435 resultados para Sarah Kofman


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The current research proposed a conceptual design framework for airports to obtain flexible departure layouts based on passenger activity analysis obtained from Business Process Models (BPM). BPMs available for airport terminals were used as a design tool in the current research to uncover the relationships existing between spatial layout and corresponding passenger activities. An algorithm has been developed that demonstrates the applicability of the proposed design framework by obtaining relative spatial layouts based on passenger activity analysis. The generated relative spatial layout assists architects in achieving suitable alternative layouts to meet the changing needs of an airport terminal.

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Background Studies investigating the relationship between malnutrition and post-discharge mortality following acute hip fracture yield conflicting results. This study aimed to determine whether malnutrition independently predicted 12-month post-fracture mortality after adjusting for clinically relevant covariates. Methods An ethics approved, prospective, consecutive audit was undertaken for all surgically treated hip fracture inpatients admitted to a dedicated orthogeriatric unit (November 2010–October 2011). The 12-month mortality data were obtained by a dual search of the mortality registry and Queensland Health database. Malnutrition was evaluated using the Subjective Global Assessment. Demographic (age, gender, admission residence) and clinical covariates included fracture type, time to surgery, anaesthesia type, type of surgery, post-surgery time to mobilize and post-operative complications (delirium, pulmonary and deep vein thrombosis, cardiac complications, infections). The Charlson Comorbidity Index was retrospectively applied. All diagnoses were confirmed by the treating orthogeriatrician. Results A total of 322 of 346 patients were available for audit. Increased age (P = 0.004), admission from residential care (P < 0.001), Charlson Comorbidity Index (P = 0.007), malnutrition (P < 0.001), time to mobilize >48 h (P < 0.001), delirium (P = 0.003), pulmonary embolism (P = 0.029) and cardiovascular complication (P = 0.04) were associated with 12-month mortality. Logistic regression analysis demonstrated that malnutrition (odds ratio (OR) 2.4 (95% confidence interval (CI) 1.3–4.7, P = 0.007)), in addition to admission from residential care (OR 2.6 (95% CI 1.3–5.3, P = 0.005)) and pulmonary embolism (OR 11.0 (95% CI 1.5–78.7, P = 0.017)), independently predicted 12-month mortality. Conclusions Findings substantiate malnutrition as an independent predictor of 12-month mortality in a representative sample of hip fracture inpatients. Effective strategies to identify and treat malnutrition in hip fracture should be prioritized.

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Purpose To evaluate if adding clonidine to a standard nerve root block containing local anaesthetic and steroid improved the outcome of patients with severe lumbar nerve root pain secondary to MRI proven lumbar disc prolapse. Methods We undertook a single blind, prospective, randomised controlled trial evaluating 100 consecutive patients with nerve root pain secondary to lumbar disc prolapse undergoing trans-foraminal epidural steroid injection either with or without the addition of clonidine. 50 patients were allocated to each arm of the study. The primary outcome measure was the avoidance of a second procedure- repeat injection or micro-discectomy surgery. Secondary outcome measures were also studied: pain scores for leg and back pain using a visual analogue scale (VAS), the Roland Morris Disability Questionnaire (RMDQ) and the Measure Your Own Medical Outcome Profile (MYMOP). Follow up was carried out at 6 weeks, 6 months and 1 year. Results No serious complications occurred. Of the 50 patients who received the addition of clonidine, 56% were classified as successful injections, with no further intervention required, as opposed to 40% who received the standard injection. This difference did not reach statistical significance (p=0.109, chi-squared test). All secondary measures showed no statistically significant differences between the groups except curiously, the standard group who had been classified as successful had better leg pain relief than the clonidine group (p=0.026) at 1 year. Conclusions This pilot study has shown a 16% treatment effect with adding clonidine to lumbar nerve root blocks and that it is a safe injectate for this purpose.

