Regulating C2H2/CO2 adsorption selectivity by electronic-state manipulation of iron in metal-organic frameworks

Article reports a metal electronic-state manipulation strategy to construct a pair of isostructural and interconvertible Fe-MOFs featuring open Fe centers with different electron densities for efficient C₂H₂/CO₂ separation. The authors show that the presence of Fe[II] centers with a medium-spin-state trail plays a crucial role in the enhanced C₂H₂ selective adsorption

A Comprehensive Expression Profile of MicroRNAs in Porcine Pituitary

MicroRNAs (miRNAs) are an abundant class of small RNAs that regulate expressions of most genes. miRNAs play important roles in the pituitary, the “master” endocrine organ.However, we still don't know which role miRNAs play in the development of pituitary tissue or how much they contribute to the pituitary function. By applying a combination of microarray analysis and Solexa sequencing, we detected a total of 450 miRNAs in the porcine pituitary. Verification with RT-PCR showed a high degree of confidence for the obtained data. According to the current miRBase release17.0, the detected miRNAs included 169 known porcine miRNAs, 163 conserved miRNAs not yet identified in the pig, and 12 potentially new miRNAs not yet identified in any species, three of which were revealed using Northern blot. The pituitary might contain about 80.17% miRNA types belonging to the animal. Analysis of 10 highly expressed miRNAs with the Kyoto Encyclopedia of Genes and Genomes (KEGG) indicated that the enriched miRNAs were involved not only in the development of the organ but also in a variety of inter-cell and inner cell processes or pathways that are involved in the function of the organ

Construction of a map-based reference genome sequence for barley, Hordeum vulgare L.

Barley (Hordeum vulgare L.) is a cereal grass mainly used as animal fodder and raw material for the malting industry. The map-based reference genome sequence of barley cv. `Morex' was constructed by the International Barley Genome Sequencing Consortium (IBSC) using hierarchical shotgun sequencing. Here, we report the experimental and computational procedures to (i) sequence and assemble more than 80,000 bacterial artificial chromosome (BAC) clones along the minimum tiling path of a genome-wide physical map, (ii) find and validate overlaps between adjacent BACs, (iii) construct 4,265 non-redundant sequence scaffolds representing clusters of overlapping BACs, and (iv) order and orient these BAC clusters along the seven barley chromosomes using positional information provided by dense genetic maps, an optical map and chromosome conformation capture sequencing (Hi-C). Integrative access to these sequence and mapping resources is provided by the barley genome explorer (BARLEX).Peer reviewe

A chromosome conformation capture ordered sequence of the barley genome

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Using hypothesis margin to boost centroid text classifier

Centroid Classifier is a simple and yet efficient method for text categorization. However it often suffers from the inductive bias or model misfit incurred by its assumption. In order to address this issue, training-set errors as well as training-set margins are regarded as training criterions. Based on these two criterions, an overall (or global) objective function over all training examples is constructed, and optimized to produce a refined Centroid classification model. The empirical assessment conducted on four benchmark collections evidence that proposed method performs comparably to state-of-the-art SVM classifier in classifying performance, as well as beats it in running time

An effective refinement strategy for KNN text classifier

Due to the exponential growth of documents on the Internet and the emergent need to organize them, the automated categorization of documents into predefined labels has received an ever-increased attention in the recent years. A wide range of supervised learning algorithms has been introduced to deal with text classification. Among all these classifiers, K-Nearest Neighbors (KNN) is a widely used classifier in text categorization community because of its simplicity and efficiency. However, KNN still suffers from inductive biases or model misfits that result from its assumptions, such as the presumption that training data are evenly distributed among all categories. In this paper, we propose a new refinement strategy, which we called as DragPushing, for the KNN Classifier. The experiments on three benchmark evaluation collections show that DragPushing achieved a significant improvement on the performance of the KNN Classifier

An Effective Approach to Enhance Centroid Classifier for Text Categorization

Abstract. Centroid Classifier has been shown to be a simple and yet effective method for text categorization. However, it is often plagued with model misfit (or inductive bias) incurred by its assumption. To address this issue, a novel Model Adjustment algorithm was proposed. The basic idea is to make use of some criteria to adjust Centroid Classifier model. In this work, the criteria include training-set errors as well as training-set margins. The empirical assessment indicates that proposed method performs slightly better than SVM classifier in prediction accuracy, as well as beats it in running time