Expression of apolipoprotein E by cultured vascular smooth muscle cells is controlled by growth state.

Rat vascular smooth muscle cells (SMC) in culture synthesize and secrete a approximately 38,000-Mr protein doublet or triplet that, as previously described (Majack and Bornstein. 1984. J. Cell Biol. 99:1688-1695), rapidly and reversibly accumulates in the SMC culture medium upon addition of heparin. In the present study, we show that this approximately 38,000-Mr heparin-regulated protein is electrophoretically and immunologically identical to apolipoprotein E (apo-E), a major plasma apolipoprotein involved in cholesterol transport. In addition, we show that expression of apo-E by cultured SMC varies according to growth state: while proliferating SMC produced little apo-E and expressed low levels of apo-E mRNA, quiescent SMC produced significantly more apo-E (relative to other proteins) and expressed markedly increased levels of apo-E mRNA. Northern analysis of RNA extracted from aortic tissue revealed that fully differentiated, quiescent SMC contain significant quantities of apo-E mRNA. These data establish aortic SMC as a vascular source for apo-E and suggest new functional roles for this apolipoprotein, possibly unrelated to traditional concepts of lipid metabolism

GeNN: a code generation framework for accelerated brain simulations

Large-scale numerical simulations of detailed brain circuit models are important for identifying hypotheses on brain functions and testing their consistency and plausibility. An ongoing challenge for simulating realistic models is, however, computational speed. In this paper, we present the GeNN (GPU-enhanced Neuronal Networks) framework, which aims to facilitate the use of graphics accelerators for computational models of large-scale neuronal networks to address this challenge. GeNN is an open source library that generates code to accelerate the execution of network simulations on NVIDIA GPUs, through a flexible and extensible interface, which does not require in-depth technical knowledge from the users. We present performance benchmarks showing that 200-fold speedup compared to a single core of a CPU can be achieved for a network of one million conductance based Hodgkin-Huxley neurons but that for other models the speedup can differ. GeNN is available for Linux, Mac OS X and Windows platforms. The source code, user manual, tutorials, Wiki, in-depth example projects and all other related information can be found on the project website http://genn-team.github.io/genn/

The use of routine outcome measures in two child and adolescent mental health services: a completed audit cycle

Background: Routine outcome measurement (ROM) is important for assessing the clinical effectiveness of health services and for monitoring patient outcomes. Within Child and Adolescent Mental Health Services (CAMHS) in the UK the adoption of ROM in CAMHS has been supported by both national and local initiatives (such as government strategies, local commissioning policy, and research). Methods: With the aim of assessing how these policies and initiatives may have influenced the uptake of ROM within two different CAMHS we report the findings of two case-note audits: a baseline audit conducted in January 2011 and a re-audit conducted two years later in December 2012-February 2013. Results: The findings show an increase in both the single and repeated use of outcome measures from the time of the original audit, with repeated use (baseline and follow-up) of the Health of the Nation Outcome Scale for Children and Adolescents (HoNOSCA) scale increasing from 10% to 50% of cases. Re-audited case-notes contained more combined use of different outcome measures, with greater consensus on which measures to use. Outcome measures that were applicable across a wide range of clinical conditions were more likely to be used than symptom-specific measures, and measures that were completed by the clinician were found more often than measures completed by the service user. Conclusions: The findings show a substantial improvement in the use of outcome measures within CAMHS. These increases in use were found across different service organisations which were subject to different types of local service priorities and drivers

Reductions in cardiovascular, cerebrovascular, and respiratory mortality following the national Irish smoking ban: Interrupted time-series analysis

Copyright @ 2013 Stallings-Smith et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.This article has been made available through the Brunel Open Access Publishing Fund.Background: Previous studies have shown decreases in cardiovascular mortality following the implementation of comprehensive smoking bans. It is not known whether cerebrovascular or respiratory mortality decreases post-ban. On March 29, 2004, the Republic of Ireland became the first country in the world to implement a national workplace smoking ban. The aim of this study was to assess the effect of this policy on all-cause and cause-specific, non-trauma mortality. Methods: A time-series epidemiologic assessment was conducted, utilizing Poisson regression to examine weekly age and gender-standardized rates for 215,878 non-trauma deaths in the Irish population, ages ≥35 years. The study period was from January 1, 2000, to December 31, 2007, with a post-ban follow-up of 3.75 years. All models were adjusted for time trend, season, influenza, and smoking prevalence. Results: Following ban implementation, an immediate 13% decrease in all-cause mortality (RR: 0.87; 95% CI: 0.76-0.99), a 26% reduction in ischemic heart disease (IHD) (RR: 0.74; 95% CI: 0.63-0.88), a 32% reduction in stroke (RR: 0.68; 95% CI: 0.54-0.85), and a 38% reduction in chronic obstructive pulmonary disease (COPD) (RR: 0.62; 95% CI: 0.46-0.83) mortality was observed. Post-ban reductions in IHD, stroke, and COPD mortalities were seen in ages ≥65 years, but not in ages 35-64 years. COPD mortality reductions were found only in females (RR: 0.47; 95% CI: 0.32-0.70). Post-ban annual trend reductions were not detected for any smoking-related causes of death. Unadjusted estimates indicate that 3,726 (95% CI: 2,305-4,629) smoking-related deaths were likely prevented post-ban. Mortality decreases were primarily due to reductions in passive smoking. Conclusions: The national Irish smoking ban was associated with immediate reductions in early mortality. Importantly, post-ban risk differences did not change with a longer follow-up period. This study corroborates previous evidence for cardiovascular causes, and is the first to demonstrate reductions in cerebrovascular and respiratory causes

