Correction: Resveratrol reduces the apoptosis induced by cigarette smoke extract by upregulating MFN2

Correction: Resveratrol reduces the apoptosis induced by cigarette smoke extract by upregulating MFN2</p

Confusion matrices of the SVM and LSTM classification results on the RAW and BR datasets.

(A) Results on the RAW dataset: SVM; (B) Results on the RAW dataset: LSTM; (C) Results on the BR dataset: SVM; (D) Results on the BR dataset: LSTM.</p

Accuracy comparison of LSTM and contrast models using different datasets.

Accuracy comparison of LSTM and contrast models using different datasets.</p

Detailed statistics of patient audios collected in the hospital.

Detailed statistics of patient audios collected in the hospital.</p

Structure of the LSTM classifier.

Structure of the LSTM classifier.</p

ROC curves comparison of SVM and LSTM results under different augmentation datasets.

(A) SVM; (B) LSTM.</p

Accuracy, precision, recall and F1-score comparison between SVM and contrast models on the RAW dataset.

Accuracy, precision, recall and F1-score comparison between SVM and contrast models on the RAW dataset.</p

Image2_Predicting the Geographical Distribution of Malaria-Associated Anopheles dirus in the South-East Asia and Western Pacific Regions Under Climate Change Scenarios.TIFF

Malaria occurrence is highly related to the geographical distribution of Anopheles dirus (An. dirus) in the South-East Asia Region and Western Pacific Region (SEAR/WPR). Future climate change has been shown to alter the geographical distribution of malaria vectors. However, few studies have investigated the impact of climate change on the potential distribution of An. dirus in the SEAR/WPR. We considered future climate and land-use data under two climate change scenarios for Representative Concentration Pathways (RCP 4.5 and RCP 8.5) and population data from five Shared Socioeconomic Pathways (SSPs), by using three machine learning models, namely, Random Forest (RF), Boosted Regression Trees (BRT), and Maximum entropy (Maxent) to project the geographical distribution of An. Dirus and to estimate the exposed population. A pseudo-absence dataset was generated based on the relationships between model performance and the distance from the pseudo-absence point to the occurrence point in order to improve model accuracy for projection of the Environmentally Suitable Area (ESA) and exposed human population. The results show that the pseudo-absence data corresponding to the distance of 250 km are appropriate for modeling. The RF method ultimately proved to have the highest accuracy. The predicted ESA of An. dirus would mainly be distributed across Myanmar, Thailand, the southern and eastern part of India, Vietnam, the northern part of Cambodia, and the southern part of Laos. The future ESA is estimated to be reduced under the RCP 4.5 climate change scenario. In the 2070s under RCP 8.5, the reduction of ESA is even greater, especially in Thailand (loss of 35.49 10,000 square kilometers), Myanmar (26.24), Vietnam (17.52), and India (15), which may prevent around 282.6 million people from the risk of malaria under the SSP3 scenarios in the SEAR/WPR. Our predicted areas and potential impact groups for An. dirus under future climate change may provide new insights into regional malaria transmission mechanisms and deployment of malaria control measures based on local conditions in the SEAR/WPR’s.</p

Notations.

Notations.</p

Structure of CFCS for classifying bronchitis and pneumonia in children.

Structure of CFCS for classifying bronchitis and pneumonia in children.</p