Glycolaldehyde as a Bio-Based C1 Building Block for Selective N-Formylation of Secondary Amines.

Biomass derived glycolaldehyde was employed as C1 building block for the N-formylation of secondary amines using air as oxidant. The reaction is atom economic, highly selective and proceeds under catalyst free conditions. This strategy can be used for the synthesis of cyclic and acyclic formylamines, including DMF. Mechanistic studies suggest a radical oxidation pathway.

On the activation of PhICl2 with pyridine.

It has been previously proposed that pyridines can activate PhICl2 by displacing a chloride and forming the [PhI(Pyr)(Cl)]+ cation as a reactive intermediate. Here we show that pyridine does not displace chloride, but rather forms a weak complex with the iodine via halogen bonding along the C-I bond axis. This interaction is interrogated by NMR, structural, charge density, and theoretical investigations, which all indicate that pyridine does not activate PhICl2 as proposed.

Automated breast image classification using features from its discrete cosine transform.

PURPOSE: This work aimed to improve breast screening program accuracy using automated classification. The goal was to determine if whole image features represented in the discrete cosine transform would provide a basis for classification. Priority was placed on avoiding false negative findings. METHODS: Online datasets were used for this work. No informed consent was required. Programs were developed in Mathematica and, where necessary to improve computational performance ported to C++. The use of a discrete cosine transform to separate normal from cancerous breast tissue was tested. Features (moments of the mean) were calculated in square sections of the transform centered on the origin. K-nearest neighbor and naive Bayesian classifiers were tested. RESULTS: Forty-one features were generated and tested singly, and in combination of two or three. Using a k-nearest neighbor classifier, sensitivities as high as 98% with a specificity of 66% were achieved. With a naive Bayesian classifier, sensitivities as high as 100% were achieved with a specificity of 64%. CONCLUSION: Whole image classification based on discrete cosine transform (DCT) features was effectively implemented with a high level of sensitivity and specificity achieved. The high sensitivity attained using the DCT generated feature set implied that these classifiers could be used in series with other methods to increase specificity. Using a classifier with near 100% sensitivity, such as the one developed in this project, before applying a second classifier could only boost the accuracy of that classifier.