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Traditional mechanized chestnut harvesting is too costly for small producers, non-selective, and prone to damaging nuts. Accurate, reliable detection of chestnuts on the orchard floor is crucial for developing low-cost, vision-guided automated harvesting technology. However, developing a reliable chestnut detection system faces challenges in complex environments with shading, varying natural light conditions, and interference from weeds, fallen leaves, stones, and other foreign on-ground objects, which have remained unaddressed. This study collected 319 images of chestnuts on the orchard floor, containing 6524 annotated chestnuts. A comprehensive set of 29 state-of-the-art real-time object detectors, including 14 in the YOLO (v11–v13) and 15 in the RT-DETR (v1–v4) families at various model scales, was systematically evaluated through replicated modeling experiments for chestnut detection. Experimental results show that the YOLOv12m model achieved the best mAP@0.5 of 95.1% among all the evaluated models, while RT-DETRv2-R101 was the most accurate variant among the RT-DETR models, with mAP@0.5 of 91.1%. In terms of mAP@[0.5:0.95], the YOLOv11x model achieved the best accuracy of 80.1%. All models demonstrated significant potential for real-time chestnut detection, and YOLO models outperformed RT-DETR models in terms of both detection accuracy and inference, making them better suited for on-board deployment. This work lays a foundation for developing AI-based, vision-guided intelligent chestnut harvest systems.

Background The predictive value of 18 F-fluorodeoxyglucose positron emission tomography/computed tomography ( 18 F-FDG PET/CT) metabolic parameters for predicting AIP relapse is currently unknown. This study firstly explored the value of 18 F-FDG PET/CT parameters as predictors of type 1 AIP relapse. Methods This multicenter retrospective cohort study analyzed 51 patients who received 18 F-FDG PET/CT prior to treatment and did not receive maintenance therapy after remission. The study collected baseline characteristics and clinical data and conducted qualitative and semi-quantitative analysis of pancreatic lesions and extrapancreatic organs. The study used three thresholds to select the boundaries of pancreatic lesions to evaluate metabolic parameters, including the maximum standard uptake value (SUV max ), mean standard uptake value (SUV mean ), total lesion glycolysis (TLG), metabolic tumor volume (MTV), and tumor-to-normal liver standard uptake value ratio (SUVR). Univariate and multivariate analyses were performed to identify independent predictors and build a recurrence prediction model. The model was internally validated using the bootstrap method and a nomogram was created for clinical application. Results In the univariable analysis, the relapsed group showed higher levels of SUV max (6.0 ± 1.6 vs. 5.2 ± 1.1; P  = 0.047), SUVR (2.3 [2.0–3.0] vs. 2.0 [1.6–2.4]; P  = 0.026), and TLG 2.5 (234.5 ± 149.1 vs. 139.6 ± 102.5; P  = 0.020) among the 18 F-FDG PET metabolic parameters compared to the non-relapsed group. In the multivariable analysis, serum IgG 4 (OR, 1.001; 95% CI, 1.000–1.002; P  = 0.014) and TLG 2.5 (OR, 1.007; 95% CI, 1.002–1.013; P  = 0.012) were independent predictors associated with relapse of type 1 AIP. A receiver-operating characteristic curve of the predictive model with these two predictors demonstrated an area under the curve of 0.806. Conclusion 18 F-FDG PET/CT metabolic parameters, particularly TLG 2.5 , are potential predictors for relapse in patients with type 1 AIP. A multiparameter model that includes IgG4 and TLG2.5 can enhance the ability to predict AIP relapse.

In this work, a new multi-component (CrFeMoNbZr)Ox system was developed. The thin films presented dual-phase amorphous structures, comprising a dominant amorphous alloy phase and a small amount of an amorphous oxide phase. The thin films showed higher hardness and better corrosion resistance than a commercial Zr-based alloy. The combined properties of high hardness and superior corrosion-resistance make the amorphous thin film a candidate for coating materials on commercial Zr-based alloys for engineering applications.

