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Objective To evaluate the efficacy and safety of photodynamic therapy (PDT) combined with oral isotretinoin in the treatment of moderate to severe acne. Methods This study was a single center prospective cohort clinical study. Patients with moderate to severe acne who were admitted to the clinic of Guangzhou Institute of Dermatology from January 2019 to July 2022 were enrolled in the study. All patients received ALA-PDT treatment once every other two weeks for 3 times. Patients in the observation group were also given oral isotretinoin at a dose of 10 mg twice-daily. The GAGS score, total number of acne lesions, number of inflammatory lesions, as well as the main adverse reactions were compared between the two groups. Results A total of 49 cases each were enrolled in the ALA-PDT alone and the observation groups. After 4 weeks of treatment, the total skin lesion clearance rate and inflammatory acne clearance rate in the observation group were higher than that in the control group (Z=2.45, P=0.014 for lesion clearance rate; Z=3.33, P=0.001 for inflammatory acne clearance rate).In comparison to ALA-PDT treatment alone, other significant improvements in observation group included GAGS scores (t=3.15, P=0.002), total lesion clearance rate (Z=4.01, P0.05). No patient in either group dropped out of the trial due to adverse reactions. Conclusions The efficacy of PDT combined with isotretinoin is superior to that of the ALA-PDT treatment alone in the treatment of moderate to severe acne, and there are no significant differences in safety between these two treatments.

Background: Some systematic reviews (SRs) on triple therapy (consisting of long-acting β 2 -agonist, long-acting muscarinic antagonist, and inhaled corticosteroid, LABA/LAMA/ICS) for chronic obstructive pulmonary disease (COPD) have reported conflicting results. As the number of syntheses increases, the task of identifying and interpreting evidence becomes increasingly complex and demanding. Objectives: To provide a comprehensive overview of the efficacy and safety of triple therapy for COPD. Design: Overview of SRs. Methods: Two independent reviewers conducted comprehensive searches in PubMed, Embase, Web of Science, and the Cochrane Library to identify relevant SRs that compared triple therapy with any non-triple therapy for COPD, from the inception of these databases until 1 June 2023. The AMSTAR 2 and GRADE tools were utilized to assess the quality of the included studies and the evidence for each outcome. Results: Eighteen SRs encompassing 30 original studies and involving 47,340 participants were analyzed. The overall AMSTAR 2 rating revealed that 3 SRs were of low quality, 13 SRs were of critically low quality, and 2 SRs were of high quality. No high-certainty evidence revealed a significant advantage of triple therapy in improving lung function or reducing acute exacerbations. However, all evidence, including one high certainty, supported the benefits of improving quality of life. Regarding all-cause mortality, no significant difference was found when compared to LAMA or ICS/LABA; however, high-certainty evidence confirmed its effectiveness when compared with LABA/LAMA. Notably, high-certainty evidence indicated that triple therapy was associated with a significant increase in the risk of pneumonia compared to LABA/LAMA. Conclusion: Triple therapy demonstrated notable benefits in improving lung function, reducing exacerbations, improving quality of life, and reducing all-cause mortality. However, it is important to note that it may also significantly increase the risk of pneumonia. Trial registration: This overview protocol was prospectively registered with PROSPERO (No. CRD42023431548).

LED packaging miniaturization has raised more requirements for LED materials. As a material contacting the LED chip directly, the reliability of LED non-conductive adhesive has also garnered increasing attention. This study optimized the formula for non-conductive adhesives for an imidazole curing system. The optimized composition of the adhesive is 25%wt for the curing agent and 30%wt for the silica. The prepared non-conductive adhesive has a 7-day pot life and 9-month storage stability. The shear strength reached 87 g and 72 g at 25 °C and 160 °C, respectively. The reliability of the LED modules packaged with the non-conductive adhesive was researched. The green and blue light intensity change was 4.7%, 43.7%, respectively, indicating good anti-aging properties. The blue light decay was mainly due to adhesive aging. The non-conductive adhesive effectively prevented “caterpillar” growth. This provides useful and practical guidelines for industry for applications of adhesive in different packages.

The intelligent detection of young peaches is the main technology of fruit-thinning robots, which is crucial for enhancing peach fruit quality and reducing labor costs. This study presents the lightweight YOLO-PEM model based on YOLOv8s to achieve high-precision and automatic detection of young “Okubo” peaches. Firstly, the C2f_P module was devised by partial convolution (PConv), replacing all C2f modules in YOLOv8s to achieve the model’s lightweight. Secondly, embedding the efficient multi-scale attention (EMA) module in the lightweight C2f_P_1 module of the backbone network enhanced the feature extraction capability and accuracy for young peaches. Finally, the MPDIoU loss function was utilized to replace the original CIoU loss function, which improved the detection accuracy of the bounding box while speeding up the convergence of the model. The experimental results demonstrate that the YOLO-PEM model achieved an average precision (AP) of 90.86%, F1 score of 86.70%, and model size of 16.1 MB, which was a 1.85% improvement in the AP, 0.85% improvement in the F1 score, and 5.3 MB reduction in the model size compared with YOLOv8s. The AP was 6.26%, 6.01%, 2.05%, 2.12%, and 1.87% higher compared with the other lightweight detection models YOLOv3-tiny, YOLOv4-tiny, YOLOv5s, YOLOv6s, and YOLOv7-tiny, respectively. Furthermore, the FPS of YOLO-PEM was 196.2 f·s-1, which can fulfill the demand for the real-time detection of young peaches. YOLO-PEM effectively detects young peaches in complex orchard environments and can offer a basis for the theoretical design of the vision system of the “Okubo” peach fruit-thinning robot and scientific management of orchards.

Typical metal–organic frameworks (MOFs) usually suffer from a limited visible light-trapping ability and easy recombination of charge carriers, hindering their photocatalytic applications. Acetylacetone (AA), leveraging its exceptional coordination capabilities, serves as a versatile and effective modifier for enhancing the photocatalytic activity of MOFs via a post-synthesis approach. The synthesis of diketone-anchored MOFs with AA can be achieved by first diazotizing the amino groups on the ligands of MOFs, followed by a condensation reaction between AA and the resulting azide. Gradient AA loadings ranging from 17% to 98% were obtained, showcasing the tunability of this approach. Interestingly, a sub-stoichiometric effect was exhibited between the AA loading and the visible photocatalytic performance of the modified photocatalyst. The singlet oxygen yields of MIL-125-AA-37% and MIL-125-AA-54% were about 1.3 times that of MIL-125-AA-17% and 3.0 times that of MIL-125-AA-98%. The improved photocatalytic activity could be attributed to the fact that the AA modification altered the electron density of the Ti metal center, leading to the creation of a significant amount of oxygen defects. This alteration resulted in a reduction in the recombination of charge carriers and thus a better charge separation. In short, AA modification provides a new strategy to maximize the visible photocatalytic performance of MOFs.

In this study, our aim is to find an effective method to solve the problem of disease similarity caused by multiple diseases occurring on the same leaf. This study proposes the use of an optimized RegNet model to identify seven common apple leaf diseases. We conducted comparisons and analyses on the impact of various factors, such as training methods, data expansion methods, optimizer selection, image background, and other factors, on model performance. The findings suggest that utilizing offline expansion and transfer learning to fine-tune all layer parameters can enhance the model’s classification performance, while complex image backgrounds significantly influence model performance. Additionally, the optimized RegNet network model demonstrates good generalization ability for both datasets, achieving testing accuracies of 93.85% and 99.23%, respectively. These results highlight the potential of the optimized RegNet network model to achieve high-precision identification of different diseases on the same apple leaf under complex field backgrounds. This will be of great significance for intelligent disease identification in apple orchards in the future.

Plant disease is an important factor affecting crop yield. With various types and complex conditions, plant diseases cause serious economic losses, as well as modern agriculture constraints. Hence, rapid, accurate, and early identification of crop diseases is of great significance. Recent developments in deep learning, especially convolutional neural network (CNN), have shown impressive performance in plant disease classification. However, most of the existing datasets for plant disease classification are a single background environment rather than a real field environment. In addition, the classification can only obtain the category of a single disease and fail to obtain the location of multiple different diseases, which limits the practical application. Therefore, the object detection method based on CNN can overcome these shortcomings and has broad application prospects. In this study, an annotated apple leaf disease dataset in a real field environment was first constructed to compensate for the lack of existing datasets. Moreover, the Faster R-CNN and YOLOv3 architectures were trained to detect apple leaf diseases in our dataset. Finally, comparative experiments were conducted and a variety of evaluation indicators were analyzed. The experimental results demonstrate that deep learning algorithms represented by YOLOv3 and Faster R-CNN are feasible for plant disease detection and have their own strong points and weaknesses.

