Array programming with NumPy

Discovery of potent inhibitors of α-synuclein aggregation using structure-based iterative learning

Machine learning methods hold the promise to reduce the costs and the failure rates of conventional drug discovery pipelines. This issue is especially pressing for neurodegenerative diseases, where the development of disease-modifying drugs has been particularly challenging. To address this problem, we describe here a machine learning approach to identify small molecule inhibitors of α-synuclein aggregation, a process implicated in Parkinson's disease and other synucleinopathies. Because the proliferation of α-synuclein aggregates takes place through autocatalytic secondary nucleation, we aim to identify compounds that bind the catalytic sites on the surface of the aggregates. To achieve this goal, we use structure-based machine learning in an iterative manner to first identify and then progressively optimize secondary nucleation inhibitors. Our results demonstrate that this approach leads to the facile identification of compounds two orders of magnitude more potent than previously reported ones.

Nonsurgical Gluteal Volume Correction with Hyaluronic Acid: A Retrospective Study to Assess Long-term Safety and Efficacy

Background

Augmentation and reshaping of body volume, particularly in the gluteal area, presents a significant challenge in aesthetic surgery. Hyaluronic acid (HA) fillers have emerged as an effective and safe tool for such indications, but literature examining nonsurgical gluteal reshaping with HA remains limited. This study aims to evaluate the long-term safety of using recommended volumes of HA body fillers for nonsurgical gluteal augmentation.

Methods

A retrospective, observational study was carried out across multiple centers in Italy and the United Arab Emirates. The study involved participants between 22 and 53 years of age who underwent gluteal augmentation using HA body filler (HYAcorp MLF1/2) between 2017 and 2021, with up to 4 years and 7 months of follow-up. Participants and investigators independently evaluated the procedure's effectiveness by comparing pre- and posttreatment photographs. The Global Aesthetic Improvement Scale was used to assess posttreatment satisfaction by both participants and investigators. All adverse effects (AEs) were recorded.

Results

The study included a diverse group of 91 participants. No serious adverse events were reported, with the majority of AE occurring shortly after treatment and resolving in 1 week. AEs were more frequently observed in participants with previous treatments using different substances in the treatment area.

Conclusions

The real-world application of HA body filler (HYAcorp MLF1/2) for gluteal augmentation in the participants of this study showed the treatment's effectiveness, with no severe adverse events reported among the participants. High levels of satisfaction were reported among both participants and investigators.

The "elongate chelicera problem": A virtual approach in an extinct pterygotid sea scorpion from a 3D kinematic point of view

Chelicerae, distinctive feeding appendages in chelicerates, such as spiders, scorpions, or horseshoe crabs, can be classified based on their orientation relative to the body axis simplified as either orthognathous (parallel) or labidognathous (inclined), exhibiting considerable diversity across various taxa. Among extinct chelicerates, sea scorpions belonging to the Pterygotidae represent the only chelicerates possessing markedly elongated chelicerae relative to body length. Despite various hypotheses regarding the potential ecological functions and feeding movements of these structures, no comprehensive 3D kinematic investigation has been conducted yet to test these ideas. In this study, we generated a comprehensive 3D model of the pterygotid Acutiramus, making the elongated right chelicera movable by equipping it with virtual joint axes for conducting Range of Motion analyses. Due to the absence in the fossil record of a clear indication of the chelicerae orientation and their potential lateral or ventral movements (vertical or horizontal insertion of joint axis 1), we explored the Range of Motion analyses under four distinct kinematic settings with two orientation modes (euthygnathous, klinogathous) analogous to the terminology of the terrestrial relatives. The most plausible kinematic setting involved euthygnathous chelicerae being folded ventrally over a horizontal joint axis. This configuration positioned the chelicera closest to the oral opening. Concerning the maximum excursion angle, our analysis revealed that the chela could open up to 70°, while it could be retracted against the basal element to a maximum of 145°. The maximum excursion in the proximal joint varied between 55° and 120° based on the insertion and orientation. Our findings underscore the utility of applying 3D kinematics to fossilized arthropods for addressing inquiries on functional ecology such as prey capture and handling, enabling insights into their possible behavioral patterns. Pterygotidae likely captured and processed their prey using the chelicerae, subsequently transporting it to the oral opening with the assistance of other prosomal appendages.

Fully automatic system to detect and segment the proximal femur in pelvic radiographic images for Legg-Calvé-Perthes disease

This study aimed to develop a method using computer vision techniques to accurately detect and delineate the proximal femur in radiographs of Legg-Calvé-Perthes disease (LCPD) patients. Currently, evaluating femoral head deformity, a crucial predictor of LCPD outcomes, relies on unreliable categorical and qualitative classifications. To address this limitation, we employed the pretrained object detection model YOLOv5 to detect the proximal femur on over 2000 radiographs, including images of shoulders and chests, to enhance robustness and generalizability. Subsequently, we utilized the U-Net convolutional neural network architecture for image segmentation of the proximal femur in more than 800 manually annotated images of stage IV LCPD. The results demonstrate outstanding performance, with the object detection model achieving high accuracy (mean average precision of 0.99) and the segmentation model attaining an accuracy score of 91%, dice coefficient of 0.75, and binary IoU score of 0.85 on the held-out test set. The proposed fully automatic proximal femur detection and segmentation system offers a promising approach to accurately detect and delineate the proximal femoral bone contour in radiographic images, which is essential for further image analysis in LCPD patients. Clinical significance: This study highlights the potential of computer vision techniques for enhancing the reliability of Legg-Calvé-Perthes disease staging and outcome prediction.

Spike sorting with Kilosort4

Spike sorting is the computational process of extracting the firing times of single neurons from recordings of local electrical fields. This is an important but hard problem in neuroscience, made complicated by the nonstationarity of the recordings and the dense overlap in electrical fields between nearby neurons. To address the spike-sorting problem, we have been openly developing the Kilosort framework. Here we describe the various algorithmic steps introduced in different versions of Kilosort. We also report the development of Kilosort4, a version with substantially improved performance due to clustering algorithms inspired by graph-based approaches. To test the performance of Kilosort, we developed a realistic simulation framework that uses densely sampled electrical fields from real experiments to generate nonstationary spike waveforms and realistic noise. We found that nearly all versions of Kilosort outperformed other algorithms on a variety of simulated conditions and that Kilosort4 performed best in all cases, correctly identifying even neurons with low amplitudes and small spatial extents in high drift conditions.

Modular segmentation, spatial analysis and visualization of volume electron microscopy datasets

Volume electron microscopy is the method of choice for the in situ interrogation of cellular ultrastructure at the nanometer scale, and with the increase in large raw image datasets generated, improving computational strategies for image segmentation and spatial analysis is necessary. Here we describe a practical and annotation-efficient pipeline for organelle-specific segmentation, spatial analysis and visualization of large volume electron microscopy datasets using freely available, user-friendly software tools that can be run on a single standard workstation. The procedures are aimed at researchers in the life sciences with modest computational expertise, who use volume electron microscopy and need to generate three-dimensional (3D) segmentation labels for different types of cell organelles while minimizing manual annotation efforts, to analyze the spatial interactions between organelle instances and to visualize the 3D segmentation results. We provide detailed guidelines for choosing well-suited segmentation tools for specific cell organelles, and to bridge compatibility issues between freely available open-source tools, we distribute the critical steps as easily installable Album solutions for deep learning segmentation, spatial analysis and 3D rendering. Our detailed description can serve as a reference for similar projects requiring particular strategies for single- or multiple-organelle analysis, which can be achieved with computational resources commonly available to single-user setups.

