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Abstract CO2 electroreduction is a potential pathway to achieve net-zero emissions in the chemical industry. Yet, CO2 loss, resulting from (bi)carbonate formation, renders the process energy-intensive. Acidic environments can address the issue but at the expense of compromised product Faradaic efficiencies (FEs), particularly for multi-carbon (C2+) products, as rapid diffusion and migration of protons (H+) favors competing H2 and CO production. Here, we present a strategy of tuning the 2-position substituent length on benzimidazole (BIM)-based copper (Cu) coordination polymer (CuCP) precatalyst – to enhance CO2 reduction to C2+ products in acidic environments. Lengthening the substituent from H to nonyl enhances H+ diffusion retardation and decreases Cu-Cu coordination numbers (CNs), favoring further reduction of CO. This leads to a nearly 24× enhancement of selectivity towards CO hydrogenation and C-C coupling at 60 mA cm−2. We report the highest C2+ product FE of more than 70% at 260 mA cm−2 on pentyl-CuCP and demonstrate a CO2-to-C2+ single-pass conversion (SPC) of ~54% at 180 mA cm−2 using pentyl-CuCP in zero-gap electrolyzers.

Abstract Background The stromal cell derived factor 2 (SDF2) relates closely to the occurrence and development of several kind of cancers. There are few studies to investigate the clinicopathological and prognostic significance of SDF2 in gastric cancer (GC) patients. Methods We detected SDF2 expression in GC and normal gastric tissues using bioinformatics, western blot and immunohistochemistry. Furthermore, we tested the relationship between SDF2 expression and clinicopathological characteristics and prognosis of GC patients. Results Bioinformatics, western blot and immunohistochemistry results showed that SDF2 expression in GC tissue was higher than that in normal gastric tissue (P

Abstract Distant metastasis is a primary cause of mortality and contributes to poor surgical outcomes in cancer patients. Before the development of organ-specific metastasis, the formation of a pre-metastatic niche is pivotal in promoting the spread of cancer cells. This review delves into the intricate landscape of the pre-metastatic niche, focusing on the roles of tumor-derived secreted factors, extracellular vesicles, and circulating tumor cells in shaping the metastatic niche. The discussion encompasses cellular elements such as macrophages, neutrophils, bone marrow-derived suppressive cells, and T/B cells, in addition to molecular factors like secreted substances from tumors and extracellular vesicles, within the framework of pre-metastatic niche formation. Insights into the temporal mechanisms of pre-metastatic niche formation such as epithelial-mesenchymal transition, immunosuppression, extracellular matrix remodeling, metabolic reprogramming, vascular permeability and angiogenesis are provided. Furthermore, the landscape of pre-metastatic niche in different metastatic organs like lymph nodes, lungs, liver, brain, and bones is elucidated. Therapeutic approaches targeting the cellular and molecular components of pre-metastatic niche, as well as interventions targeting signaling pathways such as the TGF-β, VEGF, and MET pathways, are highlighted. This review aims to enhance our understanding of pre-metastatic niche dynamics and provide insights for developing effective therapeutic strategies to combat tumor metastasis.

Abstract Metastasis has been one of the primary reasons for the high mortality rates associated with tumours in recent years, rendering the treatment of current malignancies challenging and representing a significant cause of recurrence in patients who have undergone surgical tumour resection. Halting tumour metastasis has become an essential goal for achieving favourable prognoses following cancer treatment. In recent years, increasing clarity in understanding the mechanisms underlying metastasis has been achieved. The concept of premetastatic niches has gained widespread acceptance, which posits that tumour cells establish a unique microenvironment at distant sites prior to their migration, facilitating their settlement and growth at those locations. Neutrophils serve as crucial constituents of the premetastatic niche, actively shaping its microenvironmental characteristics, which include immunosuppression, inflammation, angiogenesis and extracellular matrix remodelling. These characteristics are intimately associated with the successful engraftment and subsequent progression of tumour cells. As our understanding of the role and significance of neutrophils in the premetastatic niche deepens, leveraging the presence of neutrophils within the premetastatic niche has gradually attracted the interest of researchers as a potential therapeutic target. The focal point of this review revolves around elucidating the involvement of neutrophils in the formation and shaping of the premetastatic niche (PMN), alongside the introduction of emerging therapeutic approaches aimed at impeding cancer metastasis.

Abstract Cancer immunotherapy has sparked a wave of cancer research, driven by recent successful proof-of-concept clinical trials. However, barriers are emerging during its rapid development, including broad adverse effects, a lack of reliable biomarkers, tumor relapses, and drug resistance. Integration of nanomedicine may ameliorate current cancer immunotherapy. Ultra-large surface-to-volume ratio, extremely small size, and easy modification surface of nanoparticles enable them to selectively detect cells and kill cancer cells in vivo. Exciting synergistic applications of the two approaches have emerged in treating various cancers at the intersection of cancer immunotherapy and cancer nanomedicine, indicating the potential that the combination of these two therapeutic modalities can lead to new paradigms in the treatment of cancer. This review discusses the status of current immunotherapy and explores the possible opportunities that the nanomedicine platform can make cancer immunotherapy more powerful and precise by synergizing the two approaches.

