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Sample records for ranked treatments predicted

  1. Ranking of Unwarranted Variations in Healthcare Treatments

    NARCIS (Netherlands)

    Moes, Herry; Brekelmans, Ruud; Hamers, Herbert; Hasaart, F.

    2017-01-01

    In this paper, we introduce a framework designed to identify and rank possible unwarranted variation of treatments in healthcare. The innovative aspect of this framework is a ranking procedure that aims to identify healthcare institutions where unwarranted variation is most severe, and diagnosis

  2. Predicting disease risk using bootstrap ranking and classification algorithms.

    Directory of Open Access Journals (Sweden)

    Ohad Manor

    Full Text Available Genome-wide association studies (GWAS are widely used to search for genetic loci that underlie human disease. Another goal is to predict disease risk for different individuals given their genetic sequence. Such predictions could either be used as a "black box" in order to promote changes in life-style and screening for early diagnosis, or as a model that can be studied to better understand the mechanism of the disease. Current methods for risk prediction typically rank single nucleotide polymorphisms (SNPs by the p-value of their association with the disease, and use the top-associated SNPs as input to a classification algorithm. However, the predictive power of such methods is relatively poor. To improve the predictive power, we devised BootRank, which uses bootstrapping in order to obtain a robust prioritization of SNPs for use in predictive models. We show that BootRank improves the ability to predict disease risk of unseen individuals in the Wellcome Trust Case Control Consortium (WTCCC data and results in a more robust set of SNPs and a larger number of enriched pathways being associated with the different diseases. Finally, we show that combining BootRank with seven different classification algorithms improves performance compared to previous studies that used the WTCCC data. Notably, diseases for which BootRank results in the largest improvements were recently shown to have more heritability than previously thought, likely due to contributions from variants with low minimum allele frequency (MAF, suggesting that BootRank can be beneficial in cases where SNPs affecting the disease are poorly tagged or have low MAF. Overall, our results show that improving disease risk prediction from genotypic information may be a tangible goal, with potential implications for personalized disease screening and treatment.

  3. Evaluation of treatment effects by ranking

    DEFF Research Database (Denmark)

    Halekoh, U; Kristensen, K

    2008-01-01

    In crop experiments measurements are often made by a judge evaluating the crops' conditions after treatment. In the present paper an analysis is proposed for experiments where plots of crops treated differently are mutually ranked. In the experimental layout the crops are treated on consecutive...... plots usually placed side by side in one or more rows. In the proposed method a judge ranks several neighbouring plots, say three, by ranking them from best to worst. For the next observation the judge moves on by no more than two plots, such that up to two plots will be re-evaluated again...... in a comparison with the new plot(s). Data from studies using this set-up were analysed by a Thurstonian random utility model, which assumed that the judge's rankings were obtained by comparing latent continuous utilities or treatment effects. For the latent utilities a variance component model was considered...

  4. AptRank: an adaptive PageRank model for protein function prediction on   bi-relational graphs.

    Science.gov (United States)

    Jiang, Biaobin; Kloster, Kyle; Gleich, David F; Gribskov, Michael

    2017-06-15

    Diffusion-based network models are widely used for protein function prediction using protein network data and have been shown to outperform neighborhood-based and module-based methods. Recent studies have shown that integrating the hierarchical structure of the Gene Ontology (GO) data dramatically improves prediction accuracy. However, previous methods usually either used the GO hierarchy to refine the prediction results of multiple classifiers, or flattened the hierarchy into a function-function similarity kernel. No study has taken the GO hierarchy into account together with the protein network as a two-layer network model. We first construct a Bi-relational graph (Birg) model comprised of both protein-protein association and function-function hierarchical networks. We then propose two diffusion-based methods, BirgRank and AptRank, both of which use PageRank to diffuse information on this two-layer graph model. BirgRank is a direct application of traditional PageRank with fixed decay parameters. In contrast, AptRank utilizes an adaptive diffusion mechanism to improve the performance of BirgRank. We evaluate the ability of both methods to predict protein function on yeast, fly and human protein datasets, and compare with four previous methods: GeneMANIA, TMC, ProteinRank and clusDCA. We design four different validation strategies: missing function prediction, de novo function prediction, guided function prediction and newly discovered function prediction to comprehensively evaluate predictability of all six methods. We find that both BirgRank and AptRank outperform the previous methods, especially in missing function prediction when using only 10% of the data for training. The MATLAB code is available at https://github.rcac.purdue.edu/mgribsko/aptrank . gribskov@purdue.edu. 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

  5. Treatment plan ranking using physical and biological indices

    International Nuclear Information System (INIS)

    Ebert, M. A.; University of Western Asutralia, WA

    2001-01-01

    Full text: The ranking of dose distributions is of importance in several areas such as i) comparing rival treatment plans, ii) comparing iterations in an optimisation routine, and iii) dose-assessment of clinical trial data. This study aimed to investigate the influence of choice of objective function in ranking tumour dose distributions. A series of physical (mean, maximum, minimum, standard deviation of dose) dose-volume histogram (DVH) reduction indices and biologically-based (tumour-control probability - TCP; equivalent uniform dose -EUD) indices were used to rank a series of hypothetical DVHs, as well as DVHs obtained from a series of 18 prostate patients. The distribution in ranking and change in distribution with change in indice parameters were investigated. It is found that not only is the ranking of DVHs dependent on the actual model used to perform the DVH reduction, it is also found to depend on the inherent characteristics of each model (i.e., selected parameters). The adjacent figure shows an example where the 18 prostate patients are ranked (grey-scale from black to white) by EUD when an α value of 0.8 Gy -1 is used in the model. The change of ranking as α varies is evident. Conclusion: This study has shown that the characteristics of the model selected in plan optimisation or DVH ranking will have an impact on the ranking obtained. Copyright (2001) Australasian College of Physical Scientists and Engineers in Medicine

  6. Google and the mind: predicting fluency with PageRank.

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    Griffiths, Thomas L; Steyvers, Mark; Firl, Alana

    2007-12-01

    Human memory and Internet search engines face a shared computational problem, needing to retrieve stored pieces of information in response to a query. We explored whether they employ similar solutions, testing whether we could predict human performance on a fluency task using PageRank, a component of the Google search engine. In this task, people were shown a letter of the alphabet and asked to name the first word beginning with that letter that came to mind. We show that PageRank, computed on a semantic network constructed from word-association data, outperformed word frequency and the number of words for which a word is named as an associate as a predictor of the words that people produced in this task. We identify two simple process models that could support this apparent correspondence between human memory and Internet search, and relate our results to previous rational models of memory.

  7. Drug-target interaction prediction: A Bayesian ranking approach.

    Science.gov (United States)

    Peska, Ladislav; Buza, Krisztian; Koller, Júlia

    2017-12-01

    In silico prediction of drug-target interactions (DTI) could provide valuable information and speed-up the process of drug repositioning - finding novel usage for existing drugs. In our work, we focus on machine learning algorithms supporting drug-centric repositioning approach, which aims to find novel usage for existing or abandoned drugs. We aim at proposing a per-drug ranking-based method, which reflects the needs of drug-centric repositioning research better than conventional drug-target prediction approaches. We propose Bayesian Ranking Prediction of Drug-Target Interactions (BRDTI). The method is based on Bayesian Personalized Ranking matrix factorization (BPR) which has been shown to be an excellent approach for various preference learning tasks, however, it has not been used for DTI prediction previously. In order to successfully deal with DTI challenges, we extended BPR by proposing: (i) the incorporation of target bias, (ii) a technique to handle new drugs and (iii) content alignment to take structural similarities of drugs and targets into account. Evaluation on five benchmark datasets shows that BRDTI outperforms several state-of-the-art approaches in terms of per-drug nDCG and AUC. BRDTI results w.r.t. nDCG are 0.929, 0.953, 0.948, 0.897 and 0.690 for G-Protein Coupled Receptors (GPCR), Ion Channels (IC), Nuclear Receptors (NR), Enzymes (E) and Kinase (K) datasets respectively. Additionally, BRDTI significantly outperformed other methods (BLM-NII, WNN-GIP, NetLapRLS and CMF) w.r.t. nDCG in 17 out of 20 cases. Furthermore, BRDTI was also shown to be able to predict novel drug-target interactions not contained in the original datasets. The average recall at top-10 predicted targets for each drug was 0.762, 0.560, 1.000 and 0.404 for GPCR, IC, NR, and E datasets respectively. Based on the evaluation, we can conclude that BRDTI is an appropriate choice for researchers looking for an in silico DTI prediction technique to be used in drug

  8. Ranking beta sheet topologies with applications to protein structure prediction

    DEFF Research Database (Denmark)

    Fonseca, Rasmus; Helles, Glennie; Winter, Pawel

    2011-01-01

    One reason why ab initio protein structure predictors do not perform very well is their inability to reliably identify long-range interactions between amino acids. To achieve reliable long-range interactions, all potential pairings of ß-strands (ß-topologies) of a given protein are enumerated......, including the native ß-topology. Two very different ß-topology scoring methods from the literature are then used to rank all potential ß-topologies. This has not previously been attempted for any scoring method. The main result of this paper is a justification that one of the scoring methods, in particular......, consistently top-ranks native ß-topologies. Since the number of potential ß-topologies grows exponentially with the number of ß-strands, it is unrealistic to expect that all potential ß-topologies can be enumerated for large proteins. The second result of this paper is an enumeration scheme of a subset of ß-topologies...

  9. Does resident ranking during recruitment accurately predict subsequent performance as a surgical resident?

    Science.gov (United States)

    Fryer, Jonathan P; Corcoran, Noreen; George, Brian; Wang, Ed; Darosa, Debra

    2012-01-01

    While the primary goal of ranking applicants for surgical residency training positions is to identify the candidates who will subsequently perform best as surgical residents, the effectiveness of the ranking process has not been adequately studied. We evaluated our general surgery resident recruitment process between 2001 and 2011 inclusive, to determine if our recruitment ranking parameters effectively predicted subsequent resident performance. We identified 3 candidate ranking parameters (United States Medical Licensing Examination [USMLE] Step 1 score, unadjusted ranking score [URS], and final adjusted ranking [FAR]), and 4 resident performance parameters (American Board of Surgery In-Training Examination [ABSITE] score, PGY1 resident evaluation grade [REG], overall REG, and independent faculty rating ranking [IFRR]), and assessed whether the former were predictive of the latter. Analyses utilized Spearman correlation coefficient. We found that the URS, which is based on objective and criterion based parameters, was a better predictor of subsequent performance than the FAR, which is a modification of the URS based on subsequent determinations of the resident selection committee. USMLE score was a reliable predictor of ABSITE scores only. However, when we compared our worst residence performances with the performances of the other residents in this evaluation, the data did not produce convincing evidence that poor resident performances could be reliably predicted by any of the recruitment ranking parameters. Finally, stratifying candidates based on their rank range did not effectively define a ranking cut-off beyond which resident performance would drop off. Based on these findings, we recommend surgery programs may be better served by utilizing a more structured resident ranking process and that subsequent adjustments to the rank list generated by this process should be undertaken with caution. Copyright © 2012 Association of Program Directors in Surgery

  10. Ranking of microRNA target prediction scores by Pareto front analysis.

    Science.gov (United States)

    Sahoo, Sudhakar; Albrecht, Andreas A

    2010-12-01

    Over the past ten years, a variety of microRNA target prediction methods has been developed, and many of the methods are constantly improved and adapted to recent insights into miRNA-mRNA interactions. In a typical scenario, different methods return different rankings of putative targets, even if the ranking is reduced to selected mRNAs that are related to a specific disease or cell type. For the experimental validation it is then difficult to decide in which order to process the predicted miRNA-mRNA bindings, since each validation is a laborious task and therefore only a limited number of mRNAs can be analysed. We propose a new ranking scheme that combines ranked predictions from several methods and - unlike standard thresholding methods - utilises the concept of Pareto fronts as defined in multi-objective optimisation. In the present study, we attempt a proof of concept by applying the new ranking scheme to hsa-miR-21, hsa-miR-125b, and hsa-miR-373 and prediction scores supplied by PITA and RNAhybrid. The scores are interpreted as a two-objective optimisation problem, and the elements of the Pareto front are ranked by the STarMir score with a subsequent re-calculation of the Pareto front after removal of the top-ranked mRNA from the basic set of prediction scores. The method is evaluated on validated targets of the three miRNA, and the ranking is compared to scores from DIANA-microT and TargetScan. We observed that the new ranking method performs well and consistent, and the first validated targets are elements of Pareto fronts at a relatively early stage of the recurrent procedure, which encourages further research towards a higher-dimensional analysis of Pareto fronts. Copyright © 2010 Elsevier Ltd. All rights reserved.

  11. Time-Aware Service Ranking Prediction in the Internet of Things Environment.

    Science.gov (United States)

    Huang, Yuze; Huang, Jiwei; Cheng, Bo; He, Shuqing; Chen, Junliang

    2017-04-27

    With the rapid development of the Internet of things (IoT), building IoT systems with high quality of service (QoS) has become an urgent requirement in both academia and industry. During the procedures of building IoT systems, QoS-aware service selection is an important concern, which requires the ranking of a set of functionally similar services according to their QoS values. In reality, however, it is quite expensive and even impractical to evaluate all geographically-dispersed IoT services at a single client to obtain such a ranking. Nevertheless, distributed measurement and ranking aggregation have to deal with the high dynamics of QoS values and the inconsistency of partial rankings. To address these challenges, we propose a time-aware service ranking prediction approach named TSRPred for obtaining the global ranking from the collection of partial rankings. Specifically, a pairwise comparison model is constructed to describe the relationships between different services, where the partial rankings are obtained by time series forecasting on QoS values. The comparisons of IoT services are formulated by random walks, and thus, the global ranking can be obtained by sorting the steady-state probabilities of the underlying Markov chain. Finally, the efficacy of TSRPred is validated by simulation experiments based on large-scale real-world datasets.

  12. Time-Aware Service Ranking Prediction in the Internet of Things Environment

    Directory of Open Access Journals (Sweden)

    Yuze Huang

    2017-04-01

    Full Text Available With the rapid development of the Internet of things (IoT, building IoT systems with high quality of service (QoS has become an urgent requirement in both academia and industry. During the procedures of building IoT systems, QoS-aware service selection is an important concern, which requires the ranking of a set of functionally similar services according to their QoS values. In reality, however, it is quite expensive and even impractical to evaluate all geographically-dispersed IoT services at a single client to obtain such a ranking. Nevertheless, distributed measurement and ranking aggregation have to deal with the high dynamics of QoS values and the inconsistency of partial rankings. To address these challenges, we propose a time-aware service ranking prediction approach named TSRPred for obtaining the global ranking from the collection of partial rankings. Specifically, a pairwise comparison model is constructed to describe the relationships between different services, where the partial rankings are obtained by time series forecasting on QoS values. The comparisons of IoT services are formulated by random walks, and thus, the global ranking can be obtained by sorting the steady-state probabilities of the underlying Markov chain. Finally, the efficacy of TSRPred is validated by simulation experiments based on large-scale real-world datasets.

  13. A rank-based Prediction Algorithm of Learning User's Intention

    Science.gov (United States)

    Shen, Jie; Gao, Ying; Chen, Cang; Gong, HaiPing

    Internet search has become an important part in people's daily life. People can find many types of information to meet different needs through search engines on the Internet. There are two issues for the current search engines: first, the users should predetermine the types of information they want and then change to the appropriate types of search engine interfaces. Second, most search engines can support multiple kinds of search functions, each function has its own separate search interface. While users need different types of information, they must switch between different interfaces. In practice, most queries are corresponding to various types of information results. These queries can search the relevant results in various search engines, such as query "Palace" contains the websites about the introduction of the National Palace Museum, blog, Wikipedia, some pictures and video information. This paper presents a new aggregative algorithm for all kinds of search results. It can filter and sort the search results by learning three aspects about the query words, search results and search history logs to achieve the purpose of detecting user's intention. Experiments demonstrate that this rank-based method for multi-types of search results is effective. It can meet the user's search needs well, enhance user's satisfaction, provide an effective and rational model for optimizing search engines and improve user's search experience.

  14. Monte Carlo simulations guided by imaging to predict the in vitro ranking of radiosensitizing nanoparticles.

    Science.gov (United States)

    Retif, Paul; Reinhard, Aurélie; Paquot, Héna; Jouan-Hureaux, Valérie; Chateau, Alicia; Sancey, Lucie; Barberi-Heyob, Muriel; Pinel, Sophie; Bastogne, Thierry

    This article addresses the in silico-in vitro prediction issue of organometallic nanoparticles (NPs)-based radiosensitization enhancement. The goal was to carry out computational experiments to quickly identify efficient nanostructures and then to preferentially select the most promising ones for the subsequent in vivo studies. To this aim, this interdisciplinary article introduces a new theoretical Monte Carlo computational ranking method and tests it using 3 different organometallic NPs in terms of size and composition. While the ranking predicted in a classical theoretical scenario did not fit the reference results at all, in contrast, we showed for the first time how our accelerated in silico virtual screening method, based on basic in vitro experimental data (which takes into account the NPs cell biodistribution), was able to predict a relevant ranking in accordance with in vitro clonogenic efficiency. This corroborates the pertinence of such a prior ranking method that could speed up the preclinical development of NPs in radiation therapy.

  15. Ranking of hair dye substances according to predicted sensitization potency

    DEFF Research Database (Denmark)

    Søsted, H; Basketter, D A; Estrada, E

    2004-01-01

    Allergic contact dermatitis following the use of hair dyes is well known. Many chemicals are used in hair dyes and it is unlikely that all cases of hair dye allergy can be diagnosed by means of patch testing with p-phenylenediamine (PPD). The objectives of this study are to identify all hair dye...... in order to help select a number of chemically diverse hair dye substances that could be used in subsequent clinical work. Various information sources, including the Inventory of Cosmetics Ingredients, new regulations on cosmetics, data on total use and ChemId (the Chemical Search Input website provided...... by the National Library of Medicine), were used in order to identify the names and structures of the hair dyes. A QSAR model, developed with the help of experimental local lymph node assay data and topological sub-structural molecular descriptors (TOPS-MODE), was used in order to predict the likely sensitization...

  16. Discovering urban mobility patterns with PageRank based traffic modeling and prediction

    Science.gov (United States)

    Wang, Minjie; Yang, Su; Sun, Yi; Gao, Jun

    2017-11-01

    Urban transportation system can be viewed as complex network with time-varying traffic flows as links to connect adjacent regions as networked nodes. By computing urban traffic evolution on such temporal complex network with PageRank, it is found that for most regions, there exists a linear relation between the traffic congestion measure at present time and the PageRank value of the last time. Since the PageRank measure of a region does result from the mutual interactions of the whole network, it implies that the traffic state of a local region does not evolve independently but is affected by the evolution of the whole network. As a result, the PageRank values can act as signatures in predicting upcoming traffic congestions. We observe the aforementioned laws experimentally based on the trajectory data of 12000 taxies in Beijing city for one month.

  17. Facial morphology predicts male fitness and rank but not survival in Second World War Finnish soldiers.

    Science.gov (United States)

    Loehr, John; O'Hara, Robert B

    2013-08-23

    We investigated fitness, military rank and survival of facial phenotypes in large-scale warfare using 795 Finnish soldiers who fought in the Winter War (1939-1940). We measured facial width-to-height ratio-a trait known to predict aggressive behaviour in males-and assessed whether facial morphology could predict survival, lifetime reproductive success (LRS) and social status. We found no difference in survival along the phenotypic gradient, however, wider-faced individuals had greater LRS, but achieved a lower military rank.

  18. Network-based ranking methods for prediction of novel disease associated microRNAs.

    Science.gov (United States)

    Le, Duc-Hau

    2015-10-01

    Many studies have shown roles of microRNAs on human disease and a number of computational methods have been proposed to predict such associations by ranking candidate microRNAs according to their relevance to a disease. Among them, machine learning-based methods usually have a limitation in specifying non-disease microRNAs as negative training samples. Meanwhile, network-based methods are becoming dominant since they well exploit a "disease module" principle in microRNA functional similarity networks. Of which, random walk with restart (RWR) algorithm-based method is currently state-of-the-art. The use of this algorithm was inspired from its success in predicting disease gene because the "disease module" principle also exists in protein interaction networks. Besides, many algorithms designed for webpage ranking have been successfully applied in ranking disease candidate genes because web networks share topological properties with protein interaction networks. However, these algorithms have not yet been utilized for disease microRNA prediction. We constructed microRNA functional similarity networks based on shared targets of microRNAs, and then we integrated them with a microRNA functional synergistic network, which was recently identified. After analyzing topological properties of these networks, in addition to RWR, we assessed the performance of (i) PRINCE (PRIoritizatioN and Complex Elucidation), which was proposed for disease gene prediction; (ii) PageRank with Priors (PRP) and K-Step Markov (KSM), which were used for studying web networks; and (iii) a neighborhood-based algorithm. Analyses on topological properties showed that all microRNA functional similarity networks are small-worldness and scale-free. The performance of each algorithm was assessed based on average AUC values on 35 disease phenotypes and average rankings of newly discovered disease microRNAs. As a result, the performance on the integrated network was better than that on individual ones. In

  19. An improved rank based disease prediction using web navigation patterns on bio-medical databases

    Directory of Open Access Journals (Sweden)

    P. Dhanalakshmi

    2017-12-01

    Full Text Available Applying machine learning techniques to on-line biomedical databases is a challenging task, as this data is collected from large number of sources and it is multi-dimensional. Also retrieval of relevant document from large repository such as gene document takes more processing time and an increased false positive rate. Generally, the extraction of biomedical document is based on the stream of prior observations of gene parameters taken at different time periods. Traditional web usage models such as Markov, Bayesian and Clustering models are sensitive to analyze the user navigation patterns and session identification in online biomedical database. Moreover, most of the document ranking models on biomedical database are sensitive to sparsity and outliers. In this paper, a novel user recommendation system was implemented to predict the top ranked biomedical documents using the disease type, gene entities and user navigation patterns. In this recommendation system, dynamic session identification, dynamic user identification and document ranking techniques were used to extract the highly relevant disease documents on the online PubMed repository. To verify the performance of the proposed model, the true positive rate and runtime of the model was compared with that of traditional static models such as Bayesian and Fuzzy rank. Experimental results show that the performance of the proposed ranking model is better than the traditional models.

  20. Feature Subset Selection and Instance Filtering for Cross-project Defect Prediction - Classification and Ranking

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    Faimison Porto

    2016-12-01

    Full Text Available The defect prediction models can be a good tool on organizing the project's test resources. The models can be constructed with two main goals: 1 to classify the software parts - defective or not; or 2 to rank the most defective parts in a decreasing order. However, not all companies maintain an appropriate set of historical defect data. In this case, a company can build an appropriate dataset from known external projects - called Cross-project Defect Prediction (CPDP. The CPDP models, however, present low prediction performances due to the heterogeneity of data. Recently, Instance Filtering methods were proposed in order to reduce this heterogeneity by selecting the most similar instances from the training dataset. Originally, the similarity is calculated based on all the available dataset features (or independent variables. We propose that using only the most relevant features on the similarity calculation can result in more accurate filtered datasets and better prediction performances. In this study we extend our previous work. We analyse both prediction goals - Classification and Ranking. We present an empirical evaluation of 41 different methods by associating Instance Filtering methods with Feature Selection methods. We used 36 versions of 11 open source projects on experiments. The results show similar evidences for both prediction goals. First, the defect prediction performance of CPDP models can be improved by associating Feature Selection and Instance Filtering. Second, no evaluated method presented general better performances. Indeed, the most appropriate method can vary according to the characteristics of the project being predicted.

  1. Detecting determinism with improved sensitivity in time series: rank-based nonlinear predictability score.

    Science.gov (United States)

    Naro, Daniel; Rummel, Christian; Schindler, Kaspar; Andrzejak, Ralph G

    2014-09-01

    The rank-based nonlinear predictability score was recently introduced as a test for determinism in point processes. We here adapt this measure to time series sampled from time-continuous flows. We use noisy Lorenz signals to compare this approach against a classical amplitude-based nonlinear prediction error. Both measures show an almost identical robustness against Gaussian white noise. In contrast, when the amplitude distribution of the noise has a narrower central peak and heavier tails than the normal distribution, the rank-based nonlinear predictability score outperforms the amplitude-based nonlinear prediction error. For this type of noise, the nonlinear predictability score has a higher sensitivity for deterministic structure in noisy signals. It also yields a higher statistical power in a surrogate test of the null hypothesis of linear stochastic correlated signals. We show the high relevance of this improved performance in an application to electroencephalographic (EEG) recordings from epilepsy patients. Here the nonlinear predictability score again appears of higher sensitivity to nonrandomness. Importantly, it yields an improved contrast between signals recorded from brain areas where the first ictal EEG signal changes were detected (focal EEG signals) versus signals recorded from brain areas that were not involved at seizure onset (nonfocal EEG signals).

  2. SRMDAP: SimRank and Density-Based Clustering Recommender Model for miRNA-Disease Association Prediction

    Directory of Open Access Journals (Sweden)

    Xiaoying Li

    2018-01-01

    Full Text Available Aberrant expression of microRNAs (miRNAs can be applied for the diagnosis, prognosis, and treatment of human diseases. Identifying the relationship between miRNA and human disease is important to further investigate the pathogenesis of human diseases. However, experimental identification of the associations between diseases and miRNAs is time-consuming and expensive. Computational methods are efficient approaches to determine the potential associations between diseases and miRNAs. This paper presents a new computational method based on the SimRank and density-based clustering recommender model for miRNA-disease associations prediction (SRMDAP. The AUC of 0.8838 based on leave-one-out cross-validation and case studies suggested the excellent performance of the SRMDAP in predicting miRNA-disease associations. SRMDAP could also predict diseases without any related miRNAs and miRNAs without any related diseases.

  3. Effects of microwave irradiation treatment on physicochemical characteristics of Chinese low-rank coals

    International Nuclear Information System (INIS)

    Ge, Lichao; Zhang, Yanwei; Wang, Zhihua; Zhou, Junhu; Cen, Kefa

    2013-01-01

    Highlights: • Typical Chinese lignites with various ranks are upgraded through microwave. • The pore distribution extends to micropore region, BET area and volume increase. • FTIR show the change of microstructure and improvement in coal rank after upgrading. • Upgraded coals exhibit weak combustion similar to Da Tong bituminous coal. • More evident effects are obtained for raw brown coal with relative lower rank. - Abstract: This study investigates the effects of microwave irradiation treatment on coal composition, pore structure, coal rank, function groups, and combustion characteristics of typical Chinese low-rank coals. Results showed that the upgrading process (microwave irradiation treatment) significantly reduced the coals’ inherent moisture, and increased their calorific value and fixed carbon content. It was also found that the upgrading process generated micropores and increased pore volume and surface area of the coals. Results on the oxygen/carbon ratio parameter indicated that the low-rank coals were upgraded to high-rank coals after the upgrading process, which is in agreement with the findings from Fourier transform infrared spectroscopy. Unstable components in the coal were converted into stable components during the upgrading process. Thermo-gravimetric analysis showed that the combustion processes of upgraded coals were delayed toward the high-temperature region, the ignition and burnout temperatures increased, and the comprehensive combustion parameter decreased. Compared with raw brown coals, the upgraded coals exhibited weak combustion characteristics similar to bituminous coal. The changes in physicochemical characteristics became more notable when processing temperature increased from 130 °C to 160 °C or the rank of raw brown coal was lower. Microwave irradiation treatment could be considered as an effective dewatering and upgrading process

  4. Ranking prediction model using the competition record of Ladies Professional Golf Association players.

    Science.gov (United States)

    Chae, Jin Seok; Park, Jin; So, Wi-Young

    2017-07-28

    The purpose of this study was to suggest a ranking prediction model using the competition record of the Ladies Professional Golf Association (LPGA) players. The top 100 players on the tour money list from the 2013-2016 US Open were analyzed in this model. Stepwise regression analysis was conducted to examine the effect of performance and independent variables (i.e., driving accuracy, green in regulation, putts per round, driving distance, percentage of sand saves, par-3 average, par-4 average, par-5 average, birdies average, and eagle average) on dependent variables (i.e., scoring average, official money, top-10 finishes, winning percentage, and 60-strokes average). The following prediction model was suggested:Y (Scoring average) = 55.871 - 0.947 (Birdies average) + 4.576 (Par-4 average) - 0.028 (Green in regulation) - 0.012 (Percentage of sand saves) + 2.088 (Par-3 average) - 0.026 (Driving accuracy) - 0.017 (Driving distance) + 0.085 (Putts per round)Y (Official money) = 6628736.723 + 528557.907 (Birdies average) - 1831800.821 (Par-4 average) + 11681.739 (Green in regulation) + 6476.344 (Percentage of sand saves) - 688115.074 (Par-3 average) + 7375.971 (Driving accuracy)Y (Top-10 finish%) = 204.462 + 12.562 (Birdies average) - 47.745 (Par-4 average) + 1.633 (Green in regulation) - 5.151 (Putts per round) + 0.132 (Percentage of sand saves)Y (Winning percentage) = 49.949 + 3.191 (Birdies average) - 15.023 (Par-4 average) + 0.043 (Percentage of sand saves)Y (60-strokes average) = 217.649 + 13.978 (Birdies average) - 44.855 (Par-4 average) - 22.433 (Par-3 average) + 0.16 (Green in regulation)Scoring of the above five prediction models and the prediction of golf ranking in the 2016 Women's Golf Olympic competition in Rio revealed a significant correlation between the predicted and real ranking (r = 0.689, p ranking prediction model using LPGA data may help coaches and players to identify which players are likely to participate in Olympic and World competitions, based

  5. On predicting student performance using low-rank matrix factorization techniques

    DEFF Research Database (Denmark)

    Lorenzen, Stephan Sloth; Pham, Dang Ninh; Alstrup, Stephen

    2017-01-01

    Predicting the score of a student is one of the important problems in educational data mining. The scores given by an individual student reflect how a student understands and applies the knowledge conveyed in class. A reliable performance prediction enables teachers to identify weak students...... that require remedial support, generate adaptive hints, and improve the learning of students. This work focuses on predicting the score of students in the quiz system of the Clio Online learning platform, the largest Danish supplier of online learning materials, covering 90% of Danish elementary schools...... and the current version of the data set is very sparse, the very low-rank approximation can capture enough information. This means that the simple baseline approach achieves similar performance compared to other advanced methods. In future work, we will restrict the quiz data set, e.g. only including quizzes...

  6. Entropy-based gene ranking without selection bias for the predictive classification of microarray data

    Directory of Open Access Journals (Sweden)

    Serafini Maria

    2003-11-01

    Full Text Available Abstract Background We describe the E-RFE method for gene ranking, which is useful for the identification of markers in the predictive classification of array data. The method supports a practical modeling scheme designed to avoid the construction of classification rules based on the selection of too small gene subsets (an effect known as the selection bias, in which the estimated predictive errors are too optimistic due to testing on samples already considered in the feature selection process. Results With E-RFE, we speed up the recursive feature elimination (RFE with SVM classifiers by eliminating chunks of uninteresting genes using an entropy measure of the SVM weights distribution. An optimal subset of genes is selected according to a two-strata model evaluation procedure: modeling is replicated by an external stratified-partition resampling scheme, and, within each run, an internal K-fold cross-validation is used for E-RFE ranking. Also, the optimal number of genes can be estimated according to the saturation of Zipf's law profiles. Conclusions Without a decrease of classification accuracy, E-RFE allows a speed-up factor of 100 with respect to standard RFE, while improving on alternative parametric RFE reduction strategies. Thus, a process for gene selection and error estimation is made practical, ensuring control of the selection bias, and providing additional diagnostic indicators of gene importance.

  7. Feature selection for splice site prediction: A new method using EDA-based feature ranking

    Directory of Open Access Journals (Sweden)

    Rouzé Pierre

    2004-05-01

    Full Text Available Abstract Background The identification of relevant biological features in large and complex datasets is an important step towards gaining insight in the processes underlying the data. Other advantages of feature selection include the ability of the classification system to attain good or even better solutions using a restricted subset of features, and a faster classification. Thus, robust methods for fast feature selection are of key importance in extracting knowledge from complex biological data. Results In this paper we present a novel method for feature subset selection applied to splice site prediction, based on estimation of distribution algorithms, a more general framework of genetic algorithms. From the estimated distribution of the algorithm, a feature ranking is derived. Afterwards this ranking is used to iteratively discard features. We apply this technique to the problem of splice site prediction, and show how it can be used to gain insight into the underlying biological process of splicing. Conclusion We show that this technique proves to be more robust than the traditional use of estimation of distribution algorithms for feature selection: instead of returning a single best subset of features (as they normally do this method provides a dynamical view of the feature selection process, like the traditional sequential wrapper methods. However, the method is faster than the traditional techniques, and scales better to datasets described by a large number of features.

  8. Blind Pose Prediction, Scoring, and Affinity Ranking of the CSAR 2014 Dataset.

    Science.gov (United States)

    Martiny, Virginie Y; Martz, François; Selwa, Edithe; Iorga, Bogdan I

    2016-06-27

    The 2014 CSAR Benchmark Exercise was focused on three protein targets: coagulation factor Xa, spleen tyrosine kinase, and bacterial tRNA methyltransferase. Our protocol involved a preliminary analysis of the structural information available in the Protein Data Bank for the protein targets, which allowed the identification of the most appropriate docking software and scoring functions to be used for the rescoring of several docking conformations datasets, as well as for pose prediction and affinity ranking. The two key points of this study were (i) the prior evaluation of molecular modeling tools that are most adapted for each target and (ii) the increased search efficiency during the docking process to better explore the conformational space of big and flexible ligands.

  9. Predictive-property-ranked variable reduction in partial least squares modelling with final complexity adapted models: comparison of properties for ranking.

    Science.gov (United States)

    Andries, Jan P M; Vander Heyden, Yvan; Buydens, Lutgarde M C

    2013-01-14

    The calibration performance of partial least squares regression for one response (PLS1) can be improved by eliminating uninformative variables. Many variable-reduction methods are based on so-called predictor-variable properties or predictive properties, which are functions of various PLS-model parameters, and which may change during the steps of the variable-reduction process. Recently, a new predictive-property-ranked variable reduction method with final complexity adapted models, denoted as PPRVR-FCAM or simply FCAM, was introduced. It is a backward variable elimination method applied on the predictive-property-ranked variables. The variable number is first reduced, with constant PLS1 model complexity A, until A variables remain, followed by a further decrease in PLS complexity, allowing the final selection of small numbers of variables. In this study for three data sets the utility and effectiveness of six individual and nine combined predictor-variable properties are investigated, when used in the FCAM method. The individual properties include the absolute value of the PLS1 regression coefficient (REG), the significance of the PLS1 regression coefficient (SIG), the norm of the loading weight (NLW) vector, the variable importance in the projection (VIP), the selectivity ratio (SR), and the squared correlation coefficient of a predictor variable with the response y (COR). The selective and predictive performances of the models resulting from the use of these properties are statistically compared using the one-tailed Wilcoxon signed rank test. The results indicate that the models, resulting from variable reduction with the FCAM method, using individual or combined properties, have similar or better predictive abilities than the full spectrum models. After mean-centring of the data, REG and SIG, provide low numbers of informative variables, with a meaning relevant to the response, and lower than the other individual properties, while the predictive abilities are

  10. Estimating the chance of success in IVF treatment using a ranking algorithm.

    Science.gov (United States)

    Güvenir, H Altay; Misirli, Gizem; Dilbaz, Serdar; Ozdegirmenci, Ozlem; Demir, Berfu; Dilbaz, Berna

    2015-09-01

    In medicine, estimating the chance of success for treatment is important in deciding whether to begin the treatment or not. This paper focuses on the domain of in vitro fertilization (IVF), where estimating the outcome of a treatment is very crucial in the decision to proceed with treatment for both the clinicians and the infertile couples. IVF treatment is a stressful and costly process. It is very stressful for couples who want to have a baby. If an initial evaluation indicates a low pregnancy rate, decision of the couple may change not to start the IVF treatment. The aim of this study is twofold, firstly, to develop a technique that can be used to estimate the chance of success for a couple who wants to have a baby and secondly, to determine the attributes and their particular values affecting the outcome in IVF treatment. We propose a new technique, called success estimation using a ranking algorithm (SERA), for estimating the success of a treatment using a ranking-based algorithm. The particular ranking algorithm used here is RIMARC. The performance of the new algorithm is compared with two well-known algorithms that assign class probabilities to query instances. The algorithms used in the comparison are Naïve Bayes Classifier and Random Forest. The comparison is done in terms of area under the ROC curve, accuracy and execution time, using tenfold stratified cross-validation. The results indicate that the proposed SERA algorithm has a potential to be used successfully to estimate the probability of success in medical treatment.

  11. Implementation of Chaotic Gaussian Particle Swarm Optimization for Optimize Learning-to-Rank Software Defect Prediction Model Construction

    Science.gov (United States)

    Buchari, M. A.; Mardiyanto, S.; Hendradjaya, B.

    2018-03-01

    Finding the existence of software defect as early as possible is the purpose of research about software defect prediction. Software defect prediction activity is required to not only state the existence of defects, but also to be able to give a list of priorities which modules require a more intensive test. Therefore, the allocation of test resources can be managed efficiently. Learning to rank is one of the approach that can provide defect module ranking data for the purposes of software testing. In this study, we propose a meta-heuristic chaotic Gaussian particle swarm optimization to improve the accuracy of learning to rank software defect prediction approach. We have used 11 public benchmark data sets as experimental data. Our overall results has demonstrated that the prediction models construct using Chaotic Gaussian Particle Swarm Optimization gets better accuracy on 5 data sets, ties in 5 data sets and gets worse in 1 data sets. Thus, we conclude that the application of Chaotic Gaussian Particle Swarm Optimization in Learning-to-Rank approach can improve the accuracy of the defect module ranking in data sets that have high-dimensional features.

  12. Improving predicted protein loop structure ranking using a Pareto-optimality consensus method.

    Science.gov (United States)

    Li, Yaohang; Rata, Ionel; Chiu, See-wing; Jakobsson, Eric

    2010-07-20

    Accurate protein loop structure models are important to understand functions of many proteins. Identifying the native or near-native models by distinguishing them from the misfolded ones is a critical step in protein loop structure prediction. We have developed a Pareto Optimal Consensus (POC) method, which is a consensus model ranking approach to integrate multiple knowledge- or physics-based scoring functions. The procedure of identifying the models of best quality in a model set includes: 1) identifying the models at the Pareto optimal front with respect to a set of scoring functions, and 2) ranking them based on the fuzzy dominance relationship to the rest of the models. We apply the POC method to a large number of decoy sets for loops of 4- to 12-residue in length using a functional space composed of several carefully-selected scoring functions: Rosetta, DOPE, DDFIRE, OPLS-AA, and a triplet backbone dihedral potential developed in our lab. Our computational results show that the sets of Pareto-optimal decoys, which are typically composed of approximately 20% or less of the overall decoys in a set, have a good coverage of the best or near-best decoys in more than 99% of the loop targets. Compared to the individual scoring function yielding best selection accuracy in the decoy sets, the POC method yields 23%, 37%, and 64% less false positives in distinguishing the native conformation, indentifying a near-native model (RMSD Pareto optimality and fuzzy dominance, the POC method is effective in distinguishing the best loop models from the other ones within a loop model set.

  13. Usability Prediction & Ranking of SDLC Models Using Fuzzy Hierarchical Usability Model

    Science.gov (United States)

    Gupta, Deepak; Ahlawat, Anil K.; Sagar, Kalpna

    2017-06-01

    Evaluation of software quality is an important aspect for controlling and managing the software. By such evaluation, improvements in software process can be made. The software quality is significantly dependent on software usability. Many researchers have proposed numbers of usability models. Each model considers a set of usability factors but do not cover all the usability aspects. Practical implementation of these models is still missing, as there is a lack of precise definition of usability. Also, it is very difficult to integrate these models into current software engineering practices. In order to overcome these challenges, this paper aims to define the term `usability' using the proposed hierarchical usability model with its detailed taxonomy. The taxonomy considers generic evaluation criteria for identifying the quality components, which brings together factors, attributes and characteristics defined in various HCI and software models. For the first time, the usability model is also implemented to predict more accurate usability values. The proposed system is named as fuzzy hierarchical usability model that can be easily integrated into the current software engineering practices. In order to validate the work, a dataset of six software development life cycle models is created and employed. These models are ranked according to their predicted usability values. This research also focuses on the detailed comparison of proposed model with the existing usability models.

  14. A new method for class prediction based on signed-rank algorithms applied to Affymetrix® microarray experiments

    Directory of Open Access Journals (Sweden)

    Vassal Aurélien

    2008-01-01

    Full Text Available Abstract Background The huge amount of data generated by DNA chips is a powerful basis to classify various pathologies. However, constant evolution of microarray technology makes it difficult to mix data from different chip types for class prediction of limited sample populations. Affymetrix® technology provides both a quantitative fluorescence signal and a decision (detection call: absent or present based on signed-rank algorithms applied to several hybridization repeats of each gene, with a per-chip normalization. We developed a new prediction method for class belonging based on the detection call only from recent Affymetrix chip type. Biological data were obtained by hybridization on U133A, U133B and U133Plus 2.0 microarrays of purified normal B cells and cells from three independent groups of multiple myeloma (MM patients. Results After a call-based data reduction step to filter out non class-discriminative probe sets, the gene list obtained was reduced to a predictor with correction for multiple testing by iterative deletion of probe sets that sequentially improve inter-class comparisons and their significance. The error rate of the method was determined using leave-one-out and 5-fold cross-validation. It was successfully applied to (i determine a sex predictor with the normal donor group classifying gender with no error in all patient groups except for male MM samples with a Y chromosome deletion, (ii predict the immunoglobulin light and heavy chains expressed by the malignant myeloma clones of the validation group and (iii predict sex, light and heavy chain nature for every new patient. Finally, this method was shown powerful when compared to the popular classification method Prediction Analysis of Microarray (PAM. Conclusion This normalization-free method is routinely used for quality control and correction of collection errors in patient reports to clinicians. It can be easily extended to multiple class prediction suitable with

  15. Use of search engine optimization factors for Google page rank prediction

    OpenAIRE

    Tvrdi, Barbara

    2012-01-01

    Over the years, search engines have become an important tool for finding information. It is known that users select the link on the first page of search results in 62% of the cases. Search engine optimization techniques enable website improvement and therefore a better ranking in search engines. The exact specification of the factors that affect website ranking is not disclosed by search engine owners. In this thesis we tried to choose some most frequently mentioned search engine optimizatio...

  16. Predicting Rank Attainment in Political Science: What Else besides Publications Affects Promotion?

    Science.gov (United States)

    Hesli, Vicki L.; Lee, Jae Mook; Mitchell, Sara McLaughlin

    2012-01-01

    We report the results of hypotheses tests about the effects of several measures of research, teaching, and service on the likelihood of achieving the ranks of associate and full professor. In conducting these tests, we control for institutional and individual background characteristics. We focus our tests on the link between productivity and…

  17. Convergent RANK- and c-Met-mediated signaling components predict survival of patients with prostate cancer: an interracial comparative study.

    Science.gov (United States)

    Hu, Peizhen; Chung, Leland W K; Berel, Dror; Frierson, Henry F; Yang, Hua; Liu, Chunyan; Wang, Ruoxiang; Li, Qinlong; Rogatko, Andre; Zhau, Haiyen E

    2013-01-01

    We reported (PLoS One 6 (12):e28670, 2011) that the activation of c-Met signaling in RANKL-overexpressing bone metastatic LNCaP cell and xenograft models increased expression of RANK, RANKL, c-Met, and phosphorylated c-Met, and mediated downstream signaling. We confirmed the significance of the RANK-mediated signaling network in castration resistant clinical human prostate cancer (PC) tissues. In this report, we used a multispectral quantum dot labeling technique to label six RANK and c-Met convergent signaling pathway mediators simultaneously in formalin fixed paraffin embedded (FFPE) tissue specimens, quantify the intensity of each expression at the sub-cellular level, and investigated their potential utility as predictors of patient survival in Caucasian-American, African-American and Chinese men. We found that RANKL and neuropilin-1 (NRP-1) expression predicts survival of Caucasian-Americans with PC. A Gleason score ≥ 8 combined with nuclear p-c-Met expression predicts survival in African-American PC patients. Neuropilin-1, p-NF-κB p65 and VEGF are predictors for the overall survival of Chinese men with PC. These results collectively support interracial differences in cell signaling networks that can predict the survival of PC patients.

  18. Convergent RANK- and c-Met-mediated signaling components predict survival of patients with prostate cancer: an interracial comparative study.

    Directory of Open Access Journals (Sweden)

    Peizhen Hu

    Full Text Available We reported (PLoS One 6 (12:e28670, 2011 that the activation of c-Met signaling in RANKL-overexpressing bone metastatic LNCaP cell and xenograft models increased expression of RANK, RANKL, c-Met, and phosphorylated c-Met, and mediated downstream signaling. We confirmed the significance of the RANK-mediated signaling network in castration resistant clinical human prostate cancer (PC tissues. In this report, we used a multispectral quantum dot labeling technique to label six RANK and c-Met convergent signaling pathway mediators simultaneously in formalin fixed paraffin embedded (FFPE tissue specimens, quantify the intensity of each expression at the sub-cellular level, and investigated their potential utility as predictors of patient survival in Caucasian-American, African-American and Chinese men. We found that RANKL and neuropilin-1 (NRP-1 expression predicts survival of Caucasian-Americans with PC. A Gleason score ≥ 8 combined with nuclear p-c-Met expression predicts survival in African-American PC patients. Neuropilin-1, p-NF-κB p65 and VEGF are predictors for the overall survival of Chinese men with PC. These results collectively support interracial differences in cell signaling networks that can predict the survival of PC patients.

  19. D3R Grand Challenge 2: blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies

    Science.gov (United States)

    Gaieb, Zied; Liu, Shuai; Gathiaka, Symon; Chiu, Michael; Yang, Huanwang; Shao, Chenghua; Feher, Victoria A.; Walters, W. Patrick; Kuhn, Bernd; Rudolph, Markus G.; Burley, Stephen K.; Gilson, Michael K.; Amaro, Rommie E.

    2018-01-01

    The Drug Design Data Resource (D3R) ran Grand Challenge 2 (GC2) from September 2016 through February 2017. This challenge was based on a dataset of structures and affinities for the nuclear receptor farnesoid X receptor (FXR), contributed by F. Hoffmann-La Roche. The dataset contained 102 IC50 values, spanning six orders of magnitude, and 36 high-resolution co-crystal structures with representatives of four major ligand classes. Strong global participation was evident, with 49 participants submitting 262 prediction submission packages in total. Procedurally, GC2 mimicked Grand Challenge 2015 (GC2015), with a Stage 1 subchallenge testing ligand pose prediction methods and ranking and scoring methods, and a Stage 2 subchallenge testing only ligand ranking and scoring methods after the release of all blinded co-crystal structures. Two smaller curated sets of 18 and 15 ligands were developed to test alchemical free energy methods. This overview summarizes all aspects of GC2, including the dataset details, challenge procedures, and participant results. We also consider implications for progress in the field, while highlighting methodological areas that merit continued development. Similar to GC2015, the outcome of GC2 underscores the pressing need for methods development in pose prediction, particularly for ligand scaffolds not currently represented in the Protein Data Bank (http://www.pdb.org), and in affinity ranking and scoring of bound ligands.

  20. Exchange-Hole Dipole Dispersion Model for Accurate Energy Ranking in Molecular Crystal Structure Prediction II: Nonplanar Molecules.

    Science.gov (United States)

    Whittleton, Sarah R; Otero-de-la-Roza, A; Johnson, Erin R

    2017-11-14

    The crystal structure prediction (CSP) of a given compound from its molecular diagram is a fundamental challenge in computational chemistry with implications in relevant technological fields. A key component of CSP is the method to calculate the lattice energy of a crystal, which allows the ranking of candidate structures. This work is the second part of our investigation to assess the potential of the exchange-hole dipole moment (XDM) dispersion model for crystal structure prediction. In this article, we study the relatively large, nonplanar, mostly flexible molecules in the first five blind tests held by the Cambridge Crystallographic Data Centre. Four of the seven experimental structures are predicted as the energy minimum, and thermal effects are demonstrated to have a large impact on the ranking of at least another compound. As in the first part of this series, delocalization error affects the results for a single crystal (compound X), in this case by detrimentally overstabilizing the π-conjugated conformation of the monomer. Overall, B86bPBE-XDM correctly predicts 16 of the 21 compounds in the five blind tests, a result similar to the one obtained using the best CSP method available to date (dispersion-corrected PW91 by Neumann et al.). Perhaps more importantly, the systems for which B86bPBE-XDM fails to predict the experimental structure as the energy minimum are mostly the same as with Neumann's method, which suggests that similar difficulties (absence of vibrational free energy corrections, delocalization error,...) are not limited to B86bPBE-XDM but affect GGA-based DFT-methods in general. Our work confirms B86bPBE-XDM as an excellent option for crystal energy ranking in CSP and offers a guide to identify crystals (organic salts, conjugated flexible systems) where difficulties may appear.

  1. Binding mode prediction and MD/MMPBSA-based free energy ranking for agonists of REV-ERBα/NCoR.

    Science.gov (United States)

    Westermaier, Yvonne; Ruiz-Carmona, Sergio; Theret, Isabelle; Perron-Sierra, Françoise; Poissonnet, Guillaume; Dacquet, Catherine; Boutin, Jean A; Ducrot, Pierre; Barril, Xavier

    2017-08-01

    The knowledge of the free energy of binding of small molecules to a macromolecular target is crucial in drug design as is the ability to predict the functional consequences of binding. We highlight how a molecular dynamics (MD)-based approach can be used to predict the free energy of small molecules, and to provide priorities for the synthesis and the validation via in vitro tests. Here, we study the dynamics and energetics of the nuclear receptor REV-ERBα with its co-repressor NCoR and 35 novel agonists. Our in silico approach combines molecular docking, molecular dynamics (MD), solvent-accessible surface area (SASA) and molecular mechanics poisson boltzmann surface area (MMPBSA) calculations. While docking yielded initial hints on the binding modes, their stability was assessed by MD. The SASA calculations revealed that the presence of the ligand led to a higher exposure of hydrophobic REV-ERB residues for NCoR recruitment. MMPBSA was very successful in ranking ligands by potency in a retrospective and prospective manner. Particularly, the prospective MMPBSA ranking-based validations for four compounds, three predicted to be active and one weakly active, were confirmed experimentally.

  2. Improving residue-residue contact prediction via low-rank and sparse decomposition of residue correlation matrix.

    Science.gov (United States)

    Zhang, Haicang; Gao, Yujuan; Deng, Minghua; Wang, Chao; Zhu, Jianwei; Li, Shuai Cheng; Zheng, Wei-Mou; Bu, Dongbo

    2016-03-25

    Strategies for correlation analysis in protein contact prediction often encounter two challenges, namely, the indirect coupling among residues, and the background correlations mainly caused by phylogenetic biases. While various studies have been conducted on how to disentangle indirect coupling, the removal of background correlations still remains unresolved. Here, we present an approach for removing background correlations via low-rank and sparse decomposition (LRS) of a residue correlation matrix. The correlation matrix can be constructed using either local inference strategies (e.g., mutual information, or MI) or global inference strategies (e.g., direct coupling analysis, or DCA). In our approach, a correlation matrix was decomposed into two components, i.e., a low-rank component representing background correlations, and a sparse component representing true correlations. Finally the residue contacts were inferred from the sparse component of correlation matrix. We trained our LRS-based method on the PSICOV dataset, and tested it on both GREMLIN and CASP11 datasets. Our experimental results suggested that LRS significantly improves the contact prediction precision. For example, when equipped with the LRS technique, the prediction precision of MI and mfDCA increased from 0.25 to 0.67 and from 0.58 to 0.70, respectively (Top L/10 predicted contacts, sequence separation: 5 AA, dataset: GREMLIN). In addition, our LRS technique also consistently outperforms the popular denoising technique APC (average product correction), on both local (MI_LRS: 0.67 vs MI_APC: 0.34) and global measures (mfDCA_LRS: 0.70 vs mfDCA_APC: 0.67). Interestingly, we found out that when equipped with our LRS technique, local inference strategies performed in a comparable manner to that of global inference strategies, implying that the application of LRS technique narrowed down the performance gap between local and global inference strategies. Overall, our LRS technique greatly facilitates

  3. Uncertainties in model-based outcome predictions for treatment planning

    International Nuclear Information System (INIS)

    Deasy, Joseph O.; Chao, K.S. Clifford; Markman, Jerry

    2001-01-01

    plans can be ranked based on the probability that one plan is superior to another. Thus, reliability of plan ranking could also be assessed. Conclusions: A comprehensive framework for incorporating uncertainties into treatment-plan-specific outcome predictions is described. Uncertainty histograms for continuous variable endpoint models provide a straightforward method for visual review of the reliability of outcome predictions for each treatment plan

  4. Analysis of the predictive qualities of betting odds and FIFA World Ranking: evidence from the 2006, 2010 and 2014 Football World Cups.

    Science.gov (United States)

    Wunderlich, Fabian; Memmert, Daniel

    2016-12-01

    The present study aims to investigate the ability of a new framework enabling to derive more detailed model-based predictions from ranking systems. These were compared to predictions from the bet market including data from the World Cups 2006, 2010, and 2014. The results revealed that the FIFA World Ranking has essentially improved its predictive qualities compared to the bet market since the mode of calculation was changed in 2006. While both predictors were useful to obtain accurate predictions in general, the world ranking was able to outperform the bet market significantly for the World Cup 2014 and when the data from the World Cups 2010 and 2014 were pooled. Our new framework can be extended in future research to more detailed prediction tasks (i.e., predicting the final scores of a match or the tournament progress of a team).

  5. Google goes cancer: improving outcome prediction for cancer patients by network-based ranking of marker genes.

    Directory of Open Access Journals (Sweden)

    Christof Winter

    Full Text Available Predicting the clinical outcome of cancer patients based on the expression of marker genes in their tumors has received increasing interest in the past decade. Accurate predictors of outcome and response to therapy could be used to personalize and thereby improve therapy. However, state of the art methods used so far often found marker genes with limited prediction accuracy, limited reproducibility, and unclear biological relevance. To address this problem, we developed a novel computational approach to identify genes prognostic for outcome that couples gene expression measurements from primary tumor samples with a network of known relationships between the genes. Our approach ranks genes according to their prognostic relevance using both expression and network information in a manner similar to Google's PageRank. We applied this method to gene expression profiles which we obtained from 30 patients with pancreatic cancer, and identified seven candidate marker genes prognostic for outcome. Compared to genes found with state of the art methods, such as Pearson correlation of gene expression with survival time, we improve the prediction accuracy by up to 7%. Accuracies were assessed using support vector machine classifiers and Monte Carlo cross-validation. We then validated the prognostic value of our seven candidate markers using immunohistochemistry on an independent set of 412 pancreatic cancer samples. Notably, signatures derived from our candidate markers were independently predictive of outcome and superior to established clinical prognostic factors such as grade, tumor size, and nodal status. As the amount of genomic data of individual tumors grows rapidly, our algorithm meets the need for powerful computational approaches that are key to exploit these data for personalized cancer therapies in clinical practice.

  6. On predicting student performance using low-rank matrix factorization techniques

    DEFF Research Database (Denmark)

    Lorenzen, Stephan Sloth; Pham, Dang Ninh; Alstrup, Stephen

    2017-01-01

    that require remedial support, generate adaptive hints, and improve the learning of students. This work focuses on predicting the score of students in the quiz system of the Clio Online learning platform, the largest Danish supplier of online learning materials, covering 90% of Danish elementary schools....... Experimental results in the Clio Online data set confirm that the proposed initialization methods lead to very fast convergence. Regarding the prediction accuracy, surprisingly, the advanced EM method is just slightly better than the baseline approach based on the global mean score and student/quiz bias...

  7. . Facial attractiveness: ranking of end-of-treatment facial photographs by pairs of Chinese and US orthodontists.

    Science.gov (United States)

    Xu, Tian-Min; Korn, Edward L; Liu, Yan; Oh, Hee Soo; Lee, Ki Heon; Boyd, Robert L; Baumrind, Sheldon

    2008-07-01

    In this study, we assessed agreement and disagreement among pairs of Chinese and US orthodontists in the ranking for "facial attractiveness" of end-of-treatment photographs of growing Chinese and white orthodontic patients. Two groups of orthodontist-judges participated: from the University of the Pacific, School of Dentistry, in California and from Peking University School and Hospital of Stomatology in China. Each judge independently ranked standard clinical sets of profile, frontal, and frontal-smiling photographs of 43 white patients and 48 Chinese patients. Pearson correlations were generated for a total of 1980 rankings by pairs of judges. The resulting correlations ranged from +0.004 to +0.96 with a median of +0.54. Of these, 18.7% were lower than 0.4; 41.0% were lower than 0.5; 68.8% were lower than 0.6; 91.6% were lower than 0.7; and only 8.4% were greater than 0.7. As had been anticipated, correlations between judges were higher when they ranked patients of their own ethnicity than when they ranked patients of different ethnicity, but the differences were smaller than had been expected. The rankings of no pair of judges correlated negatively. This is to say that no pair of judges, whether of the same or different ethnicity, ranked the patients so that those 1 judge tended to find attractive were consistently found unattractive by the other. The distribution of levels of agreement between pairs of orthodontists did not differ substantially whether the pairs included 2 US orthodontists, 2 Chinese orthodontists, or 1 US and 1 Chinese orthodontist. As might be expected, the pairs of Chinese orthodontists agreed with each other slightly better on average when ranking Chinese patients, and the pairs of US orthodontists agreed with each other slightly better on average when ranking white American patients, but the overall differences were small. These findings appear consistent with the inference that, on average, judgments of "facial attractiveness" by

  8. Workflows and performances in the ranking prediction of 2016 D3R Grand Challenge 2: lessons learned from a collaborative effort.

    Science.gov (United States)

    Gao, Ying-Duo; Hu, Yuan; Crespo, Alejandro; Wang, Deping; Armacost, Kira A; Fells, James I; Fradera, Xavier; Wang, Hongwu; Wang, Huijun; Sherborne, Brad; Verras, Andreas; Peng, Zhengwei

    2018-01-01

    The 2016 D3R Grand Challenge 2 includes both pose and affinity or ranking predictions. This article is focused exclusively on affinity predictions submitted to the D3R challenge from a collaborative effort of the modeling and informatics group. Our submissions include ranking of 102 ligands covering 4 different chemotypes against the FXR ligand binding domain structure, and the relative binding affinity predictions of the two designated free energy subsets of 15 and 18 compounds. Using all the complex structures prepared in the same way allowed us to cover many types of workflows and compare their performances effectively. We evaluated typical workflows used in our daily structure-based design modeling support, which include docking scores, force field-based scores, QM/MM, MMGBSA, MD-MMGBSA, and MacroModel interaction energy estimations. The best performing methods for the two free energy subsets are discussed. Our results suggest that affinity ranking still remains very challenging; that the knowledge of more structural information does not necessarily yield more accurate predictions; and that visual inspection and human intervention are considerably important for ranking. Knowledge of the mode of action and protein flexibility along with visualization tools that depict polar and hydrophobic maps are very useful for visual inspection. QM/MM-based workflows were found to be powerful in affinity ranking and are encouraged to be applied more often. The standardized input and output enable systematic analysis and support methodology development and improvement for high level blinded predictions.

  9. Workflows and performances in the ranking prediction of 2016 D3R Grand Challenge 2: lessons learned from a collaborative effort

    Science.gov (United States)

    Gao, Ying-Duo; Hu, Yuan; Crespo, Alejandro; Wang, Deping; Armacost, Kira A.; Fells, James I.; Fradera, Xavier; Wang, Hongwu; Wang, Huijun; Sherborne, Brad; Verras, Andreas; Peng, Zhengwei

    2018-01-01

    The 2016 D3R Grand Challenge 2 includes both pose and affinity or ranking predictions. This article is focused exclusively on affinity predictions submitted to the D3R challenge from a collaborative effort of the modeling and informatics group. Our submissions include ranking of 102 ligands covering 4 different chemotypes against the FXR ligand binding domain structure, and the relative binding affinity predictions of the two designated free energy subsets of 15 and 18 compounds. Using all the complex structures prepared in the same way allowed us to cover many types of workflows and compare their performances effectively. We evaluated typical workflows used in our daily structure-based design modeling support, which include docking scores, force field-based scores, QM/MM, MMGBSA, MD-MMGBSA, and MacroModel interaction energy estimations. The best performing methods for the two free energy subsets are discussed. Our results suggest that affinity ranking still remains very challenging; that the knowledge of more structural information does not necessarily yield more accurate predictions; and that visual inspection and human intervention are considerably important for ranking. Knowledge of the mode of action and protein flexibility along with visualization tools that depict polar and hydrophobic maps are very useful for visual inspection. QM/MM-based workflows were found to be powerful in affinity ranking and are encouraged to be applied more often. The standardized input and output enable systematic analysis and support methodology development and improvement for high level blinded predictions.

  10. ABC gene-ranking for prediction of drug-induced cholestasis in rats

    Directory of Open Access Journals (Sweden)

    Yauheniya Cherkas

    drugs that behaved very differently, and were distinct from both non-cholestatic and cholestatic drugs (ketoconazole, dipyridamole, cyproheptadine and aniline, and many postulated human cholestatic drugs that in rat showed no evidence of cholestasis (chlorpromazine, erythromycin, niacin, captopril, dapsone, rifampicin, glibenclamide, simvastatin, furosemide, tamoxifen, and sulfamethoxazole. Most of these latter drugs were noted previously by other groups as showing cholestasis only in humans. The results of this work suggest that the ABC procedure and similar statistical approaches can be instrumental in combining data to compare toxicants across toxicogenomics databases, extract similarities among responses and reduce unexplained data varation. Keywords: Cluster analysis, Cholestasis, Gene signature, Microarray, Prediction, Toxicogenomics

  11. Rank Dynamics

    Science.gov (United States)

    Gershenson, Carlos

    Studies of rank distributions have been popular for decades, especially since the work of Zipf. For example, if we rank words of a given language by use frequency (most used word in English is 'the', rank 1; second most common word is 'of', rank 2), the distribution can be approximated roughly with a power law. The same applies for cities (most populated city in a country ranks first), earthquakes, metabolism, the Internet, and dozens of other phenomena. We recently proposed ``rank diversity'' to measure how ranks change in time, using the Google Books Ngram dataset. Studying six languages between 1800 and 2009, we found that the rank diversity curves of languages are universal, adjusted with a sigmoid on log-normal scale. We are studying several other datasets (sports, economies, social systems, urban systems, earthquakes, artificial life). Rank diversity seems to be universal, independently of the shape of the rank distribution. I will present our work in progress towards a general description of the features of rank change in time, along with simple models which reproduce it

  12. Where Do Bone-Targeted Agents RANK in Breast Cancer Treatment?

    Directory of Open Access Journals (Sweden)

    Roger von Moos

    2013-08-01

    Full Text Available Breast cancer cells preferentially metastasise to the skeleton, owing, in part, to the fertile environment provided by bone. Increased bone turnover releases growth factors that promote tumour cell growth. In turn, tumour cells release factors that stimulate further bone turnover, resulting in a vicious cycle of metastasis growth and bone destruction. The RANK-RANK ligand (RANKL pathway plays a key role in this cycle, and inhibition of RANKL using the fully-human monoclonal antibody denosumab, has demonstrated efficacy in delaying skeletal complications associated with bone metastases in three phase 3 trials. Preclinical studies suggest that the RANKL pathway also plays a role in breast cancer tumourigenesis and migration to bone. In a subgroup analysis of the negative Adjuvant Zoledronic Acid to Reduce Recurrence (AZURE trial, the bisphosphonate zoledronic acid showed potential for improving survival in patients who were postmenopausal; however, a prospective study in this patient population is required to validate this observation. Ongoing trials are examining whether adjuvant blockade of the RANKL pathway using denosumab can prevent disease recurrence in patients with high-risk breast cancer. These are building on analogous studies that have shown that denosumab improves bone metastasis-free survival in prostate cancer and suggested that it confers an overall survival benefit in non-small-cell lung cancer.

  13. Frequency and predictive values of first rank symptoms at baseline among 362 young adult patients with first-episode schizophrenia Results from the Danish OPUS study

    DEFF Research Database (Denmark)

    Thorup, Anne; Petersen, Lone; Jeppesen, Pia

    2007-01-01

    To investigate the frequency of the Schneiderian First Rank Symptoms (FRSs) in a representative group of patients with first-episode schizophrenia and to analyse the predictive value of these symptoms in relation to psychopathology, work situation, depression, dependency and admission after 2 years...

  14. Prediction of treatment response to adalimumab

    DEFF Research Database (Denmark)

    Krintel, S B; Dehlendorff, C; Hetland, M L

    2016-01-01

    At least 30% of patients with rheumatoid arthritis (RA) do not respond to biologic agents, which emphasizes the need of predictive biomarkers. We aimed to identify microRNAs (miRNAs) predictive of response to adalimumab in 180 treatment-naïve RA patients enrolled in the OPtimized treatment algori...... of low expression of miR-22 and high expression of miR-886.3p was associated with EULAR good response. Future studies to assess the utility of these miRNAs as predictive biomarkers are needed.The Pharmacogenomics Journal advance online publication, 5 May 2015; doi:10.1038/tpj.2015.30....

  15. Improved variable reduction in partial least squares modelling based on predictive-property-ranked variables and adaptation of partial least squares complexity.

    Science.gov (United States)

    Andries, Jan P M; Vander Heyden, Yvan; Buydens, Lutgarde M C

    2011-10-31

    The calibration performance of partial least squares for one response variable (PLS1) can be improved by elimination of uninformative variables. Many methods are based on so-called predictive variable properties, which are functions of various PLS-model parameters, and which may change during the variable reduction process. In these methods variable reduction is made on the variables ranked in descending order for a given variable property. The methods start with full spectrum modelling. Iteratively, until a specified number of remaining variables is reached, the variable with the smallest property value is eliminated; a new PLS model is calculated, followed by a renewed ranking of the variables. The Stepwise Variable Reduction methods using Predictive-Property-Ranked Variables are denoted as SVR-PPRV. In the existing SVR-PPRV methods the PLS model complexity is kept constant during the variable reduction process. In this study, three new SVR-PPRV methods are proposed, in which a possibility for decreasing the PLS model complexity during the variable reduction process is build in. Therefore we denote our methods as PPRVR-CAM methods (Predictive-Property-Ranked Variable Reduction with Complexity Adapted Models). The selective and predictive abilities of the new methods are investigated and tested, using the absolute PLS regression coefficients as predictive property. They were compared with two modifications of existing SVR-PPRV methods (with constant PLS model complexity) and with two reference methods: uninformative variable elimination followed by either a genetic algorithm for PLS (UVE-GA-PLS) or an interval PLS (UVE-iPLS). The performance of the methods is investigated in conjunction with two data sets from near-infrared sources (NIR) and one simulated set. The selective and predictive performances of the variable reduction methods are compared statistically using the Wilcoxon signed rank test. The three newly developed PPRVR-CAM methods were able to retain

  16. Predicting the affinity of Farnesoid X Receptor ligands through a hierarchical ranking protocol: a D3R Grand Challenge 2 case study

    Science.gov (United States)

    Réau, Manon; Langenfeld, Florent; Zagury, Jean-François; Montes, Matthieu

    2018-01-01

    The Drug Design Data Resource (D3R) Grand Challenges are blind contests organized to assess the state-of-the-art methods accuracy in predicting binding modes and relative binding free energies of experimentally validated ligands for a given target. The second stage of the D3R Grand Challenge 2 (GC2) was focused on ranking 102 compounds according to their predicted affinity for Farnesoid X Receptor. In this task, our workflow was ranked 5th out of the 77 submissions in the structure-based category. Our strategy consisted in (1) a combination of molecular docking using AutoDock 4.2 and manual edition of available structures for binding poses generation using SeeSAR, (2) the use of HYDE scoring for pose selection, and (3) a hierarchical ranking using HYDE and MM/GBSA. In this report, we detail our pose generation and ligands ranking protocols and provide guidelines to be used in a prospective computer aided drug design program.

  17. Collision prediction software for radiotherapy treatments

    Energy Technology Data Exchange (ETDEWEB)

    Padilla, Laura [Virginia Commonwealth University Medical Center, Richmond, Virginia 23298 (United States); Pearson, Erik A. [Techna Institute and the Princess Margaret Cancer Center, University Health Network, Toronto, Ontario M5G 2M9 (Canada); Pelizzari, Charles A., E-mail: c-pelizzari@uchicago.edu [Department of Radiation and Cellular Oncology, The University of Chicago, Chicago, Illinois 60637 (United States)

    2015-11-15

    Purpose: This work presents a method of collision predictions for external beam radiotherapy using surface imaging. The present methodology focuses on collision prediction during treatment simulation to evaluate the clearance of a patient’s treatment position and allow for its modification if necessary. Methods: A Kinect camera (Microsoft, Redmond, WA) is used to scan the patient and immobilization devices in the treatment position at the simulator. The surface is reconstructed using the SKANECT software (Occipital, Inc., San Francisco, CA). The treatment isocenter is marked using simulated orthogonal lasers projected on the surface scan. The point cloud of this surface is then shifted to isocenter and converted from Cartesian to cylindrical coordinates. A slab models the treatment couch. A cylinder with a radius equal to the normal distance from isocenter to the collimator plate, and a height defined by the collimator diameter is used to estimate collisions. Points within the cylinder clear through a full gantry rotation with the treatment couch at 0° , while points outside of it collide. The angles of collision are reported. This methodology was experimentally verified using a mannequin positioned in an alpha cradle with both arms up. A planning CT scan of the mannequin was performed, two isocenters were marked in PINNACLE, and this information was exported to AlignRT (VisionRT, London, UK)—a surface imaging system for patient positioning. This was used to ensure accurate positioning of the mannequin in the treatment room, when available. Collision calculations were performed for the two treatment isocenters and the results compared to the collisions detected the room. The accuracy of the Kinect-Skanect surface was evaluated by comparing it to the external surface of the planning CT scan. Results: Experimental verification results showed that the predicted angles of collision matched those recorded in the room within 0.5°, in most cases (largest deviation

  18. Impact factor analysis: combining prediction with parameter ranking to reveal the impact of behavior on health outcome

    DEFF Research Database (Denmark)

    Doryab, Afsaneh; Frost, Mads; Faurholt-Jepsen, Maria

    2015-01-01

    An increasing number of healthcare systems allow people to monitor behavior and provide feedback on health and wellness. Most applications, however, only offer feedback on behavior in form of visualization and data summaries. This paper presents a different approach—called impact factor analysis—...... in monitoring and control of mental illness, and we argue that the impact factor analysis can be useful in the design of other health and wellness systems....... ten bipolar patients, in which we were able to estimate mood values with an average mean absolute error of 0.5. This was used to rank the behavior parameters whose variations indicate changes in the mental state. The rankings acquired from our algorithms correspond to the patients’ rankings......, identifying physical activity and sleep as the highest impact parameters. These results revealed the feasibility of identifying behavioral impact factors. This data analysis motivated us to design an impact factor inference engine as part of the MONARCA system. To our knowledge, this is a novel approach...

