Integrative analysis of chemo-transcriptomic profiles for drug-feature specific gene expression signatures

One of the major challenges in systems biology is to understand the complex responses of a biological system to external perturbations or internal signalling depending on its biological conditions. Genome-wide transcriptomic profiling of cellular systems under various chemical perturbations allows the manifestation of certain features of the chemicals through their transcriptomic expression profiles. The insights obtained may help to establish the connections between human diseases, associated genes and therapeutic drugs. The main objective of this study was to systematically analyse cellular gene expression data under various drug treatments to elucidate drug-feature specific transcriptomic signatures. We first extracted drug-related information (drug features) from the collected textual description of DrugBank entries using text-mining techniques. A novel statistical method employing orthogonal least square learning was proposed to obtain drug-feature-specific signatures by integrating gene expression with DrugBank data. To obtain robust signatures from noisy input datasets, a stringent ensemble approach was applied with the combination of three techniques: resampling, leave-one-out cross validation, and aggregation. The validation experiments showed that the proposed method has the capacity of extracting biologically meaningful drug-feature-specific gene expression signatures. It was also shown that most of signature genes are connected with common hub genes by regulatory network analysis. The common hub genes were further shown to be related to general drug metabolism by Gene Ontology analysis. Each set of genes has relatively few interactions with other sets, indicating the modular nature of each signature and its drug-feature-specificity. Based on Gene Ontology analysis, we also found that each set of drug feature (DF)-specific genes were indeed enriched in biological processes related to the drug feature. The results of these experiments demonstrated the pot- ntial of the method for predicting certain features of new drugs using their transcriptomic profiles, providing a useful methodological framework and a valuable resource for drug development and characterization.

Non-parametric bill of quantities estimation of concrete road bridges' superstructure: an artificial neural networks approach

Bridge construction responds to the need for environmentally friendly design of motorways and facilitates the passage through sensitive natural areas and the bypassing of urban areas. However, according to numerous research studies, bridge construction presents substantial budget overruns. Therefore, it is necessary early in the planning process for the decision makers to have reliable estimates of the final cost based on previously constructed projects. At the same time, the current European financial crisis reduces the available capital for investments and financial institutions are even less willing to finance transportation infrastructure. Consequently, it is even more necessary today to estimate the budget of high-cost construction projects -such as road bridges- with reasonable accuracy, in order for the state funds to be invested with lower risk and the projects to be designed with the highest possible efficiency. In this paper, a Bill-of-Quantities (BoQ) estimation tool for road bridges is developed in order to support the decisions made at the preliminary planning and design stages of highways. Specifically, a Feed-Forward Artificial Neural Network (ANN) with a hidden layer of 10 neurons is trained to predict the superstructure material quantities (concrete, pre-stressed steel and reinforcing steel) using the width of the deck, the adjusted length of span or cantilever and the type of the bridge as input variables. The training dataset includes actual data from 68 recently constructed concrete motorway bridges in Greece. According to the relevant metrics, the developed model captures very well the complex interrelations in the dataset and demonstrates strong generalisation capability. Furthermore, it outperforms the linear regression models developed for the same dataset. Therefore, the proposed cost estimation model stands as a useful and reliable tool for the construction industry as it enables planners to reach informed decisions for technical and economic planning of concrete bridge projects from their early implementation stages.

Evolving techniques for monitoring geotechnical risk on the Antrim Coast Road.

The interaction between problematic geology and environmental variables along the Antrim Coast Road results in frequent instances of geotechnical instability. During such instances of instability, mudslide debris encroaches on the carriageway posing a hazard to motorists, causing lengthily tailbacks. This paper examines some of the geotechnical and spatial analysis techniques currently being implemented to monitor slope stability on this key transport route.

Detection of Bridge Dynamic Parameters using an Instrumented Vehicle

Highway structures such as bridges are subject to continuous degradation primarily due to ageing and environmental factors. A rational transport policy requires the monitoring of this transport infrastructure to provide adequate maintenance and guarantee the required levels of transport service and safety. In Europe, this is now a legal requirement - a European Directive requires all member states of the European Union to implement a Bridge Management System. However, the process is expensive, requiring the installation of sensing equipment and data acquisition electronics on the bridge. This paper investigates the use of an instrumented vehicle fitted with accelerometers on its axles to monitor the dynamic behaviour of bridges as an indicator of its structural condition. This approach eliminates the need for any on-site installation of measurement equipment. A simplified half-car vehicle-bridge interaction model is used in theoretical simulations to test the possibility of extracting the dynamic parameters of the bridge from the spectra of the vehicle accelerations. The effect of vehicle speed, vehicle mass and bridge span length on the detection of the bridge dynamic parameters are investigated. The algorithm is highly sensitive to the condition of the road profile and simulations are carried out for both smooth and rough profiles

Dynamic axle force and road profile identification using a moving vehicle

The axle forces applied by a vehicle through its wheels are a critical part of the interaction between vehicles, pavements and bridges. Therefore, the minimisation of these forces is important in order to promote long pavement life spans and ensure that bridge loads are small. Moreover, as the road surface roughness affects the vehicle dynamic forces, the monitoring of pavements for highways and bridges is an important task. This paper presents a novel algorithm to identify these dynamic interaction forces which involves direct instrumentation of a vehicle with accelerometers. The ability of this approach to predict the pavement roughness is also presented. Moving force identification theory is applied to a vehicle model in theoretical simulations in order to obtain the interaction forces and pavement roughness from the measured accelerations. The method is tested for a range of bridge spans in simulations and the influence of road roughness level on the accuracy of the results is investigated. Finally, the challenge for the real-world problem is addressed in a laboratory experiment.

