Stationary states in random walks on networks

In this thesis we dealt with the problem of describing a transportation network in which the objects in movement were subject to both finite transportation capacity and finite accomodation capacity. The movements across such a system are realistically of a simultaneous nature which poses some challenges when formulating a mathematical description. We tried to derive such a general modellization from one posed on a simplified problem based on asyncronicity in particle transitions. We did so considering one-step processes based on the assumption that the system could be describable through discrete time Markov processes with finite state space. After describing the pre-established dynamics in terms of master equations we determined stationary states for the considered processes. Numerical simulations then led to the conclusion that a general system naturally evolves toward a congestion state when its particle transition simultaneously and we consider one single constraint in the form of network node capacity. Moreover the congested nodes of a system tend to be located in adjacent spots in the network, thus forming local clusters of congested nodes.

Evidence for a cognitive control network for goal-directed attention in simple sustained attention

The deterioration of performance over time is characteristic for sustained attention tasks. This so-called "performance decrement" is measured by the increase of reaction time (RT) over time. Some behavioural and neurobiological mechanisms of this phenomenon are not yet fully understood. Behaviourally, we examined the increase of RT over time and the inter-individual differences of this performance decrement. On the neurophysiological level, we investigated the task-relevant brain areas where neural activity was modulated by RT and searched for brain areas involved in good performance (i.e. participants with no or moderate performance decrement) as compared to poor performance (i.e. participants with a steep performance decrement). For this purpose, 20 healthy, young subjects performed a carefully designed task for simple sustained attention, namely a low-demanding version of the Rapid Visual Information Processing task. We employed a rapid event-related functional magnetic resonance imaging (fMRI) design. The behavioural results showed a significant increase of RT over time in the whole group, and also revealed that some participants were not as prone to the performance decrement as others. The latter was statistically significant comparing good versus poor performers. Moreover, high BOLD-responses were linked to longer RTs in a task-relevant bilateral fronto-cingulate-insular-parietal network. Among these regions, good performance was associated with significantly higher RT-BOLD correlations in the pre-supplementary motor area (pre-SMA). We concluded that the task-relevant bilateral fronto-cingulate-insular-parietal network was a cognitive control network responsible for goal-directed attention. The pre-SMA in particular might be associated with the performance decrement insofar that good performers could sustain activity in this brain region in order to monitor performance declines and adjust behavioural output.

Mechanisms of perceptual learning of depth discrimination in random dot stereograms.

Perceptual learning is a training induced improvement in performance. Mechanisms underlying the perceptual learning of depth discrimination in dynamic random dot stereograms were examined by assessing stereothresholds as a function of decorrelation. The inflection point of the decorrelation function was defined as the level of decorrelation corresponding to 1.4 times the threshold when decorrelation is 0%. In general, stereothresholds increased with increasing decorrelation. Following training, stereothresholds and standard errors of measurement decreased systematically for all tested decorrelation values. Post training decorrelation functions were reduced by a multiplicative constant (approximately 5), exhibiting changes in stereothresholds without changes in the inflection points. Disparity energy model simulations indicate that a post-training reduction in neuronal noise can sufficiently account for the perceptual learning effects. In two subjects, learning effects were retained over a period of six months, which may have application for training stereo deficient subjects.

Streptococcus pneumoniae detects and responds to foreign bacterial peptide fragments in its environment

Streptococcus pneumoniae is an important cause of bacterial meningitis and pneumonia but usually colonizes the human nasopharynx harmlessly. As this niche is simultaneously populated by other bacterial species, we looked for a role and pathway of communication between pneumococci and other species. This paper shows that two proteins of non-encapsulated S. pneumoniae, AliB-like ORF 1 and ORF 2, bind specifically to peptides matching other species resulting in changes in the pneumococci. AliB-like ORF 1 binds specifically peptide SETTFGRDFN, matching 50S ribosomal subunit protein L4 of Enterobacteriaceae, and facilitates upregulation of competence for genetic transformation. AliB-like ORF 2 binds specifically peptides containing sequence FPPQS, matching proteins of Prevotella species common in healthy human nasopharyngeal microbiota. We found that AliB-like ORF 2 mediates the early phase of nasopharyngeal colonization in vivo. The ability of S. pneumoniae to bind and respond to peptides of other bacterial species occupying the same host niche may play a key role in adaptation to its environment and in interspecies communication. These findings reveal a completely new concept of pneumococcal interspecies communication which may have implications for communication between other bacterial species and for future interventional therapeutics.

THEODORE, a two-step heating system for the EC/OC determination of radiocarbon (14C) in the environment

Measurements of 14C in the organic carbon (OC) and elemental carbon (EC) fractions, respectively, of fine aerosol particles bear the potential to apportion anthropogenic and biogenic emission sources. For this purpose, the system THEODORE (two-step heating system for the EC/OC determination of radiocarbon in the environment) was developed. In this device, OC and EC are transformed into carbon dioxide in a stream of oxygen at 340 and 650 �C, respectively, and reduced to filamentous carbon. This is the target material for subsequent accelerator mass spectrometry (AMS) 14C measurements, which were performed on sub-milligram carbon samples at the PSI/ETH compact 500 kV AMS system. Quality assurance measurements of SRM 1649a, Urban Dust, yielded a fraction of modern fM in total carbon (TC) of 0.522 ±0.018 (n ¼ 5, 95% confidence level) in agreement with reported values. The results for OC and EC are 0.70± 0.05 (n ¼ 3) and 0.066 ± 0.020 (n ¼ 4), respectively.

