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Vervust W, Zhang DT, Ghysels A, Roet S, van Erp TS, Riccardi E. PyRETIS 3: Conquering rare and slow events without boundaries. J Comput Chem 2024; 45:1224-1234. [PMID: 38345082 DOI: 10.1002/jcc.27319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 01/16/2024] [Accepted: 01/18/2024] [Indexed: 04/19/2024]
Abstract
We present and discuss the advancements made in PyRETIS 3, the third instalment of our Python library for an efficient and user-friendly rare event simulation, focused to execute molecular simulations with replica exchange transition interface sampling (RETIS) and its variations. Apart from a general rewiring of the internal code towards a more modular structure, several recently developed sampling strategies have been implemented. These include recently developed Monte Carlo moves to increase path decorrelation and convergence rate, and new ensemble definitions to handle the challenges of long-lived metastable states and transitions with unbounded reactant and product states. Additionally, the post-analysis software PyVisa is now embedded in the main code, allowing fast use of machine-learning algorithms for clustering and visualising collective variables in the simulation data.
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Affiliation(s)
- Wouter Vervust
- IBiTech-BioMMedA Group, Ghent University, Ghent, Belgium
| | - Daniel T Zhang
- Department of Chemistry, Norwegian University of Science and Technology, Trondheim, Norway
| | - An Ghysels
- IBiTech-BioMMedA Group, Ghent University, Ghent, Belgium
| | - Sander Roet
- Department of Chemistry, Utrecht University, Utrecht, The Netherlands
| | - Titus S van Erp
- Department of Chemistry, Norwegian University of Science and Technology, Trondheim, Norway
| | - Enrico Riccardi
- Department of Energy Resources, University of Stavanger, Stavanger, Norway
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Weine J, McGrath C, Dirix P, Buoso S, Kozerke S. CMRsim-A python package for cardiovascular MR simulations incorporating complex motion and flow. Magn Reson Med 2024; 91:2621-2637. [PMID: 38234037 DOI: 10.1002/mrm.30010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 12/15/2023] [Accepted: 12/22/2023] [Indexed: 01/19/2024]
Abstract
PURPOSE To present an open-source MR simulation framework that facilitates the incorporation of complex motion and flow for studying cardiovascular MR (CMR) acquisition and reconstruction. METHODS CMRsim is a Python package that allows simulation of CMR images using dynamic digital phantoms with complex motion as input. Two simulation paradigms are available, namely, numerical and analytical solutions to the Bloch equations, using a common motion representation. Competitive simulation speeds are achieved using TensorFlow for GPU acceleration. To demonstrate the capability of the package, one introductory and two advanced CMR simulation experiments are presented. The latter showcase phase-contrast imaging of turbulent flow downstream of a stenotic section and cardiac diffusion tensor imaging on a contracting left ventricle. Additionally, extensive documentation and example resources are provided. RESULTS The Bloch simulation with turbulent flow using approximately 1.5 million particles and a sequence duration of 710 ms for each of the seven different velocity encodings took a total of 29 min on a NVIDIA Titan RTX GPU. The results show characteristic phase contrast and magnitude modulation present in real data. The analytical simulation of cardiac diffusion tensor imaging with bulk-motion phase sensitivity took approximately 10 s per diffusion-weighted image, including preparation and loading steps. The results exhibit the expected alteration of diffusion metrics due to strain. CONCLUSION CMRsim is the first simulation framework that allows one to feasibly incorporate complex motion, including turbulent flow, to systematically study advanced CMR acquisition and reconstruction approaches. The open-source package features modularity and transparency, facilitating maintainability and extensibility in support of reproducible research.
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Affiliation(s)
- Jonathan Weine
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| | - Charles McGrath
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| | - Pietro Dirix
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| | - Stefano Buoso
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| | - Sebastian Kozerke
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
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Ossetchkina E, Chernoloz O, Bromerchenkel LH, Karim M, MacHale L, Montgomery A, Hu Y, Peterson K. Paste, aggregate, or air? That is the question. J Microsc 2024; 294:191-202. [PMID: 38450781 DOI: 10.1111/jmi.13286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 02/19/2024] [Accepted: 02/20/2024] [Indexed: 03/08/2024]
Abstract
The Ambassador Bridge between Detroit, Michigan, and Windsor, Ontario, has served for almost 100 years as North America's busiest international border crossing. But in 2025, the Ambassador will be replaced by the new Gordie Howe International Bridge. The Gordie Howe is a cable-stayed bridge, with two massive 220 m tall concrete piers on opposite banks of the St. Claire River, a single clear span of 853 m, and 42 m of clearance over this busy waterway. To ensure durability in this harsh freeze-thaw environment, air-entrained concrete is specified throughout. And, to ensure the quality of air entrainment, the ASTM C 457 Procedure C, Contrast Enhanced Method is employed. While a similar automated microscopic approach has been in use for well over a decade according to EN 480-11 Determination of air void characteristics in hardened concrete, this is the first large-scale application of automated air void assessment in North American infrastructure. According to the ASTM Procedure C, the air void characteristics are determined through digital image processing, while the paste content may be determined by either mix design parameters, manual point count, or 'other means'. Of these three options, point counting is used for Gordie Howe; but in parallel, during each point count, the digital image coordinates and phase identifications for each evaluated stop are recorded. This allows for training of a neural network, for automated determination of paste content, as demonstrated here.
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Affiliation(s)
- Ekaterina Ossetchkina
- Department of Civil & Mineral Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Oleksiy Chernoloz
- Department of Civil & Mineral Engineering, University of Toronto, Toronto, Ontario, Canada
| | | | - Mahzabin Karim
- Department of Civil & Mineral Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Liam MacHale
- Department of Civil & Mineral Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Amy Montgomery
- Department of Civil & Mineral Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Yuqi Hu
- Department of Civil & Mineral Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Karl Peterson
- Department of Civil & Mineral Engineering, University of Toronto, Toronto, Ontario, Canada
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Milosz M, Nazyrova A, Mukanova A, Bekmanova G, Kuzin D, Aimicheva G. Ontological approach for competency-based curriculum analysis. Heliyon 2024; 10:e29046. [PMID: 38623249 PMCID: PMC11016605 DOI: 10.1016/j.heliyon.2024.e29046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 03/22/2024] [Accepted: 03/28/2024] [Indexed: 04/17/2024] Open
Abstract
This article is dedicated to the development of a model for competencies within an educational program and its implementation through the use of semantic technologies. The model proposed by the authors is distinctive in that competencies are organized into a hierarchical data structure with arbitrary levels of nesting. Furthermore, the article presents an original solution for modelling the input requirements for studying a course, which is defined in the form of dependencies between the competencies generated by the course and the competencies of other courses. The outcome of this work is an ontological model of a competency-based curriculum, for which the authors have developed and implemented algorithms for data addition and retrieval, as well as for analyzing the consistency of the curriculum in terms of the input requirements for studying a discipline and the learning outcomes from previous periods. The findings presented in the article will prove to be valuable in the development of educational process management information systems and educational program constructors. They will also be instrumental in aligning diverse educational programs within the context of academic mobility.
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Affiliation(s)
- Marek Milosz
- Department of Computer Science, Lublin University of Technology, 36B Nadbystrzycka Str., 20-618, Lublin, Poland
| | - Aizhan Nazyrova
- Faculty of Information Technologies, L.N. Gumilyov Eurasian National University, 2 Satpayev Str., Astana, 010008, Kazakhstan
- Higher School of Information Technology and Engineering, Astana International University, 8 Kabanbay Batyr av., Astana, 010000, Kazakhstan
| | - Assel Mukanova
- Higher School of Information Technology and Engineering, Astana International University, 8 Kabanbay Batyr av., Astana, 010000, Kazakhstan
| | - Gulmira Bekmanova
- Faculty of Information Technologies, L.N. Gumilyov Eurasian National University, 2 Satpayev Str., Astana, 010008, Kazakhstan
| | - Dmitrii Kuzin
- Higher School of Information Technology and Engineering, Astana International University, 8 Kabanbay Batyr av., Astana, 010000, Kazakhstan
| | - Gaukhar Aimicheva
- Faculty of Information Technologies, L.N. Gumilyov Eurasian National University, 2 Satpayev Str., Astana, 010008, Kazakhstan
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Kuhnen G, Class LC, Badekow S, Hanisch KL, Rohn S, Kuballa J. Python workflow for the selection and identification of marker peptides-proof-of-principle study with heated milk. Anal Bioanal Chem 2024:10.1007/s00216-024-05286-w. [PMID: 38607384 DOI: 10.1007/s00216-024-05286-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Revised: 03/26/2024] [Accepted: 04/02/2024] [Indexed: 04/13/2024]
Abstract
The analysis of almost holistic food profiles has developed considerably over the last years. This has also led to larger amounts of data and the ability to obtain more information about health-beneficial and adverse constituents in food than ever before. Especially in the field of proteomics, software is used for evaluation, and these do not provide specific approaches for unique monitoring questions. An additional and more comprehensive way of evaluation can be done with the programming language Python. It offers broad possibilities by a large ecosystem for mass spectrometric data analysis, but needs to be tailored for specific sets of features, the research questions behind. It also offers the applicability of various machine-learning approaches. The aim of the present study was to develop an algorithm for selecting and identifying potential marker peptides from mass spectrometric data. The workflow is divided into three steps: (I) feature engineering, (II) chemometric data analysis, and (III) feature identification. The first step is the transformation of the mass spectrometric data into a structure, which enables the application of existing data analysis packages in Python. The second step is the data analysis for selecting single features. These features are further processed in the third step, which is the feature identification. The data used exemplarily in this proof-of-principle approach was from a study on the influence of a heat treatment on the milk proteome/peptidome.
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Affiliation(s)
- Gesine Kuhnen
- GALAB Laboratories GmbH, Am Schleusengraben 7, 21029, Hamburg, Germany
- Department of Food Chemistry and Analysis, Institute of Food Technology and Food Chemistry, Technical University Berlin, Gustav Meyer Allee 25, 13355, Berlin, Germany
| | - Lisa-Carina Class
- GALAB Laboratories GmbH, Am Schleusengraben 7, 21029, Hamburg, Germany
- Hamburg School of Food Science, Institute of Food Chemistry, University of Hamburg, Grindelallee 117, 20146, Hamburg, Germany
| | - Svenja Badekow
- GALAB Laboratories GmbH, Am Schleusengraben 7, 21029, Hamburg, Germany
| | - Kim Lara Hanisch
- GALAB Laboratories GmbH, Am Schleusengraben 7, 21029, Hamburg, Germany
| | - Sascha Rohn
- Department of Food Chemistry and Analysis, Institute of Food Technology and Food Chemistry, Technical University Berlin, Gustav Meyer Allee 25, 13355, Berlin, Germany
| | - Jürgen Kuballa
- GALAB Laboratories GmbH, Am Schleusengraben 7, 21029, Hamburg, Germany.
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Mates-Torres E, Rimola A. Unlocking the surface chemistry of ionic minerals: a high-throughput pipeline for modeling realistic interfaces. J Appl Crystallogr 2024; 57:503-508. [PMID: 38596731 PMCID: PMC11001413 DOI: 10.1107/s1600576724001286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 02/08/2024] [Indexed: 04/11/2024] Open
Abstract
A systematic procedure is introduced for modeling charge-neutral non-polar surfaces of ionic minerals containing polyatomic anions. By integrating distance- and charge-based clustering to identify chemical species within the mineral bulk, our pipeline, PolyCleaver, renders a variety of theoretically viable surface terminations. As a demonstrative example, this approach was applied to forsterite (Mg2SiO4), unveiling a rich interface landscape based on interactions with formaldehyde, a relevant multifaceted molecule, and more particularly in prebiotic chemistry. This high-throughput method, going beyond techniques traditionally applied in the modeling of minerals, offers new insights into the potential catalytic properties of diverse surfaces, enabling a broader exploration of synthetic pathways in complex mineral systems.
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Affiliation(s)
- Eric Mates-Torres
- Departament de Química, Universitat Autònoma de Barcelona, Campus de la UAB, Bellaterra, Barcelona 08193, Spain
| | - Albert Rimola
- Departament de Química, Universitat Autònoma de Barcelona, Campus de la UAB, Bellaterra, Barcelona 08193, Spain
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Guardado M, Perez C, Jackson S, Magaña J, Campana S, Samperio E, Rojas BC, Hernandez S, Syas K, Hernandez R, Zavala EI, Rohlfs R. py_ped_sim - A flexible forward genetic simulator for complex family pedigree analysis. bioRxiv 2024:2024.03.25.586501. [PMID: 38585824 PMCID: PMC10996500 DOI: 10.1101/2024.03.25.586501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Background Large-scale family pedigrees are commonly used across medical, evolutionary, and forensic genetics. These pedigrees are tools for identifying genetic disorders, tracking evolutionary patterns, and establishing familial relationships via forensic genetic identification. However, there is a lack of software to accurately simulate different pedigree structures along with genomes corresponding to those individuals in a family pedigree. This limits simulation-based evaluations of methods that use pedigrees. Results We have developed a python command-line-based tool called py_ped_sim that facilitates the simulation of pedigree structures and the genomes of individuals in a pedigree. py_ped_sim represents pedigrees as directed acyclic graphs, enabling conversion between standard pedigree formats and integration with the forward population genetic simulator, SLiM. Notably, py_ped_sim allows the simulation of varying numbers of offspring for a set of parents, with the capacity to shift the distribution of sibship sizes over generations. We additionally add simulations for events of misattributed paternity, which offers a way to simulate half-sibling relationships. We validated the accuracy of our software by simulating genomes onto diverse family pedigree structures, showing that the estimated kinship coefficients closely approximated expected values. Conclusions py_ped_sim is a user-friendly and open-source solution for simulating pedigree structures and conducting pedigree genome simulations. It empowers medical, forensic, and evolutionary genetics researchers to gain deeper insights into the dynamics of genetic inheritance and relatedness within families.
