Cerebral vasculopathy in children with sickle cell disease A study of genetic modulators of the disease

Sickle cell disease (SCD) is a genetic disorder with recessive transmission, caused by the mutation HBB:c.20A>T. It originates hemoglobin S that forms polymers inside the erythrocyte, upon deoxygenation, deforming it and ultimately leading to premature hemolysis. The disease presents with high heterogeneity of clinical manifestations, the most devastating of which, ischemic stroke, occurs in 11% of patients until 20 years of age. In this study, we tried to identify genetic modifiers of risk and episodes of stroke by studying 66 children with SCD, grouped according to the degree of cerebral vasculopathy (Stroke, Risk and Control). Association studies were performed between the three phenotypic groups and hematological and biochemical parameters of patients, as well as with 23 polymorphic regions in genes related to vascular cell adhesion (VCAM-1, THBS-1 and CD36), vascular tonus (NOS3 and ET-1) and inflammation (TNF-α and HMOX-1). Relevant data was collected from patient’s medical records. Known genetic modulators of SCD (beta-globin cluster haplotype and HBA and BCL11A genotypes) and putative genetic modifiers of cerebral vasculopathy were characterized. Differences in their distribution among groups were assessed. VCAM-1 rs1409419 allele C and NOS3 rs207044 allele C were associated to stroke events, while VCAM-1 rs1409419 allele T was found to be protective. Alleles 4a and 4b of NOS3 27 bp VNTR appeared to be respectively associated to stroke risk and protection. HMOX-1 longer STRs seemed to predispose to stroke. Higher hemoglobin F levels were found in Control group, as a result of Senegal haplotype or of BCL11A rs11886868 allele T, and higher lactate dehydrogenase levels, marker of hemolysis, were found in Risk group. Molecular mechanisms underlying the modifier functions of the relevant genetic variants are discussed.

Control in distribution networks with demand side management

The way in which electricity networks operate is going through a period of significant change. Renewable generation technologies are having a growing presence and increasing penetrations of generation that are being connected at distribution level. Unfortunately, a renewable energy source is most of the time intermittent and needs to be forecasted. Current trends in Smart grids foresee the accommodation of a variety of distributed generation sources including intermittent renewable sources. It is also expected that smart grids will include demand management resources, widespread communications and control technologies required to use demand response are needed to help the maintenance in supply-demand balance in electricity systems. Consequently, smart household appliances with controllable loads will be likely a common presence in our homes. Thus, new control techniques are requested to manage the loads and achieve all the potential energy present in intermittent energy sources. This thesis is focused on the development of a demand side management control method in a distributed network, aiming the creation of greater flexibility in demand and better ease the integration of renewable technologies. In particular, this work presents a novel multi-agent model-based predictive control method to manage distributed energy systems from the demand side, in presence of limited energy sources with fluctuating output and with energy storage in house-hold or car batteries. Specifically, here is presented a solution for thermal comfort which manages a limited shared energy resource via a demand side management perspective, using an integrated approach which also involves a power price auction and an appliance loads allocation scheme. The control is applied individually to a set of Thermal Control Areas, demand units, where the objective is to minimize the energy usage and not exceed the limited and shared energy resource, while simultaneously indoor temperatures are maintained within a comfort frame. Thermal Control Areas are overall thermodynamically connected in the distributed environment and also coupled by energy related constraints. The energy split is performed based on a fixed sequential order established from a previous completed auction wherein the bids are made by each Thermal Control Area, acting as demand side management agents, based on the daily energy price. The developed solutions are explained with algorithms and are applied to different scenarios, being the results explanatory of the benefits of the proposed approaches.