bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Multimodal Vision Transformer for Interpretable Fusion of Functional and Structural Neuroimaging Data
Authors: Bi Yuda , Abrol Anees , Fu Zening , Calhoun Vince D. 
Published: July 2023

AbstractDeep learning models, despite their potential for increasing our understanding of intricate neuroimaging data, can be hampered by challenges related to interpretability. Multimodal neuroimaging appears to be a promising approach that allows us to extract supplementary information from various imaging modalities. It’s noteworthy that functional brain changes are often more pronounced in schizophrenia, albeit potentially less reproducible, while structural MRI effects are more replicable but usually manifest smaller effects. Instead of conducting isolated analyses for each modality, the joint analysis of these data can bolster the effects and further refine our neurobiological understanding of schizophrenia. This paper introduces a novel deep learning model, the multimodal vision transformer (MultiViT), specifically engineered to enhance the accuracy of classifying schizophrenia by using structural MRI (sMRI) and functional MRI (fMRI) data independently and simultaneously leveraging the combined information from both modalities. This study uses functional network connectivity data derived from a fully automated independent component analysis method as the fMRI features and segmented gray matter volume (GMV) as the sMRI features. These offer sensitive, high-dimensional features for learning from structural and functional MRI data. The resulting MultiViT model is lightweight and robust, outperforming unimodal analyses. Our approach has been applied to data collected from control subjects and patients with schizophrenia, with the MultiViT model achieving an AUC of 0.833, which is significantly higher than the average 0.766 AUC for unimodal baselines and 0.78 AUC for multimodal baselines. Advanced algorithmic approaches for predicting and characterizing these disorders have consistently evolved, though subject and diagnostic heterogeneity pose significant challenges. Given that each modality provides only a partial representation of the brain, we can gather more comprehensive information by harnessing both modalities than by relying on either one independently. Furthermore, we conducted a saliency analysis to gain insights into the co-alterations in structural gray matter and functional network connectivity disrupted in schizophrenia. While it’s clear that the MultiViT model demonstrates differences compared to previous multimodal methods, the specifics of how it compares to methods such as MCCA and JICA are still under investigation, and more research is needed in this area. The findings underscore the potential of interpretable multimodal data fusion models like the MultiViT, highlighting their robustness and potential in the classification and understanding of schizophrenia.
The role of squid for food web structure and community-level metabolism
Authors: Denéchère Rémy , van Denderen P. Daniël , Andersen Ken H. 
Published: July 2023

AbstractSquid differ from fish by their high growth rate, short life span and feeding behaviour. Their fast life strategy is thought to impose a high predation pressure on zooplankton, fish and other squid preys, and a rapid transfer of energy to upper trophic-levels of marine food webs. However, there is a lack of understanding of how squid’s fast life cycle affects the food-web structure, which is needed to project squid biomass across marine regions under shifting climatic conditions. Here, we examine the role of squid on community metabolism and biomass by collecting data on squid somatic growth and incorporating squid in a size- and trait-based fish community model. We show that squid have a 5 times higher average somatic growth rate than fish. Due to their high food demands, squid are constrained to regions of high pelagic secondary production. The presence of squid in these systems is associated with a reduction in total upper trophic level biomass. This decline is caused by an increase in community-level respiration losses associated with squid. Our results indicate that squid might have a large impact on ecosystem structure even at relatively low standing stock biomasses. Consequently, the recent proliferation of squid in ecosystems around the world is likely to have significant ecological and socio-economic impacts.
Expression of Bovine Annexin A4 In E.coliRescues Cytokinesis Blocked by Beta-Lactam Antibiotics
Authors: Creutz Carl E. 
Published: July 2023

