Genetic Algorithms: A Visual Search

Genetic algorithms apply the biological principles of selection, mutation, and crossover to a population set containing individuals representing target solutions to a given problem. Using these principles genetic algorithms attempt to create a migration of the individuals in subsequent generations toward the optimal solution. This project is an attempt to visually represent the progress of a genetic algorithm. The coordinate fitness program attempts to find the maximum or minimum value of a given function. It visually represents the progress of the algorithm by providing a plot of each individual in each generation in time. It is then possible to view the migration of points toward a known maximum or minimum value. Visual representation is also achieved by a plot of the highest and lowest fitness per generation, as well as average fitness per generation. The parameters of crossover rate and mutation rate can be altered. This allows experimentation in finding a good combination of these rates for a particular function, and viewing the results. Many involved in the field of genetic algorithms believe that this is an area of the subject that requires further research

Identifying gene locus associations with promyelocytic leukemia nuclear bodies using immuno-TRAP.

Important insights into nuclear function would arise if gene loci physically interacting with particular subnuclear domains could be readily identified. Immunofluorescence microscopy combined with fluorescence in situ hybridization (immuno-FISH), the method that would typically be used in such a study, is limited by spatial resolution and requires prior assumptions for selecting genes to probe. Our new technique, immuno-TRAP, overcomes these limitations. Using promyelocytic leukemia nuclear bodies (PML NBs) as a model, we used immuno-TRAP to determine if specific genes localize within molecular dimensions with these bodies. Although we confirmed a TP53 gene-PML NB association, immuno-TRAP allowed us to uncover novel locus-PML NB associations, including the ABCA7 and TFF1 loci and, most surprisingly, the PML locus itself. These associations were cell type specific and reflected the cell's physiological state. Combined with microarrays or deep sequencing, immuno-TRAP provides powerful opportunities for identifying gene locus associations with potentially any nuclear subcompartment

Changing patterns in long-acting bronchodilator trials in chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is a major cause of morbidity and mortality worldwide. Developments in the understanding of COPD have led to standard guidelines for diagnosis, treatment, and spirometry assessments, which have in turn influenced trial designs and inclusion criteria. Substantial clinical evidence has been gained from clinical trials and supports a positive approach to COPD management. However, there appear to be changing trends in recent trials. Large bronchodilator studies have reported lower improvements in trough forced expiratory volume in 1 second (FEV1) values versus placebo than were observed in earlier studies, while the rate of FEV1 decline seems to be lower in more recent trials. In addition, recent evidence has called into question the usefulness of bronchodilator reversibility testing as a trial inclusion criterion. Baseline patient populations and use of concomitant medications have also changed over recent years due to increased treatment options. The impact of these many variables on clinical trial results is explored, with a particular focus on changes in inclusion criteria and patient baseline demographics

Development of a biomarker for penconazole: a human oral dosing study and a survey of UK residents’ exposure

Penconazole is a widely used fungicide in the UK; however, to date, there have been no peer-reviewed publications reporting human metabolism, excretion or biological monitoring data. The objectives of this study were to i) develop a robust analytical method, ii) determine biomarker levels in volunteers exposed to penconazole, and, finally, to iii) measure the metabolites in samples collected as part of a large investigation of rural residents’ exposure. An LC-MS/MS method was developed for penconazole and two oxidative metabolites. Three volunteers received a single oral dose of 0.03 mg/kg body weight and timed urine samples were collected and analysed. The volunteer study demonstrated that both penconazole-OH and penconazole-COOH are excreted in humans following an oral dose and are viable biomarkers. Excretion is rapid with a half-life of less than four hours. Mean recovery of the administered dose was 47% (range 33%–54%) in urine treated with glucuronidase to hydrolyse any conjugates. The results from the residents’ study showed that levels of penconazole-COOH in this population were low with >80% below the limit of detection. Future sampling strategies that include both end of exposure and next day urine samples, as well as contextual data about the route and time of exposure, are recommended

Identification and predictability of soil quality indicators from conventional soil and vegetation classifications

The physical, chemical and biological attributes of a soil combined with abiotic factors (e.g. climate and topography) drive pedogenesis and some of these attributes have been used as proxies to soil quality. Thus, we investigated: (1) whether appropriate soil quality indicators (SQIs) could be identified in soils of Great Britain, (2) whether conventional soil classification or aggregate vegetation classes (AVCs) could predict SQIs and (3) to what extent do soil types and/ or AVCs act as major regulators of SQIs. Factor analysis was used to group 20 soil attributes into six SQI which were named as; soil organic matter (SOM), dissolved organic matter (DOM), soluble N, reduced N, microbial biomass, DOM humification (DOMH). SOM was identified as the most important SQI in the discrimination of both soil types and AVCs. Soil attributes constituting highly to the SOM factor were, microbial quotient and bulk density. The SOM indicator discriminated three soil type groupings and four aggregate vegetation class groupings. Among the soil types, only the peat soils were discriminated from other groups while among the AVCs only the heath and bog classes were isolated from others. However, the peat soil and heath and bog AVC were the only groups that were distinctly discriminated from other groups. All other groups heavily overlapped with one another, making it practically impossible to define reference values for each soil type or AVC. The two-way ANOVA showed that the AVCs were a better regulator of the SQIs than the soil types. We conclude that conventionally classified soil types cannot predict the SQIs defined from large areas with differing climatic and edaphic factors. Localised areas with similar climatic and topoedaphic factors may hold promise for the definition of SQI that may predict the soil types or AVCs