Fabrication and investigation of the physico-mechanical properties of Jute-PALF reinforced LLDPE hybrid composites: Effect of gamma irradiation.

The hybridization effect of agro-waste pineapple leaf fibre (PALF) and jute fibre as reinforcement in linear low-density polyethylene (LLDPE) composites was investigated in this work. The samples were fabricated by using the heat press compression moulding. The effect of gamma irradiation on composite physico-mechanical properties was also investigated in order to determine the best gamma dose among 2.50, 5.00, 7.50, and 10.00 kGy. The composite sample containing 40% PALF and 60% jute (with a total weight of 50% fibres) demonstrated the most feasible tensile strength (33.36 ± 0.59 MPa), tensile modulus (1494.41 ± 10.94 MPa), elongation at break (50.92 ± 0.77%), bending strength (82.58 ± 0.49 MPa), bending modulus (4932.46 ± 96.12 MPa), and impact strength (34.38 ± 0.42 kJ/m2) at 7.50 kGy irradiation. Thermogravimetric analysis (TGA) determined the thermal performance of the samples. Scanning electron microscopy (SEM) images at the tensile fracture surfaces of composites revealed the interfacial interaction between reinforcement fibres and matrix.

PanClassif: Improving pan cancer classification of single cell RNA-seq gene expression data using machine learning.

Cancer is one of the major causes of human death per year. In recent years, cancer identification and classification using machine learning have gained momentum due to the availability of high throughput sequencing data. Using RNA-seq, cancer research is blooming day by day and new insights of cancer and related treatments are coming into light. In this paper, we propose PanClassif, a method that requires a very few and effective genes to detect cancer from RNA-seq data and is able to provide performance gain in several wide range machine learning classifiers. We have taken 22 types of cancer samples from The Cancer Genome Atlas (TCGA) having 8287 cancer samples and 680 normal samples. Firstly, PanClassif uses k-Nearest Neighbour (k-NN) smoothing to smooth the samples to handle noise in the data. Then effective genes are selected by Anova based test. For balancing the train data, PanClassif applies an oversampling method, SMOTE. We have performed comprehensive experiments on the datasets using several classification algorithms. Experimental results shows that PanClassif outperform existing state-of-the-art methods available and shows consistent performance for two single cell RNA-seq datasets taken from Gene Expression Omnibus (GEO). PanClassif improves performances of a wide variety of classifiers for both binary cancer prediction and multi-class cancer classification. PanClassif is available as a python package (https://pypi.org/project/panclassif/). All the source code and materials of PanClassif are available at https://github.com/Zwei-inc/panclassif.

Predictors of Self-Reported Health Status of Ready-made Garment Workers in Bangladesh in Post Global Economic Recession Period.

Introduction There has been disagreement within academia in Bangladesh on whether the global economic recession of 2008-2009 came out as a bane or a boon to their economy and for their people, particularly workers in the ready-made garments (RMG) sector; therefore, we sought to conduct a study among currently employed and recently unemployed RMG workers to examine the influence of recession on their self-reported health status. Methods This cross-sectional study was conducted among 200 workers across 20 factories and 108 recently unemployed workers from different locations of Dhaka. Workers were selected based on a systematic sampling method from 20 randomly selected factories. Unemployed respondents were selected via snowball sampling. A questionnaire was prepared to cover different socio-demographic variables, which were then explored against an outcome variable of how the respondents rate their current health status (2009) compared with their past health status during the economic recession period (2008). A simple logistic regression was conducted for each of the independent variables with the outcome variable. Finally, all independent variables were loaded against the outcome variable, and multiple logistic regression was run. Results The only statistically significant predictor of self-reported health status was age, which indicated a 4% decrease (p = 0.05; 95% confidence interval (CI), 0.9203417 to 1.000015) in improved or better health with each year increase in age, holding other variables constant. Respondent health status was unchanged or even improved after the period of recession. The employed group had 1542.061 Taka (approximately $20) more average monthly family income than the unemployed group (two-sample t-test p-value 0.007), their health status was not affected (odds ratio (OR) 0.998; p-value 0.907). Conclusion The absence of an association between self-reported health status and economic recession is not uncommon, and explanations have been proposed for this phenomenon.