Comprehensive Analysis of Immunobiological Effects and Prognostic Significance Related to Endoplasmic Reticulum Stress in Pancreatic Carcinoma.

Context: Pancreatic cancer (PC) has a poor response to the many treatments available for it, including surgery, chemotherapeutics, targeted therapy, and immunotherapy. It's crucial to investigate alternative methods of prognostic assessment and decision-making in choosing a therapy, making it necessary to explore its differentially expressed genes (DEGs). Objective: The study intended to assess the role of endoplasmic reticulum stress (ERS)-related genes (ERSRGs) in PC to create an effective, prognostic risk-prediction model and potential immunotherapy options. Design: The research team performed a genetic study. Setting: The study took place at the Affiliated Hospital of Nantong University in Nantong, Jiangsu, China. Outcome Measures: The research team: (1) performed molecular subtype identification and analysis, (2) developed a prognostic risk model, (3) evaluated the clinical features of the risk model, (4) identified the clinicopathological features affecting survival, (5) analyzed the potential immune roles in ERS, (6) constructed five gene signatures associated with ERS, (7) examined the association between different risk categories and sensitivity to GDSC drugs as a potential predictor of response to chemotherapy , and (8) identified the biological processes associated with different risk categories. Results: Significant differences existed in the survival prognosis of subtype C and subtype A or B (P < .001). The high-risk group with the lower TIDE score had a significantly better response to immunotherapy (P < .0057). The high-risk group had a significantly higher somatic mutation rate (P < .00017) and a worse survival prognosis (P < .001). Differences in mRNA expression existed for ERAP2 (P < .001), IGF2BP2 (P = .006), DSG3 (P = .001), MAPK10 (P = .006), and PRKCSH (P ≤ .015) in clinical samples. Conclusions: Through the analysis of ERS subtypes of pancreatic cancer, the study found that the infiltration abundance of stromal cells and immune cells can affected by ERS, thus changing the prognosis of patients. The predictive model provides reference values for clinical prognosis and immunotherapy for PC patients through its ability to evaluate patients' immune statuses. Clinical treatment can provide individualized guidance and can effectively predict the prognosis of PC patients.

RNA-binding motif 4 promotes angiogenesis in HCC by selectively activating VEGF-A expression.

Increased angiogenesis in the liver plays a critical role in the progression of hepatocellular carcinoma (HCC). However, the molecular mechanism underlying increased angiogenesis in HCC is not well understood. Current study was designed to identify the potential angiogenic effect of RNA-binding motif 4 (RBM4)through a small-scale overexpression screening, followed by comparison of the expression level of RBM4 in cancer and adjacent tissues in multiple malignancies to explore the relationship between RBM4 and CD31 protein expression level and related clinical indicators, and understand the role of RBM4 in the hepatocellular carcinoma. To understand the specific mechanism of RBM4 in detail, transcriptome sequencing, mass spectrometry and multiple molecular cytological studies were performed. These cellular level results were verified by experiments in animal models of nude mice. The increased expression of RBM4 in cancer tissues, suggested its use as a prognostic biomarker. The RBM4 expression was found to be strongly correlated with tumor microvessel density. Mechanistically, RBM4 mediated its effects via interaction with HNRNP-M through the latter's WDR15 domain, which then stabilized RelA/p65 mRNA. Consequently, RBM4 induced the activation of the NF-kB signaling pathway, upregulating the expression of proangiogenic factor VEGF-A. The results confirmed the mechanism by which RBM4 promotes angiogenesis in hepatocellular carcinoma suggesting RBM4 as a crucial promoter of angiogenesis in HCC, helping understand regulation of NF-kB signaling in HCC.

