Variable Weighted Ordered Subset Image Reconstruction Algorithm

We propose two variable weighted iterative reconstruction algorithms (VW-ART and VW-OS-SART) to improve the algebraic reconstruction technique (ART) and simultaneous algebraic reconstruction technique (SART) and establish their convergence. In the two algorithms, the weighting varies with the geometrical direction of the ray. Experimental results with both numerical simulation and real CT data demonstrate that the VW-ART has a significant improvement in the quality of reconstructed images over ART and OS-SART. Moreover, both VW-ART and VW-OS-SART are more promising in convergence speed than the ART and SART, respectively

A Machine Learning Protocol for Predicting Protein Infrared Spectra

© 2020 American Chemical Society. Infrared (IR) absorption provides important chemical fingerprints of biomolecules. Protein secondary structure determination from IR spectra is tedious since its theoretical interpretation requires repeated expensive quantum-mechanical calculations in a fluctuating environment. Herein we present a novel machine learning protocol that uses a few key structural descriptors to rapidly predict amide I IR spectra of various proteins and agrees well with experiment. Its transferability enabled us to distinguish protein secondary structures, probe atomic structure variations with temperature, and monitor protein folding. This approach offers a cost-effective tool to model the relationship between protein spectra and their biological/chemical properties

Effects of Perfluorooctanoic Acid on the Associated Genes Expression of Autophagy Signaling Pathway of Carassius auratus Lymphocytes in vitro

Perfluorooctanoic acid (PFOA) has been detected in various water bodies and caused harm to aquatic organisms. The aim of this study was to investigate the cytotoxicity and mechanism associated with autophagy and oxidative stress after exposure to PFOA (0, 1, 10, 100 μg/L) for 12 h on lymphocytes, which was isolated from the head kidney of Carassius auratus (C. auratus). Both of autophagy formation, cell activity, and intracellular reactive oxygen species (ROS), malondialdehyde (MDA), glutathione (GSH), and superoxide dismutase (SOD) levels were measured. The relative expression of partial autophagy-related genes autophagy related 5 (Atg 5), autophagy related 7 (Atg 7), and Beclin 1 were also cloned and detected. Homologous relationships analysis showed high identities of genes in C. auratus and other fish by blast. C. auratus lymphocytes growth inhibition rates was increased induced by PFOA. Compared with the control group, the ROS generation and the MDA content were significantly increased in all of the PFOA-treated group. Besides, decreased SOD activity and decrease of GSH activity induced by PFOA further confirmed the occurrence of oxidative stress. The number of autophagosome formations was increased in a dose-dependent manner. Compared with the control group, Atg 7 and Beclin 1 mRNA expression was elevated significantly after PFOA exposed, showing a time-dependent manner, while mRNA expression of Atg 5 was increased remarkably in 100 μg/L PFOA-treated group. Our results indicated that PFOA caused oxidative damage to lymphocytes in C. auratus and caused various autophagy signaling pathway-associated genes imbalances in the lymphocytes. Autophagy signaling pathway-associated genes imbalance could weaken antioxidant capacity and involve in the mechanism of C. auratus lymphocytes oxidative injury caused by PFOA

Hematite concave nanocubes and their superior catalytic activity for low temperature CO oxidation

Hematite (α-Fe2O3) concave nanocubes bound by high-index {1344} and {1238} facets were synthesized and their catalytic activity for CO oxidation were also investigated. ? 2014 the Partner Organisations

Machine Learning Prediction of Quantum Yields and Wavelengths of Aggregation-Induced Emission Molecules

The aggregation-induced emission (AIE) effect exhibits a significant influence on the development of luminescent materials and has made remarkable progress over the past decades. The advancement of high-performance AIE materials requires fast and accurate predictions of their photophysical properties, which is impeded by the inherent limitations of quantum chemical calculations. In this work, we present an accurate machine learning approach for the fast predictions of quantum yields and wavelengths to screen out AIE molecules. A database of about 563 organic luminescent molecules with quantum yields and wavelengths in the monomeric/aggregated states was established. Individual/combined molecular fingerprints were selected and compared elaborately to attain appropriate molecular descriptors. Different machine learning algorithms combined with favorable molecular fingerprints were further screened to achieve more accurate prediction models. The simulation results indicate that combined molecular fingerprints yield more accurate predictions in the aggregated states, and random forest and gradient boosting regression algorithms show the best predictions in quantum yields and wavelengths, respectively. Given the successful applications of machine learning in quantum yields and wavelengths, it is reasonable to anticipate that machine learning can serve as a complementary strategy to traditional experimental/theoretical methods in the investigation of aggregation-induced luminescent molecules to facilitate the discovery of luminescent materials

A high-precision sensor based on AC flux cancellation for DC bias detection in dual active bridge converters

DC bias in a transformer is a threat to the safe and efficient operation of dual active bridge (DAB) converters. Detecting a dc component in milliamperes mixed with a large ac current is difficult. This article proposes a current sensor based on ac magnetic flux (MF) cancellation. The proposed sensor utilizes a current transformer (CT) to extract the ac component in the measured current. Then, a power op-amp copies the CT signal to an additional winding to generate an ac MF opposite to that of the main ac current. Hence, the sensor range required to measure the dc is greatly reduced for a much smaller absolute measurement error. Using only a CT and a simple op-amp circuit, the proposed solution features low complexity and power consumption. The relative error in a range of 15-A ac current is 0.038%, showing a five-fold improvement compared to off-the-shelf sensors. The proposed sensor is verified on a 1-kW DAB prototype, where the dc bias is suppressed to nearly null owing to the proposed sensor