bluuebunny/crossref_metadata_2025_split huggingface dataset:
RNA3DB: A structurally-dissimilar dataset split for training and benchmarking deep learning models for RNA structure prediction
Authors: Szikszai Marcell , Magnus Marcin , Sanghi Siddhant , Kadyan Sachin , Bouatta Nazim , Rivas Elena 
Published: February 2024

AbstractWith advances in protein structure prediction thanks to deep learning models like AlphaFold, RNA structure prediction has recently received increased attention from deep learning researchers. RNAs introduce substantial challenges due to the sparser availability and lower structural diversity of the experimentally resolved RNA structures in comparison to protein structures. These challenges are often poorly addressed by the existing literature, many of which report inflated performance due to using training and testing sets with significant structural overlap. Further, the most recent Critical Assessment of Structure Prediction (CASP15) has shown that deep learning models for RNA structure are currently outperformed by traditional methods.In this paper we present RNA3DB, a dataset of structured RNAs, derived from the Protein Data Bank (PDB), that is designed for training and benchmarking deep learning models. The RNA3DB method arranges the RNA 3D chains into distinct groups (Components) that are non-redundant both with regard to sequence as well as structure, providing a robust way of dividing training, validation, and testing sets. Any split of these structurally-dissimilar Components are guaranteed to produce test and validations sets that are distinct by sequence and structure from those in the training set. We provide the RNA3DB dataset, a particular train/test split of the RNA3DB Components (in an approximate 70/30 ratio) that will be updated periodically. We also provide the RNA3DB methodology along with the source-code, with the goal of creating a reproducible and customizable tool for producing structurally-dissimilar dataset splits for structural RNAs.Graphical AbstractHighlightsWhile there is a recent surge in applying deep learning to RNA structure prediction, domain experts have raised concerns about generalization and current trends in benchmarking.Many of the concerns primarily relate to how novel RNA families–i.e. families unseen in the training set–are benchmarked, and whether the models are effective at handling such cases. Performance on bench-marks reflective of real-world applications, such as CASP15 and RNA-Puzzles, is poor for RNA deep learning models.We present a dataset–RNA3DB–that is designed for training and bench-marking deep learning models for RNA structure prediction. RNA3DB provides coverage of all RNA chains found in the Protein Data Bank (PDB).RNA3DB is clustered into groups that are both sequentially and structurally non-redundant, providing a robust way of creating training, validation, and testing sets for deep learning models. Along with the dataset, we also provide a transparent methodology as well as the source-code, making our tool both reproducible and customizable.
Synthesis of 5-Benzylamino and 5-Alkylamino-substituted Pyrimido[4,5-c]quinoline Derivatives as CSNK2A Inhibitors with Antiviral Activity
Authors: Asressu Kesatebrhan Haile , Smith Jeffery L. , Dickmander Rebekah J. , Moorman Nathaniel J. , Wellnitz James , Popov Konstantin I. , Axtman Alison D. , Willson Timothy M. 
Published: February 2024

AbstractA series of 5-benzylamine-substituted pyrimido[4,5-c]quinoline derivatives of the CSNK2A chemical probe SGC-CK2-2 were synthesized with the goal of improving kinase inhibitor cellular potency and antiviral phenotypic activity while maintaining aqueous solubility. Among the range of analogs, those bearing electron-withdrawing (4cand4g) or donating (4f) substituents on the benzyl ring as well as introduction of non-aromatic groups such as the cyclohexylmethyl (4t) were shown to maintain CSNK2A activity. The CSNK2A activity was also retained withN-methylation of SGC-CK2-2, but α-methyl substitution of the benzyl substituent led to a 10-fold reduction in potency. CSNK2A inhibition potency was restored with indene-based compound4af, with activity residing in the S-enantiomer (4ag). Analogs with the highest CSNK2A potency showed good activity for inhibition of Mouse Hepatitis Virus (MHV) replication. Conformational analysis indicated that analogs with the best CSNK2A inhibition (4t, 4ac, and4af) exhibited smaller differences between their ground state conformation and their predicted binding pose. Analogs with reduced activity (4ad, 4ae, and4ai) required more substantial conformational changes from their ground state within the CSNK2A protein pocket.
GNN4DM: A Graph Neural Network-based method to identify overlapping functional disease modules
Authors: Gézsi András , Antal Péter 
Published: February 2024

