bluuebunny/crossref_metadata_2025_split huggingface dataset:
Relationship Between Irregularities of the Nuclear Envelope and Mitochondria in HeLa cells Observed with Electron Microscopy
Authors: Brito-Pacheco D. , Karabağ C. , Brito-Loeza C. , Giannopoulos P. , Reyes-Aldasoro C.C. 
Published: November 2023

ABSTRACTThis paper describes a methodology to analyse the complexity of HeLa cells as observed with electron microscopy, in particular the relationship between mitochondria and the roughness of the nuclear envelope as reflected by the invaginations of the surface. For this purpose, several segmentation mitochondria algorithms were quantitatively compared, namely: Topology, Image Processing, Topology and Image Processing, and Deep Learning, which provided the highest accuracy. The invaginations were successfully segmented with one image processing algorithm. Metrics were extracted for both structures and correlations between the mitochondria and invaginations were explored for 25 segmented cells. It was found that there was a positive correlation between the volume of invaginations and the volume of mitochondria, and negative correlations between the number and the mean volume of mitochondria, and between the volume of the cytoplasm and the aspect ratio of mitochondria. These results suggest that there is a relationship between the shape of a cell, its nucleus and its mitochondria; as well as a relationship between the number of mitochondria and their shapes. Whilst these results were obtained from a single cell line and a relatively small number of cells, they encourage further study as the methodology proposed can be easily applied to other cells and settings.Code and data are freely available. HeLa images are available fromhttp://dx.doi.org/10.6019/EMPIAR-10094, code fromhttps://github.com/reyesaldasoro/MitoEM, and segmented nuclei, cells, invaginations and mitochondria fromhttps://github.com/reyesaldasoro/HeLa_Cell_Data.
Calibrating Bayesian decoders of neural spiking activity
Authors: Wei Ganchao , Tajik Mansouri Zeinab , Wang Xiaojing , Stevenson Ian H. 
Published: November 2023

AbstractAccurately decoding external variables from observations of neural activity is a major challenge in systems neuroscience. Bayesian decoders, that provide probabilistic estimates, are some of the most widely used. Here we show how, in many common settings, the probabilistic predictions made by traditional Bayesian decoders are overconfident. That is, the estimates for the decoded stimulus or movement variables are more certain than they should be. We then show how Bayesian decoding with latent variables, taking account of low-dimensional shared variability in the observations, can improve calibration, although additional correction for overconfidence is still needed. We examine: 1) decoding the direction of grating stimuli from spike recordings in primary visual cortex in monkeys, 2) decoding movement direction from recordings in primary motor cortex in monkeys, 3) decoding natural images from multi-region recordings in mice, and 4) decoding position from hippocampal recordings in rats. For each setting we characterize the overconfidence, and we describe a possible method to correct miscalibration post-hoc. Properly calibrated Bayesian decoders may alter theoretical results on probabilistic population coding and lead to brain machine interfaces that more accurately reflect confidence levels when identifying external variables.Significance StatementBayesian decoding is a statistical technique for making probabilistic predictions about external stimuli or movements based on recordings of neural activity. These predictions may be useful for robust brain machine interfaces or for understanding perceptual or behavioral confidence. However, the probabilities produced by these models do not always match the observed outcomes. Just as a weather forecast predicting a 50% chance of rain may not accurately correspond to an outcome of rain 50% of the time, Bayesian decoders of neural activity can be miscalibrated as well. Here we identify and measure miscalibration of Bayesian decoders for neural spiking activity in a range of experimental settings. We compare multiple statistical models and demonstrate how overconfidence can be corrected.
De novoDesign of Peptides that Bind Specific Conformers of α-Synuclein
Authors: Wallace Hailey M. , Yang Hyunjun , Tan Sophia , Pan Henry S. , Yang Rose , Xu Junyi , Jo Hyunil , Condello Carlo , Polizzi Nicholas F. , DeGrado William F. 
Published: November 2023

