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1. Sequoia: an interactive visual analytics platform for interpretation and feature extraction from nanopore sequencing datasets

   https://doi.org/10.1186/s12864-021-07791-z  

   Koonchanok, Ratanond ; Daulatabad, Swapna Vidhur ; Mir, Quoseena ; Reda, Khairi ; Janga, Sarath Chandra ( July 2021 , BMC Genomics)

   Direct-sequencing technologies, such as Oxford Nanopore’s, are delivering long RNA reads with great efficacy and convenience. These technologies afford an ability to detect post-transcriptional modifications at a single-molecule resolution, promising new insights into the functional roles of RNA. However, realizing this potential requires new tools to analyze and explore this type of data.

   Here, we present Sequoia, a visual analytics tool that allows users to interactively explore nanopore sequences. Sequoia combines a Python-based backend with a multi-view visualization interface, enabling users to import raw nanopore sequencing data in a Fast5 format, cluster sequences based on electric-current similarities, and drill-down onto signals to identify properties of interest. We demonstrate the application of Sequoia by generating and analyzing ~ 500k reads from direct RNA sequencing data of human HeLa cell line. We focus on comparing signal features from m6A and m5C RNA modifications as the first step towards building automated classifiers. We show how, through iterative visual exploration and tuning of dimensionality reduction parameters, we can separate modified RNA sequences from their unmodified counterparts. We also document new, qualitative signal signatures that characterize these modifications from otherwise normal RNA bases, which we were able to discover from the visualization.

   Sequoia’s interactive features complementmore »existing computational approaches in nanopore-based RNA workflows. The insights gleaned through visual analysis should help users in developing rationales, hypotheses, and insights into the dynamic nature of RNA. Sequoia is available athttps://github.com/dnonatar/Sequoia.

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2. Cosmic-CoNN: A Cosmic-Ray Detection Deep-learning Framework, Data Set, and Toolkit

   https://doi.org/10.3847/1538-4357/ac9d91  

   Xu, Chengyuan ; McCully, Curtis ; Dong, Boning ; Howell, D. Andrew ; Sen, Pradeep ( January 2023 , The Astrophysical Journal)

   Rejecting cosmic rays (CRs) is essential for the scientific interpretation of CCD-captured data, but detecting CRs in single-exposure images has remained challenging. Conventional CR detectors require experimental parameter tuning for different instruments, and recent deep-learning methods only produce instrument-specific models that suffer from performance loss on telescopes not included in the training data. We present Cosmic-CoNN, a generic CR detector deployed for 24 telescopes at the Las Cumbres Observatory, which has been made possible by the three contributions in this work: (1) We build a large and diverse ground-based CR data set leveraging thousands of images from a global telescope network. (2) We propose a novel loss function and a neural network optimized for telescope imaging data to train generic CR-detection models. At 95% recall, our model achieves a precision of 93.70% on Las Cumbres imaging data and maintains a consistent performance on new ground-based instruments never used for training. Specifically, the Cosmic-CoNN model trained on the Las Cumbres CR data set maintains high precisions of 92.03% and 96.69% on Gemini GMOS-N/S 1 × 1 and 2 × 2 binning images, respectively. (3) We build a suite of tools including an interactive CR mask visualization and editing interface, consolemore »commands, and Python APIs to make automatic, robust CR detection widely accessible by the community of astronomers. Our data set, open-source code base, and trained models are available athttps://github.com/cy-xu/cosmic-conn.

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3. Contact.engineering—Create, analyze and publish digital surface twins from topography measurements across many scales

   https://doi.org/10.1088/2051-672X/ac860a  

   Röttger, Michael C. ; Sanner, Antoine ; Thimons, Luke A. ; Junge, Till ; Gujrati, Abhijeet ; Monti, Joseph M. ; Nöhring, Wolfram G. ; Jacobs, Tevis D. B. ; Pastewka, Lars ( September 2022 , Surface Topography: Metrology and Properties)

