dataesr/scientific-paragraphs-categorization huggingface dataset:
EchoMRI assessment of fat and fat free mass All imaging took place in the Notre Dame Integrated Imaging Facility Friemann Life Sciences Center. Whole body fat and fat free mass was measured with an EchoMRI-130™ QMR (EchoMRI, TX, USA)  (Metzinger et al. , 2014). Two X-ray images were taken at different energy levels to assess soft tissue density and bone. Mass measurements for fat and fat free tissue were produced for all scans in the EchoMRI Body Composition Analyzer EMR-184 software (EchoMRI).
Assessment of cellular respiration in muscle fiber bundles EDL muscle fiber bundles were chemically dissociated as previously described  (Li et al. , 2016). Muscle fiber bundles were seeded onto Seahorse XF e 96 microplates in culture media (low glucose DMEM supplemented with 10% FBS and 1% Antibiotic-Antimycotic; ThermoFisher, MA, USA), and maintained overnight at 37°C with 5% CO2. Prior to the commencement of metabolic assays, muscle fiber viability was assessed using an alamarBlue cell viability assay (ThermoFisher) and used for the data normalization. Real-time assessment of bioenergetic parameters in EDL fiber bundles was performed on the XF e 96 Extracellular Flux Analyzer (Agilent Technologies, CA, USA). The dependence and capacity of EDL fibers to use glucose and fatty acid as fuel substrates was determined using the Seahorse XF Mito Fuel Flex Test Kit (Agilent Technologies).
Substrate induced maximal respiration was tested in EDL fiber bundles as previously described  (Li et al. , 2016). Briefly, plates containing muscles fibers were changed into assay media (pH 7. 4) containing 120mM NaCl, 3. 5mM KCl, 1. 3mM CaCl2, 0. 4mM KH2PO4, 1mM MgCl2, 2. 5mM D-glucose and 0. 5mM L-carnitine prior to the assay run. OCR was continuously measured for 6 cycles after sequential injections of sodium pyruvate (10mM) or palmitate-BSA (100µM) with carbonyl cyanide-p-trifluoromethoxyphenylhydrazone (FCCP, 0. 4µM), followed with antimycin/rotenone (1µM). Quantitation of Seahorse data were conducted over 8-24 technical replicates for n= 5-12 animals per group.
Statistical analysis Statistical differences were assessed using Prism 8. 0a. (GraphPad Software Inc. , La Jolla, CA, USA). Statistical comparisons, unless otherwise indicated, were performed with unpaired Student's t-test, non-parametric t-test, or two-way ANOVA followed by Bonferroni multiple comparison. Linear relationships were assessed by Pearson correlation. All graphical data are presented as mean±SEM or as a Pearson correlation. Values of P<0. 05 were considered to be statistically significant.
Acknowledgements The authors gratefully acknowledge the assistance and support of staff at the  University of Queensland Biological Resources (UQBR) , and  The Centre for Integrated Physiology at the School of Biomedical Sciences, University of Queensland. This work was funded by grants from  The University of Queensland  ( NSRFS 2012002126  to STN), the  Motor Neurone Disease Research Institute of Australia  ( Grant-in-aid, Graham Smith MND Research Grant  (to FJS, PAM, and STN) and Charcot  MND Research Grant  to FJS, JSC, PAM, RDH and STN), the  National Health and Medical Research Council  ( 1101085  to FJS and STN, 1121962 to FCG), and  AriSLA Foundation and AFM-Telethon (HyperALS and AFM-Telethon # 22509  to AF, JPL, FR and CV). TYX was supported by international postgraduate research scholarships from the  Australian Government , and  University of Queensland  centennial scholarships.
Computational Model Numerical studies were performed with COMSOL Multiphysics 5. 4 software (COMSOL France, Grenoble, France). A three-dimensional computation model with periodic boundary conditions in X and Y directions was developed. The acoustic resonator was described as a piezoelectric domain with AT-cut quartz of 330 m thickness. The solid layer load was simulated with a structural mechanics model. Solid fluid interactions were computed with the acoustic-structure interaction model. We used two different liquids, the buffer liquid (Liquid1-density 1000 kg/m 3 ; viscosity 0. 001 Pa•s) and the whole blood (Liquid2-density 1060 kg/m 3 ; viscosity 0. 003Pa•s). Although whole blood is a non-Newtonian fluid (shear thinning effect), above a shear rate of 200 s -1 , as used in this study, its viscosity may be considered constant  [29].
