bluuebunny/crossref_metadata_2025_split huggingface dataset:
Machine learning differentiation of Parkinson’s disease and normal pressure hydrocephalus using wearable sensors capturing gait impairments
Authors: Magni Stefano , Bremm Rene Peter , Verros Konstantinos , He Xin , Lecossois Sylvie , Jelke Finn , Husch Andreas , Gonçalves Jorge , Hertel Frank 
Published: January 2025

AbstractGait impairments in patients with Parkinson’s Disease (PD) and Normal Pressure Hydrocephalus (NPH) are diagnosed with visual clinical assessments. Despite standardized gait tests and clinicians’ expertise, such approaches can be subjective and challenging due to similar symptoms between the two diseases. Wearable sensors and machine learning (ML) can assist clinicians by offering objective and quantitative assessments of gait impairments that can help distinguishing between PD and NPH. This study consists of a cohort of 12 PD and 11 NPH patients that performed standardized gait tests. Gait was measured by wearable sensors embedded in patients’ shoes: a three-axis gyroscope, a three-axis accelerometer and eight pressure sensors in each insole. Sensors and computational pipeline to extract gait cycle features were validated and calibrated on 21 healthy subjects. ML approaches were employed to identify changes in gait cycle features between the PD and NPH patients groups. Twenty-seven ML classifiers were compared, leading to select linear support vector machines, resulting in a classification accuracy of 0.70 ± 0.28 and an area under the ROC curve of 0.74 ± 0.39. Combining wearable sensors with ML algorithms trained on gait cycle features from those sensors showed the potential for objective differentiation of gait patterns between PD and NPH patients.
Genome-wide analysis of screen behaviors among adolescents identifies novel loci and overlap with educational attainment and mental disorders
Authors: Frei Evgeniia , Filiz Tahir Tekin , Frei Oleksandr , Loughnan Robert , Jaholkowski Piotr , Bakken Nora R. , Birkenæs Viktoria , Shadrin Alexey A. , Ask Helga , Andreassen Ole A. , Smeland Olav B. 
Published: January 2025

AbstractTechnological devices play a central role in adolescents’ life. Despite concerns about negative effects of excessive screen time on mental health and development, there is little knowledge of fundamental features of screen behaviours and underlying genetic architecture. Using self-reports from adolescents (14-16 years old) in the Norwegian Mother, Father, and Child Cohort Study (MoBa,n= 18 490), we performed genome-wide association analysis for four screen behaviors: time spent 1) watching movies/series/TV; 2) gaming; 3) sitting/lying down with PC, mobile or tablet; and 4) communicating with friends on social media. The resulting summary statistics were analysed using the conditional false discovery rate (condFDR) approach to increase genetic discovery. We also estimated SNP-based heritabilities of the screen behaviours and the genetic correlations with six major psychiatric disorders (schizophrenia, bipolar disorder, major depressive disorder, autism spectrum disorder, attention-deficit hyperactivity disorder, alcohol use disorder), and educational attainment. The screen-based phenotypes displayed significant SNP-based heritabilities (0.048–0.12). We also observed significant genetic correlations between screen behaviours and psychiatric disorders (rgrange: 0.21–0.42). Educational attainment demonstrated significant negative genetic correlation with screen behaviours, most strongly with social media use (rg= −0.69). CondFDR analysis identified three novel loci associated with social media use. Thus, we show that screen behaviors are heritable, polygenic traits that partly share genetic signal with mental disorders and educational attainment. Future studies and larger samples are required to clarify causal relationships between these traits and disorders, and to validate the identified genetic loci associated with social media use.
Higher Brain Grey Matter Density in Mild to Moderate Chronic Low Back Pain Patients
Authors: Sean Monica , Cote Samantha , Alexia-Coulombe-Lévêque  , Huck Julia , Martel Marylie , Léonard Guillaume , Whittingstall Kevin , Tétreault Pascal 
Published: January 2025

