Explore the vast range of titles available.

Recent evidence shows that carbon emissions in China are likely to peak ahead of 2030. However, the social and economic impacts of such an early carbon peak have rarely been assessed. Here we focus on the economic costs and health benefits of different carbon mitigation pathways, considering both possible socio-economic futures and varying ambitions of climate policies. We find that an early peak before 2030 in line with the 1.5 °C target could avoid ~118,000 and ~614,000 PM 2.5 attributable deaths under the Shared Socioeconomic Pathway 1, in 2030 and 2050, respectively. Under the 2 °C target, carbon mitigation costs could be more than offset by health co-benefits in 2050, bringing a net benefit of $393–$3,017 billion (in 2017 USD value). This study not only provides insight into potential health benefits of an early peak in China, but also suggests that similar benefits may result from more ambitious climate targets in other countries. Understanding benefits of carbon mitigation is an important impetus for governments to adopt more ambitious climate targets. Here, the authors show positive air quality and health co-benefits are possible if China’s CO 2 emissions peak before 2030.

Distributed denial-of-service (DDoS) attacks pose a significant cybersecurity threat to software-defined networks (SDNs). This paper proposes a feature-engineering- and machine-learning-based approach to detect DDoS attacks in SDNs. First, the CSE-CIC-IDS2018 dataset was cleaned and normalized, and the optimal feature subset was found using an improved binary grey wolf optimization algorithm. Next, the optimal feature subset was trained and tested in Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (k-NN), Decision Tree, and XGBoost machine learning algorithms, from which the best classifier was selected for DDoS attack detection and deployed in the SDN controller. The results show that RF performs best when compared across several performance metrics (e.g., accuracy, precision, recall, F1 and AUC values). We also explore the comparison between different models and algorithms. The results show that our proposed method performed the best and can effectively detect and identify DDoS attacks in SDNs, providing a new idea and solution for the security of SDNs.

Cell death is a fundamental part of life for metazoans. To maintain the balance between cell proliferation and metabolism of human bodies, a certain number of cells need to be removed regularly. Hence, the mechanisms of cell death have been preserved during the evolution of multicellular organisms. Tumorigenesis is closely related with exceptional inhibition of cell death. Mutations or defects in cell death-related genes block the elimination of abnormal cells and enhance the resistance of malignant cells to chemotherapy. Therefore, the investigation of cell death mechanisms enables the development of drugs that directly induce tumor cell death. In the guidelines updated by the Cell Death Nomenclature Committee (NCCD) in 2018, cell death was classified into 12 types according to morphological, biochemical and functional classification, including intrinsic apoptosis, extrinsic apoptosis, mitochondrial permeability transition (MPT)-driven necrosis, necroptosis, ferroptosis, pyroptosis, PARP-1 parthanatos, entotic cell death, NETotic cell death, lysosome-dependent cell death, autophagy-dependent cell death, immunogenic cell death, cellular senescence and mitotic catastrophe. The mechanistic relationships between epigenetic controls and cell death in cancer progression were previously unclear. In this review, we will summarize the mechanisms of cell death pathways and corresponding epigenetic regulations. Also, we will explore the extensive interactions between these pathways and discuss the mechanisms of cell death in epigenetics which bring benefits to tumor therapy.

ARID1A, a component of the SWI/SNF chromatin remodeling complex, is a tumor suppressor with a high frequency of inactivating mutations in many cancers. Therefore, ARID1A deficiency has been exploited therapeutically for treating cancer. Here we show that ARID1A has a synthetic lethal interaction with aurora kinase A (AURKA) in colorectal cancer (CRC) cells. Pharmacological and genetic perturbations of AURKA selectively inhibit the growth of ARID1A-deficient CRC cells. Mechanistically, ARID1A occupies the AURKA gene promoter and negatively regulates its transcription. Cells lacking ARID1A show enhanced AURKA transcription, which leads to the persistent activation of CDC25C, a key protein for G2/M transition and mitotic entry. Inhibiting AURKA activity in ARID1A-deficient cells significantly increases G2/M arrest and induces cellular multinucleation and apoptosis. This study shows a novel synthetic lethality interaction between ARID1A and AURKA and indicates that pharmacologically inhibiting the AURKA–CDC25C axis represents a novel strategy for treating CRC with ARID1A loss-of-function mutations. ARID1A is highly inactivated in cancer. Here, the authors show that ARID1A has a synthetic lethal interaction with AURKA in colorectal cancer cells and that ARID1A deficiency activates the AURKA target CDC25C, whose inhibitors also cause cell death in the ARID1A-deficient cell lines.

