Observations on graphing paleozoological data: Suggestions for better graphs

Paleozoologists have long used graphs of diverse styles to describe, analyze, and summarize their data. Some of these graphs provide excellent visual representations of complex data and are readily deciphered. Other graph styles require close study to be interpreted. Ease of visual decoding of information contained in a graph – graph perception – varies from graph style to graph style. Historical instances of graphing paleozoological data indicate some difficult to decipher graph styles have been used for at least a century. Graphs with three-dimensions, moiré effects, superimposed lines, or segmented bars, or which demand simultaneous decipherment of position and magnitude, are ill-advised. Temporal trends in data are best graphed following the principle of superposition such that data from old material is graphed at the bottom and data from younger material is graphed at the top of the diagram.     

Using Correlation Proximity Graphs to Study Phenotypic Integration

Characterizing and comparing the covariance or correlation structure of phenotypic traits lies at the heart of studies concerned with multivariate evolution. I describe an approach that represents the geometric structure of a correlation matrix as a type of proximity graph called a Correlation Proximity graph. Correlation Proximity graphs provide a compact representation of the geometric relationships inherent in correlation matrices, and these graphs have simple and intuitive properties. I demonstrate how this framework can be used to study patterns of phenotypic integration by employing this approach to compare phenotypic and additive genetic correlation matrices within and between species. I also outline a graph-based method for testing whether an inferred correlation proximity graph is one of a number of possible models that are consistent with a “soft” biological hypothesis.     

Review of uses of network and graph theory concepts within proteomics

The size and nature of data collected on gene and protein interactions has led to a rapid growth of interest in graph theory and modern techniques for describing, characterizing and comparing networks. Simultaneously, this is a field of growth within mathematics and theoretical physics, where the global properties, and emergent behavior of networks, as a function of the local properties has long been studied. In this review, a number of approaches for exploiting modern network theory to help describe and analyze different data sets and problems associated with proteomic data are considered. This review aims to help biologists find their way towards useful ideas and references, yet may also help scientists from a mathematics and physics background to understand where they may apply their expertise.     

Next-Generation Sequence Assembly: Four Stages of Data Processing and Computational Challenges

Decoding DNA symbols using next-generation sequencers was a major breakthrough in genomic research. Despite the many advantages of next-generation sequencers, e.g., the high-throughput sequencing rate and relatively low cost of sequencing, the assembly of the reads produced by these sequencers still remains a major challenge. In this review, we address the basic framework of next-generation genome sequence assemblers, which comprises four basic stages: preprocessing filtering, a graph construction process, a graph simplification process, and postprocessing filtering. Here we discuss them as a framework of four stages for data analysis and processing and survey variety of techniques, algorithms, and software tools used during each stage. We also discuss the challenges that face current assemblers in the next-generation environment to determine the current state-of-the-art. We recommend a layered architecture approach for constructing a general assembler that can handle the sequences generated by different sequencing platforms.     

On analytical approaches to epidemics on networks

One way to describe the spread of an infection on a network is by approximating the network by a random graph. However, the usual way of constructing a random graph does not give any control over the number of triangles in the graph, while these triangles will naturally arise in many networks (e.g. in social networks). In this paper, random graphs with a given degree distribution and a given expected number of triangles are constructed. By using these random graphs we analyze the spread of two types of infection on a network: infections with a fixed infectious period and infections for which an infective individual will infect all of its susceptible neighbors or none. These two types of infection can be used to give upper and lower bounds for R(0), the probability of extinction and other measures of dynamics of infections with more general infectious periods.     

A microcomputer program for hydropathic analysis of proteins with I/O through word processing and graphics software

A BASIC program has been devised for the hydropathic analysisof protein sequences according to the method of Kyte and Doolittle(1982). The program uses sequence data from input files thatare created with a word processor and produces two types ofoutput file: one contains a bar graph of the hydropathic profilein a format that can be easily edited; the other is a tabulationof hydropathic indices along a protein's sequence that can beused as input by the program for the production of a bar graphor as input into other graphics and analysis software. An MS-DOSmicrocomputer, operating under IBM BASICA or GWBASIC and a dotmatrix printer with block graphics capabilities are the onlyhardware requirements for graphic display of hydropathy profiles.The program is capable of unattended analysis from a list ofup to 15 input files. ; accepted on March 10, 1986     

