dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The IPSL-CM6 simulations were performed using the HPC resources of TGCC under the allocation  2021-A0100107732  (project  gencmip6 ) provided by  GENCI  ( Grand Equipement National de Calcul Intensif ). This work benefited from the technical support and discussions with several researchers:  Daniel Goll  and  Sébastien Nguyen  at  LSCE (France) ,  Matteo Mastropierro  at  Ca'Foscari University (Italy ),  Jörg Schwinger  at  Bjerknes Centre for Climate Research (Norway) ,  Nico Bauer  at  Potsdam Institute for Climate Impact Research (Germany)  and  Tokuta Yokohata  at  NIES (Japan). This work benefited from State assistance managed by the  National Research Agency in France  under the  Programme d'Investissements d'Avenir  under the reference  ANR-19-MPGA-0008. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under Grant Agreement No.
Data availability The data from the CMIP6 simulations are available from the CMIP6 archive: https://esgfnode. llnl. gov/search/cmip6 58 , the LUH2 data from https://luh. umd. edu/data. shtml  59 , and the IIASA database via https://tntcat. iiasa. ac. at/SspDb/dsd?Action=htmlpage&page= 20  52. The data necessary to reproduce the figures are stored in the Zenodo archive https://zenodo. org/record/7933146.
When our study was performed, shortly after the start of the epidemic in Europe, PCR could be performed in a minority of the patients with suspected COVID-19. In contrast, virological confirmation of suspected COVID-19 is now rapidly available for all patients consulting in the ED. Hence, the excellent prognostic performances of L-POCUS in the population of patients with confirmed COVID-19 could be useful for initial risk-based triage in the ED. Moreover, recent data suggest that "thickening of the pleural lining, may be an important pattern for L-POCUS assessment of the prognosis of COVID-19 patients  [28]. The inclusion of this criterion in a revised version of the score in a future study may improve the risk-stratification performance of L-POCUS for COVID-19 patients. In terms of implementing L-POCUS as a triaging tool in every day clinical practice, we aimed to stratify the result into three risk categories.
Acknowledgments We thank all the team of the  "Maison de la Recherche Clinique" of CHU Angers  and especially  Sandra Merzeau  and  Jean-Marie Chre  ´tien. We also thank all the research team of the  Emergency Department of Angers University Hospital , and especially  Cindy Augereau ,  Chloe Ŕagueneau ,  Clothilde Aubert  and  Barbara Maquin.
Contribution This systematic review included only original material that addressed medical imaging approaches tackled via deep learning in patients with RA. Some secondary literature included feet RA along with hand imaging, as some authors have worked on RA dataset for both hand and feet images. Although these approaches provide information on the early detection of RA, they do not provide apt information about which approach may be used to aid in early detection. This literature review was conducted on December 20, 2021, using four electronic databases (IEEE Xplore, PubMed, Google Scholar, and Science Direct). The following keywords were used in the search strategy (which was identical across all databases): "rheumatoid knee," "medical imaging," "classification," "Deep learning approaches," and "rheumatoid hands.
AI, pattern recognition, and feature extraction techniques are all commonly used to process medical images. A variety of medical imaging techniques are used to deter mine the appropriate course and severity of the disease. These include X-ray imaging, thermal imaging, ultrasound imaging, and MRI. Imaging the interior of a body for clinical examination and medical intervention and a visual representation of the function of particular organs or tissues is the technique and process known as medical imaging (physiology). It is the goal of medical imaging to expose interior structures that are concealed beneath the skin and bones, as well as to detect and cure diseases. Medical imaging also helps to build a normal anatomy and physiology database, which makes it easier to spot problems later in the process.
The third challenge in conducting these comparisons is that there are so many different factors to consider, making comparative data examination difficult. Several types of data were reported, including the number of patients who had radiographic abnormalities in their joints, the number of impacted joints, the level of radiographic damage, and the course of radiographic damage, which summarize the findings of six different studies that included follow-up data. It illustrates the percentage of all patients who had joint erosions as a function of time after the onset of the disease  [69]. 70% of all patients in the six prospective cohorts studied during the early stages of RA acquired erosive disease within the first year or two of disease initiation, according to the findings of the study.
