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Rates of aerobic metabolism vary considerably across evolutionary lineages, but little is known about the proximate and ultimate factors that generate and maintain this variability. Using data for 131 teleost fish species, we performed a large-scale phylogenetic comparative analysis of how interspecific variation in resting and maximum metabolic rates (RMR and MMR, respectively) is related to several ecological and morphological variables. Mass- and temperature-adjusted RMR and MMR are highly correlated along a continuum spanning a 30- to 40-fold range. Phylogenetic generalized least squares models suggest RMR and MMR are higher in pelagic species and that species with higher trophic levels exhibit elevated MMR. This variation is mirrored at various levels of structural organization: gill surface area, muscle protein content, and caudal fin aspect ratio (a proxy for activity) are positively related with aerobic capacity. Muscle protein content and caudal fin aspect ratio are also positively correlated with RMR. Hypoxia-tolerant lineages fall at the lower end of the metabolic continuum. Different ecological lifestyles are associated with contrasting levels of aerobic capacity, possibly reflecting the interplay between selection for increased locomotor performance on one hand and tolerance to low resource availability, particularly oxygen, on the other. These results support the aerobic capacity model of the evolution of endothermy, suggesting elevated body temperatures evolved as correlated responses to selection for high activity levels. Killen et al Am Nat Table S1Data used for the analysis by Killen et al. 2016, American Naturalist.Fish_PhylogenyPhylogeny used for the analysis by Killen et al. 2016, American Naturalist.

Here you can find information about all models generated by SIMON. Models can be downloaded and re-used for predictions. Each dataset is stored in separate folder which contains all the models built for that dataset. Name format is: {modelName}.RData This file contains following information: - All training specific model data: folds, tuning parameters, etc - All predictions made with test dataset - Confusion matrix and all performance measures calculated - Features and their Variable Importance Scores Here is an example of RData file structure: List of 5 $ model_training_fit :List of 23 ..$ method : chr "bagEarth" ..$ modelInfo :List of 15 .. ..$ label : chr "Bagged MARS" .. ..$ library : chr "earth" .. ..$ type : chr [1:2] "Regression" "Classification" .. ..$ parameters:'data.frame': 2 obs. of 3 variables: .. .. ..$ parameter: Factor w/ 2 levels "degree","nprune": 2 1 .. .. ..$ class : Factor w/ 1 level "numeric": 1 1 .. .. ..