植被制图色彩和符号设计原则与全国植被图图例方案

植被图通常采用不同的颜色并结合符号来区分不同的植被类型, 以期更加直观、清晰地将植被类型信息传达给读者。如何通过色彩和符号的合理搭配来区分不同的植被类型是植被制图中的关键步骤, 尤其是对于植被类型较丰富的区域, 植被图的色彩和符号设计更为复杂。植被图图例系统建立在植被分类系统之上, 近10余年来我国的植被分类系统得到了进一步的发展和完善, 因此, 非常有必要在最新的植被分类系统基础上, 提出一套植被制图色彩和符号设计方案, 用以指导当前的植被制图工作。该研究整理了地图学、植被图以及其他专题地图色彩和符号设计的要求, 提出了植被制图的色彩和符号设计原则。在最新的植被分类系统基础上, 采用颜色三属性(色调、明度、饱和度)及符号的基本视觉变量(形状、尺寸、方向、色彩、密度、亮度)的变化及其组合, 依据色彩和符号设计的系统性、科学性、象征性等原则, 对中国植被分类系统高级分类单位中的植被亚型, 进行了标准化配色和符号设计, 旨在为植被制图工作者及新一代1:50万植被图编研提供理论依据和参考, 提升植被制图的效率和质量。     

Propensity Score Matching in Randomized Clinical Trials

Summary Cluster randomization trials with relatively few clusters have been widely used in recent years for evaluation of health‐care strategies. On average, randomized treatment assignment achieves balance in both known and unknown confounding factors between treatment groups, however, in practice investigators can only introduce a small amount of stratification and cannot balance on all the important variables simultaneously. The limitation arises especially when there are many confounding variables in small studies. Such is the case in the INSTINCT trial designed to investigate the effectiveness of an education program in enhancing the tPA use in stroke patients. In this article, we introduce a new randomization design, the balance match weighted (BMW) design, which applies the optimal matching with constraints technique to a prospective randomized design and aims to minimize the mean squared error (MSE) of the treatment effect estimator. A simulation study shows that, under various confounding scenarios, the BMW design can yield substantial reductions in the MSE for the treatment effect estimator compared to a completely randomized or matched‐pair design. The BMW design is also compared with a model‐based approach adjusting for the estimated propensity score and Robins‐Mark‐Newey E‐estimation procedure in terms of efficiency and robustness of the treatment effect estimator. These investigations suggest that the BMW design is more robust and usually, although not always, more efficient than either of the approaches. The design is also seen to be robust against heterogeneous error. We illustrate these methods in proposing a design for the INSTINCT trial.     

Design Aspects and Odds Ratio Estimation in Multicenter Case-Control Studies

In this paper we propose a method to be used in the planning stage of a case-control study. An allocation rule for controls in multicenter case-control studies is proposed which would assure a simple, efficient and unbiased estimation of the odds ratio in the pooled data. It is shown that the efficiency of the design increases with increasing correlation between study center and risk factor. Sources of bias and their implications for relative risk estimation are discussed. The method is demonstrated with data from a case-control study.     

AutoMatch: target-binding protein design and enzyme design by automatic pinpointing potential active sites in available protein scaffolds

Proteins perform their functions mainly via active sites, whereas other parts of the proteins comprise the scaffolds, which support the active sites. One strategy for protein functional design is transplanting active sites, such as catalytic sites for enzyme or binding hot spots for protein-protein interactions, onto a new scaffold. AutoMatch is a new program designed for efficiently elucidating suitable scaffolds and potential sites on the scaffolds. Backrub motions are used to treat backbone flexibility during the design process. A step-by-step checking strategy and cluster-representation examination strategy were developed to solve the large combinatorial problem for the matching of active-site conformations. In addition, a grid-based binding energy scoring method was used to filter the solutions. An enzyme design benchmark and a protein-protein interaction design benchmark were built to test the algorithm. AutoMatch could identify the hot spots in the nonbinding protein and rank them within the top five results for 8 of 10 target-binding protein design cases. In addition, among the 15 enzymes tested, AutoMatch can identify the catalytic active sites in the apoprotein and rank them within the top five results for 13 cases. AutoMatch was also tested for screening scaffold library in designing binding proteins targeting influenza hemagglutinin, HIV gp120, and epidermal growth factor receptor kinase, respectively. AutoMatch, and the two test sets, ActApo and ActFree, are available for noncommercial applications at http://mdl.ipc.pku.edu.cn/cgi-bin/down.cgi.     

