Time to Move On from Congregated Settings: A Strategy for Community Inclusion

19.6.2011 I am pleased to present the Report of the Working Group on Congregated Settings, which is the outcome and culmination of a very significant piece of data capture, research and analysis. The Report was initiated by the Primary, Community and Community Care Directorate in 2007 to develop a national plan and associated change programme for moving people from congregated settings to the community in line with Government policy. Click here to download the document

Make the first move!

This leaflet was developed in conjunction with Age Concern as part of the Actively Ageing Well initiative and provides information on physical activity aimed at retired people.

Regional-scale integration of multi-scale hydrological and geophysical data using a two-step Bayesian sequential simulation approach

Significant progress has been made with regard to the quantitative integration of geophysical and hydrological data at the local scale for the purpose of improving predictions of groundwater flow and solute transport. However, extending corresponding approaches to the regional scale still represents one of the major challenges in the domain of hydrogeophysics. To address this problem, we have developed a regional-scale data integration methodology based on a two-step Bayesian sequential simulation approach. Our objective is to generate high-resolution stochastic realizations of the regional-scale hydraulic conductivity field in the common case where there exist spatially exhaustive but poorly resolved measurements of a related geophysical parameter, as well as highly resolved but spatially sparse collocated measurements of this geophysical parameter and the hydraulic conductivity. To integrate this multi-scale, multi-parameter database, we first link the low- and high-resolution geophysical data via a stochastic downscaling procedure. This is followed by relating the downscaled geophysical data to the high-resolution hydraulic conductivity distribution. After outlining the general methodology of the approach, we demonstrate its application to a realistic synthetic example where we consider as data high-resolution measurements of the hydraulic and electrical conductivities at a small number of borehole locations, as well as spatially exhaustive, low-resolution estimates of the electrical conductivity obtained from surface-based electrical resistivity tomography. The different stochastic realizations of the hydraulic conductivity field obtained using our procedure are validated by comparing their solute transport behaviour with that of the underlying ?true? hydraulic conductivity field. We find that, even in the presence of strong subsurface heterogeneity, our proposed procedure allows for the generation of faithful representations of the regional-scale hydraulic conductivity structure and reliable predictions of solute transport over long, regional-scale distances.

Counsellor behaviours and patient language during brief motivational interventions: a sequential analysis of speech

AIMS: To investigate empirically the hypothesized relationship between counsellor motivational interviewing (MI) skills and patient change talk (CT) by analysing the articulation between counsellor behaviours and patient language during brief motivational interventions (BMI) addressing at-risk alcohol consumption. DESIGN: Sequential analysis of psycholinguistic codes obtained by two independent raters using the Motivational Interviewing Skill Code (MISC), version 2.0. SETTING: Secondary analysis of data from a randomized controlled trial evaluating the effectiveness of BMI in an emergency department. PARTICIPANTS: A total of 97 patients tape-recorded when receiving BMI. MEASUREMENTS: MISC variables were categorized into three counsellor behaviours (MI-consistent, MI-inconsistent and 'other') and three kinds of patient language (CT, counter-CT (CCT) and utterances not linked with the alcohol topic). Observed transition frequencies, conditional probabilities and significance levels based on odds ratios were computed using sequential analysis software. FINDINGS: MI-consistent behaviours were the only counsellor behaviours that were significantly more likely to be followed by patient CT. Those behaviours were significantly more likely to be followed by patient change exploration (CT and CCT) while MI-inconsistent behaviours and 'other' counsellor behaviours were significantly more likely to be followed by utterances not linked with the alcohol topic and significantly less likely to be followed by CT. MI-consistent behaviours were more likely after change exploration, whereas 'other' counsellor behaviours were more likely only after utterances not linked with the alcohol topic. CONCLUSIONS: Findings lend support to the hypothesized relationship between MI-consistent behaviours and CT, highlight the importance of patient influence on counsellor behaviour and emphasize the usefulness of MI techniques and spirit during brief interventions targeting change enhancement.

E-MOVE project : a case of successfulcooperation of European academic librariesof distance teaching universities

El Library and Learning Support Working Group (LLSWG) és una xarxa europea de professionals de biblioteca i de la informació i educadors dins de l'Associació Europea d'Universitats d'Ensenyament (EADTU). EADTU és l'organització que representa tant les universitats obertes i d'ensenyament a distància com els consorcis nacionals d'institucions d'ensenyament superior actius en els camps d'educació de distància i e-learning. El 2005 l'Associació Europea d'Universitats d'Ensenyament de Distància (EADTU) presentava a la Comissió Europea una proposta de projecte sobre Mobilitat Virtual anomenada E-MOVE. El projecte incloïa, entre d'altres, un paquet de treball referent a biblioteca i suport a l'aprenentatge al context de mobilitat virtual. Aquest fou desenvolupat pel LLSWG de l'EADTU.

