Control Centrality and Hierarchical Structure in Complex Networks

We introduce the concept of control centrality to quantify the ability of a single node to control a directed weighted network. We calculate the distribution of control centrality for several real networks and find that it is mainly determined by the network’s degree distribution. We show that in a directed network without loops the control centrality of a node is uniquely determined by its layer index or topological position in the underlying hierarchical structure of the network. Inspired by the deep relation between control centrality and hierarchical structure in a general directed network, we design an efficient attack strategy against the controllability of malicious networks.     

Calculating structural complexity in phylogenies using ancestral ontologies

Complexity is an important aspect of evolutionary biology, but there are many reasonable concepts of complexity, and its objective measurement is an elusive matter. Here we develop a simple measure of complexity based on counts of elements, incorporating the hierarchical information as represented in anatomical ontologies. Neomorphic and transformational characters are used to identify novelties and individuated morphological regions, respectively. By linking the characters to terms in an anatomical ontology a node‐driven approach is implemented, where a node ontology and a complexity score are inferred from the optimization of individual characters on each ancestral or terminal node. From the atomized vector of character scorings, the anatomical ontology is used to integrate the hierarchical structure of morphology in terminals and ancestors. These node ontologies are used to calculate a measure of complexity that can be traced on phylogenetic trees and is harmonious with usual phylogenetic operations. This strategy is compared with a terminal‐driven approach, in which the complexity scores are calculated only for terminals, and optimized as a continuous character on the internal nodes. These ideas are applied to a real dataset of 166 araneomorph spider species scored for 393 characters, using Spider Ontology (SPD, https://bioportal.bioontology.org/ontologies/SPD ); complexity scores and transitions are calculated for each node and branch, respectively. This result in a distribution of transitions skewed towards simplification; the transitions in complexity have no apparent correlation with character branch lengths. The node‐driven and terminal‐driven estimations are generally correlated in the complexity scores, but have higher divergence in the transition values. The structure of the ontology is used to provide complexity scores for organ systems and body parts of the focal groups.     

Recognition of handwritten similar Chinese characters by self-growing probabilistic decision-based neural network

Recognition of similar (confusion) characters is a difficult problem in optical character recognition (OCR). In this paper, we introduce a neural network solution that is capable of modeling minor differences among similar characters, and is robust to various personal handwriting styles. The Self-growing Probabilistic Decision-based Neural Network (SPDNN) is a probabilistic type neural network, which adopts a hierarchical network structure with nonlinear basis functions and a competitive credit-assignment scheme. Based on the SPDNN model, we have constructed a three-stage recognition system. First, a coarse classifier determines a character to be input to one of the pre-defined subclasses partitioned from a large character set, such as Chinese mixed with alphanumerics. Then a character recognizer determines the input image which best matches the reference character in the subclass. Lastly, the third module is a similar character recognizer, which can further enhance the recognition accuracy among similar or confusing characters. The prototype system has demonstrated a successful application of SPDNN to similar handwritten Chinese recognition for the public database CCL/HCCR1 (5401 characters x200 samples). Regarding performance, experiments on the CCL/HCCR1 database produced 90.12% recognition accuracy with no rejection, and 94.11% accuracy with 6.7% rejection, respectively. This recognition accuracy represents about 4% improvement on the previously announced performance. As to processing speed, processing before recognition (including image preprocessing, segmentation, and feature extraction) requires about one second for an A4 size character image, and recognition consumes approximately 0.27 second per character on a Pentium-100 based personal computer, without use of any hardware accelerator or co-processor.     

Identification of apomorphies and the role of groundpatterns in molecular systematics

Putative apomorphic character states are the only relevant phylogenetic signal contained in sets of sequence data. Using the sequence position as a character, a way to identify putative apomorphies prior to phylogenetic analysis is proposed. It is shown that distance-matrix methods use trivial characters. The concept of the asymmetrical split is presented for determination of character polarity. It is furthermore argued that groundpatterns (node sequences) should be reconstructed prior to the study of relationships between taxa of high phylogenetic age. The 'evolutionary noise'contained in groundpatterns can be illustrated with a network of distances using a split-decomposition analysis.     

