Exploring the Phytochemical Landscape of the Early-Diverging Flowering Plant Amborella trichopoda Baill.

Although the evolutionary significance of the early-diverging flowering plant Amborella (Amborella trichopoda Baill.) is widely recognized, its metabolic landscape, particularly specialized metabolites, is currently underexplored. In this work, we analyzed the metabolomes of Amborella tissues using liquid chromatography high-resolution electrospray ionization mass spectrometry (LC-HR-ESI-MS). By matching the mass spectra of Amborella metabolites with those of authentic phytochemical standards in the publicly accessible libraries, 63, 39, and 21 compounds were tentatively identified in leaves, stems, and roots, respectively. Free amino acids, organic acids, simple sugars, cofactors, as well as abundant glycosylated and/or methylated phenolic specialized metabolites were observed in Amborella leaves. Diverse metabolites were also detected in stems and roots, including those that were not identified in leaves. To understand the biosynthesis of specialized metabolites with glycosyl and methyl modifications, families of small molecule UDP-dependent glycosyltransferases (UGTs) and O-methyltransferases (OMTs) were identified in the Amborella genome and the InterPro database based on conserved functional domains. Of the 17 phylogenetic groups of plant UGTs (A-Q) defined to date, Amborella UGTs are absent from groups B, N, and P, but they are highly abundant in group L. Among the 25 Amborella OMTs, 7 cluster with caffeoyl-coenzyme A (CCoA) OMTs involved in lignin and phenolic metabolism, whereas 18 form a clade with plant OMTs that methylate hydroxycinnamic acids, flavonoids, or alkaloids. Overall, this first report of metabolomes and candidate metabolic genes in Amborella provides a starting point to a better understanding of specialized metabolites and biosynthetic enzymes in this basal lineage of flowering plants

Ultra-Scalable Spectral Clustering and Ensemble Clustering

This paper focuses on scalability and robustness of spectral clustering for extremely large-scale datasets with limited resources. Two novel algorithms are proposed, namely, ultra-scalable spectral clustering (U-SPEC) and ultra-scalable ensemble clustering (U-SENC). In U-SPEC, a hybrid representative selection strategy and a fast approximation method for K-nearest representatives are proposed for the construction of a sparse affinity sub-matrix. By interpreting the sparse sub-matrix as a bipartite graph, the transfer cut is then utilized to efficiently partition the graph and obtain the clustering result. In U-SENC, multiple U-SPEC clusterers are further integrated into an ensemble clustering framework to enhance the robustness of U-SPEC while maintaining high efficiency. Based on the ensemble generation via multiple U-SEPC's, a new bipartite graph is constructed between objects and base clusters and then efficiently partitioned to achieve the consensus clustering result. It is noteworthy that both U-SPEC and U-SENC have nearly linear time and space complexity, and are capable of robustly and efficiently partitioning ten-million-level nonlinearly-separable datasets on a PC with 64GB memory. Experiments on various large-scale datasets have demonstrated the scalability and robustness of our algorithms. The MATLAB code and experimental data are available at https://www.researchgate.net/publication/330760669.Comment: To appear in IEEE Transactions on Knowledge and Data Engineering, 201

DEA Models Involving Future Performance

Workshop 2015 -Advances in DEA Theory and Applications (December 1-2, 2015)In many practical applications, past results are not sufficient for evaluating a DMU’s performance in highly volatile operating environments, such as those with highly volatile crude oil prices and currency exchange rates. That is, in such environments, a DMU’s whole performance may be seriously distorted if its future performance, which is sensitive to crude oil price volatility and/or currency fluctuations, is ignored in the evaluation process. Hence, this research aims at developing a new system of DEA models that incorporate a DMU’s uncertain future performance, and thus can be applied to fully measure their efficiency.The workshop is supported by JSPS (Japan Society for the Promotion of Science), Grant-in-Aid for Scientific Research (B), #25282090, titled “Studies in Theory and Applications of DEA for Forecasting Purpose.本研究はJSPS科研費 基盤研究(B) 25282090の助成を受けたものです

Tetra­aqua(2,2′-bipyridine-κ2 N,N′)magnesium(II) bis­(4-bromo­benzoate)

In the complex cation of the title compound, [Mg(C10H8N2)(H2O)4](C7H4BrO2)2, the MgII atom is coordinated by two N atoms from a 2,2′-bipyridine ligand and four water O atoms in a distorted MgN2O4 octa­hedral geometry. The cation is located on a special position on a twofold rotation axis which passes through the MgII atom and the centroid of the 2,2′-bipyridine ligand. The 2,2′-bipyridine ligands exhibit nearly perfect coplanarity (r.m.s. deviation = 0.0035 Å) . In the crystal, O—H⋯O and C—H⋯O, C—H⋯Br hydrogen bonds and π–π stacking inter­actions [mean inter­planar distance of 3.475 (6) Å between adjacent 2,2′-bipyridine ligands] link the cations and anions into a three-dimensional supra­molecular network. One Br atom is disordered over two sites with occupancy factors of 0.55 and 0.45

Identifying a Transcription Factor’s Regulatory Targets from its Binding Targets

ChIP-chip data, which shows binding of transcription factors (TFs) to promoter regions in vivo, are widely used by biologists to identify the regulatory targets of TFs. However, the binding of a TF to a gene does not necessarily imply regulation. Thus, it is important to develop computational methods which can extract a TF’s regulatory targets from its binding targets. We developed a method, called REgulatory Targets Extraction Algorithm (RETEA), which uses partial correlation analysis on gene expression data to extract a TF’s regulatory targets from its binding targets inferred from ChIP-chip data. We applied RETEA to yeast cell cycle microarray data and identified the plausible regulatory targets of eleven known cell cycle TFs. We validated our predictions by checking the enrichments for cell cycle-regulated genes, common cellular processes and common molecular functions. Finally, we showed that RETEA performs better than three published methods (MA-Network, TRIA and Garten et al’s method)