A systematic review of community participation measures for people with intellectual disabilities

Background: Community participation is considered a fundamental aspect of quality of life and one of the essential goals of services for people with intellectual disabilities (ID), yet there is no agreed way of measuring community participation. Method: Two systematic searches were performed across eight electronic databases to identify measures of community participation and identify validation studies for each measure. Measures were included if they were developed for adults with ID, measured extent of participation and had published information regarding content and psychometric properties. Each measure was evaluated on the basis of psychometric properties and in relation to coverage of nine domains of community participation from the International Classification of Functioning, Disability and Health (ICF). Results: Eleven measures were selected with the quality rating scores varying substantially ranging from 2-11 of a possible 16. Conclusions: The majority of measures were not sufficiently psychometrically tested. Findings suggest a need for the development of a psychometrically robust instrument

The carbohydrate-linked phosphorylcholine of the parasitic nematode product ES-62 modulates complement activation

Parasitic nematodes manufacture various carbohydratelinked phosphorylcholine (PCh)-containing molecules, including ES-62, a protein with an N-linked glycan terminally substituted with PCh. The PCh component is biologically important because it is required for immunomodulatory effects. We showed that most ES-62 was bound to a single protein, C-reactive protein (CRP), in normal human serum, displaying a calcium-dependent, high-avidity interaction and ability to form large complexes. Unexpectedly, CRP binding to ES-62 failed to efficiently activate complement as far as the C3 convertase stage in comparison with PCh-BSA and PCh-containing Streptococcus pneumoniae cell wall polysaccharide. C1q capture assays demonstrated an ES-62-CRP-C1q interaction in serum. The three ligands all activated C1 and generated C4b to similar extents. However, a C2a active site was not generated following ES-62 binding to CRP, demonstrating that C2 cleavage was far less efficient for ES-62-containing complexes. We proposed that failure of C2 cleavage was due to the flexible nature of carbohydrate-bound PCh and that reduced proximity of the C1 complex was the reason that C2 was poorly cleaved. This was confirmed using synthetic analogues that were similar to ES-62 only in respect of having a flexible PCh. Furthermore, ES-62 was shown to deplete early complement components, such as the rate-limiting C4, following CRP interaction and thereby inhibit classical pathway activation. Thus, flexible PCh-glycan represents a novel mechanism for subversion of complement activation. These data illustrate the importance of the rate-limiting C4/C2 stage of complement activation and reveal a new addition to the repertoire of ES-62 immunomodulatory mechanisms with possible therapeutic applications

Ab-initio theory of NMR chemical shifts in solids and liquids

We present a theory for the ab-initio computation of NMR chemical shifts (sigma) in condensed matter systems, using periodic boundary conditions. Our approach can be applied to periodic systems such as crystals, surfaces, or polymers and, with a super-cell technique, to non-periodic systems such as amorphous materials, liquids, or solids with defects. We have computed the hydrogen sigma for a set of free molecules, for an ionic crystal, LiH, and for a H-bonded crystal, HF, using density functional theory in the local density approximation. The results are in excellent agreement with experimental data.Comment: to appear in Physical Review Letter

Development of the Guernsey Community Participation and Leisure Assessment – Revised (GCPLA-R).

A sufficiently psychometrically robust measure of community and leisure participation of adults with intellectual disabilities was not in existence, despite research identifying this as an important outcome and a key contributor to quality of life. The current study aimed to update the Guernsey Community Participation and Leisure Assessment (GCPLA). Adults with intellectual disabilities, carers and experts were consulted in creating a revised pool of 46 items. These were then tested and data from 326 adults with intellectual disabilities were analysed for their component structure and psychometric properties. Principal Component analysis discovered a stable set of components describing seven different clusters. This revised measure (the GCPLA-R) was demonstrated to have satisfactory reliability, and scores were related to challenging behaviour and adaptive behaviour in theoretically consistent ways and were correlated with scores on comparable measures

