Study of prevalence of insulin resistance and other metabolic abnormalities in various phenotypes of polycystic ovary syndrome in central India

Background: Till recent times, defining symptoms of PCOS remained a debatable topic. In 2012, National Institute of health consensus panel proposed diagnostic criteria based on phenotypes. Evidence showed higher incidence of diabetes mellitus, insulin resistance and compensatory hyperinsulinemia among women with PCOS. So, the present study was undertaken to compare the clinical, metabolic and hormonal profile among various phenotypes in women with PCOS and to find out the prevalence of insulin resistance among the PCOS phenotypes.Methods: The prospective, observational study was done on 292 women with PCOS-related infertility. These women were divided into 4 phenotypes. Ferriman-Gallwey score, HOMA-IR, OGTT, lipid parameters, hormonal parameters, mean ovarian volume and mean antral follicle counts were compared among the 4 phenotypic groups. One-way ANOVA followed by post-hoc Tukey was applied.Results: Mean weight, BMI, waist circumference, SBP, DBP and Ferriman-Gallwey score, fasting glucose, fasting insulin, OGTT (1 hour) and HOMA-IR was highest in phenotype A, while fasting glucose / insulin ratio was lowest in phenotype A. Total triglycerides, total cholesterol, LDL were higher and HDL was lowest, testosterone, mean ovarian volume and mean antral follicle count were highest and vitamin D was lowest in Phenotype A.Conclusions: Phenotype A is the group with all features of PCOS syndrome, while phenotype D is associated with milder metabolic profile. Women with phenotype A and B are at a higher risk for metabolic syndrome. Identifying various phenotypes will assist in providing appropriate treatment and prognosticating the patients with PCOS-related infertility

Soil moisture measurement for agriculture

Chapter 2. Whilst infrastructure projects have often focused on improving the supply of water for agriculture, there has been much less focus on managing or reducing the agricultural water demand. The net effect of increasing supply, without managing demand, is that agricultural water (and energy) consumption increases, without necessarily increasing food production. Improved agricultural Water Use Efficiency (WUE) can help address this issue, as well as contributing to reducing the pressures on water resources (NITI Aayog 2019). This chapter outlines how recent improvements in large area measurement of soil moisture and the availability of high-resolution Soil Moisture Deficit information at a fine scale can provide actionable guidance to farmers. Practical methods, demonstrated in farm pilot studies, to manage irrigation demand are discussed, along with considerations of efficient irrigation methods, with the objective of improving WUE

The Indian COSMOS Network (ICON): validating L-band remote sensing and modelled soil moisture data products

Availability of global satellite based Soil Moisture (SM) data has promoted the emergence of many applications in climate studies, agricultural water resource management and hydrology. In this context, validation of the global data set is of substance. Remote sensing measurements which are representative of an area covering 100 m2 to tens of km2 rarely match with in situ SM measurements at point scale due to scale difference. In this paper we present the new Indian Cosmic Ray Network (ICON) and compare it’s data with remotely sensed SM at different depths. ICON is the first network in India of the kind. It is operational since 2016 and consist of seven sites equipped with the COSMOS instrument. This instrument is based on the Cosmic Ray Neutron Probe (CRNP) technique which uses non-invasive neutron counts as a measure of soil moisture. It provides in situ measurements over an area with a radius of 150–250 m. This intermediate scale soil moisture is of interest for the validation of satellite SM. We compare the COSMOS derived soil moisture to surface soil moisture (SSM) and root zone soil moisture (RZSM) derived from SMOS, SMAP and GLDAS_Noah. The comparison with surface soil moisture products yield that the SMAP_L4_SSM showed best performance over all the sites with correlation (R) values ranging from 0.76 to 0.90. RZSM on the other hand from all products showed lesser performances. RZSM for GLDAS and SMAP_L4 products show that the results are better for the top layer R = 0.75 to 0.89 and 0.75 to 0.90 respectively than the deeper layers R = 0.26 to 0.92 and 0.6 to 0.8 respectively in all sites in India. The ICON network will be a useful tool for the calibration and validation activities for future SM missions like the NASA-ISRO Synthetic Aperture Radar (NISAR)

