bluuebunny/crossref_metadata_2025_split huggingface dataset:
Inferring the Joint Distribution of Structural and Functional Connectivity in the Human Brain using UNIT-DDPM
Authors: Canamedi Varun 
Published: February 2024

AbstractThe structural wiring of the brain is expected to produce a repertoire of functional networks, across time, context, individuals and vice versa. Therefore, a method to infer the joint distribution of structural and functional connectomes would be of immense value. However, existing approaches only provide deterministic snapshots of the structure-function relationship. Here we use an unpaired image translation method, UNIT-DDPM, that infers a joint distribution of structural and functional connectomes. Our approach allows estimates of variability of function for a given structure and vice versa. Furthermore, we found a significant improvement in prediction accuracy among individual brain networks, implicating a tighter coupling of structure and function than previously understood. Also, our approach has the ad-vantage of not relying on paired samples for training. This novel approach provides a means for identifying regions of consistent structure-function coupling.
Explicit and implicit locomotor learning in individuals with chronic hemiparetic stroke
Authors: Wood Jonathan M. , Thompson Elizabeth , Wright Henry , Festa Liam , Morton Susanne M. , Reisman Darcy S. , Kim Hyosub E. 
Published: February 2024

AbstractMotor learning involves both explicit and implicit processes that are fundamental for acquiring and adapting complex motor skills. However, stroke may damage the neural substrates underlying explicit and/or implicit learning, leading to deficits in overall motor performance. While both learning processes are typically used in concert in daily life and rehabilitation, no gait studies have determined how these processes function together after stroke when tested during a task that elicits dissociable contributions from both. Here, we compared explicit and implicit locomotor learning in individuals with chronic stroke to age- and sex-matched neurologically intact controls. We assessed implicit learning using split-belt adaptation (where two treadmill belts move at different speeds). We assessed explicit learning (i.e., strategy-use) using visual feedback during split-belt walking to help individuals explicitly correct for step length errors created by the split-belts. The removal of visual feedback after the first 40 strides of split-belt walking, combined with task instructions, minimized contributions from explicit learning for the remainder of the task. We utilized a multi-rate state-space model to characterize individual explicit and implicit process contributions to overall behavioral change. The computational and behavioral analyses revealed that, compared to controls, individuals with chronic stroke demonstrated deficits in both explicit and implicit contributions to locomotor learning, a result that runs counter to prior work testing each process individually during gait. Since post-stroke locomotor rehabilitation involves interventions that rely on both explicit and implicit motor learning, future work should determine how locomotor rehabilitation interventions can be structured to optimize overall motor learning.New and noteworthyMotor learning involves both implicit and explicit processes, the underlying neural substrates of which could be damaged by after stroke. While both learning processes are typically used in concert in daily life and rehabilitation, no gait studies have determined how these processes function together after stroke. Using a locomotor task that elicits dissociable contributions from both processes and computational modeling, we found evidence that chronic stroke causes deficits in both explicit and implicit locomotor learning.
Frequency of Dengue Virus-Specific T Cells is related to Infection Outcome in Endemic Settings
Authors: Gálvez Rosa Isela , Martínez-Pérez Amparo , Alexandar Escarrega E. , Singh Tulika , Zambrana José Víctor , Balmaseda Ángel , Harris Eva , Weiskopf Daniela 
Published: February 2024

AbstractDengue is widespread in tropical and subtropical regions globally and leads to a considerable burden of disease. Annually, dengue virus (DENV) causes up to 400 million infections, of which ~25% present with clinical symptoms ranging from mild to fatal. Despite its significance as a growing public health concern, the development of effective DENV vaccines has been highly challenging. One of the reasons is the lack of comprehensive understanding of the influence exerted by prior DENV infections and immune responses with cross-reactive properties. To investigate this, we collected samples from a pediatric cohort study in dengue-endemic Managua, Nicaragua. We characterized T cell responses in a group of 71 healthy children who had previously experienced one or more natural DENV infections and who, within one year after sample collection, had a subsequent DENV infection that was either symptomatic (n=25) or inapparent (n=46, absence of clinical disease). Thus, our study was designed to investigate the impact of pre-existing DENV specific T cell responses on the clinical outcomes of subsequent DENV infection. We assessed the DENV specific T cell responses using an activation-induced marker assay (AIM). Children who had experienced only one prior DENV infection displayed heterogeneous DENV specific CD4+and CD8+T cell frequencies. In contrast, children who had experienced two or more DENV infections showed significantly higher frequencies of DENV specific CD4+and CD8+T cells that were associated with inapparent as opposed to symptomatic outcomes in the subsequent DENV infection. Taken together, these findings demonstrate the protective role of DENV specific T cells against symptomatic DENV infection and constitute an advancement toward identifying protective immune correlates against dengue fever and clinical disease.Graphical Abstract
Feasibility of decoding covert speech in ECoG with a Transformer trained on overt speech
Authors: Komeiji Shuji , Mitsuhashi Takumi , Iimura Yasushi , Suzuki Hiroharu , Sugano Hidenori , Shinoda Koichi , Tanaka Toshihisa 
Published: February 2024

