不同灌溉条件下小麦冠层的反射光谱特征及其模糊聚类研究

应用傅立叶滤波和导数法等信号处理技术对获得的不同灌溉条件小麦冠层的反射光谱进行处理,选出最能有效区分小麦不同灌溉条件的波长,用这些波长对应的反射率为指标,用模糊聚类（ＦＣＡ）方法对不同灌溉条件的小麦进行区分。     

New Docking Algorithm Based on Fuzzy Set Theory

A new docking algorithm based on shape complementarity is presented. The algorithm is based on Fuzzy Logic strategies. A small number of possible docking configurations is selected using partial ordering of a fuzzy measure of topographical properties between complementary surface patches. The algorithm was tested with the structures of three protein-protein-complexes of serin proteases. In one case the components determined separately by x-ray investigations whereas in the other cases the components obtained from the known complex structure were used.Electronic Supplementary Material available.     

Whence tit-for-tat?

Summary In theoretical and empirical studies of the evolution of cooperation, the tit-for-tat strategy (i.e. cooperate unless your partner did not cooperate in the previous interaction) is widely considered to be of central importance. Nevertheless, surprisingly little is known about the conditions in which tit-for-tat appears and disappears across generations in a population of interacting individuals. Here, we apply a newly developed classifier-system model (EvA) in addressing this issue when the key features of interactions are caricatured using the iterated prisoner's dilemma game. Our simple representation of behavioural strategies as algorithms composed of two interacting rules allowed us to determine conditions in which tit-for-tat can replace noncooperative strategies and vice versa. Using direct game-theoretic analysis and simulations with the EvA model, we determined that no strategy is evolutionarily stable, but larger population sizes and longer sequences of interactions between individuals can yield transient dominance by tit-for-tat. Genetic drift among behaviourally equivalent strategies is the key mechanism underlying this dominance. Our analysis suggests that tit-for-tat could be important in nature for cognitively simple organisms of limited memory capacity, in strongly kin-selected or group-selected populations, when interaction sequences between individuals are relatively short, in moderate-sized populations of widely interacting individuals and when defectors appear in the population with moderate frequency.     

Sustainable transportation systems performance evaluation using fuzzy logic

Sustainability has become an overarching concern for transportation policy and planning around the world. This article presents an approach for urban transport sustainability performance evaluation using fuzzy logic. This article presents a model for transport sustainability performance evaluation. Appropriate transport sustainability indicators were identified based on literature. The model addresses all major dimensions of transport sustainability such as Economic Sustainability, Social Sustainability, Environmental Sustainability and Transportation System Effectiveness. Transport sustainability index has been computed as (5.05, 6.62, 8.12) and weaker transport sustainability attributes were found. Transport sustainability index highlights the question how far toward becoming transport sustainable is an enterprise or region? While, weaker transport sustainability attributes reveals that how can an enterprise or region improve its transport sustainability effectively? Appropriate actions were initiated to improve urban transport sustainability performance. The results indicate that the model is capable of effectively assessing transport sustainability and has practical relevance. An example is also used to illustrate the approach developed. The results obtained using fuzzy approach has been validated with conventional crisp approach. 20 transport sustainability attributes out of 60 are found to be weaker and appropriate actions were derived to improve the weaker attributes.     

Fuzzy systems vegetation theory

Fuzzy systems vegetation theory is a comprehensive framework for the expression of vegetation theory and conceptual models, as well as the development of vegetation analyses. It is applicable to vegetation/environment relations, vegetation dynamics, and the effects of environmental dynamics on vegetation composition. Fuzzy systems vegetation theory is a fuzzy set generalization of dynamical systems theory and incorporates a formal logic and mathematics. This paper presents the elements of fuzzy systems vegetation theory and discusses the relationship of the fuzzy systems theory to the geometric concepts generally employed in vegetation theory.     