Accurate detection of muskmelon fruit ripeness is crucial to ensure fruit quality, optimize picking time, and enhance economic benefits. This study proposes an improved lightweight YOLO-RFEW model based on YOLOv8n, aiming to address the challenges of low efficiency in muskmelon fruit ripeness detection and the complexity of deploying a target detection model to a muskmelon picking robot. Firstly, the RFAConv replaces the Conv in the backbone part of YOLOv8n, allowing the network to focus more on regions with significant contributions in feature extraction. Secondly, the feature extraction and fusion capability are enhanced by improving the C2f module into a C2f-FE module based on FasterNet and an Efficient Multi-Scale attention (EMA) mechanism within the lightweight model. Finally, Weighted Intersection over Union (WIoU) is optimized as the loss function to improve target frame prediction capability and enhance target detection accuracy. The experimental results demonstrate that the YOLO-RFEW model achieves high accuracy, with precision, recall, F1 score, and mean Average Precision (mAP) values of 93.16%, 83.22%, 87.91%, and 90.82%, respectively. Moreover, it maintains a lightweight design and high efficiency with a model size of 4.75 MB and an inference time of 1.5 ms. Additionally, in the two types of maturity tests (M-u and M-r), APs of 87.70% and 93.94% are obtained, respectively, by the YOLO-RFEW model. Compared to YOLOv8n, significant improvements in detection accuracy have been achieved while reducing both model size and computational complexity using the proposed approach for muskmelon picking robots’ real-time detection requirements. Furthermore, when compared to lightweight models such as YOLOv3-Tiny, YOLOv4-Tiny, YOLOv5s, YOLOv7-Tiny, YOLOv8s, and YOLOv8n, the YOLO-RFEW model demonstrates superior performance with only 28.55%, 22.42%, 24.50%, 40.56%, 22.12%, and 79.83% of their respective model sizes, respectively, while achieving the highest F1 score and mAP values among these seven models. The feasibility and effectiveness of our improved scheme are verified through comparisons between thermograms generated by YOLOv8n and YOLO-RFEW as well as detection images. In summary, the YOLO-RFEW model not only improves the accuracy rate of muskmelon ripeness detection but also successfully realizes the lightweight and efficient performance, which has important theoretical support and application value in the field of muskmelon picking robot development.

Cerasus Humilis fruit is susceptible to rapid color changes post-harvest, which degrades its quality. This research utilized hyperspectral imaging technology to detect and visually analyze the soluble solid content (SSC) and firmness of the fruit, aiming to improve quality and achieve optimal pricing. Four maturity stages (color turning stage, coloring stage, maturity stage, and fully ripe stage) of Cerasus Humilis fruit were examined using hyperspectral images (895–1700 nm) alongside data collection on SSC and firmness. These samples were divided into a calibration set and a validation set with a ratio of 3:1 by sample set partitioning based on the joint X-Y distances (SPXY) method. The original spectral data was processed by a spectral preprocessing method. Multiple linear regression (MLR) and nonlinear least squares support vector machine (LS-SVM) detection models were established using feature wavelengths selected by the successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), uninformative variable elimination (UVE), and two combined downscaling algorithms (UVE-SPA and UVE-CARS), respectively. For SSC and firmness detection, the best models were the SNV-SPA-LS-SVM model with 18 feature wavelengths and the original spectra-UVE-CARS-LS-SVM model with eight feature wavelengths, respectively. For SSC, the correlation coefficient of prediction (Rp) was 0.8526, the root mean square error of prediction (RMSEP) was 0.9703, and the residual prediction deviation (RPD) was 1.9017. For firmness, Rp was 0.7879, RMSEP was 1.1205, and RPD was 2.0221. Furthermore, the optimal model was employed to retrieve the distribution of SSC and firmness within Cerasus Humilis fruit. This retrieved information facilitated visual inspection, enabling a more intuitive and comprehensive assessment of SSC and firmness at each pixel level. These findings demonstrated the effectiveness of hyperspectral imaging technology for determining SSC and firmness in Cerasus Humilis fruit. This paves the way for online monitoring of fruit quality, ultimately facilitating timely harvesting.

Effective sulfur dioxide (SO2) management is crucial in winemaking to minimize oxidative changes in wine flavor during storage. This study explored the impact of various SO2 management techniques on Solaris white wine’s flavor components and sensory properties. Five treatments were administered: ‘SO2 in juice’ (50 mg/L SO2 added to juice pre-fermentation), ‘Control’ (60 mg/L SO2 added post-fermentation), ‘Low SO2’ (50 mg/L SO2 post-fermentation), ‘High SO2’ (100 mg/L SO2 post-fermentation), and ‘No SO2’ (no SO2 added). The ‘Control’ followed a standard procedure, in which the achieved level of free sulfite is measured and extra SO2 added to reach the recommended level of free sulfite for the pH of the wine. Here, 50 + 10 mg/L was added. Volatile compounds were analyzed using dynamic headspace sampling coupled with gas chromatography–mass spectrometry after 0, 3, 6, and 12 months of storage. Sensory evaluation by a trained panel after 12 months revealed stronger perceptions of ‘overall impression’, ‘chemical’, ‘bitter’, ‘overripe fruit’, and ‘honey’ notes in the ‘No SO2’ and ‘SO2 in juice’ wines. The data underscore the significant influence of SO2 management on the flavor stability of Solaris white wines, emphasizing the need for strategic SO2 interventions during winemaking to enhance sensory quality over time.

As part of the genus Enteropathogenic Coronaviruses, Porcine Epidemic Diarrhea Virus (PEDV) is an important cause of early diarrhea and death in piglets, and one of the most difficult swine diseases to prevent and control in the pig industry. Previously, we found that PEDV can block Na+ absorption and induce diarrhea in piglets by inhibiting the activity of the sodium-hydrogen ion transporter NHE3 in pig intestinal epithelial cells, but the mechanism needs to be further explored. The epidermal growth factor receptor (EGFR) has been proved to be one of the co-receptors involved in many viral infections and a key protein involved in the regulation of NHE3 activity in response to various pathological stimuli. Based on this, our study used porcine intestinal epithelial cells (IPEC-J2) as an infection model to investigate the role of EGFR in regulating NHE3 activity after PEDV infection. The results showed that EGFR mediated viral invasion by interacting with PEDV S1, and activated EGFR regulated the downstream EGFR/ERK signaling pathway, resulting in decreased expression of NHE3 and reduced NHE3 mobility at the plasma membrane, which ultimately led to decreased NHE3 activity. The low level of NHE3 expression in intestinal epithelial cells may be a key factor leading to PEDV-induced diarrhea in newborn piglets. This study reveals the importance of EGFR in the regulation of NHE3 activity by PEDV and provides new targets and clues for the prevention and treatment of PEDV-induced diarrhea in piglets.

Porcine epidemic diarrhea virus (PEDV) mainly invades the small intestine and promotes an inflammatory response, eventually leading to severe diarrhea, vomiting, dehydration, and even death of piglets, which seriously threatens the economic development of pig farming. In recent years, researchers have found that probiotics can improve the intestinal microenvironment and reduce diarrhea. At the same time, certain probiotics have been shown to have antiviral effects; however, their mechanisms are different. Herein, we aimed to investigate the inhibitory effect of Lactiplantibacillus plantarum supernatant (LP-1S) on PEDV and its mechanism. We used IPEC-J2 cells as a model to assess the inhibitory effect of LP-1S on PEDV and to further investigate the relationship between LP-1S, Ca2+, and PEDV. The results showed that a divalent cation chelating agent (EGTA) and calcium channel inhibitors (Bepridil hydrochloride and BAPTA-acetoxymethylate) could inhibit PEDV proliferation while effectively reducing the intracellular Ca2+ concentration. Furthermore, LP-1S could reduce PEDV-induced loss of calcium channel proteins (TRPV6 and PMCA1b), alleviate intracellular Ca2+ accumulation caused by PEDV infection, and promote the balance of intra- and extracellular Ca2+ concentrations, thereby inhibiting PEDV proliferation. In summary, we found that LP-1S has potential therapeutic value against PEDV, which is realized by modulating Ca2+. This provides a potential new drug to treat PEDV infection.

Abstract Introduction Sepsis has the characteristics of high incidence, high mortality of ICU patients. Early assessment of disease severity and risk stratification of death in patients with sepsis, and further targeted intervention are very important. The purpose of this study was to develop machine learning models based on sequential organ failure assessment (SOFA) components to early predict in-hospital mortality in ICU patients with sepsis and evaluate model performance. Methods Patients admitted to ICU with sepsis diagnosis were extracted from MIMIC-IV database for retrospective analysis, and were randomly divided into training set and test set in accordance with 2:1. Six variables were included in this study, all of which were from the scores of 6 organ systems in SOFA score. The machine learning model was trained in the training set and evaluated in the validation set. Six machine learning methods including linear regression analysis, least absolute shrinkage and selection operator (LASSO), Logistic regression analysis (LR), Gaussian Naive Bayes (GNB) and support vector machines (SVM) were used to construct the death risk prediction models, and the accuracy, area under the receiver operating characteristic curve (AUROC), Decision Curve Analysis (DCA) and K-fold cross-validation were used to evaluate the prediction performance of developed models. Result A total of 23,889 patients with sepsis were enrolled, of whom 3659 died in hospital. Three feature variables including renal system score, central nervous system score and cardio vascular system score were used to establish prediction models. The accuracy of the LR, GNB, SVM were 0.851, 0.844 and 0.862, respectively, which were better than linear regression analysis (0.123) and LASSO (0.130). The AUROCs of LR, GNB and SVM were 0.76, 0.76 and 0.67, respectively. K-fold cross validation showed that the average AUROCs of LR, GNB and SVM were 0.757 ± 0.005, 0.762 ± 0.006, 0.630 ± 0.013, respectively. For the probability threshold of 5–50%, LY and GNB models both showed positive net benefits. Conclusion The two machine learning-based models (LR and GNB models) based on SOFA components can be used to predict in-hospital mortality of septic patients admitted to ICU.