  19. University Rankings: The Web Ranking

    Science.gov (United States)

    Aguillo, Isidro F.

    2012-01-01

    The publication in 2003 of the Ranking of Universities by Jiao Tong University of Shanghai has revolutionized not only academic studies on Higher Education, but has also had an important impact on the national policies and the individual strategies of the sector. The work gathers the main characteristics of this and other global university…

  20. Low-rank coal research

    Energy Technology Data Exchange (ETDEWEB)

    Weber, G. F.; Laudal, D. L.

    1989-01-01

    This work is a compilation of reports on ongoing research at the University of North Dakota. Topics include: Control Technology and Coal Preparation Research (SO{sub x}/NO{sub x} control, waste management), Advanced Research and Technology Development (turbine combustion phenomena, combustion inorganic transformation, coal/char reactivity, liquefaction reactivity of low-rank coals, gasification ash and slag characterization, fine particulate emissions), Combustion Research (fluidized bed combustion, beneficiation of low-rank coals, combustion characterization of low-rank coal fuels, diesel utilization of low-rank coals), Liquefaction Research (low-rank coal direct liquefaction), and Gasification Research (hydrogen production from low-rank coals, advanced wastewater treatment, mild gasification, color and residual COD removal from Synfuel wastewaters, Great Plains Gasification Plant, gasifier optimization).

  1. Methodology for Designing Models Predicting Success of Infertility Treatment

    OpenAIRE

    Alireza Zarinara; Mohammad Mahdi Akhondi; Hojjat Zeraati; Koorsh Kamali; Kazem Mohammad

    2016-01-01

    Abstract Background: The prediction models for infertility treatment success have presented since 25 years ago. There are scientific principles for designing and applying the prediction models that is also used to predict the success rate of infertility treatment. The purpose of this study is to provide basic principles for designing the model to predic infertility treatment success. Materials and Methods: In this paper, the principles for developing predictive models are explained and...

  2. Statistical control process to compare and rank treatment plans in radiation oncology: impact of heterogeneity correction on treatment planning in lung cancer.

    Science.gov (United States)

    Chaikh, Abdulhamid; Balosso, Jacques

    2016-12-01

    This study proposes a statistical process to compare different treatment plans issued from different irradiation techniques or different treatment phases. This approach aims to provide arguments for discussion about the impact on clinical results of any condition able to significantly alter dosimetric or ballistic related data. The principles of the statistical investigation are presented in the framework of a clinical example based on 40 fields of radiotherapy for lung cancers. Two treatment plans were generated for each patient making a change of dose distribution due to variation of lung density correction. The data from 2D gamma index (γ) including the pixels having γ≤1 were used to determine the capability index (Cp) and the acceptability index (Cpk) of the process. To measure the strength of the relationship between the γ passing rates and the Cp and Cpk indices, the Spearman's rank non-parametric test was used to calculate P values. The comparison between reference and tested plans showed that 95% of pixels have γ≤1 with criteria (6%, 6 mm). The values of the Cp and Cpk indices were lower than one showing a significant dose difference. The data showed a strong correlation between γ passing rates and the indices with P>0.8. The statistical analysis using Cp and Cpk, show the significance of dose differences resulting from two plans in radiotherapy. These indices can be used for adaptive radiotherapy to measure the difference between initial plan and daily delivered plan. The significant changes of dose distribution could raise the question about the continuity to treat the patient with the initial plan or the need for adjustments.

  3. Personality does not predict treatment preference, treatment experience does: a study of four complementary pain treatments.

    Science.gov (United States)

    Blasche, Gerhard; Melchart, Herbert; Leitner, Daniela; Marktl, Wolfgang

    2007-10-01

    The aim of the present study was to determine the extent to which personality and treatment experience affect patients' appraisals of 4 complementary treatments for chronic pain. A total of 232 chronic pain patients (164 females, 68 males, average age 56.6 years) visiting a spa clinic in Austria returned a questionnaire on patient characteristics and personality (autonomy, depressiveness, assertiveness, self-control) as well as attitudes towards (i.e. appealing, effective, pleasant) and experience of the treatments. Results were analysed by use of linear regression analysis and confidence intervals. Although all treatments were appraised positively, the passive treatments (thermal water tub baths, classical massage) were favoured more than the active treatments (relaxation training or exercise therapy). Treatment appraisal was not predicted by any of the personality traits but to a large extent by treatment experience. Relaxing, not unpleasant treatments were the most highly esteemed treatments. How strenuous or tiring a treatment was only had a minor effect on its appraisal. Neither do dependent, passive patients prefer passive treatments, nor do conscientious patients prefer active treatments. Instead, the appraisal of treatments that induce specific somatosensory sensations is largely determined by treatment experiences, i.e. what the treatment feels like. Despite the popularity of CAM which encompasses many experientially intensive treatments, treatment experience has to date been a neglected topic of treatment research.

  4. Sparse structure regularized ranking

    KAUST Repository

    Wang, Jim Jing-Yan; Sun, Yijun; Gao, Xin

    2014-01-01

    Learning ranking scores is critical for the multimedia database retrieval problem. In this paper, we propose a novel ranking score learning algorithm by exploring the sparse structure and using it to regularize ranking scores. To explore the sparse

  5. Predicted versus executed surgical orthognathic treatment.

    Science.gov (United States)

    Falter, B; Schepers, S; Vrielinck, L; Lambrichts, I; Politis, C

    2013-10-01

    This study aimed to analyse combined surgical-orthodontic treatment plans, compare them with the actual surgery performed, and define factors resulting in changes of the original plan during orthodontic pre-surgical preparation. The clinical files of 312 orthognathic surgery patients, operated between January 2008 and December 2010, were retrospectively reviewed. Of these 312 patients, 129 had a bimaxillary operation. One hundred sixty patients had osteotomy of the lower jaw only and 23 had osteotomy of the upper jaw only. Factors analysed in the study include Angle Class malocclusion, patient sex, and age. Lip-to-incisor relationship, overjet, overbite and midline deviations of the upper and lower jaw were recorded. Effects of surgical assisted rapid palatal expansion (SARPE) on the eventual surgery were also investigated. Reasons for changing the original treatment plan at the time of the finished pre-surgical-orthodontic alignment were analysed. The original treatment plan was changed in 42 of the 312 patients (13.5%). Changes occurred generally in case of a larger interval between set-up of the first treatment plan and the eventual operation (average 22.4 versus 16.4 months for patients with changed versus unchanged treatment plan, respectively). All Class I patients had surgery performed as planned. Class III patients had a significantly higher rate of altered treatment plan (27.3%) than Class II patients (7.6%). More men (52.4%) saw their treatment plan changed, although there were more women than men in the study population (59.6 versus 40.4%). One in seven patients (13.5%) had a different operation than was planned at the start of treatment. Class III patients with small overjet and overbite commonly have a treatment plan for a monomaxillary operation that, after decompensation, needs to be adapted to a bimaxillary operation. Copyright © 2012 European Association for Cranio-Maxillo-Facial Surgery. Published by Elsevier Ltd. All rights reserved.

  6. Ranking in evolving complex networks

    Science.gov (United States)

    Liao, Hao; Mariani, Manuel Sebastian; Medo, Matúš; Zhang, Yi-Cheng; Zhou, Ming-Yang

    2017-05-01

    Complex networks have emerged as a simple yet powerful framework to represent and analyze a wide range of complex systems. The problem of ranking the nodes and the edges in complex networks is critical for a broad range of real-world problems because it affects how we access online information and products, how success and talent are evaluated in human activities, and how scarce resources are allocated by companies and policymakers, among others. This calls for a deep understanding of how existing ranking algorithms perform, and which are their possible biases that may impair their effectiveness. Many popular ranking algorithms (such as Google's PageRank) are static in nature and, as a consequence, they exhibit important shortcomings when applied to real networks that rapidly evolve in time. At the same time, recent advances in the understanding and modeling of evolving networks have enabled the development of a wide and diverse range of ranking algorithms that take the temporal dimension into account. The aim of this review is to survey the existing ranking algorithms, both static and time-aware, and their applications to evolving networks. We emphasize both the impact of network evolution on well-established static algorithms and the benefits from including the temporal dimension for tasks such as prediction of network traffic, prediction of future links, and identification of significant nodes.

  7. Bayesian CP Factorization of Incomplete Tensors with Automatic Rank Determination.

    Science.gov (United States)

    Zhao, Qibin; Zhang, Liqing; Cichocki, Andrzej

    2015-09-01

    CANDECOMP/PARAFAC (CP) tensor factorization of incomplete data is a powerful technique for tensor completion through explicitly capturing the multilinear latent factors. The existing CP algorithms require the tensor rank to be manually specified, however, the determination of tensor rank remains a challenging problem especially for CP rank . In addition, existing approaches do not take into account uncertainty information of latent factors, as well as missing entries. To address these issues, we formulate CP factorization using a hierarchical probabilistic model and employ a fully Bayesian treatment by incorporating a sparsity-inducing prior over multiple latent factors and the appropriate hyperpriors over all hyperparameters, resulting in automatic rank determination. To learn the model, we develop an efficient deterministic Bayesian inference algorithm, which scales linearly with data size. Our method is characterized as a tuning parameter-free approach, which can effectively infer underlying multilinear factors with a low-rank constraint, while also providing predictive distributions over missing entries. Extensive simulations on synthetic data illustrate the intrinsic capability of our method to recover the ground-truth of CP rank and prevent the overfitting problem, even when a large amount of entries are missing. Moreover, the results from real-world applications, including image inpainting and facial image synthesis, demonstrate that our method outperforms state-of-the-art approaches for both tensor factorization and tensor completion in terms of predictive performance.

  8. Reduced Rank Regression

    DEFF Research Database (Denmark)

    Johansen, Søren

    2008-01-01

    The reduced rank regression model is a multivariate regression model with a coefficient matrix with reduced rank. The reduced rank regression algorithm is an estimation procedure, which estimates the reduced rank regression model. It is related to canonical correlations and involves calculating...

  9. Early dropout predictive factors in obesity treatment.

    Science.gov (United States)

    Michelini, Ilaria; Falchi, Anna Giulia; Muggia, Chiara; Grecchi, Ilaria; Montagna, Elisabetta; De Silvestri, Annalisa; Tinelli, Carmine

    2014-02-01

    Diet attrition and failure of long term treatment are very frequent in obese patients. This study aimed to identify pre-treatment variables determining dropout and to customise the characteristics of those most likely to abandon the program before treatment, thus making it possible to modify the therapy to increase compliance. A total of 146 outpatients were consecutively enrolled; 73 patients followed a prescriptive diet while 73 followed a novel brief group Cognitive Behavioural Treatment (CBT) in addition to prescriptive diet. The two interventions lasted for six months. Anthropometric, demographic, psychological parameters and feeding behaviour were assessed, the last two with the Italian instrument VCAO Ansisa; than, a semi-structured interview was performed on motivation to lose weight. To identify the baseline dropout risk factors among these parameters, univariate and multivariate logistic models were used. Comparison of the results in the two different treatments showed a higher attrition rate in CBT group, despite no statistically significant difference between the two treatment arms (P = 0.127). Dropout patients did not differ significantly from those who did not dropout with regards to sex, age, Body Mass Index (BMI), history of cycling, education, work and marriage. Regardless of weight loss, the most important factor that determines the dropout appears to be a high level of stress revealed by General Health Questionnaire-28 items (GHQ-28) score within VCAO test. The identification of hindering factors during the assessment is fundamental to reduce the dropout risk. For subjects at risk, it would be useful to dedicate a stress management program before beginning a dietary restriction.

  10. Fourth-rank cosmology

    International Nuclear Information System (INIS)

    Marrakchi, A.E.L.; Tapia, V.

    1992-05-01

    Some cosmological implications of the recently proposed fourth-rank theory of gravitation are studied. The model exhibits the possibility of being free from the horizon and flatness problems at the price of introducing a negative pressure. The field equations we obtain are compatible with k obs =0 and Ω obs t clas approx. 10 20 t Planck approx. 10 -23 s. When interpreted at the light of General Relativity the treatment is shown to be almost equivalent to that of the standard model of cosmology combined with the inflationary scenario. Hence, an interpretation of the negative pressure hypothesis is provided. (author). 8 refs

  11. Modeling Jambo wastewater treatment system to predict water re ...

    African Journals Online (AJOL)

    user

    C++ programme to implement Brown's model for determining water quality usage ... predicting the re-use options of the wastewater treatment system was a ... skins from rural slaughter slabs/butchers, slaughter .... City (Karnataka State, India).

  12. Pre-treatment amygdala volume predicts electroconvulsive therapy response

    NARCIS (Netherlands)

    ten Doesschate, Freek; van Eijndhoven, Philip; Tendolkar, Indira; van Wingen, Guido A.; van Waarde, Jeroen A.

    2014-01-01

    Electroconvulsive therapy (ECT) is an effective treatment for patients with severe depression. Knowledge on factors predicting therapeutic response may help to identify patients who will benefit most from the intervention. Based on the neuroplasticity hypothesis, volumes of the amygdala and

  13. PREDICTION OF SURGICAL TREATMENT WITH POUR PERITONITIS QUANTIFYING RISK FACTORS

    Directory of Open Access Journals (Sweden)

    І. К. Churpiy

    2012-11-01

    Full Text Available Explored the possibility of quantitative assessment of risk factors of complications in the treatment of diffuse peritonitis. Highlighted 53 groups of features that are important in predicting the course of diffuse peritonitis. The proposed scheme of defining the risk of clinical course of diffuse peritonitis can quantify the severity of the source of patients and in most cases correctly predict the results of treatment of disease.

  14. Ranking Operations Management conferences

    NARCIS (Netherlands)

    Steenhuis, H.J.; de Bruijn, E.J.; Gupta, Sushil; Laptaned, U

    2007-01-01

    Several publications have appeared in the field of Operations Management which rank Operations Management related journals. Several ranking systems exist for journals based on , for example, perceived relevance and quality, citation, and author affiliation. Many academics also publish at conferences

  15. Predicting the effect of psychoeducational group treatment for hypochondriasis.

    NARCIS (Netherlands)

    Buwalda, F.M.; Bouman, T.K.

    2008-01-01

    Both individual cognitive-behavioural therapy and short-term psychoeducational courses have shown to be effective in reducing hypochondriacal complaints. However, it is unknown which patients benefit from treatment. The aim of the present study is to explore which variables predict treatment outcome

  16. Implicit Learning Abilities Predict Treatment Response in Autism Spectrum Disorders

    Science.gov (United States)

    2015-09-01

    early behavioral interventions are the most effective treatment for Autism Spectrum Disorder (ASD), but almost half of the children do not make...behavioral intervention . 2. KEYWORDS Autism Spectrum Disorder , implicit learning, associative learning, individual differences, functional Magnetic...2 AWARD NUMBER: W81XWH-14-1-0261 TITLE: Implicit Learning Abilities Predict Treatment Response in Autism Spectrum Disorders PRINCIPAL

  17. Consistent ranking of volatility models

    DEFF Research Database (Denmark)

    Hansen, Peter Reinhard; Lunde, Asger

    2006-01-01

    We show that the empirical ranking of volatility models can be inconsistent for the true ranking if the evaluation is based on a proxy for the population measure of volatility. For example, the substitution of a squared return for the conditional variance in the evaluation of ARCH-type models can...... variance in out-of-sample evaluations rather than the squared return. We derive the theoretical results in a general framework that is not specific to the comparison of volatility models. Similar problems can arise in comparisons of forecasting models whenever the predicted variable is a latent variable....

  18. Ranking Theory and Conditional Reasoning.

    Science.gov (United States)

    Skovgaard-Olsen, Niels

    2016-05-01

    Ranking theory is a formal epistemology that has been developed in over 600 pages in Spohn's recent book The Laws of Belief, which aims to provide a normative account of the dynamics of beliefs that presents an alternative to current probabilistic approaches. It has long been received in the AI community, but it has not yet found application in experimental psychology. The purpose of this paper is to derive clear, quantitative predictions by exploiting a parallel between ranking theory and a statistical model called logistic regression. This approach is illustrated by the development of a model for the conditional inference task using Spohn's (2013) ranking theoretic approach to conditionals. Copyright © 2015 Cognitive Science Society, Inc.

  19. Combining clinical variables to optimize prediction of antidepressant treatment outcomes.

    Science.gov (United States)

    Iniesta, Raquel; Malki, Karim; Maier, Wolfgang; Rietschel, Marcella; Mors, Ole; Hauser, Joanna; Henigsberg, Neven; Dernovsek, Mojca Zvezdana; Souery, Daniel; Stahl, Daniel; Dobson, Richard; Aitchison, Katherine J; Farmer, Anne; Lewis, Cathryn M; McGuffin, Peter; Uher, Rudolf

    2016-07-01

    The outcome of treatment with antidepressants varies markedly across people with the same diagnosis. A clinically significant prediction of outcomes could spare the frustration of trial and error approach and improve the outcomes of major depressive disorder through individualized treatment selection. It is likely that a combination of multiple predictors is needed to achieve such prediction. We used elastic net regularized regression to optimize prediction of symptom improvement and remission during treatment with escitalopram or nortriptyline and to identify contributing predictors from a range of demographic and clinical variables in 793 adults with major depressive disorder. A combination of demographic and clinical variables, with strong contributions from symptoms of depressed mood, reduced interest, decreased activity, indecisiveness, pessimism and anxiety significantly predicted treatment outcomes, explaining 5-10% of variance in symptom improvement with escitalopram. Similar combinations of variables predicted remission with area under the curve 0.72, explaining approximately 15% of variance (pseudo R(2)) in who achieves remission, with strong contributions from body mass index, appetite, interest-activity symptom dimension and anxious-somatizing depression subtype. Escitalopram-specific outcome prediction was more accurate than generic outcome prediction, and reached effect sizes that were near or above a previously established benchmark for clinical significance. Outcome prediction on the nortriptyline arm did not significantly differ from chance. These results suggest that easily obtained demographic and clinical variables can predict therapeutic response to escitalopram with clinically meaningful accuracy, suggesting a potential for individualized prescription of this antidepressant drug. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

  20. Predicting therapy success for treatment as usual and blended treatment in the domain of depression.

    Science.gov (United States)

    van Breda, Ward; Bremer, Vincent; Becker, Dennis; Hoogendoorn, Mark; Funk, Burkhardt; Ruwaard, Jeroen; Riper, Heleen

    2018-06-01

    In this paper, we explore the potential of predicting therapy success for patients in mental health care. Such predictions can eventually improve the process of matching effective therapy types to individuals. In the EU project E-COMPARED, a variety of information is gathered about patients suffering from depression. We use this data, where 276 patients received treatment as usual and 227 received blended treatment, to investigate to what extent we are able to predict therapy success. We utilize different encoding strategies for preprocessing, varying feature selection techniques, and different statistical procedures for this purpose. Significant predictive power is found with average AUC values up to 0.7628 for treatment as usual and 0.7765 for blended treatment. Adding daily assessment data for blended treatment does currently not add predictive accuracy. Cost effectiveness analysis is needed to determine the added potential for real-world applications.

  1. Sparse structure regularized ranking

    KAUST Repository

    Wang, Jim Jing-Yan

    2014-04-17

    Learning ranking scores is critical for the multimedia database retrieval problem. In this paper, we propose a novel ranking score learning algorithm by exploring the sparse structure and using it to regularize ranking scores. To explore the sparse structure, we assume that each multimedia object could be represented as a sparse linear combination of all other objects, and combination coefficients are regarded as a similarity measure between objects and used to regularize their ranking scores. Moreover, we propose to learn the sparse combination coefficients and the ranking scores simultaneously. A unified objective function is constructed with regard to both the combination coefficients and the ranking scores, and is optimized by an iterative algorithm. Experiments on two multimedia database retrieval data sets demonstrate the significant improvements of the propose algorithm over state-of-the-art ranking score learning algorithms.

  2. 3P: Personalized Pregnancy Prediction in IVF Treatment Process

    Science.gov (United States)

    Uyar, Asli; Ciray, H. Nadir; Bener, Ayse; Bahceci, Mustafa

    We present an intelligent learning system for improving pregnancy success rate of IVF treatment. Our proposed model uses an SVM based classification system for training a model from past data and making predictions on implantation outcome of new embryos. This study employs an embryo-centered approach. Each embryo is represented with a data feature vector including 17 features related to patient characteristics, clinical diagnosis, treatment method and embryo morphological parameters. Our experimental results demonstrate a prediction accuracy of 82.7%. We have obtained the IVF dataset from Bahceci Women Health, Care Centre, in Istanbul, Turkey.

  3. A wavelet-based technique to predict treatment outcome for Major Depressive Disorder

    Science.gov (United States)

    Xia, Likun; Mohd Yasin, Mohd Azhar; Azhar Ali, Syed Saad

    2017-01-01

    Treatment management for Major Depressive Disorder (MDD) has been challenging. However, electroencephalogram (EEG)-based predictions of antidepressant’s treatment outcome may help during antidepressant’s selection and ultimately improve the quality of life for MDD patients. In this study, a machine learning (ML) method involving pretreatment EEG data was proposed to perform such predictions for Selective Serotonin Reuptake Inhibitor (SSRIs). For this purpose, the acquisition of experimental data involved 34 MDD patients and 30 healthy controls. Consequently, a feature matrix was constructed involving time-frequency decomposition of EEG data based on wavelet transform (WT) analysis, termed as EEG data matrix. However, the resultant EEG data matrix had high dimensionality. Therefore, dimension reduction was performed based on a rank-based feature selection method according to a criterion, i.e., receiver operating characteristic (ROC). As a result, the most significant features were identified and further be utilized during the training and testing of a classification model, i.e., the logistic regression (LR) classifier. Finally, the LR model was validated with 100 iterations of 10-fold cross-validation (10-CV). The classification results were compared with short-time Fourier transform (STFT) analysis, and empirical mode decompositions (EMD). The wavelet features extracted from frontal and temporal EEG data were found statistically significant. In comparison with other time-frequency approaches such as the STFT and EMD, the WT analysis has shown highest classification accuracy, i.e., accuracy = 87.5%, sensitivity = 95%, and specificity = 80%. In conclusion, significant wavelet coefficients extracted from frontal and temporal pre-treatment EEG data involving delta and theta frequency bands may predict antidepressant’s treatment outcome for the MDD patients. PMID:28152063

  4. A wavelet-based technique to predict treatment outcome for Major Depressive Disorder.

    Science.gov (United States)

    Mumtaz, Wajid; Xia, Likun; Mohd Yasin, Mohd Azhar; Azhar Ali, Syed Saad; Malik, Aamir Saeed

    2017-01-01

    Treatment management for Major Depressive Disorder (MDD) has been challenging. However, electroencephalogram (EEG)-based predictions of antidepressant's treatment outcome may help during antidepressant's selection and ultimately improve the quality of life for MDD patients. In this study, a machine learning (ML) method involving pretreatment EEG data was proposed to perform such predictions for Selective Serotonin Reuptake Inhibitor (SSRIs). For this purpose, the acquisition of experimental data involved 34 MDD patients and 30 healthy controls. Consequently, a feature matrix was constructed involving time-frequency decomposition of EEG data based on wavelet transform (WT) analysis, termed as EEG data matrix. However, the resultant EEG data matrix had high dimensionality. Therefore, dimension reduction was performed based on a rank-based feature selection method according to a criterion, i.e., receiver operating characteristic (ROC). As a result, the most significant features were identified and further be utilized during the training and testing of a classification model, i.e., the logistic regression (LR) classifier. Finally, the LR model was validated with 100 iterations of 10-fold cross-validation (10-CV). The classification results were compared with short-time Fourier transform (STFT) analysis, and empirical mode decompositions (EMD). The wavelet features extracted from frontal and temporal EEG data were found statistically significant. In comparison with other time-frequency approaches such as the STFT and EMD, the WT analysis has shown highest classification accuracy, i.e., accuracy = 87.5%, sensitivity = 95%, and specificity = 80%. In conclusion, significant wavelet coefficients extracted from frontal and temporal pre-treatment EEG data involving delta and theta frequency bands may predict antidepressant's treatment outcome for the MDD patients.

  5. Predicting Drug Court Treatment Completion Using the MMPI-2-RF

    Science.gov (United States)

    Mattson, Curtis; Powers, Bradley; Halfaker, Dale; Akeson, Steven; Ben-Porath, Yossef

    2012-01-01

    We examined the ability of the Minnesota Multiphasic Personality Inventory-2 Restructured Form (MMPI-2-RF; Ben-Porath & Tellegen, 2008) substantive scales to predict Drug Court treatment completion in a sample of individuals identified as being at risk for failure to complete the program. Higher scores on MMPI-2-RF scales…

  6. Predicting Social Anxiety Treatment Outcome Based on Therapeutic Email Conversations.

    Science.gov (United States)

    Hoogendoorn, Mark; Berger, Thomas; Schulz, Ava; Stolz, Timo; Szolovits, Peter

    2017-09-01

    Predicting therapeutic outcome in the mental health domain is of utmost importance to enable therapists to provide the most effective treatment to a patient. Using information from the writings of a patient can potentially be a valuable source of information, especially now that more and more treatments involve computer-based exercises or electronic conversations between patient and therapist. In this paper, we study predictive modeling using writings of patients under treatment for a social anxiety disorder. We extract a wealth of information from the text written by patients including their usage of words, the topics they talk about, the sentiment of the messages, and the style of writing. In addition, we study trends over time with respect to those measures. We then apply machine learning algorithms to generate the predictive models. Based on a dataset of 69 patients, we are able to show that we can predict therapy outcome with an area under the curve of 0.83 halfway through the therapy and with a precision of 0.78 when using the full data (i.e., the entire treatment period). Due to the limited number of participants, it is hard to generalize the results, but they do show great potential in this type of information.

  7. Predicting Social Anxiety Treatment Outcome based on Therapeutic Email Conversations

    NARCIS (Netherlands)

    Hoogendoorn, M.; Berger, Thomas; Schulz, Ava; Stolz, Timo; Szolovits, Peter

    2016-01-01

    Predicting therapeutic outcome in the mental health domain is of utmost importance to enable therapists to provide the most effective treatment to a patient. Using information from the writings of a patient can potentially be a valuable source of information, especially now that more and more

  8. Balancing treatment allocations by clinician or center in randomized trials allows unacceptable levels of treatment prediction.

    Science.gov (United States)

    Hills, Robert K; Gray, Richard; Wheatley, Keith

    2009-08-01

    Randomized controlled trials are the standard method for comparing treatments because they avoid the selection bias that might arise if clinicians were free to choose which treatment a patient would receive. In practice, allocation of treatments in randomized controlled trials is often not wholly random with various 'pseudo-randomization' methods, such as minimization or balanced blocks, used to ensure good balance between treatments within potentially important prognostic or predictive subgroups. These methods avoid selection bias so long as full concealment of the next treatment allocation is maintained. There is concern, however, that pseudo-random methods may allow clinicians to predict future treatment allocations from previous allocation history, particularly if allocations are balanced by clinician or center. We investigate here to what extent treatment prediction is possible. Using computer simulations of minimization and balanced block randomizations, the success rates of various prediction strategies were investigated for varying numbers of stratification variables, including the patient's clinician. Prediction rates for minimization and balanced block randomization typically exceed 60% when clinician is included as a stratification variable and, under certain circumstances, can exceed 80%. Increasing the number of clinicians and other stratification variables did not greatly reduce the prediction rates. Without clinician as a stratification variable, prediction rates are poor unless few clinicians participate. Prediction rates are unacceptably high when allocations are balanced by clinician or by center. This could easily lead to selection bias that might suggest spurious, or mask real, treatment effects. Unless treatment is blinded, randomization should not be balanced by clinician (or by center), and clinician-center effects should be allowed for instead by retrospectively stratified analyses. © 2009 Blackwell Publishing Asia Pty Ltd and Chinese

  9. A New Prediction Model for Evaluating Treatment-Resistant Depression.

    Science.gov (United States)

    Kautzky, Alexander; Baldinger-Melich, Pia; Kranz, Georg S; Vanicek, Thomas; Souery, Daniel; Montgomery, Stuart; Mendlewicz, Julien; Zohar, Joseph; Serretti, Alessandro; Lanzenberger, Rupert; Kasper, Siegfried

    2017-02-01

    Despite a broad arsenal of antidepressants, about a third of patients suffering from major depressive disorder (MDD) do not respond sufficiently to adequate treatment. Using the data pool of the Group for the Study of Resistant Depression and machine learning, we intended to draw new insights featuring 48 clinical, sociodemographic, and psychosocial predictors for treatment outcome. Patients were enrolled starting from January 2000 and diagnosed according to DSM-IV. Treatment-resistant depression (TRD) was defined by a 17-item Hamilton Depression Rating Scale (HDRS) score ≥ 17 after at least 2 antidepressant trials of adequate dosage and length. Remission was defined by an HDRS score depressive episode, age at first antidepressant treatment, response to first antidepressant treatment, severity, suicidality, melancholia, number of lifetime depressive episodes, patients' admittance type, education, occupation, and comorbid diabetes, panic, and thyroid disorder. While single predictors could not reach a prediction accuracy much different from random guessing, by combining all predictors, we could detect resistance with an accuracy of 0.737 and remission with an accuracy of 0.850. Consequently, 65.5% of predictions for TRD and 77.7% for remission can be expected to be accurate. Using machine learning algorithms, we could demonstrate success rates of 0.737 for predicting TRD and 0.850 for predicting remission, surpassing predictive capabilities of clinicians. Our results strengthen data mining and suggest the benefit of focus on interaction-based statistics. Considering that all predictors can easily be obtained in a clinical setting, we hope that our model can be tested by other research groups. © Copyright 2017 Physicians Postgraduate Press, Inc.

  10. How to Rank Journals.

    Science.gov (United States)

    Bradshaw, Corey J A; Brook, Barry W

    2016-01-01

    There are now many methods available to assess the relative citation performance of peer-reviewed journals. Regardless of their individual faults and advantages, citation-based metrics are used by researchers to maximize the citation potential of their articles, and by employers to rank academic track records. The absolute value of any particular index is arguably meaningless unless compared to other journals, and different metrics result in divergent rankings. To provide a simple yet more objective way to rank journals within and among disciplines, we developed a κ-resampled composite journal rank incorporating five popular citation indices: Impact Factor, Immediacy Index, Source-Normalized Impact Per Paper, SCImago Journal Rank and Google 5-year h-index; this approach provides an index of relative rank uncertainty. We applied the approach to six sample sets of scientific journals from Ecology (n = 100 journals), Medicine (n = 100), Multidisciplinary (n = 50); Ecology + Multidisciplinary (n = 25), Obstetrics & Gynaecology (n = 25) and Marine Biology & Fisheries (n = 25). We then cross-compared the κ-resampled ranking for the Ecology + Multidisciplinary journal set to the results of a survey of 188 publishing ecologists who were asked to rank the same journals, and found a 0.68-0.84 Spearman's ρ correlation between the two rankings datasets. Our composite index approach therefore approximates relative journal reputation, at least for that discipline. Agglomerative and divisive clustering and multi-dimensional scaling techniques applied to the Ecology + Multidisciplinary journal set identified specific clusters of similarly ranked journals, with only Nature & Science separating out from the others. When comparing a selection of journals within or among disciplines, we recommend collecting multiple citation-based metrics for a sample of relevant and realistic journals to calculate the composite rankings and their relative uncertainty windows.

  11. How Many Alternatives Can Be Ranked? A Comparison of the Paired Comparison and Ranking Methods.

    Science.gov (United States)

    Ock, Minsu; Yi, Nari; Ahn, Jeonghoon; Jo, Min-Woo

    2016-01-01

    To determine the feasibility of converting ranking data into paired comparison (PC) data and suggest the number of alternatives that can be ranked by comparing a PC and a ranking method. Using a total of 222 health states, a household survey was conducted in a sample of 300 individuals from the general population. Each respondent performed a PC 15 times and a ranking method 6 times (two attempts of ranking three, four, and five health states, respectively). The health states of the PC and the ranking method were constructed to overlap each other. We converted the ranked data into PC data and examined the consistency of the response rate. Applying probit regression, we obtained the predicted probability of each method. Pearson correlation coefficients were determined between the predicted probabilities of those methods. The mean absolute error was also assessed between the observed and the predicted values. The overall consistency of the response rate was 82.8%. The Pearson correlation coefficients were 0.789, 0.852, and 0.893 for ranking three, four, and five health states, respectively. The lowest mean absolute error was 0.082 (95% confidence interval [CI] 0.074-0.090) in ranking five health states, followed by 0.123 (95% CI 0.111-0.135) in ranking four health states and 0.126 (95% CI 0.113-0.138) in ranking three health states. After empirically examining the consistency of the response rate between a PC and a ranking method, we suggest that using five alternatives in the ranking method may be superior to using three or four alternatives. Copyright © 2016 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

  12. On Page Rank

    NARCIS (Netherlands)

    Hoede, C.

    In this paper the concept of page rank for the world wide web is discussed. The possibility of describing the distribution of page rank by an exponential law is considered. It is shown that the concept is essentially equal to that of status score, a centrality measure discussed already in 1953 by

  13. On Rank and Nullity

    Science.gov (United States)

    Dobbs, David E.

    2012-01-01

    This note explains how Emil Artin's proof that row rank equals column rank for a matrix with entries in a field leads naturally to the formula for the nullity of a matrix and also to an algorithm for solving any system of linear equations in any number of variables. This material could be used in any course on matrix theory or linear algebra.

  14. Hitting the Rankings Jackpot

    Science.gov (United States)

    Chapman, David W.

    2008-01-01

    Recently, Samford University was ranked 27th in the nation in a report released by "Forbes" magazine. In this article, the author relates how the people working at Samford University were surprised at its ranking. Although Samford is the largest privately institution in Alabama, its distinguished academic achievements aren't even…

  15. Intravoxel incoherent motion (IVIM histogram biomarkers for prediction of neoadjuvant treatment response in breast cancer patients

    Directory of Open Access Journals (Sweden)

    Gene Y. Cho

    Full Text Available Objective: To examine the prognostic capabilities of intravoxel incoherent motion (IVIM metrics and their ability to predict response to neoadjuvant treatment (NAT. Additionally, to observe changes in IVIM metrics between pre- and post-treatment MRI. Methods: This IRB-approved, HIPAA-compliant retrospective study observed 31 breast cancer patients (32 lesions. Patients underwent standard bilateral breast MRI along with diffusion-weighted imaging before and after NAT. Six patients underwent an additional IVIM-MRI scan 12–14 weeks after initial scan and 2 cycles of treatment. In addition to apparent diffusion coefficients (ADC from monoexponential decay, IVIM mean values (tissue diffusivity Dt, perfusion fraction fp, and pseudodiffusivity Dp and histogram metrics were derived using a biexponential model. An additional filter identified voxels of highly vascular tumor tissue (VTT, excluding necrotic or normal tissue. Clinical data include histology of biopsy and clinical response to treatment through RECIST assessment. Comparisons of treatment response were made using Wilcoxon rank-sum tests. Results: Average, kurtosis, and skewness of pseudodiffusion Dp significantly differentiated RECIST responders from nonresponders. ADC and Dt values generally increased (∼70% and VTT% values generally decreased (∼20% post-treatment. Conclusion: Dp metrics showed prognostic capabilities; slow and heterogeneous pseudodiffusion offer poor prognosis. Baseline ADC/Dt parameters were not significant predictors of response. This work suggests that IVIM mean values and heterogeneity metrics may have prognostic value in the setting of breast cancer NAT. Keywords: Breast cancer, Diffusion weighted MRI, Intravoxel incoherent motion, Neoadjuvant treatment, Response evaluation criteria in solid tumors

  16. A Ranking Approach to Genomic Selection.

    Science.gov (United States)

    Blondel, Mathieu; Onogi, Akio; Iwata, Hiroyoshi; Ueda, Naonori

    2015-01-01

    Genomic selection (GS) is a recent selective breeding method which uses predictive models based on whole-genome molecular markers. Until now, existing studies formulated GS as the problem of modeling an individual's breeding value for a particular trait of interest, i.e., as a regression problem. To assess predictive accuracy of the model, the Pearson correlation between observed and predicted trait values was used. In this paper, we propose to formulate GS as the problem of ranking individuals according to their breeding value. Our proposed framework allows us to employ machine learning methods for ranking which had previously not been considered in the GS literature. To assess ranking accuracy of a model, we introduce a new measure originating from the information retrieval literature called normalized discounted cumulative gain (NDCG). NDCG rewards more strongly models which assign a high rank to individuals with high breeding value. Therefore, NDCG reflects a prerequisite objective in selective breeding: accurate selection of individuals with high breeding value. We conducted a comparison of 10 existing regression methods and 3 new ranking methods on 6 datasets, consisting of 4 plant species and 25 traits. Our experimental results suggest that tree-based ensemble methods including McRank, Random Forests and Gradient Boosting Regression Trees achieve excellent ranking accuracy. RKHS regression and RankSVM also achieve good accuracy when used with an RBF kernel. Traditional regression methods such as Bayesian lasso, wBSR and BayesC were found less suitable for ranking. Pearson correlation was found to correlate poorly with NDCG. Our study suggests two important messages. First, ranking methods are a promising research direction in GS. Second, NDCG can be a useful evaluation measure for GS.

  17. Recurrent fuzzy ranking methods

    Science.gov (United States)

    Hajjari, Tayebeh

    2012-11-01

    With the increasing development of fuzzy set theory in various scientific fields and the need to compare fuzzy numbers in different areas. Therefore, Ranking of fuzzy numbers plays a very important role in linguistic decision-making, engineering, business and some other fuzzy application systems. Several strategies have been proposed for ranking of fuzzy numbers. Each of these techniques has been shown to produce non-intuitive results in certain case. In this paper, we reviewed some recent ranking methods, which will be useful for the researchers who are interested in this area.

  18. Ranking as parameter estimation

    Czech Academy of Sciences Publication Activity Database

    Kárný, Miroslav; Guy, Tatiana Valentine

    2009-01-01

    Roč. 4, č. 2 (2009), s. 142-158 ISSN 1745-7645 R&D Projects: GA MŠk 2C06001; GA AV ČR 1ET100750401; GA MŠk 1M0572 Institutional research plan: CEZ:AV0Z10750506 Keywords : ranking * Bayesian estimation * negotiation * modelling Subject RIV: BB - Applied Statistics, Operational Research http://library.utia.cas.cz/separaty/2009/AS/karny- ranking as parameter estimation.pdf

  19. Hierarchical partial order ranking

    International Nuclear Information System (INIS)

    Carlsen, Lars

    2008-01-01

    Assessing the potential impact on environmental and human health from the production and use of chemicals or from polluted sites involves a multi-criteria evaluation scheme. A priori several parameters are to address, e.g., production tonnage, specific release scenarios, geographical and site-specific factors in addition to various substance dependent parameters. Further socio-economic factors may be taken into consideration. The number of parameters to be included may well appear to be prohibitive for developing a sensible model. The study introduces hierarchical partial order ranking (HPOR) that remedies this problem. By HPOR the original parameters are initially grouped based on their mutual connection and a set of meta-descriptors is derived representing the ranking corresponding to the single groups of descriptors, respectively. A second partial order ranking is carried out based on the meta-descriptors, the final ranking being disclosed though average ranks. An illustrative example on the prioritisation of polluted sites is given. - Hierarchical partial order ranking of polluted sites has been developed for prioritization based on a large number of parameters

  20. Regulatory T cells predict the time to initial treatment in early stage chronic lymphocytic leukemia.

    Science.gov (United States)

    Weiss, Lukas; Melchardt, Thomas; Egle, Alexander; Grabmer, Christoph; Greil, Richard; Tinhofer, Inge

    2011-05-15

    Early stage chronic lymphocytic leukemia is characterized by a highly variable course of disease. Because it is believed that regulatory T cells (T(regs) ) are potent suppressors of antitumor immunity, the authors hypothesized that increased T(regs) may favor disease progression. T(reg) levels (cluster of differentiation 3 [CD3]-positive, [CD4]-positive, CD25-positive, and CD127-negative) in peripheral blood from 102 patients were analyzed by flow cytometry. Statistical analysis was used to evaluate correlations with clinical data. The relative T(reg) numbers in CD4-positive T cells were significantly greater in patients with chronic lymphocytic leukemia compared with the numbers in a control group of 170 healthy individuals (P = .001). Patients were divided into 2 groups using a median T(reg) value of 9.7% (the percentage of CD4-positive T cells). Patients with higher T(reg) levels had a significantly shorter time to initial treatment (median, 5.9 years) compared with patients who had lower T(reg) levels (median, 11.7 years; log-rank P = .019). Furthermore, T(reg) levels (the percentage of CD4-positive T cells) had significant prognostic power to predict the time to initial treatment in univariate analysis (P = .023) and in multivariate Cox regression analysis that included the variables Rai stage, immunoglobulin heavy-chain variable region gene mutational status, chromosomal aberrations, and CD38 expression (P = .028). Higher T(reg) levels had significant and independent prognostic power for predicting the time to initial treatment in patients with low to intermediate stage chronic lymphocytic leukemia. 2010 American Cancer Society.

  1. Ranking Adverse Drug Reactions With Crowdsourcing

    KAUST Repository

    Gottlieb, Assaf

    2015-03-23

    Background: There is no publicly available resource that provides the relative severity of adverse drug reactions (ADRs). Such a resource would be useful for several applications, including assessment of the risks and benefits of drugs and improvement of patient-centered care. It could also be used to triage predictions of drug adverse events. Objective: The intent of the study was to rank ADRs according to severity. Methods: We used Internet-based crowdsourcing to rank ADRs according to severity. We assigned 126,512 pairwise comparisons of ADRs to 2589 Amazon Mechanical Turk workers and used these comparisons to rank order 2929 ADRs. Results: There is good correlation (rho=.53) between the mortality rates associated with ADRs and their rank. Our ranking highlights severe drug-ADR predictions, such as cardiovascular ADRs for raloxifene and celecoxib. It also triages genes associated with severe ADRs such as epidermal growth-factor receptor (EGFR), associated with glioblastoma multiforme, and SCN1A, associated with epilepsy. Conclusions: ADR ranking lays a first stepping stone in personalized drug risk assessment. Ranking of ADRs using crowdsourcing may have useful clinical and financial implications, and should be further investigated in the context of health care decision making.

  2. Ranking adverse drug reactions with crowdsourcing.

    Science.gov (United States)

    Gottlieb, Assaf; Hoehndorf, Robert; Dumontier, Michel; Altman, Russ B

    2015-03-23

    There is no publicly available resource that provides the relative severity of adverse drug reactions (ADRs). Such a resource would be useful for several applications, including assessment of the risks and benefits of drugs and improvement of patient-centered care. It could also be used to triage predictions of drug adverse events. The intent of the study was to rank ADRs according to severity. We used Internet-based crowdsourcing to rank ADRs according to severity. We assigned 126,512 pairwise comparisons of ADRs to 2589 Amazon Mechanical Turk workers and used these comparisons to rank order 2929 ADRs. There is good correlation (rho=.53) between the mortality rates associated with ADRs and their rank. Our ranking highlights severe drug-ADR predictions, such as cardiovascular ADRs for raloxifene and celecoxib. It also triages genes associated with severe ADRs such as epidermal growth-factor receptor (EGFR), associated with glioblastoma multiforme, and SCN1A, associated with epilepsy. ADR ranking lays a first stepping stone in personalized drug risk assessment. Ranking of ADRs using crowdsourcing may have useful clinical and financial implications, and should be further investigated in the context of health care decision making.

  3. Multiplex PageRank.

    Science.gov (United States)

    Halu, Arda; Mondragón, Raúl J; Panzarasa, Pietro; Bianconi, Ginestra

    2013-01-01

    Many complex systems can be described as multiplex networks in which the same nodes can interact with one another in different layers, thus forming a set of interacting and co-evolving networks. Examples of such multiplex systems are social networks where people are involved in different types of relationships and interact through various forms of communication media. The ranking of nodes in multiplex networks is one of the most pressing and challenging tasks that research on complex networks is currently facing. When pairs of nodes can be connected through multiple links and in multiple layers, the ranking of nodes should necessarily reflect the importance of nodes in one layer as well as their importance in other interdependent layers. In this paper, we draw on the idea of biased random walks to define the Multiplex PageRank centrality measure in which the effects of the interplay between networks on the centrality of nodes are directly taken into account. In particular, depending on the intensity of the interaction between layers, we define the Additive, Multiplicative, Combined, and Neutral versions of Multiplex PageRank, and show how each version reflects the extent to which the importance of a node in one layer affects the importance the node can gain in another layer. We discuss these measures and apply them to an online multiplex social network. Findings indicate that taking the multiplex nature of the network into account helps uncover the emergence of rankings of nodes that differ from the rankings obtained from one single layer. Results provide support in favor of the salience of multiplex centrality measures, like Multiplex PageRank, for assessing the prominence of nodes embedded in multiple interacting networks, and for shedding a new light on structural properties that would otherwise remain undetected if each of the interacting networks were analyzed in isolation.

  4. Multiplex PageRank.

    Directory of Open Access Journals (Sweden)

    Arda Halu

    Full Text Available Many complex systems can be described as multiplex networks in which the same nodes can interact with one another in different layers, thus forming a set of interacting and co-evolving networks. Examples of such multiplex systems are social networks where people are involved in different types of relationships and interact through various forms of communication media. The ranking of nodes in multiplex networks is one of the most pressing and challenging tasks that research on complex networks is currently facing. When pairs of nodes can be connected through multiple links and in multiple layers, the ranking of nodes should necessarily reflect the importance of nodes in one layer as well as their importance in other interdependent layers. In this paper, we draw on the idea of biased random walks to define the Multiplex PageRank centrality measure in which the effects of the interplay between networks on the centrality of nodes are directly taken into account. In particular, depending on the intensity of the interaction between layers, we define the Additive, Multiplicative, Combined, and Neutral versions of Multiplex PageRank, and show how each version reflects the extent to which the importance of a node in one layer affects the importance the node can gain in another layer. We discuss these measures and apply them to an online multiplex social network. Findings indicate that taking the multiplex nature of the network into account helps uncover the emergence of rankings of nodes that differ from the rankings obtained from one single layer. Results provide support in favor of the salience of multiplex centrality measures, like Multiplex PageRank, for assessing the prominence of nodes embedded in multiple interacting networks, and for shedding a new light on structural properties that would otherwise remain undetected if each of the interacting networks were analyzed in isolation.

  5. Ranking Thinning Potential of Lodgepole Pine Stands

    OpenAIRE

    United States Department of Agriculture, Forest Service

    1987-01-01

    This paper presents models for predicting edge-response of dominant and codominant trees to clearing. Procedures are given for converting predictions to a thinning response index, for ranking stands for thinning priority. Data requirements, sampling suggestions, examples of application, and suggestions for management use are included to facilitate use as a field guide.

  6. Predictive factors of dropout from inpatient treatment for anorexia nervosa.

    Science.gov (United States)

    Roux, H; Ali, A; Lambert, S; Radon, L; Huas, C; Curt, F; Berthoz, S; Godart, Nathalie

    2016-09-30

    Patients with severe Anorexia Nervosa (AN) whose condition is life-threatening or who are not receiving adequate ambulatory care are hospitalized. However, 40 % of these patients leave the hospital prematurely, without reaching the target weight set in the treatment plan, and this can compromise outcome. This study set out to explore factors predictive of dropout from hospital treatment among patients with AN, in the hope of identifying relevant therapeutic targets. From 2009 to 2011, 180 women hospitalized for AN (DSM-IV diagnosis) in 10 centres across France were divided into two groups: those under 18 years (when the decision to discharge belongs to the parents) and those aged 18 years and over (when the patient can legally decide to leave the hospital). Both groups underwent clinical assessment using the Morgan & Russell Global Outcome State questionnaire and the Eating Disorders Examination Questionnaire (EDE-Q) for assessment of eating disorder symptoms and outcome. Psychological aspects were assessed via the evaluation of anxiety and depression using the Hospital Anxiety and Depression Scale (HADS). Socio-demographic data were also collected. A number of factors identified in previous research as predictive of dropout from hospital treatment were tested using stepwise descending Cox regressions. We found that factors predictive of dropout varied according to age groups (being under 18 as opposed to 18 and over). For participants under 18, predictive factors were living in a single-parent family, severe intake restriction as measured on the "dietary restriction" subscale of the Morgan & Russell scale, and a low patient-reported score on the EDE-Q "restraint concerns" subscale. For those over 18, dropout was predicted from a low depression score on the HADS, low level of concern about weight on the EDE-Q subscale, and lower educational status. To prevent dropout from hospitalization for AN, the appropriate therapeutic measures vary according to whether

  7. Groundwater contaminant plume ranking

    International Nuclear Information System (INIS)

    1988-08-01

    Containment plumes at Uranium Mill Tailings Remedial Action (UMTRA) Project sites were ranked to assist in Subpart B (i.e., restoration requirements of 40 CFR Part 192) compliance strategies for each site, to prioritize aquifer restoration, and to budget future requests and allocations. The rankings roughly estimate hazards to the environment and human health, and thus assist in determining for which sites cleanup, if appropriate, will provide the greatest benefits for funds available. The rankings are based on the scores that were obtained using the US Department of Energy's (DOE) Modified Hazard Ranking System (MHRS). The MHRS and HRS consider and score three hazard modes for a site: migration, fire and explosion, and direct contact. The migration hazard mode score reflects the potential for harm to humans or the environment from migration of a hazardous substance off a site by groundwater, surface water, and air; it is a composite of separate scores for each of these routes. For ranking the containment plumes at UMTRA Project sites, it was assumed that each site had been remediated in compliance with the EPA standards and that relict contaminant plumes were present. Therefore, only the groundwater route was scored, and the surface water and air routes were not considered. Section 2.0 of this document describes the assumptions and procedures used to score the groundwater route, and Section 3.0 provides the resulting scores for each site. 40 tabs

  8. Multistrain models predict sequential multidrug treatment strategies to result in less antimicrobial resistance than combination treatment

    DEFF Research Database (Denmark)

    Ahmad, Amais; Zachariasen, Camilla; Christiansen, Lasse Engbo

    2016-01-01

    generated by a mathematical model of the competitive growth of multiple strains of Escherichia coli.Results: Simulation studies showed that sequential use of tetracycline and ampicillin reduced the level of double resistance, when compared to the combination treatment. The effect of the cycling frequency...... frequency did not play a role in suppressing the growth of resistant strains, but the specific order of the two antimicrobials did. Predictions made from the study could be used to redesign multidrug treatment strategies not only for intramuscular treatment in pigs, but also for other dosing routes.......Background: Combination treatment is increasingly used to fight infections caused by bacteria resistant to two or more antimicrobials. While multiple studies have evaluated treatment strategies to minimize the emergence of resistant strains for single antimicrobial treatment, fewer studies have...

  9. Multistrain models predict sequential multidrug treatment strategies to result in less antimicrobial resistance than combination treatment

    DEFF Research Database (Denmark)

    Ahmad, Amais; Zachariasen, Camilla; Christiansen, Lasse Engbo

    2016-01-01

    Background: Combination treatment is increasingly used to fight infections caused by bacteria resistant to two or more antimicrobials. While multiple studies have evaluated treatment strategies to minimize the emergence of resistant strains for single antimicrobial treatment, fewer studies have...... the sensitive fraction of the commensal flora.Growth parameters for competing bacterial strains were estimated from the combined in vitro pharmacodynamic effect of two antimicrobials using the relationship between concentration and net bacterial growth rate. Predictions of in vivo bacterial growth were...... (how frequently antibiotics are alternated in a sequential treatment) of the two drugs was dependent upon the order in which the two drugs were used.Conclusion: Sequential treatment was more effective in preventing the growth of resistant strains when compared to the combination treatment. The cycling...

  10. Ranking economic history journals

    DEFF Research Database (Denmark)

    Di Vaio, Gianfranco; Weisdorf, Jacob Louis

    2010-01-01

    This study ranks-for the first time-12 international academic journals that have economic history as their main topic. The ranking is based on data collected for the year 2007. Journals are ranked using standard citation analysis where we adjust for age, size and self-citation of journals. We also...... compare the leading economic history journals with the leading journals in economics in order to measure the influence on economics of economic history, and vice versa. With a few exceptions, our results confirm the general idea about what economic history journals are the most influential for economic...... history, and that, although economic history is quite independent from economics as a whole, knowledge exchange between the two fields is indeed going on....

  11. Ranking Economic History Journals

    DEFF Research Database (Denmark)

    Di Vaio, Gianfranco; Weisdorf, Jacob Louis

    This study ranks - for the first time - 12 international academic journals that have economic history as their main topic. The ranking is based on data collected for the year 2007. Journals are ranked using standard citation analysis where we adjust for age, size and self-citation of journals. We...... also compare the leading economic history journals with the leading journals in economics in order to measure the influence on economics of economic history, and vice versa. With a few exceptions, our results confirm the general idea about what economic history journals are the most influential...... for economic history, and that, although economic history is quite independent from economics as a whole, knowledge exchange between the two fields is indeed going on....

  12. Dynamic Matrix Rank

    DEFF Research Database (Denmark)

    Frandsen, Gudmund Skovbjerg; Frandsen, Peter Frands

    2009-01-01

    We consider maintaining information about the rank of a matrix under changes of the entries. For n×n matrices, we show an upper bound of O(n1.575) arithmetic operations and a lower bound of Ω(n) arithmetic operations per element change. The upper bound is valid when changing up to O(n0.575) entries...... in a single column of the matrix. We also give an algorithm that maintains the rank using O(n2) arithmetic operations per rank one update. These bounds appear to be the first nontrivial bounds for the problem. The upper bounds are valid for arbitrary fields, whereas the lower bound is valid for algebraically...... closed fields. The upper bound for element updates uses fast rectangular matrix multiplication, and the lower bound involves further development of an earlier technique for proving lower bounds for dynamic computation of rational functions....

  13. High energy transurethral thermotherapy in the treatment of benign prostatic hyperplasia: criteria to predict treatment outcome

    NARCIS (Netherlands)

    D'Ancona, F. C. H.; Francisca, E. A. E.; Hendriks, J. C. M.; Debruyne, F. M. J.; de la Rosette, J. J. M. C. H.

    1999-01-01

    In this study we analyzed the individual value of baseline parameters to predict the outcome of high energy transurethral microwave thermotherapy in the treatment of patients with lower urinary tract symptoms and benign prostatic hyperplasia. Two hundred and forty-seven patients with symptomatic

  14. Predictive tools for designing new insulins and treatment regimens

    DEFF Research Database (Denmark)

    Klim, Søren

    The thesis deals with the development of "Predictive tools for designing new insulins and treatments regimens" and consists of two parts: A model based approach for bridging properties of new insulin analogues from glucose clamp experiments to meal tolerance tests (MTT) and a second part that des......The thesis deals with the development of "Predictive tools for designing new insulins and treatments regimens" and consists of two parts: A model based approach for bridging properties of new insulin analogues from glucose clamp experiments to meal tolerance tests (MTT) and a second part...... that describes an implemented software program able to handle stochastic differential equations (SDEs) with mixed effects. The thesis is supplemented with scientific papers published during the PhD. Developing an insulin analogue from candidate molecule to a clinical drug consists of a development programme...... and efficacy are investigated. Numerous methods are used to quantify dose and efficacy in Phase II - especially of interest is the 24-hour meal tolerance test as it tries to portray near normal living conditions. Part I describes an integrated model for insulin and glucose which is aimed at simulating 24-hour...

  15. Can quantitative sensory testing predict responses to analgesic treatment?

    Science.gov (United States)

    Grosen, K; Fischer, I W D; Olesen, A E; Drewes, A M

    2013-10-01

    The role of quantitative sensory testing (QST) in prediction of analgesic effect in humans is scarcely investigated. This updated review assesses the effectiveness in predicting analgesic effects in healthy volunteers, surgical patients and patients with chronic pain. A systematic review of English written, peer-reviewed articles was conducted using PubMed and Embase (1980-2013). Additional studies were identified by chain searching. Search terms included 'quantitative sensory testing', 'sensory testing' and 'analgesics'. Studies on the relationship between QST and response to analgesic treatment in human adults were included. Appraisal of the methodological quality of the included studies was based on evaluative criteria for prognostic studies. Fourteen studies (including 720 individuals) met the inclusion criteria. Significant correlations were observed between responses to analgesics and several QST parameters including (1) heat pain threshold in experimental human pain, (2) electrical and heat pain thresholds, pressure pain tolerance and suprathreshold heat pain in surgical patients, and (3) electrical and heat pain threshold and conditioned pain modulation in patients with chronic pain. Heterogeneity among studies was observed especially with regard to application of QST and type and use of analgesics. Although promising, the current evidence is not sufficiently robust to recommend the use of any specific QST parameter in predicting analgesic response. Future studies should focus on a range of different experimental pain modalities rather than a single static pain stimulation paradigm. © 2013 European Federation of International Association for the Study of Pain Chapters.

  16. Ranking beta sheet topologies of proteins

    DEFF Research Database (Denmark)

    Fonseca, Rasmus; Helles, Glennie; Winter, Pawel

    2010-01-01

    One of the challenges of protein structure prediction is to identify long-range interactions between amino acids. To reliably predict such interactions, we enumerate, score and rank all beta-topologies (partitions of beta-strands into sheets, orderings of strands within sheets and orientations...... of paired strands) of a given protein. We show that the beta-topology corresponding to the native structure is, with high probability, among the top-ranked. Since full enumeration is very time-consuming, we also suggest a method to deal with proteins with many beta-strands. The results reported...... in this paper are highly relevant for ab initio protein structure prediction methods based on decoy generation. The top-ranked beta-topologies can be used to find initial conformations from which conformational searches can be started. They can also be used to filter decoys by removing those with poorly...

  17. Predicting children's behaviour during dental treatment under oral sedation.

    Science.gov (United States)

    Lourenço-Matharu, L; Papineni McIntosh, A; Lo, J W

    2016-06-01

    The primary aim of this study was to assess whether parents' own anxiety and their perception of their child's dental fear and child's general fear can predict preoperatively their child's behaviour during dental treatment under oral sedation. The secondary aim was to assess whether the child's age, gender and ASA classification grade are associated with a child's behaviour under oral sedation. Cross-sectional prospective study. The Corah's Dental Anxiety Scale (DAS), Children's Fear Survey Schedule Dental-Subscale (CFSS-DS) and Children's Fear Survey Schedule Short-Form (CFSS-SF) questionnaires were completed by parents of children undergoing dental treatment with oral midazolam. Behaviour was rated by a single clinician using the overall behaviour section of the Houpt-Scale and scores dichotomised into acceptable or unacceptable behaviour. Data were analysed using χ (2), t test and logistic regression analysis. In total 404 children (215 girls, 53 %) were included, with the mean age of 4.57 years, SD = 1.9. Behaviour was scored as acceptable in 336 (83 %) and unacceptable in 68 (17 %) children. The level of a child's dental fear, as perceived by their parent, was significantly associated with the behaviour outcome (p = 0.001). Logistic regression analysis revealed that if the parentally perceived child's dental fear (CFSS-DS) rating was high, the odds of the child exhibiting unacceptable behaviour under oral sedation was two times greater than if their parents scored them a low dental fear rating (OR 2.27, 95 % CI 1.33-3.88, p = 0.003). CFSS-DS may be used preoperatively to help predict behaviour outcome when children are treated under oral sedation and facilitate treatment planning.

  18. Family Factors Predict Treatment Outcome for Pediatric Obsessive Compulsive Disorder

    Science.gov (United States)

    Peris, Tara S.; Sugar, Catherine A.; Bergman, R. Lindsey; Chang, Susanna; Langley, Audra; Piacentini, John

    2012-01-01

    Objective To examine family conflict, parental blame, and poor family cohesion as predictors of treatment outcome for youth receiving family-focused cognitive behavioral therapy (FCBT) for obsessive compulsive disorder (OCD). Methods We analyzed data from a sample of youth who were randomized to FCBT (n = 49; 59% male; mean age = 12.43 years) as part of a larger randomized clinical trial. Youngsters and their families were assessed by an independent evaluator (IE) pre- and post- FCBT using a standardized battery of measures evaluating family functioning and OCD symptom severity. Family conflict and cohesion were measured via parent self-report on the Family Environment Scale (FES; Moos & Moos, 1994) and parental blame was measured using parent self-report on the Parental Attitudes and Behaviors Scale (PABS; Peris, 2008b). Symptom severity was rated by IE’s using the Children’s Yale-Brown Obsessive Compulsive Scale (CY-BOCS; Scahill et al., 1997). Results Families with lower levels of parental blame and family conflict and higher levels of family cohesion at baseline were more likely to have a child who responded to FCBT treatment even after adjusting for baseline symptom severity compared to families who endorsed higher levels of dysfunction prior to treatment. In analyses using both categorical and continuous outcome measures, higher levels of family dysfunction and difficulty in higher number of domains of family functioning were associated with lower rates of treatment response. In addition, changes in family cohesion predicted response to FCBT controlling for baseline symptom severity. Conclusions Findings speak to the role of the family in treatment for childhood OCD and highlight potential targets for future family interventions. PMID:22309471

  19. Diversifying customer review rankings.

    Science.gov (United States)

    Krestel, Ralf; Dokoohaki, Nima

    2015-06-01

    E-commerce Web sites owe much of their popularity to consumer reviews accompanying product descriptions. On-line customers spend hours and hours going through heaps of textual reviews to decide which products to buy. At the same time, each popular product has thousands of user-generated reviews, making it impossible for a buyer to read everything. Current approaches to display reviews to users or recommend an individual review for a product are based on the recency or helpfulness of each review. In this paper, we present a framework to rank product reviews by optimizing the coverage of the ranking with respect to sentiment or aspects, or by summarizing all reviews with the top-K reviews in the ranking. To accomplish this, we make use of the assigned star rating for a product as an indicator for a review's sentiment polarity and compare bag-of-words (language model) with topic models (latent Dirichlet allocation) as a mean to represent aspects. Our evaluation on manually annotated review data from a commercial review Web site demonstrates the effectiveness of our approach, outperforming plain recency ranking by 30% and obtaining best results by combining language and topic model representations. Copyright © 2015 Elsevier Ltd. All rights reserved.

  20. College Rankings. ERIC Digest.

    Science.gov (United States)

    Holub, Tamara

    The popularity of college ranking surveys published by "U.S. News and World Report" and other magazines is indisputable, but the methodologies used to measure the quality of higher education institutions have come under fire by scholars and college officials. Criticisms have focused on methodological flaws, such as failure to consider…

  1. OutRank

    DEFF Research Database (Denmark)

    Müller, Emmanuel; Assent, Ira; Steinhausen, Uwe

    2008-01-01

    Outlier detection is an important data mining task for consistency checks, fraud detection, etc. Binary decision making on whether or not an object is an outlier is not appropriate in many applications and moreover hard to parametrize. Thus, recently, methods for outlier ranking have been proposed...

  2. An updated PREDICT breast cancer prognostication and treatment benefit prediction model with independent validation.

    Science.gov (United States)

    Candido Dos Reis, Francisco J; Wishart, Gordon C; Dicks, Ed M; Greenberg, David; Rashbass, Jem; Schmidt, Marjanka K; van den Broek, Alexandra J; Ellis, Ian O; Green, Andrew; Rakha, Emad; Maishman, Tom; Eccles, Diana M; Pharoah, Paul D P

    2017-05-22

    PREDICT is a breast cancer prognostic and treatment benefit model implemented online. The overall fit of the model has been good in multiple independent case series, but PREDICT has been shown to underestimate breast cancer specific mortality in women diagnosed under the age of 40. Another limitation is the use of discrete categories for tumour size and node status resulting in 'step' changes in risk estimates on moving between categories. We have refitted the PREDICT prognostic model using the original cohort of cases from East Anglia with updated survival time in order to take into account age at diagnosis and to smooth out the survival function for tumour size and node status. Multivariable Cox regression models were used to fit separate models for ER negative and ER positive disease. Continuous variables were fitted using fractional polynomials and a smoothed baseline hazard was obtained by regressing the baseline cumulative hazard for each patients against time using fractional polynomials. The fit of the prognostic models were then tested in three independent data sets that had also been used to validate the original version of PREDICT. In the model fitting data, after adjusting for other prognostic variables, there is an increase in risk of breast cancer specific mortality in younger and older patients with ER positive disease, with a substantial increase in risk for women diagnosed before the age of 35. In ER negative disease the risk increases slightly with age. The association between breast cancer specific mortality and both tumour size and number of positive nodes was non-linear with a more marked increase in risk with increasing size and increasing number of nodes in ER positive disease. The overall calibration and discrimination of the new version of PREDICT (v2) was good and comparable to that of the previous version in both model development and validation data sets. However, the calibration of v2 improved over v1 in patients diagnosed under the age

  3. Improving Ranking Using Quantum Probability

    OpenAIRE

    Melucci, Massimo

    2011-01-01

    The paper shows that ranking information units by quantum probability differs from ranking them by classical probability provided the same data used for parameter estimation. As probability of detection (also known as recall or power) and probability of false alarm (also known as fallout or size) measure the quality of ranking, we point out and show that ranking by quantum probability yields higher probability of detection than ranking by classical probability provided a given probability of ...

  4. Using Chemical Reaction Kinetics to Predict Optimal Antibiotic Treatment Strategies.

    Science.gov (United States)

    Abel Zur Wiesch, Pia; Clarelli, Fabrizio; Cohen, Ted

    2017-01-01

    Identifying optimal dosing of antibiotics has proven challenging-some antibiotics are most effective when they are administered periodically at high doses, while others work best when minimizing concentration fluctuations. Mechanistic explanations for why antibiotics differ in their optimal dosing are lacking, limiting our ability to predict optimal therapy and leading to long and costly experiments. We use mathematical models that describe both bacterial growth and intracellular antibiotic-target binding to investigate the effects of fluctuating antibiotic concentrations on individual bacterial cells and bacterial populations. We show that physicochemical parameters, e.g. the rate of drug transmembrane diffusion and the antibiotic-target complex half-life are sufficient to explain which treatment strategy is most effective. If the drug-target complex dissociates rapidly, the antibiotic must be kept constantly at a concentration that prevents bacterial replication. If antibiotics cross bacterial cell envelopes slowly to reach their target, there is a delay in the onset of action that may be reduced by increasing initial antibiotic concentration. Finally, slow drug-target dissociation and slow diffusion out of cells act to prolong antibiotic effects, thereby allowing for less frequent dosing. Our model can be used as a tool in the rational design of treatment for bacterial infections. It is easily adaptable to other biological systems, e.g. HIV, malaria and cancer, where the effects of physiological fluctuations of drug concentration are also poorly understood.

