Identifying the ecological environment suitable for the growth of Thuja sutchuenensis and predicting other potential distribution areas are essential to protect this endangered species. After selecting 24 environmenta...Identifying the ecological environment suitable for the growth of Thuja sutchuenensis and predicting other potential distribution areas are essential to protect this endangered species. After selecting 24 environmental factors thatcould affect the distribution of T. sutchuenensis, including climate, topography, soil and Normalized DifferenceVegetation Index (NDVI), we adopted the Random Forest-MaxEnt integrated model to analyze our data. Basedon the Random Forest study, the contribution of the mean temperature of the warmest quarter, mean temperatureof the coldest quarter, annual mean temperature and mean temperature of the driest quarter was large. Based onMaxEnt model prediction outputs, the potential distribution map not only identified areas that originallyrecorded T. sutchuenensis, such as Xuanhan County, Kai County and Chengkou County, but also identified highlysuitable distribution areas where T. sutchuenensis may exist, including Wanyuan County, Sichuan Province, andthe junction of Chongqing and Hubei Province. This provides a more explicit geographic range for ex situ conservation and reintroduction of T. sutchuenensis. Our results also indicate that, in addition to climate factors,topography and soil factors are also important environmental factors that affect distribution. This provides a theoretical basis for subsequent laboratory construction to simulate the indoor growth of T. sutchuenensis.展开更多
Accurate prediction of the internal corrosion rates of oil and gas pipelines could be an effective way to prevent pipeline leaks.In this study,a proposed framework for predicting corrosion rates under a small sample o...Accurate prediction of the internal corrosion rates of oil and gas pipelines could be an effective way to prevent pipeline leaks.In this study,a proposed framework for predicting corrosion rates under a small sample of metal corrosion data in the laboratory was developed to provide a new perspective on how to solve the problem of pipeline corrosion under the condition of insufficient real samples.This approach employed the bagging algorithm to construct a strong learner by integrating several KNN learners.A total of 99 data were collected and split into training and test set with a 9:1 ratio.The training set was used to obtain the best hyperparameters by 10-fold cross-validation and grid search,and the test set was used to determine the performance of the model.The results showed that theMean Absolute Error(MAE)of this framework is 28.06%of the traditional model and outperforms other ensemblemethods.Therefore,the proposed framework is suitable formetal corrosion prediction under small sample conditions.展开更多
Background:Sclerosing adenosis(SA)and breast cancer(BC)often exhibit overlapping clinical,imaging,and pathological characteristics,making them difficult to differentiate.SA may also coexist with BC(SA+BC),including du...Background:Sclerosing adenosis(SA)and breast cancer(BC)often exhibit overlapping clinical,imaging,and pathological characteristics,making them difficult to differentiate.SA may also coexist with BC(SA+BC),including ductal carcinoma in situ(SA-DCIS)and invasive breast cancer(SA-IBC),which complicates diagnosis even when core-needle biopsy(CNB)suggests SA.This study aimed to develop interpretable AI-based binary and ternary classification models that leverage clinical and imaging features to distinguish SA-only from SA+BC and to further differentiate among SA-only,SA-DCIS,and SA-IBC.Methods:We retrospectively analyzed a cohort of 726 patients with SA(January 2006 to December 2021),comprising 537 SA-only and 189 SA+BC cases(90 SA-DCIS,99 SA-IBC).Multiple machine learning algorithms-logistic regression,support vector machine,decision tree,XGBoost,and random forest-were compared using AUC,accuracy,F1-score,and C-index.Model interpretability was assessed with SHAP to elucidate feature contributions and identify key predictors.Additionally,we incorporated an independent external validation cohort consisting of 113 patients to verify the model's effectiveness.展开更多
文摘Identifying the ecological environment suitable for the growth of Thuja sutchuenensis and predicting other potential distribution areas are essential to protect this endangered species. After selecting 24 environmental factors thatcould affect the distribution of T. sutchuenensis, including climate, topography, soil and Normalized DifferenceVegetation Index (NDVI), we adopted the Random Forest-MaxEnt integrated model to analyze our data. Basedon the Random Forest study, the contribution of the mean temperature of the warmest quarter, mean temperatureof the coldest quarter, annual mean temperature and mean temperature of the driest quarter was large. Based onMaxEnt model prediction outputs, the potential distribution map not only identified areas that originallyrecorded T. sutchuenensis, such as Xuanhan County, Kai County and Chengkou County, but also identified highlysuitable distribution areas where T. sutchuenensis may exist, including Wanyuan County, Sichuan Province, andthe junction of Chongqing and Hubei Province. This provides a more explicit geographic range for ex situ conservation and reintroduction of T. sutchuenensis. Our results also indicate that, in addition to climate factors,topography and soil factors are also important environmental factors that affect distribution. This provides a theoretical basis for subsequent laboratory construction to simulate the indoor growth of T. sutchuenensis.
基金supported by the National Natural Science Foundation of China(Grant No.52174062).
文摘Accurate prediction of the internal corrosion rates of oil and gas pipelines could be an effective way to prevent pipeline leaks.In this study,a proposed framework for predicting corrosion rates under a small sample of metal corrosion data in the laboratory was developed to provide a new perspective on how to solve the problem of pipeline corrosion under the condition of insufficient real samples.This approach employed the bagging algorithm to construct a strong learner by integrating several KNN learners.A total of 99 data were collected and split into training and test set with a 9:1 ratio.The training set was used to obtain the best hyperparameters by 10-fold cross-validation and grid search,and the test set was used to determine the performance of the model.The results showed that theMean Absolute Error(MAE)of this framework is 28.06%of the traditional model and outperforms other ensemblemethods.Therefore,the proposed framework is suitable formetal corrosion prediction under small sample conditions.
基金National High Level Hospital Clinical Research Funding,Grant/Award Numbers:2025-PUMCH-A-147,2022-PUMCH-B-039Chinese Academy of Medical Sciences(CAMS)Innovation Fund for Medical Sciences(CIFM),Grant/Award Number:2021-I2M-1-014。
文摘Background:Sclerosing adenosis(SA)and breast cancer(BC)often exhibit overlapping clinical,imaging,and pathological characteristics,making them difficult to differentiate.SA may also coexist with BC(SA+BC),including ductal carcinoma in situ(SA-DCIS)and invasive breast cancer(SA-IBC),which complicates diagnosis even when core-needle biopsy(CNB)suggests SA.This study aimed to develop interpretable AI-based binary and ternary classification models that leverage clinical and imaging features to distinguish SA-only from SA+BC and to further differentiate among SA-only,SA-DCIS,and SA-IBC.Methods:We retrospectively analyzed a cohort of 726 patients with SA(January 2006 to December 2021),comprising 537 SA-only and 189 SA+BC cases(90 SA-DCIS,99 SA-IBC).Multiple machine learning algorithms-logistic regression,support vector machine,decision tree,XGBoost,and random forest-were compared using AUC,accuracy,F1-score,and C-index.Model interpretability was assessed with SHAP to elucidate feature contributions and identify key predictors.Additionally,we incorporated an independent external validation cohort consisting of 113 patients to verify the model's effectiveness.
