Common metrics for cellular automata models of complex systems

The creation and use of models is critical not only to the scientific process, but also to life in general. Selected features of a system are abstracted into a model that can then be used to gain knowledge of the workings of the observed system and even anticipate its future behaviour. A key feature of the modelling process is the identification of commonality. This allows previous experience of one model to be used in a new or unfamiliar situation. This recognition of commonality between models allows standards to be formed, especially in areas such as measurement. How everyday physical objects are measured is built on an ingrained acceptance of their underlying commonality. Complex systems, often with their layers of interwoven interactions, are harder to model and, therefore, to measure and predict. Indeed, the inability to compute and model a complex system, except at a localised and temporal level, can be seen as one of its defining attributes. The establishing of commonality between complex systems provides the opportunity to find common metrics. This work looks at two dimensional cellular automata, which are widely used as a simple modelling tool for a variety of systems. This has led to a very diverse range of systems using a common modelling environment based on a lattice of cells. This provides a possible common link between systems using cellular automata that could be exploited to find a common metric that provided information on a diverse range of systems. An enhancement of a categorisation of cellular automata model types used for biological studies is proposed and expanded to include other disciplines. The thesis outlines a new metric, the C-Value, created by the author. This metric, based on the connectedness of the active elements on the cellular automata grid, is then tested with three models built to represent three of the four categories of cellular automata model types. The results show that the new C-Value provides a good indicator of the gathering of active cells on a grid into a single, compact cluster and of indicating, when correlated with the mean density of active cells on the lattice, that their distribution is random. This provides a range to define the disordered and ordered state of a grid. The use of the C-Value in a localised context shows potential for identifying patterns of clusters on the grid

Communicating knowledge: how and why UK researchers publish and disseminate their findings

Communicating knowledge: how and why UK researchers publish and disseminate their finding

Supplementary information files for Relationships between exposure to gestational diabetes treatment and neonatal anthropometry: Evidence from the Born in Bradford (BiB)

© the authors, CC-BY NCSupplementary files for article Relationships between exposure to gestational diabetes treatment and neonatal anthropometry: Evidence from the Born in Bradford (BiB)Objectives To examine the relationships between gestational diabetes mellitus (GDM) treatment and neonatal anthropometry.Methods Covariate-adjusted multivariable linear regression analyses were used in 9,907 offspring of the Born in Bradford cohort. GDM treatment type (lifestyle changes advice only, lifestyle changes and insulin or lifestyle changes and metformin) was the exposure, offspring not exposed to GDM the control, and birth weight, head, mid-arm and abdominal circumference, and subscapular and triceps skinfold thickness the outcomes.Results Lower birth weight in offspring exposed to insulin (-117.2g (95% CI -173.8,-60.7)) and metformin (-200.3g (-328.5,-72.1)) than offspring not exposed to GDM was partly attributed to lower gestational age at birth and greater proportion of Pakistani mothers in the treatment groups. Higher subscapular skinfolds in offspring exposed to treatment compared to offspring not exposed to GDM was partly attributed to higher maternal glucose concentrations at diagnosis. In fully adjusted analyses, GDM treatment was associated with lower weight, smaller abdominal circumference and skinfolds at birth than offspring not exposed to GDM. Metformin was associated with smaller mid-arm circumference (-0.3cm (-0.6,-0.07)) than insulin in fully adjusted models with no other differences found.Conclusions for Practice Offspring exposed to GDM treatment were lighter and smaller at birth than offspring not exposed to GDM. Metformin-exposed offspring had largely comparable birth anthropometric characteristics to those exposed to insulin.</p

Description of BMI data in the five UK birth cohort studies.

<p>BMI: Body Mass Index, IOTF: International Obesity Task Force, IQR: Inter-Quartile Range, UK: United Kingdom, NSHD: Medical Research Council National Survey of Health and Development, NCDS National Child Development Study, BCS: British Cohort Study, ALSPAC: Avon Longitudinal Study of Parents and Children, MCS: Millennium Cohort Study</p><p>^aThinness, overweight, and obesity between 2–18 years of age were defined according to the IOTF cut-offs, which are centiles that link with the adulthood cut-offs at age 18 years (e.g., the 90.5th IOTF centile is used to define overweight in boys as this centile equals 25 kg/m², the adulthood cut-off, at age 18 years).</p><p>Description of BMI data in the five UK birth cohort studies.</p

The 98th, 91st, and 50th childhood BMI centiles from sex- and study-stratified LMS models plotted against the IOTF cut-offs.

