Tugas 4

Background. Recruitment of monocytes and low-grade inflammation process are both involved in obesity and in atherosclerosis. Thus, the aim of this study was to evaluate the correlation among indicators of adiposity, monocyte subtypes, and inflammatory markers in patients with stable coronary artery disease (CAD). Methods. This was a cross-sectional study including 97 patients with stable CAD aged >40 years. Traditional anthropometric indicators of adiposity (body mass index (BMI); waist, hip, and neck circumferences; and waist-hip ratio) and nontraditional anthropometric indicators of adiposity (lipid accumulation product index (LAP), visceral adiposity index (VAI), and deep-abdominal-adipose-tissue index (DAAT)) were determined. Immunoprecipitation, turbidimetry, coagulometric method, and CBA were used for the evaluation of inflammatory markers (hs-CRP, IL-2, IL-4, IL-6, IL-10, and INF-γ). Monocyte subtypes were identified by flow cytometry and defined as CD14++ CD16− (Mon1), CD...

El objetivo principal de nuestro grupo de investigación en epistemología de la imaginación –que es una propuesta teórica del primer autor de este artículo–, es cambiar el proceso educativo en veterinaria desde una perspectiva del papel de la imaginación en la enseñanza de la ciencia. Nuestra propuesta tiene como marco de referencia la construcción de acciones representativas que conlleva la imaginación de acciones basadas en el pensamiento geométrico, en el marco del sistema de ejes X, Y y Z. Con este planteamiento, las acciones representativas, a las que hacemos referencia como representa-acciones, están basadas en acciones imaginarias, a las que les damos el nombre de imagina-acciones. Desde el punto de vista epistemológico, la Caverna de Platón, en la que la gente mira sombras de hombres y animales proyectados en una pared, es llevada más allá de la alegoría, planteándola como la materialización de la imaginación expresada como arte.

The ability of machine learning techniques to make accurate predications is increasing. The aim of this work is to apply machine learning techniques such as Support Vector Machine, Naïve Bayes, Decision Tree, Logistic Regression, and K-Nearest Neighbour algorithms to predict the shelf life of Okra. Predicting the shelf life of Okra is important because Okra becomes harmful for human consumption if consumed after its shelf life. Okra parameters such as weight loss, firmness, Titrable Acid, Total Soluble Solids, Vitamin C/Ascorbic acid content, and PH were used as inputs into these machine learning techniques. Support Vector Machine, Naïve Bayes and Decision Tree each accurately predicted the shelf life of Okra with accuracies of 100%. However, the Logistic Regression and K-Nearest Neighbour achieved 88.89% and 88.33% accuracies, respectively. These results showed that machine learning techniques especially Support Vector Machine, Naïve Bayes and Decision Tree can be effectively applied for the prediction of Okra shelf life.

Human papillomavirus (HPV) gene expression is dramatically altered during cervical carcinogenesis. Because dysregulated genes frequently show abnormal patterns of DNA methylation, we hypothesized that comprehensive mapping of the HPV methylomes in cervical samples at different stages of progression would reveal patterns of clinical significance. To test this hypothesis, thirteen HPV16-positive samples were obtained from women undergoing routine cervical cancer screening. Complete methylation data were obtained for 98.7% of the HPV16 CpGs in all samples by bisulfite-sequencing. Most HPV16 CpGs were unmethylated or methylated in only one sample. The other CpGs were methylated at levels ranging from 11% to 100% of the HPV16 copies per sample. The results showed three major patterns and two variants of one pattern. The patterns showed minimal or no methylation (A), low level methylation in the E1 and E6 genes (B), and high level methylation at many CpGs in the E5/L2/L1 region (C). Gener...