Strain imaging as an early predictor in acute myocardial infarction - An augmented cross-sectional study.

INTRODUCTION: Cardiac fibres are affected invariably in myocardial infarction, with longitudinal strain being the earliest to be detected in the ischaemic cascade. The present study aims to assess strain imaging in acute myocardial infarction (AMI) patients admitted to the cardiology department at our institute and correlate GLS and DESL findings with other markers for myocardial function. METHODS AND MATERIAL: This augmented cross-sectional study was conducted amongst the patients admitted with diagnosis of AMI. During the study period, 157 subjects were sampled through convenience sampling, and examined as well as tested with routine investigations at baseline. The subjects were then followed through at first, third and six months, and findings noted. Chi-square was used to assess the crude association between sample characteristics. Pearson correlation and student t-test were used to find association between continuous variables. RESULTS: After screening 564 patients, 157 patients were included in the study after fulfilment of inclusion and exclusion criteria. A significant difference was found in baseline GLS scores and NTproBNP levels at 6 months in alive patients with STEMI, t (21.728) = -5.717, p < .001. Out of the 50 NSTEMI patients, 35 (70 %) were positive for ESL, similarly out of 43 STEMI patients without any RWMA, ESL was positive in 39 (90.02 %) patients. CONCLUSIONS: GLS by STE has good correlation with LVEF, WMSI and NT pro-BNP and it is an independent predictor of mortality and heart failure among patients with AMI.

Remote sensing-based drought hazard monitoring and assessment in a coastal plain: A principal component approach.

Accurate drought information is essential for preventing agricultural and societal losses. The indicators of how severe a drought is the deficiency in precipitation, soil moisture, and vegetation stress. The indicators were evaluated using the Precipitation Condition Index (PCI), Vegetation Condition Index (VCI), and Temperature Condition Index (TCI).The indices were combined using Principal Component Analysis to create the Synthetic Drought Index (SDI) for the evaluation of drought severity. The indices were estimated using multi-source remote sensing data from the Tropical Rainfall Measuring Mission (TRMM) and Operational Land Imager (OLI) of various years. Temporal analysis showed that the district is drought-prone and deficiency of 65% of precipitation in northeast monsoon of 2016 and below average non-monsoon rainfall in 2017, caused drought and affected 223.5 Km2 in 2017. Below average precipitation in northeast monsoon of 2018 and below average non-monsoon rainfall in 2019, caused drought and affected 423 Km2 in 2019. The northeast coastal regions of Ottapidaram, Thoothukudi, and Vilathikulam taluks of the district were more severely prone to drought. Failure of monsoon is the root cause of water deficit in water bodies. The semi-arid coastal climate accelerates the evaporation of water in water bodies and causes soil moisture deficit that leads to drought in the coastal district. A sequential evaluation of this index can be used to identify the onset of drought and mitigate the effect of drought.

Integrated flood risk assessment of properties and associated population at county scale for Nebraska, USA.

Risk assessment of properties and associated population was conducted for the state of Nebraska, leveraging only open-source datasets. The flood risk framework consisted of interactions among drivers, i.e. hazard, exposure, vulnerability, and response, to assess the risks related to properties and associated populations. To quantify hazard on a county scale, we considered properties at risk of flooding based on a flood score (a higher score represents a greater chance of flooding). Exposure was quantified by considering population density at the county level. We quantified vulnerability under four categories: social, ecological, economic, and health. Response, a relatively newer component in flood risk assessment, was also quantified under three distinct categories: structural, non-structural, and emergency. Overall, we found that counties in eastern Nebraska (Sarpy, Dakota, Wayne, and Adams) have a higher risk of flooding consequences due to more exposure to vulnerable assets such as population and property. The assessment also observed that counties in eastern Nebraska are in the process of improving their flood control measures with dams, levees, and higher insurance coverage that can subdue the risks associated with flooding. The results from this study are anticipated to guide water managers and policymakers in making more effective and locally relevant policies and measures to mitigate flood risks and consequences.

An autoencoder-based snow drought index.

In several regions across the globe, snow has a significant impact on hydrology. The amounts of water that infiltrate the ground and flow as runoff are driven by the melting of snow. Therefore, it is crucial to study the magnitude and effect of snowmelt. Snow droughts, resulting from reduced snow storage, can drastically impact the water supplies in basins where snow predominates, such as in the western United States. Hence, it is important to detect the time and severity of snow droughts efficiently. We propose the Snow Drought Response Index or SnoDRI, a novel indicator that could be used to identify and quantify snow drought occurrences. Our index is calculated using cutting-edge ML algorithms from various snow-related variables. The self-supervised learning of an autoencoder is combined with mutual information in the model. In this study, we use Random Forests for feature extraction for SnoDRI and assess the importance of each variable. We use reanalysis data (NLDAS-2) from 1981 to 2021 for the Pacific United States to study the efficacy of the new snow drought index. We evaluate the index by confirming the coincidence of its interpretation and the actual snow drought incidents.

