The regulation of AMP-activated protein kinase by vascular endothelial growth factor

The function of the vascular endothelium is to maintain vascular homeostasis, by providing an anti-thrombotic, anti-inflammatory and vasodilatory interface between circulating blood and the vessel wall, meanwhile facilitating the selective passage of blood components such as signaling molecules and immune cells. Dysfunction of the vascular endothelium is implicated in a number of pathological states including atherosclerosis and hypertension, and is thought to precede atherogenesis by a number of years. Vascular endothelial growth factor A (VEGF) is a crucial mitogenic signaling molecule, not only essential for embryonic development, but also in the adult for regulating both physiological and pathological angiogenesis. Previous studies by our laboratory have demonstrated that VEGF-A activates AMP-activated protein kinase (AMPK), the downstream component of a signaling cascade important in the regulation of whole body and cellular energy status. Furthermore, studies in our laboratory have indicated that AMPK is essential for VEGF-A-stimulated vascular endothelial cell proliferation. AMPK activation typically stimulates anabolic processes and inhibits catabolic processes including cell proliferation, with the ultimate aim of redressing energy imbalance, and as such is an attractive therapeutic target for the treatment of obesity, metabolic syndromes, and type 2 diabetes. Metabolic diseases are associated with adverse cardiovascular outcomes and AMPK activation is reported to have beneficial effects on the vascular endothelium. The mechanism by which VEGF-A stimulates AMPK, and the functional consequences of VEGF-A-stimulated AMPK activation remain uncertain. The present study therefore aimed to identify the specific mechanism(s) by which VEGF-A regulates the activity of AMPK in endothelial cells, and how this might differ from the activation of AMPK by other agents. Furthermore, the role of AMPK in the pro-proliferative actions of VEGF-A was further examined. Human aortic and umbilical vein endothelial cells were therefore used as a model system to characterise the specific effect(s) of VEGF-A stimulation on AMPK activation. The present study reports that AMPK α1 containing AMPK complexes account for the vast majority of both basal and VEGF-A-stimulated AMPK activity. Furthermore, AMPK α1 is localized to the endoplasmic reticulum when sub-confluent, but translocated to the Golgi apparatus when cells are cultured to confluence. AMPK α2 appears to be associated with a structural cellular component, but neither α1 nor α2 complexes appear to translocate in response to VEGF-A stimulation. The present study confirms previous reports that when measured using the MTS cell proliferation assay, AMPK is required for VEGF-A-stimulated endothelial cell proliferation. However, parallel experiments measuring cell proliferation using the Real-Time Cell Analyzer xCELLigence system, do not agree with these previous reports, suggesting that AMPK may in fact be required for an aspect of mitochondrial metabolism which is enhanced by VEGF-A. Studies into the mitochondrial activity of endothelial cells have proved inconclusive at this time, but further studies into this are warranted. During previous studies in our laboratory, it was suggested that VEGF-A-stimulated AMPK activation may be mediated via the diacylglycerol (DAG)-sensitive transient receptor potential cation channel (TRPCs -3, -6 or -7) family of ion channels. The present study can neither confirm, nor exclude the expression of TRPCs in vascular endothelial cells, nor rule out their involvement in VEGF-A-stimulated AMPK activation; more specific investigative tools are required in order to characterise their involvement. Furthermore, nicotinic acid adenine dinucleotide phosphate (NAADP)-stimulated Ca2+ release from acidic intracellular organelles is not required for AMPK activation by VEGF-A. Despite what is known about the mechanisms by which AMPK is activated, far less is known concerning the downregulation of AMPK activity, as observed in human and animal models of metabolic disease. Phosphorylation of AMPK α1 Ser485 (α2 Ser491) has recently been characterised as a mechanism by which the activity of AMPK is negatively regulated. We report here for the first time that VEGF-A stimulates AMPK α1 Ser485 phosphorylation independently of the previously reported AMPK α1 Ser485 kinases Akt (protein kinase B) and ERK1/2 (extracellular signal-regulated kinase 1/2). Furthermore, inhibition of protein kinase C (PKC), the activity of which is reported to be elevated in metabolic disease, attenuates VEGF-A- and phorbol 12-myristate 13-acetate (PMA)-stimulated AMPK α1 Ser485 phosphorylation, and increases basal AMPK activity. In contrast to this, PKC activation reduces AMPK activity in human vascular endothelial cells. Attempts to identify the PKC isoform responsible for inhibiting AMPK activity suggest that it is one (or more) of the Ca2+-regulated DAG-sensitive isoforms of PKC, however cross regulation of PKC isoform expression has limited the present study. Furthermore, AMPK α1 Ser485 phosphorylation was inversely correlated with human muscle insulin sensitivity. As such, enhanced AMPK α1 Ser485 phosphorylation, potentially mediated by increased PKC activation may help explain some of the reduced AMPK activity observed in metabolic disease.

