bluuebunny/crossref_metadata_2025_split huggingface dataset:
Applying Machine-Learning and Deep-Learning to Predict Depression from Brain MRI and Identify Depression-Related Brain Biology
Authors: Jiang Jiayue-Clara , Brianceau Camille , Delzant Elise , Colle Romain , Bottemanne Hugo , Corruble Emmanuelle , Wray Naomi R , Colliot Olivier , Shah Sonia , Couvy-Duchesne Baptiste 
Published: February 2025

AbstractThe accuracy of grey-matter predictors of depression has remained limited. In this study, brain-based predictors of major depressive disorder (MDD) were trained using machine-learning (Best Linear Unbiased Predictors [BLUP]) and deep-learning (ResNet3D) techniques applied to high-dimensional (voxel-wise) grey-matter structure extracted from T1-weighted structural MRI. The training sample comprised 987 MDD cases and 3,934 controls from the UK Biobank. Predictors were evaluated in an independent sub-cohort of 483 MDD cases and 1,939 controls from the UK Biobank and replicated in a clinical cohort (DEP-ARREST CLIN) of 64 cases and 32 controls. In the UK Biobank, logistic regression showed a significant association between the BLUP predictor and MDD status (AUC=0.57; OR=1.28 [1.15-1.43]; p-value=1.1×10−5), which was confirmed in both males and females. By partitioning the BLUP predictor by brain regions of interest (ROI), we found nominal significance supporting the contribution of previously identified MDD-related ROIs (e.g. hippocampus and amygdala). The BLUP predictor overlapped partially with a polygenic score (PGS) of major depression but also captured a signal that was not captured by the genetic score (combined AUC=0.66, p-value=0.024 when compared to PGS alone). No association passed multiple testing correction in the DEP-ARREST CLIN cohort, likely due to the small sample size. In contrast, the deep-learning predictor did not show a significant association with MDD after multiple testing corrections. Our novel application of the BLUP method shows promising predicting accuracy and suggests new leads to overcome the remaining challenges in predicting MDD from brain imaging.
LONGITUDINAL CHANGES IN WHITE MATTER MICROSTRUCTURAL STATUS FOLLOWING QUANTIFIED HEAD-BALL IMPACTS IN SOCCER: A PRELIMINARY, PROSPECTIVE STUDY
Authors: McCloskey Hugh , McNabb Carolyn Beth , Laguna Pedro Luque , Keenan Bethany , Evans John , Jones Derek K , Palombo Marco , Barnes-Wood Megan , Adams Rhosslyn , Connelly Sean , Theobald Peter 
Published: February 2025

ABSTRACTRepetitive, sub-concussive head impacts have been associated with increased chronic traumatic encephalopathy (CTE) incidence. CTE diagnosis traditionally relies on post-mortem examination, which limits precise correlation between cause- and-effect. This prospective study embraced innovative diffusion magnetic resonance imaging, which enables in vivo quantification of acute, sub-acute and chronic changes in brain tissue microstructure. This approach was used to evaluate changes in white matter microstructural status at intervals up to 180 days following a specified soccer heading protocol. This study was approved by the university ethics panel. Twelve males (21 – 23 years) were recruited to the study and gave signed, informed consent. Six Intervention participants were university-level soccer players, with 6 Control participants drawn from university-level non-contact sports. Multi-shell diffusion-weighted MRI data were acquired on a 3T Siemens Connectom (300mT/m) scanner using the HARDI protocols. Baseline measures of fractional anisotropy, mean diffusivity and mean kurtosis were acquired at day 0. The Intervention cohort then performed 10 soccer ‘headers’ in a laboratory, with acceleration-time data captured using an instrumented mouthguard and post-processed to report common metrics. The Intervention group was then re-scanned at day 1 (n = 6), day 90 (n = 5) and day 180 (n = 4). The Control group was re-scanned at day 1 (n = 6) and day 180 (n = 3). Many brain tracts were identified as having significant (p < 0.05) changes in white matter microstructural changes at day 90, which correlated strongly with the magnitude of head impact. A smaller number of tracts had changes at day 1 and day 180. These results indicate that, within this pilot population, the magnitude of repeated soccer headers appears to correlate with the magnitude of white matter microstructural change. Additional investigation is required to determine whether the effect of such an intervention influences long-term brain health risk.
A Multimodal Framework to Uncover Drug-Responsive Subpopulations in Triple-Negative Breast Cancer
Authors: Wang Yue , Haase Santiago , Whitman Austin , Beltran Adriana , Spanheimer Philip M. , Brunk Elizabeth 
Published: February 2025