Aiming at the drawbacks of conventional methods for the digestion of total dissolved phosphorus ( TDP ) in water, a novel ozone-based photoactivated synergistic oxidative digestion device based on a high-voltage discharge ozone generator with an oxygen source and a jacketed bubbling reactor was presented. The effects of reaction temperature, reaction time, ozone flow, initial pH of solution, common inorganic anions (Cl – , , ), and total nitrogen on the digestion efficiency were investigated. Combining the improved ammonium molybdate spectrophotometric method, proposed the new determination method for TDP and verified its performance within the range of 0.1–2.0 mg/L. Results showed that this method has good linearity ( R 2 = 0.9997), precision (0.40%), accuracy (–0.50%), and a low limit of detection (0.0042 mg/L) under the optimized digestion process conditions (reaction temperature of 40°C, reaction time of 20 min, ozone flow of 0.6 L/min, initial pH of 5). This method provides a promising approach for routine analysis or online monitoring of TDP in water.

The predictive value of F-fluorodeoxyglucose positron emission tomography/computed tomography ( F-FDG PET/CT) metabolic parameters for predicting AIP relapse is currently unknown. This study firstly explored the value of F-FDG PET/CT parameters as predictors of type 1 AIP relapse. This multicenter retrospective cohort study analyzed 51 patients who received F-FDG PET/CT prior to treatment and did not receive maintenance therapy after remission. The study collected baseline characteristics and clinical data and conducted qualitative and semi-quantitative analysis of pancreatic lesions and extrapancreatic organs. The study used three thresholds to select the boundaries of pancreatic lesions to evaluate metabolic parameters, including the maximum standard uptake value (SUV ), mean standard uptake value (SUV ), total lesion glycolysis (TLG), metabolic tumor volume (MTV), and tumor-to-normal liver standard uptake value ratio (SUVR). Univariate and multivariate analyses were performed to identify independent predictors and build a recurrence prediction model. The model was internally validated using the bootstrap method and a nomogram was created for clinical application. In the univariable analysis, the relapsed group showed higher levels of SUV (6.0 ± 1.6 vs. 5.2 ± 1.1; P = 0.047), SUVR (2.3 [2.0-3.0] vs. 2.0 [1.6-2.4]; P = 0.026), and TLG (234.5 ± 149.1 vs. 139.6 ± 102.5; P = 0.020) among the F-FDG PET metabolic parameters compared to the non-relapsed group. In the multivariable analysis, serum IgG (OR, 1.001; 95% CI, 1.000-1.002; P = 0.014) and TLG (OR, 1.007; 95% CI, 1.002-1.013; P = 0.012) were independent predictors associated with relapse of type 1 AIP. A receiver-operating characteristic curve of the predictive model with these two predictors demonstrated an area under the curve of 0.806. F-FDG PET/CT metabolic parameters, particularly TLG , are potential predictors for relapse in patients with type 1 AIP. A multiparameter model that includes IgG4 and TLG2.5 can enhance the ability to predict AIP relapse.

The ability to widely tune the optical properties of amorphous alloys is highly desirable especially for their potential applications in optoelectronic devices. In this work, we demonstrate that introducing oxygen into an amorphous alloy system of Co-Fe-Ta-B enables the formation of various amorphous derivatives ranging from metals to semiconductors, and eventually to insulators. These oxygen-containing amorphous derivatives gradually become transparent with the opened bandgaps, leading to a continuous increase in their optical transmittance. Furthermore, the reflective metal-type amorphous alloy and transparent insulator-type amorphous oxide of the system can be integrated together to realize the full-color tuning over the entire visible spectral range. This provides a new way to develop large-area color coatings with high design flexibility and full-color tun-ability. We envisage that the design concept proposed in this work is also applicable to many other amorphous alloy systems, from which all types of amorphous materials including alloys, semiconductors and insulators may be developed to show unprecedented optical functionalities.

Traditional mechanized chestnut harvesting is too costly for small producers, non-selective, and prone to damaging nuts. Accurate, reliable detection of chestnuts on the orchard floor is crucial for developing low-cost, vision-guided automated harvesting technology. However, developing a reliable chestnut detection system faces challenges in complex environments with shading, varying natural light conditions, and interference from weeds, fallen leaves, stones, and other foreign on-ground objects, which have remained unaddressed. This study collected 319 images of chestnuts on the orchard floor, containing 6524 annotated chestnuts. A comprehensive set of 29 state-of-the-art real-time object detectors, including 14 in the YOLO (v11-13) and 15 in the RT-DETR (v1-v4) families at varied model scales, was systematically evaluated through replicated modeling experiments for chestnut detection. Experimental results show that the YOLOv12m model achieves the best mAP@0.5 of 95.1% among all the evaluated models, while the RT-DETRv2-R101 was the most accurate variant among RT-DETR models, with mAP@0.5 of 91.1%. In terms of mAP@[0.5:0.95], the YOLOv11x model achieved the best accuracy of 80.1%. All models demonstrate significant potential for real-time chestnut detection, and YOLO models outperformed RT-DETR models in terms of both detection accuracy and inference, making them better suited for on-board deployment. Both the dataset and software programs in this study have been made publicly available at https://github.com/AgFood-Sensing-and-Intelligence-Lab/ChestnutDetection.