AbstractChronic low back pain (CLBP) is the leading cause of disability worldwide. Structural abnormalities in the lumbar region rarely account for the pain symptoms observed. In contrast, the brain plays a central role in chronic pain, with several studies suggesting that chronic pain may result from the persistence of pain memories and/or the inability to extinguish these memories following an initial inciting injury. Previous research has reported a reduction in grey matter density (GMD) in CLBP patients, as measured by magnetic resonance imaging (MRI). Notably, lower GMD has been observed in patients with moderate to severe CLBP who are undergoing prescribed pharmacological treatments. However, it remains unclear whether these differences in GMD could be associated with a less severe condition.This study aimed to investigate whether GMD is altered in a cohort with mild to moderate CLBP symptoms, and who have not been prescribed pharmacological treatments. To achieve this, we acquired T1-weighted MRI scans from 25 healthy controls (HC) and 27 untreated individuals with mild to moderate CLBP. Scans were taken at baseline, 2 months, and 4 months after baseline. GMD analysis was carried out using the FMRIB Software.Our results consistently showed higher GMD in the CLBP group compared to HC. These findings suggest that the observed alterations in GMD may be related to the condition itself, and can occur even in patients with milder symptoms and better physical function, without the influence of opioids, anticonvulsants, or antidepressants. Given that our participants differ from those typically studied in the literature, our results imply that they may be in a distinct brain state relative to the condition. Further research is needed to elucidate the underlying biological mechanisms and confirm whether these brain alterations are indeed a characteristic feature of CLBP.
Flow-induced bending of flagella controls bacterial surface behavior
Authors: Palalay Jessica-Jae S. , Sanfilippo Joseph E. 
Published: January 2025

AbstractBacteria engage in surface-specific behaviors that are assumed to be driven by biological signaling. However, surface behaviors could be controlled by mechanical reorientation of bacterial appendages. Here, we use microfluidics and flagellar labeling to discover how shear force bends flagella to control surface behavior of the human pathogenPseudomonas aeruginosa. By imaging flagellar rotation and using mutants with paralyzed flagella, we establish that flagellar rotation promotes surface departure in host-relevant shear regimes. Our single-cell experiments reveal two distinct subpopulations in flow: cells with their flagellum positioned upstream and cells with their flagellum positioned downstream. Shear force bends upstream flagella around the cell and blocks rotation. In contrast, downstream flagella can continue to rotate after surface arrival. Cells with downstream flagella depart the surface more frequently than cells with upstream flagella, indicating how flow direction can determine bacterial cell fate on surfaces. Together, our results demonstrate how the geometric relationship between flow and cell appendages can generate subpopulations and control surface behaviors.
Mitoxantrone inhibits and downregulates ERαthrough binding at the DBD-LBD interface
Authors: Wang Han , Luo Yuxuan , Artham Sandeep , Wang Qianqian , Peng Yi , Yun Zixi , Li Xinyue , Wu Chen , Liu Zhenghao , Weber-Bonk Kristen L. , Pai Chun-Peng , Cao Yuan , Yue Jiangan , Park Sunghee , Keri Ruth A. , Geng Lisheng , McDonnell Donald P. , Kao Hung-Ying , Yang Sichun 
Published: January 2025

AbstractTargeting the estrogen receptor (ER or ERα) through competitive antagonists, receptor downregulators, or estrogen synthesis inhibition remains the primary therapeutic strategy for luminal breast cancer. We have identified a novel mechanism of ER inhibition by targeting the critical interface between its DNA-binding domain (DBD) and ligand-binding domain (LBD). We demonstrate that mitoxantrone (MTO), a topoisomerase II inhibitor, binds at this previously unexplored DBD-LBD interface. Using comprehensive computational, biophysical, biochemical, and cellular analyses, we show that independent of its DNA damage response activity, MTO binding induces distinct conformational changes in ER, leading to its cytoplasmic redistribution and subsequent proteasomal degradation. Notably, MTO effectively inhibits clinically relevant ER mutations (Y537S and D538G) that confer resistance to current endocrine therapies, outperforming fulvestrant in bothin vitroandin vivoassays. Our findings establish domain-domain interaction targeting as a viable therapeutic strategy for ER, with translational implications for other nuclear receptors.
Nondestructive Detection and Quantification of Dysprosium in Plant Tissues
Authors: Hernández-Pagán Edmaritz , Laosuntisuk Kanjana , Harris Alex T. , Haynes Allison N. , Buitrago David , Rajabu Cyprian , Kudenov Michael W. , Doherty Colleen J. 
Published: January 2025

