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Adaptive digital back-propagation (A-DBP) is a promising candidate for mitigating Kerr nonlinearity due to its ability to estimate the optimal nonlinear scaling factor adaptively. However, the adaptive process relying on the gradient-dependent algorithm is prone to fluctuation, leading to extra iterations or even divergence and resulting in huge computational efforts in A-DBP. In this paper, an improved A-DBP algorithm with long-term memory (LTM) is proposed, employing root mean square propagation (RMSProp) to achieve low-complexity and high-robustness compensation performances. The A-DBP-LTM algorithm based on RMSProp was numerically validated through the simulated transmission of 69 Gbaud DP-16QAM over 2000 km and further verified through an experiment involving 26-λ 63 Gbaud DP-16QAM transmission over 1200 km. Compared with conventional digital back-propagation and A-DBP based on a gradient-descent algorithm, our proposed method allows substantial complexity reductions of 31.35% and 58.47%, respectively. Furthermore, high robustness in only a few iterations and a 0.33 dB improvement in the optical signal–noise ratio penalty were also experimentally demonstrated.

Utilising panel data from Chinese listed companies between 2008 and 2020, this study employs propensity score matching (PSM) in conjunction with a multi-temporal difference-in-differences (DID) model to examine the causal impacts of cross-border mergers and acquisitions (M&As) on the organisational resilience of enterprises. The findings reveal that while cross-border M&As augment company risk-taking and short-term financial volatility, they also bolster long-term growth, thereby enhancing overall organisational resilience. Cross-border M&As are particularly beneficial for bolstering organisational resilience in state-owned enterprises, non-manufacturing firms, and companies located in the eastern and central regions of China. Moreover, adhering to the principles of corporate social responsibility and possessing substantial market power are found to enhance the impact of cross-border M&As on organisational resilience. The results of this research hold important practical implications for companies seeking to improve organisational resilience and achieve sustainable development.

This study adopted the genuine progress indicator (GPI) method to measure the quality of economic development of 29 provinces in China from 1979 to 2018. Next, under the empirical framework of the environmental Kuznets curve, we used a system-generalized method of moments estimation and the panel auto-regressive distributed lag model to estimate the short-term and long-term relationships between regional carbon emissions and GPI, respectively, and compare the results to that of the calculation based on GDP. The study found that: (1) Carbon dioxide has an “inverted U-shaped” relationship with both GDP and GPI; (2) When the level of pollution is climbing, if GPI is taken as the policy goal, the rate of pollution emissions is slower, and the peak of pollution emissions reached is lower. After reaching the peak, GPI and GDP will reduce emissions at almost the same speed; (3) The pursuit of GPI growth causes less environmental damage than the pursuit of GDP growth under the same conditions. The results are significant for guiding the formulation of policies that aim to optimize the index system of high-quality development and better promote sustainable development.

Neutrophil extracellular traps (NETs), intricate reticular structures released by activated neutrophils, play a pivotal regulatory role in the pathogenesis of malignant tumors. Lung cancer is one of the most prevalent malignancies globally, with persistently high incidence and mortality rates. Recent studies have revealed that NETs dynamically modulate the tumor microenvironment through unique pathological mechanisms, exhibiting complex immunoregulatory characteristics during the progression of lung cancer, and this discovery has increasingly become a focal point in tumor immunology research. This paper provides a comprehensive review of the latest advancements in NETs research related to lung cancer, offering an in-depth analysis of their impact on lung cancer progression, their potential diagnostic value, and the current state of research on targeting NETs for lung cancer prevention and treatment. The aim is to propose novel strategies to enhance therapeutic outcomes and improve the prognosis for lung cance

Macrophages have crucial roles in immune responses and tumor progression, exhibiting diverse phenotypes based on environmental cues. In the present study, the impact of cinobufagin (CB) on macrophage polarization and the consequences on tumor-associated behaviors were investigated. Morphological transformations of THP-1 cells into M0, M1 and M2 macrophages were observed, including distinct changes in the size, shape and adherence properties of these cells. CB treatment inhibited the viability of A549 and LLC cells in a concentration-dependent manner, with an IC50 of 28.8 and 30.12 ng/ml, respectively. CB at concentrations of <30 ng/ml had no impact on the viability of M0 macrophages and lung epithelial (BEAS-2B) cells. CB influenced the expression of macrophage surface markers, reducing CD206 positivity in M2 macrophages without affecting CD86 expression in M1 macrophages. CB also altered certain expression profiles at the mRNA level, notably downregulating macrophage receptor with collagenous structure (MARCO) expression in M2 macrophages and upregulating tumor necrosis factor-α and interleukin-1β in both M0 and M1 macrophages. Furthermore, ELISA analyses revealed that CB increased the levels of pro-inflammatory cytokines in M1 macrophages and reduced the levels of anti-inflammatory factors in M2 macrophages. CB treatment also attenuated the migration and invasion capacities of A549 and LLC cells stimulated by M2 macrophage-conditioned medium. Additionally, CB modulated peroxisome proliferator-activated receptor γ (PPARγ) and MARCO expression in M2 macrophages and epithelial-mesenchymal transition in A549 cells, which was partially reversed by rosiglitazone, a PPARγ agonist. Finally, CB and cisplatin treatments hindered tumor growth in vivo, with distinct impacts on animal body weight and macrophage marker expression in tumor tissues. In conclusion, the results of the present study demonstrated that CB exerted complex regulatory effects on macrophage polarization and tumor progression, suggesting its potential as a modulator of the tumor microenvironment and a therapeutic for cancer treatment.

