bluuebunny/crossref_metadata_2025_split huggingface dataset:
Divergent neural circuits for proprioceptive and exteroceptive sensing of theDrosophilaleg
Authors: Lee Su-Yee J. , Dallmann Chris J. , Cook Andrew , Tuthill John C. , Agrawal Sweta 
Published: April 2024

AbstractSomatosensory neurons provide the nervous system with information about mechanical forces originating inside and outside the body. Here, we use connectomics from electron microscopy to reconstruct and analyze neural circuits downstream of the largest somatosensory organ in theDrosophilaleg, the femoral chordotonal organ (FeCO). The FeCO has been proposed to support both proprioceptive sensing of the fly’s femur-tibia joint and exteroceptive sensing of substrate vibrations, but it was unknown which sensory neurons and central circuits contribute to each of these functions. We found that different subtypes of FeCO sensory neurons feed into distinct proprioceptive and exteroceptive pathways. Position- and movement-encoding FeCO neurons connect to local leg motor control circuits in the ventral nerve cord (VNC), indicating a proprioceptive function. In contrast, signals from the vibration-encoding FeCO neurons are integrated across legs and transmitted to mechanosensory regions in the brain, indicating an exteroceptive function. Overall, our analyses reveal the structure of specialized circuits for processing proprioceptive and exteroceptive signals from the fly leg. These findings are consistent with a growing body of work in invertebrate and vertebrate species demonstrating the existence of specialized limb mechanosensory pathways for sensing external vibrations.
Transcriptome analysis reveals role for WRKY70 in earlyN-hydroxy-pipecolic acid signaling
Authors: Foret Jessica , Kim Jung-Gun , Sattely Elizabeth S. , Mudgett Mary Beth 
Published: April 2024

AbstractN-hydroxy-pipecolic acid (NHP) is a mobile metabolite essential for inducing and amplifying systemic acquired resistance (SAR) following pathogen attack. Early phases of NHP signaling leading to immunity have remained elusive. Here we report the early transcriptional changes mediated by NHP and the role salicylic acid (SA) plays during this response. We show that distinct waves of expression within minutes to hours of NHP treatment include increased expression of WRKY transcription factors as the primary transcriptional response, followed by the induction of WRKY-regulated defense genes as the secondary response. The majority of genes induced by NHP within minutes were SA-dependent, whereas those induced within hours were SA-independent. These data suggest that NHP induces the primary transcriptional response in a low SA environment and new SA biosynthesis is dispensable for induction of the secondary transcriptional response. We demonstrate that WRKY70 is required for the induced expression of a set of genes defining some of the secondary transcriptional response, SAR protection, and NHP-dependent enhancement of ROS production in response to flagellin treatment. Taken together, our study highlights the key genes and pathways defining early NHP responses and a role for WRKY70 in the regulation of NHP-dependent transcription.
A standard area diagram for Fusarium yellows rating in sugar beet (Beta vulgaris L.)
Authors: Todd Olivia E. , Hanson Linda E. , Dorn Kevin 
Published: April 2024

ABSTRACTMembers of theFusarium oxysporumspecies complex are pathogens of sugar beet causing Fusarium yellows. Fusarium yellows can reduce plant stand, yield, and extractable sugar. Improving host plant resistance againstFusarium-induced diseases, like Fusarium yellows, represents an important long-term breeding target in sugar beet breeding programs. Current methods for rating Fusarium yellows disease severity rely on an ordinal scale, which limits precision for intermediate phenotypes. In this study, we aimed to improve the accuracy and precision of rating Fusarium yellows by developing a standard area diagram (SAD). Two SAD versions were created using images of sugar beets infected withFusarium oxysporumstrain F19. Each version was tested using inexperienced raters. Comparing both the pilot and improved version showed no statistical differences in Lin’s Concordance Correlation Coefficient (LCC) values to assess accuracy and precision between the two versions (Cb = 0.99 for both versions, ρc= 0.97 and 0.96 for version 1 and 2, respectively). In addition, five naïve Bayesian machine learning models which used pixel classification to determine disease score, were tested for congruency to human estimates in version 2. Root mean square error was lowest compared to the “true” values for the unweighted model and a model where necrotic tissue was given a 2x weight (12.4 and 12.6, respectively). The creation of this standard area diagram enables breeding programs to make consistent, accurate disease ratings regardless of personnel’s’ previous experience with Fusarium yellows. Additionally, more iterations of pixel quantification equations may overcome accuracy issues for rating Fusarium yellows.
WITHDRAWN: The Effects of Anthropogenic Electromagnetic Fields on the Behavior of Geomagnetically Displaced Skates
Authors: Newton Kyle C. , Donato Natalie H , Henkel Sarah , Chapple Taylor K 
Published: April 2024

