An asymptotic induced numerical method for the convection-diffusion-reaction equation

A parallel algorithm for the efficient solution of a time dependent reaction convection diffusion equation with small parameter on the diffusion term is presented. The method is based on a domain decomposition that is dictated by singular perturbation analysis. The analysis is used to determine regions where certain reduced equations may be solved in place of the full equation. Parallelism is evident at two levels. Domain decomposition provides parallelism at the highest level, and within each domain there is ample opportunity to exploit parallelism. Run time results demonstrate the viability of the method

Towards a Unified Framework for Adaptable Problematic Content Detection via Continual Learning

Detecting problematic content, such as hate speech, is a multifaceted and ever-changing task, influenced by social dynamics, user populations, diversity of sources, and evolving language. There has been significant efforts, both in academia and in industry, to develop annotated resources that capture various aspects of problematic content. Due to researchers' diverse objectives, the annotations are inconsistent and hence, reports of progress on detection of problematic content are fragmented. This pattern is expected to persist unless we consolidate resources considering the dynamic nature of the problem. We propose integrating the available resources, and leveraging their dynamic nature to break this pattern. In this paper, we introduce a continual learning benchmark and framework for problematic content detection comprising over 84 related tasks encompassing 15 annotation schemas from 8 sources. Our benchmark creates a novel measure of progress: prioritizing the adaptability of classifiers to evolving tasks over excelling in specific tasks. To ensure the continuous relevance of our framework, we designed it so that new tasks can easily be integrated into the benchmark. Our baseline results demonstrate the potential of continual learning in capturing the evolving content and adapting to novel manifestations of problematic content

Automata Evaluation and Text Search Protocols with Simulation Based Security

This paper presents efficient protocols for securely computing the following two problems: 1) The fundamental problem of pattern matching. This problem is defined in the two-party setting, where party $P_1$ holds a pattern and party $P_2$ holds a text. The goal of $P_1$ is to learn where the pattern appears in the text, without revealing it to $P_2$ or learning anything else about $P_2$\u27s text. This problem has been widely studied for decades due to its broad applicability. We present several protocols for several notions of security. We further generalize one of our solutions to solve additional pattern matching related problems of interest. 2) Our construction from above, in the malicious case, is based on a novel protocol for secure oblivious automata evaluation which is of independent interest. In this problem, party $P_1$ holds an automaton and party $P_2$ holds an input string, and they need to decide if the automaton accepts the input, without learning anything else. Our protocol obtains full security in the face of malicious adversaries

Synchrotron X-Ray Study of the Thickness Dependence of the Phase Diagram of Thin Liquid-Crystal Films

The phase diagram of freely suspended thin films of heptyloxybenzylidene-heptylaniline shows dramatic changes for thicknesses below 22 layers. The most surprising feature of the phase diagram is the inclusion of two phases lacking long-range crystalline order (smectic-F and hexatic-B phases) between two crystalline phases (crystalline smectic B and smectic G). Neither the smectic F nor the hexatic B occurs in bulk samples. Between sixteen and ten layers the width, in temperature, of the hexatic-B phase increases.Engineering and Applied Science

A Modeling Approach for Predicting the Effect of Corrosion on Electrical-Circuit Reliability

An analytical capability is being developed that can be used to predict the effect of corrosion on the performance of electrical circuits and systems. The availability of this ''toolset'' will dramatically improve our ability to influence device and circuit design, address and remediate field occurrences, and determine real limits for circuit service life. In pursuit of this objective, we have defined and adopted an iterative, statistical-based, top-down approach that will permit very formidable and real obstacles related to both the development and use of the toolset to be resolved as effectively as possible. An important component of this approach is the direct incorporation of expert opinion. Some of the complicating factors to be addressed involve the code/model complexity, the existence of large number of possible degradation processes, and an incompatibility between the length scales associated with device dimensions and the corrosion processes. Two of the key aspects of the desired predictive toolset are (1) a direct linkage of an electrical-system performance model with mechanistic-based, deterministic corrosion models, and (2) the explicit incorporation of a computational framework to quantify the effects of non-deterministic parameters (uncertainty). The selected approach and key elements of the toolset are first described in this paper. These descriptions are followed by some examples of how this toolset development process is being implemented

Control of intestinal stem cell function and proliferation by mitochondrial pyruvate metabolism.

