dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion on simulated datasets In summary, MowgliNNI successfully uses the reconciliation cost as additional information to resolve the uncertain parts of gene trees inferred from sequences only. Though the gene tree resolutions are partly guided by reconciliations with the species tree, they are not attracted away from the true gene trees, but are closer to them than the initial gene trees. As a result, MowgliNNI infers gene events more accurately, which is of prior importance to distinguish orthologs from paralogs and xenologs  [14].
Experiments on real data As species tree S, we chose a phylogeny covering 336 genomes of Bacteria and Archaea recently inferred by Abby et al. [30]. Then, a dataset of 29, 709 homologous gene families spanning these taxa was collected from the HOGENOM database (release 04)  [40]. Each such family contains from 3 to 312 taxa. The gene tree of each family from this dataset was reconciled with the species tree by Mowgli and MowgliNNI using costs τ = 3, δ = 3. 5, resp. λ = 1 for transfers, duplications, resp. losses. These costs were estimated on the basis of several bacteria phyla by a maximum likelihood method  [18, 41]. A threshold of 50% for branch support values was used to indicate to MowgliNNI the weak edges in the gene trees.
A decrease in the number of inferred events and reconciliation costs MowgliNNI allowed to change the gene tree, hence to lower the reconciliation cost, in 24% of the ≈ 30, 000 families. This gain is non-negligible and has a real importance as changing the gene tree topology has an important impact on the inferred events (as already shown on simulated datasets and discussed below). In turn, these inferred events may serve to predict the function of new sequences on the basis of their orthology relationships with annotated sequences, orthology following from the chosen reconciliation. Among previous reconciliation studies that allowed to modify the gene trees, Berglund-Sonnhammer et al. report that 10% of their families were improved  [21]  when allowing rearrangements on weak edges under the DL model, while Chaudhary et al.
improved all their gene trees in a pure D model when rearranging gene trees with Subtree Prune and Regraft (SPR) operations  [24]. Note that the heterogeneity of models and datasets used in these studies limit the comparison of their results, but we cite them for completeness. For gene families with a lower reconciliation cost (24% of all families), we counted the number of events of each kind (D, T, L) inferred by Mowgli and MowgliNNI. As a rule, MowgliNNI led to a decrease in the number of events in inferred evolutionary histories. In particular, the number of transfers is reduced in 88. 3% of these gene families, the number of losses being reduced in 59. 9%, while the number of duplications is almost the same (decrease in 5. 2% of the families). These results obtained in the DTL model echo those of Durand et al.
The improvement in running time due to the optimized version of MowgliNNI We measured the running time of Mowgli and that of both the naive and optimized versions of MowgliNNI (see Methods), respectively called MowgliNNI n and MowgliNNI. From the ≈ 30 k families of our dataset, we extracted a random sample of 100 families uniformly spanning from 10 to 80 taxa, and respecting the previously observed proportion of improving / nonimproving cases in the reconciliation cost (i. e. , 24% and 76% resp. ). This sample was used to run the three programs. Figure  13  reports the ratios of MowgliNNI n and MowgliNNI running times over those of Mowgli, with respect to the number of weak edges within the input gene tree. Indeed, for each gene tree, the number of tested rearrangements during the optimization search depends on the number of weak edges, which hence strongly impacts MowgliNNI n and MowgliNNI running times.
Transfers in prokaryotic phyla On our whole dataset of 29, 709 homologous gene families, we particularly studied transfers in 5 bacterial and 1 archaeal phyla: Proteobacteria (169 genomes), Actinobacteria (31 genomes), Cyanobacteria (14 genomes), Chlamydiae (7 genomes), Spirochaetes (7 genomes) and Crenarchaeota (10 genomes). We compared our results obtained with Mowgli and MowgliNNI to those of Abby et al. [30]  obtained with the Prunier method  [29]  that infers transfers in mono-copy gene families on another basis than reconciliation. In order to compare our results to the Abby et al. study, we extracted particular families from HOGENOM v4. For each of the 6 phyla of interest, we collected the list of families having at most one copy of the gene for the genomes of this phylum and separated them into two groups: families having one copy of the gene for each genome of the phylum, so-called "universal families", and families having a copy of the gene for at least 7 genomes of the phylum, so-called "non-universal families".