Estimation of Relative Risk Under M to R Matching

Estimation of the relative risk from a rare disease is carried out using M:R matching. Both conditional and unconditional likelihood methods are used, leading in each case to the same estimate; a non-iterative estimate is always available under M:R matching, M ≦2. The method also enables the testing of homogeneity even when information on the matching variate is unavailable and this is an advantage over logistic regression methods.     

A matching algorithm for catalytic residue site selection in computational enzyme design

A loop closure-based sequential algorithm, PRODA_MATCH, was developed to match catalytic residues onto a scaffold for enzyme design in silico. The computational complexity of this algorithm is polynomial with respect to the number of active sites, the number of catalytic residues, and the maximal iteration number of cyclic coordinate descent steps. This matching algorithm is independent of a rotamer library that enables the catalytic residue to take any required conformation during the reaction coordinate. The catalytic geometric parameters defined between functional groups of transition state (TS) and the catalytic residues are continuously optimized to identify the accurate position of the TS. Pseudo-spheres are introduced for surrounding residues, which make the algorithm take binding into account as early as during the matching process. Recapitulation of native catalytic residue sites was used as a benchmark to evaluate the novel algorithm. The calculation results for the test set show that the native catalytic residue sites were successfully identified and ranked within the top 10 designs for 7 of the 10 chemical reactions. This indicates that the matching algorithm has the potential to be used for designing industrial enzymes for desired reactions.     

Taxonome: a software package for linking biological species data

Online databases of biological information offer tremendous potential for evolutionary and ecological discoveries, especially if data are combined in novel ways. However, the different names and varied spellings used for many species present major barriers to linking data. Taxonome is a software tool designed to solve this problem by quickly and reproducibly matching biological names to a given reference set. It is available both as a graphical user interface (GUI) for simple interactive use, and as a library for more advanced functionality with programs written in Python. Taxonome also includes functions to standardize distribution information to a well‐defined set of regions, such as the TDWG World Geographical Scheme for Recording Plant Distributions. In combination, these tools will help biologists to rapidly synthesize disparate datasets, and to investigate large‐scale patterns in species traits.     

基于景观结构系统的人流分布预测与匹配度分析研究

景观的空间结构组织与游人行为活动的开展以及空间使用效率密切相关。1）针对园路系统与节点系统2个结构要素，基于空间句法、ArcGIS核密度分析以及UNA 3种算法，分别从园路整合度、节点密度、时空选择度3个层面对游客时空分布特征进行预测；2）通过统计预测数据和离散度计算，对3个维度的数据进行匹配度分析，探讨景观结构设计与游人时空分布之间是否具有良好的相关性；3）结合实践方案，将测算结果对应到方案设计图中，标识出匹配度较低的区域。形成从空间结构预测人流分布情况，到匹配度分析，再将分析结果反馈设计的研究思路和方法，为具有针对性的方案调整与优化设计提供参照。     

How well can fine balance work for covariate balancing

Fine balance is a matching technique to improve covariate balance in observational studies. It constrains a match to have identical distributions for some covariates without restricting who is matched to whom. However, despite its wide application and excellent performance in practice, there is very little theory indicating when the method is likely to succeed or fail and to what extent it can remove covariate imbalance. In order to answer these questions, this paper studies the limits of what is possible for covariate balancing using fine balance and near-fine balance. The investigations suggest that given the distributions of the treated and control groups, in large samples, the maximum achievable balance by using fine balance only depends on the matching ratio (ie, the ratio of the sample size of the control group to that of the treated group). In addition, the results indicate how to estimate this matching ratio threshold without knowledge of the true distributions in finite samples. The findings are also illustrated by numerical studies in this paper.     

