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As COVID-19 persists, severe acquired respiratory syndrome coronavirus-2 (SARS-CoV-2) Variants of Concern (VOCs) emerge, accumulating spike (S) glycoprotein mutations. S receptor-binding domain (RBD) comprises a free fatty acid (FFA)-binding pocket. FFA-binding stabilizes a locked S conformation, interfering with virus infectivity. We provide evidence that the pocket is conserved in pathogenic β-coronaviruses (β-CoVs) infecting humans. SARS-CoV, MERS-CoV, SARS-CoV-2 and VOCs bind the essential FFA linoleic acid (LA), while binding is abolished by one mutation in common cold-causing HCoV-HKU1. In the SARS-CoV S structure, LA stabilizes the locked conformation while the open, infectious conformation is LA-free. Electron tomography of SARS-CoV-2 infected cells reveals that LA-treatment inhibits viral replication, resulting in fewer, deformed virions. Our results establish FFA-binding as a hallmark of pathogenic β-CoV infection and replication, highlighting potential antiviral strategies.One-Sentence SummaryFree fatty acid-binding is conserved in pathogenic β-coronavirus S proteins and suppresses viral infection and replication.

Candida albicans, GlcNAc inducible gene 2 (Gig2) is important for its virulence, oxidative stress adaptation and is supposed to be a part of the undefined GlcNAc metabolic network. On the basis of sequence homology, Gig2 is classified as a putative oxidoreductase of DUF1479 family (Domain of Unknown Function) with no reported structure and function. In this work, we have elucidated the crystal structure of Gig2 protein using X-ray crystallography at 1.7 A resolution. Crystals were improved using successive macro-seeding technique. Structure was solved by molecular replacement using a template (PDBID: 2CSG) with a mere 27% identity with Gig2, followed by manual and automated model building. Gig2 exists as a monomer with a single large DUF1479 domain and is composed of a cupin like double stranded β-helix (DBSH) core fold with Iron (Fe) in the active site. This is the first report of DUF1479 family proteins which identifies and highlights its unique structural features. Crystal structure elucidation, structural comparisons, and docking studies proposes Gig2 as a non-heme Fe (II) containing 2-oxoglutarate dependent oxygenase.

Introduction: The period of transition from nursing student to professional nurse is demanding. Most often the challenges among the novice nurses are attributed to the number of patients with complex illness and co-morbidities, inaccessible mentors, performance anxiety, communication difficulties, and blame/complaint culture. Transitional challenges could result in work dissatisfaction forcing novice nursing graduates to quit their jobs that result in a high turnover rate. The study aimed to identify the transitional challenges among new nursing graduates and the role of preceptor in various transitional challenges. Methods: The study adopted descriptive correlational design. The data were collected from 314 participants working in six different tertiary level public hospitals situated in six states of India. Casey-Fink graduate nurse experience survey-revised was used to collect the data and methods of this study were in line with the guidelines of Strengthening the Reporting of Observational studies in Epidemiology (STROBE). Descriptive and Inferential statistics were calculated using SPSS software version 16. Results: The study found that new nursing graduates are uncomfortable in performing numerous procedures independently and in accordance with them increased support would help them feel more supported or integrated into the unit. The study also found positive relationship between preceptor support and organizing and prioritizing, communication/leadership, professional satisfaction, and job satisfaction. Conclusion: New nursing graduates experience various challenges during their transition period in the areas of role expectation, confidence, workload, orientation, and fears. The preceptors and the nursing administrators needs to bring forth significant strategies to address these challenges.

In this paper, we tackle the twins identification problem; a challenging task in biometric authentication. The experiments are carried out using deep learning techniques and for that we have proposed a Convolutional Siamese Network (CNN-Siamese) and a multiscale Convolutional Siamese network (MCNN-Siamese). We have also explored and evaluated three pre-trained CNNs namely, ResNet-50, VGG-16 and NASNet-Large, and utilized them in Siamese network after appropriate modifications. In addition, a simple 5-layer neural network (sNN) is also utilised as Siamese subnetwork in the experiments. We have presented compelling experimental evidences in terms of correct classification accuracy (CCR) on CASIA-IrisV4 Twins’ dataset that manifest the effectiveness of ocular biometrics in identifying twins. The results achieve the existing state-of-the-art human level accuracy. Also, the proximity of CCRs of all the models asserts that the ocular regions in twins hold a significant correlation to label them as twins. We have also quantified the cross-domain capability of the proposed subnetworks (i.e. CNN and MCNN) on ND-GFI dataset that outperforms the state-of-the-art methods. Notably, for the ocular biometrics, to the best of our knowledge, there is currently no literature available as of date that explores the association between twins and subsequently unriddles the classification using Deep Learning.

