Foetal malformations and seizure control: 52 months data of the Australian Pregnancy Registry

The Australian Pregnancy Registry, affiliated European Register of Antiepileptic drugs in Pregnancy (EURAP), recruits informed consenting women with epilepsy on treatment with antiepileptic drugs (AEDs), those untreated, and women on AEDs for other indications. Enrolment is considered prospective if it has occurred before presence or absence of major foetal malformations (FMs) are known, or retrospective, if they had occurred after the birth of infant or detection of major FM. Telephone Interviews are conducted to ascertain pregnancy outcome and collect data about seizures. To date 630 women have been enrolled, with 565 known pregnancy outcomes. Valproate (VPA) above 1100 mg/day was associated with a significantly higher incidence of FMs than other AEDs (P < 0.05). This was independent of other AED use or potentially confounding factors on multivariate analysis (OR = 7.3, P < 0.0001). Lamotrigine (LTG) monotherapy (n = 65), has so far been free of malformations. Although seizure control was not a primary outcome, we noted that more patients on LTG than on VPA required dose adjustments to control seizures. Data indicate an increased risk of FM in women taking VPA in doses > 1100 mg/day compared with other AEDs. The choice of AED for pregnant women with epilepsy requires assessment of balance of risks between teratogenicity and seizure control.

Automatic building detection using the Dempster-Shafer algorithm

An approach and strategy for automatic detection of buildings from aerial images using combined image analysis and interpretation techniques is described in this paper. It is undertaken in several steps. A dense DSM is obtained by stereo image matching and then the results of multi-band classification, the DSM, and Normalized Difference Vegetation Index (NDVI) are used to reveal preliminary building interest areas. From these areas, a shape modeling algorithm has been used to precisely delineate their boundaries. The Dempster-Shafer data fusion technique is then applied to detect buildings from the combination of three data sources by a statistically-based classification. A number of test areas, which include buildings of different sizes, shape, and roof color have been investigated. The tests are encouraging and demonstrate that all processes in this system are important for effective building detection.

Comparison of commercially available and novel West Nile virus immunoassays for detection of seroconversion in pig-tailed macaques (Macaca nemestrina)

We report the assessment and validation of an NS1 epitope-blocking enzyme-linked immunosorbent assay (ELISA) for detection of antibodies to West Nile virus (WNV) in macaques. Sera from naturally infected Macaca nemestrina were tested by ELISA and plaque reduction neutralization test (PRNT). Results were correlated with hemagglutination inhibition (HAI) data. Our results demonstrate that the blocking ELISA rapidly and specifically detects WNV infection in M. nemestrina. In addition, the diagnostic value of 7 commercially available immunoassays (PanBio immunoglobulin [Ig] M ELISA, PanBio IgG ELISA, PanBio immunofluorescence assay (IFA), InBios IgG ELISA, InBios IgM ELISA, Focus Diagnostics IgG ELISA, and Focus Diagnostics IgM ELISA) in M. nemestrina was evaluated and compared with that of the epitope-blocking ELISA. The PanBio IgG ELISA was found to effectively diagnose WNV exposure in M. nemestrina. Further, PanBio IFA slides are fast and reliable screening tools for diagnosing flaviviral exposure in M. nemestrina.

Detection and discrimination of herpes simplex viruses, Haemophilus ducreyi, Treponema pallidum, and Calymmatobacterium (Klebsiella) granulomatis from genital ulcers

Background. Genital ulcer disease (GUD) is commonly caused by pathogens for which suitable therapies exist, but clinical and laboratory diagnoses may be problematic. This collaborative project was undertaken to address the need for a rapid, economical, and sensitive approach to the detection and diagnosis of GUD using noninvasive techniques to sample genital ulcers. Methods. The genital ulcer disease multiplex polymerase chain reaction (GUMP) was developed as an inhouse nucleic acid amplification technique targeting serious causes of GUD, namely, herpes simplex viruses (HSVs), Haemophilus ducreyi, Treponema pallidum, and Klebsiella species. In addition, the GUMP assay included an endogenous internal control. Amplification products from GUMP were detected by enzyme linked amplicon hybridization assay (ELAHA). Results. GUMP-ELAHA was sensitive and specific in detecting a target microbe in 34.3% of specimens, including 1 detection of HSV-1, three detections of HSV-2, and 18 detections of T. pallidum. No H. ducreyi has been detected in Australia since 1998, and none was detected here. No Calymmatobacterium ( Klebsiella) granulomatis was detected in the study, but there were 3 detections during ongoing diagnostic use of GUMP-ELAHA in 2004 and 2005. The presence of C. granulomatis was confirmed by restriction enzyme digestion and nucleotide sequencing of the 16S rRNA gene for phylogenetic analysis. Conclusions. GUMP-ELAHA permitted comprehensive detection of common and rare causes of GUD and incorporated noninvasive sampling techniques. Data obtained by using GUMP-ELAHA will aid specific treatment of GUD and better define the prevalence of each microbe among at-risk populations with a view to the eradication of chancroid and donovanosis in Australia.