  5. 1991 Acceptance priority ranking

    International Nuclear Information System (INIS)

    1991-12-01

    The Standard Contract for Disposal of Spent Nuclear Fuel and/or High- Level Radioactive Waste (10 CFR Part 961) that the Department of Energy (DOE) has executed with the owners and generators of civilian spent nuclear fuel requires annual publication of the Acceptance Priority Ranking (APR). The 1991 APR details the order in which DOE will allocate Federal waste acceptance capacity. As required by the Standard Contract, the ranking is based on the age of permanently discharged spent nuclear fuel (SNF), with the owners of the oldest SNF, on an industry-wide basis, given the highest priority. the 1991 APR will be the basis for the annual allocation of waste acceptance capacity to the Purchasers in the 1991 Annual Capacity Report (ACR), to be issued later this year. This document is based on SNF discharges as of December 31, 1990, and reflects Purchaser comments and corrections, as appropriate, to the draft APR issued on May 15, 1991

  6. RankProdIt: A web-interactive Rank Products analysis tool

    Directory of Open Access Journals (Sweden)

    Laing Emma

    2010-08-01

    Full Text Available Abstract Background The first objective of a DNA microarray experiment is typically to generate a list of genes or probes that are found to be differentially expressed or represented (in the case of comparative genomic hybridizations and/or copy number variation between two conditions or strains. Rank Products analysis comprises a robust algorithm for deriving such lists from microarray experiments that comprise small numbers of replicates, for example, less than the number required for the commonly used t-test. Currently, users wishing to apply Rank Products analysis to their own microarray data sets have been restricted to the use of command line-based software which can limit its usage within the biological community. Findings Here we have developed a web interface to existing Rank Products analysis tools allowing users to quickly process their data in an intuitive and step-wise manner to obtain the respective Rank Product or Rank Sum, probability of false prediction and p-values in a downloadable file. Conclusions The online interactive Rank Products analysis tool RankProdIt, for analysis of any data set containing measurements for multiple replicated conditions, is available at: http://strep-microarray.sbs.surrey.ac.uk/RankProducts

  7. Prediction of residual metabolic activity after treatment in NSCLC patients

    International Nuclear Information System (INIS)

    Rios Velazquez, Emmanuel; Aerts, Hugo J.W.L.; Oberije, Cary; Ruysscher, Dirk De; Lambin, Philippe

    2010-01-01

    Purpose. Metabolic response assessment is often used as a surrogate of local failure and survival. Early identification of patients with residual metabolic activity is essential as this enables selection of patients who could potentially benefit from additional therapy. We report on the development of a pre-treatment prediction model for metabolic response using patient, tumor and treatment factors. Methods. One hundred and one patients with inoperable NSCLC (stage I-IV), treated with 3D conformal radical (chemo)-radiotherapy were retrospectively included in this study. All patients received a pre and post-radiotherapy fluorodeoxyglucose positron emission tomography-computed tomography FDG-PET-CT scan. The electronic medical record system and the medical patient charts were reviewed to obtain demographic, clinical, tumor and treatment data. Primary outcome measure was examined using a metabolic response assessment on a post-radiotherapy FDG-PET-CT scan. Radiotherapy was delivered in fractions of 1.8 Gy, twice a day, with a median prescribed dose of 60 Gy. Results. Overall survival was worse in patients with residual metabolic active areas compared with the patients with a complete metabolic response (p=0.0001). In univariate analysis, three variables were significantly associated with residual disease: larger primary gross tumor volume (GTVprimary, p=0.002), higher pre-treatment maximum standardized uptake value (SUV max , p=0.0005) in the primary tumor and shorter overall treatment time (OTT, p=0.046). A multivariate model including GTVprimary, SUV max , equivalent radiation dose at 2 Gy corrected for time (EQD2, T) and OTT yielded an area under the curve assessed by the leave-one-out cross validation of 0.71 (95% CI, 0.65-0.76). Conclusion. Our results confirmed the validity of metabolic response assessment as a surrogate of survival. We developed a multivariate model that is able to identify patients at risk of residual disease. These patients may benefit from

  8. Predictive Risk Factors in the Treatment of Gestational Diabetes Mellitus

    Directory of Open Access Journals (Sweden)

    Lebriz Hale Aktun

    2015-01-01

    Full Text Available Objective This study aims to investigate predictive risk factors in the treatment of gestational diabetes mellitus (GDM. Patients and Methods A total of 256 pregnant women who underwent 75 g oral glucose tolerance test (OGTT during 24–28 weeks of pregnancy were included according to the World Health Organization criteria. Demographic characteristics of the patients, including age, parity, family history of diabetes, body weight before pregnancy, and body weight at the diagnosis of GDM, were recorded. Fasting insulin and hemoglobin A1c (HbA1c values at the time of diagnosis were evaluated. The patients were divided into two groups: those requiring insulin treatment (insulin group, n = 89 and those receiving diet therapy (diet group, n = 167 during pregnancy according to the American Diabetes Association recommendations. Results A total of 34.76% of the pregnant women with GDM required insulin treatment. The mean age of these patients was significantly higher compared to the diet group (34.9 ± 0.6 years vs. 31.9 ± 0.6 years; P = 0.004. Body mass index before pregnancy was also significantly higher in the insulin group than that in the diet group (32 ± 0.9 kg/m 2 vs. 29 ± 0.7 kg/m 2 ; P = 0.004. Fasting blood glucose (FBG during OGTT was 105.6 ± 2.1 mg/dL and 96.7 ± 1.1 mg/dL in the insulin group and diet group, respectively ( P < 0.001. There was no significant difference in fasting plasma glucose during OGTT between the groups ( P = 0.069, while plasma glucose at two hours was 161.1 ± 6.8 mg/dL in the insulin group and 145.1 ± 3.7 mg/dL in the diet group ( P = 0.027. At the time of diagnosis, HbA1c values were significantly higher in the insulin group compared to the diet group (5.3 ± 0.1 vs. 4.9 ± 0.1; P = 0.001. There was no significant difference in FBG and homeostasis model assessment-insulin resistance values between the groups ( P = 0.908, P = 0.073. Conclusion Our study results suggest that age, family history of diabetes, body

  9. Predictive models in the diagnosis and treatment of autoimmune epilepsy.

    Science.gov (United States)

    Dubey, Divyanshu; Singh, Jaysingh; Britton, Jeffrey W; Pittock, Sean J; Flanagan, Eoin P; Lennon, Vanda A; Tillema, Jan-Mendelt; Wirrell, Elaine; Shin, Cheolsu; So, Elson; Cascino, Gregory D; Wingerchuk, Dean M; Hoerth, Matthew T; Shih, Jerry J; Nickels, Katherine C; McKeon, Andrew

    2017-07-01

    To validate predictive models for neural antibody positivity and immunotherapy response in epilepsy. We conducted a retrospective study of epilepsy cases at Mayo Clinic (Rochester-MN; Scottsdale-AZ, and Jacksonville-FL) in whom autoimmune encephalopathy/epilepsy/dementia autoantibody testing profiles were requested (06/30/2014-06/30/2016). An Antibody Prevalence in Epilepsy (APE) score, based on clinical characteristics, was assigned to each patient. Among patients who received immunotherapy, a Response to Immunotherapy in Epilepsy (RITE) score was assigned. Favorable seizure outcome was defined as >50% reduction of seizure frequency at the first follow-up. Serum and cerebrospinal fluid (CSF) from 1,736 patients were sent to the Mayo Clinic Neuroimmunology Laboratory for neural autoantibody evaluation. Three hundred eighty-seven of these patients met the diagnostic criteria for epilepsy. Central nervous system (CNS)-specific antibodies were detected in 44 patients. Certain clinical features such as new-onset epilepsy, autonomic dysfunction, viral prodrome, faciobrachial dystonic seizures/oral dyskinesia, inflammatory CSF profile, and mesial temporal magnetic resonance imaging (MRI) abnormalities had a significant association with positive antibody results. A significantly higher proportion of antibody-positive patients had an APE score ≥4 (97.7% vs. 21.6%, p < 0.01). Sensitivity and specificity of an APE score ≥4 to predict presence of specific neural auto-antibody were 97.7% and 77.9%, respectively. In the subset of patients who received immunotherapy (77), autonomic dysfunction, faciobrachial dystonic seizures/oral dyskinesia, early initiation of immunotherapy, and presence of antibodies targeting plasma membrane proteins (cell-surface antigens) were associated with favorable seizure outcome. Sensitivity and specificity of a RITE score ≥7 to predict favorable seizure outcome were 87.5% and 83.8%, respectively. APE and RITE scores can aid diagnosis

  10. Ranking Baltic States Researchers

    Directory of Open Access Journals (Sweden)

    Gyula Mester

    2017-10-01

    Full Text Available In this article, using the h-index and the total number of citations, the best 10 Lithuanian, Latvian and Estonian researchers from several disciplines are ranked. The list may be formed based on the h-index and the total number of citations, given in Web of Science, Scopus, Publish or Perish Program and Google Scholar database. Data for the first 10 researchers are presented. Google Scholar is the most complete. Therefore, to define a single indicator, h-index calculated by Google Scholar may be a good and simple one. The author chooses the Google Scholar database as it is the broadest one.

  11. University Rankings and Social Science

    OpenAIRE

    Marginson, S.

    2014-01-01

    University rankings widely affect the behaviours of prospective students and their families, university executive leaders, academic faculty, governments and investors in higher education. Yet the social science foundations of global rankings receive little scrutiny. Rankings that simply recycle reputation without any necessary connection to real outputs are of no common value. It is necessary that rankings be soundly based in scientific terms if a virtuous relationship between performance and...

  12. International Study to Predict Optimized Treatment for Depression (iSPOT-D, a randomized clinical trial: rationale and protocol

    Directory of Open Access Journals (Sweden)

    Cooper Nicholas J

    2011-01-01

    : NCT00693849 URL: http://clinicaltrials.gov/ct2/show/NCT00693849?term=International+Study+to+Predict+Optimized+Treatment+for+Depression&rank=1

  13. Edge Contrast of the FLAIR Hyperintense Region Predicts Survival in Patients with High-Grade Gliomas following Treatment with Bevacizumab.

    Science.gov (United States)

    Bahrami, N; Piccioni, D; Karunamuni, R; Chang, Y-H; White, N; Delfanti, R; Seibert, T M; Hattangadi-Gluth, J A; Dale, A; Farid, N; McDonald, C R

    2018-04-05

    Treatment with bevacizumab is standard of care for recurrent high-grade gliomas; however, monitoring response to treatment following bevacizumab remains a challenge. The purpose of this study was to determine whether quantifying the sharpness of the fluid-attenuated inversion recovery hyperintense border using a measure derived from texture analysis-edge contrast-improves the evaluation of response to bevacizumab in patients with high-grade gliomas. MRIs were evaluated in 33 patients with high-grade gliomas before and after the initiation of bevacizumab. Volumes of interest within the FLAIR hyperintense region were segmented. Edge contrast magnitude for each VOI was extracted using gradients of the 3D FLAIR images. Cox proportional hazards models were generated to determine the relationship between edge contrast and progression-free survival/overall survival using age and the extent of surgical resection as covariates. After bevacizumab, lower edge contrast of the FLAIR hyperintense region was associated with poorer progression-free survival ( P = .009) and overall survival ( P = .022) among patients with high-grade gliomas. Kaplan-Meier curves revealed that edge contrast cutoff significantly stratified patients for both progression-free survival (log-rank χ 2 = 8.3, P = .003) and overall survival (log-rank χ 2 = 5.5, P = .019). Texture analysis using edge contrast of the FLAIR hyperintense region may be an important predictive indicator in patients with high-grade gliomas following treatment with bevacizumab. Specifically, low FLAIR edge contrast may partially reflect areas of early tumor infiltration. This study adds to a growing body of literature proposing that quantifying features may be important for determining outcomes in patients with high-grade gliomas. © 2018 by American Journal of Neuroradiology.

  14. Post-treatment changes of tumour perfusion parameters can help to predict survival in patients with high-grade astrocytoma

    Energy Technology Data Exchange (ETDEWEB)

    Sanz-Requena, Roberto; Marti-Bonmati, Luis [Hospital Quironsalud Valencia, Radiology Department, Valencia (Spain); Hospital Universitari i Politecnic La Fe, Grupo de Investigacion Biomedica en Imagen, Valencia (Spain); Revert-Ventura, Antonio J.; Salame-Gamarra, Fares [Hospital de Manises, Radiology Department, Manises (Spain); Garcia-Marti, Gracian [Hospital Quironsalud Valencia, Radiology Department, Valencia (Spain); Hospital Universitari i Politecnic La Fe, Grupo de Investigacion Biomedica en Imagen, Valencia (Spain); CIBER-SAM, Instituto de Salud Carlos III, Madrid (Spain); Perez-Girbes, Alexandre [Hospital Universitari i Politecnic La Fe, Grupo de Investigacion Biomedica en Imagen, Valencia (Spain); Molla-Olmos, Enrique [Hospital La Ribera, Radiology Department, Alzira (Spain)

    2017-08-15

    Vascular characteristics of tumour and peritumoral volumes of high-grade gliomas change with treatment. This work evaluates the variations of T2*-weighted perfusion parameters as overall survival (OS) predictors. Forty-five patients with histologically confirmed high-grade astrocytoma (8 grade III and 37 grade IV) were included. All patients underwent pre- and post-treatment T2*-weighted contrast-enhanced magnetic resonance (MR) imaging. Tumour, peritumoral and control volumes were segmented. Relative variations of cerebral blood flow (CBF), cerebral blood volume (CBV), mean transit time (MTT), K{sup trans-T2*}, k{sub ep-T2*}, v{sub e-T2*} and v{sub p-T2*} were calculated. Differences regarding tumour grade and surgical resection extension were evaluated with ANOVA tests. For each parameter, two groups were defined by non-supervised clusterisation. Survival analysis were performed on these groups. For the tumour region, the 90th percentile increase or stagnation of CBV was associated with shorter survival, while a decrease related to longer survival (393 ± 189 vs 594 ± 294 days; log-rank p = 0.019; Cox hazard-ratio, 2.31; 95% confidence interval [CI], 1.12-4.74). K{sup trans-T2*} showed similar results (414 ± 177 vs 553 ± 312 days; log-rank p = 0.037; hazard-ratio, 2.19; 95% CI, 1.03-4.65). The peritumoral area values showed no relationship with OS. Post-treatment variations of the highest CBV and K{sup trans-T2*} values in the tumour volume are predictive factors of OS in patients with high-grade gliomas. (orig.)

  15. Preference Learning and Ranking by Pairwise Comparison

    Science.gov (United States)

    Fürnkranz, Johannes; Hüllermeier, Eyke

    This chapter provides an overview of recent work on preference learning and ranking via pairwise classification. The learning by pairwise comparison (LPC) paradigm is the natural machine learning counterpart to the relational approach to preference modeling and decision making. From a machine learning point of view, LPC is especially appealing as it decomposes a possibly complex prediction problem into a certain number of learning problems of the simplest type, namely binary classification. We explain how to approach different preference learning problems, such as label and instance ranking, within the framework of LPC. We primarily focus on methodological aspects, but also address theoretical questions as well as algorithmic and complexity issues.

  16. University Rankings and Social Science

    Science.gov (United States)

    Marginson, Simon

    2014-01-01

    University rankings widely affect the behaviours of prospective students and their families, university executive leaders, academic faculty, governments and investors in higher education. Yet the social science foundations of global rankings receive little scrutiny. Rankings that simply recycle reputation without any necessary connection to real…

  17. A generalization of Friedman's rank statistic

    NARCIS (Netherlands)

    Kroon, de J.; Laan, van der P.

    1983-01-01

    In this paper a very natural generalization of the two·way analysis of variance rank statistic of FRIEDMAN is given. The general distribution-free test procedure based on this statistic for the effect of J treatments in a random block design can be applied in general two-way layouts without

  18. A simple risk scoring system for prediction of relapse after inpatient alcohol treatment.

    Science.gov (United States)

    Pedersen, Mads Uffe; Hesse, Morten

    2009-01-01

    Predicting relapse after alcoholism treatment can be useful in targeting patients for aftercare services. However, a valid and practical instrument for predicting relapse risk does not exist. Based on a prospective study of alcoholism treatment, we developed the Risk of Alcoholic Relapse Scale (RARS) using items taken from the Addiction Severity Index and some basic demographic information. The RARS was cross-validated using two non-overlapping samples, and tested for its ability to predict relapse across different models of treatment. The RARS predicted relapse to drinking within 6 months after alcoholism treatment in both the original and the validation sample, and in a second validation sample it predicted admission to new treatment 3 years after treatment. The RARS can identify patients at high risk of relapse who need extra aftercare and support after treatment.

  19. Fractional cointegration rank estimation

    DEFF Research Database (Denmark)

    Lasak, Katarzyna; Velasco, Carlos

    the parameters of the model under the null hypothesis of the cointegration rank r = 1, 2, ..., p-1. This step provides consistent estimates of the cointegration degree, the cointegration vectors, the speed of adjustment to the equilibrium parameters and the common trends. In the second step we carry out a sup......-likelihood ratio test of no-cointegration on the estimated p - r common trends that are not cointegrated under the null. The cointegration degree is re-estimated in the second step to allow for new cointegration relationships with different memory. We augment the error correction model in the second step...... to control for stochastic trend estimation effects from the first step. The critical values of the tests proposed depend only on the number of common trends under the null, p - r, and on the interval of the cointegration degrees b allowed, but not on the true cointegration degree b0. Hence, no additional...

  20. Rankings, creatividad y urbanismo

    Directory of Open Access Journals (Sweden)

    JOAQUÍN SABATÉ

    2008-08-01

    Full Text Available La competencia entre ciudades constituye uno de los factores impulsores de procesos de renovación urbana y los rankings han devenido instrumentos de medida de la calidad de las ciudades. Nos detendremos en el caso de un antiguo barrio industrial hoy en vías de transformación en distrito "creativo" por medio de una intervención urbanística de gran escala. Su análisis nos descubre tres claves críticas. En primer lugar, nos obliga a plantearnos la definición de innovación urbana y cómo se integran el pasado, la identidad y la memoria en la construcción del futuro. Nos lleva a comprender que la innovación y el conocimiento no se "dan" casualmente, sino que son el fruto de una larga y compleja red en la que participan saberes, espacios, actores e instituciones diversas en naturaleza, escala y magnitud. Por último nos obliga a reflexionar sobre el valor que se le otorga a lo local en los procesos de renovación urbana.Competition among cities constitutes one ofthe main factors o furban renewal, and rankings have become instruments to indícate cities quality. Studying the transformation of an old industrial quarter into a "creative district" by the means ofa large scale urban project we highlight three main conclusions. First, itasks us to reconsider the notion ofurban innovation and hoto past, identity and memory should intégrate the future development. Second, it shows that innovation and knowledge doesn't yield per chance, but are the result ofa large and complex grid of diverse knowledges, spaces, agents and institutions. Finally itforces us to reflect about the valué attributed to the "local" in urban renewalprocesses.

  1. Ranking nodes in growing networks: When PageRank fails.

    Science.gov (United States)

    Mariani, Manuel Sebastian; Medo, Matúš; Zhang, Yi-Cheng

    2015-11-10

    PageRank is arguably the most popular ranking algorithm which is being applied in real systems ranging from information to biological and infrastructure networks. Despite its outstanding popularity and broad use in different areas of science, the relation between the algorithm's efficacy and properties of the network on which it acts has not yet been fully understood. We study here PageRank's performance on a network model supported by real data, and show that realistic temporal effects make PageRank fail in individuating the most valuable nodes for a broad range of model parameters. Results on real data are in qualitative agreement with our model-based findings. This failure of PageRank reveals that the static approach to information filtering is inappropriate for a broad class of growing systems, and suggest that time-dependent algorithms that are based on the temporal linking patterns of these systems are needed to better rank the nodes.

  2. Threat-Related Selective Attention Predicts Treatment Success in Childhood Anxiety Disorders

    NARCIS (Netherlands)

    J.S. Legerstee (Jeroen); J.H.M. Tulen (Joke); V.L. Kallen (Victor); G.C. Dieleman (Gwen); P.D.A. Treffers (Philip); F.C. Verhulst (Frank); E.M.W.J. Utens (Elisabeth)

    2009-01-01

    textabstractAbstract OBJECTIVE: The present study examined whether threat-related selective attention was predictive of treatment success in children with anxiety disorders and whether age moderated this association. Specific components of selective attention were examined in treatment responders

  3. Threat-related selective attention predicts treatment success in childhood anxiety disorders

    NARCIS (Netherlands)

    Legerstee, Jeroen S.; Tulen, Joke H. M.; Kallen, Victor L.; Dieleman, Gwen C.; Treffers, Philip D. A.; Verhulst, Frank C.; Utens, Elisabeth M. W. J.

    2009-01-01

    The present study examined whether threat-related selective attention was predictive of treatment success in children with anxiety disorders and whether age moderated this association. Specific components of selective attention were examined in treatment responders and nonresponders. Participants

  4. Neophilia Ranking of Scientific Journals.

    Science.gov (United States)

    Packalen, Mikko; Bhattacharya, Jay

    2017-01-01

    The ranking of scientific journals is important because of the signal it sends to scientists about what is considered most vital for scientific progress. Existing ranking systems focus on measuring the influence of a scientific paper (citations)-these rankings do not reward journals for publishing innovative work that builds on new ideas. We propose an alternative ranking based on the proclivity of journals to publish papers that build on new ideas, and we implement this ranking via a text-based analysis of all published biomedical papers dating back to 1946. In addition, we compare our neophilia ranking to citation-based (impact factor) rankings; this comparison shows that the two ranking approaches are distinct. Prior theoretical work suggests an active role for our neophilia index in science policy. Absent an explicit incentive to pursue novel science, scientists underinvest in innovative work because of a coordination problem: for work on a new idea to flourish, many scientists must decide to adopt it in their work. Rankings that are based purely on influence thus do not provide sufficient incentives for publishing innovative work. By contrast, adoption of the neophilia index as part of journal-ranking procedures by funding agencies and university administrators would provide an explicit incentive for journals to publish innovative work and thus help solve the coordination problem by increasing scientists' incentives to pursue innovative work.

  5. Methods to model and predict the ViewRay treatment deliveries to aid patient scheduling and treatment planning.

    Science.gov (United States)

    Liu, Shi; Wu, Yu; Wooten, H Omar; Green, Olga; Archer, Brent; Li, Harold; Yang, Deshan

    2016-03-08

    A software tool is developed, given a new treatment plan, to predict treatment delivery time for radiation therapy (RT) treatments of patients on ViewRay magnetic resonance image-guided radiation therapy (MR-IGRT) delivery system. This tool is necessary for managing patient treatment scheduling in our clinic. The predicted treatment delivery time and the assessment of plan complexities could also be useful to aid treatment planning. A patient's total treatment delivery time, not including time required for localization, is modeled as the sum of four components: 1) the treatment initialization time; 2) the total beam-on time; 3) the gantry rotation time; and 4) the multileaf collimator (MLC) motion time. Each of the four components is predicted separately. The total beam-on time can be calculated using both the planned beam-on time and the decay-corrected dose rate. To predict the remain-ing components, we retrospectively analyzed the patient treatment delivery record files. The initialization time is demonstrated to be random since it depends on the final gantry angle of the previous treatment. Based on modeling the relationships between the gantry rotation angles and the corresponding rotation time, linear regression is applied to predict the gantry rotation time. The MLC motion time is calculated using the leaves delay modeling method and the leaf motion speed. A quantitative analysis was performed to understand the correlation between the total treatment time and the plan complexity. The proposed algorithm is able to predict the ViewRay treatment delivery time with the average prediction error 0.22min or 1.82%, and the maximal prediction error 0.89 min or 7.88%. The analysis has shown the correlation between the plan modulation (PM) factor and the total treatment delivery time, as well as the treatment delivery duty cycle. A possibility has been identified to significantly reduce MLC motion time by optimizing the positions of closed MLC pairs. The accuracy of

  6. Post-treatment PET/CT and p16 status for predicting treatment outcomes in locally advanced head and neck cancer after definitive radiation

    Energy Technology Data Exchange (ETDEWEB)

    Awan, Musaddiq J.; Machtay, Mitchell; Yao, Min [Case Western Reserve University and University Hospitals, Department of Radiation Oncology, Cleveland, OH (United States); Lavertu, Pierre; Zender, Chad; Rezaee, Rod; Fowler, Nicole [University Hospitals, Department of Otolaryngology and Head and Neck Surgery, Cleveland, OH (United States); Karapetyan, Lilit; Gibson, Michael [University Hospitals, Department of Medical Oncology, Cleveland, OH (United States); Wasman, Jay [University Hospitals, Department of Pathology, Cleveland, OH (United States); Faulhaber, Peter [University Hospitals, Department of Nuclear Medicine and Radiology, Cleveland, OH (United States)

    2017-06-15

    To retrospectively review post-treatment (post-tx) FDG-PET/CT scans in patients with advanced head and neck squamous cell carcinoma (HNSCC) and known p16 status, treated with definitive (chemo)radiation (RT). A total of 108 eligible patients had N2A or greater HNSCC treated with chemoRT from August 1, 2008, to February 28, 2015, with post-tx PET/CT within 6 months after RT. Kaplan-Meier curves, log-rank statistics, and Cox proportional hazards regression were used for statistical analysis. Median follow-up was 2.38 years. Sixty-eight (63.0%) patients had p16+ and 40 (37.0%) had p16- status. Two-year overall survival and recurrence-free survival were 93.4% and 77.8%, respectively. The negative predictive value (NPV) of PET/CT for local recurrence (LR) was 100%. The NPV for regional recurrence (RR) was 96.5% for all patients, 100% for p16+ patients, and 88.5% for p16- patients. The positive predictive value (PPV) of PET/CT for recurrence was 77.3% for all patients, 50.0% for p16+, and 78.6% for p16-. The PPV for LR was 72.7% for all patients, 50.0% for p16+ patients, and 72.7% for p16- patients. The PPV for RR was 50.0% for all patients, 33% for p16+, and 66.6% for p16-. Post-tx PET/CT and p16 status were independent predictors of recurrence-free survival (p < 0.01). Post-tx PET/CT predicts treatment outcomes in both p16 + and p16- patients, and does so independently of p16 status. P16- patients with negative PET have a 10% risk of nodal recurrence, and closer follow-up in these patients is warranted. (orig.)

  7. Inhibition of osteoclastogenesis by RNA interference targeting RANK

    Directory of Open Access Journals (Sweden)

    Ma Ruofan

    2012-08-01

    Full Text Available Abstract Background Osteoclasts and osteoblasts regulate bone resorption and formation to allow bone remodeling and homeostasis. The balance between bone resorption and formation is disturbed by abnormal recruitment of osteoclasts. Osteoclast differentiation is dependent on the receptor activator of nuclear factor NF-kappa B (RANK ligand (RANKL as well as the macrophage colony-stimulating factor (M-CSF. The RANKL/RANK system and RANK signaling induce osteoclast formation mediated by various cytokines. The RANK/RANKL pathway has been primarily implicated in metabolic, degenerative and neoplastic bone disorders or osteolysis. The central role of RANK/RANKL interaction in osteoclastogenesis makes RANK an attractive target for potential therapies in treatment of osteolysis. The purpose of this study was to assess the effect of inhibition of RANK expression in mouse bone marrow macrophages on osteoclast differentiation and bone resorption. Methods Three pairs of short hairpin RNAs (shRNA targeting RANK were designed and synthesized. The optimal shRNA was selected among three pairs of shRNAs by RANK expression analyzed by Western blot and Real-time PCR. We investigated suppression of osteoclastogenesis of mouse bone marrow macrophages (BMMs using the optimal shRNA by targeting RANK. Results Among the three shRANKs examined, shRANK-3 significantly suppressed [88.3%] the RANK expression (p Conclusions These findings suggest that retrovirus-mediated shRNA targeting RANK inhibits osteoclast differentiation and osteolysis. It may appear an attractive target for preventing osteolysis in humans with a potential clinical application.

  8. Ranking Specific Sets of Objects.

    Science.gov (United States)

    Maly, Jan; Woltran, Stefan

    2017-01-01

    Ranking sets of objects based on an order between the single elements has been thoroughly studied in the literature. In particular, it has been shown that it is in general impossible to find a total ranking - jointly satisfying properties as dominance and independence - on the whole power set of objects. However, in many applications certain elements from the entire power set might not be required and can be neglected in the ranking process. For instance, certain sets might be ruled out due to hard constraints or are not satisfying some background theory. In this paper, we treat the computational problem whether an order on a given subset of the power set of elements satisfying different variants of dominance and independence can be found, given a ranking on the elements. We show that this problem is tractable for partial rankings and NP-complete for total rankings.

  9. Wikipedia ranking of world universities

    Science.gov (United States)

    Lages, José; Patt, Antoine; Shepelyansky, Dima L.

    2016-03-01

    We use the directed networks between articles of 24 Wikipedia language editions for producing the wikipedia ranking of world Universities (WRWU) using PageRank, 2DRank and CheiRank algorithms. This approach allows to incorporate various cultural views on world universities using the mathematical statistical analysis independent of cultural preferences. The Wikipedia ranking of top 100 universities provides about 60% overlap with the Shanghai university ranking demonstrating the reliable features of this approach. At the same time WRWU incorporates all knowledge accumulated at 24 Wikipedia editions giving stronger highlights for historically important universities leading to a different estimation of efficiency of world countries in university education. The historical development of university ranking is analyzed during ten centuries of their history.

  10. Anhedonia Predicts Poorer Recovery among Youth with Selective Serotonin Reuptake Inhibitor Treatment-Resistant Depression

    Science.gov (United States)

    McMakin, Dana L.; Olino, Thomas M.; Porta, Giovanna; Dietz, Laura J.; Emslie, Graham; Clarke, Gregory; Wagner, Karen Dineen; Asarnow, Joan R.; Ryan, Neal D.; Birmaher, Boris; Shamseddeen, Wael; Mayes, Taryn; Kennard, Betsy; Spirito, Anthony; Keller, Martin; Lynch, Frances L.; Dickerson, John F.; Brent, David A.

    2012-01-01

    Objective: To identify symptom dimensions of depression that predict recovery among selective serotonin reuptake inhibitor (SSRI) treatment-resistant adolescents undergoing second-step treatment. Method: The Treatment of Resistant Depression in Adolescents (TORDIA) trial included 334 SSRI treatment-resistant youth randomized to a medication…

  11. Statistical methods for ranking data

    CERN Document Server

    Alvo, Mayer

    2014-01-01

    This book introduces advanced undergraduate, graduate students and practitioners to statistical methods for ranking data. An important aspect of nonparametric statistics is oriented towards the use of ranking data. Rank correlation is defined through the notion of distance functions and the notion of compatibility is introduced to deal with incomplete data. Ranking data are also modeled using a variety of modern tools such as CART, MCMC, EM algorithm and factor analysis. This book deals with statistical methods used for analyzing such data and provides a novel and unifying approach for hypotheses testing. The techniques described in the book are illustrated with examples and the statistical software is provided on the authors’ website.

  12. Preoperative distress predicts persistent pain after breast cancer treatment

    DEFF Research Database (Denmark)

    Mejdahl, Mathias Kvist; Mertz, Birgitte Goldschmidt; Bidstrup, Pernille Envold Hansen

    2015-01-01

    PURPOSE: Persistent pain after breast cancer treatment (PPBCT) affects 25% to 60% of breast cancer survivors and is recognized as a clinical problem, with 10% to 15% reporting moderate to severe pain several years after treatment. Psychological comorbidity is known to influence pain perception...

  13. Clinical prediction models in reproductive medicine: applications in untreated subfertility and in IVF treatment

    NARCIS (Netherlands)

    C.C. Hunault

    2006-01-01

    textabstractThis thesis deals with two prediction problems in reproductive medicine. The first is the prediction in infertile couples of the chance to conceive without treatment. The second deals with the prediction of the chance of conception in couples treated with in vitro fertilization (IVF).

  14. Ranking nodes in growing networks: When PageRank fails

    Science.gov (United States)

    Mariani, Manuel Sebastian; Medo, Matúš; Zhang, Yi-Cheng

    2015-11-01

    PageRank is arguably the most popular ranking algorithm which is being applied in real systems ranging from information to biological and infrastructure networks. Despite its outstanding popularity and broad use in different areas of science, the relation between the algorithm’s efficacy and properties of the network on which it acts has not yet been fully understood. We study here PageRank’s performance on a network model supported by real data, and show that realistic temporal effects make PageRank fail in individuating the most valuable nodes for a broad range of model parameters. Results on real data are in qualitative agreement with our model-based findings. This failure of PageRank reveals that the static approach to information filtering is inappropriate for a broad class of growing systems, and suggest that time-dependent algorithms that are based on the temporal linking patterns of these systems are needed to better rank the nodes.

  15. Returns to Tenure: Time or Rank?

    DEFF Research Database (Denmark)

    Buhai, Ioan Sebastian

    -specific investment, efficiency-wages or adverse-selection models. However, rent extracting arguments as suggested by the theory of internal labor markets, indicate that the relative position of the worker in the seniority hierarchy of the firm, her 'seniority rank', may also explain part of the observed returns...... relative to their peer workers), as predicted by theories on unionized and insider-outsider markets....

  16. Predictability of uncontrollable multifocal seizures - towards new treatment options

    Science.gov (United States)

    Lehnertz, Klaus; Dickten, Henning; Porz, Stephan; Helmstaedter, Christoph; Elger, Christian E.

    2016-04-01

    Drug-resistant, multifocal, non-resectable epilepsies are among the most difficult epileptic disorders to manage. An approach to control previously uncontrollable seizures in epilepsy patients would consist of identifying seizure precursors in critical brain areas combined with delivering a counteracting influence to prevent seizure generation. Predictability of seizures with acceptable levels of sensitivity and specificity, even in an ambulatory setting, has been repeatedly shown, however, in patients with a single seizure focus only. We did a study to assess feasibility of state-of-the-art, electroencephalogram-based seizure-prediction techniques in patients with uncontrollable multifocal seizures. We obtained significant predictive information about upcoming seizures in more than two thirds of patients. Unexpectedly, the emergence of seizure precursors was confined to non-affected brain areas. Our findings clearly indicate that epileptic networks, spanning lobes and hemispheres, underlie generation of seizures. Our proof-of-concept study is an important milestone towards new therapeutic strategies based on seizure-prediction techniques for clinical practice.

  17. Factors that might be predictive of completion of vaginismus treatment.

    Science.gov (United States)

    Özdel, Kadir; Yılmaz Özpolat, Ayşegül; Çeri, Özge; Kumbasar, Hakan

    2012-01-01

    Vaginismus is defined as a recurrent or persistent involuntary spasm of the musculature of the outer third of the vagina that interferes with sexual intercourse. The aim of this study was to assess the level of symptoms of depression, anxiety, obsessive-compulsive symptoms, and perfectionism among patients with vaginismus, as well as to determine if these clinical variables are related to the completion of treatment. The study included 20 women with vaginismus and their spouses that were referred as outpatients to Ankara University, School of Medicine, Department of Psychiatry, Consultation and Liaison Unit. All couples underwent cognitive behavioral therapy, which was administered as 40-60-min weekly sessions. At the first (assessment) session, the female patients were assessed using a sociodemographic evaluation form, the Hamilton Rating Scale for Depression (HAM-D), the Hamilton Rating Scale for Anxiety (HAM-A), the Maudsley Obsessive-Compulsive Inventory (MOCI), the Multidimensional Perfectionism Scale (MPS), and the Golombok Rust Inventory of Sexual Satisfaction (GRISS). The male spouses were evaluated using the GRISS. The same scales were administered after the completion of treatment to those that completed the treatment. The correlation between completion of treatment, and an elevated level of anxiety and self-oriented perfectionism was significant (P Vaginismus is not only a sexual dysfunction, but it is related to multiple components of mental health. Anxiety and a perfectionist personality trait were important factors associated with the completion of treatment; therefore, these factors should be evaluated before treatment.

  18. Poor Response to Periodontal Treatment May Predict Future Cardiovascular Disease.

    Science.gov (United States)

    Holmlund, A; Lampa, E; Lind, L

    2017-07-01

    Periodontal disease has been associated with cardiovascular disease (CVD), but whether the response to the treatment of periodontal disease affects this association has not been investigated in any large prospective study. Periodontal data obtained at baseline and 1 y after treatment were available in 5,297 individuals with remaining teeth who were treated at a specialized clinic for periodontal disease. Poor response to treatment was defined as having >10% sites with probing pocket depth >4 mm deep and bleeding on probing at ≥20% of the sites 1 y after active treatment. Fatal/nonfatal incidence rate of CVD (composite end point of myocardial infarction, stroke, and heart failure) was obtained from the Swedish cause-of-death and hospital discharge registers. Poisson regression analysis was performed to analyze future risk of CVD. During a median follow-up of 16.8 y (89,719 person-years at risk), those individuals who did not respond well to treatment (13.8% of the sample) had an increased incidence of CVD ( n = 870) when compared with responders (23.6 vs. 15.3%, P 4 mm, and number of teeth, the incidence rate ratio for CVD among poor responders was 1.28 (95% CI, 1.07 to 1.53; P = 0.007) as opposed to good responders. The incidence rate ratio among poor responders increased to 1.39 (95% CI, 1.13 to 1.73; P = 0.002) for those with the most remaining teeth. Individuals who did not respond well to periodontal treatment had an increased risk for future CVD, indicating that successful periodontal treatment might influence progression of subclinical CVD.

  19. PageRank tracker: from ranking to tracking.

    Science.gov (United States)

    Gong, Chen; Fu, Keren; Loza, Artur; Wu, Qiang; Liu, Jia; Yang, Jie

    2014-06-01

    Video object tracking is widely used in many real-world applications, and it has been extensively studied for over two decades. However, tracking robustness is still an issue in most existing methods, due to the difficulties with adaptation to environmental or target changes. In order to improve adaptability, this paper formulates the tracking process as a ranking problem, and the PageRank algorithm, which is a well-known webpage ranking algorithm used by Google, is applied. Labeled and unlabeled samples in tracking application are analogous to query webpages and the webpages to be ranked, respectively. Therefore, determining the target is equivalent to finding the unlabeled sample that is the most associated with existing labeled set. We modify the conventional PageRank algorithm in three aspects for tracking application, including graph construction, PageRank vector acquisition and target filtering. Our simulations with the use of various challenging public-domain video sequences reveal that the proposed PageRank tracker outperforms mean-shift tracker, co-tracker, semiboosting and beyond semiboosting trackers in terms of accuracy, robustness and stability.

  20. Motivation and treatment credibility predict alliance in cognitive behavioral treatment for youth with anxiety disorders in community clinics.

    Science.gov (United States)

    Fjermestad, K W; Lerner, M D; McLeod, B D; Wergeland, G J H; Haugland, B S M; Havik, O E; Öst, L-G; Silverman, W K

    2017-11-16

    We examined whether motivation and treatment credibility predicted alliance in a 10-session cognitive behavioral treatment delivered in community clinics for youth anxiety disorders. Ninety-one clinic-referred youths (mean age  = 11.4 years, standard deviation = 2.1, range 8-15 years, 49.5% boys) with anxiety disorders-rated treatment motivation at pretreatment and perceived treatment credibility after session 1. Youths and therapists (YT) rated alliance after session 3 (early) and session 7 (late). Hierarchical linear models were applied to examine whether motivation and treatment credibility predicted YT early alliance, YT alliance change, and YT alliance agreement. Motivation predicted high early YT alliance, but not YT alliance change or alliance agreement. Youth-rated treatment credibility predicted high early youth alliance and high YT positive alliance change, but not early therapist alliance or alliance agreement. Conclusion Efforts to enhance youth motivation and treatment credibility early in treatment could facilitate the formation of a strong YT alliance. © 2017 Wiley Periodicals, Inc.

  1. Predicting relapse of Graves' disease following treatment with antithyroid drugs

    Science.gov (United States)

    LIU, LIN; LU, HONGWEN; LIU, YANG; LIU, CHANGSHAN; XUN, CHU

    2016-01-01

    The aim of the present study was to monitor long term antithyroid drug treatments and to identify prognostic factors for Graves' disease (GD). A total of 306 patients with GD who were referred to the Endocrinology Clinic at Weifang People's Hospital (Weifang, China) between August 2005 and June 2009 and treated with methimazole were included in the present study. Following treatment, patients were divided into non-remission, including recurrence and constant treatment subgroups, and remission groups. Various prognosis factors were analyzed and compared, including: Patient age, gender, size of thyroid prior to and following treatment, thyroid hormone levels, disease relapse, hypothyroidism and drug side-effects, and states of thyrotropin suppression were observed at 3, 6 and 12 months post-treatment. Sixty-five patients (21.2%) were male, and 241 patients (78.8%) were female. The mean age was 42±11 years, and the follow-up was 31.5±6.8 months. Following long-term treatment, 141 patients (46%) demonstrated remission of hyperthyroidism with a mean duration of 18.7±1.9 months. The average age at diagnosis was 45.6±10.3 years in the remission group, as compared with 36.4±8.8 years in the non-remission group (t=3.152; P=0.002). Free thyroxine (FT)3 levels were demonstrated to be 25.2±8.9 and 18.7±9.4 pmol/l in the non-remission and remission groups, respectively (t=3.326, P=0.001). The FT3/FT4 ratio and thyrotrophin receptor antibody (TRAb) levels were both significantly higher in the non-remission group (t=3.331, 3.389, P=0.001), as compared with the remission group. Logistic regression analysis demonstrated that elevated thyroid size, FT3/FT4 ratio and TRAb at diagnosis were associated with poor outcomes. The ratio of continued thyrotropin suppression in the recurrent subgroup was significantly increased, as compared with the remission group (P=0.001), as thyroid function reached euthyroid state at 3, 6 and 12 months post-treatment. Patients with GD exhibiting

  2. Predicting chromium (VI) adsorption rate in the treatment of liquid ...

    African Journals Online (AJOL)

    The adsorption rate of chromium (VI) on commercial activated carbon during the treatment of the flocculation effluent of liquid-phase oil-based drill-cuttings has been investigated in terms of contact time and initial chromium (VI) ion concentration. Homogenizing 1 g of the activated carbon with 100 ml of the flocculation ...

  3. Erratum: Probabilistic application of a fugacity model to predict triclosan fate during wastewater treatment.

    Science.gov (United States)

    Bock, Michael; Lyndall, Jennifer; Barber, Timothy; Fuchsman, Phyllis; Perruchon, Elyse; Capdevielle, Marie

    2010-10-01

    The fate and partitioning of the antimicrobial compound, triclosan, in wastewater treatment plants (WWTPs) is evaluated using a probabilistic fugacity model to predict the range of triclosan concentrations in effluent and secondary biosolids. The WWTP model predicts 84% to 92% triclosan removal, which is within the range of measured removal efficiencies (typically 70% to 98%). Triclosan is predominantly removed by sorption and subsequent settling of organic particulates during primary treatment and by aerobic biodegradation during secondary treatment. Median modeled removal efficiency due to sorption is 40% for all treatment phases and 31% in the primary treatment phase. Median modeled removal efficiency due to biodegradation is 48% for all treatment phases and 44% in the secondary treatment phase. Important factors contributing to variation in predicted triclosan concentrations in effluent and biosolids include influent concentrations, solids concentrations in settling tanks, and factors related to solids retention time. Measured triclosan concentrations in biosolids and non-United States (US) effluent are consistent with model predictions. However, median concentrations in US effluent are over-predicted with this model, suggesting that differences in some aspect of treatment practices not incorporated in the model (e.g., disinfection methods) may affect triclosan removal from effluent. Model applications include predicting changes in environmental loadings associated with new triclosan applications and supporting risk analyses for biosolids-amended land and effluent receiving waters. © 2010 SETAC.

  4. Probabilistic application of a fugacity model to predict triclosan fate during wastewater treatment.

    Science.gov (United States)

    Bock, Michael; Lyndall, Jennifer; Barber, Timothy; Fuchsman, Phyllis; Perruchon, Elyse; Capdevielle, Marie

    2010-07-01

    The fate and partitioning of the antimicrobial compound, triclosan, in wastewater treatment plants (WWTPs) is evaluated using a probabilistic fugacity model to predict the range of triclosan concentrations in effluent and secondary biosolids. The WWTP model predicts 84% to 92% triclosan removal, which is within the range of measured removal efficiencies (typically 70% to 98%). Triclosan is predominantly removed by sorption and subsequent settling of organic particulates during primary treatment and by aerobic biodegradation during secondary treatment. Median modeled removal efficiency due to sorption is 40% for all treatment phases and 31% in the primary treatment phase. Median modeled removal efficiency due to biodegradation is 48% for all treatment phases and 44% in the secondary treatment phase. Important factors contributing to variation in predicted triclosan concentrations in effluent and biosolids include influent concentrations, solids concentrations in settling tanks, and factors related to solids retention time. Measured triclosan concentrations in biosolids and non-United States (US) effluent are consistent with model predictions. However, median concentrations in US effluent are over-predicted with this model, suggesting that differences in some aspect of treatment practices not incorporated in the model (e.g., disinfection methods) may affect triclosan removal from effluent. Model applications include predicting changes in environmental loadings associated with new triclosan applications and supporting risk analyses for biosolids-amended land and effluent receiving waters. (c) 2010 SETAC.

  5. Universal scaling in sports ranking

    International Nuclear Information System (INIS)

    Deng Weibing; Li Wei; Cai Xu; Bulou, Alain; Wang Qiuping A

    2012-01-01

    Ranking is a ubiquitous phenomenon in human society. On the web pages of Forbes, one may find all kinds of rankings, such as the world's most powerful people, the world's richest people, the highest-earning tennis players, and so on and so forth. Herewith, we study a specific kind—sports ranking systems in which players' scores and/or prize money are accrued based on their performances in different matches. By investigating 40 data samples which span 12 different sports, we find that the distributions of scores and/or prize money follow universal power laws, with exponents nearly identical for most sports. In order to understand the origin of this universal scaling we focus on the tennis ranking systems. By checking the data we find that, for any pair of players, the probability that the higher-ranked player tops the lower-ranked opponent is proportional to the rank difference between the pair. Such a dependence can be well fitted to a sigmoidal function. By using this feature, we propose a simple toy model which can simulate the competition of players in different matches. The simulations yield results consistent with the empirical findings. Extensive simulation studies indicate that the model is quite robust with respect to the modifications of some parameters. (paper)

  6. Predicting Treatment Response for Oppositional Defiant and Conduct Disorder Using Pre-treatment Adrenal and Gonadal Hormones.

    Science.gov (United States)

    Shenk, Chad E; Dorn, Lorah D; Kolko, David J; Susman, Elizabeth J; Noll, Jennie G; Bukstein, Oscar G

    2012-12-01

    Variations in adrenal and gonadal hormone profiles have been linked to increased rates of oppositional defiant disorder (ODD) and conduct disorder (CD). These relationships suggest that certain hormone profiles may be related to how well children respond to psychological treatments for ODD and CD. The current study assessed whether pre-treatment profiles of adrenal and gonadal hormones predicted response to psychological treatment of ODD and CD. One hundred five children, 6 - 11 years old, participating in a randomized, clinical trial provided samples for cortisol, testosterone, dehydroepiandrosterone, and androstenedione. Diagnostic interviews of ODD and CD were administered up to three years post-treatment to track treatment response. Group-based trajectory modeling identified two trajectories of treatment response: 1) a High-response trajectory where children demonstrated lower rates of an ODD or CD diagnosis throughout follow-up, and 2) a Low-response trajectory where children demonstrated higher rates of an ODD or CD diagnosis throughout follow-up. Hierarchical logistic regression predicting treatment response demonstrated that children with higher pre-treatment concentrations of testosterone were four times more likely to be in the Low-response trajectory. No other significant relationship existed between pre-treatment hormone profiles and treatment response. These results suggest that higher concentrations of testosterone are related to how well children diagnosed with ODD or CD respond to psychological treatment over the course of three years.

  7. Prediction of Infertility Treatment Outcomes Using Classification Trees

    Directory of Open Access Journals (Sweden)

    Milewska Anna Justyna

    2016-12-01

    Full Text Available Infertility is currently a common problem with causes that are often unexplained, which complicates treatment. In many cases, the use of ART methods provides the only possibility of getting pregnant. Analysis of this type of data is very complex. More and more often, data mining methods or artificial intelligence techniques are appropriate for solving such problems. In this study, classification trees were used for analysis. This resulted in obtaining a group of patients characterized most likely to get pregnant while using in vitro fertilization.

  8. In-treatment stroke volume predicts cardiovascular risk in hypertension

    DEFF Research Database (Denmark)

    Lønnebakken, Mai T; Gerdts, Eva; Boman, Kurt

    2011-01-01

    , the prespecified primary study endpoint, was assessed in Cox regression analysis using data from baseline and annual follow-up visits in 855 patients during 4.8 years of randomized losartan-based or atenolol-based treatment in the Losartan Intervention For Endpoint reduction in hypertension (LIFE) echocardiography...... with higher risk of cardiovascular events {hazard ratio 1.69 per 1 SD (6 ml/m2.04) lower stroke volume [95% confidence interval (CI) 1.35–2.11], P secondary model also independent of stress-corrected midwall shortening......, hence, adds information on cardiovascular risk in treated hypertensive patients beyond assessment of left ventricular structure alone....

  9. Barriers to Quitting Smoking among Substance Dependent Patients Predict Smoking Cessation Treatment Outcome

    OpenAIRE

    Martin, Rosemarie A.; Cassidy, Rachel; Murphy, Cara M.; Rohsenow, Damaris J.

    2016-01-01

    For smokers with substance use disorders (SUD), perceived barriers to quitting smoking include concerns unique to effects on sobriety as well as usual concerns. We expanded our Barriers to Quitting Smoking in Substance Abuse Treatment (BQS-SAT) scale, added importance ratings, validated it, and then used the importance scores to predict smoking treatment response in smokers with substance use disorders (SUD) undergoing smoking treatment in residential treatment programs in two studies (n = 18...

  10. Prepotent response inhibition predicts treatment outcome in attention deficit/hyperactivity disorder

    NARCIS (Netherlands)

    van der Oord, S.; Geurts, H.M.; Prins, P.J.M.; Emmelkamp, P.M.G.; Oosterlaan, J.

    2012-01-01

    Objective: Inhibition deficits, including deficits in prepotent response inhibition and interference control, are core deficits in ADHD. The predictive value of prepotent response inhibition and interference control was assessed for outcome in a 10-week treatment trial with methylphenidate. Methods:

  11. Dexamethasone-suppressed cortisol awakening response predicts treatment outcome in posttraumatic stress disorder

    NARCIS (Netherlands)

    Nijdam, M. J.; van Amsterdam, J. G. C.; Gersons, B. P. R.; Olff, M.

    2015-01-01

    Posttraumatic stress disorder (PTSD) has been associated with several alterations in the neuroendocrine system, including enhanced cortisol suppression in response to the dexamethasone suppression test. The aim of this study was to examine whether specific biomarkers of PTSD predict treatment

  12. PageRank of integers

    International Nuclear Information System (INIS)

    Frahm, K M; Shepelyansky, D L; Chepelianskii, A D

    2012-01-01

    We up a directed network tracing links from a given integer to its divisors and analyze the properties of the Google matrix of this network. The PageRank vector of this matrix is computed numerically and it is shown that its probability is approximately inversely proportional to the PageRank index thus being similar to the Zipf law and the dependence established for the World Wide Web. The spectrum of the Google matrix of integers is characterized by a large gap and a relatively small number of nonzero eigenvalues. A simple semi-analytical expression for the PageRank of integers is derived that allows us to find this vector for matrices of billion size. This network provides a new PageRank order of integers. (paper)

  13. Freudenthal ranks: GHZ versus W

    International Nuclear Information System (INIS)

    Borsten, L

    2013-01-01

    The Hilbert space of three-qubit pure states may be identified with a Freudenthal triple system. Every state has an unique Freudenthal rank ranging from 1 to 4, which is determined by a set of automorphism group covariants. It is shown here that the optimal success rates for winning a three-player non-local game, varying over all local strategies, are strictly ordered by the Freudenthal rank of the shared three-qubit resource. (paper)

  14. Ranking Queries on Uncertain Data

    CERN Document Server

    Hua, Ming

    2011-01-01

    Uncertain data is inherent in many important applications, such as environmental surveillance, market analysis, and quantitative economics research. Due to the importance of those applications and rapidly increasing amounts of uncertain data collected and accumulated, analyzing large collections of uncertain data has become an important task. Ranking queries (also known as top-k queries) are often natural and useful in analyzing uncertain data. Ranking Queries on Uncertain Data discusses the motivations/applications, challenging problems, the fundamental principles, and the evaluation algorith

  15. Does cognitive flexibility predict treatment gains in Internet-delivered psychological treatment of social anxiety disorder, depression, or tinnitus?

    Directory of Open Access Journals (Sweden)

    Philip Lindner

    2016-04-01

    Full Text Available Little is known about the individual factors that predict outcomes in Internet-administered psychological treatments. We hypothesized that greater cognitive flexibility (i.e. the ability to simultaneously consider several concepts and tasks and switch effortlessly between them in response to changes in environmental contingencies would provide a better foundation for learning and employing the cognitive restructuring techniques taught and exercised in therapy, leading to greater treatment gains. Participants in three trials featuring Internet-administered psychological treatments for depression (n = 36, social anxiety disorder (n = 115 and tinnitus (n = 53 completed the 64-card Wisconsin Card Sorting Test (WCST prior to treatment. We found no significant associations between perseverative errors on the WCST and treatment gains in any group. We also found low accuracy in the classification of treatment responders. We conclude that lower cognitive flexibility, as captured by perseverative errors on the WCST, should not impede successful outcomes in Internet-delivered psychological treatments.

  16. PREDICTION OF SURGICAL TREATMENT WITH POUR PERITONITIS TAKING INTO ACCOUNT QUANTIFYING RISK FACTORS

    Directory of Open Access Journals (Sweden)

    І. К. Churpiy

    2012-11-01

    Full Text Available There was investigated the possibility of quantitative assessment of risk factors of complications in the treatment of diffuse peritonitis. There were ditermined 70 groups of features that are important in predicting the course of diffuse peritonitis. The proposed scheme is the definition of risk clinical course of diffuse peritonitis can quantify the severity of the original patients and in most cases is correctly to predict the results of treatment of disease.

  17. Metabolomics biomarkers to predict acamprosate treatment response in alcohol-dependent subjects.

    Science.gov (United States)

    Hinton, David J; Vázquez, Marely Santiago; Geske, Jennifer R; Hitschfeld, Mario J; Ho, Ada M C; Karpyak, Victor M; Biernacka, Joanna M; Choi, Doo-Sup

    2017-05-31

    Precision medicine for alcohol use disorder (AUD) allows optimal treatment of the right patient with the right drug at the right time. Here, we generated multivariable models incorporating clinical information and serum metabolite levels to predict acamprosate treatment response. The sample of 120 patients was randomly split into a training set (n = 80) and test set (n = 40) five independent times. Treatment response was defined as complete abstinence (no alcohol consumption during 3 months of acamprosate treatment) while nonresponse was defined as any alcohol consumption during this period. In each of the five training sets, we built a predictive model using a least absolute shrinkage and section operator (LASSO) penalized selection method and then evaluated the predictive performance of each model in the corresponding test set. The models predicted acamprosate treatment response with a mean sensitivity and specificity in the test sets of 0.83 and 0.31, respectively, suggesting our model performed well at predicting responders, but not non-responders (i.e. many non-responders were predicted to respond). Studies with larger sample sizes and additional biomarkers will expand the clinical utility of predictive algorithms for pharmaceutical response in AUD.

  18. The predictive value of diagnostic sonography for the effectiveness of conservative treatment of tennis elbow

    NARCIS (Netherlands)

    Struijs, P. A. A.; Spruyt, M.; Assendelft, W. J. J.; van Dijk, C. N.

    2005-01-01

    OBJECTIVE. Tennis elbow is a common complaint. Several treatment strategies have been described, but an optimal strategy has not been identified. Sonographic imaging as a predictive,factor has never been studied. The aim of our study was to determine the value of sonographic findings in predicting

  19. Ranking stability and super-stable nodes in complex networks.

    Science.gov (United States)

    Ghoshal, Gourab; Barabási, Albert-László

    2011-07-19

    Pagerank, a network-based diffusion algorithm, has emerged as the leading method to rank web content, ecological species and even scientists. Despite its wide use, it remains unknown how the structure of the network on which it operates affects its performance. Here we show that for random networks the ranking provided by pagerank is sensitive to perturbations in the network topology, making it unreliable for incomplete or noisy systems. In contrast, in scale-free networks we predict analytically the emergence of super-stable nodes whose ranking is exceptionally stable to perturbations. We calculate the dependence of the number of super-stable nodes on network characteristics and demonstrate their presence in real networks, in agreement with the analytical predictions. These results not only deepen our understanding of the interplay between network topology and dynamical processes but also have implications in all areas where ranking has a role, from science to marketing.

  20. An Obesity Dietary Quality Index Predicts Abdominal Obesity in Women: Potential Opportunity for New Prevention and Treatment Paradigms

    Directory of Open Access Journals (Sweden)

    Dolores M. Wolongevicz

    2010-01-01

    Full Text Available Background. Links between dietary quality and abdominal obesity are poorly understood. Objective. To examine the association between an obesity-specific dietary quality index and abdominal obesity risk in women. Methods. Over 12 years, we followed 288 Framingham Offspring/Spouse Study women, aged 30–69 years, without metabolic syndrome risk factors, cardiovascular disease, cancer, or diabetes at baseline. An 11-nutrient obesity-specific dietary quality index was derived using mean ranks of nutrient intakes from 3-day dietary records. Abdominal obesity (waist circumference >88 cm was assessed during follow-up. Results. Using multiple logistic regression, women with poorer dietary quality were more likely to develop abdominal obesity compared to those with higher dietary quality (OR 1.87; 95% CI, 1.01, 3.47; P for trend =.048 independent of age, physical activity, smoking, and menopausal status. Conclusions. An obesity-specific dietary quality index predicted abdominal obesity in women, suggesting targets for dietary quality assessment, intervention, and treatment to address abdominal adiposity.

  1. Barriers to Quitting Smoking Among Substance Dependent Patients Predict Smoking Cessation Treatment Outcome.

    Science.gov (United States)

    Martin, Rosemarie A; Cassidy, Rachel N; Murphy, Cara M; Rohsenow, Damaris J

    2016-05-01

    For smokers with substance use disorders (SUD), perceived barriers to quitting smoking include concerns unique to effects on sobriety as well as usual concerns. We expanded our Barriers to Quitting Smoking in Substance Abuse Treatment (BQS-SAT) scale, added importance ratings, validated it, and then used the importance scores to predict smoking treatment response in smokers with substance use disorders (SUD) undergoing smoking treatment in residential treatment programs in two studies (n=184 and 340). Both components (general barriers, weight concerns) were replicated with excellent internal consistency reliability. Construct validity was supported by significant correlations with pretreatment nicotine dependence, smoking variables, smoking self-efficacy, and expected effects of smoking. General barriers significantly predicted 1-month smoking abstinence, frequency and heaviness, and 3-month smoking frequency; weight concerns predicted 1-month smoking frequency. Implications involve addressing barriers with corrective information in smoking treatment for smokers with SUD. Copyright © 2016 Elsevier Inc. All rights reserved.

  2. Barriers to Quitting Smoking among Substance Dependent Patients Predict Smoking Cessation Treatment Outcome

    Science.gov (United States)

    Martin, Rosemarie A.; Cassidy, Rachel; Murphy, Cara M.; Rohsenow, Damaris J.

    2016-01-01

    For smokers with substance use disorders (SUD), perceived barriers to quitting smoking include concerns unique to effects on sobriety as well as usual concerns. We expanded our Barriers to Quitting Smoking in Substance Abuse Treatment (BQS-SAT) scale, added importance ratings, validated it, and then used the importance scores to predict smoking treatment response in smokers with substance use disorders (SUD) undergoing smoking treatment in residential treatment programs in two studies (n = 184 and 340). Both components (General Barriers, Weight Concerns) were replicated with excellent internal consistency reliability. Construct validity was supported by significant correlations with pretreatment nicotine dependence, smoking variables, smoking self-efficacy, and expected effects of smoking. General Barriers significantly predicted 1-month smoking abstinence, frequency and heaviness, and 3-month smoking frequency; Weight Concerns predicted 1-month smoking frequency. Implications involve addressing barriers with corrective information in smoking treatment for smokers with SUD. PMID:26979552

  3. Three results on the PageRank vector: eigenstructure, sensitivity, and the derivative

    OpenAIRE

    Gleich, David; Glynn, Peter; Golub, Gene; Greif, Chen

    2007-01-01

    The three results on the PageRank vector are preliminary but shed light on the eigenstructure of a PageRank modified Markov chain and what happens when changing the teleportation parameter in the PageRank model. Computations with the derivative of the PageRank vector with respect to the teleportation parameter show predictive ability and identify an interesting set of pages from Wikipedia.

  4. Factors Predicting Treatment Failure in Patients Treated with Iodine-131 for Graves’ Disease

    International Nuclear Information System (INIS)

    Manohar, Kuruva; Mittal, Bhagwant Rai; Bhoil, Amit; Bhattacharya, Anish; Dutta, Pinaki; Bhansali, Anil

    2013-01-01

    Treatment of Graves' disease with iodine-131 ( 131 I) is well-known; however, all patients do not respond to a single dose of 131 I and may require higher and repeated doses. This study was carried out to identify the factors, which can predict treatment failure to a single dose of 131 I treatment in these patients. Data of 150 patients with Graves' disease treated with 259-370 MBq of 131 I followed-up for at least 1-year were retrospectively analyzed. Logistic regression analysis was used to predict factors which can predict treatment failure, such as age, sex, duration of disease, grade of goiter, duration of treatment with anti-thyroid drugs, mean dosage of anti-thyroid drugs used, 99m Tc-pertechnetate ( 99m TcO 4 - ) uptake at 20 min, dose of 131 I administered, total triiodothyronine and thyroxine levels. Of the 150 patients, 25 patients required retreatment within 1 year of initial treatment with 131 I. Logistic regression analysis revealed that male sex and 99m TcO 4 - uptake were associated with treatment failure. On receiver operating characteristic (ROC) curve analysis, area under the curve (AUC) was significant for 99m TcO 4 - uptake predicting treatment failure (AUC = 0.623; P = 0.039). Optimum cutoff for 99m TcO 4 - uptake was 17.75 with a sensitivity of 68% and specificity of 66% to predict treatment failure. Patients with >17.75% 99m TcO 4 - uptake had odds ratio of 3.14 (P = 0.014) for treatment failure and male patients had odds ratio of 1.783 for treatment failure. Our results suggest that male patients and patients with high pre-treatment 99m TcO 4 - uptake are more likely to require repeated doses of 131 I to achieve complete remission

  5. Predicting therapy success for treatment as usual and blended treatment in the domain of depression

    NARCIS (Netherlands)

    van Breda, Ward; Bremer, Vincent; Becker, Dennis; Hoogendoorn, Mark; Funk, Burkhardt; Ruwaard, Jeroen; Riper, Heleen

    2017-01-01

    In this paper, we explore the potential of predicting therapy success for patients in mental health care. Such predictions can eventually improve the process of matching effective therapy types to individuals. In the EU project E-COMPARED, a variety of information is gathered about patients

  6. RANK and RANK ligand expression in primary human osteosarcoma

    Directory of Open Access Journals (Sweden)

    Daniel Branstetter

    2015-09-01

    Our results demonstrate RANKL expression was observed in the tumor element in 68% of human OS using IHC. However, the staining intensity was relatively low and only 37% (29/79 of samples exhibited≥10% RANKL positive tumor cells. RANK expression was not observed in OS tumor cells. In contrast, RANK expression was clearly observed in other cells within OS samples, including the myeloid osteoclast precursor compartment, osteoclasts and in giant osteoclast cells. The intensity and frequency of RANKL and RANK staining in OS samples were substantially less than that observed in GCTB samples. The observation that RANKL is expressed in OS cells themselves suggests that these tumors may mediate an osteoclastic response, and anti-RANKL therapy may potentially be protective against bone pathologies in OS. However, the absence of RANK expression in primary human OS cells suggests that any autocrine RANKL/RANK signaling in human OS tumor cells is not operative, and anti-RANKL therapy would not directly affect the tumor.

  7. Ranking structures and rank-rank correlations of countries: The FIFA and UEFA cases

    Science.gov (United States)

    Ausloos, Marcel; Cloots, Rudi; Gadomski, Adam; Vitanov, Nikolay K.

    2014-04-01

    Ranking of agents competing with each other in complex systems may lead to paradoxes according to the pre-chosen different measures. A discussion is presented on such rank-rank, similar or not, correlations based on the case of European countries ranked by UEFA and FIFA from different soccer competitions. The first question to be answered is whether an empirical and simple law is obtained for such (self-) organizations of complex sociological systems with such different measuring schemes. It is found that the power law form is not the best description contrary to many modern expectations. The stretched exponential is much more adequate. Moreover, it is found that the measuring rules lead to some inner structures in both cases.

  8. QSAR enabled predictions in water treatment: from data to mechanisms and vice-versa

    NARCIS (Netherlands)

    Vries, D.; Wols, B.A.; de Voogt, P.

    2012-01-01

    The efficiency of water treatment systems to remove emerging (chemical) substances is often unknown. Consequently, the prediction of the removal of contaminants in the treatment and supply chain of drinking water is of great interest. By collecting and processing existing chemical properties of

  9. Nonverbal interpersonal attunement and extravert personality predict outcome of light treatment in seasonal affective disorder

    NARCIS (Netherlands)

    Geerts, E; Kouwert, E; Bouhuys, N; Meesters, Y; Jansen, J

    We investigated whether personality and nonverbal interpersonal processes can predict the subsequent response to light treatment in seasonal affective disorder (SAD) patients. In 60 SAD patients, Neuroticism and Extraversion were assessed prior to light treatment (4 days with 30 min of 10.000 lux).

  10. Can Assessment Reactivity Predict Treatment Outcome among Adolescents with Alcohol and Other Substance Use Disorders?

    Science.gov (United States)

    Kaminer, Yifrah; Burleson, Joseph A.; Burke, Rebecca H.

    2008-01-01

    The objectives of this paper are two-fold: to examine first, if the change from positive to negative alcohol and any other substance use status from baseline assessment to the onset of the first session (i.e., pre-treatment phase) occurs in adolescents, that is, Assessment Reactivity (AR); second, whether AR predicts treatment outcome.…

  11. Startle and spider phobia : Unilateral probes and the prediction of treatment effects

    NARCIS (Netherlands)

    de Jong, Peter J.; Visser, Sylvia; Merckelbach, Harald

    1996-01-01

    The present study explored two issues: (1) the predictive value of startle responses for treatment success and (2) the lateralization of affect-modulated startle responses. Approximately 40 days before behavioral treatment, monaural startle probes were presented to 20 women who were spider phobic

  12. Functional network architecture predicts psychologically mediated analgesia related to treatment in chronic knee pain patients.

    Science.gov (United States)

    Hashmi, Javeria Ali; Kong, Jian; Spaeth, Rosa; Khan, Sheraz; Kaptchuk, Ted J; Gollub, Randy L

    2014-03-12

    Placebo analgesia is an indicator of how efficiently the brain translates psychological signals conveyed by a treatment procedure into pain relief. It has been demonstrated that functional connectivity between distributed brain regions predicts placebo analgesia in chronic back pain patients. Greater network efficiency in baseline brain networks may allow better information transfer and facilitate adaptive physiological responses to psychological aspects of treatment. Here, we theorized that topological network alignments in resting state scans predict psychologically conditioned analgesic responses to acupuncture treatment in chronic knee osteoarthritis pain patients (n = 45). Analgesia was induced by building positive expectations toward acupuncture treatment with verbal suggestion and heat pain conditioning on a test site of the arm. This procedure induced significantly more analgesia after sham or real acupuncture on the test site than in a control site. The psychologically conditioned analgesia was invariant to sham versus real treatment. Efficiency of information transfer within local networks calculated with graph-theoretic measures (local efficiency and clustering coefficients) significantly predicted conditioned analgesia. Clustering coefficients in regions associated with memory, motivation, and pain modulation were closely involved in predicting analgesia. Moreover, women showed higher clustering coefficients and marginally greater pain reduction than men. Overall, analgesic response to placebo cues can be predicted from a priori resting state data by observing local network topology. Such low-cost synchronizations may represent preparatory resources that facilitate subsequent performance of brain circuits in responding to adaptive environmental cues. This suggests a potential utility of network measures in predicting placebo response for clinical use.

  13. Testing the predictive value of peripheral gene expression for nonremission following citalopram treatment for major depression.

    Science.gov (United States)

    Guilloux, Jean-Philippe; Bassi, Sabrina; Ding, Ying; Walsh, Chris; Turecki, Gustavo; Tseng, George; Cyranowski, Jill M; Sibille, Etienne

    2015-02-01

    Major depressive disorder (MDD) in general, and anxious-depression in particular, are characterized by poor rates of remission with first-line treatments, contributing to the chronic illness burden suffered by many patients. Prospective research is needed to identify the biomarkers predicting nonremission prior to treatment initiation. We collected blood samples from a discovery cohort of 34 adult MDD patients with co-occurring anxiety and 33 matched, nondepressed controls at baseline and after 12 weeks (of citalopram plus psychotherapy treatment for the depressed cohort). Samples were processed on gene arrays and group differences in gene expression were investigated. Exploratory analyses suggest that at pretreatment baseline, nonremitting patients differ from controls with gene function and transcription factor analyses potentially related to elevated inflammation and immune activation. In a second phase, we applied an unbiased machine learning prediction model and corrected for model-selection bias. Results show that baseline gene expression predicted nonremission with 79.4% corrected accuracy with a 13-gene model. The same gene-only model predicted nonremission after 8 weeks of citalopram treatment with 76% corrected accuracy in an independent validation cohort of 63 MDD patients treated with citalopram at another institution. Together, these results demonstrate the potential, but also the limitations, of baseline peripheral blood-based gene expression to predict nonremission after citalopram treatment. These results not only support their use in future prediction tools but also suggest that increased accuracy may be obtained with the inclusion of additional predictors (eg, genetics and clinical scales).

  14. Stigma Predicts Treatment Preferences and Care Engagement among Veterans Affairs Primary Care Patients with Depression

    Science.gov (United States)

    Campbell, Duncan G.; Bonner, Laura M.; Bolkan, Cory R.; Lanto, Andrew B.; Zivin, Kara; Waltz, Thomas J.; Klap, Ruth; Rubenstein, Lisa V.; Chaney, Edmund F.

    2016-01-01

    Background Whereas stigma regarding mental health concerns exists, the evidence for stigma as a depression treatment barrier among patients in Veterans Affairs (VA) primary care (PC) is mixed. Purpose To test whether stigma, defined as depression label avoidance, predicted patients' preferences for depression treatment providers, patients' prospective engagement in depression care, and care quality. Methods We conducted cross-sectional and prospective analyses of existing data from 761 VA PC patients with probable major depression. Results Relative to low stigma patients, those with high stigma were less likely to prefer treatment from mental health specialists. In prospective controlled analyses, high stigma predicted lower likelihood of the following: taking medications for mood, treatment by mental health specialists, treatment for emotional concerns in PC, and appropriate depression care. Conclusions High stigma is associated with lower preferences for care from mental health specialists and confers risk for minimal depression treatment engagement. PMID:26935310

  15. A novel method for prediction of dynamic smiling expressions after orthodontic treatment: a case report.

    Science.gov (United States)

    Dai, Fanfan; Li, Yangjing; Chen, Gui; Chen, Si; Xu, Tianmin

    2016-02-01

    Smile esthetics has become increasingly important for orthodontic patients, thus prediction of post-treatment smile is necessary for a perfect treatment plan. In this study, with a combination of three-dimensional craniofacial data from the cone beam computed tomography and color-encoded structured light system, a novel method for smile prediction was proposed based on facial expression transfer, in which dynamic facial expression was interpreted as a matrix of facial depth changes. Data extracted from the pre-treatment smile expression record were applied to the post-treatment static model to realize expression transfer. Therefore smile esthetics of the patient after treatment could be evaluated in pre-treatment planning procedure. The positive and negative mean values of error for prediction accuracy were 0.9 and - 1.1 mm respectively, with the standard deviation of ± 1.5 mm, which is clinically acceptable. Further studies would be conducted to reduce the prediction error from both the static and dynamic sides as well as to explore automatically combined prediction from the two sides.

  16. Topical Treatment with Xiaozheng Zhitong Paste (XZP Alleviates Bone Destruction and Bone Cancer Pain in a Rat Model of Prostate Cancer-Induced Bone Pain by Modulating the RANKL/RANK/OPG Signaling

    Directory of Open Access Journals (Sweden)

    Yanju Bao

    2015-01-01

    Full Text Available To explore the effects and mechanisms of Xiaozheng Zhitong Paste (XZP on bone cancer pain, Wistar rats were inoculated with vehicle or prostate cancer PC-3 into the tibia bone and treated topically with inert paste, XZP at 15.75, 31.5, or 63 g/kg twice per day for 21 days. Their bone structural damage, nociceptive behaviors, bone osteoclast and osteoblast activity, and the levels of OPG, RANL, RNAK, PTHrP, IGF-1, M-CSF, IL-8, and TNF-α were examined. In comparison with that in the placebo group, significantly reduced numbers of invaded cancer cells, decreased levels of bone damage and mechanical threshold and paw withdrawal latency, lower levels of serum TRACP5b, ICTP, PINP, and BAP, and less levels of bone osteoblast and osteoclast activity were detected in the XZP-treated rats (P<0.05. Moreover, significantly increased levels of bone OPG but significantly decreased levels of RANL, RNAK, PTHrP, IGF-1, M-CSF, IL-8, and TNF-α were detected in the XZP-treated rats (P<0.05 for all. Together, XZP treatment significantly mitigated the cancer-induced bone damage and bone osteoclast and osteoblast activity and alleviated prostate cancer-induced bone pain by modulating the RANKL/RANK/OPG pathway and bone cancer-related inflammation in rats.

  17. Basal blood DHEA-S/cortisol levels predicts EMDR treatment response in adolescents with PTSD.

    Science.gov (United States)

    Usta, Mirac Baris; Gumus, Yusuf Yasin; Say, Gokce Nur; Bozkurt, Abdullah; Şahin, Berkan; Karabekiroğlu, Koray

    2018-04-01

    In literature, recent evidence has shown that the hypothalamic-pituitary-adrenal (HPA) axis can be dysregulated in patients with post-traumatic stress disorder (PTSD) and HPA axis hormones may predict the psychotherapy treatment response in patients with PTSD. In this study, it was aimed to investigate changing cortisol and DHEA-S levels post-eye movement desensitization and reprocessing (EMDR) therapy and the relationship between treatment response and basal cortisol, and DHEA-S levels before treatment. The study group comprised 40 adolescents (age, 12-18 years) with PTSD. The PTSD symptoms were assessed using the Child Depression Inventory (CDI) and Child Post-traumatic Stress Reaction Index (CPSRI) and the blood cortisol and DHEA-S were measured with the chemiluminescence method before and after treatment. A maximum of six sessions of EMDR therapy were conducted by an EMDR level-1 trained child psychiatry resident. Treatment response was measured by the pre- to post-treatment decrease in self-reported and clinical PTSD severity. Pre- and post-treatment DHEA-S and cortisol levels did not show any statistically significant difference. Pre-treatment CDI scores were negatively correlated with pre-treatment DHEA-S levels (r: -0.39). ROC analysis demonstrated that the DHEA-S/cortisol ratio predicts treatment response at a medium level (AUC: 0.703, p: .030, sensitivity: 0.65, specificity: 0.86). The results of this study suggested that the DHEA-S/cortisol ratio may predict treatment response in adolescents with PTSD receiving EMDR therapy. The biochemical parameter of HPA-axis activity appears to be an important predictor of positive clinical response in adolescent PTSD patients, and could be used in clinical practice to predict PTSD treatment in the future.

