Biology subjects

Keasar, C.

Publications and source records attributed to Keasar, C..

3 recordsLinked to original sources

bioRxiv2024

Spillover of managed bumble bees from Mediterranean orchards during mass flowering causes minor short-term ecological impacts

Commercial bumble bee colonies are routinely used for crop pollination in greenhouses, and are increasingly introduced into orchards as well. Bumble bee spillover to natural habitats neighboring the orchards may interfere with local wild bees and impact the pollination of non-crop plants. Concurrently, foraging in natural habitats may diversify the bumble bees diets and improve colony development. To evaluate these potential effects, we placed commercial Bombus terrestris colonies in blooming Rosaceae orchards, 25-125 m away from the margins. We recorded the colonies mass gain, population sizes, composition of stored pollen, and temperature regulation. We monitored bee activity, and seed sets of the non-crop plant Eruca sativa, along transects in a semi-natural shrubland up to 100 m away from the orchards, with managed bumble bees either present or absent. Rosaceae pollen comprised [~]1/3 of the colonies pollen stores at all distances from the orchard margins. Colonies placed closest to the margins showed prolonged development, produced fewer reproductive individuals, and had poorer thermoregulation than colonies closer to the orchards center. Possibly, abiotic stressors inhibited the bumble bees development near orchard borders. Wild bees were as active during the colonies deployment as after their removal. E. sativas seed sets decreased after bumble bee removal, but similar declines also occurred near a control orchard without managed bumble bees. Altogether, we found no short-term spillover effects of managed bumble bees on nearby plant-bee communities during the orchards two-week flowering. The colonies prompt removal after blooming can reduce longer-term ecological risks associated with managed bumble bees.

ecology↗

bioRxiv2019

Loss-functions matter, on optimizing score functions for the estimation of protein models accuracy

MotivationMethods for protein structure prediction (PSP) generate multiple alternative structural models (aka decoys). Thus, supervised learning methods for the evaluation and ranking of these models are crucial elements of PSP. Supervised learning involves optimization of loss functions, but their influence on performance is typically overlooked. Here we put the loss functions in the spotlight, and study their effect on prediction performance.\n\nResultsHere we report the performances of three variants of MESHI-score, a supervised learning method for the estimation of model accuracy (EMA). Each variant was trained with a different loss function and showed better performance in different aspects of the EMA problem. Most importantly, better discrimination between models of the same target, is gained by target centered loss functions.\n\nAvailabilityAll data is available at http://meshi1.cs.bgu.ac.il/SidiAndKeasar2018Data_download/. The MESHI-package (version 9.412) is available at https://github.com/meshiprot/meshi/releases).\n\nContactchen.keasar@gmail.com

bioinformatics↗

bioRxiv2017

Methods For Estimation Of Model Accuracy In CASP12

Methods for reliably estimating the quality of 3D models of proteins are essential drivers for the wide adoption and serious acceptance of protein structure predictions by life scientists. In this paper, the most successful groups in CASP12 describe their latest methods for Estimates of Model Accuracy (EMA). We show that pure single model accuracy estimation methods has shown clear progress since CASP11; the three top methods (MESHI, ProQ3, SVMQA) all perform better than the top method of CASP11 (ProQ2). The pure single model accuracy estimation methods outperform quasi-single (ModFOLD6 variations) and consensus methods (Pcons, ModFOLDclust2, Pcomb-domain and Wallner) in model selection, but are still not as good as those methods in absolute model quality estimation and predictions of local quality. Finally, we show that when using contact based model quality measures (CAD, 1DDT) the single model quality methods perform relatively better.

bioinformatics↗