Robust clustering of multi-type relational data via a heterogeneous manifold ensemble

High-Order Co-Clustering (HOCC) methods have attracted high attention in recent years because of their ability to cluster multiple types of objects simultaneously using all available information. During the clustering process, HOCC methods exploit object co-occurrence information, i.e., inter-type relationships amongst different types of objects as well as object affinity information, i.e., intra-type relationships amongst the same types of objects. However, it is difficult to learn accurate intra-type relationships in the presence of noise and outliers. Existing HOCC methods consider the p nearest neighbours based on Euclidean distance for the intra-type relationships, which leads to incomplete and inaccurate intra-type relationships. In this paper, we propose a novel HOCC method that incorporates multiple subspace learning with a heterogeneous manifold ensemble to learn complete and accurate intra-type relationships. Multiple subspace learning reconstructs the similarity between any pair of objects that belong to the same subspace. The heterogeneous manifold ensemble is created based on two-types of intra-type relationships learnt using p-nearest-neighbour graph and multiple subspaces learning. Moreover, in order to make sure the robustness of clustering process, we introduce a sparse error matrix into matrix decomposition and develop a novel iterative algorithm. Empirical experiments show that the proposed method achieves improved results over the state-of-art HOCC methods for FScore and NMI.

Edge-weighting of gene expression graphs

In recent years, considerable research efforts have been directed to micro-array technologies and their role in providing simultaneous information on expression profiles for thousands of genes. These data, when subjected to clustering and classification procedures, can assist in identifying patterns and providing insight on biological processes. To understand the properties of complex gene expression datasets, graphical representations can be used. Intuitively, the data can be represented in terms of a bipartite graph, with weighted edges corresponding to gene-sample node couples in the dataset. Biologically meaningful subgraphs can be sought, but performance can be influenced both by the search algorithm, and, by the graph-weighting scheme and both merit rigorous investigation. In this paper, we focus on edge-weighting schemes for bipartite graphical representation of gene expression. Two novel methods are presented: the first is based on empirical evidence; the second on a geometric distribution. The schemes are compared for several real datasets, assessing efficiency of performance based on four essential properties: robustness to noise and missing values, discrimination, parameter influence on scheme efficiency and reusability. Recommendations and limitations are briefly discussed. Keywords: Edge-weighting; weighted graphs; gene expression; bi-clustering

Crystal structures and hydrogen-bonding in the co-crystalline adducts of 3,5-dinitrobenzoic acid with 4-aminosalicylic acid and 2-hydroxy-3-(1H-indol-3-yl)propenoic acid

The structures of the cocrystalline adducts of 3,5-dinitrobenzoic acid (3,5-DNBA) with 4-aminosalicylic acid (PASA), the 1:1 partial hydrate, C7H4N2O6 .C7H7NO3 . 2H2O, (I) and 2-hydroxy-3-(1H-indol-3-yl)propenoic acid (HIPA) and the 1:1:1 d6-dimethylsulfoxide solvate, C7H4N2O6 . C11H9NO3 . C2D6OS, (II) are reported. The crystal substructure of (I) comprises two centrosymmetric hydrogen-bonded R2/2(8) homodimers, one with 3,5-DNBA, the other with PASA, and an R2/2(8) 3,5-DNBA-PASA heterodimer. In the crystal, inter-unit amine N-H...O and water O-H...O hydrogen bonds generate a three-dimensional supramolecular structure. In (II), the asymmetric unit consists of the three constituent molecules which form an essentially planar cyclic hydrogen-bonded heterotrimer unit [graph set R2/3(17)] through carboxyl, hydroxy and amino groups. These units associate across a crystallographic inversion centre through the HIPA carboxylic acid group in an R2/2~(8) hydrogen-bonding association, giving a zero-dimensional structure lying parallel to (100). In both structures, pi--pi interactions are present [minimum ring centroid separations: 3.6471(18)A in (I) and 3.5819(10)A in (II)].

