Time-varying metasurface driven broadband radar jamming and deceptions.

Conventional radar jamming and deception systems typically necessitate the custom design of complex circuits and algorithms to transmit an additional radio signal toward a detector. Consequently, they are often cumbersome, energy-intensive, and difficult to operate in broadband electromagnetic environment. With the ongoing trend of miniaturization of various devices and the improvement of radar system performance, traditional techniques no longer meet the requirements for broadband, seamless integration, and energy efficiency. Time-varying metasurfaces, capable of manipulating electromagnetic parameters in both temporal and spatial domains, have thus inspired many contemporary research studies to revisit established fields. In this paper, we introduce a time-varying metasurface driven radar jamming and deception system (TVM-RJD), which can perfectly overcome the aforementioned intrinsic challenges. Leveraging a programmable bias voltage, the TVM-RJD can alter the spectrum distribution of incident waves, thereby deceiving radar into making erroneous judgments about the target's location. Experimental outcomes affirm that the accuracy deviation of the TVM-RJD system is less than 0.368 meters, while achieving a remarkable frequency conversion efficiency of up to 96.67%. The TVM-RJD heralds the expansion into a wider application of electromagnetic spatiotemporal manipulation, paving the way for advancements in electromagnetic illusion, radar invisibility, etc.

Coating of maize seeds with acephate for precision agriculture: Safety assessment in earthworms, bees, and soil microorganisms.

Acephate is commonly used as a seed treatment (ST) in precision agriculture, but its impact on pollinators, earthworms, and soil microorganisms remains unclear. This study aimed to compare the fate of acephate seed dressing (SD) and seed coating (SC) treatments and assess potential risks to bees, earthworms, and soil microorganisms. Additionally, a follow-up study on maize seeds treated with acephate in a greenhouse was conducted to evaluate the maize growth process and the dissipation dynamics of the insecticide. The results indicated that acephate SC led to greater uptake and translocation in maize plants, resulting in lower residue levels in the soil. However, high concentrations of acephate metabolites in the soil had a negative impact on the body weight of earthworms, whereas acephate itself did not. The potential risk to bees from exposure to acephate ST was determined to be low, but dose-dependent effects were observed. Furthermore, acephate ST had no significant effect on soil bacterial community diversity and abundance compared to a control. This study provides valuable insights into the uptake and translocation of acephate SD and SC, and indicates that SC is safer than SD in terms of adverse effects on bees and nontarget soil organisms.

Green synthesized lignin nanoparticles for the sustainable delivery of pyraclostrobin to control strawberry diseases caused by Botrytis cinerea.

Lignin, renowned for its renewable, biocompatible, and environmentally benign characteristics, holds immense potential as a sustainable feedstock for agrochemical formulations. In this study, raw dealkaline lignin (DAL) underwent a purification process involving two sequential solvent extractions. Subsequently, an enzyme-responsive nanodelivery system (Pyr@DAL-NPs), was fabricated through the solvent self-assembly method, with pyraclostrobin (Pyr) loaded into lignin nanoparticles. The Pyr@DAL-NPs shown an average particle size of 250.4 nm, demonstrating a remarkable loading capacity of up to 54.70 % and an encapsulation efficiency of 86.15 %. Notably, in the presence of cellulase and pectinase at a concentration of 2 mg/mL, the release of Pyr from the Pyr@DAL-NPs reached 92.66 % within 120 h. Furthermore, the photostability of Pyr@DAL-NPs was significantly improved, revealing a 2.92-fold enhancement compared to the commercially available fungicide suspension (Pyr SC). Bioassay results exhibited that the Pyr@DAL-NPs revealed superior fungicidal activity against Botrytis cinerea over Pyr SC, with an EC50 value of 0.951 mg/L. Additionally, biosafety assessments indicated that the Pyr@DAL-NPs effectively declined the acute toxicity of Pyr towards zebrafish and posed no negative effects on the healthy growth of strawberry plants. In conclusion, this study presents a viable and promising strategy for developing environmentally friendly controlled-release systems for pesticides, offering the unique properties of lignin.

A Semi-Supervised Learning Framework for Classifying Colorectal Neoplasia Based on the NICE Classification.

