Behavioural and neurochemical correlates of drugs acting at imidazoline and a2-adrenoceptor binding sites

A number of agents with differing selectivity profiles for the non-a2 adrenoceptor binding site (NAIBS), imidazoline preferring receptor (IPR) and a2-adrenoceptor were employed in a series of behavioural and neurochemical experiments to determine a functional role for the former two sites. The highly selective NAIBS ligand RX801 077 produced an increase in rat brain extracellular noradrenaline (NA) levels, as determined by the technique of in vivo microdialysis, which may underlie its ability to produce a discriminable cue in the same species. This increase in NA may be due to a suggested link between the NAIBS and the monoamine oxidase inhibitor (MAOI) activity of RX801 077. For instance, the RX801 077 cue was substituted for by the MAOI drugs pargyline and moclobemide, which themselves down regulate NAIBS when administered chronically. RX811 059 substituted for the RX801 077 cue which may be due its ability to stimulate NA release via its activity as a highly selective a2-adrenoceptor antagonist. An effect upon NA output may also explain the ability of RX801 077 to 'mimic' the anti-immobility effect of the antidepressant drug desmethylimipramine (DMJ) in the forced swimming test. Further studies are therefore required to examine a possible role for the NAIBS in the treatment of depression. Discriminable cues were also produced by RX811 059 and the a2- adrenoceptor agonist clonidine, probably as a consequence of their respective ability to stimulate and inhibit NA output via their opposing activity at a2-adrenoceptors. The IPR has been suggested to play a role in mediating the hypotensive effect of clonidine, although a precise role was unable to be established for this site in the present studies due to the unavailability of highly selective IPA agents

Ensemble Forecasting of Major Solar Flares: Methods for Combining Models

One essential component of operational space weather forecasting is the prediction of solar flares. With a multitude of flare forecasting methods now available online it is still unclear which of these methods performs best, and none are substantially better than climatological forecasts. Space weather researchers are increasingly looking towards methods used by the terrestrial weather community to improve current forecasting techniques. Ensemble forecasting has been used in numerical weather prediction for many years as a way to combine different predictions in order to obtain a more accurate result. Here we construct ensemble forecasts for major solar flares by linearly combining the full-disk probabilistic forecasts from a group of operational forecasting methods (ASAP, ASSA, MAG4, MOSWOC, NOAA, and MCSTAT). Forecasts from each method are weighted by a factor that accounts for the method's ability to predict previous events, and several performance metrics (both probabilistic and categorical) are considered. It is found that most ensembles achieve a better skill metric (between 5\% and 15\%) than any of the members alone. Moreover, over 90\% of ensembles perform better (as measured by forecast attributes) than a simple equal-weights average. Finally, ensemble uncertainties are highly dependent on the internal metric being optimized and they are estimated to be less than 20\% for probabilities greater than 0.2. This simple multi-model, linear ensemble technique can provide operational space weather centres with the basis for constructing a versatile ensemble forecasting system -- an improved starting point to their forecasts that can be tailored to different end-user needs

Ensemble Forecasting of Major Solar Flares: Methods for Combining Models

One essential component of operational space weather forecasting is the prediction of solar flares. With a multitude of flare forecasting methods now available online it is still unclear which of these methods performs best, and none are substantially better than climatological forecasts. Space weather researchers are increasingly looking towards methods used by the terrestrial weather community to improve current forecasting techniques. Ensemble forecasting has been used in numerical weather prediction for many years as a way to combine different predictions in order to obtain a more accurate result. Here we construct ensemble forecasts for major solar flares by linearly combining the full-disk probabilistic forecasts from a group of operational forecasting methods (ASAP, ASSA, MAG4, MOSWOC, NOAA, and MCSTAT). Forecasts from each method are weighted by a factor that accounts for the method's ability to predict previous events, and several performance metrics (both probabilistic and categorical) are considered. It is found that most ensembles achieve a better skill metric (between 5\% and 15\%) than any of the members alone. Moreover, over 90\% of ensembles perform better (as measured by forecast attributes) than a simple equal-weights average. Finally, ensemble uncertainties are highly dependent on the internal metric being optimized and they are estimated to be less than 20\% for probabilities greater than 0.2. This simple multi-model, linear ensemble technique can provide operational space weather centres with the basis for constructing a versatile ensemble forecasting system -- an improved starting point to their forecasts that can be tailored to different end-user needs.Comment: Accepted for publication in the Journal of Space Weather and Space Climat

Active Region Photospheric Magnetic Properties Derived from Line-of-sight and Radial Fields

