N-(2-Chloro­pyrimidin-4-yl)-N,2-di­methyl-2H-indazol-6-amine

In the title compound, C13H12ClN5, which is a derivative of the anti­tumor agent pazopanib {systematic name: 5-[[4-[(2,3-di­methyl-2H-indazol-6-yl)methylamino]-2-pyrimidinyl]amino]-2-methylbenzolsulfonamide}, the indazole and pyrim­idine fragments form a dihedral angle of 62.63 (5)°. In the crystal, pairs of mol­ecules related by twofold rotational symmetry are linked into dimers through π–π inter­actions between the indazole ring systems [centroid–centroid distance = 3.720 (2) Å]. Weak inter­molecular C—H⋯N hydrogen bonds further assemble these dimers into columns propagated in [001]

Impact of inclusive leadership on employee innovative behavior : Perceived organizational support as a mediator

This research was financially supported by the National Social Science Foundation (14BGL073), Ministry of Education Humanities and Social Sciences Research Planning Fund Project (19YJA0056), Shandong Social Science Planning Fund Program (17CLYJ26), Major Program of Humanities and Social Sciences of Shandong University (17RWZD21), Bing Liu as the funding recipients.Peer reviewedPublisher PD

MiR-148a inhibits angiogenesis by targeting ERBB3.

MicroRNAs (miRNAs) play an important role in carcinogenesis in various solid cancers including breast cancer. Down-regulation of microRNA-148a (miR-148a) has been reported in certain cancer types. However, the biological role of miR-148a and its related targets in breast cancer are unknown yet. In this study, we showed that the level of miR-148a was lower in MCF7 cells than that in MCF10A cells. V-erb-b2 erythroblastic leukemia viral oncogene homolog 3 (ERBB3) is a direct target of miR-148a in human breast cancer cells through direct binding of miR-148a to ERBB3 3\u27-UTR region. Overexpression of miR-148a in MCF7 cells inhibited ERBB3 expression, blocked the downstream pathway activation including activation of AKT, ERK1/2, and p70S6K1, and decreased HIF-1α expression. Furthermore, forced expression of miR-148a attenuated tumor angiogenesis in vivo. Our results identify ERBB3 as a direct target of miR-148a, and provide direct evidence that miR-148a inhibits tumor angiogenesis through ERBB3 and its downstream signaling molecules. This information would be helpful for targeting the miR-148a/ERBB3 pathway for breast cancer prevention and treatment in the future

Pulse shape discrimination based on the Tempotron: a powerful classifier on GPU

This study introduces the Tempotron, a powerful classifier based on a third-generation neural network model, for pulse shape discrimination. By eliminating the need for manual feature extraction, the Tempotron model can process pulse signals directly, generating discrimination results based on learned prior knowledge. The study performed experiments using GPU acceleration, resulting in over a 500 times speedup compared to the CPU-based model, and investigated the impact of noise augmentation on the Tempotron's performance. Experimental results showed that the Tempotron is a potent classifier capable of achieving high discrimination accuracy. Furthermore, analyzing the neural activity of Tempotron during training shed light on its learning characteristics and aided in selecting the Tempotron's hyperparameters. The dataset used in this study and the source code of the GPU-based Tempotron are publicly available on GitHub at https://github.com/HaoranLiu507/TempotronGPU.Comment: 14 pages,7 figure

Production of dibaryon dNΩd_{N\Omega} in kaon induced reactions

In this work, we propose to investigate the $d_{N\Omega}$ dibaryon production in the process $K^- p \rightarrow d_{N\Omega} \bar{\Xi}^0$ by utilizing the kaon beam with the typical momentum to be around 10 GeV, which may be available at COMPASS, OKA@U-70 and SPS@CERN. The cross sections for $K^- p \rightarrow d_{N\Omega} \bar{\Xi}^0$ are estimated and in particular, the cross sections can reach up to $577.20\ \mathrm{\mu b}$ at $P_{K}=20$ GeV. Considering that $d_{N\Omega}$ dominantly decay into $\Xi \Lambda$ and $\Xi \Sigma$, we also estimate the cross sections for $K^- p \to \Xi^0 \Lambda \bar{\Xi}^0$ and $K^- p \to \Xi^+ \Sigma^- \bar{\Xi}^0$, which can reach up to $134.89$ and $5.93 \ \mathrm{\mu b}$, respectively, at$P_K=20$ GeV.Comment: 7 pages, 4 figure

2-Methyl-6-nitro-2H-indazole

In the title compound, C8H7N3O2, the mol­ecular skeleton is almost planar with a maximum deviation of 0.0484 (9) Å for the methyl C atom. In the crystal, weak inter­molecular C—H⋯N and C—H⋯O hydrogen bonds help to establish the packing

Time-history simulation of civil architecture earthquake disaster relief- based on the three-dimensional dynamic finite element method

Earthquake action is the main external factor which influences long-term safe operation of civil construction, especially of the high-rise building. Applying time-history method to simulate earthquake response process of civil construction foundation surrounding rock is an effective method for the anti-knock study of civil buildings. Therefore, this paper develops a civil building earthquake disaster three-dimensional dynamic finite element numerical simulation system. The system adopts the explicit central difference method. Strengthening characteristics of materials under high strain rate and damage characteristics of surrounding rock under the action of cyclic loading are considered. Then, dynamic constitutive model of rock mass suitable for civil building aseismic analysis is put forward. At the same time, through the earthquake disaster of time-history simulation of Shenzhen Children’s Palace, reliability and practicability of system program is verified in the analysis of practical engineering problems

Realistic Noise Synthesis with Diffusion Models

Deep learning-based approaches have achieved remarkable performance in single-image denoising. However, training denoising models typically requires a large amount of data, which can be difficult to obtain in real-world scenarios. Furthermore, synthetic noise used in the past has often produced significant differences compared to real-world noise due to the complexity of the latter and the poor modeling ability of noise distributions of Generative Adversarial Network (GAN) models, resulting in residual noise and artifacts within denoising models. To address these challenges, we propose a novel method for synthesizing realistic noise using diffusion models. This approach enables us to generate large amounts of high-quality data for training denoising models by controlling camera settings to simulate different environmental conditions and employing guided multi-scale content information to ensure that our method is more capable of generating real noise with multi-frequency spatial correlations. In particular, we design an inversion mechanism for the setting, which extends our method to more public datasets without setting information. Based on the noise dataset we synthesized, we have conducted sufficient experiments on multiple benchmarks, and experimental results demonstrate that our method outperforms state-of-the-art methods on multiple benchmarks and metrics, demonstrating its effectiveness in synthesizing realistic noise for training denoising models