Crystal structures and hydrogen bonding in the anhydrous tryptaminium salts of the isomeric (2,4-dichlorophenoxy)acetic and (3,5-dichlorophenoxy)acetic acids

The anhydrous salts of 1H-indole-3-ethanamine (tryptamine) with isomeric (2,4-dichlorophenoxy)acetic acid (2,4-D) and (3,5-dichlorophenoxy)acetic (3,5-D), C10H13N2+ (C8H5Cl2O3)-, [(I) and (II), respectively] have been determined and their one-dimensional hydrogen-bonded polymeric structures are described. In the crystal of (I),the aminium H-atoms are involved in three separate inter-species N-H...O hydrogen-bonding interactions, two with carboxyl O-atom acceptors and the third in an asymmetric three-centre bidentate carboxyl O,O' chelate [graph set R2/1(4)]. The indole H-atom forms an N-H...O~carboxyl~ hydrogen bond, extending the chain structure along the b axial direction. In (II), two of the three aminium H-atoms are also involved in N-H...O(carboxyl) hydrogen bonds similar to (I) but with the third, a three-centre asymmetric interaction with carboxyl and phenoxy O-atoms is found [graph set R2/1(5)]. The chain polymeric extension is also along b. There are no pi--pi ring interactions in either of the structures. The aminium side chain conformations differ significantly between the two structures, reflecting the conformational ambivalence of the tryptaminium cation, as found also in the benzoate salts.

Development of brain structural connectivity between ages 12 and 30: A 4-Tesla diffusion imaging study in 439 adolescents and adults

Understanding how the brain matures in healthy individuals is critical for evaluating deviations from normal development in psychiatric and neurodevelopmental disorders. The brain's anatomical networks are profoundly re-modeled between childhood and adulthood, and diffusion tractography offers unprecedented power to reconstruct these networks and neural pathways in vivo. Here we tracked changes in structural connectivity and network efficiency in 439 right-handed individuals aged 12 to 30 (211 female/126 male adults, mean age=23.6, SD=2.19; 31 female/24 male 12 year olds, mean age=12.3, SD=0.18; and 25 female/22 male 16 year olds, mean age=16.2, SD=0.37). All participants were scanned with high angular resolution diffusion imaging (HARDI) at 4 T. After we performed whole brain tractography, 70 cortical gyral-based regions of interest were extracted from each participant's co-registered anatomical scans. The proportion of fiber connections between all pairs of cortical regions, or nodes, was found to create symmetric fiber density matrices, reflecting the structural brain network. From those 70 × 70 matrices we computed graph theory metrics characterizing structural connectivity. Several key global and nodal metrics changed across development, showing increased network integration, with some connections pruned and others strengthened. The increases and decreases in fiber density, however, were not distributed proportionally across the brain. The frontal cortex had a disproportionate number of decreases in fiber density while the temporal cortex had a disproportionate number of increases in fiber density. This large-scale analysis of the developing structural connectome offers a foundation to develop statistical criteria for aberrant brain connectivity as the human brain matures.

Changes in anatomical brain connectivity between ages 12 and 30: A HARDI study of 467 adolescents and adults

Graph theory can be applied to matrices that represent the brain's anatomical connections, to better understand global properties of anatomical networks, such as their clustering, efficiency and "small-world" topology. Network analysis is popular in adult studies of connectivity, but only one study - in just 30 subjects - has examined how network measures change as the brain develops over this period. Here we assessed the developmental trajectory of graph theory metrics of structural brain connectivity in a cross-sectional study of 467 subjects, aged 12 to 30. We computed network measures from 70×70 connectivity matrices of fiber density generated using whole-brain tractography in 4-Tesla 105-gradient high angular resolution diffusion images (HARDI). We assessed global efficiency and modularity, and both age and age 2 effects were identified. HARDI-based connectivity maps are sensitive to the remodeling and refinement of structural brain connections as the human brain develops.

Genetics of path lengths in brain connectivity networks: HARDI-based maps in 457 adults

Brain connectivity analyses are increasingly popular for investigating organization. Many connectivity measures including path lengths are generally defined as the number of nodes traversed to connect a node in a graph to the others. Despite its name, path length is purely topological, and does not take into account the physical length of the connections. The distance of the trajectory may also be highly relevant, but is typically overlooked in connectivity analyses. Here we combined genotyping, anatomical MRI and HARDI to understand how our genes influence the cortical connections, using whole-brain tractography. We defined a new measure, based on Dijkstra's algorithm, to compute path lengths for tracts connecting pairs of cortical regions. We compiled these measures into matrices where elements represent the physical distance traveled along tracts. We then analyzed a large cohort of healthy twins and show that our path length measure is reliable, heritable, and influenced even in young adults by the Alzheimer's risk gene, CLU.

