Modelling the effects of social networks on activity and travel behaviour

Activity-based models of transport demand are increasingly used by governments, engineering firms and consultants to predict the impact of various design and planning decisions on travel and consequently on noise emissions, energy consumption, accessibility and other performance indicators. In this context, non-discretionary activities, such as work and school, can be relatively easily explained by the traveller’s sociodemographic characteristics and generalised travel costs. However, participation in, and scheduling of, discretionary and joint activities are not so easily redicted. Understanding the social network that lies on top of the spatial network could lead to better prediction of social activity schedules and better forecasts of travel patterns for joint activities. Existing models of activity-travel behaviour do not consider joint activities in detail, except within households to a limited extent. A recent attempt developed at ETH Zurich to incorporate social networks in a single-day optimisation scheduling model did not model joint activities as such, rather rewarding individuals for scheduling activities at the same location and at the same time as their friends. Realistic social networks were also not incorporated. The aim of this thesis is to contribute to this rapidly expanding field by developing a simulation of activity and travel behaviour incorporating social processes and joint activities to investigate the effects on activity and travel behaviour over a simulated period of weeks. The model developed is intended as a proof-of-concept. In order to achieve this aim, an agent-based simulation was designed, implemented in Java, and calibrated and partly verified with real-world data. The model generates activities on a daily basis, including the time of day and duration of the activity. An interaction protocol has been developed to model the activity decision process. Data collected in Eindhoven on social and joint activities and social networks has been used for calibration and verification. Alongside the model development, several issues are addressed, such as exploring which parameters are useful and their effects, the data required for the validation of agent-based travel behaviour models, and whether the addition of social networks to models of this type makes adifference. Sensitivity testing was undertaken to explore the effects of parameters, which was applied to increasingly more complex versions of the model (starting from one day of outputs with no interactions between individuals and finishing with full interactions over many days). This showed that the model performed as expected when certain parameters were altered. Due to the components included in the model, scenarios of interest to policy makers (such as changes in population, land-use changes, and changes in institutional contexts) can be explored. Altering the structure of the in- put social networks and the interaction protocols showed that these inputs do have a difference on the outputs of the model. As a result, these elements of the model require data collection on the social network structure and the decision processes for each local instantiation. Two more "traditional" transport planning policy scenarios, an increase in free time and an increase in travel cost, showed that the model performs as expected for these scenarios. It is shown that the use of agent-based modelling is useful in permitting the incorporation of social networks. The social network can have a significant impact on model results and therefore the decisions made by planners and stakeholders. The model can be extended further in several different directions as new theories are developed and data sets are collected

The window period of NEUROGENIN3 during human gestation

The basic helix-loop-helix transcription factor, NEUROG3, is critical in causing endocrine commitment from a progenitor cell population in the developing pancreas. In human, NEUROG3 has been detected from 8 weeks postconception (wpc). However, the profile of its production and when it ceases to be detected is unknown. In this study we have defined the profile of NEUROG3 detection in the developing pancreas to give insight into when NEUROG3- dependent endocrine commitment is possible in the human fetus. Immunohistochemistry allowed counting of cells with positively stained nuclei from 7 wpc through to term. mRNA was also isolated from sections of human fetal pancreas and NEUROG3 transcription analyzed by quantitative reverse transcription and polymerase chain reaction. NEUROG3 was detected as expected at 8 wpc. The number of NEUROG3-positive cells increased to peak levels between 10 wpc and 14 wpc. It declined at and after 18 wpc such that it was not detected in human fetal pancreas at 35-41 wpc. Analysis of NEUROG3 transcription corroborated this profile by demonstrating very low levels of transcript at 35-41 wpc, more than 10-fold lower than levels at 12-16 wpc. These data define the appearance, peak and subsequent disappearance of the critical transcription factor, NEUROG3, in human fetal pancreas for the first time. By inference, the window for pancreatic endocrine differentiation via NEUROG3 action opens at 8 wpc and closes between 21 and 35 wpc

Bioenergetics in fibroblasts of patients with Huntington disease are associated with age at onset

