bluuebunny/crossref_metadata_2025_split huggingface dataset:
Binocular Listing’s Law For Human Eyes’ Misaligned Optical Components
Authors: Turski Jacek 
Published: June 2024

AbstractHuman eyes’ optical components are misaligned. This study presents the geometric construction of ocular torsion in the binocular system, in which the eye model incorporates the fovea that is not located on and the lens that is tilted away from the eye’s optical axis. The ocular torsion computation involves Euler’s rotation theorem in the framework of Rodrigue’s vector. When the eyes’ binocular posture changes, each eye’s torsional orientation transformations are visualized inGeoGebra’sdynamic geometry environment. Listing’s law, which originally restricts single-eye torsional positions and has imprecise binocular extensions, is formulatedab initiofor binocular fixations using Euler’s rotation theorem. It replaces Listing’s plane and related perpendicular primary direction with the empirical horopter’s abathic distance fixation, which has a straight frontal line configuration at this fixation. Notably, it corresponds to the eye muscles’ natural tonus resting position, which serves as a zero-reference level for convergence effort, providing the missing neurophysiological significance of Listing’s plane. Thus, Listing’s law use in clinical diagnosis and management of strabismus should be updated.
Discovery of functional NLRs using expression level, high-throughput transformation, and large-scale phenotyping
Authors: Brabham Helen J. , Hernández-Pinzón Inmaculada , Yanagihara Chizu , Ishikawa Noriko , Komori Toshiyuki , Matny Oadi N. , Hubbard Amelia , Witek Kamil , Feist Alexis , Numazawa Hironobu , Green Phon , Dreiseitl Antonín , Takemori Naoki , Komari Toshihiko , Freedman Roger P. , Steffenson Brian , van Esse H. Peter , Moscou Matthew J. 
Published: June 2024

AbstractProtecting crops from pests and diseases is vital for the sustainable agricultural systems needed for food security. Introducing functional resistance genes to enhance the plant immune system is an effective method of disease control, but identifying new immune receptors is time-consuming and resource intensive. We observed that functional immune receptors of the NLR class show a signature of high expression in uninfected plants across both monocot and dicot species. Here we show that this signature, combined with high throughput crop transformation, can be used to rapidly identify candidate NLRs from diverse plant species and validate pathogen resistance directly in crop plants. As a proof of concept, we generated a wheat transgenic library carrying 995 NLRs from 18 grass species. Screening the collection with the stem rust pathogenPuccinia graminis, which is a major threat to wheat production, we confirm 19 new resistance genes. This pipeline facilitates resistance gene discovery, unlocking a large gene pool of diverse and non-domesticated plant species and providingin-plantagene validation of disease resistance directly in crops.
Faster and more accurate assessment of differential transcript expression with Gibbs sampling and edgeR v4
Authors: Baldoni Pedro L. , Chen Lizhong , Smyth Gordon K. 
Published: June 2024

AbstractDifferential transcript expression analysis of RNA-seq data is an increasingly popular tool to assess changes in expression of individual transcripts between biological conditions. Software designed for transcript-level differential expression analyses account for the uncertainty of transcript quantification, the read-to-transcript ambiguity (RTA), in statistical analyses via resampling methods. Bootstrap sampling is a popular resampling method that is implemented in the RNA-seq quantification tools kallisto and Salmon. However, bootstrapping is computationally intensive and provides replicate counts with low resolution when the number of sequence reads originating from a gene is low. For lowly expressed genes, bootstrap sampling results in noisy replicate counts for the associated transcripts, which in turn leads to non reproducible and unrealistically high RTA-dispersion for those transcripts. Gibbs sampling is a more efficient and high resolution algorithm implemented in Salmon. Here we leverage the developments of edgeR v4 to present an improved differential transcript expression analysis pipeline with Salmon’s Gibbs sampling algorithm. The new bias-corrected quasi-likelihood method with adjusted deviances for small counts from edgeR, combined with the efficient Gibbs sampling algorithm from Salmon, provides faster and more accurate DTE analyses of RNA-seq data. Comprehensive simulations and test data show that the presented analysis pipeline is more powerful and efficient than previous differential transcript expression pipelines while providing correct control of the false discovery rate.
Fast-scBatch: Batch Effect Correction Using Neural Network-Driven Distance Matrix Adjustment
Authors: Chen Fu , Tian Leqi , Fei Teng , Yu Tianwei 
Published: June 2024

