bluuebunny/crossref_metadata_2025_split huggingface dataset:
Integrative Analysis of Single-cell and Bulk RNA Sequencing Reveals Macrophage Heterogeneity in Lyme Arthritis
Authors: Dong Yan , Luo Yanshuang , Liu Meng , Chen Yantong , Song Chao , Liu Xiaorong , Yang Fusong , Liang Zhi , Zi Liye , Gao Hongbing , Cao Zhichao , Gao Fucun , Chen Xuesong , Zhou Guozhong 
Published: February 2025

AbstractIntroductionLyme arthritis (LA) is a chronic inflammatory joint disease caused byBorrelia burgdorferiinfection, with approximately 10-20% of patients developing antibiotic resistance. Macrophages in the synovial microenvironment play a crucial role in disease progression, but their precise regulatory mechanisms remain unclear.MethodsscRNA-seq data of synovial tissue from LA patients was obtained from the GEO database (GSE233850) and integrated with transcriptome data from our research team’s LA mouse model and the GSE125503 dataset. Multiple analytical strategies, including cell clustering, differential gene analysis, GO and KEGG enrichment analysis, pseudotime trajectory analysis, and cell-cell communication analysis, were employed to systematically investigate macrophage characteristics in LA. In vitro experiments using RAW cell lines were conducted to validate the role of key genes in response toBorrelia burgdorferiinfection.ResultsThrough single-cell transcriptome analysis of 78,015 cells in the GSE233850 dataset, we identified and annotated 27,843 macrophages, further classifying them into 4 functional subgroups. Differential expression analysis revealed 387 macrophage-specific genes. Cell-cell communication network analysis showed that macrophages interact with other immune cells, including T cells and B cells, through multiple signaling pathways. GO and KEGG enrichment analyses indicated that these differentially expressed genes were mainly enriched in biological processes such as inflammatory response regulation, pathogen recognition, and cytokine signaling. By integrating transcriptome data from our team’s LA mouse model and the GSE125503 dataset, we screened and validated three potential diagnostic marker genes (SIRPB1C, FABP5, and PEPD). In vitro experiments using the RAW264.7 macrophage cell line showed significant expression changes in these genes followingBorrelia burgdorferistimulation (P< 0.05).ConclusionThis study provides the first systematic characterization of macrophage transcriptome in the LA synovial microenvironment, revealing their functional heterogeneity and identifying potential diagnostic markers, offering new targets for targeted therapeutic strategies.
GerNA-Bind: Geometric-enhanced RNA-ligand Binding Specificity Prediction with Deep Learning
Authors: Xia Yunpeng , Li Jiayi , Chu Yi-Ting , Rao Jiahua , Chen Jing , Hua Chenqing , Yu Dong-Jun , Chen Xiu-Cai , Zheng Shuangjia 
Published: February 2025

ABSTRACTRNA molecules are essential regulators of biological processes and promising therapeutic targets for various diseases. Discovering small molecules that selectively bind to specific RNA conformations remains challenging due to RNA’s structural complexity and the limited availability of high-resolution data. Herein, we introduce GerNA-Bind, a geometric deep learning framework to predict RNA-ligand binding specificity by integrating multi-state RNA-ligand representations and interactions. GerNA-Bind achieves state-of-the-art performance on multiple benchmark datasets and excels in predicting interactions for low-homology RNA-ligand pairs. It achieves a 20.8% improvement in precision for binding site prediction compared to AlphaFold3. Furthermore, it offers informative, well-calibrated predictions with built-in uncertainty quantification. In a large-scale virtual screening application, GerNA-Bind identified 18 structurally diverse compounds targeting the oncogenic MALAT1 RNA, with experimentally confirmed submicromolar affinities. Among them, one lead compound selectively binds the MALAT1 triple-helix, reduces its transcript levels, and inhibits cancer cell migration. These findings highlight GerNA-Bind’s potential as a powerful tool for RNA-focused drug discovery, offering both accuracy and biological insight.
Occupational fall incidence associated with heated tobacco product smoking and lifestyle behaviors: a nationwide cross-sectional study in Japan
Authors: Tsushima Saki , Watanabe Kazuhiko , Hirohashi Sora , Yoshimi Tomohiro , Fujino Yoshihisa , Tabuchi Takahiro , Zaitsu Masayoshi 
Published: February 2025