ABSTRACTBackgroundThe growing demand for rare-earth elements (REEs), particularly dysprosium (Dy), in part driven by clean energy technologies, underscores the need for sustainable extraction methods. Recovery of Dy, particularly from geographically distributed waste sources is challenging. This gap positions phytomining—a technique using plants to accumulate metals— as a promising alternative. However, plant species differ in their ability to accumulate metals in high concentrations, necessitating efficient screening methods. In this study, we developed a high-throughput fluorescence-based assay to detect and quantify Dy uptake in plant tissues.ResultsOur Dy detection method exploits Dy’s unique spectroscopic properties for sensitive and efficient analysis, enabling detection of concentrations as low as 0.3 µM. By incorporating sodium tungstate (Na WO) as a fluorescence enhancer, we achieved robust emissions at 480 and 580 nm, facilitating Dy quantification in complex plant matrices. Additionally, time-resolved fluorescence techniques reduced background autofluorescence from plant tissues, enhancing signal specificity. Validation against Inductively Coupled Plasma Mass Spectrometry (ICP-MS) demonstrated strong correlation. Greenhouse trials confirmed the method’s utility for screening Dy accumulation in living plants and highlight the potential for rapid standoff detection.ConclusionsThis fluorescence-based approach offers a scalable, efficient tool for identifying Dy-accumulating plants, advancing phytomining as a sustainable strategy for REE recovery.
Distinct Control of histone H1 expression within the Histone Locus body by CRAMP1
Authors: Bodner Justin , Vadlamani Pranathi , Helmin Kathryn A. , Liu Qianli , Mendillo Marc L. , Singer Benjamin D. , Srivastava Shashank , Foltz Daniel R. 
Published: January 2025

AbstractProper histone gene expression is critical to cell viability and maintaining genomic integrity. Multiple histone genes organized into three genomic loci encode for replication coupled core and linker histones. Histone gene expression and transcript processing is orchestrated in the histone locus body (HLB) within the nucleus. We identified human CRAMP1 as a selective regulator of linker histone H1 expression. CRAMP1 is recruited to the HLB in RPE1hTERTcells. Affinity purification shows that CRAMP1 physically associates the HLB component GON4L (a.k.a. YARP). We show that the PAH domains of GON4L interact with CRAMP1. CRAMP1 disruption results in a loss of histone H1 expression and a reduction in H1 protein. CRAMP1 occupies the unmethylated promoters of the replication coupled linker histone genes that reside within the histone locus body, and the replication independent histone H1 loci, which reside in a region of the genome without other histone genes. Together these data identify CRAMP1 as a novel and selective regulator of histone H1 gene expression.
A Single-Graph Visualization to Reveal Hidden Explainability Patterns of SHAP Feature Interactions in Machine Learning for Biomedical Issues
Authors: Furger Félix , Aligon Julien , Thomas Miguel , Doumard Emmanuel , Delpierre Cyrille , Casteilla Louis , Monsarrat Paul 
Published: January 2025

AbstractBackgroundIn the last decades, the utility of Machine Learning (ML) in the biomedical domain has been demonstrated repeatedly. Their inherent opacity need augmenting ML with explainability techniques. A common practice in model explainability however, is to focus solely on the explanatory values themselves without accounting for both the main and interaction effects. While this approach simplifies interpretation, it potentially overlooks critical medical information since the nature of the interactions may provide clues to the underlying biological mechanisms.ResultsThis article introduces a novel method for analyzing explanatory values of machine learning (ML) models, in the form of a comprehensive graphical visualization. The method not only emphasises the individual contributions of the features but also gives insights about the interactions they share with one another. Designed for local additive explanation methods, the proposed tool effectively translates the complex and multidimensional nature of these values into an intuitive single-graph format. It offers a clear window into how feature interactions contribute to the overall prediction of the ML model while aiding in the identification of various interaction types, such as mutual attenuation, positive/negative synergies or dominance of one feature over another.ConclusionsThis approach provides insights for generating hypotheses, improving the transparency of ML models, particularly in the context of biology and medicine since living organisms are characterised by a multitude of parameters in complex interactions, a complexity that ensures the “stability” and robustness of structures and functions.
Using artificial intelligence-based software for an unbiased discrimination of immune cell subtypes in the fracture hematoma and bone marrow of non-osteoporotic and osteoporotic mice
Authors: Fischer Verena , Ignatius Anita , Schmidt-Bleek Katharina , Duda Georg , Haffner-Luntzer Melanie 
Published: January 2025

