Streptotrichoseの1例

PDB structures of complexes formed between beta-Catenin and its partners. (PDF 13 kb

Additional file 2: Table S2. of Self-regulation of functional pathways by motifs inside the disordered tails of beta-catenin

List of beta-Catenin proteins from eight species. (PDF 8 kb

Comparison of true predictions between every two individual predictors for all the 163 positive samples in the D163 dataset.

<p>Comparison of true predictions between every two individual predictors for all the 163 positive samples in the D163 dataset.</p

Comparison of true predictions between every two individual predictors for all the 1679 positive samples in the D1679 dataset.

<p>Comparison of true predictions between every two individual predictors for all the 1679 positive samples in the D1679 dataset.</p

Prediction accuracies of five individual predictors in the D163 and D1679 datasets.

<p>Prediction accuracies of five individual predictors in the D163 and D1679 datasets.</p

Performance of meta-predictors under multi-fold cross validation and in independent dataset under preprocess-II transformation strategy.

<p>Performance of meta-predictors under multi-fold cross validation and in independent dataset under preprocess-II transformation strategy.</p

Infrastructure of the meta-predictor.

<p>Query sequence is input into each individual predictor. The outputs of individual predictors are preprocessed and then fed into an ANN to make a new prediction, which is the output of meta-predictor. Therefore, the meta-predictor is composed of individual predictors, preprocessing modules, and ANN. The parameters of ANN will be trained using datasets containing both positive and negative samples of miRNAs. Although five individual predictors were shown in the figure, the meta-predictor could be made from any number of individual predictors out of five. The total number of possible meta-predictors is 26. The final meta-predictor mirMeta contains all five individual predictors.</p

Using Linoleic Acid Embedded Cellulose Acetate Membranes to in Situ Monitor Polycyclic Aromatic Hydrocarbons in Lakes and Predict Their Bioavailability to Submerged Macrophytes

To date no passive sampler has been used to predict bioavailability of contaminants to macrophytes. Here a novel passive sampler, linoleic acid embedded cellulose acetate membrane (LAECAM), was developed and used to in situ measure the freely dissolved concentrations of ten polycyclic aromatic hydrocarbons in the sediment porewaters and the water columns of two lakes in both winter and summer and predict their bioavailability to the shoots of resident submerged macrophytes (Potamogeton malainus, Myriophyllum spicata, Najas minor All., and Vallisneria natans (Lour.) Hara). PAH sampling by LAECAMs could reach equilibrium within 21 days. The influence of temperature on LAECAM-water partition coefficients was 0.0008–0.0116 log units/°C. The method of LAECAM was comparable with the active sampling methods of liquid–liquid extraction combined with <i>f</i>_DOC adjustment, centrifugation/solid-phase extraction (SPE), and filtration/SPE but had several advantages. After lipid normalization, concentrations of the PAHs in LAECAMs were not significantly different from those in the macrophytes. In contrast, concentrations of the PAHs in the triolein containing passive sampler (TECAM) deployed simultaneously with LAECAM were much higher. The results suggest that linoleic acid is more suitable than triolein as the model lipid for passive samplers to predict bioavailability of PAHs to submerged macrophytes

Performance of meta-predictors using preprocess-I transformation under multi-fold cross validation and in independent dataset.

<p>Performance of meta-predictors using preprocess-I transformation under multi-fold cross validation and in independent dataset.</p

Comparison of true predictions between every two individual predictors for all the 168 negative samples in the D163 dataset.

<p>Comparison of true predictions between every two individual predictors for all the 168 negative samples in the D163 dataset.</p