Search

Abstract Background It is worthwhile to establish a prognostic prediction model based on microenvironment cells (MCs) infiltration and explore new treatment strategies for triple-negative breast cancer (TNBC). Methods The xCell algorithm was used to quantify the cellular components of the TNBC microenvironment based on bulk RNA sequencing (bulk RNA-seq) data. The MCs index (MCI) was constructed using the least absolute shrinkage and selection operator Cox (LASSO-Cox) regression analysis. Single-cell RNA sequencing (scRNA-seq), spatially resolved transcriptomics (SRT), and multiplex immunofluorescence (mIF) staining analyses verified MCI. The mechanism of action of the MCI was investigated in tumor-bearing mice. Results MCI consists of the six types of MCs, which can precisely predict the prognosis of the TNBC patients. scRNA-seq, SRT, and mIF analyses verified the existence and proportions of these cells. Furthermore, combined with the spatial distribution characteristics of the six types of MCs, an MCI-enhanced (MCI-e) model was constructed, which could predict the prognosis of the TNBC patients more accurately. More importantly, inhibition of the insulin signaling pathway activated in the cancer cells of the MCIhigh the TNBC patients significantly prolonged the survival time of tumor-bearing mice. Conclusions Overall, our results demonstrate that MCs infiltration can be exploited as a novel indicator for the prognosis and therapeutic regimen selection of the TNBC patients.

The HECT‐type E3 ubiquitin WWP1 (also known as NEDD4‐like E3 ubiquitin‐protein ligase WWP1) acts as an oncogenic factor in acute myeloid leukemia (AML) cells. WWP1 overexpression in AML confers a proliferative advantage to leukemic blasts (abnormal immature white blood cells) and counteracts apoptotic cell death and differentiation. In an effort to elucidate the molecular basis of WWP1 oncogenic activities, we identified WWP1 as a previously unknown negative regulator of thioredoxin‐interacting protein (TXNIP)‐mediated reactive oxygen species (ROS) production in AML cells. TXNIP inhibits the disulfide reductase enzymatic activity of thioredoxin (Trx), impairing its antioxidant function and, ultimately, leading to the disruption of cellular redox homeostasis. In addition, TXNIP restricts cell growth and survival by blocking glucose uptake and metabolism. Here, we found that WWP1 directly interacts with TXNIP, thus promoting its ubiquitin‐dependent proteasomal proteolysis. As a result, accumulation of TXNIP in response to WWP1 inactivation in AML blasts reduces Trx activity and increases ROS production, hence inducing cellular oxidative stress. Increased ROS generation in WWP1‐depleted cells culminates in DNA strand breaks and subsequent apoptosis. Coherently with TXNIP stabilization following WWP1 inactivation, we also observed an impairment of both glucose up‐take and consumption. Hence, a contribution to the increased cell death observed in WWP1‐depleted cells also possibly arises from the attenuation of glucose up‐take and glycolytic flux resulting from TXNIP accumulation. Future studies are needed to establish whether TXNIP‐dependent deregulation of redox homeostasis in WWP1‐overexpressing blasts may affect the response of leukemic cells to chemotherapeutic drugs.

Abstract Per- and polyfluoroalkyl substances (PFASs) have recently garnered considerable concerns regarding their impacts on human and ecological health. Despite the important roles of polyamide membranes in remediating PFASs-contaminated water, the governing factors influencing PFAS transport across these membranes remain elusive. In this study, we investigate PFAS rejection by polyamide membranes using two machine learning (ML) models, namely XGBoost and multimodal transformer models. Utilizing the Shapley additive explanation method for XGBoost model interpretation unveils the impacts of both PFAS characteristics and membrane properties on model predictions. The examination of the impacts of chemical structure involves interpreting the multimodal transformer model incorporated with simplified molecular input line entry system strings through heat maps, providing a visual representation of the attention score assigned to each atom of PFAS molecules. Both ML interpretation methods highlight the dominance of electrostatic interaction in governing PFAS transport across polyamide membranes. The roles of functional groups in altering PFAS transport across membranes are further revealed by molecular simulations. The combination of ML with computer simulations not only advances our knowledge of PFAS removal by polyamide membranes, but also provides an innovative approach to facilitate data-driven feature selection for the development of high-performance membranes with improved PFAS removal efficiency.

Abstract Façade elements, such as windows, doors, balconies, sculptures, and totems, in architectural heritage images with incomplete structures should be automatically reconstructed for applications in 3D analysis, 3D modeling, virtual tourism, city planning, and the protection and reconstruction of architectural heritage. This study segments façade elements of architectural heritage semantically using YOLOv9. A parameterized expression for the semantic façade model is designed. In addition, the façade layer graph (FLG) and element layer graph (ELG) algorithms are developed based on topological, geometric, and structural constraints to automatically reconstruct the semantic façade model for architectural heritages. The results showed that the average precision (AP) and mean intersection over union (MIoU) achieved using YOLOv9 + FLG-ELG are 86.91% and 85.63%, respectively, on the dataset concerning façade elements of architectural heritages. The AP values obtained from the proposed method are 98.5% on the ECP2011 dataset and 95.3% on the Graz2012 dataset. The YOLOv9 + FLG-ELG method automatically reconstructs regular, irregular, and complex façade layouts with high accuracy and robustness.

Probability-based methodologies have gained widespread recognition for their pivotal role in steering decision-making in contexts marked by uncertainty or vagueness. In order to guarantee that decisions made in these circumstances are both significant and impactful, various methodologies focused on probability have been devised and utilized. This study seeks to significantly enrich the existing literature by proposing a new probability model termed the weighted sine exponentiated Weibull distribution. Certain characteristics are obtained for the suggested model. Additionally, the parameter estimation method and simulation studies related to the suggested model are also provided. Two distinct data sets, acquired from the disciplines of physical education and reliability, were effectively implemented using the proposed model, resulting in a successful outcome. Through the utilization of the p-value and various tests, it becomes apparent that the weighted sine exponentiated Weibull model consistently surpasses its competitor distributions in terms of statistical significance. The proposed model’s practical illustration highlights its effectiveness and potential for extensive use in both physical education and reliability research and practice.

Metadevices have emerged as a new element or system in recent years, from optics to mechanical science, showing superior performance and powerful application potential. In this study, a mechanical metadevice that capable of low-frequency vibration isolation, which is called metamaterial springs or metasprings, is proposed. Meanwhile, a modular design method is reported to obtain the customizable quasi-zero stiffness characteristic of the designed metaspring. As proof-of-concept, we demonstrate, both in simulations and experiments, the quasi-zero stiffness characteristics of the proposed metasprings using 3D-printed experimental specimens. Moreover, the low-frequency vibration isolation properties of the proposed metasprings is demonstrated both in vibration tests and automotive vibration tests. This work provides a new mechanical metadevice, that is, metasprings for low-frequency vibration isolation, as well as a modular design method for designing metasprings, which may revolutionize vibration isolation devices in the field of low-frequency vibration isolation.

