dataesr/scientific-paragraphs-categorization huggingface dataset:
Microscopy Bacteria were imaged using an automated inverted epifluorescence microscope Nikon Ti-E/B equipped with the "perfect focus system" (PFS, Nikon), a phase contrast objective (CFI Plan Apo Lambda DM 100X, NA1. 45), Semrock filter sets for GFP (Ex: 482BP35; DM: 506; Em: 536BP40) and mCherry (Ex: 562BP40; DM: 593; Em: 640BP75), a LED light source (Spectra X Light Engine, Lumencor) and a cCMOS camera (Neo sCMOS, Andor). Image acquisition was performed using the Volocity software package. Images were analyzed using the open-source software Oufti  (Paintdakhi et al. , 2016). Cells were automatically detected using the phase contrast image and delineated to allow visual inspection, which permitted disambiguation when cells were juxtaposed or forming chains. The long axis of each cells was determined automatically for subsequent calculation.
ACKNOWLEDGMENTS We thank  J. -P. Kleman  for assistance with the microscopy at  IBS ,  G. Paboeuf  for assistance with AFM performed at 2CBioMIF facility (ScanMAT, UMS 2001  CNRS -University of Rennes 1 ),  A. Maestro  for access to the monolayer compression equipment at  ILL, Grenoble , and  E. Breukink in Utrecht  for the gift of lipid II-NBD.
FUNDING This work was supported by Grant ORBiMP  ANR-14-CE14-0003-01  from the  Agence Nationale de la Recherche  and used the platforms of the  Grenoble Instruct Center  ( UMS 3518 CNRS-CEA-UJF-EMBL ) with support from  FRISBI  Grant  ANR-10-INSB-05-02  and  GRAL  grant  ANR-10-LABX-49-01  within the  Grenoble Partnership for Structural Biology.
HS-AFM experiments Before immobilization, the ESCRT tubes were treated for 30 min with 1 mM tobacco etch virus protease to remove the MBP tag from CHMP2AC. After removal of MBP, 2 l of ESCRT tubes was incubated on the previously prepared lipid bilayer for 15 min. After the incubation time, the surface was cleaned gently with recording buffer. The glass rod was then attached to the piezo head of the HS-AFM with a small amount of wax. All HS-AFM data were taken in amplitude modulation mode using a sample scanning HS-AFM [Research Institute of Biomolecule Metrology (RIBM), Japan] (47, 56). Short cantilevers (USC-F1. 2-k0. 15, NanoWorld, Switzerland) with spring constant of 0. 15 N/m, resonance frequency around 0. 6 MHz, and a quality factor of ∼2 in buffer were used. The cantilever-free amplitude is 3 nm, and the set-point amplitude for the cantilever oscillation was set around 2.
Data analysis and image processing All the HS-AFM images were processed primarily with Igor Pro with a built-in script from RIBM (Japan) and ImageJ software with additional home-written plugins. The HS-AFM images/movies were only processed minimally through tilt correction, drift correction, and brightness correction. The kymographs were obtained from the cross section at a fixed position (marked for each image) over the entire movie. It represents the height distribution along the line cross section as a function of time. For each experimental condition, at least four tubes were investigated. For the control experiments with VPS4B E235Q and VPS4B I124E, the number of local deformation sites was measured for at least nine tubes. For the VPS4B I124E, we found several filaments without any local deformation; therefore, the calculation of the filament length per deformation for this particular case is underestimated, and the real value should be higher.
Primary and secondary efficacy endpoints The primary efficacy endpoint was change from baseline to Day 2 or Day 90 in cerebrovascular reactivity as measured by the natural logarithm of the slope of the visual stimulus-driven fMRI BOLD response. The slope was chosen as the primary endpoint based on a linear discriminant analysis of previous study data  10  where the modelled amplitude of the hemodynamic response function divided by the time to reach the peak amplitude (on a logarithmic scale) was found to provide better differentiation from controls by incorporating both the reduced amplitude and delayed time to peak associated with CAA. The secondary efficacy endpoints were change from baseline to Day 2 or to Day 90 in cerebrovascular reactivity measured by the individual parameters for time to peak, amplitude, and time to baseline from the time-course of the visual stimulus-driven fMRI hemodynamic response.