ABSTRACTBatch effect is a frequent challenge in deep sequencing data analysis that can lead to misleading conclusions. Existing methods do not correct batch effects satisfactorily, especially with single-cell RNA sequencing (scRNA-seq) data. To address this challenge, we introduce fast-scBatch, a novel and efficient two-phase algorithm for batch-effect correction in scRNA-seq data, designed to handle non-linear and complex batch effects. Specifically, this method utilizes the inherent correlation structure of the data for batch effect correction and employs a neural network to expedite the process. It outputs a corrected expression matrix, facilitating downstream analyses. We validated fast-scBatch through simulation studies and on two scRNA-seq datasets, demonstrating its superior performance in batch-effect correction compared to current methods, as evidenced by visualization using UMAP plots, and metrics including Adjusted Rand Index (ARI) and Adjusted Mutual Information (AMI).
Acute aerobic exercise does not modulate pain potentially due to differences in fitness levels and sex effects – results from a pharmacological fMRI study
Authors: Nold Janne I. , Fadai Tahmine , Büchel Christian 
Published: June 2024

AbstractExercise might lead to a release of endogenous opioids, potentially resulting in pain relief. However, the neurobiological underpinnings of this effect remain unclear. Using a pharmacological within-subject fMRI study with the opioid antagonist naloxone and different levels of aerobic exercise and pain we investigated exercise-induced hypoalgesia (N= 39, 21 female). Overall, we were unable to detect reduced pain after high-intensity aerobic exercise as compared to low-intensity aerobic exercise. Accordingly, we observed no significant changes in the descending pain modulatory system. The µ-opioid antagonist naloxone significantly increased overall pain ratings but showed no interaction with exercise intensity. An exploratory analysis suggested an influence of fitness level and sex where males showed greater hypoalgesia after high-intensity exercise with increasing fitness levels. This effect was attenuated by naloxone and mirrored by fMRI signal changes in the medial frontal cortex, where activation also varied with fitness level and sex, and was reversed by naloxone. These results indicate that aerobic exercise has no overall effect on pain in a mixed population sample, but individual factors such as fitness level and sex might play a role.
Vancomycin impairs macrophage fungal killing by disrupting mitochondrial morphology and function
Authors: Bojang Ebrima , Sheriff Lozan , Fu Man Shun , Wellings Chloe , Abdissa Ketema , Stavrou Victoria , Clark Callum , Southam Andrew D. , Dunn Warwick B. , Bending David , Hombrebueno Jose R. , Jacobsen Ilse , Dimeloe Sarah , Hall Rebecca A. , Drummond Rebecca A. 
Published: June 2024

AbstractVancomycin is a widely prescribed antibiotic used in the treatment of Gram-positive bacterial infections. We recently showed that this antibiotic disrupted protective anti-fungal immune responses via microbiome dysbiosis, enhancing susceptibility to invasive candidiasis. Antibiotics are an independent risk factor for developing this life-threatening fungal infection, but whether microbiota-independent mechanisms also drive this association is not clear. Here, we show that vancomycin directly impairs macrophage responses toCandida albicans, the main causative agent of invasive candidiasis. Vancomycin-treated macrophages were less able to killC. albicansdespite normal phagocytosis rates and were hyper-inflammatory and more likely to die during infection. We found that vancomycin bound to macrophage mitochondria, leading to depolarisation, reduced respiratory capacity and a hyper-fragmented morphology associated with increased ROS production. Taken together, this work demonstrates direct effects of vancomycin on mammalian immune cells, helping us to understand pro-inflammatory effects of this drug and how it promotes susceptibility to life-threatening fungal infection.
Role of individual and population heterogeneity in shaping dynamics of multi-pathogen excretion in an island endemic bat
Authors: Aguillon Samantha , Turpin Magali , Le Minter Gildas , Lebarbenchon Camille , Hoarau Axel O. G. , Toty Céline , Duchet Avril , Joffrin Léa , Ramanantsalama Riana V. , Tortosa Pablo , Mavingui Patrick , Dietrich Muriel 
Published: June 2024

