dataesr/scientific-paragraphs-categorization huggingface dataset:
com/Chimera-tools/ ChimBench). We also provide new realistic simulated data, as well as junction coordinates for validated fusion genes from 2 extensively used gold-standard chimera datasets  [25, 45]. We think that, in addition to ChimPipe, both these programs and these data can be very useful in future chimera detection assessments. Science and Education (MICINN). This publication has also been written with the support of the Agreenskills fellowship program which has received funding from the EU's Seventh Framework Program under grant agreement No FP7-609398, and with the support of an institutional grant from Fundación Ramón Areces attributed to CBMSO. Note that the content of this paper is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Availability of data, programs and material The publicly available data used in this study is the following: • Edgren and Berger cancer datasets described in  [25, 45, 49]  • 106 ENCODE RNA-seq experiments described in  [47]  • 36 ENCODE RNA-seq experiments and 60 Mouse ENCODE experiments described in  [56]. We have also generated the following data: • exact junction coordinates for RT-PCR validated gene fusions from 2 cancer datasets  [25, 45, 49] : we provide them as Additional file 1: Table  S2  • simulated PE RNA-seq data of 3 different read lengths: we provide them as Additional file 2 (see below) • RT-PCR, cloning and sequencing of 4 chimeras identified using ChimPipe on ENCODE data: we provide their results in Additional files 1, 4 and 5 (see below). We are also making the following programs available to the community: • The ChimPipe chimera detection program: source code and basic documentation at https://github.
Acknowledgements We would like to thank  Thomas Gingeras  for insightful comments about ChimPipe results,  Vincent Lacroix  for initial discussion about chimera mechanisms,  David Torrents  for critical comments about benchmarking and  Rory Johnson  for fruitful discussion about chimera validation. We would also like to thank  Carmen Arnan Ros  for technical support in the experimental validation, and  Rory Johnson  and  Kylie Munyard  for reviewing this manuscript.
We also develop a detailed assessment methodology and use it to evaluate the performance of several correction algorithms. We identify two approaches that can correct for the presence of even strong photodestruction, one of which can be implemented directly in the SOFI calculation software. The diffraction limit in far-field optical imaging, first described by Ernst Abbe 1 , is no longer an insurmountable barrier to the spatial resolution of fluorescence microscopy. During the last decades, several revolutionary advances have been made to observe structures with increased spatial resolution 2-5. Techniques were developed based on either repeated single-molecule localization (e. g. PALM 6, 7 , STORM 8 ) or patterned illumination (e. g. SIM 9 , ISM 10 ) possibly in combination with saturation effects (e.
required to produce an image with a comparable SNR. Since any dataset is necessarily of finite duration, the highest order of analysis that still results in interpretable images will dictate the achievable resolution  25. An important limitation is that the blinking of the fluorophores must be resolvable. Given the typical temporal resolution of tens of milliseconds available in many camera-based imaging systems, this means that only blinking on these time scales or slower can be used for SOFI. In practice, SOFI has been found to work well for dyes ranging from quantum dots  16  and organic dyes  23, 26  to fluorescent proteins  18, 27, 28. Recently, it was also shown that SOFI can be used to read out a new class of fluorescent biosensors  29, 30  , resulting in the first approach that can visualize reversible signalling activity in live cells with a spatial resolution better than the diffraction limit  30.
The amplitude of the plateau for large τ relative to the cumulant value for τ = 0 allows the direct assessment of how much of the signal is contaminated due to photodestruction. Quantifying the effect of photodestruction using simulated data. We turned to simulated datasets with known ground-truth sample structure to rigorously quantify the effect of photodestruction. We started by simulating fluorescence images based on an actual SOFI image showing mitochondria, in the presence and absence of photodestruction. The simulated fluorophores obeyed the photophysical scheme shown in Fig. 2b  (experimental procedure detailed in the methods section). In this photophysical model, k bl is a microscopic rate constant for bleaching, while the observed overall fluorescence intensity decays as exp(-t/τ bl ).
