Coriatone and corianlactone, two novel sesquiterpenes from Coriaria nepalensis

Both coriatone (1). a novel highly oxygenated picrotoxane-type sesquiterpene, and corianlactone (2). with an unprecedented sesquiterpene basic skeleton, named coriane, were isolated from Coriaria nepalensis Wall. The structures of 1 and 2 were determined by analysis of their two-dimensional NMR data, and the structure of 2 was confirmed by X-ray analysis. Compounds 1 and 2 showed no remarkable inhibitory activity toward K(562) cells. They are cytotoxic with IC(50) > 50 microg/mL (cis-platinim: IC(50) = 0.49 microg/mL).     

β-环糊精用于盐酸金刚烷胺的示波极谱测定

在0.2mol·L-1NaOH底液中,β-环糊精能够产生灵敏切口,其深度随盐酸金刚烷胺-β-环糊精包结物量的增加而变浅。根据β-环糊精的示波特性,建立了用二次微分简易示波伏安法间接测定片剂中盐酸金刚烷胺含量的新方法。盐酸金刚烷胺的浓度在1.0×10-6-3.6×10-5mol·L-1范围内呈线性,回归方程h(V)=97.66+7.27×105c(mol·L-1),相关系数为0.995 6,检出限为4.0×10-7mol·L-1。对于2.5×10-5mol·L-1盐酸金刚烷胺钠5次测定的RSD为3.1％。与其他方法相比,此法具有仪器简单、快速、无需通氮除氧等特点。     

A generalized series approach to MR spectroscopic imaging

The problem of precise spatial localization of spectral information in magnetic resonance (MR) spectroscopic imaging is addressed. A novel method, called GSLIM (generalized spectral location by imaging), is proposed to make possible the marriage of high-resolution proton imaging with spectroscopic imaging and localization. This method improves on the conventional Fourier series inversion method used in chemical shift imaging (CSI) and the compartmental modeling method used in SLIM by using a generalized series framework for optimal representation of the spectral function. In this way, a priori information extracted from proton imaging can be used, as in SLIM, and the robustness and data consistency of CSI are also retained. Simulation results show that GSLIM can significantly reduce spectral leakage in CSI and inhomogeneity errors in SLIM. It can also reveal compartmental inhomogeneities, and can easily be extended to handle other a priori constraints when necessary. This approach, with some further development, may achieve an optimal combination of sensitivity, quantitative accuracy, speed, and flexibility for in vivo spectroscopy.     

Differential operator Lie algebras on the ring of Laurent polynomials

A class of differential operator Lie algebras on the unit circle is introduced and discussed. They are the natural generalizations of the Witt algebra and the Virasoro algebra. Among them are the higher-spin algebrasW ₁₊ andW which occur in the physics literature.     

Analysis of light propagation characteristic in the aero-optic flow field of cone-headed vehicle with side window

Aero-optic imaging deviation research is carried out for infrared-guided vehicle with cone-head side window, with a focus on the propagation characteristics of light in an aero-optic flow field. When the light entering the aero-optic flow field from the free-stream should be close to the normal, numerous data indicate that the light is refracted away from the normal. This paper divides the aero-optic flow field into two parts and uses the gas density distribution in the aero-optic flow field to ...     

基于双解码路径DD-UNet的脑肿瘤图像分割算法

针对医学图像中病灶区域尺度不一、边界模糊和周围组织强度不均匀所导致的分割精度降低问题,提出了一种基于双解码器的脑肿瘤图像分割模型。为了增强特征的表征力,提出了高阶微分残差模块并使用不同空洞率的扩张卷积用于提取特征编码,提高了网络模型的分割性能;引入上下文语义信息感知模块(multi scale dilation, MSD),从不同的目标尺度中提取更多的精细信息,提高了对结构细节信息的捕获能力,同时减少了编解码器之间的特征差异;在空间解码路径中使用选择性聚合空间注意力模块(spatial aggregation attention module, SAAM),增加了对有效空间特征的权重比例,减少了无效的特征干扰。在脑肿瘤数据集上进行了实验验证,实验结果表明,所提算法的Dice系数、平均交并比、敏感性、特异性、准确率等指标分别为：93.35%、90.71%、91.15%、99.94%、96.75%。     

Vehicle and pedestrian detection method based on improved YOLOv4-tiny

Aiming at the problem of low detection accuracy of vehicle and pedestrian detection models, this paper proposes an improved you only look once v4 (YOLOv4)-tiny vehicle and pedestrian target detection algorithm. Convolutional block attention module (CBAM) is introduced into cross stage partial Darknet-53 (CSPDarknet53)-tiny module to enhance feature extraction capabilities. In addition, the cross stage partial dense block layer (CSP-DBL) module is used to replace the original simple convolutional module superposition, which compensates for the high-resolution characteristic information and further improves the detection accuracy of the network. Finally, the test results on the BDD100K traffic dataset show that the mean average precision (mAP) value of the final network of the proposed method is 88.74%, and the detection speed reaches 63 frames per second (FPS), which improves the detection accuracy of the network and meets the real-time detection speed.