Breakdown and recovery in traffic flow models

Most car-following models show a transition from laminar to ``congested'' flow and vice versa. Deterministic models often have a density range where a disturbance needs a sufficiently large critical amplitude to move the flow from the laminar into the congested phase. In stochastic models, it may be assumed that the size of this amplitude gets translated into a waiting time, i.e.\ until fluctuations sufficiently add up to trigger the transition. A recently introduced model of traffic flow however does not show this behavior: in the density regime where the jam solution co-exists with the high-flow state, the intrinsic stochasticity of the model is not sufficient to cause a transition into the jammed regime, at least not within relevant time scales. In addition, models can be differentiated by the stability of the outflow interface. We demonstrate that this additional criterion is not related to the stability of the flow. The combination of these criteria makes it possible to characterize commonalities and differences between many existing models for traffic in a new way

Incorporating topological information for predicting robust cancer subnetwork markers in human protein-protein interaction network

BACKGROUND: Discovering robust markers for cancer prognosis based on gene expression data is an important yet challenging problem in translational bioinformatics. By integrating additional information in biological pathways or a protein-protein interaction (PPI) network, we can find better biomarkers that lead to more accurate and reproducible prognostic predictions. In fact, recent studies have shown that, “modular markers,” that integrate multiple genes with potential interactions can improve disease classification and also provide better understanding of the disease mechanisms. RESULTS: In this work, we propose a novel algorithm for finding robust and effective subnetwork markers that can accurately predict cancer prognosis. To simultaneously discover multiple synergistic subnetwork markers in a human PPI network, we build on our previous work that uses affinity propagation, an efficient clustering algorithm based on a message-passing scheme. Using affinity propagation, we identify potential subnetwork markers that consist of discriminative genes that display coherent expression patterns and whose protein products are closely located on the PPI network. Furthermore, we incorporate the topological information from the PPI network to evaluate the potential of a given set of proteins to be involved in a functional module. Primarily, we adopt widely made assumptions that densely connected subnetworks may likely be potential functional modules and that proteins that are not directly connected but interact with similar sets of other proteins may share similar functionalities. CONCLUSIONS: Incorporating topological attributes based on these assumptions can enhance the prediction of potential subnetwork markers. We evaluate the performance of the proposed subnetwork marker identification method by performing classification experiments using multiple independent breast cancer gene expression datasets and PPI networks. We show that our method leads to the discovery of robust subnetwork markers that can improve cancer classification. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-016-1224-1) contains supplementary material, which is available to authorized users

A critical evaluation of network and pathway based classifiers for outcome prediction in breast cancer

Recently, several classifiers that combine primary tumor data, like gene expression data, and secondary data sources, such as protein-protein interaction networks, have been proposed for predicting outcome in breast cancer. In these approaches, new composite features are typically constructed by aggregating the expression levels of several genes. The secondary data sources are employed to guide this aggregation. Although many studies claim that these approaches improve classification performance over single gene classifiers, the gain in performance is difficult to assess. This stems mainly from the fact that different breast cancer data sets and validation procedures are employed to assess the performance. Here we address these issues by employing a large cohort of six breast cancer data sets as benchmark set and by performing an unbiased evaluation of the classification accuracies of the different approaches. Contrary to previous claims, we find that composite feature classifiers do not outperform simple single gene classifiers. We investigate the effect of (1) the number of selected features; (2) the specific gene set from which features are selected; (3) the size of the training set and (4) the heterogeneity of the data set on the performance of composite feature and single gene classifiers. Strikingly, we find that randomization of secondary data sources, which destroys all biological information in these sources, does not result in a deterioration in performance of composite feature classifiers. Finally, we show that when a proper correction for gene set size is performed, the stability of single gene sets is similar to the stability of composite feature sets. Based on these results there is currently no reason to prefer prognostic classifiers based on composite features over single gene classifiers for predicting outcome in breast cancer

Virtual versus Physical Channel for Sex Networking in Men Having Sex with Men of Sauna Customers in the City of Hong Kong

BACKGROUND: Advances in communication technology may affect networking pattern, thereby influencing the dynamics of sex partnership. The aim of the study is to explore the impacts of partner sourcing through internet and related channels on exposure risk to sexually transmitted infections (STI) including HIV. METHODS: Using venue-based sampling, a cross-sectional questionnaire survey was conducted at saunas frequented by men having sex with men (MSM) in Hong Kong. Comparison was made between MSM sourcing partners through physical venues alone versus concomitant users of physical and virtual channels, the latter referring to internet and smart-phone applications, using bivariate logistic regression. RESULTS: Over a 7-week study period, 299 MSM were recruited from 9 saunas. Three main types of sex partners were distinguished: steady (46.8%), regular (26.4%) and casual (96.0%) partners. Users of sauna (n = 78) were compared with concomitant users of saunas and virtual channels (n = 179) for partner sourcing. Sauna-visiting virtual channel users were younger and inclined to use selected physical venues for sourcing partners. Smart-phone users (n = 90) were not different from other internet-users in terms of age, education level and single/mixed self-identified body appearance. Classifying respondents into high risk and low risk MSM by their frequency of condom use, concomitant use of both sauna and virtual channels accounted for a higher proportion in the high risk category (71.6% vs. 58.2%, OR = 1.81, p<0.05). In virtual channel users, partner sourcing through smart-phone was not associated with a higher practice of unprotected sex. CONCLUSION: MSM sauna customers commonly use virtual channels for sex partner sourcing. Unprotected sex is more prevalent in sauna customers who use virtual channel for sex partner sourcing. While the popularity of smart-phone is rising, its use is not associated with increased behavioural risk for HIV/STI transmission