$ label : Factor w/ 2 levels "#Terms","Product Degree": 1 2 .. ..$ grid :function (x, y, len = NULL, search = "grid") .. ..$ loop :function (grid) .. ..$ fit :function (x, y, wts, param, lev, last, classProbs, ...) .. ..$ predict :function (modelFit, newdata, submodels = NULL) .. ..$ prob :function (modelFit, newdata, submodels = NULL) .. ..$ predictors:function (x, ...) .. ..$ varImp :function (object, ...) .. ..$ levels :function (x) .. ..$ tags : chr [1:5] "Multivariate Adaptive Regression Splines" "Ensemble Model" "Implicit Feature Selection" "Bagging" ... .. ..$ sort :function (x) .. ..$ oob :function (x) ..$ modelType : chr "Classification" ..$ results :'data.frame': 3 obs. of 24 variables: .. ..$ degree : num [1:3] 1 1 1 .. ..$ nprune : num [1:3] 2 10 18 .. ..$ logLoss : num [1:3] 1.27 1.84 1.66 .. ..$ AUC : num [1:3] 0.694 0.75 0.695 .. ..$ Accuracy : num [1:3] 0.623 0.698 0.657 .. ..$ Kappa : num [1:3] 0.12 0.36 0.262 .. ..$ F1 : num [1:3] 0.46 0.614 0.542 .. ..$ Sensitivity : num [1:3] 0.217 0.589 0.517 .. ..$ Specificity : num [1:3] 0.895 0.765 0.743 .. ..$ Pos_Pred_Value : num [1:3] 0.606 0.655 0.6 .. ..$ Neg_Pred_Value : num [1:3] 0.636 0.76 0.715 .. ..$ Detection_Rate : num [1:3] 0.0864 0.238 0.2098 .. ..$ Balanced_Accuracy : num [1:3] 0.556 0.677 0.63 .. ..$ logLossSD : num [1:3] 0.188 0.693 0.562 .. ..$ AUCSD : num [1:3] 0.19 0.146 0.157 .. ..$ AccuracySD : num [1:3] 0.0922 0.1339 0.1302 .. ..$ KappaSD : num [1:3] 0.217 0.28 0.279 .. ..$ F1SD : num [1:3] 0.099 0.174 0.176 .. ..$ SensitivitySD : num [1:3] 0.204 0.246 0.266 .. ..$ SpecificitySD : num [1:3] 0.12 0.194 0.182 .. ..$ Pos_Pred_ValueSD : num [1:3] 0.369 0.257 0.235 .. ..$ Neg_Pred_ValueSD : num [1:3] 0.0711 0.1264 0.137 .. ..$ Detection_RateSD : num [1:3] 0.0818 0.1114 0.1167 .. ..$ Balanced_AccuracySD: num [1:3] 0.0996 0.1358 0.1406 ..$ pred :'data.frame': 720 obs. of 8 variables: .. ..$ pred : Factor w/ 2 levels "high","low": 2 1 2 2 2 2 1 2 2 2 ... .. ..$ obs : Factor w/ 2 levels "high","low": 1 1 2 2 2 2 1 1 1 2 ... .. ..$ rowIndex: int [1:720] 4 26 34 39 43 47 65 4 26 34 ... .. ..$ high : num [1:720] 0.415 0.822 0.39 0.276 0.135 ... .. ..$ low : num [1:720] 0.585 0.178 0.61 0.724 0.865 ... .. ..$ degree : num [1:720] 1 1 1 1 1 1 1 1 1 1 ... .. ..$ nprune : num [1:720] 18 18 18 18 18 18 18 2 2 2 ... .. ..$ Resample: chr [1:720] "Fold01.Rep1" "Fold01.Rep1" "Fold01.Rep1" "Fold01.Rep1" ... ..$ bestTune :'data.frame': 1 obs. of 2 variables: .. ..$ nprune: num 10 .. ..$ degree: num 1 ..$ call : language train.formula(form = factor(outcome) ~ ., data = training, method = model, trControl = trControl, preProcess = NU| __truncated__ ..$ dots : list() ..$ metric : chr "Accuracy" ..$ control :List of 27 .. ..