bluuebunny/crossref_metadata_2025_split huggingface dataset:
Extraction of biological terms using large language models enhances the usability of metadata in the BioSample database
Authors: Ikeda Shuya , Zou Zhaonan , Bono Hidemasa , Moriya Yuki , Kawashima Shuichi , Katayama Toshiaki , Oki Shinya , Ohta Tazro 
Published: February 2025

AbstractBioSample is a comprehensive repository of experimental sample metadata, playing a crucial role in providing a comprehensive archive and enabling experiment searches regardless of type. However, the difficulty in comprehensively defining the rules for describing metadata and limited user awareness of best practices for metadata have resulted in substantial variability depending on the submitter. This inconsistency poses significant challenges to the findability and reusability of the data. Given the vast scale of BioSample, which hosts over 40 million records, manual curation is impractical. Rule-based automatic ontology mapping methods have been proposed to address this issue, but their effectiveness is limited by the heterogeneity of BioSample metadata. Recently, large language models (LLMs) have gained attention in natural language processing and have been expected as promising tools for automating metadata curation. In this study, we evaluated the performance of LLMs in extracting cell line names from BioSample descriptions using a gold-standard dataset derived from ChIP-Atlas, a secondary database of epigenomics experiment data, which manually curates samples. Our results demonstrated that LLM-assisted methods outperformed traditional approaches, achieving higher accuracy and coverage. We further extended this approach to extraction of information about experimentally manipulated genes from metadata where manual curation had not yet been applied in ChIP-Atlas. This also yielded successful results for the usage of the database, which facilitates more precise filtering of data and prevents misinterpretation caused by inclusion of unintended data. These findings underscore the potential of LLMs to improve the findability and reusability of experimental data in general, significantly reducing user workload and enabling more effective scientific data management.
A pan-gene catalogue of Asian cultivated rice
Authors: Contreras-Moreira Bruno , Sharma Eshan , Saraf Shradha , Naamati Guy , Gupta Parul , Elser Justin , Chebotarov Dmytro , Chougule Kapeel , Lu Zhenyuan , Wei Sharon , Olson Andrew , Tsang Ian , Lodha Disha , Zhou Yong , Yu Zhichao , Zhao Wen , Zhang Jianwei , Amberkar Sandeep , Sue-Ob Kawinnat , Martin Maria , McNally Kenneth L. , Ware Doreen , Sun Zhi , Deutsch Eric W , Copetti Dario , Wing Rod A. , Jaiswal Pankaj , Dyer Sarah , Jones Andrew R 
Published: February 2025

AbstractThe rice genome underpins fundamental research and breeding, but the Nipponbare (japonica) reference does not fully encompass the genetic diversity of Asian rice. To address this gap, the Rice Population Reference Panel (RPRP) was developed, comprising high-quality assemblies of 16 rice cultivars to representjaponica,indica,aus, andaromaticvarietal groups.The RPRP has been consistently annotated, supported by extensive experimental data and here we report the computational assignment, characterization and dissemination of stably identified pan-genes. We identified 25,178 core pan-genes shared across all cultivars, alongside cultivar-specific and family-enriched genes. Core genes exhibit higher gene expression and proteomic evidence, higher confidence protein domains and AlphaFold structures, while cultivar-specific genes were enriched for domains under selective breeding pressure, such as for disease resistance. This resource, integrated into public databases, enables researchers to explore genetic and functional diversity via a population-aware “reference guide” across rice genomes, advancing both basic and applied research.
Tracking changes in corticospinal excitability during visuomotor paired associative stimulation to predict motor resonance rewriting
Authors: Guidali Giacomo , Bolognini Nadia 
Published: February 2025