The vulnerabilities of a Biometric Authentication System (BAS) to spoof attacks have gained substantial attention over the years. Iris recognition, one of the most reliable and accurate BASs is vulnerable to various kinds of presentation attacks such as by printed iris or contact lenses. The presentation attack detection (PAD) becomes even more challenging when an attacker imposes variations by carrying out multiple spoofing attacks. To address this challenge, an end-to-end Deep Supervised Class Encoding (DSCE) approach for iris-PAD is proposed in this paper. This is to deal with three main attacks by (i) printed iris images, (ii) contact lenses, and (iii) synthetically generated iris images. DSCE is an autoencoder based supervised feature learning approach that exploits the class information, and minimizes the reconstruction and classification errors simultaneously during the training phase. DSCE is employed to design an iris-PAD framework termed as DeepI, to perceive counterfeit access to an iris-BAS. Experimental results on different benchmark databases show that DSCE based DeepI outperforms the current state-of-the-art iris-PAD methods. Also, in cross-database training-testing settings, the proposed approach manifests a promising generalization capability.

The inner boundary of iris represents the pupil’s edge. Hence, to work an Iris Recognition System (IRS) and the gaze tracking system expeditiously it is important to locate it as precisely as possible in a significant amout of time. In the presence of non-ideal constraints e.g. non-uniform illumination, poor contrast, eyelashes, hairs, glasses, off-angle orientation, these systems may not work well. In this paper we present an adaptive pupil localization method based on the roundness criteria. First, it applies a gray level inversion to suppress the reflections, then it performs Gray level co-occurrence matrix (GLCM) based contrast estimation. If this estimated contrast is lower than a certain threshold, the input image is made to undergo gamma correction to adjust the contrast. Subsequently, anisotropic diffusion filtering followed by log transformation is applied, which suppresses the effect of eyelash occlusion, limits the creation of small regions and highlight the dark pixels. Afterwards, a clean binary image with few regions is acquired using adaptive thresholding and some morphological operations. Finally, the roundness metric is computed for each of these regions and the region with largest roundness metric, also being greater than a prescribed threshold, declared as pupil. Experiments were carried out on few well known databases, NICE1, CASIA V3 lamp, MMU, WVU and IITD. The results are grounded upon subjective and objective evaluation; which in turn, indicate that our method outperforms a state-of-the-art approach and a deep learning approach in terms of localization capability in some unconstrained scenarios and shorter processing time. After assessing the performance of the proposed algorithm, it is manifested that it ensures a fast and robust localization of pupil in the presence of corneal reflection, poor contrast, glasses and eyelash occlusion.

The β-clamp is the processivity-promoting factor for most of the enzymes in prokaryotic DNA replication; hence, it is a crucial drug target. In the present study, we investigated the β-clamp from Helicobacter pylori, aiming to seek potential drug molecules against this gastric-cancer-causing bacterium. An in silico screening of Food and Drug Administration (FDA) approved drugs against the H. pylori β-clamp, followed by its in vitro inhibition using a surface competition approach, yielded the drug diflunisal as a positive initial hit. Diflunisal inhibits the growth of H. pylori in the micromolar range. We determined the structure of diflunisal in complex with the β-clamp to show that the drug binds at subsite I, which is a protein-protein interaction site. Successful identification of FDA-approved molecules against H. pylori may lead to better and faster drug development.

The main aim of this paper is to analyse the iris texture which becomes the basis of feature extraction. In Iris Recognition System (IRS), feature extraction is one of the significant steps which is followed by a matching process. The proposed method takes into account three techniques: DSIFT, HOG and DCT. These techniques are individually employed on the blocks that correspond to the regions of interest, not occluded by noise. A block-wise matching of obtained feature vectors is carried out using a standard measure. The true match of the system relies on the three aforementioned techniques which make the system more robust. The experimental results are reported on CASIA-IrisV1 database. The obtained outcomes indicate that the performance of the system increased significantly due to the score level fusion of the matching that makes it suitable for the iris recognition related applications.

