1
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Moujaes F, Ji JL, Rahmati M, Burt JB, Schleifer C, Adkinson BD, Savic A, Santamauro N, Tamayo Z, Diehl C, Kolobaric A, Flynn M, Rieser N, Fonteneau C, Camarro T, Xu J, Cho Y, Repovs G, Fineberg SK, Morgan PT, Seifritz E, Vollenweider FX, Krystal JH, Murray JD, Preller KH, Anticevic A. Ketamine induces multiple individually distinct whole-brain functional connectivity signatures. eLife 2024; 13:e84173. [PMID: 38629811 PMCID: PMC11023699 DOI: 10.7554/elife.84173] [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: 10/12/2022] [Accepted: 02/15/2024] [Indexed: 04/19/2024] Open
Abstract
Background Ketamine has emerged as one of the most promising therapies for treatment-resistant depression. However, inter-individual variability in response to ketamine is still not well understood and it is unclear how ketamine's molecular mechanisms connect to its neural and behavioral effects. Methods We conducted a single-blind placebo-controlled study, with participants blinded to their treatment condition. 40 healthy participants received acute ketamine (initial bolus 0.23 mg/kg, continuous infusion 0.58 mg/kg/hr). We quantified resting-state functional connectivity via data-driven global brain connectivity and related it to individual ketamine-induced symptom variation and cortical gene expression targets. Results We found that: (i) both the neural and behavioral effects of acute ketamine are multi-dimensional, reflecting robust inter-individual variability; (ii) ketamine's data-driven principal neural gradient effect matched somatostatin (SST) and parvalbumin (PVALB) cortical gene expression patterns in humans, while the mean effect did not; and (iii) behavioral data-driven individual symptom variation mapped onto distinct neural gradients of ketamine, which were resolvable at the single-subject level. Conclusions These results highlight the importance of considering individual behavioral and neural variation in response to ketamine. They also have implications for the development of individually precise pharmacological biomarkers for treatment selection in psychiatry. Funding This study was supported by NIH grants DP5OD012109-01 (A.A.), 1U01MH121766 (A.A.), R01MH112746 (J.D.M.), 5R01MH112189 (A.A.), 5R01MH108590 (A.A.), NIAAA grant 2P50AA012870-11 (A.A.); NSF NeuroNex grant 2015276 (J.D.M.); Brain and Behavior Research Foundation Young Investigator Award (A.A.); SFARI Pilot Award (J.D.M., A.A.); Heffter Research Institute (Grant No. 1-190420) (FXV, KHP); Swiss Neuromatrix Foundation (Grant No. 2016-0111) (FXV, KHP); Swiss National Science Foundation under the framework of Neuron Cofund (Grant No. 01EW1908) (KHP); Usona Institute (2015 - 2056) (FXV). Clinical trial number NCT03842800.
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Affiliation(s)
- Flora Moujaes
- Department of Psychiatry, Yale University School of MedicineNew HavenUnited States
- Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital for Psychiatry ZurichZurichSwitzerland
| | - Jie Lisa Ji
- Department of Psychiatry, Yale University School of MedicineNew HavenUnited States
| | - Masih Rahmati
- Department of Psychiatry, Yale University School of MedicineNew HavenUnited States
| | - Joshua B Burt
- Department of Physics, Yale UniversityBostonUnited States
| | - Charles Schleifer
- David Geffen School of Medicine, University of California, Los AngelesLos AngelesUnited States
| | - Brendan D Adkinson
- Interdepartmental Neuroscience Program, Yale UniversityNew HavenUnited States
| | | | - Nicole Santamauro
- Department of Psychiatry, Yale University School of MedicineNew HavenUnited States
| | - Zailyn Tamayo
- Department of Psychiatry, Yale University School of MedicineNew HavenUnited States
| | - Caroline Diehl
- Department of Psychology, University of California, Los AngelesLos AngelesUnited States
| | | | - Morgan Flynn
- Department of Psychiatry, Vanderbilt University Medical CenterNashvilleUnited States
| | - Nathalie Rieser
- Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital for Psychiatry ZurichZurichSwitzerland
| | - Clara Fonteneau
- Department of Psychiatry, Yale University School of MedicineNew HavenUnited States
| | - Terry Camarro
- Magnetic Resonance Research Center, Yale University School of MedicineNew HavenUnited States
| | - Junqian Xu
- Department of Radiology and Psychiatry, Baylor College of MedicineHoustonUnited States
| | - Youngsun Cho
- Department of Psychiatry, Yale University School of MedicineNew HavenUnited States
- Child Study Center, Yale University School of MedicineNew HavenUnited States
| | - Grega Repovs
- Department of Psychology, University of LjubljanaLjubljanaSlovenia
| | - Sarah K Fineberg
- Department of Psychiatry, Yale University School of MedicineNew HavenUnited States
| | - Peter T Morgan
- Department of Psychiatry, Yale University School of MedicineNew HavenUnited States
- Department of Psychiatry, Bridgeport HospitalBridgeportUnited States
| | - Erich Seifritz
- Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital for Psychiatry ZurichZurichSwitzerland
| | - Franz X Vollenweider
- Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital for Psychiatry ZurichZurichSwitzerland
| | - John H Krystal
- Department of Psychiatry, Yale University School of MedicineNew HavenUnited States
| | - John D Murray
- Department of Psychiatry, Yale University School of MedicineNew HavenUnited States
- Department of Physics, Yale UniversityBostonUnited States
- Department of Psychology, Yale UniversityNew HavenUnited States
| | - Katrin H Preller
- Department of Psychiatry, Yale University School of MedicineNew HavenUnited States
- Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital for Psychiatry ZurichZurichSwitzerland
| | - Alan Anticevic
- Department of Psychiatry, Yale University School of MedicineNew HavenUnited States
- Interdepartmental Neuroscience Program, Yale UniversityNew HavenUnited States
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2
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Rahmani V, Nawaz S, Pennicard D, Graafsma H. Robust image descriptor for machine learning based data reduction in serial crystallography. J Appl Crystallogr 2024; 57:413-430. [PMID: 38596725 PMCID: PMC11001400 DOI: 10.1107/s160057672400147x] [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: 09/29/2023] [Accepted: 02/13/2024] [Indexed: 04/11/2024] Open
Abstract
Serial crystallography experiments at synchrotron and X-ray free-electron laser (XFEL) sources are producing crystallographic data sets of ever-increasing volume. While these experiments have large data sets and high-frame-rate detectors (around 3520 frames per second), only a small percentage of the data are useful for downstream analysis. Thus, an efficient and real-time data classification pipeline is essential to differentiate reliably between useful and non-useful images, typically known as 'hit' and 'miss', respectively, and keep only hit images on disk for further analysis such as peak finding and indexing. While feature-point extraction is a key component of modern approaches to image classification, existing approaches require computationally expensive patch preprocessing to handle perspective distortion. This paper proposes a pipeline to categorize the data, consisting of a real-time feature extraction algorithm called modified and parallelized FAST (MP-FAST), an image descriptor and a machine learning classifier. For parallelizing the primary operations of the proposed pipeline, central processing units, graphics processing units and field-programmable gate arrays are implemented and their performances compared. Finally, MP-FAST-based image classification is evaluated using a multi-layer perceptron on various data sets, including both synthetic and experimental data. This approach demonstrates superior performance compared with other feature extractors and classifiers.
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Affiliation(s)
- Vahid Rahmani
- Deutsches Elektronen-Synchrotron (DESY), Notkestraße 85, Hamburg, 22607, Germany
| | - Shah Nawaz
- Deutsches Elektronen-Synchrotron (DESY), Notkestraße 85, Hamburg, 22607, Germany
| | - David Pennicard
- Deutsches Elektronen-Synchrotron (DESY), Notkestraße 85, Hamburg, 22607, Germany
| | - Heinz Graafsma
- Deutsches Elektronen-Synchrotron (DESY), Notkestraße 85, Hamburg, 22607, Germany
- Mid-Sweden University, Sundsvall, Sweden
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3
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Galchenkova M, Tolstikova A, Klopprogge B, Sprenger J, Oberthuer D, Brehm W, White TA, Barty A, Chapman HN, Yefanov O. Data reduction in protein serial crystallography. IUCrJ 2024; 11:190-201. [PMID: 38327201 PMCID: PMC10916297 DOI: 10.1107/s205225252400054x] [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: 08/07/2023] [Accepted: 01/15/2024] [Indexed: 02/09/2024]
Abstract
Serial crystallography (SX) has become an established technique for protein structure determination, especially when dealing with small or radiation-sensitive crystals and investigating fast or irreversible protein dynamics. The advent of newly developed multi-megapixel X-ray area detectors, capable of capturing over 1000 images per second, has brought about substantial benefits. However, this advancement also entails a notable increase in the volume of collected data. Today, up to 2 PB of data per experiment could be easily obtained under efficient operating conditions. The combined costs associated with storing data from multiple experiments provide a compelling incentive to develop strategies that effectively reduce the amount of data stored on disk while maintaining the quality of scientific outcomes. Lossless data-compression methods are designed to preserve the information content of the data but often struggle to achieve a high compression ratio when applied to experimental data that contain noise. Conversely, lossy compression methods offer the potential to greatly reduce the data volume. Nonetheless, it is vital to thoroughly assess the impact of data quality and scientific outcomes when employing lossy compression, as it inherently involves discarding information. The evaluation of lossy compression effects on data requires proper data quality metrics. In our research, we assess various approaches for both lossless and lossy compression techniques applied to SX data, and equally importantly, we describe metrics suitable for evaluating SX data quality.
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Affiliation(s)
- Marina Galchenkova
- Center for Free-Electron Laser Science CFEL, Deutsche Elektronen-Synchrotron DESY, Notkestr. 85, 22607 Hamburg, Germany
| | | | - Bjarne Klopprogge
- Center for Free-Electron Laser Science CFEL, Deutsche Elektronen-Synchrotron DESY, Notkestr. 85, 22607 Hamburg, Germany
| | - Janina Sprenger
- Center for Free-Electron Laser Science CFEL, Deutsche Elektronen-Synchrotron DESY, Notkestr. 85, 22607 Hamburg, Germany
| | - Dominik Oberthuer
- Center for Free-Electron Laser Science CFEL, Deutsche Elektronen-Synchrotron DESY, Notkestr. 85, 22607 Hamburg, Germany
| | - Wolfgang Brehm
- Center for Free-Electron Laser Science CFEL, Deutsche Elektronen-Synchrotron DESY, Notkestr. 85, 22607 Hamburg, Germany
| | - Thomas A. White
- Deutsches Elektronen-Synchrotron DESY, Notkestr. 85, 22607 Hamburg, Germany
| | - Anton Barty
- Deutsches Elektronen-Synchrotron DESY, Notkestr. 85, 22607 Hamburg, Germany
| | - Henry N. Chapman
- Center for Free-Electron Laser Science CFEL, Deutsche Elektronen-Synchrotron DESY, Notkestr. 85, 22607 Hamburg, Germany
- Department of Physics, Universität Hamburg, Luruper Chaussee 149, 22761 Hamburg, Germany
- Universität Hamburg, Luruper Chaussee 149, 22761 Hamburg, Germany
| | - Oleksandr Yefanov
- Center for Free-Electron Laser Science CFEL, Deutsche Elektronen-Synchrotron DESY, Notkestr. 85, 22607 Hamburg, Germany
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4
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Łącka M, Łubczonek J. Methodology for Creating a Digital Bathymetric Model Using Neural Networks for Combined Hydroacoustic and Photogrammetric Data in Shallow Water Areas. Sensors (Basel) 2023; 24:175. [PMID: 38203036 PMCID: PMC10781209 DOI: 10.3390/s24010175] [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] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Revised: 12/13/2023] [Accepted: 12/15/2023] [Indexed: 01/12/2024]
Abstract
This study uses a neural network to propose a methodology for creating digital bathymetric models for shallow water areas that are partially covered by a mix of hydroacoustic and photogrammetric data. A key challenge of this approach is the preparation of the training dataset from such data. Focusing on cases in which the training dataset covers only part of the measured depths, the approach employs generalized linear regression for data optimization followed by multilayer perceptron neural networks for bathymetric model creation. The research assessed the impact of data reduction, outlier elimination, and regression surface-based filtering on neural network learning. The average values of the root mean square (RMS) error were successively obtained for the studied nearshore, middle, and deep water areas, which were 0.12 m, 0.03 m, and 0.06 m, respectively; moreover, the values of the mean absolute error (MAE) were 0.11 m, 0.02 m, and 0.04 m, respectively. Following detailed quantitative and qualitative error analyses, the results indicate variable accuracy across different study areas. Nonetheless, the methodology demonstrated effectiveness in depth calculations for water bodies, although it faces challenges with respect to accuracy, especially in preserving nearshore values in shallow areas.
