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Willin.n.ss of Greek gen.ral population.to get a COVID-19 vaccin.

Global Health Research an. Policy 2021年第1期6卷 478-487页

作者： Georgia Kourlaba Elen. Kourkoun. Stefan.a Maistreli Christin.-Grammatiki Tsopela n.fsika-Maria Molocha Christos Trian.afyllou Markela Kon.ordou Ioan.is Kopsidas evangelia Chorian.poulou Stefan.a Maroudi-Man.a Dimitrios Filippou Theoklis E.Zaoutis Cen.er for Clin.cal Epidemiology an. Outcomes Research(CLEO) AthensGreece ECOn.ARE LP AthensGreece Departmen. of An.tomy an. Surgical An.tomy Medical SchoolNational and Kapodistrian University of AthensAthensGreece Division.of In.ectious Diseases The Children’s Hospital of PhiladelphiaPerelman School of Medicine at the University of PennsylvaniaPhiladelphiaUSA

Backgroun.:Epidemiological data in.icate that a large part of population.n.eds to be vaccin.ted to achieve herd ***,it is of high importan.e for public health officials to kn.w whether people are goin. to get vaccin.ted for *** objective of the presen. study was to examin. the willin.n.ss of adult residen.s in.Greece to receive a COVID-19 ***:A cross-section.l was survey con.ucted amon. the adult gen.ral population.of Greece between.April 28,2020 to May 03,2020(last week of lockdown.,usin. a mixed methodology for data collection.Computer Assisted Telephon. In.erviewin.(CATI)an. Computer Assisted web In.erviewin.(CAWI).Usin. a sample size calculator,the target sample size was foun. to be aroun. 1000 *** en.ure a n.tion.lly represen.ative sample of the urban.rural population.accordin. to the Greek cen.us 2011,a proportion.te stratified by region.systematic samplin. procedure was used to recruit *** collection.was guided through a structured *** willin.n.ss to COVID-19 vaccin.tion.participan.s were asked to an.wer the followin. question.“If there was a vaccin. available for the n.vel coron.virus,would you do it?”Results:Of 1004 respon.en.s on.y 57.7%stated that they are goin. to get vaccin.ted for *** aged>65 years old,those who either themselves or a member of their household belon.ed to a vuln.rable group,those believin. that the COVID-19 virus was n.t developed in.laboratories by human.,those believin. that coron.virus is far more con.agious an. lethal compared to the H1n. virus,an. those believin. that n.xt waves are comin. were statistically sign.fican.ly more likely to be willin. to get a COVID-19 *** kn.wledge score regardin. symptoms,tran.mission.routes an. preven.ion.an. con.rol measures again.t COVID-19 was sign.fican.ly associated with higher willin.n.ss of respon.en.s to get ***:A sign.fican. proportion.of in.ividuals in.the gen.ral popu

关键词： Vaccin. hesitan.y COVID-19 Europe Vaccin. coverage

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PredSL: A Tool for the n.termin.l Sequen.e-based Prediction.of Protein.Subcellular Localization

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Gen.mics, Proteomics & Bioin.ormatics 2006年第1期4卷 48-55页

作者： evangelia I. Petsalaki Pan.elis G. Bagos Zoi I. Litou Stavros J. Hamodrakas Departmen. of Cell Biology an. Biophysics Faculty of Biology University of Athens Panepistimiopolis Athens15701 Greece.

The ability to predict the subcellular localization.of a protein.from its sequen.e is of great importan.e, as it provides in.ormation.about the protein.s fun.tion. We presen. a computation.l tool, PredSL, which utilizes n.ural n.tworks, Markov chain., profile hidden.Markov models, an. scorin. matrices for the prediction.of the subcellular localization.of protein. in.eukaryotic cells from the n.termin.l amin. acid sequen.e. It aims to classify protein. in.o five groups： chloroplast, thylakoid, mitochon.rion. secretory pathway, an. ＂other＂. When.tested in.a fivefold cross-validation.procedure, PredSL demon.trates 86.7% an. 87.1% overall accuracy for the plan. an. n.n.plan. datasets, respectively. Compared with TargetP, which is the most widely used method to date, an. Lumen., the results of PredSL are comparable in.most cases. When.tested on.the experimen.ally verified protein. of the Saccharomyces cerevisiae gen.me, PredSL performs comparably if n.t better than.an. available algorithm for the same task. Furthermore, PredSL is the on.y method capable for the prediction.of these subcellular localization. that is available as a stan.-alon. application.through the URL： http：//***/PredSL/.

关键词： subcellular localization prediction target peptide tran.it peptide sign.l peptide

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