bluuebunny/crossref_metadata_2025_split huggingface dataset:
Assessment of an AI virtual staining model performance across same and serial tissue sections using CD3+T cell ground truth
Authors: Azam Abu Bakr , Wee Felicia , Väyrynen Juha P. , Yim Willa Wen-You , Xue Yue Zhen , Chua Bok Leong , Lim Jeffrey Chun Tatt , Tan Daniel Shao Weng , Takano Angela , Chow Chun Yuen , Khor Li Yan , Lim Tony Kiat Hon , Yeong Joe , Lau Mai Chan , Cai Yiyu 
Published: November 2023

AbstractImmunophenotyping via multi-marker assays significantly contributes to patient selection, therapeutic monitoring, biomarker discovery, and personalized treatments. Despite its potential, the multiplex immunofluorescence (mIF) technique faces adoption challenges due to technical and financial constraints. Alternatively, hematoxylin and eosin (H&E)-based prediction models of cell phenotypes can provide crucial insights into tumor-immune cell interactions and advance immunotherapy. Current methods mostly rely on manually annotated cell label ground truths, with limitations including high variability and substantial labor costs. To mitigate these issues, researchers are increasingly turning to digitized cell-level data for accurate in-situ cell type prediction. Typically, immunohistochemical (IHC) staining is applied to a tissue section serial to one stained with H&E. However, this method may introduce distortions and tissue section shifts, challenging the assumption of consistent cellular locations. Conversely, mIF overcomes these limitations by allowing for mIF and H&E staining on the same tissue section. Importantly, the multiplexing capability of mIF allows for a thorough analysis of the tumor microenvironment by quantifying multiple cell markers within the same tissue section. In this study, we introduce a Pix2Pix generative adversarial network (P2P-GAN)-based virtual staining model, using CD3+T-cells in lung cancer as a proof-of-concept. Using an independent CD3 IHC-stained lung cohort, we demonstrate that the model trained with cell label ground-truth from the same tissue section as H&E staining performed significantly better in both CD3+and CD3-T-cell prediction. Moreover, the model also displayed prognostic significance on a public lung cohort, demonstrating its potential clinical utility. Notably, our proposed P2P-GAN virtual staining model facilitates image-to-image translation, enabling further spatial analysis of the predicted immune cells, deepening our understanding of tumor-immune interactions, and propelling advancements in personalized immunotherapy. This concept holds potential for the prediction of other cell phenotypes, including CD4+, CD8+, and CD20+cells.
Mechanical force of uterine occupation enables large vesicle extrusion from proteostressed maternal neurons
Authors: Wang Guoqiang , Guasp Ryan , Salam Sangeena , Chuang Edward , Morera Andrés , Smart Anna J , Jimenez David , Shekhar Sahana , Friedman Emily , Melentijevic Ilija , Nguyen Ken C , Hall David H , Grant Barth D , Driscoll Monica 
Published: November 2023

AbstractLarge vesicle extrusion from neurons may contribute to spreading pathogenic protein aggregates and promoting inflammatory responses, two mechanisms leading to neurodegenerative disease. Factors that regulate extrusion of large vesicles, such as exophers produced by proteostressedC. eleganstouch neurons, are poorly understood. Here we document that mechanical force can significantly potentiate exopher extrusion from proteostressed neurons. Exopher production from theC. elegansALMR neuron peaks at adult day 2 or 3, coinciding with theC. elegansreproductive peak. Genetic disruption ofC. elegansgermline, sperm, oocytes, or egg/early embryo production can strongly suppress exopher extrusion from the ALMR neurons during the peak period. Conversely, restoring egg production at the late reproductive phase through mating with males or inducing egg retention via genetic interventions that block egg-laying can strongly increase ALMR exopher production. Overall, genetic interventions that promote ALMR exopher production are associated with expanded uterus lengths and genetic interventions that suppress ALMR exopher production are associated with shorter uterus lengths. In addition to the impact of fertilized eggs, ALMR exopher production can be enhanced by filling the uterus with oocytes, dead eggs, or even fluid, supporting that distention consequences, rather than the presence of fertilized eggs, constitute the exopher-inducing stimulus. We conclude that the mechanical force of uterine occupation potentiates exopher extrusion from proximal proteostressed maternal neurons. Our observations draw attention to the potential importance of mechanical signaling in extracellular vesicle production and in aggregate spreading mechanisms, making a case for enhanced attention to mechanobiology in neurodegenerative disease.
High quality, high throughput, and low-cost simultaneous video recording of 60 animals in operant chambers using PiRATeMC
Authors: Ramborger Jarryd , Kalra Sumay , Smith Alexander C.W. , George Olivier 
Published: November 2023

