Standard random walks and trapping on the Koch network with scale-free behavior and small-world effect

A vast variety of real-life networks display the ubiquitous presence of scale-free phenomenon and small-world effect, both of which play a significant role in the dynamical processes running on networks. Although various dynamical processes have been investigated in scale-free small-world networks, analytical research about random walks on such networks is much less. In this paper, we will study analytically the scaling of the mean first-passage time (MFPT) for random walks on scale-free small-world networks. To this end, we first map the classical Koch fractal to a network, called Koch network. According to this proposed mapping, we present an iterative algorithm for generating the Koch network, based on which we derive closed-form expressions for the relevant topological features, such as degree distribution, clustering coefficient, average path length, and degree correlations. The obtained solutions show that the Koch network exhibits scale-free behavior and small-world effect. Then, we investigate the standard random walks and trapping issue on the Koch network. Through the recurrence relations derived from the structure of the Koch network, we obtain the exact scaling for the MFPT. We show that in the infinite network order limit, the MFPT grows linearly with the number of all nodes in the network. The obtained analytical results are corroborated by direct extensive numerical calculations. In addition, we also determine the scaling efficiency exponents characterizing random walks on the Koch network.Comment: 12 pages, 8 figures. Definitive version published in Physical Review

The Structure of Climate Variability Across Scales

One of the most intriguing facets of the climate system is that it exhibits variability across all temporal and spatial scales; pronounced examples are temperature and precipitation. The structure of this variability, however, is not arbitrary. Over certain spatial and temporal ranges it can be described by scaling relationships in the form of power‐laws in probability density distributions and autocorrelation functions. These scaling relationships can be quantified by scaling exponents which measure how the variability changes across scales and how the intensity changes with frequency of occurrence. Scaling determines the relative magnitudes and persistence of natural climate fluctuations. Here, we review various scaling mechanisms and their relevance for the climate system. We show observational evidence of scaling and discuss the application of scaling properties and methods in trend detection, climate sensitivity analyses, and climate predictio

Understanding the Impacts of Outdoor Air Pollution on Social Inequality: Advancing a Just Transition Framework

Outdoor air pollution is a major environmental risk that caused over 4.2 million premature deaths worldwide in 2016. In this article, we investigate how groups with lower social-economic status are disproportionally affected by outdoor air pollution. Based on a comparative case study of two heavily polluted urban areas around Beijing and Delhi, we find that people's economic welfare and political rights are affected disproportionally not only by toxic air pollutants, but also through various policy interventions, market activities, and social practices designed to reduce or adapt to air pollution. Drawing on the concepts of environmental justice and just transitions, we present an analytical framework for investigating the links between outdoor air pollution and social inequalities. The framework enables a better understanding of structural constraints, political constraints and protective constraints in the context of outdoor air pollution and their impacts on social vulnerabilities with particular relevance to fast industrialising countries. We also provide recommendations on how to design and implement air pollution policies and social interventions in a socially inclusive manner

Prediction of overall survival for patients with metastatic castration-resistant prostate cancer : development of a prognostic model through a crowdsourced challenge with open clinical trial data

Background Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease. Methods Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interest-namely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a fifth trial-ENTHUSE M1-in which 266 patients with metastatic castration-resistant prostate cancer were treated with placebo alone. Findings 50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge. The top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function. Overall, ePCR outperformed all other methods (iAUC 0.791; Bayes factor >5) and surpassed the reference model (iAUC 0.743; Bayes factor >20). Both the ePCR model and reference models stratified patients in the ENTHUSE 33 trial into high-risk and low-risk groups with significantly different overall survival (ePCR: hazard ratio 3.32, 95% CI 2.39-4.62, p Interpretation Novel prognostic factors were delineated, and the assessment of 50 methods developed by independent international teams establishes a benchmark for development of methods in the future. The results of this effort show that data-sharing, when combined with a crowdsourced challenge, is a robust and powerful framework to develop new prognostic models in advanced prostate cancer.Peer reviewe

Permian-Triassic boundary microbialites (PTBMs) in soutwest China: implications for paleoenvironment reconstruction

Permian–Triassic boundary microbialites (PTBMs) are commonly interpreted to be a sedimentary response to upwelling of anoxic alkaline seawater and indicate a harsh marine environment in the Permian–Triassic transition. However, recent studies propose that PTBMs may instead be developed in an oxic environment, therefore necessitating the need to reassess the paleoenvironment of formation of PTBMs. This paper is an integrated study of the PTBM sequence at Yudongzi, northwest Sichuan Basin, which is one of the thickest units of PTBMs in south China. Analysis of conodont biostratigraphy, mega- to microscopic microbialite structures, stratigraphic variations in abundance and size of metazoan fossils, and total organic carbon (TOC) and total sulfur (TS) contents within the PTBM reveals the following results: (1) the microbialites occur mainly in the Hindeodus parvus Zone but may cross the Permian–Triassic boundary, and are comprised of, from bottom to top: lamellar thrombolites, dendritic thrombolites and lamellar-reticular thrombolites; (2) most metazoan fossils of the microbialite succession increase in abundance upsection, so does the sizes of bivalve and brachiopod fossils; (3) TOC and TS values of microbialites account respectively for 0.07 and 0.31 wt% on average, both of which are very low. The combination of increase in abundance and size of metazoan fossils upsection, together with the low TOC and TS contents, is evidence that the Yudongzi PTBMs developed in oxic seawater. We thus dispute the previous view, at least for the Chinese sequences, of low-oxygen seawater for microbialite growth, and question whether it is now appropriate to associate PTBMs with anoxic, harsh environments associated with the end-Permian extinction. Instead, we interpret those conditions as fully oxygenated.13th Five-Year Plan National Scientific and Technology Major Project (2016ZX05004002-001); National Natural Science Foundation of China (41602166)

Dpp/Gbb signaling is required for normal intestinal regeneration during infection

10.1016/j.ydbio.2014.12.017Developmental Biology3992189-20