Identification of an unknown b-agonist in feed by liquid chromatography/bioassay/quadrupole time-of-flight tandem mass spectrometry with accurate mass measurement.

A new approach to the search for residues of unknown growth promoting agents such as anabolic steroids and -agonists in feed is presented. Following primary extraction and clean-up, samples are separated using gradient liquid chromatography (LC). The effluent is split towards two identical 96-well fraction collectors and an optional electrospray quadrupole time-of-flight mass spectrometry (QTOFMS) system for accurate mass measurement. One 96-well plate is used for a bioassay (enzyme-immuno assay, receptor assay) and will detect the bioactivity and position of the relevant peak in the chromatogram. The positive well in the second 96-well plate is used for identification by LC/QTOFMS/MS. The value of this LC/bioassay/QTOFMS/MS methodology is highlighted by the finding and structure elucidation of a new -agonist in a feed extract.

Communal larval rearing of European lobster (Homarus gammarus): family identification by microsatellite DNA profiling an offspring fitness.

Stock enhancement experiments of European lobster (Homarus gammarus) have been carried out around the Kvitsoy Islands in south-western Norway since 1990. In addition to releases of coded wire tagged lobster juveniles (cultured) and subsequent monitoring of commercial fishery, a lobster hatchery was established in 1997. Several experiments were made on the communal-rearing approach where the performance of mixed larval groups (families) was evaluated under identical conditions. Berried females of wild and cultured origin and their respective fertilised eggs were screened by using microsatellite DNA profiling involving a multiplex set of six lobster specific primers, thereby allowing determination of both parental genotypes. Each female were kept separately during hatching, and the offspring were later mixed and raised in a communal rearing system. The early-larval survival was estimated at stage IV (bottom stage), and the survivors were identified to family and group by microsatellite profiling. Five different communal experiments were conducted, representing offspring from 65 berried females. Of the surviving larvae, 6.3% could not be assigned to family due to degraded DNA and no PCR amplification. Significant differences in early survival between offspring of wild and cultured origin were found in the experiments. No differences between the groups were found in stage IV larval size. Based on the pooled data on survival (as a measure of early larvae fitness) offspring of cultured females displayed a relative fitness of 60% in comparison to offspring from wild females. Large variation in survival was also observed among families within the wild and cultured groups, suggesting a genetic component for these traits and a potential for selective breeding.

The use of morphological characteristics and texture analysis in the identification of tissue composition in prostatic neoplasia

Quantitative examination of prostate histology offers clues in the diagnostic classification of lesions and in the prediction of response to treatment and prognosis. To facilitate the collection of quantitative data, the development of machine vision systems is necessary. This study explored the use of imaging for identifying tissue abnormalities in prostate histology. Medium-power histological scenes were recorded from whole-mount radical prostatectomy sections at × 40 objective magnification and assessed by a pathologist as exhibiting stroma, normal tissue (nonneoplastic epithelial component), or prostatic carcinoma (PCa). A machine vision system was developed that divided the scenes into subregions of 100 × 100 pixels and subjected each to image-processing techniques. Analysis of morphological characteristics allowed the identification of normal tissue. Analysis of image texture demonstrated that Haralick feature 4 was the most suitable for discriminating stroma from PCa. Using these morphological and texture measurements, it was possible to define a classification scheme for each subregion. The machine vision system is designed to integrate these classification rules and generate digital maps of tissue composition from the classification of subregions; 79.3% of subregions were correctly classified. Established classification rates have demonstrated the validity of the methodology on small scenes; a logical extension was to apply the methodology to whole slide images via scanning technology. The machine vision system is capable of classifying these images. The machine vision system developed in this project facilitates the exploration of morphological and texture characteristics in quantifying tissue composition. It also illustrates the potential of quantitative methods to provide highly discriminatory information in the automated identification of prostatic lesions using computer vision.

Pelophylaxins: novel antimicrobial peptide homologs from the skin secretion of the Fukien gold-striped pond frog, Pelophylax plancyi fukienensis: identification by “shotgun” cDNA cloning and sequence analysis.

Amphibian skin secretions are rich in antimicrobial peptides that act as important components of an innate immune system. Here, we describe a novel “shotgun” skin peptide precursor cloning technique that facilitates rapid access to these genetically encoded molecules and effects their subsequent identification and structural characterization from the secretory peptidome. Adopting this approach on a skin secretion-derived library from a hitherto unstudied Chinese species of frog, we identified a family of novel antimicrobial peptide homologs, named pelophylaxins, that belong to previously identified families (ranatuerins, brevinins and temporins) found predominantly in the skin secretions from frogs of the genus Rana. These data further substantiate the scientifically robust nature of applying parallel transcriptome and peptidome analyses on frog defensive skin secretions that can be obtained in a non-invasive, non-destructive manner. In addition, the present data illustrate that rapid structural characterization of frog skin secretion peptides can be achieved from an unstudied species without prior knowledge of primary structures of endogenous peptides.