Image analysis Functional image series were first registered to each participant's high-resolution T1-weighted structural image, then warped into Montreal Neurological Institute (MNI) space, and resampled at 2 mm using IRTK software (BioMediaIA, London, UK). Preprocessing continued in the FSL software toolkit (Oxford Centre for Functional MRI of the Brain, Oxford, UK) with motion correction, nonlinear noise reduction (SUSAN), spatial smoothing with a 5-mm Gaussian kernel, temporal filtering, and prewhitening. The FLOBS tool in FSL was then used to generate a hemodynamic response function basis set with three components that had sufficient flexibility to account for a large array of temporal delays expected in the data, and a general linear model used to fit the time-series data with the FSL tool FEAT.
Region of Interest (ROI) analyses were performed using the FSL tool Featquery within two functionally defined regions in the occipital lobe. These ROIs (Fig. 1 ) were derived from an independent dataset from CAA participants acquired at 1. 5 T,  10  then mapped into MNI space for use across participants. The primary and secondary ROIs for the analysis were defined by the voxels activated in at least 50% or at least 25%, of the runs across all CAA participants, respectively. The primary ROI for the analysis, ROI1, was used to extract the mean of the modeled time-courses within the ROI. A larger secondary ROI, ROI2, was used to extract a z-score-weighted mean of the modeled voxel-wise time-courses within the ROI. To generate summary metrics of the hemodynamic response, trial-averaged mean and z-score-weighted mean modeled time courses from ROI1 and ROI2, respectively, were fit with a trapezoid.
Statistical methods Sample size calculations were based on an estimate of the variability obtained from previous study data. 10  In addition to the variability estimate, these data also provided informative priors for placebo effects at 1 day and 3 months. The Bayesian approach, a statistical technique to incorporate prior information into data analysis, was utilized in the study, and the inclusion of the derived priors was expected to provide an equivalent of nine additional evaluable placebo participants for the study. Based on the primary endpoint of cerebrovascular reactivity measured by the natural logarithm of the slope in visual stimulus-driven fMRI and utilizing the Bayesian informative prior on placebo, it was estimated that a sample size of 30 evaluable study participants -20 ponezumab and ten placebo (approximately 1:1 ratio expected in analysis combined with the informative placebo prior)was required to provide enough precision for a 2-part predefined criteria for efficacy: C1: point estimate of ponezumab versus placebo effect >20% increase (improvement) in slope; C2: standard error of ponezumab versus placebo effect <60% of the point estimate.
e. outliers included), were also performed. The analysis was performed separately for Day 2 and Day 90. Analysis was performed on a natural logarithm (log e ) scale. For the analysis of secondary efficacy endpoints, analysis of change from baseline in time to peak, amplitude, and time to return to baseline was performed on a log e scale. Change from baseline in log e (time to peak) for Days 2 and 90 was analyzed using the same Bayesian method using an observed case approach (i. e. , missing data excluded). Analyses were based on the FAS and used an outlier-robust model. The other secondary endpoints from the visual stimulus-driven fMRI (change from baseline in log e [amplitude] and log e [time to return to baseline]) were analyzed using ANCOVA with log e (baseline value) as a covariate. These analyses were also based on the FAS and analyses were performed separately for Days 2 and 90.
16 µL of FastStart Taq DNA Polymerase (Merck). Linearity of the PCR amplifications was established experimentally. For CHIP-F/CHIP-R the following conditions were used: an initial step of 4 min at 95 • C, followed by 35 cycles of 30 s at 95 • C, 30 s at 62 • C, 30 s at 72 • C, and a final extension step of 5 min at 72 • C. For TFB2M-F/TFB2M-R the following conditions were used: an initial step of 4 min at 95 • C, followed by 31 cycles of 30 s at 95 • C, 30 s at 59 • C, 30 s at 72 • C, and a final extension step of 5 min at 72 • C. For 36B4-F/36B4-R the following conditions were used: an initial step of 10 min at 95 • C, followed by 32 cycles of 30 s at 95 • C, 1 min s at 60 • C. PCR products were run on 1. 5% agarose gel and stained with ethidium bromide (BET). Signal quantification was done using ImageJ software (National Institutes of Health, Bethesda, MD, USA).