  18. The role of attachment in predicting CBT treatment outcome in children with anxiety disorders

    DEFF Research Database (Denmark)

    Walczak, Monika Anna; Normann, Nicoline; Tolstrup, Marie

    2015-01-01

    Introduction: Child’s insecure attachment to parents and insecure parental attachment has been linked to childhood anxiety (Brumariu & Kerns, 2010; Manassis et al.,1994).Whether attachment patterns can predict treatment outcome, is yet to be investigated. We examined the role of children......’s attachment to parents, and parental attachment in predicting treatment outcome in anxious children receiving cognitive-behavioral treatment. Method: A total of 69 children aged 7-13 years were diagnosed at intake and post-treatment, using Anxiety Disorders Interview Schedule for DSM-IV (Silverman and Albano...... style in responders and non-responders in the present sample. We found a significant difference in maternal attachment anxiety scale (p=.011), with mothers of non-responders showing significantly higher attachment anxiety. Binominal logistic regression analysis was used to measure a predictive value...

  19. Predicting post-traumatic stress disorder treatment response in refugees: Multilevel analysis.

    Science.gov (United States)

    Haagen, Joris F G; Ter Heide, F Jackie June; Mooren, Trudy M; Knipscheer, Jeroen W; Kleber, Rolf J

    2017-03-01

    Given the recent peak in refugee numbers and refugees' high odds of developing post-traumatic stress disorder (PTSD), finding ways to alleviate PTSD in refugees is of vital importance. However, there are major differences in PTSD treatment response between refugees, the determinants of which are largely unknown. This study aimed at improving PTSD treatment for adult refugees by identifying PTSD treatment response predictors. A prospective longitudinal multilevel modelling design was used to predict PTSD severity scores over time. We analysed data from a randomized controlled trial with pre-, post-, and follow-up measurements of the safety and efficacy of eye movement desensitization and reprocessing and stabilization in asylum seekers and refugees suffering from PTSD. Lack of refugee status, comorbid depression, demographic, trauma-related and treatment-related variables were analysed as potential predictors of PTSD treatment outcome. Treatment outcome data from 72 participants were used. The presence (B = 6.5, p = .03) and severity (B = 6.3, p disorder predicted poor treatment response and explained 39% of the variance between individuals. Refugee patients who suffer from PTSD and severe comorbid depression benefit less from treatment aimed at alleviating PTSD. Results highlight the need for treatment adaptations for PTSD and comorbid severe depression in traumatized refugees, including testing whether initial targeting of severe depressive symptoms increases PTSD treatment effectiveness. There are differences in post-traumatic stress disorder (PTSD) treatment response between traumatized refugees. Comorbid depressive disorder and depression severity predict poor PTSD response. Refugees with PTSD and severe depression may not benefit from PTSD treatment. Targeting comorbid severe depression before PTSD treatment is warranted. This study did not correct for multiple hypothesis testing. Comorbid depression may differentially impact alternative PTSD treatments

  20. Ranking species in mutualistic networks

    Science.gov (United States)

    Domínguez-García, Virginia; Muñoz, Miguel A.

    2015-02-01

    Understanding the architectural subtleties of ecological networks, believed to confer them enhanced stability and robustness, is a subject of outmost relevance. Mutualistic interactions have been profusely studied and their corresponding bipartite networks, such as plant-pollinator networks, have been reported to exhibit a characteristic ``nested'' structure. Assessing the importance of any given species in mutualistic networks is a key task when evaluating extinction risks and possible cascade effects. Inspired in a recently introduced algorithm -similar in spirit to Google's PageRank but with a built-in non-linearity- here we propose a method which -by exploiting their nested architecture- allows us to derive a sound ranking of species importance in mutualistic networks. This method clearly outperforms other existing ranking schemes and can become very useful for ecosystem management and biodiversity preservation, where decisions on what aspects of ecosystems to explicitly protect need to be made.

  1. University rankings in computer science

    DEFF Research Database (Denmark)

    Ehret, Philip; Zuccala, Alesia Ann; Gipp, Bela

    2017-01-01

    This is a research-in-progress paper concerning two types of institutional rankings, the Leiden and QS World ranking, and their relationship to a list of universities’ ‘geo-based’ impact scores, and Computing Research and Education Conference (CORE) participation scores in the field of computer...... science. A ‘geo-based’ impact measure examines the geographical distribution of incoming citations to a particular university’s journal articles for a specific period of time. It takes into account both the number of citations and the geographical variability in these citations. The CORE participation...... score is calculated on the basis of the number of weighted proceedings papers that a university has contributed to either an A*, A, B, or C conference as ranked by the Computing Research and Education Association of Australasia. In addition to calculating the correlations between the distinct university...

  2. Subtracting a best rank-1 approximation may increase tensor rank

    NARCIS (Netherlands)

    Stegeman, Alwin; Comon, Pierre

    2010-01-01

    It has been shown that a best rank-R approximation of an order-k tensor may not exist when R >= 2 and k >= 3. This poses a serious problem to data analysts using tensor decompositions it has been observed numerically that, generally, this issue cannot be solved by consecutively computing and

  3. Predicting treatment noncompliance among criminal justice-mandated clients: a theoretical and empirical exploration.

    Science.gov (United States)

    Sung, Hung-En; Belenko, Steven; Feng, Li; Tabachnick, Carrie

    2004-01-01

    Compliance with therapeutic regimens constitutes an important but infrequently studied precursor of treatment engagement and is a necessary condition of successful treatment. This study builds on recent treatment process research and provides a theory-driven analysis of treatment compliance. Five hypotheses are formulated to predict treatment noncompliance among criminal justice-mandated clients. These hypotheses tap different determinants of treatment progress, including physical prime, supportive social network, conventional social involvement, treatment motivation, and risk-taking propensity. Data from 150 addicted felons participating in a diversion program are analyzed to test the hypotheses. Predictors related to these hypotheses correctly identify 58% of the fully compliant clients and 55-88% of the noncompliant clients. Most hypotheses are at least partially corroborated and a few strong correlates emerge across analyses. Clients in their physical prime, those with poorer social support, and those lacking internal desires for change were found especially likely to violate treatment program rules. Clinical implications are discussed.

  4. Predicting Time Spent in Treatment in a Sample of Danish Survivors of Child Sexual Abuse.

    Science.gov (United States)

    Fletcher, Shelley; Elklit, Ask; Shevlin, Mark; Armour, Cherie

    2017-07-01

    The aim of this study was to identify significant predictors of length of time spent in treatment. In a convenience sample of 439 Danish survivors of child sexual abuse, predictors of time spent in treatment were examined. Assessments were conducted on a 6-month basis over a period of 18 months. A multinomial logistic regression analysis revealed that the experience of neglect in childhood and having experienced rape at any life stage were associated with less time in treatment. Higher educational attainment and being male were associated with staying in treatment for longer periods of time. These factors may be important for identifying those at risk of terminating treatment prematurely. It is hoped that a better understanding of the factors that predict time spent in treatment will help to improve treatment outcomes for individuals who are at risk of dropping out of treatment at an early stage.

  5. Predictive value of early {sup 18}F-FDG PET/CT studies for treatment response evaluation to ipilimumab in metastatic melanoma: preliminary results of an ongoing study

    Energy Technology Data Exchange (ETDEWEB)

    Sachpekidis, Christos; Pan, Leyun; Dimitrakopoulou-Strauss, Antonia [German Cancer Research Center, Clinical Cooperation Unit Nuclear Medicine, Heidelberg (Germany); Larribere, Lionel [German Cancer Research Center, Clinical Cooperation Unit Dermato-Oncology, Heidelberg (Germany); Haberkorn, Uwe [German Cancer Research Center, Clinical Cooperation Unit Nuclear Medicine, Heidelberg (Germany); University of Heidelberg, Division of Nuclear Medicine, Heidelberg (Germany); Hassel, Jessica C. [University Hospital Heidelberg, Skin Cancer Center, Department of Dermatology, Heidelberg (Germany); National Center for Tumor Diseases Heidelberg, Heidelberg (Germany)

    2014-10-31

    significant for both early and late responses (log-rank p < 0.001). Median OS among patients with late PMD was 9.1 months (mean 11.2 months) and among those with late SMD 9.8 months (mean 10.7 months). The difference in OS between the two groups was statistically significant (log-rank p = 0.013). The median OS among patients with early PMD was 8.8 months (mean 12.0 months) and among those with early SMD 9.8 months (mean 10.0 months). The difference in OS between the two groups was statistically significant (log-rank p < 0.001). {sup 18}F-FDG PET/CT after two cycles of ipilimumab is highly predictive of the final treatment outcome in patients with PMD and SMD. (orig.)

  6. Fourth-rank gravity and cosmology

    International Nuclear Information System (INIS)

    Marrakchi, A.L.; Tapia, V.

    1992-07-01

    We consider the consequences of describing the metric properties of space-time through a quartic line element. The associated ''metric'' is a fourth-rank tensor G μυλπ . In order to recover a Riemannian behaviour of the geometry it is necessary to have G μυλπ = g (μυ g λπ) . We construct a theory for the gravitational field based on the fourth-rank metric G μυλπ . In the absence of matter the fourth-rank metric becomes separable and the theory coincides with General Relativity. In the presence of matter we can maintain Riemmanianicity, but now gravitation couples, as compared to General Relativity, in a different way to matter. We develop a simple cosmological model based on a FRW metric with matter described by a perfect fluid. For the present time the field equations are compatible with k OBS = O and Ω OBS t CLAS approx. 10 20 t PLANCK approx. 10 -23 s. Our final and most important result is the fact that the entropy is an increasing function of time. When interpreted at the light of General Relativity the treatment is shown to be almost equivalent to that of the standard model of cosmology combined with the inflationary scenario. (author). 16 refs, 1 fig

  7. Personality traits predict treatment outcome with an antidepressant in patients with functional gastrointestinal disorder.

    Science.gov (United States)

    Tanum, L; Malt, U F

    2000-09-01

    We investigated the relationship between personality traits and response to treatment with the tetracyclic antidepressant mianserin or placebo in patients with functional gastrointestinal disorder (FGD) without psychopathology. Forty-eight patients completed the Buss-Durkee Hostility Inventory, Neuroticism Extroversion Openness -Personality Inventory (NEO-PI), and Eysenck Personality Questionnaire (EPQ), neuroticism + lie subscales, before they were consecutively allocated to a 7-week double-blind treatment study with mianserin or placebo. Treatment response to pain and target symptoms were recorded daily with the Visual Analogue Scale and Clinical Global Improvement Scale at every visit. A low level of neuroticism and little concealed aggressiveness predicted treatment outcome with the antidepressant drug mianserin in non-psychiatric patients with FGD. Inversely, moderate to high neuroticism and marked concealed aggressiveness predicted poor response to treatment. These findings were most prominent in women. Personality traits were better predictors of treatment outcome than serotonergic sensitivity assessed with the fenfluramine test. Assessment of the personality traits negativism, irritability, aggression, and neuroticism may predict response to drug treatment of FGD even when serotonergic sensitivity is controlled for. If confirmed in future studies, the findings point towards a more differential psychopharmacologic treatment of FGD.

  8. Complete hazard ranking to analyze right-censored data: An ALS survival study.

    Directory of Open Access Journals (Sweden)

    Zhengnan Huang

    2017-12-01

    Full Text Available Survival analysis represents an important outcome measure in clinical research and clinical trials; further, survival ranking may offer additional advantages in clinical trials. In this study, we developed GuanRank, a non-parametric ranking-based technique to transform patients' survival data into a linear space of hazard ranks. The transformation enables the utilization of machine learning base-learners including Gaussian process regression, Lasso, and random forest on survival data. The method was submitted to the DREAM Amyotrophic Lateral Sclerosis (ALS Stratification Challenge. Ranked first place, the model gave more accurate ranking predictions on the PRO-ACT ALS dataset in comparison to Cox proportional hazard model. By utilizing right-censored data in its training process, the method demonstrated its state-of-the-art predictive power in ALS survival ranking. Its feature selection identified multiple important factors, some of which conflicts with previous studies.

  9. Complete hazard ranking to analyze right-censored data: An ALS survival study.

    Science.gov (United States)

    Huang, Zhengnan; Zhang, Hongjiu; Boss, Jonathan; Goutman, Stephen A; Mukherjee, Bhramar; Dinov, Ivo D; Guan, Yuanfang

    2017-12-01

    Survival analysis represents an important outcome measure in clinical research and clinical trials; further, survival ranking may offer additional advantages in clinical trials. In this study, we developed GuanRank, a non-parametric ranking-based technique to transform patients' survival data into a linear space of hazard ranks. The transformation enables the utilization of machine learning base-learners including Gaussian process regression, Lasso, and random forest on survival data. The method was submitted to the DREAM Amyotrophic Lateral Sclerosis (ALS) Stratification Challenge. Ranked first place, the model gave more accurate ranking predictions on the PRO-ACT ALS dataset in comparison to Cox proportional hazard model. By utilizing right-censored data in its training process, the method demonstrated its state-of-the-art predictive power in ALS survival ranking. Its feature selection identified multiple important factors, some of which conflicts with previous studies.

  10. Bias and Stability of Single Variable Classifiers for Feature Ranking and Selection.

    Science.gov (United States)

    Fakhraei, Shobeir; Soltanian-Zadeh, Hamid; Fotouhi, Farshad

    2014-11-01

    Feature rankings are often used for supervised dimension reduction especially when discriminating power of each feature is of interest, dimensionality of dataset is extremely high, or computational power is limited to perform more complicated methods. In practice, it is recommended to start dimension reduction via simple methods such as feature rankings before applying more complex approaches. Single Variable Classifier (SVC) ranking is a feature ranking based on the predictive performance of a classifier built using only a single feature. While benefiting from capabilities of classifiers, this ranking method is not as computationally intensive as wrappers. In this paper, we report the results of an extensive study on the bias and stability of such feature ranking method. We study whether the classifiers influence the SVC rankings or the discriminative power of features themselves has a dominant impact on the final rankings. We show the common intuition of using the same classifier for feature ranking and final classification does not always result in the best prediction performance. We then study if heterogeneous classifiers ensemble approaches provide more unbiased rankings and if they improve final classification performance. Furthermore, we calculate an empirical prediction performance loss for using the same classifier in SVC feature ranking and final classification from the optimal choices.

  11. A network-based dynamical ranking system for competitive sports

    Science.gov (United States)

    Motegi, Shun; Masuda, Naoki

    2012-12-01

    From the viewpoint of networks, a ranking system for players or teams in sports is equivalent to a centrality measure for sports networks, whereby a directed link represents the result of a single game. Previously proposed network-based ranking systems are derived from static networks, i.e., aggregation of the results of games over time. However, the score of a player (or team) fluctuates over time. Defeating a renowned player in the peak performance is intuitively more rewarding than defeating the same player in other periods. To account for this factor, we propose a dynamic variant of such a network-based ranking system and apply it to professional men's tennis data. We derive a set of linear online update equations for the score of each player. The proposed ranking system predicts the outcome of the future games with a higher accuracy than the static counterparts.

  12. Let Us Rank Journalism Programs

    Science.gov (United States)

    Weber, Joseph

    2014-01-01

    Unlike law, business, and medical schools, as well as universities in general, journalism schools and journalism programs have rarely been ranked. Publishers such as "U.S. News & World Report," "Forbes," "Bloomberg Businessweek," and "Washington Monthly" do not pay them much mind. What is the best…

  13. On Rank Driven Dynamical Systems

    Science.gov (United States)

    Veerman, J. J. P.; Prieto, F. J.

    2014-08-01

    We investigate a class of models related to the Bak-Sneppen (BS) model, initially proposed to study evolution. The BS model is extremely simple and yet captures some forms of "complex behavior" such as self-organized criticality that is often observed in physical and biological systems. In this model, random fitnesses in are associated to agents located at the vertices of a graph . Their fitnesses are ranked from worst (0) to best (1). At every time-step the agent with the worst fitness and some others with a priori given rank probabilities are replaced by new agents with random fitnesses. We consider two cases: The exogenous case where the new fitnesses are taken from an a priori fixed distribution, and the endogenous case where the new fitnesses are taken from the current distribution as it evolves. We approximate the dynamics by making a simplifying independence assumption. We use Order Statistics and Dynamical Systems to define a rank-driven dynamical system that approximates the evolution of the distribution of the fitnesses in these rank-driven models, as well as in the BS model. For this simplified model we can find the limiting marginal distribution as a function of the initial conditions. Agreement with experimental results of the BS model is excellent.

  14. PageRank (II): Mathematics

    African Journals Online (AJOL)

    maths/stats

    ... GAUSS SEIDEL'S. NUMERICAL ALGORITHMS IN PAGE RANK ANALYSIS. ... The convergence is guaranteed, if the absolute value of the largest eigen ... improved Gauss-Seidel iteration algorithm, based on the decomposition. U. L. D. M. +. +. = ..... This corresponds to determine the eigen vector of T with eigen value 1.

  15. Multiple graph regularized protein domain ranking

    KAUST Repository

    Wang, Jim Jing-Yan

    2012-11-19

    Background: Protein domain ranking is a fundamental task in structural biology. Most protein domain ranking methods rely on the pairwise comparison of protein domains while neglecting the global manifold structure of the protein domain database. Recently, graph regularized ranking that exploits the global structure of the graph defined by the pairwise similarities has been proposed. However, the existing graph regularized ranking methods are very sensitive to the choice of the graph model and parameters, and this remains a difficult problem for most of the protein domain ranking methods.Results: To tackle this problem, we have developed the Multiple Graph regularized Ranking algorithm, MultiG-Rank. Instead of using a single graph to regularize the ranking scores, MultiG-Rank approximates the intrinsic manifold of protein domain distribution by combining multiple initial graphs for the regularization. Graph weights are learned with ranking scores jointly and automatically, by alternately minimizing an objective function in an iterative algorithm. Experimental results on a subset of the ASTRAL SCOP protein domain database demonstrate that MultiG-Rank achieves a better ranking performance than single graph regularized ranking methods and pairwise similarity based ranking methods.Conclusion: The problem of graph model and parameter selection in graph regularized protein domain ranking can be solved effectively by combining multiple graphs. This aspect of generalization introduces a new frontier in applying multiple graphs to solving protein domain ranking applications. 2012 Wang et al; licensee BioMed Central Ltd.

  16. Multiple graph regularized protein domain ranking

    KAUST Repository

    Wang, Jim Jing-Yan; Bensmail, Halima; Gao, Xin

    2012-01-01

    Background: Protein domain ranking is a fundamental task in structural biology. Most protein domain ranking methods rely on the pairwise comparison of protein domains while neglecting the global manifold structure of the protein domain database. Recently, graph regularized ranking that exploits the global structure of the graph defined by the pairwise similarities has been proposed. However, the existing graph regularized ranking methods are very sensitive to the choice of the graph model and parameters, and this remains a difficult problem for most of the protein domain ranking methods.Results: To tackle this problem, we have developed the Multiple Graph regularized Ranking algorithm, MultiG-Rank. Instead of using a single graph to regularize the ranking scores, MultiG-Rank approximates the intrinsic manifold of protein domain distribution by combining multiple initial graphs for the regularization. Graph weights are learned with ranking scores jointly and automatically, by alternately minimizing an objective function in an iterative algorithm. Experimental results on a subset of the ASTRAL SCOP protein domain database demonstrate that MultiG-Rank achieves a better ranking performance than single graph regularized ranking methods and pairwise similarity based ranking methods.Conclusion: The problem of graph model and parameter selection in graph regularized protein domain ranking can be solved effectively by combining multiple graphs. This aspect of generalization introduces a new frontier in applying multiple graphs to solving protein domain ranking applications. 2012 Wang et al; licensee BioMed Central Ltd.

  17. 14 CFR 1214.1105 - Final ranking.

    Science.gov (United States)

    2010-01-01

    ... 14 Aeronautics and Space 5 2010-01-01 2010-01-01 false Final ranking. 1214.1105 Section 1214.1105... Recruitment and Selection Program § 1214.1105 Final ranking. Final rankings will be based on a combination of... preference will be included in this final ranking in accordance with applicable regulations. ...

  18. Multiple graph regularized protein domain ranking.

    Science.gov (United States)

    Wang, Jim Jing-Yan; Bensmail, Halima; Gao, Xin

    2012-11-19

    Protein domain ranking is a fundamental task in structural biology. Most protein domain ranking methods rely on the pairwise comparison of protein domains while neglecting the global manifold structure of the protein domain database. Recently, graph regularized ranking that exploits the global structure of the graph defined by the pairwise similarities has been proposed. However, the existing graph regularized ranking methods are very sensitive to the choice of the graph model and parameters, and this remains a difficult problem for most of the protein domain ranking methods. To tackle this problem, we have developed the Multiple Graph regularized Ranking algorithm, MultiG-Rank. Instead of using a single graph to regularize the ranking scores, MultiG-Rank approximates the intrinsic manifold of protein domain distribution by combining multiple initial graphs for the regularization. Graph weights are learned with ranking scores jointly and automatically, by alternately minimizing an objective function in an iterative algorithm. Experimental results on a subset of the ASTRAL SCOP protein domain database demonstrate that MultiG-Rank achieves a better ranking performance than single graph regularized ranking methods and pairwise similarity based ranking methods. The problem of graph model and parameter selection in graph regularized protein domain ranking can be solved effectively by combining multiple graphs. This aspect of generalization introduces a new frontier in applying multiple graphs to solving protein domain ranking applications.

  19. Multiple graph regularized protein domain ranking

    Directory of Open Access Journals (Sweden)

    Wang Jim

    2012-11-01

    Full Text Available Abstract Background Protein domain ranking is a fundamental task in structural biology. Most protein domain ranking methods rely on the pairwise comparison of protein domains while neglecting the global manifold structure of the protein domain database. Recently, graph regularized ranking that exploits the global structure of the graph defined by the pairwise similarities has been proposed. However, the existing graph regularized ranking methods are very sensitive to the choice of the graph model and parameters, and this remains a difficult problem for most of the protein domain ranking methods. Results To tackle this problem, we have developed the Multiple Graph regularized Ranking algorithm, MultiG-Rank. Instead of using a single graph to regularize the ranking scores, MultiG-Rank approximates the intrinsic manifold of protein domain distribution by combining multiple initial graphs for the regularization. Graph weights are learned with ranking scores jointly and automatically, by alternately minimizing an objective function in an iterative algorithm. Experimental results on a subset of the ASTRAL SCOP protein domain database demonstrate that MultiG-Rank achieves a better ranking performance than single graph regularized ranking methods and pairwise similarity based ranking methods. Conclusion The problem of graph model and parameter selection in graph regularized protein domain ranking can be solved effectively by combining multiple graphs. This aspect of generalization introduces a new frontier in applying multiple graphs to solving protein domain ranking applications.

  20. First rank symptoms for schizophrenia.

    Science.gov (United States)

    Soares-Weiser, Karla; Maayan, Nicola; Bergman, Hanna; Davenport, Clare; Kirkham, Amanda J; Grabowski, Sarah; Adams, Clive E

    2015-01-25

    Early and accurate diagnosis and treatment of schizophrenia may have long-term advantages for the patient; the longer psychosis goes untreated the more severe the repercussions for relapse and recovery. If the correct diagnosis is not schizophrenia, but another psychotic disorder with some symptoms similar to schizophrenia, appropriate treatment might be delayed, with possible severe repercussions for the person involved and their family. There is widespread uncertainty about the diagnostic accuracy of First Rank Symptoms (FRS); we examined whether they are a useful diagnostic tool to differentiate schizophrenia from other psychotic disorders. To determine the diagnostic accuracy of one or multiple FRS for diagnosing schizophrenia, verified by clinical history and examination by a qualified professional (e.g. psychiatrists, nurses, social workers), with or without the use of operational criteria and checklists, in people thought to have non-organic psychotic symptoms. We conducted searches in MEDLINE, EMBASE, and PsycInfo using OvidSP in April, June, July 2011 and December 2012. We also searched MEDION in December 2013. We selected studies that consecutively enrolled or randomly selected adults and adolescents with symptoms of psychosis, and assessed the diagnostic accuracy of FRS for schizophrenia compared to history and clinical examination performed by a qualified professional, which may or may not involve the use of symptom checklists or based on operational criteria such as ICD and DSM. Two review authors independently screened all references for inclusion. Risk of bias in included studies were assessed using the QUADAS-2 instrument. We recorded the number of true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN) for constructing a 2 x 2 table for each study or derived 2 x 2 data from reported summary statistics such as sensitivity, specificity, and/or likelihood ratios. We included 21 studies with a total of 6253 participants

  1. Predicting CD4 count changes among patients on antiretroviral treatment: Application of data mining techniques.

    Science.gov (United States)

    Kebede, Mihiretu; Zegeye, Desalegn Tigabu; Zeleke, Berihun Megabiaw

    2017-12-01

    To monitor the progress of therapy and disease progression, periodic CD4 counts are required throughout the course of HIV/AIDS care and support. The demand for CD4 count measurement is increasing as ART programs expand over the last decade. This study aimed to predict CD4 count changes and to identify the predictors of CD4 count changes among patients on ART. A cross-sectional study was conducted at the University of Gondar Hospital from 3,104 adult patients on ART with CD4 counts measured at least twice (baseline and most recent). Data were retrieved from the HIV care clinic electronic database and patients` charts. Descriptive data were analyzed by SPSS version 20. Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology was followed to undertake the study. WEKA version 3.8 was used to conduct a predictive data mining. Before building the predictive data mining models, information gain values and correlation-based Feature Selection methods were used for attribute selection. Variables were ranked according to their relevance based on their information gain values. J48, Neural Network, and Random Forest algorithms were experimented to assess model accuracies. The median duration of ART was 191.5 weeks. The mean CD4 count change was 243 (SD 191.14) cells per microliter. Overall, 2427 (78.2%) patients had their CD4 counts increased by at least 100 cells per microliter, while 4% had a decline from the baseline CD4 value. Baseline variables including age, educational status, CD8 count, ART regimen, and hemoglobin levels predicted CD4 count changes with predictive accuracies of J48, Neural Network, and Random Forest being 87.1%, 83.5%, and 99.8%, respectively. Random Forest algorithm had a superior performance accuracy level than both J48 and Artificial Neural Network. The precision, sensitivity and recall values of Random Forest were also more than 99%. Nearly accurate prediction results were obtained using Random Forest algorithm. This algorithm could be

  2. A study on the predictability of acute lymphoblastic leukaemia response to treatment using a hybrid oncosimulator.

    Science.gov (United States)

    Ouzounoglou, Eleftherios; Kolokotroni, Eleni; Stanulla, Martin; Stamatakos, Georgios S

    2018-02-06

    Efficient use of Virtual Physiological Human (VPH)-type models for personalized treatment response prediction purposes requires a precise model parameterization. In the case where the available personalized data are not sufficient to fully determine the parameter values, an appropriate prediction task may be followed. This study, a hybrid combination of computational optimization and machine learning methods with an already developed mechanistic model called the acute lymphoblastic leukaemia (ALL) Oncosimulator which simulates ALL progression and treatment response is presented. These methods are used in order for the parameters of the model to be estimated for retrospective cases and to be predicted for prospective ones. The parameter value prediction is based on a regression model trained on retrospective cases. The proposed Hybrid ALL Oncosimulator system has been evaluated when predicting the pre-phase treatment outcome in ALL. This has been correctly achieved for a significant percentage of patient cases tested (approx. 70% of patients). Moreover, the system is capable of denying the classification of cases for which the results are not trustworthy enough. In that case, potentially misleading predictions for a number of patients are avoided, while the classification accuracy for the remaining patient cases further increases. The results obtained are particularly encouraging regarding the soundness of the proposed methodologies and their relevance to the process of achieving clinical applicability of the proposed Hybrid ALL Oncosimulator system and VPH models in general.

  3. A Survey on PageRank Computing

    OpenAIRE

    Berkhin, Pavel

    2005-01-01

    This survey reviews the research related to PageRank computing. Components of a PageRank vector serve as authority weights for web pages independent of their textual content, solely based on the hyperlink structure of the web. PageRank is typically used as a web search ranking component. This defines the importance of the model and the data structures that underly PageRank processing. Computing even a single PageRank is a difficult computational task. Computing many PageRanks is a much mor...

  4. MR Imaging in Monitoring and Predicting Treatment Response in Multiple Sclerosis.

    Science.gov (United States)

    Río, Jordi; Auger, Cristina; Rovira, Àlex

    2017-05-01

    MR imaging is the most sensitive tool for identifying lesions in patients with multiple sclerosis (MS). MR imaging has also acquired an essential role in the detection of complications arising from these treatments and in the assessment and prediction of efficacy. In the future, other radiological measures that have shown prognostic value may be incorporated within the models for predicting treatment response. This article examines the role of MR imaging as a prognostic tool in patients with MS and the recommendations that have been proposed in recent years to monitor patients who are treated with disease-modifying drugs. Copyright © 2017 Elsevier Inc. All rights reserved.

  5. Time evolution of Wikipedia network ranking

    Science.gov (United States)

    Eom, Young-Ho; Frahm, Klaus M.; Benczúr, András; Shepelyansky, Dima L.

    2013-12-01

    We study the time evolution of ranking and spectral properties of the Google matrix of English Wikipedia hyperlink network during years 2003-2011. The statistical properties of ranking of Wikipedia articles via PageRank and CheiRank probabilities, as well as the matrix spectrum, are shown to be stabilized for 2007-2011. A special emphasis is done on ranking of Wikipedia personalities and universities. We show that PageRank selection is dominated by politicians while 2DRank, which combines PageRank and CheiRank, gives more accent on personalities of arts. The Wikipedia PageRank of universities recovers 80% of top universities of Shanghai ranking during the considered time period.

  6. Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis.

    Science.gov (United States)

    Sieberts, Solveig K; Zhu, Fan; García-García, Javier; Stahl, Eli; Pratap, Abhishek; Pandey, Gaurav; Pappas, Dimitrios; Aguilar, Daniel; Anton, Bernat; Bonet, Jaume; Eksi, Ridvan; Fornés, Oriol; Guney, Emre; Li, Hongdong; Marín, Manuel Alejandro; Panwar, Bharat; Planas-Iglesias, Joan; Poglayen, Daniel; Cui, Jing; Falcao, Andre O; Suver, Christine; Hoff, Bruce; Balagurusamy, Venkat S K; Dillenberger, Donna; Neto, Elias Chaibub; Norman, Thea; Aittokallio, Tero; Ammad-Ud-Din, Muhammad; Azencott, Chloe-Agathe; Bellón, Víctor; Boeva, Valentina; Bunte, Kerstin; Chheda, Himanshu; Cheng, Lu; Corander, Jukka; Dumontier, Michel; Goldenberg, Anna; Gopalacharyulu, Peddinti; Hajiloo, Mohsen; Hidru, Daniel; Jaiswal, Alok; Kaski, Samuel; Khalfaoui, Beyrem; Khan, Suleiman Ali; Kramer, Eric R; Marttinen, Pekka; Mezlini, Aziz M; Molparia, Bhuvan; Pirinen, Matti; Saarela, Janna; Samwald, Matthias; Stoven, Véronique; Tang, Hao; Tang, Jing; Torkamani, Ali; Vert, Jean-Phillipe; Wang, Bo; Wang, Tao; Wennerberg, Krister; Wineinger, Nathan E; Xiao, Guanghua; Xie, Yang; Yeung, Rae; Zhan, Xiaowei; Zhao, Cheng; Greenberg, Jeff; Kremer, Joel; Michaud, Kaleb; Barton, Anne; Coenen, Marieke; Mariette, Xavier; Miceli, Corinne; Shadick, Nancy; Weinblatt, Michael; de Vries, Niek; Tak, Paul P; Gerlag, Danielle; Huizinga, Tom W J; Kurreeman, Fina; Allaart, Cornelia F; Louis Bridges, S; Criswell, Lindsey; Moreland, Larry; Klareskog, Lars; Saevarsdottir, Saedis; Padyukov, Leonid; Gregersen, Peter K; Friend, Stephen; Plenge, Robert; Stolovitzky, Gustavo; Oliva, Baldo; Guan, Yuanfang; Mangravite, Lara M; Bridges, S Louis; Criswell, Lindsey; Moreland, Larry; Klareskog, Lars; Saevarsdottir, Saedis; Padyukov, Leonid; Gregersen, Peter K; Friend, Stephen; Plenge, Robert; Stolovitzky, Gustavo; Oliva, Baldo; Guan, Yuanfang; Mangravite, Lara M

    2016-08-23

    Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in ∼one-third of patients. No biomarker currently exists that identifies non-responders before treatment. A rigorous community-based assessment of the utility of SNP data for predicting anti-TNF treatment efficacy in RA patients was performed in the context of a DREAM Challenge (http://www.synapse.org/RA_Challenge). An open challenge framework enabled the comparative evaluation of predictions developed by 73 research groups using the most comprehensive available data and covering a wide range of state-of-the-art modelling methodologies. Despite a significant genetic heritability estimate of treatment non-response trait (h(2)=0.18, P value=0.02), no significant genetic contribution to prediction accuracy is observed. Results formally confirm the expectations of the rheumatology community that SNP information does not significantly improve predictive performance relative to standard clinical traits, thereby justifying a refocusing of future efforts on collection of other data.

  7. Pre-treatment cortisol awakening response predicts symptom reduction in posttraumatic stress disorder after treatment

    NARCIS (Netherlands)

    Rapcencu, A.E.; Gorter, R.; Kennis, Mitzy; van Rooij, S.J.H.; Geuze, E.

    2017-01-01

    Dysfunction of the HPA-axis has frequently been found in the aftermath of trauma exposure with or without PTSD. Decreasing HPA-axis reactivity to different stress cues has been reported during PTSD treatment. The cortisol awakening response (CARi) is a well-validated, standardized measure of

  8. Pre-treatment cortisol awakening response predicts symptom reduction in posttraumatic stress disorder after treatment

    NARCIS (Netherlands)

    Rapcencu, A E; Gorter, R; Kennis, M; van Rooij, S J H; Geuze, E

    Dysfunction of the HPA-axis has frequently been found in the aftermath of trauma exposure with or without PTSD. Decreasing HPA-axis reactivity to different stress cues has been reported during PTSD treatment. The cortisol awakening response (CARi) is a well-validated, standardized measure of

  9. Threat-Related Selective Attention Predicts Treatment Success in Childhood Anxiety Disorders

    OpenAIRE

    Legerstee, Jeroen; Tulen, Joke; Kallen, Victor; Dieleman, Gwen; Treffers, Philip; Verhulst, Frank; Utens, Elisabeth

    2009-01-01

    textabstractAbstract OBJECTIVE: The present study examined whether threat-related selective attention was predictive of treatment success in children with anxiety disorders and whether age moderated this association. Specific components of selective attention were examined in treatment responders and nonresponders. METHOD: Participants consisted of 131 children with anxiety disorders (aged 8-16 years), who received standardized cognitive-behavioral therapy. At pretreatment, a pictorial dot-pr...

  10. Predictive Treatment Management: Incorporating a Predictive Tumor Response Model Into Robust Prospective Treatment Planning for Non-Small Cell Lung Cancer

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Pengpeng, E-mail: zhangp@mskcc.org [Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York (United States); Yorke, Ellen; Hu, Yu-Chi; Mageras, Gig [Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York (United States); Rimner, Andreas [Department of Radiation Oncology, Memorial Sloan-Kettering Cancer Center, New York, New York (United States); Deasy, Joseph O. [Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York (United States)

    2014-02-01

    Purpose: We hypothesized that a treatment planning technique that incorporates predicted lung tumor regression into optimization, predictive treatment planning (PTP), could allow dose escalation to the residual tumor while maintaining coverage of the initial target without increasing dose to surrounding organs at risk (OARs). Methods and Materials: We created a model to estimate the geometric presence of residual tumors after radiation therapy using planning computed tomography (CT) and weekly cone beam CT scans of 5 lung cancer patients. For planning purposes, we modeled the dynamic process of tumor shrinkage by morphing the original planning target volume (PTV{sub orig}) in 3 equispaced steps to the predicted residue (PTV{sub pred}). Patients were treated with a uniform prescription dose to PTV{sub orig}. By contrast, PTP optimization started with the same prescription dose to PTV{sub orig} but linearly increased the dose at each step, until reaching the highest dose achievable to PTV{sub pred} consistent with OAR limits. This method is compared with midcourse adaptive replanning. Results: Initial parenchymal gross tumor volume (GTV) ranged from 3.6 to 186.5 cm{sup 3}. On average, the primary GTV and PTV decreased by 39% and 27%, respectively, at the end of treatment. The PTP approach gave PTV{sub orig} at least the prescription dose, and it increased the mean dose of the true residual tumor by an average of 6.0 Gy above the adaptive approach. Conclusions: PTP, incorporating a tumor regression model from the start, represents a new approach to increase tumor dose without increasing toxicities, and reduce clinical workload compared with the adaptive approach, although model verification using per-patient midcourse imaging would be prudent.

  11. Do treatment quality indicators predict cardiovascular outcomes in patients with diabetes?

    Directory of Open Access Journals (Sweden)

    Grigory Sidorenkov

    Full Text Available BACKGROUND: Landmark clinical trials have led to optimal treatment recommendations for patients with diabetes. Whether optimal treatment is actually delivered in practice is even more important than the efficacy of the drugs tested in trials. To this end, treatment quality indicators have been developed and tested against intermediate outcomes. No studies have tested whether these treatment quality indicators also predict hard patient outcomes. METHODS: A cohort study was conducted using data collected from >10.000 diabetes patients in the Groningen Initiative to Analyze Type 2 Treatment (GIANTT database and Dutch Hospital Data register. Included quality indicators measured glucose-, lipid-, blood pressure- and albuminuria-lowering treatment status and treatment intensification. Hard patient outcome was the composite of cardiovascular events and all-cause death. Associations were tested using Cox regression adjusting for confounding, reporting hazard ratios (HR with 95% confidence intervals. RESULTS: Lipid and albuminuria treatment status, but not blood pressure lowering treatment status, were associated with the composite outcome (HR = 0.77, 0.67-0.88; HR = 0.75, 0.59-0.94. Glucose lowering treatment status was associated with the composite outcome only in patients with an elevated HbA1c level (HR = 0.72, 0.56-0.93. Treatment intensification with glucose-lowering but not with lipid-, blood pressure- and albuminuria-lowering drugs was associated with the outcome (HR = 0.73, 0.60-0.89. CONCLUSION: Treatment quality indicators measuring lipid- and albuminuria-lowering treatment status are valid quality measures, since they predict a lower risk of cardiovascular events and mortality in patients with diabetes. The quality indicators for glucose-lowering treatment should only be used for restricted populations with elevated HbA1c levels. Intriguingly, the tested indicators for blood pressure-lowering treatment did not predict patient

  12. Serum level of CD26 predicts time to first treatment in early B-chronic lymphocytic leukemia.

    Science.gov (United States)

    Molica, Stefano; Digiesi, Giovanna; Mirabelli, Rosanna; Cutrona, Giovanna; Antenucci, Anna; Molica, Matteo; Giannarelli, Diana; Sperduti, Isabella; Morabito, Fortunato; Neri, Antonino; Baldini, Luca; Ferrarini, Manlio

    2009-09-01

    We analyzed the correlation between well-established biological parameters of prognostic relevance in B-cell chronic lymphocytic leukemia (CLL) [i.e. mutational status of the immunoglobulin heavy chain variable region (IgV(H)), ZAP-70- and CD38-expression] and serum levels of CD26 (dipeptidyl peptidase IV, DPP IV) by evaluating the impact of these variables on the time to first treatment (TFT) in a series of 69 previously untreated Binet stage A B-cell CLL patients. By using a commercial ELISA we found that with exception of a borderline significance for ZAP-70 (P = 0.07) and CD38 (P = 0.08), circulating levels of CD26 did not correlate with either Rai substages (P = 0.520) or other biomarker [beta2-microglobulin (P = 0.933), LDH (P = 0.101), mutational status of IgV(H) (P = 0.320)]. Maximally selected log-rank statistic plots identified a CD26 serum concentration of 371 ng/mL as the best cut-off. This threshold allowed the identification of two subsets of patients with CD26 serum levels higher and lower that 371 ng/mL respectively, whose clinical outcome was different with respect to TFT (i.e. 46% and 71% at 5 yr respectively; P = 0.005). Along with higher serum levels of CD26, the univariate Cox proportional hazard model identified absence of mutation in IgV(H) (P IgV(H,)P IgV(H) can be adequately used to predict clinical behavior of patients with low risk disease.

  13. Validating rankings in soccer championships

    Directory of Open Access Journals (Sweden)

    Annibal Parracho Sant'Anna

    2012-08-01

    Full Text Available The final ranking of a championship is determined by quality attributes combined with other factors which should be filtered out of any decision on relegation or draft for upper level tournaments. Factors like referees' mistakes and difficulty of certain matches due to its accidental importance to the opponents should have their influence reduced. This work tests approaches to combine classification rules considering the imprecision of the number of points as a measure of quality and of the variables that provide reliable explanation for it. Two home-advantage variables are tested and shown to be apt to enter as explanatory variables. Independence between the criteria is checked against the hypothesis of maximal correlation. The importance of factors and of composition rules is evaluated on the basis of correlation between rank vectors, number of classes and number of clubs in tail classes. Data from five years of the Brazilian Soccer Championship are analyzed.

  14. Prediction of immunophenotype, treatment response, and relapse in childhood acute lymphoblastic leukemia using DNA microarrays

    DEFF Research Database (Denmark)

    Willenbrock, Hanni; Juncker, Agnieszka; Schmiegelow, K.

    2004-01-01

    Gene expression profiling is a promising tool for classification of pediatric acute lymphoblastic leukemia ( ALL). We analyzed the gene expression at the time of diagnosis for 45 Danish children with ALL. The prediction of 5-year event-free survival or relapse after treatment by NOPHO-ALL92 or 2000...

  15. Do Assault-Related Variables Predict Response to Cognitive Behavioral Treatment for PTSD?

    Science.gov (United States)

    Hembree, Elizabeth A.; Street, Gordon P.; Riggs, David S.; Foa, Edna B.

    2004-01-01

    This study examined the hypothesis that variables such as history of prior trauma, assault severity, and type of assault, previously found to be associated with natural recovery, would also predict treatment outcome. Trauma-related variables were examined as predictors of posttreatment posttraumatic stress disorder (PTSD) severity in a sample of…

  16. RAMAN SPECTROSCOPIC STUDY ON PREDICTION OF TREATMENT RESPONSE IN CERVICAL CANCERS

    Directory of Open Access Journals (Sweden)

    S. RUBINA

    2013-04-01

    Full Text Available Concurrent chemoradiotherapy (CCRT is the choice of treatment for locally advanced cervical cancers; however, tumors exhibit diverse response to treatment. Early prediction of tumor response leads to individualizing treatment regimen. Response evaluation criteria in solid tumors (RECIST, the current modality of tumor response assessment, is often subjective and carried out at the first visit after treatment, which is about four months. Hence, there is a need for better predictive tool for radioresponse. Optical spectroscopic techniques, sensitive to molecular alteration, are being pursued as potential diagnostic tools. Present pilot study aims to explore the fiber-optic-based Raman spectroscopy approach in prediction of tumor response to CCRT, before taking up extensive in vivo studies. Ex vivo Raman spectra were acquired from biopsies collected from 11 normal (148 spectra, 16 tumor (201 spectra and 13 complete response (151 CR spectra, one partial response (8 PR spectra and one nonresponder (8 NR spectra subjects. Data was analyzed using principal component linear discriminant analysis (PC-LDA followed by leave-one-out cross-validation (LOO-CV. Findings suggest that normal tissues can be efficiently classified from both pre- and post-treated tumor biopsies, while there is an overlap between pre- and post-CCRT tumor tissues. Spectra of CR, PR and NR tissues were subjected to principal component analysis (PCA and a tendency of classification was observed, corroborating previous studies. Thus, this study further supports the feasibility of Raman spectroscopy in prediction of tumor radioresponse and prospective noninvasive in vivo applications.

  17. Threat Related Selective Attention Predicts Treatment Success in Childhood Anxiety Disorders

    Science.gov (United States)

    Legerstee, Jeroen S.; Tulen, Joke H. M.; Kallen, Victor L.; Dieleman, Gwen C.; Treffers, Philip D. A.; Verhulst, Frank C.; Utens, Elisabeth M. W. J.

    2009-01-01

    Threat-related selective attention was found to predict the success of the treatment of childhood anxiety disorders through administering a pictorial dot-probe task to 131 children with anxiety disorders prior to cognitive behavioral therapy. The diagnostic status of the subjects was evaluated with a semistructured clinical interview at both pre-…

  18. Identification of Predictive Response Markers and Novel Treatment Targets for Gliomas

    NARCIS (Netherlands)

    L. Erdem-Eraslan (Lale)

    2016-01-01

    markdownabstractGliomas are the most frequent primary brain tumors in adults. Despite multimodality treatment strategies, the survival of patients with a diffuse glioma remains poor. There has been an increasing use of molecular markers to assist diagnosis and predict prognosis and response to

  19. Prediction of treatment outcomes to exercise in patients with nonremitted major depressive disorder.

    Science.gov (United States)

    Rethorst, Chad D; South, Charles C; Rush, A John; Greer, Tracy L; Trivedi, Madhukar H

    2017-12-01

    Only one-third of patients with major depressive disorder (MDD) achieve remission with initial treatment. Consequently, current clinical practice relies on a "trial-and-error" approach to identify an effective treatment for each patient. The purpose of this report was to determine whether we could identify a set of clinical and biological parameters with potential clinical utility for prescription of exercise for treatment of MDD in a secondary analysis of the Treatment with Exercise Augmentation in Depression (TREAD) trial. Participants with nonremitted MDD were randomized to one of two exercise doses for 12 weeks. Participants were categorized as "remitters" (≤12 on the IDS-C), nonresponders (drop in IDS-C), or neither. The least absolute shrinkage and selection operator (LASSO) and random forests were used to evaluate 30 variables as predictors of both remission and nonresponse. Predictors were used to model treatment outcomes using logistic regression. Of the 122 participants, 36 were categorized as remitters (29.5%), 56 as nonresponders (45.9%), and 30 as neither (24.6%). Predictors of remission were higher levels of brain-derived neurotrophic factor (BDNF) and IL-1B, greater depressive symptom severity, and higher postexercise positive affect. Predictors of treatment nonresponse were low cardiorespiratory fitness, lower levels of IL-6 and BDNF, and lower postexercise positive affect. Models including these predictors resulted in predictive values greater than 70% (true predicted remitters/all predicted remitters) with specificities greater than 25% (true predicted remitters/all remitters). Results indicate feasibility in identifying patients who will either remit or not respond to exercise as a treatment for MDD utilizing a clinical decision model that incorporates multiple patient characteristics. © 2017 Wiley Periodicals, Inc.

  20. Cross-trial prediction of treatment outcome in depression: a machine learning approach.

    Science.gov (United States)

    Chekroud, Adam Mourad; Zotti, Ryan Joseph; Shehzad, Zarrar; Gueorguieva, Ralitza; Johnson, Marcia K; Trivedi, Madhukar H; Cannon, Tyrone D; Krystal, John Harrison; Corlett, Philip Robert

    2016-03-01

    Antidepressant treatment efficacy is low, but might be improved by matching patients to interventions. At present, clinicians have no empirically validated mechanisms to assess whether a patient with depression will respond to a specific antidepressant. We aimed to develop an algorithm to assess whether patients will achieve symptomatic remission from a 12-week course of citalopram. We used patient-reported data from patients with depression (n=4041, with 1949 completers) from level 1 of the Sequenced Treatment Alternatives to Relieve Depression (STAR*D; ClinicalTrials.gov, number NCT00021528) to identify variables that were most predictive of treatment outcome, and used these variables to train a machine-learning model to predict clinical remission. We externally validated the model in the escitalopram treatment group (n=151) of an independent clinical trial (Combining Medications to Enhance Depression Outcomes [COMED]; ClinicalTrials.gov, number NCT00590863). We identified 25 variables that were most predictive of treatment outcome from 164 patient-reportable variables, and used these to train the model. The model was internally cross-validated, and predicted outcomes in the STAR*D cohort with accuracy significantly above chance (64·6% [SD 3·2]; p<0·0001). The model was externally validated in the escitalopram treatment group (N=151) of COMED (accuracy 59·6%, p=0.043). The model also performed significantly above chance in a combined escitalopram-buproprion treatment group in COMED (n=134; accuracy 59·7%, p=0·023), but not in a combined venlafaxine-mirtazapine group (n=140; accuracy 51·4%, p=0·53), suggesting specificity of the model to underlying mechanisms. Building statistical models by mining existing clinical trial data can enable prospective identification of patients who are likely to respond to a specific antidepressant. Yale University. Copyright © 2016 Elsevier Ltd. All rights reserved.

  1. Towards personalized therapy for multiple sclerosis: prediction of individual treatment response.

    Science.gov (United States)

    Kalincik, Tomas; Manouchehrinia, Ali; Sobisek, Lukas; Jokubaitis, Vilija; Spelman, Tim; Horakova, Dana; Havrdova, Eva; Trojano, Maria; Izquierdo, Guillermo; Lugaresi, Alessandra; Girard, Marc; Prat, Alexandre; Duquette, Pierre; Grammond, Pierre; Sola, Patrizia; Hupperts, Raymond; Grand'Maison, Francois; Pucci, Eugenio; Boz, Cavit; Alroughani, Raed; Van Pesch, Vincent; Lechner-Scott, Jeannette; Terzi, Murat; Bergamaschi, Roberto; Iuliano, Gerardo; Granella, Franco; Spitaleri, Daniele; Shaygannejad, Vahid; Oreja-Guevara, Celia; Slee, Mark; Ampapa, Radek; Verheul, Freek; McCombe, Pamela; Olascoaga, Javier; Amato, Maria Pia; Vucic, Steve; Hodgkinson, Suzanne; Ramo-Tello, Cristina; Flechter, Shlomo; Cristiano, Edgardo; Rozsa, Csilla; Moore, Fraser; Luis Sanchez-Menoyo, Jose; Laura Saladino, Maria; Barnett, Michael; Hillert, Jan; Butzkueven, Helmut

    2017-09-01

    Timely initiation of effective therapy is crucial for preventing disability in multiple sclerosis; however, treatment response varies greatly among patients. Comprehensive predictive models of individual treatment response are lacking. Our aims were: (i) to develop predictive algorithms for individual treatment response using demographic, clinical and paraclinical predictors in patients with multiple sclerosis; and (ii) to evaluate accuracy, and internal and external validity of these algorithms. This study evaluated 27 demographic, clinical and paraclinical predictors of individual response to seven disease-modifying therapies in MSBase, a large global cohort study. Treatment response was analysed separately for disability progression, disability regression, relapse frequency, conversion to secondary progressive disease, change in the cumulative disease burden, and the probability of treatment discontinuation. Multivariable survival and generalized linear models were used, together with the principal component analysis to reduce model dimensionality and prevent overparameterization. Accuracy of the individual prediction was tested and its internal validity was evaluated in a separate, non-overlapping cohort. External validity was evaluated in a geographically distinct cohort, the Swedish Multiple Sclerosis Registry. In the training cohort (n = 8513), the most prominent modifiers of treatment response comprised age, disease duration, disease course, previous relapse activity, disability, predominant relapse phenotype and previous therapy. Importantly, the magnitude and direction of the associations varied among therapies and disease outcomes. Higher probability of disability progression during treatment with injectable therapies was predominantly associated with a greater disability at treatment start and the previous therapy. For fingolimod, natalizumab or mitoxantrone, it was mainly associated with lower pretreatment relapse activity. The probability of

  2. Minkowski metrics in creating universal ranking algorithms

    Directory of Open Access Journals (Sweden)

    Andrzej Ameljańczyk

    2014-06-01

    Full Text Available The paper presents a general procedure for creating the rankings of a set of objects, while the relation of preference based on any ranking function. The analysis was possible to use the ranking functions began by showing the fundamental drawbacks of commonly used functions in the form of a weighted sum. As a special case of the ranking procedure in the space of a relation, the procedure based on the notion of an ideal element and generalized Minkowski distance from the element was proposed. This procedure, presented as universal ranking algorithm, eliminates most of the disadvantages of ranking functions in the form of a weighted sum.[b]Keywords[/b]: ranking functions, preference relation, ranking clusters, categories, ideal point, universal ranking algorithm

  3. Functional Multiplex PageRank

    Science.gov (United States)

    Iacovacci, Jacopo; Rahmede, Christoph; Arenas, Alex; Bianconi, Ginestra

    2016-10-01

    Recently it has been recognized that many complex social, technological and biological networks have a multilayer nature and can be described by multiplex networks. Multiplex networks are formed by a set of nodes connected by links having different connotations forming the different layers of the multiplex. Characterizing the centrality of the nodes in a multiplex network is a challenging task since the centrality of the node naturally depends on the importance associated to links of a certain type. Here we propose to assign to each node of a multiplex network a centrality called Functional Multiplex PageRank that is a function of the weights given to every different pattern of connections (multilinks) existent in the multiplex network between any two nodes. Since multilinks distinguish all the possible ways in which the links in different layers can overlap, the Functional Multiplex PageRank can describe important non-linear effects when large relevance or small relevance is assigned to multilinks with overlap. Here we apply the Functional Page Rank to the multiplex airport networks, to the neuronal network of the nematode C. elegans, and to social collaboration and citation networks between scientists. This analysis reveals important differences existing between the most central nodes of these networks, and the correlations between their so-called pattern to success.

  4. Low rank magnetic resonance fingerprinting.

    Science.gov (United States)

    Mazor, Gal; Weizman, Lior; Tal, Assaf; Eldar, Yonina C

    2016-08-01

    Magnetic Resonance Fingerprinting (MRF) is a relatively new approach that provides quantitative MRI using randomized acquisition. Extraction of physical quantitative tissue values is preformed off-line, based on acquisition with varying parameters and a dictionary generated according to the Bloch equations. MRF uses hundreds of radio frequency (RF) excitation pulses for acquisition, and therefore high under-sampling ratio in the sampling domain (k-space) is required. This under-sampling causes spatial artifacts that hamper the ability to accurately estimate the quantitative tissue values. In this work, we introduce a new approach for quantitative MRI using MRF, called Low Rank MRF. We exploit the low rank property of the temporal domain, on top of the well-known sparsity of the MRF signal in the generated dictionary domain. We present an iterative scheme that consists of a gradient step followed by a low rank projection using the singular value decomposition. Experiments on real MRI data demonstrate superior results compared to conventional implementation of compressed sensing for MRF at 15% sampling ratio.

  5. Ranking Support Vector Machine with Kernel Approximation

    Directory of Open Access Journals (Sweden)

    Kai Chen

    2017-01-01

    Full Text Available Learning to rank algorithm has become important in recent years due to its successful application in information retrieval, recommender system, and computational biology, and so forth. Ranking support vector machine (RankSVM is one of the state-of-art ranking models and has been favorably used. Nonlinear RankSVM (RankSVM with nonlinear kernels can give higher accuracy than linear RankSVM (RankSVM with a linear kernel for complex nonlinear ranking problem. However, the learning methods for nonlinear RankSVM are still time-consuming because of the calculation of kernel matrix. In this paper, we propose a fast ranking algorithm based on kernel approximation to avoid computing the kernel matrix. We explore two types of kernel approximation methods, namely, the Nyström method and random Fourier features. Primal truncated Newton method is used to optimize the pairwise L2-loss (squared Hinge-loss objective function of the ranking model after the nonlinear kernel approximation. Experimental results demonstrate that our proposed method gets a much faster training speed than kernel RankSVM and achieves comparable or better performance over state-of-the-art ranking algorithms.

  6. Ranking Support Vector Machine with Kernel Approximation.

    Science.gov (United States)

    Chen, Kai; Li, Rongchun; Dou, Yong; Liang, Zhengfa; Lv, Qi

    2017-01-01

    Learning to rank algorithm has become important in recent years due to its successful application in information retrieval, recommender system, and computational biology, and so forth. Ranking support vector machine (RankSVM) is one of the state-of-art ranking models and has been favorably used. Nonlinear RankSVM (RankSVM with nonlinear kernels) can give higher accuracy than linear RankSVM (RankSVM with a linear kernel) for complex nonlinear ranking problem. However, the learning methods for nonlinear RankSVM are still time-consuming because of the calculation of kernel matrix. In this paper, we propose a fast ranking algorithm based on kernel approximation to avoid computing the kernel matrix. We explore two types of kernel approximation methods, namely, the Nyström method and random Fourier features. Primal truncated Newton method is used to optimize the pairwise L2-loss (squared Hinge-loss) objective function of the ranking model after the nonlinear kernel approximation. Experimental results demonstrate that our proposed method gets a much faster training speed than kernel RankSVM and achieves comparable or better performance over state-of-the-art ranking algorithms.

  7. An IL28B genotype-based clinical prediction model for treatment of chronic hepatitis C.

    Directory of Open Access Journals (Sweden)

    Thomas R O'Brien

    Full Text Available Genetic variation in IL28B and other factors are associated with sustained virological response (SVR after pegylated-interferon/ribavirin treatment for chronic hepatitis C (CHC. Using data from the HALT-C Trial, we developed a model to predict a patient's probability of SVR based on IL28B genotype and clinical variables.HALT-C enrolled patients with advanced CHC who had failed previous interferon-based treatment. Subjects were re-treated with pegylated-interferon/ribavirin during trial lead-in. We used step-wise logistic regression to calculate adjusted odds ratios (aOR and create the predictive model. Leave-one-out cross-validation was used to predict a priori probabilities of SVR and determine area under the receiver operator characteristics curve (AUC.Among 646 HCV genotype 1-infected European American patients, 14.2% achieved SVR. IL28B rs12979860-CC genotype was the strongest predictor of SVR (aOR, 7.56; p10% (43.3% of subjects had an SVR rate of 27.9% and accounted for 84.8% of subjects actually achieving SVR. To verify that consideration of both IL28B genotype and clinical variables is required for treatment decisions, we calculated AUC values from published data for the IDEAL Study.A clinical prediction model based on IL28B genotype and clinical variables can yield useful individualized predictions of the probability of treatment success that could increase SVR rates and decrease the frequency of futile treatment among patients with CHC.

  8. Consumer factors predicting level of treatment response to illness management and recovery.

    Science.gov (United States)

    White, Dominique A; McGuire, Alan B; Luther, Lauren; Anderson, Adrienne I; Phalen, Peter; McGrew, John H

    2017-12-01

    This study aims to identify consumer-level predictors of level of treatment response to illness management and recovery (IMR) to target the appropriate consumers and aid psychiatric rehabilitation settings in developing intervention adaptations. Secondary analyses from a multisite study of IMR were conducted. Self-report data from consumer participants of the parent study (n = 236) were analyzed for the current study. Consumers completed prepost surveys assessing illness management, coping, goal-related hope, social support, medication adherence, and working alliance. Correlations and multiple regression analyses were run to identify self-report variables that predicted level of treatment response to IMR. Analyses revealed that goal-related hope significantly predicted level of improved illness self-management, F(1, 164) = 10.93, p consumer-level predictors of level of treatment response have not been explored for IMR. Although 2 significant predictors were identified, study findings suggest more work is needed. Future research is needed to identify additional consumer-level factors predictive of IMR treatment response in order to identify who would benefit most from this treatment program. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  9. Endovascular Treatment of Malignant Superior Vena Cava Syndrome: Results and Predictive Factors of Clinical Efficacy

    Energy Technology Data Exchange (ETDEWEB)

    Fagedet, Dorothee, E-mail: DFagedet@chu-grenoble.fr [CHU de Grenoble, Clinique universitaire de medecine interne, Pole Pluridisciplinaire de Medecine (France); Thony, Frederic, E-mail: FThony@chu-grenoble.fr [CHU de Grenoble, Clinique universitaire de radiologie et imagerie medicale, Pole d' Imagerie (France); Timsit, Jean-Francois, E-mail: JFTimsit@chu-grenoble.fr [CHU de Grenoble, Clinique universitaire de reanimation, Pole Medecine Aiguee Communautaire (France); Rodiere, Mathieu, E-mail: MRodiere@chu-grenoble.fr [CHU de Grenoble, Clinique universitaire de radiologie et imagerie medicale, Pole d' Imagerie (France); Monnin-Bares, Valerie, E-mail: v-monnin@chu-montpellier.fr [CHRU Arnaud de Villeneuve, Imagerie Medicale Thoracique Cardiovasculaire (France); Ferretti, Gilbert R., E-mail: GFerretti@chu-grenoble.fr [CHU de Grenoble, Clinique universitaire de radiologie et imagerie medicale, Pole d' Imagerie (France); Vesin, Aurelien; Moro-Sibilot, Denis, E-mail: DMoro.pneumo@chu-grenoble.fr [University Grenoble 1 e Albert Bonniot Institute, Inserm U823 (France)

    2013-02-15

    To demonstrate the effectiveness of endovascular treatment (EVT) with self-expandable bare stents for malignant superior vena cava syndrome (SVCS) and to analyze predictive factors of EVT efficacy. Retrospective review of the 164 patients with malignant SVCS treated with EVT in our hospital from August 1992 to December 2007 and followed until February 2009. Endovascular treatment includes angioplasty before and after stent placement. We used self-expandable bare stents. We studied results of this treatment and looked for predictive factors of clinical efficacy, recurrence, and complications by statistical analysis. Endovascular treatment was clinically successful in 95% of cases, with an acceptable rate of early mortality (2.4%). Thrombosis of the superior vena cava was the only independent factor for EVT failure. The use of stents over 16 mm in diameter was a predictive factor for complications (P = 0.008). Twenty-one complications (12.8%) occurred during the follow-up period. Relapse occurred in 36 patients (21.9%), with effective restenting in 75% of cases. Recurrence of SVCS was significantly increased in cases of occlusion (P = 0.01), initial associated thrombosis (P = 0.006), or use of steel stents (P = 0.004). Long-term anticoagulant therapy did not influence the risk of recurrence or complications. In malignancy, EVT with self-expandable bare stents is an effective SVCS therapy. These results prompt us to propose treatment with stents earlier in the clinical course of patients with SVCS and to avoid dilatation greater than 16 mm.

  10. Endovascular Treatment of Malignant Superior Vena Cava Syndrome: Results and Predictive Factors of Clinical Efficacy

    International Nuclear Information System (INIS)

    Fagedet, Dorothée; Thony, Frederic; Timsit, Jean-François; Rodiere, Mathieu; Monnin-Bares, Valérie; Ferretti, Gilbert R.; Vesin, Aurélien; Moro-Sibilot, Denis

    2013-01-01

    To demonstrate the effectiveness of endovascular treatment (EVT) with self-expandable bare stents for malignant superior vena cava syndrome (SVCS) and to analyze predictive factors of EVT efficacy. Retrospective review of the 164 patients with malignant SVCS treated with EVT in our hospital from August 1992 to December 2007 and followed until February 2009. Endovascular treatment includes angioplasty before and after stent placement. We used self-expandable bare stents. We studied results of this treatment and looked for predictive factors of clinical efficacy, recurrence, and complications by statistical analysis. Endovascular treatment was clinically successful in 95% of cases, with an acceptable rate of early mortality (2.4%). Thrombosis of the superior vena cava was the only independent factor for EVT failure. The use of stents over 16 mm in diameter was a predictive factor for complications (P = 0.008). Twenty-one complications (12.8%) occurred during the follow-up period. Relapse occurred in 36 patients (21.9%), with effective restenting in 75% of cases. Recurrence of SVCS was significantly increased in cases of occlusion (P = 0.01), initial associated thrombosis (P = 0.006), or use of steel stents (P = 0.004). Long-term anticoagulant therapy did not influence the risk of recurrence or complications. In malignancy, EVT with self-expandable bare stents is an effective SVCS therapy. These results prompt us to propose treatment with stents earlier in the clinical course of patients with SVCS and to avoid dilatation greater than 16 mm.

  11. Social class rank, threat vigilance, and hostile reactivity.

    Science.gov (United States)

    Kraus, Michael W; Horberg, E J; Goetz, Jennifer L; Keltner, Dacher

    2011-10-01

    Lower-class individuals, because of their lower rank in society, are theorized to be more vigilant to social threats relative to their high-ranking upper-class counterparts. This class-related vigilance to threat, the authors predicted, would shape the emotional content of social interactions in systematic ways. In Study 1, participants engaged in a teasing interaction with a close friend. Lower-class participants--measured in terms of social class rank in society and within the friendship--more accurately tracked the hostile emotions of their friend. As a result, lower-class individuals experienced more hostile emotion contagion relative to upper-class participants. In Study 2, lower-class participants manipulated to experience lower subjective socioeconomic rank showed more hostile reactivity to ambiguous social scenarios relative to upper-class participants and to lower-class participants experiencing elevated socioeconomic rank. The results suggest that class affects expectations, perception, and experience of hostile emotion, particularly in situations in which lower-class individuals perceive their subordinate rank.

  12. Early prediction of hypothyroidism following 131I treatment for Graves' disease

    International Nuclear Information System (INIS)

    Wilson, R.; McKillop, J.H.; Jenkins, C.; Thomson, J.A.

    1988-01-01

    The aim of this study was twofold. Firstly to assess the post treatment predictive value of various biochemical and immunological tests for early hypothyroidism after 131 I therapy of Graves' disease, and secondly to determine whether or not pretreatment with Carbimazole protects against post treatment hypothyroidism. The early changes observed in serum T 3 , T 4 , TSH, thyroid microsomal and thyroglobulin antibody levels were found to be of no predictive value. A sharp rise, around 2 months, in TRAb levels following 131 I therapy indicated that hypothyroidism was likely to occur. This rise was thought to reflect a greater degree of thyroid damage. Lower levels of thyroglobulin in patients who had become hypothyroid by 12 months after treatment would support this view. Five weeks Carbimazole pretreatment in this relatively small group of patients did not appear to protect against hypothyroidism. (orig.)

  13. Empirically derived pain-patient MMPI subgroups: prediction of treatment outcome.

    Science.gov (United States)

    Moore, J E; Armentrout, D P; Parker, J C; Kivlahan, D R

    1986-02-01

    Fifty-seven male chronic pain patients admitted to an inpatient multimodal pain treatment program at a Midwestern Veterans Administration hospital completed the MMPI, Profile of Mood States (POMS), Tennessee Self-Concept Scale (TSCS), Rathus Assertiveness Schedule (RAS), activity diaries, and an extensive pain questionnaire. All patients were assessed both before and after treatment, and most also were assessed 2-5 months prior to treatment. No significant changes occurred during the baseline period, but significant improvements were evident at posttreatment on most variables: MMPI, POMS, TSCS, RAS, pain severity, sexual functioning, and activity diaries. MMPI subgroup membership, based on a hierarchical cluster analysis in a larger sample, was not predictive of differential treatment outcome. Possible reasons for comparable treatment gains among these subgroups, which previously have been shown to differ on many psychological and behavioral factors, are discussed.

  14. Predicting objective function weights from patient anatomy in prostate IMRT treatment planning

    International Nuclear Information System (INIS)

    Lee, Taewoo; Hammad, Muhannad; Chan, Timothy C. Y.; Craig, Tim; Sharpe, Michael B.

    2013-01-01

    Purpose: Intensity-modulated radiation therapy (IMRT) treatment planning typically combines multiple criteria into a single objective function by taking a weighted sum. The authors propose a statistical model that predicts objective function weights from patient anatomy for prostate IMRT treatment planning. This study provides a proof of concept for geometry-driven weight determination. Methods: A previously developed inverse optimization method (IOM) was used to generate optimal objective function weights for 24 patients using their historical treatment plans (i.e., dose distributions). These IOM weights were around 1% for each of the femoral heads, while bladder and rectum weights varied greatly between patients. A regression model was developed to predict a patient's rectum weight using the ratio of the overlap volume of the rectum and bladder with the planning target volume at a 1 cm expansion as the independent variable. The femoral head weights were fixed to 1% each and the bladder weight was calculated as one minus the rectum and femoral head weights. The model was validated using leave-one-out cross validation. Objective values and dose distributions generated through inverse planning using the predicted weights were compared to those generated using the original IOM weights, as well as an average of the IOM weights across all patients. Results: The IOM weight vectors were on average six times closer to the predicted weight vectors than to the average weight vector, usingl 2 distance. Likewise, the bladder and rectum objective values achieved by the predicted weights were more similar to the objective values achieved by the IOM weights. The difference in objective value performance between the predicted and average weights was statistically significant according to a one-sided sign test. For all patients, the difference in rectum V54.3 Gy, rectum V70.0 Gy, bladder V54.3 Gy, and bladder V70.0 Gy values between the dose distributions generated by the

  15. Pareto Optimization Identifies Diverse Set of Phosphorylation Signatures Predicting Response to Treatment with Dasatinib.

    Science.gov (United States)

    Klammer, Martin; Dybowski, J Nikolaj; Hoffmann, Daniel; Schaab, Christoph

    2015-01-01

    Multivariate biomarkers that can predict the effectiveness of targeted therapy in individual patients are highly desired. Previous biomarker discovery studies have largely focused on the identification of single biomarker signatures, aimed at maximizing prediction accuracy. Here, we present a different approach that identifies multiple biomarkers by simultaneously optimizing their predictive power, number of features, and proximity to the drug target in a protein-protein interaction network. To this end, we incorporated NSGA-II, a fast and elitist multi-objective optimization algorithm that is based on the principle of Pareto optimality, into the biomarker discovery workflow. The method was applied to quantitative phosphoproteome data of 19 non-small cell lung cancer (NSCLC) cell lines from a previous biomarker study. The algorithm successfully identified a total of 77 candidate biomarker signatures predicting response to treatment with dasatinib. Through filtering and similarity clustering, this set was trimmed to four final biomarker signatures, which then were validated on an independent set of breast cancer cell lines. All four candidates reached the same good prediction accuracy (83%) as the originally published biomarker. Although the newly discovered signatures were diverse in their composition and in their size, the central protein of the originally published signature - integrin β4 (ITGB4) - was also present in all four Pareto signatures, confirming its pivotal role in predicting dasatinib response in NSCLC cell lines. In summary, the method presented here allows for a robust and simultaneous identification of multiple multivariate biomarkers that are optimized for prediction performance, size, and relevance.

