bluuebunny/crossref_metadata_2025_split huggingface dataset:
NK Cell Exhaustion in Wilson’s Disease Revealed by Single-cell RNA Sequencing Predicts the Prognosis of Cholecystitis
Authors: Jin Yong , Xing Jiayu , Dai Chenyu , Jin Lei , Zhang Wanying , Tao Qianqian , Hou Mei , Li Ziyi , Yang Wen , Feng Qiyu , Wang Hongyang , Yu Qingsheng 
Published: May 2024

AbstractMetabolic abnormalities associated with liver disease have a significant impact on the risk and prognosis of cholecystitis. However, the underlying mechanism is complicated and remains to be elucidated. In particular, the effect of metabolic abnormalities on the progression of cholecystitis through the regulation of immune cell function is poorly understood. In this study, we investigated this issue using Wilson’s disease (WD) as a model. Wilson’s disease is a genetic disorder characterized by impaired mitochondrial function and abnormal copper metabolism. Our retrospective clinical study of over 600 patients with WD found that they have a significantly higher incidence of cholecystitis and a poorer prognosis. The immune cell landscape in the hepatic mesenchymal stromal microenvironment of WD patients was shown using single-cell RNA. A major change is the constitution and function of the innate immune system, including enhanced antigen presentation process, activation of the immune response, and activation of lymphocytes. The proportion of mononuclear phagocytes and natural killer (NK) cells is increased, and the primary characteristic and function of macrophage, Kupffer cell, neutrophil, and NK cell are altered. Exhaustion of NK cell is the fundamental factor, supported by an increase in the expression of the inhibitory receptors KLRC1 and TIGIT and a decrease in the expression of cytotoxic molecules. Clinical tissue and blood samples verified increased KLRC1+and TIGIT+NK cells and decreased IFNγ+NK cells in WD. Further bioinformatic analysis has confirmed a positive correlation between NK cell exhaustion and poor prognosis in cholecystitis and other inflammatory diseases. The study demonstrated abnormal function of liver mesenchymal immune cells triggered by specific metabolic dysfunction in WD, with a focus on the correlation between NK cell exhaustion and poor healing of cholecystitis. Our findings highlight the immune cell dysfunction due to metabolic changes in hepatocytes and provide new insights into the improvement of inflammatory diseases by assessing immune cell function.
Formation of Multinucleated Osteoclasts Depends on an Oxidized Species of Cell Surface Associated La Protein
Authors: Leikina Evgenia , Whitlock Jarred M. , Melikov Kamran , Zhang Wendy , Bachmann Michael P. , Chernomordik Leonid V. 
Published: May 2024

SummaryThe bone-resorbing activity of osteoclasts plays a critical role in the life-long remodeling of our bones that is perturbed in many bone loss diseases. Multinucleated osteoclasts are formed by the fusion of precursor cells, and larger cells - generated by an increased number of cell fusion events - have higher resorptive activity. We find that osteoclast fusion and bone-resorption are promoted by reactive oxygen species (ROS) signaling and by an unconventional low molecular weight species of La protein, located at the osteoclast surface. Here, we develop the hypothesis that La’s unique regulatory role in osteoclast multinucleation and function is controlled by a ROS switch in La trafficking. Using antibodies that recognize reduced or oxidized species of La, we find that differentiating osteoclasts enrich an oxidized species of La at the cell surface, which is distinct from the reduced La species conventionally localized within cell nuclei. ROS signaling triggers the shift from reduced to oxidized La species, its dephosphorylation and delivery to the surface of osteoclasts, where La promotes multinucleation and resorptive activity. Moreover, intracellular ROS signaling in differentiating osteoclasts oxidizes critical cysteine residues in the C-terminal half of La, producing this unconventional La species that promotes osteoclast fusion. Our findings suggest that redox signaling induces changes in the location and function of La and may represent a promising target for novel skeletal therapies.
Predicting Differentially Methylated Cytosines in TET and DNMT3 Knockout Mutants via a Large Language Model
Authors: Sereshki Saleh , Lonardi Stefano 
Published: May 2024

