Population change of Common Buzzards Buteo buteo in central southern England between 2011 and 2016

Capsule: Distance sampling identified an increase in estimated population size of Common Buzzards Buteo buteo in central southern England between 2011 and 2016 of more than 50%. The rate of population growth slowed in later years. Aims: To assess the utility of a targeted distance sampling protocol to derive seasonal and annual population estimates for Common Buzzards across an area of southern England. Methods: We used a line transect survey methodology and multiple covariate distance sampling to assess population density and abundance of Common Buzzards in spring and autumn between 2011 and 2016 across a 2600 km2 area of central southern England. Results: Estimated population size increased by more than 50%, from approximately 2900 to 4500 individuals, across the period in a trend similar to that shown by Breeding Bird Survey data. Discussion: A slowing of the growth in population size of Common Buzzards in central southern England suggests that the species may be approaching carrying capacity in this area. These results also suggest that currently employed broad scale survey methodologies adequately reflect the general population trends for this species. Our data provide the first published estimates of the Common Buzzard population in central southern England derived from direct empirical assessment

Comprehensive analysis of epigenetic clocks reveals associations between disproportionate biological ageing and hippocampal volume

The concept of age acceleration, the difference between biological age and chronological age, is of growing interest, particularly with respect to age-related disorders, such as Alzheimer’s Disease (AD). Whilst studies have reported associations with AD risk and related phenotypes, there remains a lack of consensus on these associations. Here we aimed to comprehensively investigate the relationship between five recognised measures of age acceleration, based on DNA methylation patterns (DNAm age), and cross-sectional and longitudinal cognition and AD-related neuroimaging phenotypes (volumetric MRI and Amyloid-β PET) in the Australian Imaging, Biomarkers and Lifestyle (AIBL) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Significant associations were observed between age acceleration using the Hannum epigenetic clock and cross-sectional hippocampal volume in AIBL and replicated in ADNI. In AIBL, several other findings were observed cross-sectionally, including a significant association between hippocampal volume and the Hannum and Phenoage epigenetic clocks. Further, significant associations were also observed between hippocampal volume and the Zhang and Phenoage epigenetic clocks within Amyloid-β positive individuals. However, these were not validated within the ADNI cohort. No associations between age acceleration and other Alzheimer’s disease-related phenotypes, including measures of cognition or brain Amyloid-β burden, were observed, and there was no association with longitudinal change in any phenotype. This study presents a link between age acceleration, as determined using DNA methylation, and hippocampal volume that was statistically significant across two highly characterised cohorts. The results presented in this study contribute to a growing literature that supports the role of epigenetic modifications in ageing and AD-related phenotypes

Use of Bilinear Structures for Modelling a Brewery Fermentation Process

This paper presents the results of an investigative study with the aim being to obtain and assess the appropriateness of bilinear model structures for replicating the characteristics of a brewery fermentation process. Based on realtime data taken from a brewery fermentation plant, it is shown that a discrete-time twin-bilinear model, which simultaneously relates temperature to specific gravity and specific gravity to temperature, provides an adequate input/output reconstruction. The ability of the twin-bilinear model structure is discussed and possibilities for its utilization with an adaptive closed loop system are considered. </jats:p

A Hybrid Genetic Algorithm For System Identification

The problem considered is that of approximating an unknown system (plant) by learning the coefficients of a linear model from data collected on the plant performance, using the criterion of minimizing the sum of squared errors between predicted and actual performance. It is well known that the error surface is not unimodal, and that the estimated model may converge to a locally optimal solution. Furthermore, the solution may be unstable. In this paper a genetic algorithm (GA) is described which attempts to deal with each of these problems. The question of stability is handled by encoding the solution vector in terms of the radii and angles of the poles and zeros directly. The problem of premature convergence to a local optimum is tackled by hybridizing a dynamically varying GA with the classical Golden Section method of search. Results will be presented which show that this approach is capable of very high accuracy for an artificial test problem, and that it also outperforms ordinary l..