Data-driven models of water and methane

In the field of materials modelling, traditional atomistic models seldom achieve high accuracy and speed at the same time. Recent developments using high-dimensional fits to approximate the quantum chemical potential energy surface (PES) have overcome this problem. This thesis presents such models for methane–water mixtures, in particular for methane clathrates. Since the discovery of their existence on Earth about half a century ago, methane clathrates have been subject to numerous studies motivated by industrial and environmental perspectives. This project develops atomistic models that describe methane–water interactions with high accuracy. The model development in this work focuses on the dimer and the trimer PESs, which are fitted to quantum mechanical data. The fitting methods used are the Gaussian Approximation Potentials (GAP) [1, 2] and the permutationally invariant polynomials (PIP) [3] methods, the latter applied in collaboration. The long-range electrostatic interactions are calculated using a classical force field, the modified TTM4F [4]. The resulting models are validated against quantum mechanical and experimental data. A clathrate phase diagram is calculated in the quasi-harmonic approximation using the model based on PIPs. As the fitted level, CCSD(T)-F12, is not applicable to larger systems, we compare the calculations to DMC results for the larger clusters and periodic systems. However, small systematic differences are found between the developed models and DMC; comparing different CCSD(T)-F12 versions against DMC, this inconsistency is confirmed to arise from the differences between the two quantum chemical methods. In another collaboration [5], different potential fitting methods are also compared using the same datasets and found to achieve similar accuracies when applied to only the energy differences.Peterhouse Research Studentship, BP International Centre for Advanced Materials (BP-ICAM

Children's emotional functioning in the preschool period: Emotion recognition, temparament and their links with early risk factors : The Generation R Study

Emotions are essential aspects of our adaptation to the social and physical environment. All emotions, even the negative ones, have significantly beneficial effects on our behavior, well-being, and adaptation. Conversely, inappropriate and uncontrolled emotional responses are implicated in many forms of psychopathology and even in physical illness. In young children, temperament and the ability to accurately recognize emotional signals are key aspects of emotional functioning. The present thesis examines environmental and genetic correlates of children’s temperament and emotion recognition accuracy in the early preschool years, a period during which temperament begins to stabilize and children develop a solid foundation for the accurate perception and labeling of emotions. All studies included in this thesis were conducted within the context of The Generation R Study, a large-scale population-based prospective child cohort from fetal life onward in Rotterdam, the Netherlands

Bilateral comparison of traditional and alternate electrodermal measurement sites

Abstract Advances in mobile and wireless technology have expanded the scope of electrodermal research. Since traditional electrodermal measurement sites are not always suitable for laboratory research and are rarely appropriate for ambulatory measurements, there is a need to explore and contrast alternate measurement locations. We evaluated bilateral electrodermal activity (EDA) from five measurement sites (fingers, feet, wrists, shoulders, and calves). In a counterbalanced, randomized, within-subjects design study, participants (N = 115) engaged in a 4-min-long breathing exercise and were exposed to emotionally laden and neutral stimuli. High within-subject correlations were found between the EDA measured from fingers bilaterally (r = .89), between the left fingers and both feet (r = .72). Moderate correlations were found between EDA measured from the left fingers and wrists (r = .30 and r = .33), low correlations between the left fingers and the shoulders (r = ?.03 and r = ?.06) or calves (r = .05 and r = .14). Response latency was the shortest on the fingers while it was the longest on the lower body. Short response windows would miss some of the responses from the palmar surfaces and a substantial number from other evaluated locations. The fingers and the feet are the most reliable locations to measure from, followed by the wrists. We suggest setting site-specific response windows for different measurement locations. An investigation of repeatability showed that within-subject correlations, response frequencies, response amplitudes show a similar pattern from the first measurement time to a later one

Genetic associations with childhood brain growth, defined in two longitudinal cohorts

Genome-wide association studies (GWASs) are unraveling the genetics of adult brain neuroanatomy as measured by cross-sectional anatomic magnetic resonance imaging (aMRI). However, the genetic mechanisms that shape childhood brain development are, as yet, largely unexplored. In this study we identify common genetic variants associated with childhood brain development as defined by longitudinal aMRI. Genome-wide single nucleotide polymorphism (SNP) data were determined in two cohorts: one enriched for attention-deficit/hyperactivity disorder (ADHD) (LONG cohort: 458 participants; 119 with ADHD) and the other from a population-based cohort (Generation R: 257 participants). The growth of the brain's major regions (cerebral cortex, white matter, basal ganglia, and cerebellum) and one region of interest (the right lateral prefrontal cortex) were defined on all individuals from two aMRIs, and a GWAS and a pathway analysis were performed. In addition, association between polygenic risk for ADHD and brain growth was determined for the LONG cohort. For white matter growth, GWAS meta-analysis identified a genome-wide significant intergenic SNP (rs12386571, P = 9.09 × 10-9 ), near AKR1B10. This gene is part of the aldo-keto reductase superfamily and shows neural expression. No enrichment of neural pathways was detected and polygenic risk for ADHD was not associated with the brain growth phenotypes in the LONG cohort that was enriched for the diagnosis of ADHD. The study illustrates the use of a novel brain growth phenotype defined in vivo for further study