AbstractDNA cytosine methylation is an epigenetic marker which regulates many cellular processes. Mammalian genomes typically maintain consistent methylation patterns over time, except in specific regulatory regions like promoters and certain types of enhancers. The dynamics of DNA methylation is controlled by a complex cellular machinery, in which the enzymes DNMT3 and TET play a major role. This study explores the identification of differentially methylated cytosines (DMCs) in TET and DNMT3 knockout mutants in mice and human embryonic stem cells. We investigate (i) whether a large language model can be trained to recognize DMCs in human and mouse from the sequence surrounding the cytosine of interest, (ii) whether a classifier trained on human knockout data can predict DMCs in the mouse genome (and vice versa), (iii) whether a classifier trained on DNMT3 knockout can predict DMCs for TET knockout (and vice versa). Our study identifies statistically significant motifs associated with the prediction of DMCs each mutant, casting a new light on the understanding of DNA methylation dynamics in stem cells. Our software tool is available athttps://github.com/ucrbioinfo/dmc_prediction.
Fast, Accurate, and Versatile Data Analysis Platform for the Quantification of Molecular Spatiotemporal Signals
Authors: Mi Xuelong , Chen Alex Bo-Yuan , Duarte Daniela , Carey Erin , Taylor Charlotte R. , Braaker Philipp N. , Bright Mark , Almeida Rafael G. , Lim Jing-Xuan , Ruetten Virginia M. S. , Zheng Wei , Wang Mengfan , Reitman Michael E. , Wang Yizhi , Poskanzer Kira E. , Lyons David A. , Nimmerjahn Axel , Ahrens Misha B. , Yu Guoqiang 
Published: May 2024

SUMMARYOptical recording of intricate molecular dynamics is becoming an indispensable technique for biological studies, accelerated by the development of new or improved biosensors and microscopy technology. This creates major computational challenges to extract and quantify biologically meaningful spatiotemporal patterns embedded within complex and rich data sources, many of which cannot be captured with existing methods. Here, we introduce Activity Quantification and Analysis (AQuA2), a fast, accurate, and versatile data analysis platform built upon advanced machine learning techniques. It decomposes complex live imaging-based datasets into elementary signaling events, allowing accurate and unbiased quantification of molecular activities and identification of consensus functional units. We demonstrate applications across a wide range of biosensors, cell types, organs, animal models, and imaging modalities. As exemplar findings, we show how AQuA2 identified drug-dependent interactions between neurons and astroglia, and distinct sensorimotor signal propagation patterns in the mouse spinal cord.
Sleep and Activity Patterns in Autism Spectrum Disorder
Authors: Weissenkampen J. Dylan , Ghorai Arpita , Fasolino Maria , Gehringer Brielle , Rajan Maya , Dow Holly C. , Sebro Ronnie , Rader Daniel J. , Keenan Brendan T. , Almasy Laura , Brodkin Edward S. , Bucan Maja 
Published: May 2024

