Risk prediction of late-onset Alzheimer’s disease implies an oligogenic architecture

Genetic association studies have identified 44 common genome-wide significant risk loci for late-onset Alzheimer's disease (LOAD). However, LOAD genetic architecture and prediction are unclear. Here we estimate the optimal P-threshold (P) of a genetic risk score (GRS) for prediction of LOAD in three independent datasets comprising 676 cases and 35,675 family history proxy cases. We show that the discriminative ability of GRS in LOAD prediction is maximised when selecting a small number of SNPs. Both simulation results and direct estimation indicate that the number of causal common SNPs for LOAD may be less than 100, suggesting LOAD is more oligogenic than polygenic. The best GRS explains approximately 75% of SNP-heritability, and individuals in the top decile of GRS have ten-fold increased odds when compared to those in the bottom decile. In addition, 14 variants are identified that contribute to both LOAD risk and age at onset of LOAD

A blood-based biomarker panel indicates IL-10 and IL-12/23p40 are jointly associated as predictors of β-amyloid load in an AD cohort

Alzheimer\u27s Disease (AD) is the most common form of dementia, characterised by extracellular amyloid deposition as plaques and intracellular neurofibrillary tangles of tau protein. As no current clinical test can diagnose individuals at risk of developing AD, the aim of this project is to evaluate a blood-based biomarker panel to identify individuals who carry this risk. We analysed the levels of 22 biomarkers in clinically classified healthy controls (HC), mild cognitive impairment (MCI) and Alzheimer\u27s participants from the well characterised Australian Imaging, Biomarker and Lifestyle (AIBL) study of aging. High levels of IL-10 and IL-12/23p40 were significantly associated with amyloid deposition in HC, suggesting that these two biomarkers might be used to detect at risk individuals. Additionally, other biomarkers (Eotaxin-3, Leptin, PYY) exhibited altered levels in AD participants possessing the APOE ϵ4 allele. This suggests that the physiology of some potential biomarkers may be altered in AD due to the APOE ϵ4 allele, a major risk factor for AD. Taken together, these data highlight several potential biomarkers that can be used in a blood-based panel to allow earlier identification of individuals at risk of developing AD and/or early stage AD for which current therapies may be more beneficial

Transient Response Improvement of Microgrids Exploiting the Inertia of a Doubly-Fed Induction Generator (DFIG)

Storage devices are introduced in microgrids in order to secure their power quality, power regularity and offer ancillary services in a transient period. In the transition period of a low voltage microgrid, from the connected mode of operation to the islanded mode of operation, the power unbalance can be partly covered by the inertia energy of the existing power sources. This paper proposes fuzzy local controllers exploiting the inertia of a Wind Turbine (WT) with a Doubly Fed Induction Generator (DFIG), if such a machine exists in the microgrid, in order to decrease the necessary storage devices and the drawbacks that arise. The proposed controllers are based in fuzzy logic due to the non linear and stochastic behavior of the system. Two cases are studied and compared during the transient period where the microgrid architecture and the DFIG controller differ. In the first case, the understudy microgrid includes a hybrid fuel cell system (FCS)-battery system and a WT with a DFIGURE. The DFIG local controller in this case is also based in fuzzy logic and follows the classical optimum power absorption scenario for the WT. The transition of the microgrid from the connected mode of operation to the islanded mode is evaluated and, especially, the battery contribution is estimated. In the second case, the battery is eliminated. The fuzzy controller of the DFIG during the transition provides primary frequency control and local bus voltage support exploiting the WT inertia. The response of the system is estimated in both cases using MATLAB/Simulink software package

Algorithm for Appropriate Design of Hydroelectric Turbines as Replacements for Pressure Reduction Valves in Water Distribution Systems

Gravity flow water distribution systems (WDSs) are used to permit water flow from an input point of highest elevation (source) to the terminal points of a system (consumers). In such WDSs, typically, there is no need for external power to maintain the flow due to the typical high gradients that exist. However, those gradients cause high water velocity and pressure to some network areas that could potentially lead to pipes bursting. Currently, the only means to regulate pressure within allowable range are Pressure Reduction Valves (PRVs). They are installed at various locations, but they cannot utilize the existing hydropower potential in terms of electrical energy production. This paper provides a methodology for hydro-turbines dimensioning, so that they mimic PRV operation in terms of pressure regulation while producing power efficiently. This includes an algorithmic process within which the proper turbine design and performance prediction are initially determined, and then, via an interconnection between EPANET and MATLAB, the hydraulic behavior of their operation within the water network is modeled and simulated. The methodology has been tested with simulations of a typical urban WDS. The results indicate that the produced turbine geometries mimic PRV action at the respective locations with more than 1% accuracy during 70% of the time (2% maximum error), while producing electric energy with hydraulic efficiency over 60%

Transient response improvement of microgrids exploiting the inertia of a Doubly-Fed Induction Generator (DFIG

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