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Machine-learning-based data-driven discovery of nonlinear phase-field dynamics
No full textOne of the main questions regarding complex systems at large scales concerns the effective interactions and driving forces that emerge from the detailed microscopic properties. Coarse-grained models aim to describe complex systems in terms of coarse-scale equations with a reduced number of degrees of freedom. Recent developments in machine-learning algorithms have significantly empowered the discovery process of governing equations directly from data. However, it remains difficult to discover partial differential equations (PDEs) with high-order derivatives. In this paper, we present data-driven architectures based on a multilayer perceptron, a convolutional neural network (CNN), and a combination of a CNN and long short-term memory structures for discovering the nonlinear equations of motion for phase-field models with nonconserved and conserved order parameters. The well-known Allen-Cahn, Cahn-Hilliard, and phase-field crystal models were used as test cases. Two conceptually different types of implementations were used: (a) guided by physical intuition (such as the local dependence of the derivatives) and (b) in the absence of any physical assumptions (black-box model). We show that not only can we effectively learn the time derivatives of the field in both scenarios, but we can also use the data-driven PDEs to propagate the field in time and achieve results in good agreement with the original PDEs
Platr4 is an early embryonic lncRNA that exerts its function downstream on cardiogenic mesodermal lineage commitment
No full textThe mammalian genome encodes thousands of long non-coding RNAs (lncRNAs), many of which are developmentally regulated and differentially expressed across tissues, suggesting their potential roles in cellular differentiation. Despite this expression pattern, little is known about how lncRNAs influence lineage commitment at the molecular level. Here, we demonstrate that perturbation of an embryonic stem cell/early embryonic lncRNA, pluripotency-associated transcript 4 (Platr4), directly influences the specification of cardiac-mesoderm-lineage differentiation. We show that Platr4 acts as a molecular scaffold or chaperone interacting with the Hippo-signaling pathway molecules Yap and Tead4 to regulate the expression of a downstream target gene, Ctgf, which is crucial to the cardiac-lineage program. Importantly, Platr4 knockout mice exhibit myocardial atrophy and valve mucinous degeneration, which are both associated with reduced cardiac output and sudden heart failure. Together, our findings provide evidence that Platr4 is required in cardiac-lineage specification and adult heart function in mice
Cell environment shapes TDP-43 function with implications in neuronal and muscle disease
Full text linkTDP-43 (TAR DNA-binding protein 43) aggregation and redistribution are recognised as a hallmark of amyotrophic lateral sclerosis and frontotemporal dementia. As TDP-43 inclusions have recently been described in the muscle of inclusion body myositis patients, this highlights the need to understand the role of TDP-43 beyond the central nervous system. Using RNA-seq, we directly compare TDP-43-mediated RNA processing in muscle (C2C12) and neuronal (NSC34) mouse cells. TDP-43 displays a cell-type-characteristic behaviour targeting unique transcripts in each cell-type, which is due to characteristic expression of RNA-binding proteins, that influence TDP-43's performance and define cell-type specific splicing. Among splicing events commonly dysregulated in both cell lines, we identify some that are TDP-43-dependent also in human cells. Inclusion levels of these alternative exons are altered in tissues of patients suffering from FTLD and IBM. We therefore propose that TDP-43 dysfunction contributes to disease development either in a common or a tissue-specific manner
Newly Discovered Alleles of the Tomato Antiflorigen Gene SELF PRUNING Provide a Range of Plant Compactness and Yield
