Study on Neutrophil Lymphocyte ratio and Platelet lymphocyte ratio in COVID-19 from our prospective. A cross sectional study

Background : Novel corona Virus disease 2019 has been declared as pandemic by WHO.It started from Wuhan, China and within less times spread has occurred through out the world .The whole world is facing a huge challenge to prevent the spread of the disease.Though there was a strong response and preventive measures taken at the onset of the pandemic but still its one of the fastest growing pandemic in India at present. Only a clear knowledge regarding the risk and complication of covid19 to the population helps in fighting out this pandemics. Methods : A cross sectional study of 50 COVID 19 cases confirmed by Real Time Reverse Transcriptase Polymerase Chain Reaction. In the current study , prevalance and severity was assessed at 95% confidence interval. Independent statistical T test and chi- square were used to test significance( p<0.05). Result: Out of 50 cases 20 are severe and 30 are non severe.Lymphocytopenia is one of the most important cardinal hematological feature seen in 60% of patients. It was observed in the current study that NLR & PLR was increased in severe cases.The Neutrophil to Lymphocyte ratio (NLR). is higher in severe cases (5.87±1.92) when compared to non severe cases (2.86±1.20) was statistically significant (p = 0.043). Platelet to Lymphocyte ratio (PLR) was higher in severe cases (282.08±57.85) as compared to non severe cases(216.24±36.35) and is statistically significant (p value =0.007). Conclusion: Current study declares, Neutrophil to Lymphocyte ratio (NLR)and Platelet to Lymphocyte ratio (PLR) may be considered as good diagnostic and prognostic risk evaluators in assessing the severity and progression of the novel corona disease 201

Impacts of introducing and lifting non-pharmaceutical interventions on COVID-19 daily growth rate and compliance in the US

We evaluate the impacts of implementing and lifting non-pharmaceutical interventions (NPIs) in US counties on the daily growth rate of COVID-19 cases and compliance, measured through the percentage of devices staying home, and evaluate whether introducing and lifting NPIs protecting selective populations is an effective strategy. We use difference-in-differences methods, leveraging on daily county-level data and exploit the staggered introduction and lifting of policies across counties over time. We also assess heterogenous impacts due to counties’ population characteristics, namely ethnicity and household income. Results show that introducing NPIs led to a reduction in cases through the percentage of devices staying home. When counties lifted NPIs, they benefited from reduced mobility outside of the home during the lockdown, but only for a short period. In the long-term, counties experienced diminished health and mobility gains accrued from previously implemented policies. Notably, we find heterogenous impacts due to population characteristics implying that measures can mitigate the disproportionate burden of COVID-19 on marginalized populations and find that selectively targeting populations may not be effective

Development and Integration of Metocean Data Interoperability for Intelligent Operations and Automation Using Machine Learning: A Review

The current oil industry is moving towards digitalization, which is a good opportunity that will bring value to all its stakeholders. The digitalization of oil and gas discovery, which are production-based industries, is driven by enabling technologies which include machine learning (ML) and big data analytics. However, the existing Metocean system generates data manually using sensors such as the wave buoy, anemometer, and acoustic doppler current profiler (ADCP). Additionally, these data which appear in ASCII format to the Metocean system are also manual and silos. This slows down provisioning, while the monitoring element of the Metocean data path is partial. In this paper, we demonstrate the capabilities of ML for the development of Metocean data integration interoperability based on intelligent operations and automation. A comprehensive review of several research studies, which explore the needs of ML in oil and gas industries by investigating the in-depth integration of Metocean data interoperability for intelligent operations and automation using an ML-based approach, is presented. A new model integrated with the existing Metocean data system using ML algorithms to monitor and interoperate with maximum performance is proposed. The study reveals that ML is one of the crucial and key enabling tools that the oil and gas industries are now focused on for implementing digital transformation, which allows the industry to automate, enhance production, and have less human capacity. Lastly, user recommendations for potential future investigations are offered

Design and Development of Heat Recovery System in Water Cooler

More than 15% of total energy consumption of the world is for the cooling and airconditioning application. Beign the huge contributor in consumption of electricity, these systems have the scope of developments in terms of optimum utilization of the resources. The cooling systems are improved over the years by means of design, use of different cooling mediums and th performance. The conventional water coolers are less energy efficient and the wastage of water is also the issue to be addressed. Authors have presented the improved design in terms of energy efficiency and the waste water utilization in this paper. The design fo the system components in Solid Works software is presented in the paper along with the parameter calcutions. Water and electricity are important aspects to be saved. Optimal utlistion of the electricity and water results in saving the environment, cost, and environmental hazards

AHI1, a pivotal neurodevelopmental gene, and C6orf217 are associated with susceptibility to schizophrenia

Schizophrenia, a severe neuropsychiatric disorder, is believed to involve multiple genetic factors. A significant body of evidence supports a pivotal role for abnormalities of brain development in the disorder. Linkage signals for schizophrenia map to human chromosome 6q. To obtain a finer localization, we genotyped 180 single nucleotide polymorphisms (SNPs) in a young, inbred Arab-Israeli family sample with a limited number of founders. The SNPs were mostly within a approximately 7 Mb region around the strong linkage peak at 136.2 Mb that we had previously mapped. The most significant genetic association with schizophrenia for single SNPs and haplotypes was within a 500 kb genomic region of high linkage disequilibrium (LD) at 135.85 Mb. In a different, outbred, nuclear family sample that was not appropriate for linkage analysis, under-transmitted haplotypes incorporating the same SNPs (but not the individual SNPs) were significantly associated with schizophrenia. The implicated genomic region harbors the Abelson Helper Integration Site 1 (AHI1) gene, which showed the strongest association signal, and an adjacent, primate-specific gene, C6orf217. Mutations in human AHI1 underlie the autosomal recessive Joubert Syndrome with brain malformation and mental retardation. Previous comparative genomic analysis has suggested accelerated evolution of AHI1 in the human lineage. C6orf217 has multiple splice isoforms and is expressed in brain but does not seem to encode a functional protein. The two genes appear in opposite orientations and their regulatory upstream regions overlap, which might affect their expression. Both, AHI1 and C6orf217 appear to be highly relevant candidate genes for schizophrenia