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PurposeThe aim of the paper is to measure the service quality, satisfaction, service value and behavioral intentions of Gen Z in leading global Quick Service Restaurants (QSRs) in India by integrating QUICKSERV into an established model of consumer behavior.Design/methodology/approachA cross-sectional study design was used for the hypothesis testing. Service quality perceptions with satisfaction, service value and behavioral intentions were measured using structural equation modeling.FindingsThe outcomes suggest a direct effect of the service quality of QSRs on the satisfaction, service value and behavioral intentions of Gen Z customers. Satisfaction further influenced customers' behavioral intentions. However, customer satisfaction and behavioral intentions were not directly influenced by service value. Finally, the association between service quality and behavioral intentions was mediated by satisfaction.Practical implicationsManagers should encourage a pleasant attitude, good grooming and friendliness in QSR employees as Gen Z highly values these aspects. At the same time, QSRs should focus to elevate the service value of Gen Z customers by lowering their sacrifice perceptions and fostering initiatives.Originality/valueAlthough many studies have considered millennials along with Gen Z to analyze the relationship between service quality and behavioral intentions in different service settings, few researchers have considered the impact of Gen Z consumer features in service quality research separately. The findings of the study will help both practitioners of different QSR brands and facilitators in hospitality academia to better understand the nuances and uniqueness of Gen Z consumer behavior in the QSRs.

Presently, the world needs safe and effective vaccines to overcome the COVID-19 pandemic. Our work has focused on formulating two types of mRNA vaccines that differ in capacity to copy themselves inside the cell. These are non-amplifying mRNA (NRM) and self-amplifying mRNA (SAM) vaccines. Both the vaccine candidates encode an engineered viral replicon which can provoke an immune response. Hence we predicted and screened twelve epitopes from the spike glycoprotein of SARS-CoV-2. We used five CTL, four HTL, and three B-cell-activating epitopes to formulate each mRNA vaccine. Molecular docking revealed that these epitopes could combine with HLA molecules that are important for boosting immunogenicity. The B-cell epitopes were adjoined with GPGPG linkers, while CTL and HTL epitopes were linked with KK linkers. The entire protein chain was reverse translated to develop a specific NRM-based vaccine. We incorporate gene encoding replicase in the upstream region of CDS encoding antigen to design the SAM vaccine. Subsequently, signal sequences were added to human mRNA to formulate vaccines. Both vaccine formulations translated to produce the epitopes in host cells, initiate a protective immune cascade, and generate immunogenic memory, which can counter future SARS-CoV-2 viral exposures before the onset of infection. Supplementary Information The online version contains supplementary material available at 10.1007/s12033-021-00432-6.

Newly emerging significant SARS-CoV-2 variants such as B.1.1.7, B.1.351, and B.1.1.28 are the variant of concern (VOC) for the human race. These variants are getting challenging to contain from spreading worldwide. Because of these variants, the second wave has started in various countries and is threatening human civilization. Thus, we require efficient vaccines that can combat all emerging variants of SARS-CoV-2. Therefore, we took the initiative to develop a peptide-based next-generation vaccine using four variants (Wuhan variant, B.1.1.7, B.1.351, and B.1.1.28) that could potentially combat SARS-CoV-2 variants. We applied a series of computational tools, servers, and software to identify the most significant epitopes present on the mutagenic regions of SARS-CoV-2 variants. The immunoinformatics approaches were used to identify common B cell derived T cell epitopes, influencing the host immune system. Consequently, to develop a novel vaccine candidate, the antigenic epitopes were linked with a flexible and stable peptide linker, and the adjuvant was added at the N-terminal end. 3D vaccine candidate structure was refined, and quality was assessed using web servers. The physicochemical properties and safety parameters of the vaccine construct were assessed through bioinformatics and immunoinformatics tools. The molecular docking analysis between TLR4/MD2 and the proposed vaccine candidate demonstrated a satisfactory interaction. The molecular dynamics studies confirmed the stability of the vaccine candidate. Finally, we optimized the proposed vaccine through codon optimization and in silico cloning to study the expression. Our multi-epitopic next-generation peptide vaccine construct can boost immunity against the Wuhan variant and all significant mutant variants of SARS-CoV-2.