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Affiliation(s)
- Małgorzata Łącka
- Maritime University of Szczecin, Waly Chrobrego 1–2, 70-500 Szczecin, Poland;
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5
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Peixoto MC, Castro NF, Crispim Romão M, Oliveira MGJ, Ochoa I. Fitting a collider in a quantum computer: tackling the challenges of quantum machine learning for big datasets. Front Artif Intell 2023; 6:1268852. [PMID: 38162833 PMCID: PMC10755015 DOI: 10.3389/frai.2023.1268852] [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: 07/28/2023] [Accepted: 11/20/2023] [Indexed: 01/03/2024] Open
Abstract
Current quantum systems have significant limitations affecting the processing of large datasets with high dimensionality, typical of high energy physics. In the present paper, feature and data prototype selection techniques were studied to tackle this challenge. A grid search was performed and quantum machine learning models were trained and benchmarked against classical shallow machine learning methods, trained both in the reduced and the complete datasets. The performance of the quantum algorithms was found to be comparable to the classical ones, even when using large datasets. Sequential Backward Selection and Principal Component Analysis techniques were used for feature's selection and while the former can produce the better quantum machine learning models in specific cases, it is more unstable. Additionally, we show that such variability in the results is caused by the use of discrete variables, highlighting the suitability of Principal Component analysis transformed data for quantum machine learning applications in the high energy physics context.
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Affiliation(s)
- Miguel Caçador Peixoto
- LIP—Laboratório de Instrumentação e Física Experimental de Partículas, Escola de Ciências, Universidade do Minho, Braga, Portugal
| | - Nuno Filipe Castro
- LIP—Laboratório de Instrumentação e Física Experimental de Partículas, Escola de Ciências, Universidade do Minho, Braga, Portugal
- Departamento de Física, Escola de Ciências, Universidade do Minho, Braga, Portugal
| | - Miguel Crispim Romão
- LIP—Laboratório de Instrumentação e Física Experimental de Partículas, Escola de Ciências, Universidade do Minho, Braga, Portugal
- Department of Physics and Astronomy, University of Southampton, Southampton, United Kingdom
| | - Maria Gabriela Jordão Oliveira
- LIP—Laboratório de Instrumentação e Física Experimental de Partículas, Escola de Ciências, Universidade do Minho, Braga, Portugal
| | - Inês Ochoa
- LIP—Laboratório de Instrumentação e Física Experimental de Partículas, Lisbon, Portugal
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6
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Kim S, Beane Freeman LE, Albert PS. A latent functional approach for modeling the effects of multidimensional exposures on disease risk. Stat Med 2023; 42:4776-4793. [PMID: 37635131 DOI: 10.1002/sim.9888] [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: 04/13/2022] [Revised: 07/28/2023] [Accepted: 08/14/2023] [Indexed: 08/29/2023]
Abstract
Understanding the relationships between exposure and disease incidence is an important problem in environmental epidemiology. Typically, a large number of these exposures are measured, and it is found either that a few exposures transmit risk or that each exposure transmits a small amount of risk, but, taken together, these may pose a substantial disease risk. Further, these exposure effects can be nonlinear. We develop a latent functional approach, which assumes that the individual effect of each exposure can be characterized as one of a series of unobserved functions, where the number of latent functions is less than or equal to the number of exposures. We propose Bayesian methodology to fit models with a large number of exposures and show that existing Bayesian group LASSO approaches are a special case of the proposed model. An efficient Markov chain Monte Carlo sampling algorithm is developed for carrying out Bayesian inference. The deviance information criterion is used to choose an appropriate number of nonlinear latent functions. We demonstrate the good properties of the approach using simulation studies. Further, we show that complex exposure relationships can be represented with only a few latent functional curves. The proposed methodology is illustrated with an analysis of the effect of cumulative pesticide exposure on cancer risk in a large cohort of farmers.
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Affiliation(s)
- Sungduk Kim
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA
| | - Laura E Beane Freeman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA
| | - Paul S Albert
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA
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7
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Khouchen M, Klar PB, Chintakindi H, Suresh A, Palatinus L. Optimal estimated standard uncertainties of reflection intensities for kinematical refinement from 3D electron diffraction data. Acta Crystallogr A Found Adv 2023; 79:427-439. [PMID: 37578439 PMCID: PMC10483590 DOI: 10.1107/s2053273323005053] [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: 02/09/2023] [Accepted: 06/07/2023] [Indexed: 08/15/2023] Open
Abstract
Estimating the error in the merged reflection intensities requires a full understanding of all the possible sources of error arising from the measurements. Most diffraction-spot integration methods focus mainly on errors arising from counting statistics for the estimation of uncertainties associated with the reflection intensities. This treatment may be incomplete and partly inadequate. In an attempt to fully understand and identify all the contributions to these errors, three methods are examined for the correction of estimated errors of reflection intensities in electron diffraction data. For a direct comparison, the three methods are applied to a set of organic and inorganic test cases. It is demonstrated that applying the corrections of a specific model that include terms dependent on the original uncertainty and the largest intensity of the symmetry-related reflections improves the overall structure quality of the given data set and improves the final Rall factor. This error model is implemented in the data reduction software PETS2.
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Affiliation(s)
- Malak Khouchen
- Institute of Physics of the Czech Academy of Sciences, Prague, Czech Republic
| | | | | | - Ashwin Suresh
- Institute of Physics of the Czech Academy of Sciences, Prague, Czech Republic
| | - Lukas Palatinus
- Institute of Physics of the Czech Academy of Sciences, Prague, Czech Republic
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8
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Mujta W, Wlodarczyk-Sielicka M, Stateczny A. Testing the Effect of Bathymetric Data Reduction on the Shape of the Digital Bottom Model. Sensors (Basel) 2023; 23:5445. [PMID: 37420612 DOI: 10.3390/s23125445] [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: 03/16/2023] [Revised: 06/06/2023] [Accepted: 06/07/2023] [Indexed: 07/09/2023]
Abstract
Depth data and the digital bottom model created from it are very important in the inland and coastal water zones studies and research. The paper undertakes the subject of bathymetric data processing using reduction methods and examines the impact of data reduction according to the resulting representations of the bottom surface in the form of numerical bottom models. Data reduction is an approach that is meant to reduce the size of the input dataset to make it easier and more efficient for analysis, transmission, storage and similar. For the purposes of this article, test datasets were created by discretizing a selected polynomial function. The real dataset, which was used to verify the analyzes, was acquired using an interferometric echosounder mounted on a HydroDron-1 autonomous survey vessel. The data were collected in the ribbon of Lake Klodno, Zawory. Data reduction was conducted in two commercial programs. Three equal reduction parameters were adopted for each algorithm. The research part of the paper presents the results of the conducted analyzes of the reduced bathymetric datasets based on the visual comparison of numerical bottom models, isobaths, and statistical parameters. The article contains tabular results with statistics, as well as the spatial visualization of the studied fragments of numerical bottom models and isobaths. This research is being used in the course of work on an innovative project that aims to develop a prototype of a multi-dimensional and multi-temporal coastal zone monitoring system using autonomous, unmanned floating platforms at a single survey pass.
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Affiliation(s)
| | | | - Andrzej Stateczny
- Faculty of Civil and Environmental Engineering, Gdansk University of Technology, Gabriela Narutowicza 11-12, 80-233 Gdansk, Poland
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9
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Burel G, Radoi E, Gautier R, Le Jeune D. Wideband Spectrum Sensing Using Modulated Wideband Converter and Data Reduction Invariant Algorithms. Sensors (Basel) 2023; 23:2263. [PMID: 36850865 PMCID: PMC9960934 DOI: 10.3390/s23042263] [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] [Received: 12/08/2022] [Revised: 02/13/2023] [Accepted: 02/13/2023] [Indexed: 06/18/2023]
Abstract
Wideband spectrum sensing is a challenging problem in the framework of cognitive radio and spectrum surveillance, mainly because of the high sampling rates required by standard approaches. In this paper, a compressed sensing approach was considered to solve this problem, relying on a sub-Nyquist or Xsampling scheme, known as a modulated wideband converter. First, the data reduction at its output is performed in order to enable a highly effective processing scheme for spectrum reconstruction. The impact of this data transformation on the behavior of the most popular sparse reconstruction algorithms is then analyzed. A new mathematical approach is proposed to demonstrate that greedy reconstruction algorithms, such as Orthogonal Matching Pursuit, are invariant with respect to the proposed data reduction. Relying on the same formalism, a data reduction invariant version of the LASSO (least absolute shrinkage and selection operator) reconstruction algorithm was also introduced. It is finally demonstrated that the proposed algorithm provides good reconstruction results in a wideband spectrum sensing scenario, using both synthetic and measured data.
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Affiliation(s)
- Gilles Burel
- Univ Brest, CNRS, Lab-STICC, CS 93837, 6 avenue Le Gorgeu, CEDEX 3, 29238 Brest, France
| | - Emanuel Radoi
- Univ Brest, CNRS, Lab-STICC, CS 93837, 6 avenue Le Gorgeu, CEDEX 3, 29238 Brest, France
| | - Roland Gautier
- Univ Brest, CNRS, Lab-STICC, CS 93837, 6 avenue Le Gorgeu, CEDEX 3, 29238 Brest, France
| | - Denis Le Jeune
- ENSTA Bretagne, CNRS, Lab-STICC, 2 rue François Verny, CEDEX 9, 29806 Brest, France
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Rahmani V, Nawaz S, Pennicard D, Setty SPR, Graafsma H. Data reduction for X-ray serial crystallography using machine learning. J Appl Crystallogr 2023; 56:200-213. [PMID: 36777143 PMCID: PMC9901916 DOI: 10.1107/s1600576722011748] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [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: 05/12/2022] [Accepted: 12/07/2022] [Indexed: 01/25/2023] Open
Abstract
Serial crystallography experiments produce massive amounts of experimental data. Yet in spite of these large-scale data sets, only a small percentage of the data are useful for downstream analysis. Thus, it is essential to differentiate reliably between acceptable data (hits) and unacceptable data (misses). To this end, a novel pipeline is proposed to categorize the data, which extracts features from the images, summarizes these features with the 'bag of visual words' method and then classifies the images using machine learning. In addition, a novel study of various feature extractors and machine learning classifiers is presented, with the aim of finding the best feature extractor and machine learning classifier for serial crystallography data. The study reveals that the oriented FAST and rotated BRIEF (ORB) feature extractor with a multilayer perceptron classifier gives the best results. Finally, the ORB feature extractor with multilayer perceptron is evaluated on various data sets including both synthetic and experimental data, demonstrating superior performance compared with other feature extractors and classifiers.
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Affiliation(s)
- Vahid Rahmani
- Deutsches Elektronen-Synchrotron DESY, Notkestraße 85, 22607 Hamburg, Germany
| | - Shah Nawaz
- Deutsches Elektronen-Synchrotron DESY, Notkestraße 85, 22607 Hamburg, Germany
| | - David Pennicard
- Deutsches Elektronen-Synchrotron DESY, Notkestraße 85, 22607 Hamburg, Germany
| | | | - Heinz Graafsma
- Deutsches Elektronen-Synchrotron DESY, Notkestraße 85, 22607 Hamburg, Germany.,Mid-Sweden University, Sundsvall, Sweden
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11
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Harlow GS, Pfaff S, Abbondanza G, Hegedüs Z, Lienert U, Lundgren E. HAT: a high-energy surface X-ray diffraction analysis toolkit. J Appl Crystallogr 2023; 56:312-321. [PMID: 36777142 PMCID: PMC9901923 DOI: 10.1107/s1600576723000092] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [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: 06/29/2022] [Accepted: 01/03/2023] [Indexed: 02/04/2023] Open
Abstract
This work introduces the high-energy surface X-ray diffraction analysis toolkit (HAT), an open-source cross-platform software package written in Python to allow the extraction and processing of high-energy surface X-ray diffraction (HESXRD) data sets. Thousands of large-area detector images are collected in a single HESXRD scan, corresponding to billions of pixels and hence reciprocal space positions. HAT is an optimized reciprocal space binner that implements a graphical user interface to allow the easy and interactive exploration of HESXRD data sets. Regions of reciprocal space can be selected with movable and resizable masks in multiple views and are projected onto different axes to allow the creation of reciprocal space maps and the extraction of crystal truncation rods. Current and future versions of HAT can be downloaded and used free of charge.
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Affiliation(s)
- Gary S. Harlow
- Department of Chemistry and Biochemistry and the Oregon Center for Electrochemistry, University of Oregon, Eugene, OR 97403, USA,Division of Synchrotron Radiation Research, Lund University, Lund SE-22100, Sweden,Correspondence e-mail:
| | - Sebastian Pfaff
- Division of Combustion Physics, Lund University, Lund SE-22100, Sweden
| | - Giuseppe Abbondanza
- Division of Synchrotron Radiation Research, Lund University, Lund SE-22100, Sweden
| | - Zoltan Hegedüs
- Deutsches Elektronen-Synchrotron (DESY), Notkestrasse 85, 22607 Hamburg, Germany
| | - Ulrich Lienert
- Deutsches Elektronen-Synchrotron (DESY), Notkestrasse 85, 22607 Hamburg, Germany
| | - Edvin Lundgren
- Division of Synchrotron Radiation Research, Lund University, Lund SE-22100, Sweden
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12
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Støckler LJ, Krause L, Svane B, Tolborg K, Richter B, Takahashi S, Fujita T, Kasai H, Sugahara M, Inoue I, Nishibori E, Iversen BB. Towards pump-probe single-crystal XFEL refinements for small-unit-cell systems. IUCrJ 2023; 10:103-117. [PMID: 36598506 PMCID: PMC9812214 DOI: 10.1107/s2052252522011782] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 12/09/2022] [Indexed: 06/17/2023]
Abstract
Serial femtosecond crystallography for small-unit-cell systems has so far seen very limited application despite obvious scientific possibilities. This is because reliable data reduction has not been available for these challenging systems. In particular, important intensity corrections such as the partiality correction critically rely on accurate determination of the crystal orientation, which is complicated by the low number of diffraction spots for small-unit-cell crystals. A data reduction pipeline capable of fully automated handling of all steps of data reduction from spot harvesting to merged structure factors has been developed. The pipeline utilizes sparse indexing based on known unit-cell parameters, seed-skewness integration, intensity corrections including an overlap-based combined Ewald sphere width and partiality correction, and a dynamically adjusted post-refinement routine. Using the pipeline, data measured on the compound K4[Pt2(P2O5H2)4]·2H2O have been successfully reduced and used to solve the structure to an R1 factor of ∼9.1%. It is expected that the pipeline will open up the field of small-unit-cell serial femtosecond crystallography experiments and allow investigations into, for example, excited states and reaction intermediate chemistry.
