The clustering of galaxies in the completed SDSS-III Baryon Oscillation Spectroscopic Survey: combining correlated Gaussian posterior distributions

DOE PAGES

Sánchez, Ariel G.; Grieb, Jan Niklas; Salazar-Albornoz, Salvador; ...

2016-09-30

The cosmological information contained in anisotropic galaxy clustering measurements can often be compressed into a small number of parameters whose posterior distribution is well described by a Gaussian. Here, we present a general methodology to combine these estimates into a single set of consensus constraints that encode the total information of the individual measurements, taking into account the full covariance between the different methods. We also illustrate this technique by applying it to combine the results obtained from different clustering analyses, including measurements of the signature of baryon acoustic oscillations and redshift-space distortions, based on a set of mock cataloguesmore » of the final SDSS-III Baryon Oscillation Spectroscopic Survey (BOSS). Our results show that the region of the parameter space allowed by the consensus constraints is smaller than that of the individual methods, highlighting the importance of performing multiple analyses on galaxy surveys even when the measurements are highly correlated. Our paper is part of a set that analyses the final galaxy clustering data set from BOSS. The methodology presented here is used in Alam et al. to produce the final cosmological constraints from BOSS.« less

Highly preserved consensus gene modules in human papilloma virus 16 positive cervical cancer and head and neck cancers.

PubMed

Zhang, Xianglan; Cha, In-Ho; Kim, Ki-Yeol

2017-12-26

In this study, we investigated the consensus gene modules in head and neck cancer (HNC) and cervical cancer (CC). We used a publicly available gene expression dataset, GSE6791, which included 42 HNC, 14 normal head and neck, 20 CC and 8 normal cervical tissue samples. To exclude bias because of different human papilloma virus (HPV) types, we analyzed HPV16-positive samples only. We identified 3824 genes common to HNC and CC samples. Among these, 977 genes showed high connectivity and were used to construct consensus modules. We demonstrated eight consensus gene modules for HNC and CC using the dissimilarity measure and average linkage hierarchical clustering methods. These consensus modules included genes with significant biological functions, including ATP binding and extracellular exosome. Eigengen network analysis revealed the consensus modules were highly preserved with high connectivity. These findings demonstrate that HPV16-positive head and neck and cervical cancers share highly preserved consensus gene modules with common potentially therapeutic targets.

Highly preserved consensus gene modules in human papilloma virus 16 positive cervical cancer and head and neck cancers

PubMed Central

Zhang, Xianglan; Cha, In-Ho; Kim, Ki-Yeol

2017-01-01

In this study, we investigated the consensus gene modules in head and neck cancer (HNC) and cervical cancer (CC). We used a publicly available gene expression dataset, GSE6791, which included 42 HNC, 14 normal head and neck, 20 CC and 8 normal cervical tissue samples. To exclude bias because of different human papilloma virus (HPV) types, we analyzed HPV16-positive samples only. We identified 3824 genes common to HNC and CC samples. Among these, 977 genes showed high connectivity and were used to construct consensus modules. We demonstrated eight consensus gene modules for HNC and CC using the dissimilarity measure and average linkage hierarchical clustering methods. These consensus modules included genes with significant biological functions, including ATP binding and extracellular exosome. Eigengen network analysis revealed the consensus modules were highly preserved with high connectivity. These findings demonstrate that HPV16-positive head and neck and cervical cancers share highly preserved consensus gene modules with common potentially therapeutic targets. PMID:29371966

Exit, cohesion, and consensus: social psychological moderators of consensus among adolescent peer groups

PubMed Central

Fisher, Jacob C.

2017-01-01

Virtually all social diffusion work relies on a common formal basis, which predicts that consensus will develop among a connected population as the result of diffusion. In spite of the popularity of social diffusion models that predict consensus, few empirical studies examine consensus, or a clustering of attitudes, directly. Those that do either focus on the coordinating role of strict hierarchies, or on the results of online experiments, and do not consider how consensus occurs among groups in situ. This study uses longitudinal data on adolescent social networks to show how meso-level social structures, such as informal peer groups, moderate the process of consensus formation. Using a novel method for controlling for selection into a group, I find that centralized peer groups, meaning groups with clear leaders, have very low levels of consensus, while cohesive peer groups, meaning groups where more ties hold the members of the group together, have very high levels of consensus. This finding is robust to two different measures of cohesion and consensus. This suggests that consensus occurs either through central leaders’ enforcement or through diffusion of attitudes, but that central leaders have limited ability to enforce when people can leave the group easily. PMID:29335675

Personalising care of adults with asthma from Asia: a modified e-Dephi consensus study to inform management tailored to attitude and control profiles.

PubMed

Chisholm, Alison; Price, David B; Pinnock, Hilary; Lee, Tan Tze; Roa, Camilo; Cho, Sang-Heon; David-Wang, Aileen; Wong, Gary; van der Molen, Thys; Ryan, Dermot; Castillo-Carandang, Nina; Yong, Yee Vern

2017-01-05

REALISE Asia-an online questionnaire-based study of Asian asthma patients-identified five patient clusters defined in terms of their control status and attitude towards their asthma (categorised as: 'Well-adjusted and at least partly controlled'; 'In denial about symptoms'; 'Tolerating with poor control'; 'Adrift and poorly controlled'; 'Worried with multiple symptoms'). We developed consensus recommendations for tailoring management of these attitudinal-control clusters. An expert panel undertook a three-round electronic Delphi (e-Delphi): Round 1: panellists received descriptions of the attitudinal-control clusters and provided free text recommendations for their assessment and management. Round 2: panellists prioritised Round 1 recommendations and met (or joined a teleconference) to consolidate the recommendations. Round 3: panellists voted and prioritised the remaining recommendations. Consensus was defined as Round 3 recommendations endorsed by >50% of panellists. Highest priority recommendations were those receiving the highest score. The multidisciplinary panellists (9 clinicians, 1 pharmacist and 1 health social scientist; 7 from Asia) identified consensus recommendations for all clusters. Recommended pharmacological (e.g., step-up/down; self-management; simplified regimen) and non-pharmacological approaches (e.g., trigger management, education, social support; inhaler technique) varied substantially according to each cluster's attitude to asthma and associated psychosocial drivers of behaviour. The attitudinal-control clusters defined by REALISE Asia resonated with the international panel. Consensus was reached on appropriate tailored management approaches for all clusters. Summarised and incorporated into a structured management pathway, these recommendations could facilitate personalised care. Generalisability of these patient clusters should be assessed in other socio-economic, cultural and literacy groups and nationalities in Asia.

Oral toxicity management in head and neck cancer patients treated with chemotherapy and radiation: Dental pathologies and osteoradionecrosis (Part 1) literature review and consensus statement.

PubMed

Buglione, Michela; Cavagnini, Roberta; Di Rosario, Federico; Sottocornola, Lara; Maddalo, Marta; Vassalli, Lucia; Grisanti, Salvatore; Salgarello, Stefano; Orlandi, Ester; Paganelli, Corrado; Majorana, Alessandra; Gastaldi, Giorgio; Bossi, Paolo; Berruti, Alfredo; Pavanato, Giovanni; Nicolai, Piero; Maroldi, Roberto; Barasch, Andrei; Russi, Elvio G; Raber-Durlacher, Judith; Murphy, Barbara; Magrini, Stefano M

2016-01-01

Radiotherapy alone or in combination with chemotherapy and/or surgery is the typical treatment for head and neck cancer patients. Acute side effects (such as oral mucositis, dermatitis, salivary changes, taste alterations, etc.), and late toxicities in particular (such as osteo-radionecrosis, hypo-salivation and xerostomia, trismus, radiation caries etc.), are often debilitating. These effects tend to be underestimated and insufficiently addressed in the medical community. A multidisciplinary group of head and neck cancer specialists met in Milan with the aim of reaching a consensus on clinical definitions and management of these toxicities. The Delphi Appropriateness method was used for developing the consensus, and external experts evaluated the conclusions. This paper contains 10 clusters of statements about the clinical definitions and management of head and neck cancer treatment sequels (dental pathologies and osteo-radionecroses) that reached consensus, and offers a review of the literature about these topics. The review was split into two parts: the first part dealt with dental pathologies and osteo-radionecroses (10 clusters of statements), whereas this second part deals with trismus and xerostomia. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Automating the expert consensus paradigm for robust lung tissue classification

NASA Astrophysics Data System (ADS)

Rajagopalan, Srinivasan; Karwoski, Ronald A.; Raghunath, Sushravya; Bartholmai, Brian J.; Robb, Richard A.

2012-03-01

Clinicians confirm the efficacy of dynamic multidisciplinary interactions in diagnosing Lung disease/wellness from CT scans. However, routine clinical practice cannot readily accomodate such interactions. Current schemes for automating lung tissue classification are based on a single elusive disease differentiating metric; this undermines their reliability in routine diagnosis. We propose a computational workflow that uses a collection (#: 15) of probability density functions (pdf)-based similarity metrics to automatically cluster pattern-specific (#patterns: 5) volumes of interest (#VOI: 976) extracted from the lung CT scans of 14 patients. The resultant clusters are refined for intra-partition compactness and subsequently aggregated into a super cluster using a cluster ensemble technique. The super clusters were validated against the consensus agreement of four clinical experts. The aggregations correlated strongly with expert consensus. By effectively mimicking the expertise of physicians, the proposed workflow could make automation of lung tissue classification a clinical reality.

Scientific consensus, the law, and same sex parenting outcomes.

PubMed

adams, Jimi; Light, Ryan

2015-09-01

While the US Supreme Court was considering two related cases involving the constitutionality of same-sex marriage, one major question informing that decision was whether scientific research had achieved consensus regarding how children of same-sex couples fare. Determining the extent of consensus has become a key aspect of how social science evidence and testimony is accepted by the courts. Here, we show how a method of analyzing temporal patterns in citation networks can be used to assess the state of social scientific literature as a means to inform just such a question. Patterns of clustering within these citation networks reveal whether and when consensus arises within a scientific field. We find that the literature on outcomes for children of same-sex parents is marked by scientific consensus that they experience "no differences" compared to children from other parental configurations. Copyright © 2015 Elsevier Inc. All rights reserved.

Evaluation of Nine Consensus Indices in Delphi Foresight Research and Their Dependency on Delphi Survey Characteristics: A Simulation Study and Debate on Delphi Design and Interpretation.

PubMed

Birko, Stanislav; Dove, Edward S; Özdemir, Vural

2015-01-01

The extent of consensus (or the lack thereof) among experts in emerging fields of innovation can serve as antecedents of scientific, societal, investor and stakeholder synergy or conflict. Naturally, how we measure consensus is of great importance to science and technology strategic foresight. The Delphi methodology is a widely used anonymous survey technique to evaluate consensus among a panel of experts. Surprisingly, there is little guidance on how indices of consensus can be influenced by parameters of the Delphi survey itself. We simulated a classic three-round Delphi survey building on the concept of clustered consensus/dissensus. We evaluated three study characteristics that are pertinent for design of Delphi foresight research: (1) the number of survey questions, (2) the sample size, and (3) the extent to which experts conform to group opinion (the Group Conformity Index) in a Delphi study. Their impacts on the following nine Delphi consensus indices were then examined in 1000 simulations: Clustered Mode, Clustered Pairwise Agreement, Conger's Kappa, De Moivre index, Extremities Version of the Clustered Pairwise Agreement, Fleiss' Kappa, Mode, the Interquartile Range and Pairwise Agreement. The dependency of a consensus index on the Delphi survey characteristics was expressed from 0.000 (no dependency) to 1.000 (full dependency). The number of questions (range: 6 to 40) in a survey did not have a notable impact whereby the dependency values remained below 0.030. The variation in sample size (range: 6 to 50) displayed the top three impacts for the Interquartile Range, the Clustered Mode and the Mode (dependency = 0.396, 0.130, 0.116, respectively). The Group Conformity Index, a construct akin to measuring stubbornness/flexibility of experts' opinions, greatly impacted all nine Delphi consensus indices (dependency = 0.200 to 0.504), except the Extremity CPWA and the Interquartile Range that were impacted only beyond the first decimal point (dependency = 0.087 and 0.083, respectively). Scholars in technology design, foresight research and future(s) studies might consider these new findings in strategic planning of Delphi studies, for example, in rational choice of consensus indices and sample size, or accounting for confounding factors such as experts' variable degrees of conformity (stubbornness/flexibility) in modifying their opinions.

Evaluation of Nine Consensus Indices in Delphi Foresight Research and Their Dependency on Delphi Survey Characteristics: A Simulation Study and Debate on Delphi Design and Interpretation

PubMed Central

Birko, Stanislav; Dove, Edward S.; Özdemir, Vural

2015-01-01

The extent of consensus (or the lack thereof) among experts in emerging fields of innovation can serve as antecedents of scientific, societal, investor and stakeholder synergy or conflict. Naturally, how we measure consensus is of great importance to science and technology strategic foresight. The Delphi methodology is a widely used anonymous survey technique to evaluate consensus among a panel of experts. Surprisingly, there is little guidance on how indices of consensus can be influenced by parameters of the Delphi survey itself. We simulated a classic three-round Delphi survey building on the concept of clustered consensus/dissensus. We evaluated three study characteristics that are pertinent for design of Delphi foresight research: (1) the number of survey questions, (2) the sample size, and (3) the extent to which experts conform to group opinion (the Group Conformity Index) in a Delphi study. Their impacts on the following nine Delphi consensus indices were then examined in 1000 simulations: Clustered Mode, Clustered Pairwise Agreement, Conger’s Kappa, De Moivre index, Extremities Version of the Clustered Pairwise Agreement, Fleiss’ Kappa, Mode, the Interquartile Range and Pairwise Agreement. The dependency of a consensus index on the Delphi survey characteristics was expressed from 0.000 (no dependency) to 1.000 (full dependency). The number of questions (range: 6 to 40) in a survey did not have a notable impact whereby the dependency values remained below 0.030. The variation in sample size (range: 6 to 50) displayed the top three impacts for the Interquartile Range, the Clustered Mode and the Mode (dependency = 0.396, 0.130, 0.116, respectively). The Group Conformity Index, a construct akin to measuring stubbornness/flexibility of experts’ opinions, greatly impacted all nine Delphi consensus indices (dependency = 0.200 to 0.504), except the Extremity CPWA and the Interquartile Range that were impacted only beyond the first decimal point (dependency = 0.087 and 0.083, respectively). Scholars in technology design, foresight research and future(s) studies might consider these new findings in strategic planning of Delphi studies, for example, in rational choice of consensus indices and sample size, or accounting for confounding factors such as experts’ variable degrees of conformity (stubbornness/flexibility) in modifying their opinions. PMID:26270647

Clustering of self-organizing map identifies five distinct medulloblastoma subgroups.

PubMed

Cao, Changjun; Wang, Wei; Jiang, Pucha

2016-01-01

Medulloblastoma is one the most malignant paediatric brain tumours. Molecular subgrouping these medulloblastomas will not only help identify specific cohorts for certain treatment but also improve confidence in prognostic prediction. Currently, there is a consensus of the existences of four distinct subtypes of medulloblastoma. We proposed a novel bioinformatics method, clustering of self-organizing map, to determine the subgroups and their molecular diversity. Microarray expression profiles of 46 medulloblastoma samples were analysed and five clusters with distinct demographics, clinical outcome and transcriptional profiles were identified. The previously reported Wnt subgroup was identified as expected. Three other novel subgroups were proposed for later investigation. Our findings underscore the value of SOM clustering for discovering the medulloblastoma subgroups. When the suggested subdivision has been confirmed in large cohorts, this method should serve as a part of routine classification of clinical samples.

Unsupervised consensus cluster analysis of [18F]-fluoroethyl-L-tyrosine positron emission tomography identified textural features for the diagnosis of pseudoprogression in high-grade glioma

PubMed Central

Kebir, Sied; Khurshid, Zain; Gaertner, Florian C.; Essler, Markus; Hattingen, Elke; Fimmers, Rolf; Scheffler, Björn; Herrlinger, Ulrich; Bundschuh, Ralph A.; Glas, Martin

2017-01-01

Rationale Timely detection of pseudoprogression (PSP) is crucial for the management of patients with high-grade glioma (HGG) but remains difficult. Textural features of O-(2-[18F]fluoroethyl)-L-tyrosine positron emission tomography (FET-PET) mirror tumor uptake heterogeneity; some of them may be associated with tumor progression. Methods Fourteen patients with HGG and suspected of PSP underwent FET-PET imaging. A set of 19 conventional and textural FET-PET features were evaluated and subjected to unsupervised consensus clustering. The final diagnosis of true progression vs. PSP was based on follow-up MRI using RANO criteria. Results Three robust clusters have been identified based on 10 predominantly textural FET-PET features. None of the patients with PSP fell into cluster 2, which was associated with high values for textural FET-PET markers of uptake heterogeneity. Three out of 4 patients with PSP were assigned to cluster 3 that was largely associated with low values of textural FET-PET features. By comparison, tumor-to-normal brain ratio (TNRmax) at the optimal cutoff 2.1 was less predictive of PSP (negative predictive value 57% for detecting true progression, p=0.07 vs. 75% with cluster 3, p=0.04). Principal Conclusions Clustering based on textural O-(2-[18F]fluoroethyl)-L-tyrosine PET features may provide valuable information in assessing the elusive phenomenon of pseudoprogression. PMID:28030820

Superior ab initio identification, annotation and characterisation of TEs and segmental duplications from genome assemblies.

PubMed

Zeng, Lu; Kortschak, R Daniel; Raison, Joy M; Bertozzi, Terry; Adelson, David L

2018-01-01

Transposable Elements (TEs) are mobile DNA sequences that make up significant fractions of amniote genomes. However, they are difficult to detect and annotate ab initio because of their variable features, lengths and clade-specific variants. We have addressed this problem by refining and developing a Comprehensive ab initio Repeat Pipeline (CARP) to identify and cluster TEs and other repetitive sequences in genome assemblies. The pipeline begins with a pairwise alignment using krishna, a custom aligner. Single linkage clustering is then carried out to produce families of repetitive elements. Consensus sequences are then filtered for protein coding genes and then annotated using Repbase and a custom library of retrovirus and reverse transcriptase sequences. This process yields three types of family: fully annotated, partially annotated and unannotated. Fully annotated families reflect recently diverged/young known TEs present in Repbase. The remaining two types of families contain a mixture of novel TEs and segmental duplications. These can be resolved by aligning these consensus sequences back to the genome to assess copy number vs. length distribution. Our pipeline has three significant advantages compared to other methods for ab initio repeat identification: 1) we generate not only consensus sequences, but keep the genomic intervals for the original aligned sequences, allowing straightforward analysis of evolutionary dynamics, 2) consensus sequences represent low-divergence, recently/currently active TE families, 3) segmental duplications are annotated as a useful by-product. We have compared our ab initio repeat annotations for 7 genome assemblies to other methods and demonstrate that CARP compares favourably with RepeatModeler, the most widely used repeat annotation package.

Superior ab initio identification, annotation and characterisation of TEs and segmental duplications from genome assemblies

PubMed Central

Zeng, Lu; Kortschak, R. Daniel; Raison, Joy M.

2018-01-01

Transposable Elements (TEs) are mobile DNA sequences that make up significant fractions of amniote genomes. However, they are difficult to detect and annotate ab initio because of their variable features, lengths and clade-specific variants. We have addressed this problem by refining and developing a Comprehensive ab initio Repeat Pipeline (CARP) to identify and cluster TEs and other repetitive sequences in genome assemblies. The pipeline begins with a pairwise alignment using krishna, a custom aligner. Single linkage clustering is then carried out to produce families of repetitive elements. Consensus sequences are then filtered for protein coding genes and then annotated using Repbase and a custom library of retrovirus and reverse transcriptase sequences. This process yields three types of family: fully annotated, partially annotated and unannotated. Fully annotated families reflect recently diverged/young known TEs present in Repbase. The remaining two types of families contain a mixture of novel TEs and segmental duplications. These can be resolved by aligning these consensus sequences back to the genome to assess copy number vs. length distribution. Our pipeline has three significant advantages compared to other methods for ab initio repeat identification: 1) we generate not only consensus sequences, but keep the genomic intervals for the original aligned sequences, allowing straightforward analysis of evolutionary dynamics, 2) consensus sequences represent low-divergence, recently/currently active TE families, 3) segmental duplications are annotated as a useful by-product. We have compared our ab initio repeat annotations for 7 genome assemblies to other methods and demonstrate that CARP compares favourably with RepeatModeler, the most widely used repeat annotation package. PMID:29538441

ConsDock: A new program for the consensus analysis of protein-ligand interactions.

PubMed

Paul, Nicodème; Rognan, Didier

2002-06-01

Protein-based virtual screening of chemical libraries is a powerful technique for identifying new molecules that may interact with a macromolecular target of interest. Because of docking and scoring limitations, it is more difficult to apply as a lead optimization method because it requires that the docking/scoring tool is able to propose as few solutions as possible and all of them with a very good accuracy for both the protein-bound orientation and the conformation of the ligand. In the present study, we present a consensus docking approach (ConsDock) that takes advantage of three widely used docking tools (Dock, FlexX, and Gold). The consensus analysis of all possible poses generated by several docking tools is performed sequentially in four steps: (i) hierarchical clustering of all poses generated by a docking tool into families represented by a leader; (ii) definition of all consensus pairs from leaders generated by different docking programs; (iii) clustering of consensus pairs into classes, represented by a mean structure; and (iv) ranking the different means starting from the most populated class of consensus pairs. When applied to a test set of 100 protein-ligand complexes from the Protein Data Bank, ConsDock significantly outperforms single docking with respect to the docking accuracy of the top-ranked pose. In 60% of the cases investigated here, ConsDock was able to rank as top solution a pose within 2 A RMSD of the X-ray structure. It can be applied as a postprocessing filter to either single- or multiple-docking programs to prioritize three-dimensional guided lead optimization from the most likely docking solution. Copyright 2002 Wiley-Liss, Inc.

Identification and application of self-binding zipper-like sequences in SARS-CoV spike protein.

PubMed

Zhang, Si Min; Liao, Ying; Neo, Tuan Ling; Lu, Yanning; Liu, Ding Xiang; Vahlne, Anders; Tam, James P

2018-05-22

Self-binding peptides containing zipper-like sequences, such as the Leu/Ile zipper sequence within the coiled coil regions of proteins and the cross-β spine steric zippers within the amyloid-like fibrils, could bind to the protein-of-origin through homophilic sequence-specific zipper motifs. These self-binding sequences represent opportunities for the development of biochemical tools and/or therapeutics. Here, we report on the identification of a putative self-binding β-zipper-forming peptide within the severe acute respiratory syndrome-associated coronavirus spike (S) protein and its application in viral detection. Peptide array scanning of overlapping peptides covering the entire length of S protein identified 34 putative self-binding peptides of six clusters, five of which contained octapeptide core consensus sequences. The Cluster I consensus octapeptide sequence GINITNFR was predicted by the Eisenberg's 3D profile method to have high amyloid-like fibrillation potential through steric β-zipper formation. Peptide C6 containing the Cluster I consensus sequence was shown to oligomerize and form amyloid-like fibrils. Taking advantage of this, C6 was further applied to detect the S protein expression in vitro by fluorescence staining. Meanwhile, the coiled-coil-forming Leu/Ile heptad repeat sequences within the S protein were under-represented during peptide array scanning, in agreement with that long peptide lengths were required to attain high helix-mediated interaction avidity. The data suggest that short β-zipper-like self-binding peptides within the S protein could be identified through combining the peptide scanning and predictive methods, and could be exploited as biochemical detection reagents for viral infection. Copyright © 2018. Published by Elsevier Ltd.

Global tracking of space debris via CPHD and consensus

NASA Astrophysics Data System (ADS)

Wei, Baishen; Nener, Brett; Liu, Weifeng; Ma, Liang

2017-05-01

Space debris tracking is of great importance for safe operation of spacecraft. This paper presents an algorithm that achieves global tracking of space debris with a multi-sensor network. The sensor network has unknown and possibly time-varying topology. A consensus algorithm is used to effectively counteract the effects of data incest. Gaussian Mixture-Cardinalized Probability Hypothesis Density (GM-CPHD) filtering is used to estimate the state of the space debris. As an example of the method, 45 clusters of sensors are used to achieve global tracking. The performance of the proposed approach is demonstrated by simulation experiments.

MaRaCluster: A Fragment Rarity Metric for Clustering Fragment Spectra in Shotgun Proteomics.

PubMed

The, Matthew; Käll, Lukas

2016-03-04

Shotgun proteomics experiments generate large amounts of fragment spectra as primary data, normally with high redundancy between and within experiments. Here, we have devised a clustering technique to identify fragment spectra stemming from the same species of peptide. This is a powerful alternative method to traditional search engines for analyzing spectra, specifically useful for larger scale mass spectrometry studies. As an aid in this process, we propose a distance calculation relying on the rarity of experimental fragment peaks, following the intuition that peaks shared by only a few spectra offer more evidence than peaks shared by a large number of spectra. We used this distance calculation and a complete-linkage scheme to cluster data from a recent large-scale mass spectrometry-based study. The clusterings produced by our method have up to 40% more identified peptides for their consensus spectra compared to those produced by the previous state-of-the-art method. We see that our method would advance the construction of spectral libraries as well as serve as a tool for mining large sets of fragment spectra. The source code and Ubuntu binary packages are available at https://github.com/statisticalbiotechnology/maracluster (under an Apache 2.0 license).

Effects of heterogeneous convergence rate on consensus in opinion dynamics

NASA Astrophysics Data System (ADS)

Huang, Changwei; Dai, Qionglin; Han, Wenchen; Feng, Yuee; Cheng, Hongyan; Li, Haihong

2018-06-01

The Deffuant model has attracted much attention in the study of opinion dynamics. Here, we propose a modified version by introducing into the model a heterogeneous convergence rate which is dependent on the opinion difference between interacting agents and a tunable parameter κ. We study the effects of heterogeneous convergence rate on consensus by investigating the probability of complete consensus, the size of the largest opinion cluster, the number of opinion clusters, and the relaxation time. We find that the decrease of the convergence rate is favorable to decreasing the confidence threshold for the population to always reach complete consensus, and there exists optimal κ resulting in the minimal bounded confidence threshold. Moreover, we find that there exists a window before the threshold of confidence in which complete consensus may be reached with a nonzero probability when κ is not too large. We also find that, within a certain confidence range, decreasing the convergence rate will reduce the relaxation time, which is somewhat counterintuitive.

Expert consensus for performing right heart catheterisation for suspected pulmonary arterial hypertension in systemic sclerosis: a Delphi consensus study with cluster analysis.

PubMed

Avouac, Jérôme; Huscher, Dörte; Furst, Daniel E; Opitz, Christian F; Distler, Oliver; Allanore, Yannick

2014-01-01

To establish an expert consensus on which criteria are the most appropriate in clinical practice to refer patients with systemic sclerosis (SSc) for right heart catheterisation (RHC) when pulmonary hypertension (PH) is suspected. A three stage internet based Delphi consensus exercise involving worldwide PH experts was designed. In the first stage, a comprehensive list of domains and items combining evidence based indications and expert opinions were obtained. In the second and third stages, experts were asked to rate each item selected in the list. After each of stages 2 and 3, the number of items and criteria were reduced according to a cluster analysis. A literature search and the opinions of 47 experts participating in Delphi stage 1 provided a list of seven domains containing 142 criteria. After stages 2 and 3, these domains and tools were reduced to three domains containing eight tools: clinical (progressive dyspnoea over the past 3 months, unexplained dyspnoea, worsening of WHO dyspnoea functional class, any finding on physical examination suggestive of elevated right heart pressures and any sign of right heart failure), echocardiography (systolic pulmonary artery pressure >45 mm Hg and right ventricle dilation) and pulmonary function tests (diffusion lung capacity for carbon monoxide <50% without pulmonary fibrosis). Among experts in pulmonary arterial hypertension-SSc, a core set of criteria for clinical practice to refer SSc patients for RHC has been defined by Delphi consensus methods. Although these indications are recommended by this expert group to be used as an interim tool, it will be necessary to formally validate the present tools in further studies.

ISSR, ERIC and RAPD techniques to detect genetic diversity in the aphid pathogen Pandora neoaphidis.

PubMed

Tymon, Anna M; Pell, Judith K

2005-03-01

The entomopathogenic fungus Pandora neoaphidis is an important natural enemy of aphids. ISSR, ERIC (Enterobacterial Repetitive Intergenic Consensus) and RAPD PCR-based DNA fingerprint analyses were undertaken to study intra-specific variation amongst 30 isolates of P. neoaphidis worldwide, together with six closely related species of Entomophthorales. All methods yielded scorable binary characters, and distance matrices were constructed from both individual and combined data sets. Neighbour-joining was used to construct consensus phylogenetic trees which showed that although P. neoaphidis isolates were highly polymorphic they separated into a monophyletic group compared with the other Entomophthorales tested. Three distinct subclades were found, with UK isolates occupying two of these. No specific correlation with aphid host species was established for any of the isolates apart from those in one cluster which contained isolates obtained from nettle aphid, Microlophium carnosum. ERIC, ISSR and RAPD analysis allowed the rapid genetic characterisation and differentiation of isolates with the generation of potential isolate- and cluster specific-diagnostic DNA markers.

A Decentralized Eigenvalue Computation Method for Spectrum Sensing Based on Average Consensus

NASA Astrophysics Data System (ADS)

Mohammadi, Jafar; Limmer, Steffen; Stańczak, Sławomir

2016-07-01

This paper considers eigenvalue estimation for the decentralized inference problem for spectrum sensing. We propose a decentralized eigenvalue computation algorithm based on the power method, which is referred to as generalized power method GPM; it is capable of estimating the eigenvalues of a given covariance matrix under certain conditions. Furthermore, we have developed a decentralized implementation of GPM by splitting the iterative operations into local and global computation tasks. The global tasks require data exchange to be performed among the nodes. For this task, we apply an average consensus algorithm to efficiently perform the global computations. As a special case, we consider a structured graph that is a tree with clusters of nodes at its leaves. For an accelerated distributed implementation, we propose to use computation over multiple access channel (CoMAC) as a building block of the algorithm. Numerical simulations are provided to illustrate the performance of the two algorithms.

Fast Constrained Spectral Clustering and Cluster Ensemble with Random Projection

PubMed Central

Liu, Wenfen

2017-01-01

Constrained spectral clustering (CSC) method can greatly improve the clustering accuracy with the incorporation of constraint information into spectral clustering and thus has been paid academic attention widely. In this paper, we propose a fast CSC algorithm via encoding landmark-based graph construction into a new CSC model and applying random sampling to decrease the data size after spectral embedding. Compared with the original model, the new algorithm has the similar results with the increase of its model size asymptotically; compared with the most efficient CSC algorithm known, the new algorithm runs faster and has a wider range of suitable data sets. Meanwhile, a scalable semisupervised cluster ensemble algorithm is also proposed via the combination of our fast CSC algorithm and dimensionality reduction with random projection in the process of spectral ensemble clustering. We demonstrate by presenting theoretical analysis and empirical results that the new cluster ensemble algorithm has advantages in terms of efficiency and effectiveness. Furthermore, the approximate preservation of random projection in clustering accuracy proved in the stage of consensus clustering is also suitable for the weighted k-means clustering and thus gives the theoretical guarantee to this special kind of k-means clustering where each point has its corresponding weight. PMID:29312447

Quantitative consensus of supervised learners for diffuse lung parenchymal HRCT patterns

NASA Astrophysics Data System (ADS)

Raghunath, Sushravya; Rajagopalan, Srinivasan; Karwoski, Ronald A.; Bartholmai, Brian J.; Robb, Richard A.

2013-03-01

Automated lung parenchymal classification usually relies on supervised learning of expert chosen regions representative of the visually differentiable HRCT patterns specific to different pathologies (eg. emphysema, ground glass, honey combing, reticular and normal). Considering the elusiveness of a single most discriminating similarity measure, a plurality of weak learners can be combined to improve the machine learnability. Though a number of quantitative combination strategies exist, their efficacy is data and domain dependent. In this paper, we investigate multiple (N=12) quantitative consensus approaches to combine the clusters obtained with multiple (n=33) probability density-based similarity measures. Our study shows that hypergraph based meta-clustering and probabilistic clustering provides optimal expert-metric agreement.

Spatiotemporal multistage consensus clustering in molecular dynamics studies of large proteins.

PubMed

Kenn, Michael; Ribarics, Reiner; Ilieva, Nevena; Cibena, Michael; Karch, Rudolf; Schreiner, Wolfgang

2016-04-26

The aim of this work is to find semi-rigid domains within large proteins as reference structures for fitting molecular dynamics trajectories. We propose an algorithm, multistage consensus clustering, MCC, based on minimum variation of distances between pairs of Cα-atoms as target function. The whole dataset (trajectory) is split into sub-segments. For a given sub-segment, spatial clustering is repeatedly started from different random seeds, and we adopt the specific spatial clustering with minimum target function: the process described so far is stage 1 of MCC. Then, in stage 2, the results of spatial clustering are consolidated, to arrive at domains stable over the whole dataset. We found that MCC is robust regarding the choice of parameters and yields relevant information on functional domains of the major histocompatibility complex (MHC) studied in this paper: the α-helices and β-floor of the protein (MHC) proved to be most flexible and did not contribute to clusters of significant size. Three alleles of the MHC, each in complex with ABCD3 peptide and LC13 T-cell receptor (TCR), yielded different patterns of motion. Those alleles causing immunological allo-reactions showed distinct correlations of motion between parts of the peptide, the binding cleft and the complementary determining regions (CDR)-loops of the TCR. Multistage consensus clustering reflected functional differences between MHC alleles and yields a methodological basis to increase sensitivity of functional analyses of bio-molecules. Due to the generality of approach, MCC is prone to lend itself as a potent tool also for the analysis of other kinds of big data.

Genomic Variability of Haemophilus influenzae Isolated from Mexican Children Determined by Using Enterobacterial Repetitive Intergenic Consensus Sequences and PCR

PubMed Central

Gomez-De-Leon, Patricia; Santos, Jose I.; Caballero, Javier; Gomez, Demostenes; Espinosa, Luz E.; Moreno, Isabel; Piñero, Daniel; Cravioto, Alejandro

2000-01-01

Genomic fingerprints from 92 capsulated and noncapsulated strains of Haemophilus influenzae from Mexican children with different diseases and healthy carriers were generated by PCR using the enterobacterial repetitive intergenic consensus (ERIC) sequences. A cluster analysis by the unweighted pair-group method with arithmetic averages based on the overall similarity as estimated from the characteristics of the genomic fingerprints, was conducted to group the strains. A total of 69 fingerprint patterns were detected in the H. influenzae strains. Isolates from patients with different diseases were represented by a variety of patterns, which clustered into two major groups. Of the 37 strains isolated from cases of meningitis, 24 shared patterns and were clustered into five groups within a similarity level of 1.0. One fragment of 1.25 kb was common to all meningitis strains. H. influenzae strains from healthy carriers presented fingerprint patterns different from those found in strains from sick children. Isolates from healthy individuals were more variable and were distributed differently from those from patients. The results show that ERIC-PCR provides a powerful tool for the determination of the distinctive pathogenicity potentials of H. influenzae strains and encourage its use for molecular epidemiology investigations. PMID:10878033

Metagenome assembly through clustering of next-generation sequencing data using protein sequences.

PubMed

Sim, Mikang; Kim, Jaebum

2015-02-01

The study of environmental microbial communities, called metagenomics, has gained a lot of attention because of the recent advances in next-generation sequencing (NGS) technologies. Microbes play a critical role in changing their environments, and the mode of their effect can be solved by investigating metagenomes. However, the difficulty of metagenomes, such as the combination of multiple microbes and different species abundance, makes metagenome assembly tasks more challenging. In this paper, we developed a new metagenome assembly method by utilizing protein sequences, in addition to the NGS read sequences. Our method (i) builds read clusters by using mapping information against available protein sequences, and (ii) creates contig sequences by finding consensus sequences through probabilistic choices from the read clusters. By using simulated NGS read sequences from real microbial genome sequences, we evaluated our method in comparison with four existing assembly programs. We found that our method could generate relatively long and accurate metagenome assemblies, indicating that the idea of using protein sequences, as a guide for the assembly, is promising. Copyright © 2015 Elsevier B.V. All rights reserved.

Molecular phylogeny of 21 tropical bamboo species reconstructed by integrating non-coding internal transcribed spacer (ITS1 and 2) sequences and their consensus secondary structure.

PubMed

Ghosh, Jayadri Sekhar; Bhattacharya, Samik; Pal, Amita

2017-06-01

The unavailability of the reproductive structure and unpredictability of vegetative characters for the identification and phylogenetic study of bamboo prompted the application of molecular techniques for greater resolution and consensus. We first employed internal transcribed spacer (ITS1, 5.8S rRNA and ITS2) sequences to construct the phylogenetic tree of 21 tropical bamboo species. While the sequence alone could grossly reconstruct the traditional phylogeny amongst the 21-tropical species studied, some anomalies were encountered that prompted a further refinement of the phylogenetic analyses. Therefore, we integrated the secondary structure of the ITS sequences to derive individual sequence-structure matrix to gain more resolution on the phylogenetic reconstruction. The results showed that ITS sequence-structure is the reliable alternative to the conventional phenotypic method for the identification of bamboo species. The best-fit topology obtained by the sequence-structure based phylogeny over the sole sequence based one underscores closer clustering of all the studied Bambusa species (Sub-tribe Bambusinae), while Melocanna baccifera, which belongs to Sub-Tribe Melocanneae, disjointedly clustered as an out-group within the consensus phylogenetic tree. In this study, we demonstrated the dependability of the combined (ITS sequence+structure-based) approach over the only sequence-based analysis for phylogenetic relationship assessment of bamboo.

Comparison and Evaluation of Clustering Algorithms for Tandem Mass Spectra.

PubMed

Rieder, Vera; Schork, Karin U; Kerschke, Laura; Blank-Landeshammer, Bernhard; Sickmann, Albert; Rahnenführer, Jörg

2017-11-03

In proteomics, liquid chromatography-tandem mass spectrometry (LC-MS/MS) is established for identifying peptides and proteins. Duplicated spectra, that is, multiple spectra of the same peptide, occur both in single MS/MS runs and in large spectral libraries. Clustering tandem mass spectra is used to find consensus spectra, with manifold applications. First, it speeds up database searches, as performed for instance by Mascot. Second, it helps to identify novel peptides across species. Third, it is used for quality control to detect wrongly annotated spectra. We compare different clustering algorithms based on the cosine distance between spectra. CAST, MS-Cluster, and PRIDE Cluster are popular algorithms to cluster tandem mass spectra. We add well-known algorithms for large data sets, hierarchical clustering, DBSCAN, and connected components of a graph, as well as the new method N-Cluster. All algorithms are evaluated on real data with varied parameter settings. Cluster results are compared with each other and with peptide annotations based on validation measures such as purity. Quality control, regarding the detection of wrongly (un)annotated spectra, is discussed for exemplary resulting clusters. N-Cluster proves to be highly competitive. All clustering results benefit from the so-called DISMS2 filter that integrates additional information, for example, on precursor mass.

Dynamical organization towards consensus in the Axelrod model on complex networks

NASA Astrophysics Data System (ADS)

Guerra, Beniamino; Poncela, Julia; Gómez-Gardeñes, Jesús; Latora, Vito; Moreno, Yamir

2010-05-01

We analyze the dynamics toward cultural consensus in the Axelrod model on scale-free networks. By looking at the microscopic dynamics of the model, we are able to show how culture traits spread across different cultural features. We compare the diffusion at the level of cultural features to the growth of cultural consensus at the global level, finding important differences between these two processes. In particular, we show that even when most of the cultural features have reached macroscopic consensus, there are still no signals of globalization. Finally, we analyze the topology of consensus clusters both for global culture and at the feature level of representation.

A guide to enterotypes across the human body: meta-analysis of microbial community structures in human microbiome datasets.

PubMed

Koren, Omry; Knights, Dan; Gonzalez, Antonio; Waldron, Levi; Segata, Nicola; Knight, Rob; Huttenhower, Curtis; Ley, Ruth E

2013-01-01

Recent analyses of human-associated bacterial diversity have categorized individuals into 'enterotypes' or clusters based on the abundances of key bacterial genera in the gut microbiota. There is a lack of consensus, however, on the analytical basis for enterotypes and on the interpretation of these results. We tested how the following factors influenced the detection of enterotypes: clustering methodology, distance metrics, OTU-picking approaches, sequencing depth, data type (whole genome shotgun (WGS) vs.16S rRNA gene sequence data), and 16S rRNA region. We included 16S rRNA gene sequences from the Human Microbiome Project (HMP) and from 16 additional studies and WGS sequences from the HMP and MetaHIT. In most body sites, we observed smooth abundance gradients of key genera without discrete clustering of samples. Some body habitats displayed bimodal (e.g., gut) or multimodal (e.g., vagina) distributions of sample abundances, but not all clustering methods and workflows accurately highlight such clusters. Because identifying enterotypes in datasets depends not only on the structure of the data but is also sensitive to the methods applied to identifying clustering strength, we recommend that multiple approaches be used and compared when testing for enterotypes.

A Guide to Enterotypes across the Human Body: Meta-Analysis of Microbial Community Structures in Human Microbiome Datasets

PubMed Central

Waldron, Levi; Segata, Nicola; Knight, Rob; Huttenhower, Curtis; Ley, Ruth E.

2013-01-01

Recent analyses of human-associated bacterial diversity have categorized individuals into ‘enterotypes’ or clusters based on the abundances of key bacterial genera in the gut microbiota. There is a lack of consensus, however, on the analytical basis for enterotypes and on the interpretation of these results. We tested how the following factors influenced the detection of enterotypes: clustering methodology, distance metrics, OTU-picking approaches, sequencing depth, data type (whole genome shotgun (WGS) vs.16S rRNA gene sequence data), and 16S rRNA region. We included 16S rRNA gene sequences from the Human Microbiome Project (HMP) and from 16 additional studies and WGS sequences from the HMP and MetaHIT. In most body sites, we observed smooth abundance gradients of key genera without discrete clustering of samples. Some body habitats displayed bimodal (e.g., gut) or multimodal (e.g., vagina) distributions of sample abundances, but not all clustering methods and workflows accurately highlight such clusters. Because identifying enterotypes in datasets depends not only on the structure of the data but is also sensitive to the methods applied to identifying clustering strength, we recommend that multiple approaches be used and compared when testing for enterotypes. PMID:23326225

A Cluster of Legionella-Associated Pneumonia Cases in a Population of Military Recruits

DTIC Science & Technology

2007-06-01

this cluster may suggest a previously unrecognized suscep- FIG. 1. Phylogenic analysis of the training center strain (represented by the MCRD consensus...military recruits during population- based surveillance for pneumonia pathogens. Results were confirmed by sequence analysis . Cases cluster tightly...17 April 2007 A Legionella cluster was identified through retrospective PCR analysis of 240 throat swab samples from X-ray-confirmed pneumonia cases

QMEANclust: estimation of protein model quality by combining a composite scoring function with structural density information.

PubMed

Benkert, Pascal; Schwede, Torsten; Tosatto, Silvio Ce

2009-05-20

The selection of the most accurate protein model from a set of alternatives is a crucial step in protein structure prediction both in template-based and ab initio approaches. Scoring functions have been developed which can either return a quality estimate for a single model or derive a score from the information contained in the ensemble of models for a given sequence. Local structural features occurring more frequently in the ensemble have a greater probability of being correct. Within the context of the CASP experiment, these so called consensus methods have been shown to perform considerably better in selecting good candidate models, but tend to fail if the best models are far from the dominant structural cluster. In this paper we show that model selection can be improved if both approaches are combined by pre-filtering the models used during the calculation of the structural consensus. Our recently published QMEAN composite scoring function has been improved by including an all-atom interaction potential term. The preliminary model ranking based on the new QMEAN score is used to select a subset of reliable models against which the structural consensus score is calculated. This scoring function called QMEANclust achieves a correlation coefficient of predicted quality score and GDT_TS of 0.9 averaged over the 98 CASP7 targets and perform significantly better in selecting good models from the ensemble of server models than any other groups participating in the quality estimation category of CASP7. Both scoring functions are also benchmarked on the MOULDER test set consisting of 20 target proteins each with 300 alternatives models generated by MODELLER. QMEAN outperforms all other tested scoring functions operating on individual models, while the consensus method QMEANclust only works properly on decoy sets containing a certain fraction of near-native conformations. We also present a local version of QMEAN for the per-residue estimation of model quality (QMEANlocal) and compare it to a new local consensus-based approach. Improved model selection is obtained by using a composite scoring function operating on single models in order to enrich higher quality models which are subsequently used to calculate the structural consensus. The performance of consensus-based methods such as QMEANclust highly depends on the composition and quality of the model ensemble to be analysed. Therefore, performance estimates for consensus methods based on large meta-datasets (e.g. CASP) might overrate their applicability in more realistic modelling situations with smaller sets of models based on individual methods.

A consensus embedding approach for segmentation of high resolution in vivo prostate magnetic resonance imagery

NASA Astrophysics Data System (ADS)

Viswanath, Satish; Rosen, Mark; Madabhushi, Anant

2008-03-01

Current techniques for localization of prostatic adenocarcinoma (CaP) via blinded trans-rectal ultrasound biopsy are associated with a high false negative detection rate. While high resolution endorectal in vivo Magnetic Resonance (MR) prostate imaging has been shown to have improved contrast and resolution for CaP detection over ultrasound, similarity in intensity characteristics between benign and cancerous regions on MR images contribute to a high false positive detection rate. In this paper, we present a novel unsupervised segmentation method that employs manifold learning via consensus schemes for detection of cancerous regions from high resolution 1.5 Tesla (T) endorectal in vivo prostate MRI. A significant contribution of this paper is a method to combine multiple weak, lower-dimensional representations of high dimensional feature data in a way analogous to classifier ensemble schemes, and hence create a stable and accurate reduced dimensional representation. After correcting for MR image intensity artifacts, such as bias field inhomogeneity and intensity non-standardness, our algorithm extracts over 350 3D texture features at every spatial location in the MR scene at multiple scales and orientations. Non-linear dimensionality reduction schemes such as Locally Linear Embedding (LLE) and Graph Embedding (GE) are employed to create multiple low dimensional data representations of this high dimensional texture feature space. Our novel consensus embedding method is used to average object adjacencies from within the multiple low dimensional projections so that class relationships are preserved. Unsupervised consensus clustering is then used to partition the objects in this consensus embedding space into distinct classes. Quantitative evaluation on 18 1.5 T prostate MR data against corresponding histology obtained from the multi-site ACRIN trials show a sensitivity of 92.65% and a specificity of 82.06%, which suggests that our method is successfully able to detect suspicious regions in the prostate.

A filtering method to generate high quality short reads using illumina paired-end technology.

PubMed

Eren, A Murat; Vineis, Joseph H; Morrison, Hilary G; Sogin, Mitchell L

2013-01-01

Consensus between independent reads improves the accuracy of genome and transcriptome analyses, however lack of consensus between very similar sequences in metagenomic studies can and often does represent natural variation of biological significance. The common use of machine-assigned quality scores on next generation platforms does not necessarily correlate with accuracy. Here, we describe using the overlap of paired-end, short sequence reads to identify error-prone reads in marker gene analyses and their contribution to spurious OTUs following clustering analysis using QIIME. Our approach can also reduce error in shotgun sequencing data generated from libraries with small, tightly constrained insert sizes. The open-source implementation of this algorithm in Python programming language with user instructions can be obtained from https://github.com/meren/illumina-utils.

Cluster Detection Tests in Spatial Epidemiology: A Global Indicator for Performance Assessment

PubMed Central

Guttmann, Aline; Li, Xinran; Feschet, Fabien; Gaudart, Jean; Demongeot, Jacques; Boire, Jean-Yves; Ouchchane, Lemlih

2015-01-01

In cluster detection of disease, the use of local cluster detection tests (CDTs) is current. These methods aim both at locating likely clusters and testing for their statistical significance. New or improved CDTs are regularly proposed to epidemiologists and must be subjected to performance assessment. Because location accuracy has to be considered, performance assessment goes beyond the raw estimation of type I or II errors. As no consensus exists for performance evaluations, heterogeneous methods are used, and therefore studies are rarely comparable. A global indicator of performance, which assesses both spatial accuracy and usual power, would facilitate the exploration of CDTs behaviour and help between-studies comparisons. The Tanimoto coefficient (TC) is a well-known measure of similarity that can assess location accuracy but only for one detected cluster. In a simulation study, performance is measured for many tests. From the TC, we here propose two statistics, the averaged TC and the cumulated TC, as indicators able to provide a global overview of CDTs performance for both usual power and location accuracy. We evidence the properties of these two indicators and the superiority of the cumulated TC to assess performance. We tested these indicators to conduct a systematic spatial assessment displayed through performance maps. PMID:26086911

Computational cluster validation for microarray data analysis: experimental assessment of Clest, Consensus Clustering, Figure of Merit, Gap Statistics and Model Explorer.

PubMed

Giancarlo, Raffaele; Scaturro, Davide; Utro, Filippo

2008-10-29

Inferring cluster structure in microarray datasets is a fundamental task for the so-called -omic sciences. It is also a fundamental question in Statistics, Data Analysis and Classification, in particular with regard to the prediction of the number of clusters in a dataset, usually established via internal validation measures. Despite the wealth of internal measures available in the literature, new ones have been recently proposed, some of them specifically for microarray data. We consider five such measures: Clest, Consensus (Consensus Clustering), FOM (Figure of Merit), Gap (Gap Statistics) and ME (Model Explorer), in addition to the classic WCSS (Within Cluster Sum-of-Squares) and KL (Krzanowski and Lai index). We perform extensive experiments on six benchmark microarray datasets, using both Hierarchical and K-means clustering algorithms, and we provide an analysis assessing both the intrinsic ability of a measure to predict the correct number of clusters in a dataset and its merit relative to the other measures. We pay particular attention both to precision and speed. Moreover, we also provide various fast approximation algorithms for the computation of Gap, FOM and WCSS. The main result is a hierarchy of those measures in terms of precision and speed, highlighting some of their merits and limitations not reported before in the literature. Based on our analysis, we draw several conclusions for the use of those internal measures on microarray data. We report the main ones. Consensus is by far the best performer in terms of predictive power and remarkably algorithm-independent. Unfortunately, on large datasets, it may be of no use because of its non-trivial computer time demand (weeks on a state of the art PC). FOM is the second best performer although, quite surprisingly, it may not be competitive in this scenario: it has essentially the same predictive power of WCSS but it is from 6 to 100 times slower in time, depending on the dataset. The approximation algorithms for the computation of FOM, Gap and WCSS perform very well, i.e., they are faster while still granting a very close approximation of FOM and WCSS. The approximation algorithm for the computation of Gap deserves to be singled-out since it has a predictive power far better than Gap, it is competitive with the other measures, but it is at least two order of magnitude faster in time with respect to Gap. Another important novel conclusion that can be drawn from our analysis is that all the measures we have considered show severe limitations on large datasets, either due to computational demand (Consensus, as already mentioned, Clest and Gap) or to lack of precision (all of the other measures, including their approximations). The software and datasets are available under the GNU GPL on the supplementary material web page.

An adaptive clustering algorithm for image matching based on corner feature

NASA Astrophysics Data System (ADS)

Wang, Zhe; Dong, Min; Mu, Xiaomin; Wang, Song

2018-04-01

The traditional image matching algorithm always can not balance the real-time and accuracy better, to solve the problem, an adaptive clustering algorithm for image matching based on corner feature is proposed in this paper. The method is based on the similarity of the matching pairs of vector pairs, and the adaptive clustering is performed on the matching point pairs. Harris corner detection is carried out first, the feature points of the reference image and the perceived image are extracted, and the feature points of the two images are first matched by Normalized Cross Correlation (NCC) function. Then, using the improved algorithm proposed in this paper, the matching results are clustered to reduce the ineffective operation and improve the matching speed and robustness. Finally, the Random Sample Consensus (RANSAC) algorithm is used to match the matching points after clustering. The experimental results show that the proposed algorithm can effectively eliminate the most wrong matching points while the correct matching points are retained, and improve the accuracy of RANSAC matching, reduce the computation load of whole matching process at the same time.

Development of Geriatric Competencies for Emergency Medicine Residents Using an Expert Consensus Process

PubMed Central

Hogan, Teresita M.; Losman, Eve D.; Carpenter, Christopher R.; Sauvigne, Karen; Irmiter, Cheryl; Emanuel, Linda; Leipzig, Rosanne M.

2011-01-01

Background The emergency department (ED) visit rate for older patients exceeds that of all age groups other than infants. The aging population will increase elder ED patient utilization to 35% to 60% of all visits. Older patients can have complex clinical presentations and be resource-intensive. Evidence indicates that emergency physicians fail to provide consistent high-quality care for elder ED patients, resulting in poor clinical outcomes. Objectives The objective was to develop a consensus document, “Geriatric Competencies for Emergency Medicine Residents,” by identified experts. This is a minimum set of behaviorally based performance standards that all residents should be able to demonstrate by completion of their residency training. Methods This consensus-based process utilized an inductive, qualitative, multiphase method to determine the minimum geriatric competencies needed by emergency medicine (EM) residents. Assessments of face validity and reliability were used throughout the project. Results In Phase I, participants (n = 363) identified 12 domains and 300 potential competencies. In Phase II, an expert panel (n = 24) clustered the Phase I responses, resulting in eight domains and 72 competencies. In Phase III, the expert panel reduced the competencies to 26. In Phase IV, analysis of face validity and reliability yielded a 100% consensus for eight domains and 26 competencies. The domains identified were atypical presentation of disease; trauma, including falls; cognitive and behavioral disorders; emergent intervention modifications; medication management; transitions of care; pain management and palliative care; and effect of comorbid conditions. Conclusions The Geriatric Competencies for EM Residents is a consensus document that can form the basis for EM residency curricula and assessment to meet the demands of our aging population. PMID:20370765

Clumpak: a program for identifying clustering modes and packaging population structure inferences across K.

PubMed

Kopelman, Naama M; Mayzel, Jonathan; Jakobsson, Mattias; Rosenberg, Noah A; Mayrose, Itay

2015-09-01

The identification of the genetic structure of populations from multilocus genotype data has become a central component of modern population-genetic data analysis. Application of model-based clustering programs often entails a number of steps, in which the user considers different modelling assumptions, compares results across different predetermined values of the number of assumed clusters (a parameter typically denoted K), examines multiple independent runs for each fixed value of K, and distinguishes among runs belonging to substantially distinct clustering solutions. Here, we present Clumpak (Cluster Markov Packager Across K), a method that automates the postprocessing of results of model-based population structure analyses. For analysing multiple independent runs at a single K value, Clumpak identifies sets of highly similar runs, separating distinct groups of runs that represent distinct modes in the space of possible solutions. This procedure, which generates a consensus solution for each distinct mode, is performed by the use of a Markov clustering algorithm that relies on a similarity matrix between replicate runs, as computed by the software Clumpp. Next, Clumpak identifies an optimal alignment of inferred clusters across different values of K, extending a similar approach implemented for a fixed K in Clumpp and simplifying the comparison of clustering results across different K values. Clumpak incorporates additional features, such as implementations of methods for choosing K and comparing solutions obtained by different programs, models, or data subsets. Clumpak, available at http://clumpak.tau.ac.il, simplifies the use of model-based analyses of population structure in population genetics and molecular ecology. © 2015 John Wiley & Sons Ltd.

Nucleotide sequence of a cluster of early and late genes in a conserved segment of the vaccinia virus genome.

PubMed Central

Plucienniczak, A; Schroeder, E; Zettlmeissl, G; Streeck, R E

1985-01-01

The nucleotide sequence of a 7.6 kb vaccinia DNA segment from a genomic region conserved among different orthopox virus has been determined. This segment contains a tight cluster of 12 partly overlapping open reading frames most of which can be correlated with previously identified early and late proteins and mRNAs. Regulatory signals used by vaccinia virus have been studied. Presumptive promoter regions are rich in A, T and carry the consensus sequences TATA and AATAA spaced at 20-24 base pairs. Tandem repeats of a CTATTC consensus sequence are proposed to be involved in the termination of early transcription. PMID:2987815

Towards global consensus on core outcomes for hidradenitis suppurativa research: an update from the HISTORIC consensus meetings I and II*

PubMed Central

Thorlacius, L.; Garg, A.; Ingram, J.R.; Villumsen, B.; Riis, P. Theut; Gottlieb, A.B.; Merola, J.F.; Dellavalle, R.; Ardon, C.; Baba, R.; Bechara, F.G.; Cohen, A.D.; Daham, N.; Davis, M.; Emtestam, L.; Fernández-Peñas, P.; Filippelli, M.; Gibbons, A.; Grant, T.; Guilbault, S.; Gulliver, S.; Harris, C; Harvent, C.; Houston, K.; Kirby, J.S.; Matusiak, L.; Mehdizadeh, A.; Mojica, T.; Okun, M.; Orgill, D.; Pallack, L.; Parks-Miller, A.; Prens, E.P.; Randell, S.; Rogers, C.; Rosen, C.F.; Choon, S.E.; van der Zee, H.H.; Christensen, R.; Jemec, G.B.E.

2018-01-01

Summary Background A core outcomes set (COS) is an agreed minimum set of outcomes that should be measured and reported in all clinical trials for a specific condition. Hidradenitis suppurativa (HS) has no agreed-upon COS. A central aspect in the COS development process is to identify a set of candidate outcome domains from a long list of items. Our long list had been developed from patient interviews, a systematic review of the literature and a healthcare professional survey, and initial votes had been cast in two e-Delphi surveys. In this manuscript, we describe two in-person consensus meetings of Delphi participants designed to ensure an inclusive approach to generation of domains from related items. Objectives To consider which items from a long list of candidate items to exclude and which to cluster into outcome domains. Methods The study used an international and multistakeholder approach, involving patients, dermatologists, surgeons, the pharmaceutical industry and medical regulators. The study format was a combination of formal presentations, small group work based on nominal group theory and a subsequent online confirmation survey. Results Forty-one individuals from 13 countries and four continents participated. Nine items were excluded and there was consensus to propose seven domains: disease course, physical signs, HS-specific quality of life, satisfaction, symptoms, pain and global assessments. Conclusions The HISTORIC consensus meetings I and II will be followed by further e-Delphi rounds to finalize the core domain set, building on the work of the in-person consensus meetings. PMID:29080368

Unsupervised consensus cluster analysis of [18F]-fluoroethyl-L-tyrosine positron emission tomography identified textural features for the diagnosis of pseudoprogression in high-grade glioma.

PubMed

Kebir, Sied; Khurshid, Zain; Gaertner, Florian C; Essler, Markus; Hattingen, Elke; Fimmers, Rolf; Scheffler, Björn; Herrlinger, Ulrich; Bundschuh, Ralph A; Glas, Martin

2017-01-31

Timely detection of pseudoprogression (PSP) is crucial for the management of patients with high-grade glioma (HGG) but remains difficult. Textural features of O-(2-[18F]fluoroethyl)-L-tyrosine positron emission tomography (FET-PET) mirror tumor uptake heterogeneity; some of them may be associated with tumor progression. Fourteen patients with HGG and suspected of PSP underwent FET-PET imaging. A set of 19 conventional and textural FET-PET features were evaluated and subjected to unsupervised consensus clustering. The final diagnosis of true progression vs. PSP was based on follow-up MRI using RANO criteria. Three robust clusters have been identified based on 10 predominantly textural FET-PET features. None of the patients with PSP fell into cluster 2, which was associated with high values for textural FET-PET markers of uptake heterogeneity. Three out of 4 patients with PSP were assigned to cluster 3 that was largely associated with low values of textural FET-PET features. By comparison, tumor-to-normal brain ratio (TNRmax) at the optimal cutoff 2.1 was less predictive of PSP (negative predictive value 57% for detecting true progression, p=0.07 vs. 75% with cluster 3, p=0.04). Clustering based on textural O-(2-[18F]fluoroethyl)-L-tyrosine PET features may provide valuable information in assessing the elusive phenomenon of pseudoprogression.

The threshold bootstrap clustering: a new approach to find families or transmission clusters within molecular quasispecies.

PubMed

Prosperi, Mattia C F; De Luca, Andrea; Di Giambenedetto, Simona; Bracciale, Laura; Fabbiani, Massimiliano; Cauda, Roberto; Salemi, Marco

2010-10-25

Phylogenetic methods produce hierarchies of molecular species, inferring knowledge about taxonomy and evolution. However, there is not yet a consensus methodology that provides a crisp partition of taxa, desirable when considering the problem of intra/inter-patient quasispecies classification or infection transmission event identification. We introduce the threshold bootstrap clustering (TBC), a new methodology for partitioning molecular sequences, that does not require a phylogenetic tree estimation. The TBC is an incremental partition algorithm, inspired by the stochastic Chinese restaurant process, and takes advantage of resampling techniques and models of sequence evolution. TBC uses as input a multiple alignment of molecular sequences and its output is a crisp partition of the taxa into an automatically determined number of clusters. By varying initial conditions, the algorithm can produce different partitions. We describe a procedure that selects a prime partition among a set of candidate ones and calculates a measure of cluster reliability. TBC was successfully tested for the identification of type-1 human immunodeficiency and hepatitis C virus subtypes, and compared with previously established methodologies. It was also evaluated in the problem of HIV-1 intra-patient quasispecies clustering, and for transmission cluster identification, using a set of sequences from patients with known transmission event histories. TBC has been shown to be effective for the subtyping of HIV and HCV, and for identifying intra-patient quasispecies. To some extent, the algorithm was able also to infer clusters corresponding to events of infection transmission. The computational complexity of TBC is quadratic in the number of taxa, lower than other established methods; in addition, TBC has been enhanced with a measure of cluster reliability. The TBC can be useful to characterise molecular quasipecies in a broad context.

Molecular identification and characterization of clustered regularly interspaced short palindromic repeat (CRISPR) gene cluster in Taylorella equigenitalis.

PubMed

Hara, Yasushi; Hayashi, Kyohei; Nakajima, Takuya; Kagawa, Shizuko; Tazumi, Akihiro; Moore, John E; Matsuda, Motoo

2013-09-01

Clustered regularly interspaced short palindromic repeats (CRISPRs), of approximately 10,000 base pairs (bp) in length, were shown to occur in the Japanese Taylorella equigenitalis strain, EQ59. The locus was composed of the putative CRISPRs-associated with 5 (cas5), RAMP csd1, csd2, recB, cas1, a leader region, 13 CRISPR consensus sequence repeats (each 32 bp; 5'-TCAGCCACGTTCGCGTGGCTGTGTGTTTAAAG-3'). These were in turn separated by 12 non repetitive unique spacer regions of similar length. In addition, a leader region, a transposase/IS protein, a leader region, and cas3 were also seen. All seven putative open reading frames carry their ribosome binding sites. Promoter consensus sequences at the -35 and -10 regions and putative intrinsic ρ-independent transcription terminator regions also occurred. A possible long overlap of 170 bp in length occurred between the recB and cas1 loci. Positive reverse transcription PCR signals of cas5, RAMP csd1, csd2-recB/cas1, and cas3 were generated. A putative secondary structure of the CRISPR consensus repeats was constructed. Following this, CRISPR results of the T. equigenitalis EQ59 isolate were subsequently compared with those from the Taylorella asinigenitalis MCE3 isolate.

AMS 4.0: consensus prediction of post-translational modifications in protein sequences.

PubMed

Plewczynski, Dariusz; Basu, Subhadip; Saha, Indrajit

2012-08-01

We present here the 2011 update of the AutoMotif Service (AMS 4.0) that predicts the wide selection of 88 different types of the single amino acid post-translational modifications (PTM) in protein sequences. The selection of experimentally confirmed modifications is acquired from the latest UniProt and Phospho.ELM databases for training. The sequence vicinity of each modified residue is represented using amino acids physico-chemical features encoded using high quality indices (HQI) obtaining by automatic clustering of known indices extracted from AAindex database. For each type of the numerical representation, the method builds the ensemble of Multi-Layer Perceptron (MLP) pattern classifiers, each optimising different objectives during the training (for example the recall, precision or area under the ROC curve (AUC)). The consensus is built using brainstorming technology, which combines multi-objective instances of machine learning algorithm, and the data fusion of different training objects representations, in order to boost the overall prediction accuracy of conserved short sequence motifs. The performance of AMS 4.0 is compared with the accuracy of previous versions, which were constructed using single machine learning methods (artificial neural networks, support vector machine). Our software improves the average AUC score of the earlier version by close to 7 % as calculated on the test datasets of all 88 PTM types. Moreover, for the selected most-difficult sequence motifs types it is able to improve the prediction performance by almost 32 %, when compared with previously used single machine learning methods. Summarising, the brainstorming consensus meta-learning methodology on the average boosts the AUC score up to around 89 %, averaged over all 88 PTM types. Detailed results for single machine learning methods and the consensus methodology are also provided, together with the comparison to previously published methods and state-of-the-art software tools. The source code and precompiled binaries of brainstorming tool are available at http://code.google.com/p/automotifserver/ under Apache 2.0 licensing.

Coarse Point Cloud Registration by Egi Matching of Voxel Clusters

NASA Astrophysics Data System (ADS)

Wang, Jinhu; Lindenbergh, Roderik; Shen, Yueqian; Menenti, Massimo

2016-06-01

Laser scanning samples the surface geometry of objects efficiently and records versatile information as point clouds. However, often more scans are required to fully cover a scene. Therefore, a registration step is required that transforms the different scans into a common coordinate system. The registration of point clouds is usually conducted in two steps, i.e. coarse registration followed by fine registration. In this study an automatic marker-free coarse registration method for pair-wise scans is presented. First the two input point clouds are re-sampled as voxels and dimensionality features of the voxels are determined by principal component analysis (PCA). Then voxel cells with the same dimensionality are clustered. Next, the Extended Gaussian Image (EGI) descriptor of those voxel clusters are constructed using significant eigenvectors of each voxel in the cluster. Correspondences between clusters in source and target data are obtained according to the similarity between their EGI descriptors. The random sampling consensus (RANSAC) algorithm is employed to remove outlying correspondences until a coarse alignment is obtained. If necessary, a fine registration is performed in a final step. This new method is illustrated on scan data sampling two indoor scenarios. The results of the tests are evaluated by computing the point to point distance between the two input point clouds. The presented two tests resulted in mean distances of 7.6 mm and 9.5 mm respectively, which are adequate for fine registration.

Fast Geometric Consensus Approach for Protein Model Quality Assessment

PubMed Central

Adamczak, Rafal; Pillardy, Jaroslaw; Vallat, Brinda K.

2011-01-01

Abstract Model quality assessment (MQA) is an integral part of protein structure prediction methods that typically generate multiple candidate models. The challenge lies in ranking and selecting the best models using a variety of physical, knowledge-based, and geometric consensus (GC)-based scoring functions. In particular, 3D-Jury and related GC methods assume that well-predicted (sub-)structures are more likely to occur frequently in a population of candidate models, compared to incorrectly folded fragments. While this approach is very successful in the context of diversified sets of models, identifying similar substructures is computationally expensive since all pairs of models need to be superimposed using MaxSub or related heuristics for structure-to-structure alignment. Here, we consider a fast alternative, in which structural similarity is assessed using 1D profiles, e.g., consisting of relative solvent accessibilities and secondary structures of equivalent amino acid residues in the respective models. We show that the new approach, dubbed 1D-Jury, allows to implicitly compare and rank N models in O(N) time, as opposed to quadratic complexity of 3D-Jury and related clustering-based methods. In addition, 1D-Jury avoids computationally expensive 3D superposition of pairs of models. At the same time, structural similarity scores based on 1D profiles are shown to correlate strongly with those obtained using MaxSub. In terms of the ability to select the best models as top candidates 1D-Jury performs on par with other GC methods. Other potential applications of the new approach, including fast clustering of large numbers of intermediate structures generated by folding simulations, are discussed as well. PMID:21244273

Combining analytical hierarchy process and agglomerative hierarchical clustering in search of expert consensus in green corridors development management.

PubMed

Shapira, Aviad; Shoshany, Maxim; Nir-Goldenberg, Sigal

2013-07-01

Environmental management and planning are instrumental in resolving conflicts arising between societal needs for economic development on the one hand and for open green landscapes on the other hand. Allocating green corridors between fragmented core green areas may provide a partial solution to these conflicts. Decisions regarding green corridor development require the assessment of alternative allocations based on multiple criteria evaluations. Analytical Hierarchy Process provides a methodology for both a structured and consistent extraction of such evaluations and for the search for consensus among experts regarding weights assigned to the different criteria. Implementing this methodology using 15 Israeli experts-landscape architects, regional planners, and geographers-revealed inherent differences in expert opinions in this field beyond professional divisions. The use of Agglomerative Hierarchical Clustering allowed to identify clusters representing common decisions regarding criterion weights. Aggregating the evaluations of these clusters revealed an important dichotomy between a pragmatist approach that emphasizes the weight of statutory criteria and an ecological approach that emphasizes the role of the natural conditions in allocating green landscape corridors.

Diffusion Geometry Unravels the Emergence of Functional Clusters in Collective Phenomena.

PubMed

De Domenico, Manlio

2017-04-21

Collective phenomena emerge from the interaction of natural or artificial units with a complex organization. The interplay between structural patterns and dynamics might induce functional clusters that, in general, are different from topological ones. In biological systems, like the human brain, the overall functionality is often favored by the interplay between connectivity and synchronization dynamics, with functional clusters that do not coincide with anatomical modules in most cases. In social, sociotechnical, and engineering systems, the quest for consensus favors the emergence of clusters. Despite the unquestionable evidence for mesoscale organization of many complex systems and the heterogeneity of their interconnectivity, a way to predict and identify the emergence of functional modules in collective phenomena continues to elude us. Here, we propose an approach based on random walk dynamics to define the diffusion distance between any pair of units in a networked system. Such a metric allows us to exploit the underlying diffusion geometry to provide a unifying framework for the intimate relationship between metastable synchronization, consensus, and random search dynamics in complex networks, pinpointing the functional mesoscale organization of synthetic and biological systems.

Diffusion Geometry Unravels the Emergence of Functional Clusters in Collective Phenomena

NASA Astrophysics Data System (ADS)

De Domenico, Manlio

2017-04-01

Collective phenomena emerge from the interaction of natural or artificial units with a complex organization. The interplay between structural patterns and dynamics might induce functional clusters that, in general, are different from topological ones. In biological systems, like the human brain, the overall functionality is often favored by the interplay between connectivity and synchronization dynamics, with functional clusters that do not coincide with anatomical modules in most cases. In social, sociotechnical, and engineering systems, the quest for consensus favors the emergence of clusters. Despite the unquestionable evidence for mesoscale organization of many complex systems and the heterogeneity of their interconnectivity, a way to predict and identify the emergence of functional modules in collective phenomena continues to elude us. Here, we propose an approach based on random walk dynamics to define the diffusion distance between any pair of units in a networked system. Such a metric allows us to exploit the underlying diffusion geometry to provide a unifying framework for the intimate relationship between metastable synchronization, consensus, and random search dynamics in complex networks, pinpointing the functional mesoscale organization of synthetic and biological systems.

Combining Analytical Hierarchy Process and Agglomerative Hierarchical Clustering in Search of Expert Consensus in Green Corridors Development Management

NASA Astrophysics Data System (ADS)

Shapira, Aviad; Shoshany, Maxim; Nir-Goldenberg, Sigal

2013-07-01

Environmental management and planning are instrumental in resolving conflicts arising between societal needs for economic development on the one hand and for open green landscapes on the other hand. Allocating green corridors between fragmented core green areas may provide a partial solution to these conflicts. Decisions regarding green corridor development require the assessment of alternative allocations based on multiple criteria evaluations. Analytical Hierarchy Process provides a methodology for both a structured and consistent extraction of such evaluations and for the search for consensus among experts regarding weights assigned to the different criteria. Implementing this methodology using 15 Israeli experts—landscape architects, regional planners, and geographers—revealed inherent differences in expert opinions in this field beyond professional divisions. The use of Agglomerative Hierarchical Clustering allowed to identify clusters representing common decisions regarding criterion weights. Aggregating the evaluations of these clusters revealed an important dichotomy between a pragmatist approach that emphasizes the weight of statutory criteria and an ecological approach that emphasizes the role of the natural conditions in allocating green landscape corridors.

Non-consensus Opinion Models on Complex Networks

NASA Astrophysics Data System (ADS)

Li, Qian; Braunstein, Lidia A.; Wang, Huijuan; Shao, Jia; Stanley, H. Eugene; Havlin, Shlomo

2013-04-01

Social dynamic opinion models have been widely studied to understand how interactions among individuals cause opinions to evolve. Most opinion models that utilize spin interaction models usually produce a consensus steady state in which only one opinion exists. Because in reality different opinions usually coexist, we focus on non-consensus opinion models in which above a certain threshold two opinions coexist in a stable relationship. We revisit and extend the non-consensus opinion (NCO) model introduced by Shao et al. (Phys. Rev. Lett. 103:01870, 2009). The NCO model in random networks displays a second order phase transition that belongs to regular mean field percolation and is characterized by the appearance (above a certain threshold) of a large spanning cluster of the minority opinion. We generalize the NCO model by adding a weight factor W to each individual's original opinion when determining their future opinion (NCO W model). We find that as W increases the minority opinion holders tend to form stable clusters with a smaller initial minority fraction than in the NCO model. We also revisit another non-consensus opinion model based on the NCO model, the inflexible contrarian opinion (ICO) model (Li et al. in Phys. Rev. E 84:066101, 2011), which introduces inflexible contrarians to model the competition between two opinions in a steady state. Inflexible contrarians are individuals that never change their original opinion but may influence the opinions of others. To place the inflexible contrarians in the ICO model we use two different strategies, random placement and one in which high-degree nodes are targeted. The inflexible contrarians effectively decrease the size of the largest rival-opinion cluster in both strategies, but the effect is more pronounced under the targeted method. All of the above models have previously been explored in terms of a single network, but human communities are usually interconnected, not isolated. Because opinions propagate not only within single networks but also between networks, and because the rules of opinion formation within a network may differ from those between networks, we study here the opinion dynamics in coupled networks. Each network represents a social group or community and the interdependent links joining individuals from different networks may be social ties that are unusually strong, e.g., married couples. We apply the non-consensus opinion (NCO) rule on each individual network and the global majority rule on interdependent pairs such that two interdependent agents with different opinions will, due to the influence of mass media, follow the majority opinion of the entire population. The opinion interactions within each network and the interdependent links across networks interlace periodically until a steady state is reached. We find that the interdependent links effectively force the system from a second order phase transition, which is characteristic of the NCO model on a single network, to a hybrid phase transition, i.e., a mix of second-order and abrupt jump-like transitions that ultimately becomes, as we increase the percentage of interdependent agents, a pure abrupt transition. We conclude that for the NCO model on coupled networks, interactions through interdependent links could push the non-consensus opinion model to a consensus opinion model, which mimics the reality that increased mass communication causes people to hold opinions that are increasingly similar. We also find that the effect of interdependent links is more pronounced in interdependent scale free networks than in interdependent Erdős Rényi networks.

Designing and evaluating the MULTICOM protein local and global model quality prediction methods in the CASP10 experiment

PubMed Central

2014-01-01

Background Protein model quality assessment is an essential component of generating and using protein structural models. During the Tenth Critical Assessment of Techniques for Protein Structure Prediction (CASP10), we developed and tested four automated methods (MULTICOM-REFINE, MULTICOM-CLUSTER, MULTICOM-NOVEL, and MULTICOM-CONSTRUCT) that predicted both local and global quality of protein structural models. Results MULTICOM-REFINE was a clustering approach that used the average pairwise structural similarity between models to measure the global quality and the average Euclidean distance between a model and several top ranked models to measure the local quality. MULTICOM-CLUSTER and MULTICOM-NOVEL were two new support vector machine-based methods of predicting both the local and global quality of a single protein model. MULTICOM-CONSTRUCT was a new weighted pairwise model comparison (clustering) method that used the weighted average similarity between models in a pool to measure the global model quality. Our experiments showed that the pairwise model assessment methods worked better when a large portion of models in the pool were of good quality, whereas single-model quality assessment methods performed better on some hard targets when only a small portion of models in the pool were of reasonable quality. Conclusions Since digging out a few good models from a large pool of low-quality models is a major challenge in protein structure prediction, single model quality assessment methods appear to be poised to make important contributions to protein structure modeling. The other interesting finding was that single-model quality assessment scores could be used to weight the models by the consensus pairwise model comparison method to improve its accuracy. PMID:24731387

Designing and evaluating the MULTICOM protein local and global model quality prediction methods in the CASP10 experiment.

PubMed

Cao, Renzhi; Wang, Zheng; Cheng, Jianlin

2014-04-15

Protein model quality assessment is an essential component of generating and using protein structural models. During the Tenth Critical Assessment of Techniques for Protein Structure Prediction (CASP10), we developed and tested four automated methods (MULTICOM-REFINE, MULTICOM-CLUSTER, MULTICOM-NOVEL, and MULTICOM-CONSTRUCT) that predicted both local and global quality of protein structural models. MULTICOM-REFINE was a clustering approach that used the average pairwise structural similarity between models to measure the global quality and the average Euclidean distance between a model and several top ranked models to measure the local quality. MULTICOM-CLUSTER and MULTICOM-NOVEL were two new support vector machine-based methods of predicting both the local and global quality of a single protein model. MULTICOM-CONSTRUCT was a new weighted pairwise model comparison (clustering) method that used the weighted average similarity between models in a pool to measure the global model quality. Our experiments showed that the pairwise model assessment methods worked better when a large portion of models in the pool were of good quality, whereas single-model quality assessment methods performed better on some hard targets when only a small portion of models in the pool were of reasonable quality. Since digging out a few good models from a large pool of low-quality models is a major challenge in protein structure prediction, single model quality assessment methods appear to be poised to make important contributions to protein structure modeling. The other interesting finding was that single-model quality assessment scores could be used to weight the models by the consensus pairwise model comparison method to improve its accuracy.

An image mosaic method based on corner

NASA Astrophysics Data System (ADS)

Jiang, Zetao; Nie, Heting

2015-08-01

In view of the shortcomings of the traditional image mosaic, this paper describes a new algorithm for image mosaic based on the Harris corner. Firstly, Harris operator combining the constructed low-pass smoothing filter based on splines function and circular window search is applied to detect the image corner, which allows us to have better localisation performance and effectively avoid the phenomenon of cluster. Secondly, the correlation feature registration is used to find registration pair, remove the false registration using random sampling consensus. Finally use the method of weighted trigonometric combined with interpolation function for image fusion. The experiments show that this method can effectively remove the splicing ghosting and improve the accuracy of image mosaic.

Molecular Predictors of 3D Morphogenesis by Breast Cancer Cell Lines in 3D Culture

DOE Office of Scientific and Technical Information (OSTI.GOV)

Han, Ju; Chang, Hang; Giricz, Orsi

Correlative analysis of molecular markers with phenotypic signatures is the simplest model for hypothesis generation. In this paper, a panel of 24 breast cell lines was grown in 3D culture, their morphology was imaged through phase contrast microscopy, and computational methods were developed to segment and represent each colony at multiple dimensions. Subsequently, subpopulations from these morphological responses were identified through consensus clustering to reveal three clusters of round, grape-like, and stellate phenotypes. In some cases, cell lines with particular pathobiological phenotypes clustered together (e.g., ERBB2 amplified cell lines sharing the same morphometric properties as the grape-like phenotype). Next, associationsmore » with molecular features were realized through (i) differential analysis within each morphological cluster, and (ii) regression analysis across the entire panel of cell lines. In both cases, the dominant genes that are predictive of the morphological signatures were identified. Specifically, PPAR? has been associated with the invasive stellate morphological phenotype, which corresponds to triple-negative pathobiology. PPAR? has been validated through two supporting biological assays.« less

Toward a Conceptualization of the Content of Psychosocial Screening in Living Organ Donors: An Ethical Legal Psychological Aspects of Transplantation Consensus.

PubMed

Ismail, Sohal Y; Duerinckx, Nathalie; van der Knoop, Marieke M; Timmerman, Lotte; Weimar, Willem; Dobbels, Fabienne; Massey, Emma K; Busschbach, Jan J J V

2015-11-01

Across Europe, transplant centers vary in the content of the psychosocial evaluation for eligible living organ donors. To identify whether a common framework underlies this variation in this evaluation, we studied which psychosocial screening items are most commonly used and considered as most important in current psychosocial screening programs of living organ donors. A multivariate analytic method, concept mapping, was used to generate a visual representation of the "psychosocial" screening items of living kidney and liver donors. A list of 75 potential screening items was derived from a systematic literature review and sorted and rated for their importance and commonness by multidisciplinary affiliated health care professionals from across Europe. Results were discussed and fine-tuned during a consensus meeting. The analyses resulted in a 6-cluster solution. The following clusters on psychosocial screening items were identified, listed from most to least important: (1) personal resources, (2) motivation and decision making, (3) psychopathology, (4) social resources, (5) ethical and legal factors, and (6) information and risk processing. We provided a conceptual framework of the essential elements in psychosocial evaluation of living donors which can serve as a uniform basis for the selection of relevant psychosocial evaluation tools, which can be further tested in prospective studies.

UNCLES: method for the identification of genes differentially consistently co-expressed in a specific subset of datasets.

