dataesr/scientific-paragraphs-categorization huggingface dataset:
Single-particle tracking hotspot analysis Hotspot analysis was done on the localizations of sptPALM data processed by Thunderstorm  (Ovesny ´et al. , 2014)  with a wavelet of 3px. Four to eight ROIs of the same size were made per cell wherein cell edges were avoided in the quantification, and localization tables exported per ROI. Each ROI was analyzed with DBSCAN in the SR Tesseler  (Levet et al. , 2015)  platform with a distance of 80 nm, a min localization density of 20 and minimum localizations per hotspot of 18 to account for more than two short tracks, which are the most abundant. The number of tracks per hotspot was measured manually.
Acknowledgements The work in this manuscript was funded through  VIB, KU Leuven  ( IDO/12/020 , to WA and HM;  C16/ 15/073  to WA), the  FWO  (to WA:  S006617N ,  G078117N  and  G056017N ), the  FWO-Hercules foundation  (to WA:  AKUL/08/58, -/11/30 , and  -/13/39 ) and  SAO-FRA  (S# 14017 ). The VIB Bioimaging Core acknowledges the Flemish government for acquiring the Zeiss ElyraS. 1. SptPALM was done in the  Bordeaux Imaging Center (BIC ) a service unit of the  CNRS-INSERM and Bordeaux University , member of the national infrastructure  France BioImaging  supported by the  French National Research Agency  ( ANR-10-INBS-04 ). AE-A was a recipient of a short-term  EMBO  fellowship (# 8358 ) to visit the BIC. The authors thank  Pieter Baatsen  of the  VIB-EM core facility  for help with scanning EM,  Christel Poujol  from  BIC ;  Paja Reisch  (PicoQuant) for measurements;  Haisen Ta  (PicoQuant) and  Kiran Kumar Vudya Sethu  for MATLAB scripts;  Z Debyser  for the plasmid pCHMWS-GFP-ires-hygro; the  VIB-KU Leuven FACS  facility core, Sam Lismont ,  Christine Michiels  and  David Demedts  for technical support;  Jean-Baptiste Sibarita  ( Bordeaux University ),  Jesse Aaron  from the  Advanced Imaging Center (AIC)  and  Chris Obara  of the  Lippincott-Schwartz lab  (both at  Janelia Research campus) ,  Susana Rocha  and  Peter Dedecker  ( Science & Technology, KU Leuven ),  Marine Bretou  and  Rosanne Wouters  for constructive discussions and members of the  Annaert lab  for critically reading of the manuscript.
Efficient biodiversity conservation requires that limited resources be allocated in accordance with national responsibilities and priorities. Without appropriate computational tools, the process of determining these national responsibilities and conservation priorities is time intensive when considering many species across geographic scales. Here, we have developed a computational tool as a module for the ArcGIS geographic information system. The ArcGIS National Responsibility Assessment Tool (NRA-Tool) can be used to create hierarchical lists of national responsibilities and priorities for global species conservation. Our tool will allow conservationists to prioritize conservation efforts and to focus limited resources on relevant species and regions. We showcase our tool with data on 258 bird species and various biophysical regions, including Environmental Zones in 58 Asian countries and regions.
The ArcGIS National Responsibility Assessment Tool (NRA-Tool) uses freely available shapefiles of species distributions, environmental zones, and political borders to automate the analysis for a large set of species for which data are available. Data sources include the Spatial Data & Mapping Resources and the Red List database from the IUCN  [41, 42]. We explain the GIS module's user interface and illustrate the general concept and functionality with data from four species of shrews, which have their distribution center either only in Europe or are widespread across Eurasia. As a case study, we also provide national responsibility assessments for 258 bird species of Asia.
Files downloaded from the IUCN webserver require additional processing so that data for each species is separated into a unique shapefile, and renamed by adding the prescript "s-" (following the naming convention described in more detail in the section "Technical requirements"). These shapefiles must be accompanied by an ASCII text file that contains the species names separated by semicolons (see S3 File for example). Focal area and reference area. The focal area is the geographical area for which conservation priorities are determined. For national responsibility assessments, the borders of an administrative unit, such as a country or province, should be used to demarcate the focal area. The focal area must be completely enclosed by the reference area. The reference area defines the extent and serves as a reference to determine whether the focal area contains a higher percentage of the distribution range than expected by the size of the focal area.
