Development of ELISAs for detecting domoic acid, okadaic acid, and saxitoxin and their applicability for the detection of marine toxins in samples collected in Belgium

Okadaic acid, a diarrhetic shellfish poison, domoic acid, an amnesic shellfish poison, and saxitoxin, a paralytic shellfish poison, are three of the best-known marine biotoxins. The mouse bioassay is the method most widely used to detect many of these toxins in shellfish samples, but animal welfare concerns have prompted researchers to seek alternative methods of detection. In this study, three direct competitive enzyme-linked immunosorbent assays (ELISAs), each based on antibodies raised in rabbits against a conjugate of the analyte of interest, were developed for marine biotoxin detection in mussel, oyster, and scallop. One assay was for okadaic acid, one for saxitoxin, and one for domoic acid usually detected and quantified by high-performance liquid chromatography-ultraviolet light (HPLC-UV). All three compounds and a number of related toxins were extracted quickly and simply from the shellfish matrices with a 9 : 1 mixture of ethanol and water before analysis. The detection capabilities (CC values) of the developed ELISAs were 150 mu g kg-1 for okadaic acid, 50 mu g kg-1 for domoic acid, and 5 mu g kg-1 or less for saxitoxin. The assays proved satisfactory when used over a 4-month period for the analysis of 110 real samples collected in Belgium.

Reliability of tissue microarrays in detecting protein expression and gene amplification in breast cancer

Tissue microarrays allow high throughput molecular profiling of diagnostic or predictive markers in cancer specimens and rapid validation of novel potential candidates identified from genomic and proteomic analyses in a large number of tumor samples. To validate the use of tissue microarray technology for all the main biomarkers routinely used to decide breast cancer prognostication and postsurgical adjuvant therapy, we constructed a tissue microarray from 97 breast tumors, with a single 0.6 mm core per specimen. Inummostaining; of tissue microarray sections and conventional full sections of each tumor were performed using well-characterized prognostic markers (estrogen receptor ER, progesterone receptor PR and c-erbB2). The full section versus tissue microarray concordance for these stains was 97% for ER, 98% for PR, and 97% for c-erbB2, respectively, with a strong statistical association (kappa value more than 0.90). Fluorescence in situ hybridization analysis for HER-2/neu gene amplification from the single-core tissue microarray was technically successful in about 90% (87/97) of the cases, with a concordance of 95% compared with parallel analyses with the full sections. The correlation with other pathological parameters was not significantly different between full-section and array-based results. It is concluded that the constructed tissue microarray with a single core per specimen ensures full biological representativeness to identify the associations between biomarkers and clinicopathological parameters, with no significant associated sampling bias.

Detecting Ca2+ sparks on stationary and varying baselines.

Studies concerning the physiological significance of Ca2+ sparks often depend on the detection and measurement of large populations of events in noisy microscopy images. Automated detection methods have been developed to quickly and objectively distinguish potential sparks from noise artifacts. However, previously described algorithms are not suited to the reliable detection of sparks in images where the local baseline fluorescence and noise properties can vary significantly, and risk introducing additional bias when applied to such data sets. Here, we describe a new, conceptually straightforward approach to spark detection in linescans that addresses this issue by combining variance stabilization with local baseline subtraction. We also show that in addition to greatly increasing the range of images in which sparks can be automatically detected, the use of a more accurate noise model enables our algorithm to achieve similar detection sensitivities with fewer false positives than previous approaches when applied both to synthetic and experimental data sets. We propose, therefore, that it might be a useful tool for improving the reliability and objectivity of spark analysis in general, and describe how it might be further optimized for specific applications.

Detecting Anomalies in Graphs with Numeric Labels

This paper presents Yagada, an algorithm to search labelled graphs for anomalies using both structural data and numeric attributes. Yagada is explained using several security-related examples and validated with experiments on a physical Access Control database. Quantitative analysis shows that in the upper range of anomaly thresholds, Yagada detects twice as many anomalies as the best-performing numeric discretization algorithm. Qualitative evaluation shows that the detected anomalies are meaningful, representing a com- bination of structural irregularities and numerical outliers.

Automated interpulse-duration assessment (AIDA): A new technique for detecting disturbances in cardiac activity in selected macroinvertebrates

An Automated Interpulse Duration Assessment system (AIDA) is described which permits detection of irregularities in cardiac rhythms in selected invertebrates. The sensitivity of AIDA was demonstrated by its ability to detect handling stress in mussels (Mytilus edulis) that was not evident when measuring heart rate alone. Changes in cardiac activity patterns of crabs (Carcinus maenas) held in the laboratory for up to 10 wk was also examined using the new technique. The frequency distribution of interpulse duration changed significantly as the nutritional state changed. Potential applications of the AIDA system are discussed.

Detecting the effects of diversity on measures of ecosystem function: experimental design, null models and empirical observations

Here we present a novel experimental approach to examine the relationship between diversity and ecosystem Function. We develop four null predictive models, with which to differentiate between the 'sampling effect' - the chance inclusion of a highly productive species, and 'species complementarity' - the complementary use of resources by species that differ in their niche or resource use. We investigate the effects of manipulating species and functional richness on ecosystem function in marine benthic system and using empirical data from our own experiments we illustrate the application of these models.

Detecting Deception in Movement: The Case of the Side-Step in Rugby

Although coordinated patterns of body movement can be used to communicate action intention, they can also be used to deceive. Often known as deceptive movements, these unpredictable patterns of body movement can give a competitive advantage to an attacker when trying to outwit a defender. In this particular study, we immersed novice and expert rugby players in an interactive virtual rugby environment to understand how the dynamics of deceptive body movement influence a defending player’s decisions about how and when to act. When asked to judge final running direction, expert players who were found to tune into prospective tau-based information specified in the dynamics of ‘honest’ movement signals (Centre of Mass), performed significantly better than novices who tuned into the dynamics of ‘deceptive’ movement signals (upper trunk yaw and out-foot placement) (p<.001). These findings were further corroborated in a second experiment where players were able to move as if to intercept or ‘tackle’ the virtual attacker. An analysis of action responses showed that experts waited significantly longer before initiating movement (p<.001). By waiting longer and picking up more information that would inform about future running direction these experts made significantly fewer errors (p<.05). In this paper we not only present a mathematical model that describes how deception in body-based movement is detected, but we also show how perceptual expertise is manifested in action expertise. We conclude that being able to tune into the ‘honest’ information specifying true running action intention gives a strong competitive advantage.