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As the Western world’s policy towards China has been changing in recent years, the international public opinion, that China comes into contact with, is also deteriorating, which has a serious negative impact on forming the international image of China and on shaping soft power. In the context of the rapid development of modern information technology, sports events like the Winter Olympics are often accompanied by large-scale media coverage and propaganda, and it is important to deal with the negative coverage of the Olympics involving China in the Western media and to build a good national image through the Winter Olympics. This study focuses on the specific content of Western media reports on China during the 2022 Beijing Winter Olympics cycle, and summarizes and analyzes the characteristics of “distorted facts”, “far-fetched” and “prominent political demands” in the negative reports on China in the Western media during the Winter Olympics. Based on the importance of guiding international public opinion and covering by media of such large sporting events as the Olympic Games, it is urgent and necessary to improve China’s international public opinion crisis response mechanism and enhance national and international media capacity. In this regard, this paper proposes four specific response strategies: strengthening the international communication capacity of mainstream media, enhancing the role of the government in guiding and making decisions, strengthening cooperation with international media; and making full use of our own media platforms.

Aim Materials and Methods Results Conclusions To investigate the association between metabolically healthy obesity (MHO) and left ventricular geometric remodelling in Chinese children.This cross‐sectional study used data from two population‐based samples in China, including 2871 children aged 6–11 years. Weight status was defined based on body mass index according to the World Health Organization growth chart. Metabolic status was defined based on the 2018 consensus‐based criteria proposed by Damanhoury et al. Obes Rev 2018;19:1476–1491 (blood pressure, lipids and glucose). Left ventricular geometric remodelling was determined as concentric remodelling, eccentric hypertrophy, and concentric hypertrophy. Multinomial logistic regression analysis was used to determine odds ratios (ORs) and 95% confidence intervals (CIs) for the association between categories of weight and metabolic status and left ventricular geometric remodelling.Compared with children with metabolically healthy normal weight, those with MHO had higher odds of left ventricular geometric remodelling, with adjusted ORs (95% CIs) of 2.01 (1.23–3.28) for concentric remodelling, 6.36 (4.03–10.04) for eccentric hypertrophy, and 17.07 (7.97–36.58) for concentric hypertrophy. Corresponding ORs (95% CIs) were 2.35 (1.47–3.75), 10.85 (7.11–16.55), and 18.56 (8.63–39.94), respectively, for children with metabolically unhealthy obesity. In contrast, metabolically unhealthy normal weight was not associated with higher odds of left ventricular geometric remodelling. Findings were consistent in sensitivity analyses that used different definitions of weight and metabolic status and left ventricular geometric remodelling.Children with MHO had higher odds of left ventricular geometric remodelling than their metabolically healthy normal weight counterparts. Our findings suggest MHO may not be a benign condition for cardiac health in children. [ABSTRACT FROM AUTHOR]

Dearomatization has emerged as a powerful tool for rapid construction of 3D molecular architectures from simple, abundant, and planar (hetero)arenes. The field has evolved beyond simple dearomatization driven by new synthetic technology development. With the renaissance of photocatalysis and expansion of the activation mode, the last few years have witnessed impressive developments in innovative photochemical dearomatization methodologies, enabling skeletal modifications of dearomatized structures. They offer truly efficient and useful tools for facile construction of highly complex structures, which are viable for natural product synthesis and drug discovery. In this review, we aim to provide a mechanistically insightful overview on these innovations based on the degree of skeletal alteration, categorized into dearomative functionalization and skeletal editing, and to highlight their synthetic utilities. [ABSTRACT FROM AUTHOR]

A New Concept to Study Substitute Structures in Economics and Operations Models In "S-Convexity and Gross Substitutability," Chen and Li propose a novel concept of S-convex functions defined on continuous spaces, which extends a key concept of M-natural-convex functions in discrete convex analysis. They develop a host of fundamental properties and characterizations of S-convex functions. In a parametric maximization model with a box constraint, they show that the set of optimal solutions is nonincreasing in the parameters if the objective function is S-concave and prove the necessity of S-concavity under some conditions. The monotonicity result finds notable inventory models. Interestingly, the authors show that S-concavity is the correct notion characterizing gross substitutability, an important concept in economics for markets with divisible goods. We propose a new concept of S-convex functions (and its variant, semistrictly quasi-S- (SSQS)-convex functions) to study substitute structures in economics and operations models with continuous variables. We develop a host of fundamental properties and characterizations of S-convex functions, including various preservation properties, conjugate relationships with submodular and convex functions, and characterizations using Hessians. For a divisible market, we show that the utility function satisfies gross substitutability if and only if it is S-concave under mild regularity conditions. In a parametric maximization model with a box constraint, we show that the set of optimal solutions is nonincreasing in the parameters if the objective function is (SSQS-) S-concave. Furthermore, we prove that S-convexity is necessary for the property of nonincreasing optimal solutions under some conditions. Our monotonicity result is applied to analyze two notable inventory models: a single-product inventory model with multiple unreliable suppliers and a classic multiproduct dynamic inventory model with lost sales. Funding: This work was supported by the National Science Foundation [Grants CMMI-1538451 and CMMI-1635160] and the gift fund from JD.com. Supplemental Material: The online companion is available at https://doi.org/10.1287/opre.2022.2394. [ABSTRACT FROM AUTHOR]

