Structure des caboxines: Alcaloïdes oxindoliques du Cabucala fasciculata

The structures of three new 11-monomethoxy pentacyclic oxindole alkaloids have been elucidated by chemical correlations with reserpinine: caboxine-A was assigned to the allo C₁₉-méthyl α series: 3S, 4R, 7S, 19S; isocaboxine-A and B to the epi-allo C₁₉-methyl α series and have, respectively, the following configurations 3R, 4S, 7S, 19S and 3R, 4S, 7R, 19S.     

Artificial neural network and response surface methodology: a comparative analysis for optimizing rice straw pretreatment and saccharification

Abstract

The present study demonstrates a comparative analysis between the artificial neural network (ANN) and response surface methodology (RSM) as optimization tools for pretreatment and enzymatic hydrolysis of lignocellulosic rice straw. The efficacy for both the processes, that is, pretreatment and enzymatic hydrolysis was evaluated using correlation coefficient (R²) & mean squared error (MSE). The values of R² obtained by ANN after training, validation, and testing were 1, 0.9005, and 0.997 for pretreatment and 0.962, 0.923, and 0.9941 for enzymatic saccharification, respectively. On the other hand, the R² values obtained with RSM were 0.9965 for cellulose recovery and 0.9994 for saccharification efficiency. Thus, ANN and RSM together successfully identify the substantial process conditions for rice straw pretreatment and enzymatic saccharification. The percentage of error for ANN and RSM were 0.009 and 0.01 for cellulose recovery and for 0.004 and 0.005 for saccharification efficiency, respectively, which showed the authority of ANN in exemplifying the non-linear behavior of the system.     

Rapid reversal of a potentially constraining genetic covariance between leaf and flower traits in Silene latifolia

Genetic covariance between two traits generates correlated responses to selection, and may either enhance or constrain adaptation. Silene latifolia exhibits potentially constraining genetic covariance between specific leaf area (SLA) and flower number in males. Flower number is likely to increase via fecundity selection but the correlated increase in SLA increases mortality, and SLA is under selection to decrease in dry habitats. We selected on trait combinations in two selection lines for four generations to test whether genetic covariance could be reduced without significantly altering trait means. In one selection line, the genetic covariance changed sign and eigenstructure changed significantly, while in the other selection line eigenstructure remained similar to the control line. Changes in genetic variance–covariance structure are therefore possible without the introduction of new alleles, and the responses we observed suggest that founder effects and changes in frequency of alleles of major effect may be acting to produce the changes.     

草莓种苗壮苗指数模型的构建与质量评价

随着我国草莓栽培面积逐年增加,草莓种苗需求量越来越大,为了确保种苗育苗质量,亟需开展壮苗评价的研究。本研究以生长40 d的‘红颜’穴盘苗为对象,在测定地上部和地下部生长、鲜重、干重等16项指标的基础上,分别构建单项指标隶属函数,使用加权模糊评判法计算种苗综合评价指数;利用主成分分析筛选的关键指标组成多个壮苗指数模型,与种苗综合评价指数进行相关性分析后,确定最佳草莓壮苗指数模型并进行验证。结果表明: 随机选取的320株草莓种苗16项指标存在显著差异,综合评价指数为0.165～0.817,可作为壮苗指数模型构建和种苗质量评价的依据。主成分分析将16项指标划分为地上部相关指标、地下部相关指标和色素指标3个主成分,累计贡献率达到79.7%;从每个主成分中选择贡献值最大的3个指标随机组成27种壮苗指数模型,通过相关性分析筛选出与综合评价指数相关性最大的5个壮苗指数模型,其中“地上干重×根系表面积×叶绿素a”的相关性最高,用‘红颜’、‘香野’和‘甜查理’种苗验证相关系数均最大,分别为0.879、0.924和0.975,确定可作为草莓壮苗指数计算模型。以综合评价指数为种苗质量分级依据,可将种苗健壮程度分为3个等级:等级Ⅰ(综合评价指数≥0.5,壮苗指数≥4.0)为优质苗,等级Ⅱ(综合评价指数0.3～0.5,壮苗指数0.5～4.0)为合格苗,等级Ⅲ(综合评价指数≤0.3,壮苗指数≤0.5)为弱苗。研究结果可为草莓或其他种苗壮苗指数计算和种苗健壮程度评价提供理论依据与科学方法。