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Abstract Background To explore the association between periodontitis and all-cause as well as cause-specific mortality rates in U.S. adults with hyperlipidemia. Methods Participants were extracted from NHANES during 1988–1994, 1999–2004 and 2009–2014 periods. To assess the association between moderate-to-severe periodontitis and mortality rates for both all-cause and cause-specific mortality, hazard ratios (HRs), time ratios (TRs), and their respective 95% confidence intervals (CIs) were calculated using Cox proportional hazards and Weibull accelerated failure time (AFT) models. Results Over a median follow-up duration of 11.83 years, 4,623 deaths of 16,848 participants were recorded. Multivariate Cox regression and AFT analyses showed moderate-to-severe periodontitis were associated with an increased risk of all-cause (HR: 1.31, 95% CI: 1.20–1.44, P

Abstract Malignant tumors remain a primary cause of human mortality. Among the various treatment modalities for neoplasms, tumor vaccines have consistently shown efficacy and promising potential. These vaccines offer advantages such as specificity, safety, and tolerability, with mRNA vaccines representing promising platforms. By introducing exogenous mRNAs encoding antigens into somatic cells and subsequently synthesizing antigens through gene expression systems, mRNA vaccines can effectively induce immune responses. Katalin Karikó and Drew Weissman were awarded the 2023 Nobel Prize in Physiology or Medicine for their great contributions to mRNA vaccine research. Compared with traditional tumor vaccines, mRNA vaccines have several advantages, including rapid preparation, reduced contamination, nonintegrability, and high biodegradability. Tumor-targeted therapy is an innovative treatment modality that enables precise targeting of tumor cells, minimizes damage to normal tissues, is safe at high doses, and demonstrates great efficacy. Currently, targeted therapy has become an important treatment option for malignant tumors. The application of mRNA vaccines in tumor-targeted therapy is expanding, with numerous clinical trials underway. We systematically outline the targeted delivery mechanism of mRNA vaccines and the mechanism by which mRNA vaccines induce anti-tumor immune responses, describe the current research and clinical applications of mRNA vaccines in tumor-targeted therapy, and forecast the future development trends of mRNA vaccine application in tumor-targeted therapy.

In the field of autonomous vehicles (AVs), accurately discerning commander intent and executing linguistic commands within a visual context presents a significant challenge. This paper introduces a sophisticated encoder-decoder framework, developed to address visual grounding in AVs. Our Context-Aware Visual Grounding (CAVG) model is an advanced system that integrates five core encoders—Text, Emotion, Image, Context, and Cross-Modal—with a multimodal decoder. This integration enables the CAVG model to adeptly capture contextual semantics and to learn human emotional features, augmented by state-of-the-art Large Language Models (LLMs) including GPT-4. The architecture of CAVG is reinforced by the implementation of multi-head cross-modal attention mechanisms and a Region-Specific Dynamic (RSD) layer for attention modulation. This architectural design enables the model to efficiently process and interpret a range of cross-modal inputs, yielding a comprehensive understanding of the correlation between verbal commands and corresponding visual scenes. Empirical evaluations on the Talk2Car dataset, a real-world benchmark, demonstrate that CAVG establishes new standards in prediction accuracy and operational efficiency. Notably, the model exhibits exceptional performance even with limited training data, ranging from 50% to 75% of the full dataset. This feature highlights its effectiveness and potential for deployment in practical AV applications. Moreover, CAVG has shown remarkable robustness and adaptability in challenging scenarios, including long-text command interpretation, low-light conditions, ambiguous command contexts, inclement weather conditions, and densely populated urban environments.

Abstract Both historical observations and recent modeling studies reveal a faster warming in the Arctic compared to the Antarctic. To understand the role of the Antarctic Circumpolar Circulation (ACC) in this warming asymmetry, we simulate the climate mean state and climate response to doubled CO2 under different climate mean ACC states by closing or opening the Drake Passage (DP) with the Community Earth System Model. From closed to open DP, a stronger climate mean ACC leads to a stronger climate mean Atlantic Meridional Overturning Circulation (AMOC), as well as a colder Antarctic but a warmer Arctic in the climate mean state. The less climate mean sea ice coverage in a warmer Arctic implies less extensive sea ice melting under global warming. This causes a reduced asymmetry in warming between the two poles in response to the doubled CO2.