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Affiliation(s)
- Miguel Guardado
- San Francisco State University, Department of Mathematics, San Francisco CA, 94132, USA
- University of California San Francisco, Biological and Medical Informatics Graduate Program. San Francisco CA, 94158
- Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA; San Francisco, 94134, CA, USA
- University of Oregon; Department of Data Science; Eugene, OR, 97403, USA
| | - Cynthia Perez
- San Francisco State University, Department of Biology, San Francisco CA, 94132, USA
| | - Shalom Jackson
- San Francisco State University, Department of Biology, San Francisco CA, 94132, USA
| | - Joaquín Magaña
- San Francisco State University, Department of Biology, San Francisco CA, 94132, USA
| | - Sthen Campana
- San Francisco State University, Department of Biology, San Francisco CA, 94132, USA
| | - Emily Samperio
- San Francisco State University, Department of Biology, San Francisco CA, 94132, USA
| | | | - Selena Hernandez
- San Francisco State University, Department of Biology, San Francisco CA, 94132, USA
| | - Kaela Syas
- San Francisco State University, Department of Mathematics, San Francisco CA, 94132, USA
| | - Ryan Hernandez
- Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA; San Francisco, 94134, CA, USA
| | - Elena I. Zavala
- San Francisco State University, Department of Biology, San Francisco CA, 94132, USA
- University of California, Berkeley, Department of Molecular and Cell Biology, Berkeley, CA, 94720, USA
| | - Rori Rohlfs
- San Francisco State University, Department of Biology, San Francisco CA, 94132, USA
- University of Oregon; Department of Data Science; Eugene, OR, 97403, USA
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Huang J, Zhao Y, Meng B, Lu A, Wei Y, Dong L, Fang X, An D, Dai X. SEAOP: a statistical ensemble approach for outlier detection in quantitative proteomics data. Brief Bioinform 2024; 25:bbae129. [PMID: 38557674 PMCID: PMC10982946 DOI: 10.1093/bib/bbae129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 02/01/2024] [Accepted: 03/07/2024] [Indexed: 04/04/2024] Open
Abstract
Quality control in quantitative proteomics is a persistent challenge, particularly in identifying and managing outliers. Unsupervised learning models, which rely on data structure rather than predefined labels, offer potential solutions. However, without clear labels, their effectiveness might be compromised. Single models are susceptible to the randomness of parameters and initialization, which can result in a high rate of false positives. Ensemble models, on the other hand, have shown capabilities in effectively mitigating the impacts of such randomness and assisting in accurately detecting true outliers. Therefore, we introduced SEAOP, a Python toolbox that utilizes an ensemble mechanism by integrating multi-round data management and a statistics-based decision pipeline with multiple models. Specifically, SEAOP uses multi-round resampling to create diverse sub-data spaces and employs outlier detection methods to identify candidate outliers in each space. Candidates are then aggregated as confirmed outliers via a chi-square test, adhering to a 95% confidence level, to ensure the precision of the unsupervised approaches. Additionally, SEAOP introduces a visualization strategy, specifically designed to intuitively and effectively display the distribution of both outlier and non-outlier samples. Optimal hyperparameter models of SEAOP for outlier detection were identified by using a gradient-simulated standard dataset and Mann-Kendall trend test. The performance of the SEAOP toolbox was evaluated using three experimental datasets, confirming its reliability and accuracy in handling quantitative proteomics.
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Affiliation(s)
- Jinze Huang
- College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China
| | - Yang Zhao
- Technology Innovation Center of Mass Spectrometry for State Market Regulation, Center for Advanced Measurement Science, National Institute of Metrology, Beijing 100029, China
| | - Bo Meng
- Technology Innovation Center of Mass Spectrometry for State Market Regulation, Center for Advanced Measurement Science, National Institute of Metrology, Beijing 100029, China
| | - Ao Lu
- College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China
| | - Yaoguang Wei
- College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China
| | - Lianhua Dong
- Technology Innovation Center of Mass Spectrometry for State Market Regulation, Center for Advanced Measurement Science, National Institute of Metrology, Beijing 100029, China
| | - Xiang Fang
- Technology Innovation Center of Mass Spectrometry for State Market Regulation, Center for Advanced Measurement Science, National Institute of Metrology, Beijing 100029, China
| | - Dong An
- College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China
| | - Xinhua Dai
- Technology Innovation Center of Mass Spectrometry for State Market Regulation, Center for Advanced Measurement Science, National Institute of Metrology, Beijing 100029, China
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Ister R, Sternak M, Škokić S, Gajović S. suMRak: a multi-tool solution for preclinical brain MRI data analysis. Front Neuroinform 2024; 18:1358917. [PMID: 38595906 PMCID: PMC11002116 DOI: 10.3389/fninf.2024.1358917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 02/26/2024] [Indexed: 04/11/2024] Open
Abstract
Introduction Magnetic resonance imaging (MRI) is invaluable for understanding brain disorders, but data complexity poses a challenge in experimental research. In this study, we introduce suMRak, a MATLAB application designed for efficient preclinical brain MRI analysis. SuMRak integrates brain segmentation, volumetry, image registration, and parameter map generation into a unified interface, thereby reducing the number of separate tools that researchers may require for straightforward data handling. Methods and implementation All functionalities of suMRak are implemented using the MATLAB App Designer and the MATLAB-integrated Python engine. A total of six helper applications were developed alongside the main suMRak interface to allow for a cohesive and streamlined workflow. The brain segmentation strategy was validated by comparing suMRak against manual segmentation and ITK-SNAP, a popular open-source application for biomedical image segmentation. Results When compared with the manual segmentation of coronal mouse brain slices, suMRak achieved a high Sørensen-Dice similarity coefficient (0.98 ± 0.01), approaching manual accuracy. Additionally, suMRak exhibited significant improvement (p = 0.03) when compared to ITK-SNAP, particularly for caudally located brain slices. Furthermore, suMRak was capable of effectively analyzing preclinical MRI data obtained in our own studies. Most notably, the results of brain perfusion map registration to T2-weighted images were shown, improving the topographic connection to anatomical areas and enabling further data analysis to better account for the inherent spatial distortions of echoplanar imaging. Discussion SuMRak offers efficient MRI data processing of preclinical brain images, enabling researchers' consistency and precision. Notably, the accelerated brain segmentation, achieved through K-means clustering and morphological operations, significantly reduces processing time and allows for easier handling of larger datasets.
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Affiliation(s)
- Rok Ister
- Croatian Institute for Brain Research, University of Zagreb School of Medicine, Zagreb, Croatia
| | - Marko Sternak
- Croatian Institute for Brain Research, University of Zagreb School of Medicine, Zagreb, Croatia
| | - Siniša Škokić
- Croatian Institute for Brain Research, University of Zagreb School of Medicine, Zagreb, Croatia
| | - Srećko Gajović
- Croatian Institute for Brain Research, University of Zagreb School of Medicine, Zagreb, Croatia
- BIMIS—Biomedical Research Center Šalata, University of Zagreb School of Medicine, Zagreb, Croatia
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Xu G, Li X, Liu X, Han J, Shao K, Yang H, Fan F, Zhang X, Dou J. Bibliometric insights into the evolution of uranium contamination reduction research topics: Focus on microbial reduction of uranium. Sci Total Environ 2024; 917:170397. [PMID: 38307284 DOI: 10.1016/j.scitotenv.2024.170397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Revised: 01/09/2024] [Accepted: 01/21/2024] [Indexed: 02/04/2024]
Abstract
Confronting the threat of environment uranium pollution, decades of research have yielded advanced and significant findings in uranium bioremediation, resulting in the accumulation of tremendous amount of high-quality literature. In this study, we analyzed over 10,000 uranium reduction-related papers published from 1990 to the present in the Web of Science based on bibliometrics, and revealed some critical information on knowledge structure, thematic evolution and additional attention. Methods including contribution comparison, co-occurrence and temporal evolution analysis are applied. The results of the distribution and impact analysis of authors, sources, and journals indicated that the United States is a leader in this field of research and China is on the rise. The top keywords remained stable, primarily focused on chemicals (uranium, iron, plutonium, nitrat, carbon), characters (divers, surfac, speciat), and microbiology (microbial commun, cytochrome, extracellular polymeric subst). Keywords related to new strains, reduction mechanisms and product characteristics demonstrated the strongest uptrend, while some keywords related to mechanism and performance were clearly emerging in the past 5 years. Furthermore, the evolution of the thematic progression can be categorized into three stages, commencing with the discovery of the enzymatic reduction of hexavalent uranium to tetravalent uranium, developing in the groundwater remediation process at uranium-contaminated sites, and delving into the research on microbial reduction mechanisms of uranium. For future research, enhancing the understanding of mechanisms, improving uranium removal performance, and exploring practical applications can be considered. This study provides unique insights into microbial uranium reduction research, providing valuable references for related studies in this field.
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Affiliation(s)
- Guangming Xu
- Engineering Research Center of Ministry of Education on Groundwater Pollution Control and Remediation, College of Water Sciences, Beijing Normal University, Beijing 100875, PR China
| | - Xindai Li
- Engineering Research Center of Ministry of Education on Groundwater Pollution Control and Remediation, College of Water Sciences, Beijing Normal University, Beijing 100875, PR China
| | - Xinyao Liu
- Engineering Research Center of Ministry of Education on Groundwater Pollution Control and Remediation, College of Water Sciences, Beijing Normal University, Beijing 100875, PR China
| | - Juncheng Han
- Engineering Research Center of Ministry of Education on Groundwater Pollution Control and Remediation, College of Water Sciences, Beijing Normal University, Beijing 100875, PR China
| | - Kexin Shao
- Engineering Research Center of Ministry of Education on Groundwater Pollution Control and Remediation, College of Water Sciences, Beijing Normal University, Beijing 100875, PR China
| | - Haotian Yang
- Engineering Research Center of Ministry of Education on Groundwater Pollution Control and Remediation, College of Water Sciences, Beijing Normal University, Beijing 100875, PR China
| | - Fuqiang Fan
- Advanced Institute of Natural Sciences, Beijing Normal University at Zhuhai, Zhuhai 519087, PR China.
| | - Xiaodong Zhang
- Analytical and Testing Center of BNU, Beijing Normal University, Beijing 100875, PR China
| | - Junfeng Dou
- Engineering Research Center of Ministry of Education on Groundwater Pollution Control and Remediation, College of Water Sciences, Beijing Normal University, Beijing 100875, PR China.
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Ross-Veitía BD, Palma-Ramírez D, Arias-Gilart R, Conde-García RE, Espinel-Hernández A, Nuñez-Alvarez JR, Hernández-Herrera H, Llosas-Albuerne YE. Machine learning regression algorithms to predict emissions from steam boilers. Heliyon 2024; 10:e26892. [PMID: 38434324 PMCID: PMC10904275 DOI: 10.1016/j.heliyon.2024.e26892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 01/30/2024] [Accepted: 02/21/2024] [Indexed: 03/05/2024] Open
Abstract
Currently, the modeling of complex chemical-physical processes is drastically influencing industrial development. Therefore, the analysis and study of the combustion process of the boilers using machine learning (ML) techniques are vital to increase the efficiency with which this equipment operates and reduce the pollution load they contribute to the environment. This work aims to predict the emissions of CO, CO2, NOx, and the temperature of the exhaust gases of industrial boilers from real data. Different ML algorithms for regression analysis are discussed. The following are input variables: ambient temperature, working pressure, steam production, and the type of fuel used in around 20 industrial boilers. Each boiler's emission data was collected using a TESTO 350 Combustion Gas Analyzer. The modeling, with a machine learning approach using the Gradient Boosting Regression algorithm, showed better performance in the predictions made on the test data, outperforming all other models studied. It was achieved with predicted values showing a mean absolute error of 0.51 and a coefficient of determination of 99.80%. Different regression models (DNN, MLR, RFR, GBR) were compared to select the most optimal. Compared to models based on Linear Regression, the DNN model has better prediction performance. The proposed model provides a new method to predict CO2, CO, NOx emissions, and exhaust gas outlet temperature.
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Affiliation(s)
- Bárbara D. Ross-Veitía
- National Center for Applied Electromagnetism (CNEA), Universidad de Oriente, Ave. de Las Américas s/n, 90100, Santiago de Cuba, Cuba
| | - Dayana Palma-Ramírez
- National Center for Applied Electromagnetism (CNEA), Universidad de Oriente, Ave. de Las Américas s/n, 90100, Santiago de Cuba, Cuba
| | - Ramón Arias-Gilart
- National Center for Applied Electromagnetism (CNEA), Universidad de Oriente, Ave. de Las Américas s/n, 90100, Santiago de Cuba, Cuba
| | - Rebeca E. Conde-García
- National Center for Applied Electromagnetism (CNEA), Universidad de Oriente, Ave. de Las Américas s/n, 90100, Santiago de Cuba, Cuba
| | - Alejandro Espinel-Hernández
- National Center for Applied Electromagnetism (CNEA), Universidad de Oriente, Ave. de Las Américas s/n, 90100, Santiago de Cuba, Cuba
| | - José R. Nuñez-Alvarez
- Energy Department, Universidad de la Costa, (CUC), Calle 58 # 55-66, Barranquilla, 080002, Colombia
| | - Hernan Hernández-Herrera
- Faculty of Engineering, Universidad Simón Bolívar, Carrera 59 #59-132, Barranquilla, 080002, Colombia
| | - Yolanda E. Llosas-Albuerne
- Electrical Engineering Department, Universidad Técnica de Manabí (UTM), Portoviejo, Manabí, 130105, Ecuador
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12
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Garma LD, Osório NS. Demystifying dimensionality reduction techniques in the 'omics' era: A practical approach for biological science students. Biochem Mol Biol Educ 2024; 52:165-178. [PMID: 37937712 DOI: 10.1002/bmb.21800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 10/16/2023] [Accepted: 10/23/2023] [Indexed: 11/09/2023]
Abstract
Dimensionality reduction techniques are essential in analyzing large 'omics' datasets in biochemistry and molecular biology. Principal component analysis, t-distributed stochastic neighbor embedding, and uniform manifold approximation and projection are commonly used for data visualization. However, these methods can be challenging for students without a strong mathematical background. In this study, intuitive examples were created using COVID-19 data to help students understand the core concepts behind these techniques. In a 4-h practical session, we used these examples to demonstrate dimensionality reduction techniques to 15 postgraduate students from biomedical backgrounds. Using Python and Jupyter notebooks, our goal was to demystify these methods, typically treated as "black boxes", and empower students to generate and interpret their own results. To assess the impact of our approach, we conducted an anonymous survey. The majority of the students agreed that using computers enriched their learning experience (67%) and that Jupyter notebooks were a valuable part of the class (66%). Additionally, 60% of the students reported increased interest in Python, and 40% gained both interest and a better understanding of dimensionality reduction methods. Despite the short duration of the course, 40% of the students reported acquiring research skills necessary in the field. While further analysis of the learning impacts of this approach is needed, we believe that sharing the examples we generated can provide valuable resources for others to use in interactive teaching environments. These examples highlight advantages and limitations of the major dimensionality reduction methods used in modern bioinformatics analysis in an easy-to-understand way.
