Mechanisms of the interaction between Pr(DNR)3 and Herring-Sperm DNA

Research on the interaction mechanism of drugs with DNA is essential to understand their pharmacokinetics. The interaction between rare earth complexes Pr(DNR)3 and Herring-Sperm DNA was studied in Tris-HCl buffer solution (pH 7.4) by absorption and fluorescence spectroscopy and viscosity measurements. The results showed that the modes of interaction between Pr(DNR)3 and Herring-Sperm DNA were electrostatic and intercalation. The binding ratio was nPr(DNA)3 ׃ nDNA = 5׃1 and the binding constant was KΘ292K = 4.34×10exp3 L mol-1. Furthermore, according to the double reciprocal method and the thermodynamic equation, the intercalative interaction was cooperatively driven by an enthalpy effect and an entropy effect

A Multi-Stage Framework for the 2022 Multi-Structure Segmentation for Renal Cancer Treatment

Three-dimensional (3D) kidney parsing on computed tomography angiography (CTA) images is of great clinical significance. Automatic segmentation of kidney, renal tumor, renal vein and renal artery benefits a lot on surgery-based renal cancer treatment. In this paper, we propose a new nnhra-unet network, and use a multi-stage framework which is based on it to segment the multi-structure of kidney and participate in the KiPA2022 challenge

Intergrated Segmentation and Detection Models for Dentex Challenge 2023

Dental panoramic x-rays are commonly used in dental diagnosing. With the development of deep learning, auto detection of diseases from dental panoramic x-rays can help dentists to diagnose diseases more efficiently.The Dentex Challenge 2023 is a competition for automatic detection of abnormal teeth along with their enumeration ids from dental panoramic x-rays. In this paper, we propose a method integrating segmentation and detection models to detect abnormal teeth as well as obtain their enumeration ids.Our codes are available at https://github.com/xyzlancehe/DentexSegAndDet

Inferior Alveolar Nerve Segmentation in CBCT images using Connectivity-Based Selective Re-training

Inferior Alveolar Nerve (IAN) canal detection in CBCT is an important step in many dental and maxillofacial surgery applications to prevent irreversible damage to the nerve during the procedure.The ToothFairy2023 Challenge aims to establish a 3D maxillofacial dataset consisting of all sparse labels and partial dense labels, and improve the ability of automatic IAN segmentation. In this work, in order to avoid the negative impact brought by sparse labeling, we transform the mixed supervised problem into a semi-supervised problem. Inspired by self-training via pseudo labeling, we propose a selective re-training framework based on IAN connectivity. Our method is quantitatively evaluated on the ToothFairy verification cases, achieving the dice similarity coefficient (DSC) of 0.7956, and 95\% hausdorff distance (HD95) of 4.4905, and wining the champion in the competition. Code is available at https://github.com/GaryNico517/SSL-IAN-Retraining.Comment: technical paper for Miccai ToothFairy2023 Challeng

A facile approach to fabricate highly sensitive, flexible strain sensor based on elastomeric/graphene platelet composite film

This work developed a facile approach to fabricate highly sensitive and flexible polyurethane/graphene platelets composite film for wearable strain sensor. The composite film was fabricated via layer-by-layer laminating method which is simple and cost-effective; it exhibited outstanding electrical conductivity of 1430 ± 50 S/cm and high sensitivity to strain (the gauge factor is up to 150). In the sensor application test, the flexible strain sensor achieves real-time monitoring accurately for five bio-signals such as pulse movement, finger movement, and cheek movement giving a great potential as wearable-sensing device. In addition, the developed strain sensor shows response to pressure and temperature in a certain region. A multifaceted comparison between reported flexible strain sensors and our strain sensor was made highlighting the advantages of the current work in terms of (1) high sensitivity (gauge factor) and flexibility, (2) facile approach of fabrication, and (3) accurate monitoring for body motions

A temporal Convolutional Network for EMG compressed sensing reconstruction

Electromyography (EMG) plays a vital role in detecting medical abnormalities and analyzing the biomechanics of human or animal movements. However, long-term EMG signal monitoring will increase the bandwidth requirements and transmission system burden. Compressed sensing (CS) is attractive for resource-limited EMG signal monitoring. However, traditional CS reconstruction algorithms require prior knowledge of the signal, and the reconstruction process is inefficient. To solve this problem, this paper proposed a reconstruction algorithm based on deep learning, which combines the Temporal Convolutional Network (TCN) and the fully connected layer to learn the mapping relationship between the compressed measurement value and the original signal, and it has been verified in the Ninapro database. The results show that, for the same subject, compared with the traditional reconstruction algorithms orthogonal matching pursuit (OMP), basis pursuit (BP), and Modified Compressive Sampling Matching Pursuit (MCo), the reconstruction quality and efficiency of the proposed method is significantly improved under various compression ratios (CR)

Public Service Reform in China

The article reviews the history of public service reform in China. It shows the special path of public service reform in a country transformed from a planned socialist mechanism to a market economy. The public service reform in China is distinctive by its incremental nature. The article is based on the theory of historical institutionalism. The analysis of path dependence of the reform is made from the perspectives of political context, economic foundation, and globalization. The qualitative research method is employed. The article concludes with a summary of current problems and prediction for the future of public service reform in China