基因工程技术及其在林木培育中的应用

概述了基因工程在国内外林木培育中的应用现状及发展趋势 ,综述了我国十多年来林木在抗虫、抗病、抗逆境、品质改良、育性等基因工程的研究与应用研究取得的一些进展及今后的发展方向。     

初等树变换的算法改造

本文利用王氏代数对用初等树变换生成树的方法进行了改造,提出了一种新的初等树变换的算法。新算法较之原法简洁、直观、运算方便、概念清楚,并保留了原法的所有优点。     

Seasonal host alternation by the andromeda lace bug,Stephanitis takeyai (Heteroptera: Tingidae) between its two main host-plant species

I studied the seasonal occurrence of the andromeda lace bug,Stephanitis takeyai, on its two main host-plant species. In a secondary forest in Kyoto, this bug altered its hosts seasonally, i.e., from an evergreen shrub,Pieris japonica, in winter to a deciduous shrub,Lyonia elliptica, in summer. In contrast, in Nara park where fewL. elliptica were available, the bug exploited onlyP. japonica. Thus, seasonal host alternation by this bug is not obligate. A comparison of adult longevity and fecundity on the two host-plant species demonstrated the higher quality ofL. elliptica as a food resource. Corresponding to this difference in host quality, there was a dramatic difference in the seasonal population growth in the two study sites. In Nara, the population size at the beginning of the 2nd generation was almost the same as in the overwintered generation, whereas in Kyoto the population size in the 2nd generation was approximately one hundred times as large as in the overwintered generation. Thus seasonal host alternation is adaptive for the bug. In a previous study, I reported that overwintering as eggs in living leaves of their hosts is likely to be common among all the related species of this bug. Thus, this trait can be considered to be a phylogenetic constraint to the group. I speculate that host alternation by this bug has been derived because it is more adaptive from autoecy on an evergreen plant, similar to the pattern currently found in Nara, and that this bug can not only exploit deciduous host due to a phylogenetic constraint.     

Binary Event String Analysis: A Compact Numerical Representation of the Event Tree

While the event tree is a useful diagrammatic aid to an appreciation of various event sequence possibilities, it is of a nature that suggests no obvious manner in which the associated probability data may be compiled as computer input. Here, we propose a complementary numerical representation of scenario possibilities which incorporates probability data in a succinct fashion. While its mnemonic properties facilitate the logical development of a system's characteristics, its compactness and unambiguity permit its utilization directly as computer input.     

Tree-structured subgroup analysis for censored survival data: Validation of computationally inexpensive model selection criteria

The performance of computationally inexpensive model selection criteria in the context of tree-structured subgroup analysis is investigated. It is shown through simulation that no single model selection criterion exhibits a uniformly superior performance over a wide range of scenarios. Therefore, a two-stage approach for model selection is proposed and shown to perform satisfactorily. Applied example of subgroup analysis is presented. Problems associated with tree-structured subgroup analysis are discussed and practical solutions are suggested.     

清代树木栽培技术及相关文献探析

中国的树木栽培技术源远流长,曾取得过辉煌的成就。清朝时期在对前人的树木栽培技术进行系统总结和归纳的基础上,结合树木栽培的生产实践,编纂出版了几部学术价值高、影响大、实用性强的文献资料。同时这一时期在植物嫁接成活的生理机制研究、嫁接技术、树木移栽、良种选育与新品种培育、实生苗繁殖技术等方面取得了一定的成就,使清代的树木栽培技术更加完善和成熟。     

关于图P_1(∨P_4)T_n的优美性

本文研究了一类图P1∨P4T n的优美性.利用构造函数标号的方法,证明了下面结论:对于任意正整数n,T n是n个顶点的优美树,则当n≥2时,P1∨P4T n是优美图.其中,P1是1个顶点的平凡图,P4是4个顶点的路,图P1∨P4T n是把P1之中的顶点与T n之中的每一个顶点之间都通过一个P4连接起来所得到的.     

大学校园中植物多样性调查———以福州地区为例简

当前大学校园环境的塑造广泛引起关注,一个大学校园环境塑造的好坏与其植物配置密切相关。对福州地区十个校园环境的植物进行调查,对其多样性进行总结。为植物配置、植材选择及校园环境塑造给予一定的参考及建议。     

Multi-Step Classification Trees

Many algorithms originated from decision trees have been developed for classification problems. Although they are regarded as good algorithms, most of them suffer from loss of prediction accuracy, namely high misclassification rates when there are many irrelevant variables. We propose multi-step classification trees with adaptive variable selection (the multi-step GUIDE classification tree (MG) and the multi-step CRUISE classification tree (MC) to handle this problem. The variable selection step and the fitting step comprise the multi-step method.

We compare the performance of classification trees in the presence of irrelevant variables. MG and MC perform better than Random Forest and C4.5 with an extremely noisy dataset. Furthermore, the prediction accuracy of our proposed algorithm is relatively stable even when the number of irrelevant variables increases, while that of other algorithms worsens.