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Tea bud target detection is essential for mechanized selective harvesting. To address the challenges of low detection precision caused by the complex backgrounds of tea leaves, this paper introduces a novel model called Tea-YOLOv8s. First, multiple data augmentation techniques are employed to increase the amount of information in the images and improve their quality. Then, the Tea-YOLOv8s model combines deformable convolutions, attention mechanisms, and improved spatial pyramid pooling, thereby enhancing the model’s ability to learn complex object invariance, reducing interference from irrelevant factors, and enabling multi-feature fusion, resulting in improved detection precision. Finally, the improved YOLOv8 model is compared with other models to validate the effectiveness of the proposed improvements. The research results demonstrate that the Tea-YOLOv8s model achieves a mean average precision of 88.27% and an inference time of 37.1 ms, with an increase in the parameters and calculation amount by 15.4 M and 17.5 G, respectively. In conclusion, although the proposed approach increases the model’s parameters and calculation amount, it significantly improves various aspects compared to mainstream YOLO detection models and has the potential to be applied to tea buds picked by mechanization equipment.

The present work introduces a 0.6-volt, all-digital, synthesizable temperature sensor characterized by reduced sensitivity to supply voltage variations. The design incorporates two distinct logic delay lines that are distinguished by their equivalent transistor lengths. These variations in transistor lengths result in varying threshold voltages and thermal dependencies. The difference in thermal dependency is detected through the ratio of their charging currents, which are subsequently transformed into digital outputs via their propagation delays. By employing two types of delay lines, the sensor achieves an eightfold reduction in power supply sensitivity compared to configurations utilizing a single delay line and also obviates the necessity for an external clock. Fabricated with 55 nm CMOS technology, the proposed sensor exhibits an inaccuracy of ±1 °C, evaluated through global linear fitting and two-point calibration across five chips, within a temperature range of 20 to 90 °C. The all-digital temperature sensor consumes 2 nanojoules (nJ) for each conversion, with a conversion duration of 0.8 milliseconds (ms) and a resolution of 0.2 °C. The prototype’s physical dimensions are 37 × 31 μm2. Additionally, synthesis on a Cyclone IV FPGA reveals similar characteristics in terms of supply sensitivity reduction.

Objectives This study aimed to establish a reliable diagnostic scoring model for the preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients based on gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)–enhanced magnetic resonance imaging (MRI) and biochemical indicators. Methods This retrospective study included 129 patients with HCC at our hospital from 2014 to 2020. Based on the intratumoral and peritumoral features on Gd-EOB-DTPA MRI and biochemical indicators, a scoring model was developed for preoperative prediction of MVI, and examined for diagnostic efficacy according to postoperative pathological results. The scoring model was further externally validated in an independent cohort of 63 HCC patients. Results Logistic regression analysis was performed to identify five parameters related to MVI, including maximum tumor diameter, peritumoral low intensity in the hepatobiliary phase, incomplete capsule, apparent diffusion coefficient (ADC), and [alkaline phosphatase (ALP) (U/L) + gamma-glutamyl transpeptidase (GGT) (U/L)] / lymphocyte count (× 10 9 /L) ratio (AGLR). Based on these five parameters, a scoring model was developed, and the accuracy, sensitivity, specificity, PPV, and NPV in predicting MVI were 93.6%, 94.7%, 93.2%, 85.7%, and 97.6%, respectively, with a score > 8 set as the threshold. Conclusion The scoring model based on Gd-EOB-DTPA MRI and biochemical indicators provides a reliable tool for preoperative prediction of MVI in HCC patients. Key Points • The scoring model based on Gd-EOB-DTPA MRI and biochemical indicators is practical for preoperative prediction of MVI in HCC patients . • AGLR is an independent risk factor for MVI . • The scoring model could help implement more appropriate interventions, potentially leading to precise and individualized treatments based on the biological characteristics of the tumor .

