No abstract

Protein phosphatases are critical regulators in eukaryotic cells. For example, the budding yeast Saccharomyces cerevisiae dual specificity protein phosphatase (DSP) ScYvh1 regulates growth, sporulation, and glycogen accumulation. Despite such importance, functions of Yvh1 proteins in filamentous fungi are not well understood. In this study, we characterized putative protein phosphatase MoYvh1, an Yvh1 homolog in the rice blast fungus Magnaporthe oryzae. Deletion of the MoYVH1 gene resulted in significant reductions in vegetative growth, conidial production, and virulence. The ΔMoyvh1 mutant also displayed defects in cell-wall integrity and was hyposensitive to the exogenous osmotic stress. Further examination revealed that the ΔMoyvh1 mutant had defects in appressorium function and invasive hyphae growth, resulting attenuated pathogenicity. Interestingly, we found that MoYvh1 affects the scavenging of host-derived reactive oxygen species that promotes M. oryzae infection. Finally, overexpression of the phosphodiesterase MoPDEH suppressed the defects in conidia formation and pathogenicity of the ΔMoyvh1 mutant, suggesting MoYvh1 could regulate MoPDEH for its function. Our study reveals not only the importance of MoYvh1 proteins in growth, differentiation, and virulence of the rice blast fungus but, also, a genetic link between MoYvh1 and MoPDEH-cAMP signaling in this fungus.

Microsomal epoxide hydrolase (mEH) is a bifunctional protein that plays a central role in carcinogen metabolism and is also able to mediate the sodium-dependent uptake of bile acids into hepatocytes. Studies have identified a subject (S-1) with extremely elevated serum bile salt levels in the absence of observable hepatocellular injury, suggesting a defect in bile acid uptake. In this individual, mEH protein and mEH mRNA levels were reduced by approximately 95% and 85%, respectively, whereas the expression and amino acid sequence of another bile acid transport protein (NTCP) was unaffected. Sequence analysis of the mEH gene (EPHX1) revealed a point mutation at an upstream HNF-3 site (allele I) and in intron 1 (allele II), which resulted in a significant decrease in EPHX1 promoter activity in transient transfection assays. Gel shift assays using a radiolabeled oligonucleotide from each region resulted in specific transcription factor binding patterns, which were altered in the presence of the mutation. These studies demonstrate that the expression of mEH is greatly reduced in a patient with hypercholanemia, suggesting that mEH participates in sodium-dependent bile acid uptake in human liver where its absence may contribute to the etiology of this disease.

Machine Learning (ML) and Internet of Things (IoT) are complementary advances: ML techniques unlock the potential of IoT with intelligence, and IoT applications increasingly feed data collected by sensors into ML models, thereby employing results to improve their business processes and services. Hence, orchestrating ML pipelines that encompass model training and implication involved in the holistic development lifecycle of an IoT application often leads to complex system integration. This paper provides a comprehensive and systematic survey of the development lifecycle of ML-based IoT applications. We outline the core roadmap and taxonomy, and subsequently assess and compare existing standard techniques used at individual stages.

As our dependence on intelligent machines continues to grow, so does the demand for more transparent and interpretable models. In addition, the ability to explain the model generally is now the gold standard for building trust and deployment of Artificial Intelligence (AI) systems in critical domains. Explainable Artificial Intelligence (XAI) aims to provide a suite of machine learning (ML) techniques that enable human users to understand, appropriately trust, and produce more explainable models. Selecting an appropriate approach for building an XAI-enabled application requires a clear understanding of the core ideas within XAI and the associated programming frameworks. We survey state-of-the-art programming techniques for XAI and present the different phases of XAI in a typical ML development process. We classify the various XAI approaches and using this taxonomy, discuss the key differences among the existing XAI techniques. Furthermore, concrete examples are used to describe these techniques that are mapped to programming frameworks and software toolkits. It is the intention that this survey will help stakeholders in selecting the appropriate approaches, programming frameworks, and software toolkits by comparing them through the lens of the presented taxonomy.