Supplementary Table. S1: Same as Table 1, but for scenarios considered compatible with 1.5°C and 2°C warming in the 5th assessment report of IPCC (Xxxxxx et al. 2014, Xxxxxx et al. 2015), including projections of changes in regional climate associated with resulting global temperature levels derived following previous studies (Seneviratne et al. 2016, Xxxxxxxxxxxx et al. 2017) SCEN_1p5C Emissions pathways currently considered in line with keeping warming below 1.5°C in 2100 with 66% chance (allowing for a higher peak in temperature earlier) SCEN_2C Emissions pathways currently considered in line with keeping warming below 2°C during the entire 21st century with 66% chance “probable” (66th percentile) outcomea “worst-case” 10% (90th percentile) outcomeb “probable” (66th percentile) outcomea “worst-case” 10% (90th percentile) outcomeb General characteristics of pathway Overshoot 1.5°C in 21st century with >50% likelihoodc Yes (8/8) Yes (8/8) Yes (60/60) Yes (60/60) Overshoot 2°C in 21st century with >50% likelihood No (0/8) Yes (4/8) No (0/60) Yes (60/60) Cumulative CO2 emissions up to peak warming (relative to 2016)d 510 (490, 560) 470 (410, 520) 930 (790, 1050) 900 (750, 1040) Cumulative CO2 emissions up to 2100 (relative to 2016)d [GtCO2] -40 (-100, 10) 850 (520, 1000) Global GHG emissions in 2030d [GtCO2 y-1] 19 (17, 21) 28 (23, 32) Years of global net zero CO2 emissionsd 2061 (2061, 2063) 2084 (2079, 2086) Possible climate range at peak warming (reg+glob) Global mean temperature anomaly at peak warming [°C] 1.65°C (1.61, 1.68°C) 2.01°C (1.95, 2.03°C) 1.90°C (1.86, 1.95°C) 2.35°C (2.29, 2.48°C) Warming in the Arctice (TNnf) [°C] 4.75 °C (4.09, 5.44) 5.90 °C (4.97, 6.85) 5.63 °C (4.68, 6.59) 6.97 °C (6.13, 8.38) Warming in the contiguous United Statese (TXxf) [°C] 2.39 °C (1.90, 2.84) 2.97 °C (2.36, 3.40) 2.77 °C (2.20, 3.30) 3.51 °C (3.05, 4.11) Warming in Central Brazile (TXxf) [°C] 2.55 °C (2.12, 2.97) 3.12 °C (2.66, 3.76) 2.96 °C (2.58, 3.55) 3.66 °C (3.31, 4.21) Drying in the Mediterranean regione [stdf] (-1: dry; -2: severely dry; -3: very severely dry) -1.00 (-2.12, -0.39) -1.25 (-2.21, -0.51) -1.11 (-2.18, -0.51) -1.36 (-2.93, -0.69) Increase in heavy precipitation eventsf in Southern Asiae [%] 9.78 % (6.52, 13.63) 11.56 % (7.04, 18.50) 10.27 % (6.50, 17.40) 16.74 % (9.60, 23.44) Possible climate range in 2100 (reg+glob) Global mean temperature warming in 2100 [°C] 1.41°C (1.39— 1.43°C) 1.84°C (1.81— 1.90°C) 1.84°C (1.76— 1.89°C) 2.30°C (2.21— 2.46°C) Warming in the A...
