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CTLA-4 is critical for balancing protective immunity with self-tolerance. Here the authors identify homozygous DEF6 mutations in patients with severe autoimmunity, one of which received and responds to CTLA-4-Ig, and show that DEF6 is crucial for CTLA-4 cell surface trafficking and immune regulatory function.

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The immune suppressive protein CTLA-4 is constitutively expressed by Tregs and induced in effector T cells upon activation. Its crucial role in adaptive immunity is apparent from the fatal autoimmune pathology seen in CTLA-4 knockout mice. However, little is known regarding factors that regulate CTLA-4 expression and their effect upon its function to remove CD80 and CD86 from antigen presenting cells by transendocytosis. Th17 cells are emerging as significant players in autoimmunity as well as other diseases. Therefore, in this study we have examined the effects of Th17 polarising conditions on CTLA-4 expression and function in human T cells and show that Th17 conditions can suppress the expression of CTLA-4 and its transendocytic function. In contrast to Th17 cells, vitamin D is inversely associated with autoimmune disease. We have previously shown a striking ability of 1,25 dihydroxyvitamin D3 (1,25(OH)2D3) to enhance CTLA-4, however, its effects upon B7 transendocytosis and its activity in the context of inflammation remained unknown. Here we show that induction of CTLA-4 by 1,25(OH)2D3 can actually be enhanced in the presence of Th17 polarising cytokines. Furthermore, its transendocytic function was maintained such that T cells generated in the presence of Th17 conditions and 1,25(OH)2D3 were highly effective at capturing CTLA-4 ligands from antigen presenting cells and suppressing T cell division. Taken together, these data reveal an inhibitory effect of Th17 polarising conditions upon CTLA-4-mediated regulation and show that 1,25(OH)2D3 counteracts this effect. Given the importance of CTLA-4-mediated suppression in the control of autoimmune diseases, our novel data highlight the importance of vitamin D in inflammatory settings.

Abstract Underwater images collected are often of low clarity and suffer from severe color distortion due to the marine environment and Illumination conditions. This directly impacts tasks such as marine ecological monitoring and underwater target detection, which rely on image processing. Therefore, enhancing Underwater images to improve their quality is necessary. A generative adversarial network with an encoder-decoder structure is proposed to improve the quality of Underwater images. The network consists of a generative network and an adversarial network. The generative network is responsible for enhancing the images, while the adversarial network determines whether the input is an enhanced image or a real high-quality image. In the generative network, we first design a residual convolution module to extract more texture and edge information from underwater images. Next, we design a multi-scale dilated convolution module to capture underwater features at different scales. Then, we design a feature fusion adaptive attention module to reduce the interference of redundant features and enhance the local perception capabilities. Finally, we construct the generative network using these modules along with conventional modules. In the adversarial network, we first design a multi-scale feature extraction module to improve the feature extraction ability. We then use the multi-scale feature extraction module along with conventional convolution modules to design the adversarial network. Additionally, we propose an improved loss function by introducing color loss into the conventional loss function. The improved loss function can better measure the color discrepancy between the enhanced image and the real image. It is useful to reduce color distortion in the enhanced images. In experimental simulations, the images enhanced by the proposed methods have the highest PSNR, SSIM, and UIQM values, indicating that the proposed method has superior Underwater image enhancement capabilities compared to other methods.

Abstract Distributed optical fiber sensor (DAS) is an emerging acquisition technique and has begun to be widely applied in seismic exploration owing to its advantages in acquisition and deployment. Nonetheless, DAS record has a low signal-to-noise ratio (SNR) due to the intense background noise. How to suppress the DAS background noise and increase the SNR of the DAS records has gradually become one of the hot issues in the field of seismic data processing. To solve the challenging tasks in intense DAS noise suppression, a multiscale sparse asymmetric attention convolutional neural network (MSAACNN) is proposed. The network uses dilated convolutions to expand the receptive field and map more feature information. Moreover, asymmetric convolutions are introduced to form an asymmetric unit, aiming to strengthen the feature extraction ability and realize the interaction between feature information of different scales. Finally, a pyramid attention module is used to enhance the primary features and improve the network denoising performance. The experimental results show that MSAACNN can effectively suppress the complex background noise in DAS records, compared with the traditional denoising methods and typical convolutional neural network (CNN) architecture. Additionally, the recovered signal components in the processing results are clear and complete, with significantly improved SNR.

Abstract Purpose DNA methylation prominently inactivates tumor suppressor genes and facilitates oncogenesis. Previously, we delineated a chromosome 18 deletion encompassing the erythrocyte membrane protein band 4.1-like 3 (EPB41L3) gene, a progenitor for the tumor suppressor that is differentially expressed in adenocarcinoma of the lung-1 (DAL-1) in gastric cancer (GC). Methods Our current investigation aimed to elucidate EPB41L3 expression and methylation in GC, identify regulatory transcription factors, and identify affected downstream pathways. Immunohistochemistry demonstrated that DAL-1 expression is markedly reduced in GC tissues, with its downregulation serving as an independent prognostic marker. Results High-throughput bisulfite sequencing of 70 GC patient tissue pairs revealed that higher methylation of non-CpGs in the EPB41L3 promoter was correlated with more malignant tumor progression and higher-grade tissue classification. Such hypermethylation was shown to diminish DAL-1 expression, thus contributing to the malignancy of GC phenotypes. The DNA methyltransferase inhibitor 5-aza-2’-deoxycytidine (5-aza-CdR) was found to partially restore DAL-1 expression. Moreover, direct binding of the transcription factor CDC5L to the upstream region of the EPB41L3 promoter was identified via chromosome immunoprecipitation (ChIP)-qPCR and luciferase reporter assays. Immunohistochemistry confirmed the positive correlation between CDC5L and DAL-1 protein levels. Subsequent RNA-seq analysis revealed that DAL-1 significantly influences the extracellular matrix and space-related pathways. GC cell RNA-seq post-5-Aza-CdR treatment and single-cell RNA-seq data of GC tissues confirmed the upregulation of AREG and COL17A1, pivotal tumor suppressors, in response to EPB41L3 demethylation or overexpression in GC epithelial cells. Conclusion In conclusion, this study elucidates the association between non-CpG methylation of EPB41L3 and GC progression and identifies the key transcription factors and downstream molecules involved. These findings enhance our understanding of the role of EPB41L3 in gastric cancer and provide a solid theoretical foundation for future research and potential clinical applications.

