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At least 703 records · Page 39Linked to original sources
PubMed2001

World Wide Web scaling exponent from Simon's 1955 model.

The statistical properties of the World Wide Web have attracted considerable attention recently since self-similar regimes were first observed in the scaling of its link structure. One characteristic quantity is the number of (in-)links k that point to a particular web page. Its probability distribution P(k) shows a pronounced power-law scaling P(k) approximately k(-gamma) that is not readily explained by standard random graph theory. Here, we recall a simple and elegant model for scaling phenomena in general copy- and growth-processes as proposed by Simon in 1955. When combined with an experimental measurement of network growth in the World Wide Web, this classical model is able to model the in-link dynamics and predicts the scaling exponent gamma=2.1 in accordance with observation.

Journal Article↗
PubMedDate not supplied

[The role of social networks in exclusive breastfeeding].

OBJECTIVE: The purpose of the study was to explore the network that the social actors form around the mother with a neonate and the roles they play, either as support or as an obstacle of exclusive breast-feeding. MATERIAL AND METHODS: We applied a social network survey in two stages: in the first one, we asked mothers with babies under six months of age who had helped them breast-feed their babies and who had had a bearing on not letting them do it. Later, we asked other mothers who had told them to give formula, water, tea or solid food to the babies. With the answers we formed two "cases by actors" matrices. We computed the frequencies to find out those people that most influenced the mother for and against exclusive breast-feeding. We also graphed the results from the matrix to visualize the answers. RESULTS: The maternal grandmother of the child, the physician and the paternal grandmother appeared as those who most positively contributed with exclusive breast-feeding. Paradoxically, they are the same who most influenced its interruption. DISCUSSION: We were looking for a social network to support exclusive breast-feeding and another one to hinder it, but the two networks turned out to be very similar. The same characters who help a mother to breast-feed are the ones who make her stop. The implication for an intervention program is that it is necessary to strengthen some knowledge and inhibit some other within the same group of people.

Breast Feeding↗
PubMed2004

An enriched unified medical language system semantic network with a multiple subsumption hierarchy.

OBJECTIVE: The Unified Medical Language System's (UMLS's) Semantic Network's (SN's) two-tree structure is restrictive because it does not allow a semantic type to be a specialization of several other semantic types. In this article, the SN is expanded into a multiple subsumption structure with a directed acyclic graph (DAG) IS-A hierarchy, allowing a semantic type to have multiple parents. New viable IS-A links are added as warranted. DESIGN: Two methodologies are presented to identify and add new viable IS-A links. The first methodology is based on imposing the characteristic of connectivity on a previously presented partition of the SN. Four transformations are provided to find viable IS-A links in the process of converting the partition's disconnected groups into connected ones. The second methodology identifies new IS-A links through a string matching process involving names and definitions of various semantic types in the SN. A domain expert is needed to review all the results to determine the validity of the new IS-A links. RESULTS: Nineteen new IS-A links are added to the SN, and four new semantic types are also created to support the multiple subsumption framework. The resulting network, called the Enriched Semantic Network (ESN), exhibits a DAG-structured hierarchy. A partition of the ESN containing 19 connected groups is also derived. CONCLUSION: The ESN is an expanded abstraction of the UMLS compared with the original SN. Its multiple subsumption hierarchy can accommodate semantic types with multiple parents. Its representation thus provides direct access to a broader range of subsumption knowledge.

Algorithms↗
PubMed2005

Biochemical networks with uncertain parameters.

The modelling of biochemical networks becomes delicate if kinetic parameters are varying, uncertain or unknown. Facing this situation, we quantify uncertain knowledge or beliefs about parameters by probability distributions. We show how parameter distributions can be used to infer probabilistic statements about dynamic network properties, such as steady-state fluxes and concentrations, signal characteristics or control coefficients. The parameter distributions can also serve as priors in Bayesian statistical analysis. We propose a graphical scheme, the 'dependence graph', to bring out known dependencies between parameters, for instance, due to the equilibrium constants. If a parameter distribution is narrow, the resulting distribution of the variables can be computed by expanding them around a set of mean parameter values. We compute the distributions of concentrations, fluxes and probabilities for qualitative variables such as flux directions. The probabilistic framework allows the study of metabolic correlations, and it provides simple measures of variability and stochastic sensitivity. It also shows clearly how the variability of biological systems is related to the metabolic response coefficients.

