Self-avoiding fractional Brownian motion - The Edwards model

In this work we extend Varadhan's construction of the Edwards polymer model to the case of fractional Brownian motions in $\R^d$, for any dimension $d\geq 2$, with arbitrary Hurst parameters $H\leq 1/d$.Comment: 14 page

DeBi: Discovering Differentially Expressed Biclusters using a Frequent Itemset Approach

<p>Abstract</p> <p>Background</p> <p>The analysis of massive high throughput data via clustering algorithms is very important for elucidating gene functions in biological systems. However, traditional clustering methods have several drawbacks. Biclustering overcomes these limitations by grouping genes and samples simultaneously. It discovers subsets of genes that are co-expressed in certain samples. Recent studies showed that biclustering has a great potential in detecting marker genes that are associated with certain tissues or diseases. Several biclustering algorithms have been proposed. However, it is still a challenge to find biclusters that are significant based on biological validation measures. Besides that, there is a need for a biclustering algorithm that is capable of analyzing very large datasets in reasonable time.</p> <p>Results</p> <p>Here we present a fast biclustering algorithm called DeBi (Differentially Expressed BIclusters). The algorithm is based on a well known data mining approach called frequent itemset. It discovers maximum size homogeneous biclusters in which each gene is strongly associated with a subset of samples. We evaluate the performance of DeBi on a yeast dataset, on synthetic datasets and on human datasets.</p> <p>Conclusions</p> <p>We demonstrate that the DeBi algorithm provides functionally more coherent gene sets compared to standard clustering or biclustering algorithms using biological validation measures such as Gene Ontology term and Transcription Factor Binding Site enrichment. We show that DeBi is a computationally efficient and powerful tool in analyzing large datasets. The method is also applicable on multiple gene expression datasets coming from different labs or platforms.</p

A visual analytics approach for understanding biclustering results from microarray data

Abstract Background Microarray analysis is an important area of bioinformatics. In the last few years, biclustering has become one of the most popular methods for classifying data from microarrays. Although biclustering can be used in any kind of classification problem, nowadays it is mostly used for microarray data classification. A large number of biclustering algorithms have been developed over the years, however little effort has been devoted to the representation of the results. Results We present an interactive framework that helps to infer differences or similarities between biclustering results, to unravel trends and to highlight robust groupings of genes and conditions. These linked representations of biclusters can complement biological analysis and reduce the time spent by specialists on interpreting the results. Within the framework, besides other standard representations, a visualization technique is presented which is based on a force-directed graph where biclusters are represented as flexible overlapped groups of genes and conditions. This microarray analysis framework (BicOverlapper), is available at http://vis.usal.es/bicoverlapper Conclusion The main visualization technique, tested with different biclustering results on a real dataset, allows researchers to extract interesting features of the biclustering results, especially the highlighting of overlapping zones that usually represent robust groups of genes and/or conditions. The visual analytics methodology will permit biology experts to study biclustering results without inspecting an overwhelming number of biclusters individually.</p

Differences in bleeding behavior after endoscopic band ligation: a retrospective analysis

<p>Abstract</p> <p>Background</p> <p>Endoscopic band ligation (EBL) is generally accepted as the treatment of choice for bleeding from esophageal varices. It is also used for secondary prophylaxis of esophageal variceal hemorrhage. However, there is no data or guidelines concerning endoscopic control of ligation ulcers. We conducted a retrospective study of EBL procedures analyzing bleeding complications after EBL.</p> <p>Methods</p> <p>We retrospectively analyzed data from patients who underwent EBL. We analyzed several data points, including indication for the procedure, bleeding events and the time interval between EBL and bleeding.</p> <p>Results</p> <p>255 patients and 387 ligation sessions were included in the analysis. We observed an overall bleeding rate after EBL of 7.8%. Bleeding events after elective treatment (3.9%) were significantly lower than those after treatment for acute variceal hemorrhage (12.1%). The number of bleeding events from ligation ulcers and variceal rebleeding was 14 and 15, respectively. The bleeding rate from the ligation site in the group who underwent emergency ligation was 7.1% and 0.5% in the group who underwent elective ligation. Incidence of variceal rebleeding did not vary significantly. Seventy-five percent of all bleeding episodes after elective treatment occurred within four days after EBL. 20/22 of bleeding events after emergency ligation occured within 11 days after treatment. Elective EBL has a lower risk of bleeding from treatment-induced ulceration than emergency ligation.</p> <p>Conclusions</p> <p>Patients who underwent EBL for treatment of acute variceal bleeding should be kept under medical surveillance for 11 days. After elective EBL, it may be reasonable to restrict the period of surveillance to four days or even perform the procedure in an out-patient setting.</p