Proposed wavelet-neurofuzzy combined system for power qualityviolations detection and diagnosis

A system for the identification of power quality violations is proposed. It is a two-stage system that employs the potentials of the wavelet transform and the adaptive neurofuzzy networks. For the first stage, the wavelet multiresolution signal analysis is exploited to denoise and then decompose the monitored signals of the power quality events to extract its detailed information. A new optimal feature-vector is suggested and adopted in learning the neurofuzzy classifier. Thus, the amount of needed training data is extensively reduced. A modified organisation map of the neurofuzzy classifier has significantly improved the diagnosis efficiency. Simulation results confirm the aptness and the capability of the proposed system in power quality violations detection and automatic diagnosis

Modelling the MAPK signalling pathway using a two-stage identification algorithm

Signal transduction pathways describe the dynamics of cellular response to input signalling molecules at receptors on the cell membrane. The Mitogen-Activated Protein Kinase (MAPK) cascade is one of such pathways that are involved in many important cellular processes including cell growth and proliferation. This paper describes a black-box model of this pathway created using an advanced two-stage identification algorithm. Identification allows us to capture the unique features and dynamics of the pathway and also opens up the possibility of regulatory control design. In the approach described, an optimal model is obtained by performing model subset selection in two stages, where the terms are first determined by a forward selection method and then modified using a backward selection model refinement. The simulation results demonstrate that the model selected using the two-stage algorithm performs better than with the forward selection method alone.

On velocity-based local model networks for nonlinear identification

This paper exposes the strengths and weaknesses of the recently proposed velocity-based local model (LM) network. The global dynamics of the velocity-based blended representation are directly related to the dynamics of the underlying local models, an important property in the design of local controller networks. Furthermore, the sub-models are continuous-time and linear providing continuity with established linear theory and methods. This is not true for the conventional LM framework, where the global dynamics are only weakly related to the affine sub-models. In this paper, a velocity-based multiple model network is identified for a highly nonlinear dynamical system. The results show excellent dynamical modelling performances, highlighting the value of the velocity-based approach for the design and analysis of LM based control. Three important practical issues are also addressed. These relate to the blending of the velocity-based local models, the use of normalised Gaussian basis functions and the requirement of an input derivative.

Towards an intelligent score following system: Handling of mistakes and jumps encountered during piano practicing

Score following has been an important area of research in AI and music since the mid 80's. Various systems were developed, but they were predominantly for providing automated accompaniment to live concert performances, dealing mostly with issues relating to pitch detection and identification of embellished melodies. They have a big potential in the area of education where student performers benefit in practice situations. Current accompaniment systems are not designed to deal with errors that may occur during practising. In this paper we present a system developed to provide accompaniment for students practising at home. First a survey of score following will be given. Then the capabilities of the system will be explained, and the results from the first experiments of the monophonic score following system will be presented.

Host-based P2P flow identification and use in real-time

Data identification is a key task for any Internet Service Provider (ISP) or network administrator. As port fluctuation and encryption become more common in P2P traffic wishing to avoid identification, new strategies must be developed to detect and classify such flows. This paper introduces a new method of separating P2P and standard web traffic that can be applied as part of a data mining process, based on the activity of the hosts on the network. Unlike other research, our method is aimed at classifying individual flows rather than just identifying P2P hosts or ports. Heuristics are analysed and a classification system proposed. The accuracy of the system is then tested using real network traffic from a core internet router showing over 99% accuracy in some cases. We expand on this proposed strategy to investigate its application to real-time, early classification problems. New proposals are made and the results of real-time experiments compared to those obtained in the data mining research. To the best of our knowledge this is the first research to use host based flow identification to determine a flows application within the early stages of the connection.