Ubiquitin-specific protease 7 is a druggable target that is essential for pancreatic cancer growth and chemoresistance.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers, and most patients die within one year after diagnosis. This cancer is resistant to almost all current therapies, so there is an urgent need to identify novel druggable targets. Ubiquitin-specific protease 7 (USP7) is a deubiquitinase that functions in carcinogenesis, but its role in PDAC is unknown. Our experiments indicated that several subtypes of PDAC cells are sensitive to USP7 inhibition. In particular, pharmaceutical inhibition of USP7 by the small molecule P22077 attenuated PDAC cell growth and induced cell death in vitro and in vivo. Pharmaceutical inhibition of USP7 in P22077-resistant PDAC cells allowed them to overcome chemoresistance. Genetic silencing experiments supported the importance of USP7 in the pathogenesis of PDAC. In particular, genetic disruption of USP7 greatly reduced cell proliferation and chemoresistance in vitro and prevented PDAC growth in vivo. Protein profiling by mass spectrometry (MS) indicated USP7 was associated 4 ontology terms: translation, localization and protein transporting, nucleotide or ribonucleotide binding, and ubiquitin-dependent catabolic processes. Puromycin labeling indicated that P22077 greatly reduced protein synthesis, and transcriptional analysis indicated that P22077 significantly altered the extracellular space matrix. In summary, we provided multiple lines of evidence which indicate that USP7 plays a critical role in PDAC, and may therefore be a suitable target for treatment of this cancer.

Orchestrating a biomarker panel with lncRNAs and mRNAs for predicting survival in pancreatic ductal adenocarcinoma.

The low survival of patients with pancreatic ductal adenocarcinoma (PDAC) makes the treatment of this disease one of the most challenging task in modern medicine. Here, by mining a large-scale cancer genome atlas data set of pancreatic cancer tissues, we identified 21 long noncoding RNAs (lncRNAs) that significantly associated with overall survival in patients with PDAC (P < .01). Further analysis revealed that 8 lncRNAs turned out to be independently correlated with patients' overall survival, and the risk score could be calculated based on their expression. To obtain a better predicting power, we integrated lncRNA data with a total of 410 differently expressed messenger RNAs (mRNAs) screened from PDAC and normal tissues in gene expression omnibus (GEO) database. The integration resulted in a much better panel including 8 lncRNAs (RP3.470B24.5, CTA.941F9.9, RP11.557H15.3, LINC00960, AP000479.1, LINC00635, LINC00636, and AC073133.1) and 8 mRNAs (DHRS9, ONECUT1, OR8D4, MT1M, TCN1, MMP9, DPYSL3, and TTN) to predict prognosis. A functional evaluation showed that these lncRNAs might play roles in pancreatic secretion, cell adhesion, and proteolysis. Using normal and pancreatic cancer cell lines, we confirmed that a majority of identified lncRNAs and mRNAs showed altered expressions in pancreatic cancer cells. Especially, LINC01589, LINC00960, TCN1, and MT1M showed a profoundly increased expression in pancreatic cancer cells, which suggests their potentially important role in pancreatic cancer. The results of our work indicate that lncRNAs have vital roles in PADC and provide new insights to integrate multiple kinds of markers in clinical practices.

RACK1 overexpression associates with pancreatic ductal adenocarcinoma growth and poor prognosis.

OBJECTIVES: The receptor for activated protein kinase C (RACK1) is a scaffold protein involved in multiple intracellular signal pathways. Previous studies have shown that RACK1 is associated with the progression of multiple cancer types, including hepatocellular carcinoma and gastric cancer. However, the role of RACK1 in human pancreatic ductal adenocarcinoma (PDAC) remains unclear. METHODS: In this study, the expression of RACK1 was evaluated by Western blot analysis in 8 paired fresh PDAC tissues and immunohistochemistry on 179 paraffin-embedded slices. Then, we used Fisher exact test to analyze the correlation between RACK1 expression and clinicopathological characteristics. Starvation and re-feeding assay was used to assess cell cycle. Western blot, CCK8, flow cytometry assays, and colony formation analyses demonstrated that RACK1 played an essential role in PDAC development. Annexin-V/PI apoptotic assay and western blot showed that RACK1 was involved in regulating the apoptosis of PDAC cells. RESULTS: RACK1 was highly expressed in PDAC tissues and cell lines and was significantly associated with multiple clinicopathological factors. Univariate and multivariate analyses showed that high RACK1 expression was identified to be an independent prognostic factor for PDAC patients' survival. In vitro, serum starvation-refeeding experiment suggested that RACK1 was upregulated in proliferating PDAC cells, together with the percentage of cells at the S phase, and was correlated with the expression of Cyclin D1. Moreover, Overexpression of RACK1 facilitated the proliferation and cell cycle progression of PDAC cells, while downregulation of RACK1 induced growth impairment, G1/S cell cycle arrest and apoptosis in PDAC cells. Silencing RACK1 decreased bcl-2 expression, increased cleaved caspase3 expression level and induced the apoptosis of PDAC cells. CONCLUSIONS: Our results suggest that RACK1 could play an important role in the tumorigenesis of PDAC and serve as a potential therapeutical target in PDAC treatment.