Full text linkIn tomato cultivation, a rare natural mutation in the flowering repressor antiflorigen gene SELF-PRUNING (sp-classic) induces precocious shoot termination and is the foundation in determinate tomato breeding for open field production. Heterozygous single flower truss (sft) mutants in the florigen SFT gene in the background of sp-classic provide a heterosis-like effect by delaying shoot termination, suggesting the subtle suppression of determinacy by genetic modification of the florigen-antiflorigen balance could improve yield. Here, we isolated three new sp alleles from the tomato germplasm that show modified determinate growth compared to sp-classic, including one allele that mimics the effect of sft heterozygosity. Two deletion alleles eliminated functional transcripts and showed similar shoot termination, determinate growth, and yields as sp-classic. In contrast, amino acid substitution allele sp-5732 showed semi-determinate growth with more leaves and sympodial shoots on all shoots. This translated to greater yield compared to the other stronger alleles by up to 42%. Transcriptome profiling of axillary (sympodial) shoot meristems (SYM) from sp-classic and wild type plants revealed six mis-regulated genes related to the floral transition, which were used as biomarkers to show that the maturation of SYMs in the weaker sp-5732 genotype is delayed compared to sp-classic, consistent with delayed shoot termination and semi-determinate growth. Assessing sp allele frequencies from over 500 accessions indicated that one of the strong sp alleles (sp-2798) arose in early breeding cultivars but was not selected. The newly discovered sp alleles are potentially valuable resources to quantitatively manipulate shoot growth and yield in determinate breeding programs, with sp-5732 providing an opportunity to develop semi-determinate field varieties with higher yields
Recent Advances at the Interface of Neuroscience and Artificial Neural Networks
No full textBiological neural networks adapt and learn in diverse behavioral contexts. Artificial neural networks (ANNs) have exploited biological properties to solve complex problems. However, despite their effectiveness for specific tasks, ANNs are yet to realize the flexibility and adaptability of biological cognition. This review highlights recent advances in computational and experimental research to advance our understanding of biological and artificial intelligence. In particular, we discuss critical mechanisms from the cellular, systems, and cognitive neuroscience fields that have contributed to refining the architecture and training algorithms of ANNs. Additionally, we discuss how recent work used ANNs to understand complex neuronal correlates of cognition and to process high throughput behavioral data
TCR signal strength defines distinct mechanisms of T cell dysfunction and cancer evasion
No full textT cell receptor (TCR) signal strength is a key determinant of T cell responses. We developed a cancer mouse model in which tumor-specific CD8 T cells (TST cells) encounter tumor antigens with varying TCR signal strength. High-signal-strength interactions caused TST cells to up-regulate inhibitory receptors (IRs), lose effector function, and establish a dysfunction-associated molecular program. TST cells undergoing low-signal-strength interactions also up-regulated IRs, including PD1, but retained a cell-intrinsic functional state. Surprisingly, neither high- nor low-signal-strength interactions led to tumor control in vivo, revealing two distinct mechanisms by which PD1hi TST cells permit tumor escape; high signal strength drives dysfunction, while low signal strength results in functional inertness, where the signal strength is too low to mediate effective cancer cell killing by functional TST cells. CRISPR-Cas9-mediated fine-tuning of signal strength to an intermediate range improved anti-tumor activity in vivo. Our study defines the role of TCR signal strength in TST cell function, with important implications for T cell-based cancer immunotherapies
SciApps: An Automated Platform for Processing and Distribution of Plant Genomics Data
No full textSciApps is an open-source, web-based platform for processing, storing, visualizing, and distributing genomic data and analysis results. Built upon the Tapis (formerly Agave) platform, SciApps brings users TB-scale of data storage via CyVerse Data Store and over one million CPUs via the Extreme Science and Engineering Discovery Environment (XSEDE) resources at Texas Advanced Computing Center (TACC). SciApps provides users ways to chain individual jobs into automated and reproducible workflows in a distributed cloud and provides a management system for data, associated metadata, individual analysis jobs, and multi-step workflows. This chapter provides examples of how to (1) submitting, managing, constructing workflows, (2) using public workflows for Bulked Segregant Analysis (BSA), (3) constructing a Data Analysis Center (DAC), and Data Coordination Center (DCC) for the plant ENCODE project
CyVerse for Reproducible Research: RNA-Seq Analysis
