A new hybrid estimation method for the generalized pareto distribution

<p>The generalized Pareto distribution (GPD) is important in the analysis of extreme values, especially in modeling exceedances over thresholds. Most of the existing methods for estimating the scale and shape parameters of the GPD suffer from theoretical and/or computational problems. A new hybrid estimation method is proposed in this article, which minimizes a goodness-of-fit measure and incorporates some useful likelihood information. Compared with the maximum likelihood method and other leading methods, our new hybrid estimation method retains high efficiency, reduces the estimation bias, and is computation friendly.</p

A yeast transcriptional sub-network (upper) and the decomposition tree constructed by the BCD algorithm (lower)

<p><b>Copyright information:</b></p><p>Taken from "Consistent dissection of the protein interaction network by combining global and local metrics"</p><p>http://genomebiology.com/2007/8/12/R271</p><p>Genome Biology 2007;8(12):R271-R271.</p><p>Published online 21 Dec 2007</p><p>PMCID:PMC2246273.</p><p></p> Predicted protein modules are highlighted with colored bars (lower panel) and protein nodes in the network (upper panel) are colored accordingly. The module names in the upper panel are inferred from their members' annotation information. Singletons are colored red

Additional file 1: of Transcriptomic analysis of adaptive mechanisms in response to sudden salinity drop in the mud crab, Scylla paramamosain

Table S1. The gene-specific primers used in this study, Table S2. Clean reads quality metrics from the gill of S. paramamosain, Table S3. Quality metrics of unigenes from the gill of S. paramamosain, Table S4. DEGs annotation, Table S5. Human Diseases pathways and DEGs involved, Table S6. Environmental Information Processing pathways and DEGs involved, Table S7. Genetic Information Processing pathways and DEGs involved, Table S8. Metabolism pathways and DEGs involved, Figure S1. Distribution of base quality on clean reads from the gill of S. paramamosain, Figure S2. Venn diagram between NR, KOG, KEGG, Swissprot and Interpro. Figure S3. The distribution of DEGs in GO analysis, Figure S4. Validity of DEGs in Transcriptomic data. (DOCX 1505 kb

Study flow chart.

<p>Study flow chart.</p

The uKIM-1 level in the transient AKI group was significantly higher than that of the non transient AKI group among elderly AKI patients during the occurrence of AKI.

<p>The uKIM-1 level in the transient AKI group was significantly higher than that of the non transient AKI group among elderly AKI patients during the occurrence of AKI.</p

Comparison between different age groups.

<p>Comparison between different age groups.</p

Logistic regression shows that the uKIM-1 level is a risk factor for non transient AKI.

<p>Logistic regression shows that the uKIM-1 level is a risk factor for non transient AKI.</p

Comparison between the transient AKI group and the non transient AKI group among elderly patients with AKI.

<p>Comparison between the transient AKI group and the non transient AKI group among elderly patients with AKI.</p

The uKIM-1 level and kidney prognosis. A uKIM-1 level > 2.46 ng/mg is positively related to poor prognosis.

<p>The uKIM-1 level and kidney prognosis. A uKIM-1 level > 2.46 ng/mg is positively related to poor prognosis.</p

The uKIM-1 level of the progressive deterioration of renal function group was significantly higher than that of the renal function stable group among elderly patients with AKI.

<p>The uKIM-1 level of the progressive deterioration of renal function group was significantly higher than that of the renal function stable group among elderly patients with AKI.</p