Acknowledgements We thank  Gergely J. Szöll  ősi for providing event costs of the real dataset. This work was funded by the  french Agence Nationale de la Recherche Investissements d'avenir / Bioinformatique  ( ANR-10-BINF-01-02 , Ancestrome),  Programme 6ème Extinction  ( ANR-09-PEXT-000 PhyloSpace ) and by the  Institut de Biologie Computationnelle.
Background: Digital pathology images are increasingly used both for diagnosis and research, because slide scanners are nowadays broadly available and because the quantitative study of these images yields new insights in systems biology. However, such virtual slides build up a technical challenge since the images occupy often several gigabytes and cannot be fully opened in a computer's memory. Moreover, there is no standard format. Therefore, most common open source tools such as ImageJ fail at treating them, and the others require expensive hardware while still being prohibitively slow. Results: We have developed several cross-platform open source software tools to overcome these limitations. The NDPITools provide a way to transform microscopy images initially in the loosely supported NDPI format into one or several standard TIFF files, and to create mosaics (division of huge images into small ones, with or without overlap) in various TIFF and JPEG formats.
They can be driven through ImageJ plugins. The LargeTIFFTools achieve similar functionality for huge TIFF images which do not fit into RAM. We test the performance of these tools on several digital slides and compare them, when applicable, to standard software. A statistical study of the cells in a tissue sample from an oligodendroglioma was performed on an average laptop computer to demonstrate the efficiency of the tools. Conclusions: Our open source software enables dealing with huge images with standard software on average computers. Our tools are cross-platform, independent of proprietary libraries, and very modular, allowing them to be used in other open source projects. They have excellent performance in terms of execution speed and RAM requirements. They open promising perspectives both to the clinician who wants to study a single slide and to the research team or data centre who do image analysis of many slides on a computer cluster.
Background Virtual microscopy has become routinely used over the last few years for the transmission of pathology images (the so-called virtual slides), for both telepathology and teaching  [1, 2]. In more and more hospitals, virtual slides are even attached to the patient's file  [3, 4]. They have also a great potential for research, especially in the context of multidisciplinary projects involving e. g. mathematicians and clinicians who do not work at the same location. Quantitative histology is a promising new field, involving computer-based morphometry or statistical analysis of tissues  [5] [6] [7] [8] [9]. A growing number of works report the pertinence of such images for diagnosis and classification of diseases, e. g. tumours  [10] [11] [12] [13] [14]. Databases of clinical cases  [15]  will include more and more digitized tissue images.
An accurate, non-pixelated determination of the perimeters of the cell nuclei is needed for morphometry and statistics. However, this technique involves the manipulation of huge images (of the order of 10 billions of pixels for a full-size slide at magnification 40x with a single focus level) for which the approach taken by most standard software, loading and decompressing the full image into RAM, is impossible (a single slice of a full-size slide needs of the order of 30 GiB of RAM). As a result, standard open-source software such as ImageJ  [17] , ImageMagick  [18]  or Graphics-Magick  [19]  completely fails or is prohibitively slow when used on these images. Of course, commercially available software exists  [16] , but it is usually quite expensive, and very often restricted to a single oper-ating system.
It uses proprietary source code, which is a problem if one wants to control or check the algorithms and their parameters when doing image analysis for research. In addition, many automated microscopes or slide scanners store the images which they produce into proprietary or poorly documented file formats, and the software provided by vendors is often specific to some operating system. This leads to several concerns. First, it makes research based on digital pathology technically more difficult. Even when a project is led on a single site, one has often to use clusters of computers to achieve largescale studies of many full-size slides from several patients  [20]. Since clusters of computers are typically run by open source software such as Linux, pathology images stored in non-standard file formats are a problem.
