An equilibrium approach to modelling social interaction

The aim of this work is to put forward a statistical mechanics theory of social interaction, generalizing econometric discrete choice models. After showing the formal equivalence linking econometric multinomial logit models to equilibrium statical mechanics, a multi- population generalization of the Curie-Weiss model for ferromagnets is considered as a starting point in developing a model capable of describing sudden shifts in aggregate human behaviour. Existence of the thermodynamic limit for the model is shown by an asymptotic sub-additivity method and factorization of correlation functions is proved almost everywhere. The exact solution for the model is provided in the thermodynamical limit by nding converging upper and lower bounds for the system's pressure, and the solution is used to prove an analytic result regarding the number of possible equilibrium states of a two-population system. The work stresses the importance of linking regimes predicted by the model to real phenomena, and to this end it proposes two possible procedures to estimate the model's parameters starting from micro-level data. These are applied to three case studies based on census type data: though these studies are found to be ultimately inconclusive on an empirical level, considerations are drawn that encourage further refinements of the chosen modelling approach, to be considered in future work.

Interaction between APOE4 genotype and environmental risk factors in Alzheimer's disease

Alzheimer's disease (AD) is probably caused by both genetic and environmental risk factors. The major genetic risk factor is the E4 variant of apolipoprotein E gene called apoE4. Several risk factors for developing AD have been identified including lifestyle, such as dietary habits. The mechanisms behind the AD pathogenesis and the onset of cognitive decline in the AD brain are presently unknown. In this study we wanted to characterize the effects of the interaction between environmental risk factors and apoE genotype on neurodegeneration processes, with particular focus on behavioural studies and neurodegenerative processes at molecular level. Towards this aim, we used 6 months-old apoE4 and apoE3 Target Replacement (TR) mice fed on different diets (high intake of cholesterol and high intake of carbohydrates). These mice were evaluated for learning and memory deficits in spatial reference (Morris Water Maze (MWM)) and contextual learning (Passive Avoidance) tasks, which involve the hippocampus and the amygdala, respectively. From these behavioural studies we found that the initial cognitive impairments manifested as a retention deficit in apoE4 mice fed on high carbohydrate diet. Thus, the genetic risk factor apoE4 genotype associated with a high carbohydrate diet seems to affect cognitive functions in young mice, corroborating the theory that the combination of genetic and environmental risk factors greatly increases the risk of developing AD and leads to an earlier onset of cognitive deficits. The cellular and molecular bases of the cognitive decline in AD are largely unknown. In order to determine the molecular changes for the onset of the early cognitive impairment observed in the behavioural studies, we performed molecular studies, with particular focus on synaptic integrity and Tau phosphorylation. The most relevant finding of our molecular studies showed a significant decrease of Brain-derived Neurotrophic Factor (BDNF) in apoE4 mice fed on high carbohydrate diet. Our results may suggest that BDNF decrease found in apoE4 HS mice could be involved in the earliest impairment in long-term reference memory observed in behavioural studies. The second aim of this thesis was to study possible involvement of leptin in AD. There is growing evidence that leptin has neuroprotective properties in the Central Nervous System (CNS). Recent evidence has shown that leptin and its receptors are widespread in the CNS and may provide neuronal survival signals. However, there are still numerous questions, regarding the molecular mechanism by which leptin acts, that remain unanswered. Thus, given to the importance of the involvement of leptin in AD, we wanted to clarify the function of leptin in the pathogenesis of AD and to investigate if apoE genotype affect leptin levels through studies in vitro, in mice and in human. Our findings suggest that apoE4 TR mice showed an increase of leptin in the brain. Leptin levels are also increased in the cerebral spinal fluid of AD patients and apoE4 carriers with AD have higher levels of leptin than apoE3 carriers. Moreover, leptin seems to be expressed by reactive glial cells in AD brains. In vitro, ApoE4 together with Amyloid beta increases leptin production by microglia and astrocytes. Taken together, all these findings suggest that leptin replacement might not be a good strategy for AD therapy. Our results show that high leptin levels were found in AD brains. These findings suggest that, as high leptin levels do not promote satiety in obese individuals, it might be possible that they do not promote neuroprotection in AD patients. Therefore, we hypothesized that AD brain could suffer from leptin resistance. Further studies will be critical to determine whether or not the central leptin resistance in SNC could affect its potential neuroprotective effects.