Abstract Background Studies have shown that CCR7, an important inflammatory factor, can promote the proliferation and metastasis of oral squamous cell carcinoma (OSCC), but its role in the tumor microenvironment (TME) remains unclear. This paper explores the role of CCR7 in the TME of OSCC. Methods In this work, we constructed CCR7 gene knockout mice and OSCC mouse models. Single-cell RNA sequencing (scRNA-seq) and bioinformatics were used to analyze the differences in the OSCC microenvironment between three CCR7 gene knockout mice (KO) and three wild-type mice (WT). Immunohistochemistry, immunofluorescence staining, and flow cytometry were used to analyze the expression of key genes in significantly different cell types between the KO and WT groups. An in vitro experiment was used to verify the effect of CCR7 on M2 macrophage polarization. Results In the mouse OSCC models, the tumor growth rate in the KO group was significantly lower than that in the WT group. Eight main cell types (including tumor cells, fibroblasts, macrophages, granulocytes, T cells, endothelial cells, monocytes, and B cells) were identified by Seurat analysis. The scRNA-seq results showed that the proportion of tumor cells was lower, but the proportion of inflammatory cells was significantly higher in the KO group than in the WT group. CellPhoneDB analysis results indicated a strong interaction relationship between tumor cells and macrophages, T cells, fibroblasts, and endothelial cells. Functional enrichment results indicated that the expression level of the Dusp1 gene in the KO group was generally higher than that in the WT group in various cell types. Macrophage subclustering results indicated that the proportion of M2 macrophages in the KO group was lower than that in the WT group. In vitro experimental results showed that CCR7 can promote M2 macrophage polarization, thus promoting the proliferation, invasion and migration of OSCC cells. Conclusions CCR7 gene knockout can significantly inhibit the growth of mouse oral squamous cell carcinoma by promoting the polarization of M2 macrophages.

Background: Head and neck cancer is the sixth most common malignancy worldwide, and oral squamous cell carcinoma (OSCC) is the most common head and neck cancer, being one of the leading causes of cancer morbidity and mortality worldwide. CC Chemokine receptor 7(CCR7) is a multifunctional G protein-coupled trans-membrane chemokine that affects immune cell chemotaxis, migration, and cancer progression through its interaction with its ligands C-C motif chemokine ligand 19(CCL19) and C-C motif chemokine ligand 21(CCL21). Numerous studies have demonstrated the involvement of CCR7 in the malignant progression of a variety of cancers, reflecting the pro-cancer properties of CCR7. The Cancer Genome Atlas data suggests CCR7 has elevated expression in oral cancer. Specifically, CCR7 expression in tumor microenvironment (TME) may regulate the ability of some immune cells to engage in anti-tumor immune responses. Since CD8+ T cells have become a key immunotherapeutic target, the role of CCR7 in antitumor immune response of naïve CD8+ T cells in TME has not been thoroughly investigated. Methods: A CCR7 knockout mouse model was constructed, and the mechanism of ccr7 on the regulation of tumor microenvironment by naïve CD8+ T cells was verified under the guidance of single-cell RNA sequencing combined with in vivo animal experiments and in vitro cell experiments. Results: CCR7 is knocked out with impaired tumor growth and altered CD8+ T cell profiles, revealing the importance of this protein in OSCC. Conclusions: Inhibition of CCR7 enhances CD8+ T cell activation, proliferation, and anti-tumor function, suggesting its potential as a therapeutic target.

Abstract Quantitative precipitation forecasting in numerical weather prediction (NWP) models is contingent upon physicals parameterization schemes. However, uncertainties abound due to limited knowledge of the precipitating processes, leading to degraded forecasting skills. In light of this, our study explores the application of a Swin‐Transformer based deep learning (DL) model as a supplementary tool for enhancing the mapping trajectory between the NWP fundamental variables and the most downstream variable precipitation. Constrained by the observational satellite precipitation product from NOAA CPC Morphing Technique (CMORPH), the DL model serves as the post‐processing tool that can better resolve the precipitation patterns compared to solely based on NWP estimation. Compared to the baseline Weather Research and Forecasting simulation, the DL post‐processing effectively extracts features over meteorological variables, leading to improved precipitation skill scores of 21.7%, 60.5%, and 45.5% for light rain, moderate rain, and heavy rain, respectively, on an hourly basis. We also evaluate two case studies under different driven synoptic conditions and show promising results in estimating heavy precipitation during strong convective precipitation events. Overall, the proposed DL model can provide a vital reference for capturing precipitation‐triggering mechanisms and enhancing precipitation forecasting skills. Additionally, we discuss the sensitivities of the fundamental meteorological variables used in this study, training strategies, and performance limitations.

