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Comparison of fatty acid composition in total lipid of diapause and non-diapause larvae of Cydia pomonella （lepidoptera： Tortricidae）

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Insect Science 2007年第2期14卷 125-131页

作者： ABBAS KHANI SAEID MOHARRAMIPOUR MOHSEN barzegar HOSSEIN NADERI-MANESH Department of Entomology College of Agriculture Tarbiat Modares UniversityTarbiat Modares University Tehran Iran Department of Food Science and Technology College of Agriculture Tarbiat Modares University Tarbiat Modares University Tehran Iran Department of Biophysics College of Basic Sciences Tarbiat Modares University Tehran Iran

Seasonal changes in the fatty acid composition of the total lipid extracted from the whole body of Cydia pomonella l. larvae were determined by gas chromatography. The six most abundant fatty acids in both non-diapause and diapause larvae of codling moth were oleic （35%-39%）, palmitic （23%-33%）, linoleic （16%-30%）, palmitoleic （5%-10%）, stearic （1.5%-3.0%） and linolenic acids （1.0%-2.5%）. This represents a typical complement of lepidopteran fatty acids. The fatty acid composition of total lipid of C. pomonella larvae was related to diapause. In similarity to most other reports, the proportion of unsaturated fatty acids increased in diapause initiation state. The total lipid of diapause larvae contained more linoleic acid （25.8% vs. 16.1%） and less palmitic acid （24.7% vs. 33.4%）, than that of non-diapause larvae. The weight percentage of linoleic acid （C 18：2） increased from 16% to 26% from early-August through early-September during transition to diapause, while palmitic acid （C16：0） decreased from 33% to 25% at the same time. These changes resulted in an increase in the ratio of unsaturated to saturated fatty acids （UFA/SFA） from 1.72 in non-diapause larvae to 2.63 in diapause larvae.

关键词： codling moth Cydia pomonella diapause fatty acid composition lipid

Multi-step ahead soil temperature forecasting at different depths based on meteorological data:Integrating resampling algorithms and machine learning models

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Pedosphere 2023年第3期33卷 479-495页

作者： Khabat KHOSRAVI Ali GOlKARIAN Rahim barzegar Mohammad T.AAlAMI Salim HEDDAM Ebrahim OMIDVAR Saskia D.KEESSTRA Manuel lÓPEZ-VICENTE Department of Earth and Environment Florida International UniversityMiami 33199(USA) Department of Watershed Management Engineering Ferdowsi University of MashhadMashhad 91779-48974(Iran) Department of Bioresource Engineering McGill University21111 LakeshoreSte Anne de BellevueMontreal H9X 3V9(Canada) Department of Geography&Environmental Studies Wilfrid Laurier UniversityWaterloo N2L 3G1(Canada) Faculty of Civil Engineering University of Tabriz29 Bahman BlvTabriz 51666-16471(Iran) laboratory of Research in Biodiversity Interaction Ecosystem and Biotechnology University 20 Août 1955Route El HadaikBP 26Skikda 21000(Algeria) Department of Watershed Management Engineering University of KashanKashan 87317-53153(Iran) Team Soil Water and Land UseWageningen Environmental ResearchDroevendaalsesteeg 3Wageningen 6708RC(Netherlands) Departamento de Análisis Geográfico Regional y Geografía Física Facultad de Filosofía y LetrasCampus Universitario de CartujaUniversidad de GranadaGranada 18071(Spain) Group Aquaterra CICAUniversidade da CoruñaAs Carballeiras s/nCampus de ElviñaA Coruña 15071(Spain)

