Search

Background: Early detection of spinal cord injury (SCI) is conducive to improving patient outcomes. In addition, many studies have revealed the role of immune cells in the progression or treatment of SCI. The objective of this study was to identify the early signature genes and clarify how they are related to immune cell infiltration in SCI. Methods: We analysed and identified early signature genes associated with SCI via bioinformatics analysis of the GSE151371 dataset from the GEO database. These genes were subsequently verified in the GSE33886 dataset and qRTPCR. Finally, the CIBERSORT algorithm was used to examine the immune cell infiltration in SCI and its relationship with signature genes. Results: Seven SCI-related signature genes, including ARG1, RETN, BPI, GGH, CCNB1, HIST1H2AC, and HIST1H2BJ, were identified, and their expression was verified via an external validation cohort and qRTPCR. Moreover, the ROC curves revealed the diagnostic value of these genes. In addition, on the basis of immune cell infiltration analysis, plasma cells, M0 macrophages, activated CD4+ memory T cells, γδ T cells, naive CD4+ T cells, and resting CD4+ memory T cells may participate in the progression of SCI. Conclusion: This study identified seven early signature genes of SCI that may serve as biomarkers for the early diagnosis of SCI and contribute to our understanding of immune changes during the pathology of SCI.

Highlights Low-temperature fabrication of black-phase CsPbI3 perovskite films is first demonstrated by using diphenylphosphinic chloride additive under 30–50 °C, arising from the steric effect and chloride insertion engineering. Large-area high-quality all-inorganic perovskite films with fewer defects enhanced crystallographic orientation, and excellent environmental stability is fabricated. The record performances are demonstrated for flexible wearable photodetectors with a responsivity of 42.1 A W−1, a detectivity of 1.3 × 1014 Jones, high-fidelity image, photoplethysmography sensor functions, and high mechanical stability.

Abstract Rheumatoid arthritis (RA) is a debilitating autoimmune disease characterized by chronic joint inflammation and cartilage damage. Current therapeutic strategies often result in side effects, necessitating the development of targeted and safer treatment options. This study introduces a novel nanotherapeutic system, 2-APB@DGP-MM, which utilizes macrophage membrane (MM)-encapsulated nanoparticles (NPs) for the targeted delivery of 2-Aminoethyl diphenylborinate (2-APB) to inflamed joints more effectively. The NPs are designed with a matrix metalloproteinase (MMP)-cleavable peptide, allowing for MMP-responsive drug release within RA microenvironment. Comprehensive in vitro and in vivo assays confirmed the successful synthesis and loading of 2-APB into the DSPE-GPLGVRGC-PEG (DGP) NPs, as well as their ability to repolarize macrophages from a pro-inflammatory M1 to an anti-inflammatory M2 phenotype. The NPs demonstrated high biocompatibility, low cytotoxicity, and enhanced cellular uptake. In a collagen-induced arthritis (CIA) mouse model, intra-articular injection of 2-APB@DGP-MM significantly reduced synovial inflammation and cartilage destruction. Histological analysis corroborated these findings, demonstrating marked improvements in joint structure and delayed disease progression. Above all, the 2-APB@DGP-MM nanotherapeutic system offers a promising and safe approach for RA treatment by modulating macrophage polarization and delivering effective agents to inflamed joints. Graphical Abstract

Background: Heat shock proteins (HSPs) play a critical role in cellular stress responses and have been implicated in numerous diseases, including Parkinson's disease (PD) and various cancers. Understanding the differential expression and functional implications of HSPs in these conditions is crucial for identifying potential therapeutic targets and biomarkers for diagnosis and prognosis. Methods: We utilized combined datasets (GSE6613 and GSE72267) to identify and analyze the heat shock-related genes differentially expressed in PD. Gene Set Variation Analysis (GSVA) was performed to explore functional profiles, while LASSO regression was employed to screen potential PD biomarkers. In glioma, prognostic value, immune infiltration, and pathway enrichment associated with HSPA1L gene expression were assessed via Kaplan-Meier plots, ssGSEA, and enrichment analyses. Results: In PD, we identified 17 differentially expressed HSPs. Enrichment analysis revealed significant pathways related to protein homeostasis and cellular stress responses. LASSO regression pinpointed 12 genes, including HSPA1L, as significant markers for PD, with nomogram and calibration plots indicating predictive accuracy. Stratification based on HSPA1L expression in PD highlighted differentially active biological processes, immune responses, and metabolic disruptions. In the pan-cancer analysis, HSPA1L showed variable expression across cancer types and a significant correlation with patient survival and immune infiltration. In glioma, low HSPA1L expression was associated with worse overall survival, distinct immune infiltration patterns, and altered pathway activities. Conclusion: This integrative study reveals the substantial role of HSPs, especially HSPA1L, in the pathogenesis and prognosis of PD and glioma. Our findings offer new perspectives on the molecular mechanisms underlying these diseases and propose HSPA1L as a potential prognostic biomarker and a target for therapeutic intervention.

BackgroundIt has been reported that COVID-19 patients have an increased risk of developing IBS; however, the underlying genetic mechanisms of these associations remain largely unknown. The aim of this study was to investigate potential shared SNPs, genes, proteins, and biological pathways between COVID-19 and IBS by assessing pairwise genetic correlations and cross-trait genetic analysis.Materials and methodsWe assessed the genetic correlation between three COVID-19 phenotypes and IBS using linkage disequilibrium score regression (LDSC) and high-definition likelihood (HDL) methods. Two different sources of IBS data were combined using METAL, and the Multi-trait analysis of GWAS (MTAG) method was applied for multi-trait analysis to enhance statistical robustness and discover new genetic associations. Independent risk loci were examined using genome-wide complex trait analysis (GCTA)-conditional and joint analysis (COJO), multi-marker analysis of genomic annotation (MAGMA), and functional mapping and annotation (FUMA), integrating various QTL information and methods to further identify risk genes and proteins. Gene set variation analysis (GSVA) was employed to compute pleiotropic gene scores, and combined with immune infiltration algorithms, IBS patients were categorized into high and low immune infiltration groups.ResultsWe found a positive genetic correlation between COVID-19 infection, COVID-19 hospitalization, and IBS. Subsequent multi-trait analysis identified nine significantly associated genomic loci. Among these, eight genetic variants were closely related to the comorbidity of IBS and COVID-19. The study also highlighted four genes and 231 proteins associated with the susceptibility to IBS identified through various analytical strategies and a stratification approach for IBS risk populations.ConclusionsOur study reveals a shared genetic architecture between these two diseases, providing new insights into potential biological mechanisms and laying the groundwork for more effective interventions.

Abstract Special attention was drawn to the heavy metals contained in coal, due to it will cause harm to the environment during coal processing and utilization. The sequential chemical extraction of Shanxi coal (SX coal) and Wulanchabu coal (WLCB coal) was carried out to investigate the distribution of arsenic (As) in coals. Two raw coals were pyrolyzed at 300–900 °C in horizontal tubular furnace to investigate release behavior of As during pyrolysis process. The results showed that As in SX coal mainly existed in aluminosilicate-bound state (40.25%) and disulfide-bound state (32.51%), followed by carbonate-bound state and organic-bound state. The As in WLCB coal mainly existed in aluminosilicate-bound state (62.50%), followed by disulfide-bound state (19.10%). The As contents of water-soluble, ion-exchange and residue states in the two coals were less than others. The modes of occurrence of As had great influence on its volatilization behavior. As in organic part was easy to volatilize at low temperature. Sulfide-bound state would escape with the decomposition of pyrite. Because SX coal contained higher organic state and sulfide-bound state, the volatilization rate of As was higher than WLCB coal at any temperature, and the difference was more obvious at low temperature. In addition, FactSage simulation value was basically consistent with the experimental value.

