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\"Functional Pavements is a collection of papers presented at the 6th Chinese-European Workshop (CEW) on Functional Pavement Design (Nanjing, China, October 18-21, 2020). The focus of the CEW series is on field tests, laboratory test methods and advanced analysis techniques, and cover analysis, material development and production, experimental characterization, design and construction of pavements. The main areas covered by the book include: Asphalt binders for flexible pavements Asphalt mixture evaluation and performance Pavement construction and maintenance Pavement Surface Properties and Vehicle Interaction Cementitious materials for rigid pavements Pavement geotechnics and environment Functional Pavements aims at contributing to the establishment of a new generation of pavement design methodologies in which rational mechanics principles, advanced constitutive models and advanced material characterization techniques shall constitute the backbone of the design process. The book will be much of interest to professionals, academics and practitioners in pavement engineering and related disciplines as it should assist them in providing improved road pavement infrastructure to their stakeholders.\"-- Provided by publisher.

Autologous chimeric antigen receptor (CAR) T cell therapies targeting CD19 have high efficacy in large B cell lymphomas (LBCLs), but long-term remissions are observed in less than half of patients, and treatment-associated adverse events, such as immune effector cell-associated neurotoxicity syndrome (ICANS), are a clinical challenge. We performed single-cell RNA sequencing with capture-based cell identification on autologous axicabtagene ciloleucel (axi-cel) anti-CD19 CAR T cell infusion products to identify transcriptomic features associated with efficacy and toxicity in 24 patients with LBCL. Patients who achieved a complete response by positron emission tomography/computed tomography at their 3-month follow-up had three-fold higher frequencies of CD8 T cells expressing memory signatures than patients with partial response or progressive disease. Molecular response measured by cell-free DNA sequencing at day 7 after infusion was significantly associated with clinical response ( P = 0.008), and a signature of CD8 T cell exhaustion was associated ( q = 2.8 × 10 −149 ) with a poor molecular response. Furthermore, a rare cell population with monocyte-like transcriptional features was associated ( P = 0.0002) with high-grade ICANS. Our results suggest that heterogeneity in the cellular and molecular features of CAR T cell infusion products contributes to variation in efficacy and toxicity after axi-cel therapy in LBCL, and that day 7 molecular response might serve as an early predictor of CAR T cell efficacy. Single-cell transcriptomics reveals that the heterogeneity of anti-CD19 CAR T cell infusion products contributes to variability in clinical response, early molecular response and development of immune effector cell-associated neurotoxicity syndrome in patients with large B cell lymphomas.

Genes that drive the proliferation, survival, invasion and metastasis of malignant cells have been identified for many human cancers 1 – 4 . Independent studies have identified cell death pathways that eliminate cells for the good of the organism 5 , 6 . The coexistence of cell death pathways with driver mutations suggests that the cancer driver could be rewired to activate cell death using chemical inducers of proximity (CIPs). Here we describe a new class of molecules called transcriptional/epigenetic CIPs (TCIPs) that recruit the endogenous cancer driver, or a downstream transcription factor, to the promoters of cell death genes, thereby activating their expression. We focused on diffuse large B cell lymphoma, in which the transcription factor B cell lymphoma 6 (BCL6) is deregulated 7 . BCL6 binds to the promoters of cell death genes and epigenetically suppresses their expression 8 . We produced TCIPs by covalently linking small molecules that bind BCL6 to those that bind to transcriptional activators that contribute to the oncogenic program, such as BRD4. The most potent molecule, TCIP1, increases binding of BRD4 by 50% over genomic BCL6-binding sites to produce transcriptional elongation at pro-apoptotic target genes within 15 min, while reducing binding of BRD4 over enhancers by only 10%, reflecting a gain-of-function mechanism. TCIP1 kills diffuse large B cell lymphoma cell lines, including chemotherapy-resistant, TP53 -mutant lines, at EC 50 of 1–10 nM in 72 h and exhibits cell-specific and tissue-specific effects, capturing the combinatorial specificity inherent to transcription. The TCIP concept also has therapeutic applications in regulating the expression of genes for regenerative medicine and developmental disorders. A new class of molecules can recruit downstream transcription factors or endogenous cancer drivers to cell death promoters and activate the expression of these genes.