Labelling medical images is an arduous and costly task that necessitates clinical expertise and large numbers of qualified images. Insufficient samples can lead to underfitting during training and poor performance of supervised learning models. In this study, we aim to develop a SimCLR-based semi-supervised learning framework to classify colorectal neoplasia based on the NICE classification. First, the proposed framework was trained under self-supervised learning using a large unlabelled dataset; subsequently, it was fine-tuned on a limited labelled dataset based on the NICE classification. The model was evaluated on an independent dataset and compared with models based on supervised transfer learning and endoscopists using accuracy, Matthew's correlation coefficient (MCC), and Cohen's kappa. Finally, Grad-CAM and t-SNE were applied to visualize the models' interpretations. A ResNet-backboned SimCLR model (accuracy of 0.908, MCC of 0.862, and Cohen's kappa of 0.896) outperformed supervised transfer learning-based models (means: 0.803, 0.698, and 0.742) and junior endoscopists (0.816, 0.724, and 0.863), while performing only slightly worse than senior endoscopists (0.916, 0.875, and 0.944). Moreover, t-SNE showed a better clustering of ternary samples through self-supervised learning in SimCLR than through supervised transfer learning. Compared with traditional supervised learning, semi-supervised learning enables deep learning models to achieve improved performance with limited labelled endoscopic images.

Magnetic Field-Optimized Paramagnetic Nanoprobe for T2/T1 Switchable Histopathological-Level MRI.

Traditional magnetic resonance imaging (MRI) contrast agents (CAs) are a type of "always on" system that accelerates proton relaxation regardless of their enrichment region. This "always on" feature leads to a decrease in signal differences between lesions and normal tissues, hampering their applications in accurate and early diagnosis. Herein, we report a strategy to fabricate glutathione (GSH)-responsive one-dimensional (1-D) manganese oxide nanoparticles (MONPs) with improved T2 relaxivities and achieve effective T2/T1 switchable MRI imaging of tumors. Compared to traditional contrast agents with high saturation magnetization to enhance T2 relaxivities, 1-D MONPs with weak Ms effectively increase the inhomogeneity of the local magnetic field and exhibit obvious T2 contrast. The inhomogeneity of the local magnetic field of 1-D MONPs is highly dependent on their number of primary particles and surface roughness according to Landau-Lifshitz-Gilbert simulations and thus eventually determines their T2 relaxivities. Furthermore, the GSH responsiveness ensures 1-D MONPs with sensitive switching from the T2 to T1 mode in vitro and subcutaneous tumors to clearly delineate the boundary of glioma and metastasis margins, achieving precise histopathological-level MRI. This study provides a strategy to improve T2 relaxivity of magnetic nanoparticles and construct switchable MRI CAs, offering high tumor-to-normal tissue contrast signal for early and accurate diagnosis.

The development and validation of automated machine learning models for predicting lymph node metastasis in Siewert type II T1 adenocarcinoma of the esophagogastric junction.

Background: Lymph node metastasis (LNM) is considered an essential prognosis factor for adenocarcinoma of the esophagogastric junction (AEG), which also affects the treatment strategies of AEG. We aimed to evaluate automated machine learning (AutoML) algorithms for predicting LNM in Siewert type II T1 AEG. Methods: A total of 878 patients with Siewert type II T1 AEG were selected from the Surveillance, Epidemiology, and End Results (SEER) database to develop the LNM predictive models. The patients from two hospitals in Suzhou were collected as the test set. We applied five machine learning algorithms to develop the LNM prediction models. The performance of predictive models was assessed using various metrics including accuracy, sensitivity, specificity, the area under the curve (AUC), and receiver operating characteristic (ROC) curve. Results: Patients with LNM exhibited a higher proportion of male individuals, a poor degree of differentiation, and submucosal infiltration, with statistical differences. The deep learning (DL) model demonstrated relatively good accuracy (0.713) and sensitivity (0.868) among the five models. Moreover, the DL model achieved the highest AUC (0.781) and sensitivity (1.000) in the test set. Conclusion: The DL model showed good predictive performance among five AutoML models, indicating the advantage of AutoML in modeling LNM prediction in patients with Siewert type II T1 AEG.

Deep learning in the radiologic diagnosis of osteoporosis: a literature review.

