dataesr/scientific-paragraphs-categorization huggingface dataset:
Synchrotron X-ray atomic pair distribution function (PDF) measurements Temperature dependent X-ray total scattering data were collected at 28-ID-1 beamline of the National Synchrotron Light Source II (NSLS II) at Brookhaven National Laboratory. Finely ground powders of 1T-TaS 2 were sealed in 1 mm (outer diameter) polyimide capillary (referred to as experiment 1 or exp 1) and 1. 5 mm (outer diameter) quartz capillary (experiment 2 or exp 2) within a glovebox under light vacuum. Measurements were carried out in capillary transmission geometry using a 2D PerkinElmer amorphous silicon area detector (2048 × 2048 pixels with 200 μm 2 pixel size) placed ~204 mm downstream of the sample. The setup utilized a monochromatic X-ray beam with 74. 5 keV energy (λ = 0. 1665 Å). Sample temperature control was achieved using a Cryo Industries of America cryostat (exp 1, 15 K ≤ T ≤ 500 K) and a FMB Oxford Hot Air Blower model GSB1300 (exp 2, 300 K ≤ T ≤ 915 K).
Data for each experiment were collected in 5 K steps using 5 K/min temperature ramp, 2 min thermalization, and 2 minutes data collection at each temperature. In exp1 data in temperature range 15 K ≤ T ≤ 300 K were collected on cooling, while these in temperature range 300 K ≤ T ≤ 500 K were collected on warming and cooling. In exp 2 data were collected on warming.
PDF data processing and analysis Calibrations of the experimental geometry, momentum transfer range, and detector orientation were carried out by utilizing nickel standard measurements performed under the same conditions. Appropriate masking of the beam-stop shadow, inactive and outlier pixels, and subsequent azimuthal integration of the 2D images to obtain 1D diffraction patterns of intensity versus Q data were done using pyFAI software package  68, 69  Standardized corrections to the data for experimental effects to obtain the reduced total scattering structure function, F(Q), and the subsequent sine Fourier transforms to obtain experimental PDFs, G(r), with Q max = 25 Å -1 (exp 1) and Q max = 20 Å -1 (exp 2) were carried out using the PDFgetX3 program within the xPDFsuite software package  70  The PDF analysis was carried out using the PDFgui 71 modeling platform.
Multiphase treatment The fits using only the P 3 phase in the NC regime were noticeably worse compared to these in the C state, with observable increase of fit residual, as shown in inset to Fig. 3c. This is consistent with the presence of discommensuration domain walls and lower SoD density in the NC state whose atomic structure is less distorted and not accounted for in our simple distorted P 3 model. We approximated domain wall contribution by adding an undistorted P 3m structure as a secondary (minority) phase to account for local environment of the discommensuration regions. We utilized this approach to fit the PDF data in the IC and M regimes as well, where the distorted and undistorted phases swap roles and the distorted phase becomes a minority phase.
Acknowledgements We wish to acknowledge  Igor Vaskivskyi ,  Jaka Vodeb  and  Viktor Kabanov  for valuable discussions. Work at  Brookhaven National Laboratory  is supported by the  Office of Basic Energy Sciences, Materials Sciences, and Engineering Division, U. S. Department of Energy (DOE)  under Contract No. DE-SC0012704. This research used beamline 28-ID-1 of the  National Synchrotron Light Source II , a  U. S. Department of Energy (DOE) Office of Science User Facility  operated for the  DOE Office of Science  by  Brookhaven National Laboratory  under Contract No. DE-SC0012704. DM and PS wish to acknowledge funding from  ARRS. The work at the  Jozef Stefan Institute  was supported by the  Slovenian Research Agency  ( P1-0040 ).
g. , athletics, judo, team sports). We analyzed psychological measures (TSK-CF), functional knee assessment, (KOOS), (IKDC), and knee flexor and extensor isokinetic muscle strength (i. e. , limb symmetry index [LSI], peak torque [PT], total work, hamstring-quadriceps ratio [R-HQ], and ratio of total work [R-TW]) at different angular velocities of athletes' post-surgery of ACLR in elite athletes. Based on relevant literature data and similar to the previous studies conducted with female athletes  [2-5, 8, 9, 12-14] , we hypothesized that combined plyometric and eccentric strength training would lead to greater improvements in measures of psychological kinesiophobia status, functional knee assessment and isokinetic muscle strength performance compared with singlemode plyometric or single-mode eccentric training in male athletes with ACLR.
