2-Meth­oxy-N′-(2-methoxy­benzyl­idene)benzohydrazide

The title Schiff base compound, C16H16N2O3, was derived from the condensation of 2-methoxy­benzaldehyde with 2-methoxy­benzohydrazide in an ethanol solution. The dihedral angle between the two aromatic rings is 87.5 (3)°. In the crystal structure, the mol­ecules are linked into chains running parallel to the a axis by inter­molecular N—H⋯O hydrogen bonds

3D Instances as 1D Kernels

We introduce a 3D instance representation, termed instance kernels, where instances are represented by one-dimensional vectors that encode the semantic, positional, and shape information of 3D instances. We show that instance kernels enable easy mask inference by simply scanning kernels over the entire scenes, avoiding the heavy reliance on proposals or heuristic clustering algorithms in standard 3D instance segmentation pipelines. The idea of instance kernel is inspired by recent success of dynamic convolutions in 2D/3D instance segmentation. However, we find it non-trivial to represent 3D instances due to the disordered and unstructured nature of point cloud data, e.g., poor instance localization can significantly degrade instance representation. To remedy this, we construct a novel 3D instance encoding paradigm. First, potential instance centroids are localized as candidates. Then, a candidate merging scheme is devised to simultaneously aggregate duplicated candidates and collect context around the merged centroids to form the instance kernels. Once instance kernels are available, instance masks can be reconstructed via dynamic convolutions whose weights are conditioned on instance kernels. The whole pipeline is instantiated with a dynamic kernel network (DKNet). Results show that DKNet outperforms the state of the arts on both ScanNetV2 and S3DIS datasets with better instance localization. Code is available: https://github.com/W1zheng/DKNet.Comment: Appearing in ECCV, 202

MicroRNA 27b-3p Modulates SYK in Pediatric Asthma Induced by Dust Mites

The PI3K-AKT pathway is known to regulate cytokines in dust mite-induced pediatric asthma. However, the underlying molecular steps involved are not clear. In order to clarify further the molecular steps, this study investigated the expression of certain genes and the involvement of miRNAs in the PI3K-AKT pathway, which might affect the resultant cytokine-secretion. in-vivo and in-vitro ELISA, qRT-PCR and microarrays analyses were used in this study. A down-expression of miRNA-27b-3p in dust mite induced asthma group (group D) was found by microarray analysis. This was confirmed by qRT-PCR that found the miRNA-27b-3p transcripts that regulated the expression of SYK and EGFR were also significantly decreased (p < 0.01) in group D. The transcript levels of the SYK and PI3K genes were higher, while those of EGFR were lower in the former group. Meanwhile, we found significant differences in plasma concentrations of some cytokines between the dust mite-induced asthma subjects and the healthy controls. On the other hand, this correlated with the finding that the transcripts of SYK and its downstream PI3K were decreased in HBE transfected with miRNA-27b-3p, but were increased in HBE transfected with the inhibitor in vitro. Our results indicate that the differential expression of the miRNAs in dust mite-induced pediatric asthma may regulate their target gene SYK and may have an impact on the PI3K-AKT pathway associated with the production of cytokines. These findings should add new insight into the pathogenesis of pediatric asthma

Improving the Accuracy of Density Functional Theory (DFT) Calculation for Homolysis Bond Dissociation Energies of Y-NO Bond: Generalized Regression Neural Network Based on Grey Relational Analysis and Principal Component Analysis

We propose a generalized regression neural network (GRNN) approach based on grey relational analysis (GRA) and principal component analysis (PCA) (GP-GRNN) to improve the accuracy of density functional theory (DFT) calculation for homolysis bond dissociation energies (BDE) of Y-NO bond. As a demonstration, this combined quantum chemistry calculation with the GP-GRNN approach has been applied to evaluate the homolysis BDE of 92 Y-NO organic molecules. The results show that the ull-descriptor GRNN without GRA and PCA (F-GRNN) and with GRA (G-GRNN) approaches reduce the root-mean-square (RMS) of the calculated homolysis BDE of 92 organic molecules from 5.31 to 0.49 and 0.39 kcal mol−1 for the B3LYP/6-31G (d) calculation. Then the newly developed GP-GRNN approach further reduces the RMS to 0.31 kcal mol−1. Thus, the GP-GRNN correction on top of B3LYP/6-31G (d) can improve the accuracy of calculating the homolysis BDE in quantum chemistry and can predict homolysis BDE which cannot be obtained experimentally