dataesr/scientific-paragraphs-categorization huggingface dataset:
Human samples All human biological samples were obtained from informed consenting patients treated at Toulouse Hospital (Centre Hospitalier Universitaire de Toulouse) after receiving ethics committee approval under protocol numbers CAPITOL RC31/21/038, COLIC DC-2015-2443, NCT05251415 and BACAP NCT02818829  (Canivet et al. 2018)  approved by the relevant Comité de Protection des Personnes (CPP Sud-Ouest et Outre-Mer I et II). Human colonic mucus was obtained from macroscopically healthy zones, taken during surgical resections in patients with colorectal cancer or inflammatory bowel diseases. Upon reception, tissues were mechanically cleaned of any residual fecal material and the surface of the mucosa was gently scraped using a cell scraper to harvest the mucus. The viscous material obtained was homogenized for 30 seconds using a Benchmark D1000 homogenizer, in a buffer containing 100 mM Tris-HCl pH8, 1 mM CaCl2, 1mM CHAPS.
Acknowledgments Acknowledgements are due to the members of the animal housing facilities US006 (Toulouse):  M. Lulka ,  T. Bernal , and  R. Balouzat. We are grateful to  Fabrice Pierre  from  UMR1331 Toxalim INRAE  for providing CMT93 cells. The authors also acknowledge the  U1220-IRSD Organoids Core facility  for assistance in the collection of human mucus samples. We are grateful to members of  NV-CD  team for help and discussions.
Funding statement The doctoral salary of S. G was supported by  Medical Research Foundation  to NV ( FRM -FDT202204014931 ) and  National Research Institute for agriculture , feed and environment (INRAE) to CB. This study was supported by  Paul Sabatier University , a grant of the  European Research Council  ( ERC-310973 PIPE ) to NV, the  ANR  project  TITAN  ( ANR-18-CE18-0019 ), the  ANR  project  PARCURE  ( ANR-20-CE18-0024 ) and a grant from the  Canadian Institutes of Health Research (CIHR)  to MDH.
Flow imaging microscopy The aggregate size was studied by flow imaging microscopy, on a Flowcell FC200S+HR (Occhio). Briefly, this apparatus captured images of the sample flowing in a cell, and the software Callisto (Occhio) analyzed the shape and the size of each particle individually. A 100 µmthick spacer was used, to be sure that even the largest aggregates could pass through the cell and the samples were magnified 6 times. Before each analysis, a background calibration was performed on ultrapure water. Furthermore, the sample initially at 1 mg/mL was diluted in ultrapure water to a concentration equal to 0. 5 mg/mL. The diluted sample was then deposited on top of the cell and pumped through it. From the captured images, size and shape information were extracted. As the aggregates looked anisotropic, "Length" was chosen as the most relevant size parameter for further descriptions.
5 % bovine serum albumin (BSA) and stained with monoclonal antibodies (mAbs) in the dark, on ice for 20 min. The following mAbs were used: allophycocyanin (APC)-labeled mouse anti-human CD86 (BD Biosciences, Cat# 555660); Phycoerythrin (PE)-labeled mouse anti-human CD83 (BD Biosciences, Cat# 556855). Appropriate isotype controls (mouse IgG1κ, BD Biosciences) were used at the same concentration to determine nonspecific staining. Cells were analyzed on an Attune Nxt (Invitrogen) using FlowJo software (version 10; FlowJo LLC), and we used a gating strategy to exclude dead cells, based on the FSC/SSC criteria. The data acquisitions were performed on a minimum of 10,000 living cells. Results were expressed as the relative fluorescence intensity (RFI), using the corrected mean fluorescence intensity (cMFI), as follows: cMFI=M-FI-MFI of isotype control; RFI = cMFI of treated cells/cMFI of untreated cells).
Elevated temperature stress induces a time-dependent formation of micrometer-size infliximab aggregates To evaluate the role of size and quantity of antibody aggregates on DC and specific T-cell response, diluted IFX (1 mg/mL) was submitted to different durations of stress through incubation at an elevated temperature (S) at 55 • C. Increasing times of stress (1, 2, 3, 4, 6 and 24 h) were applied to test the stress duration effect on IFX aggregation. Endotoxin levels were quantified on three independent samples and were always under the acceptable limit of 0. 25 EU/mL (data not shown). Aggregates were first characterized by two complementary methods and on three independent samples (with three measures for each sample) to verify reproducibility. Native and aggregated proteins were first characterized by size exclusion chromatography.
