dataesr/scientific-paragraphs-categorization huggingface dataset:
Following the implantation procedure, scans were taken 1 week before the second operation, just after implantation, and every 4 weeks for 8 weeks. Three-dimensional renderings were extracted from the data After a healing period of 5 weeks, the 36 rats in the second step underwent the following procedure: under general anesthesia, the intraperitoneal injection of a mixture of ketamine (90 mg/kg) and xylazine (6 mg/kg) was made in the supine position, under apnea, after locating the right alveolar crest, along with a subperiosteal infiltration of 0. 3 mL of Lidocaine HCL 2%. The initial incision was reopened with a cold blade. Subperiosteal detachment with rugine took place. Silastic sheet was removed. Bone defect was filled with cellularized biomaterial (n = 12), biomaterial alone (n = 12) or left empty (n = 12).
Following the implantation procedure, scans were taken 1 week before the second operation, just after implantation, and every 4 weeks for 8 weeks. Three-dimensional renderings were extracted from the data frames using DataViewers software (Bruker). A global grey value thresholding (55-255) was performed to separate mineralized elements from background noise. Defects and regenerated bone were measured using CT-scan analyzer software (Bruker). A global volume of interest (VOI) in the defect area was set by extrapolating 2D regions of interest over consecutive sections, including the remodeled bone defect area. The following parameters were analyzed: bone volume fraction (bone volume (BV)/tissue volume (TV), in %) and bone mineral density (BMD, in g/cm 3 hydroxyapatite). These parameters were normalized to the volume of bone in the VOI prior to surgery.
Statistical Analysis The statistical analysis was performed using the Prism version 8 software (GraphPad Software, Boston, MA, USA). The data were expressed as mean ± standard deviation. The normality of the data was tested using the Shapiro-Wilk test. The comparison of the means was carried out using the Student's t-test for paired data or the Mann-Whitney test for non-parametric data. The level of significance was set at p < 0. 05.
Phenotype Analysis Three Wharton's jelly samples were collected from 3 different donors and analyzed against BM-MSC samples. Stem cell phenotypes were analyzed by flow cytometry prior to differentiation. All expressed the CD90, C73, CD105, and CD44 markers on their surface. CD19, CD39, CD34 and CD45 were not expressed (data not shown). Three cell adhesion proteins, (CD106, CD146 and CD166) were heterogeneously expressed depending on (i) the cell type and (ii) the donor. While BM-MSCs expressed all three proteins, all WJ-MSCs expressed CD166 on their surface, but only two expressed CD146 and none expressed CD106 (Figure  3 ).
Observaram ainda que níveis pressóricos mais baixos, comuns na CCDC, poderiam ter levado à subutilização de alguns medicamentos nesse contexto. Outro estudo avaliou a proporção de pacientes com CCDC randomizados em dois ensaios clínicos recentes (PARADIGM-HF e ATMOSPHERE), reportando que apenas 7,6% dos pacientes randomizados na América Latina tinham essa etiologia. 592 álise de subgrupo post-hoc do PARADIGM-HF sugeriu que o sacubitril-valsartana, em comparação com o enalapril, poderia levar a redução semelhante ou até maior (37%) de morte e hospitalização em pacientes com CCDC, comparativamente àqueles sem essa etiologia de IC, apesar de ausência de significância estatística e imprecisão de estimativa de efeito. 618 Sob o acrônimo PARACHUTE (ClinicalTrials. gov Identifier: NCT04023227), está em andamento estudo exclusivo de pacientes com IC de etiologia da CCDC.
5 bar). The injector and the detector were at 315 °C and 345 °C, respectively. Liver ceramide, sphingomyelin, sphingosine, and sphinganine were extracted, as previously described  (Barbacini et al. , 2019) , with chloroform/ water/methanol (2. 5:1:5 v/v/v) in the presence of the following internal standards: ceramide d18:1/12:0 (16 ng), sphingomyelin d18:1/12:0 (16 ng), sphingosine 17:0, and sphinganine 17:0 and sphingosine-1-phosphate 17:0. Sphingolipids and internal standards were analyzed by liquid chromatography mass spectrometry (LC-MS) with an Acquity ultra highperformance liquid chromatography (UHPLC) system (Waters, USA) connected to a Time of Flight (LCT Premier XE, Waters, USA) Detector or a triple quadrupole mass spectrometer (Xevo, Waters, USA). The final data were calculated as pmol/mg of protein.
1 H NMR spectra were acquired at 300 K with a standard, onedimensional noesypr1D pulse sequence with water presaturation and a total spin-echo delay (2 ns) of 100 ms. Data were analyzed by applying an exponential window function with a 0. 3-Hz line broadening, prior to Fourier transformation. The resulting spectra were phased, baselinecorrected, and calibrated to TSP (0. 00 ppm) manually with Mnova NMR (version 9. 0; Mestrelab Research S. L. ). The spectra were subsequently imported into MatLab (R2014a; MathWorks, Inc. ). All data were analyzed with the use of full-resolution spectra. The 1 H NMR peak assignments of aqueous phase extracts from liver are presented in Supplementary Table  2. The region containing the water resonance (4. 6-5. 2 ppm) was removed, and the spectra were normalized to the probabilistic quotient  (Dieterle et al.
