Search

Abstract Esophageal squamous cell carcinoma (ESCC) is one of the most aggressive cancers and is highly prevalent in China, exhibiting resistance to current treatments. ΔNP63α, the main isoform of p63, is frequently amplified in ESCC and contributes to therapeutic resistance, although the molecular mechanisms remain unknown. Here, we report that ΔNP63α is highly expressed in ESCC and is associated with radioresistance by reducing ROS level. Furthermore, ΔNP63α plays a critical role in radioresistance by directly transactivating the expression of PLEC. PLEC competitively interacts with KEAP1, resulting in the release of NRF2 from KEAP1 and its translocation from the cytosol to the nucleus, where it activates gene expression to facilitate ROS elimination. Additionally, radiotherapy-induced ROS also activates ΔNP63α expression via NRF2. Pharmacologic inhibition of NRF2 effectively improves radiosensitivity in nude mice. Collectively, our results strongly suggest that the ΔNp63α/PLEC/NRF2 axis plays a key role in radioresistance in ESCC, indicating that targeting NRF2 is a promising therapeutic approach for ESCC treatment.

Abstract Background With the increasing incidence of renal lesions, pretreatment differentiation between benign and malignant lesions is crucial for optimized management. This study aimed to develop a machine learning model utilizing radiomic features extracted from various regions of interest (ROIs), intratumoral ecological diversity features, and clinical factors to classify renal lesions. Methods CT images (arterial phase) of 1,795 renal lesions with confirmed pathology from three hospital sites were split into development (1184, 66%) and test (611, 34%) cohorts by surgery date. Conventional radiomic features were extracted from eight ROIs of arterial phase images. Intratumoral ecological diversity features were derived from intratumoral subregions. The combined model incorporating these features with clinical factors was developed, and its performance was compared with radiologists’ interpretation. Results Combining intratumoral and peritumoral radiomic features, along with ecological diversity features yielded the highest AUC of 0.929 among all combinations of features extracted from CT scans. After incorporating clinical factors into the features extracted from CT images, our combined model outperformed the interpretation of radiologists in the whole (AUC = 0.946 vs 0.823, P

ObjectiveTo observe the differences in brain network activation and functional connectivity of patients with post-stroke cognitive impairment (PSCI) of different severity using functional near-infrared spectroscopy (fNIRS).MethodsA total of 40 stroke patients in the outpatient and inpatient Rehabilitation Department in the Pudong District of Huashan Hospital, Fudan University from April to November 2020 were recruited and divided into normal group (n=14), mild to moderate group (n=11) and severe group (n=15) according to the mini-mental state examination (MMSE) score. Fugl-Meyer assessment scale (FMA) and functional comprehensive assessment (FCA) were used to evaluate the motor function of upper and lower limbs; 64-channel fNIRS functional imaging device was used to collect the resting-state brain network data of patients. The whole brain functional connectivity was analyzed based on oxyhemoglobin (HbO2) and deoxyhemoglobin (HbR). Differences in functional connectivity strengths between homologous and heterologous brain networks in dorsal attention network (DAN), sensorimotor network (SEN), ventral attention network (VAN), frontoparietal network (FPN), default mode network (DMN) and visual network (VIS) regions were analyzed.Results(1) Motor function: there was no significant difference in FMA and FCA scores in three groups (P>0.05). (2) Whole brain functional connectivity and strength of homologous brain network functional connectivity: there was no significant difference in whole brain functional connectivity based on HbO2 and HbR in three groups (P>0.05). Compared with the normal group, there were significant differences in the strength of functional connectivity of the in the SEN and FPN regions of the HbO2-based homologous brain network in the severe group (PP>0.05). (3) Functional connectivity strength of heterologous brain networks: compared with the normal group, the functional connectivity of SEN-VAN, SEN-DMN, SEN-FPN, VAN-FPN and DMN-FPN in the severe group decreased significantly, and the differences were statistically significant (PPP>0.05). (4) Correlation analysis between SEN-FPN heterologous functional connectivity and MMSE, FCA scores: MMSE score (r=0.32, Pr=0.38, P

Abstract To understand the ecology of species and promote biotechnology through beneficial strain selection for improving starch yield in maize wet-milling steeping, bacterial diversity and community structure during the counter-current steeping process in a commercial steeping system were characterized and investigated. The microbial diversity in the steeping liquor, which consisted of 16 phyla, 131 families, and 290 genera, was more abundant compared to those present on the surface of unsteeped maize. As the counter-current steeping progressed, exposing newer maize to the older steepwater, Lactobacillus dominated, replacing Rahnella, Pseudomonas, Pantoea, and Serratia. The thermophilic and acidophilic microbial consortia were enriched through adaptive evolution engineering and employed to improve starch yield. Several steeping strategies were evaluated, including water alone, SO2 alone, mono-culture of B. coagulans, microbial consortia, and a combination of consortium and SO2. Combining the microbial consortium with SO2 significantly increased the starch yield to, about 66.4 ± 0.5%, a 22% and 46% increase over SO2 alone and the consortium alone, respectively. Scanning electron microscope (SEM) of steeped maize structure indicated that the combination of consortium and SO2 disrupted the protein matrix and widened gaps between starch granules in maize endosperm. This released proteins into the steepwater and left starch granules in the aleurone layer. The steeping strategy of using thermophilic and acidophilic microbial consortium as additives shows potential application as an environmentally friendly alternative to conventional maize steeping procedures.

Abstract Background Interleukin 24 (IL-24) has been implicated in the nociceptive signaling. However, direct evidence and the precise molecular mechanism underlying IL-24’s role in peripheral nociception remain unclear. Methods Using patch clamp recording, molecular biological analysis, immunofluorescence labeling, siRNA-mediated knockdown approach and behavior tests, we elucidated the effects of IL-24 on sensory neuronal excitability and peripheral pain sensitivity mediated by T-type Ca2+ channels (T-type channels). Results IL-24 enhances T-type channel currents (T-currents) in trigeminal ganglion (TG) neurons in a reversible and dose-dependent manner, primarily by activating the interleukin-22 receptor 1 (IL-22R1). Furthermore, we found that the IL-24-induced T-type channel response is mediated through tyrosine-protein kinase Lyn, but not its common downstream target JAK1. IL-24 application significantly activated protein kinase A; this effect was independent of cAMP and prevented by Lyn antagonism. Inhibition of PKA prevented the IL-24-induced T-current response, whereas inhibition of protein kinase C or MAPK kinases had no effect. Functionally, IL-24 increased TG neuronal excitability and enhanced pain sensitivity to mechanical stimuli in mice, both of which were suppressed by blocking T-type channels. In a trigeminal neuropathic pain model induced by chronic constriction injury of the infraorbital nerve, inhibiting IL-22R1 signaling alleviated mechanical allodynia, which was reversed by blocking T-type channels or knocking down Cav3.2. Conclusion Our findings reveal that IL-24 enhances T-currents by stimulating IL-22R1 coupled to Lyn-dependent PKA signaling, leading to TG neuronal hyperexcitability and pain hypersensitivity. Understanding the mechanism of IL-24/IL-22R1 signaling in sensory neurons may pave the way for innovative therapeutic strategies in pain management.

