dataesr/scientific-paragraphs-categorization huggingface dataset:
45B, the result of the MATLAB code is equal to, then gradually becomes higher than that of Matlock. Finally, at 0. 6B, the normalized lateral capacity of the MATLAB code is 10. 57, which is 104. 6% of that of  Matlock (10. 28). In the MATLAB code, p is calculated by integrating the normal and tangential stresses along the circumference of the pile or the soil-pile interface. The two results are compared in Figure  5 , which reveals that the p-y curves are the same before 0. 4B and have a small difference (1. 38 ~2. 23%) at 0. 4 ~0. 6B. Even if the p-y curve obtained from the MATLAB code presents a shape which is not identical to those from the API and Matlock empirical equations, the normalized horizontal reaction force does not exceed the empirical values when y < 0. 35B, then it is characterized with smaller difference to the Matlock curve up until y/B = 0.
6. Furthermore, the validation procedure reveals that in engineering design, the DP model leads to lower p-y curves than the API and Matlock equations at y < 0. 35B (Figure  4 ). However, at horizontal displacement larger than 0. 35B, the MATLAB code generates lateral reaction force that is close to that of the Matlock formulation. The integrated results from the pile and soil-pile interface are the same. Both confirm that the p-y curves from the MATLAB code are reliable and consistent with the Matlock design method.
Interface Thickness To check the influence of the interface thickness on the numerical results, the following different interface-thickness values are considered: 5 mm, 7 mm, 1 cm, 2. 5 cm, 5 cm, and 10 cm. The input parameters of the simulations are listed in Table  2. Figure  7  presents the effect of interface thickness on the soil reaction curves. All the p-y curves are independent of the interface thickness at the horizontal displacement considered 0 ~0. 4B (Figure  7 ). From 5 mm to 10 cm interface width, the resulting normalized p-y curves have a close shape and almost the same value of the lateral bearing capacity factor at y = 0. 4B: 8. 58, 8. 69, 8. 55, 8. 75, 8. 81, and 8. 67. The difference between the final normalized p of the six curves from the MATLAB code is 0. 02 ~0. 26. These values are higher than the one calculated by the API method (8.
3. The p-y curves are independent of interface thicknesses from 5 mm to 10 cm. 4. Clay content influences the p-y curve results. When y < 0. 15B, the same lateral capacity values are resulted at clay contents of 27. 5% and 55%, and they are higher than the curve of the sand. The normalized p-y curves show a decreasing trend with increasing clay content after y > 0. 15B. The primary results in this paper shed light on the relationship between p-y curves and interface parameters. However, the FE modeling of p-y curves in this study is performed by an implemented code using only the DP model, and the input parameters concern just three clay contents. Therefore, further numerical studies should involve more advanced constitutive laws, water, 3-D conditions, and the possibility to have gaps between the pile and the soil.
This paper presents a prototype for the visualization of food-drug interactions implemented in the MIAM project, whose objective is to develop methods for the extraction and representation of these interactions and to make them available in the Thériaque database. The prototype provides users with a graphical visualization showing the hierarchies of drugs and foods in front of each other and the links between them representing the existing interactions as well as additional details about them, including the number of articles reporting the interaction. The prototype is interactive in the following ways: hierarchies can be easily folded and unfolded, a filter can be applied to view only certain types of interactions, and details about a given interaction are displayed when the mouse is moved over the corresponding link.
Introduction The French ANR PRCE MIAM project  [1]  (2017-2020) aims to identify and model fooddrug interactions. It focuses on the problems that can emerge when an interaction occurs between a food and a drug. In practice, the consumption of a food associated with the intake of a drug can have three effects: (i) the attenuation or suppression of the effect of a drug, (ii) the increase of the effect of a drug, and (iii) the occurrence of unknown adverse effects. This information, when available, is described in scientific publications or knowledge bases in an unstructured manner. Thus, MIAM partners have used natural language processing methods to extract such information and made it available within the Thériaque database  [2]. This database has been approved by the French National Authority for Health (HAS) and aims at providing health professionals and patients with exhaustive and reliable information about the drugs available in France.
