dataesr/scientific-paragraphs-categorization huggingface dataset:
These latter two programs rely on genomic signatures and have been developed to predict plasmid sequences in metagenomic samples. Compared to PlaScope (recall=0. 87), PlasFlow achieves roughly the same recall value on our dataset (recall=0. 85), whereas cBar performed less well (recall=0. 74). However, regarding other criteria such as precision, specificity and accuracy, PlaScope outperformed the others due to its highly specific database. cBar and PlasFlow are able to identify mobile elements in many bacterial species owing to their very diverse taxonomic database. However, when focusing on a species, the targeted approach of PlaScope gave indisputably better results in terms of both recall and precision as indicated by F1 score (PlaScope: 0. 91; PlasFlow: 0. 41; cBar: 0. 32). Using PlaScope, we were able to recover almost all plasmids from the analysed strains, with very high precision, specificity and accuracy.
Furthermore, in one of the seven strains described as non-bearing plasmid strains in the NCBI database we were able to identify a mobile element: a typical plasmid F in E. coli K-12. In a second analysis, we challenged our approach on more concrete data by looking at specific genes. Analysing clinical or environmental strains, it could be of great interest to detect specific clones with particular genetic backgrounds. Indeed, the plasmid location of a resistance or virulence gene does not have the same impact from an epidemiological point of view and from the capacity of transmission of the strain in a particular environment. For example, plasmid outbreaks can occur when a gene that confers resistance against a wide-spectrum antibiotic is carried by such a mobile element. Conversely, if the same gene integrates in the chromosome of an already highly virulent strain, it can lead to the emergence of a well-adapted and dangerous clone.
To highlight this, we chose a genome dataset of E. coli wherein many strains exhibited a chromosomal integration of the blaCTX-M-15 coding gene, one of the main enzymes responsible for resistance to wide-spectrum antibiotics such as cephalosporins in E. coli  [15]. Using PlaScope we accurately identified 20/21 of these chromosomal insertions. In addition, we predicted the location of virulence genes and iron metabolism operons in agreement with the literature. This demonstrates that PlaScope may be particularly useful to locate operons such as aerobactin or salmochellin, which can be plasmidic as well as chromosomal and have, like other iron-metabolism-related systems, major impact on virulence and/or fitness  [27, 31]. We also built a Klebsiella database and assessed our workflow on K. pneumoniae clinical strains  [30].
g. Enterobacteriaceae), it will require the database to be enriched to keep it up to date. Finally, it would also be interesting to create other databases for well-known bacteria with many complete genomes available, such as Staphylococcus aureus, Enterococcus or Bacillus species. Five reasons to publish your next article with a Microbiology Society journal 1. The Microbiology Society is a not-for-profit organization. 2. We offer fast and rigorous peer reviewaverage time to first decision is 4-6 weeks. 3. Our journals have a global readership with subscriptions held in research institutions around the world. 4. 80% of our authors rate our submission process as 'excellent' or 'very good'. 5. Your article will be published on an interactive journal platform with advanced metrics. Find out more and submit your article at microbiologyresearch.
Data bibliography 1. Falgenhauer L, Imirzalioglu C, Ghosh H, Gwozdzinski K, Schmiedel J et al. Bioproject PRJEB9568 (2016). 2. Simner PJ, Antar AAR, Hao S, Gurtowski J, Tamma PD et al. Bioproject PRJNA392824 (2017).
e. , Allergan Microcell and Allergan Biocell) to quantify pore density, pore opening area, surface openness, and texture depth. Details of the methods used in this experiment can be found in the Supplementary material. X-ray CT was used to determine the surface area of the breast implant textures. Eight 10-mm diameter disks were cut from the shell of each breast implant device, four from the anterior and four from the posterior of the shell. The entire geometry of each disk was acquired by taking a series of 2-dimensional X-ray images (slices) while the implant disk was concentrically rotated 360°in the X-ray beam. These slices containing information about the implant disk's position (with 15 µm voxel resolution) and density (gray scale) were used as the basis for digital 3-dimensional reconstruction of the sample's volume data (Fig.