AbstractBackgroundThe development of Raspberry Pi-based recording devices for video analyses of drug self-administration studies has shown to be promising in terms of affordability, customizability, and capacity to extract in-depth behavioral patterns. Yet, most video recording systems are limited to a few cameras making them incompatible with large-scale studies.New MethodWe expanded the PiRATeMC (Pi-based Remote Acquisition Technology for Motion Capture) recording system by increasing its scale, modifying its code, and adding equipment to accommodate large-scale video acquisition, accompanied by data on the throughput capabilities, video fidelity, synchronicity of devices, and comparisons between the Raspberry Pi 3B+ and 4B models.ResultsUsing PiRATeMC default recording parameters resulted in minimal storage (∼350MB/h), high throughput (< ∼120 seconds/Pi), high video fidelity, and synchronicity within ∼0.02 seconds, affording the ability to simultaneously record 60 animals in individual self-administration chambers at a fraction of current commercial costs. No consequential differences were found between Raspberry Pi 3B+ and 4B models.Comparison with Existing Method(s)This system allows greater acquisition of video data simultaneously than other video recording systems by an order of magnitude with less storage needs and lower costs. Additionally, we report in-depth quantitative assessments of throughput, fidelity, and synchronicity, displaying real-time system capabilities.ConclusionsThe system presented is able to be fully installed in a month’s time by a single technician and provides a scalable, low cost, and quality-assured procedure with a high-degree of customization and synchronicity between recording devices, capable of recording a large number of subjects with high turnover in a variety of species and settings.
Population size shapes trade-off dilution and adaptation to a marginal niche unconstrained by sympatric habitual conditions
Authors: Chavhan Yashraj , Malusare Sarthak , Dey Sutirth 
Published: November 2023

AbstractNiche expansion remains significantly understudied in sympatric scenarios where marginal and habitual niches are simultaneously available. Devoid of spatial constraints, such conditions impose selection to maintain fitness in habitual (high-productivity) niches while adapting to marginal (low-productivity) niches. Hence, habitual niche composition should constrain adaptation to marginal niches. This remains untested. Similarly, it is unknown if larger populations expand their niches better. We tested these hypotheses using experimental evolution withEscherichia coliand found that larger populations consistently adapted better to both marginal and habitual niches. Whereas the habitual niche composition (constant versus fluctuating environments; environmental fluctuations varying in both predictability and speed) significantly shaped fitness in habitual niches, surprisingly, it failed to constrain adaptation to the marginal niche. Curiously, two negatively correlated habitual niches can still each be positively correlated with the marginal niche. This allows the marginal niche to dilute trade-offs between habitual niches, thereby enabling costless niche expansion.
Modeling recent positive selection in Americans of European ancestry
Authors: Temple Seth D. , Waples Ryan K. , Browning Sharon R. 
Published: November 2023

