Osmotolerantní kvasinka Zygosaccharomyces rouxii- konstrukce nástrojů genového inženýrství a charakterizace specifických vlastností

Zygosaccharomyces rouxii, Kluyveromyces, propriétés osmotolérantes, synthèse du glycérol, outils génétiques et moléculaires, séquençage, annotationsThe objectives of the thesis were fulfilled. A set of genetic tools was constructed comprising of techniques, mutant strains and plasmids, which will serve to study Z. rouxii specific features. The exploration of Z. rouxii properties revealed interesting differences among the two most studied wild-type strains concerning their osmotolerant and cell wall properties. The yeast Na+/H+-antiporters were studied. By comparing the sequences of different yeast alcali metal cation/H+ antiporters, regions important for the antiporter functions were identified. A new Z. rouxii gene coding for Na+/H+-antiporter was isolated, and its product characterized using the constructed genetic tools. Z. rouxii was shown to possess two Na+/H+-antiporters – one with a substrate specificity for K+, Na+ and Li+, the other one only for Na+ and Li+. In the future, we would like to study the two Z. rouxii antiporters to more details, e.g. examine the regulation of their expression or explore their biogenesis/degradation in different conditions. The thesis was complemented by contributing to the process of annotations of Z. rouxii and three other yeast genomes involved in the Génolevures 3 project. The availability of tools for its genetic manipulation as well as the complete genomic sequence, which will soon be publicly available, make Z. rouxii an ideal model organism for studying yeast osmotolerance. Moreover, as it's ancestor most probably did not undergo the whole genome duplication, Z. rouxii represents also an interesting preduplication model

Osmotolerant yeast Zygosaccharomyces rouxii, construction of genetic tools and characterization of specific features

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Osmotolerant yeast Zygosaccharomyces rouxii, construction of genetic tools and characterization of specific features

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Osmotolerantni kvasinka Zygosaccharomyces rouxii (Konstrukce nastroju genového inzenyrstvi a charakterizace specifickych vlastnosti)

STRASBOURG-Sc. et Techniques (674822102) / SudocSudocFranceCzech RepublicFRC

Stručná historie matematické populační dynamiky

Auto-éditionInternational audienceTato kniha sleduje historii populační dynamiky - teoretického oboru úzce souvisejícího s genetikou, ekologií, epidemiologií a demografií - do kterého matematika přinesla významné vhledy. Sleduje vznik některých důležitých témat: exponenciální růst od Eulera a Malthuse až po čínskou politiku jednoho dítěte; vývoj stochastických modelů od Mendelových zákonů a otázky vymírání příjmení po šíření epidemií; prolínání nutnosti a náhody v chaotické populaci

Comparative genomics of protoploid Saccharomycetaceae

Our knowledge of yeast genomes remains largely dominated by the extensive studies on Saccharomyces cerevisiae and the consequences of its ancestral duplication, leaving the evolution of the entire class of hemiascomycetes only partly explored. We concentrate here on five species of Saccharomycetaceae, a large subdivision of hemiascomycetes, that we call “protoploid” because they diverged from the S. cerevisiae lineage prior to its genome duplication. We determined the complete genome sequences of three of these species: Kluyveromyces (Lachancea) thermotolerans and Saccharomyces (Lachancea) kluyveri (two members of the newly described Lachancea clade), and Zygosaccharomyces rouxii. We included in our comparisons the previously available sequences of Kluyveromyces lactis and Ashbya (Eremothecium) gossypii. Despite their broad evolutionary range and significant individual variations in each lineage, the five protoploid Saccharomycetaceae share a core repertoire of approximately 3300 protein families and a high degree of conserved synteny. Synteny blocks were used to define gene orthology and to infer ancestors. Far from representing minimal genomes without redundancy, the five protoploid yeasts contain numerous copies of paralogous genes, either dispersed or in tandem arrays, that, altogether, constitute a third of each genome. Ancient, conserved paralogs as well as novel, lineage-specific paralogs were identified