Application of mathematical models to mycotoxins children risk assessment: a case study of Portuguese children exposure to co-occurring mycotoxins in processed cereal-based foods

People, animals and the environment can be exposed to multiple chemicals at once from a variety of sources, but current risk assessment is usually carried out based on one chemical substance at a time. In human health risk assessment, ingestion of food is considered a major route of exposure to many contaminants, namely mycotoxins, a wide group of fungal secondary metabolites that are known to potentially cause toxicity and carcinogenic outcomes. Mycotoxins are commonly found in a variety of foods including those intended for consumption by infants and young children and have been found in processed cereal-based foods available in the Portuguese market. The use of mathematical models, including probabilistic approaches using Monte Carlo simulations, constitutes a prominent issue in human health risk assessment in general and in mycotoxins exposure assessment in particular. The present study aims to characterize, for the first time, the risk associated with the exposure of Portuguese children to single and multiple mycotoxins present in processed cereal-based foods (CBF). Portuguese children (0-3 years old) food consumption data (n=103) were collected using a 3 days food diary. Contamination data concerned the quantification of 12 mycotoxins (aflatoxins, ochratoxin A, fumonisins and trichothecenes) were evaluated in 20 CBF samples marketed in 2014 and 2015 in Lisbon; samples were analyzed by HPLC-FLD, LC-MS/MS and GC-MS. Daily exposure of children to mycotoxins was performed using deterministic and probabilistic approaches. Different strategies were used to treat the left censored data. For aflatoxins, as carcinogenic compounds, the margin of exposure (MoE) was calculated as a ratio of BMDL (benchmark dose lower confidence limit) to the aflatoxin exposure. The magnitude of the MoE gives an indication of the risk level. For the remaining mycotoxins, the output of exposure was compared to the dose reference values (TDI) in order to calculate the hazard quotients (ratio between exposure and a reference dose, HQ). For the cumulative risk assessment of multiple mycotoxins, the concentration addition (CA) concept was used. The combined margin of exposure (MoET) and the hazard index (HI) were calculated for aflatoxins and the remaining mycotoxins, respectively. 71% of CBF analyzed samples were contaminated with mycotoxins (with values below the legal limits) and approximately 56% of the studied children consumed CBF at least once in these 3 days. Preliminary results showed that children exposure to single mycotoxins present in CBF were below the TDI. Aflatoxins MoE and MoET revealed a reduced potential risk by exposure through consumption of CBF (with values around 10000 or more). HQ and HI values for the remaining mycotoxins were below 1. Children are a particularly vulnerable population group to food contaminants and the present results point out an urgent need to establish legal limits and control strategies regarding the presence of multiple mycotoxins in children foods in order to protect their health. The development of packaging materials with antifungal properties is a possible solution to control the growth of moulds and consequently to reduce mycotoxin production, contributing to guarantee the quality and safety of foods intended for children consumption.