Application of a U-Net Neural Network to the Puccinia sorghi-Maize Pathosystem

Computer vision approaches to analyze plant disease data can be both faster and more reliable than traditional, manual methods. However, the requirement of manually annotating training data for the majority of machine learning applications can present a challenge for pipeline development. Here, we describe a machine learning approach to quantify Puccinia sorghi incidence on maize leaves utilizing U-Net convolutional neural network models. We analyzed several U-Net models with increasing amounts of training image data, either randomly chosen from a large data pool or randomly chosen from a subset of disease time course data. As the training dataset size increases, the models perform better, but the rate of performance decreases. Additionally, the use of a diverse training dataset can improve model performance and reduce the amount of annotated training data required for satisfactory performance. Models with as few as 48 whole-leaf training images are able to replicate the ground truth results within our testing dataset. The final model utilizing our entire training dataset performs similarly to our ground truth data, with an intersection over union value of 0.5002 and an F1 score of 0.6669. This work illustrates the capacity of U-Nets to accurately answer real-world plant pathology questions related to quantification and estimation of plant disease symptoms. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Sexually dimorphic control of affective state processing and empathic behaviors

Recognizing the affective states of social counterparts and responding appropriately fosters successful social interactions. However, little is known about how the affective states are expressed and perceived and how they influence social decisions. Here, we show that male and female mice emit distinct olfactory cues after experiencing distress. These cues activate distinct neural circuits in the piriform cortex (PiC) and evoke sexually dimorphic empathic behaviors in observers. Specifically, the PiC → PrL pathway is activated in female observers, inducing a social preference for the distressed counterpart. Conversely, the PiC → MeA pathway is activated in male observers, evoking excessive self-grooming behaviors. These pathways originate from non-overlapping PiC neuron populations with distinct gene expression signatures regulated by transcription factors and sex hormones. Our study unveils how internal states of social counterparts are processed through sexually dimorphic mechanisms at the molecular, cellular, and circuit levels and offers insights into the neural mechanisms underpinning sex differences in higher brain functions.

Approximate Bayesian Computation applied to time series of population genetic data disentangles rapid genetic changes and demographic variations in a pathogen population

Adaptation can occur at remarkably short timescales in natural populations, leading to drastic changes in phenotypes and genotype frequencies over a few generations only. The inference of demographic parameters can allow understanding how evolutionary forces interact and shape the genetic trajectories of populations during rapid adaptation. Here we propose a new Approximate Bayesian Computation (ABC) framework that couples a forward and individual-based model with temporal genetic data to disentangle genetic changes and demographic variations in a case of rapid adaptation. We test the accuracy of our inferential framework and evaluate the benefit of considering a dense versus sparse sampling. Theoretical investigations demonstrate high accuracy in both model and parameter estimations, even if a strong thinning is applied to time series data. Then, we apply our ABC inferential framework to empirical data describing the population genetic changes of the poplar rust pathogen following a major event of resistance overcoming. We successfully estimate key demographic and genetic parameters, including the proportion of resistant hosts deployed in the landscape and the level of standing genetic variation from which selection occurred. Inferred values are in accordance with our empirical knowledge of this biological system. This new inferential framework, which contrasts with coalescent-based ABC analyses, is promising for a better understanding of evolutionary trajectories of populations subjected to rapid adaptation.

Early prediction of long hospital stay for Intensive Care units readmission patients using medication information

OBJECTIVE

Predicting Intensive Care Unit (ICU) Length of Stay (LOS) accurately can improve patient wellness, hospital operations, and the health system's financial status. This study focuses on predicting the prolonged ICU LOS (≥3 days) of the 2nd admission, utilizing short historical data (1st admission only) for early-stage prediction, as well as incorporating medication information.

MATERIALS AND METHODS

We selected 18,572 ICU patients' records from the MIMIC-IV database for this study. We applied five machine learning classifiers: Logistic regression (LR), Random Forest (RF), Support Vector Machine (SVM), AdaBoost (AB) and XGBoost (XGB). We computed both the sum dose and the average dose for the medication and included them in our model.

RESULTS

The performance of the RF model demonstrates the highest level of accuracy compared to other models, as indicated by an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.716 and an Expected Calibration Error (ECE) of 0.023.

DISCUSSION

The calibration improved all five classifiers (LR, RF, SVC, AB, XGB) in terms of ECE. The most important two features for RF are the length of 1st admission and the patient's age when they visited the hospital. The most important medication features are Phytonadione and Metoprolol Succinate XL. Also, both the sum and the average dose for the medication features contributed to the prediction task.

CONCLUSION

Our model showed the capability to predict the prolonged ICU LOS of the 2nd admission by utilizing the demographic, diagnosis, and medication information from the 1st admission. This method can potentially support the prevention of patient complications and enhance resource allocation in hospitals.

Universal Digital High-Resolution Melt Analysis for the Diagnosis of Bacteremia

Fast and accurate diagnosis of bloodstream infection is necessary to inform treatment decisions for septic patients, who face hourly increases in mortality risk. Blood culture remains the gold standard test but typically requires approximately 15 hours to detect the presence of a pathogen. We, therefore, assessed the potential for universal digital high-resolution melt (U-dHRM) analysis to accomplish faster broad-based bacterial detection, load quantification, and species-level identification directly from whole blood. Analytical validation studies demonstrated strong agreement between U-dHRM load measurement and quantitative blood culture, indicating that U-dHRM detection is highly specific to intact organisms. In a pilot clinical study of 17 whole blood samples from pediatric patients undergoing simultaneous blood culture testing, U-dHRM achieved 100% concordance when compared with blood culture and 88% concordance when compared with clinical adjudication. Moreover, U-dHRM identified the causative pathogen to the species level in all cases where the organism was represented in the melt curve database. These results were achieved with a 1-mL sample input and sample-to-answer time of 6 hours. Overall, this pilot study suggests that U-dHRM may be a promising method to address the challenges of quickly and accurately diagnosing a bloodstream infection.

On the Accurate Estimation of Information-Theoretic Quantities from Multi-Dimensional Sample Data

Using information-theoretic quantities in practical applications with continuous data is often hindered by the fact that probability density functions need to be estimated in higher dimensions, which can become unreliable or even computationally unfeasible. To make these useful quantities more accessible, alternative approaches such as binned frequencies using histograms and k-nearest neighbors (k-NN) have been proposed. However, a systematic comparison of the applicability of these methods has been lacking. We wish to fill this gap by comparing kernel-density-based estimation (KDE) with these two alternatives in carefully designed synthetic test cases. Specifically, we wish to estimate the information-theoretic quantities: entropy, Kullback-Leibler divergence, and mutual information, from sample data. As a reference, the results are compared to closed-form solutions or numerical integrals. We generate samples from distributions of various shapes in dimensions ranging from one to ten. We evaluate the estimators' performance as a function of sample size, distribution characteristics, and chosen hyperparameters. We further compare the required computation time and specific implementation challenges. Notably, k-NN estimation tends to outperform other methods, considering algorithmic implementation, computational efficiency, and estimation accuracy, especially with sufficient data. This study provides valuable insights into the strengths and limitations of the different estimation methods for information-theoretic quantities. It also highlights the significance of considering the characteristics of the data, as well as the targeted information-theoretic quantity when selecting an appropriate estimation technique. These findings will assist scientists and practitioners in choosing the most suitable method, considering their specific application and available data. We have collected the compared estimation methods in a ready-to-use open-source Python 3 toolbox and, thereby, hope to promote the use of information-theoretic quantities by researchers and practitioners to evaluate the information in data and models in various disciplines.

Semantic integration of diverse data in materials science: Assessing Orowan strengthening

This study applies Semantic Web technologies to advance Materials Science and Engineering (MSE) through the integration of diverse datasets. Focusing on a 2000 series age-hardenable aluminum alloy, we correlate mechanical and microstructural properties derived from tensile tests and dark-field transmission electron microscopy across varied aging times. An expandable knowledge graph, constructed using the Tensile Test and Precipitate Geometry Ontologies aligned with the PMD Core Ontology, facilitates this integration. This approach adheres to FAIR principles and enables sophisticated analysis via SPARQL queries, revealing correlations consistent with the Orowan mechanism. The study highlights the potential of semantic data integration in MSE, offering a new approach for data-centric research and enhanced analytical capabilities.