Abstract Precipitation over the Tibetan Plateau is modulated by both the South Asian summer monsoon and the mid-latitude westerly winds. Using observations and numerical simulations, this study highlights the out-of-phase relationships between the mid-latitude westerly wind speeds and the west-east migration of Tibetan Plateau rainy season. When the westerly jet shifts northward before July, the weakening of westerly winds over the Tibetan Plateau leads to a westward shift of low-level warm air center and a westward extension of moist air convergence. Consequently, rainy season advances westward. Conversely, the southward shift of westerly jet after August leads to a strengthening of westerly winds and an eastward retreat of rainy season. Numerical simulations confirm the dominant role of mid-latitude westerly winds on the rain belt migration over the Tibetan Plateau, and further indicate that the timing of the westward extension of rain belt is determined by the weakening of mid-latitude westerly winds.

In recent years, jamming strategies for Synthetic Aperture Radar (SAR) pertaining to target detection and identification, such as the creation of false targets, electromagnetic (EM) deception, and signal spoofing, have been increasingly emphasized. Distinct from traditional SAR jamming approaches, the introduction of an innovative artificial material cloak in SAR target jamming presents augmented capabilities. These methods demonstrate a proficient redirection of incident EM waves in specific or arbitrary directions, effectively masking the vital information linked to critical targets. This study introduces a broadband SAR target jamming system employing an information metasurface that incorporates intelligent information processing algorithms in conjunction with a space-time-coding digital metasurface, endowing it with the capacity to adeptly modulate incident EM waves. This integration facilitates a versatile approach to jamming, enabling the deployment of multi-mode protective measures against critical targets. The conducted simulation and experiment results validate the system’s ability to adjustably produce EM deception and generate multiple false targets independently of the SAR system. The outcomes of this research significantly advance the practicality of SAR protection strategies, pushing the boundaries toward more dynamic, broadband, and controllable scenarios, thereby substantially improving the concealment of critical targets in highly sensitive conflict areas.

The authors identify a predictable component of the South Asian summer monsoon that is associated with the variability of the monsoon trough modulated by tropical sea surface temperatures.

Synthetic aperture radar (SAR) is a high-resolution imaging radar. With the deteriorating electromagnetic environment, SAR systems are susceptible to various forms of electromagnetic interference, including deception jamming. This jamming notably impacts SAR signal processing and subsequently worsens the quality of acquired images. Chaotic frequency modulation (CFM) signals could effectively counteract deception jamming. Nevertheless, due to the inadequate band-limited performance of CFM signals, higher oversampling factors are needed for achieving optimal low sidelobe levels, leading to increased system costs and excessively high data rates. Additionally, not all chaotic sentences meet the CFM signal requirements. In response, this paper proposes a novel signal modulation method called chaotic-nonlinear frequency modulation (C-NLFM) that enhances band-limited performance. The optimum parameters for C-NLFM signals are selected using the particle swarm optimization (PSO) algorithm. In this way, C-NLFM signals attain ideal low sidelobe levels even when faced with reduced oversampling factors. Meanwhile, this chaotic coding mode retains its capability to effectively suppress deception jamming. Moreover, C-NLFM signals demonstrate versatility in adapting to various chaotic sequences, thereby enhancing their flexibility to modify the signals as required. Through comprehensive simulations, data analysis, and a semi-physical experiment, the effectiveness and superiority of this proposed method have been confirmed.

In the context of resource constraints, how different dimensions of entrepreneurial alertness affect the entrepreneurial opportunity recognition of new ventures is an important issue worth studying. From entrepreneurial cognition theory and bricolage theory perspectives, we systematically investigate the intrinsic relationships among entrepreneurial alertness, entrepreneurial bricolage, entrepreneurial passion, and entrepreneurial opportunity recognition. Further, it explored the intrinsic mechanism of role in exploring entrepreneurial opportunity recognition. This study applied stepwise regression analyses and the Bootstrap method to test the hypotheses on a sample of 295 questionnaires of the new venture. The findings revealed that entrepreneurial alertness is positively related to entrepreneurial opportunity recognition. Entrepreneurial bricolage is positively related to entrepreneurial opportunity recognition. Entrepreneurial bricolage partially mediates between entrepreneurial alertness and entrepreneurial opportunity recognition. Entrepreneurial passion positively moderates the relationship between entrepreneurial bricolage and entrepreneurial opportunity identification. The study guides new ventures to enhance entrepreneurial alertness and reasonably use entrepreneurial bricolage to explore entrepreneurial opportunities.

