dataesr/scientific-paragraphs-categorization huggingface dataset:
The GFN allows for direct experimental assessment of the effects of multiple nutrients and their calorie intakes on performance traits and ultimately fitness expression (e. g. , Figure  1A, B )  [4]. This has brought key insights into the nutritional factors of a wide variety of physiological and behavioural phenomena across feeding guilds (e. g. , herbivores, carnivores, and omnivores) and taxonomic groups (e. g. , slime moulds, insects, fish, birds, and mammals)  [2, 5]. These progresses in nutrition research also have far reaching implications in various areas of biology, ecology and evolution, including to our understanding of contemporary diseases affecting human societies  [6]. Here, we argue that making GFN data open access has considerable benefits to advancing the broad field of nutrition research and tackling open questions on the ecology and evolution of nutritional processes through quantitative comparative approaches of complex nutritional systems across ecological contexts and scales of organisations.
To date, however, performance surface data have not been provided in more than 60% of the GFN studies (see meta-analysis in Data S1). We believe that making such raw GFN data open access and published alongside studies (irrespective of the journal's policy) will allow for the development of a truly integrated and collaborative community of nutritional ecologists with accessible datasets from which quantitative analyses can be performed and new methods for data analysis can be designed and tested  [16, 17].
2. If publishing in an open-access journal, authors are likely (although not always) to be required to deposit the raw data in a public data repository (e. g. , Dryad). If publishing in a subscription journal, we encourage authors to make raw data available as Supplementary Material. In both cases, it is important that the data format is also standardised (Figure  1C ) and any additional information needed for the understanding of the dataset is also provided (e. g. , README file). These steps will benefit the visibility and citations of the original paper.
Measurements The following data have been collected: sex, age, body mass index (BMI), TBSA, full-thickness body surface area (BSA) burned, mechanism of injury and patients' characteristics, Abbreviated Burn Severity Index (ABSI)  [9] , Unit Burn Standard (UBS)  [10] , Sequential Organ Failure Assessment (SOFA) score  [11] , 28-and 90-day mortality, and AKI. Patients were resuscitated according to the Intensive Care Burn Unit resuscitation protocol  [12]. A transthoracic or transesophageal echocardiography was performed at admission of patients according to the decision of the physician in charge. When performed, left ventricular ejection fraction (LVEF) was visually evaluated and systolic cardiac dysfunction was defined by a LVEF < 50%  [13]. Circulatory failure was defined as a need for hemodynamic support with inotrope and/or a vasopressor (i.
For continuous variables, hazard ratios (HR) or odds ratios (OR), as appropriate, were standardized to describe the HR/OR for a change of one IQR. HR (Cox regression) are used if time-to-event data is available; OR (logistic regression) are used if endpoints have event data (yes/no) only. Survival curves plotted by the Kaplan-Meier method were used for illustrative purposes. For dichotomous endpoints, receiver operating characteristic (ROC) curves were constructed for illustration. All statistical tests were 2-tailed and a two-sided p-value of 0. 05 was considered for significance. The statistical analyses were performed using R version 3. 4. 3 (http://www. r-project. org, library rms, Hmisc, ROCR) and Statistical Package for the Social Sciences (SPSS) version 22. 0 (SPSS Inc. , Chicago, Illinois, USA).
The testing of SRAM cells was used to verify the spatial resolution of the BSM system. Step size of 0. 18 μm was used for the testing of the Regular_11T SRAM cell. The results were consistent with what was predicted by circuit analysis and further confirmed by results from the TFIT simulation software. Furthermore, the results confirm that the actual spatial resolution of a laser beam in mapping testing can be much smaller than the calculated spot size (0. 36-μm resolution vs. 2. 7-μm spot size). This result can extensively extend the application of pulsed lasers in SEE testing. Another significant and easily neglected factor was keeping the SRAM testing system in strictly stable condition throughout the mapping testing process. Because the SRAM cell in testing has a size as The sensitivity distribution shown by the TFIT simulation indicates that the most sensitive areas (indicated by pink & red color) were located at N1, P2 and part of A1.