ABSTRACTTreatment of bacteria with beta-lactam antibiotics can impair the process of cytokinesis, the final step in cell division, leading to the formation of a filamentous form of the bacteria. The expression of a mammalian calcium-dependent, membrane-binding protein, bovine annexin A4, inE. coliwas found to reverse the inhibitory effects on cytokinesis of the beta-lactam antibiotics ampicillin, piperacillin, and cephalexin. This novel activity of the annexin was blocked by mutation of calcium binding sites in the annexin, indicating roles for calcium binding to the annexin and the binding of the annexin to membranes in restoring cytokinesis. The filamentous form of the bacteria has been reported to be more resistant to phagocytosis by cells of the immune system in eukaryotic hosts. Therefore, expression of annexins in pathogenic bacteria, by promoting the breakdown of the bacterial filaments, might serve as an adjuvant to enhance the efficacy of beta-lactam antibiotics.
Predicting Fixation Paths in the Moran Process:A Machine Learning Approach
Authors: Hajihashemi Mahdi 
Published: July 2023

AbstractPath of Fixation in evolutionary process highly depends on structure of underlying population. In this paper, we apply a machine learning method to predict the path of fixation in several complex graphs and two regular graphs. In our approach, the path of fixation is not used as the target variable in the machine learning model. Rather, we focus on predicting the probability of progression forward (referred to asλin the literature) using the machine learning model. By using previous achievements in determining the fixation path for the Moran process, obtaining the path of fixation becomes straightforward. Due to the time and computational resources required for simulating an evolutionary process in a large population, utilizing a machine learning method can help us save both of these valuable resources. This approach can provide insights to researchers studying evolutionary processes in the context of meta-population problems.
POPSHIFT: A THERMODYNAMICALLY SOUND APPROACH TO ESTIMATE BINDING FREE ENERGIES BY ACCOUNTING FOR LIGAND-INDUCED POPULATION SHIFTS FROM A LIGAND-FREE MSM
Authors: Smith Louis G. , Novak Borna , Osato Meghan , Mobley David L. , Bowman Gregory R. 
Published: July 2023

AbstractObtaining accurate binding free energies fromin silicoscreens has been a longstanding goal for the computational chemistry community. However, accuracy and computational cost are at odds with one another, limiting the utility of methods that perform this type of calculation. Many methods achieve massive scale by explicitly or implicitly assuming that the target protein adopts a single structure, or undergoes limited fluctuations around that structure, to minimize computational cost. Others simulate each protein-ligand complex of interest, accepting lower throughput in exchange for better predictions of binding affinities. Here, we present the PopShift framework for accounting for the ensemble of structures a protein adopts and their relative probabilities. Protein degrees of freedom are enumerated once, and then arbitrarily many molecules can be screened against this ensemble. Specifically, we use Markov state models (MSMs) as a compressed representation of a protein’s thermodynamic ensemble. We start with a ligand-free MSM and then calculate how addition of a ligand shifts the populations of each protein conformational state based on the strength of the interaction between that protein conformation and the ligand. In this work we use docking to estimate the affinity between a given protein structure and ligand, but any estimator of binding affinities could be used in the PopShift framework. We test PopShift on the classic benchmark pocket T4 Lysozyme L99A. We find that PopShift is more accurate than common strategies, such as docking to a single structure and traditional ensemble docking—producing results that compare favorably with alchemical binding free energy calculations in terms of RMSE but not correlation—and may have a more favorable computational cost profile in some applications. In addition to predicting binding free energies and ligand poses, PopShift also provides insight into how the probability of different protein structures is shifted upon addition of various concentrations of ligand, providing a platform for predicting affinities and allosteric effects of ligand binding. Therefore, we expect PopShift will be valuable for hit finding and for providing insight into phenomena like allostery.
Dynamical properties of self-sustained and driven neural networks
Authors: Bouté Jules , Destexhe Alain 
Published: July 2023

