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Sorption-based atmospheric water harvesting has the potential to realize water production anytime, anywhere, but reaching a hundred-gram high water yield in semi-arid climates is still challenging, although state-of-the-art sorbents have been used. Here, we report a portable and modularized water harvester with scalable, low-cost, and lightweight LiCl-based hygroscopic composite (Li-SHC) sorbents. Li-SHC achieves water uptake capacity of 1.18, 1.79, and 2.93 g g −1 at 15%, 30%, and 60% RH, respectively. Importantly, considering the large mismatch between water capture and release rates, a rationally designed batch processing mode is proposed to pursue maximum water yield in a single diurnal cycle. Together with the advanced thermal design, the water harvester shows an exceptional water yield of 311.69 g day −1 and 1.09 g g sorbent −1 day −1 in the semi-arid climate with the extremely low RH of ~15%, demonstrating the adaptability and possibility of achieving large-scale and reliable water production in real scenarios. Sorption-based atmospheric water harvesting has the potential to realize flexible water production but reaching high water yield in semi-arid climate is still challenging. Here, the authors report a portable and modularized water harvester with scalable, low-cost, and lightweight hygroscopic composite sorbent which shows an exceptional water production in semi-arid climate with low relative humidity of 15%.

The greenhouse effect and plastic pollution caused by the accumulation of plastics have led to a global concern for environmental protection, as well as the development and application of biodegradable materials. Polypropylene carbonate (PPC) is a biodegradable polymer with the function of “carbon sequestration”, which has the potential to mitigate the greenhouse effect and the plastic crisis. It has the advantages of good ductility, oxygen barrier and biocompatibility. However, the mechanical and thermal properties of PPC are poor, especially the low thermal degradation temperature, which limits its industrial use. In order to overcome this problem, PPC can be modified using environmentally friendly materials, which can also reduce the cost of PPC-based products to a certain extent and enhance their competitiveness in terms of improving their mechanical and thermal properties. In this paper, we present different perspectives on the synthesis, properties, degradation, modification and post-modification applications of PPC. The modification part mainly introduces the influence of inorganic materials, natural polymer materials and degradable polymers on the performance of PPC. It is hoped that this work will serve as a reference for the early promotion of PPC.

Mangroves play an important role in many aspects of ecosystem services. Mangroves should be accurately extracted from remote sensing imagery to dynamically map and monitor the mangrove distribution area. However, popular mangrove extraction methods, such as the object-oriented method, still have some defects for remote sensing imagery, such as being low-intelligence, time-consuming, and laborious. A pixel classification model inspired by deep learning technology was proposed to solve these problems. Three modules in the proposed model were designed to improve the model performance. A multiscale context embedding module was designed to extract multiscale context information. Location information was restored by the global attention module, and the boundary of the feature map was optimized by the boundary fitting unit. Remote sensing imagery and mangrove distribution ground truth labels obtained through visual interpretation were applied to build the dataset. Then, the dataset was used to train deep convolutional neural network (CNN) for extracting the mangrove. Finally, comparative experiments were conducted to prove the potential for mangrove extraction. We selected the Sentinel-2A remote sensing data acquired on 13 April 2018 in Hainan Dongzhaigang National Nature Reserve in China to conduct a group of experiments. After processing, the data exhibited 2093 × 2214 pixels, and a mangrove extraction dataset was generated. The dataset was made from Sentinel-2A satellite, which includes five original bands, namely R, G, B, NIR, and SWIR-1, and six multispectral indices, namely normalization difference vegetation index (NDVI), modified normalized difference water index (MNDWI), forest discrimination index (FDI), wetland forest index (WFI), mangrove discrimination index (MDI), and the first principal component (PCA1). The dataset has a total of 6400 images. Experimental results based on datasets show that the overall accuracy of the trained mangrove extraction network reaches 97.48%. Our method benefits from CNN and achieves a more accurate intersection and union ratio than other machine learning and pixel classification methods by analysis. The designed model global attention module, multiscale context embedding, and boundary fitting unit are helpful for mangrove extraction.