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Affiliation(s)
- Leonardo D Garma
- Breast Cancer Clinical Research Unit, Centro Nacional de Investigaciones Oncológicas - CNIO, Madrid, Spain
| | - Nuno S Osório
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B's -PT Government Associate Laboratory, Braga, Portugal
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13
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Hanaoka T, Matoba H, Nakayama J, Ono S, Ikegawa K, Okada M. A spatio-temporal image analysis for growth of indeterminate pulmonary nodules detected by CT scan. Radiol Phys Technol 2024; 17:71-82. [PMID: 37889460 DOI: 10.1007/s12194-023-00750-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 09/29/2023] [Accepted: 10/05/2023] [Indexed: 10/28/2023]
Abstract
The objective is to evaluate the performance of computational image classification for indeterminate pulmonary nodules (IPN) chronologically detected by CT scan. Total 483 patients with 670 abnormal pulmonary nodules, who were taken chest thin-section CT (TSCT) images at least twice and resected as suspicious nodules in our hospital, were enrolled in this study. Nodular regions from the initial and the latest TSCT images were cut manually for each case, and approached by Python development environment, using the open-source cv2 library, to measure the nodular change rate (NCR). These NCRs were statistically compared with clinico-pathological factors, and then, this discriminator was evaluated for clinical performance. NCR showed significant differences among the nodular consistencies. In terms of histological subtypes, NCR of invasive adenocarcinoma (ADC) were significantly distinguishable from other lesions, but not from minimally invasive ADC. Only for cancers, NCR was significantly associated with loco-regional invasivity, p53-immunoreactivity, and Ki67-immunoreactivity. Regarding Epidermal Growth Factor Receptor gene mutation of ADC-related nodules, NCR showed a significant negative correlation. On staging of lung cancer cases, NCR was significantly increased with progression from pTis-stage 0 up to pT1b-stage IA2. For clinical shared decision-making (SDM) whether urgent resection or watchful-waiting, receiver operating characteristic (ROC) analysis showed that area under the ROC curve was 0.686. For small-sized IPN detected by CT scan, this approach shows promise as a potential navigator to improve work-up for life-threatening cancer screening and assist SDM before surgery.
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Affiliation(s)
- Takaomi Hanaoka
- Department of Thoracic Surgery, JA Nagano North Alps Medical Center Azumi Hospital, 3207-1 Ikeda, Ikeda-machi, Kita-azumi-gun, Nagano, 399-8605, Japan.
| | - Hisanori Matoba
- Department of Molecular Pathology, Shinshu University School of Medicine, 3-1-1 Asahi, Matsumoto, Nagano, 390-8621, Japan
| | - Jun Nakayama
- Department of Molecular Pathology, Shinshu University School of Medicine, 3-1-1 Asahi, Matsumoto, Nagano, 390-8621, Japan
- Department of Pathology, JA Nagano North Alps Medical Center Azumi Hospital, 3207-1 Ikeda, Ikeda-Machi, Kita-azumi-gun, Nagano, 399-8605, Japan
| | - Shotaro Ono
- Department of Thoracic Surgery, JA Nagano North Alps Medical Center Azumi Hospital, 3207-1 Ikeda, Ikeda-machi, Kita-azumi-gun, Nagano, 399-8605, Japan
| | - Kayoko Ikegawa
- Department of Respirology, JA Nagano North Alps Medical Center Azumi Hospital, 3207-1 Ikeda, Ikeda-machi, Kita-azumi-gun, Nagano, 399-8605, Japan
| | - Mitsuyo Okada
- Department of Respirology, JA Nagano North Alps Medical Center Azumi Hospital, 3207-1 Ikeda, Ikeda-machi, Kita-azumi-gun, Nagano, 399-8605, Japan
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14
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Islam MS, Nayem AZ, Hoque KN. Finite element-based optimization procedure for an irregular domain with unstructured mesh. Heliyon 2024; 10:e25994. [PMID: 38384509 PMCID: PMC10878951 DOI: 10.1016/j.heliyon.2024.e25994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Revised: 01/03/2024] [Accepted: 02/06/2024] [Indexed: 02/23/2024] Open
Abstract
At present, structural optimization is a highly demanding area of research in engineering. Engineers aim to minimize material in a body while maintaining its usability and safety at the same time. Developing a user-friendly program to optimize a structure using the finite element method (FEM) is the goal of the current study. With the advent of additive manufacturing, the production of complex-shaped designs is showing promise. A detailed optimization algorithm based on solid isotropic material with penalization (SIMP) is presented in this paper. UnTop2D: An object-oriented Python program with a graphical user interface (GUI) has been developed, which can be applied to structures with both structured and unstructured meshes. The mesh is not required to be topologically ball and can be imported from professional meshing software. Any selected element can be frozen to prevent its removal during optimization, and wall elements can also be frozen for real-world scenarios. The optimized structure can be exported as an Abaqus input file for structural analysis and STL file for 3D printing. This paper presents several examples to demonstrate the effectiveness of the proposed procedure.
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Affiliation(s)
- Md Shahidul Islam
- Department of Naval Architecture and Marine Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh
| | - Ali Zulkar Nayem
- Department of Naval Architecture and Marine Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh
| | - Kazi Naimul Hoque
- Department of Naval Architecture and Marine Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh
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15
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Cao Y, Cheung NA, Giustini D, LeDue J, Murphy TH. Scholar Metrics Scraper (SMS): automated retrieval of citation and author data. Front Res Metr Anal 2024; 9:1335454. [PMID: 38456123 PMCID: PMC10917922 DOI: 10.3389/frma.2024.1335454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 02/06/2024] [Indexed: 03/09/2024] Open
Abstract
Academic departments, research clusters and evaluators analyze author and citation data to measure research impact and to support strategic planning. We created Scholar Metrics Scraper (SMS) to automate the retrieval of bibliometric data for a group of researchers. The project contains Jupyter notebooks that take a list of researchers as an input and exports a CSV file of citation metrics from Google Scholar (GS) to visualize the group's impact and collaboration. A series of graph outputs are also available. SMS is an open solution for automating the retrieval and visualization of citation data.
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Affiliation(s)
- Yutong Cao
- Department of Psychiatry, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Nicole A. Cheung
- Department of Psychiatry, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Dean Giustini
- Biomedical Branch Library, University of British Columbia, Vancouver, BC, Canada
| | - Jeffrey LeDue
- Department of Psychiatry, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Timothy H. Murphy
- Department of Psychiatry, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada
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16
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Allwright M, Guennewig B, Hoffmann AE, Rohleder C, Jieu B, Chung LH, Jiang YC, Lemos Wimmer BF, Qi Y, Don AS, Leweke FM, Couttas TA. ReTimeML: a retention time predictor that supports the LC-MS/MS analysis of sphingolipids. Sci Rep 2024; 14:4375. [PMID: 38388524 PMCID: PMC10883992 DOI: 10.1038/s41598-024-53860-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 02/06/2024] [Indexed: 02/24/2024] Open
Abstract
The analysis of ceramide (Cer) and sphingomyelin (SM) lipid species using liquid chromatography-tandem mass spectrometry (LC-MS/MS) continues to present challenges as their precursor mass and fragmentation can correspond to multiple molecular arrangements. To address this constraint, we developed ReTimeML, a freeware that automates the expected retention times (RTs) for Cer and SM lipid profiles from complex chromatograms. ReTimeML works on the principle that LC-MS/MS experiments have pre-determined RTs from internal standards, calibrators or quality controls used throughout the analysis. Employed as reference RTs, ReTimeML subsequently extrapolates the RTs of unknowns using its machine-learned regression library of mass-to-charge (m/z) versus RT profiles, which does not require model retraining for adaptability on different LC-MS/MS pipelines. We validated ReTimeML RT estimations for various Cer and SM structures across different biologicals, tissues and LC-MS/MS setups, exhibiting a mean variance between 0.23 and 2.43% compared to user annotations. ReTimeML also aided the disambiguation of SM identities from isobar distributions in paired serum-cerebrospinal fluid from healthy volunteers, allowing us to identify a series of non-canonical SMs associated between the two biofluids comprised of a polyunsaturated structure that confers increased stability against catabolic clearance.
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Affiliation(s)
- Michael Allwright
- ForeFront, Brain and Mind Centre, The University of Sydney, Sydney, Australia
| | - Boris Guennewig
- ForeFront, Brain and Mind Centre, The University of Sydney, Sydney, Australia
| | - Anna E Hoffmann
- Translational Research Collective, Brain and Mind Centre, The University of Sydney, Sydney, NSW, 2006, Australia
- Endosane Pharmaceuticals GmbH, Berlin, Germany
| | - Cathrin Rohleder
- Translational Research Collective, Brain and Mind Centre, The University of Sydney, Sydney, NSW, 2006, Australia
- Endosane Pharmaceuticals GmbH, Berlin, Germany
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Beverly Jieu
- Translational Research Collective, Brain and Mind Centre, The University of Sydney, Sydney, NSW, 2006, Australia
| | - Long H Chung
- Centenary Institute, The University of Sydney, Sydney, Australia
| | - Yingxin C Jiang
- Centenary Institute, The University of Sydney, Sydney, Australia
| | - Bruno F Lemos Wimmer
- Translational Research Collective, Brain and Mind Centre, The University of Sydney, Sydney, NSW, 2006, Australia
| | - Yanfei Qi
- Centenary Institute, The University of Sydney, Sydney, Australia
| | - Anthony S Don
- School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia
| | - F Markus Leweke
- Translational Research Collective, Brain and Mind Centre, The University of Sydney, Sydney, NSW, 2006, Australia
- Endosane Pharmaceuticals GmbH, Berlin, Germany
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Timothy A Couttas
- Translational Research Collective, Brain and Mind Centre, The University of Sydney, Sydney, NSW, 2006, Australia.
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17
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Di Y, Qiao ZB, Ye HY, Li XY, Luo WT, Fang WY, Qiao T. Digital measuring the ocular morphological parameters of guinea pig eye in vivo with Python. Int J Ophthalmol 2024; 17:239-246. [PMID: 38371268 PMCID: PMC10827618 DOI: 10.18240/ijo.2024.02.03] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Accepted: 12/01/2023] [Indexed: 02/20/2024] Open
Abstract
AIM To quantitatively measure ocular morphological parameters of guinea pig with Python technology. METHODS Thirty-six eyeballs of eighteen 3-week-old guinea pigs were measured with keratometer and photographed to obtain the horizontal, coronal, and sagittal planes respectively. The corresponding photo pixels-actual length ratio was acquired by a proportional scale. The edge coordinates were identified artificially by ginput function. Circle and conic curve fitting were applied to fit the contour of the eyeball in the sagittal, coronal and horizontal view. The curvature, curvature radius, eccentricity, tilt angle, corneal diameter, and binocular separation angle were calculated according to the geometric principles. Next, the eyeballs were removed, canny edge detection was applied to identify the contour of eyeball in vitro. The results were compared between in vivo and in vitro. RESULTS Regarding the corneal curvature and curvature radius on the horizontal and sagittal planes, no significant differences were observed among results in vivo, in vitro, and the keratometer. The horizontal and vertical binocular separation angles were 130.6°±6.39° and 129.8°±9.58° respectively. For the corneal curvature radius and eccentricity in vivo, significant differences were observed between horizontal and vertical planes. CONCLUSION The Graphical interface window of Python makes up the deficiency of edge detection, which requires too much definition in Matlab. There are significant differences between guinea pig and human beings, such as exotropic eye position, oblique oval eyeball, and obvious discrepancy of binoculus. This study helps evaluate objectively the ocular morphological parameters of small experimental animals in emmetropization research.
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Affiliation(s)
- Yue Di
- Department of Ophthalmology, Shanghai Children's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200062, China
| | - Zhong-Bao Qiao
- Department of Ophthalmology, Shanghai Children's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200062, China
| | - Hai-Yun Ye
- Department of Ophthalmology, Shanghai Children's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200062, China
| | - Xin-Yue Li
- Department of Ophthalmology, Shanghai Children's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200062, China
| | - Wen-Ting Luo
- Department of Ophthalmology, Shanghai Children's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200062, China
| | - Wang-Yi Fang
- Department of Ophthalmology, Shanghai Children's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200062, China
| | - Tong Qiao
- Department of Ophthalmology, Shanghai Children's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200062, China
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18
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Agarwal M, Pelegri AA. An Ogden hyperelastic 3D micromechanical model to depict Poynting effect in brain white matter. Heliyon 2024; 10:e25379. [PMID: 38371981 PMCID: PMC10873664 DOI: 10.1016/j.heliyon.2024.e25379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 01/11/2024] [Accepted: 01/25/2024] [Indexed: 02/20/2024] Open
Abstract
Shear and torsional load on soft solids such as brain white matter purportedly exhibits the Poynting Effect. It is a typical nonlinear phenomenon associated with soft materials whereby they tend to elongate (positive Poynting effect) or contract (negative Poynting effect) in a direction perpendicular to the shearing or twisting plane. In this research, a novel 3D micromechanical Finite Element Model (FEM) has been formulated to describe the Poynting effect in bi-phasic modeled brain white matter (BWM) representative volume element (RVE) with axons tracts embedded in surrounding extracellular matrix (ECM) for simulating brain matter's response to pure and simple shear. In the presented BWM 3D FEM, nonlinear Ogden hyper-elastic material model is deployed to interpret axons and ECM material phases. The modeled bi-phasic RVEs have axons tied to the surrounding ECM. In this proof-of-concept (POC) FEM, three simple shear loading configurations and a pure shear case were analyzed. Root mean square deviation (RMSD) was calculated for stress and deformation response plots to understand the effect of axon-ECM orientations and loading conditions on the degree of Poynting behavior. Variations in normal stresses (S11, S22, or S33) perpendicular to the shear plane underscored the significance of axonal fiber-matrix interactions. From the simulated ensemble of cases, a transitional dominance trend was noticed, as simple sheared axons showed pronounced Poynting behavior, but shear deformation build-up in the purely sheared brain model exhibited the highest Poynting behavior at higher strain % limits. At lower strain limits, simple shear imparted across and perpendicular to axonal tract directions emerged as the dominant Poynting effect configurations. At high strains, the stress-strain% plots manifested mild strain stiffening effects and bending stresses in purely sheared axons, substantiated the strong non-linearity in brain tissues' response.
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Affiliation(s)
- Mohit Agarwal
- Mechanical and Aerospace Engineering Rutgers, The State University of New Jersey, New Brunswick, NJ, USA
| | - Assimina A. Pelegri
- Mechanical and Aerospace Engineering Rutgers, The State University of New Jersey, New Brunswick, NJ, USA
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van der Werff J, Ravignani A, Jadoul Y. thebeat: A Python package for working with rhythms and other temporal sequences. Behav Res Methods 2024:10.3758/s13428-023-02334-8. [PMID: 38308146 DOI: 10.3758/s13428-023-02334-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/27/2023] [Indexed: 02/04/2024]
Abstract
thebeat is a Python package for working with temporal sequences and rhythms in the behavioral and cognitive sciences, as well as in bioacoustics. It provides functionality for creating experimental stimuli, and for visualizing and analyzing temporal data. Sequences, sounds, and experimental trials can be generated using single lines of code. thebeat contains functions for calculating common rhythmic measures, such as interval ratios, and for producing plots, such as circular histograms. thebeat saves researchers time when creating experiments, and provides the first steps in collecting widely accepted methods for use in timing research. thebeat is an open-source, on-going, and collaborative project, and can be extended for use in specialized subfields. thebeat integrates easily with the existing Python ecosystem, allowing one to combine our tested code with custom-made scripts. The package was specifically designed to be useful for both skilled and novice programmers. thebeat provides a foundation for working with temporal sequences onto which additional functionality can be built. This combination of specificity and plasticity should facilitate research in multiple research contexts and fields of study.
