The cumulative effect of core lifestyle behaviours on the prevalence of hypertension and dyslipidemia

Background: Most cardiovascular disease (CVD) occurs in the presence of traditional risk factors, including hypertension and dyslipidemia, and these in turn are influenced by behavioural factors such as diet and lifestyle. Previous research has identified a group at low risk of CVD based on a cluster of inter-related factors: body mass index (BMI) < 25 Kg/m2, moderate exercise, alcohol intake, non-smoking and a favourable dietary pattern. The objective of this study was to determine whether these factors are associated with a reduced prevalence of hypertension and dyslipidemia in an Irish adult population. Methods: The study was a cross-sectional survey of 1018 men and women sampled from 17 general practices. Participants completed health, lifestyle and food frequency questionnaires and provided fasting blood samples for analysis of glucose and insulin. We defined a low risk group based on the following protective factors: BMI <25 kg/m2; waist-hip ratio (WHR) <0.85 for women and <0.90 for men; never smoking status; participants with medium to high levels of physical activity; light alcohol consumption (3.5–7 units of alcohol/week) and a "prudent" diet. Dietary patterns were assessed by cluster analysis. Results: We found strong significant inverse associations between the number of protective factors and systolic blood pressure, diastolic blood pressure and dyslipidemia. The prevalence odds ratio of hypertension in persons with 1, 2, 3, ≥ 4 protective factors relative to those with none, were 1.0, 0.76, 0.68 and 0.34 (trend p < 0.01). The prevalence odds ratio of dyslipidemia in persons with 1, 2, 3, ≥ 4 protective factors relative to those with none were 0.83, 0.98, 0.49 and 0.24 (trend p = 0.001). Conclusion: Our findings of a strong inverse association between low risk behaviours and two of the traditional risk factors for CVD highlight the importance of 'the causes of the causes' and the potential for behaviour modification in CVD prevention at a population level.

Bifidobacterium longum 1714 as a translational psychobiotic: modulation of stress, electrophysiology and neurocognition in healthy volunteers

The emerging concept of psychobiotics—live microorganisms with a potential mental health benefit—represents a novel approach for the management of stress-related conditions. The majority of studies have focused on animal models. Recent preclinical studies have identified the B. longum 1714 strain as a putative psychobiotic with an impact on stress-related behaviors, physiology and cognitive performance. Whether such preclinical effects could be translated to healthy human volunteers remains unknown. We tested whether psychobiotic consumption could affect the stress response, cognition and brain activity patterns. In a within-participants design, healthy volunteers (N=22) completed cognitive assessments, resting electroencephalography and were exposed to a socially evaluated cold pressor test at baseline, post-placebo and post-psychobiotic. Increases in cortisol output and subjective anxiety in response to the socially evaluated cold pressor test were attenuated. Furthermore, daily reported stress was reduced by psychobiotic consumption. We also observed subtle improvements in hippocampus-dependent visuospatial memory performance, as well as enhanced frontal midline electroencephalographic mobility following psychobiotic consumption. These subtle but clear benefits are in line with the predicted impact from preclinical screening platforms. Our results indicate that consumption of B. longum 1714 is associated with reduced stress and improved memory. Further studies are warranted to evaluate the benefits of this putative psychobiotic in relevant stress-related conditions and to unravel the mechanisms underlying such effects.

The emerging strategic partnership between the European Union and the African Union