3 ). Our previous result (Fig. 2 ) had clearly highlighted the need to evaluate both the accuracy (the correctness of the structure) and the precision of the imaging (the SNR). Accordingly, we quantified the accuracy by calculating the root mean square deviation (RMSD) between each SOFI image and the correct sample structure, where the correct structure was given by a SOFI image calculated on a dataset without photodestruction and with a large number of simulated fluorescence images. RMSD is an appropriate metric for this comparison because we correct for any scaling factors, and did not observe any absolute offsets in the SOFI signals. We quantified the precision by repeating each simulation 100 times and calculating the signal-to-noise ratio associated with the images, following the procedure described in ref.
Similar concerns have been encountered in other techniques that make use of the statistical analysis of data that is assumed to be stationary, such as fluorescence correlation spectroscopy, potentially allowing us to adapt these solutions to SOFI. One strategy that has been proposed is the use of batching  24, 26, 35. In this approach, the input image sequence is divided into a number of smaller image sequences, also known as sub-sequences or batches, that are analysed independently. This results in multiple SOFI images for every dataset, that are then averaged to yield a single SOFI image. Typically the sizes of these batches are set to some default value, e. g. grouping 25, 50, or 100 fluorescence images into a single batch. The effect of photodestruction will be strongly reduced if the duration of these sub-sequences is longer than the blinking correlation time, but shorter than the photodestruction time.
4  and Supplementary Figs  S2, S3 , S5 and S6. When analysing the SOFI signal from different label densities for the various correction methods, the subtraction method is the best at restoring the linear correlation between SOFI signal and label density (Supplementary Fig. S4 ). It also causes the least distortions when applied to data without bleaching (both lines overlap in Supplementary Fig. S4 ). A close second is the moment-preserving method, while the multiplication correction is very ineffective. Batching is also capable of retrieving this linear dependency, particularly when using batch sizes of 25 frames or smaller. Experimental data. We applied the best performing correction methods to an experimental dataset where Dronpa was fused to keratin-19 (krt19) and expressed in HeLa cells (as detailed in the methods section).
Using this framework, we proposed and assessed a variety of correction methods. We find that the subtraction method proposed here performed very well, closely followed by the batching method. Because batching presents an easy to use and simple solution that requires no additional calculations or manipulations of the data, but greatly increases SOFI image quality (see e. g. Fig. 5 ), we recommend its general use as a necessary and sufficient correction for photodestruction. While the batch size depends on the specifics of the photodestruction and blinking kinetics, our results suggest that a batch size of 25 to 50 frames will work under the majority of experimental conditions. We have now updated our Localizer software package to make use of batching with a default batch size of 50 frames.
5  displayed a τ bl of about 9 s. Typical simulated intensity traces are visualized in Fig. 6. From each simulated dataset, we derived corrected datasets by applying the described correction methods. We then calculated SOFI images for each of these using the Localizer software package  39. To evaluate the batching method we examined batch sizes of 100, 50, 25, 10, and 5 frames. Different label densities were simulated in a single image by dividing a 32 × 32 pixel detector in nine 10 × 10 squares with label densities ranging from 70 to 2800 emitters/μm 2. After SOFI calculations, the average SOFI signal of a 6 × 6 pixel square at the centre of each 10 × 10 square was calculated to exclude edge effects. Simulations were repeated 10-fold. A τ bl value of 1. 1 s was used for simulations with photodestruction.
In addition, data sets without photodestruction but with the same amount of fluorescence images as data with photodestruction (500 images for 2 nd order and 4,000 images for 3 rd order) were also simulated, allowing us to identify the best possible SNR and RMSD when confined to these sequence sizes. Prior to RMSD calculation, scale factors were corrected by equalizing the image averages. We used Igor Pro (WaveMetrics, Inc. , Lake Oswego, OR, USA) and the Localizer software package  39  to run all simulations, calculations and data analysis.
