bluuebunny/crossref_metadata_2025_split huggingface dataset:
Predicting Car Accident Severity in Northwest Ethiopia: A Machine Learning Approach Leveraging Driver, Environmental, and Road Conditions
Authors: Mengistu Abraham Keffale , Gedefaw Andualem Enyew , Baykemagn Nebebe Demis , Walle Agmasie Damtew , Yehuala Tirualem Zeleke , Alemayehu Meron Asmamaw , Messelu Mengistu Abebe , Assaye Bayou Tilahun 
Published: January 2025

AbstractBackgroundCar accidents in Northwest Ethiopia have significantly increased in severity, with increasing impacts on public safety. This study aims to predict car accident severity in the region by considering driver behavior, environmental conditions, and road characteristics, utilizing machine learning models to enhance traffic safety measures.MethodsThis study used a dataset comprising accident records, weather conditions, road types, traffic volume, and driver characteristics. Various machine learning models, including logistic regression, decision trees, random forests, gradient boosting, XGBoost, LightGBM, support vector machines, K-nearest neighbors, multilayer perceptron (neural networks), and naive Bayes, have been employed to predict accident severity. Model performance was assessed in terms of accuracy, precision, recall, and the F1 score.ResultsDriver-related factors, including age and behavior, were found to significantly influence accident severity. Environmental factors, such as weather and road type, also play crucial roles in determining outcomes. Among the evaluated models, the random forest classifier demonstrated superior performance, achieving an initial accuracy of 78% in predicting fatal accidents. Its performance was improved to 82% through hyperparameter tuning, highlighting its strong predictive ability.ConclusionsThis research highlights the importance of environmental and driver-related factors in predicting accident severity in Northwest Ethiopia. The random forest model proves to be an effective tool for forecasting accident severity, which could inform policies and interventions to improve road safety in the region. Future work should focus on expanding the dataset and exploring additional models for better prediction accuracy.
Contrastive Learning for Omics-guided Whole-slide Visual Embedding Representation
Authors: Yu Suwan , Kim Yooeun , Kim Hoeyoung , Lee Sangseon , Kim Kwangsoo 
Published: January 2025

AbstractWhile computational pathology has transformed cancer diagnosis and prognosis prediction, existing computational methods remain limited in their ability to decipher the complex molecular characteristics within tumors. We present CLOVER (Contrastive Learning for Omics-guided whole-slide Visual Embedding Representation), a novel deep learning framework that leverages self-supervised contrastive learning to integrate multi-omics data (genomics, epigenomics, and transcriptomics) with slide representations, connecting the morphological and molecular features of tumors. Using the breast cancer cohorts comprising diagnostic slides and multi-omics paired data from 610 breast cancer patients, we validated CLOVER’s excellence by demonstrating its ability to generate effective slide-level representations that consider molecular states of cancer. CLOVER outperforms existing methods in few-shot learning scenarios, particularly in cancer subtype classification and clinical biomarker prediction tasks (ER, PR, and HER2 status). Through comprehensive interpretability analysis, we identified tumor microenvironment components within slides and revealed molecular features associated with breast cancer. Our results demonstrate that CLOVER enables detailed molecular characterization from single slide analysis, suggesting its potential for effective utilization in future cancer diagnosis and prognosis prediction studies.
Atlas of the Bacterial Serine-Threonine Kinases expands the functional diversity of the kinome
Authors: O’Boyle Brady , Yeung Wayland , Lu Jason D. , Katiyar Samiksha , Yaron-Barir Tomer M. , Johnson Jared L. , Cantley Lewis C. , Kannan Natarajan 
Published: January 2025

