Supervised walking in comparison with fitness training for chronic back pain in physiotherapy: results of the SWIFT single-blinded randomized controlled trial (ISRCTN17592092)

Effectiveness of brief/minimal contact self-activation interventions that encourage participation in physical activity (PA) for chronic low back pain (CLBP >12 weeks) is unproven. The primary objective of this assessor-blinded randomized controlled trial was to investigate the difference between an individualized walking programme (WP), group exercise class (EC), and usual physiotherapy (UP, control) in mean change in functional disability at 6 months. A sample of 246 participants with CLBP aged 18 to 65 years (79 men and 167 women; mean age ± SD: 45.4 ± 11.4 years) were recruited from 5 outpatient physiotherapy departments in Dublin, Ireland. Consenting participants completed self-report measures of functional disability, pain, quality of life, psychosocial beliefs, and PA were randomly allocated to the WP (n = 82), EC (n = 83), or UP (n = 81) and followed up at 3 (81%; n = 200), 6 (80.1%; n = 197), and 12 months (76.4%; n = 188). Cost diaries were completed at all follow-ups. An intention-to-treat analysis using a mixed between-within repeated-measures analysis of covariance found significant improvements over time on the Oswestry Disability Index (Primary Outcome), the Numerical Rating Scale, Fear Avoidance-PA scale, and the EuroQol EQ-5D-3L Weighted Health Index (P < 0.05), but no significant between-group differences and small between-group effect sizes (WP: mean difference at 6 months, 6.89 Oswestry Disability Index points, 95% confidence interval [CI] -3.64 to -10.15; EC: -5.91, CI: -2.68 to -9.15; UP: -5.09, CI: -1.93 to -8.24). The WP had the lowest mean costs and the highest level of adherence. Supervised walking provides an effective alternative to current forms of CLBP management.

Complexity of Inferences in Polytree-shaped Semi-Qualitative Probabilistic Networks

Semi-qualitative probabilistic networks (SQPNs) merge two important graphical model formalisms: Bayesian networks and qualitative probabilistic networks. They provide a very general modeling framework by allowing the combination of numeric and qualitative assessments over a discrete domain, and can be compactly encoded by exploiting the same factorization of joint probability distributions that are behind the Bayesian networks. This paper explores the computational complexity of semi-qualitative probabilistic networks, and takes the polytree-shaped networks as its main target. We show that the inference problem is coNP-Complete for binary polytrees with multiple observed nodes. We also show that inferences can be performed in linear time if there is a single observed node, which is a relevant practical case. Because our proof is constructive, we obtain an efficient linear time algorithm for SQPNs under such assumptions. To the best of our knowledge, this is the first exact polynomial-time algorithm for SQPNs. Together these results provide a clear picture of the inferential complexity in polytree-shaped SQPNs.

Semi-Qualitative Probabilistic Networks in Computer Vision Problems

This paper explores the application of semi-qualitative probabilistic networks (SQPNs) that combine numeric and qualitative information to computer vision problems. Our version of SQPN allows qualitative influences and imprecise probability measures using intervals. We describe an Imprecise Dirichlet model for parameter learning and an iterative algorithm for evaluating posterior probabilities, maximum a posteriori and most probable explanations. Experiments on facial expression recognition and image segmentation problems are performed using real data.

Belief Updating and Learning in Semi-Qualitative Probabilistic Networks

This paper explores semi-qualitative probabilistic networks (SQPNs) that combine numeric and qualitative information. We first show that exact inferences with SQPNs are NPPP-Complete. We then show that existing qualitative relations in SQPNs (plus probabilistic logic and imprecise assessments) can be dealt effectively through multilinear programming. We then discuss learning: we consider a maximum likelihood method that generates point estimates given a SQPN and empirical data, and we describe a Bayesian-minded method that employs the Imprecise Dirichlet Model to generate set-valued estimates.