Examining the growth and evolution of magnetic fields in clusters of galaxies: a numerical perspective

Magnetic fields are ubiquitous in galaxy cluster atmospheres and have a variety of astrophysical and cosmological consequences. Magnetic fields can contribute to the pressure support of clusters, affect thermal conduction, and modify the evolution of bubbles driven by active galactic nuclei. However, we currently do not fully understand the origin and evolution of these fields throughout cosmic time. Furthermore, we do not have a general understanding of the relationship between magnetic field strength and topology and other cluster properties, such as mass and X-ray luminosity. We can now begin to answer some of these questions using large-scale cosmological magnetohydrodynamic (MHD) simulations of the formation of galaxy clusters including the seeding and growth of magnetic fields. Using large-scale cosmological simulations with the FLASH code combined with a simplified model of the acceleration of cosmic rays responsible for the generation of radio halos, we find that the galaxy cluster frequency distribution and expected number counts of radio halos from upcoming low-frequency sur- veys are strongly dependent on the strength of magnetic fields. Thus, a more complete understanding of the origin and evolution of magnetic fields is necessary to understand and constrain models of diffuse synchrotron emission from clusters. One favored model for generating magnetic fields is through the amplification of weak seed fields in active galactic nuclei (AGN) accretion disks and their subsequent injection into cluster atmospheres via AGN-driven jets and bubbles. However, current large-scale cosmological simulations cannot directly include the physical processes associated with the accretion and feedback processes of AGN or the seeding and merging of the associated SMBHs. Thus, we must include these effects as subgrid models. In order to carefully study the growth of magnetic fields in clusters via AGN-driven outflows, we present a systematic study of SMBH and AGN subgrid models. Using dark-matter only cosmological simulations, we find that many important quantities, such as the relationship between SMBH mass and galactic bulge velocity dispersion and the merger rate of black holes, are highly sensitive to the subgrid model assumptions of SMBHs. In addition, using MHD calculations of an isolated cluster, we find that magnetic field strengths, extent, topology, and relationship to other gas quantities such as temperature and density are also highly dependent on the chosen model of accretion and feedback. We use these systematic studies of SMBHs and AGN inform and constrain our choice of subgrid models, and we use those results to outline a fully cosmological MHD simulation to study the injection and growth of magnetic fields in clusters of galaxies. This simulation will be the first to study the birth and evolution of magnetic fields using a fully closed accretion-feedback cycle, with as few assumptions as possible and a clearer understanding of the effects of the various parameter choices.

The third dimension: new developments in cell culture models for colorectal research

Cellular models are important tools in various research areas related to colorectal biology and associated diseases. Herein, we review the most widely used cell lines and the different techniques to grow them, either as cell monolayer, polarized two-dimensional epithelia on membrane filters, or as three-dimensional spheres in scaffoldfree or matrix-supported culture conditions. Moreover, recent developments, such as gut-on-chip devices or the ex vivo growth of biopsy-derived organoids, are also discussed. We provide an overview on the potential applications but also on the limitations for each of these techniques, while evaluating their contribution to provide more reliable cellular models for research, diagnostic testing, or pharmacological validation related to colon physiology and pathophysiology.

Hyperspectral sensing to detect the impact of herbicide drift on cotton growth and yield

Yield loss in crops is often associated with plant disease or external factors such as environment, water supply and nutrient availability. Improper agricultural practices can also introduce risks into the equation. Herbicide drift can be a combination of improper practices and environmental conditions which can create a potential yield loss. As traditional assessment of plant damage is often imprecise and time consuming, the ability of remote and proximal sensing techniques to monitor various bio-chemical alterations in the plant may offer a faster, non-destructive and reliable approach to predict yield loss caused by herbicide drift. This paper examines the prediction capabilities of partial least squares regression (PLS-R) models for estimating yield. Models were constructed with hyperspectral data of a cotton crop sprayed with three simulated doses of the phenoxy herbicide 2,4-D at three different growth stages. Fibre quality, photosynthesis, conductance, and two main hormones, indole acetic acid (IAA) and abscisic acid (ABA) were also analysed. Except for fibre quality and ABA, Spearman correlations have shown that these variables were highly affected by the chemical. Four PLS-R models for predicting yield were developed according to four timings of data collection: 2, 7, 14 and 28 days after the exposure (DAE). As indicated by the model performance, the analysis revealed that 7 DAE was the best time for data collection purposes (RMSEP = 2.6 and R2 = 0.88), followed by 28 DAE (RMSEP = 3.2 and R2 = 0.84). In summary, the results of this study show that it is possible to accurately predict yield after a simulated herbicide drift of 2,4-D on a cotton crop, through the analysis of hyperspectral data, thereby providing a reliable, effective and non-destructive alternative based on the internal response of the cotton leaves.