AbstractUnderstanding how individual cancer cells adapt to drug treatment is a fundamental challenge limiting precision medicine cancer therapy strategies. While single-cell technologies have advanced our understanding of cellular heterogeneity, efforts to connect the behavior of individual cells to broader tumor drug responses and uncover global trends across diverse systems remain limited. There is a growing availability of single-cell and bulk omics data, but a lack of centralized tools and repositories makes it difficult to study drug response globally, especially at the level of single-cell adaptation. To address this, we present a multimodal framework that integrates bulk and single-cell treated and untreated transcriptomics data to identify drug responsive cell populations in triple-negative breast cancer (TNBC). Our framework leverages population-scale bulk transcriptomics data from TNBC samples to define seven main “identities”, each representing unique combinations of biologically relevant genes. These identities are dynamic and trackable, allowing us to map them onto single cells and uncover global patterns of how cell populations respond to drug treatment. Unlike static classifications, this approach captures the evolving nature of cellular states, revealing that a select few identities dominate and drive population-level responses during treatment. Crucially, our ability to decode these trends through the inherent noise of single-cell data provides a clearer picture of how heterogeneous cell populations adapt to therapy. By identifying the dominant identities and their dynamics, we can better predict how entire tumors respond to treatment. This insight is essential for designing precise combination therapies tailored to the unique heterogeneity of patient tumors, addressing the single-cell variations that ultimately determine therapeutic outcomes.
Investigating Facial Expression Processing during Fast Periodic Visual Stimulation using Highly Variable Stimuli
Authors: Vandenheever David , Davidson Haleigh , Kemp Jennifer , Murphy Zack , Kujawa Autumn , Shi Catherine , Nadorff Michael , Bates-Brantley Kayla , Sidwell MacKenzie 
Published: February 2025

AbstractFacial expression recognition is a fundamental aspect of human social interaction, enabling effective communication and emotional understanding. Fast Periodic Visual Stimulation (FPVS) paradigms have recently emerged as a powerful approach for studying facial expression processing. However, previous studies often utilized identical base stimuli, making it difficult to disentangle neural responses to low-level perceptual differences from those reflecting conceptual discrimination of emotion. By introducing variability in our stimuli, we aimed to overcome these limitations and investigate neural responses to facial expressions of anger, fear, happiness, and sadness. Using EEG, robust oddball responses were observed across participants at both individual and group levels, demonstrating the paradigm’s sensitivity even with brief recordings and limited post-processing. Significant neural responses were detected across key regions of interest, with the right occipito-temporal region showing the strongest activity, consistent with its role in high-level facial expression processing. This study highlights the effectiveness of the FPVS paradigm for examining emotional processing using naturalistic stimuli and provides a framework for future research into neural mechanisms underlying facial emotion recognition in diverse and pathological populations.
PDBCleanV2: A Python Library for Generating Consistent Structure Datasets
Authors: Pardo-Avila Fátima , Weiner Liv , Cabral Paulina , Corsepius Nicholas , Poitevin Frédéric , Levitt Michael 
Published: February 2025

AbstractThe number of structures in the Protein Data Bank has grown rapidly in recent years. Nonetheless, comparing structures of a specific system often proves challenging due to variant nomenclature, origins from different species, presence of various ligands, and missing atoms or chains. To address these issues, we have developed PDBCleanV2, a Python package that enables users to create consistent datasets of structures, simplifying comparison among structures. This library generates individual files for each molecule in a structure file, corrects labeling errors, and standardizes chain names and numbering. Our aim is to provide researchers with consistent datasets that streamline their analysis. The source code, installation instructions, and tutorials can be found in PDBCleanV2’s GitHub repository and Zenodo accessible athttps://github.com/fatipardo/PDBClean-0.0.2/andhttps://doi.org/10.5281/zenodo.14014241, respectively.
The tree labeling polytope: a unified approach to ancestral reconstruction problems
Authors: Schmidt Henri , Raphael Benjamin J. 
Published: February 2025

