bluuebunny/crossref_metadata_2025_split huggingface dataset:
DAMM for the detection and tracking of multiple animals within complex social and environmental settings
Authors: Kaul Gaurav , McDevitt Jonathan , Johnson Justin , Eban-Rothschild Ada 
Published: January 2024

AbstractAccurate detection and tracking of animals across diverse environments are crucial for behavioral studies in various disciplines, including neuroscience. Recently, machine learning and computer vision techniques have become integral to the neuroscientist’s toolkit, enabling high-throughput behavioral studies. Despite advancements in localizing individual animals in simple environments, the task remains challenging in complex conditions due to intra-class visual variability and environmental diversity. These limitations hinder studies in ethologically- relevant conditions, such as when animals are concealed within nests or in obscured environments. Moreover, current tools are laborious and time-consuming to employ, requiring extensive, setup-specific annotation and model training/validation procedures. To address these challenges, we introduce the ’Detect Any Mouse Model’ (DAMM), a pretrained object detector for localizing mice in complex environments, capable of robust performance with zero to minimal additional training on new experimental setups. Our approach involves collecting and annotating a diverse dataset that encompasses single and multi-housed mice in various lighting conditions, experimental setups, and occlusion levels. We utilize the Mask R-CNN architecture for instance segmentation and validate DAMM’s performance with no additional training data (zero-shot inference) and with few examples for fine-tuning (few-shot inference). DAMM excels in zero- shot inference, detecting mice, and even rats, in entirely unseen scenarios and further improves with minimal additional training. By integrating DAMM with the SORT algorithm, we demonstrate robust tracking, competitively performing with keypoint-estimation-based methods. Finally, to advance and simplify behavioral studies, we made DAMM accessible to the scientific community with a user-friendly Python API, shared model weights, and a Google Colab implementation.SignificancePresent deep learning tools for animal localization require extensive laborious annotation and time-consuming training for the creation of setup-specific models, slowing scientific progress. Additionally, the effectiveness of these tools in naturalistic settings is impeded by visual variability of objects and environmental diversity, hindering animal detection in complex environments. Our study presents the ’Detect Any Mouse Model’ (DAMM), a robustly validated object detector designed for localizing mice in complex environments. DAMM excels in generalization, robustly performing with zero to minimal additional training on previously unseen setups and multi-animal scenarios. Its integration with the SORT algorithm permits robust tracking, competitively performing with keypoint-estimation-based tools. These developments, along with our dissemination of DAMM, mark a significant step forward in streamlining ethologically-relevant animal behavioral studies.
Thermodynamic analog of integrate-and-fire neuronal networks by maximum entropy modelling
Authors: Simões T. S. A. N. , Filho C. I. N. Sampaio , Herrmann H. J. , Andrade J. S. , de Arcangelis L. 
Published: January 2024

AbstractRelying on maximum entropy arguments, certain aspects of time-averaged experimental neuronal data have been recently described using Ising-like models, allowing the study of neuronal networks under an analogous thermodynamical framework. Here, we apply for the first time the Maximum Entropy method to an Integrate-and-fire (IF) model that can be tuned at criticality, offering a controlled setting for a systematic study of criticality and finite-size effects in spontaneous neuronal activity, as opposed to experiments. We show that generalized Ising models that accurately predict the average local activities and correlation functions between neurons of the IF model networks in the critical state exhibit a spin glass phase for low temperatures, having mostly negative intrinsic fields and a bimodal distribution of interaction constants that tends to become unimodal for larger networks. Results appear to be affected by sample-to-sample connectivity variations and subsampling. Furthermore, we also found that networks with higher percentage of inhibitory neurons lead to Ising-like systems with reduced thermal fluctuations. Finally, considering only neuronal pairs associated with the largest correlation functions allows the study of larger system sizes.Author summaryBrain activity, either stimulated or spontaneous,in vivoorin vitro, exhibits complex spatiotemporal behavior. Trying to make sense of it, several research groups have analyzed time-averaged experimental neuronal data using maximum entropy arguments, mapping the neuronal dynamics into a generalized Ising-like model and allowing to study neuronal data using tools typical of critical phenomena. However, the intricacy of real biological networks in experimental settings pose challenges in the precision and reliability of the neuronal measurements. Here, we apply for the first time the Maximum Entropy Method to an Integrate-and-fire model with synaptic plasticity, providing a foundation for a more systematic and comprehensive study of spontaneous brain activity. We show that generalized Ising models are able to reproduce the numerical time-averaged data of local activities and correlation functions of integrate-and-fire neurons and predict qualitatively higher-order quantities such as the three-point correlation functions across triplets of neurons. We show that subsampling affects the efficiency of the mapping and that the analogous thermodynamics functions of the Ising-like models depend on sample-to-sample network variations and on the presence of inhibition in the neural network.
CellDemux:coherent genetic demultiplexing in single-cell and single-nuclei experiments
Authors: Zoodsma Martijn , Zhan Qiuyao , Kumar Saumya , Botey-Bataller Javier , Li Wenchao , Zhou Liang , Alaswad Ahmed , Liu Zhaoli , Zhang Zhenhua , Zhang Bowen , Xu Cheng-Jian , Li Yang 
Published: January 2024