Infliximab aggregates modulate moDC phenotype in a size and quantity-dependent manner To investigate the effect of these preparations with increasing quantity and size of IFX aggregates on moDC phenotype, we analyzed the expression of surface markers CD83 and CD86 as well as the secretion of cytokines IL-6, IL-8, IL-10, IL-12p40 and TNF-α and chemokines CCL3 and CCL4 after 24 h of stimulation with native or aggregated IFX. These markers were chosen for their implication in moDC activation in response to antibody aggregates  (Gallais et al. , 2017; Morgan et al. , 2019; Nabhan et al. , 2020a). We first studied the expression of CD83 and CD86 on moDCs after stimulation with native or aggregated IFX (S1, S2, S3, S4, S6 or S24) at 100 μg/mL, or LPS at 25 ng/mL as positive control. The cytoxicity of native and aggregated antibodies was first analyzed using propidium iodide staining, and we observed that cell viability was never less than 80% (data not shown).
Fundings This work was supported by the  Innovative Medicines Initiative Joint Undertaking  under grant agreement  115303 ,  ABIRISK  " Anti-Biopharmaceutical Immunization: Prediction and Analysis of Clinical Relevance to Minimize the Risk ," resources of which are comprised of a financial contribution from the  European Union 's  Seventh Framework Program  ( FP7/2007-2013 ) and in-kind contributions from  European Federation of Pharmaceutical Industries and Associations  member companies. This work was also supported by a  French  government grant managed by the  Agence Nationale de la Recherche  under the  France 2030 program , with the reference  ANR-22-PEBI-0009.
Introduction: OpenPlanetary, or simply "OP", is an international non-profit organisation that promotes open research in the planetary science and exploration communities: sharing ideas and collaborating on planetary research and data analysis problems, new challenges, and opportunities  [1]. OpenPlanetary started in 2015 as a way for participants of the ESA's Planetary GIS Workshop to stay connected and exchange information related to and beyond this workshop. It expanded further by playing a similar role for the second USGS-hosted Planetary Data Workshop (PDW) in 2017. OpenPlanetary has continued to support the biannual PDW and provides a more persistent forum for participants to highlight presented topics and discussions from the workshops. In 2018, we established OpenPlanetary as a non-profit organisation  (Association under 1901 French Law, [2] ) in order to provide us with a legal framework to sustainably fund our community framework, projects and activities, and to better serve the planetary science community as a whole.
OpenPlanetary is governed by a Board of Directors, elected for two years, which (1) define the policy and general orientation, (2) initiate, endorse, lead, or contribute to the projects and activities, and (3) can make use of the funds of the Association for any endorsed project or activity; the Bureau contains a 3-person subset of the Board members (a president, treasurer, and secretary) and serves as the executive body of the Association. Mission: Our mission is to promote and facilitate the open practice of planetary science and data analysis for professionals and amateurs. We do so by organizing events and conducting collaborative projects aimed at creating scientific, technical and educational resources, tools and data accessible to all.
All model edits performed in the Studio would then be saved in the XML configuration file. We chose Qt (https://www. qt. io/) as the preferred GUI library for the desktop tool for multiple reasons. It is used by several other desktop (scientific) applications, runs on the three major operating systems (Windows, macOS, and Linux) that PhysiCell supports, and has Python APIs. Undergraduate students explored the C++ API to Qt, along with Qt Designer (https://doc. qt. io/qt-6/qtdesigner-manual. html) to create a primitive prototype of the desktop Studio. Others explored a Python API to Qt (https://pypi. org/project/PyQt5/) to do the same. In the end, we selected the Python API for two reasons: 1) the development cycle seemed faster (no compilation required), and 2) we believed the PhysiCell community (who might further develop the Studio) would be more familiar with Python than C++ (this was also true for undergraduate and graduate students).
Software Engineering We have adopted a software engineering workflow that uses GitHub (https://github. com/PhysiCell-Tools/PhysiCell-Studio) and takes advantage of several features it offers: hosting and distributing software, discussing issues, submitting pull requests, developing code in staged repositories, and automated testing. Most of our community is already familiar with GitHub, but for those who are not, we help them learn the basics. For anyone who wants to contribute code to the Studio, we ask that they fork the repository into their own account, make edits, test (on at least one of the three supported operating systems), and make pull requests to the development branch of the Studio repository. Community discussion about bugs (and hopefully proposed solutions) or new features for the Studio is encouraged via Slack channels and GitHub Issues.
