A linear programming approach for estimating the structure of a sparse linear genetic network from transcript profiling data

Background: A genetic network can be represented as a directed graph in which a node corresponds to a gene and a directed edge specifies the direction of influence of one gene on another. The reconstruction of such networks from transcript profiling data remains an important yet challenging endeavor. A transcript profile specifies the abundances of many genes in a biological sample of interest. Prevailing strategies for learning the structure of a genetic network from high-dimensional transcript profiling data assume sparsity and linearity. Many methods consider relatively small directed graphs, inferring graphs with up to a few hundred nodes. This work examines large undirected graphs representations of genetic networks, graphs with many thousands of nodes where an undirected edge between two nodes does not indicate the direction of influence, and the problem of estimating the structure of such a sparse linear genetic network (SLGN) from transcript profiling data. Results: The structure learning task is cast as a sparse linear regression problem which is then posed as a LASSO (l1-constrained fitting) problem and solved finally by formulating a Linear Program (LP). A bound on the Generalization Error of this approach is given in terms of the Leave-One-Out Error. The accuracy and utility of LP-SLGNs is assessed quantitatively and qualitatively using simulated and real data. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) initiative provides gold standard data sets and evaluation metrics that enable and facilitate the comparison of algorithms for deducing the structure of networks. The structures of LP-SLGNs estimated from the INSILICO1, INSILICO2 and INSILICO3 simulated DREAM2 data sets are comparable to those proposed by the first and/or second ranked teams in the DREAM2 competition. The structures of LP-SLGNs estimated from two published Saccharomyces cerevisae cell cycle transcript profiling data sets capture known regulatory associations. In each S. cerevisiae LP-SLGN, the number of nodes with a particular degree follows an approximate power law suggesting that its degree distributions is similar to that observed in real-world networks. Inspection of these LP-SLGNs suggests biological hypotheses amenable to experimental verification. Conclusion: A statistically robust and computationally efficient LP-based method for estimating the topology of a large sparse undirected graph from high-dimensional data yields representations of genetic networks that are biologically plausible and useful abstractions of the structures of real genetic networks. Analysis of the statistical and topological properties of learned LP-SLGNs may have practical value; for example, genes with high random walk betweenness, a measure of the centrality of a node in a graph, are good candidates for intervention studies and hence integrated computational – experimental investigations designed to infer more realistic and sophisticated probabilistic directed graphical model representations of genetic networks. The LP-based solutions of the sparse linear regression problem described here may provide a method for learning the structure of transcription factor networks from transcript profiling and transcription factor binding motif data.

Scalable multi-dimensional user intent identification using tree structured distributions

The problem of identifying user intent has received considerable attention in recent years, particularly in the context of improving the search experience via query contextualization. Intent can be characterized by multiple dimensions, which are often not observed from query words alone. Accurate identification of Intent from query words remains a challenging problem primarily because it is extremely difficult to discover these dimensions. The problem is often significantly compounded due to lack of representative training sample. We present a generic, extensible framework for learning the multi-dimensional representation of user intent from the query words. The approach models the latent relationships between facets using tree structured distribution which leads to an efficient and convergent algorithm, FastQ, for identifying the multi-faceted intent of users based on just the query words. We also incorporated WordNet to extend the system capabilities to queries which contain words that do not appear in the training data. Empirical results show that FastQ yields accurate identification of intent when compared to a gold standard.

Zinc oxide based photocatalysis: tailoring surface-bulk structure and related interfacial charge carrier dynamics for better environmental applications

As an alternative to the gold standard TiO2 photocatalyst, the use of zinc oxide (ZnO) as a robust candidate for wastewater treatment is widespread due to its similarity in charge carrier dynamics upon bandgap excitation and the generation of reactive oxygen species in aqueous suspensions with TiO2. However, the large bandgap of ZnO, the massive charge carrier recombination, and the photoinduced corrosion-dissolution at extreme pH conditions, together with the formation of inert Zn(OH)(2) during photocatalytic reactions act as barriers for its extensive applicability. To this end, research has been intensified to improve the performance of ZnO by tailoring its surface-bulk structure and by altering its photogenerated charge transfer pathways with an intention to inhibit the surface-bulk charge carrier recombination. For the first time, the several strategies, such as tailoring the intrinsic defects, surface modification with organic compounds, doping with foreign ions, noble metal deposition, heterostructuring with other semiconductors and modification with carbon nanostructures, which have been successfully employed to improve the photoactivity and stability of ZnO are critically reviewed. Such modifications enhance the charge separation and facilitate the generation of reactive oxygenated free radicals, and also the interaction with the pollutant molecules. The synthetic route to obtain hierarchical nanostructured morphologies and study their impact on the photocatalytic performance is explained by considering the morphological influence and the defect-rich chemistry of ZnO. Finally, the crystal facet engineering of polar and non-polar facets and their relevance in photocatalysis is outlined. It is with this intention that the present review directs the further design, tailoring and tuning of the physico-chemical and optoelectronic properties of ZnO for better applications, ranging from photocatalysis to photovoltaics.

