bluuebunny/crossref_metadata_2025_split huggingface dataset:
KidneyChain: Leveraging Blockchain & Artificial Intelligence for a Streamlined Organ Donation Solution
Authors: Panda Kapil , Mazumder Anirudh 
Published: June 2024

AbstractCurrently the kidney organ transplantation process is a manual and cumbersome process, resulting in organ wastage and reoccurring issues in patients due to a lack of organ health screening prior to transplantation. To solve these issues in the industry, we utilize blockchain technology and artificial intelligence to create a streamlined kidney transplantation process. Utilizing blockchain technology, we automated the organ matching process, and matched donors and patients based on the same metrics that UNOS clinicians use, resulting in efficient and faster matching. Furthermore, using artificial intelligence, we created a model with perfect accuracy that screens organs pre-transplantation to predict possible risk for common kidney diseases such as chronic kidney disease(CKD), acute kidney injury(AKI), and polycystic kidney disease(PKD), to prevent recurring issues in patients, post-transplantation. Furthermore, a tapped delay line convolutional neural network provides cybersecurity by identifying valid blockchain transactions from fake patterns with 100% accuracy, ensuring full data privacy. By synergizing blockchain, AI, and cybersecurity, this research creates an efficient, secure platform that could expand patient access to life-saving transplants, prevent transplant failures, and save thousands of lives currently lost due to inefficiencies and wait times.
Pou4F2/Brn3B Overexpression Promotes the Genesis of Retinal Ganglion Cell-Like Projection Neurons from Late Progenitors
Authors: Oliveira-Valença V.M. , Roberts J.M , Fernandes-Cerqueira V.M. , Colmerauer C.H. , Toledo B.C. , Santos-França P.L. , Linden R. , Martins R.A.P. , Rocha-Martins M , Bosco A. , Vetter M.L. , Silveira M.S. 
Published: June 2024

ABSTRACTRetinal ganglion cells (RGCs) are the projection neurons of the retina. In early retinal progenitor cells (RPCs),Atoh7orchestrates the developmental RGC program and regulates the expression of critical downstream targets, includingPou4ffactors. The absence ofPou4f2or more POU4F family genes results in defects in RGC differentiation, aberrant axonal elaboration and ultimately RGC death, confirming the requirement of POU4F factors for RGC development and survival, with a critical role in regulating RGC axon outgrowth and pathfinding. Here, we investigatedin vivowhether ectopicPou4f2expression in late retinal progenitor cells (late RPCs) is sufficient to induce the generation of cells with RGC properties, including projecting axons to the brain. Using a strong ubiquitous promoter to inducePou4f2overexpression in neonates, we observed a change in targeted cell distribution in the retinal tissue, including the presence of cells in the ganglion cell layer and inner plexiform layer with high density of GFP+processes along the retina. Similar results on the induction of neuron processes were obtained when we overexpressedPou4f2inAtoh7knockout mice, suggesting that POU4F2 is sufficient to induce them. Single-cell RNA sequencing (scRNA-seq) analysis shows that several RGC-genes (such asRbpms,Gap-43,Hs6st3, andFoxp2) are upregulated afterPou4f2overexpression. Additionally, gene ontology analysis indicates the induction of genes related to axonogenesis and neuronal differentiation. Imaging throughout the visual pathway revealed high density of axons projecting toward the optic nerve head and extending to brain regions, such as the superior colliculus and geniculate nucleus. Thus,Pou4f2induced neurons with specific RGC characteristics that share similarities with resident RGCs and notably project axons that reach brain targets. In conclusion, these results showed that POU4F2 alone was sufficient to promote critical properties of projection neurons from retinal progenitors outside their developmental window.
Concurrent emergence of view invariance, sensitivity to critical features, and identity face classification through visual experience: Insights from deep learning algorithms
Authors: Rosemblaum Mandy , Guy Nitzan , Grosbard Idan , Kliger Libi , Abudarham Naphtali , Yovel Galit 
Published: June 2024