AbstractBacterial serine-threonine protein kinases (STKs) regulate diverse cellular processes associated with cell growth, virulence, and pathogenicity. They are evolutionarily related to the druggable eukaryotic STKs. However, an incomplete knowledge of how bacterial STKs differ from their eukaryotic counterparts and how they have diverged to regulate diverse bacterial signaling functions presents a bottleneck in targeting them for drug discovery efforts. Here, we classified over 300,000 bacterial STK sequences from the NCBI RefSeq non-redundant and UniProt protein databases into 35 canonical and seven non-canonical (pseudokinase) families based on the patterns of evolutionary constraints in the conserved catalytic domain and flanking regulatory domains. Through statistical comparisons, we identified distinguishing features of bacterial STKs, including a distinctive arginine residue in a regulatory helix (C-Helix) that dynamically couples ATP and substrate binding lobes of the kinase domain. Biochemical and peptide-library screens demonstrated that constrained residues contribute to substrate specificity and kinase activation in theMycobacterium tuberculosiskinase PknB. Collectively, these findings open new avenues for investigating bacterial STK functions in cellular signaling and for the development of selective bacterial STK inhibitors.
Percolation and lifestyle transition in microbial metabolism
Authors: Supo-Escalante Rydberg , Plata Germán , Usmanova Dinara R. , Vitkup Dennis 
Published: January 2025

AbstractMicrobial species exhibit a remarkable diversity in their metabolic properties, genome composition, and ecological distribution. A central challenge of systems biology is to understand the relationships between genomic, metabolic, and phenotypic properties of bacteria. However, it is currently not well understood how the structure of metabolic network defines and reflects the lifestyle of diverse bacterial species across the tree of life. By analyzing thousands of genome-scale metabolic models of bacteria, we found a percolation-like transition in their ability to grow on independent carbon sources at around 800 metabolic reactions or about 2000 protein-coding genes. The observed transition is characterized by significant changes in metabolic network functional connectivity primarily associated with the completion of central carbon metabolism and the TCA cycle. Strikingly, experimentally observed phenotypic properties of bacteria also exhibit two markedly different regimes below and above the transition. Species with metabolic network sizes below the transition are typically obligate symbionts and require complex minimal media for their growth. In contrast, species with networks above the transition are primarily free- living generalists. The observed percolation transition is also reflected in multiple other genomic properties, such as a substantial decrease in the fraction of regulatory genes below the transition and higher evolvability for new metabolic phenotypes above the transition. Furthermore, we find that the distribution of bacterial genome sizes from unbiased environmental metagenomic sequencing also reflects genomic clusters corresponding to the observed transition. Overall, our work identifies two qualitatively different regimes in microbial metabolism and lifestyles characterized by distinct structural and functional properties of their metabolic networks.
Molecular detection of a cryptic salamander: Development of an eDNA assay for the detection of the mud salamander (Pseudotriton montanus)
Authors: Brammell Ben F. , Brewer Sara A. , Hoogerheide Ethan A. , Johnson Mary R. 
Published: January 2025

AbstractThe mud salamander (Pseudotriton montanus) is a notoriously cryptic semi-aquatic Plethodontid found throughout much of the eastern United States, reports of decades passing between observations of this species in areas of known occurrence are common. Although it is listed as imperiled or in need of conservation throughout much of its range, with extirpation suspected in many areas, relatively little is known of its current distribution due to its secretive nature. We developed a species-specific qPCR assay for use in eDNA detection ofPseudotriton montanus. Primers and probe were designed based on cytochrome b sequences obtained from specimens collected in central and eastern KY, compared to published sequences throughout the species’ range, and screenedin silico(twenty-seven species) andin vitro(seventeen species) for specificity against sympatric salamander species. The developed assay was field tested via the collection of water samples at sites known or suspected to containP.montanusin Kentucky, Ohio, and Tennessee. Of the 68 samples collected,P. montanuseDNA was detected in eight, including all sites (six) in whichP. montanuslarvae were observed in the field. Sequencing of each environmentally-obtained amplicon confirmed detection ofP. montanus diastictus. This work provides thoroughly vetted tools that should prove useful for future monitoring and range delineation of this threatened and cryptic species.
Towards AI-based Precision Rehabilitation via Contextual Model-based Reinforcement Learning
Authors: Ye Dongze , Luo Haipeng , Winstein Carolee , Schweighofer Nicolas 
Published: January 2025