AbstractIn the awake brain, cerebral cortex displays asynchronous-irregular (AI) states, where neurons fire irregularly and with low correlation. Neural networks can display AI states that are self-sustained through recurrent connections, or in some cases, need an external input to sustain activity. In this paper, we aim at comparing these two dynamics and their consequences on responsiveness. We first show that the first Lyapunov exponent (FLE) can differ between self-sustained and driven networks, the former displaying a higher FLE than the late. Next, we show that this impact the dynamics of the system, leading to a tendency for self-sustained networks to be more responsive, both properties that can also be captured by mean-field models. We conclude that there is a dynamical and excitability difference between the two types of networks besides their apparent similar collective firing. The model predicts that calculating FLE from population activities in experimental data could provide a way to identify if real neural networks are self-sustained or driven.
Cell networks in the mouse liver during partial hepatectomy
Authors: Li Bin , Rodrigo-Torres Daniel , Pelz Carl , Innes Brendan , Canaday Pamela , Chai Sunghee , Zandstra Peter , Bader Gary D. , Grompe Markus 
Published: July 2023

SummaryIn solid tissues homeostasis and regeneration after injury involve a complex interplay between many different cell types. The mammalian liver harbors numerous epithelial and non-epithelial cells and little is known about the global signaling networks that govern their interactions. To better understand the hepatic cell network, we isolated and purified 10 different cell populations from normal and regenerative mouse livers. Their transcriptomes were analyzed by bulk RNA-seq and a computational platform was used to analyze the cell-cell and ligand-receptor interactions among the 10 populations. Over 50,000 potential cell-cell interactions were found in both the ground state and after partial hepatectomy. Importantly, about half of these differed between the two states, indicating massive changes in the cell network during regeneration. Our study provides the first comprehensive database of potential cell-cell interactions in mammalian liver cell homeostasis and regeneration. With the help of this prediction model, we identified and validated two previously unknown signaling interactions involved in accelerating and delaying liver regeneration. Overall, we provide a novel platform for investigating autocrine/paracrine pathways in tissue regeneration, which can be adapted to other complex multicellular systems.HighlightsA platform predicting cell-cell interactions in liver regeneration was establishedThis platform identified the BMP4 pathway antagonist Fstl1 as a stimulator of hepatocyte proliferationThis platform also discovered the role of Wnt pathway inhibitor Sfrp1 delaying liver regeneration
Habitat correlates of cave-dwelling: A radiation-scale analysis of skin traits and comparative transcriptomics of theSinocyclocheiluscavefish
Authors: Luo Xiayue , Chen Bing , Mao Tingru , Liu Yewei , Yang Jian , Meegaskumbura Madhava 
Published: July 2023

ABSTRACTWith 78 species,Sinocyclocheiluscavefish constitute the largest cavefish radiation in the world. They exhibit remarkable morphological diversity across three habitat types: surface (Surface morphs, Normal-eyed, variably colored), exclusively-cave-dwelling (Stygobitic morphs, Eyeless, depigmented), and intermediate between cave and surface (Stygophilic morphs, Micro-eyed, partially depigmented). Distinctive traits ofSinocyclocheilusinclude variations in eye and skin conditions associated with their habitat, despite the role of the skin in sensing environmental changes, its habitat correlates are less understood, compared to the well-studied eye conditions. Here, we analyzed the correlation betweenSinocyclocheilusskin morphology and its habitat, utilizing morphological and transcriptomics-based methods. We generated RNA-sequencing data for nine species and integrated those with existing data from five additional species. These 14 species represent the primary clades and major habitats of these cavefish. Data on skin color and scale morphology were generated and 7374 orthologous genes were identified. Using a comparative transcriptomics approach, we identified 1,348 differentially expressed genes (DEGs) in the three morphotypes. GO and KEGG enrichment analyses suggest that these species have evolved different strategies for energy metabolism, immunity, and oxidative stress in different habitats. We also found 329 positive selection genes (PSGs) in the skin of these species that are mainly involved in immunity, apoptosis, and necrosis, indicating potential adaptations to their habitats. The maximum likelihood phylogenetic tree, based on 1369 single-copy orthologous genes of the species, was largely concordant with the currently established RAD-seq and mt-DNA based phylogenies, but with a few exceptions. Species with higher cave dependence present lighter coloration, fewer dark blotches, and diminished scale morphology and coverage. PCA and cluster analysis suggested that cave-dwelling species, characterized by the absence of black blotches, have similar expression patterns, indicating convergence in cave adaptation. Variations in tyrosine metabolism may explain pigmentation differences among species in diverse habitats. Our study highlights the significance of habitat in shaping skin metabolism, pigmentation variation, and morphology while offering insights into the molecular mechanisms driving these habitat-specific adaptations inSinocyclocheilus. These findings underscore the transcriptional variation in adapting to diverse environments and contribute to future studies on the evolution and ecology of cavefish.
Intranasal CRMP2-Ubc9 Inhibitor Regulates NaV1.7 to Alleviate Trigeminal Neuropathic Pain
Authors: Loya-Lopez Santiago I. , Allen Heather N. , Duran Paz , Calderon-Rivera Aida , Gomez Kimberly , Kumar Upasana , Shields Rory , Zeng Rui , Dwivedi Akshat , Saurabh Saumya , Korczeniewska Olga A. , Khanna Rajesh 
Published: July 2023

