bluuebunny/crossref_metadata_2025_split huggingface dataset:
Selection, Design and Immunogenicity Studies of ASFV Antigens for Subunit mRNA Cocktail Vaccines with Specific Immune Response Profiles
Authors: Yuan Fangfeng , Cui Junru , Wang Tianlei , Qin Jane , Jeon Ju Hyeong , Ding Huiming , Whittaker Charles A. , Xu Renhuan , Cao Helen , Chen Jianzhu 
Published: October 2024

AbstractDevelopment of safe and effective subunit vaccines for controlling African Swine Fever Virus (ASFV) infection has been hampered by a lack of protective viral antigens, complex virion structures, and multiple mechanisms of infection. Here, we selected ASFV antigens based on their localization on the virion, known functions, and homologies to the subunits of the protective vaccinia virus vaccine. We also engineered viral capsid proteins for inducing optimal antibody responses and designed T cell-directed antigen for inducing broad and robust cellular immunity. The selected antigens in lipid nanoparticle-mRNA formulations were evaluated for immunogenicity in both mice and pigs with concordant results. Different antigens induced divergent immune response profiles, including the levels of IgG and T cell responses and effector functions of anti-sera. We further developed a computational approach to combine antigens into cocktails for inducing specific immune response profiles and validated candidate cocktail vaccines in mice. Our results provide a basis for further evaluating candidate subunit mRNA vaccines in challenge studies.TeaserNovel strategies to develop subunit vaccines for ASFV and other complex large DNA viruses.
The Role of Semantic Inhibition in Aesthetic Appreciation: fNIRS Evidence from Poetry Reading
Authors: Liu Huishu , Xu Xiaomeng , Sun Wanyan , Zhang Dan , Zhang Yu 
Published: October 2024

AbstractBackgroundAesthetic education is pivotal in shaping a comprehensive and harmonious humanity. However, the transition from semantic comprehension to aesthetic appreciation remains poorly understood. This study, informed by transactional theory, sought to elucidate the cognitive mechanisms of aesthetic reading by examining its neural dynamics through functional Near-Infrared Spectroscopy.MethodsParticipants were tasked with reading Tang poetry aesthetically, with fNIRS monitoring brain activity in frontal and temporal regions.ResultsCompared to an efferent reading task, the aesthetic reading task revealed a distinct three-stage neural dynamic pattern. Initially, aesthetic reading showed similar HbO activation in all regions, likely indicating the semantic processing phase. This was followed by a divergence, with a decrease in HbO over the left primary somatosensory cortex and the left superior, inferior, and middle temporal gyri, suggesting inhibition of semantic processing. Finally, a resurgence of activity in these areas was observed, along with an increase in HbO over the left dorsolateral prefrontal cortex, which could be associated with memory, imagination, and empathy. This reactivation correlated with participants’ self-reported aesthetic appreciation scores.ContributionThe findings reveal the temporal and spatial dynamics of brain activity during aesthetic reading, enhancing our comprehension of underlying cognitive processes.
Time-dependent computational model of post-traumatic osteoarthritis to estimate how mechanoinflammatory mechanisms impact cartilage aggrecan content
Authors: Eskelinen ASA , Kosonen JP , Hamada M , Esrafilian A , Florea C , Grodzinsky AJ , Tanska P , Korhonen RK 
Published: October 2024

AbstractDegenerative musculoskeletal diseases like osteoarthritis can be initiated by joint injury. Injurious overloading-induced mechanical straining of articular cartilage and subsequent biological responses may trigger cartilage degradation. One early sign of degradation is loss of aggrecan content which is potentially accelerated near chondral lesions under physiological loading. Yet, the mechanoinflammatory mechanisms explaining time-dependent degradation in regions with disparate mechanical loading are unclear and challenging to assess with experiments alone. Here, we developed computational models unraveling potential mechanisms behind aggrecan content adaptation. Incorporating mechanical strain-driven cell damage and downstream proteolytic enzyme release, fluid flow-driven aggrecan depletion, and fluid pressure-stimulated regulation of aggrecan biosynthesis, the models agreed with experiments and exhibited 14%-points greater near-lesion aggrecan loss after 12 days of physiological loading compared to without loading. This significant advancement in mechanistic understanding incorporated into cartilage adaptation model can help in development and guidance of personalized therapies, such as rehabilitation protocols and tissue- engineered constructs.
Accelerated multi-shell diffusion MRI with Gaussian process estimated reconstruction of multi-band imaging
Authors: Ye Xinyu , Miller Karla , Wu Wenchuan 
Published: October 2024