AbstractUnderstanding processes driving pathogen transmission in bats is critical to prevent spillovers and emergence events. However, it requires fine-scale ecological investigations, which are rare because of the complexity and heterogeneity of bat populations. Here, based on the monitoring of 5,949 Reunion free-tailed bats (Mormopterus francoismoutoui) in 17 roosts over 24 months, we studied the simultaneous excretion of paramyxovirus andLeptospirabacteria in urine, and herpesvirus in saliva. We analysed the influence of individual and roost-level factors on co-excretion dynamics. We evidenced all year long excretion with high prevalence (37% - 87%), with low levels of temporal synchrony among roosts and no signal of density- dependent transmission. Recapture data support persistent infections for all infectious agents, alongside with clearance followed by reinfection for paramyxovirus andLeptospira. Our results suggest that sex-specific social behaviour or physiological adaptations drive herpesvirus excretion, while paramyxovirus andLeptospiraexcretion reactivate during reproduction. Our data also suggest the protection of juveniles by maternal antibodies, but different waning dynamics, much shorter for HSV infection, that could lead to a pathogen-dependent role of juveniles in the spread of infection. Interestingly, the excretion of one infectious agent was positively correlated to the excretion of a second one. However, the probability of excreting a third one was reduced for individuals already bi-excreting, suggesting negative interactions for cumulative infections. Connecting such spatio-temporal data with ecosystem alterations in Reunion Island could further contribute to our understanding of the links between human-mediated changes in the ecology of bats and infectious diseases emergence processes.Significance StatementUnderstanding risks of bat-borne pathogen spillover is challenging because of the difficulty in studying excretion dynamics in wild bat populations. Here, we used an original island-endemic model to build up a fine-scale spatio-temporal analysis of the excretion of two viruses (paramyxovirus and herpesvirus) and a bacterium (Leptospira) at both population- (roost) and individual- (through recaptured bats) levels. Excretion patterns are specific to each infectious agent and driven by the age and the reproduction of both females and males. Results also suggest that a combination of viral persistence and clearance, as well as interactions between infectious agents, are important within-host processes that contribute to the transmission of infections in bat populations. This information is crucial to better predict shedding pulses and prevent potential future spillovers.
Seasonal and hourly diversity patterns of anthropophagous female mosquito species in a semi-conserved area at the southern Mexico
Authors: Canales-Delgadillo Julio César , Vázquez-Pérez Nallely , Viveros-Santos Vicente , Pérez-Ceballos Rosela , Cardoso-Mohedano José Gilberto , Zaldívar-Jiménez Arturo , Celis-Hernández Omar , Gómez-Ponce Alejandro , Merino-Ibarra Martín 
Published: June 2024

AbstractMosquitoes are the most dangerous organisms on Earth because they spread causal agents of diseases. However, mosquito populations need to be better known in coastal areas of the Yucatan Peninsula to understand and prevent the spread of diseases effectively. To increase the knowledge about the mosquito community in the southern Gulf of Mexico region, we determined the diversity of mosquito species in Isla del Carmen Campeche. We trapped adult mosquitoes using buccal aspirators on monthly surveys (September 2019 to December 2020) in mangrove and low-semideciduous forest patches in three climate seasons. The sampling sessions consisted of 60 minutes of trapping performed every four hours (at 09, 13, 17, 21, 01, and 05 hrs) until a 24-hour cycle was completed. The trapped individuals were identified at the species level. We calculated Hill numbers using incidence data for each season and sampling hour. Abundances were compared through Kruskal-Wallis tests. A literature review determined the diseases associated with the species found. We collected 21,424 mosquito individuals from 11 genera, 26 species and four morphospecies. Seasonally, greater mosquito abundance and richness (n= 26) occurred during the norte season (χ2= 7.23, df = 2,p= 0.026) and between the 09:00 and 13:00 hrs (χ2= 15.25, df = 5,p= 0.009). Many species in this study are reported as disease vectors of medical and veterinary relevance.Conclusions:Isla del Carmen contributes to the Yucatan Peninsula’s mosquito diversity, and several of its species are vectors of pathogens to humans and wildlife.
Altered thymic niche synergistically drives the massive proliferation of malignant thymocytes
Authors: Tsingos Erika , Dick Advaita M. , Bajoghli Baubak 
Published: June 2024