Coronavirus disease 2019 (COVID-19) is a severe global public health emergency that has caused a major crisis in the safety of human life, health, global economy, and social order. Moreover, COVID-19 poses significant challenges to healthcare systems worldwide. The prediction and early warning of infectious diseases on a global scale are the premise and basis for countries to jointly fight epidemics. However, because of the complexity of epidemics, predicting infectious diseases on a global scale faces significant challenges. In this study, we developed the second version of Global Prediction System for Epidemiological Pandemic (GPEP-2), which combines statistical methods with a modified epidemiological model. The GPEP-2 introduces various parameterization schemes for both impacts of natural factors (seasonal variations in weather and environmental impacts) and human social behaviors (government control and isolation, personnel gathered, indoor propagation, virus mutation, and vaccination). The GPEP-2 successfully predicted the COVID-19 pandemic in over 180 countries with an average accuracy rate of 82.7%. It also provided prediction and decision-making bases for several regional-scale COVID-19 pandemic outbreaks in China, with an average accuracy rate of 89.3%. Results showed that both anthropogenic and natural factors can affect virus spread and control measures in the early stages of an epidemic can effectively control the spread. The predicted results could serve as a reference for public health planning and policymaking.

Corona virus disease 2019 (COVID-19) has exerted a profound adverse impact on human health. Studies have demonstrated that aerosol transmission is one of the major transmission routes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Pathogenic microorganisms such as SARS-CoV-2 can survive in the air and cause widespread infection among people. Early monitoring of pathogenic microorganism transmission in the atmosphere and accurate epidemic prediction are the frontier guarantee for preventing large-scale epidemic outbreaks. Monitoring of pathogenic microorganisms in the air, especially in densely populated areas, may raise the possibility to detect viruses before people are widely infected and contain the epidemic at an earlier stage. The multi-scale coupled accurate epidemic prediction system can provide support for governments to analyze the epidemic situation, allocate health resources, and formulate epidemic response policies. This review first elaborates on the effects of the atmospheric environment on pathogenic microorganism transmission, which lays a theoretical foundation for the monitoring and prediction of epidemic development. Secondly, the monitoring technique development and the necessity of monitoring pathogenic microorganisms in the atmosphere are summarized and emphasized. Subsequently, this review introduces the major epidemic prediction methods and highlights the significance to realize a multi-scale coupled epidemic prediction system by strengthening the multidisciplinary cooperation of epidemiology, atmospheric sciences, environmental sciences, sociology, demography, etc. By summarizing the achievements and challenges in monitoring and prediction of pathogenic microorganism transmission in the atmosphere, this review proposes suggestions for epidemic response, namely, the establishment of an integrated monitoring and prediction platform for pathogenic microorganism transmission in the atmosphere.

In the global challenge of Coronavirus disease 2019 (COVID-19) pandemic, accurate prediction of daily new cases is crucial for epidemic prevention and socioeconomic planning. In contrast to traditional local, one-dimensional time-series data-based infection models, the study introduces an innovative approach by formulating the short-term prediction problem of new cases in a region as multidimensional, gridded time series for both input and prediction targets. A spatial-temporal depth prediction model for COVID-19 (ConvLSTM) is presented, and further ConvLSTM by integrating historical meteorological factors (Meteor-ConvLSTM) is refined, considering the influence of meteorological factors on the propagation of COVID-19. The correlation between 10 meteorological factors and the dynamic progression of COVID-19 was evaluated, employing spatial analysis techniques (spatial autocorrelation analysis, trend surface analysis, etc.) to describe the spatial and temporal characteristics of the epidemic. Leveraging the original ConvLSTM, an artificial neural network layer is introduced to learn how meteorological factors impact the infection spread, providing a 5-day forecast at a 0.01° × 0.01° pixel resolution. Simulation results using real dataset from the 3.15 outbreak in Shanghai demonstrate the efficacy of Meteor-ConvLSTM, with reduced RMSE of 0.110 and increased R2 of 0.125 (original ConvLSTM: RMSE = 0.702, R2 = 0.567; Meteor-ConvLSTM: RMSE = 0.592, R2 = 0.692), showcasing its utility for investigating the epidemiological characteristics, transmission dynamics, and epidemic development.

Abstract Background This research strived to construct a new signature utilizing disulfidptosis-related ferroptosis (SRF) genes to anticipate response to immunotherapy, prognosis, and drug sensitivity in individuals with colorectal cancer (CRC). Methods The data for RNA sequencing as well as corresponding clinical information of individuals with CRC, were extracted from The Cancer Genome Atlas (TCGA) dataset. SRF were constructed with the help of the random forest (RF), least absolute shrinkage and selection operator (LASSO), and stepwise regression algorithms. To validate the SRF model, we applied it to an external cohort, GSE38832. Prognosis, immunotherapy response, drug sensitivity, molecular functions of genes, and somatic mutations of genes were compared across the high- and low-risk groups (categories). Following this, all statistical analyses were conducted with the aid of the R (version 4.23) software and various packages of the Cytoscape (version 3.8.0) tool. Results SRF was developed based on five genes (ATG7, USP7, MMD, PLIN4, and THDC2). Both univariate and multivariate Cox regression analyses established SRF as an independent, prognosis-related risk factor. Individuals from the high-risk category had a more unfavorable prognosis, elevated tumor mutational burden (TMB), and significant immunosuppressive status. Hence, they might have better outcomes post-immunotherapy and might benefit from the administration of pazopanib, lapatinib, and sunitinib. Conclusion In conclusion, SRF can act as a new biomarker for prognosis assessment. Moreover, it is also a good predictor of drug sensitivity and immunotherapy response in CRC but should undergo optimization before implementation in clinical settings.

Abstract Climate change is creating a new era of infectious disease crises, further exacerbated by extreme weather. However, the relationship between extreme weather and infectious disease remain unclear. Here, we provide a new quantitative study on the impact of cold wave on COVID‐19 as an example. We found that during cold waves, extreme cold temperatures coupled with rapid aerosol transport accelerated COVID‐19 outbreaks. It directly increased the number of COVID‐19 cases in Beijing by 28.1% in the winter of year 2022. More urgently, cold temperatures led to a higher risk of death during infectious disease outbreaks, with a 7.07% increase in confirmed deaths and a 16.61% increase in excess mortality. Our findings emphasize the urgent need to promote a synergistic policy for responding to infectious diseases during cold wave disasters in order to minimize the risk of death among the elderly and those with underlying diseases.

Coal mining machine is the core equipment of completely automated working face, and the research and development of intelligent coal mining robot is crucial for achieving the intellectualization of fully mechanized working face. This paper comprehensively analyzes the current research status of sensing detection, position and attitude control, speed control, cutting trajectory planning, and tracking control in the coal mining machine roboticization process, and proposes five key technologies that must be solved in the development of intelligent shearer robots, including sensing and detection, pose control, velocity control, cutting trajectory planning and tracking control. Aiming at the problem of intelligent perception, this paper proposes the construction thought of a coal mining robot intelligent perception system, as well as the architecture of a coal mining robot intelligent per-ception system. The architecture of the intelligent perception system for coal mining robots is outlined, enabling a comprehensive sensing of running state, posture, environment, and so on, thereby ensuring the safe and reliable operation of intelligent coal mining robots. In terms of the position and attitude control problem of intelligent coal mining robots, the intelligent PID position and attitude control thought is proposed, along with an improved genetic algorithm-based PID pose control method, enabling precise pose control for the coal mining robot. As to the problem of velocity control, the thought of cutting load measurement based on the fusion of “force-electricity” heterogeneous data is proposed. Additionally, a neural network-based algorithm for cutting load measurement is presented, achieving an accurate load measurement. Furthermore, a traction and cutting speed adaptive control approach is proposed, including an artificial intelligence-based decision-making method for traction and cutting speed and a sliding mode control method for traction and cutting speed with disturbance rejection. This approach enables a precise and adaptive speed control for the coal mining robot. Regarding the problem of cutting trajectory planning and tracking control, the precise cutting trajectory planning thought is proposed, incorporating geological data and historical cutting data into a cutting trajectory planning model. The precise cutting trajectory tracking control thought is proposed, and an intelligent interpolation algorithm-based cutting trajectory tracking control method is given, achieving a high-precision trajectory planning and accurate tracking control for the coal mining robot. Considering the “position-attitude-velocity” collaborative control problem, the intelligent optimization idea of "position-attitude-velocity" collaborative control parameters is proposed, which utilizes an improved particle swarm optimization method based on multi-system constraints to optimize the coordinated control parameters, resulting in intelligent and efficient operation of the coal mining robot. The in-depth investigation of these five key technologies for intelligent coal mining robot provides some valuable insights for accelerating the development of high-performance, efficient, and reliable intelligent coal mining robot.