Withdrawal StatementThe authors have withdrawn this manuscript because while we stand behind this important work, we are addressing significant peer-review recommendations to provide a more robust product and will provide an updated version soon. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.
Interpolation of Microbiome Composition in Longitudinal Datasets
Authors: Peleg Omri , Borenstein Elhanan 
Published: April 2024

AbstractThe human gut microbiome significantly impacts health, prompting a rise in longitudinal studies that capture microbiome samples at multiple time points. Such studies allow researchers to characterize microbiome changes over time, but importantly, also present major analytical challenges due to incomplete or irregular sampling. To address this challenge, longitudinal microbiome studies often employ various interpolation methods, aiming to infer missing microbiome data. However, to date, a comprehensive assessment of such microbiome interpolation techniques, as well as best practice guidelines for interpolating microbiome data, are still lacking.This work aims to fill this gap, rigorously implementing and systematically evaluating a large array of interpolation methods, spanning several different categories, for longitudinal microbiome interpolation. To assess each method and its ability to accurately infer microbiome composition at missing time points, we used three longitudinal microbiome datasets that follow individuals over a long period of time, and a leave-one-out approach.Overall, our analysis demonstrated that the K-nearest neighbors algorithm consistently outperforms other methods in interpolation accuracy, yet, accuracy varied widely across datasets, individuals, and time. Factors such as microbiome stability, sample size, and the time gap between interpolated and adjacent samples significantly influenced accuracy, allowing us to develop a model for predicting the expected interpolation accuracy at a missing time point.Our findings, combined, suggest that accurate interpolation in longitudinal microbiome data is feasible, especially in dense cohorts. Furthermore, using our predictive model, future studies can interpolate data only in time points where the expected interpolation accuracy is high.
Pre-clinical proof-of-concept of anti-fibrotic activity of caveolin-1 scaffolding domain peptide LTI-03 inex vivoprecision cut lung slices from patients with Idiopathic Pulmonary Fibrosis
Authors: MacKenzie BreAnne , Mahavadi Poornima , Jannini-Sa Yago Amigo Pinho , Creyns Brecht , Coelho Ana Lucia , Espindola Milena , Ruppert Clemens , Hötzenecker Konrad , Hogaboam Cory , Guenther Andreas 
Published: April 2024

ABSTRACTRationale:While rodent lung fibrosis models are routinely used to evaluate novel antifibrotics, these models have largely failed to predict clinical efficacy of novel drug candidates for Idiopathic Pulmonary Fibrosis (IPF). Moreover, single target therapeutic strategies for IPF have failed and current multi-target standard of care drugs are not curative. Caveolin-1 (CAV-1) is an integral membrane protein, which, via its caveolin scaffolding domain (CSD), interacts with caveolin binding domains (CBD). CAV-1 regulates homeostasis, and its expression is decreased in IPF lungs. LTI-03 is a seven amino acid peptide derived from the CSD and formulated for dry powder inhalation; it was well tolerated in normal volunteers (NCT04233814) and a safety trial is underway in IPF patients (NCT05954988).Objectives:Anti-fibrotic efficacy of LTI-03 and other CSD peptides has been observed in IPF lung monocultures, and rodent pulmonary, dermal, and heart fibrosis models. This study aimed to characterize progressive fibrotic activity in IPF PCLS explants and to evaluate the antifibrotic effects of LTI-03 and nintedanib in this model.Methods:First, CBD regions were identified in IPF signaling proteins usingin silicoanalysis. Then, IPF PCLS (n=8) were characterized by COL1A1 immunostaining, multiplex immunoassays, and bulk RNA sequencing following treatment every 12hrs with LTI-03 at 0.5, 3.0, or 10 μM; nintedanib at 0.1 μM or 1 μM; or control peptide (CP) at 10 μM.Measurements and Main Results:CBDs were present in proteins implicated in IPF, including VEGFR, FGFR and PDGFR. Increased expression of profibrotic mediators indicated active fibrotic activity in IPF PCLS over five days. LTI-03 dose dependently decreased COL1A1 staining, and like nintedanib, decreased profibrotic proteins and transcripts. Unlike nintedanib, LTI-03 did not induce cellular necrosis signals.Conclusion:IPF PCLS explants demonstrate molecular activity indicative of fibrosis during 5 days in culture and LTI-03 broadly attenuated pro-fibrotic proteins and pathways, further supporting the potential therapeutic effectiveness of LTI-03 for IPF.
Light-induced Extracellular Vesicle Adsorption
Authors: Hisey Colin L. , Rima Xilal Y. , Doon-Ralls Jacob , Nagaraj Chiranth K. , Mayone Sophia , Nguyen Kim T. , Wiggins Sydney , Dorayappan Kalpana D.P. , Selvendiran Karuppaiyah , Wood David , Hu Chunyu , Patel Divya , Palmer Andre , Hansford Derek , Reategui Eduardo 
Published: April 2024