ABSTRACTObjectivesTo identify the association between heated tobacco product (HTP) smoking and occupational falls, particularly combined with other lifestyle variables, including sleeping duration and comorbidities.MethodsThis web-based nationwide cross-sectional study (September–November 2023) involved 18,440 current workers (mean age, 43 years; women, 43.9%) in Japan. The primary outcome was occupational fall incidence in the past year. The secondary outcome was fall-related fractures. Smoking status was categorized as never, former, or current smokers; current smokers further classified into exclusive cigarette, exclusive HTP, and dual smokers (both cigarettes and HTPs). Other behavioral factors and covariates included alcohol consumption, sleep duration, physical activity, comorbidities, body mass index, hypnotics/anxiolytics use, and sociodemographic variables. Incidence ratios (IRs) and 95% confidence intervals (CIs) for occupational falls were estimated using multilevel Poisson regression with robust variance.ResultsOccupational falls and fall-related fractures were reported by 7.3% and 2.8% participants, respectively, in the past year. Younger workers were more likely to experience occupational falls. Compared with never smokers, current smokers had higher occupational fall incidence (IR = 1.36, 95% CI: 1.20–1.54), with exclusive HTP (IR = 1.78) and dual smokers (IR = 1.64) showing particularly elevated incidences. Similar patterns were observed for fall-related fractures. Elevated IRs were also observed for other lifestyle variables, including short sleep duration and diabetes, with the strongest associations found among younger workers.ConclusionsCurrent smoking, particularly HTP use, and lifestyle behaviors were significantly associated with occupational falls in Japan. These findings highlight the importance of lifestyle modifications to prevent occupational falls.KEY MESSAGESWhat is already known on this topicIndividual lifestyle behaviors, including physical activity, sedentary behaviors, and sleep quality, are associated with the risk of falls. However, the association between the risk of falls and smoking habits, including heated tobacco product (HTP) use, which has emerged as a public health concern in Japan, remains poorly understood.What this study addsCurrent smoking, particularly HTP use, is associated with occupational falls.How this study might affect research, practice or policySmoking cessation can be considered a modifiable behavior for occupational fall prevention. Further studies are needed to investigate the relationship between smoking and the incidence of falls.
Robust single-trial estimates of electrocortical generalized aversive conditioning: Validation of a Bayesian multilevel learning model
Authors: Farkas Andrew H. , Escobar Judith Cediel , Gilbert Faith E. , Ding Mingzhou , Keil Andreas 
Published: February 2025

AbstractAversive conditioning changes visuocortical responses to conditioned cues, and the generalization of these changes to perceptually similar cues may provide mechanistic insights into anxiety and fear disorders. Yet, neuroimaging conditioning paradigms are challenged by poor single-trial signal-to-noise ratios (SNR), missing trials, and inter-individual differences in learning. Here, we address these issues with the validation of a steady-state visual evoked potential (ssVEP) generalization paradigm in conjunction with a Rescorla-Wagner inspired Bayesian multilevel learning model. A preliminary group of observers (N=24) viewed circular gratings varying in grating orientation, with only one orientation paired with an aversive outcome (noxious electric pulse). Gratings were flickered at 15 Hz to evoke ssVEPs recorded with 31 channels of EEG in an MRI scanner. The multilevel structure of the Bayesian model learning model informs and constrains estimates per participant providing an interpretable generative model. It led to superior cross-validation accuracy and insights into individual participant dynamics than simpler models. It also isolates the generalized effects of conditioning, providing improved statistical certainty. Lastly, the present report demonstrates that missing trials are interpolated and weighted appropriately using the full model’s structure. This is a critical aspect for single-trial analyses of simultaneously recorded physiological measures, because each added measure will typically increase the number of trials missing a complete set of observations. The present technical report validates a limited version of a learning model to illustrate the utility of this analytical framework. It shows how models may be iteratively built and compared in a modern Bayesian workflow. Future models may use different conceptualizations of learning, allow integration of clinically relevant factors, and enable the fusion of different simultaneous physiological recordings.
Neutralizing antibodies elicited by Nipah virus G-head nanoparticle target diverse sites and inhibit receptor binding and fusion
Authors: Zhou Dan , Wang Yong , Yao Yanfeng , Kuang Wenhua , Cheng Rao , Zhang Gan , Liu Hang , Li Xin , Chiu Sandra , Deng Zengqin , Zhao Haiyan 
Published: February 2025