Discussion The proposed prototype provides an original and graphical way to explore food-drug interactions. It will be integrated within the Thériaque database in the coming weeks. It should be noted that the exploration of the drug hierarchy requires familiarity with the ATC system, and in particular its codes. This is of course a limitation to the use of the prototype by patients, who are also a target audience for Thériaque. An obvious perspective is to proceed to the evaluation of the prototype with the different user groups to which the Thériaque database is addressed. Moreover, the food hierarchy is based on the alphabet whereas the use of a hierarchy with semantic meaning would be more relevant. In this framework, the food hierarchy described in FIDEO  [7]  could be used. In our future work, we also plan to recover other food-drug interactions.
1 and 22. 7 for C1 and C2 conditions respectively) that led to a background signal which was faulty called positive. However, an exhaustive analysis of the amplification curves obtained by the Idylla C2 condition led to the identification of a non-valid amplification curve for an exon 19 deletion. Thus, we could also consider that the Cobas could have missed this variant if not included in the panel covered or present with very low frequency. For cases with a secondary resistance mutation, the Idylla system succeeded in retrieving the T790M mutation obtained by the reference method in two out of the three clinical samples tested (#13-#14) and missed it in one case (#15). In this latter case however, a thorough analysis of the amplification curves was performed and the T790M mutation was identified in the C1 and C2 conditions of the Idylla assay as a borderline negative result filtered by the software during the analysis process.
The Cobas EGFR mutation test v2 (Roche Diagnostics) is a real-time PCR assay for the qualitative detection of 42 hotspots mutations in exons 18-21 of the EGFR gene  43. The Cobas EGFR mutation test v2 assay was performed following the manufacturer's indications. Analysis of each sample was performed in three separate assays. For each assay, the PCR reaction mixture was prepared using standard input of 25 µL of extracted cfDNA, 20 µL of master mix solution and 5 µL of magnesium acetate. Negative and positive controls included in the kit as well as a no-template control were systematically tested in each run. PCR amplification was performed on the Cobas z480 analyzer and results were treated using the cobas 4800 software. A semiquantitative index (SQI) was calculated for every EGFR mutation detected in cfDNA and estimates the proportion of EGFR-mutated versus EGFR wild-type copies  44.
In this study, 2 mL of plasma were used for each assay and two conditions were tested for each sample (Fig. 1 ). -The C1 condition consisted in the addition of 200 µL of proteinase K (20 mg/mL) (reference 19133, Qiagen) into the lysis pad of the cartridge then the split addition of the 2 mL of plasma sample into the cartridge. -The C2 condition resumed the previous protocol followed by an incubation in the cartridge for 30 min at room temperature. For both conditions, the cartridge was finally sealed and inserted into the instrument to launch the analysis. Inside the single-use cartridge, DNA was prepared and amplified by real-time PCR then detected using a fluorophore-based system. PCR amplification curves were analysed by the Idylla console software and cycle of quantification (Cq) values were determined.
Data analysis. The clinical performance of the Idylla ctEGFR mutation assay was evaluated based on EGFR genotyping for the 38 plasma samples, using Cobas EGFR mutation Test v2 or capture-based NGS approaches as the gold standard. The sensitivity, specificity and overall agreement of the Idylla ctEGFR mutation test were calculated by considering EGFR-TKI sensitizing mutations as following. • Sensitivity (Se) = proportion of EGFR-positive samples obtained by Idylla assay among the EGFR-positive samples according to the standard reference method. • Specificity (Sp) = proportion of EGFR-negative samples obtained by Idylla assay among the EGFR-negative samples according to the standard reference method. • Overall agreement = number of samples with concordant EGFR mutation status between the Idylla assay and the standard reference method out of the overall number of samples.
