Structure-guided design of potent JAK1-selective inhibitors based on 4-amino-7H-pyrrolo[2,3-d]pyrimidine with anti-inflammatory efficacy.

In a continuous effort to develop Janus kinase 1 (JAK1)-selective inhibitors, a novel series of 4-amino-7H-pyrrolo[2,3-d]pyrimidine derivatives bearing the piperidinyl fragment were designed and synthesized according to a combination strategy. Through enzymatic assessments, the superior compound 12a with an IC50 value of 12.6 nM against JAK1 was identified and a 10.7-fold selectivity index over JAK2 was achieved. It was indicated that 12a displayed considerable effect in inhibiting the pro-inflammatory NO generated from lipopolysaccharide (LPS)-induced RAW264.7 macrophages, while on normal RAW264.7 cells, 12a exerted a weak cytotoxicity effect (IC50 = 143.3 µM). Furthermore, H&E stain assay demonstrated the conspicuous capacity of 12a to suppress CCl4-induced hepatic fibrosis levels in a dose-dependent manner in vivo. The binding model of 12a ideally reflects the excellent activity of JAK1 over the homologous kinase JAK2. Overall, 12a, a JAK1-selective inhibitor, exhibited potential for liver fibrosis and inflammatory diseases.

Uneven Strain Distribution Induces Consecutive Dislocation Slipping, Plane Gliding, and Subsequent Detwinning of Penta-Twinned Nanoparticles.

Twin structures possess distinct physical and chemical properties by virtue of their specific twin configuration. However, twinning and detwinning processes are not fully understood on the atomic scale. Integrating in situ high resolution transmission electron microscopy and molecular dynamic simulations, we find tensile strain in the asymmetrical 5-fold twins of Au nanoparticles leads to twin boundary migration through dislocation sliding (slipping of an atomic layer) along twin boundaries and dislocation reactions at the 5-fold axis under an electron beam. Migration of one or two layers of twin planes is governed by energy barriers, but overall, the total energy, including surface, lattice strain, and twin boundary energy, is relaxed after consecutive twin boundary migration, leading to a detwinning process. In addition, surface rearrangement of 5-fold twinned nanoparticles can aid in the detwinning process.

Diffusion growth mechanism of penta-twinned Ag nanocrystals from decahedral seeds.

Crystals with penta-twinned structures can be produced from diverse fcc metals, but the mechanisms that control the final product shapes are still not well understood. By using the theory of absorbing Markov chains to account for the growth of penta-twinned decahedral seeds via atom deposition and surface diffusion, we predicted the formation of various types of products: decahedra, nanorods, and nanowires. We showed that the type of product depends on the morphology of the seed and that small differences between various seed morphologies can lead to significantly different products. For the case of uncapped decahedra seeds, we compared predictions from our model to nanowire morphologies obtained in two different experiments and obtained favorable agreement. Possible extensions of our model are indicated.

MLRD-Net: 3D multiscale local cross-channel residual denoising network for MRI-based brain tumor segmentation.

The precise segmentation of multimodal MRI images is the primary stage of tumor diagnosis and treatment. Current segmentation strategies often underutilize multiscale features, which can easily lead to loss of contextual information, reduction of low-level features and noise interference. To overcome these issues, a 3D multiscale local cross-channel residual denoising network (MLRD-Net) for an MRI-based brain tumor segmentation algorithm is proposed in this paper. Specifically, we employ encoder-decoder structure to connect local and global features, and enhance the receptive field of the network. Random slice operation has been conducted to enhance robustness. Then, residual blocks with pre-activation operation are developed in down-sampling stage, which effectively improves signal propagation along the network and alleviates network overfitting. Finally, the local cross-channel denoising mechanism is established to eliminate unimportant features without dimensionality reduction. Our proposal was evaluated in Brain Tumor Segmentation 2020 dataset (BraTS 2020), obtaining significantly improved results with mean Dice Similarity Coefficient metric of 0.91, 0.79, and 0.73 for the complete, tumor core, and enhancing tumor regions respectively. Besides, we conduct further practice on BraTS 2019, with the mean Dice Similarity Coefficient metric of 0.89, 0.80, and 0.75. Massive experiments demonstrate that our method is powerful and reliable. It increases little model complexity while achieving very competitive performance.

OptiBoost: A method for choosing a safe and efficient boost for the bond-boost method in accelerated molecular dynamics simulations with hyperdynamics.