One size does not fit all: Customizing MCMC methods for hierarchical models using NIMBLE

Improved efficiency of Markov chain Monte Carlo facilitates all aspects of statistical analysis with Bayesian hierarchical models. Identifying strategies to improve MCMC performance is becoming increasingly crucial as the complexity of models, and the run times to fit them, increases. We evaluate different strategies for improving MCMC efficiency using the open‐source software NIMBLE (R package nimble) using common ecological models of species occurrence and abundance as examples. We ask how MCMC efficiency depends on model formulation, model size, data, and sampling strategy. For multiseason and/or multispecies occupancy models and for N‐mixture models, we compare the efficiency of sampling discrete latent states vs. integrating over them, including more vs. fewer hierarchical model components, and univariate vs. block‐sampling methods. We include the common MCMC tool JAGS in comparisons. For simple models, there is little practical difference between computational approaches. As model complexity increases, there are strong interactions between model formulation and sampling strategy on MCMC efficiency. There is no one‐size‐fits‐all best strategy, but rather problem‐specific best strategies related to model structure and type. In all but the simplest cases, NIMBLE's default or customized performance achieves much higher efficiency than JAGS. In the two most complex examples, NIMBLE was 10–12 times more efficient than JAGS. We find NIMBLE is a valuable tool for many ecologists utilizing Bayesian inference, particularly for complex models where JAGS is prohibitively slow. Our results highlight the need for more guidelines and customizable approaches to fit hierarchical models to ensure practitioners can make the most of occupancy and other hierarchical models. By implementing model‐generic MCMC procedures in open‐source software, including the NIMBLE extensions for integrating over latent states (implemented in the R package nimbleEcology), we have made progress toward this aim.     

网络云交互技术辅助外科实践教学的创新应用探索

目的:探索传统教学模式结合网络云交互技术在外科实践教学中的应用效果。方法:于我校2011级五年制临床医学专业本科生中选取76人,随机平均分为两组,分别采取单纯传统大课教学模式、传统教学结合网络云交互技术教学模式;课程结束后进行闭卷理论考试和实践操作考试,综合两项成绩进行评定。同时向两组学员发放调查问卷,以此评价使用网络云交互技术辅助教学的效果。结果:采取传统教学结合网络云交互技术教学模式组的成绩优于采取单纯传统教学模式组,两组成绩差异具有统计学意义(P0.05)。结论:在传统教学模式的基础上添加网络云交互技术更能激发学生的学习兴趣,大大提高了学习效率,更有利于本科生外科实践的教学以及教学资源的整合及教学档案的建立。     

Facilitating memory for novel characters by reducing neural repetition suppression in the left fusiform cortex

Background

The left midfusiform and adjacent regions have been implicated in processing and memorizing familiar words, yet its role in memorizing novel characters has not been well understood.

Methodology/Principal Findings

Using functional MRI, the present study examined the hypothesis that the left midfusiform is also involved in memorizing novel characters and spaced learning could enhance the memory by enhancing the left midfusiform activity during learning. Nineteen native Chinese readers were scanned while memorizing the visual form of 120 Korean characters that were novel to the subjects. Each character was repeated four times during learning. Repetition suppression was manipulated by using two different repetition schedules: massed learning and spaced learning, pseudo-randomly mixed within the same scanning session. Under the massed learning condition, the four repetitions were consecutive (with a jittered inter-repetition interval to improve the design efficiency). Under the spaced learning condition, the four repetitions were interleaved with a minimal inter-repetition lag of 6 stimuli. Spaced learning significantly improved participants'' performance during the recognition memory test administered one hour after the scan. Stronger left midfusiform and inferior temporal gyrus activities during learning (summed across four repetitions) were associated with better memory of the characters, based on both within- and cross-subjects analyses. Compared to massed learning, spaced learning significantly reduced neural repetition suppression and increased the overall activities in these regions, which were associated with better memory for novel characters.