Cryo-EM data collection, structure determination and modelling Low dose movies were collected manually on a 300 kV Tecnai G2 Polara (FEI) microscope equipped with a Quantum energy filter and K2 Summit direct electron detector (Gatan) in counting mode, with a total dose in each of 30 e -/Å 2 fractioned into 50 frames at a pixel size of 1. 39 Å/px. 364 and 284 movies were collected for N+UmKin5 and UmKin5 respectively. Initial frame alignment was performed using IMOD  (Kremer et al. , 1996). A second local alignment step was performed with Scipion using the optical flow method (de la  Rosa-Trevin et al. , 2016). The final reconstruction of MTs decorated with N +UmKin5 was calculated from particles containing all frames resulting in a total dose of 30 e -/Å 2 , while for the structure of MTs decorated with UmKin5, only frames 2-21 were included resulting in a total dose of 12 e -/Å 2.
For the N+UmKin5-MT dataset, 8210 MT segments were selected in 908 Å 2 boxes in Boxer  (Ludtke et al. , 1999)  using the helix option and choosing an overlap that left three tubulin dimers (240 Å) unique in each box. Of the 785 MTs that were initially boxed from 364 motioncorrected movies, 397 MTs with 13_3 architecture were selected. The final 3D-reconstruction was generated from 164,177 asymmetric units using a previously described semi-automated single particles approach for pseudo-helical assemblies in SPIDER and FREALIGN  (Sindelar and Downing, 2007). The initial reference was a 15 Å low-pass filtered synthetic map generated from an atomic model of a kinesin motor domain-bound MT  (Sindelar and Downing, 2007). In the subsequent refinement round, the reference was low-pass filtered to 15 Å, and in the remaining three refinement rounds to 10 Å.
The final reconstruction was automatically B-factor sharpened in RELION with a calculated Bfactor of -124  (Scheres, 2012) , a mask generated in RELION was applied at a threshold such that all parts of the density were includedwidened by 3 pixels and a 3 pixels soft edge -and the sharpened map was subsequently filtered to 5. 0 Å. The final overall resolution of the masked reconstruction was 4. 5 Å (0. 143 FSC cutoff, 5. 9 Å using 0. 5 FSC cutoff, Fig. S1A ). For the UmKin5-MT dataset, 9520 MT segments were selected as for N+UmKin5. Of the 672 MTs that were initially boxed from 284 motion-corrected movies, 306 MTs with 13_3 architecture were selected. The final 3D-reconstruction was calculated from 176,553 asymmetric units and calculated as for N+UmKin5. The final reconstruction was automatically B-factor sharpened in RELION with a calculated B-factor of -154  (Scheres, 2012) , a mask generated in RE-LION was applied at a threshold such that all parts of the density were included (as above) and the sharpened map was subsequently filtered to 4.
C) The UmKin5 loop8 insertion (orange) is long enough to contact β-tubulin in the adjacent protofilament; however, density is incomplete in this region of the reconstruction (dashed lines), suggesting such connectivity is flexible; viewed from the MT plus end, adjacent protofilaments are labelled pf1 and pf2, with each pf bound by a UmKin5 molecule (UmKin5_1, UmKin5_2); D) Close up view of the distinct conformation of UmKin5 loop5 (pink) protruding from the motor domain above the nucleotide binding pocket, compared to that of Cut7 (dark grey, PDB: 5MLV) and human kinesin-5 (light grey, PDB: 3HQD).
Data deposition The cryo-EM reconstructions that support the findings of this study has been deposited in the Electron Microscopy Data Bank, accession numbers 3529 (UmKin5) and 3530 (N+ N+UmKin5). The docked coordinates reported in this paper have been deposited in the Protein Data Bank, www. pdb. org, accession numbers 5MM4 (UmKin5) and 5MM7 (N+UmKin5).
2. 3. Biochemical data Calcium, phosphate, bicarbonate, and IDMS-standardized creatinine were assessed using routine techniques. Estimated glomerular filtration rate (eGFR) was calculated with the 2009 Schwartz formula. CKD stages followed the National Kidney Foundation definitions, as follows: CKD stage 3 when eGFR is between 30 and 59, stage 4 between 15 and 29, and stage.
