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Serum (C Reactive Protein) CRP is a good predictor of vascular events and has been linked to polycystic ovarian syndrome (PCOS). The increased CRP may be an early marker of upcoming atherosclerotic diseases like coronary artery disease and stroke. To identify the disease of PCOS and its interrelation with increased CRP may work as a clue for its disease progression. The purpose of this study is to evaluate the high-sensitivity C- reactive protein, which is a characteristic of individuals with PCOS of reproductive age. The sample size for this prospective randomized study was 200 people, divided into two equal groups (with or without PCOS). This study included 100 obese women with the polycystic ovarian syndrome and 100 matched controls. These women's blood samples were tested for serum lipid profile and hsCRP.. The demographic variables were similar between the groups. Our findings revealed that patients with PCOS had high LDL and triglyceride levels. HDL levels were extremely low. This indicates a poor lipid profile in PCOS patients. (The p-value is 0.001). The hs CRP was 9.3 mg compared to 2.9 mg in the control group. According to our findings, hs CRP may provide information about the presence of the disease. As a result, more research is needed to explain the potential role of C-reactive protein in the disease incidence and progression of polycystic ovarian syndrome. Even though there are a lot of studies linking these two, we have done for Hs CRP and PCOS. The sample size is on the higher side in our study.

Wildfire changes the hydrologic and geomorphic response of watersheds, resulting in a cascade of increased hazards for sediment-laden floods, shallow landslides, and debris flows. This phenomenon has long been studied in regions where landscape evolution is driven by repeated fire-flood sequences such as Southern California. However, comparing mass movement hazards across multiple regions presents a challenge because most landslide inventories are limited to local or regional spatial scales. This study seeks to identify unique triggering characteristics of post-fire landslides through a comparison of the precipitation characteristics preceding landslides at both burned and unburned locations spanning six global regions. Regional inter-comparison was facilitated by selecting landslide events from the NASA Global Landslide Catalog (GLC), and then establishing fire and precipitation histories for each site using MODIS global burned area and CHIRPS precipitation data. In addition, since the GLC did not contain a sufficient percentage of burned locations in any part of Europe, a parallel analysis incorporates nationally maintained landslide inventories from several European countries. Analysis of normalized seven-day accumulated precipitation for sites across all regions shows that post-wildfire landslides are preceded by less precipitation than landslides without antecedent wildfire events. This supports the hypothesis that fire increases the rainfall-driven landslide hazards. A regional examination of landslide susceptibility using normalized triggering storm volumes as a proxy indicator reveals a distinct sensitivity to fire across several regions of the western US. However, in other regions wildfire appears to have a limited or even opposite impact on landslide susceptibility. Unburned locations also tend to see a sharper ramp-up of precipitation leading up to the date of the landslide relative to burned sites in regions of the Western US. In other regions the storm timing is similar or, a in Central America, even longer in burned locations. The landslide-triggering storms of post-fire landslides also exhibit different seasonality from other rainfall-triggered landslides, with a variety of seasonal shifts ranging from approximately six months in the Pacific Northwest of the US to one week in the Himalayas. These results suggest that the apparent inconsistency in the impact of wildfire on landslide hazards may be due to a combination of two factors: interactions between fire and precipitation seasonality and variability in soils and vegetation. For example, in the Intermountain West of the US and Southeast Asia landslides are preceded by a thirty-day or longer dry period while in all other regions there is no difference in the precipitation prior to the triggering storm event, which suggests different landslide mechanisms such as dry ravel, which rely on low rather than high soil moisture levels, may be a factor. Overall, this work offers an exploration of regional differences in the characteristics of rainfall-triggered landslides over a broad spatial scale encompassing a variety of climates, terrains, and ecoregions.