Acanthopanax versus 3-Methyladenine Ameliorates Sodium Taurocholate-Induced Severe Acute Pancreatitis by Inhibiting the Autophagic Pathway in Rats.

Objectives. To observe the therapeutic effects of Acanthopanax and 3-methyladenine against severe acute pancreatitis (SAP). Methods. Sodium taurocholate-induced SAP rats were equally randomized into a SAP group, an Acanthopanax group, and a 3-methyladenine group. Serum amylase levels were determined by ELISA; protein and mRNA expression levels of nucleus nuclear factor kappa B (NF-κB) p65, light chain 3II (LC3-II), and Beclin-1 and mRNA expression levels of Class III phosphatidylinositol 3-kinase (PI3K-III) in pancreas tissue were detected by Western blot and quantitative real-time PCR, respectively; mortality and pathological change of the pancreas were observed at 3, 12, and 24 h after operation. Results. There was no significant difference in mortality between SAP group and both treatment groups (P > 0.05). Serum amylase levels, protein, and mRNA expression levels of nucleus NF-κB p65, LC3-II, and Beclin-1 protein, mRNA expression levels of PI3K-III, and pathological score of the pancreas in both treatment groups were significantly lower than those in SAP group at 12 and 24 h after operation (P < 0.05 or 0.01). The number of autophagosomes and autophagolysosomes of pancreatic acinar cells in both treatment groups was smaller than that in SAP group at 12 and 24 h. Conclusions. Acanthopanax and 3-methyladenine had similar therapeutic effects against SAP in rats. The mechanism may be through inhibiting abnormal autophagy activation of pancreatic acinar cells.

High expression of ErbB3 binding protein 1 (EBP1) predicts poor prognosis of pancreatic ductal adenocarcinoma (PDAC).

Recent studies have identified that ErbB3 binding protein 1 (EBP1) is broadly expressed in various cancer tissues and critically involved in plenty of biological processes in this regard. However, the functional role of EBP1 in pancreatic ductal adenocarcinoma (PDAC) has never been elucidated. In this study, we found that EBP1 could serve as a prognostic biomarker of PDAC. Western blot analysis revealed that EBP1 was remarkably upregulated in PDAC tissues and cell lines. Using immunohistochemical analysis, we showed that the expression of EBP1 was correlated with tumor size (P = 0.004), histological differentiation (P = 0.041), and tumor node metastasis (TNM) stage (P = 0.000). Notably, Kaplan-Meier curve showed that high expression of EBP1 predicted significantly worsened prognosis of PDAC patients (P = 0.001). In addition, knockdown of EBP1 expression suppressed PDAC cell proliferation and retarded cell cycle progression. Furthermore, depletion of EBP1 induced the apoptosis of Panc-1 cells. Of great interest, we found that EBP1 interacted with anti-apoptotic protein, Bcl-xL, and promoted its accumulation. In summary, our results suggest that EBP1 is a novel prognostic indicator and potential therapeutic target of PDAC, shedding new insights into the important role of EBP1 in cancer development.

NF45 overexpression is associated with poor prognosis and enhanced cell proliferation of pancreatic ductal adenocarcinoma.