Growing demand for seafood and reduced fishery harvests have raised intensive farming of marine aquaculture in coastal regions, which may cause severe coastal water problems without adequate environmental management. Effective mapping of mariculture areas is essential for the protection of coastal environments. However, due to the limited spatial coverage and complex structures, it is still challenging for traditional methods to accurately extract mariculture areas from medium spatial resolution (MSR) images. To solve this problem, we propose to use the full resolution cascade convolutional neural network (FRCNet), which maintains effective features over the whole training process, to identify mariculture areas from MSR images. Specifically, the FRCNet uses a sequential full resolution neural network as the first-level subnetwork, and gradually aggregates higher-level subnetworks in a cascade way. Meanwhile, we perform a repeated fusion strategy so that features can receive information from different subnetworks simultaneously, leading to rich and representative features. As a result, FRCNet can effectively recognize different kinds of mariculture areas from MSR images. Results show that FRCNet obtained better performance than other classical and recently proposed methods. Our developed methods can provide valuable datasets for large-scale and intelligent modeling of the marine aquaculture management and coastal zone planning.

Context Hedysari Radix Praeparata Cum Melle (HRPCM) and Astragali Radix Praeparata Cum Melle (ARPCM) are used interchangeably in clinics to treat spleen-qi deficiency (SQD) symptom mainly including gastrointestinal dysfunction and decreased immunity, which has unknown differences in efficacy.Objective To investigate the differences between HRPCM and ARPCM on intervening gastrointestinal- and immune-function with SQD syndrome.Materials and methods After the SQD model was established, the Sprague–Dawley (SD) rats were randomly divided into nine groups (n = 10): normal; model; Bu-Zhong-Yi-Qi Pills; 18.9, 12.6 and 6.3 g/kg dose groups of HRPCM and ARPCM. Gastrointestinal function including d-xylose, gastrin, amylase vasoactive intestinal peptide, motilin, pepsin, H+/K+-ATPase, Na+/K+-ATPase, sodium-glucose cotransporter 1 (SGLT1), glucose transporter 2 (GLUT2) and immune function including spleen and thymus index, blood routine, interleukin (IL)-2, IL-6, interferon-γ (IFN-γ), tumour necrosis factor-α (TNF-α), immunoglobulin (Ig) M, IgA, IgG and delayed-type hypersensitivity (DTH) were detected. Finally, the efficacy differences were analysed comprehensively by the fuzzy matter-element method.Results In regulating immune, the doses differences in efficacy between HRPCM and ARPCM showed in the high-dose (18.9 g/kg), but there were no differences in the middle- and low- dose (12.6 and 6.3 g/kg); the efficacy differences were primarily reflected in levels of IL-6, IFN-γ, TNF-α and IgM in serum, and the mRNA expression of IL-6 and IFN-γ in the spleen. In regulating gastrointestinal, the efficacy differences were primarily reflected in the levels of D-xylose, MTL, and GAS in serum, and the mRNA and protein expression of SGLT1 and GLUT2 in jejunum and ileum.Discussion and conclusions HRPCM is more effective than ARPCM on regulating gastrointestinal function and immune function with SQD syndrome. Therefore, we propose that HRPCM should be mainly used to treat SQD syndrome in the future.

Abstract Background Currently, the rate of morbidity and mortality in acute respiratory distress syndrome (ARDS) remains high. One of the potential reasons for the poor and ineffective therapies is the lack of early and credible indicator of risk prediction that would help specific treatment of severely affected ARDS patients. Nevertheless, assessment of the clinical outcomes with transcriptomics of ARDS by alveolar macrophage has not been performed. Methods The expression data GSE116560 was obtained from the Gene Expression Omnibus databases (GEO) in NCBI. This dataset consists of 68 BAL samples from 35 subjects that were collected within 48 h of ARDS. Differentially expressed genes (DEGs) of different outcomes were analyzed using R software. The top 10 DEGs that were up- or down-regulated were analyzed using receiver operating characteristic (ROC) analysis. Kaplan–Meier survival analysis within two categories according to cut-off and the value of prediction of the clinical outcomes via DEGs was verified. GO enrichment, KEGG pathway analysis, and protein–protein interaction were also used for functional annotation of key genes. Results 24,526 genes were obtained, including 235 up-regulated and 292 down-regulated DEGs. The gene ADORA3 was chosen as the most obvious value to predict the outcome according to the ROC and survival analysis. For functional annotation, ADORA3 was significantly augmented in sphingolipid signaling pathway, cGMP-PKG signaling pathway, and neuroactive ligand-receptor interaction. Four genes (ADORA3, GNB1, NTS, and RHO), with 4 nodes and 6 edges, had the highest score in these clusters in the protein–protein interaction network. Conclusions Our results show that the prognostic prediction of early biomarkers of transcriptomics as identified in alveolar macrophage in ARDS can be extended for mechanically ventilated critically ill patients. In the long term, generalizing the concept of biomarkers of transcriptomics in alveolar macrophage could add to improving precision-based strategies in the ICU patients and may also lead to identifying improved strategy for critically ill patients.

Background:. The role of thioredoxin-interacting protein (TXNIP) in lipopolysaccharide-induced liver injury in mice has been reported, but the underlying mechanisms are poorly understood. Methods:. We overexpressed deubiquitinase in cells overexpressing TXNIP and then detected the level of TXNIP to screen out the deubiquitinase regulating TXNIP; the interaction between TXNIP and deubiquitinase was verified by coimmunoprecipitation. After knockdown of a deubiquitinase and overexpression of TXNIP in Huh7 and HepG2 cells, lipopolysaccharide was used to establish a cellular inflammatory model to explore the role of deubiquitinase and TXNIP in hepatocyte inflammation. Results:. In this study, we discovered that ubiquitin-specific protease 5 (USP5) interacts with TXNIP and stabilizes it through deubiquitylation in Huh-7 and HepG2 cells after treatment with lipopolysaccharide. In lipopolysaccharide-treated Huh-7 and HepG2 cells, USP5 knockdown increased cell viability, reduced apoptosis, and decreased the expression of inflammatory factors, including NLRP3, IL-1β, IL-18, ASC, and procaspase-1. Overexpression of TXNIP reversed the phenotype induced by knockdown USP5. Conclusions:. In summary, USP5 promotes lipopolysaccharide-induced apoptosis and inflammatory response by stabilizing the TXNIP protein.

Base editing using CRISPR technologies is an invaluable tool for crop breeding. One of the major base editors, the adenine base editor (ABE), has been successfully used in both model plants and many crops. However, owing to limited editing efficiency, the ABE has been difficult to apply in polyploid crops such as allohexaploid bread wheat that often require simultaneous mutation of multiple alleles for fast breeding. We have designed a wheat high-efficiency ABE (WhieABE), using the newly developed high-activity adenosine deaminase TadA8e. In vivo and in vitro analysis demonstrated the improved applicability of TadA8e over the commonly used TadA7.10. Dinitroaniline is a widely used herbicide with high effectiveness and low toxicity to animals. However, wheat cultivars with tolerance to dinitroaniline are rare, limiting the application of dinitroaniline in wheat planting. Using A-to-G editing with WhieABE, we found that a Met-to-Thr mutation in wheat tubulin alleles located on chromosomes 1A, 1B, 1D, 4A, and 4D increased the resistance of wheat to dinitroaniline, revealing a dosage effect of edited tubulins in resistance. The WhieABE promises to be a valuable editing tool for accelerating crop improvement and developing herbicide-resistant wheat germplasm.

