An effective stacked autoencoder based depth separable convolutional neural network model for face mask detection.

The COVID-19 pandemic has been infecting the entire world over the past years. To prevent the spread of COVID-19, people have acclimatised to the new normal, which includes working from home, communicating online, and maintaining personal cleanliness. There are numerous tools required to prepare to compact transmissions in the future. One of these elements for protecting individuals from fatal virus transmission is the mask. Studies have indicated that wearing a mask may help to reduce the risk of viral transmission of all kinds. It causes many public places to take efforts to ensure that its guests wear adequate face masks and keep a safe distance from one another. Screening systems need to be installed at the doors of businesses, schools, government buildings, private offices, and/or other important areas. A variety of face detection models have been designed using various algorithms and techniques. Most of the articles in the previously published research have not worked on dimensionality reduction in conjunction with depth-wise separable neural networks. The necessity of determining the identities of people who do not cover their faces when they are in public is the driving factor for the development of this methodology. This research work proposes a deep learning technique to determine if a person is wearing mask or not and identifies whether it is properly worn or not. Stacked Auto Encoder (SAE) technique is implemented by stacking the following components: Principal Component Analysis (PCA) and Depth-wise Separable Convolutional Neural Network (DWSC-NN). PCA is used to reduce the irrelevant features in the images and resulted high true positive rate in the detection of mask. We achieved an accuracy score of 94.16% and an F1 score of 96.009% by the application of the method described in this research.

Analysis of the chemical composition of root essential oil from Indian sarsaparilla (Hemidesmus indicus) and its application as an ecofriendly insecticide and pharmacological agent.

The Indian sarsaparilla (Hemidesmus indicus) is a commonly used plant in Indian traditional medicine of Ayurveda for the preparation of various non-alcoholic beverages. However, limited studies are available on the essential oil of H. indicus roots (HRO); therefore, the study evaluated the antioxidant, anti-inflammatory and antidiabetic activities of H. indicus root essential oil as well as insecticide potential against the common pests of stored food materials (Sitophilus oryzae, Callosobruchus maculatus and Tribolium castaneum). The repellant efficacy of HRO was found to be high against S. oryzae (8.21 ± 0.55 µg/mL). Likewise, the fumigant potential was also observed for HRO against these pests; the higher activities were observed against S. oryzae and C. maculatus (32.46 ± 1.42 and 35.18 ± 1.62 µg/L). Besides, the essential oil was also found to be active as a contact poison, however, against all the three pests, the toxicity was above 100 µg/mm3, being the highest against C. maculatus (122.8 ± 3.57 µg/mm3). To analyze the possible effect of the essential oil on grains, the different grains were allowed to germinate and compared to that of normal; thus, the non-toxic nature of HRO against the stored products is also confirmed. The essential oil shown to have DPPH hydrogen peroxide and ABTS radical scavenging, nitric oxide scavenging potential, and inhibition of lipoxgenase, alpha-amylase and alpha-glucosidase. Overall, the present study concludes that the H. indicus may be a suitable repellant and fumigant agent against different pests of stored products and a possible antioxidant, anti-inflammatory, and anti-diabetic agent.

Phytochemical profiling of Azima tetracantha Lam. leaf methanol extract and elucidation of its potential as a chain-breaking antioxidant, anti-inflammatory and anti-proliferative agent.

Azima tetracantha, a traditional medicinal plant included in the order Brassicales and family Salvadoraceae, is widely used as a dietary supplement in folklore medicines. The plant is also used for the treatment of rheumatism, diarrhea and other inflammatory disorders. The present investigation focused on the phytochemical composition, radical scavenging, reducing potential and anti-proliferative activities of the A. tetracantha leaves. Quantitative estimation of the polyphenols and flavonoids revealed significantly elevated levels in the methanol extract. Corroborating with this, methanol extract exhibited higher in vitro anti-radical scavenging effect against 2,2-diphenyl-1- picrylhydrazyl (34.14 ± 2.19 µg/mL), and hydrogen peroxide (44.96 ± 1.77 µg/mL), as well as ferric reducing properties (58.24 ± 6.98 µg/mL). The methanolic extract also showed strong lipoxygenase (71.42 ± 6.36 µg/mL) and nitric oxide inhibitory activities (94.23 ± 8.11 µg/mL). Cytotoxic activity against MCF7 cells was found to be higher (IC50= 37.62 ± 2.94 µg/mL), than that of MDAMB231 cells (IC50= 69.11 ± 5.02 µg/mL). The qPCR-based analysis indicated dose-dependent increase in the expression of the pro-apoptotic genes such as executioner caspases and apoptotic protease activating factor-1. Overall, the results indicated the possible use of methanol extract of A. tetracantha leaves as a chain-breaking antioxidant molecule and are capable of inhibiting inflammatory enzymes and the proliferative potential of breast cancer cells.