Current trends in production, morphology, and real-world environmental applications of biochar for the promotion of sustainability.

Biochar has sparked a lot of interest in recent years for various applications such as contaminant removal, carbon sequestration and soil amendment. However, laboratory scale studies dominates over the real-world applications. In this study, we first enumerated on the various ongoing uses of biochar along with adsorption mechanism; and then identified the most suitable source, and efficient method for biochar production; and finally proposed real-world practical applications of biochar as an additive to control diffuse micropollutant pollution along the highways and railway tracks; as well as the pollution through agricultural runoff. We demonstrated that directly or indirectly, biochar can definitely help to achieve all the 17 Sustainable Development Goals. The study on the practical use of biochar is an emerging area, because a lot of research needs are there to realise the full-potential of biochar with ways of attaining sustainable development goals towards waste, energy and pollution management.

A novel multiscale and multipath convolutional neural network based age-related macular degeneration detection using OCT images.

BACKGROUND AND OBJECTIVE: One of the significant retinal diseases that affected older people is called Age-related Macular Degeneration (AMD). The first stage creates a blur effect on vision and later leads to central vision loss. Most people overlooked the primary stage blurring and converted it into an advanced stage. There is no proper treatment to cure the disease. So the early detection of AMD is essential to prevent its extension into the advanced stage. This paper proposes a novel deep Convolutional Neural Network (CNN) architecture to automate AMD diagnosis early from Optical Coherence Tomographic (OCT) images. METHODS: The proposed architecture is a multiscale and multipath CNN with six convolutional layers. The multiscale convolution layer permits the network to produce many local structures with various filter dimensions. The multipath feature extraction permits CNN to merge more features regarding the sparse local and fine global structures. The performance of the proposed architecture is evaluated through ten-fold cross-validation methods using different classifiers like support vector machine, multi-layer perceptron, and random forest. RESULTS: The proposed CNN with the random forest classifier gives the best classification accuracy results. The proposed method is tested on data set 1, data set 2, data set 3, data set 4, and achieved an accuracy of 0.9666, 0.9897, 0.9974, and 0.9978 respectively, with random forest classifier. Also, we tested the combination of first three data sets and achieved an accuracy of 0.9902. CONCLUSIONS: An efficient algorithm for detecting AMD from OCT images is proposed based on a multiscale and multipath CNN architecture. Comparison with other approaches produced results that exhibit the efficiency of the proposed algorithm in the detection of AMD. The proposed architecture can be applied in rapid screening of the eye for the early detection of AMD. Due to less complexity and fewer learnable parameters.

Traditionally practiced medicinal plant extracts inhibit the ergosterol biosynthesis of clinically isolated dermatophytic pathogens.

OBJECTIVE: Dermatophytes are resistant to some available antibiotics. Development of new plant drugs to control drug resistant microbes is urgently needed. This study evaluates the antidermatophytic potential of 18 selected medicinal plants used by traditional healers in Theni and Virudhunagar Districts of Tamil Nadu, India. MATERIALS AND METHODS: Selected plant parts were collected, shade dried and powdered. Plant powders were extracted with ethanol and their antifungal potency was investigated against and clinical dermatophytes. The antioxidant effect of the extracts was screened using DPPH assay. Minimal Inhibitory Concentration (MIC) and Minimal Fungicidal Concentration (MFC) were estimated for the extracts. Ten plant extracts showed maximum MFC and they were selected to study their efficacy in interfering with ergosterol biosynthesis. Fluconazole-35µg/mL known fungicide was used as control. The most active extracts were taken for biocompatibility studies using 3T3-L1 fibroblast cell lines. RESULTS: The ethanol extract of Phyllanthus reticulates leaves showed good antifungal activity compared to other plant extracts. The MIC and MFC for Phyllanthus reticulatus were 62.5 and 250µg/mL against M. pachydermatitis and T. rubrum respectively. The ethanol extracts of Phyllanthus reticulatus, Coldenia procumbens, Thespesia populnea and Senna alata significantly lowered the release of ergosterol by 16.37, 19.53, 24.79, and 21.44%, respectively. The ethanol extract of Phyllanthus reticulatus leaves was more biocompatible to host cells than other active extracts. CONCLUSION: Our study indicated that the ethanol extract of Phyllanthus reticulates leaves showed promising activity against dermatophytes. It could be a potential material for future development of antidermatophytic agents.