dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction The detection of RNA modifications has recently re-emerged as a very timely topic of current research. Coupled to new detection methods came new insights into the function of RNA modifications in the regulation of RNA stability  [1] , regulation of gene expression  [2] [3] [4] [5] , and immunity  [6]. RNA modifications are structurally highly diverse, and among the approximately 150 chemically different structures in the Modomics database  [7] , all major classes of natural product compounds can be found  [8, 9]. Furthermore, there is evidence that the diversity may yet increase with the discovery of more modifications  [10]. Despite this high diversity, some common denominators apply to both function and detection. Here, two important features for detection are reverse transcription (RT) arrest and misincorporation during complementary DNA (cDNA) synthesis.
Collection  [7]  and curation  [21]  of RNA sequences containing modifications underline a central problem in the field, arising from the vast number of candidate sites in large datasets. Because of these vast numbers, experimental verification of candidate sites by independent methods must typically be restricted to a small subset. Before engaging in such an endeavor, the experimentalist, and potential user of the software presented here, may want to assess the significance of an identification event, and visually inspect parameters at a given site. In principle, a variety of so-called alignment viewers like IGV, Tablet, Savant, UGENE and Persephone provide more or less detailed graphical representations of mapping results, typically resolving the base composition and orientation of reads covering a reference sequence.
A typical CAn-session, conceived to identify, highlight, and visually inspect modification candidates, is depicted in Figure  1. The user is required to input a dataset in SAM format, containing RNA sequencing (RNA Seq) reads mapped to a genome or transcriptome. This is converted to the internally used Profile format by an automated pipeline (Figure  1a ). To optimally detect stalled RT events, a parameter called CSA was introduced, which queries a local background arrest rate near the inspection site and takes it into account. CSA was defined as the fold change of a site i's arrest rate A  [22]  with respect to the median A of its sequence environment of r bases up-and r bases downstream (here r = 5): The CSA feature, since it maps cDNA from abortive RT events, can only be meaningfully applied to data from library preparation protocols that specifically include such reads.
Whether or not a protocol does so, typically hinges upon the incorporation step that introduces the second primer binding site. The installation package provides test datasets which were obtained by a library preparation protocol that captures abortive RT reads by ligation of a second adapter to the cDNA, as described in  [22]. After selection of an RNA sequence of interest (Figure  1b ), the software displays the sequence for visual inspection in a window (Figure  1c(i) ). Events are labeled (yellow triangles), where values for misincorporation or peaks of CSA exceed adjustable thresholds. Additional profiles of the same sequence can be loaded and displayed in parallel plots (Figure  1c (ii)) for comparison of samples of variegated modification status, for example a wild-type RNA preparation versus one from The CSA feature, since it maps cDNA from abortive RT events, can only be meaningfully applied to data from library preparation protocols that specifically include such reads.
Whether or not a protocol does so, typically hinges upon the incorporation step that introduces the second primer binding site. The installation package provides test datasets which were obtained by a library preparation protocol that captures abortive RT reads by ligation of a second adapter to the cDNA, as described in  [22]. After selection of an RNA sequence of interest (Figure  1b ), the software displays the sequence for visual inspection in a window (Figure  1c(i) ). Events are labeled (yellow triangles), where values for misincorporation or peaks of CSA exceed adjustable thresholds. Additional profiles of the same sequence can be loaded and displayed in parallel plots (Figure  1c (ii)) for comparison of samples of variegated modification status, for example a wild-type RNA preparation versus one from a knockout organism lacking a certain modification activity  [22].
Another application of interest is exemplified by the included test dataset, namely a chemical treatment suspected to alter the profile of certain modifications. With test data 1 as the naive sample in window (i) and test data 2 treated with an agent causing partial deamination of 5-methylcytidines in window (ii); a differential plot was generated in window (iii), where differences are displayed according to self-defined threshold criteria, and combinations thereof. The candidate casting tab (snippet shown in Figure  1d ) offers a formula editor to generate filter rules of arbitrary complexity using thresholds combined by Boolean expressions and brackets. The resulting candidates files can be submitted to batch plotting for fast visual inspection of many candidate positions. With high flexibility in image dimensions, parameters, and legend details, these data can be exported as publication-ready images.