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Affiliation(s)
- Lise Joost Støckler
- Center for Integrated Materials Research, Department of Chemistry, Aarhus University, Langelandsgade 140, Aarhus 8000, Denmark
| | - Lennard Krause
- Center for Integrated Materials Research, Department of Chemistry, Aarhus University, Langelandsgade 140, Aarhus 8000, Denmark
| | - Bjarke Svane
- Center for Integrated Materials Research, Department of Chemistry, Aarhus University, Langelandsgade 140, Aarhus 8000, Denmark
| | - Kasper Tolborg
- Center for Integrated Materials Research, Department of Chemistry, Aarhus University, Langelandsgade 140, Aarhus 8000, Denmark
- Department of Materials, Imperial College London, Exhibition Road, London SW7 2AZ, United Kingdom
| | - Bo Richter
- Center for Integrated Materials Research, Department of Chemistry, Aarhus University, Langelandsgade 140, Aarhus 8000, Denmark
| | - Seiya Takahashi
- Department of Physics, Faculty of Pure and Applied Sciences and TREMS, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8571, Japan
| | - Tomoki Fujita
- Department of Physics, Faculty of Pure and Applied Sciences and TREMS, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8571, Japan
| | - Hidetaka Kasai
- Department of Physics, Faculty of Pure and Applied Sciences and TREMS, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8571, Japan
| | - Michihiro Sugahara
- RIKEN SPring-8 Center, 1-1-1 Kouto, Sayo-cho, Sayo-gun, Hyogo 679-5148, Japan
| | - Ichiro Inoue
- RIKEN SPring-8 Center, 1-1-1 Kouto, Sayo-cho, Sayo-gun, Hyogo 679-5148, Japan
| | - Eiji Nishibori
- Department of Physics, Faculty of Pure and Applied Sciences and TREMS, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8571, Japan
| | - Bo Brummerstedt Iversen
- Center for Integrated Materials Research, Department of Chemistry, Aarhus University, Langelandsgade 140, Aarhus 8000, Denmark
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13
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Jensen AB, Christensen TEK, Weninger C, Birkedal H. Very large-scale diffraction investigations enabled by a matrix-multiplication facilitated radial and azimuthal integration algorithm: MatFRAIA. J Synchrotron Radiat 2022; 29:1420-1428. [PMID: 36345750 PMCID: PMC9641557 DOI: 10.1107/s1600577522008232] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [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: 11/03/2021] [Accepted: 08/17/2022] [Indexed: 05/22/2023]
Abstract
As synchrotron facilities continue to generate increasingly brilliant X-rays and detector speeds increase, swift data reduction from the collected area detector images to more workable 1D diffractograms becomes of increasing importance. This work reports an integration algorithm that can integrate diffractograms in real time on modern laptops and can reach 10 kHz integration speeds on modern workstations using an efficient pixel-splitting and parallelization scheme. This algorithm is limited not by the computation of the integration itself but is rather bottlenecked by the speed of the data transfer to the processor, the data decompression and/or the saving of results. The algorithm and its implementation is described while the performance is investigated on 2D scanning X-ray diffraction/fluorescence data collected at the interface between an implant and forming bone.
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Affiliation(s)
| | - Thorbjørn Erik Køppen Christensen
- Department of Chemistry and iNANO, Aarhus University, Gustav Wieds Vej 14, 8000 Aarhus, Denmark
- Sino-Danish College (SDC), University of Chinese Academy of Sciences, People’s Republic of China
| | | | - Henrik Birkedal
- Department of Chemistry and iNANO, Aarhus University, Gustav Wieds Vej 14, 8000 Aarhus, Denmark
- Correspondence e-mail:
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14
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Yu F, Shao L, Liu S, Xu W, Xiao D, Liu H, Shum PP. Data Reduction in Phase-Sensitive OTDR with Ultra-Low Sampling Resolution and Undersampling Techniques. Sensors (Basel) 2022; 22:6386. [PMID: 36080845 PMCID: PMC9459960 DOI: 10.3390/s22176386] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Revised: 08/20/2022] [Accepted: 08/22/2022] [Indexed: 06/15/2023]
Abstract
Data storage is a problem that cannot be ignored in the long-term monitoring of a phase-sensitive optical time-domain reflectometry (Φ-OTDR) system. In this paper, we proposed a data-reduction approach for heterodyne Φ-OTDR using an ultra-low sampling resolution and undersampling techniques. The operation principles were demonstrated and experiments with different sensing configurations were carried out to verify the proposed method. The results showed that the vibration signal could be accurately reconstructed from the undersampled 1-bit data. A space saving ratio of 98.75% was achieved by converting 128 MB of data (corresponding to 268.44 ms of sensing time) to 1.6 MB. The proposed method led to a potentially new data-reduction approach for heterodyne Φ-OTDR, which also provided economical guidance for the selection of the data-acquisition device.
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Affiliation(s)
- Feihong Yu
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Liyang Shao
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen 518055, China
- Peng Cheng Laboratory, Shenzhen 518005, China
| | - Shuaiqi Liu
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen 518055, China
- State Key Laboratory of Analog and Mixed-Signal VLSI, University of Macau, Macau 999078, China
| | - Weijie Xu
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Dongrui Xiao
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Huanhuan Liu
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Perry Ping Shum
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen 518055, China
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15
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Vaher K, Galdi P, Blesa Cabez M, Sullivan G, Stoye DQ, Quigley AJ, Thrippleton MJ, Bogaert D, Bastin ME, Cox SR, Boardman JP. General factors of white matter microstructure from DTI and NODDI in the developing brain. Neuroimage 2022; 254:119169. [PMID: 35367650 DOI: 10.1016/j.neuroimage.2022.119169] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [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: 11/29/2021] [Revised: 03/15/2022] [Accepted: 03/30/2022] [Indexed: 10/18/2022] Open
Abstract
Preterm birth is closely associated with diffuse white matter dysmaturation inferred from diffusion MRI and neurocognitive impairment in childhood. Diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) are distinct dMRI modalities, yet metrics derived from these two methods share variance across tracts. This raises the hypothesis that dimensionality reduction approaches may provide efficient whole-brain estimates of white matter microstructure that capture (dys)maturational processes. To investigate the optimal model for accurate classification of generalised white matter dysmaturation in preterm infants we assessed variation in DTI and NODDI metrics across 16 major white matter tracts using principal component analysis and structural equation modelling, in 79 term and 141 preterm infants at term equivalent age. We used logistic regression models to evaluate performances of single-metric and multimodality general factor frameworks for efficient classification of preterm infants based on variation in white matter microstructure. Single-metric general factors from DTI and NODDI capture substantial shared variance (41.8-72.5%) across 16 white matter tracts, and two multimodality factors captured 93.9% of variance shared between DTI and NODDI metrics themselves. General factors associate with preterm birth and a single model that includes all seven DTI and NODDI metrics provides the most accurate prediction of microstructural variations associated with preterm birth. This suggests that despite global covariance of dMRI metrics in neonates, each metric represents information about specific (and additive) aspects of the underlying microstructure that differ in preterm compared to term subjects.
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Affiliation(s)
- Kadi Vaher
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh EH16 4TJ, United Kingdom
| | - Paola Galdi
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh EH16 4TJ, United Kingdom
| | - Manuel Blesa Cabez
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh EH16 4TJ, United Kingdom
| | - Gemma Sullivan
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh EH16 4TJ, United Kingdom
| | - David Q Stoye
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh EH16 4TJ, United Kingdom
| | - Alan J Quigley
- Department of Paediatric Radiology, Royal Hospital for Children and Young People, Edinburgh EH16 4TJ, United Kingdom
| | - Michael J Thrippleton
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, United Kingdom
| | - Debby Bogaert
- Centre for Inflammation Research, University of Edinburgh, Edinburgh EH16 4TJ, United Kingdom
| | - Mark E Bastin
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, United Kingdom
| | - Simon R Cox
- Lothian Birth Cohort Studies group, Department of Psychology, University of Edinburgh, Edinburgh EH8 9JZ, United Kingdom
| | - James P Boardman
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh EH16 4TJ, United Kingdom; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH16 4SB, United Kingdom.
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16
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Ismaiel E, Zátonyi A, Fekete Z. Dimensionality Reduction and Prediction of Impedance Data of Biointerface. Sensors (Basel) 2022; 22:4191. [PMID: 35684818 PMCID: PMC9185537 DOI: 10.3390/s22114191] [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] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 05/24/2022] [Accepted: 05/30/2022] [Indexed: 06/15/2023]
Abstract
Electrochemical impedance spectroscopy (EIS) is the golden tool for many emerging biomedical applications that describes the behavior, stability, and long-term durability of physical interfaces in a specific range of frequency. Impedance measurements of any biointerface during in vivo and clinical applications could be used for assessing long-term biopotential measurements and diagnostic purposes. In this paper, a novel approach to predicting impedance behavior is presented and consists of a dimensional reduction procedure by converting EIS data over many days of an experiment into a one-dimensional sequence of values using a novel formula called day factor (DF) and then using a long short-term memory (LSTM) network to predict the future behavior of the DF. Three neural interfaces of different material compositions with long-term in vitro aging tests were used to validate the proposed approach. The results showed good accuracy in predicting the quantitative change in the impedance behavior (i.e., higher than 75%), in addition to good prediction of the similarity between the actual and the predicted DF signals, which expresses the impedance fluctuations among soaking days. The DF approach showed a lower computational time and algorithmic complexity compared with principal component analysis (PCA) and provided the ability to involve or emphasize several important frequencies or impedance range in a more flexible way.
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17
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Gascón A, Casas R, Buldain D, Marco Á. Providing Fault Detection from Sensor Data in Complex Machines That Build the Smart City. Sensors (Basel) 2022; 22:s22020586. [PMID: 35062547 PMCID: PMC8781749 DOI: 10.3390/s22020586] [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] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 01/07/2022] [Accepted: 01/11/2022] [Indexed: 02/06/2023]
Abstract
Household appliances, climate control machines, vehicles, elevators, cash counting machines, etc., are complex machines with key contributions to the smart city. Those devices have limited memory and processing power, but they are not just actuators; they embed tens of sensors and actuators managed by several microcontrollers and microprocessors communicated by control buses. On the other hand, predictive maintenance and the capability of identifying failures to avoid greater damage of machines is becoming a topic of great relevance in Industry 4.0, and the large amount of data to be processed is a concern. This article proposes a layered methodology to enable complex machines with automatic fault detection or predictive maintenance. It presents a layered structure to perform the collection, filtering and extraction of indicators, along with their processing. The aim is to reduce the amount of data to work with, and to optimize them by generating indicators that concentrate the information provided by data. To test its applicability, a prototype of a cash counting machine has been used. With this prototype, different failure cases have been simulated by introducing defective elements. After the extraction of the indicators, using the Kullback–Liebler divergence, it has been possible to visualize the differences between the data associated with normal and failure operation. Subsequently, using a neural network, good results have been obtained, being able to correctly classify the failure in 90% of the cases. The result of this application demonstrates the proper functioning of the proposed approach in complex machines.
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Affiliation(s)
- Alberto Gascón
- Aragon Institute of Engineering Research, University of Zaragoza, 50018 Zaragoza, Spain; (A.G.); (D.B.); (Á.M.)
| | - Roberto Casas
- Aragon Institute of Engineering Research, University of Zaragoza, 50018 Zaragoza, Spain; (A.G.); (D.B.); (Á.M.)
- Correspondence: ; Tel.: +34-976-762-856
| | - David Buldain
- Aragon Institute of Engineering Research, University of Zaragoza, 50018 Zaragoza, Spain; (A.G.); (D.B.); (Á.M.)
| | - Álvaro Marco
- Aragon Institute of Engineering Research, University of Zaragoza, 50018 Zaragoza, Spain; (A.G.); (D.B.); (Á.M.)
