Estimating biophysical variable dependences with kernels

This paper introduces a nonlinear measure of dependence between random variables in the context of remote sensing data analysis. The Hilbert-Schmidt Independence Criterion (HSIC) is a kernel method for evaluating statistical dependence. HSIC is based on computing the Hilbert-Schmidt norm of the cross-covariance operator of mapped samples in the corresponding Hilbert spaces. The HSIC empirical estimator is very easy to compute and has good theoretical and practical properties. We exploit the capabilities of HSIC to explain nonlinear dependences in two remote sensing problems: temperature estimation and chlorophyll concentration prediction from spectra. Results show that, when the relationship between random variables is nonlinear or when few data are available, the HSIC criterion outperforms other standard methods, such as the linear correlation or mutual information.

Sequential therapist interventions and the therapeutic alliance: a pilot study

(from the journal abstract) Scientific interest for the concept of alliance has been maintained and stimulated by repeated findings that a strong alliance is associated with facilitative treatment process and favourable treatment outcome. However, because the alliance is not in itself a therapeutic technique, these findings were unsuccessful in bringing about significant improvements in clinical practice. An essential issue in modern psychotherapeutic research concerns the relation between common factors which are known to explain great variance in empirical results and the specific therapeutic techniques which are the primary basis of clinical training and practice. This pilot study explored sequences in therapist interventions over four sessions of brief psychodynamic investigation. It aims at determining if patterns of interventions can be found during brief psychodynamic investigation and if these patterns can be associated with differences in the therapeutic alliance. Therapist interventions where coded using the Psychodynamic Intervention Rating Scale (PIRS) which enables the classification of each therapist utterance into one of 9 categories of interpretive interventions (defence interpretation, transference interpretation), supportive interventions (question, clarification, association, reflection, supportive strategy) or interventions about the therapeutic frame (work-enhancing statement, contractual arrangement). Data analysis was done using lag sequential analysis, a statistical procedure which identifies contingent relationships in time among a large number of behaviours. The sample includes N = 20 therapist-patient dyads assigned to three groups with: (1) a high and stable alliance profile, (2) a low and stable alliance profile and (3) an improving alliance profile. Results suggest that therapists most often have one single intention when interacting with patients. Large sequences of questions, associations and clarifications were found, which indicate that if a therapist asks a question, clarifies or associates, there is a significant probability that he will continue doing so. A single theme sequence involving frame interventions was also observed. These sequences were found in all three alliance groups. One exception was found for mixed sequences of interpretations and supportive interventions. The simultaneous use of these two interventions was associated with a high or an improving alliance over the course of treatment, but not with a low and stable alliance where only single theme sequences of interpretations were found. In other words, in this last group, therapists were either supportive or interpretative, whereas with high or improving alliance, interpretations were always given along with supportive interventions. This finding provides evidence that examining therapist interpretation individually can only yield incomplete findings. How interpretations were given is important for alliance building. It also suggests that therapists should carefully dose their interpretations and be supportive when necessary in order to build a strong therapeutic alliance. And from a research point of view, to study technical interventions, we must look into dynamic variables such as dosage, the supportive quality of an intervention, and timing. (PsycINFO Database Record (c) 2005 APA, all rights reserved)

Percentage of nonoverlapping corrected data

The present study proposes a modification in one of the most frequently applied effect size procedures in single-case data analysis the percent of nonoverlapping data. In contrast to other techniques, the calculus and interpretation of this procedure is straightforward and it can be easily complemented by visual inspection of the graphed data. Although the percent of nonoverlapping data has been found to perform reasonably well in N = 1 data, the magnitude of effect estimates it yields can be distorted by trend and autocorrelation. Therefore, the data correction procedure focuses on removing the baseline trend from data prior to estimating the change produced in the behavior due to intervention. A simulation study is carried out in order to compare the original and the modified procedures in several experimental conditions. The results suggest that the new proposal is unaffected by trend and autocorrelation and can be used in case of unstable baselines and sequentially related measurements.

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.