Real-time PCR assays for detection of bocavirus in human specimens

The recently discovered human bocavirus (HBoV) is the first member of the family Parpoviridae, genus Bocavirus, to be potentially associated with human disease. Several studies have identified HBoV in respiratory specimens from children with acute respiratory disease, but the full spectrum of clinical disease and the epidemiology of HBoV infection remain unclear. The availability of rapid and reliable molecular diagnostics would therefore aid future studies of this novel virus. To address this, we developed two sensitive and specific real-time TaqMan PCR assays that target the HBoV NS1 and NP-1 genes. Both assays could reproducibly detect 10 copies of a recombinant DNA plasmid containing a partial region of the HBoV genome, with a dynamic range of 8 log units (10(1) to 10(8) copies). Eight blinded clinical specimen extracts positive for HBoV by an independent PCR assay were positive by both real-time assays. Among 1,178 NP swabs collected from hospitalized pneumonia patients in Sa Kaeo Province, Thailand, 53 (4.5%) were reproducibly positive for HBoV by one or both targets. Our data confirm the possible association of HBoV infection with pneumonia and demonstrate the utility of these real-time PCR assays for HBoV detection.

Tradeoffs of different types of species occurrence data for use in systematic conservation planning

Data on the occurrence of species are widely used to inform the design of reserve networks. These data contain commission errors (when a species is mistakenly thought to be present) and omission errors (when a species is mistakenly thought to be absent), and the rates of the two types of error are inversely related. Point locality data can minimize commission errors, but those obtained from museum collections are generally sparse, suffer from substantial spatial bias and contain large omission errors. Geographic ranges generate large commission errors because they assume homogenous species distributions. Predicted distribution data make explicit inferences on species occurrence and their commission and omission errors depend on model structure, on the omission of variables that determine species distribution and on data resolution. Omission errors lead to identifying networks of areas for conservation action that are smaller than required and centred on known species occurrences, thus affecting the comprehensiveness, representativeness and efficiency of selected areas. Commission errors lead to selecting areas not relevant to conservation, thus affecting the representativeness and adequacy of reserve networks. Conservation plans should include an estimation of commission and omission errors in underlying species data and explicitly use this information to influence conservation planning outcomes.

Comparison of cultures from rectoanal-junction mucosal swabs and feces for detection of Escherichia coli 0157 in dairy heifers

Fecal culture for Escherichia coli 0157:H7 was compared to rectoanal mucosal swab (RAMS) culture in dairy heifers over a 1-year period. RAMS enrichment culture was as sensitive as fecal culture using immunomagnetic separation (IMS) (P = 0.98, as determined by a chi-square test). RAMS culture is less costly than fecal IMS culture and can yield quantitative data.

Presence-absence versus abundance data for monitoring threatened species

Effective detection of population trend is crucial for managing threatened species. Little theory exists, however, to assist managers in choosing the most cost-effective monitoring techniques for diagnosing trend. We present a framework for determining the optimal monitoring strategy by simulating a manager collecting data on a declining species, the Chestnut-rumped Hylacola (Hylacola pyrrhopygia parkeri), to determine whether the species should be listed under the IUCN (World Conservation Union) Red List. We compared the efficiencies of two strategies for detecting trend, abundance, and presence-absence surveys, underfinancial constraints. One might expect the abundance surveys to be superior under all circumstances because more information is collected at each site. Nevertheless, the presence-absence data can be collected at more sites because the surveyor is not obliged to spend a fixed amount of time at each site. The optimal strategy for monitoring was very dependent on the budget available. Under some circumstances, presence-absence surveys outperformed abundance surveys for diagnosing the IUCN Red List categories cost-effectively. Abundance surveys were best if the species was expected to be recorded more than 16 times/year; otherwise, presence-absence surveys were best. The relationship between the strategies we investigated is likely to be relevant for many comparisons of presence-absence or abundance data. Managers of any cryptic or low-density species who hope to maximize their success of estimating trend should find an application for our results.