Purpose Epithelial to mesenchymal transition (EMT) can contribute to gastric cancer (GC) progression and recurrence following therapy. Tumor-associated neutrophils (TANs) are associated with poor outcomes in a variety of cancers. However, it is not clear whether TANs interact with the EMT process during GC development. Methods Immunohistochemistry was performed to examine the distribution and levels of CD66 + neutrophils in samples from 327 patients with GC. CD66b + TANs were isolated either directly from GC cell suspensions or were conditioned from healthy donor peripheral blood polymorphonuclear neutrophils (PMNs) stimulated with tumor tissue culture supernatants (TTCS) and placed into co-culture with MKN45 or MKN74 cells, after which migration, invasion and EMT were measured. Interleukin-17a (IL-17a) was blocked with a polyclonal antibody, and the STAT3 pathway was blocked with the specific inhibitor AG490. Results Neutrophils were widely distributed in gastric tissues of patients with GC and were enriched predominantly at the invasion margin. Neutrophil levels at the invasion margin were an independent predictor of poor disease-free survival (DFS) and disease-specific survival (DSS). IL-17a + neutrophils constituted a large portion of IL-17a-producing cells in GC, and IL-17a was produced at the highest levels in co-culture compared with that in TANs not undergoing co-culture. TANs enhanced the migration, invasion and EMT of GC cells through the secretion of IL-17a, which activated the Janus kinase 2/signal transducers and activators of transcription (JAK2/STAT3) pathway in GC cells, while deprivation of IL-17a using a neutralizing antibody or inhibition of the JAK2/STAT3 pathway with AG490 markedly reversed these TAN-induced phenotypes in GC cells induced by TANs. Conclusions Neutrophils correlate with tumor stage and predict poor prognosis in GC. TANs produce IL-17a, which promotes EMT of GC cells through JAK2/STAT3 signalling. Blockade of IL-17a signalling with a neutralizing antibody inhibits TAN-stimulated activity in GC cells. Therefore, IL-17a-targeted therapy might be used to treat patients with GC.

Chloride corrosion is a key factor affecting the life of marine concrete, and surface chloride concentration is the main parameter for analyzing its durability. In this paper, we first introduce six erosion mechanism models for surface chloride ion concentration, reveal the convection effect in the diffusion behavior of chloride ions, and then introduce the corrosion mechanisms that occur in different marine exposure environments. On this basis, the analysis is carried out using empirical formulations and machine learning methods, which provides a clearer understanding of the research characteristics and differences between empirical formulas and emerging machine learning techniques. This paper summarizes the time-varying model and multifactor coupling model on the basis of empirical analysis. It is found that the exponential function and the reciprocal function are more consistent with the distribution law of chloride ion concentration, the multifactor model containing the time-varying law is the most effective, and the Chen model is the most reliable. Machine learning, as an emerging method, has been widely used in concrete durability research. It can make up for the shortcomings of the empirical formula method and solve the multifactor coupling problem of surface chloride ion concentration with strong prediction ability. In addition, the difficulty of data acquisition is also a major problem that restricts the development of machine learning and incorporating concrete maintenance conditions into machine learning is a future development direction. Through this study, researchers can systematically understand the characteristics and differences of different research methods and their respective models and choose appropriate techniques to explore the durability of concrete structures. Moreover, intelligent computing will certainly occupy an increasingly important position in marine concrete research.

Accurate diagnosis and prognostic assessment of gastric cancer are critical for improving patient outcomes. The application of advanced machine learning methods, particularly the XGBoost algorithm, offers a promising approach for enhancing prognostic evaluations. In this study, data from 2,270 patients with gastric cancer were analysed to develop a predictive model for prognosis using the XGBoost algorithm and 20 key clinical features. Comprehensive data collection, preprocessing, and feature selection were conducted to ensure robust model construction and validation. The model demonstrated strong predictive performance in the test cohort, achieving an area under the curve (AUC) of 0.855, and it effectively differentiated patients at high-risk from those at low-risk. Feature importance analysis revealed that pTNM stage and CA125 level were the most influential prognostic factors. This study successfully implemented a machine learning-based model integrating the XGBoost algorithm and critical clinical indicators to predict the five-year survival rate of patients with gastric cancer. The findings highlight the potential of such approaches in supporting personalised treatment strategies and advancing cancer prognosis assessment methodologies.

Background Emerging evidences suggests that Diosbulbin-B (DB) is effective to improve cisplatin (DDP)-sensitivity in gastric cancer (GC), but its molecular mechanisms were not fully delineated, and this study managed to investigate this issue. Methods Genes expressions were determined by Real-Time qPCR and Western Blot at transcriptional and translational levels. Cell proliferation and viability were evaluated by cell counting kit-8 (CCK-8) and trypan blue staining assay. Annexin V-FITC/PI double staining assay was used to examine cell apoptosis. The Spheroid formation assay was used to evaluated cell stemness. The xenograft tumor-bearing mice models were established, and the tumors were monitored and the immunohistochemistry (IHC) was employed to examine the expressions and localization of Ki67 protein in mice tumor tissues. Results Low-dose DB (12.5 μM) downregulated PD-L1 to activate NLRP3-mediated pyroptosis, and inhibited cancer stem cells (CSCs) properties, to sensitize cisplatin-resistant GC (CR-GC) cells to cisplatin. Mechanistically, the CR-GC cells were obtained, and either low-dose DB or cisplatin alone had little effects on cell viability in CR-GC cells, while low-dose DB significantly induced apoptotic cell death in cisplatin treated CR-GC cells. In addition, low-dose DB triggered cell pyroptosis in CR-GC cells co-treated with cisplatin, which were abrogated by silencing NLRP3. Next, CSCs tended to be enriched in CR-GC cells, instead of their parental cisplatin-sensitive GC (CS-GC) cells, and low-dose DB inhibited spheroid formation and stemness biomarkers (SOX2, OCT4 and Nanog) expressions to eliminate CSCs in CR-GC cells, which were reversed by upregulating programmed death ligand-1 (PD-L1). Furthermore, we proved that PD-L1 negatively regulated NLRP3 in CR-GC cells, and low-dose DB activated NLRP3-mediated pyroptotic cell death in cisplatin treated CR-GC cells by downregulating PD-L1. Also, low-dose DB aggravated the inhibiting effects of cisplatin on tumorigenesis of CR-GC cells in vivo. Conclusions Collectively, low-dose DB regulated intrinsic PD-L1/NLRP3 pathway to improve cisplatin-sensitivity in CR-GC cells, and this study provided alternative therapy treatments for GC.

