bluuebunny/crossref_metadata_2025_split huggingface dataset:
Early detection of non-small cell lung cancer using electronic health record data
Authors: Li Xiudi , Yuan Erin Y. , Kuperberg Stephen J. , Bonzel Clara-Lea , Jeffway Mary I. , Cai Tianrun , Liao Katherine P. , Aguiar-Ibáñez Raquel , Kao Yu-Han , Santorelli Melissa L. , Christiani David C. , Cai Tianxi , Duan Rui 
Published: October 2024

AbstractRationaleSpecific patient characteristics increase the risk of cancer, necessitating personalized healthcare approaches. For high-risk individuals, tailored clinical management ensures proactive monitoring and timely interventions. Electronic Health Records (EHR) data are crucial for supporting these personalized approaches, improving cancer prevention and early diagnosis.ObjectivesWe leverage EHR data and build a prediction model for early detection of non-small cell lung cancer (NSCLC).MethodsWe utilize data from Mass General Brigham’s EHR and implement a three-stage ensemble learning approach. Initially, we generate risk scores using multivariate logistic regression in a self-control and case-control design to distinguish between cases and controls. Subsequently, these risk scores are integrated and calibrated using a prospective Cox model to develop the risk prediction model.ResultsWe identified 127 EHR-derived features predictive for early detection of NSCLC. The highly predictive features include smoking, relevant lab test results, and chronic lung diseases. The predictive model reached area under the ROC curve (AUC) of 0.801 (positive predictive value (PPV) 0.0173 with specificity 0.02) for predicting one-year NSCLC risk in a population aged 18 and above, and AUC of 0.757 (PPV 0.0196 with specificity 0.02) in a population aged 40 and above.ConclusionsThis study identified EHR derived features which are predictive of early NSCLC diagnosis. The developed risk prediction model exhibits superior performance for early detection of NSCLC compared to a baseline model that only relies on demographic and smoking information, demonstrating the potential of incorporating EHR derived features for personalized cancer screening recommendations and early detection.
Revisiting Aristotle’s observation on bees: High floral constancy is common among bees but it is shaped by the locally abundant flowering species
Authors: Shrotri Saket , Kaur Sukhraj , Nawge Viraj , Sandhya S , Dandavate Rohan , Gowda Vinita 
Published: October 2024

ABSTRACTFloral constancy is the tendency of a pollinator to sequentially visit flowers of the same species despite the availability of other rewarding plants. In a phytodiverse community, resource assurance may lead to pollinators displaying floral constancy to the most abundant plant species. We tested this by investigating if pollinators are floral constant on the abundant or the non-abundant plants within a seasonally flowering tropical community. We quantified floral constancy in three social (Apisspp.) and two solitary (non-Apis) Indian native bees using three approaches, that is by manually tracking the bees, analysing their pollen load, and examining pollen sacs of returning bees at their hive. We next examined inApis cerana indicaif constancy in individual bees translated to hive-level constancy. We found that in our community with distinct co-flowering patches, bees were constant to the most abundant species within a localised patch, and not to the most abundant species in the landscape. While the pollen loads from both the social and solitary bees suggested that they show high floral constancy (> 70% uni-dominant pollen), their values differed significantly (p< 0.0001). Finally, approximately 90% of individuals within a hive showed floral constancy (monolectic), but collectively, a hive displayed polylectic foraging. Our findings highlight that the foraging patterns of native pollinators has been understudied and is a critical first step towards connecting reproductive assurances to plant-pollinator dependencies in large landscapes.
TreeFormer: A transformer-based tree rearrangement operation for phylogenetic reconstruction
Authors: Ly-Trong Nhan , Matsen Frederick A. , Minh Bui Quang 
Published: October 2024

AbstractPhylogenetic inference is a fundamental problem in biology, which studies the origins and evolutionary relationships among species. Popular phylogenetic inference methods, such as IQ-TREE, RAxML, and PHYML, typically utilize heuristic tree search algorithms to seek a phylogenetic tree that maximizes the likelihood of the observed genetic data. However, tree search is time-consuming and often prone to local optima. To address these issues, we introduce TreeFormer, a new Transformer-based tree rearrangement operation for tree search. Experimental results show that TreeFormer achieves higher accuracy than FastTree 2 when reconstructing trees from real alignments with fewer than 1000 sites.
Clathrin-independent endocytosis and retrograde transport in cancer cells promote cytotoxic CD8 T cell activation
Authors: Xu Shiqiang , Buridant Alix , Hirsch Thibault , Shafaq-Zadah Massiullah , Dransart Estelle , Ledoux Benjamin , Johannes Ludger , van der Bruggen Pierre , Morsomme Pierre , Renard Henri-François 
Published: October 2024

