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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.

The phase change material (PCM) for thermal energy storage (TES) has the characteristics of high heat storage density and great thermal storage capacity, and can effectively store solar energy resources. To put it simply a thermal energy storage captures heat from a hot fluid, and stores the energy for later use. The primary components in the thermal energy storage consists of the following: Storage medium, heat transfer fluid, and the insulation. The heat transfer fluid captures heat from solar irradiation, and transfers portion of the heat to the storage medium. When the heat transfer fluid goes cold after sunset, the storage medium replaces the sun as a heat source and heats the fluid.

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 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.

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.

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.

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.