Preventive Effects of Zoledronic Acid on Bone Metastasis in Mice Injected with Human Breast Cancer Cells

Bisphosphonates are used routinely to reduce bone-related events in breast cancer patients with bone metastasis. We evaluated the effects of zoledronic acid, a third generation, nitrogen-containing bisphosphonate, to prevent bone metastasis in breast cancer. Zoledronic acid or vehicle alone was administered to nude mice either simultaneously or after intracardiac injection of human breast cancer MDA-MB-231 cells. Nude mice treated with zoledronic acid at early time points showed a lower incidence of bone metastases than did vehicle-treated nude mice, but these differences were not statistically significant. Only 37.5% of mice treated with zoledronic acid at the time of tumor cell inoculation developed bone metastases compared to over 51.8% of mice receiving vehicle alone (P = 0.304). Cell count of apoptosis confirmed by immunohistochemical staining in metastatic bone tissue significantly increased in the zoledronic acid-treated groups compared to non-treated group (1,018.3 vs 282.0; P = 0.046). However, metastatic tumor cells, which invade soft tissue around the bone, did not show extensive apoptosis; there were no differences between the zoledronic acid-treated and control groups. These results suggest that zoledronic acid increases apoptosis of metastatic breast tumor cells in the bone and could therefore reduce metastatic tumor burden. These results support the use of zoledronic acid to reduce the incidence of bone metastasis in breast cancer

Heterologous Tissue Culture Expression Signature Predicts Human Breast Cancer Prognosis

BACKGROUND: Cancer patients have highly variable clinical outcomes owing to many factors, among which are genes that determine the likelihood of invasion and metastasis. This predisposition can be reflected in the gene expression pattern of the primary tumor, which may predict outcomes and guide the choice of treatment better than other clinical predictors. METHODOLOGY/PRINCIPAL FINDINGS: We developed an mRNA expression-based model that can predict prognosis/outcomes of human breast cancer patients regardless of microarray platform and patient group. Our model was developed using genes differentially expressed in mouse plasma cell tumors growing in vivo versus those growing in vitro. The prediction system was validated using published data from three cohorts of patients for whom microarray and clinical data had been compiled. The model stratified patients into four independent survival groups (BEST, GOOD, BAD, and WORST: log-rank test p = 1.7×10(−8)). CONCLUSIONS: Our model significantly improved the survival prediction over other expression-based models and permitted recognition of patients with different prognoses within the estrogen receptor-positive group and within a single pathological tumor class. Basing our predictor on a dataset that originated in a different species and a different cell type may have rendered it less sensitive to proliferation differences and endowed it with wide applicability. SIGNIFICANCE: Prognosis prediction for patients with breast cancer is currently based on histopathological typing and estrogen receptor positivity. Yet both assays define groups that are heterogeneous in survival. Gene expression profiling allows subdivision of these groups and recognition of patients whose tumors are very unlikely to be lethal and those with much grimmer outlooks, which can augment the predictive power of conventional tumor analysis and aid the clinician in choosing relaxed vs. aggressive therapy

Essential and checkpoint functions of budding yeast ATM and ATR during meiotic prophase are facilitated by differential phosphorylation of a meiotic adaptor protein, Hop1

A hallmark of the conserved ATM/ATR signalling is its ability to mediate a wide range of functions utilizing only a limited number of adaptors and effector kinases. During meiosis, Tel1 and Mec1, the budding yeast ATM and ATR, respectively, rely on a meiotic adaptor protein Hop1, a 53BP1/Rad9 functional analog, and its associated kinase Mek1, a CHK2/Rad53-paralog, to mediate multiple functions: control of the formation and repair of programmed meiotic DNA double strand breaks, enforcement of inter-homolog bias, regulation of meiotic progression, and implementation of checkpoint responses. Here, we present evidence that the multi-functionality of the Tel1/Mec1-to-Hop1/Mek1 signalling depends on stepwise activation of Mek1 that is mediated by Tel1/Mec1 phosphorylation of two specific residues within Hop1: phosphorylation at the threonine 318 (T318) ensures the transient basal level Mek1 activation required for viable spore formation during unperturbed meiosis. Phosphorylation at the serine 298 (S298) promotes stable Hop1-Mek1 interaction on chromosomes following the initial phospho-T318 mediated Mek1 recruitment. In the absence of Dmc1, the phospho-S298 also promotes Mek1 hyper-activation necessary for implementing meiotic checkpoint arrest. Taking these observations together, we propose that the Hop1 phospho-T318 and phospho-S298 constitute key components of the Tel1/Mec1- based meiotic recombination surveillance (MRS) network and facilitate effective coupling of meiotic recombination and progression during both unperturbed and challenged meiosis

Notch signaling during human T cell development

Notch signaling is critical during multiple stages of T cell development in both mouse and human. Evidence has emerged in recent years that this pathway might regulate T-lineage differentiation differently between both species. Here, we review our current understanding of how Notch signaling is activated and used during human T cell development. First, we set the stage by describing the developmental steps that make up human T cell development before describing the expression profiles of Notch receptors, ligands, and target genes during this process. To delineate stage-specific roles for Notch signaling during human T cell development, we subsequently try to interpret the functional Notch studies that have been performed in light of these expression profiles and compare this to its suggested role in the mouse