Cathedral engagement with young people

The Archbishops’ Commission on Cathedrals (1994) identified education as among the crucial purposes of cathedrals. This chapter analyzes the websites of fifteen cathedrals within the most urban dioceses of the Church of England and the Church in Wales in order to ascertain the variety of ways in which cathedrals are advancing the educational work of the Church in urban areas. The analysis distinguishes between four primary areas of activity, characterized as concerning school-related education, faith-related education, visitor-related education, and music-related education. Each of these four areas is illustrated by a case study profiling current practice

Mechanism and timing of Mcm2–7 ring closure during DNA replication origin licensing

The opening and closing of two ring-shaped Mcm2-7 DNA helicases is necessary to license eukaryotic origins of replication, although the mechanisms controlling these events are unclear. The origin-recognition complex (ORC), Cdc6 and Cdt1 facilitate this process by establishing a topological link between each Mcm2-7 hexamer and origin DNA. Using colocalization single-molecule spectroscopy and single-molecule Förster resonance energy transfer (FRET), we monitored ring opening and closing of Saccharomyces cerevisiae Mcm2-7 during origin licensing. The two Mcm2-7 rings were open during initial DNA association and closed sequentially, concomitant with the release of their associated Cdt1. We observed that ATP hydrolysis by Mcm2-7 was coupled to ring closure and Cdt1 release, and failure to load the first Mcm2-7 prevented recruitment of the second Mcm2-7. Our findings identify key mechanisms controlling the Mcm2-7 DNA-entry gate during origin licensing, and reveal that the two Mcm2-7 complexes are loaded via a coordinated series of events with implications for bidirectional replication initiation and quality control.National Institutes of Health (U.S.) (Grant R01 GM52339)National Institutes of Health (U.S.) (Pre-Doctoral Training Grant GM007287)National Cancer Institute (U.S.) (Koch Institute Support Grant P30-CA14051

3D Airway changes using cone beam computed tomography in patients following mandibular advancement surgery with and without constriction

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149306/1/ocr12292.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149306/2/ocr12292_am.pd

3D Airway changes using CBCT in patients following mandibular setback surgery ± maxillary advancement

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149310/1/ocr12291_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149310/2/ocr12291.pd

Phenotypic characterization and 16S rDNA identification of culturable non-obligate halophilic bacterial communities from a hypersaline lake, La Sal del Rey, in extreme South Texas (USA)

Background: La Sal del Rey ( the King’s Salt”) is one of several naturally-occurring salt lakes in Hidalgo County, Texas and is part of the Lower Rio Grande Valley National Wildlife Refuge. The research objective was to isolate and characterize halophilic microorganisms from La Sal del Rey. Water samples were collected from the lake and a small creek that feeds into the lake. Soil samples were collected from land adjacent to the water sample locations. Sample salinity was determined using a refractometer. Samples were diluted and cultured on a synthetic saline medium to grow halophilic bacteria. The density of halophiles was estimated by viable plate counts. A collection of isolates was selected, gram-stained, tested for catalase, and characterized using API 20E® test strips. Isolates were putatively identified by sequencing the 16S rDNA. Carbon source utilization by the microbial community from each sample site was examined using EcoPlate™ assays and the carbon utilization total activity of the community was determined. Results: Results showed that salinity ranged from 4 parts per thousand (ppt) at the lake water source to 420 ppt in water samples taken just along the lake shore. The density of halophilic bacteria in water samples ranged from 1.2 × 102 - 5.2 × 103 colony forming units per ml (cfu ml-1) whereas the density in soil samples ranged from 4.0 × 105 - 2.5 × 106 colony forming units per gram (cfu g-1). In general, as salinity increased the density of the bacterial community decreased. Microbial communities from water and soil samples were able to utilize 12 - 31 carbon substrates. The greatest number of substrates utilized was by water-borne communities compared to soil-based communities, especially at lower salinities. The majority of bacteria isolated were gram-negative, catalase-positive, rods. Biochemical profiles constructed from API 20E® test strips showed that bacterial isolates from low-salinity water samples (4 ppt) showed the greatest phenotypic diversity with regards to the types and number of positive tests from the strip. Isolates taken from water samples at the highest salinity (420 ppt) tended to be less diverse and have only a limited number of positive tests. Sequencing of 16S DNA displayed the presence of members of bacterial genera Bacillus, Halomonas, Pseudomonas, Exiguobacterium and others. The genus Bacillus was most commonly identified. None of the isolates were members of the Archaea probably due to dilution of salts in the samples. Conclusions: The La Sal del Rey ecosystem supports a robust and diverse bacterial community despite the high salinity of the lake and soil. However, salinity does appear to a limiting factor with

Finding the “Dark Matter” in Human and Yeast Protein Network Prediction and Modelling

Accurate modelling of biological systems requires a deeper and more complete knowledge about the molecular components and their functional associations than we currently have. Traditionally, new knowledge on protein associations generated by experiments has played a central role in systems modelling, in contrast to generally less trusted bio-computational predictions. However, we will not achieve realistic modelling of complex molecular systems if the current experimental designs lead to biased screenings of real protein networks and leave large, functionally important areas poorly characterised. To assess the likelihood of this, we have built comprehensive network models of the yeast and human proteomes by using a meta-statistical integration of diverse computationally predicted protein association datasets. We have compared these predicted networks against combined experimental datasets from seven biological resources at different level of statistical significance. These eukaryotic predicted networks resemble all the topological and noise features of the experimentally inferred networks in both species, and we also show that this observation is not due to random behaviour. In addition, the topology of the predicted networks contains information on true protein associations, beyond the constitutive first order binary predictions. We also observe that most of the reliable predicted protein associations are experimentally uncharacterised in our models, constituting the hidden or “dark matter” of networks by analogy to astronomical systems. Some of this dark matter shows enrichment of particular functions and contains key functional elements of protein networks, such as hubs associated with important functional areas like the regulation of Ras protein signal transduction in human cells. Thus, characterising this large and functionally important dark matter, elusive to established experimental designs, may be crucial for modelling biological systems. In any case, these predictions provide a valuable guide to these experimentally elusive regions

