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.     

Mapping QTL for production traits in segregating Piétrain pig populations using genome‐wide association study results of F2 crosses

In this study, genome‐wide association study (GWAS) results of porcine F2 crosses were used to map QTL in outcross Piétrain populations. For this purpose, two F2 crosses (Piétrain × Meishan, n = 304; Piétrain × Wild Boar, n = 291) were genotyped with the PorcineSNP60v2 BeadChip and phenotyped for the dressing yield, carcass length, daily gain and drip loss traits. GWASs were conducted in the pooled F2 cross applying single marker mixed linear models. For the investigated traits, between two and five (in total 15) QTL core regions, spanning 250 segregating SNPs around a significant trait‐associated peak SNP, were identified. The SNPs within the QTL core regions were subsequently tested for trait association in two outcross Piétrain populations consisting of 771 progeny‐tested boars and 210 sows with their own performance records. In the sow (boar) dataset, five (eight) of the 15 mapped QTL were validated. Hence, many QTL mapped in the F2 crosses (with Piétrain as a common founder breed) are still segregating in the current Piétrain breed. This confirms the usefulness of existing F2 crosses for mapping QTL that are still segregating in the recent founder breed generation. The approach utilizes the high power of an F2 cross to map QTL in a breeding population for which it is not guaranteed that they would be found using a GWAS in this population.     

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.     

Integrative structure and function of the yeast exocyst complex

Exocyst is an evolutionarily conserved hetero‐octameric tethering complex that plays a variety of roles in membrane trafficking, including exocytosis, endocytosis, autophagy, cell polarization, cytokinesis, pathogen invasion, and metastasis. Exocyst serves as a platform for interactions between the Rab, Rho, and Ral small GTPases, SNARE proteins, and Sec1/Munc18 regulators that coordinate spatial and temporal fidelity of membrane fusion. However, its mechanism is poorly described at the molecular level. Here, we determine the molecular architecture of the yeast exocyst complex by an integrative approach, based on a 3D density map from negative‐stain electron microscopy (EM) at ~16 Å resolution, 434 disuccinimidyl suberate and 1‐ethyl‐3‐(3‐dimethylaminopropyl)carbodiimide hydrochloride cross‐links from chemical‐crosslinking mass spectrometry, and partial atomic models of the eight subunits. The integrative structure is validated by a previously determined cryo‐EM structure, cross‐links, and distances from in vivo fluorescence microscopy. Our subunit configuration is consistent with the cryo‐EM structure, except for Sec5. While not observed in the cryo‐EM map, the integrative model localizes the N‐terminal half of Sec3 near the Sec6 subunit. Limited proteolysis experiments suggest that the conformation of Exo70 is dynamic, which may have functional implications for SNARE and membrane interactions. This study illustrates how integrative modeling based on varied low‐resolution structural data can inform biologically relevant hypotheses, even in the absence of high‐resolution data.     

基于不同可加性方法的黑龙江省红松人工林林分生物量模型

大尺度估算森林生物量一直是人们关注的焦点,而构建林分水平的生物量模型是一种估算森林乔木层生物量的方法。本研究基于聚合法1、聚合法2、平差法、分解法构建红松人工林林分生物量模型,并对比分析4种可加性方法的预测精度,为黑龙江省红松人工林的生物量预测提供科学依据。各模型均使用权函数来消除各模型的异方差,并以留一交叉验证法(LOOCV)作为各模型的检验方法。结果表明: 平差法的整体预测能力略优于聚合法1、聚合法2和分解法,预测精度排序为平差法>聚合法1>聚合法2>分解法;分别对比不同林分断面积的预测能力时,4种可加性方法的预测精度不一致。当红松人工林的林分断面积分布于0～10或50～60 m²·hm^-2区间时,建议采用分解法的参数估计值,而林分断面积分布于其他区间时,建议采用平差法的参数估计值。     

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.