Direct soil temperature(ST)measurement is time-consuming and costly;thus,the use of simple and cost-effective machine learning(Ml)tools is *** this study,Ml approaches,including KStar,instance-based K-nearest learning(IBK),and locally weighted learning(lWl),coupled with resampling algorithms of bagging(BA)and dagging(DA)(BA-IBK,BA-KStar,BA-lWl,DA-IBK,DA-KStar,and DA-lWl)were developed and tested for multi-step ahead(3,6,and 9 d ahead)ST *** addition,a linear regression(lR)model was used as a benchmark to evaluate the results.A dataset was established,with daily ST time-series at 5 and 50 cm soil depths in a farmland as models’output and meteorological data as models’input,including mean(T_(mean)),minimum(Tmin),and maximum(T_(max))air temperatures,evaporation(Eva),sunshine hours(SSH),and solar radiation(SR),which were collected at Isfahan Synoptic Station(Iran)for 13 years(1992–2005).Six different input combination scenarios were selected based on Pearson’s correlation coefficients between inputs and outputs and fed into the *** used 70%of the data to train the models,with the remaining 30%used for model evaluation via multiple visual and quantitative ***?ndings showed that T_(mean)was the most effective input variable for ST forecasting in most of the developed models,while in some cases the combinations of variables,including T_(mean)and T_(max)and T_(mean),T_(max),Tmin,Eva,and SSH proved to be the best input *** the evaluated models,BA-KStar showed greater compatibility,while in most cases,BA-IBK and-lWl provided more accurate results,depending on soil *** the 5 cm soil depth,BA-KStar had superior performance(i.e.,Nash-Sutcliffe efficiency(NSE)=0.90,0.87,and 0.85 for 3,6,and 9 d ahead forecasting,respectively);for the 50 cm soil depth,DA-KStar outperformed the other models(i.e.,NSE=0.88,0.89,and 0.89 for 3,6,and 9 d ahead forecasting,respectively).The results con?rmed that all hybrid models had higher prediction

关键词： bootstrap aggregating algorithm data mining disjoint aggregating algorithm ensemble modeling hybrid model

Pistachio green hull extract as natural antioxidant incorporated to omega-3 rich kappa-carrageenan oleogel in dry fermented sausage

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Food Bioscience 2022年第6期50卷 100-106页

作者： Zohre Noorolahi Mohammad Ali Sahari Hassan Ahmadi Gavlighi Mohsen barzegar Department of Food Science and Technology Faculty of AgricultureTarbiat Modares UniversityP.O.Box 14115-336TehranIran

The purpose of this study was to investigate the effects of pistachio green hull extract(PGH)as natural antioxidant and preservative on the quality ofω3 rich fermented *** were prepared by linseed oil gelled emulsion and treated with PGH-extract(lGE-PGH),with synthetic antioxidant(lGE-BHA),and without antioxidant(lGE-NON)and properties of them were studied during the ripening and storage for two *** results showed that the moisture and aw values of lGE-PGH were higher than ***,lGE-PGH had lower TBARS values and higherω3 and polyunsaturated fatty acids(PUFA)values compared to *** PGH-extract also reduced l*and b*,increased a*and hue in fermented ***,the PGH-extract can be used as a natural preservative ofω3 rich dry fermented sausages without causing undesirable effects on it.

关键词： Omega-3 rich dry fermented sausage Pistachio green hull extract linseed oil Kappa-carrageenan Antioxidant effect

Spontaneous twisting of a collapsed carbon nanotube

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Nano Research 2017年第6期10卷 1942-1949页

作者： Hamid Reza barzegar Aiming Yan Sinisa Coh Eduardo Gracia-Espino Claudia Ojeda-Aristizabal Gabriel Dunn Marvin l. Cohen Steven G. louie Thomas Wagberg Alex Zettl Department of Physics University of California Berkeley CA 94720 USA Department of Physics Umea University 90187 Umea Sweden Materials Sciences Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA Kavli Energy NanoSciences Institute at the University of California Berkeley and the Lawrence Berkeley National Laboratory Berkeley CA 94720 USA

We study the collapsing and subsequent spontaneous twisting of a carbon nanotube by in situ transmission electron microscopy （TEM）. A custom-sized nanotube is first created in the microscope by selectively extracting shells from a parent multi-walled tube. The few-walled, large-diameter daughter nanotube is driven to collapse via mechanical stimulation, after which the ribbon-like collapsed tube spontaneously twists along its long axis. In situ diffraction experiments fully characterize the uncollapsed and collapsed tubes. The experimental observations and associated theoretical analysis indicate that the origin of the twisting is compressive strain.

