bluuebunny/crossref_metadata_2025_split huggingface dataset:
Leveraging genomic large language models to enhance causal genotype-brain-clinical pathways in Alzheimer’s disease
Authors: Liu Qiao , Zeng Wanwen , Zhu Hongtu , Li Lexin , Wong Wing Hung ,   Alzheimer’s Disease Neuroimaging Initiative
Published: October 2024

AbstractGenome-wide association studies (GWAS) have identified numerous Alzheimer’s disease (AD)- associated variants. However, how these variants contribute to the etiology of AD remains largely elusive. Recent advances in genomic large language models (LLMs) offer new opportunities to interpret the genetic variation observed in personal genome. In this study, we propose epiBrainLLM, a novel computational framework that leverages genomic LLM to enhance our understanding of the causal pathways from genotypes to brain measures to AD-related clinical phenotypes. epiBrainLLM will first convert the personal DNA sequence into a diverse set of genomic and epigenomic features using a pretrained genomic LLM and then use these features to further predict phenotypes. Across various experimental settings, epiBrainLLM significantly improves causal analysis compared to traditional genotype association approach. We conclude that epiBrainLLM provides a novel perspective for understanding the regulatory mechanisms underlying the AD disease etiology, potentially offering insights into complex disease mechanisms beyond AD.
VITRUVIUS: A conversational agent for real-time evidence based medical question answering
Authors: Villa Maria Camila , Llano Isabella , Castano-Villegas Natalia , Martinez Julian , Guevara Maria Fernanda , Zea Jose , Velásquez Laura 
Published: October 2024

ABSTRACTBackgroundThe application of Large Language Models (LLMs) to create conversational agents (CAs) that can aid health professionals in their daily practice is increasingly popular, mainly due to their ability to understand and communicate in natural language. Conversational agents can manage enormous amounts of information, comprehend and reason with clinical questions, extract information from reliable sources and produce accurate answers to queries. This presents an opportunity for better access to updated and trustworthy clinical information in response to medical queries.ObjectiveWe present the design and initial evaluation of Vitruvius, an agent specialized in answering queries in healthcare knowledge and evidence-based medical research.MethodologyThe model is based on a system containing 5 LLMs; each is instructed with precise tasks that allow the algorithms to automatically determine the best search strategy to provide an evidence-based answer. We assessed our system’s comprehension, reasoning, and retrieval capabilities using the public clinical question-answer dataset MedQA-USMLE. The model was improved accordingly, and three versions were manufactured.ResultsWe present the performance assessment for the three versions of Vitruvius, using a subset of 288 QA (Accuracy V1 86%, V2 90%, V3 93%) and the complete dataset of 1273 QA (Accuracy V2 85%, V3 90.3%). We also evaluate intra-inter-class variability and agreement. The final version of Vitruvius (V3) obtained a Cohen’s kappa of 87% and a state-of-the-art (SoTA) performance of 90.26%, surpassing current SoTAs for other LLMs using the same database.ConclusionsVitruvius demonstrates excellent performance in medical QA compared to standard database responses and other popular LLMs. Future investigations will focus on testing the model in a real-world clinical environment. While it enhances productivity and aids healthcare professionals, it should not be utilized by individuals unqualified to reason with medical data to ensure that critical decision-making remains in the hands of trained professionals.
Exploring the trade-off between deep-learning and explainable models for brain-machine interfaces
Authors: Cubillos Luis H. , Revach Guy , Mender Matthew J. , Costello Joseph T. , Temmar Hisham , Hite Aren , Zutshi Diksha , Wallace Dylan M. , Ni Xiaoyong , Kelberman Madison M. , Willsey Matthew S. , van Sloun Ruud J.G. , Shlezinger Nir , Patil Parag , Draelos Anne , Chestek Cynthia A. 
Published: October 2024

