Improved detection of microbial ureteral stent colonisation by sonication.

PURPOSE: The diagnosis of microbial ureteral stent colonisation (MUSC) is difficult, since routine diagnostic techniques do not accurately detect microorganisms embedded in biofilms. New methods may improve diagnostic yield and understanding the pathophysiology of MUSC. The aim of the present study was to evaluate the potential of sonication in the detection of MUSC and to identify risk factors for device colonisation. METHODS: Four hundred and eight polyurethane ureteral stents of 300 consecutive patients were prospectively evaluated. Conventional urine culture (CUC) was obtained prior to stent placement and device removal. Sonication was performed to dislodge adherent microorganisms. Data of patient sex and age, indwelling time and indication for stent placement were recorded. RESULTS: Sonicate-fluid culture detected MUSC in 36%. Ureteral stents inserted during urinary tract infection (UTI) were more frequently colonised (59%) compared to those placed in sterile urine (26%; P < 0.001). Female sex (P < 0.001) and continuous stenting (P < 0.005) were significant risk factors for MUSC; a similar trend was observed in patients older than 50 years (P = 0.16). MUSC and indwelling time were positively correlated (P < 0.005). MUSC was accompanied by positive CUC in 36%. Most commonly isolated microorganisms were Coagulase-negative staphylococci (18.3%), Enterococci (17.9%) and Enterobacteriaceae (16.9%). CONCLUSIONS: Sonication is a promising approach in the diagnosis of MUSC. Significant risk factors for MUSC are UTI at the time of stent insertion, female sex, continuous stenting and indwelling time. CUC is a poor predictor of MUSC. The clinical relevance of MUSC needs further evaluation to classify isolated microorganism properly as contaminants or pathogens.

Kernel-based methods for change detection in remote sensing images

Nowadays, the joint exploitation of images acquired daily by remote sensing instruments and of images available from archives allows a detailed monitoring of the transitions occurring at the surface of the Earth. These modifications of the land cover generate spectral discrepancies that can be detected via the analysis of remote sensing images. Independently from the origin of the images and of type of surface change, a correct processing of such data implies the adoption of flexible, robust and possibly nonlinear method, to correctly account for the complex statistical relationships characterizing the pixels of the images. This Thesis deals with the development and the application of advanced statistical methods for multi-temporal optical remote sensing image processing tasks. Three different families of machine learning models have been explored and fundamental solutions for change detection problems are provided. In the first part, change detection with user supervision has been considered. In a first application, a nonlinear classifier has been applied with the intent of precisely delineating flooded regions from a pair of images. In a second case study, the spatial context of each pixel has been injected into another nonlinear classifier to obtain a precise mapping of new urban structures. In both cases, the user provides the classifier with examples of what he believes has changed or not. In the second part, a completely automatic and unsupervised method for precise binary detection of changes has been proposed. The technique allows a very accurate mapping without any user intervention, resulting particularly useful when readiness and reaction times of the system are a crucial constraint. In the third, the problem of statistical distributions shifting between acquisitions is studied. Two approaches to transform the couple of bi-temporal images and reduce their differences unrelated to changes in land cover are studied. The methods align the distributions of the images, so that the pixel-wise comparison could be carried out with higher accuracy. Furthermore, the second method can deal with images from different sensors, no matter the dimensionality of the data nor the spectral information content. This opens the doors to possible solutions for a crucial problem in the field: detecting changes when the images have been acquired by two different sensors.

Prenatal ultrasonographic detection of gastrointestinal obstruction: results from 18 European congenital anomaly registries.