AbstractVisual experience is known to play a critical role in face recognition. This experience is believed to enable the formation of a view-invariant representation, by learning which features are critical for face identification across views. Discovering these critical features and the type of experience that is needed to uncover them is challenging. We have recently revealed a subset of facial features that are critical for human face recognition. We further revealed that deep convolutional neural networks (DCNNs) that are trained on face classification, but not on object categorization, are sensitive to these facial features, highlighting the importance of experience with faces for the system to reveal these critical features. These findings enable us now to ask what type of experience with faces is required for the network to become sensitive to these human-like critical features and whether it is associated with the formation of a view-invariant representation and face classification performance. To that end, we systematically manipulated the number of within-identity and between-identity face images and examined its effect on the network performance on face classification, view-invariant representation, and sensitivity to human-like critical facial features. Results show that increasing the number of images per identity as well as the number of identities were both required for the simultaneous development of a view-invariant representation, sensitivity to human-like critical features, and successful identity classification. The concurrent emergence of sensitivity to critical features, view invariance and classification performance through experience implies that they depend on similar features. Overall, we show how systematic manipulation of the training diet of DCNNs can shed light on the role of experience on the generation of human-like representations.
Single-cell transcriptome unravels spermatogonial stem cells and dynamic heterogeneity of spermatogenesis in seasonal breeding teleost
Authors: Yang Yang , Zhou Yinan , Wessel Gary , Hu Weihua , Xu Dongdong 
Published: June 2024

AbstractSeasonal spermatogenesis in fish is driven by spermatogonial stem cells (SSCs), which undergo a complex cellular process to differentiate into mature sperm. In this study, we characterized spermatogenesis in the large yellow croaker (Larimichthys crocea), a marine fish of significant commercial value, based on a high-resolution single-cell RNA-seq atlas of testicular cells from three distinct developmental stages- juvenile, adult differentiating and regressed testes. We detailed continuous developmental trajectory of spermatogenic cells, from spermatogonia to spermatids, elucidating the molecular events involved in spermatogenesis. We uncovered dynamic heterogeneity in cellular compositions throughout the annual reproductive cycle, accompanied by strong molecular signatures within specific testicular cells. Notably, we identified a distinct population of SSCs and observed a critical metabolic transition from glycolysis to oxidative phosphorylation, enhancing our understanding of the biochemical and molecular characteristics of SSCs. Additionally, we elucidated the interactions between somatic cells and spermatogonia, illuminating the mechanisms that regulate SSCs development. Overall, this work enhances our understanding of spermatogenesis in seasonal breeding teleost and provides essential insights for the further conservation and culture of SSCs.Summary statementOur study reveals new insights into the development of spermatogonial stem cells (SSCs), potentially impacting further conservation and culture of SSCs in teleost.
A tool for CRISPR-Cas9 gRNA evaluation based on computational models of gene expression
Authors: Cohen Shai , Bergman Shaked , Lynn Nicolas , Tuller Tamir 
Published: June 2024

ABSTRACTCRISPR based technologies have revolutionized all biomedical fields as it enables efficient genomic editing. These technologies are often used to silence genes by inducing mutations that are expected to nullify their expression. To this end, dozens of computational tools have been developed to design gRNAs, CRISPR’s gene-targeting molecular guide, with high cutting efficiency and no off-target effect. However, these tools do not consider the induced mutation’s effect on the gene’s expression, which is the actual objective that should be optimized. This fact can often lead to failures in the design, as an efficient cutting of the DNA does not ensure the desired effect in protein production. Therefore, we developed EXPosition, a computational tool for gRNA design. It is the first tool designed to improve the true objective of using CRISPR: the effect it has on gene expression. To this end, we used predictive deep-learning models for the relevant gene expression steps: transcription, splicing, and translation initiation. We validated our tool by demonstrating that it can classify sites as “silencing” or “non-silencing” better than models that consider only the cutting efficiency. We believe that this tool will significantly improve both the efficiency and accuracy of genome editing endeavors. EXPosition is available at http://www.cs.tau.ac.il/~tamirtul/EXPosition.
Single-Cell Transcriptomic Analysis Highlights Specific Cell Types of Wheat Manipulated byFusarium graminearumLeading to Susceptibility
Authors: Wei Wan-Qian , Li Shuang , Zhang Dong , Tang Wei-Hua 
Published: June 2024