模糊ＩＳＯＤＡＴＡ在草地植物群落分类上的应用

 模糊集合理论在元素与集合的关系中引入了连续变化的隶属度,因而能适应植物群落作连续和明显间断分布同时存在的情况。本文运用以模糊集合理论为基础的ISODATA聚类分析方法对松嫩平原草地植物群落进行了数量分类,结果说明模糊ISODATA聚类比传统的分类方法有更为客观、灵活的特点,是一种较为理想的植被数量分类方法。     

A Transcriptome-wide Translational Program Defined by LIN28B Expression Level

1. Download high-res image (155KB)
2. Download full-size image

     

Interpretable gene expression classifier with an accurate and compact fuzzy rule base for microarray data analysis

An accurate classifier with linguistic interpretability using a small number of relevant genes is beneficial to microarray data analysis and development of inexpensive diagnostic tests. Several frequently used techniques for designing classifiers of microarray data, such as support vector machine, neural networks, k-nearest neighbor, and logistic regression model, suffer from low interpretabilities. This paper proposes an interpretable gene expression classifier (named iGEC) with an accurate and compact fuzzy rule base for microarray data analysis. The design of iGEC has three objectives to be simultaneously optimized: maximal classification accuracy, minimal number of rules, and minimal number of used genes. An "intelligent" genetic algorithm IGA is used to efficiently solve the design problem with a large number of tuning parameters. The performance of iGEC is evaluated using eight commonly-used data sets. It is shown that iGEC has an accurate, concise, and interpretable rule base (1.1 rules per class) on average in terms of test classification accuracy (87.9%), rule number (3.9), and used gene number (5.0). Moreover, iGEC not only has better performance than the existing fuzzy rule-based classifier in terms of the above-mentioned objectives, but also is more accurate than some existing non-rule-based classifiers.     

Predicting protein subcellular location by fusing multiple classifiers

One of the fundamental goals in cell biology and proteomics is to identify the functions of proteins in the context of compartments that organize them in the cellular environment. Knowledge of subcellular locations of proteins can provide key hints for revealing their functions and understanding how they interact with each other in cellular networking. Unfortunately, it is both time-consuming and expensive to determine the localization of an uncharacterized protein in a living cell purely based on experiments. With the avalanche of newly found protein sequences emerging in the post genomic era, we are facing a critical challenge, that is, how to develop an automated method to fast and reliably identify their subcellular locations so as to be able to timely use them for basic research and drug discovery. In view of this, an ensemble classifier was developed by the approach of fusing many basic individual classifiers through a voting system. Each of these basic classifiers was trained in a different dimension of the amphiphilic pseudo amino acid composition (Chou [2005] Bioinformatics 21: 10-19). As a demonstration, predictions were performed with the fusion classifier for proteins among the following 14 localizations: (1) cell wall, (2) centriole, (3) chloroplast, (4) cytoplasm, (5) cytoskeleton, (6) endoplasmic reticulum, (7) extracellular, (8) Golgi apparatus, (9) lysosome, (10) mitochondria, (11) nucleus, (12) peroxisome, (13) plasma membrane, and (14) vacuole. The overall success rates thus obtained via the resubstitution test, jackknife test, and independent dataset test were all significantly higher than those by the existing classifiers. It is anticipated that the novel ensemble classifier may also become a very useful vehicle in classifying other attributes of proteins according to their sequences, such as membrane protein type, enzyme family/sub-family, G-protein coupled receptor (GPCR) type, and structural class, among many others. The fusion ensemble classifier will be available at www.pami.sjtu.edu.cn/people/hbshen.     

Surface enhanced laser desorption ionization spectrometry reveals biomarkers for drug treatment but not dose

Here we describe the use of SELDI-MS to detect dose-dependent peptide changes in plasma from mice treated with vehicle or rosiglitazone at one of two doses (10 and 30 mg/kg). SELDI features differentiating spectra from the three conditions were found and used to train classifiers. Samples treated with vehicle could be reliably distinguished from samples treated with either dose, but samples treated with the different doses could not be reliably distinguished from one another. We conclude that while SELDI-TOF mass spectra can be used to distinguish treated from untreated samples, the reproducibility and information content of SELDI-TOF are currently not sufficient as a pharmacodynamic readout to distinguish between mice treated with 10 or 30 mg/kg of rosiglitazone. This raises more general questions about whether SELDI's sensitivity is sufficient for detecting dose-dependent changes in plasma.