For all these items, a 95% confidence interval (95% CI) was calculated with exact Clopper-Pearson method. Agreement between the two conditions of the Idylla assay was investigated by computing a Kappa Value and its 95% CI. A Kappa value greater than 0. 6 was considered as good agreement and greater than 0. 8 as excellent agreement. Analyses was performed with SAS software version 9. 4 (SAS Institute Inc. , Cary, NC, USA).
CtDNA/cfDNA samples with different mutant allele frequencies were obtained using 2 mL of plasma collected from healthy donors (reference S4180-500, Dutscher, Brumath, France) spiked with different volumes of DNA solutions (see Supplementary Table S1 online). The LOD was defined for each of the seven mutations detected by the Idylla system as the lowest mutant allele frequency yielding an "EGFR mutation detected" result. CfDNA fragment distribution was determined using the DNF-464-0500 High Sensitivity Large Fragment 50 kb Kit (ranging from 1 to 200,000 base pairs (bp)) and the Fragment analyzer instrument (Agilent, Santa Clara, CA, USA) following the supplier's recommendations (see Supplementary Fig. S1 online ). Data were analysed using the PROSize 2. 0 2. 0. 0. 51 software. Before its use, the commercial plasma was thawed and centrifuged for 10 min at 6000g.
We present the open-source python package DockOnSurf which automates the generation and optimization of low-energy adsorption configurations of molecules on extended surfaces and nanoparticles. DockOnSurf is especially geared towards handling polyfunctional flexible adsorbates. The use of this high-throughput workflow allows to carry out the screening of adsorbate-surface configurations in a systematic, customizable and traceable way, while keeping the focus on the chemically relevant structures. The screening strategy consists on splitting the exploration of the adsorbate-surface configurational space into chemically meaningful domains, i. e. , by choosing among different conformers to adsorb, surface adsorption sites, adsorbate anchoring points, orientations and allowing dissociation of (acidic) protons.
27 e growing concern for making science more reproducible, transparent and systematic  28, 29  together with the increase in computational power motivate the development of automatic high-throughput frameworks and workflows. Their applications in the field of interface chemistry span from the calculation of adsorption energies for atoms/small molecules  30  to the force-field parametrization and screening of soft matter,  31  passing by the creation and manipulation of nanoparticle interfaces. 32 Still, we are not aware of a generally applicable screening framework to investigate the (dissociative) adsorption modes of flexible molecules on surfaces and nanoparticles. Our DockOnSurf package addresses the challenge to efficiently and automatically explore the adsorption modes of flexible molecules on surfaces.
39 e adsorption on nanoparticles is, on the other side, inspired by reports pointing towards a strong relation between the adsorption energy and the coordination number of the adsorption site. 40 For this last topic, it is the capability of DockOnSurf to detect the surface normal at each adsorption site that enable systematic and automatic high-throughput computations to probe the accuracy of such structure-property relations. DockOnSurf  41  is an open-source program written in Python 3. To promote testability, adaptability, extensibility and ease of debugging, DockOnSurf is built in a modular fashion. Furthermore, the algorithms that constitute the code have been implemented following the Python Enhancement Proposals (PEP) 8  42  and PEP 257  43  guidelines for code readability and documentation.
Moreover, in order to increase the code efficiency and the modularity, the routines composing the program make extensive use of the popular chemistry-related libraries Atomic Simulation Environment (ASE)  44  and Rational Discovery toolkit (RDKit). 45 e source code is available on https://gitlab. com/lch_interfaces/dockonsurf. A full DockOnSurf execution is composed of three independent steps: (i) isolated, (ii) screening and (iii) refinement. For the sake of flexibility, the input file (see section S2 in the SI) allows to select only one or two of these steps for execution. The execution of DockOnSurf is highly customizable via the input file. The reproducibility and traceability of the results are guaranteed by recording all actions taken, as well as possible warnings/errors, together with their corresponding timestamp in a log file.