$ method : chr "repeatedcv" .. ..$ number : num 10 .. ..$ repeats : num 3 .. ..$ search : chr "grid" .. ..$ p : num 0.75 .. ..$ initialWindow : NULL .. ..$ horizon : num 1 .. ..$ fixedWindow : logi TRUE .. ..$ skip : num 0 .. ..$ verboseIter : logi FALSE .. ..$ returnData : logi TRUE .. ..$ returnResamp : chr "final" .. ..$ savePredictions : chr "all" .. ..$ classProbs : logi TRUE .. ..$ summaryFunction :function (data, lev = NULL, model = NULL) .. ..$ selectionFunction: chr "best" .. ..$ preProcOptions :List of 6 .. .. ..$ thresh : num 0.95 .. .. ..$ ICAcomp : num 3 .. .. ..$ k : num 5 .. .. ..$ freqCut : num 19 .. .. ..$ uniqueCut: num 10 .. .. ..$ cutoff : num 0.9 .. ..$ sampling : NULL .. ..$ index :List of 30 .. .. ..$ Fold01.Rep1: int [1:73] 1 2 3 5 6 7 8 9 10 11 ... .. .. ..$ Fold02.Rep1: int [1:72] 1 2 3 4 5 6 7 8 9 10 ... .. .. ..$ Fold03.Rep1: int [1:72] 1 2 3 4 5 6 7 8 9 10 ... .. .. ..$ Fold04.Rep1: int [1:71] 1 2 3 4 5 7 8 9 10 11 ... .. .. ..$ Fold05.Rep1: int [1:72] 1 2 3 4 5 6 7 8 9 10 ... .. .. ..$ Fold06.Rep1: int [1:72] 1 2 4 6 7 8 9 10 11 12 ... .. .. ..$ Fold07.Rep1: int [1:73] 1 3 4 5 6 7 8 9 10 11 ... .. .. ..$ Fold08.Rep1: int [1:71] 1 2 3 4 5 6 7 9 10 11 ... .. .. ..$ Fold09.Rep1: int [1:72] 1 2 3 4 5 6 7 8 10 11 ... .. .. ..$ Fold10.Rep1: int [1:72] 2 3 4 5 6 8 9 12 13 14 ... .. .. ..$ Fold01.Rep2: int [1:72] 1 2 4 5 6 7 8 9 10 11 ... .. .. ..$ Fold02.Rep2: int [1:72] 1 2 3 4 5 6 7 8 9 10 ... .. .. ..$ Fold03.Rep2: int [1:72] 1 2 3 4 5 6 7 9 10 11 ... .. .. ..$ Fold04.Rep2: int [1:72] 1 2 3 4 5 6 7 8 9 10 ... .. .. ..$ Fold05.Rep2: int [1:71] 1 2 3 4 5 6 7 8 9 11 ... .. .. ..$ Fold06.Rep2: int [1:71] 1 2 3 5 6 7 8 9 10 11 ... .. .. ..$ Fold07.Rep2: int [1:73] 1 3 4 5 6 8 9 10 11 12 ... .. .. ..$ Fold08.Rep2: int [1:73] 2 3 4 5 6 7 8 9 10 11 ... .. .. ..$ Fold09.Rep2: int [1:72] 1 2 3 4 5 6 7 8 10 12 ... .. .. ..$ Fold10.Rep2: int [1:72] 1 2 3 4 7 8 9 10 11 12 ... .. .. ..$ Fold01.Rep3: int [1:72] 1 3 4 6 7 8 9 10 11 12 ... .. .. ..$ Fold02.Rep3: int [1:73] 1 2 3 4 5 6 7 8 10 11 ... .. .. ..$ Fold03.Rep3: int [1:72] 1 2 3 4 5 6 7 8 9 10 ... .. .. ..$ Fold04.Rep3: int [1:72] 1 2 3 5 6 7 8 9 10 11 ... .. .. ..$ Fold05.Rep3: int [1:72] 2 3 4 5 6 7 8 9 10 11 ... .. .. ..$ Fold06.Rep3: int [1:72] 1 2 3 4 5 6 7 9 10 12 ... .. .. ..$ Fold07.Rep3: int [1:72] 1 2 3 4 5 6 8 9 10 11 ... .. .. ..$ Fold08.Rep3: int [1:71] 1 2 4 5 7 8 9 10 11 13 ... .. .. ..$ Fold09.Rep3: int [1:72] 1 2 3 4 5 6 7 8 9 10 ... .. .. ..$ Fold10.Rep3: int [1:72] 1 2 3 4 5 6 7 8 9 11 ... .. ..$ indexOut :List of 30 .. .. ..$ Resample01: int [1:7] 4 26 34 39 43 47 65 .. .. ..$ Resample02: int [1:8] 24 28 45 56 64 69 72 78 .. .. ..$ Resample03: int [1:8] 20 23 27 40 50 53 57 66 .. .. ..