Active Brownian Particles. From Individual to Collective Stochastic Dynamics

We review theoretical models of individual motility as well as collective dynamics and pattern formation of active particles. We focus on simple models of active dynamics with a particular emphasis on nonlinear and stochastic dynamics of such self-propelled entities in the framework of statistical mechanics. Examples of such active units in complex physico-chemical and biological systems are chemically powered nano-rods, localized patterns in reaction-diffusion system, motile cells or macroscopic animals. Based on the description of individual motion of point-like active particles by stochastic differential equations, we discuss different velocity-dependent friction functions, the impact of various types of fluctuations and calculate characteristic observables such as stationary velocity distributions or diffusion coefficients. Finally, we consider not only the free and confined individual active dynamics but also different types of interaction between active particles. The resulting collective dynamical behavior of large assemblies and aggregates of active units is discussed and an overview over some recent results on spatiotemporal pattern formation in such systems is given.Comment: 161 pages, Review, Eur Phys J Special-Topics, accepte

What Patients with Mild-to-Moderate Kidney Disease Know, Think, and Feel about Their Disease: An In-Depth Interview Study

Contains fulltext : 196344.pdf (Publisher’s version ) (Closed access)INTRODUCTION: It is unknown what patients in primary care with mild-to-moderate chronic kidney disease (CKD) know, think, and feel about their diagnoses and how they value the information provided. The aim of the study was to explore their knowledge, thoughts, and experiences concerning their CKD and the information given to them. METHOD: Qualitative interview study with patients with mild-to-moderate CKD who know their diagnoses and are treated mainly by family physicians. RESULTS: Four themes arose: CKD literacy, coping with anxiety, prerequisites for self-management, and reciprocity in information provision. The participants filled deficiencies in their CKD knowledge with misconceptions and half-truth about causes, symptoms, and treatment. The anxiety about CKD at the time of diagnosis versus the feeling of irrelevance later on was due to the absence of CKD symptoms and their physicians' minimization of the seriousness of CKD. Participants failed to connect lifestyle and cardiovascular disease with CKD. Not all participants were well informed about the consequences that CKD might have. CKD literacy and willingness to change were both necessary to accept lifestyle changes. Further, the participants felt that it would be helpful when information comes with empathy and is tailored to patients' personal needs. CONCLUSIONS: Patients have various perceptions about their CKD. Exploring these perceptions could help match their needs with better-tailored information. Doctors should be aware that they can deliver inaccurate signals about CKD severity, so that patients fail to realize the potential impact of CKD. This makes them less open to lifestyle changes and improving their self-management

Simultaneous Inference of Cancer Pathways and Tumor Progression from Cross-Sectional Mutation Data

Recent cancer sequencing studies provide a wealth of somatic mutation data from a large number of patients. One of the most intriguing and challenging questions arising from this data is to determine whether the temporal order of somatic mutations in a cancer follows any common progression. Since we usually obtain only one sample from a patient, such inferences are commonly made from cross-sectional data from different patients. This analysis is complicated by the extensive variation in the somatic mutations across different patients, variation that is reduced by examining combinations of mutations in various pathways. Thus far, methods to reconstruct tumor progression at the pathway level have restricted attention to known, a priori defined pathways. In this work we show how to simultaneously infer pathways and the temporal order of their mutations from cross-sectional data, leveraging on the exclusivity property of driver mutations within a pathway. We define the pathway linear progression model, and derive a combinatorial formulation for the problem of finding the optimal model from mutation data. We show that with enough samples the optimal solution to this problem uniquely identifies the correct model with high probability even when errors are present in the mutation data. We then formulate the problem as an integer linear program (ILP), which allows the analysis of datasets from recent studies with large numbers of samples. We use our algorithm to analyze somatic mutation data from three cancer studies, including two studies from The Cancer Genome Atlas (TCGA) on large number of samples on colorectal cancer and glioblastoma. The models reconstructed with our method capture most of the current knowledge of the progression of somatic mutations in these cancer types, while also providing new insights on the tumor progression at the pathway level