<p>BMI: Body Mass Index, IOTF: International Obesity Task Force, LMS: Lambda-Mu-Sigma, NSHD: Medical Research Council National Survey of Health and Development, NCDS National Child Development Study, BCS: British Cohort Study, ALSPAC: Avon Longitudinal Study of Parents and Children, MCS: Millennium Cohort Study.</p

The 98^th, 91^st, and 50^th adulthood BMI centiles from sex- and study-stratified LMS models plotted against the normal cut-offs.

<p>BMI: Body Mass Index, LMS: Lambda-Mu-Sigma, NSHD: Medical Research Council National Survey of Health and Development, NCDS National Child Development Study, BCS: British Cohort Study.</p

Trajectories of the probability of overweight or obesity (versus normal weight) from sex- and study-stratified multilevel logistic regression models.

<p>NSHD: Medical Research Council National Survey of Health and Development, NCDS National Child Development Study, BCS: British Cohort Study, ALSPAC: Avon Longitudinal Study of Parents and Children, MCS: Millennium Cohort Study.</p

Study-stratified box plots for height, weight, and BMI Z-scores according to the UK-WHO chart at 10 or 11 years of age.

<p>BMI: Body Mass Index, UK-WHO: United Kingdom-World Health Organisation, NSHD: Medical Research Council National Survey of Health and Development, NCDS National Child Development Study, BCS: British Cohort Study, ALSPAC: Avon Longitudinal Study of Parents and Children, MCS: Millennium Cohort Study.</p

Socioeconomic Inequalities in Body Mass Index across Adulthood: Coordinated Analyses of Individual Participant Data from Three British Birth Cohort Studies Initiated in 1946, 1958 and 1970

<div><p>Background</p><p>High body mass index (BMI) is an important contributor to the global burden of ill-health and health inequality. Lower socioeconomic position (SEP) in both childhood and adulthood is associated with higher adult BMI, but how these associations have changed across time is poorly understood. We used longitudinal data to examine how childhood and adult SEP relates to BMI across adulthood in three national British birth cohorts.</p><p>Methods and Findings</p><p>The sample comprised up to 22,810 participants with 77,115 BMI observations in the 1946 MRC National Survey of Health and Development (ages 20 to 60–64), the 1958 National Child Development Study (ages 23 to 50), and the 1970 British Cohort Study (ages 26 to 42). Harmonized social class-based SEP data (Registrar General’s Social Class) was ascertained in childhood (father’s class at 10/11 y) and adulthood (42/43 years), and BMI repeatedly across adulthood, spanning 1966 to 2012. Associations between SEP and BMI were examined using linear regression and multilevel models.</p><p>Lower childhood SEP was associated with higher adult BMI in both genders, and differences were typically larger at older ages and similar in magnitude in each cohort. The strength of association between adult SEP and BMI did not vary with age in any consistent pattern in these cohorts, but were more evident in women than men, and inequalities were larger among women in the 1970 cohort compared with earlier-born cohorts. For example, mean differences in BMI at 42/43 y amongst women in the lowest compared with highest social class were 2.0 kg/m² (95% CI: −0.1, 4.0) in the 1946 NSHD, 2.3 kg/m² (1.1, 3.4) in the 1958 NCDS, and 3.9 kg/m² (2.3, 5.4) the in the 1970 BCS; mean (SD) BMI in the highest and lowest social classes were as follows: 24.9 (0.8) versus 26.8 (0.7) in the 1946 NSHD, 24.2 (0.4) versus 26.5 (0.4) in the 1958 NCDS, and 24.2 (0.3) versus 28.1 (0.8) in the 1970 BCS. Findings did not differ whether using overweight or obesity as an outcome.</p><p>Limitations of this work include the use of social class as the sole indicator of SEP—while it was available in each cohort in both childhood and adulthood, trends in BMI inequalities may differ according to other dimensions of SEP such as education or income. Although harmonized data were used to aid inferences about birth cohort differences in BMI inequality, differences in other factors may have also contributed to findings—for example, differences in missing data.</p><p>Conclusions</p><p>Given these persisting inequalities and their public health implications, new and effective policies to reduce inequalities in adult BMI that tackle inequality with respect to both childhood and adult SEP are urgently required</p></div

Male BMI across adulthood in relation to own social class (42/43 y) in the 1946 NSHD, 1958 NCDS, and 1970 BCS British birth cohort studies.

<p>Note: lines show estimated BMI along with 95% confidence intervals at each age, estimated using multilevel general linear regression models (age terms not included in the 1970 BCS due to only 1 age of measurement).</p