bluuebunny/crossref_metadata_2025_split huggingface dataset:
Age-Normalized Testosterone Peaks at Series B for Male Startup Founders
Authors: Moradian Jordan , Dubrovsky Michael , Sama Megha , Korecky Pavel , Kulkarni Sidarth , Goder Yaniv , Vermeulen Diedrik 
Published: November 2024

AbstractIn a study of 107 male Y Combinator founders, a surprising correlation between age-normalized testosterone and company stage was uncovered. Testosterone, a hormone associated with confidence, dominance, and drive, increased by 55.7% from pre-seed to seed funding, peaking at the Series B stage, where levels were 99.6% higher than pre-seed. After series B funding, testosterone was observed to drop by 42.2%, coinciding with a spike in cortisol levels. This age-normalized biomarker analysis supports the dual-hormone hypothesis, illustrating that early startup success fosters feelings of dominance and confidence, while later-stage pressures and stresses erode these feelings. An alternative interpretation of the data, which suggests the opportunity for a longitudinal study, is that male founders with higher testosterone are more likely to raise larger rounds of funding.
Single-cell resolution spatial analysis of antigen-presenting cancer-associated fibroblast niches
Authors: Chen Xiongfeng , Zhou Zhuan , Yazgan Zeynep , Xie Luyu , Rossi Francesca , Liu Yang , Zhang Bo , Polanco Patricio M. , Zeh Herbert J. , Kim Alex C. , Huang Huocong 
Published: November 2024

SummaryRecent studies have identified a unique subtype of cancer-associated fibroblasts (CAFs) termed antigen-presenting CAFs (apCAFs), which remain the least understood CAF subtype. To gain a comprehensive understanding of the origin and function apCAFs, we construct a fibroblast molecular atlas across 14 types of solid tumors. Our integration study unexpectedly reveals two distinct apCAF lineages present in most cancer types: one associated with mesothelial-like cells and the other with fibrocytes. Using a high-resolution single-cell spatial imaging platform, we characterize the spatial niches of these apCAF lineages. We find that mesothelial-like apCAFs are located near cancer cells, while fibrocyte-like apCAFs are associated with tertiary lymphoid structures. Additionally, we discover that both apCAF lineages can up-regulate the secreted protein SPP1, which facilitates primary tumor formation and peritoneal metastasis. Taken together, this study offers an unprecedented resolution in analyzing apCAF lineages and their spatial niches.
AI-aided chronic mixture risk assessment along a small European river reveals multiple sites at risk and pharmaceuticals being the main risk drivers
Authors: Weichert Fabian G. , Inostroza Pedro A. , Ahlheim Jörg , Backhaus Thomas , Brack Werner , Brauns Mario , Fink Patrick , Krauss Martin , Svedberg Patrik , Hollert Henner 
Published: November 2024

AbstractThe vast amount of registered chemicals leads to a high diversity of substances occurring in the environment and the creation of new substances outpaces chemical risk assessment as well as monitoring strategies. Hence, risk assessment strategies need to be modified ensuring that they remain aligned with the rapid development and marketing of new substances. Here we performed a longitudinal chronic mixture risk assessment considering a real-world case study scenario with diverse anthropogenic impact types characterised by different land uses along a river in Central Germany. We sampled river water using large-volume solid phase extraction at six selected sampling sites. Following chemical analysis using liquid chromatography-high resolution mass spectrometry, we quantified 192 substances. For 34% of them, we obtained empirical chronic effect data for freshwater organisms. Furthermore, we used the open-source artificial intelligence (AI) model TRIDENT to predict chronic toxicity for all substances. A multi-scenario mixture risk assessment was conducted for three taxonomic groups, using the concentration-addition concept and considering various hazard and exposure scenarios. The results showed that the chronic risk estimates for all taxonomic groups were considerably higher when the empirical data was amended with data from in silico modelling. We identified hot spots of chemical pollution and our analysis indicated that fish were the most vulnerable taxonomic group, with pharmaceuticals being the most relevant risk drivers. Our study exemplifies the application of an AI model to predict chronic risk for aquatic organisms in combination with the consideration of multiple risk scenarios, that may complement future risk assessment strategies.Highlights192 organic chemicals were quantified in six surface water samples along a river.Multiple hazard and exposure scenarios were considered in mixture risk assessment.Artificial intelligence was used to fill data gaps and predict chronic ecotoxicity.Fish were identified as the most vulnerable taxonomic group for chronic toxicity.Pharmaceuticals were the most prevalent mixture risk drivers.Graphical abstract
Global risk mapping of highly pathogenic avian influenza H5N1 and H5Nx in the light of epidemic episodes occurring from 2020 onward
Authors: Dupas Marie-Cécile , Vincenti-Gonzalez Maria F. , Dhingra Madhur , Guinat Claire , Vergne Timothée , Wint William , Hendrickx Guy , Marsboom Cedric , Gilbert Marius , Dellicour Simon 
Published: November 2024