AbstractInsoluble amyloids rich in cross-β fibrils are observed in a number of neurodegenerative diseases. Depending on the clinicopathology, the amyloids can adopt distinct supramolecular assemblies, termed conformational strains. However, rapid methods to study amyloid in a conformationally specific manner are lacking. We introduce a novel computational method forde novodesign of peptides that tile the surface of α-synuclein fibrils in a conformationally specific manner. Our method begins by identifying surfaces that are unique to the conformational strain of interest, which becomes a “target backbone” for the design of a peptide binder. Next, we interrogate structures in the PDB database with high geometric complementarity to the target. Then, we identify secondary structural motifs that interact with this target backbone in a favorable, highly occurring geometry. This method produces monomeric helical motifs with a favorable geometry for interaction with the strands of the underlying amyloid. Each motif is then symmetrically replicated to form a monolayer that tiles the amyloid surface. Finally, amino acid sequences of the peptide binders are computed to provide a sequence with high geometric and physicochemical complementarity to the target amyloid. This method was applied to a conformational strain of α-synuclein fibrils, resulting in a peptide with high specificity for the target relative to other amyloids formed by α-synuclein, tau, or Aβ40. This designed peptide also markedly slowed the formation of α-synuclein amyloids. Overall, this method offers a new tool for examining conformational strains of amyloid proteins.
Robust parametric UMAP for the analysis of single-cell data
Authors: Zhang Guangzheng , Xu Bingxian 
Published: November 2023

AbstractThe increasing throughput of single-cell technologies and the pace of data generation are enhancing the resolution at which we observe cell state transitions. The characterization and visualization of these transitions rely on the construction of a low dimensional embedding, which is usually done via non-parametric methods such as t-SNE or UMAP. However, existing approaches become more and more inefficient as the size of the data gets larger and larger. Here, we test the viability of using parametric methods for the fact that they can be trained with a small subset of the data and be applied to future data when needed. We observed that the recently developed parametric version of UMAP is generalizable and robust to dropout. Additionally, to certify the robustness of the model, we use the theoretical upper and lower bounds of the mapped coordinates in the UMAP space to regularize the training process.
Interpretingcis-regulatory mechanisms from genomic deep neural networks using surrogate models
Authors: Seitz Evan E , McCandlish David M , Kinney Justin B , Koo Peter K 
Published: November 2023

ABSTRACTDeep neural networks (DNNs) have greatly advanced the ability to predict genome function from sequence. Interpreting genomic DNNs in terms of biological mechanisms, however, remains difficult. Here we introduce SQUID, a genomic DNN interpretability framework based on surrogate modeling. SQUID approximates genomic DNNs in user-specified regions of sequence space using surrogate models, i.e., simpler models that are mechanistically interpretable. Importantly, SQUID removes the confounding effects that nonlinearities and heteroscedastic noise in functional genomics data can have on model interpretation. Benchmarking analysis on multiple genomic DNNs shows that SQUID, when compared to established interpretability methods, identifies motifs that are more consistent across genomic loci and yields improved single-nucleotide variant-effect predictions. SQUID also supports surrogate models that quantify epistatic interactions within and betweencis-regulatory elements. SQUID thus advances the ability to mechanistically interpret genomic DNNs.
A Method for Calculating the Least Mutated Sequence in DNA Alignment Based on Point Mutation Sites
Authors: Shen Libing 
Published: November 2023

AbstractIt is hard to decide the root of a phylogenetic tree when no suitable outgroup sequence is available, especially in a high mutation rate system. Here we propose a bioinformatics method for calculating the least mutated sequence in DNA alignment based on point mutation sites. By parsimony principle, the least mutated sequence should be the ancestor (phylogenetic root) of the other sequences in an alignment result. The intuition suggests that our method would be sound only if enough sequences were collected. In this study, we used DNA mutation simulation data to test our method. The simulation results show that our method is very reliable and just a small proportion of sequences is required to find the least mutated sequence when the sampling is random. When the ancestral sequence is present in the dataset, the least mutated sequence calculated by our method is the ancestral sequence itself, i.e., the phylogenetic root of the dataset. When the ancestral sequence is not in the dataset, the least mutated sequence calculated by our method is the one closest to the phylogenetic root. Furthermore, the simulation demonstrates that our method is robust against reverse mutation and saturation mutation. The performance of our method improves with the increasing number of sequences. We developed the program based on this method which also calculates the transition/transversion ratio for each sequence in an DNA alignment. It can be used as a rough measure for estimating selection pressure and evolutionary stability for a sequence. In conclusion, our method and the corresponding programs could be a useful auxiliary tool for bioinformaticians and evolutionary biologists.
Pathogenicity Prediction of GABAAReceptor Missense Variants
Authors: Wang Ya-Juan , Vu Giang H. , Mu Ting-Wei 
Published: November 2023