In order to solve the problem of grasping failure or missing grasping due to the change of target gangue position and posture caused by belt slip, deviation and belt speed fluctuation of belt conveyor when the gangue sorting robot sorts gangue, an improved ORB-FLANN efficient matching method of gangue recognition image and sorting image is proposed. An improved ORB feature point detection method is proposed to detect the feature points in the recognition image and sorting image of coal gangue, so as to realize fast detection of image feature points; An improved FLANN matching algorithm is proposed to match the image feature points to achieve efficient matching between the recognition image of coal gangue and the sorting image. Aiming at the problem of long time and low repetition rate of traditional ORB method for coal gangue image feature detection, an improved ORB feature detection method is proposed to improve the speed and repetition rate of image feature point detection; Aiming at the low accuracy of traditional FLANN matching method for coal gangue image matching, an improved FLANN matching method integrating PROSAC algorithm is proposed to eliminate the wrong feature matching point pairs and improve the accuracy of image matching. The method, SURF feature matching method, HU moment invariant matching method, SIFT feature matching method and ORB feature matching method are applied on the experimental platform of the double mechanical arm truss type gangue sorting robot independently developed by the team to carry out gangue matching experiments under different belt speeds, scales and rotation angles. The results show that the matching rate of the method in this paper is 98.2%, and the matching time is 141 ms. It has the characteristics of high matching rate, good real-time performance and strong robustness, It can meet the requirements of efficient and accurate matching of gangue recognition image and sorting image.

Abstract Macroscopic fibres assembled from two-dimensional (2D) nanosheets are new and impressing type of fibre materials besides those from one-dimensional (1D) polymers, such as graphene fibres. However, the preparation and property-enhancing technologies of these fibres follow those from 1D polymers by improving the orientation along the fibre axis, leading to non-optimized microstructures and low integrated performances. Here, we show a concept of bidirectionally promoting the assembly order, making graphene fibres achieve synergistically improved mechanical and thermal properties. Concentric arrangement of graphene oxide sheets in the cross-section and alignment along fibre axis are realized by multiple shear-flow fields, which bidirectionally promotes the sheet-order of graphene sheets in solid fibres, generates densified and crystalline graphitic structures, and produces graphene fibres with ultrahigh modulus (901 GPa) and thermal conductivity (1660 W m−1 K−1). We believe that the concept would enhance both scientific and technological cognition of the assembly process of 2D nanosheets.

Rheumatoid arthritis (RA) is an autoimmune disease with a complex etiology. Monocyte-derived macrophages (MDMs) infiltration are associated with RA severity. We have reported the deletion of G-protein-coupled receptor kinase 2 (GRK2) reprograms macrophages toward an anti-inflammatory phenotype by recovering G-protein-coupled receptor signaling. However, as more GRK2-interacting proteins were discovered, the GRK2 interactome mechanisms in RA have been understudied. Thus, in the collagen-induced arthritis mouse model, we performed genetic GRK2 deletion using GRK2f/fLyz2-Cre+/− mice. Synovial inflammation and M1 polarization were improved in GRK2f/fLyz2-Cre+/− mice. Supporting experiments with RNA-seq and dual-luciferase reporter assays identified peroxisome proliferator-activated receptor γ (PPARγ) as a new GRK2-interacting protein. We further confirmed that fms-related tyrosine kinase 1 (Flt-1), which promoted macrophage migration to induce angiogenesis, was inhibited by GRK2-PPARγ signaling. Mechanistically, excess GRK2 membrane recruitment in CIA MDMs reduced the activation of PPARγ ligand-binding domain and enhanced Flt-1 transcription. Furthermore, the treatment of mice with GRK2 activity inhibitor resulted in significantly diminished CIA pathology, Flt-1+ macrophages induced-synovial inflammation, and angiogenesis. Altogether, we anticipate to facilitate the elucidation of previously unappreciated details of GRK2-specific intracellular signaling. Targeting GRK2 activity is a viable strategy to inhibit MDMs infiltration, affording a distinct way to control joint inflammation and angiogenesis of RA.

Remote sensing spatiotemporal fusion technology can provide abundant data source information for assimilating crop growth model data, enhancing crop growth monitoring, and providing theoretical support for crop irrigation management. This study focused on the winter wheat planting area in the southeastern part of the Loess Plateau, a typical semi-arid region, specifically the Linfen Basin. The SEBAL and ESTARFM were used to obtain 8 d, 30 m evapotranspiration (ET) for the growth period of winter wheat. Then, based on the ‘localization’ of the CERES-Wheat model, the fused results were incorporated into the data assimilation process to further determine the optimal assimilation method. The results indicate that (1) ESTARFM ET can accurately capture the spatial details of SEBAL ET (R > 0.9, p < 0.01). (2) ESTARFM ET can accurately capture the spatial details of SEBAL ET (R > 0.9, p < 0.01). The calibrated CERES-Wheat ET characteristic curve effectively reflects the ET variation throughout the winter wheat growth period while being consistent with the trend and magnitude of ESTARFM ET variation. (3) The correlation between Ensemble Kalman filter (EnKF) ET and ESTARFM ET (R2 = 0.7119, p < 0.01) was significantly higher than that of Four-Dimensional Variational data assimilation (4DVar) ET (R2 = 0.5142, p < 0.01) and particle filter (PF) ET (R2 = 0.5596, p < 0.01). The results of the study provide theoretical guidance to improve the yield and water use efficiency of winter wheat in the region, which will help promote sustainable agricultural development.

Aiming at the problem that it is difficult for an orbital photovoltaic panel cleaning robot to span a large distance between photovoltaic panels, a method of designing and optimizing a non-coplanar orbit based on Bezier curves is proposed. Firstly, the robot’s motion law is analyzed to obtain trajectory data for a single work cycle. Then, Bezier curves are utilized for trajectory design to ensure a smooth transition during the spanning motion phase. Thirdly, with the average value of the minimum distance between the Bezier curve and the point set data of the spanning motion phase as the optimization objective function, the nonlinear planning based on the SQP algorithm was adopted for the optimization of the upper and lower trajectories. Finally, the results of the case calculations indicate that the standard deviation of the optimized upper and lower trajectories was reduced by 35.63% and 40.57%, respectively. Additionally, the ADAMS simulation validation demonstrates that the trajectory errors of the four wheels decreased by a maximum of 8.79 mm, 23.78 mm, 10.11 mm, and 14.97 mm, respectively, thereby confirming the effectiveness of the trajectory optimization.