AbstractPhytopathogenic fungi can be recognized by the plant immune system and trigger host defenses, but adapted pathogens cause susceptibility. How different cell types cooperate and orchestrate biological processes in response to heterogeneous colonization of organs by adapted and non-adapted pathogens remains largely unknown. Here we employed single-cell RNA sequencing to dissect the responses of wheat coleoptiles to infection by the adapted fungal pathogenFusarium graminearum(Fgr) and the non-adapted fungal pathogenFusarium oxysporumf. sp.cubense(Foc) at 1-, 2-, and 3-days post-inoculation. We profiled the transcriptomes of over 90,000 cells and identified eight major cell types in coleoptiles: stomata, epidermis, chlorenchyma, parenchyma, outer sheath, inner sheath, phloem, and procambium. Differential expression analyses showed that the capacity of different cell types to respond to fungal infection varied. The upregulation of immune pathways was compartmentalized in nonhost resistance toFoc, but widespread in susceptible interaction withFgr. Pseudotime analyses revealed continuous cell state transitions in the disease progression of infected cell types. Our work indicates that the phloem and outer sheath are specific cell types that collaborate for the rapid onset of nonhost resistance.Fgrinduces a state of low transcriptional activity in the chlorenchyma. Cell trajectory analysis suggests that the competition between immune and susceptible processes in parenchyma results in specific cell states that are favored by the adapted pathogenFgr. Overall, this work explains how cell types collaborate and are manipulated during fungal infections, providing insight into the intercellular mechanisms of plant immunity.
Neural circuit-selective, multiplexed pharmacological targeting of prefrontal cortex-projecting locus coeruleus neurons drives antinociception
Authors: Kuo Chao-Cheng , McCall Jordan G. 
Published: June 2024

AbstractSelective manipulation of neural circuits using optogenetics and chemogenetics holds great translational potential but requires genetic access to neurons. Here, we demonstrate a general framework for identifying genetic tool-independent, pharmacological strategies for neural circuit-selective modulation. We developed an economically accessible calcium imaging-based approach for large-scale pharmacological scans of endogenous receptor-mediated neural activity. As a testbed for this approach, we used the mouse locus coeruleus due to the combination of its widespread, modular efferent neural circuitry and its wide variety of endogenously expressed GPCRs. Using machine learning-based action potential deconvolution and retrograde tracing, we identified an agonist cocktail that selectively inhibits medial prefrontal cortex-projecting locus coeruleus neurons.In vivo, this cocktail produces synergistic antinociception, consistent with selective pharmacological blunting of this neural circuit. This framework has broad utility for selective targeting of other neural circuits under different physiological and pathological states, facilitating non-genetic translational applications arising from cell type-selective discoveries.
DnaA modulates the gene expression and morphology of the Lyme disease spirochete
Authors: Krusenstjerna Andrew C. , Jusufovic Nerina , Saylor Timothy C. , Stevenson Brian 
Published: June 2024

ABSTRACTAll bacteria encode a multifunctional DNA-binding protein, DnaA, which initiates chromosomal replication. Despite having the most complex, segmented bacterial genome, little is known aboutBorrelia burgdorferiDnaA and its role in maintaining the spirochete’s physiology. In this work we utilized inducible CRISPR-interference and overexpression to modulate cellular levels of DnaA to better understand this essential protein. Dysregulation of DnaA, either up or down, increased or decreased cell lengths, respectively, while also significantly slowing replication rates. Using fluorescent microscopy, we found the DnaA CRISPRi mutants had increased numbers of chromosomes with irregular spacing patterns. DnaA-depleted spirochetes also exhibited a significant defect in helical morphology. RNA-seq of the conditional mutants showed significant changes in the levels of transcripts involved with flagellar synthesis, elongation, cell division, virulence, and other functions. These findings demonstrate that the DnaA plays a commanding role in maintaining borrelial growth dynamics and protein expression, which are essential for the survival of the Lyme disease spirochete.IMPORTANCELyme disease is the most prevalent tick-borne infection in the Northern Hemisphere.Borrelia burgdorferi, the causative spirochete bacteria, has been maintained in nature for millennia in a consistent enzootic cycle betweenIxodesticks and various small vertebrate hosts. During the tick’s blood meal,B. burgdorferisubstantially increases its replication rate, alters its repertoire of outer surface proteins, and disseminates into the new vertebrate host. Across eubacteria, DnaA is the master regulatory protein that initiates chromosomal replication and acts as a transcription factor to regulate specific pathways. Here, we describe the roles thatB. burgdorferiDnaA has on the physiology and gene expression of this medically important pathogen.
Predicting RNA Structure and Dynamics with Deep Learning and Solution Scattering
Authors: Patt Edan , Classen Scott , Hammel Michal , Schneidman-Duhovny Dina 
Published: June 2024

