dataesr/scientific-paragraphs-categorization huggingface dataset:
Though it presented a low sensitivity, our experimental model demonstrated that HRV indexes multivariate analysis showed good specificity, PPV and NPV to distinguished pH ≤ 7. 10 and pH > 7. 10. This experimental study confirmed the results from a study performed on human fetuses' CTG records where Gatellier et al. demonstrated that a multivariate model including FSI, STV and LTV allowed better discrimination than the FIGO classification with ROC AUC = 0. 713 and 0. 569 respectively  43. However, even if it performed better than FIGO, this model presented a low sensitivity/specificity. Any interpretation of our results must consider the low number of events in the "acidosis" group. It would be interesting to test the various models on a larger dataset and to use more complex ML models. However, animal experimentation, limited in the number of animals because of ethical considerations, does not lend itself well to the realization of large-scale tests.
Acknowledgments The authors would like to express their sincerest gratitude to all the partners and participants of the  GATEKEEPER  project. The  GATEKEEPER  project team comprises  Alexandra Bargiota ,  Clio Yuen- man  Cheng ,  Ioanna Drympeta ,  Eugenio Gaeta ,  Kai Gand ,  Alba Gallego ,  Eleni Georga ,  Germán Gutierrez ,  Piotr Klimczak ,  Maria Krini ,  Pawel Lewek ,  Francisco Lupiáñez-Villanueva ,  Alessia Maccaro ,  Ersi Papayianni ,  Davide Piaggio ,  Francesco Ricciardi ,  Manuel Sanchez-de-la-Torre , and  Maria Vlachava. This project has received funding from the  European Union 's  Horizon 2020 research and innovation program  (under grant  857223 ), cofunded by the  European Regional Development Fund (ERDF) , "A way to make Europe. " JdB acknowledges receiving financial support from  Instituto de Salud Carlos III (ISCIII ;  Miguel Servet   2019 :  CP19/00108 ), co-funded by the  European Social Fund (ESF) , " Investing in your future.
Data collection A total of one medical doctor in pediatrics rehabilitation and five physiotherapists participated in the study. Among this population, only one physiotherapist had no clinical activity at the moment of the interview, but had clinical experience. Three of the physiotherapists had participated in school scoliosis screening campaigns during their career as evaluators or organizers. The medical doctor in pediatrics rehabilitation had an expensive knowledge about spine pathologies. The interviewees had a mean of 14±12 years of experience. The interviews were conducted between January and March 2021, by videoconference. The interviews lasted 31 minutes in average, with a minimum of 25 and a maximum 35 minutes. The categories identified after transcripts analysis were closely related to the themes of the guide.
These concepts will lead to the following requirements: • Low physical and software setup time • Low capture and data treatment time • Small learning curve In a more general way, healthcare professional expects a new tool to integrates as well as possible in the already existing workflow, for example an already existing patient database: • Good integration in the already existing workflow If a lack of precision is sometimes a hindrance to explore a specific clinical or research question, the most concerning point for health professionals is the available tools' reliability. Many sources can influence reliability: the tool itself, the recommended methodology or the observer and his training. Some professionals doubt themselves when conducting a measurement, or feel like they're more efficient after they accumulated some experience.
The Epionics spine device is able to provide excellent data about the range of motion of the spine but is not suited to screenings, as it necessitates to equip the patient with the system. A laser-triangulation system was able to identify the spine on a capture of the back but the system is complex to install and control. Each system answered to a part of the specifications, but the choice was finally a solution based on a depth camera, as it answers most of the defined requirements. The depth camera can be mounted on a tripod to be placed anywhere at the right height, and only necessitates a USB 3. 0 to be connected to a computer. Once connected, the camera can capture an FBT without additional times, except for the start and the end of the recording, answering time-related requirements. Depth camera are easily accessible technically and financially, with models such as the Microsoft Kinect.