In the upcoming 6-generation (6G) revolution, the achievement of low power consumption has become a key objective in research concerning terahertz devices. As an important component of passive devices, there are very few low-loss dielectric ceramics in the terahertz range. To elucidate the mechanism of loss and promote the application of microwave dielectric ceramics for future 6G technology (covering microwave and terahertz frequencies), the terahertz responses of ANb2O6 (A = Zn, Co, Mn, and Ni) columbite niobates were studied. The influences of magnetic loss on the Q×f values in the microwave range with different transition metal ions in the A-site were reasonably analyzed. Moreover, due to the weakened magnetic relaxation properties in the terahertz range, the samples all exhibited low loss and approximate transparency, especially for MnNb2O6 and NiNb2O6 (tanδ < 0.01 and absorption coefficient < 10 cm−1 below 1.2 THz), which subverted the definition of traditional low-loss microwave dielectric ceramics. Ultimately, based on Mie theory, we designed a prototypical broadband metamaterial reflector to validate the applicability of the ANb2O6 system in the terahertz band, which is highly important for the development of terahertz ceramic-based passive devices.

Metalenses with achromatic performance offer a new opportunity for high-quality imaging with an ultra-compact configuration; however, they suffer from complex fabrication processes and low focusing efficiency. In this study, we propose an efficient design method for achromatic microlenses on a wavelength scale using materials with low dispersion, an adequately designed convex surface, and a thickness profile distribution. By taking into account the absolute chromatic aberration, relative focal length shift (FLS), and numerical aperture (NA), microlens with a certain focal length can be realized through our realized map of geometric features. Accordingly, the designed achromatic microlenses with low-dispersion fused silica were fabricated using a focused ion beam, and precise surface profiles were obtained. The fabricated microlenses exhibited a high average focusing efficiency of 65% at visible wavelengths of 410–680 nm and excellent achromatic capability via white light imaging. Moreover, the design exhibited the advantages of being polarization-insensitive and near-diffraction-limited. These results demonstrate the effectiveness of our proposed achromatic microlens design approach, which expands the prospects of miniaturized optics such as virtual and augmented reality, ultracompact microscopes, and biological endoscopy.

Abstract Nonreciprocal quantum transport effect is mainly governed by the symmetry breaking of the material systems and is gaining extensive attention in condensed matter physics. Realizing electrical switching of the polarity of the nonreciprocal transport without external magnetic field is essential to the development of nonreciprocal quantum devices. However, electrical switching of superconducting nonreciprocity remains yet to be achieved. Here, we report the observation of field-free electrical switching of nonreciprocal Ising superconductivity in Fe3GeTe2/NbSe2 van der Waals (vdW) heterostructure. By taking advantage of this electrically switchable superconducting nonreciprocity, we demonstrate a proof-of-concept nonreciprocal quantum neuronal transistor, which allows for implementing the XOR logic gate and faithfully emulating biological functionality of a cortical neuron in the brain. Our work provides a promising pathway to realize field-free and electrically switchable nonreciprocity of quantum transport and demonstrate its potential in exploring neuromorphic quantum devices with both functionality and performance beyond the traditional devices.

Abstract Terahertz (THz) emission spectroscopy (TES) has emerged as a highly effective and versatile technique for investigating the photoelectric properties of diverse materials and nonlinear physical processes in the past few decades. Concurrently, research on two-dimensional (2D) materials has experienced substantial growth due to their atomically thin structures, exceptional mechanical and optoelectronic properties, and the potential for applications in flexible electronics, sensing, and nanoelectronics. Specifically, these materials offer advantages such as tunable bandgap, high carrier mobility, wideband optical absorption, and relatively short carrier lifetime. By applying TES to investigate the 2D materials, their interfaces and heterostructures, rich information about the interplay among photons, charges, phonons and spins can be unfolded, which provides fundamental understanding for future applications. Thus it is timely to review the nonlinear processes underlying THz emission in 2D materials including optical rectification, photon-drag, high-order harmonic generation and spin-to-charge conversion, showcasing the rich diversity of the TES employed to unravel the complex nature of these materials. Typical applications based on THz emissions, such as THz lasers, ultrafast imaging and biosensors, are also discussed. Step further, we analyzed the unique advantages of spintronic terahertz emitters and the future technological advancements in the development of new THz generation mechanisms leading to advanced THz sources characterized by wide bandwidth, high power and integration, suitable for industrial and commercial applications. The continuous advancement and integration of TES with the study of 2D materials and heterostructures promise to revolutionize research in different areas, including basic materials physics, novel optoelectronic devices, and chips for post-Moore’s era.

Abstract Photochromic glass shows great promise for 3D optical information encryption and storage applications. The formation of Ag nanoclusters by light irradiation has been a significant development in the field of photochromic glass research. However, extending this approach to other metal nanoclusters remains a challenge. In this study, we present a pioneering method for crafting photochromic glass with reliably adjustable dual‐mode luminescence in both the NIR and visible spectra. This was achieved by leveraging bimetallic clusters of bismuth, resulting in a distinct and novel photochromic glass. When rare‐earth‐doped, bismuth‐based glass is irradiated with a 473 nm laser, and it undergoes a color transformation from yellow to red, accompanied by visible and broad NIR luminescence. This phenomenon is attributed to the formation of laser‐induced (Bi+, Bi0) nanoclusters. We achieved reversible manipulation of the NIR luminescence of these nanoclusters and visible rare‐earth luminescence by alternating exposure to a 473 nm laser and thermal stimulation. Information patterns can be inscribed and erased on a glass surface or in 3D space, and the readout is enabled by modulating visible and NIR luminescence. This study introduces a pioneering strategy for designing photochromic glasses with extensive NIR luminescence and significant potential for applications in high‐capacity information encryption, optical data storage, optical communication, and NIR imaging. The exploration of bimetallic cluster formation in Bi represents a vital contribution to the advancement of multifunctional glass systems with augmented optical functionalities and versatile applications.

Large-sized titanium-containing inclusion clusters seriously affect the continuous casting and performance of high titanium wear-resistant steel. In the current work, thermodynamic calculations were firstly conducted to predict the formation of various complex TiN inclusions, viz, TiOx-TiN, Al2O3–TiN and LaAlO3–TiN. Based on that, laboratory-scale experiments were designed to prepare samples containing a single type of complex TiN inclusions. The agglomeration characterizes of different complex inclusions were compared by combing in-situ observation based on confocal laser scanning microscopy and Newtonian motion equation. Furthermore, the Kralchevsky-Paunov model was used to study the influences of density and size on the agglomeration behavior of complex TiN inclusions. The result shows that the attractive force between TiOx-TiN complex inclusions increases from 1.93 × 10−18 to 4.34 × 10−17 N, while it was 3.41 × 10−17 to 5.12 × 10−16 N for Al2O3–TiN complex inclusions and 6.42 × 10−18 to 1.14 × 10−16 N for LaAlO3–TiN inclusions. Based on the experimental results and theoretical calculation, the agglomeration tendency of Al2O3–TiN is the strongest due to the considered size and compared to the TiOx-TiN inclusions, the stronger agglomeration tendency of LaAlO3–TiN ones can be attributed to their higher density.