Association between Age and the 7 Repeat Allele of the Dopamine D4 Receptor Gene

Longevity is in part (25%) inherited, and genetic studies aim to uncover allelic variants that play an important role in prolonging life span. Results to date confirm only a few gene variants associated with longevity, while others show inconsistent results. However, GWAS studies concentrate on single nucleotide polymorphisms, and there are only a handful of studies investigating variable number of tandem repeat variations related to longevity. Recently, Grady and colleagues (2013) reported a remarkable (66%) accumulation of those carrying the 7 repeat allele of the dopamine D4 receptor gene in a large population of 90-109 years old Californian centenarians, as compared to an ancestry-matched young population. In the present study we demonstrate the same association using continuous age groups in an 18-97 years old Caucasian sample (N = 1801, p = 0.007). We found a continuous pattern of increase from 18-75, however frequency of allele 7 carriers decreased in our oldest age groups. Possible role of gene-environment interaction effects driven by historical events are discussed. In accordance with previous findings, we observed association preferentially in females (p = 0.003). Our results underlie the importance of investigating non-disease related genetic variants as inherited components of longevity, and confirm, that the 7-repeat allele of the dopamine D4 receptor gene is a longevity enabling genetic factor, accumulating in the elderly female population

General psychopathology, internalising and externalising in children and functional outcomes in late adolescence

Background: Internalising and externalising problems commonly co-occur in childhood. Yet, few developmental models describing the structure of child psychopathology appropriately account for this comorbidity. We evaluate a model of childhood psychopathology that separates the unique and shared contribution of individual psychological symptoms into specific internalising, externalising and general psychopathology factors and assess how these general and specific factors predict long-term outcomes concerning criminal behaviour, academic achievement and affective symptoms in three independent cohorts. Methods: Data were drawn from independent birth cohorts (Avon Longitudinal Study of Parents and Children (ALSPAC), N = 11,612; Generation R, N = 7,946; Maternal Adversity, Vulnerability and Neurodevelopment (MAVAN), N = 408). Child psychopathology was assessed between 4 and 8 years using a range of diagnostic and questionnaire-based measures, and multiple informants. First, structural equation models were used to assess the fit of hypothesised models of shared and unique components of psychopathology in all cohorts. Once the model was chosen, linear/logistic regressions were used to investigate whether these factors were associated with important outcomes such as criminal behaviour, academic achievement and well-being from late adolescence/early adulthood. Results: The model that included specific factors for internalising/externalising and a general psychopathology factor capturing variance shared between symptoms regardless of their classification fits well for all of the cohorts. As hypothesised, general psychopathology factor scores were predictive of all outcomes of later functioning, while specific internalising factor scores predicted later internalising outcomes. Specific externalising factor scores, capturing variance not shared by any other psychological symptoms, were not predictive of later outcomes. Conclusions: Early symptoms of psychopathology carry information that is syndrome-specific as well as indicative of general vulnerability and the informant reporting on the child. The ‘general psychopathology factor’ might be more relevant for long-term outcomes than specific symptoms. These findings emphasise the importance of considering the co-occurrence of common internalising and externalising problems in childhood when considering long-term impact

Glial Cell Line-Derived Neurotrophic Factor (GDNF) as a Novel Candidate Gene of Anxiety.

Glial cell line-derived neurotrophic factor (GDNF) is a neurotrophic factor for dopaminergic neurons with promising therapeutic potential in Parkinson's disease. A few association analyses between GDNF gene polymorphisms and psychiatric disorders such as schizophrenia, attention deficit hyperactivity disorder and drug abuse have also been published but little is known about any effects of these polymorphisms on mood characteristics such as anxiety and depression. Here we present an association study between eight (rs1981844, rs3812047, rs3096140, rs2973041, rs2910702, rs1549250, rs2973050 and rs11111) GDNF single nucleotide polymorphisms (SNPs) and anxiety and depression scores measured by the Hospital Anxiety and Depression Scale (HADS) on 708 Caucasian young adults with no psychiatric history. Results of the allele-wise single marker association analyses provided significant effects of two single nucleotide polymorphisms on anxiety scores following the Bonferroni correction for multiple testing (p = 0.00070 and p = 0.00138 for rs3812047 and rs3096140, respectively), while no such result was obtained on depression scores. Haplotype analysis confirmed the role of these SNPs; mean anxiety scores raised according to the number of risk alleles present in the haplotypes (p = 0.00029). A significant sex-gene interaction was also observed since the effect of the rs3812047 A allele as a risk factor of anxiety was more pronounced in males. In conclusion, this is the first demonstration of a significant association between the GDNF gene and mood characteristics demonstrated by the association of two SNPs of the GDNF gene (rs3812047 and rs3096140) and individual variability of anxiety using self-report data from a non-clinical sample