Bioactive components,physicochemical and starch characteristics of different parts of lotus (Nelumbo nucifera Gaertn.) plant: a review

Lotus plant parts such as flower, seed, root, leaf and stem or rhizome have a great potential for food and non-food applications. This review recognises the nutritional and therapeutic benefits, functional behaviour, post-harvest processing and applications of the lotus plant. Its unique bioactive constituents, such as phenolic compounds, alkaloids, flavonoids and essential oils, have proven efficacy in improvement of health. The good amount of dietary fibre, starch, vitamins and minerals suggest that it has potentially an appropriate opportunity for future valorisation and application as functional additive and ingredient in the food industry. As a functional ingredient for use in the food industry like flour in bakery industry, as an antioxidant, for incorporation in baby foods, extruded products, beverages etc. there is a need to study the characteristics of lotus plant especially the rhizome part.     

The Potential Role of Flavonoids in Ameliorating Diabetic Cardiomyopathy via Alleviation of Cardiac Oxidative Stress,Inflammation and Apoptosis

Diabetic cardiomyopathy is one of the major mortality risk factors among diabetic patients worldwide. It has been established that most of the cardiac structural and functional alterations in the diabetic cardiomyopathy condition resulted from the hyperglycemia-induced persistent oxidative stress in the heart, resulting in the maladaptive responses of inflammation and apoptosis. Flavonoids, the most abundant phytochemical in plants, have been reported to exhibit diverse therapeutic potential in medicine and other biological activities. Flavonoids have been widely studied for their effects in protecting the heart against diabetes-induced cardiomyopathy. The potential of flavonoids in alleviating diabetic cardiomyopathy is mainly related with their remedial actions as anti-hyperglycemic, antioxidant, anti-inflammatory, and anti-apoptotic agents. In this review, we summarize the latest findings of flavonoid treatments on diabetic cardiomyopathy as well as elucidating the mechanisms involved.     

Probabilistic Hierarchical Model Using First Person Vision for Scenario Recognition

Wireless Personal Communications - Smart homes are becoming a growing need to prepare for a comfortable life style for the elderly and make things easy for the caretakers of the future. One...     

Artificial intelligence density model for oxide glasses

A comprehensive study to perform glass density prediction employing artificial intelligence using a dataset of 6630 oxide glass samples. The prediction is done based on Ionic packing ratio as the independent variable and experimental densities from the dataset as the dependent variable. Random forest regression and artificial neural networks were observed as the best models training the density datasets. The random forest regression had the least average R² score for large datasets. Artificial neural networks employing sigmoid and ReLU activation functions dominate in predicting the glass density as compared to tanh and identity activation functions. Based on this study we can theoretically predict the density of any oxide glass to an extent of maximum accuracy for a known glass composition.     

Applications of FT-NIRS combined with PLS multivariate methods for the detection & quantification of saccharin adulteration in commercial fruit juices

Detection of adulteration in carbohydrate-rich foods like fruit juices is particularly difficult because of the variety of the commercial sweeteners available that match the concentration profiles of the major carbohydrates in the foods. In present study, a new sensitive and robust assay using Fourier Transform Near-Infrared Spectroscopy (FT-NIRS) combined with partial least square (PLS) multivariate methods has been developed for detection and quantification of saccharin adulteration in different commercial fruit juice samples. For this investigation, six different commercially available fruit juice samples were intentionally adulterated with saccharin at the following percentage levels: 0%, 0.10%, 0.30%, 0.50%, 0.70%, 0.90%, 1.10%, 1.30%, 1.50%, 1.70% and 2.00% (weight/volume). Altogether, 198 samples were used including 18 pure juice samples (unadulterated) and 180 juice samples adulterated with saccharin. PLS multivariate methods including partial least-squares discriminant analysis (PLS-DA) and partial least-squares regressions (PLSR) were applied to the obtained spectral data to build models. The PLS-DA model was employed to differentiate between pure fruit juice samples and those adulterated with saccharin. The R² value obtained for the PLS-DA model was 97.90% with an RMSE error of 0.67%. Similarly, a PLS regression model was also developed to quantify the amount of saccharin adulterant in juice samples. The R² value obtained for the PLSR model was 97.04% with RMSECV error of 0.88%. The employed model was then cross-validated by using a test set which included 30% of the total adulterated juice samples. The excellent performance of the model was proved by the low root mean squared error of prediction value of 0.92% and the high correlation factor of 0.97. This newly developed method is robust, nondestructive, highly sensitive and economical.     

Impact of population and latrines on fecal contamination of ponds in rural Bangladesh

A majority of households in Bangladesh rely on pond water for hygiene. Exposure to pond water fecal contamination could therefore still contribute to diarrheal disease despite the installation of numerous tubewells for drinking. The objectives of this study are to determine the predominant sources (human or livestock) of fecal pollution in ponds and examine the association between local population, latrine density, latrine quality and concentrations of fecal bacteria and pathogens in pond water. Forty-three ponds were analyzed for E. coli using culture-based methods and E. coli, Bacteroidales and adenovirus using quantitative PCR. Population and sanitation spatial data were collected and measured against pond fecal contamination. Humans were the dominant source of fecal contamination in 79% of the ponds according to Bacteroidales measurements. Ponds directly receiving latrine effluent had the highest concentrations of fecal indicator bacteria (up to 10⁶ Most Probable Number (MPN) of culturable E. coli per 100 mL). Concentrations of fecal indicator bacteria correlated with population surveyed within a distance of 30-70 m (p < 0.05) and total latrines surveyed within 50-70 m (p < 0.05). Unsanitary latrines (visible effluent or open pits) within the pond drainage basin were also significantly correlated to fecal indicator concentrations (p < 0.05). Water in the vast majority of the surveyed ponds contained unsafe levels of fecal contamination attributable primarily to unsanitary latrines, and to lesser extent, to sanitary latrines and cattle. Since the majority of fecal pollution is derived from human waste, continued use of pond water could help explain the persistence of diarrheal disease in rural South Asia.