Non-invasive gene-expression-based detection of well-developed collateral function in individuals with and without coronary artery disease

BACKGROUND: In patients with coronary artery disease (CAD), a well grown collateral circulation has been shown to be important. The aim of this prospective study using peripheral blood monocytes was to identify marker genes for an extensively grown coronary collateral circulation. METHODS: Collateral flow index (CFI) was obtained invasively by angioplasty pressure sensor guidewire in 160 individuals (110 patients with CAD, and 50 individuals without CAD). RNA was extracted from monocytes followed by microarray-based gene-expression analysis. 76 selected genes were analysed by real-time polymerase chain reaction (PCR). A receiver operating characteristics analysis based on differential gene expression was then performed to separate individuals with poor (CFI<0.21) and well-developed collaterals (CFI>or=0.21) Thereafter, the influence of the chemokine MCP-1 on the expression of six selected genes was tested by PCR. RESULTS: The expression of 203 genes significantly correlated with CFI (p = 0.000002-0.00267) in patients with CAD and 56 genes in individuals without CAD (p = 00079-0.0430). Biological pathway analysis revealed 76 of those genes belonging to four different pathways: angiogenesis, integrin-, platelet-derived growth factor-, and transforming growth factor beta-signalling. Three genes in each subgroup differentiated with high specificity among individuals with low and high CFI (>or=0.21). Two out of these genes showed pronounced differential expression between the two groups after cell stimulation with MCP-1. CONCLUSIONS: Genetic factors play a role in the formation and the preformation of the coronary collateral circulation. Gene expression analysis in peripheral blood monocytes can be used for non-invasive differentiation between individuals with poorly and with well grown collaterals. MCP-1 can influence the arteriogenic potential of monocytes.

MODULARITY BASED FUZZY COMMUNITY DETECTION IN SOCIAL NETWORKS

Fuzzy community detection is to identify fuzzy communities in a network, which are groups of vertices in the network such that the membership of a vertex in one community is in [0,1] and that the sum of memberships of vertices in all communities equals to 1. Fuzzy communities are pervasive in social networks, but only a few works have been done for fuzzy community detection. Recently, a one-step forward extension of Newman’s Modularity, the most popular quality function for disjoint community detection, results into the Generalized Modularity (GM) that demonstrates good performance in finding well-known fuzzy communities. Thus, GMis chosen as the quality function in our research. We first propose a generalized fuzzy t-norm modularity to investigate the effect of different fuzzy intersection operators on fuzzy community detection, since the introduction of a fuzzy intersection operation is made feasible by GM. The experimental results show that the Yager operator with a proper parameter value performs better than the product operator in revealing community structure. Then, we focus on how to find optimal fuzzy communities in a network by directly maximizing GM, which we call it Fuzzy Modularity Maximization (FMM) problem. The effort on FMM problem results into the major contribution of this thesis, an efficient and effective GM-based fuzzy community detection method that could automatically discover a fuzzy partition of a network when it is appropriate, which is much better than fuzzy partitions found by existing fuzzy community detection methods, and a crisp partition of a network when appropriate, which is competitive with partitions resulted from the best disjoint community detections up to now. We address FMM problem by iteratively solving a sub-problem called One-Step Modularity Maximization (OSMM). We present two approaches for solving this iterative procedure: a tree-based global optimizer called Find Best Leaf Node (FBLN) and a heuristic-based local optimizer. The OSMM problem is based on a simplified quadratic knapsack problem that can be solved in linear time; thus, a solution of OSMM can be found in linear time. Since the OSMM algorithm is called within FBLN recursively and the structure of the search tree is non-deterministic, we can see that the FMM/FBLN algorithm runs in a time complexity of at least O (n2). So, we also propose several highly efficient and very effective heuristic algorithms namely FMM/H algorithms. We compared our proposed FMM/H algorithms with two state-of-the-art community detection methods, modified MULTICUT Spectral Fuzzy c-Means (MSFCM) and Genetic Algorithm with a Local Search strategy (GALS), on 10 real-world data sets. The experimental results suggest that the H2 variant of FMM/H is the best performing version. The H2 algorithm is very competitive with GALS in producing maximum modularity partitions and performs much better than MSFCM. On all the 10 data sets, H2 is also 2-3 orders of magnitude faster than GALS. Furthermore, by adopting a simply modified version of the H2 algorithm as a mutation operator, we designed a genetic algorithm for fuzzy community detection, namely GAFCD, where elite selection and early termination are applied. The crossover operator is designed to make GAFCD converge fast and to enhance GAFCD’s ability of jumping out of local minimums. Experimental results on all the data sets show that GAFCD uncovers better community structure than GALS.