Such cameras can capture the patient back without needing any instrumentation or preparation  [28]. For the volume needed, the camera must be placed at about 0. 8m from the patient. Research papers showed that this technology could measure the back surface with a good precision and could extract interesting anatomical landmarks  [25, 26, 27]. Considering captures and measurements, the Kinect V2 camera can capture depth image at a rate of 30 frame per second, with a depth resolution of 512x424 pixels and a depth precision of a few millimetres. The available bibliography notably showed that a depth camera has a higher precision than a clinician palpation  [27]. Thus, using this depth image to obtain significant data with an improved precision compared to the tools available to health professionals was considered possible.
Software The software developed to process and analyze the Kinect data was based on the work of Bonnet et al. [27]  and Xu et al. [28]. In these papers, researchers were able to extract anatomical landmarks from the back, and notably the spine. The workflow used to obtain this result is detailed in Figure  3. The first step is to obtain data from the Kinect, by connecting it to computer via USB. The depth data is then translated to a specific format to make it exploitable by the Point Cloud Library (PCL)  [29]. In this format, each pixel contains x, y and z coordinates, in meter. The region of the interest is the back of the patient, so it is the only region that will be kept for further analysis, after the background suppression. For this step, every point with a Z coordinate value higher than 1,5m was deleted, and a band of a width of 0.
[28] , for every frame and with a similar precision of 2-3 degrees. The available data would also be used to measure the orientation of the ribcage, similarly to a scoliometer, for every slice. This would give information on the whole back instead of on a few chosen points, and would give information at different level of inclination of the patient, instead at only the lower point of the FBT. It is hypothesized that it could be used to screen with a higher precision.
Objectives: To describe the 3-month prevalence and correlates of self-reported physical health conditions in persons with spinal cord injury (SCI) worldwide. Study design: Multinational cross-sectional survey. Subjects: Community-living persons with traumatic or non-traumatic SCI aged >18 years from 21 countries representing all the 6 World Health Organization regions. Methods: The study used data from 11,058 participants in the International SCI Community Survey (InSCI). The survey, based on the International Classification of Functioning, Disability and Health (ICF) Core Sets for SCI, was conducted in 2017-19 simultaneously in the participating countries. The health conditions were reported on a modified version of the SCI Secondary Conditions Scale. Results: Overall, 95. 8% of the participants reported having experienced 1 or more health problems secondary to SCI.
Study design and setting The International SCI Community Survey (InSCI) is a cross-sectional study comprising data from 22 countries worldwide representing all the 6 WHO regions  (14). The participating countries per WHO region were: Africa (South Africa); Americas ( Brazil and USA); Eastern Mediterranean (Morocco); Europe (France, Germany, Greece, Italy, Lithuania, the Netherlands, Norway, Poland, Romania, Spain, and Switzerland); South East Asia (Indonesia and Thailand); Western Pacific (Australia, China, Japan, Malaysia, and South Korea). The InSCI study allowed for using either random or convenience sampling frames, based on local conditions  (15). Random sampling based on predefined sampling frames was applied in 7 of the included countries with access to hospital or patient organization databases.
Participants Eligible for the study were individuals with traumatic or non-traumatic SCI (including cauda equina syndrome) who were aged 18 years or older, living in the community, being able to respond in 1 of the available language versions of the questionnaire, provided informed consent, and were residents in 1 of the participating countries. Those with congenital aetiologies (e. g. spina bifida), neurodegenerative disorders (e. g. multiple sclerosis, amyotrophic lateral sclerosis), or peripheral nerve damage (e. g. Guillain-Barré syndrome) were excluded  (15). The total InSCI-sample comprises 12,591 participants from 22 countries. The current study, however, included data from only 11,058 participants, as 1 country (Switzerland) used different response options from the other countries for the health conditions outcome (see below).
Measures The InSCI survey comprised a 125-item self-report questionnaire covering components of body function and structures, activities and participation, environmental and personal factors, lesion characteristics, and appraisal of health and well-being. The InSCI questionnaire was translated into national languages, in which the requisite cross-cultural adaptation process was based on the guidelines proposed by Epstein et al. (16). The current study used data on body function, namely health conditions, and demographic and lesion characteristics. A comprehensive description of the components of the questionnaire and the InSCI data model have been presented previously  (15, 17).
