Multicomposition EPSR: toward transferable potentials to model chalcogenide glass structures

The structure of xAs40Se60–(1 – x)As40S60 glasses, where x = 1.000, 0.667, 0.500, 0.333, 0.250, and 0.000, is investigated using a combination of neutron and X-ray diffraction coupled with computational modeling using multicomposition empirical potential structure refinement (MC-EPSR). Traditional EPSR (T-EPSR) produces a set of empirical potentials that drive a structural model of a particular composition to agreement with diffraction experiments. The work presented here establishes the shortcomings in generating such a model for a ternary chalcogenide glass composition. In an enhancement to T-EPSR, MC-EPSR produces a set of pair potentials that generate robust structural models across a range of glass compositions. The structures obtained vary with composition in a much more systematic way than those taken from T-EPSR. For example, the average arsenic–sulfur bonding distances vary between 2.28 and 2.46 Å in T-EPSR but are 2.29 ± 0.02 Å in MC-EPSR. Similarly, the arsenic–selenium bond lengths from T-EPSR vary between 2.28 and 2.43 Å but are consistently 2.40 ± 0.02 Å in the MC-EPSR results. Analysis of these models suggests that the average separation of the chalcogen (S or Se) atoms is the structural origin of the changes in nonlinear refractive index with glass composition

Changes over time in mental well-being, fruit and vegetable consumption and physical activity in a community-based lifestyle intervention: a before and after study

Objectives: There is a theoretical basis for believing that healthy lifestyle interventions can improve mental well-being and evidence to show that mental well-being is protective of future health. This study contributes to the evidence base by examining changes in mental well-being associated with the One Body One Life (OBOL) healthy lifestyle programme in a community setting in the West Midlands. Study design: Quantitative, before and after the evaluation. Methods: We conducted a before and after study of the lifestyle intervention ‘OBOL’, a multi component intervention that includes exercise and healthy eating education. Mental wellbeing was measured with the Warwick- Edinburgh Mental Well-being Scale. Physical activity and fruit and vegetable consumption were self-reported. Measures were collected before and after the 12-week intervention and three months post completion. Nonparametric tests were used to assess differences between groups, and linear mixed models were used to assess change over time. Results: Four hundred and eighty-one (81% of attendees) adult participants completed a valid Warwick-Edinburgh Mental Well-being Scale before starting OBOL; of whom, 63.8%completed the Warwick-Edinburgh Mental Well-being Scale immediately post intervention and 25.2% at three months. Mental well-being levels increased significantly (P < 0.001)over the course of the intervention and were sustained at the three-month follow-up(baseline median Warwick- Edinburgh Mental Well-being Scale score ¼ 48 [interquartile range 41e55], completion ¼ 53 [interquartile range 46e57], 3-month follow-up ¼ 52[interquartile range 46e56]). Change in mental well-being was clinically significant after accounting for age and gender. Changes in both fruit and vegetable consumption and physical activity appeared to explain some but not all of the variation in mental well-being. Conclusion: We found significant improvements in mental well-being among participants directly after the intervention which were sustained at the three-month follow-up. These findings contribute to a growing body of knowledge on the contribution of lifestyle interventions to promoting and sustaining mental well-being

Duke Activity Status Index and Liver Frailty Index predict mortality in ambulatory patients with advanced chronic liver disease:A prospective, observational study

BACKGROUND: There remains a lack of consensus on how to assess functional exercise capacity and physical frailty in patients with advanced chronic liver disease (CLD) being assessed for liver transplantation (LT). Aim To investigate prospectively the utility of the Duke Activity Status Index (DASI) and Liver Frailty Index (LFI) in ambulatory patients with CLD.AIM: To investigate prospectively the utility of the Duke Activity Status Index (DASI) and Liver Frailty Index (LFI) in ambulatory patients with CLD.METHODS: We recruited patients from outpatient clinics at University Hospitals Birmingham, UK (2018-2019). We prospectively collated the DASI and LFI to identify the prevalence of, respectively, functional capacity and physical frailty, and to evaluate their accuracy in predicting overall and pre-LT mortality.RESULTS: We studied 307 patients (57% male; median age 54 years; UKELD 52). Median DASI score was 28.7 (IQR 16.2-50.2), mean LFI was 3.82 (SD = 0.72), and 81% were defined either 'pre-frail' or 'frail'. Female sex and hyponatraemia were significant independent predictors of both DASI and LFI. Age and encephalopathy were significant independent predictors of LFI, while BMI significantly predicted DASI. DASI and LFI were significantly related to overall (HR 0.97, p = 0.001 [DASI], HR 2.04, p = 0.001 [LFI]) and pre-LT mortality (HR 0.96, p = 0.02 [DASI], HR 1.94, p = 0.04 [LFI]).CONCLUSIONS: Poor functional exercise capacity and physical frailty are highly prevalent among ambulatory patients with CLD who are being assessed for LT. The DASI and LFI are simple, low-cost tools that predict overall and pre-LT mortality. Implementation of both should be considered in all outpatients with CLD to highlight those who may benefit from targeted nutritional and exercise interventions.</p