The study aims to delineate the factors affecting the behavioral intentions of hotel interns of the reputed Institute of Hotel Management (IHMs). Also, the study intends to identify the association...

Aim: COVID-19 is currently the biggest threat to mankind. Recently, ivermectin (a US FDA-approved antiparasitic drug) has been explored as an anti-SARS-CoV-2 agent. Herein, we have studied the possible mechanism of action of ivermectin using in silico approaches. Materials & methods: Interaction of ivermectin against the key proteins involved in SARS-CoV-2 pathogenesis were investigated through molecular docking and molecular dynamic simulation. Results: Ivermectin was found as a blocker of viral replicase, protease and human TMPRSS2, which could be the biophysical basis behind its antiviral efficiency. The antiviral action and ADMET profile of ivermectin was on par with the currently used anticorona drugs such as hydroxychloroquine and remdesivir. Conclusion: Our study enlightens the candidature of ivermectin as an effective drug for treating COVID-19.

Marine fishes are one of the important factors to stabilize the local aquatic ecosystem and regulating socio-economy of local fishermen. In this study we emphasized upon finding out the interrelationship in the water quality, anthropogenic activity, and fish habitat through the 31km stretch of East Medinipur coast in West Bengal, India which is known for its tourist destinations. The study was conducted monthly on different trawler fish landing sites from Dec 2018 to Dec 2021. During this period, we took fish samples and identified them. We took water quality data such as SST, Concentration of Chl-a, Turbidity, and DO for further correlation between the water quality and species diversity. Total 154 numbers of commercially important marine fish species were documented. Among them, 12 species are from Elasmobranch and the rest is Actinopterygii. As per the IUCN database, 13% of the total fish species are under the red list category and 16% of the species showing decreasing population trend. After analyzing the water quality data, we found out that DO, SST, Turbidity, and Chlorophyll-A correlate with the species richness and it also differs during the seasons.

Helicobacter pylori is a highly potential pathogen to colonize in the human stomach. This bacterial strain is now alarming serious health concern all over the world. Combating through available drugs is a difficult task due to lack of appropriate common targets against genetically diverse strains. Therefore, the developments of effective targets vaccines require alternative strategies to eliminate the H. pylori infection. In this study, we developed a novel vaccine construct using B-cell derived T-cell epitopes from four target antigenic proteins (HpaA, FlaA, FlaB and Omp18), and found the induction of possible immune response using advanced immunoinformatics approaches. In order to boost immune system, we tagged adjuvant (50S ribosomal protein L7/L12) with a suitable linker at the N-terminus side of vaccine sequence. Protein–protein docking between human Toll like receptor 5 (TLR5) and vaccine construct help to predict the way of inductive signaling that leads to immune-response. The calculated negative score (− 151.4, + / − 8.7) of molecular docking complex signify the best binding interface. Molecular dynamics simulation studies confirmed the proper docking between TLR5 and vaccine candidate. Moreover, Normal mode analysis (NMA) calculates the molecular motion of the docking complex. The low eigenvalue (2.935e−05) indicates the stable and flexible molecular motion in the binding interaction side. Finally, in-silico cloning of vaccine candidate was performed using expression vector pET28b (+) with the optimized restriction sites. Supplementary Information The online version of this article (10.1007/s10989-020-10157-w) contains supplementary material, which is available to authorized users.

Recently, a novel coronavirus (SARS‐COV‐2) emerged which is responsible for the recent outbreak in Wuhan, China. Genetically, it is closely related to SARS‐CoV and MERS‐CoV. The situation is getting worse and worse, therefore, there is an urgent need for designing a suitable peptide vaccine component against the SARS‐COV‐2. Here, we characterized spike glycoprotein to obtain immunogenic epitopes. Next, we chose 13 Major Histocompatibility Complex‐(MHC) I and 3 MHC‐II epitopes, having antigenic properties. These epitopes are usually linked to specific linkers to build vaccine components and molecularly dock on toll‐like receptor‐5 to get binding affinity. Therefore, to provide a fast immunogenic profile of these epitopes, we performed immunoinformatics analysis so that the rapid development of the vaccine might bring this disastrous situation to the end earlier., Highlights The potential epitopes of coronavirus (SARS‐CoV‐2) are identified.The docking complex of the construct vaccine and TLR5 is described.Peptide‐based vaccine developed and in silico validation is provided.Common epitopes of coronavirus (SARS‐CoV‐2) against B‐cells and T‐cells are listed.