ABSTRACTSeveral attempts for speech brain–computer interfacing (BCI) have been made to decode phonemes, sub-words, words, or sentences using invasive measurements, such as the electrocorticogram (ECoG), during auditory speech perception, overt speech, or imagined (covert) speech. Decoding sentences from covert speech is a challenging task. Sixteen epilepsy patients with intracranially implanted electrodes participated in this study, and ECoGs were recorded during overt speech and covert speech of eight Japanese sentences, each consisting of three tokens. In particular, Transformer neural network model was applied to decode text sentences from covert speech, which was trained using ECoGs obtained during overt speech. We first examined the proposed Transformer model using the same task for training and testing, and then evaluated the model’s performance when trained with overt task for decoding covert speech. The Transformer model trained on covert speech achieved an average token error rate (TER) of 46.6% for decoding covert speech, whereas the model trained on overt speech achieved a TER of 46.3% (p >0.05;d= 0.07). Therefore, the challenge of collecting training data for covert speech can be addressed using overt speech. The performance of covert speech can improve by employing several overt speeches.
DecipheringStaphylococcus aureus-host dynamics using dual activity-based protein profiling of ATP-interacting proteins
Authors: Ahator Stephen Dela , Hegstad Kristin , Lentz Christian S. , Johannessen Mona 
Published: February 2024

AbstractThe utilization of ATP within cells plays a fundamental role in cellular processes that are essential for the regulation of host-pathogen dynamics and the subsequent immune response. This study focuses on ATP-binding proteins to dissect the complex interplay betweenStaphylococcus aureusand human cells, particularly macrophages (THP-1) and keratinocytes (HaCaT), during an intracellular infection. A snapshot of the various protein activity and function is provided using a desthiobiotin-ATP probe, which targets ATP-interacting proteins. InS. aureus, we observe enrichment in pathways required for nutrient acquisition, biosynthesis and metabolism of amino acids and energy metabolism when located inside human cells. Additionally, the direct profiling of the protein activity revealed specific adaptations ofS. aureusto the keratinocytes and macrophages. Mapping the differentially activated proteins to biochemical pathways in the human cells with intracellular bacteria revealed cell-type specific adaptations to bacterial challenges where THP-1 cells prioritized immune defenses, autophagic cell death, and inflammation. In contrast, HaCaT cells emphasized barrier integrity and immune activation. We also observe bacterial modulation of host processes and metabolic shifts. These findings offer valuable insights into the dynamics ofS. aureus-host cell interactions, shedding light on modulating host immune responses toS. aureus, which could involve developing immunomodulatory therapies.ImportanceThis study uses a chemoproteomics approach to target active ATP-interacting proteins and examines the dynamic proteomic interactions betweenS. aureusand human cell lines THP-1 and HaCaT. It uncovers the distinct responses of macrophages and keratinocytes during bacterial infection.S. aureusdemonstrated a tailored response to the intracellular environment of each cell type and adaptation during exposure to professional and non-professional phagocytes. It also highlights strategies employed byS. aureusto persist within host cells. This study offers significant insights into the human cell response toS. aureusinfection, illuminating the complex proteomic shifts that underlie the defense mechanisms of macrophages and keratinocytes. Notably, the study underscores the nuanced interplay between the host’s metabolic reprogramming and immune strategy, suggesting potential therapeutic targets for enhancing host defense and inhibiting bacterial survival. The findings enhance our understanding of host-pathogen interactions and can inform the development of targeted therapies againstS. aureusinfections.Graphical Abstract
Deep Normative Tractometry for Identifying Joint White Matter Macro- and Micro-structural Abnormalities in Alzheimer’s Disease
Authors: Feng Yixue , Chandio Bramsh Q. , Villalon-Reina Julio E. , Benavidez Sebastian , Chattopadhyay Tamoghna , Chehrzadeh Sasha , Laltoo Emily , Thomopoulos Sophia I. , Joshi Himanshu , Venkatasubramanian Ganesan , John John P. , Jahanshad Neda , Thompson Paul M. 
Published: February 2024