Computational methods for the detection of facial palsy

We are developing a telemedicine application which offers automated diagnosis of facial (Bell's) palsy through a Web service. We used a test data set of 43 images of facial palsy patients and 44 normal people to develop the automatic recognition algorithm. Three different image pre-processing methods were used. Machine learning techniques (support vector machine, SVM) were used to examine the difference between the two halves of the face. If there was a sufficient difference, then the SVM recognized facial palsy. Otherwise, if the halves were roughly symmetrical, the SVM classified the image as normal. It was found that the facial palsy images had a greater Hamming Distance than the normal images, indicating greater asymmetry. The median distance in the normal group was 331 (interquartile range 277-435) and the median distance in the facial palsy group was 509 (interquartile range 334-703). This difference was significant (P

The utility of aminoterminal pro-B-type natriuretic peptide for the detection of preclinical systolic and diastolic dysfunction in the community

Background: There is scant data regarding methods to identify subjects in the community with preclinical left ventricular (LV) systolic and diastolic dysfunction. Methods: A population-based sample of 1229 older adults underwent examination with transthoracic echocardiography and measurement of circulating aminoterminal pro-Btype natriuretic peptide (N-BNP) levels. Heart failure status was ascertained according to past history and clinical examination. The ability of N-BNP to detect preclinical LV ejection fraction (EF)

Load profiling and data mining techniques in electricity deregulated market

This paper presents load profiles of electricity customers, using the knowledge discovery in databases (KDD) procedure, a data mining technique, to determine the load profiles for different types of customers. In this paper, the current load profiling methods are compared using data mining techniques, by analysing and evaluating these classification techniques. The objective of this study is to determine the best load profiling methods and data mining techniques to classify, detect and predict non-technical losses in the distribution sector, due to faulty metering and billing errors, as well as to gather knowledge on customer behaviour and preferences so as to gain a competitive advantage in the deregulated market. This paper focuses mainly on the comparative analysis of the classification techniques selected; a forthcoming paper will focus on the detection and prediction methods.

Multisensorial obstacles detection and classification for ADAS and autonomous driving

A reliable perception of the real world is a key-feature for an autonomous vehicle and the Advanced Driver Assistance Systems (ADAS). Obstacles detection (OD) is one of the main components for the correct reconstruction of the dynamic world. Historical approaches based on stereo vision and other 3D perception technologies (e.g. LIDAR) have been adapted to the ADAS first and autonomous ground vehicles, after, providing excellent results. The obstacles detection is a very broad field and this domain counts a lot of works in the last years. In academic research, it has been clearly established the essential role of these systems to realize active safety systems for accident prevention, reflecting also the innovative systems introduced by industry. These systems need to accurately assess situational criticalities and simultaneously assess awareness of these criticalities by the driver; it requires that the obstacles detection algorithms must be reliable and accurate, providing: a real-time output, a stable and robust representation of the environment and an estimation independent from lighting and weather conditions. Initial systems relied on only one exteroceptive sensor (e.g. radar or laser for ACC and camera for LDW) in addition to proprioceptive sensors such as wheel speed and yaw rate sensors. But, current systems, such as ACC operating at the entire speed range or autonomous braking for collision avoidance, require the use of multiple sensors since individually they can not meet these requirements. It has led the community to move towards the use of a combination of them in order to exploit the benefits of each one. Pedestrians and vehicles detection are ones of the major thrusts in situational criticalities assessment, still remaining an active area of research. ADASs are the most prominent use case of pedestrians and vehicles detection. Vehicles should be equipped with sensing capabilities able to detect and act on objects in dangerous situations, where the driver would not be able to avoid a collision. A full ADAS or autonomous vehicle, with regard to pedestrians and vehicles, would not only include detection but also tracking, orientation, intent analysis, and collision prediction. The system detects obstacles using a probabilistic occupancy grid built from a multi-resolution disparity map. Obstacles classification is based on an AdaBoost SoftCascade trained on Aggregate Channel Features. A final stage of tracking and fusion guarantees stability and robustness to the result.