The testing of SRAM cells was used to verify the spatial resolution of the BSM system. Step size of 0. 18 µm was used for the testing of the Regular_11T SRAM cell. The results were consistent with what was predicted by circuit analysis and further confirmed by results from the TFIT simulation software. Furthermore, the results confirm that the actual spatial resolution of a laser beam in mapping testing can be much smaller than the calculated spot size (0. 36-µm resolution vs. 2. 7-µm spot size). This result can extensively extend the application of pulsed lasers in SEE testing. Another significant and easily neglected factor was keeping the SRAM testing system in strictly stable condition throughout the mapping testing process. Because the SRAM cell in testing has a size as small as about 1 micron, any slight vibration of the system can introduce unacceptable offsets to the result.
Based on the presented nominal models, mathematical models were determined and calculations were made for a beam having a turnout switch point with a radius of 1200 m, and the forces that arise when the rail vehicle travels through the turnout on stock and closure rail were taken into account. The results of simulation tests were performed in the ANSYS software. For computer simulations, a curved beam with a variable cross-section in the shape of a rail with the profile: 49 E1 (S49), 60E1 (UIC60) of steel grade 900A was used. Technical data of the UIC60 profile can be found in publication  [51]. In the numerical model under study, rounding and chamfering of the switch point were omitted to reduce the calculation time. On the curved beam model with a variable cross-section, a tetra-type finite element grid of 24082 elements was applied.
The vibration analysis was obtained by combining strength and frequency analysis. The results of calculations from the strength analysis were implemented as input data for frequency analysis  [52]. To induce mechanical loads, the model was fixed on the left side and the supports were added, one fixed and one movable. The parameters used in the simulation are shown in Table  1. The calculations were carried out in one bending case: • Bending in the direction of (x axis). The beam is curved in the plane of action of the concentrated force with the value F 0 = 12 kN. The force is applied at the middle connector of the top edge of the free end of the beam and works in the direction of the x axis (negative). Theoretical value of the maximum displacement in this direction according to the work is-0.
The obtained results of vibrations and deflections indicate the influence of the load on the formation of strains and displacements in the analyzed case. The numerical simulation performed with the ANSYS program allowed us to determine displacements at the load acting on the upper surface of the curved beam with a variable cross-section. The maximum deflection of the beam, i. e. , elastic displacement in the x-axis direction, occurs at the end of the tested element and amounts to 1. 1398 mm, whereas the maximum longitudinal displacements of the beam with a value of 3. 2194 mm appear at the end of it. Simulation research was also carried out based on work  [52]. Own forms were determined: first, second, third, and fourth for x equal to the length of the switch point, for x = 0 the switch point is free (Figure  9 ).
The purpose of the calculations was to determine the vibrations caused by the action of a 12 kN load. Figure  10  shows the waveforms of individual harmonics (from the first to the sixth one) obtained from computer simulations. However, the distribution of beam deflections is shown in Figure  11. The obtained results of vibrations and deflections indicate the influence of the load on the formation of strains and displacements in the analyzed case. The numerical simulation performed with the ANSYS program allowed us to determine displacements at the load acting on the upper surface of the curved beam with a variable cross-section. The maximum deflection of the beam, i. e. , elastic displacement in the x-axis direction, occurs at the end of the tested element and amounts to 1. 1398 mm, whereas the maximum longitudinal displacements of the beam with a value of 3.
Conclusions The numerical analysis carried out in this work made it possible to determine the displacements and vibrations of higher harmonics in the model of a curved beam with variable cross-section subjected to load. The presented method can be used for testing the dynamics of a rail vehicle moving on the switch point with variable cross-section, variable stiffness, and variable inertia moment for greater radii of curvature. The results obtained in the conducted simulations enable the comparison of the switch point mode frequency with own vibration frequency of a rail vehicle mass, which may lead to the creation of dynamic couplings between the turnout and the rail vehicle (Medelsztam theory). The presented results of the numerical analysis were obtained in the ANSYS 16 Workbench module.