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Affiliation(s)
- J van der Werff
- Comparative Bioacoustics Group, Max Planck Institute for Psycholinguistics, Wundtlaan 1, Nijmegen, The Netherlands.
- Department of Human Neurosciences, Sapienza University of Rome, Piazzale Aldo Moro, 5, Rome, Italy.
| | - Andrea Ravignani
- Comparative Bioacoustics Group, Max Planck Institute for Psycholinguistics, Wundtlaan 1, Nijmegen, The Netherlands
- Department of Human Neurosciences, Sapienza University of Rome, Piazzale Aldo Moro, 5, Rome, Italy
- Center for Music in the Brain, Aarhus University, Universitetsbyen 3, Aarhus, Denmark
| | - Yannick Jadoul
- Comparative Bioacoustics Group, Max Planck Institute for Psycholinguistics, Wundtlaan 1, Nijmegen, The Netherlands
- Department of Human Neurosciences, Sapienza University of Rome, Piazzale Aldo Moro, 5, Rome, Italy
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Sun J, Wu L, Fang N, Liu L. IFM calculator: An algorithm for interfragmentary motion calculation in finite element analysis. Comput Methods Programs Biomed 2024; 244:107996. [PMID: 38176328 DOI: 10.1016/j.cmpb.2023.107996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Revised: 12/12/2023] [Accepted: 12/25/2023] [Indexed: 01/06/2024]
Abstract
BACKGROUND Interfragmentary motion (IFM) is a complex state that significantly impacts the healing process of fractures following implant placement. It is crucial to fully consider the IFM state after implantation in the design and biomechanical testing of implants. However, current finite element analysis software lacks direct tools for calculating IFM, and existing IFM tools do not offer a comprehensive solution in terms of accuracy, functionality, and visualization. METHODS In our study, we developed a Python-based algorithm for calculating IFM that addresses limitations. Our algorithm automatically calculated IFM distances, sliding distances, gaps, as well as the angles and rotation of the two fracture surfaces using all nodes on both sides of the fracture ends. Researchers could input data and selected desired parameters in the interface. The algorithm then performed the necessary calculations and presented the results in a clear and concise manner. The algorithm also provided comprehensive data export capabilities, allowing researchers to customize analyses based on specific needs.To provide a more intuitive demonstration of the calculation process and usage of IFM-Cal, we conducted simulations in Ansys using two rectangular blocks to compare the accuracy and function of three different methods (Point based method, contact tool and IFM-Cal). RESULTS The point-based method and the contact tool could not accurately calculate IFA, while IFM-Cal could provide a comprehensive evaluation of IFA. In simulation 1, the IFM distances calculated using the point sampling method, contact tool, and IFM-Cal were 2.00 mm, 3.15 mm, and 2.00 mm, respectively. In simulation 2, both the point sampling method and contact tool failed to calculate the interfragmentary angle (IFA), while the IFM-Cal algorithm estimated an angle of -7.87°, which had a small error compared to the ground-truth value of 7.9°. CONCLUSION We have developed an algorithm for computing IFM which can be utilized in finite element analysis and biomechanical experiments. By conducting comparative simulations with other existing algorithms, we have demonstrated the superior accuracy and expanded evaluation capabilities of our algorithm.
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Affiliation(s)
- Jun Sun
- Department of Orthopedics, Shanghai East Hospital, School of Medicine, Tongji University, 150 Jimo road, Pudong new district, Shanghai, China 200120
| | - Le Wu
- Department of Orthopedics, Shanghai East Hospital, School of Medicine, Tongji University, 150 Jimo road, Pudong new district, Shanghai, China 200120
| | - Nan Fang
- Department of Orthopedics, Shanghai East Hospital, School of Medicine, Tongji University, 150 Jimo road, Pudong new district, Shanghai, China 200120
| | - Lifeng Liu
- Department of Orthopedics, Shanghai East Hospital, School of Medicine, Tongji University, 150 Jimo road, Pudong new district, Shanghai, China 200120.
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Valli G, Ritsche P, Casolo A, Negro F, De Vito G. Tutorial: Analysis of central and peripheral motor unit properties from decomposed High-Density surface EMG signals with openhdemg. J Electromyogr Kinesiol 2024; 74:102850. [PMID: 38065045 DOI: 10.1016/j.jelekin.2023.102850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 10/05/2023] [Accepted: 11/28/2023] [Indexed: 01/29/2024] Open
Abstract
High-Density surface Electromyography (HD-sEMG) is the most established technique for the non-invasive analysis of single motor unit (MU) activity in humans. It provides the possibility to study the central properties (e.g., discharge rate) of large populations of MUs by analysis of their firing pattern. Additionally, by spike-triggered averaging, peripheral properties such as MUs conduction velocity can be estimated over adjacent regions of the muscles and single MUs can be tracked across different recording sessions. In this tutorial, we guide the reader through the investigation of MUs properties from decomposed HD-sEMG recordings by providing both the theoretical knowledge and practical tools necessary to perform the analyses. The practical application of this tutorial is based on openhdemg, a free and open-source community-based framework for the automated analysis of MUs properties built on Python 3 and composed of different modules for HD-sEMG data handling, visualisation, editing, and analysis. openhdemg is interfaceable with most of the available recording software, equipment or decomposition techniques, and all the built-in functions are easily adaptable to different experimental needs. The framework also includes a graphical user interface which enables users with limited coding skills to perform a robust and reliable analysis of MUs properties without coding.
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Affiliation(s)
- Giacomo Valli
- Department of Biomedical Sciences, University of Padova, Padova, Italy; Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy.
| | - Paul Ritsche
- Department of Sport, Exercise and Health, University of Basel, Basel, Switzerland.
| | - Andrea Casolo
- Department of Biomedical Sciences, University of Padova, Padova, Italy.
| | - Francesco Negro
- Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy.
| | - Giuseppe De Vito
- Department of Biomedical Sciences, University of Padova, Padova, Italy.
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Firdaus AA, Yudhana A, Riadi I, Mahsun. Indonesian presidential election sentiment: Dataset of response public before 2024. Data Brief 2024; 52:109993. [PMID: 38226041 PMCID: PMC10788203 DOI: 10.1016/j.dib.2023.109993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 12/15/2023] [Accepted: 12/15/2023] [Indexed: 01/17/2024] Open
Abstract
Indonesia is one of the countries that is currently entering the political year for the election of President, Regional Heads, and Members of the Legislative in 2024. This has become a hot topic on social media, especially about the Presidential Election. Twitter is one of the platforms with the largest users in Indonesia. It is interesting to see the alignment of Twitter users towards presidential candidates who already have a carrying party, namely Ganjar Pranowo, Prabowo Subianto, and Anies Baswedan based on a sentiment analysis approach. User feedback data about Indonesian Presidential candidates are obtained from the Twitter platform using Twitter API with Python programming language. The data obtained was 30,000 data with each candidate as many as 10,000 data. Data is pulled in April 2023 with specific keywords. The time for data withdrawal is chosen based on the announcement of Presidential Candidates carried by political parties before the schedule for determining or campaigning for Presidential candidates. Current data can potentially be used again as a comparison of analysis of presidential candidates on campaign time spans and after campaigns or actual calculation results. The data that can be accessed is in CSV format and has gone through several stages such as labelling using Language experts, removing spam Tweets & empty cells and preprocessing.
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Affiliation(s)
| | - Anton Yudhana
- Department of Electrical Engineering, Universitas Ahmad Dahlan, Yogyakarta 55166, Indonesia
| | - Imam Riadi
- Department of Information System, Universitas Ahmad Dahlan, Yogyakarta 55166, Indonesia
| | - Mahsun
- Department of Indonesian Language and Literature Education, Universitas Mataram, Mataram 83125, Indonesia
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23
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Rybacki P, Niemann J, Derouiche S, Chetehouna S, Boulaares I, Seghir NM, Diatta J, Osuch A. Convolutional Neural Network (CNN) Model for the Classification of Varieties of Date Palm Fruits ( Phoenix dactylifera L.). Sensors (Basel) 2024; 24:558. [PMID: 38257650 PMCID: PMC10818393 DOI: 10.3390/s24020558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 12/29/2023] [Accepted: 01/10/2024] [Indexed: 01/24/2024]
Abstract
The popularity and demand for high-quality date palm fruits (Phoenix dactylifera L.) have been growing, and their quality largely depends on the type of handling, storage, and processing methods. The current methods of geometric evaluation and classification of date palm fruits are characterised by high labour intensity and are usually performed mechanically, which may cause additional damage and reduce the quality and value of the product. Therefore, non-contact methods are being sought based on image analysis, with digital solutions controlling the evaluation and classification processes. The main objective of this paper is to develop an automatic classification model for varieties of date palm fruits using a convolutional neural network (CNN) based on two fundamental criteria, i.e., colour difference and evaluation of geometric parameters of dates. A CNN with a fixed architecture was built, marked as DateNET, consisting of a system of five alternating Conv2D, MaxPooling2D, and Dropout classes. The validation accuracy of the model presented in this study depended on the selection of classification criteria. It was 85.24% for fruit colour-based classification and 87.62% for the geometric parameters only; however, it increased considerably to 93.41% when both the colour and geometry of dates were considered.
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Affiliation(s)
- Piotr Rybacki
- Department of Agronomy, Poznań University of Life Sciences, Dojazd 11, 60-632 Poznań, Poland
| | - Janetta Niemann
- Department of Genetics and Plant Breeding, Poznań University of Life Sciences, Dojazd 11, 60-632 Poznań, Poland;
| | - Samir Derouiche
- Department of Cellular and Molecular Biology, Faculty of Natural Sciences and Life, University of El Oued, El Oued 39000, Algeria; (S.D.); (I.B.)
- Laboratory of Biodiversity and Application of Biotechnology in the Agricultural Field, Faculty of Natural Sciences and Life, University of El Oued, El Oued 39000, Algeria;
| | - Sara Chetehouna
- Department of Microbiology and Biochemistry, Faculty of Sciences, Mohamed Boudiaf-M’sila University, M’sila 28000, Algeria;
| | - Islam Boulaares
- Department of Cellular and Molecular Biology, Faculty of Natural Sciences and Life, University of El Oued, El Oued 39000, Algeria; (S.D.); (I.B.)
- Laboratory of Biodiversity and Application of Biotechnology in the Agricultural Field, Faculty of Natural Sciences and Life, University of El Oued, El Oued 39000, Algeria;
| | - Nili Mohammed Seghir
- Laboratory of Biodiversity and Application of Biotechnology in the Agricultural Field, Faculty of Natural Sciences and Life, University of El Oued, El Oued 39000, Algeria;
- Department of Agricultural Sciences, University of El Oued, El Oued 39000, Algeria
| | - Jean Diatta
- Department of Agricultural Chemistry and Environmental Biogeochemistry, Poznań University of Life Sciences, Ul. Wojska Polskiego 71F, 60-625 Poznań, Poland;
| | - Andrzej Osuch
- Department of Biosystems Engineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-637 Poznań, Poland;
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Han J, Lee SJ, Yun HS, Kim KB, Bae SW. PyRINEX: a new multi-purpose Python package for GNSS RINEX data. PeerJ Comput Sci 2024; 10:e1800. [PMID: 38259899 PMCID: PMC10803049 DOI: 10.7717/peerj-cs.1800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 12/15/2023] [Indexed: 01/24/2024]
Abstract
Since the first receiver independent exchange format (RINEX) version was released in 1989, it has gone through several versions, making the existing software, such as TEQC, incompatible with certain later versions. This study proposes a new Python package named PyRINEX, which is developed to batch process the most generally used versions of RINEX files, namely 2.0 and 3.0. The proposed package can be used to manage and edit numerous RINEX files as well as perform a data quality check function. PyRINEX can be easily imported into any Python IDE similar to any other open-source Python package, it also makes secondary development easy for users.
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Affiliation(s)
- Jinzhen Han
- Department of Civil, Architectural & Environment Engineering, Sungkyunkwan University, Suwon, Korea
| | - Seung Jun Lee
- Department of Civil, Architectural & Environment Engineering, Sungkyunkwan University, Suwon, Korea
| | - Hong Sik Yun
- Department of Civil, Architectural & Environment Engineering, Sungkyunkwan University, Suwon, Korea
| | - Kwang Bae Kim
- Department of Civil, Architectural & Environment Engineering, Sungkyunkwan University, Suwon, Korea
| | - Sang Won Bae
- Korea Ministry of the Interior and Safety, Sejong, Korea
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25
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Woelk LM, Kovacevic D, Husseini H, Förster F, Gerlach F, Möckl F, Altfeld M, Guse AH, Diercks BP, Werner R. DARTS: an open-source Python pipeline for Ca 2+ microdomain analysis in live cell imaging data. Front Immunol 2024; 14:1299435. [PMID: 38274810 PMCID: PMC10809147 DOI: 10.3389/fimmu.2023.1299435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 12/26/2023] [Indexed: 01/27/2024] Open
Abstract
Ca2+ microdomains play a key role in intracellular signaling processes. For instance, they mediate the activation of T cells and, thus, the initial adaptive immune system. They are, however, also of utmost importance for activation of other cells, and a detailed understanding of the dynamics of these spatially localized Ca2+ signals is crucial for a better understanding of the underlying signaling processes. A typical approach to analyze Ca2+ microdomain dynamics is live cell fluorescence microscopy imaging. Experiments usually involve imaging a larger number of cells of different groups (for instance, wild type and knockout cells), followed by a time consuming image and data analysis. With DARTS, we present a modular Python pipeline for efficient Ca2+ microdomain analysis in live cell imaging data. DARTS (Deconvolution, Analysis, Registration, Tracking, and Shape normalization) provides state-of-the-art image postprocessing options like deep learning-based cell detection and tracking, spatio-temporal image deconvolution, and bleaching correction. An integrated automated Ca2+ microdomain detection offers direct access to global statistics like the number of microdomains for cell groups, corresponding signal intensity levels, and the temporal evolution of the measures. With a focus on bead stimulation experiments, DARTS provides a so-called dartboard projection analysis and visualization approach. A dartboard projection covers spatio-temporal normalization of the bead contact areas and cell shape normalization onto a circular template that enables aggregation of the spatiotemporal information of the microdomain detection results for the individual cells of the cell groups of interest. The dartboard visualization allows intuitive interpretation of the spatio-temporal microdomain dynamics at the group level. The application of DARTS is illustrated by three use cases in the context of the formation of initial Ca2+ microdomains after cell stimulation. DARTS is provided as an open-source solution and will be continuously extended upon the feedback of the community. Code available at: 10.5281/zenodo.10459243.