Abstract In low-light environments, the amount of light captured by the camera sensor is reduced, resulting in lower image brightness. This makes it difficult to recognize or completely lose details in the image, which affects subsequent processing of low-light images. Low-light image enhancement methods can increase image brightness while better-restoring color and detail information. A generative adversarial network is proposed for low-quality image enhancement to improve the quality of low-light images. This network consists of a generative network and an adversarial network. In the generative network, a multi-scale feature extraction module, which consists of dilated convolutions, regular convolutions, max pooling, and average pooling, is designed. This module can extract low-light image features from multiple scales, thereby obtaining richer feature information. Secondly, an illumination attention module is designed to reduce the interference of redundant features. This module assigns greater weight to important illumination features, enabling the network to extract illumination features more effectively. Finally, an encoder-decoder generative network is designed. It uses the multi-scale feature extraction module, illumination attention module, and other conventional modules to enhance low-light images and improve quality. Regarding the adversarial network, a dual-discriminator structure is designed. This network has a global adversarial network and a local adversarial network. They determine if the input image is actual or generated from global and local features, enhancing the performance of the generator network. Additionally, an improved loss function is proposed by introducing color loss and perceptual loss into the conventional loss function. It can better measure the color loss between the generated image and a normally illuminated image, thus reducing color distortion during the enhancement process. The proposed method, along with other methods, is tested using both synthesized and real low-light images. Experimental results show that, compared to other methods, the images enhanced by the proposed method are closer to normally illuminated images for synthetic low-light images. For real low-light images, the images enhanced by the proposed method retain more details, are more apparent, and exhibit higher performance metrics. Overall, compared to other methods, the proposed method demonstrates better image enhancement capabilities for both synthetic and real low-light images.

AbstractBackground To investigate the effectiveness of the intervention with critical value management and push short messaging service (SMS), and to determine improvement in the referral rate of patients with positive hepatitis C antibody (anti-HCV).Methods No intervention was done for patients with positive anti-HCV screening results from 1 January 2015 to 31 October 2021. Patients with positive anti-HCV results at our hospital from 1 November 2021 to 31 July 2022 were informed vide critical value management and push SMS. For inpatients, a competent physician was requested to liaise with the infectious disease physician for consultation, and patients seen in the OPD (outpatient department) were asked to visit the liver disease clinic. The Chi-square correlation test, one-sided two-ratio test and linear regression were used to test the relationship between intervention and referral rate.Results A total of 638,308 cases were tested for anti-hepatitis C virus (HCV) in our hospital and 5983 of them were positive. 51.8% of the referred patients were aged 18–59 years and 10.8% were aged ≥75 years. The result of Chi-square correlation test between intervention and referral was p = .0000, p

Purpose Heavy metal exposure can cause impaired or reduced pathology in the kidneys, lungs, liver, and other vital organs. However, the relationship between heavy metal exposure and kidney stones has not been determined. The goal of this research was to determine the association between heavy metal exposure and kidney stones in a population of American adults in general.Materials and methods We evaluated 29,201 individuals (≥20 years) from the National Health and Nutrition Examination Survey (NHANES). The association between heavy metal exposure and kidney stones was verified by multiple logistic regression and restricted cubic spline (RCS) regression. Dose–response curves were generated to analyze the relationship between heavy metal concentrations and the occurrence of kidney stones. Moreover, we used propensity score matching (PSM) to exclude the effect of confounding variables.Results After a rigorous enrollment screening process, we included 8518 participants. Logistic regression showed that urinary cadmium (U-Cd) and urinary cobalt (U-Co) concentrations were significantly different in the kidney stone group before PSM (p < 0.001). Dose–response curves revealed that the occurrence of kidney stones increased significantly with increasing U-Cd and U-Co concentrations. After adjustment for covariates, only biomarkers of U-Co were linked to the occurrence of kidney stones. When the lowest quartile was used as a reference, the 95% confidence intervals (95% CIs) for kidney stones across the other quartiles were 1.015 (0.767–1.344), 1.409 (1.059–1.875), and 2.013 (1.505–2.693) for U-Cos (p < 0.001).Conclusion In the U.S. population, high U-Co levels are positively correlated with the potential risk of kidney stones.

Abstract Background Avian influenza virus (AIV) not only causes huge economic losses to the poultry industry, but also threatens human health. Reverse transcription recombinase-aided amplification (RT-RAA) is a novel isothermal nucleic acid amplification technology. This study aimed to improve the detection efficiency of H5, H7, and H9 subtypes of AIV and detect the disease in time. This study established RT-RAA-LFD and real-time fluorescence RT-RAA (RF-RT-RAA) detection methods, which combined RT-RAA with lateral flow dipstick (LFD) and exo probe respectively, while primers and probes were designed based on the reaction principle of RT-RAA. Results The results showed that RT-RAA-LFD could specifically amplify H5, H7, and H9 subtypes of AIV at 37 °C, 18 min, 39 °C, 20 min, and 38 °C, 18 min, respectively. The sensitivity of all three subtypes for RT-RAA-LFD was 102 copies/µL, which was 10 ∼100 times higher than that of reverse transcription polymerase chain reaction (RT-PCR) agarose electrophoresis method. RF-RT-RAA could specifically amplify H5, H7, and H9 subtypes of AIV at 40 °C, 20 min, 38 °C, 16 min, and 39 °C, 17 min, respectively. The sensitivity of all three subtypes for RF-RT-RAA was 101 copies/µL, which was consistent with the results of real-time fluorescence quantification RT-PCR, and 100 ∼1000 times higher than that of RT-PCR-agarose electrophoresis method. The total coincidence rate of the two methods and RT-PCR-agarose electrophoresis in the detection of clinical samples was higher than 95%. Conclusions RT-RAA-LFD and RF-RT-RAA were successfully established in this experiment, with quick response, simple operation, strong specificity, high sensitivity, good repeatability, and stability. They are suitable for the early and rapid diagnosis of Avian influenza and they have positive significance for the prevention, control of the disease, and public health safety.

Abstract Remote sensing is gradually playing an important role in the detection of ground information. However, the quality of remote-sensing images has always suffered from unexpected natural conditions, such as intense haze phenomenon. Recently, convolutional neural networks (CNNs) have been applied to deal with dehazing problems, and some important findings have been obtained. Unfortunately, the performance of these classical CNN-based methods still needs further enhancement owing to their limited feature extraction capability. As a critical branch of CNNs, the generative adversarial network (GAN), composed of a generator and discriminator, has become a hot research topic and is considered a feasible approach to solving the dehazing problems. In this study, a novel dehazed generative adversarial network (GAN) is proposed to reconstruct the clean images from the hazy ones. For the generator network of the proposed GAN, the color and luminance feature extraction module and the high-frequency feature extraction module aim to extract multi-scale features and color space characteristics, which help the network to acquire texture, color, and luminance information. Meanwhile, a color loss function based on hue saturation value (HSV) is also proposed to enhance the performance in color recovery. For the discriminator network, a parallel structure is designed to enhance the extraction of texture and background information. Synthetic and real hazy images are used to check the performance of the proposed method. The experimental results demonstrate that the performance can significantly improve the image quality with a significant increment in peak-signal-to-noise ratio (PSNR). Compared with other popular methods, the dehazing results of the proposed method closely resemble haze-free images.

