Validation of membrane protein topology models by oxidative labeling and mass spectrometry

Computer-assisted topology predictions are widely used to build low-resolution structural models of integral membrane proteins (IMPs). Experimental validation of these models by traditional methods is labor intensive and requires modifications that might alter the IMP native conformation. This work employs oxidative labeling coupled with mass spectrometry (MS) as a validation tool for computer-generated topology models. ·OH exposure introduces oxidative modifications in solvent-accessible regions, whereas buried segments (e.g., transmembrane helices) are non-oxidizable. The Escherichia coli protein WaaL (O-antigen ligase) is predicted to have 12 transmembrane helices and a large extramembrane domain (Pérez et al., Mol. Microbiol. 2008, 70, 1424). Tryptic digestion and LC-MS/MS were used to map the oxidative labeling behavior of WaaL. Met and Cys exhibit high intrinsic reactivities with ·OH, making them sensitive probes for solvent accessibility assays. Overall, the oxidation pattern of these residues is consistent with the originally proposed WaaL topology. One residue (M151), however, undergoes partial oxidation despite being predicted to reside within a transmembrane helix. Using an improved computer algorithm, a slightly modified topology model was generated that places M151 closer to the membrane interface. On the basis of the labeling data, it is concluded that the refined model more accurately reflects the actual topology of WaaL. We propose that the combination of oxidative labeling and MS represents a useful strategy for assessing the accuracy of IMP topology predictions, supplementing data obtained in traditional biochemical assays. In the future, it might be possible to incorporate oxidative labeling data directly as constraints in topology prediction algorithms.

Hierarchical Bayesian models in ecology: Reconstructing species interaction networks from non-homogeneous species abundance data

The relationships among organisms and their surroundings can be of immense complexity. To describe and understand an ecosystem as a tangled bank, multiple ways of interaction and their effects have to be considered, such as predation, competition, mutualism and facilitation. Understanding the resulting interaction networks is a challenge in changing environments, e.g. to predict knock-on effects of invasive species and to understand how climate change impacts biodiversity. The elucidation of complex ecological systems with their interactions will benefit enormously from the development of new machine learning tools that aim to infer the structure of interaction networks from field data. In the present study, we propose a novel Bayesian regression and multiple changepoint model (BRAM) for reconstructing species interaction networks from observed species distributions. The model has been devised to allow robust inference in the presence of spatial autocorrelation and distributional heterogeneity. We have evaluated the model on simulated data that combines a trophic niche model with a stochastic population model on a 2-dimensional lattice, and we have compared the performance of our model with L1-penalized sparse regression (LASSO) and non-linear Bayesian networks with the BDe scoring scheme. In addition, we have applied our method to plant ground coverage data from the western shore of the Outer Hebrides with the objective to infer the ecological interactions. (C) 2012 Elsevier B.V. All rights reserved.

Hierarchical zeolites prepared by organosilane templating: A study of the synthesis mechanism and catalytic activity

The crystallization of hierarchical ZSM-5 in the presence of the organosilane octadecyl-dimethyl-(3-trimethoxysilyl-propyl)-ammonium chloride as the mesoporogen was investigated as a function of time and temperature. The synthesis by this method proceeds in two steps. The rapid formation of a predominantly amorphous disordered mesoporous aluminosilicate precursor phase is followed by the formation of globular highly mesoporous zeolite particles involving dissolution of the precursor phase. It is difficult to completely convert the initial phase into the final hierarchical zeolite. This limits the amount of aluminium built into the MFI network and the resulting Bronsted acidity. In the presence of iron, more crystalline hierarchical zeolite is obtained. These Fe-containing zeolites are excellent catalysts for the selective oxidation of benzene to phenol. Their hierarchical pore structure leads to higher reaction rates due to increased mass transfer and increased catalyst longevity despite more substantial coke formation. (C) 2011 Elsevier B.V. All rights reserved.

Hierarchical VHDL libraries for DSP ASIC design

Methods are presented for the rapid design of DSP ASICs based on the use of hierarchical VHDL libraries. These are portable across many silicon foundries and allow complex DSP silicon systems to be developed in a fraction of the time normally required. Resulting designs are highly competitive with ones created using conventional methods. The approach is illustrated by its application to ADPCM codec and DCT cores.

Rapid design of DSP ASIC cores using hierarchical VHDL libraries

Methods are presented for the rapid design of DSP ASICs based on the use of a series of hierarchical VHDL libraries which are portable across many silicon foundries. These allows complex DSP silicon systems to be developed in a small fraction of the time normally required. Resulting designs are highly competitive with those developed using more conventional methods. The approach is illustrated using several examples. These include ADPCM codecs, as well as DCT and FFT cores.

