Measuring the efficiency in Spanish municipal refuse collection services

The objective of this study is to analyse the technical or productive efficiency ofthe refuse collection services in 75 municipalities located in the Spanish regionof Catalonia. The analysis has been carried out using various techniques. Firstly we have calculated a deterministic parametric frontier, then a stochastic parametric frontier, and finally, various non-parametric approaches (DEA and FDH). Concerning the results, these naturally differ according to the technique used to approach the frontier. Nevertheless, they have an appearance of solidity, at least with regard to the ordinal concordance among the indices of efficiency obtained by the different approaches, as is demonstrated by the statistical tests used. Finally, we have attempted to search for any relation existing between efficiency and the method (public or private) of managing the services. No significant relation was found between the type of management and efficiencyindices

The Dyadic and Triadic Therapeutic Alliance in Cross-cultural Health Care: The case of Hispanic American Patients

This research project conducted in the Psychology Department of the University of Lausanne (Switzerland) evaluated the therapeutic alliance with Hispanic American Patients. From the patient's perspective, the therapeutic alliance was explored in two types of frameworks: the dyadic and the triadic setting. The dyadic setting is the encounter between a therapist (health professional) and a patient who ideally share the same language. The triadic setting is the encounter of a therapist and a patient who speak different languages, but are able to interact using the help of an interpreter. My specific interest focuses on studying the therapeutic alliance in a cross- cultural setting through a mixed methodology. As part of the quantitative phase, non- parametric tests were used to analyze 55 questionnaires of the Therapeutic Alliance for Migrants - Health Professionals' version (QALM-PS). For the qualitative phase, a thematic analysis was used to analyze 20 transcript interviews. While no differences were found concerning the strength of the therapeutic alliance between the triadic and dyadic settings, results showed that the factors that enrich the therapeutic alliance with migrant patients depend more on an emotional alliance (bond) than on a rational alliance (agreements). Indeed, the positive relationship with the interpreter, and especially with the therapist, relies considerably on human qualities and moral values, bringing the conception of humanity as an important need when meeting foreign patients in health care settings. In addition, the quality of communication, which could be attributed to the type of interpreter in the triadic setting, plays an important role in the establishment of a positive therapeutic relationship. Ce projet de recherche mené au Département de psychologie de l'Université de Lausanne (Suisse) a évalué l'alliance thérapeutique avec les patients hispano-américains. Du point de vue du patient, l'alliance thérapeutique a été étudiée dans deux types de dispositifs: le cadre dyadique et triadique. Le cadre dyadique est la rencontre d'un thérapeute (professionnel de la santé) et d'un patient qui, idéalement, partagent la même langue. Le cadre triadique est la rencontre d'un thérapeute et d'un patient qui parlent différentes langues, mais sont capables d'interagir grâce à l'aide d'un interprète. Mon intérêt porte en particulier sur l'étude de l'alliance thérapeutique dans un cadre interculturel au travers d'une méthodologie mixte. Dans la phase quantitative, des tests non paramétriques ont été utilisés pour les analyses des 55 questionnaires de l'alliance thérapeutique pour les migrants, version - professionnels de la santé (QALM-PS). Pour la phase qualitative, une analyse thématique a été utilisée pour l'analyse des 20 entretiens transcrits. Bien qu'aucune différence n'a été constatée en ce qui concerne la force de l'alliance thérapeutique entre les cadres dyadiques et triadiques, les résultats montrent que les facteurs qui enrichissent l'alliance thérapeutique avec les patients migrants dépendent plus de l'alliance émotionnelle (lien) que sur une alliance rationnelle (accords). En effet, la relation positive avec l'interprète, et en particulier avec le thérapeute, repose en grande partie sur des qualités humaines et des valeurs morales, ce qui porte la conception de l'humanité comme un besoin important lors de la rencontre des patients étrangers dans un cadre de santé. En outre, la qualité de la communication, qui pourrait être attribuée au type d'interprète dans le cadre triadique, joue un rôle important dans l'établissement d'une relation thérapeutique positive.