AbstractAvian influenza (AI) is a highly contagious viral disease affecting poultry and wild water birds, posing significant global challenges due to its high mortality rates and economic impacts. Highly pathogenic avian influenza (HPAI) outbreaks, particularly those caused by H5N1 and its variants, have surged since their first occurrence in 1959. The HPAI H5N1 clade 2.3.4.4b viruses have notably expanded its geographical reach, affecting numerous countries, diverse avian species, and now wild and domestic mammals. Using an ecological niche modelling approach, this study aims to elucidate the environmental factors associated with the increased HPAI H5 cases since 2020, investigate potential shifts in ecological niches, and predict new areas suitable for local viral circulation. Focusing on H5N1 and H5Nx strains, we have developed ecological niche models for HPAI case in both wild and domestic birds while considering two distinct periods: 2015-2020 and 2020-2022. Key environmental predictors include chicken and duck population density, human density, distance to water bodies, and several land cover variables. Post-2020, we observe a notable increase in the relative importance of some of these predictors, such as intensive chicken population density and cultivated vegetation. The resulting risk maps reveal notable ecological suitability for local HPAI H5 circulation in Europe, Asia, as well as North and South America, with notable expansions of the areas at risk post-2020. The spatial distribution of HPAI H5 occurrences in wild birds appears to be primarily influenced by urban areas and open water regions. Overall, global risk maps derived from our models identify regions at risk where surveillance and control measures should be prioritised. Finally, our analyses also highlight a shift in the diversity of species affected by HPAI outbreaks, with a higher variety of avian species, particularly sea birds, being impacted post-2020. This increased diversity suggests that ecological shifts in HPAI H5 circulation may be accompanied by a broader range of susceptible species. Overall, these results further contribute to the understanding of HPAI epidemiology.
Automatic Discovery of Optimal Discrete Character Models
Authors: Boyko James D. 
Published: November 2024

AbstractModeling discrete character evolution in a Markovian framework has become the standard in phylogenetic comparative methods. The increasing size and complexity of these models reflects a trend of analyses to include more taxa and more discrete characters. However, as complexity of the models increase, so do the number of potential model structures and number of estimable parameters, making it nearly impossible to consider all modeling options for a given dataset. To overcome this issue, I apply a combination of regularization and parameter sharing optimization to models of discrete character evolution. This allows for the automatic searching and optimization across different model structures without user specification. I test this framework under several simulation scenarios including hidden rates and multiple discrete characters. The results indicate that regularized models significantly outperform traditional approaches, yielding a far lower variance and a nearly tenfold reduction in the overall error of parameter estimates in the most extreme scenarios. I illustrate the power of automatic model selection by revisiting the ancestral state estimation of concealed ovulation and mating systems in Old-World monkeys. Using the dredge algorithm, I discover a previously unexamined model structure which has both better statistical performance and a differing ancestral state reconstruction when compared to default model sets. In general, these results highlight the dangers of an over-reliance on default model sets. The combination of automatic model selection and regularization help overcome problems of over-parameterization, and these results demonstrate that when inferences are drawn from a larger model space, they are both more statistically robust and biologically realistic.
Extracellular matrix phenotyping by imaging mass cytometry defines distinct cellular matrix environments associated with allergic airway inflammation
Authors: Parkinson J E , Ghafoor M , Dodd R J , Tompkins H E , Fergie M , Rattray M , Allen J E , Sutherland T E 
Published: November 2024

