Erasable and Field Programmable DNA Circuits Based on Configurable Logic Blocks.

DNA is commonly employed as a substrate for the building of artificial logic networks due to its excellent biocompatibility and programmability. Till now, DNA logic circuits are rapidly evolving to accomplish advanced operations. Nonetheless, nowadays, most DNA circuits remain to be disposable and lack of field programmability and thereby limits their practicability. Herein, inspired by the Configurable Logic Block (CLB), the CLB-based erasable field-programmable DNA circuit that uses clip strands as its operation-controlling signals is presented. It enables users to realize diverse functions with limited hardware. CLB-based basic logic gates (OR and AND) are first constructed and demonstrated their erasability and field programmability. Furthermore, by adding the appropriate operation-controlling strands, multiple rounds of programming are achieved among five different logic operations on a two-layer circuit. Subsequently, a circuit is successfully built to implement two fundamental binary calculators: half-adder and half-subtractor, proving that the design can imitate silicon-based binary circuits. Finally, a comprehensive CLB-based circuit is built that enables multiple rounds of switch among seven different logic operations including half-adding and half-subtracting. Overall, the CLB-based erasable field-programmable circuit immensely enhances their practicability. It is believed that design can be widely used in DNA logic networks due to its efficiency and convenience.

Knowledge mapping of graph neural networks for drug discovery: a bibliometric and visualized analysis.

Introduction: In recent years, graph neural network has been extensively applied to drug discovery research. Although researchers have made significant progress in this field, there is less research on bibliometrics. The purpose of this study is to conduct a comprehensive bibliometric analysis of graph neural network applications in drug discovery in order to identify current research hotspots and trends, as well as serve as a reference for future research. Methods: Publications from 2017 to 2023 about the application of graph neural network in drug discovery were collected from the Web of Science Core Collection. Bibliometrix, VOSviewer, and Citespace were mainly used for bibliometric studies. Results and Discussion: In this paper, a total of 652 papers from 48 countries/regions were included. Research interest in this field is continuously increasing. China and the United States have a significant advantage in terms of funding, the number of publications, and collaborations with other institutions and countries. Although some cooperation networks have been formed in this field, extensive worldwide cooperation still needs to be strengthened. The results of the keyword analysis clarified that graph neural network has primarily been applied to drug-target interaction, drug repurposing, and drug-drug interaction, while graph convolutional neural network and its related optimization methods are currently the core algorithms in this field. Data availability and ethical supervision, balancing computing resources, and developing novel graph neural network models with better interpretability are the key technical issues currently faced. This paper analyzes the current state, hot spots, and trends of graph neural network applications in drug discovery through bibliometric approaches, as well as the current issues and challenges in this field. These findings provide researchers with valuable insights on the current status and future directions of this field.

Computer-Aided Intraoperative Toric Intraocular Lens Positioning and Alignment During Cataract Surgery.

Cataract causes more than half of all blindness worldwide. The most effective treatment is surgery, where cataract is often replaced by intraocular lens (IOL). Beyond saving vision, toric IOL implantation is becoming increasingly popular to correct corneal astigmatism. It is important to precisely position and align the axis of IOL during surgery to achieve optimal post-operative astigmatism correction. Comparing with conventional manual marking, automated markerless IOL alignment can be faster, more accurate and non-invasive. Here we propose a framework for computer-assisted intraoperative IOL positioning and alignment based on detection and tracking. Firstly, the iris boundary was segmented and the eye center was determined. A statistical sampling method was developed to segment iris and generate training labels, and both conventional algorithms and deep convolutional neural network (CNN) methods were evaluated. Then, regions of interests (ROIs) containing high density of scleral capillaries were used for tracking eye rotations. Both correlation filter and CNN methods were evaluated for tracking. Cumulative errors during long-term tracking were corrected using a reference image. Validation studies against manual labeling using 7 clinical cataract surgical videos demonstrated that the proposed algorithm achieved an average position error around 0.2 mm, an axis alignment error of < 1 °, and a frame rate of > 25 FPS, and can be potentially used intraoperatively for markerless IOL positioning and alignment during cataract surgery.

Research Progress of Coronavirus Based on Bibliometric Analysis.

BACKGROUND: COVID-19 has become one of the most serious global epidemics in the 21st Century. This study aims to explore the distribution of research capabilities of countries, institutions, and researchers, and the hotspots and frontiers of coronavirus research in the past two decades. In it, references for funding support of urgent projects and international cooperation among research institutions are provided. METHOD: the Web of Science core collection database was used to retrieve the documents related to coronavirus published from 2003 to 2020. Citespace.5.6.R2, VOSviewer1.6.12, and Excel 2016 were used for bibliometric analysis. RESULTS: 11,036 documents were retrieved, of which China and the United States have contributed the most coronavirus studies, Hong Kong University being the top contributor. Regarding journals, the JournalofVirology has contributed the most, while in terms of researchers, Yuen Kwok Yung has made the most contributions. The proportion of documents published by international cooperation has been rising for decades. Vaccines for SARS-CoV-2 are under development, and clinical trials of several drugs are ongoing. CONCLUSIONS: international cooperation is an important way to accelerate research progress and achieve success. Developing corresponding vaccines and drugs are the current hotspots and research directions.

A novel dark septate fungal endophyte positively affected blueberry growth and changed the expression of plant genes involved in phytohormone and flavonoid biosynthesis.

Dark septate endophytes (DSEs) are one of the most studied groups of root fungal endophytes in recent years. However, the effects of DSE on host plant are still under debate, and the molecular mechanisms are poorly understood. In this study, we identified a DSE fungus of the genus Anteaglonium, named T010, from the wild blueberry. When inoculated into Vaccinium corymbosum L. plants, T010 could enhance root growth and promote shoot branching, leading to increased plant growth. By comparative transcriptome analysis, we obtained 1948 regulated differentially expressed genes (DEGs) from the V. corymbosum plants treated by T010. Further functional enrichment analysis identified a series of DEGs enriched in transcriptional regulation, material transport, phytohormone biosynthesis and flavonoid biosynthesis. Moreover, the comparative analysis of liquid chromatography-mass spectrometry verified that T010 treatment induced the changes in the contents of various phytohormones and flavonoids. This is the first report on the isolation of DSE fungi of the genus Anteaglonium from blueberry roots. Moreover, our results suggested that T010 colonization could result in a series of changes in cell metabolism, biosynthesis and signal pathways, thereby promoting plant growth. Particularly, the changes of phytohormone and flavonoid metabolism induced by T010 colonization might contribute to the promotion of blueberry growth. Our results will provide new insights into understanding of the interaction of DSE fungi and host plants, as well as the development and utilization of DSE preparations.