Abstract Background It is essential to find reliable biomarkers for early diagnosis and prognosis of acute heart failure (AHF) for its mitigation. Currently, increasing attention is paid to the role of microRNAs (miRNAs/miRs) as diagnostic or prognostic markers for cardiovascular diseases. Since plasma miR-106a-5p has been observed to be downregulated in AHF, its value in the diagnosis and prognostic assessment of AHF deserves further exploration. Accordingly, this study analyzed the diagnostic and prognostic value of plasma miR-106a-5p in AHF patients. Methods Prospectively, this study included 127 AHF patients who met the 2021 European Society of Cardiology Guidelines and 127 control individuals. Plasma miR-106a-5p levels were determined with RT-qPCR. Spearman correlation analysis was performed to evaluate the correlation of plasma miR-106a-5p levels with NT-proBNP and hs-CRP levels in AHF patients. All AHF patients were followed up for 1 year and allocated into poor and good prognosis groups, and plasma miR-106a-5p levels were compared. The diagnostic and prognostic value of plasma miR-106a-5p for AHF was assessed with a receiver-operating characteristic curve. Results Plasma miR-106a-5p was lowly expressed in AHF patients versus controls (0.53 ± 0.26 vs. 1.09 ± 0.46) and showed significant negative correlations with NT-proBNP and hs-CRP levels. Plasma miR-106a-5p level

Traffic-related air pollutants (TRAPs) pose significant health risks in megacities, yet fixed monitoring sites often fail to capture their complexity. To characterize the TRAP concentrations which fixed sites cannot address, we employed a mobile platform to effectively capture real-time hyperlocal-scale TRAP variations in Chengdu, China. A 17-day sampling campaign was conducted covering the National Holiday of China and collected ~1.2 × 105 1 Hz paired data. We measured particle number concentration (PNC), black carbon (BC), and nitrogen oxides (NOx) across urban and rural freeway environments to assess the impact of reduced heavy-duty diesel vehicles (HDDVs) during the holiday (i.e., holiday effect). No clear impact of wind direction on TRAP concentrations was found in this study. However, substantial differences (two times) were observed when comparing non-holiday to holiday campaigns. Spearman correlations (0.21–0.56) between TRAPs persistently exceeded Pearson correlations (0.14–0.41), indicating non-linear relationships and suggesting the necessity for data transformations (e.g., logarithms) in TRAP analysis. The comparison of the background subtracted TRAPs concentrations between non-holiday and holidays, revealing approximately a 50% reduction in TRAPs across microenvironments. Among the TRAPs, NOx emerged as a reliable indicator of HDDV emissions. The study provides insights into vehicle fleet composition impacts, paving the way for enhanced exposure assessment strategies.

With advancements in mobile robot technology, wheel-legged robots have emerged as promising next-generation mobile solutions, reducing design costs and enhancing adaptability in unstructured environments. As underactuated systems, their balance control has become a prominent research focus. Despite there being numerous control approaches, challenges remain. Balance control methods for wheel-legged robots are influenced by hardware characteristics, such as motor friction, which can induce oscillations and hinder dynamic convergence. This paper presents a friction feedforward Linear Quadratic Regulator (LQR) balance control method. Specifically, a basic LQR controller is developed based on the dynamics model of the wheel-legged robot, and a Stribeck friction model is established to characterize motor friction. A constant-speed excitation trajectory is designed to gather data for friction identification, and the Particle Swarm Optimization (PSO) algorithm is applied to determine the optimal friction parameters. The identified friction model is subsequently incorporated as feedforward compensation for the LQR controller’s torque output, resulting in the proposed friction feedforward LQR balance control algorithm. The minimum standard deviation for friction identification is approximately 0.30, and the computed friction model values closely match the actual values, indicating effective and accurate identification results. Balance experiments demonstrate that under diverse conditions—such as flat ground, single-sided bridges, and disturbance scenarios—the convergence performance of the friction feedforward LQR algorithm markedly surpasses that of the baseline LQR, effectively reducing oscillations, accelerating convergence, and improving the robot’s stability and robustness.

Avian leukemia virus (ALV) is one of the main pathogens of poultry tumor diseases, and has caused significant economic losses to the poultry industry since its discovery. Therefore, establishing a rapid detection method is essential to effectively prevent and control the spread of ALV. In this study, specific CRISPR RNA (crRNA) and recombinase-aided amplification (RAA) primers with T7 promoter were designed based on the relatively conserved sequence of avian leukemia virus. When crRNA recognized the target sequence, Cas13a protein was activated to cut the reporting probes, and then the detection results were read by using lateral flow dipstick (LFD). The RAA-CRISPR/Cas13a-LFD reaction system was constructed. The RAA amplification time, Cas13a protein concentration, crRNA concentration and CRISPR reaction time were optimized to evaluate the specificity, sensitivity and reproducibility of the system. Finally, RAA-CRISPR/Cas13a-LFD method was compared with Polymerase chain reaction (PCR)-Agarose electrophoresis method and qPCR method in the detection of clinical samples, and the reliability of RAA-CRISPR/Cas13a-LFD method was evaluated. The results showed that the RAA-CRISPR/Cas13a-LFD method could effectively amplify the target gene at 37°C for 40 min, and the test results could be determined by LFD visual observation. The method had good specificity and no cross-reaction with Marek’s disease virus (MDV), Fowl adenovirus (FAdV), Infectious bursal disease virus (IBDV), Newcastle disease virus (NDV), Infectious laryngotracheitis virus (ILTV), and Infectious bronchitis virus (IBV). The minimum detection limit of the method was 100 copies/μL, and it had good repeatability and stability. The coincidence rate of clinical detection reached 97.69% and 99.23%. In summary, this study established a simple, efficient, accurate and visualized ALV detection method, which can be used for the prevention and rapid clinical diagnosis of avian leukosis (AL).

This article reports the design and experiment of a novel air-assisted nozzle for pesticide application in orchard. A novel air-assisted nozzle was designed based on the transverse jet atomization pattern. This article conducted the performance and deposition experiments and established the mathematical model of volume median diameter (D50) and liquid flow rate with the nozzle design parameters. The D50 of this air-assisted nozzle ranged from 52.45 μm to 113.67 μm, and the liquid flow rate ranged from 142.6 ml/min to 1,607.8 ml/min within the designed conditions. These performances meet the low-volume and ultra-low-volume pesticide application in orchard. The droplet deposition experiment results demonstrated that the droplet coverage distribution in different layers and columns is relatively uniform, and the predicted value of spray penetration (SP) numbers SPiA, SPiB, and SPiC (i = 1, 2, and 3) are approximately 70%, 60%, and 70%, respectively. The droplet deposits on the foliage of the canopy (inside and outside) uniformly bring benefit for plant protection and pesticide saving. Compared with the traditional air-assisted nozzle that adopts a coaxial flow atomization pattern, the atomization efficiency of this air-assisted nozzle is higher. Moreover, the nozzle air pressure and liquid flow rate are considerably lower and greater than the traditional air-assisted nozzle, and these results proved that this air-assisted nozzle has great potential in orchard pesticide application. The relationship between the D50 and nozzle liquid pressure of this air-assisted nozzle differs from that of traditional air-assisted nozzles due to the atomization pattern and process. While this article provides an explanation for this relationship, further study about the atomization process and mechanism is needed so as to improve the performance.