Computational Model Numerical studies were performed with COMSOL Multiphysics 5. 4 software (COMSOL France, Grenoble, France). A three-dimensional computation model with periodic boundary conditions in X and Y directions was developed. The acoustic resonator was described as a piezoelectric domain with AT-cut quartz of 330 µm thickness. The solid layer load was simulated with a structural mechanics model. Solid fluid interactions were computed with the acoustic-structure interaction model. We used two different liquids, the buffer liquid (Liquid1-density 1000 kg/m 3 ; viscosity 0. 001 Pa•s) and the whole blood (Liquid2-density 1060 kg/m 3 ; viscosity 0. 003Pa•s). Although whole blood is a non-Newtonian fluid (shear thinning effect), above a shear rate of 200 s -1 , as used in this study, its viscosity may be considered constant  [29].
This dependence for the studied conditions is described as a 'balanced' growth of platelet deposits. Experimental results that are shown in orange dots represent two different cases of 'localized' and 'spread' deposits. The cases above the curve correspond to 'localized' ones where the thickness of platelet deposits is higher than the regression value for the same coverage. That types of deposits were obtained at high shear rate values. The 'spread' state of platelets deposits (below the curve) corresponds to the cases when the platelet deposits of similar average thickness show higher coverage. These deposits were obtained at low shear rate conditions. Figure  9  shows the same data points but this time plotting average thickness versus frequency shift reduced by negative control. We applied linear regression to all obtained data points by using the least squares method.
This dependence for the studied conditions is described as a 'balanced' growth of platelet deposits. Experimental results that are shown in orange dots represent two different cases of 'localized' and 'spread' deposits. The cases above the curve correspond to 'localized' ones where the thickness of platelet deposits is higher than the regression value for the same coverage. That types of deposits were obtained at high shear rate values. The 'spread' state of platelets deposits (below the curve) corresponds to the cases when the platelet deposits of similar average thickness show higher coverage. These deposits were obtained at low shear rate conditions. The results show that the frequency shift exhibits similar tendency (blue dots R 2 = 0. 8562, all the data R 2 = 0. 3032) than the average thickness/coverage regression shown in Figure  8.
Acknowledgments: Valuable contribution of  Guillaume Mourey  ( Etablissement Français du Sang (EFS), Besançon, France ) and  Emmanuel de Maistre (Centre Hospitalier Universitaire de Dijon-Bourgogne, Haemostasis Lab, Dijon, France ) are gratefully acknowledged. This work was partly supported by the  French RENATECH network  and its FEMTO-ST technological facility and by the  CLIPP platform member of IBISA French network. The fabrication part of the work was assisted by  FEMTO-Engineering. The authors would like also to thank  Alain Rouleau ,  Ophélie Bourgeois ,  Franck Lardet-Vieudrin  and  Rabah Zeggari.
Results Our study included 19 412 postmenopausal women, with 2369 (12. 2%) participants having prevalent diabetes. The data indicate that the majority of participants was non-Hispanic White (47. 0%), aged 50-79 years, had some college, vocational training, or associates degree (39. 4%), and earned an income between $20 000 and $34 999 (25. 9%) (Table  1 ). higher, and a reported annual income of $20 000-$34 999. For those with MUHNW, the mean age was 65. 9 years, ethnicity mainly White (not of Hispanic origin), education being some college, vocational training, or associates degree, and an annual income of less than $19 999. In contrast, those with MHO averaged 62. 4 years old, and were mostly Black or African American, had some college, vocational training, or associates degree, and reported an annual income of $20 000-$34 999.
Acknowledgements The  WHI program  is funded by the  National Heart, Lung, and Blood Institute, National Institutes of Health, US Department of Health and Human Services  through contracts  HHSN268201600018C ,  HHSN268201600001C ,  HHSN268201600002C ,  HHSN268201600003C , and  HHSN268201600004C. A. R. C. H. , H. A. -C. , and N. D. W. were involved in study concept and design. A. R. C. H. , S. L. A. , B. X. , D. V. P. , H. A. -C. , and N. D. W. were involved in acquisition, analysis, or interpretation of data and administrative, technical, or material support. A. R. C. H. , B. X. , and N. D. W. were involved in statistical analysis. S. L. A. , B. X. , D. V. P. , H. A. -C. , and N. D. W. were involved in study supervision. All authors involved in drafting of the manuscript and critical revision of the manuscript for important intellectual content.