Tourism demand modelling and forecasting with artificial neural network models: the Mozambique case study

This study is aimed to model and forecast the tourism demand for Mozambique for the period from January 2004 to December 2013 using artificial neural networks models. The number of overnight stays in Hotels was used as representative of the tourism demand. A set of independent variables were experimented in the input of the model, namely: Consumer Price Index, Gross Domestic Product and Exchange Rates, of the outbound tourism markets, South Africa, United State of America, Mozambique, Portugal and the United Kingdom. The best model achieved has 6.5% for Mean Absolute Percentage Error and 0.696 for Pearson correlation coefficient. A model like this with high accuracy of forecast is important for the economic agents to know the future growth of this activity sector, as it is important for stakeholders to provide products, services and infrastructures and for the hotels establishments to adequate its level of capacity to the tourism demand.

Explaining economic growth in developed economies after 1980

We use the Aguion and Howitt (2009) theoretical model of endogenous economic growth to explain the declining economic growth in developed economies in the period 1981-2009. Aguion and Howitt theoretical framework combines Solownian and Schumpeterian elements in a single scenario, so that labor-augmenting technological progress and capital accumulation per efficiency unit of labor are both caused not only by exogenous changes in the investment rate but also by shocks to the degree of efficiency in the Research and Development (R&D) expenditure process. Empirical results revealed that per worker output growth rates and capital stock per efficiency unit of labor growth rates both have a common panel unit root. Since the panel cointegration tests and estimates revealed a statistical significant negative long-run relationship between per worker output growth rate and capital stock per efficiency unit of labor, the interpretation of the econometric results analized from the Aguion and Howitt ́s theoretical perspective is that labor-augmenting technological progress is endogenously falling over time mainly because of an exogenous deterioration of the environment conditions for the transformation of the investment rate and R&D expenditures in technological progress.

Coarse-graining and renormalisation group methods for the elucidation of the kinetics of complex nucleation and growth processes

We review our work on generalisations of the Becker-Doring model of cluster-formation as applied to nucleation theory, polymer growth kinetics, and the formation of upramolecular structures in colloidal chemistry. One valuable tool in analysing mathematical models of these systems has been the coarse-graining approximation which enables macroscopic models for observable quantities to be derived from microscopic ones. This permits assumptions about the detailed molecular mechanisms to be tested, and their influence on the large-scale kinetics of surfactant self-assembly to be elucidated. We also summarise our more recent results on Becker-Doring systems, notably demonstrating that cross-inhibition and autocatalysis can destabilise a uniform solution and lead to a competitive environment in which some species flourish at the expense of others, phenomena relevant in models of the origins of life.

Usefulness of the Hepatocyte Growth Factor as a Predictor of Mortality in Patients Hospitalized With Acute Heart Failure Regardless of Ejection Fraction

Hepatocyte growth factor (HGF) plays a role in the improvement of cardiac function and remodeling. Their serum levels are strongly related with mortality in chronic systolic heart failure (HF). The aim of this study was to study prognostic value of HGF in acute HF, interaction with ejection fraction, renal function, and natriuretic peptides. We included 373 patients (age 76 ± 10 years, left ventricular ejection fraction [LVEF] 46 ± 14%, 48% men) consecutively admitted for acute HF. Blood samples were obtained at admission. All patients were followed up until death or close of study (>1 year, median 371 days). HGF concentrations were determined using a commercial enzyme-linked immunosorbent assay (human HGF immunoassay). The predictive power of HGF was estimated by Cox regression with calculation of Harrell C-statistic. HGF had a median of 1,942 pg/ml (interquartile rank 1,354). According to HGF quartiles, mortality rates (per 1,000 patients/year) were 98, 183, 375, and 393, respectively (p <0.001). In Cox regression analysis, HGF (hazard ratio1SD = 1.5, 95% confidence interval 1.1 to 2.1, p = 0.002) and N-terminal pro b-type natriuretic peptide (NT-proBNP; hazard ratio1SD = 1.8, 95% confidence interval 1.2 to 2.6, p = 0.002) were independent predictors of mortality. Interaction between HGF and LVEF, origin, and renal function was nonsignificant. The addition of HGF improved the predictive ability of the models (C-statistic 0.768 vs 0.741, p = 0.016). HGF showed a complementary value over NT-proBNP (p = 0.001): mortality rate was 490 with both above the median versus 72 with both below. In conclusion, in patients with acute HF, serum HGF concentrations are elevated and identify patients at higher risk of mortality, regardless of LVEF, ischemic origin, or renal function. HGF had independent and additive information over NT-proBNP.

