Solid-phase synthesis of bleomycin group antibiotics. Elaboration of deglycobleomycin A(5)

The solid-phase syntheses of two deglycobleomycin A(5) analogues were achieved using a commercially available polystyrene resin containing triphenylmethyl-linked spermidine. The final products were deblocked and released from the resin, analyzed, and purified by C(18) reversed phase HPLC and characterized by high-field (1)H NMR spectroscopy and mass spectrometry. The purified products relaxed supercoiled plasmid DNA in a concentration-dependent fashion and to the same extent as authentic material derived from natural BLM A(5).     

The first three anisotropy constants of nickel

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.     

Deglycobleomycin: solid-phase synthesis and DNA cleavage by the resin-bound ligand

[structure: see text] A greatly improved solid-phase synthesis of deglycobleomycin using a Dde-based linker is reported. The resin-bound deglycobleomycin could be completely deblocked and assayed for DNA plasmid relaxation, sequence-selective DNA cleavage, and light production from a molecular beacon.     

Manifold preprocessing for laser‐induced breakdown spectroscopy under Mars conditions

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.