bluuebunny/crossref_metadata_2025_split huggingface dataset:
Extending tactile space with hand-held tools: A re-analysis and review
Authors: Miller Luke E. , Farnè Alessandro 
Published: April 2024

AbstractTools can extend the sense of touch beyond the body, allowing the user to extract sensory information about distal objects in their environment. Though research on this topic has trickled in over the last few decades, little is known about the neurocomputational mechanisms of extended touch. In 2016, along with our late collaborator Vincent Hayward, we began a series of studies that attempted to fill this gap. We specifically focused on the ability to localize touch on the surface of a rod, as if it were part of the body. We have conducted eight behavioral experiments over the last several years, all of which have found that humans are incredibly accurate at tool-extended tactile localization. In the present article, we perform a model-driven reanalysis of these findings with an eye towards estimating the underlying parameters that map sensory input into spatial perception. This reanalysis revealed that users can almost perfectly localize touch on hand-held tools. This raises the question of how humans can be so good at localizing touch on an inert non-corporeal object. The remainder of the paper focuses on three aspects of this process that occupied much of our collaboration with Vincent: the mechanical information used by participants for localization; the speed by which the nervous system can transform this information into a spatial percept; and whether body-based computations are repurposed for tool-extended touch. In all, these studies underscore the special relationship between bodies and tools.
TAU FILAMENTS WITH THE CHRONIC TRAUMATIC ENCEPHALOPATHY FOLD IN A CASE OF VACUOLAR TAUOPATHY WITHVCPMUTATION D395G
Authors: Qi Chao , Kobayashi Ryota , Kawakatsu Shinobu , Kametani Fuyuki , Scheres Sjors H.W. , Goedert Michel , Hasegawa Masato 
Published: April 2024

ABSTRACTDominantly inherited mutation D395G in the gene encoding valosin-containing protein causes vacuolar tauopathy, a type of behavioural-variant frontotemporal dementia, with marked vacuolation and abundant filamentous tau inclusions made of all six brain isoforms. Here we report that tau inclusions were concentrated in layers II/III of the frontotemporal cortex in a case of vacuolar tauopathy. By electron cryo-microscopy, tau filaments had the chronic traumatic encephalopathy (CTE) fold. Tau inclusions of vacuolar tauopathy share this cortical location and the tau fold with CTE, subacute sclerosing panencephalitis and amyotrophic lateral sclerosis/parkinsonism-dementia complex, which are believed to be environmentally induced. Vacuolar tauopathy is the first inherited disease with the CTE tau fold.
Redefining the Game: MVAE-DFDPnet’s Low-Dimensional Embeddings for Superior Drug-Protein Interaction Predictions
Authors: Xia Liang-Yong , Wu Yu , Zhao Longfei , Chen Leying , Zhang Shiyi , Wang Mengdi , Luo Jie 
Published: April 2024

AbstractPrecisely predicting drug-protein interactions (DPIs) is pivotal for drug discovery and advancing precision medicine. A significant challenge in this domain is the high-dimensional and heterogeneous data characterizing drug and protein attributes, along with their intricate interactions. In our study, we introduce a novel deep learning architecture: theMulti-viewVariationalAuto-Encoder embedded within a cascadeDeepForest (MVAE-DFDPnet). This framework adeptly learns ultra-low-dimensional embedding for drugs and proteins. Notably, our t-SNE analysis reveals that two-dimensional embedding can clearly define clusters corresponding to diverse drug classes and protein families. These ultra-low-dimensional embedding likely contribute to the enhanced robustness and generalizability of our MVAE-DFDPnet. Impressively, our model surpasses current leading methods on benchmark datasets, functioning in significantly reduced dimensional spaces. The model’s resilience is further evidenced by its sustained accuracy in predicting interactions involving novel drugs, proteins, and drug classes. Additionally, we have corroborated several newly identified DPIs with experimental evidence from the scientific literature. The code used to generate and analyze these results can be accessed fromhttps://github.com/Macau-LYXia/MVAE-DFDPnet-V2.
Serial fermentation in milk generates functionally diverse community lineages with different degrees of structure stabilization
Authors: Gapp Chloé , Dijamentiuk Alexis , Mangavel Cécile , Callon Cécile , Theil Sébastien , Revol-Junelles Anne-Marie , Chassard Christophe , Borges Frédéric 
Published: April 2024