Experimental analysis of air flow profiles in a double skin facade in a maritime climate

Glazed Double Skin Facades (DSF) offer the potential to improve the performance of all-glass building skins, common to commercial office buildings in which full facade glazing has almost become the standard. Single skin glazing results in increased heating and cooling costs over opaque walls, due to lower thermal resistance of glass, and the increased impact of solar gain through it. However, the performance benefit of DSF technology continues to be questioned and its operation poorly understood, particularly the nature of airflow through the cavity. This paper deals specifically with the experimental analysis of the air flow characteristics in an automated double skin façade. The benefit of the DSF as a thermal buffer, and to limit overheating is evaluated through analysis of an extensive set of parameters including air and surface temperatures at each level in the DSF, airflow readings in the cavity and at the inlet and outlet, solar and wind data, and analytically derived pressure differentials. The temperature and air-flow are monitored in the cavity of a DSF using wireless sensors and hot wire anemometers respectively. Automated louvre operation and building set-points are monitored via the BMS. Thermal stratification and air flow variation during changing weather conditions are shown to effect the performance of the DSF considerably and hence the energy performance of the building. The relative pressure effects due to buoyancy and wind are analysed and quantified. This research aims to developed and validate models of DSFs in the maritime climate, using multi-season data from experimental monitoring. This extensive experimental study provides data for training and validation of models.

Predicting axial velocity profiles within a diffusing marine propeller jet

A full understanding of the hydrodynamic processes within the jet produced by a manoeuvring ship’s propeller is essential in the development and maintenance of ports, docks and harbours. In this study the predominant axial velocity component of a diffusing propeller jet was studied. The flow fields formed by four propellers, each operating at four power levels (speeds of rotation), were investigated under bollard pull conditions within a large free surface tank using Laser Doppler Anemometry. Comparison were made to existing methodologies by which a prediction of the magnitudes of the axial velocity can be made, and where deficient modifications to the methodologies have been developed. The jets were found to produce a maximum axial velocity along the initial efflux plane at a location near the blade mid-span. The position and magnitude of the axial velocity was seen to decrease as the jet entrained more flow and transitioned from the zone of flow establishment into the zone of established flow.

Associations between intensive diabetes therapy and NMR-determined lipoprotein subclass profiles in Type 1 diabetes

Our objective is to define differences in circulating lipoprotein subclasses between intensive vs. conventional management of Type 1 diabetes during the randomization phase of the Diabetes Control and Complications Trial (DCCT). Nuclear magnetic resonance-determined lipoprotein subclass profiles (NMR-LSP), which estimate molar subclass concentrations and mean particle diameters, were determined in 1,294 DCCT subjects after a median of five (interquartile range: four, six) years following randomization to intensive or conventional diabetes management. In cross-sectional analyses, we compared standard lipids and NMR-LSP between treatment groups. Standard total-, LDL- and HDL-cholesterol levels were similar between randomization groups, while triglyceride levels were lower in the intensively treated group. NMR-LSP showed that intensive therapy was associated with larger LDL diameter (20.7 vs. 20.6 nm, p=0.01) and lower levels of small LDL (median: 465 vs. 552 nmol/l, p=0.007), total IDL/LDL (mean: 1000 vs. 1053 nmol/l, p=0.01), and small HDL (mean: 17.3 vs. 18.6 μmol/l, p<0.0001), the latter accounting for reduced total HDL (mean: 33.8 vs. 34.8 μmol/l, p=0.01). In conclusion, intensive diabetes therapy was associated with potentially favorable changes in LDL and HDL subclasses in sera. Further research will determine whether these changes contribute to the beneficial effects of intensive diabetes management on vascular complications.

Review of João M. Cabrita, Mozambique. The Tortuous Road to Democracy.

Book review in Lusotopie, 2002, no.1 of João M. CABRITA, Mozambique. The Tortuous Road to Democracy, Basingstoke (G.-B.) et New York (E.U.A.), Palgrave (St. Martin’s Press), 2000, 311p., ISBN 0-333-92001-5.