NF45, also referred to as nuclear factor of activated T cells, has been reported to promote the progression of multiple cancer types. However, the expression and physiological significance of NF45 in pancreatic ductal adenocarcinoma (PDAC) remain largely elusive. In this study, we investigated the clinical relevance and potential role of NF45 expression in PDAC development. Western blot analysis revealed that NF45 was remarkably upregulated in PDAC tissues, compared with the adjacent non-tumorous ones. In addition, the expression of NF45 in 122 patients with PDAC was evaluated using immunohistochemistry. In this way, we found that NF45 was abundantly expressed in PDAC tissues, and the expression of NF45 was correlated with tumor size (p = 0.007), histological differentiation (p = 0.033), and TNM stage (p = 0.001). Importantly, patients with low levels of NF45 expression exhibited better postoperative prognosis as compared with those with high NF45 expression. Furthermore, using PDAC cell cultures, we found that interference of NF45 expression using siRNA oligos suppressed PDAC cell proliferation and retarded cell cycle progression. Moreover, depletion of NF45 impaired the levels of cellular cyclin E and proliferating cell nuclear antigen (PCNA). Conversely, overexpression of NF45 facilitated the cell growth and accelerated cell cycle progression. Our results establish NF45 as an important indicator of PDAC prognosis with potential utility as a therapeutic target in this lethal disease.

Significance of viral status on recurrence of hepatitis B-related hepatocellular carcinoma after curative therapy: A meta-analysis.

AIM: The impact of viral status on recurrence of hepatitis B-related hepatocellular carcinoma (HCC) after curative therapy remains controversial. This meta-analysis aimed to determine whether the presence of viral load, genotype, specific mutation and antiviral therapy influenced HCC recurrence after curative therapy. METHODS: We performed a meta-analysis including 20 studies to assess the effect of viral status and antiviral therapy with nucleoside analog on recurrence of HCC after curative therapy. The pooled odds ratios (OR) were calculated using a random or fixed effects model. PUBMED, MEDLINE, EMBASE and the Cochrane Database were searched for articles published from 1990 to December 2012. RESULTS: Our results showed that the presence of high viral load significantly increased overall HCC recurrence risk after curative therapy. Pooled data from four studies on the recurrence rate among patients with genotype C infection compared with genotype B showed an increased risk of recurrence. Basal core promoter (BCP) mutation was associated with a significant risk in the recurrence of HCC. The pooled estimate of treatment effect was significantly in favor of a preventive effectiveness of antiviral therapy. CONCLUSION: The present study suggested that HCC patients with high viral load, genotype C and BCP mutation had a significantly higher risk of recurrence. Antiviral therapy has potential beneficial effects after the curative treatment of HCC in terms of tumor recurrence.

Research on the epigenetic modification of pancreatic cancer vaccine.

Pancreatic cancer is characterized as a type of gastrointestinal tumor with a poor prognosis and high degree of malignancy. CIITA gene was found highly methylated in pancreatic carcinoma cell line PANC-1 and responsible for the low expression of MHC-II that may lead to immune evasion. Here, we tried to prepare pancreatic cancer vaccine with PANC-1 cells via epigenetic modification to enhance the MHC-II expression. Then the vaccine was injected into C57BL/6J mice and the effect was examined. Our study found that the vaccine could promote the proliferation of antigen-specific T cells, enhance the killing activity of cytotoxic lymphocytes (CTL), promote Th1-type cells mediated secretion of cytokines IFN-gamma and IL-2 while inhibiting Th2-type cells mediated secretion of IL-4, and inhibit the secretion of TGF-beta. Generally, the epigenetically modified vaccine could enhance the body's anti-tumor immune response, providing feasibility research on cancer vaccine for therapy of pancreatic cancer.

Purse-string suture with nylon cords and metal clips for the treatment of duodenal fistulae under the endoscope: a case report.