Animals↗
PubMed2005

Loops structure of the Internet at the autonomous system level.

We present here a study of the clustering and loops in a graph of the Internet at the autonomous systems level. We show that, even if the whole structure is changing with time, the statistical distributions of loops of order 3, 4, and 5 remain stable during the evolution. Moreover, we will bring evidence that the Internet graphs show characteristic Markovian signatures, since the structure is very well described by two-point correlations between the degrees of the vertices. This indeed proves that the Internet belongs to a class of network in which the two-point correlation is sufficient to describe their whole local (and thus global) structure. Data are also compared to present Internet models.

Journal Article↗
PubMed2006

A study of evolutionary multiagent models based on symbiosis.

Multiagent Systems with Symbiotic Learning and Evolution (Masbiole) has been proposed and studied, which is a new methodology of Multiagent Systems (MAS) based on symbiosis in the ecosystem. Masbiole employs a method of symbiotic learning and evolution where agents can learn or evolve according to their symbiotic relations toward others, i.e., considering the benefits/losses of both itself and an opponent. As a result, Masbiole can escape from Nash Equilibria and obtain better performances than conventional MAS where agents consider only their own benefits. This paper focuses on the evolutionary model of Masbiole, and its characteristics are examined especially with an emphasis on the behaviors of agents obtained by symbiotic evolution. In the simulations, two ideas suitable for the effective analysis of such behaviors are introduced; "Match Type Tile-world (MTT)" and "Genetic Network Programming (GNP)". MTT is a virtual model where tile-world is improved so that agents can behave considering their symbiotic relations. GNP is a newly developed evolutionary computation which has the directed graph type gene structure and enables to analyze the decision making mechanism of agents easily. Simulation results show that Masbiole can obtain various kinds of behaviors and better performances than conventional MAS in MTT by evolution.

Algorithms↗
PubMedDate not supplied

Oriental medicine: an introduction.

This article covers introductory concepts regarding Oriental medicine, its historical context, yin and yang principles, and Five-Phase theory. It discusses organ theory, patient examination, diagnosis, the meridian network system, and treatment modalities such as acupuncture and Chinese herbal medicine, giving a brief overview of the different acupuncture and Oriental medical styles practiced in the United States. Included also are helpful tables, graphs, and photographs to provide a better visual context for Oriental medicine.

Acupuncture Therapy↗
PubMed2004

Elucidating protein thermodynamics from the three-dimensional structure of the native state using network rigidity.

Given the three-dimensional structure of a protein, its thermodynamic properties are calculated using a recently introduced distance constraint model (DCM) within a mean-field treatment. The DCM is constructed from a free energy decomposition that partitions microscopic interactions into a variety of constraint types, i.e., covalent bonds, salt-bridges, hydrogen-bonds, and torsional-forces, each associated with an enthalpy and entropy contribution. A Gibbs ensemble of accessible microstates is defined by a set of topologically distinct mechanical frameworks generated by perturbing away from the native constraint topology. The total enthalpy of a given framework is calculated as a linear sum of enthalpy components over all constraints present. Total entropy is generally a nonadditive property of free energy decompositions. Here, we calculate total entropy as a linear sum of entropy components over a set of independent constraints determined by a graph algorithm that builds up a mechanical framework one constraint at a time, placing constraints with lower entropy before those with greater entropy. This procedure provides a natural mechanism for enthalpy-entropy compensation. A minimal DCM with five phenomenological parameters is found to capture the essential physics relating thermodynamic response to network rigidity. Moreover, two parameters are fixed by simultaneously fitting to heat capacity curves for histidine binding protein and ubiquitin at five different pH conditions. The three free parameter DCM provides a quantitative characterization of conformational flexibility consistent with thermodynamic stability. It is found that native hydrogen bond topology provides a key signature in governing molecular cooperativity and the folding-unfolding transition.

Computer Simulation↗
PubMed2005

Self-organizing hierarchies in sensor and communication networks.