AbstractMicrobial communities offer considerable potential for tackling environmental challenges by improving the functioning of ecosystems. Top-down community engineering is a promising strategy that could be used to obtain communities of desired function. However, the ecological factors that control the balance between community shaping and propagation are not well understood. Dairy backslopping can be used as a model engineering approach to investigate the dynamics of communities during serial propagations. In this study, 26 raw milk samples were used to generate lineages of 6 communities obtained by serial propagation. Bacterial community structures were analyzed by metabarcoding and acidification was recorded by pH monitoring. The results revealed that different types of community lineages could be obtained in terms of taxonomic composition and dynamics. Five lineages reached a repeatable community structure in a few propagation steps, with little variation between the final generations, giving rise to stable acidification kinetics. Moreover, these stabilized communities presented a high inter-lineage variability of community structures as well as diverse acidification properties. Besides, the other lineages were characterized by different levels of dynamics leading to parallel or divergent trajectories. The functional properties and dynamics of the communities were mainly related to the relative abundance and the taxonomic composition of lactic acid bacteria within the communities. These findings highlight that short-term schemes of serial fermentation can produce communities with a wide range of dynamics and that the balance between community shaping and propagation is intimately linked to community structure.ImportanceMicrobiome applications require approaches for shaping and propagating microbial communities. Shaping allows the selection of communities with desired taxonomic and functional properties, while propagation allows the production of the biomass required to inoculate the engineered communities in the target ecosystem. In top-down community engineering, where communities are obtained from a pool of mixed microorganisms by acting on environmental variables, a major challenge is to master the balance between shaping and propagation. However, the ecological factors that favor high dynamics of community structure and, conversely, those that favor stability during propagation are not well understood. In this work, short-term dairy blacksloping was used to investigate the key role of the taxonomic composition and structure of bacterial communities on their dynamics. The results obtained open up interesting prospects for the biotechnological use of microbiomes, particularly in the field of dairy fermentation, to diversify approaches for injecting microbial biodiversity into cheesemaking processes.
lcUMAPtSNE: Use of non-linear dimensionality reduction techniques with genotype likelihoods
Authors: Uzel Kerem , Grossen Christine , Çilingir F. Gözde 
Published: April 2024

AbstractUnderstanding population structure is essential for conservation genetics, as it provides insights into population connectivity and supports the development of targeted strategies to preserve genetic diversity and adaptability.T-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP) have proven effective for revealing population genetic structures in human and model organisms using hard-called genotypes, but their application in wild species using genotype likelihoods from low coverage sequencing (as a cost-saving measure) remains underexplored.Here, we present a Jupyter Notebook-based workflow that facilitates the use of UMAP and t-SNE on genotype likelihood-derived principal components.This workflow is demonstrated using medium to low-coverage whole-genome sequencing data from scimitar-horned oryx, which has been reintroduced into the wild and faces multiple conservation challenges.Detailed guidance on hyperparameter tuning and practical implementation is also provided, enhancing the application of these methods in wildlife genetics to potentially support biodiversity conservation.
Deterministic Formulas and Procedures for Stochastic Trait Introgression Prediction
Authors: Ajayi Temitayo , LaCombe Jason , Ince Güven , Yeats Trevor 
Published: April 2024