Comparative modular analysis of gene expression in vertebrate development

The focus of my PhD research was the concept of modularity. In the last 15 years, modularity has become a classic term in different fields of biology. On the conceptual level, a module is a set of interacting elements that remain mostly independent from the elements outside of the module. I used modular analysis techniques to study gene expression evolution in vertebrates. In particular, I identified ``natural'' modules of gene expression in mouse and human, and I showed that expression of organ-specific and system-specific genes tends to be conserved between such distance vertebrates as mammals and fishes. Also with a modular approach, I studied patterns of developmental constraints on transcriptome evolution. I showed that none of the two commonly accepted models of the evolution of embryonic development (``evo-devo'') are exclusively valid. In particular, I found that the conservation of the sequences of regulatory regions is highest during mid-development of zebrafish, and thus it supports the ``hourglass model''. In contrast, events of gene duplication and new gene introduction are most rare in early development, which supports the ``early conservation model''. In addition to the biological insights on transcriptome evolution, I have also discussed in detail the advantages of modular approaches in large-scale data analysis. Moreover, I re-analyzed several studies (published in high-ranking journals), and showed that their conclusions do not hold out under a detailed analysis. This demonstrates that complex analysis of high-throughput data requires a co-operation between biologists, bioinformaticians, and statisticians.

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.

Quantitative integration of high-resolution hydrogeophysical data through Monte-Carlo-type conditional simulations

Geophysical techniques can help to bridge the inherent gap with regard to spatial resolution and the range of coverage that plagues classical hydrological methods. This has lead to the emergence of the new and rapidly growing field of hydrogeophysics. Given the differing sensitivities of various geophysical techniques to hydrologically relevant parameters and their inherent trade-off between resolution and range the fundamental usefulness of multi-method hydrogeophysical surveys for reducing uncertainties in data analysis and interpretation is widely accepted. A major challenge arising from such endeavors is the quantitative integration of the resulting vast and diverse database in order to obtain a unified model of the probed subsurface region that is internally consistent with all available data. To address this problem, we have developed a strategy towards hydrogeophysical data integration based on Monte-Carlo-type conditional stochastic simulation that we consider to be particularly suitable for local-scale studies characterized by high-resolution and high-quality datasets. Monte-Carlo-based optimization techniques are flexible and versatile, allow for accounting for a wide variety of data and constraints of differing resolution and hardness and thus have the potential of providing, in a geostatistical sense, highly detailed and realistic models of the pertinent target parameter distributions. Compared to more conventional approaches of this kind, our approach provides significant advancements in the way that the larger-scale deterministic information resolved by the hydrogeophysical data can be accounted for, which represents an inherently problematic, and as of yet unresolved, aspect of Monte-Carlo-type conditional simulation techniques. We present the results of applying our algorithm to the integration of porosity log and tomographic crosshole georadar data to generate stochastic realizations of the local-scale porosity structure. Our procedure is first tested on pertinent synthetic data and then applied to corresponding field data collected at the Boise Hydrogeophysical Research Site near Boise, Idaho, USA.

Standardizing admission and discharge processes to improve patient flow: A cross sectional study

Background: The aim of this study was to evaluate how hospital capacity was managed focusing on standardizing the admission and discharge processes. Methods: This study was set in a 900-bed university affiliated hospital of the National Health Service, near Barcelona (Spain). This is a cross-sectional study of a set of interventions which were gradually implemented between April and December 2008. Mainly, they were focused on standardizing the admission and discharge processes to improve patient flow. Primary administrative data was obtained from the 2007 and 2009 Hospital Database. Main outcome measures were median length of stay, percentage of planned discharges, number of surgery cancellations and median number of delayed emergency admissions at 8:00 am. For statistical bivariate analysis, we used a Chi-squared for linear trend for qualitative variables and a Wilcoxon signed ranks test and a Mann–Whitney test for non-normal continuous variables. Results: The median patients’ global length of stay was 8.56 days in 2007 and 7.93 days in 2009 (p<0.051). The percentage of patients admitted the same day as surgery increased from 64.87% in 2007 to 86.01% in 2009 (p<0.05). The number of cancelled interventions due to lack of beds was 216 patients in 2007 and 42 patients in 2009. The median number of planned discharges went from 43.05% in 2007 to 86.01% in 2009 (p<0.01). The median number of emergency patients waiting for an in-hospital bed at 8:00 am was 5 patients in 2007 and 3 patients in 2009 (p<0.01). Conclusions: In conclusion, standardization of admission and discharge processes are largely in our control. There is a significant opportunity to create important benefits for increasing bed capacity and hospital throughput.

