Results and Interpretation. The results of the factor analysis are shown in Table 18. Factor Participant ID 1 2 3 4 1 skill15 0.2630 -0.0868 -0.1118 0.7496 2 skill17 -0.0939 0.3014 0.3820 0.0950 3 skill20 0.5590 -0.0734 0.3492 0.3598 4 skill21 0.1664 -0.0993 0.1433 0.8463 5 skill22 -0.1248 -0.1626 0.0668 0.4416 6 skill23 0.1634 0.4008 0.1056 -0.0563 7 skill31 0.6360 0.3745 0.1663 0.0080 8 skill33 -0.0237 0.3652 -0.1836 0.4604 9 skill34 0.1250 -0.2650 0.3215 0.6514 10 skill35 0.4610 0.3104 0.0518 -0.5006 11 skill36 0.6264 0.2823 -0.0311 -0.2911 12 skill37 -0.1402 0.0003 0.4169 0.4803 13 skill40 0.0859 0.4128 0.7966 0.0852 14 skill41 0.3934 0.3499 0.2453 0.3769 15 skill42 0.1324 0.0439 0.5028 -0.0516 16 skill44 -0.1260 0.1372 0.0339 0.5010 17 skill45 0.0263 0.6897 -0.0630 -0.1106 18 skill47 0.0466 -0.1206 0.7917 0.2808 19 skill48 0.0874 0.3786 0.3740 0.1658 20 skill50 0.0485 0.3220 0.0983 -0.0197 21 skill51 0.3887 0.1382 0.3210 0.3132 00 xxxxx00 0.0000 -0.0000 -0.0000 0.0049 23 skill59 0.0247 0.6996 -0.0161 -0.1230 24 skill60 0.3177 0.0821 -0.5631 0.1440 % Variance Explained 9 10 12 14 Table 18. Factor analysis scores for the four extracted factors against each of the sorts competed. Bold type indicates sorts that load onto the respective factor (defining sorts). The analysis identified four shared viewpoints which were statistically distinct from each other. Nevertheless, there were two statements that none of the groups differed on statistically. These statements did not elicit any strength of opinion from any of the perspectives: The cultural mentor (or colleagues if you did not have a designated mentor) helped me to adjust to the culture of my placement organisation. (S8; +1, +2, 0, +1). The private reflections in my reflective log were quite different from the public reflections. (S17; +2, -0, -0, -0).
Results and Interpretation. The results from the geomechanical system simulations are shown in Figure 15. The simulation with the rock mass modelled homogeneous poroelastic indicates a large reactivated area on the southern fault, a smaller reactivated area on the fault in-between the injection and depletion well, and almost no reactivation on the northern fault. This is supported by the formation of iso-surfaces of the maximum normal stress (blue, (S1-S3)/2) and the maximum shear stress (red, (S1-S3)/2) in the rock. A large volume of rock mass on the production side is subject to increased normal stresses that extend even into the seal. The injection side on the other hand shows increased shear stresses, which are cut-off at the interface to the seal. Increased shear and normal stresses are indications for de- and stabilization of defects inside the rock mass, respectively. For the simulation with the rock mass modelled with random flaws it was to be expected that the greatest number of reactivated flaws is on the injection side. Despite this observation, a similar distribution of reactivated areas can be observed as in the poroelastic case without random flaws. The iso-surfaces of the maximum normal and shear stress indicate a smaller stabilized and a larger destabilized volume of rock around the xxxxx, respectively. The results of the simulation containing a large hydraulic fracture show a large reactivated area on the southern fault, a smaller reactivated area on the middle fault in-between the injection and depletion well, and almost no reactivation on the northern fault. Furthermore, the hydraulic fracture cuts the cloud of increased shear stress, while the stabilizing normal stress volume is similar to the poroelastic solution. Interestingly the simulations consistently not only show a volume of increased differential stresses around the injection well, indicating increased shear loading of the fractures in the rock mass, but also in the rock mass close to the faults. These ‘sheets’ of destabilisation may be interpreted as the result of movement of the faults due to the changed stress conditions during reservoir operation and need to be considered as relevant to fault reactivation understanding also.
Results and Interpretation. This hypothesis predicts that overweight/obese Hispanic women assigned to the lifestyle intervention program will have higher compliance with IOM guidelines for weight gain than women receiving standard care. In addition, we anticipate that women in the intervention group will increase their PA. We anticipate that by limiting excessive weight gain, appropriated healthy eating habits, and increasing physical activity, the intervention group will reduce the risk of carbohydrate intolerance and GDM (aim 2) and will reduce the risk of maternal and neonatal complications (see aim 3). Results from the intervention group will be sent annually to the CDC Prevent T2 program for evaluation.
Results and Interpretation. Data from 105 individuals from the 2016 paper, and from 409 individuals from the 2017 datasets was analyzed. Five individuals (arabusta-427, CIRAD3, CIRAD7, CIRAD8, and CIRAD35) from the 2017 dataset were not included in the analysis as their data was not reported by the sequencing company. From the 4,021 SNPs analyzed in the 2016 paper and the new 19,457 SNPs supplied by the sequencing company, only 2,719 were common in the two datasets and passed all the filters applied. The data retained and used for the analysis, along with their corresponding sequences and positions in the C. canephora reference genome are given in the “Data_2016-2017.xlsx” file. Genetic structure of the C. canephora collection Analysis Individuals In order to interpret C. canephora diversity in a whole genome context, the DArTseq SNP data from the “Analysis Individuals" (113 C. canephora accessions, 33 from the 2016 paper and 80 from the 2017 dataset) was analyzed using a DAPC multivariate analysis. The first six principal components of the Principal Component Analysis (PCA), which explained 27.7%, 20.3%, 5.3%, 3.3% 2.7% and 2.2% of the variance, respectively, were retained for the discriminant analysis with the DAPC function. The first four Discriminant Functions (Discriminant axes – DA) were retained afterwards. Seven genetic clusters were identified after the analysis (Figure DAPC_analysis_individuals.pdf), with 22, 11, 15, 21, 16, 16 and 12 individuals, respectively (Table “Analysis_individuals” from the “Identified_groups_12-10.xlsx” file). The position of the individuals on the four DAs are presented in the “Analysis_ind_coordinates” table from the “DAPC_coordinates_12-10.xlsx” file and the “DAPC_analysis_individuals.pdf” file. Membership probabilities for each accession were calculated, and are shown in the “Membership_analysis_ind” table from the “Membership_probability_12-10.xlsx” file, and in the “Membership_probabilities” PDF files. In order to identify the genomic regions contributing to the population structure found in To obtain a more complete picture of the genetic relationships linking the C. canephora accessions evaluated, an unrooted NJ tree was constructed using the data from the “Analysis individuals”. (File “NJ_Analysis_Individuals_12-10_fig.pdf”). The pairwise Euclidean distance calculated between the “Analysis individuals” is included in the “Euclid_dist_Analysis_ind” table from the “Euclidean_distances_12-10”, while the number of loci for which individuals d...
Results and Interpretation