AbstractNipah virus (NiV) and Hendra virus (HeV), two highly pathogenic Henipaviruses (HNVs), pose a significant public health threat. The attachment glycoprotein (G) plays a crucial role in viral attachment and entry, making it an attractive target for vaccine and therapeutic antibody development. However, the antigenic landscape and neutralization sensitivity of the G protein remain poorly defined. Here, we systematically characterize 27 monoclonal antibodies (mAbs) elicited by NiV G head (GH) nanoparticle-immunized mice. Among these, 25 mAbs exhibit neutralizing activity against two major NiV strains, NiV-Malaysia and NiV-Bangladesh, with five mAbs also cross-inhibiting HeV infection. Although all mAbs target GH, they inhibit viral infection through distinct mechanisms, either by blocking receptor ephrin-B2 engagement with the G protein or by preventing viral membrane fusion. Notably, mAbs from two distinct groups conferred complete protection to hamsters against lethal NiV-Malaysia infection. Structural analysis of NiV GHin complex with four representative Fabs reveals four non-overlapping epitopes, including two novel antigenic sites and one shared protective epitope identified across species. Our study provides a comprehensive map of the antigenic determinants of NiV GH, elucidates the relationship between epitopes, neutralization mechanisms, and protection, and offers new insights and opportunities for antibody-based therapies and vaccine development.HighlightsMost mAbs induced in G head nanoparticle-immunized mice are potent neutralizers MAbs recognizing the G head block receptor binding or viral fusionStructural analysis shows mAbs target four distinct epitopes on G head proteinReceptor competition site on G head is a shared protective antigenic site in human and multiple animals
Efficient Inference of Macrophylogenies: Insights from the Avian Tree of Life
Authors: Zhao Min , Thom Gregory , Faircloth Brant C. , Andersen Michael J. , Barker F. Keith , Benz Brett W. , Braun Michael J. , Bravo Gustavo A. , Brumfield Robb T. , Chesser R. Terry , Derryberry Elizabeth P. , Glenn Travis C. , Harvey Michael G. , Hosner Peter A. , Imfeld Tyler S. , Joseph Leo , Manthey Joseph D. , McCormack John E. , McCullough Jenna M. , Moyle Robert G. , Oliveros Carl H. , Carreiro Noor D. White , Winker Kevin , Field Daniel J. , Ksepka Daniel T. , Braun Edward L. , Kimball Rebecca T. , Smith Brian Tilston 
Published: February 2025

AbstractThe exponential growth of molecular sequence data over the past decade has enabled the construction of numerous clade-specific phylogenies encompassing hundreds or thousands of taxa. These independent studies often include overlapping data, presenting a unique opportunity to build macrophylogenies (phylogenies sampling > 1,000 taxa) for entire classes across the Tree of Life. However, the inference of large trees remains constrained by logistical, computational, and methodological challenges. The Avian Tree of Life provides an ideal model for evaluating strategies to robustly infer macrophylogenies from intersecting datasets derived from smaller studies. In this study, we leveraged a comprehensive resource of sequence capture datasets to evaluate the phylogenetic accuracy and computational costs of four methodological approaches: (1) supermatrix approaches using concatenation, including the “fast” maximum likelihood (ML) methods, (2) filtering datasets to reduce heterogeneity, (3) supertree estimation based on published phylogenomic trees, and (4) a “divide-and-conquer” strategy, wherein smaller ML trees were estimated and subsequently combined using a supertree approach. Additionally, we examined the impact of these methods on divergence time estimation using a dataset that includes newly vetted fossil calibrations for the Avian Tree of Life. Our findings highlight that recently developed fast tree search approaches offer a reasonable compromise between computational efficiency and phylogenetic accuracy, facilitating inference of macrophylogenies.
Neural and Behavioral Adaptation to Listening with a Unilateral Cochlear Implant (CI)
Authors: Wöstmann Malte , Meineke Hannah Marie , Schönweiler Rainer , Hollfelder Daniela , Bruchhage Karl-Ludwig , Leichtle Anke , Obleser Jonas 
Published: February 2025