AbstractKey messageWe derive formulas for the background noise during trait introgression programs and use these formulas to quickly predict noise for up to five future generations without using simulation.Trait introgression is a common method for introducing valuable traits into breeding populations and inbred cultivars. The process involves recurrent backcrossing of a donor individual (and its descendants) with a desirable, inbred line that lacks the aforementioned traits. The process typically concludes with a final generation of selfing in order to recover lines with the traits of interest fixed in the homozygous state. The particular breeding scheme is usually designed to maximize the genetic similarity of the converted lines to the recurrent parent while minimizing a breeders’ cost and time to recovering the near isogenic lines. Thus, key variables include the number of generations, number of crosses, and how to apply genotyping and selection during the process. In this paper, we derive analytical formulas that characterize the stochastic nature of residual donor geneome (i.e., “background noise”) during trait introgression. We use these formulas to predict the background noise in simulated trait introgression programs for five generations of progeny, as well as to construct a novel mathematical program to optimally allocate progeny to available parents. This provides a framework for the design of optimal breeding schemes for trait introgression involving one or more traits subject to the requirements of specific crops and breeding programs.
A dynamic epigenetic perspective on above and below-ground phenotypic responses to drought: insights from global DNA methylation inErodium cicutarium
Authors: Alonso Conchita , Medrano Mónica , Herrera Carlos M. 
Published: April 2024

AbstractPremise of the study. Mounting evidence supports the view that the responses of plants to environmental stress are mediated by epigenetic factors, including DNA methylation. Understanding the relationships between DNA methylation, plant development and individual fitness under contrasting environments is key to uncover the potential impact of epigenetic regulation on plant adaptation. Experimental approaches that combine a controlled alteration of epigenetic features with exposure to some relevant stress factor can contribute to this end.Methods. We combined the experimental application of a demethylating agent (5-azacytidine) with recurrent drought, and recorded their effects on above- and below-ground phenotypic traits related to early development, phenology and fitness inErodium cicutariumfrom two provenances.Key results. We found that 5-azacytidine significantly reduced DNA methylation in leaf and root tissues. Moreover, it slowed plant development, delayed flowering, and reduced the number of inflorescences produced, and such detrimental effects occurred independently of water regime. Recurrent drought reduced final above- and below-ground biomass and total inflorescence production, and such negative effects were unaffected by artificial changes in DNA methylation. Increased fruit and seed-set were the only adaptive responses to drought observed inE. cicutarium, together with an increased number of flowers per inflorescence recorded in water stressed plants previously treated with 5-azacytidine.Conclusion. Epigenetic effects can desynchronize plant growth, flowering and senescence among individual plants in both favourable and adverse environments. Future studies should focus on understanding intraspecific variation in the ability to change plant methylome in response to stress.
Mechanical forces pattern endocardial Notch activation via mTORC2-PKC pathway
Authors: Mu Yunfei , Hu Shijia , Liu Xiangyang , Tang Xin , Lin Jiayi , Shi Hongjun 
Published: April 2024

SummaryNotch signaling has been identified as a key regulatory pathway in patterning the endocardium through activation of endothelial-to-mesenchymal transition (EMT) in the atrioventricular canal (AVC) and proximal outflow tract (OFT) region. However, the precise mechanism underlying Notch activation remains elusive. By transiently blocking the heartbeat of E9.5 mouse embryos, we found that Notch activation in the arterial endothelium was dependent on its ligand Dll4, whereas the reduced expression of Dll4 in the endocardium led to a ligand-depleted field, enabling Notch to be specifically activated in AVC and OFT by regional increased shear stress. The strong shear stress altered the membrane lipid microdomain structure of endocardial cells, which activated mTORC2 and PKC and promoted Notch1 cleavage even in the absence of strong ligand stimulation. These findings highlight the role of mechanical forces as a primary cue for endocardial patterning and provide insights into the mechanisms underlying congenital heart diseases of endocardial origin.
Mechano-regulation of GLP-1 production by Piezo1 in intestinal L cells
Authors: Huang Yanling , Mo Haocong , Yang Jie , Gao Luyang , Tao Tian , Shu Qing , Guo Wenying , Zhao Yawen , Lyu Jingya , Wang Qimeng , Guo Jinghui , Zhai Hening , Zhu Linyan , Chen Hui , Xu Geyang 
Published: April 2024

