bluuebunny/crossref_metadata_2025_split huggingface dataset:
Decoding Poultry Vocalizations - Natural Language Processing and Transformer Models for Semantic and Emotional Analysis
Authors: Manikandan Venkatraman , Neethirajan Suresh 
Published: December 2024

AbstractDeciphering the acoustic “language” of chickens opens new frontiers in animal welfare and ecological informatics, illuminating how subtle vocal signals encode health status, emotional states, and interactions within ecosystems. By uncovering the semantics of these vocalizations, we gain a powerful tool for interpreting their functional vocabulary—how each call serves a purpose within the social and environmental context. Here, we leverage state-of-the-art Natural Language Processing (NLP) and transformer-based models to translate bioacoustic data into meaningful insights. Our approach integrates Wave2Vec 2.0 for raw audio feature extraction with a fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model, pretrained on a broad corpus of animal sounds and adapted to poultry-specific tasks. This novel pipeline decodes poultry vocalizations into interpretable categories—such as distress calls, feeding signals, and mating vocalizations—while revealing subtle emotional nuances often overlooked by traditional spectrogram-based analyses. Achieving 92% accuracy in classifying key vocalization types, our methodology demonstrates the feasibility of real-time, automated monitoring of flock health and stress levels. By continuously tracking this functional vocabulary, farmers can respond proactively to environmental or behavioral changes, enhancing poultry welfare, reducing stress-induced productivity losses, and promoting more sustainable farm management. Beyond its direct agricultural applications, this work enriches our understanding of computational ecology. Gaining access to the semantic foundation of animal calls provides a window into the ecological networks of which poultry are a part, potentially serving as indicators of biodiversity, environmental stressors, and species interactions. In bridging animal behavior, machine learning, and ecosystem analysis, our framework lays a foundation for integrative studies that harness acoustic data to inform ecological decision-making and develop more resilient, ethically aligned agricultural systems.
CD4+ T-cells create a stable mechanical environment for force-sensitive TCR:pMHC interactions
Authors: Schrangl Lukas , Kellner Florian , Platzer René , Mühlgrabner Vanessa , Hubinger Paul , Wieland Josephine , Obst Reinhard , Toca-Herrera José L. , Huppa Johannes B. , Schütz Gerhard J. , Göhring Janett 
Published: December 2024

AbstractMechanical forces acting on ligand-engaged T-cell receptors (TCRs) have previously been implicated in T-cell antigen recognition and ligand discrimination, yet their magnitude, frequency, and impact remain unclear. We quantitatively assess forces across various TCR:pMHC pairs with different bond lifetimes at single-molecule resolution, both before and during T-cell activation, on platforms that either include or exclude tangential force registration. Our results imply that CD4+ T-cell TCRs experience significantly lower forces than previously estimated, with only a small fraction of ligand-engaged TCRs being subjected to these forces during antigen scanning. These rare and minute mechanical forces do not impact the global lifetime distribution of the TCR:ligand bond. We propose that the immunological synapse is created as biophysically stable environment to prevent pulling forces from disturbing antigen recognition.
A hybrid micro-ECoG for functionally targeted multi-site and multi-scale investigation
Authors: Jendritza Patrick , Liljemalm Rickard , Stieglitz Thomas , Fries Pascal , Lewis Christopher Murphy 
Published: December 2024

