Advancements in boron difluoride formazanate dyes for biological imaging.

In the past decade, boron difluoride formazanate dyes have gained considerable attention due to their redox activity, high absorption and emission intensities, chemical stability across a broad range of conditions, and the ease to fine-tune their optical and electronic characteristics. Over the past five years, boron difluoride formazanate dyes have demonstrated their extended emission wavelengths in the near-infrared region, suggesting their potential applications in the field of biological imaging. This review provides an overview of the evolution of boron difluoride formazanate dyes, encompassing the structural variations and corresponding optical properties, while also highlighting their current applications in biological imaging fields.

Visible light-activated prodrug system with a novel heavy-atom-free photosensitizer.

The use of light to activate prodrugs offers a promising method for the precise control of drug release, reducing drug-related side effects, and enhancing therapeutic effectiveness. We have created a novel prodrug system that utilizes a unique, heavy-atom-free photosensitizer to produce singlet oxygen, which then triggers the conversion of the prodrug into its active form. This system has been successfully demonstrated through the creation of "photo-unclick" prodrugs of paclitaxel (PTX), combretastatin A-4 (CA-4), and 10-hydroxy-7-ethylcamptothecin (SN-38). These prodrugs show decreased toxicity in the absence of light, but exhibit increased toxicity when exposed to red light.

A Topic Recognition Method of News Text Based on Word Embedding Enhancement.

Topic recognition technology has been commonly applied to identify different categories of news topics from the vast amount of web information, which has a wide application prospect in the field of online public opinion monitoring, news recommendation, and so on. However, it is very challenging to effectively utilize key feature information such as syntax and semantics in the text to improve topic recognition accuracy. Some researchers proposed to combine the topic model with the word embedding model, whose results had shown that this approach could enrich text representation and benefit natural language processing downstream tasks. However, for the topic recognition problem of news texts, there is currently no standard way of combining topic model and word embedding model. Besides, some existing similar approaches were more complex and did not consider the fusion between topic distribution of different granularity and word embedding information. Therefore, this paper proposes a novel text representation method based on word embedding enhancement and further forms a full-process topic recognition framework for news text. In contrast to traditional topic recognition methods, this framework is designed to use the probabilistic topic model LDA, the word embedding models Word2vec and Glove to fully extract and integrate the topic distribution, semantic knowledge, and syntactic relationship of the text, and then use popular classifiers to automatically recognize the topic categories of news based on the obtained text representation vectors. As a result, the proposed framework can take advantage of the relationship between document and topic and the context information, which improves the expressive ability and reduces the dimensionality. Based on the two benchmark datasets of 20NewsGroup and BBC News, the experimental results verify the effectiveness and superiority of the proposed method based on word embedding enhancement for the news topic recognition problem.

A Method of Short Text Representation Fusion with Weighted Word Embeddings and Extended Topic Information.

Short text representation is one of the basic and key tasks of NLP. The traditional method is to simply merge the bag-of-words model and the topic model, which may lead to the problem of ambiguity in semantic information, and leave topic information sparse. We propose an unsupervised text representation method that involves fusing word embeddings and extended topic information. Following this, two fusion strategies of weighted word embeddings and extended topic information are designed: static linear fusion and dynamic fusion. This method can highlight important semantic information, flexibly fuse topic information, and improve the capabilities of short text representation. We use classification and prediction tasks to verify the effectiveness of the method. The testing results show that the method is valid.