Workflow. The workflow (Fig  7 ) starts with the selection of the taxonomic unit (TU) to be analyzed from the dataset of all taxonomic units. The distribution pattern is then classified as "local," "regional," or "wide" based on the shapefiles of the reference area and the focal areas, and the distribution of the taxonomic unit with respect to the focal species. In the second step, the NRA-Tool calculates the expected and observed distribution areas and determines national responsibility scores and conservation priorities for the focal species as described above. As an output, conservation priority classes are assigned to all or selected countries or regions for the focal species. The results are provided as maps (shapefiles) with respective attribute tables containing the values of national responsibility scores and/or conservation priority of the assessed focal species.
Technical requirements. The NRA-Tool is a module that can be added to the ArcGIS Toolbox. It was developed in ArcGIS 10. x and C#  [57]  using Visual Studio 17  [58]  with. NET 4. 6. 1  [59] , ArcGIS runtime SDK for. NET  [60] , and ArcObjects SDK  [61]. Files to be used with the NRA-Tool must be named according to the following convention: The species distribution file names must start with a lowercase "s" followed by a hyphen. After the hyphen, the name can be chosen freely (e. g. , "s-vulpes_vulpes. shp" for the red fox). For (global) ecological units or reference areas, file names must start with a lowercase "g" followed by a hyphen (e. g. , "g-biomes. shp" and "g-border_of_asia"). Focal area file names must start with a lowercase "f" followed by a hyphen (e. g. , "f-asian_countries. shp").
This task is computationally demanding and the runtime may be many hours or even days, especially if the analyses consider the whole globe as the reference area. Further development. In addition to the development and improvement of the ArcGIS tool, we have started the development of a similar tool as an extension for the Open Source GIS QGIS  [63] , which will be made available as Open Source in future. The Polygon Area-Approach currently only uses information about the range/extent of the species distributions and should be expanded to allow for the use of abundance data. Additionally, it might be possible to integrate more options for particularly spatially-oriented conservation priority assessments with similar approaches, e. g. [31]. Availability of software and data. The NRA-Tool can be downloaded directly from  [64, 65]  or the interactive representation of the SCALES-project  [66]  called SCALETOOL  [66] [67] [68].
The GEnZ can be found and downloaded from  [62]. The publicly available species distribution dataset for selected shrew species is derived from IUCN Red List data and can be downloaded from  [44]  (not for commercial use). World continent data are available from ESRI  [43] , while the geographical data for administrative units (country areas, borderlines) from EuroGeographics can be downloaded from  [45]. For the background map we used cross-blended hypsometric tints with relief, water, drains, and ocean bottom in scale of 1:10 million that is publicly available under the Creative Commons license from Natural Earth  [46]. Case studies of bird species in Asia by Global Environmental Zones counts and Global Environmental Zones area. Here, we selected 58 Asian countries and regions as the focal areas or reference areas and categorized them according to their biogeographic characteristics: (1) tropical or temperate climate, (2) small or large area, (3) island or mainland.
Birds that are (1) widespread and (2) migratory are considered too, although their distribution ranges exceed or lie outside of the reference area, respectively. However, it is not the aim of this study to discuss special aspects of national responsibility assessments related to migration or animal movement; our purpose is simply to demonstrate our tool, using this dataset as an example. We determined the national responsibility scores and conservation priorities for 258 bird species (from BirdLife International http://www. birdlife. org) in the 58 Asian focal countries (see S3 File). Nine of the bird species were vulnerable (VU); eight were near-threatened (NT), and 241 were of least concern (LC). We performed two analyses, both of which used GEnZ  [50] , but differed by the choice of either the 'By biome-area' option in the panel (Polygon Area-Approach) or the 'By biome count' (Polygon Count-Approach) option.
Discussion The standardized determination of national responsibilities and conservation priorities across regions with multiple jurisdictions and administrative borders is an important step to advance and coordinate international conservation efforts. Here, we presented a software tool to assist in the determination of national responsibilities and conservation priorities. We showcased our tool with an example dataset of 258 Asian bird species. Our tool improves geoprocessing procedures for calculating national responsibilities and conservation priorities using a large spatial dataset. The tool also provides users with an interface and attractive visual outputs to facilitate the inclusion of national responsibility and conservation priority assessments in national and regional biodiversity reports.
It is also possible to use it in combination with software tools for conservation prioritization assessments based on different algorithmic foundations like MARXAN with Zones  [17]  and ZONATION  [16, 15] , or multivariate statistical analyses  [33]. These avenues of integration ensure that our tool can provide important assistance in making informed policy decisions  [1]. Our results show that the national responsibility assessment is sensitive to the size of focal areas and focal species ranges when area and biophysical regions, such as the GenZ, are used to classify focal species distributions. Both global and regional assessment scales  [36]  are strongly dependent on spatial distributions, the extent of overlap between focal species distribution and focal area, and the number of biogeographical units in the focal area.