Conclusion In conclusion, assessment of bone health is challenging in pediatric populations with ongoing growth. The use of HR-pQCT in clinical pediatric practice is promising because of its noninvasiveness and its low-radiation exposure, but there are still two main problems for its accurate interpretation in these populations: there are no international guidelines for ROI positioning and no validated normative microarchitecture databases  (34). With this study, we believe that we provide a potential new analytic tool to improve bone assessment in children and more generally in patients: adapting the ROI to leg length enables a better assessment of bone structure, especially when height discrepancies exist between controls and patients.
1) Objectives Term Extraction or Automatic Term Recognition (ATR) is a field in language technology that involves "extraction of technical terms from domain-specific language corpora"  [1] , or identifying term candidates in texts of lists of words  [2]. The original interest lies not in creating terminology resources, but in extracting words or expressions that identify topics in a document. This use is typical when working with medical texts  [3] , as a tool for information extraction and text mining  [4]. Different NLP techniques are described in detail in Moreno Sandoval and Redondo, 2016  [5]. In order to detect new terms and concepts, texts that are recent and also representative are required. Corpus Linguistics, with an ever-growing influence in recent years due to the availability of large datasets, has the compilation of texts of a given domain as one of the main objectives.
Documents must be digital, so searches or other computational handling can be performed, such as morphosyntactic annotation and statistical analysis. Once the medical corpus is created, the automatic recognizer will extract a number of candidate terms. In Terminology there are well-established methodological traditions to enhance lexicography resources and build data banks following standard procedures  [6]. However, the speed at which new terms (neologisms) are created in certain knowledge areas makes this approach extremely costly. It is at this precise point where systems for automatic extraction of terms are of great help, but always considering that the final "word" lies in the hands of the area expert.
In general, an effective strategy can be achieved if work focuses on a language with Greek and Latin bases to create new terms. This, however, is not the case in all domains nor all languages  [21]. With respect to statistics-based techniques, the foundation lies in measuring the degree of distinctiveness  [22]  of a word or lemma in a specialized context in contrast with their frequency in a general corpus. The two most common are the log-likelihood ratio test  [23]  and the logDice metrics used in The Sketch Engine  [24]. The central idea of these techniques is to know which words or terms over-or under-used in the corpus for analysis when compared to the frequency of the same words in a reference corpus. In our case we take a corpus of medical terms (MultiMedica) and compare it to the Reference Corpus of Current Spanish (Corpus de Referencia del Español Actual -CREA), that contains a balanced set of texts coming from different domains and linguistic registers.
1) Preselect Terms Following each Method Each method for term candidate extraction is not based on a similar strategy, and consequently the list obtained from each has a different size, although it is applied to the same data set. However, obtaining more candidates does not mean that the rate of success increases. The first method uses a morphological tagger. It is an example of the rule-based type: the analyzer contains a set of recognition rules and analysis of words in Spanish. Here only words with the tag "unknown" (desconocido) are of interest, because medical terms are assumed to have a morphological structure not included in the analyzer used: GRAMPAL  [37]  covers a lexicon with more than 50,000 lemmas of general use and is capable of analyzing more than 500,000 inflection forms. Obviously, GRAMPAL contains a large number of medical terms that have found their way into the common lexicon, as would be collected in any reference dictionary (DRAE or Maria Moliner being the most typical ones).
This test is always used in programs checking agreement (such as, Wordsmith or AntConc) to extract keywords in a text. The process performs a comparison of the occurrence frequency between the words in a given corpus with those in a reference corpus. In this case, MultiMedica was compared with the CREA version already preprocessed (see above). To achieve 99. 9% of confidence rate, we applied a threshold of significance in 10. 83. As a result, the list of candidate terms contains only words with a test value above 10, which renders a list of just 8,667 candidate terms. Several natural language processing (NLP) techniques were utilized. First, each collection was processed and tags for part-of-speech were included. The Spanish subcorpus was tagged by using GRAMPAL [39], already mentioned. The tagging process is semisupervised, as it requires manual revision to ensure annotation quality.