ABSTRACTMultiplexed single-cell experiment designs are superior in terms of reduced batch effects, increased cost-effectiveness, throughput and statistical power. However, current computational strategies using genetics to demultiplex single-cell (sc) libraries are limited when applied to single-nuclei (sn) sequencing data (e.g., snATAC-seq and snMultiome). Here, we presentCellDemux: a computational framework for genetic demultiplexing within and across data modalities, including single-cell, single-nuclei and paired snMultiome measurements.CellDemuxuses a consensus approach, leveraging modality-specific tools to robustly identify non-empty oil droplets and singlets, which are subsequently demultiplexed to donors. Notable,CellDemuxdemonstrates good performance in demultiplexing snMultiome data and is generalizable to single modalities, i.e. snATAC-seq and sc/snRNA-seq libraries. We benchmarkCellDemuxon 187 genetically multiplexed libraries from 800 samples (scRNA-seq, snATAC-seq, CITE-seq and snMultiome), confidently identifying and assigning cells to 88% of donors. In paired snMultiome libraries,CellDemuxachieves consistent demultiplexing across data modalities. Moreover, analysis of 38 snATAC libraries from 149 samples shows thatCellDemuxretains more genetically demultiplexed nuclei for downstream analyses compared to existing methods. In summary,CellDemuxis a modular and robust framework that deconvolves donors from genetically multiplexed single-cell and single-nuclei RNA/ATAC/Multiome libraries.
PYK2 in the dorsal striatum of Huntington’s disease R6/2 mouse model
Authors: Al-Massadi Omar , Pins Benoit de , Longueville Sophie , Giralt Albert , Irinopoulou Theano , Savariradjane Mythili , Subashi Enejda , Ginés Silvia , Caboche Jocelyne , Betuing Sandrine , Girault Jean-Antoine 
Published: January 2024

AbstractHuntington’s disease (HD) is a devastating disease due to autosomal dominant mutation in theHTTgene. Its pathophysiology involves multiple molecular alterations including transcriptional defects. We previously showed that in HD patients and mouse model, the protein levels of the non-receptor tyrosine kinase PYK2 were decreased in the hippocampus and that viral expression of PYK2 improved the hippocampal phenotype. Here, we investigated the possible contribution of PYK2 in the striatum, a major brain region altered in HD. PYK2 mRNA levels were decreased in the striatum and hippocampus of R6/2 mice, a severe HD model. PYK2 protein levels were also decreased in the dorsal striatum of R6/2 mice and in the putamen of human patients. PYK2 knockout by itself did not result in motor symptoms observed in HD mouse models. Yet, we examined whether PYK2 deficiency participated in the R6/2 mice phenotype by expressing PYK2 in the dorsal striatum using AAV vectors. With an AAV1/Camk2apromoter, we did not observe significant improvement of body weight, clasping, motor activity and coordination (rotarod) alterations observed in R6/2 mice. With an AAV9/SYN1promoter we found an improvement of body weight loss and a tendency to better rotarod performance. DARPP-32 immunofluorescence was increased following AAV9/SYN1-PYK2 injection compared to AAV9/SYN1-GFP, suggesting a possible partial beneficial effect on striatal projection neurons. We conclude that PYK2 mRNA and protein levels are decreased in the striatum as in hippocampus of HD patients and mouse models. However, in contrast to hippocampus, striatal viral expression of PYK2 has only a slight effect on the R6/2 model striatal motor phenotype.HighlightsHuntington’s disease is a lethal genetic disease altering striatum, cortex, and hippocampusRestoring PYK2 levels in hippocampus improved hippocampal phenotype of a Huntington mouse modelWe show that PYK2 levels are decreased in the striatum of R6/2 mice and human patientsViral expression of PYK2 in the striatum has only a small effect on R6/2 mouse model motor phenotype but improves weight loss
A novel glutathione peroxidase-based biosensor disentangles differential subcellular accumulation of H2O2 and lipid hydroperoxides
Authors: Exposito-Rodriguez Marino , Reeder Brandon , Brooke Greg N. , Hough Michael A. , Laissue Philippe P. , Mullineaux Philip M. 
Published: January 2024