OUT-OF-STEP DETECTION BASED ON ZUBOV’S APPROXIMATION BOUNDARY METHOD

Disturbances in power systems may lead to electromagnetic transient oscillations due to mismatch of mechanical input power and electrical output power. Out-of-step conditions in power system are common after the disturbances where the continuous oscillations do not damp out and the system becomes unstable. Existing out-of-step detection methods are system specific as extensive off-line studies are required for setting of relays. Most of the existing algorithms also require network reduction techniques to apply in multi-machine power systems. To overcome these issues, this research applies Phasor Measurement Unit (PMU) data and Zubov’s approximation stability boundary method, which is a modification of Lyapunov’s direct method, to develop a novel out-of-step detection algorithm. The proposed out-of-step detection algorithm is tested in a Single Machine Infinite Bus system, IEEE 3-machine 9-bus, and IEEE 10-machine 39-bus systems. Simulation results show that the proposed algorithm is capable of detecting out-of-step conditions in multi-machine power systems without using network reduction techniques and a comparative study with an existing blinder method demonstrate that the decision times are faster. The simulation case studies also demonstrate that the proposed algorithm does not depend on power system parameters, hence it avoids the need of extensive off-line system studies as needed in other algorithms.

IS711-based real-time PCR assay as a tool for detection of Brucella spp. in wild boars and comparison with bacterial isolation and serology

BACKGROUND: Control of brucellosis in livestock, wildlife and humans depends on the reliability of the methods used for detection and identification of bacteria. In the present study, we describe the evaluation of the recently established real-time PCR assay based on the Brucella-specific insertion sequence IS711 with blood samples from 199 wild boars (first group of animals) and tissue samples from 53 wild boars (second group of animals) collected in Switzerland. Results from IS711 real-time PCR were compared to those obtained by bacterial isolation, Rose Bengal Test (RBT), competitive ELISA (c-ELISA) and indirect ELISA (i-ELISA). RESULTS: In the first group of animals, IS711 real-time PCR detected infection in 11.1% (16/144) of wild boars that were serologically negative. Serological tests showed different sensitivities [RBT 15.6%, c-ELISA 7.5% and i-ELISA 5.5%] and only 2% of blood samples were positive with all three tests, which makes interpretation of the serological results very difficult. Regarding the second group of animals, the IS711 real-time PCR detected infection in 26% of animals, while Brucella spp. could be isolated from tissues of only 9.4% of the animals. CONCLUSION: The results presented here indicate that IS711 real-time PCR assay is a specific and sensitive tool for detection of Brucella spp. infections in wild boars. For this reason, we propose the employment of IS711 real-time PCR as a complementary tool in brucellosis screening programs and for confirmation of diagnosis in doubtful cases.

Method of collaborative detection of autonomous transport vehicles based on laser rangefinder data

To master changing performance demands, autonomous transport vehicles are deployed to make inhouse material flow applications more flexible. The socalled cellular transport system consists of a multitude of small scale transport vehicles which shall be able to form a swarm. Therefore the vehicles need to detect each other, exchange information amongst each other and sense their environment. By provision of peripherally acquired information of other transport entities, more convenient decisions can be made in terms of navigation and collision avoidance. This paper is a contribution to collective utilization of sensor data in the swarm of cellular transport vehicles.