AbstractThe extracellular matrix (ECM) forms the scaffold in which cells reside and interact. The composition of this scaffold guides the development of local immune responses and tissue function. With the advent of multiplexed spatial imaging methodologies, investigating the intricacies of cellular spatial organisation are more accessible than ever. However, the relationship between cellular organisation and ECM composition has been broadly overlooked. Using imaging mass cytometry, we investigated the association between cellular niches and their surrounding matrix environment during allergic airway inflammation in two commonly used mouse strains. By first classifying cells according to their canonical intracellular markers and then by developing a novel analysis pipeline to independently characterise a cells ECM environment, we integrated analysis of both intracellular and extracellular data. Applying this methodology to three distinct tissue regions we reveal disparate and restricted responses. Recruited neutrophils were dispersed within the alveolar parenchyma, alongside a loss of alveolar type I cells and an expansion of alveolar type II cells. This activated parenchyma was associated with increased proximity to hyaluronan and chondroitin sulphate. In contrast, infiltrating CD11b+and MHCII+cells accumulated in the adventitial cuff and aligned with an expansion of the subepithelial layer. This expanded subepithelial region was enriched for closely interacting stromal and CD11b+immune cells which overlaid regions enriched for type-I and type-III collagen. The cell-cell and cell-matrix interactions identified here will provide a greater understanding of the mechanisms and regulation of allergic disease progression across different inbred mouse strains and provide specific pathways to target aspects of remodelling during allergic pathology.Graphical Abstract
HSP90 buffers deleterious genetic variations inBRCA1
Authors: Gracia Brant , Montes Patricia , Huang Min , Chen Junjie , Karras Georgios Ioannis 
Published: November 2024

SUMMARYProtein-folding chaperone HSP90 buffers genetic variation in diverse organisms, but the clinical significance of HSP90 buffering in disease remains unclear. Here, we show that HSP90 buffers mutations in the BRCT domain of BRCA1. HSP90-bufferedBRCA1mutations encode protein variants that retain interactions with partner proteins and rely on HSP90 for protein stability and function in cell survival. Moreover, HSP90-buffered BRCA1 variants confer PARP inhibitor resistance in cancer cell lines. Low-level HSP90 inhibition alleviates this resistance, revealing a cryptic and mutant-specific HSP90-contingent synthetic lethality. Hence, by stabilizing metastable variants across the entirety of the BRCT domain, HSP90 reduces the clinical severity of BRCA1 mutations allowing them to accumulate in populations. We estimate that HSP90 buffers 11% to 28% of known human BRCA1- BRCT missense mutations. Our work extends the clinical significance of HSP90 buffering to a prevalent class of variations inBRCA1, pioneering its importance in cancer predisposition and therapy resistance.
OncotreeVIS – an interactive graphical user interface for visualizing mutation tree cohorts
Authors: Baciu-Drăgan Monica-Andreea , Beerenwinkel Niko 
Published: November 2024