$ Resample04: int [1:9] 6 21 38 46 49 51 54 67 77 .. .. ..$ Resample05: int [1:8] 14 17 42 48 52 62 76 79 .. .. ..$ Resample06: int [1:8] 3 5 15 18 19 36 37 73 .. .. ..$ Resample07: int [1:7] 2 29 33 58 59 71 80 .. .. ..$ Resample08: int [1:9] 8 13 22 30 31 32 35 61 68 .. .. ..$ Resample09: int [1:8] 9 12 44 55 60 70 74 75 .. .. ..$ Resample10: int [1:8] 1 7 10 11 16 25 41 63 .. .. ..$ Resample11: int [1:8] 3 24 27 28 39 53 55 77 .. .. ..$ Resample12: int [1:8] 14 16 36 41 46 59 69 73 .. .. ..$ Resample13: int [1:8] 8 17 31 50 63 70 71 80 .. .. ..$ Resample14: int [1:8] 19 25 35 52 54 58 65 72 .. .. ..$ Resample15: int [1:9] 10 12 13 23 32 38 48 76 78 .. .. ..$ Resample16: int [1:9] 4 21 22 33 34 44 64 67 75 .. .. ..$ Resample17: int [1:7] 2 7 42 49 51 60 79 .. .. ..$ Resample18: int [1:7] 1 15 26 29 37 40 57 .. .. ..$ Resample19: int [1:8] 9 11 18 45 47 56 62 66 .. .. ..$ Resample20: int [1:8] 5 6 20 30 43 61 68 74 .. .. ..$ Resample21: int [1:8] 2 5 34 38 49 53 54 74 .. .. ..$ Resample22: int [1:7] 9 19 26 27 32 70 78 .. .. ..$ Resample23: int [1:8] 17 33 36 46 48 52 64 73 .. .. ..$ Resample24: int [1:8] 4 13 18 21 35 58 63 71 .. .. ..$ Resample25: int [1:8] 1 20 24 28 30 50 55 65 .. .. ..$ Resample26: int [1:8] 8 11 15 22 62 66 72 75 .. .. ..$ Resample27: int [1:8] 7 14 25 31 40 47 59 79 .. .. ..$ Resample28: int [1:9] 3 6 12 42 43 60 69 77 80 .. .. ..$ Resample29: int [1:8] 23 29 41 45 56 57 67 68 .. .. ..$ Resample30: int [1:8] 10 16 37 39 44 51 61 76 .. ..$ indexFinal : NULL .. ..$ timingSamps : num 0 .. ..$ predictionBounds : logi [1:2] FALSE FALSE .. ..$ seeds :List of 31 .. .. ..$ : int [1:9] 114 622 609 999 858 638 10 231 661 .. .. ..$ : int [1:9] 515 693 544 282 920 291 833 285 265 .. .. ..$ : int [1:9] 187 232 316 302 159 40 218 805 522 .. .. ..$ : int [1:9] 915 831 46 455 265 304 505 180 754 .. .. ..$ : int [1:9] 202 259 991 805 552 644 310 618 328 .. .. ..$ : int [1:9] 502 677 485 244 763 74 308 713 501 .. .. ..$ : int [1:9] 153 504 493 749 174 845 860 42 315 .. .. ..$ : int [1:9] 14 239 706 308 507 52 562 121 886 .. .. ..$ : int [1:9] 15 783 90 518 383 70 319 664 919 .. .. ..$ : int [1:9] 472 143 544 196 895 388 310 159 890 .. .. ..$ : int [1:9] 167 900 134 132 105 510 299 27 308 .. .. ..$ : int [1:9] 743 36 564 280 204 134 324 154 129 .. .. ..$ : int [1:9] 436 39 712 101 947 122 219 907 939 .. .. ..$ : int [1:9] 280 124 796 743 913 990 937 483 282 .. .. ..$ : int [1:9] 252 503 496 318 959 631 127 421 908 .. .. ..$ : int [1:9] 468 908 597 630 866 501 978 323 478 .. .. ..$ : int [1:9] 357 627 741 565 977 574 437 227 82 .. .. ..$ : int [1:9] 851 235 987 601 995 374 552 427 572 .. .. ..$ : int [1:9] 433 225 85 636 430 73 798 324 752 .. .. ..$ : int [1:9] 585 709 427 343 757 422 558 116 301 .. .. ..