ABSTRACTThe discovery of genetic alterations in patient samples over the last 20 years has promoted a cell-autonomous view of proliferative expansion during T-cell acute lymphoblastic leukemia (T-ALL) development in the thymus. However, the potential contribution of non-cell-autonomous factors, particularly the impact of thymic epithelial cells (TECs) within the thymic niche during the initiation phase, remains unexplored. In this study, we employ a unique combination of a cell-based computational model of the thymus andin vivoexperiments. We systematically analyze the impact of 12 cell-autonomous and non-autonomous factors, either alone or in combinations, on the proliferation of normal and malignant thymocytes with interleukin-7 receptor (IL7R) gain-of-function mutations or elevated IL7R levels, as observed in T-ALL patients. By simulating over 1500 scenarios, we show that while a dense TEC network favored the proliferation of normal thymocytes, it inhibited the proliferation of malignant lineages, which achieved their maximal proliferative capacity when TECs were sparsely distributed. Ourin silicomodel predicts that certain mutations could accelerate proliferative expansion within a few days. This was experimentally validated, revealing rapid onset of thymus lymphoma and infiltration of malignant T-cells into other organs within 8 days of medaka (Oryzias latipes) embryonic development, thus revealing that modifications in the thymic niche and oncogenes in thymocytes together accelerate the disease development. Our results also suggest that negative feedback from the proliferative state inhibits differentiation of thymocytes, thereby prolonging the proliferative state and further fueling malignant expansion. Overall, this work reveals the critical impact of TEC-thymocyte interactions in both the initiation and progression of disease.
My friend MIROSLAV: A Hackable Open-Source Hardware and Software Platform for High-Throughput Rodent Activity Monitoring in the Home Cage
Authors: Virag Davor , Homolak Jan , Kodvanj Ivan , Vargantolić Ana-Marija , Babić Perhoč Ana , Meglić Patrik , Šoštarić Mužić Petra , Knezović Ana , Osmanović Barilar Jelena , Cifrek Mario , Trkulja Vladimir , Šalković-Petrišić Melita 
Published: June 2024

AbstractWhile conventional behavioural tests offer valuable insight into rodent behaviour in specific paradigms, rodents spend most of their time in a safe, undisturbed environment – their home cage. However, home cage monitoring (HCM) is often impractical at scale over prolonged periods of time without significant loss of data.To achieve this, we developed MIROSLAV, the Multicage InfraRed Open Source Locomotor Activity eValuator, a home cage activity and environmental monitoring device designed to be open, adaptable, and robust. Its transparent and modular design allows the device to be tailored to varying experimental requirements and environmental conditions, while its wireless operation and multiple redundancies minimise loss of data and animal disturbance. Data quality is maintained by a modular software workflow for data preparation (Prepare-a-SLAV) and cleaning (TidySLAV), followed by exploratory (MIROSine – MIRO The Explorer) and statistical (MIROSine – StatistiSLAV) analysis of circadian periodicity.Here, using MIROSLAV, we demonstrate circadian dysrhythmia and a disrupted response to regular stimuli (e.g. behavioural testing, bedding change) in a rat model of sporadic Alzheimer’s disease, showcasing MIROSLAV’s utility in a typical animal study of disease.In accordance with the 3Rs, every rodent study and laboratory performing them can benefit from HCM, provided the existence of transparent and customisable tools which allow robust deployment to tens or hundreds of cages in various conditions. MIROSLAV provides this opportunity to researchers in a cost-effective manner.
Dissecting the temporal phenomics and genomics of maize canopy cover using UAV mediated image capture
Authors: Cooper Julian , Sweet Dorothy D. , Tirado Sara B. , Springer Nathan M. , Hirsch Candice N. , Hirsch Cory D. 
Published: June 2024