AbstractPurposeThis work aims to propose a robust reconstruction method exploiting shared information across shells to increase the acquisition speed of multi-shell diffusion MRI, enabling rapid tissue microstructure mapping.Theory and MethodsLocal q-space points share similar information. Gaussian Process can exploit the q-space smoothness in a data-driven way and provide q-space signal estimation based on the signals from a q-space neighborhood. The Diffusion Acceleration with Gaussian process Estimated Reconstruction (DAGER) method uses the signal estimation from Gaussian process as a prior in a joint k-q reconstruction and improves image quality under high acceleration factors compared to conventional (k-only) reconstruction. In this work, we extend the DAGER method by introducing a multi-shell covariance function and correcting for Rician noise distribution in magnitude data when fitting the Gaussian process model. The method was evaluated with both simulation and in vivo data.ResultsSimulated and in-vivo results demonstrate that the proposed method can significantly improve the image quality of reconstructed dMRI data with high acceleration both in-plane and slice-wise, achieving a total acceleration factor of 12. The improvement of image quality allows more robust diffusion model fitting compared to conventional reconstruction methods, enabling advanced multi-shell diffusion analysis within much shorter scan time.ConclusionThe proposed method enables highly accelerated dMRI which can shorten the scan time of multi-shell dMRI without sacrificing quality compared to conventional practice. This may facilitate a wider application of advanced dMRI models in basic and clinical neuroscience.
DeepPaint: A deep-learning package for Cell Painting Image Classification
Authors: Luna Diego , Johnson Erik C. , Dunphy Laura J. , McQuillen Ryan 
Published: October 2024

AbstractRecent developments in the high-content imaging (HCI) space have allowed for the production of large Cell Painting datasets. These datasets are typically derived from cells exposed to a set of biological perturbants including proteins, small molecules, or even pathogens. While the method of Cell Painting has shown utility for drug discovery and hazard evaluation purposes, traditional analyses pipelines applied Cell Painting datasets typically require the segmentation of single cells from thousands to millions of images, a process that is time-consuming and subject to noise and experimental variability. Here we present DeepPaint, a Python package that uses a deep learning framework to perform image analysis of cell painting images including treatment classification and latent space analysis, circumventing the need for image segmentation. DeepPaint is easily tunable to different HCI setups and datasets and can be applied to classify broad types of biological perturbations. Here we demonstrate that DeepPaint can generate highly accurate neural networks for binary and multiclass classification of cell painting images. The DeepPaint package and example notebooks are freely available athttps://github.com/jhuapl-bio/DeepPaint.
Overcoming ploidy barriers: the role of triploid bridges in the genetic introgression ofCardamine amara
Authors: Bartolić P , Voltrová A , Macková L , Šrámková G , Šlenker M , Mandáková T , García N Padilla , Marhold K , Kolář F 
Published: October 2024

