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\"Drawing upon numerous manuscripts from China and Central Asia, the articles presented in this volume by leading scholars in the field examine a broad range of topics on the multi-lingual, multi-religious, and multi-ethnic communities along the Silk Road in the medieval period, and cover such topics as the social history of Kucha, book history in Dunhuang, the spread of Manichaeism, the political history of Turkic and Khotanese Kingdoms, and the travelogue of the Buddhist pilgrim Xuanzang. They demonstrate that Han Chinese, Khotanese, Sogdians, Tocharians, Tibetans, and Uyghurs have all contributed to constructing a sophisticated international network across Asia. Contributors are: Bi Bo, Chao-jung Ching, Jean Pierre Drège, Ogihara Hirotoshi, Xiaohe Ma, Nicholas Sims-Williams, Xinjiang Rong, Tokio Takata, Xiaofu Wang, Wenkan Xu, Yutaka Yoshida, Lishuang Zhu, Peter Zieme.\"--Provided by publisher.

Satellite observations reveal a substantial decline in September Arctic sea ice extent since 1979, which has played a leading role in the observed recent Arctic surface warming and has often been attributed, in large part, to the increase in greenhouse gases. However, the most rapid decline occurred during the recent global warming hiatus period. Previous studies are often focused on a single mechanism for changes and variations of summer Arctic sea ice extent, and many are based on short observational records. The key players for summer Arctic sea ice extent variability at multidecadal/centennial time scales and their contributions to the observed summer Arctic sea ice decline are not well understood. Here a multiple regression model is developed for the first time, to the author’s knowledge, to provide a framework to quantify the contributions of three key predictors (Atlantic/Pacific heat transport into the Arctic, and Arctic Dipole) to the internal low-frequency variability of Summer Arctic sea ice extent, using a 3,600-y-long control climate model simulation. The results suggest that changes in these key predictors could have contributed substantially to the observed summer Arctic sea ice decline. If the ocean heat transport into the Arctic were to weaken in the near future due to internal variability, there might be a hiatus in the decline of September Arctic sea ice. The modeling results also suggest that at multidecadal/centennial time scales, variations in the atmosphere heat transport across the Arctic Circle are forced by anticorrelated variations in the Atlantic heat transport into the Arctic. Significance The observed decline in summer Arctic sea ice has often been attributed, in large part, to the increase in greenhouse gases. However, the contributions from internal low-frequency variability in the climate system are not well understood. Here a multiple regression model is developed for the first time, to the author’s knowledge, to quantify the contributions of three key predictors on the internal low-frequency variability of summer Arctic sea ice extent. If the ocean heat transport into the Arctic were to weaken in the near future due to internal variability, there might be a hiatus in the decline of September Arctic sea ice, and a delay in attaining a summer ice-free Arctic. This plausible scenario with broad ecological and economic impacts should not be ignored.

This paper aims to propose a table tennis target detection (TD) method based on deep learning (DL) and multi-scale feature fusion (MFF) to improve the detection accuracy of the ball in table tennis competition, optimize the training process of athletes, and improve the technical level. In this paper, DL technology is used to improve the accuracy of table tennis TD through MFF guidance. Initially, based on the FAST Region-based Convolutional Neural Network (FAST R-CNN), the TD is carried out in the table tennis match. Then, through the method of MFF guidance, different levels of feature information are fused, which improves the accuracy of TD. Through the experimental verification on the test set, it is found that the mean Average Precision (mAP) value of the target detection algorithm (TDA) proposed here reaches 87.3%, which is obviously superior to other TDAs and has higher robustness. The DL TDA combined with the proposed MFF can be applied to various detection fields and can help the application of TD in real life.

