Abstract. The study of yeast cell morphology requires consistent identification of cell cycle phases based on cell bud size. A computer-based image processing algorithm is designed to automatically classify microscopic images of yeast cells in a microfluidic channel environment. The images were enhanced to reduce background noise, and a robust segmentation algorithm is developed to extract geometrical features including compactness, axis ratio, and bud size. The features are then used for classification, and the accuracy of various machine-learning classifiers is compared. The linear support vector machine, distance-based classification, and k-nearest-neighbor algorithm were the classifiers used in this experiment. The performance of the system under various illumination and focusing conditions were also tested. The results suggest it is possible to automatically classify yeast cells based on their morphological characteristics with noisy and low-contrast images. C 2011 Society of Photo-Optical Instrumentation Engineers (SPIE).
To explore the true identity of palladium-catalyzed Sonogashira coupling reaction, montmorillonite (MMT)-supported transition metal nanoparticles (MMT@M, M=Pd, Cu, Fe, and Ni) were prepared, characterized, and evaluated systematically. Among all MMT@M catalysts, MMT@Pd/Cu showed the highest activity, and it was successfully extended to 20 examples with 57%-97% yields. The morphology characterization of MMT@Pd/Cu revealed that the crystalline bimetallic particles were dispersed on a MMT layer as nanoalloy with diameters ranged from 10 to 11 nm. In situ IR analysis using CO as molecular probe and XPS characterization found that the surface of Pd/Cu particles consisted of both catalytic active sites of Pd(0) and Cu(I). The experiments on the catalytic activities of MMT@M found that Pd/Cu catalyst system exhibited high activity only in nanoalloy form. Therefore, the Pd/Cu nanoalloy was identified as catalyst, on which the interatom Pd/Cu transmetalation between surfaces was proposed to be responsible for its synergistic activity.
A neat palladium-catalyzed alkynylation reaction was developed with "super-active ester" as the carbonyl electrophile, which provides a clean and efficient synthetic protocol for a broad array of ynone compounds under CO-, Cu-, ligand-, and base-free conditions. The superior activity of triazine ester was rationalized by the strong electron-withdrawing ability and the unique affinity of triazine on palladium. A mechanistic experiment clearly demonstrated that the N-Pd coordination of triazine plays a crucial role for the highly efficient C-O activation.
With the rapid increase of protein sequences in the post-genomic age, it is challenging to develop accurate and automated methods for reliably and quickly predicting their subcellular localizations. Till now, many efforts have been tried, but most of which used only a single algorithm. In this paper, we proposed an ensemble classifier of KNN (k-nearest neighbor) and SVM (support vector machine) algorithms to predict the subcellular localization of eukaryotic proteins based on a voting system. The overall prediction accuracies by the one-versus-one strategy are 78.17%, 89.94% and 75.55% for three benchmark datasets of eukaryotic proteins. The improved prediction accuracies reveal that GO annotations and hydrophobicity of amino acids help to predict subcellular locations of eukaryotic proteins.
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