Yutao Wang, China Medical University, ChinaThe tumor microenvironment (TME) has been shown to impact the prognosis of tumors in patients including cutaneous melanoma (CM); however, not all components of TME are important. Given the aforementioned situation, the functional immune cell contents correlated with CM patient prognosis are needed to optimize present predictive models and reflect the overall situation of TME. We developed a novel risk score named core tumor-infiltrating immune cell score (cTICscore), which showed certain advantages over existing biomarkers or TME-related signatures in predicting the prognosis of CM patients. Furthermore, we explored a new gene signature named cTILscore−related module gene score (cTMGs), based on four identified TME-associated genes (GCH1, GZMA, PSMB8, and PLAAT4) showing a close correlation with the cTICscore, which was generated by weighted gene co-expression network analysis and least absolute shrinkage and selection operator analysis to facilitate clinical application. Patients with low cTMGs had significantly better overall survival (OS, P = 0.002,< 0.001, = 0.002, and = 0.03, respectively) in the training and validating CM datasets. In addition, the area under the curve values used to predict the immune response in four CM cohorts were 0.723, 0.723, 0.754, and 0.792, respectively, and that in one gastric cohort was 0.764. Therefore, the four-gene signature, based on cTICscore, might improve prognostic information, serving as a predictive tool for CM patients receiving immunotherapy.cutaneous melanoma, tumor microenvironment, prognosis, immunotherapy, cTICscore

Abstract Capturing TC intensity change remains a great challenge for most state‐of‐the‐art operational forecasting systems. Recent studies found TC intensity forecasts are sensitive to three‐dimensional ocean dynamics and air‐sea interface processes beneath extreme winds. By performing a series of numerical simulations based on hierarchical atmosphere‐wave‐ocean (AWO) coupling configurations, we showed how atmosphere‐ocean and atmosphere‐sea wave coupling can affect the intensity of super typhoon Mangkhut (2018). The AWO coupled model can simulate TC‐related strong winds, oceanic cold wake, and wind waves with high fidelity. With atmosphere‐ocean (AO) coupling implemented, the simulated maximum surface wind speed is reduced by 7 m s−1 compared to the atmosphere‐only run, due to TC‐induced oceanic cold wakes in the former experiment. In the fully coupled AWO simulations, the wind speed deficit can be completely compensated by the wave‐air coupling effect. Further analyses showed that, in the AWO experiment, two mechanisms contribute to the improvement of TC intensity. First, in the high wind scenario (>28 m s−1), the surface drag coefficient reaches an asymptotic level, assisting extreme wind speed to be maintained within the eyewall. Second, the wind speed distribution is modulated and becomes broader; higher wind within the TC area helps to offset the negative effect due to leveling off of the heat exchange coefficient as wind speed increases. Overall, the simulated TC in the AWO run can extract 8–9% more total heat energy from the ocean to maintain its strength, compared to that from the AO experiment.

Abstract There has been an interdecadal shift towards a less humid state in Sahel summer rainfall since the 1960s. The decreased Sahel summer rainfall was associated with enhanced summer latent heating over the South Asian and western Pacific summer monsoon region and anomalous zonal-vertical cell of the Asian summer monsoon circulation, indicating that the latent heating plays a significant role in the change in Sahel rainfall. The effects of the latent heating over different monsoon domains on the Sahel rainfall are investigated through several model experiments. Results show that the remote monsoon heating mainly affects Sahel rainfall by generating changes in the zonal-vertical atmospheric circulation.

Teleconnections between different flavours of El Niño and Arctic climate change are inherently uncertain in the context of warming. Here, the authors show that Central Pacific El Niño contributes to summer Arctic cooling and sea-ice increase via an Equator-Arctic teleconnection on interannual timescales.

In order to explore the influence factors on perceived waiting time, a multiple linear regression model has been used to quantitatively describe the relationship between perceived waiting time and various factors. The model is established with 234 data, which is surveyed with questionnaire at three stops in Harbin, China. The results show that several certain factors (“trip purpose - where to”, “presence of a companion - whether one has a companion or not”, “having a timing device - whether one has a timing device or not”, “riding frequency - how many times one takes one line per week” and “waiting behaviour - what one does while waiting for a bus”) have significant influence on perceived waiting time, which confirms previous findings and supports transferability of results. The significance of “waiting mood - how about the mood while waiting for a bus” and “reserved waiting time - how long one will wait” are confirmed for the first time in this study. In contrast to previous studies, “waiting time interval - for how long in one day” is a negative variable and socioeconomic variables are non-significant. And it is found that the relationship between perceived waiting time and passengers’ satisfaction with the waiting time follows a decreasing exponential distribution. With this model, the variation trend of the section, where passenger satisfaction value is larger than 0 is obviously steeper than the section smaller than 0. Such result proves that passenger mood with short waiting time is more sensitive than with longer waiting time. And the borderline perceived waiting time, distinguishing satisfied from dissatisfied passengers is proven to be 7.87 minutes when assignment interval of satisfaction is (-25.25], when satisfaction is positive (larger than 0), the accuracy being 70.30%, while the accuracy is 82.71% for negative satisfaction (less than 0).