Community Support The PhysiCell Studio User Guide (github. com/PhysiCell-Tools/Studio-Guide/blob/main/README. md) should help new users get started. Additional support is possible using Slack channels and GitHub Issues (github. com/PhysiCell-Tools/PhysiCell-Studio/issues). An introductory video from a recent PhysiCell workshop is at https://youtu. be/jkbPP1yDzME. More details about defining Rules using the constrained grammar for cell behaviors can be found in that paper's Supplemental section  [9]. We are always open to new ideas for learning how to use PhysiCell Studio and welcome community contributions.
Acknowledgements We thank the entire  PhysiCell community  for providing helpful feedback and contributions to the Studio, including several undergraduate students over the past few years:  Daniel Mishler ,  Tyler Zhang ,  Eric Bower ,  Carlos Juarez ,  Jay Thilking ,  Nicholas Goh ,  Yuchen Yang ,  Drew Willis ,  Adam Morrow ,  Grant Waldow ,  Kimberly Crèvecoeur ,  Dylan Taylor ,  Kali Konstantinopoulos ,  Marshal Gress , and  Eric Freeman , as well as graduate students:  John Metzcar ,  Elmar Bucher ,  Furkan Kurtoglu ,  Aneequa Sundus ,  Yafei Wang ,  Supriya Bidanta , and postdoc  Michael Getz. We also thank  Steven Clark ,  Daniel Mejia ,  Martin Hunt , and  Lynn Zentner  for their support with nanoHUB. Finally, we thank the  ParaView community  for their support.
Funding We thank the  National Science Foundation  (Awards  1642070  and  1720625 ), the  National Institutes of Health  ( U01-CA232137-01 ), the  Breast Cancer Research Foundation , and the  Jayne Koskinas Ted Giovanis Foundation for Health and Policy. This work was also supported by the  European Commission  under the  PerMedCoE  project [ H2020-ICT-951773 ] and  Inserm amorçage  project.
Implementation details We implemented our branch-and-price algorithm with spatial branching in C++ and run all the experiments on an AMD Ryzen 5 PRO 4650GE at 3. 3 GHz, with a time limit equal to 10,800 CPU seconds per run (3 hours). At the root node, an initial upper bound is computed by solving the single-stage version of ARCCFLP where all decisions are taken here and now. This bound is obtained by solving (39) -(  41 ) without convexifying the second-stage feasible region in constraint (41). At each node of the algorithm, we solve the restricted master problem by using Mosek 10. 0. 36, and the pricing problem by means of Gurobi 10. 0. This combination of solvers turned out to be the most numerically stable on our instances. Our code and instances are publicly available on GitHub. 1.
The textural properties of the carbon materials were studied using N 2 and H 2 adsorption, the isotherms of which were obtained at -196 • C with a 3Flex manometric sorption analyzer (Micromeritics, Atlanta, GA). Prior to adsorption measurements, the samples were outgassed under secondary vacuum at 110 • C for 24 h. The BET area (A BET ) was calculated by applying the Brunauer-Emmet-Teller theory, using the adequate relative pressure range of the N 2 adsorption isotherm  [32, 33]. The NLDFT surface area (S NLDFT ) was calculated from the combination of both N 2 and H 2 adsorption isotherms using a 2D non-local density functional model (2D-NLDFT) available in SAIEUS software (Micromeritics, Atlanta, GA). Pore size distributions (PSDs) and cumulative pore volumes were also calculated from the combination of the two isotherms.
Acknowledgements The  ANR-15-IDEX-04- LUE  project and the  TALiSMAN and TALiS-MAN2  projects funded by the  European Regional Development Fund (ERDF)  are gratefully acknowledged. The authors would also like to thank project  PID2021-123079OB-I00  funded by  MCIN/AEI/10. 13039/ 501100011033  and " ERDF A way of making Europe ". Finally, JQB thanks the  Ministerio de Universidades , the  European Union , and the  University of Alicante  for their financial support ( MARSALAS21-21 ).
The Brunauer-Emmet-Teller (BET) specific area (A BET ) was obtained using the Microactive Ò software (Micromeritics). The total pore volume (V 0. 97 ) was registered at a relative pressure (P/P 0 ) of 0. 97 and the micropore volume was calculated by the Dubinin-Radushkevich equation (V DR ). The SAIEUS Ò software (Micromeritics) was used to elucidate the pore size distribution (PSD) by applying the 2D nonlocalized density functional theory for heterogeneous surfaces (2D-NLDFT-HS) method. This model was also used to determine the specific surface area (S NLDFT ), the total pore volume (V NLDFT ), and the micropore volume (V l,NLDFT ). X-ray diffraction (XRD) patterns were acquired using a Bruker D8 Advance diffractometer (USA) operating at a counting step of 0. 05°. X-ray photoelectron spectra (XPS) were recorded using an ESCAPlus OMICRON system operating with an Al anode (1486.