   The optimization of surface finish to improve performance, such as adhesion, friction, wear, fatigue life, or interfacial transport, occurs largely through trial and error, despite significant advancements in the relevant science. There are three central challenges that account for this disconnect: (1) the challenge of integration of many different types of measurement for the same surface to capture the multi-scale nature of roughness; (2) the technical complexity of implementing spectral analysis methods, and of applying mechanical or numerical models to describe surface performance; (3) a lack of consistency between researchers and industries in how surfaces are measured, quantified, and communicated. Here we present a freely-available internet-based application (available athttps://contact.engineering) which attempts to overcome all three challenges. First, the application enables the user to upload many different topography measurements taken from a single surface, including using different techniques, and then integrates all of them together to create a digital surface twin. Second, the application calculates many of the commonly used topography metrics, such as root-mean-square parameters, power spectral density (PSD), and autocorrelation function (ACF), as well as implementing analytical and numerical calculations, such as boundary element modeling (BEM) for elastic and plastic deformation. Third, the application servesmore »as a repository for users to securely store surfaces, and if they choose, to share these with collaborators or even publish them (with a digital object identifier) for all to access. The primary goal of this application is to enable researchers and manufacturers to quickly and easily apply cutting-edge tools for the characterization and properties-modeling of real-world surfaces. An additional goal is to advance the use of open-science principles in surface engineering by providing a FAIR database where researchers can choose to publish surface measurements for all to use.

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4. Symmetry-aware recursive image similarity exploration for materials microscopy

   https://doi.org/10.1038/s41524-021-00637-y  

   Nguyen, Tri N. M. ; Guo, Yichen ; Qin, Shuyu ; Frew, Kylie S. ; Xu, Ruijuan ; Agar, Joshua C. ( October 2021 , npj Computational Materials)

   In pursuit of scientific discovery, vast collections of unstructured structural and functional images are acquired; however, only an infinitesimally small fraction of this data is rigorously analyzed, with an even smaller fraction ever being published. One method to accelerate scientific discovery is to extract more insight from costly scientific experiments already conducted. Unfortunately, data from scientific experiments tend only to be accessible by the originator who knows the experiments and directives. Moreover, there are no robust methods to search unstructured databases of images to deduce correlations and insight. Here, we develop a machine learning approach to create image similarity projections to search unstructured image databases. To improve these projections, we develop and train a model to include symmetry-aware features. As an exemplar, we use a set of 25,133 piezoresponse force microscopy images collected on diverse materials systems over five years. We demonstrate how this tool can be used for interactive recursive image searching and exploration, highlighting structural similarities at various length scales. This tool justifies continued investment in federated scientific databases with standardized metadata schemas where the combination of filtering and recursive interactive searching can uncover synthesis-structure-property relations. We provide a customizable open-source package (https://github.com/m3-learning/Recursive_Symmetry_Aware_Materials_Microstructure_Explorer) of thismore »interactive tool for researchers to use with their data.

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5. Offsets between X-Ray and Radio Components in X-Ray Jets: The AtlasX

   https://doi.org/10.3847/1538-4365/aca321  

   Reddy, Karthik ; Georganopoulos, Markos ; Meyer, Eileen T. ; Keenan, Mary ; Kollmann, Kassidy E. ( February 2023 , The Astrophysical Journal Supplement Series)

   The X-ray emission mechanism of powerful extragalactic jets—which has important implications for their environmental impacts—is poorly understood. The X-ray/radio positional offsets in the individual features of jets provide important clues. Extending previous work in Reddy et al., we present a detailed comparison between X-ray maps, deconvolved using the Low-count Image Reconstruction and Analysis tool, and radio maps of 164 components from 77 Chandra-detected X-ray jets. We detect 94 offsets (57%), with 58 new detections. In FR II–type jet knots, the X-rays peak and decay before the radio in about half the cases, disagreeing with the predictions of one-zone models. While a similar number of knots lack statistically significant offsets, we argue that projection and distance effects result in offsets below the detection level. Similar deprojected offsets imply that X-rays could be more compact than radio for most knots, and we qualitatively reproduce this finding with a “moving-knot” model. The bulk Lorentz factor (Γ) derived for knots under this model is consistent with previous radio-based estimates, suggesting that kiloparsec-scale jets are only mildly relativistic. An analysis of the X-ray/radio flux ratio distributions does not support the commonly invoked mechanism of X-ray production from inverse Compton scattering of the cosmic microwavemore »background, but does show a marginally significant trend of declining flux ratio as a function of the distance from the core. Our results imply the need for multi-zone models to explain the X-ray emission from powerful jets. We provide an interactive list of our X-ray jet sample athttp://astro.umbc.edu/Atlas-X.

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