Microarray-based detection of 90 antibiotic resistance genes of gram-positive bacteria

A disposable microarray was developed for detection of up to 90 antibiotic resistance genes in gram-positive bacteria by hybridization. Each antibiotic resistance gene is represented by two specific oligonucleotides chosen from consensus sequences of gene families, except for nine genes for which only one specific oligonucleotide could be developed. A total of 137 oligonucleotides (26 to 33 nucleotides in length with similar physicochemical parameters) were spotted onto the microarray. The microarrays (ArrayTubes) were hybridized with 36 strains carrying specific antibiotic resistance genes that allowed testing of the sensitivity and specificity of 125 oligonucleotides. Among these were well-characterized multidrug-resistant strains of Enterococcus faecalis, Enterococcus faecium, and Lactococcus lactis and an avirulent strain of Bacillus anthracis harboring the broad-host-range resistance plasmid pRE25. Analysis of two multidrug-resistant field strains allowed the detection of 12 different antibiotic resistance genes in a Staphylococcus haemolyticus strain isolated from mastitis milk and 6 resistance genes in a Clostridium perfringens strain isolated from a calf. In both cases, the microarray genotyping corresponded to the phenotype of the strains. The ArrayTube platform presents the advantage of rapidly screening bacteria for the presence of antibiotic resistance genes known in gram-positive bacteria. This technology has a large potential for applications in basic research, food safety, and surveillance programs for antimicrobial resistance.

Identification and detection of Actinobacillus pleuropneumoniae by PCR based on the gene apxIVA

The apxIVA gene, a recently discovered RTX determinant of Actinobacillus pleuropneumoniae, was shown to be species-specific. DNA hybridization experiments using probes for various regions of apxIVA revealed that the 3'-terminus of this gene was present in all 14 serotypes of A. pleuropneumoniae but absent from phylogenetically related species. A primer pair spanning this region specifically amplified a 422bp fragment in PCR experiments with DNA from the reference strains of the 14 serotypes and 194 field strains isolated from various geographic locations worldwide. DNA sequence analysis of PCR products derived from all serotypes were identical except in serotypes 3, 8, and 10, which showed minor differences. The PCR did not amplify any product when DNA from 17 different bacterial species closely related to A. pleuropneumoniae was used as template. In addition, the PCR was negative with DNA of several Actinobacillus sp. which were initially characterized as A. pleuropneumoniae using routine phenotypic and serological analyses but which were subsequently shown by 16S rRNA sequence analysis to belong to yet undefined Actinobacillus species. The sensitivity of the PCR was determined to be 10pg of A. pleuropneumoniae DNA. A set of nested primers amplified a 377bp fragment specifically with A. pleuropneumoniae DNA. DNA titration experiments using the flanking and nested primer pairs showed an improved level of sensitivity to approximately 10fg of genomic DNA. The nested PCR was used to monitor the spread of A. pleuropneumoniae in pigs experimentally infected with a virulent serotype 1 strain and housed in a controlled environment facility. A. pleuropneumoniae DNA could be detected by nested PCR in nasal swab samples of infected pigs receiving either a high dose (5x10(5)) or a low dose (1x10(4)) challenge and in unchallenged cohorts that were contact-infected by the inoculated animals. Furthermore, PCR confirmed the presence of A. pleuropneumoniae in 16/17 homogenates from necrotic lung lesions, while the bacterium was successfully recovered from 13 of these lesions by culture.

