Modification of TiO_2 Nanoparticles with Organodiboron Molecules Inducing Stable Surface Ti^(3+) Complex

As one of the most promising semiconductor oxide materials, titanium dioxide (TiO_2) absorbs ultraviolet (UV) light but not visible light. To address this limitation, the introduction of Ti^(3+) defects represents a common strategy to render TiO_2 visible-light-responsive. Unfortunately, current hurdles in Ti^(3+) generation technologies impeded the widespread application of Ti^(3+) modified materials. Herein, we demonstrate a simple and mechanistically distinct approach to generating abundant surface-Ti^(3+) sites without leaving behind oxygen vacancy and sacrificing one-off electron donors. In particular, upon adsorption of organodiboron reagents onto TiO_2 nanoparticles, spontaneous electron injection from the dibron-bound O^(2-) site to adjacent Ti^(4+) site leads to an extremely stable blue surface Ti^(3+)‒O^(-•) complex. Notably, this defect generation protocol is also applicable to other semiconductor oxides including ZnO, SnO_2, Nb_2O_5 and In_2O_3. Furthermore, the as-prepared photoelectronic device using this strategy affords 10^3 fold higher visible light response, and the fabricated perovskite solar cell shows an enhanced performance

Modification of TiO_2 Nanoparticles with Organodiboron Molecules Inducing Stable Surface Ti^(3+) Complex

As one of the most promising semiconductor oxide materials, titanium dioxide (TiO_2) absorbs ultraviolet (UV) light but not visible light. To address this limitation, the introduction of Ti^(3+) defects represents a common strategy to render TiO_2 visible-light-responsive. Unfortunately, current hurdles in Ti^(3+) generation technologies impeded the widespread application of Ti^(3+) modified materials. Herein, we demonstrate a simple and mechanistically distinct approach to generating abundant surface-Ti^(3+) sites without leaving behind oxygen vacancy and sacrificing one-off electron donors. In particular, upon adsorption of organodiboron reagents onto TiO_2 nanoparticles, spontaneous electron injection from the dibron-bound O^(2-) site to adjacent Ti^(4+) site leads to an extremely stable blue surface Ti^(3+)‒O^(-•) complex. Notably, this defect generation protocol is also applicable to other semiconductor oxides including ZnO, SnO_2, Nb_2O_5 and In_2O_3. Furthermore, the as-prepared photoelectronic device using this strategy affords 10^3 fold higher visible light response, and the fabricated perovskite solar cell shows an enhanced performance

Molecular and Antigenic Characterization of Reassortant H3N2 Viruses from Turkeys with a Unique Constellation of Pandemic H1N1 Internal Genes

Triple reassortant (TR) H3N2 influenza viruses cause varying degrees of loss in egg production in breeder turkeys. In this study we characterized TR H3N2 viruses isolated from three breeder turkey farms diagnosed with a drop in egg production. The eight gene segments of the virus isolated from the first case submission (FAV-003) were all of TR H3N2 lineage. However, viruses from the two subsequent case submissions (FAV-009 and FAV-010) were unique reassortants with PB2, PA, nucleoprotein (NP) and matrix (M) gene segments from 2009 pandemic H1N1 and the remaining gene segments from TR H3N2. Phylogenetic analysis of the HA and NA genes placed the 3 virus isolates in 2 separate clades within cluster IV of TR H3N2 viruses. Birds from the latter two affected farms had been vaccinated with a H3N4 oil emulsion vaccine prior to the outbreak. The HAl subunit of the H3N4 vaccine strain had only a predicted amino acid identity of 79% with the isolate from FAV-003 and 80% for the isolates from FAV-009 and FAV-0010. By comparison, the predicted amino acid sequence identity between a prototype TR H3N2 cluster IV virus A/Sw/ON/33853/2005 and the three turkey isolates from this study was 95% while the identity between FAV-003 and FAV-009/10 isolates was 91%. When the previously identified antigenic sites A, B, C, D and E of HA1 were examined, isolates from FAV-003 and FAV-009/10 had a total of 19 and 16 amino acid substitutions respectively when compared with the H3N4 vaccine strain. These changes corresponded with the failure of the sera collected from turkeys that received this vaccine to neutralize any of the above three isolates in vitro

A Sparse Reconstruction Approach for Identifying Gene Regulatory Networks Using Steady-State Experiment Data

<div><p>Motivation</p><p>Identifying gene regulatory networks (GRNs) which consist of a large number of interacting units has become a problem of paramount importance in systems biology. Situations exist extensively in which causal interacting relationships among these units are required to be reconstructed from measured expression data and other a priori information. Though numerous classical methods have been developed to unravel the interactions of GRNs, these methods either have higher computing complexities or have lower estimation accuracies. Note that great similarities exist between identification of genes that directly regulate a specific gene and a sparse vector reconstruction, which often relates to the determination of the number, location and magnitude of nonzero entries of an unknown vector by solving an underdetermined system of linear equations <i>y</i> = Φ<i>x</i>. Based on these similarities, we propose a novel framework of sparse reconstruction to identify the structure of a GRN, so as to increase accuracy of causal regulation estimations, as well as to reduce their computational complexity.</p><p>Results</p><p>In this paper, a sparse reconstruction framework is proposed on basis of steady-state experiment data to identify GRN structure. Different from traditional methods, this approach is adopted which is well suitable for a large-scale underdetermined problem in inferring a sparse vector. We investigate how to combine the noisy steady-state experiment data and a sparse reconstruction algorithm to identify causal relationships. Efficiency of this method is tested by an artificial linear network, a mitogen-activated protein kinase (MAPK) pathway network and the <i>in silico</i> networks of the DREAM challenges. The performance of the suggested approach is compared with two state-of-the-art algorithms, the widely adopted total least-squares (TLS) method and those available results on the DREAM project. Actual results show that, with a lower computational cost, the proposed method can significantly enhance estimation accuracy and greatly reduce false positive and negative errors. Furthermore, numerical calculations demonstrate that the proposed algorithm may have faster convergence speed and smaller fluctuation than other methods when either estimate error or estimate bias is considered.</p></div

Reconstruction performance for the DREAM3 and DREAM4 in the size 100 subchallenges.

<p>Reconstruction performance for the DREAM3 and DREAM4 in the size 100 subchallenges.</p

Comparison of the ROC and PR curves in the DREAM3 identification using the SubLM1, SubLM2, TLS, SmOMP and StOMP algorithms.

<p>(a) ROC curves of Yeast2. (b) PR curves of Yeast2.</p

Comparison of the boundary of success phase at several values of indeterminacy <i>δ</i>.

<p>Comparison of the boundary of success phase at several values of indeterminacy <i>δ</i>.</p

Comparison of the averaged ROC and PR curves in the MAPK network identification using the SubLM1, SubLM2, TLS, SmOMP and StOMP algorithms.

<p>(a) Averaged ROC curves. (b) Averaged PR curves.</p

Reconstruction performance of the StOMP and SmOMP algorithms with <i>m</i> = 80, <i>σ</i> = 0.3 for the artificial network inference.

<p>(a) Comparison of averaged ROC curves. (b) Comparison of averaged PR curves.</p

Reconstruction performance of the SmOMP, SubLM1, SubLM2 and TLS algorithms with <i>m</i> = 1000, <i>σ</i> = 2.0 for the artificial network inference.

<p>(a) Comparison of averaged ROC curves. (b) Comparison of averaged PR curves.</p