OBJECTIVES: We evaluated the prenatal detection of gastrointestinal obstruction (GIO, including atresia, stenosis, absence or fistula) by routine ultrasonographic examination in an unselected population all over Europe. METHODS: Data from 18 congenital malformation registries in 11 European countries were analysed. These multisource registries used the same methodology. All fetuses/neonates with GIO confirmed within 1 week after birth who had prenatal sonography and were born during the study period (1 July 1996 to 31 December 1998) were included. RESULTS: There were 670 793 births in the area covered and 349 fetuses/neonates had GIO. The prenatal detection rate of GIO was 34%; of these 40% were detected < or = 24 weeks of gestation (WG). A total of 31% (60/192) of the isolated GIO were detected prenatally, as were 38% (59/157) of the associated GIO (p=0.26). The detection rate was 25% for esophageal obstruction (31/122), 52% for duodenal obstruction (33/64), 40% for small intestine obstruction (27/68) and 29% for large intestine obstruction (28/95) (p=0.002). The detection rate was higher in countries with a policy of routine obstetric ultrasound. Fifteen percent of pregnancies were terminated (51/349). Eleven of these had chromosomal anomalies, 31 multiple malformations, eight non-chromosomal recognized syndromes, and one isolated GIO. The participating registries reflect the various national policies for termination of pregnancy (TOP), but TOPs after 24 WG (11/51) do not appear to be performed more frequently in countries with a liberal TOP policy. CONCLUSION: This European study shows that the detection rate of GIO depends on the screening policy and on the sonographic detectability of GIO subgroups.

Compact portable biosensor for arsenic detection in aqueous samples with Escherichia coli bioreporter cells.

We present a compact portable biosensor to measure arsenic As(III) concentrations in water using Escherichia coli bioreporter cells. Escherichia coli expresses green fluorescent protein in a linearly dependent manner as a function of the arsenic concentration (between 0 and 100 μg/L). The device accommodates a small polydimethylsiloxane microfluidic chip that holds the agarose-encapsulated bacteria, and a complete optical illumination/collection/detection system for automated quantitative fluorescence measurements. The device is capable of sampling water autonomously, controlling the whole measurement, storing and transmitting data over GSM networks. We demonstrate highly reproducible measurements of arsenic in drinking water at 10 and 50 μg/L within 100 and 80 min, respectively.

Multisensor change detection with nonlinear canonical correlation

The analysis of multi-modal and multi-sensor images is nowadays of paramount importance for Earth Observation (EO) applications. There exist a variety of methods that aim at fusing the different sources of information to obtain a compact representation of such datasets. However, for change detection existing methods are often unable to deal with heterogeneous image sources and very few consider possible nonlinearities in the data. Additionally, the availability of labeled information is very limited in change detection applications. For these reasons, we present the use of a semi-supervised kernel-based feature extraction technique. It incorporates a manifold regularization accounting for the geometric distribution and jointly addressing the small sample problem. An exhaustive example using Landsat 5 data illustrates the potential of the method for multi-sensor change detection.

On-farm animal welfare assessment in dairy cattle: improved lameness detection

Estudi realitzat a partir d’una estada a la University of British Columbia, Canada, entre 2010 i 2012. Primerament es va desenvolupar una escala per mesurar coixeses (amb valors de l’1 al 5). Aquesta escala es va utilitzar per estudiar l’associació entre factors de risc a nivell de granja (disseny de le instal.lacions i maneig) i la prevalencia de coixeses a Nord America. Les dades es van recollir en un total de 40 granges al Nord Est dels E.E.U.U (NE) i 39 a California (CA) . Totes les vaques del group mes productiu es van categoritzar segons la severitat de les coixeses: sanes, coixes i severament coixes. La prevalencia de coixeses en general fou del 55 % a NE i del 31% a CA. La prevalencia de coixeses severes fou del 8% a NE i del 4% a Ca. A NE, les coixeses en general increntaren amb la presencia de serradura als llits i disminuiren en granjes grans, amb major quantitat de llit i acces a pastura. Les coixeses mes severes incrementaren amb la falta d’higiene als llit i amb la presencia de serradura als llits, i disminuiren amb la quantitat de llit proveit, l’us de sorra als llits i amb la mida de la granja. A CA, les coixeses en general incrementaren amb la falta d’higiene al llit, i disminuiren amb la mida de la granja, la presencia de terres de goma, l’increment d’espai als cubicles , l’espai a l’abeuredor i la desinfeccio de les peulles. Les coixeses severes incrementaren amb la falta d’higiene al llit i disminuixen amb la frequencia de neteja del corral. En conclusio, canvis en el maneig i el disseny de les instal.lacions poden ajudar a disminuir la prevalencia de coixeses, tot i que les estrategies a seguir variaran segons la regio.