The need for cross-comparison and validation of all-weather Land Surface Temperature (LST) products has arisen due to the release of multiple such products aimed at providing comprehensive all-weather monitoring capabilities. In this study, we focus on validating two well-established all-weather LST products (i.e. MLST-AS and TRIMS LST) against in-situ measurements obtained from four high-quality LST validation sites: Evora, Gobabeb, KIT-Forest, and Lake Constance. For the land sites, MLST-AS exhibits better accuracy, with RMSEs ranging from 1.6 K to 2.1 K, than TRIMS LST, the RMSEs of which range from 1.9 K to 3.1 K. Because MLST-AS pixels classified as “inland water” are masked out, the validation over Lake Constance is limited to TRIMS LST: it yields a RMSE of 1.6 K. Furthermore, the validation results show that MLST-AS and TRIMS LST exhibit better accuracy under clear-sky conditions than unclear-sky conditions across all sites. Since the accuracy of the all-weather LST products is considerably affected by the input clear-sky LST products, we further compare the all-weather LST with the corresponding input clear-sky LST to conduct an error source analysis. Considering the clear-sky pixels on MLST-AS directly using the estimates from MLST, the error source analysis is limited to examining TRIMS LST and its input (i.e. MODIS LST). The findings indicate that TRIMS LST is highly correlated with MODIS LST. The investigation and validation of the two selected all-weather LST products objectively evaluate their accuracy and stability, which provides important information for applications of these all-weather LST products.

Abstract Introduction Despite older adults being more vulnerable to sepsis, most preclinical research on sepsis has been conducted using young animals. This results in decreased scientific validity since age is an independent predictor of poor outcome. In this study, we explored the impact of aging on the host response to sepsis using the fecal-induced peritonitis (FIP) model developed by the National Preclinical Sepsis Platform (NPSP). Methods C57BL/6 mice (3 or 12 months old) were injected intraperitoneally with rat fecal slurry (0.75 mg/g) or a control vehicle. To investigate the early stage of sepsis, mice were culled at 4 h, 8 h, or 12 h to investigate disease severity, immunothrombosis biomarkers, and organ injury. Mice received buprenorphine at 4 h post-FIP. A separate cohort of FIP mice were studied for 72 h (with buprenorphine given at 4 h, 12 h, and then every 12 h post-FIP and antibiotics/fluids starting at 12 h post-FIP). Organs were harvested, plasma levels of Interleukin (IL)-6, IL-10, monocyte chemoattract protein (MCP-1)/CCL2, thrombin-antithrombin (TAT) complexes, cell-free DNA (CFDNA), and ADAMTS13 activity were quantified, and bacterial loads were measured. Results In the 12 h time course study, aged FIP mice demonstrated increased inflammation and injury to the lungs compared to young FIP mice. In the 72 h study, aged FIP mice exhibited a higher mortality rate (89%) compared to young FIP mice (42%) (p

This study highlights the importance of unmanned aerial vehicle (UAV) multispectral (MS) imagery for the accurate delineation and analysis of wetland ecosystems, which is crucial for their conservation and management. We present an enhanced semantic segmentation algorithm designed for UAV MS imagery, which incorporates thermal infrared (TIR) data to improve segmentation outcomes. Our approach, involving meticulous image preprocessing, customized network architecture, and iterative training procedures, aims to refine wetland boundary delineation. The algorithm demonstrates strong segmentation results, including a mean pixel accuracy (MPA) of 90.35% and a mean intersection over union (MIOU) of 73.87% across different classes, with a pixel accuracy (PA) of 95.42% and an intersection over union (IOU) of 90.46% for the wetland class. The integration of TIR data with MS imagery not only enriches the feature set for segmentation but also, to some extent, helps address data imbalance issues, contributing to a more refined ecological analysis. This approach, along with the development of a comprehensive dataset that reflects the diversity of wetland environments and advances the utility of remote sensing technologies in ecological monitoring. This research lays the groundwork for more detailed and informative UAV-based evaluations of wetland health and integrity.

Wood, a widely used eco-friendly material in ancient architecture, has endured for thousands of years. However, environmental changes over time cause varying degrees of internal damage, degrading mechanical properties and compromising overall building safety. Investigating wood's mechanical properties and damage mechanisms in ancient structures is crucial. This study simulates real conditions of ancient wooden structures by subjecting three common fresh woods (beech, maple, sylvestris pine) to extreme environments: high-temperature treatments (100, 150, 200, and 250 °C for 15, 30, 45 min), water immersion, and exposure to acidic/alkaline environments. Using the MTS instrument, longitudinal compression and bending tests are conducted, analyzing compressive strength and flexural performance. DIC tracks both test processes, and SEM provides a microscopic analysis of damage mechanisms. Results show that beech and maple outperform sylvestris pine under high temperatures and acidic/alkaline conditions. In extreme environments, wood's mechanical strength correlates with moisture content and hemicellulose decomposition. High temperatures enhance wood's mechanical properties through dehydration reactions, but prolonged exposure leads to continuous hemicellulose, cellulose, and lignin decomposition, ultimately reducing wood's mechanical performance.

Summary: Timely adjustments of antibiotic and corticosteroid treatments are vital for patients with diffuse parenchymal lung diseases (DPLDs). In this study, 41 DPLD patients with negative metagenomic next-generation sequencing (mNGS) results who were responsive to corticosteroids were enrolled. Among these patients, about 26.8% suffered from drug-induced DPLD, while 9.8% presented autoimmune-related DPLD. Following the report of the negative mNGS results, in 34 patients with complete antibiotics administration profiles, 79.4% (27/34) patients discontinued antibiotics after receiving negative mNGS results. Moreover, 70.7% (29/41) patients began or increased the administration of corticosteroid upon receipt of negative mNGS results. In the microbiota analysis, Staphylococcus and Stenotrophomonas showed higher detection rates in patients with oxygenation index (OI) below 300, while Escherichia and Stenotrophomonas had higher abundance in patients with pleural effusion. In summary, our findings demonstrated the clinical significance of mNGS in assisting the antibiotic and corticosteroid treatment adjustments in corticosteroid-responsive DPLD. Lung microbiota may imply the severity of the disease.

Deep learning, which has exhibited considerable potential and effectiveness in forest resource assessment, is vital for comprehending and managing forest resources and ecosystems. However, extensive assessment of forest resources is highly challenging due to the complex and varied nature of forest types sourced from diverse remote sensing platforms, which include images, point clouds, and fusion data. To facilitate further study, we systematically review the current status, applications and prospects of deep learning technologies for different types of forest remote sensing data. After considering more than two hundred forest-related papers published over the past decade, we introduce sensors and devices for forest data acquisition, classify deep learning methods based on their data processing methods and operational principles, and categorize diverse instances of these methods with various forest applications. Moreover, we summarize available datasets related primarily to forest data and examine the global geographic distribution of the relevant literature. Comprehensive insights into the advantages and limitations of each method are described, offering a forward-looking perspective on the trend of applying deep learning technology to forest research. In this paper, we aim to provide an overview of the current trends and challenges of deep learning techniques applied to forest research, creating a comprehensive picture for use as a reference by both academia and industry professionals.

