A systems biology analysis of brain microvascular endothelial cell lipotoxicity.

BackgroundNeurovascular inflammation is associated with a number of neurological diseases including vascular dementia and Alzheimer's disease, which are increasingly important causes of morbidity and mortality around the world. Lipotoxicity is a metabolic disorder that results from accumulation of lipids, particularly fatty acids, in non-adipose tissue leading to cellular dysfunction, lipid droplet formation, and cell death.ResultsOur studies indicate for the first time that the neurovascular circulation also can manifest lipotoxicity, which could have major effects on cognitive function. The penetration of integrative systems biology approaches is limited in this area of research, which reduces our capacity to gain an objective insight into the signal transduction and regulation dynamics at a systems level. To address this question, we treated human microvascular endothelial cells with triglyceride-rich lipoprotein (TGRL) lipolysis products and then we used genome-wide transcriptional profiling to obtain transcript abundances over four conditions. We then identified regulatory genes and their targets that have been differentially expressed through analysis of the datasets with various statistical methods. We created a functional gene network by exploiting co-expression observations through a guilt-by-association assumption. Concomitantly, we used various network inference algorithms to identify putative regulatory interactions and we integrated all predictions to construct a consensus gene regulatory network that is TGRL lipolysis product specific.ConclusionSystem biology analysis has led to the validation of putative lipid-related targets and the discovery of several genes that may be implicated in lipotoxic-related brain microvascular endothelial cell responses. Here, we report that activating transcription factors 3 (ATF3) is a principal regulator of TGRL lipolysis products-induced gene expression in human brain microvascular endothelial cell

The relationship between Higher Education and labour market in Greece : the weakest link?

The high level of graduate unemployment, even though it is acknowledged as one of the most distinctive characteristics of the Greek labour market, it has not attracted enough attention in the academic literature. This paper utilizes micro-data from the Labour Force Survey in order to investigate how the employment situation of young (aged 35 and below) graduates varies across fields of study. The findings suggest that graduates of disciplines that have high levels of private sector employment, such as Polytechnics and Computer Science, are in general better off in the Greek labour market. On the other hand, graduates of disciplines that are traditionally related to the needs of the public sector, such as Sociology and Humanities, face poor employment prospects. The findings of this study highlight the need for drastic reforms of the Higher Education system

Authentication with Weaker Trust Assumptions for Voting Systems

Some voting systems are reliant on external authentication services. Others use cryptography to implement their own. We combine digital signatures and non-interactive proofs to derive a generic construction for voting systems with their own authentication mechanisms, from systems that rely on external authentication services. We prove that our construction produces systems satisfying ballot secrecy and election verifiability, assuming the underlying voting system does. Moreover, we observe that works based on similar ideas provide neither ballot secrecy nor election verifiability. Finally, we demonstrate applicability of our results by applying our construction to the Helios voting system

Perturbations of Gauss-Bonnet Black Strings in Codimension-2 Braneworlds

We derive the Lichnerowicz equation in the presence of the Gauss-Bonnet term. Using the modified Lichnerowicz equation we study the metric perturbations of Gauss-Bonnet black strings in Codimension-2 Braneworlds.Comment: 26 pages, no figures, clarifying comments and one reference added, to be published in JHE

Instability of brane cosmological solutions with flux compactifications

We discuss the stability of the higher-dimensional de Sitter (dS) brane solutions with two-dimensional internal space in the Einstein-Maxwel theory. We show that an instability appears in the scalar-type perturbations with respect to the dS spacetime. We derive a differential relation which has the very similar structure to the ordinary laws of thermodynamics as an extension of the work for the six-dimensional model [20]. In this relation, the area of dS horizon (integrated over the two internal dimensions) exactly behaves as the thermodynamical entropy. The dynamically unstable solutions are in the thermodynamically unstable branch. An unstable dS compactification either evolves toward a stable configuration or two-dimensional internal space is decompactified. These dS brane solutions are equivalent to the accelerating cosmological solutions in the six-dimensional Einstein-Maxwell-dilaton theory via dimensional reduction. Thus, if the seed higher-dimensional solution is unstable, the corresponding six-dimensional solution is also unstable. From the effective four-dimensional point of view, a cosmological evolution from an unstable cosmological solution in higher dimensions may be seen as a process of the transition from the initial cosmological inflation to the current dark energy dominated Universe.Comment: 11 pages, 3 figures, references added, to appear in CQ