Purse-string suture with nylon cords and metal clips under the endoscope is a novel therapeutic technique which is minimally invasive and it is particularly indicated for the closure and repair of gastrointestinal fistula or perforations such as duodenal fistulae. Duodenal fistulae are often caused by medical manipulation, disease progression or trauma. Once this occurs, it leads to a series of pathophysiologic changes and a variety of complications. In most cases, these complications will exacerbate the damage to the organism, and the complications are difficult to treat and can lead to infections, nutrient loss, multi-organ dysfunction and many other adverse effects. In this case report, the use of endoscopic nylon cords combined with purse-string suture and metal clips in the treatment of duodenal fistula is presented and discussed. The patient was treated with endoscopic purse-string suture and the duodenal fistula was significantly improved. The results indicate that endoscopic purse-string suture is an effective strategy for the treatment of duodenal fistulae.

PMANet: a time series forecasting model for Chinese stock price prediction.

Forecasting stock movements is a crucial research endeavor in finance, aiding traders in making informed decisions for enhanced profitability. Utilizing actual stock prices and correlating factors from the Wind platform presents a potent yet intricate forecasting approach. While previous methodologies have explored this avenue, they encounter challenges including limited comprehension of interrelations among stock data elements, diminished accuracy in extensive series, and struggles with anomaly points. This paper introduces an advanced hybrid model for stock price prediction, termed PMANet. PMANet is founded on Multi-scale Timing Feature Attention, amalgamating Multi-scale Timing Feature Convolution and Ant Particle Swarm Optimization. The model elevates the understanding of dependencies and interrelations within stock data sequences through Probabilistic Positional Attention. Furthermore, the Encoder incorporates Multi-scale Timing Feature Convolution, augmenting the model's capacity to discern multi-scale and significant features while adeptly managing lengthy input sequences. Additionally, the model's proficiency in addressing anomaly points in stock sequences is enhanced by substituting the optimizer with Ant Particle Swarm Optimization. To ascertain the model's efficacy and applicability, we conducted an empirical study using stocks from four pivotal industries in China. The experimental outcomes demonstrate that PMANet is both feasible and versatile in its predictive capability, yielding forecasts closely aligned with actual values, thereby fulfilling application requirements more effectively.

Mechanism of Nicotiflorin in San-Ye-Qing rhizome for anti-inflammatory effect in ulcerative colitis.

BACKGROUND: The incidence of ulcerative colitis (UC) is on the rise globally and the development of drugs targeting UC is urgent. Finding the target of action of natural products is important for drug discovery, elucidation of drug action mechanism, and disease mechanism. San-Ye-Qing (SYQ), is an ancient herbal medicine, but whether the powder of its rhizome has pharmacological effects against UC and its mechanism of action are not clear. PURPOSE: To evaluate the therapeutic effectiveness of rhizome powder of SYQ in treating UC, and conduct an isolation and characterization of the chemical constituents of the powder. Further, screen the most potent compounds among them and determine the potential mechanism for treating UC. METHODS: In vivo, the therapeutic effect of SYQ's rhizome powder on UC was assessed by mice's body weight, DAI score, colon length, tissue MPO activity, serum inflammatory markers, etc. Additionally, HPLC was used to isolate and identify the specific chemical components of SYQ's rhizome powder. Then, the most effective compounds and their therapeutic targets were analysed and screened in SYQ rhizome powder using network pharmacology, combined with CCK-8 assay, NO release assay and molecular docking assay, in conjunction with CETSA, DARTS, SPR and enzyme activity assay. Finally, the biological effects of the key compound on the targets were validated using Western blot and ELISA. RESULTS: In vivo, SYQ rhizome powder effectively restored mice's body weight, lowered DAI and pathological score, downregulated the expression of inflammatory biomarkers, and restored colon length, as well as the colonic epithelial and mucus barriers. Afterward, 9 compounds were isolated and identified from the powder of the rhizomes of SYQ by HPLC. Nicotiflorin is the primary compound in SYQ with the highest concentration. According to both CCK-8 and NO release tests, Nicotiflorin is also the most efficacious compound. Combined with network pharmacological prediction, molecular docking analysis, CETSA, DARTS, SPR and enzyme activity assay, Nicotiflorin may ultimately suppress inflammation by targeting p65 and inhibiting the NF-κB pathway, thereby attenuating the activation of NLRP3 inflammasome. To verify this conclusion, Western blot and ELISA experiments were conducted. CONCLUSIONS: Our results suggest that the extract from SYQ rhizomes has therapeutic properties for UC. Its active ingredient Nicotiflorin exerted potent anti-UC effects by binding to p65 and inhibiting the activation of NF-κB and NLRP3 inflammasomes.

