CI, confidence interval; C-Index, concordance index; NRI, net reclassification index. (XLSX)

Aggregation of generic tables describing the Noise Zones, for an infrastructure, the type of infrastructure concerned ROUTE (R), card type C and LD index.

Road infrastructure concerned: A68, C1_albi, C1_castres, D100, D1012, D13, D612, D622, D630, D631, D69, D800, D81, D84, D87, D88, D912, D926, D968, D988, D999A, D999, N112, N126, N88

Limit value exceedance maps (or “type c” maps) maps to be made within the framework of the CBS pursuant to Article 3-II-1°-c of the Decree of 24 March 2006. These are two maps representing the areas where the Lden limit values are exceeded for the year in which the maps are drawn up.

Lden sound level indicator means Level Day-Evening-Night. It corresponds to an equivalent 24-hour sound level in which evening and night noise levels are increased by 5 and 10 dB(A), respectively, to reflect greater discomfort during these periods.

Aggregation obtained by the QGIS MIZOGEO plugin made available by CEREMA.

Data source by infrastructure: CEREMA.

R Code. This R-file provides the underlying functions to reproduce the results of the simulation and the breast cancer analysis. (R 21 kb)

Risk stratification is essential for treatment decision in myelodysplastic neoplasms (MDS). Molecular international prognostic scoring system (M-IPSS) has been recently developed combining somatic mutations and clinical information being used in the revised international prognostic scoring system (R-IPSS). We aimed to explore the performances of M-IPSS and R-IPSS in Thai patients with MDS. MDS patients were stratified into risk categories using R-IPSS and M-IPSS scores. The performance of both models were evaluated for prognostic prediction. One hundred and sixty-two MDS patients were recruited from the multicenter study. Survival analysis revealed that both R-IPSS and M-IPSS were good prediction models with the Concordance Index (C-index) of 0.71 (95% Confidence interval [CI] 0.64–0.78) and 0.75 (95% CI 0.69–0.80), respectively (p = 0.22). Comparing the two, 13 of 162 (8%) cases were re-staged between lower and higher risks which would have affected treatment decisions. Our study showed that R-IPSS score can be used for risk stratification in most Thai patients. A prediction model using somatic mutations specifically in Asian patients should be formulated in the future.

Models’ hyperparameters: Machine learning models parametrized using random search optimization algorithm of 20 different parameter settings with a 5-fold cross validation to maximize the C-index.

Selected features and models combined performance using repeated 5-fold cross-validation, C-index with 95% confidence interval(95% CI).

Maps to be produced in the framework of the Strategic Noise Cards (CBS) pursuant to Article 3-II-1°-a of the Decree of 24 March 2006 and the European Directive 2002/49/EC on the management of environmental noise. Noise maps are not prescriptive. Aggregation of generic tables describing the Noise Zones, for an infrastructure, the type of infrastructure concerned ROUTE (R), card type C and LD index. Approved by prefectural decree of 08/08/2018. Road infrastructure concerned: A51, RN85, RD4, RN4a, RD4b, RD4085, RD4096, RD907 Limit value exceedance maps (or “type c” maps) These are two maps representing the areas where the Lden limit values are exceeded for the year in which the maps are drawn up. Lden: sound level indicator means Level Day-Evening-Night. It corresponds to an equivalent 24-hour sound level in which evening and night noise levels are increased by 5 and 10 dB(A), respectively, to reflect greater discomfort during these periods. The layers are produced by road axis in accordance with the COVADIS standard validated on 29 May 2017. Aggregation obtained by the QGIS MIZOGEO plugin made available by CEREMA. Data source by infrastructure: CEREMA and VINCI for A51 Strategic noise maps are reviewed every 5 years

