排序方式: 共有20条查询结果,搜索用时 0 毫秒
1.
2.
3.
Christopher J. Arnusch 《Tetrahedron letters》2004,45(21):4153-4156
The synthesis and NMR analysis of a novel highly constrained scaffold is described. The 14-membered macrocyclic ring structure was inspired by many medicinally relevant natural products that also contain the bi-aryl ether moiety. The synthesis required only commercially available starting materials and involved a base mediated SNAr cyclization. A conformational search was performed, which indicated a strong preference for a single conformation, which was consistent with observed ROE signals by NMR. 相似文献
4.
5.
6.
The technique of ferromagnetic resonance at 23 GHz has been used to determine the first three anisotropy constants of pure Ni down to 4.2K. A temperature and orientation dependent linewidth has also been observed. 相似文献
7.
Branderhorst HM Kooij R Salminen A Jongeneel LH Arnusch CJ Liskamp RM Finne J Pieters RJ 《Organic & biomolecular chemistry》2008,6(8):1425-1434
A galabiose disaccharide building block was synthesized by an efficient pectinase cleavage of polygalacturonic acid and subsequent chemical functional group transformations. Besides the disaccharide, the corresponding trisaccharide was also obtained and modified. The compounds were subsequently conjugated to dendrimers with up to eight end groups using 'click' chemistry. The compounds were evaluated as inhibitors of adhesion of the pathogen Streptococcus suis in a hemagglutination assay and strong inhibition was observed for the tetra- and octavalent galabiose compound with MIC values in the low nanomolar range. The corresponding octavalent trisaccharide was a ca. 20-fold weaker inhibitor. 相似文献
8.
9.
10.
Thomas Boucher CJ Carey Melinda Darby Dyar Sridhar Mahadevan Samuel Clegg Roger Wiens 《Journal of Chemometrics》2015,29(9):484-491
Laser‐induced breakdown spectroscopy (LIBS) is currently being used onboard the Mars Science Laboratory rover Curiosity to predict elemental abundances in dust, rocks, and soils using a partial least squares regression model developed by the ChemCam team. Accuracy of that model is constrained by the number of samples needed in the calibration, which grows exponentially with the dimensionality of the data, a phenomenon known as the curse of dimensionality. LIBS data are very high dimensional, and the number of ground‐truth samples (i.e., standards) recorded with the ChemCam before departing for Mars was small compared with the dimensionality, so strategies to optimize prediction accuracy are needed. In this study, we first use an existing machine learning algorithm, locally linear embedding (LLE), to combat the curse of dimensionality by embedding the data into a low‐dimensional manifold subspace before regressing. LLE constructs its embedding by maintaining local neighborhood distances and discarding large global geodesic distances between samples, in an attempt to preserve the underlying geometric structure of the data. We also introduce a novel supervised version, LLE for regression (LLER), which takes into account the known chemical composition of the training data when embedding. LLER is shown to outperform traditional LLE when predicting most major elements. We show the effectiveness of both algorithms using three different LIBS datasets recorded under Mars‐like conditions. Copyright © 2015 John Wiley & Sons, Ltd. 相似文献