Supplementary Table. 5: TRIPOD reporting scores for articles published after TRIPOD statement in journals that require adherence to the TRIPOD statement and journals that do not require to the adherence statement 201 Supplementary Table 6: Percentage articles reporting TRIPOD items in supplementary material TRIPOD item Supplement % TRIPOD item Supplement % 1 0 10d 1 2 0 10e 0 3a 0 11 4 3b 0 12 24 4a 7 13a 21 4b 7 13b 30 5a 10 3c 17 5b 6 14a 2 5c 33 14b 40 6a 13 15a 40 6b 0 15b 35 7a 36 16 0 7b 0 17 17 8 3 18 0 9 9 19a 0 10a 12 19b 0 10b 13 20 0 10c 14 22 1 Supplementary figure 1: Average overall TRIPOD reporting levels in percentage per year 85 80 81,7 79,2 75,2 75 70 65 70,6 71,5 2012 2013 2014 2016 2017 Chapter 7 202 Supplementary Table 7: Performance measures and missing data in all studies Calibration Before 2015 (n=32) Number (%) After 2016 (n=38) Number (%) Plot 16 (50%) 31 (82%) Intercept and Slope 0 (0%) 0 (0%) Calibration in-the-large 1 (3%) 0 (0%) Slope 0 (0%) 1 (3%) Test 4 (13%) 1 (3%) Not 11 (34%) 5 (13%) TRIPOD statement: a preliminary pre-post analysis of reporting and methods of prediction models Discrimination C-statistic / AUC 29 (91%) 38 (100%) D-statistic 5 (16%) 5 (13%) Not 3 (9%) 0 (0%) Classification IDI 5 (16%) 4 (11%) NRI 8 (25%) 7 (18%) Sens. Spec. PPV. NPV. LR. ROC 20 (63%) 19 (50%) Not reported 10 (31%) 16 (42%) Clinical usefulness Decision curve analysis 2 (6%) 8 (21%) Not reported 30 (94%) 30 (79%) Overall performance Brier 2 (6%) 3 (8%) R2 4 (13%) 5 (13%) Adequacy statistic 1 (3%) 2 (5%) Not reported 26 (81%) 28 (74%) Missing data reporting Per variable 17 (53%) 13 (34%) Overall 2 (6%) 14 (37%) Not reported 13 (41%) 11 (29%) Type and reason of missing data Type reported 3 (9%) 2 (5%) Reason reported 5 (16%) 2 (5%) Missing data handling Complete-case analysis 5 (16%) 6 (16%) Multiple Imputation 12 (38%) 19 (50%) Other methods 6 (19%) 4 (11%) Not reported 9 (28%) 9 (24%) 203 Supplementary Table 8: Model development and presentation Sample per candidate predictor Before 2015 (n=24) Number (%) After 2016 (n=27) Number (%) Chapter 7 204 <10 8 (33%) 8 (30%) 10-100 8 (33%) 9 (33%) 100-1000 8 (17%) 5 (19%) >1000 3 (13%) 3 (11%) Unknown number of predictors 0 (0%) 1 (4%) Unknown number of outcomes 1 (4%) 1 (4%) Model Type Linear 1 (4%) 0 (0%) Logistic 16 (67%) 15 (56%) Cox 6 (25%) 10 (37%) Points 0 (0%) 2 (7%) Other 1 (4%) 0 (0%) Predictor Selection A priori knowledge / based on literature 11 (46%) 17 (63%) Statistically 4 (17%) 2 (7%) Not reported 9 (38%)...
Supplementary Table. 5: Regression equations developed for the prediction of MET values based on acceleration outputs from GENEActiv (GA) and ActiGraph (AG). GA non-dominant AG non-dominant AG dominant AG waist Equation 1 α (95%CI) 1.812 (1.689 to 1.936) 1.938 (1.815 to 2.060) 1.883 (1.766 to 2.001) 1.514 (1.418 to 1.610) β (95%CI) 0.011 (0.010 to 0.012) 0.012 (0.011 to 0.013) 0.011 (0.010 to 0.012) 0.020 (0.019 to 0.021) MET 1.812 + 0.011 mg 1.938 + 0.012 mg 1.883 + 0.011 mg 1.514 + 0.020 mg Adjusted R2 76.1 73.7 74.0 84.2 Equation 2 α (95%CI) 1.898 (1.767 to 2.030) 2.032 (1.900 to 2.163) 2.012 (1.885 to 2.139) 1.533 (1.428 to 1.638) β (95%CI) 0.011 (0.010 to 0.012) 0.011 (0.010 to 0.012) 0.010 (0.009 to 0.011) 0.020 (0.019 to 0.021) MET 1.898+ 0.011 mg 2.032 + 0.011mg 2.012 + 0.010 mg 1.533 + 0.020 mg Adjusted R2 80.4 78.3 77.9 86.4 Equation 3 α (95%CI) 1.260 (1.152 to 1.368) 1.363 (1.256 to 1.471) 1.351 (1.242 to 1.461) 1.117 (1.010 to 1.224) β1 (95%CI) 0.020 (0.019 to 0.021) 0.022 (0.021 to 0.023) 0.020 (0.019 to 0.021) 0.031 (0.029 to 0.032) β2 (95%CI) x10-5 -1.1 (-1.2 to -1.0) -1.4 (-1.5 to -1.3) -1.2 (-1.3 to -1.1) -2.6 (-2.9 to -2.2) MET 1.260 + 0.020 mg + (-1.1 mg2 x 10-5) 1.363 + 0.022 mg + (-1.4 mg2 x 10-5) 1.351 + 0.020 mg + (-1.2 mg2 x 10-5) 1.117 + 0.031 mg + (-2.6 mg2 x 10-5) Adjusted R2 89.8 88.8 87.6 89.5 Equation 1: only outputs from 8 activities were included (similar activities used by Xxxxxxxxxx et al); Equation 2: 9 activities were included (Xxxxxxxxxx + intermittent running) Equation 3: Equation 2 and inclusion of a quadratic term for acceleration. Figures presented in the main document were based on this equation.