Defecation care for disabled patients is a major challenge in health management. Traditional post-defecation treatment will bring physical injury and negative emotions to patients, while existing pre-defecation forecasting care methods are physically intrusive. On the basis of exploring the mechanism of defecation intention generation, and based on the characteristic analysis and clinical application of bowel sounds, it is found that the generation of desire to defecate and bowel sounds are correlated to a certain extent. Therefore, a deep learning-based bowel sound recognition method is proposed for human defecation prediction. The wavelet domain based Wiener filter is used to filter the bowel sound data to reduce other noise. Statistical analysis, fast Fourier transform and wavelet packet transform are used to extract the integrated features of bowel sound in time, frequency and time-frequency domain. In particular, an audio signal expansion data algorithm based on the Informer model is proposed to solve the problem of poor generalization of the training model caused by the difficulty of collecting bowel sound in reality. An improved one-dimensional residual network model (1D-IResNet) for defecation classification prediction is designed based on multi-domain features. The experimental results show that the proposed bowel sound augmentation strategy can effectively improve the data sample size and increase the sample diversity. Under the augmented dataset, the training speed of the 1D-IResNet model is accelerated, and the classification accuracy reaches 90.54 %, the F1 score reaches 83.88 %, which achieves a relatively good classification stability while maintaining a high classification index.

To address the problem that traditional force control methods have difficulty obtaining stable forces during a robot massage, a robot force control algorithm based on residual reinforcement learning is proposed. An initial strategy of the robot massage force is first constructed with impedance control, but the massage force often fluctuates when the skin environment is unknown to the robot. A reinforcement learning algorithm is then used to analyze the relationship between the robot contact state and offset displacement and compensate for the residuals of the impedance controller. To speed up the search for the compensation strategy, a neural network is constructed to fit a dynamic model of reinforcement learning with the data from the initial strategy, which can simulate the contact between the robot and skin. Offline training in a simulated environment can reduce the number of actual interactions in reinforcement learning and improve the practicability of the algorithm; to integrate the two algorithms, the output of the residual reinforcement learning strategy is smoothed. The experimental results show that the robot force control algorithm based on residual reinforcement learning converges after approximately 80 offline iterations. The force error in the online experiment is basically within ±0.2 N.

Abstract Multimodal transportation is a modern way of cargo transportation. With the increasing demand for cargo transportation, higher requirements are being placed on multimodal transportation multi-objective routing optimization. In multimodal transportation multi-objective routing optimization, in response to the limitations of classical algorithms in solving large-scale problems with multiple nodes and modes of transport, the limitations of directed transportation networks in the application, and the uncertainty of transport time, this paper proposes an optimization framework based on multi-objective weighted sum Q-learning, combined with the proposed undirected multiple-node network, and characterizes the uncertainty of time with a positively skewed distribution. The undirected multiple-node transportation network can better simulate cargo transportation and characterize transfer information, facilitate the modification of origin and destination, and avoid suboptimal solutions due to the manual setting of wrong route directions. The network is combined with weighted sum Q-learning to solve multimodal transportation multi-objective routing optimization problems faster and better. When modeling the uncertainty of transport time, a positively skewed distribution is used. The three objectives of transport cost, carbon emission cost, and transport time were studied and compared with PSO, GA, AFO, NSGA-II, and MOPSO. The experimental results show that compared with PSO, GA, and AFO using a directed transportation network, the proposed method has a significant improvement in optimization results and running time, and the running time is shortened by 26 times. The proposed method can better solve the boundary of the Pareto front and dominate the partial solutions of NSGA-II and MOPSO. The effect of time uncertainty on the performance of the algorithm is more significant in transport orders with high time weight. With the increase in uncertainty, the reliability of the route decreases. The effectiveness of the proposed method is verified.

Pre-harvest water deficit (PHWD) plays an important role in sugar accumulation of citrus fruit. However, the mechanism is not known well. Here, it was confirmed that PHWD promoted sucrose accumulation of citrus fruit, but had limited effect on fructose, glucose and total acid. A sucrose transporter, CsSUT1, which localizes to the plasma membrane, was demonstrated to function in sucrose transport induced by PHWD. Compared to wild-type, CsSUT1 overexpression in citrus calli stimulated sucrose, fructose and glucose accumulation, while its silencing in juice sacs reduced sucrose accumulation. Increased sugar accumulation in transgenic lines enhanced plant drought tolerance, and resulted in decreased electrolyte leakage, malondialdehyde and hydrogen peroxide contents, as well as increased superoxide dismutase activity and proline contents. An abscisic acid (ABA)-responsive transcription factor, CsABF3, was found with a same expression pattern with CsSUT1 under PHWD. Yeast one-hybrid, electrophoretic mobility shift assay and dual-luciferase assays all revealed that CsABF3 directly bound with the CsSUT1 promoter by ABA responsive elements. When CsABF3 was overexpressed in citrus calli, the sucrose, fructose and glucose concentration increased correspondingly. Further, transgenic studies demonstrated that CsABF3 could affect sucrose accumulation by regulating CsSUT1. Overall, this study revealed a regulation of CsABF3 promoting CsSUT1 expression and sucrose accumulation in response to PHWD. Our results provide a detail insight into the quality formation of citrus fruit.

In recent years, remote sensing (RS) image scene classification methods have experienced notable development due to the powerful feature extraction ability of deep learning. However, current methods for RS image scene classification (RSSC) tasks struggle with handling unseen scene categories because of their reliance on large amounts of high-quality labeled data. To address this issue, zero-shot learning has been introduced into RSSC tasks, leading to the emergence of zero-shot remote sensing image scene classification (ZSRSSC) approaches. These methods refer to inference and recognition of unseen category images by comprehending seen category image features and combining category semantic information in a multimodal learning paradigm. The inference capability possessed by ZSRSSC methods can significantly improve the accuracy and applicability of RSSC in dealing with large amounts of data in the RS domain. However, this newly emerging field has produced numerous results without a comprehensive survey to organize research progress systematically. Therefore, this article systematically reviews the research progress made on these approaches, summarizes commonly used datasets and experimental setups, and compares the results of different methods. Specifically, the definition of zero-shot learning (ZSL) is reviewed first, and the mainstream development of ZSL classification methods is briefly introduced. Then, the definition of ZSRSSC is outlined, followed by an introduction to the prevalent ZSRSSC approaches and their categorization. Finally, the critical issues in these methods are analyzed, and their future development directions are discussed.

Feline calicivirus (FCV) is one of the most common viral pathogens in domestic cats worldwide, which mainly causes upper respiratory tract infections in felines and seriously threatens the health of felines. Consequently, it is crucial to establish a rapid detection method to efficiently take control and prevent the spread of FCV. To construct the Cas13a-RAA-LFD reaction system, this study specifically designed recombinase-aided amplification (RAA) primers added with a T7 promoter and CRISPR RNA (crRNA), which were both based on the FCV relatively conserved sequence. The Cas13a protein cleaved the reporting probes only when crRNA recognized the target sequence. The results could be directly observed by lateral flow dipsticks (LFDs). To evaluate this system, factors such as RAA amplification time, Cas13a protein concentration, crRNA concentration, and CRISPR reaction time were optimized. Then, a comparison of the coincidence rate for clinical samples between this method and the polymerase chain reaction (PCR) agarose electrophoresis method was performed to evaluate the reliability of the method. Eventually, the results indicated that the target gene could be effectively amplified by the Cas13a-RAA-LFD method, and the results could be visually observed by LFD. The method could detect FCV specifically, whilst having no cross-reaction with other common viruses which infect felines, such as feline parvovirus (FPV), feline coronavirus (FCoV) and feline herpesvirus (FHV). This method is extremely sensitive and has been validated to detect viral nucleic acids down to 100 copies/μL. The good reproducibility and stability of the method were also verified by this study. Testing of clinical samples proved that the coincidence rate of clinical detection reached 96.39%. In summary, this study established a simplistic, efficient, accurate, and visualized FCV detection method, which can be utilized for early prevention and control of FCV.

