Highly compressed water structure observed in a perchlorate aqueous solution

The discovery by the Phoenix Lander of calcium and magnesium perchlorates in Martian soil samples has fueled much speculation that flows of perchlorate brines might be the cause of the observed channeling and weathering in the surface. Here, we study the structure of a mimetic of Martian water, magnesium perchlorate aqueous solution at its eutectic composition, using neutron diffraction in combination with hydrogen isotope labeling and empirical potential structure refinement. We find that the tetrahedral structure of water is heavily perturbed, the effect being equivalent to pressurizing pure water to pressures of order 2 GPa or more. The Mg2+ and ClO4− ions appear charge-ordered, confining the water on length scales of order 9 Å, preventing ice formation at low temperature. This may explain the low evaporation rates and high deliquescence of these salt solutions, which are essential for stability within the low relative humidity environment of the Martian atmosphere

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

Temperature-Dependent Segregation in Alcohol-Water Binary Mixtures Is Driven by Water Clustering

Previous neutron scattering work, combined with computer simulated structure analysis, has established that binary mixtures of methanol and water partially segregate into water-rich and alcohol-rich components. It has furthermore been noted that, between methanol mole fractions of 0.27 and 0.54, both components, water and methanol, simultaneously form percolating clusters. This partial segregation is enhanced with decreasing temperature. The mole fraction of 0.27 also corresponds to the point of maximum excess entropy for ethanol–water mixtures. Here, we study the degree of molecular segregation in aqueous ethanol solutions at a mole fraction of 0.27 and compare it with that in methanol–water solutions at the same concentration. Structural information is extracted for these solutions using neutron diffraction coupled with empirical potential structure refinement. We show that ethanol, like methanol, bi-percolates at this concentration and that, in a similar manner to methanol, alcohol segregation, as measured by the proximity of neighboring methyl sidechains, is increased upon cooling the solution. Water clustering is found to be significantly enhanced in both alcohol solutions compared to the water clustering that occurs for random, hard sphere-like, mixing with no hydrogen bonds between molecules. Alcohol clustering via the hydrophobic groups is, on the other hand, only slightly sensitive to the water hydrogen bond network. These results support the idea that it is the water clustering that drives the partial segregation of the two components, and hence the observed excess entropy of mixing

Enhancing supervised classifications with metamorphic relations

We report on a novel use of metamorphic relations (MRs) in machine learning: instead of conducting metamorphic testing, we use MRs for the augmentation of the machine learning algorithms themselves. In particular, we report on how MRs can enable enhancements to an image classification problem of images containing hidden visual markers ("Artcodes"). Working on an original classifier, and using the characteristics of two different categories of images, two MRs, based on separation and occlusion, were used to improve the performance of the classifier. Our experimental results show that the MR-augmented classifier achieves better performance than the original classifier, algorithms, and extending the use of MRs beyond the context of software testing

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

Critical care resources in the Solomon Islands: a cross-sectional survey

<p>Abstract</p> <p>Background</p> <p>There are minimal data available on critical care case-mix, care processes and outcomes in lower and middle income countries (LMICs). The objectives of this paper were to gather data in the Solomon Islands in order to gain a better understanding of common presentations of critical illness, available hospital resources, and what resources would be helpful in improving the care of these patients in the future.</p> <p>Methods</p> <p>This study used a mixed methods approach, including a cross sectional survey of respondents' opinions regarding critical care needs, ethnographic information and qualitative data.</p> <p>Results</p> <p>The four most common conditions leading to critical illness in the Solomon Islands are malaria, diseases of the respiratory system including pneumonia and influenza, diabetes mellitus and tuberculosis. Complications of surgery and trauma less frequently result in critical illness. Respondents emphasised the need for basic critical care resources in LMICs, including equipment such as oximeters and oxygen concentrators; greater access to medications and blood products; laboratory services; staff education; and the need for at least one national critical care facility.</p> <p>Conclusions</p> <p>A large degree of critical illness in LMICs is likely due to inadequate resources for primary prevention and healthcare; however, for patients who fall through the net of prevention, there may be simple therapies and context-appropriate resources to mitigate the high burden of morbidity and mortality. Emphasis should be on the development and acquisition of simple and inexpensive tools rather than complicated equipment, to prevent critical care from unduly diverting resources away from other important parts of the health system.</p

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