$ : int [1:9] 479 345 600 76 953 23 837 629 308 .. .. ..$ : int [1:9] 743 639 991 128 880 807 817 829 727 .. .. ..$ : int [1:9] 984 639 660 527 317 765 524 728 306 .. .. ..$ : int [1:9] 405 205 984 565 280 185 754 563 925 .. .. ..$ : int [1:9] 639 701 479 848 421 32 257 333 133 .. .. ..$ : int [1:9] 500 802 337 508 493 794 564 106 999 .. .. ..$ : int [1:9] 568 213 749 307 488 985 422 243 216 .. .. ..$ : int [1:9] 690 980 477 772 573 962 793 529 592 .. .. ..$ : int [1:9] 264 280 65 562 262 4 586 517 838 .. .. ..$ : int [1:9] 30 600 268 121 101 745 16 50 742 .. .. ..$ : int 358 .. ..$ adaptive :List of 4 .. .. ..$ min : num 5 .. .. ..$ alpha : num 0.05 .. .. ..$ method : chr "gls" .. .. ..$ complete: logi TRUE .. ..$ trim : logi FALSE .. ..$ allowParallel : logi TRUE ..$ trainingData:'data.frame': 80 obs. of 13 variables: .. ..$ .outcome : Factor w/ 2 levels "high","low": 2 2 1 1 1 1 1 1 1 1 ... .. ..$ CD161_pos_CD45RA_pos_Tregs : num [1:80] 1.68 0.84 0.43 0.56 0.73 0.64 0.53 1.15 0.51 1.38 ... .. ..$ CD27_pos_CD8_pos_T_cells : num [1:80] 85.2 71.9 84.5 83 74.8 66.4 87.7 64.1 87.3 89.5 ... .. ..$ CD85j_pos_CD8_pos_T_cells : num [1:80] 17.7 25.8 17.1 19.1 19.1 28.6 8.31 18.8 11 6.95 ... .. ..$ CD94_pos_CD8_pos_T_cells : num [1:80] 4.31 14.2 3.94 4.48 10.1 25.8 20.3 11 4.16 2.74 ... .. ..$ central_memory_CD8_pos_T_cells: num [1:80] 1.96 3.27 2.77 6.31 7.59 6.02 8.54 5.64 6.36 2.93 ... .. ..$ effector_CD8_pos_T_cells : num [1:80] 14.7 26.9 13.4 11.7 21 18.8 10.6 14.4 6.82 7.72 ... .. ..$ L50_EOTAXIN : num [1:80] -0.14 1.3 0.28 -0.76 0.16 0.4 0.17 0.88 0.73 0.84 ... .. ..$ L50_HGF : num [1:80] -0.06 1.45 -0.14 -1.12 -0.36 0.19 0.1 0.82 1.14 1.35 ... .. ..$ L50_IL7 : num [1:80] -0.11 1.49 -0.1 -0.88 0.07 0.23 0.18 0.99 0.97 1.26 ... .. ..$ L50_MCP3 : num [1:80] -1.38 2 -0.17 0.48 -0.54 1.03 0.8 0.43 1.06 0.77 ... .. ..$ L50_TRAIL : num [1:80] 0.17 1.8 0.21 -1.56 0.34 0.96 0 -0.59 1.43 1.65 ... .. ..$ monocytes : num [1:80] 17.1 12 20.5 21.2 13.4 15.9 18.2 12.7 14 14.6 ... ..$ resample :'data.frame': 30 obs. of 12 variables: .. ..$ logLoss : num [1:30] 2.89 2.01 1.22 3.06 1.56 ... .. ..$ AUC : num [1:30] 0.933 0.867 0.833 1 0.8 ... .. ..$ Accuracy : num [1:30] 0.875 0.625 0.857 0.889 0.625 ... .. ..$ Kappa : num [1:30] 0.714 0.143 0.696 0.769 0.143 ... .. ..$ F1 : num [1:30] 0.8 0.4 0.8 0.857 0.4 ... .. ..$ Sensitivity : num [1:30] 0.667 0.333 0.667 0.75 0.333 ... .. ..$ Specificity : num [1:30] 1 0.8 1 1 0.8 0.6 0.6 1 0.6 1 ... .. ..$ Pos_Pred_Value : num [1:30] 1 0.5 1 1 0.5 ... .. ..$ Neg_Pred_Value : num [1:30] 0.833 0.667 0.8 0.833 0.667 ... .. ..$ Detection_Rate : num [1:30] 0.25 0.125 0.286 0.333 0.125 ... .. ..$ Balanced_Accuracy: num [1:30] 0.833 0.567 0.833 0.875 0.567 ... .. ..