Bilateral sequential nonarteritic anterior ischemic optic neuropathy: a comparison of visual outcomes in fellow eyes using quantitative analysis of goldmann visual fields.

OBJECTIVE To better define the concordance of visual loss in patients with nonarteritic anterior ischemic optic neuropathy (NAION). METHODS The medical records of 86 patients with bilateral sequential NAION were reviewed retrospectively, and visual function was assessed using visual acuity, Goldmann visual fields, color vision, and relative afferent papillary defect. A quantitative total visual field score and score per quadrant were analyzed for each eye using the numerical Goldmann visual field scoring method. RESULTS Outcome measures were visual acuity, visual field, color vision, and relative afferent papillary defect. A statistically significant correlation was found between fellow eyes for multiple parameters, including logMAR visual acuity (P = .01), global visual field (P < .001), superior visual field (P < .001), and inferior visual field (P < .001). The mean deviation of total (P < .001) and pattern (P < .001) deviation analyses was significantly less between fellow eyes than between first and second eyes of different patients. CONCLUSIONS Visual function between fellow eyes showed a fair to moderate correlation that was statistically significant. The pattern of vision loss was also more similar in fellow eyes than between eyes of different patients. These results may help allow better prediction of visual outcome for the second eye in patients with NAION.

Combination of trastuzumab and letrozole after resistance to sequential trastuzumab and aromatase inhibitor monotherapies in patients with estrogen receptor-positive, HER-2-positive advanced breast cancer: a proof-of-concept trial (SAKK 23/03).

A sequential treatment design was chosen in this trial to ensure complete resistance to single-agent non-steroidal aromatase inhibitor (AI) and trastuzumab both given as monotherapy before receiving the combination of a non-steroidal AI and trastuzumab. Key eligibility criteria included postmenopausal patients with advanced, measurable, human epidermal growth factor receptor-2 (HER-2)-positive disease (assessed by FISH, ratio (≥2)), hormone receptor (HR)-positive disease, and progression on prior treatment with a non-steroidal AI, e.g. letrozole or anastrozole, either in the adjuvant or in the advanced setting. Patients received standard dose trastuzumab monotherapy in step 1 and upon disease progression continued trastuzumab in combination with letrozole in step 2. The primary endpoint was clinical benefit rate (CBR) in step 2. Totally, 13 patients were enrolled. In step 1, six patients (46%) achieved CBR. Median time to progression (TTP) was 161 days (95% confidence interval (CI): 82-281). In step 2, CBR was observed in eight out of the 11 evaluable patients (73%), including one patient with partial response. Median TTP for all the 11 patients was 188 days (95% CI: 77-not reached). Results of this proof-of-concept trial suggest that complete resistance to both AI and trastuzumab can be overcome in a proportion of patients by combined treatment of AI and trastuzumab, as all patients served as their own control. Our results appear promising for a new treatment strategy that offers a chemotherapy-free option for at least a subset of patients with HR-positive, HER-2-positive breast cancer over a clinically relevant time period.

Decision fusion for the classification of hyperspectral data: Outcome of the 2008 GRS-S data fusion contest

The 2008 Data Fusion Contest organized by the IEEE Geoscience and Remote Sensing Data Fusion Technical Committee deals with the classification of high-resolution hyperspectral data from an urban area. Unlike in the previous issues of the contest, the goal was not only to identify the best algorithm but also to provide a collaborative effort: The decision fusion of the best individual algorithms was aiming at further improving the classification performances, and the best algorithms were ranked according to their relative contribution to the decision fusion. This paper presents the five awarded algorithms and the conclusions of the contest, stressing the importance of decision fusion, dimension reduction, and supervised classification methods, such as neural networks and support vector machines.

Sequential analysis of trans-SNARE formation in intracellular membrane fusion.