SummaryBrain function relies on the coordination of activity across a wide range of spatial and temporal scales. The activity of single neurons depends on their unique pattern of local and long-range connectivity and thus on the coordination of local activity with large-scale patterns of distributed activity across brain-wide networks. Understanding integrated, whole brain function requires new tools capable of recording from anatomically connected populations in distributed brain areas to bridge local and global dynamics. Here, we present high-density, micro-electrocorticography arrays that facilitate multi-scale studies of brain activity. The arrays are hybrid designs that integrate the desirable features of silicone elastomers and polyimide films. The silicone elastomer superstructure provides optical transparency and permits repeated mechanical penetration with rigid linear electrode arrays. The polyimide films provide the capacity for fine feature definition through photolithography. This combination facilitates high-throughput functional mapping of areas of interest to target functionally characterized populations for refined, dense sampling. We demonstrate the suitability of the technique for functional mapping of cortical regions in rats, cats and marmosets and the benefit of the resulting functional maps for targeting functionally defined populations for dense, multi-area laminar recordings. Finally, we demonstrate the utility of the hybrid µECoG to localize optogenetically evoked feedforward excitation in down-stream cortical regions to investigate cortico-cortical interactions. Together, these capabilities make the hybrid µECoG a compelling tool for systems neuroscience.
Comprehensive Analysis of Human Dendritic Spine Morphology and Density: Exploring Diversity of Human Dendritic Spines
Authors: Schünemann Kerstin D , Hattingh Roxanne M , Verhoog Matthijs B , Yang Danqing , Bak Aniella , Peter Sabrina , van Loo Karen M.J. , Wolking Stefan , Kronenberg-Versteeg Deborah , Weber Yvonne , Schwarz Niklas , Raimondo Joseph V , Melvill Roger , Tromp Sean , Butler James , Höllig Anke , Delev Daniel , Wuttke Thomas V , Kampa Björn M , Koch Henner 
Published: December 2024

AbstractDendritic spines, small protrusions on neuronal dendrites, play a crucial role in brain function by changing shape and size in response to neural activity. So far,in depthanalysis of dendritic spines in human brain tissue is lacking. This study presents a comprehensive analysis of human dendritic spine morphology and density using a unique dataset from human brain tissue from 27 patients (8 females, 19 males, aged 18-71) undergoing tumor or epilepsy surgery at three neurosurgery sites. We used acute slices and organotypic brain slice cultures to examine dendritic spines, classifying them into the three main morphological subtypes: Mushroom, Thin, and Stubby, via 3D reconstruction using ZEISS arivis Pro software. A deep learning model, trained on 39 diverse datasets, automated spine segmentation and 3D reconstruction, achieving a 74% F1-score and reducing processing time by over 50%. We show significant differences in spine density by sex, dendrite type, and tissue condition. Females had higher spine densities than males, and apical dendrites were denser in spines than basal ones. Acute tissue showed higher spine densities compared to cultured human brain tissue. With time in culture, Mushroom spines decreased, while Stubby and Thin spine percentages increased, particularly from 7-9 to 14 daysin vitro, reflecting potential synaptic plasticity changes. Our study underscores the importance of using human brain tissue to understand unique synaptic properties and shows that integrating deep learning with traditional methods enables efficient large-scale analysis, revealing key insights into sex- and tissue-specific dendritic spine dynamics relevant to neurological diseases.New and NoteworthyThis study presents a dataset of nearly 4,000 morphologically reconstructed human dendritic spines across different ages, gender, and tissue conditions. The dataset was further used to evaluate a deep learning algorithm for three-dimensional spine reconstruction, offering a scalable method for semi-automated spine analysis across various tissues and microscopy setups. The findings enhance understanding of human neurology, indicating potential connections between spine morphology, brain function, and the mechanisms of neurological and psychiatric diseases.
Impact of early life antibiotic and probiotic treatment on gut microbiome and resistome of very-low-birth-weight preterm infants
Authors: Kiu Raymond , Darby Elizabeth M , Alcon-Giner Cristina , Acuna-Gonzalez Antia , Camargo Anny , Lamberte Lisa E , Phillips Sarah , Sim Kathleen , Shaw Alexander G , Clarke Paul , Schaik Willem van , Kroll J Simon , Hall Lindsay J 
Published: December 2024