ABSTRACTHydrogen peroxide (H2O2) and lipid hydroperoxides (LOOH) are initiators and transducers of inter- and intra-cellular signaling in response to diverse environmental, pathological and developmental cues. The accumulation of both H2O2and LOOH is often temporally and spatially coincident in tissues, but it is unknown if this coincidence extends to subcellular compartments. If distinct accumulation of different peroxides occurs at this smaller spatial scale, then it would be an important factor in signaling specificity. Fusion of the redox-sensitive (ro)GFP2 to theSaccharomyces cerevisiae(yeast) OXIDANT RECEPTOR PEROXIDASE1 (ORP1), also known as GLUTATHIONE PEROXIDASE3 (GPX3), created a now widely used biosensor that is assumed to detect H2O2in vivo.This is despite monomeric GPX enzymes, such as ORP1/GPX3, possessing wide peroxide substrate specificities. Consequently, we confirmedin vitrothat roGFP2-ORP1 is not only oxidized by H2O2, but also by phospholipid fatty acid peroxides generated in lecithin-derived liposomes by lipoxygenase-catalyzed peroxidation. This led us to doubt that roGFP2-ORP1in vivois specific for H2O2. To address this issue of peroxide specificity, we constructed a modified biosensor called roGFP2-synORP1. This version has greatly diminished reactivity towards phospholipid fatty acid peroxides but retains high sensitivity for H2O2. These two roGFP2-based biosensors, targeted to chloroplasts, cytosol and the nucleus, were quantitatively imaged in parallel inNicotiana benthamianaabaxial epidermal cells experiencing high light- and herbicide-induced photo-oxidative stress. From differential patterns of oxidation of these probes, we inferred that the chloroplasts accumulated both peroxide types. In contrast, LOOH and H2O2accumulated exclusively in the cytosol and nucleus respectively. Therefore, this suggests that the signalling networks initiated by different peroxides will have a distinct spatial component.
Fructose vs. Glucose: Modulating Stem Cell Growth and Function Through Sugar Supplementation
Authors: Elsaid Salaheldeen , Wu Xiangdong , Tee Sui Seng 
Published: January 2024

AbstractIn multicellular organisms, stem cells are impacted by microenvironmental resources such as nutrient availability and oxygen tension for their survival, growth, and differentiation1,2. However, the acessibility of these resources in the pericellular environment greatly varies from organ to organ3–5. This divergence in resource availability leads to variations in the potency and differentiation potential of stem cells4,6. Moreover, hexose and oxygen levels modulate cytokine production which is crucial for cell-cell communication7,8as well as growth, and differentiation of stem cell9,10.Hence, this study aims to explore the distinct effects of glucose and fructose, as well as different oxygen tensions, on the growth dynamics, cytokine production, and differentiation of stem cells.
Strategy for unlimited cycles of scarless oligonucleotide directed gene editing inChlamydomonas reinhardtii
Authors: Ross Ian L. , Budiman Sabar , Le Hong Phuong , Xiong Dake , Hemker Fritz , Millen Elizabeth A. , Oey Melanie , Hankamer Ben 
Published: January 2024

