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Sand and dust storms are significant natural disasters which affect East Asia and China in spring, occurring from March to May. Performances of CMA-CUACE/Dust, an operational Asian sand and dust storm numerical forecasting system of CMA since 2006, are analyzed. It’s found that the model overestimates in Central Asia, underestimates in northern Mongolia, and diffuses too quickly in downwind areas far away from the source area, especially in Northern China, Korean Peninsula and Japan, resulting in low peak values or less lingering time there for very extreme sand and dust storm events. A scale-adaptive dust emission scheme is applied by resolving the mean wind speed of the model grid into sectional one which can account for values larger than the mean value by Weibull integration function. This significant aspect is crucial because the dust emission is the third power of the wind in the dust emission scheme. New wind erosion database is also adopted which consists of the updated desertification by using twenty-year surface data and new parameters deduced from site observations in the heart of Gobi Desert, determining the size distribution of the emitted dust together with the soil texture data sampling from main deserts in China.After evaluation for the strongest sand and dust episode of 13-17 in March 2021 in East Asia in the past decade, and the consistent run in the same operational environment from 1 March to 31 May in 2023, it is found that the updated CMA-CUACE/Dust effectively improves disadvantages such as overestimation in Central Asia, underestimation in northern Mongolia, and rapid dissipation in China. The predicted peak dust concentration of the extreme episode closely matches observations in China both in the source area and in Shanghai after a 4-day transportation. Threat score (TS) of three-month forecast run also indicates that the improved model shows good consistency and continuity in forecast results across various forecast lengths. TS for 1-5 days with different forecast lengths is significantly higher than that of the previous operational system and surpasses those of Korean dust model—ADAM (the Asian Dust Aerosol Model). Furthermore, the missing rate is significantly reduced, while the false alarm rate remains almost unchanged. TS for episodes beyond the level of sand and storms are all above 0.3, with some exceeding 0.5. All these findings show that the improved model performs much better than the previous one.

Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease, with a global prevalence of 25%. Patients with NAFLD are more likely to suffer from advanced liver disease, cardiovascular disease, or type II diabetes. However, unfortunately, there is still a shortage of FDA-approved therapeutic agents for NAFLD. Lian-Mei-Yin (LMY) is a traditional Chinese medicine formula used for decades to treat liver disorders. It has recently been applied to type II diabetes which is closely related to insulin resistance. Given that NAFLD is another disease involved in insulin resistance, we hypothesize that LMY might be a promising formula for NAFLD therapy. Herein, we verify that the LMY formula effectively reduces hepatic steatosis in diet-induced zebrafish and NAFLD model mice in a time- and dose-dependent manner. Mechanistically, LMY suppresses Yap1-mediated Foxm1 activation, which is crucial for the occurrence and development of NAFLD. Consequently, lipogenesis is ameliorated by LMY administration. In summary, the LMY formula alleviates diet-induced NAFLD in zebrafish and mice by inhibiting Yap1/Foxm1 signaling-mediated NAFLD pathology.

Umbilical cord blood (UCB) is an advantageous source for hematopoietic stem/progenitor cell (HSPC) transplantation, yet the current strategies for large-scale and cost-effective UCB-HSPC preparation are still unavailable. To overcome these obstacles, we systematically evaluate the feasibility of our newly identified CH02 peptide for ex vivo expansion of CD34+ UCB-HSPCs. We herein report that the CH02 peptide is specifically enriched in HSPC proliferation via activating the FLT3 signaling. Notably, the CH02-based cocktails are adequate for boosting 12-fold ex vivo expansion of UCB-HSPCs. Meanwhile, CH02-preconditioned UCB-HSPCs manifest preferable efficacy upon wound healing in diabetic mice via bidirectional orchestration of proinflammatory and anti-inflammatory factors. Together, our data indicate the advantages of the CH02-based strategy for ex vivo expansion of CD34+ UCB-HSPCs, which will provide new strategies for further development of large-scale HSPC preparation for clinical purposes.

In malignancies, fibroblast growth factor receptors (FGFRs) signaling is reinforced through overexpression of fibroblast growth factors (FGFs) or their receptors. FGFR2 has been proposed as a target for cancer therapy, because both the expression and activation of FGFR2 are boosted in various malignant carcinomas. Although several chemicals have been designed against FGFR2, they did not exhibit enough specificity and might bring potential accumulated toxicity. In this study, we developed an epitope peptide (P5) and its cyclic derivative (DcP5) based on the structure of FGF2 to limit the activation of FGFR2. The anticancer activities of P5 and DcP5 were examined in vitro and in vivo. Our results demonstrated that P5 significantly inhibited the cell proliferation in FGFR2-dependent manner in DU145 cells and retarded tumor growth in DU145 xenograft model with negligible toxicity toward normal organs. Further investigations found that the Gln4 and Glu6 residues of P5 bind to FGFR2 to abolish its activation. Moreover, we developed the P5 cyclic derivative, DcP5, which achieved reinforced stability and anticancer activity in vivo. Our findings suggest P5 and its cyclic derivative DcP5 as potential candidates for anticancer therapy.

