Angiography-based coronary flow reserve: The feasibility of automatic computation by artificial intelligence

Background: Coronary flow reserve (CFR) has prognostic value in patients with coronary artery disease. However, its measurement is complex, and automatic methods for CFR computation are scarcely available. We developed an automatic method for CFR computation based on coronary angiography and assessed its feasibility. Methods: Coronary angiographies from the Corelab database were annotated by experienced analysts. A convolutional neural network (CNN) model was trained for automatic segmentation of the main coronary arteries during contrast injection. The segmentation performance was evaluated using 5-fold cross-validation. Subsequently, the CNN model was implemented into a prototype software package for automatic computation of the CFR (CFRauto) and applied on a different sample of patients with angiographies performed both at rest and during maximal hyperemia, to assess the feasibility of CFRauto and its agreement with the manual computational method based on frame count (CFRmanual). Results: Altogether, 137,126 images of 5913 angiographic runs from 2407 patients were used to develop and evaluate the CNN model. Good segmentation performance was observed. CFRauto was successfully computed in 136 out of 149 vessels (91.3%). The average analysis time to derive CFRauto was 18.1 ± 10.3 s per vessel. Moderate correlation (r = 0.51, p < 0.001) was observed between CFRauto and CFRmanual, with a mean difference of 0.12 ± 0.53. Conclusions: Automatic computation of the CFR based on coronary angiography is feasible. This method might facilitate wider adoption of coronary physiology in the catheterization laboratory to assess microcirculatory function

Pseudo‐measurement‐based state estimation for railway power supply systems with renewable energy resources

Abstract State estimation is critical for railway power supply systems (RPSSs). Pseudo‐measurement is commonly used in state estimation. However, the fluctuations of renewable generations and railway traction loads in RPSS may introduce data noise, which will jeopardize the accuracy of the generated pseudo‐measurements and thus impact the state estimation. Additionally, when learning the historical measurement data sequences, the traditional pseudo‐measurement model is likely to have overfitting, which will further impact the accuracy of pseudo‐measurements, thereby affecting the accuracy of state estimation. To address these issues, this paper proposes a high‐accuracy pseudo‐measurement‐based state estimation approach for RPSSs. Firstly, a denoising autoencoder‐based method is used to mitigate the impact of data noise on the accuracy of pseudo measurements, and a gated recurrent unit‐based method is used to adaptively learn the historical measurement data sequence, thereby improving the accuracy of pseudo measurements. Next, the pseudo‐measurement weights are obtained by generating pseudo‐measurement variances using the Gaussian mixture model. Finally, the pseudo measurements and real‐time measurements are integrated by weighted least squares to realize the state estimation of RPSS. The effectiveness and accuracy of the proposed method are verified by simulation on a modified IEEE 33‐node system which includes a railway traction substation and renewable generations

Facile Preparation of <i>N</i>‑Vinylisobutyramide and <i>N</i>‑Vinyl-2-pyrrolidinone

A facile synthesis of <i>N</i>-vinylakylamides from commercially available <i>N</i>-vinylformamide and corresponding acyl chlorides was developed and exemplified by the preparation of <i>N</i>-vinylisobutyramide (NVIBA) and <i>N</i>-vinyl-2-pyrrolidinone (NVP) in high yields (80–89%). Both NVIBA and NVP are valuable monomers for water-soluble polymers with an array of applications in personal care, pharmaceutical, agricultural, and industrial products

Series And Parallel Resonance Problem Of Wideband Frequency Harmonic And Its Elimination Strategy

