Self-regulated learning:Validating a task-specific questionnaire for children in elementary school

This paper describes the development and initial validation of the Cognition and Emotion/Motivation Regulation (CEMOR) questionnaire, a task-specific questionnaire for upper elementary school students that measures self-regulated learning (SRL). Using a multistep procedure, 22 items were developed, divided over five theory-informed dimensions (Planning, Monitoring, Cognition Control, Emotion/Motivation Control, and Reflecting). The CEMOR was applied in a math context. Children from grades 3–6 (N = 547, 54.7 % females) completed the CEMOR. Confirmatory factor analyses indicated that the five proposed scales have adequate to good model fit, with factor loadings ranging from .54 to .83, and acceptable to good composite reliability (ρ range = .75–.85). To find further validity support, the SRL scales were correlated with students’ performance on a math task, experienced emotions, and level of motivation during the task. Most correlations were statistically significant and in the expected direction. Hence, the CEMOR questionnaire shows promise as a new SRL instrument for elementary education

Self-regulated learning:Validating a task-specific questionnaire for children in elementary school

This paper describes the development and initial validation of the Cognition and Emotion/Motivation Regulation (CEMOR) questionnaire, a task-specific questionnaire for upper elementary school students that measures self-regulated learning (SRL). Using a multistep procedure, 22 items were developed, divided over five theory-informed dimensions (Planning, Monitoring, Cognition Control, Emotion/Motivation Control, and Reflecting). The CEMOR was applied in a math context. Children from grades 3–6 (N = 547, 54.7 % females) completed the CEMOR. Confirmatory factor analyses indicated that the five proposed scales have adequate to good model fit, with factor loadings ranging from .54 to .83, and acceptable to good composite reliability (ρ range = .75–.85). To find further validity support, the SRL scales were correlated with students’ performance on a math task, experienced emotions, and level of motivation during the task. Most correlations were statistically significant and in the expected direction. Hence, the CEMOR questionnaire shows promise as a new SRL instrument for elementary education

Nonalcoholic steatohepatitis and hepatocellular carcinoma: Brazilian survey

OBJECTIVE: The majority of cases of hepatocellular carcinoma have been reported in individuals with cirrhosis due to chronic viral hepatitis and alcoholism, but recently, the prevalence has become increasingly related to nonalcoholic steatohepatitis around the world. The study aimed to evaluate the clinical and histophatological characteristics of hepatocellular carcinoma in Brazilians' patients with nonalcoholic steatohepatitis at the present time. METHODS: Members of the Brazilian Society of Hepatology were invited to complete a survey regarding patients with hepatocellular carcinoma related to nonalcoholic steatohepatitis. Patients with a history of alcohol intake (>;20 g/day) and other liver diseases were excluded. Hepatocellular carcinoma diagnosis was performed by liver biopsy or imaging methods according to the American Association for the Study of Liver Diseases’ 2011 guidelines. RESULTS: The survey included 110 patients with a diagnosis of hepatocellular carcinoma and nonalcoholic fatty liver disease from nine hepatology units in six Brazilian states (Bahia, Minas Gerais, Rio de Janeiro, São Paulo, Paraná and Rio Grande do Sul). The mean age was 67±11 years old, and 65.5% were male. Obesity was observed in 52.7% of the cases; diabetes, in 73.6%; dyslipidemia, in 41.0%; arterial hypertension, in 60%; and metabolic syndrome, in 57.2%. Steatohepatitis without fibrosis was observed in 3.8% of cases; steatohepatitis with fibrosis (grades 1-3), in 27%; and cirrhosis, in 61.5%. Histological diagnosis of hepatocellular carcinoma was performed in 47.2% of the patients, with hepatocellular carcinoma without cirrhosis accounting for 7.7%. In total, 58 patients with cirrhosis had their diagnosis by ultrasound confirmed by computed tomography or magnetic resonance imaging. Of these, 55% had 1 nodule; 17%, 2 nodules; and 28%, ≥3 nodules. CONCLUSIONS: Nonalcoholic steatohepatitis is a relevant risk factor associated with hepatocellular carcinoma in patients with and without cirrhosis in Brazil. In this survey, hepatocellular carcinoma was observed in elevated numbers of patients with steatohepatitis without cirrhosis