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Affiliation(s)
- Lena-Marie Woelk
- Department of Applied Medical Informatics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Department of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Center for Biomedical Artificial Intelligence (bAIome), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Dejan Kovacevic
- The Calcium Signalling Group, Department of Biochemistry and Molecular Cell Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Hümeyra Husseini
- Department of Applied Medical Informatics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Department of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Center for Biomedical Artificial Intelligence (bAIome), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Fritz Förster
- Department of Applied Medical Informatics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Department of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Center for Biomedical Artificial Intelligence (bAIome), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Fynn Gerlach
- The Calcium Signalling Group, Department of Biochemistry and Molecular Cell Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Franziska Möckl
- The Calcium Signalling Group, Department of Biochemistry and Molecular Cell Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Marcus Altfeld
- Institute for Immunology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Andreas H. Guse
- The Calcium Signalling Group, Department of Biochemistry and Molecular Cell Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Björn-Philipp Diercks
- The Calcium Signalling Group, Department of Biochemistry and Molecular Cell Biology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - René Werner
- Department of Applied Medical Informatics, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Department of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Center for Biomedical Artificial Intelligence (bAIome), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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26
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Zietz M, Himmelstein DS, Kloster K, Williams C, Nagle MW, Greene CS. The probability of edge existence due to node degree: a baseline for network-based predictions. Gigascience 2024; 13:giae001. [PMID: 38323677 PMCID: PMC10848215 DOI: 10.1093/gigascience/giae001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 09/25/2023] [Accepted: 01/02/2024] [Indexed: 02/08/2024] Open
Abstract
Important tasks in biomedical discovery such as predicting gene functions, gene-disease associations, and drug repurposing opportunities are often framed as network edge prediction. The number of edges connecting to a node, termed degree, can vary greatly across nodes in real biomedical networks, and the distribution of degrees varies between networks. If degree strongly influences edge prediction, then imbalance or bias in the distribution of degrees could lead to nonspecific or misleading predictions. We introduce a network permutation framework to quantify the effects of node degree on edge prediction. Our framework decomposes performance into the proportions attributable to degree and the network's specific connections using network permutation to generate features that depend only on degree. We discover that performance attributable to factors other than degree is often only a small portion of overall performance. Researchers seeking to predict new or missing edges in biological networks should use our permutation approach to obtain a baseline for performance that may be nonspecific because of degree. We released our methods as an open-source Python package (https://github.com/hetio/xswap/).
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Affiliation(s)
- Michael Zietz
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA
| | - Daniel S Himmelstein
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Related Sciences, Denver, CO 80202, USA
| | - Kyle Kloster
- Carbon, Inc., Redwood City, CA 94063, USA
- Department of Computer Science, North Carolina State University, Raleigh, NC 27606, USA
| | - Christopher Williams
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Michael W Nagle
- Internal Medicine Research Unit, Pfizer Worldwide Research, Development, and Medical, Cambridge, MA 02139, USA
- Integrative Biology, Internal Medicine Research Unit, Worldwide Research, Development, and Medicine, Pfizer Inc., Cambridge, MA 02139, USA
- Human Biology Integration Foundation, Deep Human Biology Learning, Eisai Inc., Cambridge, MA 02140, USA
| | - Casey S Greene
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Biochemistry and Molecular Genetics, University of Colorado School of Medicine, Aurora, CO 80045, USA
- Center for Health AI, University of Colorado School of Medicine, Aurora, CO 80045, USA
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27
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Samuel S, Mietchen D. Computational reproducibility of Jupyter notebooks from biomedical publications. Gigascience 2024; 13:giad113. [PMID: 38206590 PMCID: PMC10783158 DOI: 10.1093/gigascience/giad113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 08/09/2023] [Accepted: 12/08/2023] [Indexed: 01/12/2024] Open
Abstract
BACKGROUND Jupyter notebooks facilitate the bundling of executable code with its documentation and output in one interactive environment, and they represent a popular mechanism to document and share computational workflows, including for research publications. The reproducibility of computational aspects of research is a key component of scientific reproducibility but has not yet been assessed at scale for Jupyter notebooks associated with biomedical publications. APPROACH We address computational reproducibility at 2 levels: (i) using fully automated workflows, we analyzed the computational reproducibility of Jupyter notebooks associated with publications indexed in the biomedical literature repository PubMed Central. We identified such notebooks by mining the article's full text, trying to locate them on GitHub, and attempting to rerun them in an environment as close to the original as possible. We documented reproduction success and exceptions and explored relationships between notebook reproducibility and variables related to the notebooks or publications. (ii) This study represents a reproducibility attempt in and of itself, using essentially the same methodology twice on PubMed Central over the course of 2 years, during which the corpus of Jupyter notebooks from articles indexed in PubMed Central has grown in a highly dynamic fashion. RESULTS Out of 27,271 Jupyter notebooks from 2,660 GitHub repositories associated with 3,467 publications, 22,578 notebooks were written in Python, including 15,817 that had their dependencies declared in standard requirement files and that we attempted to rerun automatically. For 10,388 of these, all declared dependencies could be installed successfully, and we reran them to assess reproducibility. Of these, 1,203 notebooks ran through without any errors, including 879 that produced results identical to those reported in the original notebook and 324 for which our results differed from the originally reported ones. Running the other notebooks resulted in exceptions. CONCLUSIONS We zoom in on common problems and practices, highlight trends, and discuss potential improvements to Jupyter-related workflows associated with biomedical publications.
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Affiliation(s)
- Sheeba Samuel
- Heinz-Nixdorf Chair for Distributed Information Systems, Friedrich Schiller University Jena, Jena 07743, Germany
- Michael Stifel Center Jena, Jena 07743, Germany
| | - Daniel Mietchen
- Ronin Institute, Montclair 07043-2314, NJ, United States
- Institute for Globally Distributed Open Research and Education (IGDORE)
- FIZ Karlsruhe—Leibniz Institute for Information Infrastructure, Berlin 76344, Germany
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28
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Mei J, Luo R, Xu L, Zhao W, Wen S, Wang K, Xiao X, Meng J, Huang Y, Tang J, Cheng L, Xu M, Ming D. MetaBCI: An open-source platform for brain-computer interfaces. Comput Biol Med 2024; 168:107806. [PMID: 38081116 DOI: 10.1016/j.compbiomed.2023.107806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 11/29/2023] [Accepted: 11/29/2023] [Indexed: 01/10/2024]
Abstract
BACKGROUND Recently, brain-computer interfaces (BCIs) have attracted worldwide attention for their great potential in clinical and real-life applications. To implement a complete BCI system, one must set up several links to translate the brain intent into computer commands. However, there is not an open-source software platform that can cover all links of the BCI chain. METHOD This study developed a one-stop open-source BCI software, namely MetaBCI, to facilitate the construction of a BCI system. MetaBCI is written in Python, and has the functions of stimulus presentation (Brainstim), data loading and processing (Brainda), and online information flow (Brainflow). This paper introduces the detailed information of MetaBCI and presents four typical application cases. RESULTS The results showed that MetaBCI was an extensible and feature-rich software platform for BCI research and application, which could effectively encode, decode, and feedback brain activities. CONCLUSIONS MetaBCI can greatly lower the BCI's technical threshold for BCI beginners and can save time and cost to build up a practical BCI system. The source code is available at https://github.com/TBC-TJU/MetaBCI, expecting new contributions from the BCI community.
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Affiliation(s)
- Jie Mei
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, 300072, People's Republic of China; Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, 300072, People's Republic of China.
| | - Ruixin Luo
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, 300072, People's Republic of China; Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, 300072, People's Republic of China.
| | - Lichao Xu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, 300072, People's Republic of China
| | - Wei Zhao
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, 300072, People's Republic of China; Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, 300072, People's Republic of China
| | - Shengfu Wen
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, 300072, People's Republic of China
| | - Kun Wang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, 300072, People's Republic of China; Haihe Laboratory of Brain-computer Interaction and Human-machine Integration, Tianjin, 300392, People's Republic of China
| | - Xiaolin Xiao
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, 300072, People's Republic of China; Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, 300072, People's Republic of China; Haihe Laboratory of Brain-computer Interaction and Human-machine Integration, Tianjin, 300392, People's Republic of China
| | - Jiayuan Meng
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, 300072, People's Republic of China; Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, 300072, People's Republic of China; Haihe Laboratory of Brain-computer Interaction and Human-machine Integration, Tianjin, 300392, People's Republic of China
| | - Yongzhi Huang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, 300072, People's Republic of China; Haihe Laboratory of Brain-computer Interaction and Human-machine Integration, Tianjin, 300392, People's Republic of China
| | - Jiabei Tang
- Haihe Laboratory of Brain-computer Interaction and Human-machine Integration, Tianjin, 300392, People's Republic of China; Tiankai Suishi (Tianjin) Intelligence Ltd., Tianjin, 300192, People's Republic of China
| | - Longlong Cheng
- China Electronics Cloud Brain (Tianjin) Technology Co., Ltd., Tianjin, 300392, People's Republic of China
| | - Minpeng Xu
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, 300072, People's Republic of China; Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, 300072, People's Republic of China; Haihe Laboratory of Brain-computer Interaction and Human-machine Integration, Tianjin, 300392, People's Republic of China.
| | - Dong Ming
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, 300072, People's Republic of China; Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, 300072, People's Republic of China; Haihe Laboratory of Brain-computer Interaction and Human-machine Integration, Tianjin, 300392, People's Republic of China
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29
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Akhmetzianova LU, Davletkulov TM, Sakhabutdinova AR, Chemeris AV, Gubaydullin IM, Garafutdinov RR. LAMPrimers iQ: New primer design software for loop-mediated isothermal amplification (LAMP). Anal Biochem 2024; 684:115376. [PMID: 37924966 DOI: 10.1016/j.ab.2023.115376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 10/31/2023] [Accepted: 11/01/2023] [Indexed: 11/06/2023]
Abstract
Nucleic acids amplification is a widely used technique utilized for different manipulations with DNA and RNA. Although, polymerase chain reaction (PCR) remains the most popular amplification method, isothermal approaches are gained more attention last decades. Among these, loop-mediated isothermal amplification (LAMP) became an excellent alternative to PCR. LAMP requires an increased number of primers and, therefore, is considered a highly specific amplification reaction compared to PCR. LAMP primers design is still a non-trivial task, and all niceties should be taken into account during their selection. Here, we report on a new program called LAMPrimers iQ destined for high-quality LAMP primers design. LAMPrimers iQ is based on an original algorithm considering rigorous criteria for primers selection. Unlike alternative programs, LAMPrimers iQ can process long DNA or RNA sequences, and completely avoid primers that can form homo- and heterodimers. The quality of the primers designed was checked using SARS-CoV-2 coronavirus RNA as a model target. It was shown that primers selected with LAMPrimers iQ provide higher specificity and reliable detection of viral RNA compared to those obtained by alternative programs. The program is available at https://github.com/Restily/LAMPrimers-iQ.
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Affiliation(s)
- Liana U Akhmetzianova
- Institute of Petrochemistry and Catalysis, Ufa Federal Research Center, Russian Academy of Sciences, 450075, prosp. Oktyabrya, 141, Ufa, Bashkortostan, Russian Federation; Ufa State Petroleum Technological University, 450064, st. Cosmonauts, 1, Ufa, Bashkortostan, Russian Federation.
| | - Timur M Davletkulov
- Ufa State Petroleum Technological University, 450064, st. Cosmonauts, 1, Ufa, Bashkortostan, Russian Federation.
| | - Assol R Sakhabutdinova
- Institute of Biochemistry and Genetics, Ufa Federal Research Center, Russian Academy of Sciences, 450054, prosp. Oktyabrya, 71, Ufa, Bashkortostan, Russian Federation.
| | - Alexey V Chemeris
- Institute of Biochemistry and Genetics, Ufa Federal Research Center, Russian Academy of Sciences, 450054, prosp. Oktyabrya, 71, Ufa, Bashkortostan, Russian Federation.
| | - Irek M Gubaydullin
- Institute of Petrochemistry and Catalysis, Ufa Federal Research Center, Russian Academy of Sciences, 450075, prosp. Oktyabrya, 141, Ufa, Bashkortostan, Russian Federation; Ufa State Petroleum Technological University, 450064, st. Cosmonauts, 1, Ufa, Bashkortostan, Russian Federation.
| | - Ravil R Garafutdinov
- Institute of Biochemistry and Genetics, Ufa Federal Research Center, Russian Academy of Sciences, 450054, prosp. Oktyabrya, 71, Ufa, Bashkortostan, Russian Federation.
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30
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Kumar P, Paul RK, Roy HS, Yeasin M, Ajit, Paul AK. Big Data Analysis in Computational Biology and Bioinformatics. Methods Mol Biol 2024; 2719:181-197. [PMID: 37803119 DOI: 10.1007/978-1-0716-3461-5_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/08/2023]
Abstract
Advancements in high-throughput technologies, genomics, transcriptomics, and metabolomics play an important role in obtaining biological information about living organisms. The field of computational biology and bioinformatics has experienced significant growth with the advent of high-throughput sequencing technologies and other high-throughput techniques. The resulting large amounts of data present both opportunities and challenges for data analysis. Big data analysis has become essential for extracting meaningful insights from the massive amount of data. In this chapter, we provide an overview of the current status of big data analysis in computational biology and bioinformatics. We discuss the various aspects of big data analysis, including data acquisition, storage, processing, and analysis. We also highlight some of the challenges and opportunities of big data analysis in this area of research. Despite the challenges, big data analysis presents significant opportunities like development of efficient and fast computing algorithms for advancing our understanding of biological processes, identifying novel biomarkers for breeding research and developments, predicting disease, and identifying potential drug targets for drug development programs.
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Affiliation(s)
- Prakash Kumar
- ICAR-Indian Agricultural Statistics Research Institute, Pusa, New Delhi, India
| | - Ranjit Kumar Paul
- ICAR-Indian Agricultural Statistics Research Institute, Pusa, New Delhi, India
| | - Himadri Shekhar Roy
- ICAR-Indian Agricultural Statistics Research Institute, Pusa, New Delhi, India
| | - Md Yeasin
- ICAR-Indian Agricultural Statistics Research Institute, Pusa, New Delhi, India
| | - Ajit
- ICAR-Indian Agricultural Statistics Research Institute, Pusa, New Delhi, India
| | - Amrit Kumar Paul
- ICAR-Indian Agricultural Statistics Research Institute, Pusa, New Delhi, India
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31
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Khound P, Pandya H, Patel R, Patel N, Darji SA, Trivedi P, Mehta V, Raulji A, Banerjee D. An Approach to Track and Analyze the Trend of Antimicrobial Resistance Using Python: A Pilot Study for Anand, Gujarat, India-May 2022-August 2023. Microb Drug Resist 2024; 30:1-20. [PMID: 38150701 DOI: 10.1089/mdr.2023.0057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2023] Open
Abstract
The present work deals with the analysis and monitoring of bacterial resistance in using Python for the state of Gujarat, India, where occurrences of drug-resistant bacteria are prevalent. This will provide an insight into the portfolio of drug-resistant bacteria reported, which can be used to track resistance behavior and to suggest a treatment regime for the particular bacteria. The present analysis has been done using Python on Jupyter Notebook as the integrated development environment and its data analysis libraries such as Pandas, Seaborn, and Matplotlib. The data have been loaded from excel file using Pandas and cleaned to transform features into required format. Seaborn and Matplotlib have been used to create data visualizations and represent the data inexplicable manner using graphs, plots, and tables. This program can be used to study disaster epidemiology, tracking, analyzing, and surveillance of antimicrobial resistance with a proper system integration approach.