Background and objective: Robot-assisted radical prostatectomy (RARP) is widely used because of the many advantages of a robotic approach. The da Vinci Si robot is one of the most commonly used surgical robot systems, but it may be associated with higher costs owing to the use of consumable surgical supplies. Our aim was to conduct a preliminary investigation of the capability of the MP1000 system for RARP. Methods: In this prospective, multicentre, single-blinded study, we randomly assigned 42 patients scheduled to undergo RARP between April and September 2021 to a da Vinci Si group (control) or an MP1000 group (intervention). Patients underwent RARP performed using the assigned robotic system and were followed up at 3-mo intervals. The primary outcome was the rate of conversion to open/laparoscopic surgery. Secondary outcomes were installation and operation times, intraoperative blood loss, postoperative surgical margin status, hospital stay, incontinence, complications, safety indicators, and surgeon ergonomics. Key findings and limitations: All procedures were successfully completed without conversion to open/laparascopic surgery or major complications. Secondary outcomes, including oncological and ergonomic indicators, did not differ significantly between the groups over the study period. One patient in the control group experienced dysuria (Clavien-Dindo grade 3). No patients had incontinence at 3 mo. A limitation of the study is the small sample size. Conclusions and clinical implications: RARP with the MP1000 system is feasible, safe, and effective in the management of localised prostate cancer. Patient summary: We assessed the effectiveness and safety of the new MP1000 robot system for robot-assisted removal of the prostate in comparison to the da Vinci Si robot. We found no difference in effectiveness or safety among 42 patients with prostate cancer who were assigned randomly to one of the two systems. We conclude that the MP1000 is a suitable robot for this surgery.

IntroductionSuspended particulate matter (SPM) is a critical indicator of water quality and has a significant impact on the nearshore ecological environment. Consequently, the quantitative evaluation of SPM concentrations is essential for managing nearshore environments and planning marine resources.MethodsThis study utilized Sentinel-2’s single band and water index variables to develop a remote sensing inversion model for oceanic SPM in the estuary of the Pinglu Canal in China. Six machine learning algorithms were employed: K-nearest neighbor regression (KNNR), AdaBoost regression (ABR), random forest (RF), gradient boosting regression (GBR), extreme gradient boosting regression (XGBR), and light generalized boosted regression (LGBM). The model with the optimal performance was then selected for further analysis. This research applied the established model to investigate the spatial-temporal dynamics of SPM from 2021 to 2023.ResultsThe findings indicated that (1) the XGBR algorithm exhibited superior performance (R2 = 0.9042, RMSE = 3.0258 mg/L), with LGBM (R2 =0.8258, RMSE = 4.0813 mg/L) and GBR (R2 = 0.823, RMSE = 4.3477 mg/L) also demonstrating effective fitting. However, the ABR, RF, and KNNR algorithms produced less satisfactory fitting results. (2) Additionally, the study revealed that the combination of input variables in the XGBR algorithm was more accurate than single-variable inputs. (3) The contribution of single-band variables to the XGBR algorithm surpassed that of water index variables, with B12, B4, and B11 emerging as the top three influential variables in the model. (4) The annual SPM concentration in the study area exhibited an overall increasing trend, while its spatial distribution generally decreased from the estuary toward the Maowei Sea and Qinzhou Bay.DiscussionThe combination of Sentinel-2 data and XGBR model has shown good performance in retrieving SPM concentration, providing a new method and approach for large-scale estimation of SPM concentration.

ABSTRACT Background Carnivore parvovirus is commonly detected in canines and felines and results in a range of symptoms, including diarrhoea, with a high lethality rate. In recent years, the gradual restoration of urban ecosystems coupled with a lack of natural predators means that wild raccoon dogs have gradually entered residential neighbourhoods in Shanghai, and the carnivore parvovirus carried by such canines may seriously affect their health and that of dogs and cats in these neighbourhoods. Objectives Therefore, it is essential to monitor wild raccoon dogs for carnivore parvovirus. Methods The wild raccoon samples were collected from the residential area of Shanghai and screened for parvovirus, followed by virus isolation and sequence comparison analysis. Results In this study, three strains of parvovirus, YSH1‐2022, YSH2‐2022 and GSW1‐2023, were isolated from anal and pharyngeal swab mixtures of 42 live raccoon dogs and two dead raccoon dogs in Shanghai, China. After alignment, it was found that the three strains of parvovirus were all located in the same branch as feline panleukopenia virus (FPV), and the three VP2 genes had 100% homology with FPV VP2 genes isolated from Shanghai and Yangzhou, Jiangsu Province. The homology of the three NS1 genes with FPV NS1 isolated from Shanghai and other places was 99.9%–100%. Conclusion This is the first report of FPV isolated from raccoon dogs. In this study, the host range and risk of infection of parvovirus were evaluated, providing data to support wildlife management and policy formulation in Shanghai.

Research on ecosystem carbon sinks is vital for developing policies to reduce emissions and enhance carbon sequestration. Analyzing the temporal and spatial trends of carbon sinks and their driving mechanisms is crucial for guiding policy and measures. Traditional methods for estimating carbon sinks in karst regions focus primarily on vegetation-soil carbon sinks, mainly comprising net primary productivity (NPP) and soil heterotrophic respiration (Rh). However, these methods overlook the unique karst carbon sink (KCS), leading to significant uncertainties in carbon sink evaluations. This study focuses on the karst peak cluster depression basin in southwest Guangxi and proposes a Karst Ecosystem Carbon Sink Assessment System (KECAS) based on the “water-rock-soil-air-biology” coupling framework, considering lithological characteristics. Theil-Sen trend analysis and restricted cubic spline (RCS) regression were used to explore the temporal and spatial variations and driving mechanisms of carbon sinks in the area from 2000 to 2022. The results show the following: (1) The total carbon sink flux (TCSF) in the study area has shown an upward trend over time, with a 23-year average ranging from 538 to 929 g CO2 m−2 a−1 and a growth rate of 3.1039a−1. This means TCSF increases by 6.78 g CO2 m−2 a−1 each year. (2) Spatially, areas with increasing carbon sinks cover 34.17 % of the study area, mainly in Chongzuo, Nanning, and Wenshan Prefecture. Areas with decreasing carbon sinks cover 11.79 % of the total area, mainly in Chongzuo, with smaller areas in Nanning and Baise. The mean TCSF distribution is higher in the northwest and lower in the southeast. (3) The critical thresholds for influencing factors were identified using RCS regression: precipitation (1430 mm), temperature (17.9 °C), evapotranspiration (855 mm), NDVI (0.63), DEM (637 m), slope (16.3°), nighttime light index (6.21), and population density (421 people/km2). (4) Areas with high carbon sink supply potential, based on natural and human thresholds, account for 25.09 % and 92.97 % of the southwestern Guangxi study area, respectively. When considering both natural and human conditions, high potential areas account for approximately 22.31 % of the total study area. These findings provide a scientific basis for carbon sink protection and management and offer valuable references for government decision-making.