AbstractPreterm infants (<37 weeks’ gestation) are often administered broad-spectrum antibiotics in hospitals due to their vulnerability to severe morbidity, including necrotising enterocolitis and sepsis. However, antibiotics can disrupt the development of early-life microbiota, potentially impairing gut immunity and colonisation resistance. Evidence shows that probiotics (e.g., certainBifidobacteriumstrains) may help restore healthy gut microbiota. In this study, we examined the effects of probiotics and antibiotics on the preterm gut microbiome and resistome in two unique cohorts of 34 very-low-birth-weight, human-milk- fed preterm infants (moderate to very preterm), with one cohort receiving probiotics. Within each group, some infants were treated with antibiotics (benzylpenicillin and/or gentamicin) while others served as non-antibiotic treated controls. We performed shotgun metagenomic sequencing on 93 longitudinal faecal samples from 34 infants, generated >300 metagenome- assembled genomes, and obtained ∼90 isolate genomes through targeted culturomics, enabling analysis of the microbiome/resistome at species and strain levels. Additionally, we investigatedin vitrohorizontal gene transfer (HGT) capacity of preterm infant-derived multidrug-resistant (MDR) pathogenEnterococcusvia neonatal gut models. Overall, probiotic supplementation significantly reduced antibiotic resistance gene prevalence, MDR pathogen load, and helped restore a typical early-life microbiota. However, the persistence of MDR pathogens likeEnterococcus, with high HGT potential, highlights the need for ongoing surveillance in neonatal care. Our findings underscore the complex interactions between antibiotics, probiotics, and HGT in shaping the neonatal microbiome and support further research into probiotics for antimicrobial stewardship in preterm populations.
Visualizing Functional Network Connectivity Differences Using an Explainable Machine-learning Method
Authors: Sendi Mohammad S. E. , Itkyal Vaibhavi S. , Edwards-Swart Sabrina J. , Chun Ji Ye , Mathalon Daniel H. , Ford Judith M. , Preda Adrian , van Erp Theo G.M. , Pearlson Godfrey D. , Turner Jessica A. , Calhoun Vince D. 
Published: December 2024

AbstractFunctional network connectivity (FNC) estimated from resting-state functional magnetic resonance imaging showed great information about the neural mechanism in different brain disorders. But previous research has mainly focused on standard statistical learning approaches to find FNC features separating patients from control. Although machine learning approaches provide better models separating controls from patients, it is not straightforward for these approaches to provide intuition on the model and the underlying neural process of each disorder. Explainable machine learning offers a solution to this problem by applying machine learning to understand the neural process behind brain disorders. In this study, we introduce a novel framework leveraging SHapley Additive exPlanations (SHAP) to identify crucial Functional Network Connectivity (FNC) features distinguishing between two distinct population classes. Initially, we validate our approach using synthetic data. Subsequently, applying our framework, we ascertain FNC biomarkers distinguishing between, controls and schizophrenia patients with accuracy of 81.04% as well as middle aged adults and old aged adults with accuracy 71.38%, respectively, employing Random Forest (RF), XGBoost, and CATBoost models. Our analysis underscores the pivotal role of the cognitive control network (CCN), subcortical network (SCN), and somatomotor network (SMN) in discerning individuals with schizophrenia from controls. In addition, our platform found CCN and SCN as the most important networks separating young adults from older.
Tonic ERK signaling regulate the basal lactate production in prepubertal rat Sertoli cell
Authors: Gautam Mukesh , Pradhan Bhola Shankar , Majumdar Subeer S 
Published: December 2024

AbstractSertoli cells are the major nurse cells in the testis which regulate the division and differentiation of germ cell by secreting various factors like inhibin B, transferrin and lactate. In this study, we demonstrated that inhibition of extracellular signal-regulated kinase (ERK) leads to an increase in the basal level of lactate. This increase in lactate production was due to the rise in lactate dehydrogenase (LDH) activity and glucose uptake, simultaneously. Moreover, an increase in lactate production wasnot due to the change in mRNA level ofGLU1,GLUT3andLDHA. Importantly, the lactate production was independent of the level of cAMP suggesting that the tonic signaling of ERK is important for the growth factor mediated lactate production. These findings suggest that ERK signaling inhibits lactate production in the absence of any stimulant in rat Sertoli cells to maintain metabolic homeostasis.
Novel approaches to label the surface ofS. aureuswith DBCO for click chemistry-mediated deposition of sensitive cargo
Authors: Baryakova Tsvetelina H. , Segatori Laura , McHugh Kevin J. 
Published: December 2024