Conclusions/Significance

These results demonstrated a strong link between cortical activity in the left midfusiform and memory for novel characters, and thus challenge the visual word form area (VWFA) hypothesis. Our results also shed light on the neural mechanisms of the spacing effect in memorizing novel characters.     

Lewis Carroll's Doublets Net of English Words: Network Heterogeneity in a Complex System

Lewis Carroll''s English word game Doublets is represented as a system of networks with each node being an English word and each connectivity edge confirming that its two ending words are equal in letter length, but different by exactly one letter. We show that this system, which we call the Doublets net, constitutes a complex body of linguistic knowledge concerning English word structure that has computable multiscale features. Distributed morphological, phonological and orthographic constraints and the language''s local redundancy are seen at the node level. Phonological communities are seen at the network level. And a balancing act between the language''s global efficiency and redundancy is seen at the system level. We develop a new measure of intrinsic node-to-node distance and a computational algorithm, called community geometry, which reveal the implicit multiscale structure within binary networks. Because the Doublets net is a modular complex cognitive system, the community geometry and computable multi-scale structural information may provide a foundation for understanding computational learning in many systems whose network structure has yet to be fully analyzed.     

Stability of characters and construction of phylogenetic trees.

Parsimony methods infer phylogenetic trees by minimizing number of character changes required to explain observed character states. From the perspective of applicability of parsimony methods, it is important to assess whether the characters used to infer phylogeny are likely to provide a correct tree. We introduce a graph theoretical characterization that helps to assess whether given set of characters is appropriate to use with parsimony methods. Given a set of characters and a set of taxa, we construct a network called character overlap graph. We show that the character overlap graph for characters that are appropriate to use in parsimony methods is characterized by significant under-representation of subnetworks known as holes, and provide a validation for this observation. This characterization explains success in constructing evolutionary trees using parsimony method for some characters (e.g., protein domains) and lack of such success for other characters (e.g., introns). In the latter case, the understanding of obstacles to applying parsimony methods in a direct way has lead us to a new approach for detecting inconsistent and/or noisy data. Namely, we introduce the concept of stable characters which is similar but less restrictive than the well known concept of pairwise compatible characters. Application of this approach to introns produces the evolutionary tree consistent with the Coelomata hypothesis.     

Statistics of weighted brain networks reveal hierarchical organization and Gaussian degree distribution

Whole brain weighted connectivity networks were extracted from high resolution diffusion MRI data of 14 healthy volunteers. A statistically robust technique was proposed for the removal of questionable connections. Unlike most previous studies our methods are completely adapted for networks with arbitrary weights. Conventional statistics of these weighted networks were computed and found to be comparable to existing reports. After a robust fitting procedure using multiple parametric distributions it was found that the weighted node degree of our networks is best described by the normal distribution, in contrast to previous reports which have proposed heavy tailed distributions. We show that post-processing of the connectivity weights, such as thresholding, can influence the weighted degree asymptotics. The clustering coefficients were found to be distributed either as gamma or power-law distribution, depending on the formula used. We proposed a new hierarchical graph clustering approach, which revealed that the brain network is divided into a regular base-2 hierarchical tree. Connections within and across this hierarchy were found to be uncommonly ordered. The combined weight of our results supports a hierarchically ordered view of the brain, whose connections have heavy tails, but whose weighted node degrees are comparable.     