Due to the dark red surface of ripe fresh peaches, their internal injury defects cannot be detected using the naked eye and conventional images. The rapid and accurate detection of fresh peach defects can improve the efficiency of fresh peach classification. The goal of this paper was to develop a nondestructive approach to simultaneously detecting internal injury defects and external injuries in fresh peaches. First, we collected spectral data from 347 Kubo peach samples using hyperspectral imaging technology (900–1700 nm) and carried out pretreatment. Four methods (the competitive adaptive reweighting algorithm (CARS), the combination of CARS and the average influence value algorithm (CARS-MIV), the combination of CARS and the successive projections algorithm (CARS-SPA), and the combination of CARS and uninformative variable elimination (CARS-UVE)) were used to extract the characteristic wavelength. Based on the characteristic wavelength extracted using the above methods, a genetic algorithm optimization support vector machine (GA-SVM) model and a least-squares support vector machine (LS-SVM) model were used to establish classification models. The results show that the combination of CARS and other feature wavelength extraction methods can effectively improve the prediction accuracy of the model when the number of wavelengths is small. Among them, the discriminant accuracy of the CARS-MIV-GA-SVM model reaches 93.15%. In summary, hyperspectral imaging technology can accomplish the accurate detection of Kubo peaches defects, and provides feasible ideas for the automatic classification of Kubo peaches.

To detect quickly and accurately “Yuluxiang” pear fruits in non-structural environments, a lightweight YOLO-GEW detection model is proposed to address issues such as similar fruit color to leaves, fruit bagging, and complex environments. This model improves upon YOLOv8s by using GhostNet as its backbone for extracting features of the “Yuluxiang” pears. Additionally, an EMA attention mechanism was added before fusing each feature in the neck section to make the model focus more on the target information of “Yuluxiang” pear fruits, thereby improving target recognition ability and localization accuracy. Furthermore, the CIoU Loss was replaced with the WIoUv3 Loss as the loss function, which enhances the capability of bounding box fitting and improves model performance without increasing its size. Experimental results demonstrated that the enhanced YOLO-GEW achieves an F1 score of 84.47% and an AP of 88.83%, while only occupying 65.50% of the size of YOLOv8s. Compared to lightweight algorithms such as YOLOv8s, YOLOv7-Tiny, YOLOv6s, YOLOv5s, YOLOv4-Tiny, and YOLOv3-Tiny; there are improvements in AP by 2.32%, 1.51%, 2.95%, 2.06%, 2.92%, and 5.38% respectively. This improved model can efficiently detect “Yuluxiang” pears in non-structural environments in real-time and provides a theoretical basis for recognition systems used by picking robots.

Selenium (Se) is required for the normal growth and development of aquatic animals. However, excessive Se can have various toxic effects on aquatic animals. In this study, we performed transcriptome analysis to determine the mechanism by which immunity and growth of abalone are affected by toxic dietary concentrations of Se. Four experimental diets were administered to supplement organic Se at concentrations of 0.00 (Control), 0.15 (Se15), 0.30 (Se30), and 0.60 mg/kg (Se60) to provide 0.16 (insufficient), 0.32 (optimal), 0.44 (super nutritional), and 0.60 mg/kg (toxic) Se concentrations in the diet. After feeding for 100 days, the results of the Gene Ontology (GO) analysis showed that the enriched GO terms of differentially expressed genes (DEGs) in the Se15 and Se30 groups were mainly focused on the cellular components, whereas the GO terms enriched in the Se60 group were mainly related to the metabolism of nutrients and the activities of various enzymes. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that the DEGs of the Se30 group were mainly enriched in various disease pathways, whereas the DEGs of the Se60 treatment were associated with the metabolism of various nutrients. Further analysis of DEGs showed that toxic dietary concentrations of Se decreased the immunity of abalone by affecting the biosynthesis and metabolism pathways of various nutrients. Additionally, toxic dietary concentrations of Se retarded the growth of abalone by decreasing the expression of various genes associated with shell formation, including lustrin A, perlucin, perlustrin, dermatopontin, chitin synthase, and chitinase as well as the genes associated with muscle production, including collagen, myosin heavy chain, myosin essential light chain, and paramyosin. In conclusion, the mechanism of the decrease of immunity and growth of abalone by toxic dietary concentrations of Se has been preliminary revealed in this study.

BackgroundMedian nerve stimulation (MNS) diminishes regional myocardial ischemia and ventricular arrhythmia; however, the underlying mechanism has not been elucidated.MethodsIn this study, we randomly categorized 22 adult mongrel dogs into a control group, MNS group 1, and MNS group 2. After a 4-week experimental myocardial infarction (MI), ventricular electrophysiology was measured in the MNS group 1 before and after 30 min of MNS. The same measurements were performed in the MNS group 2 dogs via bilateral vagotomy. Venous blood and ventricular tissue were collected to detect molecular indicators related to inflammation and cholinergic pathways by enzyme-linked immunosorbent assay (ELISA), immunohistochemistry (IHC), and Western blot (WB).ResultsNo significant changes were reported in the ventricular effective refractory period (ERP) in the MNS group 1 and MNS group 2 dogs before and after MNS. The ventricular fibrillation threshold (VFT) in the MNS group 1 was significantly higher than that in the MNS group 2 (20.3 ± 3.7 V vs. 8.7 ± 2.9 V, P < 0.01). The levels of tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), and nuclear transcription factor-κB (NF-κB) were lower (P < 0.01), whereas the levels of Ach were higher in the peri-infarct zone tissues in the MNS group 1 dogs than those in the MNS group 2 dogs (P < 0.01).ConclusionThis study demonstrated that MNS increases VFT in a canine model with MI. The effects of MNS on VFT are potentially associated with the cholinergic anti-inflammatory pathway.

Abstract Background Identifying the biological subclasses of septic shock might provide specific targeted therapies for the treatment and prognosis of septic shock. It might be possible to find biological markers for the early prediction of septic shock prognosis. Methods The data were obtained from the Gene Expression Omnibus databases (GEO) in NCBI. GO enrichment and KEGG pathway analyses were performed to investigate the functional annotation of up- and downregulated DEGs. ROC curves were drawn, and their areas under the curves (AUCs) were determined to evaluate the predictive value of the key genes. Results 117 DEGs were obtained, including 36 up- and 81 downregulated DEGs. The AUC for the MME gene was 0.879, as a key gene with the most obvious upregulation in septic shock. The AUC for the THBS1 gene was 0.889, as a key downregulated gene with the most obvious downregulation in septic shock. Conclusions The upregulation of MME via the renin-angiotensin system pathway and the downregulation of THBS1 through the PI3K–Akt signaling pathway might have implications for the early prediction of prognosis of septic shock in patients with pneumopathies.

PurposeA meta-analysis of cohort studies was performed to evaluate the association between COPD and the risk of dementia or cognitive impairment.MethodsCohort studies that evaluated the association between COPD and the risk of dementia or cognitive impairment were identified by a systematic search of PubMed, Embase, Web of Science, and Cochrane Library databases. The search time frame was from database establishment to April 12, 2022, with two reviewers independently screening the literature and extracting data. The Newcastle-Ottawa Quality Assessment Scale (NOS) was used to conduct the quality evaluation. Then, a meta-analysis was performed using Stata 15.1 software.ResultsSix cohort studies including 428,030 participants were included. The overall quality of the included studies was high, with an average NOS score of over 7. Meta-analysis showed that compared to those without COPD at baseline, patients with COPD were associated with a significant increased risk of dementia (RR = 1.24, 95% CI = 1.03 ~ 1.50, I2 = 96.6%, z = 2.25, p = 0.024) and cognitive impairment (RR = 1.30, 95% CI = 1.13 ~ 1.49, I2 = 50.1%, z = 3.72, p < 0.001). Subgroup analysis suggested no significant difference in the risk of dementia among COPD patients of different genders. Nevertheless, in terms of age, the risk of dementia varied among COPD patients of different ages, which was most distinguished in patients younger than 65 years.ConclusionCOPD patients have a higher risk of developing dementia or cognitive impairment compared to those without COPD, and this risk is not affected by gender but seems to be associated with age.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42022325832.

Background: Immunotherapy has become a new direction of current research because the effect of traditional radiotherapy and chemotherapy on clear cell renal cell carcinoma (ccRCC) is not satisfactory. T cell proliferation-related genes (TRGs) play a pivotal role in tumor progression by regulating the proliferation, activity, and function of immune cells. The purpose of our study is to construct and verify a prognostic model based on TRGs and to identify tumor subtypes that may guide treatment through comprehensive bioinformatics analyses.Methods: RNA sequencing data, clinical information, and somatic mutation data of ccRCC are obtained from The Cancer Genome Atlas (TCGA) database. We identified the prognosis-related TRGs which were differentially expressed between normal and tumor tissues. After dividing the patients into a train set and a test set according to proportion 1:1 randomly, the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis were performed to construct a risk-stratified model. Its prediction performance was verified. Then, Gene Set Enrichment Analysis (GSEA), principal component analysis (PCA), tumor microenvironment (TME) analysis, and the half-maximal inhibitory concentration (IC50) prediction were performed between the different groups of patients. To further discuss the immunotherapy between hot and cold tumors, we divided all patients into two clusters based on TRGs through unsupervised learning. Analyzing the gene mutation and calculating the tumor mutation burden (TMB), we further explored the relationship between somatic mutations and grouping or clustering.Results: Risk-stratified model and nomogram predict the prognosis of ccRCC patients accurately. Functional enrichment analyses suggested that TRGs mainly focused on the biological pathways related to tumor progression and immune response. Different tumor microenvironment, drug resistance, and TMB can be distinguished clearly according to both risk stratification and tumor subtype clustering.Conclusion: In this study, a new stratification model of ccRCC based on TRGs was established, which can accurately predict the prognosis of patients. IC50 prediction may guide the application of anti-tumor drugs. The distinction between hot and cold tumors provides a reference for clinical immunotherapy.

