Environmental cues and constraints affecting the seasonality of dominant calanoid copepods in brackish, coastal waters: a case study of Acartia, Temora and Eurytemora species in the south-west Baltic

Information on physiological rates and tolerances helps one gain a cause-and-effect understanding of the role that some environmental (bottom–up) factors play in regulating the seasonality and productivity of key species. We combined the results of laboratory experiments on reproductive success and field time series data on adult abundance to explore factors controlling the seasonality of Acartia spp., Eurytemora affinis and Temora longicornis, key copepods of brackish, coastal and temperate environments. Patterns in laboratory and field data were discussed using a metabolic framework that included the effects of ‘controlling’, ‘masking’ and ‘directive’ environmental factors. Over a 5-year period, changes in adult abundance within two south-west Baltic field sites (Kiel Fjord Pier, 54°19′89N, 10°09′06E, 12–21 psu, and North/Baltic Sea Canal NOK, 54°20′45N, 9°57′02E, 4–10 psu) were evaluated with respect to changes in temperature, salinity, day length and chlorophyll a concentration. Acartia spp. dominated the copepod assemblage at both sites (up to 16,764 and 21,771 females m−3 at NOK and Pier) and was 4 to 10 times more abundant than E. affinis (to 2,939 m−3 at NOK) and T. longicornis (to 1,959 m−3 at Pier), respectively. Species-specific salinity tolerance explains differences in adult abundance between sampling sites whereas phenological differences among species are best explained by the influence of species-specific thermal windows and prey requirements supporting survival and egg production. Multiple intrinsic and extrinsic (environmental) factors influence the production of different egg types (normal and resting), regulate life-history strategies and influence match–mismatch dynamics

A novel method for measuring patients' adherence to insulin dosing guidelines: introducing indicators of adherence

<p>Abstract</p> <p>Background</p> <p>Diabetic type 1 patients are often advised to use dose adjustment guidelines to calculate their doses of insulin. Conventional methods of measuring patients' adherence are not applicable to these cases, because insulin doses are not determined in advance. We propose a method and a number of indicators to measure patients' conformance to these insulin dosing guidelines.</p> <p>Methods</p> <p>We used a database of logbooks of type 1 diabetic patients who participated in a summer camp. Patients used a guideline to calculate the doses of insulin lispro and glargine four times a day, and registered their injected doses in the database. We implemented the guideline in a computer system to calculate recommended doses. We then compared injected and recommended doses by using five indicators that we designed for this purpose: absolute agreement (AA): the two doses are the same; relative agreement (RA): there is a slight difference between them; extreme disagreement (ED): the administered and recommended doses are merely opposite; Under-treatment (UT) and over-treatment (OT): the injected dose is not enough or too high, respectively. We used weighted linear regression model to study the evolution of these indicators over time.</p> <p>Results</p> <p>We analyzed 1656 insulin doses injected by 28 patients during a three weeks camp. Overall indicator rates were AA = 45%, RA = 30%, ED = 2%, UT = 26% and OT = 30%. The highest rate of absolute agreement is obtained for insulin glargine (AA = 70%). One patient with alarming behavior (AA = 29%, RA = 24% and ED = 8%) was detected. The monitoring of these indicators over time revealed a crescendo curve of adherence rate which fitted well in a weighted linear model (slope = 0.85, significance = 0.002). This shows an improvement in the quality of therapeutic decision-making of patients during the camp.</p> <p>Conclusion</p> <p>Our method allowed the measurement of patients' adherence to their insulin adjustment guidelines. The indicators that we introduced were capable of providing quantitative data on the quality of patients' decision-making for the studied population as a whole, for each individual patient, for all injections, and for each time of injection separately. They can be implemented in monitoring systems to detect non-adherent patients.</p

Human Pathogen Shown to Cause Disease in the Threatened Eklhorn Coral Acropora palmata

Coral reefs are in severe decline. Infections by the human pathogen Serratia marcescens have contributed to precipitous losses in the common Caribbean elkhorn coral, Acropora palmata, culminating in its listing under the United States Endangered Species Act. During a 2003 outbreak of this coral disease, called acroporid serratiosis (APS), a unique strain of the pathogen, Serratia marcescens strain PDR60, was identified from diseased A. palmata, human wastewater, the non-host coral Siderastrea siderea and the corallivorous snail Coralliophila abbreviata. In order to examine humans as a source and other marine invertebrates as vectors and/or reservoirs of the APS pathogen, challenge experiments were conducted with A. palmata maintained in closed aquaria to determine infectivity of strain PDR60 from reef and wastewater sources. Strain PDR60 from wastewater and diseased A. palmata caused disease signs in elkhorn coral in as little as four and five days, respectively, demonstrating that wastewater is a definitive source of APS and identifying human strain PDR60 as a coral pathogen through fulfillment of Koch's postulates. A. palmata inoculated with strain PDR60 from C. abbreviata showed limited virulence, with one of three inoculated fragments developing APS signs within 13 days. Strain PDR60 from non-host coral S. siderea showed a delayed pathogenic effect, with disease signs developing within an average of 20 days. These results suggest that C. abbreviata and non-host corals may function as reservoirs or vectors of the APS pathogen. Our results provide the first example of a marine “reverse zoonosis” involving the transmission of a human pathogen (S. marcescens) to a marine invertebrate (A. palmata). These findings underscore the interaction between public health practices and environmental health indices such as coral reef survival

Functional Analysis: Evaluation of Response Intensities - Tailoring ANOVA for Lists of Expression Subsets

Background: Microarray data is frequently used to characterize the expression profile of a whole genome and to compare the characteristics of that genome under several conditions. Geneset analysis methods have been described previously to analyze the expression values of several genes related by known biological criteria (metabolic pathway, pathology signature, co-regulation by a common factor, etc.) at the same time and the cost of these methods allows for the use of more values to help discover the underlying biological mechanisms. Results: As several methods assume different null hypotheses, we propose to reformulate the main question that biologists seek to answer. To determine which genesets are associated with expression values that differ between two experiments, we focused on three ad hoc criteria: expression levels, the direction of individual gene expression changes (up or down regulation), and correlations between genes. We introduce the FAERI methodology, tailored from a two-way ANOVA to examine these criteria. The significance of the results was evaluated according to the self-contained null hypothesis, using label sampling or by inferring the null distribution from normally distributed random data. Evaluations performed on simulated data revealed that FAERI outperforms currently available methods for each type of set tested. We then applied the FAERI method to analyze three real-world datasets on hypoxia response. FAERI was able to detect more genesets than other methodologies, and the genesets selected were coherent with current knowledge of cellular response to hypoxia. Moreover, the genesets selected by FAERI were confirmed when the analysis was repeated on two additional related datasets. Conclusions: The expression values of genesets are associated with several biological effects. The underlying mathematical structure of the genesets allows for analysis of data from several genes at the same time. Focusing on expression levels, the direction of the expression changes, and correlations, we showed that two-step data reduction allowed us to significantly improve the performance of geneset analysis using a modified two-way ANOVA procedure, and to detect genesets that current methods fail to detect