Development of Fertilizer Prescription Targeted Yield-Equation for Carrot Crop Based on Soil Test Values

A field experiment was conducted on red soils (Kandic paleustalfs) of Zonal Agricultural Research Station, GKVK, Bangalore during kharif 2008-09 to develop a targeted yield equation for carrot crop. After developing three levels of fertility gradient with respect to available NPK in soil, the main experiment was conducted by taking carrot as a test crop. Initial soil data, carrot root yield and NPK uptake by carrot crop were used for obtaining four important basic parameters, viz., nutrients required to produce a quintal of carrot roots (NR%), contribution of nutrients from fertilizers (CF%), contribution of nutrients from soil (CS%) and contribution of nutrients from organic matter (%C-OM). These parameters were used for developing fertilizer-adjustment targeted yield equation. Comparison of the present soil testing laboratory method with Soil Test Crop Response approach of fertilizer recommendation clearly indicated superiority of STCR targeted yield approach for efficient and economic use of fertilizers to attain the required yield target

Pore scale numerical modeling of bacterial growth and decay in Cr VI reactive transport

To understand the mechanism of bacteria growth in a reactive transport model inside a bio barrier for bioremediation, numerical modeling in the pore scale has to be done. An attempt has been made to understand and simulate the growth and decay of indigenous bacteria in a saturated porous medium at pore scale having a known concentration of Cr VI and to predict the enhanced microbial activity for the transformation of Cr VI to Cr III. Darcy scale models previously developed failed to address the hydrodynamics of the system, and contaminant degradation rates were over-predicted. In this study, the pore scale model combines processes such as fluid flow, solute transport with advection & diffusion equations of Cr VI reactive transport. Along with biotransformation of Cr VI and substrate consumption, the model majorly incorporates bacterial growth and decay on a pore scale. The model is divided into three portions, one for each of the processes and operated on different time scales. The mathematical equations are solved using FEM with appropriate initial and boundary conditions. The model developed can predict the velocity and pressure profiles developed fairly well. It can also predict the movement and, consumption of the substrate and biotransformation of Cr (VI) by bacteria along with the changes in its concentration

Prevalence of psychiatric co morbidities in bronchial asthma and chronic obstructive pulmonary disease patients in north Indian population cohort

Background: Psychiatric co morbidities tend to occur quite frequently in patients of chronic respiratory diseases mainly bronchial asthma and Chronic Obstructive Pulmonary Disease (COPD) but still it is highly under diagnosed. Aim and objective of the study was to find out the prevalence of psychiatric co morbidities in asthma and COPD and to correlate them with disease severity according to Global Initiative against Obstructive Lung Disease (GOLD) and Global Initiative against Asthma (GINA) guidelines.Methods: Study was conducted in Department of TB and Chest in association with Department of Psychiatry of Punjab Institute of Medical Sciences, a secondary care medical college in north India. A total 204 patients, 68 of bronchial asthma, 68 0f COPD and 68 were controls included in the study. Diagnosis and severity of respiratory diseases was assessed by spirometry. Evaluation of psychiatric co morbidities was done using the MINI international neuropsychiatric interview questionnaire.Results: The frequency of psychiatric co morbidities in COPD patients was significantly higher (32.4%) compared to patients of bronchial asthma (20.6%). The most common co morbidity in both arms was generalized anxiety disorder (17.6% in COPD patients and 10.3% in patients of bronchial asthma.Conclusions: COPD patients have a higher frequency of psychiatric co morbidities compared to bronchial asthma patients and control population. These should be properly screened and treated.

Ovine pulmonary adenocarcinoma (OPA) in sheep: an update on epidemiology, pathogenesis and diagnosis