- GeoSpatium Lab S.L., Carlos Marx 6, 50015 Zaragoza, Spain
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18
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Pioli L, Dorneles CF, de Macedo DDJ, Dantas MAR. An overview of data reduction solutions at the edge of IoT systems: a systematic mapping of the literature. Computing 2022; 104. [PMCID: PMC8958485 DOI: 10.1007/s00607-022-01073-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Internet of Things (IoT) is a technology that connects devices of different types and characteristics through a network. The massive quantity of the heterogeneous generated data by the sensors imposes many challenges in making these data available to IoT applications. Data reduction and preprocessing are promising concepts that help to handle these data efficiently before storing them. Applying data reduction methods at the edge has emerged as an efficient solution. In such context, this systematic mapping is intended to investigate the data reduction solutions performed exclusively at the edge through a set of research questions. To reach this objective, we performed a Systematic Literature Mapping (SLM) in which 35 papers were strictly analyzed among a total of 853 articles. Finally, we present the results of these analyses answering questions that relate to the researcher’s used techniques, hardware technologies, used data type, and contributed objects to perform the data reduction techniques on the edge of the IoT systems.
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Affiliation(s)
- Laércio Pioli
- Computer Science, Universidade Federal de Santa Catarina (UFSC), Florianópolis, Santa Catarina 88040-900 Brazil
| | - Carina F. Dorneles
- Computer Science, Universidade Federal de Santa Catarina (UFSC), Florianópolis, Santa Catarina 88040-900 Brazil
| | - Douglas D. J. de Macedo
- Computer Science, Universidade Federal de Santa Catarina (UFSC), Florianópolis, Santa Catarina 88040-900 Brazil
| | - Mario A. R. Dantas
- Computer Science, Universidade Federal de Juiz de Fora (UFJF), Juiz de Fora, Minas Gerais 36036-900 Brazil
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19
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Koch RJ, Roth N, Liu Y, Ivashko O, Dippel AC, Petrovic C, Iversen BB, V Zimmermann M, Bozin ES. On single-crystal total scattering data reduction and correction protocols for analysis in direct space. Acta Crystallogr A Found Adv 2021; 77:611-636. [PMID: 34726636 DOI: 10.1107/s2053273321010159] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [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: 05/17/2021] [Accepted: 09/30/2021] [Indexed: 11/10/2022] Open
Abstract
Data reduction and correction steps and processed data reproducibility in the emerging single-crystal total-scattering-based technique of three-dimensional differential atomic pair distribution function (3D-ΔPDF) analysis are explored. All steps from sample measurement to data processing are outlined using a crystal of CuIr2S4 as an example, studied in a setup equipped with a high-energy X-ray beam and a flat-panel area detector. Computational overhead as pertains to data sampling and the associated data-processing steps is also discussed. Various aspects of the final 3D-ΔPDF reproducibility are explicitly tested by varying the data-processing order and included steps, and by carrying out a crystal-to-crystal data comparison. Situations in which the 3D-ΔPDF is robust are identified, and caution against a few particular cases which can lead to inconsistent 3D-ΔPDFs is noted. Although not all the approaches applied herein will be valid across all systems, and a more in-depth analysis of some of the effects of the data-processing steps may still needed, the methods collected herein represent the start of a more systematic discussion about data processing and corrections in this field.
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Affiliation(s)
- Robert J Koch
- Condensed Matter Physics and Materials Science Division, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - Nikolaj Roth
- Center for Materials Crystallography, Department of Chemistry and iNANO, Aarhus University, DK-8000, Aarhus, Denmark
| | - Yiu Liu
- Condensed Matter Physics and Materials Science Division, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - Oleh Ivashko
- Deutsches Elektronen-Synchrotron DESY, 22607 Hamburg, Germany
| | | | - Cedomir Petrovic
- Condensed Matter Physics and Materials Science Division, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - Bo B Iversen
- Center for Materials Crystallography, Department of Chemistry and iNANO, Aarhus University, DK-8000, Aarhus, Denmark
| | | | - Emil S Bozin
- Condensed Matter Physics and Materials Science Division, Brookhaven National Laboratory, Upton, NY 11973, USA
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20
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Lee JW, Lee SJ, Kee SH. Evaluation of a Concrete Slab Track with Debonding at the Interface between Track Concrete Layer and Hydraulically Stabilized Base Course Using Multi-Channel Impact-Echo Testing. Sensors (Basel) 2021; 21:7091. [PMID: 34770398 DOI: 10.3390/s21217091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2021] [Revised: 10/15/2021] [Accepted: 10/22/2021] [Indexed: 11/18/2022]
Abstract
Multi-channel Impact-echo (IE) testing was used to evaluate debonding defects at the interface between track concrete layer, TCL, and hydraulically stabilized base course, HSB, in a real scale mockup model of concrete slab tracks for Korea high-speed railway (KHSR) system. The mockup model includes three debonding defects that were fabricated by inserting three 400 mm by 400 mm (length and width) thin plastic foam boards with three different thicknesses of 5 mm, 10 mm, and 15 mm, before casting concrete in TCL. Multi-channel IE signals obtained over solid concrete and debonding defects were reduced to three critical IE testing parameters (the velocity of concrete, peak frequency, and Q factor). Bilinear classification models were used to evaluate the individual and a combination of the characteristic parameters. It was demonstrated that the best evaluation performance was obtained by using average peak frequency or the combination of average peak frequency and average Q factor, obtained by eight accelerometers in the multi-channel IE device. The results and discussion in this study would improve the understanding of characteristics of multiple IE testing parameters in concrete slab tracks and provide a fundamental basis to develop an effective prediction model of non-destructive evaluation for debonding defects at the interface between TCL and HSB in concrete slab tracks.
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21
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Fawzy D, Moussa S, Badr N. The Spatiotemporal Data Fusion (STDF) Approach: IoT-Based Data Fusion Using Big Data Analytics. Sensors (Basel) 2021; 21:s21217035. [PMID: 34770342 PMCID: PMC8588564 DOI: 10.3390/s21217035] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [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: 07/28/2021] [Revised: 09/28/2021] [Accepted: 09/29/2021] [Indexed: 11/17/2022]
Abstract
Enormous heterogeneous sensory data are generated in the Internet of Things (IoT) for various applications. These big data are characterized by additional features related to IoT, including trustworthiness, timing and spatial features. This reveals more perspectives to consider while processing, posing vast challenges to traditional data fusion methods at different fusion levels for collection and analysis. In this paper, an IoT-based spatiotemporal data fusion (STDF) approach for low-level data in–data out fusion is proposed for real-time spatial IoT source aggregation. It grants optimum performance through leveraging traditional data fusion methods based on big data analytics while exclusively maintaining the data expiry, trustworthiness and spatial and temporal IoT data perspectives, in addition to the volume and velocity. It applies cluster sampling for data reduction upon data acquisition from all IoT sources. For each source, it utilizes a combination of k-means clustering for spatial analysis and Tiny AGgregation (TAG) for temporal aggregation to maintain spatiotemporal data fusion at the processing server. STDF is validated via a public IoT data stream simulator. The experiments examine diverse IoT processing challenges in different datasets, reducing the data size by 95% and decreasing the processing time by 80%, with an accuracy level up to 90% for the largest used dataset.
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22
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Cofre-Martel S, Lopez Droguett E, Modarres M. Big Machinery Data Preprocessing Methodology for Data-Driven Models in Prognostics and Health Management. Sensors (Basel) 2021; 21:6841. [PMID: 34696058 PMCID: PMC8537368 DOI: 10.3390/s21206841] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 10/08/2021] [Accepted: 10/12/2021] [Indexed: 11/16/2022]
Abstract
Sensor monitoring networks and advances in big data analytics have guided the reliability engineering landscape to a new era of big machinery data. Low-cost sensors, along with the evolution of the internet of things and industry 4.0, have resulted in rich databases that can be analyzed through prognostics and health management (PHM) frameworks. Several data-driven models (DDMs) have been proposed and applied for diagnostics and prognostics purposes in complex systems. However, many of these models are developed using simulated or experimental data sets, and there is still a knowledge gap for applications in real operating systems. Furthermore, little attention has been given to the required data preprocessing steps compared to the training processes of these DDMs. Up to date, research works do not follow a formal and consistent data preprocessing guideline for PHM applications. This paper presents a comprehensive step-by-step pipeline for the preprocessing of monitoring data from complex systems aimed for DDMs. The importance of expert knowledge is discussed in the context of data selection and label generation. Two case studies are presented for validation, with the end goal of creating clean data sets with healthy and unhealthy labels that are then used to train machinery health state classifiers.
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Affiliation(s)
- Sergio Cofre-Martel
- Center for Risk and Reliability, Department of Mechanical Engineering, University of Maryland, College Park, MD 20742, USA;
| | - Enrique Lopez Droguett
- Department of Civil and Environmental Engineering, Garrick Institute for the Risk Sciences, University of California, Los Angeles, CA 90095, USA;
| | - Mohammad Modarres
- Center for Risk and Reliability, Department of Mechanical Engineering, University of Maryland, College Park, MD 20742, USA;
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23
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Hadian-Jazi M, Sadri A, Barty A, Yefanov O, Galchenkova M, Oberthuer D, Komadina D, Brehm W, Kirkwood H, Mills G, de Wijn R, Letrun R, Kloos M, Vakili M, Gelisio L, Darmanin C, Mancuso AP, Chapman HN, Abbey B. Data reduction for serial crystallography using a robust peak finder. J Appl Crystallogr 2021; 54:1360-1378. [PMID: 34667447 PMCID: PMC8493619 DOI: 10.1107/s1600576721007317] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [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: 03/16/2021] [Accepted: 07/14/2021] [Indexed: 11/23/2022] Open
Abstract
This article focuses on the challenges of hit finding and data reduction in serial crystallography (SX). An effective and reliable Bragg-peak-finding method, called robust peak finder (RPF), has been developed. RPF is based on the principle of robust statistics and can be used for SX data analysis. A peak-finding algorithm for serial crystallography (SX) data analysis based on the principle of ‘robust statistics’ has been developed. Methods which are statistically robust are generally more insensitive to any departures from model assumptions and are particularly effective when analysing mixtures of probability distributions. For example, these methods enable the discretization of data into a group comprising inliers (i.e. the background noise) and another group comprising outliers (i.e. Bragg peaks). Our robust statistics algorithm has two key advantages, which are demonstrated through testing using multiple SX data sets. First, it is relatively insensitive to the exact value of the input parameters and hence requires minimal optimization. This is critical for the algorithm to be able to run unsupervised, allowing for automated selection or ‘vetoing’ of SX diffraction data. Secondly, the processing of individual diffraction patterns can be easily parallelized. This means that it can analyse data from multiple detector modules simultaneously, making it ideally suited to real-time data processing. These characteristics mean that the robust peak finder (RPF) algorithm will be particularly beneficial for the new class of MHz X-ray free-electron laser sources, which generate large amounts of data in a short period of time.