Abstract Esophageal and gastric varices (EGV) bleeding is a dangerous side effect of liver cirrhosis. Ascites may affect the effectiveness of carvedilol in preventing EGV rebleeding. A retrospective analysis was done on patients with EGV bleeding who visited our gastroenterology department between January 1, 2015, and October 29, 2020, and were given carvedilol therapy again. Patients were classified based on whether they had ascites. The primary outcome was EGV rebleeding. A total of 286 patients were included, with a median follow‐up of 24.0 (19.0–42.0) months, comprising those without ascites (N = 155) and those with ascites (N = 131). The mean age of the patients was 55.15 ± 12.44 years, and 177 (61.9%) of them were men. There were 162 (56.6%) Child‐Pugh A grades. The etiology of cirrhosis included 135 (47.2%) cases of hepatitis B. After carvedilol therapy, the patient's portal vein diameter (DPV) was widened (p

ObjectiveThe aim of this study was to develop a nomogram, using serum thymidine kinase 1 protein (STK1p) combined with ultrasonography parameters, to early predict central lymph node metastasis (CLNM) in patients with papillary thyroid carcinoma (PTC) pre-surgery.MethodsPatients with PTC pre-surgery in January 2021 to February 2023 were divided into three cohorts: the observation cohort (CLNM, n = 140), the control cohort (NCLNM, n = 128), and the external verification cohort (CLNM, n = 50; NCLNM, n = 50). STK1p was detected by an enzyme immunodot-blot chemiluminescence analyzer and clinical parameters were evaluated by ultrasonography.ResultsA suitable risk threshold value for STK1p of 1.7 pmol/L was selected for predicting CLNM risk by receiver operating characteristic (ROC) curve analysis. Multivariate analysis identified the following six independent risk factors for CLNM: maximum tumor size >1 cm [odds ratio (OR) = 2.406, 95% confidence interval (CI) (1.279–4.526), p = 0.006]; capsule invasion [OR = 2.664, 95% CI (1.324–5.360), p = 0.006]; irregular margin [OR = 2.922; 95% CI (1.397–6.111), p = 0.004]; CLN flow signal [OR = 3.618, 95% CI (1.631–8.027), p = 0.002]; tumor-foci number ≥2 [OR = 4.064, 95% CI (2.102–7.859), p < 0.001]; and STK1p ≥1.7 pmol/L [OR = 7.514, 95% CI (3.852–14.660), p < 0.001]. The constructed nomogram showed that the area under the ROC curve for the main dataset was 0.867 and that for the validation dataset was 0.830, exhibiting effectivity, and was recalculated to a total score of approximately 383. Through monitoring the response post-surgery, all patients were assessed as tumor-free at 12 months post-surgery, which was significantly associated with a reduction in STK1p to disease-free levels.ConclusionWe demonstrate for the first time that a novel nomogram including STK1p combined with ultrasonography can assist in the clinical prevention of CLNM, by facilitating timely, individualized prophylactic CLNM dissection, thereby reducing the risk of secondary surgery and the probability of recurrence.

Abstract Objective This study aims to identify the most effective diagnostic method for distinguishing pathogenic and non-pathogenic Gram-negative bacteria (GNB) in suspected pneumonia cases using metagenomic next-generation sequencing (mNGS) on bronchoalveolar lavage fluid (BALF) samples. Methods The effectiveness of mNGS was assessed on BALF samples collected from 583 patients, and the results were compared with those from microbiological culture and final clinical diagnosis. Three interpretational approaches were evaluated for diagnostic accuracy. Results mNGS outperformed culture significantly. Among the interpretational approaches, Clinical Interpretation (CI) demonstrated the best diagnostic performance with a sensitivity of 87.3%, specificity of 100%, positive predictive value of 100%, and negative predictive value of 98.3%. CI’s specificity was significantly higher than Simple Interpretation (SI) at 37.9%. Additionally, CI excluded some microorganisms identified as putative pathogens by SI, including Haemophilus parainfluenzae, Haemophilus parahaemolyticus, and Klebsiella aerogenes. Conclusion Proper interpretation of mNGS data is crucial for accurately diagnosing respiratory infections caused by GNB. CI is recommended for this purpose.

Abstract To investigate the frequency of monocytic myeloid-derived suppressor cells (M-MDSCs) in type 2 diabetes mellitus (T2DM) patients and explore the potential associations between M-MDSCs, glycemic control, and the occurrence of infections and tumor. 102 healthy and 77 T2DM individuals were enrolled. We assessed the M-MDSCs frequency, levels of fasting plasma glucose (FPG), haemoglobin A1c (HbA1c), and other relevant indicators. Each patient underwent a follow-up of at least 6 months after M-MDSCs detection. The M-MDSCs frequency was significantly higher in patients with poor glycemic control (PGC) compared to the healthy population (P 0.05). There was a positive correlation between the M-MDSCs frequency and FPG, HbA1c (R = 0.517 and 0.315, P

The reliability of conventional Pilot Differential Protection (PDP) may decrease when applied to outgoing lines that integrate Inverter-interfaced Renewable Energy Generator (IIREG) into the grid. Therefore, it is necessary to develop new principles of the PDP according to the fault characteristics of the IIREG station. This paper begins by analyzing the transient differential current characteristics under internal and external faults of outgoing lines. The theoretical analysis reveals notable differences in the Dominant Frequency Band (DFB) energy of the differential current between internal and external faults. Additionally, factors influencing the DFB of differential current energy, such as fault resistance and inner current loop control of inverters, are examined. Furthermore, Variational Mode Decomposition (VMD), optimized by the Northern Goshawk Optimization algorithm, was employed to extract modes from different frequency bands in the differential current. Finally, the Frequency-domain Energy (FDE) of the modes is computed using Fast Fourier Transform (FFT), and the DFB is obtained. A criterion for PDP based on the FDE of the DFB is presented. Simulation experiments confirm that the proposed scheme can operate correctly under various fault types and locations, demonstrating strong resilience to fault resistance and disturbances.

The green concretes industry benefits from utilizing gel to replace parts of the cement in concretes. However, measuring the compressive strength of geo-polymer concretes (CSGPoC) needs a significant amount of work and expenditure. Therefore, the best idea is predicting CSGPoC with a high level of accuracy. To do this, the base learner and super learner machine learning models were proposed in this study to anticipate CSGPoC. The decision tree (DT) is applied as base learner, and the random forest and extreme gradient boosting (XGBoost) techniques are used as super learner system. In this regard, a database was provided involving 259 CSGPoC data samples, of which four-fifths of is considered for the training model and one-fifth is selected for the testing models. The values of fly ash, ground-granulated blast-furnace slag (GGBS), Na2SiO3, NaOH, fine aggregate, gravel 4/10 mm, gravel 10/20 mm, water/solids ratio, and NaOH molarity were considered as input of the models to estimate CSGPoC. To evaluate the reliability and performance of the decision tree (DT), XGBoost, and random forest (RF) models, 12 performance evaluation metrics were determined. Based on the obtained results, the highest degree of accuracy is achieved by the XGBoost model with mean absolute error (MAE) of 2.073, mean absolute percentage error (MAPE) of 5.547, Nash–Sutcliffe (NS) of 0.981, correlation coefficient (R) of 0.991, R2 of 0.982, root mean square error (RMSE) of 2.458, Willmott's index (WI) of 0.795, weighted mean absolute percentage error (WMAPE) of 0.046, Bias of 2.073, square index (SI) of 0.054, p of 0.027, mean relative error (MRE) of -0.014, and a20 of 0.983 for the training model and MAE of 2.06, MAPE of 6.553, NS of 0.985, R of 0.993, R2 of 0.986, RMSE of 2.307, WI of 0.818, WMAPE of 0.05, Bias of 2.06, SI of 0.056, p of 0.028, MRE of -0.015, and a20 of 0.949 for the testing model. By importing the testing set into trained models, values of 0.8969, 0.9857, and 0.9424 for R2 were obtained for DT, XGBoost, and RF, respectively, which show the superiority of the XGBoost model in CSGPoC estimation. In conclusion, the XGBoost model is capable of more accurately predicting CSGPoC than DT and RF models.