OBJECTIVE: Osteoporosis is a systemic bone disease characterized by low bone mass, damaged bone microstructure, increased bone fragility, and susceptibility to fractures. With the rapid development of artificial intelligence, a series of studies have reported deep learning applications in the screening and diagnosis of osteoporosis. The aim of this review was to summary the application of deep learning methods in the radiologic diagnosis of osteoporosis. METHODS: We conducted a two-step literature search using the PubMed and Web of Science databases. In this review, we focused on routine radiologic methods, such as X-ray, computed tomography, and magnetic resonance imaging, used to opportunistically screen for osteoporosis. RESULTS: A total of 40 studies were included in this review. These studies were divided into three categories: osteoporosis screening (n = 20), bone mineral density prediction (n = 13), and osteoporotic fracture risk prediction and detection (n = 7). CONCLUSIONS: Deep learning has demonstrated a remarkable capacity for osteoporosis screening. However, clinical commercialization of a diagnostic model for osteoporosis remains a challenge.

Development and Validation of Multimodal Models to Predict the 30-Day Mortality of ICU Patients Based on Clinical Parameters and Chest X-Rays.

We aimed to develop and validate multimodal ICU patient prognosis models that combine clinical parameters data and chest X-ray (CXR) images. A total of 3798 subjects with clinical parameters and CXR images were extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and an external hospital (the test set). The primary outcome was 30-day mortality after ICU admission. Automated machine learning (AutoML) and convolutional neural networks (CNNs) were used to construct single-modal models based on clinical parameters and CXR separately. An early fusion approach was used to integrate both modalities (clinical parameters and CXR) into a multimodal model named PrismICU. Compared to the single-modal models, i.e., the clinical parameter model (AUC = 0.80, F1-score = 0.43) and the CXR model (AUC = 0.76, F1-score = 0.45) and the scoring system APACHE II (AUC = 0.83, F1-score = 0.77), PrismICU (AUC = 0.95, F1 score = 0.95) showed improved performance in predicting the 30-day mortality in the validation set. In the test set, PrismICU (AUC = 0.82, F1-score = 0.61) was also better than the clinical parameters model (AUC = 0.72, F1-score = 0.50), CXR model (AUC = 0.71, F1-score = 0.36), and APACHE II (AUC = 0.62, F1-score = 0.50). PrismICU, which integrated clinical parameters data and CXR images, performed better than single-modal models and the existing scoring system. It supports the potential of multimodal models based on structured data and imaging in clinical management.

Transport and transformations of cadmium in water-biofilm-sediment phases as affected by hydrodynamic conditions.

Hydrodynamic conditions play a crucial role in governing the fate, transport, and risks of metal elements. However, the contribution of hydrodynamic conditions to the fate and transport of heavy metals among water, sediment, and biofilm phases is poorly understood. In our study, we conducted experiments in controlled hydrodynamic conditions using a total of 6 two-phase and 9 three-phase mesocosms consisting of water, biofilm, and sediment. We also measured Cd (cadmium) specification in different phases to assess how hydrodynamic forces control Cd bioavailability. We found that turbulent flow destroyed the surface morphology of the biofilm and significantly decreased the content of extracellular polymeric substances (p < 0.05). This led to a decrease in the biofilm's adsorption capacity for Cd, with the maximum adsorption capacity (0.124 mg/g) being one-tenth of that under static conditions (1.256 mg/g). The Cd chemical forms in the biofilm and sediment were significantly different, with the highest amount of Cd in the biofilm being acid-exchangeable, accounting for up to 95.1% of the total Cd content. Cd was more easily released in the biofilm due to its weak binding state, while Cd in the sediment existed in more stable chemical forms. Hydrodynamic conditions altered the migration behavior and distribution characteristics of Cd in the system by changing the adsorption capacity of the biofilm and sediment for Cd. Cd mobility increased in laminar flow but decreased in turbulent flow. These results enhance our understanding of the underlying mechanisms that control the mobility and bioavailability of metals in aquatic environments with varying hydrodynamic conditions.

Molecular mechanisms of pancreatic cancer liver metastasis: the role of PAK2.