Rapid and precise detection of cucumbers is a key element in enhancing the capability of intelligent harvesting robots. Problems such as near-color background interference, branch and leaf occlusion of fruits, and target scale diversity in greenhouse environments posed higher requirements for cucumber target detection algorithms. Therefore, a lightweight YOLOv5s-Super model was proposed based on the YOLOv5s model. First, in this study, the bidirectional feature pyramid network (BiFPN) and C3CA module were added to the YOLOv5s-Super model with the goal of capturing cucumber shoulder features of long-distance dependence and dynamically fusing multi-scale features in the near-color background. Second, the Ghost module was added to the YOLOv5s-Super model to speed up the inference time and floating-point computation speed of the model. Finally, this study visualized different feature fusion methods for the BiFPN module; independently designed a C3SimAM module for comparison between parametric and non-parametric attention mechanisms. The results showed that the YOLOv5s-Super model achieves mAP of 87.5%, which was 4.2% higher than the YOLOv7-tiny and 1.9% higher than the YOLOv8s model. The improved model could more accurately and robustly complete the detection of multi-scale features in complex near-color backgrounds while the model met the requirement of being lightweight. These results could provide technical support for the implementation of intelligent cucumber picking.

Aflatoxins are potent carcinogenic compounds, mainly produced by fungi species of the genus Aspergillus in the soil. Because of their stability, they are difficult to remove completely, even under extreme conditions. Aflatoxin contamination is one of the main causes of safety in peanuts, maize, wheat and other agricultural products. Aflatoxin contamination originates from the soil. Through the investigation of soil properties and soil microbial distribution, the sources of aflatoxin are identified, aflatoxin contamination is classified and analysed, and post-harvest crop detoxification and corresponding contamination prevention measures are identified. This includes the team’s recent development of the biofungicide ARC-BBBE (Aflatoxin Rhizobia Couple-B. amyloliquefaciens, B. laterosporu, B. mucilaginosus, E. ludwiggi) for field application and nanomaterials for post-production detoxification of cereals and oilseed crops, providing an effective and feasible approach for the prevention and control of aflatoxin contamination. Finally, it is hoped that effective preventive and control measures can be applied to a large number of cereal and oilseed crops.

Abalone (Haliotis discus hannai Ino) is one of the most vital economic shellfish cultured in China. However, bacterial and viral infection often leads to its high mortality and serious economic losses. In this study, transcriptome sequencing was performed on gill of abalone challenged by Vibrio parahaemolyticus for 0 h (control group, A0h), 8 h (A8h), and 48 h (A48h), respectively. Each library has an average of 53,361,675 clean reads. Approximately 85.71% reads were mapped to the reference genome of Haliotis discus hannai Ino. In comparison with A0h group, 7,656 and 2,789 genes were differentially expressed in the A8h and A48h groups, respectively. The differentially expressed genes (DEGs) were mostly enriched in oxidation–reduction process and oxidoreductase activity from GO analysis as well as immunity and metabolism-related pathways from Kyoto Encyclopedia of Genes and Genomes analysis. Cluster analysis demonstrated that all DEGs were categorized into eight subclusters by adopting the gene expression model. Specifically, a total of 1,070 significantly downregulated genes from two clusters were related to some metabolic pathways, ABC transporters, and cell adhesion. Moreover, 212 upregulated genes in one subcluster were enriched in several immune-related pathways, including nucleotide-binding oligomerization domain (NOD)-like receptor signaling pathway and apoptosis. Some members of the pattern recognition receptors family, which can initiate inflammatory signaling and induce apoptosis, were stimulated to upregulate in gill after challenge including C1q, perlucin, and low-density lipoprotein receptor–related protein. In addition, caspase-dependent apoptosis pathway was inactive, whereas caspase-independent apoptosis pathway was stimulating in response to Vibrio challenge. To eliminate invading pathogen, the immune effectors such as interleukin-17, matrix metalloproteinase, lysozyme, superoxide, and ferritin were also stimulated in gill. Moreover, this study also provides more information for comprehending the immunity and defense mechanisms against V. parahaemolyticus infection in abalone.

The impoundment of the Three Gorges Reservoir (TGR) has greatly altered the hydrological regime and thus formed a distinctive riparian zone with anti-seasonal inundation and exposure, which may affect the soil aggregate properties in this riparian zone. Yet, the soil aggregate size distribution and stability influenced by the hydrological regime along the step-impounded elevation have rarely been documented. This study aimed to elucidate how the hydrological regime of the TGR affected the aggregate size distribution and stability in the riparian zone. Based on the step-impounded elevation, topsoil samples were collected from four elevation-dependent transects in a middle section of the TGR. Dry-sieving and wet-sieving methods were employed. The results showed that, with a decrease in the elevation gradient, the mass percentage of the >5 mm aggregates significantly decreased, while the proportions of the other size classes presented an increasing trend. Additionally, the mean weight diameter (MWD), geometric mean diameter (GMD), aggregate stability rate (ASR), and percentage of aggregate destruction (PAD) of the fractal dimension showed a successive decrease with a decrease in the elevation gradient, whereas PADMWD, PADGMD, PADASR, and the fractal dimension demonstrated a reverse trend. It can thus be deduced that the hydrological regime of the TGR significantly modified the aggregate size distribution and dramatically reduced the aggregate stability, which may provide a crucial basis for assessing the soil erosion in similar riparian zones.

Various coronaviruses have emerged as a result of cross-species transmission among humans and domestic animals. Porcine epidemic diarrhea virus (PEDV; family Coronaviridae, genus Alphacoronavirus) causes acute diarrhea, vomiting, dehydration, and high mortality in neonatal piglets. Porcine small intestinal epithelial cells (IPEC-J2 cells) can be used as target cells for PEDV infection. However, the origin of PEDV in pigs, the host range, and cross-species infection of PEDV remain unclear. To determine whether PEDV has the ability to infect human cells in vitro, human small intestinal epithelial cells (FHs 74 Int cells) were inoculated with PEDV LJX and PEDV CV777 strains. The results indicated that PEDV LJX, but not PEDV CV777, could infect FHs 74 Int cells. Furthermore, we observed M gene mRNA transcripts and N protein expression in infected FHs 74 Int cells. A one-step growth curve showed that the highest viral titer of PEDV occurred at 12 h post infection. Viral particles in vacuoles were observed in FHs 74 Int cells at 24 h post infection. The results proved that human small intestinal epithelial cells are susceptible to PEDV infection, suggesting the possibility of cross-species transmission of PEDV.

Detection of skin defects in Cerasus humilis fruit is a critical process to guarantee its quality and price. This study presents a valid method for the detection of defective features in Cerasus humilis fruits based on hyperspectral imaging. A total of 420 sample images were acquired that included three types of natural defects and undamaged samples. After acquiring hyperspectral images of Cerasus humilis fruits, the spectral data were extracted from the region of interest (ROI). Five spectral preprocessing methods were used to preprocess the original spectral data, including Savitsky–Golay (S-G), standard normal variate (SNV), multiplicative scatter correction (MSC), baseline correction (BC), and de-trending (De-T). Regression coefficient (RC), successive projections algorithm (SPA), and competitive adaptive reweighed sampling (CARS) were conducted to select optimal sensitive wavelengths (SWs); as a result, 11 SWs, 17 SWs, and 13 SWs were selected, respectively. Then, the least squares-support vector machine (LS-SVM) discrimination model was established using the selected SWs. The results showed that the discriminate accuracy of the CARS-LS-SVM method was 91.43%. Based on the characteristics of image information, images corresponding to eight sensitive wavebands (950, 994, 1071, 1263, 1336, 1457, 1542, and 1628 nm) selected by CARS were subjected to principal component analysis (PCA). Then, an effective approach for detecting the defective features was exploited based on the imfill function, canny operator, region growing algorithm, bwareaopen function, and the images of PCA. The location and area of defect feature of 105 Cerasus humilis fruits could be recognized; the detect precision was 88.57%. This investigation demonstrated that hyperspectral imaging combined with an image processing technique could achieve the rapid identification of undamaged samples and natural defects in Cerasus humilis fruit. This provides a theoretical basis for the development of Cerasus humilis fruit grading and sorting equipment.

Deep learning is a branch of artificial intelligence. In recent years, with the advantages of automatic learning and feature extraction, it has been widely concerned by academic and industrial circles. It has been widely used in image and video processing, voice processing, and natural language processing. At the same time, it has also become a research hotspot in the field of agricultural plant protection, such as plant disease recognition and pest range assessment, etc. The application of deep learning in plant disease recognition can avoid the disadvantages caused by artificial selection of disease spot features, make plant disease feature extraction more objective, and improve the research efficiency and technology transformation speed. This review provides the research progress of deep learning technology in the field of crop leaf disease identification in recent years. In this paper, we present the current trends and challenges for the detection of plant leaf disease using deep learning and advanced imaging techniques. We hope that this work will be a valuable resource for researchers who study the detection of plant diseases and insect pests. At the same time, we also discussed some of the current challenges and problems that need to be resolved.