Graph-Theoretical Analysis of Cleavage Pattern: Graph Developmental System and Its Application to Cleavage Pattern of Ascidian Egg

The skeletal structure of the embryo is represented by the graph. In the graph, the cells and the connectivities between the cells are reduced to the nodes and edges, respectively. Along cleavage history, the series of graphs is obtained. In this paper I propose a new graph developmental system(GDS) which develops the series of graphs. And I represent and analyze the cleavage pattern of the ascidian egg by GDS. In order to represent it by GDS, at first, the connectivities between the cells are labeled according to the developments of the connectivities at the next time step, and next the cells are labeled. But there are two ways of labeling the cells, then two types of GDS are defined: (1) to label according to the pattern of the connectivities of their descendant cells after two time steps (G-GDS), (2) to label according to the cell fates (C-GDS). The C-GDS of the ascidian egg produces 16 cleavage patterns at 64-cell stage non-deterministically. If some labels are distinguished at 16-cell stage, the C-GDS becomes deterministic at 64-cell stage.
GDS will be useful to simulate morphogenesis by the computer graphics.     

MAMBAs: A real-time graphics environment for QSAR

MAMBAs (Multivariate Analysis Methods in Biomechanistic Activity Studies) is an integrated workstation-based graphics program designed for the investigation of quantitative structure activity relationships (QSAR). It combines many of the commonly used statistical techniques with an extensive database of substituent constants, a variety of molecular and substituent property calculations and detailed graphicsbased table and graph editors. Graphical representations of standard substituent generation and optimization techniques are also included. These are all utilized within a state-of-the -art real-time graphics environment.     

基于ComGIS的区域景观格局监测信息系统

从区域景观生态数据科学管理和景观格局动态分析的需要入手,以陕北纸坊沟流域作为设计和应用实例,采用地理信息系统组件MapX与可视化编程语言Delphi相结合的方式研制和开发了区域景观格局监测信息系统;文中首先对系统设计路线和建立方法进行简要说明,然后详细介绍了系统的基本结构和功能:该系统主要由数据管理模块、查询管理模块、景观指标分析和景观预测模块组成,实现了景观属性和景观图形数据的有机集成和高效管理,提供景观空间信息的显示、定位和交互查询,在此基础上计算景观类型和整个景观层次上的各种景观指数来实现景观格局综合分析,并且能够对景观发展趋势进行模拟预测,同时可以动态生成统计图表,形象直观地反映出不同时期内景观格局的发展变化规律.     

A graph spectral analysis of the structural similarity network of protein chains

We present a simple method for the analysis of large networks based on their graph spectral properties. One of the advantages of this method is that it uses a single numerical computation to identify subclusters in a connected graph, which can significantly simplify the complexity involved in analyzing large graphs. This is illustrated using a network of protein chains constructed on the basis of their structural similarities. The large-scale network properties and the cluster and subcluster organization of the protein chain network are presented. We summarize the results of structural and functional analyses of the nodes present in these clusters and elucidate the implications of structural similarity in the protein chain universe.     

Conceptual graph operations for formal visual reasoning in the medical domain

ObjectiveConceptual graphs (CGs) are used to represent clinical guidelines because they support visual reasoning with a logical background, making them a potentially valuable representation for guidelines.Materials and methodsConceptual graph formalism has an essential and basic component: a formal vocabulary that drives all of the other mechanisms, notably specialization and projection. The graph's theoretical operations, such as projection, rules, derivation, constraints, probabilities and uncertainty, support diagrammatic reasoning.ResultsA conceptual graph's graphical user interface includes a multilingual vocabulary management, some query and decision-making facilities and visual graph representations that are simple and interesting for user interactions. The described proposition using the Conceptual Graph user interface (CoGui) improves the performance of the actors in the diagnostic context of heart failure with preserved ejection fraction.DiscussionCGs capture the essential features of the medical processes underlying clinical reasoning. CGs are indeed useful as a way for the physician to represent guidelines, and well-defined semantic representations allow users to have a maximal understanding of the knowledge reasoning process.ConclusionCG operations of visual representations that uncover some of the actual complexities of clinicians’ reasoning have been tested in clinical guideline comprehension and used to translate text and diagrammatic guidelines into computer interpretable representations.