Addressing pandemic-wide systematic errors in the SARS-CoV-2 phylogeny

The SARS-CoV-2 genome occupies a unique place in infection biology - it is the most highly sequenced genome on earth (making up over 20% of public sequencing datasets) with fine scale information on sampling date and geography, and has been subject to unprecedented intense analysis. As a result, these phylogenetic data are an incredibly valuable resource for science and public health. However, the vast majority of the data was sequenced by tiling amplicons across the full genome, with amplicon schemes that changed over the pandemic as mutations in the viral genome interacted with primer binding sites. In combination with the disparate set of genome assembly workflows and lack of consistent quality control (QC) processes, the current genomes have many systematic errors that have evolved with the virus and amplicon schemes. These errors have significant impacts on the phylogeny, and therefore over the last few years, many thousands of hours of researchers time has been spent in "eyeballing" trees, looking for artefacts, and then patching the tree. Given the huge value of this dataset, we therefore set out to reprocess the complete set of public raw sequence data in a rigorous amplicon-aware manner, and build a cleaner phylogeny. Here we provide a global tree of 3,960,704 samples, built from a consistently assembled set of high quality consensus sequences from all available public data as of March 2023, viewable at https://viridian.taxonium.org. Each genome was constructed using a novel assembly tool called Viridian (https://github.com/iqbal-lab-org/viridian), developed specifically to process amplicon sequence data, eliminating artefactual errors and mask the genome at low quality positions. We provide simulation and empirical validation of the methodology, and quantify the improvement in the phylogeny. Phase 2 of our project will address the fact that the data in the public archives is heavily geographically biased towards the Global North. We therefore have contributed new raw data to ENA/SRA from many countries including Ghana, Thailand, Laos, Sri Lanka, India, Argentina and Singapore. We will incorporate these, along with all public raw data submitted between March 2023 and the current day, into an updated set of assemblies, and phylogeny. We hope the tree, consensus sequences and Viridian will be a valuable resource for researchers.

Discovery and Quantification of Long-Range RNA Base Pairs in Coronavirus Genomes with SEARCH-MaP and SEISMIC-RNA

RNA molecules perform a diversity of essential functions for which their linear sequences must fold into higher-order structures. Techniques including crystallography and cryogenic electron microscopy have revealed 3D structures of ribosomal, transfer, and other well-structured RNAs; while chemical probing with sequencing facilitates secondary structure modeling of any RNAs of interest, even within cells. Ongoing efforts continue increasing the accuracy, resolution, and ability to distinguish coexisting alternative structures. However, no method can discover and quantify alternative structures with base pairs spanning arbitrarily long distances - an obstacle for studying viral, messenger, and long noncoding RNAs, which may form long-range base pairs. Here, we introduce the method of Structure Ensemble Ablation by Reverse Complement Hybridization with Mutational Profiling (SEARCH-MaP) and software for Structure Ensemble Inference by Sequencing, Mutation Identification, and Clustering of RNA (SEISMIC-RNA). We use SEARCH-MaP and SEISMIC-RNA to discover that the frameshift stimulating element of SARS coronavirus 2 base-pairs with another element 1 kilobase downstream in nearly half of RNA molecules, and that this structure competes with a pseudoknot that stimulates ribosomal frameshifting. Moreover, we identify long-range base pairs involving the frameshift stimulating element in other coronaviruses including SARS coronavirus 1 and transmissible gastroenteritis virus, and model the full genomic secondary structure of the latter. These findings suggest that long-range base pairs are common in coronaviruses and may regulate ribosomal frameshifting, which is essential for viral RNA synthesis. We anticipate that SEARCH-MaP will enable solving many RNA structure ensembles that have eluded characterization, thereby enhancing our general understanding of RNA structures and their functions. SEISMIC-RNA, software for analyzing mutational profiling data at any scale, could power future studies on RNA structure and is available on GitHub and the Python Package Index.

Development of equation of motion deciphering locomotion including omega turns of Caenorhabditis elegans

Locomotion is a fundamental behavior of Caenorhabditis elegans (C. elegans). Previous works on kinetic simulations of animals helped researchers understand the physical mechanisms of locomotion and the muscle-controlling principles of neuronal circuits as an actuator part. It has yet to be understood how C. elegans utilizes the frictional forces caused by the tension of its muscles to perform sequenced locomotive behaviors. Here, we present a two-dimensional rigid body chain model for the locomotion of C. elegans by developing Newtonian equations of motion for each body segment of C. elegans. Having accounted for friction-coefficients of the surrounding environment, elastic constants of C. elegans, and its kymogram from experiments, our kinetic model (ElegansBot) reproduced various locomotion of C. elegans such as, but not limited to, forward-backward-(omega turn)-forward locomotion constituting escaping behavior and delta-turn navigation. Additionally, ElegansBot precisely quantified the forces acting on each body segment of C. elegans to allow investigation of the force distribution. This model will facilitate our understanding of the detailed mechanism of various locomotive behaviors at any given friction-coefficients of the surrounding environment. Furthermore, as the model ensures the performance of realistic behavior, it can be used to research actuator-controller interaction between muscles and neuronal circuits.

Blind source separation of event-related potentials using a recurrent neural network

Event-related potentials (ERPs) are a superposition of electric potential differences generated by neurophysiological activity associated with psychophysical events. Spatiotemporal dissociation of these signal sources can supplement conventional ERP analysis and improve source localization. However, results from established source separation methods applied to ERPs can be challenging to interpret. Hence, we have developed a recurrent neural network (RNN) method for blind source separation. The RNN transforms input step pulse signals representing events into corresponding ERP difference waveforms. Source waveforms are obtained from penultimate layer units and scalp maps are obtained from feed-forward output layer weights that project these source waveforms onto EEG electrode amplitudes. An interpretable, sparse source representation is achieved by incorporating L1 regularization of signals obtained from the penultimate layer of the network during training. This RNN method was applied to four ERP difference waveforms (MMN, N170, N400, P3) from the open-access ERP CORE database, and independent component analysis (ICA) was applied to the same data for comparison. The RNN decomposed these ERPs into eleven spatially and temporally separate sources that were less noisy, tended to be more ERP-specific, and were less similar to each other than ICA-derived sources. The RNN sources also had less ambiguity between source waveform amplitude, scalp potential polarity, and equivalent current dipole orientation than ICA sources. In conclusion, the proposed RNN blind source separation method can be effectively applied to grand-average ERP difference waves and holds promise for further development as a computational model of event-related neural signals.

The tale of HORTON: Lessons learned in a decade of scientific software development

HORTON is a free and open-source electronic-structure package written primarily in Python 3 with some underlying C++ components. While HORTON's development has been mainly directed by the research interests of its leading contributing groups, it is designed to be easily modified, extended, and used by other developers of quantum chemistry methods or post-processing techniques. Most importantly, HORTON adheres to modern principles of software development, including modularity, readability, flexibility, comprehensive documentation, automatic testing, version control, and quality-assurance protocols. This article explains how the principles and structure of HORTON have evolved since we started developing it more than a decade ago. We review the features and functionality of the latest HORTON release (version 2.3) and discuss how HORTON is evolving to support electronic structure theory research for the next decade.

Interoperable workflows by exchanging grid-based data between quantum-chemical program packages

Quantum-chemical subsystem and embedding methods require complex workflows that may involve multiple quantum-chemical program packages. Moreover, such workflows require the exchange of voluminous data that go beyond simple quantities, such as molecular structures and energies. Here, we describe our approach for addressing this interoperability challenge by exchanging electron densities and embedding potentials as grid-based data. We describe the approach that we have implemented to this end in a dedicated code, PyEmbed, currently part of a Python scripting framework. We discuss how it has facilitated the development of quantum-chemical subsystem and embedding methods and highlight several applications that have been enabled by PyEmbed, including wave-function theory (WFT) in density-functional theory (DFT) embedding schemes mixing non-relativistic and relativistic electronic structure methods, real-time time-dependent DFT-in-DFT approaches, the density-based many-body expansion, and workflows including real-space data analysis and visualization. Our approach demonstrates, in particular, the merits of exchanging (complex) grid-based data and, in general, the potential of modular software development in quantum chemistry, which hinges upon libraries that facilitate interoperability.