BackgroundAs part of the TNM (tumor-node-metastasis) staging system, T staging based on tumor depth is crucial for developing treatment plans. Previous studies have constructed a deep learning model based on computed tomographic (CT) radiomic signatures to predict the number of lymph node metastases and survival in patients with resected gastric cancer (GC). However, few studies have reported the combination of deep learning and radiomics in predicting T staging in GC. ObjectiveThis study aimed to develop a CT-based model for automatic prediction of the T stage of GC via radiomics and deep learning. MethodsA total of 771 GC patients from 3 centers were retrospectively enrolled and divided into training, validation, and testing cohorts. Patients with GC were classified into mild (stage T1 and T2), moderate (stage T3), and severe (stage T4) groups. Three predictive models based on the labeled CT images were constructed using the radiomics features (radiomics model), deep features (deep learning model), and a combination of both (hybrid model). ResultsThe overall classification accuracy of the radiomics model was 64.3% in the internal testing data set. The deep learning model and hybrid model showed better performance than the radiomics model, with overall classification accuracies of 75.7% (P=.04) and 81.4% (P=.001), respectively. On the subtasks of binary classification of tumor severity, the areas under the curve of the radiomics, deep learning, and hybrid models were 0.875, 0.866, and 0.886 in the internal testing data set and 0.820, 0.818, and 0.972 in the external testing data set, respectively, for differentiating mild (stage T1~T2) from nonmild (stage T3~T4) patients, and were 0.815, 0.892, and 0.894 in the internal testing data set and 0.685, 0.808, and 0.897 in the external testing data set, respectively, for differentiating nonsevere (stage T1~T3) from severe (stage T4) patients. ConclusionsThe hybrid model integrating radiomics features and deep features showed favorable performance in diagnosing the pathological stage of GC.

The peak-cluster depression is one of China’s most ecologically fragile areas, with extensive karst development. However, existing models for assessing karst ecosystem services often fail to consider the unique geological context of karst peak-cluster depressions, making it challenging to apply general international models to this area. To address these challenges, this study focused on the karst basin of Southwest China and evaluated ecosystem service in 2000 and 2020 by revising carbon fixation and soil erosion models. Using interpretable machine learning model (XGBoost-SHAP, eXtreme Gradient Boosting and SHapley Additive exPlanations), we quantified the nonlinear characteristics and threshold effects of ecosystem service trade-offs and synergies. Our findings include the following: (1) Carbon fixation increased from 753.99 tCO2∙km−2∙a-1 in 2000 to 756.70 tCO2∙km−2∙a-1 in 2020; however, soil erosion decreased from 16.56 t∙hm−2∙a-1 to 15.12 t∙hm−2∙a-1. (2) At the basin scale, carbon fixation and soil erosion exhibited both trade-offs and synergistic relationships, with 63.3 % of the area showing a trade-off and 36.7 % showing a synergistic relationship. Trade-off relationships were prevalent in the upper and lower reaches, while the middle reaches demonstrated synergistic relationships. (3) Normalized Difference Vegetation Index (NDVI) emerged as the primary driver of changes in ecosystem service trade-offs, with NDVI, precipitation, temperature, evapotranspiration, elevation, and lithology as the most significant explanatory factors. These factors impact ecosystem service trade-offs in a nonlinear manner and exhibit pronounced threshold effects. (4) Climate factors contributed 31.65 % to ecosystem service trade-offs, geomorphic factors contributed 14.81 %, soil factors contributed 5.72 %, and human activities contributed 5.39 %. (5) Local interpretability SHAP values indicated substantial differences in the contributions of drivers at different scales to ecosystem service trade-offs. The methodology implemented in this study offers a practical approach for the sustainable and differentiated management of karst ecosystem services by integrating karst ecosystem service assessment models with interpretable machine learning methods.

Dendritic cells (DCs) are the most potent professional antigen-presenting cells (APCs), which play a pivotal role in inducing either inflammatory or tolerogenic response based on their subtypes and environmental signals. DCs are critical for initiation and progression of multiple sclerosis (MS). Induction of tolerogenic DCs (tolDCs) with powerful therapeutic potential has been well - established to combat autoimmune responses in laboratory models and early clinical trials. In contrast to in vitro tolDCs induction, in vivo elicitation by specifically targeting multiple cell - surface receptors has shown greater promise with more advantages. Here, we summarize the role of DCs in governing immune tolerance and in the process of initiating and perpetuating MS. We then highlight the most promising cell-surface receptors expressed on DCs currently being explored as the viable pharmacological targets through antigen delivery to generate tolDCs in vivo, to guide feasible immunotherapeutic strategies.

Abstract Since probability hypothesis density (PHD) filters do not need explicit data association, they have recently been widely used in radar multi-target tracking (MTT). However, in existing PHD filters, sampling times are generally considered the same for all targets. Due to the limitation of antenna beam width in radar applications, the same sampling time for all targets will lead to a mismatch between the predicted data and measurement data, reducing the accuracy of radar MTT. In order to eliminate the estimation error with less computational cost, a radar nonlinear multi-target tracking method with a parallel PHD filter is proposed in this article. The measurement area is divided into several subspaces according to the beam width of the radar antenna, and the PHD of all subspaces is calculated in parallel. Then, multi-feature information in radar echo assists tracking and improves real-time performance. Experimental results in various scenarios illustrate that the proposed method can eliminate the estimation errors introduced by sampling time diversity at the cost of less computation cost, especially in cluttered environments.

ABSTRACT Lung squamous cell carcinoma (LUSC) is characterized by a high rate of metastasis and recurrence, leading to a poor prognosis for affected patients. Intestinal metastasis of LUSC is a rare clinical occurrence. Treatment options for LUSC patients with intestinal metastasis are limited, and no standard therapy guidelines exist for managing these cases. In this review, we discuss the clinical features, diagnosis, and treatment of LUSC patients with intestinal metastasis and present a rare case of LUSC with intestinal metastasis. We describe a patient who presented with a severe cough and chest pain and diagnosed with LUSC and bone tumor. Initially, the primary LUSC and bone tumor were controlled with standard treatments. However, the primary LUSC reoccurred shortly after treatment, this time with intestinal metastasis, for which effective treatments are lacking. Our observation from the case suggests that LUSC metastasizing to intestinal tract is associated with a poorer prognosis.