AbstractGlucagon-like peptide 1 (GLP-1) is a gut-derived hormone secreted by intestinal L cells and vital for postprandial glycemic control. As open-type enteroendocrine cells, whether L cells can sense mechanical stimuli caused by chyme and thus regulate GLP-1 synthesis and secretion is unexplored. Our study showed expression of Piezo1 in intestinal L cells. Its level varied in different energy status and correlates with blood glucose and GLP-1 levels. Mice with L cell-specific loss of Piezo1 (IntL-Piezo1-/-) exhibited impaired glucose tolerance, increased body weight, reduced GLP-1 production and decreased CaMKKβ/CaMKIV-mTORC1 signaling pathway under normal chow diet or high fed diet. Activation of the intestinal Piezo1 by its agonist Yoda1 or intestinal bead implantation increased the synthesis and secretion of GLP-1, thus alleviated glucose intolerance in diet-induced-diabetic mice. Overexpression of Piezo1, Yoda1 treatment or stretching stimulated GLP-1 production and CaMKKβ/CaMKIV-mTORC1 signaling pathway, which could be abolished by knockdown or blockage of Piezo1 in primary cultured mouse L cells and STC-1 cells. These findings suggest a previously undiscovered mechano-regulation of GLP-1 production in L cells, which may shed new light on the treatments of diabetes.
Predicting The Pathway Involvement Of Metabolites Based on Combined Metabolite and Pathway Features
Authors: Huckvale Erik D. , Moseley Hunter N.B. 
Published: April 2024

AbstractA major limitation of most metabolomics datasets is the sparsity of pathway annotations of detected metabolites. It is common for less than half of identified metabolites in these datasets to have known metabolic pathway involvement. Trying to address this limitation, machine learning models have been developed to predict the association of a metabolite with a “pathway category”, as defined by one of the metabolic knowledgebases like the Kyoto Encyclopedia of Gene and Genomes. Most of these models are implemented as a single binary classifier specific to a single pathway category, requiring a set of binary classifiers for generating predictions for multiple pathway categories. This single binary classifier per pathway category approach both multiplies the computational resources necessary for training while diluting the positive entries in gold standard datasets needed for training. To address the limitations of training separate classifiers, we propose a generalization of the metabolic pathway prediction problem using a single binary classifier that accepts both features representing a metabolite and features representing a generic pathway category and then predicts whether the given metabolite is involved in the corresponding pathway category. We demonstrate that this metabolite-pathway features-pair approach is not only competitive with the combined performance of training separate binary classifiers, but it outperforms the previous benchmark models.
Cross-correlation increases sampling in diffusion-based super-resolution optical fluctuation imaging
Authors: Antarasen Jeanpun , Wellnitz Benjamin , Kramer Stephanie N. , Chatterjee Surajit , Kisley Lydia 
Published: April 2024

AbstractCorrelation signal processing of optical three-dimensional (x, y, t) data can produce super-resolution images. The second order cross-correlation functionXC2has been documented to produce super-resolution imaging with static and blinking emitters but not for diffusing emitters. Here, we both analytically and numerically demonstrate cross-correlation analysis for diffusing particles. We then expand our fluorescence correlation spectroscopy super-resolution optical fluctuation imaging (fcsSOFI) analysis to use cross-correlation as a post-processing computational technique to extract both dynamic and structural information of particle diffusion in nanoscale structures simultaneously. We further show how this method increases sampling rates and reduces aliasing for spatial information in both simulated and experimental data. Our work demonstrates how fcsSOFI with cross-correlation can be a powerful signal-processing tool to resolve the nanoscale dynamics and structure in samples relevant to biological and soft materials.TOC Graphic
Intrapartum antibiotic prophylaxis selects for mutators in group B streptococci among persistently colonized patients
Authors: Pell Macy E. , Blankenship Heather M. , Gaddy Jennifer A. , Davies H. Dele , Manning Shannon D. 
Published: April 2024