Semi-interpenetrating polymer networks incorporating polygalacturonic acid for medical device applications

Background Over 20 million people in the US are living with an implantable medical device [ADDIN RW.CITE{{3114 Higgins,DavidM 2009}}1], with similar figures anticipated for Europe. Complications in the use of medical implants include the Foreign Body Response (FBR) characterised by macrophage adherence and fusion, and device-related infection due to bacterial biofilm formationADDIN RW.CITE{{3124 Harding,JacquelineL 2014}}2. Both can have detrimental consequences on the structural and functional integrity of the medical device [ADDIN RW.CITE{{3101 Anderson,JamesM 2008; 3124 Harding,JacquelineL 2014}}2,3], often necessitating removal; a painful and expensive procedure [ADDIN RW.CITE{{3121 Mah,Thien-FahC 2001}}4]. Materials are sought to attenuate both the FBR and device-related infection, leading to medical devices with improved biocompatibility and performance. Objectives The present work involves development of a semi-interpenetrating network (SIPN) hydrogel containing polygalacturonic acid (PGA), a biopolysaccharide similar in structure to hyaluronic acid. We aim to synthesise, characterise and determine the in vitro biocompatibility of the developed SIPN. Results & Discussion We have successfully incorporated PGA into a poly(HEMA) based hydrogel, which shows favourable swelling and wettability. The surface topography appears altered in comparison to the control material, with pronounced micrometer-scale features. In terms of in vitro performance, the SIPN showed increased protein adsorption, and biofilm formation (Staphylococcus epidermidis and Escherichia coli, up to 1 Log CFU/sample greater than control). However the SIPN displayed minimal cytotoxicity towards L929 fibroblasts, and was resistant to the adherence of RAW 264.7 macrophages. Conclusions The PGA incorporated SIPN lacks cytotoxicity and shows reduced macrophage adherence, however the increased biofilm formation highlights a concern regarding possible device related infection in clinical use. Future work will focus on strategies to reduce bacterial adherence, while maintaining biocompatibility.

Vertex Clustering of Augmented Graph Streams

In this paper we propose a graph stream clustering algorithm with a unied similarity measure on both structural and attribute properties of vertices, with each attribute being treated as a vertex. Unlike others, our approach does not require an input parameter for the number of clusters, instead, it dynamically creates new sketch-based clusters and periodically merges existing similar clusters. Experiments on two publicly available datasets reveal the advantages of our approach in detecting vertex clusters in the graph stream. We provide a detailed investigation into how parameters affect the algorithm performance. We also provide a quantitative evaluation and comparison with a well-known offline community detection algorithm which shows that our streaming algorithm can achieve comparable or better average cluster purity.

Tele-Supervised FES-Assisted Exercise for Hemiplegic Upper Limb

Stroke survivors often have upper limb (UL) hemiparesis, limiting their ability to perform activities of daily life (ADLs). Intensive, task-oriented exercise therapy (ET) can improve UL function, but motivation to perform sufficient ET is difficult to maintain. Here we report on a trial in which a workstation was deployed in the homes of chronic stroke survivors to enable tele-coaching of ET in the guise of computer games. Participants performed 6 weeks of 1 hour/day, 5 days/week ET. Hand opening and grasp were assisted with functional electrical stimulation (FES). The primary outcome measure was the Action Research Arm Test (ARAT). Secondary outcome measures included a quantitative test of UL function performed on the workstation, grasp force measurements and transcranial magnetic stimulation (TMS). Improvements were seen in the functional tests, but surprisingly, not in the TMS responses. An important finding was that participants commencing with intermediate functional scores improved the most.

CONCLUSIONS: 1) Daily, tele-supervised FES-ET in chronic stroke survivors is feasible with commercially-available technology. 2) The intervention can significantly improve UL function, particularly in people who start with an intermediate level of function. 3) Significant improvements in UL function can occur in the absence of changes in TMS responses.

