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Smart farms are crucial in modern agriculture, but current object detection algorithms cannot detect chili Phytophthora blight accurately. To solve this, we introduced the YOLOv8-GDCI model, which can detect the disease on leaves, fruits, and stem bifurcations. The model uses RepGFPN for feature fusion, Dysample upsampling for accuracy, CA attention for feature capture, and Inner-MPDIoU loss for small object detection. In addition, we also created a dataset of chili Phytophthora blight on leaves, fruits, and stem bifurcations, and conducted comparative experiments. The results manifest that the YOLOv8-GDCI model demonstrates outstanding performance across a gamut of comprehensive indicators. In comparison with the YOLOv8n model, the YOLOv8-GDCI model demonstrates an improvement of 0.9% in precision, an increase of 1.8% in recall, and a remarkable enhancement of 1.7% in average precision. Although the FPS decreases slightly, it still exceeds the industry standard for real-time object detection (FPS > 60), thus meeting the requirements for real-time detection. [ABSTRACT FROM AUTHOR]

Objective: Accurately predicting knee osteoarthritis (KOA) is essential for early detection and personalized treatment. We aimed to develop and test a magnetic resonance imaging (MRI)‐based joint space (JS) radiomic model (RM) to predict radiographic KOA incidence through neural networks by integrating meniscus and femorotibial cartilage radiomic features. Methods: In the Osteoarthritis Initiative cohort, participants with knees without radiographic KOA at baseline but at high risk for radiographic KOA were included. Patients' knees developed radiographic KOA, whereas control knees did not over four years. We randomly split the participants into development and test cohorts (8:2) and extracted features from baseline three‐dimensional double‐echo steady‐state sequence MRI. Model performance was evaluated using an area under the receiver operating characteristic curve (AUC), sensitivity, and specificity in both cohorts. Nine resident surgeons performed the reader experiment without/with the JS‐RM aid. Results: Our study included 549 knees in the development cohort (275 knees of patients with KOA vs 274 knees of controls) and 137 knees in the test cohort (68 knees of patients with KOA vs 69 knees of controls). In the test cohort, JS‐RM had a favorable accuracy for predicting the radiographic KOA incidence with an AUC of 0.931 (95% confidence interval [CI] 0.876–0.963), a sensitivity of 84.4% (95% CI 83.9%–84.9%), and a specificity of 85.6% (95% CI 85.2%–86.0%). The mean specificity and sensitivity of resident surgeons through MRI reading in predicting radiographic KOA incidence were increased from 0.474 (95% CI 0.333–0.614) and 0.586 (95% CI 0.429–0.743) without the assistance of JS‐RM to 0.874 (95% CI 0.847–0.901) and 0.812 (95% CI 0.742–0.881) with JS‐RM assistance, respectively (P < 0.001). Conclusion: JS‐RM integrating the features of the meniscus and cartilage showed improved predictive values in radiographic KOA incidence. [ABSTRACT FROM AUTHOR]

Addressing the issue of incorrect and missed detections caused by the complex types, uneven scales, and small sizes of defect targets in transmission lines, this paper proposes a defect-detection method based on cross-scale feature fusion, PGE-YOLO. Firstly, feature extraction is enriched by replacing the convolutional blocks in the backbone network that need to be cascaded and fused using the Par_C2f network module, which incorporates a parallel network (ParNet). Secondly, a four-layer efficient multi-scale attention (EMA) mechanism is incorporated into the network's neck to address long and short dependency issues. This enhancement aims to improve global information retention by employing parallel substructures and integrating cross-space feature information. Finally, the paradigm of generalized feature fusion (GFPN) is introduced and reconfigured to develop a novel CE-GFPN. This model effectively integrates shallow feature information with deep feature information to enhance the capability of feature fusion and improve detection performance. Using a real transmission line multi-defect dataset from UAV aerial photography and the CPLID dataset, ablation and comparison experiments with various models demonstrated that our model achieved superior results. Compared to the initial YOLOv8n model, our model increased the detection accuracy by 6.6% and 1.2%, respectively, while ensuring there is no surge in the number of parameters. This ensures that the real-time and accuracy requirements for defect detection in the industry are satisfied. [ABSTRACT FROM AUTHOR]

