New Granada Medium for detection and identification of group B streptococci.

A methotrexate-containing medium for the detection of beta-hemolytic group B streptococci from clinical specimens on the basis of detection of pigment is described. The medium contained peptone, starch, serum, MgSO4, glucose, pyruvate, methotrexate (as pigment enhancer), phosphate-morpholine-propanesulfonic acid buffer, and selective agents. The recovery of beta-hemolytic group B streptococci was comparable to that obtained with selective broth.

Multi-modal Change Detection, Application to the Detection of Flooded Areas: Outcome of the 2009-2010 data fusion contest

The 2009-2010 Data Fusion Contest organized by the Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society was focused on the detection of flooded areas using multi-temporal and multi-modal images. Both high spatial resolution optical and synthetic aperture radar data were provided. The goal was not only to identify the best algorithms (in terms of accuracy), but also to investigate the further improvement derived from decision fusion. This paper presents the four awarded algorithms and the conclusions of the contest, investigating both supervised and unsupervised methods and the use of multi-modal data for flood detection. Interestingly, a simple unsupervised change detection method provided similar accuracy as supervised approaches, and a digital elevation model-based predictive method yielded a comparable projected change detection map without using post-event data.

Protein phosphatase inhibition assays for okadaic acid detection in shellfish: matrix effects, applicability and comparison with LC-MS/MS analysis

The applicability of the protein phosphatase inhibition assay (PPIA) to the determination of okadaic acid (OA) and its acyl derivatives in shellfish samples has been investigated, using a recombinant PP2A and a commercial one. Mediterranean mussel, wedge clam, Pacific oyster and flat oyster have been chosen as model species. Shellfish matrix loading limits for the PPIA have been established, according to the shellfish species and the enzyme source. A synergistic inhibitory effect has been observed in the presence of OA and shellfish matrix, which has been overcome by the application of a correction factor (0.48). Finally, Mediterranean mussel samples obtained from Rı´a de Arousa during a DSP closure associated to Dinophysis acuminata, determined as positive by the mouse bioassay, have been analysed with the PPIAs. The OA equivalent contents provided by the PPIAs correlate satisfactorily with those obtained by liquid chromatography–tandem mass spectrometry (LC–MS/MS).

Risk Evaluation for Spoofing against a Sensor Supplied with Liveness Detection

The aim of this paper is to evaluate the risks associated with the use of fake fingerprints on a livescan supplied with a method of liveness detection. The method is based on optical properties of the skin. The sensor uses several polarizations and illuminations to capture the information of the different layers of the human skin. These experiments also allow for the determination under which conditions the system is deceived and if there is an influence respectively of the nature of the fake, the mould used for the production or the individuals involved in the attack. These experiments showed that current multispectral sensors can be deceived by the use of fake fingerprints created with or without the cooperation of the subject. Fakes created from direct casts perform better than those produced by fakes created from indirect casts. The results showed that the success of the attack is influenced by two main factors. The first is the quality of the fakes, and by extension the quality of the original fingerprint. The second is the combination of the general patterns involved in the attacks since an appropriate combination can strongly increase the rates of successful attacks.

Illuminating the detection chain of bacterial bioreporters.

Engineering bacteria for measuring chemicals of environmental or toxicological concern (bioreporter bacteria) has grown slowly into a mature research area. Despite many potential advantages, current bioreporters do not perform well enough to comply with environmental detection standards. Basically, the reasons for this are the lack of engineering principles in the detection chain in the bioreporters. Here, we dissect critical steps in the detection chain and illustrate how bioreporter design could be improved by mutagenizing specificity and selectivity of the sensing and regulatory proteins, by newer expression strategies and application of different signalling networks. Furthermore, we describe how redesigning bioreporter assays with respect to pollutant transport into the cells and application of other detection devices can decrease detection limits and increase the speed of detection.