AbstractThe role of extracellular vesicles (EVs) in human health and disease has garnered considerable attention over the past two decades. However, while several types of EVs are known to interact dynamically with the extracellular matrix and there is great potential value in producing high-fidelity EV micropatterns, there are currently no label-free, high-resolution, and tunable platform technologies with this capability. We introduce Light-induced Extracellular Vesicle Adsorption (LEVA) as a powerful solution to rapidly advance the study of matrix- and surface-bound EVs and other particles. The versatility of LEVA is demonstrated using commercial GFP-EV standards, EVs from glioblastoma bioreactors, and E. coli outer membrane vesicles (OMVs), with the resulting patterns used for single EV characterization, single cell migration on migrasome-mimetic trails, and OMV-mediated neutrophil swarming. LEVA will enable rapid advancements in the study of matrix- and surface-bound EVs and other particles, and should encourage researchers from many disciplines to create novel diagnostic, biomimetic, immunoengineering, and therapeutic screening assays.
mastR: Marker Automated Screening Tool for multi-omics data
Authors: Chen Jinjin , Mohamed Ahmed , Bhuva Dharmesh D. , Davis Melissa J. , Tan Chin Wee 
Published: April 2024

AbstractMotivationBiomarker discovery and utilization is important and offers insight into potential underlying mechanisms of disease. Existing marker identification methods primarily focus on single cell RNA sequencing (scRNA-seq) data, with no specific automated methods designed to learn from the bulk RNA-seq data. Furthermore, when adapting scRNA-seq methods to bulk RNA-seq, the background expressions of non-targeted cell types are not accounted for. Here we bridge this gap with an automated marker identification method that works for bulk RNA sequencing data.ResultsWe developedmastR, a novel computational tool for accurate marker identification from omics data. It leverages robust pipelines fromedgeRandlimmaR/Bioconductor packages, performing pairwise comparisons between groups, and aggregating the results through rank-product-based permutation test. A signal-to-noise ratio approach is implemented to minimize background signals. We assess the performance of amastR-derived NK cell signature against curated published signatures and find our derived signature performs as well if not better than published signatures. We also demonstrate the utility ofmastRon simulated scRNA sequencing data and provide examples ofmastRoutperformingSeuratin marker selection.Availability and implementationAll statistical analyses were carried out using R (version 4.3.0 or higher) and Bioconductor (version 3.17 and higher).MastRis available as an R/Bioconductor package with a comprehensive vignette for ease of use (https://bioconductor.org/packages/release/bioc/html/mastR.html) and a guide hosted on GitHub:https://davislaboratory.github.io/mastR/.
Linkage: an interactive shiny app and R package for linking of DNA regulatory peaks to genes
Authors: Liu Zenghui , Chen Shaodong , Li Tianting , Zhang Chao , Luo Yuyan , Zheng Junxi , Lu Zixiao , Yang Jin , Xu Siwen 
Published: April 2024