Background: Pancreatic cancer remains an extremely malignant digestive tract tumor, posing a significant global public health burden. Patients with pancreatic cancer, once metastasis occurs, lose all hope of cure, and prognosis is extremely poor. It is important to investigate liver metastasis of Pancreatic cancer in depth, not just because it is the most common form of metastasis in pancreatic cancer, but also because it is crucial for treatment planning and prognosis assessment. This study aims to delve into the mechanisms of pancreatic cancer liver metastasis, with the goal of providing crucial scientific groundwork for the development of future treatment methods and drugs. Methods: We explored the mechanisms of pancreatic cancer liver metastasis using single-cell sequencing data (GSE155698 and GSE154778) and bulk data (GSE71729, GSE19279, TCGA-PAAD). Initially, Seurat package was employed for single-cell data processing to obtain expression matrices for primary pancreatic cancer lesions and liver metastatic lesions. Subsequently, high-dimensional weighted gene co-expression network analysis (hdWGCNA) was used to identify genes associated with liver metastasis. Machine learning algorithms and COX regression models were employed to further screen genes related to patient prognosis. Informed by both biological understanding and the outcomes of algorithms, we meticulously identified the ultimate set of liver metastasis-related gene (LRG). In the study of LRG genes, various databases were utilized to validate their association with pancreatic cancer liver metastasis. In order to analyze the effects of these agents on tumor microenvironment, we conducted an in-depth analysis, including changes in signaling pathways (GSVA), cell differentiation (pseudo-temporal analysis), cell communication networks (cell communication analysis), and downstream transcription factors (transcription factor activity prediction). Additionally, drug sensitivity analysis and metabolic analysis were performed to reveal the effects of LRG on gemcitabine resistance and metabolic pathways. Finally, functional experiments were conducted by silencing the expression of LRG in PANC-1 and Bx-PC-3 cells to validate its influence to proliferation and invasiveness on PANC-1 and Bx-PC-3 cells. Results: Through a series of algorithmic filters, we identified PAK2 as a key gene promoting pancreatic cancer liver metastasis. GSVA analysis elucidated the activation of the TGF-beta signaling pathway by PAK2 to promote the occurrence of liver metastasis. Pseudo-temporal analysis revealed a significant correlation between PAK2 expression and the lower differentiation status of pancreatic cancer cells. Cell communication analysis revealed that overexpression of PAK2 promotes communication between cancer cells and the tumor microenvironment. Transcription factor activity prediction displayed the transcription factor network regulated by PAK2. Drug sensitivity analysis and metabolic analysis revealed the impact of PAK2 on gemcitabine resistance and metabolic pathways. CCK8 experiments showed that silencing PAK2 led to a decrease in the proliferative capacity of pancreatic cancer cells and scratch experiments demonstrated that low expression of PAK2 decreased invasion capability in pancreatic cancer cells. Flow cytometry reveals that PAK2 significantly inhibited apoptosis in pancreatic cancer cell lines. Molecules related to the TGF-beta pathway decreased with the inhibition of PAK2, and there were corresponding significant changes in molecules associated with EMT. Conclusion: PAK2 facilitated the angiogenic potential of cancer cells and promotes the epithelial-mesenchymal transition process by activating the TGF-beta signaling pathway. Simultaneously, it decreased the differentiation level of cancer cells, consequently enhancing their malignancy. Additionally, PAK2 fostered communication between cancer cells and the tumor microenvironment, augments cancer cell chemoresistance, and modulates energy metabolism pathways. In summary, PAK2 emerged as a pivotal gene orchestrating pancreatic cancer liver metastasis. Intervening in the expression of PAK2 may offer a promising therapeutic strategy for preventing liver metastasis of pancreatic cancer and improving its prognosis.

Few-shot learning for the classification of intestinal tuberculosis and Crohn's disease on endoscopic images: A novel learn-to-learn framework.

Background and aim: Standard deep learning methods have been found inadequate in distinguishing between intestinal tuberculosis (ITB) and Crohn's disease (CD), a shortcoming largely attributed to the scarcity of available samples. In light of this limitation, our objective is to develop an innovative few-shot learning (FSL) system, specifically tailored for the efficient categorization and differential diagnosis of CD and ITB, using endoscopic image data with minimal sample requirements. Methods: A total of 122 white-light endoscopic images (99 CD images and 23 ITB images) were collected (one ileum image from each patient). A 2-way, 3-shot FSL model that integrated dual transfer learning and metric learning strategies was devised. Xception architecture was selected as the foundation and then underwent a dual transfer process utilizing oesophagitis images sourced from HyperKvasir. Subsequently, the eigenvectors derived from the Xception for each query image were converted into predictive scores, which were calculated using the Euclidean distances to six reference images from the support sets. Results: The FSL model, which leverages dual transfer learning, exhibited enhanced performance metrics (AUC 0.81) compared to a model relying on single transfer learning (AUC 0.56) across three evaluation rounds. Additionally, its performance surpassed that of a less experienced endoscopist (AUC 0.56) and even a more seasoned specialist (AUC 0.61). Conclusions: The FSL model we have developed demonstrates efficacy in distinguishing between CD and ITB using a limited dataset of endoscopic imagery. FSL holds value for enhancing the diagnostic capabilities of rare conditions.