SNARE complexes are required for membrane fusion in the endomembrane system. They contain coiled-coil bundles of four helices, three (Q(a), Q(b), and Q(c)) from target (t)-SNAREs and one (R) from the vesicular (v)-SNARE. NSF/Sec18 disrupts these cis-SNARE complexes, allowing reassembly of their subunits into trans-SNARE complexes and subsequent fusion. Studying these reactions in native yeast vacuoles, we found that NSF/Sec18 activates the vacuolar cis-SNARE complex by selectively displacing the vacuolar Q(a) SNARE, leaving behind a Q(bc)R subcomplex. This subcomplex serves as an acceptor for a Q(a) SNARE from the opposite membrane, leading to Q(a)-Q(bc)R trans-complexes. Activity tests of vacuoles with diagnostic distributions of inactivating mutations over the two fusion partners confirm that this distribution accounts for a major share of the fusion activity. The persistence of the Q(bc)R cis-complex and the formation of the Q(a)-Q(bc)R trans-complex are both sensitive to the Rab-GTPase inhibitor, GDI, and to mutations in the vacuolar tether complex, HOPS (HOmotypic fusion and vacuolar Protein Sorting complex). This suggests that the vacuolar Rab-GTPase, Ypt7, and HOPS restrict cis-SNARE disassembly and thereby bias trans-SNARE assembly into a preferred topology.

Multi-modal Change Detection, Application to the Detection of Flooded Areas: Outcome of the 2009-2010 data fusion contest

The 2009-2010 Data Fusion Contest organized by the Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society was focused on the detection of flooded areas using multi-temporal and multi-modal images. Both high spatial resolution optical and synthetic aperture radar data were provided. The goal was not only to identify the best algorithms (in terms of accuracy), but also to investigate the further improvement derived from decision fusion. This paper presents the four awarded algorithms and the conclusions of the contest, investigating both supervised and unsupervised methods and the use of multi-modal data for flood detection. Interestingly, a simple unsupervised change detection method provided similar accuracy as supervised approaches, and a digital elevation model-based predictive method yielded a comparable projected change detection map without using post-event data.

Adaptive search and information updating in sequential mate choice

Classical treatments of problems of sequential mate choice assume that the distribution of the quality of potential mates is known a priori. This assumption, made for analytical purposes, may seem unrealistic, opposing empirical data as well as evolutionary arguments. Using stochastic dynamic programming, we develop a model that includes the possibility for searching individuals to learn about the distribution and in particular to update mean and variance during the search. In a constant environment, a priori knowledge of the parameter values brings strong benefits in both time needed to make a decision and average value of mate obtained. Knowing the variance yields more benefits than knowing the mean, and benefits increase with variance. However, the costs of learning become progressively lower as more time is available for choice. When parameter values differ between demes and/or searching periods, a strategy relying on fixed a priori information might lead to erroneous decisions, which confers advantages on the learning strategy. However, time for choice plays an important role as well: if a decision must be made rapidly, a fixed strategy may do better even when the fixed image does not coincide with the local parameter values. These results help in delineating the ecological-behavior context in which learning strategies may spread.

Sequential estimation of multipath MIMO-OFDM channels

Wireless “MIMO” systems, employing multiple transmit and receive antennas, promise a significant increase of channel capacity, while orthogonal frequency-division multiplexing (OFDM) is attracting a good deal of attention due to its robustness to multipath fading. Thus, the combination of both techniques is an attractive proposition for radio transmission. The goal of this paper is the description and analysis of a new and novel pilot-aided estimator of multipath block-fading channels. Typical models leading to estimation algorithms assume the number of multipath components and delays to be constant (and often known), while their amplitudes are allowed to vary with time. Our estimator is focused instead on the more realistic assumption that the number of channel taps is also unknown and varies with time following a known probabilistic model. The estimation problem arising from these assumptions is solved using Random-Set Theory (RST), whereby one regards the multipath-channel response as a single set-valued random entity.Within this framework, Bayesian recursive equations determine the evolution with time of the channel estimator. Due to the lack of a closed form for the solution of Bayesian equations, a (Rao–Blackwellized) particle filter (RBPF) implementation ofthe channel estimator is advocated. Since the resulting estimator exhibits a complexity which grows exponentially with the number of multipath components, a simplified version is also introduced. Simulation results describing the performance of our channel estimator demonstrate its effectiveness.

Sequential estimation of time-varying multipath channel for MIMO-OFDM systems

In this paper, we introduce a pilot-aided multipath channel estimator for Multiple-Input Multiple-Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) systems. Typical estimation algorithms assume the number of multipath components and delays to be known and constant, while theiramplitudes may vary in time. In this work, we focus on the more realistic assumption that also the number of channel taps is unknown and time-varying. The estimation problem arising from this assumption is solved using Random Set Theory (RST), which is a probability theory of finite sets. Due to the lack of a closed form of the optimal filter, a Rao-Blackwellized Particle Filter (RBPF) implementation of the channel estimator is derived. Simulation results demonstrate the estimator effectiveness.