Probabilistic Super-Resolution of Solar Magnetograms: Generating Many Explanations and Measuring Uncertainties

Machine learning techniques have been successfully applied to super-resolution tasks on natural images where visually pleasing results are sufficient. However in many scientific domains this is not adequate and estimations of errors and uncertainties are crucial. To address this issue we propose a Bayesian framework that decomposes uncertainties into epistemic and aleatoric uncertainties. We test the validity of our approach by super-resolving images of the Sun's magnetic field and by generating maps measuring the range of possible high resolution explanations compatible with a given low resolution magnetogram

Single-Frame Super-Resolution of Solar Magnetograms: Investigating Physics-Based Metrics \& Losses

Breakthroughs in our understanding of physical phenomena have traditionally followed improvements in instrumentation. Studies of the magnetic field of the Sun, and its influence on the solar dynamo and space weather events, have benefited from improvements in resolution and measurement frequency of new instruments. However, in order to fully understand the solar cycle, high-quality data across time-scales longer than the typical lifespan of a solar instrument are required. At the moment, discrepancies between measurement surveys prevent the combined use of all available data. In this work, we show that machine learning can help bridge the gap between measurement surveys by learning to \textbf{super-resolve} low-resolution magnetic field images and \textbf{translate} between characteristics of contemporary instruments in orbit. We also introduce the notion of physics-based metrics and losses for super-resolution to preserve underlying physics and constrain the solution space of possible super-resolution outputs

A Recurrent Mutation in KCNA2 as a Novel Cause of Hereditary Spastic Paraplegia and Ataxia

The hereditary spastic paraplegias (HSPs) are heterogeneous neurodegenerative disorders with over 50 known causative genes. We identified a recurrent mutation in KCNA2 (c.881G>A, p.R294H), encoding the voltage-gated K+-channel, K(V)1.2, in two unrelated families with HSP, intellectual disability (ID), and ataxia. Follow-up analysis of >2,000 patients with various neurological phenotypes identified a de novo p.R294H mutation in a proband with ataxia and ID. Two-electrode voltage-clamp recordings of Xenopus laevis oocytes expressing mutant KV1.2 channels showed loss of function with a dominant-negative effect. Our findings highlight the phenotypic spectrum of a recurrent KCNA2 mutation, implicating ion channel dysfunction as a novel HSP disease mechanism.Peer reviewe

SLCO5A1 and synaptic assembly genes contribute to impulsivity in juvenile myoclonic epilepsy

Elevated impulsivity is a key component of attention-deficit hyperactivity disorder (ADHD), bipolar disorder and juvenile myoclonic epilepsy (JME). We performed a genome-wide association, colocalization, polygenic risk score, and pathway analysis of impulsivity in JME (n = 381). Results were followed up with functional characterisation using a drosophila model. We identified genome-wide associated SNPs at 8q13.3 (P = 7.5 × 10−9) and 10p11.21 (P = 3.6 × 10−8). The 8q13.3 locus colocalizes with SLCO5A1 expression quantitative trait loci in cerebral cortex (P = 9.5 × 10−3). SLCO5A1 codes for an organic anion transporter and upregulates synapse assembly/organisation genes. Pathway analysis demonstrates 12.7-fold enrichment for presynaptic membrane assembly genes (P = 0.0005) and 14.3-fold enrichment for presynaptic organisation genes (P = 0.0005) including NLGN1 and PTPRD. RNAi knockdown of Oatp30B, the Drosophila polypeptide with the highest homology to SLCO5A1, causes over-reactive startling behaviour (P = 8.7 × 10−3) and increased seizure-like events (P = 6.8 × 10−7). Polygenic risk score for ADHD genetically correlates with impulsivity scores in JME (P = 1.60 × 10−3). SLCO5A1 loss-of-function represents an impulsivity and seizure mechanism. Synaptic assembly genes may inform the aetiology of impulsivity in health and disease

Proceedings of the 3rd Biennial Conference of the Society for Implementation Research Collaboration (SIRC) 2015: advancing efficient methodologies through community partnerships and team science

It is well documented that the majority of adults, children and families in need of evidence-based behavioral health interventionsi do not receive them [1, 2] and that few robust empirically supported methods for implementing evidence-based practices (EBPs) exist. The Society for Implementation Research Collaboration (SIRC) represents a burgeoning effort to advance the innovation and rigor of implementation research and is uniquely focused on bringing together researchers and stakeholders committed to evaluating the implementation of complex evidence-based behavioral health interventions. Through its diverse activities and membership, SIRC aims to foster the promise of implementation research to better serve the behavioral health needs of the population by identifying rigorous, relevant, and efficient strategies that successfully transfer scientific evidence to clinical knowledge for use in real world settings [3]. SIRC began as a National Institute of Mental Health (NIMH)-funded conference series in 2010 (previously titled the “Seattle Implementation Research Conference”; $150,000 USD for 3 conferences in 2011, 2013, and 2015) with the recognition that there were multiple researchers and stakeholdersi working in parallel on innovative implementation science projects in behavioral health, but that formal channels for communicating and collaborating with one another were relatively unavailable. There was a significant need for a forum within which implementation researchers and stakeholders could learn from one another, refine approaches to science and practice, and develop an implementation research agenda using common measures, methods, and research principles to improve both the frequency and quality with which behavioral health treatment implementation is evaluated. SIRC’s membership growth is a testament to this identified need with more than 1000 members from 2011 to the present.ii SIRC’s primary objectives are to: (1) foster communication and collaboration across diverse groups, including implementation researchers, intermediariesi, as well as community stakeholders (SIRC uses the term “EBP champions” for these groups) – and to do so across multiple career levels (e.g., students, early career faculty, established investigators); and (2) enhance and disseminate rigorous measures and methodologies for implementing EBPs and evaluating EBP implementation efforts. These objectives are well aligned with Glasgow and colleagues’ [4] five core tenets deemed critical for advancing implementation science: collaboration, efficiency and speed, rigor and relevance, improved capacity, and cumulative knowledge. SIRC advances these objectives and tenets through in-person conferences, which bring together multidisciplinary implementation researchers and those implementing evidence-based behavioral health interventions in the community to share their work and create professional connections and collaborations