AbstractMotivationReconstructing unobserved ancestral states of a phylogenetic tree provides insight into the history of evolving systems and is one of the fundamental problems in phylogenetics. For a fixed phylogenetic tree, the most parsimonious ancestral reconstruction – a solution to the small parsimony problem – can be efficiently found using the dynamic programming algorithms of Fitch-Hartigan and Sankoff. Ancestral reconstruction is important in many applications including inferring the routes of metastases in cancer, deriving the transmission history of viruses, determining the direction of cellular differentiation in organismal development, and detecting recombination and horizontal gene transfer in phylogenetic networks. However, most of these applications impose additionalglobalconstraints on the reconstructed ancestral states, which break the local structure required in the recurrences of Fitch-Hartigan and Sankoff.ResultsWe introduce an alternative, polyhedral approach to ancestral reconstruction problems using thetree labeling polytope, a geometric object whose vertices represent the feasible ancestral labelings of a tree. This framework yields a polynomial-time linear programming algorithm for thesmall parsimony problem. More importantly, the tree labeling polytope facilitates the incorporation of additional constraints that arise in modern ancestral reconstruction problems. We demonstrate the utility of our approach by deriving mixed-integer programming algorithms with a small number of integer variables and strong linear relaxations for three such problems: the parsimonious migration history problem, the softwired small parsimony problem on phylogenetic networks, and the convex recoloring problem on trees. Our algorithms outperform existing state-of-the-art methods on both simulated and real datasets. For instance, our algorithm scales to trace routes of cancer metastases in trees with thousands of leaves, enabling the analysis of large trees generated by recent single-cell sequencing technologies. On a mouse model of metastatic lung adenocarcinoma, the tree labeling polytope allows us to infer simpler migration histories compared to previous results.AvailabilityPython implementations of the algorithms provided in this work are available at:github.com/raphael-group/tree-labeling-polytope.
Learning Latent Trajectories in Developmental Time Series with Hidden-Markov Optimal Transport
Authors: Halmos Peter , Gold Julian , Liu Xinhao , Raphael Benjamin J. 
Published: February 2025

AbstractDeriving the sequence of transitions between cell types, or differentiation events, that occur during organismal development is one of the fundamental challenges in developmental biology. Single-cell and spatial sequencing of samples from different developmental timepoints provide data to investigate differentiation but inferring a sequence of differentiation events requires: (1) finding trajectories, or ancestor:descendant relationships, between cells from consecutive timepoints; (2) coarse-graining these trajectories into adifferentiation map, or collection of transitions betweencell types, rather than individual cells. We introduce Hidden-Markov Optimal Transport (HM-OT), an algorithm that simultaneously groups cells into cell types and learns transitions between these cell types from developmental transcriptomics time series.HM-OTuses low-rank optimal transport to simultaneously align samples in a time series and learn a sequence of clusterings and a differentiation map with minimal total transport cost. We assume that the law governing cell-type trajectories is characterized by the joint law on consecutive time points, tantamount to a Markov assumption on these latent trajectories.HM-OTcan learn these clusterings in a fully unsupervised manner or can generate the least-cost cell type differentiation map consistent with a given set of cell type labels. We validate the unsupervised clusters and cell type differentiation map output byHM-OTon a Stereo-seq dataset of zebrafish development, and we demonstrate the scalability ofHM-OTto a massive Stereo-seq dataset of mouse embryonic development.Code availabilitySoftware is available athttps://github.com/raphael-group/HM-OT
3D finite element models reveal regional fatty infiltration modulates tibialis anterior force generating capacity in FSHD
Authors: McCrady Allison N. , Friedman Seth D. , Wang Leo , Shaw Dennis W , Tawil Rabi , Statland Jeffery M , Tapscott Stephen J. , Blemker Silvia S. 
Published: February 2025