MotivationIdentifying disease modules within molecular interaction networks is an essential exploratory step in computational biology, offering insights into disease mechanisms and potential therapeutic targets. Traditional methods often struggle with the inherent complexity and overlapping nature of biological networks, and they are limited in effectively leveraging the vast amount of available genomic data and biological knowledge. This limitation underscores the need for more effective, automated approaches to integrate these rich data sources.ResultsIn this work, we propose GNN4DM, a novel graph neural network-based structured model that automates the discovery of overlapping functional disease modules. GNN4DM effectively integrates network topology with genomic data to learn the representations of the genes corresponding to functional modules and align these with known biological pathways for enhanced interpretability. Following the DREAM benchmark evaluation setting and extending with three independent data sources (GWAS Atlas, FinnGen, and DisGeNET), we show that GNN4DM performs better than several state-of-the-art methods in detecting biologically meaningful modules. Furthermore, we highlight the method’s applicability through its discovery of two novel multimorbidity-modules significantly enriched in a wide array of apparently unrelated diseases.Availability and ImplementationSource code, all training data, and all identified disease modules are freely available for download athttps://github.com/gezsi/gnn4dm. GNN4DM was implemented in Python.Contactgezsi.andras@vik.bme.hu
Transcriptional responses to priority effects in nectar yeast
Authors: Chappell Callie R. , Goddard Pagé C. , Golden Lexi-Ann , Hernandez Jonathan , Chavez Daniela Ortiz , Hossine Marziiah , Paredes Sur Herrera , Fukami Tadashi , Dhami Manpreet K. 
Published: February 2024

AbstractPriority effects, where the order and timing of species arrival influence the assembly of ecological communities, have been observed in a variety of taxa and habitats. However, the genetic and molecular basis of priority effects remains unclear in most cases, hindering the mechanistic understanding of priority effects. We sought to gain such understanding for the common nectar yeast Metschnikowiareukaufii, which engages in strong priority effects with other species of nectar yeast, includingM. rancensis, another species commonly found in the nectar of our study plant, the hummingbird-pollinatedDiplacus(Mimulus)aurantiacus. After inoculation into two contrasting types of synthetic nectar simulating early arrival ofM. rancensis, we conducted whole-transcriptome sequencing of 108 genetically diverse strains ofM. reukaufii. We found that several genes were differentially expressed inM. reukaufiistrains when the nectar had been conditioned by growth ofM. rancensis. Many of these genes were associated with amino acid metabolism, consistent with our previous finding that early-arriving species limit late-arriving species’ growth by reducing amino acid availability. Furthermore, investigation of expression quantitative trait loci (eQTLs) revealed that genes involved in amino acid transport and resistance to antifungal compounds were enriched in genetic variants, with differing effects on gene expression based on priority effects ofM. rancensis. These results demonstrate that intraspecific variation in the ability of nectar yeasts to respond to nutrient limitation and direct fungal competition may underpin the molecular mechanisms of priority effects.
A natural language processing system for the efficient extraction of cell markers
Authors: Cheng Peng , Peng Yan , Zhang Xiao-Ling , Chen Sheng , Fang Bin-Bin , Li Yan-Ze , Sun Yi-Min 
Published: February 2024

1.AbstractBackgroundIn the last few years, single-cell RNA sequencing (scRNA-seq) has been widely used in various species and tissues. The construction of the cellular landscape for a given species or tissue requires precise annotation of cell types, which relies on the quality and completeness of existing empirical knowledge or manually curated cell marker databases. The natural language processing (NLP) technique is a potent tool in text mining that enables the rapid extraction of entities of interest and relationships between them by parsing the syntax structure.Methods and resultsWe developed MarkerGeneBERT, an NLP-based system designed to automatically extract information about species, tissues, cell types and cell marker genes by parsing the full texts of the literature from single-cell sequencing studies. As a result, 8873 cell markers of 1733 cell types in 435 human tissues/subtissues and 9064 cell markers of 1832 cell types in 492 mouse tissues/subtissues were collected from 3987 single-cell sequencing-related studies. By comparison with the marker genes of existing manual curated cell marker databases, our method achieved 76% completeness and 75% accuracy. Furthermore, within the same literature, we found 89 cell types and 183 marker genes for which the cell marker database was not available. Finally, we annotated brain tissue single-cell sequencing data directly using the compiled list of brain tissue marker genes from our software, and the results were consistent with those of the original studies. Taken together, the results of this study illustrate for the first time how systematic application of NLP-based methods could expedite and enhance the annotation and interpretation of scRNA-seq data.
Relative contribution of COVID-19 vaccination and SARS-CoV-2 infection to population-level seroprevalence of SARS-CoV-2 spike antibodies in a large integrated health system
Authors: Chervo Tyler C. , Elkin Eric P. , Nugent Joshua R. , Valice Emily , Amsden Laura B. , Ergas Isaac J. , Munneke Julie R. , Flores Monica , Saelee Gina N. , Hsiao Crystal A. , Schapiro Jeffery M. , Quesenberry Charles P. , Corley Douglas A. , Habel Laurel A. , Kushi Lawrence H. , Skarbinski Jacek 
Published: February 2024