On the biaxiality of smectic C and ferroelectric liquid crystals

Ferroelectric liquid crystals (FLCs) were a major topic for research in the 1980s and 1990s, to which George Gray and his research family played a fundamental role in developing the field. The famous symbiotic relationship between the chemists at Hull University and device physicists at the Royal Signals and Radar Establishment (RSRE) continued throughout this period, providing the basis for the τVmin mode of FLC operation. The principal of this mode relies on the dielectric biaxiality inherent to the smectic C and ferroelectric smectic C* liquid crystal phases. As with nematics before, new materials and device physics developed hand-in-hand, allowing materials to be formulated with addressing times of 12μs at voltages below 30 V. After reviewing the material physics behind these devices, new measurements of the biaxial refractive indices and permittivities are presented, from which the biaxial order parameter C is determined

Serum Amyloid A, C-Reactive Protein, and Retinal Microvascular Changes in Hypertensive Diabetic and Nondiabetic Individuals: An Anglo-Scandinavian Cardiac Outcomes Trial (ASCOT) substudy

To study the association of the inflammatory markers serum amyloid A (SAA) and C-reactive protein (CRP) with retinal microvascular parameters in hypertensive individuals with and without type 2 diabetes

Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia, 1980–2015

Quantification of stillbirth risk has potential to support clinical decision-making. Studies that have attempted to quantify stillbirth risk have been hampered by small event rates, a limited range of predictors that typically exclude obstetric history, lack of validation, and restriction to a single classifier (logistic regression). Consequently, predictive performance remains low, and risk quantification has not been adopted into antenatal practice. The study population consisted of all births to women in Western Australia from 1980 to 2015, excluding terminations. After all exclusions there were 947,025 livebirths and 5,788 stillbirths. Predictive models for stillbirth were developed using multiple machine learning classifiers: regularised logistic regression, decision trees based on classification and regression trees, random forest, extreme gradient boosting (XGBoost), and a multilayer perceptron neural network. We applied 10-fold cross-validation using independent data not used to develop the models. Predictors included maternal socio-demographic characteristics, chronic medical conditions, obstetric complications and family history in both the current and previous pregnancy. In this cohort, 66% of stillbirths were observed for multiparous women. The best performing classifier (XGBoost) predicted 45% (95% CI: 43%, 46%) of stillbirths for all women and 45% (95% CI: 43%, 47%) of stillbirths after the inclusion of previous pregnancy history. Almost half of stillbirths could be potentially identified antenatally based on a combination of current pregnancy complications, congenital anomalies, maternal characteristics, and medical history. Greatest sensitivity is achieved with addition of current pregnancy complications. Ensemble classifiers offered marginal improvement for prediction compared to logistic regression

Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia, 1980–2015

Quantification of stillbirth risk has potential to support clinical decision-making. Studies that have attempted to quantify stillbirth risk have been hampered by small event rates, a limited range of predictors that typically exclude obstetric history, lack of validation, and restriction to a single classifier (logistic regression). Consequently, predictive performance remains low, and risk quantification has not been adopted into antenatal practice. The study population consisted of all births to women in Western Australia from 1980 to 2015, excluding terminations. After all exclusions there were 947,025 livebirths and 5,788 stillbirths. Predictive models for stillbirth were developed using multiple machine learning classifiers: regularised logistic regression, decision trees based on classification and regression trees, random forest, extreme gradient boosting (XGBoost), and a multilayer perceptron neural network. We applied 10-fold cross-validation using independent data not used to develop the models. Predictors included maternal socio-demographic characteristics, chronic medical conditions, obstetric complications and family history in both the current and previous pregnancy. In this cohort, 66% of stillbirths were observed for multiparous women. The best performing classifier (XGBoost) predicted 45% (95% CI: 43%, 46%) of stillbirths for all women and 45% (95% CI: 43%, 47%) of stillbirths after the inclusion of previous pregnancy history. Almost half of stillbirths could be potentially identified antenatally based on a combination of current pregnancy complications, congenital anomalies, maternal characteristics, and medical history. Greatest sensitivity is achieved with addition of current pregnancy complications. Ensemble classifiers offered marginal improvement for prediction compared to logistic regression