Changes in capture fisheries and harvesting of wild marine invertebrates.

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Automatic identification of variables in epidemiological datasets using logic regression

textabstractBackground: For an individual participant data (IPD) meta-analysis, multiple datasets must be transformed in a consistent format, e.g. using uniform variable names. When large numbers of datasets have to be processed, this can be a time-consuming and error-prone task. Automated or semi-automated identification of variables can help to reduce the workload and improve the data quality. For semi-automation high sensitivity in the recognition of matching variables is particularly important, because it allows creating software which for a target variable presents a choice of source variables, from which a user can choose the matching one, with only low risk of having missed a correct source variable. Methods: For each variable in a set of target variables, a number of simple rules were manually created. With logic regression, an optimal Boolean combination of these rules was searched for every target variable, using a random subset of a large database of epidemiological and clinical cohort data (construction subset). In a second subset of this database (validation subset), this optimal combination rules were validated. Results: In the construction sample, 41 target variables were allocated on average with a positive predictive value (PPV) of 34%, and a negative predictive value (NPV) of 95%. In the validation sample, PPV was 33%, whereas NPV remained at 94%. In the construction sample, PPV was 50% or less in 63% of all variables, in the validation sample in 71% of all variables. Conclusions: We demonstrated that the application of logic regression in a complex data management task in large epidemiological IPD meta-analyses is feasible. However, the performance of the algorithm is poor, which may require backup strategies

Local distribution patterns of macroalgae in relation to environmental variables in the northern Baltic Proper

The relationship between macroalgal assemblages and abiotic factors was quantified by gradient analyses in an area where longterm changes in macroalgal depth distributions have previously been documented. Biomass data from 4, 6, 8 and 10 m depth in an area of similar salinity (5) and substrate (rock) in the northern Baltic Proper was constrained by a set of environmental variables defining different aspects of abiotic control of species distributions (sediment cover. effective fetch, clarity index, the curvature and slope of the bottom, and direction of exposure) in multivariate analyses at different scales. Furus vesiculosus dominated the biomass at 4, 6 and 8 m depth, and Furcellaria lumbricalis at 10 m. The applied models explained 30.7-53.3% of the total variance in community structure, and 49.3-60.9% when analysed separately for each depth. A separate analysis of species depth distributions demonstrated that effective fetch was most strongly related to upper limits of the algal belts. sediment cover to the lower limit and density of the F. vesiculosus belt, and clarity index to the lower limits of F. vesiculosus, perennial red algae. and of the red algal and Sphacelaria spp. belts. The results show a strong correlation between environmental variables and vegetation structure even on a small, local scale in the northern Baltic Proper, indicating a high suitability of the phytobenthic zone for environmental monitoring. The results add to previous studies that show a strong importance of abiotic factors on large-scale variation in phytobentic community composition in the Baltic Sea. (C) 2004 Elsevier Ltd. All rights reserved

A single-step competitive binding assay for mapping of single DNA molecules

Optical mapping of genomic DNA is of relevance for a plethora of applications such as scaffolding for sequencing and detection of structural variations as well as identification cif pathogens like bacteria and viruses. For future clinical applications it is desirable to have a fast and robust mapping method based on as few steps as possible. We here demonstrate a single-step method to obtain a DNA barcode that is directly visualized using nanofluidic devices and fluorescence microscopy. Using a mixture of YOYO-1, a bright DNA dye, and netropsin, a natural antibiotic with very high AT specificity, we obtain a DNA map with a fluorescence intensity profile along the DNA that reflects the underlying sequence. The netropsin binds to AT-tetrads and blocks these binding sites from YOYO-1 binding which results in lower fluorescence intensity from AT-rich regions of the DNA. We thus obtain a DNA barcode that is dark in AT-rich regions and bright in GC-rich regions with kilobasepair resolution. We demonstrate the versatility of the method by obtaining a barcode on DNA from the phage T4 that captures its circular permutation and agrees well with its known sequence. (C) 2011 Elsevier Inc. All rights reserved