PubMed

Abu-Jamous, Basel; Fa, Rui; Roberts, David J; Nandi, Asoke K

2015-06-04

Collective analysis of the increasingly emerging gene expression datasets are required. The recently proposed binarisation of consensus partition matrices (Bi-CoPaM) method can combine clustering results from multiple datasets to identify the subsets of genes which are consistently co-expressed in all of the provided datasets in a tuneable manner. However, results validation and parameter setting are issues that complicate the design of such methods. Moreover, although it is a common practice to test methods by application to synthetic datasets, the mathematical models used to synthesise such datasets are usually based on approximations which may not always be sufficiently representative of real datasets. Here, we propose an unsupervised method for the unification of clustering results from multiple datasets using external specifications (UNCLES). This method has the ability to identify the subsets of genes consistently co-expressed in a subset of datasets while being poorly co-expressed in another subset of datasets, and to identify the subsets of genes consistently co-expressed in all given datasets. We also propose the M-N scatter plots validation technique and adopt it to set the parameters of UNCLES, such as the number of clusters, automatically. Additionally, we propose an approach for the synthesis of gene expression datasets using real data profiles in a way which combines the ground-truth-knowledge of synthetic data and the realistic expression values of real data, and therefore overcomes the problem of faithfulness of synthetic expression data modelling. By application to those datasets, we validate UNCLES while comparing it with other conventional clustering methods, and of particular relevance, biclustering methods. We further validate UNCLES by application to a set of 14 real genome-wide yeast datasets as it produces focused clusters that conform well to known biological facts. Furthermore, in-silico-based hypotheses regarding the function of a few previously unknown genes in those focused clusters are drawn. The UNCLES method, the M-N scatter plots technique, and the expression data synthesis approach will have wide application for the comprehensive analysis of genomic and other sources of multiple complex biological datasets. Moreover, the derived in-silico-based biological hypotheses represent subjects for future functional studies.

Cluster analysis and subgrouping to investigate inter-individual variability to non-invasive brain stimulation: a systematic review.

PubMed

Pellegrini, Michael; Zoghi, Maryam; Jaberzadeh, Shapour

2018-01-12

Cluster analysis and other subgrouping techniques have risen in popularity in recent years in non-invasive brain stimulation research in the attempt to investigate the issue of inter-individual variability - the issue of why some individuals respond, as traditionally expected, to non-invasive brain stimulation protocols and others do not. Cluster analysis and subgrouping techniques have been used to categorise individuals, based on their response patterns, as responder or non-responders. There is, however, a lack of consensus and consistency on the most appropriate technique to use. This systematic review aimed to provide a systematic summary of the cluster analysis and subgrouping techniques used to date and suggest recommendations moving forward. Twenty studies were included that utilised subgrouping techniques, while seven of these additionally utilised cluster analysis techniques. The results of this systematic review appear to indicate that statistical cluster analysis techniques are effective in identifying subgroups of individuals based on response patterns to non-invasive brain stimulation. This systematic review also reports a lack of consensus amongst researchers on the most effective subgrouping technique and the criteria used to determine whether an individual is categorised as a responder or a non-responder. This systematic review provides a step-by-step guide to carrying out statistical cluster analyses and subgrouping techniques to provide a framework for analysis when developing further insights into the contributing factors of inter-individual variability in response to non-invasive brain stimulation.

Population Structure of the Primary Malaria Vector in South America, Anopheles darlingi, Using Isozyme, Random Amplified Polymorphic DNA, Internal Transcribed Spacer 2, and Morphologic Markers

DTIC Science & Technology

1999-01-01

distances and identities and Roger?’ genetic distances were clustered by the unweighted pair group method using arithmetic average ( UPGMA ) to produce...Seattle, WA) using the NEIGHBOR program with the UPGMA option and a phenogram was produced with DRAWGRAM, also in PHYLIP 3.X RAPDBOOT5’ was used to...generate 100 pseudoreplicate distance matrices, which were collapsed to form 100 trees with UPGMA . The bootstrap consensus tree was derived from the 100

Consensus-based methodology for detection communities in multilayered networks

NASA Astrophysics Data System (ADS)

Karimi-Majd, Amir-Mohsen; Fathian, Mohammad; Makrehchi, Masoud

2018-03-01

Finding groups of network users who are densely related with each other has emerged as an interesting problem in the area of social network analysis. These groups or so-called communities would be hidden behind the behavior of users. Most studies assume that such behavior could be understood by focusing on user interfaces, their behavioral attributes or a combination of these network layers (i.e., interfaces with their attributes). They also assume that all network layers refer to the same behavior. However, in real-life networks, users' behavior in one layer may differ from their behavior in another one. In order to cope with these issues, this article proposes a consensus-based community detection approach (CBC). CBC finds communities among nodes at each layer, in parallel. Then, the results of layers should be aggregated using a consensus clustering method. This means that different behavior could be detected and used in the analysis. As for other significant advantages, the methodology would be able to handle missing values. Three experiments on real-life and computer-generated datasets have been conducted in order to evaluate the performance of CBC. The results indicate superiority and stability of CBC in comparison to other approaches.

Large-scale model quality assessment for improving protein tertiary structure prediction.

PubMed

Cao, Renzhi; Bhattacharya, Debswapna; Adhikari, Badri; Li, Jilong; Cheng, Jianlin

2015-06-15

Sampling structural models and ranking them are the two major challenges of protein structure prediction. Traditional protein structure prediction methods generally use one or a few quality assessment (QA) methods to select the best-predicted models, which cannot consistently select relatively better models and rank a large number of models well. Here, we develop a novel large-scale model QA method in conjunction with model clustering to rank and select protein structural models. It unprecedentedly applied 14 model QA methods to generate consensus model rankings, followed by model refinement based on model combination (i.e. averaging). Our experiment demonstrates that the large-scale model QA approach is more consistent and robust in selecting models of better quality than any individual QA method. Our method was blindly tested during the 11th Critical Assessment of Techniques for Protein Structure Prediction (CASP11) as MULTICOM group. It was officially ranked third out of all 143 human and server predictors according to the total scores of the first models predicted for 78 CASP11 protein domains and second according to the total scores of the best of the five models predicted for these domains. MULTICOM's outstanding performance in the extremely competitive 2014 CASP11 experiment proves that our large-scale QA approach together with model clustering is a promising solution to one of the two major problems in protein structure modeling. The web server is available at: http://sysbio.rnet.missouri.edu/multicom_cluster/human/. © The Author 2015. Published by Oxford University Press.

A 1,681-locus consensus genetic map of cultivated cucumber including 67 NB-LRR resistance gene homolog and ten gene loci

PubMed Central

2013-01-01

Background Cucumber is an important vegetable crop that is susceptible to many pathogens, but no disease resistance (R) genes have been cloned. The availability of whole genome sequences provides an excellent opportunity for systematic identification and characterization of the nucleotide binding and leucine-rich repeat (NB-LRR) type R gene homolog (RGH) sequences in the genome. Cucumber has a very narrow genetic base making it difficult to construct high-density genetic maps. Development of a consensus map by synthesizing information from multiple segregating populations is a method of choice to increase marker density. As such, the objectives of the present study were to identify and characterize NB-LRR type RGHs, and to develop a high-density, integrated cucumber genetic-physical map anchored with RGH loci. Results From the Gy14 draft genome, 70 NB-containing RGHs were identified and characterized. Most RGHs were in clusters with uneven distribution across seven chromosomes. In silico analysis indicated that all 70 RGHs had EST support for gene expression. Phylogenetic analysis classified 58 RGHs into two clades: CNL and TNL. Comparative analysis revealed high-degree sequence homology and synteny in chromosomal locations of these RGH members between the cucumber and melon genomes. Fifty-four molecular markers were developed to delimit 67 of the 70 RGHs, which were integrated into a genetic map through linkage analysis. A 1,681-locus cucumber consensus map including 10 gene loci and spanning 730.0 cM in seven linkage groups was developed by integrating three component maps with a bin-mapping strategy. Physically, 308 scaffolds with 193.2 Mbp total DNA sequences were anchored onto this consensus map that covered 52.6% of the 367 Mbp cucumber genome. Conclusions Cucumber contains relatively few NB-LRR RGHs that are clustered and unevenly distributed in the genome. All RGHs seem to be transcribed and shared significant sequence homology and synteny with the melon genome suggesting conservation of these RGHs in the Cucumis lineage. The 1,681-locus consensus genetic-physical map developed and the RGHs identified and characterized herein are valuable genomics resources that may have many applications such as quantitative trait loci identification, map-based gene cloning, association mapping, marker-assisted selection, as well as assembly of a more complete cucumber genome. PMID:23531125

Consensus embedding: theory, algorithms and application to segmentation and classification of biomedical data

PubMed Central

2012-01-01

Background Dimensionality reduction (DR) enables the construction of a lower dimensional space (embedding) from a higher dimensional feature space while preserving object-class discriminability. However several popular DR approaches suffer from sensitivity to choice of parameters and/or presence of noise in the data. In this paper, we present a novel DR technique known as consensus embedding that aims to overcome these problems by generating and combining multiple low-dimensional embeddings, hence exploiting the variance among them in a manner similar to ensemble classifier schemes such as Bagging. We demonstrate theoretical properties of consensus embedding which show that it will result in a single stable embedding solution that preserves information more accurately as compared to any individual embedding (generated via DR schemes such as Principal Component Analysis, Graph Embedding, or Locally Linear Embedding). Intelligent sub-sampling (via mean-shift) and code parallelization are utilized to provide for an efficient implementation of the scheme. Results Applications of consensus embedding are shown in the context of classification and clustering as applied to: (1) image partitioning of white matter and gray matter on 10 different synthetic brain MRI images corrupted with 18 different combinations of noise and bias field inhomogeneity, (2) classification of 4 high-dimensional gene-expression datasets, (3) cancer detection (at a pixel-level) on 16 image slices obtained from 2 different high-resolution prostate MRI datasets. In over 200 different experiments concerning classification and segmentation of biomedical data, consensus embedding was found to consistently outperform both linear and non-linear DR methods within all applications considered. Conclusions We have presented a novel framework termed consensus embedding which leverages ensemble classification theory within dimensionality reduction, allowing for application to a wide range of high-dimensional biomedical data classification and segmentation problems. Our generalizable framework allows for improved representation and classification in the context of both imaging and non-imaging data. The algorithm offers a promising solution to problems that currently plague DR methods, and may allow for extension to other areas of biomedical data analysis. PMID:22316103

Recombination radius of a Frenkel pair and capture radius of a self-interstitial atom by vacancy clusters in bcc Fe

NASA Astrophysics Data System (ADS)

Nakashima, Kenichi; Stoller, Roger E.; Xu, Haixuan

2015-08-01

The recombination radius of a Frenkel pair is a fundamental parameter for the object kinetic Monte Carlo (OKMC) and mean field rate theory (RT) methods that are used to investigate irradiation damage accumulation in irradiated materials. The recombination radius in bcc Fe has been studied both experimentally and numerically, however there is no general consensus about its value. The detailed atomistic processes of recombination also remain uncertain. Values from 1.0a0 to 3.3a0 have been employed as a recombination radius in previous studies using OKMC and RT. The recombination process of a Frenkel pair is investigated at the atomic level using the self-evolved atomistic kinetic Monte Carlo (SEAKMC) method in this paper. SEAKMC calculations reveal that a self-interstitial atom recombines with a vacancy in a spontaneous reaction from several nearby sites following characteristic pathways. The recombination radius of a Frenkel pair is estimated to be 2.26a0 by taking the average of the recombination distances from 80 simulation cases. In addition, we apply these procedures to the capture radius of a self-interstitial atom by a vacancy cluster. The capture radius is found to gradually increase with the size of the vacancy cluster. The fitting curve for the capture radius is obtained as a function of the number of vacancies in the cluster.

Consensus-Based Sorting of Neuronal Spike Waveforms

PubMed Central

Fournier, Julien; Mueller, Christian M.; Shein-Idelson, Mark; Hemberger, Mike

2016-01-01

Optimizing spike-sorting algorithms is difficult because sorted clusters can rarely be checked against independently obtained “ground truth” data. In most spike-sorting algorithms in use today, the optimality of a clustering solution is assessed relative to some assumption on the distribution of the spike shapes associated with a particular single unit (e.g., Gaussianity) and by visual inspection of the clustering solution followed by manual validation. When the spatiotemporal waveforms of spikes from different cells overlap, the decision as to whether two spikes should be assigned to the same source can be quite subjective, if it is not based on reliable quantitative measures. We propose a new approach, whereby spike clusters are identified from the most consensual partition across an ensemble of clustering solutions. Using the variability of the clustering solutions across successive iterations of the same clustering algorithm (template matching based on K-means clusters), we estimate the probability of spikes being clustered together and identify groups of spikes that are not statistically distinguishable from one another. Thus, we identify spikes that are most likely to be clustered together and therefore correspond to consistent spike clusters. This method has the potential advantage that it does not rely on any model of the spike shapes. It also provides estimates of the proportion of misclassified spikes for each of the identified clusters. We tested our algorithm on several datasets for which there exists a ground truth (simultaneous intracellular data), and show that it performs close to the optimum reached by a support vector machine trained on the ground truth. We also show that the estimated rate of misclassification matches the proportion of misclassified spikes measured from the ground truth data. PMID:27536990

Clusternomics: Integrative context-dependent clustering for heterogeneous datasets

PubMed Central

Wernisch, Lorenz

2017-01-01

Integrative clustering is used to identify groups of samples by jointly analysing multiple datasets describing the same set of biological samples, such as gene expression, copy number, methylation etc. Most existing algorithms for integrative clustering assume that there is a shared consistent set of clusters across all datasets, and most of the data samples follow this structure. However in practice, the structure across heterogeneous datasets can be more varied, with clusters being joined in some datasets and separated in others. In this paper, we present a probabilistic clustering method to identify groups across datasets that do not share the same cluster structure. The proposed algorithm, Clusternomics, identifies groups of samples that share their global behaviour across heterogeneous datasets. The algorithm models clusters on the level of individual datasets, while also extracting global structure that arises from the local cluster assignments. Clusters on both the local and the global level are modelled using a hierarchical Dirichlet mixture model to identify structure on both levels. We evaluated the model both on simulated and on real-world datasets. The simulated data exemplifies datasets with varying degrees of common structure. In such a setting Clusternomics outperforms existing algorithms for integrative and consensus clustering. In a real-world application, we used the algorithm for cancer subtyping, identifying subtypes of cancer from heterogeneous datasets. We applied the algorithm to TCGA breast cancer dataset, integrating gene expression, miRNA expression, DNA methylation and proteomics. The algorithm extracted clinically meaningful clusters with significantly different survival probabilities. We also evaluated the algorithm on lung and kidney cancer TCGA datasets with high dimensionality, again showing clinically significant results and scalability of the algorithm. PMID:29036190

Clusternomics: Integrative context-dependent clustering for heterogeneous datasets.

PubMed

Gabasova, Evelina; Reid, John; Wernisch, Lorenz

2017-10-01

Integrative clustering is used to identify groups of samples by jointly analysing multiple datasets describing the same set of biological samples, such as gene expression, copy number, methylation etc. Most existing algorithms for integrative clustering assume that there is a shared consistent set of clusters across all datasets, and most of the data samples follow this structure. However in practice, the structure across heterogeneous datasets can be more varied, with clusters being joined in some datasets and separated in others. In this paper, we present a probabilistic clustering method to identify groups across datasets that do not share the same cluster structure. The proposed algorithm, Clusternomics, identifies groups of samples that share their global behaviour across heterogeneous datasets. The algorithm models clusters on the level of individual datasets, while also extracting global structure that arises from the local cluster assignments. Clusters on both the local and the global level are modelled using a hierarchical Dirichlet mixture model to identify structure on both levels. We evaluated the model both on simulated and on real-world datasets. The simulated data exemplifies datasets with varying degrees of common structure. In such a setting Clusternomics outperforms existing algorithms for integrative and consensus clustering. In a real-world application, we used the algorithm for cancer subtyping, identifying subtypes of cancer from heterogeneous datasets. We applied the algorithm to TCGA breast cancer dataset, integrating gene expression, miRNA expression, DNA methylation and proteomics. The algorithm extracted clinically meaningful clusters with significantly different survival probabilities. We also evaluated the algorithm on lung and kidney cancer TCGA datasets with high dimensionality, again showing clinically significant results and scalability of the algorithm.

Consensus-Based Sorting of Neuronal Spike Waveforms.

PubMed

Fournier, Julien; Mueller, Christian M; Shein-Idelson, Mark; Hemberger, Mike; Laurent, Gilles

2016-01-01

Optimizing spike-sorting algorithms is difficult because sorted clusters can rarely be checked against independently obtained "ground truth" data. In most spike-sorting algorithms in use today, the optimality of a clustering solution is assessed relative to some assumption on the distribution of the spike shapes associated with a particular single unit (e.g., Gaussianity) and by visual inspection of the clustering solution followed by manual validation. When the spatiotemporal waveforms of spikes from different cells overlap, the decision as to whether two spikes should be assigned to the same source can be quite subjective, if it is not based on reliable quantitative measures. We propose a new approach, whereby spike clusters are identified from the most consensual partition across an ensemble of clustering solutions. Using the variability of the clustering solutions across successive iterations of the same clustering algorithm (template matching based on K-means clusters), we estimate the probability of spikes being clustered together and identify groups of spikes that are not statistically distinguishable from one another. Thus, we identify spikes that are most likely to be clustered together and therefore correspond to consistent spike clusters. This method has the potential advantage that it does not rely on any model of the spike shapes. It also provides estimates of the proportion of misclassified spikes for each of the identified clusters. We tested our algorithm on several datasets for which there exists a ground truth (simultaneous intracellular data), and show that it performs close to the optimum reached by a support vector machine trained on the ground truth. We also show that the estimated rate of misclassification matches the proportion of misclassified spikes measured from the ground truth data.

LoCuSS: weak-lensing mass calibration of galaxy clusters

NASA Astrophysics Data System (ADS)

Okabe, Nobuhiro; Smith, Graham P.

2016-10-01

We present weak-lensing mass measurements of 50 X-ray luminous galaxy clusters at 0.15 ≤ z ≤ 0.3, based on uniform high-quality observations with Suprime-Cam mounted on the 8.2-m Subaru telescope. We pay close attention to possible systematic biases, aiming to control them at the ≲4 per cent level. The dominant source of systematic bias in weak-lensing measurements of the mass of individual galaxy clusters is contamination of background galaxy catalogues by faint cluster and foreground galaxies. We extend our conservative method for selecting background galaxies with (V - I') colours redder than the red sequence of cluster members to use a colour-cut that depends on cluster-centric radius. This allows us to define background galaxy samples that suffer ≤1 per cent contamination, and comprise 13 galaxies per square arcminute. Thanks to the purity of our background galaxy catalogue, the largest systematic that we identify in our analysis is a shape measurement bias of 3 per cent, that we measure using simulations that probe weak shears up to g = 0.3. Our individual cluster mass and concentration measurements are in excellent agreement with predictions of the mass-concentration relation. Equally, our stacked shear profile is in excellent agreement with the Navarro Frenk and White profile. Our new Local Cluster Substructure Survey mass measurements are consistent with the Canadian Cluster Cosmology Project and Cluster Lensing And Supernova Survey with Hubble surveys, and in tension with the Weighing the Giants at ˜1σ-2σ significance. Overall, the consensus at z ≤ 0.3 that is emerging from these complementary surveys represents important progress for cluster mass calibration, and augurs well for cluster cosmology.

Optimising Regionalisation Techniques: Identifying Centres of Endemism in the Extraordinarily Endemic-Rich Cape Floristic Region

PubMed Central

Bradshaw, Peter L.; Colville, Jonathan F.; Linder, H. Peter

2015-01-01

We used a very large dataset (>40% of all species) from the endemic-rich Cape Floristic Region (CFR) to explore the impact of different weighting techniques, coefficients to calculate similarity among the cells, and clustering approaches on biogeographical regionalisation. The results were used to revise the biogeographical subdivision of the CFR. We show that weighted data (down-weighting widespread species), similarity calculated using Kulczinsky’s second measure, and clustering using UPGMA resulted in the optimal classification. This maximized the number of endemic species, the number of centres recognized, and operational geographic units assigned to centres of endemism (CoEs). We developed a dendrogram branch order cut-off (BOC) method to locate the optimal cut-off points on the dendrogram to define candidate clusters. Kulczinsky’s second measure dendrograms were combined using consensus, identifying areas of conflict which could be due to biotic element overlap or transitional areas. Post-clustering GIS manipulation substantially enhanced the endemic composition and geographic size of candidate CoEs. Although there was broad spatial congruence with previous phytogeographic studies, our techniques allowed for the recovery of additional phytogeographic detail not previously described for the CFR. PMID:26147438

A consensus opinion model based on the evolutionary game

NASA Astrophysics Data System (ADS)

Yang, Han-Xin

2016-08-01

We propose a consensus opinion model based on the evolutionary game. In our model, both of the two connected agents receive a benefit if they have the same opinion, otherwise they both pay a cost. Agents update their opinions by comparing payoffs with neighbors. The opinion of an agent with higher payoff is more likely to be imitated. We apply this model in scale-free networks with tunable degree distribution. Interestingly, we find that there exists an optimal ratio of cost to benefit, leading to the shortest consensus time. Qualitative analysis is obtained by examining the evolution of the opinion clusters. Moreover, we find that the consensus time decreases as the average degree of the network increases, but increases with the noise introduced to permit irrational choices. The dependence of the consensus time on the network size is found to be a power-law form. For small or larger ratio of cost to benefit, the consensus time decreases as the degree exponent increases. However, for moderate ratio of cost to benefit, the consensus time increases with the degree exponent. Our results may provide new insights into opinion dynamics driven by the evolutionary game theory.

Molecular typing of uropathogenic E. coli strains by the ERIC-PCR method.

PubMed

Ardakani, Maryam Afkhami; Ranjbar, Reza

2016-04-01

Escherichia coli (E. coli) is the most common cause of urinary infections in hospitals. The aim of this study was to evaluate the ERIC-PCR method for molecular typing of uropathogenic E. coli strains isolated from hospitalized patients. In a cross sectional study, 98 E. coli samples were collected from urine samples taken from patients admitted to Baqiyatallah Hospital from June 2014 to January 2015. The disk agar diffusion method was used to determine antibiotic sensitivity. DNA proliferation based on repetitive intergenic consensus was used to classify the E. coli strains. The products of proliferation were electrophoresed on 1.5% agarose gel, and their dendrograms were drawn. The data were analyzed by online Insillico software. The method used in this research proliferated numerous bands (4-17 bands), ranging from 100 to 3000 base pairs. The detected strains were classified into six clusters (E1-E6) with 70% similarity between them. In this study, uropathogenic E. coli strains belonged to different genotypic clusters. It was found that ERIC-PCR had good differentiation power for molecular typing of uropathogenic E. coli strains isolated from the patients in the study.

Automatic extraction of discontinuity orientation from rock mass surface 3D point cloud

NASA Astrophysics Data System (ADS)

Chen, Jianqin; Zhu, Hehua; Li, Xiaojun

2016-10-01

This paper presents a new method for extracting discontinuity orientation automatically from rock mass surface 3D point cloud. The proposed method consists of four steps: (1) automatic grouping of discontinuity sets using an improved K-means clustering method, (2) discontinuity segmentation and optimization, (3) discontinuity plane fitting using Random Sample Consensus (RANSAC) method, and (4) coordinate transformation of discontinuity plane. The method is first validated by the point cloud of a small piece of a rock slope acquired by photogrammetry. The extracted discontinuity orientations are compared with measured ones in the field. Then it is applied to a publicly available LiDAR data of a road cut rock slope at Rockbench repository. The extracted discontinuity orientations are compared with the method proposed by Riquelme et al. (2014). The results show that the presented method is reliable and of high accuracy, and can meet the engineering needs.

Consensus, Polarization and Clustering of Opinions in Social Networks

DTIC Science & Technology

2013-06-01

values of τ , and consensus at larger values. Fig. 6 compares the phase transitions for three different network configurations: RGG, Erdos- Renyi graph and...Erdos- Renyi graph [25] is generated uniformly at random from the collection of all graphs which have n = 50 nodes and M = 120 edges. The small- world...0.6 0.8 1 Threshold τ N or m al iz ed A lg eb ra ic C on ne ct iv ity RGG Erdos− Renyi Small−World Fig. 6. Phase transitions using three

Characterization of Hepatitis C Virus (HCV) Envelope Diversification from Acute to Chronic Infection within a Sexually Transmitted HCV Cluster by Using Single-Molecule, Real-Time Sequencing

PubMed Central

Ho, Cynthia K. Y.; Raghwani, Jayna; Koekkoek, Sylvie; Liang, Richard H.; Van der Meer, Jan T. M.; Van Der Valk, Marc; De Jong, Menno; Pybus, Oliver G.

2016-01-01

ABSTRACT In contrast to other available next-generation sequencing platforms, PacBio single-molecule, real-time (SMRT) sequencing has the advantage of generating long reads albeit with a relatively higher error rate in unprocessed data. Using this platform, we longitudinally sampled and sequenced the hepatitis C virus (HCV) envelope genome region (1,680 nucleotides [nt]) from individuals belonging to a cluster of sexually transmitted cases. All five subjects were coinfected with HIV-1 and a closely related strain of HCV genotype 4d. In total, 50 samples were analyzed by using SMRT sequencing. By using 7 passes of circular consensus sequencing, the error rate was reduced to 0.37%, and the median number of sequences was 612 per sample. A further reduction of insertions was achieved by alignment against a sample-specific reference sequence. However, in vitro recombination during PCR amplification could not be excluded. Phylogenetic analysis supported close relationships among HCV sequences from the four male subjects and subsequent transmission from one subject to his female partner. Transmission was characterized by a strong genetic bottleneck. Viral genetic diversity was low during acute infection and increased upon progression to chronicity but subsequently fluctuated during chronic infection, caused by the alternate detection of distinct coexisting lineages. SMRT sequencing combines long reads with sufficient depth for many phylogenetic analyses and can therefore provide insights into within-host HCV evolutionary dynamics without the need for haplotype reconstruction using statistical algorithms. IMPORTANCE Next-generation sequencing has revolutionized the study of genetically variable RNA virus populations, but for phylogenetic and evolutionary analyses, longer sequences than those generated by most available platforms, while minimizing the intrinsic error rate, are desired. Here, we demonstrate for the first time that PacBio SMRT sequencing technology can be used to generate full-length HCV envelope sequences at the single-molecule level, providing a data set with large sequencing depth for the characterization of intrahost viral dynamics. The selection of consensus reads derived from at least 7 full circular consensus sequencing rounds significantly reduced the intrinsic high error rate of this method. We used this method to genetically characterize a unique transmission cluster of sexually transmitted HCV infections, providing insight into the distinct evolutionary pathways in each patient over time and identifying the transmission-associated genetic bottleneck as well as fluctuations in viral genetic diversity over time, accompanied by dynamic shifts in viral subpopulations. PMID:28077634

SAM-VI RNAs selectively bind S-adenosylmethionine and exhibit similarities to SAM-III riboswitches.

PubMed

Mirihana Arachchilage, Gayan; Sherlock, Madeline E; Weinberg, Zasha; Breaker, Ronald R

2018-03-04

Five distinct riboswitch classes that regulate gene expression in response to the cofactor S-adenosylmethionine (SAM) or its metabolic breakdown product S-adenosylhomocysteine (SAH) have been reported previously. Collectively, these SAM- or SAH-sensing RNAs constitute the most abundant collection of riboswitches, and are found in nearly every major bacterial lineage. Here, we report a potential sixth member of this pervasive riboswitch family, called SAM-VI, which is predominantly found in Bifidobacterium species. SAM-VI aptamers selectively bind the cofactor SAM and strongly discriminate against SAH. The consensus sequence and structural model for SAM-VI share some features with the consensus model for the SAM-III riboswitch class, whose members are mainly found in lactic acid bacteria. However, there are sufficient differences between the two classes such that current bioinformatics methods separately cluster representatives of the two motifs. These findings highlight the abundance of RNA structures that can form to selectively recognize SAM, and showcase the ability of RNA to utilize diverse strategies to perform similar biological functions.

Development of geriatric competencies for emergency medicine residents using an expert consensus process.

PubMed

Hogan, Teresita M; Losman, Eve D; Carpenter, Christopher R; Sauvigne, Karen; Irmiter, Cheryl; Emanuel, Linda; Leipzig, Rosanne M

2010-03-01

The emergency department (ED) visit rate for older patients exceeds that of all age groups other than infants. The aging population will increase elder ED patient utilization to 35% to 60% of all visits. Older patients can have complex clinical presentations and be resource-intensive. Evidence indicates that emergency physicians fail to provide consistent high-quality care for elder ED patients, resulting in poor clinical outcomes. The objective was to develop a consensus document, "Geriatric Competencies for Emergency Medicine Residents," by identified experts. This is a minimum set of behaviorally based performance standards that all residents should be able to demonstrate by completion of their residency training. This consensus-based process utilized an inductive, qualitative, multiphase method to determine the minimum geriatric competencies needed by emergency medicine (EM) residents. Assessments of face validity and reliability were used throughout the project. In Phase I, participants (n=363) identified 12 domains and 300 potential competencies. In Phase II, an expert panel (n=24) clustered the Phase I responses, resulting in eight domains and 72 competencies. In Phase III, the expert panel reduced the competencies to 26. In Phase IV, analysis of face validity and reliability yielded a 100% consensus for eight domains and 26 competencies. The domains identified were atypical presentation of disease; trauma, including falls; cognitive and behavioral disorders; emergent intervention modifications; medication management; transitions of care; pain management and palliative care; and effect of comorbid conditions. The Geriatric Competencies for EM Residents is a consensus document that can form the basis for EM residency curricula and assessment to meet the demands of our aging population. Copyright (c) 2010 by the Society for Academic Emergency Medicine.

The Development of the Croatian Competency Framework for Pharmacists.

PubMed

Mucalo, Iva; Hadžiabdić, Maja Ortner; Govorčinović, Tihana; Šarić, Martina; Bruno, Andreia; Bates, Ian

2016-10-25

Objective. To adjust and validate the Global Competency Framework (GbCF) to be relevant for Croatian community and hospital pharmacists. Methods. A descriptive study was conducted in three steps: translation, consensus development, and validation by an expert panel and public consultation. Panel members were representatives from community pharmacies, hospital pharmacies, regulatory and professional bodies, academia, and industry. Results. The adapted framework consists of 96 behavioral statements organized in four clusters: Pharmaceutical Public Health, Pharmaceutical Care, Organization and Management, and Personal and Professional Competencies. When mapped against the 100 statements listed in the GbCF, 27 matched, 39 were revised, 30 were introduced, and 24 were excluded from the original framework. Conclusions. The adaptation and validation proved that GbCF is adaptable to local needs, the Croatian Competency Framework that emerged from it being an example. Key amendments were made within Organization and Management and Pharmaceutical Care clusters, demonstrating that these issues can be country specific.

Consensus properties and their large-scale applications for the gene duplication problem.

PubMed

Moon, Jucheol; Lin, Harris T; Eulenstein, Oliver

2016-06-01

Solving the gene duplication problem is a classical approach for species tree inference from gene trees that are confounded by gene duplications. This problem takes a collection of gene trees and seeks a species tree that implies the minimum number of gene duplications. Wilkinson et al. posed the conjecture that the gene duplication problem satisfies the desirable Pareto property for clusters. That is, for every instance of the problem, all clusters that are commonly present in the input gene trees of this instance, called strict consensus, will also be found in every solution to this instance. We prove that this conjecture does not generally hold. Despite this negative result we show that the gene duplication problem satisfies a weaker version of the Pareto property where the strict consensus is found in at least one solution (rather than all solutions). This weaker property contributes to our design of an efficient scalable algorithm for the gene duplication problem. We demonstrate the performance of our algorithm in analyzing large-scale empirical datasets. Finally, we utilize the algorithm to evaluate the accuracy of standard heuristics for the gene duplication problem using simulated datasets.

The quality of reporting in cluster randomised crossover trials: proposal for reporting items and an assessment of reporting quality.

PubMed

Arnup, Sarah J; Forbes, Andrew B; Kahan, Brennan C; Morgan, Katy E; McKenzie, Joanne E

2016-12-06

The cluster randomised crossover (CRXO) design is gaining popularity in trial settings where individual randomisation or parallel group cluster randomisation is not feasible or practical. Our aim is to stimulate discussion on the content of a reporting guideline for CRXO trials and to assess the reporting quality of published CRXO trials. We undertook a systematic review of CRXO trials. Searches of MEDLINE, EMBASE, and CINAHL Plus as well as citation searches of CRXO methodological articles were conducted to December 2014. Reporting quality was assessed against both modified items from 2010 CONSORT and 2012 cluster trials extension and other proposed quality measures. Of the 3425 records identified through database searching, 83 trials met the inclusion criteria. Trials were infrequently identified as "cluster randomis(z)ed crossover" in title (n = 7, 8%) or abstract (n = 21, 25%), and a rationale for the design was infrequently provided (n = 20, 24%). Design parameters such as the number of clusters and number of periods were well reported. Discussion of carryover took place in only 17 trials (20%). Sample size methods were only reported in 58% (n = 48) of trials. A range of approaches were used to report baseline characteristics. The analysis method was not adequately reported in 23% (n = 19) of trials. The observed within-cluster within-period intracluster correlation and within-cluster between-period intracluster correlation for the primary outcome data were not reported in any trial. The potential for selection, performance, and detection bias could be evaluated in 30%, 81%, and 70% of trials, respectively. There is a clear need to improve the quality of reporting in CRXO trials. Given the unique features of a CRXO trial, it is important to develop a CONSORT extension. Consensus amongst trialists on the content of such a guideline is essential.

Statistical discovery of site inter-dependencies in sub-molecular hierarchical protein structuring

PubMed Central

2012-01-01

Background Much progress has been made in understanding the 3D structure of proteins using methods such as NMR and X-ray crystallography. The resulting 3D structures are extremely informative, but do not always reveal which sites and residues within the structure are of special importance. Recently, there are indications that multiple-residue, sub-domain structural relationships within the larger 3D consensus structure of a protein can be inferred from the analysis of the multiple sequence alignment data of a protein family. These intra-dependent clusters of associated sites are used to indicate hierarchical inter-residue relationships within the 3D structure. To reveal the patterns of associations among individual amino acids or sub-domain components within the structure, we apply a k-modes attribute (aligned site) clustering algorithm to the ubiquitin and transthyretin families in order to discover associations among groups of sites within the multiple sequence alignment. We then observe what these associations imply within the 3D structure of these two protein families. Results The k-modes site clustering algorithm we developed maximizes the intra-group interdependencies based on a normalized mutual information measure. The clusters formed correspond to sub-structural components or binding and interface locations. Applying this data-directed method to the ubiquitin and transthyretin protein family multiple sequence alignments as a test bed, we located numerous interesting associations of interdependent sites. These clusters were then arranged into cluster tree diagrams which revealed four structural sub-domains within the single domain structure of ubiquitin and a single large sub-domain within transthyretin associated with the interface among transthyretin monomers. In addition, several clusters of mutually interdependent sites were discovered for each protein family, each of which appear to play an important role in the molecular structure and/or function. Conclusions Our results demonstrate that the method we present here using a k-modes site clustering algorithm based on interdependency evaluation among sites obtained from a sequence alignment of homologous proteins can provide significant insights into the complex, hierarchical inter-residue structural relationships within the 3D structure of a protein family. PMID:22793672

Statistical discovery of site inter-dependencies in sub-molecular hierarchical protein structuring.

PubMed

Durston, Kirk K; Chiu, David Ky; Wong, Andrew Kc; Li, Gary Cl

2012-07-13

Much progress has been made in understanding the 3D structure of proteins using methods such as NMR and X-ray crystallography. The resulting 3D structures are extremely informative, but do not always reveal which sites and residues within the structure are of special importance. Recently, there are indications that multiple-residue, sub-domain structural relationships within the larger 3D consensus structure of a protein can be inferred from the analysis of the multiple sequence alignment data of a protein family. These intra-dependent clusters of associated sites are used to indicate hierarchical inter-residue relationships within the 3D structure. To reveal the patterns of associations among individual amino acids or sub-domain components within the structure, we apply a k-modes attribute (aligned site) clustering algorithm to the ubiquitin and transthyretin families in order to discover associations among groups of sites within the multiple sequence alignment. We then observe what these associations imply within the 3D structure of these two protein families. The k-modes site clustering algorithm we developed maximizes the intra-group interdependencies based on a normalized mutual information measure. The clusters formed correspond to sub-structural components or binding and interface locations. Applying this data-directed method to the ubiquitin and transthyretin protein family multiple sequence alignments as a test bed, we located numerous interesting associations of interdependent sites. These clusters were then arranged into cluster tree diagrams which revealed four structural sub-domains within the single domain structure of ubiquitin and a single large sub-domain within transthyretin associated with the interface among transthyretin monomers. In addition, several clusters of mutually interdependent sites were discovered for each protein family, each of which appear to play an important role in the molecular structure and/or function. Our results demonstrate that the method we present here using a k-modes site clustering algorithm based on interdependency evaluation among sites obtained from a sequence alignment of homologous proteins can provide significant insights into the complex, hierarchical inter-residue structural relationships within the 3D structure of a protein family.

Comparison of pulsed-field gel electrophoresis and enterobacterial repetitive intergenic consensus PCR and biochemical tests to characterize Lactococcus garvieae.

PubMed

Ture, M; Altinok, I; Capkin, E

2015-01-01

Biochemical test, pulsed-field gel electrophoresis (PFGE) and enterobacterial repetitive intergenic consensus sequence PCR (ERIC-PCR) were used to compare 42 strains of Lactococcus garvieae isolated from different regions of Turkey, Italy, France and Spain. Twenty biotypes of L. garvieae were formed based on 54 biochemical tests. ERIC-PCR of genomic DNA from different L. garvieae strains resulted in amplification of multiple fragments of DNA in sizes ranging between 200 and 5000 bp with various band intensities. After cutting DNA with ApaI restriction enzyme and running on the PFGE, 11–22 resolvable bands ranging from 2 to 194 kb were observed. Turkish isolates were grouped into two clusters, and only A58 (Italy) strain was connected with Turkish isolates. Similarities between Turkish, Spanish, Italian and French isolates were <50% except 216-6 Rize strain. In Turkey, first lactococcosis occurred in Mugla, and then, it has been spread all over the country. Based on ERIC-PCR, Spanish and Italian strains of L. garvieae were related to Mugla strains. Therefore, after comparing PFGE profiles, ERIC-PCR profiles and phenotypic characteristics of 42 strains of L. garvieae, there were no relationships found between these three typing methods. PFGE method was more discriminative than the other methods. © 2014 John Wiley & Sons Ltd.

amoA-based consensus phylogeny of ammonia-oxidizing archaea and deep sequencing of amoA genes from soils of four different geographic regions

PubMed Central

Pester, Michael; Rattei, Thomas; Flechl, Stefan; Gröngröft, Alexander; Richter, Andreas; Overmann, Jörg; Reinhold-Hurek, Barbara; Loy, Alexander; Wagner, Michael

2012-01-01

Ammonia-oxidizing archaea (AOA) play an important role in nitrification and many studies exploit their amoA genes as marker for their diversity and abundance. We present an archaeal amoA consensus phylogeny based on all publicly available sequences (status June 2010) and provide evidence for the diversification of AOA into four previously recognized clusters and one newly identified major cluster. These clusters, for which we suggest a new nomenclature, harboured 83 AOA species-level OTU (using an inferred species threshold of 85% amoA identity). 454 pyrosequencing of amoA amplicons from 16 soils sampled in Austria, Costa Rica, Greenland and Namibia revealed that only 2% of retrieved sequences had no database representative on the species-level and represented 30–37 additional species-level OTUs. With the exception of an acidic soil from which mostly amoA amplicons of the Nitrosotalea cluster were retrieved, all soils were dominated by amoA amplicons from the Nitrososphaera cluster (also called group I.1b), indicating that the previously reported AOA from the Nitrosopumilus cluster (also called group I.1a) are absent or represent minor populations in soils. AOA richness estimates on the species level ranged from 8–83 co-existing AOAs per soil. Presence/absence of amoA OTUs (97% identity level) correlated with geographic location, indicating that besides contemporary environmental conditions also dispersal limitation across different continents and/or historical environmental conditions might influence AOA biogeography in soils. PMID:22141924

Is There a Consensus on Consensus Methodology? Descriptions and Recommendations for Future Consensus Research.

PubMed

Waggoner, Jane; Carline, Jan D; Durning, Steven J

2016-05-01

The authors of this article reviewed the methodology of three common consensus methods: nominal group process, consensus development panels, and the Delphi technique. The authors set out to determine how a majority of researchers are conducting these studies, how they are analyzing results, and subsequently the manner in which they are reporting their findings. The authors conclude with a set of guidelines and suggestions designed to aid researchers who choose to use the consensus methodology in their work.Overall, researchers need to describe their inclusion criteria. In addition to this, on the basis of the current literature the authors found that a panel size of 5 to 11 members was most beneficial across all consensus methods described. Lastly, the authors agreed that the statistical analyses done in consensus method studies should be as rigorous as possible and that the predetermined definition of consensus must be included in the ultimate manuscript. More specific recommendations are given for each of the three consensus methods described in the article.

Recombination radius of a Frenkel pair and capture radius of a self-interstitial atom by vacancy clusters in bcc Fe

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nakashima, Kenichi; Stoller, Roger E.; Xu, Haixuan

The recombination radius of a Frenkel pair is a fundamental parameter for the object kinetic Monte Carlo (OKMC) and mean field rate theory (RT) methods that are used to investigate irradiation damage accumulation in neutron irradiated nuclear materials. The recombination radius in bcc Fe has been studied both experimentally and numerically, however there is no general consensus about its value. The detailed atomistic processes of recombination also remain uncertain. Values from 1:0a₀ to 3:3a₀ have been employed as a recombination radius in previous studies using OKMC and RT. The recombination process of a Frenkel pair is investigated at the atomicmore » level using the self-evolved atomistic kinetic Monte Carlo (SEAKMC) method in this paper. SEAKMC calculations reveal that a self-interstitial atom recombines with a vacancy in a spontaneous reaction from several nearby sites following characteristic pathways. The recombination radius of a Frenkel pair is estimated to be 2.26a₀ by taking the average of the recombination distances from 80 simulation cases. This value agrees well with the experimental estimate. In addition, we apply these procedures to the capture radius of a self-interstitial atom by a vacancy cluster. The capture radius is found to gradually increase with the size of the vacancy cluster. The fitting curve for the capture radius is obtained as a function of the number of vacancies in the cluster.« less

Recombination radius of a Frenkel pair and capture radius of a self-interstitial atom by vacancy clusters in bcc Fe

DOE PAGES

Nakashima, Kenichi; Stoller, Roger E.; Xu, Haixuan

2015-08-04

The recombination radius of a Frenkel pair is a fundamental parameter for the object kinetic Monte Carlo (OKMC) and mean field rate theory (RT) methods that are used to investigate irradiation damage accumulation in neutron irradiated nuclear materials. The recombination radius in bcc Fe has been studied both experimentally and numerically, however there is no general consensus about its value. The detailed atomistic processes of recombination also remain uncertain. Values from 1:0a₀ to 3:3a₀ have been employed as a recombination radius in previous studies using OKMC and RT. The recombination process of a Frenkel pair is investigated at the atomicmore » level using the self-evolved atomistic kinetic Monte Carlo (SEAKMC) method in this paper. SEAKMC calculations reveal that a self-interstitial atom recombines with a vacancy in a spontaneous reaction from several nearby sites following characteristic pathways. The recombination radius of a Frenkel pair is estimated to be 2.26a₀ by taking the average of the recombination distances from 80 simulation cases. This value agrees well with the experimental estimate. In addition, we apply these procedures to the capture radius of a self-interstitial atom by a vacancy cluster. The capture radius is found to gradually increase with the size of the vacancy cluster. The fitting curve for the capture radius is obtained as a function of the number of vacancies in the cluster.« less

Consensus on core phenomena and statements describing Basic Body Awareness Therapy within the movement awareness domain in physiotherapy.

PubMed

Skjaerven, L H; Mattsson, M; Catalan-Matamoros, D; Parker, A; Gard, G; Gyllensten, A Lundvik

2018-02-26

Physiotherapists are facing complex health challenges in the treatment of persons suffering from long-lasting musculoskeletal disorders and mental health problems. Basic Body Awareness Therapy (BBAT) is a physiotherapy approach within the movement awareness domain developed to bridge physical, mental, and relational health challenges. The purpose of this study was to reach a consensus on core phenomena and statements describing BBAT. A consensus-building process was conducted using the nominal group technique (NGT). Twenty-one BBAT experts from 10 European countries participated in a concentrated weekend workshop of 20 hours. All participants signed informed consent. Participants reached a consensus on 138 core phenomena, clustered in three overarching categories: clinical core, historical roots, and research and evaluation phenomena. Of the 106 clinical core phenomena, the participants agreed on three categories of phenomena: movement quality, movement awareness practice, and movement awareness therapy and pedagogy. Furthermore, the participants reached 100 percent consensus on 16 of 30 statements describing BBAT. This study provides a consensus on core phenomena and statements describing BBAT. The data reveal phenomena implemented when promoting movement quality through movement awareness. Data provide clarity in some aspects of the vocabulary as fundamental theory. Further reearch will be developed.

A fully resolved consensus between fully resolved phylogenetic trees.

PubMed

Quitzau, José Augusto Amgarten; Meidanis, João

2006-03-31

Nowadays, there are many phylogeny reconstruction methods, each with advantages and disadvantages. We explored the advantages of each method, putting together the common parts of trees constructed by several methods, by means of a consensus computation. A number of phylogenetic consensus methods are already known. Unfortunately, there is also a taboo concerning consensus methods, because most biologists see them mainly as comparators and not as phylogenetic tree constructors. We challenged this taboo by defining a consensus method that builds a fully resolved phylogenetic tree based on the most common parts of fully resolved trees in a given collection. We also generated results showing that this consensus is in a way a kind of "median" of the input trees; as such it can be closer to the correct tree in many situations.

Phylogenetic study of metallo-β-lactamase producing multidrug resistant Pseudomonas aeruginosa isolates from burn patients.

PubMed

Jena, Jayanti; Debata, Nagen Kumar; Sahoo, Rajesh Kumar; Subudhi, Enketeswara

2015-12-01

The present study was carried out to understand the clonal relationship using enterobacteriaceae repetitive intergenic consensus polymerase chain reaction (ERIC-PCR) among metallo-β-lactamase (MBL) producing multidrug resistant Pseudomonas aeruginosa isolates from burn victims and their susceptibility to commonly used anti-pseudomonal agents. In the present study 94 non-duplicate P. aeruginosa strains from the wound samples of burn patients were included. Identification of the isolates was done by biochemical methods and antibiotic sensitivity was done by disc diffusion method following CLSI (Clinical Laboratory Standard Institute) guidelines. By using imipenem (IPM)-EDTA disk diffusion/double disc synergy method carbapenem resistant organisms were tested for MBL. To define the clonal relationship ERIC-PCR was used. Of the 94 isolates, 18 (19.14%) were found resistant to IPM and MBL production was shown 11 (11.70%) by the IPM-EDTA disc diffusion method. From dendrogram of the ERIC-PCR profile four major clusters were obtained (A, B, C and D). Cluster B contained the majority of the isolates (6 strains 1, 4, 8, 9, 10 and 11). This study using ERIC-PCR of randomly collected isolates exhibits high genetic diversity which rules out cross contamination frequency. Copyright © 2015 Elsevier Ltd and ISBI. All rights reserved.

Sensory analysis of characterising flavours: evaluating tobacco product odours using an expert panel.

PubMed

Krüsemann, Erna J Z; Lasschuijt, Marlou P; de Graaf, C; de Wijk, René A; Punter, Pieter H; van Tiel, Loes; Cremers, Johannes W J M; van de Nobelen, Suzanne; Boesveldt, Sanne; Talhout, Reinskje

2018-05-23

Tobacco flavours are an important regulatory concept in several jurisdictions, for example in the USA, Canada and Europe. The European Tobacco Products Directive 2014/40/EU prohibits cigarettes and roll-your-own tobacco having a characterising flavour. This directive defines characterising flavour as 'a clearly noticeable smell or taste other than one of tobacco […]'. To distinguish between products with and without a characterising flavour, we trained an expert panel to identify characterising flavours by smelling. An expert panel (n=18) evaluated the smell of 20 tobacco products using self-defined odour attributes, following Quantitative Descriptive Analysis. The panel was trained during 14 attribute training, consensus training and performance monitoring sessions. Products were assessed during six test sessions. Principal component analysis, hierarchical clustering (four and six clusters) and Hotelling's T-tests (95% and 99% CIs) were used to determine differences and similarities between tobacco products based on odour attributes. The final attribute list contained 13 odour descriptors. Panel performance was sufficient after 14 training sessions. Products marketed as unflavoured that formed a cluster were considered reference products. A four-cluster method distinguished cherry-flavoured, vanilla-flavoured and menthol-flavoured products from reference products. Six clusters subdivided reference products into tobacco leaves, roll-your-own and commercial products. An expert panel was successfully trained to assess characterising odours in cigarettes and roll-your-own tobacco. This method could be applied to other product types such as e-cigarettes. Regulatory decisions on the choice of reference products and significance level are needed which directly influences the products being assessed as having a characterising odour. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Hierarchical structure and importance of patients' reasons for treatment choices in knee and hip osteoarthritis: a concept mapping study.

PubMed

Selten, Ellen M H; Geenen, Rinie; van der Laan, Willemijn H; van der Meulen-Dilling, Roelien G; Schers, Henk J; Nijhof, Marc W; van den Ende, Cornelia H M; Vriezekolk, Johanna E

2017-02-01

To improve patients' use of conservative treatment options of hip and knee OA, in-depth understanding of reasons underlying patients' treatment choices is required. The current study adopted a concept mapping method to thematically structure and prioritize reasons for treatment choice in knee and hip OA from a patients' perspective. Multiple reasons for treatment choices were previously identified using in-depth interviews. In consensus meetings, experts derived 51 representative reasons from the interviews. Thirty-six patients individually sorted the 51 reasons in two card-sorting tasks: one based on content similarity, and one based on importance of reasons. The individual sortings of the first card-sorting task provided input for a hierarchical cluster analysis (squared Euclidian distances, Ward's method). The importance of the reasons and clusters were examined using descriptive statistics. The hierarchical structure of reasons for treatment choices showed a core distinction between two categories of clusters: barriers [subdivided into context (e.g. the healthcare system) and disadvantages] and outcome (subdivided into treatment and personal life). At the lowest level, 15 clusters were identified of which the clusters Physical functioning, Risks and Prosthesis were considered most important when making a treatment decision for hip or knee OA. Patients' treatment choices in knee and hip OA are guided by contextual barriers, disadvantages of the treatment, outcomes of the treatment and consequences for personal life. The structured overview of reasons can be used to support shared decision-making. © The Author 2016. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

a Novel 3d Intelligent Fuzzy Algorithm Based on Minkowski-Clustering

NASA Astrophysics Data System (ADS)

Toori, S.; Esmaeily, A.

2017-09-01

Assessing and monitoring the state of the earth surface is a key requirement for global change research. In this paper, we propose a new consensus fuzzy clustering algorithm that is based on the Minkowski distance. This research concentrates on Tehran's vegetation mass and its changes during 29 years using remote sensing technology. The main purpose of this research is to evaluate the changes in vegetation mass using a new process by combination of intelligent NDVI fuzzy clustering and Minkowski distance operation. The dataset includes the images of Landsat8 and Landsat TM, from 1989 to 2016. For each year three images of three continuous days were used to identify vegetation impact and recovery. The result was a 3D NDVI image, with one dimension for each day NDVI. The next step was the classification procedure which is a complicated process of categorizing pixels into a finite number of separate classes, based on their data values. If a pixel satisfies a certain set of standards, the pixel is allocated to the class that corresponds to those criteria. This method is less sensitive to noise and can integrate solutions from multiple samples of data or attributes for processing data in the processing industry. The result was a fuzzy one dimensional image. This image was also computed for the next 28 years. The classification was done in both specified urban and natural park areas of Tehran. Experiments showed that our method worked better in classifying image pixels in comparison with the standard classification methods.

Effect of sample stratification on dairy GWAS results

PubMed Central

2012-01-01

Background Artificial insemination and genetic selection are major factors contributing to population stratification in dairy cattle. In this study, we analyzed the effect of sample stratification and the effect of stratification correction on results of a dairy genome-wide association study (GWAS). Three methods for stratification correction were used: the efficient mixed-model association expedited (EMMAX) method accounting for correlation among all individuals, a generalized least squares (GLS) method based on half-sib intraclass correlation, and a principal component analysis (PCA) approach. Results Historical pedigree data revealed that the 1,654 contemporary cows in the GWAS were all related when traced through approximately 10–15 generations of ancestors. Genome and phenotype stratifications had a striking overlap with the half-sib structure. A large elite half-sib family of cows contributed to the detection of favorable alleles that had low frequencies in the general population and high frequencies in the elite cows and contributed to the detection of X chromosome effects. All three methods for stratification correction reduced the number of significant effects. EMMAX method had the most severe reduction in the number of significant effects, and the PCA method using 20 principal components and GLS had similar significance levels. Removal of the elite cows from the analysis without using stratification correction removed many effects that were also removed by the three methods for stratification correction, indicating that stratification correction could have removed some true effects due to the elite cows. SNP effects with good consensus between different methods and effect size distributions from USDA’s Holstein genomic evaluation included the DGAT1-NIBP region of BTA14 for production traits, a SNP 45kb upstream from PIGY on BTA6 and two SNPs in NIBP on BTA14 for protein percentage. However, most of these consensus effects had similar frequencies in the elite and average cows. Conclusions Genetic selection and extensive use of artificial insemination contributed to overlapped genome, pedigree and phenotype stratifications. The presence of an elite cluster of cows was related to the detection of rare favorable alleles that had high frequencies in the elite cluster and low frequencies in the remaining cows. Methods for stratification correction could have removed some true effects associated with genetic selection. PMID:23039970

Different Strategies for Molecular Differentiation of Mycobacterium bovis Strains Isolated in Sardinia, Italy

PubMed Central

Sechi, Leonardo A.; Leori, Guido; Lollai, Stefano A.; Duprè, Ilaria; Molicotti, Paola; Fadda, Giovanni; Zanetti, Stefania

1999-01-01

Different genetic markers were used to analyze 22 Mycobacterium bovis strains isolated from cattle in Sardinia and one human isolate. IS6110 DNA fingerprinting differentiated the strains into six patterns, whereas with enterobacterial repetitive consensus sequence primers produced seven clusters. PCR ribotyping followed by digestion with HaeIII and PvuII produced five and seven patterns, respectively. PCR with the (GTG)5 oligonucleotide primer showed the best discriminatory power, generating eight clusters among the strains analyzed. PMID:10103282

Delineation of gravel-bed clusters via factorial kriging

NASA Astrophysics Data System (ADS)

Wu, Fu-Chun; Wang, Chi-Kuei; Huang, Guo-Hao

2018-05-01

Gravel-bed clusters are the most prevalent microforms that affect local flows and sediment transport. A growing consensus is that the practice of cluster delineation should be based primarily on bed topography rather than grain sizes. Here we present a novel approach for cluster delineation using patch-scale high-resolution digital elevation models (DEMs). We use a geostatistical interpolation method, i.e., factorial kriging, to decompose the short- and long-range (grain- and microform-scale) DEMs. The required parameters are determined directly from the scales of the nested variograms. The short-range DEM exhibits a flat bed topography, yet individual grains are sharply outlined, making the short-range DEM a useful aid for grain segmentation. The long-range DEM exhibits a smoother topography than the original full DEM, yet groupings of particles emerge as small-scale bedforms, making the contour percentile levels of the long-range DEM a useful tool for cluster identification. Individual clusters are delineated using the segmented grains and identified clusters via a range of contour percentile levels. Our results reveal that the density and total area of delineated clusters decrease with increasing contour percentile level, while the mean grain size of clusters and average size of anchor clast (i.e., the largest particle in a cluster) increase with the contour percentile level. These results support the interpretation that larger particles group as clusters and protrude higher above the bed than other smaller grains. A striking feature of the delineated clusters is that anchor clasts are invariably greater than the D90 of the grain sizes even though a threshold anchor size was not adopted herein. The average areal fractal dimensions (Hausdorff-Besicovich dimensions of the projected areas) of individual clusters, however, demonstrate that clusters delineated with different contour percentile levels exhibit similar planform morphologies. Comparisons with a compilation of existing field data show consistency with the cluster properties documented in a wide variety of settings. This study thus points toward a promising, alternative DEM-based approach to characterizing sediment structures in gravel-bed rivers.

Freezing period strongly impacts the emergence of a global consensus in the voter model

PubMed Central

Wang, Zhen; Liu, Yi; Wang, Lin; Zhang, Yan; Wang, Zhen

2014-01-01

It is well known that human beings do not always change opinions or attitudes, since the duration of interaction with others usually has a significant impact on one's decision-making. Based on this observation, we introduce a freezing period into the voter model, in which the frozen individuals have a weakened opinion switching ability. We unfold the presence of an optimal freezing period, which leads to the fastest consensus, using computation simulations as well as theoretical analysis. We demonstrate that the essence of an accelerated consensus is attributed to the biased random walk of the interface between adjacent opinion clusters. The emergence of an optimal freezing period is robust against the size of the system and the number of distinct opinions. This study is instructive for understanding human collective behavior in other relevant fields. PMID:24398458

Protonation States of Homocitrate and Nearby Residues in Nitrogenase Studied by Computational Methods and Quantum Refinement.

PubMed

Cao, Lili; Caldararu, Octav; Ryde, Ulf

2017-09-07

Nitrogenase is the only enzyme that can break the triple bond in N 2 to form two molecules of ammonia. The enzyme has been thoroughly studied with both experimental and computational methods, but there is still no consensus regarding the atomic details of the reaction mechanism. In the most common form, the active site is a MoFe 7 S 9 C(homocitrate) cluster. The homocitrate ligand contains one alcohol and three carboxylate groups. In water solution, the triply deprotonated form dominates, but because the alcohol (and one of the carboxylate groups) coordinate to the Mo ion, this may change in the enzyme. We have performed a series of computational calculations with molecular dynamics (MD), quantum mechanical (QM) cluster, combined QM and molecular mechanics (QM/MM), QM/MM with Poisson-Boltzmann and surface area solvation, QM/MM thermodynamic cycle perturbations, and quantum refinement methods to settle the most probable protonation state of the homocitrate ligand in nitrogenase. The results quite conclusively point out a triply deprotonated form (net charge -3) with a proton shared between the alcohol and one of the carboxylate groups as the most stable at pH 7. Moreover, we have studied eight ionizable protein residues close to the active site with MD simulations and determined the most likely protonation states.

Defining syndromes using cattle meat inspection data for syndromic surveillance purposes: a statistical approach with the 2005-2010 data from ten French slaughterhouses.

PubMed

Dupuy, Céline; Morignat, Eric; Maugey, Xavier; Vinard, Jean-Luc; Hendrikx, Pascal; Ducrot, Christian; Calavas, Didier; Gay, Emilie

2013-04-30

The slaughterhouse is a central processing point for food animals and thus a source of both demographic data (age, breed, sex) and health-related data (reason for condemnation and condemned portions) that are not available through other sources. Using these data for syndromic surveillance is therefore tempting. However many possible reasons for condemnation and condemned portions exist, making the definition of relevant syndromes challenging.The objective of this study was to determine a typology of cattle with at least one portion of the carcass condemned in order to define syndromes. Multiple factor analysis (MFA) in combination with clustering methods was performed using both health-related data and demographic data. Analyses were performed on 381,186 cattle with at least one portion of the carcass condemned among the 1,937,917 cattle slaughtered in ten French abattoirs. Results of the MFA and clustering methods led to 12 clusters considered as stable according to year of slaughter and slaughterhouse. One cluster was specific to a disease of public health importance (cysticercosis). Two clusters were linked to the slaughtering process (fecal contamination of heart or lungs and deterioration lesions). Two clusters respectively characterized by chronic liver lesions and chronic peritonitis could be linked to diseases of economic importance to farmers. Three clusters could be linked respectively to reticulo-pericarditis, fatty liver syndrome and farmer's lung syndrome, which are related to both diseases of economic importance to farmers and herd management issues. Three clusters respectively characterized by arthritis, myopathy and Dark Firm Dry (DFD) meat could notably be linked to animal welfare issues. Finally, one cluster, characterized by bronchopneumonia, could be linked to both animal health and herd management issues. The statistical approach of combining multiple factor analysis with cluster analysis showed its relevance for the detection of syndromes using available large and complex slaughterhouse data. The advantages of this statistical approach are to i) define groups of reasons for condemnation based on meat inspection data, ii) help grouping reasons for condemnation among a list of various possible reasons for condemnation for which a consensus among experts could be difficult to reach, iii) assign each animal to a single syndrome which allows the detection of changes in trends of syndromes to detect unusual patterns in known diseases and emergence of new diseases.

Consensus methods: review of original methods and their main alternatives used in public health

PubMed Central

Bourrée, Fanny; Michel, Philippe; Salmi, Louis Rachid

2008-01-01

Summary Background Consensus-based studies are increasingly used as decision-making methods, for they have lower production cost than other methods (observation, experimentation, modelling) and provide results more rapidly. The objective of this paper is to describe the principles and methods of the four main methods, Delphi, nominal group, consensus development conference and RAND/UCLA, their use as it appears in peer-reviewed publications and validation studies published in the healthcare literature. Methods A bibliographic search was performed in Pubmed/MEDLINE, Banque de Données Santé Publique (BDSP), The Cochrane Library, Pascal and Francis. Keywords, headings and qualifiers corresponding to a list of terms and expressions related to the consensus methods were searched in the thesauri, and used in the literature search. A search with the same terms and expressions was performed on Internet using the website Google Scholar. Results All methods, precisely described in the literature, are based on common basic principles such as definition of subject, selection of experts, and direct or remote interaction processes. They sometimes use quantitative assessment for ranking items. Numerous variants of these methods have been described. Few validation studies have been implemented. Not implementing these basic principles and failing to describe the methods used to reach the consensus were both frequent reasons contributing to raise suspicion regarding the validity of consensus methods. Conclusion When it is applied to a new domain with important consequences in terms of decision making, a consensus method should be first validated. PMID:19013039

The use of Delphi and Nominal Group Technique in nursing education: A review.

PubMed

Foth, Thomas; Efstathiou, Nikolaos; Vanderspank-Wright, Brandi; Ufholz, Lee-Anne; Dütthorn, Nadin; Zimansky, Manuel; Humphrey-Murto, Susan

2016-08-01

Consensus methods are used by healthcare professionals and educators within nursing education because of their presumed capacity to extract the profession's' "collective knowledge" which is often considered tacit knowledge that is difficult to verbalize and to formalize. Since their emergence, consensus methods have been criticized and their rigour has been questioned. Our study focuses on the use of consensus methods in nursing education and seeks to explore how extensively consensus methods are used, the types of consensus methods employed, the purpose of the research and how standardized the application of the methods is. A systematic approach was employed to identify articles reporting the use of consensus methods in nursing education. The search strategy included keyword search in five electronic databases [Medline (Ovid), Embase (Ovid), AMED (Ovid), ERIC (Ovid) and CINAHL (EBSCO)] for the period 2004-2014. We included articles published in English, French, German and Greek discussing the use of consensus methods in nursing education or in the context of identifying competencies. A standardized extraction form was developed using an iterative process with results from the search. General descriptors such as type of journal, nursing speciality, type of educational issue addressed, method used, geographic scope were recorded. Features reflecting methodology such as number, selection and composition of panel participants, number of rounds, response rates, definition of consensus, and feedback were recorded. 1230 articles were screened resulting in 101 included studies. The Delphi was used in 88.2% of studies. Most were reported in nursing journals (63.4%). The most common purpose to use these methods was defining competencies, curriculum development and renewal, and assessment. Remarkably, both standardization and reporting of consensus methods was noted to be generally poor. Areas where the methodology appeared weak included: preparation of the initial questionnaire; the selection and description of participants; number of rounds and number of participants remaining after each round; formal feedback of group ratings; definitions of consensus and a priori definition of numbers of rounds; and modifications to the methodology. The findings of this study are concerning if interpreted within the context of the structural critiques because our findings lend support to these critiques. If consensus methods should continue being used to inform best practices in nursing education, they must be rigorous in design. Copyright © 2016 Elsevier Ltd. All rights reserved.

Assessment of Yellow Fever Epidemic Risk: An Original Multi-criteria Modeling Approach

PubMed Central

Briand, Sylvie; Beresniak, Ariel; Nguyen, Tim; Yonli, Tajoua; Duru, Gerard; Kambire, Chantal; Perea, William

2009-01-01

Background Yellow fever (YF) virtually disappeared in francophone West African countries as a result of YF mass vaccination campaigns carried out between 1940 and 1953. However, because of the failure to continue mass vaccination campaigns, a resurgence of the deadly disease in many African countries began in the early 1980s. We developed an original modeling approach to assess YF epidemic risk (vulnerability) and to prioritize the populations to be vaccinated. Methods and Findings We chose a two-step assessment of vulnerability at district level consisting of a quantitative and qualitative assessment per country. Quantitative assessment starts with data collection on six risk factors: five risk factors associated with “exposure” to virus/vector and one with “susceptibility” of a district to YF epidemics. The multiple correspondence analysis (MCA) modeling method was specifically adapted to reduce the five exposure variables to one aggregated exposure indicator. Health districts were then projected onto a two-dimensional graph to define different levels of vulnerability. Districts are presented on risk maps for qualitative analysis in consensus groups, allowing the addition of factors, such as population migrations or vector density, that could not be included in MCA. The example of rural districts in Burkina Faso show five distinct clusters of risk profiles. Based on this assessment, 32 of 55 districts comprising over 7 million people were prioritized for preventive vaccination campaigns. Conclusion This assessment of yellow fever epidemic risk at the district level includes MCA modeling and consensus group modification. MCA provides a standardized way to reduce complexity. It supports an informed public health decision-making process that empowers local stakeholders through the consensus group. This original approach can be applied to any disease with documented risk factors. PMID:19597548

Seizure clusters: characteristics and treatment.

PubMed

Haut, Sheryl R

2015-04-01

Many patients with epilepsy experience 'clusters' or flurries of seizures, also termed acute repetitive seizures (ARS). Seizure clustering has a significant impact on health and quality of life. This review summarizes recent advances in the definition and neurophysiologic understanding of clustering, the epidemiology and risk factors for clustering and both inpatient and outpatient clinical implications. New treatments for seizure clustering/ARS are perhaps the area of greatest recent progress. Efforts have focused on creating a uniform definition of a seizure cluster. In neurophysiologic studies of refractory epilepsy, seizures within a cluster appear to be self-triggering. Clinical progress has been achieved towards a more precise prevalence of clustering, and consensus guidelines for epilepsy monitoring unit safety. The greatest recent advances are in the study of nonintravenous route of benzodiazepines as rescue medications for seizure clusters/ARS. Rectal benzodiazepines have been very effective but barriers to use exist. New data on buccal, intramuscular and intranasal preparations are anticipated to lead to a greater number of approved treatments. Progesterone may be effective for women who experience catamenial clusters. Seizure clustering is common, particularly in the setting of medically refractory epilepsy. Clustering worsens health and quality of life, and the field requires greater focus on clarifying of definition and clinical implications. Progress towards the development of nonintravenous routes of benzodiazepines has the potential to improve care in this area.

Applying Cluster Analysis to Physics Education Research Data

ERIC Educational Resources Information Center

Springuel, R. Padraic

2010-01-01

One major thrust of Physics Education Research (PER) is the identification of student ideas about specific physics concepts, both correct ideas and those that differ from the expert consensus. Typically the research process of eliciting the spectrum of student ideas involves the administration of specially designed questions to students. One major…

Model of Decision Making through Consensus in Ranking Case

NASA Astrophysics Data System (ADS)

Tarigan, Gim; Darnius, Open

2018-01-01

The basic problem to determine ranking consensus is a problem to combine some rankings those are decided by two or more Decision Maker (DM) into ranking consensus. DM is frequently asked to present their preferences over a group of objects in terms of ranks, for example to determine a new project, new product, a candidate in a election, and so on. The problem in ranking can be classified into two major categories; namely, cardinal and ordinal rankings. The objective of the study is to obtin the ranking consensus by appying some algorithms and methods. The algorithms and methods used in this study were partial algorithm, optimal ranking consensus, BAK (Borde-Kendal)Model. A method proposed as an alternative in ranking conssensus is a Weighted Distance Forward-Backward (WDFB) method, which gave a little difference i ranking consensus result compare to the result oethe example solved by Cook, et.al (2005).

A national stakeholder consensus study of challenges and priorities for clinical learning environments in postgraduate medical education.

PubMed

Kilty, Caroline; Wiese, Anel; Bergin, Colm; Flood, Patrick; Fu, Na; Horgan, Mary; Higgins, Agnes; Maher, Bridget; O'Kane, Grainne; Prihodova, Lucia; Slattery, Dubhfeasa; Stoyanov, Slavi; Bennett, Deirdre

2017-11-22

High quality clinical learning environments (CLE) are critical to postgraduate medical education (PGME). The understaffed and overcrowded environments in which many residents work present a significant challenge to learning. The purpose of this study was to develop a national expert group consensus amongst stakeholders in PGME to; (i) identify important barriers and facilitators of learning in CLEs and (ii) indicate priority areas for improvement. Our objective was to provide information to focus efforts to provide high quality CLEs. Group Concept Mapping (GCM) is an integrated mixed methods approach to generating expert group consensus. A multi-disciplinary group of experts were invited to participate in the GCM process via an online platform. Multi-dimensional scaling and hierarchical cluster analysis were used to analyse participant inputs in regard to barriers, facilitators and priorities. Participants identified facilitators and barriers in ten domains within clinical learning environments. Domains rated most important were those which related to residents' connection to and engagement with more senior doctors. Organisation and conditions of work and Time to learn with senior doctors during patient care were rated as the most difficult areas in which to make improvements. High quality PGME requires that residents engage and connect with senior doctors during patient care, and that they are valued and supported both as learners and service providers. Academic medicine and health service managers must work together to protect these elements of CLEs, which not only shape learning, but impact quality of care and patient safety.

Authorship attribution based on Life-Like Network Automata.

PubMed

Machicao, Jeaneth; Corrêa, Edilson A; Miranda, Gisele H B; Amancio, Diego R; Bruno, Odemir M

2018-01-01

The authorship attribution is a problem of considerable practical and technical interest. Several methods have been designed to infer the authorship of disputed documents in multiple contexts. While traditional statistical methods based solely on word counts and related measurements have provided a simple, yet effective solution in particular cases; they are prone to manipulation. Recently, texts have been successfully modeled as networks, where words are represented by nodes linked according to textual similarity measurements. Such models are useful to identify informative topological patterns for the authorship recognition task. However, there is no consensus on which measurements should be used. Thus, we proposed a novel method to characterize text networks, by considering both topological and dynamical aspects of networks. Using concepts and methods from cellular automata theory, we devised a strategy to grasp informative spatio-temporal patterns from this model. Our experiments revealed an outperformance over structural analysis relying only on topological measurements, such as clustering coefficient, betweenness and shortest paths. The optimized results obtained here pave the way for a better characterization of textual networks.

Reaching consensus on reporting patient and public involvement (PPI) in research: methods and lessons learned from the development of reporting guidelines

PubMed Central

Brett, Jo; Staniszewska, Sophie; Simera, Iveta; Seers, Kate; Mockford, Carole; Goodlad, Susan; Altman, Doug; Moher, David; Barber, Rosemary; Denegri, Simon; Entwistle, Andrew Robert; Littlejohns, Peter; Suleman, Rashida; Thomas, Victoria; Tysall, Colin

2017-01-01

Introduction Patient and public involvement (PPI) is inconsistently reported in health and social care research. Improving the quality of how PPI is reported is critical in developing a higher quality evidence base to gain a better insight into the methods and impact of PPI. This paper describes the methods used to develop and gain consensus on guidelines for reporting PPI in research studies (updated version of the Guidance for Reporting Patient and Public Involvement (GRIPP2)). Methods There were three key stages in the development of GRIPP2: identification of key items for the guideline from systematic review evidence of the impact of PPI on health research and health services, a three-phase online Delphi survey with a diverse sample of experts in PPI to gain consensus on included items and a face-to-face consensus meeting to finalise and reach definitive agreement on GRIPP2. Challenges and lessons learnt during the development of the reporting guidelines are reported. Discussion The process of reaching consensus is vital within the development of guidelines and policy directions, although debate around how best to reach consensus is still needed. This paper discusses the critical stages of consensus development as applied to the development of consensus for GRIPP2 and discusses the benefits and challenges of consensus development. PMID:29061613

Flocking dynamics with voter-like interactions

NASA Astrophysics Data System (ADS)

Baglietto, Gabriel; Vazquez, Federico

2018-03-01

We study the collective motion of a large set of self-propelled particles subject to voter-like interactions. Each particle moves on a 2D space at a constant speed in a direction that is randomly assigned initially. Then, at every step of the dynamics, each particle adopts the direction of motion of a randomly chosen neighboring particle. We investigate the time evolution of the global alignment of particles measured by the order parameter φ, until complete order \\varphi=1.0 is reached (polar consensus). We find that φ increases as t 1/2 for short times and approaches 1.0 exponentially fast for longer times. Also, the mean time to consensus τ varies non-monotonically with the density of particles ρ, reaching a minimum at some intermediate density ρmin . At ρmin , the mean consensus time scales with the system size N as τmin ∼ N0.765 , and thus the consensus is faster than in the case of all-to-all interactions (large ρ) where τ=2N . We show that the fast consensus, also observed at intermediate and high densities, is a consequence of the segregation of the system into clusters of equally-oriented particles which breaks the balance of transitions between directional states in well mixed systems.

Creating neighbourhood groupings based on built environment features to facilitate health promotion activities.

PubMed

Schopflocher, Donald; VanSpronsen, Eric; Spence, John C; Vallianatos, Helen; Raine, Kim D; Plotnikoff, Ronald C; Nykiforuk, Candace I J

2012-07-26

Detailed assessments of the built environment often resist data reduction and summarization. This project sought to develop a method of reducing built environment data to an extent that they can be effectively communicated to researchers and community stakeholders. We aim to help in an understanding of how these data can be used to create neighbourhood groupings based on built environment characteristics and how the process of discussing these neighbourhoods with community stakeholders can result in the development of community-informed health promotion interventions. We used the Irvine Minnesota Inventory (IMI) to assess 296 segments of a semi-rural community in Alberta. Expert raters "created" neighbourhoods by examining the data. Then, a consensus grouping was developed using cluster analysis, and the number of IMI variables to characterize the neighbourhoods was reduced by multiple discriminant function analysis. The 296 segments were reduced to a consensus set of 10 neighbourhoods, which could be separated from each other by 9 functions constructed from 24 IMI variables. Biplots of these functions were an effective means of summarizing and presenting the results of the community assessment, and stimulated community action. It is possible to use principled quantitative methods to reduce large amounts of information about the built environment into meaningful summaries. These summaries, or built environment neighbourhoods, were useful in catalyzing action with community stakeholders and led to the development of health-promoting built environment interventions.

Comparing Effects of Cluster-Coupled Patterns on Opinion Dynamics

NASA Astrophysics Data System (ADS)

Liu, Yun; Si, Xia-Meng; Zhang, Yan-Chao

2012-07-01

Community structure is another important feature besides small-world and scale-free property of complex networks. Communities can be coupled through specific fixed links between nodes, or occasional encounter behavior. We introduce a model for opinion evolution with multiple cluster-coupled patterns, in which the interconnectivity denotes the coupled degree of communities by fixed links, and encounter frequency controls the coupled degree of communities by encounter behaviors. Considering the complicated cognitive system of people, the CODA (continuous opinions and discrete actions) update rules are used to mimic how people update their decisions after interacting with someone. It is shown that, large interconnectivity and encounter frequency both can promote consensus, reduce competition between communities and propagate some opinion successfully across the whole population. Encounter frequency is better than interconnectivity at facilitating the consensus of decisions. When the degree of social cohesion is same, small interconnectivity has better effects on lessening the competence between communities than small encounter frequency does, while large encounter frequency can make the greater degree of agreement across the whole populations than large interconnectivity can.

Update in outpatient general internal medicine: practice-changing evidence published in 2014.

PubMed

Sundsted, Karna K; Wieland, Mark L; Szostek, Jason H; Post, Jason A; Mauck, Karen F

2015-10-01

The practice of outpatient general internal medicine requires a diverse and evolving knowledge base. General internists must identify practice-changing shifts in the literature and reflect on their impact. Accordingly, we conducted a review of practice-changing articles published in outpatient general internal medicine in 2014. To identify high-quality, clinically relevant publications, we reviewed all titles and abstracts published in the following primary data sources in 2014: New England Journal of Medicine, Journal of the American Medical Association (JAMA), Annals of Internal Medicine, JAMA Internal Medicine, and the Cochrane Database of Systematic Reviews. All 2014 primary data summaries from Journal Watch-General Internal Medicine and ACP JournalWise also were reviewed. The authors used a modified Delphi method to reach consensus on inclusion of 8 articles using the following criteria: clinical relevance to outpatient internal medicine, potential for practice change, and strength of evidence. Clusters of important articles around one clinical question were considered as a single-candidate series. The article merits were debated until consensus was reached on the final 8, spanning a variety of topics commonly encountered in outpatient general internal medicine. Copyright © 2015 Elsevier Inc. All rights reserved.

Species identification and molecular typing of human Brucella isolates from Kuwait.

PubMed

Mustafa, Abu S; Habibi, Nazima; Osman, Amr; Shaheed, Faraz; Khan, Mohd W

2017-01-01

Brucellosis is a zoonotic disease of major concern in Kuwait and the Middle East. Human brucellosis can be caused by several Brucella species with varying degree of pathogenesis, and relapses are common after apparently successful therapy. The classical biochemical methods for identification of Brucella are time-consuming, cumbersome, and provide information limited to the species level only. In contrast, molecular methods are rapid and provide differentiation at intra-species level. In this study, four molecular methods [16S rRNA gene sequencing, real-time PCR, enterobacterial repetitive intergenic consensus (ERIC)-PCR and multilocus variable-number tandem-repeat analysis (MLVA)-8, MLVA-11 and MLVA-16 were evaluated for the identification and typing of 75 strains of Brucella isolated in Kuwait. 16S rRNA gene sequencing of all isolates showed 90-99% sequence identity with B. melitensis and real-time PCR with genus- and species- specific primers identified all isolates as B. melitensis. The results of ERIC-PCR suggested the existence of 75 ERIC genotypes of B. melitensis with a discriminatory index of 0.997. Cluster classification of these genotypes divided them into two clusters, A and B, diverging at ~25%. The maximum number of genotypes (n = 51) were found in cluster B5. MLVA-8 analysis identified all isolates as B. melitensis, and MLVA-8, MLVA-11 and MLVA-16 typing divided the isolates into 10, 32 and 71 MLVA types, respectively. Furthermore, the combined minimum spanning tree analysis demonstrated that, compared to MLVA types discovered all over the world, the Kuwaiti isolates were a distinct group of MLVA-11 and MLVA-16 types in the East Mediterranean Region.

Species identification and molecular typing of human Brucella isolates from Kuwait

PubMed Central

Osman, Amr; Shaheed, Faraz; Khan, Mohd W.

2017-01-01

Brucellosis is a zoonotic disease of major concern in Kuwait and the Middle East. Human brucellosis can be caused by several Brucella species with varying degree of pathogenesis, and relapses are common after apparently successful therapy. The classical biochemical methods for identification of Brucella are time-consuming, cumbersome, and provide information limited to the species level only. In contrast, molecular methods are rapid and provide differentiation at intra-species level. In this study, four molecular methods [16S rRNA gene sequencing, real-time PCR, enterobacterial repetitive intergenic consensus (ERIC)-PCR and multilocus variable-number tandem-repeat analysis (MLVA)-8, MLVA-11 and MLVA-16 were evaluated for the identification and typing of 75 strains of Brucella isolated in Kuwait. 16S rRNA gene sequencing of all isolates showed 90–99% sequence identity with B. melitensis and real-time PCR with genus- and species- specific primers identified all isolates as B. melitensis. The results of ERIC-PCR suggested the existence of 75 ERIC genotypes of B. melitensis with a discriminatory index of 0.997. Cluster classification of these genotypes divided them into two clusters, A and B, diverging at ~25%. The maximum number of genotypes (n = 51) were found in cluster B5. MLVA-8 analysis identified all isolates as B. melitensis, and MLVA-8, MLVA-11 and MLVA-16 typing divided the isolates into 10, 32 and 71 MLVA types, respectively. Furthermore, the combined minimum spanning tree analysis demonstrated that, compared to MLVA types discovered all over the world, the Kuwaiti isolates were a distinct group of MLVA-11 and MLVA-16 types in the East Mediterranean Region. PMID:28800594

Design of a Phase III cluster randomized trial to assess the efficacy and safety of a malaria transmission blocking vaccine.

PubMed

Delrieu, Isabelle; Leboulleux, Didier; Ivinson, Karen; Gessner, Bradford D

2015-03-24

Vaccines interrupting Plasmodium falciparum malaria transmission targeting sexual, sporogonic, or mosquito-stage antigens (SSM-VIMT) are currently under development to reduce malaria transmission. An international group of malaria experts was established to evaluate the feasibility and optimal design of a Phase III cluster randomized trial (CRT) that could support regulatory review and approval of an SSM-VIMT. The consensus design is a CRT with a sentinel population randomly selected from defined inner and buffer zones in each cluster, a cluster size sufficient to assess true vaccine efficacy in the inner zone, and inclusion of ongoing assessment of vaccine impact stratified by distance of residence from the cluster edge. Trials should be conducted first in areas of moderate transmission, where SSM-VIMT impact should be greatest. Sample size estimates suggest that such a trial is feasible, and within the range of previously supported trials of malaria interventions, although substantial issues to implementation exist. Copyright © 2015 Elsevier Ltd. All rights reserved.

Anchoring the Distance Scale via X-Ray/Infrared Data for Cepheid Clusters: SU Cas

NASA Astrophysics Data System (ADS)

Majaess, D.; Turner, D. G.; Gallo, L.; Gieren, W.; Bonatto, C.; Lane, D. J.; Balam, D.; Berdnikov, L.

2012-07-01

New X-ray (XMM-Newton) and JHKs (Observatoire du Mont-Mégantic) observations for members of the star cluster Alessi 95, which Turner et al. discovered hosts the classical Cepheid SU Cas, were used in tandem with UCAC3 (proper motion) and Two Micron All Sky Survey observations to determine precise cluster parameters: E(J - H) = 0.08 ± 0.02 and d = 405 ± 15 pc. The ensuing consensus among cluster, pulsation, and trigonometric distances (d=414+/- 5(\\sigma _{\\bar{x}}) +/- 10 (\\sigma) pc) places SU Cas in a select group of nearby fundamental Cepheid calibrators (δ Cep, ζ Gem). High-resolution X-ray observations may be employed to expand that sample as the data proved pertinent for identifying numerous stars associated with SU Cas. Acquiring X-ray observations of additional fields may foster efforts to refine Cepheid calibrations used to constrain H 0.