AbstractEndophilin A3-mediated clathrin-independent endocytosis (EndoA3-mediated CIE) mediates the internalization of immunoglobulin-like proteins, including key immune synapse components. Here, we identify ICAM1 as a novel EndoA3-dependent cargo, alongside ALCAM. We demonstrate that both proteins subsequently undergo retromer-dependent retrograde transport to thetrans-Golgi network (TGN) in cancer cells. From there, they undergo polarized redistribution to the plasma membrane, where they contribute to immune synapse formation between cancer cells and cytotoxic CD8 T cells. Disruption of EndoA3 or retromer components significantly impairs the activation of autologous cytotoxic CD8 T cells, as demonstrated by decreased cytokine production. Concomitantly, we observed a reduced localization of ICAM1 at the immune synapse, indicating impaired immune synapse integrity. Indeed, cancer cells lacking EndoA3-mediated CIE or retromer form enlarged immune synapses that fail to restore full T cell activation, suggesting a compensatory attempt by T cells to overcome the defective synapse. Together, these findings reveal that EndoA3-mediated CIE and retrograde transport act in concert in cancer cells to relocate immune synapse components via the Golgi, thereby promoting the activation of cytotoxic CD8 T cells. Our study paves the way for the design of future therapeutic strategies targeting these pathways to enhance T cell-mediated anti-tumor immunity.Significance StatementThis study uncovers a novel mechanism by which clathrin-independent endocytosis (CIE) and retrograde transport collaborate to regulate immune synapse dynamics in cancer cells. We identify ICAM1 as a new cargo of Endophilin A3-mediated CIE, highlighting its role in the polarized redistribution of immune synapse components critical for cytotoxic CD8 T cell activation. By linking a specific CIE mechanism and retrograde transport to immune synapse function, our findings provide new insights into cancer-immunity interactions and suggest potential therapeutic strategies to enhance immune responses by targeting protein trafficking pathways.
Exploring the adhesion properties of extracellular vesicles for functional assays
Authors: Pachane Bianca C. , Carlson Bess , Queen Suzanne E. , Selistre-de-Araujo Heloisa S. , Witwer Kenneth W. 
Published: October 2024

AbstractThe “stickiness” of extracellular vesicles (EVs) can pose challenges for EV processing and storage, but adhesive properties may also be exploited to immobilize EVs directly on surfaces for various measurement techniques, including super-resolution microscopy. Direct adhesion to surfaces may allow examination of broader populations of EVs than molecular affinity approaches, which can also involve specialized, expensive affinity reagents. Here, we report on the interaction of EVs with borosilicate glass and quartz coverslips and on the effects of pre-coating coverslips with poly-L-lysine (PLL), a reagent commonly used to facilitate interactions between negatively charged surfaces of cells and amorphous surfaces. Additionally, we compared two mounting media conditions for super-resolution microscopy (SRM) imaging and used immobilized EVs for a B-cell interaction test. Our findings suggest that borosilicate glass coverslips immobilize EVs better than quartz glass coverslips. We also found that PLL is not strictly required for EV retention but contributes to the uniform distribution of EVs on borosilicate glass coverslips. Overall, these findings suggest that standard lab materials like borosilicate glass coverslips, with or without PLL, can be effectively used for the immobilization of EVs in specific imaging techniques.
Identification of distinct subtypes of post-stroke and neurotypical gait behaviors using neural network analysis of kinematic time series data
Authors: Kuch Andrian , Schweighofer Nicolas , Finley James M. , McKenzie Alison , Wen Yuxin , Sánchez Natalia 
Published: October 2024