1a )  (ASTM International, 2011; Landis and Keane, 2010). All internal and external surfaces of the implant sample were extracted from this CT volume data. The spatial precision of the CT projection data was checked by a certified CT test standard (ruby bar with a length of 4. 0432 ± 0. 0020 mm; GE Sensing & Inspection Technologies, GmbH, Wunstorf, Germany). A vertical cross section of the X-ray CT image was used to measure the thickness of the non-textured area, which was defined as the location starting from the bottom of the disk to the flat area near the top of the disk or the lowest point of any protrusions present on the surface (Fig. 1b ). The thickness of the non-textured area was measured in three areas of the cross section and averaged. The average thickness of the non-textured area was used to calculate the surface area of the nontextured area (sides and bottom of disk) according to the formula for the area of a cylinder based on the assumption that the bottom of the disk was a flat surface.
The resulting surface area of the non-textured surface was subtracted from the total surface area of the disk (obtained using CT software) to produce a surface area measurement for the textured surface (top of disk) (Fig. 1c ). The surface area of the textured surface was calculated in terms of mm 2 as well as the percentage higher than that of a flat surface. The textured surface of the disk can be seen as the top circle of a cylinder; therefore, the surface area of a flat surface texture would be the surface area of a circle with a 5 mm radius (i. e. , 79 mm 2 ).
Statistical analyses The surface area of the implant textures determined by X-ray CT was evaluated using descriptive statistics. Comparisons between the Fig. 1. Method for calculating surface area of the textured surface of a 10-mm diameter disk taken from the shell of a breast implant. (a) The implant shell disk was imaged using X-ray CT, and with the CT software, a threshold applied to distinguish between material and air was used to produce a value for total surface area of the disk. (b) The thickness of the non-textured portion of the shell was measured and used to calculate the surface area of the non-textured area (A = 2πrh + 2πr 2 , where A is surface area, r is radius, and h is height. ). (c) The surface area of texture was calculated by subtracting the surface area of the non-textured area from the total surface area based on the assumption that the bottom of the disk was a flat surface.
There is a widening gap between the fast advancement of computational methods for tomographic reconstruction and their successful implementation in production software at various synchrotron facilities. This is due in part to the lack of readily available instrument datasets and phantoms representative of real materials for validation and comparison of new numerical methods. Recent advancements in detector technology have made sub-second and multi-energy tomographic data collection possible (Gibbs et al 2015 Sci. Rep. 5 11824), but have also increased the demand to develop new reconstruction methods able to handle in situ  (Pelt and Batenburg 2013.
Introduction Current tomographic experiments include a broad range of advanced setups, combining new imaging modes and specialized sample environments, yielding datasets that are far larger and more complex than what has been designed for standard while, on the other hand, these datasets contain limited data, in the sense that for each time-frame, the available data is noisy or contains just a limited set of images. Besides the experimental part, the quality of the resulting images depends crucially on the algorithms that are employed when processing and reconstructing the data. As experiments increase in complexity, more complex and sophisticated algorithms  [3]  are needed to obtain the highest possible quality reconstructions leading to better quality data and, ultimately, to faster scientific discoveries.
The existence of the gap between sophisticated algorithms and experimental x-ray imaging became clear during these workshops and motivated a group of synchrotron facility scientists to informally collect and share challenging tomographic datasets. These datasets were grouped under various 'challenge' categories, including round-Robin appendix A. 1. 1, in situ studies appendix A. 1. 2, dynamic systems appendix A. 1. 3, small field of view appendix A. 1. 4, phase contrast appendix A. 1. 5, etc to include the most challenging experimental scenarios like limited view, noisy projection, sample motion, instrument instability, beam hardening effects, strong ring artefacts etc.
