Serving children: the impact of poverty on children's experiences of services

This study arose from the identification of a gap in knowledge and corresponding need for the development of a better contemporary understanding of children's experiences of poverty. Focusing on children aged 10 - 14 years, the study aimed to provide a perspective on the lives of children and young people affected by poverty in Scotland through comparing the experiences of children living in poverty with those more economically advantaged

Estimating heterogeneous treatment effects with right-censored data via causal survival forests

There is fast-growing literature on estimating heterogeneous treatment effects via random forests in observational studies. However, there are few approaches available for right-censored survival data. In clinical trials, right-censored survival data are frequently encountered. Quantifying the causal relationship between a treatment and the survival outcome is of great interest. Random forests provide a robust, nonparametric approach to statistical estimation. In addition, recent developments allow forest-based methods to quantify the uncertainty of the estimated heterogeneous treatment effects. We propose causal survival forests that directly target on estimating the treatment effect from an observational study. We establish consistency and asymptotic normality of the proposed estimators and provide an estimator of the asymptotic variance that enables valid confidence intervals of the estimated treatment effect. The performance of our approach is demonstrated via extensive simulations and data from an HIV study

Bayesian log-Gaussian Cox process regression: applications to meta-analysis of neuroimaging working memory studies

Working memory (WM) was one of the first cognitive processes studied with functional magnetic resonance imaging. With now over 20 years of studies on WM, each study with tiny sample sizes, there is a need for meta-analysis to identify the brain regions that are consistently activated by WM tasks, and to understand the interstudy variation in those activations. However, current methods in the field cannot fully account for the spatial nature of neuroimaging meta-analysis data or the heterogeneity observed among WM studies. In this work, we propose a fully Bayesian random-effects metaregression model based on log-Gaussian Cox processes, which can be used for meta-analysis of neuroimaging studies. An efficient Markov chain Monte Carlo scheme for posterior simulations is presented which makes use of some recent advances in parallel computing using graphics processing units. Application of the proposed model to a real data set provides valuable insights regarding the function of the WM

Drawing inferences for high‐dimensional linear models: A selection‐assisted partial regression and smoothing approach

Drawing inferences for high‐dimensional models is challenging as regular asymptotic theories are not applicable. This article proposes a new framework of simultaneous estimation and inferences for high‐dimensional linear models. By smoothing over partial regression estimates based on a given variable selection scheme, we reduce the problem to low‐dimensional least squares estimations. The procedure, termed as Selection‐assisted Partial Regression and Smoothing (SPARES), utilizes data splitting along with variable selection and partial regression. We show that the SPARES estimator is asymptotically unbiased and normal, and derive its variance via a nonparametric delta method. The utility of the procedure is evaluated under various simulation scenarios and via comparisons with the de‐biased LASSO estimators, a major competitor. We apply the method to analyze two genomic datasets and obtain biologically meaningful results.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151307/1/biom13013.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151307/2/biom13013-sup-0001-SuppData.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151307/3/biom13013_am.pd

Collaborative Deep Learning for Recommender Systems

Collaborative filtering (CF) is a successful approach commonly used by many recommender systems. Conventional CF-based methods use the ratings given to items by users as the sole source of information for learning to make recommendation. However, the ratings are often very sparse in many applications, causing CF-based methods to degrade significantly in their recommendation performance. To address this sparsity problem, auxiliary information such as item content information may be utilized. Collaborative topic regression (CTR) is an appealing recent method taking this approach which tightly couples the two components that learn from two different sources of information. Nevertheless, the latent representation learned by CTR may not be very effective when the auxiliary information is very sparse. To address this problem, we generalize recent advances in deep learning from i.i.d. input to non-i.i.d. (CF-based) input and propose in this paper a hierarchical Bayesian model called collaborative deep learning (CDL), which jointly performs deep representation learning for the content information and collaborative filtering for the ratings (feedback) matrix. Extensive experiments on three real-world datasets from different domains show that CDL can significantly advance the state of the art

