RESUMEN
Aryl sulfides are common and ubiquitous motifs in natural products and pharmaceuticals. Presented herein is the first example of the synthesis of diaryl sulfide derivatives via dehydroaromatization under simple basic conditions. Dehydroaromatization reactions between indolines or cyclohexanones with aryl thiols are performed in an environmentally benign manner by the use of air (molecular oxygen) as the oxidant, with producing water as the only byproduct. The methodology provides a simple and practical route to diaryl sulfides with wide functional groups in good to excellent yields. Preliminary mechanistic studies suggest that a radical process is involved in the transformation.
RESUMEN
BACKGROUND: Quantitative Trait Locus (QTL) analysis and Genome-Wide Association Studies (GWAS) have the power to identify variants that capture significant levels of phenotypic variance in complex traits. However, effort and time are required to select the best methods and optimize parameters and pre-processing steps. Although machine learning approaches have been shown to greatly assist in optimization and data processing, applying them to QTL analysis and GWAS is challenging due to the complexity of large, heterogenous datasets. Here, we describe proof-of-concept for an automated machine learning approach, AutoQTL, with the ability to automate many complicated decisions related to analysis of complex traits and generate solutions to describe relationships that exist in genetic data. RESULTS: Using a publicly available dataset of 18 putative QTL from a large-scale GWAS of body mass index in the laboratory rat, Rattus norvegicus, AutoQTL captures the phenotypic variance explained under a standard additive model. AutoQTL also detects evidence of non-additive effects including deviations from additivity and 2-way epistatic interactions in simulated data via multiple optimal solutions. Additionally, feature importance metrics provide different insights into the inheritance models and predictive power of multiple GWAS-derived putative QTL. CONCLUSIONS: This proof-of-concept illustrates that automated machine learning techniques can complement standard approaches and have the potential to detect both additive and non-additive effects via various optimal solutions and feature importance metrics. In the future, we aim to expand AutoQTL to accommodate omics-level datasets with intelligent feature selection and feature engineering strategies.
RESUMEN
It is sparse and inconclusive that research on the subject whether the fatigue life of the structure will be reduced by shot peening strengthening before shot peen forming (S + F), and this study investigates accordingly. First, the crack growth rate test of the machine-processing plate and shot peening strengthening before shot peen forming plate demonstrate that both plates' final crack growth rate and length are similar. However, the test shows the "fluctuation phenomenon" of crack growth rate and the "intersection phenomenon" in the Paris curve. This study is based on a self-developed simulation plugin for crack growth paths. The results verify that "fluctuation" causes the differential distribution of the overall stress intensity factor in the strengthened (4.5% increase compared to machine-processing) and formed (9.8% decrease compared to machine-processing) crater areas of the shot peening strengthening before shot peen forming plate. Comparing to the full coverage strengthening area, the forming area (only 30% coverage) in the early stage of growth as well as the gain amplitude of the residual stress in the late stage of growth gradually decrease and tend to be the same as that of the machine-processing, as validated by the "intersection phenomenon".
RESUMEN
Background: Quantitative Trait Locus (QTL) analysis and Genome-Wide Association Studies (GWAS) have the power to identify variants that capture significant levels of phenotypic variance in complex traits. However, effort and time are required to select the best methods and optimize parameters and pre-processing steps. Although machine learning approaches have been shown to greatly assist in optimization and data processing, applying them to QTL analysis and GWAS is challenging due to the complexity of large, heterogenous datasets. Here, we describe proof-of-concept for an automated machine learning approach, AutoQTL, with the ability to automate many complex decisions related to analysis of complex traits and generate diverse solutions to describe relationships that exist in genetic data. Results: Using a dataset of 18 putative QTL from a large-scale GWAS of body mass index in the laboratory rat, Rattus norvegicus , AutoQTL captures the phenotypic variance explained under a standard additive model while also providing evidence of non-additive effects including deviations from additivity and 2-way epistatic interactions from simulated data via multiple optimal solutions. Additionally, feature importance metrics provide different insights into the inheritance models and predictive power of multiple GWAS-derived putative QTL. Conclusions: This proof-of-concept illustrates that automated machine learning techniques can be applied to genetic data and has the potential to detect both additive and non-additive effects via various optimal solutions and feature importance metrics. In the future, we aim to expand AutoQTL to accommodate omics-level datasets with intelligent feature selection strategies.
RESUMEN
Rete pegs are finger-like structures that are formed during the development and wound healing process of the skin and oral mucosa, and they provide better mechanical resistance and nutritional supply between the epithelium and dermis. An increasing number of studies have shown that rete pegs have physiological functions, such as resisting bacterial invasion, body fluid loss, and other harmful changes, which indicate that rete pegs are important structures in natural skin and oral mucosa. Although a great deal of progress has been made in scaffold materials and construction methods for tissue-engineered skin and oral mucosa in recent years, construction of the oral mucosa with functional rete pegs remains a major challenge. In this review, we summarized current research on the progress on formation of rete pegs in human oral mucosa as well as its molecular basis and regulatory mechanism, which might provide new ideas for functional construction of tissue-engineered skin and oral mucosa.