Protocol to identify biomarkers in patients with post-COVID condition using multi-omics and machine learning analysis of human plasma

Here, we present a workflow for analyzing multi-omics data of plasma samples in patients with post-COVID condition (PCC). Applicable to various diseases, we outline steps for data preprocessing and integrating diverse assay datasets. Then, we detail statistical analysis to unveil plasma profile changes and identify biomarker-clinical variable associations. The last two steps discuss machine learning techniques for unsupervised clustering of patients based on their inherent molecular similarities and feature selection to identify predictive biomarkers. For complete details on the use and execution of this protocol, please refer to Wang et al.1.

Simultaneous profiling of chromatin accessibility and DNA methylation in complete plant genomes using long-read sequencing

Epigenetic regulations, including chromatin accessibility, nucleosome positioning and DNA methylation intricately shape genome function. However, current chromatin profiling techniques relying on short-read sequencing technologies fail to characterise highly repetitive genomic regions and cannot detect multiple chromatin features simultaneously. Here, we performed Simultaneous Accessibility and DNA Methylation Sequencing (SAM-seq) of purified plant nuclei. Thanks to the use of long-read nanopore sequencing, SAM-seq enables high-resolution profiling of m6A-tagged chromatin accessibility together with endogenous cytosine methylation in plants. Analysis of naked genomic DNA revealed significant sequence preference biases of m6A-MTases, controllable through a normalisation step. By applying SAM-seq to Arabidopsis and maize nuclei we obtained fine-grained accessibility and DNA methylation landscapes genome-wide. We uncovered crosstalk between chromatin accessibility and DNA methylation within nucleosomes of genes, TEs, and centromeric repeats. SAM-seq also detects DNA footprints over cis-regulatory regions. Furthermore, using the single-molecule information provided by SAM-seq we identified extensive cellular heterogeneity at chromatin domains with antagonistic chromatin marks, suggesting that bivalency reflects cell-specific regulations. SAM-seq is a powerful approach to simultaneously study multiple epigenetic features over unique and repetitive sequences, opening new opportunities for the investigation of epigenetic mechanisms.

Machine learning for hand pose classification from phasic and tonic EMG signals during bimanual activities in virtual reality

Myoelectric prostheses have recently shown significant promise for restoring hand function in individuals with upper limb loss or deficiencies, driven by advances in machine learning and increasingly accessible bioelectrical signal acquisition devices. Here, we first introduce and validate a novel experimental paradigm using a virtual reality headset equipped with hand-tracking capabilities to facilitate the recordings of synchronized EMG signals and hand pose estimation. Using both the phasic and tonic EMG components of data acquired through the proposed paradigm, we compare hand gesture classification pipelines based on standard signal processing features, convolutional neural networks, and covariance matrices with Riemannian geometry computed from raw or xDAWN-filtered EMG signals. We demonstrate the performance of the latter for gesture classification using EMG signals. We further hypothesize that introducing physiological knowledge in machine learning models will enhance their performances, leading to better myoelectric prosthesis control. We demonstrate the potential of this approach by using the neurophysiological integration of the "move command" to better separate the phasic and tonic components of the EMG signals, significantly improving the performance of sustained posture recognition. These results pave the way for the development of new cutting-edge machine learning techniques, likely refined by neurophysiology, that will further improve the decoding of real-time natural gestures and, ultimately, the control of myoelectric prostheses.

Advanced MRI metrics improve the prediction of baseline disease severity for individuals with degenerative cervical myelopathy

BACKGROUND CONTEXT

Degenerative cervical myelopathy (DCM) is the most common form of atraumatic spinal cord injury globally. Degeneration of spinal discs, bony osteophyte growth and ligament pathology results in physical compression of the spinal cord contributing to damage of white matter tracts and grey matter cellular populations. This results in an insidious neurological and functional decline in patients which can lead to paralysis. Magnetic resonance imaging (MRI) confirms the diagnosis of DCM and is a prerequisite to surgical intervention, the only known treatment for this disorder. Unfortunately, there is a weak correlation between features of current commonly acquired MRI scans ("community MRI, cMRI") and the degree of disability experienced by a patient.

PURPOSE

This study examines the predictive ability of current MRI sequences relative to "advanced MRI" (aMRI) metrics designed to detect evidence of spinal cord injury secondary to degenerative myelopathy. We hypothesize that the utilization of higher fidelity aMRI scans will increase the effectiveness of machine learning models predicting DCM severity and may ultimately lead to a more efficient protocol for identifying patients in need of surgical intervention.

STUDY DESIGN/SETTING

Single institution analysis of imaging registry of patients with DCM.

PATIENT SAMPLE

A total of 296 patients in the cMRI group and 228 patients in the aMRI group.

OUTCOME MEASURES

Physiologic measures: accuracy of machine learning algorithms to detect severity of DCM assessed clinically based on the modified Japanese Orthopedic Association (mJOA) scale.

METHODS

Patients enrolled in the Canadian Spine Outcomes Research Network registry with DCM were screened and 296 cervical spine MRIs acquired in cMRI were compared with 228 aMRI acquisitions. aMRI acquisitions consisted of diffusion tensor imaging, magnetization transfer, T2-weighted, and T2*-weighted images. The cMRI group consisted of only T2-weighted MRI scans. Various machine learning models were applied to both MRI groups to assess accuracy of prediction of baseline disease severity assessed clinically using the mJOA scale for cervical myelopathy.

RESULTS

Through the utilization of Random Forest Classifiers, disease severity was predicted with 41.8% accuracy in cMRI scans and 73.3% in the aMRI scans. Across different predictive model variations tested, the aMRI scans consistently produced higher prediction accuracies compared to the cMRI counterparts.

CONCLUSIONS

aMRI metrics perform better in machine learning models at predicting disease severity of patients with DCM. Continued work is needed to refine these models and address DCM severity class imbalance concerns, ultimately improving model confidence for clinical implementation.

Deep convolutional generative adversarial network for generation of computed tomography images of discontinuously carbon fiber reinforced polymer microstructures

Computed tomography images are of utmost importance when characterizing the heterogeneous and complex microstructure of discontinuously fiber reinforced polymers. However, the devices are expensive and the scans are time- and energy-intensive. Through recent advances in generative adversarial networks, the instantaneous generation of endless numbers of images that are representative of the input images and hold physical significance becomes possible. Hence, this work presents a deep convolutional generative adversarial network trained on approximately 30,000 input images from carbon fiber reinforced polyamide 6 computed tomography scans. The challenge lies in the low contrast between the two constituents caused by the close proximity of the density of polyamide 6 and carbon fibers as well as the small fiber diameter compared to the necessary resolution of the images. In addition, the stochastic, heterogeneous microstructure does not follow any logical or predictable rules exacerbating their generation. The quality of the images generated by the trained network of 256 pixel × 256 pixel was investigated through the Fréchet inception distance and nearest neighbor considerations based on Euclidean distance and structural similarity index measure. Additional visual qualitative assessment ensured the realistic depiction of the complex mixed single fiber and fiber bundle structure alongside flow-related physically feasible positioning of the fibers in the polymer. The authors foresee additionally huge potential in creating three-dimensional representative volume elements typically used in composites homogenization.