Accelerated molecular-dynamics (MD) simulations based on hyperdynamics (HD) can significantly improve the efficiency of MD simulations of condensed-phase systems that evolve via rare events. However, such simulations are not generally easy to apply since appropriate boosts are usually unknown. In this work, we developed a method called OptiBoost to adjust the value of the boost in HD simulations based on the bond-boost method. We demonstrated the OptiBoost method in simulations on a cosine potential and applied it in three different systems involving Ag diffusion on Ag(100) in vacuum and in ethylene glycol solvent. In all cases, OptiBoost was able to predict safe and effective values of the boost, indicating that the OptiBoost protocol is an effective way to advance the applicability of HD simulations.

Solution-Phase Growth of Cu Nanowires with Aspect Ratios Greater Than 1000: Multiscale Theory.

Penta-twinned metal nanowires are finding widespread application in existing and emerging technologies. However, little is known about their growth mechanisms. We probe the origins of chloride- and alkylamine-mediated, solution-phase growth of penta-twinned Cu nanowires from first-principles using multiscale theory. Using quantum density functional theory (DFT) calculations, we characterize the binding and surface diffusion of Cu atoms on chlorine-covered Cu(100) and Cu(111) surfaces. We find stronger binding and slower diffusion of Cu atoms on chlorinated Cu(111) than on chlorinated Cu(100), which is a reversal of the trend for bare Cu surfaces. We also probe interfacet diffusion and find that this proceeds faster from Cu(100) to Cu(111) than the reverse. Using the DFT rates for hopping between individual sites at Ångstrom scales, we calculate coarse-grained, interfacet rates for nanowires of various lengths─up to hundreds of micrometers─and diameters in the 10 nm range. We predict nanowires with aspect ratios of ∼100, based on surface diffusion alone. We also account for the influence of a self-assembled alkylamine layer that covers most of the {100} facets, but is absent or thin and disordered on the {111} facets and in an "end zone" near the {100}/{111} boundary. With an end zone, we predict a wide range of nanowire aspect ratios in the experimental ranges. Our work reveals the mechanisms by which a halide─chloride─promotes the growth of high-aspect-ratio nanowires.

DLSTM-Based Successive Cancellation Flipping Decoder for Short Polar Codes.

Polar code has been adopted as the control channel coding scheme for the fifth generation (5G), and the performance of short polar codes is receiving intensive attention. The successive cancellation flipping (SC flipping) algorithm suffers a significant performance loss in short block lengths. To address this issue, we propose a double long short-term memory (DLSTM) neural network to locate the first error bit. To enhance the prediction accuracy of the DLSTM network, all frozen bits are clipped in the output layer. Then, Gaussian approximation is applied to measure the channel reliability and rank the flipping set to choose the least reliable position for multi-bit flipping. To be robust under different codewords, padding and masking strategies aid the network architecture to be compatible with multiple block lengths. Numerical results indicate that the error-correction performance of the proposed algorithm is competitive with that of the CA-SCL algorithm. It has better performance than the machine learning-based multi-bit flipping SC (ML-MSCF) decoder and the dynamic SC flipping (DSCF) decoder for short polar codes.

Extrapolating microdomain Ca(2+) dynamics using BK channels as a Ca(2+) sensor.

Ca(2+) ions play crucial roles in mediating physiological and pathophysiological processes, yet Ca(2+) dynamics local to the Ca(2+) source, either from influx via calcium permeable ion channels on plasmic membrane or release from internal Ca(2+) stores, is difficult to delineate. Large-conductance calcium-activated K(+) (BK-type) channels, abundantly distribute in excitable cells and often localize to the proximity of voltage-gated Ca(2+) channels (VGCCs), spatially enabling the coupling of the intracellular Ca(2+) signal to the channel gating to regulate membrane excitability and spike firing patterns. Here we utilized the sensitivity and dynamic range of BK to explore non-uniform Ca(2+) local transients in the microdomain of VGCCs. Accordingly, we applied flash photolysis of caged Ca(2+) to activate BK channels and determine their intrinsic sensitivity to Ca(2+). We found that uncaging Ca(2+) activated biphasic BK currents with fast and slow components (time constants being τf ≈ 0.2 ms and τs ≈ 10 ms), which can be accounted for by biphasic Ca(2+) transients following light photolysis. We estimated the Ca(2+)-binding rate constant kb (≈1.8 × 10(8) M(-1) s(-1)) for mSlo1 and further developed a model in which BK channels act as a calcium sensor capable of quantitatively predicting local microdomain Ca(2+) transients in the vicinity of VGCCs during action potentials.