ABSTRACTMirror properties of the action observation network (AON) can be modulated through Hebbian-like associative plasticity using paired associative stimulation (PAS). We recently introduced a visuomotor protocol (mirror-PAS, m-PAS), which pairs transcranial magnetic stimulation (TMS) over the primary motor cortex (M1) with visual stimuli of ipsilateral (to TMS) movements, leading to atypical corticospinal excitability (CSE) facilitation (i.e., motor resonance) during PAS-conditioned action observation. While m-PAS aftereffects are robust, little is known about markers of associative plasticity during its administration and their predictive value for subsequent motor resonance rewriting.In the present study, we analyzed CSE dynamics in 81 healthy participants undergoing the m-PAS before and after passively observing left- or right-hand index finger movements. Here, typical and PAS-conditioned motor resonance was assessed with TMS over the right M1. We examined CSE changes during the m-PAS and used linear regression models to explore their relationship with motor resonance modulations.Results showed that the m-PAS transiently reshaped both typical and PAS-conditioned motor resonance. Importantly, we found a gradual increase of CSE during m-PAS, which predicted the loss of typical motor resonance but not the emergence of atypical responses after the protocol’s administration.Our findings suggest that the motor resonance reshaping induced by the m-PAS is not fully predictable by CSE online modulations. Likely, this rewriting is the product of a large-scale reorganization of the AON rather than a phenomenon restricted to the PAS-stimulated motor cortex. This study underlines that monitoring CSE during non-invasive brain stimulation protocols could provide valuable insight into some, but not all, their plastic outcomes.
Triazole 187 is a biased KOR agonist that suppresses itch without sedation and induces anxiolytic-like behaviors in mice
Authors: Volf Allison , Brust Tarsis F. , Kobylski Robin R. , Czekner Kerri M. , Stahl Edward L. , Cameron Michael D. , Trojniak Ashley E. , Aubé Jeffrey , Bohn Laura M. 
Published: February 2025

ABSTRACTKappa opioid receptor agonists are clinically used to treat pruritis and have therapeutic potential for the treatment of pain and neuropsychiatric disorders. We have previously shown that triazole 1.1 is a G protein signaling-biased KOR agonist, that can suppress itch without producing signs of sedation in mice. This profile was recapitulated in rats and non-human primates however, triazole 1.1 had limited potency as an antipruritic. Here we describe a more potent, G protein signaling-biased agonist, triazole 187. Triazole 187 is a potent antipruritic agent and does not decrease spontaneous locomotor activity; interestingly, it produces anxiolytic-like behaviors in mice, an effect not observed for triazole 1.1. In addition to curbing sedation, triazole 187 produces only mild diuresis, resulting in 30% of urine output induced by U50,488H at dose that is 188-fold the antipruritic potency dose. Compounds like triazole 187 may present a means to treat anxiety accompanied by persistent chronic itch while avoiding sedation and diuresis accompanied by typical KOR agonists.Abstract FigureGraphic Abstract
Alterations in retinal tau phosphorylation in Alzheimer’s disease patients identified by mass spectrometry
Authors: Reis Santiago Jessica Dos , Poceviciute Dovile , Vogel Jacob ,   The Netherlands Brain Bank, Brinkmalm Gunnar , Wennström Malin 
Published: February 2025

AbstractBackgroundMost neurodegenerative diseases, including Alzheimer’s disease (AD) and multiple sclerosis (MS), are associated with abnormal post-translational phosphorylation of tau (p-tau) in the brain. Studies using immunostaining techniques have revealed an accumulation of p-tau also in the AD retina and suggested this p-tau accumulation may reflect tau pathology in the brain. However, immunostainings are dependent on antibody specificity and tissue processing. Hence, further validation using additional methods is needed to identify the p-tau species in the retina and verify their relationship to AD pathology.MethodsWe used mass spectrometry to measure p-tau peptides in retinal and hippocampal samples from non-demented controls (NC, n=8), AD ( n=12), and MS (n=4). Peptides were first extracted with Lysis buffer to capture extracellular components and cytoplasm, and then with Ripa buffer to isolate nuclear and organelle proteins. We then analysed the differences in p-tau levels between diagnoses and explored how retinal p-tau variants correlate with hippocampal p-tau and neuropathological changes.ResultsThe mass spectrometry analysis of the retina revealed peaks corresponding to tau peptides phosphorylated at T181, S199/S202, T231, S396 + T403/S404, and T403/S404. These p-tau peptides were also detected in the hippocampal samples, along with additional p-tau peptides such as T217 and S262. Total tau phosphorylation and phosphorylation at S199/S202 and T231 were significantly higher in the retina of AD cases compared to NC. These two peptides, along with peptides phosphorylated at S396+T403/S404 and T403/S404, were also higher in cases with high amyloid-beta (Aβ) Braak stages compared to those with low Aβ Braak stages. Further analysis showed that higher Aβ Braak stages were associated with higher mass spectrometry peak intensities of peptides phosphorylated at S199/S202 and S396+T403/S404. Additionally, retinal p-tau peptides at S396+T403/S404 and T403/S404 correlated with neurofibrillary tangle (NFT) Braak stages, and p-tau peptides S396+T403/S404 in the retina were linked to corresponding phosphorylation in the CA1 region.ConclusionsThese findings underscore the connection between retinal and brain tau pathology and highlight the potential of retinal tau as a biomarker for AD diagnosis and monitoring while also deepening our understanding of tauopathies in both the retina and brain.
Consequences of phenological shifts are determined by the number of generations per season
Authors: Zou Heng-Xing , Rudolf Volker H.W. 
Published: February 2025