In this study, four previously undescribed flavonoids, named epimesatines P (1), Q (2), R (3), and S (4), were isolated from the aerial parts of Epimedium sagittatum Maxim. Their structures and absolute configurations were confirmed via spectroscopic analyses, quantum chemical electronic circular dichroism (ECD) calculations, Mo2(OAc)4–induced ECD, and Rh2(OCOCF3)4–induced ECD experiments. Epimesatines Q and R were characterized by the presence of furan rings. A cytotoxicity assay demonstrated that epimesatines P–S exhibited significant inhibitory effects on the viability of MCF-7 human breast cancer cells, with IC50 values ranging from 1.27 to 50.3 μM. Notably, epimesatines Q and R exhibited superior efficacy against MCF-7 cells compared to epimesatines P and S, suggesting that the presence of furan rings may enhance their activity against MCF-7 cells. Specifically, epimesatine Q displayed a more potent inhibitory effect at 1.27 μM compared to a positive control, docetaxel, which had an IC50 of 2.13 μM, highlighting its potential as a therapeutic agent for breast cancer. Importantly, none of the tested compounds exhibited obvious toxicity toward MCF-10A human breast epithelial cells. Furthermore, compounds 1, 3, and 4 were found to significantly inhibit the expression of sphingosine kinase 1 (Sphk1) in MCF-7 cells.

The centrosome is a multifunctional organelle that is known primarily for its microtubule organising function. Centrosomal defects caused by changes in centrosomal structure or number have been associated with human diseases ranging from congenital defects to cancer. We are only beginning to appreciate how the non-microtubule organising roles of the centrosome are related to these clinical conditions. In this review, we will discuss the historical evidence that led to the proposal that the centrosome participates in cell cycle regulation. We then summarize the body of work that describes the involvement of the mammalian centrosome in triggering cell cycle progression and checkpoint signalling. Then we will highlight work from the fission yeast model organism, revealing the molecular details that explain how the spindle pole body (SPB, the yeast functional equivalent of the centrosome), participates in these cell cycle transitions. Importantly, we will discuss some of the emerging questions from recent discoveries related to the role of the centrosome as a cell cycle regulator.

Objectives Noninvasive evaluation and treatment of vessels encapsulating tumor cluster (VETC) HCCs remain challenging. Herein, a new Clinic-Radiologic-Intratumor Radiomics (CRIR) model was investigated for the preoperative prediction of VETC-HCCs and prognosis based on CE-CT, then compared therapeutic outcomes between predicted VETC and nonVETC-HCCs after different treatment methods. Methods Total 456 HCC patients who underwent radical resection (RR), liver transplantation (LT) or TACE were retrospectively included in this multicenter (Center 1–4) study between January 2014 and November 2022. The intratumor and 1 cm peritumor VOI were segmented in the three phases of CE-CT imaging. Radiomics features were selected using LASSO and multivariable logistic regression (LR) to filtered the useful features. Clinical, radiological qualitative and quantitative features, intratumor, peritumor and combined radiomics, were established using LR into Clinic-radiological (CR), intratumor radiomics (IR), peritumor radiomics (PR), CRIR, Clinic-radiological- peritumor radiomics (CRPR)and CR-intra and peritumor radiomics (CRIPR) models. Diagnostic performance was calculated and compared for the models. Kaplan–Meier survival analysis was used to assess progression state in model-predicted VETC-HCCs and non-VETC-HCCs in TACE, or early recurrence in both pathologic and model-predicted in RR or LT groups. Additionally, outcomes between the RR and LT groups were compared to determine the optimal treatment approach. Results Neutrophil-to-lymphocyte ratio (NLR) ( P  = 0.031), gamma-glutamyl transferase (GGT) ( P  = 0.043), intratumor necrosis ( P  = 0.026), Arterial enhancement fraction (AEF) ( P   =  0.038), and intra-tumoral artery ( P  = 0.035) were independent predictors of VETC-HCC. CRIR model showed best area under the ROC curve value (0.85-080 across training, internal test, and external test), statistically significant improvement over the clinico-radiologic model, but not the CRIPR model. In patients with pathologic VETC-HCC, those treated with RR exhibited higher early recurrence rate compared to those treated with LT ( P  = 0.029). On the contrast, the early recurrence rates in patients with pathologic non-VETC-HCC, were similar between the RR and LT groups ( P  > 0.05). Similarly, the early recurrence rates in patients with CRIR model predicted VETC-HCC or non-VETC-HCC presented same trend as pathologic group. In application (TACE) group, CRIR model predicted VETC-HCC had lower tumor response rate (50.00% vs. 75.56%, P  < 0.001) and worse PFS (17 months vs. 30 months; P  = 0 0.039) than those with non-VETC HCCs. Conclusion The CRIR model provides accurate preoperative identification of VETC-HCC and offers prognostic value for early recurrence following RR or LT, tumor response after TACE and surgical approach selection between RR and LT in solitary HCC.