Prostate cancer is a prevalent cancer in elderly men, and immunotherapy has emerged as a promising treatment approach in recent years. The aim of immunotherapy is to stimulate the body’s immune system to target and destroy cancer cells. Cancer vaccines that are highly specific, safe, and capable of creating long-lasting immune responses are a key focus in cancer immunotherapy research. Despite progress in clinical trials showing positive results, the practical use of cancer vaccines still encounters various obstacles. The complexity of the immune microenvironment and variations in the immune systems of individual patients have hindered the progress of research on prostate cancer vaccines. This review examines the history and mechanisms of cancer vaccines, summarizes recent clinical research findings, and explores future directions in the development of prostate cancer vaccines.

Vascular stents have many applications in treating arterial stenosis and other vascular-related diseases. The ideal vascular stent for clinical application should have radial support and axial bending mechanical properties that meet the requirements of vascular deformation coordination. The materials used for vascular stents implanted in the human body should have corresponding biocompatibility to ensure that the stents do not cause coagulation, hemolysis, and other reactions in the blood. This study fabricated four types of vascular stents, including inner hexagon, arrowhead, quadrilateral, and outer hexagonal, using fused filament fabrication technology and thermoplastic polyurethane (TPU) as materials. By evaluating the effects of edge width and wall thickness on the radial support and axial bending performance, it was found that the inner hexagonal stent exhibited the best radial support and axial bending performance under the same conditions. The design and fabrication of vascular stents based on 3D printing technology have promising application prospects in personalized customized vascular repair therapy.

Raindrops can scatter and absorb light, causing images to become blurry or distorted. To improve image quality by reducing the impact of raindrops, this paper proposes a novel generative adversarial network for image de-raining. The network comprises two parts: a generative network and an adversarial network. The generative network performs image de-raining. The adversarial network determines whether the input image is rain-free or de-rained. The generative network comprises two branches: the A branch, which follows a traditional convolutional network structure, and the U branch, which utilizes a U-Net architecture. The A branch includes a multi-scale module for extracting information at different scales and a residual attention module to reduce redundant information interference. The U branch contains an encoder module designed to address the loss of details and local information caused by conventional down-sampling. To improve the performance of the generative network in image de-raining, this paper employs a relative discriminator incorporating a mean squared error loss. This discriminator better measures the differences between rainy and rain-free images while effectively preventing the occurrence of gradient vanishing. Finally, this study performs visual and quantitative comparisons of the proposed method and existing methods on three established rain image datasets. In the quantitative experiments, the proposed method outperforms existing methods regarding PSNR, SSIM, and VIF metrics. Specifically, our method achieves an average PSNR, SSIM, and VIF of approximately 5%, 3%, and 4% higher than the MFAA-GAN method, respectively. These results indicate that the de-rained images generated via the proposed method are closer to rain-free images.

Abstract Background To investigate the regulatory role of microRNA (miR)-148a-3p in mouse corpus cavernous pericyte (MCPs)-derived extracellular vesicles (EVs) in the treatment of diabetes-induced erectile dysfunction (ED). Methods Mouse corpus cavernous tissue was used for MCP primary culture and EV isolation. Small-RNA sequencing analysis was performed to assess the type and content of miRs in MCPs-EVs. Four groups of mice were used: control nondiabetic mice and streptozotocin-induced diabetic mice receiving two intracavernous injections (days − 3 and 0) of phosphate buffered saline, MCPs-EVs transfected with reagent control, or MCPs-EVs transfected with a miR-148a-3p inhibitor. miR-148a-3p function in MCPs-EVs was evaluated by tube-formation assay, migration assay, TUNEL assay, intracavernous pressure, immunofluorescence staining, and Western blotting. Results We extracted EVs from MCPs, and small-RNA sequencing analysis showed miR-148a-3p enrichment in MCPs-EVs. Exogenous MCPs-EV administration effectively promoted mouse cavernous endothelial cell (MCECs) tube formation, migration, and proliferation, and reduced MCECs apoptosis under high-glucose conditions. These effects were significantly attenuated in miR-148a-3p-depleted MCPs-EVs, which were extracted after inhibiting miR-148a-3p expression in MCPs. Repetitive intracavernous injections of MCPs-EVs improved erectile function by inducing cavernous neurovascular regeneration in diabetic mice. Using online bioinformatics databases and luciferase report assays, we predicted that pyruvate dehydrogenase kinase-4 (PDK4) is a potential target gene of miR-148a-3p. Conclusions Our findings provide new and reliable evidence that miR-148a-3p in MCPs-EVs significantly enhances cavernous neurovascular regeneration by inhibiting PDK4 expression in diabetic mice.

Recent advancements in space exploration technology have significantly increased the number of diverse satellites in orbit. This surge in space-related information has posed considerable challenges in developing space target surveillance and situational awareness systems. However, existing detection algorithms face obstacles such as complex space backgrounds, varying illumination conditions, and diverse target sizes. To address these challenges, we propose an innovative end-to-end Attention-Guided Encoder DETR (AgeDETR) model, since artificial intelligence technology has progressed swiftly in recent years. Specifically, AgeDETR integrates Efficient Multi-Scale Attention (EMA) Enhanced FasterNet block (EF-Block) within a ResNet18 (EF-ResNet18) backbone. This integration enhances feature extraction and computational efficiency, providing a robust foundation for accurately identifying space targets. Additionally, we introduce the Attention-Guided Feature Enhancement (AGFE) module, which leverages self-attention and channel attention mechanisms to effectively extract and reinforce salient target features. Furthermore, the Attention-Guided Feature Fusion (AGFF) module optimizes multi-scale feature integration and produces highly expressive feature representations, which significantly improves recognition accuracy. The proposed AgeDETR framework achieves outstanding performance metrics, i.e., 97.9% in mAP0.5 and 85.2% in mAP0.5:0.95, on the SPARK2022 dataset, outperforming existing detectors and demonstrating superior performance in space target detection.