Pectin-Coated Iron-Based Metal-Organic Framework Nanoparticles for Enhanced Foliar Adhesion and Targeted Delivery of Fungicides.

Conventional agrochemicals are underutilized due to their large particle sizes, poor foliar retention rates, and difficult translocation in plants, and the development of functional nanodelivery carriers with high adhesion to the plant body surface and efficient uptake and translocation in plants remains challenging. In this study, a nanodelivery system based on a pectin-encapsulated iron-based MOF (TF@Fe-MOF-PT NPs) was constructed to enhance the utilization of thifluzamide (TF) in rice plants by taking advantage of the pectin affinity for plant cell walls. The prepared TF@Fe-MOF-PT NPs exhibited an average particle size of 126.55 nm, a loading capacity of 27.41%, and excellent dual-stimulus responses to reactive oxygen species and pectinase. Foliar washing experiments showed that the TF@Fe-MOF-PT NPs were efficiently adhered to the surfaces of rice leaves and stems. Confocal laser scanning microscopy showed that fluorescently labeled TF@Fe-MOF-PT NPs were bidirectionally delivered through vascular bundles in rice plants. The in vitro bactericidal activity of the TF@Fe-MOF-PT NPs showed better inhibitory activity than that of a TF suspension (TF SC), with an EC50 of 0.021 mg/L. A greenhouse test showed that the TF@Fe-MOF-PT NPs were more effective than TF SC at 7 and 14 d, with control effects of 85.88 and 78.59%, respectively. It also reduced the inhibition of seed stem length and root length by TF SC and promoted seedling growth. These results demonstrated that TF@Fe-MOF-PT NPs can be used as a pesticide nanodelivery system for efficient delivery and intelligent release in plants and applied for sustainable control of pests and diseases.

Development and validation of a multimodal model in predicting severe acute pancreatitis based on radiomics and deep learning.

OBJECTIVE: Aim to establish a multimodal model for predicting severe acute pancreatitis (SAP) using machine learning (ML) and deep learning (DL). METHODS: In this multicentre retrospective study, patients diagnosed with acute pancreatitis at admission were enrolled from January 2017 to December 2021. Clinical information within 24 h and CT scans within 72 h of admission were collected. First, we trained Model α based on clinical features selected by least absolute shrinkage and selection operator analysis. Second, radiomics features were extracted from 3D-CT scans and Model ß was developed on the features after dimensionality reduction using principal component analysis. Third, Model γ was trained on 2D-CT images. Lastly, a multimodal model, namely PrismSAP, was constructed based on aforementioned features in the training set. The predictive accuracy of PrismSAP was verified in the validation and internal test sets and further validated in the external test set. Model performance was evaluated using area under the curve (AUC), accuracy, sensitivity, specificity, recall, precision and F1-score. RESULTS: A total of 1,221 eligible patients were randomly split into a training set (n = 864), a validation set (n = 209) and an internal test set (n = 148). Data of 266 patients were for external testing. In the external test set, PrismSAP performed best with the highest AUC of 0.916 (0.873-0.960) among all models [Model α: 0.709 (0.618-0.800); Model ß: 0.749 (0.675-0.824); Model γ: 0.687 (0.592-0.782); MCTSI: 0.778 (0.698-0.857); RANSON: 0.642 (0.559-0.725); BISAP: 0.751 (0.668-0.833); SABP: 0.710 (0.621-0.798)]. CONCLUSION: The proposed multimodal model outperformed any single-modality models and traditional scoring systems.

Deep learning to predict esophageal variceal bleeding based on endoscopic images.

OBJECTIVE: Esophageal varix (EV) bleeding is a particularly serious complications of cirrhosis. Prediction of EV bleeding requires extensive endoscopy experience; it remains unreliable and inefficient. This retrospective cohort study evaluated the feasibility of using deep learning (DL) to predict the 12-month risk of EV bleeding based on endoscopic images. METHODS: Six DL models were trained to perform binary classification of endoscopic images of EV bleeding. The models were subsequently validated using an external test dataset, then compared with classifications performed by two endoscopists. RESULTS: In the validation dataset, EfficientNet had the highest accuracy (0.910), followed by ConvMixer (0.898) and Xception (0.875). In the test dataset, EfficientNet maintained the highest accuracy (0.893), which was better than the endoscopists (0.800 and 0.763). Notably, one endoscopist displayed higher recall (0.905), compared with EfficientNet (0.870). When their predictions were assisted by artificial intelligence, the accuracies of the two endoscopists increased by 17.3% and 19.0%. Moreover, statistical agreement among the models was dependent on model architecture. CONCLUSIONS: This study demonstrated the feasibility of using DL to predict the 12-month risk of EV bleeding based on endoscopic images. The findings suggest that artificial intelligence-aided diagnosis will be a useful addition to cirrhosis management.