The effect of using two representations of the normal-to-surface magnetic field to calculate photospheric measures that are related to active region (AR) potential for flaring is presented. Several AR properties were computed using line-of-sight (Blos) and spherical-radial (Br) magnetograms from the Spaceweather HMI Active Region Patch (SHARP) products of the Solar Dynamics Observatory, characterizing the presence and features of magnetic polarity inversion lines, fractality, and magnetic connectivity of the AR photospheric field. The data analyzed corresponds to ≈4,000 AR observations, achieved by randomly selecting 25% of days between September 2012 and May 2016 for analysis at 6-hr cadence. Results from this statistical study include: i) the Br component results in a slight upwards shift of property values in a manner consistent with a field-strength underestimation by the Blos component; ii) using the Br component results in significantly lower inter-property correlation in one-third of the cases, implying more independent information about the state of the AR photospheric magnetic field; iii) flaring rates for each property vary between the field components in a manner consistent with the differences in property-value ranges resulting from the components; iv)flaring rates generally increase for higher values of properties, except Fourier spectral power index that has flare rates peaking around a value of 5=3. These findings indicate that there may be advantages in using Br rather than Blos in calculating flare-related AR magnetic properties, especially for regions located far from central meridian

Forecasting Solar Flares Using Magnetogram-based Predictors and Machine Learning

We propose a forecasting approach for solar flares based on data from Solar Cycle 24, taken by the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO) mission. In particular, we use the Spaceweather HMI Active Region Patches (SHARP) product that facilitates cut-out magnetograms of solar active regions (AR) in the Sun in near-realtime (NRT), taken over a five-year interval (2012 – 2016). Our approach utilizes a set of thirteen predictors, which are not included in the SHARP metadata, extracted from line-of-sight and vector photospheric magnetograms. We exploit several Machine Learning (ML) and Conventional Statistics techniques to predict flares of peak magnitude >M1 and >C1, within a 24 h forecast window. The ML methods used are multi-layer perceptrons (MLP), support vector machines (SVM) and random forests (RF). We conclude that random forests could be the prediction technique of choice for our sample, with the second best method being multi-layer perceptrons, subject to an entropy objective function. A Monte Carlo simulation showed that the best performing method gives accuracy ACC=0.93(0.00), true skill statistic TSS=0.74(0.02) and Heidke skill score HSS=0.49(0.01) for >M1 flare prediction with probability threshold 15% and ACC=0.84(0.00), TSS=0.60(0.01) and HSS=0.59(0.01) for >C1 flare prediction with probability threshold 35%

Photospheric Shear Flows in Solar Active Regions and Their Relation to Flare Occurrence

Solar active regions (ARs) that produce major flares typically exhibit strong plasma shear flows around photospheric magnetic polarity inversion lines (MPILs). It is therefore important to quantitatively measure such photospheric shear flows in ARs for a better understanding of their relation to flare occurrence. Photospheric flow fields were determined by applying the Differential Affine Velocity Estimator for Vector Magnetograms (DAVE4VM) method to a large data set of 2,548 co-aligned pairs of AR vector magnetograms with 12-min separation over the period 2012-2016. From each AR flow-field map, three shear-flow parameters were derived corresponding to the mean (), maximum (S_max) and integral (S_sum) shear-flow speeds along strong-gradient, strong-field MPIL segments. We calculated flaring rates within 24 hr as a function of each shear-flow parameter, and also investigated the relation between the parameters and the waiting time ({\tau}) until the next major flare (class M1.0 or above) after the parameter observation. In general, it is found that the larger S_sum an AR has, the more likely it is for the AR to produce flares within 24 hr. It is also found that among ARs which produce major flares, if one has a larger value of S_sum then {\tau} generally gets shorter. These results suggest that large ARs with widespread and/or strong shear flows along MPILs tend to not only be more flare productive, but also produce major flares within 24 hr or less.Comment: 19 pages, 8 figures, accepted for publication in Solar Physic

Ecosystem Health Education: Teaching Leadership Through Team-Based Assignments

The health and sustainability of humans, animals, and environments are interdependent. The relationship between climate change, disease emergence, and food security on sustainability of ecosystem services is embodied in the sustainable development goals (SDGs). A diverse workforce needs to be equipped with leadership skills to function in a transdisciplinary, team-based environment. Ecosystem health (ESH) provides a critical and innovative approach to solving these complex challenges and offers a toolbox to actualize SDGs. This article outlines the development of a course detailing the process of framing a new academic approach in ESH as a training pathway for undergraduate and graduate students

Genetic modifiers and subtypes in schizophrenia: Investigations of age at onset, severity, sex and family history

Schizophrenia is a genetically and clinically heterogeneous disorder. Genetic risk factors for the disorder may differ between the sexes or between multiply affected families compared to cases with no family history. Additionally, limited data support a genetic basis for variation in onset and severity, but specific loci have not been identified. We performed genome-wide association studies (GWAS) examining genetic influences on age at onset (AAO) and illness severity as well as specific risk by sex or family history status using up to 2762 cases and 3187 controls from the International Schizophrenia Consortium (ISC)