AbstractThe order and timing of species’ arrival in a community can often change species interactions. Termed priority effects, this phenomenon arises in communities with drastically different life histories (e.g., bacteria, insects, and amphibians), yet how such life history traits affect consequences of priority effects in seasonal systems remains understudied. Here, we test how a key aspect of life history, numbers of generations in a growing season, interact with priority effects using two competing flour beetles,Tribolium castaneumandTribolium confusum. We manipulated the sequence and timing of arrivals and collected all adults after one, two, or three generations, simulating phenological shifts in communities with different growing season lengths. The early species always reached a higher population, and in general, its per-capita competition towards the late arriver was stronger, indicating priority effects. However, fitting competition models to the data revealed that more generations in a season led to smaller ranges of the calculated stabilization potentials and fitness differences between species and more positive stabilization potentials, indicating that overlapping generations buffered interactions against strong priority effects from different arrival times. These results suggest that effects of phenological shifts (e.g. due to climate change) on species coexistence mechanisms are contingent on the life-histories of interacting species.
HIV-1 derived replication intermediates/oligonucleotides induce a type I IFN-dependent immune suppression via STING activation that can be restored by targeting IFNARI
Authors: Svanberg Cecilia , Mukku Ravi Prasad , Besler Özkan , Hopkins Francis R. , Sjöwall Christopher , Nyström Sofia , Shankar Esaki M. , Larsson Marie 
Published: February 2025

AbstractThe hallmark of HIV-1 infection is the progressive development of multicellular and systemic immune dysfunction, culminating in AIDS. Dendritic cells (DCs) play a pivotal role in HIV dissemination to CD4+ T cells, which are subsequently depleted by the virus leading to HIV disease progression. Type I interferons (IFNs) are critical for host defense during acute infection but contribute to chronic immune activation during the later stages of HIV disease. This persistent activation leads to immune cell exhaustion. HIV-1 can activate type I IFN responses via several pathways, including the STING pathway, which is activated by e.g., virus-derived oligonucleotides. Here, we investigated the underlying mechanisms creating HIV-1-mediated immune dysfunction and role of type I IFNs using a DC and T cell co-culture model. HIV-1 exposure in the DC-T cell co-culture promoted the expansion of suppressive T cells with diminished proliferation and effector functions. The impairment required type I IFNs and subsequent IFN-α/β receptor signaling, initiated by HIV-derived ssDNA activation of IFI16/cGAS followed by STING signaling in the DCs. Targeting IFNAR1 with Anifrolumab restored the immune functions of both DCs and T cells, as well as T cell proliferation and T cell effector functions, including their secretion of IL-2, IFN-γ, and granzyme B. Our findings support that the immune impairments existing in untreated or antiretroviral therapy (ART) treated HIV-infected individuals are mediated, if not fully in part by type I IFN’s negative effect on DC and T cells. Therapeutics targeting IFN-α/β receptors, such as Anifrolumab, hold potential as combination treatment alongside ART, to achieve a more complete immune restoration and contribute to improved quality of life among people living with HIV.
The nematodeCaenorhabditis eleganscan benefit from its coexisting fungal microbiome memberBarnettozyma californica
Authors: Petersen Carola , Griem-Krey Hanne , Christophersen Christina Martínez , Schulenburg Hinrich , Habig Michael 
Published: February 2025