AbstractBackgroundAutism spectrum disorder (ASD) is a highly heritable and heterogeneous neurodevelopmental disorder characterized by impaired social interactions, repetitive behaviors, and a wide range of comorbidities. Between 44-83% of autistic individuals report sleep disturbances, which may share an underlying neurodevelopmental basis with ASD.MethodsWe recruited 382 ASD individuals and 223 of their family members to obtain quantitative ASD-related traits and wearable device-based accelerometer data spanning three consecutive weeks. An unbiased approach identifying traits associated with ASD was achieved by applying the elastic net machine learning algorithm with five-fold cross-validation on 6,878 days of data. The relationship between sleep and physical activity traits was examined through linear mixed-effects regressions using each night of data.ResultsThis analysis yielded 59 out of 242 actimetry measures associated with ASD status in the training set, which were validated in a test set (AUC: 0.777). For several of these traits (e.g. total light physical activity), the day-to-day variability, in addition to the mean, was associated with ASD. Individuals with ASD were found to have a stronger correlation between physical activity and sleep, where less physical activity decreased their sleep more significantly than that of their non-ASD relatives.ConclusionsThe average duration of sleep/physical activity and the variation in the average duration of sleep/physical activity strongly predict ASD status. Physical activity measures were correlated with sleep quality, traits, and regularity, with ASD individuals having stronger correlations. Interventional studies are warranted to investigate whether improvements in both sleep and increased physical activity may improve the core symptoms of ASD.
Strengths and limitations of using participatory science data to characterize a wildlife mass mortality event
Authors: Taylor Liam U. , Barychka Tatsiana , McKeon Seabird , Bartolotta Natasha , Avery-Gomm Stephanie 
Published: May 2024

ABSTRACTLarge participatory science (i.e., “community science” or “citizen science”) platforms are increasingly used at every level of ecological and conservation research, including disease monitoring. Here, we used a comprehensive, ground-truthed mortality dataset to judge how well participatory science data from iNaturalist represented the magnitude, taxonomic, temporal, and spatial patterns of waterbird mortality associated with a mass mortality event following the incursion of Highly Pathogenic Avian Influenza in eastern Canada in 2022. The iNaturalist dataset was effective at identifying species with high mortality (especially Northern Gannets,Morus bassanus), along with the time period and spatial regions with high concentrations of avian deaths. However, iNaturalist data severely underestimated the magnitude, overestimated the taxonomic breadth, and poorly represented the full geographic scope of disease-related deaths. Our results suggest iNaturalist can be used to identify the species, timing, and location of relatively high mortality in situations where no other information is available, and to supplement conventional sources of data. However, iNaturalist alone can neither quantify the magnitude nor pinpoint the mechanisms of mortality and therefore is not a viable substitute for comprehensive mortality assessments.
Multilevel clinical fingerprinting: uncovering longitudinal changes in the functional connectome of the brain along the migraine cycle
Authors: Esteves Inês , Fouto Ana R. , Ruiz-Tagle Amparo , Caetano Gina , Nunes Rita G. , da Silva Nuno A. , Vilela Pedro , Gil-Gouveia Raquel , Martins Isabel Pavão , Caballero-Gaudes César , Figueiredo Patrícia 
Published: May 2024

AbstractMigraine is a common neurological disorder characterized by recurrent headache episodes alternating with symptom-free periods, which has been associated with alterations across large-scale functional brain networks albeit with variable findings. Critically, despite the cyclic nature of the disorder, longitudinal studies spanning the various phases of the migraine cycle are scarce. Here, we leverage the identifiability of individual functional connectomes (FC) to investigate changes along the migraine cycle. For this purpose, we employ a case-control longitudinal design to study a group of 10 patients with episodic menstrual or menstrual-related migraine without aura, in the 4 phases of their spontaneous migraine cycle (preictal, ictal, postictal, interictal), and a group of 14 healthy controls in corresponding phases of the menstrual cycle, using resting-state fMRI. We propose a novel multilevel clinical fingerprinting approach to analyse the differential FC identifiability within-subject, as well as within-session and within-group. The individual FC matrices are then reconstructed with 19 principal components maximizing identifiability at all levels, and analyzed with Network-Based Statistic to identify significant changes in FC strength. We observe decreased FC identifiability for patients in the preictal phase relative to controls, which increases with the progression of the attack and becomes comparable to controls in the interictal phase. Regarding the FC strength, is increased in the ictal and postictal phases relative to controls across several networks. Our novel multilevel clinical fingerprinting approach captures FC variations along the migraine cycle in a case-control longitudinal study, bringing new insights into the cyclic nature of the disorder.
High fat diet consumption and social instability stress impair stress adaptation and maternal care in C57Bl/6 mice
Authors: Bucknor Morgan C , Gururajan Anand , Dale Russell C. , Hofer Markus J 
Published: May 2024