Multi-scale label interaction to improve object detection and segmentation

The project aims at advancing the state of the art in the use of context information for classification of image and video data. The use of context in the classification of images has been showed of great importance to improve the performance of actual object recognition systems. In our project we proposed the concept of Multi-scale Feature Labels as a general and compact method to exploit the local and global context. The feature extraction from the discriminative probability or classification confidence label field is of great novelty. Moreover the use of a multi-scale representation of the feature labels lead to a compact and efficient description of the context. The goal of the project has been also to provide a general-purpose method and prove its suitability in different image/video analysis problem. The two-year project generated 5 journal publications (plus 2 under submission), 10 conference publications (plus 2 under submission) and one patent (plus 1 pending). Of these publications, a relevant number make use of the main result of this project to improve the results in detection and/or segmentation of objects.

Human synthetic lethal inference as potential anti-cancer target gene detection

Background: Two genes are called synthetic lethal (SL) if mutation of either alone is not lethal, but mutation of both leads to death or a significant decrease in organism's fitness. The detection of SL gene pairs constitutes a promising alternative for anti-cancer therapy. As cancer cells exhibit a large number of mutations, the identification of these mutated genes' SL partners may provide specific anti-cancer drug candidates, with minor perturbations to the healthy cells. Since existent SL data is mainly restricted to yeast screenings, the road towards human SL candidates is limited to inference methods. Results: In the present work, we use phylogenetic analysis and database manipulation (BioGRID for interactions, Ensembl and NCBI for homology, Gene Ontology for GO attributes) in order to reconstruct the phylogenetically-inferred SL gene network for human. In addition, available data on cancer mutated genes (COSMIC and Cancer Gene Census databases) as well as on existent approved drugs (DrugBank database) supports our selection of cancer-therapy candidates.Conclusions: Our work provides a complementary alternative to the current methods for drug discovering and gene target identification in anti-cancer research. Novel SL screening analysis and the use of highly curated databases would contribute to improve the results of this methodology.

Effects of long-term averaging of quantitative blood pressure traits on the detection of genetic associations.

Blood pressure (BP) is a heritable, quantitative trait with intraindividual variability and susceptibility to measurement error. Genetic studies of BP generally use single-visit measurements and thus cannot remove variability occurring over months or years. We leveraged the idea that averaging BP measured across time would improve phenotypic accuracy and thereby increase statistical power to detect genetic associations. We studied systolic BP (SBP), diastolic BP (DBP), mean arterial pressure (MAP), and pulse pressure (PP) averaged over multiple years in 46,629 individuals of European ancestry. We identified 39 trait-variant associations across 19 independent loci (p < 5 × 10(-8)); five associations (in four loci) uniquely identified by our LTA analyses included those of SBP and MAP at 2p23 (rs1275988, near KCNK3), DBP at 2q11.2 (rs7599598, in FER1L5), and PP at 6p21 (rs10948071, near CRIP3) and 7p13 (rs2949837, near IGFBP3). Replication analyses conducted in cohorts with single-visit BP data showed positive replication of associations and a nominal association (p < 0.05). We estimated a 20% gain in statistical power with long-term average (LTA) as compared to single-visit BP association studies. Using LTA analysis, we identified genetic loci influencing BP. LTA might be one way of increasing the power of genetic associations for continuous traits in extant samples for other phenotypes that are measured serially over time.

Multiuser detection in a dynamic environment — Part II: Joint user identification and parameter estimation

The problem of jointly estimating the number, the identities, and the data of active users in a time-varying multiuser environment was examined in a companion paper (IEEE Trans. Information Theory, vol. 53, no. 9, September 2007), at whose core was the use of the theory of finite random sets on countable spaces. Here we extend that theory to encompass the more general problem of estimating unknown continuous parameters of the active-user signals. This problem is solved here by applying the theory of random finite sets constructed on hybrid spaces. We doso deriving Bayesian recursions that describe the evolution withtime of a posteriori densities of the unknown parameters and data.Unlike in the above cited paper, wherein one could evaluate theexact multiuser set posterior density, here the continuous-parameter Bayesian recursions do not admit closed-form expressions. To circumvent this difficulty, we develop numerical approximationsfor the receivers that are based on Sequential Monte Carlo (SMC)methods (“particle filtering”). Simulation results, referring to acode-divisin multiple-access (CDMA) system, are presented toillustrate the theory.