Statistical analyses Statistical analyses were conducted using SPSS v26. 0 (IBM Corp. Armonk, New York, United States). The level of statistical significance was set at p = 0. 05. Descriptive statistics (mean and standard deviation (SD) or median and quartiles), and frequency distributions (n and percentage) are used to describe the study population, e. g. demographics and SCI characteristics, and the outcomes. The 3-month prevalence measures are presented as proportions with 95% confidence intervals (95% CI). Differences in prevalence of self-reported health problems across socio-demographic and SCI variables were evaluated using cross-tabulations with Pearson χ 2 tests, and with Bonferroni-corrections when performing pairwise multiple comparisons of proportions. Prevalence of health conditions were based on the reporting on the 5-point NRS from 1 (no problem) to 5 (extreme problem).
Methodological considerations This study represents the largest and most comprehensive data-set on health conditions among communityliving persons with SCI worldwide. Nevertheless, caution should be exercised when interpreting the current results, as there are certain study limitations to be considered. First, the self-reported data on health conditions were not verified by medical professionals, which may have introduced reporting bias. Secondly, the variation in the sampling frame might have introduced selection bias. The InSCI study was planned as a cross-country comparison study, and as such population-based random samples would be ideal. This was, however, feasible in only 7 of the countries, while most of the countries had to rely on convenience sampling, mainly due to lack of available and updated databases.
ACKNOWLEDGEMENTS This study is based on data from the  International Spinal Cord Injury (InSCI) Community Survey , providing the evidence for the  Learning Health System for Spinal Cord Injury (LHS-SCI , see Am J Phys Med Rehabil 25 2017;96(Suppl): S23-S34 ). The  LHS-SCI  is an effort to implement the recommendations described in the WHO report  International Perspectives on Spinal Cord Injury  (Bickenbach J et al. Geneva: WHO Press; 2013). The members of the  InSCI Steering Committee  are:  Julia Patrick Engkasan  (ISPRM representative; Malaysia),  James Middleton  (ISCoS representative; Member Scientific Committee; Australia),  Gerold Stucki  (Chair Scientific Committee; Switzerland),  Mirjam Brach  ( Representative Coordinating Institute; Switzerland ),  Jerome Bickenbach  (Member Scientific Committee; Switzerland),  Christine Fekete  (Member Scientific Committee; Switzerland),  Christine Thyrian  ( Representative Study Center; Switzerland ),  Linamara Battistella  (Brazil),  Jianan Li  (China),  Brigitte Perrouin-Verbe (France) ,  Christoph Gutenbrunner  (Member Scientific Committee; Germany),  Christina-Anastasia Rapidi (Greece), Luh Karunia Wahyuni (Indonesia) ,  Mauro Zampolini  (Italy),  Eiichi Saitoh  (Japan),  Bum Suk Lee (Korea), Alvydas Juocevicius (Lithuania), Nazirah Hasnan (Malaysia), Abderrazak Hajjioui (Morocco) ,  Marcel W.
2. 2)  (Stanke et al. , 2006)  was selected. Before gene prediction with AUGUSTUS, we collected CHO cDNA sequences and performed RNA transcript assembly from RNA-seq data (Supporting Information: Supplemental Table  4 ) that was used to train the AUGUSTUS gene models. This refined AUGUSTUS (version 3. 2. 2) gene model was then used to predict genes at a genome scale in the CHOZN ® GS -/-assembly v2. 3, by integrating extrinsic evidence from RNA-seq data. The final gene set includes 20,414 genes. The predicted genes were then annotated using tools including InterPro, pfam, gene ontology (GO), and KEGG pathway. To examine how well our predicted genes are conserved in the mouse genome, we employed OrthoMCL (L. Li et al. , 2003)  and mutual best hits to identify orthologous genes in the mouse genome with stringent criteria.
Several pieces of evidence suggest that there are significant differences between the genome of C. griseus and CHO cells as well as important differences between the diverse CHO host cell lines. For example, at the chromosome level, spectral karyotyping suggests there are significant variations in the chromosome content of C. griseus, CHO-S and CHO-K1 host cell lines  (Vcelar et al. , 2018). Likewise, whole-genome sequencing indicates that while C. griseus is largely a diploid organism, there is significant gene copy number variability amongst different CHO host cell lines  (Kaas et al. , 2015). As a result of this genomic uncertainty, it has been more difficult to develop large-scale 'omics tools that will be useful across the entire bioprocessing industry, including accurate genomic databases as well as large scale genome editing resources.
