Heliophysics Discovery Tools for the 21st Century: Data Science and Machine Learning Structures and Recommendations for 2020-2050

Three main points: 1. Data Science (DS) will be increasingly important to heliophysics; 2. Methods of heliophysics science discovery will continually evolve, requiring the use of learning technologies [e.g., machine learning (ML)] that are applied rigorously and that are capable of supporting discovery; and 3. To grow with the pace of data, technology, and workforce changes, heliophysics requires a new approach to the representation of knowledge.Comment: 4 pages; Heliophysics 2050 White Pape

AI-ready data in space science and solar physics: problems, mitigation and action plan

In the domain of space science, numerous ground-based and space-borne data of various phenomena have been accumulating rapidly, making analysis and scientific interpretation challenging. However, recent trends in the application of artificial intelligence (AI) have been shown to be promising in the extraction of information or knowledge discovery from these extensive data sets. Coincidentally, preparing these data for use as inputs to the AI algorithms, referred to as AI-readiness, is one of the outstanding challenges in leveraging AI in space science. Preparation of AI-ready data includes, among other aspects: 1) collection (accessing and downloading) of appropriate data representing the various physical parameters associated with the phenomena under study from different repositories; 2) addressing data formats such as conversion from one format to another, data gaps, quality flags and labeling; 3) standardizing metadata and keywords in accordance with NASA archive requirements or other defined standards; 4) processing of raw data such as data normalization, detrending, and data modeling; and 5) documentation of technical aspects such as processing steps, operational assumptions, uncertainties, and instrument profiles. Making all existing data AI-ready within a decade is impractical and data from future missions and investigations exacerbates this. This reveals the urgency to set the standards and start implementing them now. This article presents our perspective on the AI-readiness of space science data and mitigation strategies including definition of AI-readiness for AI applications; prioritization of data sets, storage, and accessibility; and identifying the responsible entity (agencies, private sector, or funded individuals) to undertake the task

Origins of the Ambient Solar Wind: Implications for Space Weather

The Sun's outer atmosphere is heated to temperatures of millions of degrees, and solar plasma flows out into interplanetary space at supersonic speeds. This paper reviews our current understanding of these interrelated problems: coronal heating and the acceleration of the ambient solar wind. We also discuss where the community stands in its ability to forecast how variations in the solar wind (i.e., fast and slow wind streams) impact the Earth. Although the last few decades have seen significant progress in observations and modeling, we still do not have a complete understanding of the relevant physical processes, nor do we have a quantitatively precise census of which coronal structures contribute to specific types of solar wind. Fast streams are known to be connected to the central regions of large coronal holes. Slow streams, however, appear to come from a wide range of sources, including streamers, pseudostreamers, coronal loops, active regions, and coronal hole boundaries. Complicating our understanding even more is the fact that processes such as turbulence, stream-stream interactions, and Coulomb collisions can make it difficult to unambiguously map a parcel measured at 1 AU back down to its coronal source. We also review recent progress -- in theoretical modeling, observational data analysis, and forecasting techniques that sit at the interface between data and theory -- that gives us hope that the above problems are indeed solvable.Comment: Accepted for publication in Space Science Reviews. Special issue connected with a 2016 ISSI workshop on "The Scientific Foundations of Space Weather." 44 pages, 9 figure

Basement membrane components are key players in specialized extracellular matrices

More than three decades ago, basement membranes (BMs) were described as membrane-like structures capable of isolating a cell from and connecting a cell to its environment. Since this time, it has been revealed that BMs are specialized extracellular matrices (sECMs) with unique components that support important functions including differentiation, proliferation, migration, and chemotaxis of cells during development. The composition of these sECM is as unique as the tissues to which they are localized, opening the possibility that such matrices can fulfill distinct functions. Changes in BM composition play significant roles in facilitating the development of various diseases. Furthermore, tissues have to provide sECM for their stem cells during development and for their adult life. Here, we briefly review the latest research on these unique sECM and their components with a special emphasis on embryonic and adult stem cells and their niches

Development of low-density oligonucleotide microarrays for detecting mutations causing Wilson′s disease

Background & objectives : Wilson′s disease (WD) is an autosomal recessive disorder caused by mutations in ATP7B, a copper transporter gene, leading to hepatic and neuropsychiatric manifestations due to copper accumulation. If diagnosed early, WD patients can be managed by medicines reducing morbidity and mortality. Diagnosis of this disease requires a combination of tests and at times is inconclusive due to overlap of the symptoms with other disorders. Genetic testing is the preferred alternative in such cases particularly for individuals with a family history. Use of DNA microarray for detecting mutations in ATP7B gene is gaining popularity because of the advantages it offers in terms of throughput and sensitivity. This study attempts to establish the quality analysis procedures for microarray based diagnosis of Wilson′s disease. Methods: A home-made microarrayer was used to print oligonucleotide based low-density microarrays for addressing 62 mutations causing Wilson′s disease reported from Indian population. Inter- and intra- array comparisons were used to study quality of the arrays. The arrays were validated by using mutant samples generated by site directed mutagenesis. Results: The hybridization reaction were found to be consistent across the surface of a given microarray. Our results have shown that 52 °C post-hybridization wash yields better reproducibility across experiments compared to 42 °C. Our arrays have shown > 80 per cent sensitivity in detecting these 62 mutations. Interpretation & conclusions: The present results demonstrate the design and evaluation of a low-density microarray for the detection of 62 mutations in ATP7B gene, and show that a microarray based approach can be cost-effective for detecting a large number of mutations simultaneously. This study also provides information on some of the important parameters required for microarray based diagnosis of genetic disorders