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Affiliation(s)
| | - Himanshu Pandya
- Department of Microbiology, Pramukhswami Medical College, Bhaikaka University, Karamsad Medical College, Anand, Gujarat, India
| | - Rupal Patel
- Department of Microbiology, Pramukhswami Medical College, Bhaikaka University, Karamsad Medical College, Anand, Gujarat, India
| | - Naimika Patel
- Department of Microbiology, Pramukhswami Medical College, Bhaikaka University, Karamsad Medical College, Anand, Gujarat, India
| | - Siddhi A Darji
- School of Sciences, GSFC University, Vadodara, Gujarat, India
- Dr. Vikram Sarabhai Institute of Cell and Molecular Biology Department, The M S University of Baroda, Vadodara, Gujarat, India
| | - Purvi Trivedi
- School of Sciences, GSFC University, Vadodara, Gujarat, India
| | - Vandan Mehta
- School of Sciences, GSFC University, Vadodara, Gujarat, India
| | - Avani Raulji
- Department of Microbiology, Pramukhswami Medical College, Bhaikaka University, Karamsad Medical College, Anand, Gujarat, India
| | - Devjani Banerjee
- School of Sciences, GSFC University, Vadodara, Gujarat, India
- Dr. Vikram Sarabhai Institute of Cell and Molecular Biology Department, The M S University of Baroda, Vadodara, Gujarat, India
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Xu Q, Zhao X, Qin Y, Gianchandani YB. Control Software Design for a Multisensing Multicellular Microscale Gas Chromatography System. Micromachines (Basel) 2023; 15:95. [PMID: 38258214 PMCID: PMC10818470 DOI: 10.3390/mi15010095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 12/12/2023] [Accepted: 12/20/2023] [Indexed: 01/24/2024]
Abstract
Microscale gas chromatography (μGC) systems are miniaturized instruments that typically incorporate one or several microfabricated fluidic elements; such systems are generally well suited for the automated sampling and analysis of gas-phase chemicals. Advanced μGC systems may incorporate more than 15 elements and operate these elements in different coordinated sequences to execute complex operations. In particular, the control software must manage the sampling and analysis operations of the μGC system in a time-sensitive manner; while operating multiple control loops, it must also manage error conditions, data acquisition, and user interactions when necessary. To address these challenges, this work describes the investigation of multithreaded control software and its evaluation with a representative μGC system. The μGC system is based on a progressive cellular architecture that uses multiple μGC cells to efficiently broaden the range of chemical analytes, with each cell incorporating multiple detectors. Implemented in Python language version 3.7.3 and executed by an embedded single-board computer, the control software enables the concurrent control of heaters, pumps, and valves while also gathering data from thermistors, pressure sensors, capacitive detectors, and photoionization detectors. A graphical user interface (UI) that operates on a laptop provides visualization of control parameters in real time. In experimental evaluations, the control software provided successful operation and readout for all the components, including eight sets of thermistors and heaters that form temperature feedback loops, two sets of pressure sensors and tunable gas pumps that form pressure head feedback loops, six capacitive detectors, three photoionization detectors, six valves, and an additional fixed-flow gas pump. A typical run analyzing 18 chemicals is presented. Although the operating system does not guarantee real-time operation, the relative standard deviations of the control loop timings were <0.5%. The control software successfully supported >1000 μGC runs that analyzed various chemical mixtures.
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Affiliation(s)
- Qu Xu
- Center for Wireless Integrated MicroSensing and Systems (WIMS), University of Michigan, Ann Arbor, MI 48109, USA; (Q.X.); (X.Z.)
- Department of Integrative Systems + Design, University of Michigan, Ann Arbor, MI 48109, USA
| | - Xiangyu Zhao
- Center for Wireless Integrated MicroSensing and Systems (WIMS), University of Michigan, Ann Arbor, MI 48109, USA; (Q.X.); (X.Z.)
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, USA
| | - Yutao Qin
- Center for Wireless Integrated MicroSensing and Systems (WIMS), University of Michigan, Ann Arbor, MI 48109, USA; (Q.X.); (X.Z.)
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, USA
| | - Yogesh B. Gianchandani
- Center for Wireless Integrated MicroSensing and Systems (WIMS), University of Michigan, Ann Arbor, MI 48109, USA; (Q.X.); (X.Z.)
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, USA
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Maidannyk VA, Simonov Y, McCarthy NA, Ho QT. Water Effective Diffusion Coefficient in Dairy Powder Calculated by Digital Image Processing and through Machine Learning Algorithms of CLSM Micrographs. Foods 2023; 13:94. [PMID: 38201123 PMCID: PMC10778944 DOI: 10.3390/foods13010094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 12/22/2023] [Accepted: 12/25/2023] [Indexed: 01/12/2024] Open
Abstract
Rehydration of dairy powders is a complex and essential process. A relatively new quantitative mechanism for monitoring powders' rehydration process uses the effective diffusion coefficient. This research focused on modifying a previously used labor-intensive method that will be able to automatically measure the real-time water diffusion coefficient in dairy powders based on confocal microscopy techniques. Furthermore, morphological characteristics and local hydration of individual particles were identified using an imaging analysis procedure written in Matlab©-R2023b and image analysis through machine learning algorithms written in Python™-3.11. The first model includes segmentation into binary images and labeling particles during water diffusion. The second model includes the expansion of data set selection, neural network training and particle markup. For both models, the effective diffusion follows Fick's second law for spherical geometry. The effective diffusion coefficient on each particle was computed from the dye intensity during the rehydration process. The results showed that effective diffusion coefficients for water increased linearly with increasing powder particle size and are in agreement with previously used methods. In summary, the models provide two independent machine measurements of effective diffusion coefficient based on the same set of micrographs and may be useful in a wide variety of high-protein powders.
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Affiliation(s)
- Valentyn A. Maidannyk
- Food Chemistry & Technology Department, Teagasc Food Research Centre, Moorepark, Fermoy, P61 C996 County Cork, Ireland; (N.A.M.); (Q.T.H.)
| | - Yuriy Simonov
- Independent Researcher, 6511 Nijmegen, The Netherlands;
| | - Noel A. McCarthy
- Food Chemistry & Technology Department, Teagasc Food Research Centre, Moorepark, Fermoy, P61 C996 County Cork, Ireland; (N.A.M.); (Q.T.H.)
| | - Quang Tri Ho
- Food Chemistry & Technology Department, Teagasc Food Research Centre, Moorepark, Fermoy, P61 C996 County Cork, Ireland; (N.A.M.); (Q.T.H.)
- Institute of Marine Research, 5003–5268 Bergen, Norway
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Banach M. Structural Outlier Detection and Zernike-Canterakis Moments for Molecular Surface Meshes-Fast Implementation in Python. Molecules 2023; 29:52. [PMID: 38202635 PMCID: PMC10779519 DOI: 10.3390/molecules29010052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 12/06/2023] [Accepted: 12/12/2023] [Indexed: 01/12/2024] Open
Abstract
Object retrieval systems measure the degree of similarity of the shape of 3D models. They search for the elements of the 3D model databases that resemble the query model. In structural bioinformatics, the query model is a protein tertiary/quaternary structure and the objective is to find similarly shaped molecules in the Protein Data Bank. With the ever-growing size of the PDB, a direct atomic coordinate comparison with all its members is impractical. To overcome this problem, the shape of the molecules can be encoded by fixed-length feature vectors. The distance of a protein to the entire PDB can be measured in this low-dimensional domain in linear time. The state-of-the-art approaches utilize Zernike-Canterakis moments for the shape encoding and supply the retrieval process with geometric data of the input structures. The BioZernike descriptors are a standard utility of the PDB since 2020. However, when trying to calculate the ZC moments locally, the issue of the deficiency of libraries readily available for use in custom programs (i.e., without relying on external binaries) is encountered, in particular programs written in Python. Here, a fast and well-documented Python implementation of the Pozo-Koehl algorithm is presented. In contrast to the more popular algorithm by Novotni and Klein, which is based on the voxelized volume, the PK algorithm produces ZC moments directly from the triangular surface meshes of 3D models. In particular, it can accept the molecular surfaces of proteins as its input. In the presented PK-Zernike library, owing to Numba's just-in-time compilation, a mesh with 50,000 facets is processed by a single thread in a second at the moment order 20. Since this is the first time the PK algorithm is used in structural bioinformatics, it is employed in a novel, simple, but efficient protein structure retrieval pipeline. The elimination of the outlying chain fragments via a fast PCA-based subroutine improves the discrimination ability, allowing for this pipeline to achieve an 0.961 area under the ROC curve in the BioZernike validation suite (0.997 for the assemblies). The correlation between the results of the proposed approach and of the 3D Surfer program attains values up to 0.99.
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Affiliation(s)
- Mateusz Banach
- Department of Bioinformatics and Telemedicine, Faculty of Medicine, Jagiellonian University Medical College, Medyczna 7, 30-688 Kraków, Poland
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Han S, Kwak IY. Mastering data visualization with Python: practical tips for researchers. J Minim Invasive Surg 2023; 26:167-175. [PMID: 38098348 PMCID: PMC10728683 DOI: 10.7602/jmis.2023.26.4.167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 10/17/2023] [Accepted: 11/10/2023] [Indexed: 12/22/2023]
Abstract
Big data have revolutionized the way data are processed and used across all fields. In the past, research was primarily conducted with a focus on hypothesis confirmation using sample data. However, in the era of big data, this has shifted to gaining insights from the collected data. Visualizing vast amounts of data to derive insights is crucial. For instance, leveraging big data for visualization can help identify and predict characteristics and patterns related to various infectious diseases. When data are presented in a visual format, patterns within the data become clear, making it easier to comprehend and provide deeper insights. This study aimed to comprehensively discuss data visualization and the various techniques used in the process. It also sought to enable researchers to directly use Python programs for data visualization. By providing practical visualization exercises on GitHub, this study aimed to facilitate their application in research endeavors.
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Affiliation(s)
- Soyul Han
- Department of Applied Statistics, Chung-Ang University, Seoul, Korea
| | - Il-Youp Kwak
- Department of Applied Statistics, Chung-Ang University, Seoul, Korea
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Shi J, Bendig D, Vollmar HC, Rasche P. Mapping the Bibliometrics Landscape of AI in Medicine: Methodological Study. J Med Internet Res 2023; 25:e45815. [PMID: 38064255 PMCID: PMC10746970 DOI: 10.2196/45815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 08/16/2023] [Accepted: 09/30/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI), conceived in the 1950s, has permeated numerous industries, intensifying in tandem with advancements in computing power. Despite the widespread adoption of AI, its integration into medicine trails other sectors. However, medical AI research has experienced substantial growth, attracting considerable attention from researchers and practitioners. OBJECTIVE In the absence of an existing framework, this study aims to outline the current landscape of medical AI research and provide insights into its future developments by examining all AI-related studies within PubMed over the past 2 decades. We also propose potential data acquisition and analysis methods, developed using Python (version 3.11) and to be executed in Spyder IDE (version 5.4.3), for future analogous research. METHODS Our dual-pronged approach involved (1) retrieving publication metadata related to AI from PubMed (spanning 2000-2022) via Python, including titles, abstracts, authors, journals, country, and publishing years, followed by keyword frequency analysis and (2) classifying relevant topics using latent Dirichlet allocation, an unsupervised machine learning approach, and defining the research scope of AI in medicine. In the absence of a universal medical AI taxonomy, we used an AI dictionary based on the European Commission Joint Research Centre AI Watch report, which emphasizes 8 domains: reasoning, planning, learning, perception, communication, integration and interaction, service, and AI ethics and philosophy. RESULTS From 2000 to 2022, a comprehensive analysis of 307,701 AI-related publications from PubMed highlighted a 36-fold increase. The United States emerged as a clear frontrunner, producing 68,502 of these articles. Despite its substantial contribution in terms of volume, China lagged in terms of citation impact. Diving into specific AI domains, as the Joint Research Centre AI Watch report categorized, the learning domain emerged dominant. Our classification analysis meticulously traced the nuanced research trajectories across each domain, revealing the multifaceted and evolving nature of AI's application in the realm of medicine. CONCLUSIONS The research topics have evolved as the volume of AI studies increases annually. Machine learning remains central to medical AI research, with deep learning expected to maintain its fundamental role. Empowered by predictive algorithms, pattern recognition, and imaging analysis capabilities, the future of AI research in medicine is anticipated to concentrate on medical diagnosis, robotic intervention, and disease management. Our topic modeling outcomes provide a clear insight into the focus of AI research in medicine over the past decades and lay the groundwork for predicting future directions. The domains that have attracted considerable research attention, primarily the learning domain, will continue to shape the trajectory of AI in medicine. Given the observed growing interest, the domain of AI ethics and philosophy also stands out as a prospective area of increased focus.
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Affiliation(s)
- Jin Shi
- Institute for Entrepreneurship, University of Münster, Münster, Germany
| | - David Bendig
- Institute for Entrepreneurship, University of Münster, Münster, Germany
| | | | - Peter Rasche
- Department of Healthcare, University of Applied Science - Hochschule Niederrhein, Krefeld, Germany
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Kunnakkattu IR, Choudhary P, Pravda L, Nadzirin N, Smart OS, Yuan Q, Anyango S, Nair S, Varadi M, Velankar S. PDBe CCDUtils: an RDKit-based toolkit for handling and analysing small molecules in the Protein Data Bank. J Cheminform 2023; 15:117. [PMID: 38042830 PMCID: PMC10693035 DOI: 10.1186/s13321-023-00786-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 11/17/2023] [Indexed: 12/04/2023] Open
Abstract
While the Protein Data Bank (PDB) contains a wealth of structural information on ligands bound to macromolecules, their analysis can be challenging due to the large amount and diversity of data. Here, we present PDBe CCDUtils, a versatile toolkit for processing and analysing small molecules from the PDB in PDBx/mmCIF format. PDBe CCDUtils provides streamlined access to all the metadata for small molecules in the PDB and offers a set of convenient methods to compute various properties using RDKit, such as 2D depictions, 3D conformers, physicochemical properties, scaffolds, common fragments, and cross-references to small molecule databases using UniChem. The toolkit also provides methods for identifying all the covalently attached chemical components in a macromolecular structure and calculating similarity among small molecules. By providing a broad range of functionality, PDBe CCDUtils caters to the needs of researchers in cheminformatics, structural biology, bioinformatics and computational chemistry.
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Affiliation(s)
- Ibrahim Roshan Kunnakkattu
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Preeti Choudhary
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Lukas Pravda
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Nurul Nadzirin
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Oliver S Smart
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Qi Yuan
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Stephen Anyango
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Sreenath Nair
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Mihaly Varadi
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Sameer Velankar
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.
