Differences in Characteristics of Reserve Network Selection Using Population Data Versus Habitat Surrogates

The use of species data versus environmental surrogates used in lieu of species data in systematic reserve site selection is still highly debated. We analyse in a case study whether and how the results of reserve network selection are affected by the use of species data versus habitat surrogates (habitat models) for qualitative (presence/absence) and quantitative (population size/habitat quality) information. In a model region, the post-mining landscape south of Leipzig/Germany, we used iterative algorithms to select a network for 29 animal target species from a basic set of 127 sites. The network results differ markedly for the two information types: depending on the representation goal, 18–45% of the selected sites chosen in response to one information type do not appear in the results for the other type. Given the availability of quantitative and hence deeper information, evaluation rules can be used to filter out the best habitats and the largest populations. In our model study, 0–40% less suitable areas were selected when instead of quantitative details only qualitative data were used. In view of various advantages and limitations of the two information types, we propose improving the methodological approach to the selection of networks for animal species by combining different information types.     

青岛市湿地生态网络评价与构建

在快速城市化背景下,城市社会经济发展与空间扩张不可避免,往往造成栖息地数量、面积、质量锐减、栖息地之间联系破碎等后果。此外,全球气候变化也加剧了栖息地恶化过程。生态网络通过保护恢复重点栖息地及构建栖息地之间物质、信息及能量传播的连接廊道,在整体上维持生态系统的动态平衡。青岛市位于东部沿海经济发达地区,地处海洋与陆地生态系统的交汇处,生态环境极为脆弱,易遭受外力破坏,且难以恢复。选取青岛地区湿地作为研究对象,在RS,GIS技术支持下,基于最小成本路径法构建青岛市湿地生态网络,并利用关联长度指数、介数指数对网络的整体结构及斑块重要程度进行评价。结果表明,不同路径成本阈值水平下,网络的连通性有很大差别;核心斑块中,胶州湾湿地、大沽河等具有较高重要程度;歇脚石斑块中,重要程度较高的斑块位于以胶州湾湿地为中心,以胶莱河、潍河河口,白马河、潮河河口及丁字湾为端点的连线上。在青岛市及周边地区划定"一心、二轴、一环"的湿地生态网络控制框架的策略,为青岛市湿地生态系统保护与城市发展空间选择提供科学方法与量化依据。     

A cooperative particle swarm optimizer with migration of heterogeneous probabilistic models

Particle Swarm Optimization (PSO) is a stochastic optimization approach that originated from simulations of bird flocking, and that has been successfully used in many applications as an optimization tool. Estimation of distribution algorithms (EDAs) are a class of evolutionary algorithms which perform a two-step process: building a probabilistic model from which good solutions may be generated and then using this model to generate new individuals. Two distinct research trends that emerged in the past few years are the hybridization of PSO and EDA algorithms and the parallelization of EDAs to exploit the idea of exchanging the probabilistic model information. In this work, we propose the use of a cooperative PSO/EDA algorithm based on the exchange of heterogeneous probabilistic models. The model is heterogeneous because the cooperating PSO/EDA algorithms use different methods to sample the search space. Three different exchange approaches are tested and compared in this work. In all these approaches, the amount of information exchanged is adapted based on the performance of the two cooperating swarms. The performance of the cooperative model is compared to the existing state-of-the-art PSO cooperative approaches using a suite of well-known benchmark optimization functions.     

Theory of interface: Category theory,directed networks and evolution of biological networks

Biological networks have two modes. The first mode is static: a network is a passage on which something flows. The second mode is dynamic: a network is a pattern constructed by gluing functions of entities constituting the network. In this paper, first we discuss that these two modes can be associated with the category theoretic duality (adjunction) and derive a natural network structure (a path notion) for each mode by appealing to the category theoretic universality. The path notion corresponding to the static mode is just the usual directed path. The path notion for the dynamic mode is called lateral path which is the alternating path considered on the set of arcs. Their general functionalities in a network are transport and coherence, respectively. Second, we introduce a betweenness centrality of arcs for each mode and see how the two modes are embedded in various real biological network data. We find that there is a trade-off relationship between the two centralities: if the value of one is large then the value of the other is small. This can be seen as a kind of division of labor in a network into transport on the network and coherence of the network. Finally, we propose an optimization model of networks based on a quality function involving intensities of the two modes in order to see how networks with the above trade-off relationship can emerge through evolution. We show that the trade-off relationship can be observed in the evolved networks only when the dynamic mode is dominant in the quality function by numerical simulations. We also show that the evolved networks have features qualitatively similar to real biological networks by standard complex network analysis.     