Pelatihan digital marketing kopi kare Madiun

Pentingnya transformasi digitalisasi menuntut pelaku usaha untuk selalu up to date dalam melakukan pemasaran mengikuti perkembangan zaman yang didukung kuantitas dan kualitas produksi hasil usaha Kopi Kare Madiun. Tujuan pelaksanaan kegiatan ini untuk memperluas jangkauan pemasaran, meningkatkan kuantitas dan kualitas kemasan Kopi Kare Madiun dalam memenuhi permintaan konsumen. Metode pelaksanaan kegiatan dengan tahapan: (a) pra kegiatan tim melakukan (survei dan observasi untuk pemetaan masalah, mengetahui penyebab akibat, pemberian solusi, pembuatan proposal kegiatan); (b) pelaksanaan kegiatan; (c) evaluasi kegiatan. Hasil kegiatan menunjukkan bahwa jangkauan perluasan pasar selain di Jawa Timur, permintaan Kopi Kare Madiun sampai di Bali, Jambi. Jumlah dan kualitas kemasan Kopi Kare Madiun lebih meningkat dengan menggunakan mesin pres sealer dalam memenuhi permintaan konsumen

Predicting a small molecule-kinase interaction map: A machine learning approach

<p>Abstract</p> <p>Background</p> <p>We present a machine learning approach to the problem of protein ligand interaction prediction. We focus on a set of binding data obtained from 113 different protein kinases and 20 inhibitors. It was attained through ATP site-dependent binding competition assays and constitutes the first available dataset of this kind. We extract information about the investigated molecules from various data sources to obtain an informative set of features.</p> <p>Results</p> <p>A Support Vector Machine (SVM) as well as a decision tree algorithm (C5/See5) is used to learn models based on the available features which in turn can be used for the classification of new kinase-inhibitor pair test instances. We evaluate our approach using different feature sets and parameter settings for the employed classifiers. Moreover, the paper introduces a new way of evaluating predictions in such a setting, where different amounts of information about the binding partners can be assumed to be available for training. Results on an external test set are also provided.</p> <p>Conclusions</p> <p>In most of the cases, the presented approach clearly outperforms the baseline methods used for comparison. Experimental results indicate that the applied machine learning methods are able to detect a signal in the data and predict binding affinity to some extent. For SVMs, the binding prediction can be improved significantly by using features that describe the active site of a kinase. For C5, besides diversity in the feature set, alignment scores of conserved regions turned out to be very useful.</p

Cell-active small molecule inhibitors validate the SNM1A DNA repair nuclease as a cancer target

The three human SNM1 metallo-β-lactamase fold nucleases (SNM1A–C) play key roles in DNA damage repair and in maintaining telomere integrity. Genetic studies indicate that they are attractive targets for cancer treatment and to potentiate chemo- and radiation-therapy. A high-throughput screen for SNM1A inhibitors identified diverse pharmacophores, some of which were shown by crystallography to coordinate to the di-metal ion centre at the SNM1A active site. Structure and turnover assay-guided optimization enabled the identification of potent quinazoline–hydroxamic acid containing inhibitors, which bind in a manner where the hydroxamic acid displaces the hydrolytic water and the quinazoline ring occupies a substrate nucleobase binding site. Cellular assays reveal that SNM1A inhibitors cause sensitisation to, and defects in the resolution of, cisplatin-induced DNA damage, validating the tractability of MBL fold nucleases as cancer drug targets

A comparison of machine learning algorithms for chemical toxicity classification using a simulated multi-scale data model