Genome-wide scan of healthy human connectome discovers SPON1 gene variant influencing dementia severity

Aberrant connectivity is implicated in many neurological and psychiatric disorders, including Alzheimer's disease and schizophrenia. However, other than a few disease-associated candidate genes, we know little about the degree to which genetics play a role in the brain networks; we know even less about specific genes that influence brain connections. Twin and family-based studies can generate estimates of overall genetic influences on a trait, but genome-wide association scans (GWASs) can screen the genome for specific variants influencing the brain or risk for disease. To identify the heritability of various brain connections, we scanned healthy young adult twins with high-field, highangular resolution diffusion MRI. We adapted GWASs to screen the brain's connectivity pattern, allowing us to discover genetic variants that affect the human brain's wiring. The association of connectivity with the SPON1 variant at rs2618516 on chromosome 11 (11p15.2) reached connectome-wide, genome-wide significance after stringent statistical corrections were enforced, and it was replicated in an independent subsample. rs2618516 was shown to affect brain structure in an elderly population with varying degrees of dementia. Older people who carried the connectivity variant had significantly milder clinical dementia scores and lower risk of Alzheimer's disease. As a posthoc analysis, we conducted GWASs on several organizational and topological network measures derived from the matrices to discover variants in and around genes associated with autism (MACROD2), development (NEDD4), and mental retardation (UBE2A) significantly associated with connectivity. Connectome-wide, genome-wide screening offers substantial promise to discover genes affecting brain connectivity and risk for brain diseases.

New graph-based algorithms to efficiently solve large scale open pit mining optimisation problems

In the mining optimisation literature, most researchers focused on two strategic-level and tactical-level open-pit mine optimisation problems, which are respectively termed ultimate pit limit (UPIT) or constrained pit limit (CPIT). However, many researchers indicate that the substantial numbers of variables and constraints in real-world instances (e.g., with 50-1000 thousand blocks) make the CPIT’s mixed integer programming (MIP) model intractable for use. Thus, it becomes a considerable challenge to solve the large scale CPIT instances without relying on exact MIP optimiser as well as the complicated MIP relaxation/decomposition methods. To take this challenge, two new graph-based algorithms based on network flow graph and conjunctive graph theory are developed by taking advantage of problem properties. The performance of our proposed algorithms is validated by testing recent large scale benchmark UPIT and CPIT instances’ datasets of MineLib in 2013. In comparison to best known results from MineLib, it is shown that the proposed algorithms outperform other CPIT solution approaches existing in the literature. The proposed graph-based algorithms leads to a more competent mine scheduling optimisation expert system because the third-party MIP optimiser is no longer indispensable and random neighbourhood search is not necessary.

Spanish version of the screening Orebro Musculoskeletal Pain Questionnaire: A cross-cultural adaptation and validation

Background Spanish is one of the five most spoken languages in the world. There is currently no published Spanish version of the Örebro Musculoskeletal Pain Questionnaire (OMPQ). The aim of the present study is to describe the process of translating the OMPQ into Spanish and to perform an analysis of reliability, internal structure, internal consistency and concurrent criterion-related validity. Methods Design: Translation and psychometric testing. Procedure: Two independent translators translated the OMPQ into Spanish. From both translations a consensus version was achieved. A backward translation was made to verify and resolve any semantic or conceptual problems. A total of 104 patients (67 men/37 women) with a mean age of 53.48 (±11.63), suffering from chronic musculoskeletal disorders, twice completed a Spanish version of the OMPQ. Statistical analysis was performed to evaluate the reliability, the internal structure, internal consistency and concurrent criterion-related validity with reference to the gold standard questionnaire SF-12v2. Results All variables except “Coping” showed a rate above 0.85 on reliability. The internal structure calculation through exploratory factor analysis indicated that 75.2% of the variance can be explained with six components with an eigenvalue higher than 1 and 52.1% with only three components higher than 10% of variance explained. In the concurrent criterion-related validity, several significant correlations were seen close to 0.6, exceeding that value in the correlation between general health and total value of the OMPQ. Conclusions The Spanish version of the screening questionnaire OMPQ can be used to identify Spanish patients with musculoskeletal pain at risk of developing a chronic disability.

Fractal and complex network analyses of protein molecular dynamics

Based on protein molecular dynamics, we investigate the fractal properties of energy, pressure and volume time series using the multifractal detrended fluctuation analysis (MF-DFA) and the topological and fractal properties of their converted horizontal visibility graphs (HVGs). The energy parameters of protein dynamics we considered are bonded potential, angle potential, dihedral potential, improper potential, kinetic energy, Van der Waals potential, electrostatic potential, total energy and potential energy. The shape of the h(q)h(q) curves from MF-DFA indicates that these time series are multifractal. The numerical values of the exponent h(2)h(2) of MF-DFA show that the series of total energy and potential energy are non-stationary and anti-persistent; the other time series are stationary and persistent apart from series of pressure (with H≈0.5H≈0.5 indicating the absence of long-range correlation). The degree distributions of their converted HVGs show that these networks are exponential. The results of fractal analysis show that fractality exists in these converted HVGs. For each energy, pressure or volume parameter, it is found that the values of h(2)h(2) of MF-DFA on the time series, exponent λλ of the exponential degree distribution and fractal dimension dBdB of their converted HVGs do not change much for different proteins (indicating some universality). We also found that after taking average over all proteins, there is a linear relationship between 〈h(2)〉〈h(2)〉 (from MF-DFA on time series) and 〈dB〉〈dB〉 of the converted HVGs for different energy, pressure and volume.