Brain orchestra under spontaneous conditions: Identifying communication modules from the functional architecture of area V1

We used two-photon imaging to record from granular and supragranular layers in mouse primary visual cortex (V1) under spontaneous conditions and applied an extension of the spike time tiling coefficient (STTC; introduced by Cutts and Eglen) to map functional connectivity architecture within and across layers. We made several observations: Approximately, 19-34% of neuronal pairs within 300 μm of each other exhibit statistically significant functional connections, compared to ~10% at distances of 1mm or more. As expected, neuronal pairs with similar tuning functions exhibit a significant, though relatively small, increase in the fraction of functional inter-neuronal correlations. In contrast, internal state as reflected by pupillary diameter or aggregate neuronal activity appears to play a much stronger role in determining inter-neuronal correlation distributions and topography. Overall, inter-neuronal correlations appear to be slightly more prominent in L4. The first-order functionally connected (i.e., direct) neighbors of neurons determine the hub structure of the V1 microcircuit. L4 exhibits a nearly flat degree of connectivity distribution, extending to higher values than seen in supragranular layers, whose distribution drops exponentially. In all layers, functional connectivity exhibits small-world characteristics and network robustness. The probability of firing of L2/3 pyramidal neurons can be predicted as a function of the aggregate activity in their first-order functionally connected partners within L4, which represent their putative input group. The functional form of this prediction conforms well to a ReLU function, reaching up to firing probability one in some neurons. Interestingly, the properties of L2/3 pyramidal neurons differ based on the size of their L4 functional connectivity group. Specifically, L2/3 neurons with small layer-4 degrees of connectivity appear to be more sensitive to the firing of their L4 functional connectivity partners, suggesting they may be more effective at transmitting synchronous activity downstream from L4. They also appear to fire largely independently from each other, compared to neurons with high layer-4 degrees of connectivity, and are less modulated by changes in pupil size and aggregate population dynamics. Information transmission is best viewed as occurring from neuronal ensembles in L4 to neuronal ensembles in L2/3. Under spontaneous conditions, we were able to identify such candidate neuronal ensembles, which exhibit high sensitivity, precision, and specificity for L4 to L2/3 information transmission. In sum, functional connectivity analysis under spontaneous activity conditions reveals a modular neuronal ensemble architecture within and across granular and supragranular layers of mouse primary visual cortex. Furthermore, modules with different degrees of connectivity appear to obey different rules of engagement and communication across the V1 columnar circuit.

Scalable computation of anisotropic vibrations for large macromolecular assemblies

The Normal Mode Analysis (NMA) is a standard approach to elucidate the anisotropic vibrations of macromolecules at their folded states, where low-frequency collective motions can reveal rearrangements of domains and changes in the exposed surface of macromolecules. Recent advances in structural biology have enabled the resolution of megascale macromolecules with millions of atoms. However, the calculation of their vibrational modes remains elusive due to the prohibitive cost associated with constructing and diagonalizing the underlying eigenproblem and the current approaches to NMA are not readily adaptable for efficient parallel computing on graphic processing unit (GPU). Here, we present eigenproblem construction and diagonalization approach that implements level-structure bandwidth-reducing algorithms to transform the sparse computation in NMA to a globally-sparse-yet-locally-dense computation, allowing batched tensor products to be most efficiently executed on GPU. We map, optimize, and compare several low-complexity Krylov-subspace eigensolvers, supplemented by techniques such as Chebyshev filtering, sum decomposition, external explicit deflation and shift-and-inverse, to allow fast GPU-resident calculations. The method allows accurate calculation of the first 1000 vibrational modes of some largest structures in PDB ( > 2.4 million atoms) at least 250 times faster than existing methods.

Improved Resolution and Image Quality of Musculoskeletal Magnetic Resonance Imaging using Deep Learning-based Denoising Reconstruction: A Prospective Clinical Study

OBJECTIVE

To prospectively evaluate a deep learning-based denoising reconstruction (DLR) for improved resolution and image quality in musculoskeletal (MSK) magnetic resonance imaging (MRI).

METHODS

Images from 137 contrast-weighted sequences in 40 MSK patients were evaluated. Each sequence was performed twice, first with the routine parameters and reconstructed with a routine reconstruction filter (REF), then with higher resolution and reconstructed with DLR, and with three conventional reconstruction filters (NL2, GA43, GA53). The five reconstructions (REF, DLR, NL2, GA43, and GA53) were de-identified, randomized, and blindly reviewed by three MSK radiologists using eight scoring criteria and a forced ranking. Quantitative SNR, CNR, and structure's full width at half maximum (FWHM) for resolution assessment were measured and compared. To account for repeated measures, Generalized Estimating Equations (GEE) with Bonferroni adjustment was used to compare the reader's scores, SNR, CNR, and FWHM between DLR vs. NL2, GA43, GA53, and REF.

RESULTS

Compared to the routine REF images, the resolution was improved by 47.61% with DLR from 0.39 ± 0.15 mm2 to 0.20 ± 0.06 mm2 (p < 0.001). Per-sequence average scan time was shortened by 7.93% with DLR from 165.58 ± 21.86 s to 152.45 ± 25.65 s (p < 0.001). Based on the average scores, DLR images were rated significantly higher in all image quality criteria and the forced ranking (p < 0.001).

CONCLUSION

This prospective clinical evaluation demonstrated that DLR allows approximately two times finer resolution and improved image quality compared to the standard-of-care images.

Retention of the RNA ends provides the molecular memory for maintaining the activation of the Csm complex

The type III CRISPR-Cas effector complex Csm functions as a molecular Swiss army knife that provides multilevel defense against foreign nucleic acids. The coordinated action of three catalytic activities of the Csm complex enables simultaneous degradation of the invader's RNA transcripts, destruction of the template DNA and synthesis of signaling molecules (cyclic oligoadenylates cAn) that activate auxiliary proteins to reinforce CRISPR-Cas defense. Here, we employed single-molecule techniques to connect the kinetics of RNA binding, dissociation, and DNA hydrolysis by the Csm complex from Streptococcus thermophilus. Although single-stranded RNA is cleaved rapidly (within seconds), dual-color FCS experiments and single-molecule TIRF microscopy revealed that Csm remains bound to terminal RNA cleavage products with a half-life of over 1 hour while releasing the internal RNA fragments quickly. Using a continuous fluorescent DNA degradation assay, we observed that RNA-regulated single-stranded DNase activity decreases on a similar timescale. These findings suggest that after fast target RNA cleavage the terminal RNA cleavage products stay bound within the Csm complex, keeping the Cas10 subunit activated for DNA destruction. Additionally, we demonstrate that during Cas10 activation, the complex remains capable of RNA turnover, i.e. of ongoing degradation of target RNA.

The effect of time normalization and biomechanical signal processing techniques of ground reaction force curves on deep-learning model performance

Time-series data are common in biomechanical studies. These data often undergo pre-processing steps such as time normalization or filtering prior to use in further analyses, including deep-learning classification. In this context, it remains unclear how these preprocessing steps affect deep-learning model performance. Thus, the aim of this study is to assess the effect of time-normalization and filtering on the performance of deep-learning classification models. We also investigated the effect of amplitude scaling. Using a public dataset (Gutenburg Gait Database, a ground reaction force database of level overground walking at self-selected walking speed involving 350 healthy individuals), we trained convolutional neural network (CNN) and long short-term memory (LSTM) models to predict binary sex (male, female) using three-dimensional ground-reaction forces to which we applied different processing approaches: zero padding, interpolation to 100% of signal, filtering, and scaling (min-max, body mass). The results show that transformations resulted in differences in model performances. Highest performance was obtained using unfiltered data, zero-padding, and min-max amplitude scaling (F1-score of 91 and 87% for CNN and LSTM, respectively). Not filtering data and using min-max scaling generally improve performance for both model architectures. For interpolation, results are not consistent across model architectures. This study suggests that processing steps must be considered in applications where deep-learning classification performance is relevant.

Toward Improving the Generation Quality of Autoregressive Slot VAEs

Unconditional scene inference and generation are challenging to learn jointly with a single compositional model. Despite encouraging progress on models that extract object-centric representations ("slots") from images, unconditional generation of scenes from slots has received less attention. This is primarily because learning the multiobject relations necessary to imagine coherent scenes is difficult. We hypothesize that most existing slot-based models have a limited ability to learn object correlations. We propose two improvements that strengthen object correlation learning. The first is to condition the slots on a global, scene-level variable that captures higher-order correlations between slots. Second, we address the fundamental lack of a canonical order for objects in images by proposing to learn a consistent order to use for the autoregressive generation of scene objects. Specifically, we train an autoregressive slot prior to sequentially generate scene objects following a learned order. Ordered slot inference entails first estimating a randomly ordered set of slots using existing approaches for extracting slots from images, then aligning those slots to ordered slots generated autoregressively with the slot prior. Our experiments across three multiobject environments demonstrate clear gains in unconditional scene generation quality. Detailed ablation studies are also provided that validate the two proposed improvements.