This paper presents a convolutional neural network (CNN)-based deep learning model, inspired from UNet with series of encoder and decoder units with skip connections, for the simulation of microwave-plasma interaction. The microwave propagation characteristics in complex plasma medium pertaining to transmission, absorption and reflection primarily depends on the ratio of electromagnetic (EM) wave frequency and electron plasma frequency, and the plasma density profile. The scattering of a plane EM wave with fixed frequency (1 GHz) and amplitude incident on a plasma medium with different gaussian density profiles (in the range of $1\times 10^{17}-1\times 10^{22}{m^{-3}}$) have been considered. The training data associated with microwave-plasma interaction has been generated using 2D-FDTD (Finite Difference Time Domain) based simulations. The trained deep learning model is then used to reproduce the scattered electric field values for the 1GHz incident microwave on different plasma profiles with error margin of less than 2\%. We propose a complete deep learning (DL) based pipeline to train, validate and evaluate the model. We compare the results of the network, using various metrics like SSIM index, average percent error and mean square error, with the physical data obtained from well-established FDTD based EM solvers. To the best of our knowledge, this is the first effort towards exploring a DL based approach for the simulation of complex microwave plasma interaction. The deep learning technique proposed in this work is significantly fast as compared to the existing computational techniques, and can be used as a new, prospective and alternative computational approach for investigating microwave-plasma interaction in a real time scenario.

Bunyamwera orthobunyavirus and its serogroup can cause several diseases in humans, cattle, ruminants, and birds. The viral M-polyprotein helps the virus to enter the host body. Therefore, this protein might serve as a potential vaccine target against Bunyamwera orthobunyavirus. The present study applied the immunoinformatics technique to design an epitopic vaccine component that could protect against Bunyamwera infection. Phylogenetic analysis revealed the presence of conserved patterns of M-polyprotein within the viral serogroup. Three epitopes common for both B-cell and T-cell were identified, i.e., YQPTELTRS, YKAHDKEET, and ILGTGTPKF merged with a specific linker peptide to construct an active vaccine component. The low atomic contact energy value of docking complex between human TLR4 (TLR4/MD2 complex) and vaccine construct confirms the elevated protein–protein binding interaction. Molecular dynamic simulation and normal mode analysis illustrate the docking complex’s stability, especially by the higher Eigenvalue. In silico cloning of the vaccine construct was applied to amplify the desired vaccine component. Structural allocation of both the vaccine and epitopes also show the efficacy of the developed vaccine. Hence, the computational research design outcomes support that the peptide-based vaccine construction is a crucial drive target to limit the infection of Bunyamwera orthobunyavirus to an extent. Supplementary Information The online version contains supplementary material available at 10.1007/s10989-021-10322-9.

Though hospitality education relies strongly on experiential learning, the COVID-19 pandemic has compelled all the higher educational institutions including the institute of hotel managements (IHMs) to restrict on-campus learning. As the only possible solution to deliver uninterrupted knowledge and skills to the students under these adverse circumstances, the management of these IHMs has quickly retorted to virtual classrooms. Many virtual platforms such as Google Meet, Microsoft Teams, Zoom, Cisco Webex, etc. emerged as the elixir for the institutions with customized features to fulfil the learning needs of the students. This necessitates the need to not only examine and compare the perceptions of these platforms based on virtual classroom service quality, perceived ease of use, and perceived usefulness but also to understand the impact of these perceptions on the future scope in terms of satisfaction and behavioral intentions of the hospitality students in IHMs.