Despite the potential and increasing interest in physically based hydrological models for streamflow forecasting applications, they are constrained in terms of agility to generate ensembles. Hence, we develop and test a Bayesian hierarchical model (BHM) to postprocess physically based hydrologic model simulations at multiple sites on a river network, with the aim to generate probabilistic information (i.e., ensembles) and improve raw model skill. We apply our BHM framework to daily summer (July–August) streamflow simulations at five stations located in the Narmada River basin in central India, forcing the Variable Infiltration Capacity (VIC) model with observed rainfall. In this approach, daily observed streamflow at each station is modeled with a conditionally independent probability density function with time varying distribution parameters, which are modeled as a linear function of potential covariates that include VIC outputs and meteorological variables. Using suitable priors on the parameters, posterior parameters and predictive posterior distributions—and thus ensembles—of daily streamflow are obtained. The best BHM model considers a gamma distribution and uses VIC streamflow and a nonlinear covariate formulated as the product of VIC streamflow and 2-day precipitation spatially averaged across the area between the current and upstream station. The second covariate enables correcting the time delay in flow peaks and nonsystematic biases in VIC streamflow. The results show that the BHM postprocessor increases probabilistic skill in 60% compared to raw VIC simulations, providing reliable ensembles for most sites. This modeling approach can be extended to combine forecasts from multiple sources and provide skillful multimodel ensemble forecasts.

We performed a systematic space-time analysis of monsoon seasonal (Jun-Sep) rainfall and extremes (3-day maximum rainfall) over India for the period 1951–2019. Employing Partition Around Medoid (PAM) clustering technique on the seasonal rainfall and extremes, six spatially coherent regions (clusters) were identified that are contiguous in space and consistent with the topography, which are: Central-West India (CW), Northwest and Northern India (NW), (WG) Western Ghats (WG), Deccan Plateau (DP), Central-East India including Indo-Gangetic plain (CE) and Northeast India (NE). Integrated Vertical Transport (IVT) of moisture composites for wet and dry years for each cluster indicated that Bay of Bengal is the major source of moisture for extreme rainfall for all of India, except for WG. Arabian Sea and Bay of Bengal both provide moisture for the seasonal rainfall for western and eastern halves of India, respectively. Trend analysis revealed decline in seasonal rainfall over CE, NE and WG clusters and increase in extreme rainfall over CW region. These are consistent with increasing IVT trends over Bay of Bengal and decreasing over Arabian Sea and Indian Ocean. Teleconnections to tropical Pacific Sea surface temperatures (SSTs) were reminiscent of El Nino Southern Oscillation (ENSO) patterns, with cooler SSTs in central and eastern Pacific favoring stronger monsoon rainfall and to a lesser extent the extremes. Further, warmer Indian Ocean in recent decades is likely a mediator in the moisture transport by reducing seasonal rainfall and enhancing the extremes. These interesting insights brighten the prospects for skillful forecast of monsoon rainfall and extremes.

The Prairie Pothole Region (PPR) experiences considerable space–time variability in temperature and precipitation, and this variability is expected to increase. The PPR is sensitive to this variability—it plays a large role in the water availability of the region. Thousands of wetlands in the region, sometimes containing ponds, provide habitats and breeding grounds for various species. Many wildlife management decisions are planned and executed on subseasonal-to-seasonal time scales and would benefit from knowledge of seasonal conditions at longer lead times. Therefore, it is important to understand potential driving mechanisms and teleconnections behind space–time climate variability in the PPR. We performed principal component analysis on summer precipitation of the southeastern PPR (SEPPR) to determine the leading principal components (PCs) of variability. These PCs were used to establish teleconnections to large-scale climate variables and indices. They were also used to determine potential mechanisms driving the precipitation variability. There were teleconnections to Pacific and Atlantic Ocean sea surface temperatures (SST) resembling the Pacific decadal oscillation and El Niño–Southern Oscillation, low 500-hPa heights over the western United States, and the Palmer drought severity index over the SEPPR. A large-scale low pressure region over the northwestern United States and a pattern like the Great Plains low-level jet, observed in the 500- and 850-hPa heights and winds, are a potential mechanism of the precipitation variability by increasing precipitation during wet PC1 years. These findings can inform management actions to maintain and restore wildlife habitat and the resources used for those actions in the PPR.

Introduction: PCOS is one of the common endocrine disorders in women leading to various complications like infertility, menstrual, psychological disturbances and metabolic syndrome. The objectives of this study were to study the clinical and biochemical parameters of metabolic syndrome in women of reproductive age group with PCOS. Materials and Methods: This cross-sectional study was conducted in a tertiary care centre from September 2018 to June 2020. Fifty six women diagnosed with PCOS, underwent clinical and laboratory assessments for the diagnosis of metabolic syndrome, as per international diabetes federation (IDF) criteria. Results: 14.3% subjects had metabolic syndrome. The prevalence of individual components of the metabolic syndrome among PCOS patients were: waist circumference >80cms in 100%, HDL 150mg/dl in 75%, blood pressure >130/85mmhg in 12.5%, fasting plasma glucose > 100 mg /dl in 87.5%. Subjects with metabolic syndrome had significantly higher BMI compared to those without metabolic syndrome (p=0.043). Conclusion: The metabolic syndrome and its individual components, like waist circumference and decreased HDL were found to be more common among PCOS patients in this study. As the risk of MetS increases with age and BMI, early screening and timely interventions like lifestyle changes will prevent metabolic complications like cardiovascular diseases and type II diabetes mellitus. Keywords: Polycystic ovarian syndrome, Metabolic syndrome, Central obesity, Insulin resistance, Hyperandrogenism.