AbstractPeople with brain or spinal cord-related paralysis often need to rely on others for basic tasks, limiting their independence. A potential solution is brain-machine interfaces (BMIs), which could allow them to voluntarily control external devices (e.g., robotic arm) by decoding brain activity to movement commands. In the past decade, deep-learning decoders have achieved state-of-the-art results in most BMI applications, ranging from speech production to finger control. However, the ‘black-box’ nature of deep-learning decoders could lead to unexpected behaviors, resulting in major safety concerns in real-world physical control scenarios. In these applications, explainable but lower-performing decoders, such as the Kalman filter (KF), remain the norm. In this study, we designed a BMI decoder based on KalmanNet, an extension of the KF that augments its operation with recurrent neural networks to compute the Kalman gain. This results in a varying “trust” that shifts between inputs and dynamics. We used this algorithm to predict finger movements from the brain activity of two monkeys. We compared KalmanNet results offline (pre-recorded data,n= 13 days) and online (real-time predictions,n= 5 days) with a simple KF and two recent deep-learning algorithms with state-of-the-art results: tcFNN and LSTM. KalmanNet achieved comparable or better results than other deep learning models in offline and online modes, relying on the dynamical model for stopping while depending more on neural inputs for initiating movements. We further validated this mechanism by implementing a heteroscedastic KF that used the same strategy, and it also approached state-of-the-art performance while remaining in the explainable domain of standard KFs. However, we also see two downsides to KalmanNet. KalmanNet shares the limited generalization ability of existing deep-learning decoders, and its usage of the KF as an inductive bias limits its performance in the presence of unseen noise distributions. Despite this trade-off, our analysis successfully integrates traditional controls and modern deep-learning approaches to motivate high-performing yet still explainable BMI designs.
Mechanobiological cues to bone cells during early metastasis drive later osteolysis: a computational mechanoregulation framework prediction
Authors: Verbruggen Anneke S.K. , McCarthy Elan C. , Dwyer Roisin , McNamara Laoise M. 
Published: October 2024

AbstractBone cells contribute to tumour metastasis by producing biochemical factors that stimulate tumour cell homing and proliferation, but also by resorbing bone matrix (osteolysis) that releases further stimulatory factors for tumour growth in a vicious cycle. Changes in the local mechanical environment of bone tissue occur during early metastasis, which might activate mechanobiological responses by resident bone cells (osteocytes) to activate resorption (osteoclasts) and thereby contribute to tumour invasion. The objective of this study is to investigate whether bone osteolysis is driven by early changes in the bone mechanical environment during metastasis by (a) implementing subject-specific FE models of metastatic femora to predict the mechanical environment within bone tissue during early metastasis (3-weeks after tumour inoculation) and then (b) applying mechanoregulation theory to predict bone tissue remodelling as a function of the evolving mechanical environment within bone tissue during breast cancer-bone metastasis. We implemented a global resorption rate derived from an experimental model, but the mechanoregulation algorithm predicted localised bone loss in the greater trochanter region, the same region where osteolysis was prevalent after three weeks of metastasis development in the animal model. Moreover, the mechanical environment evolved in a similar manner to that reported in separate subject-specific finite element models of these same animals by 6 weeks. Thus, we propose that early changes in the physical environment of bone tissue during metastasis may elicit mechanobiological cues for bone cells and activate later osteolytic bone destruction.
Gene-by-environment interactions and adaptive body size variation in mice from the Americas
Authors: Mack Katya L. , Landino Nico P. , Tertyshnaia Mariia , Longo Tiffany C. , Vera Sebastian A. , Crew Lilia A. , McDonald Kristi , Phifer-Rixey Megan 
Published: October 2024