Remote sensing image scene classification is an important task of remote sensing image interpretation, which has recently been well addressed by the convolutional neural network owing to its powerful learning ability. However, due to the multiple types of geographical information and redundant background information of the remote sensing images, most of the CNN-based methods, especially those based on a single CNN model and those ignoring the combination of global and local features, exhibit limited performance on accurate classification. To compensate for such insufficiency, we propose a new dual-model deep feature fusion method based on an attention cascade global–local network (ACGLNet). Specifically, we use two popular CNNs as the feature extractors to extract complementary multiscale features from the input image. Considering the characteristics of the global and local features, the proposed ACGLNet filters the redundant background information from the low-level features through the spatial attention mechanism, followed by which the locally attended features are fused with the high-level features. Then, bilinear fusion is employed to produce the fused representation of the dual model, which is finally fed to the classifier. Through extensive experiments on four public remote sensing scene datasets, including UCM, AID, PatternNet, and OPTIMAL-31, we demonstrate the feasibility of the proposed method and its superiority over the state-of-the-art scene classification methods.

Multiple epigenetic regulatory mechanisms exert critical roles in tumor development, and understanding the interactions and impact of diverse epigenetic modifications on gene expression in cancer is crucial for the development of precision medicine. We found that methyltransferase‐like 14 (METTL14) was significantly downregulated in non‐small‐cell lung cancer (NSCLC) tissues. Functional experiments demonstrated that overexpression of METTL14 inhibited the proliferation and migration of NSCLC cells both in vivo and in vitro, and the colorimetric m6A quantification assay also showed that knockdown of METTL14 notably reduced global m6A modification levels in NSCLC cells. By using the methylated‐RNA immunoprecipitation‐qPCR and dual‐luciferase reporter assays, we verified that long noncoding RNA LINC02747 was a target of METTL14 and was regulated by METTL14‐mediated m6A modification, and silencing LINC02747 inhibited the malignant progression of NSCLC by modulating the PI3K/Akt and CDK4/Cyclin D1 signaling pathway. Further studies revealed that overexpression of METTL14 promoted m6A methylation and accelerated the decay of LINC02747 mRNA via increased recognition of the “GAACU” binding site by YTHDC2. Additionally, histone demethylase lysine‐specific histone demethylase 5B (KDM5B) mediated the demethylation of histone H3 lysine 4 tri‐methylation (H3K4me3) in the METTL14 promoter region and repressed its transcription. In summary, KDM5B downregulated METTL14 expression at the transcriptional level in a H3K4me3‐dependent manner, while METTL14 modulated LINC02747 expression via m6A modification. Our results demonstrate a synergy of multiple mechanisms in regulating the malignant phenotype of NSCLC, revealing the complex regulation involved in the occurrence and development of cancer. The m6A methyltransferase methyltransferase‐like 14 (METTL14) is downregulated in non‐small‐cell lung cancer (NSCLC), which can decrease the expression of LINC02747 through m6A modification and subsequently lower the malignant phenotype of NSCLC cells. Interestingly, we found that the low expression of METTL14 in NSCLC is due to the histone demethylase histone demethylase lysine‐specific histone demethylase 5B, which suppresses the transcription of METTL14 in a histone H3 lysine 4 tri‐methylation‐dependent manner. The correlation between the m6A modification mediated by METTL14 and histone modifications in NSCLC provides new insights into NSCLC therapy.

Richter Transformation (RT) develops in CLL as an aggressive, therapy-resistant, diffuse large B cell lymphoma (RT-DLBCL), commonly clonally-related (CLR) to the concomitant CLL. Lack of available pre-clinical human models has hampered the development of novel therapies for RT-DLBCL. Here, we report the profiles of genetic alterations, chromatin accessibility and active enhancers, gene-expressions and anti-lymphoma drug-sensitivity of three newly established, patient-derived, xenograft (PDX) models of RT-DLBCLs, including CLR and clonally-unrelated (CLUR) to concomitant CLL. The CLR and CLUR RT-DLBCL cells display active enhancers, higher single-cell RNA-Seq-determined mRNA, and protein expressions of IRF4, TCF4, and BCL2, as well as increased sensitivity to BET protein inhibitors. CRISPR knockout of IRF4 attenuated c-Myc levels and increased sensitivity to a BET protein inhibitor. Co-treatment with BET inhibitor or BET-PROTAC and ibrutinib or venetoclax exerted synergistic in vitro lethality in the RT-DLBCL cells. Finally, as compared to each agent alone, combination therapy with BET-PROTAC and venetoclax significantly reduced lymphoma burden and improved survival of immune-depleted mice engrafted with CLR-RT-DLBCL. These findings highlight a novel, potentially effective therapy for RT-DLBCL.