The re-emerging mpox (formerly monkeypox) virus (MPXV), a member of Orthopoxvirus genus together with variola virus (VARV) and vaccinia virus (VACV), has led to public health emergency of international concern since July 2022. Inspired by the unprecedent success of coronavirus disease 2019 (COVID-19) mRNA vaccines, the development of a safe and effective mRNA vaccine against MPXV is of high priority. Based on our established lipid nanoparticle (LNP)-encapsulated mRNA vaccine platform, we rationally constructed and prepared a panel of multicomponent MPXV vaccine candidates encoding different combinations of viral antigens including M1R, E8L, A29L, A35R, and B6R. In vitro and in vivo characterization demonstrated that two immunizations of all mRNA vaccine candidates elicit a robust antibody response as well as antigen-specific Th1-biased cellular response in mice. Importantly, the penta- and tetra-component vaccine candidates AR-MPXV5 and AR-MPXV4a showed superior capability of inducing neutralizing antibodies as well as of protecting from VACV challenge in mice. Our study provides critical insights to understand the protection mechanism of MPXV infection and direct evidence supporting further clinical development of these multicomponent mRNA vaccine candidates.

The re-emergence of Zika virus (ZIKV) in the Western Hemisphere has resulted in global public health crisis since 2015. ZIKV preferentially infects and targets human neural progenitor cells (hNPCs) and causes fetal microcephaly upon maternal infection. hNPCs not only play critical roles during fetal brain development, but also persist in adult brain throughout life. Yet the mechanism of innate antiviral immunity in hNPCs remains largely unknown. Here, we show that ZIKV infection triggers the abundant production of virus-derived small interfering RNAs in hNPCs, but not in the more differentiated progenies or somatic cells. Ablation of key RNAi machinery components significantly enhances ZIKV replication in hNPCs. Furthermore, enoxacin, a broad-spectrum antibiotic that is known as an RNAi enhancer, exerts potent anti-ZIKV activity in hNPCs and other RNAi-competent cells. Strikingly, enoxacin treatment completely prevents ZIKV infection and circumvents ZIKV-induced microcephalic phenotypes in brain organoid models that recapitulate human fetal brain development. Our findings highlight the physiological importance of RNAi-mediated antiviral immunity during the early stage of human brain development, uncovering a novel strategy to combat human congenital viral infections through enhancing RNAi.

A purely data‐driven and transformer‐based model with a novel self‐attention mechanism (3D‐Geoformer) is used to make predictions by adopting a rolling predictive manner similar to that in dynamical coupled models. The 3D‐Geoformer yields a successful prediction of the 2021 second‐year cooling conditions that followed the 2020 La Niña event, including covarying anomalies of surface wind stress and three‐dimensional (3D) upper‐ocean temperature, the reoccurrence of negative subsurface temperature anomalies in the eastern equatorial Pacific and a corresponding turning point of sea surface temperature (SST) evolution in mid‐2021. The reasons for the successful prediction with interpretability are explored comprehensively by performing sensitivity experiments with modulating effects on SST due to wind and subsurface thermal forcings being separately considered in the input predictors for prediction. A comparison is also conducted with physics‐based modeling, illustrating the suitability and effectiveness of 3D‐Geoformer as a new platform for El Niño and Southern Oscillation studies. Plain Language Summary The tropical Pacific experienced the prolonged cooling conditions during 2020–2022 (often called a triple La Niña), which exerted great impacts on the weather and climate globally. However, physics‐derived coupled models still have difficulty in accurately making long‐lead real‐time predictions for sea surface temperature (SST) evolution in the tropical Pacific. With the rapid development of deep learning‐based modeling, purely data‐driven models provide an innovative way for SST predictions. Here, a transformer‐based deep learning model is used to evaluate its performance in predicting the evolution of SST in the tropical Pacific during 2020–2022 and explore process representations that are important for SST evolution during 2021, including subsurface thermal effect and surface wind forcing on SST, the crucial factors determining the second‐year prolonged La Niña conditions and turning point of SST evolution. A comparison is made between the completely differently constructed physics‐derived dynamical coupled model and the pure‐data driven deep learning model, showing they both can be used for predictions of SST evolution in the 2021 second‐year cooling conditions. This indicates that it is necessary to adequately represent the thermocline feedback in predictive models, either in dynamical coupled models or purely data‐driven models, so that El Niño and Southern Oscillation predictions can be improved. Key Points A transformer‐based deep learning model is used for El Niño‐Southern Oscillation multivariate prediction in a rolling predictive manner The purely data‐driven model successfully predicts the 2021 second‐year La Niña and turning point of temperature evolution in mid‐2021 Applications of purely data‐driven model for process representations and understanding are demonstrated as in dynamical coupled models