These simple geometric collision detector/corrector schemes could, of course, be superseded in the future by more advanced functions: (a) The use of internal angles is ideally suited for constructing (either on the fly or based on databases of previous explorations) Bayesian estimators for the most promising values. 64 (b) The use of simplified force fields, for instance based on the nuclear charges or on Lennard-Jones potentials permit to rapidly discriminate unphysical structures and to determine a rigid-body translation and rotation gradient towards lower-energy arrangements. However, this goes beyond the scope of the present scheme. Depending on the computational resources available and the combination of parameters set in the input file, the number of configurations generated can easily lead to an overwhelming number of computations.
Supported software The actual quantum chemical computations are performed using the user-provided CP2K or VASP input files. These computations can either be run locally or in a computing center by using a batch queuing system. For the moment, Simple Linux Utility for Resource Management (SLURM), Platform Load Sharing Facility (LSF) and Sun Grid Engine (SGE) are supported. The user can also specify a maximum number of simultaneous jobs to be either on queue, running or the sum of both. The magnetism of hematite (one of our test systems) plays a fundamental role in the correct description of the surface properties. 66, 67 In order to properly deal with the antiferromagnetic coupling, the definition of two kinds of Fe atoms with different spin-orbital occupations is needed. In practice, such a definition comes, necessarily, with a different labeling of the two Fe atom types.
Acknowledgement S. B. gratefully acknowledges Total MS and the  ANRT  for her PhD fellowship. The authors thank the  SYSPROD  project and  AXELERA Pôle de Compétitivité  for financial support (PSMN Data Center). This work was also granted access to the HPC resources of CINES and IDRIS under the allocation 0800609 made by  GENCI. We thank  Paul Clabaut  for helpful discussions.
Data and Software Availability The software described herein (DockOnSurf) is freely available on https://gitlab. com/ lch_interfaces/dockonsurf and documentation under https://dockonsurf. readthedocs. io/. The software used for the electronic structure computations, CP2K, is available on https://github. com/cp2k/cp2k/releases/. The generated structures, together with the corresponding results of the geomertry optimization with CP2K are available in the SI as a zip archive.
UniCiCles -Ice Sheet Model The core of the ISM used in UKESM1 configurations with ice sheet dynamics is the Berkeley Ice Sheet Initiative for Climate at Extreme Scales model (BISICLES,  Cornford et al. , 2013). UniCiCles (Unified Model-CISM-BISICLES) is a package combining BISICLES with an interface that obtains boundary conditions from Unified Model or JULES data, using code derived from the Glint interface of the Glimmer-CISM ISM  (Lipscomb et al. , 2019; Rutt et al. , 2009). We regard this interface code as part of the surface coupling, and describe its functions in Section 4. BISICLES uses the adaptive-mesh Chombo libraries  (Adams et al. , 2019). Adaptive meshing allows areas of the ice sheet where the accurate representation of the dynamics is critically reliant on fine resolution in small areas (e.
Coupling Framework Of all the Earth System components in UKESM1, the atmosphere dynamics require the shortest timestep, at 20 min. The other sub-models, excluding UniCiCles, communicate once in every few of these timesteps, on timescales from minutes to hours. Structurally, this communication is achieved either by having the model subroutines compiled into the same executable (e. g. , UKCA, JULES, TRIFFID and the UM are compiled together, and MEDUSA and CICE are part of the NEMO executable) or using the OASIS coupling software  (Craig et al. , 2017)  to pass information between separate executables (Figure  2 ). On our computing platforms UKESM1 is run in individually submitted, sequential jobs simulating between one and three months each, and in each such job both OASIS and subroutine coupling pathways are used many times.
Because of these considerations, we couple UniCiCles to the rest of UKESM1 by using Cylc to coordinate the exchange of boundary condition files in between the individual UKESM1 jobs in the sequence. The standard diagnostic systems in each component are used to accumulate and output files of time averaged fluxes or instantaneous locations of boundaries (see Table  2 ). Cylc runs scripts to turn these outputs into input files for the other components, which are read upon initialization of the next job when boundaries can be changed. The relative infrequency of the ISM coupling means that the computational overhead of this rather brute-force approach is negligible. All our ice-coupled UKESM1 configurations pass information from the atmosphere and ocean components to UniCiCles once per year of climate model simulation, in northern hemisphere winter.