A categorical study on iris localization, its challenges and techniques is carried out here. This survey comprehends the historical evolution and present state-of-the-art in iris localization. Most research publications include their primary contribution in iris localization based on tools like Hough Transform, Integro-differential Operator, Active Contour and Deep Learning etc. Majority of the techniques discussed in this paper are popular and efficient enough, however some are unique in their ways. Vast and growing horizon of iris biometric research forces this work to be selective. Our work will be of assistance to someone who is new to iris biometrics and wants to acquire a quick knowledge about iris localization, hurdles before it and various methods to achieve this.

In this study Bacillus sp. MTCC5877 was explored for the production of biosurfactant (BSs) and various carbon sources 1% (w/v), 0.5% (w/v) nitrogen sources were tested at different pH, and temperature. Yield was measured in terms of Emulsification index (EI), Oil Displacement Area (ODA) and Drop Collapse Area (DCA) and maximum emulsification activities of BSs were found (E-24) 50%, 76% and 46%, respectively, and maximum ODA of 5.0, 6.2 and 4.7 cm, were shown respectively. The BS was able to reduce the surface tension of water from 72 to 30 mN/m and 72 to 32 mN/m. Structural compositions of BS were confirmed by FTIR, GC-MS and NMR. Anti-adhesive property of BS was determined and found effective against biofilm formation. It could remove 73% Cd from vegetable which confirms its application in food industry. (C) 2016 Elsevier Ltd. All rights reserved.

Drinking water is a basic need for survival and good health of human beings as well as for any form of life on earth. In the absence of potable water, there is a high risk of transmission of serious diseases among people, like diarrhea, dysentery, cholera, and typhoid. Despite significant progress on community water schemes, worldwide still 2.2 billion people are out of access to safely managed water services. This study was conducted during the period from February to July 2020. Published literature such as paper, thesis, manuals, database, reports, and standards were collected from different sources and went on thoroughly. The conclusions are drawn based on studied materials. The study found that about 829 thousand people are estimated to die each year from diarrhea because of unsafe contaminated drinking water, sanitation, and hand hygiene. In South Asia, over 134 million people still do not have access to clean drinking water. Various studies have shown that progress on basic water supply in Nepal has reached up to 89% of the population by the end of 2019. Community drinking water schemes in Nepal are facing various types of problems, such as technical problems related to, gender equity and social inclusion (GESI), cost recovery and affordability, operation and maintenance, institutional, and legal framework. Because those problems on water schemes are not timely and appropriately addressed, there is always a high impact on un-functionality, poor service, and unsustainability. The study would like to suggest that the operation and maintenance funds should be managed after the implementation of the project. The capacity building training for the water users committee should be conducted before the handover of the schemes.

This paper presents a multilevel image thresholding approach which relies on Tsallis entropy using Fuzzy partition with a novel threshold selection technique. In order to compute the optimal threshold values, Differential Evolution (DE) has been employed. The proposed method can further be exploited in image segmentation which is considered to be a critical step in image processing. Our proposed threshold selection technique is based on Tsallis-Fuzzy entropy and the results are compared with Shannon entropy (or fuzzy entropy) and Tsallis entropy based existing threshold selection techniques. The experiments are performed on two different sets of images and the results have been compared with that of existing state-of-the-art methods, namely, Patch Levy Bees' Algorithm (PLBA), Bacterial Foraging optimization (BFO), modified Bacterial Foraging optimization (MBFO) and Bees' Algorithm (BA). Quantitative analysis is carried out based on three image quality metrics viz SSIM, PSNR and SNR. Standard deviation and CPU time for convergence of the objective function have been calculated for performance evaluation. Furthermore, the statistical significance of our method has been estimated using Friedman test and Wilcoxon test. The experimental results manifest that our method produces results superior to the methods in comparison.