The extensive use of pulse width modulation control technology in smart grid will lead to prominent enlargement of high-frequency harmonics. The effects of the distributed capacitances of transmission line and transformer that are neglected previously will be very obvious. The performance of the traditional harmonic eliminating method for wideband harmonic is limited, which will lead to huge challenge to the analysis, evaluation, and elimination of harmonics as well as series and parallel resonance problem. In this paper, to accurately describe the influence of wideband harmonic on smart grid, the multiterminal analysis model of harmonic degradation in smart grid is established, especially the distributed capacitances of the transmission line and the transformer are considered. Then, a novel topology of hybrid active power filter (HAPF) for resonance damping and multitype harmonic eliminating is proposed. The resonance damping model of the new topology is established; analysis results indicate that the proposed HAPF has a good harmonic resonance damping characteristic. Both simulation and experimental results have validated the validity of the theoretical analysis in this paper. © 2013 IEEE

Effects of Extraction Conditions on the Characteristics of Ethanol Organosolv Lignin from Bamboo (Phyllostachys pubescens Mazel)

The structure and properties of ethanol organosolv lignin (EOL) extracted from bamboo under various conditions were characterized. EOL yield increased at high temperatures of 160 to 200 °C and a reaction time of 1 to 3 h. The nitrogen content in lignin was low, with a maximum of 0.62%. The carbon content increased with increasing temperature and prolonged time, whereas oxygen content showed an inverse trend. EOL extracted from bamboo showed high purity levels (more than 95.5% Klason lignin) with low impurity contents (carbohydrate and ash). The severity of the process increased the carboxylic acid and phenolic hydroxyl group contents and also decreased the methoxyl group content. The molecular weight of EOL varied depending on the extraction condition. The FT-IR and NMR spectra revealed that the main structure did not significantly change. From the spectra, it is clear that EOL extracted from bamboo can be classified as an HGS (H--p-hydroxyphenyl, G--guaiacyl, and S--syringyl, respectively) type. Clear β-O-4, β-β, and β-5’ linkages were observed

B cell memory: from generation to reactivation: a multipronged defense wall against pathogens

Abstract Development of B cell memory is a conundrum that scientists are still exploring. Studies have been conducted in vitro and using advanced animal models to elucidate the mechanism underlying the generation of memory B cells (MBCs), the precise roles of MBCs against pathogens, and their protective functions against repeated infections throughout life. Lifelong immunity against invading diseases is mainly the result of overcoming a single infection. This protection is largely mediated by the two main components of B cell memory—MBCs and long-lived plasma cells (PCs). The chemical and cellular mechanisms that encourage fat selection for MBCs or long-lived PCs are an area of active research. Despite the fact that nearly all available vaccinations rely on the capacity to elicit B-cell memory, we have yet to develop successful vaccines that can induce broad-scale protective MBCs against some of the deadliest diseases, including malaria and AIDS. A deeper understanding of the specific cellular and molecular pathways that govern the generation, function, and reactivation of MBCs is critical for overcoming the challenges associated with vaccine development. Here, we reviewed literature on the development of MBCs and their reactivation, interaction with other cell types, strategies against invading pathogens, and function throughout life and discussed the recent advances regarding the key signals and transcription factors which regulate B cell memory and their relevance to the quest for vaccine development

Suppression strategy for the inrush current of a solid-state transformer caused by the reclosing process

Abstract The automatic reclosing strategy is an effective measure to improve the reliability of a distribution network. It can quickly clear instantaneous faults in the grid. The traditional transformer has proven to be reliable and robust during the reclosing process. However, the influence of the reclosing process on the operational characteristics and reliability of solid-state transformers (SST) is still unclear. The reclosing action may generate a huge inrush current, resulting in shutdown and even damage of the SST. To address this problem, this paper proposes an inrush current suppression strategy. First, the operational performance of the SST under a reclosing process is discussed, and the inrush current generation mechanism is analyzed in detail. Then, considering the controllability of distributed generation (DG), a novel DG-supported inrush current suppression strategy is proposed. The suppression ability of the DG on inrush current in different initial conditions is analyzed. Finally, the effectiveness of the proposed strategy is verified by simulation and experiment. These show that the proposed strategy can help to enhance the FRT capability of the SST, as well as support the SST to maintain continuous power supply and physical integrity during grid faults