In recent years several nicotine products have been introduced that aim to offer smokers an alternative to cigarettes. As well as having fewer toxicants than combustible cigarettes, such nicotine products must be able to deliver nicotine efficiently. The aim of this study was to determine and compare the pharmacokinetics of nicotine absorption from nine oral tobacco-free smokeless nicotine pouches with varying nicotine content and flavours.

Traditional grass-roofed dwellings are important components of Chinese vernacular architecture. Building a comprehensive nationwide database of traditional grass-roofed dwellings is crucial for the inherence of this cultural heritage and its traditional ecological technologies. This study proposes classifying traditional Chinese grass-roofed dwellings into three types according to recognizable appearance features. Based on the YOLOv8 deep learning framework, a recognition model is constructed to recognize and spatially locate various grass-roofed dwellings from the image dataset on a county-level. Further, by conducting spatial overlap analysis with a variety of natural and socio-environmental factors on ArcGIS, their influences on the distribution pattern of traditional grass-roofed dwellings were examined. The study findings are as follows: (1) Traditional grass-roofed dwellings are concentrated on the southeast side of the Hu Line with different distribution patterns according to their types. (2) The natural environment influences the original construction and distribution of traditional grass-roofed dwellings in terms of the growth of grass resources and the ecological adaptability of grass material. (3) The development of economy, population, and urbanization pose challenges to the retention of grass-roofed dwellings. This research provides useful references for the precise preservation of various grass-roofed dwellings and introduced a novel approach for the classification of traditional buildings.

Abstract Mitochondrial dysfunction contributes to the development of secondary brain injury (SBI) following intracerebral hemorrhage (ICH) and represents a promising therapeutic target. Celastrol, the primary active component of Tripterygium wilfordii, is a natural product that exhibits mitochondrial and neuronal protection in various cell types. This study aims to investigate the neuroprotective effects of celastrol against ICH‐induced SBI and explore its underlying mechanisms. Celastrol improves neurobehavioral and cognitive abilities in mice with autologous blood‐induced ICH, reduces neuronal death in vivo and in vitro, and promotes mitochondrial function recovery in neurons. Single‐cell nuclear sequencing reveals that the cyclic adenosine monophosphate (cAMP)/cAMP‐activated exchange protein‐1 (EPAC‐1) signaling pathways are impacted by celastrol. Celastrol binds to cNMP (a domain of EPAC‐1) to inhibit its interaction with voltage‐dependent anion‐selective channel protein 1 (VDAC1) and blocks the opening of mitochondrial permeability transition pores. After neuron‐specific knockout of EPAC1, the neuroprotective effects of celastrol are diminished. In summary, this study demonstrates that celastrol, through its interaction with EPAC‐1, ameliorates mitochondrial dysfunction in neurons, thus potentially improving SBI induced by ICH. These findings suggest that targeting EPAC‐1 with celastrol can be a promising therapeutic approach for treating ICH‐induced SBI.

Abstract Although beta-endorphinergic neurons in the hypothalamic arcuate nucleus (ARC) synthesize beta-endorphin (β-EP) to alleviate nociceptive behaviors, the underlying regulatory mechanisms remain unknown. Here, we elucidated an epigenetic pathway driven by microRNA regulation of β-EP synthesis in ARC neurons to control neuropathic pain. In pain-injured rats miR-203a-3p was the most highly upregulated miRNA in the ARC. A similar increase was identified in the cerebrospinal fluid of trigeminal neuralgia patients. Mechanistically, we found histone deacetylase 9 was downregulated following nerve injury, which decreased deacetylation of histone H3 lysine-18, facilitating the binding of NR4A2 transcription factor to the miR-203a-3p gene promoter, thereby upregulating miR-203a-3p expression. Further, increased miR-203a-3p was found to maintain neuropathic pain by targeting proprotein convertase 1, an endopeptidase necessary for the cleavage of proopiomelanocortin, the precursor of β-EP. The identified mechanism may provide an avenue for the development of new therapeutic targets for neuropathic pain treatment.

Cracks are the most typical faults of concrete structures, and their extension can lead to structural fracture. However, when cracks develop inside a structure, the most important depth information is invisible and difficult to measure. The tracer electromagnetic method is an effective technique for detecting the depth of concrete cracks, but since concrete is a multiphase stochastic composite material, its complex internal structure often interferes with the radar detection results, making the conventional radar interpretation technique difficult. In this study, the detection results for concrete crack depth detection based on the tracer electromagnetic method were comprehensively analyzed by combining the complex signal analysis technique, using transient information such as amplitude, phase, and frequency in order to improve the precision and accuracy of radar signal interpretation. In this study, a numerical model was established to determine whether typical cracks such as vertical cracks and diagonal cracks contain indicators or not, and the ground-penetrating radar forward simulation software was used to perform forward simulation of the numerical model and analyze the forward results. The complex signal analysis technique was used to obtain the response characteristics of typical cracks when they did and did not contain the indicator, and the complex signal was finally analyzed by combining it with the actual crack depth detection data. The results show that the tracer electromagnetic method can significantly improve the crack bottom’s reflection ability for radar signals, and when the crack bottom contains an indicator, the amplitude of the reflected signal at the bottom of the crack is enhanced, the phase is reversed, and the frequency is reduced. The distribution of the crack morphology and the location of the crack bottom can be analyzed more conveniently by using the complex signal analysis technique.

Extensive use of pesticides in agricultural production has been causing serious health threats to humans and animals. Among them, phorate is a highly toxic organophosphorus insecticide that has been widely used in planting. Due to its harmful effects on human and animal health, it has been restricted for use in many countries. Analytical methods for the rapid and sensitive detection of phorate residues in agricultural products are urgently needed. In this study, a new method was developed by combining surface-enhanced Raman spectroscopy (SERS) and immunochromatography assay (ICA). Hybrid magnetic Fe3O4@Au@DTNB-Ab nanoprobes were prepared by modifying and growing Au nanoseeds on an Fe3O4 core. SERS activity of the nanoprobe was optimized by adjusting the concentration of the Au precursor. A rapid and sensitive assay was established by replacing the traditional colloidal gold-based ICA with hybrid SERS nanoprobes for SERS-ICA. After optimizing parameters including coating antibody concentrations and the composition and pH of the buffer solution, the limit of detection (LOD) for phorate could reach 1 ng/mL, with a linear range of 5~100 ng/mL. This LOD is remarkably lower than the maximum residue limit in vegetables and fruits set by the Chinese government. The feasibility of this method was further examined by conducting a spiking test with celery as the real sample. The result demonstrated that this method could serve as a promising platform for rapid and sensitive detection of phorate in agricultural products.