ABSTRACTThe strain-promoted alkyne-azide cycloaddition (SPAAC) reaction can be used to modify the surface of bacteria for a variety of applications, including drug delivery, biosensing, and imaging. This is usually accomplished by first installing a small azide group within the peptidoglycan and then delivering exogenous cargo (e.g., a protein or nanoparticle) modified with a cyclooctyne group, such as dibenzocyclooctyne (DBCO), forin situconjugation. However, DBCO is comparatively bulky and hydrophobic, increasing the propensity for some payloads to aggregate. In this study, we sought to invert this paradigm by exploring two novel strategies for incorporating DBCO into the peptidoglycan ofStaphylococcus aureusand compared them to an established approach using DBCO-vancomycin. We demonstrate that DBCO-modified small molecules belonging to all three classes – a sortase peptide substrate (LPETG), two D-alanine derivatives, and vancomycin – can selectively label theS. aureussurface to varying degrees. In contrast to DBCO-vancomycin, the DBCO-D-alanine variants do not adversely affect the growth ofS. aureusor lead to off-target labeling or toxicity in HEK293T cells, even at high concentrations. Finally, we show that, unlike IgG3-Fc labeled with DBCO groups, IgG3-Fc labeled with azide groups is stable (i.e., remains water-soluble) under normal storage conditions, retains its ability to bind the immune receptor CD64, and can be successfully attached to the surface of DBCO-modifiedS. aureus. We believe the labeling strategies explored herein will expand the paradigm of specific, nontoxic SPAAC-mediated labeling of the surface ofS. aureusand other gram-positive bacteria, opening the door for new applications using azido-modified cargo.GRAPHICAL ABSTRACT
Impact of COVID-19 vaccine doses and viral waves on inflammatory and immunological responses to COVID-19 infections in India
Authors: Sharma Kamal Kant , Partap Uttara , Marathe Yogesh , Shaikh Sanaa , D’Costa Pradeep , Gupta Gaurav , Wang Molin , Fawzi Wafaie W , Kain Kevin C , Mistry Nerges , Dholakia Yatin 
Published: December 2024

AbstractBackgroundInvestigation of the effect of SARS-CoV-2 variants and COVID-19 vaccination on inflammatory and immune response to SARS-CoV-2 infection is limited in South Asia.ObjectivesWe aimed to examine the impact of COVID-19 vaccination and waves of COVID- 19 on inflammatory and immunological biomarkers among COVID-19 patients in India.MethodsThis cross-sectional analysis used baseline data from a randomized controlled trial of vitamin D and zinc during COVID-19 infection in India (N=181). Blood samples and data regarding vaccination doses were collected. The second (Delta) or third (Omicron) wave was determined by date of enrolment. Mixed effects linear regression with robust standard errors was used to examine associations between COVID-19 vaccination dose or wave at enrolment and C-Reactive Protein (CRP), ferritin, lactate dehydrogenase (LDH), D-dimer, interleukin-6 (IL-6), angiopoietin-2 (Ang-2), soluble triggering receptor expressed on myeloid cells-1 (sTREM-1), immunoglobulin G (IgG) and immunoglobulin M (IgM).ResultsCompared to no vaccination, full vaccination was associated with lower LDH (P<0.001), D-dimer (P=0.521) and Ang-2 (P=0.046), and higher IgG levels (P<0.001). Partial vaccination was associated with lower IL-6 (P=0.040) and higher IgG (P<0.001). Enrolment during the third wave was associated with lower IL-6 (P<0.001), CRP (P=0.056), IgM (P=0.013), and IgG (P<0.001), but higher D-dimer levels (P<0.001).ConclusionsCOVID-19 vaccination status and SARS-CoV-2 variant influence the inflammatory and immunologic response during SARS-CoV-2 infection, contributing to the severity of clinical presentation.
Single-cell transcriptome-wide Mendelian randomization and colocalization analyses uncover cell-specific mechanisms in atherosclerotic cardiovascular disease
Authors: Ray Anushree , Alabarse Paulo , Malik Rainer , Sargurupremraj Muralidharan , Bernhagen Jürgen , Dichgans Martin , Baumeister Sebastian-Edgar , Georgakis Marios K. 
Published: December 2024