Bootstrapping pairs in Distance-Based Regression

La regressió basada en distàncies és un mètode de predicció que consisteix en dos passos: a partir de les distàncies entre observacions obtenim les variables latents, les quals passen a ser els regressors en un model lineal de mínims quadrats ordinaris. Les distàncies les calculem a partir dels predictors originals fent us d'una funció de dissimilaritats adequada. Donat que, en general, els regressors estan relacionats de manera no lineal amb la resposta, la seva selecció amb el test F usual no és possible. En aquest treball proposem una solució a aquest problema de selecció de predictors definint tests estadístics generalitzats i adaptant un mètode de bootstrap no paramètric per a l'estimació dels p-valors. Incluim un exemple numèric amb dades de l'assegurança d'automòbils.

Benchmarking therapeutic drug monitoring software: A systematic evaluation of available computer tools

Introduction: Therapeutic drug monitoring (TDM) aims at optimizing treatment by individualizing dosage regimen based on measurement of blood concentrations. Maintaining concentrations within a target range requires pharmacokinetic and clinical capabilities. Bayesian calculation represents a gold standard in TDM approach but requires computing assistance. In the last decades computer programs have been developed to assist clinicians in this assignment. The aim of this benchmarking was to assess and compare computer tools designed to support TDM clinical activities.¦Method: Literature and Internet search was performed to identify software. All programs were tested on common personal computer. Each program was scored against a standardized grid covering pharmacokinetic relevance, user-friendliness, computing aspects, interfacing, and storage. A weighting factor was applied to each criterion of the grid to consider its relative importance. To assess the robustness of the software, six representative clinical vignettes were also processed through all of them.¦Results: 12 software tools were identified, tested and ranked. It represents a comprehensive review of the available software's characteristics. Numbers of drugs handled vary widely and 8 programs offer the ability to the user to add its own drug model. 10 computer programs are able to compute Bayesian dosage adaptation based on a blood concentration (a posteriori adjustment) while 9 are also able to suggest a priori dosage regimen (prior to any blood concentration measurement), based on individual patient covariates, such as age, gender, weight. Among those applying Bayesian analysis, one uses the non-parametric approach. The top 2 software emerging from this benchmark are MwPharm and TCIWorks. Other programs evaluated have also a good potential but are less sophisticated (e.g. in terms of storage or report generation) or less user-friendly.¦Conclusion: Whereas 2 integrated programs are at the top of the ranked listed, such complex tools would possibly not fit all institutions, and each software tool must be regarded with respect to individual needs of hospitals or clinicians. Interest in computing tool to support therapeutic monitoring is still growing. Although developers put efforts into it the last years, there is still room for improvement, especially in terms of institutional information system interfacing, user-friendliness, capacity of data storage and report generation.

Learning wind fields with multiple kernels

This paper presents multiple kernel learning (MKL) regression as an exploratory spatial data analysis and modelling tool. The MKL approach is introduced as an extension of support vector regression, where MKL uses dedicated kernels to divide a given task into sub-problems and to treat them separately in an effective way. It provides better interpretability to non-linear robust kernel regression at the cost of a more complex numerical optimization. In particular, we investigate the use of MKL as a tool that allows us to avoid using ad-hoc topographic indices as covariables in statistical models in complex terrains. Instead, MKL learns these relationships from the data in a non-parametric fashion. A study on data simulated from real terrain features confirms the ability of MKL to enhance the interpretability of data-driven models and to aid feature selection without degrading predictive performances. Here we examine the stability of the MKL algorithm with respect to the number of training data samples and to the presence of noise. The results of a real case study are also presented, where MKL is able to exploit a large set of terrain features computed at multiple spatial scales, when predicting mean wind speed in an Alpine region.

How single-trial electrical neuroimaging contributes to multisensory research.