  16. Toward an online cognitive and emotional battery to predict treatment remission in depression

    Directory of Open Access Journals (Sweden)

    Gordon E

    2015-02-01

    Full Text Available Evian Gordon,1 A John Rush,2 Donna M Palmer,3,4 Taylor A Braund,3 William Rekshan1 1Brain Resource, San Francisco, CA, USA; 2Duke-NUS, Singapore; 3Brain Resource, Sydney, NSW, Australia; 4Brain Dynamics Center, Sydney Medical School – Westmead and Westmead Millennium Institute, The University of Sydney, Sydney, NSW, Australia Purpose: To evaluate the performance of a cognitive and emotional test battery in a representative sample of depressed outpatients to inform likelihood of remission over 8 weeks of treatment with each of three common antidepressant medications. Patients and methods: Outpatients 18–65 years old with nonpsychotic major depressive disorder (17 sites were randomized to escitalopram, sertraline or venlafaxine-XR (extended release. Participants scored ≥12 on the baseline 16-item Quick Inventory of Depressive Symptomatology – Self-Report and completed 8 weeks of treatment. The baseline test battery measured cognitive and emotional status. Exploratory multivariate logistic regression models predicting remission (16-item Quick Inventory of Depressive Symptomatology – Self-Report score ≤5 at 8 weeks were developed independently for each medication in subgroups stratified by age, sex, or cognitive and emotional test performance. The model with the highest cross-validated accuracy determined the participant proportion in each arm for whom remission could be predicted with an accuracy ≥10% above chance. The proportion for whom a prediction could be made with very high certainty (positive predictive value and negative predictive value exceeding 80% was calculated by incrementally increasing test battery thresholds to predict remission/non-remission. Results: The test battery, individually developed for each medication, improved identification of remitting and non-remitting participants by ≥10% beyond chance for 243 of 467 participants. The overall remission rates were escitalopram: 40.8%, sertraline: 30.3%, and

  17. Eigentumors for prediction of treatment failure in patients with early-stage breast cancer using dynamic contrast-enhanced MRI: a feasibility study

    Science.gov (United States)

    Chan, H. M.; van der Velden, B. H. M.; E Loo, C.; Gilhuijs, K. G. A.

    2017-08-01

    We present a radiomics model to discriminate between patients at low risk and those at high risk of treatment failure at long-term follow-up based on eigentumors: principal components computed from volumes encompassing tumors in washin and washout images of pre-treatment dynamic contrast-enhanced (DCE-) MR images. Eigentumors were computed from the images of 563 patients from the MARGINS study. Subsequently, a least absolute shrinkage selection operator (LASSO) selected candidates from the components that contained 90% of the variance of the data. The model for prediction of survival after treatment (median follow-up time 86 months) was based on logistic regression. Receiver operating characteristic (ROC) analysis was applied and area-under-the-curve (AUC) values were computed as measures of training and cross-validated performances. The discriminating potential of the model was confirmed using Kaplan-Meier survival curves and log-rank tests. From the 322 principal components that explained 90% of the variance of the data, the LASSO selected 28 components. The ROC curves of the model yielded AUC values of 0.88, 0.77 and 0.73, for the training, leave-one-out cross-validated and bootstrapped performances, respectively. The bootstrapped Kaplan-Meier survival curves confirmed significant separation for all tumors (P  <  0.0001). Survival analysis on immunohistochemical subgroups shows significant separation for the estrogen-receptor subtype tumors (P  <  0.0001) and the triple-negative subtype tumors (P  =  0.0039), but not for tumors of the HER2 subtype (P  =  0.41). The results of this retrospective study show the potential of early-stage pre-treatment eigentumors for use in prediction of treatment failure of breast cancer.

  18. Serum albumin level predicts initial intravenous immunoglobulin treatment failure in Kawasaki disease.

    Science.gov (United States)

    Kuo, Ho-Chang; Liang, Chi-Di; Wang, Chih-Lu; Yu, Hong-Ren; Hwang, Kao-Pin; Yang, Kuender D

    2010-10-01

    Kawasaki disease (KD) is a systemic vasculitis primarily affecting children who are initial IVIG treatment. This study was conducted to investigate the risk factors for initial IVIG treatment failure in KD. Children who met KD diagnosis criteria and were admitted for IVIG treatment were retrospectively enrolled for analysis. Patients were divided into IVIG-responsive and IVIG-resistant groups. Initial laboratory data before IVIG treatment were collected for analysis. A total of 131 patients were enrolled during the study period. At 48 h after completion of initial IVIG treatment, 20 patients (15.3%) had an elevated body temperature. Univariate analysis showed that patients who had initial findings of high neutrophil count, abnormal liver function, low serum albumin level (≤2.9 g/dL) and pericardial effusion were at risk for IVIG treatment failure. Multivariate analysis with a logistic regression procedure showed that serum albumin level was considered the independent predicting factor of IVIG resistance in patients with KD (p = 0.006, OR = 40, 95% CI: 52.8-562). There was no significant correlation between age, gender, fever duration before IVIG treatment, haemoglobin level, total leucocyte and platelet counts, C-reactive protein level, or sterile pyuria and initial IVIG treatment failure. The specificity and sensitivity for prediction of IVIG treatment failure in this study were 96% and 34%, respectively. Pre-IVIG treatment serum albumin levels are a useful predictor of IVIG resistance in patients with KD. © 2010 The Author(s)/Journal Compilation © 2010 Foundation Acta Paediatrica.

  19. Structural MRI-Based Predictions in Patients with Treatment-Refractory Depression (TRD.

    Directory of Open Access Journals (Sweden)

    Blair A Johnston

    Full Text Available The application of machine learning techniques to psychiatric neuroimaging offers the possibility to identify robust, reliable and objective disease biomarkers both within and between contemporary syndromal diagnoses that could guide routine clinical practice. The use of quantitative methods to identify psychiatric biomarkers is consequently important, particularly with a view to making predictions relevant to individual patients, rather than at a group-level. Here, we describe predictions of treatment-refractory depression (TRD diagnosis using structural T1-weighted brain scans obtained from twenty adult participants with TRD and 21 never depressed controls. We report 85% accuracy of individual subject diagnostic prediction. Using an automated feature selection method, the major brain regions supporting this significant classification were in the caudate, insula, habenula and periventricular grey matter. It was not, however, possible to predict the degree of 'treatment resistance' in individual patients, at least as quantified by the Massachusetts General Hospital (MGH-S clinical staging method; but the insula was again identified as a region of interest. Structural brain imaging data alone can be used to predict diagnostic status, but not MGH-S staging, with a high degree of accuracy in patients with TRD.

  20. SibRank: Signed bipartite network analysis for neighbor-based collaborative ranking

    Science.gov (United States)

    Shams, Bita; Haratizadeh, Saman

    2016-09-01

    Collaborative ranking is an emerging field of recommender systems that utilizes users' preference data rather than rating values. Unfortunately, neighbor-based collaborative ranking has gained little attention despite its more flexibility and justifiability. This paper proposes a novel framework, called SibRank that seeks to improve the state of the art neighbor-based collaborative ranking methods. SibRank represents users' preferences as a signed bipartite network, and finds similar users, through a novel personalized ranking algorithm in signed networks.

  1. Threat-related selective attention predicts treatment success in childhood anxiety disorders.

    Science.gov (United States)

    Legerstee, Jeroen S; Tulen, Joke H M; Kallen, Victor L; Dieleman, Gwen C; Treffers, Philip D A; Verhulst, Frank C; Utens, Elisabeth M W J

    2009-02-01

    The present study examined whether threat-related selective attention was predictive of treatment success in children with anxiety disorders and whether age moderated this association. Specific components of selective attention were examined in treatment responders and nonresponders. Participants consisted of 131 children with anxiety disorders (aged 8-16 years), who received standardized cognitive-behavioral therapy. At pretreatment, a pictorial dot-probe task was administered to assess selective attention. Both at pretreatment and posttreatment, diagnostic status of the children was evaluated with a semistructured clinical interview (the Anxiety Disorders Interview Schedule for Children). Selective attention for severely threatening pictures at pretreatment assessment was predictive of treatment success. Examination of the specific components of selective attention revealed that nonresponders showed difficulties to disengage their attention away from severe threat. Treatment responders showed a tendency not to engage their attention toward severe threat. Age was not associated with selective attention and treatment success. Threat-related selective attention is a significant predictor of treatment success in children with anxiety disorders. Clinically anxious children with difficulties disengaging their attention away from severe threat profit less from cognitive-behavioral therapy. For these children, additional training focused on learning to disengage attention away from anxiety-arousing stimuli may be beneficial.

  2. Predicting tDCS treatment outcomes of patients with major depressive disorder using automated EEG classification.

    Science.gov (United States)

    Al-Kaysi, Alaa M; Al-Ani, Ahmed; Loo, Colleen K; Powell, Tamara Y; Martin, Donel M; Breakspear, Michael; Boonstra, Tjeerd W

    2017-01-15

    Transcranial direct current stimulation (tDCS) is a promising treatment for major depressive disorder (MDD). Standard tDCS treatment involves numerous sessions running over a few weeks. However, not all participants respond to this type of treatment. This study aims to investigate the feasibility of identifying MDD patients that respond to tDCS treatment based on resting-state electroencephalography (EEG) recorded prior to treatment commencing. We used machine learning to predict improvement in mood and cognition during tDCS treatment from baseline EEG power spectra. Ten participants with a current diagnosis of MDD were included. Power spectral density was assessed in five frequency bands: delta (0.5-4Hz), theta (4-8Hz), alpha (8-12Hz), beta (13-30Hz) and gamma (30-100Hz). Improvements in mood and cognition were assessed using the Montgomery-Åsberg Depression Rating Scale and Symbol Digit Modalities Test, respectively. We trained the classifiers using three algorithms (support vector machine, extreme learning machine and linear discriminant analysis) and a leave-one-out cross-validation approach. Mood labels were accurately predicted in 8 out of 10 participants using EEG channels FC4-AF8 (accuracy=76%, p=0.034). Cognition labels were accurately predicted in 10 out of 10 participants using channels pair CPz-CP2 (accuracy=92%, p=0.004). Due to the limited number of participants (n=10), the presented results mainly aim to serve as a proof of concept. These finding demonstrate the feasibility of using machine learning to identify patients that will respond to tDCS treatment. These promising results warrant a larger study to determine the clinical utility of this approach. Copyright © 2016 Elsevier B.V. All rights reserved.

  3. Automatic figure ranking and user interfacing for intelligent figure search.

    Directory of Open Access Journals (Sweden)

    Hong Yu

    2010-10-01

    Full Text Available Figures are important experimental results that are typically reported in full-text bioscience articles. Bioscience researchers need to access figures to validate research facts and to formulate or to test novel research hypotheses. On the other hand, the sheer volume of bioscience literature has made it difficult to access figures. Therefore, we are developing an intelligent figure search engine (http://figuresearch.askhermes.org. Existing research in figure search treats each figure equally, but we introduce a novel concept of "figure ranking": figures appearing in a full-text biomedical article can be ranked by their contribution to the knowledge discovery.We empirically validated the hypothesis of figure ranking with over 100 bioscience researchers, and then developed unsupervised natural language processing (NLP approaches to automatically rank figures. Evaluating on a collection of 202 full-text articles in which authors have ranked the figures based on importance, our best system achieved a weighted error rate of 0.2, which is significantly better than several other baseline systems we explored. We further explored a user interfacing application in which we built novel user interfaces (UIs incorporating figure ranking, allowing bioscience researchers to efficiently access important figures. Our evaluation results show that 92% of the bioscience researchers prefer as the top two choices the user interfaces in which the most important figures are enlarged. With our automatic figure ranking NLP system, bioscience researchers preferred the UIs in which the most important figures were predicted by our NLP system than the UIs in which the most important figures were randomly assigned. In addition, our results show that there was no statistical difference in bioscience researchers' preference in the UIs generated by automatic figure ranking and UIs by human ranking annotation.The evaluation results conclude that automatic figure ranking and user

  4. Quantifying Treatment Benefit in Molecular Subgroups to Assess a Predictive Biomarker.

    Science.gov (United States)

    Iasonos, Alexia; Chapman, Paul B; Satagopan, Jaya M

    2016-05-01

    An increased interest has been expressed in finding predictive biomarkers that can guide treatment options for both mutation carriers and noncarriers. The statistical assessment of variation in treatment benefit (TB) according to the biomarker carrier status plays an important role in evaluating predictive biomarkers. For time-to-event endpoints, the hazard ratio (HR) for interaction between treatment and a biomarker from a proportional hazards regression model is commonly used as a measure of variation in TB. Although this can be easily obtained using available statistical software packages, the interpretation of HR is not straightforward. In this article, we propose different summary measures of variation in TB on the scale of survival probabilities for evaluating a predictive biomarker. The proposed summary measures can be easily interpreted as quantifying differential in TB in terms of relative risk or excess absolute risk due to treatment in carriers versus noncarriers. We illustrate the use and interpretation of the proposed measures with data from completed clinical trials. We encourage clinical practitioners to interpret variation in TB in terms of measures based on survival probabilities, particularly in terms of excess absolute risk, as opposed to HR. Clin Cancer Res; 22(9); 2114-20. ©2016 AACR. ©2016 American Association for Cancer Research.

  5. Prediction of treatment response and metastatic disease in soft tissue sarcoma

    Science.gov (United States)

    Farhidzadeh, Hamidreza; Zhou, Mu; Goldgof, Dmitry B.; Hall, Lawrence O.; Raghavan, Meera.; Gatenby, Robert A.

    2014-03-01

    Soft tissue sarcomas (STS) are a heterogenous group of malignant tumors comprised of more than 50 histologic subtypes. Based on spatial variations of the tumor, predictions of the development of necrosis in response to therapy as well as eventual progression to metastatic disease are made. Optimization of treatment, as well as management of therapy-related side effects, may be improved using progression information earlier in the course of therapy. Multimodality pre- and post-gadolinium enhanced magnetic resonance images (MRI) were taken before and after treatment for 30 patients. Regional variations in the tumor bed were measured quantitatively. The voxel values from the tumor region were used as features and a fuzzy clustering algorithm was used to segment the tumor into three spatial regions. The regions were given labels of high, intermediate and low based on the average signal intensity of pixels from the post-contrast T1 modality. These spatially distinct regions were viewed as essential meta-features to predict the response of the tumor to therapy based on necrosis (dead tissue in tumor bed) and metastatic disease (spread of tumor to sites other than primary). The best feature was the difference in the number of pixels in the highest intensity regions of tumors before and after treatment. This enabled prediction of patients with metastatic disease and lack of positive treatment response (i.e. less necrosis). The best accuracy, 73.33%, was achieved by a Support Vector Machine in a leave-one-out cross validation on 30 cases predicting necrosis treatment and metastasis.

  6. Rank Two Affine Manifolds in Genus 3

    OpenAIRE

    Aulicino, David; Nguyen, Duc-Manh

    2016-01-01

    We complete the classification of rank two affine manifolds in the moduli space of translation surfaces in genus three. Combined with a recent result of Mirzakhani and Wright, this completes the classification of higher rank affine manifolds in genus three.

  7. Does impulsivity predict outcome in treatment for binge eating disorder? A multimodal investigation.

    Science.gov (United States)

    Manasse, Stephanie M; Espel, Hallie M; Schumacher, Leah M; Kerrigan, Stephanie G; Zhang, Fengqing; Forman, Evan M; Juarascio, Adrienne S

    2016-10-01

    Multiple dimensions of impulsivity (e.g., affect-driven impulsivity, impulsive inhibition - both general and food-specific, and impulsive decision-making) are associated with binge eating pathology cross-sectionally, yet the literature on whether impulsivity predicts treatment outcome is limited. The present pilot study explored impulsivity-related predictors of 20-week outcome in a small open trial (n = 17) of a novel treatment for binge eating disorder. Overall, dimensions of impulsivity related to emotions (i.e., negative urgency) and food cues emerged as predictors of treatment outcomes (i.e., binge eating frequency and global eating pathology as measured by the Eating Disorders Examination), while more general measures of impulsivity were statistically unrelated to global eating pathology or binge frequency. Specifically, those with higher levels of negative urgency at baseline experienced slower and less pronounced benefit from treatment, and those with higher food-specific impulsivity had more severe global eating pathology at baseline that was consistent at post-treatment and follow-up. These preliminary findings suggest that patients high in negative urgency and with poor response inhibition to food cues may benefit from augmentation of existing treatments to achieve optimal outcomes. Future research will benefit from replication with a larger sample, parsing out the role of different dimensions of impulsivity in treatment outcome for eating disorders, and identifying how treatment can be improved to accommodate higher levels of baseline impulsivity. Copyright © 2016 Elsevier Ltd. All rights reserved.

  8. Analysis model for forecasting extreme temperature using refined rank set pair

    Directory of Open Access Journals (Sweden)

    Qiao Ling-Xia

    2013-01-01

    Full Text Available In order to improve the precision of forecasting extreme temperature time series, a refined rank set pair analysis model with a refined rank transformation function is proposed to improve precision of its prediction. The measured values of the annual highest temperature of two China’s cities, Taiyuan and Shijiazhuang, in July are taken to examine the performance of a refined rank set pair model.

  9. The Privilege of Ranking: Google Plays Ball.

    Science.gov (United States)

    Wiggins, Richard

    2003-01-01

    Discussion of ranking systems used in various settings, including college football and academic admissions, focuses on the Google search engine. Explains the PageRank mathematical formula that scores Web pages by connecting the number of links; limitations, including authenticity and accuracy of ranked Web pages; relevancy; adjusting algorithms;…

  10. A Comprehensive Analysis of Marketing Journal Rankings

    Science.gov (United States)

    Steward, Michelle D.; Lewis, Bruce R.

    2010-01-01

    The purpose of this study is to offer a comprehensive assessment of journal standings in Marketing from two perspectives. The discipline perspective of rankings is obtained from a collection of published journal ranking studies during the past 15 years. The studies in the published ranking stream are assessed for reliability by examining internal…

  11. Treatment credibility, expectancy, and preference: Prediction of treatment engagement and outcome in a randomized clinical trial of hatha yoga vs. health education as adjunct treatments for depression.

    Science.gov (United States)

    Uebelacker, Lisa A; Weinstock, Lauren M; Battle, Cynthia L; Abrantes, Ana M; Miller, Ivan W

    2018-06-02

    Hatha yoga may be helpful for alleviating depression symptoms. The purpose of this analysis is to determine whether treatment program preference, credibility, or expectancy predict engagement in depression interventions (yoga or a control class) or depression symptom severity over time. This is a secondary analysis of a randomized controlled trial (RCT) of hatha yoga vs. a health education control group for treatment of depression. Depressed participants (n = 122) attended up to 20 classes over a period of 10 weeks, and then completed additional assessments after 3 and 6 months. We assessed treatment preference prior to randomization, and treatment credibility and expectancy after participants attended their first class. Treatment "concordance" indicated that treatment preference matched assigned treatment. Treatment credibility, expectancy, and concordance were not associated with treatment engagement. Treatment expectancy moderated the association between treatment group and depression. Depression severity over time differed by expectancy level for the yoga group but not for the health education group. Controlling for baseline depression, participants in the yoga group with an average or high expectancy for improvement showed lower depression symptoms across the acute intervention and follow-up period than those with a low expectancy for improvement. There was a trend for a similar pattern for credibility. Concordance was not associated with treatment outcome. This is a secondary, post-hoc analysis and should be considered hypothesis-generating. Results suggest that expectancy improves the likelihood of success only for a intervention thought to actively target depression (yoga) and not a control intervention. Copyright © 2018. Published by Elsevier B.V.

  12. An integrated prediction and optimization model of biogas production system at a wastewater treatment facility.

    Science.gov (United States)

    Akbaş, Halil; Bilgen, Bilge; Turhan, Aykut Melih

    2015-11-01

    This study proposes an integrated prediction and optimization model by using multi-layer perceptron neural network and particle swarm optimization techniques. Three different objective functions are formulated. The first one is the maximization of methane percentage with single output. The second one is the maximization of biogas production with single output. The last one is the maximization of biogas quality and biogas production with two outputs. Methane percentage, carbon dioxide percentage, and other contents' percentage are used as the biogas quality criteria. Based on the formulated models and data from a wastewater treatment facility, optimal values of input variables and their corresponding maximum output values are found out for each model. It is expected that the application of the integrated prediction and optimization models increases the biogas production and biogas quality, and contributes to the quantity of electricity production at the wastewater treatment facility. Copyright © 2015 Elsevier Ltd. All rights reserved.

  13. Grooming Up the Hierarchy: The Exchange of Grooming and Rank-Related Benefits in a New World Primate

    OpenAIRE

    Tiddi, Barbara; Aureli, Filippo; Schino, Gabriele

    2012-01-01

    Seyfarth’s model assumes that female primates derive rank-related benefits from higher-ranking females in exchange for grooming. As a consequence, the model predicts females prefer high-ranking females as grooming partners and compete for the opportunity to groom them. Therefore, allogrooming is expected to be directed up the dominance hierarchy and to occur more often between females with adjacent ranks. Although data from Old World primates generally support the model, studies o...

  14. Pain drawings predict outcome of surgical treatment for degenerative disc disease in the cervical spine.

    Science.gov (United States)

    MacDowall, Anna; Robinson, Yohan; Skeppholm, Martin; Olerud, Claes

    2017-08-01

    Pain drawings have been frequently used in the preoperative evaluation of spine patients. For lumbar conditions comprehensive research has established both the reliability and predictive value, but for the cervical spine most of this knowledge is lacking. The aims of this study were to validate pain drawings for the cervical spine, and to investigate the predictive value for treatment outcome of four different evaluation methods. We carried out a post hoc analysis of a randomized controlled trial, comparing cervical disc replacement to fusion for radiculopathy related to degenerative disc disease. A pain drawing together with Neck Disability Index (NDI) was completed preoperatively, after 2 and 5 years. The inter- and intraobserver reliability of four evaluation methods was tested using κ statistics, and its predictive value investigated by correlation to change in NDI. Included were 151 patients, mean age of 47 years, female/male: 78/73. The interobserver reliability was fair for the modified Ransford and Udén methods, good for the Gatchel method, and very good for the modified Ohnmeiss method. Markings in the shoulder and upper arm region on the pain drawing were positive predictors of outcome after 2 years of follow-up, and markings in the upper arm region remained a positive predictor of outcome even after 5 years of follow-up. Pain drawings were a reliable tool to interpret patients' pain prior to cervical spine surgery and were also to some extent predictive for treatment outcome.

  15. Prediction of successful treatment by extracorporeal shock wave lithotripsy based on crystalluriacomposition correlations of urinary calculi

    Directory of Open Access Journals (Sweden)

    Nadia Messaoudi

    2015-12-01

    Full Text Available Objective: To provide correlations between crystalluria and chemical structure of calculi in situ to help making decision in the use of the extracorporeal shock wave lithotripsy (ESWL. Methods: A crystalluria study was carried out on 644 morning urines of 172 nephrolithiasis patients (111 males and 61 females, and 235 of them were in situ stone carriers. After treating by ESWL, the recovered calculi have been analyzed by Fourier transform infrared spectroscopy and their compositions were correlated to the nature of urinary crystals. Results: We obtained successful treatment for 109 patients out of 157 and 63 patients out of 78 with stones had a treatment failure (33.2%. The correlations showed that for the overwhelming crystalluria containing calcium oxalate dihydrate (COD with mixed crystals without calcium oxalate monohydrate, we should have 68% to 88 % success rate. However, the obtained result was 79%. Similarly, for crystalluria with COD + calcium oxalate monohydrate ± carbapatite, the prediction was 11% to 45% and the result was approximately 39%. When the majority of crystalluria was calcium phosphate, the prediction of 50% to 80% was confirmed by 71% success rate. For those majority containing magnesium ammonium phosphate hexahydrate (struvite ± diammonium urate ± COD, we predicted between 80% to 100%, and the result gave a success rate of 84%. Conclusions: The analysis of crystalluria of morning urine can help to know the composition of calculi in situ and can predict the success rate of ESWL for maximum efficiency.

  16. Predictive value of lidocaine for treatment success of oxcarbazepine in patients with neuropathic pain syndrome.

    Science.gov (United States)

    Schipper, Sivan; Gantenbein, Andreas R; Maurer, Konrad; Alon, Eli; Sándor, Peter S

    2013-06-01

    Pharmacotherapy in patients with neuropathic pain syndromes (NPS) can be associated with long periods of trial and error before reaching satisfactory analgesia. The aim of this study was to investigate whether a short intravenous (i.v.) infusion of lidocaine may have a predictive value for the efficacy of oxcarbazepine. In total, 16 consecutive patients with NPS were studied in a prospective, uncontrolled, open-label study design. Each patient received i.v. lidocaine (5 mg/kg) within 30 min followed by a long-term oral oxcarbazepine treatment (900-1,500 mg/day). During an observation period of 28 days, treatment response was documented by a questionnaire including the average daily pain score documented on a numeric rating scale (NRS). A total of 6 out of 16 patients (38%) were lidocaine responders (defined as pain reduction >50% during the infusion), and 4 of 16 (25%) were oxcarbazepine responders. In total, 6 out of 16 participants (38%) discontinued oxcarbazepine treatment due to side effects. In an interim analysis predictive value of the lidocaine infusion was low with a Kendall's tau correlation coefficient of 0.29 and coefficient of determination R(2) of 0.119 (95% confidence interval -0.29 to 0.72). As a consequence of this low correlation, the study was discontinued for ethical reasons. In conclusion, lidocaine infusion has a low predictive value for effectiveness of oxcarbazepine-if at all.

  17. Computerized property prediction and process planning in heat treatment of steels

    Energy Technology Data Exchange (ETDEWEB)

    Gergely, M. (Steel Advisory Centre for Industrial Technologies (SACIT), Budapest (Hungary)); Somogyi, S. (Steel Advisory Centre for Industrial Technologies (SACIT), Budapest (Hungary)); Kohlheb, R. (Steel Advisory Centre for Industrial Technologies (SACIT), Budapest (Hungary))

    1994-01-01

    Recent years have seen widespread interest in the establishment of prediction methods, based on phenomenological description and computer simulation of transformation processes during heat treatment, and in the introduction of software for technological planning. The steady development of this approach is aimed at meeting the requirement of metallurgists, design engineers dealing with material selection and dimensioning, and technologists planning heat treatment processes. Research in this field of computer simulation has been concentrated so far on two main areas of interest: . Modelling of transformation processes and the prediction of microstructures and/or properties, . Developing program packages to help solve concrete tasks such as material selection, on-line process control and monitoring, and the design of heat-treating operations. During the last two decades in the field of heat treatment, various mathematical models with different accuracy and complexity have been developed. In this paper, an attempt is made to outline some important results in computer simulation and computerized property prediction without aiming at completeness. The topic is restricted to quenched and tempered, and case-hardened steels. (orig.)

  18. Identifying a predictive model for response to atypical antipsychotic monotherapy treatment in south Indian schizophrenia patients.

    Science.gov (United States)

    Gupta, Meenal; Moily, Nagaraj S; Kaur, Harpreet; Jajodia, Ajay; Jain, Sanjeev; Kukreti, Ritushree

    2013-08-01

    Atypical antipsychotic (AAP) drugs are the preferred choice of treatment for schizophrenia patients. Patients who do not show favorable response to AAP monotherapy are subjected to random prolonged therapeutic treatment with AAP multitherapy, typical antipsychotics or a combination of both. Therefore, prior identification of patients' response to drugs can be an important step in providing efficacious and safe therapeutic treatment. We thus attempted to elucidate a genetic signature which could predict patients' response to AAP monotherapy. Our logistic regression analyses indicated the probability that 76% patients carrying combination of four SNPs will not show favorable response to AAP therapy. The robustness of this prediction model was assessed using repeated 10-fold cross validation method, and the results across n-fold cross-validations (mean accuracy=71.91%; 95%CI=71.47-72.35) suggest high accuracy and reliability of the prediction model. Further validations of these results in large sample sets are likely to establish their clinical applicability. Copyright © 2013 Elsevier Inc. All rights reserved.

  19. Prediction of methylphenidate treatment outcome in adults with attention-deficit/hyperactivity disorder (ADHD).

    Science.gov (United States)

    Retz, Wolfgang; Retz-Junginger, Petra

    2014-11-01

    Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent mental disorder of childhood, which often persists in adulthood. Methylphenidate (MPH) is one of the most effective medications to treat ADHD, but also few adult patients show no sufficient response to this drug. In this paper, we give an overview regarding genetic, neuroimaging, clinical and other studies which have tried to reveal the reasons for non-response in adults with ADHD, based on a systematic literature search. Although MPH is a well-established treatment for adults with ADHD, research regarding the prediction of treatment outcome is still limited and has resulted in inconsistent findings. No reliable neurobiological markers of treatment response have been identified so far. Some findings from clinical studies suggest that comorbidity with substance use disorders and personality disorders has an impact on treatment course and outcome. As MPH is widely used in the treatment of adults with ADHD, much more work is needed regarding positive and negative predictors of long-term treatment outcome in order to optimize the pharmacological treatment of adult ADHD patients.

  20. Accumulating Data to Optimally Predict Obesity Treatment (ADOPT): Recommendations from the Biological Domain.

    Science.gov (United States)

    Rosenbaum, Michael; Agurs-Collins, Tanya; Bray, Molly S; Hall, Kevin D; Hopkins, Mark; Laughlin, Maren; MacLean, Paul S; Maruvada, Padma; Savage, Cary R; Small, Dana M; Stoeckel, Luke

    2018-04-01

    The responses to behavioral, pharmacological, or surgical obesity treatments are highly individualized. The Accumulating Data to Optimally Predict obesity Treatment (ADOPT) project provides a framework for how obesity researchers, working collectively, can generate the evidence base needed to guide the development of tailored, and potentially more effective, strategies for obesity treatment. The objective of the ADOPT biological domain subgroup is to create a list of high-priority biological measures for weight-loss studies that will advance the understanding of individual variability in response to adult obesity treatments. This list includes measures of body composition, energy homeostasis (energy intake and output), brain structure and function, and biomarkers, as well as biobanking procedures, which could feasibly be included in most, if not all, studies of obesity treatment. The recommended high-priority measures are selected to balance needs for sensitivity, specificity, and/or comprehensiveness with feasibility to achieve a commonality of usage and increase the breadth and impact of obesity research. The accumulation of data on key biological factors, along with behavioral, psychosocial, and environmental factors, can generate a more precise description of the interplay and synergy among them and their impact on treatment responses, which can ultimately inform the design and delivery of effective, tailored obesity treatments. © 2018 The Obesity Society.

  1. Ranking REACH registered neutral, ionizable and ionic organic chemicals based on their aquatic persistency and mobility.

    Science.gov (United States)

    Arp, H P H; Brown, T N; Berger, U; Hale, S E

    2017-07-19

    The contaminants that have the greatest chances of appearing in drinking water are those that are mobile enough in the aquatic environment to enter drinking water sources and persistent enough to survive treatment processes. Herein a screening procedure to rank neutral, ionizable and ionic organic compounds for being persistent and mobile organic compounds (PMOCs) is presented and applied to the list of industrial substances registered under the EU REACH legislation as of December 2014. This comprised 5155 identifiable, unique organic structures. The minimum cut-off criteria considered for PMOC classification herein are a freshwater half-life >40 days, which is consistent with the REACH definition of freshwater persistency, and a log D oc water distribution coefficient). Experimental data were given the highest priority, followed by data from an array of available quantitative structure-activity relationships (QSARs), and as a third resort, an original Iterative Fragment Selection (IFS) QSAR. In total, 52% of the unique REACH structures made the minimum criteria to be considered a PMOC, and 21% achieved the highest PMOC ranking (half-life > 40 days, log D oc freshwater persistency, which was also the parameter that QSARs performed the most poorly at predicting. Several prioritized drinking water contaminants in the EU and USA, and other contaminants of concern, were identified as PMOCs. This identification and ranking procedure for PMOCs can be part of a strategy to better identify contaminants that pose a threat to drinking water sources.

  2. Endoscopic assessment and prediction of prostate urethral disintegration after histotripsy treatment in a canine model.

    Science.gov (United States)

    Schade, George R; Styn, Nicholas R; Hall, Timothy L; Roberts, William W

    2012-02-01

    Histotripsy is a nonthermal focused ultrasound technology that uses acoustic cavitation to homogenize tissue. Previous research has demonstrated that the prostatic urethra is more resistant to histotripsy effects than prostate parenchyma, a finding that may complicate the creation of transurethral resection of the prostate-like treatment cavities. The purpose of this study was to characterize the endoscopic appearance of the prostatic urethra during and after histotripsy treatment and to identify features that are predictive of urethral disintegration. Thirty-five histotripsy treatments were delivered in a transverse plane traversing the prostatic urethra in 17 canine subjects (1-3/prostate ≥1 cm apart). Real-time endoscopy was performed in the first four subjects to characterize development of acute urethral treatment effect (UTE). Serial postprocedure endoscopy was performed in all subjects to assess subsequent evolution of UTE. Endoscopy during histotripsy was feasible with observation of intraurethral cavitation, allowing characterization of the real-time progression of UTE from normal to frank urethral disintegration. While acute urethral fragmentation occurred in 3/35 (8.6%) treatments, frank urethral disintegration developed in 24/35 (68.5%) within 14 days of treatment. Treating until the appearance of hemostatic pale gray shaggy urothelium was the best predictor of achieving urethral fragmentation within 14 days of treatment with positive and negative predictive values of 0.91 and 0.89, respectively. Endoscopic assessment of the urethra may be a useful adjunct to prostatic histotripsy to help guide therapy to ensure urethral disintegration, allowing drainage of the homogenized adenoma and effective tissue debulking.

  3. Qualitative assessment of awake nasopharyngoscopy for prediction of oral appliance treatment response in obstructive sleep apnoea.

    Science.gov (United States)

    Sutherland, Kate; Chan, Andrew S L; Ngiam, Joachim; Darendeliler, M Ali; Cistulli, Peter A

    2018-01-23

    Clinical methods to identify responders to oral appliance (OA) therapy for obstructive sleep apnoea (OSA) are needed. Awake nasopharyngoscopy during mandibular advancement, with image capture and subsequent processing and analysis, may predict treatment response. A qualitative assessment of awake nasopharyngoscopy would be simpler for clinical practice. We aimed to determine if a qualitative classification system of nasopharyngoscopic observations reflects treatment response. OSA patients were recruited for treatment with a customised two-piece OA. A custom scoring sheet was used to record observations of the pharyngeal airway (velopharynx, oropharynx, hypopharynx) during supine nasopharyngoscopy in response to mandibular advancement and performance of the Müller manoeuvre. Qualitative scores for degree ( 75%), collapse pattern (concentric, anteroposterior, lateral) and diameter change (uniform, anteroposterior, lateral) were recorded. Treatment outcome was confirmed by polysomnography after a titration period of 14.6 ± 9.8 weeks. Treatment response was defined as (1) Treatment AHI  50% AHI reduction and (3) > 50% AHI reduction. Eighty OSA patients (53.8% male) underwent nasopharyngoscopy. The most common naspharyngoscopic observation with mandibular advancement was a small ( 75% velopharyngeal collapse on performance of the Müller manoeuvre. Mandibular advancement reduced the observed level of pharyngeal collapse at all three pharyngeal regions (p < 0.001). None of the nasopharyngoscopic qualitative scores differed between responder and non-responder groups. Qualitative assessment of awake nasopharyngoscopy appears useful for assessing the effect of mandibular advancement on upper airway collapsibility. However, it is not sensitive enough to predict oral appliance treatment outcome.

  4. Error-related brain activity predicts cocaine use after treatment at 3-month follow-up.

    Science.gov (United States)

    Marhe, Reshmi; van de Wetering, Ben J M; Franken, Ingmar H A

    2013-04-15

    Relapse after treatment is one of the most important problems in drug dependency. Several studies suggest that lack of cognitive control is one of the causes of relapse. In this study, a relative new electrophysiologic index of cognitive control, the error-related negativity, is investigated to examine its suitability as a predictor of relapse. The error-related negativity was measured in 57 cocaine-dependent patients during their first week in detoxification treatment. Data from 49 participants were used to predict cocaine use at 3-month follow-up. Cocaine use at follow-up was measured by means of self-reported days of cocaine use in the last month verified by urine screening. A multiple hierarchical regression model was used to examine the predictive value of the error-related negativity while controlling for addiction severity and self-reported craving in the week before treatment. The error-related negativity was the only significant predictor in the model and added 7.4% of explained variance to the control variables, resulting in a total of 33.4% explained variance in the prediction of days of cocaine use at follow-up. A reduced error-related negativity measured during the first week of treatment was associated with more days of cocaine use at 3-month follow-up. Moreover, the error-related negativity was a stronger predictor of recent cocaine use than addiction severity and craving. These results suggest that underactive error-related brain activity might help to identify patients who are at risk of relapse as early as in the first week of detoxification treatment. Copyright © 2013 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

  5. Modified Phenomena Identification and Ranking Table (PIRT) for Uncertainty Analysis

    International Nuclear Information System (INIS)

    Gol-Mohamad, Mohammad P.; Modarres, Mohammad; Mosleh, Ali

    2006-01-01

    This paper describes a methodology of characterizing important phenomena, which is also part of a broader research by the authors called 'Modified PIRT'. The methodology provides robust process of phenomena identification and ranking process for more precise quantification of uncertainty. It is a two-step process of identifying and ranking methodology based on thermal-hydraulics (TH) importance as well as uncertainty importance. Analytical Hierarchical Process (AHP) has been used for as a formal approach for TH identification and ranking. Formal uncertainty importance technique is used to estimate the degree of credibility of the TH model(s) used to represent the important phenomena. This part uses subjective justification by evaluating available information and data from experiments, and code predictions. The proposed methodology was demonstrated by developing a PIRT for large break loss of coolant accident LBLOCA for the LOFT integral facility with highest core power (test LB-1). (authors)

  6. Ranking of concern, based on environmental indexes, for pharmaceutical and personal care products: an application to the Spanish case.

    Science.gov (United States)

    Ortiz de García, Sheyla; Pinto, Gilberto Pinto; García-Encina, Pedro A; Irusta Mata, Rubén I

    2013-11-15

    A wide range of Pharmaceuticals and Personal Care Products (PPCPs) are present in the environment, and many of their adverse effects are unknown. The emergence of new compounds or changes in regulations have led to dynamical studies of occurrence, impact and treatment, which consider geographical areas and trends in consumption and innovation in the pharmaceutical industry. A Quantitative study of Structure-Activity Relationship ((Q)SAR) was performed to assess the possible adverse effects of ninety six PPCPs and metabolites with negligible experimental data and establish a ranking of concern, which was supported by the EPA EPI Suite™ interface. The environmental and toxicological indexes, the persistence (P), the bioaccumulation (B), the toxicity (T) (extensive) and the occurrence in Spanish aquatic environments (O) (intensive) were evaluated. The most hazardous characteristics in the largest number of compounds were generated by the P index, followed by the T and B indexes. A high number of metabolites has a concern score equal to or greater than their parent compounds. Three PBT and OPBT rankings of concern were proposed using the total and partial ranking method (supported by a Hasse diagram) by the Decision Analysis by Ranking Techniques (DART) tool, which was recently recommended by the European Commission. An analysis of the sensibility of the relative weights of these indexes has been conducted. Hormones, antidepressants (and their metabolites), blood lipid regulators and all of the personal care products considered in this study were at the highest levels of risk according to the PBT and OPBT total rankings. Furthermore, when the OPBT partial ranking was performed, X-ray contrast media, H2 blockers and some antibiotics were included at the highest level of concern. It is important to improve and incorporate useful indexes for the predicted environmental impact of PPCPs and metabolites and thus focus experimental analysis on the compounds that require

  7. Two-dimensional ranking of Wikipedia articles

    Science.gov (United States)

    Zhirov, A. O.; Zhirov, O. V.; Shepelyansky, D. L.

    2010-10-01

    The Library of Babel, described by Jorge Luis Borges, stores an enormous amount of information. The Library exists ab aeterno. Wikipedia, a free online encyclopaedia, becomes a modern analogue of such a Library. Information retrieval and ranking of Wikipedia articles become the challenge of modern society. While PageRank highlights very well known nodes with many ingoing links, CheiRank highlights very communicative nodes with many outgoing links. In this way the ranking becomes two-dimensional. Using CheiRank and PageRank we analyze the properties of two-dimensional ranking of all Wikipedia English articles and show that it gives their reliable classification with rich and nontrivial features. Detailed studies are done for countries, universities, personalities, physicists, chess players, Dow-Jones companies and other categories.

  8. Do symptom-specific stages of change predict eating disorder treatment outcome?

    Science.gov (United States)

    Ackard, Diann M; Cronemeyer, Catherine L; Richter, Sara; Egan, Amber

    2015-03-01

    Interview methods to assess stages of change (SOC) in eating disorders (ED) indicate that SOC are positively correlated with symptom improvement over time. However, interviews require significant time and staff training and global measures of SOC do not capture varying levels of motivation across ED symptoms. This study used a self-report, ED symptom-specific SOC measure to determine prevalence of stages across symptoms and identify if SOC predict treatment outcome. Participants [N = 182; age 13-58 years; 92% Caucasian; 96% female; average BMI 21.7 (SD = 5.9); 50% ED not otherwise specified (EDNOS), 30.8% bulimia nervosa (BN), 19.2% anorexia nervosa (AN)] seeking ED treatment at a diverse-milieu multi-disciplinary facility in the United States completed stages of change, behavioral (ED symptom use and frequency) and psychological (ED concerns, anxiety, depression) measures at intake assessment and at 3, 6 and 12 months thereafter. Descriptive summaries were generated using ANOVA or Kruskal-Wallis (continuous) and χ (2) (categorical) tests. Repeated measures linear regression models with autoregressive correlation structure predicted treatment outcome. At intake assessment, 53.3% of AN, 34.0% of BN and 18.1% of EDNOS patients were in Preparation/Action. Readiness to change specific symptoms was highest for binge-eating (57.8%) and vomiting (56.5%). Frequency of fasting and restricting behaviors, and scores on all eating disorder and psychological measures improved over time regardless of SOC at intake assessment. Symptom-specific SOC did not predict reductions in ED symptom frequency. Overall SOC predicted neither improvement in Eating Disorder Examination Questionnaire (EDE-Q) scores nor reduction in depression or trait anxiety; however, higher overall SOC predicted lower state anxiety across follow-up. Readiness to change ED behaviors varies considerably. Most patients reduced eating disorder behaviors and increased psychological functioning regardless of stages

  9. Do health care providers' attitudes towards back pain predict their treatment recommendations? Differential predictive validity of implicit and explicit attitude measures

    NARCIS (Netherlands)

    Houben, R.M.A.; Gijsen, A.; Peterson, J.; de Jong, P.J.; Vlaeyen, J.W.S.

    The current study aimed to measure the differential predictive value of implicit and explicit attitude measures on treatment behaviour of health care providers. Thirty-six physiotherapy students completed a measure of explicit treatment attitude (Pain Attitudes And Beliefs Scale For

  10. The potential of predictive analytics to provide clinical decision support in depression treatment planning.

    Science.gov (United States)

    Kessler, Ronald C

    2018-01-01

    To review progress developing clinical decision support tools for personalized treatment of major depressive disorder (MDD). Over the years, a variety of individual indicators ranging from biomarkers to clinical observations and self-report scales have been used to predict various aspects of differential MDD treatment response. Most of this work focused on predicting remission either with antidepressant medications versus psychotherapy, some antidepressant medications versus others, some psychotherapies versus others, and combination therapies versus monotherapies. However, to date, none of the individual predictors in these studies has been strong enough to guide optimal treatment selection for most patients. Interest consequently turned to decision support tools made up of multiple predictors, but the development of such tools has been hampered by small study sample sizes. Design recommendations are made here for future studies to address this problem. Recommendations include using large prospective observational studies followed by pragmatic trials rather than smaller, expensive controlled treatment trials for preliminary development of decision support tools; basing these tools on comprehensive batteries of inexpensive self-report and clinical predictors (e.g., self-administered performance-based neurocognitive tests) versus expensive biomarkers; and reserving biomarker assessments for targeted studies of patients not well classified by inexpensive predictor batteries.

  11. Rank Dynamics of Word Usage at Multiple Scales

    Directory of Open Access Journals (Sweden)

    José A. Morales

    2018-05-01

    Full Text Available The recent dramatic increase in online data availability has allowed researchers to explore human culture with unprecedented detail, such as the growth and diversification of language. In particular, it provides statistical tools to explore whether word use is similar across languages, and if so, whether these generic features appear at different scales of language structure. Here we use the Google Books N-grams dataset to analyze the temporal evolution of word usage in several languages. We apply measures proposed recently to study rank dynamics, such as the diversity of N-grams in a given rank, the probability that an N-gram changes rank between successive time intervals, the rank entropy, and the rank complexity. Using different methods, results show that there are generic properties for different languages at different scales, such as a core of words necessary to minimally understand a language. We also propose a null model to explore the relevance of linguistic structure across multiple scales, concluding that N-gram statistics cannot be reduced to word statistics. We expect our results to be useful in improving text prediction algorithms, as well as in shedding light on the large-scale features of language use, beyond linguistic and cultural differences across human populations.

  12. 24 CFR 599.401 - Ranking of applications.

    Science.gov (United States)

    2010-04-01

    ... 24 Housing and Urban Development 3 2010-04-01 2010-04-01 false Ranking of applications. 599.401... Communities § 599.401 Ranking of applications. (a) Ranking order. Rural and urban applications will be ranked... applications ranked first. (b) Separate ranking categories. After initial ranking, both rural and urban...

  13. On the Schwartz space isomorphism theorem for rank one ...

    Indian Academy of Sciences (India)

    Abstract. In this paper we give a simpler proof of the Lp-Schwartz space isomorphism. (0 < p ≤ 2) under the Fourier transform for the class of functions of left δ-type on a. Riemannian symmetric space of rank one. Our treatment rests on Anker's [2] proof of the corresponding result in the case of left K-invariant functions on X.

  14. Brain anatomy and chemistry may predict treatment response in paediatric obsessive--compulsive disorder.

    Science.gov (United States)

    Rosenberg, D R; MacMillan, S N; Moore, G J

    2001-06-01

    Obsessive--compulsive disorder (OCD) is a severe, highly prevalent and often chronically disabling illness with frequent onset in childhood and adolescence. This underscores the importance of studying the illness during childhood near the onset of illness to minimize potential confounds of long-term illness duration and treatment intervention as well as to examine the developmental underpinnings of the illness. In this review, the authors focus on an integrated series of brain-imaging studies in paediatric OCD suggesting a reversible glutamatergically mediated thalamo-cortical--striatal dysfunction in OCD and their relevance for improved diagnosis and treatment of the condition. Developmental neurobiological models for OCD are presented and particular attention is devoted to evaluating neuroimaging studies designed to test these models and how they may help predict treatment response in paediatric OCD.

  15. Agro-tourism and ranking

    Science.gov (United States)

    Cioca, L. I.; Giurea, R.; Precazzini, I.; Ragazzi, M.; Achim, M. I.; Schiavon, M.; Rada, E. C.

    2018-05-01

    Nowadays the global tourism growth has caused a significant interest in research focused on the impact of the tourism on environment and community. The purpose of this study is to introduce a new ranking for the classification of tourist accommodation establishments with the functions of agro-tourism boarding house type by examining the sector of agro-tourism based on a research aimed to improve the economic, socio-cultural and environmental performance of agrotourism structures. This paper links the criteria for the classification of agro-tourism boarding houses (ABHs) to the impact of agro-tourism activities on the environment, enhancing an eco-friendly approach on agro-tourism activities by increasing the quality reputation of the agro-tourism products and services. Taking into account the impact on the environment, agrotourism can play an important role by protecting and conserving it.

  16. Validation and uncertainty analysis of a pre-treatment 2D dose prediction model

    Science.gov (United States)

    Baeza, Jose A.; Wolfs, Cecile J. A.; Nijsten, Sebastiaan M. J. J. G.; Verhaegen, Frank

    2018-02-01

    Independent verification of complex treatment delivery with megavolt photon beam radiotherapy (RT) has been effectively used to detect and prevent errors. This work presents the validation and uncertainty analysis of a model that predicts 2D portal dose images (PDIs) without a patient or phantom in the beam. The prediction model is based on an exponential point dose model with separable primary and secondary photon fluence components. The model includes a scatter kernel, off-axis ratio map, transmission values and penumbra kernels for beam-delimiting components. These parameters were derived through a model fitting procedure supplied with point dose and dose profile measurements of radiation fields. The model was validated against a treatment planning system (TPS; Eclipse) and radiochromic film measurements for complex clinical scenarios, including volumetric modulated arc therapy (VMAT). Confidence limits on fitted model parameters were calculated based on simulated measurements. A sensitivity analysis was performed to evaluate the effect of the parameter uncertainties on the model output. For the maximum uncertainty, the maximum deviating measurement sets were propagated through the fitting procedure and the model. The overall uncertainty was assessed using all simulated measurements. The validation of the prediction model against the TPS and the film showed a good agreement, with on average 90.8% and 90.5% of pixels passing a (2%,2 mm) global gamma analysis respectively, with a low dose threshold of 10%. The maximum and overall uncertainty of the model is dependent on the type of clinical plan used as input. The results can be used to study the robustness of the model. A model for predicting accurate 2D pre-treatment PDIs in complex RT scenarios can be used clinically and its uncertainties can be taken into account.

  17. Predictive models of pain following root canal treatment: a prospective clinical study.

    Science.gov (United States)

    Arias, A; de la Macorra, J C; Hidalgo, J J; Azabal, M

    2013-08-01

    To determine the probability of the incidence, intensity, duration and triggering of post-endodontic pain, considering factors related to the patient (age, gender, medical evaluation) and to the affected tooth (group, location, number of canals, pulp vitality, preoperative pain, periapical radiolucencies, previous emergency access, presence of occlusal contacts with antagonist). A total of 500 one-visit root canal treatments (RCTs) were performed on patients referred to an endodontist. Shaping of root canals was performed manually with Gates-Glidden drills and K-Flexofiles, and apical patency was maintained with a size 10 file. A 5% NaOCl solution was used for irrigation, and canals were filled with lateral compaction and AH-Plus sealer. Independent factors were recorded during the treatment, and characteristics of post-endodontic pain (incidence, intensity, type and duration) were later surveyed through questionnaires. Of the 500 questionnaires, 374 were properly returned and split in two groups for two different statistical purposes: 316 cases were used to adjust the logistic regression models to predict each characteristic of post-endodontic pain using predictive factors, and the remaining 58 cases were used to test the validity of each model. The predictive models showed that the incidence of post-endodontic pain was significantly lower when the treated tooth was not a molar (P = 0.003), demonstrated periapical radiolucencies (P = 0.003), had no history of previous pain (P = 0.006) or emergency endodontic treatment (P = 0.045) and had no occlusal contact (P endodontic pain were generated and validated taking account of the interrelation of multiple concomitant clinical factors. A predictive model for triggering post-endodontic pain could not be established. © 2013 International Endodontic Journal. Published by John Wiley & Sons Ltd.

  18. The predictive value of extensor grip test for the effectiveness of treatment for tennis elbow

    International Nuclear Information System (INIS)

    Zehtab, Mohammad J.; Mirghasemi, A.; Majlesara, A.; Siavashi, B.; Tajik, P.

    2008-01-01

    Objective was to compare the effectiveness of 5 different modalities and determine the usefulness of recently proposed extensor grip test (EGT) in predicting the response to treatment. In a randomized controlled clinical trial, 92 of 98 tennis elbow patients in Sina Hospital Tehran, Iran between 2006 and 2007 fulfilled the trial entry criteria. Among these patients 56 (60.9%) had positive EGT results, were randomly allocated to 5 treatment groups: brace, physiotherapy, brace plus physiotherapy, injection and injection plus physiotherapy. Patients with a positive EGT result had better response to treatments. Among them, injection plus physiotherapy was the most successful, then brace plus physiotherapy was the worst treatment modality. Response to treatment was comparable in all groups between EGT positive and negative patients except bracing, in which positive EGT was correlated with dramatic response to treatment. In all patients, injection plus physiotherapy and the brace plus physiotherapy is recommended, but in EGT negatives, bracing seems to be of no use. Injection alone is not recommended in either group. (author)

  19. Esophageal stasis in achalasia patients without symptoms after treatment does not predict symptom recurrence.

    Science.gov (United States)

    van Hoeij, F B; Smout, A J P M; Bredenoord, A J

    2017-08-01

    After achalasia treatment, a subset of patients has poor esophageal emptying without having symptoms. There is no consensus on whether to pre-emptively treat these patients. We hypothesized that, if left untreated, these patients will experience earlier symptom recurrence than patients without stasis. 99 treated achalasia patients who were in clinical remission (Eckardt ≤3) at 3 months after treatment were divided into two groups, based on presence or absence of esophageal stasis on a timed barium esophagogram performed after 3 months. Two years after initial treatment, patients with stasis after treatment still had a wider esophagus (3 cm; IQR: 2.2-3.8) and more stasis (3.5 cm; IQR: 1.9-5.6) than patients without stasis (1.8 cm wide and 0 cm stasis; both Ptreatment also had a higher degree of stasis and a more dilated esophagus, compared to patients without stasis, they did not have a higher chance of requiring retreatment. We conclude that stasis in symptom-free achalasia patients after treatment does not predict treatment failure within 2 years and can therefore not serve as a sole reason for retreatment. © 2017 John Wiley & Sons Ltd.

  20. The Accumulating Data to Optimally Predict Obesity Treatment (ADOPT) Core Measures Project: Rationale and Approach.

    Science.gov (United States)

    MacLean, Paul S; Rothman, Alexander J; Nicastro, Holly L; Czajkowski, Susan M; Agurs-Collins, Tanya; Rice, Elise L; Courcoulas, Anita P; Ryan, Donna H; Bessesen, Daniel H; Loria, Catherine M

    2018-04-01

    Individual variability in response to multiple modalities of obesity treatment is well documented, yet our understanding of why some individuals respond while others do not is limited. The etiology of this variability is multifactorial; however, at present, we lack a comprehensive evidence base to identify which factors or combination of factors influence treatment response. This paper provides an overview and rationale of the Accumulating Data to Optimally Predict obesity Treatment (ADOPT) Core Measures Project, which aims to advance the understanding of individual variability in response to adult obesity treatment. This project provides an integrated model for how factors in the behavioral, biological, environmental, and psychosocial domains may influence obesity treatment responses and identify a core set of measures to be used consistently across adult weight-loss trials. This paper provides the foundation for four companion papers that describe the core measures in detail. The accumulation of data on factors across the four ADOPT domains can inform the design and delivery of effective, tailored obesity treatments. ADOPT provides a framework for how obesity researchers can collectively generate this evidence base and is a first step in an ongoing process that can be refined as the science advances. © 2018 The Obesity Society.

  1. Empirically derived personality subtyping for predicting clinical symptoms and treatment response in bulimia nervosa.

    Science.gov (United States)

    Haynos, Ann F; Pearson, Carolyn M; Utzinger, Linsey M; Wonderlich, Stephen A; Crosby, Ross D; Mitchell, James E; Crow, Scott J; Peterson, Carol B

    2017-05-01

    Evidence suggests that eating disorder subtypes reflecting under-controlled, over-controlled, and low psychopathology personality traits constitute reliable phenotypes that differentiate treatment response. This study is the first to use statistical analyses to identify these subtypes within treatment-seeking individuals with bulimia nervosa (BN) and to use these statistically derived clusters to predict clinical outcomes. Using variables from the Dimensional Assessment of Personality Pathology-Basic Questionnaire, K-means cluster analyses identified under-controlled, over-controlled, and low psychopathology subtypes within BN patients (n = 80) enrolled in a treatment trial. Generalized linear models examined the impact of personality subtypes on Eating Disorder Examination global score, binge eating frequency, and purging frequency cross-sectionally at baseline and longitudinally at end of treatment (EOT) and follow-up. In the longitudinal models, secondary analyses were conducted to examine personality subtype as a potential moderator of response to Cognitive Behavioral Therapy-Enhanced (CBT-E) or Integrative Cognitive-Affective Therapy for BN (ICAT-BN). There were no baseline clinical differences between groups. In the longitudinal models, personality subtype predicted binge eating (p = 0.03) and purging (p = 0.01) frequency at EOT and binge eating frequency at follow-up (p = 0.045). The over-controlled group demonstrated the best outcomes on these variables. In secondary analyses, there was a treatment by subtype interaction for purging at follow-up (p = 0.04), which indicated a superiority of CBT-E over ICAT-BN for reducing purging among the over-controlled group. Empirically derived personality subtyping appears to be a valid classification system with potential to guide eating disorder treatment decisions. © 2016 Wiley Periodicals, Inc.(Int J Eat Disord 2017; 50:506-514). © 2016 Wiley Periodicals, Inc.

  2. Impulsive suicide attempts predict post-treatment relapse in alcohol-dependent patients.

    Science.gov (United States)

    Wojnar, Marcin; Ilgen, Mark A; Jakubczyk, Andrzej; Wnorowska, Anna; Klimkiewicz, Anna; Brower, Kirk J

    2008-10-01

    The present study was designed to examine the influence of suicidality on relapse in alcohol-dependent patients. Specifically, a lifetime suicide attempt at baseline was used to predict relapse in the year after treatment. Also, the unique contribution of impulsive suicide attempts was examined. A total of 154 patients with alcohol dependence, consecutively admitted to four addiction treatment facilities in Warsaw, Poland participated in the study. Of the 154 eligible patients, 118 (76.6%) completed a standardized follow-up assessment at 12 months. Previous suicide attempts were common in adults treated for alcohol dependence with 43% patients in the present sample reporting an attempt at some point during their lifetime. Additionally, more than 62% of those with a lifetime suicide attempt reported making an impulsive attempt. Lifetime suicide attempts were not associated with post-treatment relapse (chi-square=2.37, d.f.=1, p=0.124). However, impulsive suicide attempts strongly predicted relapse (OR=2.81, 95% CI=1.13-6.95, p=0.026) and time to relapse (OR=2.10, 95% CI=1.18-3.74, p=0.012) even after adjusting for other measures of baseline psychopathology, depression, impulsivity, hopelessness and alcohol use severity. This study is the first to document the relationship between pre-treatment impulsive suicide attempts and higher likelihood of post-treatment relapse in alcohol-dependent patents. Clinicians should routinely conduct an assessment for previous suicide attempts in patients with alcohol use disorders, and when impulsive suicidality is reported, they should recognize the increased risk for relapse and formulate their patients' treatment plans accordingly with the goals of reducing both alcoholic relapse and suicide rates.

  3. Long-term response to recombinant human growth hormone treatment: a new predictive mathematical method.

    Science.gov (United States)

    Migliaretti, G; Ditaranto, S; Guiot, C; Vannelli, S; Matarazzo, P; Cappello, N; Stura, I; Cavallo, F

    2018-07-01

    Recombinant GH has been offered to GH-deficient (GHD) subjects for more than 30 years, in order to improve height and growth velocity in children and to enhance metabolic effects in adults. The aim of our work is to describe the long-term effect of rhGH treatment in GHD pediatric patients, suggesting a growth prediction model. A homogeneous database is defined for diagnosis and treatment modalities, based on GHD patients afferent to Hospital Regina Margherita in Turin (Italy). In this study, 232 GHD patients are selected (204 idiopathic GHD and 28 organic GHD). Each measure is shown in terms of mean with relative standard deviations (SD) and 95% confidence interval (95% CI). To estimate the final height of each patient on the basis of few measures, a mathematical growth prediction model [based on Gompertzian function and a mixed method based on the radial basis functions (RBFs) and the particle swarm optimization (PSO) models] was performed. The results seem to highlight the benefits of an early start of treatment, further confirming what is suggested by the literature. Generally, the RBF-PSO method shows a good reliability in the prediction of the final height. Indeed, RMSE is always lower than 4, i.e., in average the forecast will differ at most of 4 cm to the real value. In conclusion, the large and accurate database of Italian GHD patients allowed us to assess the rhGH treatment efficacy and compare the results with those obtained in other Countries. Moreover, we proposed and validated a new mathematical model forecasting the expected final height after therapy which was validated on our cohort.

  4. Early perfusion changes within 1 week of systemic treatment measured by dynamic contrast-enhanced MRI may predict survival in patients with advanced hepatocellular carcinoma

    Energy Technology Data Exchange (ETDEWEB)

    Chen, Bang-Bin; Yu, Chih-Wei; Liang, Po-Chin [National Taiwan University College of Medicine and Hospital, Department of Medical Imaging and Radiology, Taipei City (China); Hsu, Chao-Yu [National Taiwan University College of Medicine and Hospital, Department of Medical Imaging and Radiology, Taipei City (China); Taipei Hospital, Ministry of Health and Welfare, Department of Radiology, New Taipei City (China); Hsu, Chiun; Hsu, Chih-Hung; Cheng, Ann-Lii [National Taiwan University College of Medicine and Hospital, Department of Oncology, Taipei City (China); Shih, Tiffany Ting-Fang [National Taiwan University College of Medicine and Hospital, Department of Medical Imaging and Radiology, Taipei City (China); Taipei City Hospital, Department of Medical Imaging, Taipei City (China); National Taiwan University Hospital, Department of Medical Imaging, Taipei (China)

    2017-07-15

    To correlate early changes in the parameters of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) within 1 week of systemic therapy with overall survival (OS) in patients with advanced hepatocellular carcinoma (HCC). Eighty-nine patients with advanced HCC underwent DCE-MRI before and within 1 week following systemic therapy. The relative changes of six DCE-MRI parameters (Peak, Slope, AUC, Ktrans, Kep and Ve) of the tumours were correlated with OS using the Kaplan-Meier model and the double-sided log-rank test. All patients died and the median survival was 174 days. Among the six DCE-MRI parameters, reductions in Peak, AUC, and Ktrans, were significantly correlated with one another. In addition, patients with a high Peak reduction following treatment had longer OS (P = 0.023) compared with those with a low Peak reduction. In multivariate analysis, a high Peak reduction was an independent favourable prognostic factor in all patients [hazard ratio (HR), 0.622; P = 0.038] after controlling for age, sex, treatment methods, tumour size and stage, and Eastern Cooperative Oncology Group performance status. Early perfusion changes within 1 week following systemic therapy measured by DCE-MRI may aid in the prediction of the clinical outcome in patients with advanced HCC. (orig.)

  5. Do personality traits predict outcome of psychodynamically oriented psychosomatic inpatient treatment beyond initial symptoms?

    Science.gov (United States)

    Steinert, Christiane; Klein, Susanne; Leweke, Frank; Leichsenring, Falk

    2015-03-01

    Whether personality characteristics have an impact on treatment outcome is an important question in psychotherapy research. One of the most common approaches for the description of personality is the five-factor model of personality. Only few studies investigated whether patient personality as measured with the NEO-Five-Factor Inventory (NEO-FFI, Costa & McCrae [1992b]. Revised NEO-PI-R and NEO-FFI. Professional manual. Odessa, FL: Psychological Assessment Recources) predicts outcome. Results were inconsistent. Studies reporting personality to be predictive of outcome did not control for baseline symptoms, while studies controlling initial symptoms could not support these findings. We hypothesized that after taking into account baseline symptoms, the NEO-FFI would not predict outcome and tested this in a large sample of inpatients at a psychosomatic clinic. Naturalistic, non-controlled study using patients' data for multiple regression analysis to identify predictors of outcome. Data of 254 inpatients suffering primarily from depressive, anxiety, stress, and somatoform disorders were analysed. Personality was assessed at the beginning of therapy. For psychotherapy outcome, changes in anxiety and depression (Hospital Anxiety and Depression Scale; HADS), overall psychopathology (Symptom Checklist-90-R Global Severity Index [GSI]), and interpersonal problems (Inventory of Interpersonal Problems; IIP) were measured. The treatment resulted in significant decreases on all outcome measures corresponding to moderate to large effect sizes (HADS: d = 1.03; GSI: d = 0.90; IIP: d = 0.38). Consistent with our hypothesis, none of the personality domains predicted outcome when baseline symptoms were controlled for. Personality assessment at baseline does not seem to have an added value in the prediction of inpatient psychotherapy outcome beyond initial symptoms. Clinical implications Personality dimensions overlap with symptomatic distress. Rather than serve as predictors of

  6. Predicting Bacillus coagulans spores inactivation in tomato pulp under nonisothermal heat treatments.

    Science.gov (United States)

    Zimmermann, Morgana; Longhi, Daniel A; Schaffner, Donald W; Aragão, Gláucia M F

    2014-05-01

    The knowledge and understanding of Bacillus coagulans inactivation during a thermal treatment in tomato pulp, as well as the influence of temperature variation during thermal processes are essential for design, calculation, and optimization of the process. The aims of this work were to predict B. coagulans spores inactivation in tomato pulp under varying time-temperature profiles with Gompertz-inspired inactivation model and to validate the model's predictions by comparing the predicted values with experimental data. B. coagulans spores in pH 4.3 tomato pulp at 4 °Brix were sealed in capillary glass tubes and heated in thermostatically controlled circulating oil baths. Seven different nonisothermal profiles in the range from 95 to 105 °C were studied. Predicted inactivation kinetics showed similar behavior to experimentally observed inactivation curves when the samples were exposed to temperatures in the upper range of this study (99 to 105 °C). Profiles that resulted in less accurate predictions were those where the range of temperatures analyzed were comparatively lower (inactivation profiles starting at 95 °C). The link between fail prediction and both lower starting temperature and magnitude of the temperature shift suggests some chemical or biological mechanism at work. Statistical analysis showed that overall model predictions were acceptable, with bias factors from 0.781 to 1.012, and accuracy factors from 1.049 to 1.351, and confirm that the models used were adequate to estimate B. coagulans spores inactivation under fluctuating temperature conditions in the range from 95 to 105 °C. How can we estimate Bacillus coagulans inactivation during sudden temperature shifts in heat processing? This article provides a validated model that can be used to predict B. coagulans under changing temperature conditions. B. coagulans is a spore-forming bacillus that spoils acidified food products. The mathematical model developed here can be used to predict the spoilage

  7. Volatility and change in chronic pain severity predict outcomes of treatment for prescription opioid addiction.

    Science.gov (United States)

    Worley, Matthew J; Heinzerling, Keith G; Shoptaw, Steven; Ling, Walter

    2017-07-01

    Buprenorphine-naloxone (BUP-NLX) can be used to manage prescription opioid addiction among persons with chronic pain, but post-treatment relapse is common and difficult to predict. This study estimated whether changes in pain over time and pain volatility during BUP-NLX maintenance would predict opioid use during the taper BUP-NLX taper. Secondary analysis of a multi-site clinical trial for prescription opioid addiction, using data obtained during a 12-week BUP-NLX stabilization and 4-week BUP-NLX taper. Community clinics affiliated with a national clinical trials network in 10 US cities. Subjects with chronic pain who entered the BUP-NLX taper phase (n = 125) with enrollment occurring from June 2006 to July 2009 (52% male, 88% Caucasian, 31% married). Outcomes were weekly biologically verified and self-reported opioid use from the 4-week taper phase. Predictors were estimates of baseline severity, rate of change and volatility in pain from weekly self-reports during the 12-week maintenance phase. Controlling for baseline pain and treatment condition, increased pain [odds ratio (OR) = 2.38, P = 0.02] and greater pain volatility (OR = 2.43, P = 0.04) predicted greater odds of positive opioid urine screen during BUP-NLX taper. Increased pain (IRR = 1.40, P = 0.04) and greater pain volatility [incidence-rate ratio (IRR) = 1.66, P = 0.009] also predicted greater frequency of self-reported opioid use. Adults with chronic pain receiving out-patient treatment with buprenorphine-naloxone (BUP-NLX) for prescription opioid addiction have an elevated risk for opioid use when tapering off maintenance treatment. Those with relative persistence in pain over time and greater volatility in pain during treatment are less likely to sustain abstinence during BUP-NLX taper. © 2017 Society for the Study of Addiction.

  8. Predicting Retrograde Autobiographical Memory Changes Following Electroconvulsive Therapy: Relationships between Individual, Treatment, and Early Clinical Factors.

    Science.gov (United States)

    Martin, Donel M; Gálvez, Verònica; Loo, Colleen K

    2015-06-19

    Loss of personal memories experienced prior to receiving electroconvulsive therapy is common and distressing and in some patients can persist for many months following treatment. Improved understanding of the relationships between individual patient factors, electroconvulsive therapy treatment factors, and clinical indicators measured early in the electroconvulsive therapy course may help clinicians minimize these side effects through better management of the electroconvulsive therapy treatment approach. In this study we examined the associations between the above factors for predicting retrograde autobiographical memory changes following electroconvulsive therapy. Seventy-four depressed participants with major depressive disorder were administered electroconvulsive therapy 3 times per week using either a right unilateral or bitemporal electrode placement and brief or ultrabrief pulse width. Verbal fluency and retrograde autobiographical memory (assessed using the Columbia Autobiographical Memory Interview - Short Form) were tested at baseline and after the last electroconvulsive therapy treatment. Time to reorientation was measured immediately following the third and sixth electroconvulsive therapy treatments. Results confirmed the utility of measuring time to reorientation early during the electroconvulsive therapy treatment course as a predictor of greater retrograde amnesia and the importance of assessing baseline cognitive status for identifying patients at greater risk for developing later side effects. With increased number of electroconvulsive therapy treatments, older age was associated with increased time to reorientation. Consistency of verbal fluency performance was moderately correlated with change in Columbia Autobiographical Memory Interview - Short Form scores following right unilateral electroconvulsive therapy. Electroconvulsive therapy treatment techniques associated with lesser cognitive side effects should be particularly considered for

  9. Effect of foam on temperature prediction and heat recovery potential from biological wastewater treatment.

    Science.gov (United States)

    Corbala-Robles, L; Volcke, E I P; Samijn, A; Ronsse, F; Pieters, J G

    2016-05-15

    Heat is an important resource in wastewater treatment plants (WWTPs) which can be recovered. A prerequisite to determine the theoretical heat recovery potential is an accurate heat balance model for temperature prediction. The insulating effect of foam present on the basin surface and its influence on temperature prediction were assessed in this study. Experiments were carried out to characterize the foam layer and its insulating properties. A refined dynamic temperature prediction model, taking into account the effect of foam, was set up. Simulation studies for a WWTP treating highly concentrated (manure) wastewater revealed that the foam layer had a significant effect on temperature prediction (3.8 ± 0.7 K over the year) and thus on the theoretical heat recovery potential (30% reduction when foam is not considered). Seasonal effects on the individual heat losses and heat gains were assessed. Additionally, the effects of the critical basin temperature above which heat is recovered, foam thickness, surface evaporation rate reduction and the non-absorbed solar radiation on the theoretical heat recovery potential were evaluated. Copyright © 2016 Elsevier Ltd. All rights reserved.