Supramolecular Polymers in DNA Nanotechnology

Creation of biocompatible functional materials is an important task in supramolecular chemistry. In this contribution, we report on noncovalent synthesis of DNA-grafted supramolecular polymers (SPs). DNA-grafted SPs enable programmed arrangement of oligonucleotides in a regular, tightly packed one-dimensional array. Further interactions of DNA-grafted SPs with complementary DNA strands leads to the formation of networks through highly cooperative G-C blunt-end stacking interactions. The structural changes in the polymeric core enable to monitor spectroscopically the stepwise formation of networks. Such stimuli-responsive supramolecular networks may lead to the development of DNA-based smart materials.

BAYESIAN STATISTICAL METHODS IN GENE-ENVIRONMENT AND GENE-GENE INTERACTION STUDIES

Complex diseases such as cancer result from multiple genetic changes and environmental exposures. Due to the rapid development of genotyping and sequencing technologies, we are now able to more accurately assess causal effects of many genetic and environmental factors. Genome-wide association studies have been able to localize many causal genetic variants predisposing to certain diseases. However, these studies only explain a small portion of variations in the heritability of diseases. More advanced statistical models are urgently needed to identify and characterize some additional genetic and environmental factors and their interactions, which will enable us to better understand the causes of complex diseases. In the past decade, thanks to the increasing computational capabilities and novel statistical developments, Bayesian methods have been widely applied in the genetics/genomics researches and demonstrating superiority over some regular approaches in certain research areas. Gene-environment and gene-gene interaction studies are among the areas where Bayesian methods may fully exert its functionalities and advantages. This dissertation focuses on developing new Bayesian statistical methods for data analysis with complex gene-environment and gene-gene interactions, as well as extending some existing methods for gene-environment interactions to other related areas. It includes three sections: (1) Deriving the Bayesian variable selection framework for the hierarchical gene-environment and gene-gene interactions; (2) Developing the Bayesian Natural and Orthogonal Interaction (NOIA) models for gene-environment interactions; and (3) extending the applications of two Bayesian statistical methods which were developed for gene-environment interaction studies, to other related types of studies such as adaptive borrowing historical data. We propose a Bayesian hierarchical mixture model framework that allows us to investigate the genetic and environmental effects, gene by gene interactions (epistasis) and gene by environment interactions in the same model. It is well known that, in many practical situations, there exists a natural hierarchical structure between the main effects and interactions in the linear model. Here we propose a model that incorporates this hierarchical structure into the Bayesian mixture model, such that the irrelevant interaction effects can be removed more efficiently, resulting in more robust, parsimonious and powerful models. We evaluate both of the 'strong hierarchical' and 'weak hierarchical' models, which specify that both or one of the main effects between interacting factors must be present for the interactions to be included in the model. The extensive simulation results show that the proposed strong and weak hierarchical mixture models control the proportion of false positive discoveries and yield a powerful approach to identify the predisposing main effects and interactions in the studies with complex gene-environment and gene-gene interactions. We also compare these two models with the 'independent' model that does not impose this hierarchical constraint and observe their superior performances in most of the considered situations. The proposed models are implemented in the real data analysis of gene and environment interactions in the cases of lung cancer and cutaneous melanoma case-control studies. The Bayesian statistical models enjoy the properties of being allowed to incorporate useful prior information in the modeling process. Moreover, the Bayesian mixture model outperforms the multivariate logistic model in terms of the performances on the parameter estimation and variable selection in most cases. Our proposed models hold the hierarchical constraints, that further improve the Bayesian mixture model by reducing the proportion of false positive findings among the identified interactions and successfully identifying the reported associations. This is practically appealing for the study of investigating the causal factors from a moderate number of candidate genetic and environmental factors along with a relatively large number of interactions. The natural and orthogonal interaction (NOIA) models of genetic effects have previously been developed to provide an analysis framework, by which the estimates of effects for a quantitative trait are statistically orthogonal regardless of the existence of Hardy-Weinberg Equilibrium (HWE) within loci. Ma et al. (2012) recently developed a NOIA model for the gene-environment interaction studies and have shown the advantages of using the model for detecting the true main effects and interactions, compared with the usual functional model. In this project, we propose a novel Bayesian statistical model that combines the Bayesian hierarchical mixture model with the NOIA statistical model and the usual functional model. The proposed Bayesian NOIA model demonstrates more power at detecting the non-null effects with higher marginal posterior probabilities. Also, we review two Bayesian statistical models (Bayesian empirical shrinkage-type estimator and Bayesian model averaging), which were developed for the gene-environment interaction studies. Inspired by these Bayesian models, we develop two novel statistical methods that are able to handle the related problems such as borrowing data from historical studies. The proposed methods are analogous to the methods for the gene-environment interactions on behalf of the success on balancing the statistical efficiency and bias in a unified model. By extensive simulation studies, we compare the operating characteristics of the proposed models with the existing models including the hierarchical meta-analysis model. The results show that the proposed approaches adaptively borrow the historical data in a data-driven way. These novel models may have a broad range of statistical applications in both of genetic/genomic and clinical studies.