AbstractThis study introduces the Deep Normative Tractometry (DNT) framework, that encodes the joint distribution of both macrostructural and microstructural profiles of the brain white matter tracts through a variational autoencoder (VAE). By training on data from healthy controls, DNT learns the normative distribution of tract data, and can delineate along-tract micro- and macro-structural abnormalities. Leveraging a large sample size via generative pre-training, we assess DNT’s generalizability using transfer learning on data from an independent cohort acquired in India. Our findings demonstrate DNT’s capacity to detect widespread diffusivity abnormalities along tracts in mild cognitive impairment and Alzheimer’s disease, aligning closely with results from the Bundle Analytics (BUAN) tractometry pipeline. By incorporating tract geometry information, DNT may be able to distinguish disease-related abnormalities in anisotropy from tract macrostructure, and shows promise in enhancing fine-scale mapping and detection of white matter alterations in neurodegenerative conditions.
Sleep need driven oscillation of glutamate synaptic phenotype
Authors: Vogt K.E. , Kulkarni A. , Pandey R. , Dehnad M. , Konopka G. , Greene R.W. 
Published: February 2024

AbstractSleep loss increases AMPA-synaptic strength and number in the neocortex. However, this is only part of the synaptic sleep loss response. We report increased AMPA/NMDA EPSC ratio in frontal-cortical pyramidal neurons of layers 2-3. Silent synapses are absent, decreasing the plastic potential to convert silent NMDA to active AMPA synapses. These sleep loss changes are recovered by sleep. Sleep genes are enriched for synaptic shaping cellular components controlling glutamate synapse phenotype, overlap with autism risk genes and are primarily observed in excitatory pyramidal neurons projecting intra-telencephalically. These genes are enriched with genes controlled by the transcription factor, MEF2c and its repressor, HDAC4. Sleep genes can thus provide a framework within which motor learning and training occurs mediated by sleep-dependent oscillation of glutamate-synaptic phenotypes.
Identifying Interpretable Latent Factors with Sparse Component Analysis
Authors: Zimnik Andrew J. , Ames K. Cora , An Xinyue , Driscoll Laura , Lara Antonio H. , Russo Abigail A. , Susoy Vladislav , Cunningham John P. , Paninski Liam , Churchland Mark M. , Glaser Joshua I. 
Published: February 2024

AbstractIn many neural populations, the computationally relevant signals are posited to be a set of ‘latent factors’ – signals shared across many individual neurons. Understanding the relationship between neural activity and behavior requires the identification of factors that reflect distinct computational roles. Methods for identifying such factors typically require supervision, which can be suboptimal if one is unsure how (or whether) factors can be grouped into distinct, meaningful sets. Here, we introduce Sparse Component Analysis (SCA), an unsupervised method that identifies interpretable latent factors. SCA seeks factors that are sparse in time and occupy orthogonal dimensions. With these simple constraints, SCA facilitates surprisingly clear parcellations of neural activity across a range of behaviors. We applied SCA to motor cortex activity from reaching and cycling monkeys, single-trial imaging data fromC. elegans, and activity from a multitask artificial network. SCA consistently identified sets of factors that were useful in describing network computations.
Combining Unity with machine vision to create low latency, flexible, and simple virtual realities
Authors: Ogawa Yuri , Aoukar Raymond , Leibbrandt Richard , Manger Jake S , Bagheri Zahra M , Turnbull Luke , Johnston Chris , Kaushik Pavan K , Hemmi Jan M , Nordström Karin 
Published: February 2024