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Kvalsund AS, Winkler D. Development of an Arduino-based, open-control interface for hardware in the loop applications. HardwareX 2023; 16:e00488. [PMID: 38020544 PMCID: PMC10679478 DOI: 10.1016/j.ohx.2023.e00488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 10/11/2023] [Accepted: 11/09/2023] [Indexed: 12/01/2023]
Abstract
This article presents a flexible control interface based on low-cost hardware solutions for electric drives which classically come either with a proprietary hardware solution or a high-cost interface solution. The interface presented can be used to connect a standard PC with an electric drive to enable testing simulation and control applications. The control interface is developed based on the open-source Python scripting language and Arduino's open-source and accessible hardware. The new interface communicates with the test stand through its I/O terminals via developed electronic amplifiers and creates a solid base for further development towards more extensive hardware in the loop simulations.
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Veríssimo GC, Pantaleão SQ, Fernandes PDO, Gertrudes JC, Kronenberger T, Honorio KM, Maltarollo VG. MASSA Algorithm: an automated rational sampling of training and test subsets for QSAR modeling. J Comput Aided Mol Des 2023; 37:735-754. [PMID: 37804393 DOI: 10.1007/s10822-023-00536-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 09/14/2023] [Indexed: 10/09/2023]
Abstract
QSAR models capable of predicting biological, toxicity, and pharmacokinetic properties were widely used to search lead bioactive molecules in chemical databases. The dataset's preparation to build these models has a strong influence on the quality of the generated models, and sampling requires that the original dataset be divided into training (for model training) and test (for statistical evaluation) sets. This sampling can be done randomly or rationally, but the rational division is superior. In this paper, we present MASSA, a Python tool that can be used to automatically sample datasets by exploring the biological, physicochemical, and structural spaces of molecules using PCA, HCA, and K-modes. The proposed algorithm is very useful when the variables used for QSAR are not available or to construct multiple QSAR models with the same training and test sets, producing models with lower variability and better values for validation metrics. These results were obtained even when the descriptors used in the QSAR/QSPR were different from those used in the separation of training and test sets, indicating that this tool can be used to build models for more than one QSAR/QSPR technique. Finally, this tool also generates useful graphical representations that can provide insights into the data.
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Affiliation(s)
- Gabriel Corrêa Veríssimo
- Department of Pharmaceutical Products, Faculty of Pharmacy, Federal University of Minas Gerais, Belo Horizonte, MG, 31270-901, Brazil
| | | | - Philipe de Olveira Fernandes
- Department of Pharmaceutical Products, Faculty of Pharmacy, Federal University of Minas Gerais, Belo Horizonte, MG, 31270-901, Brazil
| | - Jadson Castro Gertrudes
- Department of Computing, Institute of Exact and Biological Sciences, Federal University of Ouro Preto, Ouro Preto, MG, 35400-000, Brazil
| | - Thales Kronenberger
- Department of Pharmaceutical and Medicinal Chemistry, University of Tübingen, Tübingen, BW, 72076, Germany
| | - Kathia Maria Honorio
- Federal University of ABC, Santo André, SP, 09210-170, Brazil
- School of Arts, Sciences and Humanities, University of São Paulo, São Paulo, SP, 03828-000, Brazil
| | - Vinícius Gonçalves Maltarollo
- Department of Pharmaceutical Products, Faculty of Pharmacy, Federal University of Minas Gerais, Belo Horizonte, MG, 31270-901, Brazil.
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Belhaj OE, Belhaj S, Bellahsaouia M, Sadeq Y, Hadouachi M, Laazouzi K, Arectout A, Boukhal H, El mahjoub C, Khoukhi TE. GUI development for SSDL to calibrate photon measuring equipment. MethodsX 2023; 11:102408. [PMID: 37854710 PMCID: PMC10579526 DOI: 10.1016/j.mex.2023.102408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 09/27/2023] [Indexed: 10/20/2023] Open
Abstract
Current legislation mandates the inspection and calibration of operational survey radiation monitoring instruments used in nuclear medicine, radiotherapy departments, and other fields utilizing ionizing radiation sources. To comply with national and international radiation protection standards, Morocco's National Secondary Standard Dosimetry Laboratory provides reliable calibration results with high accuracy and covers various measurement ranges using attenuators provided by the automated Gamma G10 irradiator or validated beam qualities produced by the X-ray irradiator type X80-320 kV. This study aims to develop a digital graphical user interface using Python programming language, designed for calibrating radiation protection measuring instruments . The interface is intended to facilitate all operations and calculations related to determining calibration factors and measurement uncertainties in accordance with the ISO 4037 standard, ensuring minimal processing time and minimizing potential error sources . The interface enables calculations to be recorded, as well as the establishment and electronic archiving of the calibration certificate and the report in PDF format using the Hypertext Preprocessor FPDF library (PHP FPDF). With the development of this interface, multiple instruments can be processed per day with high accuracy, streamlining the calibration process and improving efficiency.•The importance of compliance with international standards to ensure the quality and reliability of measurements in radiation protection was examined.•Description of X-ray and Gamma-ray irradiators designed for the calibration of radiation protection measuring instruments within the Secondary Dosimetry Calibration Laboratory (SSDL) which is a member of the WHO/IAEA network within the National Center for Radiation Protection of Morocco•Graphical User Interface using python for the calibration of photon measurement instruments for radiation protection purposes was developped.
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Affiliation(s)
| | | | | | | | - Maryam Hadouachi
- ERSN, Faculty of Sciences, Abdelmalek Essaadi University, Tetouan, Morocco
| | - Khaoula Laazouzi
- ERSN, Faculty of Sciences, Abdelmalek Essaadi University, Tetouan, Morocco
| | - Assia Arectout
- ERSN, Faculty of Sciences, Abdelmalek Essaadi University, Tetouan, Morocco
| | - Hamid Boukhal
- ERSN, Faculty of Sciences, Abdelmalek Essaadi University, Tetouan, Morocco
| | | | - Tahar El Khoukhi
- National Center for Energy, Science and Nuclear Technology (CNETSEN), Morocco
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Bernard M, Poli M, Karadayi J, Dupoux E. Shennong: A Python toolbox for audio speech features extraction. Behav Res Methods 2023; 55:4489-4501. [PMID: 36750521 DOI: 10.3758/s13428-022-02029-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/17/2022] [Indexed: 02/09/2023]
Abstract
We introduce Shennong, a Python toolbox and command-line utility for audio speech features extraction. It implements a wide range of well-established state-of-the-art algorithms: spectro-temporal filters such as Mel-Frequency Cepstral Filterbank or Predictive Linear Filters, pre-trained neural networks, pitch estimators, speaker normalization methods, and post-processing algorithms. Shennong is an open source, reliable and extensible framework built on top of the popular Kaldi speech processing library. The Python implementation makes it easy to use by non-technical users and integrates with third-party speech modeling and machine learning tools from the Python ecosystem. This paper describes the Shennong software architecture, its core components, and implemented algorithms. Then, three applications illustrate its use. We first present a benchmark of speech features extraction algorithms available in Shennong on a phone discrimination task. We then analyze the performances of a speaker normalization model as a function of the speech duration used for training. We finally compare pitch estimation algorithms on speech under various noise conditions.
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Affiliation(s)
- Mathieu Bernard
- Cognitive Machine Learning, PSL Research University, CNRS, EHESS, ENS, Inria, Paris, France.
- EconomiX (UMR 7235), Université Paris Nanterre, CNRS, Nanterre, France.
| | - Maxime Poli
- Cognitive Machine Learning, PSL Research University, CNRS, EHESS, ENS, Inria, Paris, France
| | - Julien Karadayi
- Cognitive Machine Learning, PSL Research University, CNRS, EHESS, ENS, Inria, Paris, France
| | - Emmanuel Dupoux
- Cognitive Machine Learning, PSL Research University, CNRS, EHESS, ENS, Inria, Paris, France
- Meta AI Research, Paris, France
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Ramanauskas K, Igić B. kakapo: easy extraction and annotation of genes from raw RNA-seq reads. PeerJ 2023; 11:e16456. [PMID: 38034874 PMCID: PMC10688300 DOI: 10.7717/peerj.16456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 10/23/2023] [Indexed: 12/02/2023] Open
Abstract
kakapo (kākāpō) is a Python-based pipeline that allows users to extract and assemble one or more specified genes or gene families. It flexibly uses original RNA-seq read or GenBank SRA accession inputs without performing global assembly of entire transcriptomes or metatranscriptomes. The pipeline identifies open reading frames in the assembled gene transcripts and annotates them. It optionally filters raw reads for ribosomal, plastid, and mitochondrial reads, or reads belonging to non-target organisms (e.g., viral, bacterial, human). kakapo can be employed for targeted assembly, to extract arbitrary loci, such as those commonly used for phylogenetic inference in systematics or candidate genes and gene families in phylogenomic and metagenomic studies. We provide example applications and discuss how its use can offset the declining value of GenBank's single-gene databases and help assemble datasets for a variety of phylogenetic analyses.
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Affiliation(s)
- Karolis Ramanauskas
- Department of Biological Sciences, University of Illinois at Chicago, Chicago, IL, United States of America
| | - Boris Igić
- Department of Biological Sciences, University of Illinois at Chicago, Chicago, IL, United States of America
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Gottlieb D, Asadipour B, Kostina P, Ung TPL, Stringari C. FLUTE: A Python GUI for interactive phasor analysis of FLIM data. Biol Imaging 2023; 3:e21. [PMID: 38487690 PMCID: PMC10936343 DOI: 10.1017/s2633903x23000211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 09/16/2023] [Accepted: 10/25/2023] [Indexed: 03/17/2024]
Abstract
Fluorescence lifetime imaging microscopy (FLIM) is a powerful technique used to probe the local environment of fluorophores. The fit-free phasor approach to FLIM data is increasingly being used due to its ease of interpretation. To date, no open-source graphical user interface (GUI) for phasor analysis of FLIM data is available in Python, thus limiting the widespread use of phasor analysis in biomedical research. Here, we present Fluorescence Lifetime Ultimate Explorer (FLUTE), a Python GUI that is designed to fill this gap. FLUTE simplifies and automates many aspects of the analysis of FLIM data acquired in the time domain, such as calibrating the FLIM data, performing interactive exploration of the phasor plot, displaying phasor plots and FLIM images with different lifetime contrasts simultaneously, and calculating the distance from known molecular species. After applying desired filters and thresholds, the final edited datasets can be exported for further user-specific analysis. FLUTE has been tested using several FLIM datasets including autofluorescence of zebrafish embryos and in vitro cells. In summary, our user-friendly GUI extends the advantages of phasor plotting by making the data visualization and analysis easy and interactive, allows for analysis of large FLIM datasets, and accelerates FLIM analysis for non-specialized labs.
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Affiliation(s)
- Dale Gottlieb
- Laboratory for Optics and Biosciences, École Polytechnique, CNRS, INSERM, Institut Polytechnique de Paris, 91128 Palaiseau, France
| | - Bahar Asadipour
- Laboratory for Optics and Biosciences, École Polytechnique, CNRS, INSERM, Institut Polytechnique de Paris, 91128 Palaiseau, France
| | - Polina Kostina
- Laboratory for Optics and Biosciences, École Polytechnique, CNRS, INSERM, Institut Polytechnique de Paris, 91128 Palaiseau, France
| | - Thi Phuong Lien Ung
- Laboratory for Optics and Biosciences, École Polytechnique, CNRS, INSERM, Institut Polytechnique de Paris, 91128 Palaiseau, France
| | - Chiara Stringari
- Laboratory for Optics and Biosciences, École Polytechnique, CNRS, INSERM, Institut Polytechnique de Paris, 91128 Palaiseau, France
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Guo Z, Guo F, Zhang Y, He J, Li G, Yang Y, Zhang X. A python system for regional landslide susceptibility assessment by integrating machine learning models and its application. Heliyon 2023; 9:e21542. [PMID: 38027891 PMCID: PMC10660045 DOI: 10.1016/j.heliyon.2023.e21542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 10/23/2023] [Accepted: 10/23/2023] [Indexed: 12/01/2023] Open
Abstract
Landslide susceptibility assessment is considered the first step in landslide risk assessment, but current studies mostly rely on GIS platforms or other software for data preprocessing. The modeling process is relatively complicated and multi-models cannot be integrated. With regard to this issue, this study develops a Python system for automatic assessment of regional landslide susceptibility. The Python system implements landslide susceptibility assessment through three modules: geographic data processing, machine learning modeling and result evaluation analysis. For geographic data processing, ten landslide influencing factors can be used to construct an evaluation factor dataset and reclassify the thematic maps based on the frequency ratio method. Four built-in machine learning models (logistic regression (LR), multi-layer perceptron (MLP), support vector machine (SVM) and extreme gradient boosting (XGBoost)) are integrated into the system to complete susceptibility modeling and calculation. Additionally, receiver operating characteristic (ROC) curves can be automatically generated to evaluate the accuracy. The system was then applied into Lantian County in Shaanxi Province as a demonstration example. The results show that the areas under the ROC curve (AUC) of the four models are 0.838 (LR)、0.882 (SVM)、0.809 (MLP) and 0.812 (XGBoost), respectively, indicating that the SVM model was the most suitable model for landslide susceptibility assessment in Lantian County in the Loess Plateau of China. The system has now been made open source on Github, which can effectively improve the efficiency of regional landslide susceptibility assessment, especially provide tools for data processing and modeling for non-professionals.
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Affiliation(s)
- Zizheng Guo
- Hubei Key Laboratory of Disaster Prevention and Mitigation (China Three Gorges University), Yichang, 443002, China
- Key Laboratory of Geological Hazards on Three Gorges Reservoir Area (China Three Gorges University), Ministry of Education, Yichang, 443002, China
- School of Civil and Transportation Engineering, Hebei University of Technology, Tianjin, 300401, China
| | - Fei Guo
- Hubei Key Laboratory of Disaster Prevention and Mitigation (China Three Gorges University), Yichang, 443002, China
- Key Laboratory of Geological Hazards on Three Gorges Reservoir Area (China Three Gorges University), Ministry of Education, Yichang, 443002, China
| | - Yu Zhang
- Zhejiang Geology and Mineral Technology Co. LTD, Hangzhou, 310007, China
- Wenzhou Engineering Survey Institute Co., LTD, Wenzhou, 325006, China
| | - Jun He
- School of Civil and Transportation Engineering, Hebei University of Technology, Tianjin, 300401, China
| | - Guangming Li
- Tianjin Municipal Engineering Design & Research Institute (TMEDI), Tianjin, 300392, China
| | - Yufei Yang
- School of Civil and Transportation Engineering, Hebei University of Technology, Tianjin, 300401, China
| | - Xiaobo Zhang
- Beijing Glory PKPM Technology Co.,Ltd., Beijing, 100013, China
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Laky DJ, Casas-Orozco D, Abdi M, Feng X, Wood E, Reklaitis GV, Nagy ZK. Using PharmaPy with Jupyter Notebook to teach digital design in pharmaceutical manufacturing. Comput Appl Eng Educ 2023; 31:1662-1677. [PMID: 38314247 PMCID: PMC10838379 DOI: 10.1002/cae.22660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 06/03/2023] [Indexed: 02/06/2024]
Abstract
The use of digital tools in pharmaceutical manufacturing has gained traction over the past two decades. Whether supporting regulatory filings or attempting to modernize manufacturing processes to adopt new and quickly evolving Industry 4.0 standards, engineers entering the workforce must exhibit proficiency in modeling, simulation, optimization, data processing, and other digital analysis techniques. In this work, a course that addresses digital tools in pharmaceutical manufacturing for chemical engineers was adjusted to utilize a new tool, PharmaPy, instead of traditional chemical engineering simulation tools. Jupyter Notebook was utilized as an instructional and interactive environment to teach students to use PharmaPy, a new, open-source pharmaceutical manufacturing process simulator. Students were then surveyed to see if PharmaPy was able to meet the learning objectives of the course. During the semester, PharmaPy's model library was used to simulate both individual unit operations as well as multiunit pharmaceutical processes. Through the initial survey results, students indicated that: (i) through Jupyter Notebook, learning Python and PharmaPy was approachable from varied coding experience backgrounds and (ii) PharmaPy strengthened their understanding of pharmaceutical manufacturing through active pharmaceutical ingredient process design and development.