Biclustering via optimal re-ordering of data matrices in systems biology: rigorous methods and comparative studies

<p>Abstract</p> <p>Background</p> <p>The analysis of large-scale data sets via clustering techniques is utilized in a number of applications. Biclustering in particular has emerged as an important problem in the analysis of gene expression data since genes may only jointly respond over a subset of conditions. Biclustering algorithms also have important applications in sample classification where, for instance, tissue samples can be classified as cancerous or normal. Many of the methods for biclustering, and clustering algorithms in general, utilize simplified models or heuristic strategies for identifying the "best" grouping of elements according to some metric and cluster definition and thus result in suboptimal clusters.</p> <p>Results</p> <p>In this article, we present a rigorous approach to biclustering, OREO, which is based on the Optimal RE-Ordering of the rows and columns of a data matrix so as to globally minimize the dissimilarity metric. The physical permutations of the rows and columns of the data matrix can be modeled as either a network flow problem or a traveling salesman problem. Cluster boundaries in one dimension are used to partition and re-order the other dimensions of the corresponding submatrices to generate biclusters. The performance of OREO is tested on (a) metabolite concentration data, (b) an image reconstruction matrix, (c) synthetic data with implanted biclusters, and gene expression data for (d) colon cancer data, (e) breast cancer data, as well as (f) yeast segregant data to validate the ability of the proposed method and compare it to existing biclustering and clustering methods.</p> <p>Conclusion</p> <p>We demonstrate that this rigorous global optimization method for biclustering produces clusters with more insightful groupings of similar entities, such as genes or metabolites sharing common functions, than other clustering and biclustering algorithms and can reconstruct underlying fundamental patterns in the data for several distinct sets of data matrices arising in important biological applications.</p

Telephone Cognitive-Behavioral Therapy for Subthreshold Depression and Presenteeism in Workplace: A Randomized Controlled Trial

Subthreshold depression is highly prevalent in the general population and causes great loss to society especially in the form of reduced productivity while at work (presenteeism). We developed a highly-structured manualized eight-session cognitive-behavioral program with a focus on subthreshold depression in the workplace and to be administered via telephone by trained psychotherapists (tCBT).We conducted a parallel-group, non-blinded randomized controlled trial of tCBT in addition to the pre-existing Employee Assistance Program (EAP) versus EAP alone among workers with subthreshold depression at a large manufacturing company in Japan. The primary outcomes were depression severity as measured with Beck Depression Inventory-II (BDI-II) and presenteeism as measured with World Health Organization Health and Work Productivity Questionnaire (HPQ). In the course of the trial the follow-up period was shortened in order to increase acceptability of the study.The planned sample size was 108 per arm but the trial was stopped early due to low accrual. Altogether 118 subjects were randomized to tCBT+EAP (n = 58) and to EAP alone (n = 60). The BDI-II scores fell from the mean of 17.3 at baseline to 11.0 in the intervention group and to 15.7 in the control group after 4 months (p<0.001, Effect size = 0.69, 95%CI: 0.32 to 1.05). However, there was no statistically significant decrease in absolute and relative presenteeism (p = 0.44, ES = 0.15, -0.21 to 0.52, and p = 0.50, ES = 0.02, -0.34 to 0.39, respectively).Remote CBT, including tCBT, may provide easy access to quality-assured effective psychotherapy for people in the work force who present with subthreshold depression. Further studies are needed to evaluate the effectiveness of this approach in longer terms. The study was funded by Sekisui Chemicals Co. Ltd.ClinicalTrials.gov NCT00885014