  10. Effect of heteroscedasticity treatment in residual error models on model calibration and prediction uncertainty estimation

    Science.gov (United States)

    Sun, Ruochen; Yuan, Huiling; Liu, Xiaoli

    2017-11-01

    The heteroscedasticity treatment in residual error models directly impacts the model calibration and prediction uncertainty estimation. This study compares three methods to deal with the heteroscedasticity, including the explicit linear modeling (LM) method and nonlinear modeling (NL) method using hyperbolic tangent function, as well as the implicit Box-Cox transformation (BC). Then a combined approach (CA) combining the advantages of both LM and BC methods has been proposed. In conjunction with the first order autoregressive model and the skew exponential power (SEP) distribution, four residual error models are generated, namely LM-SEP, NL-SEP, BC-SEP and CA-SEP, and their corresponding likelihood functions are applied to the Variable Infiltration Capacity (VIC) hydrologic model over the Huaihe River basin, China. Results show that the LM-SEP yields the poorest streamflow predictions with the widest uncertainty band and unrealistic negative flows. The NL and BC methods can better deal with the heteroscedasticity and hence their corresponding predictive performances are improved, yet the negative flows cannot be avoided. The CA-SEP produces the most accurate predictions with the highest reliability and effectively avoids the negative flows, because the CA approach is capable of addressing the complicated heteroscedasticity over the study basin.

  11. One Layer Nonlinear Economic Closed-Loop Generalized Predictive Control for a Wastewater Treatment Plant

    Directory of Open Access Journals (Sweden)

    Hicham El bahja

    2018-04-01

    Full Text Available The main scope of this paper is the proposal of a new single layer Nonlinear Economic Closed-Loop Generalized Predictive Control (NECLGPC as an efficient advanced control technique for improving economics in the operation of nonlinear plants. Instead of the classic dual-mode MPC (model predictive controller schemes, where the terminal control law defined in the terminal region is obtained offline solving a linear quadratic regulator problem, here the terminal control law in the NECLGPC is determined online by an unconstrained Nonlinear Generalized Predictive Control (NGPC. In order to make the optimization problem more tractable two considerations have been made in the present work. Firstly, the prediction model consisting of a nonlinear phenomenological model of the plant is expressed with linear structure and state dependent matrices. Secondly, instead of including the nonlinear economic cost in the objective function, an approximation of the reduced gradient of the economic function is used. These assumptions allow us to design an economic unconstrained nonlinear GPC analytically and to state the NECLGPC allow for the design of an economic problem as a QP (Quadratic Programing problem each sampling time. Four controllers based on GPC that differ in designs and structures are compared with the proposed control technique in terms of process performance and energy costs. Particularly, the methodology is implemented in the N-Removal process of a Wastewater Treatment Plant (WWTP and the results prove the efficiency of the method and that it can be used profitably in practical cases.

  12. Contextual effects on the perceived health benefits of exercise: the exercise rank hypothesis.

    Science.gov (United States)

    Maltby, John; Wood, Alex M; Vlaev, Ivo; Taylor, Michael J; Brown, Gordon D A

    2012-12-01

    Many accounts of social influences on exercise participation describe how people compare their behaviors to those of others. We develop and test a novel hypothesis, the exercise rank hypothesis, of how this comparison can occur. The exercise rank hypothesis, derived from evolutionary theory and the decision by sampling model of judgment, suggests that individuals' perceptions of the health benefits of exercise are influenced by how individuals believe the amount of exercise ranks in comparison with other people's amounts of exercise. Study 1 demonstrated that individuals' perceptions of the health benefits of their own current exercise amounts were as predicted by the exercise rank hypothesis. Study 2 demonstrated that the perceptions of the health benefits of an amount of exercise can be manipulated by experimentally changing the ranked position of the amount within a comparison context. The discussion focuses on how social norm-based interventions could benefit from using rank information.

  13. Novel enzymatic assay predicts minoxidil response in the treatment of androgenetic alopecia.

    Science.gov (United States)

    Goren, Andy; Castano, Juan Antonio; McCoy, John; Bermudez, Fernando; Lotti, Torello

    2014-01-01

    Topical minoxidil is the most common drug used for the treatment of androgenetic alopecia (AGA) in men and women. Although topical minoxidil exhibits a good safety profile, the efficacy in the overall population remains relatively low at 30-40%. To observe significant improvement in hair growth, minoxidil is typically used daily for a period of at least 3-4 months. Due to the significant time commitment and low response rate, a biomarker for predicting patient response prior to therapy would be advantageous. Minoxidil is converted in the scalp to its active form, minoxidil sulfate, by the sulfotransferase enzyme SULT1A1. We hypothesized that SULT1A1 enzyme activity in the hair follicle correlates with minoxidil response for the treatment of AGA. Our preliminary retrospective study of a SULT1A1 activity assay demonstrates 95% sensitivity and 73% specificity in predicting minoxidil treatment response for AGA. A larger prospective study is now under way to further validate this novel assay. © 2013 Wiley Periodicals, Inc.

  14. Tissue Biomarkers in Predicting Response to Sunitinib Treatment of Metastatic Renal Cell Carcinoma.

    Science.gov (United States)

    Trávníček, Ivan; Branžovský, Jindřich; Kalusová, Kristýna; Hes, Ondřej; Holubec, Luboš; Pele, Kevin Bauleth; Ürge, Tomáš; Hora, Milan

    2015-10-01

    To identify tissue biomarkers that are predictive of the therapeutic effect of sunitinib in treatment of metastatic clear cell renal cell carcinoma (mCRCC). Our study included 39 patients with mCRCC treated with sunitinib. Patients were stratified into two groups based on their response to sunitinib treatment: non-responders (progression), and responders (stable disease, regression). The effect of treatment was measured by comparing imaging studies before the initiation treatment with those performed at between 3rd and 7th months of treatment, depending on the patient. Histological samples of tumor tissue and healthy renal parenchyma, acquired during surgery of the primary tumor, were examined with immunohistochemistry to detect tissue targets involved in the signaling pathways of tumor growth and neoangiogenesis. We selected mammalian target of rapamycine, p53, vascular endothelial growth factor, hypoxia-inducible factor 1 and 2 and carbonic anhydrase IX. We compared the average levels of biomarker expression in both, tumor tissue, as well as in healthy renal parenchyma. Results were evaluated using the Student's t-test. For responders, statistically significant differences in marker expression in tumor tissue versus healthy parenchyma were found for mTOR (4%/16.7%; p=0.01031), p53 (4%/12.7%; p=0.042019), VEGF (62.7%/45%; p=0.019836) and CAIX (45%/15.33%; p=0.001624). A further significant difference was found in the frequency of high expression (more than 60%) between tumor tissue and healthy parenchyma in VEGF (65%/35%; p=0.026487) and CAIX (42%/8%; p=0.003328). CAIX was expressed at high levels in the tumor tissue in both evaluated groups. A significantly higher expression of VEGF in CRCC in comparison to healthy parenchyma can predict a better response to sunitinib. Copyright© 2015 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved.

  15. The preoperative manometric pattern predicts the outcome of surgical treatment for esophageal achalasia.

    Science.gov (United States)

    Salvador, Renato; Costantini, Mario; Zaninotto, Giovanni; Morbin, Tiziana; Rizzetto, Christian; Zanatta, Lisa; Ceolin, Martina; Finotti, Elena; Nicoletti, Loredana; Da Dalt, Gianfranco; Cavallin, Francesco; Ancona, Ermanno

    2010-11-01

    A new manometric classification of esophageal achalasia has recently been proposed that also suggests a correlation with the final outcome of treatment. The aim of this study was to investigate this hypothesis in a large group of achalasia patients undergoing laparoscopic Heller-Dor myotomy. We evaluated 246 consecutive achalasia patients who underwent surgery as their first treatment from 2001 to 2009. Patients with sigmoid-shaped esophagus were excluded. Symptoms were scored and barium swallow X-ray, endoscopy, and esophageal manometry were performed before and again at 6 months after surgery. Patients were divided into three groups: (I) no distal esophageal pressurization (contraction wave amplitude 30 mmHg); and (III) rapidly propagating pressurization attributable to spastic contractions. Treatment failure was defined as a postoperative symptom score greater than the 10th percentile of the preoperative score (i.e., >7). Type III achalasia coincided with a longer overall lower esophageal sphincter (LES) length, a lower symptom score, and a smaller esophageal diameter. Treatment failure rates differed significantly in the three groups: I = 14.6% (14/96), II = 4.7% (6/127), and III = 30.4% (7/23; p = 0.0007). At univariate analysis, the manometric pattern, a low LES resting pressure, and a high chest pain score were the only factors predicting treatment failure. At multivariate analysis, the manometric pattern and a LES resting pressure achalasia subtypes: patients with panesophageal pressurization have the best outcome after laparoscopic Heller-Dor myotomy.

  16. Motivation and Treatment Credibility Predicts Dropout, Treatment Adherence, and Clinical Outcomes in an Internet-Based Cognitive Behavioral Relaxation Program: A Randomized Controlled Trial.

    Science.gov (United States)

    Alfonsson, Sven; Olsson, Erik; Hursti, Timo

    2016-03-08

    In previous research, variables such as age, education, treatment credibility, and therapeutic alliance have shown to affect patients' treatment adherence and outcome in Internet-based psychotherapy. A more detailed understanding of how such variables are associated with different measures of adherence and clinical outcomes may help in designing more effective online therapy. The aims of this study were to investigate demographical, psychological, and treatment-specific variables that could predict dropout, treatment adherence, and treatment outcomes in a study of online relaxation for mild to moderate stress symptoms. Participant dropout and attrition as well as data from self-report instruments completed before, during, and after the online relaxation program were analyzed. Multiple linear and logistical regression analyses were conducted to predict early dropout, overall attrition, online treatment progress, number of registered relaxation exercises, posttreatment symptom levels, and reliable improvement. Dropout was significantly predicted by treatment credibility, whereas overall attrition was associated with reporting a focus on immediate consequences and experiencing a low level of intrinsic motivation for the treatment. Treatment progress was predicted by education level and treatment credibility, whereas number of registered relaxation exercises was associated with experiencing intrinsic motivation for the treatment. Posttreatment stress symptoms were positively predicted by feeling external pressure to participate in the treatment and negatively predicted by treatment credibility. Reporting reliable symptom improvement after treatment was predicted by treatment credibility and therapeutic bond. This study confirmed that treatment credibility and a good working alliance are factors associated with successful Internet-based psychotherapy. Further, the study showed that measuring adherence in different ways provides somewhat different results, which

  17. Parkinsonian motor impairment predicts personality domains related to genetic risk and treatment outcomes in schizophrenia.

    Science.gov (United States)

    Molina, Juan L; Calvó, María; Padilla, Eduardo; Balda, Mara; Alemán, Gabriela González; Florenzano, Néstor V; Guerrero, Gonzalo; Kamis, Danielle; Rangeon, Beatriz Molina; Bourdieu, Mercedes; Strejilevich, Sergio A; Conesa, Horacio A; Escobar, Javier I; Zwir, Igor; Cloninger, C Robert; de Erausquin, Gabriel A

    2017-01-01

    Identifying endophenotypes of schizophrenia is of critical importance and has profound implications on clinical practice. Here we propose an innovative approach to clarify the mechanims through which temperament and character deviance relates to risk for schizophrenia and predict long-term treatment outcomes. We recruited 61 antipsychotic naïve subjects with chronic schizophrenia, 99 unaffected relatives, and 68 healthy controls from rural communities in the Central Andes. Diagnosis was ascertained with the Schedules of Clinical Assessment in Neuropsychiatry; parkinsonian motor impairment was measured with the Unified Parkinson's Disease Rating Scale; mesencephalic parenchyma was evaluated with transcranial ultrasound; and personality traits were assessed using the Temperament and Character Inventory. Ten-year outcome data was available for ~40% of the index cases. Patients with schizophrenia had higher harm avoidance and self-transcendence (ST), and lower reward dependence (RD), cooperativeness (CO), and self-directedness (SD). Unaffected relatives had higher ST and lower CO and SD. Parkinsonism reliably predicted RD, CO, and SD after correcting for age and sex. The average duration of untreated psychosis (DUP) was over 5 years. Further, SD was anticorrelated with DUP and antipsychotic dosing at follow-up. Baseline DUP was related to antipsychotic dose-years. Further, 'explosive/borderline', 'methodical/obsessive', and 'disorganized/schizotypal' personality profiles were associated with increased risk of schizophrenia. Parkinsonism predicts core personality features and treatment outcomes in schizophrenia. Our study suggests that RD, CO, and SD are endophenotypes of the disease that may, in part, be mediated by dopaminergic function. Further, SD is an important determinant of treatment course and outcome.

  18. Invisalign® treatment in the anterior region: were the predicted tooth movements achieved?

    Science.gov (United States)

    Krieger, Elena; Seiferth, Jörg; Marinello, Ivana; Jung, Britta A; Wriedt, Susanne; Jacobs, Collin; Wehrbein, Heinrich

    2012-09-01

    Based on our previous pilot study, the objective of this extended study was to compare (a) casts to their corresponding digital ClinCheck® models at baseline and (b) the tooth movement achieved at the end of aligner therapy (Invisalign®) to the predicted movement in the anterior region. Pre- and post-treatment casts as well as initial and final ClinChecks® models of 50 patients (15-63 years of age) were analyzed. All patients were treated with Invisalign® (Align Technology, Santa Clara, CA, USA). Evaluated parameters were: upper/lower anterior arch length and intercanine distance, overjet, overbite, dental midline shift, and the irregularity index according to Little. The comparison achieved/predicted tooth movement was tested for equivalence [adjusted 98.57% confidence interval (- 1.00; + 1.00)]. Before treatment the anterior crowding, according to Little, was on average 5.39 mm (minimum 1.50 mm, maximum 14.50 mm) in the upper dentition and 5.96 mm (minimum 2.00 mm, maximum 11.50 mm) in the lower dentition. After treatment the values were reduced to 1.57 mm (minimum 0 mm, maximum 4.5 mm) in the maxilla and 0.82 mm (minimum 0 mm, maximum 2.50 mm) in the mandible. We found slight deviations between pretreatment casts and initialClinCheck® ranging on average from -0.08 mm (SD ± 0.29) for the overjet and up to -0.28 mm (SD ± 0.46) for the upper anterior arch length. The difference between achieved/predicted tooth movements ranged on average from 0.01 mm (SD ± 0.48) for the lower anterior arch length, up to 0.7 mm (SD ± 0.87) for the overbite. All parameters were significantly equivalent except for the overbite (-1.02; -0.39). Performed with aligners (Invisalign®), the resolvement of the partly severe anterior crowding was successfully accomplished. Resolving lower anterior crowding by protrusion of the anterior teeth (i.e., enlargement of the anterior arch length) seems well predictable. The initial ClinCheck® models provided high accuracy compared to the

  19. Error analysis of stochastic gradient descent ranking.

    Science.gov (United States)

    Chen, Hong; Tang, Yi; Li, Luoqing; Yuan, Yuan; Li, Xuelong; Tang, Yuanyan

    2013-06-01

    Ranking is always an important task in machine learning and information retrieval, e.g., collaborative filtering, recommender systems, drug discovery, etc. A kernel-based stochastic gradient descent algorithm with the least squares loss is proposed for ranking in this paper. The implementation of this algorithm is simple, and an expression of the solution is derived via a sampling operator and an integral operator. An explicit convergence rate for leaning a ranking function is given in terms of the suitable choices of the step size and the regularization parameter. The analysis technique used here is capacity independent and is novel in error analysis of ranking learning. Experimental results on real-world data have shown the effectiveness of the proposed algorithm in ranking tasks, which verifies the theoretical analysis in ranking error.

  20. Can radiation therapy treatment planning system accurately predict surface doses in postmastectomy radiation therapy patients?

    International Nuclear Information System (INIS)

    Wong, Sharon; Back, Michael; Tan, Poh Wee; Lee, Khai Mun; Baggarley, Shaun; Lu, Jaide Jay

    2012-01-01

    Skin doses have been an important factor in the dose prescription for breast radiotherapy. Recent advances in radiotherapy treatment techniques, such as intensity-modulated radiation therapy (IMRT) and new treatment schemes such as hypofractionated breast therapy have made the precise determination of the surface dose necessary. Detailed information of the dose at various depths of the skin is also critical in designing new treatment strategies. The purpose of this work was to assess the accuracy of surface dose calculation by a clinically used treatment planning system and those measured by thermoluminescence dosimeters (TLDs) in a customized chest wall phantom. This study involved the construction of a chest wall phantom for skin dose assessment. Seven TLDs were distributed throughout each right chest wall phantom to give adequate representation of measured radiation doses. Point doses from the CMS Xio® treatment planning system (TPS) were calculated for each relevant TLD positions and results correlated. There were no significant difference between measured absorbed dose by TLD and calculated doses by the TPS (p > 0.05 (1-tailed). Dose accuracy of up to 2.21% was found. The deviations from the calculated absorbed doses were overall larger (3.4%) when wedges and bolus were used. 3D radiotherapy TPS is a useful and accurate tool to assess the accuracy of surface dose. Our studies have shown that radiation treatment accuracy expressed as a comparison between calculated doses (by TPS) and measured doses (by TLD dosimetry) can be accurately predicted for tangential treatment of the chest wall after mastectomy.

  1. SU-E-T-629: Prediction of the ViewRay Radiotherapy Treatment Time for Clinical Logistics

    Energy Technology Data Exchange (ETDEWEB)

    Liu, S; Wooten, H; Wu, Y; Yang, D [Washington University in St Louis, St Louis, MO (United States)

    2015-06-15

    Purpose: An algorithm is developed in our clinic, given a new treatment plan, to predict treatment delivery time for radiation therapy (RT) treatments of patients on ViewRay magnetic resonance-image guided radiation therapy (MR-IGRT) delivery system. This algorithm is necessary for managing patient treatment appointments, and is useful as an indicator to assess the treatment plan complexity. Methods: A patient’s total treatment delivery time, not including time required for localization, may be described as the sum of four components: (1) the treatment initialization time; (2) the total beam-on time; (3) the gantry rotation time; and (4) the multileaf collimator (MLC) motion time. Each of the four components is predicted separately. The total beam-on time can be calculated using both the planned beam-on time and the decay-corrected delivery dose rate. To predict the remaining components, we quantitatively analyze the patient treatment delivery record files. The initialization time is demonstrated to be random since it depends on the final gantry angle and MLC leaf positions of the previous treatment. Based on modeling the relationships between the gantry rotation angles and the corresponding rotation time, and between the furthest MLC leaf moving distance and the corresponding MLC motion time, the total delivery time is predicted using linear regression. Results: The proposed algorithm has demonstrated the feasibility of predicting the ViewRay treatment delivery time for any treatment plan of any patient. The average prediction error is 0.89 minutes or 5.34%, and the maximal prediction error is 2.09 minutes or 13.87%. Conclusion: We have developed a treatment delivery time prediction algorithm based on the analysis of previous patients’ treatment delivery records. The accuracy of our prediction is sufficient for guiding and arranging patient treatment appointments on a daily basis. The predicted delivery time could also be used as an indicator to assess the

  2. SU-E-T-629: Prediction of the ViewRay Radiotherapy Treatment Time for Clinical Logistics

    International Nuclear Information System (INIS)

    Liu, S; Wooten, H; Wu, Y; Yang, D

    2015-01-01

    Purpose: An algorithm is developed in our clinic, given a new treatment plan, to predict treatment delivery time for radiation therapy (RT) treatments of patients on ViewRay magnetic resonance-image guided radiation therapy (MR-IGRT) delivery system. This algorithm is necessary for managing patient treatment appointments, and is useful as an indicator to assess the treatment plan complexity. Methods: A patient’s total treatment delivery time, not including time required for localization, may be described as the sum of four components: (1) the treatment initialization time; (2) the total beam-on time; (3) the gantry rotation time; and (4) the multileaf collimator (MLC) motion time. Each of the four components is predicted separately. The total beam-on time can be calculated using both the planned beam-on time and the decay-corrected delivery dose rate. To predict the remaining components, we quantitatively analyze the patient treatment delivery record files. The initialization time is demonstrated to be random since it depends on the final gantry angle and MLC leaf positions of the previous treatment. Based on modeling the relationships between the gantry rotation angles and the corresponding rotation time, and between the furthest MLC leaf moving distance and the corresponding MLC motion time, the total delivery time is predicted using linear regression. Results: The proposed algorithm has demonstrated the feasibility of predicting the ViewRay treatment delivery time for any treatment plan of any patient. The average prediction error is 0.89 minutes or 5.34%, and the maximal prediction error is 2.09 minutes or 13.87%. Conclusion: We have developed a treatment delivery time prediction algorithm based on the analysis of previous patients’ treatment delivery records. The accuracy of our prediction is sufficient for guiding and arranging patient treatment appointments on a daily basis. The predicted delivery time could also be used as an indicator to assess the

  3. Evaluation of candidate biomarkers to predict cancer cell sensitivity or resistance to PARP-1 inhibitor treatment

    DEFF Research Database (Denmark)

    Oplustilova, L.; Wolanin, K.; Bartkova, J.

    2012-01-01

    combinations with camptothecin or ionizing radiation. Furthermore, monitoring pARsylation and Rad51 foci formation as surrogate markers for PARP activity and HR, respectively, supported their candidacy for biomarkers of PARP-1i responses. As to resistance mechanisms, we confrmed the role of the multidrug......(ADp-ribose) polymerase-1 (PARP-1), an enzyme critical for repair pathways alternative to HR. While promising, treatment with PARP-1 inhibitors (PARP-1i) faces some hurdles, including (1) acquired resistance, (2) search for other sensitizing, non-BRCA1/2 cancer defects and (3) lack of biomarkers to predict response...

  4. Circulating HER2 DNA after trastuzumab treatment predicts survival and response in breast cancer

    DEFF Research Database (Denmark)

    Sorensen, Boe S; Mortensen, Lise S; Andersen, Jørn

    2010-01-01

    BACKGROUND: Only a subset of breast cancer patients responds to the HER2 inhibitor trastuzumab, and methods to identify responders are needed. PATIENTS AND METHODS: We studied 28 patients with metastatic breast cancer that had amplified human epidermal growth factor receptor 2 (HER2) genes...... in their primary tumour and were treated with a combination of trastuzumab and chemotherapy. Plasma was collected and amplification of the HER2 gene in circulating DNA and the amounts of the extracellular domain (ECD) of HER2 were measured just before first treatment (n=28) and just before second treatment three...... response (p=0.02), and overall survival (p=0.05). HER2 ECD kinetics did not correlate to clinical data. CONCLUSION: We suggest that a decrease in HER2 gene amplification in the plasma predicts a more favourable response to trastuzumab....

  5. Methodology for ranking restoration options

    International Nuclear Information System (INIS)

    Hedemann Jensen, Per

    1999-04-01

    The work described in this report has been performed as a part of the RESTRAT Project FI4P-CT95-0021a (PL 950128) co-funded by the Nuclear Fission Safety Programme of the European Commission. The RESTRAT project has the overall objective of developing generic methodologies for ranking restoration techniques as a function of contamination and site characteristics. The project includes analyses of existing remediation methodologies and contaminated sites, and is structured in the following steps: characterisation of relevant contaminated sites; identification and characterisation of relevant restoration techniques; assessment of the radiological impact; development and application of a selection methodology for restoration options; formulation of generic conclusions and development of a manual. The project is intended to apply to situations in which sites with nuclear installations have been contaminated with radioactive materials as a result of the operation of these installations. The areas considered for remedial measures include contaminated land areas, rivers and sediments in rivers, lakes, and sea areas. Five contaminated European sites have been studied. Various remedial measures have been envisaged with respect to the optimisation of the protection of the populations being exposed to the radionuclides at the sites. Cost-benefit analysis and multi-attribute utility analysis have been applied for optimisation. Health, economic and social attributes have been included and weighting factors for the different attributes have been determined by the use of scaling constants. (au)

  6. Citation graph based ranking in Invenio

    CERN Document Server

    Marian, Ludmila; Rajman, Martin; Vesely, Martin

    2010-01-01

    Invenio is the web-based integrated digital library system developed at CERN. Within this framework, we present four types of ranking models based on the citation graph that complement the simple approach based on citation counts: time-dependent citation counts, a relevancy ranking which extends the PageRank model, a time-dependent ranking which combines the freshness of citations with PageRank and a ranking that takes into consideration the external citations. We present our analysis and results obtained on two main data sets: Inspire and CERN Document Server. Our main contributions are: (i) a study of the currently available ranking methods based on the citation graph; (ii) the development of new ranking methods that correct some of the identified limitations of the current methods such as treating all citations of equal importance, not taking time into account or considering the citation graph complete; (iii) a detailed study of the key parameters for these ranking methods. (The original publication is ava...

  7. Communities in Large Networks: Identification and Ranking

    DEFF Research Database (Denmark)

    Olsen, Martin

    2008-01-01

    We study the problem of identifying and ranking the members of a community in a very large network with link analysis only, given a set of representatives of the community. We define the concept of a community justified by a formal analysis of a simple model of the evolution of a directed graph. ...... and its immediate surroundings. The members are ranked with a “local” variant of the PageRank algorithm. Results are reported from successful experiments on identifying and ranking Danish Computer Science sites and Danish Chess pages using only a few representatives....

  8. Ranking Entities in Networks via Lefschetz Duality

    DEFF Research Database (Denmark)

    Aabrandt, Andreas; Hansen, Vagn Lundsgaard; Poulsen, Bjarne

    2014-01-01

    then be ranked according to how essential their positions are in the network by considering the effect of their respective absences. Defining a ranking of a network which takes the individual position of each entity into account has the purpose of assigning different roles to the entities, e.g. agents......, in the network. In this paper it is shown that the topology of a given network induces a ranking of the entities in the network. Further, it is demonstrated how to calculate this ranking and thus how to identify weak sub-networks in any given network....

  9. Predictive Factors and Treatment Outcomes of Tuberculous Pleural Effusion in Patients With Cancer and Pleural Effusion.

    Science.gov (United States)

    Lee, Jaehee; Lee, Yong Dae; Lim, Jae Kwang; Lee, Deok Heon; Yoo, Seung Soo; Lee, Shin Yup; Cha, Seung Ick; Park, Jae Yong; Kim, Chang Ho

    2017-08-01

    Patients with cancer are at an increased risk of tuberculosis. As pleural effusion has great clinical significance in patients with cancer, the differential diagnosis between tuberculous pleural effusion (TPE) and malignant pleural effusion (MPE) is important. However, the predictive factors and treatment outcomes of TPE in patients with cancer have rarely been studied. Confirmed TPE cases identified at cancer diagnosis and during anticancer management from 2008-2015 were retrospectively investigated. Patients in the study included coexisting TPE and cancer (n = 20), MPE (n = 40) and TPE without cancer (n = 40). Control groups were patients with MPE, and patients with TPE without cancer. Clinical, laboratory and pleural fluid characteristics were compared among groups. Treatment outcomes were compared between patients with TPE with and without cancer. In the final analysis, serum C-reactive protein (S-CRP) ≥3.0mg/dL and pleural fluid adenosine deaminase (ADA) ≥40U/L were independent predictors for identifying TPE in patients with cancer having pleural effusion. The combination of S-CRP with pleural fluid ADA using an "or" rule achieved a sensitivity of 100%, whereas both parameters combined in an "and" rule had a specificity of 98%. Treatment outcomes were not different between the TPE groups with and without cancer. S-CRP and pleural fluid ADA levels may be helpful for predicting TPE in patients with cancer with pleural effusion. The combination of these biomarkers provides better information for distinguishing between TPE and MPE in these patients. Treatment outcomes of TPE in patients with cancer are comparable to those in patients without cancer. Copyright © 2017 Southern Society for Clinical Investigation. Published by Elsevier Inc. All rights reserved.

  10. Sparse reduced-rank regression with covariance estimation

    KAUST Repository

    Chen, Lisha

    2014-12-08

    Improving the predicting performance of the multiple response regression compared with separate linear regressions is a challenging question. On the one hand, it is desirable to seek model parsimony when facing a large number of parameters. On the other hand, for certain applications it is necessary to take into account the general covariance structure for the errors of the regression model. We assume a reduced-rank regression model and work with the likelihood function with general error covariance to achieve both objectives. In addition we propose to select relevant variables for reduced-rank regression by using a sparsity-inducing penalty, and to estimate the error covariance matrix simultaneously by using a similar penalty on the precision matrix. We develop a numerical algorithm to solve the penalized regression problem. In a simulation study and real data analysis, the new method is compared with two recent methods for multivariate regression and exhibits competitive performance in prediction and variable selection.

  11. Sparse reduced-rank regression with covariance estimation

    KAUST Repository

    Chen, Lisha; Huang, Jianhua Z.

    2014-01-01

    Improving the predicting performance of the multiple response regression compared with separate linear regressions is a challenging question. On the one hand, it is desirable to seek model parsimony when facing a large number of parameters. On the other hand, for certain applications it is necessary to take into account the general covariance structure for the errors of the regression model. We assume a reduced-rank regression model and work with the likelihood function with general error covariance to achieve both objectives. In addition we propose to select relevant variables for reduced-rank regression by using a sparsity-inducing penalty, and to estimate the error covariance matrix simultaneously by using a similar penalty on the precision matrix. We develop a numerical algorithm to solve the penalized regression problem. In a simulation study and real data analysis, the new method is compared with two recent methods for multivariate regression and exhibits competitive performance in prediction and variable selection.

  12. Using behavioral economics to predict opioid use during prescription opioid dependence treatment.

    Science.gov (United States)

    Worley, Matthew J; Shoptaw, Steven J; Bickel, Warren K; Ling, Walter

    2015-03-01

    Research grounded in behavioral economics has previously linked addictive behavior to disrupted decision-making and reward-processing, but these principles have not been examined in prescription opioid addiction, which is currently a major public health problem. This study examined whether pre-treatment drug reinforcement value predicted opioid use during outpatient treatment of prescription opioid addiction. Secondary analyses examined participants with prescription opioid dependence who received 12 weeks of buprenorphine-naloxone and counseling in a multi-site clinical trial (N=353). Baseline measures assessed opioid source and indices of drug reinforcement value, including the total amount and proportion of income spent on drugs. Weekly urine drug screens measured opioid use. Obtaining opioids from doctors was associated with lower pre-treatment drug spending, while obtaining opioids from dealers/patients was associated with greater spending. Controlling for demographics, opioid use history, and opioid source frequency, patients who spent a greater total amount (OR=1.30, peconomic resources to drugs, reflects propensity for continued opioid use during treatment among individuals with prescription opioid addiction. Future studies should examine disrupted decision-making and reward-processing in prescription opioid users more directly and test whether reinforcer pathology can be remediated in this population. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  13. The predicted effectiveness of noble metal treatment at the Chinshan boiling water reactor

    International Nuclear Information System (INIS)

    Yeh Tsungkuang; Chu Fang; Chang Ching; Huang Chiashen

    2000-01-01

    The technique of noble metal treatment (NMT) available in a form of noble metal cooling (NMC) or noble metal chemical addition (NMCA), was introduced to enhance effectiveness of hydrogen water chemistry. Since it is technically difficult to gain access to an entire primary heat transport circuit (PHTC) of a BWR and monitor variation on electrochemical corrosion potential (ECP), a question whether the NMC technology is indeed effective for lowering the ECP of every location in a BWR is not still well understood at the moment. Then, computer modeling is so far the best tool to help investigate effectiveness of the NMT along PHCT of the BWR. Here was discussed on how the computer model was calibrated by using measured chemistry data obtained from No. 2 unit (BWR) in the Kuosheng Plant. The effect of noble metal treatment coupled with hydrogen water chemistry has been quantitatively molded, on a base of two different sets of ECD enhancement data. It was predicted that No. 1 unit in the Chinshan could be protected by noble metal treatment with lower [H 2 ] FW . In the case of competitive enhancing factors for the ECDs of oxygen reduction, hydrogen peroxide reduction, and hydrogen oxidation reactions, HWC had always to be present for noble metal treatment to be effective for protecting a reactor. Otherwise, according to a model calculation based upon the results from Kim's work, the ECP might instead be increased due to the enhanced reduction reaction rate of oxygen and hydrogen peroxide, especially in the near core regions. (G.K.)

  14. Accumulating Data to Optimally Predict Obesity Treatment (ADOPT) Core Measures: Behavioral Domain.

    Science.gov (United States)

    Lytle, Leslie A; Nicastro, Holly L; Roberts, Susan B; Evans, Mary; Jakicic, John M; Laposky, Aaron D; Loria, Catherine M

    2018-04-01

    The ability to identify and measure behaviors that are related to weight loss and the prevention of weight regain is crucial to understanding the variability in response to obesity treatment and the development of tailored treatments. The overarching goal of the Accumulating Data to Optimally Predict obesity Treatment (ADOPT) Core Measures Project is to provide obesity researchers with guidance on a set of constructs and measures that are related to weight control and that span and integrate obesity-related behavioral, biological, environmental, and psychosocial domains. This article describes how the behavioral domain subgroup identified the initial list of high-priority constructs and measures to be included, and it describes practical considerations for assessing the following four behavioral areas: eating, activity, sleep, and self-monitoring of weight. Challenges and considerations for advancing the science related to weight loss and maintenance behaviors are also discussed. Assessing a set of core behavioral measures in combination with those from other ADOPT domains is critical to improve our understanding of individual variability in response to adult obesity treatment. The selection of behavioral measures is based on the current science, although there continues to be much work needed in this field. © 2018 The Obesity Society.

  15. CADrx for GBM Brain Tumors: Predicting Treatment Response from Changes in Diffusion-Weighted MRI

    Directory of Open Access Journals (Sweden)

    Matthew S. Brown

    2009-11-01

    Full Text Available The goal of this study was to develop a computer-aided therapeutic response (CADrx system for early prediction of drug treatment response for glioblastoma multiforme (GBM brain tumors with diffusion weighted (DW MR images. In conventional Macdonald assessment, tumor response is assessed nine weeks or more post-treatment. However, we will investigate the ability of DW-MRI to assess response earlier, at five weeks post treatment. The apparent diffusion coefficient (ADC map, calculated from DW images, has been shown to reveal changes in the tumor’s microenvironment preceding morphologic tumor changes. ADC values in treated brain tumors could theoretically both increase due to the cell kill (and thus reduced cell density and decrease due to inhibition of edema. In this study, we investigated the effectiveness of features that quantify changes from pre- and post-treatment tumor ADC histograms to detect treatment response. There are three parts to this study: first, tumor regions were segmented on T1w contrast enhanced images by Otsu’s thresholding method, and mapped from T1w images onto ADC images by a 3D region of interest (ROI mapping tool using DICOM header information; second, ADC histograms of the tumor region were extracted from both pre- and five weeks post-treatment scans, and fitted by a two-component Gaussian mixture model (GMM. The GMM features as well as standard histogram-based features were extracted. Finally, supervised machine learning techniques were applied for classification of responders or non-responders. The approach was evaluated with a dataset of 85 patients with GBM under chemotherapy, in which 39 responded and 46 did not, based on tumor volume reduction. We compared adaBoost, random forest and support vector machine classification algorithms, using ten-fold cross validation, resulting in the best accuracy of 69.41% and the corresponding area under the curve (Az of 0.70.

  16. Prediction factors for failure to seek treatment following traumatic dental injuries to primary teeth

    Directory of Open Access Journals (Sweden)

    Ramon Targino Firmino

    2014-06-01

    Full Text Available The objective of this study was to evaluate prediction factors for failure to seek treatment following a traumatic dental injury (TDI to primary teeth among preschool children in the city of Campina Grande, Brazil. A cross-sectional study was carried out involving 277 children 3 to 5 years of age, with TDI, enrolled in public and private preschools. Parents filled out a form addressing demographic data and whether or not they had sought treatment. Clinical examinations were performed by three dentists who had undergone a calibration exercise (Kappa: 0.85 to 0.90 for the evaluation of TDI. Bivariate and multivariate Poisson regression models were constructed (α = 5%. Enamel fracture was the most prevalent type of TDI (48.7% and the upper central incisors were the most affected teeth (88.4%. The frequency of seeking dental treatment was low (9.7%. The following variables were associated with failure to seek treatment following TDI: a household income greater than one minimum wage (PR = 1.170; 95%CI 1.018-1.341, parents/caregivers’ perception of a child’s oral health as poor (PR = 1.100; 95%CI 1.026-1.176, and the non-perception of TDI by parents/caregivers (PR = 1.250; 95%CI 1.142-1.360. In the present study, the frequency of seeking treatment following TDI was low, and parents/caregivers with a higher income, a poor perception of their child’s oral health and a lack of awareness regarding the trauma were more likely to fail to seek treatment following TDI to primary teeth.

  17. Maintenance treatment with azathioprine in ulcerative colitis: outcome and predictive factors after drug withdrawal.

    Science.gov (United States)

    Cassinotti, Andrea; Actis, Giovanni C; Duca, Piergiorgio; Massari, Alessandro; Colombo, Elisabetta; Gai, Elisa; Annese, Vito; D'Albasio, Giuseppe; Manes, Gianpiero; Travis, Simon; Porro, Gabriele Bianchi; Ardizzone, Sandro

    2009-11-01

    Whether the duration of maintenance treatment with azathioprine (AZA) affects the outcome of ulcerative colitis (UC) is unclear. We investigated clinical outcomes and any predictive factors after withdrawal of AZA in UC. In this multicenter observational retrospective study, 127 Italian UC patients, who were in steroid-free remission at the time of withdrawal of AZA, were followed-up for a median of 55 months or until relapse. The frequency of clinical relapse or colectomy after AZA withdrawal was analyzed according to demographic, clinical, and endoscopic variables. After drug withdrawal, a third of the patients relapsed within 12 months, half within 2 years and two-thirds within 5 years. After multivariable analysis, predictors of relapse after drug withdrawal were lack of sustained remission during AZA maintenance (hazard ratio, HR 2.350, confidence interval, CI 95% 1.434-3.852; P=0.001), extensive colitis (HR 1.793, CI 95% 1.064-3.023, P=0.028 vs. left-sided colitis; HR 2.024, CI 95% 1.103-3.717, P=0.023 vs. distal colitis), and treatment duration, with short treatments (3-6 months) more disadvantaged than >48-month treatments (HR 2.783, CI 95% 1.267-6.114, P=0.008). Concomitant aminosalicylates were the only predictors of sustained remission during AZA therapy (P=0.009). The overall colectomy rate was 10%. Predictors of colectomy were drug-related toxicity as the cause of AZA withdrawal (P=0.041), no post-AZA drug therapy (P=0.031), and treatment duration (P<0.0005). Discontinuation of AZA while UC is in remission is associated with a high relapse rate. Disease extent, lack of sustained remission during AZA, and discontinuation due to toxicity could stratify relapse risk. Concomitant aminosalicylates were advantageous. Prospective randomized controlled trials are needed to confirm whether treatment duration is inversely associated with outcome.

  18. Global DNA methylation is altered by neoadjuvant chemoradiotherapy in rectal cancer and may predict response to treatment - A pilot study.

    LENUS (Irish Health Repository)

    Tsang, J S

    2014-07-28

    In rectal cancer, not all tumours display a response to neoadjuvant treatment. An accurate predictor of response does not exist to guide patient-specific treatment. DNA methylation is a distinctive molecular pathway in colorectal carcinogenesis. Whether DNA methylation is altered by neoadjuvant treatment and a potential response predictor is unknown. We aimed to determine whether DNA methylation is altered by neoadjuvant chemoradiotherapy (CRT) and to determine its role in predicting response to treatment.

  19. Bayesian additive decision trees of biomarker by treatment interactions for predictive biomarker detection and subgroup identification.

    Science.gov (United States)

    Zhao, Yang; Zheng, Wei; Zhuo, Daisy Y; Lu, Yuefeng; Ma, Xiwen; Liu, Hengchang; Zeng, Zhen; Laird, Glen

    2017-10-11

    Personalized medicine, or tailored therapy, has been an active and important topic in recent medical research. Many methods have been proposed in the literature for predictive biomarker detection and subgroup identification. In this article, we propose a novel decision tree-based approach applicable in randomized clinical trials. We model the prognostic effects of the biomarkers using additive regression trees and the biomarker-by-treatment effect using a single regression tree. Bayesian approach is utilized to periodically revise the split variables and the split rules of the decision trees, which provides a better overall fitting. Gibbs sampler is implemented in the MCMC procedure, which updates the prognostic trees and the interaction tree separately. We use the posterior distribution of the interaction tree to construct the predictive scores of the biomarkers and to identify the subgroup where the treatment is superior to the control. Numerical simulations show that our proposed method performs well under various settings comparing to existing methods. We also demonstrate an application of our method in a real clinical trial.

  20. Citric Acid Metabolism in Resistant Hypertension: Underlying Mechanisms and Metabolic Prediction of Treatment Response.

    Science.gov (United States)

    Martin-Lorenzo, Marta; Martinez, Paula J; Baldan-Martin, Montserrat; Ruiz-Hurtado, Gema; Prado, Jose Carlos; Segura, Julian; de la Cuesta, Fernando; Barderas, Maria G; Vivanco, Fernando; Ruilope, Luis Miguel; Alvarez-Llamas, Gloria

    2017-11-01

    Resistant hypertension (RH) affects 9% to 12% of hypertensive adults. Prolonged exposure to suboptimal blood pressure control results in end-organ damage and cardiovascular risk. Spironolactone is the most effective drug for treatment, but not all patients respond and side effects are not negligible. Little is known on the mechanisms responsible for RH. We aimed to identify metabolic alterations in urine. In addition, a potential capacity of metabolites to predict response to spironolactone was investigated. Urine was collected from 29 patients with RH and from a group of 13 subjects with pseudo-RH. For patients, samples were collected before and after spironolactone administration and were classified in responders (n=19) and nonresponders (n=10). Nuclear magnetic resonance was applied to identify altered metabolites and pathways. Metabolites were confirmed by liquid chromatography-mass spectrometry. Citric acid cycle was the pathway most significantly altered ( P citric acid cycle and deregulation of reactive oxygen species homeostasis control continue its activation after hypertension was developed. A metabolic panel showing alteration before spironolactone treatment and predicting future response of patients is shown. These molecular indicators will contribute optimizing the rate of control of RH patients with spironolactone. © 2017 American Heart Association, Inc.

  1. Stathmin protein level, a potential predictive marker for taxane treatment response in endometrial cancer.

    Directory of Open Access Journals (Sweden)

    Henrica M J Werner

    Full Text Available Stathmin is a prognostic marker in many cancers, including endometrial cancer. Preclinical studies, predominantly in breast cancer, have suggested that stathmin may additionally be a predictive marker for response to paclitaxel. We first evaluated the response to paclitaxel in endometrial cancer cell lines before and after stathmin knock-down. Subsequently we investigated the clinical response to paclitaxel containing chemotherapy in metastatic endometrial cancer in relation to stathmin protein level in tumors. Stathmin level was also determined in metastatic lesions, analyzing changes in biomarker status on disease progression. Knock-down of stathmin improved sensitivity to paclitaxel in endometrial carcinoma cell lines with both naturally higher and lower sensitivity to paclitaxel. In clinical samples, high stathmin level was demonstrated to be associated with poor response to paclitaxel containing chemotherapy and to reduced disease specific survival only in patients treated with such combination. Stathmin level increased significantly from primary to metastatic lesions. This study suggests, supported by both preclinical and clinical data, that stathmin could be a predictive biomarker for response to paclitaxel treatment in endometrial cancer. Re-assessment of stathmin level in metastatic lesions prior to treatment start may be relevant. Also, validation in a randomized clinical trial will be important.

  2. Predicting stone composition before treatment – can it really drive clinical decisions?

    Science.gov (United States)

    Bres–Niewada, Ewa; Radziszewski, Piotr

    2014-01-01

    Introduction Determination of stone composition is considered to be crucial for the choice of an optimal treatment algorithm. It is especially important for uric acid stones, which can be dissolved by oral chemolysis and for renal stones smaller than 2 cm, which can be treated with extracorporeal shockwave lithotripsy (ESWL). Material and methods This short review identifies the latest papers on radiological assessment of stone composition and presents a comprehensive evaluation of current scientific findings. Results Stone chemical composition is difficult to predict using standard CT imaging, however, attenuation index measured in Hounsfield units (HU) is related to ESWL outcome. Stone density >1000 HU can be considered predictive for ESWL failure. It seems that stone composition is meaningless in determining the outcome of ureterolithotripsy and percutaneous surgery. Alternative imaging techniques such as Dual–Energy CT or analysis of shape, density and homogeneity of stones on plain X–rays are used as promising methods of predicting stone composition and ESWL outcome. Conclusions New imaging techniques facilitate the identification of uric acid stones and ESWL–resistant stones. Therefore, they may help in selecting the best therapeutic option. PMID:25667761

  3. HIV-1 DNA predicts disease progression and post-treatment virological control

    Science.gov (United States)

    Williams, James P; Hurst, Jacob; Stöhr, Wolfgang; Robinson, Nicola; Brown, Helen; Fisher, Martin; Kinloch, Sabine; Cooper, David; Schechter, Mauro; Tambussi, Giuseppe; Fidler, Sarah; Carrington, Mary; Babiker, Abdel; Weber, Jonathan

    2014-01-01

    In HIV-1 infection, a population of latently infected cells facilitates viral persistence despite antiretroviral therapy (ART). With the aim of identifying individuals in whom ART might induce a period of viraemic control on stopping therapy, we hypothesised that quantification of the pool of latently infected cells in primary HIV-1 infection (PHI) would predict clinical progression and viral replication following ART. We measured HIV-1 DNA in a highly characterised randomised population of individuals with PHI. We explored associations between HIV-1 DNA and immunological and virological markers of clinical progression, including viral rebound in those interrupting therapy. In multivariable analyses, HIV-1 DNA was more predictive of disease progression than plasma viral load and, at treatment interruption, predicted time to plasma virus rebound. HIV-1 DNA may help identify individuals who could safely interrupt ART in future HIV-1 eradication trials. Clinical trial registration: ISRCTN76742797 and EudraCT2004-000446-20 DOI: http://dx.doi.org/10.7554/eLife.03821.001 PMID:25217531

  4. Quality of Acute Psychedelic Experience Predicts Therapeutic Efficacy of Psilocybin for Treatment-Resistant Depression.

    Science.gov (United States)

    Roseman, Leor; Nutt, David J; Carhart-Harris, Robin L

    2017-01-01

    Introduction: It is a basic principle of the "psychedelic" treatment model that the quality of the acute experience mediates long-term improvements in mental health. In the present paper we sought to test this using data from a clinical trial assessing psilocybin for treatment-resistant depression (TRD). In line with previous reports, we hypothesized that the occurrence and magnitude of Oceanic Boundlessness (OBN) (sharing features with mystical-type experience) and Dread of Ego Dissolution (DED) (similar to anxiety) would predict long-term positive outcomes, whereas sensory perceptual effects would have negligible predictive value. Materials and Methods: Twenty patients with treatment resistant depression underwent treatment with psilocybin (two separate sessions: 10 and 25 mg psilocybin). The Altered States of Consciousness (ASC) questionnaire was used to assess the quality of experiences in the 25 mg psilocybin session. From the ASC, the dimensions OBN and DED were used to measure the mystical-type and challenging experiences, respectively. The Self-Reported Quick Inventory of Depressive Symptoms (QIDS-SR) at 5 weeks served as the endpoint clinical outcome measure, as in later time points some of the subjects had gone on to receive new treatments, thus confounding inferences. In a repeated measure ANOVA, Time was the within-subject factor (independent variable), with QIDS-SR as the within-subject dependent variable in baseline, 1-day, 1-week, 5-weeks. OBN and DED were independent variables. OBN-by-Time and DED-by-Time interactions were the primary outcomes of interest. Results: For the interaction of OBN and DED with Time (QIDS-SR as dependent variable), the main effect and the effects at each time point compared to baseline were all significant ( p = 0.002 and p = 0.003, respectively, for main effects), confirming our main hypothesis. Furthermore, Pearson's correlation of OBN with QIDS-SR (5 weeks) was specific compared to perceptual dimensions of the ASC ( p

  5. Quality of Acute Psychedelic Experience Predicts Therapeutic Efficacy of Psilocybin for Treatment-Resistant Depression

    Directory of Open Access Journals (Sweden)

    Leor Roseman

    2018-01-01

    Full Text Available Introduction: It is a basic principle of the “psychedelic” treatment model that the quality of the acute experience mediates long-term improvements in mental health. In the present paper we sought to test this using data from a clinical trial assessing psilocybin for treatment-resistant depression (TRD. In line with previous reports, we hypothesized that the occurrence and magnitude of Oceanic Boundlessness (OBN (sharing features with mystical-type experience and Dread of Ego Dissolution (DED (similar to anxiety would predict long-term positive outcomes, whereas sensory perceptual effects would have negligible predictive value.Materials and Methods: Twenty patients with treatment resistant depression underwent treatment with psilocybin (two separate sessions: 10 and 25 mg psilocybin. The Altered States of Consciousness (ASC questionnaire was used to assess the quality of experiences in the 25 mg psilocybin session. From the ASC, the dimensions OBN and DED were used to measure the mystical-type and challenging experiences, respectively. The Self-Reported Quick Inventory of Depressive Symptoms (QIDS-SR at 5 weeks served as the endpoint clinical outcome measure, as in later time points some of the subjects had gone on to receive new treatments, thus confounding inferences. In a repeated measure ANOVA, Time was the within-subject factor (independent variable, with QIDS-SR as the within-subject dependent variable in baseline, 1-day, 1-week, 5-weeks. OBN and DED were independent variables. OBN-by-Time and DED-by-Time interactions were the primary outcomes of interest.Results: For the interaction of OBN and DED with Time (QIDS-SR as dependent variable, the main effect and the effects at each time point compared to baseline were all significant (p = 0.002 and p = 0.003, respectively, for main effects, confirming our main hypothesis. Furthermore, Pearson's correlation of OBN with QIDS-SR (5 weeks was specific compared to perceptual dimensions of the

  6. MRI evaluation of anterior knee pain: predicting response to nonoperative treatment

    International Nuclear Information System (INIS)

    Wittstein, Jocelyn R.; Garrett, William E.; O'Brien, Seth D.; Vinson, Emily N.

    2009-01-01

    Tibial tubercle lateral deviation and patellofemoral chondromalacia are associated with anterior knee pain (AKP). We hypothesized that increased tibial tubercle lateral deviation and patellofemoral chondromalacia on magnetic resonance imaging correlates with the presence of AKP and with failure of nonoperative management. In this retrospective comparative study, a blinded musculoskeletal radiologist measured tibial tubercle lateral deviation relative to the trochlear groove in 15 controls, 15 physical therapy responders with AKP, and 15 physical therapy nonresponders with AKP. Patellar and trochlear cartilage was assessed for signal abnormality, irregularity, and defects. The mean tibial tubercle lateral deviation in controls, physical therapy responders, and physical therapy nonresponders were 9.32 ± 0.68, 13.01 ± 0.82, and 16.07 ± 1.16 mm, respectively (data are mean ± standard deviation). The correlation coefficients for tubercle deviation, chondromalacia patellae, and trochlear chondromalacia were 0.51 (P < 0.01), 0.44 (P < 0.01), and 0.28 (P < 0.05), respectively. On analysis of variance, tubercle deviation and chondromalacia patellae contributed significantly to prediction of AKP and response to physical therapy. The presence of chondromalacia patellae and a tubercle deviation greater than 14.6 mm is 100% specific and 67% sensitive with a positive predictive value of 100% and negative predictive value of 75% for failure of nonoperative management. Subjects with AKP have more laterally positioned tibial tubercles and are more likely to have patellar chondromalacia. Patients with AKP, chondromalacia patellae, and a tubercle deviation greater than 14.6 mm are unlikely to respond to nonoperative treatment. Knowledge of tibial tubercle lateralization and presence of chondromalacia patellae may assist clinicians in determining patient prognosis and selecting treatment options. (orig.)

  7. MRI evaluation of anterior knee pain: predicting response to nonoperative treatment

    Energy Technology Data Exchange (ETDEWEB)

    Wittstein, Jocelyn R.; Garrett, William E. [Duke University Medical Center, Division of Orthopaedic Surgery, Durham, NC (United States); O' Brien, Seth D. [Brooke Army Medical Center, Department of Radiology, San Antonio, TX (United States); Vinson, Emily N. [Duke University Medical Center, Department of Radiology, Durham, NC (United States)

    2009-09-15

    Tibial tubercle lateral deviation and patellofemoral chondromalacia are associated with anterior knee pain (AKP). We hypothesized that increased tibial tubercle lateral deviation and patellofemoral chondromalacia on magnetic resonance imaging correlates with the presence of AKP and with failure of nonoperative management. In this retrospective comparative study, a blinded musculoskeletal radiologist measured tibial tubercle lateral deviation relative to the trochlear groove in 15 controls, 15 physical therapy responders with AKP, and 15 physical therapy nonresponders with AKP. Patellar and trochlear cartilage was assessed for signal abnormality, irregularity, and defects. The mean tibial tubercle lateral deviation in controls, physical therapy responders, and physical therapy nonresponders were 9.32 {+-} 0.68, 13.01 {+-} 0.82, and 16.07 {+-} 1.16 mm, respectively (data are mean {+-} standard deviation). The correlation coefficients for tubercle deviation, chondromalacia patellae, and trochlear chondromalacia were 0.51 (P < 0.01), 0.44 (P < 0.01), and 0.28 (P < 0.05), respectively. On analysis of variance, tubercle deviation and chondromalacia patellae contributed significantly to prediction of AKP and response to physical therapy. The presence of chondromalacia patellae and a tubercle deviation greater than 14.6 mm is 100% specific and 67% sensitive with a positive predictive value of 100% and negative predictive value of 75% for failure of nonoperative management. Subjects with AKP have more laterally positioned tibial tubercles and are more likely to have patellar chondromalacia. Patients with AKP, chondromalacia patellae, and a tubercle deviation greater than 14.6 mm are unlikely to respond to nonoperative treatment. Knowledge of tibial tubercle lateralization and presence of chondromalacia patellae may assist clinicians in determining patient prognosis and selecting treatment options. (orig.)

  8. Age, gender and tumour size predict work capacity after surgical treatment of vestibular schwannomas.

    Science.gov (United States)

    Al-Shudifat, Abdul Rahman; Kahlon, Babar; Höglund, Peter; Soliman, Ahmed Y; Lindskog, Kristoffer; Siesjo, Peter

    2014-01-01

    The aim of the present study was to identify predictive factors for outcome after surgery of vestibular schwannomas. This is a retrospective study with partially collected prospective data of patients who were surgically treated for vestibular schwannomas at a single institution from 1979 to 2000. Patients with recurrent tumours, NF2 and those incapable of answering questionnaires were excluded from the study. The short form 36 (SF36) questionnaire and a specific questionnaire regarding neurological status, work status and independent life (IL) status were sent to all eligible patients. The questionnaires were sent to 430 eligible patients (out of 537) and 395 (93%) responded. Scores for work capacity (WC) and IL were compared with SF36 scores as outcome estimates. Patients were divided into two groups (group age, gender and tumour diameter were independent predictive factors for postoperative WC in multivariate analysis. A high-risk group was identified in women with age >50 years and tumour diameter >25 mm. In patients ≥64, gender and tumour diameter were significant predictive factors for IL in univariate analysis. Perioperative and postoperative objective factors as length of surgery, blood loss and complications did not predict outcome in the multivariable analysis for any age group. Patients' assessment of change in balance function was the only neurological factor that showed significance both in univariate and multivariable analysis in both age cohorts. While SF36 scores were lower in surgically treated patients in relation to normograms for the general population, they did not correlate significantly to WC and IL. The SF36 questionnaire did not correlate to outcome measures as WC and IL in patients undergoing surgery for vestibular schwannomas. Women and patients above 50 years with larger tumours have a high risk for reduced WC after surgical treatment. These results question the validity of quality of life scores in assessment of outcome after surgery

  9. Robust and predictive fuzzy key performance indicators for condition-based treatment of squats in railway infrastructures

    NARCIS (Netherlands)

    Jamshidi, A.; Nunez Vicencio, Alfredo; Dollevoet, R.P.B.J.; Li, Z.

    2017-01-01

    This paper presents a condition-based treatment methodology for a type of rail surface defect called squat. The proposed methodology is based on a set of robust and predictive fuzzy key performance indicators. A fuzzy Takagi-Sugeno interval model is used to predict squat evolution for different

  10. Ranking scientific publications: the effect of nonlinearity

    Science.gov (United States)

    Yao, Liyang; Wei, Tian; Zeng, An; Fan, Ying; di, Zengru

    2014-10-01

    Ranking the significance of scientific publications is a long-standing challenge. The network-based analysis is a natural and common approach for evaluating the scientific credit of papers. Although the number of citations has been widely used as a metric to rank papers, recently some iterative processes such as the well-known PageRank algorithm have been applied to the citation networks to address this problem. In this paper, we introduce nonlinearity to the PageRank algorithm when aggregating resources from different nodes to further enhance the effect of important papers. The validation of our method is performed on the data of American Physical Society (APS) journals. The results indicate that the nonlinearity improves the performance of the PageRank algorithm in terms of ranking effectiveness, as well as robustness against malicious manipulations. Although the nonlinearity analysis is based on the PageRank algorithm, it can be easily extended to other iterative ranking algorithms and similar improvements are expected.

  11. Ranking scientific publications: the effect of nonlinearity.

    Science.gov (United States)

    Yao, Liyang; Wei, Tian; Zeng, An; Fan, Ying; Di, Zengru

    2014-10-17

    Ranking the significance of scientific publications is a long-standing challenge. The network-based analysis is a natural and common approach for evaluating the scientific credit of papers. Although the number of citations has been widely used as a metric to rank papers, recently some iterative processes such as the well-known PageRank algorithm have been applied to the citation networks to address this problem. In this paper, we introduce nonlinearity to the PageRank algorithm when aggregating resources from different nodes to further enhance the effect of important papers. The validation of our method is performed on the data of American Physical Society (APS) journals. The results indicate that the nonlinearity improves the performance of the PageRank algorithm in terms of ranking effectiveness, as well as robustness against malicious manipulations. Although the nonlinearity analysis is based on the PageRank algorithm, it can be easily extended to other iterative ranking algorithms and similar improvements are expected.

  12. Neural Ranking Models with Weak Supervision

    NARCIS (Netherlands)

    Dehghani, M.; Zamani, H.; Severyn, A.; Kamps, J.; Croft, W.B.

    2017-01-01

    Despite the impressive improvements achieved by unsupervised deep neural networks in computer vision and NLP tasks, such improvements have not yet been observed in ranking for information retrieval. The reason may be the complexity of the ranking problem, as it is not obvious how to learn from

  13. A Rational Method for Ranking Engineering Programs.

    Science.gov (United States)

    Glower, Donald D.

    1980-01-01

    Compares two methods for ranking academic programs, the opinion poll v examination of career successes of the program's alumni. For the latter, "Who's Who in Engineering" and levels of research funding provided data. Tables display resulting data and compare rankings by the two methods for chemical engineering and civil engineering. (CS)

  14. Lerot: An Online Learning to Rank Framework

    NARCIS (Netherlands)

    Schuth, A.; Hofmann, K.; Whiteson, S.; de Rijke, M.

    2013-01-01

    Online learning to rank methods for IR allow retrieval systems to optimize their own performance directly from interactions with users via click feedback. In the software package Lerot, presented in this paper, we have bundled all ingredients needed for experimenting with online learning to rank for

  15. Adaptive distributional extensions to DFR ranking

    DEFF Research Database (Denmark)

    Petersen, Casper; Simonsen, Jakob Grue; Järvelin, Kalervo

    2016-01-01

    -fitting distribution. We call this model Adaptive Distributional Ranking (ADR) because it adapts the ranking to the statistics of the specific dataset being processed each time. Experiments on TREC data show ADR to outperform DFR models (and their extensions) and be comparable in performance to a query likelihood...

  16. Contests with rank-order spillovers

    NARCIS (Netherlands)

    M.R. Baye (Michael); D. Kovenock (Dan); C.G. de Vries (Casper)

    2012-01-01

    textabstractThis paper presents a unified framework for characterizing symmetric equilibrium in simultaneous move, two-player, rank-order contests with complete information, in which each player's strategy generates direct or indirect affine "spillover" effects that depend on the rank-order of her

  17. Classification of rank 2 cluster varieties

    DEFF Research Database (Denmark)

    Mandel, Travis

    We classify rank 2 cluster varieties (those whose corresponding skew-form has rank 2) according to the deformation type of a generic fiber U of their X-spaces, as defined by Fock and Goncharov. Our approach is based on the work of Gross, Hacking, and Keel for cluster varieties and log Calabi...

  18. Using centrality to rank web snippets

    NARCIS (Netherlands)

    Jijkoun, V.; de Rijke, M.; Peters, C.; Jijkoun, V.; Mandl, T.; Müller, H.; Oard, D.W.; Peñas, A.; Petras, V.; Santos, D.

    2008-01-01

    We describe our participation in the WebCLEF 2007 task, targeted at snippet retrieval from web data. Our system ranks snippets based on a simple similarity-based centrality, inspired by the web page ranking algorithms. We experimented with retrieval units (sentences and paragraphs) and with the

  19. Mining Feedback in Ranking and Recommendation Systems

    Science.gov (United States)

    Zhuang, Ziming

    2009-01-01

    The amount of online information has grown exponentially over the past few decades, and users become more and more dependent on ranking and recommendation systems to address their information seeking needs. The advance in information technologies has enabled users to provide feedback on the utilities of the underlying ranking and recommendation…

  20. Entity Ranking using Wikipedia as a Pivot

    NARCIS (Netherlands)

    R. Kaptein; P. Serdyukov; A.P. de Vries (Arjen); J. Kamps

    2010-01-01

    htmlabstractIn this paper we investigate the task of Entity Ranking on the Web. Searchers looking for entities are arguably better served by presenting a ranked list of entities directly, rather than a list of web pages with relevant but also potentially redundant information about

  1. Entity ranking using Wikipedia as a pivot

    NARCIS (Netherlands)

    Kaptein, R.; Serdyukov, P.; de Vries, A.; Kamps, J.; Huang, X.J.; Jones, G.; Koudas, N.; Wu, X.; Collins-Thompson, K.

    2010-01-01

    In this paper we investigate the task of Entity Ranking on the Web. Searchers looking for entities are arguably better served by presenting a ranked list of entities directly, rather than a list of web pages with relevant but also potentially redundant information about these entities. Since

  2. Rank 2 fusion rings are complete intersections

    DEFF Research Database (Denmark)

    Andersen, Troels Bak

    We give a non-constructive proof that fusion rings attached to a simple complex Lie algebra of rank 2 are complete intersections.......We give a non-constructive proof that fusion rings attached to a simple complex Lie algebra of rank 2 are complete intersections....

  3. A Ranking Method for Evaluating Constructed Responses

    Science.gov (United States)

    Attali, Yigal

    2014-01-01

    This article presents a comparative judgment approach for holistically scored constructed response tasks. In this approach, the grader rank orders (rather than rate) the quality of a small set of responses. A prior automated evaluation of responses guides both set formation and scaling of rankings. Sets are formed to have similar prior scores and…

  4. Ranking Music Data by Relevance and Importance

    DEFF Research Database (Denmark)

    Ruxanda, Maria Magdalena; Nanopoulos, Alexandros; Jensen, Christian Søndergaard

    2008-01-01

    Due to the rapidly increasing availability of audio files on the Web, it is relevant to augment search engines with advanced audio search functionality. In this context, the ranking of the retrieved music is an important issue. This paper proposes a music ranking method capable of flexibly fusing...

  5. The Rankings Game: Who's Playing Whom?

    Science.gov (United States)

    Burness, John F.

    2008-01-01

    This summer, Forbes magazine published its new rankings of "America's Best Colleges," implying that it had developed a methodology that would give the public the information that it needed to choose a college wisely. "U.S. News & World Report," which in 1983 published the first annual ranking, just announced its latest ratings last week--including…

  6. Dynamic collective entity representations for entity ranking

    NARCIS (Netherlands)

    Graus, D.; Tsagkias, M.; Weerkamp, W.; Meij, E.; de Rijke, M.

    2016-01-01

    Entity ranking, i.e., successfully positioning a relevant entity at the top of the ranking for a given query, is inherently difficult due to the potential mismatch between the entity's description in a knowledge base, and the way people refer to the entity when searching for it. To counter this

  7. Brain structural properties predict psychologically mediated hypoalgesia in an 8-week sham acupuncture treatment for migraine.

    Science.gov (United States)

    Liu, Jixin; Mu, Junya; Liu, Qianqian; Dun, Wanghuan; Zhang, Ming; Tian, Jie

    2017-09-01

    Neuroimaging studies described brain structural changes that comprise the mechanisms underlying individual differences in migraine development and maintenance. However, whether such interindividual variability in migraine was observed in a pretreatment scan is a predisposition for subsequent hypoalgesia to placebo treatment that remains largely unclear. Using T1-weighted imaging, we investigated this issue in 50 healthy controls (HC) and 196 patients with migraine without aura (MO). An 8-week double-blinded, randomized, placebo-controlled acupuncture was used, and we only focused on the data from the sham acupuncture group. Eighty patients participated in an 8-weeks sham acupuncture treatment, and were subdivided (50% change in migraine days from baseline) into recovering (MOr) and persisting (MOp) patients. Optimized voxel-based morphometry (VBM) and functional connectivity analysis were performed to evaluate brain structural and functional changes. At baseline, MOp and MOr had similar migraine activity, anxiety and depression; reduced migraine days were accompanied by decreased anxiety in MOr. In our findings, the MOr group showed a smaller volume in the left medial prefrontal cortex (mPFC), and decreased mPFC-related functional connectivity was found in the default mode network. Additionally, the reduction in migraine days after placebo treatment was significantly associated with the baseline gray matter volume of the mPFC which could also predict post-treatment groups with high accuracy. It indicated that individual differences for the brain structure in the pain modulatory system at baseline served as a substrate on how an individual facilitated or diminished hypoalgesia responses to placebo treatment in migraineurs. Hum Brain Mapp 38:4386-4397, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  8. Comparing classical and quantum PageRanks

    Science.gov (United States)

    Loke, T.; Tang, J. W.; Rodriguez, J.; Small, M.; Wang, J. B.

    2017-01-01

    Following recent developments in quantum PageRanking, we present a comparative analysis of discrete-time and continuous-time quantum-walk-based PageRank algorithms. Relative to classical PageRank and to different extents, the quantum measures better highlight secondary hubs and resolve ranking degeneracy among peripheral nodes for all networks we studied in this paper. For the discrete-time case, we investigated the periodic nature of the walker's probability distribution for a wide range of networks and found that the dominant period does not grow with the size of these networks. Based on this observation, we introduce a new quantum measure using the maximum probabilities of the associated walker during the first couple of periods. This is particularly important, since it leads to a quantum PageRanking scheme that is scalable with respect to network size.

  9. Universal emergence of PageRank

    Energy Technology Data Exchange (ETDEWEB)

    Frahm, K M; Georgeot, B; Shepelyansky, D L, E-mail: frahm@irsamc.ups-tlse.fr, E-mail: georgeot@irsamc.ups-tlse.fr, E-mail: dima@irsamc.ups-tlse.fr [Laboratoire de Physique Theorique du CNRS, IRSAMC, Universite de Toulouse, UPS, 31062 Toulouse (France)

    2011-11-18

    The PageRank algorithm enables us to rank the nodes of a network through a specific eigenvector of the Google matrix, using a damping parameter {alpha} Element-Of ]0, 1[. Using extensive numerical simulations of large web networks, with a special accent on British University networks, we determine numerically and analytically the universal features of the PageRank vector at its emergence when {alpha} {yields} 1. The whole network can be divided into a core part and a group of invariant subspaces. For {alpha} {yields} 1, PageRank converges to a universal power-law distribution on the invariant subspaces whose size distribution also follows a universal power law. The convergence of PageRank at {alpha} {yields} 1 is controlled by eigenvalues of the core part of the Google matrix, which are extremely close to unity, leading to large relaxation times as, for example, in spin glasses. (paper)

  10. Universal emergence of PageRank

    International Nuclear Information System (INIS)

    Frahm, K M; Georgeot, B; Shepelyansky, D L

    2011-01-01

    The PageRank algorithm enables us to rank the nodes of a network through a specific eigenvector of the Google matrix, using a damping parameter α ∈ ]0, 1[. Using extensive numerical simulations of large web networks, with a special accent on British University networks, we determine numerically and analytically the universal features of the PageRank vector at its emergence when α → 1. The whole network can be divided into a core part and a group of invariant subspaces. For α → 1, PageRank converges to a universal power-law distribution on the invariant subspaces whose size distribution also follows a universal power law. The convergence of PageRank at α → 1 is controlled by eigenvalues of the core part of the Google matrix, which are extremely close to unity, leading to large relaxation times as, for example, in spin glasses. (paper)

  11. Predicting client attendance at further treatment following drug and alcohol detoxification: Theory of Planned Behaviour and Implementation Intentions.

    Science.gov (United States)

    Kelly, Peter J; Leung, Joanne; Deane, Frank P; Lyons, Geoffrey C B

    2016-11-01

    Despite clinical recommendations that further treatment is critical for successful recovery following drug and alcohol detoxification, a large proportion of clients fail to attend treatment after detoxification. In this study, individual factors and constructs based on motivational and volitional models of health behaviour were examined as predictors of post-detoxification treatment attendance. The sample consisted of 220 substance-dependent individuals participating in short-term detoxification programs provided by The Australian Salvation Army. The Theory of Planned Behaviour and Implementation Intentions were used to predict attendance at subsequent treatment. Follow-up data were collected for 177 participants (81%), with 104 (80%) of those participants reporting that they had either attended further formal treatment (e.g. residential rehabilitation programs, outpatient counselling) or mutual support groups in the 2 weeks after leaving the detoxification program. Logistic regression examined the predictors of further treatment attendance. The full model accounted for 21% of the variance in treatment attendance, with attitude and Implementation Intentions contributing significantly to the prediction. Findings from the present study would suggest that assisting clients to develop a specific treatment plan, as well as helping clients to build positive perceptions about subsequent treatment, will promote greater attendance at further treatment following detoxification. [Kelly PJ, Leung J, Deane FP, Lyons GCB. Predicting client attendance at further treatment following drug and alcohol detoxification: Theory of Planned Behaviour and Implementation Intentions. Drug Alcohol Rev 2016;35:678-685]. © 2015 Australasian Professional Society on Alcohol and other Drugs.

  12. Brain natriuretic peptide precursor (NT-pro-BNP) levels predict for clinical benefit to sunitinib treatment in patients with metastatic renal cell carcinoma

    International Nuclear Information System (INIS)

    Papazisis, Konstantinos T; Kortsaris, Alexandros H; Kontovinis, Lukas F; Papandreou, Christos N; Kouvatseas, George; Lafaras, Christos; Antonakis, Evangelos; Christopoulou, Maria; Andreadis, Charalambos; Mouratidou, Despoina

    2010-01-01

    Sunitinib is an oral, multitargeted tyrosine kinase inhibitor that has been approved for the treatment of metastatic renal cell carcinoma. Although the majority of sunitinib-treated patients receive a clinical benefit, almost a third of the patients will not respond. Currently there is no available marker that can predict for response in these patients. We estimated the plasma levels of NT-pro-BNP (the N-terminal precursor of brain natriuretic peptide) in 36 patients that were treated with sunitinib for metastatic clear-cell renal carcinoma. From the 36 patients, 9 had progressive disease and 27 obtained a clinical benefit (objective response or disease stabilization). Increases in plasma NT-pro-BNP were strongly correlated to clinical outcome. Patients with disease progression increased plasma BNP at statistically significant higher levels than patients that obtained a clinical benefit, and this was evident from the first 15 days of treatment (a three-fold increase in patients with progressive disease compared to stable NT-pro-BNP levels in patients with clinical benefit, p < 0.0001). Median progression-free survival was 12.0 months in patients with less than 1.5 fold increases (n = 22) and 3.9 months in patients with more than 1.5 fold increases in plasma NT-pro-BNP (n = 13) (log-rank test, p = 0.001). This is the first time that a potential 'surrogate marker' has been reported with such a clear correlation to clinical benefit at an early time of treatment. Due to the relative small number of accessed patients, this observation needs to be further addressed on larger cohorts. More analyses, including multivariate analyses are needed before such an observation can be used in clinical practice

  13. Nerve Ultrasound Predicts Treatment Response in Chronic Inflammatory Demyelinating Polyradiculoneuropathy-a Prospective Follow-Up.