The role of the NG2 proteoglycan in migration and characterization of the interaction with the intracellular binding partner, Syntenin

NG2 is a transmembrane proteoglycan with two N-terminal LNS domains and a C-terminal PDZ-binding motif. It is expressed in the developing and adult CNS by oligodendroglial precursor cells and subpopulations of perisynaptic glia and elsewhere by many immature cell types. In order to elucidate the functions of the protein and the heterogenous cell population which expresses it, we undertook to identify and characterise interaction partners of the molecule. The presence of the C-terminal PDZ recognition site in NG2 suggested PDZ-domain proteins as intracellular binding partners. In this work, interaction between the PDZ protein Syntenin and NG2 has been characterised. Syntenin is known to be involved in plasma membrane dynamics, metastasis and adhesion. Syntenin may thus link NG2 to the cytoskeleton, mediating migration of developing oligodendrocytes to axonal tracts prior to myelination, as well as process movement of NG2+ perisynaptic glia. NG2 is involved in cell spreading and polyclonal antibodies against NG2 inhibit the migration of immature glia and cell lines expressing the molecule. In this work we have characterised the segments of the extracellular portion of NG2 that are involved in migration. We found that the extracellular region immediately preceding the transmembrane segment is most important for cell motility. As part of this thesis, biochemical approaches to identify a trans-binding ligand interacting with the extracellular part of NG2 was also explored.

Extracellular glutamate-GABA balance in the developing neocortex

It has been shown in the study that glutamate transporters (EAAT) are capable to modulate GABA transports (GAT). Here we also report that DL-TBOA, a non-transportable glutamate uptake blocker, eliminates GAT-mediated GABA release, while D-aspartate, an EAAT substrate, does not block the latter. The strength or even the operating mode of GABA uptake/release could be influenced by the work of EAATs. Considering the interaction between EAATs and GATs we can conclude that ambient glutamate and GABA levels are mutually dependent. The EAAT-GAT crosstalk observed in this work is mediated by EAAT1 and GAT-2/3. Since both transporters are Na+ dependent and mainly glial, next we investigated the role of [Na+]i in astrocytic-mediated glutamate uptake. We tested whether [Na+]i changes affect paired-pulse plasticity of STCs recorded from cortical layer 2/3 astrocytes. We report that an elevation of [Na+]i induced either by using a high [Na+]i intrapipette solution or by application of GABA slows STCs kinetics and decrease paired-pulse facilitation (PPF) of STCs at short inter-stimulus intervals. Moreover, GAT inhibitors decrease PPF of STCs under control conditions, suggesting that endogenous GABA operating via GATs influences EAAT-mediated transport