Objective Percutaneous vertebroplasty (PVP) is currently the most common surgical procedure for unstable Kummell disease (KD), but cement loosening or displacement often occurs after PVP. We had been using percutaneous pediculoplasty (PPP) or a self-developed bone cement bridging screw system to avoid this severe complication. This study intends to compare these novel surgical procedures through a 2-year follow-up evaluation. Methods From May 2017 to May 2021, 77 patients with single-level unstable KD were included in the PPP group, and 42 patients received the PVP-bone cement bridging screw system were included in the screw group. The changes in the vertebral body index (VBI), bisegmental Cobb angle, visual analogue scale (VAS) and Oswestry Disability Index (ODI) and the cement loosening rate and displacement rate at different follow-up time points were used to evaluate the clinical efficacy. Results There was no significant difference in VBI or bisegmental Cobb angle between the 2 groups (p > 0.05) before operation, immediately after operation and at 6-month follow-up, while at 1-year and 2-year postoperative evaluations, the screw group had higher VBI and bisegmental Cobb angle than the PPP group (p < 0.05). Before operation, immediately after operation, at 6-month and 1-year follow-up, there was no significant difference in VAS or ODI score between the 2 groups (p > 0.05), while at 2-year follow-up, the screw group still had higher VAS and ODI scores than the PPP group (p < 0.05). No bone cement displacement occurred in both groups, but the rate of bone cement loosening was 14.29% in group PPP, and 0 in screw group (p < 0.05). Conclusion This 2-year follow-up study shows that the PVP-bone cement bridging screw system combined therapy had better midterm treatment efficacy than the PVP-PPP combined therapy in patients with unstable KD, and the bone cement bridging screw system is a preferred therapy with better anti cement loosening ability.

Abstract Background Artemisia selengensis, classified within the genus Artemisia of the Asteraceae family, is a perennial herb recognized for its dual utility in culinary and medicinal domains. There are few studies on the chloroplast genome of A. selengensis, and the phylogeographic classification is vague, which makes phylogenetic analysis and evolutionary studies very difficult. Results The chloroplast genomes of 10 A. selengensis in this study were highly conserved in terms of gene content, gene order, and gene intron number. The genome lengths ranged from 151,148 to 151,257 bp and were typical of a quadripartite structure with a total GC content of approximately 37.5%. The chloroplast genomes of all species encode 133 genes, including 88 protein-coding genes, 37 tRNA genes, and 8 rRNA genes. Due to the contraction and expansion of the inverted repeats (IR), the overlap of ycf1 and ndhF genes occurred at the inverted repeats B (IRB) and short single copy sequence (SSC) boundaries. According to a codon use study, the frequent base in the chloroplast genome of A. selengensis’ third codon position was A/T. The number of SSR repeats was 42–44, most of which were single nucleotide A/T repeats. Sequence alignment analysis of the chloroplast genome showed that variable regions were mainly distributed in single copy regions, nucleotide diversity values of 0 to 0.009 were calculated by sliding window analysis, 8 mutation hotspot regions were detected, and coding regions were more conserved than non-coding regions. Analysis of non-synonymous substitution (Ka) and synonymous substitution (Ks) revealed that accD, rps12, petB, and atpF genes were affected by positive selection and no genes were affected by neutral selection. Based on the findings of the phylogenetic analysis, Artemisia selengensis was sister to the genus Artemisia Chrysanthemum and formed a monophyletic group with other Artemisia genera. Conclusions In this research, the present study systematically compared the chloroplast genomic features of A. selengensis and provided important information for the study of the chloroplast genome of A. selengensis and the evolutionary relationships among Asteraceae species.

Background: Cystitis glandularis is a chronic inflammatory disease of the urinary system characterized by high recurrence rates, the reasons for which are still unknown. Objectives: This study aims to identify potential factors contributing to recurrence and propose a simple and feasible prognostic model through nomogram construction. Design: Patients with confirmed recurrence based on outpatient visits or readmissions were included in this study, which was subsequently divided into training and validation cohorts. Methods: Machine learning techniques were utilized to screen for the most important predictors, and these were then employed to construct the nomogram. The reliability of the nomogram was assessed through receiver operating characteristic curve analysis, decision curve analysis, and calibration curves. Results: A total of 252 patients met the screening criteria and were enrolled in this study. Over the 12-month follow-up period, the relapse rate was found to be 57.14% ( n = 144). The five final predictors identified through machine learning were urinary infections, urinary calculi, eosinophil count, lymphocyte count, and serum magnesium. The area under curve values for all three time points assessing recurrence exceeded 0.75. Furthermore, both calibration curves and decision curve analyses indicated good performance of the nomogram. Conclusion: We have developed a reliable machine learning-based nomogram for predicting recurrence in cystitis glandularis

Abstract Macrophages, as pivotal cells within the tumour microenvironment, significantly influence the impact of and reactions to treatments for solid tumours. The rapid evolution of bioengineering technology has revealed the vast potential of engineered macrophages in immunotherapy, disease diagnosis, and tissue engineering. Given this landscape, the goal of harnessing and innovating macrophages as a novel strategy for solid tumour immunotherapy cannot be overstated. The diverse strategies for engineered macrophages in the realm of cancer immunotherapy, encompassing macrophage drug delivery systems, chimeric antigen receptor macrophage therapy, and synergistic treatment approaches involving bacterial outer membrane vesicles and macrophages, are meticulously examined in this review. These methodologies are designed to enhance the therapeutic efficacy of macrophages against solid tumours, particularly those that are drug-resistant and metastatic. Collectively, these immunotherapies are poised to supplement and refine current solid tumour treatment paradigms, thus heralding a new frontier in the fight against malignant tumours.

Abstract The voltage-gated calcium channel CaV1.2 is essential for cardiac and vessel smooth muscle contractility and brain function. Accumulating evidence demonstrates that malfunctions of CaV1.2 are involved in brain and heart diseases. Pharmacological inhibition of CaV1.2 is therefore of therapeutic value. Here, we report cryo-EM structures of CaV1.2 in the absence or presence of the antirheumatic drug tetrandrine or antihypertensive drug benidipine. Tetrandrine acts as a pore blocker in a pocket composed of S6II, S6III, and S6IV helices and forms extensive hydrophobic interactions with CaV1.2. Our structure elucidates that benidipine is located in the DIII-DIV fenestration site. Its hydrophobic sidechain, phenylpiperidine, is positioned at the exterior of the pore domain and cradled within a hydrophobic pocket formed by S5DIII, S6DIII, and S6DIV helices, providing additional interactions to exert inhibitory effects on both L-type and T-type voltage gated calcium channels. These findings provide the structural foundation for the rational design and optimization of therapeutic inhibitors of voltage-gated calcium channels.