$ Resample : chr [1:30] "Fold03.Rep1" "Fold02.Rep1" "Fold01.Rep1" "Fold04.Rep1" ... ..$ resampledCM :'data.frame': 90 obs. of 7 variables: .. ..$ degree : num [1:90] 1 1 1 1 1 1 1 1 1 1 ... .. ..$ nprune : num [1:90] 18 2 10 18 2 10 18 2 10 18 ... .. ..$ cell1 : num [1:90] 2 0 2 1 1 1 1 1 2 2 ... .. ..$ cell2 : num [1:90] 1 3 1 2 2 2 2 2 1 2 ... .. ..$ cell3 : num [1:90] 0 0 0 1 1 1 1 0 0 0 ... .. ..$ cell4 : num [1:90] 4 4 4 4 4 4 4 5 5 5 ... .. ..$ Resample: chr [1:90] "Fold01.Rep1" "Fold01.Rep1" "Fold01.Rep1" "Fold02.Rep1" ... ..$ perfNames : chr [1:11] "logLoss" "AUC" "Accuracy" "Kappa" ... ..$ maximize : logi TRUE ..$ yLimits : NULL ..$ times :List of 3 .. ..$ everything: 'proc_time' Named num [1:5] 2.25 0.56 13.92 156.52 8.29 .. .. ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ... .. ..$ final : 'proc_time' Named num [1:5] 0.776 0.004 0.783 0 0 .. .. ..- attr(*, "names")= chr [1:5] "user.self" "sys.self" "elapsed" "user.child" ... .. ..$ prediction: logi [1:3] NA NA NA ..$ levels : chr [1:2] "high" "low" .. ..- attr(*, "ordered")= logi FALSE ..$ terms :Classes 'terms', 'formula' language factor(outcome) ~ CD161_pos_CD45RA_pos_Tregs + CD27_pos_CD8_pos_T_cells + CD85j_pos_CD8_pos_T_cells + CD94_pos_CD| __truncated__ $ model_prediction :List of 2 ..$ pred_prob:'data.frame': 25 obs. of 2 variables: .. ..$ high: num [1:25] 0.237 0.52 0.625 0.857 0.452 ... .. ..$ low : num [1:25] 0.763 0.48 0.375 0.143 0.548 ... ..$ pred_raw : Factor w/ 2 levels "high","low": 2 1 1 1 2 2 1 1 1 2 ... $ roc_auc :List of 2 ..$ roc_p:List of 15 .. ..$ percent : logi FALSE .. ..$ sensitivities : num [1:26] 1 1 1 0.933 0.867 ... .. ..$ specificities : num [1:26] 0 0.1 0.2 0.2 0.2 0.3 0.4 0.5 0.5 0.5 ... .. ..$ thresholds : num [1:26] -Inf 0.0625 0.0688 0.0753 0.122 ... .. ..$ direction : chr "<" .. ..$ cases : num [1:15] 0.52 0.625 0.452 0.55 0.735 ... .. ..$ controls : num [1:10] 0.237 0.857 0.238 0.354 0.167 ... .. ..$ fun.sesp :function (thresholds, controls, cases, direction) .. ..$ auc : 'auc' num 0.7 .. .. ..- attr(*, "partial.auc")= logi FALSE .. .. ..- attr(*, "percent")= logi FALSE .. .. ..- attr(*, "roc")=List of 15 .. .. .. ..$ percent : logi FALSE .. .. .. ..$ sensitivities : num [1:26] 1 1 1 0.933 0.867 ... .. .. .. ..$ specificities : num [1:26] 0 0.1 0.2 0.2 0.2 0.3 0.4 0.5 0.5 0.5 ... .. .. .. ..$ thresholds : num [1:26] -Inf 0.0625 0.0688 0.0753 0.122 ... .. .. .. ..$ direction : chr "<" .. .. .. ..$ cases : num [1:15] 0.52 0.625 0.452 0.55 0.735 ... .. .. .. ..$ controls : num [1:10] 0.237 0.857 0.238 0.354 0.167 ... .. .. .. ..$ fun.sesp :function (thresholds, controls, cases, direction) .. .. .. ..$ auc : 'auc' num 0.7 .. .. .. .. ..- attr(*, "partial.auc")= logi FALSE .. .. .. .. ..