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Affiliation(s)
- Marjan Hadian-Jazi
- ARC Centre of Excellence in Advanced Molecular Imaging, La Trobe Institute for Molecular Sciences, La Trobe University, Melbourne, Australia.,Australian Nuclear Science and Technology Organisation (ANSTO), Australia.,European XFEL, Holzkoppel 4, 22869 Schenefeld, Germany
| | - Alireza Sadri
- Center for Free-Electron Laser Science, Deutsches Elektronen-Synchrotron (DESY), Notkestrasse 85, 22607 Hamburg, Germany
| | - Anton Barty
- Center for Free-Electron Laser Science, Deutsches Elektronen-Synchrotron (DESY), Notkestrasse 85, 22607 Hamburg, Germany
| | - Oleksandr Yefanov
- Center for Free-Electron Laser Science, Deutsches Elektronen-Synchrotron (DESY), Notkestrasse 85, 22607 Hamburg, Germany
| | - Marina Galchenkova
- Center for Free-Electron Laser Science, Deutsches Elektronen-Synchrotron (DESY), Notkestrasse 85, 22607 Hamburg, Germany
| | - Dominik Oberthuer
- Center for Free-Electron Laser Science, Deutsches Elektronen-Synchrotron (DESY), Notkestrasse 85, 22607 Hamburg, Germany
| | - Dana Komadina
- Center for Free-Electron Laser Science, Deutsches Elektronen-Synchrotron (DESY), Notkestrasse 85, 22607 Hamburg, Germany
| | - Wolfgang Brehm
- Center for Free-Electron Laser Science, Deutsches Elektronen-Synchrotron (DESY), Notkestrasse 85, 22607 Hamburg, Germany
| | | | - Grant Mills
- European XFEL, Holzkoppel 4, 22869 Schenefeld, Germany
| | | | - Romain Letrun
- European XFEL, Holzkoppel 4, 22869 Schenefeld, Germany
| | - Marco Kloos
- European XFEL, Holzkoppel 4, 22869 Schenefeld, Germany
| | | | - Luca Gelisio
- Center for Free-Electron Laser Science, Deutsches Elektronen-Synchrotron (DESY), Notkestrasse 85, 22607 Hamburg, Germany
| | - Connie Darmanin
- ARC Centre of Excellence in Advanced Molecular Imaging, La Trobe Institute for Molecular Sciences, La Trobe University, Melbourne, Australia
| | - Adrian P Mancuso
- European XFEL, Holzkoppel 4, 22869 Schenefeld, Germany.,Department of Chemistry and Physics, La Trobe Institute for Molecular Science, La Trobe University, Melbourne, Victoria, Australia
| | - Henry N Chapman
- Center for Free-Electron Laser Science, Deutsches Elektronen-Synchrotron (DESY), Notkestrasse 85, 22607 Hamburg, Germany.,Department of Physics, Universität Hamburg, Luruper Chaussee 149, 22761 Hamburg, Germany.,The Hamburg Centre for Ultrafast Imaging, Luruper Chaussee 149, 22761 Hamburg, Germany
| | - Brian Abbey
- ARC Centre of Excellence in Advanced Molecular Imaging, La Trobe Institute for Molecular Sciences, La Trobe University, Melbourne, Australia
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24
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Missale F, Bugatti M, Mattavelli D, Lonardi S, Lombardi D, Nicolai P, Piazza C, Battocchio S, Bozzola AM, Calza S, Vermi W. Metavariables Resuming Host Immune Features and Nodal Involvement Are Associated with Oncological Outcomes in Oral Cavity Squamous Cell Carcinoma. Cells 2021; 10:2203. [PMID: 34571850 PMCID: PMC8472482 DOI: 10.3390/cells10092203] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2021] [Revised: 08/13/2021] [Accepted: 08/19/2021] [Indexed: 11/16/2022] Open
Abstract
Oral cavity squamous cell carcinoma (OSCC) is a common head and neck cancer characterized by a poor prognosis associated with locoregional or distant failure. Among the predictors of prognosis, a dense infiltration of adaptive immune cells is protective and associated with improved clinical outcomes. However, few tools are available to integrate immune contexture variables into clinical settings. By using digital microscopy analysis of a large retrospective OSCC cohort (n = 182), we explored the clinical significance of tumor-infiltrating CD8+ T-cells. To this end, CD8+ T-cells counts were combined with well-established clinical variables and peripheral blood immune cell parameters. Through variable clustering, five metavariables (MV) were obtained and included descriptors of nodal (NODALMV) and primary tumor (TUMORMV) involvement, the frequency of myeloid (MYELOIDMV) or lymphoid (LYMPHOIDMV) peripheral blood immune cell populations, and the density of tumor-infiltrating CD8+ T-cells (TI-CD8MV). The clinical relevance of the MV was evaluated in the multivariable survival models. The NODALMV was significantly associated with all tested outcomes (p < 0.001), the LYMPHOIDMV showed a significant association with the overall, disease-specific and distant recurrence-free survival (p < 0.05) and the MYELOIDMV with the locoregional control only (p < 0.001). Finally, TI-CD8MV was associated with distant recurrence-free survival (p = 0.029). Notably, the performance in terms of survival prediction of the combined effect of NODALMV and immune metavariables (LYMPHOIDMV, MYELOIDMV and TI-CD8MV) was superior to the TNM stage for most of the outcomes analyzed. These findings indicate that the analysis of the baseline host immune features are promising tools to complement clinical features, in stratifying the risk of recurrences.
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Affiliation(s)
- Francesco Missale
- Department of Molecular and Translational Medicine, University of Brescia, 25125 Brescia, Italy
- Department of Head & Neck Oncology & Surgery Otorhinolaryngology, Antoni Van Leeuwenhoek, Nederlands Kanker Instituut, 1066 Amsterdam, The Netherlands
| | - Mattia Bugatti
- Unit of Pathology, ASST Spedali Civili di Brescia, 25100 Brescia, Italy; (M.B.); (S.L.); (S.B.); (A.M.B.)
| | - Davide Mattavelli
- Unit of Otorhinolaryngology—Head and Neck Surgery, Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, 25123 Brescia, Italy; (D.M.); (D.L.); (C.P.)
| | - Silvia Lonardi
- Unit of Pathology, ASST Spedali Civili di Brescia, 25100 Brescia, Italy; (M.B.); (S.L.); (S.B.); (A.M.B.)
| | - Davide Lombardi
- Unit of Otorhinolaryngology—Head and Neck Surgery, Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, 25123 Brescia, Italy; (D.M.); (D.L.); (C.P.)
| | - Piero Nicolai
- Section of Otorhinolaryngology—Head and Neck Surgery, Department of Neurosciences, University of Padua, Via Giustiniani, 2-35128 Padua, Italy;
| | - Cesare Piazza
- Unit of Otorhinolaryngology—Head and Neck Surgery, Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, 25123 Brescia, Italy; (D.M.); (D.L.); (C.P.)
| | - Simonetta Battocchio
- Unit of Pathology, ASST Spedali Civili di Brescia, 25100 Brescia, Italy; (M.B.); (S.L.); (S.B.); (A.M.B.)
| | - Anna Maria Bozzola
- Unit of Pathology, ASST Spedali Civili di Brescia, 25100 Brescia, Italy; (M.B.); (S.L.); (S.B.); (A.M.B.)
| | - Stefano Calza
- Unit of Biostatistics, Department of Molecular and Translational Medicine, University of Brescia, 25125 Brescia, Italy;
- BDbiomed, Big and Open Data Innovation Laboratory, University of Brescia, 25125 Brescia, Italy
| | - William Vermi
- Department of Molecular and Translational Medicine, University of Brescia, 25125 Brescia, Italy
- Unit of Pathology, ASST Spedali Civili di Brescia, 25100 Brescia, Italy; (M.B.); (S.L.); (S.B.); (A.M.B.)
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63130, USA
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Marczyk A, Ghio A, Lalain M, Rebourg M, Fredouille C, Woisard V. Optimizing linguistic materials for feature-based intelligibility assessment in speech impairments. Behav Res Methods 2021. [PMID: 34100199 DOI: 10.3758/s13428-021-01610-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/02/2021] [Indexed: 11/08/2022]
Abstract
Assessing the intelligibility of speech-disordered individuals generally involves asking them to read aloud texts such as word lists, a procedure that can be time-consuming if the materials are lengthy. This paper seeks to optimize such elicitation materials by identifying an optimal trade-off between the quantity of material needed for assessment purposes and its capacity to elicit a robust intelligibility metrics. More specifically, it investigates the effect of reducing the number of pseudowords used in a phonetic-acoustic decoding task in a speech-impaired population in terms of the subsequent impact on the intelligibility classifier as quantified by accuracy indexes (AUC of ROC, Balanced Accuracy index and F-scores). A comparison of obtained accuracy indexes shows that when reduction of the amount of elicitation material is based on a phonetic criterion-here, related to phonotactic complexity-the classifier has a higher classifying ability than when the material is arbitrarily reduced. Crucially, downsizing the material to about 30% of the original dataset does not diminish the classifier's performance nor affect its stability. This result is of significant interest to clinicians as well as patients since it validates a tool that is both reliable and efficient.
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Zhou Q, Gao ZQ, Dong Z, Jiang YM, She Z, Geng Z, Dong YH. A reference-based multi-lattice indexing method integrating prior information correction and iterative refinement in protein crystallography. Acta Crystallogr A Found Adv 2021; 77:277-288. [PMID: 34196290 DOI: 10.1107/s2053273321003521] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 04/01/2021] [Indexed: 11/10/2022]
Abstract
A new multi-lattice indexing method based on the principle of whole-pattern matching given cell dimensions and space-group symmetry is presented for macromolecular crystallography. The proposed method, termed the multi-crystal data processing suite (MCDPS), features a local correction for prior information accompanied by iterative refinement of experimental parameters, both of which are numerically and experimentally demonstrated to be critical for accurately identifying multiple crystal lattices. Further analysis of data reduction and structure determination with conventional single-crystal programs reveals that the processed multi-lattice data sets are comparable in quality to typical single-crystal ones in terms of crystallographic metrics. Importantly, it is confirmed that careful exclusion of overlapping reflections prior to scaling is necessary to guarantee an accurate data reduction result. The potential for multi-lattice indexing in solving the general macroscopic twinning problem is also explored.
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Affiliation(s)
- Qiang Zhou
- Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, 100049, People's Republic of China
| | - Zeng Qiang Gao
- Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, 100049, People's Republic of China
| | - Zheng Dong
- Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, 100049, People's Republic of China
| | - Yu Meng Jiang
- Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, 100049, People's Republic of China
| | - Zhun She
- Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, 100049, People's Republic of China
| | - Zhi Geng
- Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, 100049, People's Republic of China
| | - Yu Hui Dong
- Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, 100049, People's Republic of China
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Beattie JR, Esmonde-White FWL. Exploration of Principal Component Analysis: Deriving Principal Component Analysis Visually Using Spectra. Appl Spectrosc 2021; 75:361-375. [PMID: 33393349 DOI: 10.1177/0003702820987847] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Spectroscopy rapidly captures a large amount of data that is not directly interpretable. Principal component analysis is widely used to simplify complex spectral datasets into comprehensible information by identifying recurring patterns in the data with minimal loss of information. The linear algebra underpinning principal component analysis is not well understood by many applied analytical scientists and spectroscopists who use principal component analysis. The meaning of features identified through principal component analysis is often unclear. This manuscript traces the journey of the spectra themselves through the operations behind principal component analysis, with each step illustrated by simulated spectra. Principal component analysis relies solely on the information within the spectra, consequently the mathematical model is dependent on the nature of the data itself. The direct links between model and spectra allow concrete spectroscopic explanation of principal component analysis , such as the scores representing "concentration" or "weights". The principal components (loadings) are by definition hidden, repeated and uncorrelated spectral shapes that linearly combine to generate the observed spectra. They can be visualized as subtraction spectra between extreme differences within the dataset. Each PC is shown to be a successive refinement of the estimated spectra, improving the fit between PC reconstructed data and the original data. Understanding the data-led development of a principal component analysis model shows how to interpret application specific chemical meaning of the principal component analysis loadings and how to analyze scores. A critical benefit of principal component analysis is its simplicity and the succinctness of its description of a dataset, making it powerful and flexible.
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Raudonis V, Paulauskaite-Taraseviciene A, Sutiene K. Fast Multi-Focus Fusion Based on Deep Learning for Early-Stage Embryo Image Enhancement. Sensors (Basel) 2021; 21:863. [PMID: 33525420 PMCID: PMC7865517 DOI: 10.3390/s21030863] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 01/22/2021] [Accepted: 01/25/2021] [Indexed: 11/16/2022]
Abstract
BACKGROUND Cell detection and counting is of essential importance in evaluating the quality of early-stage embryo. Full automation of this process remains a challenging task due to different cell size, shape, the presence of incomplete cell boundaries, partially or fully overlapping cells. Moreover, the algorithm to be developed should process a large number of image data of different quality in a reasonable amount of time. METHODS Multi-focus image fusion approach based on deep learning U-Net architecture is proposed in the paper, which allows reducing the amount of data up to 7 times without losing spectral information required for embryo enhancement in the microscopic image. RESULTS The experiment includes the visual and quantitative analysis by estimating the image similarity metrics and processing times, which is compared to the results achieved by two wellknown techniques-Inverse Laplacian Pyramid Transform and Enhanced Correlation Coefficient Maximization. CONCLUSION Comparatively, the image fusion time is substantially improved for different image resolutions, whilst ensuring the high quality of the fused image.
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Affiliation(s)
- Vidas Raudonis
- Department of Automation, Kaunas University of Technology, Studentu 48, 51367 Kaunas, Lithuania;
| | | | - Kristina Sutiene
- Department of Mathematical Modelling, Kaunas University of Technology, Studentu 50, 51368 Kaunas, Lithuania;
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Ryan S, Kempton T, Coutts AJ. Data Reduction Approaches to Athlete Monitoring in Professional Australian Football. Int J Sports Physiol Perform 2020; 16:59-65. [PMID: 33152687 DOI: 10.1123/ijspp.2020-0083] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Revised: 02/17/2020] [Accepted: 02/28/2020] [Indexed: 11/18/2022]
Abstract
PURPOSE To apply data reduction methods to athlete-monitoring measures to address the issue of data overload for practitioners of professional Australian football teams. METHODS Data were collected from 45 professional Australian footballers from 1 club during the 2018 Australian Football League season. External load was measured in training and matches by 10-Hz OptimEye S5 and ClearSky T6 GPS units. Internal load was measured via the session rate of perceived exertion method. Perceptual wellness was measured via questionnaires completed before training sessions with players providing a rating (1-5 Likert scale) of muscle soreness, sleep quality, fatigue, stress, and motivation. Percentage of maximum speed was calculated relative to individual maximum velocity recorded during preseason testing. Derivative external training load measures (total daily, weekly, and monthly) were calculated. Principal-component analyses (PCAs) were conducted for Daily and Chronic measures, and components were identified via scree plot inspection (eigenvalue > 1). Components underwent orthogonal rotation with a factor loading redundancy threshold of 0.70. RESULTS The Daily PCA identified components representing external load, perceived wellness, and internal load. The Chronic PCA identified components representing 28-d speed exposure, 28-d external load, 7-d external load, and 28-d internal load. Perceived soreness did not meet the redundancy threshold. CONCLUSIONS Monitoring player exposure to maximum speed is more appropriate over chronic than short time frames to capture variations in between-matches training-cycle duration. Perceived soreness represents a distinct element of a player's perception of wellness. Summed-variable and single-variable approaches are novel methods of data reduction following PCA of athlete monitoring data.