Background: Patients with inflammatory bowel disease (IBD) have an increased risk of developing immune-mediated diseases. However, the genetic basis of IBD is complex, and an integrated approach should be used to elucidate the complex genetic relationship between IBD and immune-mediated diseases. Methods: The genetic relationship between IBD and 16 immune-mediated diseases was examined using linkage disequilibrium score regression. GWAS data were synthesized from two IBD databases using the METAL, and multi-trait analysis of genome-wide association studies was performed to enhance statistical robustness and identify novel genetic associations. Independent risk loci were meticulously examined using conditional and joint genome-wide multi-trait analysis, multi-marker analysis of genomic annotation, and functional mapping and annotation of significant genetic loci, integrating the information of quantitative trait loci and different methodologies to identify risk-related genes and proteins. Results: The results revealed four immune-mediated diseases (AS, psoriasis, iridocyclitis, and PsA) with a significant relationship with IBD. The multi-trait analysis revealed 909 gene loci of statistical significance. Of these loci, 28 genetic variants were closely related to IBD, and 7 single-nucleotide polymorphisms represented novel independent risk loci. In addition, 14 genes and 514 proteins were found to be associated with susceptibility to immune-mediated diseases. Notably, IL1RL1 emerged as a key player, present within pleiotropic genes across multiple protein databases, highlighting its potential as a therapeutic target. Conclusions: This study suggests that the common polygenic determinants between IBD and immune-mediated diseases are widely distributed across the genome. The findings not only support a shared genetic relationship between IBD and immune-mediated diseases but also provide novel therapeutic targets for these diseases.

Purpose: Colorectal cancer (CRC) is a common malignancy with a low survival rate as well as a low response rate to immunotherapy. This study aims to develop a risk model based on tertiary lymphoid structure (TLS)-associated gene signatures to enhance predictions of prognosis and immunotherapy response. Methods: TLS-associated gene data were obtained from TCGA-CRC and GEO cohorts. A comprehensive analysis using univariate Cox regression identified TLS-associated genes with significant prognostic implications. Subsequently, multiple algorithms were employed to select the most influential genes, and a stepwise Cox regression model was constructed. The model’s predictive performance was validated using independent datasets (GSE39582, GSE17536, and GSE38832). To further investigate the immune microenvironment, immune cell infiltration in high-risk (HRG) and low-risk (LRG) groups was assessed using the CIBERSORT and ssGSEA algorithms. Additionally, we evaluated the model’s potential to predict immune checkpoint blockade therapy response using data from The Cancer Imaging Archive, the TIDE algorithm, and external immunotherapy cohorts (GSE35640, GSE78200, and PRJEB23709). Immunohistochemistry (IHC) was employed to characterize TLS presence and CCL2 gene expression. Results: A three-gene (CCL2, PDCD1, and ICOS) TLS-associated model was identified as strongly associated with prognosis and demonstrated predictive power for CRC patient outcomes and immunotherapy efficacy. Notably, patients in the low-risk group (LRG) had a higher overall survival rate as well as a higher re-response rate to immunotherapy compared to the high-risk group (HRG). Finally, IHC results confirmed significantly elevated CCL2 expression in the TLS regions. Conclusions: The multi-algorithm-integrated model demonstrated robust performance in predicting patient prognosis and immunotherapy response, offering a novel perspective for assessing immunotherapy efficacy. CCL2 may function as a TLS modulator and holds potential as a therapeutic target in CRC.

In the planning of public charging facilities and the charging activity network of users, there is a decision-making conflict among three stakeholders: the government, charging station enterprises, and electric vehicle users. Previous studies have described the tripartite game relationship in a relatively simplistic manner, and when designing charging facility planning schemes, they did not consider scenarios where users’ choice preferences undergo continuous random changes. In order to reduce the impacts of queuing phenomenon and resource idleness on the three participants, we introduce a bilateral matching algorithm combined with the dynamic Huff model as a strategy for EV charging selection in the passenger flow problem based on the three-dimensional activity network of time–space–energy of users. Meanwhile, the Dirichlet distribution is utilized to control the selection preferences on the user side, constructing uncertain scenarios for the choice of user charging activities. In this study, we establish a bilevel programming model that takes into account the uncertainty in social responsibility and user charging selection behavior. Solutions for the activity network and facility planning schemes can be derived based on the collaborative relationships among the three parties. The model employs a robust optimization method to collaboratively design the charging activity network and facility planning scheme. For this mixed-integer nonlinear multi-objective multi-constraint optimization problem, the model is solved by the NSGA-II algorithm, and the optimal compromise scheme is determined by using the EWM-TOPSIS comprehensive evaluation method for the Pareto solution set. Finally, the efficacy of the model and the solution algorithm is illustrated by a simulation example in a real urban space.

COVID-19 has posed formidable challenges as a significant global health crisis. Its complexity stems from factors like viral contagiousness, population density, social behaviors, governmental regulations, and environmental conditions, with interpersonal interactions and large-scale activities being particularly pivotal. To unravel these complexities, we used a modified SEIR epidemiological model to simulate various outbreak scenarios during the holiday season, incorporating both inter-regional and intra-regional human mobility effects into the parameterization scheme. In addition, evaluation metrics were used to evaluate the accuracy of the model simulation by comparing the congruence between simulated results and recorded confirmed cases. The findings suggested that intra-city mobility led to an average surge of 57.35% in confirmed cases of China, while inter-city mobility contributed to an average increase of 15.18%. In the simulation for Tianjin, China, a one-week delay in human mobility attenuated the peak number of cases by 34.47% and postponed the peak time by 6 days. The simulation for the United States revealed that human mobility played a more pronounced part in the outbreak, with a notable disparity in peak cases when mobility was considered. This study highlights that while inter-regional mobility acted as a trigger for the epidemic spread, the diffusion effect of intra-regional mobility was primarily responsible for the outbreak. We have a better understanding on how human mobility and infectious disease epidemics interact, and provide empirical evidence that could contribute to disease prevention and control measures.

Highlights Multidimensional detection of intensity, wavelength, polarization, and angle of the incidence light significantly accelerates the development of optical information technology and artificial intelligence fields. The first comprehensive overview of the advancement of multifunctional photodetectors for perovskite semiconductors ranging from polarized light detection, spectral detection, and angle-sensing detection to self-powered detection is summarized. The existing problems and perspectives are discussed which can inspire more researchers to rationally design new perovskite materials and micro/nano-structure for high-performance multifunctional photodetectors.

Abstract Congenital heart disease (CHD) affecting the conotruncal region of the heart, occurs in 40–50% of patients with 22q11.2 deletion syndrome (22q11.2DS). This syndrome is a rare disorder with relative genetic homogeneity that can facilitate identification of genetic modifiers. Haploinsufficiency of TBX1, encoding a T-box transcription factor, is one of the main genes responsible for the etiology of the syndrome. We suggest that genetic modifiers of conotruncal defects in patients with 22q11.2DS may be in the TBX1 gene network. To identify genetic modifiers, we analyzed rare, predicted damaging variants in whole genome sequence of 456 cases with conotruncal defects and 537 controls, with 22q11.2DS. We then performed gene set approaches and identified chromatin regulatory genes as modifiers. Chromatin genes with recurrent damaging variants include EP400, KAT6A, KMT2C, KMT2D, NSD1, CHD7 and PHF21A. In total, we identified 37 chromatin regulatory genes, that may increase risk for conotruncal heart defects in 8.5% of 22q11.2DS cases. Many of these genes were identified as risk factors for sporadic CHD in the general population. These genes are co-expressed in cardiac progenitor cells with TBX1, suggesting that they may be in the same genetic network. The genes KAT6A, KMT2C, CHD7 and EZH2, have been previously shown to genetically interact with TBX1 in mouse models. Our findings indicate that disturbance of chromatin regulatory genes impact the TBX1 gene network serving as genetic modifiers of 22q11.2DS and sporadic CHD, suggesting that there are some shared mechanisms involving the TBX1 gene network in the etiology of CHD.