- attr(*, "percent")= logi FALSE .. .. .. .. ..- attr(*, "roc")=List of 8 .. .. .. .. .. ..$ percent : logi FALSE .. .. .. .. .. ..$ sensitivities: num [1:26] 1 1 1 0.933 0.867 ... .. .. .. .. .. ..$ specificities: num [1:26] 0 0.1 0.2 0.2 0.2 0.3 0.4 0.5 0.5 0.5 ... .. .. .. .. .. ..$ thresholds : num [1:26] -Inf 0.0625 0.0688 0.0753 0.122 ... .. .. .. .. .. ..$ direction : chr "<" .. .. .. .. .. ..$ cases : num [1:15] 0.52 0.625 0.452 0.55 0.735 ... .. .. .. .. .. ..$ controls : num [1:10] 0.237 0.857 0.238 0.354 0.167 ... .. .. .. .. .. ..$ fun.sesp :function (thresholds, controls, cases, direction) .. .. .. .. .. ..- attr(*, "class")= chr "roc" .. .. .. ..$ call : language roc.default(response = testing$outcome, predictor = predict_model[, "high"], levels = levels(testing$outcome)) .. .. .. ..$ original.predictor: num [1:25] 0.237 0.52 0.625 0.857 0.452 ... .. .. .. ..$ original.response : Factor w/ 2 levels "high","low": 1 2 2 1 2 1 2 2 2 2 ... .. .. .. ..$ predictor : num [1:25] 0.237 0.52 0.625 0.857 0.452 ... .. .. .. ..$ response : Factor w/ 2 levels "high","low": 1 2 2 1 2 1 2 2 2 2 ... .. .. .. ..$ levels : chr [1:2] "high" "low" .. .. .. ..- attr(*, "class")= chr "roc" .. ..$ call : language roc.default(response = testing$outcome, predictor = predict_model[, "high"], levels = levels(testing$outcome)) .. ..$ original.predictor: num [1:25] 0.237 0.52 0.625 0.857 0.452 ... .. ..$ original.response : Factor w/ 2 levels "high","low": 1 2 2 1 2 1 2 2 2 2 ... .. ..$ predictor : num [1:25] 0.237 0.52 0.625 0.857 0.452 ... .. ..$ response : Factor w/ 2 levels "high","low": 1 2 2 1 2 1 2 2 2 2 ... .. ..$ levels : chr [1:2] "high" "low" .. ..- attr(*, "class")= chr "roc" ..$ auc_p: num 0.7 $ confusion_matrix :List of 6 ..$ positive: chr "high" ..$ table : 'table' int [1:2, 1:2] 7 8 8 2 .. ..- attr(*, "dimnames")=List of 2 .. .. ..$ : chr [1:2] "low" "high" .. .. ..$ reference: chr [1:2] "low" "high" ..$ overall : Named num [1:7] 0.36 -0.333 0.18 0.575 0.6 ... .. ..- attr(*, "names")= chr [1:7] "Accuracy" "Kappa" "AccuracyLower" "AccuracyUpper" ... ..$ byClass : Named num [1:11] 0.2 0.467 0.2 0.467 0.2 ... .. ..- attr(*, "names")= chr [1:11] "Sensitivity" "Specificity" "Pos Pred Value" "Neg Pred Value" ... ..$ mode : chr "sens_spec" ..$ dots : list() ..- attr(*, "class")= chr "confusionMatrix" $ variable_importance:'data.frame': 12 obs. of 4 variables: ..$ score_perc: num [1:12] 100 78.8 63.1 47.6 34.5 ... ..$ features : chr [1:12] "L50_EOTAXIN" "central_memory_CD8_pos_T_cells" "CD94_pos_CD8_pos_T_cells" "L50_TRAIL" ... ..$ rank : int [1:12] 1 2 3 4 5 6 7 8 9 10 ... ..$ score_no : num [1:12] 99.3 78.2 62.7 47.3 34.3 ... File was compressed using 7-Zip available at https://www.7-zip.org/.