The immunosuppressive tumor microenvironment (TME) in lung cancer, driven in part by M2-polarized tumor-associated macrophages (TAMs), contributes to worse prognosis and supports tumor progression. Cinobufagin (CB), an active compound in cinobufotalin injections, has demonstrated potential antitumor effects by modulating macrophage activity. This study investigated the mechanism by which CB influences glucose metabolism and polarization in M2 TAMs by focusing on the regulation of HIF-1α. Human THP-1 monocytes were differentiated into M2 macrophages by stimulation with interleukin-4 at 20 ng/mL and then treated with cinobufagin at 2 μM, either alone or together with the HIF-1α activator DMOG at 1 mM. HIF-1α hydroxylation and ubiquitination were evaluated by Western blot and co-immunoprecipitation. Glycolytic activity was determined by measuring uptake of the glucose analogue 2-NBDG, extracellular lactate levels and expression of GLUT1, PKM2, LDHA and MCT1. M2 polarization markers CD206, Arg-1 and IL-10 were quantified by qRT-PCR, and TGF-β and IL-10 secretion was measured by ELISA. PD-L1 expression was assessed by Western blot, immunofluorescence and chromatin immunoprecipitation. Finally, conditioned media from treated macrophages were applied to A549 cells to evaluate migration through wound-healing assays and invasion using Transwell inserts, and to HUVECs to quantify tube formation. Using DMOG, an HIF-1α activator, we stimulated glycolysis in M2 macrophages, promoting their immunosuppressive polarization and elevating PD-L1 expression, a checkpoint protein associated with immune evasion. CB treatment reversed this effect by increasing HIF-1α hydroxylation and ubiquitination, leading to decreased HIF-1α stability, glucose uptake, and lactate production in M2 macrophages. Additionally, CB pre-treatment of M2 macrophages reduced the secretion of the cytokines TGF-β and IL-10, thereby limiting lung cancer cell migration, invasion, and angiogenesis. These findings suggest that CB suppresses M2 macrophage-mediated tumor support by targeting HIF-1α and glycolysis, thereby reprogramming the TME toward an anti-tumor state. This highlights CB's potential of CB in the treatment of lung cancer by countering immunosuppressive macrophage activity.

中性粒细胞胞外诱捕网（neutrophil extracellular traps, NETs）作为中性粒细胞活化后释放的网状复合结构，在恶性肿瘤病理进程中发挥关键调控作用。肺癌是全球范围内最常见的恶性肿瘤之一，发病率和死亡率均居高不下。近年研究发现，NETs通过其独特的病理机制参与肿瘤微环境动态调控，在肺癌演进过程中呈现出复杂的免疫调节特征，这一发现已逐渐成为肿瘤免疫学研究的热点领域。本文系统地梳理了NETs在肺癌领域的最新研究进展，深度剖析了NETs对肺癌发生发展的影响和在肺癌诊断中的潜在价值，以及靶向NETs防治肺癌的研究现状，旨在为提高肺癌患者治疗疗效和改善预后提供新的思路。

Current on-board chips usually have different computing power, which means multiple training processes are needed for adapting the same learning-based algorithm to different chips, costing huge computing resources. The situation becomes even worse for 3D perception methods with large models. Previous vision-centric 3D perception approaches are trained with regular grid-represented feature maps of fixed resolutions, which is not applicable to adapt to other grid scales, limiting wider deployment. In this paper, we leverage the Polar representation when constructing the BEV feature map from images in order to achieve the goal of training once for multiple deployments. Specifically, the feature along rays in Polar space can be easily adaptively sampled and projected to the feature in Cartesian space with arbitrary resolutions. To further improve the adaptation capability, we make multi-scale contextual information interact with each other to enhance the feature representation. Experiments on a large-scale autonomous driving dataset show that our method outperforms others as for the good property of one training for multiple deployments.

Robotic perception requires the modeling of both 3D geometry and semantics. Existing methods typically focus on estimating 3D bounding boxes, neglecting finer geometric details and struggling to handle general, out-of-vocabulary objects. 3D occupancy prediction, which estimates the detailed occupancy states and semantics of a scene, is an emerging task to overcome these limitations. To support 3D occupancy prediction, we develop a label generation pipeline that produces dense, visibility-aware labels for any given scene. This pipeline comprises three stages: voxel densification, occlusion reasoning, and image-guided voxel refinement. We establish two benchmarks, derived from the Waymo Open Dataset and the nuScenes Dataset, namely Occ3D-Waymo and Occ3D-nuScenes benchmarks. Furthermore, we provide an extensive analysis of the proposed dataset with various baseline models. Lastly, we propose a new model, dubbed Coarse-to-Fine Occupancy (CTF-Occ) network, which demonstrates superior performance on the Occ3D benchmarks. The code, data, and benchmarks are released at https://tsinghua-mars-lab.github.io/Occ3D/.

Current state-of-the-art point cloud-based perception methods usually rely on large-scale labeled data, which requires expensive manual annotations. A natural option is to explore the unsupervised methodology for 3D perception tasks. However, such methods often face substantial performance-drop difficulties. Fortunately, we found that there exist amounts of image-based datasets and an alternative can be proposed, i.e., transferring the knowledge in the 2D images to 3D point clouds. Specifically, we propose a novel approach for the challenging cross-modal and cross-domain adaptation task by fully exploring the relationship between images and point clouds and designing effective feature alignment strategies. Without any 3D labels, our method achieves state-of-the-art performance for 3D point cloud semantic segmentation on SemanticKITTI by using the knowledge of KITTI360 and GTA5, compared to existing unsupervised and weakly-supervised baselines.