Abstract Acinetobacters pose a significant threat to human health, especially those with weakened immune systems. Type IV pili of acinetobacters play crucial roles in virulence and antibiotic resistance. Single-stranded RNA bacteriophages target the bacterial retractile pili, including type IV. Our study delves into the interaction between Acinetobacter phage AP205 and type IV pili. Using cryo-electron microscopy, we solve structures of the AP205 virion with an asymmetric dimer of maturation proteins, the native Acinetobacter type IV pili bearing a distinct post-translational pilin cleavage, and the pili-bound AP205 showing its maturation proteins adapted to pilin modifications, allowing each phage to bind to one or two pili. Leveraging these results, we develop a 20-kilodalton AP205-derived protein scaffold targeting type IV pili in situ, with potential for research and diagnostics.

Abstract Background The TGF-β signalling pathway is intricately associated with the progression of glioblastoma (GBM). The objective of this study was to examine the role of circRNAs in the TGF-β signalling pathway. Methods In our research, we used transcriptome analysis to search for circRNAs that were activated by TGF-β. After confirming the expression pattern of the selected circRYK, we carried out in vitro and in vivo cell function assays. The underlying mechanisms were analysed via RNA pull-down, luciferase reporter, and RNA immunoprecipitation assays. Results CircRYK expression was markedly elevated in GBM, and this phenotype was strongly associated with a poor prognosis. Functionally, circRYK promotes epithelial-mesenchymal transition and GSC maintenance in GBM. Mechanistically, circRYK sponges miR-330-5p and promotes the expression of the oncogene VLDLR. In addition, circRYK could enhance the stability of VLDLR mRNA via the RNA-binding protein HuR. Conclusion Our findings show that TGF-β promotes epithelial-mesenchymal transition and GSC maintenance in GBM through the circRYK-VLDLR axis, which may provide a new therapeutic target for the treatment of GBM.

As a green building material, recycled aggregate concrete (RAC) can reduce construction solid waste and carbon emissions. In this paper, a combination of macroscale physical experiments of thermal performance and mesoscale numerical simulations of heat transfer were used to investigate the thermal conductivity (TC) of RAC. Firstly, the TC of RAC was measured using the steady-state method. Secondly, the mesoscale numerical analysis method of steady-state heat transfer of RAC was established by self-programming. Finally, by comparing with experimental results, the validity of the mesoscale numerical analysis method in this paper is verified. The results indicates that the TC of RAC is positively correlated with the recycled coarse aggregate dosage and the TC of each component, while it is negatively correlated with the water-binder ratio, the recycled fine aggregate replacement ratio, the old mortar adhesion ratio, the interface transition zone thickness, and the porosity. The six-phase mesoscale model of random convex polygonal aggregate for RAC constructed in this paper can effectively predict the TC of RAC, which provides a new idea for analyzing the TC of RAC.

Key parameters describing the spatial variability of soil properties based on the random field theory are the scale of fluctuation (SOF) and coefficient of variation (COV). To characterize the spatial variability of soil properties, reducing the impact of these errors and uncertainties is necessary. To accomplish this, for the five main layers of soil, we collected 18 cone penetration test (CPT) data from a highly heterogeneous region in Lianyungang New Airport, Jiangsu Province, China, and used the control variable method to analyze the influence of the estimation method of tendency, its function type and outliers. The results show that, compared with the ordinary least square method (OLSM), the least absolute deviation method (LADM) can more truly reflect the trend component of CPT parameters in the vertical direction, and the influence of other factors on SOF and COV is also studied, such as outliers and estimation functions of trend components. On this basis, a reasonable calculation process of SOF and COV is summarized, which provides a reference for the calculation of SOF and COV in vertical direction in the future. By comparing the SOF calculated by different models, the results show that the squared exponential (SQX) model has the highest SOF in 68.3% of the evaluation, and the single exponential (SNX) model has the lowest SOF in 64.4% of the evaluation. Moreover, we compared the SOF and COV of cone tip resistance (qc) and sleeve friction (fs), which showed that SOF of qc and COV of qc is lower than that of fs in 54.4 and 73.3% of all evaluations, respectively.

Abstract Prohexadione-Calcium (Pro-Ca) plays key roles in improving fruit quality and yield by regulating various aspects of plant growth. However, the effects of how Pro-Ca regulates the regulation of sugar and acid balance and its impact on the production of volatile aroma substances during fruit growth and development are poorly understood. In this study, the Pro-Ca solutions developed at concentrations of 200, 400, 600 and 800 mg·L-1 were sprayed on the entire “Chardonnay” grape tree 22, 42, 62 and 82 days after initial flowering. The values of endogenous hormones, sugar and acid content, enzyme activities and flavor content were then measured in grapes 45, 65, 85 and 105 days (ripeness stage) after the initial flowering. The results showed that Pro-Ca had significant effects on fruits during development, including reducing ABA content, increasing ZT, GA3 and IAA levels, promoting fruit ripening and enhancing enzymes, which are involved in sugar and acid synthesis. Consequently, these effects led to an increase in sugar and acid content in the berries. Particularly during the ripening phase, the application of 600 mg L-1 Pro-Ca resulted in an increase in soluble sugar content of 11.28% and a significant increase in citric acid and malic acid content of 97.80% and 68.86%, respectively. Additionally, Pro-Ca treatment enhanced both the variety and quantity of aroma compounds present in the berries, with the 600 mg·L-1 Pro-Ca treatment showcasing the most favorable impact on volatile aroma compounds in ‘Chardonnay’ grapes. The levels of aldehydes, esters, alcohols, phenols, acids, ketones, and terpenes were significantly higher under the 600 mg·L-1 Pro-Ca treatment compared to those of control with 51.46 − 423.85% increase. In conclusion, Pro-Ca can regulate the content of endogenous hormones and the activities of enzymes related to sugar and acid metabolism in fruit, thereby increasing the content of soluble sugar and organic acid in fruit and the diversity and concentration of fruit aroma substances. Among them, foliar spraying 600 mg · L-1 Pro-Ca has the best effect. In the future, we need to further understand the molecular mechanism of Pro-Ca in grape fruit to lay a solid foundation for quality improvement breeding.