Several types of RNase protein has been identified and characterized from different group of organism using advanced biocomputational techniques. Some of these RNase have crucial role for understanding and discovering the practical mechanism of cellular angiogenesis. To characterize the zebrafish (Danio rerio) RNase5 for its angiogenic properties multiple modern bioinformatics tools and server applied in present research. For accurate structural profiling of RNase5 protein through Ramachandran plot of PROCHECK and ProSA-web servers were applied. Subsequently, the CABS-flex server has been introduced to compute the Route Mean Square Fluctuation of all atoms for dynamic system simulation with a minimal residue fluctuation. Prediction of RNase5 protein’s sub-cellular localization, the TMHMM server also applied and confirmed its extracellular or secretory nature. Moreover, the sequence alignment with human angiogenin protein authenticates higher level of sequence similarity and reveals the conserved regions within protein of interest. Additionally, molecular docking with very low ACE value − 131.67 between the VEGF-binding domain of FLT-1 protein and zebrafish RNase5 able to generate the effective confirmation of cellular angiogenic activity. The molecular dynamic simulation by Normal Mode Analysis (NMA) also performed for functional mobility and structural stability of docking complex. Therefore, such In silico efforts definitely support the proper understanding of RNase5 enzyme mediated angiogenic activity. This effort certainly provides like a potential aid towards the future study of angiogenesis mechanism, as an important phenomenon of cancer metastasis in vertebrate model.

The research tries to define “Process Map” for a Unified Data Platform for the power sector and its interoperations. Data mining and analytics are playing a key role in every modern industrial and process system. The tremendous growth in the field of information technology, communication protocols and support of high performing processors of modern day computer has led to a layer of data and information which can be utilized for management and operational excellence. The different data system, communication protocols, hardware and software applications were studied to identify the factors for designing the process map.The information layer added to the conventional power sector network is studied which is an ever increasing source of data. Additional data is being added by the new generation smartgrid with its automation system that are having numerous sensors embedded in the existing power system for controlling, monitoring and operational activities. The research was important as the last few decades have seen an organic growth in the power sector data which needs a defined framework and process map for Operational Intelligence and Analysis.

The research tries to identify the various factors that need to be integrated to implement a Unified Data Platform (UDP) for the power sector and its interoperations.Data and tools from generation, transmission, distribution and trading were readily available but in silos and we added to it the complexity of interoperability between stakeholders affecting the organizations, applications, information systems, social, economic, regulatory and technical infrastructure. The different data silos were studied to identify the factors which need to be analysed and integrated for these silos to interoperate with each other for attaining operational excellence in a competitive environment.The research was important as the last few decades have seen an organic growth of the power sector in India. This growth inherently bought with it the requirement for humongous enhancement of existing systems, restructuring of the sector, modification of the policies and develop frameworks to handle the scale of future capacities. The integration and interconnection of different stakeholders in the sector has increased the quantum of software, hardware, tools and data by manifold. The unification of data at a higher level will improve the operational efficiencies and reduce the losses at silos level due to lack of data unification.

Leprosy is a significant universal health problem that is remarkably still a concern in developing countries due to infection frequency. New therapeutic molecules and next-generation vaccines are urgently needed to accelerate the leprosy-free world. In this direction, the present study was performed using two routes: proteome-mediated therapeutic target identification and mapping as well as multi-epitopic peptide-based novel vaccine development using state of the art of computational biology for the TN strain of M. leprae. The TN strain was selected from 65 Mycobacterium strains, and TN strain proteome mediated 83 therapeutic protein targets were mapped and characterized according to subcellular localization. Also, drug molecules were mapped with respect to protein targets localization. The Druggability potential of proteins was also evaluated. For multi-epitope peptide-based vaccine development, the four common types of B and T cell epitopes were identified (SLFQSHNRK, VVGIGQHAA, MMHRSPRTR, LGVDQTQPV) and combined with the suitable peptide linker. The vaccine component had an acceptable protective antigenic score (0.9751). The molecular docking of vaccine components with TLR4/MD2 complex exhibited a low ACE value (-244.12) which signifies the proper binding between the two molecules. The estimated free Gibbs binding energy ensured accurate protein-protein interactions (-112.46 kcal/mol). The vaccine was evaluated through adaptive immunity stimulation as well as immune interactions. The molecular dynamic simulation was carried out by using CHARMM topology-based parameters to minimize the docked complex. Subsequently, the Normal Mode Analysis in the internal coordinates showed a low eigen-value (1.3982892e-05), which also signifies the stability of molecular docking. Finally, the vaccine components were adopted for reverse transcription and codon optimization in E. coli strain K12 for the pGEX-4T1 vector, which supports in silico cloning of the vaccine components against the pathogen. The study directs the experimental study for therapeutics molecules discovery and vaccine candidate development with higher reliability.