Cross-validation of protein structural class prediction using statistical clustering and neural networks.

We present an approach to predicting protein structural class that uses amino acid composition and hydrophobic pattern frequency information as input to two types of neural networks: (1) a three-layer back-propagation network and (2) a learning vector quantization network. The results of these methods are compared to those obtained from a modified Euclidean statistical clustering algorithm. The protein sequence data used to drive these algorithms consist of the normalized frequency of up to 20 amino acid types and six hydrophobic amino acid patterns. From these frequency values the structural class predictions for each protein (all-alpha, all-beta, or alpha-beta classes) are derived. Examples consisting of 64 previously classified proteins were randomly divided into multiple training (56 proteins) and test (8 proteins) sets. The best performing algorithm on the test sets was the learning vector quantization network using 17 inputs, obtaining a prediction accuracy of 80.2%. The Matthews correlation coefficients are statistically significant for all algorithms and all structural classes. The differences between algorithms are in general not statistically significant. These results show that information exists in protein primary sequences that is easily obtainable and useful for the prediction of protein structural class by neural networks as well as by standard statistical clustering algorithms.     

De Novo Assembly of the Complete Genome of an Enhanced Electricity-Producing Variant of Geobacter sulfurreducens Using Only Short Reads

State-of-the-art DNA sequencing technologies are transforming the life sciences due to their ability to generate nucleotide sequence information with a speed and quantity that is unapproachable with traditional Sanger sequencing. Genome sequencing is a principal application of this technology, where the ultimate goal is the full and complete sequence of the organism of interest. Due to the nature of the raw data produced by these technologies, a full genomic sequence attained without the aid of Sanger sequencing has yet to be demonstrated.We have successfully developed a four-phase strategy for using only next-generation sequencing technologies (Illumina and 454) to assemble a complete microbial genome de novo. We applied this approach to completely assemble the 3.7 Mb genome of a rare Geobacter variant (KN400) that is capable of unprecedented current production at an electrode. Two key components of our strategy enabled us to achieve this result. First, we integrated the two data types early in the process to maximally leverage their complementary characteristics. And second, we used the output of different short read assembly programs in such a way so as to leverage the complementary nature of their different underlying algorithms or of their different implementations of the same underlying algorithm.The significance of our result is that it demonstrates a general approach for maximizing the efficiency and success of genome assembly projects as new sequencing technologies and new assembly algorithms are introduced. The general approach is a meta strategy, wherein sequencing data are integrated as early as possible and in particular ways and wherein multiple assembly algorithms are judiciously applied such that the deficiencies in one are complemented by another.     

Linking landscape-level change to habitat quality: an evaluation of restoration actions on the freshwater habitat of spring-run Chinook salmon

1. Conservation planning is often hampered by the lack of causal quantitative links between landscape characteristics, restoration actions and habitat conditions that impact the status of imperilled species. Here we present a first step toward linking actions on the landscape to the population status of endangered stream‐type Chinook salmon (Oncorhynchus tshawytscha). 2. We developed relationships between land use, landscape characteristics and freshwater habitat of spring Chinook salmon in the Wenatchee River basin. Available data allowed us to find relationships that described water temperatures at several life stages (prespawning, egg incubation and summer rearing) and substratum characteristics, including fine sediments, cobble and embeddedness. Predictors included altitude, gradient, mean annual precipitation, total and riparian forest cover, road density, impervious surface and alluvium. We used a model averaging approach to account for parameter and model selection uncertainty. Key predictors were total forest cover and impervious surface area for prespawning and summer rearing temperatures; precipitation and stream gradients were important predictors of the percent of fine sediments in stream substrata. 3. We estimated habitat conditions using these relationships in three alternative landscape scenarios: historical, no restoration and one that included a set of restoration actions from local conservation planning. We found that prespawning and summer temperatures were estimated to be slightly higher historically relative to current conditions in dry sparsely forested areas, but lower in some important Chinook salmon spawning and rearing areas and lower in those locations under the restoration scenario. Fine sediments were lower in the historical scenario and were reduced as a consequence of restoration actions in two areas currently unoccupied by Chinook salmon that contain reaches with some potential for high quality spawning and rearing. Cobble and embeddedness in general were predicted to be higher historically and changed little as a result of restoration actions relative to current conditions. 4. This modelling framework converts suites of restoration actions into changes in habitat condition, thereby enabling restoration planners to evaluate alternative combinations of proposed actions. It also provides inputs to models linking habitat conditions to population status. This approach represents a first step in estimating impacts of restoration strategies, and can provide key information for conservation managers and planners.     

