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Material, dust content, temperature, water, and other factors were studied in the performance of asphalt surface treatment. Results show that asphalt surface treatment performance varied with material types and temperature. The loss of the aggregate percentage increases with dust content. Precoated aggregate effectively improves the performance of asphalt surface treatment. Temperature has a positive influence on the performance of asphalt surface treatment. The aggregate residual rate of asphalt surface treatment decreased with the increase in immersion time.

Ratiometric luminescent oxygen sensing based on dual fluorescence and phosphorescence emission in a single matrix is highly desirable, yet the designed synthesis remains challenging. Silver-chalcogenolate-cluster-based metal-organic frameworks that combine the advantages of silver clusters and metal-organic frameworks have displayed unique luminescent properties. Herein, we rationally introduce −NH 2 groups on the linkers of a silver-chalcogenolate-cluster-based metal-organic framework (Ag 12 bpy-NH 2 ) to tune the intersystem crossing, achieving a dual fluorescence-phosphorescence emission from the same linker chromophore. The blue fluorescence component has a 100-nm gap in wavelength and 8,500,000-fold difference in lifetime relative to a yellow phosphorescence component. Ag 12 bpy-NH 2 quantifies oxygen during hypoxia with the limit of detection of as low as 0.1 ppm and 0.3 s response time, which is visualized by the naked eye. Our work shows that metal cluster-based MOFs have great potential in luminescent sensing, and the longer-lived charge-separated states could find more photofunctional applications in solar energy transformation and photocatalysis. Incorporating dual fluorescence and phosphorescence into a single matrix is attractive for oxygen sensing, but material design is challenging. Here the authors achieve dual fluorescence-phosphorescence from a single linker chromophore in a silver chalcogenolate-cluster-based metal-organic framework.

BRAF and KRAS are two key oncogenes in the RAS/RAF/MEK/MAPK signaling pathway. Concomitant mutations in both KRAS and BRAF genes have been identified in non-small cell lung cancer (NSCLC). They lead to the proliferation, differentiation, and apoptosis of tumor cells by activating the RAS/RAF/MEK/ERK signaling pathway. To date, agents that target RAS/RAF/MEK/ERK signaling pathway have been investigated in NSCLC patients harboring BRAF mutations. BRAF and MEK inhibitors have gained approval for the treatment of patients with NSCLC. According to the reported findings, the combination of MEK inhibitors with chemotherapy, immune checkpoint inhibitors, epidermal growth factor receptor-tyrosine kinase inhibitors or BRAF inhibitors is highly significant for improving clinical efficacy and causing delay in the occurrence of drug resistance. This review summarized the existing experimental results and presented ongoing clinical studies as well. However, further researches need to be conducted to indicate how we can combine other drugs with MEK inhibitors to significantly increase therapeutic effects on patients with lung cancer.

At present, convolutional neural networks (CNNs) have been widely applied to the task of skin disease image segmentation due to the fact of their powerful information discrimination abilities and have achieved good results. However, it is difficult for CNNs to capture the connection between long-range contexts when extracting deep semantic features of lesion images, and the resulting semantic gap leads to the problem of segmentation blur in skin lesion image segmentation. In order to solve the above problems, we designed a hybrid encoder network based on transformer and fully connected neural network (MLP) architecture, and we call this approach HMT-Net. In the HMT-Net network, we use the attention mechanism of the CTrans module to learn the global relevance of the feature map to improve the network’s ability to understand the overall foreground information of the lesion. On the other hand, we use the TokMLP module to effectively enhance the network’s ability to learn the boundary features of lesion images. In the TokMLP module, the tokenized MLP axial displacement operation strengthens the connection between pixels to facilitate the extraction of local feature information by our network. In order to verify the superiority of our network in segmentation tasks, we conducted extensive experiments on the proposed HMT-Net network and several newly proposed Transformer and MLP networks on three public datasets (ISIC2018, ISBI2017, and ISBI2016) and obtained the following results. Our method achieves 82.39%, 75.53%, and 83.98% on the Dice index and 89.35%, 84.93%, and 91.33% on the IOU. Compared with the latest skin disease segmentation network, FAC-Net, our method improves the Dice index by 1.99%, 1.68%, and 1.6%, respectively. In addition, the IOU indicators have increased by 0.45%, 2.36%, and 1.13%, respectively. The experimental results show that our designed HMT-Net achieves state-of-the-art performance superior to other segmentation methods.

Treatment of non-small cell lung cancer (NSCLC) harboring epidermal growth factor receptor (EGFR) activating mutation with EGFR-TKIs has achieved great success, yet faces the development of acquired resistance as the major obstacle to long-term disease remission in the clinic. MET (or c-MET ) gene amplification has long been known as an important resistance mechanism to first- or second-generation EGFR-TKIs in addition to the appearance of T790 M mutation. Recent preclinical and clinical studies have suggested that MET amplification and/or protein hyperactivation is likely to be a key mechanism underlying acquired resistance to third-generation EGFR-TKIs such as osimertinib as well, particularly when used as a first-line therapy. EGFR-mutant NSCLCs that have relapsed from first-generation EGFR-TKI treatment and have MET amplification and/or protein hyperactivation should be insensitive to osimertinib monotherapy. Therefore, combinatorial therapy with osimertinib and a MET or even a MEK inhibitor should be considered for these patients with resistant NSCLC carrying MET amplification and/or protein hyperactivation.