SummaryPolyploidisation is a significant reproductive barrier, yet genetic evidence indicates that interploidy admixture is more common than previously thought. Theoretical models and controlled crosses support the ‘triploid bridge’ hypothesis supposing that hybrids of intermediate ploidy facilitate gene flow. However, comprehensive evidence combining experimental and genetic data is missing.In this study, we investigated the rates and directions of gene flow within a diploid– autotetraploid contact zone ofCardamine amara, a species with abundant natural triploids. We cytotyped over 400 wild individuals, conducted reciprocal interploidy crosses and inferred gene flow based on genome-wide sequencing of 84 individuals.Triploids represent a conspicuous entity in mixed-ploidy populations (5%), yet only part of them arose through interploidy hybridisation. Despite being rarely formed, triploid hybrids can backcross with their parental cytotypes, producing viable offspring that are often euploid (in 42% of cases). In correspondence, we found a significant genome-wide signal of gene flow for sympatric, but not allopatric, diploids and tetraploids. Coalescent simulations demonstrated significant bidirectional introgression which is stronger in the direction towards the tetraploid cytotype.Triploids, though rare, play a key role in overcoming polyploidy-related reproductive barriers. We present integrative evidence for bidirectional interploidy gene flow mediated by a triploid bridge in natural populations.
Regulating Human T Lymphocytes Through Magnetogenetic Tools
Authors: Helalat Seyed Hossein , Téllez Rodrigo C. , Kristinsdóttir Helga Thora , Petersen Astrid Dolinger , Islam Seder Fayyad , Pardo Carlos Rodriguez , Sun Yi 
Published: October 2024

AbstractThe field of synthetic biology has expanded the possibilities for controlling cellular functions, particularly in the development of mammalian cells for therapeutic applications. This study explored the application of magnetogenetic tools to regulate T cell activity, a crucial aspect of developing advanced immunotherapies. Magnetogenetic tools use magnetic fields to remotely control engineered ion channels and protein domains, providing non-invasive, deep-tissue stimulation that overcomes the limitations of traditional methods. We investigated the effects of three magnetogenetic tools - engineered TRPV1 (TRP1-Fer) and TRPV4 (TRP4-Fer) channels, and Electromagnetic Perceptive Gene (EPG) - in Jurkat cells. First, calcium concentration measurements confirmed the activity of these tools within the cells. Using qPCR and proteomics analysis, we then analyzed their impact on T cell activation, calcium signaling, mitochondrial function, membrane integrity, and gene expression under both stimulated (with antigens) and non-stimulated conditions. Our results revealed significant upregulation of activation and calcium-handling proteins in stimulated cells, indicating enhanced activation and cytoskeletal dynamics compared to controls. However, in non-stimulated cells, the magnetogenetic tools unexpectedly led to deactivation of T cells. This investigation showed that while magnetic induction alone deactivated the cells, antigen stimulation in conjunction with magnetic induction amplified cell activation.This study highlights the potential of magnetogenetics to precisely modulate T cell functions, presenting promising avenues for more effective and controlled immunotherapies. However, the findings also underscore the need for careful optimization to mitigate potential adverse effects on cellular integrity and function.
A novel epigenetic clock for rhesus macaques unveils an association between early life adversity and epigenetic age acceleration
Authors: Bronk Gabriel , Lardenoije Roy , Koolman Laura , Klengel Claudia , Dan Shu , Howell Brittany R. , Morin Elyse L. , Meyer Jerrold S. , Wilson Mark E. , Ethun Kelly F. , Alvarado Maria C. , Raper Jessica , Bravo-Rivera Hector , Kenwood Margaux M. , Roseboom Patrick H. , Quirk Gregory J. , Kalin Ned H. , Binder Elisabeth B. , Sanchez Mar M. , Klengel Torsten 
Published: October 2024

AbstractBecause DNA methylation changes reliably with age, machine learning models called epigenetic clocks can estimate an individual’s age based on their DNA methylation profile. This epigenetic measure of age can deviate from one’s true age, and the difference between the epigenetic age and true age, known as epigenetic age acceleration (EAA), has been found to directly correlate with morbidity and mortality in adults. Emerging evidence suggests that EAA is also associated with aberrant health outcomes in children, making epigenetic clocks useful tools for studying aging and development. We developed two highly accurate epigenetic clocks for the rhesus macaque, utilizing 1,008 blood samples from 690 macaques between 2 days and 23.4 years of age with diverse genetic backgrounds and exposure to environmental conditions. The first clock, which is trained on all samples, achieves a Pearson correlation between true age and predicted age of 0.983 and median absolute error of 0.210 years. To study phenotypes during development, the second clock is optimized for macaques younger than 6 years and achieves a Pearson correlation of 0.974 and a median absolute error of 0.148 years. Using the latter clock, we investigated whether epigenetic aging is affected by early life adversity in the form of infant maltreatment. Our data suggests that maltreatment and increased hair cortisol levels are associated with epigenetic age acceleration right after the period of maltreatment.
Leveraging Transfer Learning for Predicting Protein-Small Molecule Interactions
Authors: Wang Jian , Dokholyan Nikolay V. 
Published: October 2024