AbstractThe human auditory system must encode sounds and separate relevant input from noise. Hearing loss hurts both processes, but profound hearing loss and deafness can be treated with a Cochlear Implant (CI). Here, we probe the human brain’s adaptation to electrical hearing with a CI in unilaterally implanted CI users with normal or aided contralateral hearing, in the first and seventh month after CI activation. In addition to clinical measures of speech comprehension, electroencephalography (EEG) recordings during passive listening to amplitude-modulated (AM) sounds and an active spatial listening task with competing talkers were analyzed. We demonstrate that, first, adaptation to hearing with a CI is accompanied by decreasing cortical overrepresentation and increasing auditory temporal coding fidelity: After six month of CI use, auditory phase-locking during passive listening relatively decreased for 4-Hz AM sounds but increased for 40-Hz AM sounds. Second, strongest benefits of listening with the CI (i.e., CI on vs. off) in the spatial-listening task occurred when the CI ear was primarily exposed to target sound. Here, the power of lateralized alpha oscillations (∼10 Hz) served as a robust signature of spatial attention, independent of time (1stvs 7thmonth) and listening modality (with vs. without CI). Stronger lateralized alpha modulation in the hemisphere contralateral to the non-implanted ear suggests asymmetrical allocation of auditory spatial attention, operating preferentially on the input to the non-implanted ear. These findings highlight CI-related plasticity in auditory perception and a persistent attentional bias toward the non-implanted ear, emphasizing the need to consider bilateral auditory input in CI rehabilitation.
Quantification of SARM1 activity in human peripheral blood mononuclear cells
Authors: Dabill Lila , Shen Ivana , Brazill Jennifer , Neiner Alicia , Sasaki Yo , Scheller Erica L 
Published: February 2025

ABSTRACTSARM1 (sterile α and TIR motif-containing protein-1) is an NADase enzyme that has been identified as the central executioner of Wallerian axon degeneration. Given this, SARM1 is of high interest as a candidate therapeutic target and SARM1 inhibitors are currently in clinical trials for prevention and treatment of neurodegeneration. Beyond neuroscience, emerging studies reveal that SARM1 activation may also drive aspects of bone fragility, liver pathology, adipose tissue expansion, and insulin resistance in settings of metabolic disease. However, we lack methods to quantify SARM1 activation in humans using clinical isolates to better define patients at high risk of SARM1-mediated tissue damage, informing the future clinical application of SARM1 inhibitors. Unlike neurons, peripheral blood mononuclear cells (PBMCs) represent an easily accessible population of cells for clinical screening. We hypothesized that by pairing activators and inhibitors of SARM1 with analysis of downstream changes in cellular metabolites, we could quantify both the basal SARM1 activity and the SARM1 activation potential of human PBMCs. Our results reveal that SARM1 agonist pyrinuron, also known as Vacor, activates a dose-dependent increase in cAPDR and the cADPR:ADPR ratio that is arrested when paired with SARM1 inhibitor DSRM-3716. Various changes in secondary metabolites were also characterized and reported herein. Overall, these findings demonstrate that human PBMCs have detectable SARM1 activation potential and could be leveraged as a clinical readout of SARM1 expression and activity across diverse disease contexts.
Neuromodulation in neural organoids with shell MEAs
Authors: Acha Chris , George Derosh , Diaz Lauren C. , Ouyang Ziwei , Alam El Din Dowlette-Mary M. , Surlekar Hrishikesh , Moghadas Babak , Loftus Eva , Mangalvedhekar Gandhali M. , Rayasam Pratyush Sai R. , Lai Yu-Chiao , Smirnova Lena , Caffo Brian S. , Johnson Erik C. , Gracias David H. 
Published: February 2025

