Application of connectivity mapping in predictive toxicology based on gene-expression similarity.

Connectivity mapping is the process of establishing connections between different biological states using gene-expression profiles or signatures. There are a number of applications but in toxicology the most pertinent is for understanding mechanisms of toxicity. In its essence the process involves comparing a query gene signature generated as a result of exposure of a biological system to a chemical to those in a database that have been previously derived. In the ideal situation the query gene-expression signature is characteristic of the event and will be matched to similar events in the database. Key criteria are therefore the means of choosing the signature to be matched and the means by which the match is made. In this article we explore these concepts with examples applicable to toxicology.

Predictive Current Control of Doubly Fed Induction Generators

This paper presents a predictive current control strategy for doubly-fed induction generators (DFIG). The method predicts the DFIG’s rotor current variations in the synchronous reference frame fixed to the stator flux within a fixed sampling period. This is then used to directly calculate the required rotor voltage to eliminate the current errors at the end of the following sampling period. Space vector modulation is used to generate the required switching pulses within the fixed sampling period. The impact of sampling delay on the accuracy of the sampled rotor current is analyzed and detailed compensation methods are proposed to improve the current control accuracy and system stability. Experimental results for a 1.5 kW DFIG system illustrate the effectiveness and robustness of the proposed control strategy during rotor current steps and rotating speed variation. Tests during negative sequence current injection further demonstrate the excellent dynamic performance of the proposed PCC method.

Genetic Algorithm Search for Predictive Patterns in Multidimensional Time Series

Based on an algorithm for pattern matching in character strings, we implement a pattern matching machine that searches for occurrences of patterns in multidimensional time series. Before the search process takes place, time series are encoded in user-designed alphabets. The patterns, on the other hand, are formulated as regular expressions that are composed of letters from these alphabets and operators. Furthermore, we develop a genetic algorithm to breed patterns that maximize a user-defined fitness function. In an application to financial data, we show that patterns bred to predict high exchange rates volatility in training samples retain statistically significant predictive power in validation samples.

Body Mass Index Is More Predictive of Progenitor Number in Bone Marrow Stromal Cell Population Than Age in Men: Expanding the Predictors of the Progenitor Compartment

Research has focused on in vitro expansion of bone marrow stromal cells with the aim of developing cell-based therapies or tissue-engineered constructs. There is debate over whether there is a reduction in stem cells/osteoprogenitors in the bone marrow compartment with increasing age. The aim of this study was to investigate patient factors that affect the progenitor pool in bone marrow samples. Six milliliters of marrow aspirate was obtained from the femoral canal of 38 primary hip replacement patients (aged 28-91). Outcome measures were total nucleated cell count, colony-forming efficiency, alkaline phosphatase expression, and expression of stem cell markers. There was a nonsignificant negative correlation between age and both colony-forming efficiency and stem cell marker expression. However, body mass index showed a positive, significant correlation with colony area and number in men-accounting for up to 75% of the variation. In conclusion, body mass index, not age, was highly predictive of the number of progenitors found in bone marrow, and this relationship was sex specific. These results may inform the clinician's treatment choice when considering bone marrow-based therapies. Further, it highlights the need to widen research into patient factors that affect the adult stem cell population beyond age and reinforces the need to consider sexes separately.

Psychosocial impact of breast/ovarian (BRCA 1/2) cancer-predictive genetic testing in a UK multi-centre clinical cohort

This multi-centre UK study assesses the impact of predictive testing for breast and ovarian cancer predisposition genes (BRCA 1/2) in the clinical context. In the year following predictive testing, 261 adults (59 male) from nine UK genetics centres participated; 9 I gene mutation carriers and 170 noncarriers. Self-report questionnaires were completed at baseline (pre-genetic testing) and 1, 4 and 12 months following the genetic test result. Men were assessed for general mental health (by general health questionnaire (GHQ)) and women for general mental health, cancer-related worry, intrusive and avoidant thoughts, perception of risk and risk management behaviour. Main comparisons were between female carriers and noncarriers on all measures and men and women for general mental health. Female noncarriers benefited psychologically, with significant reductions in cancer-related worry following testing (P