We consider a hierarchical multicellular sensing and communication network, embedded in an ageless aerospace vehicle that is expected to detect and react to multiple impacts and damage over a wide range of impact energies. In particular, we investigate self-organization of impact boundaries enclosing critically damaged areas, and impact networks connecting remote cells that have detected noncritical impacts. Each level of the hierarchy is shown to have distinct higher-order emergent properties, desirable in self-monitoring and self-repairing vehicles. In addition, cells and communication messages are shown to need memory (hysteresis) in order to retain desirable emergent behavior within and between various hierarchical levels. Spatiotemporal robustness of self-organizing hierarchies is quantitatively measured with graph-theoretic and information-theoretic techniques, such as the Shannon entropy. This allows us to clearly identify phase transitions separating chaotic dynamics from ordered and robust patterns.

Algorithms↗
PubMed2026

Knowledge-driven interpretable neural networks provide mechanistic insight.

Analyzing omics data in the context of pathway knowledge is critical for understanding the molecular mechanisms underlying pathological changes. However, current pathway analysis methods do not model the detailed mechanistic nature of biological interactions, limiting the understanding of pathway behavior to a relatively shallow level. To address this issue, we present a knowledge-driven machine learning framework that embeds features into pathway graphs and models reactions analytically, producing interpretable feature hierarchies and subnetworks in which functional associations are estimated to model biological interactions. The approach is agnostic to feature selection, enabling the use of full omics data sets without discarding weak signals. Applications to breast cancer microRNA-gene regulation data and COVID-19 metabolomic data highlight immune and metabolic pathways relevant to disease progression. This framework bridges predictive modeling with mechanistic interpretation and offers a foundation for integrative pathway analysis.

Humans↗
PubMed2005

Bis(2,9-dimethyl-1,10-phenanthroline-kappa(2)N,N')copper(I) hydrogen squarate hemihydrate.

In the crystal structure of the synthetically prepared title compound, [Cu(C14H12N2)2](C4HO4) x 0.5H2O or [Cu(dmphen)2](HSq) x 0.5H2O (dmphen is 2,9-dimethyl-1,10-phenanthroline or neocuproine and HSq is hydrogen squarate), the Cu(I) centre has distorted tetrahedral coordination geometry comprised of four N atoms from two bidentate dmphen ligands. The squarate monoanions form a ten-membered dimer, graph set R(2)(2)(10), linked by two strong intermolecular O-H...O hydrogen bonds. These squarate dimers are linked into chains that propagate along the [100] direction. An extensive three-dimensional network of C-H...O hydrogen bonds and pi-pi interactions is responsible for stabilization of the crystal structure.

Journal Article↗
PubMed2005

A theoretical and experimental investigation of graph theoretical measures for land development in satellite imagery.

Today's commercial satellite images enable experts to classify region types in great detail. In previous work, we considered discriminating rural and urban regions [23]. However, a more detailed classification is required for many purposes. These fine classifications assist government agencies in many ways including urban planning, transportation management, and rescue operations. In a step toward the automation of the fine classification process, this paper explores graph theoretical measures over grayscale images. The graphs are constructed by assigning photometric straight line segments to vertices, while graph edges encode their spatial relationships. We then introduce a set of measures based on various properties of the graph. These measures are nearly monotonic (positively correlated) with increasing structure (organization) in the image. Thus, increased cultural activity and land development are indicated by increases in these measures-without explicit extraction of road networks, buildings, residences, etc. These latter, time consuming (and still only partially automated) tasks can be restricted only to "promising" image regions, according to our measures. In some applications our measures may suffice. We present a theoretical basis for the measures followed by extensive experimental results in which the measures are first compared to manual evaluations of land development. We then present and test a method to focus on, and (pre)extract, suburban-style residential areas. These are of particular importance in many applications, and are especially difficult to extract. In this work, we consider commercial IKONOS data. These images are orthorectified to provide a fixed resolution of 1 meter per pixel on the ground. They are, therefore, metric in the sense that ground distance is fixed in scale to pixel distance. Our data set is large and diverse, including sea and coastline, rural, forest, residential, industrial, and urban areas.

Algorithms↗
PubMed2002

Theoretical neuroanatomy and the connectivity of the cerebral cortex.

Over recent years, a wealth of neuroanatomical information on the pattern of interconnections between segregated areas of the cerebral cortex has become available. Here, we describe a set of structural measures, based on graph theory, which can be used to analyze these anatomical patterns. We describe relationships between these structural measures and measures based on patterns of functional connectivity, i.e. patterns of correlations in neural activity. We find that networks capable of producing highly complex functional dynamics share common structural motifs. These motifs are also found in cortical connection matrices, which are characterized by the existence of densely linked groups of areas, low potential wiring length, and a high abundance of reciprocal connections and short cycles. An analysis of cortical functional connectivity demonstrates the existence of functional clusters of highly interactive areas, producing highly complex dynamics. The combined structural and functional analysis outlined in this chapter provides insight into the large-scale functional organization of distributed cortical systems.