Abstract Background Adipokines, including adiponectin, are implicated in nociceptive pain; however, the underlying cellular and molecular mechanisms remain unknown. Methods Using electrophysiological recording, immunostaining, molecular biological approaches and animal behaviour tests, we elucidated a pivotal role of adiponectin in regulating membrane excitability and pain sensitivity by manipulating Cav3.2 channels in trigeminal ganglion (TG) neurons. Results Adiponectin enhanced T-type Ca2+ channel currents (I T) in TG neurons through the activation of adiponectin receptor 1 (adipoR1) but independently of heterotrimeric G protein-mediated signaling. Coimmunoprecipitation revealed a physical association between AdipoR1 and casein kinase II alpha-subunits (CK2α) in the TG, and inhibiting CK2 activity by chemical inhibitor or siRNA targeting CK2α prevented the adiponectin-induced I T response. Adiponectin significantly activated protein kinase C (PKC), and this effect was abrogated by CK2α knockdown. Adiponectin increased the membrane abundance of PKC beta1 (PKCβ1). Blocking PKCβ1 pharmacologically or genetically abrogated the adiponectin-induced I T increase. In heterologous expression systems, activation of adipoR1 induced a selective enhancement of Cav3.2 channel currents, dependent on PKCβ1 signaling. Functionally, adiponectin increased TG neuronal excitability and induced mechanical pain hypersensitivity, both attenuated by T-type channel blockade. In a trigeminal neuralgia model induced by chronic constriction injury of infraorbital nerve, blockade of adipoR1 signaling suppressed mechanical allodynia, which was prevented by silencing Cav3.2. Conclusion Our study elucidates a novel signaling cascade wherein adiponectin stimulates TG Cav3.2 channels via adipoR1 coupled to a novel CK2α-dependent PKCβ1. This process induces neuronal hyperexcitability and pain hypersensitivity. Insight into adipoR-Cav3.2 signaling in sensory neurons provides attractive targets for pain treatment.

Discrete dislocation dynamics (DDD) simulations reveal the evolution of dislocation structures and the interaction of dislocations. This study investigated the compression behavior of single-crystal copper micropillars using few-shot machine learning with data provided by DDD simulations. Two types of features are considered: external features comprising specimen size and loading orientation and internal features involving dislocation source length, Schmid factor, the orientation of the most easily activated dislocations and their distance from the free boundary. The yielding stress and stress-strain curves of single-crystal copper micropillar are predicted well by incorporating both external and internal features of the sample as separate or combined inputs. It is found that the machine learning accuracy predictions for single-crystal micropillar compression can be improved by incorporating easily activated dislocation features with external features. However, the effect of easily activated dislocation on yielding is less important compared to the effects of specimen size and Schmid factor which includes information of orientation but becomes more evident in small-sized micropillars. Overall, incorporating internal features, especially the information of most easily activated dislocations, improves predictive capabilities across diverse sample sizes and orientations.

Xanthomonas citri pv. citri (Xcc) causes the devastating citrus canker disease. Xcc is known to have been introduced into Florida, USA in at least three different events in 1915, 1986 and 1995 with the first two claimed to be eradicated. It was questioned whether the Xcc introduction in 1986 has been successfully eradicated. Furthermore, it is unknown how Xcc has spread throughout the citrus groves in Florida. In this study, we investigated the population structure of Xcc to address these questions. We sequenced the whole genome of 343 Xcc strains collected from Florida groves between 1997 and 2016. Our analysis revealed two distinct clusters of Xcc. Our data strongly indicate that the claimed eradication of the 1986 Xcc introduction was not successful and Xcc strains from 1986 introduction were present in samples from at least 8 counties collected after 1994. Importantly, our data revealed that the Cluster 2 strains, which are present in all 20 citrus-producing counties sampled in Florida, originated from the Xcc introduction event in the Miami area in 1995. Our data suggest that Polk County is the epicenter of the dispersal of Cluster 2 Xcc strains, which is consistent with the fact that three major hurricanes passed through Polk County in 2004. As copper-based products have been extensively used to control citrus canker, we also investigated whether Xcc strains have developed resistance to copper. Notably, none of the 343 strains contained known copper resistance genes. Twenty randomly selected Xcc strains displayed sensitivity to copper. Overall, this study provides valuable insights into the introduction, eradication, spread, and copper resistance of Xcc in Florida.

Parachute design is challenging to achieve innovative progress if the dominant role of testing continues, as it will be an increasingly expensive and time-consuming work. The aim of this study is to establish a reliable and efficient design tool using existing advanced numerical modeling methods. This paper presents a numerical method based on two-way coupled fluid-structure interaction (FSI) strategies for predicting aerodynamic and flight performance for parafoil design optimization. The nonlinear finite element method was used for the canopy fabric model and flow field, and the fluid dynamics were solved by Reynolds-averaged Navier-Stokes with the Spalart-Allmaras turbulence model. The FSI simulations are performed to assess the aerodynamic performance and structural deformations of full-scale parafoil canopies. The equilibrium shape of the parafoil canopy under steady gliding states and the relevant flow field were analyzed to enhance confidence and understanding in the performance prediction of new parachutes. Three-dimensional FSI simulation results of parafoils show that the inflation caused flexible bulges of canopy cells, and the maximum lift coefficient increased more than 16% with a higher stall angle of attack than that of the rigid body model. A parafoil with a smaller leading edge inlet or a scaling down area can improve the aerodynamic performance, mainly manifested in a higher lift-to-drag ratio and better anti-stall performance. Finally, the prediction results of parafoil glide performance were verified by flight test data, and the prediction accuracy of the flexible model is more than 10% higher than that of the rigid model. This work makes the simulation tools a step closer to practical application.

Abstract Background Trace amines, such as tyramine, are endogenous amino acid metabolites that have been hypothesized to promote headache. However, the underlying cellular and molecular mechanisms remain unknown. Methods Using patch-clamp recording, immunostaining, molecular biological approaches and behaviour tests, we elucidated a critically functional role of tyramine in regulating membrane excitability and pain sensitivity by manipulating Kv1.4 channels in trigeminal ganglion (TG) neurons. Results Application of tyramine to TG neurons decreased the A-type K+ current (I A) in a manner dependent on trace amine-associated receptor 1 (TAAR1). Either siRNA knockdown of Gαo or chemical inhibition of βγ subunit (Gβγ) signaling abrogated the response to tyramine. Antagonism of protein kinase C (PKC) prevented the tyramine-induced I A response, while inhibition of conventional PKC isoforms or protein kinase A elicited no such effect. Tyramine increased the membrane abundance of PKCθ in TG neurons, and either pharmacological or genetic inhibition of PKCθ blocked the TAAR1-mediated I A decrease. Furthermore, PKCθ-dependent I A suppression was mediated by Kv1.4 channels. Knockdown of Kv1.4 abrogated the TAAR1-induced I A decrease, neuronal hyperexcitability, and pain hypersensitivity. In a mouse model of migraine induced by electrical stimulation of the dura mater surrounding the superior sagittal sinus, blockade of TAAR1 signaling attenuated mechanical allodynia; this effect was occluded by lentiviral overexpression of Kv1.4 in TG neurons. Conclusion These results suggest that tyramine induces Kv1.4-mediated I A suppression through stimulation of TAAR1 coupled to the Gβγ-dependent PKCθ signaling cascade, thereby enhancing TG neuronal excitability and mechanical pain sensitivity. Insight into TAAR1 signaling in sensory neurons provides attractive targets for the treatment of headache disorders such as migraine.