Examples of Supplementary Table in a sentence
Then, NMR=distances, dihedral restraints, and structure statistics are given for structure refinement (Supplementary Table S6).
In all cases cross-assay performance was lower as expected, and the more complex model (ECBLSTM) performed better than the simplest model (DeepBind), in addition having better same-assay performance (see Supplementary Table S5).
All the deep learning methods performed better than iONMF which uses multiple sources of data, including k-mer frequency, sec- ondary structure and GO annotations (see Supplementary Table S4).
Association results for all index variants are given in Supplementary Table 3.
However, to compare with earlier findings, we included subgroup analyses of maternal bereavement and job control analysed separately for mothers with ‘high’ and ‘low’ psychological job demands (Supplementary Table 3, available as Supplementary data at IJE online).
TG measurements were available for 3,202 individuals with sequence data, including 1,497 ALSPAC children (mean age 10 years, 50% females) and 1,705 TwinsUK adults, respectively (mean year 56 years, all females, Supplementary Table 1).
Supplementary Table 1 lists the pool of mining rigs constructed by the authors, from which they randomly assigned rigs (i.e., with equal probability) to solve blocks in 2017 via Eq. 1.
Classifier agreementClassifiers trained on the gold standard datasets perform very simi- larly, see Figure 1 and Supplementary Table S1.
Zoom into individual nodes for Spectrum (S) and Tocris (T) plate number and well identity (cross- reference to Supplementary Table S1).
RT-PCR reactions (22–26,28,32) and 3r RACE (23,28) were performed on total RNA preparations of NZ9800OltrB and MMS372 harbouring various intron constructs (primers in Supplementary Table S2).
More Definitions of Supplementary Table
Supplementary Table. 6: Average confusion matrix indicating the ability of thresholds to accurately classify intensities (% accurately classified). Columns indicate actual intensity, while rows indicate predicted intensity. Sedentary a n (%) Light b n (%) Moderate n (%) Vigorous n (%) Kappa (SE) GA non-dominant 0.63 (0.02) Sedentary 151 (58.8) 104 (40.5) 2 (0.8) 0 (0.0) Light 2 (2.2) 70 (70.1) 20 (21.7) 0 (0.0) Moderate 0 (0.0) 6 (4.1) 117 (80.7) 22 (15.2) Vigorous 0 (0.0) 0 (0.0) 18 (13.2) 118 (86.7) AG non-dominant 0.72 (0.02) Sedentary 144 (77.4) 42 (22.6) 0 (0.0) 0 (0.0) Light 21 (10.9) 146 (75.7) 26 (13.5) 0 (0.0) Moderate 0 (0.0) 10 (5.8) 130 (75.1) 33 (19.1) Vigorous 0 (0.0) 0 (0.0) 14 (10.1) 125 (89.9) AG dominant 0.66 (0.02) Sedentary 171 (65.5) 88 (33.7) 2 (0.8) 0 (0.0) Light 11 (7.1) 116 (74.4) 29 (18.6) 0 (0.0) Moderate 0 (0.0) 11 (6.0) 138 (75.0) 35 (19.0) Vigorous 0 (0.0) 0 (0.0) 17 (11.0) 138 (89.0) AG waist 0.53 (0.02) Sedentary 185 (51.8) 171 (47.9) 1 (0.3) 0 (0.0) Light 1 (2.6) 23 (60.5) 14 (36.8) 0 (0.0) Moderate 0 (0.0) 26 (12.8) 146 (71.6) 32 (15.7) Vigorous 0 (0.0) 0 (0.0) 27 (15.7) 145 (84.3)