Mammalian Fe-S proteins: definition of a consensus motif recognized by the co-chaperone HSC20.

PubMed

Maio, N; Rouault, T A

2016-10-01

Iron-sulfur (Fe-S) clusters are inorganic cofactors that are fundamental to several biological processes in all three kingdoms of life. In most organisms, Fe-S clusters are initially assembled on a scaffold protein, ISCU, and subsequently transferred to target proteins or to intermediate carriers by a dedicated chaperone/co-chaperone system. The delivery of assembled Fe-S clusters to recipient proteins is a crucial step in the biogenesis of Fe-S proteins, and, in mammals, it relies on the activity of a multiprotein transfer complex that contains the chaperone HSPA9, the co-chaperone HSC20 and the scaffold ISCU. How the transfer complex efficiently engages recipient Fe-S target proteins involves specific protein interactions that are not fully understood. This mini review focuses on recent insights into the molecular mechanism of amino acid motif recognition and discrimination by the co-chaperone HSC20, which guides Fe-S cluster delivery.

Identification of uncommon objects in containers

DOEpatents

Bremer, Peer-Timo; Kim, Hyojin; Thiagarajan, Jayaraman J.

2017-09-12

A system for identifying in an image an object that is commonly found in a collection of images and for identifying a portion of an image that represents an object based on a consensus analysis of segmentations of the image. The system collects images of containers that contain objects for generating a collection of common objects within the containers. To process the images, the system generates a segmentation of each image. The image analysis system may also generate multiple segmentations for each image by introducing variations in the selection of voxels to be merged into a segment. The system then generates clusters of the segments based on similarity among the segments. Each cluster represents a common object found in the containers. Once the clustering is complete, the system may be used to identify common objects in images of new containers based on similarity between segments of images and the clusters.

Defining syndromes using cattle meat inspection data for syndromic surveillance purposes: a statistical approach with the 2005–2010 data from ten French slaughterhouses

PubMed Central

2013-01-01

Background The slaughterhouse is a central processing point for food animals and thus a source of both demographic data (age, breed, sex) and health-related data (reason for condemnation and condemned portions) that are not available through other sources. Using these data for syndromic surveillance is therefore tempting. However many possible reasons for condemnation and condemned portions exist, making the definition of relevant syndromes challenging. The objective of this study was to determine a typology of cattle with at least one portion of the carcass condemned in order to define syndromes. Multiple factor analysis (MFA) in combination with clustering methods was performed using both health-related data and demographic data. Results Analyses were performed on 381,186 cattle with at least one portion of the carcass condemned among the 1,937,917 cattle slaughtered in ten French abattoirs. Results of the MFA and clustering methods led to 12 clusters considered as stable according to year of slaughter and slaughterhouse. One cluster was specific to a disease of public health importance (cysticercosis). Two clusters were linked to the slaughtering process (fecal contamination of heart or lungs and deterioration lesions). Two clusters respectively characterized by chronic liver lesions and chronic peritonitis could be linked to diseases of economic importance to farmers. Three clusters could be linked respectively to reticulo-pericarditis, fatty liver syndrome and farmer’s lung syndrome, which are related to both diseases of economic importance to farmers and herd management issues. Three clusters respectively characterized by arthritis, myopathy and Dark Firm Dry (DFD) meat could notably be linked to animal welfare issues. Finally, one cluster, characterized by bronchopneumonia, could be linked to both animal health and herd management issues. Conclusion The statistical approach of combining multiple factor analysis with cluster analysis showed its relevance for the detection of syndromes using available large and complex slaughterhouse data. The advantages of this statistical approach are to i) define groups of reasons for condemnation based on meat inspection data, ii) help grouping reasons for condemnation among a list of various possible reasons for condemnation for which a consensus among experts could be difficult to reach, iii) assign each animal to a single syndrome which allows the detection of changes in trends of syndromes to detect unusual patterns in known diseases and emergence of new diseases. PMID:23628140

METHODS FOR CLUSTERING TIME SERIES DATA ACQUIRED FROM MOBILE HEALTH APPS.

PubMed

Tignor, Nicole; Wang, Pei; Genes, Nicholas; Rogers, Linda; Hershman, Steven G; Scott, Erick R; Zweig, Micol; Yvonne Chan, Yu-Feng; Schadt, Eric E

2017-01-01

In our recent Asthma Mobile Health Study (AMHS), thousands of asthma patients across the country contributed medical data through the iPhone Asthma Health App on a daily basis for an extended period of time. The collected data included daily self-reported asthma symptoms, symptom triggers, and real time geographic location information. The AMHS is just one of many studies occurring in the context of now many thousands of mobile health apps aimed at improving wellness and better managing chronic disease conditions, leveraging the passive and active collection of data from mobile, handheld smart devices. The ability to identify patient groups or patterns of symptoms that might predict adverse outcomes such as asthma exacerbations or hospitalizations from these types of large, prospectively collected data sets, would be of significant general interest. However, conventional clustering methods cannot be applied to these types of longitudinally collected data, especially survey data actively collected from app users, given heterogeneous patterns of missing values due to: 1) varying survey response rates among different users, 2) varying survey response rates over time of each user, and 3) non-overlapping periods of enrollment among different users. To handle such complicated missing data structure, we proposed a probability imputation model to infer missing data. We also employed a consensus clustering strategy in tandem with the multiple imputation procedure. Through simulation studies under a range of scenarios reflecting real data conditions, we identified favorable performance of the proposed method over other strategies that impute the missing value through low-rank matrix completion. When applying the proposed new method to study asthma triggers and symptoms collected as part of the AMHS, we identified several patient groups with distinct phenotype patterns. Further validation of the methods described in this paper might be used to identify clinically important patterns in large data sets with complicated missing data structure, improving the ability to use such data sets to identify at-risk populations for potential intervention.

Bioethics and the whole: pluralism, consensus, and the transmutation of bioethical methods into gold.

PubMed

Martin, P A

1999-01-01

Arguing that a consensus-based method of bioethical decision making can transform ethical pluralism into an ethical whole, author examines the theory of three consensus-based models--clinical pragmatism, ethics facilitation, and mediation--and develops a practical guide to ethics facilitation that includes a hypothetical case.

Contradictions and Consensus--Clusters of Opinions on E-Books

ERIC Educational Resources Information Center

Shrimplin, Aaron K.; Revelle, Andy; Hurst, Susan; Messner, Kevin

2011-01-01

Q methodology was used to determine attitudes and opinions about e-books among a group of faculty, graduate students, and undergraduates at Miami University of Ohio. Oral interviews formed the basis for a collection of opinion statements concerning e-books versus print. These statements were then ranked by a second group of research participants.…

Local communities obstruct global consensus: Naming game on multi-local-world networks

NASA Astrophysics Data System (ADS)

Lou, Yang; Chen, Guanrong; Fan, Zhengping; Xiang, Luna

2018-02-01

Community structure is essential for social communications, where individuals belonging to the same community are much more actively interacting and communicating with each other than those in different communities within the human society. Naming game, on the other hand, is a social communication model that simulates the process of learning a name of an object within a community of humans, where the individuals can generally reach global consensus asymptotically through iterative pair-wise conversations. The underlying network indicates the relationships among the individuals. In this paper, three typical topologies, namely random-graph, small-world and scale-free networks, are employed, which are embedded with the multi-local-world community structure, to study the naming game. Simulations show that (1) the convergence process to global consensus is getting slower as the community structure becomes more prominent, and eventually might fail; (2) if the inter-community connections are sufficiently dense, neither the number nor the size of the communities affects the convergence process; and (3) for different topologies with the same (or similar) average node-degree, local clustering of individuals obstruct or prohibit global consensus to take place. The results reveal the role of local communities in a global naming game in social network studies.

Hot spot analysis for driving the development of hits into leads in fragment based drug discovery

PubMed Central

Hall, David R.; Ngan, Chi Ho; Zerbe, Brandon S.; Kozakov, Dima; Vajda, Sandor

2011-01-01

Fragment based drug design (FBDD) starts with finding fragment-sized compounds that are highly ligand efficient and can serve as a core moiety for developing high affinity leads. Although the core-bound structure of a protein facilitates the construction of leads, effective design is far from straightforward. We show that protein mapping, a computational method developed to find binding hot spots and implemented as the FTMap server, provides information that complements the fragment screening results and can drive the evolution of core fragments into larger leads with a minimal loss or, in some cases, even a gain in ligand efficiency. The method places small molecular probes, the size of organic solvents, on a dense grid around the protein, and identifies the hot spots as consensus clusters formed by clusters of several probes. The hot spots are ranked based on the number of probe clusters, which predicts the binding propensity of the subsites and hence their importance for drug design. Accordingly, with a single exception the main hot spot identified by FTMap binds the core compound found by fragment screening. The most useful information is provided by the neighboring secondary hot spots, indicating the regions where the core can be extended to increase its affinity. To quantify this information, we calculate the density of probes from mapping, which describes the binding propensity at each point, and show that the change in the correlation between a ligand position and the probe density upon extending or repositioning the core moiety predicts the expected change in ligand efficiency. PMID:22145575

A statistically compiled test battery for feasible evaluation of knee function after rupture of the Anterior Cruciate Ligament – derived from long-term follow-up data

PubMed Central

2017-01-01

Purpose Clinical test batteries for evaluation of knee function after injury to the Anterior Cruciate Ligament (ACL) should be valid and feasible, while reliably capturing the outcome of rehabilitation. There is currently a lack of consensus as to which of the many available assessment tools for knee function that should be included. The present aim was to use a statistical approach to investigate the contribution of frequently used tests to avoid redundancy, and filter them down to a proposed comprehensive and yet feasible test battery for long-term evaluation after ACL injury. Methods In total 48 outcome variables related to knee function, all potentially relevant for a long-term follow-up, were included from a cross-sectional study where 70 ACL-injured (17–28 years post injury) individuals were compared to 33 controls. Cluster analysis and logistic regression were used to group variables and identify an optimal test battery, from which a summarized estimator of knee function representing various functional aspects was derived. Results As expected, several variables were strongly correlated, and the variables also fell into logical clusters with higher within-correlation (max ρ = 0.61) than between clusters (max ρ = 0.19). An extracted test battery with just four variables assessing one-leg balance, isokinetic knee extension strength and hop performance (one-leg hop, side hop) were mathematically combined to an estimator of knee function, which acceptably classified ACL-injured individuals and controls. This estimator, derived from objective measures, correlated significantly with self-reported function, e.g. Lysholm score (ρ = 0.66; p<0.001). Conclusions The proposed test battery, based on a solid statistical approach, includes assessments which are all clinically feasible, while also covering complementary aspects of knee function. Similar test batteries could be determined for earlier phases of ACL rehabilitation or to enable longitudinal monitoring. Such developments, established on a well-grounded consensus of measurements, would facilitate comparisons of studies and enable evidence-based rehabilitation. PMID:28459885

Hybrid Collaborative Learning for Classification and Clustering in Sensor Networks

NASA Technical Reports Server (NTRS)

Wagstaff, Kiri L.; Sosnowski, Scott; Lane, Terran

2012-01-01

Traditionally, nodes in a sensor network simply collect data and then pass it on to a centralized node that archives, distributes, and possibly analyzes the data. However, analysis at the individual nodes could enable faster detection of anomalies or other interesting events as well as faster responses, such as sending out alerts or increasing the data collection rate. There is an additional opportunity for increased performance if learners at individual nodes can communicate with their neighbors. In previous work, methods were developed by which classification algorithms deployed at sensor nodes can communicate information about event labels to each other, building on prior work with co-training, self-training, and active learning. The idea of collaborative learning was extended to function for clustering algorithms as well, similar to ideas from penta-training and consensus clustering. However, collaboration between these learner types had not been explored. A new protocol was developed by which classifiers and clusterers can share key information about their observations and conclusions as they learn. This is an active collaboration in which learners of either type can query their neighbors for information that they then use to re-train or re-learn the concept they are studying. The protocol also supports broadcasts from the classifiers and clusterers to the rest of the network to announce new discoveries. Classifiers observe an event and assign it a label (type). Clusterers instead group observations into clusters without assigning them a label, and they collaborate in terms of pairwise constraints between two events [same-cluster (mustlink) or different-cluster (cannot-link)]. Fundamentally, these two learner types speak different languages. To bridge this gap, the new communication protocol provides four types of exchanges: hybrid queries for information, hybrid "broadcasts" of learned information, each specified for classifiers-to-clusterers, and clusterers-to-classifiers. The new capability has the potential to greatly expand the in situ analysis abilities of sensor networks. Classifiers seeking to categorize incoming data into different types of events can operate in tandem with clusterers that are sensitive to the occurrence of new kinds of events not known to the classifiers. In contrast to current approaches that treat these operations as independent components, a hybrid collaborative learning system can enable them to learn from each other.

NCC-RANSAC: a fast plane extraction method for 3-D range data segmentation.

PubMed

Qian, Xiangfei; Ye, Cang

2014-12-01

This paper presents a new plane extraction (PE) method based on the random sample consensus (RANSAC) approach. The generic RANSAC-based PE algorithm may over-extract a plane, and it may fail in case of a multistep scene where the RANSAC procedure results in multiple inlier patches that form a slant plane straddling the steps. The CC-RANSAC PE algorithm successfully overcomes the latter limitation if the inlier patches are separate. However, it fails if the inlier patches are connected. A typical scenario is a stairway with a stair wall where the RANSAC plane-fitting procedure results in inliers patches in the tread, riser, and stair wall planes. They connect together and form a plane. The proposed method, called normal-coherence CC-RANSAC (NCC-RANSAC), performs a normal coherence check to all data points of the inlier patches and removes the data points whose normal directions are contradictory to that of the fitted plane. This process results in separate inlier patches, each of which is treated as a candidate plane. A recursive plane clustering process is then executed to grow each of the candidate planes until all planes are extracted in their entireties. The RANSAC plane-fitting and the recursive plane clustering processes are repeated until no more planes are found. A probabilistic model is introduced to predict the success probability of the NCC-RANSAC algorithm and validated with real data of a 3-D time-of-flight camera-SwissRanger SR4000. Experimental results demonstrate that the proposed method extracts more accurate planes with less computational time than the existing RANSAC-based methods.

NCC-RANSAC: A Fast Plane Extraction Method for 3-D Range Data Segmentation

PubMed Central

Qian, Xiangfei; Ye, Cang

2015-01-01

This paper presents a new plane extraction (PE) method based on the random sample consensus (RANSAC) approach. The generic RANSAC-based PE algorithm may over-extract a plane, and it may fail in case of a multistep scene where the RANSAC procedure results in multiple inlier patches that form a slant plane straddling the steps. The CC-RANSAC PE algorithm successfully overcomes the latter limitation if the inlier patches are separate. However, it fails if the inlier patches are connected. A typical scenario is a stairway with a stair wall where the RANSAC plane-fitting procedure results in inliers patches in the tread, riser, and stair wall planes. They connect together and form a plane. The proposed method, called normal-coherence CC-RANSAC (NCC-RANSAC), performs a normal coherence check to all data points of the inlier patches and removes the data points whose normal directions are contradictory to that of the fitted plane. This process results in separate inlier patches, each of which is treated as a candidate plane. A recursive plane clustering process is then executed to grow each of the candidate planes until all planes are extracted in their entireties. The RANSAC plane-fitting and the recursive plane clustering processes are repeated until no more planes are found. A probabilistic model is introduced to predict the success probability of the NCC-RANSAC algorithm and validated with real data of a 3-D time-of-flight camera–SwissRanger SR4000. Experimental results demonstrate that the proposed method extracts more accurate planes with less computational time than the existing RANSAC-based methods. PMID:24771605

What do international ethics guidelines say in terms of the scope of medical research ethics?

PubMed

Bernabe, Rosemarie D L C; van Thiel, Ghislaine J M W; van Delden, Johannes J M

2016-04-26

In research ethics, the most basic question would always be, "which is an ethical issue, which is not?" Interestingly, depending on which ethics guideline we consult, we may have various answers to this question. Though we already have several international ethics guidelines for biomedical research involving human participants, ironically, we do not have a harmonized document which tells us what these various guidelines say and shows us the areas of consensus (or lack thereof). In this manuscript, we attempted to do just that. We extracted the imperatives from five internationally-known ethics guidelines and took note where the imperatives came from. In doing so, we gathered data on how many guidelines support a specific imperative. We found that there is no consensus on the majority of the imperatives and that in only 8.2% of the imperatives were there at least moderate consensus (i.e., consensus of at least 3 of the 5 ethics guidelines). Of the 12 clusters (Basic Principles; Research Collaboration; Social Value; Scientific Validity; Participant Selection; Favorable Benefit/Risk Ratio; Independent Review; Informed Consent; Respect for Participants; Publication and Registration; Regulatory Sanctions; and Justified Research on the Vulnerable Population), Informed Consent has the highest level of consensus and Research Collaboration and Regulatory Sanctions have the least. There was a lack of consensus in the majority of imperatives from the five internationally-known ethics guidelines. This may be partly explained by the differences among the guidelines in terms of their levels of specification as well as conceptual/ideological differences.

Comprehensive Genomic Characterization of Upper Tract Urothelial Carcinoma.

PubMed

Moss, Tyler J; Qi, Yuan; Xi, Liu; Peng, Bo; Kim, Tae-Beom; Ezzedine, Nader E; Mosqueda, Maribel E; Guo, Charles C; Czerniak, Bogdan A; Ittmann, Michael; Wheeler, David A; Lerner, Seth P; Matin, Surena F

2017-10-01

Upper urinary tract urothelial cancer (UTUC) may have unique etiologic and genomic factors compared to bladder cancer. To characterize the genomic landscape of UTUC and provide insights into its biology using comprehensive integrated genomic analyses. We collected 31 untreated snap-frozen UTUC samples from two institutions and carried out whole-exome sequencing (WES) of DNA, RNA sequencing (RNAseq), and protein analysis. Adjusting for batch effects, consensus mutation calls from independent pipelines identified DNA mutations, gene expression clusters using unsupervised consensus hierarchical clustering (UCHC), and protein expression levels that were correlated with relevant clinical variables, The Cancer Genome Atlas, and other published data. WES identified mutations in FGFR3 (74.1%; 92% low-grade, 60% high-grade), KMT2D (44.4%), PIK3CA (25.9%), and TP53 (22.2%). APOBEC and CpG were the most common mutational signatures. UCHC of RNAseq data segregated samples into four molecular subtypes with the following characteristics. Cluster 1: no PIK3CA mutations, nonsmokers, high-grade

Teaching the physical examination: a longitudinal strategy for tomorrow's physicians.

PubMed

Uchida, Toshiko; Farnan, Jeanne M; Schwartz, Jennifer E; Heiman, Heather L

2014-03-01

The physical examination is an essential clinical skill. The traditional approach to teaching the physical exam has involved a comprehensive "head-to-toe" checklist, which is often used to assess students before they begin their clinical clerkships. This method has been criticized for its lack of clinical context and for promoting rote memorization without critical thinking. In response to these concerns, Gowda and colleagues surveyed a national sample of clinical skills educators in order to develop a consensus "core" physical exam, which they report in this issue. The core physical exam is intended to be performed for every patient admitted by students during their medicine clerkships and to be supplemented by symptom-driven "clusters" of additional history and physical exam maneuvers.In this commentary, the authors review the strengths and limitations of this Core + Clusters technique as well as the head-to-toe approach. They propose that the head-to-toe still has a place in medical education, particularly for beginning students with little knowledge of pathophysiology and for patients with vague or multiple symptoms. The authors suggest that the ideal curriculum would include teaching both the head-to-toe and the Core + Clusters exams in sequence. This iterative approach to physical exam teaching would allow a student to assess a patient in a comprehensive manner while incorporating more clinical reasoning as further medical knowledge is acquired.

Effect of Explicit Evaluation on Neural Connectivity Related to Listening to Unfamiliar Music

PubMed Central

Liu, Chao; Brattico, Elvira; Abu-jamous, Basel; Pereira, Carlos S.; Jacobsen, Thomas; Nandi, Asoke K.

2017-01-01

People can experience different emotions when listening to music. A growing number of studies have investigated the brain structures and neural connectivities associated with perceived emotions. However, very little is known about the effect of an explicit act of judgment on the neural processing of emotionally-valenced music. In this study, we adopted the novel consensus clustering paradigm, called binarisation of consensus partition matrices (Bi-CoPaM), to study whether and how the conscious aesthetic evaluation of the music would modulate brain connectivity networks related to emotion and reward processing. Participants listened to music under three conditions – one involving a non-evaluative judgment, one involving an explicit evaluative aesthetic judgment, and one involving no judgment at all (passive listening only). During non-evaluative attentive listening we obtained auditory-limbic connectivity whereas when participants were asked to decide explicitly whether they liked or disliked the music excerpt, only two clusters of intercommunicating brain regions were found: one including areas related to auditory processing and action observation, and the other comprising higher-order structures involved with visual processing. Results indicate that explicit evaluative judgment has an impact on the neural auditory-limbic connectivity during affective processing of music. PMID:29311874

A consensus least squares support vector regression (LS-SVR) for analysis of near-infrared spectra of plant samples.

PubMed

Li, Yankun; Shao, Xueguang; Cai, Wensheng

2007-04-15

Consensus modeling of combining the results of multiple independent models to produce a single prediction avoids the instability of single model. Based on the principle of consensus modeling, a consensus least squares support vector regression (LS-SVR) method for calibrating the near-infrared (NIR) spectra was proposed. In the proposed approach, NIR spectra of plant samples were firstly preprocessed using discrete wavelet transform (DWT) for filtering the spectral background and noise, then, consensus LS-SVR technique was used for building the calibration model. With an optimization of the parameters involved in the modeling, a satisfied model was achieved for predicting the content of reducing sugar in plant samples. The predicted results show that consensus LS-SVR model is more robust and reliable than the conventional partial least squares (PLS) and LS-SVR methods.

The SOS-framework (Systems of Sedentary behaviours): an international transdisciplinary consensus framework for the study of determinants, research priorities and policy on sedentary behaviour across the life course: a DEDIPAC-study.

PubMed

Chastin, Sebastien F M; De Craemer, Marieke; Lien, Nanna; Bernaards, Claire; Buck, Christoph; Oppert, Jean-Michel; Nazare, Julie-Anne; Lakerveld, Jeroen; O'Donoghue, Grainne; Holdsworth, Michelle; Owen, Neville; Brug, Johannes; Cardon, Greet

2016-07-15

Ecological models are currently the most used approaches to classify and conceptualise determinants of sedentary behaviour, but these approaches are limited in their ability to capture the complexity of and interplay between determinants. The aim of the project described here was to develop a transdisciplinary dynamic framework, grounded in a system-based approach, for research on determinants of sedentary behaviour across the life span and intervention and policy planning and evaluation. A comprehensive concept mapping approach was used to develop the Systems Of Sedentary behaviours (SOS) framework, involving four main phases: (1) preparation, (2) generation of statements, (3) structuring (sorting and ranking), and (4) analysis and interpretation. The first two phases were undertaken between December 2013 and February 2015 by the DEDIPAC KH team (DEterminants of DIet and Physical Activity Knowledge Hub). The last two phases were completed during a two-day consensus meeting in June 2015. During the first phase, 550 factors regarding sedentary behaviour were listed across three age groups (i.e., youths, adults and older adults), which were reduced to a final list of 190 life course factors in phase 2 used during the consensus meeting. In total, 69 international delegates, seven invited experts and one concept mapping consultant attended the consensus meeting. The final framework obtained during that meeting consisted of six clusters of determinants: Physical Health and Wellbeing (71% consensus), Social and Cultural Context (59% consensus), Built and Natural Environment (65% consensus), Psychology and Behaviour (80% consensus), Politics and Economics (78% consensus), and Institutional and Home Settings (78% consensus). Conducting studies on Institutional Settings was ranked as the first research priority. The view that this framework captures a system-based map of determinants of sedentary behaviour was expressed by 89% of the participants. Through an international transdisciplinary consensus process, the SOS framework was developed for the determinants of sedentary behaviour through the life course. Investigating the influence of Institutional and Home Settings was deemed to be the most important area of research to focus on at present and potentially the most modifiable. The SOS framework can be used as an important tool to prioritise future research and to develop policies to reduce sedentary time.

Authorship attribution based on Life-Like Network Automata

PubMed Central

Machicao, Jeaneth; Corrêa, Edilson A.; Miranda, Gisele H. B.; Amancio, Diego R.

2018-01-01

The authorship attribution is a problem of considerable practical and technical interest. Several methods have been designed to infer the authorship of disputed documents in multiple contexts. While traditional statistical methods based solely on word counts and related measurements have provided a simple, yet effective solution in particular cases; they are prone to manipulation. Recently, texts have been successfully modeled as networks, where words are represented by nodes linked according to textual similarity measurements. Such models are useful to identify informative topological patterns for the authorship recognition task. However, there is no consensus on which measurements should be used. Thus, we proposed a novel method to characterize text networks, by considering both topological and dynamical aspects of networks. Using concepts and methods from cellular automata theory, we devised a strategy to grasp informative spatio-temporal patterns from this model. Our experiments revealed an outperformance over structural analysis relying only on topological measurements, such as clustering coefficient, betweenness and shortest paths. The optimized results obtained here pave the way for a better characterization of textual networks. PMID:29566100

Which modifiable health risk behaviours are related? A systematic review of the clustering of Smoking, Nutrition, Alcohol and Physical activity ('SNAP') health risk factors.

PubMed

Noble, Natasha; Paul, Christine; Turon, Heidi; Oldmeadow, Christopher

2015-12-01

There is a growing body of literature examining the clustering of health risk behaviours, but little consensus about which risk factors can be expected to cluster for which sub groups of people. This systematic review aimed to examine the international literature on the clustering of smoking, poor nutrition, excess alcohol and physical inactivity (SNAP) health behaviours among adults, including associated socio-demographic variables. A literature search was conducted in May 2014. Studies examining at least two SNAP risk factors, and using a cluster or factor analysis technique, or comparing observed to expected prevalence of risk factor combinations, were included. Fifty-six relevant studies were identified. A majority of studies (81%) reported a 'healthy' cluster characterised by the absence of any SNAP risk factors. More than half of the studies reported a clustering of alcohol with smoking, and half reported clustering of all four SNAP risk factors. The methodological quality of included studies was generally weak to moderate. Males and those with greater social disadvantage showed riskier patterns of behaviours; younger age was less clearly associated with riskier behaviours. Clustering patterns reported here reinforce the need for health promotion interventions to target multiple behaviours, and for such efforts to be specifically designed and accessible for males and those who are socially disadvantaged. Copyright © 2015 Elsevier Inc. All rights reserved.

Beyond Low-Rank Representations: Orthogonal clustering basis reconstruction with optimized graph structure for multi-view spectral clustering.

PubMed

Wang, Yang; Wu, Lin

2018-07-01

Low-Rank Representation (LRR) is arguably one of the most powerful paradigms for Multi-view spectral clustering, which elegantly encodes the multi-view local graph/manifold structures into an intrinsic low-rank self-expressive data similarity embedded in high-dimensional space, to yield a better graph partition than their single-view counterparts. In this paper we revisit it with a fundamentally different perspective by discovering LRR as essentially a latent clustered orthogonal projection based representation winged with an optimized local graph structure for spectral clustering; each column of the representation is fundamentally a cluster basis orthogonal to others to indicate its members, which intuitively projects the view-specific feature representation to be the one spanned by all orthogonal basis to characterize the cluster structures. Upon this finding, we propose our technique with the following: (1) We decompose LRR into latent clustered orthogonal representation via low-rank matrix factorization, to encode the more flexible cluster structures than LRR over primal data objects; (2) We convert the problem of LRR into that of simultaneously learning orthogonal clustered representation and optimized local graph structure for each view; (3) The learned orthogonal clustered representations and local graph structures enjoy the same magnitude for multi-view, so that the ideal multi-view consensus can be readily achieved. The experiments over multi-view datasets validate its superiority, especially over recent state-of-the-art LRR models. Copyright © 2018 Elsevier Ltd. All rights reserved.

Utility of action checklists as a consensus building tool

PubMed Central

KIM, Yeon-Ha; YOSHIKAWA, Etsuko; YOSHIKAWA, Toru; KOGI, Kazutaka; JUNG, Moon-Hee

2014-01-01

The present study’s objective was to determine the mechanisms for enhancing the utility of action checklists applied in participatory approach programs for workplace improvements, to identify the benefits of building consensus and to compare their applicability in Asian countries to find the most appropriate configuration for action checklists. Data were collected from eight trainees and 43 trainers with experience in Participatory Action-Oriented Training. Statistical analysis was performed in SPSS using the package PASW, version 19.0. The difference in the mean score for the degree of the utility of action checklists between countries was analyzed using ANOVA methods. Factor analysis was performed to validate the action checklists’ utility. Pearson Correlation Coefficients were then calculated to determine the direction and strength of the relationship between these factors. Using responses obtained from trainees’ in-depth interviews, we identified 33 key statements that were then classified into 11 thematic clusters. Five factors were extracted, namely “ease of application”, “practical solutions”, “group interaction”, “multifaceted perspective” and “active involvement”. The action checklist was useful for facilitating a participatory process among trainees and trainers for improving working conditions. Action checklists showed similar patterns of utility in various Asian countries; particularly when adjusted to local conditions. PMID:25224334

A Probabilistic Model of Social Working Memory for Information Retrieval in Social Interactions.

PubMed

Li, Liyuan; Xu, Qianli; Gan, Tian; Tan, Cheston; Lim, Joo-Hwee

2018-05-01

Social working memory (SWM) plays an important role in navigating social interactions. Inspired by studies in psychology, neuroscience, cognitive science, and machine learning, we propose a probabilistic model of SWM to mimic human social intelligence for personal information retrieval (IR) in social interactions. First, we establish a semantic hierarchy as social long-term memory to encode personal information. Next, we propose a semantic Bayesian network as the SWM, which integrates the cognitive functions of accessibility and self-regulation. One subgraphical model implements the accessibility function to learn the social consensus about IR-based on social information concept, clustering, social context, and similarity between persons. Beyond accessibility, one more layer is added to simulate the function of self-regulation to perform the personal adaptation to the consensus based on human personality. Two learning algorithms are proposed to train the probabilistic SWM model on a raw dataset of high uncertainty and incompleteness. One is an efficient learning algorithm of Newton's method, and the other is a genetic algorithm. Systematic evaluations show that the proposed SWM model is able to learn human social intelligence effectively and outperforms the baseline Bayesian cognitive model. Toward real-world applications, we implement our model on Google Glass as a wearable assistant for social interaction.

Developing a research agenda for promoting physical activity in Brazil through environmental and policy change

PubMed Central

Reis, Rodrigo S.; Kelly, Cheryl M.; Parra, Diana C.; Barros, Mauro; Gomes, Grace; Malta, Deborah; Schmid, Thomas; Brownson, Ross C.

2016-01-01

Objective To identify the highest priorities for research on environmental and policy changes for promoting physical activity (PA) in Brazil; to uncover any gaps between researchers' and practitioners' priorities; and to consider which tools, methods, collaborative strategies, and actions could be useful to moving a research agenda forward. Methods This was a mixed-methods study (qualitative and quantitative) conducted by Project GUIA (Guide for Useful Interventions for Activity in Brazil and Latin America) in February 2010–January 2011. A total of 240 individuals in the PA field (186 practitioners and 54 researchers) were asked to generate research ideas; 82 participants provided 266 original statements from which 52 topics emerged. Participants rated topics by “importance” and “feasibility;” a separate convenience sample of 21 individuals categorized them. Cluster analysis and multidimensional scaling were used to create concept maps and pattern matches. Results Five distinct clusters emerged from the concept mapping, of which “effectiveness and innovation in PA interventions” was rated most important by both practitioners and researchers. Pattern matching showed a divergence between the groups, especially regarding feasibility, where there was no consensus. Conclusions The study results provided the basis for a research agenda to advance the understanding of environmental and policy influences on PA promotion in Brazil and Latin America. These results should stimulate future research and, ultimately, contribute to the evidence-base of successful PA strategies in Latin America. PMID:23099869

The Use of the Delphi and Other Consensus Group Methods in Medical Education Research: A Review.

PubMed

Humphrey-Murto, Susan; Varpio, Lara; Wood, Timothy J; Gonsalves, Carol; Ufholz, Lee-Anne; Mascioli, Kelly; Wang, Carol; Foth, Thomas

2017-10-01

Consensus group methods, such as the Delphi method and nominal group technique (NGT), are used to synthesize expert opinions when evidence is lacking. Despite their extensive use, these methods are inconsistently applied. Their use in medical education research has not been well studied. The authors set out to describe the use of consensus methods in medical education research and to assess the reporting quality of these methods and results. Using scoping review methods, the authors searched the Medline, Embase, PsycInfo, PubMed, Scopus, and ERIC databases for 2009-2016. Full-text articles that focused on medical education and the keywords Delphi, RAND, NGT, or other consensus group methods were included. A standardized extraction form was used to collect article demographic data and features reflecting methodological rigor. Of the articles reviewed, 257 met the inclusion criteria. The Modified Delphi (105/257; 40.8%), Delphi (91/257; 35.4%), and NGT (23/257; 8.9%) methods were most often used. The most common study purpose was curriculum development or reform (68/257; 26.5%), assessment tool development (55/257; 21.4%), and defining competencies (43/257; 16.7%). The reporting quality varied, with 70.0% (180/257) of articles reporting a literature review, 27.2% (70/257) reporting what background information was provided to participants, 66.1% (170/257) describing the number of participants, 40.1% (103/257) reporting if private decisions were collected, 37.7% (97/257) reporting if formal feedback of group ratings was shared, and 43.2% (111/257) defining consensus a priori. Consensus methods are poorly standardized and inconsistently used in medical education research. Improved criteria for reporting are needed.

The Rise of Radicals in Bioinorganic Chemistry.

PubMed

Gray, Harry B; Winkler, Jay R

2016-10-01

Prior to 1950, the consensus was that biological transformations occurred in two-electron steps, thereby avoiding the generation of free radicals. Dramatic advances in spectroscopy, biochemistry, and molecular biology have led to the realization that protein-based radicals participate in a vast array of vital biological mechanisms. Redox processes involving high-potential intermediates formed in reactions with O 2 are particularly susceptible to radical formation. Clusters of tyrosine (Tyr) and tryptophan (Trp) residues have been found in many O 2 -reactive enzymes, raising the possibility that they play an antioxidant protective role. In blue copper proteins with plastocyanin-like domains, Tyr/Trp clusters are uncommon in the low-potential single-domain electron-transfer proteins and in the two-domain copper nitrite reductases. The two-domain muticopper oxidases, however, exhibit clusters of Tyr and Trp residues near the trinuclear copper active site where O 2 is reduced. These clusters may play a protective role to ensure that reactive oxygen species are not liberated during O 2 reduction.

Explaining opinion polarisation with opinion copulas.

PubMed

Askitas, Nikolaos

2017-01-01

An empirically founded and widely established driving force in opinion dynamics is homophily i.e. the tendency of "birds of a feather" to "flock together". The closer our opinions are the more likely it is that we will interact and converge. Models using these assumptions are called bounded confidence models (BCM) as they assume a tolerance threshold after which interaction is unlikely. They are known to produce one or more clusters, depending on the size of the bound, with more than one cluster being possible only in the deterministic case. Introducing noise, as is likely to happen in a stochastic world, causes BCM to produce consensus which leaves us with the open problem of explaining the emergence and sustainance of opinion clusters and polarisation. We investigate the role of heterogeneous priors in opinion formation, introduce the concept of opinion copulas, argue that it is well supported by findings in Social Psychology and use it to show that the stochastic BCM does indeed produce opinion clustering without the need for extra assumptions.

Explaining opinion polarisation with opinion copulas

PubMed Central

2017-01-01

An empirically founded and widely established driving force in opinion dynamics is homophily i.e. the tendency of “birds of a feather” to “flock together”. The closer our opinions are the more likely it is that we will interact and converge. Models using these assumptions are called bounded confidence models (BCM) as they assume a tolerance threshold after which interaction is unlikely. They are known to produce one or more clusters, depending on the size of the bound, with more than one cluster being possible only in the deterministic case. Introducing noise, as is likely to happen in a stochastic world, causes BCM to produce consensus which leaves us with the open problem of explaining the emergence and sustainance of opinion clusters and polarisation. We investigate the role of heterogeneous priors in opinion formation, introduce the concept of opinion copulas, argue that it is well supported by findings in Social Psychology and use it to show that the stochastic BCM does indeed produce opinion clustering without the need for extra assumptions. PMID:28829802

Mammalian Fe-S proteins: definition of a consensus motif recognized by the co-chaperone HSC20

PubMed Central

Maio, N.; Rouault, T. A.

2017-01-01

Iron-sulfur (Fe-S) clusters are inorganic cofactors that are fundamental to several biological processes in all three kingdoms of life. In most organisms, Fe-S clusters are initially assembled on a scaffold protein, ISCU, and subsequently transferred to target proteins or to intermediate carriers by a dedicated chaperone/co-chaperone system. The delivery of assembled Fe-S clusters to recipient proteins is a crucial step in the biogenesis of Fe-S proteins, and, in mammals, it relies on the activity of a multiprotein transfer complex that contains the chaperone HSPA9, the co-chaperone HSC20 and the scaffold ISCU. How the transfer complex efficiently engages recipient Fe-S target proteins involves specific protein interactions that are not fully understood. This mini review focuses on recent insights into the molecular mechanism of amino acid motif recognition and discrimination by the co-chaperone HSC20, which guides Fe-S cluster delivery. PMID:27714045

Analysis of genetic diversity in banana cultivars (Musa cvs.) from the South of Oman using AFLP markers and classification by phylogenetic, hierarchical clustering and principal component analyses*

PubMed Central

Opara, Umezuruike Linus; Jacobson, Dan; Al-Saady, Nadiya Abubakar

2010-01-01

Banana is an important crop grown in Oman and there is a dearth of information on its genetic diversity to assist in crop breeding and improvement programs. This study employed amplified fragment length polymorphism (AFLP) to investigate the genetic variation in local banana cultivars from the southern region of Oman. Using 12 primer combinations, a total of 1094 bands were scored, of which 1012 were polymorphic. Eighty-two unique markers were identified, which revealed the distinct separation of the seven cultivars. The results obtained show that AFLP can be used to differentiate the banana cultivars. Further classification by phylogenetic, hierarchical clustering and principal component analyses showed significant differences between the clusters found with molecular markers and those clusters created by previous studies using morphological analysis. Based on the analytical results, a consensus dendrogram of the banana cultivars is presented. PMID:20443211

Texas Children's Medication Algorithm Project: Update from Texas Consensus Conference Panel on Medication Treatment of Childhood Major Depressive Disorder

ERIC Educational Resources Information Center

Hughes, Carroll W.; Emslie, Graham J.; Crismon, M. Lynn; Posner, Kelly; Birmaher, Boris; Ryan, Neal; Jensen, Peter; Curry, John; Vitiello, Benedetto; Lopez, Molly; Shon, Steve P.; Pliszka, Steven R.; Trivedi, Madhukar H.

2007-01-01

Objective: To revise and update consensus guidelines for medication treatment algorithms for childhood major depressive disorder based on new scientific evidence and expert clinical consensus when evidence is lacking. Method: A consensus conference was held January 13-14, 2005, that included academic clinicians and researchers, practicing…

Reproducibility of Cognitive Profiles in Psychosis Using Cluster Analysis.

PubMed

Lewandowski, Kathryn E; Baker, Justin T; McCarthy, Julie M; Norris, Lesley A; Öngür, Dost

2018-04-01

Cognitive dysfunction is a core symptom dimension that cuts across the psychoses. Recent findings support classification of patients along the cognitive dimension using cluster analysis; however, data-derived groupings may be highly determined by sampling characteristics and the measures used to derive the clusters, and so their interpretability must be established. We examined cognitive clusters in a cross-diagnostic sample of patients with psychosis and associations with clinical and functional outcomes. We then compared our findings to a previous report of cognitive clusters in a separate sample using a different cognitive battery. Participants with affective or non-affective psychosis (n=120) and healthy controls (n=31) were administered the MATRICS Consensus Cognitive Battery, and clinical and community functioning assessments. Cluster analyses were performed on cognitive variables, and clusters were compared on demographic, cognitive, and clinical measures. Results were compared to findings from our previous report. A four-cluster solution provided a good fit to the data; profiles included a neuropsychologically normal cluster, a globally impaired cluster, and two clusters of mixed profiles. Cognitive burden was associated with symptom severity and poorer community functioning. The patterns of cognitive performance by cluster were highly consistent with our previous findings. We found evidence of four cognitive subgroups of patients with psychosis, with cognitive profiles that map closely to those produced in our previous work. Clusters were associated with clinical and community variables and a measure of premorbid functioning, suggesting that they reflect meaningful groupings: replicable, and related to clinical presentation and functional outcomes. (JINS, 2018, 24, 382-390).

An Exemplar-Based Multi-View Domain Generalization Framework for Visual Recognition.

PubMed

Niu, Li; Li, Wen; Xu, Dong; Cai, Jianfei

2018-02-01

In this paper, we propose a new exemplar-based multi-view domain generalization (EMVDG) framework for visual recognition by learning robust classifier that are able to generalize well to arbitrary target domain based on the training samples with multiple types of features (i.e., multi-view features). In this framework, we aim to address two issues simultaneously. First, the distribution of training samples (i.e., the source domain) is often considerably different from that of testing samples (i.e., the target domain), so the performance of the classifiers learnt on the source domain may drop significantly on the target domain. Moreover, the testing data are often unseen during the training procedure. Second, when the training data are associated with multi-view features, the recognition performance can be further improved by exploiting the relation among multiple types of features. To address the first issue, considering that it has been shown that fusing multiple SVM classifiers can enhance the domain generalization ability, we build our EMVDG framework upon exemplar SVMs (ESVMs), in which a set of ESVM classifiers are learnt with each one trained based on one positive training sample and all the negative training samples. When the source domain contains multiple latent domains, the learnt ESVM classifiers are expected to be grouped into multiple clusters. To address the second issue, we propose two approaches under the EMVDG framework based on the consensus principle and the complementary principle, respectively. Specifically, we propose an EMVDG_CO method by adding a co-regularizer to enforce the cluster structures of ESVM classifiers on different views to be consistent based on the consensus principle. Inspired by multiple kernel learning, we also propose another EMVDG_MK method by fusing the ESVM classifiers from different views based on the complementary principle. In addition, we further extend our EMVDG framework to exemplar-based multi-view domain adaptation (EMVDA) framework when the unlabeled target domain data are available during the training procedure. The effectiveness of our EMVDG and EMVDA frameworks for visual recognition is clearly demonstrated by comprehensive experiments on three benchmark data sets.

The development of a consensus definition for healthcare improvement science (HIS) in seven European countries: A consensus methods approach

PubMed Central

Macrae, Rhoda; Lillo-Crespo, Manuel; Rooney, Kevin D

2017-01-01

Abstract Introduction There is a limited body of research in the field of healthcare improvement science (HIS). Quality improvement and ‘change making’ should become an intrinsic part of everyone’s job, every day in all parts of the healthcare system. The lack of theoretical grounding may partly explain the minimal transfer of health research into health policy. Methods This article seeks to present the development of the definition for healthcare improvement science. A consensus method approach was adopted with a two-stage Delphi process, expert panel and consensus group techniques. A total of 18 participants were involved in the expert panel and consensus group, and 153 answers were analysed as a part of the Delphi survey. Participants were researchers, educators and healthcare professionals from Scotland, Slovenia, Spain, Italy, England, Poland, and Romania. Results A high level of consensus was achieved for the broad definition in the 2nd Delphi iteration (86%). The final definition was agreed on by the consensus group: ‘Healthcare improvement science is the generation of knowledge to cultivate change and deliver person-centred care that is safe, effective, efficient, equitable and timely. It improves patient outcomes, health system performance and population health.’ Conclusions The process of developing a consensus definition revealed different understandings of healthcare improvement science between the participants. Having a shared consensus definition of healthcare improvement science is an important step forward, bringing about a common understanding in order to advance the professional education and practice of healthcare improvement science. PMID:28289467

Developing a research agenda for promoting physical activity in Brazil through environmental and policy change.

PubMed

Reis, Rodrigo S; Kelly, Cheryl M; Parra, Diana C; Barros, Mauro; Gomes, Grace; Malta, Deborah; Schmid, Thomas; Brownson, Ross C

2012-08-01

To identify the highest priorities for research on environmental and policy changes for promoting physical activity (PA) in Brazil; to uncover any gaps between researchers' and practitioners' priorities; and to consider which tools, methods, collaborative strategies, and actions could be useful to moving a research agenda forward. This was a mixed-methods study (qualitative and quantitative) conducted by Project GUIA (Guide for Useful Interventions for Activity in Brazil and Latin America) in February 2010-January 2011. A total of 240 individuals in the PA field (186 practitioners and 54 researchers) were asked to generate research ideas; 82 participants provided 266 original statements from which 52 topics emerged. Participants rated topics by "importance" and "feasibility;" a separate convenience sample of 21 individuals categorized them. Cluster analysis and multidimensional scaling were used to create concept maps and pattern matches. Five distinct clusters emerged from the concept mapping, of which "effectiveness and innovation in PA interventions" was rated most important by both practitioners and researchers. Pattern matching showed a divergence between the groups, especially regarding feasibility, where there was no consensus. The study results provided the basis for a research agenda to advance the understanding of environmental and policy influences on PA promotion in Brazil and Latin America. These results should stimulate future research and, ultimately, contribute to the evidence-base of successful PA strategies in Latin America.

Intrachromosomal karyotype asymmetry in Orchidaceae.

PubMed

Medeiros-Neto, Enoque; Nollet, Felipe; Moraes, Ana Paula; Felix, Leonardo P

2017-01-01

The asymmetry indexes have helped cytotaxonomists to interpret and classify plant karyotypes for species delimitation efforts. However, there is no consensus about the best method to calculate the intrachromosomal asymmetry. The present study aimed to compare different intrachromosomal asymmetry indexes in order to indicate which are more efficient for the estimation of asymmetry in different groups of orchids. Besides, we aimed to compare our results with the Orchidaceae phylogenetic proposal to test the hypothesis of Stebbins (1971). Through a literature review, karyotypes were selected and analyzed comparatively with ideal karyotypes in a cluster analysis. All karyotypes showed some level of interchromosomal asymmetry, ranging from slightly asymmetric to moderately asymmetric. The five tested intrachromosomal asymmetry indexes indicated Sarcoglottis grandiflora as the species with the most symmetrical karyotype and Christensonella pachyphylla with the most asymmetrical karyotype. In the cluster analysis, the largest number of species were grouped with the intermediary ideal karyotypes B or C. Considering our results, we recommend the combined use of at least two indexes, especially Ask% or A1 with Syi, for cytotaxonomic analysis in groups of orchids. In an evolutionary perspective, our results support Stebbins' hypothesis that asymmetric karyotypes derive from a symmetric karyotypes.

Intrachromosomal karyotype asymmetry in Orchidaceae

PubMed Central

Medeiros-Neto, Enoque; Nollet, Felipe; Moraes, Ana Paula; Felix, Leonardo P.

2017-01-01

Abstract The asymmetry indexes have helped cytotaxonomists to interpret and classify plant karyotypes for species delimitation efforts. However, there is no consensus about the best method to calculate the intrachromosomal asymmetry. The present study aimed to compare different intrachromosomal asymmetry indexes in order to indicate which are more efficient for the estimation of asymmetry in different groups of orchids. Besides, we aimed to compare our results with the Orchidaceae phylogenetic proposal to test the hypothesis of Stebbins (1971). Through a literature review, karyotypes were selected and analyzed comparatively with ideal karyotypes in a cluster analysis. All karyotypes showed some level of interchromosomal asymmetry, ranging from slightly asymmetric to moderately asymmetric. The five tested intrachromosomal asymmetry indexes indicated Sarcoglottis grandiflora as the species with the most symmetrical karyotype and Christensonella pachyphylla with the most asymmetrical karyotype. In the cluster analysis, the largest number of species were grouped with the intermediary ideal karyotypes B or C. Considering our results, we recommend the combined use of at least two indexes, especially Ask% or A1 with Syi, for cytotaxonomic analysis in groups of orchids. In an evolutionary perspective, our results support Stebbins’ hypothesis that asymmetric karyotypes derive from a symmetric karyotypes. PMID:28644507

Assessment of yellow fever epidemic risk: an original multi-criteria modeling approach.

PubMed

Briand, Sylvie; Beresniak, Ariel; Nguyen, Tim; Yonli, Tajoua; Duru, Gerard; Kambire, Chantal; Perea, William

2009-07-14

Yellow fever (YF) virtually disappeared in francophone West African countries as a result of YF mass vaccination campaigns carried out between 1940 and 1953. However, because of the failure to continue mass vaccination campaigns, a resurgence of the deadly disease in many African countries began in the early 1980s. We developed an original modeling approach to assess YF epidemic risk (vulnerability) and to prioritize the populations to be vaccinated. We chose a two-step assessment of vulnerability at district level consisting of a quantitative and qualitative assessment per country. Quantitative assessment starts with data collection on six risk factors: five risk factors associated with "exposure" to virus/vector and one with "susceptibility" of a district to YF epidemics. The multiple correspondence analysis (MCA) modeling method was specifically adapted to reduce the five exposure variables to one aggregated exposure indicator. Health districts were then projected onto a two-dimensional graph to define different levels of vulnerability. Districts are presented on risk maps for qualitative analysis in consensus groups, allowing the addition of factors, such as population migrations or vector density, that could not be included in MCA. The example of rural districts in Burkina Faso show five distinct clusters of risk profiles. Based on this assessment, 32 of 55 districts comprising over 7 million people were prioritized for preventive vaccination campaigns. This assessment of yellow fever epidemic risk at the district level includes MCA modeling and consensus group modification. MCA provides a standardized way to reduce complexity. It supports an informed public health decision-making process that empowers local stakeholders through the consensus group. This original approach can be applied to any disease with documented risk factors.

Asian Consensus Report on Functional Dyspepsia

PubMed Central

Miwa, Hiroto; Ghoshal, Uday C; Gonlachanvit, Sutep; Gwee, Kok-Ann; Ang, Tiing-Leong; Chang, Full-Young; Fock, Kwong Ming; Hongo, Michio; Hou, Xiaohua; Kachintorn, Udom; Ke, Meiyun; Lai, Kwok-Hung; Lee, Kwang Jae; Lu, Ching-Liang; Mahadeva, Sanjiv; Miura, Soichiro; Park, Hyojin; Rhee, Poong-Lyul; Sugano, Kentaro; Vilaichone, Ratha-korn; Wong, Benjamin CY

2012-01-01

Background/Aims Environmental factors such as food, lifestyle and prevalence of Helicobacter pylori infection are widely different in Asian countries compared to the West, and physiological functions and genetic factors of Asians may also be different from those of Westerners. Establishing an Asian consensus for functional dyspepsia is crucial in order to attract attention to such data from Asian countries, to articulate the experience and views of Asian experts, and to provide a relevant guide on management of functional dyspepsia for primary care physicians working in Asia. Methods Consensus team members were selected from Asian experts and consensus development was carried out using a modified Delphi method. Consensus teams collected published papers on functional dyspepsia especially from Asia and developed candidate consensus statements based on the generated clinical questions. At the first face-to-face meeting, each statement was reviewed and e-mail voting was done twice. At the second face-to-face meeting, final voting on each statement was done using keypad voting system. A grade of evidence and a strength of recommendation were applied to each statement according to the method of the GRADE Working Group. Results Twenty-nine consensus statements were finalized, including 7 for definition and diagnosis, 5 for epidemiology, 9 for pathophysiology and 8 for management. Algorithms for diagnosis and management of functional dyspepsia were added. Conclusions This consensus developed by Asian experts shows distinctive features of functional dyspepsia in Asia and will provide a guide to the diagnosis and management of functional dyspepsia for Asian primary care physicians. PMID:22523724

Comparing and Contrasting Consensus versus Empirical Domains

PubMed Central

Jason, Leonard A.; Kot, Bobby; Sunnquist, Madison; Brown, Abigail; Reed, Jordan; Furst, Jacob; Newton, Julia L.; Strand, Elin Bolle; Vernon, Suzanne D.

2015-01-01

Background Since the publication of the CFS case definition [1], there have been a number of other criteria proposed including the Canadian Consensus Criteria [2] and the Myalgic Encephalomyelitis: International Consensus Criteria. [3] Purpose The current study compared these domains that were developed through consensus methods to one obtained through more empirical approaches using factor analysis. Methods Using data mining, we compared and contrasted fundamental features of consensus-based criteria versus empirical latent factors. In general, these approaches found the domain of Fatigue/Post-exertional malaise as best differentiating patients from controls. Results Findings indicated that the Fukuda et al. criteria had the worst sensitivity and specificity. Conclusions These outcomes might help both theorists and researchers better determine which fundamental domains to be used for the case definition. PMID:26977374

Comparison of STR profiling from low template DNA extracts with and without the consensus profiling method

PubMed Central

2012-01-01

Background The consensus profiling method was introduced to overcome the exaggerated stochastic effects associated with low copy number DNA typing. However, little empirical evidence has been provided which shows that a consensus profile, derived from dividing a sample into separate aliquots and including only alleles seen at least twice, gives the most informative profile, compared to a profile obtained by amplifying the entire low template DNA extract in one reaction. Therefore, this study aimed to investigate the quality of consensus profiles compared to profiles obtained using the whole low template extract for amplification. Methods A total of 100 pg and 25 pg DNA samples were amplified with the PowerPlex® ESI 16 Kits using 30 or 34 PCR cycles. A total of 100 pg and 25 pg DNA samples were then divided into three aliquots for a 34-cycle PCR and a consensus profile derived that included alleles that appeared in at least two of the replicates. Profiles from the non-split samples were compared to the consensus profiles focusing on peak heights, allele drop out, locus drop out and allele drop in. Results Performing DNA profiling on non-split extracts produced profiles with a higher percentage of correct loci compared to the consensus profiling technique. Consensus profiling did eliminate any spurious alleles from the final profile. However, there was a notable increase in allele and locus drop out when a LTDNA sample was divided prior to amplification. Conclusions The loss of information that occurs when a sample is split for amplification indicates that consensus profiling may not be producing the most informative DNA profile for samples where the template amount is limited. PMID:22748106

75 FR 65052 - Consensus Standards, Standard Practice for Maintenance of Airplane Electrical Wiring Systems

Federal Register 2010, 2011, 2012, 2013, 2014

2010-10-21

... systems. By this notice, the FAA finds the standards to be acceptable methods and procedures for... DEPARTMENT OF TRANSPORTATION Federal Aviation Administration Consensus Standards, Standard... consensus standards and the Federal Aviation Administration (FAA) intention to accept the ASTM International...

Collaborative Clustering for Sensor Networks

NASA Technical Reports Server (NTRS)

Wagstaff. Loro :/; Green Jillian; Lane, Terran

2011-01-01

Traditionally, nodes in a sensor network simply collect data and then pass it on to a centralized node that archives, distributes, and possibly analyzes the data. However, analysis at the individual nodes could enable faster detection of anomalies or other interesting events, as well as faster responses such as sending out alerts or increasing the data collection rate. There is an additional opportunity for increased performance if individual nodes can communicate directly with their neighbors. Previously, a method was developed by which machine learning classification algorithms could collaborate to achieve high performance autonomously (without requiring human intervention). This method worked for supervised learning algorithms, in which labeled data is used to train models. The learners collaborated by exchanging labels describing the data. The new advance enables clustering algorithms, which do not use labeled data, to also collaborate. This is achieved by defining a new language for collaboration that uses pair-wise constraints to encode useful information for other learners. These constraints specify that two items must, or cannot, be placed into the same cluster. Previous work has shown that clustering with these constraints (in isolation) already improves performance. In the problem formulation, each learner resides at a different node in the sensor network and makes observations (collects data) independently of the other learners. Each learner clusters its data and then selects a pair of items about which it is uncertain and uses them to query its neighbors. The resulting feedback (a must and cannot constraint from each neighbor) is combined by the learner into a consensus constraint, and it then reclusters its data while incorporating the new constraint. A strategy was also proposed for cleaning the resulting constraint sets, which may contain conflicting constraints; this improves performance significantly. This approach has been applied to collaborative clustering of seismic and infrasonic data collected by the Mount Erebus Volcano Observatory in Antarctica. Previous approaches to distributed clustering cannot readily be applied in a sensor network setting, because they assume that each node has the same view of the data set. A view is the set of features used to represent each object. When a single data set is partitioned across several computational nodes, distributed clustering works; all objects have the same view. But when the data is collected from different locations, using different sensors, a more flexible approach is needed. This approach instead operates in situations where the data collected at each node has a different view (e.g., seismic vs. infrasonic sensors), but they observe the same events. This enables them to exchange information about the likely cluster membership relations between objects, even if they do not use the same features to represent the objects.

Development of a nationwide consensus syllabus of palliative medicine for undergraduate medical education in Japan: a modified Delphi method.

PubMed

Kizawa, Yoshiyuki; Tsuneto, Satoru; Tamba, Kaichiro; Takamiya, Yusuke; Morita, Tatsuya; Bito, Seiji; Otaki, Junji

2012-07-01

There is currently no consensus syllabus of palliative medicine for undergraduate medical education in Japan, although the Cancer Control Act proposed in 2007 covers the dissemination of palliative care. To develop a nationwide consensus syllabus of palliative medicine for undergraduate medical education in Japan using a modified Delphi method. We adopted the following three-step method: (1) a workshop to produce the draft syllabus; (2) a survey-based provisional syllabus; (3) Delphi rounds and a panel meeting (modified Delphi method) to produce the working syllabus. Educators in charge of palliative medicine from 63% of the medical schools in Japan collaborated to develop a survey-based provisional syllabus before the Delphi rounds. A panel of 32 people was then formed for the modified Delphi rounds comprising 28 educators and experts in palliative medicine, one cancer survivor, one bereaved family member, and two medical students. The final consensus syllabus consists of 115 learning objectives across seven sections as follows: basic principles; disease process and comprehensive assessment; symptom management; psychosocial care; cultural, religious, and spiritual issues; ethical issues; and legal frameworks. Learning objectives were categorized as essential or desirable (essential: 66; desirable: 49). A consensus syllabus of palliative medicine for undergraduate medical education was developed using a clear and innovative methodology. The final consensus syllabus will be made available for further dissemination of palliative care education throughout the country.

Old document image segmentation using the autocorrelation function and multiresolution analysis

NASA Astrophysics Data System (ADS)

Mehri, Maroua; Gomez-Krämer, Petra; Héroux, Pierre; Mullot, Rémy

2013-01-01

Recent progress in the digitization of heterogeneous collections of ancient documents has rekindled new challenges in information retrieval in digital libraries and document layout analysis. Therefore, in order to control the quality of historical document image digitization and to meet the need of a characterization of their content using intermediate level metadata (between image and document structure), we propose a fast automatic layout segmentation of old document images based on five descriptors. Those descriptors, based on the autocorrelation function, are obtained by multiresolution analysis and used afterwards in a specific clustering method. The method proposed in this article has the advantage that it is performed without any hypothesis on the document structure, either about the document model (physical structure), or the typographical parameters (logical structure). It is also parameter-free since it automatically adapts to the image content. In this paper, firstly, we detail our proposal to characterize the content of old documents by extracting the autocorrelation features in the different areas of a page and at several resolutions. Then, we show that is possible to automatically find the homogeneous regions defined by similar indices of autocorrelation without knowledge about the number of clusters using adapted hierarchical ascendant classification and consensus clustering approaches. To assess our method, we apply our algorithm on 316 old document images, which encompass six centuries (1200-1900) of French history, in order to demonstrate the performance of our proposal in terms of segmentation and characterization of heterogeneous corpus content. Moreover, we define a new evaluation metric, the homogeneity measure, which aims at evaluating the segmentation and characterization accuracy of our methodology. We find a 85% of mean homogeneity accuracy. Those results help to represent a document by a hierarchy of layout structure and content, and to define one or more signatures for each page, on the basis of a hierarchical representation of homogeneous blocks and their topology.

ClubSub-P: Cluster-Based Subcellular Localization Prediction for Gram-Negative Bacteria and Archaea

PubMed Central

Paramasivam, Nagarajan; Linke, Dirk

2011-01-01

The subcellular localization (SCL) of proteins provides important clues to their function in a cell. In our efforts to predict useful vaccine targets against Gram-negative bacteria, we noticed that misannotated start codons frequently lead to wrongly assigned SCLs. This and other problems in SCL prediction, such as the relatively high false-positive and false-negative rates of some tools, can be avoided by applying multiple prediction tools to groups of homologous proteins. Here we present ClubSub-P, an online database that combines existing SCL prediction tools into a consensus pipeline from more than 600 proteomes of fully sequenced microorganisms. On top of the consensus prediction at the level of single sequences, the tool uses clusters of homologous proteins from Gram-negative bacteria and from Archaea to eliminate false-positive and false-negative predictions. ClubSub-P can assign the SCL of proteins from Gram-negative bacteria and Archaea with high precision. The database is searchable, and can easily be expanded using either new bacterial genomes or new prediction tools as they become available. This will further improve the performance of the SCL prediction, as well as the detection of misannotated start codons and other annotation errors. ClubSub-P is available online at http://toolkit.tuebingen.mpg.de/clubsubp/ PMID:22073040

Cardiovascular–renal axis disorders in the domestic dog and cat: a veterinary consensus statement

PubMed Central

Pouchelon, J L; Atkins, C E; Bussadori, C; Oyama, M A; Vaden, S L; Bonagura, J D; Chetboul, V; Cowgill, L D; Elliot, J; Francey, T; Grauer, G F; Luis Fuentes, V; Sydney Moise, N; Polzin, D J; Van Dongen, A M; Van Israël, N

2015-01-01

OBJECTIVES There is a growing understanding of the complexity of interplay between renal and cardiovascular systems in both health and disease. The medical profession has adopted the term “cardiorenal syndrome” (CRS) to describe the pathophysiological relationship between the kidney and heart in disease. CRS has yet to be formally defined and described by the veterinary profession and its existence and importance in dogs and cats warrant investigation. The CRS Consensus Group, comprising nine veterinary cardiologists and seven nephrologists from Europe and North America, sought to achieve consensus around the definition, pathophysiology, diagnosis and management of dogs and cats with “cardiovascular-renal disorders” (CvRD). To this end, the Delphi formal methodology for defining/building consensus and defining guidelines was utilised. METHODS Following a literature review, 13 candidate statements regarding CvRD in dogs and cats were tested for consensus, using a modified Delphi method. As a new area of interest, well-designed studies, specific to CRS/CvRD, are lacking, particularly in dogs and cats. Hence, while scientific justification of all the recommendations was sought and used when available, recommendations were largely reliant on theory, expert opinion, small clinical studies and extrapolation from data derived from other species. RESULTS Of the 13 statements, 11 achieved consensus and 2 did not. The modified Delphi approach worked well to achieve consensus in an objective manner and to develop initial guidelines for CvRD. DISCUSSION The resultant manuscript describes consensus statements for the definition, classification, diagnosis and management strategies for veterinary patients with CvRD, with an emphasis on the pathological interplay between the two organ systems. By formulating consensus statements regarding CvRD in veterinary medicine, the authors hope to stimulate interest in and advancement of the understanding and management of CvRD in dogs and cats. The use of a formalised method for consensus and guideline development should be considered for other topics in veterinary medicine. PMID:26331869

Genotypic characterization of Escherichia coli O157:H7 isolates from different sources in the North-West Province, South Africa, using enterobacterial repetitive intergenic consensus PCR analysis.

PubMed

Ateba, Collins Njie; Mbewe, Moses

2014-05-30

In many developing countries, proper hygiene is not strictly implemented when animals are slaughtered and meat products become contaminated. Contaminated meat may contain Escherichia coli (E. coli) O157:H7 that could cause diseases in humans if these food products are consumed undercooked. In the present study, a total of 94 confirmed E. coli O157:H7 isolates were subjected to the enterobacterial repetitive intergenic consensus (ERIC) polymerase chain reaction (PCR) typing to generate genetic fingerprints. The ERIC fragments were resolved by electrophoresis on 2% (w/v) agarose gels. The presence, absence and intensity of band data were obtained, exported to Microsoft Excel (Microsoft Office 2003) and used to generate a data matrix. The unweighted pair group method with arithmetic mean (UPGMA) and complete linkage algorithms were used to analyze the percentage of similarity and matrix data. Relationships between the various profiles and/or lanes were expressed as dendrograms. Data from groups of related lanes were compiled and reported on cluster tables. ERIC fragments ranged from one to 15 per isolate, and their sizes varied from 0.25 to 0.771 kb. A large proportion of the isolates produced an ERIC banding pattern with three duplets ranging in sizes from 0.408 to 0.628 kb. Eight major clusters (I-VIII) were identified. Overall, the remarkable similarities (72% to 91%) between the ERIC profiles for the isolate from animal species and their corresponding food products indicated some form of contamination, which may not exclude those at the level of the abattoirs. These results reveal that ERIC PCR analysis can be reliable in comparing the genetic profiles of E. coli O157:H7 from different sources in the North-West Province of South Africa.

Identification of Sinorhizobium (Ensifer) medicae based on a specific genomic sequence unveiled by M13-PCR fingerprinting.

PubMed

Dourado, Ana Catarina; Alves, Paula I L; Tenreiro, Tania; Ferreira, Eugénio M; Tenreiro, Rogério; Fareleira, Paula; Crespo, M Teresa Barreto

2009-12-01

A collection of nodule isolates from Medicago polymorpha obtained from southern and central Portugal was evaluated by M13-PCR fingerprinting and hierarchical cluster analysis. Several genomic clusters were obtained which, by 16S rRNA gene sequencing of selected representatives, were shown to be associated with particular taxonomic groups of rhizobia and other soil bacteria. The method provided a clear separation between rhizobia and co-isolated non-symbiotic soil contaminants. Ten M13-PCR groups were assigned to Sinorhizobium (Ensifer) medicae and included all isolates responsible for the formation of nitrogen-fixing nodules upon re-inoculation of M. polymorpha test-plants. In addition, enterobacterial repetitive intergenic consensus (ERIC)-PCR fingerprinting indicated a high genomic heterogeneity within the major M13- PCR clusters of S. medicae isolates. Based on nucleotide sequence data of an M13-PCR amplicon of ca. 1500 bp, observed only in S. medicae isolates and spanning locus Smed_3707 to Smed_3709 from the pSMED01 plasmid sequence of S. medicae WSM419 genome's sequence, a pair of PCR primers was designed and used for direct PCR amplification of a 1399-bp sequence within this fragment. Additional in silico and in vitro experiments, as well as phylogenetic analysis, confirmed the specificity of this primer combination and therefore the reliability of this approach in the prompt identification of S. medicae isolates and their distinction from other soil bacteria.

75 FR 65051 - Consensus Standards, Standard Practice for Inspection of Airplane Electrical Wiring Systems

Federal Register 2010, 2011, 2012, 2013, 2014

2010-10-21

.... By this notice, the FAA finds the standards to be acceptable methods and procedures for inspection of... DEPARTMENT OF TRANSPORTATION Federal Aviation Administration Consensus Standards, Standard... consensus standards and the Federal Aviation Administration (FAA) intention to accept the ASTM International...

Opinion evolution in different social acquaintance networks.

PubMed

Chen, Xi; Zhang, Xiao; Wu, Zhan; Wang, Hongwei; Wang, Guohua; Li, Wei

2017-11-01

Social acquaintance networks influenced by social culture and social policy have a great impact on public opinion evolution in daily life. Based on the differences between socio-culture and social policy, three different social acquaintance networks (kinship-priority acquaintance network, independence-priority acquaintance network, and hybrid acquaintance network) incorporating heredity proportion p h and variation proportion p v are proposed in this paper. Numerical experiments are conducted to investigate network topology and different phenomena during opinion evolution, using the Deffuant model. We found that in kinship-priority acquaintance networks, similar to the Chinese traditional acquaintance networks, opinions always achieve fragmentation, resulting in the formation of multiple large clusters and many small clusters due to the fact that individuals believe more in their relatives and live in a relatively closed environment. In independence-priority acquaintance networks, similar to Western acquaintance networks, the results are similar to those in the kinship-priority acquaintance network. In hybrid acquaintance networks, similar to the Chinese modern acquaintance networks, only a few clusters are formed indicating that in modern China, opinions are more likely to reach consensus on a large scale. These results are similar to the opinion evolution phenomena in modern society, proving the rationality and applicability of network models combined with social culture and policy. We also found a threshold curve p v +2p h =2.05 in the results for the final opinion clusters and evolution time. Above the threshold curve, opinions could easily reach consensus. Based on the above experimental results, a culture-policy-driven mechanism for the opinion dynamic is worth promoting in this paper, that is, opinion dynamics can be driven by different social cultures and policies through the influence of heredity and variation in interpersonal relationship networks. This finding is of great significance for predicting opinion evolution under different acquaintance networks and formulating reasonable policies based on cultural characteristics to guide public opinion.

Opinion evolution in different social acquaintance networks

NASA Astrophysics Data System (ADS)

Chen, Xi; Zhang, Xiao; Wu, Zhan; Wang, Hongwei; Wang, Guohua; Li, Wei

2017-11-01

Social acquaintance networks influenced by social culture and social policy have a great impact on public opinion evolution in daily life. Based on the differences between socio-culture and social policy, three different social acquaintance networks (kinship-priority acquaintance network, independence-priority acquaintance network, and hybrid acquaintance network) incorporating heredity proportion ph and variation proportion pv are proposed in this paper. Numerical experiments are conducted to investigate network topology and different phenomena during opinion evolution, using the Deffuant model. We found that in kinship-priority acquaintance networks, similar to the Chinese traditional acquaintance networks, opinions always achieve fragmentation, resulting in the formation of multiple large clusters and many small clusters due to the fact that individuals believe more in their relatives and live in a relatively closed environment. In independence-priority acquaintance networks, similar to Western acquaintance networks, the results are similar to those in the kinship-priority acquaintance network. In hybrid acquaintance networks, similar to the Chinese modern acquaintance networks, only a few clusters are formed indicating that in modern China, opinions are more likely to reach consensus on a large scale. These results are similar to the opinion evolution phenomena in modern society, proving the rationality and applicability of network models combined with social culture and policy. We also found a threshold curve pv+2 ph=2.05 in the results for the final opinion clusters and evolution time. Above the threshold curve, opinions could easily reach consensus. Based on the above experimental results, a culture-policy-driven mechanism for the opinion dynamic is worth promoting in this paper, that is, opinion dynamics can be driven by different social cultures and policies through the influence of heredity and variation in interpersonal relationship networks. This finding is of great significance for predicting opinion evolution under different acquaintance networks and formulating reasonable policies based on cultural characteristics to guide public opinion.

Prioritization of in silico models and molecular descriptors for the assessment of ready biodegradability

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fernández, Alberto; Rallo, Robert; Giralt, Francesc

2015-10-15

Ready biodegradability is a key property for evaluating the long-term effects of chemicals on the environment and human health. As such, it is used as a screening test for the assessment of persistent, bioaccumulative and toxic substances. Regulators encourage the use of non-testing methods, such as in silico models, to save money and time. A dataset of 757 chemicals was collected to assess the performance of four freely available in silico models that predict ready biodegradability. They were applied to develop a new consensus method that prioritizes the use of each individual model according to its performance on chemical subsetsmore » driven by the presence or absence of different molecular descriptors. This consensus method was capable of almost eliminating unpredictable chemicals, while the performance of combined models was substantially improved with respect to that of the individual models. - Highlights: • Consensus method to predict ready biodegradability by prioritizing multiple QSARs. • Consensus reduced the amount of unpredictable chemicals to less than 2%. • Performance increased with the number of QSAR models considered. • The absence of 2D atom pairs contributed significantly to the consensus model.« less

Expert Consensus on Characteristics of Wisdom: A Delphi Method Study

ERIC Educational Resources Information Center

Jeste, Dilip V.; Ardelt, Monika; Blazer, Dan; Kraemer, Helena C.; Vaillant, George; Meeks, Thomas W.

2010-01-01

Purpose: Wisdom has received increasing attention in empirical research in recent years, especially in gerontology and psychology, but consistent definitions of wisdom remain elusive. We sought to better characterize this concept via an expert consensus panel using a 2-phase Delphi method. Design and Methods: A survey questionnaire comprised 53…

Scientific Networks on Data Landscapes: Question Difficulty, Epistemic Success, and Convergence

PubMed Central

Grim, Patrick; Singer, Daniel J.; Fisher, Steven; Bramson, Aaron; Berger, William J.; Reade, Christopher; Flocken, Carissa; Sales, Adam

2014-01-01

A scientific community can be modeled as a collection of epistemic agents attempting to answer questions, in part by communicating about their hypotheses and results. We can treat the pathways of scientific communication as a network. When we do, it becomes clear that the interaction between the structure of the network and the nature of the question under investigation affects epistemic desiderata, including accuracy and speed to community consensus. Here we build on previous work, both our own and others’, in order to get a firmer grasp on precisely which features of scientific communities interact with which features of scientific questions in order to influence epistemic outcomes. Here we introduce a measure on the landscape meant to capture some aspects of the difficulty of answering an empirical question. We then investigate both how different communication networks affect whether the community finds the best answer and the time it takes for the community to reach consensus on an answer. We measure these two epistemic desiderata on a continuum of networks sampled from the Watts-Strogatz spectrum. It turns out that finding the best answer and reaching consensus exhibit radically different patterns. The time it takes for a community to reach a consensus in these models roughly tracks mean path length in the network. Whether a scientific community finds the best answer, on the other hand, tracks neither mean path length nor clustering coefficient. PMID:24683416

Scientific Networks on Data Landscapes: Question Difficulty, Epistemic Success, and Convergence.

PubMed

Grim, Patrick; Singer, Daniel J; Fisher, Steven; Bramson, Aaron; Berger, William J; Reade, Christopher; Flocken, Carissa; Sales, Adam

2013-12-01

A scientific community can be modeled as a collection of epistemic agents attempting to answer questions, in part by communicating about their hypotheses and results. We can treat the pathways of scientific communication as a network. When we do, it becomes clear that the interaction between the structure of the network and the nature of the question under investigation affects epistemic desiderata, including accuracy and speed to community consensus. Here we build on previous work, both our own and others', in order to get a firmer grasp on precisely which features of scientific communities interact with which features of scientific questions in order to influence epistemic outcomes. Here we introduce a measure on the landscape meant to capture some aspects of the difficulty of answering an empirical question. We then investigate both how different communication networks affect whether the community finds the best answer and the time it takes for the community to reach consensus on an answer. We measure these two epistemic desiderata on a continuum of networks sampled from the Watts-Strogatz spectrum. It turns out that finding the best answer and reaching consensus exhibit radically different patterns. The time it takes for a community to reach a consensus in these models roughly tracks mean path length in the network. Whether a scientific community finds the best answer, on the other hand, tracks neither mean path length nor clustering coefficient.

Moving without a purpose: an experimental study of swarm guidance in the Western honey bee, Apis mellifera.

PubMed

Makinson, James C; Beekman, Madeleine

2014-06-01

During reproductive swarming, honey bee scouts perform two very important functions. Firstly, they find new nesting locations and return to the swarm cluster to communicate their discoveries. Secondly, once the swarm is ready to depart, informed scout bees act as guides, leading the swarm to its final destination. We have previously hypothesised that the two processes, selecting a new nest site and swarm guidance, are tightly linked in honey bees. When swarms can be laissez faire about where they nest, reaching directional consensus prior to lift off seems unnecessary. If, in contrast, it is essential that the swarm reaches a precise location, either directional consensus must be near unanimous prior to swarm departure or only a select subgroup of the scouts guide the swarm. Here, we tested experimentally whether directional consensus is necessary for the successful guidance of swarms of the Western honey bee Apis mellifera by forcing swarms into the air prior to the completion of the decision-making process. Our results show that swarms were unable to guide themselves prior to the swarm reaching the pre-flight buzzing phase of the decision-making process, even when directional consensus was high. We therefore suggest that not all scouts involved in the decision-making process attempt to guide the swarm. © 2014. Published by The Company of Biologists Ltd.

Consensus Treatments for Moderate Juvenile Dermatomyositis: Beyond the First Two Months

PubMed Central

Huber, Adam M.; Robinson, Angela B.; Reed, Ann M.; Abramson, Leslie; Bout-Tabaku, Sharon; Carrasco, Ruy; Curran, Megan; Feldman, Brian M.; Gewanter, Harry; Griffin, Thomas; Haines, Kathleen; Sanzari, Joseph M.; Hoeltzel, Mark F.; Isgro, Josephine; Kahn, Philip; Lang, Bianca; Lawler, Patti; Shaham, Bracha; Schmeling, Heinrike; Scuccimarri, Rosie; Shishov, Michael; Stringer, Elizabeth; Wohrley, Julie; Ilowite, Norman T.; Wallace, Carol

2011-01-01

Objectives To use consensus methods and the considerable expertise contained within the Children’s Arthritis and Rheumatology Research Alliance (CARRA) organization, to extend the 3 previously developed treatment plans for moderate juvenile dermatomyositis (JDM) to span the full course of treatment. Methods A consensus meeting was held in Chicago on April 23–24, 2010 involving 30 pediatric rheumatologists and 4 lay participants. Nominal group technique was used to achieve consensus on treatment plans which represented typical management of moderate JDM. A pre-conference survey of CARRA, completed by 151/272 (56%) members, was used to provide additional guidance to discussion. Results Consensus was reached on timing and rate of steroid tapering, duration of steroid therapy, and actions to be taken if patients were unchanged, worsening, experiencing medication side effects or disease complications. Of particular importance, a single, consensus steroid taper was developed. Conclusions We were able to develop consensus treatment plans which describe therapy for moderate JDM throughout the treatment course. These treatment plans can now be used clinically, and data collected prospectively regarding treatment effectiveness and toxicity. This will allow comparison of these treatment plans and facilitate the development of evidence-based treatment recommendations for moderate JDM. PMID:22076847

Congruent population structure inferred from dispersal behaviour and intensive genetic surveys of the threatened Florida scrub-jay (Aphelocoma cœrulescens)

USGS Publications Warehouse

Coulon, A.; Fitzpatrick, J.W.; Bowman, R.; Stith, B.M.; Makarewich, C.A.; Stenzler, L.M.; Lovette, I.J.

2008-01-01

The delimitation of populations, defined as groups of individuals linked by gene flow, is possible by the analysis of genetic markers and also by spatial models based on dispersal probabilities across a landscape. We combined these two complimentary methods to define the spatial pattern of genetic structure among remaining populations of the threatened Florida scrub-jay, a species for which dispersal ability is unusually well-characterized. The range-wide population was intensively censused in the 1990s, and a metapopulation model defined population boundaries based on predicted dispersal-mediated demographic connectivity. We subjected genotypes from more than 1000 individual jays screened at 20 microsatellite loci to two Bayesian clustering methods. We describe a consensus method for identifying common features across many replicated clustering runs. Ten genetically differentiated groups exist across the present-day range of the Florida scrub-jay. These groups are largely consistent with the dispersal-defined metapopulations, which assume very limited dispersal ability. Some genetic groups comprise more than one metapopulation, likely because these genetically similar metapopulations were sundered only recently by habitat alteration. The combined reconstructions of population structure based on genetics and dispersal-mediated demographic connectivity provide a robust depiction of the current genetic and demographic organization of this species, reflecting past and present levels of dispersal among occupied habitat patches. The differentiation of populations into 10 genetic groups adds urgency to management efforts aimed at preserving what remains of genetic variation in this dwindling species, by maintaining viable populations of all genetically differentiated and geographically isolated populations.

How to Choose? Using the Delphi Method to Develop Consensus Triggers and Indicators for Disaster Response.

PubMed

Lis, Rebecca; Sakata, Vicki; Lien, Onora

2017-08-01

To identify key decisions along the continuum of care (conventional, contingency, and crisis) and the critical triggers and data elements used to inform those decisions concerning public health and health care response during an emergency. A classic Delphi method, a consensus-building survey technique, was used with clinicians around Washington State to identify regional triggers and indicators. Additionally, using a modified Delphi method, we combined a workshop and single-round survey with panelists from public health (state and local) and health care coalitions to identify consensus state-level triggers and indicators. In the clinical survey, 122 of 223 proposed triggers or indicators (43.7%) reached consensus and were deemed important in regional decision-making during a disaster. In the state-level survey, 110 of 140 proposed triggers or indicators (78.6%) reached consensus and were deemed important in state-level decision-making during a disaster. The identification of consensus triggers and indicators for health care emergency response is crucial in supporting a comprehensive health care situational awareness process. This can inform the creation of standardized questions to ask health care, public health, and other partners to support decision-making during a response. (Disaster Med Public Health Preparedness. 2017;11:467-472).