AbstractIt is well established that the early inflammatory response following fracture is essential for initiating subsequent bone regeneration. An imbalance in inflammation, whether within the innate or adaptive immune response, can result in impaired fracture healing. In our previous studies, we demonstrated that, for example, mice with ovariectomy-induced osteoporosis exhibit altered immune cell populations in the early fracture hematoma and bone marrow, leading to delayed healing. These analyses were conducted using conventional FACS/flow cytometry software, where surface marker expression was assessed using a single threshold based on isotype controls—a binary “yes or no” decision. Recent advances have highlighted that immune cell populations are often more heterogeneous, with distinct phenotypic subgroups depending on their polarization status. This has been particularly well documented for macrophage subpopulations (M1, M2, and intermediate polarization states). In light of this, we employed a commercially available artificial intelligence-based clustering software (Cytolution) to more accurately and objectively identify immune cell subpopulations. We re-analyzed flow cytometry raw data from fracture hematoma and bone marrow of non-osteoporotic and osteoporotic mice at day 1 after fracture. Our findings revealed distinct subclusters for granulocytes (27 subclusters), macrophages (7 subclusters), B cells (4 subclusters), and T cells (6 subclusters) within the fracture hematoma and bone marrow. Comparing osteoporotic and non-osteoporotic mice, we observed an increased abundance of a specific B cell subpopulation in osteoporotic mice, alongside a significant reduction of a particular granulocyte subpopulation in the early fracture hematoma. Several subclusters of granulocytes, T cells, and macrophages were also altered in the bone marrow. The specific role of these immune cell subclusters remains to be investigated in the future. These results suggest that AI-based clustering may provide a powerful tool for identifying immune cell phenotypes during bone regeneration, offering a more nuanced understanding of flow cytometry data.
Enterococcus faeciumMBBL3 Exhibits Promising Probiotic Potential and Antimicrobial Efficacy Against Bovine Mastitis-AssociatedEscherichia coli and Klebsiella pneumoniae
Authors: Siddique Naim , Rahman Md. Morshedur , Hasnat Soharth , Rahman ANM Aminoor , Talukder Anup Kumar , Karim Md. Robiul , Das Ziban Chandra , Islam Tofazzal , Hoque M. Nazmul 
Published: January 2025

AbstractEnterococcus faecium, a promising probiotic, combats pathogens, supports gut health, strengthens immunity, and provides a natural approach to address the escalating global challenge of antimicrobial resistance. This study aimed to investigate the genome ofE. faeciumMBBL3, isolated from healthy cow milk, to assess its probiotic potential and antimicrobial activity against pathogens causing bovine mastitis. The strain was analyzed through whole genome sequencing, along within-vitroandin-silicoassessments were conducted to determine its antimicrobial efficacy against bovine mastitis pathogens,Klebsiella pneumoniaeMBBL2 (KpMBBL2) andEscherichia coliMBBL4 (EcMBBL4). The genome assembly and functional annotations uncovered many important probiotic traits in MBBL3, where genome comparison revealed its high genetic similarity with otherEnterococcusstrains. MBBL3 demonstrated the ability to ferment a wide range of carbohydrates and possessed 76 carbohydrate-active enzyme-related genes, including five key CAZy families namely GH73, GH18, CBM50, CE4, and AA10. It also possessed importance genes for bile salt and acid tolerance, stress resistance, and surface adhesion. Additionally, MBBL3 contained metabolite regions involved in the biosynthesis of antimicrobial compounds such as 2,4-DAPG, aborycin, enterocin NKR-5-3B, and sodorifen, and bacteriocin gene clusters forsactipeptides,Enterolysin_A, andUviB. Safety assessments indicated low pathogenic potential, whilein-vitroassays demonstrated antibiotic susceptibility and suppressed the growth ofKpMBBL2 andEcMBBL4, respectively. Its bacteriocin compound Enterolysin_A exhibited strong molecular interactions with virulence proteins of these mastitis pathogens. Therefore, the promising probiotic potential and antimicrobial efficacy ofE. faeciumMBBL3, especially against mastitis pathogens combined with its safety, position it as a valuable candidate for therapeutic applications.Key pointsE. faeciumMBBL3 genosme showed high similarity with other species of this genera.Genetic makeup of MBBL3 revealed its ability to survival and adaptation in different niches including host’s gut.In-vitroandin-silicostudy results, along with several genes linked to antimicrobials demonstrated its ability to combat against mastitis pathogens.
CellPhePy: a Python implementation of the CellPhe toolkit for automated cell phenotyping from microscopy time-lapse videos
Authors: Wiggins Laura , Lacy Stuart , Park Graeme , Marrison Joanne , Powell Ben , Cimini Beth , O’Toole Peter , Wilson Julie , Brackenbury William J. 
Published: January 2025