Electrochemical carbon monoxide reduction is a promising strategy for the production of value-added multicarbon compounds, albeit yielding diverse products with low selectivities and Faradaic efficiencies. Here, copper single atoms anchored to Ti 3 C 2 T x MXene nanosheets are firstly demonstrated as effective and robust catalysts for electrochemical carbon monoxide reduction, achieving an ultrahigh selectivity of 98% for the formation of multicarbon products. Particularly, it exhibits a high Faradaic efficiency of 71% towards ethylene at −0.7 V versus the reversible hydrogen electrode, superior to the previously reported copper-based catalysts. Besides, it shows a stable activity during the 68-h electrolysis. Theoretical simulations reveal that atomically dispersed Cu–O 3 sites favor the C–C coupling of carbon monoxide molecules to generate the key *CO-CHO species, and then induce the decreased free energy barrier of the potential-determining step, thus accounting for the high activity and selectivity of copper single atoms for carbon monoxide reduction. Electrochemical carbon monoxide reduction is a promising strategy to yield valuable multicarbon products but low selectivities and Faradaic efficiencies are common. Here the authors show single atom copper catalyst supported on MXene with high CO reduction performance and stability.

HighlightsA multiscale structure is realized through layer-by-layer deposition with atom-scale precision via atomic layer depositionFeSiAl@ZnO@Al2O3 exhibits record-high absorption properties in low-frequency bands.The corrosion resistance is improved by the unique multistage oxide barriers.Developing highly efficient magnetic microwave absorbers (MAs) is crucial, and yet challenging for anti-corrosion properties in extremely humid and salt-induced foggy environments. Herein, a dual-oxide shell of ZnO/Al2O3 as a robust barrier to FeSiAl core is introduced to mitigate corrosion resistance. The FeSiAl@ZnO@Al2O3 layer by layer hybrid structure is realized with atomic-scale precision through the atomic layer deposition technique. Owing to the unique hybrid structure, the FeSiAl@ZnO@Al2O3 exhibits record-high microwave absorbing performance in low-frequency bands covering L and S bands with a minimum reflection loss (RLmin) of -50.6 dB at 3.4 GHz. Compared with pure FeSiAl (RLmin of -13.5 dB, a bandwidth of 0.5 GHz), the RLmin value and effective bandwidth of this designed novel absorber increased up to ~ 3.7 and ~ 3 times, respectively. Furthermore, the inert ceramic dual-shells have improved 9.0 times the anti-corrosion property of FeSiAl core by multistage barriers towards corrosive medium and obstruction of the electric circuit. This is attributed to the large charge transfer resistance, increased impedance modulus |Z|0.01 Hz, and frequency time constant of FeSiAl@ZnO@Al2O3. The research demonstrates a promising platform toward the design of next-generation MAs with improved anti-corrosion properties.

Lithium-ion batteries (LIBs) are widely used in electrochemical energy storage and in other fields. However, LIBs are prone to thermal runaway (TR) under abusive conditions, which may lead to fires and even explosion accidents. Given the severity of TR hazards for LIBs, early warning and fire extinguishing technologies for battery TR are comprehensively reviewed in this paper. First, the TR reaction mechanism and hazards of LIBs are discussed. Second, the TR early warning and monitoring methods of LIBs are summarized in five aspects consisting of acoustic, heat, force, electricity, and gas. In addition, to reduce the fire and explosion hazards caused by the TR of LIBs, the highly efficient extinguishing agents for LIBs are summarized. Finally, the early warning technology and fire extinguishing agent are proposed, which provides a reference for the hazard prevention and control of energy storage systems.