Structural health monitoring (SHM) is widely used to monitor and assess the condition and performance of engineering structures such as, buildings, bridges, dams, and tunnels. Owing to sensor defects, data acquisition errors, and environmental interference, abnormal data are often collected and stored in monitoring systems. The abnormal data in this study are essentially different from so-called “abnormal state data,” which result from structural physical damage or performance degradation. Abnormal data are totally related to the external interference rather than changes in the inherent structural features. However, abnormal data can significantly affect the performance assessment of engineering structures. It is imperative to detect and remove abnormal data from measurements to avoid misjudging structural performance in SHM. This paper summarizes abnormal data detection in the SHM field and discusses relevant challenges. Moreover, background knowledge regarding abnormal data detection is introduced. Abnormal data detection methods are then classified into statistical probability methods, predictive models, and computer vision methods. The advantages, disadvantages, and scope of each method are investigated. An example of detecting abnormal monitoring data for a cable-stayed bridge is introduced. In addition, the issues of existing studies are summarized, and future study interests are discussed.

Abstract Postoperative nausea and vomiting (PONV) is a common side effect after laparoscopic surgery. The aim of the study is to investigate the variables that could predict PONV in patients who underwent laparoscopic gastrectomy. We divided patients who underwent laparoscopic gastrectomy into PONV and No-PONV groups. Propensity score matching (PSM) was applied to adjust confounding factors for further validation, and ordinal logistic regression analysis was used to identify predictors for PONV. In the ordinal logistic regression analysis, the preoperative neutrophil-to-lymphocyte ratio (NLR) (odds ratio [OR]: 3.19, 95% confidence interval [CI]: 1.38–7.38; p

Abstract Background Colorectal cancer (CRC), has a link between obesity, especially visceral fat. The body roundness index (BRI) can more accurately assess body fat and visceral fat levels. It is, however, unknown whether BRI is associated with CRC risk. Methods 53,766 participants were enrolled from the National Health and Nutrition Examination Survey (NHANES). Analysing the corelation between BRI and CRC risk was performed using logistic regression. Stratified analyses revealed the association based on the population type. Receiver operating characteristic curve (ROC) was performed for predicting CRC risk using different anthropometric indices. Results The risk of CRC mounting apparently with elevated BRI for participants with CRC compared to normal participants (P-trend

Plant height is regarded as a key indicator that is crucial for assessing the crop growth status and predicting yield. In this study, an advanced method based on solid-state LiDAR technology is proposed, which is specifically designed to accurately capture the phenotypic characteristics of plant height during the maize growth cycle. By segmenting the scanned point cloud of maize, detailed point cloud data of a single maize plant were successfully extracted, from which stem information was accurately measured to obtain accurate plant height information. In this study, we will concentrate on the analysis of individual maize plants. Leveraging the advantages of solid-state LiDAR technology in precisely capturing phenotypic information, the data processing approach for individual maize plants, as compared to an entire maize community, will better restore the maize’s original growth patterns. This will enable the acquisition of more accurate maize plant height information and more clearly demonstrate the potential of solid-state LiDAR in capturing detailed phenotypic information. To enhance the universality of the research findings, this study meticulously selected key growth stages of maize for data validation and comparison, encompassing the tasseling, silking, and maturity phases. At these crucial stages, 20 maize plants at the tasseling stage, 40 at the flowering stage, and 40 at the maturity stage were randomly selected, totaling 100 samples for analysis. Each sample not only included actual measurement values but also included plant height information extracted using point cloud technology. The observation period was set from 20 June to 20 September 2021. This period encompasses the three key growth stages of maize described above, and each growth stage included one round of data collection, with three rounds of data collection each, each spaced about a week apart, for a total of nine data collections. To ensure the accuracy and reliability of the data, all collections were performed at noon when the natural wind speed was controlled within the range of 0 to 1.5 m/s and the weather was clear. The findings demonstrate that the root mean square error (RMSE) of the maize plant height data, procured through LiDAR technology, stands at 1.27 cm, the mean absolute percentage error (MAPE) hovers around 0.77%, and the peak R2 value attained is 0.99. These metrics collectively attest to the method’s ongoing high efficiency and precision in capturing the plant height information. In the comparative study of different stem growth stages, especially at the maturity stage, the MAPE of the plant height was reduced to 0.57%, which is a significant improvement compared to the performance at the nodulation and sprouting stage. These results effectively demonstrate that the maize phenotypic information extraction method based on solid-state LiDAR technology is not only highly accurate and effective but is also effective on individual plants, which provides a reliable reference for applying the technique to a wider range of plant populations and extending it to the whole farmland.

Predicting potential risks associated with the fatigue of key structural components is crucial in engineering design. However, fatigue often involves entangled complexities of material microstructures and service conditions, making diagnosis and prognosis of fatigue damage challenging. We report a statistical learning framework to predict the growth of fatigue cracks and the life-to-failure of the components under loading conditions with uncertainties. Digital libraries of fatigue crack patterns and the remaining life are constructed by high-fidelity physical simulations. Dimensionality reduction and neural network architectures are then used to learn the history dependence and nonlinearity of fatigue crack growth. Path-slicing and re-weighting techniques are introduced to handle the statistical noises and rare events. The predicted fatigue crack patterns are self-updated and self-corrected by the evolving crack patterns. The end-to-end approach is validated by representative examples with fatigue cracks in plates, which showcase the digital-twin scenario in real-time structural health monitoring and fatigue life prediction for maintenance management decision-making.

Background: The correlation of type 2 diabetes mellitus (T2DM) with colorectal cancer (CRC) has garnered considerable attention in the scientific community. Despite this, the molecular mechanisms underlying the interaction between these two diseases are yet to be elucidated. Hence, the present investigation aims to explore the shared gene signatures, immune profiles, and drug sensitivity patterns that exist between CRC and T2DM.Methods: RNA sequences and characteristics of patients with CRC and T2DM were retrieved from The Cancer Genome Atlas and Gene Expression Omnibus databases. These were investigated using weighted gene co-expression network analysis (WGCNA) to determine the co-expression networks linked to the conditions. Genes shared between CRC and T2DM were analyzed by univariate regression, followed by risk prognosis assessment using the LASSO regression model. Various parameters were assessed through different software such as the ESTIMATE, CIBERSORT, AND SSGSEA utilized for tumor immune infiltration assessment in the high- and low-risk groups. Additionally, pRRophetic was utilized to assess the sensitivity to chemotherapeutic agents in both groups. This was followed by diagnostic modeling using logistic modeling and clinical prediction modeling using the nomogram.Results: WGCNA recognized four and five modules that displayed a high correlation with T2DM and CRC, respectively. In total, 868 genes were shared between CRC and T2DM, with 14 key shared genes being identified in the follow-up analysis. The overall survival (OS) of patients in the low-risk group was better than that of patients in the high-risk group. In contrast, the high-risk group exhibited higher expression levels of immune checkpoints The Cox regression analyses established that the risk-score model possessed independent prognostic value in predicting OS. To facilitate the prediction of OS and cause-specific survival, the nomogram was established utilizing the Cox regression model.Conclusion: The T2DM + CRC risk-score model enabled independent prediction of OS in individuals with CRC. Moreover, these findings revealed novel genes that hold promise as therapeutic targets or biomarkers in clinical settings.