AbstractRecent positive selection can result in an excess of long identity-by-descent (IBD) haplotype segments. The statistical methods that we propose here address three major objectives in studying classical selective sweeps: scanning for regions of interest, identifying possible sweeping alleles, and estimating a selection coefficients. First, we implement a selection scan to locate regions of excess IBD rate. Second, we develop a statistic to rank alleles in strong linkage disequilibrium with a putative sweeping allele. We aggregate these scores to estimate the allele frequency of the sweeping allele, even if it is not genotyped. Lastly, we propose an estimator for the selection coefficient and quantify uncertainty using the parametric bootstrap. Comparing against state-of-the-art methods in extensive simulations, we show that our methods are better at pinpointing alleles at low frequency and estimatings≥ 0.015. We apply these methods to study positive selection in inferred European ancestry samples from the TOPMed project. We find twelve loci where their IBD rates exceed four standard deviations above the population median. The excess IBD rate at LCT is thirty-five standard deviations above the population median, and our estimates of its selection coefficient imply strong selection within the past 120 to 163 generations. Overall, we present robust and accurate new approaches to study very recent adaptive evolution under mild assumptions.
A neural geometry theory comprehensively explains apparently conflicting models of visual perceptual learning
Authors: Cheng Yu-Ang , Sanayei Mehdi , Chen Xing , Jia Ke , Li Sheng , Fang Fang , Watanabe Takeo , Thiele Alexander , Zhang Ru-Yuan 
Published: November 2023

ABSTRACTVisual perceptual learning (VPL), defined as long-term improvement in a visual task, is considered a crucial tool for elucidating underlying visual and brain plasticity. However, the identification of a unified theory of VPL has long been controversial. Multiple existing models have proposed diverse mechanisms, including improved signal-to-noise ratio, changes in tuning curves, and reduction of noise correlations, as major contributors to improved neural representations associated with VPL. However, each model only accounts for specific aspects of the empirical findings, and there exists no theory that can comprehensively explain all empirical results. Here, we argue that all neural changes at single units can be conceptualized as geometric transformations of population response manifolds in a high-dimensional neural space. This approach enables conflicting major models of VPL to be quantitatively tested and compared within a unified computational theory. Following this approach, we found that changes in tuning curves and noise correlations, as emphasized by previous models, make no significant contributions to improved population representations by visual training. Instead, we identified neural manifold shrinkage due to reduced trial-by-trial neural response variability, a previously unexplored factor, as the primary mechanism underlying improved population representations. Furthermore, we showed that manifold shrinkage successfully accounts for learning effects across various domains, including artificial neural responses in deep neural networks trained on typical VPL tasks, multivariate BOLD signals in humans, and multi-unit activities in monkeys. These converging results suggest that our neural geometry theory offers a quantitative and comprehensive approach to explain a wide range of empirical results and to reconcile previously conflicting models of VPL.
The effects of inhaled corticosteroids on healthy airways
Authors: Marchi Emanuele , Hinks Timothy S.C. , Richardson Matthew , Khalfaou Latifa , Symon Fiona A. , Rajasekar Poojitha , Clifford Rachel , Hargadon Beverley , Austin Cary D. , MacIsaac Julia L. , Kobor Michael S. , Siddiqui Salman , Mar Jordan S. , Arron Joseph R. , Choy David , Bradding Peter 
Published: November 2023

ABSTRACTRationaleThe effects of inhaled corticosteroids (ICS) on healthy airways are poorly defined.ObjectivesTo delineate the effects of ICS on gene expression in healthy airways, without confounding caused by changes in disease-related genes and disease-related alterations in ICS-responsiveness.MethodsRandomised open-label bronchoscopy study of high dose ICS therapy in 30 healthy adult volunteers randomised 2:1 to i) fluticasone propionate 500 mcg bd or ii) no treatment, for 4 weeks. Laboratory staff were blinded to allocation. Biopsies and brushings were analysed by immunohistochemistry, bulk RNA sequencing, DNA methylation array and metagenomics.Measurements and main resultsICS induced small between-group differences in blood and lamina propria eosinophil numbers, but not in other immunopathological features, blood neutrophils, FeNO, FEV1, microbiome or DNA methylation. ICS treatment upregulated 72 genes in brushings and 53 genes in biopsies, and downregulated 82 genes in brushings and 416 genes in biopsies. The most downregulated genes in both tissues were canonical markers of type-2 inflammation (FCER1A, CPA3, IL33, CLEC10A, SERPINB10 and CCR5), T cell-mediated adaptive immunity (TARP, TRBC1, TRBC2, PTPN22, TRAC, CD2, CD8A, HLA-DQB2, CD96, PTPN7), B cell immunity (CD20, immunoglobulin heavy and light chains), and innate immunity, including CD48, Hobit, RANTES, Langerin and GFI1. An IL-17-dependent gene signature was not upregulated by ICS.ConclusionsIn healthy airways, 4-week ICS exposure reduces gene expression related to both innate and adaptive immunity, and reduces markers of type-2 inflammation. This implies that homeostasis in health involves tonic type-2 signalling in the airway mucosa, which is exquisitely sensitive to ICS.
ChatGPT Exhibits Gender and Racial Biases in Acute Coronary Syndrome Management
Authors: Zhang Angela , Yuksekgonul Mert , Guild Joshua , Zou James , Wu Joseph C. 
Published: November 2023

