A comparison of location of acute symptomatic vs. 'silent' small vessel lesions

Background: Acute lacunar ischaemic stroke, white matter hyperintensities, and lacunes are all features of cerebral small vessel disease. It is unclear why some small vessel disease lesions present with acute stroke symptoms, whereas others typically do not. Aim: To test if lesion location could be one reason why some small vessel disease lesions present with acute stroke, whereas others accumulate covertly. Methods: We identified prospectively patients who presented with acute lacunar stroke symptoms with a recent small subcortical infarct confirmed on magnetic resonance diffusion imaging. We compared the distribution of the acute infarcts with that of white matter hyperintensity and lacunes using computational image mapping methods. Results: In 188 patients, mean age 67 ± standard deviation 12 years, the lesions that presented with acute lacunar ischaemic stroke were located in or near the main motor and sensory tracts in (descending order): posterior limb of the internal capsule (probability density 0·2/mm3), centrum semiovale (probability density = 0·15/mm3), medial lentiform nucleus/lateral thalamus (probability density = 0·09/mm3), and pons (probability density = 0·02/mm3). Most lacunes were in the lentiform nucleus (probability density = 0·01–0·04/mm3) or external capsule (probability density = 0·05/mm3). Most white matter hyperintensities were in centrum semiovale (except for the area affected by the acute symptomatic infarcts), external capsules, basal ganglia, and brainstem, with little overlap with the acute symptomatic infarcts (analysis of variance, P < 0·01). Conclusions: Lesions that present with acute lacunar ischaemic stroke symptoms may be more likely noticed by the patient through affecting the main motor and sensory tracts, whereas white matter hyperintensity and asymptomatic lacunes mainly affect other areas. Brain location could at least partly explain the symptomatic vs. covert development of small vessel disease

Comparison of distance measures in spatial analytical modeling for health service planning

<p>Abstract</p> <p>Background</p> <p>Several methodological approaches have been used to estimate distance in health service research. In this study, focusing on cardiac catheterization services, Euclidean, Manhattan, and the less widely known Minkowski distance metrics are used to estimate distances from patient residence to hospital. Distance metrics typically produce less accurate estimates than actual measurements, but each metric provides a single model of travel over a given network. Therefore, distance metrics, unlike actual measurements, can be directly used in spatial analytical modeling. Euclidean distance is most often used, but unlikely the most appropriate metric. Minkowski distance is a more promising method. Distances estimated with each metric are contrasted with road distance and travel time measurements, and an optimized Minkowski distance is implemented in spatial analytical modeling.</p> <p>Methods</p> <p>Road distance and travel time are calculated from the postal code of residence of each patient undergoing cardiac catheterization to the pertinent hospital. The Minkowski metric is optimized, to approximate travel time and road distance, respectively. Distance estimates and distance measurements are then compared using descriptive statistics and visual mapping methods. The optimized Minkowski metric is implemented, via the spatial weight matrix, in a spatial regression model identifying socio-economic factors significantly associated with cardiac catheterization.</p> <p>Results</p> <p>The Minkowski coefficient that best approximates road distance is 1.54; 1.31 best approximates travel time. The latter is also a good predictor of road distance, thus providing the best single model of travel from patient's residence to hospital. The Euclidean metric and the optimal Minkowski metric are alternatively implemented in the regression model, and the results compared. The Minkowski method produces more reliable results than the traditional Euclidean metric.</p> <p>Conclusion</p> <p>Road distance and travel time measurements are the most accurate estimates, but cannot be directly implemented in spatial analytical modeling. Euclidean distance tends to underestimate road distance and travel time; Manhattan distance tends to overestimate both. The optimized Minkowski distance partially overcomes their shortcomings; it provides a single model of travel over the network. The method is flexible, suitable for analytical modeling, and more accurate than the traditional metrics; its use ultimately increases the reliability of spatial analytical models.</p

Performance issues in optical burst/packet switching

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-01524-3_8This chapter summarises the activities on optical packet switching (OPS) and optical burst switching (OBS) carried out by the COST 291 partners in the last 4 years. It consists of an introduction, five sections with contributions on five different specific topics, and a final section dedicated to the conclusions. Each section contains an introductive state-of-the-art description of the specific topic and at least one contribution on that topic. The conclusions give some points on the current situation of the OPS/OBS paradigms

Expiratory flow rate, breath hold and anatomic dead space influence electronic nose ability to detect lung cancer

BACKGROUND: Electronic noses are composites of nanosensor arrays. Numerous studies showed their potential to detect lung cancer from breath samples by analysing exhaled volatile compound pattern ("breathprint"). Expiratory flow rate, breath hold and inclusion of anatomic dead space may influence the exhaled levels of some volatile compounds; however it has not been fully addressed how these factors affect electronic nose data. Therefore, the aim of the study was to investigate these effects. METHODS: 37 healthy subjects (44 +/- 14 years) and 27 patients with lung cancer (60 +/- 10 years) participated in the study. After deep inhalation through a volatile organic compound filter, subjects exhaled at two different flow rates (50 ml/sec and 75 ml/sec) into Teflon-coated bags. The effect of breath hold was analysed after 10 seconds of deep inhalation. We also studied the effect of anatomic dead space by excluding this fraction and comparing alveolar air to mixed (alveolar + anatomic dead space) air samples. Exhaled air samples were processed with Cyranose 320 electronic nose. RESULTS: Expiratory flow rate, breath hold and the inclusion of anatomic dead space significantly altered "breathprints" in healthy individuals (p 0.05). These factors also influenced the discrimination ability of the electronic nose to detect lung cancer significantly. CONCLUSIONS: We have shown that expiratory flow, breath hold and dead space influence exhaled volatile compound pattern assessed with electronic nose. These findings suggest critical methodological recommendations to standardise sample collections for electronic nose measurements

Conceptualizing and measuring distance in international business research:Recurring questions and best practice guidelines

Distance is a central concept in international business research, yet there is debate about the construct as well as its operationalization. In this editorial, we address three of the most important recurring questions posed by authors, editors, and reviewers by examining the theory, methods, and data of distance research. We discuss (1) how to theorize on distance, and (2) what method and (3) what data to use when constructing a distance index. We develop practical recommendations grounded in theory, illustrating and supporting them by calculating cross-country distance indices for all available country pairs and two of the most used distance indices: cultural and institutional. We show that, whereas a specific method to calculate distance may matter to some extent, the choice for a specific cultural or institutional framework to measure cultural or institutional distance has a major impact on country-pair distances. Overall, this editorial highlights the importance of matching data and method to the theoretical argument.</p

Big Data and Causality

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Causality analysis continues to remain one of the fundamental research questions and the ultimate objective for a tremendous amount of scientific studies. In line with the rapid progress of science and technology, the age of big data has significantly influenced the causality analysis on various disciplines especially for the last decade due to the fact that the complexity and difficulty on identifying causality among big data has dramatically increased. Data mining, the process of uncovering hidden information from big data is now an important tool for causality analysis, and has been extensively exploited by scholars around the world. The primary aim of this paper is to provide a concise review of the causality analysis in big data. To this end the paper reviews recent significant applications of data mining techniques in causality analysis covering a substantial quantity of research to date, presented in chronological order with an overview table of data mining applications in causality analysis domain as a reference directory