Trends in Characteristics of Patients Listed for Liver Transplantation Will Lead to Higher Rates of Waitlist Removal Due to Clinical Deterioration

BACKGROUND: Changes in the epidemiology of end-stage liver disease may lead to increased risk of dropout from the liver transplant waitlist. Anticipating the future of liver transplant waitlist characteristics is vital when considering organ allocation policy. METHODS: We performed a discrete event simulation to forecast patient characteristics and rate of waitlist dropout. Estimates were simulated from 2015 to 2025. The model was informed by data from the Organ Procurement and Transplant Network, 2003 to 2014. National data are estimated along with forecasts for 2 regions. RESULTS: Nonalcoholic steatohepatitis will increase from 18% of waitlist additions to 22% by 2025. Hepatitis C will fall from 30% to 21%. Listings over age 60 years will increase from 36% to 48%. The hazard of dropout will increase from 41% to 46% nationally. Wait times for transplant for patients listed with a Model for End-Stage Liver Disease (MELD) between 22 and 27 will double. Region 5, which transplants at relatively higher MELD scores, will experience an increase from 53% to 64% waitlist dropout. Region 11, which transplants at lower MELD scores, will have an increase in waitlist dropout from 30% to 44%. CONCLUSIONS: The liver transplant waitlist size will remain static over the next decade due to patient dropout. Liver transplant candidates will be older, more likely to have nonalcoholic steatohepatitis and will wait for transplantation longer even when listed at a competitive MELD score. There will continue to be significant heterogeneity among transplant regions where some patients will be more likely to drop out of the waitlist than receive a transplant

Changes in cigarette smoking initiation, cessation, and relapse among U.S. adults: a comparison of two longitudinal samples

Abstract Background The tobacco epidemic in the U.S. has matured in the past decade. However, due to rapidly changing social policy and commercial environments, tailored prevention and interventions are needed to support further reduction in smoking. Methods Using Tobacco Use Supplement to the Current Population Survey (TUS-CPS) 2002–2003 and 2010–2011 longitudinal cohorts, five smoking states are defined including daily-heavy, daily-light, non-daily, former and non-smoker. We quantified the changes between smoking states for the two longitudinal cohorts, and used a series of multivariable logistic regression models to examine the association of socio-demographic attributes and initial smoking states on smoking initiation, cessation, and relapse between waves within each cohort. Results The prevalence of adult heavy smoking decreased from 9.9% (95% CI: 9.6%, 10.2%) in 2002 to 7.1% (95% CI: 6.9%, 7.4%) in 2010. Non-daily smokers were less likely to quit in the 2010–2011 cohort than the 2002–2003 cohort (37.0% vs. 44.9%). Gender, age group, smoker type, race and marital status exhibit similar patterns in terms of their association to the odds of initiation, cessation and relapse between the two cohorts, while education groups showed some inconsistent results between the two cohorts regarding the odds of cessation. Conclusions Transitions between smoking states are complex and increasingly unstable, requiring a holistic, population-based perspective to understand the stocks and flows that ultimately dictate the public health impact of cigarette smoking behavior. This knowledge helps to identify groups in need of increased tobacco control prevention and intervention efforts

Human gait recognition using patch distribution feature and locality-constrained group sparse representation

In this paper, we propose a new patch distribution feature (PDF) (i.e., referred to as Gabor-PDF) for human gait recognition. We represent each gait energy image (GEI) as a set of local augmented Gabor features, which concatenate the Gabor features extracted from different scales and different orientations together with the X-Y coordinates. We learn a global Gaussian mixture model (GMM) (i.e., referred to as the universal background model) with the local augmented Gabor features from all the gallery GEIs; then, each gallery or probe GEI is further expressed as the normalized parameters of an image-specific GMM adapted from the global GMM. Observing that one video is naturally represented as a group of GEIs, we also propose a new classification method called locality-constrained group sparse representation (LGSR) to classify each probe video by minimizing the weighted l1, 2 mixed-norm-regularized reconstruction error with respect to the gallery videos. In contrast to the standard group sparse representation method that is a special case of LGSR, the group sparsity and local smooth sparsity constraints are both enforced in LGSR. Our comprehensive experiments on the benchmark USF HumanID database demonstrate the effectiveness of the newly proposed feature Gabor-PDF and the new classification method LGSR for human gait recognition. Moreover, LGSR using the new feature Gabor-PDF achieves the best average Rank-1 and Rank-5 recognition rates on this database among all gait recognition algorithms proposed to date

Learning by Associating Ambiguously Labeled Images

We study in this paper the problem of learning classifiers from ambiguously labeled images. For instance, in the col-lection of new images, each image contains some samples of interest (e.g., human faces), and its associated caption has labels with the true ones included, while the sample-label association is unknown. The task is to learn classi-fiers from these ambiguously labeled images and general-ize to new images. An essential consideration here is how to make use of the information embedded in the relation-s between samples and labels, both within each image and across the image set. To this end, we propose a novel frame-work to address this problem. Our framework is motivated by the observation that samples from the same class repeti-tively appear in the collection of ambiguously labeled train-ing images, while they are just ambiguously labeled in each image. If we can identify samples of the same class from each image and associate them across the image set, the matrix formed by the samples from the same class would be ideally low-rank. By leveraging such a low-rank assump-tion, we can simultaneously optimize a partial permutation matrix (PPM) for each image, which is formulated in order to exploit all information between samples and labels in a principled way. The obtained PPMs can be readily used to assign labels to samples in training images, and then a s-tandard SVM classifier can be trained and used for unseen data. Experiments on benchmark datasets show the effec-tiveness of our proposed method. 1

Anisotropic fracture behavior of the 3rd generation advanced high-strength – Quenching and Partitioning steels: Experiments and simulation

Advanced high-strength steels (AHSS) have revolutionized the automotive industry by reducing weight without compromising crashworthiness. The new third-generation steels, such as quenching and partitioning (QP) steels, offer exceptional strength and ductility. However, despite the extensive strength-ductility studies, there is a wide knowledge gap in the literature on the fracture behavior of QP steels under a large range of stress states and loading conditions for material forming operations. This study aims to systematically investigate the fracture behavior of QP1000 sheet metal through a combination of experimental and numerical approaches. In addition to the classic fracture dependency on stress states, we particularly focus on the anisotropic behavior in terms of both plasticity and fracture. Mechanical tests with digital image correlation are performed along three loading directions covering stress states from simple shear to plane-strain tension. The evolving non-associated Hill48 (enHill48) model is employed to describe anisotropic plasticity, while the fracture behavior is represented by a partially coupled anisotropic fracture model and a fully anisotropic fracture model. It is concluded that the investigated QP steel shows moderate anisotropic plasticity behavior yet strong fracture anisotropy, which intensifies with the increase of stress triaxiality. The partially coupled anisotropic fracture model, which has shown success for materials with minor anisotropic plasticity, fails to describe the anisotropic fracture and a fully anisotropic model provides significantly improved predictive capability