Understanding molecular genetics through a drawing-based activity

The activity uses drawings typically found in textbooks, engaging students in physical and mental activities such as drawing, painting and figure completion — as well as finding missing words and answering guiding questions. The activity deals with three topics: the structure of DNA, DNA replication and protein synthesis. In this study we investigated students' understanding of genetics in two different groups: one used the drawingbased activity integrated in the traditional learning (experimental group) and the other, the control group, received only traditional learning. Achievements of the experimental group were compared to the achievements of the control group, using a written questionnaire. Analysis of the post-test showed that the mean score of the experimental group was significantly higher than the mean score of the control group, suggesting that engaging students in physical and mental activities promote students' learning. This positive trend was also reflected in students' responses in personal interviews. Most of the students reported they felt that the activity helped them to organise and better understand the subject matter, especially the translation process.     

Spectral partitioning of phylogenetic data sets based on compatibility

We describe two new methods to partition phylogenetic data sets of discrete characters based on pairwise compatibility. The partitioning methods make no assumptions regarding the phylogeny, model of evolution, or characteristics of the data. The methods first build a compatibility graph, in which each node represents a character in the data set. Edges in the compatibility graph may represent strict compatibility of characters or they may be weighted based on a fractional compatibility scoring procedure that measures how close the characters are to being compatible. Given the desired number of partitions, the partitioning methods then seek to cluster the characters with the highest average pairwise compatibility, so that characters in each cluster are more compatible with each other than they are with characters in the other cluster(s). Partitioning according to these criteria is computationally intractable (NP-hard); however, spectral methods can quickly provide high-quality solutions. We demonstrate that the spectral partitioning effectively identifies characters with different evolutionary histories in simulated data sets, and it is better at highlighting phylogenetic conflict within empirical data sets than previously used partitioning methods.     

生物技术专业环境微生物学课程探索与改革

本文主要介绍对生物技术专业高年级学生或研究生开设的环境微生物学课程在教学内容、教学方法与考核方式方面的改革,主要包括制定新教学大纲、选择新版中英文教材、引用创新成果更新授课内容、引导学生自主学习和注重能力考核等方面。     

Exploring the Morphospace of Communication Efficiency in Complex Networks

Graph theoretical analysis has played a key role in characterizing global features of the topology of complex networks, describing diverse systems such as protein interactions, food webs, social relations and brain connectivity. How system elements communicate with each other depends not only on the structure of the network, but also on the nature of the system''s dynamics which are constrained by the amount of knowledge and resources available for communication processes. Complementing widely used measures that capture efficiency under the assumption that communication preferentially follows shortest paths across the network (“routing”), we define analytic measures directed at characterizing network communication when signals flow in a random walk process (“diffusion”). The two dimensions of routing and diffusion efficiency define a morphospace for complex networks, with different network topologies characterized by different combinations of efficiency measures and thus occupying different regions of this space. We explore the relation of network topologies and efficiency measures by examining canonical network models, by evolving networks using a multi-objective optimization strategy, and by investigating real-world network data sets. Within the efficiency morphospace, specific aspects of network topology that differentially favor efficient communication for routing and diffusion processes are identified. Charting regions of the morphospace that are occupied by canonical, evolved or real networks allows inferences about the limits of communication efficiency imposed by connectivity and dynamics, as well as the underlying selection pressures that have shaped network topology.     

A Threshold-based Dynamic Data Replication and Parallel Job Scheduling strategy to enhance Data Grid

Data Grids provide environment for huge, data-intensive applications that produce and process enormous data. Such environments are thus asked to manage data and schedule jobs at the same time. These two important operations have to be tightly coupled to achieve the best results. Replication techniques are widely used to increase the availability of data, improving performance of query latency and load balancing in Data Grid. Also effective resource scheduling is a challenging research issue. In this paper we propose a job scheduling policy, called Parallel Job Scheduling (PJS), and a dynamic data replication strategy, called Threshold-based Dynamic Data Replication (TDDR), to improve the data access efficiencies in a hierarchical Data Grid. The PJS uses hierarchical scheduling to reduce the search time for an appropriate computing node. It considers network characteristics, number of jobs waiting in queue, file locations, and disk read speed of storage drive at data sources. The main idea of TDDR strategy is using a threshold value to determine if the requested replica needs to be copied to the node. The TDDR determines this threshold dynamically based on data request arrival rates and available storage capacities. Then, in order to overcome the problem of limited storage space in each node, we design an efficient replica replacement strategy, which is developed as a two stages process. First, it deletes those files with minimum time for transferring. Second, if space is still insufficient then it considers the last time the replica was requested, number of access, size of replica and file transfer time. Results from the simulation show that our proposed algorithms have better performance in comparison with other algorithms in terms of Mean Job Time, Number of Intercommunications, Number of Replications, Computing Resource Usage, and Effective Network Usage.     