From marine ecology to crime analysis: improving the detection of serial sexual offences using a taxonomic similarity measure

Jaccard has been the choice similarity metric in ecology and forensic psychology for comparison of sites or offences, by species or behaviour. This paper applies a more powerful hierarchical measure - taxonomic similarity (s), recently developed in marine ecology - to the task of behaviourally linking serial crime. Forensic case linkage attempts to identify behaviourally similar offences committed by the same unknown perpetrator (called linked offences). s considers progressively higher-level taxa, such that two sites show some similarity even without shared species. We apply this index by analysing 55 specific offence behaviours classified hierarchically. The behaviours are taken from 16 sexual offences by seven juveniles where each offender committed two or more offences. We demonstrate that both Jaccard and s show linked offences to be significantly more similar than unlinked offences. With up to 20% of the specific behaviours removed in simulations, s is equally or more effective at distinguishing linked offences than where Jaccard uses a full data set. Moreover, s retains significant difference between linked and unlinked pairs, with up to 50% of the specific behaviours removed. As police decision-making often depends upon incomplete data, s has clear advantages and its application may extend to other crime types. Copyright © 2007 John Wiley & Sons, Ltd.

Outlier detection with partial information:Application to emergency mapping

This paper, addresses the problem of novelty detection in the case that the observed data is a mixture of a known 'background' process contaminated with an unknown other process, which generates the outliers, or novel observations. The framework we describe here is quite general, employing univariate classification with incomplete information, based on knowledge of the distribution (the 'probability density function', 'pdf') of the data generated by the 'background' process. The relative proportion of this 'background' component (the 'prior' 'background' 'probability), the 'pdf' and the 'prior' probabilities of all other components are all assumed unknown. The main contribution is a new classification scheme that identifies the maximum proportion of observed data following the known 'background' distribution. The method exploits the Kolmogorov-Smirnov test to estimate the proportions, and afterwards data are Bayes optimally separated. Results, demonstrated with synthetic data, show that this approach can produce more reliable results than a standard novelty detection scheme. The classification algorithm is then applied to the problem of identifying outliers in the SIC2004 data set, in order to detect the radioactive release simulated in the 'oker' data set. We propose this method as a reliable means of novelty detection in the emergency situation which can also be used to identify outliers prior to the application of a more general automatic mapping algorithm. © Springer-Verlag 2007.

Pulse compression applied to the detection of F.S.K. and P.S.K. signals

The matched filter detector is well known as the optimum detector for use in communication, as well as in radar systems for signals corrupted by Additive White Gaussian Noise (A.W.G.N.). Non-coherent F.S.K. and differentially coherent P.S.K. (D.P.S.K.) detection schemes, which employ a new approach in realizing the matched filter processor, are investigated. The new approach utilizes pulse compression techniques, well known in radar systems, to facilitate the implementation of the matched filter in the form of the Pulse Compressor Matched Filter (P.C.M.F.). Both detection schemes feature a mixer- P.C.M.F. Compound as their predetector processor. The Compound is utilized to convert F.S.K. modulation into pulse position modulation, and P.S.K. modulation into pulse polarity modulation. The mechanisms of both detection schemes are studied through examining the properties of the Autocorrelation function (A.C.F.) at the output of the P.C.M.F.. The effects produced by time delay, and carrier interference on the output A.C.F. are determined. Work related to the F.S.K. detection scheme is mostly confined to verifying its validity, whereas the D.P.S.K. detection scheme has not been reported before. Consequently, an experimental system was constructed, which utilized combined hardware and software, and operated under the supervision of a microprocessor system. The experimental system was used to develop error-rate models for both detection schemes under investigation. Performances of both F. S. K. and D.P. S. K. detection schemes were established in the presence of A. W. G. N. , practical imperfections, time delay, and carrier interference. The results highlight the candidacy of both detection schemes for use in the field of digital data communication and, in particular, the D.P.S.K. detection scheme, which performed very close to optimum in a background of A.W.G.N.