Los Montes Submarinos de la Plataforma Marginal de Motril, Mar de Alborán: Interacción con las Masas de Agua Profunda

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Mortality of breast cancer (BC), and of other cancers in Swedish women from 1974 to 2003.

<p>Mortality of breast cancer (BC), and of other cancers in Swedish women from 1974 to 2003.</p

Number of breast cancer deaths in women 40 to 69 years old in six Swedish counties according to two sources of data (3; 15).

<p>Number of breast cancer deaths in women 40 to 69 years old in six Swedish counties according to two sources of data (3; 15).</p

Mortality rate of breast cancer in Sweden in women 40–79 years of age in nine counties, according to time of screening start ( = 0) in each county.

<p>The continuous line is the crude mortality rate, and dashed line is mortality rate adjusted for age, secular trend, and screening effect.</p

Additional file 1: of MutSpec: a Galaxy toolbox for streamlined analyses of somatic mutation spectra in human and mouse cancer genomes

Example of Excel file generated by MutSpec-Stat tool. The data in this file correspond to the example analysis of OSCC described herein. (XLS 37674 kb

Additional file 2: of MutSpec: a Galaxy toolbox for streamlined analyses of somatic mutation spectra in human and mouse cancer genomes

Example of NMF analysis with combined matrices from different analyses. Matrices from two different analyses may be combined in a single matrix to analyse samples from analysis 1 and 2 together. This matrix should contain a header with sample IDs and have 96 rows describing the 6 SBS types in their sequence context. The matrix should be formatted as tab-delimited text to be accepted as input of MutSpec-NMF. (PPT 348 kb

Reclassification table for age and alarm symptom model (Model2) and risk-prediction model (Model3).

*<p>Model2: age plus four alarm symptoms (weigh loss, persistent vomiting, GI bleeding, dysphagia); Model3: risk-prediction model.</p

The diagnostic characteristics of choosing different risk score cut-off levels, derived from the risk-prediction model.

£<p>The number of patients that have a RS of less than the chosen threshold.</p>€<p>The number of cancer patients that have a RS of less than the chosen threshold and consequently are missed, due to not being selected for prompt endoscopy.</p>¥<p>The probability of having cancer when the RS is exactly equal to the threshold, according to mentioned formulae in the method section.</p

Performance of three different models for predicting UGI malignancy in dyspeptic patients.

*<p>Model1: age only; Model2: age plus four alarm symptoms (weigh loss, persistent vomiting, GI bleeding, dysphagia); Model3: risk-prediction model.</p>**<p>Brier<i>_scaled</i> = 1−Brier/Brier<i>_max</i>, where Brier<i>_max</i> = mean(p)<i>×</i>(1−mean(p)); and mean(p) is mean probability of outcome prediction based on model.</p

Estimated odds ratios of demographic characteristics and alarm symptoms for upper GI malignancies, based on unadjusted and multivariable adjusted regression models.

€<p>Multivariable model is adjusted for age, gender, educational level, cigarette smoking and history of weight loss, GI bleeding, persistent vomiting and dysphagia.</p>‡<p><i>H. pylori</i> infection is detected based on Rapid Urease Test (RUT), during endoscopy.</p