Purpose - Convenience stores are the main driving force of the small retail format in China. In order to meet customer demands, maintain product quality, reduce waste, and stay competitive in the fast-paced retail environment, convenience stores conduct daily food deliveries, with transportation costs accounting for the largest share of logistics expenses. Consequently, the daily operation costs of chain convenience stores increase. Therefore, the aim of this paper is to optimize the distribution paths of chain convenience stores with multiple distribution centers in order to reduce operational costs, reduce carbon dioxide emissions, etc. Design/methodology/approach - A mathematical model of the multi-temperature joint distribution problem with multiple distribution centers was constructed to minimize the total cost. In the model, six kinds of costs were considered. In addition, a two-stage algorithm was designed. The K-means algorithm was used to cluster match the demand points with the distribution centers, and the genetic algorithm was used to solve the routing problem of each distribution center. Subsequently, the costs of the multi-temperature joint distribution and multi-vehicle distribution were compared. Findings - The results showed that the optimization rate of total cost was 26.35%, the optimization rate of other costs was greater than 45%. Note that through case solving, the K-means algorithm was used to convert the single problem of multiple distribution centers into multiple problems of one distribution center. The multi-temperature joint distribution pattern was applied to the food distribution of chain convenience stores so that they could improve the delivery time and reduce the number of vehicles, carbon dioxide emissions, and total cost. Originality/value - Previous research on multi-temperature joint deliveries has predominantly focused on distribution from a single distribution center, overlooking the study of multi-distribution center scenarios. This article addresses this gap and explores the feasibility of applying a multi-temperature joint delivery service model to food distribution for chain convenience stores with multiple distribution centers.

The high cost associated with high-fidelity computational fluid dynamics (CFD) is one of the main challenges that inhibit the design and optimisation of new fluid-flow systems. In this study, we explore the feasibility of a physics-informed deep learning approach to predict turbulence evolution and mixing without requiring a classical CFD solver. The deep learning architecture was inspired by integrating U-Net with inception modules for capturing the multi-scale nature of turbulent flows. In addition, a physics-constrained loss function was designed to enforce the mass and pressure conservation of the predicted solution. After trained, the optimised model was validated in the large eddy simulation (LES) of a forced turbulent mixing layer at two distinct Reynolds numbers ( $\mathrm {Re} =3000$ and 30000). The results demonstrate that the proposed approach achieves a promising solution accuracy and extrapolation ability with a significant reduction in computing time when compared to those obtained using a classical LES flow solver. The success in developing such a physics-informed deep learning approach not only justifies the potential of ML-based surrogate solvers for fast prototyping and design of generic fluid-flow systems but also highlights the key challenges arising from data-driven surrogate solver development for turbulence modelling.

In this paper, the discharge coefficient prediction model for this structure in a subcritical flow regime is first established by extreme learning machine (ELM) and Bayesian network, and the model's performance is analyzed and verified in detail. In addition, the global sensitivity analysis method is introduced to the optimal prediction model to analyze the sensitivity for the dimensionless parameters affecting the discharge coefficient. The results show that the Bayesian extreme learning machine (BELM) can effectively predict the discharge coefficients of the symmetric stepped labyrinth side weir. The range of 95% confidence interval [−0.055,0.040] is also significantly smaller than that of the ELM ([−0.089,0.076]) and the Kernel extreme learning machine (KELM) ([−0.091,0.081]) at the testing stage. The dimensionless parameter ratio of upstream water depth of stepped labyrinth side weir p/y1 has the greatest effect on the discharge coefficient Cd, accounting for 55.57 and 54.17% under single action and other parameter interactions, respectively. Dimensionless step number bs/L has little effect on Cd, which can be ignored. Meanwhile, when the number of steps is less (N = 4) and the internal head angle is smaller (θ = 45°), a larger discharge coefficient value can be obtained. HIGHLIGHTS The discharge coefficient model of a symmetrical stepped labyrinth side weir is developed.; The quantitative model of the discharge coefficient is established.; The discharge characteristics of a symmetrical stepped labyrinth side weir are analyzed comprehensively.; The effects of different dimensionless parameters on the discharge coefficient are compared.;

Rapid advancements in electric vehicle (EV) technology have highlighted the importance of lithium-ion (Li) batteries. These batteries are essential for safety and reliability. Battery data show non-stationarity and complex dynamics, presenting challenges for current monitoring and prediction methods. These methods often fail to manage the variability seen in real-world environments. To address these challenges, we propose a Transformer model with a wavelet transform dynamic attention mechanism (WADT). The dynamic attention mechanism uses wavelet transform. It focuses adaptively on the most informative parts of the battery data to enhance the anomaly detection accuracy. We also developed a deep learning model with an improved Transformer architecture. This architecture is tailored for the complex dynamics of battery data time series. The model accounts for temporal dependencies and adapts to non-stationary behavior. Experiments on public battery datasets show our approach’s effectiveness. Our model significantly outperforms existing technologies with an accuracy of 0.89 and an AUC score of 0.88. These results validate our method’s innovation and effectiveness.

Understanding the strength development of alkali-activated materials (AAMs) with fly ash (FA) and granulated blast furnace slag (GBFS) is crucial for designing high-performance AAMs. This study investigates the strength development mechanism of AAMs using machine learning. A total of 616 uniaxial compressive strength (UCS) data points from FA-GBFS-based AAM mixtures were collected from published literature to train four tree-based machine learning models. Among these models, Gradient Boosting Regression (GBR) demonstrated the highest prediction accuracy, with a correlation coefficient (R-value) of 0.970 and a root mean square error (RMSE) of 4.110 MPa on the test dataset. The SHapley Additive exPlanations (SHAP) analysis revealed that water content is the most influential variable in strength development, followed by curing periods. The study recommends a calcium-to-silicon ratio of around 1.3, a sodium-to-aluminum ratio slightly below 1, and a silicon-to-aluminum ratio slightly above 3 for optimal AAM performance. The proposed design model was validated through laboratory experiments with FA-GBFS-based AAM mixtures, confirming the model’s reliability. This research provides novel insights into the strength development mechanism of AAMs and offers a practical guide for elemental design, potentially leading to more sustainable construction materials.