Potentially inappropriate prescribing in two populations with differing socio-economic profiles: a cross-sectional database study using the PROMPT criteria

PURPOSE: The purpose of this study is to establish the prevalence of potentially inappropriate prescribing (PIP) in middle-aged adults (45-64 years) in two populations with differing socio-economic profiles, and to investigate factors associated with PIP, using the PROMPT (PRescribing Optimally in Middle-aged People's Treatments) criteria.METHODS: A retrospective cross-sectional study was conducted using 2012 data from the Enhanced Prescribing Database (EPD), covering the full population in Northern Ireland and the Health Services Executive Primary Care Reimbursement Service (HSE-PCRS) database, covering the most socio-economically deprived third of the population in this age group in the Republic of Ireland. The prevalence for each PROMPT criterion and overall prevalence of PIP were calculated. Logistic regression was used to investigate the association between PIP and gender, age group and polypharmacy.RESULTS: This study included 441,925 patients from the EPD and 309,748 patients from the HSE-PCRS database. Polypharmacy was common in both datasets (46.7 % in the HSE-PCRS and 20.3 % in the EPD). The prevalence of PIP was 42.9 % (95%CI 42.7, 43.1) in the HSE-PCRS and 21.1 % (95%CI 21.0, 21.2) in the EPD. Age group, female gender and polypharmacy were significantly associated with PIP in both populations (p < 0.05) and polypharmacy had the strongest association.CONCLUSIONS: PIP is common amongst middle-aged people with the risk of PIP increasing with polypharmacy. Differences in the prevalence of polypharmacy and PIP between the two populations may relate to heterogeneity in healthcare services and different socio-economic profiles, with higher rates of multimorbidity and associated polypharmacy in more deprived groups.

Magnitude differences in bioactive compounds, chemical functional groups, fatty acid profiles, nutrient degradation and digestion, molecular structure and metabolic characteristics of protein in newly developed yellow-seeded and black-seeded canola lines.

Recently, new lines of yellow-seeded (CS-Y) and black-seeded canola (CS-B) have been developed with chemical and structural alteration through modern breeding technology. However, no systematic study was found on the bioactive compounds, chemical functional groups, fatty acid profiles, inherent structure, nutrient degradation and absorption, or metabolic characteristics between the newly developed yellow- and black-seeded canola lines. This study aimed to systematically characterize chemical, structural, and nutritional features in these canola lines. The parameters accessed include bioactive compounds and antinutrition factors, chemical functional groups, detailed chemical and nutrient profiles, energy value, nutrient fractions, protein structure, degradation kinetics, intestinal digestion, true intestinal protein supply, and feed milk value. The results showed that the CS-Y line was lower (P ≤ 0.05) in neutral detergent fiber (122 vs 154 g/kg DM), acid detergent fiber (61 vs 99 g/kg DM), lignin (58 vs 77 g/kg DM), nonprotein nitrogen (56 vs 68 g/kg DM), and acid detergent insoluble protein (11 vs 35 g/kg DM) than the CS-B line. There was no difference in fatty acid profiles except C20:1 eicosenoic acid content (omega-9) which was in lower in the CS-Y line (P < 0.05) compared to the CS-B line. The glucosinolate compounds differed (P < 0.05) in terms of 4-pentenyl, phenylethyl, 3-CH3-indolyl, and 3-butenyl glucosinolates (2.9 vs 1.0 μmol/g) between the CS-Y and CS-B lines. For bioactive compounds, total polyphenols tended to be different (6.3 vs 7.2 g/kg DM), but there were no differences in erucic acid and condensed tannins with averages of 0.3 and 3.1 g/kg DM, respectively. When protein was portioned into five subfractions, significant differences were found in PA, PB1 (65 vs 79 g/kg CP), PB2, and PC fractions (10 vs 33 g/kg CP), indicating protein degradation and supply to small intestine differed between two new lines. In terms of protein structure spectral profile, there were no significant differences in functional groups of amides I and II, α helix, and β-sheet structure as well as their ratio between the two new lines, indicating no difference in protein structure makeup and conformation between the two lines. In terms of energy values, there were significant differences in total digestible nutrient (TDN; 149 vs 133 g/kg DM), metabolizable energy (ME; 58 vs 52 MJ/kg DM), and net energy for lactation (NEL; 42 vs 37 MJ/kg DM) between CS-Y and CS-B lines. For in situ rumen degradation kinetics, the two lines differed in soluble fraction (S; 284 vs 341 g/kg CP), potential degradation fraction (D; 672 vs 590 g/kg CP), and effective degraded organic matter (EDOM; 710 vs 684 g/kg OM), but no difference in degradation rate. CS-Y had higher digestibility of rumen bypass protein in the intestine than CS-B (566 vs 446 g/kg of RUP, P < 0.05). Modeling nutrient supply results showed that microbial protein synthesis (MCP; 148 vs 171 g/kg DM) and rumen protein degraded balance (DPB; 108 vs 127 g/kg DM) were lower in the CS-Y line, but there were no differences in total truly digested protein in small intestine (DVE) and feed milk value (FMV) between the two lines. In conclusion, the new yellow line had different nutritional, chemical, and structural features compared to the black line. CS-Y provided better nutrient utilization and availability.