AbstractBackgroundUnderstanding the relative contributions of SARS-CoV-2 infection-induced and vaccine- induced seroprevalence is key to measuring overall population-level seroprevalence and help guide policy decisions.MethodsUsing a series of six population-based cross-sectional surveys conducted among persons aged ≥7 years in a large health system with over 4.5 million members between May 2021 and April 2022, we combined data from the electronic health record (EHR), an electronic survey and SARS-CoV-2 spike antibody binding assay, to assess the relative contributions of infection and vaccination to population- level SARS-CoV-2 seroprevalence. EHR and survey data were incorporated to determine spike antibody positivity due to SARS-CoV-2 infection and COVID-19 vaccination. We used sampling and non-response weighting to create population-level estimates.ResultsWe enrolled 4,319 persons over six recruitment waves. SARS-CoV-2 spike antibody seroprevalence increased from 83.3% (CI 77.0-88.9) in May 2021 to 93.5% (CI 89.5-97.5) in April 2022. By April 2022, 68.5% (CI 61.9-74.3) of the population was seropositive from COVID-19 vaccination only, 13.9% (10.7-17.9) from COVID-19 vaccination and prior diagnosed SARS-CoV-2 infection, 8.2% (CI 4.5- 14.5) from prior diagnosed SARS-CoV-2 infection only and 2.9% (CI 1.1-7.6) from prior undiagnosed SARS-CoV-2 infection only. We found high agreement (≥97%) between EHR and survey data for ascertaining COVID-19 vaccination and SARS-CoV-2 infection status.ConclusionsBy April 2022, 93.5% of persons had detectable SARS-CoV-2 spike antibody, predominantly from COVID-19 vaccination. In this highly vaccinated population and over 18 months into the pandemic, SARS-CoV-2 infection without COVID-19 vaccination was a small contributor to overall population-level seroprevalence.Article summaryBy April 2022, >93% of people had antibodies to SARS-CoV-2 with COVID-19 vaccination as the main driver of overall population-level seroprevalence in our healthcare system. SARS-CoV-2 infection without vaccination made a small contribution to population-level seroprevalence in our healthcare system.
Examining the indirect effects of life satisfaction and perceived social support on selection optimization compensation and PTSD among the senior citizens of Ekiti State: A moderated mediation approach
Authors: Yarseah Dogbahgen Alphonso , Ogunsanmi Ololade Omolayo , Ogunsanmi Joyce Olufunke , Adesola Alade Foladade , Akinilabi Falana Bernard , Ibimiluyi Olu Francis , Cheeseman Viola H. 
Published: February 2024