Supervised Aggregative Feature Extraction for Big Data Time Series Regression

In many applications, and especially those where batch processes are involved, a target scalar output of interest is often dependent on one or more time series of data. With the exponential growth in data logging in modern industries such time series are increasingly available for statistical modeling in soft sensing applications. In order to exploit time series data for predictive modelling, it is necessary to summarise the information they contain as a set of features to use as model regressors. Typically this is done in an unsupervised fashion using simple techniques such as computing statistical moments, principal components or wavelet decompositions, often leading to significant information loss and hence suboptimal predictive models. In this paper, a functional learning paradigm is exploited in a supervised fashion to derive continuous, smooth estimates of time series data (yielding aggregated local information), while simultaneously estimating a continuous shape function yielding optimal predictions. The proposed Supervised Aggregative Feature Extraction (SAFE) methodology can be extended to support nonlinear predictive models by embedding the functional learning framework in a Reproducing Kernel Hilbert Spaces setting. SAFE has a number of attractive features including closed form solution and the ability to explicitly incorporate first and second order derivative information. Using simulation studies and a practical semiconductor manufacturing case study we highlight the strengths of the new methodology with respect to standard unsupervised feature extraction approaches.

Predicting conversion to dementia in a memory clinic: A standard clinical approach compared with an empirically defined clustering method (latent profile analysis) for mild cognitive impairment subtyping

Introduction
Mild cognitive impairment (MCI) has clinical value in its ability to predict later dementia. A better understanding of cognitive profiles can further help delineate who is most at risk of conversion to dementia. We aimed to (1) examine to what extent the usual MCI subtyping using core criteria corresponds to empirically defined clusters of patients (latent profile analysis [LPA] of continuous neuropsychological data) and (2) compare the two methods of subtyping memory clinic participants in their prediction of conversion to dementia.

Methods
Memory clinic participants (MCI, n = 139) and age-matched controls (n = 98) were recruited. Participants had a full cognitive assessment, and results were grouped (1) according to traditional MCI subtypes and (2) using LPA. MCI participants were followed over approximately 2 years after their initial assessment to monitor for conversion to dementia.

Results
Groups were well matched for age and education. Controls performed significantly better than MCI participants on all cognitive measures. With the traditional analysis, most MCI participants were in the amnestic multidomain subgroup (46.8%) and this group was most at risk of conversion to dementia (63%). From the LPA, a three-profile solution fit the data best. Profile 3 was the largest group (40.3%), the most cognitively impaired, and most at risk of conversion to dementia (68% of the group).

Discussion
LPA provides a useful adjunct in delineating MCI participants most at risk of conversion to dementia and adds confidence to standard categories of clinical inference.

A Novel Semi-Automated Method of Tracking Fetal Movements

We have designed software that can â€â€™look’’ at recorded ultrasound sequences. We analyzed fifteen video sequences representing recorded ultrasound scans of nine fetuses. Our method requires a small amount of user labelled pixels for processing the first frame. These initialize GrowCut 1 , a background removal algorithm, which was used for separating the fetus from its surrounding environment (segmentation). For each subsequent frame, user input is no longer necessary as some of the pixels will inherit labels from the previously processed frame. This results in our software’s ability to track movement. Two sonographers rated the results of our computer’s â€vision’ on a scale from 1 (poor fit) to 10 (excellent fit). They assessed tracking accuracy for the entire video as well as segmentation accuracy (the ability to identify fetus from non-fetus) for every 100th processed frame. There was no appreciable deterioration in the software’s ability to track the fetus over time. I

Interpretable and Reconfigurable Clustering of Document Datasets by Deriving Word-based Rules

Clusters of text documents output by clustering algorithms are often hard to interpret. We describe motivating real-world scenarios that necessitate reconfigurability and high interpretability of clusters and outline the problem of generating clusterings with interpretable and reconfigurable cluster models. We develop two clustering algorithms toward the outlined goal of building interpretable and reconfigurable cluster models. They generate clusters with associated rules that are composed of conditions on word occurrences or nonoccurrences. The proposed approaches vary in the complexity of the format of the rules; RGC employs disjunctions and conjunctions in rule generation whereas RGC-D rules are simple disjunctions of conditions signifying presence of various words. In both the cases, each cluster is comprised of precisely the set of documents that satisfy the corresponding rule. Rules of the latter kind are easy to interpret, whereas the former leads to more accurate clustering. We show that our approaches outperform the unsupervised decision tree approach for rule-generating clustering and also an approach we provide for generating interpretable models for general clusterings, both by significant margins. We empirically show that the purity and f-measure losses to achieve interpretability can be as little as 3 and 5%, respectively using the algorithms presented herein.