Flooding and its relationship with land cover change, population growth, and road density

Bangladesh experiences frequent hydro-climatic disasters such as flooding.These disasters are believed to be associated with land use changes and climate variability.However,identifying the factors that lead to flooding is challenging.This study mapped flood susceptibility in the northeast region of Bangladesh using Bayesian regularization back propagation(BRBP)neural network,classification and regression trees(CART),a statistical model(STM)using the evidence belief function(EBF),and their ensemble models(EMs)for three time periods(2000,2014,and 2017).The accuracy of machine learning algorithms(MLAs),STM,and EMs were assessed by considering the area under the curve—receiver operating characteristic(AUC-ROC).Evaluation of the accuracy levels of the aforementioned algorithms revealed that EM4(BRBP-CART-EBF)outperformed(AUC>90%)standalone and other ensemble models for the three time periods analyzed.Furthermore,this study investigated the relationships among land cover change(LCC),population growth(PG),road density(RD),and relative change of flooding(RCF)areas for the period between 2000 and 2017.The results showed that areas with very high susceptibility to flooding increased by 19.72%between 2000 and 2017,while the PG rate increased by 51.68%over the same period.The Pearson correlation coefficient for RCF and RD was calculated to be 0.496.These findings highlight the significant association between floods and causative factors.The study findings could be valuable to policymakers and resource managers as they can lead to improvements in flood management and reduction in flood damage and risks.

Mem Brain: An Easy-to-Use Online Webserver for Transmembrane Protein Structure Prediction

Membrane proteins are an important kind of proteins embedded in the membranes of cells and play crucial roles in living organisms, such as ion channels,transporters, receptors. Because it is difficult to determinate the membrane protein's structure by wet-lab experiments,accurate and fast amino acid sequence-based computational methods are highly desired. In this paper, we report an online prediction tool called Mem Brain, whose input is the amino acid sequence. Mem Brain consists of specialized modules for predicting transmembrane helices, residue–residue contacts and relative accessible surface area of a-helical membrane proteins. Mem Brain achieves aprediction accuracy of 97.9% of ATMH, 87.1% of AP,3.2 ± 3.0 of N-score, 3.1 ± 2.8 of C-score. Mem BrainContact obtains 62%/64.1% prediction accuracy on training and independent dataset on top L/5 contact prediction,respectively. And Mem Brain-Rasa achieves Pearson correlation coefficient of 0.733 and its mean absolute error of13.593. These prediction results provide valuable hints for revealing the structure and function of membrane proteins.Mem Brain web server is free for academic use and available at www.csbio.sjtu.edu.cn/bioinf/Mem Brain/.

Models and estimators linking individual-based and sample-based rarefaction, extrapolation and comparison of assemblages

Aims In ecology and conservation biology,the number of species counted in a biodiversity study is a key metric but is usually a biased underestimate of total species richness because many rare species are not detected.Moreover,comparing species richness among sites or samples is a statistical challenge because the observed number of species is sensitive to the number of individuals counted or the area sampled.For individual-based data,we treat a single,empirical sample of species abundances from an investigator-defined species assemblage or community as a reference point for two estimation objectives under two sampling models:estimating the expected number of species(and its unconditional variance)in a random sample of(i)a smaller number of individuals(multinomial model)or a smaller area sampled(Poisson model)and(ii)a larger number of individuals or a larger area sampled.For sample-based incidence(presence–absence)data,under a Bernoulli product model,we treat a single set of species incidence frequencies as the reference point to estimate richness for smaller and larger numbers of sampling units.Methods The first objective is a problem in interpolation that we address with classical rarefaction(multinomial model)and Coleman rarefaction(Poisson model)for individual-based data and with sample-based rarefaction(Bernoulli product model)for incidence frequencies.The second is a problem in extrapolation that we address with sampling-theoretic predictors for the number of species in a larger sample(multinomial model),a larger area(Poisson model)or a larger number of sampling units(Bernoulli product model),based on an estimate of asymptotic species richness.Although published methods exist for many of these objectives,we bring them together here with some new estimators under a unified statistical and notational framework.This novel integration of mathematically distinct approaches allowed us to link interpolated(rarefaction)curves and extrapolated curves to plot a unified species accumulation curve for empirical examples.We provide new,unconditional variance estimators for classical,individual-based rarefaction and for Coleman rarefaction,long missing from the toolkit of biodiversity measurement.We illustrate these methods with datasets for tropical beetles,tropical trees and tropical ants.Important Findings Surprisingly,for all datasets we examined,the interpolation(rarefaction)curve and the extrapolation curve meet smoothly at the reference sample,yielding a single curve.Moreover,curves representing 95%confidence intervals for interpolated and extrapolated richness estimates also meet smoothly,allowing rigorous statistical comparison of samples not only for rarefaction but also for extrapolated richness values.The confidence intervals widen as the extrapolation moves further beyond the reference sample,but the method gives reasonable results for extrapolations up to about double or triple the original abundance or area of the reference sample.We found that the multinomial and Poisson models produced indistinguishable results,in units of estimated species,for all estimators and datasets.For sample-based abundance data,which allows the comparison of all three models,the Bernoulli product model generally yields lower richness estimates for rarefied data than either the multinomial or the Poisson models because of the ubiquity of non-random spatial distributions in nature.

Influence of colour changes and moisture content during banana drying on laser backscattering

Pre-drying treatments are frequently employed to preserve fruit quality.The objective of this research was to monitor colour changes of banana during drying by laser backscattering and to determine the influence of the fruit discolouration on the light distribution into banana tissue.Moreover,to examine the influence of drying on the laser backscatter,the relationship between moisture content and relative laser area of banana slices was analyzed with different degrees of colour degradation.The experiments were conducted at drying air temperature of 63℃with various pre-treatments like chilling,soaking in ascorbic/citric acid and dipping in distilled water.An untreated sample was used as a control.A laser diode emitting at 670 nm with 3 mW power was used as light source.The backscattering relative laser area was used as an indicator for the light absorption into the tissue.The high result achieved on coefficient of determination R^(2)(>0.93)confirmed linear relationship between relative laser area and moisture content.Treatment with ascorbic acid gave the best prediction of the moisture content with the standard error of 5.7 and 8.8 for the estimated intercept and slope.The results showed a significant difference of lightness(L*values)during drying according to the different treatments.As a result,colour degradation did not have a significant influence on the absorption of light at 670 nm wavelength.