AbstractFacioscapulohumeral muscular dystrophy (FSHD) is a progressive neuromuscular disorder characterized by muscle damage, fibro-fatty infiltration, and ultimately weakness. The tibialis anterior (TA), very often involved relatively early in FSHD, is a primary dorsiflexor and important for ambulation. Recent work using magnetic resonance imaging to quantify fat infiltration in the TA volume observed a steep decline in force generation after fat reached ~20% in volume. Additional imaging studies have identified regional fat infiltration patterns that may contribute to the non-linear relationship between fat volume and muscle strength due to the distribution of fat within the muscle structure. The goals of this study were to 1) develop a pipeline for creating subject-specific models of the TA that include fat infiltration patterns measured from MRI and predict force generation, 2) compare models created using this pipeline with clinical measures of muscle strength, and 3) use the models to investigate the impact of regional fat distribution on muscle force generation. Twelve subject-specific models were created, and the model-predicted forces strongly correlate to clinical measures of strength in the same subjects (manual muscle testing (MMT): r=0.75, and quantitative muscle testing (QMT): r=0.54). The models showed fat amount accounts for 48% and muscle volume accounts for 74% of the variation in force. To investigate the impact of fat distribution, we developed eight pseudo maps to systematically vary fat location and amount in all subject-specific geometries. The models revealed that fat location modulates force generation, with the middle region involvement having the greatest impact in reducing force. This work highlights the need to characterize and understand the impact of intra-muscular fat distributions in neuromuscular diseases.
Clinical Progression on CDR-SB©: Progression Free Time at Each 0.5-unit Level in Dominantly Inherited and Sporadic Alzheimer’s Disease Populations
Authors: Wang Guoqiao , Li Yan , McDade Eric , Xiong Chengjie , Hartz Sarah M. , Bateman Randall J. , Morris John C. , Schneider Lon S. 
Published: February 2025

ABSTRACTINTRODUCTIONCDR-SB is a reliable and clinically meaningful composite for assessing treatment effects in Alzheimer’s disease (AD) clinical trials. Small CDR-SB differences at the end of a trial often lead to controversy in deriving clinically meaningful interpretations.METHODSWe estimated progression-free time participants remained at each 0.5-unit CDR-SB increment in dominantly inherited AD (DIAD) and sporadic AD populations, evaluating its potential as an alternative measure of treatment effects.RESULTSProgression-free time is longer at CDR-SB ≤ 2.0 (1–2 years) and shorter at CDR-SB ≥ 5 (0.33 or less) in the ADNI cohort. The DIAD cohort showed similar but shorter times. Using progression-free time, continuous lecanemab treatment for three years is estimated to delay disease progression by 0.7 years in the sporadic population.DISCUSSIONProgression-free time provides a benchmark for expressing clinical progression and treatment effects and can be applied particularly during open-label extensions and singlearm trials without placebo comparisons.
Changed epidemiology of influenza and RSV hospitalizations after the emergence of SARS-CoV-2 in Norway, 2017 - 2024
Authors: Bøås Håkon , Seppälä Elina , Veneti Lamprini , Stålcrantz Jeanette , Berild Jacob Dag , Dahl Jesper , Paulsen Trine Hessevik 
Published: February 2025

AbstractBackgroundMany countries reported missing and atypical influenza and RSV seasons during the COVID-19 pandemic. Here we describe the incidence and seasonality of COVID-19, influenza, and RSV hospitalizations in Norway between 2017-2024, and the disease burden between 2022-2024.MethodsUsing nationwide data on ICD-10 discharge codes, procedure codes and laboratory results, we calculate the incidence of COVID-19, influenza, and RSV hospitalizations, by age group, week and season between January 2017 to April 2024, and report proportions receiving intensive care, proportion of deaths and length of stay between 2022-2024.ResultsThe transmission of influenza and RSV was interrupted the first year of the COVID-19 pandemic and reemerged with epidemics outside of the normal seasonality in 2021/2022, after COVID-19 restrictions were removed. Between 2022-2024, COVID-19 was a greater contributor to hospitalizations than influenza and RSV, with higher mortality rate within two weeks of admission. The use of ventilatory support/intensive care admission was highest among patients hospitalized with RSV.ConclusionThe transmission of influenza and RSV was interrupted during the first year of the COVID-19 pandemic, followed by an unusual seasonality. Although many hospitalizations are caused by RSV and influenza, COVID-19 was the largest contributor of these three to hospital burden in the first years with co-circulation.
A systems-level insight into PHB-driven metabolic adaptation orchestrated by the PHB-binding transcriptional regulator AniA (PhaR)
Authors: Lagares Antonio , Krol Elizaveta , Müller Tina , Glatter Timo , Becker Anke 
Published: February 2025