The IKDC score (international knee documentation committee 2000) The IKDC 2000 normative database can be used as a standard for positive patient-reported outcomes  [26]. The original form contained 18 questions, in which total scores are expressed as a percentage from 0-100%, with higher scores representing better knee function and fewer symptoms. The IKDC 2000 is a reliable selfreport with valid outcome measures  [27] , consisting of a 10-question rating converted to a 100-point scale with a 95% confidence interval. This form has been tested on a large number of patients, with various knee disorders, and is valid  [27].
Statistical analysis Data were presented as group means values and standard deviations (SD). Normal distribution of data was assessed, and confirmed, using the Shapiro-Wilk test. One-way analysis of variance (ANOVA) with repeated measures was used to evaluate differences between groups in means differences (MD = operated -non operated legs) ratio peak torque and total work and to evaluate differences between groups in index LSI (ILSI = LSI peak torque or LSI total work -100) peak torque and total work on knee extensors and flexors. Post-hoc comparisons were calculated using the Tukey-HSD test. The effect size (ESs) was calculated for all ANOVA using partial eta-squared (η2). The values of 0. 01, 0. 06, and 0. 15 were considered small, medium, and large cut-off points, respectively  [38, 39].
the grand mean of the specified contrasts. Indeed, post-hoc analyses, while useful, do not necessarily yield sufficient insight into multiple levels or detailing patterns in response; whereas, contrast analysis allows researchers to test theory-driven expectations directly against an empirically derived group or cell means  [40]. Statistical analyses were performed using SPSS (SPSS Inc. , Chicago, IL, version. 20. 0), and significance was accepted, a priori at p < 0. 05.
Thin GO was compared to thicker FLG in order to understand the importance of the carbon to oxygen ratio as well as material thickness in these processes. Two lateral dimensions for each type of GBMs were also tested to evaluate the importance of size. A single exposure scenario was specifically chosen to investigate the potential of the different materials to degrade in cells over 28 days after exposure and track the administered materials in the lungs. We measured the cell loading and intracellular degradation of the selected GBMs using Raman microscopy, in all cells present in the alveolar space in vivo. Using this data set, we could also determine their kinetics of elimination from the alveolar space over the recovery period (from day 1 to 28). Finally, we examined the biological effects in respect to GBMs' physicochemical characteristics and correlated the biological effects with material fate, in order to determine the combination of features leading to the best biocompatibility profile in the lungs.
Immune cell infiltrates and granuloma-like structures were identified, and the bronchial thickness was measured. All the histopathological analyses were performed using CaseViewer (software version 2. 4. 0. 11902, 3DHistech Ltd). Evaluation of GBM Presence and Degradation Using Raman Spectroscopy: To evaluate the distribution of GBMs in lung sections and BAL cells, Raman spectroscopy-based confocal microscopy (XploRA Plus, HORIBA) was performed using GBM specific Raman signatures. Prior to scanning, lung sections embedded in paraffin on glass slides (Superfrost plus, Thermo Fisher Scientific) were dewaxed, whereas ice-cold methanol fixed BAL cells cyto-spun on glass slides were prepared as mentioned above. In both cases, scanning was performed by Raman microscopy at a wavelength of 638 nm and a grating of 600, with 3 μm of distance between each scanning point.
Acknowledgements This project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement nos. 785219  ( H2020-FET-GrapheneCore2 ) and 881603 ( H2020-FET-GrapheneCore3 ). A. F. would like to acknowledge studentship co-funded by the  Lloyds Register Foundation funded International Consortium on Nanotechnology (LRF-ICON ) and the  UK Research and Innovation Engineering and Physical Sciences Research Council  ( UKRI-EPSRC) Centre for Doctoral Training Programme Graphene NOWNANO  ( EP/L01548X/1 ). The authors also thank  Dr Adrian Esteban Arranz  for contributing to the synthesis of the GO materials used in the present study. At the  Faculty of Biology, Medicine and Health , the  University of Manchester, UK , authors would like to thank  P.