The data in this folder comprises all data necessary to produce the Figures presented in our paper (Hirt et al, 2020, in review, Quarterly Journal of the Royal Meteorological Society). Corresponding Jupyter notebooks, which were used to analyse and plot the data, are available at https://github.com/HirtM/cold_pool_driven_convection_initiation. The datasets are netcdf files and should contain all relevant metadata. cp_aggregates2*: These datasets contain different variables of cold pool objects. For each variable, several different statistics are available, e.g. the average/median/some percentile over the area of each cold pool object. Note that the data does not contain tracked cold pools. Any sequence of cold pool indices is hence meaningless. Each cold pool index does not only have information about its cold pool, but also its edges (see mask dimension). P_ci_* These datasets contain information on convection initiation within cold pool areas, cold pool edge areas or no cold pool areas. No single cold pool objects are identified here. prec_* As P_ci_*, but for precipitation. synoptic_conditions_variables.nc This dataset contains domain averaged (total domain, not cold pool objects) timeseries of selected variables. The selected variables were chosen in order to describe the synoptic and diurnal conditions of the days of interest. This dataset is used for the causal regression analysis. All the data here is derived from the ICON-LEM simulation conducted within HDCP2: http://hdcp2.eu/index.php?id=5013 Heinze, R., Dipankar, A., Carbajal Henken, C., Moseley, C., Sourdeval, O., Trömel, S., Xie, X., Adamidis, P., Ament, F., Baars, H., Barthlott, C., Behrendt, A., Blahak, U., Bley, S., Brdar, S., Brueck, M., Crewell, S., Deneke, H., Di Girolamo, P., Evaristo, R., Fischer, J., Frank, C., Friederichs, P., Göcke, T., Gorges, K., Hande, L., Hanke, M., Hansen, A., Hege, H.-C., Hoose, C., Jahns, T., Kalthoff, N., Klocke, D., Kneifel, S., Knippertz, P., Kuhn, A., van Laar, T., Macke, A., Maurer, V., Mayer, B., Meyer, C. I., Muppa, S. K., Neggers, R. A. J., Orlandi, E., Pantillon, F., Pospichal, B., Röber, N., Scheck, L., Seifert, A., Seifert, P., Senf, F., Siligam, P., Simmer, C., Steinke, S., Stevens, B., Wapler, K., Weniger, M., Wulfmeyer, V., Zängl, G., Zhang, D. and Quaas, J. (2016): Large-eddy simulations over Germany using ICON: A comprehensive evaluation. Q.J.R. Meteorol. Soc., doi:10.1002/qj.2947 M.Hirt, 9 Jan 2020

Adrenocortical carcinoma (ACC) is a rare malignancy with poor prognosis. Thus, we aimed to establish a potential gene model for prognosis prediction of patients with ACC. First, weighted gene co-expression network (WGCNA) was constructed to screen two key modules (blue: P = 5e-05, R^2 = 0.65; red: P = 4e-06, R^2 = −0.71). Second, 93 survival-associated genes were identified. Third, 11 potential prognosis models were constructed, and two models were further selected. Survival analysis, receiver operating characteristic curve (ROC), Cox regression analysis, and calibrate curve were performed to identify the best model with great prognostic value. Model 2 was further identified as the best model [training set: P < 0.0001; the area under curve (AUC) value was higher than in any other models showed]. We further explored the prognostic values of genes in the best model by analyzing their mutations and copy number variations (CNVs) and found that MKI67 altered the most (12%). CNVs of the 14 genes could significantly affect the relative mRNA expression levels and were associated with survival of ACC patients. Three independent analyses indicated that all the 14 genes were significantly associated with the prognosis of patients with ACC. Six hub genes were further analyzed by constructing a PPI network and validated by AUC and concordance index (C-index) calculation. In summary, we constructed and validated a prognostic multi-gene model and found six prognostic biomarkers, which may be useful for predicting the prognosis of ACC patients.

Uncover historical ownership history and changes over time by performing a reverse Whois lookup for the company nellarete-di-castagna-r-&-c.-Sas.

This record was harvested by RDA at 2025-09-11T16:10:37.805841+10:00 from NCI's Data Catalogue where it was last modified at 2018-04-11T02:27:08.