AbstractRecent breakthroughs in large language models (LLMs) have led to their rapid dissemination and widespread use. One early application has been to medicine, where LLMs have been investigated to streamline clinical workflows and facilitate clinical analysis and decision-making. However, a leading barrier to the deployment of Artificial Intelligence (AI) and in particular LLMs has been concern for embedded gender and racial biases. Here, we evaluate whether a leading LLM, ChatGPT 3.5, exhibits gender and racial bias in clinical management of acute coronary syndrome (ACS). We find that specifying patients as female, African American, or Hispanic resulted in a decrease in guideline recommended medical management, diagnosis, and symptom management of ACS. Most notably, the largest disparities were seen in the recommendation of coronary angiography or stress testing for the diagnosis and further intervention of ACS and recommendation of high intensity statins. These disparities correlate with biases that have been observed clinically and have been implicated in the differential gender and racial morbidity and mortality outcomes of ACS and coronary artery disease. Furthermore, we find that the largest disparities are seen during unstable angina, where fewer explicit clinical guidelines exist. Finally, we find that through asking ChatGPT 3.5 to explain its reasoning prior to providing an answer, we are able to improve clinical accuracy and mitigate instances of gender and racial biases. This is among the first studies to demonstrate that the gender and racial biases that LLMs exhibit do in fact affect clinical management. Additionally, we demonstrate that existing strategies that improve LLM performance not only improve LLM performance in clinical management, but can also be used to mitigate gender and racial biases.
Image guided construction of a common coordinate framework for spatial transcriptome data
Authors: Lais Peter , Mishra Shawn , Xiong Kun , Atwal Gurinder S. , Bai Yu 
Published: November 2023

AbstractSpatial transcriptomics is a powerful technology for high-resolution mapping of gene expression in tissue samples, enabling a molecular level understanding of tissue architecture. The acquisition entails dissecting and profiling micron-thick tissue slices, with multiple slices often needed for a comprehensive study. However, the lack of a common coordinate framework (CCF) among slices, due to slicing and displacement variations, can hinder data analysis, making data comparison and integration challenging, and potentially compromising analysis accuracy. Here we present a deep learning algorithm STaCker that unifies the coordinates of transcriptomic slices via an image registration process. STaCker derives a composite image representation by integrating tissue image and gene expressions that are transformed to be resilient to noise and batch effects. Trained exclusively on diverse synthetic data, STaCker overcomes the training data scarcity and is applicable to any tissue type. Its performance on various benchmarking datasets shows a significant increase in spatial concordance in aligned slices, surpassing existing methods. STaCker also successfully harmonizes multiple real spatial transcriptome datasets. These results indicate that STaCker is a valuable computational tool for constructing a CCF with spatial transcriptome data.
Diverse rhizosphere-associatedPseudomonasgenomes isolated along the marine-terrestrial transition zone of a Wadden island salt march
Authors: Mei Siyu , Wang Miao , Falcão Salles Joana , Hackl Thomas 
Published: November 2023