Furthermore, research projects are now commonly performed in parallel in several sites, not to say in several countries, thanks to technology such as Grid  [21] , and there is ongoing efforts towards the interoperability of information systems used in pathology  [3, 22]. Second, proprietary formats may hinder the development of shared clinical databases  [15]  and access of the general public to knowledge, whereas the citizen should receive benefit of public investments. Finally, they may also raise financial concerns and conflicts of interest  [23]. There have been recent attempts to define open, documented, vendor-independent software  [24, 25] , which partly address this problem. However, very large images stored in the NDPI file format produced by some slide scanners manufactured by Hamamatsu, such as the NanoZoomer, are not yet fully supported by such software.
For instance, LOCI Bio-Formats  [25]  is presently unable to open images, one dimension of which is larger than 65k, and does not deal properly with NDPI files of more than 4 GiB. OpenSlide  [24]  does not currently support the NDPI format. NDPI-Splitter  [26]  needs to be run on Windows and depends on a proprietary library. To address these problems, we have developed open source tools which achieve two main goals: reading and converting images in the NDPI file format into standard open formats such as TIFF, and splitting a huge image, without decompressing it entirely into RAM, into a mosaic of much smaller pieces (tiles), each of which can be easily opened or processed by standard software. All this is realized with high treatment speed on all platforms.
Implementation Overview The main software is implemented in the C programming language as separate, command-line driven executables. It is independent of any proprietary library. This ensures portability on a large number of platforms (we have tested several versions of Mac OS X, Linux and Windows), modularity and ease of integration into scripts or other software projects. It is complemented by a set of plugins for the public domain software ImageJ  [17] , implemented in Java, which call the main executables in an automatic way to enable an interactive use. The LargeTIFFTools and NDPITools are based on the open source TIFF  [27]  and JPEG  [28]  or libjpeg-turbo  [29]  libraries. The NDPITools plugins for ImageJ are based on the Java API of Im-ageJ  [17, 30]  and on the open source software Image-IO  [31] , and use the Java Advanced Imaging 1.
Standalone Our tools can be used through the command line (POSIX-like shell or Windows command interpreter), and therefore can be very easily integrated into scripts or other programs. Depending on the tool, the paths and file names of one or several files, in NDPI or TIFF format, have to be provided. Options can be added with their arguments on the command line to modify the behavior of the programs from its default. They are explained in the messages printed by the programs run without arguments, in Unix-style man pages, and on the web pages of the project (see below in the Availability and requirements Section). Under the Windows OS, one can click-and-drag the NDPI file icon onto the icon of ndpi2tiff or ndpisplit. We provide precompiled binaries where frequently-used options are turned on by default: e.
ImageJ integration In addition to command line use, the ndpisplit program can be driven through the NDPITools plugins in ImageJ with a point-and-click interface, so that previewing the content of a NDPI file at low resolution, selecting a portion, extracting it at high resolution and finally opening it in ImageJ to apply further treatments can be done in an easy and graphical way. Figure  2  shows a screen shot of ImageJ 1. 47m after extraction of a rectangular zone from a NDPI file. Figure  3  explains what happens when the NDPI file contains several levels of focalization: the preview image is displayed as a stack. When producing a mosaic, the user can request that pieces be JPEG files. Since the File > Open command of versions 1. x of ImageJ is unable to open TIFF files with JPEG compression (one has to use plugins), this is way to produce mosaics which can be opened by click-and-drag onto the window or icon of ImageJ while still saving disk space thanks to efficient compression.