Ovine pulmonary adenocarcinoma (OPA) is a spontaneous lung tumor in sheep caused by Jaagsiekte sheep retrovirus (JSRV) belonging to the Retroviridae. The primary aim of this review work is to give brief insights into the epidemiological aspects of OPA based on a meta-analysis of available research work. This review article also discussed pathogenesis, diagnostic tests and control strategies available for OPA in Sheep. This will help in developing future strategies for disease-free status in India. This disease is endemic in Europe, Africa, Asia, and American continents, causing significant economic losses due to chronic respiratory illness and persistent infections in flocks. The virus is unique among retroviruses with selective affinity to lungs and is the only virus known to cause spontaneous lung tumors in sheep. The incubation time ranges for sheep with naturally occurring OPA ranged from one to four years. There are two pathological forms of the disease: classical and atypical. At an early stage, OPA is difficult to detect in sheep due to a lack of preclinical diagnostic methods, as JSRV is poorly immunogenic and doesn't induce an immune response. PCR, histopathology, and immunohistochemistry are recommended methods for OIE diagnosis. To become a JSRV-free country, mandatory surveillance, detection, and removal of positive animals are required, as OPA is difficult to control due to a lack of vaccines and preclinical diagnostic tests. Due to its similar histological and molecular pathogenesis to that of human lung cancer, OPA is considered an ideal large animal model of human lung adenocarcinoma

Recent Developments in Lattice QCD

I review the current status of lattice QCD results. I concentrate on new analytical developments and on numerical results relevant to phenomenology.Comment: 35 pages, 4 figures (Figures are excerpted from others' work and are not included) Uses harvmac.te

Emergency Department Pediatric Readiness Among US Trauma Centers: A Machine Learning Analysis of Components Associated With Survival.

OBJECTIVE: We used machine learning to identify the highest impact components of emergency department (ED) pediatric readiness for predicting in-hospital survival among children cared for in US trauma centers. BACKGROUND: ED pediatric readiness is associated with improved short-term and long-term survival among injured children and part of the national verification criteria for US trauma centers. However, the components of ED pediatric readiness most predictive of survival are unknown. METHODS: This was a retrospective cohort study of injured children below 18 years treated in 458 trauma centers from January 1, 2012, through December 31, 2017, matched to the 2013 National ED Pediatric Readiness Assessment and the American Hospital Association survey. We used machine learning to analyze 265 potential predictors of survival, including 152 ED readiness variables, 29 patient variables, and 84 ED-level and hospital-level variables. The primary outcome was in-hospital survival. RESULTS: There were 274,756 injured children, including 4585 (1.7%) who died. Nine ED pediatric readiness components were associated with the greatest increase in survival: policy for mental health care (+8.8% change in survival), policy for patient assessment (+7.5%), specific respiratory equipment (+7.2%), policy for reduced-dose radiation imaging (+7.0%), physician competency evaluations (+4.9%), recording weight in kilograms (+3.2%), life support courses for nursing (+1.0%-2.5%), and policy on pediatric triage (+2.5%). There was a 268% improvement in survival when the 5 highest impact components were present. CONCLUSIONS: ED pediatric readiness components related to specific policies, personnel, and equipment were the strongest predictors of pediatric survival and worked synergistically when combined

Genetic variant effects on gene expression in human pancreatic islets and their implications for T2D

Most signals detected by genome-wide association studies map to non-coding sequence and their tissue-specific effects influence transcriptional regulation. However, key tissues and cell-types required for functional inference are absent from large-scale resources. Here we explore the relationship between genetic variants influencing predisposition to type 2 diabetes (T2D) and related glycemic traits, and human pancreatic islet transcription using data from 420 donors. We find: (a) 7741 cis-eQTLs in islets with a replication rate across 44 GTEx tissues between 40% and 73%; (b) marked overlap between islet cis-eQTL signals and active regulatory sequences in islets, with reduced eQTL effect size observed in the stretch enhancers most strongly implicated in GWAS signal location; (c) enrichment of islet cis-eQTL signals with T2D risk variants identified in genome-wide association studies; and (d) colocalization between 47 islet cis-eQTLs and variants influencing T2D or glycemic traits, including DGKB and TCF7L2. Our findings illustrate the advantages of performing functional and regulatory studies in disease relevant tissues.Peer reviewe

Influence of genetic variants on gene expression in human pancreatic islets – implications for type 2 diabetes