ABSTRACTThrough vaginal colonization, GBS causes severe pregnancy outcomes including neonatal sepsis and meningitis. Although intrapartum antibiotic prophylaxis (IAP) has reduced early-onset disease rates, persistent GBS colonization has been observed in patients following prophylaxis. To determine whether IAP selects for genomic signatures that enhance GBS survival and persistence in the vaginal tract, whole-genome sequencing was performed on 97 isolates from 58 patients before (prenatal) and after (postpartum) IAP/childbirth. Core-gene mutation analysis identified 7,025 mutations between the paired isolates. Three postpartum isolates accounted for 98% of mutations and were classified as “mutators” because of point mutations within DNA repair systems.In vitroassays revealed stronger biofilms in two mutators. These findings suggest that antibiotics select for mutations that promote survivalin vivo, which increases the likelihood of transmission to neonates. They also demonstrate how mutators can provide a reservoir of beneficial mutations that enhance fitness and genetic diversity in the GBS population.
Cryo-electron tomographic investigation of native hippocampal glutamatergic synapses
Authors: Matsui Aya , Spangler Cathy J. , Elferich Johannes , Shiozaki Momoko , Jean Nikki , Zhao Xiaowei , Qin Maozhen , Zhong Haining , Yu Zhiheng , Gouaux Eric 
Published: April 2024

SummaryChemical synapses are the major sites of communication between neurons in the nervous system and mediate either excitatory or inhibitory signaling [1]. At excitatory synapses, glutamate is the primary neurotransmitter and upon release from presynaptic vesicles, is detected by postsynaptic glutamate receptors, which include ionotropic AMPA and NMDA receptors [2, 3]. Here we have developed methods to identify glutamatergic synapses in brain tissue slices, label AMPA receptors with small gold nanoparticles (AuNPs), and prepare lamella for cryo-electron tomography studies. The targeted imaging of glutamatergic synapses in the lamella is facilitated by fluorescent pre- and postsynaptic signatures, and the subsequent tomograms allow for identification of key features of chemical synapses, including synaptic vesicles, the synaptic cleft and AuNP-labeled AMPA receptors. These methods pave the way for imaging brain regions at high resolution, using unstained, unfixed samples preserved under near-native conditions.
RosetteArray®Platform for Quantitative High-Throughput Screening of Human Neurodevelopmental Risk
Authors: Lundin Brady F. , Knight Gavin T. , Fedorchak Nikolai J. , Krucki Kevin , Iyer Nisha , Maher Jack E. , Izban Nicholas R. , Roberts Abilene , Cicero Madeline R. , Robinson Joshua F. , Iskandar Bermans J. , Willett Rebecca , Ashton Randolph S. 
Published: April 2024

SUMMARYNeural organoids have revolutionized how human neurodevelopmental disorders (NDDs) are studied. Yet, their utility for screening complex NDD etiologies and in drug discovery is limited by a lack of scalable and quantifiable derivation formats. Here, we describe the RosetteArray®platform’s ability to be used as an off-the-shelf, 96-well plate assay that standardizes incipient forebrain and spinal cord organoid morphogenesis as micropatterned, 3-D, singularly polarized neural rosette tissues (>9000 per plate). RosetteArrays are seeded from cryopreserved human pluripotent stem cells, cultured over 6-8 days, and immunostained images can be quantified using artificial intelligence-based software. We demonstrate the platform’s suitability for screening developmental neurotoxicity and genetic and environmental factors known to cause neural tube defect risk. Given the presence of rosette morphogenesis perturbation in neural organoid models of NDDs and neurodegenerative disorders, the RosetteArray platform could enable quantitative high-throughput screening (qHTS) of human neurodevelopmental risk across regulatory and precision medicine applications.