Public Imaging Datasets of Gastrointestinal Endoscopy for Artificial Intelligence: a Review.

With the advances in endoscopic technologies and artificial intelligence, a large number of endoscopic imaging datasets have been made public to researchers around the world. This study aims to review and introduce these datasets. An extensive literature search was conducted to identify appropriate datasets in PubMed, and other targeted searches were conducted in GitHub, Kaggle, and Simula to identify datasets directly. We provided a brief introduction to each dataset and evaluated the characteristics of the datasets included. Moreover, two national datasets in progress were discussed. A total of 40 datasets of endoscopic images were included, of which 34 were accessible for use. Basic and detailed information on each dataset was reported. Of all the datasets, 16 focus on polyps, and 6 focus on small bowel lesions. Most datasets (n = 16) were constructed by colonoscopy only, followed by normal gastrointestinal endoscopy and capsule endoscopy (n = 9). This review may facilitate the usage of public dataset resources in endoscopic research.

Green synthesis of a chlorfenapyr chitosan nanopesticide for maize root application: Reducing environmental pollution and risks to nontarget organisms.

Chlorfenapyr (CHL) is a pyrrole insecticide with a novel structure that is used to control resistant pests. However, its weak systemic activity limits its application to crop roots. Herein, a novel CHL formulation with improved effective utilization rates and suitability for root application is developed to avoid or reduce contamination caused by pesticide spraying. Accordingly, we prepared CHL@CS/CMCS nanoparticle (NP) suspensions with a particle size of approximately 100 nm using chitosan (CS) and carboxymethyl chitosan (CMCS). These suspensions exhibited better thermal stability, adhesion, permeability and systemic activity than a CHL suspension concentrate (CHL-SC). The nanoformulation deposition rate on maize leaves after spraying was 12.28 mg/kg, significantly higher than that of CHL-SC. The nanosuspension was effectively absorbed and transported by roots after irrigation and was suitable for root application. The efficacy was 89.46-92.36 % against Spodoptera frugiperda at 7 d, 7.5-17.5 times higher than that of CHL-SC. Furthermore, the CHL@CS/CMCS nanosuspension was safer for earthworms. These results suggest that chitosan-based nanoformulations improve the efficacy, utilization efficiency and active period of CHL control, providing a new approach for CHL application, reducing pollutant dispersal and the environmental impacts of pesticide application and facilitating sustainable agricultural production.

Efficacy of drip irrigation with thiamethoxam on control of Monolepta hieroglyphica, and uptake, translocation and dietary risk of thiamethoxam in maize.

BACKGROUND: Monolepta hieroglyphica (Motschulsky) is an important agricultural pest that causes significant economic losses in terms of crop production. Conventional pesticide spraying treatments can result in pesticide drift, endanger nontarget organisms and cause pests to fly away, resulting in unsatisfactory prevention and control effects. To study the effect of thiamethoxam on the control of maize M. hieroglyphica, a field experiment was conducted to determine the optimal thiamethoxam application dose, its spatial and temporal distribution dynamics, and its dietary risk based on its control effect when applied by spray and drip irrigation. RESULTS: The results of the field trials showed that compared with spray irrigation, drip irrigation resulted in greater control starting from Day 5. This result was a consequence of the hysteresis effect of thiamethoxam being first absorbed by the roots and then continuously transferred upward, where it accumulates. After 30 days of drip irrigation with 75 and 150 g a.i. ha-1 thiamethoxam, the control effect on M. hieroglyphica was 32.41-49.44% and 69.77-80.57%, respectively. The results of the dietary risk assessment showed that the risk of thiamethoxam ingestion through maize kernels was acceptable regarding its effect on human health. CONCLUSIONS: Drip irrigation with thiamethoxam can improve the effective utilization rate of pesticides, achieve precise control of maize M. hieroglyphica, and provide a new method for sustainable agricultural production. © 2023 Society of Chemical Industry.

