Effective recognition design in 8-ball billiards vision systems for training purposes based on Xception network modified by improved Chaos African Vulture Optimizer.

This research paper presents a comprehensive investigation into the utilization of color image processing technologies and deep learning algorithms in the development of a robot vision system specifically designed for 8-ball billiards. The sport of billiards, with its various games and ball arrangements, presents unique challenges for robotic vision systems. The proposed methodology addresses these challenges through two main components: object detection and ball pattern recognition. Initially, a robust algorithm is employed to detect the billiard balls using color space transformation and thresholding techniques. This is followed by determining the position of the billiard table through strategic cropping and isolation of the primary table area. The crucial phase involves the intricate task of recognizing ball patterns to differentiate between solid and striped balls. To achieve this, a modified convolutional neural network is utilized, leveraging the Xception network optimized by an innovative algorithm known as the Improved Chaos African Vulture Optimization (ICAVO) algorithm. The ICAVO algorithm enhances the Xception network's performance by efficiently exploring the solution space and avoiding local optima. The results of this study demonstrate a significant enhancement in recognition accuracy, with the Xception/ICAVO model achieving remarkable recognition rates for both solid and striped balls. This paves the way for the development of more sophisticated and efficient billiards robots. The implications of this research extend beyond 8-ball billiards, highlighting the potential for advanced robotic vision systems in various applications. The successful integration of color image processing, deep learning, and optimization algorithms shows the effectiveness of the proposed methodology. This research has far-reaching implications that go beyond just billiards. The cutting-edge robotic vision technology can be utilized for detecting and tracking objects in different sectors, transforming industrial automation and surveillance setups. By combining color image processing, deep learning, and optimization algorithms, the system proves its effectiveness and flexibility. The innovative approach sets the stage for creating advanced and productive robotic vision systems in various industries.

An Online Model for Central Lymph Node Metastases in Papillary Thyroid Carcinoma With BRAF V600E Mutation.

BACKGROUND: To construct a predictive model to direct the dissection of the central lymph nodes in papillary thyroid cancer (PTC) with BRAF V600E mutation by identifying the risk variables for central lymph node metastases (CLNM). METHODS: Data from 466 PTC patients with BRAF V600E mutations underwent thyroid surgery was collected and analyzed retrospectively. For these patients, we conducted univariate and multivariate logistic regression analysis to find risk variables for CLNM. To construct a nomogram, the independent predictors were chosen. The calibration, discrimination, and clinical utility of the predictive model were assessed by training and validation data. RESULTS: CLNM was present in 323/466 PTC patients with BRAF V600E mutations. By using univariate and multivariate logistic regression, we discovered that gender, age, tumor size, multifocality, and pathological subtype were all independent predictors of CLNM in PTC patients with BRAF V600E mutations. A predictive nomogram was created by combining these variables. In both training and validation groups, the nomogram demonstrated great calibration capacities. The training and validation groups' areas under the curve (AUC) were 0.772 (specificity 0.694, sensitivity 0.728, 95% CI: 0.7195-0.8247) and 0.731 (specificity 0.778, sensitivity 0.653, 95% CI: 0.6386-0.8232) respectively. According to the nomogram's decision curve analysis (DCA), the nomogram might be beneficial. As well, an online dynamic calculator was developed to make the application of this nomogram easier in the clinic. CONCLUSION: An online nomogram model based on the 5 predictors included gender, age, pathological subtype, multifocality, and tumor size was confirmed to predict CLNM and guide the central lymph nodes dissection in PTC patients with BRAF V600E mutations.

A Web-based Prediction Model for Early Death in Patients With Metastatic Triple-negative Breast Cancer.

BACKGROUND: Triple-negative breast cancer (TNBC) is a subtype of breast cancer characterized by the absence of expression of estrogen receptor, progesterone receptor, or human epidermal growth factor receptor 2. This subtype of breast cancer is known for its high aggressiveness, high metastatic potential, tendency for recurrence, and poor prognosis. Patients with metastatic TNBC (mTNBC) have a poorer prognosis and a higher likelihood of early death (survival time ≤3 months). Therefore, the development of effective individualized survival prediction tools, such as prediction nomograms and web-based survival calculators, is of great importance for predicting the probability of early death in patients with metastatic TNBC. METHODS: Patients diagnosed with mTNBC in the Surveillance, Epidemiology, and End Results database between 2010 and 2015 were included in the model construction. Univariate and multivariate logistic regression analysis was performed to identify risk factors associated with early death in patients with mTNBC and predictive prognostic nomograms were constructed. The accuracy of the nomograms was verified using receiver operating characteristic curves, and GiViTi Calibration belt plots were used to evaluate the model consistency. The clinical applicability of the nomograms was evaluated using decision curve analysis. On the basis of the predictive prognostic nomograms, a network survival rate calculator was developed for individualized survival prediction in patients with mTNBC. RESULTS: A total of 2230 patients diagnosed with mTNBC were included in the Surveillance, Epidemiology, and End Results database for this study. After strict exclusion criteria, 1428 patients were found to be eligible for the study. All the patients were randomly divided into a training cohort and a validation cohort in a ratio of 7:3. Independent risk factors for mTNBC, including age, tumor size, brain metastasis, liver metastasis, surgery, and chemotherapy, were identified and integrated to construct the prediction nomogram and survival calculator. Results of receiver operating characteristic curves, calibration curves, and decision curve analysis curves from the training and validation cohort confirmed that the developed nomogram and web-based survival calculator in this study could accurately predict the probability of early death in patients with mTNBC. CONCLUSIONS: In this study, we developed a reliable prediction nomogram and web-based survival calculator for predicting the probability of early death in patients with mTNBC. These tools can assist clinical physicians in identifying high-risk patients and developing personalized treatment plans as early as possible.