NETWORK TOPOLOGY IN HUMAN PROTEIN INTERACTION DATA PREDICTS FUNCTIONAL ASSOCIATION

High-throughput assays, such as yeast two-hybrid system, have generated a huge amount of protein-protein interaction (PPI) data in the past decade. This tremendously increases the need for developing reliable methods to systematically and automatically suggest protein functions and relationships between them. With the available PPI data, it is now possible to study the functions and relationships in the context of a large-scale network. To data, several network-based schemes have been provided to effectively annotate protein functions on a large scale. However, due to those inherent noises in high-throughput data generation, new methods and algorithms should be developed to increase the reliability of functional annotations. Previous work in a yeast PPI network (Samanta and Liang, 2003) has shown that the local connection topology, particularly for two proteins sharing an unusually large number of neighbors, can predict functional associations between proteins, and hence suggest their functions. One advantage of the work is that their algorithm is not sensitive to noises (false positives) in high-throughput PPI data. In this study, we improved their prediction scheme by developing a new algorithm and new methods which we applied on a human PPI network to make a genome-wide functional inference. We used the new algorithm to measure and reduce the influence of hub proteins on detecting functionally associated proteins. We used the annotations of the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) as independent and unbiased benchmarks to evaluate our algorithms and methods within the human PPI network. We showed that, compared with the previous work from Samanta and Liang, our algorithm and methods developed in this study improved the overall quality of functional inferences for human proteins. By applying the algorithms to the human PPI network, we obtained 4,233 significant functional associations among 1,754 proteins. Further comparisons of their KEGG and GO annotations allowed us to assign 466 KEGG pathway annotations to 274 proteins and 123 GO annotations to 114 proteins with estimated false discovery rates of <21% for KEGG and <30% for GO. We clustered 1,729 proteins by their functional associations and made pathway analysis to identify several subclusters that are highly enriched in certain signaling pathways. Particularly, we performed a detailed analysis on a subcluster enriched in the transforming growth factor β signaling pathway (P<10-50) which is important in cell proliferation and tumorigenesis. Analysis of another four subclusters also suggested potential new players in six signaling pathways worthy of further experimental investigations. Our study gives clear insight into the common neighbor-based prediction scheme and provides a reliable method for large-scale functional annotations in this post-genomic era.

Glutamate and nitric oxide as regulatory transmitters in developing rabbit retina

Glutamate is the major excitatory neurotransmitter in the retina and serves as the synaptic messenger for the three classes of neurons which constitute the vertical pathway--the photoreceptors, bipolar cells and ganglion cells. In addition, the glutamate system has been localized morphologically, pharmacologically as well as molecularly during the first postnatal week of development before synaptogenesis occurs. The role which glutamate plays in the maturing visual system is complex but ranges from mediating developmental neurotoxicity to inducing neurite outgrowth.^ Nitric oxide/cGMP is a novel intercellular messenger which is thought to act in concert with the glutamate system in regulating a variety of cellular processes in the brain as well as retina, most notably neurotoxicity. Several developmental activities including programmed cell death, synapse elimination and synaptic reorganization are possible functions of cellular regulation modulated by nitric oxide as well as glutamate.^ The purpose of this thesis is to (1) biochemically characterize the endogenous pools of glutamate and determine what fraction exists extracellularly; (2) examine the morphological expression of NO producing cells in developing retina; (3) test the functional coupling of the NMDA subtype of glutamate receptor to the NO system by examining neurotoxicity which has roles in both the maturing and adult retina.^ Biochemical sampling of perfusates collected from the photoreceptor surface of ex vivo retina demonstrated that although the total pool of glutamate present at birth is relatively modest, a high percentage resides in extracellular pools. As a result, immature neurons without significant synaptic connections survive and develop in a highly glutamatergic environment which has been shown to be toxic in the adult retina.^ The interaction of the glutamate system with the NO system has been postulated to regulate neuronal survival. We therefore examined the developmental expression of the enzyme responsible for producing NO, nitric oxide synthase (NOS), using an antibody to the constitutive form of NOS found in the brain. The neurons thought to produce the majority of NO in the adult retina, a subpopulation of widefield amacrine cells, were not immunoreactive until the end of the second postnatal week. However, a unique developmental expression was observed in the ganglion cell layer and developing outer nuclear layer of the retina during the first postnatal week. We postulate NO producing neurons may not be present in a mature configuration therefore permitting neuronal survival in a highly glutamatergic microenvironment and allowing NO to play a development-specific role at this time.^ The next set of experiments constituted a functional test of the hypothesis that the absence of the prototypic NO producing cells in developing retina protects immature neurons against glutamate toxicity. An explant culture system developed in order to examine cellular responses of immature and adult neurons to glutamate toxicity showed that immature neurons were affected by NMDA but were less responsive to NMDA and NO mediated toxicity. In contrast, adult explants exhibited significant NMDA toxicity which was attenuated by NMDA antagonists, 2-amino-5-phosphonovaleric acid (APV), dextromethorphan (Dex) and N$\rm\sp{G}$-D-methyl arginine (metARG). These results indicated that pan-retinal neurotoxicity via the NMDA receptor and/or NO activation occurred in the adult retina but was not significant in the neonate. (Abstract shortened by UMI.) ^

The Ste20-like kinase misshapen functions together with Bicaudal-D and dynein in driving nuclear migration in the developing drosophila eye.