AbstractThe relationship between genotype and phenotype is often mediated by the environment. Moreover, gene-by-environment (GxE) interactions can contribute to variation in phenotypes and, in turn, fitness. Nevertheless, understanding the impact of GxE interactions in wild systems remains challenging. In the last 500 years, house mice have invaded the Americas. Despite their short residence time, there is evidence of rapid climate adaptation, including shifts in body size and aspects of metabolism with latitude. Previous studies in this system have identified candidate genes for metabolic adaptation using selection scans, however, environmental variation in diet as well as GxE interactions affecting metabolism are likely important factors in shaping body mass variation in wild populations. Here, we investigate the role of the environment and GxE interactions in shaping adaptive phenotypic variation with an experimental manipulation of diet. Using new locally adapted inbred strains from North and South America, we evaluated response to a high-fat diet, finding that sex, strain, diet, and the interaction between strain and diet contribute significantly to variation in aspects of body size. We also found that transcriptional response to diet is largely strain-specific, indicating that GxE interactions affecting gene expression are pervasive. Next, we used crosses between strains from contrasting climates (New York x Brazil and New York x Florida) to characterize gene expression regulatory divergence on a standard breeder diet and on a high-fat diet. We found that gene regulatory divergence is often condition-specific, particularly fortrans-acting changes. Finally, we find evidence for lineage-specific selection oncis-regulatory variation involved in diverse processes, including lipid metabolism. Overlap with scans for selection identified candidate genes for environmental adaptation with diet-specific effects. Together, our results underscore the importance of environmental variation and GxE interactions to adaptive variation in complex traits.
3D Reconstruction Enables High-Throughput Phenotyping and Quantitative Genetic Analysis of Phyllotaxy
Authors: Davis Jensina M. , Gaillard Mathieu , Tross Michael C. , Shrestha Nikee , Ostermann Ian , Grove Ryleigh J. , Li Bosheng , Benes Bedrich , Schnable James C. 
Published: October 2024

AbstractDifferences in canopy architecture play a role in determining both the light and water use efficiency. Canopy architecture is determined by several component traits, including leaf length, width, number, angle, and phyllotaxy. Phyllotaxy may be among the most difficult of the leaf canopy traits to measure accurately across large numbers of individual plants. As a result, in simulations of the leaf canopies of grain crops such as maize and sorghum, this trait is frequently approximated as alternating 180° angles between sequential leaves. We explore the feasibility of extracting direct measurements of the phyllotaxy of sequential leaves from 3D reconstructions of individual sorghum plants generated from 2D calibrated images and test the assumption of consistently alternating phyllotaxy across a diverse set of sorghum genotypes. Using a voxel-carving-based approach, we generate 3D reconstructions from multiple calibrated 2D images of 366 sorghum plants representing 236 sorghum genotypes from the sorghum association panel. The correlation between automated and manual measurements of phyllotaxy is only modestly lower than the correlation between manual measurements of phyllotaxy generated by two different individuals. Automated phyllotaxy measurements exhibited a repeatability ofR2= 0.41 across imaging timepoints separated by a period of two days. A resampling based genome wide association study (GWAS) identified several putative genetic associations with lower-canopy phyllotaxy in sorghum. This study demonstrates the potential of 3D reconstruction to enable both quantitative genetic investigation and breeding for phyllotaxy in sorghum and other grain crops with similar plant architectures.
Pathological PNPase variants with altered RNA binding and degradation activity affect the phenotype of bacterial and human cell models
Authors: Pizzoccheri R. , Falchi F. A. , Alloni A. , Caldarulo M. , Camboni T. , Zambelli F. , Pavesi G. , Visentin C. , Camilloni C. , Sertic S. , Briani F. 
Published: October 2024

ABSTRACTHuman PNPase (hPNPase) is an essential RNA exonuclease located in mitochondria, where it contributes to RNA import from the cytoplasm, degradation of mitochondrial RNA, and R-loop homeostasis. Biallelic mutations in the hPNPasePNPT1gene cause different genetic diseases, ranging from hereditary hearing loss to Leigh syndrome. In this work, we used anEscherichia colimodel we recently developed to test the effects of four pathologicalPNPT1mutations associated with diseases of different severity. Moreover, we generated a new human cell model by introducingPNPT1mutations into 293T cells via CRISPR-Cas editing. Notably, the bacterial cells expressing the different mutant alleles exhibited similar phenotypes consistent with hPNPase loss of function. In contrast, the human cell model responded differently to the two mutations tested, with responses correlating with the severity of the respective pathologies. We interpreted the data derived from both models in the light of thein vitroRNA binding and degradation activity of the purified wild-type and mutated hPNPase variants. We found that all pathogenic mutations tested caused defects in protein assembly and affected the degradation and RNA binding efficiency to varying degrees. However, the severity of the conditions caused by different mutations did not correlate with the catalytic activity of the mutant proteins. Also, we demonstrated that human PNPase, like its bacterial orthologue, is capable of trimming ssDNA and some mutations inhibit such activity.
Accurate genotyping of three major respiratory bacterial pathogens with ONT R10.4.1 long-read sequencing
Authors: Zidane Nora , Rodrigues Carla , Bouchez Valérie , Rethoret-Pasty Martin , Passet Virginie , Brisse Sylvain , Crestani Chiara 
Published: October 2024