A compact photoacoustic spectroscopy system integrated with a non-coaxial multi-pass cell was developed for improving the instrument performance in the measurement of methane. The multi-pass cell with compact light spot mode was proposed for concentrating the light radiation within a limited space, which effectively reduces the instrument dimension. A distributed feedback (DFB) laser with a central wavelength of 1653 nm was employed to excite the photoacoustic signal of methane. A total of 21 round trips of reflection were achieved in an acoustic resonant cavity with a radius of 4 mm and a length of 36 mm. Four microphones were installed around the cavity to collect the signal. An 11-fold enhancement of the photoacoustic signal was achieved through the multi-pass cell, compared to a single-pass cell with dimension of 10 cm. The system was used to measure different concentrations of methane, which showed good linearity. The continuous detection of 10 ppm methane gas was carried out for 6000 s. The Allan standard deviation analysis indicates that the limit of detection of the system was 5.7 ppb with an optimum integration time of 300 s.

Ethnopharmacological relevance: Murrayae Folium et Cacumen (MFC) is a plant considered to be a traditional Chinese medicine with culinary value as well. The dry leaves and twigs of Murraya paniculata and M. exotica are used to treat stomach aches, rheumatism, toothaches, swelling, and insect and snake bites. They are also used to prepare spicy chicken dishes.Aim of the review: This review comprehensively summarizes the available information on the botanical characterization, phytochemistry, pharmacological activities, pharmacodynamics, pharmacokinetics, and toxicity of MFC.Methods: Relevant scientific literature up to August 2023 was included in the study. Chinese and English studies on MFC were collected from databases, including PubMed, Elsevier, Web of Science, Springer, Science Direct, Wiley, ACS, and CNKI (Chinese). Doctoral and Master’s dissertations were also included.Results: In total, 720 compounds have been identified and reported in the literature, including flavonoids, coumarins, alkaloids, sterols, phenylpropenols, organic acids, spirocyclopentenones, and volatile oils. Flavonoids and coumarins are the two most important bioactive compounds responsible for these pharmacological activities. MFC has anti-inflammatory, anti-bacterial, anti-microbial, anti-diabetic, anti-tumor, anti-oxidant, anti-depressant, potential anti-Alzheimer’s disease, chondroprotective, and analgesic properties. The pharmacological effects include interrupting the STAT3/NF-κB/COX-2 and EGFR signaling pathways, downregulating EpCAM expression, inhibiting NF-κB and ERK signals, inhibiting the EP/cAMP/PKA signaling pathway and miR-29a/Wnt/β-catenin signaling activity, and upregulating Foxo3a expression.Conclusion: This review demonstrates that the chemical constituents, pharmacological activities, pharmacodynamics, pharmacokinetics, and toxicity of MFC support its use in traditional Chinese botanical medicines. MFC contains a wide range of chemical compounds. Flavonoids and coumarins promote strong pharmacological activity and, are low-toxicity natural phytomedicines that are widely used in medicine, food, ornamentation, and cosmetics, making MFC a promising compound for development and use in the treatment of several medical conditions.

Personalized repair of bone defects remains a tremendous clinical challenge. Developing 3D-printed bioceramics with excellent osteogenic potential is an effective therapeutic strategy to overcome this challenge. In this study, we prepared composite bioceramics (designated as CSC-DBM) with immunomodulatory, vascularization, and osteogenesis based on calcium silicate (CS) bioceramics, calcium sulfate (CSH), and decalcified bone matrix (DBM). CSC-DBM bioceramics has an unmatched biomineralization potential due to the synergistic promotion of CS and CSH. Meanwhile, CSC-DBM bioceramics could effectively modulate the immune response, induce M2 macrophage polarization, promote vascular regeneration, and further enhance new bone formation. In summary, this composite bioceramics made of CS bioceramics, CSH, and DBM can provide a new solution for bone defect repair in the clinic.

To rapidly, specifically, and sensitively detect avian influenza virus (AIV), this research established a visual detection method of recombinase-aided amplification (RAA) based on Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) and CRISPR associated proteins 13a (Cas13a) system. In this study, specific primers and CRISPR RNA (crRNA) were designed according to the conservative sequence of AIV Nucleprotein (NP) gene. RAA technology was used to amplify the target sequence, and the amplification products were visually detected by lateral flow dipstick (LFD). The specificity, sensitivity, and reproducibility of RAA-CRISPR-Cas13a-LFD were evaluated. At the same time, this method and polymerase chain reaction (PCR)-agarose electrophoresis method were used to detect clinical samples, and the coincidence rate of the two detection methods was calculated. The results showed that the RAA-CRISPR-Cas13a-LFD method could achieve specific amplification of the target gene fragments, and the detection results could be visually observed through the LFD. Meanwhile, there was no cross-reaction with infectious bronchitis virus (IBV), infectious laryngotracheitis virus (ILTV), and Newcastle disease virus (NDV). The sensitivity reached 100 copies/μL, which was 1,000-fold higher than that of PCR-agarose electrophoresis method. The coincidence rate of clinical tests was 98.75 %, and the total reaction time was ~1 h. The RAA-CRISPR-Cas13a-LFD method established in this study had the advantages of rapid, simple, strong specificity, and high sensitivity, which provided a new visual method for AIV detection.

To address traditional impedance control methods' difficulty with obtaining stable forces during robot-skin contact, a force control based on the Gaussian mixture model/Gaussian mixture regression (GMM/GMR) algorithm fusing different compensation strategies is proposed. The contact relationship between a robot end effector and human skin is established through an impedance control model. To allow the robot to adapt to flexible skin environments, reinforcement learning algorithms and a strategy based on the skin mechanics model compensate for the impedance control strategy. Two different environment dynamics models for reinforcement learning that can be trained offline are proposed to quickly obtain reinforcement learning strategies. Three different compensation strategies are fused based on the GMM/GMR algorithm, exploiting the online calculation of physical models and offline strategies of reinforcement learning, which can improve the robustness and versatility of the algorithm when adapting to different skin environments. The experimental results show that the contact force obtained by the robot force control based on the GMM/GMR algorithm fusing different compensation strategies is relatively stable. It has better versatility than impedance control, and the force error is within ~±0.2 N.

Abstract Background Given their potent antioxidation properties, carotenoids play a role in delaying and preventing dementia and mild cognitive impairment (MCI). However, observational studies have found inconsistent results regarding the associations between blood carotenoid levels and the risk of dementia and MCI. We conducted this systematic review and meta-analysis to investigate the relationship between blood carotenoid levels and the risk of dementia and MCI. Methods A systematic search was performed in the Web of Science, PubMed, Embase, and Cochrane Library electronic databases to retrieve relevant English articles published from their inception until February 23, 2023. Study quality was assessed by the Newcastle-Ottawa scale. Standardized mean differences (SMDs) and 95% confidence intervals (CIs) were pooled using random-effect meta-analyses. Ultimately, 23 studies (n = 6610) involving 1422 patients with dementia, 435 patients with MCI, and 4753 controls were included. Results Our meta-analysis showed that patients with dementia had lower blood lycopene (SMD: -0.521; 95%CI: -0.741, -0.301), α-carotene (SMD: -0.489; 95%CI: -0.697, -0.281), β-carotene (SMD: -0.476; 95%CI: -0.784, -0.168), lutein (SMD: -0.516; 95%CI: -0.753, -0.279), zeaxanthin (SMD: -0.571; 95%CI: -0.910, -0.232) and β-cryptoxanthin (SMD: -0.617; 95%CI: -0.953, -0.281) than the controls. Our results indicated that blood carotenoid levels were significantly lower in patients with dementia than in controls, despite high heterogeneity across the studies. Owing to insufficient data, we did not observe a similar and stable relationship between blood carotenoid levels and MCI. Conclusions Our meta-analysis indicated that lower blood carotenoid levels may be a risk factor for dementia and MCI.