BackgroundStroke is predominately attributed to left atrial thrombus (LAT) in patients with non-valvular atrial fibrillation (NVAF), however, its detection rate in real clinical practice has been few reported in China.ObjectiveThis study aimed to investigate the prevalence and associated factors of LAT in patients with NVAF in China.MethodsAll adult NVAF patients undergoing transesophageal echocardiography (TEE) in the China Atrial Fibrillation Center database from January 2017 to January 2022 were enrolled in this study. The prevalence of LAT was calculated, and associated factors were identified.ResultsA total of 36,007 NVAF inpatients from 602 hospitals in 30 provinces/autonomous regions/municipalities were included in the final analysis, with a median age of 66 years and 39.4% were female. LAT was present in 1,467 (4.1%) patients overall, 2.7, 5.7, and 6.8% in patients with paroxysmal, persistent, and long-standing persistent AF, respectively. In subgroup analysis, including age ≥ 65 years, CHA2DS2-VASC score ≥ 2, left atrial diameter (LAD) ≥ 50 mm, left ventricular ejection fraction (LVEF) < 50%, and anticoagulation, patients with paroxysmal AF always had the lowest LAT prevalence, followed by patients with persistent and long-standing persistent AF. Patients treated with anticoagulants had less prevalent LAT than those without anticoagulation (2.1 vs. 5.0%, p < 0.001). In multivariate analysis, AF pattern (both persistent AF and long-standing persistent AF), hypertension, chronic heart failure, coronary heart disease, transient ischemic attack/stroke, diabetes mellitus, and LAD (per 5 mm) were associated with an increased prevalence of LAT. However, LVEF (per 5%) and anticoagulation were associated with a reduced prevalence of LAT.ConclusionLAT was found in 4.1% of Chinese adult NVAF inpatients underwent TEE in real-world experience. The prevalence of LAT mainly associated with non-paroxysmal AF, cardiovascular diseases, diabetes mellitus, enlarged left atrium, lower LVEF, and lack of anticoagulation therapy.

Abstract Background PhasiRNAs (phased secondary siRNAs) play important regulatory roles in the development processes and biotic or abiotic stresses in plants. Some of phasiRNAs involve in the reproductive development in grasses, which include two categories, 21-nt (nucleotide) and 24-nt phasiRNAs. They are triggered by miR2118 and miR2275 respectively, in premeiotic and meiotic anthers of rice, maize and other grass species. Wheat (Triticum aestivum) with three closely related subgenomes (subA, subB and subD), is a model of allopolyploid in plants. Knowledge about the role of phasiRNAs in the inflorescence development of wheat is absent until now, and the evolution of PHAS loci in polyploid plants is also unavailable. Results Using 261 small RNA expression datasets from various tissues, a batch of PHAS (phasiRNA precursors) loci were identified in the young spike of wheat, most of which were regulated by miR2118 and miR2275 in their target site regions. Dissection of PHAS and their trigger miRNAs among the diploid (AA and DD), tetraploid (AABB) and hexaploid (AABBDD) genomes of Triticum indicated that distribution of PHAS loci were dominant randomly in local chromosomes, while miR2118 was dominant only in the subB genome. The diversity of PHAS loci in the three subgenomes of wheat and their progenitor genomes (AA, DD and AABB) suggested that they originated or diverged at least before the occurrence of the tetraploid AABB genome. The positive correlation between the PHAS loci or the trigger miRNAs and the ploidy of genome indicated the expansion of genome was the major drive force for the increase of PHAS loci and their trigger miRNAs in Triticum. In addition, the expression profiles of the PHAS transcripts suggested they responded to abiotic stresses such as cold stress in wheat. Conclusions Altogether, non-coding phasiRNAs are conserved transcriptional regulators that display quick plasticity in Triticum genome. They may be involved in reproductive development and abiotic stress in wheat. It could be referred to molecular research on male reproductive development in Triticum.

The ripeness phases of jujube fruits are one factor mitigating against fruit detection, in addition to uneven environmental conditions such as illumination variation, leaf occlusion, overlapping fruits, colors or brightness, similar plant appearance to the background, and so on. Therefore, a method called YOLO-Jujube was proposed to solve these problems. With the incorporation of the networks of Stem, RCC, Maxpool, CBS, SPPF, C3, PANet, and CIoU loss, YOLO-Jujube was able to detect jujube fruit automatically for ripeness inspection. Having recorded params of 5.2 m, GFLOPs of 11.7, AP of 88.8%, and a speed of 245 fps for detection performance, including the sorting and counting process combined, YOLO-Jujube outperformed the network of YOLOv3-tiny, YOLOv4-tiny, YOLOv5s, and YOLOv7-tiny. YOLO-Jujube is robust and applicable to meet the goal of a computer vision-based understanding of images and videos.

The stable bearing capacity of roadside backfill body (RBB) is the key to control the surrounding rock deformation of gob-side entry retaining (GER) in deep mining. In this paper, slump and uniaxial compressive strength tests were conducted to study the changes of the slump and RBB mechanical properties of RBB fresh mixture under the influence of different percentages of fly ash (FA), river sand, and amounts of steel fiber (SF) admixture. The results show that (1) the slump first increases and then decreases with the increase of the FA proportion, while 20% of FA proportion is the critical point. In addition, SF has little influence on slump. However, when the proportion of FA is 60% and the content of SF is 0.4%, the slump reaches the minimum value of 130 mm. (2) The peak strength and residual strength increase with the increase of the FA proportion, and the strength gain starts to decrease when the FA proportion exceeds 40%. Moreover, SF only has a small effect on the peak strength when the dosage is 0.8%. (3) The post-peak strain difference decreases with the increase of the FA proportion and increases with the increase of the SF content. The addition of SF can make up for the reduced capacity of RBB to post-peak deformation caused by the addition of FA to a certain extent. (4) The addition of fly ash does not change the brittle failure mode of RBB. Furthermore, after the addition of SF, RBB changes from brittle to ductile failure mode. This study can provide experimental and theoretical basis for improving the bearing capacity of RBB and maintaining the stability of GER.

Apple leaf diseases seriously affect the sustainable production of apple fruit. Early infection monitoring of apple leaves and timely disease control measures are the key to ensuring the regular growth of apple fruits and achieving a high-efficiency economy. Consequently, disease detection schemes based on computer vision can compensate for the shortcomings of traditional disease detection methods that are inaccurate and time-consuming. Nowadays, to solve the limitations ranging from complex background environments to dense and small characteristics of apple leaf diseases, an improved Faster region-based convolutional neural network (Faster R-CNN) method was proposed. The advanced Res2Net and feature pyramid network architecture were introduced as the feature extraction network for extracting reliable and multi-dimensional features. Furthermore, RoIAlign was also employed to replace RoIPool so that accurate candidate regions will be produced to address the object location. Moreover, soft non-maximum suppression was applied for precise detection performance of apple leaf disease when making inferences to the images. The improved Faster R-CNN structure behaves effectively in the annotated apple leaf disease dataset with an accuracy of 63.1% average precision, which is higher than other object detection methods. The experiments proved that our improved Faster R-CNN method provides a highly precise apple leaf disease recognition method that could be used in real agricultural practice.

Using the triaxial shear or compressive strength as a single index of the resistance of frozen soils to failure does not always meet frozen soil engineering requirements for the comprehensive evaluation of the resistance. In this study, triaxial compression experiments were carried out on undisturbed ice-rich frozen clay samples with various levels of water content under different confining pressures to study the characteristics of the failure strain energy density of the samples. The results indicate that as the confining pressure increased, the failure strain energy density first increased and then decreased. The failure strain energy density reached a maximum at a critical confining pressure of 2.00 MPa for 13.25–25.76% water content and 1.00 MPa for 26.02–45.82% water content. The failure strain energy density increased as the water content increased at low confining pressures (0.05–0.50 MPa) but then declined slightly at intermediate confining pressures (1.00–2.00 MPa). At a high confined pressure of 3.00 MPa, the failure strain energy density decreased overall as the water content increased. There were similarities and differences between the change characteristics of the compressive strength and the failure strain energy density. The failure strain energy density can be used as a supplementary reference index of the resistance of frozen soils to damage. The variation characteristics of the failure strain energy density of undisturbed frozen clay are essentially consistent with those of remolded frozen sandy soils. However, there are also clear differences between the characteristics of the failure strain energy density of these two types of frozen soil.