Animals↗
PubMedDate not supplied

Automation of protein 2D proton NMR assignment by means of fuzzy mathematics and graph theory.

The novel methodology for protein 2D NMR assignment presented in this paper is based upon protein spin coupling graph theory analysis, fuzzy graph pattern recognition, and tree searching. The method required to formalize the whole assignment procedure into a logical system which can be properly processed by computer software is also discussed. Solutions for peak overlaps, spin coupling network overlaps, and details related to the automated assignment of BPTI are reported as well.

Algorithms↗
PubMed2004

Network dynamics: jamming is limited in scale-free systems.

A large number of complex networks are scale-free--that is, they follow a power-law degree distribution. Here we propose that the emergence of many scale-free networks is tied to the efficiency of transport and flow processing across these structures. In particular, we show that for large networks on which flows are influenced or generated by gradients of a scalar distributed on the nodes, scale-free structures will ensure efficient processing, whereas structures that are not scale-free, such as random graphs, will become congested.

Journal Article↗
PubMed1978

Integrated stereological and biochemical studies on hepatocytic membranes. II. Correction of section thickness effect on volume and surface density estimates.

The basic stereological formulas for estimating volume (Vv) and surface (Sv) densities are strictly valid only for true infinitely thin sections; the use of "ultrathin" sections of finite thickness T introduces systematic errors, mostly in the sense of overestimation of the parameters. These errors depend on the size and shape of the structural elements and on T. Correction factors for this effect of T are derived by considering model structures that simulate the shape and arrangement of subcellular organelles: (a) spherical vesicles, (b) disks as models for rough endoplasmic reticulum (RER) cisternae, (c) cylindrical tublules as models for smooth endoplasmic reticulum (SER) tubules, microvilli, etc. For vesicles, a model of discrete convex spherical particles is assumed; the correction factors consider loss of caps due to grazing sections and size distribution of the vesicles. The disk and tubule models are used in connection with the new integral geometric formulas of R.E. Miles which consider random aggregates of "inter-penetrating" particles so that the resultant structure is non-convex and thus approximates in nature the networks characteristic of endoplasmic reticulum (ER). Some practical examples relative to liver cells show that the errors due to section thickness may be of the order of 20-40% or more. Computation formulas as well as graphs are given for the determination of the correction factors for Vv and Sv.

Animals↗
PubMed2005

Study of coordinative gene expression at the biological process level.

MOTIVATION: Cellular processes are not isolated groups of events. Nevertheless, in most microarray analyses, they tend to be treated as standalone units. To shed light on how various parts of the interlocked biological processes are coordinated at the transcription level, there is a need to study the between-unit expressional relationship directly. RESULTS: We approach this issue by constructing an index of correlation function to convey the global pattern of coexpression between genes from one process and genes from the entire genome. Processes with similar signatures are then identified and projected to a process-to-process association graph. This top-down method allows for detailed gene-level analysis between linked processes to follow up. Using the cell-cycle gene-expression profiles for Saccharomyces cerevisiae, we report well-organized networks of biological processes that would be difficult to find otherwise. Using another dataset, we report a sharply different network structure featuring cellular responses under environmental stress. SUPPLEMENTARY INFORMATION: http://kiefer.stat.ucla.edu/lap2/download/KL_supplement.pdf.

Algorithms↗
PubMedDate not supplied

Information content in organic molecules: quantification and statistical structure via Brownian processing.

Information and organic molecules were the subject of two previous works from this lab (Graham and Schacht, J. Chem. Inf. Comput. Sci. 2000, 40, 187; Graham, J. Chem. Inf. Computer Sci. 2002, 42, 215). We delve further in this paper by examining organic structure graphs as objects of Brownian information processing. In so doing, tools are introduced which quantify and correlate molecular information to several orders. When the results are combined with energy data, an enhanced informatic view of covalent bond networks is obtained. The information properties of select molecules and libraries are illustrated. Notably, Brownian processing accommodates all possible compounds and libraries, not just ones registered in chemical databases. This approach establishes important features of the statistical structure underlying carbon chemistry.

Journal Article↗