As a result, we may need to build a more efficient algorithm to achieve better outcomes. Researchers in arthritis detection have conducted several studies. However, it continues to be challenging to detect the existence of the disease at an early stage in the data to validate their results. Previous research  [36, 53]  have given no information about their model pipeline. In the study by Hemalatha et al. [40] , there is no information regarding the dataset used. A lot of information is vague. The weakness of such system is that there may not be a difference in the texture feature values of the normal and abnormal images. In the study by Huan et al. [42 there is no mention of methodology or how they have found the results. Dang and Allison  [47]  have not mentioned anything about the time taken.
Research gaps The literature work that has been reviewed in this work holds some research gaps. There are a few prevalent problems in most of the papers: (1) unavailability of good datasets. Some of the authors  [20, 38]  have used very small sample size that is insufficient to prove the actual working of their model. (2) Use of k-fold cross validation technique. k-fold cross validation  [32, 39, 43, 46]  is a technique in which the training algorithm has to be rerun k times from scratch. Thus, the model would be extremely time consuming. (3) Missing data. Some literature have not given sufficient proof.
Potential solutions for RA The reviewed literature on deep learning approaches for detecting RA has highlighted some research gaps that need to be addressed to improve the accuracy and effectiveness of the models. One potential solution is to ensure the availability of good datasets with sufficient sample size to validate the models. Researchers could also consider using alternative cross validation techniques that are less timeconsuming than the k-fold cross validation technique. To address missing data issues, authors could provide more detailed information about their model pipeline and the datasets used. Additionally, researchers could choose appropriate filters that are efficient and effective in reducing noise without compromising on the details of the image. By addressing these research gaps, the deep learning models for detecting RA could be improved, leading to better diagnosis and treatment of the disease.
Discussion of the limitations of possible areas for improvement While there are potential solutions to address the research gaps in the reviewed literature on deep learning approaches for detecting RA, there are also some limitations and possible areas for improvement. First, while increasing the sample size of datasets could improve the accuracy of the models, it may not always be possible due to the limited availability of high-quality data. Therefore, researchers could consider using data augmentation techniques to generate additional training data and improve the robustness of the models. Second, while using alternative cross validation techniques may reduce the training time of the models, it may also result in overfitting and lead to poor generalization performance. Therefore, researchers need to carefully choose the most appropriate cross validation technique that balances the trade-off between computational efficiency and generalization performance.
Third, providing more detailed information about the model pipeline and the datasets used may not always be feasible due to confidentiality and privacy concerns. However, researchers could consider providing a more detailed description of the data preprocessing steps, model architecture, and hyperparameter settings to facilitate reproducibility and transparency. Finally, while choosing appropriate filters is critical for reducing noise and preserving image details, it may also require some domain expertise and knowledge of the imaging modalities. Therefore, researchers could consider collaborating with domain experts and radiologists to choose the most appropriate filters for their specific imaging modality. Overall, while there are limitations and challenges in improving the deep learning models for detecting RA, addressing these issues could lead to more accurate and effective diagnosis and treatment of the disease.
Future scope The development of fully automated method for diagnosing arthritis will be required shortly. Researchers should improve visualization, quantify their findings, and reduce manual interactions. It is necessary to design a non-invasive type diagnosis tool in a hybrid environment that is implemented using advanced programming languages such as Python and Oracle. In order to extract more usable information from the images, it is necessary to upgrade the existing image processing and machine learning techniques. The researchers should concentrate their efforts on producing a device containing a combination of hardware and software packages within a single kit. So, an in-depth investigation into various machine learning and image processing algorithms for detecting and classifying OA and RA has been conducted with integrity.
Confocal laser scanning microscopy Specimens were mounted on stage of confocal microscope TCS SP8 (Leica Microsystems, Vienna, Austria) and images were acquired with a 63× glycerol (N. A. 1. 3) objective in a stack of 1 mm thickness (z-interval of 170 nm). In total, five images from every coverslip were taken randomly. DAPI staining of the nucleus was performed to facilitate orientation in individual cells. Every experiment included different patient fibroblast lines and control fibroblast lines. Additionally, in every experiment negative controls without primary antibody were included. The images were deconvoluted using the standard "Deconvolution Express" algorithm in Huygens Software (version 18. 10; SVI, Hilversum, Netherlands). For image processing, applications like channel selection and color changing were used.
