AmylPepPred: Amyloidogenic Peptide Prediction tool

We present an efficient computational architecture designed using supervised machine learning model to predict amyloid fibril forming protein segments, named AmylPepPred. The proposed prediction model is based on bio-physio-chemical properties of primary sequences and auto-correlation function of their amino acid indices. AmylPepPred provides a user friendly web interface for the researchers to easily observe the fibril forming and non-fibril forming hexmers in a given protein sequence. We expect that this stratagem will be highly encouraging in discovering fibril forming regions in proteins thereby benefit in finding therapeutic agents that specifically aim these sequences for the inhibition and cure of amyloid illnesses

Modeling Daily Reference Evapotranspiration from Climate Variables: Assessment of Bagging and Boosting Regression Approaches

AbstractThe increasing frequency of droughts and floods due to climate change has severely affected water resources across the globe in recent years. An optimal design for the scheduling and management of irrigation is thus urgently needed to adapt agricultural activities to the changing climate. The accurate estimation of reference crop evapotranspiration (ET0), a vital hydrological component of the water balance and crop water need, is a tiresome task if all the relevant climatic variables are unavailable. This study investigates the potential of four ensemble techniques for estimating precise values of the daily ET0 at representative stations in 10 agro-climatic zones in the state of Karnataka, India, from 1979 to 2014. The performance of these models was evaluated by using several combinations of climatic variables as inputs by using tenfold cross-validation. The outcomes indicated that predictions of ET0 by all four ensemble models based on all climatic variables were the most accurate in comparison with other input combinations. The random forest regressor was found to deliver the best performance among the four models on all measures considered (Nash–Sutcliffe efficiency, 1.0, root-mean-squared error, 0.016 mm/day, and mean absolute error, 0.011 mm/day). However, it incurred the highest computational cost, whereas the computational cost of the bagging model for linear regression was the lowest. The extreme gradient-boosting model delivered the most stable performance with a modified training dataset. The work here shows that these models can be recommended for daily ET0 estimation based on the users’ interests.</jats:p

A study on the status of fluoride ion in groundwater of coastal hard rock aquifers of south India

India has an increasing incidence of fluorosis, dental and skeletal, with nearly about 62 million people at risk. High fluoride groundwaters are present especially in the hard rock areas of the country. This paper analyzes the most extensive database on fluoride and other chemical constituent distribution in the coastal hard rock aquifers of Thoothukudi district. A total of 135 samples were collected and analyzed for major cations and anions to assess the geochemical process. The fluoride concentration in drinking waters varied from BDL to 3.2 mg l-1 in the study area. Majority of the samples do not comply with WHO standards for most of the water quality parameters. The saturation index of fluorite saturation index was used to correlate with F- to identify their relationship to increase of fluoride levels. The correlation between the F- concentration and the water type was also attempted. Spatial distribution of fluoride in groundwater was studied to understand the influencing factors. The relationship of F- with HCO- 3, Na+ and pH concentrations were studied and found that HCO- 3, has good correlation with F- than the other parameters