With the rapid development of modern science and technology, deep learning and artificial intelligence have greatly promoted the progress of computer vision, among which image restoration is a key field of computer vision, aiming to repair images that are damaged or missing important parts of the main body. Although traditional interpolation and region filling techniques are effective in some environments, they often have difficulty handling complex scenes that require image restoration in today's world. In contrast, modern methods such as GANs and Diffusion models have significantly improved the quality and reliability of restoration. However, GANs are hindered by problems such as instability and mode collapse, and although diffusion models can generate high-quality images, they are computationally demanding. To address these challenges, this review explores the hybrid diffusion GANs framework and focuses on the basic conceptual restoration principles of the above three models and compares the performance of the GANs and Diffusion model and other traditional models by comparing indicators such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Furthermore, although Diffusion-GANs model is mainly applied to image generation, we discuss their great potential for image inpainting, providing new possibilities for future improvements in image restoration and generation.

B cell lymphoma is a clinically heterogeneous and pathologically diverse group of diseases with a strong epigenetic component. The B cell lymphoma 6 ( BCL6 ) gene encodes a transcription factor that is critical for normal germinal center reaction B cell development by maintaining an epigenetic and transcriptional state that is permissive for cellular proliferation and DNA damage. The activity of BCL6 can be deregulated by a variety of mechanisms and contributes to the development of B-cell lymphoma. Here we review the direct and indirect mechanisms BCL6 dysregulation in B cell lymphoma, including transcriptional and post-translational regulation of BCL6 expression and activity, and the perturbation of BCL6-regulated epigenetic programs by cooperating chromatin modifying gene mutations. We underscore the critical importance of BCL6 and its associated epigenetic programs in the development of B-cell lymphoma, and discuss avenues for the therapeutic targeting of BCL6 in this context.

Tunnel boring machines (TBMs) accumulate vast operational data crucial for analyzing complex rock‐machine interactions during construction. Recent advancements in big data mining and machine learning (ML) have spurred significant artificial intelligence (AI) research in tunnel construction. The effectiveness of ML models depends on high‐quality tunneling data, prompting increased research emphasis on big data preprocessing. This review synthesizes current practices in data preprocessing for tunnel construction, covering data characteristics, methods for handling anomalies such as discrete values, missing data, and noise. It also addresses challenges and future directions in big data processing. These findings establish a comprehensive foundation for further research in big data preprocessing and ML modeling in TBM construction.

Lung carcinogenesis is a complex process that occurs in unregulated inflammatory environment. EGCG has been extensively investigated as a multi-targeting anti-tumor and anti-inflammatory compound. In this study, we demonstrated a novel mechanism by which EGCG reverses the neutrophil elastase-induced migration of A549 cells. We found that neutrophil elastase directly triggered human adenocarcinoma A549 cell migration and that EGCG suppressed the elevation of tumor cell migration induced by neutrophil elastase. We observed that EGCG directly binds to neutrophil elastase and inhibits its enzymatic activity based on the CDOCKER algorithm, MD stimulation by GROMACS, SPR assay and elastase enzymatic activity assay. As the natural inhibitor of neutrophil elastase, α1-antitrypsin is synthesized in tumor cells. We further demonstrated that the expression of α1-antitrypsin was up-regulated after EGCG treatment in neutrophil elastase-treated A549 cells. We preliminarily discovered that the EGCG-mediated induction of α1-antitrypsin expression might be correlated with the regulatory effect of EGCG on the PI3K/Akt pathway. Overall, our results suggest that EGCG ameliorates the neutrophil elastase-induced migration of A549 cells. The mechanism underlying this effect may include two processes: EGCG directly binds to neutrophil elastase and inhibits its enzymatic activity; EGCG enhances the expression of α1-antitrypsin by regulating the PI3K/AKT pathway.