Hybrid breeding for biomass yield in winter triticale: II. Combining ability and hybrid prediction

Accurate hybrid prediction and knowledge about the relative contribution of general (GCA) and specific combining ability (SCA) are of utmost importance for efficient hybrid breeding. We therefore evaluated 91 triticale single-cross hybrids in field trials at seven environments for plant height, heading time, fresh biomass, dry matter content and dry biomass. Fresh and dry biomass showed the highest proportion (23%) of variance due to SCA. Prediction accuracies based on GCA were slightly higher than based on mid-parent values. Utilizing parental kinship information yielded the highest prediction accuracies when both parental lines have been tested in other hybrid combinations, but still moderate-to-low prediction accuracies for two untested parents. Thus, hybrid prediction for biomass traits in triticale is currently promising based on mid-parent values as emphasized by our simulation study, but can be expected to shift to GCA-based prediction with an increasing importance of GCA due to selection in hybrid breeding. Moreover, the performance of potential hybrids between newly developed lines can be predicted with moderate accuracy using genomic relationship information.     

加速广义Laguerre多项式级数的Laplace变换的数值反演方法

本文利用Levin t-变换迭代法，对加速广义Laguerre多项式级数，提出了一种新的Laplace变换的数值反演方法，这种方法在精度上和数值稳定性上的效果都较好。     

两种一年生植物在替代试验中的相互竞争关系

（1）用替代试验研究了燕麦（Aneva sativa）和箭舌豌豆（Vicia sativa）在混播中的竞争相互作用。为了攻混播比例对两种植物在混播条件下的行为影响，田间试验设为5个混播总密度和5种混播比例，共了5次。（2）方差分析表明，相对总生物量（RYT）和竞争平衡指数（CBI）对总密度，混播比例和生长时间的反应敏感。总密度对RYT和CBI的影响高度显著，在大多数情况下，混播比例对RYT和CBI的影响不显著。（3）本文研究的结果表明，持续时间对RYT和CBI很强的影响，在第1次测定时，RYT值接近1而BI值接近0，这一事实表明，种间无竞争或互惠作用。而在生长旺盛期，种间竞争较为剧烈。（4）本研究结果表明，箭舌豌豆的生长严惩受到燕麦的抑制作用，这说明箭舌豌豆在与燕麦混播时表现为竞争上的弱者。     

基于Matlab的时序数据两种建模和预测方法比较

利用Matlab工具箱,对时序数据进行了两种不同形式的建模和预测。一是利用系统辨识方法来建立AR模型,利用所建模型进行预测；二是利用单隐层BP神经网络,对数据进行非线性拟合和预测。通过可视化,比较了两种方法的优劣。     

小麦条锈病流行程度趋势预测专家系统雏型

本文运用知识工程语言 M·1构造了一个用于小麦条锈病流行程度趋势预测的专家系统雏型。专家的知识用产生式规则来表示。知识库中的知识规则可相对独立,便于知识库的扩充和完善。     

GI PiCO2: Tissue Specific Monitoring For Improving Patient Outcomes

Improvements in human patient monitoring despite their development in animals, do not always find their way into veterinary clinical use due to financial constraints. Gastrointestinal intraluminal CO₂ partial pressure (Gip₁CO₂) monitoring, however, is not only proving very beneficial in human trauma and critical patient care but is also very likely to become relatively inexpensive. By providing information on the perfusion adequacy of a high risk, critically important tissue, the GI mucosa, GI P₁CO₂ monitoring offers an easily accesible indicator of the efficacy and adequacy of resuscitative interventions. The potential for decreasing morbidity and mortality is enormous. Therefore, the practicing veterinarian should become familiar with GI P₁CO₂ monitoring theory and technology so he or she can be better prepared to incorporate it into practice when in becomes available.     

应用积分GM(1,1)模型进行杉木炭疽病预测预报的研究

以积分生成代替传统的累加生成方法 ,并用双向差分建立积分GM (1,1)灰色预测模型进行杉木炭疽病预测预报的研究 ,结果表明积分GM (1,1)模型能较好地预测杉木炭疽病在三明市的发生     

小麦矮秆育种中性状间关系的多元分析

以7个株高梯度系列的35个系统及5个亲本品种为材料,采用典范分析和逐步回归分析研究了株高及其构成因子与产量因子、产量生理性状间的关系。结果表明:①株高因子通过产量生理性状影响产量因子,一定的生物学产量是获得高产的基础。②矮秆品种的高化对提高产量、千粒重和收获指数有利,对蛋白质含量的影响不明显;高秆品种矮化能显著提高收获指数,对籽粒的蛋白质含量影响不大。③在主要性状符合育种目标的前提下,选择倒二节间较长、第一和第二叶间距略长、穗颈和穗部较长的类型可能对提高产量有利。     

Trends in NE Atlantic landings (southern Portugal): identifying the relative importance of fisheries and environmental variables

Time series of commercial landings from the Algarve (southern Portugal) from 1982 to 1999 were analyzed using min/max autocorrelation factor analysis (MAFA) and dynamic factor analysis (DFA). These techniques were used to identify trends and explore the relationships between the response variables (annual landings of 12 species) and explanatory variables [sea surface temperature, rainfall, an upwelling index, Guadiana river (south‐east Portugal) flow, the North Atlantic oscillation, the number of licensed fishing vessels and the number of commercial fishermen]. Landings were more highly correlated with non‐lagged environmental variables and in particular with Guadiana river flow. Both techniques gave coherent results, with the most important trend being a steady decline over time. A DFA model with two explanatory variables (Guadiana river flow and number of fishermen) and three common trends (smoothing functions over time) gave good fits to 10 of the 12 species. Results of other models indicated that river flow is the more important explanatory variable in this model. Changes in the mean flow and discharge regime of the Guadiana river resulting from the construction of the Alqueva dam, completed in 2002, are therefore likely to have a significant and deleterious impact on Algarve fisheries landings.