This study focuses on the identification of aerodynamic coefficients through airdrop data based on a known six-degree-of-freedom (6-DOF) model. Since the precise modelling of the parafoil system is a prerequisite for design of guidance, navigation and control systems, the accurate calculation of aerodynamic coefficients is vital. This paper first presents a rigid 6-DOF nonlinear model and simplifies it by linearizing the equations around the equilibrium point, which facilitates the acquisition of aerodynamic parameters next. Then, the recursive weighted least square method is applied to identify roll and yaw coefficients from airdrop test data. At last, simulations in the wind environment are implemented to analyse the dynamics of gliding and turning. The airdrop experiment also verifies the effectiveness of the dynamic model and the accuracy of identification.

Osteoarthritis is a common degenerative disease in which endoplasmic reticulum stress(ER stress) plays an important role in the pathogenesis of chondrocyte apoptosis. To limit the adverse effects of endoplasmic reticulum stress, cells activate the unfolded protein response (UPR). However, when endoplasmic reticulum stress persists beyond the maximum tolerance of UPR, cells may trigger endoplasmic reticulum dysfunction through three pathways of UPR leading to chondrocyte apoptosis.

The 19th century saw the important transformation of modern Western concepts into Chinese lexical resources. The missionaries were the initiators and important driving force for the translation of Western books into Chinese in modern China. They promoted ‘translating terms’ and ‘coining terms’ in their translations of Western books and the compilation of dictionaries with the cooperation of Chinese intellectuals. Their work provided a tangible ‘word’ carrier of ‘concepts’ for disseminating modern knowledge from the West to the East. Compiled by missionaries, the English–Chinese bilingual dictionaries introduced a brand-new concept of dictionary compilation and changed China’s history of having zidian (字典, character dictionaries) but no cidian (辞典, specialized dictionaries). In particular, John Fryer applied the translation method of creating new words or characters in the translation of chemical terminology. Members of the School and Textbook Series Committee, including John Fryer and Calvin Wilson Mateer, made great contributions to theories and strategies for translation, which keep inspiring Chinese–English translation of terminology and its theoretical construction.

This paper takes a low-energy building in Changchun, China, as an object to test and study the characteristics of two heating modes, AC/DC (Alternative current/Direct current) switching and AC/DC synthesis, from the perspectives of temperature change, irradiation intensity, power generation, electricity consumption, etc. Firstly, the experimental research was conducted under two heating cable modes by establishing mathematical models and a test rig, and it was found that the photoelectric conversion efficiency on sunny, cloudy, and overcast days was 18%, 14.5%, and 12%, respectively. A simulation model was established by TRNSYS to run an ultra-low-energy building throughout the year. It was found that the highest and lowest monthly power generation occurred in February and July, respectively. The annual power generation of the system was 6614 kWh, and the heating season power generation was 3293.42 kWh. In the current research, the DC electricity consumption was slightly higher than the AC electricity consumption. Under conditions of similar radiation intensity and power generation, the indoor temperature of the AC/DC synthesis cable heating mode were 1.38% higher than the AC/DC switching heating able mode, and the electricity consumption were 10.9% and 4.76% higher, respectively, than those of the AC switching heating cable mode. This is of great significance for clean-energy heating, energy savings, and emissions reduction in northern China.

High‐performance beam‐steering devices play a crucial role for optical wireless communication (OWC), which can meet the demand for increasing number of wireless mobile devices and emerging high‐speed multimedia applications. Conventional beam‐steering optical components like spatial light modulator and digital micromirror device are limited to realize a small steering angle (typ. several degrees), thus dramatically limits the spatial scope of OWC. Herein, a beam‐steering metadevice utilizing an ultracompact metasurface assisted with a spatial light modulator is presented, which can significantly increase the beam‐steering angle without at the cost of complicated optical setup like conventionally used angle magnifier. Based on the actively tunable beam‐steering metadevice, an intelligent bidirectional optical broadcasting communication system is designed and experimentally demonstrated, which exhibits nine broadcasting areas covering a large field‐of‐view of 20° × 20°, with user‐defined dynamic beam‐steering ability in each area, and each user has a data rate of 10 Gbps for both upstream and downstream transmission. The proposed metadevices can meet the demand of intelligent optical wireless communication which requires both high‐performance and low‐cost for practical purpose, under currently available technical architectures.

The Fourier transform between the time and frequency signals of light enables highly efficient band usage in optical communications. Analogously, the Fourier transform between the wavefield and wavevector distributions in the optical spatial diffraction is useful to understand the bandwidth considered in the diffraction simulations. In this work, we thoroughly investigate the numerical techniques for controlling the bandwidth in the design of computer-generated holography. The experiments reveal the effects of bandwidth control on both accuracy improvement of diffraction equations and suppression of higher hologram orders. A general formula for controlling the bandwidth directly using the spatial-frequency components is concluded.

Abstract This paper aims to propose an approach to automatically identify historic villages from remote sensing images based on deep learning algorithm and accurately calculate the villages’ geographical coordinates. Experimental datasets of Conghua, a typical region in fast development that retains many historic villages, are designated for training and testing. Comparison experiments of two recognition models, image classification and object detection, are designed to obtain the most suitable identification algorithm. GIS platform is adopted to visualize the distribution of the historic villages. The results show that first, the recognition accuracy of the image classification algorithm is 90.79%. However, visualization of test results shows the identified area is not a village but a surrounding. Second, the recognition accuracy of an object detection algorithm can reach 95.61%, which indicates that the algorithm is accurate and efficient. Third, by using the Historical-Modern tag as a filter, a village with a certain proportion of historic features according to specific requirements may be discriminated. Finally, 1531 historic villages in Conghua area were identified by the preferred algorithm, and their spatial locations were marked. This research will extend the detection of remote sensing image targets of deep learning algorithms from single buildings to group patterns and complex ground objects, so as to promote the integration of heritage conservation and artificial intelligence research. This time-efficiency approach can provide strong support for the discovery and field investigation of historic villages facing fast development and provide a scientific basis for the formulation of conservation policies. Article Highlights Deep learning is applied to the protection of the cultural heritage of historic villages. Comparative experiments of different algorithms are designed to analyse their applicability in historic village recognition. A recognition rate of up to 95.61% is achieved. The visualization of recognition results is important for understanding the relationship between historic villages and nature, and historic village conservation.