BACKGROUND: Postoperative hypotension is associated with adverse outcomes, but intraoperative prediction of postanaesthesia care unit (PACU) hypotension is not routine in anaesthesiology workflow. Although machine learning models may support clinician prediction of PACU hypotension, clinician acceptance of prediction models is poorly understood. METHODS: We developed a clinically informed gradient boosting machine learning model using preoperative and intraoperative data from 88 446 surgical patients from 2015 to 2019. Nine anaesthesiologists each made 192 predictions of PACU hypotension using a web-based visualisation tool with and without input from the machine learning model. Questionnaires and interviews were analysed using thematic content analysis for model acceptance by anaesthesiologists. RESULTS: The model predicted PACU hypotension in 17 029 patients (area under the receiver operating characteristic [AUROC] 0.82 [95% confidence interval {CI}: 0.81–0.83] and average precision 0.40 [95% CI: 0.38–0.42]). On a random representative subset of 192 cases, anaesthesiologist performance improved from AUROC 0.67 (95% CI: 0.60–0.73) to AUROC 0.74 (95% CI: 0.68–0.79) with model predictions and information on risk factors. Anaesthesiologists perceived more value and expressed trust in the prediction model for prospective planning, informing PACU handoffs, and drawing attention to unexpected cases of PACU hypotension, but they doubted the model when predictions and associated features were not aligned with clinical judgement. Anaesthesiologists expressed interest in patient-specific thresholds for defining and treating postoperative hypotension. CONCLUSIONS: The ability of anaesthesiologists to predict PACU hypotension was improved by exposure to machine learning model predictions. Clinicians acknowledged value and trust in machine learning technology. Increasing familiarity with clinical use of model predictions is needed for effective integration into perioperative workflows.

COVID-19 caused by SARS-CoV-2 was declared a global pandemic by WHO (World Health Organization) in March, 2020. Within 6 months, nearly 750,000 deaths are claimed by COVID-19 across the globe. This called for immediate social, scientific, technological, public and community interventions. Considering the severity of infection and the associated mortalities, global efforts are underway to develop preventive measures against SARS-CoV-2. Among the SARS-CoV-2 target proteins, Spike (S) glycoprotein (a.k.a S Protein) is the most studied target known to trigger strong host immune response. A detailed analysis of S protein-based epitopes enabled us to design a novel B-cell-derived T-cell Multi-epitope-based peptide (MEBP) vaccine candidate. This involved a systematic and comprehensive computational protocol consisting of prediction of dual-purpose epitopes and designing an MEBP vaccine construct. This was followed by 3D structure validation, MEBP complex interaction studies, in silico cloning and vaccine dose-based immune response simulation to evaluate the immunogenic potency of the vaccine construct. The dual-purpose epitope prediction protocol was designed such that the same epitope elicits both humoral and cellular immune response unlike the earlier designs. Further, the epitopes predicted were screened against stringent criteria to ensure selection of a potent candidate with maximum antigen coverage and best immune response. The vaccine dose-based immune response simulation studies revealed a rapid antigen clearance through antibody generation and elevated levels of cell-mediated immunity during repeated exposure of the vaccine. The favourable results of the analysis strongly indicate that the vaccine construct is indeed a potent vaccine candidate and ready to proceed to the next steps of experimental validation and efficacy studies. Supplementary Information The online version contains supplementary material available at 10.1007/s13205-020-02574-x.

