A time-resolved proteomic and prognostic map of COVID-19

COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection to severe organ damage and death. We characterized the time-dependent progression of the disease in 139 COVID-19 inpatients by measuring 86 accredited diagnostic parameters, such as blood cell counts and enzyme activities, as well as untargeted plasma proteomes at 687 sampling points. We report an initial spike in a systemic inflammatory response, which is gradually alleviated and followed by a protein signature indicative of tissue repair, metabolic reconstitution, and immunomodulation. We identify prognostic marker signatures for devising risk-adapted treatment strategies and use machine learning to classify therapeutic needs. We show that the machine learning models based on the proteome are transferable to an independent cohort. Our study presents a map linking routinely used clinical diagnostic parameters to plasma proteomes and their dynamics in an infectious disease

Niche Modeling of the economical important Mahanarva species in South and Central America (HEMIPTERA, CERCOPIDAE)

Submitted by PPG Zoologia ([email protected]) on 2018-04-20T12:08:51Z No. of bitstreams: 1 Disserta??o corre??o final - Christian.pdf: 2735367 bytes, checksum: 73fdc67c9898d886fdbd0e0cce1dfad3 (MD5)Approved for entry into archive by Caroline Xavier ([email protected]) on 2018-05-08T16:47:52Z (GMT) No. of bitstreams: 1 Disserta??o corre??o final - Christian.pdf: 2735367 bytes, checksum: 73fdc67c9898d886fdbd0e0cce1dfad3 (MD5)Made available in DSpace on 2018-05-08T16:53:43Z (GMT). No. of bitstreams: 1 Disserta??o corre??o final - Christian.pdf: 2735367 bytes, checksum: 73fdc67c9898d886fdbd0e0cce1dfad3 (MD5) Previous issue date: 2018-02-22Conselho Nacional de Pesquisa e Desenvolvimento Cient?fico e Tecnol?gico - CNPqMahanarva fimbriolata, M. spectabilis, M. liturata and M. posticata (Hemiptera: Cercopidae) s?o conhecidas como pragas de planta??es de cana-de-a??car e pastagem em todo Brasil. Por alimentarem-se diretamente da seiva das plantas, esses cercop?deos causam fitotoxicidade e devido a isso diminuem a produ??o. A modelagem da distribui??o de esp?cies permite analisar a poss?vel occurencia das quatro esp?cies na Am?rica do Sul e Central. Para criar modelos de distribui??o de esp?cies foram utilizados em R, os algoritmos Bioclim, Domain, diferentes modelos lineares generalizados e Maxent. Nesses modelos foram utilizadas vari?veis bioclim?ticas atuais e futuras, al?m da eleva??o e outras vari?veis agr?colas. As vari?veis clim?ticas futuras s?o para os anos 2050 e 2070 com diferentes repentant concentration pathways. As esp?cies apresentam habitats adequados em diferentes pa?ses da Am?rica do Sul e Central, onde as planta??es de cana-de-a??car s?o abundantes. Os resultados das an?lises clim?ticas futuras n?o apresentaram diferen?as em rela??o ?s an?lises clim?ticas atuais. No geral, o algoritmo Maxent mostrou os maiores valores de AUC e o Bioclim os menores. As vari?veis que mais contribu?ram para os modelos s?o: eleva??o, isothermality e diferentes vari?veis de precipita??o. As mudan?as clim?ticas e ciclos de vida de insetos adicionais n?o t?m impacto em habitats adequados dos insetos. Em geral, o Maxent ? o melhor algoritmo para realizar modelos de distribui??o de esp?cies com um n?mero baixo de pontos de ocorr?ncia e an?lises de mudan?as clim?ticas.Mahanarva fimbriolata, M. spectabilis, M. liturata and M. posticata (Hemiptera: Cercopidae) are known pests for sugarcane and pasture plantations throughout Brazil. By direct sap feeding on the plants they cause phytotoxicity and due to this they decrease the production of plantations. With species distribution modeling it is possible to analyze the possible occurence of the four species in South and Central America. To create species distribution models the algorithms Bioclim, Domain, different generalized linear models and Maxent were used in R. For those models current and future bioclimatic variables as well as elevation and other agricultural variables were used. The future climatic variables are for the years 2050 and 2070 with different repentant concentration pathways. The species show suitable habitats in different countries in South and Central America where sugarcane plantations are abundant. The results of the future climate analyzes do not show differences compared to the current climate analyzes. Overall the Maxent algorithm showed the highest AUC scores and Bioclim the lowest. The variables which contributed the most to the models are elevation, isothermality and different precipitation variables. Climate change and therefore additional insect lifecycles do not have an impact on the insect?s suitable habitats. Overall Maxent is the best algorithm to perform species distribution models with a low number of occurrence points and for climate change analyzes