Repeated analysis of subsets of 500 units; the number of knots is based on AIC; estimates (Brier score, c index, calibration intercept and slope) are obtained on the data not included in the model estimation, power is evaluated on training data.

*, data was the training data of that model. Bold, the best one for each data. Values in the parentheses represent the 95% confidence interval, derived from 1000 bootstrap replicates. Vax(grp) was significantly different from all competitor models across three data (Wilcoxon Signed-Rank Test, all P < 0.001).

Background: Biochemical recurrence (BCR) after radical prostatectomy indicates poor prognosis in patients with prostate cancer (PCA). DNA methylation (DNAm) is a critical factor in tumorigenesis and has attracted attention as a biomarker for the diagnosis, treatment, and prognosis of PCA. However, the predictive value of DNAm-derived differentially expressed genes (DMGs) in PCA with BCR remains elusive.Methods: We filtered the methylated genes and the differentially expressed genes (DGEs) for more than 1,000 clinical samples from the TCGA cohort using the chAMP and DESeq2 packages of R language, respectively. Next, we integrated the DNAm beta value and gene expression data with the Mithymix package of R language to obtain the DMGs. Then, 1,000 times Cox LASSO regression with 10-fold cross validation was performed to screen signature DMGs and establish a predictive classifier. Univariate and multivariate cox regressive analyses were used to identify the prognostic factors to build a predictive model, and its performance was measured by receiver operating characteristic, calibration curves, and Harrell’s concordance index (C-index). Additionally, a GEO dataset was used to validate the prognostic classifier.Results: One hundred DMGs were mined using the chAMP and Methymix packages of R language. Of these, seven DMGs (CCK, CD38, CYP27A1, EID3, HABP2, LRRC4, and LY6G6D) were identified to build the prognostic classifier (Classifier) through LASSO analysis. Moreover, univariate and multivariate Cox regression analysis determined that the Classifier and pathological T stage (pathological_T) were independent predictors of BCR (hazard ratio (HR 2.2), (95% CI 1.4–3.5), p < 0.0012, and (HR 1.8), (95% CI 1.0–3.2), p < 0.046). A nomogram based on the Classifier was constructed, with high prediction accuracy for BCR-free survival in TCGA and GEO datasets. GSEA enrichment analysis showed that the DMGs were mainly enriched in the metabolism pathways.Conclusion: We identified and validated the nomogram of BCR-free survival for PCA patients, which has the potential to guide treatment decisions for patients at differing risks of BCR. Our study deepens the understanding of DMGs in the pathogenesis of PCA.

Comparisons among ToPs/R, regression methods, and machine learning benchmarks for pre-transplantation survival prediction using C-index and AUC (at horizons of 3-months, 1-year, 3-years, and 10-years).

Train on 2005-2009; predict on 2010-2015.

Objective: We built and externally validated a nomogram for predicting the overall survival (OS) probability of advanced gastric cancer patients receiving second-line treatment. Methods: The nomogram was developed on a set of 320 Italian patients and validated on two independent sets (295 Italian and 172 Korean patients). Putative prognostic variables were selected using a random forest model and included in the multivariable Cox model. The nomogram’s performance was evaluated by calibration plot and C index. Results: ECOG performance status, neutrophils to lymphocytes ratio, and peritoneal involvement were selected and included into the multivariable model. The C index was 0.72 (95% CI 0.68–0.75) in the development set, 0.69 (95% CI 0.65–0.73) in the Italian validation set, but only 0.57 (95% CI 0.52–0.62) in the Korean set. While Italian calibrations were quite good, the Korean one was poor. Regarding 6-month OS predictions, calibration was best in both Caucasian cohorts and worst the in Asian one. Conclusions: Our nomogram may be a useful tool to predict 3- or 6-month OS in Caucasian gastric cancer patients eligible for second-line therapy. Based on three easy-to-collect variables, the Gastric Life nomogram may help clinicians improve patient selection for second-line treatments and assist in clinical trial enrollment.