Combined node and link partitions method for finding overlapping communities in complex networks

PubMed Central

Jin, Di; Gabrys, Bogdan; Dang, Jianwu

2015-01-01

Community detection in complex networks is a fundamental data analysis task in various domains, and how to effectively find overlapping communities in real applications is still a challenge. In this work, we propose a new unified model and method for finding the best overlapping communities on the basis of the associated node and link partitions derived from the same framework. Specifically, we first describe a unified model that accommodates node and link communities (partitions) together, and then present a nonnegative matrix factorization method to learn the parameters of the model. Thereafter, we infer the overlapping communities based on the derived node and link communities, i.e., determine each overlapped community between the corresponding node and link community with a greedy optimization of a local community function conductance. Finally, we introduce a model selection method based on consensus clustering to determine the number of communities. We have evaluated our method on both synthetic and real-world networks with ground-truths, and compared it with seven state-of-the-art methods. The experimental results demonstrate the superior performance of our method over the competing ones in detecting overlapping communities for all analysed data sets. Improved performance is particularly pronounced in cases of more complicated networked community structures. PMID:25715829

Building a Consensus on Community Health Workers’ Scope of Practice: Lessons From New York

PubMed Central

Matos, Sergio; Hicks, April L.; Campbell, Ayanna; Moore, Addison; Diaz, Diurka

2012-01-01

Objectives. We evaluated efforts in New York to build a consensus between community health workers (CHWs) and employers on CHWs’ scope of practice, training standards, and certification procedures. Methods. We conducted multiple-choice surveys in 2008 and 2010 with 226 CHWs and 44 employers. We compared CHWs’ and employers’ recommendations regarding 28 scope of practice elements. The participatory ranking method was used to identify consensus scope of practice recommendations. Results. There was consensus on 5 scope of practice elements: outreach and community organizing, case management and care coordination, home visits, health education and coaching, and system navigation. For each element, 3 to 4 essential skills were identified, giving a total of 27 skills. These included all skills recommended in national CHW studies, along with 3 unique to New York: computer skills, participatory research methods, and time management. Conclusions. CHWs and employers in New York were in consensus on CHWs’ scope of practice on virtually all of the detailed core competency skills. The CHW scope of practice recommendations of these groups can help other states refine their scope of practice elements. PMID:22897548

Collaboration patterns in the German political science co-authorship network.

PubMed

Leifeld, Philip; Wankmüller, Sandra; Berger, Valentin T Z; Ingold, Karin; Steiner, Christiane

2017-01-01

Research on social processes in the production of scientific output suggests that the collective research agenda of a discipline is influenced by its structural features, such as "invisible colleges" or "groups of collaborators" as well as academic "stars" that are embedded in, or connect, these research groups. Based on an encompassing dataset that takes into account multiple publication types including journals and chapters in edited volumes, we analyze the complete co-authorship network of all 1,339 researchers in German political science. Through the use of consensus graph clustering techniques and descriptive centrality measures, we identify the ten largest research clusters, their research topics, and the most central researchers who act as bridges and connect these clusters. We also aggregate the findings at the level of research organizations and consider the inter-university co-authorship network. The findings indicate that German political science is structured by multiple overlapping research clusters with a dominance of the subfields of international relations, comparative politics and political sociology. A small set of well-connected universities takes leading roles in these informal research groups.

Collaboration patterns in the German political science co-authorship network

PubMed Central

Wankmüller, Sandra; Berger, Valentin T. Z.; Ingold, Karin; Steiner, Christiane

2017-01-01

Research on social processes in the production of scientific output suggests that the collective research agenda of a discipline is influenced by its structural features, such as “invisible colleges” or “groups of collaborators” as well as academic “stars” that are embedded in, or connect, these research groups. Based on an encompassing dataset that takes into account multiple publication types including journals and chapters in edited volumes, we analyze the complete co-authorship network of all 1,339 researchers in German political science. Through the use of consensus graph clustering techniques and descriptive centrality measures, we identify the ten largest research clusters, their research topics, and the most central researchers who act as bridges and connect these clusters. We also aggregate the findings at the level of research organizations and consider the inter-university co-authorship network. The findings indicate that German political science is structured by multiple overlapping research clusters with a dominance of the subfields of international relations, comparative politics and political sociology. A small set of well-connected universities takes leading roles in these informal research groups. PMID:28388621

Graph partitions and cluster synchronization in networks of oscillators

PubMed Central

Schaub, Michael T.; O’Clery, Neave; Billeh, Yazan N.; Delvenne, Jean-Charles; Lambiotte, Renaud; Barahona, Mauricio

2017-01-01

Synchronization over networks depends strongly on the structure of the coupling between the oscillators. When the coupling presents certain regularities, the dynamics can be coarse-grained into clusters by means of External Equitable Partitions of the network graph and their associated quotient graphs. We exploit this graph-theoretical concept to study the phenomenon of cluster synchronization, in which different groups of nodes converge to distinct behaviors. We derive conditions and properties of networks in which such clustered behavior emerges, and show that the ensuing dynamics is the result of the localization of the eigenvectors of the associated graph Laplacians linked to the existence of invariant subspaces. The framework is applied to both linear and non-linear models, first for the standard case of networks with positive edges, before being generalized to the case of signed networks with both positive and negative interactions. We illustrate our results with examples of both signed and unsigned graphs for consensus dynamics and for partial synchronization of oscillator networks under the master stability function as well as Kuramoto oscillators. PMID:27781454

Gene expression profiles reveal key genes for early diagnosis and treatment of adamantinomatous craniopharyngioma.

PubMed

Yang, Jun; Hou, Ziming; Wang, Changjiang; Wang, Hao; Zhang, Hongbing

2018-04-23

Adamantinomatous craniopharyngioma (ACP) is an aggressive brain tumor that occurs predominantly in the pediatric population. Conventional diagnosis method and standard therapy cannot treat ACPs effectively. In this paper, we aimed to identify key genes for ACP early diagnosis and treatment. Datasets GSE94349 and GSE68015 were obtained from Gene Expression Omnibus database. Consensus clustering was applied to discover the gene clusters in the expression data of GSE94349 and functional enrichment analysis was performed on gene set in each cluster. The protein-protein interaction (PPI) network was built by the Search Tool for the Retrieval of Interacting Genes, and hubs were selected. Support vector machine (SVM) model was built based on the signature genes identified from enrichment analysis and PPI network. Dataset GSE94349 was used for training and testing, and GSE68015 was used for validation. Besides, RT-qPCR analysis was performed to analyze the expression of signature genes in ACP samples compared with normal controls. Seven gene clusters were discovered in the differentially expressed genes identified from GSE94349 dataset. Enrichment analysis of each cluster identified 25 pathways that highly associated with ACP. PPI network was built and 46 hubs were determined. Twenty-five pathway-related genes that overlapped with the hubs in PPI network were used as signatures to establish the SVM diagnosis model for ACP. The prediction accuracy of SVM model for training, testing, and validation data were 94, 85, and 74%, respectively. The expression of CDH1, CCL2, ITGA2, COL8A1, COL6A2, and COL6A3 were significantly upregulated in ACP tumor samples, while CAMK2A, RIMS1, NEFL, SYT1, and STX1A were significantly downregulated, which were consistent with the differentially expressed gene analysis. SVM model is a promising classification tool for screening and early diagnosis of ACP. The ACP-related pathways and signature genes will advance our knowledge of ACP pathogenesis and benefit the therapy improvement.

Application of the principles of evidence-based practice in decision making among senior management in Nova Scotia's addiction services agencies.

PubMed

Murphy, Matthew; MacCarthy, M Jayne; McAllister, Lynda; Gilbert, Robert

2014-12-05

Competency profiles for occupational clusters within Canada's substance abuse workforce (SAW) define the need for skill and knowledge in evidence-based practice (EBP) across all its members. Members of the Senior Management occupational cluster hold ultimate responsibility for decisions made within addiction services agencies and therefore must possess the highest level of proficiency in EBP. The objective of this study was to assess the knowledge of the principles of EBP, and use of the components of the evidence-based decision making (EBDM) process in members of this occupational cluster from selected addiction services agencies in Nova Scotia. A convenience sampling method was used to recruit participants from addiction services agencies. Semi-structured qualitative interviews were conducted with eighteen Senior Management. The interviews were audio-recorded, transcribed verbatim and checked by the participants. Interview transcripts were coded and analyzed for themes using content analysis and assisted by qualitative data analysis software (NVivo 9.0). Data analysis revealed four main themes: 1) Senior Management believe that addictions services agencies are evidence-based; 2) Consensus-based decision making is the norm; 3) Senior Management understand the principles of EBP and; 4) Senior Management do not themselves use all components of the EBDM process when making decisions, oftentimes delegating components of this process to decision support staff. Senior Management possess an understanding of the principles of EBP, however, when making decisions they often delegate components of the EBDM process to decision support staff. Decision support staff are not defined as an occupational cluster in Canada's SAW and have not been ascribed a competency profile. As such, there is no guarantee that this group possesses competency in EBDM. There is a need to advocate for the development of a defined occupational cluster and associated competency profile for this critical group.

The development of a consensus definition for healthcare improvement science (HIS) in seven European countries: A consensus methods approach.

PubMed

Skela-Savič, Brigita; Macrae, Rhoda; Lillo-Crespo, Manuel; Rooney, Kevin D

2017-06-01

There is a limited body of research in the field of healthcare improvement science (HIS). Quality improvement and 'change making' should become an intrinsic part of everyone's job, every day in all parts of the healthcare system. The lack of theoretical grounding may partly explain the minimal transfer of health research into health policy. This article seeks to present the development of the definition for healthcare improvement science. A consensus method approach was adopted with a two-stage Delphi process, expert panel and consensus group techniques. A total of 18 participants were involved in the expert panel and consensus group, and 153 answers were analysed as a part of the Delphi survey. Participants were researchers, educators and healthcare professionals from Scotland, Slovenia, Spain, Italy, England, Poland, and Romania. A high level of consensus was achieved for the broad definition in the 2nd Delphi iteration (86%). The final definition was agreed on by the consensus group: 'Healthcare improvement science is the generation of knowledge to cultivate change and deliver person-centred care that is safe, effective, efficient, equitable and timely. It improves patient outcomes, health system performance and population health.' The process of developing a consensus definition revealed different understandings of healthcare improvement science between the participants. Having a shared consensus definition of healthcare improvement science is an important step forward, bringing about a common understanding in order to advance the professional education and practice of healthcare improvement science.

The Causal Evaluation of Acute Recurrent and Chronic Pancreatitis in Children: Consensus From the INSPPIRE Group

PubMed Central

Gariepy, Cheryl E.; Heyman, Melvin B.; Lowe, Mark E.; Pohl, John F.; Werlin, Steven L.; Wilschanski, Michael; Barth, Bradley; Fishman, Douglas S.; Freedman, Steven D.; Giefer, Matthew J.; Gonska, Tanja; Himes, Ryan; Husain, Sohail Z.; Morinville, Veronique D.; Ooi, Chee Y.; Schwarzenberg, Sarah Jane; Troendle, David M.; Yen, Elizabeth; Uc, Aliye

2016-01-01

Acute recurrent pancreatitis (ARP) and chronic pancreatitis (CP) have been diagnosed in children at increasing rates over the past decade. However, as pediatric ARP and CP are still relatively rare conditions, little quality evidence is available on which to base the diagnosis and determination of etiology. Objectives: To review the current state of the literature regarding the etiology of these disorders and to developed a consensus among a panel of clinically active specialists caring for children with these disorders to help guide the diagnostic evaluation and identify areas most in need of future research. Methods: A systematic review of the literature was performed and scored for quality, then consensus statements developed and scored by each individual in the group for level of agreement and strength of the supporting data using a modified Delphi method. Scores were analyzed for the level of consensus achieved by the group. Results: The panel reached consensus on 27 statements covering the definitions of pediatric ARP and CP, evaluation for potential etiologies of these disorders, and long-term monitoring. Statements for which the group reached consensus to make no recommendation or could not reach consensus are discussed. Conclusion: This consensus helps define the minimal diagnostic evaluation and monitoring of children with ARP and CP. Even in areas in which we reached consensus, the quality of the evidence is weak, highlighting the need for further research. Improved understanding of the underlying cause will facilitate treatment development and targeting. PMID:27782962

Improving consensus contact prediction via server correlation reduction.

PubMed

Gao, Xin; Bu, Dongbo; Xu, Jinbo; Li, Ming

2009-05-06

Protein inter-residue contacts play a crucial role in the determination and prediction of protein structures. Previous studies on contact prediction indicate that although template-based consensus methods outperform sequence-based methods on targets with typical templates, such consensus methods perform poorly on new fold targets. However, we find out that even for new fold targets, the models generated by threading programs can contain many true contacts. The challenge is how to identify them. In this paper, we develop an integer linear programming model for consensus contact prediction. In contrast to the simple majority voting method assuming that all the individual servers are equally important and independent, the newly developed method evaluates their correlation by using maximum likelihood estimation and extracts independent latent servers from them by using principal component analysis. An integer linear programming method is then applied to assign a weight to each latent server to maximize the difference between true contacts and false ones. The proposed method is tested on the CASP7 data set. If the top L/5 predicted contacts are evaluated where L is the protein size, the average accuracy is 73%, which is much higher than that of any previously reported study. Moreover, if only the 15 new fold CASP7 targets are considered, our method achieves an average accuracy of 37%, which is much better than that of the majority voting method, SVM-LOMETS, SVM-SEQ, and SAM-T06. These methods demonstrate an average accuracy of 13.0%, 10.8%, 25.8% and 21.2%, respectively. Reducing server correlation and optimally combining independent latent servers show a significant improvement over the traditional consensus methods. This approach can hopefully provide a powerful tool for protein structure refinement and prediction use.

Relevance of Web Documents:Ghosts Consensus Method.

ERIC Educational Resources Information Center

Gorbunov, Andrey L.

2002-01-01

Discusses how to improve the quality of Internet search systems and introduces the Ghosts Consensus Method which is free from the drawbacks of digital democracy algorithms and is based on linear programming tasks. Highlights include vector space models; determining relevant documents; and enriching query terms. (LRW)

Neuromodulation of chronic headaches: position statement from the European Headache Federation

PubMed Central

2013-01-01

The medical treatment of patients with chronic primary headache syndromes (chronic migraine, chronic tension-type headache, chronic cluster headache, hemicrania continua) is challenging as serious side effects frequently complicate the course of medical treatment and some patients may be even medically intractable. When a definitive lack of responsiveness to conservative treatments is ascertained and medication overuse headache is excluded, neuromodulation options can be considered in selected cases. Here, the various invasive and non-invasive approaches, such as hypothalamic deep brain stimulation, occipital nerve stimulation, stimulation of sphenopalatine ganglion, cervical spinal cord stimulation, vagus nerve stimulation, transcranial direct current stimulation, repetitive transcranial magnetic stimulation, and transcutaneous electrical nerve stimulation are extensively published although proper RCT-based evidence is limited. The European Headache Federation herewith provides a consensus statement on the clinical use of neuromodulation in headache, based on theoretical background, clinical data, and side effect of each method. This international consensus further gives recommendations for future studies on these new approaches. In spite of a growing field of stimulation devices in headaches treatment, further controlled studies to validate, strengthen and disseminate the use of neurostimulation are clearly warranted. Consequently, until these data are available any neurostimulation device should only be used in patients with medically intractable syndromes from tertiary headache centers either as part of a valid study or have shown to be effective in such controlled studies with an acceptable side effect profile. PMID:24144382

Opinion Formation Models on a Gradient

PubMed Central

Gastner, Michael T.; Markou, Nikolitsa; Pruessner, Gunnar; Draief, Moez

2014-01-01

Statistical physicists have become interested in models of collective social behavior such as opinion formation, where individuals change their inherently preferred opinion if their friends disagree. Real preferences often depend on regional cultural differences, which we model here as a spatial gradient g in the initial opinion. The gradient does not only add reality to the model. It can also reveal that opinion clusters in two dimensions are typically in the standard (i.e., independent) percolation universality class, thus settling a recent controversy about a non-consensus model. However, using analytical and numerical tools, we also present a model where the width of the transition between opinions scales , not as in independent percolation, and the cluster size distribution is consistent with first-order percolation. PMID:25474528

Adaptive consensus of scale-free multi-agent system by randomly selecting links

NASA Astrophysics Data System (ADS)

Mou, Jinping; Ge, Huafeng

2016-06-01

This paper investigates an adaptive consensus problem for distributed scale-free multi-agent systems (SFMASs) by randomly selecting links, where the degree of each node follows a power-law distribution. The randomly selecting links are based on the assumption that every agent decides to select links among its neighbours according to the received data with a certain probability. Accordingly, a novel consensus protocol with the range of the received data is developed, and each node updates its state according to the protocol. By the iterative method and Cauchy inequality, the theoretical analysis shows that all errors among agents converge to zero, and in the meanwhile, several criteria of consensus are obtained. One numerical example shows the reliability of the proposed methods.

“One for all and all for one”: consensus-building within communities in rural India on their health microinsurance package

PubMed Central

Dror, David M; Panda, Pradeep; May, Christina; Majumdar, Atanu; Koren, Ruth

2014-01-01

Introduction This study deals with consensus by poor persons in the informal sector in rural India on the benefit-package of their community-based health insurance (CBHI). In this article we describe the process of involving rural poor in benefit-package design and assess the underlying reasons for choices they made and their ability to reach group consensus. Methods The benefit-package selection process entailed four steps: narrowing down the options by community representatives, plus three Choosing Healthplans All Together (CHAT) rounds conducted among female members of self-help groups. We use mixed-methods and four sources of data: baseline study, CHAT exercises, in-depth interviews, and evaluation questionnaires. We define consensus as a community resolution reached by discussion, considering all opinions, and to which everyone agrees. We use the coefficient of unalikeability to express consensus quantitatively (as variability of categorical variables) rather than just categorically (as a binomial Yes/No). Findings The coefficient of unalikeability decreased consistently over consecutive CHAT rounds, reaching zero (ie, 100% consensus) in two locations, and confirmed gradual adoption of consensus. Evaluation interviews revealed that the wish to be part of a consensus was dominant in all locations. The in-depth interviews indicated that people enjoyed the participatory deliberations, were satisfied with the selection, and that group decisions reflected a consensus rather than majority. Moreover, evidence suggests that pre-selectors and communities aimed to enhance the likelihood that many households would benefit from CBHI. Conclusion The voluntary and contributory CBHI relies on an engaging experience with others to validate perceived priorities of the target group. The strongest motive for choice was the wish to join a consensus (more than price or package-composition) and the intention that many members should benefit. The degree of consensus improved with iterative CHAT rounds. Harnessing group consensus requires catalytic intervention, as the process is not spontaneous. PMID:25120378

Molecular Control of Polyene Macrolide Biosynthesis

PubMed Central

Santos-Aberturas, Javier; Vicente, Cláudia M.; Guerra, Susana M.; Payero, Tamara D.; Martín, Juan F.; Aparicio, Jesús F.

2011-01-01

Control of polyene macrolide production in Streptomyces natalensis is mediated by the transcriptional activator PimM. This regulator, which combines an N-terminal PAS domain with a C-terminal helix-turn-helix motif, is highly conserved among polyene biosynthetic gene clusters. PimM, truncated forms of the protein without the PAS domain (PimMΔPAS), and forms containing just the DNA-binding domain (DBD) (PimMDBD) were overexpressed in Escherichia coli as GST-fused proteins. GST-PimM binds directly to eight promoters of the pimaricin cluster, as demonstrated by electrophoretic mobility shift assays. Assays with truncated forms of the protein revealed that the PAS domain does not mediate specificity or the distinct recognition of target genes, which rely on the DBD domain, but significantly reduces binding affinity up to 500-fold. Transcription start points were identified by 5′-rapid amplification of cDNA ends, and the binding regions of PimMDBD were investigated by DNase I protection studies. In all cases, binding took place covering the −35 hexamer box of each promoter, suggesting an interaction of PimM and RNA polymerase to cause transcription activation. Information content analysis of the 16 sequences protected in target promoters was used to deduce the structure of the PimM-binding site. This site displays dyad symmetry, spans 14 nucleotides, and adjusts to the consensus TVGGGAWWTCCCBA. Experimental validation of this binding site was performed by using synthetic DNA duplexes. Binding of PimM to the promoter region of one of the polyketide synthase genes from the Streptomyces nodosus amphotericin cluster containing the consensus binding site was also observed, thus proving the applicability of the findings reported here to other antifungal polyketides. PMID:21187288

Proposal of a Consensus Set of Hypervariable Mycobacterial Interspersed Repetitive-Unit–Variable-Number Tandem-Repeat Loci for Subtyping of Mycobacterium tuberculosis Beijing Isolates

PubMed Central

Allix-Béguec, Caroline; Wahl, Céline; Hanekom, Madeleine; Nikolayevskyy, Vladyslav; Drobniewski, Francis; Maeda, Shinji; Campos-Herrero, Isolina; Mokrousov, Igor; Niemann, Stefan; Kontsevaya, Irina; Rastogi, Nalin; Samper, Sofia; Sng, Li-Hwei; Warren, Robin M.

2014-01-01

Mycobacterium tuberculosis Beijing strains represent targets of special importance for molecular surveillance of tuberculosis (TB), especially because they are associated with spread of multidrug resistance in some world regions. Standard 24-locus mycobacterial interspersed repetitive-unit–variable-number tandem-repeat (MIRU-VNTR) typing lacks resolution power for accurately discriminating closely related clones that often compose Beijing strain populations. Therefore, we evaluated a set of 7 additional, hypervariable MIRU-VNTR loci for better resolution and tracing of such strains, using a collection of 535 Beijing isolates from six world regions where these strains are known to be prevalent. The typeability and interlaboratory reproducibility of these hypervariable loci were lower than those of the 24 standard loci. Three loci (2163a, 3155, and 3336) were excluded because of their redundant variability and/or more frequent noninterpretable results compared to the 4 other markers. The use of the remaining 4-locus set (1982, 3232, 3820, and 4120) increased the number of types by 52% (from 223 to 340) and reduced the clustering rate from 58.3 to 36.6%, when combined with the use of the standard 24-locus set. Known major clonal complexes/24-locus-based clusters were all subdivided, although the degree of subdivision varied depending on the complex. Only five single-locus variations were detected among the hypervariable loci of an additional panel of 92 isolates, representing 15 years of clonal spread of a single Beijing strain in a geographically restricted setting. On this calibrated basis, we propose this 4-locus set as a consensus for subtyping Beijing clonal complexes and clusters, after standard typing. PMID:24172154

Proposal of a consensus set of hypervariable mycobacterial interspersed repetitive-unit-variable-number tandem-repeat loci for subtyping of Mycobacterium tuberculosis Beijing isolates.

PubMed

Allix-Béguec, Caroline; Wahl, Céline; Hanekom, Madeleine; Nikolayevskyy, Vladyslav; Drobniewski, Francis; Maeda, Shinji; Campos-Herrero, Isolina; Mokrousov, Igor; Niemann, Stefan; Kontsevaya, Irina; Rastogi, Nalin; Samper, Sofia; Sng, Li-Hwei; Warren, Robin M; Supply, Philip

2014-01-01

Mycobacterium tuberculosis Beijing strains represent targets of special importance for molecular surveillance of tuberculosis (TB), especially because they are associated with spread of multidrug resistance in some world regions. Standard 24-locus mycobacterial interspersed repetitive-unit-variable-number tandem-repeat (MIRU-VNTR) typing lacks resolution power for accurately discriminating closely related clones that often compose Beijing strain populations. Therefore, we evaluated a set of 7 additional, hypervariable MIRU-VNTR loci for better resolution and tracing of such strains, using a collection of 535 Beijing isolates from six world regions where these strains are known to be prevalent. The typeability and interlaboratory reproducibility of these hypervariable loci were lower than those of the 24 standard loci. Three loci (2163a, 3155, and 3336) were excluded because of their redundant variability and/or more frequent noninterpretable results compared to the 4 other markers. The use of the remaining 4-locus set (1982, 3232, 3820, and 4120) increased the number of types by 52% (from 223 to 340) and reduced the clustering rate from 58.3 to 36.6%, when combined with the use of the standard 24-locus set. Known major clonal complexes/24-locus-based clusters were all subdivided, although the degree of subdivision varied depending on the complex. Only five single-locus variations were detected among the hypervariable loci of an additional panel of 92 isolates, representing 15 years of clonal spread of a single Beijing strain in a geographically restricted setting. On this calibrated basis, we propose this 4-locus set as a consensus for subtyping Beijing clonal complexes and clusters, after standard typing.

The Delphi Method Online: Medical Expert Consensus Via the Internet

PubMed Central

Cam, Kenneth M.; McKnight, Patrick E.; Doctor, Jason N.

2002-01-01

Delphi is an expert consensus method. The theory behind the Delphi method is that the interaction of experts may lead to a reduction in individual bias. We have developed software that carries out all aspects of the Delphi method via the Internet. The Delphi method online consists of three components: 1) authorship, 2) interactive polling, and 3) reporting/results. We hope that researchers use this tool in future medical expert systems.

Principles for a Successful Computerized Physician Order Entry Implementation

PubMed Central

Ash, Joan S.; Fournier, Lara; Stavri, P. Zoë; Dykstra, Richard

2003-01-01

To identify success factors for implementing computerized physician order entry (CPOE), our research team took both a top-down and bottom-up approach and reconciled the results to develop twelve overarching principles to guide implementation. A consensus panel of experts produced ten Considerations with nearly 150 sub-considerations, and a three year project using qualitative methods at multiple successful sites for a grounded theory approach yielded ten general themes with 24 sub-themes. After reconciliation using a meta-matrix approach, twelve Principles, which cluster into groups forming the mnemonic CPOE emerged. Computer technology principles include: temporal concerns; technology and meeting information needs; multidimensional integration; and costs. Personal principles are: value to users and tradeoffs; essential people; and training and support. Organizational principles include: foundational underpinnings; collaborative project management; terms, concepts and connotations; and improvement through evaluation and learning. Finally, Environmental issues include the motivation and context for implementing such systems. PMID:14728129

"Heroes" and "villains" of world history across cultures.

PubMed

Hanke, Katja; Liu, James H; Sibley, Chris G; Paez, Dario; Gaines, Stanley O; Moloney, Gail; Leong, Chan-Hoong; Wagner, Wolfgang; Licata, Laurent; Klein, Olivier; Garber, Ilya; Böhm, Gisela; Hilton, Denis J; Valchev, Velichko; Khan, Sammyh S; Cabecinhas, Rosa

2015-01-01

Emergent properties of global political culture were examined using data from the World History Survey (WHS) involving 6,902 university students in 37 countries evaluating 40 figures from world history. Multidimensional scaling and factor analysis techniques found only limited forms of universality in evaluations across Western, Catholic/Orthodox, Muslim, and Asian country clusters. The highest consensus across cultures involved scientific innovators, with Einstein having the most positive evaluation overall. Peaceful humanitarians like Mother Theresa and Gandhi followed. There was much less cross-cultural consistency in the evaluation of negative figures, led by Hitler, Osama bin Laden, and Saddam Hussein. After more traditional empirical methods (e.g., factor analysis) failed to identify meaningful cross-cultural patterns, Latent Profile Analysis (LPA) was used to identify four global representational profiles: Secular and Religious Idealists were overwhelmingly prevalent in Christian countries, and Political Realists were common in Muslim and Asian countries. We discuss possible consequences and interpretations of these different representational profiles.

Management of intrathecal baclofen therapy for severe acquired brain injury: consensus and recommendations for good clinical practice.

PubMed

De Tanti, Antonio; Scarponi, Federico; Bertoni, Michele; Gasperini, Giulio; Lanzillo, Bernardo; Molteni, Franco; Posteraro, Federico; Vitale, Dino Francesco; Zanpolini, Mauro

2017-08-01

Although widespread in the treatment of generalised spasticity due to severe acquired brain injury, clinical use of intrathecal baclofen administered through an implanted catheter is not yet supported by full scientific evidence. The aim of the study is to provide recommendations for good clinical practice regarding intrathecal baclofen therapy. We used a modified RAND Delphi method to develop consensus-based medical guidelines, involving clinicians who use intrathecal baclofen therapy throughout Italy. The clinicians were asked 38 questions grouped in six areas (patient selection, contraindications for implant, tests prior to implant, method of implant and management of therapy, efficacy evaluation and goal setting, and management of complications). To establish consensus, 75% agreement was required in answers to every question. Consensus was reached on the second round of the Delphi process on 27/38 questions (71%), specifically those regarding identification of objectives, efficacy evaluation, and method of implant and management of therapy, whereas management of complications and contraindications for implant remained critical areas. Despite the limits of our method, a set of recommendations was drawn up for clinical practice in this sector. The study also revealed residual critical areas and indicated future lines of research necessary to reach evidence-based consensus.

The Efficacy of Consensus Tree Methods for Summarizing Phylogenetic Relationships from a Posterior Sample of Trees Estimated from Morphological Data.

PubMed

O'Reilly, Joseph E; Donoghue, Philip C J

2018-03-01

Consensus trees are required to summarize trees obtained through MCMC sampling of a posterior distribution, providing an overview of the distribution of estimated parameters such as topology, branch lengths, and divergence times. Numerous consensus tree construction methods are available, each presenting a different interpretation of the tree sample. The rise of morphological clock and sampled-ancestor methods of divergence time estimation, in which times and topology are coestimated, has increased the popularity of the maximum clade credibility (MCC) consensus tree method. The MCC method assumes that the sampled, fully resolved topology with the highest clade credibility is an adequate summary of the most probable clades, with parameter estimates from compatible sampled trees used to obtain the marginal distributions of parameters such as clade ages and branch lengths. Using both simulated and empirical data, we demonstrate that MCC trees, and trees constructed using the similar maximum a posteriori (MAP) method, often include poorly supported and incorrect clades when summarizing diffuse posterior samples of trees. We demonstrate that the paucity of information in morphological data sets contributes to the inability of MCC and MAP trees to accurately summarise of the posterior distribution. Conversely, majority-rule consensus (MRC) trees represent a lower proportion of incorrect nodes when summarizing the same posterior samples of trees. Thus, we advocate the use of MRC trees, in place of MCC or MAP trees, in attempts to summarize the results of Bayesian phylogenetic analyses of morphological data.

The Efficacy of Consensus Tree Methods for Summarizing Phylogenetic Relationships from a Posterior Sample of Trees Estimated from Morphological Data

PubMed Central

O’Reilly, Joseph E; Donoghue, Philip C J

2018-01-01

Abstract Consensus trees are required to summarize trees obtained through MCMC sampling of a posterior distribution, providing an overview of the distribution of estimated parameters such as topology, branch lengths, and divergence times. Numerous consensus tree construction methods are available, each presenting a different interpretation of the tree sample. The rise of morphological clock and sampled-ancestor methods of divergence time estimation, in which times and topology are coestimated, has increased the popularity of the maximum clade credibility (MCC) consensus tree method. The MCC method assumes that the sampled, fully resolved topology with the highest clade credibility is an adequate summary of the most probable clades, with parameter estimates from compatible sampled trees used to obtain the marginal distributions of parameters such as clade ages and branch lengths. Using both simulated and empirical data, we demonstrate that MCC trees, and trees constructed using the similar maximum a posteriori (MAP) method, often include poorly supported and incorrect clades when summarizing diffuse posterior samples of trees. We demonstrate that the paucity of information in morphological data sets contributes to the inability of MCC and MAP trees to accurately summarise of the posterior distribution. Conversely, majority-rule consensus (MRC) trees represent a lower proportion of incorrect nodes when summarizing the same posterior samples of trees. Thus, we advocate the use of MRC trees, in place of MCC or MAP trees, in attempts to summarize the results of Bayesian phylogenetic analyses of morphological data. PMID:29106675

Observer-based output consensus of a class of heterogeneous multi-agent systems with unmatched disturbances

NASA Astrophysics Data System (ADS)

Zhang, Jiancheng; Zhu, Fanglai

2018-03-01

In this paper, the output consensus of a class of linear heterogeneous multi-agent systems with unmatched disturbances is considered. Firstly, based on the relative output information among neighboring agents, we propose an asymptotic sliding-mode based consensus control scheme, under which, the output consensus error can converge to zero by removing the disturbances from output channels. Secondly, in order to reach the consensus goal, we design a novel high-order unknown input observer for each agent. It can estimate not only each agent's states and disturbances, but also the disturbances' high-order derivatives, which are required in the control scheme aforementioned above. The observer-based consensus control laws and the convergence analysis of the consensus error dynamics are given. Finally, a simulation example is provided to verify the validity of our methods.

1 CFR 21.21 - General requirements: References.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 1 General Provisions 1 2010-01-01 2010-01-01 false General requirements: References. 21.21 Section... to test methods or consensus standards produced by a Federal agency that have replaced or preempted private or voluntary test methods or consensus standards in a subject matter area. (5) The reference is to...

Distributed k-Means Algorithm and Fuzzy c-Means Algorithm for Sensor Networks Based on Multiagent Consensus Theory.

PubMed

Qin, Jiahu; Fu, Weiming; Gao, Huijun; Zheng, Wei Xing

2016-03-03

This paper is concerned with developing a distributed k-means algorithm and a distributed fuzzy c-means algorithm for wireless sensor networks (WSNs) where each node is equipped with sensors. The underlying topology of the WSN is supposed to be strongly connected. The consensus algorithm in multiagent consensus theory is utilized to exchange the measurement information of the sensors in WSN. To obtain a faster convergence speed as well as a higher possibility of having the global optimum, a distributed k-means++ algorithm is first proposed to find the initial centroids before executing the distributed k-means algorithm and the distributed fuzzy c-means algorithm. The proposed distributed k-means algorithm is capable of partitioning the data observed by the nodes into measure-dependent groups which have small in-group and large out-group distances, while the proposed distributed fuzzy c-means algorithm is capable of partitioning the data observed by the nodes into different measure-dependent groups with degrees of membership values ranging from 0 to 1. Simulation results show that the proposed distributed algorithms can achieve almost the same results as that given by the centralized clustering algorithms.

Defining consensus: a systematic review recommends methodologic criteria for reporting of Delphi studies.

PubMed

Diamond, Ivan R; Grant, Robert C; Feldman, Brian M; Pencharz, Paul B; Ling, Simon C; Moore, Aideen M; Wales, Paul W

2014-04-01

To investigate how consensus is operationalized in Delphi studies and to explore the role of consensus in determining the results of these studies. Systematic review of a random sample of 100 English language Delphi studies, from two large multidisciplinary databases [ISI Web of Science (Thompson Reuters, New York, NY) and Scopus (Elsevier, Amsterdam, NL)], published between 2000 and 2009. About 98 of the Delphi studies purported to assess consensus, although a definition for consensus was only provided in 72 of the studies (64 a priori). The most common definition for consensus was percent agreement (25 studies), with 75% being the median threshold to define consensus. Although the authors concluded in 86 of the studies that consensus was achieved, consensus was only specified a priori (with a threshold value) in 42 of these studies. Achievement of consensus was related to the decision to stop the Delphi study in only 23 studies, with 70 studies terminating after a specified number of rounds. Although consensus generally is felt to be of primary importance to the Delphi process, definitions of consensus vary widely and are poorly reported. Improved criteria for reporting of methods of Delphi studies are required. Copyright © 2014 Elsevier Inc. All rights reserved.

Report on ISCTM Consensus Meeting on Clinical Assessment of Response to Treatment of Cognitive Impairment in Schizophrenia

PubMed Central

Keefe, Richard S. E.; Haig, George M.; Marder, Stephen R.; Harvey, Philip D.; Dunayevich, Eduardo; Medalia, Alice; Davidson, Michael; Lombardo, Ilise; Bowie, Christopher R.; Buchanan, Robert W.; Bugarski-Kirola, Dragana; Carpenter, William T.; Csernansky, John T.; Dago, Pedro L.; Durand, Dante M.; Frese, Frederick J.; Goff, Donald C.; Gold, James M.; Hooker, Christine I.; Kopelowicz, Alex; Loebel, Antony; McGurk, Susan R.; Opler, Lewis A.; Pinkham, Amy E.; Stern, Robert G.

2016-01-01

If treatments for cognitive impairment are to be utilized successfully, clinicians must be able to determine whether they are effective and which patients should receive them. In order to develop consensus on these issues, the International Society for CNS Clinical Trials and Methodology (ISCTM) held a meeting of experts on March 20, 2014, in Washington, DC. Consensus was reached on several important issues. Cognitive impairment and functional disability were viewed as equally important treatment targets. The group supported the notion that sufficient data are not available to exclude patients from available treatments on the basis of age, severity of cognitive impairment, severity of positive symptoms, or the potential to benefit functionally from treatment. The group reached consensus that cognitive remediation is likely to provide substantial benefits in combination with procognitive medications, although a substantial minority believed that medications can be administered without nonpharmacological therapy. There was little consensus on the best methods for assessing cognitive change in clinical practice. Some participants supported the view that performance-based measures are essential for measurement of cognitive change; others pointed to their cost and time requirements as evidence of impracticality. Interview-based measures of cognitive and functional change were viewed as more practical, but lacking validity without informant involvement or frequent contact from clinicians. The lack of consensus on assessment methods was viewed as attributable to differences in experience and education among key stakeholders and significant gaps in available empirical data. Research on the reliability, validity, sensitivity, and practicality of competing methods will facilitate consensus. PMID:26362273

Global finite-time attitude consensus tracking control for a group of rigid spacecraft

NASA Astrophysics Data System (ADS)

Li, Penghua

2017-10-01

The problem of finite-time attitude consensus for multiple rigid spacecraft with a leader-follower architecture is investigated in this paper. To achieve the finite-time attitude consensus, at the first step, a distributed finite-time convergent observer is proposed for each follower to estimate the leader's attitude in a finite time. Then based on the terminal sliding mode control method, a new finite-time attitude tracking controller is designed such that the leader's attitude can be tracked in a finite time. Finally, a finite-time observer-based distributed control strategy is proposed. It is shown that the attitude consensus can be achieved in a finite time under the proposed controller. Simulation results are given to show the effectiveness of the proposed method.

The role of recombination in the origin and evolution of Alu subfamilies.

PubMed

Teixeira-Silva, Ana; Silva, Raquel M; Carneiro, João; Amorim, António; Azevedo, Luísa

2013-01-01

Alus are the most abundant and successful short interspersed nuclear elements found in primate genomes. In humans, they represent about 10% of the genome, although few are retrotransposition-competent and are clustered into subfamilies according to the source gene from which they evolved. Recombination between them can lead to genomic rearrangements of clinical and evolutionary significance. In this study, we have addressed the role of recombination in the origin of chimeric Alu source genes by the analysis of all known consensus sequences of human Alus. From the allelic diversity of Alu consensus sequences, validated in extant elements resulting from whole genome searches, distinct events of recombination were detected in the origin of particular subfamilies of AluS and AluY source genes. These results demonstrate that at least two subfamilies are likely to have emerged from ectopic Alu-Alu recombination, which stimulates further research regarding the potential of chimeric active Alus to punctuate the genome.

Characterization of an Avipoxvirus From a Bald Eagle ( Haliaeetus leucocephalus ) Using Novel Consensus PCR Protocols for the rpo147 and DNA-Dependent DNA Polymerase Genes.

PubMed

Stephen, Alexa A; Leone, Angelique M; Toplon, David E; Archer, Linda L; Wellehan, James F X

2016-12-01

A juvenile female bald eagle ( Haliaeetus leucocephalus ) was presented with emaciation and proliferative periocular lesions. The eagle did not respond to supportive therapy and was euthanatized. Histopathologic examination of the skin lesions revealed plaques of marked epidermal hyperplasia parakeratosis, marked acanthosis and spongiosis, and eosinophilic intracytoplasmic inclusion bodies. Novel polymerase chain reaction (PCR) assays were done to amplify and sequence DNA polymerase and rpo147 genes. The 4b gene was also analyzed by a previously developed assay. Bayesian and maximum likelihood phylogenetic analyses of the obtained sequences found it to be poxvirus of the genus Avipoxvirus and clustered with other raptor isolates. Better phylogenetic resolution was found in rpo147 rather than the commonly used DNA polymerase. The novel consensus rpo147 PCR assay will create more accurate phylogenic trees and allow better insight into poxvirus history.

Identification of consensus biomarkers for predicting non-genotoxic hepatocarcinogens

PubMed Central

Huang, Shan-Han; Tung, Chun-Wei

2017-01-01

The assessment of non-genotoxic hepatocarcinogens (NGHCs) is currently relying on two-year rodent bioassays. Toxicogenomics biomarkers provide a potential alternative method for the prioritization of NGHCs that could be useful for risk assessment. However, previous studies using inconsistently classified chemicals as the training set and a single microarray dataset concluded no consensus biomarkers. In this study, 4 consensus biomarkers of A2m, Ca3, Cxcl1, and Cyp8b1 were identified from four large-scale microarray datasets of the one-day single maximum tolerated dose and a large set of chemicals without inconsistent classifications. Machine learning techniques were subsequently applied to develop prediction models for NGHCs. The final bagging decision tree models were constructed with an average AUC performance of 0.803 for an independent test. A set of 16 chemicals with controversial classifications were reclassified according to the consensus biomarkers. The developed prediction models and identified consensus biomarkers are expected to be potential alternative methods for prioritization of NGHCs for further experimental validation. PMID:28117354

Systemic risk and spatiotemporal dynamics of the US housing market.

PubMed

Meng, Hao; Xie, Wen-Jie; Jiang, Zhi-Qiang; Podobnik, Boris; Zhou, Wei-Xing; Stanley, H Eugene

2014-01-13

Housing markets play a crucial role in economies and the collapse of a real-estate bubble usually destabilizes the financial system and causes economic recessions. We investigate the systemic risk and spatiotemporal dynamics of the US housing market (1975-2011) at the state level based on the Random Matrix Theory (RMT). We identify richer economic information in the largest eigenvalues deviating from RMT predictions for the housing market than for stock markets and find that the component signs of the eigenvectors contain either geographical information or the extent of differences in house price growth rates or both. By looking at the evolution of different quantities such as eigenvalues and eigenvectors, we find that the US housing market experienced six different regimes, which is consistent with the evolution of state clusters identified by the box clustering algorithm and the consensus clustering algorithm on the partial correlation matrices. We find that dramatic increases in the systemic risk are usually accompanied by regime shifts, which provide a means of early detection of housing bubbles.

Spectral Archives: Extending Spectral Libraries to Analyze both Identified and Unidentified Spectra

PubMed Central

Frank, Ari M.; Monroe, Matthew E.; Shah, Anuj R.; Carver, Jeremy J.; Bandeira, Nuno F.; Moore, Ronald J.; Anderson, Gordon A.; Smith, Richard D.; Pevzner, Pavel A.

2011-01-01

MS/MS experiments generate multiple, nearly identical spectra of the same peptide in various laboratories, but proteomics researchers typically do not leverage the unidentified spectra produced in other labs to decode spectra generated in their own labs. We propose a spectral archives approach that clusters MS/MS datasets, representing similar spectra by a single consensus spectrum. Spectral archives extend spectral libraries by analyzing both identified and unidentified spectra in the same way and maintaining information about spectra of peptides shared across species and conditions. Thus archives offer both traditional library spectrum similarity-based search capabilities along with novel ways to analyze the data. By developing a clustering tool, MS-Cluster, we generated a spectral archive from ~1.18 billion spectra that greatly exceeds the size of existing spectral repositories. We advocate that publicly available data should be organized into spectral archives, rather than be analyzed as disparate datasets, as is mostly the case today. PMID:21572408

Image Registration Algorithm Based on Parallax Constraint and Clustering Analysis

NASA Astrophysics Data System (ADS)

Wang, Zhe; Dong, Min; Mu, Xiaomin; Wang, Song

2018-01-01

To resolve the problem of slow computation speed and low matching accuracy in image registration, a new image registration algorithm based on parallax constraint and clustering analysis is proposed. Firstly, Harris corner detection algorithm is used to extract the feature points of two images. Secondly, use Normalized Cross Correlation (NCC) function to perform the approximate matching of feature points, and the initial feature pair is obtained. Then, according to the parallax constraint condition, the initial feature pair is preprocessed by K-means clustering algorithm, which is used to remove the feature point pairs with obvious errors in the approximate matching process. Finally, adopt Random Sample Consensus (RANSAC) algorithm to optimize the feature points to obtain the final feature point matching result, and the fast and accurate image registration is realized. The experimental results show that the image registration algorithm proposed in this paper can improve the accuracy of the image matching while ensuring the real-time performance of the algorithm.

Application of constrained k-means clustering in ground motion simulation validation

NASA Astrophysics Data System (ADS)

Khoshnevis, N.; Taborda, R.

2017-12-01

The validation of ground motion synthetics has received increased attention over the last few years due to the advances in physics-based deterministic and hybrid simulation methods. Unlike for low frequency simulations (f ≤ 0.5 Hz), for which it has become reasonable to expect a good match between synthetics and data, in the case of high-frequency simulations (f ≥ 1 Hz) it is not possible to match results on a wiggle-by-wiggle basis. This is mostly due to the various complexities and uncertainties involved in earthquake ground motion modeling. Therefore, in order to compare synthetics with data we turn to different time series metrics, which are used as a means to characterize how the synthetics match the data on qualitative and statistical sense. In general, these metrics provide GOF scores that measure the level of similarity in the time and frequency domains. It is common for these scores to be scaled from 0 to 10, with 10 representing a perfect match. Although using individual metrics for particular applications is considered more adequate, there is no consensus or a unified method to classify the comparison between a set of synthetic and recorded seismograms when the various metrics offer different scores. We study the relationship among these metrics through a constrained k-means clustering approach. We define 4 hypothetical stations with scores 3, 5, 7, and 9 for all metrics. We put these stations in the category of cannot-link constraints. We generate the dataset through the validation of the results from a deterministic (physics-based) ground motion simulation for a moderate magnitude earthquake in the greater Los Angeles basin using three velocity models. The maximum frequency of the simulation is 4 Hz. The dataset involves over 300 stations and 11 metrics, or features, as they are understood in the clustering process, where the metrics form a multi-dimensional space. We address the high-dimensional feature effects with a subspace-clustering analysis, generate a final labeled dataset of stations, and discuss the within-class statistical characteristics of each metric. Labeling these stations is the first step towards developing a unified metric to evaluate ground motion simulations in an application-independent manner.

Simulation-Based Evaluation of Hybridization Network Reconstruction Methods in the Presence of Incomplete Lineage Sorting

PubMed Central

Kamneva, Olga K; Rosenberg, Noah A

2017-01-01

Hybridization events generate reticulate species relationships, giving rise to species networks rather than species trees. We report a comparative study of consensus, maximum parsimony, and maximum likelihood methods of species network reconstruction using gene trees simulated assuming a known species history. We evaluate the role of the divergence time between species involved in a hybridization event, the relative contributions of the hybridizing species, and the error in gene tree estimation. When gene tree discordance is mostly due to hybridization and not due to incomplete lineage sorting (ILS), most of the methods can detect even highly skewed hybridization events between highly divergent species. For recent divergences between hybridizing species, when the influence of ILS is sufficiently high, likelihood methods outperform parsimony and consensus methods, which erroneously identify extra hybridizations. The more sophisticated likelihood methods, however, are affected by gene tree errors to a greater extent than are consensus and parsimony. PMID:28469378

Effect of denoising on supervised lung parenchymal clusters

NASA Astrophysics Data System (ADS)

Jayamani, Padmapriya; Raghunath, Sushravya; Rajagopalan, Srinivasan; Karwoski, Ronald A.; Bartholmai, Brian J.; Robb, Richard A.

2012-03-01

Denoising is a critical preconditioning step for quantitative analysis of medical images. Despite promises for more consistent diagnosis, denoising techniques are seldom explored in clinical settings. While this may be attributed to the esoteric nature of the parameter sensitve algorithms, lack of quantitative measures on their ecacy to enhance the clinical decision making is a primary cause of physician apathy. This paper addresses this issue by exploring the eect of denoising on the integrity of supervised lung parenchymal clusters. Multiple Volumes of Interests (VOIs) were selected across multiple high resolution CT scans to represent samples of dierent patterns (normal, emphysema, ground glass, honey combing and reticular). The VOIs were labeled through consensus of four radiologists. The original datasets were ltered by multiple denoising techniques (median ltering, anisotropic diusion, bilateral ltering and non-local means) and the corresponding ltered VOIs were extracted. Plurality of cluster indices based on multiple histogram-based pair-wise similarity measures were used to assess the quality of supervised clusters in the original and ltered space. The resultant rank orders were analyzed using the Borda criteria to nd the denoising-similarity measure combination that has the best cluster quality. Our exhaustive analyis reveals (a) for a number of similarity measures, the cluster quality is inferior in the ltered space; and (b) for measures that benet from denoising, a simple median ltering outperforms non-local means and bilateral ltering. Our study suggests the need to judiciously choose, if required, a denoising technique that does not deteriorate the integrity of supervised clusters.

Molecular identification and characterization of clustered regularly interspaced short palindromic repeats (CRISPRs) in a urease-positive thermophilic Campylobacter sp. (UPTC).

PubMed

Tasaki, E; Hirayama, J; Tazumi, A; Hayashi, K; Hara, Y; Ueno, H; Moore, J E; Millar, B C; Matsuda, M

2012-02-01

Novel clustered regularly-interspaced short palindromic repeats (CRISPRs) locus [7,500 base pairs (bp) in length] occurred in the urease-positive thermophilic Campylobacter (UPTC) Japanese isolate, CF89-12. The 7,500 bp gene loci consisted of the 5'-methylaminomethyl-2-thiouridylate methyltransferase gene, putative (P) CRISPR associated (p-Cas), putative open reading frames, Cas1 and Cas2, leader sequence region (146 bp), 12 CRISPRs consensus sequence repeats (each 36 bp) separated by a non-repetitive unique spacer region of similar length (26-31 bp) and the phosphatidyl glycerophosphatase A gene. When the CRISPRs loci in the UPTC CF89-12 and five C. jejuni isolates were compared with one another, these six isolates contained p-Cas, Cas1 and Cas2 within the loci. Four to 12 CRISPRs consensus sequence repeats separated by a non-repetitive unique spacer region occurred in six isolates and the nucleotide sequences of those repeats gave approximately 92-100% similarity with each other. However, no sequence similarity occurred in the unique spacer regions among these isolates. The putative σ(70) transcriptional promoter and the hypothetical ρ-independent terminator structures for the CRISPRs and Cas were detected. No in vivo transcription of p-Cas, Cas1 and Cas2 was confirmed in the UPTC cells.

Post-transplant Pneumocystis jirovecii pneumonia--a re-emerged public health problem?

PubMed

Chapman, Jeremy R; Marriott, Deborrah J; Chen, Sharon C-A; MacDonald, Peter S

2013-08-01

Pneumocystis jirovecii is a unicellular organism that in individuals with impaired immunity may cause pneumonia that can progress from minor illness to severe inflammatory pneumonia (PCP) with respiratory failure and death. Despite antimicrobial prophylaxis, which has reduced the incidence of PCP, clusters of late infections have been reported among kidney transplant recipients worldwide. A nosocomial PCP cluster was first recognized in 2010 at a Sydney hospital, but PCP clusters have since occurred in almost half of the renal transplant units on the eastern Australian seaboard, refocussing attention on optimal prophylaxis regimens and the likelihood of patient-to-patient transmission. A consensus meeting was conducted to derive the lessons from this experience for responding to PCP outbreaks. These included: (1) acting quickly--clusters of PCP in kidney transplant recipients with patient-to-patient transmission required transplant programs to act quickly to institute prophylactic and treatment measures; (2) instituting universal prophylaxis for all patients seen in the affected unit; (3) reducing patient-to-patient transmission via airborne droplets in the outpatient waiting areas; (4) examining the P. jirovecii genotypes. The meeting also considered recommendations for the duration of prophylaxis following de novo transplant and, for the individuals in whom long term prophylaxis is required, separating units with and without clusters of PCP.

Stability of formation control using a consensus protocol under directed communications with two time delays and delay scheduling

NASA Astrophysics Data System (ADS)

Cepeda-Gomez, Rudy; Olgac, Nejat

2016-01-01

We consider a linear algorithm to achieve formation control in a group of agents which are driven by second-order dynamics and affected by two rationally independent delays. One of the delays is in the position and the other in the velocity information channels. These delays are taken as constant and uniform throughout the system. The communication topology is assumed to be directed and fixed. The formation is attained by adding a supplementary control term to the stabilising consensus protocol. In preparation for the formation control logic, we first study the stability of the consensus, using the recent cluster treatment of characteristic roots (CTCR) paradigm. This effort results in a unique depiction of the non-conservative stability boundaries in the domain of the delays. However, CTCR requires the knowledge of the potential stability switching loci exhaustively within this domain. The creation of these loci is done in a new surrogate coordinate system, called the 'spectral delay space (SDS)'. The relative stability is also investigated, which has to do with the speed of reaching consensus. This step leads to a paradoxical control design concept, called the 'delay scheduling', which highlights the fact that the group behaviour may be enhanced by increasing the delays. These steps lead to a control strategy to establish a desired group formation that guarantees spacing among the agents. Example case studies are presented to validate the underlying analytical derivations.

How to use the nominal group and Delphi techniques.

PubMed

McMillan, Sara S; King, Michelle; Tully, Mary P

2016-06-01

Introduction The Nominal Group Technique (NGT) and Delphi Technique are consensus methods used in research that is directed at problem-solving, idea-generation, or determining priorities. While consensus methods are commonly used in health services literature, few studies in pharmacy practice use these methods. This paper provides an overview of the NGT and Delphi technique, including the steps involved and the types of research questions best suited to each method, with examples from the pharmacy literature. Methodology The NGT entails face-to-face discussion in small groups, and provides a prompt result for researchers. The classic NGT involves four key stages: silent generation, round robin, clarification and voting (ranking). Variations have occurred in relation to generating ideas, and how 'consensus' is obtained from participants. The Delphi technique uses a multistage self-completed questionnaire with individual feedback, to determine consensus from a larger group of 'experts.' Questionnaires have been mailed, or more recently, e-mailed to participants. When to use The NGT has been used to explore consumer and stakeholder views, while the Delphi technique is commonly used to develop guidelines with health professionals. Method choice is influenced by various factors, including the research question, the perception of consensus required, and associated practicalities such as time and geography. Limitations The NGT requires participants to personally attend a meeting. This may prove difficult to organise and geography may limit attendance. The Delphi technique can take weeks or months to conclude, especially if multiple rounds are required, and may be complex for lay people to complete.

On Federated and Proof Of Validation Based Consensus Algorithms In Blockchain

NASA Astrophysics Data System (ADS)

Ambili, K. N.; Sindhu, M.; Sethumadhavan, M.

2017-08-01

Almost all real world activities have been digitized and there are various client server architecture based systems in place to handle them. These are all based on trust on third parties. There is an active attempt to successfully implement blockchain based systems which ensures that the IT systems are immutable, double spending is avoided and cryptographic strength is provided to them. A successful implementation of blockchain as backbone of existing information technology systems is bound to eliminate various types of fraud and ensure quicker delivery of the item on trade. To adapt IT systems to blockchain architecture, an efficient consensus algorithm need to be designed. Blockchain based on proof of work first came up as the backbone of cryptocurrency. After this, several other methods with variety of interesting features have come up. In this paper, we conduct a survey on existing attempts to achieve consensus in block chain. A federated consensus method and a proof of validation method are being compared.

Low template STR typing: effect of replicate number and consensus method on genotyping reliability and DNA database search results.

PubMed

Benschop, Corina C G; van der Beek, Cornelis P; Meiland, Hugo C; van Gorp, Ankie G M; Westen, Antoinette A; Sijen, Titia

2011-08-01

To analyze DNA samples with very low DNA concentrations, various methods have been developed that sensitize short tandem repeat (STR) typing. Sensitized DNA typing is accompanied by stochastic amplification effects, such as allele drop-outs and drop-ins. Therefore low template (LT) DNA profiles are interpreted with care. One can either try to infer the genotype by a consensus method that uses alleles confirmed in replicate analyses, or one can use a statistical model to evaluate the strength of the evidence in a direct comparison with a known DNA profile. In this study we focused on the first strategy and we show that the procedure by which the consensus profile is assembled will affect genotyping reliability. In order to gain insight in the roles of replicate number and requested level of reproducibility, we generated six independent amplifications of samples of known donors. The LT methods included both increased cycling and enhanced capillary electrophoresis (CE) injection [1]. Consensus profiles were assembled from two to six of the replications using four methods: composite (include all alleles), n-1 (include alleles detected in all but one replicate), n/2 (include alleles detected in at least half of the replicates) and 2× (include alleles detected twice). We compared the consensus DNA profiles with the DNA profile of the known donor, studied the stochastic amplification effects and examined the effect of the consensus procedure on DNA database search results. From all these analyses we conclude that the accuracy of LT DNA typing and the efficiency of database searching improve when the number of replicates is increased and the consensus method is n/2. The most functional number of replicates within this n/2 method is four (although a replicate number of three suffices for samples showing >25% of the alleles in standard STR typing). This approach was also the optimal strategy for the analysis of 2-person mixtures, although modified search strategies may be needed to retrieve the minor component in database searches. From the database searches follows the recommendation to specifically mark LT DNA profiles when entering them into the DNA database. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

A psychophysical imaging method evidencing auditory cue extraction during speech perception: a group analysis of auditory classification images.

PubMed

Varnet, Léo; Knoblauch, Kenneth; Serniclaes, Willy; Meunier, Fanny; Hoen, Michel

2015-01-01

Although there is a large consensus regarding the involvement of specific acoustic cues in speech perception, the precise mechanisms underlying the transformation from continuous acoustical properties into discrete perceptual units remains undetermined. This gap in knowledge is partially due to the lack of a turnkey solution for isolating critical speech cues from natural stimuli. In this paper, we describe a psychoacoustic imaging method known as the Auditory Classification Image technique that allows experimenters to estimate the relative importance of time-frequency regions in categorizing natural speech utterances in noise. Importantly, this technique enables the testing of hypotheses on the listening strategies of participants at the group level. We exemplify this approach by identifying the acoustic cues involved in da/ga categorization with two phonetic contexts, Al- or Ar-. The application of Auditory Classification Images to our group of 16 participants revealed significant critical regions on the second and third formant onsets, as predicted by the literature, as well as an unexpected temporal cue on the first formant. Finally, through a cluster-based nonparametric test, we demonstrate that this method is sufficiently sensitive to detect fine modifications of the classification strategies between different utterances of the same phoneme.

Using the modified Delphi method to establish a new Chinese clinical consensus of the treatments for cervical radiculopathy.

PubMed

Zang, Lei; Fan, Ning; Hai, Yong; Lu, S B; Su, Q J; Yang, J C; Du, Peng; Gao, Y J

2015-06-01

Although cervical radiculopathy is very common, there is no standard treatment for this condition, with little high-level evidence available to guide the treatment choice. Thus, this study aimed to review the current data on the management of cervical radiculopathy; and, further, to establish a new Chinese clinical consensus of the treatments for cervical radiculopathy using the Delphi method. First, a systematic review of the previously established treatment guidelines and of articles related to cervical radiculopathy was conducted to establish a protocol for the clinical consensus of the treatment for cervical radiculopathy. Second, from February 2012 to June 2014, we performed a modified Delphi survey in which the current professional opinions from 30 experienced experts, representing almost all of the Chinese provinces, were gathered. Three rounds were performed, and consensus was defined as ≥70% agreement. Consensus of the treatments for cervical radiculopathy was reached on seven aspects, including the proportion of patients requiring only non-surgical therapies; the effectiveness of neck immobilization, physiotherapy, pharmacologic treatment; surgical indications; contraindications; surgery. The modified Delphi study conducted herein reached a consensus concerning several treatment issues for cervical radiculopathy. In the absence of high-level evidence, at present, these expert opinion findings will help guide health care providers to define the appropriate treatment in their regions. Items with no consensus provide excellent areas for future research.

Using the modified Delphi method to establish clinical consensus for the diagnosis and treatment of patients with rotator cuff pathology.

PubMed

Eubank, Breda H; Mohtadi, Nicholas G; Lafave, Mark R; Wiley, J Preston; Bois, Aaron J; Boorman, Richard S; Sheps, David M

2016-05-20

Patients presenting to the healthcare system with rotator cuff pathology do not always receive high quality care. High quality care occurs when a patient receives care that is accessible, appropriate, acceptable, effective, efficient, and safe. The aim of this study was twofold: 1) to develop a clinical pathway algorithm that sets forth a stepwise process for making decisions about the diagnosis and treatment of rotator cuff pathology presenting to primary, secondary, and tertiary healthcare settings; and 2) to establish clinical practice guidelines for the diagnosis and treatment of rotator cuff pathology to inform decision-making processes within the algorithm. A three-step modified Delphi method was used to establish consensus. Fourteen experts representing athletic therapy, physiotherapy, sport medicine, and orthopaedic surgery were invited to participate as the expert panel. In round 1, 123 best practice statements were distributed to the panel. Panel members were asked to mark "agree" or "disagree" beside each statement, and provide comments. The same voting method was again used for round 2. Round 3 consisted of a final face-to-face meeting. In round 1, statements were grouped and reduced to 44 statements that met consensus. In round 2, five statements reached consensus. In round 3, ten statements reached consensus. Consensus was reached for 59 statements representing five domains: screening, diagnosis, physical examination, investigations, and treatment. The final face-to-face meeting was also used to develop clinical pathway algorithms (i.e., clinical care pathways) for three types of rotator cuff pathology: acute, chronic, and acute-on-chronic. This consensus guideline will help to standardize care, provide guidance on the diagnosis and treatment of rotator cuff pathology, and assist in clinical decision-making for all healthcare professionals.

Report on ISCTM Consensus Meeting on Clinical Assessment of Response to Treatment of Cognitive Impairment in Schizophrenia.

PubMed

Keefe, Richard S E; Haig, George M; Marder, Stephen R; Harvey, Philip D; Dunayevich, Eduardo; Medalia, Alice; Davidson, Michael; Lombardo, Ilise; Bowie, Christopher R; Buchanan, Robert W; Bugarski-Kirola, Dragana; Carpenter, William T; Csernansky, John T; Dago, Pedro L; Durand, Dante M; Frese, Frederick J; Goff, Donald C; Gold, James M; Hooker, Christine I; Kopelowicz, Alex; Loebel, Antony; McGurk, Susan R; Opler, Lewis A; Pinkham, Amy E; Stern, Robert G

2016-01-01

If treatments for cognitive impairment are to be utilized successfully, clinicians must be able to determine whether they are effective and which patients should receive them. In order to develop consensus on these issues, the International Society for CNS Clinical Trials and Methodology (ISCTM) held a meeting of experts on March 20, 2014, in Washington, DC. Consensus was reached on several important issues. Cognitive impairment and functional disability were viewed as equally important treatment targets. The group supported the notion that sufficient data are not available to exclude patients from available treatments on the basis of age, severity of cognitive impairment, severity of positive symptoms, or the potential to benefit functionally from treatment. The group reached consensus that cognitive remediation is likely to provide substantial benefits in combination with procognitive medications, although a substantial minority believed that medications can be administered without nonpharmacological therapy. There was little consensus on the best methods for assessing cognitive change in clinical practice. Some participants supported the view that performance-based measures are essential for measurement of cognitive change; others pointed to their cost and time requirements as evidence of impracticality. Interview-based measures of cognitive and functional change were viewed as more practical, but lacking validity without informant involvement or frequent contact from clinicians. The lack of consensus on assessment methods was viewed as attributable to differences in experience and education among key stakeholders and significant gaps in available empirical data. Research on the reliability, validity, sensitivity, and practicality of competing methods will facilitate consensus. © The Author 2015. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center.

Priority target conditions for algorithms for monitoring children's growth: Interdisciplinary consensus.

PubMed

Scherdel, Pauline; Reynaud, Rachel; Pietrement, Christine; Salaün, Jean-François; Bellaïche, Marc; Arnould, Michel; Chevallier, Bertrand; Piloquet, Hugues; Jobez, Emmanuel; Cheymol, Jacques; Bichara, Emmanuelle; Heude, Barbara; Chalumeau, Martin

2017-01-01

Growth monitoring of apparently healthy children aims at early detection of serious conditions through the use of both clinical expertise and algorithms that define abnormal growth. Optimization of growth monitoring requires standardization of the definition of abnormal growth, and the selection of the priority target conditions is a prerequisite of such standardization. To obtain a consensus about the priority target conditions for algorithms monitoring children's growth. We applied a formal consensus method with a modified version of the RAND/UCLA method, based on three phases (preparatory, literature review, and rating), with the participation of expert advisory groups from the relevant professional medical societies (ranging from primary care providers to hospital subspecialists) as well as parent associations. We asked experts in the pilot (n = 11), reading (n = 8) and rating (n = 60) groups to complete the list of diagnostic classification of the European Society for Paediatric Endocrinology and then to select the conditions meeting the four predefined criteria of an ideal type of priority target condition. Strong agreement was obtained for the 8 conditions selected by the experts among the 133 possible: celiac disease, Crohn disease, craniopharyngioma, juvenile nephronophthisis, Turner syndrome, growth hormone deficiency with pituitary stalk interruption syndrome, infantile cystinosis, and hypothalamic-optochiasmatic astrocytoma (in decreasing order of agreement). This national consensus can be used to evaluate the algorithms currently suggested for growth monitoring. The method used for this national consensus could be re-used to obtain an international consensus.

Early assembly of the most massive galaxies.

PubMed

Collins, Chris A; Stott, John P; Hilton, Matt; Kay, Scott T; Stanford, S Adam; Davidson, Michael; Hosmer, Mark; Hoyle, Ben; Liddle, Andrew; Lloyd-Davies, Ed; Mann, Robert G; Mehrtens, Nicola; Miller, Christopher J; Nichol, Robert C; Romer, A Kathy; Sahlén, Martin; Viana, Pedro T P; West, Michael J

2009-04-02

The current consensus is that galaxies begin as small density fluctuations in the early Universe and grow by in situ star formation and hierarchical merging. Stars begin to form relatively quickly in sub-galactic-sized building blocks called haloes which are subsequently assembled into galaxies. However, exactly when this assembly takes place is a matter of some debate. Here we report that the stellar masses of brightest cluster galaxies, which are the most luminous objects emitting stellar light, some 9 billion years ago are not significantly different from their stellar masses today. Brightest cluster galaxies are almost fully assembled 4-5 billion years after the Big Bang, having grown to more than 90 per cent of their final stellar mass by this time. Our data conflict with the most recent galaxy formation models based on the largest simulations of dark-matter halo development. These models predict protracted formation of brightest cluster galaxies over a Hubble time, with only 22 per cent of the stellar mass assembled at the epoch probed by our sample. Our findings suggest a new picture in which brightest cluster galaxies experience an early period of rapid growth rather than prolonged hierarchical assembly.

Fingerprints of resistant Escherichia coli O157:H7 from vegetables and environmental samples.

PubMed

Abakpa, Grace Onyukwo; Umoh, Veronica J; Kamaruzaman, Sijam; Ibekwe, Mark

2018-01-01

Some routes of transmission of Escherichia coli O157:H7 to fresh produce include contaminated irrigation water and manure polluted soils. The aim of the present study was to determine the genetic relationships of E. coli O157:H7 isolated from some produce growing region in Nigeria using enterobacterial repetitive intergenic consensus (ERIC) DNA fingerprinting analysis. A total of 440 samples comprising leafy greens, irrigation water, manure and soil were obtained from vegetable producing regions in Kano and Plateau States, Nigeria. Genes coding for the quinolone resistance-determinant (gyrA) and plasmid (pCT) coding for multidrug resistance (MDR) were determined using polymerase chain reaction (PCR) in 16 isolates that showed MDR. Cluster analysis of the ERIC-PCR profiles based on band sizes revealed six main clusters from the sixteen isolates analysed. The largest cluster (cluster 3) grouped isolates from vegetables and manure at a similarity coefficient of 0.72. The present study provides data that support the potential transmission of resistant strains of E. coli O157:H7 from vegetables and environmental sources to humans with potential public health implications, especially in developing countries. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

Individualization as Driving Force of Clustering Phenomena in Humans

PubMed Central

Mäs, Michael; Flache, Andreas; Helbing, Dirk

2010-01-01

One of the most intriguing dynamics in biological systems is the emergence of clustering, in the sense that individuals self-organize into separate agglomerations in physical or behavioral space. Several theories have been developed to explain clustering in, for instance, multi-cellular organisms, ant colonies, bee hives, flocks of birds, schools of fish, and animal herds. A persistent puzzle, however, is the clustering of opinions in human populations, particularly when opinions vary continuously, such as the degree to which citizens are in favor of or against a vaccination program. Existing continuous opinion formation models predict “monoculture” in the long run, unless subsets of the population are perfectly separated from each other. Yet, social diversity is a robust empirical phenomenon, although perfect separation is hardly possible in an increasingly connected world. Considering randomness has not overcome the theoretical shortcomings so far. Small perturbations of individual opinions trigger social influence cascades that inevitably lead to monoculture, while larger noise disrupts opinion clusters and results in rampant individualism without any social structure. Our solution to the puzzle builds on recent empirical research, combining the integrative tendencies of social influence with the disintegrative effects of individualization. A key element of the new computational model is an adaptive kind of noise. We conduct computer simulation experiments demonstrating that with this kind of noise a third phase besides individualism and monoculture becomes possible, characterized by the formation of metastable clusters with diversity between and consensus within clusters. When clusters are small, individualization tendencies are too weak to prohibit a fusion of clusters. When clusters grow too large, however, individualization increases in strength, which promotes their splitting. In summary, the new model can explain cultural clustering in human societies. Strikingly, model predictions are not only robust to “noise”—randomness is actually the central mechanism that sustains pluralism and clustering. PMID:20975937

Phylogenetic Analysis of Prevalent Tuberculosis and Non-Tuberculosis Mycobacteria in Isfahan, Iran, Based on a 360 bp Sequence of the rpoB Gene

PubMed Central

Nasr Esfahani, Bahram; Moghim, Sharareh; Ghasemian Safaei, Hajieh; Moghoofei, Mohsen; Sedighi, Mansour; Hadifar, Shima

2016-01-01

Background Taxonomic and phylogenetic studies of Mycobacterium species have been based around the 16sRNA gene for many years. However, due to the high strain similarity between species in the Mycobacterium genus (94.3% - 100%), defining a valid phylogenetic tree is difficult; consequently, its use in estimating the boundaries between species is limited. The sequence of the rpoB gene makes it an appropriate gene for phylogenetic analysis, especially in bacteria with limited variation. Objectives In the present study, a 360bp sequence of rpoB was used for precise classification of Mycobacterium strains isolated in Isfahan, Iran. Materials and Methods From February to October 2013, 57 clinical and environmental isolates were collected, subcultured, and identified by phenotypic methods. After DNA extraction, a 360bp fragment was PCR-amplified and sequenced. The phylogenetic tree was constructed based on consensus sequence data, using MEGA5 software. Results Slow and fast-growing groups of the Mycobacterium strains were clearly differentiated based on the constructed tree of 56 common Mycobacterium isolates. Each species with a unique title in the tree was identified; in total, 13 nods with a bootstrap value of over 50% were supported. Among the slow-growing group was Mycobacterium kansasii, with M. tuberculosis in a cluster with a bootstrap value of 98% and M. gordonae in another cluster with a bootstrap value of 90%. In the fast-growing group, one cluster with a bootstrap value of 89% was defined, including all fast-growing members present in this study. Conclusions The results suggest that only the application of the rpoB gene sequence is sufficient for taxonomic categorization and definition of a new Mycobacterium species, due to its high resolution power and proper variation in its sequence (85% - 100%); the resulting tree has high validity. PMID:27284397

Biclustering as a method for RNA local multiple sequence alignment.

PubMed

Wang, Shu; Gutell, Robin R; Miranker, Daniel P

2007-12-15

Biclustering is a clustering method that simultaneously clusters both the domain and range of a relation. A challenge in multiple sequence alignment (MSA) is that the alignment of sequences is often intended to reveal groups of conserved functional subsequences. Simultaneously, the grouping of the sequences can impact the alignment; precisely the kind of dual situation biclustering is intended to address. We define a representation of the MSA problem enabling the application of biclustering algorithms. We develop a computer program for local MSA, BlockMSA, that combines biclustering with divide-and-conquer. BlockMSA simultaneously finds groups of similar sequences and locally aligns subsequences within them. Further alignment is accomplished by dividing both the set of sequences and their contents. The net result is both a multiple sequence alignment and a hierarchical clustering of the sequences. BlockMSA was tested on the subsets of the BRAliBase 2.1 benchmark suite that display high variability and on an extension to that suite to larger problem sizes. Also, alignments were evaluated of two large datasets of current biological interest, T box sequences and Group IC1 Introns. The results were compared with alignments computed by ClustalW, MAFFT, MUCLE and PROBCONS alignment programs using Sum of Pairs (SPS) and Consensus Count. Results for the benchmark suite are sensitive to problem size. On problems of 15 or greater sequences, BlockMSA is consistently the best. On none of the problems in the test suite are there appreciable differences in scores among BlockMSA, MAFFT and PROBCONS. On the T box sequences, BlockMSA does the most faithful job of reproducing known annotations. MAFFT and PROBCONS do not. On the Intron sequences, BlockMSA, MAFFT and MUSCLE are comparable at identifying conserved regions. BlockMSA is implemented in Java. Source code and supplementary datasets are available at http://aug.csres.utexas.edu/msa/

Brainstorming: weighted voting prediction of inhibitors for protein targets.

PubMed

Plewczynski, Dariusz

2011-09-01

The "Brainstorming" approach presented in this paper is a weighted voting method that can improve the quality of predictions generated by several machine learning (ML) methods. First, an ensemble of heterogeneous ML algorithms is trained on available experimental data, then all solutions are gathered and a consensus is built between them. The final prediction is performed using a voting procedure, whereby the vote of each method is weighted according to a quality coefficient calculated using multivariable linear regression (MLR). The MLR optimization procedure is very fast, therefore no additional computational cost is introduced by using this jury approach. Here, brainstorming is applied to selecting actives from large collections of compounds relating to five diverse biological targets of medicinal interest, namely HIV-reverse transcriptase, cyclooxygenase-2, dihydrofolate reductase, estrogen receptor, and thrombin. The MDL Drug Data Report (MDDR) database was used for selecting known inhibitors for these protein targets, and experimental data was then used to train a set of machine learning methods. The benchmark dataset (available at http://bio.icm.edu.pl/∼darman/chemoinfo/benchmark.tar.gz ) can be used for further testing of various clustering and machine learning methods when predicting the biological activity of compounds. Depending on the protein target, the overall recall value is raised by at least 20% in comparison to any single machine learning method (including ensemble methods like random forest) and unweighted simple majority voting procedures.

Current understanding on aflatoxin biosynthesis and future perspective in reducing aflatoxin contamination.

PubMed

Yu, Jiujiang

2012-10-25

Traditional molecular techniques have been used in research in discovering the genes and enzymes that are involved in aflatoxin formation and genetic regulation. We cloned most, if not all, of the aflatoxin pathway genes. A consensus gene cluster for aflatoxin biosynthesis was discovered in 2005. The factors that affect aflatoxin formation have been studied. In this report, the author summarized the current status of research progress and future possibilities that may be used for solving aflatoxin contamination.

Metabolic syndrome in children and adolescents: Old concepts in a young population.

PubMed

Titmuss, Angela Therese; Srinivasan, Shubha

2016-10-01

Many years after first being described, there is still no clear consensus on diagnostic criteria for metabolic syndrome, particularly in children. However, identification of this cluster of cardiovascular risk factors especially in children with co-morbidities, is important in order to reduce their future risk of chronic disease and morbidity. Sustained multidisciplinary and family-based early intervention is required, aiming primarily at life-style change. © 2016 Paediatrics and Child Health Division (The Royal Australasian College of Physicians).

Effects of Complex System Structure and External Field in Opinion Formation

NASA Astrophysics Data System (ADS)

Guo, Long; Cai, Xu

Around us, the society structure and external field, such as government policy, the newspaper, the internet and other mass media, play a special role in shaping the attitudes, beliefs and public opinion. For studying the role of the society structure and the external field, we propose a new opinion model based on the former models. With computer simulations of opinion dynamics, we find that the smaller the clustering coefficient and the society size, the easier the consensus phase is reached and other interesting results.

Current Understanding on Aflatoxin Biosynthesis and Future Perspective in Reducing Aflatoxin Contamination

PubMed Central

Yu, Jiujiang

2012-01-01

Traditional molecular techniques have been used in research in discovering the genes and enzymes that are involved in aflatoxin formation and genetic regulation. We cloned most, if not all, of the aflatoxin pathway genes. A consensus gene cluster for aflatoxin biosynthesis was discovered in 2005. The factors that affect aflatoxin formation have been studied. In this report, the author summarized the current status of research progress and future possibilities that may be used for solving aflatoxin contamination. PMID:23202305

Document clustering methods, document cluster label disambiguation methods, document clustering apparatuses, and articles of manufacture

DOEpatents

Sanfilippo, Antonio [Richland, WA; Calapristi, Augustin J [West Richland, WA; Crow, Vernon L [Richland, WA; Hetzler, Elizabeth G [Kennewick, WA; Turner, Alan E [Kennewick, WA

2009-12-22

Document clustering methods, document cluster label disambiguation methods, document clustering apparatuses, and articles of manufacture are described. In one aspect, a document clustering method includes providing a document set comprising a plurality of documents, providing a cluster comprising a subset of the documents of the document set, using a plurality of terms of the documents, providing a cluster label indicative of subject matter content of the documents of the cluster, wherein the cluster label comprises a plurality of word senses, and selecting one of the word senses of the cluster label.

Opinion formation models in static and dynamic social networks

NASA Astrophysics Data System (ADS)

Singh, Pramesh

We study models of opinion formation on static as well as dynamic networks where interaction among individuals is governed by widely accepted social theories. In particular, three models of competing opinions based on distinct interaction mechanisms are studied. A common feature in all of these models is the existence of a tipping point in terms of a model parameter beyond which a rapid consensus is reached. In the first model that we study on a static network, a node adopts a particular state (opinion) if a threshold fraction of its neighbors are already in that state. We introduce a few initiator nodes which are in state '1' in a population where every node is in state '0'. Thus, opinion '1' spreads through the population until no further influence is possible. Size of the spread is greatly affected by how these initiator nodes are selected. We find that there exists a critical fraction of initiators pc that is needed to trigger global cascades for a given threshold phi. We also study heuristic strategies for selecting a set of initiator nodes in order to maximize the cascade size. The structural properties of networks also play an important role in the spreading process. We study how the dynamics is affected by changing the clustering in a network. It turns out that local clustering is helpful in spreading. Next, we studied a model where the network is dynamic and interactions are homophilic. We find that homophily-driven rewiring impedes the reaching of consensus and in the absence of committed nodes (nodes that are not influenceable on their opinion), consensus time Tc diverges exponentially with network size N . As we introduce a fraction of committed nodes, beyond a critical value, the scaling of Tc becomes logarithmic in N. We also find that slight change in the interaction rule can produce strikingly different scaling behaviors of T c . However, introducing committed agents in the system drastically improves the scaling of the consensus time regardless of the interaction rules considered. Finally, a three-state (leftist, rightist, centrist) model that couples the dynamics of social balance with an external deradicalizing field is studied. The mean-field analysis shows that for a weak external field, the system exhibits a metastable fixed point and a saddle point in addition to a stable fixed point. However, if the strength of the external field is sufficiently large (larger than a critical value), there is only one (stable) fixed point which corresponds to an all-centrist consensus state (absorbing state). In the weak-field regime, the convergence time to the absorbing state is evaluated using the quasi-stationary(QS) distribution and is found to be in good agreement with the results obtained by numerical simulations.

Inferring gene and protein interactions using PubMed citations and consensus Bayesian networks.

PubMed

Deeter, Anthony; Dalman, Mark; Haddad, Joseph; Duan, Zhong-Hui

2017-01-01

The PubMed database offers an extensive set of publication data that can be useful, yet inherently complex to use without automated computational techniques. Data repositories such as the Genomic Data Commons (GDC) and the Gene Expression Omnibus (GEO) offer experimental data storage and retrieval as well as curated gene expression profiles. Genetic interaction databases, including Reactome and Ingenuity Pathway Analysis, offer pathway and experiment data analysis using data curated from these publications and data repositories. We have created a method to generate and analyze consensus networks, inferring potential gene interactions, using large numbers of Bayesian networks generated by data mining publications in the PubMed database. Through the concept of network resolution, these consensus networks can be tailored to represent possible genetic interactions. We designed a set of experiments to confirm that our method is stable across variation in both sample and topological input sizes. Using gene product interactions from the KEGG pathway database and data mining PubMed publication abstracts, we verify that regardless of the network resolution or the inferred consensus network, our method is capable of inferring meaningful gene interactions through consensus Bayesian network generation with multiple, randomized topological orderings. Our method can not only confirm the existence of currently accepted interactions, but has the potential to hypothesize new ones as well. We show our method confirms the existence of known gene interactions such as JAK-STAT-PI3K-AKT-mTOR, infers novel gene interactions such as RAS- Bcl-2 and RAS-AKT, and found significant pathway-pathway interactions between the JAK-STAT signaling and Cardiac Muscle Contraction KEGG pathways.

Development of Consensus Treatment Plans for Juvenile Localized Scleroderma

PubMed Central

Li, Suzanne C.; Torok, Kathryn S.; Pope, Elena; Dedeoglu, Fatma; Hong, Sandy; Jacobe, Heidi T.; Rabinovich, C. Egla; Laxer, Ronald M.; Higgins, Gloria C.; Ferguson, Polly J.; Lasky, Andrew; Baszis, Kevin; Becker, Mara; Campillo, Sarah; Cartwright, Victoria; Cidon, Michael; Inman, Christi J; Jerath, Rita; O'Neil, Kathleen M.; Vora, Sheetal; Zeft, Andrew; Wallace, Carol A.; Ilowite, Norman T.; Fuhlbrigge, Robert C

2013-01-01

Objective To develop standardized treatment plans, clinical assessments, and response criteria for active, moderate to high severity juvenile localized scleroderma (jLS). Background jLS is a chronic inflammatory skin disorder associated with substantial morbidity and disability. Although a wide range of therapeutic strategies have been reported in the literature, a lack of agreement on treatment specifics and accepted methods for clinical assessment of have made it difficult to compare approaches and identify optimal therapy. Methods A core group of pediatric rheumatologists, dermatologists and a lay advisor was engaged by the Childhood Arthritis and Rheumatology Research Alliance (CARRA) to develop standardized treatment plans and assessment parameters for jLS using consensus methods/nominal group techniques. Recommendations were validated in two face-to-face conferences with a larger group of practitioners with expertise in jLS and with the full membership of CARRA, which encompasses the majority of pediatric rheumatologists in the U.S and Canada. Results Consensus was achieved on standardized treatment plans that reflect the prevailing treatment practices of CARRA members. Standardized clinical assessment methods and provisional treatment response criteria were also developed. Greater than 90% of pediatric rheumatologists responding to a survey (67% of CARRA membership) affirmed the final recommendations and agreed to utilize these consensus plans to treat patients with jLS. Conclusions Using consensus methodology, we have developed standardized treatment plans and assessment methods for jLS. The high level of support among pediatric rheumatologists will support future comparative effectiveness studies and enable the development of evidence-based guidelines for the treatment of jLS. PMID:22505322

Stability switches of arbitrary high-order consensus in multiagent networks with time delays.