Impact of Eastern Redcedar encroachment on water resources in the Nebraska Sandhills.

Worldwide, tree or shrub dominated woodlands have encroached into herbaceous dominated grasslands. While very few studies have evaluated the impact of Eastern Redcedar (redcedar) encroachment on the water budget, none have analyzed the impact on water quality. In this study, we evaluated the impact of redcedar encroachment on the water budget in the Nebraska Sand Hills and how the decreased streamflow would increase nitrate and atrazine concentrations in the Platte River. We calibrated a Soil and Water Assessment Tool (SWAT model) for streamflow, recharge, and evapotranspiration. Using a moving window with a dilate morphological filter, encroachment scenarios of 11.9 %, 16.1 %, 28.0 %, 40.6 %, 57.5 %, 72.5 % and 100 % were developed and simulated by the calibrated model. At 11.9 % and 100 % encroachment, streamflow was reduced by 4.6 % and 45.5 %, respectively in the Upper Middle Loup River, a tributary to the Platte River. Percolation and deep aquifer recharge increased by 27 % and 26 % at 100 % encroachment. Streamflow in the Platte River, a major water source for Omaha and Lincoln, would decrease by 2.6 %, 5.5 % and 10.5 % for 28 %, 57.5 %, and 100 % encroachment of the Loup River watershed, respectively. This reduction in streamflow could increase nitrate and atrazine concentrations in the Platte River by 4 to 15 % and 4 to 30 %, respectively. While the density of redcedar is minimal, it is important to manage their encroachment to prevent reductions in streamflow and potential increases in pollutant concentrations.

Great Lakes Runoff Intercomparison Project Phase 3: Lake Erie (GRIP-E).

Hydrologic model intercomparison studies help to evaluate the agility of models to simulate variables such as streamflow, evaporation, and soil moisture. This study is the third in a sequence of the Great Lakes Runoff Intercomparison Projects. The densely populated Lake Erie watershed studied here is an important international lake that has experienced recent flooding and shoreline erosion alongside excessive nutrient loads that have contributed to lake eutrophication. Understanding the sources and pathways of flows is critical to solve the complex issues facing this watershed. Seventeen hydrologic and land-surface models of different complexity are set up over this domain using the same meteorological forcings, and their simulated streamflows at 46 calibration and seven independent validation stations are compared. Results show that: (1) the good performance of Machine Learning models during calibration decreases significantly in validation due to the limited amount of training data; (2) models calibrated at individual stations perform equally well in validation; and (3) most distributed models calibrated over the entire domain have problems in simulating urban areas but outperform the other models in validation.

Computing Accurate Probabilistic Estimates of One-D Entropy from Equiprobable Random Samples.

We develop a simple Quantile Spacing (QS) method for accurate probabilistic estimation of one-dimensional entropy from equiprobable random samples, and compare it with the popular Bin-Counting (BC) and Kernel Density (KD) methods. In contrast to BC, which uses equal-width bins with varying probability mass, the QS method uses estimates of the quantiles that divide the support of the data generating probability density function (pdf) into equal-probability-mass intervals. And, whereas BC and KD each require optimal tuning of a hyper-parameter whose value varies with sample size and shape of the pdf, QS only requires specification of the number of quantiles to be used. Results indicate, for the class of distributions tested, that the optimal number of quantiles is a fixed fraction of the sample size (empirically determined to be ~0.25-0.35), and that this value is relatively insensitive to distributional form or sample size. This provides a clear advantage over BC and KD since hyper-parameter tuning is not required. Further, unlike KD, there is no need to select an appropriate kernel-type, and so QS is applicable to pdfs of arbitrary shape, including those with discontinuous slope and/or magnitude. Bootstrapping is used to approximate the sampling variability distribution of the resulting entropy estimate, and is shown to accurately reflect the true uncertainty. For the four distributional forms studied (Gaussian, Log-Normal, Exponential and Bimodal Gaussian Mixture), expected estimation bias is less than 1% and uncertainty is low even for samples of as few as 100 data points; in contrast, for KD the small sample bias can be as large as -10% and for BC as large as -50%. We speculate that estimating quantile locations, rather than bin-probabilities, results in more efficient use of the information in the data to approximate the underlying shape of an unknown data generating pdf.

Useful & Reliable: Technological Transformation in Colonial India.

The proposition that Useful and Reliable Knowledge (URK) produced divergent patterns of long-term economic growth implies that such knowledge had weak agency in countries that fell behind. This article rejects such a theory on the evidence of colonial India. Indo-European contacts activated transfer, transplantation, and adaptation in URK; however, the impact was uneven within India. Despite boosting productivity in manufacturing and consumption, large areas were left untouched. These contrasts highlight the differentiated impact of URK on production conditions in India and question its transferability and mode of knowledge exchange under colonialism.