IalAccuracy 65 60 60Sensitivity 1 33 58 60Specificity two one hundred 62

IalAccuracy 65 60 60Sensitivity 1 33 58 60Specificity two one hundred 62 60Percentage of predictions properly classified as DON contamination 200 kg-1 . 2 Percentage of predictions correctly classified as DON contamination 200 kg-1 .The other models have been much less precise, with accuracy of about 605 (Table three). For the DT model, by far the most important variables have been the area where the spring wheat was grown as well as the sum of precipitation about milk development/dough development/ripening. The most crucial variables for the RF-based model have been RH, PREC and VPD throughout germination and seedling development, wind speed throughout tillering and stem elongation, precipitation and flowering, and PREC and Tmax in the milk development/dough stage (Figures eight and 9).Toxins 2021, 13,11 ofFigure eight. Variable significance within the Random Forest-based model for Sweden grown spring wheat. PREC-precipitation, RH-relative humidity, Tmax-daily maximum temperature, WS-wind speed, WD-wind path, VPD-vapour stress deficit. PREC_022-PREC 22.045.05, PREC_085-PREC 24.067.07, RH _029-RH 29.042.05, RH_036-RH 06.059.05, Tmax_099-Tmax 08.071.07, VPD_036-VPD 06.059.05, WS_008-WS 08.041.04, WS_050-WS 20.052.06, WS_057-WS 27.059.06, WS_092-WS 01.074.07.Figure 9. Distribution from the minimal depth of your variable and its mean in the Random Forest-based model for Sweden grown spring wheat. PREC-precipitation, RH-relative humidity, Tmax-daily maximum temperature, WS-wind speed, WD-wind direction, VPD-vapour pressure deficit. RH_036-RH 06.059.05, PREC_106-PREC 15.078.07, WS_050-WS 20.052.06, WD_099-WD 08.071.07, WS_057-WS 27.059.06, WS_092-WS 01.074.07, VPD_036-VPD 06.059.05, PREC_085-PREC 24.067.07, Tmax_099-Tmax 08.071.07, RH _001-RH 01.044.04.two.two.2. KRP-297 Cell Cycle/DNA Damage Lithuania For Lithuanian spring wheat, the model based on DT had the highest accuracy (95 ) and also the highest potential for correct classification of MRS1334 site samples with high and low DON contamination (accuracy 100 and 93 , respectively) (Table four). The other models performedToxins 2021, 13,12 ofslightly less nicely, with accuracy ranging amongst 84 and 90 , and had been significantly weaker in classifying samples using a DON content material 1250 kg-1 (Table four).Table 4. Efficiency (accuracy, sensitivity and specificity) on the 4 models utilized to predict the threat of a deoxynivalenol (DON) contamination level 1250 kg-1 in Lithuanian spring wheat, depending on the test information set. Model Selection Tree Random Forest Help Vector Machine Linear Help Vector Machine RadialAccuracy 95 84 90Sensitivity 1 100 74 83Specificity two 93 88 93Percentage of predictions properly classified as DON contamination 1250 kg-1 . 2 Percentage of predictions correctly classified as DON contamination 1250 kg-1 .The DT-based model accurately classified samples determined by Tmean around sowing and precipitation during stem elongation. As outlined by the RF-based model, the most essential stages during the growing season have been sowing and flowering, when Tmean and precipitation had been the most crucial variables, and milk development/dough development/ripening, when Tmean strongly impacted the DON contamination inside the grain at harvest (Figures ten and 11).Figure ten. Variable value inside the Random Forest-based model for Lithuania grown spring wheat. PREC-precipitation, Tmean-daily mean temperature. PREC_022-PREC 22.045.05, Tmean_008-Tmean 08.041.04, Tmean_015-Tmean 15.048.04, Tmean_022-Tmean 22.045.05, Tmean_029-Tmean 29.042.05, Tmean_36-Tmean 06.059.05, Tmean_085Tmean 24.067.07, Tmean_092-Tme.