Introduction: Pregnancy is a diabetogenic state which places the mother as well as the foetus at high risk for certain complications even though the woman is neither GDM nor overt diabetic. This study aims at estimation of HbA1c value after 24 weeks of pregnancy and its correlation with the pregnancy outcome. Materials and Methods: The study was done among 60 healthy pregnant women who are between 18-35 years. HbA1c was estimated and they were followed up for assessing pregnancy outcome. Birth weight >90th percentile, primary C-section, neonatal hypoglycaemia, any NICU admissions and neonate’s condition at discharge were assessed. Results: The mean of haemoglobin, OGTT (fasting, 1-hour and 2-hours) and HbA1c were 11.25mg/dL, 81.73mg/dL, 120.75mg/dL, 104.13mg/dL and 5.24% respectively. The trimester specific HbA1c levels in the second and third trimesters in the present study were found to be 5.11% and 5.27% respectively. Among the study subjects 51 (85%) women had term delivery whereas 9 (15%) women had preterm delivery. 42 women (70%) had normal delivery, 1(1.7%) patient had operative vaginal and the remaining 17(28.3%) patients had emergency caesarean section. It was observed that 5(8.3%) subjects had LBW and 55(91.7%) had normal birth weight neonates. Two neonates among the study subjects had NICU admission for other reasons. Conclusion: The results obtained by this study showed low or no significant relationship between HbA1c levels estimation during pregnancy and pregnancy as well as foetal outcome, also adverse pregnancy outcomes have multifactorial influences rather than maternal glycaemic status alone. Keywords: HbA1c, Gestational diabetes mellitus, Pregnancy outcome.

The timing of melt onset in the Arctic plays a key role in the evolution of sea ice throughout Spring, Summer and Autumn. A major catalyst of early melt onset is increased downwelling longwave radiation, associated with increased levels of moisture in the atmosphere. Determining the atmospheric moisture pathways that are tied to increased downwelling longwave radiation and melt onset is therefore of keen interest. We employed Self Organizing Maps (SOM) on the daily sea level pressure for the period 1979–2018 over the Arctic during the melt season (April–July) and identified distinct circulation patterns. Melt onset dates were mapped on to these SOM patterns. The dominant moisture transport to much of the Arctic is enabled by a broad low pressure region stretching over Siberia and a high pressure over northern North America and Greenland. This configuration, which is reminiscent of the North American-Eurasian Arctic dipole pattern, funnels moisture from lower latitudes and through the Bering and Chukchi Seas. Other leading patterns are variations of this which transport moisture from North America and the Atlantic to the Central Arctic and Canadian Arctic Archipelago. Our analysis further indicates that most of the early and late melt onset timings in the Arctic are strongly related to the strong and weak emergence of these preferred circulation patterns, respectively.

A few open source software (OSS) products exhibit an abrupt and significant increase in downloads. However, the majority of OSS products fail to gain much interest. Identifying early success is important for catalyzing growth in OSS markets. However, previous OSS research has not examined early product success dynamics and assumes adoption to be a continuous process. We propose OSS takeoff in adoptions as a measure of eventual product success. Takeoff is a nonlinear inflection point separating the early development from the growth phase in the product lifecycle. Using arguments from the signaling literature, we propose that community activities send signals about product quality and reduce information asymmetry faced by potential adopters of OSS products. Estimating a Cox proportional hazard model using a large sample of OSS products from SourceForge, we find that takeoff times are significantly associated with signals of quality deficiency and improvement. Further, we find that target audience and product innovativeness moderate this relationship.