The infrapatellar fat pad (IPFP) and synovium play essential roles in maintaining knee joint homeostasis and in the progression of osteoarthritis (OA). The cellular and transcriptional mechanisms regulating the function of these specialized tissues under healthy and diseased conditions are largely unknown. Here, single-cell and single-nuclei RNA sequencing of human IPFP and synovial tissues were performed to elucidate the cellular composition and transcriptional profile. Computational trajectory analysis revealed that dipeptidyl peptidase 4+ mesenchymal cells function as a common progenitor for IPFP adipocytes and synovial lining layer fibroblasts, suggesting that IPFP and synovium represent an integrated tissue unit. OA induced a profibrotic and inflammatory phenotype in mesenchymal lineage cells with biglycan+ intermediate fibroblasts as a major contributor to OA fibrosis. Apolipoprotein E (APOE) signaling from intermediate fibroblasts and macrophages was identified as a critical regulatory factor. Ex vivo incubation of human cartilage with soluble APOE accelerated proteoglycan degeneration. Inhibition of APOE signaling by intra-articular injection of an anti-APOE neutralizing antibody attenuated the progression of collagenase-induced OA in mice, demonstrating a detrimental effect of APOE on cartilage. Our studies provide a framework for designing further therapeutic strategies for OA by describing the cellular and transcriptional landscape of human IPFP and synovium in healthy versus OA joints. Editor's summary: Osteoarthritis (OA) is a disease of the entire joint; however, the combined role of the infrapatellar fat pad and synovium is largely unknown. Tang et al. established a single-cell transcriptomic profile of the human fat pad and synovium in knee joints and described changes in mesenchymal and immune populations between healthy and OA conditions. In silico reconstruction of cell-cell communication in the joint implicated apolipoprotein E (APOE) from intermediate fibroblasts and inflammatory macrophages in the infrapatellar fat pad as a potential driver of disease progression. Antibody-mediated blockade of APOE in a murine model of collagenase-induced arthritis further supported a role for APOE in OA pathology and warrants further study. —Molly Ogle [ABSTRACT FROM AUTHOR]

Infrapatellar fat pad (IPFP) is closely associated with the development and progression of knee osteoarthritis (OA), but the underlying mechanism remains unclear. Here, it is find that IPFP from OA patients can secret small extracellular vesicles (sEVs) and deliver them into articular chondrocytes. Inhibition the release of endogenous osteoarthritic IPFP‐sEVs by GW4869 significantly alleviated IPFP‐sEVs‐induced cartilage destruction. Functional assays in vitro demonstrated that IPFP‐sEVs significantly promoted chondrocyte extracellular matrix (ECM) catabolism and induced cellular senescence. It is further demonstrated that IPFP‐sEVs induced ECM degradation in human and mice cartilage explants and aggravated the progression of experimental OA in mice. Mechanistically, highly enriched let‐7b‐5p and let‐7c‐5p in IPFP‐sEVs are essential to mediate detrimental effects by directly decreasing senescence negative regulator, lamin B receptor (LBR). Notably, intra‐articular injection of antagomirs inhibiting let‐7b‐5p and let‐7c‐5p in mice increased LBR expression, suppressed chondrocyte senescence and ameliorated the progression of experimental OA model. This study uncovers the function and mechanism of the IPFP‐sEVs in the progression of OA. Targeting IPFP‐sEVs cargoes of let‐7b‐5p and let‐7c‐5p can provide a potential strategy for OA therapy. [ABSTRACT FROM AUTHOR]

The therapeutic efficacy of bone marrow mesenchymal stem cells (BMSCs) in myocardial infarction (MI) is hindered by poor cell survival. This study explored the role of N6-methyladenosine (m6A) regulation, specifically YTHDC1, in improving BMSC transplantation for MI. By screening m6A-related regulators in hypoxia and serum deprivation (HSD)-induced BMSC apoptosis, YTHDC1 was found to be downregulated. Overexpression of Ythdc1 in BMSCs reduced apoptosis markers, reactive oxygen species (ROS) release, and improved cell survival under HSD conditions. Conversely, Ythdc1 knockdown enhanced apoptosis. In rat MI models, transplantation of Ythdc1-overexpressing BMSCs improved cardiac function and reduced myocardial fibrosis. Mechanistically, YTHDC1 interacts with nuclear factor kappa B (NF-κB) inhibitor-alpha mRNA, suggesting its involvement in BMSC survival pathways. This study identifies YTHDC1 as a potential target to enhance BMSC efficacy in MI therapy. [ABSTRACT FROM AUTHOR]