AbstractHigh-throughput massive parallel sequencing has significantly improved bacterial pathogen genomics, diagnostics, and epidemiology. Despite its high accuracy, short-read sequencing struggles with complete genome reconstruction and assembly of extrachromosomal elements such as plasmids. Long-read sequencing with Oxford Nanopore Technologies (ONT) presents an alternative that offers benefits like real-time sequencing and cost-efficiency, particularly useful in resource-limited settings. However, the higher error rates of ONT have so far limited its application in high-precision genomic typing. The recent release of ONT’s R10.4.1 chemistry, with significantly improved raw read accuracy (Q20+), offers a potential solution to this problem.The aim of this study was to evaluate the performance of ONT’s latest chemistry for bacterial genomic typing against the gold standard Illumina technology, focusing on three respiratory pathogens of public health importance,Klebsiella pneumoniae, Bordetella pertussis, andCorynebacterium diphtheriae, and their related species. Using the Rapid Barcoding Kit V14, we generated and analyzed genome assemblies with different basecalling tools and models, at different simulated depths of coverage. ONT assemblies were compared to the Illumina reference for completeness and core genome multilocus sequence typing (cgMLST) accuracy (number of allelic mismatches).Our results show that genomes obtained from raw data basecalled with Dorado (with both simplex and duplex reads) SUP v0.7.1, assembled with Flye, and with a minimum coverage depth of 30×, optimized the accuracy for all bacterial species tested. The error rates were consistently below 1% of each cgMLST scheme, indicating that ONT R10.4.1 data is suitable for high-resolution genomic typing applied to outbreak investigations and public health surveillance.
Cross-species orthology detection of long non-coding RNAs (lncRNA) through 13 species using genomic and functional annotations
Authors: Degalez Fabien , Allain Coralie , Lagoutte Laetitia , Lecerf Frédéric , Lagarrigue Sandrine 
Published: October 2024

ABSTRACTLong non-coding RNAs (lncRNAs), defined by a length of over 200 nucleotides and limited protein-coding potential, have emerged as key regulators of gene expression. However, their evolutionary conservation and functional roles remain largely unexplored. Comparative genomics, particularly through sequence conservation analysis, offers a promising approach to infer lncRNA functions. Traditional methods focusing on protein-coding genes (PCGs) fall short due to the rapid evolutionary divergence of lncRNA sequences. To address this, a workflow combining syntenic methods and motif analysis via the Mercator- Pecan genome alignment was developed and applied across 13 vertebrate species, from zebrafish to various amniotes and birds. Further analyses to infer functionality revealed co-expression patterns through 17 shared tissues between human and chicken but also functional short-motif enrichment across the 13 species using the LncLOOM tool, exemplified by the human OTX2-AS1 and its counterparts in other species. The study expanded the catalog of conserved lncRNAs, providing insights into their evolutionary conservation and information related to potential functions. The workflow presented serves as a robust tool for investigating lncRNA conservation across species, supporting future research in molecular biology to elucidate the roles of these enigmatic transcripts.
Unraveling Integration-Segregation Imbalances in Schizophrenia Through Topological High-Order Functional Connectivity
Authors: Li Qiang , Huang Wei , Qiao Chen , Chen Huafu 
Published: October 2024