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Introduction Malorientation of the socket contributes to instability after hip arthroplasty but the optimal orientation of the cup in relation to the pelvis has not been unequivocally described. Large radiological studies are few and problems occur with film standardisation, measurement methodology used and alternative definitions of describing acetabular orientation. Methods A cohort of 1,578 patients from a single institution is studied where all patient data was collected prospectively. Risk factors for patients undergoing surgery are analysed. Radiological data was compared between a series of non-dislocating hips and dislocating cases matched 2:1 by operation type, age and diagnosis. Results The overall dislocation rate for all 1,578 cases was 3.23% but the rate varied according to the type of surgery performed. The rate in uncomplicated primary cases was 2.4% which increased to 9.3% for second stage implantation for a two stage procedure for infection. There was no significant difference in the variability of the dislocating and non-dislocating groups for either inclination (p = 0.393) or anteversion (p = 0.661). Conclusions A “safe zone” for socket orientation to avoid dislocation could not be defined. The cause of dislocation is multifactorial, re-establishing the anatomic centre of rotation, balancing soft tissues and avoidance of impingement around the hip are important considerations.

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Historically, two-dimensional (2D) cell culture has been the preferred method of producing disease models in vitro. Recently, there has been a move away from 2D culture in favor of generating three-dimensional (3D) multicellular structures, which are thought to be more representative of the in vivo environment. This transition has brought with it an influx of technologies capable of producing these structures in various ways. However, it is becoming evident that many of these technologies do not perform well in automated in vitro drug discovery units. We believe that this is a result of their incompatibility with high-throughput screening (HTS). In this study, we review a number of technologies, which are currently available for producing in vitro 3D disease models. We assess their amenability with high-content screening and HTS and highlight our own work in attempting to address many of the practical problems that are hampering the successful deployment of 3D cell systems in mainstream research.

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Head motion (HM) is a well known confound in analyses of functional MRI (fMRI) data. Neuroimaging researchers therefore typically treat HM as a nuisance covariate in their analyses. Even so, it is possible that HM shares a common genetic influence with the trait of interest. Here we investigate the extent to which this relationship is due to shared genetic factors, using HM extracted from resting-state fMRI and maternal and self report measures of Inattention and Hyperactivity-Impulsivity from the Strengths and Weaknesses of ADHD Symptoms and Normal Behaviour (SWAN) scales. Our sample consisted of healthy young adult twins (N = 627 (63% females) including 95 MZ and 144 DZ twin pairs, mean age 22, who had mother-reported SWAN; N = 725 (58% females) including 101 MZ and 156 DZ pairs, mean age 25, with self reported SWAN). This design enabled us to distinguish genetic from environmental factors in the association between head movement and ADHD scales. HM was moderately correlated with maternal reports of Inattention (r = 0.17, p-value = 7.4E-5) and Hyperactivity-Impulsivity (r = 0.16, p-value = 2.9E-4), and these associations were mainly due to pleiotropic genetic factors with genetic correlations [95% CIs] of rg = 0.24 [0.02, 0.43] and rg = 0.23 [0.07, 0.39]. Correlations between self-reports and HM were not significant, due largely to increased measurement error. These results indicate that treating HM as a nuisance covariate in neuroimaging studies of ADHD will likely reduce power to detect between-group effects, as the implicit assumption of independence between HM and Inattention or Hyperactivity-Impulsivity is not warranted. The implications of this finding are problematic for fMRI studies of ADHD, as failing to apply HM correction is known to increase the likelihood of false positives. We discuss two ways to circumvent this problem: censoring the motion contaminated frames of the RS-fMRI scan or explicitly modeling the relationship between HM and Inattention or Hyperactivity-Impulsivity

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As part of an ongoing project to explore the design of behaviour-change technology for smoking cessation, we analysed a successful community who come together on the popular Reddit website to discuss quitting and to encourage each other's quit attempts. We found that users remain anonymous but identify according to their quit stage. We examined the form and content of posts, finding that narratives about people and events are more common than other rhetorical forms. Many speak of ongoing struggles with quit attempts. Our analysis reveals forms of sociality spontaneously enacted in a self-managed community of quitters. We compare our results with earlier work on social media and behaviour change.

