Quantifying ethnic segregation in cities through random walks

Socioeconomic segregation has an important role in the emergence of large-scale inequalities in urban areas. Most of the available measures of spatial segregation depend on the scale and size of the system under study, or neglect large-scale spatial correlations, or rely on ad-hoc parameters, making it hard to compare different systems on equal grounds. We propose here a family of non-parametric measures for spatial distributions, based on the statistics of the trajectories of random walks on graphs associated to a spatial system. These quantities provide a consistent estimation of segregation in synthetic spatial patterns, and we use them to analyse the ethnic segregation of metropolitan areas in the US and the UK. We show that the spatial diversity of ethnic distributions, as measured through diffusion on graphs, allow us to compare the ethnic segregation of urban areas having different size, shape, or peculiar microscopic characteristics, and exhibits a strong association with socio-economic deprivation

Influence of a Corpus Luteum Tissue Extract on Rabbit Ovarian Mesothelial Cells

This study investigates rabbit ovarian mesothelial (OM) cells exposed in vitro to a crude corpus luteum extract (CLE; 60 μg/ml). The growth of OM cells was evaluated by measuring the change in cell number (mean % ± standard error of mean, SEM), the number of cell population doublings (CPD ± SEM), and the cell population doubling time in hours (CPDT ± SEM) after 7.5 days of culture in a serum-poor medium. Quantitative estimates of surface morphology changes were obtained by analyzing the total number (mean no. ± SEM), density (mean no./100 μ.m2 ± SEM), and length-to-diameter ratio (mean L/D ± SEM) of microvilli. OM cells in control medium formed loosely cohesive monolayers, and grew 152.53 ± 11.01% with a CPD of 0.59 ± 0.08 and a CPDT of 117.29 ± 6.43 hours. The exposed surface area of these cells was over 8,000 μ.m2 and was covered in its epinuclear region by long and slender microvilli with a L/D of 6.01 ± 0.29. The total number of microvilli in each control cell was 1977.52 ± 120.49 with a density of 0.58 ± 0.03/100 μ.m2 in the epinuclear region and of 0.05 ± 0.003/150 μ.m2 in the remaining surface area (5,161.62 ± 354.43 μ.m2). In contrast, CLE-rich cells cultures grew 329.57 ± 16.65%, with a CPD of 1.71 ± 0.07 and a CPDT of 53.43 + 2.93 hours. These cells formed confluent monolayers of smaller (2104.86 ± 103.71 μ.m2), tightly juxtaposed epithelioid cells with a microvillar density of 0.70 ± 0.03/100 μ.m2 in over 78% of their surface. These data support the existence of an intra-ovarian factor capable of enhancing growth and differentiation of OM cells

Trends Prediction Using Social Diffusion Models

The importance of the ability of predict trends in social media has been growing rapidly in the past few years with the growing dominance of social media in our everyday's life. Whereas many works focus on the detection of anomalies in networks, there exist little theoretical work on the prediction of the likelihood of anomalous network pattern to globally spread and become "trends". In this work we present an analytic model the social diffusion dynamics of spreading network patterns. Our proposed method is based on information diffusion models, and is capable of predicting future trends based on the analysis of past social interactions between the community's members. We present an analytic lower bound for the probability that emerging trends would successful spread through the network. We demonstrate our model using two comprehensive social datasets - the "Friends and Family" experiment that was held in MIT for over a year, where the complete activity of 140 users was analyzed, and a financial dataset containing the complete activities of over 1.5 million members of the "eToro" social trading community.Comment: 6 Pages + Appendi

Dynamic communicability predicts infectiousness

Using real, time-dependent social interaction data, we look at correlations between some recently proposed dynamic centrality measures and summaries from large-scale epidemic simulations. The evolving network arises from email exchanges. The centrality measures, which are relatively inexpensive to compute, assign rankings to individual nodes based on their ability to broadcast information over the dynamic topology. We compare these with node rankings based on infectiousness that arise when a full stochastic SI simulation is performed over the dynamic network. More precisely, we look at the proportion of the network that a node is able to infect over a fixed time period, and the length of time that it takes for a node to infect half the network.We find that the dynamic centrality measures are an excellent, and inexpensive, proxy for the full simulation-based measures

Modularity measure of networks with overlapping communities

In this paper we introduce a non-fuzzy measure which has been designed to rank the partitions of a network's nodes into overlapping communities. Such a measure can be useful for both quantifying clusters detected by various methods and during finding the overlapping community-structure by optimization methods. The theoretical problem referring to the separation of overlapping modules is discussed, and an example for possible applications is given as well