American Telemedicine Association’s Principles for Delivering Telerehabilitation Services

Telehealth is a broad term used to describe the use of electronic or digital information and communications technologies to support clinical healthcare, patient and professional health related education, and public health and health administration. Telerehabilitation refers to the delivery of rehabilitation and habilitation services via information and communication technologies (ICT), also commonly referred to as” telehealth” technologies. Telerehabilitation services can include evaluation, assessment, monitoring, prevention, intervention, supervision, education, consultation, and coaching. Telerehabilitation services can be deployed across all patient populations and multiple healthcare settings including clinics, homes, schools, or community-based worksites. This document was adapted from the American Telemedicine Association’s (ATA) “A Blueprint for Telerehabilitation Guidelines” (2010) and reflects the current utilization of telerehabilitation services. It was developed collaboratively by members of the ATA Telerehabilitation Special Interest Group, with input and guidance from other practitioners in the field, strategic stakeholders, and ATA staff. Its purpose is to inform and assist practitioners in providing effective and secure services that are based on client needs, current empirical evidence, and available technologies. Rehabilitation professionals, in conjunction with professional associations and other organizations are encouraged to use this document as a resource for developing discipline-specific standards, guidelines, and practice requirements.Keywords: American Telemedicine Association, Habilitation, Rehabilitation, Telehealth, Telepractice

Duke Activity Status Index and Liver Frailty Index predict mortality in ambulatory patients with advanced chronic liver disease:A prospective, observational study

BACKGROUND: There remains a lack of consensus on how to assess functional exercise capacity and physical frailty in patients with advanced chronic liver disease (CLD) being assessed for liver transplantation (LT). Aim To investigate prospectively the utility of the Duke Activity Status Index (DASI) and Liver Frailty Index (LFI) in ambulatory patients with CLD.AIM: To investigate prospectively the utility of the Duke Activity Status Index (DASI) and Liver Frailty Index (LFI) in ambulatory patients with CLD.METHODS: We recruited patients from outpatient clinics at University Hospitals Birmingham, UK (2018-2019). We prospectively collated the DASI and LFI to identify the prevalence of, respectively, functional capacity and physical frailty, and to evaluate their accuracy in predicting overall and pre-LT mortality.RESULTS: We studied 307 patients (57% male; median age 54 years; UKELD 52). Median DASI score was 28.7 (IQR 16.2-50.2), mean LFI was 3.82 (SD = 0.72), and 81% were defined either 'pre-frail' or 'frail'. Female sex and hyponatraemia were significant independent predictors of both DASI and LFI. Age and encephalopathy were significant independent predictors of LFI, while BMI significantly predicted DASI. DASI and LFI were significantly related to overall (HR 0.97, p = 0.001 [DASI], HR 2.04, p = 0.001 [LFI]) and pre-LT mortality (HR 0.96, p = 0.02 [DASI], HR 1.94, p = 0.04 [LFI]).CONCLUSIONS: Poor functional exercise capacity and physical frailty are highly prevalent among ambulatory patients with CLD who are being assessed for LT. The DASI and LFI are simple, low-cost tools that predict overall and pre-LT mortality. Implementation of both should be considered in all outpatients with CLD to highlight those who may benefit from targeted nutritional and exercise interventions.</p