The development of plastic brings world-shattering changes in industrialization, commercialization, and mankind and is now the most predominant widely used essential product. The disposal and degradation of plastic leads to the generation of microplastic and is a potential and crucial pollutant in our ecosystem. The discharge of microplastic to the aquatic bodies and finally its migration to the marine ecosystem draw attention of many researchers toward its noxious effects. The primary and secondary sources of microplastic that makes its path toward the riverine ecosystem causing pollution and interference with the aquatic organism resulting in contamination and toxicity is primarily discussed in this chapter. The impacts of microplastic in aquatic organisms affect its biological ingestion process causing clogging and indigestion and in adverse condition hinder the biological processes resulting in toxicity, immune dysfunction, reproductive abnormalities, alteration of behaviors and feeding habit, and finally death. In addition to this, the absorption of microplastic results in bioaccumulation and biomagnification across the food chain, making its route to the human body. Furthermore, this chapter also emphasizes the various remediation and pollution management strategies based on the present approach and provides a research assessment for future research and advancement.

An advanced personal health monitoring medicine box, analysis, and control system along with medical monitoring is proposed in this chapter. The system focuses on improving the current standing position of the internet of things (IoT) in biomedical applications. The principle thought overwhelming the current patterns is the IoT in well-being because of its integration with individual social insurance. This chapter presents an IoT-based health observing framework that altogether allows to keep up the best possible soundness of the populace in a country. The information will be stored in the cloud. The status of a patient’s health and the previous records associated with it will be handed over to the contacts linked to the android app. The medicine box consists of an emergency button that when pressed generates an alert message to the linked contacts. The entire hardware consists of three modules such as wristband, data recorder, and medicine box. The hardware uses noninvasive methods to collect the health-related data of the user. The data collected is then stored in the cloud and also relayed to the doctor. The medicine is used to monitor the medicine consumption of the user.

The fluid-based simulation of high power millimeter(HPM) wave interaction with plasma is a computationally challenging problem. The complex phenomenon is a multi-physics and a multi-scale problem due to the involvement of coupled EM wave and plasma solvers with different space and time scales. The simulation requires a proper resolution in both space and time to accommodate the gradients in the fields as well as the plasma density, making it a computationally intensive problem. In this paper, we have implemented a mesh refinement based algorithm with selective refined grids in the desirable region with steep gradients in plasma density and electric(E) field. We have tested the algorithm on a test-bed problem, involving the formation of self-organized plasma filaments, for three different refinement factors (r_f = 2, 4, 8) and achieved a speedup of 4 − 100 compared to a serial implementation with uniform mesh without any refinement. We have investigated the dependence of speedup and accuracy on the refinement factor for different cases.

In spite of the tremendous efforts of the World Health Organization, scientific and medical community to eradicate lymphatic filariasis (LF) within 2020, the disease is still taking a huge toll on mankind throughout the globe. The current therapeutic strategies and solution measures against this alarming condition are suffering from a number of limitations such as inadequate effectiveness of the drugs against the adult-stage parasites, low bioavailability, and emergence of resistance. Considering this situation, development of the new therapeutics are urgently needed to combat human LF, especially targeting the adult filarial nematodes. Brugia malayi, the causative parasite for the human brugian filariasis majorly found in the countries of the South-Asia. In this study, we have designed a vaccine candidate using B-cell and T-cell epitopes derived from the aspartic protease of B. malayi (BmASP-1) and found to display significant humoral and cell mediated immune responses using in-silico approaches. Protein-protein docking between the human Toll-like receptor 4 (TLR4) and the vaccine candidate helped us to predict the way of inductive signaling that leads to immune-response. Molecular dynamics (MD) simulation studies further confirmed the proper docking between the TLR4 and vaccine candidate. Moreover, in-silico cloning of the vaccine element within the expression vector was found useful to optimize the restriction sites as well as to determine the primer location. Taken together, the in-silico vaccine candidate depicted in this study promises could be a useful therapeutic option for treating LF and experimental validation of this study is expected to strengthen the candidature of the said vaccine in the future.