Many among the emerging generation of political elites in Africa see the role the European Union (EU) plays in the maintenance of an unprecedented period of peace in Western Europe as an inspirational example of the manner in which the African Union (AU) can contribute to peace and stability in Africa. This doctoral thesis examines security cooperation between the EU and the AU, with a particular focus on the nature and substance of that cooperation. It suggests that despite the establishment of various EU–AU institutions and ties with a role in security policy and cooperation, such security cooperation is limited in substance. This study argues that EU–AU security cooperation is especially constrained by the emergence of alternative partners, most notably China, and by failures of implementation and follow-through. Two case studies, the first dealing with EU–AU cooperation in peacekeeping, and the second addressing the silent water crisis along with the link between water and security, have been analysed in detail to determine the effectiveness and sustainability of the EU–AU partnership. A number of important lessons for regionalism, interregionalism and multilateralism are drawn from the bond between the EU and the AU. This doctoral thesis will prove that, despite an emphasis on the problematic term ‘strategic’ by both EU and AU policymakers, EU–AU cooperation is limited and somewhat lacking in strategic direction. The cooperation between the EU and the AU focuses mainly on EU financial support for AU peacekeeping and specific projects in Africa (e.g. in the water sector), as well as on a limited political dialogue. Nonetheless, the EU–AU link represents the most comprehensive partnership the AU has with any non-African actor. This study will furthermore demonstrate that the United Nations (UN) is an indispensable third-party to their relationship and it is therefore more appropriate to speak of the AU–EU–UN nexus. This doctoral thesis concludes that the AU–EU–UN nexus is an important example of interregionalism in a global context and that such interregionalism is an important emerging part of global governance.

Non-equilibrium induction of tin in germanium: towards direct bandgap Ge1-xSnx nanowires

The development of non-equilibrium group IV nanoscale alloys is critical to achieving new functionalities, such as the formation of a direct bandgap in a conventional indirect bandgap elemental semiconductor. Here, we describe the fabrication of uniform diameter, direct bandgap Ge1-xSnx alloy nanowires, with a Sn incorporation up to 9.2[thinsp]at.%, far in excess of the equilibrium solubility of Sn in bulk Ge, through a conventional catalytic bottom-up growth paradigm using noble metal and metal alloy catalysts. Metal alloy catalysts permitted a greater inclusion of Sn in Ge nanowires compared with conventional Au catalysts, when used during vapour-liquid-solid growth. The addition of an annealing step close to the Ge-Sn eutectic temperature (230[thinsp][deg]C) during cool-down, further facilitated the excessive dissolution of Sn in the nanowires. Sn was distributed throughout the Ge nanowire lattice with no metallic Sn segregation or precipitation at the surface or within the bulk of the nanowires. The non-equilibrium incorporation of Sn into the Ge nanowires can be understood in terms of a kinetic trapping model for impurity incorporation at the triple-phase boundary during growth.

Ultrahigh Purcell factor in low-threshold nanolaser based on asymmetric hybrid plasmonic cavity

A low-threshold nanolaser with all three dimensions at the subwavelength scale is proposed and investigated. The nanolaser is constructed based on an asymmetric hybrid plasmonic F-P cavity with Ag-coated end facets. Lasing characteristics are calculated using finite element method at the wavelength of 1550 nm. The results show that owing to the low modal loss, large modal confinement factor of the asymmetric plasmonic cavity structure, in conjunction with the high reflectivity of the Ag reflectors, a minimum threshold gain of 240 cm−1 is predicted. Furthermore, the Purcell factor as large as 2518 is obtained with optimized structure parameters to enhance rates of spontaneous and stimulated emission.

Comparative survey of the relative impact of mRNA features on local ribosome profiling read density

Ribosome profiling (Ribo-seq), a promising technology for exploring ribosome decoding rates, is characterized by the presence of infrequent high peaks in ribosome footprint density and by long alignment gaps. Here, to reduce the impact of data heterogeneity we introduce a simple normalization method, Ribo-seq Unit Step Transformation (RUST). RUST is robust and outperforms other normalization techniques in the presence of heterogeneous noise. We illustrate how RUST can be used for identifying mRNA sequence features that affect ribosome footprint densities globally. We show that a few parameters extracted with RUST are sufficient for predicting experimental densities with high accuracy. Importantly the application of RUST to 30 publicly available Ribo-seq data sets revealed a substantial variation in sequence determinants of ribosome footprint frequencies, questioning the reliability of Ribo-seq as an accurate representation of local ribosome densities without prior quality control. This emphasizes our incomplete understanding of how protocol parameters affect ribosome footprint densities.