Brain metastasis (BM) is the most common and serious complication of breast cancer (BC). There is significant interest in investigating the crosstalk between BC cells and immune cells. β-elemene is the main pharmacodynamic component of , a traditional Chinese medicine that is commonly used for the clinical treatment and prevention of various tumors. However, the specific underlying mechanism of β-elemene in BC-BM is still unclear. An intracardiac (ICT) injection model was used to establish specific BC-BM cells, then an intracarotid (ICD) injection model was used to verify the inhibitory effect of β-elemene in BC-BM. Tumor-cell-conditioned media, a primary microglia co-culture model, and an recruitment experiment were used to explore crosstalk between BC cells and immune cells. TMT-based quantitative proteomic, ELISA, IF, and other molecular biotechnologies were used to investigate the mechanisms. The BC-BM cells established in our study not only increased BM rates but also exhibit mesenchymal phenotype and activated the JAK-STAT signaling pathway. Microglia, particularly M2 microglia, were enriched in BM lesions and secreted high levels of both IL6 and CCL2. Hypersecretory IL6 reversed the MET process of BC cells by regulating the JAK2/STAT3 signaling pathway to promote colonization in the brain. Increased levels of CCL2 significantly recruited monocytic myeloid-derived suppressor cells (M-MDSCs) to induce an immunosuppressive brain microenvironment. β-elemene could significantly inhibit BC-BM in mice by regulating the IL6/STAT3 signaling pathway and suppressing the M-MDSC recruitment. Our work first demonstrated that β-elemene regulated the IL6/STAT3 axis and M-MDSC recruitment to reconstruct immunosuppressive brain microenvironment to suppress BC-BM.

AMOC variations are often thought to propagate with the Kelvin wave speed, resulting in a short time lead between high and low latitudes AMOC variations. However as shown in this paper using a coupled climate model (GFDL CM2.1), with the existence of interior pathways of North Atlantic Deep Water (NADW) from Flemish Cap to Cape Hatteras as that observed recently, AMOC variations estimated in density space propagate with the advection speed in this region, resulting in a much longer time lead (several years) between subpolar and subtropical AMOC variations and providing a more useful predictability. The results suggest that AMOC variations have significant meridional coherence in density space, and monitoring AMOC variations in density space at higher latitudes might reveal a stronger signal with a several‐year time lead.

NOABSTRACTThis study aimed to explore undergraduates’ knowledge, attitude, and practice/behavior of human papillomavirus (HPV) vaccination, as well as the essential influencing factors for vaccination decision-making.Through cluster and convenience sampling, 2000 undergraduates from the Nursing and Language department of a university in Shanghai were sent a self-designed questionnaire. Chi-square tests, independent sample t-test/ANOVE, and multiple linear regression were used to investigate participants’ knowledge and attitude on HPV vaccination, as well as the factors that predicted potential action to receive and promote HPV vaccination in the future.The mean HPV knowledge score was 5.027 out of 10. Health science students showed a significantly higher knowledge mean score than the non-health science students (P < 0.000). There was a statistically difference in HPV vaccination attitude among undergraduates in different grades (P < 0.05). Awareness of cervical cancer and worries about the risk of cervical cancer were the significant predictors of willingness to receive and promote HPV vaccination in the future.It would take time for a new health product to be aware, understood, accepted, and received. Education providing and information sharing are expected to break the dawn and make the procedure processed.

NOABSTRACTTo explore the effect of peer education on improving the knowledge, attitude, practice level (KAP) to prevent human papilloma virus (HPV) infection among college students.The knowledge, attitude/belief, and practice level of 536 grade I college students from a university in Shanghai were surveyed and analyzed using a self-designed questionnaire—the HPV Infection and Prevention KAP Questionnaire.Many college students heard about HPV vaccine (49.70% before peer education and 100% after peer education); however, few of them were vaccinated (2.82% before peer education and 5.23% after peer education). Knowledge, attitude/belied, and practice of HPV infection prevention were positively related (P < 0.05). Peer education was effective in improving college students’ KAP level of HPV infection prevention (P < 0.05).Peer education could be used as a strategy in colleges to improve student’s KAP level of HPV infection prevention. College students may also be encouraged to spread their influence to society.