Discussion CAn is a tool that allows the visualization and assisted inspection of deep sequencing data in the search for RNA modifications. Perusal of vast amounts of data is facilitated by a toolbox that allows to automatically highlight sites, where noticeably unusual combinations of RT arrest and misincorporation hint at the potential presence of modifications. Of note, there are no predefined thresholds that the program uses to flag unusual instances. Rather, it is up to the user to define threshold values for different parameters, and to combine them by Boolean operators. CAn is not meant to predict a modification event, or even to decipher the chemical structure of a potential modification. The program is rather designed to point attention to special candidate sites for its visual inspection.
Inspection of large datasets automatically increases the statistical likelihood of the occurrence of conspicuous signals without a biochemical cause. Therefore, it is prudent to increase the stringency in such a case. While it is left to the user to decide how p-values are used to gauge the significance of findings, we recommend to use techniques like the Bonferroni correction  [25]  in order to account for the number of tested positions. In addition, the False Discovery Rate (FDR) can be controlled in the manner of Benjamini and Hochberg  [26]. Outside these two approaches, which are rooted in statistics, a number of experimental approaches are open to the user to improve confidence by experimentally validating the candidate sites proposed by CAn. We urgently propose to call and treat these sites as "candidates" until validated by further experiments, e.
Implementation The graphical user interface (GUI) and the core of CAn are written in Java. The Miniconda Python based plotting component uses Matplotlib  [29] , Numpy  [30]  and Scipy. The software is distributed as self-extracting archive (~100 MB) for Windows (64-bit) and as zip files installed via included script setup routines for Linux and Mac OS X. Dependencies are downloaded automatically. On Mac OS X, latest Homebrew is installed to setup SAMtools  [31]. Java 1. 7+ is expected to be installed by the user, whereas Linux version installs dependencies via apt-get. Test data, a getting-started screencast and a user manual are included.
Conclusions CAn was developed as a cross-platform open-source software running on most current computers. It allows efficient inspection of RNA Seq profiles for RT signatures of modifications, such as m 1 A  [22]. The user is provided with assistance to identify unusual patterns, to compare different datasets containing the same sequence, and to perform significance-based candidate calling. Important to the field is the implementation of both misincorporation patterns and RT arrest, including also the CSA format as defined during our recent extraction of RT signatures by machine learning  [22]. CAn is highly conductive to the extraction of complete RT signatures, by providing full control of all thresholds for visualization, identification and discrimination to the user. Supplementary Materials: CoverageAnalyzer setup files (incl.
Acknowledgments: This work was supported by the  DFG   SPP1784 ,  MH3397/12-1 ,  MH3397/14-1  to M. H. ; by an  ANR  HTRNAMod  ANR-13-ISV8-0001  grant to Y. M. and by a fellowship to R. H. from the  International PhD Programme (IPP)  at the  Institute of Molecular Biology (IMB) Mainz , funded by the  Boehringer Ingelheim Foundation.
Therefore, they are considered equal to the isotropic value of 1. 0. In brief, the number of parameters to be identified for the CPB06 yield criterion is eight: seven anisotropy parameters and k. For the YLD91 yield criterion this number reduces to four, i. e. the anisotropy parameters, since 8 m . Both yield criteria were implemented in the in-house code DD3MAT, enabling the use of all the experimental data defined in Eq. (11)  in the parameters identification procedure. The optimization algorithm implemented in DD3MAT is based on a downhill simplex method, which is a derivative free method. For the CPB06 yield criterion, the identification procedure assumes a range of   1,1  for k and a range of    to RD.
Acknowledgements The authors gratefully acknowledge the financial support of the  Portuguese Foundation for Science and Technology (FCT)  under projects with reference  PTDC/EMS-TEC/0702/2014  ( POCI-01-0145-FEDER-016779 ) and  PTDC/EMS-TEC/6400/2014  ( POCI-01-0145-FEDER-016876 ) by  UE/FEDER  through the program  COMPETE 2020. The first author is also grateful to the  FCT  for the PhD grant  SFRH/BD/98545/2013.