Nuclear translocation, driven by the motility apparatus consisting of the cytoplasmic dynein motor and microtubules, is essential for cell migration during embryonic development. Bicaudal-D (Bic-D), an evolutionarily conserved dynein-interacting protein, is required for developmental control of nuclear migration in Drosophila. Nothing is known about the signaling events that coordinate the function of Bic-D and dynein during development. Here, we show that Misshapen (Msn), the fly homolog of the vertebrate Nck-interacting kinase is a component of a novel signaling pathway that regulates photoreceptor (R-cell) nuclear migration in the developing Drosophila compound eye. Msn, like Bic-D, is required for the apical migration of differentiating R-cell precursor nuclei. msn displays strong genetic interaction with Bic-D. Biochemical studies demonstrate that Msn increases the phosphorylation of Bic-D, which appears to be necessary for the apical accumulation of both Bic-D and dynein in developing R-cell precursor cells. We propose that Msn functions together with Bic-D to regulate the apical localization of dynein in generating directed nuclear migration within differentiating R-cell precursor cells.

A health disparities perspective on developing emerging infectious disease preparedness

A systematic review of the literature yielded 10 articles that explored the interaction between race/ethnicity, citizenship, socioeconomic status, and health literacy domains with respect to preparedness agenda development. Current emerging infectious disease (EID) preparedness plans do not adequately address the needs of vulnerable populations for the events before, during, and after an epidemic. Central to the disadvantage of most vulnerable populations are various health disparity domains that persist as barriers for individuals and communities alike to engage in preparedness efforts. Seven out of the ten articles discussed the importance of including health disparity domains in preparedness policy. Two proposed frameworks for an emerging infectious disease framework that considers health disparities are presented in this study. ^ Framework 1 is beneficial for the evaluation phase after a disaster has struck and preparedness efforts have been initiated. It considers several existing disparities and remediation strategies at the individual, community, and system levels to reach adequate restructuring of preparedness aims. Framework 2 serves as a "how to" carry out preparedness during a disaster event. It is a revision of a framework proposed by Blumenshine et al. (2008) and explores those characteristics central to pandemic preparedness plan development/deployment. Although two frameworks were devised, no one framework will adequately address the needs of vulnerable populations during an epidemic. However, the two frameworks propose to demonstrate the inclusion of important health disparity domains in preparedness plan development. ^ The National Consensus Panel for Emergency Preparedness and Cultural Diversity has released guidelines that are considered the leading strategies necessary to reorient preparedness infrastructure. In order for vulnerable populations to benefit from ample protection during a disaster, inclusion of health disparity domains in the development phases of preparedness must occur prior to full deployment in communities. Although "promising practices" and other methods at the frontier of exploring these multidimensional constraints has entered the research arena, new studies on adequate preparedness merit further investigation and support.^

