Arsenic tolerances in rice (Oryza sativa) have a predominant role in transcriptional regulation of a set of genes including sulphur assimilation pathway and antioxidant system

World wide arsenic (As) contamination of rice has raised much concern as it is the staple crop for millions. Four most commonly cultivated rice cultivars, Triguna, IR-36, PNR-519 and IET-4786, of the West Bengal region were taken for a hydroponic study to examine the effect of arsenate (As^V) and arsenite (As^III) on growth response, expression of genes and antioxidants vis-à-vis As accumulation. The rice genotypes responded differentially under As^V and As^III stress in terms of gene expression and antioxidant defences. Some of the transporters were up-regulated in all rice cultivars at lower doses of As species, except IET-4786. Phytochelatin synthase, GST and γ-ECS showed considerable variation in their expression pattern in all genotypes, however in IET-4786 they were generally down-regulated in higher As^III stress. Similarly, most of antioxidants such as superoxide dismutase (SOD), ascorbate peroxidase (APX), guaiacol peroxidase (GPX), catalase (CAT), monodehydroascorbate reductase (MDHAR) and dehydroascorbate reductase (DHAR) increased significantly in Triguna, IR-36 and PNR-519 and decreased in IET-4786. Our study suggests that Triguna, IR-36 and PNR-519 are tolerant rice cultivars accumulating higher arsenic; however IET-4786 is susceptible to As-stress and accumulates less arsenic than other cultivars.     

Emission of volatile organic compounds from religious and ritual activities in India

Worshipping activity is a customary practice related with many religions and cultures in various Asian countries, including India. Smoke from incense burning in religious and ritual places produces a large number of health-damaging and carcinogenic air pollutants include volatile organic compounds (VOCs) such as formaldehyde, benzene, 1,3 butadiene, styrene, etc. This study evaluates real-world VOCs emission conditions in contrast to other studies that examined emissions from specific types of incense or biomass material. Sampling was conducted at four different religious places in Raipur City, District Raipur, Chhattisgarh, India: (1) Hindu temples, (2) Muslim graveyards (holy shrines), (3) Buddhist temples, and (4) marriage ceremony. Concentrations of selected VOCs, respirable particulate matter (aerodynamic diameter, <5 μm), carbon dioxide, and carbon monoxide were sampled from the smoke plumes. Benzene has shown highest emission factor (EF) among selected volatile organic compounds in all places. All the selected religious and ritual venues have shown different pattern of VOC EFs compared to laboratory-based controlled chamber studies.     

Enhancement of nutritional value of fried fish using an artificial intelligence approach

Frying affects the nutritional quality of fish detrimentally. In this study, using Catla catla and mustard oil, experiments were carried out in varying temperatures (140–240 °C), times (5–20 min), and oil amounts (25–100 ml/kg of fish) which established drastic reduction of 44.97% and 99.40% for polyunsaturated fatty acid (PUFA)/saturated fatty acids (SFA) and index of atherogenicity (IA) profile, respectively. Artificial neural network (ANN) was implemented successfully to provide an association between the independent inputs with dependent outputs (values of R² were 0.99 and 0.98; RMSE were 0.038 and 0.046; and performance were 0.038 and 0.067 for PUFA/SFA and IA, respectively) by exhaustive search of various algorithms and activation functions available in literature. ANN model–based meta-heuristic, stochastic optimization formalisms, genetic algorithm (GA) and particle swarm optimization (PSO), were applied to optimize the combination of cooking parameters for improving the nutritional quality of food which improved the nutritional value by maximizing the PUFA/SFA profile up to 63.05% and minimizing the IA profile to 99.64%. Multi-objective genetic algorithm (MOGA) was also employed to tune the inputs by maintaining a balance between the contrasting outputs and enhance the overall food value simultaneously with multi-objective (beneficial for health, economic, and environment-friendly) proposal. MOGA was able to improve the PUFA/SFA profile up to 44.76% and reduce the IA profile to 92.94% concurrently with the reduction of wastage of culinary media and energy consumption, following the optimized cooking condition (118.92 °C, 6.06 min, 40 ml oil/kg of fish).