TomoBank In this paper, we formalize that effort by introducing tomoBank, a tomographic data bank aiming to provide applied mathematicians and computational scientists with an extensive catalog of challenging x-ray tomography raw datasets from different x-ray instruments, as well as softwaregenerated phantoms that more closely represent material and live science samples compared to the standard phantoms used in the mathematical community. The distribution of raw data will enable computational scientists to access all information available as collected during the measurement. This is critical as new algorithms sometimes need more/different information than existing standard algorithms require.
Comparison with other data-banks Many of the existing data banks focus on the science surrounding the sample by providing data of biological systems  [10, 11] , materials  [12] [13] [14]  and simulations  [15] [16] [17] [18]  but are rarely focused on the science surrounding the experimental techniques  [19] [20] [21]  and/or targeted to computational experts in that technique. TomoBank provides tomography technique specific datasets collected at different facilities and is directly aimed at computational scientists by including a 'reference' catalog containing datasets and phantoms to be used for algorithm validation and comparison and a 'challenge' catalog containing datasets requiring, in most cases, the development of new algorithms to obtain good quality reconstructions. While datasets transition from 'challenge' to 'reference' catalogs we also aim to create a network through tomoBank in which computational scientists can not only use existing data but also request a new ad hoc measurement to validate a new method or vice-versa to optimize the experiment design.
Data format Datasets are collected and distributed in their original file format e. g. multiple TIFFs  [22] , dxfile  [23] , EDF  [24] , XRM  [25] , etc. However, for standard equally spaced angular scans, they are also converted and distributed as dxfile (a single HDF5 file containing raw, dark, and flat field arrays with all the required meta-data). All parallel beam tomography datasets and phantoms are also distributed with a Python script to read and perform a basic tomographic reconstruction using tomoPy  [4].
Simulated phantoms Although having access to a variety of experimental data sets will be helpful, knowing the ground truth may be even more valuable when evaluating the performance of novel reconstruction algorithms or other post-processing techniques. This is why tomoBank also includes a selection of simulated phantoms constructed with XDesign  [26]  appendix A. 2. XDesign is a Python module which can simulate the interaction of an x-ray probe with simulated phantoms constructed from simple geometric shapes. Developers testing new or on-the-fly reconstruction algorithms that may rely on unconventional data acquisition schemes will find these more useful.
Browsing tomoBank Access to tomoBank can be found at  [27]. All datasets and phantoms are listed with a unique ID number and can be sorted by facility, instrument, or 'challenge' category. Once a dataset or phantom is selected to be part of tomoBank, a digital object identifier [28] will be assigned to point to a permanent dataset web page containing its meta data (authors, supported article, data citation, description and the data download link, etc). To efficiently download tomographic datasets, tomoBank relies on Globus  [29].
Contributing data to tomoBank TomoBank provides links to published tomographic datasets valuable for the advancement of computational methods. To submit a dataset follow the instruction at  [27]  by providing author list, dataset title, description, DOI number, license, link to the raw data and, if the dataset was used to support a scientific publication, the reference to the supported publication. For datasets hosted on Argonne National Laboratory servers, the DOI number and link to a permanent download web page can be provided by the Argonne Research Library (an example is at  [30] ).
A. 1. Datasets A. 1. 1. Round-Robin. As in a round-Robin tournament where each player faces every other player, these are series datasets of specimens imaged at multiple facilities. The Hornby-Blakely datasets (tomo_00001 to tomo_00006)  [30]  consist of two shale samples obtained from the North Sea and the Upper Barnett Formation in Texas  [32]  (see figure  A1 ). These specimens were imaged under similar conditions at the advanced photon source (APS) of Argonne National Laboratory, USA, at the swiss light source (SLS) of the Paul Scherrer Institut, Switzerland and at the advanced light source (ALS) of Lawrence Berkeley National Laboratory, USA. The goals of this round-Robin project were to (i) characterize microstructures and porosity on the micrometer scale, (ii) compare results measured at three synchrotron facilities, and (iii) identify optimal experimental conditions of high-resolution tomography (microCT) for fine-grained materials.