IntroductionThe association between red blood cell distribution width (RDW) and psoriasis among the US adults is still unknown. We aimed to assess whether RDW is associated with psoriasis in the US adults.MethodWe conducted a cross-sectional study consisting of 14,089 participants from National Health and Nutrition Examination Survey (NHANES) 2009–2014. Psoriasis status were assessed by self-reported questionnaire. We evaluated the association between RDW and risk of psoriasis using multivariate regression models. Subgroup and interaction analysis were performed.ResultsThe higher RDW level was associated with an increased risk of psoriasis (OR = 1.10 [95% CI, 1.01, 1.19]; p = 0.025) after adjusting for confounders in female. However, there is no significant association between RDW and risk of psoriasis among male (OR = 0.99 [95% CI, 0.87, 1.15]; p = 0.992). Subgroup and interaction analysis found that the strongest positive association mainly exists in female participants with BMD greater than 29.9 kg/m2 (OR = 1.20 [95% CI, 1.09, 1.32], Pint = 0.004).DiscussionIn conclusion, we found that increased RDW levels were associated with an increased risk of psoriasis in females, which could provide clinicians with auxiliary data for the early diagnosis of psoriasis.

The images captured underwater are usually degraded due to the effects of light absorption and scattering. Degraded underwater images exhibit color distortion, low contrast, and blurred details, which in turn reduce the accuracy of marine biological monitoring and underwater object detection. To address this issue, a generative adversarial network with multi-scale and an attention mechanism is proposed to improve the quality of underwater images. To extract more effective features within the generative network, several modules are introduced: a multi-scale dilated convolution module, a novel attention module, and a residual module. These modules are utilized to design a generative network with a U-shaped structure. The multi-scale dilated convolution module is designed to extract features at multiple scales and expand the receptive field to capture more global information. The attention module directs the network’s focus towards important features, thereby reducing the interference from redundant feature information. To improve the discriminative power of the adversarial network, a multi-scale discriminator is designed. It has two output feature maps with different scales. Additionally, an improved loss function for the generative adversarial network is proposed. This improvement involves incorporating the total variation loss into the traditional loss function. The performance of different methods for enhancing underwater images is evaluated using the EUVP dataset and UIEB dataset. The experimental results demonstrate that the enhanced underwater images exhibit better quality and visual effects compared to other methods.

Agricultural chemicals are commonly used to control pests and weeds, but cause pesticide waste problems. Oil-based emulsions are often used as pesticide formulations to improve pesticide utilization. In this study, the spray visualization experiment of the water and oil-based emulsion butachlor is carried out using an ST flat fan nozzle at 0.1–0.5 MPa pressure. The dimensionless method is used to analyze the difference in liquid sheet fragmentation morphology and disintegration process and the influence of different fragmentation methods on droplet size. It is found that the hydrophobic components in pesticide have a significant effect on the morphology and process of atomization fragmentation. When spray liquid is water, the liquid sheet breaks up into liquid ligaments due to the Rayleigh instability, then the ligaments break up into droplets. The side view of a liquid sheet is a large-amplitude wave disturbance. When the spray liquid is the emulsion butachlor, holes are generated on the liquid sheet, then the holes break up into droplets. The fragmentation method of emulsion spray is the perforation mechanism. Compared with water spray, the presence of the pesticide butachlor increases the droplet size and spray angle and improves the uniformity of droplet size distribution but reduces the breakup length. The spray angle shows a power law dependence of the Weber number with a power of 0.17 for all conditions tested here. At 0.3 MPa, DV50 increases 25%, and span decreases from 1.187 to 1.172. This study could provide reference for the addition of agricultural additives, the improvement of spray operation efficiency, and the establishment of spray fragmentation mechanism.

There are still limitations in artificial bone materials used in clinical practice, such as difficulty in repairing large bone defects, the mismatch between the degradation rate and tissue growth, difficulty in vascularization, an inability to address bone defects of various shapes, and risk of infection. To solve these problems, our group designed stereolithography (SLA) 3D-printed calcium silicate artificial bone improved by a calcium sulfate-Cu2+ delivery system. SLA technology endows the scaffold with a three-dimensional tunnel structure to induce cell migration to the center of the bone defect. The calcium sulfate-Cu2+ delivery system was introduced to enhance the osteogenic activity of calcium silicate. Rapid degradation of calcium sulfate (CS) induces early osteogenesis in the three-dimensional tunnel structure. Calcium silicate (CSi) which degrades slowly provides mechanical support and promotes bone formation in bone defect sites for a long time. The gradient degradation of these two components is perfectly matched to the rate of repair in large bone defects. On the other hand, the calcium sulfate delivery system can regularly release Cu2+ in the temporal and spatial dimensions, exerting a long-lasting antimicrobial effect and promoting vascular growth. This powerful 3D-printed calcium silicate artificial bone which has rich osteogenic activity is a promising material for treating large bone defects and has excellent potential for clinical application.

Understanding of orchard nutrition status can facilitate the proper management of orchards for higher fruit yield and quality. From 2011 to 2019, the nutrition status of orchards in Hunan Province, a major citrus-producing area in china, were assessed. A total of 361 soil samples, 378 leaf samples and 285 fruit samples of sweet oranges including navel orange (citrus sinensis l. Osbeck ‘newhall’) and common sweet orange (citrus sinensis l. Osbeck ‘bingtangcheng’), and mandarins including satsuma mandarin (citrus unshiu marc. ‘miyagawa’) and ponkan (citrus reticulata blanco ‘xinnu’) were collected. These samples were from112 sweet orange orchards and 140 mandarin orchards. Parameters of soil, leaf and fruit nutrition and fruit quality were analyzed based on these samples. The results revealed that soil acidification occurred in 79.0% of sweet orange orchards (ph 3.56 to 8.12) and 74.7% of mandarin orchards (ph 2.75 to 8.15). Soil organic matter (om) was abundant in both sweet orange orchards (21.3 g/kg on average) and mandarin orchards (19.77 g/kg on average). Nutrient analysis demonstrated deficiencies of nitrogen (n), potassium (k) and boron (b) in soils and leaves of sweet orange and mandarin orchards. Zinc (zn) was abundant in soils but deficient in leaves, suggesting a low zn utilization efficiency in these orchards. Besides, excessive iron (fe), magnesium (mn) and copper (cu) were found in sweet orange and mandarin leaves, which may be associated with low soil ph and fungicides application in this area. The average contents of n, phosphorus (p) and k in the fruit were about 0.80%, 0.11% and 0.51%, respectively, with a ratio close to 1:0.14:0.6. Multiple linear regression (mlr) analysis revealed that leaf npk and molybdenum (mo) significantly affect fruit fresh fruit weight (ffw) and total soluble solids (tss), and leaf p is closely correlated with fruit titratable acid (ta).