AbstractPoor maternal diet and psychosocial stress represent two environmental factors that can significantly impact maternal health during pregnancy. While various mouse models have been developed to study the relationship between maternal health and offspring development, few incorporate multiple sources of stress that mirror the complexity of human experiences. Maternal high-fat diet (HF) models in rodents are well-established, whereas maternal psychosocial stress models are still emerging. The social instability stress (SIS) paradigm, serves as a chronic and unpredictable form of social stress. To evaluate the combined effects of a poor maternal diet and social stress on maternal health and behaviour, we developed a novel maternal stress model in adult female C57Bl/6 mice. We observed that all HF+ mice demonstrated rapid weight gain, elevated fasting blood glucose levels and impaired glucose tolerance independent of the presence (+) or absence (-) of SIS. Behavioural testing revealed anxiety-like behaviors remained across all groups prior to pregnancy. However, we did observe a trend of poorer nest quality among all HF+ mice compared to HF-mice following nest building testing. Unlike the other HF+ and HF-stress groups, which exhibited significantly reduced plasma ACTH and corticosterone levels following SIS exposure, we did not observe this reduction in HF+/SIS+ females. In addition, HF+/SIS+ females demonstrated significant postpartum maternal neglect, resulting in fewer numbers of live offspring. These findings suggest that prolonged maternal HF diet consumption, coupled with SIS, places a significant burden on the maternal stress response system, resulting in reduced parental investment and negative postpartum behaviour towards offspring.
A New Differential Gene Expression Based Simulated Annealing for Solving Gene Selection Problem: A Case Study on Eosinophilic Esophagitis and Few Other Gastro-Intestinal Diseases
Authors: Sinha Koushiki , Chakraborty Sanchari , Bardhan Arohit , Saha Riju , Chakraborty Srijan , Biswas Surama 
Published: May 2024

AbstractBackgroundIdentifying the set of disease-causing genes is crucial for understanding pathogenesis and developing therapies. This is particularly important to understand the pathophysiology of Eosinophilic Esophagitis (EoE) and other gastrointestinal diseases. Comparing and contrasting gene selection methods across these diseases can enhance our knowledge to identify potential therapeutic targets.MethodsThis study introduces two approaches for gene selection in gastrointestinal diseases: the Ranked Variance (RV) method and Differential Gene Expression Based Simulated Annealing (DGESA). RV acts as an initial screener by prioritizing genes based on variance. DGESA refines gene selection further by employing simulated annealing with differential expression data. We compared the outcomes of both methods through a case study on EoE and other gastrointestinal diseases.ResultsResult finds greater number of genes with negative fold changes compared to those with positive fold change in differential EoE dataset. RV Ranks top 40 genes with high variance of EoE which overlaps with the disease-causing gene set of EoE from DGESA. 40 gene pathways for each of EoE, Crohn’s Disease (CD), and Ulcerative Colitis (UC) were identified as execution outcome of our method DGESA. Among these, 10 genes for EoE, 8 for CD, and 7 for UC were confirmed in the literature for their connection with respective diseases. For EoE, 10 such confirmed genes include KRT79, CRISP2, IL36G, SPRR2B, SPRR2D and SPRR2E. For CD, the literature confirmed set encompasses NPDC1, SLC2A4RG, LGALS8, CDKN1A, XAF1, and CYBA. The validated genes in UC final gene set includes TRAF3, BAG6, CCDC80, CDC42SE2, and HSPA9.ConclusionThe RV method, serving as an initial screener, and the more refined DGESA both effectively elucidate molecular signatures in gastrointestinal diseases. Identifying and validating genes like SPRR2B, SPRR2D, SPRR2E and STAT6 for EoE showcase efficacy of DGESA. Other genes in the same pathway are interesting targets for future laboratory validation.
Factors associated with tobacco consumption behavior of adolescent students
Authors: Mandal Anil Kumar 
Published: May 2024