The ideal vaccine against viruses such as influenza and SARS-CoV-2 must provide a robust, durable and broad immune protection against multiple viral variants. However, antibody responses to current vaccines often lack robust cross-reactivity. Here we describe a polymeric Toll-like receptor 7 agonist nanoparticle (TLR7-NP) adjuvant, which enhances lymph node targeting, and leads to persistent activation of immune cells and broad immune responses. When mixed with alum-adsorbed antigens, this TLR7-NP adjuvant elicits cross-reactive antibodies for both dominant and subdominant epitopes and antigen-specific CD8+ T-cell responses in mice. This TLR7-NP-adjuvanted influenza subunit vaccine successfully protects mice against viral challenge of a different strain. This strategy also enhances the antibody response to a SARS-CoV-2 subunit vaccine against multiple viral variants that have emerged. Moreover, this TLR7-NP augments antigen-specific responses in human tonsil organoids. Overall, we describe a nanoparticle adjuvant to improve immune responses to viral antigens, with promising implications for developing broadly protective vaccines.A nanoparticle-based adjuvant incorporating a Toll-like receptor 7 agonist elicits cross-reactive antibodies for both dominant and subdominant epitopes and enhances immune responses against multiple variants of influenza and SARS-CoV-2.

When the workpiece surface exhibits strong reflectivity, it becomes challenging to obtain accurate key measurements using non-contact, visual measurement techniques due to poor image quality. In this paper, we propose a high-precision measurement method shaft diameter based on an enhanced quality stripe image. By capturing two stripe images with different exposure times, we leverage their different characteristics. The results extracted from the low-exposure image are used to perform grayscale correction on the high-exposure image, improving the distribution of stripe grayscale and resulting in more accurate extraction results for the center points. The incorporation of different measurement positions and angles further enhanced measurement precision and robustness. Additionally, ellipse fitting is employed to derive shaft diameter. This method was applied to the profiles of different cross-sections and angles within the same shaft segment. To reduce the shape error of the shaft measurement, the average of these measurements was taken as the estimate of the average diameter for the shaft segment. In the experiments, the average shaft diameters determined by averaging elliptical estimations were compared with shaft diameters obtained using a coordinate measuring machine (CMM) the maximum error and the minimum error were respectively 18 μm and 7 μm; the average error was 11 μm; and the root mean squared error of the multiple measurement results was 10.98 μm. The measurement accuracy achieved is six times higher than that obtained from the unprocessed stripe images.

The neutrophil-lymphocyte ratio (NLR) and the platelet-lymphocyte ratio (PLR) are markers of systemic inflammation. However, there is little evidence of the value of inflammation in the early diagnosis of gastric cancer (GC). A total of 2,606 patients diagnosed with GC in the past three years and 3,219 healthy controls over the same period were included in this study. Peripheral blood samples were obtained to analyze the NLR, PLR, carcinoembryonic antigen (CEA), and carbohydrate antigen 19-9 (CA19-9). The optimal cutoff levels for the NLR and PLR were defined by receiver operating characteristic (ROC) curve analysis (NLR=2.258, PLR=147.368). The value of different biomarkers for diagnosing GC was compared by the area under the curve (AUC). The NLR and PLR showed diagnostic sensitivity in GC (AUC=0.715, AUC=0.707). Using the Bonferroni correction, the NLR and PLR were superior to CEA and CA19-9 in the diagnosis of GC (P<0.0001). The systemic inflammatory markers were significantly higher in the early stage of GC than tumor markers. After grouping patients and healthy controls by gender, we found that the diagnostic significance of combined NLR and PLR for GC was greater in male patients than in female patients (P<0.0001). The diagnostic value of the NLR and PLR in GC is higher than that of the traditional tumor markers CEA and CA19-9. Systemic markers of inflammation are more valuable in male than female patients.