Potent Antioxidant and Anti-Tyrosinase Activity of Butein and Homobutein Probed by Molecular Kinetic and Mechanistic Studies.

Butein (BU) and homobutein (HB) are bioactive polyhydroxylated chalcones widespread in dietary plants, whose antioxidant properties require mechanistic definition. They were investigated by inhibited autoxidation kinetic studies of methyl linoleate in Triton™ X-100 micelles at pH 7.4, 37 °C. Butein had kinh = (3.0 ± 0.9) × 104 M-1s-1 showing a chain-breaking mechanism with higher antioxidant activity than reference α-tocopherol (kinh = (2.2 ± 0.6) × 104 M-1s-1), particularly concerning the stoichiometry or peroxyl radical trapping n = 3.7 ± 1.1 vs. 2.0 for tocopherol. Homobutein had kinh = (2.8 ± 0.9) × 103 M-1s-1, pairing the relative BDEOH measured by radical equilibration EPR as 78.4 ± 0.2 kcal/mol for BU and estimated as 82.6 kcal/mol for HB. The inhibition of mushroom tyrosinase (mTYR) by HB and BU was also investigated. BU gives a reversible uncompetitive inhibition of monophenolase reaction with KI' = 9.95 ± 2.69 µM and mixed-type diphenolase inhibition with KI = 3.30 ± 0.75 µM and KI' = 18.75 ± 5.15 µM, while HB was nearly competitive toward both mono- and diphenolase with respective KI of 2.76 ± 0.70 µM and 2.50 ± 1.56 µM. IC50 values (monophenolase/diphenolase at 1 mM substrate) were 10.88 ± 2.19 µM/15.20 ± 1.25 µM, 14.78 ± 1.05 µM/12.36 ± 2.00 µM, and 33.14 ± 5.03 µM/18.27 ± 3.42 µM, respectively, for BU, HB, and reference kojic acid. Molecular docking studies confirmed the mechanism. Results indicate very potent antioxidant activity for BU and potent anti-tyrosinase activity for both chalcones, which is discussed in relation to bioactivity toward protection from skin disorders and food oxidative spoilage.

Efficacy of apatinib+radiotherapy vs radiotherapy alone in patients with advanced multiline therapy failure for non small cell lung cancer with brain metastasis.

OBJECTIVE: Lung cancer is the leading cause of cancer-associated mortality worldwide. Central nervous system (CNS) metastasis is a prevalent and serious complication. The most common treatment for brain metastasis (BM) is still radiation therapy (RT). An increasing number of drugs have been shown to have intracranial activity or to sensitize tumours to radiotherapy. METHODS: Consecutive advanced multiline therapy failure in patients with non-small-cell lung cancer (NSCLC) with BM at the authors' hospital were retrospectively reviewed. Eligible patients were divided into two groups: Apatinib+RT group and RT group. Intracranial progression-free survival (PFS) and overall survival (OS) were analysed using the Kaplan-Meier method. RESULTS: The median intracranial PFS for the RT group and Apatinib+RT group was 5.83 months and 11.81 months (p = 0.034). The median OS for the RT group and Apatinib+RT group was 9.02 months and 13.62 months (p = 0.311). The Apatinib+RT group had a better intracranial PFS, but there were no significant differences between the two arms in OS. The Apatinib+RT group had significantly reduced symptoms caused by BM. CONCLUSION: RT combined with apatinib could help to control intracranial metastases. The Apatinib+RT group had significantly reduced symptoms caused by BM and improved quality of life for patients, the safety of the two treatments was similar. ADVANCES IN KNOWLEDGE: Here, we propose that RT combined with apatinib can significantly relieve brain symptoms and tolerate side-effects without affecting OS in patients with BM following failure of multiline therapy for NSCLC. Of course, this paper is a retrospective origin study, and more powerful evidence is needed to demonstrate.

C6-structural optimizations of 2-aryl-1H-pyrazole-S-DABOs: From anti-HIV to anti-DENV activity.

Both HIV and DENV are serious threats to human life, health and social economy today. So far, no vaccine for either HIV or DENV has been developed successfully. The research on anti-HIV or DENV drugs is still of great significance. In this study we developed a series of novel 2-Aryl-1H-pyrazole-S-DABOs with C6-strucutral optimizations as potent NNRTIs, among which, 8 compounds had low cytotoxicity and EC50 values in the range of 0.0508 âˆ¼ 0.0966 µM, and their selectivity index was SI > 1415 âˆ¼ 3940. In particular, two compounds 4a and 4b were identified to have good inhibitory effects on DENV of four serotypes. The EC50 of compound 4a and 4b against DENV-II (13.2 µM and 9.23 µM, respectively) were better than that of the positive control ribavirin (EC50 = 40.78 µM). In addition, the effect of C-6 substituents on the anti-HIV or anti-DENV activity of these compounds was also discussed.