No full textPosing complex research questions poses complex reproducibility challenges. Datasets may need to be managed over long periods of time. Reliable and secure repositories are needed for data storage. Sharing big data requires advance planning and becomes complex when collaborators are spread across institutions and countries. Many complex analyses require the larger compute resources only provided by cloud and high-performance computing infrastructure. Finally at publication, funder and publisher requirements must be met for data availability and accessibility and computational reproducibility. For all of these reasons, cloud-based cyberinfrastructures are an important component for satisfying the needs of data-intensive research. Learning how to incorporate these technologies into your research skill set will allow you to work with data analysis challenges that are often beyond the resources of individual research institutions. One of the advantages of CyVerse is that there are many solutions for high-powered analyses that do not require knowledge of command line (i.e., Linux) computing. In this chapter we will highlight CyVerse capabilities by analyzing RNA-Seq data. The lessons learned will translate to doing RNA-Seq in other computing environments and will focus on how CyVerse infrastructure supports reproducibility goals (e.g., metadata management, containers), team science (e.g., data sharing features), and flexible computing environments (e.g., interactive computing, scaling)
Interrupting the nitrosative stress fuels tumor-specific cytotoxic T lymphocytes in pancreatic cancer
Full text linkBACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest tumors owing to its robust desmoplasia, low immunogenicity, and recruitment of cancer-conditioned, immunoregulatory myeloid cells. These features strongly limit the success of immunotherapy as a single agent, thereby suggesting the need for the development of a multitargeted approach. The goal is to foster T lymphocyte infiltration within the tumor landscape and neutralize cancer-triggered immune suppression, to enhance the therapeutic effectiveness of immune-based treatments, such as anticancer adoptive cell therapy (ACT). METHODS: We examined the contribution of immunosuppressive myeloid cells expressing arginase 1 and nitric oxide synthase 2 in building up a reactive nitrogen species (RNS)-dependent chemical barrier and shaping the PDAC immune landscape. We examined the impact of pharmacological RNS interference on overcoming the recruitment and immunosuppressive activity of tumor-expanded myeloid cells, which render pancreatic cancers resistant to immunotherapy. RESULTS: PDAC progression is marked by a stepwise infiltration of myeloid cells, which enforces a highly immunosuppressive microenvironment through the uncontrolled metabolism of L-arginine by arginase 1 and inducible nitric oxide synthase activity, resulting in the production of large amounts of reactive oxygen and nitrogen species. The extensive accumulation of myeloid suppressing cells and nitrated tyrosines (nitrotyrosine, N-Ty) establishes an RNS-dependent chemical barrier that impairs tumor infiltration by T lymphocytes and restricts the efficacy of adoptive immunotherapy. A pharmacological treatment with AT38 ([3-(aminocarbonyl)furoxan-4-yl]methyl salicylate) reprograms the tumor microenvironment from protumoral to antitumoral, which supports T lymphocyte entrance within the tumor core and aids the efficacy of ACT with telomerase-specific cytotoxic T lymphocytes. CONCLUSIONS: Tumor microenvironment reprogramming by ablating aberrant RNS production bypasses the current limits of immunotherapy in PDAC by overcoming immune resistance
Spatiotemporal 3D image registration for mesoscale studies of brain development
Full text linkComparison of brain samples representing different developmental stages often necessitates registering the samples to common coordinates. Although the available software tools are successful in registering 3D images of adult brains, registration of perinatal brains remains challenging due to rapid growth-dependent morphological changes and variations in developmental pace between animals. To address these challenges, we introduce CORGI (Customizable Object Registration for Groups of Images), an algorithm for the registration of perinatal brains. First, we optimized image preprocessing to increase the algorithm's sensitivity to mismatches in registered images. Second, we developed an attention-gated simulated annealing procedure capable of focusing on the differences between perinatal brains. Third, we applied classical multidimensional scaling (CMDS) to align ("synchronize") brain samples in time, accounting for individual development paces. We tested CORGI on 28 samples of whole-mounted perinatal mouse brains (P0-P9) and compared its accuracy with other registration algorithms. Our algorithm offers a runtime of several minutes per brain on a laptop and automates such brain registration tasks as mapping brain data to atlases, comparing experimental groups, and monitoring brain development dynamics