The efforts were made to duplicate clinical procedure as much as possible. The standard air polishing nozzle, designed for supragingival application, was used with spraying distance of 3 mm, at 45 o angle between nozzle and specimens surface with spraying time of 5 s counted down via electronic control of the device. During the application the nozzle was kept in a constantly sweeping movement as in clinical practice. Then the surface roughness of each sample was remeasured, keeping the same field of observation. Specialized 3D analysis computer software -TalyMap Platinum (Taylorhobson Ltd. , USA) was used for data processing, which comprised: surface levelling, non-measured points filling (using a smooth shape calculated from the neighbours) and shape (form) removal. The following height parameters according to ISO 25178 were used to describe composite surface roughness: Sa -arithmetical mean height of the surface, Sq -root mean square height of the surface, Skukurtosis of the surface Spmaximum peak height Svmaximum pit depth Sz -maximum height of the surface, Ssk -skewness of height distribution.
| ANALYTICAL METHODS Quantitative mineral chemistry was obtained using a Cameca SX-5 Electron Probe operated at the CAMPARIS analytical facility in Paris University using standard analytical conditions (15 keV, 10 nA, beam diameter 5 µm), with a set of synthetic and natural crystals for calibration. The X-ray maps were acquired on the same instrument using analytical conditions of 15 keV, 250 nA, a dwell time of 55 ms and a step size of 2 µm. Fe 2 O 3 (Fe), MnTiO 3 (Mn, Ti), diopside (Mg, Si), CaF 2 (F), orthoclase (Al, K), anorthite (Ca) and albite (Na) were used as standards. The X-ray images were processed and quantified with the DWImager software (R. L. Torres-Roldán & A. García-Casco, unpublished data; García-Casco, 2007). A scanning electron microscope (SEM) Zeiss EVO MA10 at the Institut de Physique du Globe de Paris using internal calibration standards was used for microstructural observations and surface composition characterization.
cm Na-Amph the classification of  Morimoto (1989)  and  Hawthorne and Oberti (2007)  respectively. Atoms per formula unit is abbreviated apfu. For Rb-Sr mineral dating, two small, phlogopite-rich samples (less than 50 g) were crushed for mineral separation work. Rb-Sr isotopic data were generated at GFZ Potsdam on a Thermo Scientific TRITON thermal-ionization mass spectrometer. Sr isotopic composition was measured in dynamic multi-collection mode. Rb isotope dilution analysis was done in static multi-collection mode. The value obtained for 87 Sr/ 86 Sr in the NIST SRM 987 isotopic standard during the period of analytical work was 0. 710242 ± 0. 000020 (2σ, n = 16). For age calculation, standard uncertainties of ±0. 005% for 87 Sr/ 86 Sr and of ±1. 5% for 87 Rb/ 86 Sr ratios were assigned to the results.
Individual analytical uncertainties were consistently smaller than these values. Mineral separating technique and analytical procedures are described in greater detail in  Glodny et al. (2008). Uncertainties of isotope and age data are quoted at 2σ throughout this work. The program ISOPLOT/EX3. 71 (Ludwig, 2009)  was used to calculate the regression lines. The 87 Rb decay constant is used as recommended by  Villa et al. (2015). The 40 Ar/ 39 Ar analyses were conducted in the Noble Gas laboratory of Géosciences at the University of Montpellier 2, France, using a multicollector mass spectrometer (Thermo Scientific Argus VI MS) with a nominal mass resolution of ~200 and a sensitivity for argon measurements of 3. 55 × 10 -17 moles/fA at 200 μA trap current. The mass spectrometer houses a Nier-type source and a fixed array of five Faraday detectors and one compact discrete dynode (CDD) detector.
Air aliquots from an automated pipette system were analysed prior to the sample analyses to monitor mass discrimination and detector bias. Line blanks were analysed between every three sample analyses and were subtracted from succeeding sample data. Stepheating experiments on one to three mineral grains were conducted with a duration of heating of 60 s at each step. A custom-made software is used to control the laser intensity, the timing of extraction/purification and the data acquisition. The ArArCalc software© v2. 5. 2  (Koppers, 2002)  was used for data reduction and plotting. All the data were corrected for system blanks, radioactive decay, isotopic interferences and mass discrimination. Atmospheric 40 Ar is estimated using a value of the initial 40 Ar/ 36 Ar ratio of 298. 6  (Lee et al.