A comparison of neural network approaches for on-line prediction in IGRT

Image-guided radiation therapy aims to improve the accuracy of treatment delivery by tracking tumor position and compensating for observed movement. Due to system latency it is sometimes necessary to predict tumor trajectory evolution in order to facilitate changes in beam delivery. Neural networks (NNs) have previously been investigated for predicting future tumor position because of their ability to model non-linear systems. However, no attempt has been made to optimize the NN training algorithms, and no mention has been made of potential errors which can be caused by using NNs for extrapolation purposes. In this work, after giving a brief explanation of NN theory, a comparison is made between 4 different adaptive algorithms for training time-series prediction NNs. New error criteria are introduced which highlight error maxima. Results are obtained by training the NNs using previously published data. A hybrid algorithm combining Bayesian regularization with conjugate-gradient backpropagation is demonstrated to give the best average prediction accuracy, whilst a generalized regression NN is shown to reduce the possibility of isolated large prediction errors

Demonstration of low power and highly uniform 6-bit operation in SiO2-based memristors embedded with Pt nanoparticles

In this work, an optimized method was implemented for attaining stable multibit operation with low energy consumption in a two-terminal memory element made from the following layers: Ag/Pt nanoparticles (NPs)/SiO2/TiN in a 1-Transistor-1-Memristor configuration. Compared to the reference sample where no NPs were embedded, an enlarged memory window was recorded in conjunction with reduced variability for both switching states. A comprehensive numerical model was also applied to shed light on this enhanced performance, which was attributed to the spatial confinement effect induced by the presence of the Pt NPs and its impact on the properties of the percolating conducting filaments (CFs). Although 5-bit precision was demonstrated with the application of the incremental-step-pulse-programming (ISPP) algorithm, the reset process was unreliable and the output current increased abnormally when exceeded the value of 150 uA. As a result, the multibit operation was limited. To address this issue, a modified scheme was developed to accurately control the distance between the various resistance levels and achieve highly reliable 6-bit precision. Our work provides valuable insights for the development of energy-efficient memories for applications where a high density of conductance levels is required

Water-food-energy nexus for transboundary cooperation in Eastern Africa

This is the author accepted manuscript. The final version is available on open access from IWA Publishing via the DOI in this recordEstablishing cooperation in transboundary rivers is challenging especially with the weak or non-existent river basin institutions. A nexus-based approach is developed to explore cooperation opportunities in transboundary river basins while considering system operation and coordination under uncertain hydrologic river regimes. The proposed approach is applied to the Nile river basin with a special focus on the Grand Ethiopian Renaissance Dam (GERD), assuming two possible governance positions: with or without cooperation. A cooperation mechanism is developed to allocate additional releases from the GERD when necessary, while a unilateral position assumes that the GERD is operated to maximize hydropower generation regardless of downstream users' needs. The GERD operation modes were analysed considering operation of downstream reservoirs and varying demands in Egypt. Results show that average basin-wide hydropower generation is likely to increase by about 547 GWh/year (1%) if cooperation is adopted when compared to the unilateral position. In Sudan, hydropower generation and water supply are expected to enhance in the unilateral position and would improve further with cooperation. Furthermore, elevated low flows by the GERD are likely to improve the WFE nexus outcomes in Egypt under full cooperation governance scenario with a small reduction in GERD hydropower generation (2,000 GWh/year (19%)).Ministry of Higher Education (MoHE), EgyptUniversity of Exete

Novel fuzzy-based optimization approaches for the prediction of ultimate axial load of circular concrete-filled steel tubes

An accurate estimation of the axial compression capacity of the concrete-filled steel tubular (CFST) column is crucial for ensuring the safety of structures containing them and preventing related failures. In this article, two novel hybrid fuzzy systems (FS) were used to create a new framework for estimating the axial compression capacity of circular CCFST columns. In the hybrid models, differential evolution (DE) and firefly algorithm (FFA) techniques are employed in order to obtain the optimal membership functions of the base FS model. To train the models with the new hybrid techniques, i.e., FS-DE and FS-FFA, a substantial library of 410 experimental tests was compiled from openly available literature sources. The new model\u2019s robustness and accuracy was assessed using a variety of statistical criteria both for model development and for model validation. The novel FS-FFA and FS-DE models were able to improve the prediction capacity of the base model by 9.68% and 6.58%, respectively. Furthermore, the proposed models exhibited considerably improved performance compared to existing design code methodologies. These models can be utilized for solving similar problems in structural engineering and concrete technology with an enhanced level of accuracy