Experimental quantification of photodestruction. To evaluate the amount of photodestruction in experimental data the following procedure is followed. First an experimental dataset is loaded into the freely available Localizer software  39. At this time, for second order SOFI 10 distinct SOFI images are produced from the data using a time lag varying from 0 to 9 in the calculation. For higher order SOFI calculation, which can contain multiple different time lags, a similar procedure is followed where all time lags are set to 0 except for one, which is then varied between 0 and 9. Next, the mean value of each SOFI image is plotted against the time-lag, normalized to 100% for the image at lag 0. The curve that results can be fitted reasonably well to a mono-exponential decay for a wide array of experimental conditions (Eq.
Acknowledgements We thank  Stefan Jakobs  for kindly providing the pQE-rsCherryRev1. 4-Krt19 plasmid and  Thorsten Wohland  for suggesting the moment-preserving correction. Y. P. thanks the  Agency for Innovation by Science and Technology (IWT) Flanders  for a doctoral fellowship. P. D. acknowledges support by the  European Research Council  via  ERC Starting Grant   714688  and from the  Research Foundation-Flanders  via grants  G062616N ,  G0B8817N ,  G0A5817N , and  VS. 003. 16N. S. D. thanks the  Research Foundation-Flanders  for a postdoctoral fellowship. W. V. acknowledges support by the  Horizons2020 program  via grant  686271. T. Lukeš ,  T. Lasser  highly appreciate the partial funding by the  Swiss National Science Foundation  (SNSF, http://www. snf. ch/)under grants  200020-159945  and  205321-138305.
They are constructed on a regularly spaced ocean point-mesh grid, from sea surface to seafloor, and attributed with data from the 2013 World Ocean Atlas version 2. The point attribute data are the means of the decadal averages from a 57-year climatology of six physical and chemical environment parameters (temperature, salinity, dissolved oxygen, nitrate, phosphate, and silicate). The database includes over 52 million points that depict the global ocean in x, y, and z dimensions. The point data were statistically clustered to define the 37 EMUs, which represent physically and chemically distinct water volumes based on spatial variation in the six marine environmental characteristics used. The aspatial clustering to produce the 37 EMUs did not include point location or depth as a determinant, yet strong geographic and vertical separation was observed.
METHODS We used the complete set of variables from the 2013 World Ocean Atlas data set, version 2  (Locarnini et al. , 2013; Zweng et al. , 2013; Garcia et al. , 2014a Garcia et al. , , 2014b) )  as our source of physical and chemical environmental data for defining the ocean mesh and subsequently modeling the ecological marine units. The WOA data set is a compendium of data from a variety of ocean research and monitoring programs over the past five decades. It is an authoritative 57-year climatology that contains over 52 million points, hereafter referred to as the ocean mesh. Each point is attributed with values for temperature, salinity, dissolved oxygen, nitrate, phosphate, and silicate, and all WOA values are corrected for the effect of pressure on each variable. The WOA has a horizontal spatial resolution of ¼° × ¼° for temperature and salinity, and 1° × 1° for oxygen, nitrate, phosphate, and silicate.
The WOA data are water-column variables, but seafloor geomorphology may also be significant in influencing both these variables and species ecology. The first global digital map of seafloor geomorphic features is now available  (Harris et al. , 2014). Temporally, the WOA archive is available in seasonal, annual, and decadal resolutions. Seasonal data are not available for all points in the mesh, many of which may not have been visited regularly over the 57-year period. Moreover, data from polar regions, typically collected only during warmer summer months when access to ice-bound regions is easier, may under-report true salinity values. Decadal values of the WOA represent the average of the annual mean values for the parameters, themselves derived from the seasonal data. We used the 57-year record of the parameters, which are provided in the WOA database as archival means, derived from the decadal averages.