5 J/cm 2 at the target. Standards were analysed in duplicate every 2 hr during the mapping session, including reference glasses NIST 610  (Jochum et al. , 2011; Pearce et al. , 1997)  and GSD-1D  (Jochum et al. , 2011). A beam diameter of 51 μm was used for all standard analyses, and included five pre-ablation shots (51 μm, 75% overlap) followed by 20 s washout, 30 s background measurement and 40 s ablation time. Data acquisition was performed in time-resolved analysis mode as a single continuous experiment. Each analysis comprised a suite of 38 elements, and dwell times were as follows: 0. 01 s (Li), 0. 002 s (Na, Mg, Al, Si, K, Ca, Mn, Fe, Ni), 0. 005 s (Sc, Ti, V, Cr, Nb, Ba, Hf, Th, U) and 0. 008 s (Sr, Y, Zr, Ta, Pb, REEs). The total sweep time was 0. 297 s. Postacquisition processing was performed using the software Iolite  (Hellstrom et al.
ACKNOWLEDGEMENTS Nicolas Rividi  and  Michel Fialin  (CAMPARIS) are acknowledged for assistance with Electron Probe measurements and mapping. Michael Bostock  is acknowledged for insightful discussions. This study was supported by the  IdEx   ANR-18-IDEX-0001  through a chair attributed to SA. Oleg,  Firaz  and Katia are acknowledged for assistance during the two field expeditions. The article was prepared when working on the state budget topic  IGG UB RAS AAAA-A18-118052590032-6. Two reviewers are acknowledged for constructive comments. This is IPGP contribution # 40XX.
Relation classification (RC) aims at extracting structural information, i. e. , triplets of two entities with a relation, from free texts, which is pivotal for automatic knowledge base construction. In this paper, we investigate a fully automatic method to train a RC model which facilitates to boost the knowledge base. Traditional RC models cannot extract new relations unseen during training since they define RC as a multiclass classification problem. The recent development of few-shot learning (FSL) provides a feasible way to accommodate to fresh relation types with a handful of examples. However, it requires a moderately large amount of training data to learn a promising few-shot RC model, which consumes expensive human labor. This issue recalls a kind of weak supervision methods, dubbed distant supervision (DS), which can generate the training data automatically.
INTRODUCTION Relation Classification (RC) is defined as identifying semantic relations between entity pairs in given plain texts, which is a crucial task in automatic knowledge base (KB) construction  (Bollacker et al. , 2008). Mainstream works on this task mainly follow supervised learning, where large-scale and high-quality training data is required  (Zeng et al. , 2014; Gormley et al. , 2015). However, human-annotated data is always expensive to acquire. Subsequently, recent literature resorted to distant supervision (DS)  (Mintz et al. , 2009; Riedel et al. , 2010)  to address the sparsity issue of training data. In DS, it is assumed that sentences mentioning an entity pair instantiate the relation of the corresponding entity pair in knowledge bases. With this (untrue) heuristic, large-scale training data can be constructed automatically, but mislabeling is inevitably introduced at the same time.
For example, as shown in Figure  1 , since the triplet (soccer, publisher, nintendo) exists in a KB, two sentences mentioning the entity pair (soccer, nintendo) are assigned with the relation "publisher. " In fact, the first sentence fails to express the target relation indeed, called false positive instance, while the second one obtains a correct label, which is a true positive instance. Hence, efforts were made to restrain the impact of false positives  (Ji et al. , 2017; Qin et al. , 2018b; Wu et al. , 2019). However, these models perform well on common relations, but suffer a dramatic performance drop in classifying long-tail relations, which have few training instances; that is, even though a large amount of training data can be generated by DS, the distributions of such data over different types are unbalanced.
, 2016; Snell et al. , 2017) , where similar classes are placed close to each other. Lately, optimizationbased meta-learning is developed fast because of its fast-learning ability to learn from previous experience and generalize to new knowledge  (Finn et al. , 2017; Ravi and Larochelle, 2017). These models, especially prototypical networks, achieve promising results on several benchmarks, but almost all of them focus on image processing. Observing the lack of researches about employing FSL to natural language processing (NLP) tasks, this paper focus on the few-shot relation classification with distant supervision data. In specific, a dataset was manually labeled for FSRC, namely, FewRel and on top of it, systematic evaluation of state-of-theart FSL methods (used in computer vision) was carried out for RC  (Han et al.