Aiming at the demand for long-range and high-resolution imaging detection of small targets such as submerged submarine markers in shallow coastal waters, research on single-photon lidar imaging technology is carried out. This paper reports the sequential two-mode fusion imaging algorithm, which has a strong information extraction capability and can reconstruct scene target depth and reflection intensity images from complex signal photon counts. The algorithm consists of four steps: data preprocessing, extremely large group value estimation, noise sieving, and total variation smoothing constraints to image the target with high quality. Simulation and test results show that the imaging performance and imaging characteristics of the method are better than the current high-performance first-photon group imaging algorithm, indicating that the method has a great advantage in sparse photon counting imaging, and the method proposed in this paper constructs a clear depth and reflectance intensity image of the target scene, even in the 50,828 Lux ambient strong light and strong interference, the 0.1 Lux low-light environment, or the underwater high-attenuation environment.

Abstract Background Cancer is a thorny problem which cannot be conquered by mankind at present and recent researchers have put their focus on tumor microenviroment. Neutrophils, the prominent leukocytes in peripheral blood that accumulate in tumours, serves as frontline cells in response to tumour progression owing to the rapid development of micro biotechnology. Hence, targeted therapy with these neutrophils has made targeting treatment a promising field in cancer therapy. Main body We broadly summarise some studies on the phenotypes and functions of tumour‐associated neutrophils as well as the unique web‐like products of neutrophils that play a role in cancer progression—neutrophil extracellular traps—and the interactions between neutrophils and the tumour microenvironment. Moreover, several targeted neutrophils therapeutic studies have made some progress and provided potential strategies for the treatment of cancer. Conclusion This review aims to offer a holistic perspective on therapeutic interventions targeting neutrophils to further inspire more researches on cancer therapies.

BackgroundThe tumor microenvironment (TME) plays a pivotal role in the progression and metastasis of lung adenocarcinoma (LUAD). However, the detailed characteristics of LUAD and its associated microenvironment are yet to be extensively explored. This study aims to delineate a comprehensive profile of the immune cells within the LUAD microenvironment, including CD8+ T cells, CD4+ T cells, and myeloid cells. Subsequently, based on marker genes of exhausted CD8+ T cells, we aim to establish a prognostic model for LUAD.MethodUtilizing the Seurat and Scanpy packages, we successfully constructed an immune microenvironment atlas for LUAD. The Monocle3 and PAGA algorithms were employed for pseudotime analysis, pySCENIC for transcription factor analysis, and CellChat for analyzing intercellular communication. Following this, a prognostic model for LUAD was developed, based on the marker genes of exhausted CD8+ T cells, enabling effective risk stratification in LUAD patients. Our study included a thorough analysis to identify differences in TME, mutation landscape, and enrichment across varying risk groups. Moreover, by integrating risk scores with clinical features, we developed a new nomogram. The expression of model genes was validated via RT-PCR, and a series of cellular experiments were conducted, elucidating the potential oncogenic mechanisms of GALNT2.ResultsOur study developed a single-cell atlas for LUAD from scRNA-seq data of 19 patients, examining crucial immune cells in LUAD’s microenvironment. We underscored pDCs’ role in antigen processing and established a Cox regression model based on CD8_Tex-LAYN genes for risk assessment. Additionally, we contrasted prognosis and tumor environments across risk groups, constructed a new nomogram integrating clinical features, validated the expression of model genes via RT-PCR, and confirmed GALNT2’s function in LUAD through cellular experiments, thereby enhancing our understanding and approach to LUAD treatment.ConclusionThe creation of a LUAD single-cell atlas in our study offered new insights into its tumor microenvironment and immune cell interactions, highlighting the importance of key genes associated with exhausted CD8+ T cells. These discoveries have enabled the development of an effective prognostic model for LUAD and identified GALNT2 as a potential therapeutic target, significantly contributing to the improvement of LUAD diagnosis and treatment strategies.

Teaching point: A ruptured aorto-iliac aneurysm, complicated by an iliac arteriovenous fistula, is rare but has a possibly fatal outcome and requires prompt diagnosis and appropriate treatment.

For distributed fiber-optic sensors, slowly varying vibration signals down to 5 mHz are difficult to measure due to low signal-to-noise ratios. We propose and demonstrate a forward transmission-based distributed sensing system, combined with a polarization-generated carrier for detection bandwidth reduction, and cross-correlation for vibration positioning. By applying a higher-frequency carrier signal using a fast polarization controller, the initial phase of the known carrier frequency is monitored and analyzed to demodulate the vibration signal. Only the polarization carrier needs to be analyzed, not the arbitrary-frequency signal, which can lead to hardware issues (reduced detection bandwidth and less noise). The difference in arrival time between the two detection ends obtained through cross-correlation can determine the vibration position. Our experimental results demonstrate a sensitivity of 0.63 mrad/με and a limit of detection (LoD) of 355.6 pε/Hz1/2 at 60 Hz. A lock-in amplifier can be used on the fixed carrier to achieve a minimal LoD. The sensing distance can reach 131.5 km and the positioning accuracy is 725 m (root-mean-square error) while the spatial resolution is 105 m. The tested vibration frequency range is between 0.005 Hz and 160 Hz. A low frequency of 5 mHz for forward transmission-based distributed sensing is highly attractive for seismic monitoring applications.