ICPNet: Advanced Maize Leaf Disease Detection with Multidimensional Attention and Coordinate Depthwise Convolution.

Maize is an important crop, and the detection of maize diseases is critical for ensuring food security and improving agricultural production efficiency. To address the challenges of difficult feature extraction due to the high similarity among maize leaf disease species, the blurring of image edge features, and the susceptibility of maize leaf images to noise during acquisition and transmission, we propose a maize disease detection method based on ICPNet (Integrated multidimensional attention coordinate depthwise convolution PSO (Particle Swarm Optimization)-Integrated lion optimisation algorithm network). Firstly, we introduce a novel attention mechanism called Integrated Multidimensional Attention (IMA), which enhances the stability and responsiveness of the model in detecting small speckled disease features by combining cross-attention and spatial channel reconstruction methods. Secondly, we propose Coordinate Depthwise Convolution (CDC) to enhance the accuracy of feature maps through multi-scale convolutional processing, allowing for better differentiation of the fuzzy edges of maize leaf disease regions. To further optimize model performance, we introduce the PSO-Integrated Lion Optimisation Algorithm (PLOA), which leverages the exploratory stochasticity and annealing mechanism of the particle swarm algorithm to enhance the model's ability to handle mutation points while maintaining training stability and robustness. The experimental results demonstrate that ICPNet achieved an average accuracy of 88.4% and a precision of 87.3% on the self-constructed dataset. This method effectively extracts the tiny and fuzzy edge features of maize leaf diseases, providing a valuable reference for disease control in large-scale maize production.

A Precise Segmentation Algorithm of Pumpkin Seedling Point Cloud Stem Based on CPHNet.

Accurate segmentation of the stem of pumpkin seedlings has a great influence on the modernization of pumpkin cultivation, and can provide detailed data support for the growth of pumpkin plants. We collected and constructed a pumpkin seedling point cloud dataset for the first time. Potting soil and wall background in point cloud data often interfere with the accuracy of partial cutting of pumpkin seedling stems. The stem shape of pumpkin seedlings varies due to other environmental factors during the growing stage. The stem of the pumpkin seedling is closely connected with the potting soil and leaves, and the boundary of the stem is easily blurred. These problems bring challenges to the accurate segmentation of pumpkin seedling point cloud stems. In this paper, an accurate segmentation algorithm for pumpkin seedling point cloud stems based on CPHNet is proposed. First, a channel residual attention multilayer perceptron (CRA-MLP) module is proposed, which suppresses background interference such as soil. Second, a position-enhanced self-attention (PESA) mechanism is proposed, enabling the model to adapt to diverse morphologies of pumpkin seedling point cloud data stems. Finally, a hybrid loss function of cross entropy loss and dice loss (HCE-Dice Loss) is proposed to address the issue of fuzzy stem boundaries. The experimental results show that CPHNet achieves a 90.4% average cross-to-merge ratio (mIoU), 93.1% average accuracy (mP), 95.6% average recall rate (mR), 94.4% F1 score (mF1) and 0.03 plants/second (speed) on the self-built dataset. Compared with other popular segmentation models, this model is more accurate and stable for cutting the stem part of the pumpkin seedling point cloud.

Identification of Pepper Leaf Diseases Based on TPSAO-AMWNet.