Fast automatic two-stage nonlinear model identification based on the extreme learning machine

It is convenient and effective to solve nonlinear problems with a model that has a linear-in-the-parameters (LITP) structure. However, the nonlinear parameters (e.g. the width of Gaussian function) of each model term needs to be pre-determined either from expert experience or through exhaustive search. An alternative approach is to optimize them by a gradient-based technique (e.g. Newton’s method). Unfortunately, all of these methods still need a lot of computations. Recently, the extreme learning machine (ELM) has shown its advantages in terms of fast learning from data, but the sparsity of the constructed model cannot be guaranteed. This paper proposes a novel algorithm for automatic construction of a nonlinear system model based on the extreme learning machine. This is achieved by effectively integrating the ELM and leave-one-out (LOO) cross validation with our two-stage stepwise construction procedure [1]. The main objective is to improve the compactness and generalization capability of the model constructed by the ELM method. Numerical analysis shows that the proposed algorithm only involves about half of the computation of orthogonal least squares (OLS) based method. Simulation examples are included to confirm the efficacy and superiority of the proposed technique.

Factor structure and validity of the Alcohol Use Disorders Identification Test (AUDIT) in a sample of mentally disordered offenders

Investigations of the factor structure of the Alcohol Use Disorders Identification Test (AUDIT) have produced conflicting results. The current study assessed the factor structure of the AUDIT for a group of Mentally Disordered Offenders (MDOs) and examined the pattern of scoring in specific subgroups. The sample comprised 2005 MDOs who completed a battery of tests including the AUDIT. Confirmatory factor analyses revealed that a two-factor solution – alcohol consumption and alcohol-related consequences – provided the best data fit for AUDIT scores. A three-factor solution provided an equally good fit, but the second and third factors were highly correlated and a measure of parsimony also favoured the two-factor solution. This study provides useful information on the factor structure of the AUDIT amongst a large MDO population, while also highlighting the difficulties associated with the presence of people with mental health problems in the criminal justice system.

Accuracy of LightCycler® SeptiFast for the detection and identification of pathogens in the blood of patients with suspected sepsis: a systematic review protocol

Background: There is growing interest in the potential utility of molecular diagnostics in improving the detection of life-threatening infection (sepsis). LightCycler® SeptiFast is a multipathogen probebased real-time PCR system targeting DNA sequences of bacteria and fungi present in blood samples within a few hours. We report here the protocol of the first systematic review of published clinical diagnostic accuracy studies of this technology when compared with blood culture in the setting of suspected sepsis. Methods/design: Data sources: the Cochrane Database of Systematic Reviews, the Database of Abstracts of Reviews of Effects (DARE), the Health Technology Assessment Database (HTA), the NHS Economic Evaluation Database (NHSEED), The Cochrane Library, MEDLINE, EMBASE, ISI Web of Science, BIOSIS Previews, MEDION and the Aggressive Research Intelligence Facility Database (ARIF). Study selection: diagnostic accuracy studies that compare the real-time PCR technology with standard culture results performed on a patient's blood sample during the management of sepsis. Data extraction: three reviewers, working independently, will determine the level of evidence, methodological quality and a standard data set relating to demographics and diagnostic accuracy metrics for each study. Statistical analysis/data synthesis: heterogeneity of studies will be investigated using a coupled forest plot of sensitivity and specificity and a scatter plot in Receiver Operator Characteristic (ROC) space. Bivariate model method will be used to estimate summary sensitivity and specificity. The authors will investigate reporting biases using funnel plots based on effective sample size and regression tests of asymmetry. Subgroup analyses are planned for adults, children and infection setting (hospital vs community) if sufficient data are uncovered. Dissemination: Recommendations will be made to the Department of Health (as part of an open-access HTA report) as to whether the real-time PCR technology has sufficient clinical diagnostic accuracy potential to move forward to efficacy testing during the provision of routine clinical care.

Multiple Luminaire Identification in Airborne Images of Airport’s Approach Lighting Using Mathematical Morphology With Variable Length Structuring Element

The increasing demand for fast air transportation around the clock
has increased the number of night flights in civil aviation over
the past few decades. In night aviation, to land an aircraft, a
pilot needs to be able to identify an airport. The approach
lighting system (ALS) at an airport is used to provide
identification and guidance to pilots from a distance. ALS
consists of more than $100$ luminaires which are installed in a
defined pattern following strict guidelines by the International
Civil Aviation Organization (ICAO). ICAO also has strict
regulations for maintaining the performance level of the
luminaires. However, once installed, to date there is no automated
technique by which to monitor the performance of the lighting. We
suggest using images of the lighting pattern captured using a camera
placed inside an aircraft. Based on the information contained
within these images, the performance of the luminaires has to be
evaluated which requires identification of over $100$ luminaires
within the pattern of ALS image. This research proposes analysis
of the pattern using morphology filters which use a variable
length structuring element (VLSE). The dimension of the VLSE changes
continuously within an image and varies for different images.
A novel
technique for automatic determination of the VLSE is proposed and
it allows successful identification of the luminaires from the
image data as verified through the use of simulated and real data.