The Analysis of Association Between Traits When Differences Between Trait States Matter

Because of their elementary significance in almost all fields of science, measures of association between two variables or traits are abundant and multiform. One aspect of association that is of considerable interest, especially in population genetics and ecology, seems to be widely ignored. This aspect concerns association between complex traits that show variable and arbitrarily defined state differences. Among such traits are genetic characters controlled by many and potentially polyploid loci, species characteristics, and environmental variables, all of which may be mutually and asymmetrically associated. A concept of directed association of one trait with another is developed here that relies solely on difference measures between the states of a trait. Associations are considered at three levels: between individual states of two variables, between an individual state of one variable and the totality of the other variable, and between two variables. Relations to known concepts of association are identified. In particular, measures at the latter two levels turn out to be interpretable as measures of differentiation. Examples are given for areas of application (search for functional relationships, distribution of variation over populations, genomic associations, spatiogenetic structure)

Biologic Monitoring of Exposure to Environmental Chemicals throughout the Life Stages: Requirements and Issues for Consideration for the National Children’s Study

Biomonitoring of exposure is a useful tool for assessing environmental exposures. The matrices available for analyses include blood, urine, breast milk, adipose tissue, and saliva, among others. The sampling can be staged to represent the particular time period of concern: preconceptionally from both parents, from a pregnant woman during each of the three trimesters, during and immediately after childbirth, from the mother postnatally, and from the child as it develops to 21 years of age. The appropriate sample for biomonitoring will depend upon matrix availability, the time period of concern for a particular exposure or health effect, and the different classes of environmental chemicals to be monitored. This article describes the matrices available for biomonitoring during the life stages being evaluated in the National Children’s Study; the best biologic matrices for exposure assessment for each individual chemical class, including consideration of alternative matrices; the analytical methods used for analysis, including quality control procedures and less costly alternatives; the costs of analysis; optimal storage conditions; and chemical and matrix stability during long-term storage

Towards a quantum probability theory of similarity judgments

We review recent progress in understanding similarity judgments in cognition by means of quantum probability theory (QP) models. We begin by outlining some features of similarity judgments that have proven difficult to model by traditional approaches. We then briefly present a model of similarity judgments based on QP, and show how it can solve many of the problems faced by traditional approaches. Finally we look at some areas where the quantum model is currently less satisfactory, and discuss some open questions and areas for further work

Implementation of routine outcome measurement in child and adolescent mental health services in the United Kingdom: a critical perspective

The aim of this commentary is to provide an overview of clinical outcome measures that are currently recommended for use in UK Child and Adolescent Mental Health Services (CAMHS), focusing on measures that are applicable across a wide range of conditions with established validity and reliability, or innovative in their design. We also provide an overview of the barriers and drivers to the use of Routine Outcome Measurement (ROM) in clinical practice

Gene Function Classification Using Bayesian Models with Hierarchy-Based Priors

We investigate the application of hierarchical classification schemes to the annotation of gene function based on several characteristics of protein sequences including phylogenic descriptors, sequence based attributes, and predicted secondary structure. We discuss three Bayesian models and compare their performance in terms of predictive accuracy. These models are the ordinary multinomial logit (MNL) model, a hierarchical model based on a set of nested MNL models, and a MNL model with a prior that introduces correlations between the parameters for classes that are nearby in the hierarchy. We also provide a new scheme for combining different sources of information. We use these models to predict the functional class of Open Reading Frames (ORFs) from the E. coli genome. The results from all three models show substantial improvement over previous methods, which were based on the C5 algorithm. The MNL model using a prior based on the hierarchy outperforms both the non-hierarchical MNL model and the nested MNL model. In contrast to previous attempts at combining these sources of information, our approach results in a higher accuracy rate when compared to models that use each data source alone. Together, these results show that gene function can be predicted with higher accuracy than previously achieved, using Bayesian models that incorporate suitable prior information

Designing a complex intervention for dementia case management in primary care

Background: Community-based support will become increasingly important for people with dementia, but currently services are fragmented and the quality of care is variable. Case management is a popular approach to care co-ordination, but evidence to date on its effectiveness in dementia has been equivocal. Case management interventions need to be designed to overcome obstacles to care co-ordination and maximise benefit. A successful case management methodology was adapted from the United States (US) version for use in English primary care, with a view to a definitive trial. Medical Research Council guidance on the development of complex interventions was implemented in the adaptation process, to capture the skill sets, person characteristics and learning needs of primary care based case managers. Methods: Co-design of the case manager role in a single NHS provider organisation, with external peer review by professionals and carers, in an iterative technology development process. Results: The generic skills and personal attributes were described for practice nurses taking up the case manager role in their workplaces, and for social workers seconded to general practice teams, together with a method of assessing their learning needs. A manual of information material for people with dementia and their family carers was also created using the US intervention as its source. Conclusions: Co-design produces rich products that have face validity and map onto the complexities of dementia and of health and care services. The feasibility of the case manager role, as described and defined by this process, needs evaluation in ‘real life’ settings