Objective We aimed to assess whether differences in energy metabolism in fibroblast cell lines derived from patients with Huntington disease were associated with age at onset independent of the cytosine-adenine-guanine (CAG) repeat number in the mutant allele. Methods For this study, we selected 9 pairs of patients with Huntington disease matched for mutant CAG repeat size and sex, but with a difference of at least 10 years in age at onset, using the Leiden Huntington disease database. From skin biopsies, we isolated fibroblasts in which we (1) quantified the ATP concentration before and after a hydrogen-peroxide challenge and (2) measured mitochondrial respiration and glycolysis in real time, using the Seahorse XF Extracellular Flux Analyzer XF24. Results The ATP concentration in fibroblasts was significantly lower in patients with Huntington disease with an earlier age at onset, independent of calendar age and disease duration. Maximal respiration, spare capacity, and respiration dependent on complex II activity, and indices of mitochondrial respiration were significantly lower in patients with Huntington disease with an earlier age at onset, again independent of calendar age and disease duration. Conclusions A less efficient bioenergetics profile was found in fibroblast cells from patients with Huntington disease with an earlier age at onset independent of mutant CAG repeat size. Thus, differences in bioenergetics could explain part of the residual variation in age at onset in Huntington disease

Does Repeated Measurement of a 6-Min Walk Test Contribute to Risk Prediction in Children with Dilated Cardiomyopathy?

A single 6-min walk test (6MWT) can be used to identify children with dilated cardiomyopathy (DCM) with a high risk of death or heart transplantation. To determine if repeated 6MWT has added value in addition to a single 6MWT in predicting death or heart transplantation in children with DCM. Prospective multicenter cohort study including ambulatory DCM

Predicting outcome in children with dilated cardiomyopathy: the use of repeated measurements of risk factors for outcome

Aims: We aimed to determine whether in children with dilated cardiomyopathy repeated measurement of known risk factors for death or heart transplantation (HTx) during disease progression can identify children at the highest risk for adverse outcome. Methods and results: Of 137 children we included in a prospective cohort, 36 (26%) reached the study endpoint (SE: all-cause death or HTx), 15 (11%) died at a median of 0.09 years [inter-quartile range (IQR) 0.03–0.7] after diagnosis, and 21 (15%) underwent HTx at a median of 2.9 years [IQR 0.8–6.1] after diagnosis. Median follow-up was 2.1 years [IQR 0.8–4.3]. Twenty-three children recovered at a median of 0.6 years [IQR 0.5–1.4] after diagnosis, and 78 children had ongoing disease at the end of the study. Children who reached the SE could be distinguished from those who did not, based on the temporal evolution of four risk factors: stunting of length growth (−0.42 vs. −0.02 length Z-score per year, P < 0.001), less decrease in N-terminal pro-B-type natriu

ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors

Novel genetic loci associated with hippocampal volume

The hippocampal formation is a brain structure integrally involved in episodic memory, spatial navigation, cognition and stress responsiveness. Structural abnormalities in hippocampal volume and shape are found in several common neuropsychiatric disorders. To identify the genetic underpinnings of hippocampal structure here we perform a genome-wide association study (GWAS) of 33,536 individuals and discover six independent loci significantly associated with hippocampal volume, four of them novel. Of the novel loci, three lie within genes (ASTN2, DPP4 and MAST4) and one is found 200 kb upstream of SHH. A hippocampal subfield analysis shows that a locus within the MSRB3 gene shows evidence of a localized effect along the dentate gyrus, subiculum, CA1 and fissure. Further, we show that genetic variants associated with decreased hippocampal volume are also associated with increased risk for Alzheimer's disease (rg =-0.155). Our findings suggest novel biological pathways through which human genetic variation influences hippocampal volume and risk for neuropsychiatric illness

The genetic architecture of type 2 diabetes

The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of heritability. To test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole genome sequencing in 2,657 Europeans with and without diabetes, and exome sequencing in a total of 12,940 subjects from five ancestral groups. To increase statistical power, we expanded sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support a major role for lower-frequency variants in predisposition to type 2 diabetes