The dynamic loading of CoCrNiSi0.3 medium-entropy alloys was performed by using Split Hopkinson Pressure Bar. The compression properties at different strain rates were investigated, and found to be sensitive to strain rates and have obvious strain rate strengthening effect. Under quasi-static compression (strain rate 10-4 s-1), the strain rate sensitivity m is 0.13, while under dynamic compression (strain rate 5 150 s-1) m 0.25. The shear properties of the alloy were studied through hat shaped shear test. The results show that the quasi-static shear yield strength is 330 MPa and the dynamic shear yield strength is 630 MPa. With the increase of the width of the reserved shear region, the peak stress decreases, but the shear failure strain increases obviously. When the width of the reserved shear region is 50 μm, the shear strain is as high as 12.8. Through the shear “freezing” experiment, the effect of increasing shear deformation on the grain microstructure and texture evolution of the material was studied. The results show that the geometric dislocation density in the grain increases with the increase of deformation, and the deformation texture evolves to A and P type texture, while the Cu type texture decreases gradually. Before the formation of adiabatic shear band, the temperature rise calculated by power-heat conversion lacks accuracy, which proves that the formation of adiabatic shear band is not caused by thermal softening.

Macroscopic mechanical response and microstructure evolution of 7075-T6 aluminum alloy during dynamic compression were investigated by experiments and crystal plastic finite element method (CPFEM). The dynamic compression experiment of 7075-T6 aluminum alloy with a strain rate of 2 000 s-1 was carried out with a split Hopkinson pressure bar (SHPB). The microstructure of aluminum before and after dynamic compression was characterized by electron backscattering diffraction technique (EBSD). The internal state variables of dislocation density were introduced into the CPFEM model by modifying the strengthening model and the flow criterion. The effects of friction coefficient, dislocation multiplication coefficient, and dislocation annihilation coefficient on the macro mechanical properties of the specimens were investigated. The results show that the increase of dislocation multiplication coefficient aggravates the hardening behavior and increases the ultimate strength of the material, and the increase of dislocation annihilation coefficient reduces the ultimate strength of the material while weakens its hardening behavior. Through the comparison of experimental and simulation results, it could be seen that CPFEM accurately predicts the variation trend of the texture during dynamic compression, as more brass {110} texture and Goss {110} texture are generated.

Abstract Patients with severe fever with thrombocytopenia syndrome (SFTS) had been confirmed to have immune dysfunction and were prone to invasive pulmonary aspergillosis (IPA), which was directly related to the increased mortality. The aim of this study was to investigate the predictors for IPA in SFTS patients, and the results were expected to be helpful for early identification of IPA and initiation of anti-fungal therapy. The study was performed to review laboratory confirmed SFTS patients in two tertiary hospitals in Shandong province (Qilu Hospital of Shandong University and Shandong Public Health Clinical Center) from April 2021 to August 2022. The enrolled patients were further divided into IPA group and non-IPA group. Demographic characteristics, clinical manifestations and laboratory parameters between IPA group and non-IPA group patients were analyzed and compared to identify the independent predictors for IPA by univariate analysis and multivariable logistic regression analysis. Sensitivity and specificity of independent predictors were evaluated by receiver operating characteristic (ROC) curve analysis. In total, 67 SFTS patients were enrolled with an average age of 64.7 (± 8.4) years old. The incidence of IPA was 32.8% (22/67). Mortality of patients in IPA group was 27.3% (6/22), which was significantly higher than that in non-IPA group. Results of univariate analysis showed that uncontrolled diabetes, central nervous system symptoms, platelet

Abstract Highly efficient second‐harmonic generation (SHG) has facilitated the development of nanophotonics and sustained promising applications, ranging from electro‐optical modulation, frequency conversion, and optical frequency combs to pulse characterization. Although controllable SHG switching has been observed in nanophotonics structures and molecule systems, the relatively small SHG switching contrast impedes its application in switchable nonlinear optics. Herein, reversible phase transitions between glassy and crystalline states without material degradation are demonstrated based on solution‐processed chiral perovskite microwire arrays. Breaking of lattice inversion symmetry and high crystallinity support efficient SHG in microwire arrays. By synergy of high‐performance SHG and reversible phase transitions between glassy and crystalline states, reversible switching of SHG is demonstrated under facile conditions. The high SHG switching performances, together with a small footprint, pave the way toward the integration of switchable nonlinear devices based on microwire arrays.

Abstract Breeding new wheat varieties with salt resistance is one of the best ways to solve a constraint on the sustainability and expansion of wheat cultivation. Therefore, understanding the molecular components or genes related to salt tolerance must contribute to the cultivation of salt-tolerant varieties. The present study used a recombinant inbred line (RIL) population to genetically dissect the effects of different salt stress concentrations on wheat seed germination and seedling traits using two quantitative trait locus (QTL) mapping methods. A total of 31 unconditional and 11 conditional QTLs for salt tolerance were identified on 11 chromosomes explaining phenotypic variation (PVE) ranging from 2.01 to 65.76%. Of these, 15 major QTLs were found accounting for more than 10% PVE. QTL clusters were detected on chromosomes 2A and 3B in the marker intervals ‘wPt-8328 and wPt-2087’ and ‘wPt-666008 and wPt-3620’, respectively, involving more than one salt tolerance trait. QRdw3B and QSfw3B.2 were most consistent in two or more salt stress treatments. 16 candidate genes associated with salt tolerance were predicted in wheat. These results could be useful to improve salt tolerance by marker-assisted selection (MAS) and shed new light on understanding the genetic basis of salt tolerance in wheat.

Extract residue of coal liquefaction residue (ERCLR) is a solid mixture of high carbon, high ash, high sulfur, and complex composition, mainly including unconverted coal, inorganic minerals, and coal liquefaction catalyst. The effective use of ERCLR, which has high utilization value based on high hydrogen-to-carbon ratio, not only improves the thermal efficiency and economy of coal liquefaction process, but also reduces the emissions of pollutants. The energy efficiency analysis, environmental impact, and economic analysis of three power generation systems were calculated and analyzed by Aspen Plus. The three power generation systems are ERCLR gasification combined cycle power generation system, ERCLR combustion power generation system, and ERCLR pyrolysis combustion power generation system. The results show that the energy efficiency of ERCLR gasification combined cycle power generation system is 47.5%, which is better than that of ERCLR combustion power generation system (43%) and ERCLR pyrolysis combustion power generation system (45.3%). The flue gas emission of ERCLR gasification combined cycle power generation system has lower impact on environment than that of the other two systems. ERCLR gasification combined cycle power generation system provides a lower cost of electricity (0.44 CNY/kWh), compared with ERCLR combustion power generation system (0.48 CNY/kWh) and ERCLR pyrolysis combustion power generation system (0.69 CNY/kWh). Therefore, ERCLR gasification combined cycle power generation system has significant advantages over the other two utilization approaches. For the recovery and utilization of ERCLR, ERCLR gasification combined cycle can be given priority to power generation.

A water-soluble cube-like supramolecular cage was constructed by an engagement of six molecules through a hydrophobic effect in the water. The obtained cage could perfectly encapsulate one fullerene C60 molecule inside of the cavity and significantly improve the water-solubility of the C60 without changing the original structure. The water-soluble complex was further applied to reduce the reactive oxygen species (R.O.S.) in cardiomyocytes (FMC84) through Akt/Nrf2/HO-1 pathway. Furthermore, in the mouse model of myocardial ischemia-reperfusion injury, the application of C60 was found to be effective in reducing myocardial injury and improving cardiac function. It also reduced the levels of R.O.S. in myocardial tissue, inhibited myocardial apoptosis, and mitigated myocardial inflammatory responses. The present study provides a new guideline for constructing water-soluble C60 and verifies the important role of C60 in preventing oxidative stress-related cardiovascular disease injury.