7 eV) at 225 W (15 mA, 15 kV). A step of 0. 5 eV, a dwell of 0. 2 s and a pass energy of 50 eV were used for the survey scans, while high-resolution spectra were acquired at 0. 1 eV step, 0. 5 s dwell and 20 eV pass energy. XPS data were fitted using the software CasaXPS by applying the Shirley type background after calibration of the binding energy (BE) positions with the C1s graphitic peak (284. 5 eV). Gaussian (70%)-Lorentzian (30%) profiles were used for each component. X-ray absorption spectroscopy (XAS) measurements were carried out at room temperature at the SAMBA beamline (Synchrotron SOLEIL). The beamline is equipped with a sagittaly focusing Si(220) monochromator and two Pd-coated collimating/focusing mirrors, used to remove X-ray harmonics. The catalysts were pelletized as disks of 10 mm diameter with 1 mm thickness using graphite as a binder.
Cu-N-C-Ac0. 5, Cu-N-C-Ac1, Cu-N-C-Chl1, and Cu-N-C-Nit1 were measured in fluorescence geometry, while Cu-N-C-Ac1-BPT, Cu-N-C-Ac2, Cu foil, Cu 2 S, Cu 2 O, CuO, and CuPc were measured in transmission mode. The EXAFS data analysis was performed with the GNXAS code  [46, 47]. In the GNXAS approach the interpretation of the experimental spectrum is based on the decomposition of the EXAFS v (k) signal into a summation over n-body distribution functions c (n) calculated by means of the multiple-scattering (MS) theory. Each signal has been calculated in the Muffin-tin (MT) approximation using the Hedin-Lundqvist (HL) energy dependent exchange and correlation potential model, which includes inelastic loss effects. The copper first coordination shells have been modelled with c-like distribution functions which depend on four parameters: the coordination number N, the average distance R, the mean-square variation r 2 , and the skewness b.
The Cu-N-C-Ac0. 5 spectrum has been calculated including the first shell Cu-N c (2)  two body signal, and two Cu-C 1 and Cu-C 2 c (2) two body signals. The c (3) signal associated with the N-Cu-N three-body distributions has been included as well. Least-square fits of the EXAFS raw experimental data have been performed by minimizing a residual function of the type (Eq. (  1 )). R i k f g ð Þ ¼ X N i¼1 ½a exp E i ð Þ À a mod E i ; k 1 ; k 2 ; Á Á Á ; k p À Á 2 r 2 i ð1Þ where N is the number of experimental points, Ei = (k 1 , k 2 ,. , k p ) are the p parameters to be refined and r i 2 is the variance associated with each experimental point a exp(Ei). Additional non-structural parameters were minimized, namely E 0 (core ionization threshold energy) and S 0 2 (amplitude reduction factor) which considers intrinsic losses.
This indicates an apparent change in the faradaic efficiency at larger overpotentials, also accompanied by larger current densities (Fig. 8a ). Interestingly, the peak potential values in peak I for Cu-N-C-Nit1 are shifted towards more negative values if compared to the data from the previous section, which seems to be related to the generation of a lower CO concentration than 1 lM for this catalyst. On the other hand, the calibration was carried out with mixtures of CO/H 2 /CO 2 gases without any other organic species. Therefore, the presence of other reaction products at the Cu-N-C catalyst surfaces that are oxidized at more negative potentials than CO (e. g. , formic acid) cannot be totally discarded. In an H-cell/GC analyses (Section 3. 4) only small production of CH 4 was detected and only for the catalysts prepared with copper acetate.
Two key figures of merit, i. e. solar absorptance α sol and thermal emittance ε th are considered for the characterization of such materials  [10]. These figures are most often calculated on the basis of room temperature (RT) optical measurements performed with laboratory spectrophotometers  [11, 12]. Such spectral measurements have also been performed on some relevant receiver materials and coatings at operating temperature up to 800 • C (OT)  [13] [14] [15] [16]. Spectral emittance datasets at RT and OT are not only important for the evaluation and comparison of materials, but also for the design, calibration and operation of radiometric instrumentation, such as infrared thermography  [17, 18]. The following questions are relevant for scientists and engineers: i) are significant deviations observed for figures of merit derived from RT and OT spectral measurements? ii) Does the material exhibit a grey or a selective behavior in certain spectral ranges? iii) Are local spectral shifts observed at higher temperature, outside of known atmospheric absorption bands? iv) Does the emittance vary with the angular incidence of measurement at OT? This paper focuses on the intercomparison of spectral emittance datasets for relevant materials in CRS applications.