AbstractDysregulation of voltage-gated sodium NaV1.7 channels in sensory neurons contributes to chronic pain conditions, including trigeminal neuropathic pain. We previously reported that chronic pain results in part from increased SUMOylation of collapsin response mediator protein 2 (CRMP2), leading to an increased CRMP2/NaV1.7 interaction and increased functional activity of NaV1.7. Targeting this feed-forward regulation, we developed compound194, which inhibits CRMP2 SUMOylation mediated by the SUMO-conjugating enzyme Ubc9. We further demonstrated that194effectively reduces the functional activity of NaV1.7 channels in dorsal root ganglia neurons and alleviated inflammatory and neuropathic pain. Here, we employed a comprehensive array of investigative approaches, encompassing biochemical, pharmacological, genetic, electrophysiological, and behavioral analyses, to assess the functional implications of NaV1.7 regulation by CRMP2 in trigeminal ganglia (TG) neurons. We confirmed the expression ofScn9a,Dpysl2, andUBE2Iwithin TG neurons. Furthermore, we found an interaction between CRMP2 and NaV1.7, with CRMP2 being SUMOylated in these sensory ganglia. Disrupting CRMP2 SUMOylation with compound194uncoupled the CRMP2/NaV1.7 interaction, impeded NaV1.7 diffusion on the plasma membrane, and subsequently diminished NaV1.7 activity. Compound194also led to a reduction in TG neuron excitability. Finally, when intranasally administered to rats with chronic constriction injury of the infraorbital nerve (CCI-ION),194significantly decreased nociceptive behaviors. Collectively, our findings underscore the critical role of CRMP2 in regulating NaV1.7 within TG neurons, emphasizing the importance of this indirect modulation in trigeminal neuropathic pain.
Protective intravenous BCG vaccination induces enhanced immune signaling in the airways
Authors: Peters Joshua M. , Irvine Edward B. , Rosenberg Jacob M. , Wadsworth Marc H. , Hughes Travis K. , Sutton Matthew , Nyquist Sarah K. , Bromley Joshua D. , Mondal Rajib , Roederer Mario , Seder Robert A. , Darrah Patricia A. , Alter Galit , Flynn JoAnne L. , Shalek Alex K. , Fortune Sarah M. , Bryson Bryan D. 
Published: July 2023

