In the present manuscript we have analyzed the reliability of the predictions that were made in the CASP7 experiment and that are publicly available. It was found that most of the bioinformatics tools are able to determine if a protein is made by a single domain or if it contains more than one domain, despite a similar reliability is reached by considering only the sequence length, a much simpler strategy. Using a standard and well known statistical test, we showed that most of the predictions that can be done are not impressively better than pseudo-random predictions. It was also observed that although the reliability of the prediction methods seems to be insufficient to make them routine tools in experimental structural biology, their performance can be extremely good. When the domain is correctly identified, its boundaries are very close, within one or two residues, to the experimental ones. In conclusion, these bioinformatics applications are not yet sufficiently accurate to be used as routine tools in experimental structural biology. It is rather probable that the use of more than a single prediction method by a sort of consensus approach might improve the reliability of the predictions. Although these bioinformatics tools are still immature, progress can be expected in the future. This work was supported by the Austrian GEN-AU project BIN-II. Björn Sjöblom and Kristina Djinovic are acknowledged for helpful discussions. Financial support by Putta None is also acknowledged. One reviewer is acknowledged for a series of references that deserved citation. Scientific Abstracts   International Course on Emerging Viruses in the Amazon Region   Enhancing Time-Series Detection Algorithms for Automated Biosurveillance BioSense is a US national system that uses data from health information systems for automated disease surveillance. We studied 4 time-series algorithm modifications designed to improve sensitivity for detecting artificially added data. To test these modified algorithms, we used reports of daily syndrome visits from 308 Department of Defense (DoD) facilities and 340 hospital emergency departments (EDs). At a constant alert rate of 1%, sensitivity was improved for both datasets by using a minimum standard deviation (SD) of 1.0, a 14–28 day baseline duration for calculating mean and SD, and an adjustment for total clinic visits as a surrogate denominator. Stratifying baseline days into weekdays versus weekends to account for day-of-week effects increased sensitivity for the DoD data but not for the ED data. These enhanced methods may increase sensitivity without increasing the alert rate and may improve the ability to detect outbreaks by using automated surveillance system data. S ince the late 1990s, the threats of bioterrorist attacks, the potential for outbreaks of natural disease such as severe acute respiratory syndrome and pandemic infl uenza, and the availability of computerized data have prompted the use of automated disease surveillance systems (1) . Sources of information include clinical data, such as records of hospital emergency department visits, and nonclinical information, such as sales of over-the-counter remedies (2) .