ABSTRACTThis study addresses the escalating concern of Posttraumatic Stress Disorder (PTSD) among the elderly population in Ekiti State, Nigeria. As the country experiences a growing number of older individuals, understanding and mitigating PTSD in this demographic is of paramount importance. Despite this urgency, there is a significant gap in the literature pertaining to PTSD among older Nigerians. To fill this void, our research investigates the impact of Selection, Optimization, and Compensation (SOC) strategies on perceived social support (PSS), life satisfaction (LS), and PTSD in the elderly.Our study explores the relationships between PSS and LS, considering their potential roles in alleviating the psychological impact of trauma-related disorders. We also examine how age groups moderate the relationship between SOC and PTSD. Additionally, we explore the indirect effects of PSS and LS on the SOC-PTSD relationship.Conducting a cross-sectional study, we collected data from 321 individuals aged 65 and above (260 males, 61 females) across four Local Government Areas. Our data collection instruments included socio-demographic variables, the SOC instrument, the PTSD scale, the Multidimensional scale of PSS, and the LS index-z.Utilizing the Hayes Model 5 macro process for Structural Equation Modeling (SEM) analysis, our results highlight the significant moderating effect of age groups on the SOC-PTSD relationship. Furthermore, we find that PSS and LS partially mediate the relationships between SOC and PTSD, with SOC directly influencing both PTSD and PSS. These findings carry substantial practical implications and provide avenues for future research. In light of our study, we offer recommendations for addressing the mental health needs of Ekiti State’s elderly population, emphasizing the importance of further studies in the critical field of geropsychology.
The Impact of Digital Adherence Technologies on Health Outcomes in Tuberculosis: A Systematic Review and Meta-Analysis
Authors: Mohamed Mona S. , Zary Miranda , Kafie Cedric , Chilala Chimweta I. , Bahukudumbi Shruti , Foster Nicola , Gore Genevieve , Fielding Katherine , Subbaraman Ramnath , Schwartzman Kevin 
Published: February 2024

AbstractBackgroundSuboptimal tuberculosis (TB) treatment adherence may lead to unsuccessful treatment and relapse. Digital adherence technologies (DATs) may allow more person-centric approaches for supporting treatment. We conducted a systematic review (PROSPERO-CRD42022313166) to evaluate the impact of DATs on health outcomes in TB.MethodsWe searched MEDLINE, Embase, CENTRAL, CINAHL, Web of Science and preprints from medRxiv, Europe PMC, andclinicaltrials.govfor relevant literature from January 2000 to April 2023. We considered experimental or cohort studies reporting quantitative comparisons of clinical outcomes between a DAT and the standard of care in each setting.ResultsSeventy studies (total 58,950 participants) met inclusion criteria. They reported SMS-based interventions (k=18 studies), feature phone-based interventions (k=7), medication sleeves with phone calls (branded as “99DOTS,” k=5), video-observed therapy (VOT; k=17), smartphone-based interventions (k=5), digital pillboxes (k=18), ingestible sensors (k=1), and interventions combining 2 DATs (k=1). Overall, the use of DATs was associated with more frequent treatment success in TB disease (OR = 1.18 [1.06, 1.33]; I2= 66%, k = 46), a decrease in loss to follow up (OR = 0.71 [0.53, 0.94]; I2= 80%, k = 36) and an increase in adverse event reporting (OR = 1.53 [1.26, 1.86]; I2= 0%, k = 9). VOT was associated with an increased likelihood of treatment success in TB disease (OR 1.54 [1.09; 2.19]; I2= 0%, k = 8) and treatment completion in TB infection (OR 4.69 [2.08; 10.55]; I2= 0%, k = 2) as well as an increased frequency of adverse event reporting (OR = 1.79 [1.27; 2.52]; I2= 34%, k = 4). Other interventions involving smartphone technologies were associated with increased treatment success in TB disease (OR 1.98 [1.07; 3.65]; I2=56%, k = 5) and a decreased frequency of loss to follow up (OR = 0.31 [0.13; 0.77]; I2= 36%, k = 5). Digital pillboxes were also associated with an improvement in treatment success (OR = 1.32 [1.00; 1.73]; I2= 71%, k = 11). However, improved treatment success was only seen in high- and upper middle-income countries. SMS-based interventions, feature-phone interventions and 99DOTS were not associated with improvements in short-term clinical outcomes.ConclusionCertain DATs--notably VOT and smartphone-based interventions, in higher income settings and sometimes combined with other supportive measures—may be associated with improvements in treatment success and losses to follow-up, compared to standard care. However, evidence remains highly variable, and generalizability limited. Higher quality data are needed.
Metabolic priming of GD2TRAC-CAR T cells during manufacturing promotes memory phenotypes while enhancing persistence
Authors: Cappabianca Dan , Pham Dan , Forsberg Matthew H. , Bugel Madison , Tommasi Anna , Lauer Anthony , Vidugiriene Jolanta , Hrdlicka Brookelyn , McHale Alexandria , Sodji Quaovi , Skala Melissa C. , Capitini Christian M. , Saha Krishanu 
Published: February 2024