AbstractBackgroundHeterogeneous types of gait impairment are common post-stroke. Studies used supervised and unsupervised machine learning on discrete biomechanical features to summarize the gait cycle and identify common patterns of gait behaviors. However, discrete features cannot account for temporal variations that occur during gait. Here, we propose a novel machine-learning pipeline to identify subgroups of gait behaviors post-stroke using kinematic time series data.MethodsWe analyzed ankle and knee kinematic data during treadmill walking data in 39 individuals post- stroke and 28 neurotypical controls. The data were first input into a supervised dual-stage Convolutional Neural Network-Temporal Convolutional Network, trained to extract temporal and spatial gait features. Then, we used these features to find clusters of different gait behaviors using unsupervised time series k-means. We repeated the clustering process using 10,000 bootstrap training data samples and a Gaussian Mixture Model to identify stable clusters representative of our dataset. Finally, we assessed the kinematic differences between the identified clusters using 1D statistical parametric mapping ANOVA. We then compared gait spatiotemporal and clinical characteristics between clusters using one-way ANOVA.ResultsWe obtained five clusters: two clusters of neurotypical individuals (C1 and C2) and three clusters of individuals post-stroke (S1, S2, S3). C1 had kinematics that resembled the normative gait pattern. Individuals in C2 had a shorter stride time than C1. Individuals in S1 had mild impairment and walked with increased bilateral knee flexion during the loading response. Individuals in S2 had moderate impairment, were the slowest among the clusters, took shorter steps, had increased knee flexion during stance bilaterally and reduced paretic knee flexion during swing. Individuals in S3 had mild impairment, asymmetric swing time, had increased ankle abduction during the gait cycle and reduced dorsiflexion bilaterally during loading response and stance. Every individual was assigned to a cluster with a cluster membership likelihood above 93%.ConclusionsOur results indicate that joint kinematics in individuals post-stroke are distinct from controls, even in those individuals with mild impairment. The three subgroups post-stroke showed distinct kinematic impairments during specific phases in the gait cycle, providing additional information to clinicians for gait retraining interventions.
Improving GWAS performance in underrepresented groups by appropriate modeling of genetics, environment, and sociocultural factors
Authors: Cataldo-Ramirez Chelsea C. , Lin Meng , Mcmahon Aislinn , Gignoux Christopher R. , Weaver Timothy D. , Henn Brenna M. 
Published: October 2024

ABSTRACTGenome-wide association studies (GWAS) and polygenic score (PGS) development are typically constrained by the data available in biobank repositories in which European cohorts are vastly overrepresented. Here, we increase the utility of non-European participant data within the UK Biobank (UKB) by characterizing the genetic affinities of UKB participants who self-identify as Bangladeshi, Indian, Pakistani, “White and Asian” (WA), and “Any Other Asian” (AOA), towards creating a more robust South Asian sample size for future genetic analyses. We assess the relationships between genetic structure and self-selected ethnic identities resulting in consistent patterns of clustering used to train a support vector machine (SVM). The SVM model was utilized to reassignn= 1,853 AOA and WA participants at the subcontinental level, and increase the sample size of the UKB South Asian group by 1,381 additional participants. We then leverage these samples to assess GWAS performance and PGS development. We further include environmental covariates in the height GWAS by implementing a rigorous covariate selection procedure, and compare the outputs of two GWAS models: GWASnulland GWASenv. We show that PGS performance derived from environmentally adjusted GWAS yields comparable prediction to PGS models developed with an order of magnitude larger training dataset (R2=0.021 vs 0.026). Models with 7 - 8 environmental covariates double the variance explained by PGS alone. In summary, we demonstrate how GWAS performance can be improved by leveraging ambiguous ethnicity codes, ancestry matched imputation panels, and including environmental covariates.
Comparative spatiotemporal single cell transcriptomes reveal rewiring of pre-existing regulations during emergence of Kranz anatomy in C4grasses
Authors: Zhao Caiyao , Yang Jianzhao , Ni Anting , Chen Hanyang , Su Hong , Cheng Yating , Li Xiaoya , Zhong Liyuan , Fang Dongming , Sun Hao-Ran , Yi Juan , Wang Ying , Wang Yanjie , Lyu Ming-ju Amy , Ni Xiaoxiang , Chen Lichuan , Li Huanjin , Yu Guichao , Wang Yumeng , Chen Rulei , Yu Hanyue , Shao Wenwen , Sun Haixi , Bai Yinqi , Li Chuanshun , Dong Yang , Zou Changsong , Tu Xiaoyu , Ma Shisong , Guo Xing , Zhang Gengyun , Wang Peng , Liu Huan , Wei Tong , Gu Ying , Xu Xun , Zhu Xin-Guang 
Published: October 2024