AbstractIn recent years, virtual reality arenas have become increasingly popular for quantifying visual behaviors. By using the actions of a constrained animal to control the visual scenery, the animal is provided the perception of moving through a simulated environment. As the animal is constrained in space, this allows detailed behavioral quantification. Additionally, as the world is generally computer-generated this allows for mechanistic quantification of visual triggers of behavior.We created a novel virtual arena combining machine vision with the gaming engine Unity. For tethered flight, we enhanced an existing multi-modal virtual reality arena, MultiMoVR (Kaushik et al., 2020) but tracked hoverfly wing movements using DeepLabCut-live (DLC-live, Kane et al., 2020). For trackball experiments, we recorded the motion of a ball that a tethered crab was walking on using FicTrac (Moore et al., 2014). In both cases, real-time tracking was interfaced with Unity to control the movement of the tethered animals’ avatars in the virtual world. We developed a user-friendly Unity Editor interface, CAVE, to simplify experimental design and data storage without the need for coding.We show that both the DLC-live-Unity and the FicTrac-Unity configurations close the feedback loop effectively with small delays, less than 50 ms. Our FicTrac-Unity integration highlighted the importance of closed-loop feedback by reducing behavioral artifacts exhibited by the crabs in open-loop scenarios. We show thatEristalis tenaxhoverflies, using the DLC-live-Unity integration, navigate towards flowers. The effectiveness of our CAVE interface is shown by implementing experimental sequencing control based on avatar proximity to virtual structures.Our results show that combining Unity with machine vision tools such as DLC-live and FicTrac provides an easy and flexible virtual reality (VR) environment that can be readily adjusted to new experiments and species. This can be implemented programmatically in Unity, or by using our new tool CAVE, which allows users to design and implement new experiments without programming in code. We provide resources for replicating experiments and our interface CAVE via GitHub, together with user manuals and instruction videos, for sharing with the wider scientific community.
Percutaneous Nephrostomy Guidance by a Convolutional Neural Network Based Endoscopic Optical Coherence Tomography System
Authors: Wang Chen , Calle Paul , Yan Feng , Zhang Qinghao , Fung Kar-ming , Pan Chongle , Tang Qinggong 
Published: February 2024

AbstractPercutaneous nephrostomy (PCN) is a commonly used procedure for kidney surgeries. However, difficulties persist in precisely locating the PCN needle tip during its insertion into the kidney. Challenges for PCN needle guidance exist in two aspects: 1) Accurate tissue recognition, and 2) Renal blood vessel detection. In this study, we demonstrated an endoscopic optical coherence tomography (OCT) system for PCN needle guidance. Human kidney samples are utilized in the experiments. Different renal tissues including: 1) cortex, 2) medulla, 3) calyx, 4) fat, and 5) pelvis can be clearly distinguished based on their OCT imaging features. We conduct kidney perfusion experiments to mimic the renal blood flow. Our system can efficiently detect the blood flow in front of PCN needle using Doppler OCT function. To improve surgical guidance efficiency and alleviate the workload of radiologists, we employ convolutional neural network (CNN) methods to automate the procedure. Three CNN models including ResNet50, InceptionV3, and Xception were applied for tissue classification. All of them demonstrate promising prediction results, with InceptionV3 achieving the highest recognition accuracy of 99.6%. For automatic blood vessel detection, nnU-net was applied, and it exhibited intersection over unions (IoU) values of 0.8917 for blood vessel and 0.9916 for background.
Functional networks of reward and punishment processing and their molecular profiles predicting the severity of young adult drinking
Authors: Li Yashuang , Yang Lin , Hao Dongmei , Chen Yu , Li Bao , Liu Youjun , Ye-Lin Yiyao , Li Chiang-Shan R. , Li Guangfei 
Published: February 2024

