N/V-limit for Langevin dynamics in continuum

We construct an infinite particle/infinite volume Langevin dynamics on the space of configurations in $\R^d$ having velocities as marks. The construction is done via a limiting procedure using $N$-particle dynamics in cubes $(-\lambda,\lambda]^d$ with periodic boundary conditions. A main step to this result is to derive an (improved) Ruelle bound for the canonical correlation functions of $N$-particle systems in $(-\lambda,\lambda]^d$ with periodic boundary conditions. After proving tightness of the laws of finite particle dynamics, the identification of accumulation points as martingale solutions of the Langevin equation is based on a general study of properties of measures on configuration space (and their weak limit) fulfilling a uniform Ruelle bound. Additionally, we prove that the initial/invariant distribution of the constructed dynamics is a tempered grand canonical Gibbs measure. All proofs work for general repulsive interaction potentials $\phi$ of Ruelle type (e.g. the Lennard-Jones potential) and all temperatures, densities and dimensions $d\geq 1$

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

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