Shale was chosen for the round-Robin because it is a challenging material due to its multiphase composition, small grain size, low but significant amount of porosity, as well as strong shape-and lattice-preferred orientation. All instruments used a 10× objective lens with an effective pixel size of ∼0. 7 μm. A. 1. 2. In situ studies. Environment control cells, which can control physical parameters like pressure, temperature chemical environment etc around a sample, allow for 3D characterizations of materials and biological systems in real conditions; however, these conditions or the chamber itself can create difficult data acquisition conditions. The Limited View datasets (tomo_00007 to tomo_00021) contain nano tomography (nanoCT) data of a small particle of Ce 6 Al 4 undergoing a pressure increase (see figure  A2 ).
Data is collected at 15 pressure levels from 0. 3 GPa to 59 GPa. Because in 86 of the 359 projections, the particle partially hidden from the field of view by the high-pressure cell's load frame, the challenge of this dataset is to determine accurately the particle volume as a function of pressure. The Limited View datasets are described in tables A1 and A3 and are available for download at  [27]  under tomo_00007 to tomo_00021. The rock with and without oil (tomo_00025 to tomo_00026) datasets with experimental conditions reported in table A4 represent a microCT geo-science example (figure  A6 ) where the background fluid is replaced. A. 1. 3. Dynamic systems. Datasets in this category aim to capture fast evolving samples in 3D by adjusting several parameters including x-ray energy, exposure time, frame rate, rotary stage speed etc.
The Dorthe_F_002 dataset (tomo_00031) has an exposure time that is much shorter than what is optimal for data acquisition (figure  A7 ). The sample description and the experimental conditions for Dorthe_F_002 are reported in table A6. A. 1. 4. Small region of interest. These datasets have specimens which are larger than the field of view. Double bamboo tin wire (figure  A3 ), bamboo wire 2 (figure  A4 ), polystyrene (figure  A5 ) (tomo_00022 to tomo_00024) are microCT datasets acquired at the SYRMEP beamline of Elettra-Sincrotrone Trieste (Elettra) laboratory (Italy) in nearly-parallel beam geometry. The related sample description and the experimental conditions are reported in table  A4. Bone local, bone local stone, candie local, and smiling sample (tomo_00027 to tomo_00030) were acquired by microfocus CT at the TomoLab station of Elettra using a conebeam geometry.
Reconstructions are computed with SIRT with a non-negativity constraint using the ASTRA toolbox  [37]. tomographic reconstruction is then performed using as input data the phase-retrieved projections. [27]  includes several phase-contrast datasets (see tomo_00064 to tomo_00068). A. 1. 6. Beam hardening and ring artefacts. When using polychromatic x-ray sources for tomographic experiments, the reconstructed slices in many cases display the so-called beam hardening artefacts  [35]. These artefacts are due to the differential absorption of the x-ray spectrum by the sample and lead to a misleading recovery of the linear absorption coefficients, mainly appearing as brighter sample borders in the slices. For regular objects the effect is easily detected, but for irregular objects it is commonly difficult to differentiate between beam hardening artefacts and actual material variations because of the additional appearance of dark stripes in the images.
Often it is also possible to observe the presence of concentric rings which are typical artefacts arising from inhomogeneities in the individual pixel response of detector elements. These artefacts, named ring artefacts, seriously complicate the visibility of fine details in the sample and the post-processing of the images. Several datasets containing beam hardening and ring artefacts are available in  [27] , including data obtained by using the TomoLab station at Elettra.
A. 2. The phantoms All phantoms available in tomoBank are generated using XDesign  [26]. Each phantom is discretized to obtain the ground truth reconstructed slice and is projected on a 256 pixel detector in 512 equally spaced angles. All phantoms are saved as dxfile  [23]  and distributed together with the XDesign Python code used to generate them together with a basic reconstruction script using  [4]. Some of the phantoms included in tomoBank are listed in table A2 and shown in figure  A8.