Walker  and  G. Balko  ( Histology Facility ),  Dr P. March  ( Bioimaging Facility ),  Dr. N. Hodson  ( BioAFM Facility ), and all staff in the  Biological Services Facility. The microscopes of the Bioimaging and  BioAFM Facilities  used in the present study were purchased with grants from the  UKRI Biotechnology and Biological Sciences Research Council (UKRI-BBSRC) , the  Wellcome Trust , and the  University of Manchester Strategic Fund. This work was also partly supported by the  Agence Nationale de la Recherche (ANR)  through the  LabEx  project  Chemistry of Complex Systems  ( ANR-10-LABX-0026 CSC ). The authors wish to acknowledge the  Centre National de la Recherche Scientifique (CNRS)  and the  International Center for Frontier Research in Chemistry (icFRC). The authors wish to thank  C.
Royer  and  V. Demais  for TEM analyses at the " Plateforme Imagerie In Vitro de l'ITI Neurostra ",  CNRS UAR 3156, University of Strasbourg (Strasbourg, France ). In addition, authors would like to acknowledge the following groups at  ICN2  ( Catalan Institute of Nanoscience and Nanotechnology, Barcelona, Spain ):  Advanced Electronic Materials and Devices  led by  Prof. J. Garrido  for the AFM and Raman instrumentation. The ICN2 is funded by the  CERCA programme, Generalitat de Catalunya  and has been supported by the  Severo Ochoa Centres of Excellence program  [ SEV-2017-0706 ] and is currently supported by the  Severo Ochoa Centres of Excellence program , grant  CEX2021-001214-S , both funded by  MCIN/AEI/10. 13039. 501100011033.
Hormones The sea bass gonadotropin-inhibitory hormone-2 peptide (sbGnih-2, GenBank accession no. LN681205, SPNSTPNMPQRF-NH 2 , Paullada-Salmeroń et al. , 2016a) was synthesized by Metabion International AG (Planegg, Germany). Synthetic peptide was amidated at the C-terminal end, purified by high-performance liquid chromatography (>98% purity) and dissolved in phosphate b u ff e r s a l i n e ( P B S ) 1 x s t e r i l i z e d a c c o r d i n g t o t h e manufacturer's instructions.
Analysis of images and figure production Immunohistochemical and in situ hybridization sections were observed under a Zeiss Imager D2 photomicroscope (Zeiss, Oberkochen, Germany) and images were acquired with an Axiocam 503 camera (Zeiss) and processed using the ZEN software (Zeiss). Plates were assembled using Photoshop CS6 (Adobe Systems, San Jose, CA, USA) without alteration except for light or contrast adjustments. For the precise localization of immunoreactive sbGnih-2 and aromatase-expressing cells, a sea bass brain atlas was used  (Cerda-Reverter et al. , 2001a; Cerda ́-Reverter et al. , 2001b; Cerda-Reverter et al. , 2008) , with some changes in the nomenclature to adapt it to the current neuromeric/prosomeric models of brain organization  (Puelles, 2019).
Behavioral analysis During the reproductive period, thirty pairs of sea bass males were used to test the effect of sbGnih-2 administration on agonistic behavior and aggression levels by using the mirror test. Animals were anesthetized and sbGnih-2 was administered in the third ventricle between 8. 30 and 11:00 h with a single dose of 2 mg dissolved in 10 ml of PBS, which was revealed as the most effective dose after icv injection in our previous study performed in sea bass  (Paullada-Salmeroń et al. , 2016b) , while the control group was injected with PBS alone. Subsequently, animals were placed in the experimental tank fifteen minutes before the behavioral test for recovery. For mirror test and behavioral data analysis, the experimental procedure described by Manciocco and coworkers  (Manciocco et al.
, 2015)  was used with slight modification. Fifteen minutes after administration of sbGnih-2 or vehicle, the behavior of each pair of male sea bass was recorded for 30 minutes using a digital camera (Supplemental Material 1). The position of the camera was defined to allow video coverage of the whole surface of the experimental tank (110 x 70 x 50 cm). The camera was turned on avoiding sudden movements and no evidence of stress behavior (e. g. , no change in swimming movements or change in pigmentation) was observed. All experimental fish pairs were tested out of their home tanks, isolated from the social group, and the behavior of both fishes from each pair was recorded and analyzed (15 pairs, n = 30 for control fish; 15 pairs, n = 30 for sbGnih-2 injected fish). Behavioral analysis was performed using Noldus EthoVision XT software and some parameters defined by Noldus system (proximity, relative movement, mobility, acceleration state, velocity and distance moved, latency to the first event, C-shape bending of the body in front of the mirror) were analyzed.