AbstractBackgroundStroke is a condition marked by considerable variability in lesions, recovery trajectories, and responses to therapy. Consequently, precision medicine in rehabilitation post-stroke, which aims to deliver the “right intervention, at the right time, in the right setting, for the right person,” is essential for optimizing stroke recovery. Although Artificial Intelligence (AI) has been effectively utilized in other medical fields, such as cancer and sepsis treatments, no current AI system is designed to tailor and continuously refine rehabilitation plans post-stroke.MethodsWe propose a novel AI-based decision-support system for precision rehabilitation that uses Reinforcement Learning (RL) to personalize the treatment plan. Specifically, our system iteratively adjusts the sequential treatment plan—timing, dosage, and intensity— to maximize long-term outcomes based on a patient model that includes covariate data (the context). The system collaborates with clinicians and people with stroke to customize the recommended plan based on clinical judgment, constraints, and preferences. To achieve this goal, we proposea Contextual Markov Decision Process (CMDP)framework and a novel hierarchical Bayesian model-based RL algorithm, namedPosterior Sampling for Contextual RL(PSCRL), that discovers and continuously adjusts near-optimal sequential treatments by efficiently balancing exploitation and exploration while respecting constraints and preferences.ResultsWe implemented and validated our precision rehabilitation system in simulations with a sequence of 100 diverse, synthetic patients. Simulation results showed the system ability to continuously learn from both upcoming data from the current patient and a database of past patients via Bayesian hierarchical modeling. Specifically, the algorithm’s sequential treatment recommendations became increasingly more effective in improving functional gains for each patient over time and across the synthetic patient population.ConclusionsOur novel AI-based precision rehabilitation system based on contextual model-based reinforcement learning has the potential to play a key role in novel learning health systems in rehabilitation.
Contaminating manufacturing plasmids and disrupted vector genomes present in liver tissue following adeno-associated virus gene therapy
Authors: Buddle Sarah , Brown Li-An K , Morfopoulou Sofia , Torres Montaguth Oscar Enrique , Scoto Mariacristina , Herder Vanessa , Dhawan Anil , Brown Julianne R , Atkinson Laura , Kopec Angelika , Davis Dee , Storey Nathaniel , Campos Luis , Sebire Neil , Macpherson Hannah , Lee Jasmaine , Orton Richard , Baranello Giovanni , Asamaphan Patawee , Ilia Georgios , Karda Rajvinder , Belfield Holly ,   ISARIC 4C Investigators, Semple Malcolm Gracie , Baillie J Kenneth , Counsell John , Waddington Simon , Thomson Emma C , Muntoni Francesco , Breuer Judith 
Published: January 2025

AbstractAdeno-associated viruses (AAVs) are the most used vectors in gene therapy but can frequently cause liver complications in patients. The mechanisms underlying AAV-related liver toxicity remain poorly understood, posing challenges for effective prevention and intervention. We undertook long and short read metagenomic sequencing of liver tissue from a child with spinal muscular atrophy type 1 experiencing significant hepatitis after receiving onasemnogene abeparvovec. We identified manufacturing plasmid sequences, with evidence of complex structures and recombination. Vector genomes had extensive disruption and concatemerisation. We also identified the presence of human betaherpesvirus 6B in the liver. It is possible that presence of the manufacturing plasmid sequences or helper viruses allow replication of the vector within cells, contributing to the development of complex concatemeric structures and associated hepatitis.
Double-stranded RNA responses, neoantigen presentation and suppression of hepatocellular carcinoma through NMD inhibition via endonuclease SMG6
Authors: Arpa Enes S. , Ricci Virginie , Shapiro Ilja E. , Jokic Marija , Gonzalez-Menendez Irene , Quintanilla-Martinez Leticia , Bénéchet Alexandre P. , Rehwinkel Jan , Luther Sanjiv A. , Bassani-Sternberg Michal , Gatfield David 
Published: January 2025

SUMMARYNonsense-mediated mRNA decay (NMD) eliminates transcripts with premature termination codons, often resulting from mutations or RNA processing errors. While both pro-and anti-tumor NMD activities have been proposed, mechanistic and therapeutic insights into NMD inhibition in cancer have been hindered by the lack of a suitablein vivomodel. We address this by combining a liver-specific, conditionalSmg6mutation – disabling the endonuclease that cleaves NMD-regulated transcripts – with a genetic hepatocellular carcinoma (HCC) model. SMG6 inhibition elevates NMD-sensitive mRNA abundance and translation, promotes MHC presentation of unique immunopeptides, and enhances T-cell infiltration. Additionally, mutant SMG6 specifically activates type-I interferon signaling via double-stranded RNA (dsRNA) accumulation and dsRNA-sensor MDA5, boosting innate immune responses. These pathways converge to halt HCC at an inflammatory stage. Our findings establish SMG6 as a key regulator of dsRNA homeostasis and immune activation, offering therapeutic potential and advancing our understanding of NMD in immune regulation.
A general framework for cycles in ecology
Authors: Moura Rafael O. , Calleja-Solanas Violeta , Langa José A. , Godoy Oscar 
Published: January 2025