SqueakOut: Autoencoder-based segmentation of mouse ultrasonic vocalizations

Mice emit ultrasonic vocalizations (USVs) that are important for social communication. Despite great advancements in tools to detect USVs from audio files in the recent years, highly accurate segmentation of USVs from spectrograms (i.e., removing noise) remains a significant challenge. Here, we present a new dataset of 12,954 annotated spectrograms explicitly labeled for mouse USV segmentation. Leveraging this dataset, we developed SqueakOut, a lightweight (4.6M parameters) fully convolutional autoencoder that achieves high accuracy in supervised segmentation of USVs from spectrograms, with a Dice score of 90.22. SqueakOut combines a MobileNetV2 backbone with skip connections and transposed convolutions to precisely segment USVs. Using stochastic data augmentation techniques and a hybrid loss function, SqueakOut learns robust segmentation across varying recording conditions. We evaluate SqueakOut's performance, demonstrating substantial improvements over existing methods like VocalMat (63.82 Dice score). The accurate USV segmentations enabled by SqueakOut will facilitate novel methods for vocalization classification and more accurate analysis of mouse communication. To promote further research, we release the annotated 12,954 spectrogram USV segmentation dataset and the SqueakOut implementation publicly.

A mechanosensory feedback that uncouples external and self-generated sensory responses in the olfactory cortex

Sampling behaviors have sensory consequences that can hinder perceptual stability. In olfaction, sniffing affects early odor encoding, mimicking a sudden change in odor concentration. We examined how the inhalation speed affects the representation of odor concentration in the main olfactory cortex. Neurons combine the odor input with a global top-down signal preceding the sniff and a mechanosensory feedback generated by the air passage through the nose during inhalation. Still, the population representation of concentration is remarkably sniff invariant. This is because the mechanosensory and olfactory responses are uncorrelated within and across neurons. Thus, faster odor inhalation and an increase in concentration change the cortical activity pattern in distinct ways. This encoding strategy affords tolerance to potential concentration fluctuations caused by varying inhalation speeds. Since mechanosensory reafferences are widespread across sensory systems, the coding scheme described here may be a canonical strategy to mitigate the sensory ambiguities caused by movements.

Qubit Count Reduction by Orthogonally Constrained Orbital Optimization for Variational Quantum Excited-State Solvers

We propose a state-averaged orbital optimization scheme for improving the accuracy of excited states of the electronic structure Hamiltonian for use on near-term quantum computers. Instead of parameterizing the orbital rotation operator in the conventional fashion as an exponential of an antihermitian matrix, we parameterize the orbital rotation as a general partial unitary matrix. Whereas conventional orbital optimization methods minimize the state-averaged energy using successive Newton steps of the second-order Taylor expansion of the energy, the method presented here optimizes the state-averaged energy using an orthogonally constrained gradient projection method that does not require any expansion approximations. Through extensive benchmarking of the method on various small molecular systems, we find that the method is capable of producing more accurate results than fixed basis FCI while simultaneously using fewer qubits. In particular, we show that for H2, the method is capable of matching the accuracy of FCI in the cc-pVTZ basis (56 qubits) while only using 14 qubits.

Synaptic Information Storage Capacity Measured With Information Theory

Variation in the strength of synapses can be quantified by measuring the anatomical properties of synapses. Quantifying precision of synaptic plasticity is fundamental to understanding information storage and retrieval in neural circuits. Synapses from the same axon onto the same dendrite have a common history of coactivation, making them ideal candidates for determining the precision of synaptic plasticity based on the similarity of their physical dimensions. Here, the precision and amount of information stored in synapse dimensions were quantified with Shannon information theory, expanding prior analysis that used signal detection theory (Bartol et al., 2015). The two methods were compared using dendritic spine head volumes in the middle of the stratum radiatum of hippocampal area CA1 as well-defined measures of synaptic strength. Information theory delineated the number of distinguishable synaptic strengths based on nonoverlapping bins of dendritic spine head volumes. Shannon entropy was applied to measure synaptic information storage capacity (SISC) and resulted in a lower bound of 4.1 bits and upper bound of 4.59 bits of information based on 24 distinguishable sizes. We further compared the distribution of distinguishable sizes and a uniform distribution using Kullback-Leibler divergence and discovered that there was a nearly uniform distribution of spine head volumes across the sizes, suggesting optimal use of the distinguishable values. Thus, SISC provides a new analytical measure that can be generalized to probe synaptic strengths and capacity for plasticity in different brain regions of different species and among animals raised in different conditions or during learning. How brain diseases and disorders affect the precision of synaptic plasticity can also be probed.

From NMR to AI: Designing a Novel Chemical Representation to Enhance Machine Learning Predictions of Physicochemical Properties

A novel approach to the utilization of nuclear magnetic resonance (NMR) spectroscopy data in the prediction of logD through machine learning algorithms is shown. In the analysis, a data set of 754 chemical compounds, organized into 30 clusters, was evaluated using advanced machine learning models, such as Support Vector Regression (SVR), Gradient Boosting, and AdaBoost, and comprehensive validation and testing methods were employed, including 10-fold cross-validation, bootstrapping, and leave-one-out. The study revealed the superior performance of the Bucket Integration method for dimensionality reduction, consistently yielding the lowest root mean square error (RMSE) across all data sets and normalization schemes. The SVR prediction models demonstrated remarkable computational efficiency and low cost, with the best RMSE value reaching 0.66. Our best model outperformed existing tools like JChem Suite's logD Predictor (0.91) and CplogD (1.27), and a comparison with traditional molecular representations yielded a comparable RMSE (0.50), emphasizing the robustness of our NMR data integration. The widespread availability of NMR data in pharmaceutical and industrial research presents an untapped resource for predictive modeling, highlighting the need for accessible methodologies like ours that complement the analytical toolbox beyond conventional 2D approaches. Our approach, designed to leverage the rich spatial data from NMR spectroscopy, provides additional insights and enriches drug discovery and computational chemistry with a freely accessible tool.

Biomolecular condensates form spatially inhomogeneous network fluids

The functions of biomolecular condensates are thought to be influenced by their material properties, and these will be determined by the internal organization of molecules within condensates. However, structural characterizations of condensates are challenging, and rarely reported. Here, we deploy a combination of small angle neutron scattering, fluorescence recovery after photobleaching, and coarse-grained molecular dynamics simulations to provide structural descriptions of model condensates that are formed by macromolecules from nucleolar granular components (GCs). We show that these minimal facsimiles of GCs form condensates that are network fluids featuring spatial inhomogeneities across different length scales that reflect the contributions of distinct protein and peptide domains. The network-like inhomogeneous organization is characterized by a coexistence of liquid- and gas-like macromolecular densities that engenders bimodality of internal molecular dynamics. These insights suggest that condensates formed by multivalent proteins share features with network fluids formed by systems such as patchy or hairy colloids.

Dynamic antennal positioning allows honeybee followers to decode the dance

The honeybee waggle dance has been widely studied as a communication system, yet we know little about how nestmates assimilate the information needed to navigate toward the signaled resource. They are required to detect the dancer's orientation relative to gravity and duration of the waggle phase and translate this into a flight vector with a direction relative to the sun1 and distance from the hive.2,3 Moreover, they appear capable of doing so from varied, dynamically changing positions around the dancer. Using high-speed, high-resolution video, we have uncovered a previously unremarked correlation between antennal position and the relative body axes of dancer and follower bees. Combined with new information about antennal inputs4,5 and spatial encoding in the insect central complex,6,7 we show how a neural circuit first proposed to underlie path integration could be adapted to decoding the dance and acquiring the signaled information as a flight vector that can be followed to the resource. This provides the first plausible account of how the bee brain could support the interpretation of its dance language.

pyCOFBuilder: A Python Package for Automated Creation of Covalent Organic Framework Models Based on the Reticular Approach

Covalent organic frameworks (COFs) have gained significant popularity in recent years due to their unique ability to provide a high surface area and customizable pore geometry and chemistry, making them an ideal choice for a wide range of applications. However, exploring COFs experimentally can be arduous and time-consuming due to their immense number of potential structures. As a result, computational high-throughput studies have become an attractive option. Nevertheless, generating COF structures can also be a challenging and time-consuming task. To address this challenge, here, we introduce the pyCOFBuilder, an open-source Python package designed to facilitate the generation of COF structures for computational studies. The pyCOFBuilder software provides an easy-to-use set of functionalities to generate COF structures following the reticular approach. In this paper, we describe the implementation, main features, and capabilities of the pyCOFBuilder, demonstrating its utility for generating COF structures with varying topologies and chemical properties. pyCOFBuilder is freely available on GitHub at https://github.com/lipelopesoliveira/pyCOFBuilder.