Do the complex processes of angiogenesis during organism development ultimately lead to a near optimal coronary vasculature in the organs of adult mammals? We examine this hypothesis using a powerful and universal method, built on physical and physiological principles, for the determination of globally energetically optimal arterial trees. The method is based on simulated annealing, and can be used to examine arteries in hollow organs with arbitrary tissue geometries. We demonstrate that the approach can generate in silico vasculatures which closely match porcine anatomical data for the coronary arteries on all length scales, and that the optimized arterial trees improve systematically as computational time increases. The method presented here is general, and could in principle be used to examine the arteries of other organs. Potential applications include improvement of medical imaging analysis and the design of vascular trees for artificial organs. dataData for various parts of the paper are contained in appropriate directories, and include matlab files for plotting where appropriate.

The host praises guests and invites them to drink. 主人赞扬客人，并且让他们喝酒。 This collection contains two biographical songs, three dancing songs, three folk songs, one speech, seven weddings songs, and two welcoming songs collected in Tha rgyas Village, Rtsa zhol Township, Mol gro gung dkar County, Lhasa City, Tibet Autonomous Region, PR China by Sgrol ma lha skyid in June 2007.

Data and script for Andrews et al. 'A marker of biological ageing predicts risk preference in European starlings, Sturnus vulgaris'. Consists: One R script and three data .csv files.

Project: IPCC Assessment Report 5 and Coupled Model Intercomparison Project data sets - These data belong to two projects: 1) to the Assessment Report No 5 of the International Panel on Climate Change (IPCC-AR5) and 2) to the Coupled Model Intercomparison Project No 5 (CMIP5). CMIP5 is executed by the Program for Climate Model Diagnosis and Intercomparison (PCMDI) on behalf of the World Climate Research Programme (WCRP). Most of the data is replicated between the three data nodes at the World Data Centre for Climate (WDCC), the British Atmospheric Data Centre (BADC), and the PCMDI. The project embraces the simulations with about 30 climate models of about 20 institutes worldwide. Summary: 'historical' is an experiment of the CMIP5 - Coupled Model Intercomparison Project Phase 5 (http://cmip-pcmdi.llnl.gov/cmip5/). CMIP5 is meant to provide a framework for coordinated climate change experiments for the next five years and thus includes simulations for assessment in the AR5 as well as others that extend beyond the AR5. 3.2 historical (3.2 Historical) - Version 1: Simulation of recent past (1850 to 2005). Impose changing conditions (consistent with observations). Experiment design: http://cmip-pcmdi.llnl.gov/cmip5/docs/Taylor_CMIP5_design.pdf List of output variables: http://cmip-pcmdi.llnl.gov/cmip5/docs/standard_output.pdf Output: time series per variable in model grid spatial resolution in netCDF format Earth System model and the simulation information: CIM repository Entry name/title of data are specified according to the Data Reference Syntax (http://cmip-pcmdi.llnl.gov/cmip5/docs/cmip5_data_reference_syntax.pdf) as activity/product/institute/model/experiment/frequency/modeling realm/MIP table/ensemble member/version number/variable name/CMOR filename.nc.

GWAS hits tagged by TNFSF-related gene eQTLs (Additional file 7) were examined for risk allele effects. SNPs associated with gene expression in multiple cell types are repeated, one line per cell type. Duplicate associations from different studies were removed in plotting Fig. 5 but are retained here due to different references, p values and odds ratios. (XLSX 55 kb)

The original art work produced by Louise Clarke for the booklet was a response to a specific track through a detailed drawing. The musician Darren Hayman invited artists to select a song from his concept album Bugbear and respond by producing a visual artwork. All the images were printed in the albums booklet. Darren Hayman's LP of seventeenth century folk songs called Bugbears is encased in deluxe packaging and accompanied by a massive booklet of Darren's notes, lyrics along with artwork by various artists; the 13 songs are illustrated by 13 artists, including Ant Harding of Hefner, Jonny Helm of The Wave Pictures, Pam Berry of Black Tambourine, Dan Wilson of Withered Hand, Robert Rotifer, Sarah Lippett of Fever Dream, Louise Clarke, Joe Besford, James Paterson and Matthew Sawyer.

.mp4 video file Standing in one of the old examination halls in Cambridge, Alan Macfarlane talks about the invention of competitive university examinations (in mathematics in the earlier C19) and the central role of Cambridge as one of the biggest exam setting boards in the world. Also what Camrbdige examinations are thought to be testing.

Pulmonary blood pressure data