A random sample representing 5% of the popularized texts in Spanish was revised twice to compute the inter-annotator agreement (IAA) value. This was assessed by computing the F-measure, as exposed in  Hripcsak and Rothschild (2005)   [40] , and it was found that both annotators agreed in about 98 per cent of the texts. [41]  explain the methodology followed in the creation of the morphological tagging for the Japanese corpus. After considering three different taggers (ChaSen, Mecab and Juman), Juman was chosen, because it provides good segmentation and a wider range of morphological information. Similarly, the Arabic corpus was automatically annotated using the PoS tagger MADA+Tokan  [42]. Finally, the tagged texts were indexed for all languages to enhance online queries.
4) Developing a Term Extractor for Each Language Each language required a different approach in order to build the term extractor. The Spanish extractor uses lists of terms, medical roots and affixes, the GRAMPAL tagger, and rules for multi-words and context patterns. The processing of the input text to detect candidate terms is as follows. First, a dictionary-based method that relies on pattern matching is applied. Each item found in the gold standard list is marked as a highly reliable candidate term (e. g. , pulmón, 'lung'). Likewise, each term found in the silver standard list is selected as a medium reliable candidate term (e. g. , secundario, 'secondary'). In the third stage, those words that were not found in any list are POS-tagged through the GRAMPAL tagger. Unrecognized items (i.
IV. Future Work Biomedical Natural Language Processing (BioNLP) is receiving a growing interest from both academia and industrial specialized applications. The specific field of biomedical text mining is one of the most mature domains. Biomedical text mining, of which term extraction is just one area, is providing great advances in terms of widespread availability of expert-annotated text resources, biomedical term banks, and a great number of information extraction components. Biomedical text processing components have been published, covering various aspects, from tokenization approaches  [57]  to the creation of specialized tokenizers for biomedical texts  [58]. Equally important are special linguistic and NLP tools for biomedical texts, such as POS taggers  [59]  or dependency-based parsers  [60]  for pure syntactic analysis (Enju/Mogura  [61] , GDep), which present biomedical domain models to create graphic representations of syntactic dependency relations.
These syntactic relations are used to express bioentity relationships present in the text (such as protein-protein interactions  [62] ) in combination with recent machine learning techniques. Current and future promising trends biomedical natural language processing include the following: to rank a classification of topics of relevance in a text after term identification  [63] ; detection of different types of bioterms applying semantic roles; indexing of documents to terms and concepts from controlled vocabularies and corpora, as in the case of Multimedica, which may build bioontologies  [64]  to be applied in other domains, and extracting relationships between biomedical terms (protein or gene relations  [65] ). Another area of biomedical term extraction research field is the detection of associations between disease concepts and actual disease areas  [66] , like in the bioontologies mentioned above.
In this sense, MultiMedica is a pioneering effort in this Biomedicine domain and for this combination of languages. It has also provided an interesting typological insight into how languages behave within the medical domain. Each of the three languages presented different challenges when developing the extractor: the variation in inflection of Spanish terms, variation in the Arabic writing system or word segmentation in Japanese due to the lack of white spaces between words. Even though the initial steps of creating the corpus, tagging, and development of a medical term list was approximately equal in the three languages, the processing of the texts and the creation of the extractor had to be adapted to the specificities of each language. Looking into the future it is reasonable to expect that the corpus and online tools may provide the users with a good amount of data for future linguistic research into biomedical discourse and may be used for many other use cases.
Methods We use a regional configuration of the Massachusetts Institute of Technology general circulation model (MITgcm) with hydrostatic approximation, dynamic/thermodynamic sea-ice  44  , and thermodynamic ice shelf  45. The model domain contains the continental shelf region of the eastern Amundsen Sea and the Pine Island, Thwaites, Crosson, and Dotson ice shelves (Fig. 1 ). Nominal horizontal and vertical grid spacings are 200 m and 10 m, respectively. Model bathymetry is based on the International Bathymetric Chart of the Southern Ocean (IBCSO  46  ), with recent updates of more accurate bathymetry for the region near Pine Island, and the Crosson and Dotson ice shelves  12, 39. The trough extending towards the Pine Island Ice Shelf is well mapped  39  and we do not find obvious differences among different bathymetry datasets.