ABSTRACTVariants in the genes encoding the subunits of gamma-aminobutyric acid type A (GABAA) receptors are associated with epilepsy. To date, over 1000 clinical variants have been identified in these genes. However, the majority of these variants lack functional studies and their clinical significance is uncertain although accumulating evidence indicates that proteostasis deficiency is the major disease-causing mechanism for GABAAreceptor variants. Here, we apply two state-of-the-art modeling tools, namely AlphaMissense, which uses an artificial intelligence-based approach based on AlphaFold structures, and Rhapsody, which integrates sequence evolution and known structure-based data, to predict the pathogenicity of saturating missense variants in genes that encode the major subunits of GABAAreceptors in the central nervous system, includingGABRA1,GABRB2,GABRB3, andGABRG2. Our results demonstrate that the predicted pathogenicity correlates well between AlphaMissense and Rhapsody although AlphaMissense tends to generate higher pathogenic probability. Furthermore, almost all annotated pathogenic variants in the ClinVar clinical database are successfully identified from the prediction, whereas uncertain variants from ClinVar partially due to the lack of experimental data are differentiated into different pathogenicity groups. The pathogenicity prediction of GABAAreceptor missense variants provides a resource to the community as well as guidance for future experimental and clinical investigations.
Explainable fNIRS Based Pain Decoding Under Pharmacological Conditions via Deep Transfer Learning Approach
Authors: Eken Aykut , Erdoğan Sinem Burcu , Yükselen Gülnaz , Yüce Murat 
Published: November 2023

Problem StatementPain has a crucial function in the human body acting as an early warning signal to protect against tissue damage. However, both assessment of pain experience and its clinical diagnosis rely on highly subjective methods. Objective evaluation of the presence of pain under analgesic drug administrations becomes even more complicated.ObjectivesThe aim of this study was to propose a transfer learning (TL) based deep learning (DL) methodology for accurate detection and objective classification of the neural processing of painful and non-painful stimuli that were presented under different levels of analgesia.MethodA publicly available fNIRS dataset of 14 participants was obtained during an experimental protocol that involved painful and non-painful events. Separate fNIRS scans were taken under the same nociceptive protocol before analgesic drug (Morphine and Placebo) administration and at three different times (30,60 and 90 min) post-administration. By utilizing data from pre-drug fNIRS scans, a DL architecture for classifying painful and non-painful stimuli was constructed as a base model. Knowledge generated in pre-drug base model was transferred to 6 distinct post-drug conditions by adapting a TL approach. The DeepSHAP method was utilized to unveil the contribution weights of nine R OIs for each of the pre-drug and post-drug models.ResultsMean performance of pre-drug base model was above 95% for accuracy, sensitivity, specificity and AUC metrics. Each of the post-drug models had mean accuracy, sensitivity, specificity and AUC performance above 90%. No statistically significant difference across post-drug models were found for classification performance of any of the performance metrics. Post-placebo models had higher decoding accuracy than post-morphine models.ConclusionKnowledge obtained from a pre-drug base model could be successfully utilized to build pain decoding models for six distinct brain states that were altered with either analgesic or placebo intervention. Contribution of different cortical regions to classification performance varied across the post-drug models.ImportanceThe proposed methodology may remove the necessity to build new DL models for data collected at clinical or daily life conditions for which obtaining training data is not practical or building a new decoding model will have a computational cost. Unveiling the explanation power of different cortical regions may aid the design of more computationally efficient fNIRS based BCI system designs that target other application areas.
Gene expression overlap between neuropsychiatric disorders
Authors: Panzenhagen Alana Castro , Alves-Teixeira Alexsander , Wissmann Martina Schroeder , Girardi Carolina Saibro , Santos Lucas , Silveira Alexandre Kleber , Gelain Daniel Pens , Fonseca Moreira José Cláudio 
Published: November 2023