Data analysis All results are expressed as mean ± standard error of the mean (SEM). Statistical differences in relative gene expression levels, hormonal levels and behavioral response were examined by oneway ANOVA followed by Tukey post hoc test using Statgraphics Centurion XVI software (Statpoint Technologies, Warrenton, VA, USA) and GraphPad Prims 9 Software (San Diego, USA). Before the analysis, datasets were tested for normality and homoscedasticity of variance, and if necessary, were log-or square route-transformed. When data did not fulfil the requirements of the ANOVA, data were analyzed using the Kruskal Wallis non-parametric test. In all cases, statistical significance was established at p<0. 05.
Acknowledgments We would like to thank to staff from the " Planta de Cultivos Marinos" (University of Cadiz)  and from the  Mutualized Aquariology Service of the Oceanological Observatory of Banyulssur-Mer (Sorbonne Universite, Banyuls-sur-Mer, France ) for their assistance in the maintaining and care of experimental animals used in this study. We thank  Dr. Olivier Kah  for his critical reading of the manuscript and useful comments.
Funding Funded by grants from  PAIDI2020-Junta de Andalucıá  (Grant no  P18-RT-5152 ) and  FEDER-UCA  (Grant no  18-107538 ) to JAM-C, and a grant from the  European Union  ( FP7 -INFRASTRUCTURES -2010-1 , Grant no  262280. Ref. OOB-EMBRC-FR-AAP2018-no 2200 ) to GL, LB and JAM-C.
Results of Software Simulations Intending to observe the role of astrocytes in regulating synaptic activity and modulating neuronal firing patterns, several experiments were conducted using the MATLAB environment. First, the neuron-astrocyte interaction model used in this work was simulated in an effort to test its ability to accurately emulate the dynamic behaviour of its biological counterpart. Hence, the experiments demonstrated in Figures  4 5 6 7 aimed to evaluate both spiking and bursting activities in order to consider a wide range of dynamic neuronal behaviors. The set of parameters used for the neuron models is provided in Table  2. In addition, when the tripartite synapse was modeled, various astrocytic parameters were carefully selected in order to explore how neuronal behaviour is attuned.
Results of Hardware Implementation The proposed system was designed to target the FPGA technology, which is reconfigurable hardware suitable for application-specific implementations favoring fast prototyping. The system was developed specifically for the AMD Xilinx Zynq-7000 family of devices in order to achieve a fair comparison to other similar implementations. The design dataflow was based on the ModelSim (simulation purposes) and Xilinx Vivado 2022. 2 (implementation and bitstream generation) using basic parameters. The previous selection was made to avoid providing biased results in favor of the proposed design. The targeted board was a Zynq 7000 ZC702 Evaluation Board (for the device xc7z020clg484-1). Initially, the system was functionally verified in the simulation tool Modelsim to achieve the same functionality as that reported by the Matlab model.
Error Analysis In order to confirm the precision of the proposed digital system, comparisons using the Root Mean Square Error (RMSE) were conducted. RMSE estimates the standard deviation of the residuals; in this case, it calculates how different the results obtained by the suggested implementations are from the results obtained by the original IZH model. To this end, two comparisons were executed in the MATLAB environment, each testing the (V), (U), (G m ), and (S m ) variables. The first comparison was accomplished on the basis of two different implementations using 10. 10 and 16. 16 fixed-point representation. The first implementation was derived from  [13] , and the second was implemented for the purposes of the current work. These two hardware implementations were compared to the IZH model.
07%. Concerning the accuracy of our system, 10 bits for the integer part and 10 bits for the decimal were chosen as optimal, as after the conduction of error analysis this type of representation was considered the most efficient. In order to verify the accuracy of our hardware design and test the initial hypothesis, the output signals obtained by the FPGA implementation were validated based on the results produced by MATLAB software. These two different kinds of simulations generated almost the same values, taking into consideration the errors due to the different arithmetical representations. Based on these comparisons, it is evident that the proposed neuronastrocyte architecture generates exactly the same results for the variables being modeled by the equations for both behaviors (i. e. , tonic bursting and tonic spiking activity).