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30
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Mellors BOL, Bentley A, Spear AM, Howle CR, Dehghani H. Applications of compressive sensing in spatial frequency domain imaging. J Biomed Opt 2020; 25:JBO-200205SSR. [PMID: 33179460 PMCID: PMC7657414 DOI: 10.1117/1.jbo.25.11.112904] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 10/19/2020] [Indexed: 06/11/2023]
Abstract
SIGNIFICANCE Spatial frequency domain imaging (SFDI) is an imaging modality that projects spatially modulated light patterns to determine optical property maps for absorption and reduced scattering of biological tissue via a pixel-by-pixel data acquisition and analysis procedure. Compressive sensing (CS) is a signal processing methodology which aims to reproduce the original signal with a reduced number of measurements, addressing the pixel-wise nature of SFDI. These methodologies have been combined for complex heterogenous data in both the image detection and data analysis stage in a compressive sensing SFDI (cs-SFDI) approach, showing reduction in both the data acquisition and overall computational time. AIM Application of CS in SFDI data acquisition and image reconstruction significantly improves data collection and image recovery time without loss of quantitative accuracy. APPROACH cs-SFDI has been applied to an increased heterogenic sample from the AppSFDI data set (back of the hand), highlighting the increased number of CS measurements required as compared to simple phantoms to accurately obtain optical property maps. A novel application of CS to the parameter recovery stage of image analysis has also been developed and validated. RESULTS Dimensionality reduction has been demonstrated using the increased heterogenic sample at both the acquisition and analysis stages. A data reduction of 30% for the cs-SFDI and up to 80% for the parameter recover was achieved as compared to traditional SFDI, while maintaining an error of <10 % for the recovered optical property maps. CONCLUSION The application of data reduction through CS demonstrates additional capabilities for multi- and hyperspectral SFDI, providing advanced optical and physiological property maps.
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Affiliation(s)
- Ben O. L. Mellors
- University of Birmingham, College of Engineering and Physical Sciences, Physical Sciences for Health Doctoral Training Centre, Birmingham, United Kingdom
- University of Birmingham, College of Engineering and Physical Sciences, School of Computer Science, Birmingham, United Kingdom
| | - Alexander Bentley
- University of Birmingham, College of Engineering and Physical Sciences, Physical Sciences for Health Doctoral Training Centre, Birmingham, United Kingdom
- University of Birmingham, College of Engineering and Physical Sciences, School of Computer Science, Birmingham, United Kingdom
| | - Abigail M. Spear
- Defence Science and Technology Laboratory, Salisbury, United Kingdom
| | | | - Hamid Dehghani
- University of Birmingham, College of Engineering and Physical Sciences, School of Computer Science, Birmingham, United Kingdom
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Wlodarczyk-Sielicka M, Blaszczak-Bak W. Processing of Bathymetric Data: The Fusion of New Reduction Methods for Spatial Big Data. Sensors (Basel) 2020; 20:s20216207. [PMID: 33143323 PMCID: PMC7662608 DOI: 10.3390/s20216207] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [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: 09/29/2020] [Revised: 10/27/2020] [Accepted: 10/28/2020] [Indexed: 11/28/2022]
Abstract
Floating autonomous vehicles are very often equipped with modern systems that collect information about the situation under the water surface, e.g., the depth or type of bottom and obstructions on the seafloor. One such system is the multibeam echosounder (MBES), which collects very large sets of bathymetric data. The development and analysis of such large sets are laborious and expensive. Reduction of the spatial data obtained from bathymetric and other systems collecting spatial data is currently widely used. In commercial programs used in the development of data from hydrographic systems, methods of interpolation to a specific mesh size are very frequently used. The authors of this article previously proposed original the true bathymetric data reduction method (TBDRed) and Optimum Dataset (OptD) reduction methods, which maintain the actual position and depth for each of the measured points, without their interpolation. The effectiveness of the proposed methods has already been presented in previous articles. This article proposes the fusion of original reduction methods, which is a new and innovative approach to the problem of bathymetric data reduction. The article contains a description of the methods used and the methodology of developing bathymetric data. The proposed fusion of reduction methods allows the generation of numerical models that can be a safe, reliable source of information, and a basis for design. Numerical models can also be used in comparative navigation, during the creation of electronic navigation maps and other hydrographic products.
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Affiliation(s)
- Marta Wlodarczyk-Sielicka
- Department of Navigation, Maritime University of Szczecin, Waly Chrobrego 1-2, 70-500 Szczecin, Poland
- Correspondence: ; Tel.: +48-513-846-391
| | - Wioleta Blaszczak-Bak
- Faculty of Geoengineering, University of Warmia and Mazury in Olsztyn, Oczapowskiego 1, 10-719 Olsztyn, Poland;
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Tsapparellas G, Jin N, Dai X, Fehringer G. Laplacian Scores-Based Feature Reduction in IoT Systems for Agricultural Monitoring and Decision-Making Support. Sensors (Basel) 2020; 20:s20185107. [PMID: 32911684 PMCID: PMC7570761 DOI: 10.3390/s20185107] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [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: 07/31/2020] [Revised: 08/31/2020] [Accepted: 09/02/2020] [Indexed: 11/16/2022]
Abstract
Internet of things (IoT) systems generate a large volume of data all the time. How to choose and transfer which data are essential for decision-making is a challenge. This is especially important for low-cost and low-power designs, for example Long-Range Wide-Area Network (LoRaWan)-based IoT systems, where data volume and frequency are constrained by the protocols. This paper presents an unsupervised learning approach using Laplacian scores to discover which types of sensors can be reduced, without compromising the decision-making. Here, a type of sensor is a feature. An IoT system is designed and implemented for a plant-monitoring scenario. We have collected data and carried out the Laplacian scores. The analytical results help choose the most important feature. A comparative study has shown that using fewer types of sensors, the accuracy of decision-making remains at a satisfactory level.
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Affiliation(s)
- Giorgos Tsapparellas
- Department of Maritime and Mechanical Engineering, Liverpool John Moores University, Liverpool L3 3AF, UK;
| | - Nanlin Jin
- Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK;
- Correspondence:
| | - Xuewu Dai
- Department of Mathematics, Physics and Electrical Engineering, Northumbria University, Newcastle upon Tyne NE1 8ST, UK;
| | - Gerhard Fehringer
- Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK;
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Park J, Chung Y, Choi J. CoDR: Correlation-based Data Reduction Scheme for Efficient Gathering of Heterogeneous Driving Data. Sensors (Basel) 2020; 20:s20061677. [PMID: 32192221 PMCID: PMC7146115 DOI: 10.3390/s20061677] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [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: 12/27/2019] [Revised: 03/10/2020] [Accepted: 03/14/2020] [Indexed: 12/05/2022]
Abstract
A variety of deep learning techniques are actively employed for advanced driver assistance systems, which in turn require gathering lots of heterogeneous driving data, such as traffic conditions, driver behavior, vehicle status and location information. However, these different types of driving data easily become more than tens of GB per day, forming a significant hurdle due to the storage and network cost. To address this problem, this paper proposes a novel scheme, called CoDR, which can reduce data volume by considering the correlations among heterogeneous driving data. Among heterogeneous datasets, CoDR first chooses one set as a pivot data. Then, according to the objective of data collection, it identifies data ranges relevant to the objective from the pivot dataset. Finally, it investigates correlations among sets, and reduces data volume by eliminating irrelevant data from not only the pivot set but also other remaining datasets. CoDR gathers four heterogeneous driving datasets: two videos for front view and driver behavior, OBD-II and GPS data. We show that CoDR decreases data volume by up to 91%. We also present diverse analytical results that reveal the correlations among the four datasets, which can be exploited usefully for edge computing to reduce data volume on the spot.
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Affiliation(s)
- Junho Park
- Department of Computer Science and Engineering, Dankook University, Yongin 16890, Korea;
| | - Yoojin Chung
- Division of Computer and Electronic Systems Engineering, Hankuk University of Foreign Studies, Yongin 17035, Korea;
| | - Jongmoo Choi
- Department of Computer Science and Engineering, Dankook University, Yongin 16890, Korea;
- Correspondence: ; Tel.: +82-31-8005-3242
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Hoogerheide DP, Heinrich F, Maranville BB, Majkrzak CF. Accurate background correction in neutron reflectometry studies of soft condensed matter films in contact with fluid reservoirs. J Appl Crystallogr 2020; 53:10.1107/s160057671901481x. [PMID: 34194075 PMCID: PMC8240731 DOI: 10.1107/s160057671901481x] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [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: 10/11/2018] [Accepted: 11/01/2019] [Indexed: 11/10/2022] Open
Abstract
Neutron reflectometry (NR) is a powerful method for looking at the structures of multilayered thin films, including biomolecules on surfaces, particularly proteins at lipid interfaces. The spatial resolution of the film structure obtained through an NR experiment is limited by the maximum wavevector transfer at which the reflectivity can be measured. This maximum is in turn determined primarily by the scattering background, e.g. from incoherent scattering from a liquid reservoir or inelastic scattering from cell materials. Thus, reduction of scattering background is an important part of improving the spatial resolution attainable in NR measurements. Here, the background field generated by scattering from a thin liquid reservoir on a monochromatic reflectometer is measured and calculated. It is shown that background subtraction utilizing the entire background field improves data modeling and reduces experimental uncertainties associated with localized background subtraction.
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Affiliation(s)
- David P. Hoogerheide
- Center for Neutron Research, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA
| | - Frank Heinrich
- Center for Neutron Research, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA
- Department of Physics, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Brian B. Maranville
- Center for Neutron Research, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA
| | - Charles F. Majkrzak
- Center for Neutron Research, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA
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35
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Bolandi H, Lajnef N, Jiao P, Barri K, Hasni H, Alavi AH. A Novel Data Reduction Approach for Structural Health Monitoring Systems. Sensors (Basel) 2019; 19:E4823. [PMID: 31698686 DOI: 10.3390/s19224823] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 10/29/2019] [Accepted: 11/04/2019] [Indexed: 11/25/2022]
Abstract
The massive amount of data generated by structural health monitoring (SHM) systems usually affects the system’s capacity for data transmission and analysis. This paper proposes a novel concept based on the probability theory for data reduction in SHM systems. The beauty salient feature of the proposed method is that it alleviates the burden of collecting and analysis of the entire strain data via a relative damage approach. In this methodology, the rate of variation of strain distributions is related to the rate of damage. In order to verify the accuracy of the approach, experimental and numerical studies were conducted on a thin steel plate subjected to cyclic in-plane tension loading. Circular holes with various sizes were made on the plate to define damage states. Rather than measuring the entire strain response, the cumulative durations of strain events at different predefined strain levels were obtained for each damage scenario. Then, the distribution of the calculated cumulative times was used to detect the damage progression. The results show that the presented technique can efficiently detect the damage progression. The damage detection accuracy can be improved by increasing the predefined strain levels. The proposed concept can lead to over 2500% reduction in data storage requirement, which can be particularly important for data generation and data handling in on-line SHM systems.
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Bedrick EJ. Data reduction prior to inference: Are there consequences of comparing groups using a t-test based on principal component scores? Biometrics 2019; 76:508-517. [PMID: 31584187 DOI: 10.1111/biom.13159] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [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: 06/27/2018] [Accepted: 09/10/2019] [Indexed: 11/28/2022]
Abstract
Researchers often use a two-step process to analyze multivariate data. First, dimensionality is reduced using a technique such as principal component analysis, followed by a group comparison using a t -test or analysis of variance. Although this practice is often discouraged, the statistical properties of this procedure are not well understood, starting with the hypothesis being tested. We suggest that this approach might be considering two distinct hypotheses, one of which is a global test of no differences in the mean vectors, and the other being a focused test of a specific linear combination where the coefficients have been estimated from the data. We study the asymptotic properties of the two-sample t -statistic for these two scenarios, assuming a nonsparse setting. We show that the size of the global test agrees with the presumed level but that the test has poor power. In contrast, the size of the focused test can be arbitrarily distorted with certain mean and covariance structures. A simple method is provided to correct the size of the focused test. Data analyses and simulations are used to illustrate the results. Recommendations on the use of this two-step method and the related use of principal components for prediction are provided.
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Affiliation(s)
- Edward J Bedrick
- Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, Center for Biomedical Informatics and Biostatistics, University of Arizona, Tucson, Arizona
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Zolnierczuk PA, Holderer O, Pasini S, Kozielewski T, Stingaciu LR, Monkenbusch M. Efficient data extraction from neutron time-of-flight spin-echo raw data. J Appl Crystallogr 2019; 52:1022-1034. [PMID: 31636520 PMCID: PMC6782076 DOI: 10.1107/s1600576719010847] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [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: 05/15/2019] [Accepted: 08/02/2019] [Indexed: 11/10/2022] Open
Abstract
Neutron spin-echo spectrometers with a position-sensitive detector and operating with extended time-of-flight-tagged wavelength frames are able to collect a comprehensive set of data covering a large range of wavevector and Fourier time space with only a few instrumental settings in a quasi-continuous way. Extracting all the information contained in the raw data and mapping them to a suitable physical space in the most efficient way is a challenge. This article reports algorithms employed in dedicated software, DrSpine (data reduction for spin echo), that achieves this goal and yields reliable representations of the intermediate scattering function S(Q, t) independent of the selected 'binning'.