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Affiliation(s)
- Daniel J. Laky
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana, USA
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Daniel Casas-Orozco
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana, USA
| | - Mesfin Abdi
- Office of Pharmaceutical Quality, Center for Drug Evaluation and Research, Food & Drug Administration, Silver Spring, Maryland, USA
| | - Xin Feng
- Office of Pharmaceutical Quality, Center for Drug Evaluation and Research, Food & Drug Administration, Silver Spring, Maryland, USA
| | - Erin Wood
- Office of Pharmaceutical Quality, Center for Drug Evaluation and Research, Food & Drug Administration, Silver Spring, Maryland, USA
| | - Gintaras V. Reklaitis
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana, USA
| | - Zoltan K. Nagy
- Davidson School of Chemical Engineering, Purdue University, West Lafayette, Indiana, USA
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Osuala R, Skorupko G, Lazrak N, Garrucho L, García E, Joshi S, Jouide S, Rutherford M, Prior F, Kushibar K, Díaz O, Lekadir K. medigan: a Python library of pretrained generative models for medical image synthesis. J Med Imaging (Bellingham) 2023; 10:061403. [PMID: 36814939 PMCID: PMC9940031 DOI: 10.1117/1.jmi.10.6.061403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 01/23/2023] [Indexed: 02/22/2023] Open
Abstract
Purpose Deep learning has shown great promise as the backbone of clinical decision support systems. Synthetic data generated by generative models can enhance the performance and capabilities of data-hungry deep learning models. However, there is (1) limited availability of (synthetic) datasets and (2) generative models are complex to train, which hinders their adoption in research and clinical applications. To reduce this entry barrier, we explore generative model sharing to allow more researchers to access, generate, and benefit from synthetic data. Approach We propose medigan, a one-stop shop for pretrained generative models implemented as an open-source framework-agnostic Python library. After gathering end-user requirements, design decisions based on usability, technical feasibility, and scalability are formulated. Subsequently, we implement medigan based on modular components for generative model (i) execution, (ii) visualization, (iii) search & ranking, and (iv) contribution. We integrate pretrained models with applications across modalities such as mammography, endoscopy, x-ray, and MRI. Results The scalability and design of the library are demonstrated by its growing number of integrated and readily-usable pretrained generative models, which include 21 models utilizing nine different generative adversarial network architectures trained on 11 different datasets. We further analyze three medigan applications, which include (a) enabling community-wide sharing of restricted data, (b) investigating generative model evaluation metrics, and (c) improving clinical downstream tasks. In (b), we extract Fréchet inception distances (FID) demonstrating FID variability based on image normalization and radiology-specific feature extractors. Conclusion medigan allows researchers and developers to create, increase, and domain-adapt their training data in just a few lines of code. Capable of enriching and accelerating the development of clinical machine learning models, we show medigan's viability as platform for generative model sharing. Our multimodel synthetic data experiments uncover standards for assessing and reporting metrics, such as FID, in image synthesis studies.
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Affiliation(s)
- Richard Osuala
- Universitat de Barcelona, Barcelona Artificial Intelligence in Medicine Lab (BCN-AIM), Facultat de Matemàtiques i Informàtica, Barcelona, Spain
| | - Grzegorz Skorupko
- Universitat de Barcelona, Barcelona Artificial Intelligence in Medicine Lab (BCN-AIM), Facultat de Matemàtiques i Informàtica, Barcelona, Spain
| | - Noussair Lazrak
- Universitat de Barcelona, Barcelona Artificial Intelligence in Medicine Lab (BCN-AIM), Facultat de Matemàtiques i Informàtica, Barcelona, Spain
| | - Lidia Garrucho
- Universitat de Barcelona, Barcelona Artificial Intelligence in Medicine Lab (BCN-AIM), Facultat de Matemàtiques i Informàtica, Barcelona, Spain
| | - Eloy García
- Universitat de Barcelona, Facultat de Matemàtiques i Informàtica, Barcelona, Spain
| | - Smriti Joshi
- Universitat de Barcelona, Barcelona Artificial Intelligence in Medicine Lab (BCN-AIM), Facultat de Matemàtiques i Informàtica, Barcelona, Spain
| | - Socayna Jouide
- Universitat de Barcelona, Barcelona Artificial Intelligence in Medicine Lab (BCN-AIM), Facultat de Matemàtiques i Informàtica, Barcelona, Spain
| | - Michael Rutherford
- University of Arkansas for Medical Sciences, Department of Biomedical Informatics, Little Rock, Arkansas, United States
| | - Fred Prior
- University of Arkansas for Medical Sciences, Department of Biomedical Informatics, Little Rock, Arkansas, United States
| | - Kaisar Kushibar
- Universitat de Barcelona, Barcelona Artificial Intelligence in Medicine Lab (BCN-AIM), Facultat de Matemàtiques i Informàtica, Barcelona, Spain
| | - Oliver Díaz
- Universitat de Barcelona, Barcelona Artificial Intelligence in Medicine Lab (BCN-AIM), Facultat de Matemàtiques i Informàtica, Barcelona, Spain
| | - Karim Lekadir
- Universitat de Barcelona, Barcelona Artificial Intelligence in Medicine Lab (BCN-AIM), Facultat de Matemàtiques i Informàtica, Barcelona, Spain
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Shaukat Z, Zafar W, Ahmad W, Haq IU, Husnain G, Al-Adhaileh MH, Ghadi YY, Algarni A. Revolutionizing Diabetes Diagnosis: Machine Learning Techniques Unleashed. Healthcare (Basel) 2023; 11:2864. [PMID: 37958014 PMCID: PMC10648466 DOI: 10.3390/healthcare11212864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 10/24/2023] [Accepted: 10/27/2023] [Indexed: 11/15/2023] Open
Abstract
The intricate and multifaceted nature of diabetes disrupts the body's crucial glucose processing mechanism, which serves as a fundamental energy source for the cells. This research aims to predict the occurrence of diabetes in individuals by harnessing the power of machine learning algorithms, utilizing the PIMA diabetes dataset. The selected algorithms employed in this study encompass Decision Tree, K-Nearest Neighbor, Random Forest, Logistic Regression, and Support Vector Machine. To execute the experiments, two software tools, namely Waikato Environment for Knowledge Analysis (WEKA) version 3.8.1 and Python version 3.10, were utilized. To evaluate the performance of the algorithms, several metrics were employed, including true positive rate, false positive rate, precision, recall, F-measure, Matthew's correlation coefficient, receiver operating characteristic area, and precision-recall curves area. Furthermore, various errors such as Mean Absolute Error, Root Mean Squared Error, Relative Absolute Error, and Root Relative Squared Error were examined to assess the accuracy of the models. Upon conducting the experiments, it was observed that Logistic Regression outperformed the other techniques, exhibiting the highest precision of 81 percent using Python and 80.43 percent using WEKA. These findings shed light on the efficacy of machine learning in predicting diabetes and highlight the potential of Logistic Regression as a valuable tool in this domain.
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Affiliation(s)
- Zain Shaukat
- Department of Computer Science, Iqra National University Peshawar, Peshawar 25100, Pakistan
| | - Wisal Zafar
- Department of Computer Science, Iqra National University Peshawar, Peshawar 25100, Pakistan
| | - Waqas Ahmad
- Department of Computer Science, Iqra National University Peshawar, Peshawar 25100, Pakistan
| | - Ihtisham Ul Haq
- Department of Mechatronics Engineering, UET Peshawar, Peshawar 25000, Pakistan
| | - Ghassan Husnain
- Department of Computer Science, Iqra National University Peshawar, Peshawar 25100, Pakistan
| | | | - Yazeed Yasin Ghadi
- Department of Computer Science, Al Ain University, Abu Dhabi P.O. Box 112612, United Arab Emirates;
| | - Abdulmohsen Algarni
- Department of Computer Science, King Khalid University, Abha 61421, Saudi Arabia
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Mate-Kole EM, Margot D, Dewji SA. Mathematical solutions in internal dose assessment: A comparison of Python-based differential equation solvers in biokinetic modeling. J Radiol Prot 2023; 43:041507. [PMID: 37848023 PMCID: PMC10613827 DOI: 10.1088/1361-6498/ad0409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Revised: 09/14/2023] [Accepted: 10/17/2023] [Indexed: 10/19/2023]
Abstract
In biokinetic modeling systems employed for radiation protection, biological retention and excretion have been modeled as a series of discretized compartments representing the organs and tissues of the human body. Fractional retention and excretion in these organ and tissue systems have been mathematically governed by a series of coupled first-order ordinary differential equations (ODEs). The coupled ODE systems comprising the biokinetic models are usually stiff due to the severe difference between rapid and slow transfers between compartments. In this study, the capabilities of solving a complex coupled system of ODEs for biokinetic modeling were evaluated by comparing different Python programming language solvers and solving methods with the motivation of establishing a framework that enables multi-level analysis. The stability of the solvers was analyzed to select the best performers for solving the biokinetic problems. A Python-based linear algebraic method was also explored to examine how the numerical methods deviated from an analytical or semi-analytical method. Results demonstrated that customized implicit methods resulted in an enhanced stable solution for the inhaled60Co (Type M) and131I (Type F) exposure scenarios for the inhalation pathway of the International Commission on Radiological Protection (ICRP) Publication 130 Human Respiratory Tract Model (HRTM). The customized implementation of the Python-based implicit solvers resulted in approximately consistent solutions with the Python-based matrix exponential method (expm). The differences generally observed between the implicit solvers andexpmare attributable to numerical precision and the order of numerical approximation of the numerical solvers. This study provides the first analysis of a list of Python ODE solvers and methods by comparing their usage for solving biokinetic models using the ICRP Publication 130 HRTM and provides a framework for the selection of the most appropriate ODE solvers and methods in Python language to implement for modeling the distribution of internal radioactivity.
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Affiliation(s)
- Emmanuel Matey Mate-Kole
- Nuclear and Radiological Engineering and Medical Physics Programs, Georgia Institute of Technology, Atlanta, GA, United States of America
| | - Dmitri Margot
- Nuclear and Radiological Engineering and Medical Physics Programs, Georgia Institute of Technology, Atlanta, GA, United States of America
| | - Shaheen Azim Dewji
- Nuclear and Radiological Engineering and Medical Physics Programs, Georgia Institute of Technology, Atlanta, GA, United States of America
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Monks T, Harper A. Improving the usability of open health service delivery simulation models using Python and web apps. NIHR Open Res 2023; 3:48. [PMID: 37881450 PMCID: PMC10593330 DOI: 10.3310/nihropenres.13467.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 08/15/2023] [Indexed: 10/27/2023]
Abstract
One aim of Open Science is to increase the accessibility of research. Within health services research that uses discrete-event simulation, Free and Open Source Software (FOSS), such as Python, offers a way for research teams to share their models with other researchers and NHS decision makers. Although the code for healthcare discrete-event simulation models can be shared alongside publications, it may require specialist skills to use and run. This is a disincentive to researchers adopting Free and Open Source Software and open science practices. Building on work from other health data science disciplines, we propose that web apps offer a user-friendly interface for healthcare models that increase the accessibility of research to the NHS, and researchers from other disciplines. We focus on models coded in Python deployed as streamlit web apps. To increase uptake of these methods, we provide an approach to structuring discrete-event simulation model code in Python so that models are web app ready. The method is general across discrete-event simulation Python packages, and we include code for both simpy and ciw implementations of a simple urgent care call centre model. We then provide a step-by-step tutorial for linking the model to a streamlit web app interface, to enable other health data science researchers to reproduce and implement our method.
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Affiliation(s)
- Thomas Monks
- University of Exeter Medical School, University of Exeter, Exeter, England, UK
- NIHR Applied Research Collaboration South West Peninsula, University of Exeter, Exeter, England, UK
| | - Alison Harper
- University of Exeter Medical School, University of Exeter, Exeter, England, UK
- NIHR Applied Research Collaboration South West Peninsula, University of Exeter, Exeter, England, UK
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50
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Tumusiime AG, Eyobu OS, Mugume I, Oyana TJ. A weather features dataset for prediction of short-term rainfall quantities in Uganda. Data Brief 2023; 50:109613. [PMID: 37808539 PMCID: PMC10551829 DOI: 10.1016/j.dib.2023.109613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 09/11/2023] [Accepted: 09/19/2023] [Indexed: 10/10/2023] Open
Abstract
Weather data is of great importance to the development of weather prediction models. However, the availability and quality of this data remains a significant challenge for most researchers around the world. In Uganda, obtaining observational weather data is very challenging due to the sparse distribution of weather stations and inconsistent data records. This has created critical gaps in data availability to run and develop efficient weather prediction models. To bridge this gap, we obtained country-specific time series hourly observational weather data. The data period is from 2020 to 2022 of 11 weather stations distributed in the four regions of Uganda. The data was accessed from the Ogimet data repository using the "climate" R-package. The automated procedures in the R-programming language environment allowed us to download user-defined data at a time resolution from an hourly to an annual basis. However, the raw data acquired cannot be used to learn rainfall patterns because it includes duplicates and non-uniform data. Therefore, this article presents a prepared and cleaned dataset that can be used for the prediction of short-term rainfall quantities in Uganda.
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Affiliation(s)
| | - Odongo Steven Eyobu
- College of Computing and IS, Makerere University, P.O Box, 7062, Kampala, Uganda
| | - Isaac Mugume
- College of Agricultural and Environmental Sciences, Makerere University, P.O Box, 7062, Kampala, Uganda
| | - Tonny J. Oyana
- College of Computing and IS, Makerere University, P.O Box, 7062, Kampala, Uganda
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