Grapes are prone to various diseases throughout their growth cycle, and the failure to promptly control these diseases can result in reduced production and even complete crop failure. Therefore, effective disease control is essential for maximizing grape yield. Accurate disease identification plays a crucial role in this process. In this paper, we proposed a real-time and lightweight detection model called Fusion Transformer YOLO for 4 grape diseases detection. The primary source of the dataset comprises RGB images acquired from plantations situated in North China. Firstly, we introduce a lightweight high-performance VoVNet, which utilizes ghost convolutions and learnable downsampling layer. This backbone is further improved by integrating effective squeeze and excitation blocks and residual connections to the OSA module. These enhancements contribute to improved detection accuracy while maintaining a lightweight network. Secondly, an improved dual-flow PAN+FPN structure with Real-time Transformer is adopted in the neck component, by incorporating 2D position embedding and a single-scale Transformer Encoder into the last feature map. This modification enables real-time performance and improved accuracy in detecting small targets. Finally, we adopt the Decoupled Head based on the improved Task Aligned Predictor in the head component, which balances accuracy and speed. Experimental results demonstrate that FTR-YOLO achieves the high performance across various evaluation metrics, with a mean Average Precision (mAP) of 90.67%, a Frames Per Second (FPS) of 44, and a parameter size of 24.5M. The FTR-YOLO presented in this paper provides a real-time and lightweight solution for the detection of grape diseases. This model effectively assists farmers in detecting grape diseases.

Purpose The claudin 18.2 (CLDN18.2) antigen is frequently expressed in malignant tumors, including pancreatic ductal adenocarcinoma (PDAC). Although CLDN18.2-targeted CAR-T cells demonstrated some therapeutic efficacy in PDAC patients, further improvement is needed. One of the major obstacles might be the abundant cancer-associated fibroblasts (CAFs) in the PDAC tumor microenvironment (TME). Targeting fibroblast activation protein (FAP), a vital characteristic of CAFs provides a potential way to overcome this obstacle. In this study, we explored the combined antitumor activity of FAP-targeted and CLDN18.2-targeted CAR-T cells against PDAC. Methods Novel FAP-targeted CAR-T cells were developed. Sequential treatment of FAP-targeted and CLDN18.2-targeted CAR-T cells as well as the corresponding mechanism were explored in immunocompetent mouse models of PDAC. Results The results indicated that the priorly FAP-targeted CAR-T cells infusion could significantly eliminate CAFs and enhance the anti-PDAC efficacy of subsequently CLDN18.2-targeted CAR-T cells in vivo. Interestingly, we observed that FAP-targeted CAR-T cells could suppress the recruitment of myeloid-derived suppressor cells (MDSCs) and promote the survival of CD8 + T cells and CAR-T cells in tumor tissue. Conclusion In summary, our finding demonstrated that FAP-targeted CAR-T cells could increase the antitumor activities of sequential CAR-T therapy via remodeling TME, at least partially through inhibiting MDSCs recruitment. Sequential infusion of FAP-targeted and CLDN18.2-targeted CAR-T cells might be a feasible approach to enhance the clinical outcome of PDAC.

This study introduces a Sagnac Interferometer (SI) fiber sensor that integrates Polarization-Maintaining Fibers (PMFs) with a Tilted Fiber Bragg Grating (TFBG) for the dual-parameter measurement of strain and lateral pressure. By incorporating a 6° TFBG with PMFs into the SI sensor, its sensitivity is significantly enhanced, enabling advanced multi-parameter sensing capabilities. The sensor demonstrates a temperature sensitivity of −1.413 nm/°C and a lateral pressure sensitivity of −4.264 dB/kPa, as validated by repeated experiments. The results exhibit excellent repeatability and high precision, underscoring the sensor’s potential for robust and accurate multi-parameter sensing applications.