The mixotrophic dinoflagellate Akashiwo sanguinea is known to have acute toxic effects on multiple marine organisms, while the composition and chemical properties of its toxins remain unclear. In this study, we established a method for separation and purification of A. sanguinea toxins using chromatographic techniques. The acetone extract of A. sanguinea exhibited higher hemolytic activity and shorter incubation time compared to methanol and ethyl acetate extracts. Five fractions were obtained by solid-phase extraction (SPE), of which SPE3 (acetone/water ratio 3:2) and SPE4 (acetone/water ratio 4:1) exhibited the highest hemolytic activities and allelopathic effects. Further purification on SPE3 and SPE4 using reverse-phase high-performance liquid chromatography (RP-HPLC) coupled with a diode array detector (DAD) resulted in 11 subfractions, among which Fr4-5 displayed the strongest hemolytic activity. Nearly all active subfractions exhibited higher hemolytic activities incubated under light than those in the dark (p < 0.05), suggesting that A. sanguinea can produce both photosensitive and non-photosensitive toxins, with the former being the primary contributors to its hemolytic activity. Molecular characterization by UV-Vis, FTIR, and HRMS/MS analysis revealed that the structural features of Fr4-5 were highly consistent with porphyrin analogs and could be derived from chlorophyll c-related precursors. These findings highlight that the photosensitive toxins in A. sanguinea may serve dual roles in stress adaptation and ecological competition, potentially contributing to the formation of the blooms.

Background This study aimed to develop a computed tomography (CT) model to predict Ki-67 expression in hepatocellular carcinoma (HCC) and to examine the added value of radiomics to clinico-radiological features. Methods A total of 208 patients (training set, n  = 120; internal test set, n  = 51; external validation set, n  = 37) with pathologically confirmed HCC who underwent contrast-enhanced CT (CE-CT) within 1 month before surgery were retrospectively included from January 2014 to September 2021. Radiomics features were extracted and selected from three phases of CE-CT images, least absolute shrinkage and selection operator regression (LASSO) was used to select features, and the rad-score was calculated. CE-CT imaging and clinical features were selected using univariate and multivariate analyses, respectively. Three prediction models, including clinic-radiologic (CR) model, rad-score (R) model, and clinic-radiologic-radiomic (CRR) model, were developed and validated using logistic regression analysis. The performance of different models for predicting Ki-67 expression was evaluated using the area under the receiver operating characteristic curve (AUROC) and decision curve analysis (DCA). Results HCCs with high Ki-67 expression were more likely to have high serum α-fetoprotein levels ( P  = 0.041, odds ratio [OR] 2.54, 95% confidence interval [CI]: 1.04–6.21), non-rim arterial phase hyperenhancement ( P  = 0.001, OR 15.13, 95% CI 2.87–79.76), portal vein tumor thrombus ( P  = 0.035, OR 3.19, 95% CI: 1.08–9.37), and two-trait predictor of venous invasion ( P  = 0.026, OR 14.04, 95% CI: 1.39–144.32). The CR model achieved relatively good and stable performance compared with the R model (AUC, 0.805 [95% CI: 0.683–0.926] vs. 0.678 [95% CI: 0.536–0.839], P  = 0.211; and 0.805 [95% CI: 0.657–0.953] vs. 0.667 [95% CI: 0.495–0.839], P  = 0.135) in the internal and external validation sets. After combining the CR model with the R model, the AUC of the CRR model increased to 0.903 (95% CI: 0.849–0.956) in the training set, which was significantly higher than that of the CR model ( P  = 0.0148). However, no significant differences were found between the CRR and CR models in the internal and external validation sets ( P  = 0.264 and P  = 0.084, respectively). Conclusions Preoperative models based on clinical and CE-CT imaging features can be used to predict HCC with high Ki-67 expression accurately. However, radiomics cannot provide added value.