THE NATURAL AND ORTHOGONAL INTERACTION (NOIA) MODELS FOR QUANTITATIVE TRAITS (QTs) AND COMPLEX DISEASES

My dissertation focuses on developing methods for gene-gene/environment interactions and imprinting effect detections for human complex diseases and quantitative traits. It includes three sections: (1) generalizing the Natural and Orthogonal interaction (NOIA) model for the coding technique originally developed for gene-gene (GxG) interaction and also to reduced models; (2) developing a novel statistical approach that allows for modeling gene-environment (GxE) interactions influencing disease risk, and (3) developing a statistical approach for modeling genetic variants displaying parent-of-origin effects (POEs), such as imprinting. In the past decade, genetic researchers have identified a large number of causal variants for human genetic diseases and traits by single-locus analysis, and interaction has now become a hot topic in the effort to search for the complex network between multiple genes or environmental exposures contributing to the outcome. Epistasis, also known as gene-gene interaction is the departure from additive genetic effects from several genes to a trait, which means that the same alleles of one gene could display different genetic effects under different genetic backgrounds. In this study, we propose to implement the NOIA model for association studies along with interaction for human complex traits and diseases. We compare the performance of the new statistical models we developed and the usual functional model by both simulation study and real data analysis. Both simulation and real data analysis revealed higher power of the NOIA GxG interaction model for detecting both main genetic effects and interaction effects. Through application on a melanoma dataset, we confirmed the previously identified significant regions for melanoma risk at 15q13.1, 16q24.3 and 9p21.3. We also identified potential interactions with these significant regions that contribute to melanoma risk. Based on the NOIA model, we developed a novel statistical approach that allows us to model effects from a genetic factor and binary environmental exposure that are jointly influencing disease risk. Both simulation and real data analyses revealed higher power of the NOIA model for detecting both main genetic effects and interaction effects for both quantitative and binary traits. We also found that estimates of the parameters from logistic regression for binary traits are no longer statistically uncorrelated under the alternative model when there is an association. Applying our novel approach to a lung cancer dataset, we confirmed four SNPs in 5p15 and 15q25 region to be significantly associated with lung cancer risk in Caucasians population: rs2736100, rs402710, rs16969968 and rs8034191. We also validated that rs16969968 and rs8034191 in 15q25 region are significantly interacting with smoking in Caucasian population. Our approach identified the potential interactions of SNP rs2256543 in 6p21 with smoking on contributing to lung cancer risk. Genetic imprinting is the most well-known cause for parent-of-origin effect (POE) whereby a gene is differentially expressed depending on the parental origin of the same alleles. Genetic imprinting affects several human disorders, including diabetes, breast cancer, alcoholism, and obesity. This phenomenon has been shown to be important for normal embryonic development in mammals. Traditional association approaches ignore this important genetic phenomenon. In this study, we propose a NOIA framework for a single locus association study that estimates both main allelic effects and POEs. We develop statistical (Stat-POE) and functional (Func-POE) models, and demonstrate conditions for orthogonality of the Stat-POE model. We conducted simulations for both quantitative and qualitative traits to evaluate the performance of the statistical and functional models with different levels of POEs. Our results showed that the newly proposed Stat-POE model, which ensures orthogonality of variance components if Hardy-Weinberg Equilibrium (HWE) or equal minor and major allele frequencies is satisfied, had greater power for detecting the main allelic additive effect than a Func-POE model, which codes according to allelic substitutions, for both quantitative and qualitative traits. The power for detecting the POE was the same for the Stat-POE and Func-POE models under HWE for quantitative traits.

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.