Abstract Effective electrocatalysts are crucial for facilitating the oxygen evolution reaction (OER), the anodic reaction of water electrolysis for renewable green hydrogen production. Perovskite oxides are a group of potential catalysts featuring the lattice oxygen mechanism (LOM) for OER, where O2 formation commences via a lattice oxygen redox process. The LOM pathway breaks the thermodynamic limitation of the adsorbate evolution mechanism (AEM) and achieves a high intrinsic activity. However, perovskite oxides often suffer high OER overpotentials due to the insufficient activation of the LOM pathway. Typically, the overpotential exceeds 300 mV at 10 mA cm−2. This greatly impedes the practical applications of perovskite oxide based OER catalysts. Here, it is demonstrated that the B‐site‐metal exsolution of a La0.6Sr0.4Fe0.8Ni0.2O3‐δ perovskite increases the activity of LOM by a factor of 3.8 at 400 mV overpotential. The activated LOM pathway leads to a 36‐mV reduction in the overpotential at 10 mA cm−2 (from 310 mV to 274 mV) and a 2× increase in the turnover frequency (TOF) at 450 mV overpotential. A membrane electrode assembly (MEA) water electrolyzer equipped with this LSFN‐based catalyst offers 1 A cm−2 current density at 2.46 V and 24‐h operation stability.

Largemouth bass virus (LMBV) poses a significant threat to largemouth bass farming, leading to substantial economic losses. In December 2022, massive largemouth bass juveniles died at a fish farm in the city of Xinxiang, China. Through a series of experiments, we conclusively identified LMBV as the causative pathogen. The affected fish displayed anorexia, lethargy, and hemorrhage at the pectoral and caudal fin base. No parasites or pathogenic bacteria were detected on the body surface or gills, or isolated from the diseased fish. Severe hemorrhage, lymphocyte infiltration, and extensive necrosis were observed in the liver, spleen, intestine, and stomach of the moribund fish. The tissue homogenate from the diseased fish induced epithelioma papulosum cyprini cells (EPC) cell death, while no such effects were observed in grouper spleen (GS) cells. Sequence similarity analysis of the major capsid protein (MCP) indicated the virus shared 100% similarity with the LMBV-FS2021 strain, placing it within the Ranavirus genus. Transmission electron microscopy (TEM) observations revealed plenty of hexagonal virions accumulated in the cytoplasm of infected EPC cells. Artificial infection demonstrated that LMBV-XX01 was highly fatal to Micropterus salmoides juveniles, with an LD50 of 103.081 TCID50/fish. RT-qPCR detection confirmed that LMBV appeared in all sampled tissues of the challenged largemouth bass, with significantly higher viral loads detected in the liver and heart compared to other tissues. Additionally, we successfully obtained a highly purified recombinant MCP of LMBV and developed two strains of monoclonal antibodies targeting MCP of LMBV-XX01. Overall, our findings provide valuable materials and insights for the design of prevention strategies and the development of detection methods for LMBV.

The sand cat swarm optimization algorithm (SCSO) is a novel metaheuristic algorithm that has been proposed in recent years. The algorithm optimizes the search ability of individuals by mimicking the hunting behavior of sand cat groups in nature, thereby achieving robust optimization performance. It is characterized by few control parameters and simple operation. However, due to the lack of population diversity, SCSO is less efficient in solving complex problems and is prone to fall into local optimization. To address these shortcomings and refine the algorithm’s efficacy, an improved multi-strategy sand cat optimization algorithm (IMSCSO) is proposed in this paper. In IMSCSO, a roulette fitness–distance balancing strategy is used to select codes to replace random agents in the exploration phase and enhance the convergence performance of the algorithm. To bolster population diversity, a novel population perturbation strategy is introduced, aiming to facilitate the algorithm’s escape from local optima. Finally, a best–worst perturbation strategy is developed. The approach not only maintains diversity throughout the optimization process but also enhances the algorithm’s exploitation capabilities. To evaluate the performance of the proposed IMSCSO, we conducted experiments in the CEC 2017 test suite and compared IMSCSO with seven other algorithms. The results show that the IMSCSO proposed in this paper has better optimization performance.

Background: Gliomas are characterized by aggressive behavior, leading to severe disability and high mortality. Ubiquitin-like modifier activating enzyme 2 (UBA2) is a subunit of the E1-activating enzyme involved in the SUMOylation (SUMO, small ubiquitin-related modifier) of numerous proteins. Although the abnormality of UBA2 is linked to the progression of various tumor types, the role of UBA2 in glioma is still unknown. Methods: A bioinformatic analysis using several public databases was conducted to examine the expression level, clinicopathological correlations, and prognostic significance of UBA2 in glioma. The correlation between UBA2 expression and drug sensitivity in cancers was also explored. Multiple cellular experiments were conducted to validate the role of UBA2 in glioma. Results: Analysis of multiple databases and cellular experiments revealed that UBA2 was overexpressed in glioma tissues and cell lines, respectively. UBA2 expression in gliomas correlated with World Health Organization (WHO) grade, IDH gene status, 1p19q deletion, histological type, and immune cell infiltration in glioma. UBA2 expression in carcinomas also correlated with drug sensitivity. Kaplan-Meier analysis revealed that high expression of UBA2 predicted poorer survival in glioma patients. A nomogram model containing UBA2 expression was constructed for clinical practice. Knockdown of UBA2 was observed to suppress glioma cell progression and sensitize glioma cells to irradiation in vitro. Conclusion: Overall, this research showed that UBA2 might be involved not only in the development of glioma but also in the regulation of immunity, drug sensitivity, and radiosensitivity. Therefore, UBA2 may be a potential target for therapy and a candidate biomarker for glioma diagnosis and prognosis.