Statistical Analysis Statistical analysis was performed with GraphPad Prism Software 9. 1. 2 (GraphPad, La Jolla, CA, USA). The Wilson-Brown statistical method with a 95% confidence interval (p value < 0. 0001) was performed. A correlation test (Spearman) and receiver operating characteristic curve (ROC) were conducted using Analyse-it software v5. 65. Results with p values < 0. 05 were considered to be significant.
Data treatment and statistical analyses Data processing and statistical analyses were performed using R version 4. 2. 1 (R Development Core Team, 2015)  in Rstudio version 2023. 03. 0 Build 386 (RStudio Team, 2015). Data was screened for outliers before statistical analyses and values that were lower or higher than 2× IQR for each replicate were considered to be outliers due to measurement errors or perturbations (e. g. , when entering the domes, etc. ). The C flux partitioning and gapfilling was performed within the "REddyProc (v. 1. 3. 2)" package  35. Missing values from the N 2 O time series were replaced with the predicted values from a loess regression, with a 0. 05 span. We conducted three distinct but complementary statistical analyses, each aimed at discerning specific aspects of the ecosystem fluxes: (1) an analysis of the treatment effect on weekly averaged fluxes to capture potential differences in temporal dynamics, (2) a per crop analysis of cumulated ecosystem fluxes and (3) a whole crop sequence cumulated analysis including the data from the whole experiment.
Acknowledgements This study benefited from the  CNRS  human and technical resources allocated to the  Ecotrons Research Infrastructure  and from the state allocation " Investissement d' Avenir" AnaEE France   ANR-11-INBS-0001. We thank  Thierry Morvin  and  P. Le Roy  for their support with the soil extraction from the EFELE field experiment, part of the  SOERE-PRO  (network of long-term experiments dedicated to the study of impacts of  organic waste product recycling ) certified by  ALLENVI (Alliance Nationale de Recherche pour l'Environnement ) and integrated as a service of the '' Investment for future ''  infrastructure AnaEE-France , overseen by the  French National Research Agency  ( ANR-11-INBS-0001 ). Special thanks to  Damien Landais  for providing experimental and technical support during the whole experiment.
Introduction Schistosomes are digenean parasitic worms with a complex life cycle and perfectly adapted to both intermediate (mollusk) and definitive host (mammal)  [1, 2]. In humans, the chronic disease is caused by the inflammatory reactions elicited by the eggs trapped in the host tissues, while adult worms can live undisturbed for years in the blood flow  [1, 3]. Many molecular studies flourished after completing of Schistosoma mansoni genome sequencing  [4, 5] ,. The analysis of secreted biomolecules by eggs, adult and juvenile worms highlighted the abundance of a peculiar family of highly variable peptides/proteins, ranging from 7 to 20 kDa, without homologs in the host, called Micro-Exon Gene (MEG) proteins  [6] [7] [8]. The genetic structure of micro-exons is not unique to schistosomes, but proteins annotated in the UniProt database  [9]  (Release 2022_05) as schistosome MEG proteins, as much as 109, do not show relevant homology to other proteins in this database.
MEG 2. 1 isoform 2 corresponds to UniProt ID D7PD76. The synthesized peptide sequences of iso 2a and iso 2b are displayed in purple and petrol green respectively. MEG 2. 1 isoform 3 corresponds to UniProt ID D7PD75. https://doi. org/10. 1371/journal. pone. 0289444. g001 T) and equipped with a triple HCN cryoprobe. Each sample was prepared by dissolving the peptide in 200 μL DMSO-d6 (Eurisotop) to a concentration of 2 mM. The following experiments were recorded: 1 H-1 H TOCSY  [14]  (mixing time = 60 ms, 16 scans), 1 H-1 H NOESY (mixing time = 120 ms, 48 scans), 1 H-13 C edited HSQC  [15]  (8 scans) and sofast 1 H- 15 N HSQC  [16]  (256 scans). 2D data were processed using nmrPipe and nmrDraw  [17]  processing software. Peak assignment and visualization were performed using Sparky  [18]  and Poky  [19]  softwares.
