Choosing among techniques for quantifying single-case intervention effectiveness

If single case experimental designs are to be used to establish guidelines for evidence-based interventions in clinical and educational settings, numerical values that reflect treatment effect sizes are required. The present study compares four recently developed procedures for quantifying the magnitude of intervention effect using data with known characteristics. Monte Carlo methods were used to generate AB designs data with potential confounding variables (serial dependence, linear and curvilinear trend, and heteroscedasticity between phases) and two types of treatment effect (level and slope change). The results suggest that data features are important for choosing the appropriate procedure and, thus, inspecting the graphed data visually is a necessary initial stage. In the presence of serial dependence or a change in data variability, the Nonoverlap of All Pairs (NAP) and the Slope and Level Change (SLC) were the only techniques of the four examined that performed adequately. Introducing a data correction step in NAP renders it unaffected by linear trend, as is also the case for the Percentage of Nonoverlapping Corrected Data and SLC. The performance of these techniques indicates that professionals" judgments concerning treatment effectiveness can be readily complemented by both visual and statistical analyses. A flowchart to guide selection of techniques according to the data characteristics identified by visual inspection is provided.

A comparison of mean phase difference and generalized least squares for analyzing single-case data

The present study focuses on single-case data analysis and specifically on two procedures for quantifying differences between baseline and treatment measurements The first technique tested is based on generalized least squares regression analysis and is compared to a proposed non-regression technique, which allows obtaining similar information. The comparison is carried out in the context of generated data representing a variety of patterns (i.e., independent measurements, different serial dependence underlying processes, constant or phase-specific autocorrelation and data variability, different types of trend, and slope and level change). The results suggest that the two techniques perform adequately for a wide range of conditions and researchers can use both of them with certain guarantees. The regression-based procedure offers more efficient estimates, whereas the proposed non-regression procedure is more sensitive to intervention effects. Considering current and previous findings, some tentative recommendations are offered to applied researchers in order to help choosing among the plurality of single-case data analysis techniques.

Low Cost Techniques of Base Stabilization, HR-312, 1994

This project was initiated in 1988 to study the effectiveness of four different construction techniques for establishing a stable base on a granular surfaced roadway. After base stabilization, the roadway was then seal coated, eliminating dust problems associated with granular surfaced roads. When monies become available, the roadway can be surfaced with a more permanent structure. A 2.8 mi (4.5 km) section of the Horseshoe Road in Dubuque County was divided into four divisions for this study. This report discusses the procedures used during construction of these different divisions. Problems and possible solutions have been analyzed to better understand the capabilities of the materials and construction techniques used on the project. The project had the following results: High structural ratings and soil K factors for the BIO CAT and Consolid bases did not translate to good roadway performance; the macadam base had the best overall performance; the tensar fabric had no noticeable effect on the macadam base; and the HFE-300 performed acceptably.

Transobturator surgery for female stress incontinence: a comparative anatomical study of outside-in vs inside-out techniques.

OBJECTIVE To assess the specific risks of injury to neural and vascular structures inherent in two approaches to transobturator surgery for inserting a suburethral sling, i.e. the outside-in (standard technique) and inside-out approaches. MATERIALS AND METHODS The study comprised seven cadavers, providing 14 obturator regions. Five specimens had a tape inserted outside-in on one side, and inside-out on the other; of the remaining two cadavers, one had an inside-out tape and one an outside-in tape, bilaterally. After tape insertion, the cadavers were dissected. Particular attention was paid to the distances between the tape and the deep external pudendal vessels, and between the tape and the posterior branch of the obturator nerve. RESULTS With the inside-out technique, the safety margins were reduced, and the external pudendal vessels and the posterior branch of the obturator nerve were at greater risk of injury. CONCLUSION The two techniques are not equivalent, with a lower risk of injury to vascular and nerve structures with the outside-in technique.

