Quick and reliable estimation of BOD load of beverage industrial wastewater by developing BOD biosensor

An amperometric biosensor for determination of biochemical oxygen demand in wastewater has been developed to overcome the time consuming monitoring procedures. The performance and stability of the immobilized membrane have been investigated at 37 °C and pH 6.8. Immobilized microbial membranes maintain their stability and activity after intermittent use for 400 cycles when stored at 4 °C in sodium phosphate buffer pH 6.8. The response time of the BOD sensor was only 90 min, being independent of the concentration, and the lower detection limit was 1 mg/l. The obtained BOD values showed correlation with that of the conventional method for BOD determination (BOD5) with a deviation of ±10%. Moreover, the sensor exhibits good repeatability (3.39–4.45%) and reproducibility (1.85–2.25%). Software was added to upgrade this sensor and to make it a promising candidate for online monitoring.     

Improving the capacity,redox activities of Li-ion batteries through Si3N4@MoS2 hetero-structure design

Journal of Materials Science - To develop advanced anode materials for Li-ion batteries (LIB), an extensive research effort is being employed. The effort focuses much on silicon-based anodes due to...     

A robust similarity based deep siamese convolutional neural network for gait recognition across views

Gait recognition has been considered as the emerging biometric technology for identifying the walking behaviors of humans. The major challenges addressed in this article is significant variation caused by covariate factors such as clothing, carrying conditions and view angle variations will undesirably affect the recognition performance of gait. In recent years, deep learning technique has produced a phenomenal performance accuracy on various challenging problems based on classification. Due to an enormous amount of data in the real world, convolutional neural network will approximate complex nonlinear functions in models to develop a generalized deep convolutional neural network (DCNN) architecture for gait recognition. DCNN can handle relatively large multiview datasets with or without using any data augmentation and fine-tuning techniques. This article proposes a color-mapped contour gait image as gait feature for addressing the variations caused by the cofactors and gait recognition across views. We have also compared the various edge detection algorithms for gait template generation and chosen the best from among them. The databases considered for our work includes the most widely used CASIA-B dataset and OULP database. Our experiments show significant improvement in the gait recognition for fixed-view, crossview, and multiview compared with the recent methodologies.     

Production and characterization of glycolipid biosurfactant from Streptomyces enissocaesilis HRB1 and its evaluation for biomedical and bioremediation applications

The aim of this investigation is to produce and characterize biosurfactant from Streptomyces sp. HRB1 and to evaluate its biomedical and bioremediation potential. Biosurfactant producing property of Streptomyces sp. HRB1 isolated from petroleum contaminated soil was confirmed by hemolytic and oil spread assays. Based on the results of FT-IR spectral and GC–MS analysis, the biosurfactant was confirmed as glycolipid type. Biosurfactant from Streptomyces sp. HRB1 exhibited 71% inhibition against Pseudomonas aeruginosa biofilm formation, 77.33% quorum sensing inhibition property against Chromobacterium violeceum MTCC 2656, more than 80% inhibition in antioxidant assays namely, DPPH, ABTS, and metal chelation, promising anti-proliferative activity against leukemia and myeloma cells with low IC₅₀ values, 96% decolorization of malachite green within 48 h of reaction time, and minimal toxicity against normal cell lines in dose-dependent manner. The taxonomic position of the potential strain HRB1 was further confirmed as Streptomyces enissocaesilis HRB1 based on their phenotypic and molecular characteristics. To conclude, Streptomyces enissocaesilis HRB1 isolated from petroleum-contaminated soil is a promising source for low-cost production of glycolipid biosurfactant with potential biomedical and environmental applications such as antiphytofungal, antibiofilm, anti-quorum sensing, antioxidant, anticancer, and dye degradation properties.     

Brain tumor segmentation using a deep Shuffled-YOLO network

Brain tumor is an anomalous proliferation of cells in the brain that can evolve to malignant and benign tumors. Currently, segmentation of brain tumor is the most important surgical and pharmaceutical procedures. However, manually segmenting brain tumors is hard because it is hard to find erratically shaped tumors with only one modality; the MRI modalities are integrated to provide multi-modal images with data that can be utilized to segment tumors. The recent developments in machine learning and the accessibility of medical diagnostic imaging have made it possible to tackle the challenges of segmenting brain tumors with deep neural networks. In this work, a novel Shuffled-YOLO network has been proposed for segmenting brain tumors from multimodal MRI images. Initially, the scalable range-based adaptive bilateral filer (SCRAB) pre-processing technique was used to eliminate the noise artifacts from MRI while preserving the edges. In the segmentation phase, we propose a novel deep Shuffled-YOLO architecture for segmenting the internal tumor structures that include non-enhancing, edema, necrosis, and enhancing tumors from the multi-modality MRI sequences. The experimental fallouts reveal that the proposed Shuffled-YOLO network achieves a better accuracy range of 98.07% for BraTS 2020 and 97.04% for BraTS 2019 with very minimal computational complexity compared to the state-of-the-art models.