AbstractThe nematodeCaenorhabditis elegansis known to feed on and interact with bacteria in its environment and has become a model organism for microbiome studies. However, whether and howC. elegansinteracts with co-occurring fungi remains largely unknown, despite the presence of many fungal species in its natural habitat. Here, we isolate the yeastBarnettozyma californicafrom a mesocosm experiment withC. elegansand characterize its genome and interaction with the nematode. We find thatB. californicais ingested byC. elegansand can serve as a sole, albeit poor, food source. Yet, when present together withEscherichia coliOP50, the fungus can lead to higher population growth and altered foraging behavior, suggesting that this fungi-bacteria mixture provides a better food source than bacteria alone. This effect varied between different naturalC. elegansstrains, suggesting a genomic basis for the nematode’s interaction withB. californica. The fully assembled and annotated genome of the isolatedB. californicastrain does not indicate any obvious candidate genes for its interaction withC. elegansand/orE. coliOP50. Overall, our results provide an intriguing example of the complexity and multi-level relationship between naturally interacting fungi, bacteria, and animals.Graphical AbstractBarnettozyma californicais a member of the fungal microbiome of the model nematodeCaenorhabditiselegans and can serve as food for worms. When combined withE. coli, this fungus can increase population growth and reduce foraging behaviour dependent on C. elegans genotype, revealing a genetic influence on host-fungus-bacterium interactions.
Neutrophil arrest in myocardial capillaries drives hypoxia and impairs diastolic function in a mouse model of heart failure with preserved ejection fraction
Authors: Small David M. , Buglione Anne E. , Allan-Rahill Nathaniel H. , Locke Tyler , Elmagid Laila M. Abd , Hovasapian Soseh , Kim Rachel , Mistry Adina A. , Vaquerano Sofia A. , Schaffer Chris B. , Nishimura Nozomi 
Published: February 2025

ABSTRACTMyocardial blood flow deficits in heart failure with preserved ejection fraction (HFpEF) patients and related animal models have been recognized for decades, but the underlying mechanisms and resulting consequences for HFpEF pathogenesis remain poorly understood. Using intravital cardiac microcopy in a ‘two-hit’ mouse model of HFpEF, we identified an increased number of neutrophils in capillaries slow moving orstalled, blocking blood flow, as compared to control mice. Administration of antibodies against the neutrophil marker Ly6G reduced the number of arrested neutrophils in myocardial capillaries, leading to both a reduction in myocardial tissue hypoxia and improvement in diastolic function and exercise tolerance. This study identifies a previously uncharacterized cellular mechanism that explains myocardial blood flow deficits in mouse models of HFpEF, and demonstrates that improving myocardial blood flow improves heart function. Restoring myocardial perfusion by preventing neutrophil arrest in coronary capillaries may provide a strategy for improving heart function in HFpEF patients.
hERG-LTN: A New Paradigm in hERG Cardiotoxicity Assessment Using Neuro-Symbolic and Generative AI Embedding (MegaMolBART, Llama3.2, Gemini, DeepSeek) Approach
Authors: Hossain Delower , Al Abir Fuad , Chen Jake Y 
Published: February 2025