Objectives To evaluate the performance of a radiomics nomogram developed based on gadolinium-ethoxybenzyl-diethylenetriamine penta-acetic acid (Gd-EOB-DTPA) MRI for preoperative prediction of microvascular invasion (MVI) of hepatocellular carcinoma (HCC), and to identify patients who may benefit from the postoperative adjuvant transarterial chemoembolization (PA-TACE). Methods A total of 260 eligible patients were retrospectively enrolled from three hospitals (140, 65, and 55 in training, standardized external, and non-standardized external validation cohort). Radiomics features and image characteristics were extracted from Gd-EOB-DTPA MRI image before hepatectomy for each lesion. In the training cohort, a radiomics nomogram which incorporated the radiomics signature and radiological predictors was developed. The performance of the radiomics nomogram was assessed with respect to discrimination calibration, and clinical usefulness with external validation. A score ( m -score) was constructed to stratify the patients and explored whether it could accurately predict patient who benefit from PA-TACE. Results A radiomics nomogram integrated with the radiomics signature, max-D(iameter)  > 5.1 cm, peritumoral low intensity (PTLI), incomplete capsule, and irregular morphology had favorable discrimination in the training cohort (AUC = 0.982), the standardized external validation cohort (AUC = 0.969), and the non-standardized external validation cohort (AUC = 0.981). Decision curve analysis confirmed the clinical usefulness of the novel radiomics nomogram. The log-rank test revealed that PA-TACE significantly decreased the early recurrence in the high-risk group ( p  = 0.006) with no significant effect in the low-risk group ( p  = 0.270). Conclusions The novel radiomics nomogram combining the radiomics signature and clinical radiological features achieved preoperative non-invasive MVI risk prediction and patient benefit assessment after PA-TACE, which may help clinicians implement more appropriate interventions. Clinical relevance statement Our radiomics nomogram could represent a novel biomarker to identify patients who may benefit from the postoperative adjuvant transarterial chemoembolization, which may help clinicians to implement more appropriate interventions and perform individualized precision therapies. Key Points • The novel radiomics nomogram developed based on Gd-EOB-DTPA MRI achieved preoperative non-invasive MVI risk prediction. • An m-score based on the radiomics nomogram could stratify HCC patients and further identify individuals who may benefit from the PA-TACE. • The radiomics nomogram could help clinicians to implement more appropriate interventions and perform individualized precision therapies.

In this work, six pairs of undescribed enantiomeric prenylated flavonoids, ( ±)-epimesatines J–O ( 1a/1b–6a/6b ), were isolated from the aerial parts of Epimedium sagittatum Maxim. Their structures and absolute configurations were determined based on spectroscopic data, quantum chemical calculations of electronic circular dichroism (ECD) and 13 C NMR, as well as ECD experiments induced by Mo 2 (OAc) 4 and Rh 2 (OCOCF 3 ) 4 . The cytotoxicity assay revealed that compounds 1a/1b, 2a/2b , and 4a/4b–6a/6b demonstrated significant inhibitory effects on the viability of human breast cancer cells MCF-7 while exhibiting no obvious toxicity towards human breast epithelial cells MCF-10A. Additionally, these compounds were found to decrease the expression of sphingosine kinase 1 (Sphk1) in MCF-7 cells. Notably, compounds 4a and 5b exhibited IC 50 values of 7.45 and 8.97 μM, respectively, in MCF-7 cells. Graphical Abstract