ABSTRACTGenome-wide association studies (GWAS) have identified numerous genetic loci influencing human disease risk. However, linking these loci to causal genes remains challenging, limiting opportunities for drug target discovery. Transcriptome-wide association studies (TWAS) address this by linking variants to gene expression, but typically rely on bulk RNA sequencing, which lacks cell-specific resolution. Here, we present a single-cell TWAS pipeline combiningcis-Mendelian randomization (MR) with colocalization analyses at the single-cell level. As a case study, we examined how genetically proxied gene expression in immune cells influences atherosclerotic cardiovascular disease (ASCVD) risk. We integrated single-cell expression quantitative trait loci (sc-eQTL) for 14 immune cell types with GWAS for coronary artery disease, large artery atherosclerotic stroke, and peripheral artery disease. Single-cellcis-MR analyses revealed 440 gene-outcome associations across cell types, 84% of which were missed by bulk TWAS, despite a considerably smaller sample size of the sc-eQTL dataset. Of these associations, 17 were replicated with externalcis-eQTLs and demonstrated colocalization with ASCVD GWAS signals. Notably, genetically proxied expression ofLIPAin monocytes was associated with coronary artery disease, large artery atherosclerotic stroke, and subclinical atherosclerosis traits. These findings were confirmed in a phenome-wide association study without evidence of associations with unexpected clinical outcomes. Single-cell RNA sequencing and immunohistochemistry of human carotid plaques revealed highLIPAexpression in plaque macrophages. Our pipeline provides a solution for the discovery of cell-specific expression patterns that drive genetic predisposition to human disease, potentially impacting target selection for cell-tailored therapeutics.
Association of cumulative exposure to triglyceride and remnant cholesterol with the risk of cardiovascular disease in hypertensive patients with target LDL-C
Authors: Guo Shuang , Wang Chi , Hu Taoyu , Lan Lihua , Ge Zhen , Huang Jianxiang , Chen Shuohua , Wu Shouling , Xue Hao 
Published: December 2024

AbstractBackgroundThe associations of cumulative exposure to triglycerides (cumTG) and remnant cholesterol (cumRC) with cardiovascular disease (CVD) risk in patients with optimal low-density lipoprotein cholesterol (LDL-C) levels remain uncertain. We aimed to explore the relationship between cumTG and cumRC and the risk of CVD in hypertensive patients with target LDL-C.MethodsA total of 12,086 hypertensive participants with target LDL-C level who completed three health examinations from 2006 to 2010 were enrolled and followed until December 31, 2022. The cumulative burden was derived by multiplying the average level with the interval between two consecutive assessments. The primary outcome included a composite of CVD events (myocardial infarction, ischemic stroke, and hemorrhage stroke). Participants were divided into 4 groups based on the median values of cumTG and cumRC to assess their joint associations. Cox proportional hazard models were used to calculate the hazard ratio (HR) and 95% confidence intervals (CI) between the cumulative burden and CVD risk.ResultsDuring a median follow-up of 11.95 years, 1,392 participants developed CVD. After multivariable adjustment, compared to the low cumTG/low cumRC group, both the high cumTG/high cumRC group (HR 1.43, 95% CI 1.24‒1.64) and the low cumTG/high cumRC group (HR 1.26, 95% CI 1.07‒1.49), but not the high cumTG/low cumRC group (HR 1.01, 95% CI 0.84‒1.20), were associated with higher risk of CVD.ConclusionsIn hypertensive patients who met the target level of LDL-C, the cumulative burden of RC was a risk factor for CVD and exhibited a stronger association with CVD risk compared to cumulative triglycerides burden.