THE MOLECULAR INTERACTION BETWEEN TYPE II DIABETES AND ALZHEIMER’S DISEASE THROUGH CROSS-SEEDING OF PROTEIN MISFOLDING

With the population of the world aging, the prominence of diseases such as Type II Diabetes (T2D) and Alzheimer’s disease (AD) are on the rise. In addition, patients with T2D have an increased risk of developing AD compared to age-matched individuals, and the number of AD patients with T2D is higher than among aged-matched non-AD patients. AD is a chronic and progressive dementia characterized by amyloid-beta (Aβ) plaques, neurofibrillary tangles (NFTs), neuronal loss, brain inflammation, and cognitive impairment. T2D involves the dysfunctional use of pancreatic insulin by the body resulting in insulin resistance, hyperglycemia, hyperinsulinemia, pancreatic beta cell (β-cell) death, and other complications. T2D and AD are considered protein misfolding disorders (PMDs). PMDs are characterized by the presence of misfolded protein aggregates, such as in T2D pancreas (islet amyloid polypeptide - IAPP) and in AD brain (amyloid– Aβ) of affected individuals. The misfolding and accumulation of these proteins follows a seeding-nucleation model where misfolded soluble oligomers act as nuclei to propagate misfolding by recruiting other native proteins. Cross-seeding occurs when oligomers composed by one protein seed the aggregation of a different protein. Our hypothesis is that the pathological interactions between T2D and AD may in part occur through cross-seeding of protein misfolding. To test this hypothesis, we examined how each respective aggregate (Aβ or IAPP) affects the disparate disease pathology through in vitro and in vivo studies. Assaying Aβ aggregates influence on T2D pathology, IAPP+/+/APPSwe+/- double transgenic (DTg) mice exhibited exacerbated T2D-like pathology as seen in elevated hyperglycemia compared to controls; in addition, IAPP levels in the pancreas are highest compared to controls. Moreover, IAPP+/+/APPSwe+/- animals demonstrate abundant plaque formation and greater plaque density in cortical and hippocampal areas in comparison to controls. Indeed, IAPP+/+/APPSwe+/- exhibit a colocalization of both misfolded proteins in cerebral plaques suggesting IAPP may directly interact with Aβ and aggravate AD pathology. In conclusion, these studies suggest that cross-seeding between IAPP and Aβ may occur, and that these protein aggregates exacerbate and accelerate disease pathology, respectively. Further mechanistic studies are necessary to determine how these two proteins interact and aggravate both pancreatic and brain pathologies.

Interaction of powers in the Philippine presidential system

The executive - legislative relations in the Philippines have been described in two contrasting stories, namely the "strong president" story, and the "strong congress" story. This paper tries to consolidate the existing arguments and propose a new perspective focusing on the "compromise exchange" between the president and the congress across the different policy areas. It considers that the policy outcome is not brought by unilateral power of the president or the congress, but formed as the product of such an exchange. Interaction of powers and their complementary function are addressed. Furthermore, aside from the constitutional power, the weak party discipline is pointed out as a key factor in making the exchange possible.

Developing Accessible Mobile Phone Applications: The Case of a Contact Manager and Real Time Text Applications

Mobile phones are becoming increasingly popular and are already the first access technology to information and communication. However, people with disabilities have to face a lot of barriers when using this kind of technology. This paper presents an Accessible Contact Manager and a Real Time Text application, designed to be used by all users with disabilities. Both applications are focused to improve accessibility of mobile phones.

Developing front-end Web 2.0 technologies to access services, content and things in the future Internet

The future Internet is expected to be composed of a mesh of interoperable web services accessible from all over the web. This approach has not yet caught on since global user?service interaction is still an open issue. This paper states one vision with regard to next-generation front-end Web 2.0 technology that will enable integrated access to services, contents and things in the future Internet. In this paper, we illustrate how front-ends that wrap traditional services and resources can be tailored to the needs of end users, converting end users into prosumers (creators and consumers of service-based applications). To do this, we propose an architecture that end users without programming skills can use to create front-ends, consult catalogues of resources tailored to their needs, easily integrate and coordinate front-ends and create composite applications to orchestrate services in their back-end. The paper includes a case study illustrating that current user-centred web development tools are at a very early stage of evolution. We provide statistical data on how the proposed architecture improves these tools. This paper is based on research conducted by the Service Front End (SFE) Open Alliance initiative.

Physical characterization of laser interaction and shock generation in laser shock processing: coupled theoretical-experimental analysis

Laser Shock Processing is developing as a key technology for the improvement of surface mechanical and corrosion resistance properties of metals due to its ability to introduce intense compressive residual stresses fields into high elastic limit materials by means of an intense laser driven shock wave generated by laser with intensities exceeding the 109 W/cm2 threshold, pulse energies in the range of 1 Joule and interaction times in the range of several ns. However, because of the relatively difficult-to-describe physics of shock wave formation in plasma following laser-matter interaction in solid state, only limited knowledge is available in the way of full comprehension and predictive assessment of the characteristic physical processes and material transformations with a specific consideration of real material properties. In the present paper, an account of the physical issues dominating the development of LSP processes from a moderately high intensity laser-matter interaction point of view is presented along with the theoretical and computational methods developed by the authors for their predictive assessment and new experimental contrast results obtained at laboratory scale.