On multi-centennial timescales other assumptions used in our UKESM1 configurations, such as the fixed internal temperature of the ice sheet or lack of solid earth response, also become harder to justify and would need to be considered when interpreting the simulation results. UniCiCles runs separate domains for each ice sheet it simulates, for example a relatively small regional Greenland domain at the same time as a much larger Antarctic one for a modern climate. This means it is simple to run simulations with just one ice sheet interactive or have both active at once. Domain-specific boundary files for each ice sheet are prepared by the coupling scripts as input to the ISM, and the outputs from each ISM domain are merged into global fields for input to the climate model. The BISICLES adaptive mesh itself does not need any special treatment, since the BISICLES interface accepts boundary information on regular grids whose cells coincide with any one of the sizes of grid-box allowed by mesh in use.
When Cylc determines that the ice sheet dynamics should be run, submission of the next segment of the climate run is delayed until the ISM segment has completed, and the first of the coupling scripts is triggered. This script selects the required variables from the UM and JULES output and reformats them so they may be read by UniCiCles. The other steps that precede BISICLES dynamic time-stepping are all carried out inside UniCiCles itself, separately for each ice sheet domain. Snow and ice mass is conserved in the UM, whilst UniCiCles deals in ice volume, so in all cases the fixed ice density of 910 kg/m 3 specified by BISICLES is used to convert between mass and volume representations. First, the SMB from the e-ice tiles and snow mass from the e-rock tiles are trilinearly interpolated from the three-dimensional latitude-longitude-elevated-tile fields provided by the JULES grid to the ice sheet surface on the regular Cartesian grid of UniCiCles, using the highest spatial resolution permitted by the adaptive mesh.
The topography statistics are calculated by the program used to make standard UM boundary condition files, to ensure methodological consistency. Finally, the area fractions and snowpack properties of the elevated tiles in JULES are modified. The area fractions for the tiles reflect the horizontal and vertical distribution of glaciated and unglaciated grid-boxes in UniCiCles, remapped to the grid-boxes and elevation classes of the UM grid. Snowpack properties on the JULES elevated tiles are modified in three ways. First, by adjusting the thickness of the bottom layer, the snowpack mass on each e-ice tile is reset to its initial value (equivalent to 100 m of ice-density snow). This step balances the mass changes specified by the SMB field that were applied to UniCiCles. Second, since JULES deals with snow mass per unit area, we account for the fact that changes in tile area resulting from the evolving shape of the ice sheet alter the area-integral mass of snow lying on the JULES tiles.
While the UKESM1. ice summer albedo is lower than MAR, it is not unrealistic compared to observations. The simulated 1982-2014 mean summer albedo averaged over the tile fractions compares well with the mean taken over the same mask from the CLARA-A2 (CM SAF cLoud, Albedo and Radiation, second edition) dataset based on satellite observations  (Riihelä et al. , 2019) , with values of 0. 77 and 0. 74 respectively. To demonstrate the ability of our coupled model to cope with large changes in the extent and shape of the GrIS we carried out a multi-centennial experiment with atmospheric carbon dioxide concentration instantaneously quadrupled in 1970. The ice sheet and climate are coupled annually until , and thereafter 5 years of ice dynamics are run for every 1 year of climate (N = 5, Section 3).
Implementation of Coupling Each period of UniCiCles simulation results in a two-dimensional distribution of the mass of discharged floating ice beyond the ice margin, from which a coupling script creates two boundary condition files. One file is used in the next execution of NEMO as an annual-mean source of icebergs, which is applied spread over the next year of climate simulation. The resolution of the coastline is far more detailed in UniCiCles than in NEMO, and the regridding assigns each UniCiCles grid-box to the nearest open-ocean grid-box of NEMO, which in the case of Greenland can be a considerable distance away. The other file specifies a rate of ice mass removal on the UniCiCles grid, which is applied across the whole of the next execution of UniCi-Cles, removing the ice that has been used for icebergs in NEMO.