Genetics of Serum Dehydroepiandrosterone Sulfate and Its Relationship to Insulin in a Population-Based Cohort of Twin Subjects

Previous studies have shown a significant effect of insulin administration on serum dehydroepiandrosterone sulfate (DHEA-S) concentration and its metabolic rate, with evidence for the effect in men, but not in women. This could lead to differences in the sources of variation in serum DHEA-S between men and women and in its covariation with insulin concentration. This study aimed to test whether these hypotheses were supported in a sample of healthy adult twins. Serum DHEA-S (n=2287) and plasma insulin (n=2436) were measured in samples from adult male and female twins recruited through the Australian Twin Registry. Models of genetic and environmental sources of variation and covariation were tested against the data. DHEA-S showed substantial genetic effects in both men and women after adjustment for covariates, including sex, age, body mass index, and time since the last meal. There was no significant phenotypic or genetic correlation between DHEA-S and insulin in either men or women. Despite the experimental evidence for insulin infusion producing a reduction in serum DHEA-S and some effect of meals on the observed DHEA-S concentration, there were no associations between insulin and DHEA-S at the population level. Variations in DHEA-S are due to age, sex, obesity, and substantial polygenic genetic influences.

Structural ordering of coal char during heat treatment and its impact on reactivity

The effect of heat treatment on the structure of an Australian semi-anthracite char was studied in detail in the 850-1150degreesC temperature range using XRD, HRTEM, and electrical resistivity techniques. It was found that the carbon crystallite size in the char does not change significantly during heat treatment in the temperature range studied, for both the raw coal and its ash-free derivative obtained by acid treatment. However, the fraction of the organized carbon in the raw coal chars, determined by XRD, increased with increase of heat treatment time and temperature, while that for the ash-free coal chars remained almost unchanged. This suggests the occurrence of catalytic ordering during heat treatment, supported by the observation that the electrical resistivity of the raw coal chars decreased with heat treatment, while that of the ash-free coal chars did not vary significantly. Further confirmatory evidence was provided by high resolution transmission electron micrographs depicting well-organized carbon layers surrounding iron particles. It is also found that the fraction of organized carbon does not reach unity, but attains an apparent equilibrium value that increases with increase in temperature, providing an apparent heat of ordering of 71.7 kJ mol(-1) in the temperature range studied. Good temperature-independent correlation was found between the electrical resistivity and the organized carbon fraction, indicating that electrical resistivity is indeed structure sensitive. Good correlation was also found between the electrical resistivity and the reactivity of coal char. All these results strongly suggest that the thermal deactivation is the result of a crystallite-perfecting process, which is effectively catalyzed by the inorganic matter in the coal char. Based on kinetic interpretation of the data it is concluded that the process is diffusion controlled, most likely involving transport of iron in the inter-crystallite nanospaces in the temperature range studied. The activation energy of this transport process is found to be very low, at about 11.8 kJ mol(-1), which is corroborated by model-free correlation of the temporal variation of organized carbon fraction as well as electrical resistivity data using the superposition method, and is suggestive of surface transport of iron. (C) 2002 Elsevier Science Ltd. All rights reserved.

Heat resistance of Bacillus spores when adhered to stainless steel and its relationship to spore hydrophobicity

Twenty-one strains of Bacillus (10 B. stearothermophilus, 3 B. cereus, and 8 B. licheniformis strains) were assayed for spore surface hydrophobicity on the basis of three measures: contact angle measurement (CAM), microbial adhesion to hydrocarbons (MATH), and hydrophobic interaction chromatography (HIC). On the basis of the spore surface characteristics obtained from these assays, along with data on the heat resistance of these spores in water, eight strains of Bacillus (three B. stearothermophilus, three B. cereus, and two B. licheniformis strains) either suspended in water or adhering to stainless steel were exposed to sublethal heat treatments at 90 to 110degreesC to determine heat resistance (D-value). Significant increases in heat resistance (ranging from 3 to 400%) were observed for the eight strains adhering to stainless steel. No significant correlation was found between these heat resistance increases and spore surface characteristics as determined by the three hydrophobicity assays. There was a significant positive correlation between the hydrophobicity data obtained by the MATH assay and those obtained by the HIC assay, but these data did not correlate with those obtained by the CAM assay.