Reference samples Eighteen H230 flat sample coupons were prepared for the RT and OT test campaigns. These coupons are described in Table  2  and are shown in Fig. 1 a ). This sample lot is divided in three batches: i) oxidized H230, ii) Pyromark 2500 and iii) an industrial black coating. For each batch, three geometries were prepared (rectangular, disk, square inch), to comply  wavelengths. This requires each participating laboratory to combine spectral measurements from two complementary spectrophotometers. Some laboratory spectrophotometers and integrating sphere geometries are illustrated in Fig. 2. An inventory of instrumentation is described in Table  3 , while some spectrophotometer characteristics are listed in Table  4. Spectral ranges and raw dataset resolutions are summarized in Table  5.
All participants except PROMES-CNRS use a Pike Ltd Mid-IR gold diffuse coated integrating sphere of diameter 76. 2 mm. The light source incidence angle on the sample is also near normal (ϴ = 12 • ). PROMES-CNRS used the SOC 100 HDR hemi-ellipsoid coated with specular gold, for which the light source incidence angle is adjustable from 8 to 80 • and set to 8 • for these measurements. For each participant, there is a partial spectral overlap between UV-VIS-NIR and FT-IR datasets, at least from 2 to 2. 5 μm. A spectral mismatch may be induced during measurements, hypothetically explained by different instrument configurations (integrating sphere, detector type, calibration procedure). This mismatch should be ideally minimal for any sample.
Baseline coupons. An inventory of flat baseline coupons is provided in Table  6 , while typical reflectance spectra are shown in Fig. 3. These baseline coupons are used as reference for the calibration of spectral datasets. All participants expect CIEMAT-DLR (OPAC) used a white diffuse flat coupon (Spectralon, 99% reflectance) for UV-VIS-NIR measurements while a gold diffuse (Infragold, ~94% reflectance) or gold specular flat coupon is used for FT-IR measurements. These coupons are traceable to primary standards. CIEMAT-DLR used custom secondary standards, i. e. a black coated sample for UV-VIS-NIR and a black or solar selective coupon for FT-IR measurements. These secondary standards were calibrated by OMT Solutions, Netherlands  [11]. The selection of a baseline is a subjective decision made by the operator, depending on the sample to be measured.
e. from 0 • to 80 • , in steps of 10 • , as well as 45 • and 75 •. The spectroradiometer-goniometer optical assembly and the pyro-reflectometer were calibrated before the test campaign, in front of a blackbody which temperature is measured with a standard pyrometer. 5  and 7. Both spectral ranges overlap from 2 µm to 2. 5 μm. The spectral resolution is set to 5 nm by linear interpolation of spectral datasets.
Reflectance measurements For all RT and OT reflectance measurements, the sample SDHR (ρ sample,SDHR ) is calculated with a set of spectrophotometer measurements according to (Eq. (1)). First the sample port is left empty for a zeroline or background measurement (I, zero, meas ). A baseline coupon is then measured (I base, meas ). Its reference calibration data is referred as ρ base,calib. The sample coupon is then measured after its temperature is stabilized to the desired setpoint (I sample, meas ). ρ sample,SDHR (λ, θ, T) = I sample,meas (λ, θ, T) -I zero,meas (λ, θ, RT) I base,meas (λ, θ, RT) -I zero,meas (λ, θ, RT). ρ base,calib (1) Assuming opaque samples, the spectral absorptance and spectral emittance are derived according to Kirchhoff's law of thermal radiation (Eq. (  2 )): α(λ)= 1 -ρ(λ); α(λ)= ε(λ) (2).
3. 2 2. 3. MEDIASE setup Spectral measurements for H230 sample coupons recorded by PROMES-CNRS at MEDIASE setup from 620 • C to 805 • C are shown in Fig. 15. Weak temperature dependent spectral shifts are observed, with a lower confidence for datasets obtained below 650 • C due to the applied temperature measurement principle [29]. Local artefacts caused by atmospheric absorption bands are observed around 2. 7, 4. 2 and 5. 5 μm, despite the short range between the detector and the measured sample. These artefacts, caused by residual water vapor and carbon dioxide distort locally the emittance spectrum, which locally reaches values above 100%. The spectral behaviour is consistent for each pair of sample coupons. Spectral profiles are similar to previous measurements in Figs. 10, Figs. 11 and 12 for other experimental setups.