Statistical optimalization of α-Amylase production from Penicillium notatum NCIM 923 and kinetics study of the purified enzyme

In this study, response surface methodology (RSM) was employed to optimize the production of α-amylase by Penicillium notatum NCIM 923 through solid-state fermentation. The individual and combinational effects of the factors, i.e. substrate amount, initial moisture, fermentation time, temperature and size of inoculum were found to have significant effects on α-amylase production: the optimum values of the tested variables were 5 g, 70%, 94 h, 28 °C and 20%, respectively. The predicted amylase production (2819.24 U/g) was in good agreement with the value measured under optimized surrounding (2810.33 U/g). The molecular mass of purified α-amylase was about 52 kDa. The enzyme activity exhibited its pH optimum between pH 4.6 and 6.6, and it had maximal activity at 50 °C. The apparent Km and Vmax of α-amylase for starch were 4.1 mg/ml and 247.6 μmol/min, respectively. The activation energy (Ea) for starch hydrolysis was found to be 14.133 kJ/mol. The enzyme was thermostable with half-life (t1/2) of 110 min at 80 °C and temperature coefficient (Q10) value of 1.0. Purified enzyme was activated by Ca2+ and inhibited by Hg2+ ions. EDTA also inhibited the enzyme activity, indicating that the purified enzyme is a metalloenzyme

Eye diseases: the neglected health condition among urban slum population of Dhaka, Bangladesh

Introduction: Globally, eye diseases are considered as one of the major contributors of nonfatal disabling conditions. In Bangladesh, 1.5% of adults are blind and 21.6% have low vision. Therefore, this paper aimed to identify the community-based prevalence and associated risk factors of eye diseases among slum dwellers of Dhaka city. Methods: The study was carried out in two phases. In the first phase, a survey was conducted using multistage cluster sampling among 1320 households of three purposively selected slums in Dhaka city. From each household, one family member (≥ 18 years old) was randomly interviewed by trained data collectors using a structured questionnaire. After that, each of the participants was requested to take part in the second phase of the study. Following the request, 432 participants out of 1320 participants came into the tertiary care hospitals where they were clinically assessed by ophthalmologist for presence of eye diseases. A number of descriptive and inferential statistics were performed using Stata 13. Result: The majority of total 432 study participants were female (68.6%), married (82.6%) and Muslim (98.8%). Among them almost all (92.8%) were clinically diagnosed with eye disease. The most prevalent eye diseases were refractive error (63.2%), conjunctivitis (17.1%), visual impairment (16.4%) and cataract (7.2%). Refractive error was found significantly associated with older age, female gender and income generating work. Cataract was found negatively associated with the level of education, however, opposite relationship was found between cataract and visual impairment. Conclusion: Our study provides epidemiologic data on the prevalence of eye diseases among adult population in low-income urban community of Dhaka city. The high prevalence of refractive error, allergic conjunctivitis, visual impairment, and cataract among this group of people suggests the importance of increasing access to eye care services

Proposing novel ensemble approach of particle swarm optimized and machine learning algorithms for drought vulnerability mapping in Jharkhand, India

Drought, a natural and very complex climatic hazard, causes impacts on natural and socio-economic environments. This study aims to produce the drought vulnerability map (DVM) considering novel ensemble machine learning algorithms (MLAs) in Jharkhand, India. Forty, drought vulnerability determining factors under the categories of exposure, sensitivity, and adaptive capacity were used. Then, four machine learning and four novel ensemble approaches of particle swarm optimized (PSO) algorithms, named random forest (RF), PSO-RF, multi-layer perceptron (MLP), PSO-MLP, support vector regression (SVM), PSO-MLP, Bagging, and PSO-Bagging, were established for DVMs. The receiver operating characteristic curve (ROC), mean-absolute-error (MAE), root-mean-square-error (RMSE), precision, and K-index were utilized for judging the performance of novel ensemble MLAs. The obtained results show that the PSO-RF had the highest performance with an AUC of 0.874, followed by RF, PSO-MLP, PSO-Bagging, Bagging, MLP, PSO-SVM and SVM, respectively. Produced DVMs would be helpful for policy intervention to minimize drought vulnerability