AbstractAdvanced deep learning and statistical methods can predict structural models for RNA molecules. However, RNAs are flexible, and it remains difficult to describe their macromolecular conformations in solutions where varying conditions can induce conformational changes. Small-angle X-ray scattering (SAXS) in solution is an efficient technique to validate structural predictions by comparing the experimental SAXS profile with those calculated from predicted structures. There are two main challenges in comparing SAXS profiles to RNA structures: the absence of cations essential for stability and charge neutralization in predicted structures and the inadequacy of a single structure to represent RNA’s conformational plasticity. We introduce Solution Conformation Predictor for RNA (SCOPER) to address these challenges. This pipeline integrates kinematics-based conformational sampling with the innovative deep-learning model, IonNet, designed for predicting Mg2+ion binding sites. Validated through benchmarking against fourteen experimental datasets, SCOPER significantly improved the quality of SAXS profile fits by including Mg2+ions and sampling of conformational plasticity. We observe that an increased content of monovalent and bivalent ions leads to decreased RNA plasticity. Therefore, carefully adjusting the plasticity and ion density is crucial to avoid overfitting experimental SAXS data. SCOPER is an efficient tool for accurately validating the solution state of RNAs given an initial, sufficiently accurate structure and provides the corrected atomistic model, including ions.The method is available from:https://github.com/dina-lab3d/IonNetOur pipeline is available for use as a web server:https://bilbomd.bl1231.als.lbl.gov/Statement of SignificanceUnderstanding the behavior of RNA in solution is critical for deciphering its biological functions, yet predicting its macromolecular conformation remains challenging. While advanced computational methods can predict RNA structures, their accuracy in solution is often limited by the absence of stabilizing ions and the failure to account for RNA’s conformational flexibility. This study presents SCOPER, an innovative tool that addresses these challenges by integrating deep-learning-based ion binding site prediction with conformational sampling, offering a more reliable approach to validate and refine RNA structures against experimental SAXS data. We provide our source code and a web server that runs the pipeline.
Characterization of β-Hydroxybutyrate as a Cell Autonomous Fuel for Active Excitatory and Inhibitory Neurons
Authors: Bredvik Kirsten , Liu Charmaine , Ryan Timothy A. 
Published: June 2024

AbstractThe ketogenic diet is an effective treatment for drug-resistant epilepsy, but the therapeutic mechanisms are poorly understood. Although ketones are able to fuel the brain, it is not known whether ketones are directly metabolized by neurons on a time scale sufficiently rapid to fuel the bioenergetic demands of sustained synaptic transmission. Here, we show that nerve terminals can use the ketone β-hydroxybutyrate in a cell- autonomous fashion to support neurotransmission in both excitatory and inhibitory nerve terminals and that this flexibility relies on Ca2+dependent upregulation of mitochondrial metabolism. Using a genetically encoded ATP sensor, we show that inhibitory axons fueled by ketones sustain much higher ATP levels under steady state conditions than excitatory axons, but that the kinetics of ATP production following activity are slower when using ketones as fuel compared to lactate/pyruvate for both excitatory and inhibitory neurons.Significance StatementThe ketogenic diet is a standard treatment for drug resistant epilepsy, but the mechanism of treatment efficacy is largely unknown. Changes to excitatory and inhibitory balance is one hypothesized mechanism. Here, we determine that ATP levels are differentially higher in inhibitory neurons compared to excitatory neurons, suggesting that greater mitochondrial ATP production in inhibitory neurons could be one mechanism mediating therapeutic benefit. Further, our studies of ketone metabolism by synaptic mitochondria should inform management of side effects and risks associated with ketogenic diet treatments. These results provide novel insights that clarify the role of ketones at the cellular level in ketogenic diet treatment for intractable epilepsy and inform the use of ketogenic diets for neurologic and psychiatric conditions more broadly.
A Mechanistic Understanding of the Activity-Stability Trade-off in PETase
Authors: Chen Shuang , Akram Ekram , Qiao Weili , Zhang Yifei , Haider Shozeb , Cao Yufei 
Published: June 2024

