Artificial intelligence model validation before its application in clinical diagnosis assistance

The process of selecting an artificial intelligence(AI)model to assist clinical diagnosis of a particular pathology and its validation tests is relevant since the values of accuracy,sensitivity and specificity may not reflect the behavior of the method in a real environment.Here,we provide helpful considerations to increase the success of using an AI model in clinical practice.

Comparing independent climate-sensitive models of aboveground biomass and diame ter grow th with their compatible simultaneous model system for three larch species in China

Accurate estimate of tree biomass is essential for forest management.In recent years,several climate-sensitive allometric biomass models with diameter at breast height(D)as a predictor have been proposed for various tree species and climate zones to estimate tree aboveground biomass(AGB).But the allometric models only account for the potential effects of climate on tree biomass and do not simultaneously explain the influence of climate on D growth.In this study,based on the AGB data from 256 destructively sampled trees of three larch species randomly distributed across the five secondary climate zones in northeastern and northern China,we first developed a climate-sensitive AGB base model and a climate-sensitive D growth base model using a nonlinear least square regression separately.A compatible simultaneous model system was then developed with the climate-sensitive AGB and D growth models using a nonlinear seemingly unrelated regression.The potential effects of several temperature and precipitation variables on AGB and D growth were evaluated.The fitting results of climatic sensitive base models were compared against those of their compatible simultaneous model system.It was found that a decreased isothermality([mean of monthly(maximum temperatureminimum temperature)]/(Maximum temperature of the warmest month-Minimum temperature of the coldest month))and total growing season precipitation,and increased annual precipitation significantly increased the values of AGB;an increase of temperature seasonality(a standard deviation of the mean monthly temperature)and precipitation seasonality(a standard deviation of the mean monthly precipitation)could lead to the increase of D.The differences of the model fitting results between the compatible simultaneous system with the consideration of climate effects on both AGB and D growth and its corresponding climate-sensitive AGB and D growth base models were very small and insignificant(p>0.05).Compared to the base models,the inhere nt correlation of AGB with D was taken into account effectively by the proposed compatible model system developed with the climate-sensitive AGB and D grow th models.In addition,the compatible properties of the estimated AGB and D were also addressed substantially in the proposed model system.

Quantitative structure–biodegradability relationships for biokinetic parameter of polycyclic aromatic hydrocarbons

Prediction of the biodegradability of organic pollutants is an ecologically desirable and economically feasible tool for estimating the environmental fate of chemicals. In this paper,stepwise multiple linear regression analysis method was applied to establish quantitative structure biodegradability relationship（QSBR） between the chemical structure and a novel biodegradation activity index（qmax） of 20 polycyclic aromatic hydrocarbons（PAHs）. The frequency B3LYP/6-311＋G（2df,p） calculations showed no imaginary values, implying that all the structures are minima on the potential energy surface. After eliminating the parameters which had low related coefficient with qmax, the major descriptors influencing the biodegradation activity were screened to be Freq, D, MR, EHOMOand To IE. The evaluation of the developed QSBR mode, using a leave-one-out cross-validation procedure, showed that the relationships are significant and the model had good robustness and predictive ability. The results would be helpful for understanding the mechanisms governing biodegradation at the molecular level.

New method for estimating strike and dip based on structural expansion orientation for 3D geological modeling

Strike and dip are essential to the description of geological features and therefore play important roles in 3D geological modeling.Unevenly and sparsely measured orientations from geological field mapping pose problems for the geological modeling,especially for covered and deep areas.This study developed a new method for estimating strike and dip based on structural expansion orientation,which can be automatically extracted from both geological and geophysical maps or profiles.Specifically,strike and dip can be estimated by minimizing an objective function composed of the included angle between the strike and dip and the leave-one-out cross-validation strike and dip.We used angle parameterization to reduce dimensionality and proposed a quasi-gradient descent(QGD)method to rapidly obtain a near-optimal solution,improving the time-efficiency and accuracy of objective function optimization with the particle swarm method.A synthetic basin fold model was subsequently used to test the proposed method,and the results showed that the strike and dip estimates were close to the true values.Finally,the proposed method was applied to a real fold structure largely covered by Cainozoic sediments in Australia.The strikes and dips estimated by the proposed method conformed to the actual geological structures more than those of the vector interpolation method did.As expected,the results of 3D geological implicit interface modeling and the strike and dip vector field were much improved by the addition of estimated strikes and dips.