The role of TatD DNases as DNA repair enzymes or cell death (apoptotic) nucleases is well established in prokaryotes as well as eukaryotes. The current study aims to characterize the TatD nuclease from Bacillus anthracis (Ba TatD) and to explore its key histidine catalytic residues. Ba TatD was found to be a metal-dependent, nonspecific endonuclease which could efficiently cleave double-stranded DNA substrates. Moreover, Ba TatD nuclease was observed to be thermostable up to 55°C and act in a wide pH range indicating its industrial applicability. Diethyl pyrocarbonate-based histidine-selective alkylation of the Ba TatD resulted in a loss of its nuclease activity suggesting a crucial role of the histidine residues in its activity. The key residues of Ba TatD were predicted using sequence analysis and structure-based approaches, and then the predicted residues were further tested by mutational analysis. Upon mutational analysis, H128 and H153 have been found to be crucial for Ba TatD activity, though H153 seems to bear an important but a dispensable role for the Ba TatD nuclease. Ba TatD had a uniform expression in the cytosol of B. anthracis, which indicates a significant role of the protein in the pathogen's life cycle. This is the first study to identify and characterize the TatD DNase from B. anthracis and will be helpful in gaining more insights on the role of TatD proteins in Gram-positive bacteria where it remains unexplored.

In this paper, image contrast enhancement is achieved by combining together the local–global image statistics and multiscale morphological filtering (MMF). The proposed method has been executed on two different sets of images, and the result has been compared with that of some existing standard methods, namely histogram equalization (HE), contrast limited adaptive histogram equalization (CLAHE), and multiscale morphology in order to have an outlook on the relative performances. The experimental results manifest that the proposed method produced results superior to the methods compared.

The versatility in the recognition of various interacting proteins by the SH3 domain drives a variety of cellular functions. Here, the crystal structure of the C-terminal SH3 domain of myosin IB fromEntamoeba histolytica(EhMySH3) is reported at a resolution of 1.7 Å in native and PEG-bound states. Comparisons with other structures indicated that the PEG molecules occupy protein–protein interaction pockets similar to those occupied by the peptides in other peptide-bound SH3-domain structures. Also, analysis of the PEG-boundEhMySH3 structure led to the recognition of two additional pockets, apart from the conventional polyproline and specificity pockets, that are important for ligand interaction. Molecular-docking studies combined with various comparisons revealed structural similarity betweenEhMySH3 and the SH3 domain of β-Pix, and this similarity led to the prediction thatEhMySH3 preferentially binds targets containing type II-like PXXP motifs. These studies expand the understanding of theEhMySH3 domain and provide extensive structural knowledge, which is expected to help in predicting the interacting partners which function together with myosin IB during phagocytosis inE. histolyticainfections.

The current work is enlightened about a cost effective bioprocess using one factor at a time approach for the production of bio-surfactant through solid state fermentation. A fungal strain Penicillium chrysogenum SNP5 isolated from grease contaminated soil was reconnoitered for the production of bio-surfactant. Various physiochemical parameters i.e., substrate composition, nitrogen supplements, extraction media and pH were optimized in order to optimized the production in terms of emulsification index and oil displacement assay. Maximum oil displacement area produced using grease waste and wheat bran (1:1 w/w), waste cooking oil and wheat bran (1:1 v/w) as a substrate were 3.5 cm and 5 cm, respectively. Whereas, considered values for emulsification activity with oil and diesel were 43% and 22% during optimization of substrate composition. Variable ratios of grease waste and wheat bran were capable to enhance the emulsification activity with oil and diesel up to 45% and 24% in presence of grease and wheat bran (1.5:1). The strain also showed enhancement of emulsification activity 45% and 23% with oil and diesel respectively to utilized yeast extract as a nitrogen source and the highest emulsification activity 38% in diesel, 47% in oil and oil displacement 5.5 cm was found at pH 8 with grease and wheat bran as a substrate. Preliminary characterizations by thin layer chromatography showed that the bio-surfactant was lipopeptide in nature and was also confirmed through FTIR analysis. Metabolization of industrial grease waste through solid state fermentation has never been reported before for the production of biosurfactants therefore would be applicable in petroleum and biodiesel industry. The partially purified biosurfactants was further investigated for antimicrobial activity and enhanced oil recovery. It displayed effective zones of inhibition against both gram +ve (1.67 cm) and gram –ve (1.93 cm) as well as 16.5% enhanced recovery of oil. Both results also give a positive support to its role in pharmaceuticals as well as in petroleum and oil industry.