A novel non-peptidic agonist of the ghrelin receptor with orexigenic activity in vivo

Loss of appetite in the medically ill and ageing populations is a major health problem and a significant symptom in cachexia syndromes, which is the loss of muscle and fat mass. Ghrelin is a gut-derived hormone which can stimulate appetite. Herein we describe a novel, simple, non-peptidic, 2-pyridone which acts as a selective agonist for the ghrelin receptor (GHS-R1a). The small 2-pyridone demonstrated clear agonistic activity in both transfected human cells and mouse hypothalamic cells with endogenous GHS-R1a receptor expression. In vivo tests with the hit compound showed significant increased food intake following peripheral administration, which highlights the potent orexigenic effect of this novel GHS-R1a receptor ligand.

The right to humanitarian assistance in natural and human-made disasters: progress and challenges for an emerging international legal framework

Natural and human-made disasters cause on average 120,000 deaths and over US$140 billion in damage to property and infrastructure every year, with national, regional and international actors consistently responding to the humanitarian imperative to alleviate suffering wherever it may be found. Despite various attempts to codify international disaster laws since the 1920s, a right to humanitarian assistance remains contested, reflecting concerns regarding the relative importance of state sovereignty vis-à-vis individual rights under international law. However, the evolving acquis humanitaire of binding and non-binding normative standards for responses to humanitarian crises highlights the increasing focus on rights and responsibilities applicable in disasters; although the International Law Commission has also noted the difficulty of identifying lex lata and lex ferenda regarding the protection of persons in the event of disasters due to the “amorphous state of the law relating to international disaster response.” Therefore, using the conceptual framework of transnational legal process, this thesis analyses the evolving normative frameworks and standards for rights-holders and duty-bearers in disasters. Determining the process whereby rights are created and evolve, and their potential internalisation into domestic law and policy, provides a powerful analytical framework for examining the progress and challenges of developing accountable responses to major disasters.

A distributed architecture for the monitoring and analysis of time series data

It is estimated that the quantity of digital data being transferred, processed or stored at any one time currently stands at 4.4 zettabytes (4.4 × 2 70 bytes) and this figure is expected to have grown by a factor of 10 to 44 zettabytes by 2020. Exploiting this data is, and will remain, a significant challenge. At present there is the capacity to store 33% of digital data in existence at any one time; by 2020 this capacity is expected to fall to 15%. These statistics suggest that, in the era of Big Data, the identification of important, exploitable data will need to be done in a timely manner. Systems for the monitoring and analysis of data, e.g. stock markets, smart grids and sensor networks, can be made up of massive numbers of individual components. These components can be geographically distributed yet may interact with one another via continuous data streams, which in turn may affect the state of the sender or receiver. This introduces a dynamic causality, which further complicates the overall system by introducing a temporal constraint that is difficult to accommodate. Practical approaches to realising the system described above have led to a multiplicity of analysis techniques, each of which concentrates on specific characteristics of the system being analysed and treats these characteristics as the dominant component affecting the results being sought. The multiplicity of analysis techniques introduces another layer of heterogeneity, that is heterogeneity of approach, partitioning the field to the extent that results from one domain are difficult to exploit in another. The question is asked can a generic solution for the monitoring and analysis of data that: accommodates temporal constraints; bridges the gap between expert knowledge and raw data; and enables data to be effectively interpreted and exploited in a transparent manner, be identified? The approach proposed in this dissertation acquires, analyses and processes data in a manner that is free of the constraints of any particular analysis technique, while at the same time facilitating these techniques where appropriate. Constraints are applied by defining a workflow based on the production, interpretation and consumption of data. This supports the application of different analysis techniques on the same raw data without the danger of incorporating hidden bias that may exist. To illustrate and to realise this approach a software platform has been created that allows for the transparent analysis of data, combining analysis techniques with a maintainable record of provenance so that independent third party analysis can be applied to verify any derived conclusions. In order to demonstrate these concepts, a complex real world example involving the near real-time capturing and analysis of neurophysiological data from a neonatal intensive care unit (NICU) was chosen. A system was engineered to gather raw data, analyse that data using different analysis techniques, uncover information, incorporate that information into the system and curate the evolution of the discovered knowledge. The application domain was chosen for three reasons: firstly because it is complex and no comprehensive solution exists; secondly, it requires tight interaction with domain experts, thus requiring the handling of subjective knowledge and inference; and thirdly, given the dearth of neurophysiologists, there is a real world need to provide a solution for this domain