Most signals detected by genome-wide association studies map to non-coding sequence and their tissue-specific effects influence transcriptional regulation. However, many key tissues and cell-types required for appropriate functional inference are absent from large-scale resources such as ENCODE and GTEx. We explored the relationship between genetic variants influencing predisposition to type 2 diabetes (T2D) and related glycemic traits, and human pancreatic islet transcription using RNA-Seq and genotyping data from 420 islet donors. We find: (a) eQTLs have a variable replication rate across the 44 GTEx tissues (<73%), indicating that our study captured islet-specific cis-eQTL signals; (b) islet eQTL signals show marked overlap with islet epigenome annotation, though eQTL effect size is reduced in the stretch enhancers most strongly implicated in GWAS signal location; (c) selective enrichment of islet eQTL overlap with the subset of T2D variants implicated in islet dysfunction; and (d) colocalization between islet eQTLs and variants influencing T2D or related glycemic traits, delivering candidate effector transcripts at 23 loci, including DGKB and TCF7L2. Our findings illustrate the advantages of performing functional and regulatory studies in tissues of greatest disease-relevance while expanding our mechanistic insights into complex traits association loci activity with an expanded list of putative transcripts implicated in T2D development

Mortality and pulmonary complications in patients undergoing surgery with perioperative SARS-CoV-2 infection: an international cohort study

Background: The impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on postoperative recovery needs to be understood to inform clinical decision making during and after the COVID-19 pandemic. This study reports 30-day mortality and pulmonary complication rates in patients with perioperative SARS-CoV-2 infection. Methods: This international, multicentre, cohort study at 235 hospitals in 24 countries included all patients undergoing surgery who had SARS-CoV-2 infection confirmed within 7 days before or 30 days after surgery. The primary outcome measure was 30-day postoperative mortality and was assessed in all enrolled patients. The main secondary outcome measure was pulmonary complications, defined as pneumonia, acute respiratory distress syndrome, or unexpected postoperative ventilation. Findings: This analysis includes 1128 patients who had surgery between Jan 1 and March 31, 2020, of whom 835 (74·0%) had emergency surgery and 280 (24·8%) had elective surgery. SARS-CoV-2 infection was confirmed preoperatively in 294 (26·1%) patients. 30-day mortality was 23·8% (268 of 1128). Pulmonary complications occurred in 577 (51·2%) of 1128 patients; 30-day mortality in these patients was 38·0% (219 of 577), accounting for 81·7% (219 of 268) of all deaths. In adjusted analyses, 30-day mortality was associated with male sex (odds ratio 1·75 [95% CI 1·28–2·40], p\textless0·0001), age 70 years or older versus younger than 70 years (2·30 [1·65–3·22], p\textless0·0001), American Society of Anesthesiologists grades 3–5 versus grades 1–2 (2·35 [1·57–3·53], p\textless0·0001), malignant versus benign or obstetric diagnosis (1·55 [1·01–2·39], p=0·046), emergency versus elective surgery (1·67 [1·06–2·63], p=0·026), and major versus minor surgery (1·52 [1·01–2·31], p=0·047). Interpretation: Postoperative pulmonary complications occur in half of patients with perioperative SARS-CoV-2 infection and are associated with high mortality. Thresholds for surgery during the COVID-19 pandemic should be higher than during normal practice, particularly in men aged 70 years and older. Consideration should be given for postponing non-urgent procedures and promoting non-operative treatment to delay or avoid the need for surgery. Funding: National Institute for Health Research (NIHR), Association of Coloproctology of Great Britain and Ireland, Bowel and Cancer Research, Bowel Disease Research Foundation, Association of Upper Gastrointestinal Surgeons, British Association of Surgical Oncology, British Gynaecological Cancer Society, European Society of Coloproctology, NIHR Academy, Sarcoma UK, Vascular Society for Great Britain and Ireland, and Yorkshire Cancer Research

Study on the Prognostication of Crop Diseases using Artificial Intelligence

It is universally accepted fact that crop diseases are one of the major threats in agriculture that ultimately result in drastic reduction of food supply. The present project study aims to use artificial intelligence in building a model which is integrated with a user-friendly web application. The web application is created using the Python-based Django framework. This user interface allows the user to choose a crop name and upload an image of a leaf wherein the trained model then begins the process of feature extraction on the image and tries to make an accurate prediction. The final result is displayed to the user confirming whether the crop may be “healthy” or the “diseased “and even the name of the disease that infects the plant will be displayed. The application also suggests a suitable treatment to combat the disease. Thus, the scope of this project study is very scalable as it can be easily be used by amateur gardeners as well as by farmers. The model itself can also be extended to include more plant types along with any new diseases which may arise due to factors like climate change, pest - resistance etc