Unraveling the role of Slc10a4 in auditory processing and sensory motor gating: Implications for neuropsychiatric disorders?

BACKGROUND

Psychiatric disorders, such as schizophrenia, are complex and challenging to study, partly due to the lack of suitable animal models. However, the absence of the Slc10a4 gene, which codes for a monoaminergic and cholinergic associated vesicular transporter protein, in knockout mice (Slc10a4-/-), leads to the accumulation of extracellular dopamine. A major challenge for studying schizophrenia is the lack of suitable animal models that accurately represent the disorder. We sought to overcome this challenge by using Slc10a4-/- mice as a potential model, considering their altered dopamine levels. This makes them a potential animal model for schizophrenia, a disorder known to be associated with altered dopamine signaling in the brain.

METHODS

The locomotion, auditory sensory filtering and prepulse inhibition (PPI) of Slc10a4-/- mice were quantified and compared to wildtype (WT) littermates. Intrahippocampal electrodes were used to record auditory event-related potentials (aERPs) for quantifying sensory filtering in response to paired-clicks. The channel above aERPs phase reversal was chosen for reliably comparing results between animals, and aERPs amplitude and latency of click responses were quantified. WT and Slc10a4-/- mice were also administered subanesthetic doses of ketamine to provoke psychomimetic behavior.

RESULTS

Baseline locomotion during auditory stimulation was similar between Slc10a4-/- mice and WT littermates. In WT animals, normal auditory processing was observed after i.p saline injections, and it was maintained under the influence of 5 mg/kg ketamine, but disrupted by 20 mg/kg ketamine. On the other hand, Slc10a4-/- mice did not show significant differences between N40 S1 and S2 amplitude responses in saline or low dose ketamine treatment. Auditory gating was considered preserved since the second N40 peak was consistently suppressed, but with increased latency. The P80 component showed higher amplitude, with shorter S2 latency under saline and 5 mg/kg ketamine treatment in Slc10a4-/- mice, which was not observed in WT littermates. Prepulse inhibition was also decreased in Slc10a4-/- mice when the longer interstimulus interval of 100 ms was applied, compared to WT littermates.

CONCLUSION

The Slc10a4-/- mice responses indicate that cholinergic and monoaminergic systems participate in the PPI magnitude, in the temporal coding (response latency) of the auditory sensory gating component N40, and in the amplitude of aERPs P80 component. These results suggest that Slc10a4-/- mice can be considered as potential models for neuropsychiatric conditions.

Multi-modality deep learning model reaches high prediction accuracy in the diagnosis of ovarian cancer

We evaluated the diagnostic performance of a multimodal deep-learning (DL) model for ovarian mass differential diagnosis. This single-center retrospective study included 1,054 ultrasound (US)-detected ovarian tumors (699 benign and 355 malignant). Patients were randomly divided into training (n = 675), validation (n = 169), and testing (n = 210) sets. The model was developed using ResNet-50. Three DL-based models were proposed for benign-malignant classification of these lesions: single-modality model that only utilized US images; dual-modality model that used US images and menopausal status as inputs; and multi-modality model that integrated US images, menopausal status, and serum indicators. After 5-fold cross-validation, 210 lesions were tested. We evaluated the three models using the area under the curve (AUC), accuracy, sensitivity, and specificity. The multimodal model outperformed the single- and dual-modality models with 93.80% accuracy and 0.983 AUC. The Multimodal ResNet-50 DL model outperformed the single- and dual-modality models in identifying benign and malignant ovarian tumors.

Effect of Three-Dimensional-Printed Thermoplastics Used in Sensor Housings on Common Atmospheric Trace Gasses

Low-cost air quality sensors (LCSs) are becoming more ubiquitous as individuals and communities seek to reduce their exposure to poor air quality. Compact, efficient, and aesthetically designed sensor housings that do not interfere with the target air quality measurements are a necessary component of a low-cost sensing system. The selection of appropriate housing material can be an important factor in air quality applications employing LCSs. Three-dimensional printing, specifically fused deposition modeling (FDM), is a standard for prototyping and small-scale custom plastics production because of its low cost and ability for rapid iteration. However, little information exists about whether FDM-printed thermoplastics affect measurements of trace atmospheric gasses. This study investigates how five different FDM-printed thermoplastics (ABS, PETG, PLA, PC, and PVDF) affect the concentration of five common atmospheric trace gasses (CO, CO2, NO, NO2, and VOCs). The laboratory results show that the thermoplastics, except for PVDF, exhibit VOC off-gassing. The results also indicate no to limited interaction between all of the thermoplastics and CO and CO2 and a small interaction between all of the thermoplastics and NO and NO2.

ModDotPlot-Rapid and interactive visualization of complex repeats

Motivation

A common method for analyzing genomic repeats is to produce a sequence similarity matrix visualized via a dot plot. Innovative approaches such as StainedGlass have improved upon this classic visualization by rendering dot plots as a heatmap of sequence identity, enabling researchers to better visualize multi-megabase tandem repeat arrays within centromeres and other heterochromatic regions of the genome. However, computing the similarity estimates for heatmaps requires high computational overhead and can suffer from decreasing accuracy.

Results

In this work we introduce ModDotPlot, an interactive and alignment-free dot plot viewer. By approximating average nucleotide identity via a k-mer-based containment index, ModDotPlot produces accurate plots orders of magnitude faster than StainedGlass. We accomplish this through the use of a hierarchical modimizer scheme that can visualize the full 128 Mbp genome of Arabidopsis thaliana in under 5 minutes on a laptop. ModDotPlot is bundled with a graphical user interface supporting real-time interactive navigation of entire chromosomes.

Availability and Implementation

ModDotPlot is available at https://github.com/marbl/ModDotPlot.

Development of New Predictive Equations for the Resting Metabolic Rate (RMR) of Women with Lipedema

This study aimed to develop a novel predictive equation for calculating resting metabolic rate (RMR) in women with lipedema. We recruited 119 women diagnosed with lipedema from the Angiology Outpatient Clinic at Wroclaw Medical University, Poland. RMR was assessed using indirect calorimetry, while body composition and anthropometric measurements were conducted using standardized protocols. Due to multicollinearity among predictors, classical multiple regression was deemed inadequate for developing the new equation. Therefore, we employed machine learning techniques, utilizing principal component analysis (PCA) for dimensionality reduction and predictor selection. Regression models, including support vector regression (SVR), random forest regression (RFR), and k-nearest neighbor (kNN) were evaluated in Python's scikit-learn framework, with hyperparameter tuning via GridSearchCV. Model performance was assessed through mean absolute percentage error (MAPE) and cross-validation, complemented by Bland-Altman plots for method comparison. A novel equation incorporating body composition parameters was developed, addressing a gap in accurate RMR prediction methods. By incorporating measurements of body circumference and body composition parameters alongside traditional predictors, the model's accuracy was improved. The segmented regression model outperformed others, achieving an MAPE of 10.78%. The proposed predictive equation for RMR offers a practical tool for personalized treatment planning in patients with lipedema.

PaCS-Toolkit: Optimized Software Utilities for Parallel Cascade Selection Molecular Dynamics (PaCS-MD) Simulations and Subsequent Analyses

Parallel cascade selection molecular dynamics (PaCS-MD) is an enhanced conformational sampling method conducted as a "repetition of time leaps in parallel worlds", comprising cycles of multiple molecular dynamics (MD) simulations performed in parallel and selection of the initial structures of MDs for the next cycle. We developed PaCS-Toolkit, an optimized software utility enabling the use of different MD software and trajectory analysis tools to facilitate the execution of the PaCS-MD simulation and analyze the obtained trajectories, including the preparation for the subsequent construction of the Markov state model. PaCS-Toolkit is coded with Python, is compatible with various computing environments, and allows for easy customization by editing the configuration file and specifying the MD software and analysis tools to be used. We present the software design of PaCS-Toolkit and demonstrate applications of PaCS-MD variations: original targeted PaCS-MD to peptide folding; rmsdPaCS-MD to protein domain motion; and dissociation PaCS-MD to ligand dissociation from adenosine A2A receptor.