AbstractUnderstanding the mechanisms that promote cyclic dynamics among species is critical for predicting biodiversity patterns and community assembly. Here, we present a general framework for studying cycles in ecological systems. Using invasion graphs derived from the Lotka-Volterra equations, we discovered that cycles are primarily driven by the antisymmetric component of the interaction matrix, which captures directional imbalances in interaction strength and promotes non-hierarchical dynamics. We also generalized the concept of cycles to include multi-species equilibria and transitions between states with varying species compositions. Additionally, we found that intrinsic growth rates modulate the likelihood of cycles: homogeneous growth rates promote cycles, while greater variability, particularly with negative growth rates, inhibits them. Our results suggest that cycles are constrained to ecological systems with pronounced interaction asymmetries and relatively uniform growth rates, providing a theoretical basis for their absence in natural communities.
Eco-evolutionary dynamics between multiple competitors reduce phytoplankton coexistence but have limited impacts on community productivity
Authors: Briddon Charlotte L. , García Aurora Menéndez , Ghedini Giulia 
Published: January 2025

ABSTRACTSpecies can evolve rapidly in response to competition but how evolution within communities affects community properties is unclear. To test this, we grew three marine phytoplankton species in monoculture (alone) or polyculture (together) for 17 weeks. We then combined them in communities based on their competition history (monoculture or polyculture isolates) and tracked their composition and productivity over time. We found that species dominance was unaffected, but coexistence was reduced when species evolved together (polyculture isolates). Total biovolume was robust to changes in species relative abundances. However, polyculture isolates had greater oxygen fluxes during exponential phase and were less robust to the addition of an invader. Our results suggest that evolution within communities can strengthen competitive differences between species with uneven effects on different aspects of community functioning. Thus, we should be cautious in extrapolating the consequences of evolution on community biomass to other aspects of productivity or stability.
Evolution ofWolbachiamale-killing mechanism within a host species
Authors: Arai Hiroshi , Wijonarko Arman , Katsuma Susumu , Naka Hideshi , Kageyama Daisuke , Hornett Emily A. , Hurst Gregory D. D. 
Published: January 2025

AbstractMale-killing bacterial symbionts, prevalent in arthropods, skew population sex ratios by selectively killing male progeny, profoundly impacting ecology and evolution of their hosts. Male-killing is a convergently evolved trait, with microbes evolving diverse male-killing mechanisms across host species with widely divergent sex determination pathways. A common evolutionary response to MK presence is the spread of suppressor mutations that restore male survival. In this study, we demonstrate evolution of a novel male-killing mechanism that is insensitive to an existing male-killing suppressor.Hypolimnas bolinabutterflies from Yogyakarta, Indonesia, showed extreme female biased population sex ratio associated with high prevalence of a male-killingWolbachia. This strain,wBol1Y, shared a very recent common ancestor with the previously characterized ‘suppressed’ male-killing strain in the species,wBol1, but retained its male-killing ability in the presence of the male-killing suppressor. The genome ofwBol1Y differed from the suppressedwBol1 in carrying an additional prophage that included strong candidate genes for male-killing.In vitroandin vivodata demonstrated thatwBol1Y feminized splicing and expression of lepidopteran sex determination pathway genes, and that the geneHb-oscar– present onwBol1Y’s unique prophage insert – was sufficient to disrupt the male sex determination pathway. Our study demonstrates the diversity of male-killing mechanisms is a product both of interaction with varying insect sex determination systems and evolution of male-killing within a host species. Our data indicate male-killer and host may be involved in escalating arms races, where spread male-killing suppression drives evolution of additional systems that reestablish male killing by the symbiont.
Infant RSV infection desensitizes β2-adrenergic receptor via CXCL11-CXCR7 signaling in airway smooth muscle
Authors: Zhao Caiqi , Taliento Alice E. , Belkin Elise M , Fearns Rachel , Lerou Paul H. , Ai Xingbin , Bai Yan 
Published: January 2025