Young LINE-1 transposon 5' UTRs marked by elongation factor ELL3 function as enhancers to regulate naïve pluripotency in embryonic stem cells.

LINE-1s are the major clade of retrotransposons with autonomous retrotransposition activity. Despite the potential genotoxicity, LINE-1s are highly activated in early embryos. Here we show that a subset of young LINE-1s, L1Md_Ts, are marked by the RNA polymerase II elongation factor ELL3, and function as enhancers in mouse embryonic stem cells. ELL3 depletion dislodges the DNA hydroxymethylase TET1 and the co-repressor SIN3A from L1Md_Ts, but increases the enrichment of the Bromodomain protein BRD4, leading to loss of 5hmC, gain of H3K27ac, and upregulation of the L1Md_T nearby genes. Specifically, ELL3 occupies and represses the L1Md_T-based enhancer located within Akt3, which encodes a key regulator of AKT pathway. ELL3 is required for proper ERK activation and efficient shutdown of naïve pluripotency through inhibiting Akt3 during naïve-primed transition. Our study reveals that the enhancer function of a subset of young LINE-1s controlled by ELL3 in transcription regulation and mouse early embryo development.

Identification of gastric signet ring cell carcinoma based on endoscopic images using few-shot learning.

BACKGROUND: Deep learning (DL) models perform poorly when there are limited gastric signet ring cell carcinoma (SRCC) samples. Few-shot learning (FSL) can address the small sample problem. METHODS: EfficientNetV2-S was first pretrained on ImageNet and then pretrained on esophageal endoscopic images (i.e., base classes: normal vs. early cancer vs. advanced cancer) using transfer learning. Second, images of gastric benign ulcers, adenocarcinoma and SRCC, i.e., novel classes (n = 50 per class), were included. Image features were extracted as vectors using the dual pretrained EfficientNetV2-S. Finally, a k-nearest neighbor classifier was used to identify SRCC. The above proposed 3-way 3-shot FSL framework was conducted in three rounds. RESULTS: Dual pretrained FSL performed better than single pretrained FSL, endoscopists and traditional EfficientNetV2-S models. Dual pretrained FSL obtained the highest accuracy (79.4%), sensitivity (68.8%), recall (68.8%), precision (69.3%) and F1-score (0.691), and the senior endoscopist achieved the highest specificity of 93.6% when identifying SRCC. The macro-AUC and F1-score for dual pretraining (0.763 and 0.703, respectively) were higher than those for single pretraining (0.656 and 0.537, respectively), along with endoscopists and traditional EfficientNetV2-S models. The 2-way 3-shot FSL also performed better. CONCLUSION: The proposed FSL framework showed practical performance in the differentiation of SRCC on endoscopic images, suggesting the potential of FSL in the computer-aided diagnosis for rare diseases.

Enhanced Insecticidal Activity of Chlorfenapyr against Spodoptera frugiperda by Reshaping the Intestinal Microbial Community and Interfering with the Metabolism of Iron-Based Metal-Organic Frameworks.

Spodoptera frugiperda (S. frugiperda) is an invasive pest that threatens global crop production and food security and poses a serious threat to maize production worldwide. Metal-organic framework (MOF) nanocarriers have great potential for agricultural pest control applications. The present study successfully prepared the chemical cross-linking of iron-based metal-organic framework nanoparticles (MIL-101(Fe)-NH2 NPs) with sodium lignosulfonate (SL) as a pH/laccase double stimuli-responsive pesticide release system. The average particle size of the prepared chlorfenapyr (CF)-loaded nanoparticles (CF@MIL-101-SL NPs) was 161.54 nm, and the loading efficiency was 44.52%. Bioactivity assays showed that CF@MIL-101-SL NPs increased the toxicity of CF to S. frugiperda and caused the rupture of the peritrophic membrane and enlargement of the midgut. Data from 16S rRNA gene sequencing showed that CF@MIL-101-SL treatment reduced the resistance of S. frugiperda to pesticides and pathogens and affected nutrient and energy availability by remodeling the intestinal microbiota of S. frugiperda. The dysregulated microbial community interacted with the broken peritrophic membrane, which exacerbated damage to the host. Nontargeted metabolomic results showed that ABC transporters may be a potential mechanism for the enhanced toxicity of CF@MIL-101-SL to S. frugiperda. In summary, the present study provides effective strategies for toxicological studies of nanopesticides against insects.