Robust model matching design methodology for a stochastic synthetic gene network

Synthetic biology has shown its potential and promising applications in the last decade. However, many synthetic gene networks cannot work properly and maintain their desired behaviors due to intrinsic parameter variations and extrinsic disturbances. In this study, the intrinsic parameter uncertainties and external disturbances are modeled in a non-linear stochastic gene network to mimic the real environment in the host cell. Then a non-linear stochastic robust matching design methodology is introduced to withstand the intrinsic parameter fluctuations and to attenuate the extrinsic disturbances in order to achieve a desired reference matching purpose. To avoid solving the Hamilton-Jacobi inequality (HJI) in the non-linear stochastic robust matching design, global linearization technique is used to simplify the design procedure by solving a set of linear matrix inequalities (LMIs). As a result, the proposed matching design methodology of the robust synthetic gene network can be efficiently designed with the help of LMI toolbox in Matlab. Finally, two in silico design examples of the robust synthetic gene network are given to illustrate the design procedure and to confirm the robust model matching performance to achieve the desired behavior in spite of stochastic parameter fluctuations and environmental disturbances in the host cell.     

A Note on the Estimation of Coverage Errors in Demographic Data

A simple formula for the lower bound of coverage errors in demographic data is obtained. The formula is derived under general assumptions, the validity of which is discussed. Three numerical examples are given.     

A generalized habitat matching rule

Summary When fitness of a resource-limited animal depends only on that individual's share of the total resource in a habitat patch and individuals are free to move to the patch where their gains are highest, population density matches resource availability under the simple assumption that individual fitness increases with resource use. Previous theory on habitat matching required the stronger assumption that individual fitness was directly proportional to (rather than monotonically increasing with) resource use. The basic theory suggests conditions under which population density empirically indicates habitat quality. Extensions of this basic theory apply when individuals that are free to move among resource patches interact by interfering with each other's resource extraction or by competing unequally. Analysis of existing models of such ideal free competition yields conditions for a single general matching rule in which the logarithm of crowding is a linear function of the logarithm of resource abundance. Double logarithmic plots of empirical data on habitat use and habitat quality based on this rule furnish possible graphical indicators of the occurrence and intensity of competition in nature.     

Tools for automated acoustic monitoring within the R package monitoR

The R package monitoR contains tools for managing an acoustic-monitoring program including survey metadata, template creation and manipulation, automated detection and results management. These tools are scalable for use with small projects as well as larger long-term projects and those with expansive spatial extents. Here, we describe typical workflow when using the tools in monitoR. Typical workflow utilizes a generic sequence of functions, with the option for either binary point matching or spectrogram cross-correlation detectors.     

Prioritisation of potential agents for the biological control of the invasive alien weed,Pereskia aculeata (Cactaceae), in South Africa

Pereskia aculeata Miller (Cactaceae) is an invasive alien species in South Africa that is native in Central and South America. In South Africa, P. aculeata outcompetes native plant species leading to a reduction in biodiversity at infested sites. Herbicidal and mechanical control of the plant is ineffective and unsustainable, so biological control is considered the only potential solution. Climatic matching and genotype matching indicated that the most appropriate regions in which to collect biological control agents were Santa Catarina and Rio de Janeiro provinces in Southern Brazil. Surveys throughout the native distribution resulted in 15 natural enemy species that were associated with the plant. Field host range data, as well as previous host plant records, were used to prioritise which of the species were most likely to be suitably host specific for release in South Africa. The mode of damage was used to determine which species were most likely to be damaging and effective if released. The most promising species prioritised for further study, including host specificity and impact studies, were the stem-wilter Catorhintha schaffneri Brailovsky & Garcia (Coreidae); the stem boring species Acanthodoxus machacalis Martins & Monné (Cerambycidae), Cryptorhynchus sp. (Curculionidae) and Maracayia chlorisalis (Walker) (Crambidae) and the fruit galler Asphondylia sp. (Cecidomyiidae). By prioritising the potential biological control agents that are most likely to be host-specific and damaging, the risk of conducting host specificity testing on unsuitable or ineffective biological control agents is reduced.