The construction of efficient electrocatalysts is key in harnessing the electrochemical CO2 reduction reaction (CRR) to produce high-value chemicals. Herein, a typical bacterium containing metal–nitrogen (M−N) groups and with a rich phosphorus content, Shewanella oneidensis MR-1, was used as a single precursor to produce atomically dispersed iron–nitrogen–phosphorus active sites (P–Fe/NC-1000) for converting CO2 to CO. Excellent CO selectivity of nearly 100% at 390 mV overpotential was achieved with good stability, and a rechargeable Zn–CO2 battery provides a maximum power density of 1.32 mW cm−2. Kinetic isotope effect measurements revealed that the presence of P atoms accelerates water dissociation and transfers a proton to Fe centers to promote protonation of intermediates and the overall CRR process. This synthesis strategy based on the intrinsic structure of a precursor may inspire the development of other high-performance catalysts for diverse electrochemical reactions beyond the CRR .

Glycosylation, a dynamic modification prevalent in viruses and higher eukaryotes, is principally regulated by uridine diphosphate (UDP)-glycosyltransferases (UGTs) in plants. Although UGTs are involved in plant defense responses, their responses to most pathogens, especially plant viruses, remain unclear. Here, we aimed to identify UGTs in the whole genome of Nicotiana benthamiana (N. benthamiana) and to analyze their function in Chinese wheat mosaic virus (CWMV) infection. A total of 147 NbUGTs were identified in N. benthamiana. To conduct a phylogenetic analysis, the UGT protein sequences of N. benthamiana and Arabidopsis thaliana were aligned. The gene structure and conserved motifs of the UGTs were also analyzed. Additionally, the physicochemical properties and predictable subcellular localization were examined in detail. Analysis of cis-acting elements in the putative promoter revealed that NbUGTs were involved in temperature, defense, and hormone responses. The expression levels of 20 NbUGTs containing defense-related cis-acting elements were assessed in CWMV-infected N. benthamiana, revealing a significant upregulation of 8 NbUGTs. Subcellular localization analysis of three NbUGTs (NbUGT12, NbUGT16 and NbUGT17) revealed their predominant localization in the cytoplasm of N. benthamiana leaves, and NbUGT12 was also distributed in the chloroplasts. CWMV infection did not alter the subcellular localization of NbUGT12, NbUGT16, and NbUGT17. Transient overexpression of NbUGT12, NbUGT16, and NbUGT17 enhanced CWMV infection, whereas the knockdown of NbUGT12, NbUGT16 and NbUGT17 inhibited CWMV infection in N. benthamiana. These NbUGTs could serve as potential susceptibility genes to facilitate CWMV infection. Overall, the findings throw light on the evolution and function of NbUGTs.

BackgroundThe prognosis for patients with ovarian cancer is bleak. Clinical trials have shown the efficacy of bevacizumab in ovarian cancer treatment. However, life-threatening strokes may limit the usage of bevacizumab and require specific follow-up strategies. This study aims to systematically evaluate the risk of stroke of bevacizumab treatment in ovarian cancer.MethodsWe retrieved all relevant articles published up to December 4th, 2022, from Embase, PubMed, Web of Science, and the Cochrane Library. The risk of stroke in patients with ovarian cancer treated with bevacizumab combined with chemotherapy was analyzed. Meta-analysis was performed using the Stata 17 software and R 4.2.1 program.ResultsSix randomized controlled trials (RCTs) of bevacizumab combined with chemotherapy or chemotherapy for ovarian cancer and six single-experimental-arm trials were included in this study. The meta-analysis showed a pooled risk ratio (RR) of 2.14 [95% confidence interval (CI): 0.88–7.99] for patients with ovarian cancer treated with bevacizumab combined with chemotherapy. Subgroup analyses showed that the incidence of stroke-related adverse events in the carboplatin + paclitaxel + bevacizumab group was 0.01% (95% CI: 0.00–0.01, p < 0.01). The incidence of stroke-related adverse events was 0.01% (95% CI: 0.00–0.01, p < 0.01) in patients aged ≥60. The incidence of stroke caused by cerebral ischemia and cerebral hemorrhage was 0.01% (95% CI: 0.01–0.02, p = 0.27) and 0.01% (95% CI: 0.00–0.01, p < 0.01), respectively.ConclusionsThis meta-analysis indicates that chemotherapy combined with bevacizumab may not increase the incidence of stroke in patients with ovarian cancer. However, stroke-related adverse events may be higher in older patients. Cerebral hemorrhage might cause the incidence of stroke more than cerebral ischemia.Systematic review registrationPROSPERO (CRD42022381003).

Rational development of high-performance catalysts for selective catalytic reduction (SCR) is important for NOx removal. As the most widely used commercial catalysts, coated monolithic catalysts need to adapt to the harsh external conditions, which make researchers concerned about maintaining structural integrity, catalytic activity, and toxicity resistance in services. Herein, we seek solutions by clarifying the influences of coating process parameters on these three concerns, and trying to tackle the technical difficulties of coated monolithic catalysts in SCR applications. The insights presented in this paper are beneficial to the synthesis optimization and engineering innovations for SCR catalysts.

Rotor unbalance is the most important factor affecting the dynamic performance of aircraft engines. The existing unbalance prediction and control methods are insufficient for multi-stage rotors. The post-assembly unbalance of rotors in aircraft engines is a critical factor affecting their dynamic performance. In order to predict and reduce the unbalance of multi-stage rotors after assembly, this paper establishes a measurement model for the center-of-mass offset of aircraft engine rotors through decoupled calculations of the unbalance. Furthermore, it constructs an unbalance prediction model using the spatial transfer mechanism of combined rotor offset centers under the influence of manufacturing errors. Additionally, a method for measuring rotor unbalance during the assembly phase is proposed. The experimental results of the unbalance in multi-stage combined rotor assembly indicate that the degree of agreement between the predicted results and the experimental results is 91.3%, resulting in a reduction in the mean error of 15.3% compared to before the correction. The study also investigates the impact of manufacturing errors on unbalance. This research provides robust support for controlling the unbalance in multi-stage combined rotor assembly.

Synthesis of fully π-conjugated covalent organic frameworks (COFs) remains still challenging. Here, the authors propose a versatile synthesis method based on Knoevenagel polycondensation for the synthesis of vinylene-linked benzobisthiazole-based 2D COFs.

This paper proposes a method for estimating the speed and position of unsampled vehicles using sampled data from connected automated vehicles (CAVs). The determination of vehicle speed and position on the road is a challenging and crucial task, as they can effectively reflect traffic flow characteristics and contribute to traffic state estimation and intersection signal timing optimization. Connected automated vehicles have the capability to upload their own trajectory data while also capturing trajectory data of surrounding vehicles through onboard sensors. Therefore, this paper proposes a novel approach to estimate the speed and position of unsampled vehicles. Firstly, using real vehicle trajectory data, the correlation between the velocity of following vehicles and the velocity of leading vehicles under different densities is analyzed, leading to the development of a velocity estimation model incorporating a speed correction factor. Secondly, the correlation between time headway, the rate of change of following vehicle acceleration, and traffic density is examined. To address the issue of heterogeneous behavior in vehicle following described by the Intelligent Driver Model (IDM), a real-time optimization model for estimating vehicle position by optimizing IDM parameters is proposed. The velocity estimation model and the position estimation model are summarized as two nonlinear optimization problems. Finally, the proposed method is validated using actual vehicle trajectory data. Experimental results demonstrate that when the number of connected automated vehicles (CAVs) is 2, the proposed method reduces the average absolute error by 30.73% and the standard deviation of the average absolute error by 42.8% compared to a linear model-based speed estimation method under different density conditions. Compared to a method that estimates vehicle position by calibrating desired gaps, the proposed method reduces the average absolute error by 38.2% and the standard deviation of the average absolute error by 41.7% under different density conditions. Furthermore, the proposed method exhibits good practicality under different CAV penetration rates.