    Science.gov (United States)

    Härtig, Florian; Ross, Marlene; Dammeier, Nele Maria; Fedtke, Nadin; Heiling, Bianka; Axer, Hubertus; Décard, Bernhard F; Auffenberg, Eva; Koch, Marilin; Rattay, Tim W; Krumbholz, Markus; Bornemann, Antje; Lerche, Holger; Winter, Natalie; Grimm, Alexander

    2018-04-01

    As reliable biomarkers of disease activity are lacking, monitoring of therapeutic response in chronic inflammatory demyelinating polyradiculoneuropathy (CIDP) remains a challenge. We sought to determine whether nerve ultrasound and electrophysiology scoring could close this gap. In CIDP patients (fulfilling EFNS/PNS criteria), we performed high-resolution nerve ultrasound to determine ultrasound pattern sum scores (UPSS) and predominant echotexture nerve conduction study scores (NCSS) as well as Medical Research Council sum scores (MRCSS) and inflammatory neuropathy cause and treatment disability scores (INCAT) at baseline and after 12 months of standard treatment. We retrospectively correlated ultrasound morphology with nerve histology when available. 72/80 CIDP patients featured multifocal nerve enlargement, and 35/80 were therapy-naïve. At baseline, clinical scores correlated with NCSS (r 2  = 0.397 and r 2  = 0.443, p  50% of measured segments, possibly reflecting axonal degeneration; and 3) almost no enlargement, reflecting "burned-out" or "cured" disease without active inflammation. Clinical improvement after 12 months was best in patients with pattern 1 (up to 75% vs up to 43% in pattern 2/3, Fisher's exact test p < 0.05). Nerve ultrasound has additional value not only for diagnosis, but also for classification of disease state and may predict treatment response.

  14. Hypothyroidism as a predictive clinical marker of better treatment response to sunitinib therapy.

    Science.gov (United States)

    Kust, Davor; Prpić, Marin; Murgić, Jure; Jazvić, Marijana; Jakšić, Blanka; Krilić, Dražena; Bolanča, Ante; Kusić, Zvonko

    2014-06-01

    Tyrosine kinase inhibitors are standard treatment in patients with metastatic renal cell carcinoma (mRCC). Several studies have indicated that side-effects including hypothyroidism may serve as potential predictive biomarkers of treatment efficacy. All patients with clear cell mRCC treated with sunitinib in the first-line setting in our Center between November 2008 and October 2013 were included. Thyroid function was assessed after every 2 cycles. Prognostic factors were tested using Cox proportional hazards model for univariate analysis. During treatment, 29.3% developed hypothyroidism, with a median of peak TSH values of 34.4 mIU/L. Patients who had both TSH >4 mIU/L and were receiving substitution therapy with levothyroxine had prolonged PFS compared to all other patients (25.3 months vs. 9.0 months; p=0.042). The rate of hypothyroidism as a side-effect of sunitinib in patients with mRCC is significant. Patients with symptomatic hypothyroidism experienced significantly longer PFS, but without difference in OS. Copyright© 2014 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved.

  15. Do ictal EEG characteristics predict treatment outcomes in schizophrenic patients undergoing electroconvulsive therapy?

    Science.gov (United States)

    Simsek, Gulnihal Gokce; Zincir, Selma; Gulec, Huseyin; Eksioglu, Sevgin; Semiz, Umit Basar; Kurtulmus, Yasemin Sipka

    2015-08-01

    The aim of this study is to investigate the relationship between features of electroencephalography (EEG), including seizure time, energy threshold level and post-ictal suppression time, and clinical variables, including treatment outcomes and side-effects, among schizophrenia inpatients undergoing electroconvulsive therapy (ECT). This is a naturalistic follow-up study on schizophrenia patients, diagnosed using DSM-IV-TR criteria, treated by a psychosis inpatient service. All participants completed the Brief Psychiatric Rating Scale (BPRS), the Global Assessment of Functioning (GAF) scale, the Frontal Assessment Battery (FAB) and a Data Collection Form. Assessments were made before treatment, during ECT and after treatment. Statistically significant improvements in both clinical and cognitive outcome were noted after ECT in all patients. Predictors of improvement were sought by evaluating electrophysiological variables measured at three time points (after the third, fifth and seventh ECT sessions). Logistic regression analysis showed that clinical outcome/improvement did not differ by seizure duration, threshold energy level or post-ictal suppression time. We found that ictal EEG parameters measured at several ECT sessions did not predict clinical recovery/outcomes. This may be because our centre defensively engages in "very specific patient selection" when ECT is contemplated. ECT does not cause short-term cognitive functional impairment and indeed improves cognition, because symptoms of the schizophrenic episode are alleviated.

  16. The Functional Diffusion Map: An Imaging Biomarker for the Early Prediction of Cancer Treatment Outcome

    Directory of Open Access Journals (Sweden)

    Bradford A. Moffat

    2006-04-01

    Full Text Available Functional diffusion map (fDM has been recently reported as an early and quantitative biomarker of clinical brain tumor treatment outcome. This MRI approach spatially maps and quantifies treatment-induced changes in tumor water diffusion values resulting from alterations in cell density/cell membrane function and microenvironment. This current study was designed to evaluate the capability of fDM for preclinical evaluation of dose escalation studies and to determine if these changes were correlated with outcome measures (cell kill and overall survival. Serial T2-weighted and diffusion MRI were carried out on rodents with orthotopically implanted 9L brain tumors receiving three doses of 1,3-bis(2-chloroethyl-1-nitrosourea (6.65, 13.3, and 26.6 mg/kg, i.p.. All images were coregistered to baseline T2-weighted images for fDM analysis. Analysis of tumor fDM data on day 4 posttreatment detected dosedependent changes in tumor diffusion values, which were also found to be spatially dependent. Histologic analysis of treated tumors confirmed spatial changes in cellularity as observed by fDM. Early changes in tumor diffusion values were found to be highly correlative with drug dose and independent biologic outcome measures (cell kill and survival. Therefore, the fDM imaging biomarker for early prediction of treatment efficacy can be used in the drug development process.

  17. PageRank and rank-reversal dependence on the damping factor

    Science.gov (United States)

    Son, S.-W.; Christensen, C.; Grassberger, P.; Paczuski, M.

    2012-12-01

    PageRank (PR) is an algorithm originally developed by Google to evaluate the importance of web pages. Considering how deeply rooted Google's PR algorithm is to gathering relevant information or to the success of modern businesses, the question of rank stability and choice of the damping factor (a parameter in the algorithm) is clearly important. We investigate PR as a function of the damping factor d on a network obtained from a domain of the World Wide Web, finding that rank reversal happens frequently over a broad range of PR (and of d). We use three different correlation measures, Pearson, Spearman, and Kendall, to study rank reversal as d changes, and we show that the correlation of PR vectors drops rapidly as d changes from its frequently cited value, d0=0.85. Rank reversal is also observed by measuring the Spearman and Kendall rank correlation, which evaluate relative ranks rather than absolute PR. Rank reversal happens not only in directed networks containing rank sinks but also in a single strongly connected component, which by definition does not contain any sinks. We relate rank reversals to rank pockets and bottlenecks in the directed network structure. For the network studied, the relative rank is more stable by our measures around d=0.65 than at d=d0.

  18. PageRank and rank-reversal dependence on the damping factor.

    Science.gov (United States)

    Son, S-W; Christensen, C; Grassberger, P; Paczuski, M

    2012-12-01

    PageRank (PR) is an algorithm originally developed by Google to evaluate the importance of web pages. Considering how deeply rooted Google's PR algorithm is to gathering relevant information or to the success of modern businesses, the question of rank stability and choice of the damping factor (a parameter in the algorithm) is clearly important. We investigate PR as a function of the damping factor d on a network obtained from a domain of the World Wide Web, finding that rank reversal happens frequently over a broad range of PR (and of d). We use three different correlation measures, Pearson, Spearman, and Kendall, to study rank reversal as d changes, and we show that the correlation of PR vectors drops rapidly as d changes from its frequently cited value, d_{0}=0.85. Rank reversal is also observed by measuring the Spearman and Kendall rank correlation, which evaluate relative ranks rather than absolute PR. Rank reversal happens not only in directed networks containing rank sinks but also in a single strongly connected component, which by definition does not contain any sinks. We relate rank reversals to rank pockets and bottlenecks in the directed network structure. For the network studied, the relative rank is more stable by our measures around d=0.65 than at d=d_{0}.

  19. A tilting approach to ranking influence

    KAUST Repository

    Genton, Marc G.

    2014-12-01

    We suggest a new approach, which is applicable for general statistics computed from random samples of univariate or vector-valued or functional data, to assessing the influence that individual data have on the value of a statistic, and to ranking the data in terms of that influence. Our method is based on, first, perturbing the value of the statistic by ‘tilting’, or reweighting, each data value, where the total amount of tilt is constrained to be the least possible, subject to achieving a given small perturbation of the statistic, and, then, taking the ranking of the influence of data values to be that which corresponds to ranking the changes in data weights. It is shown, both theoretically and numerically, that this ranking does not depend on the size of the perturbation, provided that the perturbation is sufficiently small. That simple result leads directly to an elegant geometric interpretation of the ranks; they are the ranks of the lengths of projections of the weights onto a ‘line’ determined by the first empirical principal component function in a generalized measure of covariance. To illustrate the generality of the method we introduce and explore it in the case of functional data, where (for example) it leads to generalized boxplots. The method has the advantage of providing an interpretable ranking that depends on the statistic under consideration. For example, the ranking of data, in terms of their influence on the value of a statistic, is different for a measure of location and for a measure of scale. This is as it should be; a ranking of data in terms of their influence should depend on the manner in which the data are used. Additionally, the ranking recognizes, rather than ignores, sign, and in particular can identify left- and right-hand ‘tails’ of the distribution of a random function or vector.

  20. Combined-modality treatment and organ preservation in bladder cancer. Do molecular markers predict outcome?

    International Nuclear Information System (INIS)

    Weiss, C.; Roedel, F.; Wolf, I.; Sauer, R.; Roedel, C.; Papadopoulos, T.; Engehausen, D.G.; Schrott, K.M.

    2005-01-01

    Purpose: in invasive bladder cancer, several groups have reported the value of organ preservation by a combined-treatment approach, including transurethral resection (TUR-BT) and radiochemotherapy (RCT). As more experience is acquired with this organ-sparing treatment, patient selection needs to be optimized. Clinical factors are limited in their potential to identify patients most likely to respond to RCT, thus, additional molecular markers for predicting treatment response of individual lesions are sorely needed. Patients and methods: the apoptotic index (AI) and Ki-67 index were evaluated by immunohistochemistry on pretreatment biopsies of 134 patients treated for bladder cancer by TUR-BT and RCT. Expression of each marker as well as clinicopathologic factors were then correlated with initial response, local control and cancer-specific survival with preserved bladder in univariate and multivariate analysis. Results: the median AI for all patients was 1.5% (range 0.2-7.4%). The percentage of Ki-67-positive cells in the tumors ranged from 0.2% to 85% with a median of 14.2%. A significant correlation was found for AI and tumor differentiation (G1/2: AI = 1.3% vs. G3/4: AI = 1.6%; p = 0.01). A complete response at restaging TUR-BT was achieved in 76% of patients. Factors predictive of complete response included T-category (p < 0.0001), resection status (p = 0.02), lymphovascular invasion (p = 0.01), and Ki-67 index (p = 0.02). For local control, AI (p = 0.04) and Ki-67 index (p = 0.05) as well as T-category (p = 0.005), R-status (p = 0.05), and lymphatic vessel invasion (p = 0.05) reached statistical significance. Out of the molecular markers only high Ki-67 levels were associated to cause-specific survival with preserved bladder. On multivariate analysis, T-category was the strongest independent factor for initial response, local control and cancer-specific survival with preserved bladder. Conclusion: The indices of pretreatment apoptosis and Ki-67 predict a

  1. HbA1c level cannot predict the treatment outcome of smear-positive non-multi-drug-resistant HIV-negative pulmonary tuberculosis inpatients

    Science.gov (United States)

    Tashiro, Ken; Horita, Nobuyuki; Nagai, Kenjiro; Ikeda, Misako; Shinkai, Masaharu; Yamamoto, Masaki; Sato, Takashi; Hara, Yu; Nagakura, Hideyuki; Shibata, Yuji; Watanabe, Hiroki; Nakashima, Kentaro; Ushio, Ryota; Nagashima, Akimichi; Narita, Atsuya; Kobayashi, Nobuaki; Kudo, Makoto; Kaneko, Takeshi

    2017-01-01

    We conducted a single-center retrospective cohort study to evaluate whether the HbA1c level on admission could predict the in-hospital treatment outcome of smear-positive non-multi-drug-resistant HIV-negative culture-proven pulmonary tuberculosis inpatients. Our standard regimens under the direct observation were HRZE or HRE for the first two months followed by combination therapy with isoniazid and rifampicin. Our cohort consisted of consecutive 239 patients consisted of 147 men and 92 women with a median age of 73 years. The HbA1c level of patients whose HbA1c was above 7.0% on admission showed clear declining trends after admission. HbA1c on admission had no Spearman’s rank correlation with time to discharge alive (r = 0.17) and time to becoming non-infective (r = 0.17). By Kaplan-Meier curves and a log-rank trend test, HbA1c quartile subgroups showed no association with times to discharge alive (p = 0.431), becoming non-infective (p = 0.113), and in-hospital death (p = 0.427). Based on multi-variate Cox analysis, HbA1c on admission had no significant impact on time to discharge alive (hazard ratio = 1.03, 95% CI 0.89–1.20, p = 0.659), becoming non-infective (hazard ratio = 0.93, 95% CI 0.80–1.06, p = 0.277), and in-hospital death (hazard ratio = 0.68, 0.43–1.07, p = 0.097). In conclusion, the HbA1c level on admission did not seem to affect in-hospital tuberculosis treatment outcomes in Japanese cohort. PMID:28406247

  2. Outcome after surgical treatment for lumbar spinal stenosis: the lumbar extension test is not a predictive factor

    DEFF Research Database (Denmark)

    Westergaard, Lars; Hauerberg, John; Springborg, Jacob B

    2009-01-01

    STUDY DESIGN: A prospective clinical study. OBJECTIVES: To investigate the predictive value of the lumbar extension test for outcome after surgical treatment of lumbar spinal stenosis (LSS). SUMMARY OF BACKGROUND DATA: Studies have indicated that aggravation of the symptoms from LSS by extension...... of the lumbar spine has predictive value for the outcome after decompression. The aim of this study was to investigate this theory in a larger group of patients. METHODS: One hundred forty-six consecutive patients surgically treated for LSS were included in the study. The clinical condition was recorded before...... has no predictive value for the outcome after surgical treatment of LSS....

  3. A two-stage predictive model to simultaneous control of trihalomethanes in water treatment plants and distribution systems: adaptability to treatment processes.

    Science.gov (United States)

    Domínguez-Tello, Antonio; Arias-Borrego, Ana; García-Barrera, Tamara; Gómez-Ariza, José Luis

    2017-10-01

    The trihalomethanes (TTHMs) and others disinfection by-products (DBPs) are formed in drinking water by the reaction of chlorine with organic precursors contained in the source water, in two consecutive and linked stages, that starts at the treatment plant and continues in second stage along the distribution system (DS) by reaction of residual chlorine with organic precursors not removed. Following this approach, this study aimed at developing a two-stage empirical model for predicting the formation of TTHMs in the water treatment plant and subsequently their evolution along the water distribution system (WDS). The aim of the two-stage model was to improve the predictive capability for a wide range of scenarios of water treatments and distribution systems. The two-stage model was developed using multiple regression analysis from a database (January 2007 to July 2012) using three different treatment processes (conventional and advanced) in the water supply system of Aljaraque area (southwest of Spain). Then, the new model was validated using a recent database from the same water supply system (January 2011 to May 2015). The validation results indicated no significant difference in the predictive and observed values of TTHM (R 2 0.874, analytical variance distribution systems studied, proving the adaptability of the new model to the boundary conditions. Finally the predictive capability of the new model was compared with 17 other models selected from the literature, showing satisfactory results prediction and excellent adaptability to treatment processes.

  4. Can dosimetry help to predict euthyroidism after 131I radioiodine treatment of solitary thyroid nodule?

    International Nuclear Information System (INIS)

    Skanjeti, A.; Pia, A.

    2015-01-01

    Full text of publication follows. Introduction: recent SNM guidelines suggest to administer 3-8 MBq for each gram of thyroid tissue in order to reach a non hyperthyroid status, while EANM guidelines suggest to reach a dose of 100-400 Gy depending on type of disease. This second point of view is based on the principle that dosimetry, i.e. the metabolism of radioiodine within the thyroid can determine the outcome of radiation in the gland. However, although reasonable, it has not been shown unequivocally that dosimetry allows better outcome. The aim of this pilot study was to evaluate whether dosimetry and parameters that consent a dose evaluation can be useful in order to predict outcome in hyperthyroid patients with solitary nodule and successfully treated with radioiodine. Material and methods. Thirty-one consecutive patients with solitary nodule and successfully treated with 131 I radioiodine were included. In 27 patients euthyroidism was durably reached during the follow up, while in 4 hypothyroid state was the final outcome. All of them underwent Radioiodine Uptake Test (RUT) with 5 measurements (6 h, 24 h, 48 h, 72 h, and 96 h), thyroid scintigraphy to estimate gland mass and radioiodine administration. Bi-compartmental model was used to estimate residence time and dose was estimated according to EANM guidelines based on administered activities of radioiodine. Uptake at 6 h, uptake at 24 h, mass gland, dose, age, residence time, activity and activity/mass were compared in patients with stable euthyroidism versus patients with hypothyroidism in the follow up. Results: only uptake at 6 h was different in these groups of patients (p=0.05 at Welch t-test), the logistic regression seemed to confirm the significant correlation (p=0.08) between uptake at 6 h and outcome of the treatment. The other parameters were not significantly correlated with the treatment effect. Conclusion: this pilot study, performed in a very small population, did not show any significant

  5. Greater hunger and less restraint predict weight loss success with phentermine treatment.

    Science.gov (United States)

    Thomas, Elizabeth A; Mcnair, Bryan; Bechtell, Jamie L; Ferland, Annie; Cornier, Marc-Andre; Eckel, Robert H

    2016-01-01

    Phentermine is thought to cause weight loss through a reduction in hunger. It was hypothesized that higher hunger ratings would predict greater weight loss with phentermine. This is an observational pilot study in which all subjects were treated with phentermine for 8 weeks and appetite and eating behaviors were measured at baseline and week 8. Outcomes were compared in subjects with ≥5% vs. hunger (P = 0.017), desire to eat (P =0.003), and prospective food consumption (0.006) and lower baseline cognitive restraint (P = 0.01). In addition, higher baseline home prospective food consumption (P = 0.002) and lower baseline cognitive restraint (P hunger and less restraint are more likely to achieve significant weight loss with phentermine. This information can be used clinically to determine who might benefit most from phentermine treatment. © 2015 The Obesity Society.

  6. Fractionated Stereotactic Radiotherapy in the Treatment of Vestibular Schwannoma (Acoustic Neuroma): Predicting the Risk of Hydrocephalus

    International Nuclear Information System (INIS)

    Powell, Ceri; Micallef, Caroline; Gonsalves, Adam; Wharram, Bev; Ashley, Sue; Brada, Michael

    2011-01-01

    Purpose: To determine the incidence and predictive factors for the development of hydrocephalus in patients with acoustic neuromas (AN) treated with fractionated stereotactic radiotherapy. Patients and Methods: Seventy-two patients with AN were treated with fractionated stereotactic radiotherapy between 1998 and 2007 (45-50 Gy in 25-30 fractions over 5 to 6 weeks). The pretreatment MRI scan was assessed for tumor characteristics and anatomic distortion independently of subsequent outcome and correlated with the risk of hydrocephalus. Results: At a median follow-up of 49 months (range, 1-120 months), 5-year event-free survival was 95%. Eight patients (11%) developed hydrocephalus within 19 months of radiotherapy, which was successfully treated. On univariate analysis, pretreatment factors predictive of hydrocephalus were maximum diameter (p = 0.005), proximity to midline (p = 0.009), displacement of the fourth ventricle (p = 0.02), partial effacement of the fourth ventricle (p < 0.001), contact with the medulla (p = 0.005), and more brainstem structures (p = 0.004). On multivariate analysis, after adjusting for fourth ventricular effacement, no other variables remained independently associated with hydrocephalus formation. Conclusions: Fractionated stereotactic radiotherapy results in excellent tumor control of AN, albeit with a risk of developing hydrocephalus. Patients at high risk, identified as those with larger tumors with partial effacement of the fourth ventricle before treatment, should be monitored more closely during follow-up. It would also be preferable to offer treatment to patients with progressive AN while the risk of hydrocephalus is low, before the development of marked distortion of fourth ventricle before tumor diameter significantly exceeds 2 cm.

  7. Doppler laser imaging predicts response to topical minoxidil in the treatment of female pattern hair loss.

    Science.gov (United States)

    McCoy, J; Kovacevic, M; Situm, M; Stanimirovic, A; Bolanca, Z; Goren, A

    2016-01-01

    Topical minoxidil is the only drug approved by the US FDA for the treatment of female pattern hair loss. Unfortunately, following 16 weeks of daily application, less than 40% of patients regrow hair. Several studies have demonstrated that sulfotransferase enzyme activity in plucked hair follicles predicts topical minoxidil response in female pattern hair loss patients. However, due to patients’ discomfort with the procedure, and the time required to perform the enzymatic assay it would be ideal to develop a rapid, non-invasive test for sulfotransferase enzyme activity. Minoxidil is a pro-drug converted to its active form, minoxidil sulfate, by sulfotransferase enzymes in the outer root sheath of hair. Minoxidil sulfate is the active form required for both the promotion of hair regrowth and the vasodilatory effects of minoxidil. We thus hypothesized that laser Doppler velocimetry measurement of scalp blood perfusion subsequent to the application of topical minoxidil would correlate with sulfotransferase enzyme activity in plucked hair follicles. In this study, plucked hair follicles from female pattern hair loss patients were analyzed for sulfotransferase enzyme activity. Additionally, laser Doppler velocimetry was used to measure the change in scalp perfusion at 15, 30, 45, and 60 minutes, after the application of minoxidil. In agreement with our hypothesis, we discovered a correlation (r=1.0) between the change in scalp perfusion within 60 minutes after topical minoxidil application and sulfotransferase enzyme activity in plucked hairs. To our knowledge, this is the first study demonstrating the feasibility of using laser Doppler imaging as a rapid, non-invasive diagnostic test to predict topical minoxidil response in the treatment of female pattern hair loss.

  8. External validation of the ability of the DRAGON score to predict outcome after thrombolysis treatment.

    Science.gov (United States)

    Ovesen, C; Christensen, A; Nielsen, J K; Christensen, H

    2013-11-01

    Easy-to-perform and valid assessment scales for the effect of thrombolysis are essential in hyperacute stroke settings. Because of this we performed an external validation of the DRAGON scale proposed by Strbian et al. in a Danish cohort. All patients treated with intravenous recombinant plasminogen activator between 2009 and 2011 were included. Upon admission all patients underwent physical and neurological examination using the National Institutes of Health Stroke Scale along with non-contrast CT scans and CT angiography. Patients were followed up through the Outpatient Clinic and their modified Rankin Scale (mRS) was assessed after 3 months. Three hundred and three patients were included in the analysis. The DRAGON scale proved to have a good discriminative ability for predicting highly unfavourable outcome (mRS 5-6) (area under the curve-receiver operating characteristic [AUC-ROC]: 0.89; 95% confidence interval [CI] 0.81-0.96; pDRAGON scale provided good discriminative capability (AUC-ROC: 0.89; 95% CI 0.78-1.0; p=0.003) for highly unfavourable outcome. We confirmed the validity of the DRAGON scale in predicting outcome after thrombolysis treatment. Copyright © 2013 Elsevier Ltd. All rights reserved.

  9. Personality Pathology Predicts Outcomes in a Treatment-Seeking Sample with Bipolar I Disorder

    Directory of Open Access Journals (Sweden)

    Susan J. Wenze

    2014-01-01

    Full Text Available We conducted a secondary analysis of data from a clinical trial to explore the relationship between degree of personality disorder (PD pathology (i.e., number of subthreshold and threshold PD symptoms and mood and functioning outcomes in Bipolar I Disorder (BD-I. Ninety-two participants completed baseline mood and functioning assessments and then underwent 4 months of treatment for an index manic, mixed, or depressed phase acute episode. Additional assessments occurred over a 28-month follow-up period. PD pathology did not predict psychosocial functioning or manic symptoms at 4 or 28 months. However, it did predict depressive symptoms at both timepoints, as well as percent time symptomatic. Clusters A and C pathology were most strongly associated with depression. Our findings fit with the literature highlighting the negative repercussions of PD pathology on a range of outcomes in mood disorders. This study builds upon previous research, which has largely focused on major depression and which has primarily taken a categorical approach to examining PD pathology in BD.

  10. Do patient characteristics predict outcome in the outpatient treatment of chronic tinnitus?

    Science.gov (United States)

    Kröner-Herwig, Birgit; Zachriat, Claudia; Weigand, Doreen

    2006-12-06

    Various patient characteristics were assessed before offering a treatment to reduce tinnitus related distress to 57 individuals suffering from chronic idiopathic tinnitus. Patients were randomly assigned to a cognitive-behavioral tinnitus coping training (TCT) and a habituation-based training (HT) modelled after Tinnitus Retraining Therapy (TRT) as conceived by Jastreboff. Both trainings were conducted in groups. It was hypothesized that comorbidity regarding mental disorders or psychopathological symptoms (DSM-IV diagnoses, SCL-90R score) and a high level of dysfunctional cognitions relating to tinnitus would have a negative effect on therapy outcome while both trainings proved to be highly efficacious for the average patient. Also further patient features (assessed at baseline) were explored as potential predictors of outcome. None of the hypotheses was corroborated by the data. On the contrary, a higher number of diagnoses was associated with better outcome (statistical trend) and a higher extent of annoyance and interference led to a larger positive change in patients if treated by TCT. No predictor could be identified for long-term success (follow-up ≥18 months) except regarding education. The higher the educational level, the larger was the improvement in HT patients. It is concluded that therapy outcome of TCT and HT can not reliably be predicted by patient characteristics and that early variables of the therapeutic process should be analysed as potentially predicting subsequent therapeutic outcome.

  11. Evaluation of facial attractiveness from end-of-treatment facial photographs.

    Science.gov (United States)

    Shafiee, Roxanne; Korn, Edward L; Pearson, Helmer; Boyd, Robert L; Baumrind, Sheldon

    2008-04-01

    Orthodontists typically make judgments of facial attractiveness by examining groupings of profile, full-face, and smiling photographs considered together as a "triplet." The primary objective of this study was to determine the relative contributions of the 3 photographs-each considered separately-to the overall judgment a clinician forms by examining the combination of the 3. End-of-treatment triplet orthodontic photographs of 45 randomly selected orthodontic patients were duplicated. Copies of the profile, full-face, and smiling images were generated, and the images were separated and then pooled by image type for all subjects. Ten judges ranked the 45 photographs of each image type for facial attractiveness in groups of 9 to 12, from "most attractive" to "least attractive." Each judge also ranked the triplet groupings for the same 45 subjects. The mean attractiveness rankings for each type of photograph were then correlated with the mean rankings of each other and the triplets. The rankings of the 3 image types correlated highly with each other and the rankings of the triplets (P <.0001). The rankings of the smiling photographs were most predictive of the rankings of the triplets (r = 0.93); those of the profile photographs were the least predictive (r = 0.76). The difference between these correlations was highly statistically significant (P = .0003). It was also possible to test the extent to which the judges' rankings were influenced by sex, original Angle classification, and extraction status of each patient. No statistically significant preferences were found for sex or Angle classification, and only 1 marginally significant preference was found for extraction pattern. Clinician judges demonstrated a high level of agreement in ranking the facial attractiveness of profile, full-face, and smiling photographs of a group of orthodontically treated patients whose actual differences in physical dimensions were relatively small. The judges' rankings of the smiling

  12. Adiabatic quantum algorithm for search engine ranking.

    Science.gov (United States)

    Garnerone, Silvano; Zanardi, Paolo; Lidar, Daniel A

    2012-06-08

    We propose an adiabatic quantum algorithm for generating a quantum pure state encoding of the PageRank vector, the most widely used tool in ranking the relative importance of internet pages. We present extensive numerical simulations which provide evidence that this algorithm can prepare the quantum PageRank state in a time which, on average, scales polylogarithmically in the number of web pages. We argue that the main topological feature of the underlying web graph allowing for such a scaling is the out-degree distribution. The top-ranked log(n) entries of the quantum PageRank state can then be estimated with a polynomial quantum speed-up. Moreover, the quantum PageRank state can be used in "q-sampling" protocols for testing properties of distributions, which require exponentially fewer measurements than all classical schemes designed for the same task. This can be used to decide whether to run a classical update of the PageRank.

  13. Connectivity ranking of heterogeneous random conductivity models

    Science.gov (United States)

    Rizzo, C. B.; de Barros, F.

    2017-12-01

    To overcome the challenges associated with hydrogeological data scarcity, the hydraulic conductivity (K) field is often represented by a spatial random process. The state-of-the-art provides several methods to generate 2D or 3D random K-fields, such as the classic multi-Gaussian fields or non-Gaussian fields, training image-based fields and object-based fields. We provide a systematic comparison of these models based on their connectivity. We use the minimum hydraulic resistance as a connectivity measure, which it has been found to be strictly correlated with early time arrival of dissolved contaminants. A computationally efficient graph-based algorithm is employed, allowing a stochastic treatment of the minimum hydraulic resistance through a Monte-Carlo approach and therefore enabling the computation of its uncertainty. The results show the impact of geostatistical parameters on the connectivity for each group of random fields, being able to rank the fields according to their minimum hydraulic resistance.

  14. RankExplorer: Visualization of Ranking Changes in Large Time Series Data.

    Science.gov (United States)

    Shi, Conglei; Cui, Weiwei; Liu, Shixia; Xu, Panpan; Chen, Wei; Qu, Huamin

    2012-12-01

    For many applications involving time series data, people are often interested in the changes of item values over time as well as their ranking changes. For example, people search many words via search engines like Google and Bing every day. Analysts are interested in both the absolute searching number for each word as well as their relative rankings. Both sets of statistics may change over time. For very large time series data with thousands of items, how to visually present ranking changes is an interesting challenge. In this paper, we propose RankExplorer, a novel visualization method based on ThemeRiver to reveal the ranking changes. Our method consists of four major components: 1) a segmentation method which partitions a large set of time series curves into a manageable number of ranking categories; 2) an extended ThemeRiver view with embedded color bars and changing glyphs to show the evolution of aggregation values related to each ranking category over time as well as the content changes in each ranking category; 3) a trend curve to show the degree of ranking changes over time; 4) rich user interactions to support interactive exploration of ranking changes. We have applied our method to some real time series data and the case studies demonstrate that our method can reveal the underlying patterns related to ranking changes which might otherwise be obscured in traditional visualizations.

  15. Augmenting the Deliberative Method for Ranking Risks.

    Science.gov (United States)

    Susel, Irving; Lasley, Trace; Montezemolo, Mark; Piper, Joel

    2016-01-01

    The Department of Homeland Security (DHS) characterized and prioritized the physical cross-border threats and hazards to the nation stemming from terrorism, market-driven illicit flows of people and goods (illegal immigration, narcotics, funds, counterfeits, and weaponry), and other nonmarket concerns (movement of diseases, pests, and invasive species). These threats and hazards pose a wide diversity of consequences with very different combinations of magnitudes and likelihoods, making it very challenging to prioritize them. This article presents the approach that was used at DHS to arrive at a consensus regarding the threats and hazards that stand out from the rest based on the overall risk they pose. Due to time constraints for the decision analysis, it was not feasible to apply multiattribute methodologies like multiattribute utility theory or the analytic hierarchy process. Using a holistic approach was considered, such as the deliberative method for ranking risks first published in this journal. However, an ordinal ranking alone does not indicate relative or absolute magnitude differences among the risks. Therefore, the use of the deliberative method for ranking risks is not sufficient for deciding whether there is a material difference between the top-ranked and bottom-ranked risks, let alone deciding what the stand-out risks are. To address this limitation of ordinal rankings, the deliberative method for ranking risks was augmented by adding an additional step to transform the ordinal ranking into a ratio scale ranking. This additional step enabled the selection of stand-out risks to help prioritize further analysis. © 2015 Society for Risk Analysis.

  16. The hematopoietic transcription factor PU.1 regulates RANK gene expression in myeloid progenitors

    International Nuclear Information System (INIS)

    Kwon, Oh Hyung; Lee, Chong-Kil; Lee, Young Ik; Paik, Sang-Gi; Lee, Hyun-Jun

    2005-01-01

    Osteoclasts are bone resorbing cells of hematopoietic origin. The hematopoietic transcription factor PU.1 is critical for osteoclastogenesis; however, the molecular mechanisms of PU.1-regulated osteoclastogenesis have not been explored. Here, we present evidence that the receptor activator of nuclear factor κB (RANK) gene that has been shown to be crucial for osteoclastogenesis is a transcriptional target of PU.1. The PU.1 -/- progenitor cells failed to express the RANK gene and reconstitution of PU.1 in these cells induced RANK expression. Treatment of the PU.1 reconstituted cells with M-CSF and RANKL further augmented the RANK gene expression. To explore the regulatory mechanism of the RANK gene expression by PU.1, we have cloned the human RANK promoter. Transient transfection assays have revealed that the 2.2-kb RANK promoter was functional in a monocyte line RAW264.7, whereas co-transfection of PU.1 transactivated the RANK promoter in HeLa cells. Taken together, these results suggest that PU.1 regulates the RANK gene transcription and this may represent one of the key roles of PU.1 in osteoclast differentiation

  17. Communities in Large Networks: Identification and Ranking

    DEFF Research Database (Denmark)

    Olsen, Martin

    2008-01-01

    show that the problem of deciding whether a non trivial community exists is NP complete. Nevertheless, experiments show that a very simple greedy approach can identify members of a community in the Danish part of the web graph with time complexity only dependent on the size of the found community...... and its immediate surroundings. The members are ranked with a “local” variant of the PageRank algorithm. Results are reported from successful experiments on identifying and ranking Danish Computer Science sites and Danish Chess pages using only a few representatives....

  18. A Universal Rank-Size Law

    Science.gov (United States)

    2016-01-01

    A mere hyperbolic law, like the Zipf’s law power function, is often inadequate to describe rank-size relationships. An alternative theoretical distribution is proposed based on theoretical physics arguments starting from the Yule-Simon distribution. A modeling is proposed leading to a universal form. A theoretical suggestion for the “best (or optimal) distribution”, is provided through an entropy argument. The ranking of areas through the number of cities in various countries and some sport competition ranking serves for the present illustrations. PMID:27812192

  19. Aspirin plus dipyridamole has the highest surface under the cumulative ranking curves (SUCRA) values in terms of mortality, intracranial hemorrhage, and adverse event rate among 7 drug therapies in the treatment of cerebral infarction.

    Science.gov (United States)

    Zhang, Jian-Jun; Liu, Xin

    2018-03-01

    The standardization for the clinical use of drug therapy for cerebral infarction (CI) has not yet determined in some aspects. In this paper, we discussed the efficacies of different drug therapies (aspirin, aspirin plus dipyridamole, aspirin plus clopidogrel, aspirin plus warfarin, cilostazol, warfarin, and ticlopidine) for CI. We searched databases of PubMed and Cochrane Library from the inception to April, 2017, randomized controlled trials (RCTs) met the inclusion and exclusion criteria were enrolled in this study. The network meta-analysis integrated evidences of direct and indirect comparisons to assess odd ratios (OR) and surface under the cumulative ranking curves (SUCRA) value. Thirteen eligible RCTs including 7 drug therapies were included into this network meta-analysis. The network meta-analysis results showed that CI patients who received aspirin plus dipyridamole presented lower mortality when compared with those received aspirin plus clopidogrel (OR = 0.46, 95% CI = 0.18-0.99), indicating aspirin plus dipyridamole therapy had better efficacy for CI. As for intracranial hemorrhage (ICH), stroke recurrence, and adverse event (AE) rate, there were no significant differences of efficacy among 7 drug therapies. Besides, SUCRA values demonstrated that in the 7 drug therapies, aspirin plus dipyridamole therapy was more effective than others (mortality: 80.67%; ICH: 76.6%; AE rate: 90.2%). Our findings revealed that aspirin plus dipyridamole therapy might be the optimum one for patients with CI, which could help to improve the survival of CI patients.

  20. Predictive Modeling of Response to Pregabalin for the Treatment of Neuropathic Pain Using 6-Week Observational Data: A Spectrum of Modern Analytics Applications.

    Science.gov (United States)

    Emir, Birol; Johnson, Kjell; Kuhn, Max; Parsons, Bruce

    2017-01-01

    This post hoc analysis used 11 predictive models of data from a large observational study in Germany to evaluate potential predictors of achieving at least 50% pain reduction by week 6 after treatment initiation (50% pain response) with pregabalin (150-600 mg/d) in patients with neuropathic pain (NeP). The potential predictors evaluated included baseline demographic and clinical characteristics, such as patient-reported pain severity (0 [no pain] to 10 [worst possible pain]) and pain-related sleep disturbance scores (0 [sleep not impaired] to 10 [severely impaired sleep]) that were collected during clinic visits (baseline and weeks 1, 3, and 6). Baseline characteristics were also evaluated combined with pain change at week 1 or weeks 1 and 3 as potential predictors of end-of-treatment 50% pain response. The 11 predictive models were linear, nonlinear, and tree based, and all predictors in the training dataset were ranked according to their variable importance and normalized to 100%. The training dataset comprised 9187 patients, and the testing dataset had 6114 patients. To adjust for the high imbalance in the responder distribution (75% of patients were 50% responders), which can skew the parameter tuning process, the training set was balanced into sets of 1000 responders and 1000 nonresponders. The predictive modeling approaches that were used produced consistent results. Baseline characteristics alone had fair predictive value (accuracy range, 0.61-0.72; κ range, 0.17-0.30). Baseline predictors combined with pain change at week 1 had moderate predictive value (accuracy, 0.73-0.81; κ range, 0.37-0.49). Baseline predictors with pain change at weeks 1 and 3 had substantial predictive value (accuracy, 0.83-0.89; κ range, 0.54-0.71). When variable importance across the models was estimated, the best predictor of 50% responder status was pain change at week 3 (average importance 100.0%), followed by pain change at week 1 (48.1%), baseline pain score (14

  1. Dextromethorphan as a phenotyping test to predict endoxifen exposure in patients on tamoxifen treatment.

    Science.gov (United States)

    de Graan, Anne-Joy M; Teunissen, Sebastiaan F; de Vos, Filip Y F L; Loos, Walter J; van Schaik, Ron H N; de Jongh, Felix E; de Vos, Aad I; van Alphen, Robbert J; van der Holt, Bronno; Verweij, Jaap; Seynaeve, Caroline; Beijnen, Jos H; Mathijssen, Ron H J

    2011-08-20

    Tamoxifen, a widely used agent for the prevention and treatment of breast cancer, is mainly metabolized by CYP2D6 and CYP3A to form its most abundant active metabolite, endoxifen. Interpatient variability in toxicity and efficacy of tamoxifen is substantial. Contradictory results on the value of CYP2D6 genotyping to reduce the variable efficacy have been reported. In this pharmacokinetic study, we investigated the value of dextromethorphan, a known probe drug for both CYP2D6 and CYP3A enzymatic activity, as a potential phenotyping probe for tamoxifen pharmacokinetics. In this prospective study, 40 women using tamoxifen for invasive breast cancer received a single dose of dextromethorphan 2 hours after tamoxifen intake. Dextromethorphan, tamoxifen, and their respective metabolites were quantified. Exposure parameters of all compounds were estimated, log transformed, and subsequently correlated. A strong and highly significant correlation (r = -0.72; P dextromethorphan (0 to 6 hours) and endoxifen (0 to 24 hours). Also, the area under the plasma concentration-time curve of dextromethorphan (0 to 6 hours) and daily trough endoxifen concentration was strongly correlated (r = -0.70; P dextromethorphan exposure. Dextromethorphan exposure after a single administration adequately predicted endoxifen exposure in individual patients with breast cancer taking tamoxifen. This test could contribute to the personalization and optimization of tamoxifen treatment, but it needs additional validation and simplification before being applicable in future dosing strategies.

  2. Accumulating Data to Optimally Predict Obesity Treatment (ADOPT) Core Measures: Psychosocial Domain.

    Science.gov (United States)

    Sutin, Angelina R; Boutelle, Kerri; Czajkowski, Susan M; Epel, Elissa S; Green, Paige A; Hunter, Christine M; Rice, Elise L; Williams, David M; Young-Hyman, Deborah; Rothman, Alexander J

    2018-04-01

    Within the Accumulating Data to Optimally Predict obesity Treatment (ADOPT) Core Measures Project, the psychosocial domain addresses how psychosocial processes underlie the influence of obesity treatment strategies on weight loss and weight maintenance. The subgroup for the psychosocial domain identified an initial list of high-priority constructs and measures that ranged from relatively stable characteristics about the person (cognitive function, personality) to dynamic characteristics that may change over time (motivation, affect). This paper describes (a) how the psychosocial domain fits into the broader model of weight loss and weight maintenance as conceptualized by ADOPT; (b) the guiding principles used to select constructs and measures for recommendation; (c) the high-priority constructs recommended for inclusion; (d) domain-specific issues for advancing the science; and (e) recommendations for future research. The inclusion of similar measures across trials will help to better identify how psychosocial factors mediate and moderate the weight loss and weight maintenance process, facilitate research into dynamic interactions with factors in the other ADOPT domains, and ultimately improve the design and delivery of effective interventions. © 2018 The Obesity Society.

  3. Accumulating Data to Optimally Predict Obesity Treatment (ADOPT) Core Measures: Environmental Domain.

    Science.gov (United States)

    Saelens, Brian E; Arteaga, S Sonia; Berrigan, David; Ballard, Rachel M; Gorin, Amy A; Powell-Wiley, Tiffany M; Pratt, Charlotte; Reedy, Jill; Zenk, Shannon N

    2018-04-01

    There is growing interest in how environment is related to adults' weight and activity and eating behaviors. However, little is known about whether environmental factors are related to the individual variability seen in adults' intentional weight loss or maintenance outcomes. The environmental domain subgroup of the Accumulating Data to Optimally Predict obesity Treatment (ADOPT) Core Measures Project sought to identify a parsimonious set of objective and perceived neighborhood and social environment constructs and corresponding measures to include in the assessment of response to adult weight-loss treatment. Starting with the home address, the environmental domain subgroup recommended for inclusion in future weight-loss or maintenance studies constructs and measures related to walkability, perceived land use mix, food outlet accessibility (perceived and objective), perceived food availability, socioeconomics, and crime-related safety (perceived and objective) to characterize the home neighborhood environment. The subgroup also recommended constructs and measures related to social norms (perceived and objective) and perceived support to characterize an individual's social environment. The 12 neighborhood and social environment constructs and corresponding measures provide a succinct and comprehensive set to allow for more systematic examination of the impact of environment on adults' weight loss and maintenance. © 2018 The Obesity Society.

  4. Characteristics of MRI features in Alzheimer's disease patients predicting response to donepezil treatment

    International Nuclear Information System (INIS)

    Tanaka, Yuriko; Hanyu, Haruo; Sakurai, Hirofumi; Shimizu, Souichiro; Takasaki, Masaru

    2003-01-01

    We attempted to investigate whether morphological features as shown on magnetic resonance imaging (MRI) predict response to donepezil treatment in patients with Alzheimer's disease (AD). Sixty-three patients with AD were divided into responders (n=16) and non-responders (n=47) based on the changes in the mini mental state examinations (MMSE) score between baseline and endpoint. Atrophy of the substantia innominata was more pronounced in responders than non-responders. Although no significant difference in the medial temporal lobe atrophy between responders and non-responders was found, magnetization transfer ratios (MTRs) of the hippocampus and parahippocampus, indicators of structural damage, in the non-responder group were significantly reduced compared to those in the responder group. There were no significant differences in the severity of white matter lesions between the two groups. Logistic regression analysis revealed that the overall discrimination rate was 81%, with 85% of non-responders and 69% of responders, through measurement of the thickness of the substantia innominata and MTR of the hippocampus and parahippocampus. These results suggest that AD patients who show more severe cholinergic dysfunction and less severe structural damage of the hippocampus and parahippocampus as shown on MRI are likely to respond to donepezil treatment. (author)

  5. Predicting response to physiotherapy treatment for musculoskeletal shoulder pain: protocol for a longitudinal cohort study

    Science.gov (United States)

    2013-01-01

    Background Shoulder pain affects all ages, with a lifetime prevalence of one in three. The most effective treatment is not known. Physiotherapy is often recommended as the first choice of treatment. At present, it is not possible to identify, from the initial physiotherapy assessment, which factors predict the outcome of physiotherapy for patients with shoulder pain. The primary objective of this study is to identify which patient characteristics and baseline measures, typically assessed at the first physiotherapy appointment, are related to the functional outcome of shoulder pain 6 weeks and 6 months after starting physiotherapy treatment. Methods/Design Participants with musculoskeletal shoulder pain of any duration will be recruited from participating physiotherapy departments. For this longitudinal cohort study, the participants care pathway, including physiotherapy treatment will be therapist determined. Potential prognostic variables will be collected from participants during their first physiotherapy appointment and will include demographic details, lifestyle, psychosocial factors, shoulder symptoms, general health, clinical examination, activity limitations and participation restrictions. Outcome measures (Shoulder Pain and Disability Index, Quick Disability of the Arm, Shoulder and Hand, and Global Impression of Change) will be collected by postal self-report questionnaires 6 weeks and 6 months after commencing physiotherapy. Details of attendance and treatment will be collected by the treating physiotherapist. Participants will be asked to complete an exercise dairy. An initial exploratory analysis will assess the relationship between potential prognostic factors at baseline and outcome using univariate statistical tests. Those factors significant at the 5% level will be further considered as prognostic factors using a general linear model. It is estimated that 780 subjects will provide more than 90% power to detect an effect size of less than 0

  6. Paclitaxel Plasma Concentration after the First Infusion Predicts Treatment-Limiting Peripheral Neuropathy.

    Science.gov (United States)

    Hertz, Daniel L; Kidwell, Kelley M; Vangipuram, Kiran; Li, Feng; Pai, Manjunath P; Burness, Monika; Griggs, Jennifer J; Schott, Anne F; Van Poznak, Catherine; Hayes, Daniel F; Lavoie Smith, Ellen M; Henry, N Lynn

    2018-04-27

    Purpose: Paclitaxel exposure, specifically the maximum concentration ( C max ) and amount of time the concentration remains above 0.05 μmol/L ( T c >0.05 ), has been associated with the occurrence of paclitaxel-induced peripheral neuropathy. The objective of this study was to validate the relationship between paclitaxel exposure and peripheral neuropathy. Experimental Design: Patients with breast cancer receiving paclitaxel 80 mg/m 2 × 12 weekly doses were enrolled in an observational clinical study (NCT02338115). Paclitaxel plasma concentration was measured at the end of and 16-26 hours after the first infusion to estimate C max and T c >0.05 Patient-reported peripheral neuropathy was collected via CIPN20 at each dose, and an 8-item sensory subscale (CIPN8) was used in the primary analysis to test for an association with T c >0.05 Secondary analyses were conducted using C max as an alternative exposure parameter and testing each parameter with a secondary endpoint of the occurrence of peripheral neuropathy-induced treatment disruption. Results: In 60 subjects included in the analysis, the increase in CIPN8 during treatment was associated with baseline CIPN8, cumulative dose, and relative dose intensity ( P 0.05 ( P = 0.27) nor C max ( P = 0.99). In analyses of the secondary endpoint, cumulative dose (OR = 1.46; 95% confidence interval (CI), 1.18-1.80; P = 0.0008) and T c >0.05 (OR = 1.79; 95% CI, 1.06-3.01; P = 0.029) or C max (OR = 2.74; 95% CI, 1.45-5.20; P = 0.002) were associated with peripheral neuropathy-induced treatment disruption. Conclusions: Paclitaxel exposure is predictive of the occurrence of treatment-limiting peripheral neuropathy in patients receiving weekly paclitaxel for breast cancer. Studies are warranted to determine whether exposure-guided dosing enhances treatment effectiveness and/or prevents peripheral neuropathy in these patients. Clin Cancer Res; 1-9. ©2018 AACR. ©2018 American Association for Cancer Research.

  7. Predictive Clinical Parameters and Glycemic Efficacy of Vildagliptin Treatment in Korean Subjects with Type 2 Diabetes

    Directory of Open Access Journals (Sweden)

    Jin-Sun Chang

    2013-02-01

    Full Text Available BackgroundThe aims of this study are to investigate the glycemic efficacy and predictive parameters of vildagliptin therapy in Korean subjects with type 2 diabetes.MethodsIn this retrospective study, we retrieved data for subjects who were on twice-daily 50 mg vildagliptin for at least 6 months, and classified the subjects into five treatment groups. In three of the groups, we added vildagliptin to their existing medication regimen; in the other two groups, we replaced one of their existing medications with vildagliptin. We then analyzed the changes in glucose parameters and clinical characteristics.ResultsUltimately, 327 subjects were analyzed in this study. Vildagliptin significantly improved hemoglobin A1c (HbA1c levels over 6 months. The changes in HbA1c levels (ΔHbA1c at month 6 were -2.24% (P=0.000, -0.77% (P=0.000, -0.80% (P=0.001, -0.61% (P=0.000, and -0.34% (P=0.025 for groups 1, 2, 3, 4, and 5, respectively, with significance. We also found significant decrements in fasting plasma glucose levels in groups 1, 2, 3, and 4 (P<0.05. Of the variables, initial HbA1c levels (P=0.032 and history of sulfonylurea use (P=0.026 were independently associated with responsiveness to vildagliptin treatment.ConclusionVildagliptin was effective when it was used in subjects with poor glycemic control. It controlled fasting plasma glucose levels as well as sulfonylurea treatment in Korean type 2 diabetic subjects.

  8. What Predicts Patients' Willingness to Undergo Online Treatment and Pay for Online Treatment? Results from a Web-Based Survey to Investigate the Changing Patient-Physician Relationship.

    Science.gov (United States)

    Roettl, Johanna; Bidmon, Sonja; Terlutter, Ralf

    2016-02-04

    Substantial research has focused on patients' health information-seeking behavior on the Internet, but little is known about the variables that may predict patients' willingness to undergo online treatment and willingness to pay additionally for online treatment. This study analyzed sociodemographic variables, psychosocial variables, and variables of Internet usage to predict willingness to undergo online treatment and willingness to pay additionally for online treatment offered by the general practitioner (GP). An online survey of 1006 randomly selected German patients was conducted. The sample was drawn from an e-panel maintained by GfK HealthCare. Missing values were imputed; 958 usable questionnaires were analyzed. Variables with multi-item measurement were factor analyzed. Willingness to undergo online treatment and willingness to pay additionally for online treatment offered by the GP were predicted using 2 multiple regression models. Exploratory factor analyses revealed that the disposition of patients' personality to engage in information-searching behavior on the Internet was unidimensional. Exploratory factor analysis with the variables measuring the motives for Internet usage led to 2 separate factors: perceived usefulness (PU) of the Internet for health-related information searching and social motives for information searching on the Internet. Sociodemographic variables did not serve as significant predictors for willingness to undergo online treatment offered by the GP, whereas PU (B=.092, P=.08), willingness to communicate with the GP more often in the future (B=.495, Pcommunication with the GP (B=.198, Pmotive (B=.178, P=.002) were significant predictors. Age, gender, satisfaction with the GP, social motive, and trust in the GP had no significant impact on the willingness to pay additionally for online treatment, but it was predicted by health-related information-seeking personality (B=.127, P=.07), PU (B=-.098, P=.09), willingness to undergo online

  9. Scalable Faceted Ranking in Tagging Systems

    Science.gov (United States)

    Orlicki, José I.; Alvarez-Hamelin, J. Ignacio; Fierens, Pablo I.

    Nowadays, web collaborative tagging systems which allow users to upload, comment on and recommend contents, are growing. Such systems can be represented as graphs where nodes correspond to users and tagged-links to recommendations. In this paper we analyze the problem of computing a ranking of users with respect to a facet described as a set of tags. A straightforward solution is to compute a PageRank-like algorithm on a facet-related graph, but it is not feasible for online computation. We propose an alternative: (i) a ranking for each tag is computed offline on the basis of tag-related subgraphs; (ii) a faceted order is generated online by merging rankings corresponding to all the tags in the facet. Based on the graph analysis of YouTube and Flickr, we show that step (i) is scalable. We also present efficient algorithms for step (ii), which are evaluated by comparing their results with two gold standards.

  10. Superfund Hazard Ranking System Training Course

    Science.gov (United States)

    The Hazard Ranking System (HRS) training course is a four and ½ day, intermediate-level course designed for personnel who are required to compile, draft, and review preliminary assessments (PAs), site inspections (SIs), and HRS documentation records/packag

  11. Who's bigger? where historical figures really rank

    CERN Document Server

    Skiena, Steven

    2014-01-01

    Is Hitler bigger than Napoleon? Washington bigger than Lincoln? Picasso bigger than Einstein? Quantitative analysts are rapidly finding homes in social and cultural domains, from finance to politics. What about history? In this fascinating book, Steve Skiena and Charles Ward bring quantitative analysis to bear on ranking and comparing historical reputations. They evaluate each person by aggregating the traces of millions of opinions, just as Google ranks webpages. The book includes a technical discussion for readers interested in the details of the methods, but no mathematical or computational background is necessary to understand the rankings or conclusions. Along the way, the authors present the rankings of more than one thousand of history's most significant people in science, politics, entertainment, and all areas of human endeavor. Anyone interested in history or biography can see where their favorite figures place in the grand scheme of things.

  12. Ranking Forestry Investments With Parametric Linear Programming

    Science.gov (United States)

    Paul A. Murphy

    1976-01-01

    Parametric linear programming is introduced as a technique for ranking forestry investments under multiple constraints; it combines the advantages of simple tanking and linear programming as capital budgeting tools.

  13. Block models and personalized PageRank.

    Science.gov (United States)

    Kloumann, Isabel M; Ugander, Johan; Kleinberg, Jon

    2017-01-03

    Methods for ranking the importance of nodes in a network have a rich history in machine learning and across domains that analyze structured data. Recent work has evaluated these methods through the "seed set expansion problem": given a subset [Formula: see text] of nodes from a community of interest in an underlying graph, can we reliably identify the rest of the community? We start from the observation that the most widely used techniques for this problem, personalized PageRank and heat kernel methods, operate in the space of "landing probabilities" of a random walk rooted at the seed set, ranking nodes according to weighted sums of landing probabilities of different length walks. Both schemes, however, lack an a priori relationship to the seed set objective. In this work, we develop a principled framework for evaluating ranking methods by studying seed set expansion applied to the stochastic block model. We derive the optimal gradient for separating the landing probabilities of two classes in a stochastic block model and find, surprisingly, that under reasonable assumptions the gradient is asymptotically equivalent to personalized PageRank for a specific choice of the PageRank parameter [Formula: see text] that depends on the block model parameters. This connection provides a formal motivation for the success of personalized PageRank in seed set expansion and node ranking generally. We use this connection to propose more advanced techniques incorporating higher moments of landing probabilities; our advanced methods exhibit greatly improved performance, despite being simple linear classification rules, and are even competitive with belief propagation.

  14. Block models and personalized PageRank

    OpenAIRE

    Kloumann, Isabel M.; Ugander, Johan; Kleinberg, Jon

    2016-01-01

    Methods for ranking the importance of nodes in a network have a rich history in machine learning and across domains that analyze structured data. Recent work has evaluated these methods though the seed set expansion problem: given a subset $S$ of nodes from a community of interest in an underlying graph, can we reliably identify the rest of the community? We start from the observation that the most widely used techniques for this problem, personalized PageRank and heat kernel methods, operate...

  15. General correlation and partial correlation analysis in finding interactions: with Spearman rank correlation and proportion correlation as correlation measures

    OpenAIRE

    WenJun Zhang; Xin Li

    2015-01-01

    Between-taxon interactions can be detected by calculating the sampling data of taxon sample type. In present study, Spearman rank correlation and proportion correlation are chosen as the general correlation measures, and their partial correlations are calculated and compared. The results show that for Spearman rank correlation measure, in all predicted candidate direct interactions by partial correlation, about 16.77% (x, 0-45.4%) of them are not successfully detected by Spearman rank correla...

  16. Evaluation of the osteoclastogenic process associated with RANK / RANK-L / OPG in odontogenic myxomas

    Science.gov (United States)

    González-Galván, María del Carmen; Mosqueda-Taylor, Adalberto; Bologna-Molina, Ronell; Setien-Olarra, Amaia; Marichalar-Mendia, Xabier; Aguirre-Urizar, José-Manuel

    2018-01-01

    Background Odontogenic myxoma (OM) is a benign intraosseous neoplasm that exhibits local aggressiveness and high recurrence rates. Osteoclastogenesis is an important phenomenon in the tumor growth of maxillary neoplasms. RANK (Receptor Activator of Nuclear Factor κappa B) is the signaling receptor of RANK-L (Receptor activator of nuclear factor kappa-Β ligand) that activates the osteoclasts. OPG (osteoprotegerin) is a decoy receptor for RANK-L that inhibits pro-osteoclastogenesis. The RANK / RANKL / OPG system participates in the regulation of osteolytic activity under normal conditions, and its alteration has been associated with greater bone destruction, and also with tumor growth. Objectives To analyze the immunohistochemical expression of OPG, RANK and RANK-L proteins in odontogenic myxomas (OMs) and their relationship with the tumor size. Material and Methods Eighteen OMs, 4 small ( 3cm) and 18 dental follicles (DF) that were included as control were studied by means of standard immunohistochemical procedure with RANK, RANKL and OPG antibodies. For the evaluation, 5 fields (40x) of representative areas of OM and DF were selected where the expression of each antibody was determined. Descriptive and comparative statistical analyses were performed with the obtained data. Results There are significant differences in the expression of RANK in OM samples as compared to DF (p = 0.022) and among the OMSs and OMLs (p = 0.032). Also a strong association is recognized in the expression of RANK-L and OPG in OM samples. Conclusions Activation of the RANK / RANK-L / OPG triad seems to be involved in the mechanisms of bone balance and destruction, as well as associated with tumor growth in odontogenic myxomas. Key words:Odontogenic myxoma, dental follicle, RANK, RANK-L, OPG, osteoclastogenesis. PMID:29680857

  17. Dominance rank and boldness predict social attraction in great tits

    NARCIS (Netherlands)

    Snijders, L.; Naguib, M.; van Oers, K.

    2017-01-01

    Social relationships can have important fitness consequences, and how well an individual is socially connected often correlates with other behavioral traits. Whether such correlations are caused by underlying individual differences in social attraction usually remains unclear, because to identify

  18. Dominance rank and boldness predict social attraction in great tits

    NARCIS (Netherlands)

    Snijders, Lysanne; Naguib, Marc; Oers, van Kees

    2017-01-01

    Social relationships can have important fitness consequences, and how well an individual is socially connected often correlates with other behavioral traits. Whether such correlations are caused by underlying individual differences in social attraction usually remains unclear, because to identify

  19. Distinctive Facial Cues Predict Leadership Rank and Selection.

    Science.gov (United States)

    Re, Daniel E; Rule, Nicholas

    2017-09-01

    Facial appearance correlates with leadership, both in terms of who is chosen (leader selection) and how they do (leader success). Leadership theories suggest that exceptional individuals acquire positions as leaders. Exceptional traits can differ between domains, however, and so the qualities valued in leaders in one occupation may not match those valued among leaders in another. To test this, we compared the relationship between facial appearance and leadership across two domains: law firms and mafia families. Perceptions of power correlated with leadership among law executives whereas social skill correlated with leadership in organized crime. Critically, these traits were distinctive within their respective groups. Furthermore, an experimental test showed that the relative frequency of facial traits in a group can render them either an asset or liability. Perceived leadership ability is therefore enhanced by characteristics that appear unique among individuals who satisfy the basic criteria for their group.

  20. Predictive Factors for Subjective Improvement in Lumbar Spinal Stenosis Patients with Nonsurgical Treatment: A 3-Year Prospective Cohort Study.

    Directory of Open Access Journals (Sweden)

    Ko Matsudaira

    Full Text Available To assess the predictive factors for subjective improvement with nonsurgical treatment in consecutive patients with lumbar spinal stenosis (LSS.Patients with LSS were enrolled from 17 medical centres in Japan. We followed up 274 patients (151 men; mean age, 71 ± 7.4 years for 3 years. A multivariable logistic regression model was used to assess the predictive factors for subjective symptom improvement with nonsurgical treatment.In 30% of patients, conservative treatment led to a subjective improvement in the symptoms; in 70% of patients, the symptoms remained unchanged, worsened, or required surgical treatment. The multivariable analysis of predictive factors for subjective improvement with nonsurgical treatment showed that the absence of cauda equina symptoms (only radicular symptoms had an odds ratio (OR of 3.31 (95% confidence interval [CI]: 1.50-7.31; absence of degenerative spondylolisthesis/scoliosis had an OR of 2.53 (95% CI: 1.13-5.65; <1-year duration of illness had an OR of 3.81 (95% CI: 1.46-9.98; and hypertension had an OR of 2.09 (95% CI: 0.92-4.78.The predictive factors for subjective symptom improvement with nonsurgical treatment in LSS patients were the presence of only radicular symptoms, absence of degenerative spondylolisthesis/scoliosis, and an illness duration of <1 year.

  1. Rank distributions: A panoramic macroscopic outlook

    Science.gov (United States)

    Eliazar, Iddo I.; Cohen, Morrel H.

    2014-01-01

    This paper presents a panoramic macroscopic outlook of rank distributions. We establish a general framework for the analysis of rank distributions, which classifies them into five macroscopic "socioeconomic" states: monarchy, oligarchy-feudalism, criticality, socialism-capitalism, and communism. Oligarchy-feudalism is shown to be characterized by discrete macroscopic rank distributions, and socialism-capitalism is shown to be characterized by continuous macroscopic size distributions. Criticality is a transition state between oligarchy-feudalism and socialism-capitalism, which can manifest allometric scaling with multifractal spectra. Monarchy and communism are extreme forms of oligarchy-feudalism and socialism-capitalism, respectively, in which the intrinsic randomness vanishes. The general framework is applied to three different models of rank distributions—top-down, bottom-up, and global—and unveils each model's macroscopic universality and versatility. The global model yields a macroscopic classification of the generalized Zipf law, an omnipresent form of rank distributions observed across the sciences. An amalgamation of the three models establishes a universal rank-distribution explanation for the macroscopic emergence of a prevalent class of continuous size distributions, ones governed by unimodal densities with both Pareto and inverse-Pareto power-law tails.

  2. Fair ranking of researchers and research teams.

    Science.gov (United States)

    Vavryčuk, Václav

    2018-01-01

    The main drawback of ranking of researchers by the number of papers, citations or by the Hirsch index is ignoring the problem of distributing authorship among authors in multi-author publications. So far, the single-author or multi-author publications contribute to the publication record of a researcher equally. This full counting scheme is apparently unfair and causes unjust disproportions, in particular, if ranked researchers have distinctly different collaboration profiles. These disproportions are removed by less common fractional or authorship-weighted counting schemes, which can distribute the authorship credit more properly and suppress a tendency to unjustified inflation of co-authors. The urgent need of widely adopting a fair ranking scheme in practise is exemplified by analysing citation profiles of several highly-cited astronomers and astrophysicists. While the full counting scheme often leads to completely incorrect and misleading ranking, the fractional or authorship-weighted schemes are more accurate and applicable to ranking of researchers as well as research teams. In addition, they suppress differences in ranking among scientific disciplines. These more appropriate schemes should urgently be adopted by scientific publication databases as the Web of Science (Thomson Reuters) or the Scopus (Elsevier).

  3. Implicit and Explicit Attitudes Toward Spiders : Sensitivity to Treatment and Predictive Value for Generalization of Treatment Effects

    NARCIS (Netherlands)

    Huijding, Jorg; de Jong, Peter J.

    This study tested whether high spider fearful individuals' implicit and explicit attitudes toward spiders are sensitive to exposure treatment, and whether post-treatment implicit and/or explicit attitudes are related to the generalization of treatment effects. Self-reported explicit and implicit

  4. Anterior Cruciate Ligament Tear: Reliability of MR Imaging to Predict Stability after Conservative Treatment

    Energy Technology Data Exchange (ETDEWEB)

    Chung, Hye Won; Ahn, Jin Hwan; Ahn, Joong Mo; Yoon, Young Cheol; Hong, Hyun Pyo; Yoo, So Young; Kim, Seon Woo [Sungkyunkwan University School of Medicine, Seoul (Korea, Republic of)

    2007-06-15

    The aim of this study is to evaluate the reliability of MR imaging to predict the stability of the torn anterior cruciate ligament (ACL) after complete recovery of the ligament's continuity. Twenty patients with 20 knee injuries (13 males and 7 females; age range, 20 54) were enrolled in the study. The inclusion criteria were a positive history of acute trauma, diagnosis of the ACL tear by both the physical examination and the MR imaging at the initial presentation, conservative treatment, complete recovery of the continuity of the ligament on the follow up (FU) MR images and availability of the KT-2000 measurements. Two radiologists, who worked in consensus, graded the MR findings with using a 3-point system for the signal intensity, sharpness, straightness and the thickness of the healed ligament. The insufficiency of ACL was categorized into three groups according to the KT-2000 measurements. The statistic correlations between the grades of the MR findings and the degrees of ACL insufficiency were analyzed using the Cochran-Mantel-Haenszel test (p < 0.05). The p-values for each category of the MR findings according to the different groups of the KT-2000 measurements were 0.9180 for the MR signal intensity, 1.0000 for sharpness, 0.5038 for straightness and 0.2950 for thickness of the ACL. The MR findings were not significantly different between the different KT-2000 groups. MR imaging itself is not a reliable examination to predict stability of the ACL rupture outcome, even when the MR images show an intact appearance of the ACL.

  5. Significance of SYT8 For the Detection, Prediction, and Treatment of Peritoneal Metastasis From Gastric Cancer.

    Science.gov (United States)

    Kanda, Mitsuro; Shimizu, Dai; Tanaka, Haruyoshi; Tanaka, Chie; Kobayashi, Daisuke; Hayashi, Masamichi; Iwata, Naoki; Niwa, Yukiko; Yamada, Suguru; Fujii, Tsutomu; Sugimoto, Hiroyuki; Murotani, Kenta; Fujiwara, Michitaka; Kodera, Yasuhiro

    2018-03-01

    prolonged survival of mice engrafted with GC cells. SYT8 represents a promising target for the detection, prediction, and treatment of peritoneal metastasis of GC.

  6. Impact of Child Maltreatment on Attachment and Social Rank Systems: Introducing an Integrated Theory.

    Science.gov (United States)

    Sloman, Leon; Taylor, Peter

    2016-04-01

    Child maltreatment is a prevalent societal problem that has been linked to a wide range of social, psychological, and emotional difficulties. Maltreatment impacts on two putative evolved psychobiological systems in particular, the attachment system and the social rank system. The maltreatment may disrupt the child's ability to form trusting and reassuring relationships and also creates a power imbalance where the child may feel powerless and ashamed. The aim of the current article is to outline an evolutionary theory for understanding the impact of child maltreatment, focusing on the interaction between the attachment and the social rank system. We provide a narrative review of the relevant literature relating to child maltreatment and these two theories. This research highlights how, in instances of maltreatment, these ordinarily adaptive systems may become maladaptive and contribute to psychopathology. We identify a number of novel hypotheses that can be drawn from this theory, providing a guide for future research. We finally explore how this theory provides a guide for the treatment of victims of child maltreatment. In conclusion, the integrated theory provides a framework for understanding and predicting the consequences of maltreatment, but further research is required to test several hypotheses made by this theory. © The Author(s) 2015.

  7. Predicting response to physiotherapy treatment for musculoskeletal shoulder pain: a systematic review

    Science.gov (United States)

    2013-01-01

    Background People suffering from musculoskeletal shoulder pain are frequently referred to physiotherapy. Physiotherapy generally involves a multimodal approach to management that may include; exercise, manual therapy and techniques to reduce pain. At present it is not possible to predict which patients will respond positively to physiotherapy treatment. The purpose of this systematic review was to identify which prognostic factors are associated with the outcome of physiotherapy in the management of musculoskeletal shoulder pain. Methods A comprehensive search was undertaken of Ovid Medline, EMBASE, CINAHL and AMED (from inception to January 2013). Prospective studies of participants with shoulder pain receiving physiotherapy which investigated the association between baseline prognostic factors and change in pain and function over time were included. Study selection, data extraction and appraisal of study quality were undertaken by two independent assessors. Quality criteria were selected from previously published guidelines to form a checklist of 24 items. The study protocol was prospectively registered onto the International Prospective Register of Systematic Reviews. Results A total of 5023 titles were retrieved and screened for eligibility, 154 articles were assessed as full text and 16 met the inclusion criteria: 11 cohort studies, 3 randomised controlled trials and 2 controlled trials. Results were presented for the 9 studies meeting 13 or more of the 24 quality criteria. Clinical and statistical heterogeneity resulted in qualitative synthesis rather than meta-analysis. Three studies demonstrated that high functional disability at baseline was associated with poor functional outcome (p ≤ 0.05). Four studies demonstrated a significant association (p ≤ 0.05) between longer duration of shoulder pain and poorer outcome. Three studies, demonstrated a significant association (p ≤ 0.05) between increasing age and poorer function; three studies

  8. Accuracy of Dolphin visual treatment objective (VTO prediction software on class III patients treated with maxillary advancement and mandibular setback

    Directory of Open Access Journals (Sweden)

    Robert J. Peterman

    2016-06-01

    Full Text Available Abstract Background Dolphin® visual treatment objective (VTO prediction software is routinely utilized by orthodontists during the treatment planning of orthognathic cases to help predict post-surgical soft tissue changes. Although surgical soft tissue prediction is considered to be a vital tool, its accuracy is not well understood in tow-jaw surgical procedures. The objective of this study was to quantify the accuracy of Dolphin Imaging’s VTO soft tissue prediction software on class III patients treated with maxillary advancement and mandibular setback and to validate the efficacy of the software in such complex cases. Methods This retrospective study analyzed the records of 14 patients treated with comprehensive orthodontics in conjunction with two-jaw orthognathic surgery. Pre- and post-treatment radiographs were traced and superimposed to determine the actual skeletal movements achieved in surgery. This information was then used to simulate surgery in the software and generate a final soft tissue patient profile prediction. Prediction images were then compared to the actual post-treatment profile photos to determine differences. Results Dolphin Imaging’s software was determined to be accurate within an error range of +/− 2 mm in the X-axis at most landmarks. The lower lip predictions were most inaccurate. Conclusions Clinically, the observed error suggests that the VTO may be used for demonstration and communication with a patient or consulting practitioner. However, Dolphin should not be useful for precise treatment planning of surgical movements. This program should be used with caution to prevent unrealistic patient expectations and dissatisfaction.

