Residence Time Statistics for Normal and Fractional Diffusion in a Force Field

We investigate statistics of occupation times for an over-damped Brownian particle in an external force field. A backward Fokker-Planck equation introduced by Majumdar and Comtet describing the distribution of occupation times is solved. The solution gives a general relation between occupation time statistics and probability currents which are found from solutions of the corresponding problem of first passage time. This general relationship between occupation times and first passage times, is valid for normal Markovian diffusion and for non-Markovian sub-diffusion, the latter modeled using the fractional Fokker-Planck equation. For binding potential fields we find in the long time limit ergodic behavior for normal diffusion, while for the fractional framework weak ergodicity breaking is found, in agreement with previous results of Bel and Barkai on the continuous time random walk on a lattice. For non-binding potential rich physical behaviors are obtained, and classification of occupation time statistics is made possible according to whether or not the underlying random walk is recurrent and the averaged first return time to the origin is finite. Our work establishes a link between fractional calculus and ergodicity breaking.Comment: 12 page

Stochastic pump effect and geometric phases in dissipative and stochastic systems

The success of Berry phases in quantum mechanics stimulated the study of similar phenomena in other areas of physics, including the theory of living cell locomotion and motion of patterns in nonlinear media. More recently, geometric phases have been applied to systems operating in a strongly stochastic environment, such as molecular motors. We discuss such geometric effects in purely classical dissipative stochastic systems and their role in the theory of the stochastic pump effect (SPE).Comment: Review. 35 pages. J. Phys. A: Math, Theor. (in press

Assessment of cognitive self-statements during marital problem solving: A comparison of two methods

Twenty maritally distressed couples (DC) and 20 nondistressed couples (NDC) were recruited and asked to undertake 10 minutes of problem-solving discussions, which were videotaped. Each individual partner’s cognitive self-statements during the interaction were assessed using two methods: video-assisted recall (VR) and thought listing (TL). Reported cognitions from each method were content- analysed and classified into five categories: partner- referent positive, partner- referent negative, self-referent positive, self- referent negative, and other. Proportions of reported cognitions falling into each category were analysed in two separate two-way MANOVAs (marital distress/ nondistress x sex) for the VR and TL measures. Results of each MANOVA indicated a highly significant effect of marital distress on cognitions, and a significant effect of sex on the VR but not the TL measure. Discriminant analyses showed that the VR and TL methods both discriminated between DC and NDC groups. Post hoc univariate ANOVAs indicated that DC had significantly higher proportions of negative partner- referent cognitions, and lower proportions of positive partner- referent cognitions, than NDC while problem solving. The relative merits of each cognitive assessment method, and their potential use in increasing marital therapy effectiveness, are discussed

NetDiff – Bayesian model selection for differential gene regulatory network inference

Differential networks allow us to better understand the changes in cellular processes that are exhibited in conditions of interest, identifying variations in gene regulation or protein interaction between, for example, cases and controls, or in response to external stimuli. Here we present a novel methodology for the inference of differential gene regulatory networks from gene expression microarray data. Specifically we apply a Bayesian model selection approach to compare models of conserved and varying network structure, and use Gaussian graphical models to represent the network structures. We apply a variational inference approach to the learning of Gaussian graphical models of gene regulatory networks, that enables us to perform Bayesian model selection that is significantly more computationally efficient than Markov Chain Monte Carlo approaches. Our method is demonstrated to be more robust than independent analysis of data from multiple conditions when applied to synthetic network data, generating fewer false positive predictions of differential edges. We demonstrate the utility of our approach on real world gene expression microarray data by applying it to existing data from amyotrophic lateral sclerosis cases with and without mutations in C9orf72, and controls, where we are able to identify differential network interactions for further investigation

Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks

BACKGROUND. Reverse engineering cellular networks is currently one of the most challenging problems in systems biology. Dynamic Bayesian networks (DBNs) seem to be particularly suitable for inferring relationships between cellular variables from the analysis of time series measurements of mRNA or protein concentrations. As evaluating inference results on a real dataset is controversial, the use of simulated data has been proposed. However, DBN approaches that use continuous variables, thus avoiding the information loss associated with discretization, have not yet been extensively assessed, and most of the proposed approaches have dealt with linear Gaussian models. RESULTS. We propose a generalization of dynamic Gaussian networks to accommodate nonlinear dependencies between variables. As a benchmark dataset to test the new approach, we used data from a mathematical model of cell cycle control in budding yeast that realistically reproduces the complexity of a cellular system. We evaluated the ability of the networks to describe the dynamics of cellular systems and their precision in reconstructing the true underlying causal relationships between variables. We also tested the robustness of the results by analyzing the effect of noise on the data, and the impact of a different sampling time. CONCLUSION. The results confirmed that DBNs with Gaussian models can be effectively exploited for a first level analysis of data from complex cellular systems. The inferred models are parsimonious and have a satisfying goodness of fit. Furthermore, the networks not only offer a phenomenological description of the dynamics of cellular systems, but are also able to suggest hypotheses concerning the causal interactions between variables. The proposed nonlinear generalization of Gaussian models yielded models characterized by a slightly lower goodness of fit than the linear model, but a better ability to recover the true underlying connections between variables.Italian Ministry of University and Scientific Research; National Institutes of Health & National Human Genome Research Institute (HG003354-01A2); Collegio Ghislieri, Pavia Italy fellowshi