The mesh spacing matched the WOA data matrix and allowed for the structuring and symbolization of data as columnar volumes (or other shapes) that can be queried by ranges of values, and can be spatially analyzed via proximity algorithms and multivariate statistical clustering. We first constructed an "empty" ocean mesh using the 52,487,233 WOA point locations, and then attached the WOA attribute data to those points. The water column was bounded at the top by the sea surface, and at the bottom by the seafloor, as defined in the geomorphic map of  Harris et al. (2014). The set of cells intersecting or nearest to the global shoreline (or ice masses) defined the horizontal extent of the water column. We statistically clustered the points in the mesh in order to identify environmentally distinct regions in the water column.
The clustering was blind to both the depth of the point and the thickness of the depth interval at that point's vertical position in the water column. The "big data" nature of the clustering of the entire ocean volume required sophisticated spatial data processing and functionality. The clustering was implemented using SAS software (©2015 SAS Institute Inc; SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. , Cary, NC, USA). The ArcGIS platform was utilized for subsequent geospatial assessment and visualization of FIGURE 2. A vertical column of the ocean mesh framework (illustrative and not to scale), produced from World Ocean Atlas 2013 data extracted into a set of 52,487,233 points at ¼° × ¼° ( ~27 km × 27 km at the equator) horizontal resolution and variable depth z ranging from 5 m intervals near the surface to 100 m intervals near the deep seafloor.
Following the depth-blind statistical clustering, basic descriptive statistics (mean, minimum, maximum, and standard deviation) were produced for the six deterministic parameters (temperature, salinity, dissolved oxygen, nitrate, phosphate, and silicate) for each EMU. The unit-middle depth for each EMU was also calculated as the median depth of all the points allocated into an EMU. We then labeled the clusters using the naming criteria (Table  2 ) of the Coastal and Marine Ecosystem Classification Standard (CMECS), a Federal Geographic Data Committee standard for the United States (FGDC, 2012). The CMECS labels for the EMUs begin with their depth zone assignments based on their median depths, followed by a concatenation of the CMECS descriptors for temperature, salinity, and dissolved oxygen.
Interacting visually with true volumes in 3D, especially with such a large resource, is a software challenge, but it is possible with commercially available virtual globebased visualization software, as shown in Figure  6. Although the EMUs are continuous data surfaces, Figure  6  shows that they are more easily visualized as stacked bands on horizontally separated cylinders. The size and complexity of the EMU resource, a de facto example of big data with over 52 million attributed points in three dimensions, potentially presents barriers to its efficient use. To help mitigate this challenge, we developed an open-access web application, EMU Explorer (http://livingatlas. arcgis. com/ emu) that permits real-time query and visualization of both the points and the EMUs by anyone with Internet access.
Surface temperature gradients are also apparent between the pink (warmer) EMUs in the south, and the blue (colder) EMUs in the north. 30 realms were obtained from 2D clustering of occurrence records representing over 65,000 species from the Ocean Biogeographic Information System (OBIS, http://www. iobis. org) database, and they reflect global patterns of species endemicity. Initially, we note correspondence between realms and EMUs (e. g. , realms 2, 5, 7, and 30) in some areas, but in other cases a relationship is less apparent  (e. g. , realms 18, 21, and 22). In another global assessment of 11,567 species occurrences representing 13 taxonomic groups,  Tittensor et al. (2010)  identified concentrations of coastal and open-ocean species in the eastern Pacific and in mid-latitudinal belts, respectively.
Limitations and Future Work We recognize limitations in our work related to both temporal scaling dimensions and parameters selected for the clustering. The WOA data offer several native temporal resolutions (seasonal, annual, and decadal) that we did not exploit. As our aim was to use long-term historical average values for the point locations, we used the 57-year mean values for the six parameters to map EMU 2009) are similarly difficult to compare with the quantitative, statistically derived EMUs presented here.