Glyphosate (GLY) is the most widely used broad-spectrum, non-selective herbicide in the world, whose main degradation product is aminomethyl phosphonic acid (AMPA). Because of long-term and large-scale use, residual GLY and AMPA in the environment pose great environmental and human health threats. The purpose of this study is to evaluate the effects and mechanism of residual low-concentrations of GLY and AMPA in the environment on the development of zebrafish embryos. Zebrafish embryos were exposed to 0, 1, 10, 100, and 700 ng·mL−1 GLY and AMPA for 72 h (from 2 to 74 h post-fertilization). With increasing exposure dose, heart rates of both embryos and larvae showed a rising trend and obvious arrhythmia appeared. Defects in cardiac development and function of zebrafish juveniles may be related to altered transcription levels of cardiac development genes (TBX5, NKX2.5, BMP4) and apoptosis genes (Bcl-2, Bax). In addition, pericardial edema and bone deformation of zebrafish embryos may be caused by inhibition of Na+/K+-ATPase and Ca2+-ATPase after exposure to GLY and AMPA. The present results demonstrated that at typical environmental residual concentrations of GLY and AMPA had similar developmental toxicity in zebrafish embryos.

Introduction: Type 2 diabetes mellitus (T2DM) is an independent risk factor of Alzheimer’s disease (AD), and populations with mild cognitive impairment (MCI) have high incidence to suffer from AD. Therefore, discerning who may be more vulnerable to MCI, among the increasing T2DM populations, is important for early intervention and eventually decreasing the prevalence rate of AD. This study was to explore whether the change of plasma β-amyloid (Aβ) could be a biomarker to distinguish MCI (T2DM-MCI) from non-MCI (T2DM-nMCI) in T2DM patients.Methods: Eight hundred fifty-two T2DM patients collected from five medical centers were assigned randomly to training and validation cohorts. Plasma Aβ, platelet glycogen synthase kinase-3β (GSK-3β), apolipoprotein E (ApoE) genotypes, and olfactory and cognitive functions were measured by ELISA, dot blot, RT-PCR, Connecticut Chemosensory Clinical Research Center (CCCRC) olfactory test based on the diluted butanol, and Minimum Mental State Examination (MMSE) test, respectively, and multivariate logistic regression analyses were applied.Results: Elevation of plasma Aβ1-42/Aβ1-40 is an independent risk factor of MCI in T2DM patients. Although using Aβ1-42/Aβ1-40 alone only reached an AUC of 0.631 for MCI diagnosis, addition of the elevated Aβ1-42/Aβ1-40 to our previous model (i.e., activated platelet GSK-3β, ApoE ε4 genotype, olfactory decline, and aging) significantly increased the discriminating efficiency of T2DM-MCI from T2DM-nMCI, with an AUC of 0.846 (95% CI: 0.794–0.897) to 0.869 (95% CI: 0.822–0.916) in the training cohort and an AUC of 0.848 (95% CI: 0.815–0.882) to 0.867 (95% CI: 0.835–0.899) in the validation cohort, respectively.Conclusion: A combination of the elevated plasma Aβ1-42/Aβ1-40 with activated platelet GSK-3β, ApoE ε4 genotype, olfactory decline, and aging could efficiently diagnose MCI in T2DM patients. Further longitudinal studies may consummate the model for early prediction of AD.

Abstract Background High temperature is a major environmental stress that limits plant growth and agriculture productivity. Mitogen-activated protein kinases (MAPKs) are highly conserved serine and threonine protein kinases that participate in response to diverse environmental stresses in plants. A total of 16 putative SlMAPK genes are identified in tomato, and SlMAPK3 is one of the most extensively studied SlMAPKs. However, the role of SlMAPK3 in response to heat stress is not clearly understood in tomato plants. In this study, we performed functional analysis of SlMAPK3 for its possible role in response to heat stress. Results qRT-PCR analyses revealed that SlMAPK3 relative expression was depressed by heat stress. Here, wild-type (WT) tomato plants and CRISPR/Cas9-mediated slmapk3 mutant lines (L8 and L13) were used to investigate the function of SlMAPK3 in response to heat stress. Compared with WT plants, slmapk3 mutants exhibited less severe wilting and less membrane damage, showed lower reactive oxygen species (ROS) contents, and presented higher both activities and transcript levels of antioxidant enzymes, as well as elevated expressions of genes encoding heat stress transcription factors (HSFs) and heat shock proteins (HSPs). Conclusions CRISPR/Cas9-mediated slmapk3 mutants exhibited more tolerance to heat stress than WT plants, suggesting that SlMAPK3 was a negative regulator of thermotolerance. Moreover, antioxidant enzymes and HSPs/HSFs genes expression were involved in SlMAPK3-mediated heat stress response in tomato plants.

Abstract Background Building brain reserves before dementia onset could represent a promising strategy to prevent Alzheimer’s disease (AD), while how to initiate early cognitive stimulation is unclear. Given that the immature brain is more sensitive to environmental stimuli and that brain dynamics decrease with ageing, we reasoned that it would be effective to initiate cognitive stimulation against AD as early as the fetal period. Methods After conception, maternal AD transgenic mice (3 × Tg AD) were exposed to gestational environment enrichment (GEE) until the day of delivery. The cognitive capacity of the offspring was assessed by the Morris water maze and contextual fear-conditioning tests when the offspring were raised in a standard environment to 7 months of age. Western blotting, immunohistochemistry, real-time PCR, immunoprecipitation, chromatin immunoprecipitation (ChIP) assay, electrophysiology, Golgi staining, activity assays and sandwich ELISA were employed to gain insight into the mechanisms underlying the beneficial effects of GEE on embryos and 7–10-month-old adult offspring. Results We found that GEE markedly preserved synaptic plasticity and memory capacity with amelioration of hallmark pathologies in 7–10-m-old AD offspring. The beneficial effects of GEE were accompanied by global histone hyperacetylation, including those at bdnf promoter-binding regions, with robust BDNF mRNA and protein expression in both embryo and progeny hippocampus. GEE increased insulin-like growth factor 1 (IGF1) and activated its receptor (IGF1R), which phosphorylates Ca2+/calmodulin-dependent kinase IV (CaMKIV) at tyrosine sites and triggers its nuclear translocation, subsequently upregulating histone acetyltransferase (HAT) and BDNF transcription. The upregulation of IGF1 mimicked the effects of GEE, while IGF1R or HAT inhibition during pregnancy abolished the GEE-induced CaMKIV-dependent histone hyperacetylation and BDNF upregulation. Conclusions These findings suggest that activation of IGF1R/CaMKIV/HAT/BDNF signaling by gestational environment enrichment may serve as a promising strategy to delay AD progression.

Abstract Background Hexaploid bread wheat (Triticum aestivum L) arose by two polyploidisation events from three diploid species with homoeologous genomes. Nullisomic-tetrasomic (nulli-tetra or NT) lines are aneuploid wheat plants lacking two and adding two of six homoeologous chromosomes. These plants can grow normally, but with significantly morphological variations because the adding two chromosomes or the remaining four chromosomes compensate for those absent. Despite these interesting phenomena, detailed molecular mechanisms underlying dosage deletion and compensation in these useful genetic materials have not been determined. Results By sequencing the transcriptomes of leaves in two-week-old seedlings, we showed that the profiles of differentially expressed genes between NT stocks for homoeologous group 7 and the parent hexaploid Chinese Spring (CS) occurred throughout the whole genome with a subgenome and chromosome preference. The deletion effect of nulli-chromosomes was compensated partly by the tetra-chromosomes via the dose level of expressed genes, according to the types of homoeologous genes. The functions of differentially regulated genes primarily focused on carbon metabolic process, photosynthesis process, hormone metabolism, and responding to stimulus, and etc., which might be related to the defective phenotypes that included reductions in plant height, flag leaf length, spikelet number, and kernels per spike. Conclusions The perturbation of the expression levels of transcriptional genes among the NT stocks for homoeologous group 7 demonstrated the gene dosage effect of the subgenome at the genome-wide level. The gene dosage deletion and compensation can be used as a model to elucidate the functions of the subgenomes in modern polyploid plants.

Abstract Background NPR1, nonexpressor of pathogenesis-related gene 1, is a master regulator involved in plant defense response to pathogens, and its regulatory mechanism in the defense pathway has been relatively clear. However, information about the function of NPR1 in plant response to abiotic stress is still limited. Tomato is the fourth most economically crop worldwide and also one of the best-characterized model plants employed in genetic studies. Because of the lack of a stable tomato NPR1 (SlNPR1) mutant, little is known about the function of SlNPR1 in tomato response to biotic and abiotic stresses. Results Here we isolated SlNPR1 from tomato ‘Ailsa Craig’ and generated slnpr1 mutants using the CRISPR/Cas9 system. Analysis of the cis-acting elements indicated that SlNPR1 might be involved in tomato plant response to drought stress. Expression pattern analysis showed that SlNPR1 was expressed in all plant tissues, and it was strongly induced by drought stress. Thus, we investigated the function of SlNPR1 in tomato-plant drought tolerance. Results showed that slnpr1 mutants exhibited reduced drought tolerance with increased stomatal aperture, higher electrolytic leakage, malondialdehyde (MDA) and hydrogen peroxide (H2O2) levels, and lower activity levels of antioxidant enzymes, compared to wild type (WT) plants. The reduced drought tolerance of slnpr1 mutants was further reflected by the down-regulated expression of drought related key genes, including SlGST, SlDHN, and SlDREB. Conclusions Collectively, the data suggest that SlNPR1 is involved in regulating tomato plant drought response. These results aid in further understanding the molecular basis underlying SlNPR1 mediation of tomato drought sensitivity.