Understanding influent water quality variability is essential for the long-term planning of potable water systems. To quantify variability and generate realistic influent scenarios, we propose a nonparametric time series approach based on k-nearest neighbor (k-NN) bootstrap resampling. The k-NN approach resamples historical data conditioned on a “feature vector” at a given time to generate values at subsequent times. We modified this algorithm by adding random perturbations to the resampled values to generate realistic extremes unobserved in the historical record. k-NN is widely used in stochastic hydrology and hydroclimatology; however, it is adapted here for the multivariate, data-limited context of water treatment. To examine the performance of the algorithm, we applied it to an eleven-year, monthly water quality dataset of alkalinity, temperature, total organic carbon, and pH from the Cache la Poudre River in Colorado. We found that the k-NN simulations captured the relevant distributional statistics of the historical record, which suggests that the algorithm produces realistic and varied scenarios. When used in conjunction with modeling and optimization, these scenarios have the potential to improve the sustainability, resilience, and efficiency of potable water systems.

Background: Copper plays a definitive role in normal function and development of the body. Previous studies have shown that suboptimal copper intake during fetal development contributes to occurrence of birth defects in humans. Copper deficiency is more common in pregnancy especially in developing countries like India. Prevalence studies on copper levels in pregnant women in south Indian population are scanty and needs to be evaluated.Aim: The aim of this study is to assess serum copper levels during pregnancy and its association with dietary intake, socioeconomic status, gestational age and parity. Materials and methods: This cross sectional study included a total of 54 healthy pregnant women in the rural area of chengalpettu district, Tamil Nadu. A proforma was used to collect a detailed obstetric history and their average daily dietary intake in terms of copper was assessed by using a weekly standardized food frequency questionnaire. Serum Copper level was estimated using colorimetric method. Results: Serum copper levels were deficient in 3.7% of the study population with mean serum copper levels of 193.93±79.24 µg/dl in study population. The dietary intake of copper was found to be deficient

Natural and man-made lakes store the largest amount of available freshwater on Earth’s surface. Human activities and a changing climate increasingly threaten lakes, as evidenced by record-low water levels in some of the world’s largest lakes. Yet, studies that document lake volume changes are limited to a small subset of lakes or neglect to attribute the drivers of changes due to a lack of consistent long-term observations. Here, we merge three decades of high-resolution satellite observations, climate, and hydrologic models, to provide an unprecedented assessment of long-term global lake volume trends (1992-2020). Storage trends and their attributions are quantified for 1,978 large lakes that represent 95% of the total global lake volume. In support of reproducing our findings, the attached codes include constructing water body areas from Landsat satellites and deriving water body levels from the CryoSat-2 altimeter.&nbsp

Timely projections of seasonal streamflow extremes can be useful for the early implementation of annual flood risk adaptation strategies. However, predicting seasonal extremes is challenging particularly under non-stationary conditions and if extremes are connected in space. The goal of this study is to implement a space-time model for projection of seasonal streamflow extremes that considers the nonstationarity and spatio-temporal dependence of high flows. We develop a space-time model to project seasonal streamflow extremes for several lead times up to 2 months using a Bayesian Hierarchical Modelling (BHM) framework. This model is based on the assumption that streamflow extremes (3-day maxima) at a set of gauge locations are realizations of a Gaussian elliptical copula and generalized extreme value (GEV) margins with nonstationary parameters. These parameters are modeled as a linear function of suitable covariates from the previous season selected using the deviance information criterion (DIC). Finally, the copula is used to generate streamflow ensembles, which capture spatio-temporal variability and uncertainty. We apply this modelling framework to predict 3-day maximum flow in spring (May-June) at seven gauges in the Upper Colorado River Basin (UCRB) with 0 to 2 months lead time. In this basin, almost all extremes that cause severe flooding occur in spring as a result of snowmelt and precipitation. Therefore, we use regional mean snow water equivalent and temperature from the preceding winter season as well as indices of large-scale climate teleconnections – ENSO, AMO, and PDO – as potential covariates for 3-day maximum flow. Our model evaluation, which is based on the comparison of different model versions and the energy skill score, indicates that the model can capture the space-time variability of extreme flow well and that model skill increases with decreasing lead time. We also find that the use of climate variables slightly enhances skill relative to using only snow information. Median projections and their uncertainties are consistent with observations thanks to the representation of spatial dependencies through covariates in the margins and a Gaussian copula. This spatio-temporal modeling framework helps to plan seasonal adaptation and preparedness measures as predictions of extreme spring flows become available 2 months before actual flood occurrence.