Abstract Cervical cancer is the second leading cause of morbidity and mortality in women worldwide. Traditional treatment methods have become limited. Naringenin, a flavonoid abundant in various fruits and herbal medicines, has demonstrated anti-tumor properties among other effects. This research undertook to elucidate the mechanism of naringenin in the context of cervical cancer treatment by leveraging network pharmacology and performing experimental validation. Initial steps involved predicting potential naringenin targets and subsequently screening for overlaps between these targets and those related to cervical cancer, followed by analysis of their interrelationships. Molecular docking was subsequently utilized to verify the binding effect of the central target. Within the framework of network pharmacology, it was discovered that naringenin might possess anti-cancer properties specific to cervical cancer. Following this, the anti-tumor effects of naringenin on Hela cell viability, migration, and invasion were assessed employing CCK-8, transwell, wound healing assays, and western blotting. Experimental data indicated that naringenin attenuates the migration and invasion of Hela cells via downregulation EGFR/PI3K/AKT signaling pathway. Thus, our findings suggest that naringenin has therapeutic impacts on cervical cancer via multiple mechanisms, primarily by inhibiting the migration and invasion through the EGFR/PI3K/AKT/mTOR pathway. This study offers fresh insights for future clinical studies.

Highlights Presenting the first investigation into the structurally bubbling-failure mechanism of graphitic film during cyclic liquid nitrogen shocks. Proposing an innovative design about seamless heterointerface constructing a Cu-modified structure. Inventing a new ultra-stable species of highly thermally conductive films to inspire new techniques for efficient and extreme thermal management.

To study the dynamic response of multi-span mortise and tenon stone bridges under natural excitation, a bluestone multi-span stone bridge with a main span of 2.56 m in southern China was taken as the research object. Based on the collected pulsating signals of bridge piers and slabs, the natural frequencies and damping ratios of the main span bridge slab and pier were analyzed using the half-power broadband method (HPBM) and random decrement technique (RDT). Modal analysis was conducted using ANSYS, and the results were compared with those obtained from on-site experiments for further performance analysis. The research results of this article indicate that the natural frequency range of the 2.56-m bridge slab identified by measured signals is 48–49 Hz, and the damping ratio range is 33.33–36.61%. The natural frequency of the central pier is 75–76 Hz, and the damping ratio range is 26.39–27.83%. Through finite element modal analysis, the natural frequency of the bridge slab is 54.401 Hz, with an error of 10.5%. The natural frequency of the overall stone bridge is about 82.2 Hz, with an error of about 8.2%. The validated finite element model was subjected to normal water flow impact and erosion simulation. The results indicate that under erosion with fewer particles and lower flow rates, the upstream pier bottom at the center receives the highest relative erosion mass and displacement per unit area. The bridge deck near the main span also experienced relative displacement. Therefore, in the subsequent protection work, special attention should be paid to these components.

Ground filtering is crucial for airborne Light Detection and Ranging (LiDAR) data post-processing. The progressive triangulated irregular network densification (PTD) algorithm and its variants outperform others in accuracy, stability, and robustness, using grid-based seed point selection, TIN construction, and iterative rules for ground point identification. However, these methods still face limitations in removing low points and accurately preserving terrain details, primarily due to their sensitivity to grid size. To overcome this issue, a novel PTD filtering algorithm based on an adaptive grid (AGPTD) was proposed. The main contributions of the proposed method include an outlier removal method using a radius outlier removal algorithm and Kd-tree, a method for establishing an adaptive two-level grid based on point cloud density and terrain slope, and an adaptive selection method for angle and distance thresholds in the iterative densification processing. The performance of the AGPTD algorithm was assessed based on widely used benchmark datasets. Results show that the AGPTD algorithm outperforms the classical PTD algorithm in retaining ground feature points, especially in reducing Type I error and average total error significantly. In comparison with other advanced algorithms developed in recent years, the novel algorithm showed the lowest average Type I error, the minimal average total error, and the greatest average Kappa coefficient, which were 1.11%, 2.28%, and 90.86%, respectively. Additionally, the average accuracy, precision, and recall of AGPTD were 97.69%, 97.52%, and 98.98%, respectively.

Background: Chemotherapy-induced nausea and vomiting (CINV) is the most common adverse effect of chemotherapy and affects the continuation of chemotherapy in cancer patients. Electrical acupoint stimulation (EAS), which includes electroacupuncture and transcutaneous electrical stimulation (TES), has been used to treat CINV. This meta-analysis aimed to evaluate the efficacy of EAS in the treatment of CINV. Methods: Randomized controlled trials (RCTs) of EAS for CINV retrieved form five key databases. Two researchers independently performed article screening, data extraction and data integration. The Cochrane Collaboration's tool for assessing risk of bias was used to assesse the methodological quality according to Cochrane Handbook for Systematic Reviews of Interventions. RevMan 5.4 was used to perform analyses. Results: 10 RCTs with a total of 950 participants were included. The results showed that there was no significant difference between EAS compared to sham EAS in terms of increasing the rate of complete control of CINV and decreasing the overall incidence of CINV [RR = 1.26, 95 % CI (0.96, 1.66), P = 0.95; RR = 1.16, 95 % CI (0.97, 1.40), p = 0.71]. In terms of CINV severity, EAS reduced the occurrence of moderate-to-severe CINV [RR = 0.60, 95 % CI (0.38, 0.94), P = 0.03; RR = 0.50, 95 % CI (0.33, 0.76), P = 0.001]. Conclusion: EAS could improve moderate-to-severe CINV. However, EAS did not show a significant difference in reducing overall incidence and improving complete control rates compared with sham EAS. Due to limitations in the quality of the included articles, the available studies are insufficient to have sufficient evidence to confirm the efficacy of EAS for CINV. Validation with rigorously designed, large-sample, high-quality clinical trial studies may also be needed.

Overlay accuracy, one of the three fundamental indicators of lithography, is directly influenced by alignment precision. During the alignment process based on the Moiré fringe method, a slight angular misalignment between the mask and wafer will cause the Moiré fringes to tilt, thereby affecting the alignment accuracy. This paper proposes a leveling strategy based on the DQN (Deep Q-Network) algorithm. This strategy involves using four consecutive frames of wafer tilt images as the input values for a convolutional neural network (CNN), which serves as the environment model. The environment model is divided into two groups: the horizontal plane tilt environment model and the vertical plane tilt environment model. After convolution through the CNN and training with the pooling operation, the Q-value consisting of n discrete actions is output. In the DQN algorithm, the main contributions of this paper lie in three points: the adaptive application of environmental model input, parameter optimization of the loss function, and the possibility of application in the actual environment to provide some ideas. The environment model input interface can be applied to different tilt models and more complex scenes. The optimization of the loss function can match the leveling of different tilt models. Considering the application of this strategy in actual scenarios, motion calibration and detection between the mask and the wafer provide some ideas. To verify the reliability of the algorithm, simulations were conducted to generate tilted Moiré fringes resulting from tilt angles of the wafer plate, and the phase of the tilted Moiré fringes was subsequently calculated. The angle of the wafer was automatically adjusted using the DQN algorithm, and then various angles were measured. Repeated measurements were also conducted at the same angle. The angle deviation accuracy of the horizontal plane tilt environment model reached 0.0011 degrees, and the accuracy of repeated measurements reached 0.00025 degrees. The angle deviation accuracy of the vertical plane tilt environment model reached 0.0043 degrees, and repeated measurements achieved a precision of 0.00027 degrees. Moreover, in practical applications, it also provides corresponding ideas to ensure the determination of the relative position between the mask and wafer and the detection of movement, offering the potential for its application in the industry.