SARS-CoV-2 is spreading globally at a rapid pace. To contain its spread and prevent further fatalities, the development of a vaccine against SARS-CoV-2 is an urgent prerequisite. Thus, in this article, by utilizing the in silico approach, a vaccine candidate for SARS-CoV-2 has been proposed. Moreover, the effectiveness and safety measures of our proposed epitopic vaccine candidate have been evaluated by in silico tools and servers (AllerTOP and AllergenFP servers). We observed that the vaccine candidate has no allergenicity and successfully combined with Toll-like receptor (TLR) protein to elicit an inflammatory immune response. Stable, functional mobility of the vaccine-TLR protein binding interface was confirmed by the Normal Mode Analysis. The in silico cloning model demonstrated the efficacy of the construct vaccine along with the identified epitopes against SARS-CoV-2. Taken together, our proposed in silico vaccine candidate has potent efficacy against COVID-19 infection, and successive research work might validate its effectiveness in in vitro and in vivo models., Highlights • In-silico cloning of peptide based vaccine candidate SARS-CoV-2. • Validation of vaccine construct through computational biology. • Proposed In-silico vaccine construct help to develop vaccine against COVID-19.

This study was undertaken to detect the prevalence of CTX-M-producing Klebsiella spp. in healthy broiler, indigenous, and kuroiler birds reared in West Bengal (India) during November 2014–February 2015. In addition to CTX-M gene, the study was also conducted to reveal the occurrence of other β-lactamase and class I integron genes in Klebsiella spp. isolates along with their clonal relationship. A total of 321 cloacal swabs from healthy broiler, indigenous, and kuroiler birds were collected from different places of West Bengal, India. Klebsiella spp. isolation rate varies among different types of poultry birds (43.8–72.3%). In total, 33 (10.7%) Klebsiella spp. isolates were detected phenotypically as CTX-M producers and all the isolates possessed blaCTX-M in polymerase chain reaction. Whereas 17 (51.5%) and 16 (48.5%) Klebsiella spp. isolates possessed blaSHV, and blaTEM with blaCTX-M, respectively. None of the CTX-M-producing Klebsiella spp. isolates in this study possessed class I integron gene. Randomly...

Prostate cancer (PCa) is the second most diagnosed cancer in men and ranked fifth in overall cancer diagnosis. During the past decades, it has arisen as a significant life-threatening disease in men at an older age. At the early onset of illness when it is in localized form, radiation and surgical treatments are applied against this disease. In case of adverse situations androgen deprivation therapy, chemotherapy, hormonal therapy, etc. are widely used as a therapeutic element. However, studies found the occurrences of several side effects after applying these therapies. In current work, several immunoinformatic techniques were applied to formulate a multi-epitopic vaccine from the overexpressed antigenic proteins of PCa. A total of 13 epitopes were identified from the five prostatic antigenic proteins (PSA, PSMA, PSCA, STEAP, and PAP), after validation with several in silico tools. These epitopes were fused to form a vaccine element by (GGGGS)3 peptide linker. Afterward, 5, 6-dimethylxanthenone-4-acetic acid (DMXAA) was used as an adjuvant to initiate and induce STING-mediated cytotoxic cascade. In addition, molecular docking was performed between the vaccine element and HLA class I antigen with the low ACE value of −251 kcal/mol which showed a significant binding. Molecular simulation using normal mode analysis (NMA) illustrated the docking complex as a stable one. Therefore, this observation strongly indicated that our multi epitopes bases peptide vaccine molecule will be an effective candidate for the treatment of the PCa.

Specific Absorption Rate (SAR) is a widely used metric to measure the amount of non-ionizing EM radiation power absorbed by a unit mass of biological tissue, thus it helps in determining the safety standard of any EM applications. The 3D Finite Difference Time Domain (FDTD) is a commonly used computational technique for accurate SAR computation. However, it is computationally as well as memory intensive and requires high-performance computing(HPC) clusters to handle problems of large sizes encountered during high-frequency applications. This paper describes the implementation of a fast, optimized Open Source GPU accelerated FDTD based SAR calculator (available at Github)using CUDA (Code Unified Device Architecture) as an alternative that can be developed locally at a relatively small cost. On a testbed comprising a serial implementation on Intel i7-4790 CPU and parallel implementations on NVIDIA Tesla K40 graphics card, we have achieved a speedup of up to 45x in double precision mode. We have investigated the effect of increasing the frequency (larger problem sizes) and threads per block on speedup. Finally, we have compared the performance of our parallel implementation on two different GPUs (Tesla K40 and GTX 670) and analyzed the effect of hardware architecture on the performance of the SAR calculator.

