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This study was focused on extraction, radical scavenging activities, and chemical composition identification of total flavonoids in sunflower ( Helianthus annuus L.) receptacles (TFSR). We investigated the optimal extract parameters of TFSR using response surface methodology. The highest yield of TFSR was 1.04% with the ethanol concentration 58%, the material-to-liquid ratio 1:20 ( v / w ), the extraction time 2.6 h, and the extraction temperature 67 °C. The results of radical scavenging activities showed that ethyl acetate fraction (EAF) was the strongest by using 2-diphenyl-1-picrylhydrazyl (DPPH), 2, 2'-azino-bis (3-ethylbenzo thiazoline-6-sulfonic acid) (ABTS) and iron ion reducing analysis. The EAF had the highest flavonoids contents. Four fractions A, B, C and D were enrichment from EAF by polyamide resin. Fraction B had the highest flavonoids content. Thirteen chemical components of flavonoids in fraction B were first identified by Ultimate 3000 Nano LC System coupled to a Q Exactive HF benchtop Orbitrap mass spectrometer (UHPLC-HRMS/MS). Among of the thirteen chemical components, isoquercetin and daidzein were identified accurately by comparing with standard samples. Radical scavenging analysis showed that isoquercetin and EAF had strong activities. Therefore, sunflower receptacles can be used as a source of natural flavonoids. TFSR as a natural radical scavenger has potential applications in pharmaceutical industry.

Better tools are needed to enable researchers to quickly identify and explore effective and interpretable feature-based explanations for discriminating multi-class genomic datasets, e.g., healthy versus diseased samples. We develop an interactive exploration tool, GENVISAGE, which rapidly discovers the most discriminative feature pairs that separate two classes of genomic objects and then displays the corresponding visualizations. Since quickly finding top feature pairs is computationally challenging, especially for large numbers of objects and features, we propose a suite of optimizations to make GENVISAGE responsive at scale and demonstrate that our optimizations lead to a 400× speedup over competitive baselines for multiple biological datasets. We apply our rapid and interpretable tool to identify literature-supported pairs of genes whose transcriptomic responses significantly discriminate several chemotherapy drug treatments. With its generalizable optimizations and framework, GENVISAGE opens up real-time feature-based explanation generation to data from massive sequencing efforts, as well as many other scientific domains., Competing Interests: The authors declare no competing interests., (© 2020 The Authors.)

The subject of topological materials has attracted immense attention in condensed-matter physics because they host new quantum states of matter containing Dirac, Majorana, or Weyl fermions. Although Majorana fermions can only exist on the surface of topological superconductors, Dirac and Weyl fermions can be realized in both 2D and 3D materials. The latter are semimetals with Dirac/Weyl cones either not tilted (type I) or tilted (type II). Although both Dirac and Weyl fermions have massless nature with the nontrivial Berry phase, the formation of Weyl fermions in 3D semimetals require either time-reversal or inversion symmetry breaking to lift degeneracy at Dirac points. Here we demonstrate experimentally that canted antiferromagnetic BaMnSb 2 is a 3D Weyl semimetal with a 2D electronic structure. The Shubnikov-de Hass oscillations of the magnetoresistance give nearly zero effective mass with high mobility and the nontrivial Berry phase. The ordered magnetic arrangement (ferromagnetic ordering in the ab plane and antiferromagnetic ordering along the c axis below 286 K) breaks the time-reversal symmetry, thus offering us an ideal platform to study magnetic Weyl fermions in a centrosymmetric material., Competing Interests: The authors declare no conflict of interest.

The relative ease of collaborative data science and analysis has led to a proliferation of many thousands or millions of versions of the same datasets in many scientific and commercial domains, acquired or constructed at various stages of data analysis across many users, and often over long periods of time. Managing, storing, and recreating these dataset versions is a non-trivial task. The fundamental challenge here is the storage-recreation trade-off : the more storage we use, the faster it is to recreate or retrieve versions, while the less storage we use, the slower it is to recreate or retrieve versions. Despite the fundamental nature of this problem, there has been a surprisingly little amount of work on it. In this paper, we study this trade-off in a principled manner: we formulate six problems under various settings, trading off these quantities in various ways, demonstrate that most of the problems are intractable, and propose a suite of inexpensive heuristics drawing from techniques in delay-constrained scheduling, and spanning tree literature, to solve these problems. We have built a prototype version management system, that aims to serve as a foundation to our DataHub system for facilitating collaborative data science. We demonstrate, via extensive experiments, that our proposed heuristics provide efficient solutions in practical dataset versioning scenarios.