PubMed

Yang, Bo

2013-01-01

High-order consensus seeking, in which individual high-order dynamic agents share a consistent view of the objectives and the world in a distributed manner, finds its potential broad applications in the field of cooperative control. This paper presents stability switches analysis of arbitrary high-order consensus in multiagent networks with time delays. By employing a frequency domain method, we explicitly derive analytical equations that clarify a rigorous connection between the stability of general high-order consensus and the system parameters such as the network topology, communication time-delays, and feedback gains. Particularly, our results provide a general and a fairly precise notion of how increasing communication time-delay causes the stability switches of consensus. Furthermore, under communication constraints, the stability and robustness problems of consensus algorithms up to third order are discussed in details to illustrate our central results. Numerical examples and simulation results for fourth-order consensus are provided to demonstrate the effectiveness of our theoretical results.

PCAN: phenotype consensus analysis to support disease-gene association.

PubMed

Godard, Patrice; Page, Matthew

2016-12-07

Bridging genotype and phenotype is a fundamental biomedical challenge that underlies more effective target discovery and patient-tailored therapy. Approaches that can flexibly and intuitively, integrate known gene-phenotype associations in the context of molecular signaling networks are vital to effectively prioritize and biologically interpret genes underlying disease traits of interest. We describe Phenotype Consensus Analysis (PCAN); a method to assess the consensus semantic similarity of phenotypes in a candidate gene's signaling neighborhood. We demonstrate that significant phenotype consensus (p < 0.05) is observable for ~67% of 4,549 OMIM disease-gene associations, using a combination of high quality String interactions + Metabase pathways and use Joubert Syndrome to demonstrate the ease with which a significant result can be interrogated to highlight discriminatory traits linked to mechanistically related genes. We advocate phenotype consensus as an intuitive and versatile method to aid disease-gene association, which naturally lends itself to the mechanistic deconvolution of diverse phenotypes. We provide PCAN to the community as an R package ( http://bioconductor.org/packages/PCAN/ ) to allow flexible configuration, extension and standalone use or integration to supplement existing gene prioritization workflows.

Short Communication: Analysis of Minor Populations of Human Immunodeficiency Virus by Primer Identification and Insertion-Deletion and Carry Forward Correction Pipelines.

PubMed

Hughes, Paul; Deng, Wenjie; Olson, Scott C; Coombs, Robert W; Chung, Michael H; Frenkel, Lisa M

2016-03-01

Accurate analysis of minor populations of drug-resistant HIV requires analysis of a sufficient number of viral templates. We assessed the effect of experimental conditions on the analysis of HIV pol 454 pyrosequences generated from plasma using (1) the "Insertion-deletion (indel) and Carry Forward Correction" (ICC) pipeline, which clusters sequence reads using a nonsubstitution approach and can correct for indels and carry forward errors, and (2) the "Primer Identification (ID)" method, which facilitates construction of a consensus sequence to correct for sequencing errors and allelic skewing. The Primer ID and ICC methods produced similar estimates of viral diversity, but differed in the number of sequence variants generated. Sequence preparation for ICC was comparably simple, but was limited by an inability to assess the number of templates analyzed and allelic skewing. The more costly Primer ID method corrected for allelic skewing and provided the number of viral templates analyzed, which revealed that amplifiable HIV templates varied across specimens and did not correlate with clinical viral load. This latter observation highlights the value of the Primer ID method, which by determining the number of templates amplified, enables more accurate assessment of minority species in the virus population, which may be relevant to prescribing effective antiretroviral therapy.

Applications of cluster analysis to the creation of perfectionism profiles: a comparison of two clustering approaches.

PubMed

Bolin, Jocelyn H; Edwards, Julianne M; Finch, W Holmes; Cassady, Jerrell C

2014-01-01

Although traditional clustering methods (e.g., K-means) have been shown to be useful in the social sciences it is often difficult for such methods to handle situations where clusters in the population overlap or are ambiguous. Fuzzy clustering, a method already recognized in many disciplines, provides a more flexible alternative to these traditional clustering methods. Fuzzy clustering differs from other traditional clustering methods in that it allows for a case to belong to multiple clusters simultaneously. Unfortunately, fuzzy clustering techniques remain relatively unused in the social and behavioral sciences. The purpose of this paper is to introduce fuzzy clustering to these audiences who are currently relatively unfamiliar with the technique. In order to demonstrate the advantages associated with this method, cluster solutions of a common perfectionism measure were created using both fuzzy clustering and K-means clustering, and the results compared. Results of these analyses reveal that different cluster solutions are found by the two methods, and the similarity between the different clustering solutions depends on the amount of cluster overlap allowed for in fuzzy clustering.

Applications of cluster analysis to the creation of perfectionism profiles: a comparison of two clustering approaches

PubMed Central

Bolin, Jocelyn H.; Edwards, Julianne M.; Finch, W. Holmes; Cassady, Jerrell C.

2014-01-01

Although traditional clustering methods (e.g., K-means) have been shown to be useful in the social sciences it is often difficult for such methods to handle situations where clusters in the population overlap or are ambiguous. Fuzzy clustering, a method already recognized in many disciplines, provides a more flexible alternative to these traditional clustering methods. Fuzzy clustering differs from other traditional clustering methods in that it allows for a case to belong to multiple clusters simultaneously. Unfortunately, fuzzy clustering techniques remain relatively unused in the social and behavioral sciences. The purpose of this paper is to introduce fuzzy clustering to these audiences who are currently relatively unfamiliar with the technique. In order to demonstrate the advantages associated with this method, cluster solutions of a common perfectionism measure were created using both fuzzy clustering and K-means clustering, and the results compared. Results of these analyses reveal that different cluster solutions are found by the two methods, and the similarity between the different clustering solutions depends on the amount of cluster overlap allowed for in fuzzy clustering. PMID:24795683

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry combined with multidimensional scaling, binary hierarchical cluster tree and selected diagnostic masses improves species identification of Neolithic keratin sequences from furs of the Tyrolean Iceman Oetzi.

PubMed

Hollemeyer, Klaus; Altmeyer, Wolfgang; Heinzle, Elmar; Pitra, Christian

2012-08-30

The identification of fur origins from the 5300-year-old Tyrolean Iceman's accoutrement is not yet complete, although definite identification is essential for the socio-cultural context of his epoch. Neither have all potential samples been identified so far, nor there has a consensus been reached on the species identified using the classical methods. Archaeological hair often lacks analyzable hair scale patterns in microscopic analyses and polymer chain reaction (PCR)-based techniques are often inapplicable due to the lack of amplifiable ancient DNA. To overcome these drawbacks, a matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) method was used exclusively based on hair keratins. Thirteen fur specimens from his accoutrement were analyzed after tryptic digest of native hair. Peptide mass fingerprints (pmfs) from ancient samples and from reference species mostly occurring in the Alpine surroundings at his lifetime were compared to each other using multidimensional scaling and binary hierarchical cluster tree analysis. Both statistical methods highly reflect spectral similarities among pmfs as close zoological relationships. While multidimensional scaling was useful to discriminate specimens on the zoological order level, binary hierarchical cluster tree reached the family or subfamily level. Additionally, the presence and/or absence of order, family and/or species-specific diagnostic masses in their pmfs allowed the identification of mammals mostly down to single species level. Red deer was found in his shoe vamp, goat in the leggings, cattle in his shoe sole and at his quiver's closing flap as well as sheep and chamois in his coat. Canid species, like grey wolf, domestic dog or European red fox, were discovered in his leggings for the first time, but could not be differentiated to species level. This is widening the spectrum of processed fur-bearing species to at least one member of the Canidae family. His fur cap was allocated to a carnivore species, but differentiation between brown bear and a canid species could not be made with certainty. Copyright © 2012 John Wiley & Sons, Ltd.

A meta-classifier for detecting prostate cancer by quantitative integration of in vivo magnetic resonance spectroscopy and magnetic resonance imaging

NASA Astrophysics Data System (ADS)

Viswanath, Satish; Tiwari, Pallavi; Rosen, Mark; Madabhushi, Anant

2008-03-01

Recently, in vivo Magnetic Resonance Imaging (MRI) and Magnetic Resonance Spectroscopy (MRS) have emerged as promising new modalities to aid in prostate cancer (CaP) detection. MRI provides anatomic and structural information of the prostate while MRS provides functional data pertaining to biochemical concentrations of metabolites such as creatine, choline and citrate. We have previously presented a hierarchical clustering scheme for CaP detection on in vivo prostate MRS and have recently developed a computer-aided method for CaP detection on in vivo prostate MRI. In this paper we present a novel scheme to develop a meta-classifier to detect CaP in vivo via quantitative integration of multimodal prostate MRS and MRI by use of non-linear dimensionality reduction (NLDR) methods including spectral clustering and locally linear embedding (LLE). Quantitative integration of multimodal image data (MRI and PET) involves the concatenation of image intensities following image registration. However multimodal data integration is non-trivial when the individual modalities include spectral and image intensity data. We propose a data combination solution wherein we project the feature spaces (image intensities and spectral data) associated with each of the modalities into a lower dimensional embedding space via NLDR. NLDR methods preserve the relationships between the objects in the original high dimensional space when projecting them into the reduced low dimensional space. Since the original spectral and image intensity data are divorced from their original physical meaning in the reduced dimensional space, data at the same spatial location can be integrated by concatenating the respective embedding vectors. Unsupervised consensus clustering is then used to partition objects into different classes in the combined MRS and MRI embedding space. Quantitative results of our multimodal computer-aided diagnosis scheme on 16 sets of patient data obtained from the ACRIN trial, for which corresponding histological ground truth for spatial extent of CaP is known, show a marginally higher sensitivity, specificity, and positive predictive value compared to corresponding CAD results with the individual modalities.

A review of consensus test methods for established medical imaging modalities and their implications for optical coherence tomography

NASA Astrophysics Data System (ADS)

Pfefer, Joshua; Agrawal, Anant

2012-03-01

In recent years there has been increasing interest in development of consensus, tissue-phantom-based approaches for assessment of biophotonic imaging systems, with the primary goal of facilitating clinical translation of novel optical technologies. Well-characterized test methods based on tissue phantoms can provide useful tools for performance assessment, thus enabling standardization and device inter-comparison during preclinical development as well as quality assurance and re-calibration in the clinical setting. In this review, we study the role of phantom-based test methods as described in consensus documents such as international standards for established imaging modalities including X-ray CT, MRI and ultrasound. Specifically, we focus on three image quality characteristics - spatial resolution, spatial measurement accuracy and image uniformity - and summarize the terminology, metrics, phantom design/construction approaches and measurement/analysis procedures used to assess these characteristics. Phantom approaches described are those in routine clinical use and tend to have simplified morphology and biologically-relevant physical parameters. Finally, we discuss the potential for applying knowledge gained from existing consensus documents in the development of standardized, phantom-based test methods for optical coherence tomography.

A consensus reaching model for 2-tuple linguistic multiple attribute group decision making with incomplete weight information

NASA Astrophysics Data System (ADS)

Zhang, Wancheng; Xu, Yejun; Wang, Huimin

2016-01-01

The aim of this paper is to put forward a consensus reaching method for multi-attribute group decision-making (MAGDM) problems with linguistic information, in which the weight information of experts and attributes is unknown. First, some basic concepts and operational laws of 2-tuple linguistic label are introduced. Then, a grey relational analysis method and a maximising deviation method are proposed to calculate the incomplete weight information of experts and attributes respectively. To eliminate the conflict in the group, a weight-updating model is employed to derive the weights of experts based on their contribution to the consensus reaching process. After conflict elimination, the final group preference can be obtained which will give the ranking of the alternatives. The model can effectively avoid information distortion which is occurred regularly in the linguistic information processing. Finally, an illustrative example is given to illustrate the application of the proposed method and comparative analysis with the existing methods are offered to show the advantages of the proposed method.

Development of a decision aid for the treatment of benign prostatic hyperplasia: A four stage method using a Delphi consensus study.

PubMed

Lamers, Romy E D; Cuypers, Maarten; Garvelink, Mirjam M; de Vries, Marieke; Bosch, J L H Ruud; Kil, Paul J M

2016-07-01

To develop a web-based decision aid (DA) for the treatment of lower urinary tract symptoms due to benign prostatic hyperplasia (LUTS/BPH). From February-September 2014 we performed a four-stage development method: 1: Two-round Delphi consensus method among urologists, 2: Identifying patients' needs and expectations, 3: Development of DA content and structure, 4: Usability testing with LUTS/BPH patients. 1 (N=15): Dutch urologists reached consensus on 61% of the statements concerning users' criteria, decision options, structure, and medical content. 2 (N=24): Consensus was reached in 69% on statements concerning the need for improvement of information provision, the need for DA development and that the DA should clarify patients' preferences. 3: DA development based on results from stage 1 and stage 2. 4 (N=10): Pros of the DA were clear information provision, systematic design and easy to read and re-read. A LUTS/BPH DA containing VCEs(**) was developed in cooperation with urologists and patients following a structured 4 stage method and was stated to be well accepted. This method can be adopted for the development of DAs to support other medical decision issues. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Core Competencies in Natural Health Products for Canadian Pharmacy Students

PubMed Central

Byrne, Ani; Austin, Zubin; Jurgens, Tannis; Raman-Wilms, Lalitha

2010-01-01

Objective To reach consensus on core competency statements for natural health products (NHPs) for Canadian pharmacy students. Methods Four rounds of a modified Delphi method were used to achieve consensus on core competency statements for NHPs. Pharmacy educators from Canada and the United States, and representatives from Canadian pharmacy organizations ranked their agreement using a 5-point Likert scale. Results Consensus was achieved on 3 NHP-related core competency statements: (1) to incorporate NHP knowledge when providing pharmaceutical care; (2) to access and critically appraise NHP-related information sources; and (3) to provide appropriate education to patients and other health care providers on the effectiveness, potential adverse effects, and drug interactions of NHPs. Conclusions Consensus was reached among leaders in NHP education on 3 NHP-related core competency statements. Implementation of these competencies would ensure that graduating Canadian pharmacists would be able to fulfill their professional responsibilities related to NHPs. PMID:20498738

Genetic analysis reveals the identity of the photoreceptor for phototaxis in hormogonium filaments of Nostoc punctiforme.

PubMed

Campbell, Elsie L; Hagen, Kari D; Chen, Rui; Risser, Douglas D; Ferreira, Daniela P; Meeks, John C

2015-02-15

In cyanobacterial Nostoc species, substratum-dependent gliding motility is confined to specialized nongrowing filaments called hormogonia, which differentiate from vegetative filaments as part of a conditional life cycle and function as dispersal units. Here we confirm that Nostoc punctiforme hormogonia are positively phototactic to white light over a wide range of intensities. N. punctiforme contains two gene clusters (clusters 2 and 2i), each of which encodes modular cyanobacteriochrome-methyl-accepting chemotaxis proteins (MCPs) and other proteins that putatively constitute a basic chemotaxis-like signal transduction complex. Transcriptional analysis established that all genes in clusters 2 and 2i, plus two additional clusters (clusters 1 and 3) with genes encoding MCPs lacking cyanobacteriochrome sensory domains, are upregulated during the differentiation of hormogonia. Mutational analysis determined that only genes in cluster 2i are essential for positive phototaxis in N. punctiforme hormogonia; here these genes are designated ptx (for phototaxis) genes. The cluster is unusual in containing complete or partial duplicates of genes encoding proteins homologous to the well-described chemotaxis elements CheY, CheW, MCP, and CheA. The cyanobacteriochrome-MCP gene (ptxD) lacks transmembrane domains and has 7 potential binding sites for bilins. The transcriptional start site of the ptx genes does not resemble a sigma 70 consensus recognition sequence; moreover, it is upstream of two genes encoding gas vesicle proteins (gvpA and gvpC), which also are expressed only in the hormogonium filaments of N. punctiforme. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

Protocols for the Initial Treatment of Moderately Severe Juvenile Dermatomyositis: Results of a Children's Arthritis and Rheumatology Research Alliance Consensus Conference

PubMed Central

Huber, Adam M.; Giannini, Edward H.; Bowyer, Suzanne L.; Kim, Susan; Lang, Bianca; Lindsley, Carol B.; Pachman, Lauren M.; Pilkington, Clarissa; Reed, Ann M.; Rennebohm, Robert M.; Rider, Lisa G.; Wallace, Carol A.; Feldman, Brian M.

2010-01-01

Objective To use juvenile dermatomyositis (JDM) survey data and expert opinion to develop a small number of consensus treatment protocols which reflect current initial treatment of moderately severe JDM. Methods A consensus meeting was held in Toronto, Ontario, Canada on December 1-2, 2007. Nominal group technique was used to achieve consensus on treatment protocols which represented typical management of moderately severe JDM. Consensus was also reached on which patients these protocols would be applicable to (inclusion and exclusion criteria), initial investigations which should be done prior to initiating one of these protocols, data which should be collected to evaluate these protocols, concomitant interventions that would be required or recommended. Results Three protocols were developed which described the first 2 months of treatment. All protocols included corticosteroids and methotrexate. One protocol also included intravenous gammaglobulin. Consensus was achieved for all issues that were addressed by conference participants, although there were some areas of controversy Conclusions This study shows that it is possible to achieve consensus on the initial treatment of JDM, despite considerable variation in clinical practice. Once these protocols are extended beyond 2 months, these protocols will be available for clinical use. By using methods which account for differences between patients (confounding by indication), the comparative effectiveness of the protocols will be evaluated. In the future, the goal will be to identify the optimal treatment of moderately severe JDM. PMID:20191521

Developing clinical practice guidelines for Chinese herbal treatment of polycystic ovary syndrome: A mixed-methods modified Delphi study.

PubMed

Lai, Lily; Flower, Andrew; Moore, Michael; Lewith, George

2015-06-01

Preliminary evidence suggests Chinese herbal medicine (CHM) could be a viable treatment option for polycystic ovary syndrome (PCOS). Prior to conducting a clinical trial it is important to consider the characteristics of good clinical practice. This study aims to use professional consensus to establish good clinical practice guidelines for the CHM treatment of PCOS. CHM practitioners participated in a mixed-methods modified Delphi study involving three rounds of structured group communication. Round 1 involved qualitative interviews with practitioners to generate statements regarding good clinical practice. In round 2, these statements were distributed online to the same practitioners to rate their agreement using a 7-point Likert scale, where group consensus was defined as a median rating of ≥5. Statements reaching consensus were accepted for consideration onto the guideline whilst those not reaching consensus were re-distributed for consideration in round 3. Statements presented in the guidelines were graded from A (strong consensus) to D (no consensus) determined by median score and interquartile range. 11 CHM practitioners in the UK were recruited. After three Delphi rounds, 91 statement items in total had been considered, of which 89 (97.8%) reached consensus and 2 (2.2%) did not. The concluding set of guidelines consists of 85 items representing key features of CHM prescribing for PCOS. These guidelines can be viewed as an initial framework that captures fundamental principles of good clinical practice for CHM. Copyright © 2015 Elsevier Ltd. All rights reserved.

Terminal Sliding Mode-Based Consensus Tracking Control for Networked Uncertain Mechanical Systems on Digraphs.

PubMed

Chen, Gang; Song, Yongduan; Guan, Yanfeng

2018-03-01

This brief investigates the finite-time consensus tracking control problem for networked uncertain mechanical systems on digraphs. A new terminal sliding-mode-based cooperative control scheme is developed to guarantee that the tracking errors converge to an arbitrarily small bound around zero in finite time. All the networked systems can have different dynamics and all the dynamics are unknown. A neural network is used at each node to approximate the local unknown dynamics. The control schemes are implemented in a fully distributed manner. The proposed control method eliminates some limitations in the existing terminal sliding-mode-based consensus control methods and extends the existing analysis methods to the case of directed graphs. Simulation results on networked robot manipulators are provided to show the effectiveness of the proposed control algorithms.

Engineering large-scale agent-based systems with consensus

NASA Technical Reports Server (NTRS)

Bokma, A.; Slade, A.; Kerridge, S.; Johnson, K.

1994-01-01

The paper presents the consensus method for the development of large-scale agent-based systems. Systems can be developed as networks of knowledge based agents (KBA) which engage in a collaborative problem solving effort. The method provides a comprehensive and integrated approach to the development of this type of system. This includes a systematic analysis of user requirements as well as a structured approach to generating a system design which exhibits the desired functionality. There is a direct correspondence between system requirements and design components. The benefits of this approach are that requirements are traceable into design components and code thus facilitating verification. The use of the consensus method with two major test applications showed it to be successful and also provided valuable insight into problems typically associated with the development of large systems.

Membership determination of open clusters based on a spectral clustering method

NASA Astrophysics Data System (ADS)

Gao, Xin-Hua

2018-06-01

We present a spectral clustering (SC) method aimed at segregating reliable members of open clusters in multi-dimensional space. The SC method is a non-parametric clustering technique that performs cluster division using eigenvectors of the similarity matrix; no prior knowledge of the clusters is required. This method is more flexible in dealing with multi-dimensional data compared to other methods of membership determination. We use this method to segregate the cluster members of five open clusters (Hyades, Coma Ber, Pleiades, Praesepe, and NGC 188) in five-dimensional space; fairly clean cluster members are obtained. We find that the SC method can capture a small number of cluster members (weak signal) from a large number of field stars (heavy noise). Based on these cluster members, we compute the mean proper motions and distances for the Hyades, Coma Ber, Pleiades, and Praesepe clusters, and our results are in general quite consistent with the results derived by other authors. The test results indicate that the SC method is highly suitable for segregating cluster members of open clusters based on high-precision multi-dimensional astrometric data such as Gaia data.

Maastricht Delphi Consensus on Event Definitions for Classification of Recurrence in Breast Cancer Research

PubMed Central

van Roozendaal, Lori M.; Strobbe, Luc J. A.; Aebi, Stefan; Cameron, David A.; Dixon, J. Michael; Giuliano, Armando E.; Haffty, Bruce G.; Hickey, Brigid E.; Hudis, Clifford A.; Klimberg, V. Suzanne; Koczwara, Bogda; Kühn, Thorsten; Lippman, Marc E.; Lucci, Anthony; Piccart, Martine; Smith, Benjamin D.; Tjan-Heijnen, Vivianne C. G.; van de Velde, Cornelis J. H.; Van Zee, Kimberly J.; Vermorken, Jan B.; Viale, Giuseppe; Voogd, Adri C.; Wapnir, Irene L.; White, Julia R.; Smidt, Marjolein L.

2014-01-01

Background In breast cancer studies, many different endpoints are used. Definitions are often not provided or vary between studies. For instance, “local recurrence” may include different components in similar studies. This limits transparency and comparability of results. This project aimed to reach consensus on the definitions of local event, second primary breast cancer, regional and distant event for breast cancer studies. Methods The RAND-UCLA Appropriateness method (modified Delphi method) was used. A Consensus Group of international breast cancer experts was formed, including representatives of all involved clinical disciplines. Consensus was reached in two rounds of online questionnaires and one meeting. Results Twenty-four international breast cancer experts participated. Consensus was reached on 134 items in four categories. Local event is defined as any epithelial breast cancer or ductal carcinoma in situ (DCIS) in the ipsilateral breast, or skin and subcutaneous tissue on the ipsilateral thoracic wall. Second primary breast cancer is defined as epithelial breast cancer in the contralateral breast. Regional events are breast cancer in ipsilateral lymph nodes. A distant event is breast cancer in any other location. Therefore, this includes metastasis in contralateral lymph nodes and breast cancer involving the sternal bone. If feasible, tissue sampling of a first, solitary, lesion suspected for metastasis is highly recommended. Conclusion This project resulted in consensus-based event definitions for classification of recurrence in breast cancer research. Future breast cancer research projects should adopt these definitions to increase transparency. This should facilitate comparison of results and conducting reviews as well as meta-analysis. PMID:25381395

To Clone or Not To Clone: Method Analysis for Retrieving Consensus Sequences In Ancient DNA Samples

PubMed Central

Winters, Misa; Barta, Jodi Lynn; Monroe, Cara; Kemp, Brian M.

2011-01-01

The challenges associated with the retrieval and authentication of ancient DNA (aDNA) evidence are principally due to post-mortem damage which makes ancient samples particularly prone to contamination from “modern” DNA sources. The necessity for authentication of results has led many aDNA researchers to adopt methods considered to be “gold standards” in the field, including cloning aDNA amplicons as opposed to directly sequencing them. However, no standardized protocol has emerged regarding the necessary number of clones to sequence, how a consensus sequence is most appropriately derived, or how results should be reported in the literature. In addition, there has been no systematic demonstration of the degree to which direct sequences are affected by damage or whether direct sequencing would provide disparate results from a consensus of clones. To address this issue, a comparative study was designed to examine both cloned and direct sequences amplified from ∼3,500 year-old ancient northern fur seal DNA extracts. Majority rules and the Consensus Confidence Program were used to generate consensus sequences for each individual from the cloned sequences, which exhibited damage at 31 of 139 base pairs across all clones. In no instance did the consensus of clones differ from the direct sequence. This study demonstrates that, when appropriate, cloning need not be the default method, but instead, should be used as a measure of authentication on a case-by-case basis, especially when this practice adds time and cost to studies where it may be superfluous. PMID:21738625

ApiEST-DB: analyzing clustered EST data of the apicomplexan parasites.

PubMed

Li, Li; Crabtree, Jonathan; Fischer, Steve; Pinney, Deborah; Stoeckert, Christian J; Sibley, L David; Roos, David S

2004-01-01

ApiEST-DB (http://www.cbil.upenn.edu/paradbs-servlet/) provides integrated access to publicly available EST data from protozoan parasites in the phylum Apicomplexa. The database currently incorporates a total of nearly 100,000 ESTs from several parasite species of clinical and/or veterinary interest, including Eimeria tenella, Neospora caninum, Plasmodium falciparum, Sarcocystis neurona and Toxoplasma gondii. To facilitate analysis of these data, EST sequences were clustered and assembled to form consensus sequences for each organism, and these assemblies were then subjected to automated annotation via similarity searches against protein and domain databases. The underlying relational database infrastructure, Genomics Unified Schema (GUS), enables complex biologically based queries, facilitating validation of gene models, identification of alternative splicing, detection of single nucleotide polymorphisms, identification of stage-specific genes and recognition of phylogenetically conserved and phylogenetically restricted sequences.

Approaching Etuaptmumk--introducing a consensus-based mixed method for health services research.

PubMed

Chatwood, Susan; Paulette, Francois; Baker, Ross; Eriksen, Astrid; Hansen, Ketil Lenert; Eriksen, Heidi; Hiratsuka, Vanessa; Lavoie, Josée; Lou, Wendy; Mauro, Ian; Orbinski, James; Pabrum, Nathalie; Retallack, Hanna; Brown, Adalsteinn

2015-01-01

With the recognized need for health systems' improvements in the circumpolar and indigenous context, there has been a call to expand the research agenda across all sectors influencing wellness and to recognize academic and indigenous knowledge through the research process. Despite being recognized as a distinct body of knowledge in international forums and across indigenous groups, examples of methods and theories based on indigenous knowledge are not well documented in academic texts or peer-reviewed literature on health systems. This paper describes the use of a consensus-based, mixed method with indigenous knowledge by an experienced group of researchers and indigenous knowledge holders who collaborated on a study that explored indigenous values underlying health systems stewardship. The method is built on the principles of Etuaptmumk or two-eyed seeing, which aim to respond to and resolve the inherent conflicts between indigenous ways of knowing and the scientific inquiry that informs the evidence base in health care. Mixed methods' frameworks appear to provide a framing suitable for research questions that require data from indigenous knowledge sources and western knowledge. The nominal consensus method, as a western paradigm, was found to be responsive to embedding of indigenous knowledge and allowed space to express multiple perspectives and reach consensus on the question at hand. Further utilization and critical evaluation of this mixed methodology with indigenous knowledge are required.

Ethnopharmacological implications of quantitative and network analysis for traditional knowledge regarding the medicinal use of animals by indigenous people in Wolchulsan National Park, Korea.

PubMed

Kim, Geun; Kim, Hyun; Song, Mi-Jang

2018-03-01

The purpose of this study was to record, analyze, and identify ethnopharmacological implications for oral traditional knowledge regarding the medicinal use of animals by indigenous people living in Wolchulsan National Park, Korea. Data were collected through interviews, informal meetings, open and group discussions, and observations guided by semi-structured questionnaires. Data were analyzed via quantitative analysis of informant consensus factor and fidelity level, and network analysis, including centrality and clustering analysis. A total of 46 families, 59 genera, and 60 species of animals, as well as 373 methods of usage, were recorded. Fish comprised 31.7% of the total animal species recorded, followed by mammals at 20.0%, arthropods at 18.3%, and mollusks at 11.7%. Of these animals, 48.0% were utilized as food and 46.1% for medicinal use. Quantitative analysis showed that the category with the highest degree of consensus from informants was veterinary ailments (informant consensus factor value, 0.96). This was followed by poisonings (0.93), pains (0.92), genitourinary system disorders (0.91), cuts and wounds (0.89), and other medical conditions. The lowest degree of consensus was for skin diseases and disorders (0.57). There were 8 species of animals with a fidelity level of 100%, after eliminating from the animals analyzed that were mentioned only once. Finally, using network analysis, Gallus gallus domesticus and Gloydius brevicaudus were defined as species with meaningful medicinal use, while lack of vigor and lung diseases were defined as significant ailments in the study area. This study validates that local communities use animals not only for food but also for medicinal purposes as crucial therapeutic measures. Therefore, the conservation of fauna and preservation of traditional knowledge need to be seriously considered to maintain the health and well-being of the local communities. Network analysis clarified the series of ailments for which each animal species is preferentially used and helped confirm the order of priority when prescribing animal components for medicinal use. The traditional knowledge recorded in the present study will provide the basic data to develop new medicines for the bioindustry. Copyright © 2017 Elsevier B.V. All rights reserved.

Hybrid fuzzy cluster ensemble framework for tumor clustering from biomolecular data.

PubMed

Yu, Zhiwen; Chen, Hantao; You, Jane; Han, Guoqiang; Li, Le

2013-01-01

Cancer class discovery using biomolecular data is one of the most important tasks for cancer diagnosis and treatment. Tumor clustering from gene expression data provides a new way to perform cancer class discovery. Most of the existing research works adopt single-clustering algorithms to perform tumor clustering is from biomolecular data that lack robustness, stability, and accuracy. To further improve the performance of tumor clustering from biomolecular data, we introduce the fuzzy theory into the cluster ensemble framework for tumor clustering from biomolecular data, and propose four kinds of hybrid fuzzy cluster ensemble frameworks (HFCEF), named as HFCEF-I, HFCEF-II, HFCEF-III, and HFCEF-IV, respectively, to identify samples that belong to different types of cancers. The difference between HFCEF-I and HFCEF-II is that they adopt different ensemble generator approaches to generate a set of fuzzy matrices in the ensemble. Specifically, HFCEF-I applies the affinity propagation algorithm (AP) to perform clustering on the sample dimension and generates a set of fuzzy matrices in the ensemble based on the fuzzy membership function and base samples selected by AP. HFCEF-II adopts AP to perform clustering on the attribute dimension, generates a set of subspaces, and obtains a set of fuzzy matrices in the ensemble by performing fuzzy c-means on subspaces. Compared with HFCEF-I and HFCEF-II, HFCEF-III and HFCEF-IV consider the characteristics of HFCEF-I and HFCEF-II. HFCEF-III combines HFCEF-I and HFCEF-II in a serial way, while HFCEF-IV integrates HFCEF-I and HFCEF-II in a concurrent way. HFCEFs adopt suitable consensus functions, such as the fuzzy c-means algorithm or the normalized cut algorithm (Ncut), to summarize generated fuzzy matrices, and obtain the final results. The experiments on real data sets from UCI machine learning repository and cancer gene expression profiles illustrate that 1) the proposed hybrid fuzzy cluster ensemble frameworks work well on real data sets, especially biomolecular data, and 2) the proposed approaches are able to provide more robust, stable, and accurate results when compared with the state-of-the-art single clustering algorithms and traditional cluster ensemble approaches.

Mapping tobacco industry strategies in South East Asia for action planning and surveillance

PubMed Central

Stillman, F; Hoang, M; Linton, R; Ritthiphakdee, B; Trochim, W

2008-01-01

Objective: To develop a comprehensive conceptual framework of tobacco industry tactics in four countries in South East Asia for the purpose of: (1) generating consensus on key areas of importance and feasibility for regional and cross country tobacco industry monitoring and surveillance; (2) developing measures to track and monitor the effects of the tobacco industry and to design counterstrategies; and (3) building capacity to improve tobacco control planning in the participating countries. Design: A structured conceptualisation methodology known as concept mapping was used. The process included brainstorming, sorting and rating of statements describing industry activities. Statistical analyses used multidimensional scaling and cluster analysis. Interpretation of the maps was participatory, using regional tobacco control researchers, practitioners, and policy makers during a face to face meeting. Participants: 31 participants in this study come from the four countries represented in the project along with six people from the Johns Hopkins Blomberg School of Public Health. Conclusions: The map shows eight clusters of industry activities within the four countries. These were arranged into four general sectors: economics, politics, public relations and deception. For project design purposes, the map indicates areas of importance and feasibility for monitoring tobacco industry activities and serves as a basis for an initial discussion about action planning. Furthermore, the development of the map used a consensus building process across different stakeholders or stakeholder agencies and is critical when developing regional, cross border strategies for tracking and surveillance. PMID:18218787

Methodological Quality of Consensus Guidelines in Implant Dentistry

PubMed Central

Faggion, Clovis Mariano; Apaza, Karol; Ariza-Fritas, Tania; Málaga, Lilian; Giannakopoulos, Nikolaos Nikitas; Alarcón, Marco Antonio

2017-01-01

Background Consensus guidelines are useful to improve clinical decision making. Therefore, the methodological evaluation of these guidelines is of paramount importance. Low quality information may guide to inadequate or harmful clinical decisions. Objective To evaluate the methodological quality of consensus guidelines published in implant dentistry using a validated methodological instrument. Methods The six implant dentistry journals with impact factors were scrutinised for consensus guidelines related to implant dentistry. Two assessors independently selected consensus guidelines, and four assessors independently evaluated their methodological quality using the Appraisal of Guidelines for Research & Evaluation (AGREE) II instrument. Disagreements in the selection and evaluation of guidelines were resolved by consensus. First, the consensus guidelines were analysed alone. Then, systematic reviews conducted to support the guidelines were included in the analysis. Non-parametric statistics for dependent variables (Wilcoxon signed rank test) was used to compare both groups. Results Of 258 initially retrieved articles, 27 consensus guidelines were selected. Median scores in four domains (applicability, rigour of development, stakeholder involvement, and editorial independence), expressed as percentages of maximum possible domain scores, were below 50% (median, 26%, 30.70%, 41.70%, and 41.70%, respectively). The consensus guidelines and consensus guidelines + systematic reviews data sets could be compared for 19 guidelines, and the results showed significant improvements in all domain scores (p < 0.05). Conclusions Methodological improvement of consensus guidelines published in major implant dentistry journals is needed. The findings of the present study may help researchers to better develop consensus guidelines in implant dentistry, which will improve the quality and trust of information needed to make proper clinical decisions. PMID:28107405

International Society of Urological Pathology (ISUP) Consensus Conference on Handling and Staging of Radical Prostatectomy Specimens. Working group 4: seminal vesicles and lymph nodes.

PubMed

Berney, Daniel M; Wheeler, Thomas M; Grignon, David J; Epstein, Jonathan I; Griffiths, David F; Humphrey, Peter A; van der Kwast, Theo; Montironi, Rodolfo; Delahunt, Brett; Egevad, Lars; Srigley, John R

2011-01-01

The 2009 International Society of Urological Pathology Consensus Conference in Boston made recommendations regarding the standardization of pathology reporting of radical prostatectomy specimens. Issues relating to the infiltration of tumor into the seminal vesicles and regional lymph nodes were coordinated by working group 4. There was a consensus that complete blocking of the seminal vesicles was not necessary, although sampling of the junction of the seminal vesicles and prostate was mandatory. There was consensus that sampling of the vas deferens margins was not obligatory. There was also consensus that muscular wall invasion of the extraprostatic seminal vesicle only should be regarded as seminal vesicle invasion. Categorization into types of seminal vesicle spread was agreed by consensus to be not necessary. For examination of lymph nodes, there was consensus that special techniques such as frozen sectioning were of use only in high-risk cases. There was no consensus on the optimal sampling method for pelvic lymph node dissection specimens, although there was consensus that all lymph nodes should be completely blocked as a minimum. There was also a consensus that a count of the number of lymph nodes harvested should be attempted. In view of recent evidence, there was consensus that the diameter of the largest lymph node metastasis should be measured. These consensus decisions will hopefully clarify the difficult areas of pathological assessment in radical prostatectomy evaluation and improve the concordance of research series to allow more accurate assessment of patient prognosis.

Identification of carbapenem-resistant Pseudomonas aeruginosa in selected hospitals of the Gulf Cooperation Council States: dominance of high-risk clones in the region.

PubMed

Zowawi, Hosam M; Syrmis, Melanie W; Kidd, Timothy J; Balkhy, Hanan H; Walsh, Timothy R; Al Johani, Sameera M; Al Jindan, Reem Y; Alfaresi, Mubarak; Ibrahim, Emad; Al-Jardani, Amina; Al Salman, Jameela; Dashti, Ali A; Sidjabat, Hanna E; Baz, Omar; Trembizki, Ella; Whiley, David M; Paterson, David L

2018-04-17

The molecular epidemiology and resistance mechanisms of carbapenem-resistant Pseudomonas aeruginosa (CRPA) were determined in hospitals in the countries of the Gulf Cooperation Council (GCC), namely, Saudi Arabia, the United Arab Emirates, Oman, Qatar, Bahrain and Kuwait. Isolates were screened for common carbapenem-resistance genes by PCR. Relatedness between isolates was assessed using previously described genotyping methods: an informative-single nucleotide polymorphism MassARRAY iPLEX assay (iPLEX20SNP) and the enterobacterial repetitive intergenic consensus (ERIC)-PCR assay, with selected isolates being subjected to multilocus sequence typing (MLST). Ninety-five non-repetitive isolates that were found to be resistant to carbapenems were subjected to further investigation.Results/Key findings. The most prevalent carbapenemase-encoding gene, blaVIM-type, was found in 37/95 (39 %) isolates, while only 1 isolate (from UAE) was found to have blaIMP-type. None of the CRPA were found to have blaNDM-type or blaKPC-type. We found a total of 14 sequence type (ST) clusters, with 4 of these clusters being observed in more than 1 country. Several clusters belonged to the previously recognized internationally disseminated high-risk clones ST357, ST235, ST111, ST233 and ST654. We also found the less predominant ST316, ST308 and ST823 clones, and novel MLST types (ST2010, ST2011, ST2012 and ST2013), in our collection. Overall our data show that 'high-risk' CRPA clones are now detected in the region and highlight the need for strategies to limit further spread of such organisms, including enhanced surveillance, infection control precautions and further promotion of antibiotic stewardship programmes.

Organ-Level Analysis of Idioblast Patterning in Egeria densa Planch. Leaves

PubMed Central

Hara, Takuya; Kobayashi, Emi; Ohtsubo, Kohei; Kumada, Shogo; Kanazawa, Mikako; Abe, Tomoko; Itoh, Ryuuichi D.; Fujiwara, Makoto T.

2015-01-01

Leaf tissues of plants usually contain several types of idioblasts, defined as specialized cells whose shape and contents differ from the surrounding homogeneous cells. The spatial patterning of idioblasts, particularly of trichomes and guard cells, across the leaf epidermis has received considerable attention as it offers a useful biological model for studying the intercellular regulation of cell fate and patterning. Excretory idioblasts in the leaves of the aquatic monocotyledonous plant Egeria densa produced light blue autofluorescence when irradiated with ultraviolet light. The use of epifluorescence microscopy to detect this autofluorescence provided a simple and convenient method for detecting excretory idioblasts and allowed tracking of those cells across the leaf surfaces, enabling quantitative measurement of the clustering and spacing patterns of idioblasts at the whole leaf level. Occurrence of idioblasts was coordinated along the proximal–distal, medial–lateral, and adaxial–abaxial axes, producing a recognizable consensus spatial pattern of idioblast formation among fully expanded leaves. Idioblast clusters, which comprised up to nine cells aligned along the proximal–distal axis, showed no positional bias or regularity in idioblast-forming areas when compared with singlet idioblasts. Up to 75% of idioblasts existed as clusters on every leaf side examined. The idioblast-forming areas varied between leaves, implying phenotypic plasticity. Furthermore, in young expanding leaves, autofluorescence was occasionally detected in a single giant vesicle or else in one or more small vesicles, which eventually grew to occupy a large portion of the idioblast volume as a central vacuole. Differentiation of vacuoles by accumulating the fluorescence substance might be an integral part of idioblast differentiation. Red autofluorescence from chloroplasts was not detected in idioblasts of young expanding leaves, suggesting idioblast differentiation involves an arrest in chloroplast development at a very early stage, rather than transdifferentiation of chloroplast-containing epidermal cells. PMID:25742311

Antibiotic resistance and molecular typing among cockle (Anadara granosa) strains of Vibrio parahaemolyticus by polymerase chain reaction (PCR)-based analysis.

PubMed

Sahilah, A M; Laila, R A S; Sallehuddin, H Mohd; Osman, H; Aminah, A; Ahmad Azuhairi, A

2014-02-01

Genomic DNA of Vibrio parahaemolyticus were characterized by antibiotic resistance, enterobacterial repetitive intergenic consensus-polymerase chain reaction (ERIC-PCR) and random amplified polymorphic DNA-polymerase chain reaction (RAPD-PCR) analysis. These isolates originated from 3 distantly locations of Selangor, Negeri Sembilan and Melaka (East coastal areas), Malaysia. A total of 44 (n = 44) of tentatively V. parahaemolyticus were also examined for the presence of toxR, tdh and trh gene. Of 44 isolates, 37 were positive towards toxR gene; while, none were positive to tdh and trh gene. Antibiotic resistance analysis showed the V. parahaemolyticus isolates were highly resistant to bacitracin (92%, 34/37) and penicillin (89%, 33/37) followed by resistance towards ampicillin (68%, 25/37), cefuroxime (38%, 14/37), amikacin (6%, 2/37) and ceftazidime (14%, 5/37). None of the V. parahaemolyticus isolates were resistant towards chloramphenicol, ciprofloxacin, ceftriaxone, enrofloxacin, norfloxacin, streptomycin and vancomycin. Antibiogram patterns exhibited, 9 patterns and phenotypically less heterogenous when compared to PCR-based techniques using ERIC- and RAPD-PCR. The results of the ERIC- and RAPD-PCR were analyzed using GelCompare software. ERIC-PCR with primers ERIC1R and ERIC2 discriminated the V. parahaemolyticus isolates into 6 clusters and 21 single isolates at a similarity level of 80%. While, RAPD-PCR with primer Gen8 discriminated the V. parahaemolyticus isolates into 11 clusters and 10 single isolates and Gen9 into 8 clusters and 16 single isolates at the same similarity level examined. Results in the presence study demonstrated combination of phenotypically and genotypically methods show a wide heterogeneity among cockle isolates of V. parahaemolyticus.

A methodological study of genome-wide DNA methylation analyses using matched archival formalin-fixed paraffin embedded and fresh frozen breast tumors

PubMed Central

Yan, Li; Liu, Song; Tang, Li; Hu, Qiang; Morrison, Carl D.; Ambrosone, Christine B.; Higgins, Michael J.; Sucheston-Campbell, Lara E.

2017-01-01

Background DNA from archival formalin-fixed and paraffin embedded (FFPE) tissue is an invaluable resource for genome-wide methylation studies although concerns about poor quality may limit its use. In this study, we compared DNA methylation profiles of breast tumors using DNA from fresh-frozen (FF) tissues and three types of matched FFPE samples. Results For 9/10 patients, correlation and unsupervised clustering analysis revealed that the FF and FFPE samples were consistently correlated with each other and clustered into distinct subgroups. Greater than 84% of the top 100 loci previously shown to differentiate ER+ and ER– tumors in FF tissues were also FFPE DML. Weighted Correlation Gene Network Analyses (WCGNA) grouped the DML loci into 16 modules in FF tissue, with ~85% of the module membership preserved across tissue types. Materials and Methods Restored FFPE and matched FF samples were profiled using the Illumina Infinium HumanMethylation450K platform. Methylation levels (β-values) across all loci and the top 100 loci previously shown to differentiate tumors by estrogen receptor status (ER+ or ER−) in a larger FF study, were compared between matched FF and FFPE samples using Pearson's correlation, hierarchical clustering and WCGNA. Positive predictive values and sensitivity levels for detecting differentially methylated loci (DML) in FF samples were calculated in an independent FFPE cohort. Conclusions FFPE breast tumors samples show lower overall detection of DMLs versus FF, however FFPE and FF DMLs compare favorably. These results support the emerging consensus that the 450K platform can be employed to investigate epigenetics in large sets of archival FFPE tissues. PMID:28118602

Stellar systems in the direction of the Hickson Compact Group 44. I. Low surface brightness galaxies

NASA Astrophysics Data System (ADS)

Smith Castelli, A. V.; Faifer, F. R.; Escudero, C. G.

2016-11-01

Context. In spite of the numerous studies of low-luminosity galaxies in different environments, there is still no consensus about their formation scenario. In particular, a large number of galaxies displaying extremely low-surface brightnesses have been detected in the last year, and the nature of these objects is under discussion. Aims: In this paper we report the detection of two extended low-surface brightness (LSB) objects (μeffg' ≃ 27 mag) found, in projection, next to NGC 3193 and in the zone of the Hickson Compact Group (HCG) 44, respectively. Methods: We analyzed deep, high-quality, GEMINI-GMOS images with ELLIPSE within IRAF in order to obtain their brightness profiles and structural parameters. We also searched for the presence of globular clusters (GC) in these fields. Results: We have found that, if these LSB galaxies were at the distances of NGC 3193 and HCG 44, they would show sizes and luminosities similar to those of the ultra-diffuse galaxies (UDGs) found in the Coma cluster and other associations. In that case, their sizes would be rather larger than those displayed by the Local Group dwarf spheroidal (dSph) galaxies. We have detected a few unresolved sources in the sky zone occupied by these galaxies showing colors and brightnesses typical of blue globular clusters. Conclusions: From the comparison of the properties of the galaxies presented in this work with those of similar objects reported in the literature, we have found that LSB galaxies display sizes covering a quite extended continous range (reff 0.3-4.5 kpc), in contrast to "normal" early-type galaxies, which show reff 1.0 kpc with a low dispersion. This fact might point to different formation processes for both types of galaxies.

Continuous noninvasive orthostatic blood pressure measurements and their relationship with orthostatic intolerance, falls, and frailty in older people.

PubMed

Romero-Ortuno, Roman; Cogan, Lisa; Foran, Tim; Kenny, Rose Anne; Fan, Chie Wei

2011-04-01

To identify morphological orthostatic blood pressure (BP) phenotypes in older people and assess their correlation with orthostatic intolerance (OI), falls, and frailty and to compare the discriminatory performance of a morphological classification with two established orthostatic hypotension (OH) definitions: consensus (COH) and initial (IOH). Cross-sectional. Geriatric research clinic. Four hundred forty-two participants (mean age 72, 72% female) without dementia or risk factors for autonomic neuropathy. Active lying-to-standing test monitored using a continuous noninvasive BP monitor. For the morphological classification, four orthostatic systolic BP variables were extracted (delta (baseline - nadir) and maximum percentage of baseline recovered by 30 seconds and 1 and 2 minutes) using the 5-second averages method and entered in K-means cluster analysis (three clusters). Main outcomes were OI, falls (≥1 in past 6 months), and frailty (modified Fried criteria). The morphological clusters were small drop, fast overrecovery (n=112); medium drop, slow recovery (n=238); and large drop, nonrecovery (n=92). Their characterization revealed an increasing OI gradient (17.9%, 27.5%, and 44.6% respectively, P<.001) but no significant gradients in falls or frailty. The COH definition failed to reveal clinical differences between COH+ (n=416) and COH- (n=26) participants. The IOH definition resulted in a clinically meaningful separation between IOH+ (n=85) and IOH- (n=357) subgroups, as assessed according to OI (100% vs 11.5%, P<.001), falls (24.7% vs 10.4%, P<.001), and frailty (14.1% vs 5.4%, P=.005). It is recommended that the IOH definition be applied when taking continuous noninvasive orthostatic BP measurements in older people. © 2011, Copyright the Authors. Journal compilation © 2011, The American Geriatrics Society.

Systemic risk and spatiotemporal dynamics of the US housing market

PubMed Central

Meng, Hao; Xie, Wen-Jie; Jiang, Zhi-Qiang; Podobnik, Boris; Zhou, Wei-Xing; Stanley, H. Eugene

2014-01-01

Housing markets play a crucial role in economies and the collapse of a real-estate bubble usually destabilizes the financial system and causes economic recessions. We investigate the systemic risk and spatiotemporal dynamics of the US housing market (1975–2011) at the state level based on the Random Matrix Theory (RMT). We identify richer economic information in the largest eigenvalues deviating from RMT predictions for the housing market than for stock markets and find that the component signs of the eigenvectors contain either geographical information or the extent of differences in house price growth rates or both. By looking at the evolution of different quantities such as eigenvalues and eigenvectors, we find that the US housing market experienced six different regimes, which is consistent with the evolution of state clusters identified by the box clustering algorithm and the consensus clustering algorithm on the partial correlation matrices. We find that dramatic increases in the systemic risk are usually accompanied by regime shifts, which provide a means of early detection of housing bubbles. PMID:24413626

Inferring gene and protein interactions using PubMed citations and consensus Bayesian networks

PubMed Central

Dalman, Mark; Haddad, Joseph; Duan, Zhong-Hui

2017-01-01

The PubMed database offers an extensive set of publication data that can be useful, yet inherently complex to use without automated computational techniques. Data repositories such as the Genomic Data Commons (GDC) and the Gene Expression Omnibus (GEO) offer experimental data storage and retrieval as well as curated gene expression profiles. Genetic interaction databases, including Reactome and Ingenuity Pathway Analysis, offer pathway and experiment data analysis using data curated from these publications and data repositories. We have created a method to generate and analyze consensus networks, inferring potential gene interactions, using large numbers of Bayesian networks generated by data mining publications in the PubMed database. Through the concept of network resolution, these consensus networks can be tailored to represent possible genetic interactions. We designed a set of experiments to confirm that our method is stable across variation in both sample and topological input sizes. Using gene product interactions from the KEGG pathway database and data mining PubMed publication abstracts, we verify that regardless of the network resolution or the inferred consensus network, our method is capable of inferring meaningful gene interactions through consensus Bayesian network generation with multiple, randomized topological orderings. Our method can not only confirm the existence of currently accepted interactions, but has the potential to hypothesize new ones as well. We show our method confirms the existence of known gene interactions such as JAK-STAT-PI3K-AKT-mTOR, infers novel gene interactions such as RAS- Bcl-2 and RAS-AKT, and found significant pathway-pathway interactions between the JAK-STAT signaling and Cardiac Muscle Contraction KEGG pathways. PMID:29049295

Fractional discrete-time consensus models for single- and double-summator dynamics

NASA Astrophysics Data System (ADS)

Wyrwas, Małgorzata; Mozyrska, Dorota; Girejko, Ewa

2018-04-01

The leader-following consensus problem of fractional-order multi-agent discrete-time systems is considered. In the systems, interactions between opinions are defined like in Krause and Cucker-Smale models but the memory is included by taking the fractional-order discrete-time operator on the left-hand side of the nonlinear systems. In this paper, we investigate fractional-order models of opinions for the single- and double-summator dynamics of discrete-time by analytical methods as well as by computer simulations. The necessary and sufficient conditions for the leader-following consensus are formulated by proposing a consensus control law for tracking the virtual leader.

Fast and accurate de novo genome assembly from long uncorrected reads

PubMed Central

Vaser, Robert; Sović, Ivan; Nagarajan, Niranjan

2017-01-01

The assembly of long reads from Pacific Biosciences and Oxford Nanopore Technologies typically requires resource-intensive error-correction and consensus-generation steps to obtain high-quality assemblies. We show that the error-correction step can be omitted and that high-quality consensus sequences can be generated efficiently with a SIMD-accelerated, partial-order alignment–based, stand-alone consensus module called Racon. Based on tests with PacBio and Oxford Nanopore data sets, we show that Racon coupled with miniasm enables consensus genomes with similar or better quality than state-of-the-art methods while being an order of magnitude faster. PMID:28100585

A statistically compiled test battery for feasible evaluation of knee function after rupture of the Anterior Cruciate Ligament - derived from long-term follow-up data.

PubMed

Schelin, Lina; Tengman, Eva; Ryden, Patrik; Häger, Charlotte

2017-01-01

Clinical test batteries for evaluation of knee function after injury to the Anterior Cruciate Ligament (ACL) should be valid and feasible, while reliably capturing the outcome of rehabilitation. There is currently a lack of consensus as to which of the many available assessment tools for knee function that should be included. The present aim was to use a statistical approach to investigate the contribution of frequently used tests to avoid redundancy, and filter them down to a proposed comprehensive and yet feasible test battery for long-term evaluation after ACL injury. In total 48 outcome variables related to knee function, all potentially relevant for a long-term follow-up, were included from a cross-sectional study where 70 ACL-injured (17-28 years post injury) individuals were compared to 33 controls. Cluster analysis and logistic regression were used to group variables and identify an optimal test battery, from which a summarized estimator of knee function representing various functional aspects was derived. As expected, several variables were strongly correlated, and the variables also fell into logical clusters with higher within-correlation (max ρ = 0.61) than between clusters (max ρ = 0.19). An extracted test battery with just four variables assessing one-leg balance, isokinetic knee extension strength and hop performance (one-leg hop, side hop) were mathematically combined to an estimator of knee function, which acceptably classified ACL-injured individuals and controls. This estimator, derived from objective measures, correlated significantly with self-reported function, e.g. Lysholm score (ρ = 0.66; p<0.001). The proposed test battery, based on a solid statistical approach, includes assessments which are all clinically feasible, while also covering complementary aspects of knee function. Similar test batteries could be determined for earlier phases of ACL rehabilitation or to enable longitudinal monitoring. Such developments, established on a well-grounded consensus of measurements, would facilitate comparisons of studies and enable evidence-based rehabilitation.

Asian-Pacific Association for the Study of the Liver (APASL) consensus guidelines on invasive and non-invasive assessment of hepatic fibrosis: a 2016 update.

PubMed

Shiha, Gamal; Ibrahim, Alaa; Helmy, Ahmed; Sarin, Shiv Kumar; Omata, Masao; Kumar, Ashish; Bernstien, David; Maruyama, Hitushi; Saraswat, Vivek; Chawla, Yogesh; Hamid, Saeed; Abbas, Zaigham; Bedossa, Pierre; Sakhuja, Puja; Elmahatab, Mamun; Lim, Seng Gee; Lesmana, Laurentius; Sollano, Jose; Jia, Ji-Dong; Abbas, Bahaa; Omar, Ashraf; Sharma, Barjesh; Payawal, Diana; Abdallah, Ahmed; Serwah, Abdelhamid; Hamed, Abdelkhalek; Elsayed, Aly; AbdelMaqsod, Amany; Hassanein, Tarek; Ihab, Ahmed; GHaziuan, Hamsik; Zein, Nizar; Kumar, Manoj

2017-01-01

Hepatic fibrosis is a common pathway leading to liver cirrhosis, which is the end result of any injury to the liver. Accurate assessment of the degree of fibrosis is important clinically, especially when treatments aimed at reversing fibrosis are being evolved. Despite the fact that liver biopsy (LB) has been considered the "gold standard" of assessment of hepatic fibrosis, LB is not favored by patients or physicians owing to its invasiveness, limitations, sampling errors, etc. Therefore, many alternative approaches to assess liver fibrosis are gaining more popularity and have assumed great importance, and many data on such approaches are being generated. The Asian Pacific Association for the Study of the Liver (APASL) set up a working party on liver fibrosis in 2007, with a mandate to develop consensus guidelines on various aspects of liver fibrosis relevant to disease patterns and clinical practice in the Asia-Pacific region. The first consensus guidelines of the APASL recommendations on hepatic fibrosis were published in 2009. Due to advances in the field, we present herein the APASL 2016 updated version on invasive and non-invasive assessment of hepatic fibrosis. The process for the development of these consensus guidelines involved review of all available published literature by a core group of experts who subsequently proposed consensus statements followed by discussion of the contentious issues and unanimous approval of the consensus statements. The Oxford System of the evidence-based approach was adopted for developing the consensus statements using the level of evidence from one (highest) to five (lowest) and grade of recommendation from A (strongest) to D (weakest). The topics covered in the guidelines include invasive methods (LB and hepatic venous pressure gradient measurements), blood tests, conventional radiological methods, elastography techniques and cost-effectiveness of hepatic fibrosis assessment methods, in addition to fibrosis assessment in special and rare situations.

“Heroes” and “Villains” of World History across Cultures

PubMed Central

Hanke, Katja; Liu, James H.; Sibley, Chris G.; Paez, Dario; Gaines, Stanley O.; Moloney, Gail; Leong, Chan-Hoong; Wagner, Wolfgang; Licata, Laurent; Klein, Olivier; Garber, Ilya; Böhm, Gisela; Hilton, Denis J.; Valchev, Velichko; Khan, Sammyh S.; Cabecinhas, Rosa

2015-01-01

Emergent properties of global political culture were examined using data from the World History Survey (WHS) involving 6,902 university students in 37 countries evaluating 40 figures from world history. Multidimensional scaling and factor analysis techniques found only limited forms of universality in evaluations across Western, Catholic/Orthodox, Muslim, and Asian country clusters. The highest consensus across cultures involved scientific innovators, with Einstein having the most positive evaluation overall. Peaceful humanitarians like Mother Theresa and Gandhi followed. There was much less cross-cultural consistency in the evaluation of negative figures, led by Hitler, Osama bin Laden, and Saddam Hussein. After more traditional empirical methods (e.g., factor analysis) failed to identify meaningful cross-cultural patterns, Latent Profile Analysis (LPA) was used to identify four global representational profiles: Secular and Religious Idealists were overwhelmingly prevalent in Christian countries, and Political Realists were common in Muslim and Asian countries. We discuss possible consequences and interpretations of these different representational profiles. PMID:25651504

TEMPy: a Python library for assessment of three-dimensional electron microscopy density fits.

PubMed

Farabella, Irene; Vasishtan, Daven; Joseph, Agnel Praveen; Pandurangan, Arun Prasad; Sahota, Harpal; Topf, Maya

2015-08-01

Three-dimensional electron microscopy is currently one of the most promising techniques used to study macromolecular assemblies. Rigid and flexible fitting of atomic models into density maps is often essential to gain further insights into the assemblies they represent. Currently, tools that facilitate the assessment of fitted atomic models and maps are needed. TEMPy (template and electron microscopy comparison using Python) is a toolkit designed for this purpose. The library includes a set of methods to assess density fits in intermediate-to-low resolution maps, both globally and locally. It also provides procedures for single-fit assessment, ensemble generation of fits, clustering, and multiple and consensus scoring, as well as plots and output files for visualization purposes to help the user in analysing rigid and flexible fits. The modular nature of TEMPy helps the integration of scoring and assessment of fits into large pipelines, making it a tool suitable for both novice and expert structural biologists.

The Role of Recombination in the Origin and Evolution of Alu Subfamilies

PubMed Central

Teixeira-Silva, Ana; Silva, Raquel M.; Carneiro, João; Amorim, António; Azevedo, Luísa

2013-01-01

Alus are the most abundant and successful short interspersed nuclear elements found in primate genomes. In humans, they represent about 10% of the genome, although few are retrotransposition-competent and are clustered into subfamilies according to the source gene from which they evolved. Recombination between them can lead to genomic rearrangements of clinical and evolutionary significance. In this study, we have addressed the role of recombination in the origin of chimeric Alu source genes by the analysis of all known consensus sequences of human Alus. From the allelic diversity of Alu consensus sequences, validated in extant elements resulting from whole genome searches, distinct events of recombination were detected in the origin of particular subfamilies of AluS and AluY source genes. These results demonstrate that at least two subfamilies are likely to have emerged from ectopic Alu-Alu recombination, which stimulates further research regarding the potential of chimeric active Alus to punctuate the genome. PMID:23750218

Gardnerella infection as distinguished from cervical dysbacteriosis: the advantageous spin-off of cervical screening.

PubMed

Klomp, Johanna M; Ouwerkerk-Noordam, Elisabeth; Boon, Mathilde E; van Haaften, Maarten; Heintz, A Peter M

2009-01-01

To evaluate cytologic diagnoses of dysbacteriosis and Gardnerella infection and to obtain insight into the diagnostic problems of Gardnerella. One hundred randomly selected samples of each of 3 diagnostic series were rescreened by 2 pathologists, resulting in 2 rescreening diagnoses and a consensus diagnosis. A smear was considered unequivocal when the original O code and the O code of the consensus diagnoses were equal and discordant when the flora diagnoses of the 2 pathologists differed. Discordance was highest in the dysbacteriotic series (20%) and lowest in the healthy group (4%). Unequivocal diagnoses were established in 65% of the dysbacteriotic smears, 80% of the Gardnerella smears and 93% of the healthy smears. Misclassification of Gardnerella occurred in the presence of clusters of bacteria mixed with spermatozoa. Blue mountain cells in Gardnerella infection can be identified unequivocally in cervical smears. Because of the clinical importance of treating Gardnerella, such advantageous spin-offs of cervical screening should be exploited.

Multi-stakeholder perspectives in defining health-services quality in cataract care.

PubMed

Stolk-Vos, Aline C; van de Klundert, Joris J; Maijers, Niels; Zijlmans, Bart L M; Busschbach, Jan J V

2017-08-01

To develop a method to define a multi-stakeholder perspective on health-service quality that enables the expression of differences in systematically identified stakeholders' perspectives, and to pilot the approach for cataract care. Mixed-method study between 2014 and 2015. Cataract care in the Netherlands. Stakeholder representatives. We first identified and classified stakeholders using stakeholder theory. Participants established a multi-stakeholder perspective on quality of cataract care using concept mapping, this yielded a cluster map based on multivariate statistical analyses. Consensus-based quality dimensions were subsequently defined in a plenary stakeholder session. Stakeholders and multi-stakeholder perspective on health-service quality. Our analysis identified seven definitive stakeholders, as follows: the Dutch Ophthalmology Society, ophthalmologists, general practitioners, optometrists, health insurers, hospitals and private clinics. Patients, as dependent stakeholders, were considered to lack power by other stakeholders; hence, they were not classified as definitive stakeholders. Overall, 18 stakeholders representing ophthalmologists, general practitioners, optometrists, health insurers, hospitals, private clinics, patients, patient federations and the Dutch Healthcare Institute sorted 125 systematically collected indicators into the seven following clusters: patient centeredness and accessibility, interpersonal conduct and expectations, experienced outcome, clinical outcome, process and structure, medical technical acting and safety. Importance scores from stakeholders directly involved in the cataract service delivery process correlated strongly, as did scores from stakeholders not directly involved in this process. Using a case study on cataract care, the proposed methods enable different views among stakeholders concerning quality dimensions to be systematically revealed, and the stakeholders jointly agreed on these dimensions. The methods helped to unify different quality definitions and facilitated operationalisation of quality measurement in a way that was accepted by relevant stakeholders. © The Author 2017. Published by Oxford University Press in association with the International Society for Quality in Health Care. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com

Reaching Consensus on Essential Biomedical Science Learning Objectives in a Dental Curriculum.

PubMed

Best, Leandra; Walton, Joanne N; Walker, Judith; von Bergmann, HsingChi

2016-04-01

This article describes how the University of British Columbia Faculty of Dentistry reached consensus on essential basic biomedical science objectives for DMD students and applied the information to the renewal of its DMD curriculum. The Delphi Method was used to build consensus among dental faculty members and students regarding the relevance of over 1,500 existing biomedical science objectives. Volunteer panels of at least three faculty members (a basic scientist, a general dentist, and a dental specialist) and a fourth-year dental student were formed for each of 13 biomedical courses in the first two years of the program. Panel members worked independently and anonymously, rating each course objective as "need to know," "nice to know," "irrelevant," or "don't know." Panel members were advised after each round which objectives had not yet achieved a 75% consensus and were asked to reconsider their ratings. After a maximum of three rounds to reach consensus, a second group of faculty experts reviewed and refined the results to establish the biomedical science objectives for the renewed curriculum. There was consensus on 46% of the learning objectives after round one, 80% after round two, and 95% after round three. The second expert group addressed any remaining objectives as part of its review process. Only 47% of previous biomedical science course objectives were judged to be essential or "need to know" for the general dentist. The consensus reached by participants in the Delphi Method panels and a second group of faculty experts led to a streamlined, better integrated DMD curriculum to prepare graduates for future practice.

48 CFR 11.107 - Solicitation provision.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 48 Federal Acquisition Regulations System 1 2010-10-01 2010-10-01 false Solicitation provision. 11... transaction-based reporting method to report its use of voluntary consensus standards to the National... Use of Voluntary Consensus Standards and in Conformity Assessment Activities”). Use of the provision...

Approaching Etuaptmumk – introducing a consensus-based mixed method for health services research

PubMed Central

Chatwood, Susan; Paulette, Francois; Baker, Ross; Eriksen, Astrid; Hansen, Ketil Lenert; Eriksen, Heidi; Hiratsuka, Vanessa; Lavoie, Josée; Lou, Wendy; Mauro, Ian; Orbinski, James; Pabrum, Nathalie; Retallack, Hanna; Brown, Adalsteinn

2015-01-01

With the recognized need for health systems’ improvements in the circumpolar and indigenous context, there has been a call to expand the research agenda across all sectors influencing wellness and to recognize academic and indigenous knowledge through the research process. Despite being recognized as a distinct body of knowledge in international forums and across indigenous groups, examples of methods and theories based on indigenous knowledge are not well documented in academic texts or peer-reviewed literature on health systems. This paper describes the use of a consensus-based, mixed method with indigenous knowledge by an experienced group of researchers and indigenous knowledge holders who collaborated on a study that explored indigenous values underlying health systems stewardship. The method is built on the principles of Etuaptmumk or two-eyed seeing, which aim to respond to and resolve the inherent conflicts between indigenous ways of knowing and the scientific inquiry that informs the evidence base in health care. Mixed methods’ frameworks appear to provide a framing suitable for research questions that require data from indigenous knowledge sources and western knowledge. The nominal consensus method, as a western paradigm, was found to be responsive to embedding of indigenous knowledge and allowed space to express multiple perspectives and reach consensus on the question at hand. Further utilization and critical evaluation of this mixed methodology with indigenous knowledge are required. PMID:26004427

Performance Assessment of Kernel Density Clustering for Gene Expression Profile Data

PubMed Central

Zeng, Beiyan; Chen, Yiping P.; Smith, Oscar H.

2003-01-01

Kernel density smoothing techniques have been used in classification or supervised learning of gene expression profile (GEP) data, but their applications to clustering or unsupervised learning of those data have not been explored and assessed. Here we report a kernel density clustering method for analysing GEP data and compare its performance with the three most widely-used clustering methods: hierarchical clustering, K-means clustering, and multivariate mixture model-based clustering. Using several methods to measure agreement, between-cluster isolation, and withincluster coherence, such as the Adjusted Rand Index, the Pseudo F test, the r2 test, and the profile plot, we have assessed the effectiveness of kernel density clustering for recovering clusters, and its robustness against noise on clustering both simulated and real GEP data. Our results show that the kernel density clustering method has excellent performance in recovering clusters from simulated data and in grouping large real expression profile data sets into compact and well-isolated clusters, and that it is the most robust clustering method for analysing noisy expression profile data compared to the other three methods assessed. PMID:18629292

[Molecular variability in the commom shrew Sorex araneus L. from European Russia and Siberia inferred from the length polymorphism of DNA regions flanked by short interspersed elements (Inter-SINE PCR) and the relationships between the Moscow and Seliger chromosome races].

PubMed

Bannikova, A A; Bulatova, N Sh; Kramerov, D A

2006-06-01

Genetic exchange among chromosomal races of the common shrew Sorex araneus and the problem of reproductive barriers have been extensively studied by means of such molecular markers as mtDNA, microsatellites, and allozymes. In the present study, the interpopulation and interracial polymorphism in the common shrew was derived, using fingerprints generated by amplified DNA regions flanked by short interspersed repeats (SINEs)-interSINE PCR (IS-PCR). We used primers, complementary to consensus sequences of two short retroposons: mammalian element MIR and the SOR element from the genome of Sorex araneus. Genetic differentiation among eleven populations of the common shrew from eight chromosome races was estimated. The NP and MJ analyses, as well as multidimensional scaling showed that all samples examined grouped into two main clusters, corresponding to European Russia and Siberia. The bootstrap support of the European Russia cluster in the NJ and MP analyses was respectively 76 and 61%. The bootstrap index for the Siberian cluster was 100% in both analyses; the Tomsk race, included into this cluster, was separated with the bootstrap support of NJ/MP 92/95%.

Knowledge, beliefs and use of nursing methods in preventing pressure sores in Dutch hospitals.

PubMed

Halfens, R J; Eggink, M

1995-02-01

Different methods have been developed in the past to prevent patients from developing pressure sores. The consensus guidelines developed in the Netherlands make a distinction between preventive methods useful for all patients, methods useful only in individual cases, and methods which are not useful at all. This study explores the extent of use of the different methods within Dutch hospitals, and the knowledge and beliefs of nurses regarding the usefulness of these methods. A mail questionnaire was sent to a representative sample of nurses working within Dutch hospitals. A total of 373 questionnaires were returned and used for the analyses. The results showed that many methods judged by the consensus report as not useful, or only useful in individual cases, are still being used. Some methods which are judged as useful, like the use of a risk assessment scale, are used on only a few wards. The opinion of nurses regarding the usefulness of the methods differ from the guidelines of the consensus committee. Although there is agreement about most of the useful methods, there is less agreement about the methods which are useful in individual cases or methods which are not useful at all. In particular the use of massage and cream are, in the opinion of the nurses, useful in individual or in all cases.

The management of abdominal wall hernias – in search of consensus

PubMed Central

Bury, Kamil; Śmietański, Maciej

2015-01-01

Introduction Laparoscopic repair is becoming an increasingly popular alternative in the treatment of abdominal wall hernias. In spite of numerous studies evaluating this technique, indications for laparoscopic surgery have not been established. Similarly, implant selection and fixation techniques have not been unified and are the subject of scientific discussion. Aim To assess whether there is a consensus on the management of the most common ventral abdominal wall hernias among recognised experts. Material and methods Fourteen specialists representing the boards of European surgical societies were surveyed to determine their choice of surgical technique for nine typical primary ventral and incisional hernias. The access method, type of operation, mesh prosthesis and fixation method were evaluated. In addition to the laparoscopic procedures, the number of tackers and their arrangement were assessed. Results In none of the cases presented was a consensus of experts obtained. Laparoscopic and open techniques were used equally often. Especially in the group of large hernias, decisions on repair methods were characterised by high variability. The technique of laparoscopic mesh fixation was a subject of great variability in terms of both method selection and the numbers of tackers and sutures used. Conclusions Recognised experts have not reached a consensus on the management of abdominal wall hernias. Our survey results indicate the need for further research and the inclusion of large cohorts of patients in the dedicated registries to evaluate the results of different surgical methods, which would help in the development of treatment algorithms for surgical education in the future. PMID:25960793

Validation of consensus panel diagnosis in dementia.

PubMed

Gabel, Matthew J; Foster, Norman L; Heidebrink, Judith L; Higdon, Roger; Aizenstein, Howard J; Arnold, Steven E; Barbas, Nancy R; Boeve, Bradley F; Burke, James R; Clark, Christopher M; Dekosky, Steven T; Farlow, Martin R; Jagust, William J; Kawas, Claudia H; Koeppe, Robert A; Leverenz, James B; Lipton, Anne M; Peskind, Elaine R; Turner, R Scott; Womack, Kyle B; Zamrini, Edward Y

2010-12-01

The clinical diagnosis of dementing diseases largely depends on the subjective interpretation of patient symptoms. Consensus panels are frequently used in research to determine diagnoses when definitive pathologic findings are unavailable. Nevertheless, research on group decision making indicates that many factors can adversely affect panel performance. To determine conditions that improve consensus panel diagnosis. Comparison of neuropathologic diagnoses with individual and consensus panel diagnoses based on clinical scenarios only, fludeoxyglucose F 18 positron emission tomography images only, and scenarios plus images. Expert and trainee individual and consensus panel deliberations using a modified Delphi method in a pilot research study of the diagnostic utility of fludeoxyglucose F 18 positron emission tomography. Forty-five patients with pathologically confirmed Alzheimer disease or frontotemporal dementia. Statistical measures of diagnostic accuracy, agreement, and confidence for individual raters and panelists before and after consensus deliberations. The consensus protocol using trainees and experts surpassed the accuracy of individual expert diagnoses when clinical information elicited diverse judgments. In these situations, consensus was 3.5 times more likely to produce positive rather than negative changes in the accuracy and diagnostic certainty of individual panelists. A rule that forced group consensus was at least as accurate as majority and unanimity rules. Using a modified Delphi protocol to arrive at a consensus diagnosis is a reasonable substitute for pathologic information. This protocol improves diagnostic accuracy and certainty when panelist judgments differ and is easily adapted to other research and clinical settings while avoiding the potential pitfalls of group decision making.

International, Expert-Based, Consensus Statement Regarding the Management of Acute Diverticulitis.

PubMed

O'Leary, D Peter; Lynch, Noel; Clancy, Cillian; Winter, Desmond C; Myers, Eddie

2015-09-01

This Delphi study provides consensus related to many aspects of acute diverticulitis and identifies other areas in need of research. To generate an international, expert-based, consensus statement to address controversies in the management of acute diverticulitis. This study was conducted using the Delphi technique from April 3 through October 21, 2014. A survey website was used and a panel of acute diverticulitis experts was formed via the snowball method. The top 5 acute diverticulitis experts in 5 international geographic regions were identified based on their number of publications related to acute diverticulitis. The Delphi study used 3 rounds of questions, after which the consensus statement was collated. A consensus statement related to the management of acute diverticulitis. Twenty items were selected for inclusion in the consensus statement following 3 rounds of questioning. A clear definition of uncomplicated and complicated diverticulitis is provided. In uncomplicated diverticulitis, consensus was reached regarding appropriate laboratory and radiological evaluation of patients as well as nonsurgical, surgical, and follow-up strategies. A number of important topics, including antibiotic treatment, failed to reach consensus. In addition, consensus was reached regarding many nonsurgical and surgical treatment strategies in complicated diverticulitis. Controversy continues internationally regarding the management of acute diverticulitis. This study demonstrates that there is more nonconsensus among experts than consensus regarding most issues, even in the same region. It also provides insight into the status quo regarding the treatment of acute diverticulitis and provides important direction for future research.

Speaker Linking and Applications using Non-Parametric Hashing Methods

DTIC Science & Technology

2016-09-08

clustering method based on hashing—canopy- clustering . We apply this method to a large corpus of speaker recordings, demonstrate performance tradeoffs...and compare to other hash- ing methods. Index Terms: speaker recognition, clustering , hashing, locality sensitive hashing. 1. Introduction We assume...speaker in our corpus. Second, given a QBE method, how can we perform speaker clustering —each clustering should be a single speaker, and a cluster should

MQAPRank: improved global protein model quality assessment by learning-to-rank.

PubMed

Jing, Xiaoyang; Dong, Qiwen

2017-05-25

Protein structure prediction has achieved a lot of progress during the last few decades and a greater number of models for a certain sequence can be predicted. Consequently, assessing the qualities of predicted protein models in perspective is one of the key components of successful protein structure prediction. Over the past years, a number of methods have been developed to address this issue, which could be roughly divided into three categories: single methods, quasi-single methods and clustering (or consensus) methods. Although these methods achieve much success at different levels, accurate protein model quality assessment is still an open problem. Here, we present the MQAPRank, a global protein model quality assessment program based on learning-to-rank. The MQAPRank first sorts the decoy models by using single method based on learning-to-rank algorithm to indicate their relative qualities for the target protein. And then it takes the first five models as references to predict the qualities of other models by using average GDT_TS scores between reference models and other models. Benchmarked on CASP11 and 3DRobot datasets, the MQAPRank achieved better performances than other leading protein model quality assessment methods. Recently, the MQAPRank participated in the CASP12 under the group name FDUBio and achieved the state-of-the-art performances. The MQAPRank provides a convenient and powerful tool for protein model quality assessment with the state-of-the-art performances, it is useful for protein structure prediction and model quality assessment usages.

MobiDB-lite: fast and highly specific consensus prediction of intrinsic disorder in proteins.

PubMed

Necci, Marco; Piovesan, Damiano; Dosztányi, Zsuzsanna; Tosatto, Silvio C E

2017-05-01

Intrinsic disorder (ID) is established as an important feature of protein sequences. Its use in proteome annotation is however hampered by the availability of many methods with similar performance at the single residue level, which have mostly not been optimized to predict long ID regions of size comparable to domains. Here, we have focused on providing a single consensus-based prediction, MobiDB-lite, optimized for highly specific (i.e. few false positive) predictions of long disorder. The method uses eight different predictors to derive a consensus which is then filtered for spurious short predictions. Consensus prediction is shown to outperform the single methods when annotating long ID regions. MobiDB-lite can be useful in large-scale annotation scenarios and has indeed already been integrated in the MobiDB, DisProt and InterPro databases. MobiDB-lite is available as part of the MobiDB database from URL: http://mobidb.bio.unipd.it/. An executable can be downloaded from URL: http://protein.bio.unipd.it/mobidblite/. silvio.tosatto@unipd.it. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

A practical guideline for examining a uterine niche using ultrasonography in non-pregnant women: a modified Delphi method amongst European experts.

PubMed

Jordans, I P M; de Leeuw, R; Stegwee, S I; Amso, N N; Barri-Soldevila, P N; van den Bosch, T; Bourne, T; Brolmann, H A M; Donnez, O; Dueholm, M; Hehenkamp, W J K; Jastrow, N; Jurkovic, D; Mashiach, R; Naji, O; Streuli, I; Timmerman, D; Vd Voet, L F; Huirne, J A F

2018-03-14

To generate a uniform, internationally recognized guideline for detailed uterine niche evaluation by ultrasonography in non-pregnant women using a modified Delphi method amongst international experts. Fifteen international gynecological experts were recruited by their membership of the European niche taskforce group. All experts were physicians with extensive experience in niche evaluation in clinical practice and/or authors of niche studies. Relevant items for niche measurement were determined based on the results of a literature search and recommendations of a focus group. Two online questionnaires were sent to the expert panel and one group meeting was organized. Consensus was predefined as a consensus rate of at least 70%. In total 15 experts participated in this study. Consensus was reached for a total of 42 items on niche evaluation, including definitions, relevance, method of measurement and tips for visualization of the niche. All experts agreed on the proposed guideline for niche evaluation in non-pregnant women as presented in this paper. Consensus between niche experts was achieved on all items regarding ultrasonographic niche measurement. This article is protected by copyright. All rights reserved.

Earliest evidence for the structure of Homo sapiens populations in Africa

NASA Astrophysics Data System (ADS)

Scerri, Eleanor M. L.; Drake, Nick A.; Jennings, Richard; Groucutt, Huw S.

2014-10-01

Understanding the structure and variation of Homo sapiens populations in Africa is critical for interpreting multiproxy evidence of their subsequent dispersals into Eurasia. However, there is no consensus on early H. sapiens demographic structure, or its effects on intra-African dispersals. Here, we show how a patchwork of ecological corridors and bottlenecks triggered a successive budding of populations across the Sahara. Using a temporally and spatially explicit palaeoenvironmental model, we found that the Sahara was not uniformly ameliorated between ∼130 and 75 thousand years ago (ka), as has been stated. Model integration with multivariate analyses of corresponding stone tools then revealed several spatially defined technological clusters which correlated with distinct palaeobiomes. Similarities between technological clusters were such that they decreased with distance except where connected by palaeohydrological networks. These results indicate that populations at the Eurasian gateway were strongly structured, which has implications for refining the demographic parameters of dispersals out of Africa.

Thermal and Non-thermal Nature of the Soft Excess Emission from Sersic 159-03 observed with XMM-Newton

NASA Technical Reports Server (NTRS)

Bonamente, Massimiliano; Lieu, Richard; Mittaz, Jonathan P. D.; Kaastra, Jelle S.; Nevalainen, Jukka

2005-01-01

Several nearby clusters exhibit an excess of soft X-ray radiation which cannot be attributed to the hot virialized intra-cluster medium. There is no consensus to date on the origin of the excess emission: it could be either of thermal origin, or due to an inverse Compton scattering of the cosmic microwave background. Using high resolution XMM-Newton data of Sersic 159-03 we first show that strong soft excess emission is detected out to a radial distance of 0.9 Mpc. The data are interpreted using the two viable models available, i.e., by invoking a warm reservoir of thermal gas, or relativistic electrons which are part of a cosmic ray population. The thermal model leads to a better goodness-of-fit, and the emitting warm gas must be high in mass and low in metallicity.

Choosing a Cluster Sampling Design for Lot Quality Assurance Sampling Surveys

PubMed Central

Hund, Lauren; Bedrick, Edward J.; Pagano, Marcello

2015-01-01

Lot quality assurance sampling (LQAS) surveys are commonly used for monitoring and evaluation in resource-limited settings. Recently several methods have been proposed to combine LQAS with cluster sampling for more timely and cost-effective data collection. For some of these methods, the standard binomial model can be used for constructing decision rules as the clustering can be ignored. For other designs, considered here, clustering is accommodated in the design phase. In this paper, we compare these latter cluster LQAS methodologies and provide recommendations for choosing a cluster LQAS design. We compare technical differences in the three methods and determine situations in which the choice of method results in a substantively different design. We consider two different aspects of the methods: the distributional assumptions and the clustering parameterization. Further, we provide software tools for implementing each method and clarify misconceptions about these designs in the literature. We illustrate the differences in these methods using vaccination and nutrition cluster LQAS surveys as example designs. The cluster methods are not sensitive to the distributional assumptions but can result in substantially different designs (sample sizes) depending on the clustering parameterization. However, none of the clustering parameterizations used in the existing methods appears to be consistent with the observed data, and, consequently, choice between the cluster LQAS methods is not straightforward. Further research should attempt to characterize clustering patterns in specific applications and provide suggestions for best-practice cluster LQAS designs on a setting-specific basis. PMID:26125967

Choosing a Cluster Sampling Design for Lot Quality Assurance Sampling Surveys.