This study details a method to statistically determine, on a millisecond scale and for individual subjects, those brain areas whose activity differs between experimental conditions, using single-trial scalp-recorded EEG data. To do this, we non-invasively estimated local field potentials (LFPs) using the ELECTRA distributed inverse solution and applied non-parametric statistical tests at each brain voxel and for each time point. This yields a spatio-temporal activation pattern of differential brain responses. The method is illustrated here in the analysis of auditory-somatosensory (AS) multisensory interactions in four subjects. Differential multisensory responses were temporally and spatially consistent across individuals, with onset at approximately 50 ms and superposition within areas of the posterior superior temporal cortex that have traditionally been considered auditory in their function. The close agreement of these results with previous investigations of AS multisensory interactions suggests that the present approach constitutes a reliable method for studying multisensory processing with the temporal and spatial resolution required to elucidate several existing questions in this field. In particular, the present analyses permit a more direct comparison between human and animal studies of multisensory interactions and can be extended to examine correlation between electrophysiological phenomena and behavior.

Rockfall induced seismic signals: case study in Montserrat, Catalonia

After a rockfall event, a usual post event survey includes qualitative volume estimation, trajectory mapping and determination of departing zones. However, quantitative measurements are not usually made. Additional relevant quantitative information could be useful in determining the spatial occurrence of rockfall events and help us in quantifying their size. Seismic measurements could be suitable for detection purposes since they are non invasive methods and are relatively inexpensive. Moreover, seismic techniques could provide important information on rockfall size and location of impacts. On 14 February 2007 the Avalanche Group of the University of Barcelona obtained the seismic data generated by an artificially triggered rockfall event at the Montserrat massif (near Barcelona, Spain) carried out in order to purge a slope. Two 3 component seismic stations were deployed in the area about 200 m from the explosion point that triggered the rockfall. Seismic signals and video images were simultaneously obtained. The initial volume of the rockfall was estimated to be 75 m3 by laser scanner data analysis. After the explosion, dozens of boulders ranging from 10¿4 to 5 m3 in volume impacted on the ground at different locations. The blocks fell down onto a terrace, 120 m below the release zone. The impact generated a small continuous mass movement composed of a mixture of rocks, sand and dust that ran down the slope and impacted on the road 60 m below. Time, time-frequency evolution and particle motion analysis of the seismic records and seismic energy estimation were performed. The results are as follows: 1 ¿ A rockfall event generates seismic signals with specific characteristics in the time domain; 2 ¿ the seismic signals generated by the mass movement show a time-frequency evolution different from that of other seismogenic sources (e.g. earthquakes, explosions or a single rock impact). This feature could be used for detection purposes; 3 ¿ particle motion plot analysis shows that the procedure to locate the rock impact using two stations is feasible; 4 ¿ The feasibility and validity of seismic methods for the detection of rockfall events, their localization and size determination are comfirmed.

Integration of local-scale hydrological and regional-scale geophysical based on a nonlinear Bayesian sequential simulation approach

Significant progress has been made with regard to the quantitative integration of geophysical and hydrological data at the local scale. However, extending the corresponding approaches to the regional scale represents a major, and as-of-yet largely unresolved, challenge. To address this problem, we have developed a downscaling procedure based on a non-linear Bayesian sequential simulation approach. The basic objective of this algorithm is to estimate the value of the sparsely sampled hydraulic conductivity at non-sampled locations based on its relation to the electrical conductivity, which is available throughout the model space. The in situ relationship between the hydraulic and electrical conductivities is described through a non-parametric multivariate kernel density function. This method is then applied to the stochastic integration of low-resolution, re- gional-scale electrical resistivity tomography (ERT) data in combination with high-resolution, local-scale downhole measurements of the hydraulic and electrical conductivities. Finally, the overall viability of this downscaling approach is tested and verified by performing and comparing flow and transport simulation through the original and the downscaled hydraulic conductivity fields. Our results indicate that the proposed procedure does indeed allow for obtaining remarkably faithful estimates of the regional-scale hydraulic conductivity structure and correspondingly reliable predictions of the transport characteristics over relatively long distances.