, 2015)  resulting in v1. 0 of the CHOZN ® GS -/-genome. HiRise assembler (https://github. com/DovetailGenomics/HiRise_July2015_GR) was used to improve assembly v1. 0 by integration with the reads from Chicago and Hi-C libraries to obtain the CHOZN ® GS -/-genome version 2. 3. 4. 4 | CHOZN ® GS -/-RNA-seq transcriptome assembly A de novo CHOZN ® GS -/-RNA-seq assembly was performed using Trinity RNA-seq de novo assembler based on a single Illumina pairedend sequencing reads  (Grabherr et al. , 2011). The raw reads were first subjected to a quality check using FastQC to estimate the overall read quality. Next read correction was performed using Rcorrector, which is a k-mer-based error correction method for Illumina RNA-seq reads  (Song & Florea, 2015). Once the reads were corrected, a custom python script, FilterUncorrectablePEfastq.
py (https://github. com/harvardinformatics/TranscriptomeAssemblyTools) was used to further remove reads which were labeled as 'unfixable' from Rcorrector. In addition, the sequence header format was corrected to avoid any issues in downstream analysis. FASTX-Toolkit was used for quality trimming at the 3′ end of the reads, in which, the bases were removed if the quality was less than a phred score of 30. Based on the corrected and quality trimmed reads, the coverage was estimated around 55x. These corrected and quality trimmed reads were then used for de novo transcriptome assembly using Trinity with CuffFly option. The transcriptome assembly quality was further assessed using rnaQUAST (version 1. 4. 0) (Bushmanova et al. , 2016).
| Genome-scale gene prediction and annotation The genes in the assembled CHOZN ® GS -/-genome v2. 3 were predicted using AUGUSTUS (version 3. 2. 2) eukaryotic gene finding tool using two independent training and prediction steps  (Stanke et al. , 2006). During AUGUSTUS training, a gene training set was created using PASA by aligning the assembled transcriptome assembly with the assembled genomic scaffolds, and a collection of training sets with defined gene structures. The training set was used to learn gene HMM model parameters. The refined gene models were then used to predict CHOZN ® GS -/-genes, in which assembled RNA transcripts serves as extrinsic evidence to improve dictions. InterPro, pfam, KEGG, and GO were utilized to perform genomewide annotations. Two approaches were used to identify orthologs between CHOZN ® GS -/-and mouse genes including OrthoMCL (version 2.
The IgG samples were purified using immobilized Protein-A resin, washed with 20 mM citrate, 150 mM NaCl, pH 7 buffer, and eluted with 25 mM citrate buffer, pH 3. N-glycan analysis was performed using UHPLC-FLR-MS based detection where glycans are removed using PNGase F and labeled with procainamide. A BIOshell Glycan column was used for separation (15 x 2. 1 x 2. 7 µm), fluorescence signal collected using excitation at 308 nm with emission at 359 nm, and MS and MS2 data collected using a Thermo Q-Exactive Plus Mass Spectrometer. Distribution of charge variants was determined by imaged capillary isoelectric focusing (ICIEF) using the ICE3 system by (ProteinSimple) according to the manufacturer's instructions.
We used a dataset of ( λ j , J j ) points generated by combination of set of λ values ranging from 0. 5 to 2. 0 with a set of J values ranging from (0. 5) to (2. 0), where J = ( λ) represents the analytical relationship between λ and J for a Neo-Hookean material under uniaxial stress. Ten sample values are extracted for each variable, and therefore the size of the entire ( λ j , J j ) dataset is equal to 100. For each point of this dataset, we calculated the related energies stored by a Hy-perMSM and a Neo-Hookean element model respectively. The former energy is noted W H j and combines both the springs energies expressed in  (12)  and the volumetric energy expressed in  (18) , and the latter one is noted W N j and it is calculated by multiplying the energy density function given in (1) by the element's volume.