PubMed

Hund, Lauren; Bedrick, Edward J; Pagano, Marcello

2015-01-01

Lot quality assurance sampling (LQAS) surveys are commonly used for monitoring and evaluation in resource-limited settings. Recently several methods have been proposed to combine LQAS with cluster sampling for more timely and cost-effective data collection. For some of these methods, the standard binomial model can be used for constructing decision rules as the clustering can be ignored. For other designs, considered here, clustering is accommodated in the design phase. In this paper, we compare these latter cluster LQAS methodologies and provide recommendations for choosing a cluster LQAS design. We compare technical differences in the three methods and determine situations in which the choice of method results in a substantively different design. We consider two different aspects of the methods: the distributional assumptions and the clustering parameterization. Further, we provide software tools for implementing each method and clarify misconceptions about these designs in the literature. We illustrate the differences in these methods using vaccination and nutrition cluster LQAS surveys as example designs. The cluster methods are not sensitive to the distributional assumptions but can result in substantially different designs (sample sizes) depending on the clustering parameterization. However, none of the clustering parameterizations used in the existing methods appears to be consistent with the observed data, and, consequently, choice between the cluster LQAS methods is not straightforward. Further research should attempt to characterize clustering patterns in specific applications and provide suggestions for best-practice cluster LQAS designs on a setting-specific basis.

Towards a formal genealogical classification of the Lezgian languages (North Caucasus): testing various phylogenetic methods on lexical data.

PubMed

Kassian, Alexei

2015-01-01

A lexicostatistical classification is proposed for 20 languages and dialects of the Lezgian group of the North Caucasian family, based on meticulously compiled 110-item wordlists, published as part of the Global Lexicostatistical Database project. The lexical data have been subsequently analyzed with the aid of the principal phylogenetic methods, both distance-based and character-based: Starling neighbor joining (StarlingNJ), Neighbor joining (NJ), Unweighted pair group method with arithmetic mean (UPGMA), Bayesian Markov chain Monte Carlo (MCMC), Unweighted maximum parsimony (UMP). Cognation indexes within the input matrix were marked by two different algorithms: traditional etymological approach and phonetic similarity, i.e., the automatic method of consonant classes (Levenshtein distances). Due to certain reasons (first of all, high lexicographic quality of the wordlists and a consensus about the Lezgian phylogeny among Caucasologists), the Lezgian database is a perfect testing area for appraisal of phylogenetic methods. For the etymology-based input matrix, all the phylogenetic methods, with the possible exception of UMP, have yielded trees that are sufficiently compatible with each other to generate a consensus phylogenetic tree of the Lezgian lects. The obtained consensus tree agrees with the traditional expert classification as well as some of the previously proposed formal classifications of this linguistic group. Contrary to theoretical expectations, the UMP method has suggested the least plausible tree of all. In the case of the phonetic similarity-based input matrix, the distance-based methods (StarlingNJ, NJ, UPGMA) have produced the trees that are rather close to the consensus etymology-based tree and the traditional expert classification, whereas the character-based methods (Bayesian MCMC, UMP) have yielded less likely topologies.

Towards a Formal Genealogical Classification of the Lezgian Languages (North Caucasus): Testing Various Phylogenetic Methods on Lexical Data

PubMed Central

Kassian, Alexei

2015-01-01

A lexicostatistical classification is proposed for 20 languages and dialects of the Lezgian group of the North Caucasian family, based on meticulously compiled 110-item wordlists, published as part of the Global Lexicostatistical Database project. The lexical data have been subsequently analyzed with the aid of the principal phylogenetic methods, both distance-based and character-based: Starling neighbor joining (StarlingNJ), Neighbor joining (NJ), Unweighted pair group method with arithmetic mean (UPGMA), Bayesian Markov chain Monte Carlo (MCMC), Unweighted maximum parsimony (UMP). Cognation indexes within the input matrix were marked by two different algorithms: traditional etymological approach and phonetic similarity, i.e., the automatic method of consonant classes (Levenshtein distances). Due to certain reasons (first of all, high lexicographic quality of the wordlists and a consensus about the Lezgian phylogeny among Caucasologists), the Lezgian database is a perfect testing area for appraisal of phylogenetic methods. For the etymology-based input matrix, all the phylogenetic methods, with the possible exception of UMP, have yielded trees that are sufficiently compatible with each other to generate a consensus phylogenetic tree of the Lezgian lects. The obtained consensus tree agrees with the traditional expert classification as well as some of the previously proposed formal classifications of this linguistic group. Contrary to theoretical expectations, the UMP method has suggested the least plausible tree of all. In the case of the phonetic similarity-based input matrix, the distance-based methods (StarlingNJ, NJ, UPGMA) have produced the trees that are rather close to the consensus etymology-based tree and the traditional expert classification, whereas the character-based methods (Bayesian MCMC, UMP) have yielded less likely topologies. PMID:25719456

Developing syndrome definitions based on consensus and current use

PubMed Central

Dowling, John N; Baer, Atar; Buckeridge, David L; Cochrane, Dennis; Conway, Michael A; Elkin, Peter; Espino, Jeremy; Gunn, Julia E; Hales, Craig M; Hutwagner, Lori; Keller, Mikaela; Larson, Catherine; Noe, Rebecca; Okhmatovskaia, Anya; Olson, Karen; Paladini, Marc; Scholer, Matthew; Sniegoski, Carol; Thompson, David; Lober, Bill

2010-01-01

Objective Standardized surveillance syndromes do not exist but would facilitate sharing data among surveillance systems and comparing the accuracy of existing systems. The objective of this study was to create reference syndrome definitions from a consensus of investigators who currently have or are building syndromic surveillance systems. Design Clinical condition–syndrome pairs were catalogued for 10 surveillance systems across the United States and the representatives of these systems were brought together for a workshop to discuss consensus syndrome definitions. Results Consensus syndrome definitions were generated for the four syndromes monitored by the majority of the 10 participating surveillance systems: Respiratory, gastrointestinal, constitutional, and influenza-like illness (ILI). An important element in coming to consensus quickly was the development of a sensitive and specific definition for respiratory and gastrointestinal syndromes. After the workshop, the definitions were refined and supplemented with keywords and regular expressions, the keywords were mapped to standard vocabularies, and a web ontology language (OWL) ontology was created. Limitations The consensus definitions have not yet been validated through implementation. Conclusion The consensus definitions provide an explicit description of the current state-of-the-art syndromes used in automated surveillance, which can subsequently be systematically evaluated against real data to improve the definitions. The method for creating consensus definitions could be applied to other domains that have diverse existing definitions. PMID:20819870

Expert surgical consensus for prenatal counseling using the Delphi method.

PubMed

Berman, Loren; Jackson, Jordan; Miller, Kristen; Kowalski, Rebecca; Kolm, Paul; Luks, Francois I

2017-11-28

Pediatric surgeons frequently offer prenatal consultation for congenital pulmonary airway malformation (CPAM) and congenital diaphragmatic hernia (CDH); however, there is no evidence-based consensus to guide prenatal decision making and counseling for these conditions. Eliciting feedback from experts is integral to defining best practice regarding prenatal counseling and intervention. A Delphi consensus process was undertaken using a panel of pediatric surgeons identified as experts in fetal therapy to address current limitations. Areas of discrepancy in the literature on CPAM and CDH were identified and used to generate a list of content and intervention questions. Experts were invited to participate in an online Delphi survey. Items that did not reach first-round consensus were broken down into additional questions, and consensus was achieved in the second round. Fifty-four surgeons (69%) responded to at least one of the two survey rounds. During round one, consensus was reached on 54 of 89 survey questions (61%), and 45 new questions were developed. During round two, consensus was reached on 53 of 60 survey questions (88%). We determined expert consensus to establish guidelines regarding perinatal management of CPAM and CDH. Our results can help educate pediatric surgeons participating in perinatal care of these patients. V. Copyright © 2017 Elsevier Inc. All rights reserved.

Developing syndrome definitions based on consensus and current use.

PubMed

Chapman, Wendy W; Dowling, John N; Baer, Atar; Buckeridge, David L; Cochrane, Dennis; Conway, Michael A; Elkin, Peter; Espino, Jeremy; Gunn, Julia E; Hales, Craig M; Hutwagner, Lori; Keller, Mikaela; Larson, Catherine; Noe, Rebecca; Okhmatovskaia, Anya; Olson, Karen; Paladini, Marc; Scholer, Matthew; Sniegoski, Carol; Thompson, David; Lober, Bill

2010-01-01

Standardized surveillance syndromes do not exist but would facilitate sharing data among surveillance systems and comparing the accuracy of existing systems. The objective of this study was to create reference syndrome definitions from a consensus of investigators who currently have or are building syndromic surveillance systems. Clinical condition-syndrome pairs were catalogued for 10 surveillance systems across the United States and the representatives of these systems were brought together for a workshop to discuss consensus syndrome definitions. Consensus syndrome definitions were generated for the four syndromes monitored by the majority of the 10 participating surveillance systems: Respiratory, gastrointestinal, constitutional, and influenza-like illness (ILI). An important element in coming to consensus quickly was the development of a sensitive and specific definition for respiratory and gastrointestinal syndromes. After the workshop, the definitions were refined and supplemented with keywords and regular expressions, the keywords were mapped to standard vocabularies, and a web ontology language (OWL) ontology was created. The consensus definitions have not yet been validated through implementation. The consensus definitions provide an explicit description of the current state-of-the-art syndromes used in automated surveillance, which can subsequently be systematically evaluated against real data to improve the definitions. The method for creating consensus definitions could be applied to other domains that have diverse existing definitions.

Prevalence, Molecular Characterization, and Antibiotic Susceptibility of Vibrio parahaemolyticus from Ready-to-Eat Foods in China

PubMed Central

Xie, Tengfei; Xu, Xiaoke; Wu, Qingping; Zhang, Jumei; Cheng, Jianheng

2016-01-01

Vibrio parahaemolyticus is the leading cause of foodborne outbreaks, particularly outbreaks associated with consumption of fish and shellfish, and represents a major threat to human health worldwide. This bacterium harbors two main virulence factors: the thermostable direct hemolysin (TDH) and TDH-related hemolysin (TRH). Additionally, various serotypes have been identified. The extensive use of antibiotics is a contributing factor to the increasing incidence of antimicrobial-resistant V. parahaemolyticus. In the current study, we aimed to determine the incidence and features of V. parahaemolyticus in ready-to-eat (RTE) foods in China. We found 39 V. parahaemolyticus strains on Chinese RTE foods through investigation of 511 RTE foods samples from 24 cities in China. All isolates were analyzed for the presence of tdh and trh gene by PCR, serotyping was performed using multiplex PCR, antibiotic susceptibility analysis was carried out using the disk diffusion method, and molecular typing was performed using enterobacterial repetitive intergenic consensus sequence PCR (ERIC-PCR) typing and multilocus sequence typing (MLST). The results showed that none of the isolates were positive for tdh and trh. Most of the isolates (33.3%) were serotype O2. Antimicrobial susceptibility results indicated that most strains were resistant to streptomycin (89.7%), cefazolin (51.3%), and ampicillin (51.3%). The isolates were grouped into five clusters by ERIC-PCR and four clusters by MLST. We updated 10 novel loci and 33 sequence types (STs) in the MLST database. Thus, our findings demonstrated the presence of V. parahaemolyticus in Chinese RTE foods, provided insights into the dissemination of antibiotic-resistant strains, and improved our knowledge of methods of microbiological risk assessment in RTE foods. PMID:27148231

Addressing the knowledge gap in clinical recommendations for management and complete excision of clinically atypical nevi/dysplastic nevi: Pigmented Lesion Subcommittee consensus statement.

PubMed

Kim, Caroline C; Swetter, Susan M; Curiel-Lewandrowski, Clara; Grichnik, James M; Grossman, Douglas; Halpern, Allan C; Kirkwood, John M; Leachman, Sancy A; Marghoob, Ashfaq A; Ming, Michael E; Nelson, Kelly C; Veledar, Emir; Venna, Suraj S; Chen, Suephy C

2015-02-01

The management of clinically atypical nevi/dysplastic nevi (CAN/DN) is controversial, with few data to guide the process. Management recommendations for DN with positive histologic margins were developed by the Delphi method to achieve consensus among members of the Pigmented Lesion Subcommittee (PLS) of the Melanoma Prevention Working Group (MPWG) after reviewing the current evidence. To outline key issues related to the management of CAN/DN: (1) biopsies of CAN and how positive margins arise, (2) whether incompletely excised DN evolve into melanoma, (3) current data on the outcomes of DN with positive histologic margins, (4) consensus recommendations, and (5) a proposal for future studies, including a large-scale study to help guide the management of DN with positive margins. The literature, including recent studies examining management and outcomes of DN with positive margins between 2009 to 2014, was reviewed. A consensus statement by the PLS of the MPWG following review of the literature, group discussions, and a structured Delphi method consensus. This consensus statement reviews the complexities of management of CAN/DN. A review of the literature and 2 rounds of a structured Delphi consensus resulted in the following recommendations: (1) mildly and moderately DN with clear margins do not need to be reexcised, (2) mildly DN biopsied with positive histologic margins without clinical residual pigmentation may be safely observed rather than reexcised, and (3) observation may be a reasonable option for management of moderately DN with positive histologic margins without clinically apparent residual pigmentation; however, more data are needed to make definitive recommendations in this clinical scenario.

Development of consensus treatment plans for juvenile localized scleroderma: a roadmap toward comparative effectiveness studies in juvenile localized scleroderma.

PubMed

Li, Suzanne C; Torok, Kathryn S; Pope, Elena; Dedeoglu, Fatma; Hong, Sandy; Jacobe, Heidi T; Rabinovich, C Egla; Laxer, Ronald M; Higgins, Gloria C; Ferguson, Polly J; Lasky, Andrew; Baszis, Kevin; Becker, Mara; Campillo, Sarah; Cartwright, Victoria; Cidon, Michael; Inman, Christi J; Jerath, Rita; O'Neil, Kathleen M; Vora, Sheetal; Zeft, Andrew; Wallace, Carol A; Ilowite, Norman T; Fuhlbrigge, Robert C

2012-08-01

Juvenile localized scleroderma (LS) is a chronic inflammatory skin disorder associated with substantial morbidity and disability. Although a wide range of therapeutic strategies has been reported in the literature, a lack of agreement on treatment specifics and accepted methods for clinical assessment has made it difficult to compare approaches and identify optimal therapy. Our objective was to develop standardized treatment plans, clinical assessments, and response criteria for active, moderate to high severity juvenile LS. A core group of pediatric rheumatologists, dermatologists, and a lay advisor was engaged by the Childhood Arthritis and Rheumatology Research Alliance (CARRA) to develop standardized treatment plans and assessment parameters for juvenile LS using consensus methods/nominal group techniques. Recommendations were validated in 2 face-to-face conferences with a larger group of practitioners with expertise in juvenile LS and with the full membership of CARRA, which encompasses the majority of pediatric rheumatologists in the US and Canada. Consensus was achieved on standardized treatment plans that reflect the prevailing treatment practices of CARRA members. Standardized clinical assessment methods and provisional treatment response criteria were also developed. Greater than 90% of pediatric rheumatologists responding to a survey (66% of CARRA membership) affirmed the final recommendations and agreed to utilize these consensus plans to treat patients with juvenile LS. Using consensus methodology, we have developed standardized treatment plans and assessment methods for juvenile LS. The high level of support among pediatric rheumatologists will support future comparative effectiveness studies and enable the development of evidence-based guidelines for the treatment of juvenile LS. Copyright © 2012 by the American College of Rheumatology.

Semi-supervised clustering methods.

PubMed

Bair, Eric

2013-01-01

Cluster analysis methods seek to partition a data set into homogeneous subgroups. It is useful in a wide variety of applications, including document processing and modern genetics. Conventional clustering methods are unsupervised, meaning that there is no outcome variable nor is anything known about the relationship between the observations in the data set. In many situations, however, information about the clusters is available in addition to the values of the features. For example, the cluster labels of some observations may be known, or certain observations may be known to belong to the same cluster. In other cases, one may wish to identify clusters that are associated with a particular outcome variable. This review describes several clustering algorithms (known as "semi-supervised clustering" methods) that can be applied in these situations. The majority of these methods are modifications of the popular k-means clustering method, and several of them will be described in detail. A brief description of some other semi-supervised clustering algorithms is also provided.

Distributed Position-Based Consensus of Second-Order Multiagent Systems With Continuous/Intermittent Communication.

PubMed

Song, Qiang; Liu, Fang; Wen, Guanghui; Cao, Jinde; Yang, Xinsong

2017-04-24

This paper considers the position-based consensus in a network of agents with double-integrator dynamics and directed topology. Two types of distributed observer algorithms are proposed to solve the consensus problem by utilizing continuous and intermittent position measurements, respectively, where each observer does not interact with any other observers. For the case of continuous communication between network agents, some convergence conditions are derived for reaching consensus in the network with a single constant delay or multiple time-varying delays on the basis of the eigenvalue analysis and the descriptor method. When the network agents can only obtain intermittent position data from local neighbors at discrete time instants, the consensus in the network without time delay or with nonuniform delays is investigated by using the Wirtinger's inequality and the delayed-input approach. Numerical examples are given to illustrate the theoretical analysis.

Multi-Attribute Consensus Building Tool

ERIC Educational Resources Information Center

Shyyan, Vitaliy; Christensen, Laurene; Thurlow, Martha; Lazarus, Sheryl

2013-01-01

The Multi-Attribute Consensus Building (MACB) method is a quantitative approach for determining a group's opinion about the importance of each item (strategy, decision, recommendation, policy, priority, etc.) on a list (Vanderwood, & Erickson, 1994). This process enables a small or large group of participants to generate and discuss a set…

43 CFR 46.110 - Incorporating consensus-based management.

Code of Federal Regulations, 2010 CFR

2010-10-01

..., whenever practicable, use a consensus-based management approach to the NEPA process. (d) If the Responsible... to implementation of the bureau decision. It seeks to achieve agreement from diverse interests on the goals of, purposes of, and needs for bureau plans and activities, as well as the methods anticipated to...

43 CFR 46.110 - Incorporating consensus-based management.

Code of Federal Regulations, 2011 CFR

2011-10-01

..., whenever practicable, use a consensus-based management approach to the NEPA process. (d) If the Responsible... to implementation of the bureau decision. It seeks to achieve agreement from diverse interests on the goals of, purposes of, and needs for bureau plans and activities, as well as the methods anticipated to...

A spatial scan statistic for multiple clusters.

PubMed

Li, Xiao-Zhou; Wang, Jin-Feng; Yang, Wei-Zhong; Li, Zhong-Jie; Lai, Sheng-Jie

2011-10-01

Spatial scan statistics are commonly used for geographical disease surveillance and cluster detection. While there are multiple clusters coexisting in the study area, they become difficult to detect because of clusters' shadowing effect to each other. The recently proposed sequential method showed its better power for detecting the second weaker cluster, but did not improve the ability of detecting the first stronger cluster which is more important than the second one. We propose a new extension of the spatial scan statistic which could be used to detect multiple clusters. Through constructing two or more clusters in the alternative hypothesis, our proposed method accounts for other coexisting clusters in the detecting and evaluating process. The performance of the proposed method is compared to the sequential method through an intensive simulation study, in which our proposed method shows better power in terms of both rejecting the null hypothesis and accurately detecting the coexisting clusters. In the real study of hand-foot-mouth disease data in Pingdu city, a true cluster town is successfully detected by our proposed method, which cannot be evaluated to be statistically significant by the standard method due to another cluster's shadowing effect. Copyright © 2011 Elsevier Inc. All rights reserved.

Bootstrap-based methods for estimating standard errors in Cox's regression analyses of clustered event times.

PubMed

Xiao, Yongling; Abrahamowicz, Michal

2010-03-30

We propose two bootstrap-based methods to correct the standard errors (SEs) from Cox's model for within-cluster correlation of right-censored event times. The cluster-bootstrap method resamples, with replacement, only the clusters, whereas the two-step bootstrap method resamples (i) the clusters, and (ii) individuals within each selected cluster, with replacement. In simulations, we evaluate both methods and compare them with the existing robust variance estimator and the shared gamma frailty model, which are available in statistical software packages. We simulate clustered event time data, with latent cluster-level random effects, which are ignored in the conventional Cox's model. For cluster-level covariates, both proposed bootstrap methods yield accurate SEs, and type I error rates, and acceptable coverage rates, regardless of the true random effects distribution, and avoid serious variance under-estimation by conventional Cox-based standard errors. However, the two-step bootstrap method over-estimates the variance for individual-level covariates. We also apply the proposed bootstrap methods to obtain confidence bands around flexible estimates of time-dependent effects in a real-life analysis of cluster event times.

Multiple co-clustering based on nonparametric mixture models with heterogeneous marginal distributions

PubMed Central

Yoshimoto, Junichiro; Shimizu, Yu; Okada, Go; Takamura, Masahiro; Okamoto, Yasumasa; Yamawaki, Shigeto; Doya, Kenji

2017-01-01

We propose a novel method for multiple clustering, which is useful for analysis of high-dimensional data containing heterogeneous types of features. Our method is based on nonparametric Bayesian mixture models in which features are automatically partitioned (into views) for each clustering solution. This feature partition works as feature selection for a particular clustering solution, which screens out irrelevant features. To make our method applicable to high-dimensional data, a co-clustering structure is newly introduced for each view. Further, the outstanding novelty of our method is that we simultaneously model different distribution families, such as Gaussian, Poisson, and multinomial distributions in each cluster block, which widens areas of application to real data. We apply the proposed method to synthetic and real data, and show that our method outperforms other multiple clustering methods both in recovering true cluster structures and in computation time. Finally, we apply our method to a depression dataset with no true cluster structure available, from which useful inferences are drawn about possible clustering structures of the data. PMID:29049392

United3D: a protein model quality assessment program that uses two consensus based methods.

PubMed

Terashi, Genki; Oosawa, Makoto; Nakamura, Yuuki; Kanou, Kazuhiko; Takeda-Shitaka, Mayuko

2012-01-01

In protein structure prediction, such as template-based modeling and free modeling (ab initio modeling), the step that assesses the quality of protein models is very important. We have developed a model quality assessment (QA) program United3D that uses an optimized clustering method and a simple Cα atom contact-based potential. United3D automatically estimates the quality scores (Qscore) of predicted protein models that are highly correlated with the actual quality (GDT_TS). The performance of United3D was tested in the ninth Critical Assessment of protein Structure Prediction (CASP9) experiment. In CASP9, United3D showed the lowest average loss of GDT_TS (5.3) among the QA methods participated in CASP9. This result indicates that the performance of United3D to identify the high quality models from the models predicted by CASP9 servers on 116 targets was best among the QA methods that were tested in CASP9. United3D also produced high average Pearson correlation coefficients (0.93) and acceptable Kendall rank correlation coefficients (0.68) between the Qscore and GDT_TS. This performance was competitive with the other top ranked QA methods that were tested in CASP9. These results indicate that United3D is a useful tool for selecting high quality models from many candidate model structures provided by various modeling methods. United3D will improve the accuracy of protein structure prediction.

Evaluation of a combined triple method to detect causative HPV in oral and oropharyngeal squamous cell carcinomas: p16 Immunohistochemistry, Consensus PCR HPV-DNA, and In Situ Hybridization

PubMed Central

2012-01-01

Background Recent emerging evidences identify Human Papillomavirus (HPV) related Head and Neck squamous cell carcinomas (HN-SCCs) as a separate subgroup among Head and Neck Cancers with different epidemiology, histopathological characteristics, therapeutic response to chemo-radiation treatment and clinical outcome. However, there is not a worldwide consensus on the methods to be used in clinical practice. The endpoint of this study was to demonstrate the reliability of a triple method which combines evaluation of: 1. p16 protein expression by immunohistochemistry (p16-IHC); 2. HPV-DNA genotyping by consensus HPV-DNA PCR methods (Consensus PCR); and 3 viral integration into the host by in situ hybridization method (ISH). This triple method has been applied to HN-SCC originated from oral cavity (OSCC) and oropharynx (OPSCC), the two anatomical sites in which high risk (HR) HPVs have been clearly implicated as etiologic factors. Methylation-Specific PCR (MSP) was performed to study inactivation of p16-CDKN2a locus by epigenetic events. Reliability of multiple methods was measured by Kappa statistics. Results All the HN-SCCs confirmed HPV positive by PCR and/or ISH were also p16 positive by IHC, with the latter showing a very high level of sensitivity as single test (100% in both OSCC and OPSCC) but lower specificity level (74% in OSCC and 93% in OPSCC). Concordance analysis between ISH and Consensus PCR showed a faint agreement in OPSCC (κ = 0.38) and a moderate agreement in OSCC (κ = 0.44). Furthermore, the addition of double positive score (ISHpositive and Consensus PCR positive) increased significantly the specificity of HR-HPV detection on formalin-fixed paraffin embedded (FFPE) samples (100% in OSCC and 78.5% in OPSCC), but reduced the sensitivity (33% in OSCC and 60% in OPSCC). The significant reduction of sensitivity by the double method was compensated by a very high sensitivity of p16-IHC detection in the triple approach. Conclusions Although HR-HPVs detection is of utmost importance in clinical settings for the Head and Neck Cancer patients, there is no consensus on which to consider the 'golden standard' among the numerous detection methods available either as single test or combinations. Until recently, quantitative E6 RNA PCR has been considered the 'golden standard' since it was demonstrated to have very high accuracy level and very high statistical significance associated with prognostic parameters. In contrast, quantitative E6 DNA PCR has proven to have very high level of accuracy but lesser prognostic association with clinical outcome than the HPV E6 oncoprotein RNA PCR. However, although it is theoretically possible to perform quantitative PCR detection methods also on FFPE samples, they reach the maximum of accuracy on fresh frozen tissue. Furthermore, worldwide diagnostic laboratories have not all the same ability to analyze simultaneously both FFPE and fresh tissues with these quantitative molecular detection methods. Therefore, in the current clinical practice a p16-IHC test is considered as sufficient for HPV diagnostic in accordance with the recently published Head and Neck Cancer international guidelines. Although p16-IHC may serve as a good prognostic indicator, our study clearly demonstrated that it is not satisfactory when used exclusively as the only HPV detecting method. Adding ISH, although known as less sensitive than PCR-based detection methods, has the advantage to preserve the morphological context of HPV-DNA signals in FFPE samples and, thus increase the overall specificity of p16/Consensus PCR combination tests. PMID:22376902

Comparison of four morphometric definitions and a semiquantitative consensus reading for assessing prevalent vertebral fractures.

PubMed

Grados, F; Roux, C; de Vernejoul, M C; Utard, G; Sebert, J L; Fardellone, P

2001-01-01

The assessment of vertebral fracture in patients with osteoporosis by conventional radiography has been improved over the past 10 years using either the semiquantitative (SQ) method devised by Genant et al. or quantitative morphometry. However, there is still no internationally agreed definition for vertebral fracture and there have been few comparative studies between these different approaches. Our study assessed the reproducibility of the SQ method and of four commonly used morphometric algorithms (Melton's, Eastell's, Minne's and McCloskey's methods) for assessing prevalent vertebral fractures, and examined the agreement of each morphometric algorithm with a SQ consensus reading performed by three experts. With this consensus reading in place of a gold standard, we determined relative measures of sensitivity, specificity and optimal cutoff threshold for each morphometric algorithm. The study was conducted in 39 postmenopausal women who had at least one osteoporotic vertebral fracture. Normal values were derived from 84 healthy postmenopausal women with apparently normal vertebral bodies. Our results indicate that the concordance of SQ method was excellent (intraobserver agreement on serial radiographs = 96.4%, kappa = 0.91; agreement between individual readings and the consensus reading = 98%, kappa = 0.95). Three morphometric approaches demonstrated good intra- and interobserver concordance (Melton: intraobserver agreement on serial radiographs = 92.7%, kappa = 0.82, interobserver agreement = 91.1%, kappa = 0.79; Eastell: intraobserver agreement on serial radiographs = 87.6%, kappa = 0.66, interobserver agreement = 88.6%, kappa = 0.68; McCloskey: intraobserver agreement on serial radiographs = 91.5%, kappa = 0.72, interobserver agreement = 93.9%, kappa = 0.78). Except for McCloskey's method, the optimal cutoff thresholds defined in our study by highest kappa score or Youden index in comparison with the SQ consensus reading were near the cutoff thresholds that were arbitrarily fixed. The four morphometric algorithms provided a good agreement with the results of the SQ consensus reading, but the more complex algorithm did not provide better results and even if we adjusted the cutoff threshold, no morphometric algorithm agreed perfectly with the SQ consensus reading. We conclude that morphometric approaches currently used should not be employed alone to detect prevalent vertebral fractures in studies on osteoporosis, but should rather be used in combination with a visual assessment. The SQ approach that allows differential diagnosis of vertebral deformities and has demonstrated a better reproducibility can be employed alone when it is performed by experienced and well-trained readers.

Causal Evaluation of Acute Recurrent and Chronic Pancreatitis in Children: Consensus From the INSPPIRE Group.

PubMed

Gariepy, Cheryl E; Heyman, Melvin B; Lowe, Mark E; Pohl, John F; Werlin, Steven L; Wilschanski, Michael; Barth, Bradley; Fishman, Douglas S; Freedman, Steven D; Giefer, Matthew J; Gonska, Tanja; Himes, Ryan; Husain, Sohail Z; Morinville, Veronique D; Ooi, Chee Y; Schwarzenberg, Sarah J; Troendle, David M; Yen, Elizabeth; Uc, Aliye

2017-01-01

Acute recurrent pancreatitis (ARP) and chronic pancreatitis (CP) have been diagnosed in children at increasing rates during the past decade. As pediatric ARP and CP are still relatively rare conditions, little quality evidence is available on which to base the diagnosis and determination of etiology. The aim of the study was to review the current state of the literature regarding the etiology of these disorders and to developed a consensus among a panel of clinically active specialists caring for children with these disorders to help guide the diagnostic evaluation and identify areas most in need of future research. A systematic review of the literature was performed and scored for quality, followed by consensus statements developed and scored by each individual in the group for level of agreement and strength of the supporting data using a modified Delphi method. Scores were analyzed for the level of consensus achieved by the group. The panel reached consensus on 27 statements covering the definitions of pediatric ARP and CP, evaluation for potential etiologies of these disorders, and long-term monitoring. Statements for which the group reached consensus to make no recommendation or could not reach consensus are discussed. This consensus helps define the minimal diagnostic evaluation and monitoring of children with ARP and CP. Even in areas in which we reached consensus, the quality of the evidence is weak, highlighting the need for further research. Improved understanding of the underlying cause will facilitate treatment development and targeting.

Improving transmembrane protein consensus topology prediction using inter-helical interaction.

PubMed

Wang, Han; Zhang, Chao; Shi, Xiaohu; Zhang, Li; Zhou, You

2012-11-01

Alpha helix transmembrane proteins (αTMPs) represent roughly 30% of all open reading frames (ORFs) in a typical genome and are involved in many critical biological processes. Due to the special physicochemical properties, it is hard to crystallize and obtain high resolution structures experimentally, thus, sequence-based topology prediction is highly desirable for the study of transmembrane proteins (TMPs), both in structure prediction and function prediction. Various model-based topology prediction methods have been developed, but the accuracy of those individual predictors remain poor due to the limitation of the methods or the features they used. Thus, the consensus topology prediction method becomes practical for high accuracy applications by combining the advances of the individual predictors. Here, based on the observation that inter-helical interactions are commonly found within the transmembrane helixes (TMHs) and strongly indicate the existence of them, we present a novel consensus topology prediction method for αTMPs, CNTOP, which incorporates four top leading individual topology predictors, and further improves the prediction accuracy by using the predicted inter-helical interactions. The method achieved 87% prediction accuracy based on a benchmark dataset and 78% accuracy based on a non-redundant dataset which is composed of polytopic αTMPs. Our method derives the highest topology accuracy than any other individual predictors and consensus predictors, at the same time, the TMHs are more accurately predicted in their length and locations, where both the false positives (FPs) and the false negatives (FNs) decreased dramatically. The CNTOP is available at: http://ccst.jlu.edu.cn/JCSB/cntop/CNTOP.html. Copyright © 2012 Elsevier B.V. All rights reserved.

The implementation of hybrid clustering using fuzzy c-means and divisive algorithm for analyzing DNA human Papillomavirus cause of cervical cancer

NASA Astrophysics Data System (ADS)

Andryani, Diyah Septi; Bustamam, Alhadi; Lestari, Dian

2017-03-01

Clustering aims to classify the different patterns into groups called clusters. In this clustering method, we use n-mers frequency to calculate the distance matrix which is considered more accurate than using the DNA alignment. The clustering results could be used to discover biologically important sub-sections and groups of genes. Many clustering methods have been developed, while hard clustering methods considered less accurate than fuzzy clustering methods, especially if it is used for outliers data. Among fuzzy clustering methods, fuzzy c-means is one the best known for its accuracy and simplicity. Fuzzy c-means clustering uses membership function variable, which refers to how likely the data could be members into a cluster. Fuzzy c-means clustering works using the principle of minimizing the objective function. Parameters of membership function in fuzzy are used as a weighting factor which is also called the fuzzier. In this study we implement hybrid clustering using fuzzy c-means and divisive algorithm which could improve the accuracy of cluster membership compare to traditional partitional approach only. In this study fuzzy c-means is used in the first step to find partition results. Furthermore divisive algorithms will run on the second step to find sub-clusters and dendogram of phylogenetic tree. To find the best number of clusters is determined using the minimum value of Davies Bouldin Index (DBI) of the cluster results. In this research, the results show that the methods introduced in this paper is better than other partitioning methods. Finally, we found 3 clusters with DBI value of 1.126628 at first step of clustering. Moreover, DBI values after implementing the second step of clustering are always producing smaller IDB values compare to the results of using first step clustering only. This condition indicates that the hybrid approach in this study produce better performance of the cluster results, in term its DBI values.

The sampled-data consensus of multi-agent systems with probabilistic time-varying delays and packet losses

NASA Astrophysics Data System (ADS)

Sui, Xin; Yang, Yongqing; Xu, Xianyun; Zhang, Shuai; Zhang, Lingzhong

2018-02-01

This paper investigates the consensus of multi-agent systems with probabilistic time-varying delays and packet losses via sampled-data control. On the one hand, a Bernoulli-distributed white sequence is employed to model random packet losses among agents. On the other hand, a switched system is used to describe packet dropouts in a deterministic way. Based on the special property of the Laplacian matrix, the consensus problem can be converted into a stabilization problem of a switched system with lower dimensions. Some mean square consensus criteria are derived in terms of constructing an appropriate Lyapunov function and using linear matrix inequalities (LMIs). Finally, two numerical examples are given to show the effectiveness of the proposed method.

Protein contact prediction by integrating deep multiple sequence alignments, coevolution and machine learning.

PubMed

Adhikari, Badri; Hou, Jie; Cheng, Jianlin

2018-03-01

In this study, we report the evaluation of the residue-residue contacts predicted by our three different methods in the CASP12 experiment, focusing on studying the impact of multiple sequence alignment, residue coevolution, and machine learning on contact prediction. The first method (MULTICOM-NOVEL) uses only traditional features (sequence profile, secondary structure, and solvent accessibility) with deep learning to predict contacts and serves as a baseline. The second method (MULTICOM-CONSTRUCT) uses our new alignment algorithm to generate deep multiple sequence alignment to derive coevolution-based features, which are integrated by a neural network method to predict contacts. The third method (MULTICOM-CLUSTER) is a consensus combination of the predictions of the first two methods. We evaluated our methods on 94 CASP12 domains. On a subset of 38 free-modeling domains, our methods achieved an average precision of up to 41.7% for top L/5 long-range contact predictions. The comparison of the three methods shows that the quality and effective depth of multiple sequence alignments, coevolution-based features, and machine learning integration of coevolution-based features and traditional features drive the quality of predicted protein contacts. On the full CASP12 dataset, the coevolution-based features alone can improve the average precision from 28.4% to 41.6%, and the machine learning integration of all the features further raises the precision to 56.3%, when top L/5 predicted long-range contacts are evaluated. And the correlation between the precision of contact prediction and the logarithm of the number of effective sequences in alignments is 0.66. © 2017 Wiley Periodicals, Inc.

European evidence based consensus on the diagnosis and management of Crohn's disease: special situations

PubMed Central

Caprilli, R; Gassull, M A; Escher, J C; Moser, G; Munkholm, P; Forbes, A; Hommes, D W; Lochs, H; Angelucci, E; Cocco, A; Vucelic, B; Hildebrand, H; Kolacek, S; Riis, L; Lukas, M; de Franchis, R; Hamilton, M; Jantschek, G; Michetti, P; O'Morain, C; Anwar, M M; Freitas, J L; Mouzas, I A; Baert, F; Mitchell, R; Hawkey, C J

2006-01-01

This third section of the European Crohn's and Colitis Organisation (ECCO) Consensus on the management of Crohn's disease concerns postoperative recurrence, fistulating disease, paediatrics, pregnancy, psychosomatics, extraintestinal manifestations, and alternative therapy. The first section on definitions and diagnosis reports on the aims and methods of the consensus, as well as sections on diagnosis, pathology, and classification of Crohn's disease. The second section on current management addresses treatment of active disease, maintenance of medically induced remission, and surgery of Crohn's disease. PMID:16481630

Inflation data clustering of some cities in Indonesia

NASA Astrophysics Data System (ADS)

Setiawan, Adi; Susanto, Bambang; Mahatma, Tundjung

2017-06-01

In this paper, it is presented how to cluster inflation data of cities in Indonesia by using k-means cluster method and fuzzy c-means method. The data that are used is limited to the monthly inflation data from 15 cities across Indonesia which have highest weight of donations and is supplemented with 5 cities used in the calculation of inflation in Indonesia. When they are applied into two clusters with k = 2 for k-means cluster method and c = 2, w = 1.25 for fuzzy c-means cluster method, Ambon, Manado and Jayapura tend to become one cluster (high inflation) meanwhile other cities tend to become members of other cluster (low inflation). However, if they are applied into two clusters with c=2, w=1.5, Surabaya, Medan, Makasar, Samarinda, Makasar, Manado, Ambon dan Jayapura tend to become one cluster (high inflation) meanwhile other cities tend to become members of other cluster (low inflation). Furthermore, when we use two clusters with k=3 for k-means cluster method and c=3, w = 1.25 for fuzzy c-means cluster method, Ambon tends to become member of first cluster (high inflation), Manado and Jayapura tend to become member of second cluster (moderate inflation), other cities tend to become members of third cluster (low inflation). If it is applied c=3, w = 1.5, Ambon, Manado and Jayapura tend to become member of first cluster (high inflation), Surabaya, Bandung, Medan, Makasar, Banyuwangi, Denpasar, Samarinda dan Mataram tend to become members of second cluster (moderate inflation), meanwhile other cities tend to become members of third cluster (low inflation). Similarly, interpretation can be made to the results of applying 5 clusters.

Semi-supervised clustering methods

PubMed Central

Bair, Eric

2013-01-01

Cluster analysis methods seek to partition a data set into homogeneous subgroups. It is useful in a wide variety of applications, including document processing and modern genetics. Conventional clustering methods are unsupervised, meaning that there is no outcome variable nor is anything known about the relationship between the observations in the data set. In many situations, however, information about the clusters is available in addition to the values of the features. For example, the cluster labels of some observations may be known, or certain observations may be known to belong to the same cluster. In other cases, one may wish to identify clusters that are associated with a particular outcome variable. This review describes several clustering algorithms (known as “semi-supervised clustering” methods) that can be applied in these situations. The majority of these methods are modifications of the popular k-means clustering method, and several of them will be described in detail. A brief description of some other semi-supervised clustering algorithms is also provided. PMID:24729830

Fractal Clustering and Knowledge-driven Validation Assessment for Gene Expression Profiling.

PubMed

Wang, Lu-Yong; Balasubramanian, Ammaiappan; Chakraborty, Amit; Comaniciu, Dorin

2005-01-01

DNA microarray experiments generate a substantial amount of information about the global gene expression. Gene expression profiles can be represented as points in multi-dimensional space. It is essential to identify relevant groups of genes in biomedical research. Clustering is helpful in pattern recognition in gene expression profiles. A number of clustering techniques have been introduced. However, these traditional methods mainly utilize shape-based assumption or some distance metric to cluster the points in multi-dimension linear Euclidean space. Their results shows poor consistence with the functional annotation of genes in previous validation study. From a novel different perspective, we propose fractal clustering method to cluster genes using intrinsic (fractal) dimension from modern geometry. This method clusters points in such a way that points in the same clusters are more self-affine among themselves than to the points in other clusters. We assess this method using annotation-based validation assessment for gene clusters. It shows that this method is superior in identifying functional related gene groups than other traditional methods.

Core competencies for emergency medicine clerkships: results of a Canadian consensus initiative.

PubMed

Penciner, Rick; Woods, Robert A; McEwen, Jill; Lee, Richard; Langhan, Trevor; Bandiera, Glen

2013-01-01

There is no consensus on what constitutes the core competencies for emergency medicine (EM) clerkship rotations in Canada. Existing EM curricula have been developed through informal consensus and often focus on EM content to be known at the end of training rather than what is an appropriate focus for a time-limited rotation in EM. We sought to define the core competencies for EM clerkship in Canada through consensus among an expert panel of Canadian EM educators. We used a modified Delphi method and the CanMEDS 2005 Physician Competency Framework to develop a consensus among expert EM educators from across Canada. Thirty experts from nine different medical schools across Canada participated on the panel. The initial list consisted of 152 competencies organized in the seven domains of the CanMEDS 2005 Physician Competency Framework. After the second round of the Delphi process, the list of competencies was reduced to 62 (59% reduction). A complete list of competencies is provided. This study established a national consensus defining the core competencies for EM clerkship in Canada.

Interactive visual exploration and refinement of cluster assignments.

PubMed

Kern, Michael; Lex, Alexander; Gehlenborg, Nils; Johnson, Chris R

2017-09-12

With ever-increasing amounts of data produced in biology research, scientists are in need of efficient data analysis methods. Cluster analysis, combined with visualization of the results, is one such method that can be used to make sense of large data volumes. At the same time, cluster analysis is known to be imperfect and depends on the choice of algorithms, parameters, and distance measures. Most clustering algorithms don't properly account for ambiguity in the source data, as records are often assigned to discrete clusters, even if an assignment is unclear. While there are metrics and visualization techniques that allow analysts to compare clusterings or to judge cluster quality, there is no comprehensive method that allows analysts to evaluate, compare, and refine cluster assignments based on the source data, derived scores, and contextual data. In this paper, we introduce a method that explicitly visualizes the quality of cluster assignments, allows comparisons of clustering results and enables analysts to manually curate and refine cluster assignments. Our methods are applicable to matrix data clustered with partitional, hierarchical, and fuzzy clustering algorithms. Furthermore, we enable analysts to explore clustering results in context of other data, for example, to observe whether a clustering of genomic data results in a meaningful differentiation in phenotypes. Our methods are integrated into Caleydo StratomeX, a popular, web-based, disease subtype analysis tool. We show in a usage scenario that our approach can reveal ambiguities in cluster assignments and produce improved clusterings that better differentiate genotypes and phenotypes.

[Development of a consented set of criteria to evaluate post-rehabilitation support services].

PubMed

Parzanka, Susanne; Himstedt, Christian; Deck, Ruth

2015-01-01

Existing rehabilitation aftercare offers in Germany are heterogeneous, and there is a lack of transparency in terms of indications and methods as well as of (nationwide) availability and financial coverage. Also, there is no systematic and transparent synopsis. To close this gap a systematic review was conducted and a web-based database created for post-rehabilitation support. To allow a consistent assessment of the included aftercare offers, a quality profile of universally valid criteria was developed. This paper aims to outline the scientific approach. The procedure adapts the RAND/UCLA method, with the participation of the advisory board of the ReNa project. Preparations for the set included systematic searches in order to find possible criteria to assess the quality of aftercare offers. These criteria first were collected without any pre-selection involved. Every item of the adjusted collection was evaluated by every single member of the advisory board considering the topics "relevance", "feasibility" and "suitability for public coverage". Interpersonal analysis was conducted by relating the median and classification into consensus and dissent. All items that were considered to be "relevant" and "feasible" in the three stages of consensus building and deemed "suitable for public coverage" were transferred into the final set of criteria (ReNa set). A total of 82 publications were selected out of the 656 findings taken into account, which delivered 3,603 criteria of possible initial relevance. After a further removal of 2,598 redundant criteria, the panel needed to assess a set of 1,005 items. Finally we performed a quality assessment of aftercare offers using a set of 35 descriptive criteria merged into 8 conceptual clusters. The consented ReNa set of 35 items delivers a first generally valid tool to describe quality of structures, standards and processes of aftercare offers. So finally, the project developed into a complete collection of profiles characterizing each post-rehabilitation support service included in the database. Copyright © 2015. Published by Elsevier GmbH.

HIV-1 transmission linkage in an HIV-1 prevention clinical trial

DOE Office of Scientific and Technical Information (OSTI.GOV)

Leitner, Thomas; Campbell, Mary S; Mullins, James I

2009-01-01

HIV-1 sequencing has been used extensively in epidemiologic and forensic studies to investigate patterns of HIV-1 transmission. However, the criteria for establishing genetic linkage between HIV-1 strains in HIV-1 prevention trials have not been formalized. The Partners in Prevention HSV/HIV Transmission Study (ClinicaITrials.gov NCT00194519) enrolled 3408 HIV-1 serodiscordant heterosexual African couples to determine the efficacy of genital herpes suppression with acyclovir in reducing HIV-1 transmission. The trial analysis required laboratory confirmation of HIV-1 linkage between enrolled partners in couples in which seroconversion occurred. Here we describe the process and results from HIV-1 sequencing studies used to perform transmission linkage determinationmore » in this clinical trial. Consensus Sanger sequencing of env (C2-V3-C3) and gag (p17-p24) genes was performed on plasma HIV-1 RNA from both partners within 3 months of seroconversion; env single molecule or pyrosequencing was also performed in some cases. For linkage, we required monophyletic clustering between HIV-1 sequences in the transmitting and seroconverting partners, and developed a Bayesian algorithm using genetic distances to evaluate the posterior probability of linkage of participants sequences. Adjudicators classified transmissions as linked, unlinked, or indeterminate. Among 151 seroconversion events, we found 108 (71.5%) linked, 40 (26.5%) unlinked, and 3 (2.0%) to have indeterminate transmissions. Nine (8.3%) were linked by consensus gag sequencing only and 8 (7.4%) required deep sequencing of env. In this first use of HIV-1 sequencing to establish endpoints in a large clinical trial, more than one-fourth of transmissions were unlinked to the enrolled partner, illustrating the relevance of these methods in the design of future HIV-1 prevention trials in serodiscordant couples. A hierarchy of sequencing techniques, analysis methods, and expert adjudication contributed to the linkage determination process.« less

Viral Linkage in HIV-1 Seroconverters and Their Partners in an HIV-1 Prevention Clinical Trial

PubMed Central

Campbell, Mary S.; Mullins, James I.; Hughes, James P.; Celum, Connie; Wong, Kim G.; Raugi, Dana N.; Sorensen, Stefanie; Stoddard, Julia N.; Zhao, Hong; Deng, Wenjie; Kahle, Erin; Panteleeff, Dana; Baeten, Jared M.; McCutchan, Francine E.; Albert, Jan; Leitner, Thomas; Wald, Anna; Corey, Lawrence; Lingappa, Jairam R.

2011-01-01

Background Characterization of viruses in HIV-1 transmission pairs will help identify biological determinants of infectiousness and evaluate candidate interventions to reduce transmission. Although HIV-1 sequencing is frequently used to substantiate linkage between newly HIV-1 infected individuals and their sexual partners in epidemiologic and forensic studies, viral sequencing is seldom applied in HIV-1 prevention trials. The Partners in Prevention HSV/HIV Transmission Study (ClinicalTrials.gov #NCT00194519) was a prospective randomized placebo-controlled trial that enrolled serodiscordant heterosexual couples to determine the efficacy of genital herpes suppression in reducing HIV-1 transmission; as part of the study analysis, HIV-1 sequences were examined for genetic linkage between seroconverters and their enrolled partners. Methodology/Principal Findings We obtained partial consensus HIV-1 env and gag sequences from blood plasma for 151 transmission pairs and performed deep sequencing of env in some cases. We analyzed sequences with phylogenetic techniques and developed a Bayesian algorithm to evaluate the probability of linkage. For linkage, we required monophyletic clustering between enrolled partners' sequences and a Bayesian posterior probability of ≥50%. Adjudicators classified each seroconversion, finding 108 (71.5%) linked, 40 (26.5%) unlinked, and 3 (2.0%) indeterminate transmissions, with linkage determined by consensus env sequencing in 91 (84%). Male seroconverters had a higher frequency of unlinked transmissions than female seroconverters. The likelihood of transmission from the enrolled partner was related to time on study, with increasing numbers of unlinked transmissions occurring after longer observation periods. Finally, baseline viral load was found to be significantly higher among linked transmitters. Conclusions/Significance In this first use of HIV-1 sequencing to establish endpoints in a large clinical trial, more than one-fourth of transmissions were unlinked to the enrolled partner, illustrating the relevance of these methods in the design of future HIV-1 prevention trials in serodiscordant couples. A hierarchy of sequencing techniques, analysis methods, and expert adjudication contributed to the linkage determination process. PMID:21399681

Sampled-Data Consensus of Linear Multi-agent Systems With Packet Losses.

PubMed

Zhang, Wenbing; Tang, Yang; Huang, Tingwen; Kurths, Jurgen

In this paper, the consensus problem is studied for a class of multi-agent systems with sampled data and packet losses, where random and deterministic packet losses are considered, respectively. For random packet losses, a Bernoulli-distributed white sequence is used to describe packet dropouts among agents in a stochastic way. For deterministic packet losses, a switched system with stable and unstable subsystems is employed to model packet dropouts in a deterministic way. The purpose of this paper is to derive consensus criteria, such that linear multi-agent systems with sampled-data and packet losses can reach consensus. By means of the Lyapunov function approach and the decomposition method, the design problem of a distributed controller is solved in terms of convex optimization. The interplay among the allowable bound of the sampling interval, the probability of random packet losses, and the rate of deterministic packet losses are explicitly derived to characterize consensus conditions. The obtained criteria are closely related to the maximum eigenvalue of the Laplacian matrix versus the second minimum eigenvalue of the Laplacian matrix, which reveals the intrinsic effect of communication topologies on consensus performance. Finally, simulations are given to show the effectiveness of the proposed results.In this paper, the consensus problem is studied for a class of multi-agent systems with sampled data and packet losses, where random and deterministic packet losses are considered, respectively. For random packet losses, a Bernoulli-distributed white sequence is used to describe packet dropouts among agents in a stochastic way. For deterministic packet losses, a switched system with stable and unstable subsystems is employed to model packet dropouts in a deterministic way. The purpose of this paper is to derive consensus criteria, such that linear multi-agent systems with sampled-data and packet losses can reach consensus. By means of the Lyapunov function approach and the decomposition method, the design problem of a distributed controller is solved in terms of convex optimization. The interplay among the allowable bound of the sampling interval, the probability of random packet losses, and the rate of deterministic packet losses are explicitly derived to characterize consensus conditions. The obtained criteria are closely related to the maximum eigenvalue of the Laplacian matrix versus the second minimum eigenvalue of the Laplacian matrix, which reveals the intrinsic effect of communication topologies on consensus performance. Finally, simulations are given to show the effectiveness of the proposed results.

Defining the Medical Library Association research agenda: methodology and final results from a consensus process

PubMed Central

Eldredge, Jonathan D.; Harris, Martha R.; Ascher, Marie T.

2009-01-01

Objective: Using a group consensus methodology, the research sought to generate a list of the twelve to fifteen most important and answerable research questions in health sciences librarianship as part of a broader effort to implement the new Medical Library Association (MLA) research policy. Methods: The delphi method was used. The committee distributed a brief survey to all estimated 827 MLA leaders and 237 MLA Research Section members, requesting they submit what they considered to be the most important and answerable research questions facing the profession. The submitted questions were then subjected to 2 rounds of voting to produce a short list of top-ranked questions. Results: The survey produced 62 questions from 54 MLA leaders and MLA Research Section members, who responded from an estimated potential population of 1,064 targeted colleagues. These questions were considered by the process participants to be the most important and answerable research questions facing the profession. Through 2 rounds of voting, these 62 questions were reduced to the final 12 highest priority questions. Conclusion: The modified delphi method accomplished its desired survey and consensus goals. Future survey and consensus processes will be revised to generate more initial questions and to distill a larger number of ranked prioritized research questions. PMID:19626143

A comparison of heuristic and model-based clustering methods for dietary pattern analysis.

PubMed

Greve, Benjamin; Pigeot, Iris; Huybrechts, Inge; Pala, Valeria; Börnhorst, Claudia

2016-02-01

Cluster analysis is widely applied to identify dietary patterns. A new method based on Gaussian mixture models (GMM) seems to be more flexible compared with the commonly applied k-means and Ward's method. In the present paper, these clustering approaches are compared to find the most appropriate one for clustering dietary data. The clustering methods were applied to simulated data sets with different cluster structures to compare their performance knowing the true cluster membership of observations. Furthermore, the three methods were applied to FFQ data assessed in 1791 children participating in the IDEFICS (Identification and Prevention of Dietary- and Lifestyle-Induced Health Effects in Children and Infants) Study to explore their performance in practice. The GMM outperformed the other methods in the simulation study in 72 % up to 100 % of cases, depending on the simulated cluster structure. Comparing the computationally less complex k-means and Ward's methods, the performance of k-means was better in 64-100 % of cases. Applied to real data, all methods identified three similar dietary patterns which may be roughly characterized as a 'non-processed' cluster with a high consumption of fruits, vegetables and wholemeal bread, a 'balanced' cluster with only slight preferences of single foods and a 'junk food' cluster. The simulation study suggests that clustering via GMM should be preferred due to its higher flexibility regarding cluster volume, shape and orientation. The k-means seems to be a good alternative, being easier to use while giving similar results when applied to real data.

A proximity-based graph clustering method for the identification and application of transcription factor clusters.

PubMed

Spadafore, Maxwell; Najarian, Kayvan; Boyle, Alan P

2017-11-29

Transcription factors (TFs) form a complex regulatory network within the cell that is crucial to cell functioning and human health. While methods to establish where a TF binds to DNA are well established, these methods provide no information describing how TFs interact with one another when they do bind. TFs tend to bind the genome in clusters, and current methods to identify these clusters are either limited in scope, unable to detect relationships beyond motif similarity, or not applied to TF-TF interactions. Here, we present a proximity-based graph clustering approach to identify TF clusters using either ChIP-seq or motif search data. We use TF co-occurrence to construct a filtered, normalized adjacency matrix and use the Markov Clustering Algorithm to partition the graph while maintaining TF-cluster and cluster-cluster interactions. We then apply our graph structure beyond clustering, using it to increase the accuracy of motif-based TFBS searching for an example TF. We show that our method produces small, manageable clusters that encapsulate many known, experimentally validated transcription factor interactions and that our method is capable of capturing interactions that motif similarity methods might miss. Our graph structure is able to significantly increase the accuracy of motif TFBS searching, demonstrating that the TF-TF connections within the graph correlate with biological TF-TF interactions. The interactions identified by our method correspond to biological reality and allow for fast exploration of TF clustering and regulatory dynamics.

Does Civic Education Matter?: The Power of Long-Term Observation and the Experimental Method

ERIC Educational Resources Information Center

Claassen, Ryan L.; Monson, J. Quin

2015-01-01

Despite consensus regarding the civic shortcomings of American citizens, no such scholarly consensus exists regarding the effectiveness of civic education addressing political apathy and ignorance. Accordingly, we report the results of a detailed study of students enrolled in introductory American politics courses on the campuses of two large…

Collective intelligence in medical diagnosis systems: A case study.

PubMed

Hernández-Chan, Gandhi S; Ceh-Varela, Edgar Eduardo; Sanchez-Cervantes, Jose L; Villanueva-Escalante, Marisol; Rodríguez-González, Alejandro; Pérez-Gallardo, Yuliana

2016-07-01

Diagnosing a patient's condition is one of the most important and challenging tasks in medicine. We present a study of the application of collective intelligence in medical diagnosis by applying consensus methods. We compared the accuracy obtained with this method against the diagnostics accuracy reached through the knowledge of a single expert. We used the ontological structures of ten diseases. Two knowledge bases were created by placing five diseases into each knowledge base. We conducted two experiments, one with an empty knowledge base and the other with a populated knowledge base. For both experiments, five experts added and/or eliminated signs/symptoms and diagnostic tests for each disease. After this process, the individual knowledge bases were built based on the output of the consensus methods. In order to perform the evaluation, we compared the number of items for each disease in the agreed knowledge bases against the number of items in the GS (Gold Standard). We identified that, while the number of items in each knowledge base is higher, the consensus level is lower. In all cases, the lowest level of agreement (20%) exceeded the number of signs that are in the GS. In addition, when all experts agreed, the number of items decreased. The use of collective intelligence can be used to increase the consensus of physicians. This is because, by using consensus, physicians can gather more information and knowledge than when obtaining information and knowledge from knowledge bases fed or populated from the knowledge found in the literature, and, at the same time, they can keep updated and collaborate dynamically. Copyright © 2016 Elsevier Ltd. All rights reserved.

A cross-validation Delphi method approach to the diagnosis and treatment of personality disorders in older adults.

PubMed

Rosowsky, Erlene; Young, Alexander S; Malloy, Mary C; van Alphen, S P J; Ellison, James M

2018-03-01

The Delphi method is a consensus-building technique using expert opinion to formulate a shared framework for understanding a topic with limited empirical support. This cross-validation study replicates one completed in the Netherlands and Belgium, and explores US experts' views on the diagnosis and treatment of older adults with personality disorders (PD). Twenty-one geriatric PD experts participated in a Delphi survey addressing diagnosis and treatment of older adults with PD. The European survey was translated and administered electronically. First-round consensus was reached for 16 out of 18 items relevant to diagnosis and specific mental health programs for personality disorders in older adults. Experts agreed on the usefulness of establishing criteria for specific types of treatments. The majority of psychologists did not initially agree on the usefulness of pharmacotherapy. Expert consensus was reached following two subsequent rounds after clarification addressing medication use. Study results suggest consensus among regarding psychosocial treatments. Limited acceptance amongst US psychologists about the suitability of pharmacotherapy for late-life PDs contrasted with the views expressed by experts surveyed in Netherlands and Belgium studies.

Integration K-Means Clustering Method and Elbow Method For Identification of The Best Customer Profile Cluster

NASA Astrophysics Data System (ADS)

Syakur, M. A.; Khotimah, B. K.; Rochman, E. M. S.; Satoto, B. D.

2018-04-01

Clustering is a data mining technique used to analyse data that has variations and the number of lots. Clustering was process of grouping data into a cluster, so they contained data that is as similar as possible and different from other cluster objects. SMEs Indonesia has a variety of customers, but SMEs do not have the mapping of these customers so they did not know which customers are loyal or otherwise. Customer mapping is a grouping of customer profiling to facilitate analysis and policy of SMEs in the production of goods, especially batik sales. Researchers will use a combination of K-Means method with elbow to improve efficient and effective k-means performance in processing large amounts of data. K-Means Clustering is a localized optimization method that is sensitive to the selection of the starting position from the midpoint of the cluster. So choosing the starting position from the midpoint of a bad cluster will result in K-Means Clustering algorithm resulting in high errors and poor cluster results. The K-means algorithm has problems in determining the best number of clusters. So Elbow looks for the best number of clusters on the K-means method. Based on the results obtained from the process in determining the best number of clusters with elbow method can produce the same number of clusters K on the amount of different data. The result of determining the best number of clusters with elbow method will be the default for characteristic process based on case study. Measurement of k-means value of k-means has resulted in the best clusters based on SSE values on 500 clusters of batik visitors. The result shows the cluster has a sharp decrease is at K = 3, so K as the cut-off point as the best cluster.

Developing appropriate methods for cost-effectiveness analysis of cluster randomized trials.

PubMed

Gomes, Manuel; Ng, Edmond S-W; Grieve, Richard; Nixon, Richard; Carpenter, James; Thompson, Simon G

2012-01-01

Cost-effectiveness analyses (CEAs) may use data from cluster randomized trials (CRTs), where the unit of randomization is the cluster, not the individual. However, most studies use analytical methods that ignore clustering. This article compares alternative statistical methods for accommodating clustering in CEAs of CRTs. Our simulation study compared the performance of statistical methods for CEAs of CRTs with 2 treatment arms. The study considered a method that ignored clustering--seemingly unrelated regression (SUR) without a robust standard error (SE)--and 4 methods that recognized clustering--SUR and generalized estimating equations (GEEs), both with robust SE, a "2-stage" nonparametric bootstrap (TSB) with shrinkage correction, and a multilevel model (MLM). The base case assumed CRTs with moderate numbers of balanced clusters (20 per arm) and normally distributed costs. Other scenarios included CRTs with few clusters, imbalanced cluster sizes, and skewed costs. Performance was reported as bias, root mean squared error (rMSE), and confidence interval (CI) coverage for estimating incremental net benefits (INBs). We also compared the methods in a case study. Each method reported low levels of bias. Without the robust SE, SUR gave poor CI coverage (base case: 0.89 v. nominal level: 0.95). The MLM and TSB performed well in each scenario (CI coverage, 0.92-0.95). With few clusters, the GEE and SUR (with robust SE) had coverage below 0.90. In the case study, the mean INBs were similar across all methods, but ignoring clustering underestimated statistical uncertainty and the value of further research. MLMs and the TSB are appropriate analytical methods for CEAs of CRTs with the characteristics described. SUR and GEE are not recommended for studies with few clusters.

Sub-grouping patients with non-specific low back pain based on cluster analysis of discriminatory clinical items.

PubMed

Billis, Evdokia; McCarthy, Christopher J; Roberts, Chris; Gliatis, John; Papandreou, Maria; Gioftsos, George; Oldham, Jacqueline A

2013-02-01

To identify potential subgroups amongst patients with non-specific low back pain based on a consensus list of potentially discriminatory examination items. Exploratory study. A convenience sample of 106 patients with non-specific low back pain (43 males, 63 females, mean age 36 years, standard deviation 15.9 years) and 7 physiotherapists. Based on 3 focus groups and a two-round Delphi involving 23 health professionals and a random stratified sample of 150 physiotherapists, respectively, a comprehensive examination list comprising the most "discriminatory" items was compiled. Following reliability analysis, the most reliable clinical items were assessed with a sample of patients with non-specific low back pain. K-means cluster analysis was conducted for 2-, 3- and 4-cluster options to explore for meaningful homogenous subgroups. The most clinically meaningful cluster was a two-subgroup option, comprising a small group (n = 24) with more severe clinical presentation (i.e. more widespread pain, functional and sleeping problems, other symptoms, increased investigations undertaken, more severe clinical signs, etc.) and a larger less dysfunctional group (n = 80). A number of potentially discriminatory clinical items were identified by health professionals and sub-classified, based on a sample of patients with non-specific low back pain, into two subgroups. However, further work is needed to validate this classification process.

UK quantitative WB-DWI technical workgroup: consensus meeting recommendations on optimisation, quality control, processing and analysis of quantitative whole-body diffusion-weighted imaging for cancer.

PubMed

Barnes, Anna; Alonzi, Roberto; Blackledge, Matthew; Charles-Edwards, Geoff; Collins, David J; Cook, Gary; Coutts, Glynn; Goh, Vicky; Graves, Martin; Kelly, Charles; Koh, Dow-Mu; McCallum, Hazel; Miquel, Marc E; O'Connor, James; Padhani, Anwar; Pearson, Rachel; Priest, Andrew; Rockall, Andrea; Stirling, James; Taylor, Stuart; Tunariu, Nina; van der Meulen, Jan; Walls, Darren; Winfield, Jessica; Punwani, Shonit

2018-01-01

Application of whole body diffusion-weighted MRI (WB-DWI) for oncology are rapidly increasing within both research and routine clinical domains. However, WB-DWI as a quantitative imaging biomarker (QIB) has significantly slower adoption. To date, challenges relating to accuracy and reproducibility, essential criteria for a good QIB, have limited widespread clinical translation. In recognition, a UK workgroup was established in 2016 to provide technical consensus guidelines (to maximise accuracy and reproducibility of WB-MRI QIBs) and accelerate the clinical translation of quantitative WB-DWI applications for oncology. A panel of experts convened from cancer centres around the UK with subspecialty expertise in quantitative imaging and/or the use of WB-MRI with DWI. A formal consensus method was used to obtain consensus agreement regarding best practice. Questions were asked about the appropriateness or otherwise on scanner hardware and software, sequence optimisation, acquisition protocols, reporting, and ongoing quality control programs to monitor precision and accuracy and agreement on quality control. The consensus panel was able to reach consensus on 73% (255/351) items and based on consensus areas made recommendations to maximise accuracy and reproducibly of quantitative WB-DWI studies performed at 1.5T. The panel were unable to reach consensus on the majority of items related to quantitative WB-DWI performed at 3T. This UK Quantitative WB-DWI Technical Workgroup consensus provides guidance on maximising accuracy and reproducibly of quantitative WB-DWI for oncology. The consensus guidance can be used by researchers and clinicians to harmonise WB-DWI protocols which will accelerate clinical translation of WB-DWI-derived QIBs.

Coping with living in the soil: the genome of the parthenogenetic springtail Folsomia candida.

PubMed

Faddeeva-Vakhrusheva, Anna; Kraaijeveld, Ken; Derks, Martijn F L; Anvar, Seyed Yahya; Agamennone, Valeria; Suring, Wouter; Kampfraath, Andries A; Ellers, Jacintha; Le Ngoc, Giang; van Gestel, Cornelis A M; Mariën, Janine; Smit, Sandra; van Straalen, Nico M; Roelofs, Dick

2017-06-28

Folsomia candida is a model in soil biology, belonging to the family of Isotomidae, subclass Collembola. It reproduces parthenogenetically in the presence of Wolbachia, and exhibits remarkable physiological adaptations to stress. To better understand these features and adaptations to life in the soil, we studied its genome in the context of its parthenogenetic lifestyle. We applied Pacific Bioscience sequencing and assembly to generate a reference genome for F. candida of 221.7 Mbp, comprising only 162 scaffolds. The complete genome of its endosymbiont Wolbachia, was also assembled and turned out to be the largest strain identified so far. Substantial gene family expansions and lineage-specific gene clusters were linked to stress response. A large number of genes (809) were acquired by horizontal gene transfer. A substantial fraction of these genes are involved in lignocellulose degradation. Also, the presence of genes involved in antibiotic biosynthesis was confirmed. Intra-genomic rearrangements of collinear gene clusters were observed, of which 11 were organized as palindromes. The Hox gene cluster of F. candida showed major rearrangements compared to arthropod consensus cluster, resulting in a disorganized cluster. The expansion of stress response gene families suggests that stress defense was important to facilitate colonization of soils. The large number of HGT genes related to lignocellulose degradation could be beneficial to unlock carbohydrate sources in soil, especially those contained in decaying plant and fungal organic matter. Intra- as well as inter-scaffold duplications of gene clusters may be a consequence of its parthenogenetic lifestyle. This high quality genome will be instrumental for evolutionary biologists investigating deep phylogenetic lineages among arthropods and will provide the basis for a more mechanistic understanding in soil ecology and ecotoxicology.

Best practice guidelines for the molecular genetic diagnosis of Type 1 (HFE-related) hereditary haemochromatosis

PubMed Central

King, Caitriona; Barton, David E

2006-01-01

Background Hereditary haemochromatosis (HH) is a recessively-inherited disorder of iron over-absorption prevalent in Caucasian populations. Affected individuals for Type 1 HH are usually either homozygous for a cysteine to tyrosine amino acid substitution at position 282 (C282Y) of the HFE gene, or compound heterozygotes for C282Y and for a histidine to aspartic acid change at position 63 (H63D). Molecular genetic testing for these two mutations has become widespread in recent years. With diverse testing methods and reporting practices in use, there was a clear need for agreed guidelines for haemochromatosis genetic testing. The UK Clinical Molecular Genetics Society has elaborated a consensus process for the development of disease-specific best practice guidelines for genetic testing. Methods A survey of current practice in the molecular diagnosis of haemochromatosis was conducted. Based on the results of this survey, draft guidelines were prepared using the template developed by UK Clinical Molecular Genetics Society. A workshop was held to develop the draft into a consensus document. The consensus document was then posted on the Clinical Molecular Genetics Society website for broader consultation and amendment. Results Consensus or near-consensus was achieved on all points in the draft guidelines. The consensus and consultation processes worked well, and outstanding issues were documented in an appendix to the guidelines. Conclusion An agreed set of best practice guidelines were developed for diagnostic, predictive and carrier testing for hereditary haemochromatosis and for reporting the results of such testing. PMID:17134494

A two-stage method for microcalcification cluster segmentation in mammography by deformable models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Arikidis, N.; Kazantzi, A.; Skiadopoulos, S.

Purpose: Segmentation of microcalcification (MC) clusters in x-ray mammography is a difficult task for radiologists. Accurate segmentation is prerequisite for quantitative image analysis of MC clusters and subsequent feature extraction and classification in computer-aided diagnosis schemes. Methods: In this study, a two-stage semiautomated segmentation method of MC clusters is investigated. The first stage is targeted to accurate and time efficient segmentation of the majority of the particles of a MC cluster, by means of a level set method. The second stage is targeted to shape refinement of selected individual MCs, by means of an active contour model. Both methods aremore » applied in the framework of a rich scale-space representation, provided by the wavelet transform at integer scales. Segmentation reliability of the proposed method in terms of inter and intraobserver agreements was evaluated in a case sample of 80 MC clusters originating from the digital database for screening mammography, corresponding to 4 morphology types (punctate: 22, fine linear branching: 16, pleomorphic: 18, and amorphous: 24) of MC clusters, assessing radiologists’ segmentations quantitatively by two distance metrics (Hausdorff distance—HDIST{sub cluster}, average of minimum distance—AMINDIST{sub cluster}) and the area overlap measure (AOM{sub cluster}). The effect of the proposed segmentation method on MC cluster characterization accuracy was evaluated in a case sample of 162 pleomorphic MC clusters (72 malignant and 90 benign). Ten MC cluster features, targeted to capture morphologic properties of individual MCs in a cluster (area, major length, perimeter, compactness, and spread), were extracted and a correlation-based feature selection method yielded a feature subset to feed in a support vector machine classifier. Classification performance of the MC cluster features was estimated by means of the area under receiver operating characteristic curve (Az ± Standard Error) utilizing tenfold cross-validation methodology. A previously developed B-spline active rays segmentation method was also considered for comparison purposes. Results: Interobserver and intraobserver segmentation agreements (median and [25%, 75%] quartile range) were substantial with respect to the distance metrics HDIST{sub cluster} (2.3 [1.8, 2.9] and 2.5 [2.1, 3.2] pixels) and AMINDIST{sub cluster} (0.8 [0.6, 1.0] and 1.0 [0.8, 1.2] pixels), while moderate with respect to AOM{sub cluster} (0.64 [0.55, 0.71] and 0.59 [0.52, 0.66]). The proposed segmentation method outperformed (0.80 ± 0.04) statistically significantly (Mann-Whitney U-test, p < 0.05) the B-spline active rays segmentation method (0.69 ± 0.04), suggesting the significance of the proposed semiautomated method. Conclusions: Results indicate a reliable semiautomated segmentation method for MC clusters offered by deformable models, which could be utilized in MC cluster quantitative image analysis.« less

Use of the Delphi process in paediatric cataract management.

PubMed

Serafino, Massimiliano; Trivedi, Rupal H; Levin, Alex V; Wilson, M Edward; Nucci, Paolo; Lambert, Scott R; Nischal, Ken K; Plager, David A; Bremond-Gignac, Dominique; Kekunnaya, Ramesh; Nishina, Sachiko; Tehrani, Nasrin N; Ventura, Marcelo C

2016-05-01

To identify areas of consensus and disagreement in the management of paediatric cataract using a modified Delphi approach among individuals recognised for publishing in this field. A modified Delphi method. International paediatric cataract experts with a publishing record in paediatric cataract management. The process consisted of three rounds of anonymous electronic questionnaires followed by a face-to-face meeting, followed by a fourth anonymous electronic questionnaire. The executive committee created questions to be used for the electronic questionnaires. Questions were designed to have unit-based, multiple choice or true-false answers. The questionnaire included issues related to the preoperative, intraoperative and postoperative management of paediatric cataract. Consensus based on 85% of panellists being in agreement for electronic questionnaires or 80% for the face-to-face meeting, and near consensus based on 70%. Sixteen of 22 invited paediatric cataract surgeons agreed to participate. We arrived at consensus or near consensus for 85/108 (78.7%) questions and non-consensus for the remaining 23 (21.3%) questions. Those questions where consensus was not reached highlight areas of either poor evidence or contradicting evidence, and may help investigators identify possible research questions. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Update of the International Consensus on Palliative Radiotherapy Endpoints for Future Clinical Trials in Bone Metastases

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chow, Edward, E-mail: Edward.Chow@sunnybrook.ca; Hoskin, Peter; Mitera, Gunita

2012-04-01

Purpose: To update the international consensus on palliative radiotherapy endpoints for future clinical trials in bone metastases by surveying international experts regarding previous uncertainties within the 2002 consensus, changes that may be necessary based on practice pattern changes and research findings since that time. Methods and Materials: A two-phase survey was used to determine revisions and new additions to the 2002 consensus. A total of 49 experts from the American Society for Radiation Oncology, the European Society for Therapeutic Radiology and Oncology, the Faculty of Radiation Oncology of the Royal Australian and New Zealand College of Radiologists, and the Canadianmore » Association of Radiation Oncology who are directly involved in the care of patients with bone metastases participated in this survey. Results: Consensus was established in areas involving response definitions, eligibility criteria for future trials, reirradiation, changes in systemic therapy, radiation techniques, parameters at follow-up, and timing of assessments. Conclusion: An outline for trials in bone metastases was updated based on survey and consensus. Investigators leading trials in bone metastases are encouraged to adopt the revised guideline to promote consistent reporting. Areas for future research were identified. It is intended for the consensus to be re-examined in the future on a regular basis.« less

Link-Prediction Enhanced Consensus Clustering for Complex Networks (Open Access)

DTIC Science & Technology

2016-05-20

92:022816. Available from: http://link.aps.org/doi/10.1103/PhysRevE.92.022816. doi: 10. 1103 /PhysRevE.92.022816 16. Aldecoa R, Marín I. Exploring the...from: http://link.aps.org/doi/10.1103/PhysRevE.80.056117. doi: 10. 1103 /PhysRevE.80.056117 18. Dahlin J, Svenson P. Ensemble approaches for improving...046110. Available from: http://link.aps.org/doi/10.1103/PhysRevE.81.046110. doi: 10. 1103 /PhysRevE.81.046110 28. Gfeller D, Chappelier JC, De Los Rios P

A proposed approach for quantitative benefit-risk assessment in diagnostic radiology guideline development: the American College of Radiology Appropriateness Criteria Example.

PubMed

Agapova, Maria; Bresnahan, Brian B; Higashi, Mitchell; Kessler, Larry; Garrison, Louis P; Devine, Beth

2017-02-01

The American College of Radiology develops evidence-based practice guidelines to aid appropriate utilization of radiological procedures. Panel members use expert opinion to weight trade-offs and consensus methods to rate appropriateness of imaging tests. These ratings include an equivocal range, assigned when there is disagreement about a technology's appropriateness and the evidence base is weak or for special circumstances. It is not clear how expert consensus merges with the evidence base to arrive at an equivocal rating. Quantitative benefit-risk assessment (QBRA) methods may assist decision makers in this capacity. However, many methods exist and it is not clear which methods are best suited for this application. We perform a critical appraisal of QBRA methods and propose several steps that may aid in making transparent areas of weak evidence and barriers to consensus in guideline development. We identify QBRA methods with potential to facilitate decision making in guideline development and build a decision aid for selecting among these methods. This study identified 2 families of QBRA methods suited to guideline development when expert opinion is expected to contribute substantially to decision making. Key steps to deciding among QBRA methods involve identifying specific benefit-risk criteria and developing a state-of-evidence matrix. For equivocal ratings assigned for reasons other than disagreement or weak evidence base, QBRA may not be needed. In the presence of disagreement but the absence of a weak evidence base, multicriteria decision analysis approaches are recommended; and in the presence of weak evidence base and the absence of disagreement, incremental net health benefit alone or combined with multicriteria decision analysis is recommended. Our critical appraisal further extends investigation of the strengths and limitations of select QBRA methods in facilitating diagnostic radiology clinical guideline development. The process of using the decision aid exposes and makes transparent areas of weak evidence and barriers to consensus. © 2016 John Wiley & Sons, Ltd.

Research and development of methods and tools for achieving and maintaining consensus processes in the face of change within and among government oversight agencies. Progress report, October 1, 1992--March 31, 1994, Volume I

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

1994-06-01

This progress report summarizes our research activities under our consensus grant. In year five, we devoted much of our activities to completing fundamental research projects delayed because of the considerably stepped-up effort in consensus processes efforts during development of DOE`s Five Year Waste Plan (FYWP). Following our work on various procedures for bringing together groups such as the State and Tribal Government Working Group and the Stakeholders` Forum (both of which provide input to the Five Year Waste Plan), we compiled a literature overview of small-group consensus gaining and a handbook for consensus decision making. We also tested the effectivenessmore » Of group decision support software, and designed a structured observation process and its related hard- and software. We completed studies on experts and the role of personality characteristics in consensus group influence. Results of these studies are included in this final report. In consensus processes research, we were unable to continue studying consensus groups in action. However, we did study ways to improve ways to improve DOE`s technological information exchange effectiveness. We also studied how a new administration identifies what its strategic mission is and how it gets support from existing EM managers. We identified selection criteria for locating the EM exhibit, and tested our audience selection model. We also further calibrated our consensus measure. Additional conference papers and papers for journal submission were completed during year five.« less

Sleep-deprived motor vehicle operators are unfit to drive: a multidisciplinary expert consensus statement on drowsy driving.

PubMed

Czeisler, Charles A; Wickwire, Emerson M; Barger, Laura K; Dement, William C; Gamble, Karen; Hartenbaum, Natalie; Ohayon, Maurice M; Pelayo, Rafael; Phillips, Barbara; Strohl, Kingman; Tefft, Brian; Rajaratnam, Shantha M W; Malhotra, Raman; Whiton, Kaitlyn; Hirshkowitz, Max

2016-06-01

This article presents the consensus findings of the National Sleep Foundation Drowsy Driving Consensus Working Group, which was an expert panel assembled to establish a consensus statement regarding sleep-related driving impairment. The National Sleep Foundation assembled a expert panel comprised of experts from the sleep community and experts appointed by stakeholder organizations. A systematic literature review identified 346 studies that were abstracted and provided to the panelists for review. A modified Delphi RAND/UCLA Appropriateness Method with 2 rounds of voting was used to reach consensus. A final consensus was reached that sleep deprivation renders motorists unfit to drive a motor vehicle. After reviewing growing evidence of impairment and increased crash risk among drivers who obtained less than optimal sleep duration in the preceding 24 hours, the panelists recognized the need for public policy guidance as to when it is certainly unsafe to drive. Toward this end, the panelists agreed upon the following expert consensus statement: "Drivers who have slept for two hours or less in the preceding 24 hours are not fit to operate a motor vehicle." Panelists further agreed that most healthy drivers would likely be impaired with only 3 to 5 hours of sleep during the prior 24 hours. There is consensus among experts that healthy individuals who have slept for 2 hours or less in the preceding 24 hours are too impaired to safely operate a motor vehicle. Prevention of drowsy driving will require sustained and collaborative effort from multiple stakeholders. Implications and limitations of the consensus recommendations are discussed. Copyright © 2016. Published by Elsevier Inc.

Cluster mass inference via random field theory.

PubMed

Zhang, Hui; Nichols, Thomas E; Johnson, Timothy D

2009-01-01

Cluster extent and voxel intensity are two widely used statistics in neuroimaging inference. Cluster extent is sensitive to spatially extended signals while voxel intensity is better for intense but focal signals. In order to leverage strength from both statistics, several nonparametric permutation methods have been proposed to combine the two methods. Simulation studies have shown that of the different cluster permutation methods, the cluster mass statistic is generally the best. However, to date, there is no parametric cluster mass inference available. In this paper, we propose a cluster mass inference method based on random field theory (RFT). We develop this method for Gaussian images, evaluate it on Gaussian and Gaussianized t-statistic images and investigate its statistical properties via simulation studies and real data. Simulation results show that the method is valid under the null hypothesis and demonstrate that it can be more powerful than the cluster extent inference method. Further, analyses with a single subject and a group fMRI dataset demonstrate better power than traditional cluster size inference, and good accuracy relative to a gold-standard permutation test.

Hybrid Tracking Algorithm Improvements and Cluster Analysis Methods.

DTIC Science & Technology

1982-02-26

UPGMA ), and Ward’s method. Ling’s papers describe a (k,r) clustering method. Each of these methods have individual characteristics which make them...Reference 7), UPGMA is probably the most frequently used clustering strategy. UPGMA tries to group new points into an existing cluster by using an

A Novel Consensus-Based Particle Swarm Optimization-Assisted Trust-Tech Methodology for Large-Scale Global Optimization.