Stall in Canonical Autophagy-Lysosome Pathways Prompts Nucleophagy-Based Nuclear Breakdown in Neurodegeneration

The terminal stages of neuronal degeneration and death in neurodegenerative diseases remain elusive. Autophagy is an essential catabolic process frequently failing in neurodegeneration. Selective autophagy routes have recently emerged, including nucleophagy, defined as degradation of nuclear components by autophagy. Here, we show that, in a mouse model for the polyglutamine disease dentatorubral-pallidoluysian atrophy (DRPLA), progressive acquirement of an ataxic phenotype is linked to severe cerebellar cellular pathology, characterized by nuclear degeneration through nucleophagy-based LaminB1 degradation and excretion. We find that canonical autophagy is stalled in DRPLA mice and in human fibroblasts from patients of DRPLA. This is evidenced by accumulation of p62 and downregulation of LC3-I/II conversion as well as reduced Tfeb expression. Chronic autophagy blockage in several conditions, including DRPLA and Vici syndrome, an early-onset autolysosomal pathology, leads to the activation of alternative clearance pathways including Golgi membrane-associated and nucleophagy-based LaminB1 degradation and excretion. The combination of these alternative pathways and canonical autophagy blockade, results in dramatic nuclear pathology with disruption of the nuclear organization, bringing about terminal cell atrophy and degeneration. Thus, our findings identify a novel progressive mechanism for the terminal phases of neuronal cell degeneration and death in human neurodegenerative diseases and provide a link between autophagy block, activation of alternative pathways for degradation, and excretion of cellular components

Super-Resolution Maps of the Solar Magnetic Field Covering 40 Years of Space Weather Events

As modern society becomes increasingly dependent on technology, space weather events will have a farther-reaching impact than ever before. For nearly 10 years, NASA's Solar Dynamics Observatory (SDO) has continuously monitored the Sun, however, the SDO-era coincides with the weakest solar cycle of the last century: over the last 40 years, there have been nearly 500 X-class solar flares—around 10 times the number of events observed by SDO alone. It is also clear that there is no single observational survey with sufficient time coverage to enable an effective deep learning space weather forecasting application. Crucially, over the past 40 years, numerous observatories have monitored the Sun's magnetic field. However, cross calibrating magnetograms is a complex and non-trivial endeavour as the relationship between observed pixels is strongly affected by a wide range of systematics. Here we present a deep learning application that can convert magnetograms to a target survey while preserving the features and systematics of the target survey. We will first present our approach for upscaling and cross-calibrating images obtained by the Michelson Doppler Imager (MDI; on-board the Solar and Heliospheric Observatory, SOHO), to the resolution of the Helioseismic and Magnetic Imager (SDO/HMI). We will discuss the physics-based metrics, deep learning architectures, and the lessons learned along the way. This work was performed at NASA’s Frontier Development Laboratory (FDL), a public-private partnership to apply AI techniques to accelerate space science discovery and exploration

Centronuclear myopathy due to a de novo dominant mutation in the skeletal muscle ryanodine receptor (RYR1) gene.

Centronuclear myopathy is a genetically heterogeneous congenital myopathy. Whilst mutations in the myotubularin (MTM1) gene are implicated in the X-linked variant, mutations in the dynamin 2 (DNM2) gene have been recently associated with dominant inheritance. We report a 16-year-old girl with clinical features of a congenital myopathy and external ophthalmoplegia. Multiple central nuclei affecting up to 50% of fibres and central accumulation of oxidative enzyme stains were the most prominent findings on muscle biopsy obtained at 1 year. However, some core-like areas appeared on repeat biopsy 8 years later; in addition, muscle MRI was compatible with the pattern we previously reported in patients with mutations in the skeletal muscle ryanodine receptor (RYR1) gene. Mutational analysis identified a de novo dominant RYR1 missense mutation (c.12335C>T; Ser4112Leu) affecting a highly conserved domain of the protein. Our findings expand the phenotypical spectrum associated with RYR1 mutations and indicate that RYR1 screening should be considered in centronuclear myopathy patients without MTM1 or DNM2 mutations; muscle MRI may aid selection of appropriate genetic testing