Competition among plants: Concepts, individual-based modelling approaches, and a proposal for a future research strategy

Competition is a key process in plant populations and communities. We thus need, if we are to predict the responses of ecological systems to environmental change, a comprehensive and mechanistic understanding of plant competition. Considering competition, however, only at the population level is not sufficient because plant individuals usually are different, interact locally, and can adapt their behaviour to the current state of themselves and of their biotic and abiotic environment. Therefore, simulation models that are individual-based and spatially explicit are increasingly used for studying competition in plant systems. Many different individual-based modelling approaches exist to represent competition, but it is not clear how good they are in reflecting essential aspects of plant competition. We therefore first summarize current concepts and theories addressing plant competition. Then, we review individual-based approaches for modelling competition among plants. We distinguish between approaches that are used for more than 10 years and more recent ones. We identify three major gaps that need to be addressed more in the future: the effects of plants on their local environment, adaptive behaviour, and below-ground competition. To fill these gaps, the representation of plants and their interactions have to be more mechanistic than most existing approaches. Developing such new approaches is a challenge because they are likely to be more complex and to require more detailed knowledge and data on individual-level processes underlying competition. We thus need a more integrated research strategy for the future, where empirical and theoretical ecologists as well as computer scientists work together on formulating, implementing, parameterization, testing, comparing, and selecting the new approaches.     

Comparison of Approaches for Developing Distributions for Carcinogenic Slope Factors

In recent years, risk assessors have increasingly been moving away from deterministic risk assessment approaches and are applying probabilistic approaches that incorporate distributions of possible values for each input parameter. This paper reviews several approaches that are being used or that could potentially be used to develop distributions for carcinogenic slope factors (CSFs). Based on the primary tool or framework that is applied, these approaches have been divided into the following three categories: the statistical framework, the decision analysis framework, and the biological framework. Work that has been done on each approach is summarized, and the aspects of variability and uncertainty that are incorporated into each approach are examined. The implications of the resulting distributional information for calculating risk estimates or risk-based concentrations is explored. The approaches differ in their stage of development, the degree to which they diverge from the U.S. Environmental Protection Agency's (EPA) current practice in establishing CSF values, their flexibility to accommodate varying data sets or theories of carcinogenicity, and their complexity of application. In some cases, wide ranges of potential potency estimates are indicated by these approaches. Such findings suggest widely divergent risk assessment implications and the need for additional evaluation of the goals of developing CSF distributions for use in risk assessment applications and the types of information that should be reflected in such distributions. Some combination of the features offered by these approaches may best support risk assessment and risk management decisions.     

microRNA计算发现方法的研究进展

microRNA (miRNA)是近几年发现的一类长度为～21 nt的内源非编码小RNA, 在植物和动物中发挥着重要而广泛的调控功能。它的发现主要有cDNA克隆测序和计算发现两条途径。由于cDNA克隆测序方法受miRNA表达的时间和组织特异性以及表达水平的影响, 而计算发现可以弥补其不足, 因此miRNA的计算发现方法研究受到了广泛的重视。文章对近几年计算发现miRNA的研究进展进行了综述, 根据计算发现方法的本质, 将计算发现方法归纳为5类, 分别是同源片段搜索方法、基于比较基因组学的预测方法、基于序列和结构特征打分的预测方法、结合作用靶标的预测方法和基于机器学习的预测方法, 并对各类方法的原理、核心思想、优点和局限性进行了分析, 最后探讨了进一步的发展方向。     