Detection of millimetric deformation using a terrestrial laser scanner: experiment and application to a rockfall event

Abstract. Terrestrial laser scanning (TLS) is one of the most promising surveying techniques for rockslope characteriza- tion and monitoring. Landslide and rockfall movements can be detected by means of comparison of sequential scans. One of the most pressing challenges of natural hazards is com- bined temporal and spatial prediction of rockfall. An outdoor experiment was performed to ascertain whether the TLS in- strumental error is small enough to enable detection of pre- cursory displacements of millimetric magnitude. This con- sists of a known displacement of three objects relative to a stable surface. Results show that millimetric changes cannot be detected by the analysis of the unprocessed datasets. Dis- placement measurement are improved considerably by ap- plying Nearest Neighbour (NN) averaging, which reduces the error (1σ ) up to a factor of 6. This technique was ap- plied to displacements prior to the April 2007 rockfall event at Castellfollit de la Roca, Spain. The maximum precursory displacement measured was 45 mm, approximately 2.5 times the standard deviation of the model comparison, hampering the distinction between actual displacement and instrumen- tal error using conventional methodologies. Encouragingly, the precursory displacement was clearly detected by apply- ing the NN averaging method. These results show that mil- limetric displacements prior to failure can be detected using TLS.

Development of bacteria-based bioassays for arsenic detection in natural waters.

Arsenic contamination of natural waters is a worldwide concern, as the drinking water supplies for large populations can have high concentrations of arsenic. Traditional techniques to detect arsenic in natural water samples can be costly and time-consuming; therefore, robust and inexpensive methods to detect arsenic in water are highly desirable. Additionally, methods for detecting arsenic in the field have been greatly sought after. This article focuses on the use of bacteria-based assays as an emerging method that is both robust and inexpensive for the detection of arsenic in groundwater both in the field and in the laboratory. The arsenic detection elements in bacteria-based bioassays are biosensor-reporter strains; genetically modified strains of, e.g., Escherichia coli, Bacillus subtilis, Staphylococcus aureus, and Rhodopseudomonas palustris. In response to the presence of arsenic, such bacteria produce a reporter protein, the amount or activity of which is measured in the bioassay. Some of these bacterial biosensor-reporters have been successfully utilized for comparative in-field analyses through the use of simple solution-based assays, but future methods may concentrate on miniaturization using fiberoptics or microfluidics platforms. Additionally, there are other potential emerging bioassays for the detection of arsenic in natural waters including nematodes and clams.

An Interval Intelligent-based Approach for Fault Detection and Modelling

Not considered in the analytical model of the plant, uncertainties always dramatically decrease the performance of the fault detection task in the practice. To cope better with this prevalent problem, in this paper we develop a methodology using Modal Interval Analysis which takes into account those uncertainties in the plant model. A fault detection method is developed based on this model which is quite robust to uncertainty and results in no false alarm. As soon as a fault is detected, an ANFIS model is trained in online to capture the major behavior of the occurred fault which can be used for fault accommodation. The simulation results understandably demonstrate the capability of the proposed method for accomplishing both tasks appropriately

Robust fault detection using consistency techniques for uncertainty handling

Often practical performance of analytical redundancy for fault detection and diagnosis is decreased by uncertainties prevailing not only in the system model, but also in the measurements. In this paper, the problem of fault detection is stated as a constraint satisfaction problem over continuous domains with a big number of variables and constraints. This problem can be solved using modal interval analysis and consistency techniques. Consistency techniques are then shown to be particularly efficient to check the consistency of the analytical redundancy relations (ARRs), dealing with uncertain measurements and parameters. Through the work presented in this paper, it can be observed that consistency techniques can be used to increase the performance of a robust fault detection tool, which is based on interval arithmetic. The proposed method is illustrated using a nonlinear dynamic model of a hydraulic system

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.

Conjugation of genetically-engineered protein phosphatases to magnetic particles for okadaic acid detection

This work presents the functional characterisation of a protein phosphatase 2A (PP2A) catalytic subunit obtained by genetic engineering and its conjugation to magnetic particles (MPs) via metal coordination chemistry for the subsequent development of assays for diarrheic lipophilic marine toxins. Colorimetric assays with free enzyme have allowed the determination of the best enzyme activity stabiliser, which is glycerol at 10%. They have also demonstrated that the recombinant enzyme can be as sensitive towards okadaic acid (OA) (LOD=2.3μg/L) and dinophysistoxin-1 (DTX-1) (LOD=15.2μg/L) as a commercial PP2A and, moreover, it has a higher operational stability, which makes possible to perform the protein phosphatase inhibition assay (PPIA) with a lower enzyme amount. Once conjugated to MPs, the PP2A catalytic subunit still retains its enzyme activity and it can also be inhibited by OA (LOD=30.1μg/L).

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.