Transmission electron microscopy in cell biology: sample preparation techniques and image information

Transmission electron microscopy is a proven technique in the field of cell biology and a very useful tool in biomedical research. Innovation and improvements in equipment together with the introduction of new technology have allowed us to improve our knowledge of biological tissues, to visualizestructures better and both to identify and to locate molecules. Of all the types ofmicroscopy exploited to date, electron microscopy is the one with the mostadvantageous resolution limit and therefore it is a very efficient technique fordeciphering the cell architecture and relating it to function. This chapter aims toprovide an overview of the most important techniques that we can apply to abiological sample, tissue or cells, to observe it with an electron microscope, fromthe most conventional to the latest generation. Processes and concepts aredefined, and the advantages and disadvantages of each technique are assessedalong with the image and information that we can obtain by using each one ofthem.

Handbook of instrumental techniques for materials, chemical and biosciences research

This Handbook contains a collection of articles describing instrumental techniques used for Materials, Chemical and Biosciences research that are available at the Scientific and Technological Centers of theUniversity of Barcelona (CCiTUB). The CCiTUB are a group of facilities of the UB that provide both the research community and industry with ready access to a wide range of major instrumentation.Together with the latest equipment and technology, the CCiTUB provide expertise in addressing the methodological research needs of the user community and they also collaborate in R+D+i Projectswith industry. CCiTUB specialists include technical and Ph.D.-level professional staff members who are actively engaged in methodological research. Detailed information on the centers’ resources andactivities can be found at the CCiTUB website www.ccit.ub.edu ...

Advanced light microscopy techniques

This article summarizes the basic principles of light microscopy, with examples of applications in biomedicine that illustrate the capabilities of thetechnique.

Ensemble methods for environmental data modelling with support vector regression

This paper investigates the use of ensemble of predictors in order to improve the performance of spatial prediction methods. Support vector regression (SVR), a popular method from the field of statistical machine learning, is used. Several instances of SVR are combined using different data sampling schemes (bagging and boosting). Bagging shows good performance, and proves to be more computationally efficient than training a single SVR model while reducing error. Boosting, however, does not improve results on this specific problem.

Recent advances in remote sensing image processing

Remote sensing image processing is nowadays a mature research area. The techniques developed in the field allow many real-life applications with great societal value. For instance, urban monitoring, fire detection or flood prediction can have a great impact on economical and environmental issues. To attain such objectives, the remote sensing community has turned into a multidisciplinary field of science that embraces physics, signal theory, computer science, electronics, and communications. From a machine learning and signal/image processing point of view, all the applications are tackled under specific formalisms, such as classification and clustering, regression and function approximation, image coding, restoration and enhancement, source unmixing, data fusion or feature selection and extraction. This paper serves as a survey of methods and applications, and reviews the last methodological advances in remote sensing image processing.

Le dispositif mythico-rituel comme opérateur efficace (techniques magico-religieuses d'arrêt, figuration et résolution des états critiques : analyse de cas)

(Résumé de l'ouvrage) Dans cet ouvrage réunissant théologiens et philosophes, le corps contemporain est pensé par rapport à ce qui l'excède, ce qui le met en scène, ce qui le reprend, ce qui le transforme aujourd'hui. Dans une première partie, l'ouvrage propose des éclairages sur le corps à partir de ce qui met en question sa vision strictement rationnelle. Puis, trois auteurs évoquent les différentes manières dont la Bible, la philosophie et la littérature contemporaine mettent en scène les corps. Dans une troisième partie, sont abordées des questions plus spécifiquement reliées à la tradition catholique, au christianisme primitif et à la pratique de l'ascèse. Enfin, quatre contributions explorent le défi posé par la déréalisation du corps dans nos sociétés d'aujourd'hui, avec, pour clore l'ensemble, une réflexion sur le dualisme qui traverse le questionnement sur le corps.

Evaluation of a multicomponent worksite health promotion program for cardiovascular risk factors-correcting for the regression towards the mean effect.

