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The ITQ-37 mesoporous chiral zeolite (2009)

J. Sun ; C. Bonneau ; A. Cantin ; [et al.]

Nature Publishing Group (NPG)

In: Nature. 458(7242): 1154-7. doi: 10.1038/nature07957.

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Publication Date: 2009-05-02

Description: The synthesis of crystalline molecular sieves with pore dimensions that fill the gap between microporous and mesoporous materials is a matter of fundamental and industrial interest. The preparation of zeolitic materials with extralarge pores and chiral frameworks would permit many new applications. Two important steps in this direction include the synthesis of ITQ-33, a stable zeolite with 18 x 10 x 10 ring windows, and the synthesis of SU-32, which has an intrinsically chiral zeolite structure and where each crystal exhibits only one handedness. Here we present a germanosilicate zeolite (ITQ-37) with extralarge 30-ring windows. Its structure was determined by combining selected area electron diffraction (SAED) and powder X-ray diffraction (PXRD) in a charge-flipping algorithm. The framework follows the SrSi(2) (srs) minimal net and forms two unique cavities, each of which is connected to three other cavities to form a gyroidal channel system. These cavities comprise the enantiomorphous srs net of the framework. ITQ-37 is the first chiral zeolite with one single gyroidal channel. It has the lowest framework density (10.3 T atoms per 1,000 A(3)) of all existing 4-coordinated crystalline oxide frameworks, and the pore volume of the corresponding silica polymorph would be 0.38 cm(3) g(-1).〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Sun, Junliang -- Bonneau, Charlotte -- Cantin, Angel -- Corma, Avelino -- Diaz-Cabanas, Maria J -- Moliner, Manuel -- Zhang, Daliang -- Li, Mingrun -- Zou, Xiaodong -- England -- Nature. 2009 Apr 30;458(7242):1154-7. doi: 10.1038/nature07957.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Structural Chemistry and Berzelii Centre, EXSELENT on Porous Materials, Stockholm University, SE-106 91, Stockholm, Sweden.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/19407798" target="_blank"〉PubMed〈/a〉

Print ISSN: 0028-0836

Electronic ISSN: 1476-4687

Topics: Biology , Chemistry and Pharmacology , Medicine , Natural Sciences in General , Physics

Published by Nature Publishing Group (NPG)

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Robust data-driven incorporation of prior knowledge into the inference of dynamic regulatory networks (2013)

Greenfield, A., Hafemeister, C., Bonneau, R.

Oxford University Press

In: Bioinformatics

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Publication Date: 2013-04-13

Description: Motivation: Inferring global regulatory networks (GRNs) from genome-wide data is a computational challenge central to the field of systems biology. Although the primary data currently used to infer GRNs consist of gene expression and proteomics measurements, there is a growing abundance of alternate data types that can reveal regulatory interactions, e.g. ChIP-Chip, literature-derived interactions, protein–protein interactions. GRN inference requires the development of integrative methods capable of using these alternate data as priors on the GRN structure. Each source of structure priors has its unique biases and inherent potential errors; thus, GRN methods using these data must be robust to noisy inputs. Results: We developed two methods for incorporating structure priors into GRN inference. Both methods [Modified Elastic Net (MEN) and Bayesian Best Subset Regression ( BBSR )] extend the previously described Inferelator framework, enabling the use of prior information. We test our methods on one synthetic and two bacterial datasets, and show that both MEN and BBSR infer accurate GRNs even when the structure prior used has significant amounts of error (〉90% erroneous interactions). We find that BBSR outperforms MEN at inferring GRNs from expression data and noisy structure priors. Availability and implementation: Code, datasets and networks presented in this article are available at http://bonneaulab.bio.nyu.edu/software.html . Contact: bonneau@nyu.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Print ISSN: 1367-4803

Electronic ISSN: 1460-2059

Topics: Biology , Computer Science , Medicine