Biology subjects

Hakim, A.

Publications and source records attributed to Hakim, A..

3 recordsLinked to original sources

bioRxiv2018

Phylogeny and distinct properties of major intrinsic proteins in the genomes of six Phytophthora species suggest their novel functions in Phytophthora

ABSTRACTMajor intrinsic proteins (MIPs), commonly known as aquaporins, facilitate the membrane diffusion of water and some other non- polar solutes. MIPs might be involved in host-pathogen interactions. Herein, we identified 17, 24, 27, 19, 19, and 22 full-length MIPs, respectively, in the genomes of six Phytophthora species, P. infestans, P. parasitica, P. sojae, P. ramorum, P. capsici, and P. cinnamomi. These Phytophthora species are devastating plant pathogens and members of oomycetes, a distinct lineage of fungus-like eukaryotic microbes. Phylogenetic analysis showed that the Phytophthora MIPs (PMIPs) formed a completely distinct clade from their counterparts in other taxa and were clustered into nine subgroups. Sequence and structural properties indicated that the primary selectivity-related constrictions, including aromatic arginine (ar/R) selectivity filter and Froger’s positions in PMIPs were distinct from those in other taxa. The substitutions in the conserved Asn-Pro-Ala motifs in loops B and E of many PMIPs were also divergent from those in plants. We further deciphered group-specific consensus sequences/motifs in different loops and transmembrane helices of PMIPs, which were distinct from those in plants, animals, and microbes. The data collectively supported the notion that PMIPs might have novel functions.Competing Interest StatementThe authors have declared no competing interest.View Full Text

bioinformatics↗

bioRxiv2018

More is Better: Using Machine Learning Techniques and Multiple EEG Metrics to Increase Preference Prediction Above and Beyond Traditional Measurements

A basic aim of marketing research is to predict consumers preferences and the success of marketing campaigns in the general population. However, traditional behavioral measurements have various limitations, calling for novel measurements to improve predictive power. In this study, we use neural signals measured with electroencephalography (EEG) in order to overcome these limitations. We record the EEG signals of subjects, as they watched commercials of six food products. We introduce a novel approach in which instead of using one type of EEG measure, we combine several measures, and use state-of-the-art machine learning algorithms to predict subjects individual future preferences over the products and the commercials population success, as measured by their YouTube metrics. As a benchmark, we acquired measurements of the commercials effectiveness using a standard questionnaire commonly used in marketing research. We reached 68.5% accuracy in predicting between the most and least preferred items and a lower than chance RMSE score for predicting the rank order preferences of all six products. We also predicted the commercials population success better than chance. Most importantly, we demonstrate for the first time, that for all of our predictions, the EEG measurements increased the prediction power of the questionnaires. Our analyses methods and results show great promise for utilizing EEG measures by managers, marketing practitioners, and researchers, as a valuable tool for predicting subjects preferences and marketing campaigns success.

neuroscience↗

bioRxiv2017

WorMachine: Machine Learning-Based Phenotypic Analysis Tool for Worms

While Caenorhabditis elegans nematodes are powerful model organisms, quantification of visible phenotypes is still often labor-intensive, biased, and error-prone. We developed \"WorMachine\", a three-step MATLAB-based image analysis software that allows automated identification of C. elegans worms, extraction of morphological features, and quantification of fluorescent signals. The program offers machine learning techniques which should aid in studying a large variety of research questions. We demonstrate the power of WorMachine using five separate assays: scoring binary and continuous sexual phenotypes, quantifying the effects of different RNAi treatments, and measuring intercellular protein aggregation. Thus, WorMachine is a \"quick and easy\", high-throughput, automated, and unbiased analysis tool for measuring phenotypes.

bioinformatics↗