AbstractAssessing adverse drug reactions (ADRs) during drug development is essential for ensuring the safety of new compounds. The blockade of the Ether-a-go-go-related gene (hERG) channel plays a critical role in cardiac repolarization. Computational predictions of hERG inhibition can help foresee drug safety, but current data-driven approaches have limitations. Therefore, a new paradigm that bridges the gap between data and knowledge offers an alternative for advancing precision pharmacogenomics in assessing hERG cardiotoxicity. This study aims to develop a reasoning-based, in silico, robust model for predicting drug-induced hERG inhibition, facilitating new drug development by reducing time and cost, supporting downstream in vitro and in vivo testing. In this study, we constructed a new cohort, UnihERG_DB, by sourcing data from ChEMBL, PubChem, BindingDB, GTP, hERG Karim’s, and hERG Blocker’s bioactivity databases. The final dataset comprises 20,409 structures represented as SMILES (Simplified Molecular Input Line Entry System), labeled as hERG blockers (IC50 < 10 µM) or non-hERG blockers (IC50 ≥ 10 µM). Molecular features were extracted using Morgan and CDK fingerprints. Furthermore, we explored embedding feature computation using cutting-edge Large Language Models, including NVIDIA MegaMolBART, LLaMA 3.2, Gemini, and DeepSeek. Finally, we utilized the Logic Tensor Network (LTN), an advanced AI framework, to train and develop the hERG predictive model. Model performance was evaluated using two benchmarks: External Test-1 and hERG-70. The Logic Tensor Network (LTN) outperformed several models, including CardioTox, M-PNN, DeepHIT, CardPred, OCHEM Predictor-II, Pred-hERG 4.2, Random Forest, and Gradient Boosting. On the External Test-1 dataset, LTN achieved an accuracy of 0.931, a specificity of 0.928, and a sensitivity of 0.933. Furthermore, on the hERG-70 benchmark, LTN achieved an accuracy (ACC) of 0.827, a specificity (SPE) of 0.890, and a correct classification rate (CCR) of 0.833. Overall, the Neuro-Symbolic AI approach sets a new standard for hERG-related cardiotoxicity assessment, yielding competitive results with current state-of-the-art (SOTA) models, and highlights its potential for advancing precision pharmacogenomics in drug discovery and development (GitHub).
Machine learning ensemble identifies distinct age-related response to spaceflight in mammary tissue
Authors: Casaletto James A. , Zhao Tyler , Yeung Jay , Ansari Amaan , Raj Ayush , Mishra Arnav , Fry Amber , Sun Kathryn , Lendahl Sofia , Guan Willy , Lee Abigail , Sanders Lauren M. , Costes Sylvain V. 
Published: February 2025

AbstractSpaceflight presents unique environmental stressors, such as microgravity and radiation, that significantly affect biological systems at the molecular, cellular, and organismal levels. Female astronauts, in particular, face an increased risk of developing breast cancer due to exposure to ionizing radiation and other spaceflight-related factors. Age also plays a crucial role in the mammary gland’s response to these stressors, with younger organisms generally exhibiting more efficient response mechanisms than older ones. In this study, we utilized an ensemble of machine learning algorithms to analyze gene expression profiles from mammary tissue of young and old female mice exposed to spaceflight to predict age (old vs young) and condition (spaceflight vs ground control). Using the genes our ensemble identified as most predictive, we investigate the molecular pathways involved in spaceflight-related health risks, particularly in the context of breast cancer and cardiovascular health. We identified age-dependent differences in the gene expression profiles of spaceflight-exposed mice compared to ground control.Specifically, younger mice exhibited enriched pathways related to cellular structure, while older mice showed activation of pathways involved in cortisol synthesis and muscle contraction. All mice responded to spaceflight with evidence of elevated lipid metabolic function. These findings highlight the critical role of age in modulating the response to spaceflight-induced stress and suggest that these molecular pathways may contribute to differential outcomes in tissue homeostasis, cardiovascular and metabolic disorders, and breast cancer susceptibility.
SpliceSelectNet: A Hierarchical Transformer-Based Deep Learning Model for Splice Site Prediction
Authors: Miyachi Yuna , Nakai Kenta 
Published: February 2025

AbstractAccurate RNA splicing is essential for gene expression and protein function, yet the mechanisms governing splice site recognition remain incompletely understood. Aberrant splicing caused by mutations can lead to severe diseases, including cancer and genetic disorders, underscoring the need for accurate computational tools to predict splice sites and detect disruptions. Existing methods have made significant advances in splice site prediction but are often limited in handling long-range dependencies, a factor critical to splicing regulation. Moreover, many models lack interpretability, hindering efforts to elucidate the underlying biological mechanisms. Here, we present SpliceSelectNet (SSNet), a novel deep-learning model that predicts splice sites directly from DNA sequences. This model is capable of handling long-range dependencies (up to 100 kb) using a hierarchical Transformer-based architecture with both local and global attention mechanisms. SSNet offers interpretability at the single nucleotide level, making it particularly effective for identifying aberrant splicing caused by mutations. Our model surpasses the state-of-the-art (SoTA) in splice site prediction on the Gencode test dataset and demonstrates superior performance in aberrant splicing prediction on the BRCA dataset and deep intronic dataset.