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The performance of gradient and shim coils affect the quality of magnetic resonance imaging (MRI) directly. Conventional gradient/shim coils have some inherent disadvantages, such as the state of produced magnetic field is single and inflexible, many types of coils are required and the structure is complex, while the matrix gradient coils are able to overcome those problems. This paper introduces the concept and characteristics of matrix gradient coil, and reviews the current progresses in this research topic according to the structure and function of the gradient coil. The development tendency of matrix gradient coil in the future is proposed. In addition, the preliminary research on matrix gradient coil is presented.

Gradient coils generate gradient magnetic fields that are essential in the magnetic response imaging. The gradient coils are usually designed by the target field method. In this work, we attempted to design matrix gradient coils with particle swarm optimization and the genetic algorithm. Finite element simulation calculation demonstrated that the average non-uniformity in the x and y directions was 0.851%, and the average non-uniformity in the z direction was 1.013%.

The synergistic systems of photoredox and copper catalyst have already appeared as a novel formation of green synthetic chemistry, which open new avenues for chemical synthesis applications. We describe a novel strategy for the cyclization of alkyne‐tethered α‐bromocarbonyls initiated by the cleavage of C(sp3)−Br bond via the collaboration of photoredox and copper catalyst. The present protocol exhibits mildness using economical copper catalyst and visible‐light at room temperature. The gram‐scale and sunlight irradiation experiments proceeded smoothly to show the practicality of the methodology. It is notable that the newly generated oxygen in the product originates from H2O. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the regulation of histone-like nucleoid structuring protein (H-NS) on biofilm formation and cyclic diguanylate (c-di-GMP) synthesis in Vibrio parahaemolyticus RIMD2210633. Regulatory mechanisms were analyzed by the combined utilization of crystal violet staining, quantification of c-di-GMP, quantitative real-time polymerase chain reaction, LacZ fusion, and electrophoretic-mobility shift assay. The deletion of hns enhanced the biofilm formation and intracellular c-di-GMP levels in V. parahaemolyticus RIMD2210633. H-NS can bind the upstream promoter–proximal DNA regions of scrA, scrG, VP0117, VPA0198, VPA1176, VP0699, and VP2979 to repress their transcription. These genes encode a group of proteins with GGDEF and/or EAL domains associated with c-di-GMP metabolism. One of the mechanisms by which H-NS represses the biofilm formation by V. parahaemolyticus RIMD2210633 may be via repression of the production of intracellular c-di-GMP. [ABSTRACT FROM AUTHOR]

High-dimensional data and the small sample size problem occur in many modern pattern classification applications such as face recognition and gene expression data analysis. To deal with such data, one important step is dimensionality reduction. Principal component analysis (PCA) and between-group analysis (BGA) are two commonly used methods, and various extensions of these two methods exist. The principle of these two approaches comes from their best approximation property. From a pattern recognition perspective, we show that PCA, which is based on the total scatter matrix, preserves linear separability, and BGA, which is based on between-class scatter matrix, retains the distance between class centroids. Moreover, we propose an automatic nonparameter uncorrelated discriminant analysis (UDA) algorithm based on the maximum margin criterion (MMC). The extracted features via UDA are statistically uncorrelated. UDA combines rank-preserving dimensionality reduction and constraint discriminant analysis and also serves as an effective solution for the small-sample-size problem. Experiments with face images and gene expression data sets are conducted to evaluate UDA in terms of classification accuracy and robustness. Index Terms--Feature extraction, feature interpretability, small sample size problem, face recognition, microarray data classification.