AbstractCommon diseases result from a mix of genetic and environmental factors, often involving inflammation. Complex traits like diabetes and psychiatric disorders are polygenic, influenced by many genetic variants. The omnigenic model suggests all expressed genes can impact disease-related genes. This study examines blood transcriptomic variations in psychiatric and neurological disorders to understand mRNA expression profiles and address field discrepancies. Animal models are explored for similar gene expressions. This study extensively searched GEO DataSets and ArrayExpress databases, identifying gene expression profiles associated with neuropsychiatric disorders. From GEO, 10,359 samples were found, with 30 series (1,897 samples) in the qualitative synthesis, revealing 1,364 differentially expressed genes in Schizophrenia, 134 in Bipolar Disorder, 11 in Autism Spectrum Disorder, and 2,784 in Alzheimer’s Disorder. Comparisons with GWAS studies unveiled overlaps, with 81 genes for SCZ, two for BD, and 135 for ALZ. Notably, 441 genes were shared between ALZ and SCZ. Enrichment analyses indicated associations with signalling pathways. In animal models, 2,360 series were identified, with 175 in the qualitative synthesis, resulting in a meta-analysis focusing on ALZ with hippocampus tissue, revealing 14 consistently differentially expressed genes. Four overlapped with human data (ALOX5AP, P2RY13, RGS10, SH3GL1). These findings contribute to understanding shared and unique molecular signatures across neuropsychiatric disorders, bridging insights between human and animal models. The study efficiently identifies and tests consistent differentially expressed genes in psychiatric and neurological disorders, focusing on blood transcriptomes. Compared to transcriptome-wide or proteome-wide association studies, this approach analyses transcripts directly from individuals with disorders, offering real-world predictive capability. Shared genes between disorders suggest common molecular pathways, emphasizing the need for interdisciplinary approaches in understanding and treating psychiatric disorders. Limitations include sample characterization and the peripheral marker focus. Further investigations, including functional assays, are crucial for validation and extending these findings.
Waist Circumference as a potential indicator of cardiac autonomic profile in Metabolic Syndrome - A cross-sectional study
Authors: Bhasi Ankith , Puri Oshin , Pathania Monika , Saha Sarama , Dhar Meenakshi , Kulshreshta Poorvi 
Published: November 2023

AbstractIntroduction- Metabolic Syndrome (MetS), a global epidemic on the rise, affects about one-third of the world’s population. MetS poses significant cardiovascular risk often assessed through the cardiac autonomic function test Heart Rate Variability (HRV). This study attempts to identify a suitable anthropometric or biochemical proxy for resource-intensive HRV to assess cardiac autonomic profile in primary and community healthcare setups.Methodology- This cross-section study assessed relevant anthropometric, biochemical, and HRV parameters of 174 patients recently diagnosed with MetS.Results- Waist circumference (WC) had a significant (p<0.05) moderate to strong (r=0.49) positive correlation with sympathetic predominant HRV parameters - LFnu and LF/HF ratio. Parasympathetically modulated HRV parameters like Average RR (r=-0.17), rMSSD (r=-0.23), and HF (r=-0.49) had a significant (p<0.05) negative correlation with WC.Conclusion- The study identifies WC as a suitable indicator for cardiac autonomic profile in patients with MetS and by extension of cardiovascular posed by the disease.
A blind trial of Aptamarker prediction of brain amyloid based on plasma analysis
Authors: Lecocq Soizic , Bastos Filipa , Silva Mariana , Meehan Cathal , Castro Rita , Cunha João , Silva Ana Cristina , Ruano Luis , Penner Gregory 
Published: November 2023