AbstractNeural organoids (NOs) have emerged as important tissue engineering models for brain sciences and biocomputing. Establishing reliable relationships between stimulation and recording traces of electrical activity is essential to monitor the functionality of NOs, especially as it relates to realizing biocomputing paradigms such as reinforcement learning or stimulus discrimination. While researchers have demonstrated neuromodulation in NOs, they have primarily used 2D microelectrode arrays (MEAs) with limited access to the entire 3D contour of the NOs. Here, we report neuromodulation using tiny mimics of macroscale EEG caps or shell MEAs. Specifically, we observe that stimulating current within a specific range (20 to 30 µA) induced a statistically significant increase in neuron firing rate when comparing the activity five seconds before and after stimulation. We observed neuromodulatory behavior using both three– and 16-electrode shells and could generate 3D spatiotemporal maps of neuromodulatory activity around the surface of the NO. Our studies demonstrate a methodology for investigating 3D spatiotemporal neuromodulation in organoids of broad relevance to biomedical engineering and biocomputing.One-Sentence SummaryNeuromodulation, an essential intelligence feature, was observed using 3D stimulation and recording from neural organoids.
Mapping the regulatory effects of common and rare non-coding variants across cellular and developmental contexts in the brain and heart
Authors: Marderstein Andrew R. , Kundu Soumya , Padhi Evin M. , Deshpande Salil , Wang Austin , Robb Esther , Sun Ying , Yun Chang M. , Pomales-Matos Diego , Xie Yilin , Nachun Daniel , Jessa Selin , Kundaje Anshul , Montgomery Stephen B. 
Published: February 2025

AbstractWhole genome sequencing has identified over a billion non-coding variants in humans, while GWAS has revealed the non-coding genome as a significant contributor to disease. However, prioritizing causal common and rare non-coding variants in human disease, and understanding how selective pressures have shaped the non-coding genome, remains a significant challenge. Here, we predicted the effects of 15 million variants with deep learning models trained on single-cell ATAC-seq across 132 cellular contexts in adult and fetal brain and heart, producing nearly two billion context-specific predictions. Using these predictions, we distinguish candidate causal variants underlying human traits and diseases and their context-specific effects. While common variant effects are more cell-type-specific, rare variants exert more cell-type-shared regulatory effects, with selective pressures particularly targeting variants affecting fetal brain neurons. To prioritizede novomutations with extreme regulatory effects, we developed FLARE, a context-specific functional genomic model of constraint. FLARE outperformed other methods in prioritizing case mutations from autism-affected families near syndromic autism-associated genes; for example, identifying mutation outliers nearCNTNAP2that would be missed by alternative approaches. Overall, our findings demonstrate the potential of integrating single-cell maps with population genetics and deep learning-based variant effect prediction to elucidate mechanisms of development and disease–ultimately, supporting the notion that genetic contributions to neurodevelopmental disorders are predominantly rare.
Deep exploration of transcriptomic cellular identities over evolutionary time
Authors: Xu Ying , Gatt Catherine , Kaymak Esra , Kikuchi Kensei , Bourguignon Thomas , Zanini Fabio 
Published: February 2025

AbstractWhole-organism cell atlases have painted the cellular landscapes of individual species, however comparing cells across the tree of life remains challenging. Most cross-species analyses are restricted to orthologous genes, while the complexity of atlas data constitutes an access barrier for many researchers. We developed a computational strategy to accelerate the exploration of cellular identities at scale. We integrated 30 atlases detailing the expression of 861,013 genes by 2,645,508 animal and plant cells and trained a universal model of cellular transcriptomes to track cell type diversification over evolutionary times. A derived model could identify cell types of a new species within minutes on both mammals and ade novoconstructed atlas of the insectCryptocercus punctulatus. We then reduced the footprint of atlases by 100 times while retaining crucial information and developed interfaces to answer 26 query types within two seconds, accelerating atlas exploration by ∼50,000 times.