AbstractSummaryExisting studies have demonstrated that the integration analysis of transcriptomic and epigenomic data can be used to better understand the onset and progression of many diseases, as well as identify new diagnostic and prognostic biomarkers. However, such investigations on large-scale sequencing data remain challenging for researchers or clinicians with limited bioinformatics knowledge. To facilitate the interpretation of gene regulatory landscape, we developed an R Shiny application and R package [Linking ofatac-seq togeneexpression data (Linkage)] for exploring and visualizing potential cis-regulatory elements (CREs) of genes based on ATAC-seq and RNA-seq data. Linkage offers six modules to systematically identify, annotate, and interpret potential gene regulatory elements from the whole genome step by step. Linkage can provide interactive visualization for the correlation between chromatin accessibility and gene expression. More than that, Linkage identifies transcription factors (TFs) that potentially drive the chromatin changes through identifying TF binding motifs within the CREs and constructing trans-regulatory networks of the target gene set. This powerful tool enables researchers to conduct extensive multiomics integration analysis and generate visually appealing visualizations that effectively highlight the relationship between genes and corresponding regulatory elements. With Linkage, users can obtain publishable results and gain deeper insights into the gene regulatory landscape.Availability and implementation‘Linkage’ is freely available as a Shiny web application (https://xulabgdpu.org.cn/linkage) and an R package (https://github.com/XuLab-GDPU/Linkage). The documentation is available at (https://aicplane.github.io/Linkage-tutorial/).
Pervasive but lagged responses in the composition of small mammal communities to a century of climate change
Authors: Abercrombie Ethan , Myers Jonathan A. , Smith Adam B. 
Published: April 2024

AbstractClimate change has widespread effects on the distribution, abundance, and behavior of species around the world, leading to the reshuffling of ecological communities. However, it remains unclear whether individual species’ range shifts scale up to result in communities whose rate of change lag, lead, or track the rate of climate change. We capitalized on a century-old data set originally collected by Joseph Grinnell and his students, plus modern resurveys, to measure long-term compositional responses of small mammal communities to climate change in historical and modern eras across three regions in the Sierra Nevada of California (Lassen, Yosemite, and Sequoia and Kings Canyon National Parks). Across this period, mean annual temperature in each region increased and mean annual precipitation decreased. We tested whether small mammal communities have shifted their composition in favor of species more adapted to hot and dry conditions, processes known as thermophilization and mesophilization respectively. We found positive thermophilization rates (communities composed of more warm-adapted species) in all three regions and negative mesophilization rates (communities composed of dry-adapted species) in one of the three regions. Importantly, thermophilization and mesophilization rates lagged behind corresponding rates of climate change on average by 0.90-1.2 °C and 26.5-111.0 mm, but the magnitude of lags was unrelated to region, amount of climate change, or their interaction. Previous work demonstrated high intraspecific variability in range shifts across the three regions in our study. Our results suggest that the net effects of climate change will be directional at the scale of the ecological community, despite variability in individual species responses to environmental change and the varied mechanisms that govern them. Communities, like many individual species, may already be out of equilibrium with ambient climate.
A Personalized Metabolic Modelling Approach through Integrated Analysis of RNA-Seq-Based Genomic Variants and Gene Expression Levels in Alzheimer’s Disease
Authors: Uzuner Dilara , İlgün Atılay , Bozkurt Fatma Betül , Çakır Tunahan 
Published: April 2024

AbstractMotivationAlzheimer’s disease (AD) is known to cause alterations in brain metabolism. Furthermore, genomic variants in enzyme-coding genes may exacerbate AD-linked metabolic changes. Generating condition-specific metabolic models by mapping gene expression data to genome-scale metabolic models is a routine approach to elucidate disease mechanisms from a metabolic perspective. RNAseq data provides both gene expression and genomic variation information. Integrating variants that perturb enzyme functionality from the same RNAseq data may enhance model accuracy, offering insights into genome-wide AD metabolic pathology.ResultsOur study pioneers the extraction of both transcriptomic and genomic data from the same RNA-seq data to reconstruct personalized metabolic models. We mapped genes with significantly higher load of pathogenic variants in AD onto a human genome-scale metabolic network together with the gene expression data. Comparative analysis of the resulting personalized patient metabolic models with the control models showed enhanced accuracy in detecting AD-associated metabolic pathways compared to the case where only expression data was mapped on the metabolic network. Besides, several otherwise would-be missed pathways were annotated in AD by considering the effect of genomic variants.ImplementationThe scripts are available athttps://github.com/SysBioGTU/GenomicVariantsMetabolicModels.Contacttcakir@gtu.edu.tr
Antibiotic-inducedMalasseziaspp. expansion in infants promotes early-life immune dysregulation and airway inflammation in mice
Authors: Bernardes Erik van Tilburg , Gutierrez Mackenzie W. , Nguyen William N. T. , Mercer Emily M. , Ramay Hena R. , Glatthardt Thaís , Thomson Carolyn A. , Halim Tisha , Gopalakrishnan Nithya , Kalbfleish Kristen , Patel Kamala D. , McCoy Kathy D. , Freedman Stephen B. , Arrieta Marie-Claire 
Published: April 2024