A new, fast and flexible radiative transfer method for Venus general circulation models

We present a new radiation scheme for the Oxford Planetary Unified Model System for Venus, suitable for the solar and thermal bands. This new and fast radiative parameterization uses a different approach in the two main radiative wavelength bands: solar radiation (0.1-5.5 mu m) and thermal radiation (1.7-260 mu m). The solar radiation calculation is based on the delta-Eddington approximation (two-stream-type) with an adding layer method. For the thermal radiation case, a code based on an absorptivity/emissivity formulation is used. The new radiative transfer formulation implemented is intended to be computationally light, to allow its incorporation in 3D global circulation models, but still allowing for the calculation of the effect of atmospheric conditions on radiative fluxes. This will allow us to investigate the dynamical-radiative-microphysical feedbacks. The model flexibility can be also used to explore the uncertainties in the Venus atmosphere such as the optical properties in the deep atmosphere or cloud amount. The results of radiative cooling and heating rates and the global-mean radiative-convective equilibrium temperature profiles for different atmospheric conditions are presented and discussed. This new scheme works in an atmospheric column and can be easily implemented in 3D Venus global circulation models. (C) 2014 Elsevier Ltd. All rights reserved.

Mesoscale Optoelectronic Design of Wire-Based Photovoltaic and Photoelectrochemical Devices

The overarching theme of this thesis is mesoscale optical and optoelectronic design of photovoltaic and photoelectrochemical devices. In a photovoltaic device, light absorption and charge carrier transport are coupled together on the mesoscale, and in a photoelectrochemical device, light absorption, charge carrier transport, catalysis, and solution species transport are all coupled together on the mesoscale. The work discussed herein demonstrates that simulation-based mesoscale optical and optoelectronic modeling can lead to detailed understanding of the operation and performance of these complex mesostructured devices, serve as a powerful tool for device optimization, and efficiently guide device design and experimental fabrication efforts. In-depth studies of two mesoscale wire-based device designs illustrate these principles—(i) an optoelectronic study of a tandem Si|WO3 microwire photoelectrochemical device, and (ii) an optical study of III-V nanowire arrays.

The study of the monolithic, tandem, Si|WO3 microwire photoelectrochemical device begins with development and validation of an optoelectronic model with experiment. This study capitalizes on synergy between experiment and simulation to demonstrate the model’s predictive power for extractable device voltage and light-limited current density. The developed model is then used to understand the limiting factors of the device and optimize its optoelectronic performance. The results of this work reveal that high fidelity modeling can facilitate unequivocal identification of limiting phenomena, such as parasitic absorption via excitation of a surface plasmon-polariton mode, and quick design optimization, achieving over a 300% enhancement in optoelectronic performance over a nominal design for this device architecture, which would be time-consuming and challenging to do via experiment.