Cosmic-ray soil water monitoring: the development, status & potential of the COSMOS-India network

Soil moisture (SM) plays a central role in the hydrological cycle and surface energy balance and represents an important control on a range of land surface processes. Knowledge of the spatial and temporal dynamics of SM is important for applications ranging from numerical weather and climate predictions, the calibration and validation of remotely sensed data products, as well as water resources, flood and drought forecasting, agronomy and predictions of greenhouse gas fluxes. Since 2015, the Centre for Ecology and Ecology has been working in partnership with several Indian Research Institutes to develop COSMOS-India, a new network of SM monitoring stations that employ cosmic-ray soil moisture sensors (CRS) to deliver high temporal frequency, near-real time observations of SM at field scale. CRS provide continuous observations of near-surface (top 0.1 to 0.2 m) soil volumetric water content (VWC; m3 m-3) that are representative of a large footprint area (approximately 200 m in radius). To date, seven COSMOS-India sites have been installed and are operational at a range of locations that are characterised by differences in climate, soil type and land management. In this presentation, the development, current status and future potential of the COSMOS-India network will be discussed. Key results from the COSMOS-India network will be presented and analysed

Giant hydronephrosis mimicking as gross ascites in a 6-year-old boy

Giant hydronephrotic kidney is a rare form of obstructive uropathy in children and adolescents. The congenital ureteropelvic junction obstruction is the most frequent cause. The CT scan is very important for the diagnosis. We report a 6-year-old boy with progressively increasing abdominal distension since birth. There were no associated urinary or gastrointestinal symptoms. He had bilateral hydronephosis with giant hydronephrosis over right side. The cystic mass occupied the right retroperitoneal space and crossed the midline

Integrating process-related information into an artificial neural network for root-zone soil moisture prediction

International audienceAbstract. Quantification of root-zone soil moisture (RZSM) is crucial for agricultural applications and the soil sciences. RZSM impacts processes such as vegetation transpiration and water percolation. Surface soil moisture (SSM) can be assessed through active and passive microwave remote-sensing methods, but no current sensor enables direct RZSM retrieval. Spatial maps of RZSM can be retrieved via proxy observations (vegetation stress, water storage change and surface soil moisture) or via land surface model predictions. In this study, we investigated the combination of surface soil moisture information with process-related inferred features involving artificial neural networks (ANNs). We considered the infiltration process through the soil water index (SWI) computed with a recursive exponential filter and the evaporation process through the evaporation efficiency computed based on a Moderate Resolution Imaging Spectroradiometer (MODIS) remote-sensing dataset and a simplified analytical model, while vegetation growth was not modeled and was only inferred through normalized difference vegetation index (NDVI) time series. Several ANN models with different sets of features were developed. Training was conducted considering in situ stations distributed in several areas worldwide characterized by different soil and climate patterns of the International Soil Moisture Network (ISMN), and testing was applied to stations of the same data-hosting facility. The results indicate that the integration of process-related features into ANN models increased the overall performance over the reference model level in which only SSM features were considered. In arid and semiarid areas, for instance, performance enhancement was observed when the evaporation efficiency was integrated into the ANN models. To assess the robustness of the approach, the trained models were applied to observation sites in Tunisia, Italy and southern India that are not part of the ISMN. The results reveal that joint use of surface soil moisture, evaporation efficiency, NDVI and recursive exponential filter represented the best alternative for more accurate predictions in the case of Tunisia, where the mean correlation of the predicted RZSM based on SSM only sharply increased from 0.443 to 0.801 when process-related features were integrated into the ANN models in addition to SSM. However, process-related features have no to little added value in temperate to tropical conditions