Machine learning methods without tears: a primer for ecologists

Machine learning methods, a family of statistical techniques with origins in the field of artificial intelligence, are recognized as holding great promise for the advancement of understanding and prediction about ecological phenomena. These modeling techniques are flexible enough to handle complex problems with multiple interacting elements and typically outcompete traditional approaches (e.g., generalized linear models), making them ideal for modeling ecological systems. Despite their inherent advantages, a review of the literature reveals only a modest use of these approaches in ecology as compared to other disciplines. One potential explanation for this lack of interest is that machine learning techniques do not fall neatly into the class of statistical modeling approaches with which most ecologists are familiar. In this paper, we provide an introduction to three machine learning approaches that can be broadly used by ecologists: classification and regression trees, artificial neural networks, and evolutionary computation. For each approach, we provide a brief background to the methodology, give examples of its application in ecology, describe model development and implementation, discuss strengths and weaknesses, explore the availability of statistical software, and provide an illustrative example. Although the ecological application of machine learning approaches has increased, there remains considerable skepticism with respect to the role of these techniques in ecology. Our review encourages a greater understanding of machin learning approaches and promotes their future application and utilization, while also providing a basis from which ecologists can make informed decisions about whether to select or avoid these approaches in their future modeling endeavors.     

Bridging the explanatory gaps: What can we learn from a biological agency perspective?

We begin this article by delineating the explanatory gaps left by prevailing gene-focused approaches in our understanding of phenotype determination, inheritance, and the origin of novel traits. We aim not to diminish the value of these approaches but to highlight where their implementation, despite best efforts, has encountered persistent limitations. We then discuss how each of these explanatory gaps can be addressed by expanding research foci to take into account biological agency—the capacity of living systems at various levels to participate in their own development, maintenance, and function by regulating their structures and activities in response to conditions they encounter. Here we aim to define formally what agency and agents are and—just as importantly—what they are not, emphasizing that agency is an empirical property connoting neither intention nor consciousness. Lastly, we discuss how incorporating agency helps to bridge explanatory gaps left by conventional approaches, highlight scientific fields in which implicit agency approaches are already proving valuable, and assess the opportunities and challenges of more systematically incorporating biological agency into research programs.     

Metabolic engineering of <Emphasis Type="Italic">Escherichia coli</Emphasis> for L-tryptophan production

The review summarizes the main approaches applied during the creation of L-tryptophan producing strains based on Escherichia coli for the industrial production of this amino acid. In addition, some prospects for the further improvement of tryptophan producers to increase their productivity and improve their technological characteristics based on systems metabolic engineering approaches are outlined in the review. These approaches can be used to obtain the producers of other aromatic amino acids and tryptophan precursors or derivatives.     

Infectomics: genomics and proteomics of microbial infections

The completion of genomic sequences is the greatest triumph of molecular reductionism since the discovery of the DNA double helix in 1953. However, the utility of reductionism is becoming limited and holistic approaches, including theories and techniques, are desperately needed in the postgenomic era. In the field of infectious diseases there is an urgent need for global approaches that can efficiently, precisely and integratively study structural and functional genomics and proteomics of microbial infections (infectomics). The combination of new (e.g. DNA and protein microarrays) and traditional approaches (e.g. cloning, PCR, gene knockout and knockin, and antisense) will help overcome the challenges we are facing today. We assume that the global phenotypic changes (infectomes) in microbes and their host during infections are encoded by the genomes of microbial pathogens and their hosts, expressed in certain environmental conditions devoted to specific microbe-host interactions. Global drug responses (pharmacomes) in microbes and their host can be detected by genomic and proteomic approaches. Genome-wide approaches to genotyping and phenotyping or expression profiling will eventually lead to global dissection of microbial pathogenesis, efficient and rapid diagnosis of infectious diseases, and the development of novel strategies to control infections. The key fundamental issue of infectious diseases is how to globally and integratively understand the interactions between microbial pathogens and their hosts by using infectomics. In this review, we focus on the events that are considered important in infectomics. Electronic Publication     

Engineering enzymes for non-aqueous solvents.

Enzymes may be redesigned to permit catalysis in non-aqueous solvents by engineering their amino acid sequences, thereby altering their physical and chemical properties to suit the new solvent environment. The interactions that contribute to protein stability in non-aqueous solvents are discussed in the context of attempting to identify possible approaches to constructing enzymes which exhibit enhanced stability in non-aqueous media. These approaches are illustrated by several examples where protein engineering has resulted in enzymes that are better suited for catalysis in organic solvents.     