Abstract Background China is a region with a high incidence of tuberculosis, and the incidence of IBD has also been rising rapidly in recent years. Differentiating Crohn’s disease(CD) from intestinal tuberculosis (ITB) has become a very challenging issue. We aimed to develop and assess a diagnostic nomogram to differentiate between CD and ITB to improve the accuracy and practicability of the model. Methods A total of 133 patients (CD 90 and ITB 43) were analyzed retrospectively. Univariate and multivariate logistic regression analysis was included to determine the independent predictive factors and establish the regression equation. On this basis, the nomogram prediction model was constructed. The discrimination, calibration and clinical efficiency of the nomogram were assessed using area under the curve(AUC), C-index, calibration curve, decision curve analysis (DCA) and clinical impact curve. Results T-SPOT positive, cobblestone appearance, comb sign and granuloma were significant predictors in differentiating CD from ITB. Base on the above independent predictors, a diagnostic nomogram was successfully established. The sensitivity, specificity, accuracy of the prediction model are 94.4%, 93.0%, 94.0% respectively. The AUC and the C-index of the prediction model are both 0.988, which suggest that the model had a good discrimination power. The calibration curve indicated a high calibration degree of the prediction model. The DCA and clinical impact curve indicated a good clinical efficiency of the prediction model which could bring clinical benefits. Conclusion A nomogram prediction model for distinguishing CD from ITB was developed and assessed, with high discrimination, calibration and clinical efficiency. It can be used as an accurate and convenient diagnostic tool to distinguish CD from ITB, facilitating clinical decision-making.

Abstract Karst rocky desertification (KRD) has become one of the most serious ecological and environmental problems in karst areas. At present, mapping KRD with a high accuracy and on a large scale is still a difficult problem in the control of KRD. In this study, a random forest (RF) based on maximum information coefficient and correlation coefficient feature selection is proposed to predict KRD. Nine predictors stood out as feature factors to estimate KRD. Rock exposure was the most important predictor, followed by fractional vegetation cover for the prediction of KRD processes. The kappa and classification accuracy indexes were to evaluate the performance of the model. We recorded overall accuracy rate and kappa index values of 94.7% and 0.92 for the testing datasets respectively. The RF model was then used to predict the KRD in 2001, 2011, 2016, and 2020, and it was found that the KRD in the study area has exhibited a positive trend of improvement. Therefore, the use of multisource remote sensing data combined with the RF model can obtain better prediction results of KRD, thereby providing a new idea for large-scale estimation of the KRD in peak-cluster depression.

This article focuses on the large number of existing defense-dwellings built in the Ming and Qing dynasties with distinct regional characteristics in Fujian, and explores the relationship between major conflicts and defense-dwellings. A four-year field survey involving 459 historic villages was conducted. ArcGis was used to superimpose the high-resolution satellite map of Fujian with the borders of towns to classify defense-dwellings and establish the attribute database. By comparing the spatial types and their provincial distribution characteristics, and analyzing them with the conflicts in Fujian history, this paper presents a unique relationship between the unity and diversity of space forms and how they changed or were maintained. The results show: 1. Major military conflicts had a profound and lasting impact on the spatial pattern, residential form, and daily life of historic villages in Fujian; 2. The time and space difference of regional conflicts makes defense-dwellings present a distribution state of different types of central gathering and edge transition; 3. Persistent conflict for limited resources between clan groups made defense-dwellings remain vital, finally forming a typical “architectural atlas”. The results of this study are helpful in understanding the systematic view of morphological changes of historic villages in a highly conflicting social environment.

Self-similar multi-cell structures have been widely applied in engineering fields for their excellent mechanical properties and multi-functional characteristics. In this paper, self-similar inspired multi-cell tubes are constructed by replacing the substructures according to different strategies. The hybrid tubes are built by the space-filling and tessellation of these self-similar inspired multi-cell tubes. The effect of the diameter, layout and hierarchical order are experimentally investigated. The tessellation and space-filling of these multi-cell tubes are numerically and theoretically investigated. The results show that the specific energy absorption (SEA) increases with the hierarchical fractal order. The specific energy absorption of the 2nd order self-similar inspired multi-cell tubes increased by 342.5% and 116.5% compared to the 0th and 1st order multi-cell tubes. It is also found that the increase of the side length is not an effective method to improve the crashworthiness of the lower-order multi-cell tubes. Finally, a theoretical model of the angle element was constructed to predict the mean crushing force of these multi-cell tubes. In general, our research provides an effective method for the design of self-similar inspired multi-cell structures with excellent crashworthiness.

The escalating demand for processed foods has led to the widespread industrial use of glucose isomerase (GI) for high-fructose corn syrup (HFCS) production. This reliance on GIs necessitates continual Co2+ supplementation to sustain high catalytic activity across multiple reaction cycles. In this study, Serratia marcescens GI (SmGI) was immobilized onto surfaces of the metal-organic framework (MOF) material MOF (Co)-525 to generate MOF (Co)-525-GI for use in catalyzing glucose isomerization to generate fructose. Examination of MOF (Co)-525-GI structural features using scanning electron microscopy-energy dispersive spectroscopy, Fourier-transform infrared spectroscopy, and ultraviolet spectroscopy revealed no structural changes after SmGI immobilization and the addition of Co2+. Notably, MOF (Co)-525-GI exhibited optimal catalytic activity at pH 7.5 and 70 °C, with a maximum reaction rate (Vmax) of 37.24 ± 1.91 μM/min and Km value of 46.25 ± 3.03 mM observed. Remarkably, immobilized SmGI exhibited sustained high catalytic activity over multiple cycles without continuous Co2+ infusion, retaining its molecular structure and 96.38% of its initial activity after six reaction cycles. These results underscore the potential of MOF (Co)-525-GI to serve as a safer and more efficient immobilized enzyme technology compared to traditional GI-based food-processing technologies.

Intracranial stenosis is prevalent among Asians and constitutes a common cause of cerebral ischemia. While the best medical therapy carries stroke recurrence rates in excess of 10% per year, trials with intracranial stenting have been associated with unacceptable peri-procedural ischemic events. Cerebral ischemic events are strongly related to the severity of intracranial stenosis, which is high in patients with severe intracranial stenosis with poor vasodilatory reserve. Enhanced External Counter Pulsation (EECP) therapy is known to improve myocardial perfusion by facilitating the development of collateral blood vessels in the heart. In this randomized clinical trial, we evaluate whether EECP therapy may be useful in patients with severe stenosis of intracranial internal carotid (ICA) or middle cerebral artery (MCA). The review of literature, methods of evaluation, status of currently used therapeutic approaches, and trial protocol have been presented.Clinical trial registrationClinicalTrials.gov, Identifier: NCT03921827.