Highlighting Entanglement of Cultures via Ranking of Multilingual Wikipedia Articles

How different cultures evaluate a person? Is an important person in one culture is also important in the other culture? We address these questions via ranking of multilingual Wikipedia articles. With three ranking algorithms based on network structure of Wikipedia, we assign ranking to all articles in 9 multilingual editions of Wikipedia and investigate general ranking structure of PageRank, CheiRank and 2DRank. In particular, we focus on articles related to persons, identify top 30 persons for each rank among different editions and analyze distinctions of their distributions over activity fields such as politics, art, science, religion, sport for each edition. We find that local heroes are dominant but also global heroes exist and create an effective network representing entanglement of cultures. The Google matrix analysis of network of cultures shows signs of the Zipf law distribution. This approach allows to examine diversity and shared characteristics of knowledge organization between cultures. The developed computational, data driven approach highlights cultural interconnections in a new perspective.Dated: June 26, 2013     

Gene expression network reconstruction by convex feature selection when incorporating genetic perturbations

Cellular gene expression measurements contain regulatory information that can be used to discover novel network relationships. Here, we present a new algorithm for network reconstruction powered by the adaptive lasso, a theoretically and empirically well-behaved method for selecting the regulatory features of a network. Any algorithms designed for network discovery that make use of directed probabilistic graphs require perturbations, produced by either experiments or naturally occurring genetic variation, to successfully infer unique regulatory relationships from gene expression data. Our approach makes use of appropriately selected cis-expression Quantitative Trait Loci (cis-eQTL), which provide a sufficient set of independent perturbations for maximum network resolution. We compare the performance of our network reconstruction algorithm to four other approaches: the PC-algorithm, QTLnet, the QDG algorithm, and the NEO algorithm, all of which have been used to reconstruct directed networks among phenotypes leveraging QTL. We show that the adaptive lasso can outperform these algorithms for networks of ten genes and ten cis-eQTL, and is competitive with the QDG algorithm for networks with thirty genes and thirty cis-eQTL, with rich topologies and hundreds of samples. Using this novel approach, we identify unique sets of directed relationships in Saccharomyces cerevisiae when analyzing genome-wide gene expression data for an intercross between a wild strain and a lab strain. We recover novel putative network relationships between a tyrosine biosynthesis gene (TYR1), and genes involved in endocytosis (RCY1), the spindle checkpoint (BUB2), sulfonate catabolism (JLP1), and cell-cell communication (PRM7). Our algorithm provides a synthesis of feature selection methods and graphical model theory that has the potential to reveal new directed regulatory relationships from the analysis of population level genetic and gene expression data.     

Restoring species through reintroductions: strategies for source population selection

Only a quarter of reintroduction programs succeed in restoring a self‐sustaining population of an extirpated species. Optimal source population selection for restoration efforts can increase the fitness of translocated individuals and improve reintroduction success. Here, we describe the support for two strategies for selecting source populations: pre‐existing adaptation and adaptive potential. The pre‐existing adaptation strategy focuses on source populations with a high frequency of genotypes that confer adaptations, and within this strategy we detail the ancestry matching approach and environment matching approach. The adaptive potential strategy focuses on source populations with high heritable genetic variation that confer the potential to adapt, and within this strategy we detail the single source population approach and multiple source population approach. We review empirical tests of the different approaches, and find stronger support for the pre‐existing adaptation strategy than the adaptive potential strategy. We provide a framework for source population selection based on the two strategies, highlighting the importance of gathering information on key environment features in the source and restoration locations, as well as detail the knowledge gaps. Filling these knowledge gaps is important for validating and potentially revising our proposed framework, and ultimately improving the success rate of restoring extirpated populations.     