Leucine-rich repeat kinase 2 (LRRK2) is a serine–threonine protein kinase belonging to the ROCO protein family. Within the kinase domain of LRRK2, a point mutation known as LRRK2 G2019S has emerged as the most prevalent variant associated with Parkinson’s disease. Recent clinical studies have indicated that G2019S carriers have an elevated risk of cancers, including colon cancer. Despite this observation, the underlying mechanisms linking LRRK2 G2019S to colon cancer remain elusive. In this study, employing a colitis-associated cancer (CAC) model and LRRK2 G2019S knock-in (KI) mouse model, we demonstrate that LRRK2 G2019S promotes the pathogenesis of colon cancer, characterized by increased tumor number and size in KI mice. Furthermore, LRRK2 G2019S enhances intestinal epithelial cell proliferation and inflammation within the tumor microenvironment. Mechanistically, KI mice exhibit heightened susceptibility to DSS-induced colitis, with inhibition of LRRK2 kinase activity ameliorating colitis severity and CAC progression. Our investigation also reveals that LRRK2 G2019S promotes inflammasome activation and exacerbates gut epithelium necrosis in the colitis model. Notably, GSDMD inhibitors attenuate colitis in LRRK2 G2019S KI mice. Taken together, our findings offer experimental evidence indicating that the gain-of-kinase activity in LRRK2 promotes colorectal tumorigenesis, suggesting LRRK2 as a potential therapeutic target in colon cancer patients exhibiting hyper LRRK2 kinase activity.

Electric vehicles (EVs) play a major role in the energy system because they are clean and environmentally friendly and can use excess electricity from renewable sources. In order to meet the growing charging demand for EVs and overcome its negative impact on the power grid, new EV charging stations integrating photovoltaic (PV) and energy storage systems (ESSs) have emerged. However, the output of solar PV systems and the charging demand of EVs are both characterized by uncertainty and dynamics. These may lead to large power fluctuations in the grid and frequent alternation of peak and valley loads, which are not conducive to the stability of the distribution network. The study of reasonable capacity configuration and control strategy issues is conducive to the efficient use of solar energy, fast charging of EVs, stability of the distribution network, and maximization of the economic benefits of the system. In this paper, the concept, advantages, capacity allocation methods and algorithms, and control strategies of the integrated EV charging station with PV and ESSs are reviewed. On the basis of the above research, the current problems and challenges are analyzed, and corresponding solutions and ideas are proposed.

In this work, the major methods for implementing flexible sensing technology—flexible surface acoustic wave (SAW) sensors—are summarized; the working principles and device characteristics of the flexible SAW sensors are introduced; and the latest achievements of the flexible SAW sensors in the selection of the substrate materials, the development of the piezoelectric thin films, and the structural design of the interdigital transducers are discussed. This paper focuses on analyzing the research status of physical flexible SAW sensors such as temperature, humidity, and ultraviolet radiation, including the sensing mechanism, bending strain performance, device performance parameters, advantages and disadvantages, etc. It also looks forward to the development of future chemical flexible SAW sensors for gases, the optimization of the direction of the overall device design, and systematic research on acoustic sensing theory under strain. This will enable the manufacturing of multifunctional and diverse sensors that better meet human needs.

There have been many studies on surface acoustic wave (SAW) sensors for detecting sulfur-containing toxic or harmful gases. This paper aims to give an overview of the current state of polymer films used in SAW sensors for detecting deleterious gases. By covering most of the important polymer materials, the structures and types of polymers are summarized, and a variety of devices with different frequencies, such as delay lines and array sensors for detecting mustard gas, hydrogen sulfide, and sulfur dioxide, are introduced. The preparation method of polymer films, the sensitivity of the SAW gas sensor, the limit of detection, the influence of temperature and humidity, and the anti-interference ability are discussed in detail. The advantages and disadvantages of the films are analyzed, and the potential application of polymer films in the future is also forecasted.

Fluid slosh caused by residual acceleration in microgravity is a common problem encountered in space engineering. To solve this problem, the ground-based experiment research on the influence of gravity jitter and gravitational tilt on the thermocapillary convection (TCC) transition behaviour of non-axisymmetric liquid bridge has become an important issue in microgravity fluid management. Based on a mesoscale liquid bridge experimental platform which can realize gravitational tilt, the effect of gravitational tilt on TCC by using a high-speed camera equipped with a near-focus lens and a self-developed interface image recognition package. The results show that the spatio-temporal evolution of TCC by the influence of gravitational tilt is still divided into steady and oscillatory flow. In the stable TCC, the vortex core distortion of cellular flow caused by the imbalance left and right interface curvature invites cellular flow close to the free surface, and it shrinks to the intermediate height. As gravitational tilt increases, the transverse/longitudinal velocity peaks are significantly reduced, peak velocity has been reduced by 26%–27%. Meanwhile, the longitudinal velocity gradient at the free interface increases significantly. Therefore, gravitational tilt plays an important role in improving the surface flow velocity. In the oscillatory TCC, the position of vortex core is closer to the free interface at the hot/cold corner as the periodic mutual occupation of the left and right cellular flows. The TCC is obviously inhibited due to the gravitational tilt. The critical temperature difference is increased by 25% and the onset of temperature oscillation at the hot corner is delayed by 20% compared with conventional gravity condition.

In this paper, a microheater that can absorb thermal stress and has a large heating area is demonstrated by optimizing the structure and process of the microheater. Four symmetrically distributed elongated support beam structures were machined around the microheater via deep silicon etching. This design efficiently mitigates the deformation of the heated region caused by thermal expansion and enhances the structural stability of the microheater. The updated microheater no longer converts the work area into a thin film; instead, it creates a stable heating platform that can uniformly heat a work area measuring 10 × 10 mm2. The microheater is verified to have high temperature uniformity and structural stability in finite element simulation. Finally, thorough investigations of electrical–thermal–structural characterization were conducted. The test findings show that the new microheater can achieve 350 °C with a power consumption of 6 W and a thermal reaction time of 22 s. A scan of its whole plane reveals that the surface of the working area of the new microheater is flat and does not distort in response to variations in temperature, offering good structural stability.