关键词： multi-walled carbonnanotube collapsed carbon nanotube in situ TEM electron diffraction twisting graphene nanoribbons

Human chorionic gonadotropin in vitro: Effects on rat sperm motility and fertilization outcome

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Asian pacific Journal of Reproduction 2018年第5期7卷 225-228页

作者： Seyed Mansour Moosavi Hossein barzegar Ganji Hajar Ramezanikhah Mitra Arianmanesh Department of Anatomical Sciences School of MedicineZanjan University of Medical SciencesZanjanIran

Objective:To investigate the effect of human chorionic gonadotropin (hCG) on the motility of rat sperm and the fertilization rate following in vitro fertilization. Methods: hCG concentrations of 25, 50, 75 and 100 ng/ml with incubation time of 1 h and 2 h were used in medium to study sperm motility. Then, 25 and 100 ng/ml of hCG with incubation time of 2 h were applied for in vitrofertilization. After 6 h, the number of two pronuclei was counted. Obtained data was subjected to one way ANOVA and Bonferroni-Dun hoc-post ***:Total motility and progressive motility of sperms were increased significantly (Pl group with concentration of 25 ng/l and incubation time of 2 h compared to control group, however, total motility and progressive motility of sperms were decreased significantly (Plization rate was decreased significantly (Pl group with dose of 100 ng/ml compared to 25 ng/ml and control group. Conclusions:The effect of hCG on the rat sperm motility and the rate of their fertility is dose-dependent so that hCG with dose of 25 ng/ml leads to an increase in rat sperm motility and to some extent fertilization rate.

关键词： In vitro fertilization Sperm motility Fertilization rate hCG Rat cAMP

Comparing the Soil Conservation Service model with new machine learning algorithms for predicting cumulative infiltration in semi-arid regions

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Pedosphere 2022年第5期32卷 718-732页

作者： Khabat KHOSRAVI Phuong T.T.NGO Rahim barzegar John QUIlTY Mohammad T.AAlAMI Dieu T.BUI Department of Watershed Management Engineering Ferdowsi University of MashhadMashhad 93(Iran) Department of Earth and Environment Florida International UniversityMiami 33199(USA) Institute of Research and Development Duy Tan UniversityDa Nang 550000(Vietnam) Department of Bioresource Engineering McGill UniversitySte Anne de Bellevue QC H9X(Canada) Faculty of Civil Engineering University of TabrizTabriz 51(Iran) Department of Civil and Environmental Engineering University of WaterlooWaterloo N2L 3G1(Canada) Department of Business and IT University of South-Eastern NorwayNotodden 3603(Norway)

Water infiltration into soil is an important process in hydrologic cycle;however,its measurement is difficult,time-consuming and *** and physical models have been developed to predict cumulative infiltration(CI),but are often *** this study,several novel standalone machine learning algorithms(M5Prime(M5P),decision stump(DS),and sequential minimal optimization(SMO))and hybrid algorithms based on additive regression(AR)(i.e.,AR-M5P,AR-DS,and AR-SMO)and weighted instance handler wrapper(WIHW)(i.e.,WIHW-M5P,WIHW-DS,and WIHW-SMO)were developed for CI *** Soil Conservation Service(SCS)model developed by the United States Department of Agriculture(USDA),one of the most popular empirical models to predict CI,was considered as a ***,154 measurements of CI(explanatory/input variables)were taken from 16 sites in a semi-arid region of Iran(Illam and lorestan provinces).Six input variable combinations were considered based on Pearson correlations between candidate model inputs(time of measuring and soil bulk density,moisture content,and sand,clay,and silt percentages)and *** dataset was divided into two subgroups at random:70%of the data were used for model building(training dataset)and the remaining 30%were used for model validation(testing dataset).The various models were evaluated using different graphical approaches(bar charts,scatter plots,violin plots,and Taylor diagrams)and quantitative measures(root mean square error(RMSE),mean absolute error(MAE),Nash-Sutcliffe efficiency(NSE),and percent bias(PBIAS)).Time of measuring had the highest correlation with CI in the study *** best input combinations were different for different *** results showed that all hybrid algorithms enhanced the CI prediction accuracy compared to the standalone *** AR-M5P model provided the most accurate CI predictions(RMSE=0.75 cm,MAE=0.59 cm,NSE=0.98),while the SCS model had the lowest performance(RMSE=4.77 cm,MAE=2.64 cm,NSE=

关键词： additive regression hybrid algorithms empirical model soil water infiltration weighted instances handler wrapper