AbstractWe previously developed the CellPhe toolkit1, an open-source R package for automated cell phenotyping from ptychography time-lapse videos. To align with the growing adoption of python-based image analysis tools and to enhance interoperability with widely used software for cell segmentation and tracking, we developed a python implementation of CellPhe, named CellPhePy. CellPhePy preserves all of the core functionality of the original toolkit, including single-cell phenotypic feature extraction, time-series analysis, feature selection and cell type classification. In addition, CellPhePy introduces significant enhancements, such as an improved method for identifying features that differentiate cell populations and extended support for multiclass classification, broadening its analytical capabilities. Notably, the CellPhePy package supports CellPose segmentation and TrackMate tracking, meaning that a set of microscopy images are the only required input with segmentation, tracking and feature extraction fully automated for downstream analysis, without reliance on external applications. The workflow’s increased flexibility and modularity make it adaptable to different imaging modalities and fully customisable to address specific research questions. CellPhePy can be installed via PyPi or GitHub, and we also provide a CellPhePy GUI to aid user accessibility.
Identification and characterization of novel bat coronaviruses in Spain
Authors: Soriano-Tordera Clàudia , Buigues Jaime , Viñals Adrià , Muscolino Elena , Martínez-Recio Raquel , Díez Juana , Monrós Juan S , Cuevas José M. , Dufloo Jérémy , Sanjuán Rafael 
Published: January 2025

AbstractThe zoonotic transmission of bat coronaviruses poses a threat to human health. However, the diversity of bat-borne coronaviruses remains poorly characterized in many geographical areas. Here, we recovered six complete coronavirus genomes by performing a metagenomic analysis of fecal samples from hundreds of individual bats captured in Spain, a country with high bat species diversity. Three of these genomes corresponded to potentially novel coronavirus species belonging to the alphacoronavirus genus. Phylogenetic analyses revealed that some of these viruses are closely related to coronaviruses previously described in bats from other countries, suggesting the existence of a shared viral reservoir worldwide. Using viral pseudotypes, we investigated the receptor usage of the identified viruses and found that one of them can use human and bat ACE2, highlighting its zoonotic potential. However, the receptor usage of the other viruses remains unknown. This study broadens our understanding of coronavirus diversity and identifies research priorities for the prevention of zoonotic viral outbreaks.Author summaryBats carry many viruses, some of which can cross the species barrier and infect humans, a process known as zoonosis. In particular, bat-borne coronaviruses pose a significant threat to human health. To improve our pandemic preparedness, it is essential to characterize the diversity and zoonotic potential of bat coronaviruses. However, such research efforts have historically suffered from a strong geographical bias. For example, despite the rich bat diversity in Spain, few studies have searched for coronaviruses in Iberian bats. Here, we used viral metagenomics to test for the presence of coronaviruses in more than 200 bat samples collected across Spain. We detected six complete coronaviruses, three of which were proposed to be new viral species. We characterized their relationship to previously identified viruses and their ability to use known coronavirus receptors to enter cells, demonstrating that one virus could use human ACE2 as a receptor. Our results highlight the diversity of bat-borne coronaviruses in Spain, their zoonotic potential, and the need to better characterize coronavirus diversity worldwide.