Mitotic chromosomes are self-entangled and disentangle through a topoisomerase-II-dependent two-stage exit from mitosis

The topological state of chromosomes determines their mechanical properties, dynamics, and function. Recent work indicated that interphase chromosomes are largely free of entanglements. Here, we use Hi-C, polymer simulations, and multi-contact 3C and find that, by contrast, mitotic chromosomes are self-entangled. We explore how a mitotic self-entangled state is converted into an unentangled interphase state during mitotic exit. Most mitotic entanglements are removed during anaphase/telophase, with remaining ones removed during early G1, in a topoisomerase-II-dependent process. Polymer models suggest a two-stage disentanglement pathway: first, decondensation of mitotic chromosomes with remaining condensin loops produces entropic forces that bias topoisomerase II activity toward decatenation. At the second stage, the loops are released, and the formation of new entanglements is prevented by lower topoisomerase II activity, allowing the establishment of unentangled and territorial G1 chromosomes. When mitotic entanglements are not removed in experiments and models, a normal interphase state cannot be acquired.

A New Auto-Regressive Multi-Variable Modified Auto-Encoder for Multivariate Time-Series Prediction: A Case Study with Application to COVID-19 Pandemics

The SARS-CoV-2 global pandemic prompted governments, institutions, and researchers to investigate its impact, developing strategies based on general indicators to make the most precise predictions possible. Approaches based on epidemiological models were used but the outcomes demonstrated forecasting with uncertainty due to insufficient or missing data. Besides the lack of data, machine-learning models including random forest, support vector regression, LSTM, Auto-encoders, and traditional time-series models such as Prophet and ARIMA were employed in the task, achieving remarkable results with limited effectiveness. Some of these methodologies have precision constraints in dealing with multi-variable inputs, which are important for problems like pandemics that require short and long-term forecasting. Given the under-supply in this scenario, we propose a novel approach for time-series prediction based on stacking auto-encoder structures using three variations of the same model for the training step and weight adjustment to evaluate its forecasting performance. We conducted comparison experiments with previously published data on COVID-19 cases, deaths, temperature, humidity, and air quality index (AQI) in São Paulo City, Brazil. Additionally, we used the percentage of COVID-19 cases from the top ten affected countries worldwide until May 4th, 2020. The results show 80.7% and 10.3% decrease in RMSE to entire and test data over the distribution of 50 trial-trained models, respectively, compared to the first experiment comparison. Also, model type#3 achieved 4th better overall ranking performance, overcoming the NBEATS, Prophet, and Glounts time-series models in the second experiment comparison. This model shows promising forecast capacity and versatility across different input dataset lengths, making it a prominent forecasting model for time-series tasks.

3D pose estimation to detect posture transition in free-stall housed dairy cows

Free stall comfort is reflected in various indicators, including the ability for dairy cattle to display unhindered posture transition movements in the cubicles. To ensure farm animal welfare, it is instrumental for the farm management to be able to continuously monitor occurrences of abnormal motions. Advances in computer vision have enabled accurate kinematic measurements in several fields such as human, equine and bovine biomechanics. An important step upstream to measuring displacement during posture transitions is to determine that the behavior is accurately detected. In this study, we propose a framework for detecting lying to standing posture transitions from 3D pose estimation data. A multi-view computer vision system recorded posture transitions between Dec. 2021 and Apr. 2022 in a Swedish stall housing 183 individual cows. The output data consisted of the 3D coordinates of specific anatomical landmarks. Sensitivity of posture transition detection was 88.2% while precision reached 99.5%. Analyzing those transition movements, breakpoints detected the timestamp of onset of the rising motion, which was compared with that annotated by observers. Agreement between observers, measured by intra-class correlation, was 0.85 between 3 human observers and 0.81 when adding the automated detection. The intra-observer mean absolute difference in annotated timestamps ranged from 0.4s to 0.7s. The mean absolute difference between each observer and the automated detection ranged from 1.0s to 1.3s. There was a significant difference in annotated timestamp between all observer pairs but not between the observers and the automated detection, leading to the conclusion that the automated detection does not introduce a distinct bias. We conclude that the model is able to accurately detect the phenomenon of interest and that it is equatable to an observer.

Cellular geometry and epithelial-mesenchymal plasticity intersect with PIEZO1 in breast cancer cells

Differences in shape can be a distinguishing feature between different cell types, but the shape of a cell can also be dynamic. Changes in cell shape are critical when cancer cells escape from the primary tumor and undergo major morphological changes that allow them to squeeze between endothelial cells, enter the vasculature, and metastasize to other areas of the body. A shift from rounded to spindly cellular geometry is a consequence of epithelial-mesenchymal plasticity, which is also associated with changes in gene expression, increased invasiveness, and therapeutic resistance. However, the consequences and functional impacts of cell shape changes and the mechanisms through which they occur are still poorly understood. Here, we demonstrate that altering the morphology of a cell produces a remodeling of calcium influx via the ion channel PIEZO1 and identify PIEZO1 as an inducer of features of epithelial-to-mesenchymal plasticity. Combining automated epifluorescence microscopy and a genetically encoded calcium indicator, we demonstrate that activation of the PIEZO1 force channel with the PIEZO1 agonist, YODA 1, induces features of epithelial-to-mesenchymal plasticity in breast cancer cells. These findings suggest that PIEZO1 is a critical point of convergence between shape-induced changes in cellular signaling and epithelial-mesenchymal plasticity in breast cancer cells.

An overview of log likelihood ratio cost in forensic science - Where is it used and what values can we expect?

There is increasing support for reporting evidential strength as a likelihood ratio (LR) and increasing interest in (semi-)automated LR systems. The log-likelihood ratio cost (Cllr) is a popular metric for such systems, penalizing misleading LRs further from 1 more. Cllr = 0 indicates perfection while Cllr = 1 indicates an uninformative system. However, beyond this, what constitutes a "good" Cllr is unclear. Aiming to provide handles on when a Cllr is "good", we studied 136 publications on (semi-)automated LR systems. Results show Cllr use heavily depends on the field, e.g., being absent in DNA analysis. Despite more publications on automated LR systems over time, the proportion reporting Cllr remains stable. Noticeably, Cllr values lack clear patterns and depend on the area, analysis and dataset. As LR systems become more prevalent, comparing them becomes crucial. This is hampered by different studies using different datasets. We advocate using public benchmark datasets to advance the field.

Long-term disgust habituation with limited generalisation in care home workers

Countless workers handle bodily effluvia and body envelope violations every working day, and consequentially face deeply unpleasant levels of disgust. Understanding if and how they adapt can help inform policies to improve worker satisfaction and reduce staff turnover. So far, limited evidence exist that self-reported disgust is reduced (or lower to begin with) among those employed in high-disgust environments. However, it is unclear if this is due to demand effects or translates into real behavioural changes. Here, we tested healthcare assistants (N = 32) employed in UK care homes and a control sample (N = 50). We replicated reduced self-reported pathogen disgust sensitivity in healthcare workers compared to controls. We also found it negatively correlated with career duration, suggesting long-term habituation. Furthermore, we found that healthcare assistants showed no behavioural disgust avoidance on a web-based preferential looking task (equivalent to eye tracking). Surprisingly, this extended to disgust elicitors found outside care homes, suggesting generalisation of disgust habituation. While we found no difference between bodily effluvia (core disgust) and body envelope violations (gore disgust), generalisation did not extend to other domains: self-reported sexual and moral disgust sensitivity were not different between healthcare assistants and the control group, nor was there a correlation with career duration. In sum, our work confirms that people in high-frequency disgust employment are less sensitive to pathogen disgust. Crucially, we provide preliminary evidence that this is due to a process of long-term habituation with generalisation to disgust-elicitors within the pathogen domain, but not beyond it.