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Purpose: Trop2, a cell membrane glycoprotein, is overexpressed in almost all epithelial cancers. This study aimed to explore the mutational characteristics and significance of Trop2 in breast cancer (BC). Methods: Patients diagnosed with BC (n = 77) were enrolled to investigate expression level and clinical characteristics of Trop2. Database of cBioPortal and Kaplan–Meier Plotter were used to evaluate the effects of Trop2 (TACSTD2) genomic ateration and mRNA expression levels on disease-free survival (DFS) and relapse-free survival (RFS), respectively. Based on next generation sequencing analysis, the Trop2 mutation characteristics of BC patients were deeply depicted. In addition, Trop2 expression, mutation and methylation signature associated with Trop2 mutations were analyzed. Results: Trop2 mutation and high expression of Trop2 were predictive biomarker for shorter DFS and RFS in BC. The positive rate of Trop2 expression in these 77 BC patients was 96.1% (74/77). Based on the Trop2 expression level, the patients were classified into Trop2 negative group, medium expression group and high expression group. The mutation frequencies of MAP3K1, NOTCH2, PTEN and MAGI2 were significantly higher in Trop2 medium expression group than high expression group. Moreover, we investigated the effect of the Trop2 mutations on other genes, including co-expressed genes, differentially mutated genes, differentially expressed genes, gene methylation and phosphorylation. We found that MED8, DPH2, KDM4A, EBNA1BP2, USP1, IPO13, CGAS, PRKAA2, NCOA7, ASCC3 and ABRACL were differentially expressed, mutated and methylated between Trop2 mutation group and wild group. Conclusion: MAP3K1, NOTCH2, PTEN and MAGI2 mutations were significantly different between Trop2 medium expression and Trop2 high expression BC patients. The effects of Trop2 mutation on the expression, variation, methylation, and phosphorylation of other genes were comprehensively revealed. High expression level of Trop2 and Trop2 mutation were predictive biomarker for poor prognosis and targeted therapy in BC. [ABSTRACT FROM AUTHOR]

Generative molecular models generate novel molecules with desired properties by searching chemical space. Traditional combinatorial optimization methods, such as genetic algorithms, have demonstrated superior performance in various molecular optimization tasks. However, these methods do not utilize docking simulation to inform the design process, and heavy dependence on the quality and quantity of available data, as well as require additional structural optimization to become candidate drugs. To address this limitation, we propose a novel model named DockingGA that combines Transformer neural networks and genetic algorithms to generate molecules with better binding affinity for specific targets. In order to generate high quality molecules, we chose the Self-referencing Chemical Structure Strings to represent the molecule and optimize the binding affinity of the molecules to different targets. Compared to other baseline models, DockingGA proves to be the optimal model in all docking results for the top 1, 10 and 100 molecules, while maintaining 100% novelty. Furthermore, the distribution of physicochemical properties demonstrates the ability of DockingGA to generate molecules with favorable and appropriate properties. This innovation creates new opportunities for the application of generative models in practical drug discovery. [ABSTRACT FROM AUTHOR]

In the context of the accelerated pace of daily life and the development of e-commerce, online shopping is a mainstream way for consumers to access products and services. To understand their emotional expressions in facing different shopping experience scenarios, this paper presents a sentiment analysis method that combines the e-commerce review keyword-generated image with a hybrid machine learning-based model, in which the Word2Vec-TextRank is used to extract keywords that act as the inputs for generating the related images by generative Artificial Intelligence (AI). Subsequently, a hybrid Convolutional Neural Network and Support Vector Machine (CNN-SVM) model is applied for sentiment classification of those keyword-generated images. For method validation, the data randomly comprised of 5000 reviews from Amazon have been analyzed. With superior keyword extraction capability, the proposed method achieves impressive results on sentiment classification with a remarkable accuracy of up to 97.13%. Such performance demonstrates its advantages by using the text-to-image approach, providing a unique perspective for sentiment analysis in the e-commerce review data compared to the existing works. Thus, the proposed method enhances the reliability and insights of customer feedback surveys, which would also establish a novel direction in similar cases, such as social media monitoring and market trend research. [ABSTRACT FROM AUTHOR]

The advancement of Internet of Things (IoT) has enhanced the extensive usage of QR code images in various computer vision applications. Nonetheless, this has also brought forth several technical challenges. In particular, the logistics sorting system often encounters issues such as a low recognition rate and slow processing speed when dealing with QR code images under complex lighting conditions like uneven illumination. To address these difficulties, a method that focuses on achieving a fast adaptive binarization of QR code images through dynamic illumination equalization was proposed. First, an algorithm based on edge enhancement to obtain the position detection patterns within QR code images was applied, which enabled the acquisition of structural features in uneven illumination. Subsequently, QR code images with complex lighting conditions can achieve a fast adaptive binarization through dynamic illumination equalization. As for method validation, the experiments were performed on the two datasets that include QR code images influenced by strong light, weak light, and different shadow degrees. The results disclosed the benefits of the proposed method compared to the previous approaches; it produced superior recognition rates of 78.26–98.75% in various cases through commonly used decoders (Wechat and Zxing), with a faster processing speed of 0.0164 s/image, making it a proper method to satisfy real-time requirements in practical applications, such as a logistics sorting system. [ABSTRACT FROM AUTHOR]

In this work, an assistance system based on the Artificial Intelligence of Things (AIoT) framework was designed and implemented to provide convenience for visually impaired people. This system aims to be low-cost and multi-functional with object detection, obstacle distance measurement, and text recognition achieved by wearable smart glasses, heart rate detection, fall detection, body temperature measurement, and humidity-temperature monitoring offered by an intelligent walking stick. The total hardware cost is approximately $66.8, as diverse low-cost sensors and modules are embedded. Meanwhile, a voice assistant is adopted, which helps to convey detection results to users. As for the performance evaluation, the accuracies of object detection and text recognition in the wearable smart glasses experiments are 92.16% and 99.91%, respectively, and the maximum deviation rate compared to the mobile app on obstacle distance measurement is 6.32%. In addition, the intelligent walking stick experiments indicate that the maximum deviation rates compared to the commercial devices on heart rate detection, body temperature measurement, and humidity-temperature monitoring are 3.52%, 0.19%, and 3.13%, respectively, and the fall detection accuracy is 87.33%. Such results demonstrate that the proposed assistance system yields reliable performances similar to commercial devices and is impressive when considering the total cost as a primary concern. Consequently, it satisfies the fundamental requirements of daily life, benefiting the safety and well-being of visually impaired people. [ABSTRACT FROM AUTHOR]