Most differentiated cells convert glucose to pyruvate in the cytosol through glycolysis, followed by pyruvate oxidation in the mitochondria. These processes are linked by the mitochondrial pyruvate carrier (MPC), which is required for efficient mitochondrial pyruvate uptake. In contrast, proliferative cells, including many cancer and stem cells, perform glycolysis robustly but limit fractional mitochondrial pyruvate oxidation. We sought to understand the role this transition from glycolysis to pyruvate oxidation plays in stem cell maintenance and differentiation. Loss of the MPC in Lgr5-EGFP-positive stem cells, or treatment of intestinal organoids with an MPC inhibitor, increases proliferation and expands the stem cell compartment. Similarly, genetic deletion of the MPC in Drosophila intestinal stem cells also increases proliferation, whereas MPC overexpression suppresses stem cell proliferation. These data demonstrate that limiting mitochondrial pyruvate metabolism is necessary and sufficient to maintain the proliferation of intestinal stem cells

Targeted Genome-Wide Enrichment of Functional Regions

Only a small fraction of large genomes such as that of the human contains the functional regions such as the exons, promoters, and polyA sites. A platform technique for selective enrichment of functional genomic regions will enable several next-generation sequencing applications that include the discovery of causal mutations for disease and drug response. Here, we describe a powerful platform technique, termed “functional genomic fingerprinting” (FGF), for the multiplexed genomewide isolation and analysis of targeted regions such as the exome, promoterome, or exon splice enhancers. The technique employs a fixed part of a uniquely designed Fixed-Randomized primer, while the randomized part contains all the possible sequence permutations. The Fixed-Randomized primers bind with full sequence complementarity at multiple sites where the fixed sequence (such as the splice signals) occurs within the genome, and multiplex amplify many regions bounded by the fixed sequences (e.g., exons). Notably, validation of this technique using cardiac myosin binding protein-C (MYBPC3) gene as an example strongly supports the application and efficacy of this method. Further, assisted by genomewide computational analyses of such sequences, the FGF technique may provide a unique platform for high-throughput sample production and analysis of targeted genomic regions by the next-generation sequencing techniques, with powerful applications in discovering disease and drug response genes

A miRNA Signature of Prion Induced Neurodegeneration

MicroRNAs (miRNAs) are small, non-coding RNA molecules which are emerging as key regulators of numerous cellular processes. Compelling evidence links miRNAs to the control of neuronal development and differentiation, however, little is known about their role in neurodegeneration. We used microarrays and RT-PCR to profile miRNA expression changes in the brains of mice infected with mouse-adapted scrapie. We determined 15 miRNAs were de-regulated during the disease processes; miR-342-3p, miR-320, let-7b, miR-328, miR-128, miR-139-5p and miR-146a were over 2.5 fold up-regulated and miR-338-3p and miR-337-3p over 2.5 fold down-regulated. Only one of these miRNAs, miR-128, has previously been shown to be de-regulated in neurodegenerative disease. De-regulation of a unique subset of miRNAs suggests a conserved, disease-specific pattern of differentially expressed miRNAs is associated with prion–induced neurodegeneration. Computational analysis predicted numerous potential gene targets of these miRNAs, including 119 genes previously determined to be also de-regulated in mouse scrapie. We used a co-ordinated approach to integrate miRNA and mRNA profiling, bioinformatic predictions and biochemical validation to determine miRNA regulated processes and genes potentially involved in disease progression. In particular, a correlation between miRNA expression and putative gene targets involved in intracellular protein-degradation pathways and signaling pathways related to cell death, synapse function and neurogenesis was identified