AbstractAntibiotics have deleterious consequences for the gut microbiome and can increase the risk of childhood asthma. While the effects of antibiotics on the bacterial microbiome and asthma risk are well characterized, their impact on the fungal microbiome (mycobiome) remains vastly unexplored. We investigated the effect of antibiotic use on the gut mycobiome in an observational, prospective clinical study of young infants. Antibiotic treatment resulted in increased fungal abundance and expansion of the yeastMalasseziaspp. Based on these findings, germ-free mouse pups were colonized with a defined consortium of mouse-derived bacteria (Oligo-MM12) with or withoutMalassezia restricta. Colonization with this yeast increased myeloid and lymphoid intestinal immune responses deemed critical in atopy development, and elevated airway inflammation in house-dust mite (HDM)-challenged mice. Further evaluation in eosinophil-deficient mice revealed that the observed immune response is partially dependent on this cell type. This translational work demonstrates that fungal overgrowth and expansion ofMalasseziaspp. are previously overlooked collateral effects of infant antibiotic use, which may offer a potential strategy to prevent or mitigate pediatric asthma and related conditions.One Sentence Summary:Antibiotic-inducedMalasseziaspp. expansion in infants promotes early-life immune dysregulation and airway inflammation in gnotobiotic mice.
Interferon-sensitized hematopoietic progenitors dynamically alter organismal immunity
Authors: Guillamot Maria , Subudhi Ipsita , Paraskevopoulou Varvara , Prystupa Aleksandr , Sidhu Ikjot , Yeaton Anna , Laskou Maria , Hannemann Carmen , Donahoe Casey , Wiseman Destini , Aifantis Iannis , Naik Shruti , Weinstock Ada 
Published: April 2024

ABSTRACTInflammation has enduring impacts on organismal immunity. However, the precise mechanisms by which tissue-restricted inflammation conditions systemic responses are poorly understood. Here, we leveraged a highly compartmentalized model of skin inflammation and identified a surprising type I interferon (IFN)- mediated activation of hematopoietic stem/progenitor cells (HSPCs) that results in profound changes to systemic host responses. Post-inflamed mice were protected from atherosclerosis and had worse outcomes following influenza virus infection. This IFN-mediated HSPC modulation was dependent on IFNAR signaling and could be recapitulated with the administration of recombinant IFNα. Importantly, the transfer of post-inflamed HSPCs was sufficient to transmit the immune suppression phenotype. IFN modulation of HSPCs was rooted both in long-term changes in chromatin accessibility and the emergence of an IFN- responsive functional state from multiple progenitor populations. Collectively, our data reveal the profound and enduring effect of transient inflammation and more specifically type I IFN signaling and set the stage for a more nuanced understanding of HSPC functional modulation by peripheral immune signals.
Le Petit Prince Hong Kong (LPPHK): Naturalistic fMRI and EEG data from older Cantonese speakers
Authors: Momenian Mohammad , Ma Zhengwu , Wu Shuyi , Wang Chengcheng , Brennan Jonathan , Hale John , Meyer Lars , Li Jixing 
Published: April 2024

AbstractCurrently, the field of neurobiology of language is based on data from only a few Indo-European languages. The majority of this data comes from younger adults neglecting other age groups. Here we present a multimodal database which consists of task-based and resting state fMRI, structural MRI, and EEG data while participants over 65 years old listened to sections of the story The Little Prince in Cantonese. We also provide data on participants’ language history, lifetime experiences, linguistic and cognitive skills. Audio and text annotations, including time- aligned speech segmentation and prosodic information, as well as word-by-word predictors such as frequency and part-of-speech tagging derived from natural language processing (NLP) tools are included in this database. Both MRI and EEG data diagnostics revealed that the data has good quality. This multimodal database could advance our understanding of spatiotemporal dynamics of language comprehension in the older population and help us study the effects of healthy aging on the relationship between brain and behaviour.