Language delay is not predictable from available risk factors

Aims. To investigate factors associated with language delay in a cohort of 30-month-old children and determine if identification of language delay requires active contact with families. Methods. Data were collected at a pilot universal 30-month health contact. Health visitors used a simple two-item language screen. Data were obtained for 315 children; language delay was found in 33. The predictive capacity of 13 variables which could realistically be known before the 30-month contact was analysed. Results. Seven variables were significantly associated with language delay in univariate analysis, but in logistic regression only five of these variables remained significant. Conclusion. The presence of one or more risk factors had a sensitivity of 89% and specificity of 45%, but a positive predictive value of only 15%. The presence of one or more of these risk factors thus can not reliably be used to identify language delayed children, nor is it possible to define an “at risk” population because male gender was the only significant demographic factor and it had an unacceptably low specificity (52.5%). It is not possible to predict which children will have language delay at 30 months. Identification of this important ESSENCE disorder requires direct clinical contact with all families

The impact of sleep quality on cognitive functioning in Parkinson's disease

In healthy individuals and those with insomnia, poor sleep quality is associated with decrements in performance on tests of cognition, especially executive function. Sleep disturbances and cognitive deficits are both prevalent in Parkinson's disease (PD). Sleep problems occur in over 75% of patients, with sleep fragmentation and decreased sleep efficiency being the most common sleep complaints, but their relation to cognition is unknown. We examined the association between sleep quality and cognition in PD. In 35 non-demented individuals with PD and 18 normal control adults (NC), sleep was measured using 24-hr wrist actigraphy over 7 days. Cognitive domains tested included attention and executive function, memory and psychomotor function. In both groups, poor sleep was associated with worse performance on tests of attention/executive function but not memory or psychomotor function. In the PD group, attention/executive function was predicted by sleep efficiency, whereas memory and psychomotor function were not predicted by sleep quality. Psychomotor and memory function were predicted by motor symptom severity. This study is the first to demonstrate that sleep quality in PD is significantly correlated with cognition and that it differentially impacts attention and executive function, thereby furthering our understanding of the link between sleep and cognition.Published versio

Comparison of Semantic and Episodic Memory BOLD fMRI Activation in Predicting Cognitive Decline in Older Adults

Previous studies suggest that task-activated functional magnetic resonance imaging (fMRI) can predict future cognitive decline among healthy older adults. The present fMRI study examined the relative sensitivity of semantic memory (SM) versus episodic memory (EM) activation tasks for predicting cognitive decline. Seventy-eight cognitively intact elders underwent neuropsychological testing at entry and after an 18-month interval, with participants classified as cognitively “Stable” or “Declining” based on ≥1.0 SD decline in performance. Baseline fMRI scanning involved SM (famous name discrimination) and EM (name recognition) tasks. SM and EM fMRI activation, along with Apolipoprotein E (APOE) ε4 status, served as predictors of cognitive outcome using a logistic regression analysis. Twenty-seven (34.6%) participants were classified as Declining and 51 (65.4%) as Stable. APOE ε4 status alone significantly predicted cognitive decline (R2 = .106; C index = .642). Addition of SM activation significantly improved prediction accuracy (R2 = .285; C index = .787), whereas the addition of EM did not (R2 = .212; C index = .711). In combination with APOE status, SM task activation predicts future cognitive decline better than EM activation. These results have implications for use of fMRI in prevention clinical trials involving the identification of persons at-risk for age-associated memory loss and Alzheimer\u27s disease. (JINS, 2012, 18, 1–11

Control speculation for energy-efficient next-generation superscalar processors

Conventional front-end designs attempt to maximize the number of "in-flight" instructions in the pipeline. However, branch mispredictions cause the processor to fetch useless instructions that are eventually squashed, increasing front-end energy and issue queue utilization and, thus, wasting around 30 percent of the power dissipated by a processor. Furthermore, processor design trends lead to increasing clock frequencies by lengthening the pipeline, which puts more pressure on the branch prediction engine since branches take longer to be resolved. As next-generation high-performance processors become deeply pipelined, the amount of wasted energy due to misspeculated instructions will go up. The aim of this work is to reduce the energy consumption of misspeculated instructions. We propose selective throttling, which triggers different power-aware techniques (fetch throttling, decode throttling, or disabling the selection logic) depending on the branch prediction confidence level. Results show that combining fetch-bandwidth reduction along with select-logic disabling provides the best performance in terms of overall energy reduction and energy-delay product improvement (14 percent and 10 percent, respectively, for a processor with a 22-stage pipeline and 16 percent and 13 percent, respectively, for a processor with a 42-stage pipeline).Peer ReviewedPostprint (published version

Crowd-ML: A Privacy-Preserving Learning Framework for a Crowd of Smart Devices

Smart devices with built-in sensors, computational capabilities, and network connectivity have become increasingly pervasive. The crowds of smart devices offer opportunities to collectively sense and perform computing tasks in an unprecedented scale. This paper presents Crowd-ML, a privacy-preserving machine learning framework for a crowd of smart devices, which can solve a wide range of learning problems for crowdsensing data with differential privacy guarantees. Crowd-ML endows a crowdsensing system with an ability to learn classifiers or predictors online from crowdsensing data privately with minimal computational overheads on devices and servers, suitable for a practical and large-scale employment of the framework. We analyze the performance and the scalability of Crowd-ML, and implement the system with off-the-shelf smartphones as a proof of concept. We demonstrate the advantages of Crowd-ML with real and simulated experiments under various conditions

E-cigarette use among women of reproductive age: Impulsivity, cigarette smoking status, and other risk factors.