AbstractCanopy cover is an important agronomic trait influencing photosynthesis, weed suppression, biomass accumulation, and yield. Conventional methods to quantify canopy cover are time and labor-intensive. As such, little is known about how canopy cover develops over time, the stability of canopy cover across environments, or the genetic architecture of canopy cover. We used unoccupied aerial vehicle-mediated image capture to quantify plot-level canopy coverage in maize throughout the growing season. Images of 501 diverse inbred lines were acquired between 300 and 1300 growing degree days in the 2018-2021 growing seasons. We observed that the maize canopy developed following a logistic curve. Phenotypic variation in percent canopy coverage and canopy growth rate was explained by genetic and environmental factors and genotype-by-environment interactions, however the percent of variance explained by each factor varied throughout the growing season. Environmental factors explained the largest portion of trait variance during the adult vegetative growth stage and had a larger impact on canopy growth rates than percent canopy coverage. We conducted multiple genome wide association studies and found that canopy cover is a complex, polygenic trait with a diverse range of marker trait associations throughout development. The change in associations indicated that single time point phenotyping was insufficient to capture the full phenomic and genetic diversity of canopy cover in maize.
Plant height defined growth curves during vegetative development have the potential to predict end of season maize yield and assist with mid-season management decisions
Authors: Sweet Dorothy D. , Cooper Julian , Hirsch Cory D. , Hirsch Candice N. 
Published: June 2024

AbstractPrecision farming has been developing with the intention of identifying within field variability to adjust management strategies and maximize end of season yield and profitability and minimize negative environmental impacts. The development of quick, easy, and low cost methods to quantify field level variation is essential to successful implementation of precision agriculture at scale. Temporal plant height and growth rates collected with unoccupied aerial vehicles mounted with red, green, blue sensors have the potential to predict end of season grain yield, which could facilitate mid-season management decisions. Image-based plant height data was collected weekly from commercial maize fields in three growing seasons to assess variation within fields and the relationship with grain yield variation. Plant height, growth rate, and grain yield had variable relationships depending on the time point and growth environment. Models developed using temporal traits predicted grain yield variation within a commercial field up to r = 0.7, though insufficient water affected the prediction accuracy in one field due to the limited representation of drought environments in the model development. In the future, with more data from stress environments, such as drought, this method has potential for high accuracy grain yield prediction across a range of environmental conditions. This study demonstrates the potential of using unoccupied aerial vehicles to derive vegetative growth patterns and model within field variations, and has application in making mid-season management decisions.
PhyloFusion- Fast and easy fusion of rooted phylogenetic trees into rooted phylogenetic networks
Authors: Zhang Louxin , Cetinkaya Banu , Huson Daniel H. 
Published: June 2024

AbstractUnrooted phylogenetic networks are often used to represent evolutionary data when incompatibilities are present. Although rooted phylogenetic networks are better suited for explicitly depicting evolutionary histories that involve reticulate events, they have been rarely used in practice, due to a lack of appropriate methods for their calculation. Here we present PhyloFusion, a fast and easily-applicable method for calculating rooted phylogenetic networks on sets of rooted phylogenetic trees. The algorithm can handle trees with unresolved nodes (which arise when edges with low support are contracted) and missing taxa. We illustrate how to use the algorithm to explore different groups of functionally-related genes and report that the algorithm can be applied to datasets containing tens of trees and hundreds of taxa.AvailabilityAn open source implementation of PhyloFusion is available in SplitsTree,https://www.github.com/husonlab/splitstree6(GPLv3 license)
Challenges and Progress in RNA Velocity: Comparative Analysis Across Multiple Biological Contexts
Authors: Ancheta Sarah , Dorman Leah , Le Treut Guillaume , Gurung Abel , Royer Loïc A. , Granados Alejandro , Lange Merlin 
Published: June 2024

AbstractSingle-cell RNA sequencing is revolutionizing our understanding of cell state dynamics, allowing researchers to observe the progression of individual cells’ transcriptomic profiles over time. Among the computational techniques used to predict future cellular states, RNA velocity has emerged as a predominant tool for modeling transcriptional dynamics. RNA velocity leverages the mRNA maturation process to generate velocity vectors that predict the likely future state of a cell, offering insights into cellular differentiation, aging, and disease progression. Although this technique has shown promise across biological fields, the performance accuracy varies depending on the RNA velocity method and dataset. We established a comparative pipeline and analyzed the performance of five RNA velocity methods on three datasets based on local consistency, method agreement, identification of driver genes, and robustness to sequencing depth. This benchmark provides a resource for scientists to understand the strengths and limitations of different RNA velocity methods.