AbstractCRISPR/Cas9 gene editing in the model green algaChlamydomonas reinhardtiirelies on the use of selective marker genes to enrich for non-selectable target mutations. This becomes challenging when many sequential modifications are required in a single cell line, as useful markers are limited. Here we demonstrate a cyclical selection process which only requires a single marker gene to identify an almost infinite sequential series of CRISPR-based target gene modifications. TheNIA1(Nit1, NR; nitrate reductase) gene was this selectable marker. In the forward stage of the cycle, a stop codon was engineered into theNIA1gene at the CRISPR target location. Cells retaining the wild typeNIA1gene were killed by chlorate, whileNIA1knockout mutants survived. In the reverse phase of the cycle, the stop codon engineered into theNIA1gene during the forward phase was edited back to the wild type sequence. Using nitrate as the sole nitrogen source, here only the reverted wild type cells survived. By using CRISPR to specifically deactivate and reactivate theNIA1gene, a marker system was established that flipped back and forth between chlorate- and auxotrophic (nitrate) based selection. This provided a scarless cyclical marker system that enabled an indefinite series of CRISPR edits in other, non-selectable genes. Here, we demonstrate that this ‘Sequential CRISPR via Recycling Endogenous Auxotrophic Markers(SCREAM)’ technology enables an essentially limitless series of genetic modifications to be introduced to a single cell lineage ofC. reinhardtiiin a fast and efficient manner to complete complex genetic engineering.
Claudin-4 modulates autophagy via SLC1A5/LAT1 as a tolerance mechanism for genomic instability in ovarian cancer
Authors: Villagomez Fabian R. , Lang Julie , Webb Patricia , Neville Margaret , Woodruff Elizabeth R. , Bitler Benjamin G. 
Published: January 2024

AbstractGenome instability is key for tumor heterogeneity and derives from defects in cell division and DNA damage repair. Tumors show tolerance for this characteristic, but its accumulation is regulated somehow to avoid catastrophic chromosomal alterations and cell death. Claudin-4 is upregulated and closely associated with genome instability and worse patient outcome in ovarian cancer. This protein is commonly described as a junctional protein participating in processes such as cell proliferation and DNA repair. However, its biological association with genomic instability is still poorly-understood. Here, we used CRISPRi and a claudin mimic peptide (CMP) to modulate the cladudin-4 expression and its function, respectively inin-vitro(high-grade serous carcinoma cells) andin-vivo(patient-derived xenograft in a humanized-mice model) systems. We found that claudin-4 promotes a protective cellular-mechanism that links cell-cell junctions to genome integrity. Disruption of this axis leads to irregular cellular connections and cell cycle that results in chromosomal alterations, a phenomenon associated with a novel functional link between claudin-4 and SLC1A5/LAT1 in regulating autophagy. Consequently, claudin-4’s disruption increased autophagy and associated with engulfment of cytoplasm-localized DNA. Furthermore, the claudin-4/SLC1A5/LAT1 biological axis correlates with decrease ovarian cancer patient survival and targeting claudin-4in-vivowith CMP resulted in increased niraparib (PARPi) efficacy, correlating with increased tumoral infiltration of T CD8+ lymphocytes. Our results show that the upregulation of claudin-4 enables a mechanism that promotes tolerance to genomic instability and immune evasion in ovarian cancer; thus, suggesting the potential of claudin-4 as a translational target for enhancing ovarian cancer treatment.
Spaceflight alters host-gut microbiota interactions
Authors: Gonzalez E. , Lee M.D. , Tierney B.T. , Lipieta N. , Flores P. , Mishra M. , Beckett N. , Karouia F. , Barker R. , Jansen R.J. , Green S.J. , Weging S. , Broddrick J. , Kelliher J. , Singh N.K. , Bezdan D. , Galazska J. , Brereton N.J.B 
Published: January 2024

AbstractThe rodent habitat on the International Space Station has provided crucial insights into the impact of spaceflight on mammals, including observation of symptoms characteristic of liver disease, insulin resistance, osteopenia and myopathy. Although these physiological responses can involve the microbiome when observed on Earth, changes in host-microbiota interactions during spaceflight are still being elucidated. Here, NASA GeneLab multiomic data from the Rodent Research 6 mission are used to determine changes to gut microbiota and murine host colon and liver gene expression after 29 and 56-days of spaceflight. Using hybrid amplicon and whole metagenome sequencing analysis, significant spaceflight-associated alterations to 42 microbiome species were identified. These included relative reductions of bacteria associated with bile acid and butyrate metabolism, such asExtibacter murisandDysosmobacter welbionis.Functional prediction suggested over-representation of fatty acid and bile acid metabolism, extracellular matrix interactions, and antibiotic resistance genes within the gut microbiome, while host intestinal and hepatic gene expression described corresponding changes to host bile acid and energy metabolism, and immune suppression from spaceflight. Taken together, these changes imply that interactions at the host-gut microbiome interface contribute to spaceflight pathology and highlight how these interactions might critically influence human health and the feasibility of long-duration spaceflight.
Accurate inference of population history in the presence of background selection
Authors: Cousins Trevor , Tabin Daniel , Patterson Nick , Reich David , Durvasula Arun 
Published: January 2024

