COVID-19 pandemic has become a major threat to the country. Till date, well tested medication or antidote is not available to cure this disease. According to WHO reports, COVID-19 is a severe acute respiratory syndrome which is transmitted through respiratory droplets and contact routes. Analysis of this disease requires major attention by the Government to take necessary steps in reducing the effect of this global pandemic. In this study, outbreak of this disease has been analysed for India till 30 th March 2020 and predictions have been made for the number of cases for the next 2 weeks. SEIR model and Regression model have been used for predictions based on the data collected from John Hopkins University repository in the time period of 30 th January 2020 to 30 th March 2020. The performance of the models was evaluated using RMSLE and achieved 1.52 for SEIR model and 1.75 for the regression model. The RMSLE error rate between SEIR model and Regression model was found to be 2.01. Also, the value of R 0 which is the spread of the disease was calculated to be 2.02. Expected cases may rise between 5000-6000 in the next two weeks of time. This study will help the Government and doctors in preparing their plans for the next two weeks. Based on the predictions for short-term interval, these models can be tuned for forecasting in long-term intervals.

The COVID-19 pandemic has become a major threat to the whole world. Analysis of this disease requires major attention by the government in all countries to take necessary steps in reducing the effect of this global pandemic. In this study, outbreak of this disease has been analysed and trained for Indian region till 10th May, 2020, and testing has been done for the number of cases for the next three weeks. Machine learning models such as SEIR model and Regression model have been used for predictions based on the data collected from the official portal of the Government of India in the time period of 30th January, 2020, to 10th May, 2020. The performance of the models was evaluated using RMSLE and achieved 1.52 for SEIR model and 1.75 for the regression model. The RMSLE error rate between SEIR model and Regression model was found to be 2.01. Also, the value of R0, which is the spread of the disease, was calculated to be 2.84. Expected cases are predicted around 175K-200K in the three-week time period of test data, which is very close to the actual numbers. This study will help the government and doctors in preparing their plans for the future. CCS Concepts: • Computing methodolongies → Machine learning algorithms ; • Mathematics of computing → Regression analysis ;

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Study Objectives: This study was done to find whether a history of nocturia is associated with severity of obstructive sleep apnea (OSA) and also whether patients with nocturia constitute a separate phenotype of OSA. Materials and Methods: Retrospective chart review was done in consecutive OSA patients who were diagnosed in sleep laboratory of our institute. Detailed sleep history, examination, biochemical investigations, and polysomnography reports were taken for the analysis. Nocturia was defined as urine frequency ≥2/night. Results: Of 172 OSA patients, 87 (50.5%) patients had nocturia. On multivariate analysis, a history of nocturia had 2.429 times (confidence interval 1.086–5.434) more chances of having very severe OSA ( P = 0.031). Time between bedtime and first time for urination was significantly less in very severe OSA compared to severe OSA and mild-to-moderate OSA (2.4 ± 0.9, 3.1 ± 1.3, and 3.0 ± 1.1 h, respectively) ( P = 0.021). Patients with nocturia were older (52.3 ± 11.9 vs. 47.6 ± 12.1 years; P = 0.012), had higher STOP BANG scores ( P = 0.002), higher apnea–hypopnea index (AHI) (64.8 ± 35.9 vs. 43.9 ± 29.1; P < 0.001), and higher Epworth sleepiness scale (ESS) (9.2 ± 5.3 vs. 7.7 ± 4.4; P = 0.052) and were more likely to be fatigued during day ( P = 0.001). Nocturics had higher body mass index (BMI) ( P = 0.030), higher waist, and hip circumference ( P = 0.001and 0.023, respectively). Nocturic patients had lower awake SpO 2 ( P = 0.032) and lower nadir SpO 2 during sleep ( P = 0.002). Conclusions: A history of nocturia (≥2/night) predicts very severe OSA (AHI >60). Nocturic OSA is a phenotype of OSA with more severe AHI, lower oxygen levels, higher BMI, and higher ESS. We believe nocturia can be used for screening in OSA questionnaires, which needs to be validated in further community-based studies.

Disposal of sewage water in cultivated soils often containing considerable amount of potentially toxic metals such as Cu, Zn, Ni, Cd, Pb and Cr can be beneficial or harmful to plant growth, rhizobial survival, nodulation and nitrogen fixation. Soil samples from 14 such locations were collected. Symbiotic effectivity of host-Rhizobium leguminosarum symbiosis in these soils was assessed. The total metal contents of Cd, Cu, Zn and Ni in all the 14 samples collected from farmer's fields receiving sewage water ranged between 1.3 and 6.7, 55.8-353.2, 356.0-1028.0 and 90.0-199.7 mg kg(-1) of soil, respectively. In Rohtak 1 soil, levels of Cd, Cu and Zn were highest while Ni was highest in Sonipat 2 soil. The content of available Cd, Cu, Zn and Ni in these soils ranged from 1.0-29.3; 6.2-47.0; 2.4-13.5, respectively, and was 2-9 percent of their total metal contents. All the N2 fixing parameters in pea and Egyptian clover were adversely affected by the presence of heavy metals. Available Cd and Cu contents significantly affected the N contents of pea and Egyptian clover plants, whereas Ni contents were negatively correlated with the plant biomass of pea and Egyptian clover.