BACKGROUND: We assessed the impact of a multicomponent worksite health promotion program for0 reducing cardiovascular risk factors (CVRF) with short intervention, adjusting for regression towards the mean (RTM) affecting such nonexperimental study without control group. METHODS: A cohort of 4,198 workers (aged 42 +/- 10 years, range 16-76 years, 27% women) were analyzed at 3.7-year interval and stratified by each CVRF risk category (low/medium/high blood pressure [BP], total cholesterol [TC], body mass index [BMI], and smoking) with RTM and secular trend adjustments. Intervention consisted of 15 min CVRF screening and individualized counseling by health professionals to medium- and high-risk individuals, with eventual physician referral. RESULTS: High-risk groups participants improved diastolic BP (-3.4 mm Hg [95%CI: -5.1, -1.7]) in 190 hypertensive patients, TC (-0.58 mmol/l [-0.71, -0.44]) in 693 hypercholesterolemic patients, and smoking (-3.1 cig/day [-3.9, -2.3]) in 808 smokers, while systolic BP changes reflected RTM. Low-risk individuals without counseling deteriorated TC and BMI. Body weight increased uniformly in all risk groups (+0.35 kg/year). CONCLUSIONS: In real-world conditions, short intervention program participants in high-risk groups for diastolic BP, TC, and smoking improved their CVRF, whereas low-risk TC and BMI groups deteriorated. Future programs may include specific advises to low-risk groups to maintain a favorable CVRF profile.

Kernel-based Mapping of Meteorological Fields in Complex Orography

Due to the advances in sensor networks and remote sensing technologies, the acquisition and storage rates of meteorological and climatological data increases every day and ask for novel and efficient processing algorithms. A fundamental problem of data analysis and modeling is the spatial prediction of meteorological variables in complex orography, which serves among others to extended climatological analyses, for the assimilation of data into numerical weather prediction models, for preparing inputs to hydrological models and for real time monitoring and short-term forecasting of weather.In this thesis, a new framework for spatial estimation is proposed by taking advantage of a class of algorithms emerging from the statistical learning theory. Nonparametric kernel-based methods for nonlinear data classification, regression and target detection, known as support vector machines (SVM), are adapted for mapping of meteorological variables in complex orography.With the advent of high resolution digital elevation models, the field of spatial prediction met new horizons. In fact, by exploiting image processing tools along with physical heuristics, an incredible number of terrain features which account for the topographic conditions at multiple spatial scales can be extracted. Such features are highly relevant for the mapping of meteorological variables because they control a considerable part of the spatial variability of meteorological fields in the complex Alpine orography. For instance, patterns of orographic rainfall, wind speed and cold air pools are known to be correlated with particular terrain forms, e.g. convex/concave surfaces and upwind sides of mountain slopes.Kernel-based methods are employed to learn the nonlinear statistical dependence which links the multidimensional space of geographical and topographic explanatory variables to the variable of interest, that is the wind speed as measured at the weather stations or the occurrence of orographic rainfall patterns as extracted from sequences of radar images. Compared to low dimensional models integrating only the geographical coordinates, the proposed framework opens a way to regionalize meteorological variables which are multidimensional in nature and rarely show spatial auto-correlation in the original space making the use of classical geostatistics tangled.The challenges which are explored during the thesis are manifolds. First, the complexity of models is optimized to impose appropriate smoothness properties and reduce the impact of noisy measurements. Secondly, a multiple kernel extension of SVM is considered to select the multiscale features which explain most of the spatial variability of wind speed. Then, SVM target detection methods are implemented to describe the orographic conditions which cause persistent and stationary rainfall patterns. Finally, the optimal splitting of the data is studied to estimate realistic performances and confidence intervals characterizing the uncertainty of predictions.The resulting maps of average wind speeds find applications within renewable resources assessment and opens a route to decrease the temporal scale of analysis to meet hydrological requirements. Furthermore, the maps depicting the susceptibility to orographic rainfall enhancement can be used to improve current radar-based quantitative precipitation estimation and forecasting systems and to generate stochastic ensembles of precipitation fields conditioned upon the orography.