3D structure refinement of short peptides CYANA  [20]  version 2. 1 was used for automatic structure calculations based on assigned spectra ( 1 H-1 H NOESY, 1 H- 13 C HSQC and 1 H-15 N HSQC). The algorithm used by the CYANA program is based on a consistent probabilistic treatment of the NOE assignment process. The goal of the procedure is to reduce ambiguities of NOE assignments that could lead to erroneous distance restraints. The structure calculation is an iterative process over 7 cycles. The same input data are used for all cycles and comprise the assigned chemical shift list, the amino acid sequence of the peptides and a list containing the positions and the volume of the NOE cross peaks measured in the 2D 1 H-1 H NOESY spectrum. Finally, the unambiguous distance restraints are included as input of the structure calculation with simulated annealing by the fast CYANA algorithm developed by Gu ¨ntert et al.
Reconstruction of isoform 1 (25-88) structure The NMR peptide structures (iso 1a, iso 1b, iso 1c and iso 1d) obtained after CYANA treatment were built together with UCSF Chimera software using the default setting Join Models section for C-N peptide bond  [22]. The 4 amino acids FSHC were also added to the N-terminal part of the reconstructed peptide to design the complete sequence without the signal peptide (corresponding to residues 1 to 20). Such reconstructed models were subjected to energy minimization using the Chiron protein structure refinement server  [23]  that minimizes steric clashes in proteins using short discrete molecular dynamics (DMD) simulations until it attains an acceptable clash score. The resulting protein structure has normalized clash score comparable to high-resolution protein structures (< 2.
Molecular dynamics (MD) simulation MD simulations were carried out using the ACEMD3 software  [24]  with the Amberff14SB force field  [25]  and TIP3P water model  [26]. The system was minimized and equilibrated under constant pressure (1 atm) and temperature (300 K) conditions using a time step of 4 fs, a non-bonded cutoff of 9 Å, and particle-mesh Ewald long-range electrostatics with a grid of 52 × 81 × 103 with a spacing of 1 Å. The system was first equilibrated during 10 ns, then a production run of 4 μs was performed. The processing of the trajectory and the secondary structure timeline analyses were processed by using VMD  [27]  and the GROMACS dssp utilities  [28, 29]. The radius of gyration (Rg), the Root Mean Square Deviation (RMSD), and the Root Mean Square Fluctuations (RMSF) parameters were calculated by means of the corresponding GROMACS tools.
Strategy for MEG 2. 1 isoforms 1, 2, and 3 productions The sequence of isoforms 1, 2 and 3 of S. mansoni MEG 2. 1 are displayed in Fig 1. To circumvent the low yield in production, the isoform 1 (25-88) and 3  were chemically synthesized to a minimum of 95% purity in natural abundance by Genosphere Biotechnologies (https://www. genosphere-biotech. com). According to the SignalP database, residues 1-20 of isoforms 1 and 2 are predicted as a putative signal peptide and show a practically complete identity with isoform 3. Unfortunately, the company failed after three trials to synthesize isoform 2. Consequently, we decided to break the sequence of isoform 2 into two shorter chemically synthesized peptides (Fig 1 ): iso 2a (residues V19 to C36) and iso 2b (residues C37 to P52). To prevent any bottleneck in NMR analyses due to crowded signals of the 64 residues, isoform 1 was also divided into four shorter peptides, namely iso 1a (residues D25 to K43), iso 1b (residues G42 to S58), iso 1c (residues S58 to R72) and iso 1d (residues M73 to P88).
DMSO is a mild oxidant and cysteine residues can be in reduced or oxidized state. Effectively, we observe two sets of chemical shift values for Ca/Cb of C81, namely 55. 6/26. 4 ppm for the first set and 52. 3/33. 7 ppm for the second set (Figure  S6  in S1 File). Sharma et al. demonstrated that 13 C chemical shift can discriminate between the reduced and oxidized forms  [39]. Indeed, from a database of 375 Ca and 337 Cb resonances it has been reported that d Ca (S-S) < d Ca (S-H) and d Cb (S-S) > d Cb (S-H). This means that C81b is in an oxidized state whereas C81 is in reduced state. Note that the intensities of the resonances for C81 are lower than for C81b, which indicates a major proportion of the oxidized state that is consistent with the presence of the mild oxidant solvent DMSO. Finally, around 50% of the resonances of isoform 1 (25-88) are entirely absent in all the different spectra despite the use of two different high magnetic fields (14.