Assisted Diagnosis of Parkinsonism Based on the Striatal Morphology

Parkinsonism is a clinical syndrome characterized by the progressive loss of striatal dopamine. Its diagnosis is usually corroborated by neuroimaging data such as DaTSCAN neuroimages that allow visualizing the possible dopamine deficiency. During the last decade, a number of computer systems have been proposed to automatically analyze DaTSCAN neuroimages, eliminating the subjectivity inherent to the visual examination of the data. In this work, we propose a computer system based on machine learning to separate Parkinsonian patients and control subjects using the size and shape of the striatal region, modeled from DaTSCAN data. First, an algorithm based on adaptative thresholding is used to parcel the striatum. This region is then divided into two according to the brain hemisphere division and characterized with 152 measures, extracted from the volume and its three possible 2-dimensional projections. Afterwards, the Bhattacharyya distance is used to discard the least discriminative measures and, finally, the neuroimage category is estimated by means of a Support Vector Machine classifier. This method was evaluated using a dataset with 189 DaTSCAN neuroimages, obtaining an accuracy rate over 94%. This rate outperforms those obtained by previous approaches that use the intensity of each striatal voxel as a feature.This work was supported by the MINECO/ FEDER under the TEC2015-64718-R project, the Ministry of Economy, Innovation, Science and Employment of the Junta de Andaluc´ıa under the P11-TIC-7103 Excellence Project and the Vicerectorate of Research and Knowledge Transfer of the University of Granada

An empirical comparison of commercial and open‐source web vulnerability scanners

Web vulnerability scanners (WVSs) are tools that can detect security vulnerabilities in web services. Although both commercial and open-source WVSs exist, their vulnerability detection capability and performance vary. In this article, we report on a comparative study to determine the vulnerability detection capabilities of eight WVSs (both open and commercial) using two vulnerable web applications: WebGoat and Damn vulnerable web application. The eight WVSs studied were: Acunetix; HP WebInspect; IBM AppScan; OWASP ZAP; Skipfish; Arachni; Vega; and Iron WASP. The performance was evaluated using multiple evaluation metrics: precision; recall; Youden index; OWASP web benchmark evaluation; and the web application security scanner evaluation criteria. The experimental results show that, while the commercial scanners are effective in detecting security vulnerabilities, some open-source scanners (such as ZAP and Skipfish) can also be effective. In summary, this study recommends improving the vulnerability detection capabilities of both the open-source and commercial scanners to enhance code coverage and the detection rate, and to reduce the number of false-positives

Comparison of machine learning and semi-quantification algorithms for (I123)FP-CIT classification: the beginning of the end for semi-quantification?

Background Semi-quantification methods are well established in the clinic for assisted reporting of (I123) Ioflupane images. Arguably, these are limited diagnostic tools. Recent research has demonstrated the potential for improved classification performance offered by machine learning algorithms. A direct comparison between methods is required to establish whether a move towards widespread clinical adoption of machine learning algorithms is justified. This study compared three machine learning algorithms with that of a range of semi-quantification methods, using the Parkinson’s Progression Markers Initiative (PPMI) research database and a locally derived clinical database for validation. Machine learning algorithms were based on support vector machine classifiers with three different sets of features: Voxel intensities Principal components of image voxel intensities Striatal binding radios from the putamen and caudate. Semi-quantification methods were based on striatal binding ratios (SBRs) from both putamina, with and without consideration of the caudates. Normal limits for the SBRs were defined through four different methods: Minimum of age-matched controls Mean minus 1/1.5/2 standard deviations from age-matched controls Linear regression of normal patient data against age (minus 1/1.5/2 standard errors) Selection of the optimum operating point on the receiver operator characteristic curve from normal and abnormal training data Each machine learning and semi-quantification technique was evaluated with stratified, nested 10-fold cross-validation, repeated 10 times. Results The mean accuracy of the semi-quantitative methods for classification of local data into Parkinsonian and non-Parkinsonian groups varied from 0.78 to 0.87, contrasting with 0.89 to 0.95 for classifying PPMI data into healthy controls and Parkinson’s disease groups. The machine learning algorithms gave mean accuracies between 0.88 to 0.92 and 0.95 to 0.97 for local and PPMI data respectively. Conclusions Classification performance was lower for the local database than the research database for both semi-quantitative and machine learning algorithms. However, for both databases, the machine learning methods generated equal or higher mean accuracies (with lower variance) than any of the semi-quantification approaches. The gain in performance from using machine learning algorithms as compared to semi-quantification was relatively small and may be insufficient, when considered in isolation, to offer significant advantages in the clinical context