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Sixteen cotton cultivars widely planted in China were sowed under five different drought concentrations (0, 2.5, 5, 7.5, and 10%) using PEG6000 to screen the indices of drought resistance identification and explore the drought resistance of different cotton cultivars. Eighteen physiological indices including root, stem, and leaf water contents (RWC, SWC, and LWC), net photosynthetic rate (Pn), the maximum photochemical quantum yield (Fv/Fm), the actual photochemical quantum yield (ϕPSII), non-photochemical quenching coefficient (NPQ), leaf water potential (LWP), osmotic potential (ψs), leaf relative conductivity (REC), leaf proline content (Pro), leaf and root soluble protein contents (LSPC and RSPC), leaf and root malondialdehyde (MDA) contents (LMDA and RMDA), root superoxide dismutase, peroxidase, and catalase activities (RSOD, RPOD, and RCAT) were measured. Results indicated the 18 physiological indices can be converted into five or six independent comprehensive indices by principal component analysis, and nine typical indices (Fv/Fm, SWC, LWP, Pro, LMDA, RSPC, RMDA, RSOD, and RCAT) screened out by a stepwise regression method could be utilized to evaluate the drought resistance. Moreover, the 16 cotton cultivars were divided into four types: drought sensitive, drought weak sensitive, moderate drought resistant, and drought resistant types. The resistance ability of two selected cotton cultivars (drought resistant cultivar, Dexiamian 1; drought sensitive cultivar, Yuzaomian 9110) with contrasting drought sensitivities were further verified by pot experiment. Results showed that the responses of final cotton biomass, yield, and yield composition to drought were significantly different between the two cultivars. In conclusion, drought resistant cultivar Dexiamian 1 and drought sensitive cultivar Yuzaomian 9110 were screened through hydroponics experiment, which can be used as ideal experimental materials to study the mechanism of different cotton cultivars with contrasting drought sensitivities in response to drought stress.

To screen the desired criterion to identify desirable genotypes and select genotypes best suited to limited nitrogen availability in order to facilitate the practice of low-nitrogen-tolerant breeding in maize, the response of 31 maize inbred lines, containing four control inbred lines (PH6WC, PH4CV, Zheng58, and Chang7-2) and others selected from the Shaan A and Shaan B heterotic groups cultivated at Northwest A&F University (Yangling, Shaanxi, China), were evaluated. The experiment was conducted following a split plot design with two replications during three growing seasons (2015, 2016, and 2017) under both high nitrogen (HN) and low nitrogen (LN) conditions at the Yulin and Yangling in Shaanxi Province, China. Seven screening indices, based on grain yield under two contrasting nitrogen (N) conditions, the stress susceptibility index (SSI), yield stability index (YSI), mean productivity (MP), geometric mean productivity (GMP), stress tolerance index (STI), harmonic mean (HM), and low nitrogen tolerance index (LNTI), were computed to assess the overall index that accurately screened the desirable genotypes. The results of the correlation analyses and principal component analysis showed that MP, GMP, HM and STI were correlated with grain yield significantly and positively under contrasting N conditions, and were able to accurately discriminate the desirable genotypes. Compared with the control inbred lines, many inbred lines selected from the Shaan A and Shaan B groups showed a higher LN tolerance. This shows that we can effectively improve the LN tolerance of maize inbred lines through LN screening. Based on the screening indices, the three-dimensional diagram and genotype and genotype × environment (GGE) biplots are agreed with this results, and we identified KA105, KB081, KA225, 91227, and 2013KB-47 as the desired genotypes that have the potential to be used to breed a high yield and stable hybrid.

Abstract: Introduction: Placental morphological and histopathological measures can be numerous and it is very time-consuming to collect all the information. When planning a large birth cohort study, researchers often face the dilemma as to whether and what information to collect in a placenta. The aim of this study was (1) to systematically select placental measures that have significant clinical implications, and (2) to assess the accuracy of these measures. Methods: We used placental pathology information from the Collaborative Perinatal Project (CPP), in which 45,785 births had comprehensive information on placental morphology and pathology. We chose 20 major childhood diseases as outcomes. The statistical method of LASSO (least absolute shrinkage and selection operator) was used to select important placental measures that would have better predictability for outcomes. Results: LASSO selected 81 measures as candidates. After having consulted placental pathologists, we further narrowed down to the 38 and 23 most important measures to form two shortened evaluation systems that could be used in clinical practice and research. The sensitivity and specificity of these measures for composite child diseases were 68% and 34%, respectively, for the 38 measures and 48% and 53%, respectively, for the 23 measures. Conclusions: We presented a potentially useful system for pathological characterization of the placenta. The use of these relatively simple and accessible characteristics as biomarkers may be considered in large birth cohort studies. [Copyright &y& Elsevier]

To screen the desired criterion to identify desirable genotypes and select genotypes best suited to limited nitrogen availability in order to facilitate the practice of low-nitrogen-tolerant breeding in maize, the response of 31 maize inbred lines, containing four control inbred lines (PH6WC, PH4CV, Zheng58, and Chang7-2) and others selected from the Shaan A and Shaan B heterotic groups cultivated at Northwest A&F University (Yangling, Shaanxi, China), were evaluated. The experiment was conducted following a split plot design with two replications during three growing seasons (2015, 2016, and 2017) under both high nitrogen (HN) and low nitrogen (LN) conditions at the Yulin and Yangling in Shaanxi Province, China. Seven screening indices, based on grain yield under two contrasting nitrogen (N) conditions, the stress susceptibility index (SSI), yield stability index (YSI), mean productivity (MP), geometric mean productivity (GMP), stress tolerance index (STI), harmonic mean (HM), and low nitrogen tolerance index (LNTI), were computed to assess the overall index that accurately screened the desirable genotypes. The results of the correlation analyses and principal component analysis showed that MP, GMP, HM and STI were correlated with grain yield significantly and positively under contrasting N conditions, and were able to accurately discriminate the desirable genotypes. Compared with the control inbred lines, many inbred lines selected from the Shaan A and Shaan B groups showed a higher LN tolerance. This shows that we can effectively improve the LN tolerance of maize inbred lines through LN screening. Based on the screening indices, the three-dimensional diagram and genotype and genotype × environment (GGE) biplots are agreed with this results, and we identified KA105, KB081, KA225, 91227, and 2013KB-47 as the desired genotypes that have the potential to be used to breed a high yield and stable hybrid. [ABSTRACT FROM AUTHOR]