Trilobite monophyly revisited

Fortey's and Whittington's recent refutation of Lauterbach's hypothesis of a paraphyletic Trilobita is supported. However, much of the character evidence raised by Fortey and Whittington to substantiate the monophyly of the Trilobita (including, inter alia, "Olenellinae”; and Agnostoidea) is ambiguous. Of seven proposed synapomorphies, only one (dorsal cuticle calcification) may be maintained at that node after testing within a cladistic framework. The other six characters are either constrained by calcification or define nodes up or down the cladogram. As positioned by Fortey's and Whittington's characters, Agnostoidea could be regarded either as the most primitive trilobites, or as being outside that clade. Lauterbach's support for an “olenelline"‐chelicerate clade is found to include interdependent characters which are reduced here to two testable derived similarities. Only one of these may conform to general criteria indicative of homology, such as detailed similarity and topology. It is, however, rejected on the basis of parsimony. We emphasize that resolution of the chelicerate‐"olenelline"‐trilobite three‐taxon problem must be based on recognition of homologies among each of these taxa. Nectaspida are excluded from Trilobita as defined by cuticle calcification, but as ingroup “Arachnata”; (sensu Lauterbach) they are important for determining character generality in this clade.     

微生物双语教学中的实施策略与实践—交互教学提升课堂活力

探讨了微生物双语教学中采用交互式教学以发挥学生主体地位以及教师鼓励作用的教学策略.为了“有助于达到人才培养目标”又保证培养质量,精选了英文原版教材和国内选编教材;基于双语教学资源平台,了解学情,灵活把握双语教学的中英文比例;通过微生物理论与实践相结合和英文演讲辩论等提升课堂活力;适度改革双语教学考核等方式提高微生物双语教学效果.     

Technical note: A method for assignment of the weight of characters

The weight of characters is a crucial step in different population analyses. We propose a new formula to facilitate this while establishing a scale that follows the criteria of the probability of change in each character. This method is described for drawing of median‐joining networks, yet it could also be used for other methods in which the weight of the characters is required. Am J Phys Anthropol, 2010. © 2010 Wiley‐Liss, Inc.     

Individual sequences in large sets of gene sequences may be distinguished efficiently by combinations of shared sub-sequences

Background  

Most current DNA diagnostic tests for identifying organisms use specific oligonucleotide probes that are complementary in sequence to, and hence only hybridise with the DNA of one target species. By contrast, in traditional taxonomy, specimens are usually identified by 'dichotomous keys' that use combinations of characters shared by different members of the target set. Using one specific character for each target is the least efficient strategy for identification. Using combinations of shared bisectionally-distributed characters is much more efficient, and this strategy is most efficient when they separate the targets in a progressively binary way.     

细胞外基质蛋白质预测工具研究进展

细胞外基质蛋白质在细胞的一系列生物过程中发挥着重要作用,它的异常调节会导致很多重大疾病。理论细胞外基质蛋白质参考数据是实现细胞外基质蛋白质高效鉴定的基础,研究者们已经基于机器学习的方法开发出一系列的细胞外基质蛋白质预测工具。文中首先阐述了基于机器学习模型构建细胞外基质蛋白质预测工具的基本流程,之后以工具为单位总结了已有细胞外基质蛋白质预测工具的研究成果,最后提出了细胞外基质蛋白质预测工具目前面临的问题和可能的优化方法。