1AbstractAll published methods for learning about demographic history make the simplifying assumption that the genome evolves neutrally, and do not seek to account for the effects of natural selection on patterns of variation. This is a major concern, as ample work has demonstrated the pervasive effects of natural selection and in particular background selection (BGS) on patterns of genetic variation in diverse species. Simulations and theoretical work have shown that methods to infer changes in effective population size over time (Ne(t)) become increasingly inaccurate as the strength of linked selection increases. Here, we introduce an extension to the Pairwise Sequentially Markovian Coalescent (PSMC) algorithm, PSMC+, which explicitly co-models demographic history and natural selection. We benchmark our method using forward-in-time simulations with BGS and find that our approach improves the accuracy of effective population size inference. Leveraging a high resolution map of BGS in humans, we infer considerable changes in the magnitude of inferred effective population size relative to previous reports. Finally, we separately inferNe(t) on the X chromosome and on the autosomes in diverse great apes without making a correction for selection, and find that the inferred ratio fluctuates substantially through time in a way that differs across species, showing that uncorrected selection may be an important driver of signals of genetic difference on the X chromosome and autosomes.
Diencephalic and Neuropeptidergic Dysfunction in Zebrafish with Autism Risk Mutations
Authors: Capps Mary E.S. , Moyer Anna J. , Conklin Claire L. , Martina Verdion , Torija-Olson Emma G. , Klein Morgan C. , Gannaway William C. , Calhoun Caleb C.S. , Vivian Michael D. , Thyme Summer B. 
Published: January 2024

AbstractHundreds of human mutations are linked to autism and related disorders, yet the functions of many of these mutated genes during vertebrate neurodevelopment are unclear. We generated 27 zebrafish mutants with presumptive protein-truncating mutations or specific missense variants corresponding to autism-risk alleles in 17 human genes. We observed baseline and stimulus-driven behavioral changes at larval stages, as well as social behavior differences in lines tested as juveniles. Imaging whole-brain activity revealed a near identical activity map for mutations in the unrelated geneskmt5bandhdlbpa, defined by increased activity mainly in the diencephalon. Mutating 7 of the 17 risk genes resulted in substantial brain size differences. Using RNA sequencing, we further defined molecular drivers of the observed phenotypes, identifying targetable disruptions in neuropeptide signaling, neuronal maturation, and cell proliferation. This multi-modal screen nominated brain regions, cell types, and molecular pathways that may contribute to autism susceptibility.TeaserZebrafish screen uncovers diencephalon, social interaction, and neuropeptidergic signaling phenotypes in ASD risk mutants.
Strategic vaccine stockpiles for regional epidemics of emerging viruses: a geospatial modeling framework
Authors: Carlson Colin J. , Garnier Romain , Tiu Andrew , Luby Stephen , Bansal Shweta 
Published: January 2024

AbstractMultinational epidemics of emerging infectious diseases are increasingly common, due to anthropogenic pressure on ecosystems and the growing connectivity of human populations. Early and efficient vaccination can contain outbreaks and prevent mass mortality, but optimal vaccine stockpiling strategies are dependent on pathogen characteristics, reservoir ecology, and epidemic dynamics. Here, we model major regional outbreaks of Nipah virus and Middle East respiratory syndrome, and use these to develop a generalized framework for estimating vaccine stockpile needs based on spillover geography, spatially-heterogeneous healthcare capacity and spatially-distributed human mobility networks. Because outbreak sizes were highly skewed, we found that most outbreaks were readily contained (median stockpile estimate for MERS-CoV: 2,089 doses; Nipah: 1,882 doses), but the maximum estimated stockpile need in a highly unlikely large outbreak scenario was 2-3 orders of magnitude higher (MERS-CoV: ∼87,000 doses; Nipah ∼1.1 million doses). Sensitivity analysis revealed that stockpile needs were more dependent on basic epidemiological parameters (i.e., death and recovery rate) and healthcare availability than any uncertainty related to vaccine efficacy or deployment strategy. Our results highlight the value of descriptive epidemiology for real-world modeling applications, and suggest that stockpile allocation should consider ecological, epidemiological, and social dimensions of risk.