  9. Failure of 111In-labeled bleomycin tumor scanning to predict response to bleomycin (NSC-125066) treatment

    International Nuclear Information System (INIS)

    Jones, S.E.; Salmon, S.E.; Durie, B.G.M.

    1974-01-01

    The question of whether or not 111 In-labeled bleomycin is predictive of the response of tumors to bleomycin treatment is answered in the negative. The real test of the value of labeled bleomycin as a predictor of response will be possible only when a tightly labeled bleomycin with fully preserved biologic activity is synthesized. The negative results of this study do not invalidate further investigations of the predictive values of labeled anticancer drugs

  10. Predicting substance-abuse treatment providers' communication with clients about medication assisted treatment: a test of the theories of reasoned action and planned behavior.

    Science.gov (United States)

    Roberto, Anthony J; Shafer, Michael S; Marmo, Jennifer

    2014-01-01

    The purpose of this investigation is to determine if the theory of reasoned action (TRA) and theory of planned behavior (TPB) can retrospectively predict whether substance-abuse treatment providers encourage their clients to use medicated-assisted treatment (MAT) as part of their treatment plan. Two-hundred and ten substance-abuse treatment providers completed a survey measuring attitudes, subjective norms, perceived behavioral control, intentions, and behavior. Results indicate that substance-abuse treatment providers have very positive attitudes, neutral subjective norms, somewhat positive perceived behavioral control, somewhat positive intentions toward recommending MAT as part of their clients' treatment plan, and were somewhat likely to engage in the actual behavior. Further, the data fit both the TRA and TPB, but with the TPB model having better fit and predictive power for this target audience and behavior. The theoretical and practical implications for the developing messages for substance-abuse treatment providers and other health-care professionals who provide treatment to patients with substance use disorders are discussed. Copyright © 2014 Elsevier Inc. All rights reserved.

  11. Evaluation of Different Score Index for Predicting Prognosis in Gamma Knife Radiosurgical Treatment for Brain Metastasis

    International Nuclear Information System (INIS)

    Franzin, Alberto; Snider, Silvia; Picozzi, Piero; Bolognesi, Angelo; Serra, Carlo; Vimercati, Alberto; Passarin, Olga; Mortini, Pietro

    2009-01-01

    Purpose: To assess the utility of the Radiation Therapy Oncology Group Recursive Partitioning Analysis (RPA) and Score Index for Radiosurgery (SIR) stratification systems in predicting survival in patients with brain metastasis treated with Gamma Knife radiosurgery (GKRS). Methods and Materials: A total of 185 patients were included in the study. Patients were stratified according to RPA and SIR classes. The RPA and SIR classes, age, Karnofsky Performance Status (KPS), and systemic disease were correlated with survival. Results: Five patients were lost to follow-up. Median survival in patients in RPA Class 1 (30 patients) was 17 months; in Class 2 (140 patients), 10 months; and in Class 3 (10 patients), 3 months. Median survival in patients in SIR Class 1 (30 patients) was 3 months; in Class 2 (135 patients), 8 months; and in Class 3 (15 patients), 20 months. In univariate testing, age younger than 65 years (p = 0.0004), KPS higher than 70 (p = 0.0001), RPA class (p = 0.0078), SIR class (p = 0.0002), and control of the primary tumor (p = 0.02) were significantly associated with improved outcome. In multivariate analysis, KPS (p < 0.0001), SIR class (p = 0.0008), and RPA class (p = 0.03) had statistical value. Conclusions: This study supports the use of GKRS as a single-treatment modality in this selected group of patients. Stratification systems are useful in the estimation of patient eligibility for GKRS. A second-line treatment was necessary in 30% of patients to achieve distal or local brain control. This strategy is useful to control brain metastasis in long-surviving patients.

  12. Predicting the In-Hospital Responsiveness to Treatment of Alcoholics. Social Factors as Predictors of Outcome. Brain Damage as a Factor in Treatment Outcome of Chronic Alcoholic Patients.

    Science.gov (United States)

    Mascia, George V.; And Others

    The authors attempt to locate predictor variables associated with the outcome of alcoholic treatment programs. Muscia's study focuses on the predictive potential of: (1) response to a GSR conditioning procedure; (2) several personality variables; and (3) age and IQ measures. Nine variables, reflecting diverse perspectives, were selected as a basis…

  13. Serum Adiponectin, Vitamin D, and Alpha-Fetoprotein in Children with Chronic Hepatitis C: Can They Predict Treatment Response?

    Directory of Open Access Journals (Sweden)

    Mohamed Ahmed Khedr

    2015-01-01

    Full Text Available Background & Aims. The currently available treatment for chronic hepatitis C (CHC in children is costly and with much toxicity. So, predicting the likelihood of response before starting therapy is important. Methods. Serum adiponectin, vitamin D, and alpha-fetoprotein (AFP were measured before starting pegylated-interferon/ribavirin therapy for 50 children with CHC. Another 21 healthy children were recruited as controls. Results. Serum adiponectin, vitamin D, and AFP were higher in the CHC group than healthy controls (p<0.0001, p=0.071, and p=0.87, resp.. In univariate analysis, serum adiponectin was significantly higher in responders than nonresponders (p<0.0001 and at a cutoff value ≥8.04 ng/mL it can predict treatment response by 77.8% sensitivity and 92.9% specificity, while both AFP and viremia were significantly lower in responders than nonresponders, p<0.0001 and p=0.0003, respectively, and at cutoff values ≤3.265 ng/mL and ≤235,384 IU/mL, respectively, they can predict treatment response with a sensitivity of 83.3% for both and specificity of 85.7% and 78.6%, respectively. In multivariate analysis, adiponectin was found to be the only independent predictor of treatment response (p=0.044. Conclusions. The pretreatment serum level of adiponectin can predict the likelihood of treatment response, thus avoiding toxicities for those unlikely to respond to therapy.

  14. Serum Adiponectin, Vitamin D, and Alpha-Fetoprotein in Children with Chronic Hepatitis C: Can They Predict Treatment Response?

    Science.gov (United States)

    Khedr, Mohamed Ahmed; Sira, Ahmad Mohamed; Saber, Magdy Anwar; Raia, Gamal Yousef

    2015-01-01

    Background & Aims. The currently available treatment for chronic hepatitis C (CHC) in children is costly and with much toxicity. So, predicting the likelihood of response before starting therapy is important. Methods. Serum adiponectin, vitamin D, and alpha-fetoprotein (AFP) were measured before starting pegylated-interferon/ribavirin therapy for 50 children with CHC. Another 21 healthy children were recruited as controls. Results. Serum adiponectin, vitamin D, and AFP were higher in the CHC group than healthy controls (p < 0.0001, p = 0.071, and p = 0.87, resp.). In univariate analysis, serum adiponectin was significantly higher in responders than nonresponders (p < 0.0001) and at a cutoff value ≥8.04 ng/mL it can predict treatment response by 77.8% sensitivity and 92.9% specificity, while both AFP and viremia were significantly lower in responders than nonresponders, p < 0.0001 and p = 0.0003, respectively, and at cutoff values ≤3.265 ng/mL and ≤235,384 IU/mL, respectively, they can predict treatment response with a sensitivity of 83.3% for both and specificity of 85.7% and 78.6%, respectively. In multivariate analysis, adiponectin was found to be the only independent predictor of treatment response (p = 0.044). Conclusions. The pretreatment serum level of adiponectin can predict the likelihood of treatment response, thus avoiding toxicities for those unlikely to respond to therapy.

  15. High-frequency binge eating predicts weight gain among veterans receiving behavioral weight loss treatments.

    Science.gov (United States)

    Masheb, Robin M; Lutes, Lesley D; Kim, Hyungjin Myra; Holleman, Robert G; Goodrich, David E; Janney, Carol A; Kirsh, Susan; Richardson, Caroline R; Damschroder, Laura J

    2015-01-01

    To assess for the frequency of binge eating behavior and its association with weight loss in an overweight/obese sample of veterans. This study is a secondary analysis of data from the ASPIRE study, a randomized effectiveness trial of weight loss among veterans. Of the 481 enrolled veterans with overweight/obesity, binge eating frequency was obtained by survey for 392 (82%). The majority (77.6%) reported binge eating, and 6.1% reported high-frequency binge eating. Those reporting any binge eating lost 1.4% of body weight, decreased waist circumference by 2.0 cm, and had significantly worse outcomes than those reporting never binge eating who lost about double the weight (2.7%) and reduced waist circumference by twice as much (4.2 cm). The high-frequency binge group gained 1.4% of body weight and increased waist circumference by 0.3 cm. High rates of binge eating were observed in an overweight/obese sample of veterans enrolled in weight loss treatment. The presence of binge eating predicted poorer weight loss outcomes. Furthermore, high-frequency binge eating was associated with weight gain. These findings have operational and policy implications for developing effective strategies to address binge eating in the context of behavioral weight loss programs for veterans. © 2014 The Obesity Society.

  16. Predictive Model and Methodology for Heat Treatment Distortion Final Report CRADA No. TC-298-92

    Energy Technology Data Exchange (ETDEWEB)

    Nikkel, D. J. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); McCabe, J. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2017-10-16

    This project was a multi-lab, multi-partner CRADA involving LLNL, Los Alamos National Laboratory, Sandia National Laboratories, Oak Ridge National Laboratory, Martin Marietta Energy Systems and the industrial partner, The National Center of Manufacturing Sciences (NCMS). A number of member companies of NCMS participated including General Motors Corporation, Ford Motor Company, The Torrington Company, Gear Research, the Illinois Institute of Technology Research Institute, and Deformation Control Technology •. LLNL was the lead laboratory for metrology technology used for validation of the computational tool/methodology. LLNL was also the lead laboratory for the development of the software user interface , for the computational tool. This report focuses on the participation of LLNL and NCMS. The purpose of the project was to develop a computational tool/methodology that engineers would use to predict the effects of heat treatment on the _size and shape of industrial parts made of quench hardenable alloys. Initially, the target application of the tool was gears for automotive power trains.

  17. Stressful life events predict delayed functional recovery following treatment for mania in bipolar disorder.

    Science.gov (United States)

    Yan-Meier, Leslie; Eberhart, Nicole K; Hammen, Constance L; Gitlin, Michael; Sokolski, Kenneth; Altshuler, Lori

    2011-04-30

    Identifying predictors of functional recovery in bipolar disorder is critical to treatment efforts to help patients re-establish premorbid levels of role adjustment following an acute manic episode. The current study examined the role of stressful life events as potential obstacles to recovery of functioning in various roles. 65 patients with bipolar I disorder participated in a longitudinal study of functional recovery following clinical recovery from a manic episode. Stressful life events were assessed as predictors of concurrent vs. delayed recovery of role functioning in 4 domains (friends, family, home duties, work/school). Despite clinical recovery, a subset of patients experienced delayed functional recovery in various role domains. Moreover, delayed functional recovery was significantly associated with presence of one or more stressors in the prior 3 months, even after controlling for mood symptoms. Presence of a stressor predicted longer time to functional recovery in life domains, up to 112 days in work/school. Interventions that provide monitoring, support, and problem-solving may be needed to help prevent or mitigate the effects of stress on functional recovery. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

  18. PageRank as a method to rank biomedical literature by importance.

    Science.gov (United States)

    Yates, Elliot J; Dixon, Louise C

    2015-01-01

    Optimal ranking of literature importance is vital in overcoming article overload. Existing ranking methods are typically based on raw citation counts, giving a sum of 'inbound' links with no consideration of citation importance. PageRank, an algorithm originally developed for ranking webpages at the search engine, Google, could potentially be adapted to bibliometrics to quantify the relative importance weightings of a citation network. This article seeks to validate such an approach on the freely available, PubMed Central open access subset (PMC-OAS) of biomedical literature. On-demand cloud computing infrastructure was used to extract a citation network from over 600,000 full-text PMC-OAS articles. PageRanks and citation counts were calculated for each node in this network. PageRank is highly correlated with citation count (R = 0.905, P PageRank can be trivially computed on commodity cluster hardware and is linearly correlated with citation count. Given its putative benefits in quantifying relative importance, we suggest it may enrich the citation network, thereby overcoming the existing inadequacy of citation counts alone. We thus suggest PageRank as a feasible supplement to, or replacement of, existing bibliometric ranking methods.

  19. RANK/RANK-Ligand/OPG: Ein neuer Therapieansatz in der Osteoporosebehandlung

    Directory of Open Access Journals (Sweden)

    Preisinger E

    2007-01-01

    Full Text Available Die Erforschung der Kopplungsmechanismen zur Osteoklastogenese, Knochenresorption und Remodellierung eröffnete neue mögliche Therapieansätze in der Behandlung der Osteoporose. Eine Schlüsselrolle beim Knochenabbau spielt der RANK- ("receptor activator of nuclear factor (NF- κB"- Ligand (RANKL. Durch die Bindung von RANKL an den Rezeptor RANK wird die Knochenresorption eingeleitet. OPG (Osteoprotegerin sowie der für den klinischen Gebrauch entwickelte humane monoklonale Antikörper (IgG2 Denosumab blockieren die Bindung von RANK-Ligand an RANK und verhindern den Knochenabbau.

  20. Predicting Treatment Outcomes from Prefrontal Cortex Activation for Self-Harming Patients with Borderline Personality Disorder: A Preliminary Study

    Science.gov (United States)

    Ruocco, Anthony C.; Rodrigo, Achala H.; McMain, Shelley F.; Page-Gould, Elizabeth; Ayaz, Hasan; Links, Paul S.

    2016-01-01

    Self-harm is a potentially lethal symptom of borderline personality disorder (BPD) that often improves with dialectical behavior therapy (DBT). While DBT is effective for reducing self-harm in many patients with BPD, a small but significant number of patients either does not improve in treatment or ends treatment prematurely. Accordingly, it is crucial to identify factors that may prospectively predict which patients are most likely to benefit from and remain in treatment. In the present preliminary study, 29 actively self-harming patients with BPD completed brain-imaging procedures probing activation of the prefrontal cortex (PFC) during impulse control prior to beginning DBT and after 7 months of treatment. Patients that reduced their frequency of self-harm the most over treatment displayed lower levels of neural activation in the bilateral dorsolateral prefrontal cortex (DLPFC) prior to beginning treatment, and they showed the greatest increases in activity within this region after 7 months of treatment. Prior to starting DBT, treatment non-completers demonstrated greater activation than treatment-completers in the medial PFC and right inferior frontal gyrus. Reductions in self-harm over the treatment period were associated with increases in activity in right DLPFC even after accounting for improvements in depression, mania, and BPD symptom severity. These findings suggest that pre-treatment patterns of activation in the PFC underlying impulse control may be prospectively associated with improvements in self-harm and treatment attrition for patients with BPD treated with DBT. PMID:27242484

  1. Predicting Treatment Outcomes from Prefrontal Cortex Activation for Self-Harming Patients with Borderline Personality Disorder: A Preliminary Study

    Directory of Open Access Journals (Sweden)

    Anthony Charles Ruocco

    2016-05-01

    Full Text Available Self-harm is a potentially lethal symptom of borderline personality disorder (BPD that often improves with dialectical behavior therapy (DBT. While DBT is effective for reducing self-harm in many patients with BPD, a small but significant number of patients either does not improve in treatment or ends treatment prematurely. Accordingly, it is crucial to identify factors that may prospectively predict which patients are most likely to benefit from and remain in treatment. In the present preliminary study, twenty-nine actively self-harming patients with BPD completed brain-imaging procedures probing activation of the prefrontal cortex during impulse control prior to beginning DBT and after seven months of treatment. Patients that reduced their frequency of self-harm the most over treatment displayed lower levels of neural activation in the bilateral dorsolateral prefrontal cortex prior to beginning treatment, and they showed the greatest increases in activity within this region after seven months of treatment. Prior to starting DBT, treatment non-completers demonstrated greater activation than treatment-completers in the medial prefrontal cortex and right inferior frontal gyrus. Reductions in self-harm over the treatment period were associated with increases in activity in right dorsolateral prefrontal cortex even after accounting for improvements in depression, mania, and BPD symptom severity. These findings suggest that pre-treatment patterns of activation in the prefrontal cortex underlying impulse control may be prospectively associated with improvements in self-harm and treatment attrition for patients with BPD treated with DBT.

  2. Predicting Treatment Outcomes from Prefrontal Cortex Activation for Self-Harming Patients with Borderline Personality Disorder: A Preliminary Study.

    Science.gov (United States)

    Ruocco, Anthony C; Rodrigo, Achala H; McMain, Shelley F; Page-Gould, Elizabeth; Ayaz, Hasan; Links, Paul S

    2016-01-01

    Self-harm is a potentially lethal symptom of borderline personality disorder (BPD) that often improves with dialectical behavior therapy (DBT). While DBT is effective for reducing self-harm in many patients with BPD, a small but significant number of patients either does not improve in treatment or ends treatment prematurely. Accordingly, it is crucial to identify factors that may prospectively predict which patients are most likely to benefit from and remain in treatment. In the present preliminary study, 29 actively self-harming patients with BPD completed brain-imaging procedures probing activation of the prefrontal cortex (PFC) during impulse control prior to beginning DBT and after 7 months of treatment. Patients that reduced their frequency of self-harm the most over treatment displayed lower levels of neural activation in the bilateral dorsolateral prefrontal cortex (DLPFC) prior to beginning treatment, and they showed the greatest increases in activity within this region after 7 months of treatment. Prior to starting DBT, treatment non-completers demonstrated greater activation than treatment-completers in the medial PFC and right inferior frontal gyrus. Reductions in self-harm over the treatment period were associated with increases in activity in right DLPFC even after accounting for improvements in depression, mania, and BPD symptom severity. These findings suggest that pre-treatment patterns of activation in the PFC underlying impulse control may be prospectively associated with improvements in self-harm and treatment attrition for patients with BPD treated with DBT.

  3. A human fecal contamination index for ranking impaired ...

    Science.gov (United States)

    Human fecal pollution of surface water remains a public health concern worldwide. As a result, there is a growing interest in the application of human-associated fecal source identification quantitative real-time PCR (qPCR) technologies for recreational water quality risk management. The transition from a research subject to a management tool requires the integration of standardized water sampling, laboratory, and data analysis procedures. In this study, a standardized HF183/BacR287 qPCR method was combined with a water sampling strategy and Bayesian data algorithm to establish a human fecal contamination index that can be used to rank impaired recreational water sites polluted with human waste. Stability and bias of index predictions were investigated under various parameters including siteswith different pollution levels, sampling period time range (1-15 weeks), and number of qPCR replicates per sample (2-14 replicates). Sensitivity analyses were conducted with simulated data sets (100 iterations) seeded with HF183/BacR287 qPCR laboratory measurements from water samples collected from three Southern California sites (588 qPCR measurements). Findings suggest that site ranking is feasible and that all parameters tested influence stability and bias in human fecal contamination indexscoring. Trends identified by sensitivity analyses will provide managers with the information needed to design and conduct field studies to rank impaired recreational water sites based

  4. Social Feedback and the Emergence of Rank in Animal Society.

    Science.gov (United States)

    Hobson, Elizabeth A; DeDeo, Simon

    2015-09-01

    Dominance hierarchies are group-level properties that emerge from the aggression of individuals. Although individuals can gain critical benefits from their position in a hierarchy, we do not understand how real-world hierarchies form. Nor do we understand what signals and decision-rules individuals use to construct and maintain hierarchies in the absence of simple cues such as size or spatial location. A study of conflict in two groups of captive monk parakeets (Myiopsitta monachus) found that a transition to large-scale order in aggression occurred in newly-formed groups after one week, with individuals thereafter preferring to direct aggression more frequently against those nearby in rank. We consider two cognitive mechanisms underlying the emergence of this order: inference based on overall levels of aggression, or on subsets of the aggression network. Both mechanisms were predictive of individual decisions to aggress, but observed patterns were better explained by rank inference through subsets of the aggression network. Based on these results, we present a new theory, of a feedback loop between knowledge of rank and consequent behavior. This loop explains the transition to strategic aggression and the formation and persistence of dominance hierarchies in groups capable of both social memory and inference.

  5. Model assessment using a multi-metric ranking technique

    Science.gov (United States)

    Fitzpatrick, P. J.; Lau, Y.; Alaka, G.; Marks, F.

    2017-12-01

    Validation comparisons of multiple models presents challenges when skill levels are similar, especially in regimes dominated by the climatological mean. Assessing skill separation will require advanced validation metrics and identifying adeptness in extreme events, but maintain simplicity for management decisions. Flexibility for operations is also an asset. This work postulates a weighted tally and consolidation technique which ranks results by multiple types of metrics. Variables include absolute error, bias, acceptable absolute error percentages, outlier metrics, model efficiency, Pearson correlation, Kendall's Tau, reliability Index, multiplicative gross error, and root mean squared differences. Other metrics, such as root mean square difference and rank correlation were also explored, but removed when the information was discovered to be generally duplicative to other metrics. While equal weights are applied, weights could be altered depending for preferred metrics. Two examples are shown comparing ocean models' currents and tropical cyclone products, including experimental products. The importance of using magnitude and direction for tropical cyclone track forecasts instead of distance, along-track, and cross-track are discussed. Tropical cyclone intensity and structure prediction are also assessed. Vector correlations are not included in the ranking process, but found useful in an independent context, and will be briefly reported.

  6. Country-specific determinants of world university rankings

    OpenAIRE

    Pietrucha, Jacek

    2017-01-01

    This paper examines country-specific factors that affect the three most influential world university rankings (the Academic Ranking of World Universities, the QS World University Ranking, and the Times Higher Education World University Ranking). We run a cross sectional regression that covers 42–71 countries (depending on the ranking and data availability). We show that the position of universities from a country in the ranking is determined by the following country-specific variables: econom...

  7. Prediction of DVH parameter changes due to setup errors for breast cancer treatment based on 2D portal dosimetry

    International Nuclear Information System (INIS)

    Nijsten, S. M. J. J. G.; Elmpt, W. J. C. van; Mijnheer, B. J.; Minken, A. W. H.; Persoon, L. C. G. G.; Lambin, P.; Dekker, A. L. A. J.

    2009-01-01

    Electronic portal imaging devices (EPIDs) are increasingly used for portal dosimetry applications. In our department, EPIDs are clinically used for two-dimensional (2D) transit dosimetry. Predicted and measured portal dose images are compared to detect dose delivery errors caused for instance by setup errors or organ motion. The aim of this work is to develop a model to predict dose-volume histogram (DVH) changes due to setup errors during breast cancer treatment using 2D transit dosimetry. First, correlations between DVH parameter changes and 2D gamma parameters are investigated for different simulated setup errors, which are described by a binomial logistic regression model. The model calculates the probability that a DVH parameter changes more than a specific tolerance level and uses several gamma evaluation parameters for the planning target volume (PTV) projection in the EPID plane as input. Second, the predictive model is applied to clinically measured portal images. Predicted DVH parameter changes are compared to calculated DVH parameter changes using the measured setup error resulting from a dosimetric registration procedure. Statistical accuracy is investigated by using receiver operating characteristic (ROC) curves and values for the area under the curve (AUC), sensitivity, specificity, positive and negative predictive values. Changes in the mean PTV dose larger than 5%, and changes in V 90 and V 95 larger than 10% are accurately predicted based on a set of 2D gamma parameters. Most pronounced changes in the three DVH parameters are found for setup errors in the lateral-medial direction. AUC, sensitivity, specificity, and negative predictive values were between 85% and 100% while the positive predictive values were lower but still higher than 54%. Clinical predictive value is decreased due to the occurrence of patient rotations or breast deformations during treatment, but the overall reliability of the predictive model remains high. Based on our

  8. Global network centrality of university rankings

    Science.gov (United States)

    Guo, Weisi; Del Vecchio, Marco; Pogrebna, Ganna

    2017-10-01

    Universities and higher education institutions form an integral part of the national infrastructure and prestige. As academic research benefits increasingly from international exchange and cooperation, many universities have increased investment in improving and enabling their global connectivity. Yet, the relationship of university performance and its global physical connectedness has not been explored in detail. We conduct, to our knowledge, the first large-scale data-driven analysis into whether there is a correlation between university relative ranking performance and its global connectivity via the air transport network. The results show that local access to global hubs (as measured by air transport network betweenness) strongly and positively correlates with the ranking growth (statistical significance in different models ranges between 5% and 1% level). We also found that the local airport's aggregate flight paths (degree) and capacity (weighted degree) has no effect on university ranking, further showing that global connectivity distance is more important than the capacity of flight connections. We also examined the effect of local city economic development as a confounding variable and no effect was observed suggesting that access to global transportation hubs outweighs economic performance as a determinant of university ranking. The impact of this research is that we have determined the importance of the centrality of global connectivity and, hence, established initial evidence for further exploring potential connections between university ranking and regional investment policies on improving global connectivity.

  9. Diversity rankings among bacterial lineages in soil.

    Science.gov (United States)

    Youssef, Noha H; Elshahed, Mostafa S

    2009-03-01

    We used rarefaction curve analysis and diversity ordering-based approaches to rank the 11 most frequently encountered bacterial lineages in soil according to diversity in 5 previously reported 16S rRNA gene clone libraries derived from agricultural, undisturbed tall grass prairie and forest soils (n=26,140, 28 328, 31 818, 13 001 and 53 533). The Planctomycetes, Firmicutes and the delta-Proteobacteria were consistently ranked among the most diverse lineages in all data sets, whereas the Verrucomicrobia, Gemmatimonadetes and beta-Proteobacteria were consistently ranked among the least diverse. On the other hand, the rankings of alpha-Proteobacteria, Acidobacteria, Actinobacteria, Bacteroidetes and Chloroflexi varied widely in different soil clone libraries. In general, lineages exhibiting largest differences in diversity rankings also exhibited the largest difference in relative abundance in the data sets examined. Within these lineages, a positive correlation between relative abundance and diversity was observed within the Acidobacteria, Actinobacteria and Chloroflexi, and a negative diversity-abundance correlation was observed within the Bacteroidetes. The ecological and evolutionary implications of these results are discussed.

  10. Social class rank, essentialism, and punitive judgment.

    Science.gov (United States)

    Kraus, Michael W; Keltner, Dacher

    2013-08-01

    Recent evidence suggests that perceptions of social class rank influence a variety of social cognitive tendencies, from patterns of causal attribution to moral judgment. In the present studies we tested the hypotheses that upper-class rank individuals would be more likely to endorse essentialist lay theories of social class categories (i.e., that social class is founded in genetically based, biological differences) than would lower-class rank individuals and that these beliefs would decrease support for restorative justice--which seeks to rehabilitate offenders, rather than punish unlawful action. Across studies, higher social class rank was associated with increased essentialism of social class categories (Studies 1, 2, and 4) and decreased support for restorative justice (Study 4). Moreover, manipulated essentialist beliefs decreased preferences for restorative justice (Study 3), and the association between social class rank and class-based essentialist theories was explained by the tendency to endorse beliefs in a just world (Study 2). Implications for how class-based essentialist beliefs potentially constrain social opportunity and mobility are discussed.

  11. RANK und RANKL - Vom Knochen zum Mammakarzinom

    Directory of Open Access Journals (Sweden)

    Sigl V

    2012-01-01

    Full Text Available RANK („Receptor Activator of NF-κB“ und sein Ligand RANKL sind Schlüsselmoleküle im Knochenmetabolismus und spielen eine essenzielle Rolle in der Entstehung von pathologischen Knochenveränderungen. Die Deregulation des RANK/RANKL-Systems ist zum Beispiel ein Hauptgrund für das Auftreten von postmenopausaler Osteoporose bei Frauen. Eine weitere wesentliche Funktion von RANK und RANKL liegt in der Entwicklung von milchsekretierenden Drüsen während der Schwangerschaft. Dabei regulieren Sexualhormone, wie zum Beispiel Progesteron, die Expression von RANKL und induzieren dadurch die Proliferation von epithelialen Zellen der Brust. Seit Längerem war schon bekannt, dass RANK und RANKL in der Metastasenbildung von Brustkrebszellen im Knochengewebe beteiligt sind. Wir konnten nun das RANK/RANKLSystem auch als essenziellen Mechanismus in der Entstehung von hormonellem Brustkrebs identifizieren. In diesem Beitrag werden wir daher den neuesten Erkenntnissen besondere Aufmerksamkeit schenken und diese kritisch in Bezug auf Brustkrebsentwicklung betrachten.

  12. rpsftm: An R package for rank preserving structural failure time models

    OpenAIRE

    Allison, A.; White, I. R.; Bond, S.

    2017-01-01

    Treatment switching in a randomised controlled trial occurs when participants change from their randomised treatment to the other trial treatment during the study. Failure to account for treatment switching in the analysis (i.e. by performing a standard intention-to-treat analysis) can lead to biased estimates of treatment efficacy. The rank preserving structural failure time model (RPSFTM) is a method used to adjust for treatment switching in trials with survival outcomes. The RPSFTM is due ...

  13. Network information improves cancer outcome prediction.

    Science.gov (United States)

    Roy, Janine; Winter, Christof; Isik, Zerrin; Schroeder, Michael

    2014-07-01

    Disease progression in cancer can vary substantially between patients. Yet, patients often receive the same treatment. Recently, there has been much work on predicting disease progression and patient outcome variables from gene expression in order to personalize treatment options. Despite first diagnostic kits in the market, there are open problems such as the choice of random gene signatures or noisy expression data. One approach to deal with these two problems employs protein-protein interaction networks and ranks genes using the random surfer model of Google's PageRank algorithm. In this work, we created a benchmark dataset collection comprising 25 cancer outcome prediction datasets from literature and systematically evaluated the use of networks and a PageRank derivative, NetRank, for signature identification. We show that the NetRank performs significantly better than classical methods such as fold change or t-test. Despite an order of magnitude difference in network size, a regulatory and protein-protein interaction network perform equally well. Experimental evaluation on cancer outcome prediction in all of the 25 underlying datasets suggests that the network-based methodology identifies highly overlapping signatures over all cancer types, in contrast to classical methods that fail to identify highly common gene sets across the same cancer types. Integration of network information into gene expression analysis allows the identification of more reliable and accurate biomarkers and provides a deeper understanding of processes occurring in cancer development and progression. © The Author 2012. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  14. A machine learning approach using EEG data to predict response to SSRI treatment for major depressive disorder.

    Science.gov (United States)

    Khodayari-Rostamabad, Ahmad; Reilly, James P; Hasey, Gary M; de Bruin, Hubert; Maccrimmon, Duncan J

    2013-10-01

    The problem of identifying, in advance, the most effective treatment agent for various psychiatric conditions remains an elusive goal. To address this challenge, we investigate the performance of the proposed machine learning (ML) methodology (based on the pre-treatment electroencephalogram (EEG)) for prediction of response to treatment with a selective serotonin reuptake inhibitor (SSRI) medication in subjects suffering from major depressive disorder (MDD). A relatively small number of most discriminating features are selected from a large group of candidate features extracted from the subject's pre-treatment EEG, using a machine learning procedure for feature selection. The selected features are fed into a classifier, which was realized as a mixture of factor analysis (MFA) model, whose output is the predicted response in the form of a likelihood value. This likelihood indicates the extent to which the subject belongs to the responder vs. non-responder classes. The overall method was evaluated using a "leave-n-out" randomized permutation cross-validation procedure. A list of discriminating EEG biomarkers (features) was found. The specificity of the proposed method is 80.9% while sensitivity is 94.9%, for an overall prediction accuracy of 87.9%. There is a 98.76% confidence that the estimated prediction rate is within the interval [75%, 100%]. These results indicate that the proposed ML method holds considerable promise in predicting the efficacy of SSRI antidepressant therapy for MDD, based on a simple and cost-effective pre-treatment EEG. The proposed approach offers the potential to improve the treatment of major depression and to reduce health care costs. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  15. Refining Prediction in Treatment-Resistant Depression: Results of Machine Learning Analyses in the TRD III Sample.

    Science.gov (United States)

    Kautzky, Alexander; Dold, Markus; Bartova, Lucie; Spies, Marie; Vanicek, Thomas; Souery, Daniel; Montgomery, Stuart; Mendlewicz, Julien; Zohar, Joseph; Fabbri, Chiara; Serretti, Alessandro; Lanzenberger, Rupert; Kasper, Siegfried

    The study objective was to generate a prediction model for treatment-resistant depression (TRD) using machine learning featuring a large set of 47 clinical and sociodemographic predictors of treatment outcome. 552 Patients diagnosed with major depressive disorder (MDD) according to DSM-IV criteria were enrolled between 2011 and 2016. TRD was defined as failure to reach response to antidepressant treatment, characterized by a Montgomery-Asberg Depression Rating Scale (MADRS) score below 22 after at least 2 antidepressant trials of adequate length and dosage were administered. RandomForest (RF) was used for predicting treatment outcome phenotypes in a 10-fold cross-validation. The full model with 47 predictors yielded an accuracy of 75.0%. When the number of predictors was reduced to 15, accuracies between 67.6% and 71.0% were attained for different test sets. The most informative predictors of treatment outcome were baseline MADRS score for the current episode; impairment of family, social, and work life; the timespan between first and last depressive episode; severity; suicidal risk; age; body mass index; and the number of lifetime depressive episodes as well as lifetime duration of hospitalization. With the application of the machine learning algorithm RF, an efficient prediction model with an accuracy of 75.0% for forecasting treatment outcome could be generated, thus surpassing the predictive capabilities of clinical evaluation. We also supply a simplified algorithm of 15 easily collected clinical and sociodemographic predictors that can be obtained within approximately 10 minutes, which reached an accuracy of 70.6%. Thus, we are confident that our model will be validated within other samples to advance an accurate prediction model fit for clinical usage in TRD. © Copyright 2017 Physicians Postgraduate Press, Inc.

  16. Temperature Fields in Soft Tissue during LPUS Treatment: Numerical Prediction and Experiment Results

    International Nuclear Information System (INIS)

    Kujawska, Tamara; Wojcik, Janusz; Nowicki, Andrzej

    2010-01-01

    Recent research has shown that beneficial therapeutic effects in soft tissues can be induced by the low power ultrasound (LPUS). For example, increasing of cells immunity to stress (among others thermal stress) can be obtained through the enhanced heat shock proteins (Hsp) expression induced by the low intensity ultrasound. The possibility to control the Hsp expression enhancement in soft tissues in vivo stimulated by ultrasound can be the potential new therapeutic approach to the neurodegenerative diseases which utilizes the known feature of cells to increase their immunity to stresses through the Hsp expression enhancement. The controlling of the Hsp expression enhancement by adjusting of exposure level to ultrasound energy would allow to evaluate and optimize the ultrasound-mediated treatment efficiency. Ultrasonic regimes are controlled by adjusting the pulsed ultrasound waves intensity, frequency, duration, duty cycle and exposure time. Our objective was to develop the numerical model capable of predicting in space and time temperature fields induced by a circular focused transducer generating tone bursts in multilayer nonlinear attenuating media and to compare the numerically calculated results with the experimental data in vitro. The acoustic pressure field in multilayer biological media was calculated using our original numerical solver. For prediction of temperature fields the Pennes' bio-heat transfer equation was employed. Temperature field measurements in vitro were carried out in a fresh rat liver using the 15 mm diameter, 25 mm focal length and 2 MHz central frequency transducer generating tone bursts with the spatial peak temporal average acoustic intensity varied between 0.325 and 1.95 W/cm 2 , duration varied from 20 to 500 cycles at the same 20% duty cycle and the exposure time varied up to 20 minutes. The measurement data were compared with numerical simulation results obtained under experimental boundary conditions. Good agreement between the

  17. Variable importance analysis based on rank aggregation with applications in metabolomics for biomarker discovery.

    Science.gov (United States)

    Yun, Yong-Huan; Deng, Bai-Chuan; Cao, Dong-Sheng; Wang, Wei-Ting; Liang, Yi-Zeng

    2016-03-10

    Biomarker discovery is one important goal in metabolomics, which is typically modeled as selecting the most discriminating metabolites for classification and often referred to as variable importance analysis or variable selection. Until now, a number of variable importance analysis methods to discover biomarkers in the metabolomics studies have been proposed. However, different methods are mostly likely to generate different variable ranking results due to their different principles. Each method generates a variable ranking list just as an expert presents an opinion. The problem of inconsistency between different variable ranking methods is often ignored. To address this problem, a simple and ideal solution is that every ranking should be taken into account. In this study, a strategy, called rank aggregation, was employed. It is an indispensable tool for merging individual ranking lists into a single "super"-list reflective of the overall preference or importance within the population. This "super"-list is regarded as the final ranking for biomarker discovery. Finally, it was used for biomarkers discovery and selecting the best variable subset with the highest predictive classification accuracy. Nine methods were used, including three univariate filtering and six multivariate methods. When applied to two metabolic datasets (Childhood overweight dataset and Tubulointerstitial lesions dataset), the results show that the performance of rank aggregation has improved greatly with higher prediction accuracy compared with using all variables. Moreover, it is also better than penalized method, least absolute shrinkage and selectionator operator (LASSO), with higher prediction accuracy or less number of selected variables which are more interpretable. Copyright © 2016 Elsevier B.V. All rights reserved.

  18. Can we predict the outcome for people with patellofemoral pain? A systematic review on prognostic factors and treatment effect modifiers.

    Science.gov (United States)

    Matthews, M; Rathleff, M S; Claus, A; McPoil, T; Nee, R; Crossley, K; Vicenzino, B

    2017-12-01

    Patellofemoral pain (PFP) is a multifactorial and often persistent knee condition. One strategy to enhance patient outcomes is using clinically assessable patient characteristics to predict the outcome and match a specific treatment to an individual. A systematic review was conducted to determine which baseline patient characteristics were (1) associated with patient outcome (prognosis); or (2) modified patient outcome from a specific treatment (treatment effect modifiers). 6 electronic databases were searched (July 2016) for studies evaluating the association between those with PFP, their characteristics and outcome. All studies were appraised using the Epidemiological Appraisal Instrument. Studies that aimed to identify treatment effect modifiers underwent a checklist for methodological quality. The 24 included studies evaluated 180 participant characteristics. 12 studies investigated prognosis, and 12 studies investigated potential treatment effect modifiers. Important methodological limitations were identified. Some prognostic studies used a retrospective design. Studies aiming to identify treatment effect modifiers often analysed too many variables for the limiting sample size and typically failed to use a control or comparator treatment group. 16 factors were reported to be associated with a poor outcome, with longer duration of symptoms the most reported (>4 months). Preliminary evidence suggests increased midfoot mobility may predict those who have a successful outcome to foot orthoses. Current evidence can identify those with increased risk of a poor outcome, but methodological limitations make it difficult to predict the outcome after one specific treatment compared with another. Adequately designed randomised trials are needed to identify treatment effect modifiers. 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/.

  19. What factors predict individual subjects' re-learning of words during anomia treatment?

    Directory of Open Access Journals (Sweden)

    William Hayward

    2014-04-01

    Full Text Available A growing number of studies are addressing methodological approaches to treating anomia in persons with aphasia. What is missing from these studies are validated procedures for determining which words have the greatest potential for recovery. The current study evaluates the usefulness of several word-specific variables and one subject-specific measure in predicting success in re-learning problematic words. Methods: Two participants, YPR and ODH, presented with fluent aphasia and marked anomia. YPR’s Aphasia Quotient on the Western Aphasia Battery was 58.8; ODH’s AQ was 79.5. Stimuli were 96 pictures chosen individually for each participant from among those that they named incorrectly on multiple baselines. Subsequently, participants were presented with each picture and asked to indicate whether they could name it covertly, or “in their head.” Each subject completed a biweekly anomia treatment for these pictures. We performed separate statistical analyses for each subject. Dependent variables included whether each word was learned during treatment (Acquisition and the number of sessions required to learn each word (#Sessions. We used logistic regression models to evaluate the association of (self-reported covert naming success with Acquisition, and linear regression models to assess the relationship between (self-reported covert naming success and #Sessions. Starting with the predictors of covert naming accuracy, number of syllables (#syllables, number of phonemes (#phonemes, and frequency, we used backwards elimination methods to select the final regression models. Results: By the end of 25 treatment sessions, YPR had learned 90.2% (37/41 of the covertly correct words but only 70.4% (38/54 of the covertly incorrect words. In the unadjusted analysis, covert naming was significantly associated with Acquisition, OR=3.89, 95% CI: (1.19, 12.74, p=0.025. The result remained significant after adjustment for #phonemes (the only other predictor

  20. Low Rank Approximation Algorithms, Implementation, Applications

    CERN Document Server

    Markovsky, Ivan

    2012-01-01

    Matrix low-rank approximation is intimately related to data modelling; a problem that arises frequently in many different fields. Low Rank Approximation: Algorithms, Implementation, Applications is a comprehensive exposition of the theory, algorithms, and applications of structured low-rank approximation. Local optimization methods and effective suboptimal convex relaxations for Toeplitz, Hankel, and Sylvester structured problems are presented. A major part of the text is devoted to application of the theory. Applications described include: system and control theory: approximate realization, model reduction, output error, and errors-in-variables identification; signal processing: harmonic retrieval, sum-of-damped exponentials, finite impulse response modeling, and array processing; machine learning: multidimensional scaling and recommender system; computer vision: algebraic curve fitting and fundamental matrix estimation; bioinformatics for microarray data analysis; chemometrics for multivariate calibration; ...

  1. Resolution of ranking hierarchies in directed networks

    Science.gov (United States)

    Barucca, Paolo; Lillo, Fabrizio

    2018-01-01

    Identifying hierarchies and rankings of nodes in directed graphs is fundamental in many applications such as social network analysis, biology, economics, and finance. A recently proposed method identifies the hierarchy by finding the ordered partition of nodes which minimises a score function, termed agony. This function penalises the links violating the hierarchy in a way depending on the strength of the violation. To investigate the resolution of ranking hierarchies we introduce an ensemble of random graphs, the Ranked Stochastic Block Model. We find that agony may fail to identify hierarchies when the structure is not strong enough and the size of the classes is small with respect to the whole network. We analytically characterise the resolution threshold and we show that an iterated version of agony can partly overcome this resolution limit. PMID:29394278

  2. Data envelopment analysis of randomized ranks

    Directory of Open Access Journals (Sweden)

    Sant'Anna Annibal P.

    2002-01-01

    Full Text Available Probabilities and odds, derived from vectors of ranks, are here compared as measures of efficiency of decision-making units (DMUs. These measures are computed with the goal of providing preliminary information before starting a Data Envelopment Analysis (DEA or the application of any other evaluation or composition of preferences methodology. Preferences, quality and productivity evaluations are usually measured with errors or subject to influence of other random disturbances. Reducing evaluations to ranks and treating the ranks as estimates of location parameters of random variables, we are able to compute the probability of each DMU being classified as the best according to the consumption of each input and the production of each output. Employing the probabilities of being the best as efficiency measures, we stretch distances between the most efficient units. We combine these partial probabilities in a global efficiency score determined in terms of proximity to the efficiency frontier.

  3. Ranking spreaders by decomposing complex networks

    International Nuclear Information System (INIS)

    Zeng, An; Zhang, Cheng-Jun

    2013-01-01

    Ranking the nodes' ability of spreading in networks is crucial for designing efficient strategies to hinder spreading in the case of diseases or accelerate spreading in the case of information dissemination. In the well-known k-shell method, nodes are ranked only according to the links between the remaining nodes (residual links) while the links connecting to the removed nodes (exhausted links) are entirely ignored. In this Letter, we propose a mixed degree decomposition (MDD) procedure in which both the residual degree and the exhausted degree are considered. By simulating the epidemic spreading process on real networks, we show that the MDD method can outperform the k-shell and degree methods in ranking spreaders.

  4. MMPI-2 Personality Psychopathology Five (PSY-5) and prediction of treatment outcome for patients with chronic back pain

    NARCIS (Netherlands)

    Vendrig, A.A.; Derksen, J.J.L.; Mey, H.R.A. De

    2000-01-01

    This study investigated the utility of the MMPI-2-based Personality Psychopathology Five (PSY-5) scales (Harkness, McNulty, & Ben-Porath, 1995) in the outcome prediction of behaviorally oriented chronic-pain treatment. The PSY-5 is a dimensional descriptive system for personality and its disorders.

  5. Prediction of Response to Treatment in a Randomized Clinical Trial of Couple Therapy: A 2-Year Follow-Up

    Science.gov (United States)

    Baucom, Brian R.; Atkins, David C.; Simpson, Lorelei E.; Christensen, Andrew

    2009-01-01

    Many studies have examined pretreatment predictors of immediate posttreatment outcome, but few studies have examined prediction of long-term treatment response to couple therapies. Four groups of predictors (demographic, intrapersonal, communication, and other interpersonal) and 2 moderators (pretreatment severity and type of therapy) were…

  6. The predictive value of baseline variables in the treatment of benign prostatic hyperplasia using high-energy transurethral microwave thermotherapy

    NARCIS (Netherlands)

    D'Ancona, F. C.; Francisca, E. A.; Hendriks, J. C.; Debruyne, F. M.; de la Rosette, J. J.

    1998-01-01

    To evaluate the combination of patient age, prostate size, grade of outlet obstruction and total amount of energy, all independent predictive variables of treatment outcome in patients with lower urinary tract symptoms (LUTS) and benign prostatic hyperplasia (BPH) treated with high-energy

  7. A predictive model of suitability for minimally invasive parathyroid surgery in the treatment of primary hyperparathyroidism [corrected].

    LENUS (Irish Health Repository)

    Kavanagh, Dara O

    2012-05-01

    Improved preoperative localizing studies have facilitated minimally invasive approaches in the treatment of primary hyperparathyroidism (PHPT). Success depends on the ability to reliably select patients who have PHPT due to single-gland disease. We propose a model encompassing preoperative clinical, biochemical, and imaging studies to predict a patient\\'s suitability for minimally invasive surgery.

  8. SimpleTreat 3.0: a model to predict the distribution and elimination of Chemicals by Sewage Treatment Plants

    NARCIS (Netherlands)

    Struijs J; ECO

    1996-01-01

    The spreadsheet SimpelTreat 3.0 is a model to predict the distribution and elimination of chemicals by sewage treatment. Simpeltreat 3.0 is an improved version of SimpleTreat, applied in the Netherlands in the Uniform System for the Evaluation of Substances (USES version 1.0, 1994). Although in the

  9. Prehospital evaluation and economic analysis of different coronary syndrome treatment strategies - PREDICT - Rationale, Development and Implementation

    Directory of Open Access Journals (Sweden)

    Craig Alan

    2011-03-01

    Full Text Available Abstract Background A standard of prehospital care for patients presenting with ST-segment elevation myocardial infarction (STEMI includes prehospital 12-lead and advance Emergency Department notification or prehospital bypass to percutaneous coronary intervention centres. Implementation of either care strategies is variable across communities and neither may exist in some communities. The main objective is to compare prehospital care strategies for time to treatment and survival outcomes as well as cost effectiveness. Methods/Design PREDICT is a multicentre, prospective population-based cohort study of all chest pain patients 18 years or older presenting within 30 mins to 6 hours of symptom onset and treated with nitroglycerin, transported by paramedics in a number of different urban and rural regions in Ontario. The primary objective of this study is to compare the proportion of study subjects who receive reperfusion within the target door-to-reperfusion times in subjects obtained after four prehospital strategies: 12-lead ECG and advance emergency department (ED notification or 3-lead ECG monitoring and alert to dispatch prior to hospital arrival; either with or without the opportunity to bypass to a PCI centre. Discussion We anticipate four challenges to successful study implementation and have developed strategies for each: 1 diversity in the interpretation of the ethical and privacy issues across 47 research ethics boards/commiittees covering 71 hospitals, 2 remote oversight of data guardian abstraction, 3 timeliness of implementation, and 4 potential interference in the study by concurrent technological advances. Research ethics approvals from academic centres were obtained initially and submitted to non academic centre applications. Data guardians were trained by a single investigator and data entry is informed by a detailed data dictionary including variable definitions and abstraction instrucations and subjected to error and logic

  10. Clinical use of the hyperthermia treatment planning system HyperPlan to predict effectiveness and toxicity

    International Nuclear Information System (INIS)

    Sreenivasa, Geetha; Gellermann, Johanna; Rau, Beate; Nadobny, Jacek; Schlag, Peter; Deuflhard, Peter; Felix, Roland; Wust, Peter

    2003-01-01

    Purpose: The main aim is to prove the clinical practicability of the hyperthermia treatment planning system HyperPlan on a β-test level. Data and observations obtained from clinical hyperthermia are compared with the numeric methods FE (finite element) and FDTD (finite difference time domain), respectively. Methods and Materials: The planning system HyperPlan is built on top of the modular, object-oriented platform for visualization and model generation AMIRA. This system already contains powerful algorithms for image processing, geometric modeling, and three-dimensional graphics display. A number of hyperthermia-specific modules are provided, enabling the creation of three-dimensional tetrahedral patient models suitable for treatment planning. Two numeric methods, FE and FDTD, are implemented in HyperPlan for solving Maxwell's equations. Both methods base their calculations on segmented (contour based) CT or MR image data. A tetrahedral grid is generated from the segmented tissue boundaries, consisting of approximately 80,000 tetrahedrons per patient. The FE method necessitates, primarily, this tetrahedral grid for the calculation of the E-field. The FDTD method, on the other hand, calculates the E-field on a cubical grid, but also requires a tetrahedral grid for correction at electrical interfaces. In both methods, temperature distributions are calculated on the tetrahedral grid by solving the bioheat transfer equation with the FE method. Segmentation, grid generation, E-field, and temperature calculation can be carried out in clinical practice at an acceptable time expenditure of about 1-2 days. Results: All 30 patients we analyzed with cervical, rectal, and prostate carcinoma exhibit a good correlation between the model calculations and the attained clinical data regarding acute toxicity (hot spots), prediction of easy-to-heat or difficult-to-heat patients, and the dependency on various other individual parameters. We could show sufficient agreement between

  11. Sign rank versus Vapnik-Chervonenkis dimension

    Science.gov (United States)

    Alon, N.; Moran, Sh; Yehudayoff, A.

    2017-12-01

    This work studies the maximum possible sign rank of sign (N × N)-matrices with a given Vapnik-Chervonenkis dimension d. For d=1, this maximum is three. For d=2, this maximum is \\widetilde{\\Theta}(N1/2). For d >2, similar but slightly less accurate statements hold. The lower bounds improve on previous ones by Ben-David et al., and the upper bounds are novel. The lower bounds are obtained by probabilistic constructions, using a theorem of Warren in real algebraic topology. The upper bounds are obtained using a result of Welzl about spanning trees with low stabbing number, and using the moment curve. The upper bound technique is also used to: (i) provide estimates on the number of classes of a given Vapnik-Chervonenkis dimension, and the number of maximum classes of a given Vapnik-Chervonenkis dimension--answering a question of Frankl from 1989, and (ii) design an efficient algorithm that provides an O(N/log(N)) multiplicative approximation for the sign rank. We also observe a general connection between sign rank and spectral gaps which is based on Forster's argument. Consider the adjacency (N × N)-matrix of a Δ-regular graph with a second eigenvalue of absolute value λ and Δ ≤ N/2. We show that the sign rank of the signed version of this matrix is at least Δ/λ. We use this connection to prove the existence of a maximum class C\\subseteq\\{+/- 1\\}^N with Vapnik-Chervonenkis dimension 2 and sign rank \\widetilde{\\Theta}(N1/2). This answers a question of Ben-David et al. regarding the sign rank of large Vapnik-Chervonenkis classes. We also describe limitations of this approach, in the spirit of the Alon-Boppana theorem. We further describe connections to communication complexity, geometry, learning theory, and combinatorics. Bibliography: 69 titles.

  12. Rank-based Tests of the Cointegrating Rank in Semiparametric Error Correction Models

    NARCIS (Netherlands)

    Hallin, M.; van den Akker, R.; Werker, B.J.M.

    2012-01-01

    Abstract: This paper introduces rank-based tests for the cointegrating rank in an Error Correction Model with i.i.d. elliptical innovations. The tests are asymptotically distribution-free, and their validity does not depend on the actual distribution of the innovations. This result holds despite the

  13. When sparse coding meets ranking: a joint framework for learning sparse codes and ranking scores

    KAUST Repository

    Wang, Jim Jing-Yan

    2017-06-28

    Sparse coding, which represents a data point as a sparse reconstruction code with regard to a dictionary, has been a popular data representation method. Meanwhile, in database retrieval problems, learning the ranking scores from data points plays an important role. Up to now, these two problems have always been considered separately, assuming that data coding and ranking are two independent and irrelevant problems. However, is there any internal relationship between sparse coding and ranking score learning? If yes, how to explore and make use of this internal relationship? In this paper, we try to answer these questions by developing the first joint sparse coding and ranking score learning algorithm. To explore the local distribution in the sparse code space, and also to bridge coding and ranking problems, we assume that in the neighborhood of each data point, the ranking scores can be approximated from the corresponding sparse codes by a local linear function. By considering the local approximation error of ranking scores, the reconstruction error and sparsity of sparse coding, and the query information provided by the user, we construct a unified objective function for learning of sparse codes, the dictionary and ranking scores. We further develop an iterative algorithm to solve this optimization problem.

  14. Multistep bioassay to predict recolonization potential of emerging parasitoids after a pesticide treatment.

    Science.gov (United States)

    Desneux, Nicolas; Ramirez-Romero, Ricardo; Kaiser, Laure

    2006-10-01

    Neurotoxic pyrethroid insecticides are widely used for crop protection, and lethal and sublethal perturbations can be expected in beneficial insects. Under laboratory conditions, the lethal and sublethal effects of deltamethrin on the aphid parasitoid Diaeretiella rapae M'Intosh (Hymenoptera: Braconidae) were studied at the mummy stage and in emerging adults. Following a multistep bioassay, analyses were aimed at evaluating the effects of deltamethrin at various crucial steps in the recolonization process following a deltamethrin treatment: Parasitoid pupal development (emergence from the mummies), adult survival, and host-searching capacity. A four-armed olfactometer was used to investigate the effect of deltamethrin on host-searching behavior (a range of concentrations causing 0.4-79.4% mortality was tested), and a Potter tower was used to test the deltamethrin effect with a realistic application method (four concentrations were tested: 0.5, 5.0, 6.25, and 50 g active ingredient [a.i.]/ha). Deltamethrin reduced the percentage of emergence from mummies, but only when exposed to the 50 g a.i./ha concentration. However, for all concentrations tested, the insecticide induced a decrease in longevity after emergence from sprayed mummies and significant adult mortality when parasitoids walked on fresh residues on leaves. Indices were defined and predicted a high mortality and, thus, reduction of recolonization capacities. However, deltamethrin had no effect on orientation behavior toward aphid-infested plants for adults that survived a residual exposure to the insecticide. The impact of deltamethrin on recolonization via pupal emergence and interest in the methodology used are discussed.

  15. Learning to rank for information retrieval

    CERN Document Server

    Liu, Tie-Yan

    2011-01-01

    Due to the fast growth of the Web and the difficulties in finding desired information, efficient and effective information retrieval systems have become more important than ever, and the search engine has become an essential tool for many people. The ranker, a central component in every search engine, is responsible for the matching between processed queries and indexed documents. Because of its central role, great attention has been paid to the research and development of ranking technologies. In addition, ranking is also pivotal for many other information retrieval applications, such as coll

  16. Cointegration rank testing under conditional heteroskedasticity

    DEFF Research Database (Denmark)

    Cavaliere, Giuseppe; Rahbek, Anders Christian; Taylor, Robert M.

    2010-01-01

    We analyze the properties of the conventional Gaussian-based cointegrating rank tests of Johansen (1996, Likelihood-Based Inference in Cointegrated Vector Autoregressive Models) in the case where the vector of series under test is driven by globally stationary, conditionally heteroskedastic......, relative to tests based on the asymptotic critical values or the i.i.d. bootstrap, the wild bootstrap rank tests perform very well in small samples under a variety of conditionally heteroskedastic innovation processes. An empirical application to the term structure of interest rates is given....

  17. Ranking health between countries in international comparisons

    DEFF Research Database (Denmark)

    Brønnum-Hansen, Henrik

    2014-01-01

    Cross-national comparisons and ranking of summary measures of population health sometimes give rise to inconsistent and diverging conclusions. In order to minimise confusion, international comparative studies ought to be based on well-harmonised data with common standards of definitions and docum......Cross-national comparisons and ranking of summary measures of population health sometimes give rise to inconsistent and diverging conclusions. In order to minimise confusion, international comparative studies ought to be based on well-harmonised data with common standards of definitions...

  18. Compressed Sensing with Rank Deficient Dictionaries

    DEFF Research Database (Denmark)

    Hansen, Thomas Lundgaard; Johansen, Daniel Højrup; Jørgensen, Peter Bjørn

    2012-01-01

    In compressed sensing it is generally assumed that the dictionary matrix constitutes a (possibly overcomplete) basis of the signal space. In this paper we consider dictionaries that do not span the signal space, i.e. rank deficient dictionaries. We show that in this case the signal-to-noise ratio...... (SNR) in the compressed samples can be increased by selecting the rows of the measurement matrix from the column space of the dictionary. As an example application of compressed sensing with a rank deficient dictionary, we present a case study of compressed sensing applied to the Coarse Acquisition (C...

  19. Ranking mutual funds using Sortino method

    Directory of Open Access Journals (Sweden)

    Khosro Faghani Makrani

    2014-04-01

    Full Text Available One of the primary concerns on most business activities is to determine an efficient method for ranking mutual funds. This paper performs an empirical investigation to rank 42 mutual funds listed on Tehran Stock Exchange using Sortino method over the period 2011-2012. The results of survey have been compared with market return and the results have confirmed that there were some positive and meaningful relationships between Sortino return and market return. In addition, there were some positive and meaningful relationship between two Sortino methods.

  20. Rank acquisition in rhesus macaque yearlings following permanent maternal separation: The importance of the social and physical environment.

    Science.gov (United States)

    Wooddell, Lauren J; Kaburu, Stefano S K; Murphy, Ashley M; Suomi, Stephen J; Dettmer, Amanda M

    2017-11-01

    Rank acquisition is a developmental milestone for young primates, but the processes by which primate yearlings attain social rank in the absence of the mother remain unclear. We studied 18 maternally reared yearling rhesus macaques (Macaca mulatta) that differed in their social and physical rearing environments. We found that early social experience and maternal rank, but not individual traits (weight, sex, age), predicted dominance acquisition in the new peer-only social group. Yearlings also used coalitions to reinforce the hierarchy, and social affiliation (play and grooming) was likely a product, rather than a determinant, of rank acquisition. Following relocation to a familiar environment, significant rank changes occurred indicating that familiarity with a physical environment was salient in rank acquisition. Our results add to the growing body of literature emphasizing the role of the social and physical environment on behavioral development, namely social asymmetries among peers. © 2017 Wiley Periodicals, Inc.

  1. Risk-informed ranking of engineering projects

    International Nuclear Information System (INIS)

    Jyrkama, M.; Pandey, M.

    2011-01-01

    Refurbishment planning requires prudent investment decisions with respect to the various systems and components at the station. These decisions are influenced by many factors, including engineering, safety, regulatory, economic, and political constraints. From an engineering perspective, the concept of cost-benefit analysis is a common way to allocate capital among various projects. Naturally, the 'best' or optimal project should have the lowest cost and the highest benefit. In the context of risk-informed decision making (RIDM), a process that has been widely embraced by the global nuclear community, the costs and benefits must further be 'weighted' by probabilities to estimate the underlying risk associated with the various planning alternatives. The main purpose of this study is to illustrate how risk and reliability information can be integrated into the refurbishment planning process to facilitate more objective and transparent investment decisions. The methodology is based on the concept of generation risk assessment (GRA) which provides a systematic approach for balancing investment costs with the reduction in overall financial risk. In addition to reliability predictions, the model provides estimates for the level of risk reduction associated with each system/project and also the break-even point for investment. This information is vital for project ranking, and helps to address the key question of whether capital investment should be made in the most risk critical systems, or in systems that reduce the overall risk the most. The application of the proposed methodology requires only basic information regarding the current reliability of each engineering system, which should be readily available from plant records and routine condition assessments. Because the methodology can be readily implemented in a Microsoft Excel spreadsheet, all plausible (e.g., bounding) planning scenarios, with or without investment, can also be generated quickly and easily, while

  2. Research of Subgraph Estimation Page Rank Algorithm for Web Page Rank

    Directory of Open Access Journals (Sweden)

    LI Lan-yin

    2017-04-01

    Full Text Available The traditional PageRank algorithm can not efficiently perform large data Webpage scheduling problem. This paper proposes an accelerated algorithm named topK-Rank,which is based on PageRank on the MapReduce platform. It can find top k nodes efficiently for a given graph without sacrificing accuracy. In order to identify top k nodes,topK-Rank algorithm prunes unnecessary nodes and edges in each iteration to dynamically construct subgraphs,and iteratively estimates lower/upper bounds of PageRank scores through subgraphs. Theoretical analysis shows that this method guarantees result exactness. Experiments show that topK-Rank algorithm can find k nodes much faster than the existing approaches.

  3. Cephalometric variables used to predict the success of interceptive treatment with rapid maxillary expansion and face mask. A longitudinal study

    Directory of Open Access Journals (Sweden)

    Daniele Nóbrega Nardoni

    2015-02-01

    Full Text Available INTRODUCTION: Prognosis is the main limitation of interceptive treatment of Class III malocclusions. The interceptive procedures of rapid maxillary expansion (RME and face mask therapy performed in early mixed dentition are capable of achieving immediate overcorrection and maintenance of facial and occlusal morphology for a few years. Individuals presenting minimal acceptable faces at growth completion are potential candidates for compensatory orthodontic treatment, while those with facial involvement should be submitted to orthodontic decompensation for orthognathic surgery. OBJECTIVES: To investigate cephalometric variables that might predict the outcomes of orthopedic treatment with RME and face mask therapy (FM. METHODS: Cephalometric analysis of 26 Class III patients (mean age of 8 years and 4 months was performed at treatment onset and after a mean period of 6 years and 10 months at pubertal growth completion, including a subjective facial analysis. Patients was divided into two groups: success group (21 individuals and failure group (5 individuals. Discriminant analysis was applied to the cephalometric values at treatment onset. Two predictor variables were found by stepwise procedure. RESULTS: Orthopedic treatment of Class III malocclusion may have unfavorable prognosis at growth completion whenever initial cephalometric analysis reveals increased lower anterior facial height (LAFH combined with reduced angle between the condylar axis and the mandibular plane (CondAx.MP. CONCLUSION: The results of treatment with RME and face mask therapy at growth completion in Class III patients could be predicted with a probability of 88.5%.

  4. The Use of Data Mining Methods to Predict the Result of Infertility Treatment Using the IVF ET Method

    Directory of Open Access Journals (Sweden)

    Malinowski Paweł

    2014-12-01

    Full Text Available The IVF ET method is a scientifically recognized infertility treat- ment method. The problem, however, is this method’s unsatisfactory efficiency. This calls for a more thorough analysis of the information available in the treat- ment process, in order to detect the factors that have an effect on the results, as well as to effectively predict result of treatment. Classical statistical methods have proven to be inadequate in this issue. Only the use of modern methods of data mining gives hope for a more effective analysis of the collected data. This work provides an overview of the new methods used for the analysis of data on infertility treatment, and formulates a proposal for further directions for research into increasing the efficiency of the predicted result of the treatment process.

  5. Usefulness of a rapid faecal calprotectin test to predict relapse in Crohn's disease patients on maintenance treatment with adalimumab.

    Science.gov (United States)

    Ferreiro-Iglesias, Rocio; Barreiro-de Acosta, Manuel; Lorenzo-Gonzalez, Aurelio; Dominguez-Muñoz, Juan Enrique

    2016-01-01

    Predicting relapse in Crohn's disease (CD) patients by measuring non-invasive biomarkers could allow for early changes of treatment. Data are scarce regarding the utility of monitoring calprotectin to predict relapse. The aim of the study was to evaluate the predictive value of a rapid test of faecal calprotectin (FC) to predict for flares in CD patients on maintenance treatment with adalimumab (ADA). A prospective, observational cohort study was designed. Inclusion criteria were CD patients in clinical remission on a standard dose of ADA therapy. Fresh FC was measured using a rapid test. Thirty patients were included (median age 38 years, 56.7% female). After the 4 months follow-up, 70.0% patients remained in clinical remission and 30.0% had a relapse. FC concentration at inclusion was significantly higher in those patients who relapsed during the follow-up (625 μg/g) compared to those who stayed in remission (45 μg/g). The optimal cut-off for FC to predict relapse was 204 μg/g. The area under the receiver-operating characteristic curve was 0.968. Sensitivity, specificity, positive, and negative predictive value of FC to predict relapse were 100%, 85.7%, 74.1%, and 100%, respectively. In CD patients on ADA maintenance therapy, FC levels measured with a rapid test allow relapse over the following months to be predicted with high accuracy. Low FC levels exclude relapse within at least 4 months after testing, whereas high levels are associated with relapse in three out of every four patients.

  6. Fully automated treatment planning for head and neck radiotherapy using a voxel-based dose prediction and dose mimicking method

    Science.gov (United States)

    McIntosh, Chris; Welch, Mattea; McNiven, Andrea; Jaffray, David A.; Purdie, Thomas G.

    2017-08-01

    Recent works in automated radiotherapy treatment planning have used machine learning based on historical treatment plans to infer the spatial dose distribution for a novel patient directly from the planning image. We present a probabilistic, atlas-based approach which predicts the dose for novel patients using a set of automatically selected most similar patients (atlases). The output is a spatial dose objective, which specifies the desired dose-per-voxel, and therefore replaces the need to specify and tune dose-volume objectives. Voxel-based dose mimicking optimization then converts the predicted dose distribution to a complete treatment plan with dose calculation using a collapsed cone convolution dose engine. In this study, we investigated automated planning for right-sided oropharaynx head and neck patients treated with IMRT and VMAT. We compare four versions of our dose prediction pipeline using a database of 54 training and 12 independent testing patients by evaluating 14 clinical dose evaluation criteria. Our preliminary results are promising and demonstrate that automated methods can generate comparable dose distributions to clinical. Overall, automated plans achieved an average of 0.6% higher dose for target coverage evaluation criteria, and 2.4% lower dose at the organs at risk criteria levels evaluated compared with clinical. There was no statistically significant difference detected in high-dose conformity between automated and clinical plans as measured by the conformation number. Automated plans achieved nine more unique criteria than clinical across the 12 patients tested and automated plans scored a significantly higher dose at the evaluation limit for two high-risk target coverage criteria and a significantly lower dose in one critical organ maximum dose. The novel dose prediction method with dose mimicking can generate complete treatment plans in 12-13 min without user interaction. It is a promising approach for fully automated treatment

  7. TU-G-210-03: Acoustic Simulations in Transcranial MRgFUS: Treatment Prediction and Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Vyas, U. [Stanford University (United States)

    2015-06-15

    Modeling can play a vital role in predicting, optimizing and analyzing the results of therapeutic ultrasound treatments. Simulating the propagating acoustic beam in various targeted regions of the body allows for the prediction of the resulting power deposition and temperature profiles. In this session we will apply various modeling approaches to breast, abdominal organ and brain treatments. Of particular interest is the effectiveness of procedures for correcting for phase aberrations caused by intervening irregular tissues, such as the skull in transcranial applications or inhomogeneous breast tissues. Also described are methods to compensate for motion in targeted abdominal organs such as the liver or kidney. Douglas Christensen – Modeling for Breast and Brain HIFU Treatment Planning Tobias Preusser – TRANS-FUSIMO - An Integrative Approach to Model-Based Treatment Planning of Liver FUS Urvi Vyas – Acoustic Simulations in Transcranial MRgFUS: Treatment Prediction and Analysis Learning Objectives: Understand the role of acoustic beam modeling for predicting the effectiveness of therapeutic ultrasound treatments. Apply acoustic modeling to specific breast, liver, kidney and transcranial anatomies. Determine how to obtain appropriate acoustic modeling parameters from clinical images. Understand the separate role of absorption and scattering in energy delivery to tissues. See how organ motion can be compensated for in ultrasound therapies. Compare simulated data with clinical temperature measurements in transcranial applications. Supported by NIH R01 HL172787 and R01 EB013433 (DC); EU Seventh Framework Programme (FP7/2007-2013) under 270186 (FUSIMO) and 611889 (TRANS-FUSIMO)(TP); and P01 CA159992, GE, FUSF and InSightec (UV)

  8. Speaker-sensitive emotion recognition via ranking: Studies on acted and spontaneous speech☆

    Science.gov (United States)

    Cao, Houwei; Verma, Ragini; Nenkova, Ani

    2015-01-01

    We introduce a ranking approach for emotion recognition which naturally incorporates information about the general expressivity of speakers. We demonstrate that our approach leads to substantial gains in accuracy compared to conventional approaches. We train ranking SVMs for individual emotions, treating the data from each speaker as a separate query, and combine the predictions from all rankers to perform multi-class prediction. The ranking method provides two natural benefits. It captures speaker specific information even in speaker-independent training/testing conditions. It also incorporates the intuition that each utterance can express a mix of possible emotion and that considering the degree to which each emotion is expressed can be productively exploited to identify the dominant emotion. We compare the performance of the rankers and their combination to standard SVM classification approaches on two publicly available datasets of acted emotional speech, Berlin and LDC, as well as on spontaneous emotional data from the FAU Aibo dataset. On acted data, ranking approaches exhibit significantly better performance compared to SVM classification both in distinguishing a specific emotion from all others and in multi-class prediction. On the spontaneous data, which contains mostly neutral utterances with a relatively small portion of less intense emotional utterances, ranking-based classifiers again achieve much higher precision in identifying emotional utterances than conventional SVM classifiers. In addition, we discuss the complementarity of conventional SVM and ranking-based classifiers. On all three datasets we find dramatically higher accuracy for the test items on whose prediction the two methods agree compared to the accuracy of individual methods. Furthermore on the spontaneous data the ranking and standard classification are complementary and we ob