Multiway Array Decomposition Analysis of EEGs in Alzheimer’s Disease

Methods for the extraction of features from physiological datasets are growing needs as clinical investigations of Alzheimer’s disease (AD) in large and heterogeneous population increase. General tools allowing diagnostic regardless of recording sites, such as different hospitals, are essential and if combined to inexpensive non-invasive methods could critically improve mass screening of subjects with AD. In this study, we applied three state of the art multiway array decomposition (MAD) methods to extract features from electroencephalograms (EEGs) of AD patients obtained from multiple sites. In comparison to MAD, spectral-spatial average filter (SSFs) of control and AD subjects were used as well as a common blind source separation method, algorithm for multiple unknown signal extraction (AMUSE). We trained a feed-forward multilayer perceptron (MLP) to validate and optimize AD classification from two independent databases. Using a third EEG dataset, we demonstrated that features extracted from MAD outperformed features obtained from SSFs AMUSE in terms of root mean squared error (RMSE) and reaching up to 100% of accuracy in test condition. We propose that MAD maybe a useful tool to extract features for AD diagnosis offering great generalization across multi-site databases and opening doors to the discovery of new characterization of the disease.

On Automatic Diagnosis of Alzheimer's Disease based on Spontaneous Speech Analysis and Emotional Temperature

Alzheimer's disease is the most prevalent form of progressive degenerative dementia; it has a high socio-economic impact in Western countries. Therefore it is one of the most active research areas today. Alzheimer's is sometimes diagnosed by excluding other dementias, and definitive confirmation is only obtained through a post-mortem study of the brain tissue of the patient. The work presented here is part of a larger study that aims to identify novel technologies and biomarkers for early Alzheimer's disease detection, and it focuses on evaluating the suitability of a new approach for early diagnosis of Alzheimer’s disease by non-invasive methods. The purpose is to examine, in a pilot study, the potential of applying Machine Learning algorithms to speech features obtained from suspected Alzheimer sufferers in order help diagnose this disease and determine its degree of severity. Two human capabilities relevant in communication have been analyzed for feature selection: Spontaneous Speech and Emotional Response. The experimental results obtained were very satisfactory and promising for the early diagnosis and classification of Alzheimer’s disease patients.

Immunobiology of human papillomavirus infection and vaccination - implications for second generation vaccines.

Prophylactic human papillomavirus (HPV) L1 virus like particle (VLP) vaccines have been shown, in large clinical trials, to be very immunogenic, well-tolerated and highly efficacious against genital disease caused by the vaccine HPV types. However these vaccines, at the present, protect against only two of the 15 oncogenic genital HPV types, they are expensive, delivered by intramuscular injection and require a cold chain. The challenges are to develop cheap, thermo-stable vaccines that can be delivered by non-injectable methods that provide long term (decades) protection at mucosal surfaces to most, if not all, oncogenic HPV types that is as good as the current VLP vaccines. Current approaches include L1 capsomers, L2 protein and peptides, delivery via recombinant L1 bacterial and viral vectors and large-scale VLP production in plants. Rational design and successful development of such vaccines will be based on an understanding of the immune response, and particularly the 'cross talk' between the innate and adaptive responses. This will be central in the development of adjuvants and vaccine formulations that induce the response to provide effective protection.

Impact of videogame playing on glucose metabolism in children with type 1 diabetes.

Phan-Hug F, Thurneysen E, Theintz G, Ruffieux C, Grouzmann E. Impact of videogame playing on glucose metabolism in children with type 1 diabetes. Time spent playing videogames (VG) occupies a continually increasing part of children's leisure time. They can generate an important state of excitation, representing a form of mental and physical stress. This pilot study aimed to assess whether VG influences glycemic balance in children with type 1 diabetes. Twelve children with type 1 diabetes were subjected to two distinct tests at a few weeks interval: (i) a 60-min VG session followed by a 60-min rest period and (ii) a 60-min reading session followed by a 60-min rest period. Heart rate, blood pressure, glycemia, epinephrine (E), norepinephrine (NE), cortisol (F), and growth hormone (GH) were measured at 30 min intervals from -60 to +120 min. Non-parametric Wilcoxon tests for paired data were performed on Δ-values computed from baseline (0 min). Rise in heart rate (p = 0.05) and NE increase (p = 0.03) were shown to be significantly higher during the VG session when compared to the reading session and a significant difference of Δ-glycemic values was measured between the respective rest periods. This pilot study suggests that VG playing could induce a state of excitation sufficient to activate the sympathetic system and alter the course of glycemia. Dietary and insulin dose recommendations may be needed to better control glycemic excursion in children playing VG.

