Photoregulation of Gene Expression with Ligand-Modified Caged siRNAs through Host/Guest Interaction

Small interfering RNA (siRNA) can effectively silence target genes through Argonate 2 (Ago2)-induced RNA interference (RNAi). It is very important to control siRNA activity in both spatial and temporal modes. Among different masking strategies, photocaging can be used to regulate gene expression through light irradiation with spatiotemporal and dose-dependent resolution. Many different caging strategies and caging groups have been reported for light-activated siRNA gene silencing. Herein, we describe a novel caging strategy that increases the blocking effect of RISC complex formation/process through host/guest (including ligand/receptor) interactions, thereby enhancing the inhibition of caged siRNA activity until light activation. This strategy can be used as a general approach to design caged siRNAs for the photomodulation of gene silencing of exogenous and endogenous genes.     

基于阶段注意力机制的电力线提取算法

电力系统维护是电力系统稳定运行的重要保障，应用智能算法的无人机电力巡检则为电力系统维护提供便捷。电力线提取是自主电力巡检以及保障飞行器低空飞行安全的关键技术，结合深度学习理论进行电力线提取是电力巡检的重要突破点。本文将深度学习方法用于电力线提取任务，结合电力线图像特点嵌入改进的图像输入策略和注意力模块，提出一种基于阶段注意力机制的电力线提取模型（SA-Unet）。本文提出的SA-Unet模型编码阶段采用阶段输入融合策略（Stage input fusion strategy， SIFS），充分利用图像的多尺度信息减少空间位置信息丢失。解码阶段通过嵌入阶段注意力模块（Stage attention module，SAM）聚焦电力线特征，从大量信息中快速筛选出高价值信息。实验结果表明，该方法在复杂背景的多场景中具有良好的性能。     

One step method of structure engineering porous graphitic carbon nitride for efficient visible-light photocatalytic reduction of Cr(Ⅵ)

Porous g-C3N4 nanosheets (PCN) were prepared by the nickel-assisted one-step thermal polymerization method.Hydrogen (H2) which was produced by the reaction between nickel (Ni) foam and ammonia (NH3) defined the structure and properties of PCN.During the formation of PCN,the participation of H2 not only enhanced the spacing between layers but also boosted the specific surface area that more active sites were exposed.Additionally,H2 promoted pores formation in the nanosheets,which was beneficial to the transfer of photons through lamellar structure and improved the absorption efficiency of visible light.Remarkably,the obtained PCN possessed better Cr(Ⅵ) photocatalytic reduction efficiency than pure g-C3N4.The reaction rate constant (k) of PCN (0.013 min-1) was approximately twice that of bare g-C3N4 (0.007 min-1).Furthermore,the effects of original pH and concentration of Cr(Ⅵ)-containing solution on removal efficiency of Cr(Ⅵ) were explored.A possible photocatalytic mechanism was proposed based on the experiments of radical scavengers and photoelectrochemical characterizations.     

Hydrogen Production from Catalytic Steam Reforming of Bio-Oils: A Critical Review

In the future, hydrogen will be an important energy carrier and industrial raw material. Catalytic steam reforming of bio-oils is a promising and economically viable technology for hydrogen production. However, during the reforming process, the catalysts are rapidly deactivated due to coke formation and sintering. Thus, maintaining the activity and stability of catalysts is the key issue in this process. Optimized operation conditions could extend the catalyst lifetime by affecting the coke morphology or promoting coke gasification. This article summarizes the recent developments in the field of catalytic steam reforming of bio-oils, focusing on the operation conditions, the properties of the catalysts, and the effects of the catalyst supports. The expected insights into the catalytic steam reforming of bio-oils will provide further guidance for hydrogen production from bio-oils.     

基于神经网络和遗传算法的锭子弹性管性能优化

为得到减振弹性管对下锭胆的支承弹性和锭子高速运动下的稳定性等性能的最优匹配效率,依据减振弹性管的等效抗弯刚度及底部等效刚度系数公式,利用MatLab数值分析软件构建弹性管抗弯刚度和底部挠度数学模型。首先,结合Isight优化软件基于径向基神经网络构建其近似模型,且使精度达到可接受水平,并以模型的关键结构参数弹性模量、螺距、槽宽、壁厚为设计变量,结合遗传算法对弹性管抗弯刚度和底部挠度进行多目标优化设计,得到Pareto最优解集和Pareto前沿图,确定出减振弹性管结构工艺参数的优化方案。通过对优化数据进行分析发现,该方案在保证减振弹性管弹性的同时,其底部振幅明显减弱。     

铜电解精炼添加剂生产实践与精细化管理探索

添加剂是铜电解精炼生产管理的最关键要素,合适的添加剂使用方案可以有效保障电解生产系统长期稳定。结合添加剂生产实践和其它电解厂添加剂管理优点,提出探索添加剂精细化管理,进一步保障和提升铜电解添加剂管理实效。     

iRGD-reinforced,photo-transformable nanoclusters toward cooperative enhancement of intratumoral penetration and antitumor efficacy

Nano Research - Insufficient intratumoral penetration greatly hurdles the anticancer performance of nanomedicine. To realize highly efficient tumor penetration in a precisely and spatiotemporally...     

Reconstructing gene regulatory networks with a memetic-neural hybrid based on fuzzy cognitive maps

Reconstructing gene regulatory networks (GRNs) plays an important role in identifying the complicated regulatory relationships, uncovering regulatory patterns in cells, and gaining a systematic view for biological processes. In order to reconstruct large-scale GRNs accurately, in this paper, we first use fuzzy cognitive maps (FCMs), which are a kind of cognition fuzzy influence graphs based on fuzzy logic and neural networks, to model GRNs. Then, a novel hybrid method is proposed to reconstruct GRNs from time series expression profiles using memetic algorithm (MA) combined with neural network (NN), which is labeled as MANN_FCM-GRN. In MANN_FCM-GRN, the MA is used to determine regulatory connections in GRNs and the NN is used to determine the interaction strength of the regulatory connections. In the experiments, the performance of MANN_FCM-GRN is validated on both synthetic data and the benchmark dataset DREAM3 and DREAM4. The experimental results demonstrate the efficacy of MANN_FCM-GRN and show that MANN_FCM-GRN can reconstruct GRNs with high accuracy without expert knowledge. The comparison with existing algorithms also shows that MANN_FCM-GRN outperforms ant colony optimization, non-linear Hebbian learning, and real-coded genetic algorithms.