Development of a bead-based multiplex assay for the simultaneous detection of porcine inflammation markers using xMAP technology

Commercially available assays for the simultaneous detection of multiple inflammatory and cardiac markers in porcine blood samples are currently lacking. Therefore, this study was aimed at developing a bead-based, multiplexed flow cytometric assay to simultaneously detect porcine cytokines [interleukin (IL)-1β, IL-6, IL-10, and tumor necrosis factor alpha], chemokines (IL-8 and monocyte chemotactic protein 1), growth factors [basic fibroblast growth factor (bFGF), vascular endothelial growth factor, and platelet-derived growth factor-bb], and injury markers (cardiac troponin-I) as well as complement activation markers (C5a and sC5b-9). The method was based on the Luminex xMAP technology, resulting in the assembly of a 6- and 11-plex from the respective individual singleplex situation. The assay was evaluated for dynamic range, sensitivity, cross-reactivity, intra-assay and interassay variance, spike recovery, and correlation between multiplex and commercially available enzyme-linked immunosorbent assay as well as the respective singleplex. The limit of detection ranged from 2.5 to 30,000 pg/ml for all analytes (6- and 11-plex assays), except for soluble C5b-9 with a detection range of 2-10,000 ng/ml (11-plex). Typically, very low cross-reactivity (<3% and <1.4% by 11- and 6-plex, respectively) between analytes was found. Intra-assay variances ranged from 4.9 to 7.4% (6-plex) and 5.3 to 12.9% (11-plex). Interassay variances for cytokines were between 8.1 and 28.8% (6-plex) and 10.1 and 26.4% (11-plex). Correlation coefficients with singleplex assays for 6-plex as well as for 11-plex were high, ranging from 0.988 to 0.997 and 0.913 to 0.999, respectively. In this study, a bead-based porcine 11-plex and 6-plex assay with a good assay sensitivity, broad dynamic range, and low intra-assay variance and cross-reactivity was established. These assays therefore represent a new, useful tool for the analysis of samples generated from experiments with pigs.

PCR-based assay using occult blood detection cards for detection of diarrheagenic Escherichia coli in specimens from U.S. travelers to Mexico with acute diarrhea.

Large field studies of travelers' diarrhea for multiple destinations are limited by the need to perform stool cultures on site in a timely manner. A method for the collection, transport, and storage of fecal specimens that does not require immediate processing and refrigeration and that is stable for months would be advantageous. This study was designed to determine if enterotoxigenic Escherichia coli (ETEC) and enteroaggregative E. coli (EAEC) DNA could be identified from cards that were processed for the evaluation of fecal occult blood. U.S. students traveling to Mexico during 2005 to 2007 were monitored for the occurrence of diarrheal illness. When ill, students provided a stool specimen for culture and occult blood by the standard methods. Cards then were stored at room temperature prior to DNA extraction. Fecal PCR was performed to identify ETEC and EAEC in DNA extracted from stools and from occult blood cards. Significantly more EAEC cases were identified by PCR that was performed on DNA that was extracted from cards (49%) or from frozen feces (40%) than from culture methods that used HEp-2 adherence assays (13%) (P < 0.001). Similarly, more ETEC cases were detected from card DNA (38%) than from fecal DNA (30%) or by culture that was followed by hybridization (10%) (P < 0.001). The sensitivity and specificity of the card test were 75 and 62%, respectively, compared to those for EAEC by culture and were 50 and 63%, respectively, compared to those for ETEC. DNA extracted from fecal cards that was used for the detection of occult blood is of use in identifying diarrheagenic E. coli.

A novel universal DNA labeling and amplification system for rapid microarray-based detection of 117 antibiotic resistance genes in Gram-positive bacteria.