ECIL recommendations for the use of biological markers for the diagnosis of invasive fungal diseases in leukemic patients and hematopoietic SCT recipients.

As culture-based methods for the diagnosis of invasive fungal diseases (IFD) in leukemia and hematopoietic SCT patients have limited performance, non-culture methods are increasingly being used. The third European Conference on Infections in Leukemia (ECIL-3) meeting aimed at establishing evidence-based recommendations for the use of biological tests in adult patients, based on the grading system of the Infectious Diseases Society of America. The following biomarkers were investigated as screening tests: galactomannan (GM) for invasive aspergillosis (IA); β-glucan (BG) for invasive candidiasis (IC) and IA; Cryptococcus Ag for cryptococcosis; mannan (Mn) Ag/anti-mannan (A-Mn) Ab for IC, and PCR for IA. Testing for GM, Cryptococcus Ag and BG are included in the revised EORTC/MSG (European Organization for Research and Treatment of Cancer/Mycoses Study Group) consensus definitions for IFD. Strong evidence supports the use of GM in serum (A II), and Cryptococcus Ag in serum and cerebrospinal fluid (CSF) (A II). Evidence is moderate for BG detection in serum (B II), and the combined Mn/A-Mn testing in serum for hepatosplenic candidiasis (B III) and candidemia (C II). No recommendations were formulated for the use of PCR owing to a lack of standardization and clinical validation. Clinical utility of these markers for the early management of IFD should be further assessed in prospective randomized interventional studies.

Long-term changes in rice development in Southern Brazil, during the last ten decades

The objective of this work was to test long-term trends in the duration of rice development phases in Santa Maria, RS, Brazil. The duration from emergence to V3 (EM-V3), emergence to panicle differentiation (EM-R1), emergence to anthesis (EM-R4), and emergence to all grains with brown hull (EM-R9) was calculated using leaf appearance and developmental models for four rice cultivars (IRGA 421, IRGA 417, EPAGRI 109, and EEA 406), for the period from 1912 to 2011, considering three emergence dates (early, mid, and late). The trend of the time series was tested with the non-parametric Mann-Kendall test, and the magnitude of the trend was estimated with simple linear regression. Rice development has changed over the last ten decades in this location, leading to an anticipation of harvest time of 17 to 31 days, depending on the cultivar maturity group and emergence date, which is related to trends of temperature increase during the growing season. Warmer temperatures over the evaluated time period are responsible for changing rice phenology in this location, since minimum and maximum daily temperature drive the rice developmental models used.

Water, energy and early postnatal growth in preterm infants.

Non-invasive methods, including stable isotope techniques, indirect calorimetry, nutritional balance and skinfold thickness, have given a new insight into early postnatal growth in neonates. Neonates and premature infants in particular, create an unusual opportunity to study the fluid and metabolic adaptation to extrauterine life because their physical environment can be controlled, fluid and energy balance can be measured and the link between metabolism and the energetics of their postnatal growth can be assessed accurately. Thus the postnatal time course of total body water, heat production, energy cost of growth and composition of weight gain have been quantified in a series of "healthy" low-birth-weight premature infants. These results show that total body water is remarkably stable between postnatal days 3-21. Energy expenditure and heat production rates increase postnatally from mean values of 40 kcal/kg/day during the first week to 60 kcal/kg/day in the third week. An apparent energy balance deficit of 180 kcal/kg can be ascribed to premature delivery. The cost of protein metabolism is the highest energy demanding process related to growth. The fact that nitrogen balance becomes positive within 72 h after birth places the newborn in a transitional situation of dissociated balance between energy and protein metabolism during early postnatal growth: skinfold thickness, dry body mass and fat decrease, while there is a gain in protein and increase in supine length. This particular situation ends during the second postnatal week and soon thereafter the rate of weight gain matches statural growth. The goals of the following review are to summarize data on total body water and energy metabolism in premature infants and to discuss how they correlate with physiological aspects of early postnatal growth.