Standardizing admission and discharge processes to improve patient flow: A cross sectional study

Background: The aim of this study was to evaluate how hospital capacity was managed focusing on standardizing the admission and discharge processes. Methods: This study was set in a 900-bed university affiliated hospital of the National Health Service, near Barcelona (Spain). This is a cross-sectional study of a set of interventions which were gradually implemented between April and December 2008. Mainly, they were focused on standardizing the admission and discharge processes to improve patient flow. Primary administrative data was obtained from the 2007 and 2009 Hospital Database. Main outcome measures were median length of stay, percentage of planned discharges, number of surgery cancellations and median number of delayed emergency admissions at 8:00¿am. For statistical bivariate analysis, we used a Chi-squared for linear trend for qualitative variables and a Wilcoxon signed ranks test and a Mann¿Whitney test for non-normal continuous variables. Results:The median patients' global length of stay was 8.56 days in 2007 and 7.93 days in 2009 (p<0.051). The percentage of patients admitted the same day as surgery increased from 64.87% in 2007 to 86.01% in 2009 (p<0.05). The number of cancelled interventions due to lack of beds was 216 patients in 2007 and 42 patients in 2009. The median number of planned discharges went from 43.05% in 2007 to 86.01% in 2009 (p<0.01). The median number of emergency patients waiting for an in-hospital bed at 8:00¿am was 5 patients in 2007 and 3 patients in 2009 (p<0.01). Conclusions: In conclusion, standardization of admission and discharge processes are largely in our control. There is a significant opportunity to create important benefits for increasing bed capacity and hospital throughput.

La douleur prolongée chez le nouveau-né: étude de cas [Prolonged pain in neonates: retrospective analysis].

INTRODUCTION: infants hospitalised in neonatology are inevitably exposed to pain repeatedly. Premature infants are particularly vulnerable, because they are hypersensitive to pain and demonstrate diminished behavioural responses to pain. They are therefore at risk of developing short and long-term complications if pain remains untreated. CONTEXT: compared to acute pain, there is limited evidence in the literature on prolonged pain in infants. However, the prevalence is reported between 20 and 40 %. OBJECTIVE : this single case study aimed to identify the bio-contextual characteristics of neonates who experienced prolonged pain. METHODS : this study was carried out in the neonatal unit of a tertiary referral centre in Western Switzerland. A retrospective data analysis of seven infants' profile, who experienced prolonged pain ,was performed using five different data sources. RESULTS : the mean gestational age of the seven infants was 32weeks. The main diagnosis included prematurity and respiratory distress syndrome. The total observations (N=55) showed that the participants had in average 21.8 (SD 6.9) painful procedures that were estimated to be of moderate to severe intensity each day. Out of the 164 recorded pain scores (2.9 pain assessment/day/infant), 14.6 % confirmed acute pain. Out of those experiencing acute pain, analgesia was given in 16.6 % of them and 79.1 % received no analgesia. CONCLUSION: this study highlighted the difficulty in managing pain in neonates who are exposed to numerous painful procedures. Pain in this population remains underevaluated and as a result undertreated.Results of this study showed that nursing documentation related to pain assessment is not systematic.Regular assessment and documentation of acute and prolonged pain are recommended. This could be achieved with clear guidelines on the Assessment Intervention Reassessment (AIR) cyclewith validated measures adapted to neonates. The adequacy of pain assessment is a pre-requisite for appropriate pain relief in neonates.

Using Digital Video Analysis to Monitor Driver Behavior at Intersections

Commercially available instruments for road-side data collection take highly limited measurements, require extensive manual input, or are too expensive for widespread use. However, inexpensive computer vision techniques for digital video analysis can be applied to automate the monitoring of driver, vehicle, and pedestrian behaviors. These techniques can measure safety-related variables that cannot be easily measured using existing sensors. The use of these techniques will lead to an improved understanding of the decisions made by drivers at intersections. These automated techniques allow the collection of large amounts of safety-related data in a relatively short amount of time. There is a need to develop an easily deployable system to utilize these new techniques. This project implemented and tested a digital video analysis system for use at intersections. A prototype video recording system was developed for field deployment. A computer interface was implemented and served to simplify and automate the data analysis and the data review process. Driver behavior was measured at urban and rural non-signalized intersections. Recorded digital video was analyzed and used to test the system.