In order to correct the integrated nonuniformity of a lithographic illumination field, a high-precision uniformity correction method for an advanced lithographic illumination system is proposed. The method adopts the opaque finger array structure and improves correction ability and accuracy by optimizing the arrangement and structure of the unit without changing the width of each unit. The correction accuracy is expressed as the percentage of the corrected integrated nonuniformity. Through theoretical analysis and simulation, it can be seen that the correction accuracy of a staggered finger array is better than 0.22%. When staggered and layered, the correction accuracy of a finger array is better than 0.14%, which is better than that of a non-layered finger array. When staggered, layered, and chamfered of each unit, the correction accuracy of the finger array structure is better than 0.12%.

Breeding for nitrogen use efficiency (NUE) is becoming more important as global uncertainty makes the production and application of nitrogen (N) fertilizers more expensive and environmentally unfriendly. Despite this, most cereal breeding programs still use yield-related components as proxies for NUE, likely due to the prohibitive cost and time of collecting and analyzing samples through traditional lab-based methods. Drone-based NUE phenotyping provides a viable and scalable alternative as it is quicker, non-destructive, and consistent. Here, we present a study that utilized financially accessible cost-effective drones mounted with red-green-blue (RGB) image sensors coupled with the open-source AirMeasurer platform and advanced statistical analysis to exclude low-NUE lines in multi-seasonal field experiments. The method helped us to identify high N agronomic use efficiency lines but was less effective with a high N recovery efficiency line. We found that the drone-powered approach was very effective at 180 kg N per hectare (N180, an optimized N-rate) as it completely removed low-NUE wheat lines in the trial, which would facilitate breeders to quickly reduce the number of lines taken through multi-year breeding programs. Hence, this encouraging and scalable approach demonstrates its ability to conduct NUE phenotyping in wheat. With continuous refinements in field experiments, this method would be employable as an openly accessible platform to identify NUE lines at different N-rates for breeding and resource use efficiency studies in wheat.

Ancient masonry pagodas hold significant scientific, historical, and cultural importance. However, due to the complexity of masonry materials, structures, and boundary conditions, establishing finite element static and dynamic models for ancient masonry pagodas is highly challenging. This study aimed to explore the dynamic characteristics and finite element numerical simulation methods of ancient masonry pagodas in Yongzhou, Hunan province. It focused on the Huilong Pagoda in Yongzhou, where in situ test experiments under natural excitation are conducted. The SSI and NExT-ERA methods were employed to determine the ancient pagoda’s natural frequencies, vibration patterns, and damping ratios, and to validate the NExT-ERA method. The macroscopic numerical model of the Huilong Pagoda was calibrated using measured results. Subsequently, the NExT-ERA identification results were compared and analyzed with the numerical simulation results of the dynamic characteristics. The results indicate that the first three orders of natural frequencies for the ancient pagoda in the east–west direction are 1.937 Hz, 6.802 Hz, and 21.361 Hz, respectively. Similarly, the first three orders of natural frequencies in the north–south direction are 1.935 Hz, 7.439 Hz, and 21.398 Hz. The results obtained from both methods revealed that the overall structural damping ratio ranges from 0.21% to 2.89%. The numerical model was analyzed using ANSYS, and the first three orders of natural frequencies obtained were highly consistent with the measured values, exhibiting a maximum relative error of 8.54%. The numerical simulation method developed in this study can effectively simulate masonry pagodas.

Wheat (Triticum aestivum) is one of the most important staple crops worldwide. To ensure its global supply, the timing and duration of its growth cycle needs to be closely monitored in the field so that necessary crop management activities can be arranged in a timely manner. Also, breeders and plant researchers need to evaluate growth stages (GSs) for tens of thousands of genotypes at the plot level, at different sites and across multiple seasons. These indicate the importance of providing a reliable and scalable toolkit to address the challenge so that the plot-level assessment of GS can be successfully conducted for different objectives in plant research. Here, we present a multimodal deep learning model called GSP-AI, capable of identifying key GSs and predicting the vegetative-to-reproductive transition (i.e., flowering days) in wheat based on drone-collected canopy images and multiseasonal climatic datasets. In the study, we first established an open Wheat Growth Stage Prediction (WGSP) dataset, consisting of 70,410 annotated images collected from 54 varieties cultivated in China, 109 in the United Kingdom, and 100 in the United States together with key climatic factors. Then, we built an effective learning architecture based on Res2Net and long short-term memory (LSTM) to learn canopy-level vision features and patterns of climatic changes between 2018 and 2021 growing seasons. Utilizing the model, we achieved an overall accuracy of 91.2% in identifying key GS and an average root mean square error (RMSE) of 5.6 d for forecasting the flowering days compared with manual scoring. We further tested and improved the GSP-AI model with high-resolution smartphone images collected in the 2021/2022 season in China, through which the accuracy of the model was enhanced to 93.4% for GS and RMSE reduced to 4.7 d for the flowering prediction. As a result, we believe that our work demonstrates a valuable advance to inform breeders and growers regarding the timing and duration of key plant growth and development phases at the plot level, facilitating them to conduct more effective crop selection and make agronomic decisions under complicated field conditions for wheat improvement.

The six-degree-of-freedom (6-DOF) micromotion mechanism is a key component in extreme ultraviolet (EUV) projection lithography objectives to compensate for wavefront aberration and ensure performance stability. It is required to have high precision and high reliability. A 6-DOF mirror micromotion mechanism of an EUV lithography objective lens with Stewart configuration is proposed, and based on the RPY (Roll-Pitch-Yaw) angular rotation method and the second-type Lagrange method, the kinematic and dynamic models of the mechanism are constructed to realize the decoupling of the mechanism’s kinematics and the solution of the driving force. Comparative verification with finite element transient analysis shows that the response value of the dynamic model and the finite element method (FEM) differs by 4.8% in a specific posture. A hybrid optimization method, combining genetic algorithms and particle-swarm optimization (GA-PSO), was proposed to optimize the structural parameters of the initial design of the motion outriggers in a micromotion mechanism. The results demonstrate a 15.1% reduction in maximum driving force, significantly enhancing outrigger driving smoothness.

Predicting the air temperature based on spatially accurate simulations is helpful to agricultural production, commercial activities, air transportation, water transportation, power supply, and national defense. Traditional prediction is not generally based on the time series obtained from multiple meteorological stations and from a spatial perspective. In this study, a deep convolution neural network with long short-term memory (CNN-LSTM) is constructed to extract the spatiotemporal features of temperature and the correlation between meteorological elements. The accuracies of the simulated and the predicted temperatures are spatially visualized by using the Kriging interpolation method. The accuracy of air temperature obtained from the CNN-LSTM was compared with those obtained from the CNN and the LSTM to verify its performance. The results show that there were prominent spatial variations in the temperature, with a latitudinal zonal structure in the southern Ningxia and a radial zonal structure in the northern Ningxia, China. The accuracies of the simulated and predicted temperature were high in areas with a small range of the annual temperature. The accuracies of the mean monthly temperature simulated by the CNN-LSTM in spring and autumn were higher than that in the summer. The predicted annual average temperature increased each year from 2020 to 2025. The CNN-LSTM had a higher accuracy of simulating and predicting the temperature as well as a better generalization ability than the CNN and LSTM.