Fruits with various maturity levels coexist among the harvested jujubes, and have different tastes and uses. Manual grading has a low efficiency and a strong subjectivity. The number of “Hupingzao” jujubes between different maturity levels is unbalanced, which affects the performance of the classifier. To solve the above issue, the class balance loss (CB) was used to improve the MobileNet V2 network, and a transfer learning strategy was used to train the model. The model was optimized based on the selection of an optimizer and learning rate. The model achieved the best classification results using the AdamW optimizer and a learning rate of 0.0001. The application of transfer learning and class balance loss improved the model’s performance. The precision was 96.800~100.000%, the recall was 95.833~100.000%, and the F1 score was 0.963~1.000. To compare the CB-MobileNet V2 performance, the CB-AlexNet, CB-GoogLeNet, CB-ShuffleNet, CB-Inception V3, CB-ResNet 50, and CB-VGG 16 with transfer learning were used to build classification models. Achieving a validation accuracy of 99.058%, and a validation loss value of 0.055, the CB-MobileNet V2 model showed a better overall performance compared with other models. The maturity detection system of “Hupingzao” jujubes was developed to test the model. The testing accuracy of the CB-MobileNet V2 model was 99.294%. The research indicates that the CB-MobileNet V2 model improves the performance of maturity classification, and provides a theoretical basis for intelligent classification of the quality of “Hupingzao” jujubes.

With increasing consumer expectations for the quality and safety of agricultural products, intelligent quality detection and gradation have considerable significance in agricultural production. The surface defect is an important indicator of quality, but is classified mainly using inefficient manual identification for “Yuluxiang” pears. Because of the uncertainty and high difficulty of image acquisition in agriculture, the data imbalance between categories is a common problem. For the resolution of these problems, the class balance (CB) was used to re-weight the sigmoid cross-entropy loss (SGM-CE), softmax cross-entropy loss (SM-CE), focal loss (FL) functions in this study. CB-SGM-CE, CB-SM-CE, and CB-FL were used to construct a GoogLeNet network as a convolutional neural network (CNN) generalized feature extractor, and transfer learning was combined to build detection models, respectively. The results showed that CB-SGM-CE, CB-SM-CE, and CB-FL were better than SGM-CE, SM-CE, and FL, respectively. CB-FL achieved the best detection results (F1 score of 0.993–1.000) in 3 CB loss functions. Then, CB-FL was used to construct VGG 16, AlexNet, SqueezeNet, and MobileNet V2 networks based on transfer learning, respectively. Machine learning (ML) and CNN were used to build classification models in this study. Compared with ML models and the other 4 CNN models, the CB-FL-GoogLeNet model achieved the best detection results (accuracy of 99.78%). A system for surface defect detection was developed. The results showed that the testing accuracy of the CB-FL-GoogLeNet model was 95.28% based on this system. This study realizes the surface defect detection of the “Yuluxiang” pear with an unbalanced dataset, and provides a method for intelligent detection in agriculture.

To solve the failure problem of the visible/near infrared (VIS/NIR) spectroscopy model, soluble solids content (SSC) detection for fresh jujubes cultivated in different modes was carried out based on the method of variable optimization and model update. Iteratively retained informative variables (IRIV) and successive projections algorithm (SPA) algorithms were used to extract characteristic wavelengths, and least square support vector machine (LS-SVM) was used to establish detection models. Compared with IRIV, IRIV-SPA achieved better performance. Combined with the offset properties of the wavelength, repeated wavelengths were removed, and wavelength recombination was carried out to create a new combination of variables. Using these fused wavelengths, the model was recalibrated based on the Euclidean distance between samples. The LS-SVM detection model of SSC was established using the update method of wavelength fusion-Euclidean distance. Good prediction results were achieved using the proposed model. The determination coefficient (R2), root mean square error (RMSE), and residual predictive deviation (RPD) of the test set on SSC of fresh jujubes cultivated in the open field were 0.82, 1.49%, and 2.18, respectively. The R2, RMSE, and RPD of the test set on SSC of fresh jujubes cultivated in the rain shelter were 0.81, 1.44%, and 2.17, respectively. This study realized the SSC detection of fresh jujubes with different cultivation and provided a method for the establishment of a robust VIS/NIR detection model for fruit quality, effectively addressing the industry need for identifying jujubes grown in the open field.

Botrytis cinerea seriously affects the value of post-harvest grapes. Melatonin can act as an exogenous regulator in the resistance of exogenous pathogens due to its antioxidant activity. An artificial inoculation trial was conducted to research the induced resistance mechanism of melatonin treatment using the table grape “Muscat Hamburg” (Vitis vinifera L. cv). Grapes were immersed with 0.02, 0.2, and 2 mmol/L melatonin, followed by B. cinerea suspension injections after 48 h. The results showed that the mycelial growth and spore germination of B. cinerea was not significantly inhibited by melatonin at different concentrations (0.02–2 mmol/L). However, post-harvest melatonin treatment inhibited the increase of disease incidence and severity of grey mould, induced the synthesis and accumulation of total phenols and flavonoids, reduced malondialdehyde generation, and inhibited an increase in cell membrane permeability. Meanwhile, defensive enzyme activities, including superoxide dismutase (SOD), peroxidize (POD), catalase (CAT), phenylalanine ammonia-lyase (PAL), polyphenol oxidase (PPO), chitinase (CHI), and β-1,3-glucanase, were significantly increased in fruits treated with exogenous melatonin. These results suggested that exogenous melatonin treatment could activate defence responses to combat the infection of B. cinerea in post-harvest grapes.

In order to study the relationship between the distribution and aflatoxin production capacity of Aspergillus species and soil types, 35 soil samples were collected from the main peanut planting areas in Xiangyang, which has 19.7 thousand square kilometers and is located in a special area with different soil types. The soil types of peanut planting areas in Xiangyang are mainly sandy loam and clay loam, and most of the soil is acidic, providing unique nature conditions for this study. The results showed that the Aspergillus sp. population in clay loam (9050 cfu/g) was significantly larger than that in sandy loam (3080 cfu/g). The percentage of atoxigenic Aspergillus strains isolated from sandy loam samples was higher than that from clay loam samples, reaching 58.5%. Meanwhile the proportion of high toxin-producing strains from clay loam (39.7%) was much higher than that from sandy loam (7.3%). Under suitable culture conditions, the average aflatoxin production capacity of Aspergillus isolates from clay loam samples (236.97 μg/L) was higher than that of strains from sandy loam samples (80.01 μg/L). The results inferred that under the same regional climate conditions, the density and aflatoxin production capacity of Aspergillus sp. in clay loam soil were significantly higher than that in sandy loam soil. Therefore, peanuts from these planting areas are at a relatively higher risk of contamination by Aspergillus sp. and aflatoxins.

In cold regions, the long-term stability of engineering facilities is unavoidably influenced by the negative temperature, freeze-thaw process, dry-wet process, and dynamic loading conditions induced by earthquakes and traffic loads. In order to investigate the effects of different cyclic stress paths on the evolution of dynamic mechanical properties of frozen silt clay, a series of cyclic triaxial tests with variation confining pressure (VCP) or constant confining pressure (CCP) were performed. Triaxial low-temperature apparatus (MTS-810) was taken advantage of to simulating various cyclic stress paths by changing cyclic loading conditions of axial stress and confining pressure. In this paper, the evolution features of the axial resilient modulus, damping ratios, and the shape of hysteresis loops with an increase in the number of load cycles under different dynamic stress paths are comprehensively studied. The results show that the loading angle of cyclic stress path and the phase difference between cyclic axial stress and confining pressure are the main factors that remarkably affect the development characteristics of the resilient modulus and damping ratio. With increasing of the loading angle and phase difference, the resilient modulus increases, but damping ratio increases with increasing of loading angle and with decreasing of phase difference. With the continuous increase in the number of loading cycles, the samples of frozen soil show compacting and hardening characteristics. With an increase in the number of load cycles, the shape of hysteresis loop becoming narrows, the resilient modulus decreases at the initial stage and then gradually increases, and the damping ratio stably decreases. According to contrastively analyzing the evolution of dynamic parameters and the shape features of hysteresis loops under various cyclic stress paths, it can be clearly discovered that the evolution of sample microstructure and the development of dynamic characteristics of frozen samples have obvious dependence on the cyclic stress path. Therefore, the effects of variable confining pressure (VCP) on dynamic behaviors of frozen soils are nonnegligible in practical cold region engineering.