In recent decades, immune checkpoint blockade and chimeric antigen receptor T cell (CAR-T) therapy are two milestone achievements in clinical immunotherapy. However, both show limited efficacies in most solid neoplasms, which necessitates the exploration of new immunotherapeutic modalities. The failure of CAR-T and immune checkpoint blockade in several solid neoplasms is attributed to multiple factors, including low antigenicity of tumor cells, low infiltration of effector T cells, and diverse mechanisms of immunosuppression in the tumor microenvironment. New adoptive cell therapies have been attempted for solid neoplasms, including TCR-T, CAR-natural killer cells (CAR-NK), and CAR-macrophages (CAR-M). Compared to CAR-T, these new adoptive cell therapies have certain advantages in treating solid neoplasms. In this review, we summarized the 40-year evolution of adoptive cell therapies, then focused on the advances of TCR-T, CAR-NK, and CAR-M in solid neoplasms and discussed their potential clinical applications.

Genome sequencing has enabled us to find functional peptides encoded by short open read frames (sORFs) in long non-coding RNAs (lncRNAs). sORFs-encoded peptides (SEPs) regulate gene expression, signaling, and so on and have significant roles, unlike common peptides. Various computational methods have been proposed. However, there is a lack of contributive features and effective models. Therefore, a high-throughput computational method to predict SEPs is needed. We propose a computational method, SORFPP, to predict SEPs by mining feature information from multiple perspectives in an experimentally validated dataset from TranLnc. SORFPP fully extracts SEP sequence information using the protein language model ESM-2 and curated traditional encoding, including QSOrder, k-mer, etc. SORFPP uses CatBoost to solve the sparsity problem of traditional encoding. SORFPP also analyzes ESM-2 pre-training characterization information with the Self-attention model. Finally, an ensemble learning framework combines the two models and their results are fed into Logistic Regression model for accurate and robust predictions. For comparison, SORFPP outperforms other state-of-the-art models in Matthew correlation coefficient by 12.2%-24.2% on three benchmark datasets. Integrating the ensemble learning strategy with contributive traditional features and the protein language encoding methods shows better performance. Datasets and codes are accessible at https://doi.org/10.6084/m9.figshare.28079897 and http://111.229.198.94:5000/.

To ensure national food security amidst severe water shortages, agricultural irrigation must be reduced through scientific innovation and technological progress. Efficient monitoring is essential for achieving water-saving irrigation and ensuring the sustainable development of agriculture. UAV hyperspectral remote sensing has demonstrated significant potential in monitoring large-scale crop leaf water content (LWC). In this study, hyperspectral and LWC data were collected for potatoes ( Solanum tuberosum ) during the tuber formation, growth, and starch accumulation stage in both 2021 and 2022. The hyperspectral data underwent mathematical transformation by multivariate scatter correction (MSC) and standard normal transformation (SNV). Next, feature spectral bands of LWC were selected using Competitive Adaptive Reweighted Sampling (CARS) and Random Frog (RF). For comparison, both the full-band and feature band were utilized to establish the estimation models of LWC. Modeling methods included partial least squares regression (PLSR), support vector regression (SVR), and BP neural network regression (BP). Results demonstrate that MSC and SNV significantly enhance the correlation between spectral data and LWC. The efficacy of estimation models varied across different growth stages, with optimal models identified as MSC-CARS-SVR (R 2 = 0.81, RMSE = 0.51) for tuber formation, SNV-CARS-PLSR (R 2 = 0.85, RMSE = 0.42) for tuber growth, and MSC-RF-PLSR (R 2 = 0.81, RMSE = 0.55) for starch accumulation. The RPD values of the three optimal models all exceed 2, indicating their excellent predictive performance. Utilizing these optimal models, a spatial distribution map of LWC across the entire potato canopy was generated, offering valuable insights for precise potato irrigation.

With the rapid development of virtual technology and data acquisition technology, digital twin (DT) technology was proposed and gradually become one of the key research directions of intelligent manufacturing. However, the research of DT for product life cycle management is still in the theoretical stage, the application framework and application methods are not clear, and the lack of referable application cases is also a problem. In this paper, the related research and application of DT technology are systematically studied. Then the concept and characteristics of DT are interpreted from both broad sense and narrow sense. On this basis, an application framework of DT for product lifecycle management is proposed. In physical space, the total-elements information perception technology of production is discussed in detail. In the information processing layer, three main function modules, including data storage, data processing and data mapping, are constructed. In virtual space, this paper describes the implementation process of full parametric virtual modeling and the construction idea for DT application subsystems. At last, a DT case of a welding production line is built and studied. Meanwhile, the implementation scheme, application process and effect of this case are detail described to provide reference for enterprises.

The glycoprotein CD58, also known as lymphocyte-function antigen 3 (LFA-3), is a costimulatory receptor distributed on a broad range of human tissue cells. Its natural ligand CD2 is primarily expressed on the surface of T/NK cells. The CD2-CD58 interaction is an important component of the immunological synapse (IS) that induces activation and proliferation of T/NK cells and triggers a series of intracellular signaling in T/NK cells and target cells, respectively, in addition to promoting cell adhesion and recognition. Furthermore, a soluble form of CD58 (sCD58) is also present in cellular supernatant in vitro and in local tissues in vivo . The sCD58 is involved in T/NK cell-mediated immune responses as an immunosuppressive factor by affecting CD2-CD58 interaction. Altered accumulation of sCD58 may lead to immunosuppression of T/NK cells in the tumor microenvironment, allowing sCD58 as a novel immunotherapeutic target. Recently, the crucial roles of costimulatory molecule CD58 in immunomodulation seem to be reattracting the interests of investigators. In particular, the CD2-CD58 interaction is involved in the regulation of antiviral responses, inflammatory responses in autoimmune diseases, immune rejection of transplantation, and immune evasion of tumor cells. In this review, we provide a comprehensive summary of CD58 immunobiology.