INTRODUCTION: The study aim was to examine impulsivity and other risk factors for e-cigarette use among women of reproductive age comparing current daily cigarette smokers to never cigarette smokers. Women of reproductive age are of special interest because of the additional risk that tobacco and nicotine use represents should they become pregnant. METHOD: Survey data were collected anonymously online using Amazon Mechanical Turk in 2014. Participants were 800 women ages 24-44years from the US. Half (n=400) reported current, daily smoking and half (n=400) reported smokingsociodemographics, tobacco/nicotine use, and impulsivity (i.e., delay discounting & Barratt Impulsiveness Scale). Predictors of smoking and e-cigarette use were examined using logistic regression. RESULTS: Daily cigarette smoking was associated with greater impulsivity, lower education, past illegal drug use, and White race/ethnicity. E-cigarette use in the overall sample was associated with being a cigarette smoker and greater education. E-cigarette use among current smokers was associated with increased nicotine dependence and quitting smoking; among never smokers it was associated with greater impulsivity and illegal drug use. E-cigarette use was associated with hookah use, and for never smokers only with use of cigars and other nicotine products. CONCLUSIONS: E-cigarette use among women of reproductive age varies by smoking status, with use among current smokers reflecting attempts to quit smoking whereas among non-smokers use may be a marker of a more impulsive repertoire that includes greater use of alternative tobacco products and illegal drugs

Developmental pathways to autism: a review of prospective studies of infants at risk

Autism Spectrum Disorders (ASDs) are neurodevelopmental disorders characterized by impairments in social interaction and communication, and the presence of restrictive and repetitive behaviors. Symptoms of ASD likely emerge from a complex interaction between pre-existing neurodevelopmental vulnerabilities and the child's environment, modified by compensatory skills and protective factors. Prospective studies of infants at high familial risk for ASD (who have an older sibling with a diagnosis) are beginning to characterize these developmental pathways to the emergence of clinical symptoms. Here, we review the range of behavioral and neurocognitive markers for later ASD that have been identified in high-risk infants in the first years of life. We discuss theoretical implications of emerging patterns, and identify key directions for future work, including potential resolutions to several methodological challenges for the field. Mapping how ASD unfolds from birth is critical to our understanding of the developmental mechanisms underlying this disorder. A more nuanced understanding of developmental pathways to ASD will help us not only to identify children who need early intervention, but also to improve the range of interventions available to them

Risk factors for chest infection in acute stroke: a prospective cohort study

<p><b>Background and Purpose:</b> Pneumonia is a major cause of morbidity and mortality after stroke. We aimed to determine key characteristics that would allow prediction of those patients who are at highest risk for poststroke pneumonia.</p> <p><b>Methods:</b> We studied a series of consecutive patients with acute stroke who were admitted to hospital. Detailed evaluation included the modified National Institutes of Health Stroke Scale; the Abbreviated Mental Test; and measures of swallow, respiratory, and oral health status. Pneumonia was diagnosed by set criteria. Patients were followed up at 3 months after stroke.</p> <p><b>Results:</b> We studied 412 patients, 391 (94.9%) with ischemic stroke and 21 (5.1%) with hemorrhagic stroke; 78 (18.9%) met the study criteria for pneumonia. Subjects who developed pneumonia were older (mean±SD age, 75.9±11.4 vs 64.9±13.9 years), had higher modified National Institutes of Health Stroke Scale scores, a history of chronic obstructive pulmonary disease, lower Abbreviated Mental Test scores, and a higher oral cavity score, and a greater proportion tested positive for bacterial cultures from oral swabs. In binary logistic-regression analysis, independent predictors (P<0.05) of pneumonia were age >65 years, dysarthria or no speech due to aphasia, a modified Rankin Scale score ≥4, an Abbreviated Mental Test score <8, and failure on the water swallow test. The presence of 2 or more of these risk factors carried 90.9% sensitivity and 75.6% specificity for the development of pneumonia.</p> <p><b>Conclusions:</b> Pneumonia after stroke is associated with older age, dysarthria/no speech due to aphasia, severity of poststroke disability, cognitive impairment, and an abnormal water swallow test result. Simple assessment of these variables could be used to identify patients at high risk of developing pneumonia after stroke.</p&gt

Do imitation problems reflect a core characteristic in autism? Evidence from a literature review

A

A marker of biological ageing predicts adult risk preference in European starlings, Sturnus vulgaris

Why are some individuals more prone to gamble than others? Animals often show preferences between 2 foraging options with the same mean reward but different degrees of variability in the reward, and such risk preferences vary between individuals. Previous attempts to explain variation in risk preference have focused on energy budgets, but with limited empirical support. Here, we consider whether biological ageing, which affects mortality and residual reproductive value, predicts risk preference. We studied a cohort of European starlings (Sturnus vulgaris) in which we had previously measured developmental erythrocyte telomere attrition, an established integrative biomarker of biological ageing. We measured the adult birds’ preferences when choosing between a fixed amount of food and a variable amount with an equal mean. After controlling for change in body weight during the experiment (a proxy for energy budget), we found that birds that had undergone greater developmental telomere attrition were more risk averse as adults than were those whose telomeres had shortened less as nestlings. Developmental telomere attrition was a better predictor of adult risk preference than either juvenile telomere length or early-life food supply and begging effort. Our longitudinal study thus demonstrates that biological ageing, as measured via developmental telomere attrition, is an important source of lasting differences in adult risk preferences