Y-P L, T-S D, Y-Ch W, W-Y L were mainly funded by the National Science Council of Taiwan under grant NSC101-2923-I-002-001-MY2. In 2014 the NSC was renamed in Ministry of Science and Technology (MOST, http://www. most. gov. tw/? l=en) K H, D S S, R A K were funded by the project SCALES: Securing the Conservation of biodiversity across Administrative Levels and spatial, temporal, and Ecological Scales, under the European Union's Framework Program 7 (grant 226852; https:// cordis. europa. eu/project/id/226852, www. scalesproject. net).
Acknowledgments We would like to thank  BirdLife International  (http://www. birdlife. org)for provision of geographical distributions of 258 bird species, The IUCN Red List of Threatened Species™ [44] for the provision of geographical distribution for 4 shrew species, ESRI [43] for the publicly available continental borders, EuroGeographics [45] for publicly available data of administrative borders, and Natural Earth [46] for the background map. We thank the two anonymous reviewers, whose comments / suggestions have contributed significantly to the improvement and clarification of this manuscript, and  Alessandra Moyer  for linguistic proofreading.
Funding: Y-P L, T-S D, Y-Ch W, W-Y L were mainly funded by the National Science Council of Taiwan under grant NSC101-2923-I-002-001-MY2. In 2014 the NSC was renamed in Ministry of Science and Technology (MOST, http://www. most. gov. tw/? l=en) K H, D S S, R A K were funded by the project SCALES: Securing the Conservation of biodiversity across Administrative Levels and spatial, temporal, and Ecological Scales, under the European Union's Framework Program 7 (grant 226852; https:// cordis. europa. eu/project/id/226852, www. scalesproject. net).
Main Re-purposing drugs is of particular benefit for rare diseases such as DMD as it can expedite new therapeutic options since it leverages off previous safety and tolerability trials in other diseases. One example of re-purposed drugs for the treatment of DMD are the phosphodiesterase (PDE) type 5 inhibitors, sildenafil and tadalafil, both commonly prescribed for erectile dysfunction  [1]. PDE inhibition enhances blood flow by increasing the half-life of cyclic guanosine 3 ,5 -monophosphate (cGMP), the second messenger of nitric oxide (NO)  [1]. Pre-clinically, these PDE5 inhibitors prevented skeletal muscle fatigue, ischemia and damage in the dystrophin-deficient mdx mouse indicating that manipulation of the NO pathway is beneficial to dystrophic muscles  [2] [3] [4] [5] [6] [7]. Clinically, however, both have failed to modify primary endpoint measures, being delayed loss of ambulation (tadalafil; NCT01865084)  [8]  and improved cardiac function (sildenafil; NCT01168908)  [9]  in DMD patients, resulting in the termination of both trials.
Similarly, sildenafil had no effect in Becker Muscular Dystrophy patients  [9, 10]. While there are no individualized data reported for the tadalafil trial (NCT01865084), there was evidence of participant-specific responsivity in the sildenafil trial where 4/14 participants (29%) experienced worsening of cardiac function with sildenafil versus 1/8 (12. 9%) on placebo and the other 10/14 showed no response  [9]. These data suggest that other factors interplay to orchestrate the biological effects of enhanced NO signaling in DMD. In skeletal muscle, NO, produced by neuronal nitric oxide synthase (nNOS), is a critical signaling molecule that modulates key physiological processes including blood flow, contraction, glucose uptake and metabolism, among others  [7]. In dystrophic muscles, nNOS dissociates from the sarcolemma resulting in its targeted degradation  [11] [12] [13]  and consequently, a diminished capacity for NO generation  [7, 14, 15].
Two-way ANOVA was used to detect between genotype and treatment differences. When a main effect/interaction was detected, unpaired T-tests were used to identify between group differences using SPSS (version 21; Armonk, NY, USA). An α value of 0. 05 was considered significant. Despite acute NIT treatment increasing mitochondrial viability (p < 0. 01 DMD NIT vs. DMD untreated (UNTREAT); Figure  1A ) and spare respiratory capacity (p < 0. 05 DMD NIT vs. DMD UNTREAT; Figure  1B ), this did not translate to improvements in other key metabolic parameters. In fact, acute NIT treatment exacerbated the repression of ATP production (p < 0. 05 DMD NIT vs. DMD UNTREAT; Figure  1C ) and mitochondrial uncoupling (p < 0. 001 DMD NIT vs. DMD UNSUPP; Figure  1D ) already evident in DMD myoblasts. While mitochondrial uncoupling could be considered protective against oxidative stress, in DMD myoblasts it only caused severe impact to ATP synthesis without any attenuation of mitochondrial superoxide, which was 83% higher in NIT-treated compared to UNTREAT dystrophic myoblasts (p < 0.