Hair cortisol reflects socio-economic factors and hair zinc in preschoolers

This study examined the relationship between children's hair cortisol and socioeconomic status of the family, as measured by parental education and income. Low family socioeconomic status has traditionally been considered a long-term environmental stressor. Measurement of hair cortisol provides an integrated index of cumulative stress exposure across an extended period of time. The present study is the first to examine the relationship between hair cortisol and parental education as well as parental income in a representative sample of preschoolers. Data on hair cortisol, family income, and parental education were collected for a representative sample of 339 children (Mean age=4.6 years; SD=.5 years) from across 23 neighbourhoods of the city of Vancouver, Canada. As maternal education was shown previously to be associated with hair zinc level, hair zinc measurements were included as well in order to explore potential relationships between hair zinc and hair cortisol. The relationship between hair cortisol and parental education was examined using hierarchical regression, with hair zinc, gender, age, and single parenthood included as covariates. Maternal and paternal education both were correlated significantly with hair cortisol (r=-0.18; p=.001). The relationship remained statistically significant even after controlling for all demographic covariates as well as for hair zinc and after taking the neighbourhood-level clustering of the data into account. Parental income, on the other hand, was not related significantly to children's hair cortisol. This study provides evidence that lower maternal and paternal education are associated with higher hair cortisol levels. As hair cortisol provides an integrated index of cortisol exposure over an extended time period, these findings suggest a possibly stable influence of SES on the function of the hypothalamic-pituitary-adrenal (HPA) axis. Cumulative exposure to cortisol during early childhood may be greater in children from low socio-economic backgrounds, possibly through increased exposure to environmental stressors.

Self-reported teamwork in family health team practices in Ontario:Organizational and cultural predictors of team climate

Objective: To determine the organizational predictors of higher scores on team climate measures as an indicator of the functioning of a family health team (FHT). Design: Cross-sectional study using a mailed survey. Setting: Family health teams in Ontario. Participants: Twenty-one of 144 consecutively approached FHTs; 628 team members were surveyed. Main outcome measures: Scores on the team climate inventory, which assessed organizational culture type (group, developmental, rational, or hierarchical); leadership perceptions; and organizational factors, such as use of electronic medical records (EMRs), team composition, governance of the FHT, location, meetings, and time since FHT initiation. All analyses were adjusted for clustering of respondents within the FHT using a mixed random-intercepts model. Results: The response rate was 65.8% (413 of 628); 2 were excluded from analysis, for a total of 411 participants. At the time of survey completion, there was a median of 4 physicians, 11 other health professionals, and 4 management and clerical staff per FHT. The average team climate score was 3.8 out of a possible 5. In multivariable regression analysis, leadership score, group and developmental culture types, and use of more EMR capabilities were associated with higher team climate scores. Other organizational factors, such as number of sites and size of group, were not associated with the team climate score. Conclusion: Culture, leadership, and EMR functionality, rather than organizational composition of the teams (eg, number of professionals on staff, practice size), were the most important factors in predicting climate in primary care teams.

DRIFTS/MS/Isotopic Labeling Study on the NO-Moderated Decomposition of a Silica-Supported Nickel Nitrate Catalyst Precursor

In the preparation of silica-supported nickel oxide from nickel nitrate impregnation and drying, the replacement of the traditional air calcination step by a thermal treatment in 1% NO/Ar prevents agglomeration, resulting in highly dispersed NiO. The mechanism by which NO prevents agglomeration was investigated by using combined in situ diffuse reflectance infrared fourier transform (DRIFT) spectroscopy and mass spectrometry (MS). After impregnation and drying, a supported nickel hydroxynitrate phase with composition Ni(3)(NO(3))(2)(OH)(4) had been formed. Comparison of the evolution of the decomposition gases during the thermal decomposition of Ni(3)(NO(3))(2)(OH)(4) in labeled and unlabeled NO and O(2) revealed that NO scavenges oxygen radicals, forming NO(2). The DRIFT spectra revealed that the surface speciation evolved differently in the presence of NO as compared with in O(2) or Ar. It is proposed that oxygen scavenging by NO depletes the Ni(3)(NO(3))(2)(OH)(4) phase of nitrate groups, creating nucleation sites for the formation of NiO, which leads to very small (similar to 4 nm) NiO particles and prevents agglomeration.

A new method for non-labeling attomolar detection of diseases based on an individual gold nanorod immunosensor

Herein, we present the use of a single gold nanorod sensor for detection of diseases on an antibody-functionalized surface, based on antibody–antigen interaction and the localized surface plasmon resonance (LSPR) ?max shifts of the resonant Rayleigh light scattering spectra. By replacing the cetyltrimethylammonium bromide (CTAB), a tightly packed self-assembled monolayer of HS(CH2)11(OCH2CH2)6OCH2COOH(OEG6) has been successfully formed on the gold nanorod surface prior to the LSPR sensing, leading to the successful fabrication of individual gold nanorod immunosensors. Using prostate specific antigen (PSA) as a protein biomarker, the lowest concentration experimentally detected was as low as 111 aM, corresponding to a 2.79 nm LSPR ?max shift. These results indicate that the detection platform is very sensitive and outperforms detection limits of commercial tests for PSA so far. Correlatively, its detection limit can be equally compared to the assays based on DNA biobarcodes. This study shows that a gold nanorod has been used as a single nanobiosensor to detect antigens for the first time; and the detection method based on the resonant Rayleigh scattering spectrum of individual gold nanorods enables a simple, label-free detection with ultrahigh sensitivity.