Thermomechanical environment characterisation in injection moulding and its relation to the mechanical properties of talc-filled polypropylene

This study is focused on the establishment of relationships between the injection moulding processing conditions, the applied thermomechanical environment (TME) and the tensile properties of talc-filled polypropylene,adopting a new extended concept of thermomechanical indices (TMI). In this approach, TMI are calculated from computational simulations of the moulding process that characterise the TME during processing, which are then related to the mechanical properties of the mouldings. In this study, this concept is extended to both the filling and the packing phases, with new TMI defined related to the morphology developed during these phases. A design of experiments approach based on Taguchi orthogonal arrays was adopted to vary the injection moulding parameters (injection flow rate, injection temperature, mould wall temperature and holding pressure), and thus, the TME. Results from analysis of variance for injection-moulded tensile specimens have shown that among the considered processing conditions, the flow rate is the most significant parameter for the Young’s modulus; the flow rate and melt temperature are the most significant for the strain at break; and the holding pressure and flow rate are the most significant for the stress at yield. The yield stress and Young’s modulus were found to be governed mostly by the thermostress index (TSI, related to the orientation of the skin layer), whilst the strain at break depends on both the TSI and the cooling index (CI, associated to the crystallinity degree of the core region). The proposed TMI approach provides predictive capabilities of the mechanical response of injection-moulded components, which is a valuable input during their design stage.

Assessment and prediction of asthma and its severity in the pediatric community

Seventy four asthmatic children aged 7 to 11 years were examined along with controls matched by age and sex. Clinical and laboratory investigations preceded a 28-day follow-up where data about morning and evening peak expiratory flow rate (PEF), symptoms and treatment were recorded. The coefficient of variation of PEF was found to be an objective measurement of asthma severity that has statistically significant correlation with both symptoms (r s= .36) and treatment (r s= .60). Moreover, it separates mild and severe asthmatics, as confirmed by statistically significant differences (p= .008 or less) in symptoms, treatment, skin allergy and airways response to exercise. Skin allergy and airways responsiveness to exercise were found to be predictors of both disease and severity. By means of logistic regression analysis it was possible to establish the probabilities for both asthma and severe asthma when children presenting and not presenting these characteristics are compared. One single positive skin test represent a probability of 88% for the development of asthma and a probability of 70% for severe disease. A PEF reduction of 10% after an exercise test implies a probability of 73% for disease and a probability of 64% for severe disease. Increases in these variables imply geometrically increased risks and their presence together have a multiplicative effect in the final risk.

Web 2.0 and its impact on knowledge and business organizations

Today, information overload and the lack of systems that enable locating employees with the right knowledge or skills are common challenges that large organisations face. This makes knowledge workers to re-invent the wheel and have problems to retrieve information from both internal and external resources. In addition, information is dynamically changing and ownership of data is moving from corporations to the individuals. However, there is a set of web based tools that may cause a major progress in the way people collaborate and share their knowledge. This article aims to analyse the impact of ‘Web 2.0’ on organisational knowledge strategies. A comprehensive literature review was done to present the academic background followed by a review of current ‘Web 2.0’ technologies and assessment of their strengths and weaknesses. As the framework of this study is oriented to business applications, the characteristics of the involved segments and tools were reviewed from an organisational point of view. Moreover, the ‘Enterprise 2.0’ paradigm does not only imply tools but also changes the way people collaborate, the way the work is done (processes) and finally impacts on other technologies. Finally, gaps in the literature in this area are outlined.

An architecture for an effective usage of data mining in business intelligence systems

Business Intelligence (BI) is one emergent area of the Decision Support Systems (DSS) discipline. Over the last years, the evolution in this area has been considerable. Similarly, in the last years, there has been a huge growth and consolidation of the Data Mining (DM) field. DM is being used with success in BI systems, but a truly DM integration with BI is lacking. Therefore, a lack of an effective usage of DM in BI can be found in some BI systems. An architecture that pretends to conduct to an effective usage of DM in BI is presented.