Acknowledgments This research used resources of the  Advanced Photon Source , a  US Department of Energy (DOE) Office of Science User Facility  operated for the  DOE Office of Science by Argonne National Laboratory  under Contract No. DE-AC02-06CH11357. Portions of this work were performed at  GeoSoilEnviroCARS (The University of Chicago , Sector  13 ),  Advanced Photon Source (APS) ,  Argonne National Laboratory. GeoSoilEnviroCARS is supported by the  National Science Foundation-Earth Sciences  ( EAR-1128799 ) and  Department of Energy-GeoSciences  ( DE-FG02-94ER14466 ). Peter Cloetens  is acknowledged for the interesting discussion at the Lorentz workshop that lead to this paper. Networking support was provided by the  EXTREMA COST Action MP1207.
Relation Induction In the relation induction task, we are given word pairs (s 1 , t 1 ),. , (s k , t k ) that are related in some way, and the task is to decide for a number of test examples (s, t) whether they also have this relationship. Among others, this task was considered in  (Vylomova et al. , 2016) , and a ranking version of this task was studied in  (Drozd et al. , 2016). As test sets we use the Google Analogy Test Set  (Mikolov et al. , 2013a) , which contains instances of 14 different types of relations, and the DiffVec dataset, which was introduced in  (Vylomova et al. , 2016). This dataset contains instances of 36 different types of relations 4. Note that both datasets contain a mix of semantic and syntactic relations. In our evaluation, we have used 10-fold crossvalidation (or leave-one-out for relations with fewer than 10 instances).
In the experiments, we consider for each relation in the test set a separate binary classification task, which was found to be considerably more challenging than a multi-class classification setting in  (Vylomova et al. , 2016)   1  in terms of accuracy and (macro-averaged) precision, recall and F1 score. As can be observed, our model outperforms the baselines on both datasets, with the R 2 ik variant outperforming the others. To analyze the benefit of our proposed word embedding variant, Table  2  shows the results that were obtained when we use a standard word embedding model. In particular, we show results for the standard GloVe model, SkipGram and the Continuous Bag of Words (CBOW) model. As can be observed, our variant leads to better results than the original GloVe model, even for the baselines.
Measuring Degrees of Prototypicality Instances of relations can often have different degrees of prototypicality. For example, for the relation "X characteristically makes the sound Y ", the pair (dog,bark) should be considered more prototypical than the pair (floor,squeak), even though both pairs might be considered to be instances of the relation  (Jurgens et al. , 2012). A suitable relation vector should allow us to rank word pairs according to how prototypical they are as instances of that relation. We evaluate this ability using a dataset that was produced in the aftermath of SemEval 2012 Task 2. In particular, we have used the "Phase2AnswerScaled" data from the platinum rankings dataset, which is available from the SemEval 2012 Task 2 website 5. In this dataset, 79 ranked list of word pairs are provided, each of which corresponds to a particular relation.
Relation Extraction Finally, we consider the problem of relation extraction from a text corpus. Specifically, we consider the task proposed in  (Riedel et al. , 2010) , which is to extract (subject,predicate,object) triples from the New York Times (NYT) corpus. Rather than having labelled sentences as training data, the task requires using the existing triples from Freebase as a form of distant supervision, i. e. for some pairs of entities we know some of the relations that hold between them, but not which sentences assert these relationships (if any). To be consistent with published results for this task, we have used a word embedding that was trained from the NYT corpus 6  , rather than Wikipedia (using the same preprocessing and set-up). We have used the training and test data that was shared publicly for this task Following earlier work on this task, we report our results on the test set as a precisionrecall graph in Figure  1.
We present a library for generic programming in OCAML, adapting some techniques borrowed from other functional languages. The library makes use of three recent additions to OCAML: generalised abstract datatypes are essential to reflect types, extensible variants allow this reflection to be open for new additions, and extension points provide syntactic sugar and generate boiler plate code that simplify the use of the library. The building blocks of the library can be used to support many approaches to generic programming through the concept of view. Generic traversals are implemented on top of the library and provide powerful combinators to write concise definitions of recursive functions over complex tree types. Our case study is a type-safe deserialisation function that respects type abstraction.