To address this issue, we have executed a panchromatic (X-ray to mid-IR) study of the spectral energy distributions of 11 nearby planet hosting stars, the Measurements of the Ultraviolet Spectral Characteristics of Low-mass Exoplanetary Systems (MUSCLES) Treasury Survey. The MUSCLES program consists of contemporaneous observations in the X-rays with Chandra and XMM-Newton, ultraviolet observations with Hubble, and visible observations from Hubble and ground-based observatories. Infrared and astrophysically inaccessible wavelengths (EUV and Lyα) are reconstructed using stellar model spectra to fill-in gaps in the observational data. In this overview and companion papers describing the MUSCLES survey, we show that energetic radiation (X-ray and ultraviolet) is present from magnetically active stellar atmospheres at all times for stars as late as M5.
g. , Sun in Time;  Ribas et al. 2005) , M dwarfs have received less attention (see also  Guinan & Engle 2009) , despite the fact that these systems dominate the planet statistics of the Galaxy. To address the above question, we have carried out the first panchromatic survey of M and K dwarf exoplanet host stars in the solar neighborhood  (d 20 pc). We refer to this program as the Measurements of the Ultraviolet Spectral Characteristics of Low-mass Exoplanetary Systems (MUSCLES) Treasury Survey, a coordinated Xray to NIR observational effort to provide the exoplanet community with empirically-derived panchromatic irradiance spectra for the study of all types of exoplanets orbiting these stars. MUSCLES is largely based on a Hubble Space Telescope Cycle 22 treasury program and makes use of smaller guest observing programs on HST , XMM-Newton, Chandra, and several ground-based observatories.
Our survey provides a database of the chromospheric, transition region, and coronal properties of low-mass stars hosting exoplanets, providing high-quality input for models of both Jovian and Earth-like planets as vast numbers of these systems are discovered and characterized in the next decade with missions such as W F IRST , P lato, T ESS, and JW ST. While the long-term evolution of the broadband UV luminosity function of M dwarfs can be constrained with large photometric samples from GALEX (e. g. , Shkolnik & Barman 2014), a further uncertainty in the temporal behavior of low-mass exoplanet host stars is the variability on time scales of years (the stellar equivalent of the solar cycle). Given the limitations on space observatories, particularly as it is likely that we will be in the post-HST and post-Chandra era within the next 5 -10 years, it is critically important to identify a set of visible-wavelength tracers (e.
g. , Gomes da  Silva et al. 2011 ) that can be used to quantify the longer-term (years-to-decades) UV variability of these stars. The MUSCLES dataset will enable us to derive empirical relations between optical, UV, and X-ray fluxes (e. g. , FUV luminosity vs. Ca II and H-α profiles), as well as their relative behavior during flares, supporting long-term ground-based programs to study the time evolution of the energetic radiation environment. This paper provides an overview of the motivation for and the design of the MUSCLES Treasury Survey, as well as some initial quantitative results. A detailed example of the need for HST to carry out this work is given is Section 2. Section 3 describes the MUSCLES target list and the description of the observing modes used in the program. Section 4 presents the evolution of the broad-band SED with stellar effective temperature and habitable zone distance, provides scaling relations to estimate the broadband luminosity from individual spectral line measurements, and presents a first look at the intense high-energy flare behavior of these otherwise inactive stars.
Section 5 explores the interaction of the planets and host stars in these systems, and compares the UV flux measurements with predictions from coronal models. Section 6 presents a summary of the important results from this paper. Details on all aspects of the data analysis and scientific results from the program can be found in the companion papers from the MUSCLES team. Youngblood et al. (2016b)  present an analysis of the essential reconstruction of the intrinsic Lyα stellar emission lines and the calculations of the EUV irradiance ("Paper II"). Loyd et al. (2016b) , "Paper III", describe the creation of the panchromatic spectral energy distributions (SEDs), quantification of the SEDs for exoplanet atmospheric modeling, and a description of how to download the data in machine-readable format from the Milkulski Archive -A comparison of the IU E spectrum of the M3V star GJ 644 and the weakly active M3V exoplanet host star GJ 581  (France et al.