Acknowledging some overlap, the EMUs appear to be better separated, visually, at depths approximately corresponding to water column positions from 0 m to 200 m (upper water column), 200 m to 2,000 m (middle water column), and >2,000 m (lower water column). Although the EMUs are currently classified into CMECS depth zones using standardized criteria, an attempt to statistically separate EMUs into depth classes and associated quantitative determination of the depth boundaries for those groupings appears warranted. In addition, the EMUs should be assessed for their depth-based relationship to physical properties like light attenuation limits  (Stal, 2016)  and biologically mediated phenomena like respiration  (Costello and Breyer, 2017)  and carbon flux  (Reygondeau et al. , 2017). We suggest that the depth boundaries of the vertical zones and the environmental factors controlling the vertical separation of the pelagic ocean need additional analysis and further clarification.
, 2016; Wright, 2016) , but as spatial processing technologies evolve, these kinds of analyses will be rendered less computationally intense than they are at present. The clustering of oceanic data to derive EMUs was based on the six variables in the WOA data set. The addition of other variables would likely influence the oceanic partitioning we present here. The inclusion of data on particulate organic carbon (POC), carbonate contents, and ocean current patterns might influence the clustering results. POC plays a crucial role in the marine and global carbon cycle and is a primary component of oceanographic food webs  (Buesseler et al. , 2007). POC flux was one of the parameters used to subdivide the Mediterranean Sea into 63 biogeochemical regions  (Reygondeau et al. , 2017) , where it was used to quantitatively separate the mesopelagic layer from the bathypelagic layer.
We also plan to enrich the EMU resource by combining the EMU data with other data layers. This will result in the creation of new 2D layers for the sea surface and the seafloor. For example, for the seafloor, we have combined the bottom-occurring EMUs with the seafloor physiographic regions and features of  Harris et al. (2014)  in order to evaluate the influence of seafloor geomorphology on the water-column structure above it. We also have combined a 13-year average ocean color value data set (chlorophyll a from the NASA Aqua-MODIS sensor) to our surface-occurring EMUs, and plankton abundance characteristics are now available for the surface data points and the surface-occurring EMUs. We intend to continue adding associative attributes from other globally available resources, and we are exploring the relationship between EMUs and established temporally dynamic climatological classifications  (Oliver and Irwin, 2008; Kavanaugh et al.
The mapping of global ecological land units (ELUs), sponsored by the GEO commission, has now been extended to the ocean. We partitioned the global ocean water column into 37 physically and chemically distinct volumetric regions using available data from the 57-year World Ocean Atlas data set. The global map of EMUs is an initial, unsupervised, statistical classification approach to mapping ocean environmental structure in three dimensions. Based on this methodology, we can re-cluster the ocean using additional or different deterministic variables if desirable, and also cluster the global ocean in different time intervals ranging from seasonal to annual to decadal to explore the temporal geographies of EMUs. The existence of the EMUs has the potential to facilitate research on the extent to which environmental drivers control biotic distributions.
ACKNOWLEDGMENTS We are grateful for the thoughtful reviews of three  U. S. Geological Survey scientists ,  Jonathan H. Smith ,  Page C. Valentine , and  Ilsa B. Kuffner , as well as those of three anonymous reviewers. Cressie's research was partially supported by a 2015-2017  Australian Research Council Discovery Project  ( DP150104576 ). Goodin's research was partially supported by the  Langar Foundation. Kavanaugh's research was partially supported by the  National Ocean Partnership Program 's  Marine Sanctuaries  as  Sentinel Sites  for a  Demonstration Marine Biodiversity Observation Network award  ( NNX14AP62A ). Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the  U. S. Government.
[3]  describe a computation model that simulates fission chambers, named CHESTER. The retrieved quantities of interest are the neutron-induced charge spectrum, the electronic and ionic pulses, the mean current and variance and the power spectrum. It relies on the GARFIELD suite, originally developed for drift chambers, and makes use of the MAGBOLTZ code to assess the drift parameters of electrons within the filling gas, and the SRIM code to evaluate the stopping range of fission products. The effect of the gamma flux is also estimated. A good qualitative agreement is obtained when comparing the results with the experimental data available to date. Similarly, in  [1] , a model is presented for the charge creation in a miniature fission chamber (outer diameter 4 mm) in order to understand the impact of some physical parameters such as the fissile deposit thickness.