Malaria control: present situation and need for historical research

A rapid review is made of the history of malaria control, calling attention to differences between the evolution of the technical concepts, the formulated strategies and their implementation. Particular emphasis is placed on the discussion of the present situation of the world malaria problem and the difficulties faced by many endemic countries in adopting a malaria control strategy, based on primary health care, while their services are vertically organized for the performance of routines, which are irrelevant for disease control. The present malaria control strategy recognizes local variability, but it is possible to identify a limited number of types of situations, likely to respond to similar approaches. The definition not only of the control approaches but also of their conditions of applicability will become more precise as experiences are accumulated and adequately documented from different types of epidemiological situations. It is postulated that historical research on the malaria control and public health approaches, with proper attention being given to their socioeconomic and political context, in the countries which succeeded in controlling endemic malaria, will make an important contribution to such a definition.     

Resolving the roles of body size and species identity in driving functional diversity

Efforts to characterize food webs have generated two influential approaches that reduce the complexity of natural communities. The traditional approach groups individuals based on their species identity, while recently developed approaches group individuals based on their body size. While each approach has provided important insights, they have largely been used in parallel in different systems. Consequently, it remains unclear how body size and species identity interact, hampering our ability to develop a more holistic framework that integrates both approaches. We address this conceptual gap by developing a framework which describes how both approaches are related to each other, revealing that both approaches share common but untested assumptions about how variation across size classes or species influences differences in ecological interactions among consumers. Using freshwater mesocosms with dragonfly larvae as predators, we then experimentally demonstrate that while body size strongly determined how predators affected communities, these size effects were species specific and frequently nonlinear, violating a key assumption underlying both size- and species-based approaches. Consequently, neither purely species- nor size-based approaches were adequate to predict functional differences among predators. Instead, functional differences emerged from the synergistic effects of body size and species identity. This clearly demonstrates the need to integrate size- and species-based approaches to predict functional diversity within communities.     

The ways to produce biologically safe marker-free transgenic plants

The review considers the basic strategies used to produce biologically safe marker-free transgenic plants and analyzes their advantages and disadvantages. The systems of positive and negative selection as safer approaches for transformant identification are briefly described. The application of co-transformation, transposition, and site-specific recombination for production of marker-free plants is described. Special attention is paid to novel approaches to create marker-free plants initially containing no selective genes in their genomes.     

Computational intelligence approaches for pattern discovery in biological systems

Biology, chemistry and medicine are faced by tremendous challenges caused by an overwhelming amount of data and the need for rapid interpretation. Computational intelligence (CI) approaches such as artificial neural networks, fuzzy systems and evolutionary computation are being used with increasing frequency to contend with this problem, in light of noise, non-linearity and temporal dynamics in the data. Such methods can be used to develop robust models of processes either on their own or in combination with standard statistical approaches. This is especially true for database mining, where modeling is a key component of scientific understanding. This review provides an introduction to current CI methods, their application to biological problems, and concludes with a commentary about the anticipated impact of these approaches in bioinformatics.     

A comparative study of FMS tool allocation and part type selection approaches for a varying part type mix

Hankins and Rovito (1984) examined the impact of different tool policies on cutting tool inventory levels and spindle utilization for a flexible manufacturing system (FMS). This study provides a broader perspective of the impact of tool allocation approaches on flow times, tardiness, percent of orders tardy, machine utilization, and robot utilization. Part type selection procedures have been suggested for the FMS prerelease planning problem. However, very little research has specifically evaluated the part type selection procedures across different tool allocation approaches. Also, with the exception of Stecke and Kim (1988, 1991) no other known study has provided any insights on what tool allocation approaches are appropriate when processing different mixes of part types. This research is devoted to addressing those issues. Three tool allocation approaches, three production scheduling rules, and three levels of part mix are evaluated in this study through a similation model of a flexible manufacturing system. The specific impacts of the tool approaches, their interaction effects with the part type selection rules, and their effectiveness at different part type mix levels are provided through the use of a regression metamodel.     