AbstractSoil microbes are key drivers of ecosystem processes promoting nutrient cycling, system productivity, and resilience. While much is known about the roles of microbes in established systems, their impact on soil development and the successional transformation over time remains poorly understood. Here, we provide 67 diverse, rhizosphere-associatedPseudomonasdraft genomes from an undisturbed salt march primary succession spanning >100 years of soil development.Pseudomonasare cosmopolitan bacteria with a significant role in plant establishment and growth. We obtained isolates associated withLimonium vulgareandArtemisia maritima, two typical salt marsh perennial plants with roles in soil stabilization, salinity regulation, and biodiversity support. We anticipate that our data, in combination with the provided physiochemical measurements, will help identify genomic signatures associated with the different selective regimes along the successional stages, such as varying soil complexity, texture, and nutrient availability. Such findings would not only advance our understanding ofPseudomonas’role in natural soil ecosystems but also provide the basis for a better understanding of the roles of microbes throughout ecosystem transformations.
DeepMRG: a multi-label deep learning classifier for predicting bacterial metal resistance genes
Authors: Emon Muhit Islam , Zhang Liqing 
Published: November 2023

AbstractThe widespread misuse of antibiotics has escalated antibiotic resistance into a critical global public health concern. Beyond antibiotics, metals function as antibacterial agents. Metal resistance genes (MRGs) enable bacteria to tolerate metal-based antibacterials and may also foster antibiotic resistance within bacterial communities through co-selection. Thus, predicting bacterial MRGs is vital for elucidating their involvement in antibiotic resistance and metal tolerance mechanisms. The “best hit” approach is mainly utilized to identify and annotate MRGs. This method is sensitive to cutoff values and produces a high false negative rate. Other than the best hit approach, only a few antimicrobial resistance (AMR) detection tools exist for predicting MRGs. However, these tools lack comprehensive annotation for MRGs conferring resistance to multiple metals. To address such limitations, we introduce DeepMRG, a deep learning-based multi-label classifier, to predict bacterial MRGs. Because a bacterial MRG can confer resistance to multiple metals, DeepMRG is designed as a multi-label classifier capable of predicting multiple metal labels associated with an MRG. It leverages bit score-based similarity distribution of sequences with experimentally verified MRGs. To ensure unbiased model evaluation, we employed a clustering method to partition our dataset into six subsets, five for cross-validation and one for testing, with non-homologous sequences, mitigating the impact of sequence homology. DeepMRG consistently achieved high overall F1-scores and significantly reduced false negative rates across a wide range of datasets. It can be used to predict bacterial MRGs in metagenomic or isolate assemblies. The web server of DeepMRG can be accessed athttps://deepmrg.cs.vt.edu/deepmrgand the source code is available athttps://github.com/muhit-emon/DeepMRGunder the MIT license.
Small in size but huge as reservoir – insights into the virome of European white-toothed shrews
Authors: Haring Viola C. , Litz Benedikt , Jacob Jens , Brecht Michael , Bauswein Markus , Sehl-Ewert Julia , Heroldova Marta , Hoffmann Donata , Ulrich Rainer G. , Beer Martin , Pfaff Florian 
Published: November 2023

ABSTRACTWhile the virome and immune system of bats and rodents have been extensively studied, comprehensive data are lacking for insectivores. Anthropogenic land use and outdoor recreational activities may lead to an expansion of the human-shrew interface with the risk of zoonotic infections, as reported for Borna disease virus 1. We investigated the virosphere of four white-toothed shrew species from Central Europe, addressing the One Health concept of spillover prevention. A high diversity of viruses was identified, including several co-infections. Whole genomes were generated for novel species of paramyxoviruses (n=3), nairoviruses (n=2) and hepevirus. Phylogenetically, they are closely related to WHO priority diseases, such as henipaviruses. High viral loads of orthoparamyxoviruses were detected in kidneys, in well-perfused organs for orthonairoviruses, and an association with liver and intestine was identified for orthohepevirus. Our study highlights the virus diversity present in shrews, not only in biodiversity hotspots but also in industrialised countries.