0. 0  [27] , and therefore by the abundant image viewing and manipulation software which relies on LibTIFF. If the use of the BigTIFF format extension would have impeded the further exploitation of the produced TIFF file, we could have simply used ndpisplit as above. Or we could have called the ndpi2tiff command several times, each time requesting extraction of a subset of all images by specifying image numbers after the file name, separated with commas, as in a. ndpi,0,1,2,3,4. Extracting a small region from a huge image. This task can be useful to visualize at full resolution a region of interest which the user has selected on a low-magnification preview image. Therefore, it should be performed as quickly as possible. From a TIFF file. The task was to extract a rectangular region of size 256 × 256 pixels situated at the bottom right corner of huge TIFF images and to save it as an independent file.
The source images were singleimage TIFF files using JPEG compression. Table  1  compares the time needed to complete the task with tifffastcrop from our LargeTIFFTools and with several software tools, on increasingly large TIFF files. Tests were performed on a 2. 6 GHz Intel Core i7 Mac Mini computer with 16 GB of RAM and used GraphicsMagick 1. 3. 17, ImageMagick 6. 8. 0-7 and the utility tiffcrop from LibTIFF 4. 0. 3. Noticeably, when treating the largest image, Graph-icsMagick needs 50 GiB of free disk space, whereas tifffastcrop doesn't need it. From a NDPI file. The task was to extract a rectangular region of size 256 × 256 from one of the largest images of the file a. ndpi (size 180224 × 70144). On a 2. 6 GHz Intel Core i7 Mac Mini computer with 16 GB of RAM, the execution time was 0.
Integration in digital pathology image servers or virtual slide systems The NDPITools are being used in several other software projects: • in a system for automatic blur detection  [2, 4]. • in WIDE  [22] , to deal with NDPI files. WIDE is an open-source biological and digital pathology image archiving and visualization system, which allows the remote user to see images stored in a remote library in a browser. In particular, thanks to the feature of high-speed extraction of a rectangular region by ndpisplit, WIDE saves costly disk space since it doesn't need to store TIFF files converted from NDPI files in addition to the latter.
Conclusions The LargeTIFFTools, NDPITools and NDPITools plugins for ImageJ achieve efficiently some fundamental functions on large images and in particular digital slides, for which standard open source software fails or performs badly. They enable both the clinician to examine a single slide and the bioinformatics research team to perform large-scale analysis of many slides, possibly on computer grids  [20]. To date, the LargeTIFFTools have been downloaded from more than 388 different IP addresses, the NDPITools from more than 1361 addresses, and the ImageJ plugins from more than 235 addresses. Table  2  lists the distribution of the target platforms among the downloads of the binary files. It shows a broad usage of the different platforms by the community, emphasizing the importance of cross-platform, open source tools.
We have explained how the software was used to study some microscopic properties of brain tissue when invaded by an oligodendroglioma, and we have given an illustrative application to the analysis of a whole-size pathology slide. This suggests other promising applications. The dialog box shows some options which can be customized while producing a mosaic from a rectangular selection of a NDPI file preview image (here, using the file previewed in Figure  3 ). The positions of the 154240 identified nuclei were obtained from the analysis with ImageJ of the digital slide on a laptop computer. Since the slide was too large to fit into the computer's memory, it was turned into a mosaic of 16 pieces with overlap of 60 pixels, and each piece underwent automated analysis independently. Then the results were aggregated.
Acknowledgements We thank  F. Bouhidel  and  P. Bertheau  for their help with the slide scanner of the  Pathology Laboratory of the Saint-Louis Hospital in Paris , and  C. Klein  ( Imaging facility, Cordeliers Research Center -INSERM U872, Paris ) for tests and suggestions. The computer,  CPU , operating system, and programming language names quoted in this article are trademarks of their respective owners.
fr 2 Laboratoire de Chimie de la Matière Condensée de Paris, Ecole Nationale Supérieure de Chimie de Paris, UMR CNRS 7574, Paris, France Full list of author information is available at the end of the article an EPR experiment, extract the classical EPR parameters and compute their distribution in order to have an estimate of their error; compute the new R 10 parameter and its distribution from the preceding distributions. Thanks to this paper, scientists may themselves extract the R 10 parameter from EPR data and use it not only for datation purposes but also to uniquely characterize the observed EPR spectrum lineshapes. Our goal is to automate a manual process that has proved scientifically successful yet cumbersome and tedious when applied on datasets that are getting larger. In that version of our code, some of our algorithmic choices just mirror the -successful-manual process.