This paper illustrates the constructional plan of an inset feed mechanism microstrip patch antenna. The essential advantage of employing inset cut feed is not only increasing the antenna bandwidth but its return loss is also improved suitably. Our antenna has been simulated using hexagonal patch on both defected and non-defected ground plane. It has been realized that operation of the antenna, acquires development according to the size of the patch, over usual design.

Single nucleotide polymorphisms (SNPs) are the most regular type of genetic variation among human genome. The low-density lipoprotein receptors (LDLR) gene encoded receptor protein that specifically binds to low density lipoprotein (LDL), to act as prime cholesterol carriers in the blood. Unregulated removal of LDL from the circulation results from inherited mutations in the LDLR gene or, more rarely, in the apo B gene, the ligand for the LDLR causes familial hypercholesterolemia (FH). Several studies have shown that non-synonymous single nucleotide polymorphisms (nsSNPs) induce more than 50% genetic diseases. However, the growing number of SNPs in databases makes it difficult to evaluate the SNPs that come up with disease development. Therefore, we tried to identify the nsSNP within LDLR gene used mainly computational tools like: PROVEAN, Predict-SNP, SIFT, MAAP, SNAP, PhD-SNP, Polyphen v1, Polyphen v2, PANTHER and nsSNPAnalyzer. After analysing a total of 1114 missense SNPs, we found only 39 nsSNPs are most deleterious, whose conservation score ranges from grade ranges 7 to 9 that is assessed through ConSurf web server. Among 39 most significant nsSNPs, 11were predicted for involving in post translational modifications. Additionally, I-Mutant 2.0 and MUpro tool showed a decreased instability for these nsSNPs upon mutation. Protein structural analysis of these amino acid variants was performed by using I-Mutant 2.0. Stability analysis predicted 11 nsSNPs (C313W, F408K, A431P, A431S, A431T, D433H, D433Y, G546D, G546V, G546E and C697W), these are decreased protein stability. The in-silico analysis in current study may be a significant model for future research design studies.

In this paper, we have designed a microstrip patch with inset feed mechanism. The fundamental benefit of utilizing inset cut feed is not only increasing the antenna bandwidth but return loss are satisfactorily improved. The antenna has been simulated using rectangular patch on partial and non partial ground plane. It has been examined that the antenna performance acquires improvement over conventional design by partial ground plane according to the size of the patch. Here we have generated a third-order mode to get a broad bandwidth.

An efficient Phase Locked Loop (PLL) circuit with lower error rate Phase Frequency Detector (PFD), lower area, lower phase noise active inductor based Voltage Controlled Oscillator (VCO) and lesser current mismatch Charge Pump (CP) and passive loop filter was designed for a frequency synthesizer used in standard ZigBee based applications. For 2.4 GHz Industrial Scientific and Medical(ISM) band a precise synthesizer the PLL must have a better lock range and faster acquisition which is achieved by optimally designing PFD. We proposed a NAND based PFD using dynamic CMOS True Single-Phase Clock (TSPC) technique. A new Gyrator-C based active inductor with Quality factor (Q) enhancement technique for the LC-VCO to replace the on-chip spiral inductors in previous designs was proposed. This improved the phase noise at VCO's output using circuit controlled inductance with added benefit of lesser chip area. We used a linearizing circuit to lower the VCO's gain in order to improve the tuning range. The analysis was carried out on 180nm and 90nm generic process design kit (gpdk) CMOS technology and simulations on Cadence design environment. For a 2.4 GHz PLL, designed and simulated results showed the power consumption of 0.3mW for PFD and 20mW from active inductor based VCO and a Phase Noise of -110dBc/Hz. A comparative result between existing design and modified circuit has been provided.