Abstract This study details the acceleration techniques and associated performance gains in the time integration of coupled poromechanical problems using the Discrete Element Method (DEM) and a Pore scale Finite Volume (PFV) scheme in Yade open DEM software. Specifically, the model is tailored for accuracy by reducing the frequency of costly matrix factorizations (matrix factor reuse), moving the matrix factorizations to background POSIX threads (multithreaded factorization), factorizing the matrix on a GPU (accelerated factorization), and running PFV pressure and force calculations in parallel to the DEM interaction loop using OpenMP threads (parallel task management). Findings show that these four acceleration techniques combine to accelerate the numerical poroelastic oedometer solution by 170x, which enables more frequent triangulation of large scale time-dependent DEM+PFV simulations (356 thousand+ particles, 2.
Model details The DEM+PFV performances of multi-core CPU, GeForce 1050 Ti GPU, and Tesla K20 GPU conductivity matrix factorizations (Eq. 12) were evaluated using a pre-validated  (Catalano et al. , 2014)  consolidation test of a saturated soil packing (example script 1  ). The DEM sphere packing is cubically sized from 8e-6 to 3. 4e-3 m 3 (Figure  4 ) with microparameters as shown in Table  1. The fluid and mechanical boundary conditions follow traditional oedometer boundary conditions as shown in Figure  4  Both mechanical and fluid time steps are set constant to 1e-6 s and the simulation proceeds for 600 time steps with λ rm =200.
Data description A parametric sweep was performed for three device types and six problem sizes, resulting in 18 total simulations. For each parametric combination, six distinct timings were collected and averaged for each of the following seven algorithms: (1) Build the system of linear equations (2) Allocate the system to memory (3) Analyze the system (identifiy non-zero pattern and build elimination tree) (4) Factorize the system ([G] matrix decomposition) (5) Solve the system (forward/backward substitution into factor) (6) Compute pore volumes (7) Compute fluid forces (pressure and viscous forces) Additionally, the simulation speed and total time to step 600 iterations of each parametric combination was collected. In total, 972 data points were used to generate the parametric sweeps presented in Sec.
Model construction & simulation Simulations were conducted using a mesh derived from high resolution whole-torso computed tomography (CT) imaging data, previously used by our group  [11] -for full details of the mesh generation and parameterisation of the model, see that work. Briefly, CT data were segmented into major organs of interest (skin, skeletal muscles, fat, bones, lungs, spleen, liver, stomach, kidney, major blood vessels) using Seg3D software (www. sci. utah. edu/software/seg3d. html), with higher resolution contrast CT images being used to derive a more detailed representation of the heart using the Siemens Axseg v4. 11 automated segmentation tool  [12]. These two segmentations were then combined, and converted to a tetrahedral mesh using the Tarantula meshing software  [13]. The resulting mesh is illustrated in the top left panel of Fig.
The random forests analysis was implemented using the scikit-learn Python package  [42] , using a classifier to determine whether the scar was present in the LV free wall or not, or in the septum or not (these were independent, binary questions); analysis was conducted with a random seed value of 42 to ensure reproducibility. In total, 30 simulations with unique scar parameters were available to train the foests. The individual trees in the forest were created to maximise the homogeneity of the population after the question, as measured by the Gini coefficient. Random forests were trained and evaluated using both 5-fold and -fold cross-validation, for random forests of both 20 and 1000 trees; no limit was based on the number of nodes in the trees. For the former, this involved splitting the data into five segments, then performing five rounds of training/testing, with each segment being the testing set for one of the rounds.
Acknowledgements The research was funded/supported by the  National Institute for Health Research (NIHR) Biomedical Research Centre, UK  and  CRF based at Guy's and St Thomas' NHS Foundation Trust and King's College London, UK. The views expressed are those of the author(s) and not necessarily those of the  NHS , the  NIHR  or the Department of Health. This work was also supported by the  Wellcome EPSRC Centre for Medical Engineering at King's College London  ( WT 203148/Z/16/Z ) and by the  Medical Research Council, UK  ( MR/N011007/1 ), and by a  Wellcome Trust   Innovator award  to MJB ( 213342/Z/18/Z ).