Yap is the key component of Hippo pathway which plays crucial roles in tumorigenesis. Inhibition of Yap activity could promote apoptosis, suppress proliferation, and restrain metastasis of cancer cells. However, how Yap is regulated is not fully understood. Here, we reported Yap being negatively regulated by its circular RNA (circYap) through the suppression of the assembly of Yap translation initiation machinery. Overexpression of circYap in cancer cells significantly decreased Yap protein but did not affect its mRNA levels. As a consequence, it remarkably suppressed proliferation, migration and colony formation of the cells. We found that circYap could bind with Yap mRNA and the translation initiation associated proteins, eIF4G and PABP. The complex containing overexpressed circYap abolished the interaction of PABP on the poly(A) tail with eIF4G on the 5′-cap of the Yap mRNA, which functionally led to the suppression of Yap translation initiation. Individually blocking the binding sites of circYap on Yap mRNA or respectively mutating the binding sites for PABP and eIF4G derepressed Yap translation. Significantly, breast cancer tissue from patients in the study manifested dysregulation of circYap expression. Collectively, our study uncovered a novel molecular mechanism in the regulation of Yap and implicated a new function of circular RNA, supporting the pursuit of circYap as a potential tool for future cancer intervention.

Acute myeloid leukemia (AML) is a hematological malignancy with high incidence and recurrence rates. Gene expression profiling has revealed that transcriptional overexpression of glioma‐associated oncogene 1 (GLI1), a vital gene in the Hedgehog (Hh) signaling pathway, occurs in poor-prognosis AML, and high levels of phosphoinositide-3-kinase, regulatory subunit 1 (PIK3R1) and AKT3 predict shorter overall survival in AML patients. In this study, we discovered that GLI1 overexpression promotes cell proliferation and reduces chemotherapy sensitivity in AML cells while knocking down GLI1 has the opposite effect. Moreover, GLI1 promoted cell cycle progression and led to elevated protein levels of cyclins and cyclin-dependent kinases (CDKs) in AML cells. By luciferase assays and co-immunoprecipitation, we demonstrated that the PI3K/AKT pathway is directly activated by GLI1. GLI1 overexpression significantly accelerates tumor growth and upregulated p-AKT, CDK4, and cyclinD3 in vivo. Notably, the GLI1 inhibitor GANT61 and the CDK4/6 inhibitor PD 0332991 had synergistic effects in promoting Ara-c sensitivity in AML cell lines and patient samples. Collectively, our data demonstrate that GLI1 reduces drug sensitivity by regulating cell cycle through the PI3K/AKT/GSK3/CDK pathway, providing a new perspective for involving GLI1 and CDK4/6 inhibitors in relapsed/refractory (RR) patient treatment.

Dendrobium officinale is a valuable Chinese medicinal herb, but distinguishing it from other Dendrobium species after processing is challenging, leading to low classification accuracy and time-consuming analysis. This study proposes a rapid classification model based on near-infrared hyperspectral imaging (NIR-HSI), incorporating data preprocessing and feature wavelength selection. Five Dendrobium species—D. officinale, D. aphyllum, D. chrysanthum, D. fimbriatum, and D. thyrsiflorum—were used. Spectral preprocessing techniques like normalization and smoothing were applied, and Support Vector Machine (SVM) models were constructed. Normalization improved both accuracy and stability, with the full-spectrum Normalize-SVM model achieving 97% accuracy for calibration and 88% for prediction. D. chrysotoxum performed best, with all metrics reaching 100%, while D. aphyllum had poor classification (40% recall and 51.74% F1 score). To improve efficiency and performance, feature wavelength selection was performed using Competitive Adaptive Reweighted Sampling (CARS) and Successive Projections Algorithm (SPA). The CARS-Normalize-SVM model yielded the best results: 98% accuracy for calibration and 96% for prediction, improving by 1% and 8%, respectively. D. aphyllum’s classification also improved significantly, with a 100% recall rate and 95.24% F1 score. These findings highlight hyperspectral imaging’s potential for rapid Dendrobium species identification, supporting future quality control and market supervision.