The purpose of this work was to examine the influence of apolipoprotein gene variation on plasma lipid levels in a population of Mayan Indians of the Yucatan Peninsula, Mexico. Four restriction enzymes: XmnI, PstI, SstI, and PvuII, were used to detect restriction fragment length polymorphisms (RFLP) within the region of the apolipoprotein AI/CIII/AIV gene cluster. The frequencies of these polymorphisms in this Mayan population were similar to those reported for other Amerindian populations, but differed widely from those reported for Caucasian populations. The XmnI and SstI RFLPs were informative for association studies in this population, and we analyzed their influence on the quantitative variation of plasma cholesterol and triglycerides. Using a nonparametric analysis of variance, it is shown that the presence of the XmnI restriction site had a significant effect in lowering plasma cholesterol, whereas the presence of the restriction site for SstI had a significant effect in raising plasma triglycerides. Consequently, genetic indicators of both low and high risk for lipid-related diseases, such as atherosclerosis and coronary heart disease, seem to be present within the same gene region in this Mayan population.
SummaryThe precision of forecasting rainfall is vital owing to current world climate change. As deterministic weather forecasting models are usually time consuming, it becomes challenging to efficiently use this large volume of data in hand. Machine learning methods are already proven to be good replacement for traditional deterministic approaches in weather prediction. This paper presents an approach using recurrent neural networks (RNN) and long short term memory (LSTM) techniques to improve the rainfall forecast performance. This will be compared with the random forest classifier and XGBoost as well. The goal is to predict a set of hourly rainfall levels from sequences of weather radar measurements. Python libraries are utilized to forecast the time series data. The training set comprises of data from first 20 days of every month and the inference set data from the continuing days. This makes sure that both train and inference sets are more or less independent. The idea resides in implementing an end‐to‐end learning framework.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.