Development of In-Situ Detection Methods for Materials-Related Distress (MRD) in Concrete Pavements, July 2003

The purpose of this research was to summarize existing nondestructive test methods that have the potential to be used to detect materials-related distress (MRD) in concrete pavements. The various nondestructive test methods were then subjected to selection criteria that helped to reduce the size of the list so that specific techniques could be investigated in more detail. The main test methods that were determined to be applicable to this study included two stress-wave propagation techniques (impact-echo and spectral analysis of surface waves techniques), infrared thermography, ground penetrating radar (GPR), and visual inspection. The GPR technique was selected for a preliminary round of “proof of concept” trials. GPR surveys were carried out over a variety of portland cement concrete pavements for this study using two different systems. One of the systems was a state-of-the-art GPR system that allowed data to be collected at highway speeds. The other system was a less sophisticated system that was commercially available. Surveys conducted with both sets of equipment have produced test results capable of identifying subsurface distress in two of the three sites that exhibited internal cracking due to MRD. Both systems failed to detect distress in a single pavement that exhibited extensive cracking. Both systems correctly indicated that the control pavement exhibited negligible evidence of distress. The initial positive results presented here indicate that a more thorough study (incorporating refinements to the system, data collection, and analysis) is needed. Improvements in the results will be dependent upon defining the optimum number and arrangement of GPR antennas to detect the most common problems in Iowa pavements. In addition, refining highfrequency antenna response characteristics will be a crucial step toward providing an optimum GPR system for detecting materialsrelated distress.

Multiuser detection with an unknown number of active users: receiver design

In multiuser detection, the set of users active at any time may be unknown to the receiver. In these conditions, optimum reception consists of detecting simultaneously the set of activeusers and their data, problem that can be solved exactly by applying random-set theory (RST) and Bayesian recursions (BR). However, implementation of optimum receivers may be limited by their complexity, which grows exponentially with the number of potential users. In this paper we examine three strategies leading to reduced-complexity receivers.In particular, we show how a simple approximation of BRs enables the use of Sphere Detection (SD) algorithm, whichexhibits satisfactory performance with limited complexity.

Detection of four Plasmodium species in blood from humans by 18S rRNA gene subunit-based and specific real-time PCR assays

