Cloud Computing Based Network Analysis in Smart Healthcare System with Neural Network Architecture

The recent progressions in Artificial Intelligence (AI), the Internet of Things (IoT), and cloud computing transformed the traditional healthcare system into a smart healthcare system. Medical services can be improved through the incorporation of key technologies namely AI and IoT. The convergence of AI and IoT renders several openings in the healthcare system. In machine learning, deep learning can be considered a renowned topic with a wide range of applications like biomedicine, computer vision, speech recognition, drug discovery, visual object detection, natural language processing, disease prediction, bioinformatics, etc. Among these applications, medical science-related and health care applications were raised dramatically. This study develops a Cloud computing-based network analysis in the smart healthcare systems with neural network (CCNA-SHSNN) architecture. The presented CCNA-SHSNN technique assists in the decision-making process of the healthcare system in a real time cloud environment. For data pre-processing, the CCNA-SHSNN technique uses a normalization approach. Secondly, the CCNA-SHSNN technique applies the autoencoder (AE) model for healthcare data classification in the CC platform. At last, the gravitational search algorithm (GSA) is used for hyperparameter optimization of the AE model. The experimental outcomes are determined on a benchmark dataset and the outcomes signify the outperforming efficiency of the CCNA-SHSNN technique compared to existing techniques

Genomic Diversity and Evolution of Mycobacterium ulcerans Revealed by Next-Generation Sequencing

Mycobacterium ulcerans is the causative agent of Buruli ulcer, the third most common mycobacterial disease after tuberculosis and leprosy. It is an emerging infectious disease that afflicts mainly children and youths in West Africa. Little is known about the evolution and transmission mode of M. ulcerans, partially due to the lack of known genetic polymorphisms among isolates, limiting the application of genetic epidemiology. To systematically profile single nucleotide polymorphisms (SNPs), we sequenced the genomes of three M. ulcerans strains using 454 and Solexa technologies. Comparison with the reference genome of the Ghanaian classical lineage isolate Agy99 revealed 26,564 SNPs in a Japanese strain representing the ancestral lineage. Only 173 SNPs were found when comparing Agy99 with two other Ghanaian isolates, which belong to the two other types previously distinguished in Ghana by variable number tandem repeat typing. We further analyzed a collection of Ghanaian strains using the SNPs discovered. With 68 SNP loci, we were able to differentiate 54 strains into 13 distinct SNP haplotypes. The average SNP nucleotide diversity was low (average 0.06–0.09 across 68 SNP loci), and 96% of the SNP locus pairs were in complete linkage disequilibrium. We estimated that the divergence of the M. ulcerans Ghanaian clade from the Japanese strain occurred 394 to 529 thousand years ago. The Ghanaian subtypes diverged about 1000 to 3000 years ago, or even much more recently, because we found evidence that they evolved significantly faster than average. Our results offer significant insight into the evolution of M. ulcerans and provide a comprehensive report on genetic diversity within a highly clonal M. ulcerans population from a Buruli ulcer endemic region, which can facilitate further epidemiological studies of this pathogen through the development of high-resolution tools

Estimating global injuries morbidity and mortality: methods and data used in the Global Burden of Disease 2017 study

BACKGROUND: While there is a long history of measuring death and disability from injuries, modern research methods must account for the wide spectrum of disability that can occur in an injury, and must provide estimates with sufficient demographic, geographical and temporal detail to be useful for policy makers. The Global Burden of Disease (GBD) 2017 study used methods to provide highly detailed estimates of global injury burden that meet these criteria. METHODS: In this study, we report and discuss the methods used in GBD 2017 for injury morbidity and mortality burden estimation. In summary, these methods included estimating cause-specific mortality for every cause of injury, and then estimating incidence for every cause of injury. Non-fatal disability for each cause is then calculated based on the probabilities of suffering from different types of bodily injury experienced. RESULTS: GBD 2017 produced morbidity and mortality estimates for 38 causes of injury. Estimates were produced in terms of incidence, prevalence, years lived with disability, cause-specific mortality, years of life lost and disability-adjusted life-years for a 28-year period for 22 age groups, 195 countries and both sexes. CONCLUSIONS: GBD 2017 demonstrated a complex and sophisticated series of analytical steps using the largest known database of morbidity and mortality data on injuries. GBD 2017 results should be used to help inform injury prevention policy making and resource allocation. We also identify important avenues for improving injury burden estimation in the future

Comparative venom-gland transcriptomics and venom proteomics of four Sidewinder Rattlesnake (Crotalus cerastes) lineages reveal little differential expression despite individual variation

Changes in gene expression can rapidly influence adaptive traits in the early stages of lineage diversification. Venom is an adaptive trait comprised of numerous toxins used for prey capture and defense. Snake venoms can vary widely between conspecific populations, but the influence of lineage diversification on such compositional differences are unknown. To explore venom differentiation in the early stages of lineage diversification, we used RNA-seq and mass spectrometry to characterize Sidewinder Rattlesnake (Crotalus cerastes) venom. We generated the first venom-gland transcriptomes and complementary venom proteomes for eight individuals collected across the United States and tested for expression differences across life history traits and between subspecific, mitochondrial, and phylotranscriptomic hypotheses. Sidewinder venom was comprised primarily of hemorrhagic toxins, with few cases of differential expression attributable to life history or lineage hypotheses. However, phylotranscriptomic lineage comparisons more than doubled instances of significant expression differences compared to all other factors. Nevertheless, only 6.4% of toxins were differentially expressed overall, suggesting that shallow divergence has not led to major changes in Sidewinder venom composition. Our results demonstrate the need for consensus venom-gland transcriptomes based on multiple individuals and highlight the potential for discrepancies in differential expression between different phylogenetic hypotheses