SummaryMany of the world’s most productive food and bioenergy crops use C4photosynthesis, which have high photosynthetic efficiency due to a Kranz anatomy-based CO2concentrating mechanism. Here, we took a comparative transcriptomics approach using single cell spatial transcriptomes of leaf primordia for maize (Zea mays) and single cell RNA-seq (scRNA-seq) atlases of corresponding leaf tissues for three C4species (Zea mays,Sorghum bicolor,Setaria viridis) and one C3rice (Oryza sativa) to study regulatory networks involved in the development and evolution of Kranz anatomy. We show that the formation of Kranz anatomy involves extensive recruitment and modification of pre-existing regulatory modules, especially the SHR-SCR module and the auxin signaling pathway. Members of theINDETERMINATE DOMAIN(IDD) family transcription factors, includingIDD7andIDDP1, contribute to the modification of the involved modules. These extensive recruitment and modification of existing genetic regulatory programs underlie the repeated emergence of C4photosynthesis during evolution.
Unraveling the genetic landscape of susceptibility to multiple primary cancers
Authors: Middha Pooja , Kachuri Linda , Nierenberg Jovia L. , Graff Rebecca E. , Cavazos Taylor B. , Hoffmann Thomas J. , Zhang Jie , Alexeeff Stacey , Habel Laurel , Corley Douglas A. , Van Den Eeden Stephen , Kushi Lawrence H. , Ziv Elad , Sakoda Lori C. , Witte John S. 
Published: October 2024

AbstractWith advances in cancer screening and treatment, there is a growing population of cancer survivors who may develop subsequent primary cancers. While hereditary cancer syndromes account for only a portion of multiple cancer cases, we sought to explore the role of common genetic variation in susceptibility to multiple primary tumors. We conducted a cross-ancestry genome-wide association study (GWAS) and transcriptome-wide association study (TWAS) of 10,983 individuals with multiple primary cancers, 84,475 individuals with single cancer, and 420,944 cancer-free controls from two large-scale studies.Our GWAS identified six lead variants across five genomic regions that were significantly associated (P<5×10-8) with the risk of developing multiple primary tumors (overall and invasive) relative to cancer-free controls (at 3q26, 8q24, 10q24, 11q13.3, and 17p13). We also found one variant significantly associated with multiple cancers when comparing to single cancer cases (at 22q13.1). Multi-tissue TWAS detected associations with genes involved in telomere maintenance in two of these regions (ACTRT3in 3q26 andSLKandSTN1in 10q24) and the development of multiple cancers. Additionally, the TWAS also identified several novel genes associated with multiple cancers, including two immune-related genes,IRF4andTNFRSF6B. Telomere maintenance and immune dysregulation emerge as central, common pathways influencing susceptibility to multiple cancers. These findings underscore the importance of exploring shared mechanisms in carcinogenesis, offering insights for targeted prevention and intervention strategies.
Dynamics and ecology of a multi-stage expansion of Oropouche virus in Brazil
Authors: Tegally Houriiyah , Dellicour Simon , Poongavanan Jenicca , Mavian Carla , Dor Graeme , Fonseca Vagner , Tagliamonte Massimiliano S. , Dunaiski Marcel , Moir Monika , Wilkinson Eduan , de Albuquerque Carlos Frederico Campelo , Frutuoso Livia C. V. ,   CLIMADE Consortium, Holmes Edward C. , Baxter Cheryl , Lessells Richard , Kraemer Moritz U.G. , Lourenço José , Alcantara Luiz Carlos Junior , de Oliveira Tulio , Giovanetti Marta 
Published: October 2024

AbstractIn March 2024, the Pan American Health Organization (PAHO) issued an alert in response to a rapid increase in Oropouche fever cases across South America. Brazil has been particularly affected, reporting a novel reassortant lineage of the Oropouche virus (OROV) and expansion to previously non-endemic areas beyond the Amazon Basin. Utilising phylogeographic approaches, we reveal a multi-scale expansion process with both short and long-distance dispersal events, and diffusion velocities in line with human-mediated jumps. We identify forest cover, banana and cocoa cultivation, temperature, and human population density as key environmental factors associated with OROV range expansion. Using ecological niche modelling, we show that OROV circulated in areas of enhanced ecological suitability immediately preceding its explosive epidemic expansion in the Amazon. This likely resulted from the virus being introduced into simultaneously densely populated and environmentally favourable regions in the Amazon, such as Manaus, leading to an amplified epidemic and spread beyond the Amazon. Our study provides valuable insights into the dispersal and ecological dynamics of OROV, highlighting the role of human mobility in colonisation of new areas, and raising concern over high viral suitability along the Brazilian coast.
Isoform-level analyses of 6 cancers uncover extensive genetic risk mechanisms undetected at the gene-level
Authors: Chang Yung-Han , Head S. Taylor , Harrison Tabitha , Yu Yao , Huff Chad D. , Pasaniuc Bogdan , Lindström Sara , Bhattacharya Arjun 
Published: October 2024