Résumé : Un nombre croissant de cas de malaria chez les voyageurs et migrants a été rapporté. Bien que l'analyse microscopique des frottis sanguins reste traditionnellement l'outil diagnostic de référence, sa fiabilité dépend considérablement de l'expertise de l'examinateur, pouvant elle-même faire défaut sous nos latitudes. Une PCR multiplex en temps réel a donc été développée en vue d'une standardisation du diagnostic. Un ensemble d'amorces génériques ciblant une région hautement conservée du gène d'ARN ribosomial 18S du genre Plasmodium a tout d'abord été conçu, dont le polymorphisme du produit d'amplification semblait suffisant pour créer quatre sondes spécifiques à l'espèce P. falciparum, P. malariae, P. vivax et P. ovale. Ces sondes utilisées en PCR en temps réel se sont révélées capables de détecter une seule copie de plasmide de P. falciparum, P. malariae, P. vivax et P. ovale spécifiquement. La même sensibilité a été obtenue avec une sonde de screening pouvant détecter les quatre espèces. Quatre-vingt-dix-sept échantillons de sang ont ensuite été testés, dont on a comparé la microscopie et la PCR en temps réel pour 66 (60 patients) d'entre eux. Ces deux méthodes ont montré une concordance globale de 86% pour la détection de plasmodia. Les résultats discordants ont été réévalués grâce à des données cliniques, une deuxième expertise microscopique et moléculaire (laboratoire de Genève et de l'Institut Suisse Tropical de Bâle), ainsi qu'à l'aide du séquençage. Cette nouvelle analyse s'est prononcé en faveur de la méthode moléculaire pour tous les neuf résultats discordants. Sur les 31 résultats positifs par les deux méthodes, la même réévaluation a pu donner raison 8 fois sur 9 à la PCR en temps réel sur le plan de l'identification de l'espèce plasmodiale. Les 31 autres échantillons ont été analysés pour le suivi de sept patients sous traitement antimalarique. Il a été observé une baisse rapide du nombre de parasites mesurée par la PCR en temps réel chez six des sept patients, baisse correspondant à la parasitémie déterminée microscopiquement. Ceci suggère ainsi le rôle potentiel de la PCR en temps réel dans le suivi thérapeutique des patients traités par antipaludéens. Abstract : There have been reports of increasing numbers of cases of malaria among migrants and travelers. Although microscopic examination of blood smears remains the "gold standard" in diagnosis, this method suffers from insufficient sensitivity and requires considerable expertise. To improve diagnosis, a multiplex real-time PCR was developed. One set of generic primers targeting a highly conserved region of the 18S rRNA gene of the genus Plasmodium was designed; the primer set was polymorphic enough internally to design four species-specific probes for P. falciparum, P. vivax, P. malarie, and P. ovale. Real-time PCR with species-specific probes detected one plasmid copy of P. falciparum, P. vivax, P. malariae, and P. ovale specifically. The same sensitivity was achieved for all species with real-time PCR with the 18S screening probe. Ninety-seven blood samples were investigated. For 66 of them (60 patients), microscopy and real-time PCR results were compared and had a crude agreement of 86% for the detection of plasmodia. Discordant results were reevaluated with clinical, molecular, and sequencing data to resolve them. All nine discordances between 18S screening PCR and microscopy were resolved in favor of the molecular method, as were eight of nine discordances at the species level for the species-specific PCR among the 31 samples positive by both methods. The other 31 blood samples were tested to monitor the antimalaria treatment in seven patients. The number of parasites measured by real-time PCR fell rapidly for six out of seven patients in parallel to parasitemia determined microscopically. This suggests a role of quantitative PCR for the monitoring of patients receiving antimalaria therapy.

Frame detection over the semantic web

In the past, research in ontology learning from text has mainly focused on entity recognition, taxonomy induction and relation extraction. In this work we approach a challenging research issue: detecting semantic frames from texts and using them to encode web ontologies. We exploit a new generation Natural Language Processing technology for frame detection, and we enrich the frames acquired so far with argument restrictions provided by a super-sense tagger and domain specializations. The results are encoded according to a Linguistic MetaModel, which allows a complete translation of lexical resources and data acquired from text, enabling custom transformations of the enriched frames into modular ontology components.

Postoperative delirium. Part 2: detection, prevention and treatment.

To target pharmacological prevention, instruments giving an approximation of an individual patient's risk of developing postoperative delirium are available. In view of the variable clinical presentation, identifying patients in whom prophylaxis has failed (that is, who develop delirium) remains a challenge. Several bedside instruments are available for the routine ward and ICU setting. Several have been shown to have a high specificity and sensitivity when compared with the standard definitions according to DSM-IV-TR and ICD-10. The Confusion Assessment Method (CAM) and a version specifically developed for the intensive care setting (CAM-ICU) have emerged as a standard. However, alternatives allowing grading of the severity of delirium are also available. In many units, the approach to delirium follows a three-step strategy. Initially, non-pharmacological multicomponent strategies are used for primary prevention. As a second step, pharmacological prophylaxis may be added. Perioperative administration of haloperidol has been shown to reduce the severity, but not the incidence, of delirium. Perioperative administration of atypical antipsychotics has been shown to reduce the incidence of delirium in specific groups of patients. In patients with delirium, both symptomatic and causal treatment of delirium need to be considered. So far symptomatic treatment of delirium is primarily based on antipsychotics. Currently, cholinesterase inhibitors cannot be recommended and the data on dexmedetomidine are inconclusive. With the exception of alcohol-withdrawal delirium, there is no role for benzodiazepines in the treatment of delirium. It is unclear whether treating delirium prevents long-term sequelae.