Pepper is a high-economic-value agricultural crop that faces diverse disease challenges such as blight and anthracnose. These diseases not only reduce the yield of pepper but, in severe cases, can also cause significant economic losses and threaten food security. The timely and accurate identification of pepper diseases is crucial. Image recognition technology plays a key role in this aspect by automating and efficiently identifying pepper diseases, helping agricultural workers to adopt and implement effective control strategies, alleviating the impact of diseases, and being of great importance for improving agricultural production efficiency and promoting sustainable agricultural development. In response to issues such as edge-blurring and the extraction of minute features in pepper disease image recognition, as well as the difficulty in determining the optimal learning rate during the training process of traditional pepper disease identification networks, a new pepper disease recognition model based on the TPSAO-AMWNet is proposed. First, an Adaptive Residual Pyramid Convolution (ARPC) structure combined with a Squeeze-and-Excitation (SE) module is proposed to solve the problem of edge-blurring by utilizing adaptivity and channel attention; secondly, to address the issue of micro-feature extraction, Minor Triplet Disease Focus Attention (MTDFA) is proposed to enhance the capture of local details of pepper leaf disease features while maintaining attention to global features, reducing interference from irrelevant regions; then, a mixed loss function combining Weighted Focal Loss and L2 regularization (WfrLoss) is introduced to refine the learning strategy during dataset processing, enhancing the model's performance and generalization capabilities while preventing overfitting. Subsequently, to tackle the challenge of determining the optimal learning rate, the tent particle snow ablation optimizer (TPSAO) is developed to accurately identify the most effective learning rate. The TPSAO-AMWNet model, trained on our custom datasets, is evaluated against other existing methods. The model attains an average accuracy of 93.52% and an F1 score of 93.15%, demonstrating robust effectiveness and practicality in classifying pepper diseases. These results also offer valuable insights for disease detection in various other crops.

High-Accuracy Tomato Leaf Disease Image-Text Retrieval Method Utilizing LAFANet.

Tomato leaf disease control in the field of smart agriculture urgently requires attention and reinforcement. This paper proposes a method called LAFANet for image-text retrieval, which integrates image and text information for joint analysis of multimodal data, helping agricultural practitioners to provide more comprehensive and in-depth diagnostic evidence to ensure the quality and yield of tomatoes. First, we focus on six common tomato leaf disease images and text descriptions, creating a Tomato Leaf Disease Image-Text Retrieval Dataset (TLDITRD), introducing image-text retrieval into the field of tomato leaf disease retrieval. Then, utilizing ViT and BERT models, we extract detailed image features and sequences of textual features, incorporating contextual information from image-text pairs. To address errors in image-text retrieval caused by complex backgrounds, we propose Learnable Fusion Attention (LFA) to amplify the fusion of textual and image features, thereby extracting substantial semantic insights from both modalities. To delve further into the semantic connections across various modalities, we propose a False Negative Elimination-Adversarial Negative Selection (FNE-ANS) approach. This method aims to identify adversarial negative instances that specifically target false negatives within the triplet function, thereby imposing constraints on the model. To bolster the model's capacity for generalization and precision, we propose Adversarial Regularization (AR). This approach involves incorporating adversarial perturbations during model training, thereby fortifying its resilience and adaptability to slight variations in input data. Experimental results show that, compared with existing ultramodern models, LAFANet outperformed existing models on TLDITRD dataset, with top1, top5, and top10 reaching 83.3% and 90.0%, and top1, top5, and top10 reaching 80.3%, 93.7%, and 96.3%. LAFANet offers fresh technical backing and algorithmic insights for the retrieval of tomato leaf disease through image-text correlation.

A Multi-Modal Open Object Detection Model for Tomato Leaf Diseases with Strong Generalization Performance Using PDC-VLD.