PHYLOViZ: phylogenetic inference and data visualization for sequence based typing methods

Background: With the decrease of DNA sequencing costs, sequence-based typing methods are rapidly becoming the gold standard for epidemiological surveillance. These methods provide reproducible and comparable results needed for a global scale bacterial population analysis, while retaining their usefulness for local epidemiological surveys. Online databases that collect the generated allelic profiles and associated epidemiological data are available but this wealth of data remains underused and are frequently poorly annotated since no user-friendly tool exists to analyze and explore it. Results: PHYLOViZ is platform independent Java software that allows the integrated analysis of sequence-based typing methods, including SNP data generated from whole genome sequence approaches, and associated epidemiological data. goeBURST and its Minimum Spanning Tree expansion are used for visualizing the possible evolutionary relationships between isolates. The results can be displayed as an annotated graph overlaying the query results of any other epidemiological data available. Conclusions: PHYLOViZ is a user-friendly software that allows the combined analysis of multiple data sources for microbial epidemiological and population studies. It is freely available at http://www.phyloviz.net.

Evaluation of alcohol outlet density and its relation with violence

OBJECTIVES: The current study set out to investigate alcohol availability in a densely populated, residential area of suburban São Paulo associated with high levels of social deprivation and violence. Gun-related deaths and a heavy concentration of alcohol outlets are notable features of the area surveyed. Given the strong evidence for a link between alcohol availability and a number of alcohol-related problems, including violent crime, measures designed to reduce accessibility have become a favored choice for alcohol prevention programs in recent years. METHODS: The interviewers were 24 residents of the area who were trained for the study. It was selected an area of nineteen streets, covering a total distance of 3.7 km. A profile of each alcohol outlet available on the area was recorded. RESULTS: One hundred and seven alcohol outlets were recorded. The number of other properties in the same area was counted at 1,202. Two measures of outlet density may thus be calculated: the number of outlets per kilometer of roadway (29 outlets/km); and the proportion of all properties that sold alcohol (1 in 12). CONCLUSIONS: The results of this study is compared with others which are mainly from developed countries and shown that the area studied have the highest density of alcohol outlet density ever recorded in the medical literature. The implication of this data related to the violence of the region is discussed. By generating a profile of alcohol sales and selling points, it was hoped to gain a better understanding of alcohol access issues within the sample area. Future alcohol prevention policy would be well served by such knowledge.

Antioxidant activity and cytotoxicity of solubilized C60 and its conjugates with butylated hydroxytoluene

It has been described that fullerenes (C60) present interesting properties with potential application in clinical conditions related to oxidative stress. One of the most prominent features of fullerenes is the ability to quench free radicals. However, because of its poor solubility, this has been studied mostly in organic solutions, while the antioxidant activity and cytotoxicity of fullerenes and their derivates in aqueous medium is not well characterized. The antioxidant capacity of synthesised C60-conjugates has been investigated and its was higher comparing to C60 isolated. The aim of this study was to assess the viability of C60-conjugates by determining its antioxidant activity and cytotoxicity in bio-relevant media.

Data mining techniques contributions to support electrical vehicle demand response

The introduction of Electric Vehicles (EVs) together with the implementation of smart grids will raise new challenges to power system operators. This paper proposes a demand response program for electric vehicle users which provides the network operator with another useful resource that consists in reducing vehicles charging necessities. This demand response program enables vehicle users to get some profit by agreeing to reduce their travel necessities and minimum battery level requirements on a given period. To support network operator actions, the amount of demand response usage can be estimated using data mining techniques applied to a database containing a large set of operation scenarios. The paper includes a case study based on simulated operation scenarios that consider different operation conditions, e.g. available renewable generation, and considering a diversity of distributed resources and electric vehicles with vehicle-to-grid capacity and demand response capacity in a 33 bus distribution network.