AbstractWe previously described the development of a predictive blood test for brain amyloid based on the combined use of eight aptamers (Aptamarkers) and the clinical variable, patient age with a sensitivity of 88%, specificity of 76% and an overall accuracy of 81%. This test results from an agnostic application of a defined library of 4.29 billion aptamer sequences to plasma samples derived from individuals with varying amounts of brain amyloid deposition. In this report, the same eight Aptamarkers and corresponding predictive algorithm were applied to plasma samples from 36 patients diagnosed with memory impairment in a memory clinic in Portugal. The patients were all subjected to cerebrospinal fluid (CSF) analysis with Aβ40, Aβ4 peptides, p181-tau and total tau characterization. The plasma analysis with the Aptamarkers resulted in a sensitivity of 73%, specificity of 71% and overall accuracy of 72% when considering brain amyloid alone. The predictive power when considering the diagnosis of Alzheimer’s disease based on a complete CSF analysis was a sensitivity of 68%, a specificity of 76% and an overall accuracy of 72%. These results in this diagnostic accuracy study are very similar to results obtained with model building on AIBL cohort. As such, this study demonstrates the potential efficacy of this simple qPCR assay as a basis for inclusion for subsequent CSF confirmation of brain amyloid deposition in a clinical setting.
Mental health and burnout during medical school: Longitudinal evolution and covariates
Authors: Carrard Valerie , Berney Sylvie , Bourquin Céline , Ranjbar Setareh , Castelao Enrique , Schlegel Katja , Gaume Jacques , Bart Pierre-Alexandre , Schmid Mast Marianne , Preisig Martin , Berney Alexandre 
Published: November 2023

AbstractBackgroundMedical students’ rate of depression, suicidal ideation, anxiety, and burnout have been shown to be higher than those of the same-age general population. However, longitudinal studies spanning the whole course of medical school are scarce and present contradictory findings. This study aims to analyze the longitudinal evolution of mental health and burnout from the first to the last year of medical school using a wide range of indicators. Moreover, biopsychosocial covariates that can influence this evolution are explored.MethodIn an open cohort study design, 3066 annual questionnaires were filled in by 1595 different students from the first to the sixth year of the Lausanne Medical School (Switzerland). Depression symptoms, suicidal ideation, anxiety symptoms, stress, and burnout were measured along with biopsychosocial covariates. The longitudinal evolution of mental health and burnout and the impact of covariates were modelled with linear mixed models.ResultsComparison to a same-aged general population sample shows that medical students reported significantly more depression symptoms and anxiety symptoms. Medical students’ mental health improved during the course of the studies in terms of depression symptoms, suicidal ideation, and stress, although suicidal ideation increased again in the last year and anxiety symptoms remained stable. Conversely, the results regarding burnout globally showed a significant worsening from beginning to end of medical school. The covariates most strongly related to better mental health and less burnout were less emotion-focused coping, more social support, and more satisfaction with health.ConclusionBoth improvement of mental health and worsening of burnout were observed during the course of medical school. This underlines that the beginning and the end of medical school bring specific challenges with the first years’ stressors negatively impacting mental health and the last year’s difficulties negatively impacting burnout.
Heteroplasmy is rare in plant mitochondria compared to plastids despite similar mutation rates
Authors: Khachaturyan Marina , Santer Mario , Reusch Thorsten B. H. , Dagan Tal 
Published: November 2023

AbstractPlant cells harbor two membrane-bound organelles containing their own genetic material – plastids and mitochondria. Although the two organelles co-exist and co-evolve within the same plant cells, they differ in genome copy number, intracellular organization, and mode of inheritance. How these attributes determine the time to fixation, or conversely, loss of neutral alleles is currently unresolved. Here we show that mitochondria and plastids share the same mutation rate yet plastid alleles remain in a heteroplasmic state significantly longer compared to mitochondrial alleles. By analysing genetic variants across populations of the marine flowering plantZostera marina and simulating organelle allele dynamics, we examine the determinants of allele segregation and fixation time. Our results suggest that bottleneck on the cell population, e.g., during branching and seeding, and stratification of the meristematic tissue, are important determinants of mitochondrial allele dynamics. Furthermore, we suggest that the prolonged plastid allele dynamics are due to a yet unknown active plastid partition mechanism. The dissimilarity between plastid and mitochondrial novel allele fixation at different levels of organization may figure into differences in adaptation processes. Our study uncovers fundamental principles of organelle population genetics that are essential for further investigations of long-term evolution and molecular dating of divergence events.