The zona incerta is a subthalamic nucleus made up mostly of GABAergic neurons. It has wide-ranging inputs and outputs and is believed to have many integrative functions that link sensory stimuli with motor responses to guide behavior. However, its role is not well established perhaps because few studies have measured the activity of zona incerta neurons in behaving animals under different conditions. To record the activity of zona incerta neurons during exploratory and cue-driven goal-directed behaviors, we used electrophysiology in head-fixed mice moving on a spherical treadmill and fiber photometry in freely moving mice. We found two groups of neurons based on their sensitivity to movement, with a minority of neurons responding to whisker stimuli. Furthermore, zona incerta GABAergic neurons robustly code the occurrence of exploratory and goal-directed movements, but not their direction. To understand the function of these activations, we performed genetically targeted lesions and optogenetic manipulations of zona incerta GABAergic neurons during exploratory and goal-directed behaviors. The results showed that the zona incerta has a role in modulating the movement associated with these behaviors, but this has little impact on overall performance. Zona incerta neurons distribute a broad corollary signal of movement occurrence to their diverse projection sites, which regulates behavior.

Abstract The influence of achievement motives and relational goal structures embedded in schools on learning outcomes as well as their interaction has attracted much attention in educational research. In the secondary multi-level analysis of data from four areas of China (B–S–J–Z) and the United States in the PISA 2018 study, the relationship among students’ personal achievement motives (competitiveness, work mastery and fear of failure), competitive or cooperative goal structures in schools, reading achievement and reading self-concept was examined. Results revealed similar relationship across countries: both competitiveness and work mastery motives had positive influence on reading self-concept; competitive goal structure influenced reading achievement. Cross-level interactions were only observed in the Chinese (B–S–J–Z) sample, with the competitive goal structure negatively moderating the relationship between work mastery motive and learning outcomes as well as the relationship between competitiveness motive and self-concept. Implications for theory and educational practice were discussed.

Abstract Genomic MET amplification and exon 14 skipping are currently clinically recognized biomarkers for stratifying subsets of non-small cell lung cancer (NSCLC) patients according to the predicted response to c-Met inhibitors (c-Metis), yet the overall clinical benefit of this strategy is quite limited. Notably, c-Met protein overexpression, which occurs in approximately 20–25% of NSCLC patients, has not yet been clearly defined as a clinically useful biomarker. An optimized strategy for accurately classifying patients with c-Met overexpression for decision-making regarding c-Meti treatment is lacking. Herein, we found that SYK regulates the plasticity of cells in an epithelial state and is associated with their sensitivity to c-Metis both in vitro and in vivo in PDX models with c-Met overexpression regardless of MET gene status. Furthermore, TGF-β1 treatment resulted in SYK transcriptional downregulation, increased Sp1-mediated transcription of FRA1, and restored the mesenchymal state, which conferred resistance to c-Metis. Clinically, a subpopulation of NSCLC patients with c-Met overexpression coupled with SYK overexpression exhibited a high response rate of 73.3% and longer progression-free survival with c-Meti treatment than other patients. SYK negativity coupled with TGF-β1 positivity conferred de novo and acquired resistance. In summary, SYK regulates cell plasticity toward a therapy-sensitive epithelial cell state. Furthermore, our findings showed that SYK overexpression can aid in precisely stratifying NSCLC patients with c-Met overexpression regardless of MET alterations and expand the population predicted to benefit from c-Met-targeted therapy.

Abstract Nasopharyngeal carcinoma (NPC) is a cancer with a high metastatic rate and poor prognosis. Growing studies suggest that ferroptosis take part in the development of tumours. At the same time, the connection between ferroptosis-related genes (FRGs) and the prognosis of NPC remains unclear. In this study, we explored the dysregulated FRGs between normal control and tumour samples of NPC. Firstly, 14 of 36 differentially expressed FRGs were identified in NPC tissues compared to normal tissues, among which ABCC1, GLS2, CS and HMGCR were associated with poor prognosis for patients. The four ferroptosis genes were used for consensus cluster analysis and two risk-related FRGs (ABCC1 and GLS2) were used in a risk model. The ROC curve revealed the good predictive performance of this risk signature. Multivariate analysis revealed that risk score and intratumoral TILs were independent risk factors linked to prognosis. Additionally, our results suggested that the risk signature was attached to the immune microenvironment. Moreover, the NPC patients with high risk were sensitive to chemotherapeutic drugs including axitinib, docetaxel, embelin, epothilone.B, parthenolide, thapsigargin, tipifarnib, vinorelbine. Finally, the expression of ABCC1 and GLS2 was validated in NPC tissues using immunohistochemistry. Together, these results revealed ferroptosis may be a potential biomarker in NPC and representing a promising future direction in prognosis and therapeutic strategy for the treatment of NPC.

Abstract Aim To assess whether serum thymidine kinase 1 (STK1p), CEA and CA19.9 can be used as prognostic biomarkers in the primary tumor location (PTL) of colorectal carcinoma (CRC). Additional clinical factors of TNM stage, pathological grade, age and sex were also included. Methods STK1p was determined by an ECL-dot-blot assay, and CEA/CA19.9 was determined by an automatic electrochemiluminescence analyzer in a retrospective presurgery of right-colon carcinoma (R-CC, n = 90), left-colon carcinoma (L-CC, n = 128) and rectal carcinoma (RC, n = 270). Prognostic factors were evaluated by COX and overall survival (OS). Results The multivariate-COX and OS in relation to the prognostic factors of PTL in CRC were different and complex. An elevated STK1p value was significantly associated with poor OS in RC (P = 0.002) and L-CC (P = 0.037) but not in R-CC (P > 0.05). Elevated CEA (P≈.000) and CA19.9 (P≈.000) were significantly associated with poor OS in RC but not in L-CC and R-CC. Multivariate-COX showed that STK1p (P = 0.02, HR = 1.779, 95%CI 1.30–7.582), CEA (P = 0.001, HR = 2.052, 95%CI 1.320–3.189), CA19.9 (P≈.000, HR = 2.574, 95%CI 1.592–4.162) and TNM-stage (P≈.000, HR = 2.368, 95%CI 1.518–3.694) were independent prognostic factors in RC, while TNM-stage was an independent prognostic factor only in R-CC (P = 0.011, HR = 3.139, 95% CI 1.30–7.582) and L-CC (P≈.000, HR = 4.168, 95%CI 1.980–8.852). Moreover, elevated STK1p was significantly more sensitive (P

Near-surface air temperature lapse rate (NSATLR) is one of the key criteria for judging atmospheric stability. It is an important model parameter in glacier, hydrology, ecology, climate, and meteorology in high mountains such as the Tibetan Plateau (TP). However, due to the complex interaction between the atmosphere and land surface, retrieving NSATLR from remotely sensed data is challenging. In this article, we develop a novel model based on the MODIS data to estimate the daily 5-km NSATLR over the TP. The input parameters of the model include digital elevation model and the near-surface air temperature estimated from the MODIS data. By combining moving window and specified criteria, the pixel-based NASTLR with a 5-km spatial resolution over the TP is estimated. In addition, we also estimate the NSATLR based on the meteorological data over the TP (NSATLRin situ) to evaluate the accuracy of NSATLR estimation model. Results demonstrate that the NSATLR estimate agrees well with NSATLRin situ in the eastern TP, with an overall root-mean-square error of 1.51 °C/km and a mean bias error of −0.78 °C/km. Larger deviation exists in the southern TP. Thanks to the evident advantages of simplicity, the availability of input parameters and the independence from the meteorological data, the developed model can be applied to larger areas.