Biomimetics -using nature to inspire innovation- has resulted in the development of many novel materials. Wood, nature’s light-weight structural material, defies mimicry at multiple scales, but its design features have been abstracted and used to develop honeycomb composites of limited geometric complexity. In this paper we demonstrate how it is possible to more completely biomimic wood, capture its exquisite mesoscale features and reproduce its mechanical properties. We utilised micro computed tomography (XCT) to image willow wood and used a novel cross-correlation reconstruction algorithm to mimic its 3D microstructure using minimal information. The mechanical properties of the simulated wood were compared with those derived from parent XCT data using finite-element simulations and testing of 3D printed samples of both wood types. The porosity and connectivity of both sample types was also compared. Our reconstruction algorithm was able to capture critical structural elements of willow wood with a high degree of fidelity, while mechanical properties of both wood types were closely matched. We conclude that our image-based stochastic modelling biomimetics offers a unique approach to capturing the mesoscale complexity of wood and will lead to new opportunities for designing novel materials inspired by xylotomy and powered by fast and high-performance computation.

Pollution flashover is one of the main factors threatening the safety of power system. In order to prevent pollution flashover accident,it is necessary to regularly analyze the contamination of insulator surface. However,the traditional power outage sampling method has a long detection period and consumes a lot of manpower and material resources. In this paper,a strategy and a model for the surface pollution analysis of insulators using laser-induced breakdown spectroscopy are proposed. 10 glass insulators with different equivalent salt deposit density (ESDD) and non-soluble deposit density (NSDD) are taken as research object,and the glass insulator's surface can be divided into inner,middle and outer ring. The calibration of coefficient of correlation of the model under different test strategy is studied,using Na 589.592 nm,Al 396.192 nm as characteristic spectral lines. The result shows that for ESDD,the coefficient of correlation can reach 0.948 1 when inner and middle rings are selected as test objects. And the relative error of detection is within 5%. For NSDD,the coefficient of correlation can reach 0.938 3 when middle ring is selected as test object. And the relative error of detection is within 15%. The analysis method can realize the quick on-site analysis of insulator surface contamination and improve the operation ability of transmission line operation safety maintenance,which has important engineering application value.

In order to achieve an accurate airdrop in the actual environment, the influence of complex interferences, such as wind field and the terrain of the environment, must be taken into account. Aiming at this problem, a combined trajectory planning strategy of a parafoil system subjected to intricate conditions is proposed in this paper. This method divides the parafoil airdrop area into an obstacle area and a landing area, then, considering the terrain environment surface, a model for the parafoil system in the wind field is built in the obstacle area. The Gauss pseudo-spectral method is used to transform the complex terrain environment constraint into a series of nonlinear optimal control problems with complex constraints. Finally, the trajectory of the landing area is designed by means of multiphase homing, and the target parameters are solved by the improved marine predator algorithm. The simulation results show that the proposed method has better realizability than a single homing strategy, and the optimization results of the improved marine predator algorithm have higher accuracy.

Abstract Marine mammals relying on tactile perception for hunting are able to achieve a remarkably high prey capture rate without visual or acoustic perception. Here, a self-powered triboelectric palm-like tactile sensor (TPTS) is designed to build a tactile perceptual system for underwater vehicles. It is enabled by a three-dimensional structure that mimics the leathery, granular texture in the palms of sea otters, whose inner neural architecture provides additional clues indicating the importance of tactile information. With the assistance of palm structure and triboelectric nanogenerator technology, the proposed TPTS has the ability to detect and distinguish normal and shear external load in real-time and approximate the external stimulation area, especially not affected by the touch frequency, that is, it can maintain stable performance under high-frequency contact. The results show that the TPTS is a promising tool for integration into grippers mounted on underwater vehicles to complete numerous underwater tasks.

Powered parafoils, known for their impressive load-bearing capacity and extended endurance, have garnered significant interest. However, the parafoil system is a highly complex nonlinear system. It primarily relies on the steering gear to change flight direction and utilizes a thrust motor for climbing. However, achieving precise trajectory tracking control presents a challenge due to the interdependence of direction and altitude control. Furthermore, underactuation and wind disturbances bring additional difficulties for trajectory tracking control. Consequently, realizing trajectory tracking control for powered parafoils holds immense significance. In this paper, we propose a trajectory tracking method based on Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm-optimized Linear Active Disturbance Rejection Control (LADRC). Our method addresses the underactuation issue by incorporating a guiding law while utilizing two LADRC methods to achieve decoupling and compensate for disturbances. Moreover, we employ the TD3 algorithm to dynamically adjust controller parameters, thus enhancing the controller performance. The simulation results demonstrate the effectiveness of our proposed method as a trajectory tracking control approach. Additionally, since the control process is not reliant on system-specific models, our method can also provide guidance for trajectory tracking control in other aircraft.

Visible light communication has attracted significant attention due to advantages such as high transmission speed, good security, high confidentiality, environmental protection, and lack of need for spectrum authorization. Herein, the problem of absorption of light in the visible band in thin silicon films is examined, and a dual composite nanostructure array based on nanoholes of two sizes is proposed to increase visible light absorption. Light absorptivity was determined to be greater than 80% at wavelengths between 400 and 678 nm. Moreover, using finite-difference time-domain numerical simulations, the distributions of the electric field at specific wavelengths were obtained, the propagation modes at these wavelengths were examined, and the effect of these modes was analyzed according to the electric field distributions. The proposed dual composite nanostructured silicon film can be used in photovoltaic devices to improve visible light absorption.

ABSTRACT The widespread emergence of transferable extensively drug-resistant (XDR) genes, including blaNDM and blaKPC for carbapenem resistance, mcr-1 for colistin resistance, and tet(X4) and tet(X6) for tigecycline resistance, in Enterobacteriaceae poses a major threat to public health. Thus, rapid on-site detection of these XDR genes is urgently needed. We developed a cascade system with a unitary polyethylene glycol (PEG) 200-enhanced recombinase polymerase amplification (RPA) as the core, combined with a modified Chelex-100 lysis method and a horseradish peroxidase (HRP)-catalyzed lateral flow immunoassay (LFIA) biosensor, to accurately detect these genes in Enterobacteriaceae. The conventional Chelex-100 lysis method was modified to allow in situ extraction of bacterial DNA in 20 min without requiring bulky high-speed centrifuges. Using PEG 200 increased the amplification efficiency of the RPA by 13%, and the HRP-catalyzed LFIA biosensor intensified the colorimetric signal of the test line. Following optimization, the sensitivity of the cascade system was