Laser scanning 3D imaging technology, because it can obtain accurate three-dimensional surface data, has been widely used in the search for wrecks and rescue operations, underwater resource development, and other fields. At present, the conventional underwater spinning laser scanning imaging system maintains a relatively fixed light window. However, in low-light situations underwater, the rotation of the scanning device causes some degree of water fluctuation, which warps the light strip data that the system sensor receives about the object’s surface. To solve this problem, this research studies an underwater 3D scanning and imaging system that makes use of a fixed light window and a spinning laser (FWLS). A refraction error compensation algorithm is investigated that is based on the fundamentals of linear laser scanning imaging, and a dynamic refraction mathematical model is established based on the motion of the imaging device. The results of the experiment on error analysis in an optimal underwater environment indicate that the error in reconstructing the radius is decreased by 60% (from 2.5 mm to around 1 mm) when compensating for the measurement data of a standard sphere with a radius of 20 mm. Moreover, the compensated point cloud data exhibit a higher degree of correspondence with the model of the standard spherical point cloud. Furthermore, we examine the impact of physical noise, measurement distance, and partial occlusion of the object on the imaging system inside an authentic underwater setting. This study is a good starting point for looking at the refractive error of an underwater laser scanning imaging system. It also provides to us some ideas for future research on the refractive error of other scanning imaging methods.

Background: Improving job performance has a significant effect on the quality of medical services and ensuring people's health. Purpose: This study explores the influence and mechanism of the character strengths and career callings of medical staff as well as the intermediary role of such career calling. Methodology: A cross-sectional survey was conducted of 414 healthcare staff members in public hospitals in Hangzhou. Descriptive statistics and hierarchical linear regressions were used to analyze the medical staff's job performance and related factors, and structural equation modeling path analysis was used to explore and validate the influence and mechanism of character advantage and career calling on job performance. Results: The results show that medical staff character strengths have a positive impact on job performance. Path analysis shows that character strengths indirectly affect job performance, and career calling plays a partial mediating role in character strengths and job performance. Conclusion: The results show that good personalities promote job performance, and the association is more significant under a high sense of career calling. [ABSTRACT FROM AUTHOR]

This paper introduces spatio-temporal-based multi-level aggregation network (ST-MANet) for action recognition. It utilizes the correlations between different spatial positions and the correlations between different temporal positions on the feature map to explore long-range spatial and temporal dependencies, respectively, generating the spatial and temporal attention map that assigns different weights to features at different spatial and temporal locations. Additionally, a multi-scale approach is introduced, proposing a multi-scale behavior recognition framework that models various visual rhythms while capturing multi-scale spatiotemporal information. A spatial diversity constraint is then proposed, encouraging spatial attention maps at different scales to focus on distinct areas. This ensures a greater emphasis on spatial information unique to each scale, thereby incorporating more diverse spatial information into multi-scale features. Finally, ST-MANet is compared with existing approaches, demonstrating high accuracy on the three datasets. [ABSTRACT FROM AUTHOR]

This paper addresses the common orthopedic trauma of spinal vertebral fractures and aims to enhance doctors' diagnostic efficiency. Therefore, a deep-learning-based automated diagnostic systemwithmulti-label segmentation is proposed to recognize the condition of vertebral fractures. The whole spine Computed Tomography (CT) image is segmented into the fracture, normal, and background using U-Net, and the fracture degree of each vertebra is evaluated (Genant semi-qualitative evaluation). The main work of this paper includes: First, based on the spatial configuration network (SCN) structure, U-Net is used instead of the SCN feature extraction network. The attention mechanismand the residual connection between the convolutional layers are added in the local network (LN) stage. Multiple filtering is added in the global network (GN) stage, and each layer of the LN decoder feature map is filtered separately using dot product, and the filtered features are re-convolved to obtain the GN output heatmap. Second, a network model with improved SCN (M-SCN) helps automatically localize the center-of-mass position of each vertebra, and the voxels around each localized vertebra were clipped, eliminating a large amount of redundant information (e.g., background and other interfering vertebrae) and keeping the vertebrae to be segmented in the center of the image. Multilabel segmentation of the clipped portion was subsequently performed using U-Net. This paper uses VerSe'19, VerSe'20 (using only data containing vertebral fractures), and private data (provided by Guizhou Orthopedic Hospital) for model training and evaluation. Compared with the original SCN network, the M-SCN reduced the prediction error rate by 1.09% and demonstrated the effectiveness of the improvement in ablation experiments. In the vertebral segmentation experiment, the Dice Similarity Coefficient (DSC) index reached 93.50% and the Maximum Symmetry Surface Distance (MSSD) index was 4.962 mm, with accuracy and recall of 95.82% and 91.73%, respectively. Fractured vertebrae were also marked as red and normal vertebrae were marked as white in the experiment, and the semi-qualitative assessment results of Genant were provided, as well as the results of spinal localization visualization and 3D reconstructed views of the spine to analyze the actual predictive ability of the model. It provides a promising tool for vertebral fracture detection. [ABSTRACT FROM AUTHOR]

The voltage-gated calcium channel CaV1.2 is essential for cardiac and vessel smooth muscle contractility and brain function. Accumulating evidence demonstrates that malfunctions of CaV1.2 are involved in brain and heart diseases. Pharmacological inhibition of CaV1.2 is therefore of therapeutic value. Here, we report cryo-EM structures of CaV1.2 in the absence or presence of the antirheumatic drug tetrandrine or antihypertensive drug benidipine. Tetrandrine acts as a pore blocker in a pocket composed of S6II, S6III, and S6IV helices and forms extensive hydrophobic interactions with CaV1.2. Our structure elucidates that benidipine is located in the DIII-DIV fenestration site. Its hydrophobic sidechain, phenylpiperidine, is positioned at the exterior of the pore domain and cradled within a hydrophobic pocket formed by S5DIII, S6DIII, and S6DIV helices, providing additional interactions to exert inhibitory effects on both L-type and T-type voltage gated calcium channels. These findings provide the structural foundation for the rational design and optimization of therapeutic inhibitors of voltage-gated calcium channels. [ABSTRACT FROM AUTHOR]