Bcl-2 expression is altered with ovarian tumor progression: an immunohistochemical evaluation

<p>Abstract</p> <p>Background</p> <p>Ovarian cancer is the most lethal gynecologic malignancy. The ovarian tumor microenvironment is comprised of tumor cells, surrounding stroma, and circulating lymphocytes, an important component of the immune response, in tumors. Previous reports have shown that the anti-apoptotic protein Bcl-2 is overexpressed in many solid neoplasms, including ovarian cancers, and contributes to neoplastic transformation and drug-resistant disease, resulting in poor clinical outcome. Likewise, studies indicate improved clinical outcome with increased presence of lymphocytes. Therefore, we sought to examine Bcl-2 expression in normal, benign, and cancerous ovarian tissues to determine the potential relationship between epithelial and stromal Bcl-2 expression in conjunction with the presence of lymphocytes for epithelial ovarian tumor progression.</p> <p>Methods</p> <p>Ovarian tissue sections were classified as normal (n = 2), benign (n = 17) or cancerous (n = 28) and immunohistochemically stained for Bcl-2. Bcl-2 expression was assessed according to cellular localization, extent, and intensity of staining. The number of lymphocyte nests as well as the number of lymphocytes within these nests was counted.</p> <p>Results</p> <p>While Bcl-2 staining remained cytoplasmic, both percent and intensity of epithelial and stromal Bcl-2 staining decreased with tumor progression. Further, the number of lymphocyte nests dramatically increased with tumor progression.</p> <p>Conclusion</p> <p>The data suggest alterations in Bcl-2 expression and lymphocyte infiltration correlate with epithelial ovarian cancer progression. Consequently, Bcl-2 expression and lymphocyte status may be important for prognostic outcome or useful targets for therapeutic intervention.</p

A kinetic binding study to evaluate the pharmacological profile of a specific leukotriene C-4 binding site not coupled to contraction in human lung parenchyma

We report the identification of a novel pharmacological profile for the leukotriene (LT)C-4 binding site we previously identified in human lung parenchyma (HLP). We used a series of classic cysteinyl-LT (CysLT)(1) receptor antagonists belonging to different chemical classes and the dual CysLT(1)-CysLT(2) antagonist BAY u9773 for both binding and functional studies. Because the presence of (S)-decyl-glutathione interfered with cysteinyl-LT binding, with a kinetic protocol we avoided the use of this compound. By means of heterologous dissociation time courses, we demonstrated that zafirlukast, iralukast, and BAY u9773 selectively competed only for H-3-LTD4 binding sites, whereas pobilukast, pranlukast, and CGP 57698 dissociated both H-3-LTC4 and H-3-LTD4 from their binding sites. Thus, with binding studies, we have been able to identify a pharmacological profile for LTC4 distinct from that of LTD4 receptor (CysLT(1)) in HLP. On the contrary, in functional studies, all of the classic antagonists tested were able to revert both LTC4- and LTD4-induced contractions of isolated HLP strips. Thus, LTD4 and LTC4 contract isolated HLP strips through the same CysLT1 receptor. The results of kinetic binding studies, coupled to a sophisticated data analysis, confirm our hypothesis that HLP membranes contain two cysteinyl-LT high-affinity binding sites with different pharmacological profiles. In functional studies, however, LTD4- and LTC4-induced contractions are mediated by the same CysLT(1) receptor. In conclusion, the specific LTC4 high-affinity binding site cannot be classified as one of the officially recognized CysLT receptors, and it is not implicated in LTC4-induced HLP strip contractions

Trends Prediction Using Social Diffusion Models

The importance of the ability to predict trends in social media has been growing rapidly in the past few years with the growing dominance of social media in our everyday’s life. Whereas many works focus on the detection of anomalies in networks, there exist little theoretical work on the prediction of the likelihood of anomalous network pattern to globally spread and become “trends”. In this work we present an analytic model for the social diffusion dynamics of spreading network patterns. Our proposed method is based on information diffusion models, and is capable of predicting future trends based on the analysis of past social interactions between the community’s members. We present an analytic lower bound for the probability that emerging trends would successfully spread through the network. We demonstrate our model using two comprehensive social datasets — the Friends and Family experiment that was held in MIT for over a year, where the complete activity of 140 users was analyzed, and a financial dataset containing the complete activities of over 1.5 million members of the eToro social trading community