Precise disease detection is crucial in modern precision agriculture, especially in ensuring the health of tomato crops and enhancing agricultural productivity and product quality. Although most existing disease detection methods have helped growers identify tomato leaf diseases to some extent, these methods typically target fixed categories. When faced with new diseases, extensive and costly manual annotation is required to retrain the dataset. To overcome these limitations, this study proposes a multimodal model PDC-VLD based on the open-vocabulary object detection (OVD) technology within the VLDet framework, which can accurately identify new tomato leaf diseases without manual annotation by using only image-text pairs. First, we developed a progressive visual transformer-convolutional pyramid module (PVT-C) that effectively extracts tomato leaf disease features and optimizes anchor box positioning using the self-supervised learning algorithm DINO, suppressing interference from irrelevant backgrounds. Then, a context feature guided module (CFG) was adopted to address the low adaptability and recognition accuracy of the model in data-scarce environments. To validate the model's effectiveness, we constructed a tomato leaf disease image dataset containing 4 base classes and 2 new categories. Experimental results show that the PDC-VLD model achieved 61.2% on the main evaluation metric mAP novel 50 , and 56.4% on mAP novel 75 , 87.7% on mAP base 50 , 81.0% on mAP all 50 , and 45.5% on average recall, outperforming existing OVD models. Our research provides an innovative solution for efficiently and accurately detecting new diseases, substantially reducing the need for manual annotation, and offering critical technical support and practical reference for agricultural workers.

AISOA-SSformer: An Effective Image Segmentation Method for Rice Leaf Disease Based on the Transformer Architecture.

Rice leaf diseases have an important impact on modern farming, threatening crop health and yield. Accurate semantic segmentation techniques are crucial for segmenting diseased leaf parts and assisting farmers in disease identification. However, the diversity of rice growing environments and the complexity of leaf diseases pose challenges. To address these issues, this study introduces an innovative semantic segmentation algorithm for rice leaf pests and diseases based on the Transformer architecture AISOA-SSformer. First, it features the sparse global-update perceptron for real-time parameter updating, enhancing model stability and accuracy in learning irregular leaf features. Second, the salient feature attention mechanism is introduced to separate and reorganize features using the spatial reconstruction module (SRM) and channel reconstruction module (CRM), focusing on salient feature extraction and reducing background interference. Additionally, the annealing-integrated sparrow optimization algorithm fine-tunes the sparrow algorithm, gradually reducing the stochastic search amplitude to minimize loss. This enhances the model's adaptability and robustness, particularly against fuzzy edge features. The experimental results show that AISOA-SSformer achieves an 83.1% MIoU, an 80.3% Dice coefficient, and a 76.5% recall on a homemade dataset, with a model size of only 14.71 million parameters. Compared with other popular algorithms, it demonstrates greater accuracy in rice leaf disease segmentation. This method effectively improves segmentation, providing valuable insights for modern plantation management. The data and code used in this study will be open sourced at https://github.com/ZhouGuoXiong/Rice-Leaf-Disease-Segmentation-Dataset-Code.

A Precise Framework for Rice Leaf Disease Image-Text Retrieval Using FHTW-Net.

Cross-modal retrieval for rice leaf diseases is crucial for prevention, providing agricultural experts with data-driven decision support to address disease threats and safeguard rice production. To overcome the limitations of current crop leaf disease retrieval frameworks, we focused on four common rice leaf diseases and established the first cross-modal rice leaf disease retrieval dataset (CRLDRD). We introduced cross-modal retrieval to the domain of rice leaf disease retrieval and introduced FHTW-Net, a framework for rice leaf disease image-text retrieval. To address the challenge of matching diverse image categories with complex text descriptions during the retrieval process, we initially employed ViT and BERT to extract fine-grained image and text feature sequences enriched with contextual information. Subsequently, two-way mixed self-attention (TMS) was introduced to enhance both image and text feature sequences, with the aim of uncovering important semantic information in both modalities. Then, we developed false-negative elimination-hard negative mining (FNE-HNM) strategy to facilitate in-depth exploration of semantic connections between different modalities. This strategy aids in selecting challenging negative samples for elimination to constrain the model within the triplet loss function. Finally, we introduced warm-up bat algorithm (WBA) for learning rate optimization, which improves the model's convergence speed and accuracy. Experimental results demonstrated that FHTW-Net outperforms state-of-the-art models. In image-to-text retrieval, it achieved R@1, R@5, and R@10 accuracies of 83.5%, 92%, and 94%, respectively, while in text-to-image retrieval, it achieved accuracies of 82.5%, 98%, and 98.5%, respectively. FHTW-Net offers advanced technical support and algorithmic guidance for cross-modal retrieval of rice leaf diseases.