Mass spectrometry based characterization of Hb Beckman variant in a falsely elevated HbA(1c) sample

Glycated hemoglobin (HbA(1c)) is a `gold standard' biomarker for assessing the glycemic index of an individual. HbA(1c) is formed due to nonenzymatic glycosylation at N-terminal valine residue of the P-globin chain. Cation exchange based high performance liquid chromatography (CE HPLC) is mostly used to quantify HbA(1c), in blood sample. A few genetic variants of hemoglobin and post-translationally modified variants of hemoglobin interfere with CE HPLC-based quantification,. resulting in its false positive estimation. Using mass spectrometry, we analyzed a blood sample with abnormally high HbA(1c) (52.1%) in the CE HPLC method. The observed HbA(1c) did not corroborate the blood glucose level of the patient. A mass spectrometry based bottom up proteomics approach, intact globin chain mass analysis, and chemical modification of the proteolytic peptides identified the presence of Hb Beckman, a genetic variant of hemoglobin, in the experimental sample. A similar surface area to charge ratio between HbA(1c) and Hb Beckman might have resulted in the coelution of the variant with HbA(1c) in CE HPLC. Therefore, in the screening of diabetes mellitus through the estimation of HbA(1c), it is important to look for genetic variants of hemoglobin in samples that show abnormally high glycemic index, and HbA(1c) must be estimated using an alternative method. (C) 2015 Elsevier Inc. All rights reserved.

On the issues of resolving a low melting combination as a definite eutectic or an elusive cocrystal: A critical evaluation

Cocrystals and eutectics are different yet related crystalline multi-component adducts with diverse applications in pharmaceutical and materials fields. Recently, they were shown to be alternate products of cocrystallization experiments. Whereas a cocrystal shows distinct diffraction, spectroscopic and thermal signatures as compared to parent components, the hallmark of a eutectic is its low melting nature. However, in certain cases, there can be a problem when one resorts to design a cocrystal and assess its formation vis-A -vis a eutectic. In the absence of a gold standard method to make a cocrystal, it is often difficult to judge how exhaustive should the cocrystallization trials be to ensure the accomplishment of a desired/putative cocrystal. Further, a cocrystal can manifest with intermolecular interactions and/or crystal structure similar to that of its parent compounds such that the conventional diffraction and spectroscopic techniques will be of little help to conclusively infer the formation of cocrystal in the lack of single crystals. Such situations combined with low melting behavior of a combination brings the complication of resolving the combination as a cocrystal or eutectic since now both the adducts share common features. Based on the curious case of Caffeine-Benzoic acid combination, this study aims to unfold the intricate issues related to the design, formation and characterization of cocrystals and eutectics for a way forward. The utility of heteronuclear seeding methodology in establishing a given combination as a cocrystal-forming one or a eutectic-forming one in four known systems is appraised.

Growth of Nanometric Gold Particles in Solution Phase

The time evolution of colloidal gold particles in the nanometric regime has been investigated by employing electron microscopy and electronic absorption spectroscopy. The particle size distributions are essentially Gaussian and show the same time dependence for both the mean and the standard deviation, enabling us to obtain a time-independent universal curve for the particle size. Temperature dependent studies show the growth to be an activated process with a barrier of about 18 kJ mol(-1). We present a phenomenological equation for the evolution of particle size and suggest that the growth process is stochastic.

Effect of ultra-fast mixing in a microchannel due to a soft wall on the room temperature synthesis of gold nanoparticles

The room-temperature synthesis of mono-dispersed gold nanoparticles, by the reduction of chlorauric acid (HAuCl4) with tannic acid as the reducing and stabilizing agent, is carried out in a microchannel. The microchannel is fabricated with one soft wall, so that there is a spontaneous transition to turbulence, and thereby enhanced mixing, when the flow Reynolds number increases beyond a critical value. The objective of the study is to examine whether the nanoparticle size and polydispersity can be modified by enhancing the mixing in the microchannel device. The flow rates are varied in order to study nanoparticle formation both in laminar flow and in the chaotic flow after transition, and the molar ratio of the chlorauric acid to tannic acid is also varied to study the effect of molar ratio on nanoparticle size. The formation of gold nanoparticles is examined by UV-visual spectroscopy and the size distribution is determined using scanning electron microscopy. The synthesized nanoparticles size decreases from a parts per thousand yen6 nm to a parts per thousand currency sign4 nm when the molar ratio of chlorauric acid to tannic acid is increased from 1 to 20. It is found that there is no systematic variation of nanoparticle size with flow velocity, and the nanoparticle size is not altered when the flow changes from laminar to turbulent. However, the standard deviation of the size distribution decreases by about 30% after transition, indicating that the enhanced mixing results in uniformity of particle size.