AbstractMotivationIn recent years, developments in single-cell next-generation sequencing technology and computational methodology have made it possible to reconstruct, with increasing precision, the evolutionary history of tumors and their cell phylogenies, represented as mutation trees. Many mutation tree inference tools exist, but they do not support detailed visual tree inspection, nor tree comparisons or analysis at the cohort level, an important task in computational oncology.ResultsWe developed oncotreeVIS, an interactive graphical user interface for visualizing mutation tree cohorts and tree posterior distributions obtained from mutation tree inference tools. Oncotree-VIS can display mutation trees that encode single or joint genetic events, such as point mutations and copy number changes, and highlight matching subclones, conserved trajectories and drug-gene interactions at cohort level. OncotreeVIS facilitates the visual inspection of mutation tree clusters and pairwise tree distances. It is available both as a JavaScript library that can be used locally or as a web application that can be accessed online. It includes seven default datasets of public mutation tree cohorts for visualization, while new mutation trees are provided in a predefined JSON format.Availability and implementationhttps://github.com/cbg-ethz/oncotreeVIS.Web applicationhttps://cbg-ethz.github.io/oncotreeVIS.
Giving it a fair shake: A simple, fast, & efficient method to extract amatoxins from the death cap mushroom,Amanita phalloides
Authors: Adams Catharine A. , Bever Candace S. , Blake-Hedges Jacquelyn M. , Brown Sean , Thompson Mitchell G. , Behie Scott , Keasling Jay , Shih Patrick 
Published: November 2024

1.1.AbstractThe death cap mushroom,Amanita phalloides, is well known for containing amatoxins such as alpha- and beta-amanitin, which inhibit eukaryotic RNA polymerase II. While these toxins have been used in research for almost a century, they have recently garnered attention for their role in drug-antibody conjugates. The amatoxins are still largely extracted from wild mushrooms, which cannot be made to fruit in the lab.We propose simplified extraction methods that could reduce hazardous exposures to dust and expedite sample analysis without sacrificing accuracy. We recently developed a Lateral Flow Immunoassay (LFIA), for which we identified that sample maceration was not needed to extract the amatoxins and that the incubation time for extraction could be accomplished in 1 minute. In this current work, we hypothesized that these same extraction adjustments–minimal tissue maceration and reduced incubation time–could be transferable to instrumental detection methods.To test the need for sample maceration, we utilized three different techniques: 1) traditional mortar and pestle, 2) a similarly disruptive method of bead beating, and 3) no grinding, but rather hand shaking dried mushroom tissue in extraction buffer. In addition, we performed the solvent extraction step at varying times to observe if more time allows for more toxin to be removed from the tissue. Lastly, we utilized two comparable solvent evaporation methods (rotovap or speedvac) to establish if multiple samples could be processed simultaneously, thus improving sample throughput.We adjusted aspects of the typical extraction protocol, which resulted in a rapid (1 min) incubation step, along with minimal sample handling (no grinding) of the dried mushroom tissue. We present an extraction protocol that saves time, reduces equipment contamination, and minimizes risk to the researcher. The impact of this faster, safer method may help produce these important toxins faster, for both research and medical use.
The follicular lymphoma and chronic lymphocytic leukemia proliferative microenvironment at single-cell resolution
Authors: Ferreira Antonio P. , Wang Shumei , Poluben Laryssa , Brandstadter Joshua , Perkey Eric , Sotirakos Steven , Drew Mai , Pan Li , Lemieux Madeleine E. , Dorfman David M. , Shoji Brent , Maillard Ivan , Blacklow Stephen C. , Aster Jon C. 
Published: November 2024