ABSTRACTManufacturing Chimeric Antigen Receptor (CAR) T cell therapies is complex, with limited understanding of how media composition impact T-cell phenotypes. CRISPR/Cas9 ribonucleoproteins can precisely insert a CAR sequence while disrupting the endogenous T cell receptor alpha constant (TRAC) gene resulting inTRAC-CAR T cells with an enriched stem cell memory T-cell population, a process that could be further optimized through modifications to the media composition. In this study we generated anti-GD2TRAC-CAR T cells using “metabolic priming” (MP), where the cells were activated in glucose/glutamine low media and then expanded in glucose/glutamine high media. T cell products were evaluated using spectral flow cytometry, metabolic assays, cytokine production, cytotoxicity assaysin vitroand potency against human GD2+ xenograft neuroblastoma modelsin vivo. Compared to standardTRAC-CAR T cells, MPTRAC-CAR T cells showed less glycolysis, higher CCR7/CD62L expression, more bound NAD(P)H activity and reduced IFN-γ, IL-2, IP-10, IL-1β, IL-17, and TGFβ production at the end of manufacturingex vivo, with increased central memory CAR T cells and better persistence observedin vivo. Metabolic priming with media during CAR T cell biomanufacturing can minimize glycolysis and enrich memory phenotypesex vivo, which could lead to better responses against solid tumorsin vivo.
Somite-independent regeneration of the axolotl primary body axis
Authors: Masselink Wouter , Gerber Tobias , Falcon Francisco , Deshayes Tom , Papadopoulos Sofia-Christina , Pende Marko , Jamwal Vijayishwer Singh , Taniguchi-Sugiura Yuka , Lin Tzi-Yang , Kurth Thomas , Wang Jingkui , Arendt Detlev , Fei Ji-Feng , Treutlein Barbara , Tanaka Elly M. , Murawala Prayag 
Published: February 2024

AbstractPrimary body axis development is a highly conserved process that proceeds through somitogenesis and further subdivision into dermatome, myotome, and sclerotome. Defects in somitic-clock genes such asHes7lead to vertebral segmentation defects in mice and fish. Here we show that in the axolotl,Hes7is also necessary for proper embryonic vertebral segmentation but is, surprisingly, dispensable during axolotl tail regeneration. Using genetic barcoding and fate mapping we found that during regeneration, the somitic derivatives (muscle, cartilage, tendon, fibroblasts) arose from tendon-like,Lfng+multi-potent stem cells residing at the myotendonal junction that we term “asomitic stem cells”. Throughout homeostasis and regeneration these stem cells display a distinct gene regulatory state compared to developmental progenitors with comparable lineage potential. These observations contrast to axolotl limb regeneration that proceeds via fibroblast dedifferentiation and the subsequent recapitulation of a limb developmental program. Taken together our research shows that divergent strategies are deployed between limb and tail regeneration, and that regeneration of complex body parts does not necessarily involve the complete redeployment of developmental programs.
FGF2 promotes the expansion of parietal mesothelial progenitor pools and inhibits BMP4-mediated smooth muscle cell differentiation
Authors: Hwang Youngmin , Shimamura Yuko , Tanaka Junichi , Miura Akihiro , Sawada Anri , Sarmah Hemanta , Shimizu Dai , Kondo Yuri , Ninish Zurab , Yamada Kazuhiko , Mori Munemasa 
Published: February 2024

SummaryMesothelial cells, in the outermost layer of internal organs, are essential for both organ development and homeostasis. Although the parietal mesothelial cell is the primary origin of mesothelioma that may highjack developmental signaling, the signaling pathways that orchestrate developing parietal mesothelial progenitor cell (MPC) behaviors, such as MPC pool expansion, maturation, and differentiation, are poorly understood. To address it, we established a robust protocol for culturing WT1+MPCs isolated from developing pig and mouse parietal thorax. Quantitative qPCR and immunostaining analyses revealed that BMP4 facilitated MPC differentiation into smooth muscle cells (SMCs). In contrast, FGF2 significantly promoted MPC progenitor pool expansion but blocked the SMC differentiation. BMP4 and FGF2 counterbalanced these effects, but FGF2 had the dominant impact in the long-term culture. A Wnt activator, CHIR99021, was pivotal in MPC maturation to CALB2+mesothelial cells, while BMP4 or FGF2 was limited. Our results demonstrated central pathways critical for mesothelial cell behaviors.