Data mining techniques to support the classification of MV electricity customers

This paper describes a methodology that was developed for the classification of Medium Voltage (MV) electricity customers. Starting from a sample of data bases, resulting from a monitoring campaign, Data Mining (DM) techniques are used in order to discover a set of a MV consumer typical load profile and, therefore, to extract knowledge regarding to the electric energy consumption patterns. In first stage, it was applied several hierarchical clustering algorithms and compared the clustering performance among them using adequacy measures. In second stage, a classification model was developed in order to allow classifying new consumers in one of the obtained clusters that had resulted from the previously process. Finally, the interpretation of the discovered knowledge are presented and discussed.

Using data mining techniques to support DR programs definition in smart grids

In recent years, Power Systems (PS) have experimented many changes in their operation. The introduction of new players managing Distributed Generation (DG) units, and the existence of new Demand Response (DR) programs make the control of the system a more complex problem and allow a more flexible management. An intelligent resource management in the context of smart grids is of huge important so that smart grids functions are assured. This paper proposes a new methodology to support system operators and/or Virtual Power Players (VPPs) to determine effective and efficient DR programs that can be put into practice. This method is based on the use of data mining techniques applied to a database which is obtained for a large set of operation scenarios. The paper includes a case study based on 27,000 scenarios considering a diversity of distributed resources in a 32 bus distribution network.

A data-mining based methodology for wind forecasting

In many countries the use of renewable energy is increasing due to the introduction of new energy and environmental policies. Thus, the focus on the efficient integration of renewable energy into electric power systems is becoming extremely important. Several European countries have already achieved high penetration of wind based electricity generation and are gradually evolving towards intensive use of this generation technology. The introduction of wind based generation in power systems poses new challenges for the power system operators. This is mainly due to the variability and uncertainty in weather conditions and, consequently, in the wind based generation. In order to deal with this uncertainty and to improve the power system efficiency, adequate wind forecasting tools must be used. This paper proposes a data-mining-based methodology for very short-term wind forecasting, which is suitable to deal with large real databases. The paper includes a case study based on a real database regarding the last three years of wind speed, and results for wind speed forecasting at 5 minutes intervals.

A data-mining-based methodology for transmission expansion planning

In recent decades, all over the world, competition in the electric power sector has deeply changed the way this sector’s agents play their roles. In most countries, electric process deregulation was conducted in stages, beginning with the clients of higher voltage levels and with larger electricity consumption, and later extended to all electrical consumers. The sector liberalization and the operation of competitive electricity markets were expected to lower prices and improve quality of service, leading to greater consumer satisfaction. Transmission and distribution remain noncompetitive business areas, due to the large infrastructure investments required. However, the industry has yet to clearly establish the best business model for transmission in a competitive environment. After generation, the electricity needs to be delivered to the electrical system nodes where demand requires it, taking into consideration transmission constraints and electrical losses. If the amount of power flowing through a certain line is close to or surpasses the safety limits, then cheap but distant generation might have to be replaced by more expensive closer generation to reduce the exceeded power flows. In a congested area, the optimal price of electricity rises to the marginal cost of the local generation or to the level needed to ration demand to the amount of available electricity. Even without congestion, some power will be lost in the transmission system through heat dissipation, so prices reflect that it is more expensive to supply electricity at the far end of a heavily loaded line than close to an electric power generation. Locational marginal pricing (LMP), resulting from bidding competition, represents electrical and economical values at nodes or in areas that may provide economical indicator signals to the market agents. This article proposes a data-mining-based methodology that helps characterize zonal prices in real power transmission networks. To test our methodology, we used an LMP database from the California Independent System Operator for 2009 to identify economical zones. (CAISO is a nonprofit public benefit corporation charged with operating the majority of California’s high-voltage wholesale power grid.) To group the buses into typical classes that represent a set of buses with the approximate LMP value, we used two-step and k-means clustering algorithms. By analyzing the various LMP components, our goal was to extract knowledge to support the ISO in investment and network-expansion planning.