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Affiliation(s)
- P A Zolnierczuk
- Forschungszentrum Jülich GmbH, JCNS Outstation, Oak Ridge, Tennessee, USA
| | - O Holderer
- Forschungszentrum Jülich GmbH, JCNS MLZ, Garching, Germany
| | - S Pasini
- Forschungszentrum Jülich GmbH, JCNS MLZ, Garching, Germany
| | - T Kozielewski
- Forschungszentrum Jülich GmbH, JCNS-1, Jülich, Germany
| | - L R Stingaciu
- NScD, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - M Monkenbusch
- Forschungszentrum Jülich GmbH, JCNS-1, Jülich, Germany
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Dutta S, Biswas A, Ahrens J. Multivariate Pointwise Information-Driven Data Sampling and Visualization. Entropy (Basel) 2019; 21:e21070699. [PMID: 33267413 PMCID: PMC7515213 DOI: 10.3390/e21070699] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [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: 05/08/2019] [Revised: 06/25/2019] [Accepted: 07/06/2019] [Indexed: 12/05/2022]
Abstract
With increasing computing capabilities of modern supercomputers, the size of the data generated from the scientific simulations is growing rapidly. As a result, application scientists need effective data summarization techniques that can reduce large-scale multivariate spatiotemporal data sets while preserving the important data properties so that the reduced data can answer domain-specific queries involving multiple variables with sufficient accuracy. While analyzing complex scientific events, domain experts often analyze and visualize two or more variables together to obtain a better understanding of the characteristics of the data features. Therefore, data summarization techniques are required to analyze multi-variable relationships in detail and then perform data reduction such that the important features involving multiple variables are preserved in the reduced data. To achieve this, in this work, we propose a data sub-sampling algorithm for performing statistical data summarization that leverages pointwise information theoretic measures to quantify the statistical association of data points considering multiple variables and generates a sub-sampled data that preserves the statistical association among multi-variables. Using such reduced sampled data, we show that multivariate feature query and analysis can be done effectively. The efficacy of the proposed multivariate association driven sampling algorithm is presented by applying it on several scientific data sets.
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Ismael WM, Gao M, Al-Shargabi AA, Zahary A. An In-Networking Double-Layered Data Reduction for Internet of Things (IoT). Sensors (Basel) 2019; 19:s19040795. [PMID: 30781406 PMCID: PMC6412591 DOI: 10.3390/s19040795] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [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: 01/05/2019] [Revised: 02/09/2019] [Accepted: 02/11/2019] [Indexed: 11/16/2022]
Abstract
Due to the ever-increasing number and diversity of data sources, and the continuous flow of data that are inevitably redundant and unused to the cloud, the Internet of Things (IoT) brings several problems including network bandwidth, the consumption of network energy, cloud storage, especially for paid volume, and I/O throughput as well as handling huge amount of stored data in the cloud. These call for data pre-processing at the network edge before data transmission over the network takes place. Data reduction is a method for mitigating such problems. Most state-of-the-art data reduction approaches employ a single tier, such as gateways, or two tiers, such gateways and the cloud data center or sensor nodes and base station. In this paper, an approach for IoT data reduction is proposed using in-networking data filtering and fusion. The proposed approach consists of two layers that can be adapted at either a single tier or two tiers. The first layer of the proposed approach is the data filtering layer that is based on two techniques, namely data change detection and the deviation of real observations from their estimated values. The second layer is the data fusion layer. It is based on a minimum square error criterion and fuses the data of the same time domain for specific sensors deployed in a specific area. The proposed approach was implemented using Python and the evaluation of the approach was conducted based on a real-world dataset. The obtained results demonstrate that the proposed approach is efficient in terms of data reduction in comparison with Least Mean Squares filter and Papageorgiou's (CLONE) method.
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Affiliation(s)
- Waleed M Ismael
- College of Internet of Things (IoT) Engineering, Hohai University, Changzhou Campus, Changzhou 213022, China.
| | - Mingsheng Gao
- College of Internet of Things (IoT) Engineering, Hohai University, Changzhou Campus, Changzhou 213022, China.
| | - Asma A Al-Shargabi
- Faculty of Computer Science, University of Science and Technology, Sana'a 31220, Yemen.
| | - Ammar Zahary
- Faculty of Computer and IT, Sana'a University, Sana'a 31220, Yemen.
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Zhan J, Ince RAA, van Rijsbergen N, Schyns PG. Dynamic Construction of Reduced Representations in the Brain for Perceptual Decision Behavior. Curr Biol 2019; 29:319-326.e4. [PMID: 30639108 PMCID: PMC6345582 DOI: 10.1016/j.cub.2018.11.049] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2018] [Revised: 10/23/2018] [Accepted: 11/20/2018] [Indexed: 01/03/2023]
Abstract
Over the past decade, extensive studies of the brain regions that support face, object, and scene recognition suggest that these regions have a hierarchically organized architecture that spans the occipital and temporal lobes [1-14], where visual categorizations unfold over the first 250 ms of processing [15-19]. This same architecture is flexibly involved in multiple tasks that require task-specific representations-e.g. categorizing the same object as "a car" or "a Porsche." While we partly understand where and when these categorizations happen in the occipito-ventral pathway, the next challenge is to unravel how these categorizations happen. That is, how does high-dimensional input collapse in the occipito-ventral pathway to become low dimensional representations that guide behavior? To address this, we investigated what information the brain processes in a visual perception task and visualized the dynamic representation of this information in brain activity. To do so, we developed stimulus information representation (SIR), an information theoretic framework, to tease apart stimulus information that supports behavior from that which does not. We then tracked the dynamic representations of both in magneto-encephalographic (MEG) activity. Using SIR, we demonstrate that a rapid (∼170 ms) reduction of behaviorally irrelevant information occurs in the occipital cortex and that representations of the information that supports distinct behaviors are constructed in the right fusiform gyrus (rFG). Our results thus highlight how SIR can be used to investigate the component processes of the brain by considering interactions between three variables (stimulus information, brain activity, behavior), rather than just two, as is the current norm.
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Affiliation(s)
- Jiayu Zhan
- Institute of Neuroscience and Psychology, University of Glasgow, Scotland G12 8QB, United Kingdom
| | - Robin A A Ince
- Institute of Neuroscience and Psychology, University of Glasgow, Scotland G12 8QB, United Kingdom
| | - Nicola van Rijsbergen
- Institute of Neuroscience and Psychology, University of Glasgow, Scotland G12 8QB, United Kingdom
| | - Philippe G Schyns
- Institute of Neuroscience and Psychology, University of Glasgow, Scotland G12 8QB, United Kingdom; School of Psychology, University of Glasgow, 62 Hillhead Street, Glasgow, Scotland G12 8QB, United Kingdom.
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41
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Abstract
Lane changes are important behaviors to study in driving research. Automated detection of lane-change events is required to address the need for data reduction of a vast amount of naturalistic driving videos. This paper presents a method to deal with weak lane-marker patterns as small as a couple of pixels wide. The proposed method is novel in its approach to detecting lane-change events by accumulating lane-marker candidates over time. Since the proposed method tracks lane markers in temporal domain, it is robust to low resolution and many different kinds of interferences. The proposed technique was tested using 490 h of naturalistic driving videos collected from 63 drivers. The lane-change events in a 10-h video set were first manually coded and compared with the outcome of the automated method. The method's sensitivity was 94.8% and the data reduction rate was 93.6%. The automated procedure was further evaluated using the remaining 480-h driving videos. The data reduction rate was 97.4%. All 4971 detected events were manually reviewed and classified as either true or false lane-change events. Bootstrapping showed that the false discovery rate from the larger data set was not significantly different from that of the 10-h manually coded data set. This study demonstrated that the temporal processing of lane markers is an effcient strategy for detecting lane-change events involving weak lane-marker patterns in naturalistic driving.
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Affiliation(s)
- Shuhang Wang
- Schepens Eye Research Institute, Mass. Eye and Ear, Harvard Medical School, Boston, MA 02114, USA
| | - Brian R Ott
- Rhode Island Hospital, Alpert Medical School, Brown University, Providence, RI 02903, USA
| | - Gang Luo
- Schepens Eye Research Institute, Mass. Eye and Ear, Harvard Medical School, Boston, MA 02114, USA
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42
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Dauphas N, Hu MY, Baker EM, Hu J, Tissot FLH, Alp EE, Roskosz M, Zhao J, Bi W, Liu J, Lin JF, Nie NX, Heard A. SciPhon: a data analysis software for nuclear resonant inelastic X-ray scattering with applications to Fe, Kr, Sn, Eu and Dy. J Synchrotron Radiat 2018; 25:1581-1599. [PMID: 30179200 PMCID: PMC6140397 DOI: 10.1107/s1600577518009487] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2018] [Accepted: 07/02/2018] [Indexed: 06/01/2023]
Abstract
The synchrotron radiation technique of nuclear resonant inelastic X-ray scattering (NRIXS), also known as nuclear resonance vibrational spectroscopy or nuclear inelastic scattering, provides a wealth of information on the vibrational properties of solids. It has found applications in studies of lattice dynamics and elasticity, superconductivity, heme biochemistry, seismology, isotope geochemistry and many other fields. It involves probing the vibrational modes of solids by using the nuclear resonance of Mössbauer isotopes such as 57Fe, 83Kr, 119Sn, 151Eu and 161Dy. After data reduction, it provides the partial phonon density of states of the Mössbauer isotope that is investigated, as well as many other derived quantities such as the mean force constant of the chemical bonds and the Debye velocity. The data reduction is, however, not straightforward and involves removal of the elastic peak, normalization and Fourier-Log transformation. Furthermore, some of the quantities derived are highly sensitive to details in the baseline correction. A software package and several novel procedures to streamline and hopefully improve the reduction of the NRIXS data generated at sector 3ID of the Advanced Photon Source have been developed. The graphical user interface software is named SciPhon and runs as a Mathematica package. It is easily portable to other platforms and can be easily adapted for reducing data generated at other beamlines. Several tests and comparisons are presented that demonstrate the usefulness of this software, whose results have already been used in several publications. Here, the SciPhon software is used to reduce Kr, Sn, Eu and Dy NRIXS data, and potential implications for interpreting natural isotopic variations in those systems are discussed.
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Affiliation(s)
- Nicolas Dauphas
- Department of the Geophysical Sciences and Enrico Fermi Institute, The University of Chicago, 5734 South Ellis Avenue, Chicago, IL 60615, USA
| | - Michael Y. Hu
- Advanced Photon Source, Argonne National Laboratory, 9700 South Cass Avenue, Argonne, IL 60439, USA
| | - Erik M. Baker
- Department of the Geophysical Sciences and Enrico Fermi Institute, The University of Chicago, 5734 South Ellis Avenue, Chicago, IL 60615, USA
- Department of Earth and Planetary Sciences, Northwestern University, 2145 Sheridan Road, Evanston, IL 60208, USA
| | - Justin Hu
- Department of the Geophysical Sciences and Enrico Fermi Institute, The University of Chicago, 5734 South Ellis Avenue, Chicago, IL 60615, USA
| | - Francois L. H. Tissot
- Department of the Geophysical Sciences and Enrico Fermi Institute, The University of Chicago, 5734 South Ellis Avenue, Chicago, IL 60615, USA
| | - E. Ercan Alp
- Advanced Photon Source, Argonne National Laboratory, 9700 South Cass Avenue, Argonne, IL 60439, USA
| | - Mathieu Roskosz
- IMPMC-UMR CNRS 7590, Sorbonne Universités, UPMC, IRD, MNHN, Muséum National d’Histoire Naturelle, 61 Rue Buffon, 75005 Paris, France
| | - Jiyong Zhao
- Advanced Photon Source, Argonne National Laboratory, 9700 South Cass Avenue, Argonne, IL 60439, USA
| | - Wenli Bi
- Advanced Photon Source, Argonne National Laboratory, 9700 South Cass Avenue, Argonne, IL 60439, USA
| | - Jin Liu
- Department of Geological Sciences, Stanford University, Stanford, CA, USA
| | - Jung-Fu Lin
- Department of Geological Sciences, Jackson School of Geosciences, University of Texas at Austin, Austin, TX 78712, USA
| | - Nicole X. Nie
- Department of the Geophysical Sciences and Enrico Fermi Institute, The University of Chicago, 5734 South Ellis Avenue, Chicago, IL 60615, USA
| | - Andrew Heard
- Department of the Geophysical Sciences and Enrico Fermi Institute, The University of Chicago, 5734 South Ellis Avenue, Chicago, IL 60615, USA
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Howard JT, Rathje TA, Bruns CE, Wilson-Wells DF, Kachman SD, Spangler ML. The impact of truncating data on the predictive ability for single-step genomic best linear unbiased prediction. J Anim Breed Genet 2018; 135:251-262. [PMID: 29882604 DOI: 10.1111/jbg.12334] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [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: 01/12/2018] [Revised: 04/08/2018] [Accepted: 04/25/2018] [Indexed: 11/29/2022]
Abstract
Simulated and swine industry data sets were utilized to assess the impact of removing older data on the predictive ability of selection candidate estimated breeding values (EBV) when using single-step genomic best linear unbiased prediction (ssGBLUP). Simulated data included thirty replicates designed to mimic the structure of swine data sets. For the simulated data, varying amounts of data were truncated based on the number of ancestral generations back from the selection candidates. The swine data sets consisted of phenotypic and genotypic records for three traits across two breeds on animals born from 2003 to 2017. Phenotypes and genotypes were iteratively removed 1 year at a time based on the year an animal was born. For the swine data sets, correlations between corrected phenotypes (Cp) and EBV were used to evaluate the predictive ability on young animals born in 2016-2017. In the simulated data set, keeping data two generations back or greater resulted in no statistical difference (p-value > 0.05) in the reduction in the true breeding value at generation 15 compared to utilizing all available data. Across swine data sets, removing phenotypes from animals born prior to 2011 resulted in a negligible or a slight numerical increase in the correlation between Cp and EBV. Truncating data is a method to alleviate computational issues without negatively impacting the predictive ability of selection candidate EBV.