Thus, the NO-driven compensatory stimulation of glycolytic flux cannot overcome the mitochondrial dysfunction to re-balance energy homeostasis in DMD myoblasts. Mitochondrial viability was reduced in DMD myoblasts compared to CON (p < 0. 0001), but NIT supplementation increased this parameter in DMD myoblasts (p < 0. 01; A). Spare respiratory capacity, adenosine triphosphate (ATP) production and coupling efficiency (mitochondrial functional indices) were determined using the Seahorse Bioscience XF24 Analyzer with data presented per 5000 myoblasts due to different plating densities of CON and DMD myoblasts. While NIT increased the spare respiratory capacity in DMD myoblasts (p < 0. 05; B), NIT further reduced ATP production (p < 0. 05; C) and coupling efficiency (p < 0. 0001; D) in DMD myoblasts.
Accession codes Data deposition: Atomic coordinates and structure factors for the reported crystal structures have been deposited at the Protein Data Bank under the accession code 6gze (BeF 3 -) and 6s9e (AlF 3 ). nucleotide excess and equilibrated in MEDTA buffer with 1 mM GDP by a second chromatography in a cold Sephadex G-25 column (15 Â 0. 9 cm). The nucleotide content of the protein was quantified by protein precipitation and HPLC as described (Dı ´az and  Andreu, 1993)  and was found to be 0. 98 ± 0. 07 mol GTP/mol tubulin, and 0. 99 ± 0. 06 mol GDP/mol tubulin. Alternatively fully substituted GDP-tubulin was obtained from GTP-tubulin by hydrolysis. GDP tubulin was equilibrated in a 20 Â 1 cm Sephadex G-25 column equilibrated in MEDTA buffer with 3. 4 M Glycerol. The nucleotide content of the protein was measured as above and was found to be 0.
7 mM BeF 3 -or 500 mM AlCl 3 and 2 mM HKF 2 or 1. 5 mM AlCl 3 and 2 mM HKF 2 depending on the growing conditions. X-ray diffraction data were collected on beamline ID23-1 of the European Synchrotron Radiation Facility (ESRF) and beamline XALOC of ALBA Synchrotron. Diffraction intensities were indexed and integrated using XDS  (Kabsch, 2010) , and scaled using AIMLESS  (Winn et al. , 2011). Molecular replacement was performed with  PHASER (McCoy et al. , 2007)  using the previously determined structure (PDB 4o2b) as a search model. Structures were completed with cycles of manual building in COOT  (Emsley et al. , 2010)  and refined in PHENIX  (Adams et al. , 2010) , which allowed the determination of alternative conformations on different residues at main chains. Data collection and refinement statistics are summarized in Table  1.
Supernatants were separated by aspiration, and pellets were resuspended in a 1% SDS, 10 mM phosphate buffer. Tubulin concentration in pellets and supernatants was measured spectrofluorometrically by excitation at 280 nm and emission at 320 nm  (Buey et al. , 2005)  employing a Shimadzu RF-540 fluorometer (excitation and emission slits, 5 nm) calibrated with standards of known concentration prepared from the same tubulin. Apparent polymer growth equilibrium constants were estimated as the reciprocal of the critical concentrations for polymerization determined at several total protein concentrations  (Oosawa, 1975). MT shear-flow alignment and X-ray fiber diffraction experiments X-ray fiber diffraction data were collected on beamlines BL11-NDC-SWEET and BL40XU of ALBA (Spain) and SPring-8 (Japan) synchrotrons.
A total of 16-24 diffraction images were averaged for each condition and background subtracted using ImageJ (version 1. 51j8; Wayne Rasband, National Insitutes of Health, Bethesda, USA). Angular image integrations were performed using the XRTools software (obtained upon request from beamline BM26-DUBBLE of the ESRF), where the spatial calibration was obtained from Ag-Behenate powder diffraction. For data analysis, we considered MTs as cylinders with a three-start helical pattern. The diffraction pattern of MTs comprises layer lines (l) each defined by a group of Bessel functions of order n. Their structural factor F in the reciprocal space (R) is described by Equation 1  (Klug et al. , 1958) : F l;n R ð Þ ¼ J n 2pr m R ð Þf R ð Þ (1) where J n is the n th Bessel function, r m is the radius of a MT with m PFs and f(R) is the structural factor of the sphere defined by Equation 2  (Malinchik et al.