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In this paper we consider HCI's role in technology interventions for health and well-being. Three projects carried out by the authors are analysed by appropriating the idea of a value chain to chart a causal history from proximal effects generated in early episodes of design through to distal health and well-being outcomes. Responding to recent arguments that favour bounding HCI's contribution to local patterns of use, we propose an unbounded view of HCI that addresses an extended value chain of influence. We discuss a view of HCI methods as mobilising this value chain perspective in multi-disciplinary collaborations through its emphasis on early prototyping and naturalistic studies of use.

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Background The Global Burden of Diseases (GBD), Injuries, and Risk Factors study used the disability-adjusted life year (DALY) to quantify the burden of diseases, injuries, and risk factors. This paper provides an overview of injury estimates from the 2013 update of GBD, with detailed information on incidence, mortality, DALYs and rates of change from 1990 to 2013 for 26 causes of injury, globally, by region and by country. Methods Injury mortality was estimated using the extensive GBD mortality database, corrections for ill-defined cause of death and the cause of death ensemble modelling tool. Morbidity estimation was based on inpatient and outpatient data sets, 26 cause-of-injury and 47 nature-of-injury categories, and seven follow-up studies with patient-reported long-term outcome measures. Results In 2013, 973 million (uncertainty interval (UI) 942 to 993) people sustained injuries that warranted some type of healthcare and 4.8 million (UI 4.5 to 5.1) people died from injuries. Between 1990 and 2013 the global age-standardised injury DALY rate decreased by 31% (UI 26% to 35%). The rate of decline in DALY rates was significant for 22 cause-of-injury categories, including all the major injuries. Conclusions Injuries continue to be an important cause of morbidity and mortality in the developed and developing world. The decline in rates for almost all injuries is so prominent that it warrants a general statement that the world is becoming a safer place to live in. However, the patterns vary widely by cause, age, sex, region and time and there are still large improvements that need to be made.

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Summary Common variants in WNT pathway genes have been associated with bone mass and fat distribution, the latter predicting diabetes and cardiovascular disease risk. Rare mutations in the WNT co-receptors LRP5 and LRP6 are similarly associated with bone and cardiometabolic disorders. We investigated the role of LRP5 in human adipose tissue. Subjects with gain-of-function LRP5 mutations and high bone mass had enhanced lower-body fat accumulation. Reciprocally, a low bone mineral density-associated common LRP5 allele correlated with increased abdominal adiposity. Ex vivo LRP5 expression was higher in abdominal versus gluteal adipocyte progenitors. Equivalent knockdown of LRP5 in both progenitor types dose-dependently impaired β-catenin signaling and led to distinct biological outcomes: diminished gluteal and enhanced abdominal adipogenesis. These data highlight how depot differences in WNT/β-catenin pathway activity modulate human fat distribution via effects on adipocyte progenitor biology. They also identify LRP5 as a potential pharmacologic target for the treatment of cardiometabolic disorders. © 2015 The Authors.