Finally, we defined an objective minimizing function consisting of the weighted mean-squared-error of our model's calculated energies with respect to Neo-Hookean ones as follows: ∅ ( α 1 , α 2 ) = 1 N N j=1 ω i W H j λ j , J j , α 1 , α 2 -W N i λ j , J j 2 ( 23 ) where ω j is the weight assigned to each dataset point and it is equal to W N j ( J, λ) / W N j ( J, λ) ∅ ( a 1 , a 2 ) is a quadratic function in terms of both α 1 and α 2 and therefore minimizing it is straightforward. This optimization problem tries to fit the analytical Neo-Hookean energy densities with the energy densities of our model. It depends on material properties (Neo-Hookean constants and their equivalent linear elasticity constants) but it is independent of the size of the model. The function in  (23)  considers the energy densities, the stretching ratios and the volumetric ratios which are all normalized with respect to the size.
Implementation Our simulation environment was developed by using C ++ programming language. We used OpenGL for 3D rendering and the Eigen library (eigen. tuxfamily. org) for the linear algebra while solving the minimization problem of (4) according to  (5). Since we are using the Projective Dynamics as time integration solver, the time-step h should be constant throughout the simulation. The user interface provides the possibility to tune this parameter to set the simulation speed. Note that a real time simulation is achieved when h is equal to the average real calculation time of each simulation step. In the present implementation, no parallelization of the code has been applied. As for the model damping, we implemented the Laplacian damping proposed in  [42]  for the projective dynamics solver.
e. , they are very dependent on how we choose the ( λ j , J j ) dataset. In addition, beside its lower accuracy compared to the hexahedral model, the tetrahedral model sometimes shows undesirable wrinkles with very large deformations that sometimes lead to model collapse. This is a common drawback for the tetrahedral mass-spring models. Fortunately, the volume preservation constraint prevents this to happen most of the time and helps the model to recover in case if it happens. Our model inherits the advantages as well as the drawbacks of the Projective Dynamics. PD provides a constant approximation of the Hessian matrix of the minimization problem  (5). This matrix can be pre-factorized to avoid solving the linear system from scratch at each iteration. However, this requires constant system structure and constant physical parameters.
Data collection 2. 4. 1. Vegetation surveys in donor and reference grasslands In the donor grassland, vegetation surveys were conducted on 29 June 2018, and the occurrence of mature seeds was evaluated for each plant species at the harvest date. Surveys were used to establish a list of 37 potentially transferred species (hereafter: transferred species). The vegetation surveys of the eighteen potential reference sites were conducted between 21 June and 5 July 2018 by visual estimation of plant cover for each species in quadrats of 2 m × 2 m. In the donor grassland, five quadrats were sampled, and three in each of the other potential reference grasslands. Additionally, we monitored seed maturity in each plant species at the day of harvest.
Data analysis We used plant abundance, cover and richness of the entire plant community and of transferred species to run statistical tests and to calculate restoration indices. Major soil and microclimatic variables of restoration sites and potential reference sites were fitted to multivariate models (NMDS) to identify the best references of each restoration site. The identification was based on dissimilarity coefficients using Euclidean distances and is detailed in  Durbecq et al. , (2020). We compared restoration sites and these best reference sites using the restoration indices CSIInorm (normalized Community Structure Integrity Index) and HAI (Higher Abundance Index). The CSII allows measuring which proportion of the species abundance of the reference communities is represented in the restored communities, and the HAI evaluates the proportions of the species abundance in the restored communities that are higher than in the reference communities  (Jaunatre et al.
SapS Is Involved in Intramacrophage Survival of S. aureus Considering how crucial the previously identified phosphatases PtpA and PtpB are to the capacity of S. aureus to survive inside macrophages  [16, 27] , we hypothesised that SapS, which is highly conserved in S. aureus (Figure  S1 ), might have a similar impact. An in silico investigation yielded that until 5 November 2022, the sapS encoding gene of S. aureus isolate SA564 exhibited 100% coverage and 100% identity with all S. aureus genomes sequences by blast against NCBI GenBank database, represented by the 100 hitblast strains deposited previously on NCBI GenBank database. Phylogenetic analysis of the identified sapS complete sequence with the most hit-blasts including the S. aureus USA300 strain and other Staphylococcus species obtained by blast after exclusion of the S.