12 research outputs found
Effect of amikacin, cephalothin, clindamycin and vancomycin on in vitro fibroblast growth
Conhecimento dos profissionais de saúde na Unidade de Terapia Intensiva sobre prevenção de pneumonia associada à ventilação mecânica
Multivariate Genomic Hybrid Prediction with Kernels and Parental Information
Genomic selection (GS) plays a pivotal role in hybrid prediction. It can enhance the selection of parental lines, accurately predict hybrid performance, and harness hybrid vigor. Likewise, it can optimize breeding strategies by reducing field trial requirements, expediting hybrid development, facilitating targeted trait improvement, and enhancing adaptability to diverse environments. Leveraging genomic information empowers breeders to make informed decisions and significantly improve the efficiency and success rate of hybrid breeding programs. In order to improve the genomic ability performance, we explored the incorporation of parental phenotypic information as covariates under a multi-trait framework. Approach 1, referred to as Pmean, directly utilized parental phenotypic information without any preprocessing. While approach 2, denoted as BV, replaced the direct use of phenotypic values of both parents with their respective breeding values. While an improvement in prediction performance was observed in both approaches, with a minimum 4.24% reduction in the normalized root mean square error (NRMSE), the direct incorporation of parental phenotypic information in the Pmean approach slightly outperformed the BV approach. We also compared these two approaches using linear and nonlinear kernels, but no relevant gain was observed. Finally, our results increase empirical evidence confirming that the integration of parental phenotypic information helps increase the prediction performance of hybrids
Bird Community Diversity in three Habitat Types in an Ecological Corridor in the Atlantic Forest of Misiones Province, Argentina
Effect of organic carbon chemistry on sorption of atrazine and metsulfuron-methyl as determined by 13C-NMR and IR spectroscopy
Soil organic matter (SOM) content is themajor soil component affecting pesticide sorption. However,recent studies have highlighted the fact that it is notthe total carbon content of the organic matter, but itschemical structure which have a profound effect on thepesticide’s sorption. In the present study, sorption ofatrazine and metsulfuron-methyl herbicides was studiedin four SOM fractions viz. commercial humic acid,commercial lignin, as well as humic acid and huminextracted from a compost. Sorption data was fitted to theFreundlich adsorption equation. In general, theFreundlich slope (1/n) values for both the herbicideswere <1. Except for atrazine sorption on commercialhumic acid, metsulfuron-methyl was more sorbed. Desorptionresults suggested that atrazine was moredesorbed than metsulfuron-methyl. Lignin, whichshowed least sorption of both the herbicides, showedminimum desorption. Sorption of atrazine was bestpositively correlated with the alkyl carbon (adjustedR2 = 0.748) and carbonyl carbon (adjusted R2 = 0.498)but, their effect was statistically nonsignificant(P = 0.05). Metsulfuron-methyl sorption showed bestpositive correlation with carbonyl carbon (adjustedR2 = 0.960; P = 0.05) content. Sorption of both theherbicides showed negative correlation with O/N-alkylcarbon. Correlation of herbicide’s sorption with alkyland carbonyl carbon content of SOM fractions suggestedtheir contribution towards herbicide sorption.But, sorption of metsulfuron-methyl, relatively morepolar than atrazine, was mainly governed by the polargroups in SOM. IR spectra showed that H-bonds andcharge-transfer bonds between SOM fraction and herbicidesprobably operated as mechanisms of adsorption