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High bone mass (HBM) can be an incidental clinical finding; however, monogenic HBM disorders (eg, LRP5 or SOST mutations) are rare. We aimed to determine to what extent HBM is explained by mutations in known HBM genes. A total of 258 unrelated HBM cases were identified from a review of 335,115 DXA scans from 13 UK centers. Cases were assessed clinically and underwent sequencing of known anabolic HBM loci: LRP5 (exons 2, 3, 4), LRP4 (exons 25, 26), SOST (exons 1, 2, and the van Buchem's disease [VBD] 52-kb intronic deletion 3'). Family members were assessed for HBM segregation with identified variants. Three-dimensional protein models were constructed for identified variants. Two novel missense LRP5 HBM mutations ([c.518C>T; p.Thr173Met], [c.796C>T; p.Arg266Cys]) were identified, plus three previously reported missense LRP5 mutations ([c.593A>G; p.Asn198Ser], [c.724G>A; p.Ala242Thr], [c.266A>G; p.Gln89Arg]), associated with HBM in 11 adults from seven families. Individuals with LRP5 HBM ( approximately prevalence 5/100,000) displayed a variable phenotype of skeletal dysplasia with increased trabecular BMD and cortical thickness on HRpQCT, and gynoid fat mass accumulation on DXA, compared with both non-LRP5 HBM and controls. One mostly asymptomatic woman carried a novel heterozygous nonsense SOST mutation (c.530C>A; p.Ser177X) predicted to prematurely truncate sclerostin. Protein modeling suggests the severity of the LRP5-HBM phenotype corresponds to the degree of protein disruption and the consequent effect on SOST-LRP5 binding. We predict p.Asn198Ser and p.Ala242Thr directly disrupt SOST binding; both correspond to severe HBM phenotypes (BMD Z-scores +3.1 to +12.2, inability to float). Less disruptive structural alterations predicted from p.Arg266Cys, p.Thr173Met, and p.Gln89Arg were associated with less severe phenotypes (Z-scores +2.4 to +6.2, ability to float). In conclusion, although mutations in known HBM loci may be asymptomatic, they only account for a very small proportion ( approximately 3%) of HBM individuals, suggesting the great majority are explained by either unknown monogenic causes or polygenic inheritance.

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The problem of unsupervised anomaly detection arises in a wide variety of practical applications. While one-class support vector machines have demonstrated their effectiveness as an anomaly detection technique, their ability to model large datasets is limited due to their memory and time complexity for training. To address this issue for supervised learning of kernel machines, there has been growing interest in random projection methods as an alternative to the computationally expensive problems of kernel matrix construction and sup-port vector optimisation. In this paper we leverage the theory of nonlinear random projections and propose the Randomised One-class SVM (R1SVM), which is an efficient and scalable anomaly detection technique that can be trained on large-scale datasets. Our empirical analysis on several real-life and synthetic datasets shows that our randomised 1SVM algorithm achieves comparable or better accuracy to deep auto encoder and traditional kernelised approaches for anomaly detection, while being approximately 100 times faster in training and testing.

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Identifying unusual or anomalous patterns in an underlying dataset is an important but challenging task in many applications. The focus of the unsupervised anomaly detection literature has mostly been on vectorised data. However, many applications are more naturally described using higher-order tensor representations. Approaches that vectorise tensorial data can destroy the structural information encoded in the high-dimensional space, and lead to the problem of the curse of dimensionality. In this paper we present the first unsupervised tensorial anomaly detection method, along with a randomised version of our method. Our anomaly detection method, the One-class Support Tensor Machine (1STM), is a generalisation of conventional one-class Support Vector Machines to higher-order spaces. 1STM preserves the multiway structure of tensor data, while achieving significant improvement in accuracy and efficiency over conventional vectorised methods. We then leverage the theory of nonlinear random projections to propose the Randomised 1STM (R1STM). Our empirical analysis on several real and synthetic datasets shows that our R1STM algorithm delivers comparable or better accuracy to a state-of-the-art deep learning method and traditional kernelised approaches for anomaly detection, while being approximately 100 times faster in training and testing.

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Many conventional statistical machine learning al- gorithms generalise poorly if distribution bias ex- ists in the datasets. For example, distribution bias arises in the context of domain generalisation, where knowledge acquired from multiple source domains need to be used in a previously unseen target domains. We propose Elliptical Summary Randomisation (ESRand), an efficient domain generalisation approach that comprises of a randomised kernel and elliptical data summarisation. ESRand learns a domain interdependent projection to a la- tent subspace that minimises the existing biases to the data while maintaining the functional relationship between domains. In the latent subspace, ellipsoidal summaries replace the samples to enhance the generalisation by further removing bias and noise in the data. Moreover, the summarisation enables large-scale data processing by significantly reducing the size of the data. Through comprehensive analysis, we show that our subspace-based approach outperforms state-of-the-art results on several activity recognition benchmark datasets, while keeping the computational complexity significantly low.

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