AbstractPolyhydroxybutyrate (PHB) is a carbon and energy storage polymer, whose accumulation under nutrient imbalances with excess carbon is a widespread phenomenon in bacteria. PhaR is a conserved transcriptional regulator found to be associated to PHB granules in several species. Although its role in modulating PHB storage and metabolism has been extensively studied across the bacterial phylogeny, a comprehensive analysis of the PhaR regulon within the context of its dual role as a metabolic sensor and regulator remains missing. To bridge this gap, we integrated co-expression network analysis with proteome profiling across multiple mutant backgrounds (lack of PhaR (AniA) and/or PHB synthesis) in the free-living state of the PHB-accumulating alphaproteobacterial root nodule symbiontSinorhizobium meliloti. This analysis was enriched by identifying direct regulatory targets of PhaR through a regulon-centric computational multi-step search for DNA binding site motifs combined with PhaR-DNA binding and promoter-reporter assays. We confirmed that the model of accumulated PHB sequestering PhaR and thereby relieving phasin and PHB depolymerase gene repression to control cellular PHB levels also applies toS. meliloti, and showed that PhaR-mediated regulation also occurs the symbiotic state. A comprehensive picture of the impact of PHB-mediated PhaR titration on cellular functions revealed exopolysaccharide production as well as central carbon metabolism (pdhandbkd), gluconeogenesis (ppdKandpyc),entry into the TCA cycle (gltA), and the initial steps of the Entner-Doudoroff pathway (zwf, pgl and edd) as major regulatory targets, and beyond carbon metabolism several target genes of yet unknown function. Our findings highlight a pivotal role for PhaR in orchestrating carbon metabolism.
In a pathophysiologic state due to SARS-CoV-2, viscosity and cholesterol are two-edged swords
Authors: Udema Ikechukwu Iloh 
Published: February 2025

ABSTRACTMuch attention has been paid to the genetic composition and molecular biology of viral particles, infection, and micro-anatomical impacts that culminate in fatalities; vaccines have been in continuous development and production; less attention is paid to the fundamental issues of thermodynamics and activation energy characterization of viral RNA replication and cell death. The study aimed at deriving equations that can be fitted to both theoretically and empirically derived data for the quantitation of other thermodynamic parameters and its cognate dimensionless equilibrium constant; some of the derived equations addressed the issue of viscosity and the concentration of cholesterol in particular as they affect translational velocity needed for the delivery of biomolecules to the site of need. The instantaneous velocities before terminal velocity are: ∼ 0.046674 m/s (cytosol); 0.141837 m/s (water). The terminal velocities were approximately equal to 3.548614 nm/s for the cytosol and 99.590626 nm/s for the water; these values were computed using literature values of translational diffusion coefficients (Di) of glucose in cytoplasm and in water. The value in water is higher than in the cytosol because of higher cytosolic viscosity than aqueous viscosity. These support the view that cholesterol and viscosity have a dual-edged effect on the pathophysiologic state orchestrated by SARS-CoV-2; higher viscosities in the membrane and in the cytoplasm enhance binding and infection and can diminish the progress of infection, respectively. Higher feasibility and rates were observed at lower thermodynamic temperatures than at higher ones, according to the outcome of the analysis of the viral binding free energy and activation energy, respectively. The dimensionless constant values were higher at the earlier time of the infection and decreased with time, exhibiting a power law relationship. It is advised, among others, that pharmaceuticals (including airborne surfactants) and drugs in solution be given at temperatures above body temperature. Swab testing should be performed on a regular basis to detect significant infections early. Future in vitro and in vivo studies on viral infection might focus on various time periods at different temperatures, above and below body temperature.GRAPHICAL ABSTRACTWith vaccine and/or drug the viral infection can regress; without treatment there could be infection and progression into disease state; discontinuation of treatment can cause a reinfection.