The prediction accuracy of hourly air temperature is generally poor because of random changes, long time series, and the nonlinear relationship between temperature and other meteorological elements, such as air pressure, dew point, and wind speed. In this study, two deep-learning methods—a convolutional neural network (CNN) and long short-term memory (LSTM)—are integrated into a network model (CNN–LSTM) for hourly temperature prediction. The CNN reduces the dimensionality of the time-series data, while LSTM captures the long-term memory of the massive temperature time-series data. Training and validation sets are constructed using 60,133 hourly meteorological data (air temperature, dew point, air pressure, wind direction, wind speed, and cloud amount) obtained from January 2000 to October 2020 at the Yinchuan meteorological station in China. Mean absolute error (MAE), mean absolute percentage error (MAPE), and goodness of fit are used to compare the performances of the CNN, LSTM, and CNN–LSTM models. The results show that MAE, MAPE, RMSE, and PBIAS from the CNN–LSTM model for hourly temperature prediction are 0.82, 0.63, 2.05, and 2.18 in the training stage and 1.02, 0.8, 1.97, and −0.08 in the testing stage. Average goodness of fit from the CNN–LSTM model is 0.7258, higher than the CNN (0.5291), and LSTM (0.5949) models. The hourly temperatures predicted by the CNN–LSTM model are highly consistent with the measured values, especially for long time series of hourly temperature data.

Abstract Background Cyst nematodes are one of the major groups of plant-parasitic nematode, responsible for considerable crop losses worldwide. Improving genetic resources, and therefore resistant cultivars, is an ongoing focus of many pest management strategies. One of the major bottlenecks in identifying the plant genes that impact the infection, and thus the yield, is phenotyping. The current available screening method is slow, has unidimensional quantification of infection limiting the range of scorable parameters, and does not account for phenotypic variation of the host. The ever-evolving field of computer vision may be the solution for both the above-mentioned issues. To utilise these tools, a specialised imaging platform is required to take consistent images of nematode infection in quick succession. Results Here, we describe an open-source, easy to adopt, imaging hardware and trait analysis software method based on a pre-existing nematode infection screening method in axenic culture. A cost-effective, easy-to-build and -use, 3D-printed imaging device was developed to acquire images of the root system of Arabidopsis thaliana infected with the cyst nematode Heterodera schachtii, replacing costly microscopy equipment. Coupling the output of this device to simple analysis scripts allowed the measurement of some key traits such as nematode number and size from collected images, in a semi-automated manner. Additionally, we used this combined solution to quantify an additional trait, root area before infection, and showed both the confounding relationship of this trait on nematode infection and a method to account for it. Conclusion Taken together, this manuscript provides a low-cost and open-source method for nematode phenotyping that includes the biologically relevant nematode size as a scorable parameter, and a method to account for phenotypic variation of the host. Together these tools highlight great potential in aiding our understanding of nematode parasitism.

Fine particulate matter (PM2.5)-related health issues have received increasing attention as a worldwide public health problem, and PM2.5-related chronic kidney disease (CKD) has been emerging over the years. Limited research has focused on the mechanism of PM2.5-induced kidney disease. To investigate the impact of PM2.5 on the kidney and its potential mechanism, we generated a PM2.5-exposed C57BL/6 mouse model by using Shanghai Meteorological and Environment Animal Exposure System (Shanghai-METAS) for 12 weeks, urine, blood and kidney tissues were collected. The pathological changes and the function of the kidney were measured after PM2.5 exposure for 12 weeks. Along with glomerular damage, tubular damage was also severe in PM2.5-induced mice. The results of mRNA-seq indicate that pyroptosis is involved. Pyroptosis is defined as caspase-1-dependent programmed cell death in response to insults. The expression of the nucleotide-binding and oligomerization domain-like receptor family pyrin domain containing 3 (NLRP3), Caspase-1, gasdermin D (GSDMD) and IL-1β was detected. NLRP3 inflammasome activation and subsequent pyroptosis were observed in PM2.5-exposed kidney tissues and PM2.5-exposed Bumpt cells too. At the meantime, the inhibitors of NLRP3 and caspase-1 were applied to the PM2.5 exposed Bumpt cells. It turned out to have a significant rescue effect of the inhibitors. This study revealed new insights into PM2.5-induced kidney injury and specific kidney pathological damage, as well as morphological changes, and defined the important role of pyroptosis in PM2.5-induced kidney dysfunction.

High spatial resolution maps of evapotranspiration (ET) for precision agricultural irrigation, water resource management are increasingly important in the context of climate change. Here, we conducted extensive unmanned aerial vehicle (UAV) experiments in oasis-desert areas with three 2 km × 1 km regions during two growing seasons. The spatially distributed land surface temperature (LST), albedo and leaf area index (LAI) were retrieved from thermal infrared, multispectral and LiDAR (light detection and ranging) multi-sensor data, and the surface energy balance system (SEBS) model with an optimized surface roughness length for heat transfer was utilized to estimate ET with a super high resolution (SHR) of 0.2 m. The model’s outputs were validated using the measurements from the eddy covariance (EC) with a source area of hundred meters and optical-microwave scintillometer (OMS) with a source area of approximately 2 km × 1 km. They yielded mean root mean square error (RMSE) values of 53 and 45 W/m2 for sensible heat flux (H) and 70 and 65 W/m2 for latent heat flux (LE), respectively. The seasonal characteristics of LE distributions indicated the difference of LE in oasis-desert areas varied with the growing period. These sub-pixel differences in the cropland, wetland, and desert experimental areas were caused mainly by the agricultural activities, the vegetation coverage, and the topographic peaks and valleys, respectively. This study can potentially provide spatially sub-pixel information on agricultural and ecological water management and genuinely bridge the spatio-temporal scale gap between field observations and satellite remote sensing.

Abstract At present, in the process of study tour, study tutors are responsible for supervising the safety of primary and secondary schools' students, and many hidden dangers are present in safety supervision. Therefore, how to provide study tutors with intelligent information tools and thus help safety tour have become the primary issue for the construction of research bases and the normal. For this reason, this study proposes an adaptive dynamic security area generation method and dynamic security area early warning technology. By comparing the location of the dynamic security area and the dangerous area, whether the users in the area are in a safe state can be determined. On the basis of mobile smart terminals, a dynamic safety supervision system that supports the study tour of primary and secondary school students has been developed. Practical application shows that the system has perfect functions and safe, stable, and reliable performance. The system has good application prospects and promotion value and can be used in other similar field safety management and control fields.

This study investigates the vibration response and dynamic characteristics of the Shigudeng Pavilion under environmental excitation, employing a comprehensive approach that includes vibration response measurements, theoretical analysis, and finite element numerical simulation. An in-depth examination of the appearance and stress status of the Shigudeng Pavilion in Yao Ancient Village in southern Hunan was conducted, and the horizontal acceleration of different floors of the pavilion under environmental excitation was ascertained through field measurements. The NExT-ERA method was utilized to identify the modal parameters (frequency, damping ratio, and vibration pattern) of the measured timber construction. Subsequently, a finite element analysis model of the ancient timber building was developed and refined using ANSYS Workbench finite element software. This study proposes a specific repair plan addressing apparent defects and structural damage in the measured ancient timber building, offering theoretical and technical references for the conservation and repair of similar structures.