The work on III-V nanowire arrays also starts as a collaboration of experiment and simulation aimed at gaining understanding of unprecedented, experimentally observed absorption enhancements in sparse arrays of vertically-oriented GaAs nanowires. To explain this resonant absorption in periodic arrays of high index semiconductor nanowires, a unified framework that combines a leaky waveguide theory perspective and that of photonic crystals supporting Bloch modes is developed in the context of silicon, using both analytic theory and electromagnetic simulations. This detailed theoretical understanding is then applied to a simulation-based optimization of light absorption in sparse arrays of GaAs nanowires. Near-unity absorption in sparse, 5% fill fraction arrays is demonstrated via tapering of nanowires and multiple wire radii in a single array. Finally, experimental efforts are presented towards fabrication of the optimized array geometries. A hybrid self-catalyzed and selective area MOCVD growth method is used to establish morphology control of GaP nanowire arrays. Similarly, morphology and pattern control of nanowires is demonstrated with ICP-RIE of InP. Optical characterization of the InP nanowire arrays gives proof of principle that tapering and multiple wire radii can lead to near-unity absorption in sparse arrays of InP nanowires.

Demand and supply oriented input-output models: a linkage analysis for Portugal.

Doutoramento em Economia.

Understanding Co-translational Protein Targeting and Lithium Dendrite Formation through Free Energy Simulations and Coarse-Grained Models

We describe the application of alchemical free energy methods and coarse-grained models to study two key problems: (i) co-translational protein targeting and insertion to direct membrane proteins to the endoplasmic reticulum for proper localization and folding, (ii) lithium dendrite formation during recharging of lithium metal batteries. We show that conformational changes in the signal recognition particle, a central component of the protein targeting machinery, confer additional specificity during the the recognition of signal sequences. We then develop a three-dimensional coarse-grained model to study the long-timescale dynamics of membrane protein integration at the translocon and a framework for the calculation of binding free energies between the ribosome and translocon. Finally, we develop a coarse-grained model to capture the dynamics of lithium deposition and dissolution at the electrode interface with time-dependent voltages to show that pulse plating and reverse pulse plating methods can mitigate dendrite growth.

Improving Resource Management in Virtualized Data Centers using Application Performance Models

The rapid growth of virtualized data centers and cloud hosting services is making the management of physical resources such as CPU, memory, and I/O bandwidth in data center servers increasingly important. Server management now involves dealing with multiple dissimilar applications with varying Service-Level-Agreements (SLAs) and multiple resource dimensions. The multiplicity and diversity of resources and applications are rendering administrative tasks more complex and challenging. This thesis aimed to develop a framework and techniques that would help substantially reduce data center management complexity. We specifically addressed two crucial data center operations. First, we precisely estimated capacity requirements of client virtual machines (VMs) while renting server space in cloud environment. Second, we proposed a systematic process to efficiently allocate physical resources to hosted VMs in a data center. To realize these dual objectives, accurately capturing the effects of resource allocations on application performance is vital. The benefits of accurate application performance modeling are multifold. Cloud users can size their VMs appropriately and pay only for the resources that they need; service providers can also offer a new charging model based on the VMs performance instead of their configured sizes. As a result, clients will pay exactly for the performance they are actually experiencing; on the other hand, administrators will be able to maximize their total revenue by utilizing application performance models and SLAs. This thesis made the following contributions. First, we identified resource control parameters crucial for distributing physical resources and characterizing contention for virtualized applications in a shared hosting environment. Second, we explored several modeling techniques and confirmed the suitability of two machine learning tools, Artificial Neural Network and Support Vector Machine, to accurately model the performance of virtualized applications. Moreover, we suggested and evaluated modeling optimizations necessary to improve prediction accuracy when using these modeling tools. Third, we presented an approach to optimal VM sizing by employing the performance models we created. Finally, we proposed a revenue-driven resource allocation algorithm which maximizes the SLA-generated revenue for a data center.

Productivity growth and the structure of production

In this study, interactions between potential hierarchical value chains existing in the production structure and industry-wise productivity growths are sought. We applied generalized Chenery-Watanabe heuristics for matrix linearity maximization to triangulate the input-output incidence matrix for both Japan and the Republic of Korea, finding the potential directed flow of values spanning the industrial sectors of the basic (disaggregated) industry classifications for both countries. Sector specific productivity growths were measured by way of the Trönquvist index, using the 2000-2005 linked input-output tables for both Japan and Korea.