A rapid and simple DNA labeling system has been developed for disposable microarrays and has been validated for the detection of 117 antibiotic resistance genes abundant in Gram-positive bacteria. The DNA was fragmented and amplified using phi-29 polymerase and random primers with linkers. Labeling and further amplification were then performed by classic PCR amplification using biotinylated primers specific for the linkers. The microarray developed by Perreten et al. (Perreten, V., Vorlet-Fawer, L., Slickers, P., Ehricht, R., Kuhnert, P., Frey, J., 2005. Microarray-based detection of 90 antibiotic resistance genes of gram-positive bacteria. J.Clin.Microbiol. 43, 2291-2302.) was improved by additional oligonucleotides. A total of 244 oligonucleotides (26 to 37 nucleotide length and with similar melting temperatures) were spotted on the microarray, including genes conferring resistance to clinically important antibiotic classes like β-lactams, macrolides, aminoglycosides, glycopeptides and tetracyclines. Each antibiotic resistance gene is represented by at least 2 oligonucleotides designed from consensus sequences of gene families. The specificity of the oligonucleotides and the quality of the amplification and labeling were verified by analysis of a collection of 65 strains belonging to 24 species. Association between genotype and phenotype was verified for 6 antibiotics using 77 Staphylococcus strains belonging to different species and revealed 95% test specificity and a 93% predictive value of a positive test. The DNA labeling and amplification is independent of the species and of the target genes and could be used for different types of microarrays. This system has also the advantage to detect several genes within one bacterium at once, like in Staphylococcus aureus strain BM3318, in which up to 15 genes were detected. This new microarray-based detection system offers a large potential for applications in clinical diagnostic, basic research, food safety and surveillance programs for antimicrobial resistance.

A PCR based assay for the detection of enteric pathogens from Hemoccult(RTM) cards

Background. Large field studies in travelers' diarrhea (TD) in multiple destinations are limited by the need to perform stool cultures on site in a timely manner. A method for the collection, transport and storage of fecal specimens that does not require immediate processing, refrigeration and is stable for months would be advantageous. ^ Objectives. Determine if enteric pathogen bacterial DNA can be identified in cards routinely used for evaluation of fecal occult blood. ^ Methods. U.S. students traveling to Mexico in 2005-07 were followed for occurrence of diarrheal illness. When ill, students provided a stool specimen for culture and occult blood by the standard method. Cards were then stored at room temperature prior to DNA extraction. A multiplex fecal PCR was performed to identify enterotoxigenic Escherichia coli and enteroaggregative E. coli (EAEC) in DNA extracted from stools and occult blood cards. ^ Results. Significantly more EAEC cases were identified by PCR done in DNA extracted from cards (49%) or from frozen feces (40%) than by culture followed by HEp-2 adherence assays (13%). Similarly more ETEC cases were detected in card DNA (38%) than fecal DNA (30%) or culture followed by hybridization (10%). Sensitivity and specificity of the card test was 75% and 62%, respectively, and 50% and 63%, respectively, when compared to EAEC and ETEC culture, respectively, and 53% and 51%, respectively compared to EAEC multiplex fecal PCR and 56% and 70%, respectively, compared to ETEC multiplex fecal PCR. ^ Conclusions. DNA extracted from fecal cards used for detection of occult blood is of use in detecting enteric pathogens. ^

Homography-based ground plane detection using a single on-board camera

This study presents a robust method for ground plane detection in vision-based systems with a non-stationary camera. The proposed method is based on the reliable estimation of the homography between ground planes in successive images. This homography is computed using a feature matching approach, which in contrast to classical approaches to on-board motion estimation does not require explicit ego-motion calculation. As opposed to it, a novel homography calculation method based on a linear estimation framework is presented. This framework provides predictions of the ground plane transformation matrix that are dynamically updated with new measurements. The method is specially suited for challenging environments, in particular traffic scenarios, in which the information is scarce and the homography computed from the images is usually inaccurate or erroneous. The proposed estimation framework is able to remove erroneous measurements and to correct those that are inaccurate, hence producing a reliable homography estimate at each instant. It is based on the evaluation of the difference between the predicted and the observed transformations, measured according to the spectral norm of the associated matrix of differences. Moreover, an example is provided on how to use the information extracted from ground plane estimation to achieve object detection and tracking. The method has been successfully demonstrated for the detection of moving vehicles in traffic environments.