The accurate prediction of adverse drug reactions (ADRs) is essential for comprehensive drug safety evaluation. Pre-trained deep chemical language models have emerged as powerful tools capable of automatically learning molecular structural features from large-scale datasets, showing promising capabilities for the downstream prediction of molecular properties. However, the performance of pre-trained chemical language models in predicting ADRs, especially idiosyncratic ADRs induced by marketed drugs, remains largely unexplored. In this study, we propose MoLFormer-XL, a pre-trained model for encoding molecular features from canonical SMILES, in conjunction with a CNN-based model to predict drug-induced QT interval prolongation (DIQT), drug-induced teratogenicity (DIT), and drug-induced rhabdomyolysis (DIR). Our results demonstrate that the proposed model outperforms conventional models applied in previous studies for predicting DIQT, DIT, and DIR. Notably, an analysis of the learned linear attention maps highlights amines, alcohol, ethers, and aromatic halogen compounds as strongly associated with the three types of ADRs. These findings hold promise for enhancing drug discovery pipelines and reducing the drug attrition rate due to safety concerns. [ABSTRACT FROM AUTHOR]

Predicting disease-related microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) is crucial to find new biomarkers for the prevention, diagnosis, and treatment of complex human diseases. Computational predictions for miRNA/lncRNA-disease associations are of great practical significance, since traditional experimental detection is expensive and time-consuming. In this paper, we proposed a consensual machine-learning technique-based prediction approach to identify disease-related miRNAs and lncRNAs by high-order proximity preserved embedding (HOPE) and eXtreme Gradient Boosting (XGB), named HOPEXGB. By connecting lncRNA, miRNA, and disease nodes based on their correlations and relationships, we first created a heterogeneous disease-miRNA-lncRNA (DML) information network to achieve an effective fusion of information on similarities, correlations, and interactions among miRNAs, lncRNAs, and diseases. In addition, a more rational negative data set was generated based on the similarities of unknown associations with the known ones, so as to effectively reduce the false negative rate in the data set for model construction. By 10-fold cross-validation, HOPE shows better performance than other graph embedding methods. The final consensual HOPEXGB model yields robust performance with a mean prediction accuracy of 0.9569 and also demonstrates high sensitivity and specificity advantages compared to lncRNA/miRNA-specific predictions. Moreover, it is superior to other existing methods and gives promising performance on the external testing data, indicating that integrating the information on lncRNA-miRNA interactions and the similarities of lncRNAs/miRNAs is beneficial for improving the prediction performance of the model. Finally, case studies on lung, stomach, and breast cancers indicate that HOPEXGB could be a powerful tool for preclinical biomarker detection and bioexperiment preliminary screening for the diagnosis and prognosis of cancers. HOPEXGB is publicly available at https://github.com/airpamper/HOPEXGB.

Temperature sensors are one of the most fundamental sensors and are found in industrial, environmental, and biomedical applications. The traditional approach of reading the resistive response of Positive Temperature Coefficient thermistors at DC hindered their adoption as wide-range temperature sensors. Here, we present a large-area thermistor, based on a flexible and stretchable short carbon fibre incorporated Polydimethylsiloxane composite, enabled by a radio frequency sensing interface. The radio frequency readout overcomes the decades-old sensing range limit of thermistors. The composite exhibits a resistance sensitivity over 1000 °C -1 , while maintaining stability against bending (20,000 cycles) and stretching (1000 cycles). Leveraging its large-area processing, the anisotropic composite is used as a substrate for sub-6 GHz radio frequency components, where the thermistor-based microwave resonators achieve a wide temperature sensing range (30 to 205 °C) compared to reported flexible temperature sensors, and high sensitivity (3.2 MHz/°C) compared to radio frequency temperature sensors. Wireless sensing is demonstrated using a microstrip patch antenna based on a thermistor substrate, and a battery-less radio frequency identification tag. This radio frequency-based sensor readout technique could enable functional materials to be directly integrated in wireless sensing applications., (© 2024. The Author(s).)