We have chosen the Python language because of its high level, ease of development and popularity; last but not least, it also provides powerful libraries for scientific development, and speed of execution turned out not to be a key factor for our goals a. The Python code runs inside the Sage computing platform  [2] , which aims at providing a single computing environment both for numerical and symbolic computations. http://www. scfbm. org/content/8/1/15 Electron Paramagnetic Resonance (EPR) is a nondestructive and non-invasive technique which has indeed long been used for the study of paramagnetic defects (organic radicals) in carbonaceous materials. Such defects have been detected with high sensitivity in coals by pioneering EPR works  [3]. These types of radicals were therefore used for the characterization of a wide range of carbonaceous objects, ranging from coals  [4] [5] [6]  to cherts  [7]  through meteorites  [8] [9] [10] [11].
Methods All the relevant discussion about the experimental part of the work, that involves collecting EPR data on the rock samples, can be found in  [1]. In the current paper, we focus on the specific data handling and processing in order to extract the R 10 parameter from an EPR spectrum and estimate the associated error. All computations were made in the Sage computing environment  [2] , with imports from the Numeric Python library  [20]. In the spirit of reproducible research  [21] , the paper is written in the literate programming style  [22] : the code and its explanation b are intertwined in a single place, and a particular program is then used to extract either the source code for execution on a computer or the literate paper for reading by humans. Literate programming tools exist, and we use Noweb  [19]  and Org-mode  [23, 24]  within Emacs with Evil mode to enable vi commands.
We also make use of the Sagetex package that comes with the Sage distribution, that allows Sage code to be executed when compiling the LaTeX source of the paper c , and we have a home-built script that manages to combine Org-mode with Sagetex together with a Noweb output. Figures are produced either with Sage and Sagetex, or with Asymptote: it allows us to program figures, and thus make them executable, and embeddable in the LaTeX source code. The code will be made available through the team's website d.
Processing data from an EPR file Removing the background signal EPR spectra on which the R 10 factor was to be measured were selected for their symmetric and well-defined single absorption derivative signal. As usual in EPR studies, the large scale background signal was subtracted with a third degree polynomial fitted on the smooth parts of the spectrum where the signal variations are only due to noise, which in practice correspond to the first and last 10% data points in a typical spectrum. from pylab import polyfit From now on, the spectrum will be understood as the baseline corrected raw spectrum. The first two lines must be skipped when loading data: they provide the EPR acquisition parameters and the file description. EPR text files comprise three columns, giving respectively the point index (starting from one and running to the total number of points recorded), the datapoint abscissa -the magnetic field B-and the datapoint ordinate -the intensity in arbitrary units.
In detail, the layout of the paper is as follows. Section 2 summarizes the dataset and the basic measurements, and describes the selection criteria for the final galaxy sample in detail. Section 3 presents the approach utilized to correct sizes and other structural parameters for systematic biases that affect raw measurements as a function of galaxy magnitude, size, concentration and ellipticity. Section 4 presents the redshift evolution of the number densities at constant size-and surface mass density (i. e. , the size and surface mass density functions) for Q-ETGs, and thus our core result, i. e. , the constancy of the compact Q-ETG population and the emergence of a newlyquenched population of large ETGs over the z =→ 0. 2 period. Section 5 presents the size-and surface mass density functions for star-forming galaxies, and compares the number densities of the newly-quenched galaxies with the number densities of star-forming galaxies, of similar masses and sizes, that are expected to quench in the z = 1 → 0.