The X-ray observations were coordinated with the COS G130M 5 orbit monitoring program (see above) to establish the longest possible simultaneous baseline over which to explore the panchromatic properties of M and K dwarf flares. We observed GJ 667C, GJ 436, GJ 176, and GJ 876 using the Chandra ACIS-S back-illuminated S3 chip. Owing to an HST safing event in 2015 June, the GJ 876 campaign was not simultaneous. We observed ǫ Eri and GJ832 with XMM-Newton because they are optically bright. ǫ Eri is by far the brightest X-ray source in our sample and provided a high-quality RGS grating spectrum. ǫ Eri re- The MUSCLES Treasury Survey Overview   The spectra are normalized by their total bolometric luminosity. These spectra were taken primarily with the COS G230L grating observations in order to show a uniform comparison.
For the brighter K stars, we display higher resolution HST -STIS E230M and E230H observations. At higher resolution, one can resolve the absorption from interstellar Mg + ions. Dashed lines identify the Mg II rest wavelengths. quired use of the pn/MOS Thick filters, while GJ832 used the Medium filters. We also obtained photometry with the UVM2 filter for GJ 832. HD 85512 and HD 40307 were observed as part of a complementary XMM-Newton program (PI -A. Brown) using the same configuration as for GJ 832. For the stars without contemporaneous X-ray data, we obtained spectra from the Chandra and XMM-Newton archives  (Loyd et al. 2016; Brown et al. 2016 -in prep. ). The coronal model fit to the X-ray data is extended into the 50 -100 Å region where no direct observations are available. 7 -8 -7 -6 GJ1214 CII OV SiIV OIV] SiIV -8 -7 -6 GJ176 -8 -7 -6 GJ436 -8 -7 -6 Flux / F Bol GJ581 -8 -7 -6 GJ667C 1340 1360 1380 1400 Wavelength (Å) -8 -7 -6 GJ832 Optical Observations, 3200 -6000 Å -We carried out complementary ground-based observations as close in time as possible to the MUSCLES UV/Xray observations.
Our primary optical spectra came from the ARCES and DIS instruments on the Astrophysical Research Consortium (ARC) 3. 5-m telescope at Apache Point Observatory (3700 -10000 Å, depending on the mode), the 2. 15-meter Jorge Sahade telescope at the Complejo Astronomico El Leoncito  (Cincunegui & Mauas 2004) , and the FLOYDS intrument on the Las Cumbras Observatory Global Telescope Network  (Brown et al. 2013). Multi-band optical photometry was acquired with the Apache Point Observatory 0. 5-m ARCSAT telescope (with the Flarecam instrument) and the LCOGT. The optical data and their correlation with spectral and temporal behavior of the high-energy emission will be presented in a future work by the MUSCLES team  (Youngblood et al. 2016a). In order to calibrate the UV data with respect to visible/IR photospheric models and the ground-based spectra, we also acquired short optical observations with STIS G430L or G430M (depending on target brightness) during each HST campaign.
RESULTS: 5 Å TO 5 µM IRRADIANCE SPECTRA The primary goal of the MUSCLES Treasury Survey is to develop a uniform set of irradiance spectra to support the study of extrasolar planets orbiting lowmass stars. The X-ray through optical observations are described above, a description of the analysis required to complete "missing" parts of the SED (intrinsic Lyα line profiles and EUV fluxes) is presented in Paper II  (Youngblood et al. 2016b) , and a detailed discussion of the creation of the panchromatic radiation fields is given in Paper III of this series  (Loyd et al. 2016b ). In the following subsections, we present both quantitative and qualitative descriptions of the SEDs, including the behavior of the broadband fluxes as a function of stellar effective temperature and habitable zone location, the distribution of stellar emission lines observed in the HST spectra, the use of individual spectral tracers to estimate the broadband energetic radiation fluxes, and an initial description of the temporal variability of the MUSCLES Treasury dataset.