PubMed

Zhang, Yong-Feng; Chiang, Hsiao-Dong

2017-09-01

A novel three-stage methodology, termed the "consensus-based particle swarm optimization (PSO)-assisted Trust-Tech methodology," to find global optimal solutions for nonlinear optimization problems is presented. It is composed of Trust-Tech methods, consensus-based PSO, and local optimization methods that are integrated to compute a set of high-quality local optimal solutions that can contain the global optimal solution. The proposed methodology compares very favorably with several recently developed PSO algorithms based on a set of small-dimension benchmark optimization problems and 20 large-dimension test functions from the CEC 2010 competition. The analytical basis for the proposed methodology is also provided. Experimental results demonstrate that the proposed methodology can rapidly obtain high-quality optimal solutions that can contain the global optimal solution. The scalability of the proposed methodology is promising.

Peptide Array X-Linking (PAX): A New Peptide-Protein Identification Approach

PubMed Central

Okada, Hirokazu; Uezu, Akiyoshi; Soderblom, Erik J.; Moseley, M. Arthur; Gertler, Frank B.; Soderling, Scott H.

2012-01-01

Many protein interaction domains bind short peptides based on canonical sequence consensus motifs. Here we report the development of a peptide array-based proteomics tool to identify proteins directly interacting with ligand peptides from cell lysates. Array-formatted bait peptides containing an amino acid-derived cross-linker are photo-induced to crosslink with interacting proteins from lysates of interest. Indirect associations are removed by high stringency washes under denaturing conditions. Covalently trapped proteins are subsequently identified by LC-MS/MS and screened by cluster analysis and domain scanning. We apply this methodology to peptides with different proline-containing consensus sequences and show successful identifications from brain lysates of known and novel proteins containing polyproline motif-binding domains such as EH, EVH1, SH3, WW domains. These results suggest the capacity of arrayed peptide ligands to capture and subsequently identify proteins by mass spectrometry is relatively broad and robust. Additionally, the approach is rapid and applicable to cell or tissue fractions from any source, making the approach a flexible tool for initial protein-protein interaction discovery. PMID:22606326

Clustering of longitudinal data by using an extended baseline: A new method for treatment efficacy clustering in longitudinal data.

PubMed

Schramm, Catherine; Vial, Céline; Bachoud-Lévi, Anne-Catherine; Katsahian, Sandrine

2018-01-01

Heterogeneity in treatment efficacy is a major concern in clinical trials. Clustering may help to identify the treatment responders and the non-responders. In the context of longitudinal cluster analyses, sample size and variability of the times of measurements are the main issues with the current methods. Here, we propose a new two-step method for the Clustering of Longitudinal data by using an Extended Baseline. The first step relies on a piecewise linear mixed model for repeated measurements with a treatment-time interaction. The second step clusters the random predictions and considers several parametric (model-based) and non-parametric (partitioning, ascendant hierarchical clustering) algorithms. A simulation study compares all options of the clustering of longitudinal data by using an extended baseline method with the latent-class mixed model. The clustering of longitudinal data by using an extended baseline method with the two model-based algorithms was the more robust model. The clustering of longitudinal data by using an extended baseline method with all the non-parametric algorithms failed when there were unequal variances of treatment effect between clusters or when the subgroups had unbalanced sample sizes. The latent-class mixed model failed when the between-patients slope variability is high. Two real data sets on neurodegenerative disease and on obesity illustrate the clustering of longitudinal data by using an extended baseline method and show how clustering may help to identify the marker(s) of the treatment response. The application of the clustering of longitudinal data by using an extended baseline method in exploratory analysis as the first stage before setting up stratified designs can provide a better estimation of treatment effect in future clinical trials.

Comparison of integrated clustering methods for accurate and stable prediction of building energy consumption data

DOE PAGES

Hsu, David

2015-09-27

Clustering methods are often used to model energy consumption for two reasons. First, clustering is often used to process data and to improve the predictive accuracy of subsequent energy models. Second, stable clusters that are reproducible with respect to non-essential changes can be used to group, target, and interpret observed subjects. However, it is well known that clustering methods are highly sensitive to the choice of algorithms and variables. This can lead to misleading assessments of predictive accuracy and mis-interpretation of clusters in policymaking. This paper therefore introduces two methods to the modeling of energy consumption in buildings: clusterwise regression,more » also known as latent class regression, which integrates clustering and regression simultaneously; and cluster validation methods to measure stability. Using a large dataset of multifamily buildings in New York City, clusterwise regression is compared to common two-stage algorithms that use K-means and model-based clustering with linear regression. Predictive accuracy is evaluated using 20-fold cross validation, and the stability of the perturbed clusters is measured using the Jaccard coefficient. These results show that there seems to be an inherent tradeoff between prediction accuracy and cluster stability. This paper concludes by discussing which clustering methods may be appropriate for different analytical purposes.« less

A consensus view of fold space: Combining SCOP, CATH, and the Dali Domain Dictionary

PubMed Central

Day, Ryan; Beck, David A.C.; Armen, Roger S.; Daggett, Valerie

2003-01-01

We have determined consensus protein-fold classifications on the basis of three classification methods, SCOP, CATH, and Dali. These classifications make use of different methods of defining and categorizing protein folds that lead to different views of protein-fold space. Pairwise comparisons of domains on the basis of their fold classifications show that much of the disagreement between the classification systems is due to differing domain definitions rather than assigning the same domain to different folds. However, there are significant differences in the fold assignments between the three systems. These remaining differences can be explained primarily in terms of the breadth of the fold classifications. Many structures may be defined as having one fold in one system, whereas far fewer are defined as having the analogous fold in another system. By comparing these folds for a nonredundant set of proteins, the consensus method breaks up broad fold classifications and combines restrictive fold classifications into metafolds, creating, in effect, an averaged view of fold space. This averaged view requires that the structural similarities between proteins having the same metafold be recognized by multiple classification systems. Thus, the consensus map is useful for researchers looking for fold similarities that are relatively independent of the method used to compare proteins. The 30 most populated metafolds, representing the folds of about half of a nonredundant subset of the PDB, are presented here. The full list of metafolds is presented on the Web. PMID:14500873

A consensus view of fold space: combining SCOP, CATH, and the Dali Domain Dictionary.

PubMed

Day, Ryan; Beck, David A C; Armen, Roger S; Daggett, Valerie

2003-10-01

We have determined consensus protein-fold classifications on the basis of three classification methods, SCOP, CATH, and Dali. These classifications make use of different methods of defining and categorizing protein folds that lead to different views of protein-fold space. Pairwise comparisons of domains on the basis of their fold classifications show that much of the disagreement between the classification systems is due to differing domain definitions rather than assigning the same domain to different folds. However, there are significant differences in the fold assignments between the three systems. These remaining differences can be explained primarily in terms of the breadth of the fold classifications. Many structures may be defined as having one fold in one system, whereas far fewer are defined as having the analogous fold in another system. By comparing these folds for a nonredundant set of proteins, the consensus method breaks up broad fold classifications and combines restrictive fold classifications into metafolds, creating, in effect, an averaged view of fold space. This averaged view requires that the structural similarities between proteins having the same metafold be recognized by multiple classification systems. Thus, the consensus map is useful for researchers looking for fold similarities that are relatively independent of the method used to compare proteins. The 30 most populated metafolds, representing the folds of about half of a nonredundant subset of the PDB, are presented here. The full list of metafolds is presented on the Web.

A Response to Proposed Equal Employment Opportunity Commission Regulations on Employer-Sponsored Health, Safety, and Well-Being Initiatives.

PubMed

2016-03-01

The aim of this study was to identify areas of consensus in response to proposed Equal Employment Opportunity Commission Americans with Disabilities Act of 1990 and Genetic Information Nondiscrimination Act of 2008 regulations on employer-sponsored health, safety, and well-being initiatives. The consensus process included review of existing and proposed regulations, identification of key areas where consensus is needed, and a methodical consensus-building process. Stakeholders representing employees, employers, consulting organizations, and wellness providers reached consensus around five areas, including adequate privacy notice on how medical data are collected, used, and protected; effective, equitable use of inducements that influence participation in programs; observance of reasonable alternative standards; what constitutes reasonably designed programs; and the need for greater congruence between federal agency regulations. Employee health and well-being initiatives that are in accord with federal regulations are comprehensive, evidence-based, and are construed as voluntary by employees and regulators alike.

From spoken narratives to domain knowledge: mining linguistic data for medical image understanding.

PubMed

Guo, Xuan; Yu, Qi; Alm, Cecilia Ovesdotter; Calvelli, Cara; Pelz, Jeff B; Shi, Pengcheng; Haake, Anne R

2014-10-01

Extracting useful visual clues from medical images allowing accurate diagnoses requires physicians' domain knowledge acquired through years of systematic study and clinical training. This is especially true in the dermatology domain, a medical specialty that requires physicians to have image inspection experience. Automating or at least aiding such efforts requires understanding physicians' reasoning processes and their use of domain knowledge. Mining physicians' references to medical concepts in narratives during image-based diagnosis of a disease is an interesting research topic that can help reveal experts' reasoning processes. It can also be a useful resource to assist with design of information technologies for image use and for image case-based medical education systems. We collected data for analyzing physicians' diagnostic reasoning processes by conducting an experiment that recorded their spoken descriptions during inspection of dermatology images. In this paper we focus on the benefit of physicians' spoken descriptions and provide a general workflow for mining medical domain knowledge based on linguistic data from these narratives. The challenge of a medical image case can influence the accuracy of the diagnosis as well as how physicians pursue the diagnostic process. Accordingly, we define two lexical metrics for physicians' narratives--lexical consensus score and top N relatedness score--and evaluate their usefulness by assessing the diagnostic challenge levels of corresponding medical images. We also report on clustering medical images based on anchor concepts obtained from physicians' medical term usage. These analyses are based on physicians' spoken narratives that have been preprocessed by incorporating the Unified Medical Language System for detecting medical concepts. The image rankings based on lexical consensus score and on top 1 relatedness score are well correlated with those based on challenge levels (Spearman correlation>0.5 and Kendall correlation>0.4). Clustering results are largely improved based on our anchor concept method (accuracy>70% and mutual information>80%). Physicians' spoken narratives are valuable for the purpose of mining the domain knowledge that physicians use in medical image inspections. We also show that the semantic metrics introduced in the paper can be successfully applied to medical image understanding and allow discussion of additional uses of these metrics. Copyright © 2014 Elsevier B.V. All rights reserved.

Improving local clustering based top-L link prediction methods via asymmetric link clustering information

NASA Astrophysics Data System (ADS)

Wu, Zhihao; Lin, Youfang; Zhao, Yiji; Yan, Hongyan

2018-02-01

Networks can represent a wide range of complex systems, such as social, biological and technological systems. Link prediction is one of the most important problems in network analysis, and has attracted much research interest recently. Many link prediction methods have been proposed to solve this problem with various techniques. We can note that clustering information plays an important role in solving the link prediction problem. In previous literatures, we find node clustering coefficient appears frequently in many link prediction methods. However, node clustering coefficient is limited to describe the role of a common-neighbor in different local networks, because it cannot distinguish different clustering abilities of a node to different node pairs. In this paper, we shift our focus from nodes to links, and propose the concept of asymmetric link clustering (ALC) coefficient. Further, we improve three node clustering based link prediction methods via the concept of ALC. The experimental results demonstrate that ALC-based methods outperform node clustering based methods, especially achieving remarkable improvements on food web, hamster friendship and Internet networks. Besides, comparing with other methods, the performance of ALC-based methods are very stable in both globalized and personalized top-L link prediction tasks.

A new method to prepare colloids of size-controlled clusters from a matrix assembly cluster source

NASA Astrophysics Data System (ADS)

Cai, Rongsheng; Jian, Nan; Murphy, Shane; Bauer, Karl; Palmer, Richard E.

2017-05-01

A new method for the production of colloidal suspensions of physically deposited clusters is demonstrated. A cluster source has been used to deposit size-controlled clusters onto water-soluble polymer films, which are then dissolved to produce colloidal suspensions of clusters encapsulated with polymer molecules. This process has been demonstrated using different cluster materials (Au and Ag) and polymers (polyvinylpyrrolidone, polyvinyl alcohol, and polyethylene glycol). Scanning transmission electron microscopy of the clusters before and after colloidal dispersion confirms that the polymers act as stabilizing agents. We propose that this method is suitable for the production of biocompatible colloids of ultraprecise clusters.

Using Consensus Building Procedures with Expert Raters to Establish Comparison Scores of Behavior for Direct Behavior Rating

ERIC Educational Resources Information Center

Jaffery, Rose; Johnson, Austin H.; Bowler, Mark C.; Riley-Tillman, T. Chris; Chafouleas, Sandra M.; Harrison, Sayward E.

2015-01-01

To date, rater accuracy when using Direct Behavior Rating (DBR) has been evaluated by comparing DBR-derived data to scores yielded through systematic direct observation. The purpose of this study was to evaluate an alternative method for establishing comparison scores using expert-completed DBR alongside best practices in consensus building…

Involving patients in a multidisciplinary European consensus process and in the development of a 'patient summary of the consensus document for colon and rectal cancer care'.

PubMed

Boelens, Petra G; Taylor, Claire; Henning, Geoffrey; Marang-van de Mheen, Perla J; Espin, Eloy; Wiggers, Theo; Gore-Booth, Jola; Moss, Barbara; Valentini, Vincenzo; van de Velde, Cornelis J H

2014-01-01

High-quality cancer care should be accessible for patients and healthcare professionals. Involvement of patients as partners in guideline formation and consensus processes is still rarely found. EURECCA, short for European Registration of Cancer Care, is the platform to improve outcomes of cancer care by reducing variation in the diagnostic and treatment process. EURECCA acknowledges the important role of patients in implementation of consensus information in clinical practice. The aim of this article is to describe the process of involving patients in the consensus process and in developing the patient summary of the consensus for colon and rectal cancer care. The Delphi method for achieving consensus was used. Three online voting rounds and one tele-voting round were offered to an expert panel of oncology professionals and patient representatives. At four different stages, patients and/or patient representatives were involved in the process: (1) during the consensus process, (2) lecturing about the role of the patient, (3) development of the patient summary, and (4) testing the patient summary. Representatives were invited to the voting and commenting rounds of this process and given an equal vote. Although patients were not consulted during the planning stages of this process, patient involvement increased following the panel's discussion of the implementation of the consensus among the patient population. After the consensus meeting, the patient summary was written by patient representatives, oncologists and nurses. A selection of proactive patients reviewed the draft patient summary; responses were positive and several patient-reported outcomes were added. Questionnaires to evaluate the use and implementation of the patient summary in daily practice are currently being developed and tested. Patient consultation will be needed in future planning for selection of topics. The present study may function as a model for future consensus processes to involve patients at different stages and to implement both patient and healthcare professional versions in daily practice.

Core Needle Biopsy of the Thyroid: 2016 Consensus Statement and Recommendations from Korean Society of Thyroid Radiology

PubMed Central

Na, Dong Gyu; Jung, So Lyung; Kim, Ji-hoon; Sung, Jin Yong; Kim, Kyu Sun; Lee, Jeong Hyun; Shin, Jung Hee; Choi, Yoon Jung; Ha, Eun Ju; Lim, Hyun Kyung; Kim, Soo Jin; Hahn, Soo Yeon; Lee, Kwang Hwi; Choi, Young Jun; Youn, Inyoung; Kim, Young Joong; Ahn, Hye Shin; Ryu, Ji Hwa; Baek, Seon Mi; Sim, Jung Suk; Jung, Chan Kwon; Lee, Joon Hyung

2017-01-01

Core needle biopsy (CNB) has been suggested as a complementary diagnostic method to fine-needle aspiration in patients with thyroid nodules. Many recent CNB studies have suggested a more advanced role for CNB, but there are still no guidelines on its use. Therefore, the Task Force Committee of the Korean Society of Thyroid Radiology has developed the present consensus statement and recommendations for the role of CNB in the diagnosis of thyroid nodules. These recommendations are based on evidence from the current literature and expert consensus. PMID:28096731

Shear wave speed estimation by adaptive random sample consensus method.

PubMed

Lin, Haoming; Wang, Tianfu; Chen, Siping

2014-01-01

This paper describes a new method for shear wave velocity estimation that is capable of extruding outliers automatically without preset threshold. The proposed method is an adaptive random sample consensus (ARANDSAC) and the metric used here is finding the certain percentage of inliers according to the closest distance criterion. To evaluate the method, the simulation and phantom experiment results were compared using linear regression with all points (LRWAP) and radon sum transform (RS) method. The assessment reveals that the relative biases of mean estimation are 20.00%, 4.67% and 5.33% for LRWAP, ARANDSAC and RS respectively for simulation, 23.53%, 4.08% and 1.08% for phantom experiment. The results suggested that the proposed ARANDSAC algorithm is accurate in shear wave speed estimation.

Reaching consensus on communication of critical laboratory results using a collective intelligence method.

PubMed

Llovet, Maria Isabel; Biosca, Carmen; Martínez-Iribarren, Alicia; Blanco, Aurora; Busquets, Glòria; Castro, María José; Llopis, Maria Antonia; Montesinos, Mercè; Minchinela, Joana; Perich, Carme; Prieto, Judith; Ruiz, Rosa; Serrat, Núria; Simón, Margarita; Trejo, Alex; Monguet, Josep Maria; López-Pablo, Carlos; Ibarz, Mercè

2018-02-23

There is no consensus in the literature about what analytes or values should be informed as critical results and how they should be communicated. The main aim of this project is to establish consensual standards of critical results for the laboratories participating in the study. Among the project's secondary objectives, establishing consensual procedures for communication can be highlighted. Consensus was reached among all participating laboratories establishing the basis for the construction of the initial model put forward for consensus in conjunction with the clinicians. A real-time Delphi, methodology "health consensus" (HC), with motivating and participative questions was applied. The physician was expected to choose a numeric value within a scale designed for each analyte. The medians of critical results obtained represent the consensus on critical results for outpatient and inpatient care. Both in primary care and in hospital care a high degree of consensus was observed for critical values proposed in the analysis of creatinine, digoxin, phosphorus, glucose, international normalized ratio (INR), leukocytes, magnesium, neutrophils, chloride, sodium, calcium and lithium. For the rest of critical results the degree of consensus obtained was "medium high". The results obtained showed that in 72% of cases the consensual critical value coincided with the medians initially proposed by the laboratories. The real-time Delphi has allowed obtaining consensual standards for communication of critical results among the laboratories participating in the study, which can serve as a basis for other organizations.

Cluster Correspondence Analysis.

PubMed

van de Velden, M; D'Enza, A Iodice; Palumbo, F

2017-03-01

A method is proposed that combines dimension reduction and cluster analysis for categorical data by simultaneously assigning individuals to clusters and optimal scaling values to categories in such a way that a single between variance maximization objective is achieved. In a unified framework, a brief review of alternative methods is provided and we show that the proposed method is equivalent to GROUPALS applied to categorical data. Performance of the methods is appraised by means of a simulation study. The results of the joint dimension reduction and clustering methods are compared with the so-called tandem approach, a sequential analysis of dimension reduction followed by cluster analysis. The tandem approach is conjectured to perform worse when variables are added that are unrelated to the cluster structure. Our simulation study confirms this conjecture. Moreover, the results of the simulation study indicate that the proposed method also consistently outperforms alternative joint dimension reduction and clustering methods.

Gap analysis: assessing the value perception of consultant pharmacist services and the performance of consultant pharmacists.

PubMed

Clark, Thomas R

2008-09-01

To understand the importance of services provided by consultant pharmacists and to assess perception of their performance of services. Cross-sectional; nursing facility team. Random e-mail survey of consultant pharmacists; phone survey of team members. 233 consultant pharmacists (practicing in a nursing facility); 540 team members (practicing in a nursing facility, interacting with > or = 1 consultant pharmacist): 120 medical directors, 210 directors of nursing, 210 administrators. Consultant pharmacists, directors of nursing, medical directors, and administrators rating importance/performance of 21 services. Gap between teams' ratings of importance and consultant pharmacists' performance is assessed to categorize services. Importance/performance ranked on five-point scale. Mean scores used for gap analysis to cluster services into four categories. Per combined group, six services categorized as "Keep It Up" (important, good performance), consensus with individual groups, except discrepancy with medical directors, for one service. Six services each categorized as "Improve" (important, large gap) and "Improve Second" (lower importance, large gap), with varied responses by individual groups. Three different services were categorized into "Don't Worry," with consensus within individual groups. Consensus from all groups found 5 of 21 services are important and performed well by consultant pharmacists, indicating to maintain performance of services. For three services, consultant pharmacists do not need to worry about their performance. Thirteen services require improvement in consultant pharmacists' performance; various groups differ on extent of improvement needed. Results can serve as benchmark comparisons with results obtained by consultant pharmacists in their own facilities.

Developing Appropriate Methods for Cost-Effectiveness Analysis of Cluster Randomized Trials

PubMed Central

Gomes, Manuel; Ng, Edmond S.-W.; Nixon, Richard; Carpenter, James; Thompson, Simon G.

2012-01-01

Aim. Cost-effectiveness analyses (CEAs) may use data from cluster randomized trials (CRTs), where the unit of randomization is the cluster, not the individual. However, most studies use analytical methods that ignore clustering. This article compares alternative statistical methods for accommodating clustering in CEAs of CRTs. Methods. Our simulation study compared the performance of statistical methods for CEAs of CRTs with 2 treatment arms. The study considered a method that ignored clustering—seemingly unrelated regression (SUR) without a robust standard error (SE)—and 4 methods that recognized clustering—SUR and generalized estimating equations (GEEs), both with robust SE, a “2-stage” nonparametric bootstrap (TSB) with shrinkage correction, and a multilevel model (MLM). The base case assumed CRTs with moderate numbers of balanced clusters (20 per arm) and normally distributed costs. Other scenarios included CRTs with few clusters, imbalanced cluster sizes, and skewed costs. Performance was reported as bias, root mean squared error (rMSE), and confidence interval (CI) coverage for estimating incremental net benefits (INBs). We also compared the methods in a case study. Results. Each method reported low levels of bias. Without the robust SE, SUR gave poor CI coverage (base case: 0.89 v. nominal level: 0.95). The MLM and TSB performed well in each scenario (CI coverage, 0.92–0.95). With few clusters, the GEE and SUR (with robust SE) had coverage below 0.90. In the case study, the mean INBs were similar across all methods, but ignoring clustering underestimated statistical uncertainty and the value of further research. Conclusions. MLMs and the TSB are appropriate analytical methods for CEAs of CRTs with the characteristics described. SUR and GEE are not recommended for studies with few clusters. PMID:22016450

The burden of neck pain: its meaning for persons with neck pain and healthcare providers, explored by concept mapping.

PubMed

van Randeraad-van der Zee, Carlijn H; Beurskens, Anna J H M; Swinkels, Raymond A H M; Pool, Jan J M; Batterham, Roy W; Osborne, Richard H; de Vet, Henrica C W

2016-05-01

To empirically define the concept of burden of neck pain. The lack of a clear understanding of this construct from the perspective of persons with neck pain and care providers hampers adequate measurement of this burden. An additional aim was to compare the conceptual model obtained with the frequently used Neck Disability Index (NDI). Concept mapping, combining qualitative (nominal group technique and group consensus) and quantitative research methods (cluster analysis and multidimensional scaling), was applied to groups of persons with neck pain (n = 3) and professionals treating persons with neck pain (n = 2). Group members generated statements, which were organized into concept maps. Group members achieved consensus about the number and description of domains and the researchers then generated an overall mind map covering the full breadth of the burden of neck pain. Concept mapping revealed 12 domains of burden of neck pain: impaired mobility neck, neck pain, fatigue/concentration, physical complaints, psychological aspects/consequences, activities of daily living, social participation, financial consequences, difficult to treat/difficult to diagnose, difference of opinion with care providers, incomprehension by social environment, and how person with neck pain deal with complaints. All ten items of the NDI could be linked to the mind map, but the NDI measures only part of the burden of neck pain. This study revealed the relevant domains for the burden of neck pain from the viewpoints of persons with neck pain and their care providers. These results can guide the identification of existing measurements instruments for each domain or the development of new ones to measure the burden of neck pain.

Population-genetic analysis of HvABCG31 promoter sequence in wild barley (Hordeum vulgare ssp. spontaneum)

PubMed Central

2012-01-01

Background The cuticle is an important adaptive structure whose origin played a crucial role in the transition of plants from aqueous to terrestrial conditions. HvABCG31/Eibi1 is an ABCG transporter gene, involved in cuticle formation that was recently identified in wild barley (Hordeum vulgare ssp. spontaneum). To study the genetic variation of HvABCG31 in different habitats, its 2 kb promoter region was sequenced from 112 wild barley accessions collected from five natural populations from southern and northern Israel. The sites included three mesic and two xeric habitats, and differed in annual rainfall, soil type, and soil water capacity. Results Phylogenetic analysis of the aligned HvABCG31 promoter sequences clustered the majority of accessions (69 out of 71) from the three northern mesic populations into one cluster, while all 21 accessions from the Dead Sea area, a xeric southern population, and two isolated accessions (one from a xeric population at Mitzpe Ramon and one from the xeric ‘African Slope’ of “Evolution Canyon”) formed the second cluster. The southern arid populations included six haplotypes, but they differed from the consensus sequence at a large number of positions, while the northern mesic populations included 15 haplotypes that were, on average, more similar to the consensus sequence. Most of the haplotypes (20 of 22) were unique to a population. Interestingly, higher genetic variation occurred within populations (54.2%) than among populations (45.8%). Analysis of the promoter region detected a large number of transcription factor binding sites: 121–128 and 121–134 sites in the two southern arid populations, and 123–128,125–128, and 123–125 sites in the three northern mesic populations. Three types of TFBSs were significantly enriched: those related to GA (gibberellin), Dof (DNA binding with one finger), and light. Conclusions Drought stress and adaptive natural selection may have been important determinants in the observed sequence variation of HvABCG31 promoter. Abiotic stresses may be involved in the HvABCG31 gene transcription regulations, generating more protective cuticles in plants under stresses. PMID:23006777

Scoring clustering solutions by their biological relevance.

PubMed

Gat-Viks, I; Sharan, R; Shamir, R

2003-12-12

A central step in the analysis of gene expression data is the identification of groups of genes that exhibit similar expression patterns. Clustering gene expression data into homogeneous groups was shown to be instrumental in functional annotation, tissue classification, regulatory motif identification, and other applications. Although there is a rich literature on clustering algorithms for gene expression analysis, very few works addressed the systematic comparison and evaluation of clustering results. Typically, different clustering algorithms yield different clustering solutions on the same data, and there is no agreed upon guideline for choosing among them. We developed a novel statistically based method for assessing a clustering solution according to prior biological knowledge. Our method can be used to compare different clustering solutions or to optimize the parameters of a clustering algorithm. The method is based on projecting vectors of biological attributes of the clustered elements onto the real line, such that the ratio of between-groups and within-group variance estimators is maximized. The projected data are then scored using a non-parametric analysis of variance test, and the score's confidence is evaluated. We validate our approach using simulated data and show that our scoring method outperforms several extant methods, including the separation to homogeneity ratio and the silhouette measure. We apply our method to evaluate results of several clustering methods on yeast cell-cycle gene expression data. The software is available from the authors upon request.

Expert consensus on best evaluative practices in community-based rehabilitation.

PubMed

Grandisson, Marie; Thibeault, Rachel; Hébert, Michèle; Cameron, Debra

2016-01-01

The objective of this study was to generate expert consensus on best evaluative practices for community-based rehabilitation (CBR). This consensus includes key features of the evaluation process and methods, and discussion of whether a shared framework should be used to report findings and, if so, which framework should play this role. A Delphi study with two predefined rounds was conducted. Experts in CBR from a wide range of geographical areas and disciplinary backgrounds were recruited to complete the questionnaires. Both quantitative and qualitative analyses were performed to generate the recommendations for best practices in CBR evaluation. A panel of 42 experts reached consensus on 13 recommendations for best evaluative practices in CBR. In regard to the critical qualities of sound CBR evaluation processes, panellists emphasized that these processes should be inclusive, participatory, empowering and respectful of local cultures and languages. The group agreed that evaluators should consider the use of mixed methods and participatory tools, and should combine indicators from a universal list of CBR indicators with locally generated ones. The group also agreed that a common framework should guide CBR evaluations, and that this framework should be a flexible combination between the CBR Matrix and the CBR Principles. An expert panel reached consensus on key features of best evaluative practices in CBR. Knowledge transfer initiatives are now required to develop guidelines, tools and training opportunities to facilitate CBR program evaluations. CBR evaluation processes should strive to be inclusive, participatory, empowering and respectful of local cultures and languages. CBR evaluators should strongly consider using mixed methods, participatory tools, a combination of indicators generated with the local community and with others from a bank of CBR indicators. CBR evaluations should be situated within a shared, but flexible, framework. This shared framework could combine the CBR Matrix and the CBR Principles.

Inferring explicit weighted consensus networks to represent alternative evolutionary histories

PubMed Central

2013-01-01

Background The advent of molecular biology techniques and constant increase in availability of genetic material have triggered the development of many phylogenetic tree inference methods. However, several reticulate evolution processes, such as horizontal gene transfer and hybridization, have been shown to blur the species evolutionary history by causing discordance among phylogenies inferred from different genes. Methods To tackle this problem, we hereby describe a new method for inferring and representing alternative (reticulate) evolutionary histories of species as an explicit weighted consensus network which can be constructed from a collection of gene trees with or without prior knowledge of the species phylogeny. Results We provide a way of building a weighted phylogenetic network for each of the following reticulation mechanisms: diploid hybridization, intragenic recombination and complete or partial horizontal gene transfer. We successfully tested our method on some synthetic and real datasets to infer the above-mentioned evolutionary events which may have influenced the evolution of many species. Conclusions Our weighted consensus network inference method allows one to infer, visualize and validate statistically major conflicting signals induced by the mechanisms of reticulate evolution. The results provided by the new method can be used to represent the inferred conflicting signals by means of explicit and easy-to-interpret phylogenetic networks. PMID:24359207

Finding gene clusters for a replicated time course study

PubMed Central

2014-01-01

Background Finding genes that share similar expression patterns across samples is an important question that is frequently asked in high-throughput microarray studies. Traditional clustering algorithms such as K-means clustering and hierarchical clustering base gene clustering directly on the observed measurements and do not take into account the specific experimental design under which the microarray data were collected. A new model-based clustering method, the clustering of regression models method, takes into account the specific design of the microarray study and bases the clustering on how genes are related to sample covariates. It can find useful gene clusters for studies from complicated study designs such as replicated time course studies. Findings In this paper, we applied the clustering of regression models method to data from a time course study of yeast on two genotypes, wild type and YOX1 mutant, each with two technical replicates, and compared the clustering results with K-means clustering. We identified gene clusters that have similar expression patterns in wild type yeast, two of which were missed by K-means clustering. We further identified gene clusters whose expression patterns were changed in YOX1 mutant yeast compared to wild type yeast. Conclusions The clustering of regression models method can be a valuable tool for identifying genes that are coordinately transcribed by a common mechanism. PMID:24460656

[Public health: politics help those who help themselves [in health services

PubMed

Ortún, Vicente

2007-01-01

Poor countries health improves with the application of public health knowledge, but this requires from institutional capacity and political will, not automatically guaranteed by income growth alone. Generalized cost-benefit analysis, explicit establishment of priorities and even consensus (knowledge sharing) are suitable methods to select appropriate policies. Some problems, such as the increasing inequalities among countries or the global warming, may require a change of our institutions given than both market mechanisms and traditional policy intervention by nation-states may prove insufficient. could be the motto for the necessary conciliation between individual and collective actions on health. It has a similar importance to act upon the differences between individuals with similar exposures as diminishing the global risk of those social groups where misfortunes cluster. In a country such a Spain the aforementioned conciliation happens though a Welfare State capable of achieving social 'desirability' based upon democratic legitimacy and effective behavior, effectiveness that can not be obtained without the best combination of clinical and Public Health interventions.

Environmental initiative prioritization with a Delphi approach: a case study.

PubMed

Gokhale, A A

2001-08-01

India is fast finding its place in the industrialized world and that is beginning to raise its environmental consciousness. The Delphi technique was used to prioritize specific needs and articulate a sustainable urban improvement strategy for the city of Mumbai (formerly Bombay). The Delphi technique is a means of achieving consensual validity among raters by providing them feedback regarding other raters' responses. Mumbai has several indigenous environmental groups that were tapped for activists and leaders; the study was conducted using ten environmentalists. In the initial phases the responses resulted in a range of possible program alternatives. The last two stages helped to seek out information that generated a consensus on the part of the respondent group. Statistical analysis methods included a hierarchical cluster analysis, mean, median, mode, and percent of agreement calculations using SPSS software. The face-to-face discussion in phase 4 clarified some issues and helped the group as a whole to outline the strategy for putting in place the essential elements of a framework to improve the quality of life in an urban environment.

[Identification of Tibetan medicine "Dida" of Gentianaceae using DNA barcoding].

PubMed

Liu, Chuan; Zhang, Yu-Xin; Liu, Yue; Chen, Yi-Long; Fan, Gang; Xiang, Li; Xu, Jiang; Zhang, Yi

2016-02-01

The ITS2 barcode was used toidentify Tibetan medicine "Dida", and tosecure its quality and safety in medication. A total of 13 species, 151 experimental samples for the study from the Tibetan Plateau, including Gentianaceae Swertia, Halenia, Gentianopsis, Comastoma, Lomatogonium ITS2 sequences were amplified, and purified PCR products were sequenced. Sequence assembly and consensus sequence generation were performed using the CodonCode Aligner V3.7.1. The Kimura 2-Parameter (K2P) distances were calculated using MEGA 6.0. The neighbor-joining (NJ) phylogenetic trees were constructed. There are 31 haplotypes among 231 bp after alignment of all ITS2 sequence haplotypes, and the average G±C content of 61.40%. The NJ tree strongly supported that every species clustered into their own clade and high identification success rate, except that Swertia bifolia and Swertia wolfangiana could not be distinguished from each other based on the sequence divergences. DNA barcoding could be used as a fast and accurate identification method to distinguish Tibetan medicine "Dida" to ensure its safe use. Copyright© by the Chinese Pharmaceutical Association.

Update in Outpatient General Internal Medicine: Practice-Changing Evidence Published in 2015.

PubMed

Szostek, Jason H; Wieland, Mark L; Post, Jason A; Sundsted, Karna K; Mauck, Karen F

2016-08-01

Identifying new practice-changing articles is challenging. To determine the 2015 practice-changing articles most relevant to outpatient general internal medicine, 3 internists independently reviewed the titles and abstracts of original articles, synopses of single studies and syntheses, and databases of syntheses. For original articles, internal medicine journals with the 7 highest impact factors were reviewed: New England Journal of Medicine, Lancet, Journal of the American Medical Association (JAMA), British Medical Journal, Public Library of Science Medicine, Annals of Internal Medicine, and JAMA Internal Medicine. For synopses of single studies and syntheses, collections in American College of Physicians Journal Club, Journal Watch, and Evidence-Based Medicine were reviewed. For databases of synthesis, Evidence Updates and the Cochrane Library were reviewed. More than 100 articles were identified. Criteria for inclusion were as follows: clinical relevance, potential for practice change, and strength of evidence. Clusters of important articles around one topic were considered as a single-candidate series. The 5 authors used a modified Delphi method to reach consensus on inclusion of 7 topics for in-depth appraisal. Copyright © 2016 Elsevier Inc. All rights reserved.

Update in Outpatient General Internal Medicine: Practice-Changing Evidence Published in 2017.

PubMed

Wieland, Mark L; Szostek, Jason H; Wingo, Majken T; Post, Jason A; Mauck, Karen F

2018-02-26

Clinicians are challenged to identify new practice-changing articles in the medical literature. To identify the practice-changing articles published in 2017 most relevant to outpatient general internal medicine, 5 internists reviewed the following sources: 1) titles and abstracts from internal medicine journals with the 7 highest impact factors, including New England Journal of Medicine, Lancet, Journal of the American Medical Association, British Medical Journal, Public Library of Science Medicine, Annals of Internal Medicine, and JAMA Internal Medicine; 2) synopses and syntheses of individual studies, including collections in the American College of Physicians Journal Club, Journal Watch, and Evidence-Based Medicine; 3) databases of synthesis, including Evidence Updates and the Cochrane Library. Inclusion criteria were perceived clinical relevance to outpatient general medicine, potential for practice change, and strength of evidence. This process yielded 140 articles. Clusters of important articles around one topic were considered as a single-candidate series. A modified Delphi method was utilized by the 5 authors to reach consensus on 7 topics to highlight and appraise from the 2017 literature. Copyright © 2018 Elsevier Inc. All rights reserved.

Peculiar macrophagous adaptations in a new Cretaceous pliosaurid

PubMed Central

Arkhangelsky, Maxim S.; Stenshin, Ilya M.; Uspensky, Gleb N.; Zverkov, Nikolay G.

2015-01-01

During the Middle and Late Jurassic, pliosaurid plesiosaurs evolved gigantic body size and a series of craniodental adaptations that have been linked to the occupation of an apex predator niche. Cretaceous pliosaurids (i.e. Brachaucheninae) depart from this morphology, being slightly smaller and lacking the macrophagous adaptations seen in earlier forms. However, the fossil record of Early Cretaceous pliosaurids is poor, concealing the evolution and ecological diversity of the group. Here, we report a new pliosaurid from the Late Hauterivian (Early Cretaceous) of Russia. Phylogenetic analyses using reduced consensus methods recover it as the basalmost brachauchenine. This pliosaurid is smaller than other derived pliosaurids, has tooth alveoli clustered in pairs and possesses trihedral teeth with complex serrated carinae. Maximum-likelihood ancestral state reconstruction suggests early brachauchenines retained trihedral teeth from their ancestors, but modified this feature in a unique way, convergent with macrophagous archosaurs or sphenacodontoids. Our findings indicate that Early Cretaceous marine reptile teeth with serrated carinae cannot be unequivocally assigned to metriorhynchoid crocodylomorphs. Furthermore, they extend the known diversity of dental adaptations seen in Sauropterygia, the longest lived clade of marine tetrapods. PMID:27019740

Defining interdisciplinary competencies for audiological rehabilitation: findings from a modified Delphi study.

PubMed

Xue, Lina; Le Bot, Gaëlle; Van Petegem, Wim; van Wieringen, Astrid

2018-02-01

The aim of this study is to derive a consensus on an interdisciplinary competency framework regarding a holistic approach for audiological rehabilitation (AR), which includes disciplines from medicine, engineering, social sciences and humanities. We employed a modified Delphi method. In the first round survey, experts were asked to rate an initial list of 28 generic interdisciplinary competencies and to propose specific knowledge areas for AR. In the second round, experts were asked to reconsider their answers in light of the group answers of the first round. An international panel of 27 experts from different disciplines in AR completed the first round. Twenty-two of them completed the second round. We developed a competency framework consisting of 21 generic interdisciplinary competencies grouped in five domains and nine specific competencies (knowledge areas) in three clusters. Suggestions for the implementation of the generic competencies in interdisciplinary programmes were identified. This study reveals insights into the interdisciplinary competencies that are unique for AR. The framework will be useful for educators in developing interdisciplinary programmes as well as for professionals in considering their lifelong training needs in AR.

Progeny Clustering: A Method to Identify Biological Phenotypes

PubMed Central

Hu, Chenyue W.; Kornblau, Steven M.; Slater, John H.; Qutub, Amina A.

2015-01-01

Estimating the optimal number of clusters is a major challenge in applying cluster analysis to any type of dataset, especially to biomedical datasets, which are high-dimensional and complex. Here, we introduce an improved method, Progeny Clustering, which is stability-based and exceptionally efficient in computing, to find the ideal number of clusters. The algorithm employs a novel Progeny Sampling method to reconstruct cluster identity, a co-occurrence probability matrix to assess the clustering stability, and a set of reference datasets to overcome inherent biases in the algorithm and data space. Our method was shown successful and robust when applied to two synthetic datasets (datasets of two-dimensions and ten-dimensions containing eight dimensions of pure noise), two standard biological datasets (the Iris dataset and Rat CNS dataset) and two biological datasets (a cell phenotype dataset and an acute myeloid leukemia (AML) reverse phase protein array (RPPA) dataset). Progeny Clustering outperformed some popular clustering evaluation methods in the ten-dimensional synthetic dataset as well as in the cell phenotype dataset, and it was the only method that successfully discovered clinically meaningful patient groupings in the AML RPPA dataset. PMID:26267476

A spatial scan statistic for nonisotropic two-level risk cluster.

PubMed

Li, Xiao-Zhou; Wang, Jin-Feng; Yang, Wei-Zhong; Li, Zhong-Jie; Lai, Sheng-Jie

2012-01-30

Spatial scan statistic methods are commonly used for geographical disease surveillance and cluster detection. The standard spatial scan statistic does not model any variability in the underlying risks of subregions belonging to a detected cluster. For a multilevel risk cluster, the isotonic spatial scan statistic could model a centralized high-risk kernel in the cluster. Because variations in disease risks are anisotropic owing to different social, economical, or transport factors, the real high-risk kernel will not necessarily take the central place in a whole cluster area. We propose a spatial scan statistic for a nonisotropic two-level risk cluster, which could be used to detect a whole cluster and a noncentralized high-risk kernel within the cluster simultaneously. The performance of the three methods was evaluated through an intensive simulation study. Our proposed nonisotropic two-level method showed better power and geographical precision with two-level risk cluster scenarios, especially for a noncentralized high-risk kernel. Our proposed method is illustrated using the hand-foot-mouth disease data in Pingdu City, Shandong, China in May 2009, compared with two other methods. In this practical study, the nonisotropic two-level method is the only way to precisely detect a high-risk area in a detected whole cluster. Copyright © 2011 John Wiley & Sons, Ltd.

EURECCA colorectal: multidisciplinary management: European consensus conference colon & rectum.

PubMed

van de Velde, Cornelis J H; Boelens, Petra G; Borras, Josep M; Coebergh, Jan-Willem; Cervantes, Andres; Blomqvist, Lennart; Beets-Tan, Regina G H; van den Broek, Colette B M; Brown, Gina; Van Cutsem, Eric; Espin, Eloy; Haustermans, Karin; Glimelius, Bengt; Iversen, Lene H; van Krieken, J Han; Marijnen, Corrie A M; Henning, Geoffrey; Gore-Booth, Jola; Meldolesi, Elisa; Mroczkowski, Pawel; Nagtegaal, Iris; Naredi, Peter; Ortiz, Hector; Påhlman, Lars; Quirke, Philip; Rödel, Claus; Roth, Arnaud; Rutten, Harm; Schmoll, Hans J; Smith, Jason J; Tanis, Pieter J; Taylor, Claire; Wibe, Arne; Wiggers, Theo; Gambacorta, Maria A; Aristei, Cynthia; Valentini, Vincenzo

2014-01-01

Care for patients with colon and rectal cancer has improved in the last 20years; however considerable variation still exists in cancer management and outcome between European countries. Large variation is also apparent between national guidelines and patterns of cancer care in Europe. Therefore, EURECCA, which is the acronym of European Registration of Cancer Care, is aiming at defining core treatment strategies and developing a European audit structure in order to improve the quality of care for all patients with colon and rectal cancer. In December 2012, the first multidisciplinary consensus conference about cancer of the colon and rectum was held. The expert panel consisted of representatives of European scientific organisations involved in cancer care of patients with colon and rectal cancer and representatives of national colorectal registries. The expert panel had delegates of the European Society of Surgical Oncology (ESSO), European Society for Radiotherapy & Oncology (ESTRO), European Society of Pathology (ESP), European Society for Medical Oncology (ESMO), European Society of Radiology (ESR), European Society of Coloproctology (ESCP), European CanCer Organisation (ECCO), European Oncology Nursing Society (EONS) and the European Colorectal Cancer Patient Organisation (EuropaColon), as well as delegates from national registries or audits. Consensus was achieved using the Delphi method. For the Delphi process, multidisciplinary experts were invited to comment and vote three web-based online voting rounds and to lecture on the subjects during the meeting (13th-15th December 2012). The sentences in the consensus document were available during the meeting and a televoting round during the conference by all participants was performed. This manuscript covers all sentences of the consensus document with the result of the voting. The consensus document represents sections on diagnostics, pathology, surgery, medical oncology, radiotherapy, and follow-up where applicable for treatment of colon cancer, rectal cancer and metastatic colorectal disease separately. Moreover, evidence based algorithms for diagnostics and treatment were composed which were also submitted to the Delphi process. The total number of the voted sentences was 465. All chapters were voted on by at least 75% of the experts. Of the 465 sentences, 84% achieved large consensus, 6% achieved moderate consensus, and 7% resulted in minimum consensus. Only 3% was disagreed by more than 50% of the members. Multidisciplinary consensus on key diagnostic and treatment issues for colon and rectal cancer management using the Delphi method was successful. This consensus document embodies the expertise of professionals from all disciplines involved in the care for patients with colon and rectal cancer. Diagnostic and treatment algorithms were developed to implement the current evidence and to define core treatment guidance for multidisciplinary team management of colon and rectal cancer throughout Europe. Copyright © 2013 Elsevier Ltd. All rights reserved.

MULTI-K: accurate classification of microarray subtypes using ensemble k-means clustering

PubMed Central

Kim, Eun-Youn; Kim, Seon-Young; Ashlock, Daniel; Nam, Dougu

2009-01-01

Background Uncovering subtypes of disease from microarray samples has important clinical implications such as survival time and sensitivity of individual patients to specific therapies. Unsupervised clustering methods have been used to classify this type of data. However, most existing methods focus on clusters with compact shapes and do not reflect the geometric complexity of the high dimensional microarray clusters, which limits their performance. Results We present a cluster-number-based ensemble clustering algorithm, called MULTI-K, for microarray sample classification, which demonstrates remarkable accuracy. The method amalgamates multiple k-means runs by varying the number of clusters and identifies clusters that manifest the most robust co-memberships of elements. In addition to the original algorithm, we newly devised the entropy-plot to control the separation of singletons or small clusters. MULTI-K, unlike the simple k-means or other widely used methods, was able to capture clusters with complex and high-dimensional structures accurately. MULTI-K outperformed other methods including a recently developed ensemble clustering algorithm in tests with five simulated and eight real gene-expression data sets. Conclusion The geometric complexity of clusters should be taken into account for accurate classification of microarray data, and ensemble clustering applied to the number of clusters tackles the problem very well. The C++ code and the data sets tested are available from the authors. PMID:19698124

Size-guided multi-seed heuristic method for geometry optimization of clusters: Application to benzene clusters.

PubMed

Takeuchi, Hiroshi

2018-05-08

Since searching for the global minimum on the potential energy surface of a cluster is very difficult, many geometry optimization methods have been proposed, in which initial geometries are randomly generated and subsequently improved with different algorithms. In this study, a size-guided multi-seed heuristic method is developed and applied to benzene clusters. It produces initial configurations of the cluster with n molecules from the lowest-energy configurations of the cluster with n - 1 molecules (seeds). The initial geometries are further optimized with the geometrical perturbations previously used for molecular clusters. These steps are repeated until the size n satisfies a predefined one. The method locates putative global minima of benzene clusters with up to 65 molecules. The performance of the method is discussed using the computational cost, rates to locate the global minima, and energies of initial geometries. © 2018 Wiley Periodicals, Inc. © 2018 Wiley Periodicals, Inc.

Relation between financial market structure and the real economy: comparison between clustering methods.

PubMed

Musmeci, Nicoló; Aste, Tomaso; Di Matteo, T

2015-01-01

We quantify the amount of information filtered by different hierarchical clustering methods on correlations between stock returns comparing the clustering structure with the underlying industrial activity classification. We apply, for the first time to financial data, a novel hierarchical clustering approach, the Directed Bubble Hierarchical Tree and we compare it with other methods including the Linkage and k-medoids. By taking the industrial sector classification of stocks as a benchmark partition, we evaluate how the different methods retrieve this classification. The results show that the Directed Bubble Hierarchical Tree can outperform other methods, being able to retrieve more information with fewer clusters. Moreover,we show that the economic information is hidden at different levels of the hierarchical structures depending on the clustering method. The dynamical analysis on a rolling window also reveals that the different methods show different degrees of sensitivity to events affecting financial markets, like crises. These results can be of interest for all the applications of clustering methods to portfolio optimization and risk hedging [corrected].

Relation between Financial Market Structure and the Real Economy: Comparison between Clustering Methods

PubMed Central

Musmeci, Nicoló; Aste, Tomaso; Di Matteo, T.

2015-01-01

We quantify the amount of information filtered by different hierarchical clustering methods on correlations between stock returns comparing the clustering structure with the underlying industrial activity classification. We apply, for the first time to financial data, a novel hierarchical clustering approach, the Directed Bubble Hierarchical Tree and we compare it with other methods including the Linkage and k-medoids. By taking the industrial sector classification of stocks as a benchmark partition, we evaluate how the different methods retrieve this classification. The results show that the Directed Bubble Hierarchical Tree can outperform other methods, being able to retrieve more information with fewer clusters. Moreover, we show that the economic information is hidden at different levels of the hierarchical structures depending on the clustering method. The dynamical analysis on a rolling window also reveals that the different methods show different degrees of sensitivity to events affecting financial markets, like crises. These results can be of interest for all the applications of clustering methods to portfolio optimization and risk hedging. PMID:25786703

Cluster randomised crossover trials with binary data and unbalanced cluster sizes: application to studies of near-universal interventions in intensive care.

PubMed

Forbes, Andrew B; Akram, Muhammad; Pilcher, David; Cooper, Jamie; Bellomo, Rinaldo

2015-02-01

Cluster randomised crossover trials have been utilised in recent years in the health and social sciences. Methods for analysis have been proposed; however, for binary outcomes, these have received little assessment of their appropriateness. In addition, methods for determination of sample size are currently limited to balanced cluster sizes both between clusters and between periods within clusters. This article aims to extend this work to unbalanced situations and to evaluate the properties of a variety of methods for analysis of binary data, with a particular focus on the setting of potential trials of near-universal interventions in intensive care to reduce in-hospital mortality. We derive a formula for sample size estimation for unbalanced cluster sizes, and apply it to the intensive care setting to demonstrate the utility of the cluster crossover design. We conduct a numerical simulation of the design in the intensive care setting and for more general configurations, and we assess the performance of three cluster summary estimators and an individual-data estimator based on binomial-identity-link regression. For settings similar to the intensive care scenario involving large cluster sizes and small intra-cluster correlations, the sample size formulae developed and analysis methods investigated are found to be appropriate, with the unweighted cluster summary method performing well relative to the more optimal but more complex inverse-variance weighted method. More generally, we find that the unweighted and cluster-size-weighted summary methods perform well, with the relative efficiency of each largely determined systematically from the study design parameters. Performance of individual-data regression is adequate with small cluster sizes but becomes inefficient for large, unbalanced cluster sizes. When outcome prevalences are 6% or less and the within-cluster-within-period correlation is 0.05 or larger, all methods display sub-nominal confidence interval coverage, with the less prevalent the outcome the worse the coverage. As with all simulation studies, conclusions are limited to the configurations studied. We confined attention to detecting intervention effects on an absolute risk scale using marginal models and did not explore properties of binary random effects models. Cluster crossover designs with binary outcomes can be analysed using simple cluster summary methods, and sample size in unbalanced cluster size settings can be determined using relatively straightforward formulae. However, caution needs to be applied in situations with low prevalence outcomes and moderate to high intra-cluster correlations. © The Author(s) 2014.

A novel thermophilic and halophilic esterase from Janibacter sp. R02, the first member of a new lipase family (Family XVII).

PubMed

Castilla, Agustín; Panizza, Paola; Rodríguez, Diego; Bonino, Luis; Díaz, Pilar; Irazoqui, Gabriela; Rodríguez Giordano, Sonia

2017-03-01

Janibacter sp. strain R02 (BNM 560) was isolated in our laboratory from an Antarctic soil sample. A remarkable trait of the strain was its high lipolytic activity, detected in Rhodamine-olive oil supplemented plates. Supernatants of Janibacter sp. R02 displayed superb activity on transesterification of acyl glycerols, thus being a good candidate for lipase prospection. Considering the lack of information concerning lipases of the genus Janibacter, we focused on the identification, cloning, expression and characterization of the extracellular lipases of this strain. By means of sequence alignment and clustering of consensus nucleotide sequences, a DNA fragment of 1272bp was amplified, cloned and expressed in E. coli. The resulting recombinant enzyme, named LipJ2, showed preference for short to medium chain-length substrates, and displayed maximum activity at 80°C and pH 8-9, being strongly activated by a mixture of Na + and K + . The enzyme presented an outstanding stability regarding both pH and temperature. Bioinformatics analysis of the amino acid sequence of LipJ2 revealed the presence of a consensus catalytic triad and a canonical pentapeptide. However, two additional rare motifs were found in LipJ2: an SXXL β-lactamase motif and two putative Y-type oxyanion holes (YAP). Although some of the previous features could allow assigning LipJ2 to the bacterial lipase families VIII or X, the phylogenetic analysis showed that LipJ2 clusters apart from other members of known lipase families, indicating that the newly isolated Janibacter esterase LipJ2 would be the first characterized member of a new family of bacterial lipases. Published by Elsevier Inc.

Physical and in silico approaches identify DNA-PK in a Tax DNA-damage response interactome

PubMed Central

Ramadan, Emad; Ward, Michael; Guo, Xin; Durkin, Sarah S; Sawyer, Adam; Vilela, Marcelo; Osgood, Christopher; Pothen, Alex; Semmes, Oliver J

2008-01-01

Background We have initiated an effort to exhaustively map interactions between HTLV-1 Tax and host cellular proteins. The resulting Tax interactome will have significant utility toward defining new and understanding known activities of this important viral protein. In addition, the completion of a full Tax interactome will also help shed light upon the functional consequences of these myriad Tax activities. The physical mapping process involved the affinity isolation of Tax complexes followed by sequence identification using tandem mass spectrometry. To date we have mapped 250 cellular components within this interactome. Here we present our approach to prioritizing these interactions via an in silico culling process. Results We first constructed an in silico Tax interactome comprised of 46 literature-confirmed protein-protein interactions. This number was then reduced to four Tax-interactions suspected to play a role in DNA damage response (Rad51, TOP1, Chk2, 53BP1). The first-neighbor and second-neighbor interactions of these four proteins were assembled from available human protein interaction databases. Through an analysis of betweenness and closeness centrality measures, and numbers of interactions, we ranked proteins in the first neighborhood. When this rank list was compared to the list of physical Tax-binding proteins, DNA-PK was the highest ranked protein common to both lists. An overlapping clustering of the Tax-specific second-neighborhood protein network showed DNA-PK to be one of three bridge proteins that link multiple clusters in the DNA damage response network. Conclusion The interaction of Tax with DNA-PK represents an important biological paradigm as suggested via consensus findings in vivo and in silico. We present this methodology as an approach to discovery and as a means of validating components of a consensus Tax interactome. PMID:18922151

Pooled clustering of high-grade serous ovarian cancer gene expression leads to novel consensus subtypes associated with survival and surgical outcomes

PubMed Central

Wang, Chen; Armasu, Sebastian M.; Kalli, Kimberly R.; Maurer, Matthew J.; Heinzen, Ethan P.; Keeney, Gary L.; Cliby, William A.; Oberg, Ann L.; Kaufmann, Scott H.; Goode, Ellen L.

2017-01-01

Purpose Here we assess whether molecular subtyping identifies biological features of tumors that correlate with survival and surgical outcomes of high-grade serous ovarian cancer (HGSOC). Experimental Design Consensus clustering of pooled mRNA expression data from over 2,000 HGSOC cases was used to define molecular subtypes of HGSOCs. This de novo classification scheme was then applied to 381 Mayo Clinic HGSOC patients with detailed survival and surgical outcome information. Results Five molecular subtypes of HGSOC were identified. In the pooled dataset, three subtypes were largely concordant with prior studies describing proliferative, mesenchymal, and immunoreactive tumors (concordance > 70%), and the group of tumors previously described as differentiated type was segregated into two new types, one of which (anti-mesenchymal) had down-regulation of genes that were typically upregulated in the mesenchymal subtype. Molecular subtypes were significantly associated with overall survival (p<0.001) and with rate of optimal surgical debulking (≤1 cm, p=1.9E-4) in the pooled dataset. Among stage III-C or IV Mayo Clinic patients, molecular subtypes were also significantly associated with overall survival (p=0.001), as well as rate of complete surgical debulking (no residual disease; 16% in mesenchymal tumors comparing to >28% in other subtypes; p=0.02). Conclusions HGSOC tumors may be categorized into five molecular subtypes that associate with overall survival and the extent of residual disease following debulking surgery. Because mesenchymal tumors may have features that were associated with less favorable surgical outcome, molecular subtyping may have future utility in guiding neoadjuvant treatment decisions for women with HGSOC. PMID:28280090

Application of next-generation sequencing technology to study genetic diversity and identify unique SNP markers in bread wheat from Kazakhstan.

PubMed

Shavrukov, Yuri; Suchecki, Radoslaw; Eliby, Serik; Abugalieva, Aigul; Kenebayev, Serik; Langridge, Peter

2014-09-28

New SNP marker platforms offer the opportunity to investigate the relationships between wheat cultivars from different regions and assess the mechanism and processes that have led to adaptation to particular production environments. Wheat breeding has a long history in Kazakhstan and the aim of this study was to explore the relationship between key varieties from Kazakhstan and germplasm from breeding programs for other regions. The study revealed 5,898 polymorphic markers amongst ten cultivars, of which 2,730 were mapped in the consensus genetic map. Mapped SNP markers were distributed almost equally across the A and B genomes, with between 279 and 484 markers assigned to each chromosome. Marker coverage was approximately 10-fold lower in the D genome. There were 863 SNP markers identified as unique to specific cultivars, and clusters of these markers (regions containing more than three closely mapped unique SNPs) showed specific patterns on the consensus genetic map for each cultivar. Significant intra-varietal genetic polymorphism was identified in three cultivars (Tzelinnaya 3C, Kazakhstanskaya rannespelaya and Kazakhstanskaya 15). Phylogenetic analysis based on inter-varietal polymorphism showed that the very old cultivar Erythrospermum 841 was the most genetically distinct from the other nine cultivars from Kazakhstan, falling in a clade together with the American cultivar Sonora and genotypes from Central and South Asia. The modern cultivar Kazakhstanskaya 19 also fell into a separate clade, together with the American cultivar Thatcher. The remaining eight cultivars shared a single sub-clade but were categorised into four clusters. The accumulated data for SNP marker polymorphisms amongst bread wheat genotypes from Kazakhstan may be used for studying genetic diversity in bread wheat, with potential application for marker-assisted selection and the preparation of a set of genotype-specific markers.

Meta-analysis of Clear Cell Renal Cell Carcinoma Gene Expression Defines a Variant Subgroup and Identifies Gender Influences on Tumor Biology

PubMed Central

Brannon, A. Rose; Haake, Scott M.; Hacker, Kathryn E.; Pruthi, Raj S.; Wallen, Eric M.; Nielsen, Matthew E.; Rathmell, W. Kimryn

2011-01-01

Background Clear cell renal cell carcinoma (ccRCC) displays molecular and histologic heterogeneity. Previously described subsets of this disease, ccA and ccB, were defined based on multigene expression profiles, but it is unclear whether these subgroupings reflect the full spectrum of disease or how these molecular subtypes relate to histologic descriptions or gender. Objective Determine whether additional subtypes of ccRCC exist and whether these subtypes are related to von Hippel-Lindau (VHL) inactivation, hypoxia-inducible factor (HIF) 1 and 2 expression, tumor histology, or gender. Design, setting, and participants Six large, publicly available ccRCC gene expression databases were identified that cumulatively provided data for 480 tumors for meta-analysis via meta-array compilation. Measurements Unsupervised consensus clustering was performed on the meta-arrays. Tumors were examined for the relationship of multigene-defined consensus subtypes and expression signatures of VHL mutation and HIF status, tumor histology, and gender. Results and limitations Two dominant subsets of ccRCC were observed. However, a minor third cluster was revealed that correlated strongly with a wild type (WT) VHL expression profile and indications of variant histologies. When variant histologies were removed, ccA tumors naturally divided by gender. This technique is limited by the potential for persistent batch effect, tumor sampling bias, and restrictions of annotated information. Conclusions The ccA and ccB subsets of ccRCC are robust in meta-analysis among histologically conventional ccRCC tumors. A third group of tumors was identified that may represent a new variant of ccRCC. Within definitively clear cell tumors, gender may delineate tumors in such a way that it could have implications regarding current treatments and future drug development. PMID:22030119

A consensus-guided approach yields a heat-stable alkane-producing enzyme and identifies residues promoting thermostability.

PubMed

Shakeel, Tabinda; Gupta, Mayank; Fatma, Zia; Kumar, Rakesh; Kumar, Raubins; Singh, Rahul; Sharma, Medha; Jade, Dhananjay; Gupta, Dinesh; Fatma, Tasneem; Yazdani, Syed Shams

2018-06-15

Aldehyde-deformylating oxygenase (ADO) is an essential enzyme for production of long-chain alkanes as drop-in biofuels, which are compatible with existing fuel systems. The most active ADOs are present in mesophilic cyanobacteria, especially Nostoc punctiforme Given the potential applications of thermostable enzymes in biorefineries, here we generated a thermostable (Cts)-ADO based on a consensus of ADO sequences from several thermophilic cyanobacterial strains. Using an in silico design pipeline and a metagenome library containing 41 hot-spring microbial communities, we created Cts-ADO. Cts-ADO displayed a 3.8-fold increase in pentadecane production on raising the temperature from 30 to 42 °C, whereas ADO from N. punctiforme (Np-ADO) exhibited a 1.7-fold decline. 3D structure modeling and molecular dynamics simulations of Cts- and Np-ADO at different temperatures revealed differences between the two enzymes in residues clustered on exposed loops of these variants, which affected the conformation of helices involved in forming the ADO catalytic core. In Cts-ADO, this conformational change promoted ligand binding to its preferred iron, Fe2, in the di-iron cluster at higher temperature, but the reverse was observed in Np-ADO. Detailed mapping of residues conferring Cts-ADO thermostability identified four amino acids, which we substituted individually and together in Np-ADO. Among these substitution variants, A161E was remarkably similar to Cts-ADO in terms of activity optima, kinetic parameters, and structure at higher temperature. A161E was located in loop L6, which connects helices H5 and H6, and supported ligand binding to Fe2 at higher temperatures, thereby promoting optimal activity at these temperatures and explaining the increased thermostability of Cts-ADO. © 2018 by The American Society for Biochemistry and Molecular Biology, Inc.

Fast optimization of binary clusters using a novel dynamic lattice searching method.

PubMed

Wu, Xia; Cheng, Wen

2014-09-28

Global optimization of binary clusters has been a difficult task despite of much effort and many efficient methods. Directing toward two types of elements (i.e., homotop problem) in binary clusters, two classes of virtual dynamic lattices are constructed and a modified dynamic lattice searching (DLS) method, i.e., binary DLS (BDLS) method, is developed. However, it was found that the BDLS can only be utilized for the optimization of binary clusters with small sizes because homotop problem is hard to be solved without atomic exchange operation. Therefore, the iterated local search (ILS) method is adopted to solve homotop problem and an efficient method based on the BDLS method and ILS, named as BDLS-ILS, is presented for global optimization of binary clusters. In order to assess the efficiency of the proposed method, binary Lennard-Jones clusters with up to 100 atoms are investigated. Results show that the method is proved to be efficient. Furthermore, the BDLS-ILS method is also adopted to study the geometrical structures of (AuPd)79 clusters with DFT-fit parameters of Gupta potential.

Consulting the oracle: ten lessons from using the Delphi technique in nursing research.

PubMed

Keeney, Sinead; Hasson, Felicity; McKenna, Hugh

2006-01-01

The aim of this paper was to provide insight into the Delphi technique by outlining our personal experiences during its use over a 10-year period in a variety of applications. As a means of achieving consensus on an issue, the Delphi research method has become widely used in healthcare research generally and nursing research in particular. The literature on this technique is expanding, mainly addressing what it is and how it should be used. However, there is still much confusion and uncertainty surrounding it, particularly about issues such as modifications, consensus, anonymity, definition of experts, how 'experts' are selected and how non-respondents are pursued. This issues that arise when planning and carrying out a Delphi study include the definition of consensus; the issue of anonymity vs. quasi-anonymity for participants; how to estimate the time needed to collect the data, analyse each 'round', feed back results to participants, and gain their responses to this feedback; how to define and select the 'experts' who will be asked to participate; how to enhance response rates; and how many 'rounds' to conduct. Many challenges and questions are raised when using the Delphi technique, but there is no doubt that it is an important method for achieving consensus on issues where none previously existed. Researchers need to adapt the method to suit their particular study.

Comparative analysis of minimal residual disease detection using four-color flow cytometry, consensus IgH-PCR, and quantitative IgH PCR in CLL after allogeneic and autologous stem cell transplantation.

PubMed

Böttcher, S; Ritgen, M; Pott, C; Brüggemann, M; Raff, T; Stilgenbauer, S; Döhner, H; Dreger, P; Kneba, M

2004-10-01

The clinically most suitable method for minimal residual disease (MRD) detection in chronic lymphocytic leukemia is still controversial. We prospectively compared MRD assessment in 158 blood samples of 74 patients with CLL after stem cell transplantation (SCT) using four-color flow cytometry (MRD flow) in parallel with consensus IgH-PCR and ASO IgH real-time PCR (ASO IgH RQ-PCR). In 25 out of 106 samples (23.6%) with a polyclonal consensus IgH-PCR pattern, MRD flow still detected CLL cells, proving higher sensitivity of flow cytometry over PCR-genescanning with consensus IgH-primers. Of 92 samples, 14 (15.2%) analyzed in parallel by MRD flow and by ASO IgH RQ-PCR were negative by our flow cytometric assay but positive by PCR, thus demonstrating superior sensitivity of RQ-PCR with ASO primers. Quantitative MRD levels measured by both methods correlated well (r=0.93). MRD detection by flow and ASO IgH RQ-PCR were equally suitable to monitor MRD kinetics after allogeneic SCT, but the PCR method detected impending relapses after autologous SCT earlier. An analysis of factors that influence sensitivity and specificity of flow cytometry for MRD detection allowed to devise further improvements of this technique.

A preliminary score for the assessment of disease activity in hereditary recurrent fevers: results from the AIDAI (Auto-Inflammatory Diseases Activity Index) Consensus Conference

PubMed Central

Piram, Maryam; Frenkel, Joost; Gattorno, Marco; Ozen, Seza; Lachmann, Helen J; Goldbach-Mansky, Raphaela; Hentgen, Véronique; Neven, Bénédicte; Stankovic Stojanovic, Katia; Simon, Anna; Kuemmerle-Deschner, Jasmin; Hoffman, Hal; Stojanov, Silvia; Duquesne, Agnès; Pillet, Pascal; Martini, Alberto; Pouchot, Jacques; Koné-Paut, Isabelle

2012-01-01

Background The systemic autoinflammatory disorders (SAID) share many clinical manifestations, albeit with variable patterns, intensity and frequency. A common definition of disease activity would be rational and useful in the management of these lifelong diseases. Moreover, standardised disease activity scores are required for the assessment of new therapies in constant development. The aim of this study was to develop preliminary activity scores for familial Mediterranean fever, mevalonate kinase deficiency, tumour necrosis factor receptor-1-associated periodic syndrome and cryopyrin-associated periodic syndromes (CAPS). Methods The study was conducted using two well-recognised consensus formation methods: the Delphi technique and the nominal group technique. The results from a two-step survey and data from parent/patient interviews were used as preliminary data to develop the agenda for a consensus conference to build a provisional scoring system. Results 24 of 65 experts in SAID from 20 countries answered the web questionnaire and 16 attended the consensus conference. There was consensus agreement to develop separate activity scores for each disease but with a common format based on patient diaries. Fever and disease-specific clinical variables were scored according to their severity. A final score was generated by summing the score of all the variables divided by the number of days over which the diary was completed. Scores varied from 0 to 16 (0–13 in CAPS). These scores were developed for the purpose of clinical studies but could be used in clinical practice. Conclusion Using widely recognised consensus formation techniques, preliminary scores were obtained to measure disease activity in four main SAID. Further prospective validation study of this instrument will follow. PMID:21081528

Use of cultural consensus analysis to evaluate expert feedback of median safety.

PubMed

Kim, Tae-Gyu; Donnell, Eric T; Lee, Dongmin

2008-07-01

Cultural consensus analysis is a statistical method that can be used to assess participant responses to survey questions. The technique concurrently estimates the knowledge of each survey participant and estimates the culturally correct answer to each question asked, based on the existence of consensus among survey participants. The main objectives of this paper are to present the cultural consensus methodology and apply it to a set of median design and safety survey data that were collected using the Delphi method. A total of 21 Delphi survey participants were asked to answer research questions related to cross-median crashes. It was found that the Delphi panel had agreeable opinions with respect to the association of average daily traffic (ADT) and heavy vehicle percentage combination on the risk of cross-median crashes; relative importance of additional factors, other than ADT, median width, and crash history that may contribute to cross-median crashes; and, the relative importance of geometric factors that may be associated with the likelihood of cross-median crashes. Therefore, the findings from the cultural consensus analysis indicate that the expert panel selected to participate in the Delphi survey shared a common knowledge pool relative to the association between median design and safety. There were, however, diverse opinions regarding median barrier type and its preferred placement location. The panel showed a higher level of knowledge on the relative importance regarding the association of geometric factors on cross-median crashes likelihood than on other issues considered. The results of the cultural consensus analysis of the present median design and safety survey data could be used to design a focused field study of median safety.

Prioritizing quantitative trait loci for root system architecture in tetraploid wheat

PubMed Central

Maccaferri, Marco; El-Feki, Walid; Nazemi, Ghasemali; Salvi, Silvio; Canè, Maria Angela; Colalongo, Maria Chiara; Stefanelli, Sandra; Tuberosa, Roberto

2016-01-01

Optimization of root system architecture (RSA) traits is an important objective for modern wheat breeding. Linkage and association mapping for RSA in two recombinant inbred line populations and one association mapping panel of 183 elite durum wheat (Triticum turgidum L. var. durum Desf.) accessions evaluated as seedlings grown on filter paper/polycarbonate screening plates revealed 20 clusters of quantitative trait loci (QTLs) for root length and number, as well as 30 QTLs for root growth angle (RGA). Divergent RGA phenotypes observed by seminal root screening were validated by root phenotyping of field-grown adult plants. QTLs were mapped on a high-density tetraploid consensus map based on transcript-associated Illumina 90K single nucleotide polymorphisms (SNPs) developed for bread and durum wheat, thus allowing for an accurate cross-referencing of RSA QTLs between durum and bread wheat. Among the main QTL clusters for root length and number highlighted in this study, 15 overlapped with QTLs for multiple RSA traits reported in bread wheat, while out of 30 QTLs for RGA, only six showed co-location with previously reported QTLs in wheat. Based on their relative additive effects/significance, allelic distribution in the association mapping panel, and co-location with QTLs for grain weight and grain yield, the RSA QTLs have been prioritized in terms of breeding value. Three major QTL clusters for root length and number (RSA_QTL_cluster_5#, RSA_QTL_cluster_6#, and RSA_QTL_cluster_12#) and nine RGA QTL clusters (QRGA.ubo-2A.1, QRGA.ubo-2A.3, QRGA.ubo-2B.2/2B.3, QRGA.ubo-4B.4, QRGA.ubo-6A.1, QRGA.ubo-6A.2, QRGA.ubo-7A.1, QRGA.ubo-7A.2, and QRGA.ubo-7B) appear particularly valuable for further characterization towards a possible implementation of breeding applications in marker-assisted selection and/or cloning of the causal genes underlying the QTLs. PMID:26880749

Adolescence transitional care in neurogenic detrusor overactivity and the use of OnabotulinumtoxinA: A clinical algorithm from an Italian consensus statement.

PubMed

Palleschi, Giovanni; Mosiello, Giovanni; Iacovelli, Valerio; Musco, Stefania; Del Popolo, Giulio; Giannantoni, Antonella; Carbone, Antonio; Carone, Roberto; Tubaro, Andrea; De Gennaro, Mario; Marte, Antonio; Finazzi Agrò, Enrico

2018-03-01

OnabotulinumtoxinA (onaBNTa) for treating neurogenic detrusor overactivity (NDO) is widely used after its regulatory approval in adults. Although the administration of onaBNTa is still considered off-label in children, data have already been reported on its efficacy and safety. Nowadays, there is a lack of standardized protocols for treatment of NDO with onaBNTa in adolescent patients in their transition from the childhood to the adult age. With the aim to address this issue a consensus panel was obtained. A panel of leading urologists and urogynaecologists skilled in functional urology, neuro-urology, urogynaecology, and pediatric urology participated in a consensus-forming project using a Delphi method to reach national consensus on NDO-onaBNTa treatment in adolescence transitional care. In total, 11 experts participated. All panelists participated in the four phases of the consensus process. Consensus was reached if ≥70% of the experts agreed on recommendations. To facilitate a common understanding among all experts, a face-to-face consensus meeting was held in Rome in march 2015 and then with a follow-up teleconference in march 2017. By the end of the Delphi process, formal consensus was achieved for 100% of the items and an algorithm was then developed. This manuscript represents the first report on the onaBNTa in adolescents. Young adults should be treated as a distinct sub-population in policy, planning, programming, and research, as strongly sustained by national public health care. This consensus and the algorithm could support multidisciplinary communication, reduce the extent of variations in clinical practice and optimize clinical decision making. © 2017 Wiley Periodicals, Inc.

Role of Genetic Testing for Inherited Prostate Cancer Risk: Philadelphia Prostate Cancer Consensus Conference 2017.

PubMed

Giri, Veda N; Knudsen, Karen E; Kelly, William K; Abida, Wassim; Andriole, Gerald L; Bangma, Chris H; Bekelman, Justin E; Benson, Mitchell C; Blanco, Amie; Burnett, Arthur; Catalona, William J; Cooney, Kathleen A; Cooperberg, Matthew; Crawford, David E; Den, Robert B; Dicker, Adam P; Eggener, Scott; Fleshner, Neil; Freedman, Matthew L; Hamdy, Freddie C; Hoffman-Censits, Jean; Hurwitz, Mark D; Hyatt, Colette; Isaacs, William B; Kane, Christopher J; Kantoff, Philip; Karnes, R Jeffrey; Karsh, Lawrence I; Klein, Eric A; Lin, Daniel W; Loughlin, Kevin R; Lu-Yao, Grace; Malkowicz, S Bruce; Mann, Mark J; Mark, James R; McCue, Peter A; Miner, Martin M; Morgan, Todd; Moul, Judd W; Myers, Ronald E; Nielsen, Sarah M; Obeid, Elias; Pavlovich, Christian P; Peiper, Stephen C; Penson, David F; Petrylak, Daniel; Pettaway, Curtis A; Pilarski, Robert; Pinto, Peter A; Poage, Wendy; Raj, Ganesh V; Rebbeck, Timothy R; Robson, Mark E; Rosenberg, Matt T; Sandler, Howard; Sartor, Oliver; Schaeffer, Edward; Schwartz, Gordon F; Shahin, Mark S; Shore, Neal D; Shuch, Brian; Soule, Howard R; Tomlins, Scott A; Trabulsi, Edouard J; Uzzo, Robert; Vander Griend, Donald J; Walsh, Patrick C; Weil, Carol J; Wender, Richard; Gomella, Leonard G

2018-02-01

Purpose Guidelines are limited for genetic testing for prostate cancer (PCA). The goal of this conference was to develop an expert consensus-driven working framework for comprehensive genetic evaluation of inherited PCA in the multigene testing era addressing genetic counseling, testing, and genetically informed management. Methods An expert consensus conference was convened including key stakeholders to address genetic counseling and testing, PCA screening, and management informed by evidence review. Results Consensus was strong that patients should engage in shared decision making for genetic testing. There was strong consensus to test HOXB13 for suspected hereditary PCA, BRCA1/2 for suspected hereditary breast and ovarian cancer, and DNA mismatch repair genes for suspected Lynch syndrome. There was strong consensus to factor BRCA2 mutations into PCA screening discussions. BRCA2 achieved moderate consensus for factoring into early-stage management discussion, with stronger consensus in high-risk/advanced and metastatic setting. Agreement was moderate to test all men with metastatic castration-resistant PCA, regardless of family history, with stronger agreement to test BRCA1/2 and moderate agreement to test ATM to inform prognosis and targeted therapy. Conclusion To our knowledge, this is the first comprehensive, multidisciplinary consensus statement to address a genetic evaluation framework for inherited PCA in the multigene testing era. Future research should focus on developing a working definition of familial PCA for clinical genetic testing, expanding understanding of genetic contribution to aggressive PCA, exploring clinical use of genetic testing for PCA management, genetic testing of African American males, and addressing the value framework of genetic evaluation and testing men at risk for PCA-a clinically heterogeneous disease.

Endemic and Epidemic Lineages of Escherichia coli that Cause Urinary Tract Infections

PubMed Central

Tabor, Helen; Tellis, Patricia; Vincent, Caroline; Tellier, Pierre-Paul

2008-01-01

Women with urinary tract infections (UTIs) in California, USA (1999–2001), were infected with closely related or indistinguishable strains of Escherichia coli (clonal groups), which suggests point source dissemination. We compared strains of UTI-causing E. coli in California with strains causing such infections in Montréal, Québec, Canada. Urine specimens from women with community-acquired UTIs in Montréal (2006) were cultured for E. coli. Isolates that caused 256 consecutive episodes of UTI were characterized by antimicrobial drug susceptibility profile, enterobacterial repetitive intergenic consensus 2 PCR, serotyping, XbaI and NotI pulsed-field gel electrophoresis, multilocus sequence typing, and phylogenetic typing. We confirmed the presence of drug-resistant, genetically related, and temporally clustered E. coli clonal groups that caused community-acquired UTIs in unrelated women in 2 locations and 2 different times. Two clonal groups were identified in both locations. Epidemic transmission followed by endemic transmission of UTI-causing clonal groups may explain these clusters of UTI cases. PMID:18826822

TELEMAM: a cluster randomised trial to assess the use of telemedicine in multi-disciplinary breast cancer decision making.

PubMed

Kunkler, I H; Prescott, R J; Lee, R J; Brebner, J A; Cairns, J A; Fielding, R G; Bowman, A; Neades, G; Walls, A D F; Chetty, U; Dixon, J M; Smith, M E; Gardner, T W; Macnab, M; Swann, S; Maclean, J R

2007-11-01

The TELEMAM trial aimed to assess the clinical effectiveness and costs of telemedicine in conducting breast cancer multi-disciplinary meetings (MDTs). Over 12 months 473 MDT patient discussions in two district general hospitals (DGHs) were cluster randomised (2:1) to the intervention of telemedicine linkage to breast specialists in a cancer centre or to the control group of 'in-person' meetings. Primary endpoints were clinical effectiveness and costs. Economic analysis was based on a cost-minimisation approach. Levels of agreement of MDT members on a scale from 1 to 5 were high and similar in both the telemedicine and standard meetings for decision sharing (4.04 versus 4.17), consensus (4.06 versus 4.20) and confidence in the decision (4.16 versus 4.07). The threshold at which the telemedicine meetings became cheaper than standard MDTs was approximately 40 meetings per year. Telemedicine delivered breast cancer multi-disciplinary meetings have similar clinical effectiveness to standard 'in-person' meetings.

Review of methods for handling confounding by cluster and informative cluster size in clustered data

PubMed Central

Seaman, Shaun; Pavlou, Menelaos; Copas, Andrew

2014-01-01

Clustered data are common in medical research. Typically, one is interested in a regression model for the association between an outcome and covariates. Two complications that can arise when analysing clustered data are informative cluster size (ICS) and confounding by cluster (CBC). ICS and CBC mean that the outcome of a member given its covariates is associated with, respectively, the number of members in the cluster and the covariate values of other members in the cluster. Standard generalised linear mixed models for cluster-specific inference and standard generalised estimating equations for population-average inference assume, in general, the absence of ICS and CBC. Modifications of these approaches have been proposed to account for CBC or ICS. This article is a review of these methods. We express their assumptions in a common format, thus providing greater clarity about the assumptions that methods proposed for handling CBC make about ICS and vice versa, and about when different methods can be used in practice. We report relative efficiencies of methods where available, describe how methods are related, identify a previously unreported equivalence between two key methods, and propose some simple additional methods. Unnecessarily using a method that allows for ICS/CBC has an efficiency cost when ICS and CBC are absent. We review tools for identifying ICS/CBC. A strategy for analysis when CBC and ICS are suspected is demonstrated by examining the association between socio-economic deprivation and preterm neonatal death in Scotland. PMID:25087978

Distributed Cooperative Optimal Control for Multiagent Systems on Directed Graphs: An Inverse Optimal Approach.

PubMed

Zhang, Huaguang; Feng, Tao; Yang, Guang-Hong; Liang, Hongjing

2015-07-01

In this paper, the inverse optimal approach is employed to design distributed consensus protocols that guarantee consensus and global optimality with respect to some quadratic performance indexes for identical linear systems on a directed graph. The inverse optimal theory is developed by introducing the notion of partial stability. As a result, the necessary and sufficient conditions for inverse optimality are proposed. By means of the developed inverse optimal theory, the necessary and sufficient conditions are established for globally optimal cooperative control problems on directed graphs. Basic optimal cooperative design procedures are given based on asymptotic properties of the resulting optimal distributed consensus protocols, and the multiagent systems can reach desired consensus performance (convergence rate and damping rate) asymptotically. Finally, two examples are given to illustrate the effectiveness of the proposed methods.

Interactive K-Means Clustering Method Based on User Behavior for Different Analysis Target in Medicine.

PubMed

Lei, Yang; Yu, Dai; Bin, Zhang; Yang, Yang

2017-01-01

Clustering algorithm as a basis of data analysis is widely used in analysis systems. However, as for the high dimensions of the data, the clustering algorithm may overlook the business relation between these dimensions especially in the medical fields. As a result, usually the clustering result may not meet the business goals of the users. Then, in the clustering process, if it can combine the knowledge of the users, that is, the doctor's knowledge or the analysis intent, the clustering result can be more satisfied. In this paper, we propose an interactive K -means clustering method to improve the user's satisfactions towards the result. The core of this method is to get the user's feedback of the clustering result, to optimize the clustering result. Then, a particle swarm optimization algorithm is used in the method to optimize the parameters, especially the weight settings in the clustering algorithm to make it reflect the user's business preference as possible. After that, based on the parameter optimization and adjustment, the clustering result can be closer to the user's requirement. Finally, we take an example in the breast cancer, to testify our method. The experiments show the better performance of our algorithm.