ABSTRACTIntegrating genome-wide association study (GWAS) and transcriptomic datasets can help identify potential mediators for germline genetic risk of cancer. However, traditional methods have been largely unsuccessful because of an overreliance on total gene expression. These approaches overlook alternative splicing, which can produce multiple isoforms from the same gene, each with potentially different effects on cancer risk.Here, we integrate genetic and multi-tissue isoform-level gene expression data from the Genotype Tissue-Expression Project (GTEx, N = 108-574) with publicly available European-ancestry GWAS summary statistics (all N > 20,000 cases) to identify both isoform- and gene-level risk associations with six cancers (breast, endometrial, colorectal, lung, ovarian, prostate) and six related cancer subtype classifications (N = 12 total). Compared to traditional methods leveraging total gene expression, directly modeling isoform expression through transcriptome-wide association studies (isoTWAS) substantially increases discovery of transcriptomic mechanisms underlying genetic associations. Using the same RNA-seq datasets, isoTWAS identified 164% more significant unique gene associations compared to TWAS (6,163 and 2,336, respectively), with isoTWAS-prioritized genes enriched 4-fold for evolutionarily-constrained genes (P = 6.1 × 10−13). isoTWAS tags transcriptomic associations at 52% more independent GWAS loci compared to TWAS across the six cancers. Additionally, isoform expression mediates an estimated 63% greater proportion of cancer risk SNP heritability compared to gene expression when evaluating cis-genetic influence on isoform expression. We highlight several notable isoTWAS associations that demonstrate GWAS colocalization at the isoform level but not at the gene level, including,CLPTM1L(lung cancer),LAMC1(colorectal), andBABAM1(breast). These results underscore the critical importance of modeling isoform-level expression to maximize discovery of genetic risk mechanisms for cancers.
Validation of the PREDICT Breast Version 3.0 Prognostic Tool in US Breast Cancer Patients
Authors: Hsiao Yi-Wen , Wishart Gordon C. , Pharoah Paul D.P. , Peng Pei-Chen 
Published: October 2024

ABSTRACTBackgroundPREDICT Breast v3 is the latest updated prognostication tool, developed from the breast cancer registry of approximately 35,000 women diagnosed between 2000 and 2018 in the United Kingdom. However, its performance in the United States (US) population is unknown. This study aims to validate PREDICT Breast v3 using newly released Surveillance, Epidemiology, and End Results (SEER) outcome data for US breast cancer patients and to address potential health disparities.MethodsOver 860,000 female patients diagnosed between 2000 and 2018 with primary breast cancer and followed for at least 10 years were selected from the SEER database. Predicted and observed 10- and 15-year breast cancer-specific survival outcomes were compared for the overall cohort, stratified by estrogen receptor (ER) status, and predefined subgroups. Discriminatory accuracy was determined through the area under the receiver-operator curves (AUC).ResultsPREDICT Breast v3 demonstrated good calibration and discrimination for long-term breast cancer-specific mortality. It provided accurate mortality estimates (within a ±10% error range) across the entire US population for 10-year (−8% in ER-positive and 4% in ER-negative patients) and 15-year (−3 % in ER-positive and 5% in ER-negative patients) all-cause mortality, for both ER statuses. The model also showed good performance for 10- and 15-year all-cause mortality across the U.S. population, with AUC of 0.769 and 0.793 for ER-positive breast cancer as well as AUC of 0.738 and 0.746 for ER-negative breast cancer. However, recalibration is needed for specific groups, such as non-Hispanic Asian and non-Hispanic Black patients with ER-negative status.ConclusionsPREDICT v3 accurately predicts 10- and 15-year overall survival in contemporary US breast cancer patients. Future work should focus on promoting equitable care by addressing disparities that are observed in predictive tools.