Methods The cone mosaics used for this work are from previously published images of patches of real human retinas, as shown in the leftmost boxes of Figures  2 through 5 ; they were acquired from the pdf versions of the papers or html, if available, and saved as png images. The pictures are from different subjects of various ages and were obtained with different techniques, from histological tissue prepared for electronic microscopic imaging in  [15, 35, 13, 30] , to the most recent in vivo imaging techniques, using adaptive optics like deformable mirrors coupled with a wavefront sensor to compensate for the ocular aberrations of the eye  [51, 57, 55, 64]. The x and y coordinates of the cells inner segments were manually plotted using WebPlotDigitizer [50]. This preliminary work has been based on a relatively small dataset sue to the difficulty of finding wide collections of retinal images.
We understand these difficulties related also with problem of the use of different imaging techniques and tissue preparation and we hope to have larger datasets in the future. When analyzing the points distribution, the distance between the cone centers was converted in real µm on the retina by multiplying them with the appropriate scale factor of the image, determined by the size of the sample window's side. Conversion from degrees was performed according to the model from  [19] , with one degree of visual angle equal to 288 µm on the retina. Cone spacing values are compatible with Wyszecki and Styles  [65] , with the exception of the data from  [30]  exhibiting lower values, probably due to post mortem shrinkage. Retinas 6, 7-A and 7-B have been cropped during analysis because they didn't fully fill the sampling window, and would have included uncharacterized areas.
Such a minimum could be found automatically for a real measurement by e. g. a golden-section search, before processing the entire recording. We can repeat this procedure for different number of particles, see Fig. 5. One can see that the same balance can be found for all datasets of different number of particles. As expected, the optimal number of divisions (d) increases with the number of particles (m). We find that d ∝ m 0. 461±0. 005 for a 95% confidence level. The number of cells traversed, for optimal timing (and therefore divisions), scales therefore as n ∝ m 1. 461±0. 005. In Fig. 5  the optimal computing time as a function of number of particles is shown. It is found that the time t scales as t ∝ m 1. 429±0. 006 for a 95% confidence level. Which is close to the m 1. 461 scaling that was predicted above.
Acknowledgments This work was financially supported by the  Zwaartekracht programme Multiscale Catalytic Energy Conversion (MCEC) , which is part of The  Netherlands Organisation for Scientific Research (NWO)  and by the  Project IDEXLYON of the University of Lyon  in the framework of the  French program  " Programme Investissements d'Avenir"  ( ANR-16-IDEX-0005 ). We thank  Thomas Basset ,  David Dumont ,  Luuk Blaauw ,  Varghese Mathai ,  Chao Sun ,  Bianca Viggiano , and  Romain Volk  for feedback on, discussion of, and testing of the method and providing their experimental data for the example visualisations.
Pelvic intraoperative neuromonitoring (pIOM) was used to support the nerve-sparing radical hysterectomy and to confirm the intraoperative functional integrity of the inferior hypogastric plexus. The modular technology consisted of an ISIS Xpert neuromonitoring system including a neurostimulator and multichannel amplifiers for electromyographic recording (EMG). The NeuroExplorer operating software included the pIOM application, providing simultaneous stimulation and recording with the help of a hand-guided disposable 400 ball-tip stimulation probe and needle electrodes (inomed Medizintechnik GmbH, Emmendingen, Germany). The needle electrodes were placed in the internal and external sphincter. At the end of this study, PubMed and PMC (PubMed Central) were searched for studies pertaining to radical hysterectomy, radical hysterectomy and anatomic complications, anatomy of the pelvic autonomic nerve system and its connections to surrounding anatomical structures, especially the paracolpium and parametrium, and surgical challenges in radical hysterectomy for a complete literature search for comparison and reference.
Cells were washed with ice cold FACS buffer, centrifuged at 250 g for 6 min at 4 • C, and incubated with goat anti-rabbit FITC or goat anti-mouse FITC (both purchased from Southern Biotech, Birmingham, USA) for 30 min on ice in the dark. Propidium iodide 1 µg/ml (Molecular Probes, Leiden, The Netherlands) was added just prior to measurement in order to exclude apoptotic cells. Properdin binding and activated C3 deposition on non-apoptotic cells were analyzed in a FACSCalibur TM (FACSCalibur, Becton Dickinson, New Jersey, USA). Experiments were repeated independently two times. The percentage effect was calculated based on the control data (0 ng/mL Salp20 + 10 µg/mL purified properdin) in median fluorescence intensity (MFI), % inhibition = 100-(MFI test result/MFI control) × 100.