Comparison of cluster-based and source-attribution methods for estimating transmission risk using large HIV sequence databases.

PubMed

Le Vu, Stéphane; Ratmann, Oliver; Delpech, Valerie; Brown, Alison E; Gill, O Noel; Tostevin, Anna; Fraser, Christophe; Volz, Erik M

2018-06-01

Phylogenetic clustering of HIV sequences from a random sample of patients can reveal epidemiological transmission patterns, but interpretation is hampered by limited theoretical support and statistical properties of clustering analysis remain poorly understood. Alternatively, source attribution methods allow fitting of HIV transmission models and thereby quantify aspects of disease transmission. A simulation study was conducted to assess error rates of clustering methods for detecting transmission risk factors. We modeled HIV epidemics among men having sex with men and generated phylogenies comparable to those that can be obtained from HIV surveillance data in the UK. Clustering and source attribution approaches were applied to evaluate their ability to identify patient attributes as transmission risk factors. We find that commonly used methods show a misleading association between cluster size or odds of clustering and covariates that are correlated with time since infection, regardless of their influence on transmission. Clustering methods usually have higher error rates and lower sensitivity than source attribution method for identifying transmission risk factors. But neither methods provide robust estimates of transmission risk ratios. Source attribution method can alleviate drawbacks from phylogenetic clustering but formal population genetic modeling may be required to estimate quantitative transmission risk factors. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Challenges in microarray class discovery: a comprehensive examination of normalization, gene selection and clustering

PubMed Central

2010-01-01

Background Cluster analysis, and in particular hierarchical clustering, is widely used to extract information from gene expression data. The aim is to discover new classes, or sub-classes, of either individuals or genes. Performing a cluster analysis commonly involve decisions on how to; handle missing values, standardize the data and select genes. In addition, pre-processing, involving various types of filtration and normalization procedures, can have an effect on the ability to discover biologically relevant classes. Here we consider cluster analysis in a broad sense and perform a comprehensive evaluation that covers several aspects of cluster analyses, including normalization. Result We evaluated 2780 cluster analysis methods on seven publicly available 2-channel microarray data sets with common reference designs. Each cluster analysis method differed in data normalization (5 normalizations were considered), missing value imputation (2), standardization of data (2), gene selection (19) or clustering method (11). The cluster analyses are evaluated using known classes, such as cancer types, and the adjusted Rand index. The performances of the different analyses vary between the data sets and it is difficult to give general recommendations. However, normalization, gene selection and clustering method are all variables that have a significant impact on the performance. In particular, gene selection is important and it is generally necessary to include a relatively large number of genes in order to get good performance. Selecting genes with high standard deviation or using principal component analysis are shown to be the preferred gene selection methods. Hierarchical clustering using Ward's method, k-means clustering and Mclust are the clustering methods considered in this paper that achieves the highest adjusted Rand. Normalization can have a significant positive impact on the ability to cluster individuals, and there are indications that background correction is preferable, in particular if the gene selection is successful. However, this is an area that needs to be studied further in order to draw any general conclusions. Conclusions The choice of cluster analysis, and in particular gene selection, has a large impact on the ability to cluster individuals correctly based on expression profiles. Normalization has a positive effect, but the relative performance of different normalizations is an area that needs more research. In summary, although clustering, gene selection and normalization are considered standard methods in bioinformatics, our comprehensive analysis shows that selecting the right methods, and the right combinations of methods, is far from trivial and that much is still unexplored in what is considered to be the most basic analysis of genomic data. PMID:20937082

Analyzing Cultural Consensus with Proportional Reduction in Error (PRE): Beyond the Eigenvalue Ratio

ERIC Educational Resources Information Center

Lacy, Michael G.; Snodgrass, Jeffrey G.

2016-01-01

This article provides an alternate method to assess the fit of the cultural consensus model (CCM) of Romney and colleagues to the responses of a group of informants about a domain of knowledge, and thus also to evaluate the extent of shared knowledge within a group. Criteria for judging the existence of singular culture have been articulated…

Why Is It So Hard to Reach Agreement on Terminology? The Case of Developmental Language Disorder (DLD)

ERIC Educational Resources Information Center

Bishop, Dorothy V. M.

2017-01-01

A recent project entitled CATALISE used the Delphi method to reach a consensus on terminology for unexplained language problems in children. "Developmental language disorder" (DLD) was the term agreed by a panel of 57 experts. Here I reflect on points of difficulty that arose when attempting to reach a consensus, using qualitative…

Finding Culture Change in the Second Factor: Stability and Change in Cultural Consensus and Residual Agreement

ERIC Educational Resources Information Center

Dressler, William W.; Balieiro, Mauro C.; dos Santos, José Ernesto

2015-01-01

This article reports the replication after 10 years of cultural consensus analyses in four cultural domains in the city of Ribeirão Preto, Brazil. Additionally, two methods for evaluating residual agreement are applied to the data, and a new technique for evaluating how cultural knowledge is represented by residual agreement is introduced. We…

A safe an easy method for building consensus HIV sequences from 454 massively parallel sequencing data.

PubMed

Fernández-Caballero Rico, Jose Ángel; Chueca Porcuna, Natalia; Álvarez Estévez, Marta; Mosquera Gutiérrez, María Del Mar; Marcos Maeso, María Ángeles; García, Federico

2018-02-01

To show how to generate a consensus sequence from the information of massive parallel sequences data obtained from routine HIV anti-retroviral resistance studies, and that may be suitable for molecular epidemiology studies. Paired Sanger (Trugene-Siemens) and next-generation sequencing (NGS) (454 GSJunior-Roche) HIV RT and protease sequences from 62 patients were studied. NGS consensus sequences were generated using Mesquite, using 10%, 15%, and 20% thresholds. Molecular evolutionary genetics analysis (MEGA) was used for phylogenetic studies. At a 10% threshold, NGS-Sanger sequences from 17/62 patients were phylogenetically related, with a median bootstrap-value of 88% (IQR83.5-95.5). Association increased to 36/62 sequences, median bootstrap 94% (IQR85.5-98)], using a 15% threshold. Maximum association was at the 20% threshold, with 61/62 sequences associated, and a median bootstrap value of 99% (IQR98-100). A safe method is presented to generate consensus sequences from HIV-NGS data at 20% threshold, which will prove useful for molecular epidemiological studies. Copyright © 2016 Elsevier España, S.L.U. and Sociedad Española de Enfermedades Infecciosas y Microbiología Clínica. All rights reserved.

e-Bitter: Bitterant Prediction by the Consensus Voting From the Machine-Learning Methods

PubMed Central

Zheng, Suqing; Jiang, Mengying; Zhao, Chengwei; Zhu, Rui; Hu, Zhicheng; Xu, Yong; Lin, Fu

2018-01-01

In-silico bitterant prediction received the considerable attention due to the expensive and laborious experimental-screening of the bitterant. In this work, we collect the fully experimental dataset containing 707 bitterants and 592 non-bitterants, which is distinct from the fully or partially hypothetical non-bitterant dataset used in the previous works. Based on this experimental dataset, we harness the consensus votes from the multiple machine-learning methods (e.g., deep learning etc.) combined with the molecular fingerprint to build the bitter/bitterless classification models with five-fold cross-validation, which are further inspected by the Y-randomization test and applicability domain analysis. One of the best consensus models affords the accuracy, precision, specificity, sensitivity, F1-score, and Matthews correlation coefficient (MCC) of 0.929, 0.918, 0.898, 0.954, 0.936, and 0.856 respectively on our test set. For the automatic prediction of bitterant, a graphic program “e-Bitter” is developed for the convenience of users via the simple mouse click. To our best knowledge, it is for the first time to adopt the consensus model for the bitterant prediction and develop the first free stand-alone software for the experimental food scientist. PMID:29651416

e-Bitter: Bitterant Prediction by the Consensus Voting From the Machine-learning Methods

NASA Astrophysics Data System (ADS)

Zheng, Suqing; Jiang, Mengying; Zhao, Chengwei; Zhu, Rui; Hu, Zhicheng; Xu, Yong; Lin, Fu

2018-03-01

In-silico bitterant prediction received the considerable attention due to the expensive and laborious experimental-screening of the bitterant. In this work, we collect the fully experimental dataset containing 707 bitterants and 592 non-bitterants, which is distinct from the fully or partially hypothetical non-bitterant dataset used in the previous works. Based on this experimental dataset, we harness the consensus votes from the multiple machine-learning methods (e.g., deep learning etc.) combined with the molecular fingerprint to build the bitter/bitterless classification models with five-fold cross-validation, which are further inspected by the Y-randomization test and applicability domain analysis. One of the best consensus models affords the accuracy, precision, specificity, sensitivity, F1-score, and Matthews correlation coefficient (MCC) of 0.929, 0.918, 0.898, 0.954, 0.936, and 0.856 respectively on our test set. For the automatic prediction of bitterant, a graphic program “e-Bitter” is developed for the convenience of users via the simple mouse click. To our best knowledge, it is for the first time to adopt the consensus model for the bitterant prediction and develop the first free stand-alone software for the experimental food scientist.

Recording, analysis, and interpretation of spreading depolarizations in neurointensive care: Review and recommendations of the COSBID research group.

PubMed

Dreier, Jens P; Fabricius, Martin; Ayata, Cenk; Sakowitz, Oliver W; William Shuttleworth, C; Dohmen, Christian; Graf, Rudolf; Vajkoczy, Peter; Helbok, Raimund; Suzuki, Michiyasu; Schiefecker, Alois J; Major, Sebastian; Winkler, Maren Kl; Kang, Eun-Jeung; Milakara, Denny; Oliveira-Ferreira, Ana I; Reiffurth, Clemens; Revankar, Gajanan S; Sugimoto, Kazutaka; Dengler, Nora F; Hecht, Nils; Foreman, Brandon; Feyen, Bart; Kondziella, Daniel; Friberg, Christian K; Piilgaard, Henning; Rosenthal, Eric S; Westover, M Brandon; Maslarova, Anna; Santos, Edgar; Hertle, Daniel; Sánchez-Porras, Renán; Jewell, Sharon L; Balança, Baptiste; Platz, Johannes; Hinzman, Jason M; Lückl, Janos; Schoknecht, Karl; Schöll, Michael; Drenckhahn, Christoph; Feuerstein, Delphine; Eriksen, Nina; Horst, Viktor; Bretz, Julia S; Jahnke, Paul; Scheel, Michael; Bohner, Georg; Rostrup, Egill; Pakkenberg, Bente; Heinemann, Uwe; Claassen, Jan; Carlson, Andrew P; Kowoll, Christina M; Lublinsky, Svetlana; Chassidim, Yoash; Shelef, Ilan; Friedman, Alon; Brinker, Gerrit; Reiner, Michael; Kirov, Sergei A; Andrew, R David; Farkas, Eszter; Güresir, Erdem; Vatter, Hartmut; Chung, Lee S; Brennan, K C; Lieutaud, Thomas; Marinesco, Stephane; Maas, Andrew Ir; Sahuquillo, Juan; Dahlem, Markus A; Richter, Frank; Herreras, Oscar; Boutelle, Martyn G; Okonkwo, David O; Bullock, M Ross; Witte, Otto W; Martus, Peter; van den Maagdenberg, Arn Mjm; Ferrari, Michel D; Dijkhuizen, Rick M; Shutter, Lori A; Andaluz, Norberto; Schulte, André P; MacVicar, Brian; Watanabe, Tomas; Woitzik, Johannes; Lauritzen, Martin; Strong, Anthony J; Hartings, Jed A

2017-05-01

Spreading depolarizations (SD) are waves of abrupt, near-complete breakdown of neuronal transmembrane ion gradients, are the largest possible pathophysiologic disruption of viable cerebral gray matter, and are a crucial mechanism of lesion development. Spreading depolarizations are increasingly recorded during multimodal neuromonitoring in neurocritical care as a causal biomarker providing a diagnostic summary measure of metabolic failure and excitotoxic injury. Focal ischemia causes spreading depolarization within minutes. Further spreading depolarizations arise for hours to days due to energy supply-demand mismatch in viable tissue. Spreading depolarizations exacerbate neuronal injury through prolonged ionic breakdown and spreading depolarization-related hypoperfusion (spreading ischemia). Local duration of the depolarization indicates local tissue energy status and risk of injury. Regional electrocorticographic monitoring affords even remote detection of injury because spreading depolarizations propagate widely from ischemic or metabolically stressed zones; characteristic patterns, including temporal clusters of spreading depolarizations and persistent depression of spontaneous cortical activity, can be recognized and quantified. Here, we describe the experimental basis for interpreting these patterns and illustrate their translation to human disease. We further provide consensus recommendations for electrocorticographic methods to record, classify, and score spreading depolarizations and associated spreading depressions. These methods offer distinct advantages over other neuromonitoring modalities and allow for future refinement through less invasive and more automated approaches.

Recording, analysis, and interpretation of spreading depolarizations in neurointensive care: Review and recommendations of the COSBID research group

PubMed Central

Fabricius, Martin; Ayata, Cenk; Sakowitz, Oliver W; William Shuttleworth, C; Dohmen, Christian; Graf, Rudolf; Vajkoczy, Peter; Helbok, Raimund; Suzuki, Michiyasu; Schiefecker, Alois J; Major, Sebastian; Winkler, Maren KL; Kang, Eun-Jeung; Milakara, Denny; Oliveira-Ferreira, Ana I; Reiffurth, Clemens; Revankar, Gajanan S; Sugimoto, Kazutaka; Dengler, Nora F; Hecht, Nils; Foreman, Brandon; Feyen, Bart; Kondziella, Daniel; Friberg, Christian K; Piilgaard, Henning; Rosenthal, Eric S; Westover, M Brandon; Maslarova, Anna; Santos, Edgar; Hertle, Daniel; Sánchez-Porras, Renán; Jewell, Sharon L; Balança, Baptiste; Platz, Johannes; Hinzman, Jason M; Lückl, Janos; Schoknecht, Karl; Schöll, Michael; Drenckhahn, Christoph; Feuerstein, Delphine; Eriksen, Nina; Horst, Viktor; Bretz, Julia S; Jahnke, Paul; Scheel, Michael; Bohner, Georg; Rostrup, Egill; Pakkenberg, Bente; Heinemann, Uwe; Claassen, Jan; Carlson, Andrew P; Kowoll, Christina M; Lublinsky, Svetlana; Chassidim, Yoash; Shelef, Ilan; Friedman, Alon; Brinker, Gerrit; Reiner, Michael; Kirov, Sergei A; Andrew, R David; Farkas, Eszter; Güresir, Erdem; Vatter, Hartmut; Chung, Lee S; Brennan, KC; Lieutaud, Thomas; Marinesco, Stephane; Maas, Andrew IR; Sahuquillo, Juan; Dahlem, Markus A; Richter, Frank; Herreras, Oscar; Boutelle, Martyn G; Okonkwo, David O; Bullock, M Ross; Witte, Otto W; Martus, Peter; van den Maagdenberg, Arn MJM; Ferrari, Michel D; Dijkhuizen, Rick M; Shutter, Lori A; Andaluz, Norberto; Schulte, André P; MacVicar, Brian; Watanabe, Tomas; Woitzik, Johannes; Lauritzen, Martin; Strong, Anthony J; Hartings, Jed A

2016-01-01

Spreading depolarizations (SD) are waves of abrupt, near-complete breakdown of neuronal transmembrane ion gradients, are the largest possible pathophysiologic disruption of viable cerebral gray matter, and are a crucial mechanism of lesion development. Spreading depolarizations are increasingly recorded during multimodal neuromonitoring in neurocritical care as a causal biomarker providing a diagnostic summary measure of metabolic failure and excitotoxic injury. Focal ischemia causes spreading depolarization within minutes. Further spreading depolarizations arise for hours to days due to energy supply-demand mismatch in viable tissue. Spreading depolarizations exacerbate neuronal injury through prolonged ionic breakdown and spreading depolarization-related hypoperfusion (spreading ischemia). Local duration of the depolarization indicates local tissue energy status and risk of injury. Regional electrocorticographic monitoring affords even remote detection of injury because spreading depolarizations propagate widely from ischemic or metabolically stressed zones; characteristic patterns, including temporal clusters of spreading depolarizations and persistent depression of spontaneous cortical activity, can be recognized and quantified. Here, we describe the experimental basis for interpreting these patterns and illustrate their translation to human disease. We further provide consensus recommendations for electrocorticographic methods to record, classify, and score spreading depolarizations and associated spreading depressions. These methods offer distinct advantages over other neuromonitoring modalities and allow for future refinement through less invasive and more automated approaches. PMID:27317657

Unified Alignment of Protein-Protein Interaction Networks.

PubMed

Malod-Dognin, Noël; Ban, Kristina; Pržulj, Nataša

2017-04-19

Paralleling the increasing availability of protein-protein interaction (PPI) network data, several network alignment methods have been proposed. Network alignments have been used to uncover functionally conserved network parts and to transfer annotations. However, due to the computational intractability of the network alignment problem, aligners are heuristics providing divergent solutions and no consensus exists on a gold standard, or which scoring scheme should be used to evaluate them. We comprehensively evaluate the alignment scoring schemes and global network aligners on large scale PPI data and observe that three methods, HUBALIGN, L-GRAAL and NATALIE, regularly produce the most topologically and biologically coherent alignments. We study the collective behaviour of network aligners and observe that PPI networks are almost entirely aligned with a handful of aligners that we unify into a new tool, Ulign. Ulign enables complete alignment of two networks, which traditional global and local aligners fail to do. Also, multiple mappings of Ulign define biologically relevant soft clusterings of proteins in PPI networks, which may be used for refining the transfer of annotations across networks. Hence, PPI networks are already well investigated by current aligners, so to gain additional biological insights, a paradigm shift is needed. We propose such a shift come from aligning all available data types collectively rather than any particular data type in isolation from others.

Robustness in practice--the regional planning of health services.

PubMed

Best, G; Parston, G; Rosenhead, J

1986-05-01

Earlier work has criticized the dominant tendencies in operational research contributions to health services planning as characterized by optimization, implausible demands for data, depoliticization, hierarchy and inflexibility. This paper describes an effort which avoids at least some of these pitfalls. The project was to construct a planning system for a regional health council in Ontario, Canada, which would take account of the possible alternative future states of the health-care system's environment and would aim to keep options for future development open. The planning system devised is described in the paper. It is based on robustness analysis, which evaluates alternative initial action sets in terms of the useful flexibility they preserve. Other features include the explicit incorporation of pressures for change generated outside the health-care system, and a satisficing approach to the identification of both initial action sets and alternative future configurations of the health-care system. It was found possible to borrow and radically 're-use' techniques or formulations from the mainstream of O.R. contributions. Thus the 'reference projection' method was used to identify inadequacies in performance which future health-care system configurations must repair. And Delphi analysis, normally a method for generating consensus, was used in conjunction with cluster analysis of responses to generate meaningfully different alternative futures.

Improved Ant Colony Clustering Algorithm and Its Performance Study

PubMed Central

Gao, Wei

2016-01-01

Clustering analysis is used in many disciplines and applications; it is an important tool that descriptively identifies homogeneous groups of objects based on attribute values. The ant colony clustering algorithm is a swarm-intelligent method used for clustering problems that is inspired by the behavior of ant colonies that cluster their corpses and sort their larvae. A new abstraction ant colony clustering algorithm using a data combination mechanism is proposed to improve the computational efficiency and accuracy of the ant colony clustering algorithm. The abstraction ant colony clustering algorithm is used to cluster benchmark problems, and its performance is compared with the ant colony clustering algorithm and other methods used in existing literature. Based on similar computational difficulties and complexities, the results show that the abstraction ant colony clustering algorithm produces results that are not only more accurate but also more efficiently determined than the ant colony clustering algorithm and the other methods. Thus, the abstraction ant colony clustering algorithm can be used for efficient multivariate data clustering. PMID:26839533

Symptom Clusters in Advanced Cancer Patients: An Empirical Comparison of Statistical Methods and the Impact on Quality of Life.

PubMed

Dong, Skye T; Costa, Daniel S J; Butow, Phyllis N; Lovell, Melanie R; Agar, Meera; Velikova, Galina; Teckle, Paulos; Tong, Allison; Tebbutt, Niall C; Clarke, Stephen J; van der Hoek, Kim; King, Madeleine T; Fayers, Peter M

2016-01-01

Symptom clusters in advanced cancer can influence patient outcomes. There is large heterogeneity in the methods used to identify symptom clusters. To investigate the consistency of symptom cluster composition in advanced cancer patients using different statistical methodologies for all patients across five primary cancer sites, and to examine which clusters predict functional status, a global assessment of health and global quality of life. Principal component analysis and exploratory factor analysis (with different rotation and factor selection methods) and hierarchical cluster analysis (with different linkage and similarity measures) were used on a data set of 1562 advanced cancer patients who completed the European Organization for the Research and Treatment of Cancer Quality of Life Questionnaire-Core 30. Four clusters consistently formed for many of the methods and cancer sites: tense-worry-irritable-depressed (emotional cluster), fatigue-pain, nausea-vomiting, and concentration-memory (cognitive cluster). The emotional cluster was a stronger predictor of overall quality of life than the other clusters. Fatigue-pain was a stronger predictor of overall health than the other clusters. The cognitive cluster and fatigue-pain predicted physical functioning, role functioning, and social functioning. The four identified symptom clusters were consistent across statistical methods and cancer types, although there were some noteworthy differences. Statistical derivation of symptom clusters is in need of greater methodological guidance. A psychosocial pathway in the management of symptom clusters may improve quality of life. Biological mechanisms underpinning symptom clusters need to be delineated by future research. A framework for evidence-based screening, assessment, treatment, and follow-up of symptom clusters in advanced cancer is essential. Copyright © 2016 American Academy of Hospice and Palliative Medicine. Published by Elsevier Inc. All rights reserved.

Topic modeling for cluster analysis of large biological and medical datasets

PubMed Central

2014-01-01

Background The big data moniker is nowhere better deserved than to describe the ever-increasing prodigiousness and complexity of biological and medical datasets. New methods are needed to generate and test hypotheses, foster biological interpretation, and build validated predictors. Although multivariate techniques such as cluster analysis may allow researchers to identify groups, or clusters, of related variables, the accuracies and effectiveness of traditional clustering methods diminish for large and hyper dimensional datasets. Topic modeling is an active research field in machine learning and has been mainly used as an analytical tool to structure large textual corpora for data mining. Its ability to reduce high dimensionality to a small number of latent variables makes it suitable as a means for clustering or overcoming clustering difficulties in large biological and medical datasets. Results In this study, three topic model-derived clustering methods, highest probable topic assignment, feature selection and feature extraction, are proposed and tested on the cluster analysis of three large datasets: Salmonella pulsed-field gel electrophoresis (PFGE) dataset, lung cancer dataset, and breast cancer dataset, which represent various types of large biological or medical datasets. All three various methods are shown to improve the efficacy/effectiveness of clustering results on the three datasets in comparison to traditional methods. A preferable cluster analysis method emerged for each of the three datasets on the basis of replicating known biological truths. Conclusion Topic modeling could be advantageously applied to the large datasets of biological or medical research. The three proposed topic model-derived clustering methods, highest probable topic assignment, feature selection and feature extraction, yield clustering improvements for the three different data types. Clusters more efficaciously represent truthful groupings and subgroupings in the data than traditional methods, suggesting that topic model-based methods could provide an analytic advancement in the analysis of large biological or medical datasets. PMID:25350106

Topic modeling for cluster analysis of large biological and medical datasets.

PubMed

Zhao, Weizhong; Zou, Wen; Chen, James J

2014-01-01

The big data moniker is nowhere better deserved than to describe the ever-increasing prodigiousness and complexity of biological and medical datasets. New methods are needed to generate and test hypotheses, foster biological interpretation, and build validated predictors. Although multivariate techniques such as cluster analysis may allow researchers to identify groups, or clusters, of related variables, the accuracies and effectiveness of traditional clustering methods diminish for large and hyper dimensional datasets. Topic modeling is an active research field in machine learning and has been mainly used as an analytical tool to structure large textual corpora for data mining. Its ability to reduce high dimensionality to a small number of latent variables makes it suitable as a means for clustering or overcoming clustering difficulties in large biological and medical datasets. In this study, three topic model-derived clustering methods, highest probable topic assignment, feature selection and feature extraction, are proposed and tested on the cluster analysis of three large datasets: Salmonella pulsed-field gel electrophoresis (PFGE) dataset, lung cancer dataset, and breast cancer dataset, which represent various types of large biological or medical datasets. All three various methods are shown to improve the efficacy/effectiveness of clustering results on the three datasets in comparison to traditional methods. A preferable cluster analysis method emerged for each of the three datasets on the basis of replicating known biological truths. Topic modeling could be advantageously applied to the large datasets of biological or medical research. The three proposed topic model-derived clustering methods, highest probable topic assignment, feature selection and feature extraction, yield clustering improvements for the three different data types. Clusters more efficaciously represent truthful groupings and subgroupings in the data than traditional methods, suggesting that topic model-based methods could provide an analytic advancement in the analysis of large biological or medical datasets.

Study of cluster behavior in the riser of CFB by the DSMC method

NASA Astrophysics Data System (ADS)

Liu, H. P.; Liu, D. Y.; Liu, H.

2010-03-01

The flow behaviors of clusters in the riser of a two-dimensional (2D) circulating fluidized bed was numerically studied based on the Euler-Lagrangian approach. Gas turbulence was modeled by means of Large Eddy Simulation (LES). Particle collision was modeled by means of the direct simulation Monte Carlo (DSMC) method. Clusters' hydrodynamic characteristics are obtained using a cluster identification method proposed by sharrma et al. (2000). The descending clusters near the wall region and the up- and down-flowing clusters in the core were studied separately due to their different flow behaviors. The effects of superficial gas velocity on the cluster behavior were analyzed. Simulated results showed that near wall clusters flow downward and the descent velocity is about -45 cm/s. The occurrence frequency of the up-flowing cluster is higher than that of down-flowing cluster in the core of riser. With the increase of superficial gas velocity, the solid concentration and occurrence frequency of clusters decrease, while the cluster axial velocity increase. Simulated results were in agreement with experimental data. The stochastic method used in present paper is feasible for predicting the cluster flow behavior in CFBs.

Dynamic Trajectory Extraction from Stereo Vision Using Fuzzy Clustering

NASA Astrophysics Data System (ADS)

Onishi, Masaki; Yoda, Ikushi

In recent years, many human tracking researches have been proposed in order to analyze human dynamic trajectory. These researches are general technology applicable to various fields, such as customer purchase analysis in a shopping environment and safety control in a (railroad) crossing. In this paper, we present a new approach for tracking human positions by stereo image. We use the framework of two-stepped clustering with k-means method and fuzzy clustering to detect human regions. In the initial clustering, k-means method makes middle clusters from objective features extracted by stereo vision at high speed. In the last clustering, c-means fuzzy method cluster middle clusters based on attributes into human regions. Our proposed method can be correctly clustered by expressing ambiguity using fuzzy clustering, even when many people are close to each other. The validity of our technique was evaluated with the experiment of trajectories extraction of doctors and nurses in an emergency room of a hospital.

A cluster merging method for time series microarray with production values.

PubMed

Chira, Camelia; Sedano, Javier; Camara, Monica; Prieto, Carlos; Villar, Jose R; Corchado, Emilio

2014-09-01

A challenging task in time-course microarray data analysis is to cluster genes meaningfully combining the information provided by multiple replicates covering the same key time points. This paper proposes a novel cluster merging method to accomplish this goal obtaining groups with highly correlated genes. The main idea behind the proposed method is to generate a clustering starting from groups created based on individual temporal series (representing different biological replicates measured in the same time points) and merging them by taking into account the frequency by which two genes are assembled together in each clustering. The gene groups at the level of individual time series are generated using several shape-based clustering methods. This study is focused on a real-world time series microarray task with the aim to find co-expressed genes related to the production and growth of a certain bacteria. The shape-based clustering methods used at the level of individual time series rely on identifying similar gene expression patterns over time which, in some models, are further matched to the pattern of production/growth. The proposed cluster merging method is able to produce meaningful gene groups which can be naturally ranked by the level of agreement on the clustering among individual time series. The list of clusters and genes is further sorted based on the information correlation coefficient and new problem-specific relevant measures. Computational experiments and results of the cluster merging method are analyzed from a biological perspective and further compared with the clustering generated based on the mean value of time series and the same shape-based algorithm.

Protein model quality assessment prediction by combining fragment comparisons and a consensus Cα contact potential

PubMed Central

Zhou, Hongyi; Skolnick, Jeffrey

2009-01-01

In this work, we develop a fully automated method for the quality assessment prediction of protein structural models generated by structure prediction approaches such as fold recognition servers, or ab initio methods. The approach is based on fragment comparisons and a consensus Cα contact potential derived from the set of models to be assessed and was tested on CASP7 server models. The average Pearson linear correlation coefficient between predicted quality and model GDT-score per target is 0.83 for the 98 targets which is better than those of other quality assessment methods that participated in CASP7. Our method also outperforms the other methods by about 3% as assessed by the total GDT-score of the selected top models. PMID:18004783

A consensus process on management of major burns accidents: lessons learned from the café fire in Volendam, The Netherlands.

PubMed

Welling, L; Boers, M; Mackie, D P; Patka, P; Bierens, J J L M; Luitse, J S K; Kreis, R W

2006-01-01

The optimum response to the different stages of a major burns incident is still not established. The fire in a café in Volendam on New Year's Eve 2000 was the worst incident in recent Dutch history and resulted in mass burn casualties. The fire has been the subject of several investigations concerned with organisational and medical aspects. Based on the findings in these investigations, a multidisciplinary research group started a consensus study. The aim of this study was to further identify areas of improvement in the care after mass burns incidents. The consensus process comprised three postal rounds (Delphi Method) and a consensus conference (modified nominal group technique). The multidisciplinary panel consisted of 26 Dutch-speaking experts, working in influential positions within the sphere of disaster management and healthcare. In response to the postal questionnaires, consensus was reached for 66 per cent of the statements. Six topics were subsequently discussed during the consensus conference; three topics were discussed within the plenary session and three during subgroup meetings. During the conference, consensus was reached for seven statements (one subject generated two statements). In total, the panel agreed on 21 statements. These covered the following topics: registration and evaluation of disaster care, capacity planning for disasters, pre hospital care of victims of burns disasters, treatment and transportation priorities, distribution of casualties (including interhospital transports), diagnosis and treatment and education and training. In disaster medicine, the paper shows how a consensus process is a suitable tool to identify areas of improvement of care after mass burns incidents.

Second consensus on the assessment of sublingual microcirculation in critically ill patients: results from a task force of the European Society of Intensive Care Medicine.

PubMed

Ince, Can; Boerma, E Christiaan; Cecconi, Maurizio; De Backer, Daniel; Shapiro, Nathan I; Duranteau, Jacques; Pinsky, Michael R; Artigas, Antonio; Teboul, Jean-Louis; Reiss, Irwin K M; Aldecoa, Cesar; Hutchings, Sam D; Donati, Abele; Maggiorini, Marco; Taccone, Fabio S; Hernandez, Glenn; Payen, Didier; Tibboel, Dick; Martin, Daniel S; Zarbock, Alexander; Monnet, Xavier; Dubin, Arnaldo; Bakker, Jan; Vincent, Jean-Louis; Scheeren, Thomas W L

2018-03-01

Hand-held vital microscopes (HVMs) were introduced to observe sublingual microcirculatory alterations at the bedside in different shock states in critically ill patients. This consensus aims to provide clinicians with guidelines for practical use and interpretation of the sublingual microcirculation. Furthermore, it aims to promote the integration of routine application of HVM microcirculatory monitoring in conventional hemodynamic monitoring of systemic hemodynamic variables. In accordance with the Delphi method we organized three international expert meetings to discuss the various aspects of the technology, physiology, measurements, and clinical utility of HVM sublingual microcirculatory monitoring to formulate this consensus document. A task force from the Cardiovascular Dynamics Section of the European Society of Intensive Care Medicine (with endorsement of its Executive Committee) created this consensus as an update of a previous consensus in 2007. We classified consensus statements as definitions, requirements, and/or recommendations, with a minimum requirement of 80% agreement of all participants. In this consensus the nature of microcirculatory alterations is described. The nature of variables, which can be extracted from analysis of microcirculatory images, is presented and the needed dataset of variables to identify microcirculatory alterations is defined. Practical aspects of sublingual HVM measurements and the nature of artifacts are described. Eleven statements were formulated that pertained to image acquisitions and quality statements. Fourteen statements addressed the analysis of the images, and 13 statements are related to future developments. This consensus describes 25 statements regarding the acquisition and interpretation of microcirculatory images needed to guide the assessment of the microcirculation in critically ill patients.

Event-triggered consensus tracking of multi-agent systems with Lur'e nonlinear dynamics

NASA Astrophysics Data System (ADS)

Huang, Na; Duan, Zhisheng; Wen, Guanghui; Zhao, Yu

2016-05-01

In this paper, distributed consensus tracking problem for networked Lur'e systems is investigated based on event-triggered information interactions. An event-triggered control algorithm is designed with the advantages of reducing controller update frequency and sensor energy consumption. By using tools of ?-procedure and Lyapunov functional method, some sufficient conditions are derived to guarantee that consensus tracking is achieved under a directed communication topology. Meanwhile, it is shown that Zeno behaviour of triggering time sequences is excluded for the proposed event-triggered rule. Finally, some numerical simulations on coupled Chua's circuits are performed to illustrate the effectiveness of the theoretical algorithms.

Applications of Some Artificial Intelligence Methods to Satellite Soundings

NASA Technical Reports Server (NTRS)

Munteanu, M. J.; Jakubowicz, O.

1985-01-01

Hard clustering of temperature profiles and regression temperature retrievals were used to refine the method using the probabilities of membership of each pattern vector in each of the clusters derived with discriminant analysis. In hard clustering the maximum probability is taken and the corresponding cluster as the correct cluster are considered discarding the rest of the probabilities. In fuzzy partitioned clustering these probabilities are kept and the final regression retrieval is a weighted regression retrieval of several clusters. This method was used in the clustering of brightness temperatures where the purpose was to predict tropopause height. A further refinement is the division of temperature profiles into three major regions for classification purposes. The results are summarized in the tables total r.m.s. errors are displayed. An approach based on fuzzy logic which is intimately related to artificial intelligence methods is recommended.

The detection methods of dynamic objects

NASA Astrophysics Data System (ADS)

Knyazev, N. L.; Denisova, L. A.

2018-01-01

The article deals with the application of cluster analysis methods for solving the task of aircraft detection on the basis of distribution of navigation parameters selection into groups (clusters). The modified method of cluster analysis for search and detection of objects and then iterative combining in clusters with the subsequent count of their quantity for increase in accuracy of the aircraft detection have been suggested. The course of the method operation and the features of implementation have been considered. In the conclusion the noted efficiency of the offered method for exact cluster analysis for finding targets has been shown.

Acne severity grading: determining essential clinical components and features using a Delphi consensus.

PubMed

Tan, Jerry; Wolfe, Barat; Weiss, Jonathan; Stein-Gold, Linda; Bikowski, Joseph; Del Rosso, James; Webster, Guy F; Lucky, Anne; Thiboutot, Diane; Wilkin, Jonathan; Leyden, James; Chren, Mary-Margaret

2012-08-01

There are multiple global scales for acne severity grading but no singular standard. Our objective was to determine the essential clinical components (content items) and features (property-related items) for an acne global grading scale for use in research and clinical practice using an iterative method, the Delphi process. Ten acne experts were invited to participate in a Web-based Delphi survey comprising 3 iterative rounds of questions. In round 1, the experts identified the following clinical components (primary acne lesions, number of lesions, extent, regional involvement, secondary lesions, and patient experiences) and features (clinimetric properties, ease of use, categorization of severity based on photographs or text, and acceptance by all stakeholders). In round 2, consensus for inclusion in the scale was established for primary lesions, number, sites, and extent; as well as clinimetric properties and ease of use. In round 3, consensus for inclusion was further established for categorization and acceptance. Patient experiences were excluded and no consensus was achieved for secondary lesions. The Delphi panel consisted solely of the United States (U.S.)-based acne experts. Using an established method for achieving consensus, experts in acne vulgaris concluded that an ideal acne global grading scale would comprise the essential clinical components of primary acne lesions, their quantity, extent, and facial and extrafacial sites of involvement; with features of clinimetric properties, categorization, efficiency, and acceptance. Copyright © 2011 American Academy of Dermatology, Inc. Published by Mosby, Inc. All rights reserved.

Integrating conflict analysis and consensus reaching in a decision support system for water resource management.

PubMed

Giordano, R; Passarella, G; Uricchio, V F; Vurro, M

2007-07-01

The importance of shared decision processes in water management derives from the awareness of the inadequacy of traditional--i.e. engineering--approaches in dealing with complex and ill-structured problems. It is becoming increasingly obvious that traditional problem solving and decision support techniques, based on optimisation and factual knowledge, have to be combined with stakeholder based policy design and implementation. The aim of our research is the definition of an integrated decision support system for consensus achievement (IDSS-C) able to support a participative decision-making process in all its phases: problem definition and structuring, identification of the possible alternatives, formulation of participants' judgments, and consensus achievement. Furthermore, the IDSS-C aims at structuring, i.e. systematising the knowledge which has emerged during the participative process in order to make it comprehensible for the decision-makers and functional for the decision process. Problem structuring methods (PSM) and multi-group evaluation methods (MEM) have been integrated in the IDSS-C. PSM are used to support the stakeholders in providing their perspective of the problem and to elicit their interests and preferences, while MEM are used to define not only the degree of consensus for each alternative, highlighting those where the agreement is high, but also the consensus label for each alternative and the behaviour of individuals during the participative decision-making. The IDSS-C is applied experimentally to a decision process regarding the use of treated wastewater for agricultural irrigation in the Apulia Region (southern Italy).

Pruning Rogue Taxa Improves Phylogenetic Accuracy: An Efficient Algorithm and Webservice

PubMed Central

Aberer, Andre J.; Krompass, Denis; Stamatakis, Alexandros

2013-01-01

Abstract The presence of rogue taxa (rogues) in a set of trees can frequently have a negative impact on the results of a bootstrap analysis (e.g., the overall support in consensus trees). We introduce an efficient graph-based algorithm for rogue taxon identification as well as an interactive webservice implementing this algorithm. Compared with our previous method, the new algorithm is up to 4 orders of magnitude faster, while returning qualitatively identical results. Because of this significant improvement in scalability, the new algorithm can now identify substantially more complex and compute-intensive rogue taxon constellations. On a large and diverse collection of real-world data sets, we show that our method yields better supported reduced/pruned consensus trees than any competing rogue taxon identification method. Using the parallel version of our open-source code, we successfully identified rogue taxa in a set of 100 trees with 116 334 taxa each. For simulated data sets, we show that when removing/pruning rogue taxa with our method from a tree set, we consistently obtain bootstrap consensus trees as well as maximum-likelihood trees that are topologically closer to the respective true trees. PMID:22962004

Pruning rogue taxa improves phylogenetic accuracy: an efficient algorithm and webservice.

PubMed

Aberer, Andre J; Krompass, Denis; Stamatakis, Alexandros

2013-01-01

The presence of rogue taxa (rogues) in a set of trees can frequently have a negative impact on the results of a bootstrap analysis (e.g., the overall support in consensus trees). We introduce an efficient graph-based algorithm for rogue taxon identification as well as an interactive webservice implementing this algorithm. Compared with our previous method, the new algorithm is up to 4 orders of magnitude faster, while returning qualitatively identical results. Because of this significant improvement in scalability, the new algorithm can now identify substantially more complex and compute-intensive rogue taxon constellations. On a large and diverse collection of real-world data sets, we show that our method yields better supported reduced/pruned consensus trees than any competing rogue taxon identification method. Using the parallel version of our open-source code, we successfully identified rogue taxa in a set of 100 trees with 116 334 taxa each. For simulated data sets, we show that when removing/pruning rogue taxa with our method from a tree set, we consistently obtain bootstrap consensus trees as well as maximum-likelihood trees that are topologically closer to the respective true trees.

Nearest neighbor-density-based clustering methods for large hyperspectral images

NASA Astrophysics Data System (ADS)

Cariou, Claude; Chehdi, Kacem

2017-10-01

We address the problem of hyperspectral image (HSI) pixel partitioning using nearest neighbor - density-based (NN-DB) clustering methods. NN-DB methods are able to cluster objects without specifying the number of clusters to be found. Within the NN-DB approach, we focus on deterministic methods, e.g. ModeSeek, knnClust, and GWENN (standing for Graph WatershEd using Nearest Neighbors). These methods only require the availability of a k-nearest neighbor (kNN) graph based on a given distance metric. Recently, a new DB clustering method, called Density Peak Clustering (DPC), has received much attention, and kNN versions of it have quickly followed and showed their efficiency. However, NN-DB methods still suffer from the difficulty of obtaining the kNN graph due to the quadratic complexity with respect to the number of pixels. This is why GWENN was embedded into a multiresolution (MR) scheme to bypass the computation of the full kNN graph over the image pixels. In this communication, we propose to extent the MR-GWENN scheme on three aspects. Firstly, similarly to knnClust, the original labeling rule of GWENN is modified to account for local density values, in addition to the labels of previously processed objects. Secondly, we set up a modified NN search procedure within the MR scheme, in order to stabilize of the number of clusters found from the coarsest to the finest spatial resolution. Finally, we show that these extensions can be easily adapted to the three other NN-DB methods (ModeSeek, knnClust, knnDPC) for pixel clustering in large HSIs. Experiments are conducted to compare the four NN-DB methods for pixel clustering in HSIs. We show that NN-DB methods can outperform a classical clustering method such as fuzzy c-means (FCM), in terms of classification accuracy, relevance of found clusters, and clustering speed. Finally, we demonstrate the feasibility and evaluate the performances of NN-DB methods on a very large image acquired by our AISA Eagle hyperspectral imaging sensor.

Two distinct Photobacterium populations thrive in ancient Mediterranean sapropels.

PubMed

Süss, Jacqueline; Herrmann, Kerstin; Seidel, Michael; Cypionka, Heribert; Engelen, Bert; Sass, Henrik

2008-04-01

Eastern Mediterranean sediments are characterized by the periodic occurrence of conspicuous, organic matter-rich sapropel layers. Phylogenetic analysis of a large culture collection isolated from these sediments revealed that about one third of the isolates belonged to the genus Photobacterium. In the present study, 22 of these strains were examined with respect to their phylogenetic and metabolic diversity. The strains belonged to two distinct Photobacterium populations (Mediterranean cluster I and II). Strains of cluster I were isolated almost exclusively from organic-rich sapropel layers and were closely affiliated with P. aplysiae (based on their 16S rRNA gene sequences). They possessed almost identical Enterobacterial Repetitive Intergenic Consensus (ERIC) and substrate utilization patterns, even among strains from different sampling sites or from layers differing up to 100,000 years in age. Strains of cluster II originated from sapropels and from the surface and carbon-lean intermediate layers. They were related to Photobacterium frigidiphilum but differed significantly in their fingerprint patterns and substrate spectra, even when these strains were obtained from the same sampling site and layer. Temperature range for growth (4 to 33 degrees C), salinity tolerance (5 to 100 per thousand), pH requirements (5.5-9.3), and the composition of polar membrane lipids were similar for both clusters. All strains grew by fermentation (glucose, organic acids) and all but five by anaerobic respiration (nitrate, dimethyl sulfoxide, anthraquinone disulfonate, or humic acids). These results indicate that the genus Photobacterium forms subsurface populations well adapted to life in the deep biosphere.

Transcriptional Analysis of the vanC Cluster from Enterococcus gallinarum Strains with Constitutive and Inducible Vancomycin Resistance

PubMed Central

Panesso, Diana; Abadía-Patiño, Lorena; Vanegas, Natasha; Reynolds, Peter E.; Courvalin, Patrice; Arias, Cesar A.

2005-01-01

The vanC glycopeptide resistance gene cluster encodes enzymes required for synthesis of peptidoglycan precursors ending in d-Ala-d-Ser. Enterococcus gallinarum BM4174 and SC1 are constitutively and inducibly resistant to vancomycin, respectively. Analysis of peptidoglycan precursors in both strains indicated that UDP-MurNAc-tetrapeptide and UDP-MurNAc-pentapeptide[d-Ser] were synthesized in E. gallinarum SC1 only in the presence of vancomycin (4 μg/ml), whereas the “resistance” precursors accumulated in the cytoplasm of BM4174 cells under both inducing and noninducing conditions. Northern hybridization and reverse transcription-PCR experiments revealed that all the genes from the cluster, vanC-1, vanXYC, vanT, vanRC, and vanSC, were transcribed from a single promoter. In the inducible SC1 isolate, transcriptional regulation appeared to be responsible for inducible expression of resistance. Promoter mapping in E. gallinarum BM4174 revealed that the transcriptional start site was located 30 nucleotides upstream from vanC-1 and that the −10 promoter consensus sequence had high identity with that of the vanA cluster. Comparison of the deduced sequence of the vanSC genes from isolates with constitutive and inducible resistance revealed several amino acid substitutions located in the X box (R200L) and in the region between the F and G2 boxes (D312N, D312A, and G320S) of the putative sensor kinase proteins from isolates with constitutive resistance. PMID:15728903

An improved clustering algorithm based on reverse learning in intelligent transportation

NASA Astrophysics Data System (ADS)

Qiu, Guoqing; Kou, Qianqian; Niu, Ting

2017-05-01

With the development of artificial intelligence and data mining technology, big data has gradually entered people's field of vision. In the process of dealing with large data, clustering is an important processing method. By introducing the reverse learning method in the clustering process of PAM clustering algorithm, to further improve the limitations of one-time clustering in unsupervised clustering learning, and increase the diversity of clustering clusters, so as to improve the quality of clustering. The algorithm analysis and experimental results show that the algorithm is feasible.

Fine-tuning structural RNA alignments in the twilight zone.

PubMed

Bremges, Andreas; Schirmer, Stefanie; Giegerich, Robert

2010-04-30

A widely used method to find conserved secondary structure in RNA is to first construct a multiple sequence alignment, and then fold the alignment, optimizing a score based on thermodynamics and covariance. This method works best around 75% sequence similarity. However, in a "twilight zone" below 55% similarity, the sequence alignment tends to obscure the covariance signal used in the second phase. Therefore, while the overall shape of the consensus structure may still be found, the degree of conservation cannot be estimated reliably. Based on a combination of available methods, we present a method named planACstar for improving structure conservation in structural alignments in the twilight zone. After constructing a consensus structure by alignment folding, planACstar abandons the original sequence alignment, refolds the sequences individually, but consistent with the consensus, aligns the structures, irrespective of sequence, by a pure structure alignment method, and derives an improved sequence alignment from the alignment of structures, to be re-submitted to alignment folding, etc.. This circle may be iterated as long as structural conservation improves, but normally, one step suffices. Employing the tools ClustalW, RNAalifold, and RNAforester, we find that for sequences with 30-55% sequence identity, structural conservation can be improved by 10% on average, with a large variation, measured in terms of RNAalifold's own criterion, the structure conservation index.

Selecting Cooking Methods to Decrease Persistent Organic Pollutant Concentrations in Food of Animal Origin Using a Consensus Decision-Making Model.

PubMed

Tan, Xiao; Gong, Zaiwu; Huang, Minji; Wang, Zhou-Jing

2017-02-14

Persistent organic pollutants (POPs) pose serious threats to human health. Increasing attention has been paid to POPs to protect the environment and prevent disease. Humans are exposed to POPs through diet (the major route), inhaling air and dust and skin contact. POPs are very lipophilic and hydrophobic, meaning that they accumulate in fatty tissues in animals and can biomagnify. Humans can therefore be exposed to relatively high POP concentrations in food of animal origin. Cooking animal products can decrease the POP contents, and different cooking methods achieve different reduction rates. Here, a consensus decision-making model with interval preference relations is used to prioritize cooking methods for specific animal products in terms of reducing POP concentrations. Two consistency mathematical expressions ( I -consistency and I I -consistency) are defined, then the ideal interval preference relations are determined for the cooking methods with respect to different social choice principles. The objective is to minimize disparities between individual judgments and the ideal consensus judgment. Consistency is used as a constraint to determine the rationality of the consistency definitions. A numerical example indicated that baking is the best cooking method for decreasing POP concentrations in grass carp. The I -consistency results were more acceptable than the I I -consistency results.

Selecting Cooking Methods to Decrease Persistent Organic Pollutant Concentrations in Food of Animal Origin Using a Consensus Decision-Making Model

PubMed Central

Tan, Xiao; Gong, Zaiwu; Huang, Minji; Wang, Zhou-Jing

2017-01-01

Persistent organic pollutants (POPs) pose serious threats to human health. Increasing attention has been paid to POPs to protect the environment and prevent disease. Humans are exposed to POPs through diet (the major route), inhaling air and dust and skin contact. POPs are very lipophilic and hydrophobic, meaning that they accumulate in fatty tissues in animals and can biomagnify. Humans can therefore be exposed to relatively high POP concentrations in food of animal origin. Cooking animal products can decrease the POP contents, and different cooking methods achieve different reduction rates. Here, a consensus decision-making model with interval preference relations is used to prioritize cooking methods for specific animal products in terms of reducing POP concentrations. Two consistency mathematical expressions (I-consistency and II-consistency) are defined, then the ideal interval preference relations are determined for the cooking methods with respect to different social choice principles. The objective is to minimize disparities between individual judgments and the ideal consensus judgment. Consistency is used as a constraint to determine the rationality of the consistency definitions. A numerical example indicated that baking is the best cooking method for decreasing POP concentrations in grass carp. The I-consistency results were more acceptable than the II-consistency results. PMID:28216589

Multiconstrained gene clustering based on generalized projections

PubMed Central

2010-01-01

Background Gene clustering for annotating gene functions is one of the fundamental issues in bioinformatics. The best clustering solution is often regularized by multiple constraints such as gene expressions, Gene Ontology (GO) annotations and gene network structures. How to integrate multiple pieces of constraints for an optimal clustering solution still remains an unsolved problem. Results We propose a novel multiconstrained gene clustering (MGC) method within the generalized projection onto convex sets (POCS) framework used widely in image reconstruction. Each constraint is formulated as a corresponding set. The generalized projector iteratively projects the clustering solution onto these sets in order to find a consistent solution included in the intersection set that satisfies all constraints. Compared with previous MGC methods, POCS can integrate multiple constraints from different nature without distorting the original constraints. To evaluate the clustering solution, we also propose a new performance measure referred to as Gene Log Likelihood (GLL) that considers genes having more than one function and hence in more than one cluster. Comparative experimental results show that our POCS-based gene clustering method outperforms current state-of-the-art MGC methods. Conclusions The POCS-based MGC method can successfully combine multiple constraints from different nature for gene clustering. Also, the proposed GLL is an effective performance measure for the soft clustering solutions. PMID:20356386

Consensus for second-order multi-agent systems with position sampled data

NASA Astrophysics Data System (ADS)

Wang, Rusheng; Gao, Lixin; Chen, Wenhai; Dai, Dameng

2016-10-01

In this paper, the consensus problem with position sampled data for second-order multi-agent systems is investigated. The interaction topology among the agents is depicted by a directed graph. The full-order and reduced-order observers with position sampled data are proposed, by which two kinds of sampled data-based consensus protocols are constructed. With the provided sampled protocols, the consensus convergence analysis of a continuous-time multi-agent system is equivalently transformed into that of a discrete-time system. Then, by using matrix theory and a sampled control analysis method, some sufficient and necessary consensus conditions based on the coupling parameters, spectrum of the Laplacian matrix and sampling period are obtained. While the sampling period tends to zero, our established necessary and sufficient conditions are degenerated to the continuous-time protocol case, which are consistent with the existing result for the continuous-time case. Finally, the effectiveness of our established results is illustrated by a simple simulation example. Project supported by the Natural Science Foundation of Zhejiang Province, China (Grant No. LY13F030005) and the National Natural Science Foundation of China (Grant No. 61501331).

Performance Analysis of Entropy Methods on K Means in Clustering Process

NASA Astrophysics Data System (ADS)

Dicky Syahputra Lubis, Mhd.; Mawengkang, Herman; Suwilo, Saib

2017-12-01

K Means is a non-hierarchical data clustering method that attempts to partition existing data into one or more clusters / groups. This method partitions the data into clusters / groups so that data that have the same characteristics are grouped into the same cluster and data that have different characteristics are grouped into other groups.The purpose of this data clustering is to minimize the objective function set in the clustering process, which generally attempts to minimize variation within a cluster and maximize the variation between clusters. However, the main disadvantage of this method is that the number k is often not known before. Furthermore, a randomly chosen starting point may cause two points to approach the distance to be determined as two centroids. Therefore, for the determination of the starting point in K Means used entropy method where this method is a method that can be used to determine a weight and take a decision from a set of alternatives. Entropy is able to investigate the harmony in discrimination among a multitude of data sets. Using Entropy criteria with the highest value variations will get the highest weight. Given this entropy method can help K Means work process in determining the starting point which is usually determined at random. Thus the process of clustering on K Means can be more quickly known by helping the entropy method where the iteration process is faster than the K Means Standard process. Where the postoperative patient dataset of the UCI Repository Machine Learning used and using only 12 data as an example of its calculations is obtained by entropy method only with 2 times iteration can get the desired end result.

Key Features of Academic Detailing: Development of an Expert Consensus Using the Delphi Method

PubMed Central

Yeh, James S.; Van Hoof, Thomas J.; Fischer, Michael A.

2016-01-01

Background Academic detailing is an outreach education technique that combines the direct social marketing traditionally used by pharmaceutical representatives with unbiased content summarizing the best evidence for a given clinical issue. Academic detailing is conducted with clinicians to encourage evidence-based practice in order to improve the quality of care and patient outcomes. The adoption of academic detailing has increased substantially since the original studies in the 1980s. However, the lack of standard agreement on its implementation makes the evaluation of academic detailing outcomes challenging. Objective To identify consensus on the key elements of academic detailing among a group of experts with varying experiences in academic detailing. Methods This study is based on an online survey of 20 experts with experience in academic detailing. We used the Delphi process, an iterative and systematic method of developing consensus within a group. We conducted 3 rounds of online surveys, which addressed 72 individual items derived from a previous literature review of 5 features of academic detailing, including (1) content, (2) communication process, (3) clinicians targeted, (4) change agents delivering intervention, and (5) context for intervention. Nonrespondents were removed from later rounds of the surveys. For most questions, a 4-point ordinal scale was used for responses. We defined consensus agreement as 70% of respondents for a single rating category or 80% for dichotomized ratings. Results The overall survey response rate was 95% (54 of 57 surveys) and nearly 92% consensus agreement on the survey items (66 of 72 items) by the end of the Delphi exercise. The experts' responses suggested that (1) focused clinician education offering support for clinical decision-making is a key component of academic detailing, (2) detailing messages need to be tailored and provide feasible strategies and solutions to challenging cases, and (3) academic detailers need to develop specific skill sets required to overcome barriers to changing clinician behavior. Conclusion Consensus derived from this Delphi exercise can serve as a useful template of general principles in academic detailing initiatives and evaluation. The study findings are limited by the lack of standard definitions of certain terms used in the Delphi process. PMID:27066195

A clustering method of Chinese medicine prescriptions based on modified firefly algorithm.

PubMed

Yuan, Feng; Liu, Hong; Chen, Shou-Qiang; Xu, Liang

2016-12-01

This paper is aimed to study the clustering method for Chinese medicine (CM) medical cases. The traditional K-means clustering algorithm had shortcomings such as dependence of results on the selection of initial value, trapping in local optimum when processing prescriptions form CM medical cases. Therefore, a new clustering method based on the collaboration of firefly algorithm and simulated annealing algorithm was proposed. This algorithm dynamically determined the iteration of firefly algorithm and simulates sampling of annealing algorithm by fitness changes, and increased the diversity of swarm through expansion of the scope of the sudden jump, thereby effectively avoiding premature problem. The results from confirmatory experiments for CM medical cases suggested that, comparing with traditional K-means clustering algorithms, this method was greatly improved in the individual diversity and the obtained clustering results, the computing results from this method had a certain reference value for cluster analysis on CM prescriptions.

Preferences and needs of patients with a rheumatic disease regarding the structure and content of online self-management support.

PubMed

Ammerlaan, Judy W; van Os-Medendorp, Harmieke; de Boer-Nijhof, Nienke; Maat, Bertha; Scholtus, Lieske; Kruize, Aike A; Bijlsma, Johannes W J; Geenen, Rinie

2017-03-01

Aim of this study was to investigate preferences and needs regarding the structure and content of a person-centered online self-management support intervention for patients with a rheumatic disease. A four step procedure, consisting of online focus group interviews, consensus meetings with patient representatives, card sorting task and hierarchical cluster analysis was used to identify the preferences and needs. Preferences concerning the structure involved 1) suitability to individual needs and questions, 2) fit to the life stage 3) creating the opportunity to share experiences, be in contact with others, 4) have an expert patient as trainer, 5) allow for doing the training at one's own pace and 6) offer a brief intervention. Hierarchical cluster analysis of 55 content needs comprised eleven clusters: 1) treatment knowledge, 2) societal procedures, 3) physical activity, 4) psychological distress, 5) self-efficacy, 6) provider, 7) fluctuations, 8) dealing with rheumatic disease, 9) communication, 10) intimate relationship, and 11) having children. A comprehensive assessment of preferences and needs in patients with a rheumatic disease is expected to contribute to motivation, adherence to and outcome of self-management-support programs. The overview of preferences and needs can be used to build an online-line self-management intervention. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

[Interdisciplinary AWMF guideline for the treatment of primary antibody deficiencies].

PubMed

Krudewig, J; Baumann, U; Bernuth von, H; Borte, M; Burkhard-Meier, U; Dueckers, G; Foerster-Waldl, E; Franke, K; Habermehl, P; Hönig, M; Kern, W; Kösters, K; Kugel, K; Lehrnbecher, T; Liese, J; Marks, R; Müller, G A; Müller, R; Nadal, D; Peter, H-H; Pfeiffer-Kascha, D; Schneider, M; Sitter, H; Späth, P; Wahn, V; Welte, T; Niehues, T

2012-10-01

Currently, management of antibody deficient patients differs significantly among caregivers. Evidence and consensus based (S3) guidelines for the treatment of primary antibody deficiencies were developed to improve the management of these patients. Based on a thorough analysis of current evidence (systematic literature search in PubMed; deadline November 2011) 14 recommendations were finalized during a consensus meeting in Frankfurt in November 2011 using structured consensus methods (nominal group technique). Experts were nominated by their scientific societies/patient initiatives (Tab. 1). The guidelines focus on indication, practical issues and monitoring of immunoglobulin replacement therapy as well as on different routes of administration. Furthermore recommendations regarding supportive measures such as antiinfective therapy, vaccinations and physiotherapy are given. Combining literature evidence and experience of caregivers within this evidence and consensus based guidelines offers the chance to improve the quality of care for anti-body deficient patients. © Georg Thieme Verlag KG Stuttgart · New York.

Cultural Consensus Theory: Aggregating Continuous Responses in a Finite Interval

NASA Astrophysics Data System (ADS)

Batchelder, William H.; Strashny, Alex; Romney, A. Kimball

Cultural consensus theory (CCT) consists of cognitive models for aggregating responses of "informants" to test items about some domain of their shared cultural knowledge. This paper develops a CCT model for items requiring bounded numerical responses, e.g. probability estimates, confidence judgments, or similarity judgments. The model assumes that each item generates a latent random representation in each informant, with mean equal to the consensus answer and variance depending jointly on the informant and the location of the consensus answer. The manifest responses may reflect biases of the informants. Markov Chain Monte Carlo (MCMC) methods were used to estimate the model, and simulation studies validated the approach. The model was applied to an existing cross-cultural dataset involving native Japanese and English speakers judging the similarity of emotion terms. The results sharpened earlier studies that showed that both cultures appear to have very similar cognitive representations of emotion terms.

Applied Swarm-based medicine: collecting decision trees for patterns of algorithms analysis.

PubMed

Panje, Cédric M; Glatzer, Markus; von Rappard, Joscha; Rothermundt, Christian; Hundsberger, Thomas; Zumstein, Valentin; Plasswilm, Ludwig; Putora, Paul Martin

2017-08-16

The objective consensus methodology has recently been applied in consensus finding in several studies on medical decision-making among clinical experts or guidelines. The main advantages of this method are an automated analysis and comparison of treatment algorithms of the participating centers which can be performed anonymously. Based on the experience from completed consensus analyses, the main steps for the successful implementation of the objective consensus methodology were identified and discussed among the main investigators. The following steps for the successful collection and conversion of decision trees were identified and defined in detail: problem definition, population selection, draft input collection, tree conversion, criteria adaptation, problem re-evaluation, results distribution and refinement, tree finalisation, and analysis. This manuscript provides information on the main steps for successful collection of decision trees and summarizes important aspects at each point of the analysis.

[Medical expert consensus in AH on the clinical use of triple fixed-dose antihypertensive therapy in Spain].

PubMed

Mazón, P; Galve, E; Gómez, J; Gorostidi, M; Górriz, J L; Mediavilla, J D

The opinion of experts (different specialties) on the triple fixed-dose antihypertensive therapy in clinical practice may differ. Online questionnaire with controversial aspects of the triple therapy answered by panel of experts in hypertension (HT) using two-round modified Delphi method. The questionnaire was completed by 158 experts: Internal Medicine (49), Nephrology (26), Cardiology (83). Consensus was reached (agreement) on 27/45 items (60%); 7 items showed differences statistically significant. Consensus was reached regarding: Predictive factors in the need for combination therapy and its efficacy vs. increasing the dose of a pretreatment, and advantage of triple therapy (prescription/adherence/cost/pressure control) vs. free combination. This consensus provides an overview of the clinical use of triple therapy in moderate-severe and resistant/difficult to control HT. Copyright © 2016 SEH-LELHA. Publicado por Elsevier España, S.L.U. All rights reserved.

Robust consensus control with guaranteed rate of convergence using second-order Hurwitz polynomials

NASA Astrophysics Data System (ADS)

Fruhnert, Michael; Corless, Martin

2017-10-01

This paper considers homogeneous networks of general, linear time-invariant, second-order systems. We consider linear feedback controllers and require that the directed graph associated with the network contains a spanning tree and systems are stabilisable. We show that consensus with a guaranteed rate of convergence can always be achieved using linear state feedback. To achieve this, we provide a new and simple derivation of the conditions for a second-order polynomial with complex coefficients to be Hurwitz. We apply this result to obtain necessary and sufficient conditions to achieve consensus with networks whose graph Laplacian matrix may have complex eigenvalues. Based on the conditions found, methods to compute feedback gains are proposed. We show that gains can be chosen such that consensus is achieved robustly over a variety of communication structures and system dynamics. We also consider the use of static output feedback.

Constructing post-surgical discharge instructions through a Delphi consensus methodology.

PubMed

Scott, Aaron R; Sanderson, Cody J; Rush, Augustus J; Alore, Elizabeth A; Naik, Aanand D; Berger, David H; Suliburk, James W

2018-05-01

Patient education materials are a crucial part of physician-patient communication. We hypothesize that available discharge instructions are difficult to read and fail to address necessary topics. Our objective is to evaluate readability and content of surgical discharge instructions using thyroidectomy to develop standardized discharge materials. Thyroidectomy discharge materials were analyzed for readability and assessed for content. Fifteen endocrine surgeons participated in a modified Delphi consensus panel to select necessary topics. Using readability best practices, we created standardized discharge instructions which included all selected topics. The panel evaluated 40 topics, selected 23, deemed 4 inappropriate, consolidated 5, and did not reach consensus on 8 topics after 4 rounds. The evaluated instructions' reading levels ranged from grade 6.5 to 13.2; none contained all consensus topics. Current post surgical thyroidectomy discharge instructions are more difficult to read than recommended by literacy standards and omit consensus warning signs of major complications. Our easy-to-read discharge instructions cover pertinent topics and may enhance patient education. Delphi methodology is useful for developing post-surgical instructions. Patient education materials need appropriate readability levels and content. We recommend the Delphi method to select content using consensus expert opinion whenever higher level data is lacking. Copyright © 2017 Elsevier B.V. All rights reserved.

Using a Delphi process to establish consensus on emergency medicine clerkship competencies.

PubMed

Penciner, Rick; Langhan, Trevor; Lee, Richard; McEwen, Jill; Woods, Robert A; Bandiera, Glen

2011-01-01

Currently, there is no consensus on the core competencies required for emergency medicine (EM) clerkships in Canada. Existing EM curricula have been developed through informal consensus or local efforts. The Delphi process has been used extensively as a means for establishing consensus. The purpose of this project was to define core competencies for EM clerkships in Canada, to validate a Delphi process in the context of national curriculum development, and to demonstrate the adoption of the CanMEDS physician competency paradigm in the undergraduate medical education realm. Using a modified Delphi process, we developed a consensus amongst a panel of expert emergency physicians from across Canada utilizing the CanMEDS 2005 Physician Competency Framework. Thirty experts from nine different medical schools across Canada participated on the panel. The initial list consisted of 152 competencies organized in the seven domains of the CanMEDS 2005 Physician Competency Framework. After the second round of the Delphi process, the list of competencies was reduced to 62 (59% reduction). This study demonstrated that a modified Delphi process can result in a strong consensus around a realistic number of core competencies for EM clerkships. We propose that such a method could be used by other medical specialties and health professions to develop rotation-specific core competencies.

Similarities and differences of systematic consensus on disaster mental health services between Japanese and European experts.

PubMed

Fukasawa, Maiko; Suzuki, Yuriko; Nakajima, Satomi; Narisawa, Tomomi; Kim, Yoshiharu

2013-04-01

We recently developed new disaster mental health guidelines in Japan through the Delphi process, a method for building consensus among experts, using as a reference the guidelines developed by The European Network for Traumatic Stress (TENTS) in Europe. We included in our survey 30 items used in the TENTS survey, 20 of which achieved positive consensus in that survey. Here we report on the extent of agreement of 95 Japanese experts on each of these 30 items and examine the reasons for disagreements with the TENTS survey results based on the comments obtained from the participants of our survey. Of the 20 items, 12 also gained consensus in our survey and 1 additional item achieved consensus that did not achieve it in the TENTS survey. Items that did not gain consensus in our survey, but did in the TENTS survey, were recommendations for close collaboration with the media, screening volunteers for their suitability, and withholding formal screening of the affected population. The need for specialist care for specific populations was endorsed in our survey, but not in the TENTS survey. Overall, the opinion of Japanese experts was congruent with that of Western experts, but some guideline amendments would be beneficial. Copyright © 2013 International Society for Traumatic Stress Studies.

Development of a consensus method for culture of Clostridium difficile from meat and its use in a survey of U.S. retail meats.

PubMed

Limbago, Brandi; Thompson, Angela D; Greene, Sharon A; MacCannell, Duncan; MacGowan, Charles E; Jolbitado, Beverly; Hardin, Henrietta D; Estes, Stephanie R; Weese, J Scott; Songer, J Glenn; Gould, L Hannah

2012-12-01

Three previously described methods for culture of Clostridium difficile from meats were evaluated by microbiologists with experience in C. difficile culture and identification. A consensus protocol using BHI broth enrichment followed by ethanol shock and plating to selective and non-selective media was selected for use, and all participating laboratories received hands-on training in the use of this method prior to study initiation. Retail meat products (N = 1755) were cultured for C. difficile over 12 months during 2010-2011 at 9 U.S. FoodNet sites. No C. difficile was recovered, although other clostridia were isolated. Published by Elsevier Ltd.

Development of a consensus method for culture of Clostridium difficile from meat and its use in a survey of U.S. retail meats

PubMed Central

Limbago, Brandi; Thompson, Angela D.; Greene, Sharon A.; MacCannell, Duncan; MacGowan, Charles E.; Jolbitado, Beverly; Hardin, Henrietta D.; Estes, Stephanie R.; Weese, J. Scott; Songer, J. Glenn; Gould, L. Hannah

2017-01-01

Three previously described methods for culture of Clostridium difficile from meats were evaluated by microbiologists with experience in C. difficile culture and identification. A consensus protocol using BHI broth enrichment followed by ethanol shock and plating to selective and non-selective media was selected for use, and all participating laboratories received hands-on training in the use of this method prior to study initiation. Retail meat products (N = 1755) were cultured for C. difficile over 12 months during 2010-2011 at 9 U.S. FoodNet sites. No C. difficile was recovered, although other clostridia were isolated. PMID:22986214

Developing a model for effective leadership in healthcare: a concept mapping approach

PubMed Central

Hargett, Charles William; Doty, Joseph P; Hauck, Jennifer N; Webb, Allison MB; Cook, Steven H; Tsipis, Nicholas E; Neumann, Julie A; Andolsek, Kathryn M; Taylor, Dean C

2017-01-01

Purpose Despite increasing awareness of the importance of leadership in healthcare, our understanding of the competencies of effective leadership remains limited. We used a concept mapping approach (a blend of qualitative and quantitative analysis of group processes to produce a visual composite of the group’s ideas) to identify stakeholders’ mental model of effective healthcare leadership, clarifying the underlying structure and importance of leadership competencies. Methods Literature review, focus groups, and consensus meetings were used to derive a representative set of healthcare leadership competency statements. Study participants subsequently sorted and rank-ordered these statements based on their perceived importance in contributing to effective healthcare leadership in real-world settings. Hierarchical cluster analysis of individual sortings was used to develop a coherent model of effective leadership in healthcare. Results A diverse group of 92 faculty and trainees individually rank-sorted 33 leadership competency statements. The highest rated statements were “Acting with Personal Integrity”, “Communicating Effectively”, “Acting with Professional Ethical Values”, “Pursuing Excellence”, “Building and Maintaining Relationships”, and “Thinking Critically”. Combining the results from hierarchical cluster analysis with our qualitative data led to a healthcare leadership model based on the core principle of Patient Centeredness and the core competencies of Integrity, Teamwork, Critical Thinking, Emotional Intelligence, and Selfless Service. Conclusion Using a mixed qualitative-quantitative approach, we developed a graphical representation of a shared leadership model derived in the healthcare setting. This model may enhance learning, teaching, and patient care in this important area, as well as guide future research. PMID:29355249

A Weight-Adaptive Laplacian Embedding for Graph-Based Clustering.

PubMed

Cheng, De; Nie, Feiping; Sun, Jiande; Gong, Yihong

2017-07-01

Graph-based clustering methods perform clustering on a fixed input data graph. Thus such clustering results are sensitive to the particular graph construction. If this initial construction is of low quality, the resulting clustering may also be of low quality. We address this drawback by allowing the data graph itself to be adaptively adjusted in the clustering procedure. In particular, our proposed weight adaptive Laplacian (WAL) method learns a new data similarity matrix that can adaptively adjust the initial graph according to the similarity weight in the input data graph. We develop three versions of these methods based on the L2-norm, fuzzy entropy regularizer, and another exponential-based weight strategy, that yield three new graph-based clustering objectives. We derive optimization algorithms to solve these objectives. Experimental results on synthetic data sets and real-world benchmark data sets exhibit the effectiveness of these new graph-based clustering methods.

Clustering self-organizing maps (SOM) method for human papillomavirus (HPV) DNA as the main cause of cervical cancer disease

NASA Astrophysics Data System (ADS)

Bustamam, A.; Aldila, D.; Fatimah, Arimbi, M. D.

2017-07-01

One of the most widely used clustering method, since it has advantage on its robustness, is Self-Organizing Maps (SOM) method. This paper discusses the application of SOM method on Human Papillomavirus (HPV) DNA which is the main cause of cervical cancer disease, the most dangerous cancer in developing countries. We use 18 types of HPV DNA-based on the newest complete genome. By using open-source-based program R, clustering process can separate 18 types of HPV into two different clusters. There are two types of HPV in the first cluster while 16 others in the second cluster. The analyzing result of 18 types HPV based on the malignancy of the virus (the difficultness to cure). Two of HPV types the first cluster can be classified as tame HPV, while 16 others in the second cluster are classified as vicious HPV.

A Multicriteria Decision Making Approach for Estimating the Number of Clusters in a Data Set

PubMed Central

Peng, Yi; Zhang, Yong; Kou, Gang; Shi, Yong

2012-01-01

Determining the number of clusters in a data set is an essential yet difficult step in cluster analysis. Since this task involves more than one criterion, it can be modeled as a multiple criteria decision making (MCDM) problem. This paper proposes a multiple criteria decision making (MCDM)-based approach to estimate the number of clusters for a given data set. In this approach, MCDM methods consider different numbers of clusters as alternatives and the outputs of any clustering algorithm on validity measures as criteria. The proposed method is examined by an experimental study using three MCDM methods, the well-known clustering algorithm–k-means, ten relative measures, and fifteen public-domain UCI machine learning data sets. The results show that MCDM methods work fairly well in estimating the number of clusters in the data and outperform the ten relative measures considered in the study. PMID:22870181

Clustering PPI data by combining FA and SHC method.

PubMed

Lei, Xiujuan; Ying, Chao; Wu, Fang-Xiang; Xu, Jin

2015-01-01

Clustering is one of main methods to identify functional modules from protein-protein interaction (PPI) data. Nevertheless traditional clustering methods may not be effective for clustering PPI data. In this paper, we proposed a novel method for clustering PPI data by combining firefly algorithm (FA) and synchronization-based hierarchical clustering (SHC) algorithm. Firstly, the PPI data are preprocessed via spectral clustering (SC) which transforms the high-dimensional similarity matrix into a low dimension matrix. Then the SHC algorithm is used to perform clustering. In SHC algorithm, hierarchical clustering is achieved by enlarging the neighborhood radius of synchronized objects continuously, while the hierarchical search is very difficult to find the optimal neighborhood radius of synchronization and the efficiency is not high. So we adopt the firefly algorithm to determine the optimal threshold of the neighborhood radius of synchronization automatically. The proposed algorithm is tested on the MIPS PPI dataset. The results show that our proposed algorithm is better than the traditional algorithms in precision, recall and f-measure value.

Clustering PPI data by combining FA and SHC method

PubMed Central

2015-01-01

Clustering is one of main methods to identify functional modules from protein-protein interaction (PPI) data. Nevertheless traditional clustering methods may not be effective for clustering PPI data. In this paper, we proposed a novel method for clustering PPI data by combining firefly algorithm (FA) and synchronization-based hierarchical clustering (SHC) algorithm. Firstly, the PPI data are preprocessed via spectral clustering (SC) which transforms the high-dimensional similarity matrix into a low dimension matrix. Then the SHC algorithm is used to perform clustering. In SHC algorithm, hierarchical clustering is achieved by enlarging the neighborhood radius of synchronized objects continuously, while the hierarchical search is very difficult to find the optimal neighborhood radius of synchronization and the efficiency is not high. So we adopt the firefly algorithm to determine the optimal threshold of the neighborhood radius of synchronization automatically. The proposed algorithm is tested on the MIPS PPI dataset. The results show that our proposed algorithm is better than the traditional algorithms in precision, recall and f-measure value. PMID:25707632

Cluster analysis of molecular simulation trajectories for systems where both conformation and orientation of the sampled states are important.

PubMed

Abramyan, Tigran M; Snyder, James A; Thyparambil, Aby A; Stuart, Steven J; Latour, Robert A

2016-08-05

Clustering methods have been widely used to group together similar conformational states from molecular simulations of biomolecules in solution. For applications such as the interaction of a protein with a surface, the orientation of the protein relative to the surface is also an important clustering parameter because of its potential effect on adsorbed-state bioactivity. This study presents cluster analysis methods that are specifically designed for systems where both molecular orientation and conformation are important, and the methods are demonstrated using test cases of adsorbed proteins for validation. Additionally, because cluster analysis can be a very subjective process, an objective procedure for identifying both the optimal number of clusters and the best clustering algorithm to be applied to analyze a given dataset is presented. The method is demonstrated for several agglomerative hierarchical clustering algorithms used in conjunction with three cluster validation techniques. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Clustering Scientific Publications Based on Citation Relations: A Systematic Comparison of Different Methods.

PubMed

Šubelj, Lovro; van Eck, Nees Jan; Waltman, Ludo

2016-01-01

Clustering methods are applied regularly in the bibliometric literature to identify research areas or scientific fields. These methods are for instance used to group publications into clusters based on their relations in a citation network. In the network science literature, many clustering methods, often referred to as graph partitioning or community detection techniques, have been developed. Focusing on the problem of clustering the publications in a citation network, we present a systematic comparison of the performance of a large number of these clustering methods. Using a number of different citation networks, some of them relatively small and others very large, we extensively study the statistical properties of the results provided by different methods. In addition, we also carry out an expert-based assessment of the results produced by different methods. The expert-based assessment focuses on publications in the field of scientometrics. Our findings seem to indicate that there is a trade-off between different properties that may be considered desirable for a good clustering of publications. Overall, map equation methods appear to perform best in our analysis, suggesting that these methods deserve more attention from the bibliometric community.

Clustering Scientific Publications Based on Citation Relations: A Systematic Comparison of Different Methods

PubMed Central

Šubelj, Lovro; van Eck, Nees Jan; Waltman, Ludo

2016-01-01

Clustering methods are applied regularly in the bibliometric literature to identify research areas or scientific fields. These methods are for instance used to group publications into clusters based on their relations in a citation network. In the network science literature, many clustering methods, often referred to as graph partitioning or community detection techniques, have been developed. Focusing on the problem of clustering the publications in a citation network, we present a systematic comparison of the performance of a large number of these clustering methods. Using a number of different citation networks, some of them relatively small and others very large, we extensively study the statistical properties of the results provided by different methods. In addition, we also carry out an expert-based assessment of the results produced by different methods. The expert-based assessment focuses on publications in the field of scientometrics. Our findings seem to indicate that there is a trade-off between different properties that may be considered desirable for a good clustering of publications. Overall, map equation methods appear to perform best in our analysis, suggesting that these methods deserve more attention from the bibliometric community. PMID:27124610

Does aspartate 170 of the D1 polypeptide ligate the manganese cluster in photosystem II? An EPR and ESEEM Study.

PubMed

Debus, Richard J; Aznar, Constantino; Campbell, Kristy A; Gregor, Wolfgang; Diner, Bruce A; Britt, R David

2003-09-16

Aspartate 170 of the D1 polypeptide provides part of the high-affinity binding site for the first Mn(II) ion that is photooxidized during the light-driven assembly of the (Mn)(4) cluster in photosystem II [Campbell, K. A., Force, D. A., Nixon, P. J., Dole, F., Diner, B. A., and Britt, R. D. (2000) J. Am. Chem. Soc. 122, 3754-3761]. However, despite a wealth of data on D1-Asp170 mutants accumulated over the past decade, there is no consensus about whether this residue ligates the assembled (Mn)(4) cluster. To address this issue, we have conducted an EPR and ESEEM (electron spin-echo envelope modulation) study of D1-D170H PSII particles purified from the cyanobacterium Synechocystis sp. PCC 6803. The line shapes of the S(1) and S(2) state multiline EPR signals of D1-D170H PSII particles are unchanged from those of wild-type PSII particles, and the signal amplitudes correlate approximately with the lower O(2) evolving activity of the mutant PSII particles (40-60% compared to that of the wild type). These data provide further evidence that the assembled (Mn)(4) clusters in D1-D170H cells function normally, even though the assembly of the (Mn)(4) cluster is inefficient in this mutant. In the two-pulse frequency domain ESEEM spectrum of the 9.2 GHz S(2) state multiline EPR signal of D1-D170H PSII particles, the histidyl nitrogen modulation observed at 4-5 MHz is unchanged from that of wild-type PSII particles and no significant new modulation is observed. Three scenarios are presented to explain this result. (1) D1-Asp170 ligates the assembled (Mn)(4) cluster, but the hyperfine couplings to the ligating histidyl nitrogen of D1-His170 are too large or anisotropic to be detected by ESEEM analyses conducted at 9.2 GHz. (2) D1-Asp170 ligates the assembled (Mn)(4) cluster, but D1-His170 does not. (3) D1-Asp170 does not ligate the assembled (Mn)(4) cluster.

CORM: An R Package Implementing the Clustering of Regression Models Method for Gene Clustering

PubMed Central

Shi, Jiejun; Qin, Li-Xuan

2014-01-01

We report a new R package implementing the clustering of regression models (CORM) method for clustering genes using gene expression data and provide data examples illustrating each clustering function in the package. The CORM package is freely available at CRAN from http://cran.r-project.org. PMID:25452684

Three-dimensional Magnetohydrodynamical Simulations of the Morphology of Head-Tail Radio Galaxies Based on the Magnetic Tower Jet Model

NASA Astrophysics Data System (ADS)

Gan, Zhaoming; Li, Hui; Li, Shengtai; Yuan, Feng

2017-04-01

The distinctive morphology of head-tail radio galaxies reveals strong interactions between the radio jets and their intra-cluster environment, the general consensus on the morphology origin of head-tail sources is that radio jets are bent by violent intra-cluster weather. We demonstrate in this paper that such strong interactions provide a great opportunity to study the jet properties and also the dynamics of the intra-cluster medium (ICM). By three-dimensional magnetohydrodynamical simulations, we analyze the detailed bending process of a magnetically dominated jet, based on the magnetic tower jet model. We use stratified atmospheres modulated by wind/shock to mimic the violent intra-cluster weather. Core sloshing is found to be inevitable during the wind-cluster core interaction, which induces significant shear motion and could finally drive ICM turbulence around the jet, making it difficult for the jet to survive. We perform a detailed comparison between the behavior of pure hydrodynamical jets and the magnetic tower jet and find that the jet-lobe morphology could not survive against the violent disruption in all of our pure hydrodynamical jet models. On the other hand, the head-tail morphology is well reproduced by using a magnetic tower jet model bent by wind, in which hydrodynamical instabilities are naturally suppressed and the jet could always keep its integrity under the protection of its internal magnetic fields. Finally, we also check the possibility for jet bending by shock only. We find that shock could not bend the jet significantly, and thus could not be expected to explain the observed long tails in head-tail radio galaxies.