AbstractLymph nodes (LNs) are secondary lymphoid organs where lymphocytes interact with antigen presenting cells to initiate adaptive immune responses within microenvironments established by resident stromal cells. LNs are also the major site of growth of follicular lymphoma (FL) and chronic lymphocytic leukemia (CLL), B cell neoplasms that alter the stromal architecture of LNs in highly stereotypic ways. To characterize FL and CLL cells within the LN microenvironment, we developed a pipeline for single-cell RNA sequencing of all resident LN cells. We observed that proliferation of FL and CLL cells within specialized niches commences with transient upregulation ofMYC,subsequent downregulation of which may act to limit the growth potential of these indolent neoplasms. Proliferating FL cells within neoplastic follicles co-localized with follicular dendritic cells, whereas proliferating CLL cells were spatially associated with a distinct set of fibroblasts expressing CCL19 that localized to proliferation centers. We used informatic analyses and microscopy to identify and validate interacting sets of ligand-receptor pairs between proliferating neoplastic B cells, immune cells and stromal fibroblasts, including interactions involving CD74-MIF, TNFRSF13C (BAFF receptor), immunomodulatory factors such as CD55 and Galectin-9 (Gal9), and adhesion molecules. Our analyses highlight common features of these two microenvironment-dependent neoplasms and provide a roadmap for identifying vulnerabilities and new therapeutic strategies.Key pointsNodal FL and CLL cells have similar proliferative programs and predicted ligand-receptor interactions with immune and stromal cells.Proliferation of FL and CLL cells occurs in distinct lymph node niches defined by different types of fibroblasts.
Rhinovirus as a Driver of Airway T-Cell Dynamics in Children with Severe Asthma
Authors: Bryant Naomi , Muehling Lyndsey M. , Wavell Kristin , Teague W. Gerald , Woodfolk Judith A. 
Published: November 2024

AbstractSevere asthma in children is notoriously difficult to treat, and its immunopathogenesis is complex. In particular, the contribution of T cells and relationships to anti-viral immunity, remain enigmatic. Here, we coupled deep phenotyping with machine learning methods to resolve the dynamics of T cells in the diseased lower airways, and examined rhinovirus (RV) as a driver. Our strategy revealed a T-cell landscape dominated by type 1 and type 17 CD8+ signatures. Interrogation of phenotypic relationships coupled with trajectory mapping identified T-cell migratory and differentiation pathways spanning the blood and airways that culminated in tissue residency, and included transitions between type 1 and type 17 tissue-resident types. These T-cell dynamics were reflected in cytokine polyfunctionalityin situ. Use of machine learning to cross-compare T-cell populations that were enriched in the airways of RV-positive children with those induced in the blood after RV challenge in an experimental infection model, precisely pinpointed RV-responsive signatures that mapped to T-cell differentiation pathways. Despite their rarity, these signatures were detected in the airways of uninfected children. Together, our results underscore the aberrant nature of type 1 immunity in the airways of children with severe asthma, and implicate an important viral trigger as a driver.
Cross-tissue Graph Attention Networks for Semi-supervised Gene Expression Prediction
Authors: Wang Shiyu , He Mengyu , Qin Muran , Hu Yijuan , Zhao Liang , Qin Zhaohui 
Published: November 2024

AbstractHigh-throughput biotechnologies have significantly advanced precision medicine by enabling the exploitation of global gene expression patterns to enhance our understanding of disease etiology, progression, and treatment options. However, the tissue-specific nature of gene expression presents a challenge, particularly for less accessible tissues such as the brain, underscoring the need for computational methods to accurately impute gene expression in these critical but hard-to-reach tissues. While several attempts to impute gene expression in tissue-specific contexts have shown promising results, their reliance on regression analysis faces limitations due to the inability to capture complex, nonlinear relationships in gene expression patterns. In contrast, modern machine learning techniques, particularly graph neural networks, have demonstrated superior performance by efficiently modeling the intricate interactions among genes across different tissues. Therefore, we introduce gene expression imputation with Graph Attention Networks (gemGAT), a novel approach leveraging Graph Attention Networks (GATs) to enhance gene expression prediction across different tissues. gemGAT distinguishes itself by predicting the expression of all genes simultaneously, utilizing the full spectrum of genomic data to account for gene co-expressions and non-linear relationships. Validated through extensive experiments with Genotype-Tissue Expression (GTEx) data and a case study from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), gemGAT demonstrates superior performance over existing methods by efficiently capturing non-linear gene co-expressions. This advancement underscores gemGAT’s potential to significantly contribute to precision medicine, showcasing its utility in advancing our understanding of gene expression in less accessible tissues.