Modeling and formal analysis of meta-ecosystems with dynamic structure using graph transformation

The dynamics of meta-ecosystems are of crucial importance and, therefore, in theoretical ecology, various model-based approaches are employed to analyze their internal effects and to derive predictions on their future behavior and interactions. However, existing model-based approaches are limited in their support for the modeling of space structure dynamics of meta-ecosystems growing and shrinking in size over time due to intrinsic and extrinsic process as well as neighboring opportunities.In this paper, we employ the formal technique of Graph Transformation (GT) to qualitatively model the space structure dynamics and ecological dynamics of meta-ecosystems. In such GTSs, states of meta-ecosystems are given by graphs and evolutions from one state to the next are derived by applying GT rules describing local modifications of graphs. As a case study, we introduce GTSs capturing the space structure dynamics and ecological dynamics of three different savanna meta-ecosystems, which vary in their space structure dynamics. We then consider several qualitative analysis technique for GTSs such as for the existence of structural stabilities and apply them in our case study. We conclude that GTSs provide a complementary avenue to that of existing approaches to rigorously model and analyze qualitative ecological phenomena with adequate support for capturing not only ecological effects but also various forms of space structure dynamics.     

Twelve ways to confirm targets of activity-based probes in plants

Activity-based probes are powerful tools to interrogate the functional proteome. Their covalent and often irreversible labeling of proteins facilitates the purification, identification and quantification of labeled proteins. However, the detection of labeled proteins often requires a confirmation, especially when unexpected proteins are identified, or to unravel fluorescent activity profiles. Here, we review twelve approaches towards target confirmation, grouped in approaches by direct target detection, target expression or target depletion. We discuss their proper use and limitations and illustrate these approaches with examples from plant science.     

New concepts in synaptic biology derived from single-molecule imaging

Single-molecule approaches give access to the full distribution of molecule behaviors and overcome the averaging intrinsic to bulk measurement methods. They allow access to complex processes where a given molecule can have heterogeneous properties over time. Recent developments in single-molecule imaging technologies have been followed by their wide application in cellular biology and are leading to the unraveling of new mechanisms related to molecular movements. They are shaping new concepts in the dynamic equilibria of complex biological macromolecular assemblies such as synapses. These advances were made possible thanks to improvements in visualization approaches combined with new strategies to label proteins with nanoprobes. In this primer, we will review the different approaches used to track single molecules in live neurons, compare them to bulk measurements, and discuss the different concepts that have emerged from their application to synaptic biology.     

Coordinating the traditional and modern approaches in the systematics of insects

The problem of coordinating the traditional and modern approaches to systematics is ever-lasting due to the continuous development and enrichment of our knowledge of biodiversity, means of analysis, and concepts. Comparative morphology was and still is the cornerstone of studies of insect taxonomy. It gives the most extensive and diverse information on the organisms studied, particularly when it is supported by the data on embryology and functional morphology as well as by analysis of adaptive significance of morphological characters. The limitations of this approach are often related to the presence of homoplasies, reversions, etc. Comparative paleontology is the only approach providing direct evidence of the historical succession of taxa and their characters. However, this approach is fully applicable only to some groups due to the specific features of their morphology and taphonomy. All the modern approaches (molecular, cytogenetic, etc.) are very informative but also have their own limitations; they should not be contrasted with the traditional approaches and certainly should not replace them. The traditional approaches do not become obsolete; it is only their comparative importance in the set of taxonomic tools that may be reevaluated. No single approach can be considered universal for an unambiguous reconstruction of phylogeny and substantiation of the natural system of taxa. Each approach has its own advantages and limitations, and only combined use of different approaches allows a broader range of the problems to be solved. Different approaches may prevail in the studies of different groups of insects and at different levels of taxonomic hierarchy. The intuition of the taxonomist, which is so often criticized by the followers of “objective” systematics, is based on taxonomic experience and scope of knowledge of a particular taxon. It does not imply a subjective bias, but allows the taxonomist to choose the instruments adequate to a particular case.