Effects of compassion training on brain responses to suffering others

Compassion meditation (CM) is a promising intervention for enhancing compassion, although its active ingredients and neurobiological mechanisms are not well-understood. To investigate these, we conducted a three-armed placebo-controlled randomized trial (N = 57) with longitudinal functional magnetic resonance imaging (fMRI). We compared a 4-week CM program delivered by smartphone application with (i) a placebo condition, presented to participants as the compassion-enhancing hormone oxytocin, and (ii) a condition designed to control for increased familiarity with suffering others, an element of CM which may promote compassion. At pre- and post-intervention, participants listened to compassion-eliciting narratives describing suffering others during fMRI. CM increased brain responses to suffering others in the medial orbitofrontal cortex (mOFC) relative to the familiarity condition, p \u3c 0.05 family-wise error rate corrected. Among CM participants, individual differences in increased mOFC responses positively correlated with increased compassion-related feelings and attributions, r = 0.50, p = 0.04. Relative to placebo, the CM group exhibited a similar increase in mOFC activity at an uncorrected threshold of P \u3c 0.001 and 10 contiguous voxels. We conclude that the mOFC, a region closely related to affiliative affect and motivation, is an important brain mechanism of CM. Effects of CM on mOFC function are not explained by familiarity effects and are partly explained by placebo effects

Large-scale UK audit of blood transfusion requirements and anaemia in patients receiving cytotoxic chemotherapy

Cancer patients receiving cytotoxic chemotherapy often become anaemic and may require blood transfusions. A large-scale audit of patients with a variety of solid tumours receiving chemotherapy at 28 specialist centers throughout the UK was undertaken to quantify the problem. Data were available from 2719 patients receiving 3206 courses of cytotoxic chemotherapy for tumours of the breast (878), ovary (856), lung (772) or testis (213). Their mean age was 55 years (range 16–87). Overall, 33% of patients required at least one blood transfusion but the proportion varied from 19% for breast cancer to 43% for lung. Sixteen per cent of patients required more than one transfusion (7% for breast, 22% in lung). The mean proportion of patients with Hb < 11 g dl−1rose over the course of chemotherapy from 17% before the first cycle, to 38% by the sixth, despite transfusion in 33% of patients. Of the patients receiving transfusions, 25% required an inpatient admission and overnight stay. The most common symptoms reported at the time of transfusion were lethargy, tiredness and breathlessness. Further research is needed to evaluate the role of blood transfusions in patients receiving cytotoxic chemotherapy. © 2000 Cancer Research Campaig

Study of trap states in zinc oxide (ZnO) thin films for electronic applications

The electrical properties of ZnO thin films grown by pulsed laser deposition were studied. Field-effect devices with a mobility reaching 1 cm2/V s show non-linearities both in the current–voltage and in the transfer characteristics which are explained as due to the presence of trap states. These traps cause a reversible threshold voltage shift as revealed by low-frequency capacitance–voltage measurements in metal insulator semiconductor (MIS) capacitors. Thermal detrapping experiments in heterojunctions confirm the presence of a trap state located at 0.32 eV

High Return to Play Rate and Reduced Career Longevity Following Surgical Management of Athletic Pubalgia in National Basketball Association Players

PURPOSE: To assess the effects of surgical treatment of athletic pubalgia (AP) on game use and performance metrics in National Basketball Association (NBA) players. METHODS: A retrospective review of all NBA players who underwent surgical management for AP from 1996 to 2018 was performed. A matched control group was created for comparison. The index period was defined as the entire NBA season in which surgery occurred, including the corresponding offseason. Player demographics, use (games played, games started, and minutes per game) and performance (player efficiency rating) metrics were collected for all players. Statistical analysis was performed to compare data before and after return to play. RESULTS: Thirty players with a history of surgical management for AP were included in the final analysis. Following surgery for AP, NBA players were found to have a return to play (RTP) rate of 90.91% (30/33). The average RTP following surgery was 4.73 ± 2.62 months. Compared with control athletes, athletes in the AP group played significantly fewer seasons postinjury (4.17 ± 2.70 vs 5.49 ± 3.04 seasons, respectively; P = .02). During the first year following RTP, NBA players experienced significant reductions in game use and performance, both when compared with the year prior and matched control athletes (P \u3c .05). At 3-year follow-up, players continued to demonstrate significant reductions in game use (minutes per game, P \u3c .05) but not performance. CONCLUSIONS: Following surgical treatment of AP, NBA players demonstrated a high RTP rate, but shortened career. A short-term reduction in game use and performance metrics was found the year of return following surgery. However, 3-year follow-up performance metrics normalized when compared with healthy controls. STUDY DESIGN: Level III; retrospective case-control study