Moving lattice kinks and pulses: an inverse method

We develop a general mapping from given kink or pulse shaped travelling-wave solutions including their velocity to the equations of motion on one-dimensional lattices which support these solutions. We apply this mapping - by definition an inverse method - to acoustic solitons in chains with nonlinear intersite interactions, to nonlinear Klein-Gordon chains, to reaction-diffusion equations and to discrete nonlinear Schr\"odinger systems. Potential functions can be found in at least a unique way provided the pulse shape is reflection symmetric and pulse and kink shapes are at least $C^2$ functions. For kinks we discuss the relation of our results to the problem of a Peierls-Nabarro potential and continuous symmetries. We then generalize our method to higher dimensional lattices for reaction-diffusion systems. We find that increasing also the number of components easily allows for moving solutions.Comment: 15 pages, 5 figure

Templates for Convex Cone Problems with Applications to Sparse Signal Recovery

This paper develops a general framework for solving a variety of convex cone problems that frequently arise in signal processing, machine learning, statistics, and other fields. The approach works as follows: first, determine a conic formulation of the problem; second, determine its dual; third, apply smoothing; and fourth, solve using an optimal first-order method. A merit of this approach is its flexibility: for example, all compressed sensing problems can be solved via this approach. These include models with objective functionals such as the total-variation norm, ||Wx||_1 where W is arbitrary, or a combination thereof. In addition, the paper also introduces a number of technical contributions such as a novel continuation scheme, a novel approach for controlling the step size, and some new results showing that the smooth and unsmoothed problems are sometimes formally equivalent. Combined with our framework, these lead to novel, stable and computationally efficient algorithms. For instance, our general implementation is competitive with state-of-the-art methods for solving intensively studied problems such as the LASSO. Further, numerical experiments show that one can solve the Dantzig selector problem, for which no efficient large-scale solvers exist, in a few hundred iterations. Finally, the paper is accompanied with a software release. This software is not a single, monolithic solver; rather, it is a suite of programs and routines designed to serve as building blocks for constructing complete algorithms.Comment: The TFOCS software is available at http://tfocs.stanford.edu This version has updated reference

Incidence, Risk Factors, and Outcomes of Patients Who Develop Mucosal Barrier Injury-Laboratory Confirmed Bloodstream Infections in the First 100 Days after Allogeneic Hematopoietic Stem Cell Transplant

Importance: Patients undergoing hematopoietic stem cell transplant (HSCT) are at risk for bloodstream infection (BSI) secondary to translocation of bacteria through the injured mucosa, termed mucosal barrier injury-laboratory confirmed bloodstream infection (MBI-LCBI), in addition to BSI secondary to indwelling catheters and infection at other sites (BSI-other). Objective: To determine the incidence, timing, risk factors, and outcomes of patients who develop MBI-LCBI in the first 100 days after HSCT. Design, Setting, and Participants: A case-cohort retrospective analysis was performed using data from the Center for International Blood and Marrow Transplant Research database on 16875 consecutive pediatric and adult patients receiving a first allogeneic HSCT from January 1, 2009, to December 31, 2016. Patients were classified into 4 categories: MBI-LCBI (1481 [8.8%]), MBI-LCBI and BSI-other (698 [4.1%]), BSI-other only (2928 [17.4%]), and controls with no BSI (11768 [69.7%]). Statistical analysis was performed from April 5 to July 17, 2018. Main Outcomes and Measures: Demographic characteristics and outcomes, including overall survival, chronic graft-vs-host disease, and transplant-related mortality (only for patients with malignant disease), were compared among groups. Results: Of the 16875 patients in the study (9737 [57.7%] male; median [range] age, 47 [0.04-82] years) 13686 (81.1%) underwent HSCT for a malignant neoplasm, and 3189 (18.9%) underwent HSCT for a nonmalignant condition. The cumulative incidence of MBI-LCBI was 13% (99% CI, 12%-13%) by day 100, and the cumulative incidence of BSI-other was 21% (99% CI, 21%-22%) by day 100. Median (range) time from transplant to first MBI-LCBI was 8 (<1 to 98) days vs 29 (<1 to 100) days for BSI-other. Multivariable analysis revealed an increased risk of MBI-LCBI with poor Karnofsky/Lansky performance status (hazard ratio [HR], 1.21 [99% CI, 1.04-1.41]), cord blood grafts (HR, 2.89 [99% CI, 1.97-4.24]), myeloablative conditioning (HR, 1.46 [99% CI, 1.19-1.78]), and posttransplant cyclophosphamide graft-vs-host disease prophylaxis (HR, 1.85 [99% CI, 1.38-2.48]). One-year mortality was significantly higher for patients with MBI-LCBI (HR, 1.81 [99% CI, 1.56-2.12]), BSI-other (HR, 1.81 [99% CI, 1.60-2.06]), and MBI-LCBI plus BSI-other (HR, 2.65 [99% CI, 2.17-3.24]) compared with controls. Infection was more commonly reported as a cause of death for patients with MBI-LCBI (139 of 740 [18.8%]), BSI (251 of 1537 [16.3%]), and MBI-LCBI plus BSI (94 of 435 [21.6%]) than for controls (566 of 4740 [11.9%]). Conclusions and Relevance: In this cohort study, MBI-LCBI, in addition to any BSIs, were associated with significant morbidity and mortality after HSCT. Further investigation into risk reduction should be a clinical and scientific priority in this patient population

