Archives
We standardized all bioclimate variables
We standardized all bioclimate variables (current and future) with their corresponding current layer, i. e. for the projected value of each variable we subtracted the mean and then divided it by the standard deviation of the current data subset. We used the MaxLike software package [5] to generate potential distribution maps and we used the packages “maxlike” ver. 0.1–5, “raster” ver. 2.3–12, “rgdal” ver. 0.9–1, “sp” ver. 1.0–16 and “tcltk2” ver. 1.2–10, in the software R ver. 3.1.2.
Then, we randomly selected 65% of the records for training and the remaining 35% for cross-validation each of the 1000 times the process was repeated with the current conditions dataset. The resulting models were deemed adequate, according to Estrada-Contreras et al. [6], if they satisfied the following criteria: a) convergence occurred, b) they had no missing data, and c) proportion of errors of omission was less than or equal to 10. The model coefficients were then used to project the species’ future niche. The resulting models were ranked by how well they matched the relative occurrence area (ROA) [7] values. We chose 10 models around the statistical median that had an average probability of presence obtained with validation records closest to 1, since theoretically the average of this value should be 1. Then we produced a consensus map averaging these 10 maps (the same models set for current and future conditions).
The minimum value of probability of presence was considered indicative of the likely presence of Ae. aegypti, and was obtained by extracting values from the potential distribution map to current conditions with the coordinates of all the records used to generate the models (training and validation). To further evaluate the current presence model we used partial ROC [8] by randomly selecting 35% of the records used to generate the models.
Although moclobemide models were generated for surface analysis of the entire state of Veracruz, elevation increase and changes in the probability of occurrence were conducted only in the rectangle that has its diagonal vertices at points 97°35’55.78’’W and 20°28’20.67’’N, and 95°49’31.07’’W and 18°39’41.6’’N, which covers an area of 28,167.58 km2. To identify whether the analysis area has combinations of environmental variables similar to those of today, the \"Mobility-Oriented Parity\"(MOP) tool [9] was used.
Conflict of Interest
Acknowledgements
Data
Tables 1 and 2 describe the Association, Relative Risk and Odds Ratio of XRCC1 (194C>T), (280G>A), (399G>A) genotypes in disease and control groups. Table 3 summarizes the comparative table of polymorphisms of DNA repair gene XRCC1. Graphs 1 and 2 represent the best double and three-locus bar diagrams. Fig. 1 illustrates the MDR interaction information analysis of the three polymorphisms, represented in the form of a dendrogram. Pairwise linkage disequilibrium (LD) for three SNPs were calculated. The analysis has generated 8 marker combinations from three SNPs in both cases and controls (Table 4; Graph 3).
Experimental design, materials and methods
Acknowledgments
Data
A Brazilian case/control SMM cohort was studied concerning frequencies of selected inflammasome SNPs in NLRP1, NLRP3, CARD8, IL1B and IL18 genes and minor allele frequencies (MAF) with respective Hardy–Weinberg p-values were calculated.
Case/control analysis were performed and distribution of alleles for each selected SNP, as well as Odd Ratios (OR), haplotypes, Linkage disequilibrium analysis were determined.
Patients were stratified according to histological tumor type, invasiveness and skin type were represented (Fig. 1).
Experimental design, materials and methods
Refer to the associated article [2] for detailed methods (Table 1–5).
Data
Both raw and processed microarray data have been deposited in ArrayExpress with the Accession Number E-MTAB-4857 (https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-4857/). The dataset summarize changes in the transcription profile of a Mycobacterium smegmatis amtR mutant compared to M. smegmatis wild-type. Raw data files are the result of four independent biological replicates including two dye swaps and the columns IA and IB in the raw data files correspond to Cy3 and Cy5 values, respectively. The processed data file contains the combined differential gene expression data including statistical analysis.