Abstract This study aimed to evaluate differences in the clinical outcomes of different sacral neuromodulation systems (InterStim and BetterStim) used in the treatment of overactive bladder. Data from a previously established database of sacral neuromodulation in China (the InterStim system) and a 2020 clinical trial of the BetterStim system were screened. Patients with overactive bladder undergoing stage II implanted pulse generator implantation were selected for analysis and divided into InterStim and BetterStim system groups. Voiding diaries and subjective scores obtained preoperatively, after stage I tined-lead implantation (experience period), and after stage II implanted pulse generator implantation were compared between the two groups. This study included 113 patients with overactive bladder (43, InterStim system group; 70, BetterStim system group). Voiding diaries and subjective scores significantly improved in both the InterStim and BetterStim system groups over the treatment period. Specifically, the urination frequency (all P

In order to determine the effect of high pressure homogenization (HPH) on the stability and quality of not-from-concentrate (NFC) cloudy honey peach juice, the samples were subjected to HPH processing with 20~40 MPa for 1~2 successive passes. The changes in turbidity, centrifugation precipitating rate, particle size distribution, Zeta potential, water soluble pectin content, pectin methylesterase (PME) activity, pH, total soluble solids (TSS), total acidity (TA), TSS/TA, reducing sugar content, color, total phenol and VC content were evaluated. The results showed that HPH could effectively improve the stability of NFC cloudy honey peach juice. With the treatment of 30~40 MPa/1 and 20~40 MPa/2, the centrifugation precipitating rates significantly decreased by 13.49%~24.22% (P0.05). HPH decreased the pH, TSS, TA, total phenol and VC content with increasing pass number (P

Abstract Objective Gastric cancer (GC) is one of the most prevalent malignant tumors in Asian countries. Studies have proposed that lncRNAs can be used as diagnostic and prognostic indicators of GC due to the high specificity of lncRNAs expression involvement in GC. Recently, N6-methyladenosine (m6A) has also emerged as an important modulator of the expression of lncRNAs in GC. This study aimed at establishing a novel m6A-related lncRNAs prognostic signature that can be used to construct accurate models for predicting the prognosis of GC in the Asian population. Methods First, the levels of m6A modification and m6A methyltransferases expression in GC samples were determined using dot blot and western blot analyses. Next, we evaluated the lncRNAs expression profiles and the corresponding clinical data of 88 Asian GC patients retrieved from The Cancer Genome Atlas (TCGA) database. Differential expression of m6A-related lncRNAs between GC and normal tissues was investigated. The relationship between these target lncRNAs and potential immunotherapeutic signatures was also analyzed. Gene set enrichment analysis (GSEA) was performed to identify the malignancy-associated pathways. Univariate Cox regression, LASSO regression, and multivariate Cox regression analyses were performed to establish a novel prognostic m6A-related lncRNAs prognostic signature. Moreover, we constructed a predictive nomogram and determined the expression levels of nine m6A-related lncRNAs in 12 pairs of clinical samples. Results We found that m6A methylation levels were significantly increased in GC tumor samples compared to adjacent normal tissues, and the increase was positively correlated with tumor stage. Patients were then divided into two clusters (cluster 1 and cluster 2) based on the differential expression of the m6A-related lncRNAs. Results showed that there was a significant difference in survival probability between the two clusters (p = 0.018). Notably, the low survival rate in cluster 2 may be associated with high expression of immune cells (resting memory CD4+ T cells, p = 0.027; regulatory T cells, p = 0.0018; monocytes, p = 0.00095; and resting dendritic cells, p = 0.015), and low expression of immune cells (resting NK cells, p = 0.033; and macrophages M1, p = 0.045). Enrichment analysis indicated that malignancy-associated biological processes were more common in the cluster 2 subgroup. Finally, the risk model comprising of six m6A-related lncRNAs was identified as an independent predictor of prognoses, which could divide patients into high- or low-risk groups. Time-dependent ROC analysis suggested that the risk score could accurately predict the prognosis of GC patients. Patients in the high-risk group had worse outcomes compared to patients in the low-risk group, and the risk score showed a positive correlation with immune cells (resting memory CD4+ T cells, R = 0.31, P = 0.038; regulatory T cells, R = 0.42, P = 0.0042; monocytes, R = 0.42, P = 0.0043). However, M1 macrophages (R = -0.37, P = 0.012) and resting NK cells (R = -0.31, P = 0.043) had a negative correlation with risk scores. Furthermore, analysis of clinical samples validated the weak positive correlation between the risk score and tumor stage. Conclusions The risk model described here, based on the six m6A-related lncRNAs signature, and may predict the clinical prognoses and immunotherapeutic response in Asian GC patients.

Laser absorption spectroscopy for gas sensing basically employs an air pump located at the gas cell probe to draw in ambient gases, and the on-site gas sample is subsequently delivered for laboratory non-real-time analysis. In this study, an in situ all-fiber remote gas sensing strategy is proposed. The anti-resonant hollow-core fiber (AR-HCF) is used as the sensing fiber, and a 20 m middle-hole eccentric-core fiber (MH-ECF) is used as the conducting fiber. The remote ambient gases can be inhaled into the AR-HCF as a result of the negative pressure transmitted through the MH-ECF when pumping gas at the interface of the MH-ECF. Since the real-time monitoring of greenhouse gas emissions in industrial processes holds immense significance in addressing global climate change, the detection of CO2 is achieved with the TDLAS-WMS method, and the gas sensing performance of an all-fiber remote gas sensing structure (RGS) is experimentally validated. The response time t90 under the pumping condition is about 456 s, which is about 30 times faster than that of free diffusion. Allan deviation results for more than one hour of continuous monitoring indicate that the lowest detection limit for the all-fiber RGS is 0.0373% when the integration time is 184 s. The all-fiber remote gas sensing strategy also possesses the benefits of being applicable to multiplex, hazardous gas environment passive monitoring.

The fusion of hyperspectral imagery (HSI) and light detection and ranging (LiDAR) data for classification has received widespread attention and has led to significant progress in research and remote sensing applications. However, existing common CNN architectures suffer from the significant drawback of not being able to model remote sensing images globally, while transformer architectures are not able to capture local features effectively. To address these bottlenecks, this paper proposes a classification framework for multisource remote sensing image fusion. First, a spatial and spectral feature projection network is constructed based on parallel feature extraction by combining HSI and LiDAR data, which is conducive to extracting joint spatial, spectral, and elevation features from different source data. Furthermore, in order to construct local–global nonlinear feature mapping more flexibly, a network architecture coupling together multiscale convolution and a multiscale vision transformer is proposed. Moreover, a plug-and-play nonlocal feature token aggregation module is designed to adaptively adjust the domain offsets between different features, while a class token is employed to reduce the complexity of high-dimensional feature fusion. On three open-source remote sensing datasets, the performance of the proposed multisource fusion classification framework improves about 1% to 3% over other state-of-the-art algorithms.