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Affiliation(s)
- Jeremy T Howard
- Department of Animal Science, University of Nebraska-Lincoln, Lincoln, Nebraska
| | | | | | | | - Stephen D Kachman
- Department of Statistics, University of Nebraska-Lincoln, Lincoln, Nebraska
| | - Matthew L Spangler
- Department of Animal Science, University of Nebraska-Lincoln, Lincoln, Nebraska
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Schwahn K, Nikoloski Z. Data Reduction Approaches for Dissecting Transcriptional Effects on Metabolism. Front Plant Sci 2018; 9:538. [PMID: 29731765 PMCID: PMC5920133 DOI: 10.3389/fpls.2018.00538] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/26/2017] [Accepted: 04/06/2018] [Indexed: 06/08/2023]
Abstract
The availability of high-throughput data from transcriptomics and metabolomics technologies provides the opportunity to characterize the transcriptional effects on metabolism. Here we propose and evaluate two computational approaches rooted in data reduction techniques to identify and categorize transcriptional effects on metabolism by combining data on gene expression and metabolite levels. The approaches determine the partial correlation between two metabolite data profiles upon control of given principal components extracted from transcriptomics data profiles. Therefore, they allow us to investigate both data types with all features simultaneously without doing preselection of genes. The proposed approaches allow us to categorize the relation between pairs of metabolites as being under transcriptional or post-transcriptional regulation. The resulting classification is compared to existing literature and accumulated evidence about regulatory mechanism of reactions and pathways in the cases of Escherichia coli, Saccharomycies cerevisiae, and Arabidopsis thaliana.
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Affiliation(s)
- Kevin Schwahn
- Systems Biology and Mathematical Modelling Group, Max Placnk Institute of Molecular Plant Physiology, Potsdam, Germany
| | - Zoran Nikoloski
- Systems Biology and Mathematical Modelling Group, Max Placnk Institute of Molecular Plant Physiology, Potsdam, Germany
- Bioinformatics Group, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
- Center of Plant Systems Biology and Biotechnology, Plovdiv, Bulgaria
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Karge L, Gilles R, Busch S. Calibrating SANS data for instrument geometry and pixel sensitivity effects: access to an extended Q range. J Appl Crystallogr 2017; 50:1382-1394. [PMID: 29021734 PMCID: PMC5627681 DOI: 10.1107/s1600576717011463] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [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: 05/03/2017] [Accepted: 08/03/2017] [Indexed: 11/10/2022] Open
Abstract
An improved data-reduction procedure is proposed and demonstrated for small-angle neutron scattering (SANS) measurements. Its main feature is the correction of geometry- and wavelength-dependent intensity variations on the detector in a separate step from the different pixel sensitivities: the geometric and wavelength effects can be corrected analytically, while pixel sensitivities have to be calibrated to a reference measurement. The geometric effects are treated for position-sensitive 3He proportional counter tubes, where they are anisotropic owing to the cylindrical geometry of the gas tubes. For the calibration of pixel sensitivities, a procedure is developed that is valid for isotropic and anisotropic signals. The proposed procedure can save a significant amount of beamtime which has hitherto been used for calibration measurements.
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Affiliation(s)
- Lukas Karge
- Heinz Maier-Leibnitz Zentrum (MLZ), Technische Universität München, Lichtenbergstrasse 1, 85747 Garching bei München, Germany
| | - Ralph Gilles
- Heinz Maier-Leibnitz Zentrum (MLZ), Technische Universität München, Lichtenbergstrasse 1, 85747 Garching bei München, Germany
| | - Sebastian Busch
- German Engineering Materials Science Centre (GEMS) at Heinz Maier-Leibnitz Zentrum (MLZ), Helmholtz-Zentrum Geesthacht, Lichtenbergstrasse 1, 85747 Garching bei München, Germany
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46
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Abass A, Bell JS, Spang MT, Hayes S, Meek KM, Boote C. SAXS4COLL: an integrated software tool for analysing fibrous collagen-based tissues. J Appl Crystallogr 2017; 50:1235-1240. [PMID: 28808439 PMCID: PMC5541358 DOI: 10.1107/s1600576717007877] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [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: 10/12/2016] [Accepted: 05/26/2017] [Indexed: 12/02/2022] Open
Abstract
SAXS4COLL is an interactive computer program for reduction and analysis of small-angle X-ray scattering data from fibrous collagen tissues, combining data reduction, bespoke background subtraction, semi-automated peak detection and calibration. This article provides an overview of a new integrated software tool for reduction and analysis of small-angle X-ray scattering (SAXS) data from fibrous collagen tissues, with some wider applicability to other cylindrically symmetric scattering systems. SAXS4COLL combines interactive features for data pre-processing, bespoke background subtraction, semi-automated peak detection and calibration. Both equatorial and meridional SAXS peak parameters can be measured, and the former can be deconstructed into cylinder and lattice contributions. Finally, the software combines functionality for determination of collagen spatial order parameters with a rudimentary orientation plot capability.
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Affiliation(s)
- Ahmed Abass
- Structural Biophysics Group, School of Optometry and Vision Science, Cardiff University, Maindy Road, Cardiff CF24 4HQ, UK
| | - James S Bell
- Structural Biophysics Group, School of Optometry and Vision Science, Cardiff University, Maindy Road, Cardiff CF24 4HQ, UK
| | - Martin T Spang
- Structural Biophysics Group, School of Optometry and Vision Science, Cardiff University, Maindy Road, Cardiff CF24 4HQ, UK
| | - Sally Hayes
- Structural Biophysics Group, School of Optometry and Vision Science, Cardiff University, Maindy Road, Cardiff CF24 4HQ, UK
| | - Keith M Meek
- Structural Biophysics Group, School of Optometry and Vision Science, Cardiff University, Maindy Road, Cardiff CF24 4HQ, UK
| | - Craig Boote
- Structural Biophysics Group, School of Optometry and Vision Science, Cardiff University, Maindy Road, Cardiff CF24 4HQ, UK
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47
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Houdayer J, Poitevin F. Reduction of small-angle scattering profiles to finite sets of structural invariants. Acta Crystallogr A Found Adv 2017; 73:317-332. [PMID: 28660864 PMCID: PMC5571748 DOI: 10.1107/s205327331700451x] [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: 10/12/2016] [Accepted: 03/21/2017] [Indexed: 11/10/2022] Open
Abstract
This paper shows how small-angle scattering (SAS) curves can be decomposed in a simple sum using a set of invariant parameters called Kn which are related to the shape of the object of study. These Kn, together with a radius R, give a complete theoretical description of the SAS curve. Adding an overall constant, these parameters are easily fitted against experimental data giving a concise comprehensive description of the data. The pair distance distribution function is also entirely described by this invariant set and the Dmax parameter can be measured. In addition to the understanding they bring, these invariants can be used to reliably estimate structural moments beyond the radius of gyration, thereby rigorously expanding the actual set of model-free quantities one can extract from experimental SAS data, and possibly paving the way to designing new shape reconstruction strategies.
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Affiliation(s)
- Jérôme Houdayer
- Institut de Physique Théorique, Université Paris Saclay, CEA, UMR 3681 du CNRS, Gif-sur-Yvette, France
| | - Frédéric Poitevin
- Department of Structural Biology, Stanford, CA 94305, USA
- Stanford PULSE Institute, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA
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48
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Singh A, Dandapat S. Block sparsity-based joint compressed sensing recovery of multi-channel ECG signals. Healthc Technol Lett 2017; 4:50-56. [PMID: 28546862 PMCID: PMC5437710 DOI: 10.1049/htl.2016.0049] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [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: 05/25/2016] [Revised: 12/15/2016] [Accepted: 01/03/2017] [Indexed: 11/20/2022] Open
Abstract
In recent years, compressed sensing (CS) has emerged as an effective alternative to conventional wavelet based data compression techniques. This is due to its simple and energy-efficient data reduction procedure, which makes it suitable for resource-constrained wireless body area network (WBAN)-enabled electrocardiogram (ECG) telemonitoring applications. Both spatial and temporal correlations exist simultaneously in multi-channel ECG (MECG) signals. Exploitation of both types of correlations is very important in CS-based ECG telemonitoring systems for better performance. However, most of the existing CS-based works exploit either of the correlations, which results in a suboptimal performance. In this work, within a CS framework, the authors propose to exploit both types of correlations simultaneously using a sparse Bayesian learning-based approach. A spatiotemporal sparse model is employed for joint compression/reconstruction of MECG signals. Discrete wavelets transform domain block sparsity of MECG signals is exploited for simultaneous reconstruction of all the channels. Performance evaluations using Physikalisch-Technische Bundesanstalt MECG diagnostic database show a significant gain in the diagnostic reconstruction quality of the MECG signals compared with the state-of-the art techniques at reduced number of measurements. Low measurement requirement may lead to significant savings in the energy-cost of the existing CS-based WBAN systems.
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Affiliation(s)
- Anurag Singh
- Electro Medical and Speech Technology Laboratory, Department of Electronics and Electrical Engineering, Indian Institute of Technology Guwahati, Guwahati-781039, India
| | - Samarendra Dandapat
- Electro Medical and Speech Technology Laboratory, Department of Electronics and Electrical Engineering, Indian Institute of Technology Guwahati, Guwahati-781039, India
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Berberidis D, Kekatos V, Giannakis GB. Online Censoring for Large-Scale Regressions with Application to Streaming Big Data. IEEE Trans Signal Process 2016; 64:3854-3867. [PMID: 28042229 PMCID: PMC5198787 DOI: 10.1109/tsp.2016.2546225] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
On par with data-intensive applications, the sheer size of modern linear regression problems creates an ever-growing demand for efficient solvers. Fortunately, a significant percentage of the data accrued can be omitted while maintaining a certain quality of statistical inference with an affordable computational budget. This work introduces means of identifying and omitting less informative observations in an online and data-adaptive fashion. Given streaming data, the related maximum-likelihood estimator is sequentially found using first- and second-order stochastic approximation algorithms. These schemes are well suited when data are inherently censored or when the aim is to save communication overhead in decentralized learning setups. In a different operational scenario, the task of joint censoring and estimation is put forth to solve large-scale linear regressions in a centralized setup. Novel online algorithms are developed enjoying simple closed-form updates and provable (non)asymptotic convergence guarantees. To attain desired censoring patterns and levels of dimensionality reduction, thresholding rules are investigated too. Numerical tests on real and synthetic datasets corroborate the efficacy of the proposed data-adaptive methods compared to data-agnostic random projection-based alternatives.
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Tseng HW, Fan J, Kupinski MA. Design of a practical model-observer-based image quality assessment method for x-ray computed tomography imaging systems. J Med Imaging (Bellingham) 2016; 3:035503. [PMID: 27493982 DOI: 10.1117/1.jmi.3.3.035503] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [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: 01/22/2016] [Accepted: 07/01/2016] [Indexed: 11/14/2022] Open
Abstract
The use of a channelization mechanism on model observers not only makes mimicking human visual behavior possible, but also reduces the amount of image data needed to estimate the model observer parameters. The channelized Hotelling observer (CHO) and channelized scanning linear observer (CSLO) have recently been used to assess CT image quality for detection tasks and combined detection/estimation tasks, respectively. Although the use of channels substantially reduces the amount of data required to compute image quality, the number of scans required for CT imaging is still not practical for routine use. It is our desire to further reduce the number of scans required to make CHO or CSLO an image quality tool for routine and frequent system validations and evaluations. This work explores different data-reduction schemes and designs an approach that requires only a few CT scans. Three different kinds of approaches are included in this study: a conventional CHO/CSLO technique with a large sample size, a conventional CHO/CSLO technique with fewer samples, and an approach that we will show requires fewer samples to mimic conventional performance with a large sample size. The mean value and standard deviation of areas under ROC/EROC curve were estimated using the well-validated shuffle approach. The results indicate that an 80% data reduction can be achieved without loss of accuracy. This substantial data reduction is a step toward a practical tool for routine-task-based QA/QC CT system assessment.
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Affiliation(s)
- Hsin-Wu Tseng
- The University of Arizona, College of Optical Sciences, Tucson, Arizona 85721, United States; CT Engineering, GE Healthcare, Waukesha, Wisconsin 53188, United States
| | - Jiahua Fan
- CT Engineering , GE Healthcare, Waukesha, Wisconsin 53188, United States
| | - Matthew A Kupinski
- The University of Arizona , College of Optical Sciences, Tucson, Arizona 85721, United States
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