<p>Abstract</p> <p>Background</p> <p>Bioactivity profiling using high-throughput <it>in vitro </it>assays can reduce the cost and time required for toxicological screening of environmental chemicals and can also reduce the need for animal testing. Several public efforts are aimed at discovering patterns or classifiers in high-dimensional bioactivity space that predict tissue, organ or whole animal toxicological endpoints. Supervised machine learning is a powerful approach to discover combinatorial relationships in complex <it>in vitro/in vivo </it>datasets. We present a novel model to simulate complex chemical-toxicology data sets and use this model to evaluate the relative performance of different machine learning (ML) methods.</p> <p>Results</p> <p>The classification performance of Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Naïve Bayes (NB), Recursive Partitioning and Regression Trees (RPART), and Support Vector Machines (SVM) in the presence and absence of filter-based feature selection was analyzed using K-way cross-validation testing and independent validation on simulated <it>in vitro </it>assay data sets with varying levels of model complexity, number of irrelevant features and measurement noise. While the prediction accuracy of all ML methods decreased as non-causal (irrelevant) features were added, some ML methods performed better than others. In the limit of using a large number of features, ANN and SVM were always in the top performing set of methods while RPART and KNN (k = 5) were always in the poorest performing set. The addition of measurement noise and irrelevant features decreased the classification accuracy of all ML methods, with LDA suffering the greatest performance degradation. LDA performance is especially sensitive to the use of feature selection. Filter-based feature selection generally improved performance, most strikingly for LDA.</p> <p>Conclusion</p> <p>We have developed a novel simulation model to evaluate machine learning methods for the analysis of data sets in which in vitro bioassay data is being used to predict in vivo chemical toxicology. From our analysis, we can recommend that several ML methods, most notably SVM and ANN, are good candidates for use in real world applications in this area.</p

Reorganization of the nuclear lamina and cytoskeleton in adipogenesis

A thorough understanding of fat cell biology is necessary to counter the epidemic of obesity. Although molecular pathways governing adipogenesis are well delineated, the structure of the nuclear lamina and nuclear-cytoskeleton junction in this process are not. The identification of the ‘linker of nucleus and cytoskeleton’ (LINC) complex made us consider a role for the nuclear lamina in adipose conversion. We herein focused on the structure of the nuclear lamina and its coupling to the vimentin network, which forms a cage-like structure surrounding individual lipid droplets in mature adipocytes. Analysis of a mouse and human model system for fat cell differentiation showed fragmentation of the nuclear lamina and subsequent loss of lamins A, C, B1 and emerin at the nuclear rim, which coincides with reorganization of the nesprin-3/plectin/vimentin complex into a network lining lipid droplets. Upon 18 days of fat cell differentiation, the fraction of adipocytes expressing lamins A, C and B1 at the nuclear rim increased, though overall lamin A/C protein levels were low. Lamin B2 remained at the nuclear rim throughout fat cell differentiation. Light and electron microscopy of a subcutaneous adipose tissue specimen showed striking indentations of the nucleus by lipid droplets, suggestive for an increased plasticity of the nucleus due to profound reorganization of the cellular infrastructure. This dynamic reorganization of the nuclear lamina in adipogenesis is an important finding that may open up new venues for research in and treatment of obesity and nuclear lamina-associated lipodystrophy

Antioxidant pathways are up-regulated during biological nitrogen fixation to prevent ROS-induced nitrogenase inhibition in Gluconacetobacter diazotrophicus

Gluconacetobacter diazotrophicus, an endophyte isolated from sugarcane, is a strict aerobe that fixates N2. This process is catalyzed by nitrogenase and requires copious amounts of ATP. Nitrogenase activity is extremely sensitive to inhibition by oxygen and reactive oxygen species (ROS). However, the elevated oxidative metabolic rates required to sustain biological nitrogen fixation (BNF) may favor an increased production of ROS. Here, we explored this paradox and observed that ROS levels are, in fact, decreased in nitrogen-fixing cells due to the up-regulation of transcript levels of six ROS-detoxifying genes. A cluster analyses based on common expression patterns revealed the existence of a stable cluster with 99.8% similarity made up of the genes encoding the α-subunit of nitrogenase Mo–Fe protein (nifD), superoxide dismutase (sodA) and catalase type E (katE). Finally, nitrogenase activity was inhibited in a dose-dependent manner by paraquat, a redox cycler that increases cellular ROS levels. Our data revealed that ROS can strongly inhibit nitrogenase activity, and G. diazotrophicus alters its redox metabolism during BNF by increasing antioxidant transcript levels resulting in a lower ROS generation. We suggest that careful controlled ROS production during this critical phase is an adaptive mechanism to allow nitrogen fixation