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Over the past two decades, many Latin America and Caribbean (LAC) countries have deepened their economic relationship with China by accepting large amounts of Chinese investments and signing up for China's Belt and Road Initiative (BRI). However, Colombia has not followed such a path. Using cross‐border data on trade, investment, and economic agreements, this article demonstrates that Colombia's under‐engagement with China is driven by Colombia's lower competitiveness in the commodities sectors, where Chinese demand for LAC products is highest. Colombia's limited competitiveness has led to lower exports to China and lower amounts of Chinese investment. The low level of bilateral economic activities has resulted in a lack of incentives for Colombia to sign economic agreements with China, as well as general public skepticism toward China and more favorable attitudes toward the United States even though this trend has been on the decline since 2016. Colombia can benefit from strengthening ties with China, without distancing itself from the United States, on issues pertaining to energy transition and the development of rural infrastructure, which will help reduce inequality and broaden export diversification in the medium to long term. [ABSTRACT FROM AUTHOR]

Background: Dynamic contrast‐enhanced (DCE) MRI commonly outperforms diffusion‐weighted (DW) MRI in breast cancer discrimination. However, the side effects of contrast agents limit the use of DCE‐MRI, particularly in patients with chronic kidney disease. Purpose: To develop a novel deep learning model to fully exploit the potential of overall b‐value DW‐MRI without the need for a contrast agent in predicting breast cancer molecular subtypes and to evaluate its performance in comparison with DCE‐MRI. Study Type: Prospective. Subjects: 486 female breast cancer patients (training/validation/test: 64%/16%/20%). Field Strength/Sequence: 3.0 T/DW‐MRI (13 b‐values) and DCE‐MRI (one precontrast and five postcontrast phases). Assessment: The breast cancers were divided into four subtypes: luminal A, luminal B, HER2+, and triple negative. A channel‐dimensional feature‐reconstructed (CDFR) deep neural network (DNN) was proposed to predict these subtypes using pathological diagnosis as the reference standard. Additionally, a non‐CDFR DNN (NCDFR‐DNN) was built for comparative purposes. A mixture ensemble DNN (ME‐DNN) integrating two CDFR‐DNNs was constructed to identify subtypes on multiparametric MRI (MP‐MRI) combing DW‐MRI and DCE‐MRI. Statistical Tests: Model performance was evaluated using accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Model comparisons were performed using the one‐way analysis of variance with least significant difference post hoc test and the DeLong test. P < 0.05 was considered significant. Results: The CDFR‐DNN (accuracies, 0.79 ~ 0.80; AUCs, 0.93 ~ 0.94) demonstrated significantly improved predictive performance than the NCDFR‐DNN (accuracies, 0.76 ~ 0.78; AUCs, 0.92 ~ 0.93) on DW‐MRI. Utilizing the CDFR‐DNN, DW‐MRI attained the predictive performance equal (P = 0.065 ~ 1.000) to DCE‐MRI (accuracies, 0.79 ~ 0.80; AUCs, 0.93 ~ 0.95). The predictive performance of the ME‐DNN on MP‐MRI (accuracies, 0.85 ~ 0.87; AUCs, 0.96 ~ 0.97) was superior to those of both the CDFR‐DNN and NCDFR‐DNN on either DW‐MRI or DCE‐MRI. Data Conclusion: The CDFR‐DNN enabled overall b‐value DW‐MRI to achieve the predictive performance comparable to DCE‐MRI. MP‐MRI outperformed DW‐MRI and DCE‐MRI in subtype prediction. Level of Evidence: 2 Technical Efficacy Stage: 1 [ABSTRACT FROM AUTHOR]

This article reviews research on antihypertensive medications including vasodilators, β-blockers, and angiotensin II receptor antagonists that have been approved by the FDA (Food and Drug Administration) since 2011. It covers the discovery of lead compounds, mechanisms of action, structural modifications, structure-activity relationships, and synthesis methods, aiming to serve as a reference for the development of antihypertensive drugs. [ABSTRACT FROM AUTHOR]

The COVID-19, AIDS, influenza, and other infectious viral diseases have a significant impact on human health, and new antiviral drugs are continuously emerging to combat these diseases. Herein, aiming at anti-SARS-CoV-2, anti-HIV, and anti-influenza virus, the latest research progress of small-molecule drugs is reported in this paper. The discovery process, mechanism of action, synthesis process, and structure--activity relationship research of each drug are systematically expounded to promote antiviral drug research and development. [ABSTRACT FROM AUTHOR]

Permanent magnet synchronous motor (PMSM) has been widely used in industry due to the advantages of high efficiency, high power density and high reliability. In this paper, a finite-control-set model-free predictive current control (FCS-MFPCC) based on extended control set is proposed for permanent magnet synchronous motor drives. At first, the mathematical model of PMSM is analyzed and the principle of finite-control-set model predictive current control (FCS-MPCC) is elaborated. Then, the traditional FCS-MFPCC based on linear extended state observer (LESO) is introduced. In order to tackle the issue of dissatisfactory steady-state performance of the traditional FCS-MFPCC, a control-set extension method based on the discrete space vector modulation (DSVM) is utilized, by which, the number of voltage vectors can be increased to 25. Following this, a fast optimization strategy is proposed to solve the problem of high computation burden caused by the extended control set. The principle and procedure of implementation of the strategy is explained in detail. Finally, the effectiveness and superiority of the proposed control strategy are verified by comparing the traditional and the proposed FCS-MFPCC methods based on a 500 W PMSM experimental test rig. The results show that the proposed control strategy can significantly enhance the steady-state performance of the system. The total harmonic distortion of stator current can be reduced from 10.07% to 6.48%.