Methods The CarinaNet model was constructed by applying transfer learning to the RetinaNet model using an internal dataset of ICU chest radiographs. The accuracy of the model in predicting the position of the ETT tip and carina was externally validated using a dataset of 200 images extracted from the MIMIC-CXR database. Uncertainty quantification was performed using the level of confidence in the ETT-carina distance prediction. Segmentation of the ETT was carried out using edge detection and pixel clustering.
Study design The CarinaNet model was constructed by applying transfer learning to the RetinaNet model using an internal dataset composed exclusively of unselected ICU chest radiographs. This dataset called RadioICU was created especially for this work. The accuracy of the CarinaNet model in predicting the position of the ETT tip and carina on ICU chest radiographs was externally validated using a dataset extracted from the MIMIC-CXR database, a US multicenter database of chest radiographs  [15]. Uncertainty quantification was performed using the level of confidence in the ETT-carina distance prediction. Segmentation of the ETT was carried out using edge detection and pixel clustering. In accordance with the Consort-AI guidelines  [16] , the model code and weights are available at https://github.
Radio ICU dataset The RadioICU dataset was composed of all consecutive chest radiographs performed in the polyvalent ICU of Reunion Island University Hospital between 01/01/2016 and 12/31/2019. All radiographs were acquired using a Siemens (München, Germany) Mobilett XP Hybrid Analog Imager with Fuji (Tokyo, Japan) radio luminescent memory screens. Chest radiographs showing non-intubated patients were retained. Those showing intubation through tracheostomy were dropped from the dataset. Radiographs were converted from their original DICOM format to 8-bit PNG images and the PixelSpacing attribute was extracted for the conversion from pixel to centimeters. The mean resolution was 3. 0 ± 0. 5 megapixels. The images were anonymized and did not contain burned-in text. Images were independently annotated by two experienced intensivists (JA or AR), over a predetermined 3-day period.
MIMIC-CXR dataset The CarinaNet model was externally validated using a dataset extracted from the MIMIC-CXR database, a US multicenter database of chest radiographs. This external test set allowed to evaluate the generalizability of the CarinaNet model  [15]. The MIMIC-CXR dataset was created by randomly selecting 200 chest radiographs featuring an ETT from the MIMIC-CXR database. Radiographs were selected without consideration for image quality or medical report content. Unlike the RadioICU dataset, the MIMIC-CXR dataset contained images with burned-in text. The MIMIC-CXR dataset had higher resolution than the RadioICU dataset images. On a given day ( t 0 ), the chest radiographs of the MIMIC-CXR dataset were independently annotated by two experienced intensivists (NA and JA). Ten days later ( t 10 ), the chest radiographs of the MIMIC-CXR dataset were shuffled and given back to one of the two intensivists (JA) for a second blinded annotation.
The CarinaNet model: a deep learning model for the automated assessment of endotracheal tube placement on chest radiographs The CarinaNet model was constructed by applying transfer learning to the RetinaNet model, an object detection model developed by Facebook AI Research (New York, USA)  [18]. The RetinaNet model has shown state-of-the-art performance on the Common Objects in Context dataset which consists in the detection of specific objects in photographs of everyday life  [19]. It has also been used with great success in aerial imagery  [20, 21] , and medical imaging (computed tomography scans  [22] , ultrasounds  [23] , chest radiographs  [24] , etc. ). The RetinaNet architecture combines a ResNet architecture with a Feature Pyramid Network that outputs a series of feature maps at different spatial scales  [25].
Annotation of chest radiographs RadioICU dataset The RadioICU dataset was composed of 1,890 separate radiographs, 1,357 of which had annotations for both the (3)    1 0 -1. 1 0 -1    10×3 ETT and carina. The clinicians rated 12. 9% of the radiographs as acceptable quality, 70. 1% as poor quality, 15. 6% as hardly visible carina, and 1. 3% as barely readable. Clinicians generally classified the position of the ETT tip as too low when the ETT-carina distance was less than 2 cm (Fig. 4 ).
Mimic-CXR dataset The interrater agreement and intrarater agreement obtained from the annotation of 200 chest radiographs are given in Table  1. The intrarater agreement was slightly lower than the interrater agreement. The latter was used as the reference for human accuracy in interpreting chest radiographs in clinical practice. Table  1  indicates that the readings of the ETT tip and carina positions from the same intensivist (JA) at a 10-day interval were not statistically different, whereas the readings from two separate intensivists (NA and JA) were statistically different.