ABSTRACTA complex web of intermolecular interactions defines and regulates biological processes. Understanding this web has been particularly challenging because of the sheer number of actors in biological systems: ∼104proteins in a typical human cell offer a plausible 108interactions. This number grows rapidly if we consider metabolites, drugs, nutrients, and other biological molecules. The relative strength of interactions also critically affects these biological processes. However, the small and often incomplete datasets (103-104protein-ligand interactions) traditionally used for binding affinity predictions limit the ability to capture the full complexity of these interactions. To overcome this challenge, we developed Yuel 2, a novel neural network-based approach that leverages transfer learning to address the limitations of small datasets. Yuel 2 is pre-trained on a large-scale dataset to learn intricate structural features and then fine-tuned on specialized datasets like PDBbind to enhance the predictive accuracy and robustness. We show that Yuel 2 predicts multiple binding affinity metrics – Kd, Ki, and IC50 – between proteins and small molecules, offering a comprehensive representation of molecular interactions crucial for drug design and development.
Simulations reveal how touchdown kinematic variables affect top sprinting speed: implications for coaching
Authors: Haralabidis Nicos , Eaton Ashton J. , Delp Scott L. , Hicks Jennifer L. 
Published: October 2024

AbstractSprint performance is a priority for coaches and athletes. Several kinematic variables, including horizontal touchdown distance (HTD) and inter-knee touchdown distance (IKTD), are targeted by coaches to increase top sprinting speed. However, the results of past research are conflicting, potentially due to the use of experimental inter-athlete study designs where it is not possible to establish cause-effect relationships. In this study, we used a predictive simulation approach to assess cause-effect relationships between HTD and IKTD and sprinting speed. We scaled a three-dimensional musculoskeletal model to match the anthropometry of an international caliber male sprinter, and generated predictive simulations of a single symmetric step of top-speed sprinting using a direct collocation optimal control framework. We first used our simulation framework to establish the model’s top speed with minimal constraints on touchdown kinematics (the optimal simulation). Then, in additional simulations we enforced specific HTD or IKTD values (± 2, 4 and 6 cm compared to optimal). The model achieved a top speed of 11.85 m/s in the optimal simulation. Shortening HTD by 6 cm reduced speed by 7.3%, while lengthening HTD by 6 cm had a smaller impact on speed, with a 1.6% reduction. Speed in the simulation was insensitive to the IKTD changes we tested. The results of our simulations indicate there is an optimal HTD to maximize sprinting speed, providing support for coaches and athletes to adjust this technique variable. Conversely, our results do not provide evidence to support utilizing IKTD as a key technique variable for speed enhancement. We share the simulation framework so researchers can explore the effects of additional modifications on sprinting performance (https://github.com/nicos1993/Pred_Sim_Sprinting).
Decoding Spatial Tissue Architecture: A Scalable Bayesian Topic Model for Multiplexed Imaging Analysis
Authors: Peng Xiyu , Smithy James W. , Yosofvand Mohammad , Kostrzewa Caroline E. , Bleile MaryLena , Ehrich Fiona D. , Lee Jasme , Postow Michael A. , Callahan Margaret K. , Panageas Katherine S. , Shen Ronglai 
Published: October 2024