Bayesian networks as a novel tool to enhance interpretability and predictive power of ecological models

In today's world, it is becoming increasingly important to have the tools to understand, and ultimately to predict, the response of ecosystems to disturbance. However, understanding such dynamics is not simple. Ecosystems are a complex network of species interactions, and therefore any change to a population of one species will have some degree of community level effect. In recent years, the use of Bayesian networks (BNs) has seen successful applications in molecular biology and ecology, where they were able to recover plausible links in the respective systems they were applied to. The recovered network also comes with a quantifiable metric of interaction strength between variables. While the latter is an invaluable piece of information in ecology, an unexplored application of BNs would be using them as a novel variable selection tool in the training of predictive models. To this end, we evaluate the potential usefulness of BNs in two aspects: (1) we apply BN inference on species abundance data from a rocky shore ecosystem, a system with well documented links, to test the ecological validity of the revealed network; and (2) we evaluate BNs as a novel variable selection method to guide the training of an artificial neural network (ANN). Here, we demonstrate that not only was this approach able to recover meaningful species interactions networks from ecological data, but it also served as a meaningful tool to inform the training of predictive models, where there was an improvement in predictive performance in models with BN variable selection. Combining these results, we demonstrate the potential of this novel application of BNs in enhancing the interpretability and predictive power of ecological models; this has general applicability beyond the studied system, to ecosystems where existing relationships between species and other functional components are unknown.     

Immune function responds to selection for cuticular colour in Tenebrio molitor

Cuticular colour in the mealworm beetle (Tenebrio molitor) is a quantitative trait, varying from tan to black. Population level variation in cuticular colour has been linked to pathogen resistance in this species and in several other insects: darker individuals are more resistant to pathogens. Given that cuticular colour has a heritable component, we have taken an experimental evolution approach: we selected 10 lines for black and 10 lines for tan adult cuticular phenotypes over at least six generations and measured the correlated responses to selection in a range of immune effector systems. Our results show that two immune parameters related to resistance (haemocyte density and pre-immune challenge activity of phenoloxidase (PO)) were significantly higher in selection lines of black beetles compared to tan lines. This may help to explain increased resistance to pathogens in darker individuals. Cuticular colour is dependent upon melanin production, which requires the enzyme PO that is present in its inactive form inside haemocytes. Thus, the observed correlated response to selection upon cuticular colour and immune variables probably results from these traits' shared dependence on melanin production.     

Optimization of a crossing system using mate selection

A simple model based on one single identified quantitative trait locus (QTL) in a two-way crossing system was used to demonstrate the power of mate selection algorithms as a natural means of opportunistic line development for optimization of crossbreeding programs over multiple generations. Mate selection automatically invokes divergent selection in two parental lines for an over-dominant QTL and increased frequency of the favorable allele toward fixation in the sire-line for a fully-dominant QTL. It was concluded that an optimal strategy of line development could be found by mate selection algorithms for a given set of parameters such as genetic model of QTL, breeding objective and initial frequency of the favorable allele in the base populations, etc. The same framework could be used in other scenarios, such as programs involving crossing to exploit breed effects and heterosis. In contrast to classical index selection, this approach to mate selection can optimize long-term responses.     

Exploiting temporal network structures of human interaction to effectively immunize populations

Decreasing the number of people who must be vaccinated to immunize a community against an infectious disease could both save resources and decrease outbreak sizes. A key to reaching such a lower threshold of immunization is to find and vaccinate people who, through their behavior, are more likely than average to become infected and to spread the disease further. Fortunately, the very behavior that makes these people important to vaccinate can help us to localize them. Earlier studies have shown that one can use previous contacts to find people that are central in static contact networks. However, real contact patterns are not static. In this paper, we investigate if there is additional information in the temporal contact structure for vaccination protocols to exploit. We answer this affirmative by proposing two immunization methods that exploit temporal correlations and showing that these methods outperform a benchmark static-network protocol in four empirical contact datasets under various epidemic scenarios. Both methods rely only on obtainable, local information, and can be implemented in practice. For the datasets directly related to contact patterns of potential disease spreading (of sexually-transmitted and nosocomial infections respectively), the most efficient protocol is to sample people at random and vaccinate their latest contacts. The network datasets are temporal, which enables us to make more realistic evaluations than earlier studies--we use only information about the past for the purpose of vaccination, and about the future to simulate disease outbreaks. Using analytically tractable models, we identify two temporal structures that explain how the protocols earn their efficiency in the empirical data. This paper is a first step towards real vaccination protocols that exploit temporal-network structure--future work is needed both to characterize the structure of real contact sequences and to devise immunization methods that exploit these.     