For the determination of the average monomer lengths, we analyzed the intensity profile of the central-meridional signals (Figure  3 -figure supplement 1, red line). The 4 th harmonic of the first layer-line (l = 4)) was fitted to a single-peaked Lorentzian function using Sigma-Plot software (version 12. 0), in which the position of the maximum of intensity corresponds to ¼ of the average monomer length at the reciprocal space. The standard error was obtained from the standard deviation of the regression applied.
This work was supported by Ministerio de Economia y Competitividad grants BFU2013-47014-P to MAO and BFU2016-75319-R to FDP (both AEI/FEDER, UE); Ministerio de Ciencia e Innovacion RYC-2011-07900 to MAO; European Union H2020-MSCA-ITN-ETN/0582 ITN TubInTrain to FDP and AEP; Swiss National Science Foundation (31003A_166608) to MOS; JSPS KAKENHI (16K07328/17H03668) to SK; (FSE) ANR-16-CE11-0017-01 to DC and LD. JE-G was supported by Ministerio de Educacion, Cultura y Deporte FPU15-03140 and SK by French Ministry of Higher Education of Research and Innovation (IGDR). Cryo-EM data were adquired on the MRic platform (Univ. Rennes, CNRS, Inserm, BIOSIT-UMS 3480, US_S 018, F-35000 Rennes, France). The authors acknowledge networking contribution by the COST Action CM1407 "Challenging organic syntheses inspired by nature -from natural products chemistry to drug discovery".
Additional information Funding Funder Grant reference number Author Ministerio de Economı ´a y BFU2013-47014P Maria A Oliva Competitividad Ministerio de Economı ´a y RYC-2011-07900 Maria A Oliva Competitividad Ministerio de Economı ´a y BFU2016-75319-R J Fernando Dı ´az Competitividad H2020 European Research H2020-MSCA-ITN-EJD- Andrea E Prota Council 860070 J Fernando Dı ´az Swiss National Science Foun- 31003A_166608 Michel O Steinmetz dation Japan Society for the Promo- KAKENHI 16K07328/ Shinji Kamimura tion of Science 17H03668 Agence Nationale de la Re- ANR-16-C11-0017-01 Denis Chretien cherche SPring-8 Proposal 2016B1182 Shinji Kamimura SPring-8 Proposal 2019B1365 Shinji Kamimura.
Phenotype data Anthracnose severity score data collected at different time during the crop's growth period were converted to AUDPC for quantitative comparison across years. The relative area under disease progress curve data was subjected to mixed model analysis using lme4 library package implemented in R (see Equation 2 in the Supplementary Files)  [42]. Where; Y ijk = phenotypic value, µ = overall phenotypic mean, β i = effect of year i, R ij = effect of block j in year i, G k = effect of genotype k, (β i x G k ) = effect of interaction between year i and genotype k and e ijkm = residual. Block effects were added to the model as random variable to remove the spatial variation within the trial eld. Broad sense heritability was estimated from the model to assess the proportion of AU DP C = ∑ n i=1 ( ) (t i+1 -t i ) y i + y i+1 2 Y ijk = μ + β i + R ij + G K + (β I xG K ) + e ijkm phenotypic variation in data set due to genetic effects.
Snp Calling And Quality Assessment Raw data was rst ltered using a pipeline described in Scarcelli et al. [43]. Demuladapt (https://github. com/Maillol/demultadapt) was used for demultiplexing and cutadapt 1. 2. 1  [44]  used to remove the adaptors and low-quality bases reads with a mean quality score < 30 using a free perl script https://github. com/SouthGreenPlatform/arcadhts/blob/master/scripts/arcad_hts_2_Filter_Fastq_On_Mean_Quality. pl. For the nal SNP calling, GATK was used while mapping was performed using default options of Burrows-Wheeler Aligner (BWA)  [45]  using the D. alata reference genome. The SNP quality assessment was performed using vcftools [46] and plink  [47] , and SNPs with low information (such as MAF < 0. 05, depth of sequencing < 5, 20% missing data for both SNPs and genotypes, multiple alleles) were removed.