AbstractRecent progress in multiplexed tissue imaging is advancing the study of tumor microenvironments to enhance our understanding of treatment response and disease progression. Cellular neighborhood analysis is a popular computational approach for these complex image data. Despite its popularity, there are significant challenges, including high computational demands that limit feasibility for largescale applications and the lack of a principled strategy for integrative analysis across images. This absence hampers the precise and consistent identification of spatial features and tracking of their dynamics over disease progression. To overcome these challenges, we introduceSpatialTopic, a spatial topic model designed to decode high-level spatial architecture across multiplexed tissue images.SpatialTopicintegrates both cell type and spatial information within a topic modelling framework, originally developed for natural language processing and adapted for computer vision. Spatial information is incorporated into the flexible design of documents, representing densely overlapping regions in images. We employ an efficient collapsed Gibbs sampling algorithm for model inference. We benchmarked the performance against five state-of-the-art algorithms through various case studies using different single-cell spatial transcriptomic and proteomic imaging platforms across different tissue types. We show thatSpatialTopicis highly scalable on large-scale image datasets with millions of cells, along with high precision and interpretability. Our findings demonstrate thatSpatialTopicconsistently identifies biologically and clinically significant spatial “topics” such as tertiary lymphoid structures (TLSs) and tracks dynamic changes in spatial features over disease progression. Its computational efficiency and broad applicability across various molecular imaging platforms will enhance the analysis of large-scale tissue imaging datasets.
Dissecting Regulatory Non-Coding Heart Disease GWAS Loci with High-Resolution 3D Chromatin Interactions Reveals Causal Genes with Pathophysiological Relevance to Heart Failure
Authors: Gill Richard , Lu Daniel R. , Eres Ittai , Lu Jiamiao , Cui Jixin , Yu Zhongsheng , Yamawaki Tracy , Zhou Hong , Pei Baikang , Amrute Junedh M. , Ang Yen-Sin , Wang Songli , Lavine Kory J. , Ason Brandon , Li Chi-Ming , Hsu Yi-Hsiang 
Published: October 2024

SummaryHeart failure is caused in part by cardiac remodeling processes that include the death of cardiac myocytes and their replacement by cardiac fibroblasts. We hypothesized that these two cell types may harbor epigenetic contexts in which heart disease-associated non-coding SNPs perturb gene expression relevant to disease. Accordingly, we generated high-resolution Hi-C data layered with chromatin accessibility and transcriptomic information to annotate and link putative distal regulatory elements in heart disease-associated loci to gene promoters. Our analysis identified several target genes with established roles in cardiac fibrosis and/or heart disease (GJA1,TBC1D32, CXCL12,IL6R, andFURIN). Perturb-seq in cardiac fibroblasts to knock out putative regulatory elements confirmed regulatory relationships involvingGJA1,CXCL12, andFURIN, as gene editing led to changes in transcriptomic signatures associated with fibroblasts in heart failure. Our results demonstrate how integrative multi-omic approaches can delineate pathophysiologically relevant regulatory circuits that connect protein-coding genes to non-coding genetic variants associated with disease.
Influence of Cognitive Demand and auditory noise on Postural Dynamics
Authors: Carey Sam , Balasubramaniam Ramesh 
Published: October 2024

ABSTRACTThe control of human balance involves an interaction between the human motor, cognitive, and sensory systems. The dynamics of this interaction are yet to be fully understood, however, work has shown the performance of cognitive tasks to have a hampering effect on motor performance, while additive sensory noise to have a beneficial effect. The current study aims to examine whether postural control will be impacted by a concurrent working memory task, and similarly, if additive noise can counteract the expected negative influence of the added cognitive demand. Postural sway of healthy young adults was collected during the performance of a modified N-back task with varying difficulty, in the presence and absence of auditory noise. Our results show a reduction in postural stability scaled to the difficulty of the cognitive task, but this effect is less prominent in the presence of additive noise. Additionally, by separating postural sway into different frequency bands, typically used to assess the exploratory vs feedback-driven stabilizing dynamics of sway, we found a differential effect between the cognitive task and additive noise, thus demonstrating that both frequency regimes of postural sway are sensitive to high cognitive load and increased sensory information.