Abstract Background Tumor necrosis factor (TNF)-α inhibitors represented by Etanercept (a fusion protein containing soluble TNF receptor II (sTNFRII) and the Fc segment of human IgG1) play a pivotal role in Rheumatoid arthritis (RA) treatment. However, long-term use increases the risk of infection and tumors for their systemic inhibition of TNF-α, which disrupts the regular physiological function of this molecular. Mesenchymal stem cells (MSCs)-based delivery system provides new options for RA treatment with their “homing” and immune-regulation capacities, whereas inflammatory environment (especially TNF-α) is not conducive to MSCs' therapeutic effects by inducing apoptosis/autophagy. Here, we constructed a strain of sTNFRII-Fc-expressing MSCs (sTNFRII-MSC), aiming to offset the deficiency of those two interventions. Methods Constructed sTNFRII-Fc lentiviral vector was used to infect human umbilical cord-derived MSCs, and sTNFRII-MSC stable cell line was generated by monoclonal cultivation. In vitro and vivo characteristics of sTNFRII-MSC were assessed by coculture assay and an acute inflammatory model in NOD/SCID mice. The sTNFRII-MSC were transplanted into CIA model, pathological and immunological indicators were detected to evaluate the therapeutic effects of sTNFRII-MSC. The distribution of sTNFRII-MSC was determined by immunofluorescence assay. Apoptosis and autophagy were analyzed by flow cytometry, western blot and immunofluorescence. Results sTNFRII-Fc secreted by sTNFRII-MSC present biological activity both in vitro and vivo. sTNFRII-MSC transplantation effectively alleviates mice collagen-induced arthritis (CIA) via migrating to affected area, protecting articular cartilage destruction, modulating immune balance and sTNFRII-MSC showed prolonged internal retention via resisting apoptosis/autophagy induced by TNF-α. Conclusion sTNFRII-Fc modification protects MSCs against apoptosis/autophagy induced by TNF-α, in addition to releasing sTNFRII-Fc neutralizing TNF-α to block relevant immune-inflammation cascade, and thus exert better therapeutic effects in alleviating inflammatory arthritis.

Summary: Unraveling the morphological complexity of two-dimensional macromolecules allows researchers to design and fabricate high-performance, multifunctional materials. Here, we present a protocol based on statistical learning to resolve morphological complexity utilizing geometrical, topological, and physical features extracted from the strain energy heatmap and structural point cloud. We detail steps for software installation and data generation. We further describe model implementation and evaluation via unsupervised and supervised learning and discuss a theoretical description of morphological complexity including topological features.For complete details on the use and execution of this protocol, please refer to Zhao et al. (2022). : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

As urban areas continue to expand, traffic congestion has emerged as a significant challenge impacting urban governance and economic development. Frequent regional traffic congestion has become a primary factor hindering urban economic growth and social activities, necessitating improved regional traffic management. Addressing regional traffic optimization and control methods based on the characteristics of regional congestion has become a crucial and complex issue in the field of traffic management and control research. This paper focuses on the macroscopic fundamental diagram (MFD) and aims to tackle the control problem without relying on traffic determination information. To address this, we introduce the Q-learning (QL) algorithm in reinforcement learning and the Deep Deterministic Policy Gradient (DDPG) algorithm in deep reinforcement learning. Subsequently, we propose the MFD-QL perimeter control model and the MFD-DDPG perimeter control model. We conduct numerical analysis and simulation experiments to verify the effectiveness of the MFD-QL and MFD-DDPG algorithms. The experimental results show that the algorithms converge rapidly to a stable state and achieve superior control effects in optimizing regional perimeter control.

A polarization analysis method based on a multi-parameter model is proposed to address the polarization effect analysis and calibration requirements of a dual-coded snapshot spectral polarization imaging system. A full-link polarization effect model for a spectral polarization imaging system is established that includes a digital micromirror array (DMD), prism grating prism (PGP), micro-polarizer array detector (MPA), and multi-film. The influence of parameters such as the refractive index, incident angle, grating refractive index, constant, prism refractive index, vertex angle, multi-layer film complex refractive index, and film thickness on the optical transmittance of the system are analyzed. Using a dynamic data exchange mechanism to perform full-link, full-FOV, and full-pupil ray tracing on the optical system, the polarization effect distribution of the system under different degrees of polarization (DOP) and wavelengths is obtained. A calibration experiment for the controllable incident wavelength and DOP using narrowband filters and glass stacks is established. The experimental results show that in the 420 nm, 532 nm, and 635 nm wavelength bands, the MSEs of the calibrated values are 1.3924 × 10−4, 1.6852 × 10−4, and 1.6735 × 10−4, respectively. It is proven that the calibration method based on a multi-parameter model is feasible. Finally, the spectral polarization image at 532 nm is calibrated. The contrast ratio of metallic aluminum is calibrated from 7.13 to 15.33. This study provides a theoretical basis for the analysis and calibration of polarization effects in a dual-coded snapshot spectral polarization imaging system.

The utilization of unmanned aerial vehicles (UAVs) for the precise and convenient detection of litchi fruits, in order to estimate yields and perform statistical analysis, holds significant value in the complex and variable litchi orchard environment. Currently, litchi yield estimation relies predominantly on manual rough counts, which often result in discrepancies between the estimated values and the actual production figures. This study proposes a large-scene and high-density litchi fruit recognition method based on the improved You Only Look Once version 5 (YOLOv5) model. The main objective is to enhance the accuracy and efficiency of yield estimation in natural orchards. First, the PANet in the original YOLOv5 model is replaced with the improved Bi-directional Feature Pyramid Network (BiFPN) to enhance the model’s cross-scale feature fusion. Second, the P2 feature layer is fused into the BiFPN to enhance the learning capability of the model for high-resolution features. After that, the Normalized Gaussian Wasserstein Distance (NWD) metric is introduced into the regression loss function to enhance the learning ability of the model for litchi tiny targets. Finally, the Slicing Aided Hyper Inference (SAHI) is used to enhance the detection of tiny targets without increasing the model’s parameters or computational memory. The experimental results show that the overall AP value of the improved YOLOv5 model has been effectively increased by 22%, compared to the original YOLOv5 model’s AP value of 50.6%. Specifically, the APs value for detecting small targets has increased from 27.8% to 57.3%. The model size is only 3.6% larger than the original YOLOv5 model. Through ablation and comparative experiments, our method has successfully improved accuracy without compromising the model size and inference speed. Therefore, the proposed method in this paper holds practical applicability for detecting litchi fruits in orchards. It can serve as a valuable tool for providing guidance and suggestions for litchi yield estimation and subsequent harvesting processes. In future research, optimization can be continued for the small target detection problem, while it can be extended to study the small target tracking problem in dense scenarios, which is of great significance for litchi yield estimation.

Abstract Organic photochromic materials have drawn considerable attention for their potential applications in large‐scale and low‐cost optoelectronics owing to unique tunable physicochemical properties. For organic photodetectors, photochromic materials have realized optical and electrical engineering of semiconductor layers, which incorporate not only tunable performance, but also functionalities to optoelectronic devices. However, the essential challenge is to assemble large‐area photochromic micro‐ and nanostructure arrays with controllable geometry and precise alignment, which restricts the integration of multifunctional optoelectronic devices. Herein, we fabricate organic photochromic one‐dimensional (1D) arrays via a feasible solution process through the confined crystallization of organic molecules. By modulating and controlling the photoisomerization behaviors, these 1D photochromic arrays possess broad spectral tunability, which ensure tunable photoresponse. Furthermore, we investigate the crystallographic transition and electronic performance variation of these 1D photochromic arrays. By adjusting the dwell time of ultraviolet (UV) irradiation, the UV photochromic photodetectors realize tunable and repeatable responsivity from 85.6 to 0.709 mA/W. Our work provides new possibilities for optical and electrical engineering of photochromic microwires towards the integration of multifunctional optoelectronic devices.