AbstractEnzymatic degradation of poly(ethylene terephthalate) (PET) has garnered considerable interest in plastic recycling efforts. Despite numerous descriptions of both natural and engineered enzymes, the fundamental mechanism underlying PETase-catalyzed PET depolymerization at the solid-liquid interface remains elusive. This lack of understanding hampers the rational design of highly efficient depolymerases. Here, we employ multiscale simulations and experiments to elucidate the complete catalytic pathway ofIsPETase, from enzyme adsorption at the interface to PET fragment capture, conformational refinement, and ester bond cleavage. Both endo- and exo-cleavage modes of the enzyme are identified, indicating its capacity for endo- and exo-lytic activities. We discover that the trade-off between the activity and stability ofIsPETase’s PET-capturing pliers brings compromises to its PET depolymerization performance. Reshaping the loop dynamics of the enzyme can break this trade-off and enhance its stability and activity simultaneously, as demonstrated by the evolved variant HotPETase. Overall, our study offers comprehensive details into how PETase functions at the interface and provides valuable insights for engineering efficient plastic-degrading enzymes.
Multi-modal Spatial-modality Attentive Fusion for Studying Neuropsychiatric Disorders
Authors: Rahaman Md Abdur , Garg Yash , Iraji Armin , Fu Zening , Kochunov Peter , Hong L. Elliot , Van Erp Theo G. M. , Preda Adrian , Chen Jiayu , Calhoun Vince 
Published: June 2024

AbstractMulti-modal learning has emerged as a powerful technique that leverages diverse data sources to enhance learning and decision-making processes. Adapting this approach to analyzing data collected from different biological domains is intuitive, especially for studying neuropsychiatric disorders. A complex neuropsychiatric disorder like schizophrenia (SZ) can affect multiple aspects of the brain and biologies. These biological sources each present distinct yet correlated expressions of subjects’ underlying physiological processes. Joint learning from these data sources can improve our understanding of the disorder. However, combining these biological sources is challenging for several reasons: (i) observations are domains-specific, leading to data being represented in dissimilar subspaces, and (ii) fused data is often noisy and high-dimensional, making it challenging to identify relevant information. To address these challenges, we propose a multi-modal artificial intelligence (AI) model with a novel fusion module inspired by a bottleneck attention module (BAM). We use deep neural networks (DNN) to learn latent space representations of the input streams. Next, we introduce a two-dimensional (spatio-modality) attention module to regulate the intermediate fusion for SZ classification. We implement spatial attention via a dilated convolutional neural network that creates large receptive fields for extracting significant contextual patterns. The resulting joint learning framework maximizes complementarity allowing us to explore the correspondence among the modalities. We test our model on a multi-modal imaging-genetic dataset and achieve an SZ prediction accuracy of 94.10% (P < 0.0001), outperforming state-of-the-art unimodal and multi-modal models for the task. Moreover, the model provides inherent interpretability that helps identify concepts significant for the neural network’s decision and explains the underlying physiopathology of the disorder. Results also show that functional connectivity among subcortical, sensorimotor, and cognitive control domains plays an important role in characterizing SZ. Analysis of the spatio-modality attention scores suggests that structural components like the supplementary motor area, caudate, and insula play a significant role in SZ. Biclustering the attention scores discover a multi-modal cluster that includes genes CSMD1, ATK3, MOB4, and HSPE1, all of which have been identified as relevant to schizophrenia. In summary, feature attribution appears to be especially useful for probing the transient and confined but decisive patterns of complex disorders, and it shows promise for extensive applicability in future studies.