AbstractIntradermal (ID) Bacillus Calmette–Guérin (BCG) is the most widely administered vaccine in the world. However, ID-BCG fails to achieve the level of protection needed in adults to alter the course of the tuberculosis epidemic. Recent studies in non-human primates have demonstrated high levels of protection againstMycobacterium tuberculosis(Mtb) following intravenous (IV) administration of BCG. However, the protective immune features that emerge following IV BCG vaccination remain incompletely defined. Here we used single-cell RNA-sequencing (scRNAseq) to transcriptionally profile 157,114 unstimulated and purified protein derivative (PPD)-stimulated bronchoalveolar lavage (BAL) cells from 29 rhesus macaques immunized with BCG across routes of administration and doses to uncover cell composition-, gene expression-, and biological network-level signatures associated with IV BCG-mediated protection. Our analyses revealed that high-dose IV BCG drove an influx of polyfunctional T cells and macrophages into the airways. These macrophages exhibited a basal activation phenotype even in the absence of PPD-stimulation, defined in part by IFN and TNF-α signaling up to 6 months following BCG immunization. Furthermore, intercellular immune signaling pathways between key myeloid and T cell subsets were enhanced following PPD-stimulation in high-dose IV BCG-vaccinated macaques. High-dose IV BCG also engendered quantitatively and qualitatively stronger transcriptional responses to PPD-stimulation, with a robust Th1-Th17 transcriptional phenotype in T cells, and augmented transcriptional signatures of reactive oxygen species production, hypoxia, and IFN-γ response within alveolar macrophages. Collectively, this work supports that IV BCG immunization creates a unique cellular ecosystem in the airways, which primes and enables local myeloid cells to effectively clearMtbupon challenge.
Developmental Manganese Exposure Causes Lasting Attention Deficits Accompanied by Dysregulation of mTOR Signaling and Catecholaminergic Gene Expression in Brain Prefrontal Cortex
Authors: Santiago N. A. , He B. , Howard S. L. , Beaudin S. , Strupp B. J. , Smith D. R. 
Published: July 2023

AbstractElevated manganese (Mn) exposure is associated with attentional deficits in children, and is an environmental risk factor for attention deficit hyperactivity disorder (ADHD). We have shown that developmental Mn exposure causes lasting attention and sensorimotor deficits in a rat model of early childhood Mn exposure, and that these deficits are associated with a hypofunctioning catecholaminergic system in the prefrontal cortex (PFC), though the mechanistic basis for these deficits is not well understood. To address this, male Long-Evans rats were exposed orally to Mn (50 mg/kg/d) over PND 1-21 and attentional function was assessed in adulthood using the 5-Choice Serial Reaction Time Task. Targeted catecholaminergic system and epigenetic gene expression, followed by unbiased differential DNA methylation and gene regulation expression transcriptomics in the PFC, were performed in young adult littermates. Results show that developmental Mn exposure causes lasting focused attention deficits that are associated with reduced gene expression of tyrosine hydroxylase, dopamine transporter, and DNA methyltransferase 3a. Further, developmental Mn exposure causes broader lasting methylation and gene expression dysregulation associated with epigenetic regulation, inflammation, cell development, and hypofunctioning catecholaminergic neuronal systems. Pathway enrichment analyses uncovered mTOR and Wnt signaling pathway genes as significant transcriptomic regulators of the Mn altered transcriptome, and Western blot of total, C1 and C2 phospho-mTOR confirmed mTOR pathway dysregulation. Our findings deepen our understanding of the mechanistic basis of how developmental Mn exposure leads to lasting catecholaminergic dysfunction and attention deficits, which may aid future therapeutic interventions of environmental exposure associated disorders.Significance StatementAttention deficit hyperactivity disorder (ADHD) is associated with environmental risk factors, including exposure to neurotoxic agents. Here we used a rodent model of developmental manganese (Mn) exposure producing lasting attention deficits to show broad epigenetic and gene expression changes in the prefrontal cortex, and to identify disrupted mTOR and Wnt signaling pathways as a novel mechanism for how developmental Mn exposure may induce lasting attention and catecholaminergic system impairments. Importantly, our findings establish early development as a critical period of susceptibility to lasting deficits in attentional function caused by elevated environmental toxicant exposure. Given that environmental health threats disproportionately impact communities of color and low socioeconomic status, our findings can aid future studies to assess therapeutic interventions for vulnerable populations.