Despite the implementation of numerous interventions to enhance urban traffic safety, the estimation of the risk of traffic crashes resulting in life-threatening and economic costs remains a significant challenge. In light of the above, an online inference method for traffic crash risk based on the self-developed TAR-DETR and WOA-SA-SVM methods is proposed. The method's robust data inference capabilities can be applied to autonomous mobile robots and vehicle systems, enabling real-time road condition prediction, continuous risk monitoring, and timely roadside assistance. First, a self-developed dataset for urban traffic object detection, named TAR-1, is created by extracting traffic information from major roads around Hainan University in China and incorporating Russian car crash news. Secondly, we develop an innovative Context-Guided Reconstruction Feature Network-based Urban Traffic Objects Detection Model (TAR-DETR). The model demonstrates a detection accuracy of 76.8% for urban traffic objects, which exceeds the performance of other state-of-the-art object detection models. The TAR-DETR model is employed in TAR-1 to extract urban traffic risk features, and the resulting feature dataset was designated as TAR-2. TAR-2 comprises six risk features and three categories. A new inference algorithm based on WOA-SA-SVM is proposed to optimize the parameters (C, g) of the SVM, thereby enhancing the accuracy and robustness of urban traffic crash risk inference. The algorithm is developed by combining the Whale Optimization Algorithm (WOA) and Simulated Annealing (SA), resulting in a Hybrid Bionic Intelligent Optimization Algorithm. The TAR-2 dataset is inputted into a Support Vector Machine (SVM) optimized using a hybrid algorithm and used to infer the risk of urban traffic crashes. The proposed WOA-SA-SVM method achieves an average accuracy of 80% in urban traffic crash risk inference. [ABSTRACT FROM AUTHOR]

Multispectral and hyperspectral image fusion (MS/HS fusion) aims to generate a high-resolution hyperspectral (HRHS) image by fusing a high-resolution multispectral (HRMS) and a low-resolution hyperspectral (LRHS) images. The deep unfolding-based MS/HS fusion method is a representative deep learning paradigm due to its excellent performance and sufficient interpretability. However, existing deep unfolding-based MS/HS fusion methods only rely on a fixed linear degradation model, which focuses on modeling the relationships between HRHS and HRMS, as well as HRHS and LRHS. In this paper, we break free from this observation model framework and propose a new observation model. Firstly, the proposed observation model is built based on the convolutional sparse coding (CSC) technique, and then a proximal gradient algorithm is designed to solve this model. Secondly, we unfold the iterative algorithm into a deep network, dubbed as MHF-CSCNet, where the proximal operators are learned using convolutional neural networks. Finally, all trainable parameters can be automatically learned end-to-end from the training pairs. Experimental evaluations conducted on various benchmark datasets demonstrate the superiority of our method both quantitatively and qualitatively compared to other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The high resolution numerical simulation for rotor/wing-body interaction flow-field in hover is crucial for the accurate prediction of aerodynamic performance of the tilt-rotor aircraft, and is the one of the important issues of helicopter aerodynamics. Based on the moving-embedded grid technique, a CFD simulation method for predicting unsteady flow-field is established. Reynolds-averaged Navier-Stokes(RANS) equations are used as governing equations and Spalart-Allmaras turbulence model is employed, implicit LU-SGS scheme is adopted. On this basis, the unsteady interaction flow-field of the rotor/wing-body are investigated in deep research, and the special phenomenon“fountain effect” is obtained. The different preset flaps/ailerons affect on decreasing rotor/wing-body aerodynamic interference is studied. The results show that the total thrust is lossed about 16.6%, the aerodynamic interference is lowest when the preset flaps/ailerons angle is 45°, and the total thrust loss is reduced to 13.8% from 16.6%.

There are a considerable number of accuracy indexes for surrogate models to measure. However, most of these indexes are not capable of comparing surrogate models of different data types. The drawbacks of the existing indexes are firstly discussed. Then a new index named “Mean Normalized Absolute Error(MNEA)” is introduced. Kriging models of 11 different types of aerodynamic parameters from a tactical missile model are built, with the adding-points design of experiment method and cross-validation verification methods. The effectiveness of MNEA is verified by comparing the MNEA value and the correlation plots of the example, which proves that MNEA can be used to compare the accuracy of surrogate models of different data types.

Late Eocene artiodactyl and creodont tracks have been reported from the Jianchuan Basin, southeastern Tibetan Plateau, representing the first and the oldest reported mammal tracks in Yunnan Province, China. Sedimentary facies analysis indicates that tracks from the upper Baoxiangsi Formation and the lower Shuanghe Formation occurred in lake shore and flood plain environments, respectively. Based on the morphological analysis, the artiodactyl tracks from the Baoxiangsi Formation are attributed to Anoplotheriipus zeuctus. Combined with fossil records and morphological characteristics, the artiodactyl and creodont tracks were probably produced by entelodonts and mesonychids, respectively. The superimposition of artiodactyl tracks with varying sizes indicates coexistence of juveniles and adults as well as their gregarious behaviour. The co-occurrence of artiodactyl tracks and creodont tracks suggests the predator–prey relationship and ecological chain during the late Eocene. The presence of mammal tracks in the Jianchuan Basin coincides with the climate change from dry to wet in the late Eocene, implying the climatic and ecological effects of the uplift of the Tibetan Plateau. [ABSTRACT FROM AUTHOR]