AbstractAlthough consumption of tobacco is attributed to 8 million deaths of people each year in the world, the consumption of tobacco by adolescent students in Nepal is seen in alarming stage. This paper aims to describe tobacco consumption behavior of adolescent students and its associated factors. For the study, cross-sectional survey design was followed. Students of community schools of Class 10 of Hanumannagar Kankalini Municipality, Saptari were the population of the study. Probability proportional to size method was used to select students. Sample size was 235 but only 225 were included in analysis due to incompleteness of questionnaire of 10 students. Self-administered anonymous questionnaire was used to collect data. Frequency and percentage; and chi-square test were used to analyze descriptive data and inferential data, respectively. Proportion of students who consumed tobacco was 24% (boy-18.7%, girl-5.3%). Sex of students and education of parents were significantly associated with tobacco consumption behavior of students. It is concluded that sex of students, and tobacco consumption behavior of parents are associated factors for high prevalence of tobacco consumption among adolescent students. It should be minimized by promoting girl as role model and educating parents of students.
Improving Predictability, Reliability and Generalisability of Brain-Wide Associations for Cognitive Abilities via Multimodal Stacking
Authors: Tetereva Alina , Knodt Annchen R. , Melzer Tracy R. , van der Vliet William , Gibson Bryn , Hariri Ahmad R. , Whitman Ethan T. , Li Jean , Deng Jeremiah , Ireland David , Ramrakha Sandhya , Pat Narun 
Published: May 2024

AbstractBrain-wide association studies (BWASs) have attempted to relate cognitive abilities with brain phenotypes, but have been challenged by issues such as predictability, test-retest reliability, and cross-cohort generalisability. To tackle these challenges, we proposed a machine-learning “stacking” approach that draws information from whole-brain magnetic resonance imaging (MRI) across different modalities, from task-fMRI contrasts and functional connectivity during tasks and rest to structural measures, into one prediction model. We benchmarked the benefits of stacking, using the Human Connectome Projects: Young Adults (n=873, 22-35 years old) and Human Connectome Projects-Aging (n=504, 35-100 years old) and the Dunedin Multidisciplinary Health and Development Study (Dunedin Study, n=754, 45 years old). For predictability, stacked models led to out-of-sampler∼.5-.6 when predicting cognitive abilities at the time of scanning, primarily driven by task-fMRI contrasts. Notably, using the Dunedin Study, we were able to predict participants’ cognitive abilities at ages 7, 9, and 11 using their multimodal MRI at age 45, with an out-of-samplerof 0.52. For test-retest reliability, stacked models reached an excellent level of reliability (ICC>.75), even when we stacked only task-fMRI contrasts together. For generalisability, a stacked model with non-task MRI built from one dataset significantly predicted cognitive abilities in other datasets. Altogether, stacking is a viable approach to undertake the three challenges of BWAS for cognitive abilities.Significance statementFor decades, psychologists and neuroscientists have attempted to predict cognitive abilities from brain magnetic resonance imaging (MRI) data but have had limited success, casting doubt on the predictive ability of brain MRI. Here we proposed a machine learning method, called stacking, which allows us to draw information across different types of brain MRI. Using three large databases (n=2,131, 22–100 years old), we found stacking to make the prediction of cognitive abilities 1) closer to actual cognitive scores when applied to a new individual, not part of the modelling process, 2) reliable over times and 3) applicable to the data collected from different age groups and MRI scanners. Indeed, stacking, especially with fMRI task contrasts, allowed us to use MRI of people aged 45 to predict their childhood cognitive abilities reasonably well. Accordingly, stacking may help MRI realise its potential to predict cognitive abilities.PreprintbiorXiv available under a CC-BY-NC 4.0 International license.https://doi.org/10.1101/2024.05.03.589404ClassificationMajor Category: Biological SciencesMinor Category: Psychological and Cognitive Sciences