Predicción de la tasa de penetración mediante el aprendizaje de la máquina de vectores de soporte de mínimos cuadrados acoplados simulados (CSA_LSSVM) en una formación de hidrocarburos basada en parámetros de perforación

Field information analysis is the main element of reducing costs and improving drilling operations. Therefore, the development of field data analysis tools is one of the ways to improve drilling operations. This paper uses mathematical programming and optimization-based methods to present and review learning models for data classification. A comprehensive multi-objective optimization model is proposed by extracting commonalities and the same philosophy of some of the most popular mathematical optimization models in the last few years. The geometric representation of the model will be to make it easier to understand the characteristics of the proposed model. Then it is shown that a large number of models studied in the past and present are subsets, and exceptional cases of this proposed comprehensive model and how to convert the proposed comprehensive model to these methods will be examined. This seeks to bridge the gap between new multi-objective programming models and the powerful and improved CSA-LSSVM methods presented for classification in data mining and to generalize studies to improve each of these methods.El análisis de la información de campo es el elemento principal para reducir costos y mejorar las operaciones de perforación. Por lo tanto, el desarrollo de herramientas de análisis de datos de campo es una de las formas de mejorar las operaciones de perforación. Este artículo utiliza programación matemática y métodos basados ​​en optimización para presentar y revisar modelos de aprendizaje para la clasificación de datos. Se propone un modelo integral de optimización multiobjetivo extrayendo los puntos en común y la misma filosofía de algunos de los modelos matemáticos de optimización más populares en los últimos años. La representación geométrica del modelo servirá para facilitar la comprensión de las características del modelo propuesto. Luego se muestra que una gran cantidad de modelos estudiados en el pasado y el presente son subconjuntos, y se examinarán casos excepcionales de este modelo integral propuesto y cómo convertir el modelo integral propuesto a estos métodos. Esto busca cerrar la brecha entre los nuevos modelos de programación multiobjetivo y los métodos CSA-LSSVM poderosos y mejorados presentados para la clasificación en la minería de datos y generalizar los estudios para mejorar cada uno de estos métodos

Combined Ultrahigh Pressure Extraction and High-Speed Counter-Current Chromatography for Separation and Purification of Three Glycoside Compounds from Dendrobium officinale Protocorm

As an alternative to Dendrobium candidum, protocorm-like bodies (PLBs) of Dendrobium candidum are of great value due to their high yield and low cost. In this work, three glycoside compounds, β-D-glucopyranose 1-[(E)-3-(4-hydroxyphenyl)-2-propenoat] (I), β-D-glucopyranose 1-[(E)-3-(3, 4-dihydroxyphenyl)-2-propenoat] (II), and 1-O-sinapoyl glucopyranoside (III), were extracted and isolated by ultrahigh pressure extraction (UPE) coupled with high-speed counter-current chromatography (HSCCC) from PLBs of D. officinale. First, the target compounds were optimized and prepared with 50% ethanol solution at a 1:30 (g/mL) solid/liquid ratio in 2 min under 300 MPa by UPE. Then, the crude extract was chromatographed with a silica gel column, and primary separation products were obtained. In addition, the products (150 mg) were separated by HSCCC under the solvent system of MTBE-n-butyl alcohol-acetonitrile-water (5:1:2:6, v/v/v/v), yielding 31.43 mg of compound I, 10.21 mg of compound II, and 24.75 mg of compound III. Their structures were further identified by ESI-MS, 1H NMR, and 13C NMR. The antioxidant results showed that the three compounds expressed moderate effects on the DPPH· scavenging effect. Compound II had the best antioxidant capacity and its IC50 value was 0.0497 mg/mL

Molecular dynamics simulations provide insights into the origin of gleevec’s selectivity toward human tyrosine kinases

<p>Protein kinases are critical drug targets against cancer. Since the discovery of Gleevec, a specific inhibitor of Abl kinase, the capability of this drug to distinguish between Abl and other tyrosine kinases, such as Src, has been intensely investigated but the origin of Gleevec’s selectivity to Abl against Src is less studied. Here, we performed molecular dynamics (MD) simulations, dynamical cross-correlation matrices (DCCM), dynamical network analysis, and binding free energy calculations to explore Gleevec’s selectivity based on the crystal structures of Abl, Src, and their common ancestors (ANC-AS) and the two constructed mutation systems (AS→Abl and AS→Src). MD simulations revealed that the conformation of the phosphate-binding loop (P-loop) was altered significantly in the AS→Abl system. DCCM results unraveled that mutations increased anticorrelated motions in the AS→Abl system. Community network analysis suggested that the P-loop established special contacts in the AS→Abl system that are devoid in the AS→Src system. The binding free energy calculations unveiled that the affinity of Gleevec to AS→Abl increased to near the Abl level, whereas its affinity to AS→Src decreased to near the Src level. Analysis of individual residue contributions showed that the differences were located mainly at the P-loop. This study is valuable for understanding the sensitivity of Gleevec to human tyrosine kinases.</p> <p>Communicated by Ramaswamy H. Sarma</p

Multi-omics analysis of human mesenchymal stem cells shows cell aging that alters immunomodulatory activity through the downregulation of PD-L1

Abstract Mesenchymal stem cells (MSCs) possess potent immunomodulatory activity and have been extensively investigated for their therapeutic potential in treating inflammatory disorders. However, the mechanisms underlying the immunosuppressive function of MSCs are not fully understood, hindering the development of standardized MSC-based therapies for clinical use. In this study, we profile the single-cell transcriptomes of MSCs isolated from adipose tissue (AD), bone marrow (BM), placental chorionic membrane (PM), and umbilical cord (UC). Our results demonstrate that MSCs undergo a progressive aging process and that the cellular senescence state influences their immunosuppressive activity by downregulating PD-L1 expression. Through integrated analysis of single-cell transcriptomic and proteomic data, we identify GATA2 as a regulator of MSC senescence and PD-L1 expression. Overall, our findings highlight the roles of cell aging and PD-L1 expression in modulating the immunosuppressive efficacy of MSCs and implicating perinatal MSC therapy for clinical applications in inflammatory disorders