ABSTRACTBackgroundThe occurrence of brain disorders correlates with detectable dysfunctions in the specialization of brain connectomics. While extensive research has explored this relationship, there is a lack of studies specifically examining the statistical correlation between the integration and segregation of psychotic brain networks using high-order networks, given the limitations of low-order networks. Moreover, these dysfunctions are believed to be linked to information imbalances in brain functions. However, our understanding of how these imbalances give rise to specific psychotic symptoms remains limited.MethodsThis study aims to address this gap by investigating variations at the topological high-order level of the system with regard to specialization in both healthy individuals and those diagnosed with schizophrenia. By employing graph-theoretic brain network analysis, we systematically examine information integration and segregation to delineate system-level differences in the connectivity patterns of brain networks.ResultsThe findings indicate that topological high-order functional connectomics highlight differences in the connectome between healthy controls and schizophrenia, demonstrating increased cingulo-opercular task control and salience interactions, while the interaction between subcortical and default mode networks, dorsal attention and sensory/somatomotor mouth decreases in schizophrenia. Furthermore, we observed a reduction in the segregation of brain systems in healthy controls compared to individuals with schizophrenia, which means the balance between segregation and integration of brain networks is disrupted in schizophrenia, suggesting that restoring this balance may aid in the treatment of the disorder. Additionally, the increased segregation and decreased integration of brain systems in schizophrenia patients compared to healthy controls may serve as a novel indicator for early schizophrenia diagnosis.ConclusionWe discovered that topological high-order functional connectivity highlights brain network interactions compared to low-order functional connectivity. Furthermore, we observed alterations in specific brain regions associated with schizophrenia, as well as changes in brain network information integration and segregation in individuals with schizophrenia.
Sustainable Regeneration of 20 Aminoacyl-tRNA Synthetases in a Reconstituted System Toward Self-Synthesizing Artificial Systems
Authors: Hagino Katsumi , Masuda Keiko , Shimizu Yoshihiro , Ichihashi Norikazu 
Published: October 2024

AbstractIn vitro construction of self-reproducible artificial systems is a major challenge in bottom-up synthetic biology. Here, we developed a reconstituted system capable of sustainably regenerating all 20 aminoacyl-tRNA synthetases (aaRSs), which are major components of the translation system. To achieve this, we needed five types of improvements: 1) optimization of aaRS sequences for efficient translation, 2) optimization of the composition of the translation system to enhance translation, 3) employment of another bacterial AlaRS and SerRS to improve each aminoacylation activity, 4) diminishing the translational inhibition caused by certain aaRS sequences by codon optimization and EF-P addition, and 5) balancing the DNA concentrations of 20 aaRSs to match each requirement. After these improvements, we succeeded in the sustainable regeneration of all 20 aaRSs for up to 20 cycles of 2.5-fold serial dilutions. These methodologies and results provide a substantial advancement toward the realization of self-reproducible artificial systems.
AI-Driven Analysis of Telehealth Effectiveness in Youth Mental Health Services: Insights from SAMHSA Data
Authors: Mansoor Masab A. , Grindem David J. 
Published: October 2024

AbstractBackgroundThe rapid adoption of telehealth services for youth mental health care necessitates a comprehensive evaluation of its effectiveness. This study aimed to analyze the impact of telehealth on youth mental health outcomes using artificial intelligence techniques applied to large-scale public health data.MethodsWe conducted an AI-driven analysis of data from the National Survey on Drug Use and Health (NSDUH) and other SAMHSA datasets. Machine learning techniques, including Random Forest models, K-means clustering, and time series analysis, were employed to evaluate telehealth adoption patterns, predictors of effectiveness, and comparative outcomes with traditional in-person care. Natural language processing was used to analyze sentiment in user feedback.ResultsTelehealth adoption among youth increased significantly, with usage rising from 2.3 sessions per year in 2019 to 8.7 in 2022. Telehealth showed comparable effectiveness to in-person care for depressive disorders and superior effectiveness for anxiety disorders. Session frequency, age, and prior diagnosis were identified as key predictors of telehealth effectiveness. Four distinct user clusters were identified, with socioeconomic status and home environment strongly associated with positive outcomes. States with favorable reimbursement policies saw a 15% greater increase in youth telehealth utilization and a 7% greater improvement in mental health outcomes.ConclusionsTelehealth demonstrates significant potential in improving access to and effectiveness of mental health services for youth. However, addressing technological barriers and socioeconomic disparities is crucial to maximize its benefits.