Crystal structures and hydrogen bonding in the morpholinium salts of four phenoxyacetic acid analogues

The anhydrous salts morpholinium (tetrahydro-2-H-1,4-oxazine) phenxyacetate, C4H10NO+ C8H7O3- (I), (4-fluorophenoxy)acetate, C4H10NO+ C8H6FO3- (II) and isomeric morpholinium (3,5-dichlorophenoxy)acetate (3,5-D) (III) and morpholinium (2,4-dichlorophenoxy)acetate (2,4-D), C4H10NO+ C8H5Cl2O3- (IV), have been determined and their hydrogen-bonded structures are described. In the crystals of (I), (III) and (IV), one of the the aminium H atoms is involved in a three-centre asymmetric cation-anion N-H...O,O' R2/1(4) hydrogen-bonding interaction with the two carboxyl O-atom acceptors of the anion. With the structure of (II), the primary N---H...O interaction is linear. In the structures of (I), (II) and (III), the second N-H...O(carboxyl) hydrogen bond generates one-dimensional chain structures extending in all cases along [100]. With (IV), the ion pairs are linked though inversion-related N-H...O hydrogen bonds [graph set R2/4(8)], giving a cyclic heterotetrameric structure.

Automated topometric graph generation from floor plan analysis

The world is rich with information such as signage and maps to assist humans to navigate. We present a method to extract topological spatial information from a generic bitmap floor plan and build a topometric graph that can be used by a mobile robot for tasks such as path planning and guided exploration. The algorithm first detects and extracts text in an image of the floor plan. Using the locations of the extracted text, flood fill is used to find the rooms and hallways. Doors are found by matching SURF features and these form the connections between rooms, which are the edges of the topological graph. Our system is able to automatically detect doors and differentiate between hallways and rooms, which is important for effective navigation. We show that our method can extract a topometric graph from a floor plan and is robust against ambiguous cases most commonly seen in floor plans including elevators and stairwells.

On the expressive power of behavioral profiles

Behavioral profiles have been proposed as a behavioral abstraction of dynamic systems, specifically in the context of business process modeling. A behavioral profile can be seen as a complete graph over a set of task labels, where each edge is annotated with one relation from a given set of binary behavioral relations. Since their introduction, behavioral profiles were argued to provide a convenient way for comparing pairs of process models with respect to their behavior or computing behavioral similarity between process models. Still, as of today, there is little understanding of the expressive power of behavioral profiles. Via counter-examples, several authors have shown that behavioral profiles over various sets of behavioral relations cannot distinguish certain systems up to trace equivalence, even for restricted classes of systems represented as safe workflow nets. This paper studies the expressive power of behavioral profiles from two angles. Firstly, the paper investigates the expressive power of behavioral profiles and systems captured as acyclic workflow nets. It is shown that for unlabeled acyclic workflow net systems, behavioral profiles over a simple set of behavioral relations are expressive up to configuration equivalence. When systems are labeled, this result does not hold for any of several previously proposed sets of behavioral relations. Secondly, the paper compares the expressive power of behavioral profiles and regular languages. It is shown that for any set of behavioral relations, behavioral profiles are strictly less expressive than regular languages, entailing that behavioral profiles cannot be used to decide trace equivalence of finite automata and thus Petri nets.

Using a glow graph to represent data flow and dependency in event logs

The idea of extracting knowledge in process mining is a descendant of data mining. Both mining disciplines emphasise data flow and relations among elements in the data. Unfortunately, challenges have been encountered when working with the data flow and relations. One of the challenges is that the representation of the data flow between a pair of elements or tasks is insufficiently simplified and formulated, as it considers only a one-to-one data flow relation. In this paper, we discuss how the effectiveness of knowledge representation can be extended in both disciplines. To this end, we introduce a new representation of the data flow and dependency formulation using a flow graph. The flow graph solves the issue of the insufficiency of presenting other relation types, such as many-to-one and one-to-many relations. As an experiment, a new evaluation framework is applied to the Teleclaim process in order to show how this method can provide us with more precise results when compared with other representations.