ACKNOWLEDGMENTS We thank  H. M. van der Lugt  for her support for the preparation of this manuscript and  Dr. J. D. Lambris  for his generous gift of Compstatin. We also thank  R. van den Bos  and  Dr. P. Gros  ( Bijvoet Center for Biomolecular Research, Utrecht University, Utrecht, The Netherlands ) for their helpful gift of recombinant properdin and recombinant Salp20 and their support during the preparation of this manuscript.
This paper brings MAA models in synchronous languages to enrich the collection of formal models for the MAA algorithm. More precisely, in this paper, we present a formal model of the MAA with the synchronous formal language Lustre  [9]  dedicated to design embedded control systems; and with the industrial formal language SCADE (Safety Critical Application Development Environment)  [4]. We discuss implementation choices and challenges, common to both models, partly because the SCADE language is based on Lustre language. Finally, we explain how we test the two formal models with the complete set of test vectors derived from the initial specification  [6]. The remainder of the paper is organized as follows. Section 2 presents the technical perspective of the MAA. The modeling of the MAA using the synchronous language SCADE/Lustre is described in Section 3.
Modelling MAA in SCADE and Lustre We consider the MAA models LNT-18  [8]  and REC-17  [7]  and extend them to model the full MAA algorithm in the synchronous languages Lustre  [9]  and SCADE  [2]. Lustre is a synchronous language based on the dataflow model of computation, which defines a reactive system that receives inputs at each tick of a logical clock and calculates outputs. The relation between inputs and outputs is described using a system of (unordered) equations. At a given cycle, the value of inputs can be memorized to be reused in the next cycles. The outputs at a given instant may thus depend on current and past inputs, but not on future inputs. The relations between inputs and outputs are specified in nodes using constants, operators, functions, auxiliary variables, as well as other nodes, which provide for a compositional description of complex systems.
SCADE is an industrial language that merges two different academic languages: Lustre, a declarative dataflow language, and Esterel  [3]  an imperative, synchronous process calculus; more precisely, SCADE embodies a graphical variant of Esterel known as SyncCharts  [1]. Although these languages are quite different, the designers of SCADE managed to integrate both in one coherent language. In the present article, our modelling is based upon the dataflow subset of SCADE, with little or no use of the Esteral/SynchCharts features. Also, following the approach of a previous modelling of the MAA using term rewrite systems (  [7] ), we chose not to rely on predefined data types of SCADE (bits, machine words, etc. ) but to define all our types from scratch, using the type constructors provided by the language.
This function is invoked as follows (as it is done in the MAC node): X , Y , V , W , S , T = prelude (J , K ); Note that in SCADE the underscore symbol (_) is used in place of defining a local variable if the corresponding returned value is not used. In our case, we needed some outputs from the auxiliary functions for the test vectors, but not for the prelude function, which is why we 'assigned them to _' in prelude. Comparison between SCADE and Lustre implementations: In the semantic context of our MAA formal models the differences between the two languages are negligible. First, SCADE uses comma instead of a semicolon, and defines the type before the equal in the constant/function definitions. Second, using let/tel in a function composed of one statement is not permitted in SCADE, which generally reduces the number of code lines.
However, the main difference is on the compilers: the SCADE compiler enabled us to detect some potentially non-initialized variables in the MAC node and variables not used in the prelude function. The Lustre and SCADE models contain 9 types (structures), 442 constants, 100 functions, and 1 nodes each. Our complete Lustre model of the MAA (see the repository) consists of 1908 lines and our complete SCADE model of the MAA (see Appendix A) has 1762 lines and 3 additional graphical nodes. Comparison between SCADE/Lustre and earlier implementations: Our two synchronous models are executable, in a sense that they can be compiled automatically into some executable program that will be run to generate the expected results. Let us review these two synchronous models with the three earlier executable specifications; REC-17  [7] , LOTOS-18  [8] , and, LNT-18  [8].