Motivation To recap, DS can generate large-scale data but suffers from longtail relations and mislabels; meanwhile, FSRC is able to recognize new relations with few training samples, but requires moderately large amount of human labor for data annotation. Hence, we are at the frontiers of DS and FSRC, being ready to union them, in order to compensate for the downsides of the two paradigms. The combination of DS and FSRC enables the fully automatic method to develop a RC model, which can extract the relation held between two entities. Subsequently, the extracted triplets are used to boost the knowledge base automatically. In this research, we investigate the task of few-shot relation classification under distant supervision. In realization, we refine a previous DS dataset  (Zeng et al. , 2017) , which was built by aligning Wikidata with New York Times corpus, into a reconstructed dataset for FSRC.
In previous research on FSRC, prototypical networks (PN) achieve promising performance  (Han et al. , 2018)  by measuring the distances between a query and prototypes. The classic approach  (Snell et al. , 2017)  first encodes all instances into a unified vector space, then generates each prototype by averaging all support instances of a relation type. Nevertheless, the mislabeled support instance sampled from DS data may cause a huge deviation for the prototype. In this connection, we conceive a attention-based MIL method, which consists of two steps: • Denoising the query set: as discussed above, selecting a single instance that is unfortunately mislabeled as query has negative effects on the optimization of few-shot models. Thus, we take an instance bag as a query which provides enough information for the few-shot models to recognize an appropriate relation concerning an entity pair.
TASK FORMULATION Formally, the task of few-shot relation classification under distant supervision with attention-based MIL is to obtain a function F :(R, S, Q) → r, (1) given training data D, which is labeled by existing knowledge bases under the DS assumption. In specific, R = {r 1 ,. , r i ,. , r m } is the relation set, 1 ≤ i ≤ m, m = |R|, where | • | denotes the cardinality of a set. D = {D r 1 ,. , D r i ,. , D r m }, where D r i is a set of DS-labeled instance bags (all) with relation r i. S is the support set, i. e. , S = {(s r 1 1 , s r 1 2 ,. , s r 1 n 1 ),. , (s r m 1 , s r m 2 ,. , s r m n m )}, (2) where relation r i has n i support instances, each of which is randomly selected from D r i. Q = {s q 1 ,. , s q |Q| } (|Q| ≥ 1) is the query set, which is essentially an instance bag concerning an entity pair.
The query set Q gives rise to the major difference with respect to the formulation of conventional FSRC in  Han et al. (2018) ,  Fan et al. (2019), and Gao et al. (2019). As DS data tends to have mislabeled instances, if the previous formulation is followed, a mislabeled instance is likely to be sampled as the query instance; in this case, a FSRC model may be intermittently confused during training by the mislabeled query instances, as they substantially deviate from real ones. To overcome the limitation, we propose to employ in training an instance bag, instances of which concern the same entity pair and are DS-labeled with the same relation r, to replace a single query instance; by doing this, we expect that the trained model can recover the relation r given its instance bag. Then for testing, the trained model predicts the best relation, where single or multiple instances can be supplied.
Relation Classification Under Distant Supervision Most existing researches concentrate on neural models via supervised learning  (Zeng et al. , 2014; Nguyen and Grishman, 2015)  or distantly supervised learning  (Zeng et al. , 2015; Lin et al. , 2016). Supervised learning requires a large amount of annotated data, which can be fairly expensive to acquire. As a result, many neural models with supervised learning for RC suffer from data insufficiency  (Zeng et al. , 2014; dos Santos et al. , 2015). DS comes as a remedy  (Mintz et al. , 2009) , which can generate large-scale training data without human labor; whereas, it inevitably brings in mislabels and still has little coverage of long-tail relations. Riedel et al. (2010)  formulated RC under DS as a multiple instance learning problem to alleviate the influence of mislabels, which achieves remarkable improvement.
, 2017; Al-Shedivat et al. , 2018) , which either generate the model parameters directly or predicting the updating gradients for parameters. Afterwards, metric learning is proposed to project instances into a unified feature space, where instances with the same class are placed adjacent with each other  (Koch et al. , 2015; Vinyals et al. , 2016). Prototypical networks used in  Han et al. (2018)  and  Gao et al. (2019) , as well as this research is a representative method of metric learning. As introduced in section 1,  Han et al. (2018)  first formulated the task of FSRC, and a dataset FewRel for evaluating the task was created via crowdsourcing. Based on FewRel, Fan et al. presented large-margin prototypical networks with finegrained features  (Fan et al. , 2019). Wu et al. proposed a dynamic prototypes selection approach with attention to fully capture information in support set  (Wu et al.