Novel therapeutic strategies in NBIA: A gene therapy approach for PLA2G6-associated neurodegeneration

Infantile neuroaxonal dystrophy (INAD) is a debilitating, intractable and ultimately lethal neurodegenerative disorder. It is caused by mutations in the PLA2G6 gene which encodes for phospholipase A2. INAD patients present neurodegeneration-associated symptoms between six months and three years of age. Severe spasticity, progressive cognitive decline, and visual impairment typically result in death during the first decade (Morgan et al, 2006). There is no disease-modifying treatment available and palliative care focuses on quality of life. Therefore, there is an overwhelming need to develop novel therapies to treat INAD patients. To create a landscape of the behavioural and pathological deficits, we aim to first conduct an in-depth characterization of the PLA2G6 mouse model developed by Wada et al (2009). Additionally, we aim to develop an AAV-mediated gene therapy approach for the treatment of INAD and conduct a pre-clinical study in the pla2g6-inad mouse model. The objective is to be able to prevent or ameliorate both the central and peripheral nervous system phenotype and improve the lifespan and/or quality of life of the animal. Recombinant adeno-associated virus serotype 9 vector (AAV9) will be used to deliver the therapeutic human PLA2G6 gene to the neonatal pla2g6-inad mouse. The strong neuron specific synapsin-I promoter will drive the human PLA2G6 gene. The efficacy of different administration routes including intracerebroventricular (ICV), intravenous (IV) and a combination of intracerebroventricular (ICV)/ intravenous (IV) and intracerbroventricular (ICV)/intraperioteneal (IP) will be investigated in the pla2g6-inad mouse model. AAV9-hSyn1-hPLA2G6 gene therapy treated pla2g6-inad mice showed an increased lifespan with the largest improvements observed in the animal cohort that received a combined administration of AAV9-hPLA2G6. The significant increase in lifespan supplemented with significant improvements in behavioural tests validates the potential beneficial use of gene therapy for infantile neuroaxonal dystrophy (INAD)

A spatially continuous magnetization model for Mars

[1] Using a three-component magnetic field data set at over 100,000 satellite points previously compiled for spherical harmonic analysis, we have produced a continuously varying magnetization model for Mars. The magnetized layer was assumed to be 40 km thick, an average value based on previous studies of the topography and gravity field. The severe nonuniqueness in magnetization modeling is addressed by seeking the model with minimum root-mean-square (RMS) magnetization for a given fit to the data, with the trade-off between RMS magnetization and fit controlled by a damping parameter. Our preferred model has magnetization amplitudes up to 20 A/m. It is expressed as a linear combination of the Green’s functions relating each observation to magnetization at the point of interest within the crust, leading to a linear system of equations of dimension the number of data points. Although this is impractically large for direct solution, most of the matrix elements relating data to model parameters are negligibly small. We therefore apply methods applicable to sparse systems, allowing us to preserve the resolution of the original data set. Thus we produce more detailed models than any previously published, although they share many similarities. We find that tectonism in the Valles Marineris region has a magnetic signature, and we show that volcanism south of the dichotomy boundary has both a magnetic and gravity signature. The method can also be used to downward continue magnetic data, and a comparison with other leveling techniques at Mars ’ surface is favorable

Using Ensemble KalmanFiltering to improve magnetic field models during vector satellite data ‘gaps’?

Kalmanfiltering can be used to combine data optimally from different sources assuming that the error or variance of each data type is suitably understood. Typically a physical model is combined with occasional real measurements. Ensemble KalmanFilters (EnKF) extend this idea by making multiple simulations with randomly perturbed models drawn from probability distribution of fixed variance. Here we use EnKFto combine steady core surface flow models of the fluid outer core with magnetic field models derived from periods when no vector satellite data were available. We test if there is an optimal combination of flow and field that minimises the overall root-mean-square misfit to a ‘true’ magnetic field calculated after the resumption of satellite vector measurements

Forecasting changes of the magnetic field in the UK from L1 Lagrange solar wind measurements

Extreme space weather events can have large impacts on ground-based infrastructure important to technology-based societies. Machine learning techniques based on interplanetary observations have proven successful as a tool for forecasting global geomagnetic indices, however, few studies have examined local ground magnetic field perturbations. Nowcast and forecast models which predict the magnitude of the horizontal geomagnetic field, |BH|, and its time derivative, ∣∣dBHdt∣∣, at ground level would be valuable for assessing the potential space weather hazard. We attempt to predict the variation of the magnetic field at the three United Kingdom observatories (Eskdalemuir, Hartland and Lerwick) driven by L1 solar wind parameters. The horizontal magnetic field component and its time derivative are predicted from solar wind plasma and interplanetary magnetic field observations using Long Short-Term Memory (LSTM) networks and hybrid Convolutional Neural Network-LSTM models. A 5-fold grid search cross-validation is used for tuning the hyperparameters in each model. Forecasts were made with 5, 15 and 30-min lead times. Models were trained and validated with geomagnetic storm-only data from 1997 to 2016; their outputs were evaluated with the 7–9th September 2017 storms. The forecast models are only able to predict the directly driven parts of geomagnetic storms (not the substorms) and LSTM models generally perform best. We find the |BH| 15- and 30-min forecasts at Lerwick and Eskdalemuir have some predictive power. The 5-min |BH| forecast as well as all the ∣∣dBHdt∣∣ models for Eskdalemuir and all the Hartland models were found to have little or no predictive power. This suggests that the machine learning models have better forecasting power at higher latitude (closer to the auroral zones), where the ground magnetic variation field is larger and during storm onset, which is directly driven by changes in the solar wind

Investigating the location and strength of the auroral electrojets using Swarm

The auroral electrojets are a key space weather phenomenon. They are formed by horizontal Hall currents that flow within the ionospheric polar regions at an altitude of around 115 km. They form ovals around the magnetic poles but their latitudinal position, width, and strength are highly variable. These are governed by geomagnetic activity and solar wind conditions, along with a global ordering by the main magnetic field. Typically, greater geomagnetic activity will cause the electrojets to intensify and move equatorward. This is associated with greater auroral displays in more populated areas but also with potentially severe consequences both on Earth and in space: - geomagnetically Induced Currents (GICs) - disturbance to radio communications and GNSS signals - disruption to navigation applications - increased drag on satellites due to expansion of the atmosphere The auroral electrojet system can be described by the AE activity indices derived from measurements at ground-based magnetic observatories. The accuracy of the AE indices is limited by the observatories' fixed positions, which inhibits the ability to consistently locate the electrojets. Significantly, the indices only cover the Northern hemisphere so do not capture the differences between the Northern and Southern systems. Polar low-Earth orbit satellite observations offer the opportunity to overcome these limitations, by providing excellent latitudinal resolution and coverage equally over both poles. There have been several demonstrations of using satellites to monitor the auroral electrojets: Olsen (1996) using Magsat; Moretto et al (2002) using Oersted, CHAMP, and SAC-C; Juusola et al. (2009) and Vennerstrom and Moretto (2013) using CHAMP; and Hamilton and Macmillan (2013) using Magsat and CHAMP. The results presented here apply the method of Vennerstrom and Moretto (2013) to data from the Swarm mission