Temporally Resolved Spectra and Energetic Flares As discussed in the introduction, temporal variability of the energetic radiation is considerably higher for M dwarfs than for solar type stars  (Mitra-Kraev et al. 2005) , with active M stars showing disk-integrated flux increases of an order-of-magnitude or more during large flares  (Hawley & Pettersen 1991; Hawley et al. 2003; Osten et al. 2005). The MUSCLES pilot program showed that even inactive M dwarfs could show impulsive flare behavior in their UV light curves  (France et al. 2012a). The UV monitoring component of the MUS-CLES Treasury Survey was designed to present a uniform database of flares from exoplanet host stars so the flare frequency-amplitude relations could be derived for these stars and the impact of impulsive events on the atmospheres or orbiting planets could be assessed.
g. ,  Shkolnik et al. 2008; Poppenhaeger et al. 2010). The search for star-planet interaction (SPI) has predominantly focused on hot Jupiters orbiting F, G, and K stars. The MUSCLES database allows us to examine potential star-planet interactions for a range of planetary masses and semi-major axes on a new class of optically inactive low-mass star. Furthermore, the MUS-CLES database allows us to explore SPI as a function of emission line formation temperature, which can constrain the possible location of magnetic field line connection in the stellar atmosphere and subsequent location of the plasma heating. Figure  16  shows a correlation between the N V fractional luminosity and M plan /a plan. The N V -M plan /a plan relation has a Pearson correlation coefficient of 0. 83 and a statistical non-correlation likelihood of 0.
The average FUV and XUV fluxes in the habitable zones of all K and M dwarfs studied are ≈ 10 -70 erg cm -2 s -1. The spectral energy distribution of the radiation field changes dramatically for different habitable zone distances around low-mass stars, but the intensity of the high-energy radiation field does not. 5. Emission measure distribution models based on Xray (coronal) data alone underestimate the FUV transition region flux by factors of ∼ 5 -30, meaning that these models should not be used for calculating the FUV radiation field of exoplanet host stars. The MUSCLES database provides an excellent resource for emission measure modeling of the stellar atmosphere where lines formed in the chromosphere, transition region, and corona can all be taken into account. 6. We present tentative evidence for star-planet interaction by measuring the fractional emission line luminosity as a function of the star-planet interaction strength, M plan /a plan.
The semicircular duct system is part of the sensory organ of balance and essential for navigation and spatial awareness in vertebrates. Its function in detecting head rotations has been modelled with increasing sophistication, but the biomechanics of actual semicircular duct systems has rarely been analyzed, foremost because the fragile membranous structures in the inner ear are hard to visualize undistorted and in full. Here we present a new, easy-to-apply and non-invasive method for threedimensional in-situ visualization and quantification of the semicircular duct system, using X-ray micro tomography and tissue staining with phosphotungstic acid. Moreover, we introduce Ariadne, a software toolbox which provides comprehensive and improved morphological and functional analysis of any visualized duct system.
Here we present a new, comprehensive and integrated methodological approach to the study of the semicircular duct system which covers both visualization and analysis. An easy-to-apply and non-invasive visualization method based on X-ray micro tomography (micro-CT) for the first time offers high-resolution 3D imagery and quantification of the complete membranous labyrinth in-situ inside the petrosal bone. Furthermore, to process the digitized semicircular duct system we introduce Ariadne, a user-friendly software toolbox which provides detailed morphological descriptions and analyses that are the most realistic yet in representing the biomechanical response of the actual specimen. We test these methods by exploring the biomechanics of the semicircular duct system of a human, a squirrel monkey and a rhesus macaque as examples, and compare the results with evidence from neurophysiology, oculometry and previous biometric studies.
Overview of Ariadne. The Ariadne software toolbox analyses both the morphology and function of the semicircular duct system. For the latter it implements and expands upon seminal biomechanical models of the three interconnected semicircular ducts  16  (Methods section and Supplementary Notes 1, 2). The input, obtained from the CT images, consists of a digital description of the endolymphatic fill of the duct system, landmark data characterizing its central streamlines, evidence-based models of the cupulae, and the plane of symmetry of both ears (Fig. 3 ). The output consists of an accurate description of the functionally important morphological parameters, such as the lengths, cross-sectional areas, enclosed areas, and maximal response planes of the ducts, as well as a complete biomechanical analysis, including Bode analysis and spatial mapping of the mechanical sensitivity (Fig.