Trees and/or networks to display intraspecific DNA sequence variation?

Phylogenetic trees and networks are both used in the scientific literature to display DNA sequence variation at the intraspecific level. Should we rather use trees or networks? I argue that the process of inferring the most parsimonious genealogical relationships among a set of DNA sequences should be dissociated from the problem of displaying this information in a graph. A network graph is probably more appropriate than a strict consensus tree if many alternative, equally most parsimonious, genealogies are to be included. Within the maximum parsimony framework, current phylogenetic inference and network‐building algorithms are both unable to guarantee the finding of all most parsimonious (MP) connections. In fact, each approach can find MP connections that the other does not. Although it should be possible to improve at least the maximum parsimony approach, current implementations of these algorithms are such that it is advisable to use both approaches to increase the probability of finding all possible MP connections among a set of DNA sequences.     

Evaluating functional network inference using simulations of complex biological systems

MOTIVATION: Although many network inference algorithms have been presented in the bioinformatics literature, no suitable approach has been formulated for evaluating their effectiveness at recovering models of complex biological systems from limited data. To overcome this limitation, we propose an approach to evaluate network inference algorithms according to their ability to recover a complex functional network from biologically reasonable simulated data. RESULTS: We designed a simulator to generate data representing a complex biological system at multiple levels of organization: behaviour, neural anatomy, brain electrophysiology, and gene expression of songbirds. About 90% of the simulated variables are unregulated by other variables in the system and are included simply as distracters. We sampled the simulated data at intervals as one would sample from a biological system in practice, and then used the sampled data to evaluate the effectiveness of an algorithm we developed for functional network inference. We found that our algorithm is highly effective at recovering the functional network structure of the simulated system-including the irrelevance of unregulated variables-from sampled data alone. To assess the reproducibility of these results, we tested our inference algorithm on 50 separately simulated sets of data and it consistently recovered almost perfectly the complex functional network structure underlying the simulated data. To our knowledge, this is the first approach for evaluating the effectiveness of functional network inference algorithms at recovering models from limited data. Our simulation approach also enables researchers a priori to design experiments and data-collection protocols that are amenable to functional network inference.     

Using Pareto optimality to explore the topology and dynamics of the human connectome

Graph theory has provided a key mathematical framework to analyse the architecture of human brain networks. This architecture embodies an inherently complex relationship between connection topology, the spatial arrangement of network elements, and the resulting network cost and functional performance. An exploration of these interacting factors and driving forces may reveal salient network features that are critically important for shaping and constraining the brain''s topological organization and its evolvability. Several studies have pointed to an economic balance between network cost and network efficiency with networks organized in an ‘economical’ small-world favouring high communication efficiency at a low wiring cost. In this study, we define and explore a network morphospace in order to characterize different aspects of communication efficiency in human brain networks. Using a multi-objective evolutionary approach that approximates a Pareto-optimal set within the morphospace, we investigate the capacity of anatomical brain networks to evolve towards topologies that exhibit optimal information processing features while preserving network cost. This approach allows us to investigate network topologies that emerge under specific selection pressures, thus providing some insight into the selectional forces that may have shaped the network architecture of existing human brains.     

基于表达及网络拓扑结构挖掘动脉粥样硬化风险疾病基因

基于功能基因组信息、网络拓扑结构信息整合分析方法,利用基因表达谱数据和蛋白质互作数据挖掘动脉粥样硬化(AS)风险疾病基因,为从基因组层面研究动脉粥样硬化提供了新的视角．经过差异表达分析,支持向量机(SVM)的机器学习方法双重筛选,可以鉴别出可信度水平较高的风险疾病基因,对于研究动脉粥样硬化疾病基因在网络中的拓扑性质,建立基因与疾病发生发展过程的联系,提供了新的思路．得到了巨噬细胞样本中59个风险疾病基因,泡沫细胞中61个风险疾病基因．这些风险基因与已知疾病基因共享大部分动脉粥样硬化病变相关生物学过程及信号通路．并应用到对其他复杂疾病致病机理的研究中．