ABSTRACTBackgroundAlcohol misuse is associated with altered punishment and reward processing. Here, we investigated neural network responses to reward and punishment and the molecular profiles of the connectivity features predicting alcohol use severity in young adults.MethodsWe curated the Human Connectome Project data and employed connectome-based predictive modeling (CPM) to examine how functional connectivity (FC) features during wins and losses associated with alcohol use severity in 981 young adults. Alcohol use severity was quantified by the first principal component of principal component analysis of all drinking measures of the Semi-Structured Assessment for the Genetics of Alcoholism. We combined the CPM findings and JuSpace toolbox to characterize the molecular profiles of the network connectivity features of alcohol use severity.ResultsThe connectomics predicting alcohol use severity appeared specific, comprising less than 0.12% of all connectivity features. These connectivities featured the medial frontal, motor/sensory, and cerebellum/brainstem networks during punishment processing and medial frontal, fronto-parietal, and motor/sensory networks during reward processing. Spatial correlation analyses showed that these networks were associated predominantly with serotonergic and GABAa signaling.ConclusionsA distinct pattern of network connectivity predicted alcohol use severity in young adult drinkers. These network features were associated with the serotonergic and GABAa signaling. These “neural fingerprints” help in elucidating the impact of alcohol misuse on the brain and providing evidence of new targets for future intervention.
αKG-mediated carnitine synthesis promotes homologous recombination via histone acetylation
Authors: Uboveja Apoorva , Huang Zhentai , Buj Raquel , Amalric Amandine , Wang Hui , Tangudu Naveen Kumar , Cole Aidan R. , Megill Emily , Kantner Daniel , Chatoff Adam , Ahmad Hafsah , Marcinkiewicz Mariola M. , Disharoon Julie A. , Graff Sarah , Dahl Erika S. , Hempel Nadine , Stallaert Wayne , Sidoli Simone , Bitler Benjamin G. , Long David T. , Snyder Nathaniel W. , Aird Katherine M. 
Published: February 2024

AbstractHomologous recombination (HR) deficiency enhances sensitivity to DNA damaging agents commonly used to treat cancer. In HR-proficient cancers, metabolic mechanisms driving response or resistance to DNA damaging agents remain unclear. Here we identified that depletion of alpha-ketoglutarate (αKG) sensitizes HR-proficient cells to DNA damaging agents by metabolic regulation of histone acetylation. αKG is required for the activity of αKG-dependent dioxygenases (αKGDDs), and prior work has shown that changes in αKGDD affect demethylases. Using a targeted CRISPR knockout library consisting of 64 αKGDDs, we discovered that Trimethyllysine Hydroxylase Epsilon (TMLHE), the first and rate-limiting enzyme inde novocarnitine synthesis, is necessary for proliferation of HR-proficient cells in the presence of DNA damaging agents. Unexpectedly, αKG-mediated TMLHE-dependent carnitine synthesis was required for histone acetylation, while histone methylation was affected but dispensable. The increase in histone acetylation via αKG-dependent carnitine synthesis promoted HR-mediated DNA repair through site- and substrate-specific histone acetylation. These data demonstrate for the first time that HR-proficiency is mediated through αKG directly influencing histone acetylation via carnitine synthesis and provide a metabolic avenue to induce HR-deficiency and sensitivity to DNA damaging agents.
Microbiome-derived metabolite effects on intestinal barrier integrity and immune cell response to infection
Authors: Adams Lauren , Li Xiang , Burchmore Richard , Goodwin Richard , Wall Daniel M. 
Published: February 2024

AbstractThe gut microbiota exerts a significant influence on human health and disease. While compositional changes in the gut microbiota in specific diseases can easily be determined, we lack a detailed mechanistic understanding of how these changes exert effects at the cellular level. However, the putative local and systemic effects on human physiology that are attributed to the gut microbiota are clearly being mediated through molecular communication. Here we determined the effects of a number of gut microbiome-derived metabolites on the first line of defence in the gut. Usingin vitromodels of intestinal barrier integrity, and studying the interaction of macrophages with pathogenic and non-pathogenic bacteria, we could ascertain the influence of these metabolites at the cellular level at physiologically relevant concentrations. Many metabolites exerted competing influences on intestinal epithelial or immune cells, specific metabolite effects were noted on barrier function, polarised cytokine release and the lifespan of metabolite treated cells. Our findings reiterate the complexity of understanding microbiome effects on host physiology with many metabolites having contrasting effects on host cells. However, our results underline that microbiome metabolites are crucial mediators of barrier function and the innate response to infection. Understanding the effects of these metabolites at the cellular level will allow us to move towards a better mechanistic understanding of microbiome influence over host physiology, a crucial step in advancing microbiome research.