ABSTRACTRationaleAirflow obstruction refractory to β2 adrenergic receptor (β2AR) agonists is an important clinical feature of infant respiratory syncytial virus (RSV) bronchiolitis, with limited treatment options. This resistance is often linked to poor drug delivery and potential viral infection of airway smooth muscle cells (ASMCs). Whether RSV inflammation causes β2AR desensitization in infant ASMCs is unknown.ObjectivesTo investigate the interaction of RSV inflammation with the β2AR signaling pathway in infant ASMCsMethodsInfant precision-cut lung slices (PCLSs) and mouse pup models of RSV infection were subjected to airway physiological assays. Virus-free, conditioned media from RSV-infected infant bronchial epithelial cells in air-liquid interface (ALI) culture and nasopharyngeal aspirates (NPA) from infants with severe RSV bronchiolitis were collected and applied to infant PCLSs and ASMCs. Cytokines in these samples were profiled and assessed for the effects on β2AR expression, cell surface distribution, and relaxant function in ASMCs.Measurements and Main ResultsConditioned media and NPA induced similar resistance to β2AR agonists in ASMCs as RSV infection. Cytokine profiling identified CXCL11 as one of the most elevated signals following RSV infection. CXCL11 activated its receptor CXCR7 in a complex with β2AR in ASMCs to promote β2AR phosphorylation, internalization, and degradation. Blockade of CXCR7 partially restored airway relaxation in response to β2AR agonists in infant PCLSs and mouse pup models of RSV infection.ConclusionsThe CXCL11-CXCR7 pathway plays a critical role in β2AR desensitization in ASMCs during RSV infection and represents a potential therapeutic target in alleviating airflow obstruction in infant RSV bronchiolitis.
Real-time fMRI neurofeedback modulates auditory cortex activity and connectivity in schizophrenia patients with auditory hallucinations: A controlled study
Authors: Bauer Clemens C.C. , Zhang Jiahe , Morfini Francesca , Hinds Oliver , Wighton Paul , Lee Yoonji , Stone Lena , Awad Angelina , Okano Kana , Hwang Melissa , Hammoud Jude , Nestor Paul , Whitfield-Gabrieli Susan , Shinn Ann K. , Niznikiewicz Margaret A. 
Published: January 2025

AbstractBackground and HypothesisWe have reported previously a reduction in superior temporal gyrus (STG) activation and in auditory verbal hallucinations (AHs) after real-time fMRI neurofeedback (NFB) in schizophrenia patients with AHs.Study DesignWith this randomized, participant-blinded, sham-controlled trial, we expanded our previous results. Specifically, we examined neurofeedback effects from the STG, an area associated with auditory hallucinations. The effects were compared to Sham-NFB from the motor cortex, a region unrelated to hallucinations. Twenty-three adults with schizophrenia or schizoaffective disorder and frequent medication-resistant hallucinations performed mindfulness meditation to ignore pre-recorded stranger’s voices while receiving neurofeedback either from the STG (n=10, Real-NFB) or motor cortex (n=13 Sham-NFB). Individuals randomized to Sham-NFB received Real-NFB in a subsequent visit, providing a within-subject ’Real-after-Sham-NFB’ comparison.Study ResultsBoth groups showed reduced AHs after NFB, with no group differences. Compared to the Sham-NFB group, the Real-NFB group showed more reduced activation in secondary auditory cortex (AC) and more reduced connectivity between AC and cognitive control regions including dorsolateral prefrontal cortex (DLPFC) and anterior cingulate. The connectivity reduction was also observed in the Real-after-Sham-NFB condition. Secondary AC-DLPFC connectivity reduction correlated with hallucination reduction in the Real-NFB group. Replicating prior results, both groups showed reduced primary auditory cortex activation, suggesting mindfulness meditation may regulate bottom-up processes involved in hallucinations.ConclusionsOur findings emphasize delivering NFB from brain regions involved in medication-resistant AHs. They provide insights into auditory cortex and cognitive control network interactions, highlighting complex processing dynamics and top-down modulation of sensory information.