CHEK2 1100delC is prevalent in Swedish early onset familial breast cancer

<p>Abstract</p> <p>Background</p> <p>A truncating variant, 1100delC, in check point-kinase CHEK2, has been identified as a risk factor for familial and sporadic breast cancer. The prevalence in healthy non-breast cancer cases is low and varies between populations.</p> <p>Methods</p> <p>We analyzed the prevalence of <it>CHEK2 </it>1100delC in 763 breast cancer patients with a defined family history and 760 controls from the Stockholm region. The breast cancer patients originated from; a population-based cohort (n = 452) and from a familial cancer clinic (n = 311), the detailed family history was known in both groups.</p> <p>Results</p> <p>The variant was found in 2.9% of the familial cases from the population-based cohort and in 1.9% from the familial cancer clinic. In total 2.2% of the patients with a family history of breast cancer carried the variant compared to 0.7% of the controls (p = 0.03). There was no increased prevalence in sporadic patients (0.3%). The variant was most frequent in young familial patients (5.1% of cases ≤45 years, p = 0.003). The mean age at diagnosis of variant carriers was 12 years lower than in non-carriers (p = 0.001).</p> <p>Conclusion</p> <p>In conclusion, <it>CHEK2 </it>1100delC exists in the Swedish population. The prevalence is increased in familial breast cancer and the variant seems to influence age at onset.</p

FGF receptor genes and breast cancer susceptibility: results from the Breast Cancer Association Consortium

Background:Breast cancer is one of the most common malignancies in women. Genome-wide association studies have identified FGFR2 as a breast cancer susceptibility gene. Common variation in other fibroblast growth factor (FGF) receptors might also modify risk. We tested this hypothesis by studying genotyped single-nucleotide polymorphisms (SNPs) and imputed SNPs in FGFR1, FGFR3, FGFR4 and FGFRL1 in the Breast Cancer Association Consortium. Methods:Data were combined from 49 studies, including 53 835 cases and 50 156 controls, of which 89 050 (46 450 cases and 42 600 controls) were of European ancestry, 12 893 (6269 cases and 6624 controls) of Asian and 2048 (1116 cases and 932 controls) of African ancestry. Associations with risk of breast cancer, overall and by disease sub-type, were assessed using unconditional logistic regression. Results:Little evidence of association with breast cancer risk was observed for SNPs in the FGF receptor genes. The strongest evidence in European women was for rs743682 in FGFR3; the estimated per-allele odds ratio was 1.05 (95 confidence interval=1.02-1.09, P=0.0020), which is substantially lower than that observed for SNPs in FGFR2. Conclusion:Our results suggest that common variants in the other FGF receptors are not associated with risk of breast cancer to the degree observed for FGFR2. © 2014 Cancer Research UK

High-dose paclitaxel in combination with doxorubicin, cyclophosphamide and peripheral blood progenitor cell rescue in patients with high-risk primary and responding metastatic breast carcinoma: toxicity profile, relationship to paclitaxel pharmacokinetics and short-term outcome

We assessed the feasibility and pharmacokinetics of high-dose infusional paclitaxel in combination with doxorubicin, cyclophosphamide, and peripheral blood progenitor cell rescue. Between October 1995 and June 1998, 63 patients with high-risk primary [stage II with ≥ 10 axillary nodes involved, stage IIIA or stage IIIB inflammatory carcinoma (n = 53)] or with stage IV responsive breast cancer (n = 10) received paclitaxel 150–775 mg/m2infused over 24 hours, doxorubicin 165 mg/m2as a continuous infusion over 96 hours, and cyclophosphamide 100 mg kg–1. There were no treatment-related deaths. Dose-limiting toxicity was reversible, predominantly sensory neuropathy following administration of paclitaxel at the 775 mg/m2dose level. Paclitaxel pharmacokinetics were non-linear at higher dose levels; higher paclitaxel dose level, AUC, and peak concentrations were associated with increased incidence of paraesthesias. No correlation between stomatitis, haematopoietic toxicities, and paclitaxel dose or pharmacokinetics was found. Kaplan–Meier estimates of 30-month event-free and overall survival for patients with primary breast carcinoma are 65% (95% CI; 51–83%) and 77% (95% CI; 64–93%). Paclitaxel up to 725 mg/m2infused over 24 hours in combination with with doxorubicin 165 mg/m2and cyclophosphamide 100 mg kg–1is tolerable. A randomized study testing this regimen against high-dose carboplatin, thiotepa and cyclophosphamide (STAMP V) is currently ongoing. © 2001 Cancer Research Campaign http://www.bjcancer.co

Integrated Genomic Analysis of Diverse Induced Pluripotent Stem Cells from the Progenitor Cell Biology Consor tium

The rigorous characterization of distinct induced pluripotent stem cells (iPSC) derived from multiple reprogramming technologies, somatic sources, and donors is required to understand potential sources of variability and downstream potential. To achieve this goal, the Progenitor Cell Biology Consortium performed comprehensive experimental and genomic analyses of 58 iPSC from ten laboratories generated using a variety of reprogramming genes, vectors, and cells. Associated global molecular characterization studies identified functionally informative correlations in gene expression, DNA methylation, and/or copy-number variation among key developmental and oncogenic regulators as a result of donor, sex, line stability, reprogramming technology, and cell of origin. Furthermore, X-chromosome inactivation in PSC produced highly correlated differences in teratoma-lineage staining and regulator expression upon differentiation. All experimental results, and raw, processed, and metadata from these analyses, including powerful tools, are interactively accessible from a new online portal at https://www.synapse.org to serve as a reusable resource for the stem cell community