MD simulation of isoform 1 (25-88) The structure of isoform 1 (25-88) was reconstructed by assembling the peptide structures of iso 1a, iso 1b, iso 1c and iso 1d with UCSF Chimera software using the default setting Join Models section for C-N peptide bond  [40]  (see Materials and Methods for details). This modeled structure, which is mainly disordered (Fig 6A  , top) , was used as the first structure for the MD simulation run. The structure was first minimized and equilibrated for 10 ns using ACEMD3 software. Then, a production run of 4 μs was performed on the system. During the first 100 ns of simulation, the structure is mainly disordered (Fig 6B ) and gave rise to drastic conformational changes afterwards. Between 100 and 200 ns, we noticed the formation of short helices that encompass residues H23 to K33, S39 to L47, and E50 to G53.
Acknowledgments The authors thank  Dr Francesca Fiorini Tregoue  ¨t for her great help with the recombinant protein expression. We also thank  Dr. Vojtech Vacek  for the pET-22b(+) plasmid preparation and  Lukas Konecny  for the pET SUMO Champion plasmid preparation. We thank  Kristyna Peterkova  for the fruitful discussion.
As the total number of individuals who received the questionnaire is unknown, it is impossible to calculate the response rate for this study. The link included a summary of the study's purpose and instructions on how to fill out the questionnaire. The first page of the questionnaire included a set of dichotomous questions to screen for eligibility and digital informed consent. Participants were then asked about their consent for study participation through a specific dichotomous question (i. e. , Yes vs. No). To prevent participants from looking up the correct responses, a clear statement was provided at the beginning of the survey, asking participants to respond to the questions based on their current knowledge. The survey was anonymous, and no personal data such as name, IP address, email address, or any other unnecessary personal information was requested, saved, or tracked.
Data analysis All the statistical analyses were performed using the statistical software SPSS (Statistical Package for Social Sciences), version 22. 0. The normality of the distribution of knowledge scale variables was checked using the Kolmogorov-Smirnov test (K-S). Categorical variables were reported by frequencies and percentages. The Chi-square test was used to determine the associations between knowledge levels and independent variables including baseline characteristics. Multivariable logistic regression was also carried out on the significant variables in the bivariate analyses/chi-squared test with a p value < 0. 2 to identify the factors associated with the good level of knowledge among the Lebanese population. For all tests, p values < 0. 05 were considered statistically significant.
Patients An initial group of 123 patients with ascites and cirrhosis was recruited prospectively at four centers (Brest, Hyères, Nice, and Monaco) between 2010 and 2014, during a French multicenter study funded by the French national hospital-based clinical research program (forming the PHRC cohort; ClinicalTrials. gov NCT01193426)  [11]. The goal was to define a specific cytokine profile produced during SBP  [11]. Clinical and laboratory data were collected for all patients. Laboratory data included analyses of blood and ascites samples. A second group of 133 patients with ascites and cirrhosis admitted to an intensive care unit (at Saint-Antoine University Medical Center, Paris, France; the Paris cohort) was studied prospectively. The study was approved by an institutional review board (CPP IV Ile de France, Paris, France; reference 2014/04NI) and performed in accordance with the principles of the Declaration of Helsinki.
Statistical Analysis Continuous variables were expressed as the median [interquartile range]. The Shapiro-Wilk test was used to determine whether or not data were normally distributed. Groups were compared using Student's t-test (for normally distributed data) or the Mann-Whitney U-test. Categorical data were assessed using Fisher's exact test. Spearman's test was used to study the correlations between variables. The 95% confidence intervals for the performance indicators and the AUROCs were estimated from 2000 bootstrap replicates (using the pROC package in R software  [14] ). The threshold for statistical significance was set to p < 0. 05. All statistical analyses were performed with R software  [15].