Narrow band imaging is a promising approach enabling in vivo differentiation of nonmalignant from malignant laryngeal lesions by evaluating the morphology of mucosal capillaries. These results suggest endoscopic narrow band imaging may be useful in the early detection of laryngeal cancer and precancerous lesions.

Multilocus genome-wide association studies (GWAS) have become the state-of-the-art procedure to identify quantitative trait nucleotides (QTNs) associated with complex traits. However, implementation of multilocus model in GWAS is still difficult. In this study, we integrated least angle regression with empirical Bayes to perform multilocus GWAS under polygenic background control. We used an algorithm of model transformation that whitened the covariance matrix of the polygenic matrix K and environmental noise. Markers on one chromosome were included simultaneously in a multilocus model and least angle regression was used to select the most potentially associated single-nucleotide polymorphisms (SNPs), whereas the markers on the other chromosomes were used to calculate kinship matrix as polygenic background control. The selected SNPs in multilocus model were further detected for their association with the trait by empirical Bayes and likelihood ratio test. We herein refer to this method as the pLARmEB (polygenic-background-control-based least angle regression plus empirical Bayes). Results from simulation studies showed that pLARmEB was more powerful in QTN detection and more accurate in QTN effect estimation, had less false positive rate and required less computing time than Bayesian hierarchical generalized linear model, efficient mixed model association (EMMA) and least angle regression plus empirical Bayes. pLARmEB, multilocus random-SNP-effect mixed linear model and fast multilocus random-SNP-effect EMMA methods had almost equal power of QTN detection in simulation experiments. However, only pLARmEB identified 48 previously reported genes for 7 flowering time-related traits in Arabidopsis thaliana.

New zinc pimelate catalysts used for copolymerization of carbon dioxide and propylene oxide have been synthesized in high yield by a magnetic stirring method. The regular molecular structure of the zinc pimelate was confirmed by Fourier‐transform infrared spectroscopy and wide‐angle X‐ray diffraction techniques. Accordingly, poly(propylene carbonate) (PPC) can be synthesized from carbon dioxide and propylene oxide using these zinc pimelate catalysts. High catalytic efficiency (95.2 gram polymer per gram catalyst or 21 300 g of polymer per mole of zinc) was achieved by optimizing the PO/catalyst ratio. NMR measurement revealed that the PPC synthesized had an alternating copolymer structure. The thermal properties of PPC were determined by modulated differential scanning calorimetry and thermogravimetric analysis. The results demonstrated that the PPC copolymer exhibited an extremely high glass transition temperature of 44.27 °C and decomposition temperature of 257 °C, comparable with values reported in literature. Copyright © 2003 Society of Chemical Industry