Managing Decisions Regarding Rural Expressway Routes and Associated Highway Bypasses, 2009

For several years, the Iowa Department of Transportation has constructed bypasses along rural highways. Most bypasses were constructed on the state’s Commercial Industrial Network (CIN). Now that work on the CIN has been completed and the system is open to traffic, it is possible to study the impacts of bypasses. In the past, construction of highway bypasses has led community residents and business people to raise concerns about the loss of business activity. For policy development purposes, it is essential to understand the impacts that a bypass might have on safety, the community, and economics. By researching these impacts, policies can be produced to help to alleviate any negative impacts and create a better system that is ultimately more cost-effective. This study found that the use of trade area analysis does not provide proof that a bypass can positively or negatively impact the economy of a rural community. The analysis did show that, even though the population of a community may be stable for several years and per capita income is increasing, sales leakage still occurs. The literature, site visits, and data make it is apparent that a bypass can positively affect a community. Some conditions that would need to exist in order to maximize a positive impact include the installation of signage along the bypass directing travelers to businesses and services in the community, community or regional plans that include the bypass in future land development scenarios, and businesses adjusting their business plans to attract bypass users. In addition, how proactive a community is in adapting to the bypass will determine the kinds of effects felt in the community. Results of statistical safety analysis indicate that, at least when crashes are separated by severity, bypasses with at-grade accesses appear to perform more poorly than either the bypasses with fully separated accesses or with a mix of at-grade and fully separated accesses. However, the benefit in terms of improved safety of bypasses with fully separated accesses relative to bypasses with a mixed type of accesses is not statistically conclusive.

Robust Nonlinear Mapping of Soil Contamination Using Support Regression

Spatial data analysis mapping and visualization is of great importance in various fields: environment, pollution, natural hazards and risks, epidemiology, spatial econometrics, etc. A basic task of spatial mapping is to make predictions based on some empirical data (measurements). A number of state-of-the-art methods can be used for the task: deterministic interpolations, methods of geostatistics: the family of kriging estimators (Deutsch and Journel, 1997), machine learning algorithms such as artificial neural networks (ANN) of different architectures, hybrid ANN-geostatistics models (Kanevski and Maignan, 2004; Kanevski et al., 1996), etc. All the methods mentioned above can be used for solving the problem of spatial data mapping. Environmental empirical data are always contaminated/corrupted by noise, and often with noise of unknown nature. That's one of the reasons why deterministic models can be inconsistent, since they treat the measurements as values of some unknown function that should be interpolated. Kriging estimators treat the measurements as the realization of some spatial randomn process. To obtain the estimation with kriging one has to model the spatial structure of the data: spatial correlation function or (semi-)variogram. This task can be complicated if there is not sufficient number of measurements and variogram is sensitive to outliers and extremes. ANN is a powerful tool, but it also suffers from the number of reasons. of a special type ? multiplayer perceptrons ? are often used as a detrending tool in hybrid (ANN+geostatistics) models (Kanevski and Maignank, 2004). Therefore, development and adaptation of the method that would be nonlinear and robust to noise in measurements, would deal with the small empirical datasets and which has solid mathematical background is of great importance. The present paper deals with such model, based on Statistical Learning Theory (SLT) - Support Vector Regression. SLT is a general mathematical framework devoted to the problem of estimation of the dependencies from empirical data (Hastie et al, 2004; Vapnik, 1998). SLT models for classification - Support Vector Machines - have shown good results on different machine learning tasks. The results of SVM classification of spatial data are also promising (Kanevski et al, 2002). The properties of SVM for regression - Support Vector Regression (SVR) are less studied. First results of the application of SVR for spatial mapping of physical quantities were obtained by the authorsin for mapping of medium porosity (Kanevski et al, 1999), and for mapping of radioactively contaminated territories (Kanevski and Canu, 2000). The present paper is devoted to further understanding of the properties of SVR model for spatial data analysis and mapping. Detailed description of the SVR theory can be found in (Cristianini and Shawe-Taylor, 2000; Smola, 1996) and basic equations for the nonlinear modeling are given in section 2. Section 3 discusses the application of SVR for spatial data mapping on the real case study - soil pollution by Cs137 radionuclide. Section 4 discusses the properties of the modelapplied to noised data or data with outliers.

Factors predisposing to dislocation after primary total hip arthroplasty: a multivariate analysis.

We conducted this study to determine the relative influence of various mechanical and patient-related factors on the incidence of dislocation after primary total hip asthroplasty (THA). Of 2,023 THAs, 21 patients who had at least 1 dislocation were compared with a control group of 21 patients without dislocation, matched for age, gender, pathology, and year of surgery. Implant positioning, seniority of the surgeon, American Society of Anesthesiologists (ASA) score, and diminished motor coordination were recorded. Data analysis included univariate and multivariate methods. The dislocation risk was 6.9 times higher if total anteversion was not between 40 degrees and 60 degrees and 10 times higher in patients with high ASA scores. Surgeons should pay attention to total anteversion (cup and stem) of THA. The ASA score should be part of the preoperative assessment of the dislocation risk.