Low Complexity Regularization of Linear Inverse Problems

Inverse problems and regularization theory is a central theme in contemporary signal processing, where the goal is to reconstruct an unknown signal from partial indirect, and possibly noisy, measurements of it. A now standard method for recovering the unknown signal is to solve a convex optimization problem that enforces some prior knowledge about its structure. This has proved efficient in many problems routinely encountered in imaging sciences, statistics and machine learning. This chapter delivers a review of recent advances in the field where the regularization prior promotes solutions conforming to some notion of simplicity/low-complexity. These priors encompass as popular examples sparsity and group sparsity (to capture the compressibility of natural signals and images), total variation and analysis sparsity (to promote piecewise regularity), and low-rank (as natural extension of sparsity to matrix-valued data). Our aim is to provide a unified treatment of all these regularizations under a single umbrella, namely the theory of partial smoothness. This framework is very general and accommodates all low-complexity regularizers just mentioned, as well as many others. Partial smoothness turns out to be the canonical way to encode low-dimensional models that can be linear spaces or more general smooth manifolds. This review is intended to serve as a one stop shop toward the understanding of the theoretical properties of the so-regularized solutions. It covers a large spectrum including: (i) recovery guarantees and stability to noise, both in terms of $\ell^2$-stability and model (manifold) identification; (ii) sensitivity analysis to perturbations of the parameters involved (in particular the observations), with applications to unbiased risk estimation ; (iii) convergence properties of the forward-backward proximal splitting scheme, that is particularly well suited to solve the corresponding large-scale regularized optimization problem

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

School-based prevention for adolescent Internet addiction: prevention is the key. A systematic literature review

Adolescents’ media use represents a normative need for information, communication, recreation and functionality, yet problematic Internet use has increased. Given the arguably alarming prevalence rates worldwide and the increasingly problematic use of gaming and social media, the need for an integration of prevention efforts appears to be timely. The aim of this systematic literature review is (i) to identify school-based prevention programmes or protocols for Internet Addiction targeting adolescents within the school context and to examine the programmes’ effectiveness, and (ii) to highlight strengths, limitations, and best practices to inform the design of new initiatives, by capitalizing on these studies’ recommendations. The findings of the reviewed studies to date presented mixed outcomes and are in need of further empirical evidence. The current review identified the following needs to be addressed in future designs to: (i) define the clinical status of Internet Addiction more precisely, (ii) use more current psychometrically robust assessment tools for the measurement of effectiveness (based on the most recent empirical developments), (iii) reconsider the main outcome of Internet time reduction as it appears to be problematic, (iv) build methodologically sound evidence-based prevention programmes, (v) focus on skill enhancement and the use of protective and harm-reducing factors, and (vi) include IA as one of the risk behaviours in multi-risk behaviour interventions. These appear to be crucial factors in addressing future research designs and the formulation of new prevention initiatives. Validated findings could then inform promising strategies for IA and gaming prevention in public policy and education