Distributed Community Detection with the WCC Metric

Community detection has become an extremely active area of research in recent years, with researchers proposing various new metrics and algorithms to address the problem. Recently, the Weighted Community Clustering (WCC) metric was proposed as a novel way to judge the quality of a community partitioning based on the distribution of triangles in the graph, and was demonstrated to yield superior results over other commonly used metrics like modularity. The same authors later presented a parallel algorithm for optimizing WCC on large graphs. In this paper, we propose a new distributed, vertex-centric algorithm for community detection using the WCC metric. Results are presented that demonstrate the algorithm's performance and scalability on up to 32 worker machines and real graphs of up to 1.8 billion vertices. The algorithm scales best with the largest graphs, and to our knowledge, it is the first distributed algorithm for optimizing the WCC metric.Comment: 6 pages, 6 figure

Wild flies hedge their thermal preference bets in response to seasonal fluctuations

Fluctuating environmental pressures can challenge organisms by repeatedly shifting the optimum phenotype. Two contrasting evolutionary strategies to cope with these fluctuations are 1) evolution of the mean phenotype to follow the optimum (adaptive tracking) or 2) diversifying phenotypes so that at least some individuals have high fitness in the current fluctuation (bet-hedging). Bet-hedging could underlie stable differences in the behavior of individuals that are present even when genotype and environment are held constant. Instead of being simply ‘noise,’ behavioral variation across individuals may reflect an evolutionary strategy of phenotype diversification. Using geographically diverse wild-derived fly strains and high-throughput assays of individual preference, we tested whether thermal preference variation in Drosophila melanogaster could reflect a bet-hedging strategy. We also looked for evidence that populations from different regions differentially adopt bet-hedging or adaptive-tracking strategies. Computational modeling predicted regional differences in the relative advantage of bet-hedging, and we found patterns consistent with that in regional variation in thermal preference heritability. In addition, we found that temporal patterns in mean preference support bet-hedging predictions and that there is a genetic basis for thermal preference variability. Our empirical results point to bet-hedging in thermal preference as a potentially important evolutionary strategy in wild populations

Wild flies hedge their thermal preference bets in response to seasonal fluctuations

Fluctuating environmental pressures can challenge organisms by repeatedly shifting the optimum phenotype. Two contrasting evolutionary strategies to cope with these fluctuations are 1) evolution of the mean phenotype to follow the optimum (adaptive tracking) or 2) diversifying phenotypes so that at least some individuals have high fitness in the current fluctuation (bet-hedging). Bet-hedging could underlie stable differences in the behavior of individuals that are present even when genotype and environment are held constant. Instead of being simply ‘noise,’ behavioral variation across individuals may reflect an evolutionary strategy of phenotype diversification. Using geographically diverse wild-derived fly strains and high-throughput assays of individual preference, we tested whether thermal preference variation in Drosophila melanogaster could reflect a bet-hedging strategy. We also looked for evidence that populations from different regions differentially adopt bet-hedging or adaptive-tracking strategies. Computational modeling predicted regional differences in the relative advantage of bet-hedging, and we found patterns consistent with that in regional variation in thermal preference heritability. In addition, we found that temporal patterns in mean preference support bet-hedging predictions and that there is a genetic basis for thermal preference variability. Our empirical results point to bet-hedging in thermal preference as a potentially important evolutionary strategy in wild populations

Imaging and Neuro-Oncology Clinical Trials of the National Clinical Trials Network (NCTN)

Imaging in neuro-oncology clinical trials can be used to validate patient eligibility, stage at presentation, response to therapy, and radiation therapy. A number of National Clinical Trials Network trials illustrating this are presented. Through the Imaging and Radiation Oncology Core’s quality assurance processes for data acquisition and review, there are uniform data and imaging sets for review. Once the trial endpoints have been analyzed and published, the clinical trial information including pathology, imaging, and radiation therapy objects can be moved to a public archive for use by investigators interested in translational science and the application of new informatics tools for trial analysis

Acquisition and Management of Data for Translational Science in Oncology

Oncology clinical trials provide opportunity to advance care for patients with cancer. Bridging basic science with bedside care, cancer clinical trials have brought new and updated scientific knowledge at a rapid pace. Managing subject data in translation science requires a sophisticated informatics infrastructure that will enable harmonized datasets across all areas that could influence outcomes. Successful translational science requires that all relevant information be made readily available in a digital format that can be queried in a facile manner. Through a translational science prism, we look at past issues in cancer clinical trials and the new National Institutes of Health/National Cancer Institute initiative to address the need of database availability at an enterprise level

Modern Clinical Trials in Radiation Oncology

Clinical trials in radiation oncology have improved our translational science and patient care. All patients referred to departments of radiation oncology can be invited to participate in a clinical trial with multiple venues. Study endpoints can include intradepartmental endpoints to improve workflow and patient access as well as interdepartmental clinical translational trials that include the National Clinical Trials Network (NCTN) and industry. The quality of the trial is important to trial outcome and influences interpretation of the results of the study and how the results can be applied to patient care moving forward. Clinical trials in radiation oncology to date have accomplished much, however many important questions remain as patient care matures and systemic therapies become more sophisticated and associated with specific biomarkers and cellular expression products. In this chapter we review the history of clinical trials in radiation oncology and review the current status of the structure of quality assurance in clinical trials. We will review unanswered questions and areas to study in each disease area and how to design strategy for trials to address modern unmet needs in our discipline

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Pooled Safety and Efficacy Analysis Examining the Effect of Performance Status on Outcomes in Nine First-Line Treatment Trials Using Individual Data From Patients With Metastatic Colorectal Cancer

Performance status (PS) is a prognostic factor in patients with metastatic colorectal cancer. Clinical trials typically enroll less than 10% of patients with a PS of 2 (PS2); thus, the benefit of systemic chemotherapy in PS2 patients is uncertain