European Covid-19 Forecast Hub

European Covid-19 Forecast Hub

Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations

Background: Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive performance of such forecasts if multiple models are combined into an ensemble. Here, we report on the performance of ensembles in predicting COVID-19 cases and deaths across Europe between 08 March 2021 and 07 March 2022. Methods: We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported by a standardised source for 32 countries over the next 1–4 weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models’ predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models’ forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models’ past predictive performance. Results: Over 52 weeks, we collected forecasts from 48 unique models. We evaluated 29 models’ forecast scores in comparison to the ensemble model. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 83% of participating models’ forecasts of incident cases (with a total N=886 predictions from 23 unique models), and 91% of participating models’ forecasts of deaths (N=763 predictions from 20 models). Across a 1–4 week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over 4 weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast models. Conclusions: Our results support the use of combining forecasts from individual models into an ensemble in order to improve predictive performance across epidemiological targets and populations during infectious disease epidemics. Our findings further suggest that median ensemble methods yield better predictive performance more than ones based on means. Our findings also highlight that forecast consumers should place more weight on incident death forecasts than incident case forecasts at forecast horizons greater than 2 weeks. Funding: AA, BH, BL, LWa, MMa, PP, SV funded by National Institutes of Health (NIH) Grant 1R01GM109718, NSF BIG DATA Grant IIS-1633028, NSF Grant No.: OAC-1916805, NSF Expeditions in Computing Grant CCF-1918656, CCF-1917819, NSF RAPID CNS-2028004, NSF RAPID OAC-2027541, US Centers for Disease Control and Prevention 75D30119C05935, a grant from Google, University of Virginia Strategic Investment Fund award number SIF160, Defense Threat Reduction Agency (DTRA) under Contract No. HDTRA1-19-D-0007, and respectively Virginia Dept of Health Grant VDH-21-501-0141, VDH-21-501-0143, VDH-21-501-0147, VDH-21-501-0145, VDH-21-501-0146, VDH-21-501-0142, VDH-21-501-0148. AF, AMa, GL funded by SMIGE - Modelli statistici inferenziali per governare l'epidemia, FISR 2020-Covid-19 I Fase, FISR2020IP-00156, Codice Progetto: PRJ-0695. AM, BK, FD, FR, JK, JN, JZ, KN, MG, MR, MS, RB funded by Ministry of Science and Higher Education of Poland with grant 28/WFSN/2021 to the University of Warsaw. BRe, CPe, JLAz funded by Ministerio de Sanidad/ISCIII. BT, PG funded by PERISCOPE European H2020 project, contract number 101016233. CP, DL, EA, MC, SA funded by European Commission - Directorate-General for Communications Networks, Content and Technology through the contract LC-01485746, and Ministerio de Ciencia, Innovacion y Universidades and FEDER, with the project PGC2018-095456-B-I00. DE., MGu funded by Spanish Ministry of Health / REACT-UE (FEDER). DO, GF, IMi, LC funded by Laboratory Directed Research and Development program of Los Alamos National Laboratory (LANL) under project number 20200700ER. DS, ELR, GG, NGR, NW, YW funded by National Institutes of General Medical Sciences (R35GM119582; the content is solely the responsibility of the authors and does not necessarily represent the official views of NIGMS or the National Institutes of Health). FB, FP funded by InPresa, Lombardy Region, Italy. HG, KS funded by European Centre for Disease Prevention and Control. IV funded by Agencia de Qualitat i Avaluacio Sanitaries de Catalunya (AQuAS) through contract 2021-021OE. JDe, SMo, VP funded by Netzwerk Universitatsmedizin (NUM) project egePan (01KX2021). JPB, SH, TH funded by Federal Ministry of Education and Research (BMBF; grant 05M18SIA). KH, MSc, YKh funded by Project SaxoCOV, funded by the German Free State of Saxony. Presentation of data, model results and simulations also funded by the NFDI4Health Task Force COVID-19 (https://www.nfdi4health.de/task-force-covid-19-2) within the framework of a DFG-project (LO-342/17-1). LP, VE funded by Mathematical and Statistical modelling project (MUNI/A/1615/2020), Online platform for real-time monitoring, analysis and management of epidemic situations (MUNI/11/02202001/2020); VE also supported by RECETOX research infrastructure (Ministry of Education, Youth and Sports of the Czech Republic: LM2018121), the CETOCOEN EXCELLENCE (CZ.02.1.01/0.0/0.0/17-043/0009632), RECETOX RI project (CZ.02.1.01/0.0/0.0/16-013/0001761). NIB funded by Health Protection Research Unit (grant code NIHR200908). SAb, SF funded by Wellcome Trust (210758/Z/18/Z)