Background: India first detected SARS-CoV-2, causal agent of COVID-19 in late January 2020, imported from Wuhan, China. From March 2020 onwards, the importation of cases from countries in the rest of the world followed by seeding of local transmission triggered further outbreaks in India. Methods: We used ARTIC protocol-based tiling amplicon sequencing of SARS-CoV-2 (n=104) from different states of India using a combination of MinION and MinIT sequencing from Oxford Nanopore Technology to understand how introduction and local transmission occurred. Results: The analyses revealed multiple introductions of SARS-CoV-2 genomes, including the A2a cluster from Europe and the USA, A3 cluster from Middle East and A4 cluster (haplotype redefined) from Southeast Asia (Indonesia, Thailand and Malaysia) and Central Asia (Kyrgyzstan). The local transmission and persistence of genomes A4, A2a and A3 was also observed in the studied locations. The most prevalent genomes with patterns of variance (confined in a cluster) remain unclassified, and are here proposed as A4-clade based on its divergence within the A cluster. Conclusions: The viral haplotypes may link their persistence to geo-climatic conditions and host response. Multipronged strategies including molecular surveillance based on real-time viral genomic data is of paramount importance for a timely management of the pandemic.

With the advancement of wireless communication, internet of things, and big data, high performance data analytic tools and algorithms are required. Data clustering, a promising analytic technique is widely used to solve the IoT and big data based problems, since it does not require labeled datasets. Recently, meta-heuristic algorithms have been efficiently used to solve various clustering problems. However, to handle big data sets produced from IoT devices, these algorithm fail to respond within desired time due to high computation cost. This paper presents a new meta-heuristic based clustering method to solve the big data problems by leveraging the strength of MapReduce. The proposed methods leverages the searching potential of military dog squad to find the optimal centroids and MapReduce architecture to handle the big data sets. The optimization efficacy the proposed method is validated against 17 benchmark functions and the results are compared with 5 other recent algorithms namely, bat, particle swarm optimization, artificial bee colony, multiverse optimization, and whale optimization algorithm. Further, a parallel version of the proposed method is introduced using MapReduce (MR-MDBO) for clustering the big datasets produced from industrial IoT. Moreover, the performance of MR-MDBO is studied on 2 benchmark UCI datasets and 3 real IoT based datasets produced from industry. The F-measure and computation time of the MR-MDBO is compared with the 6 other state-of-the-art methods. The experimental results witness that the proposed MR-MDBO based clustering outperforms the other considered algorithms in terms of clustering accuracy and computation times.

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India first detected SARS-CoV-2, causal agent of COVID-19 in late January-2020, imported from Wuhan, China. March-2020 onwards; importation of cases from rest of the countries followed by seeding of local transmission triggered further outbreaks in India. We used ARTIC protocol based tiling amplicon sequencing of SARS-CoV-2 (n=104) from different states of India using a combination of MinION and MinIT from Oxford Nanopore Technology to understand introduction and local transmission. The analyses revealed multiple introductions of SARS-CoV-2 from Europe and Asia following local transmission. The most prevalent genomes with patterns of variance (confined in a cluster) remain unclassified, here, proposed as A4-clade based on its divergence within A-cluster. The viral haplotypes may link their persistence to geoclimatic conditions and host response. Despite the effectiveness of non-therapeutic interventions in India, multipronged strategies including molecular surveillance based on real-time viral genomic data is of paramount importance for a timely management of the pandemic. India, 2020). However, while the global focus was on China and other eastern countries like South Korea and Japan; European countries, middle-east and the USA reported a surge in cases of COVID-19, pressing the WHO to declare it as a pandemic. March 2020 onwards, India also witnessed a surge of imported cases from countries other than China which has been further assisted with local transmission. In March, imposition of nationwide lockdown checked the epidemic curve. Despite these measurements, India is at the verge of a large outbreak as the transmission is rapidly increasing with more than 100,000 cases of COVID-19 having been reported in the third week of May, 2020.We carried out WGS of SARS-CoV-2 (n=104) from Pan-India through the network of Integrated Disease Surveillance Program (IDSP) of National Centre of Disease Control (NCDC), Delhi. We report here a comprehensive and integrative genomic view of SARS-CoV-2 in the Indian subcontinent. In this study, we combine genetic and epidemiological data to understand the genetic diversity, evolution, and epidemiology of SARS-CoV-2 across India. The spectrum of variations would be an important tool towards contact tracing, effective diagnostics and backbone for drug and vaccine development. METHODS Subject recruitmentThe study was conducted jointly by the NCDC and CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB). Institutional ethical clearance was obtained at both the places prior to initiation of research. A total of 127 laboratory confirmed cases of COVID-19 from a targeted testing representing different locations (as described in Table- 1 and supplementary figure-1) were included in the study for genomic analyses. Targeted testing involved suspected cases;having symptoms (fever, cough and breathlessness) with recent travel history to high-risk countries or positive contacts of COVID-19 cases. Sample collection and Viral RNA isolationThe nasopharyngeal and oropharyngeal swabs (in ...

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Background:To determine the prevalence and pattern of cavitated carious lesions in primary dentition in children below 5 years of age in Sirsa, Haryana.Aims:The aim of this study was to evaluate the status of dental caries in primary dentition and compute data for planning anticipatory programs in children aged less than 5 years.Settings and Design:The study was conducted among children attending the outpatient department of pedodontics, JCD Dental College, Sirsa, Haryana (India) from April to December 2014.Materials and Methods:This study consisted of 576 children of both sexes (311 males and 265 females) up to 5 years of age. Dentition status and treatment proforma (WHO, 1997) was used to assess the prevalence of cavitated carious lesions. Selection of children for the study was done by simple random sampling method.Statistical Analysis:Chi-square test and t-test were used to compute data for statistical analysis.Results:33.85% of children in the study population showed presence of cavitated carious lesions. Males showed slightly higher prevalence of cavitated carious lesions than females (P = 0.35). Incidence of caries was higher in mandibular arch in both the sexes (males P = 0.9, females P = 0.7) and in posterior teeth (both sex wise and arch wise). Higher caries prevalence was noticed in maxillary anterior teeth (P = 0.04) and mandibular posterior teeth (P = 0.7). Primary second molars showed highest caries prevalence (P = 0.39) in both the arches and sexes.Conclusion:The mean prevalence of cavitated carious lesions in primary dentition was found to be 33.85%. Males were more affected than females. Mandibular molars and maxillary anterior teeth were the predominantly affected teeth. Mandibular anterior teeth were least affected. The increase in incidence of cavitated carious lesions shows that there is necessity of implementing dental health awareness programs and modifications in types of food consumed are needed to eliminate the cause of decay.