Crystal structures and binding dynamics of Odorant-Binding Protein 3 from two aphid species Megoura viciae and Nasonovia ribisnigri

Aphids use chemical cues to locate hosts and find mates. The vetch aphid Megoura viciae feeds exclusively on the Fabaceae, whereas the currant-lettuce aphid Nasonovia ribisnigri alternates hosts between the Grossulariaceae and Asteraceae. Both species use alarm pheromones to warn of dangers. For N. ribisnigri this pheromone is a single component (E)-β-farnesene but M. viciae uses a mixture of (E)-β-farnesene, (-)-α- pinene, β-pinene, and limonene. Odorant-binding proteins (OBP) are believed to capture and transport such semiochemicals to their receptors. Here, we report the first aphid OBP crystal structures and examine their molecular interactions with the alarm pheromone components. Our study reveals some unique structural features: 1) the lack of internal ligand binding site; 2) a striking groove in the surface of the proteins as a putative binding site; 3) the N-terminus rather than the C-terminus occupies the site closing off the conventional OBP pocket. The results from fluorescent binding assays, molecular docking and dynamics demonstrate that OBP3 from M. viciae can bind to all four alarm pheromone components and the differential ligand binding between these very similar OBP3s from the two aphid species is determined mainly by the direct π-π interactions between ligands and the aromatic residues of OBP3s in the binding pocket

Inference of Functional Relations in Predicted Protein Networks with a Machine Learning Approach

Background: Molecular biology is currently facing the challenging task of functionally characterizing the proteome. The large number of possible protein-protein interactions and complexes, the variety of environmental conditions and cellular states in which these interactions can be reorganized, and the multiple ways in which a protein can influence the function of others, requires the development of experimental and computational approaches to analyze and predict functional associations between proteins as part of their activity in the interactome. Methodology/Principal Findings: We have studied the possibility of constructing a classifier in order to combine the output of the several protein interaction prediction methods. The AODE (Averaged One-Dependence Estimators) machine learning algorithm is a suitable choice in this case and it provides better results than the individual prediction methods, and it has better performances than other tested alternative methods in this experimental set up. To illustrate the potential use of this new AODE-based Predictor of Protein InterActions (APPIA), when analyzing high-throughput experimental data, we show how it helps to filter the results of published High-Throughput proteomic studies, ranking in a significant way functionally related pairs. Availability: All the predictions of the individual methods and of the combined APPIA predictor, together with the used datasets of functional associations are available at http://ecid.bioinfo.cnio.es/. Conclusions: We propose a strategy that integrates the main current computational techniques used to predict functional associations into a unified classifier system, specifically focusing on the evaluation of poorly characterized protein pairs. We selected the AODE classifier as the appropriate tool to perform this task. AODE is particularly useful to extract valuable information from large unbalanced and heterogeneous data sets. The combination of the information provided by five prediction interaction prediction methods with some simple sequence features in APPIA is useful in establishing reliability values and helpful to prioritize functional interactions that can be further experimentally characterized.This work was funded by the BioSapiens (grant number LSHG-CT-2003-503265) and the Experimental Network for Functional Integration (ENFIN) Networks of Excellence (contract number LSHG-CT-2005-518254), by Consolider BSC (grant number CSD2007-00050) and by the project “Functions for gene sets” from the Spanish Ministry of Education and Science (BIO2007-66855). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Short-term prediction of threatening and violent behaviour in an Acute Psychiatric Intensive Care Unit based on patient and environment characteristics

<p>Abstract</p> <p>Background</p> <p>The aims of the present study were to investigate clinically relevant patient and environment-related predictive factors for threats and violent incidents the first three days in a PICU population based on evaluations done at admittance.</p> <p>Methods</p> <p>In 2000 and 2001 all 118 consecutive patients were assessed at admittance to a Psychiatric Intensive Care Unit (PICU). Patient-related conditions as actuarial data from present admission, global clinical evaluations by physician at admittance and clinical nurses first day, a single rating with an observer rated scale scoring behaviours that predict short-term violence in psychiatric inpatients (The Brøset Violence Checklist (BVC)) at admittance, and environment-related conditions as use of segregation or not were related to the outcome measure Staff Observation Aggression Scale-Revised (SOAS-R). A multiple logistic regression analysis with SOAS-R as outcome variable was performed.</p> <p>Results</p> <p>The global clinical evaluations and the BVC were effective and more suitable than actuarial data in predicting short-term aggression. The use of segregation reduced the number of SOAS-R incidents.</p> <p>Conclusions</p> <p>In a naturalistic group of patients in a PICU segregation of patients lowers the number of aggressive and threatening incidents. Prediction should be based on clinical global judgment, and instruments designed to predict short-term aggression in psychiatric inpatients.</p> <p>Trial registrations</p> <p><a href="http://www.clinicaltrials.gov/ct2/show/NCT00184119">NCT00184119</a>/<a href="http://www.clinicaltrials.gov/ct2/show/NCT00184132">NCT00184132</a></p