Gene expression studies Total cellular RNA was extracted with Trizol reagent (Invitrogen). Transcriptome profiles were performed with the Agilent Whole Mouse Genome microarray (4 × 44 K), according to manufacturer instructions. Microarray data and all experimental details are available in the Gene Expression Omnibus Series database (accession number GSE208735; https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE208735). Total RNA samples (2 μg) were reverse-transcribed with the highcapacity cDNA reverse transcription kit (Applied Biosystems), then analyzed with real-time quantitative polymerase chain reaction (qPCR). Primers for the Sybr Green assays are presented in Supplementary Table  1. Amplifications were performed on a Stratagene Mx3005P thermocycler (Agilent Technology). qPCR data were normalized to the endogenous level of proteasome 20S subunit beta 6 messenger RNA (mRNA) and analyzed with LinRegPCR software.
16S rRNA gene sequence analysis 16S rRNA sequences were analyzed with QIIME2version 2019 360  (Bolyen et al. , 2019). Sequences were demultiplexed and quality-filtered with the Dada2 method  (Callahan et al. , 2016). We used QIIME2 default parameters to detect and correct Illumina amplicon sequence data, and a table of Qiime 2 artifacts was generated. Next, a tree was generated with the align-to-tree-mafft-fasttree command, for analyzing phylogenetic diversity. Then, alpha and beta diversity analyses were computed with the coremetrics-phylogenetic command. We constructed principal coordinates analysis (PCoA) plots to assess the variation between experimental groups (beta diversity). To analyze the taxonomy, we assigned features to operational taxonomic units, according to a 99 % threshold of pairwise identity to the Greengenes reference database 13_8.
Acknowledgements L. D. PhD  was supported by the  INRAE Animal Health department. This work was also supported by grants from the  French National Research Agency (ANR)   Fumolip  ( ANR-16-CE21-0003 ) and the  Hepatomics FEDER program of Région Occitanie. We thank  Prof Wentzel C. Gelderblom  for generously providing the FB1 and for his interest and support in our project. B. C. laboratory is supported by a Starting Grant from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement No. ERC-2018-StG-804135 ), a  Chaire d'Excellence  from  IdEx Université de Paris  - ANR-18-IDEX-0001 , an  Innovator Award  from the  Kenneth Rainin Foundation , an  ANR  grant  EMULBIONT ANR-21-CE15-0042-01  and the national program "Microbiote" from  INSERM.
Concurrently, the presence of disease activity despite the use of a DMT mandates the switch to another, preferably more efficacious, drug  [8] [9] [10]. The best strategy (escalation vs induction) in the DMT sequence is still debated, and clinical studies addressing this are underway (clinicaltrials. gov: NCT03535298 and NCT03500328). The therapeutic options for PPMS are currently very limited with ocrelizumab being the only drug with proved efficacy and approval for this indication  [8, 9]. S1PR modulators are a class of oral DMTs for relapsing MS  [12]. In this narrative review, we present their pharmacology, efficacy, safety profile, and risk management based on the results of phase II and phase III clinical trials. We also briefly address their impact on the risk of infections and vaccines efficacy, as well as their potential roles in treating other immune-mediated or nonimmune-mediated conditions.
With this aim, we searched the English language medical literature in the PubMed database up to the 26 th of August 2022 using the search string 'sphingosine 1-phosphate receptor modulators' OR 'S1P receptor modulators' OR 'S1PR modulators' OR 'fingolimod' OR 'FTY720' OR 'siponimod' OR 'BAF312' OR 'ozanimod' OR 'RPC1063' OR 'ponesimod' OR 'ACT-128800. ' Additional sources (References of PubMed articles, Google Scholar, Congresses etc. ) were used for further researching specific topics (such as side effects and impact on vaccine responses). The emphasis of our review is on the S1PR modulators already approved for the treatment of MS by North American and European regulatory agencies, but we also discuss other relevant pharmacological products. The topic is of importance, since fingolimod, the first S1PR modulator that was developed, as well as the first oral DMT approved for adults with relapsing MS, is currently the only oral DMT approved for adolescents and children with relapsing forms of MS  [13] [14] [15] [16].
Cases of varicella-zoster virus (VZV) and herpes simplex virus (HSV) reactivation resulting in meningitis, encephalitis, or meningoencephalitis, as well as cases of fungal meningitis, including cryptococcal, have been reported. A review of cases of cryptococcal meningitis in the Novartis safety database identified until February 2020, 60 case reports (estimated rate 8/100,000 patient-years), of which 13 had a fatal outcome  [59]. Of particular interest is progressive multifocal leukoencephalopathy (PML), a demyelinating.
The identification of kinematic subpopulations is of paramount importance to understanding the biological nature of the sperm heterogeneity. Nowadays, the data of motility parameters obtained by a computer-assisted sperm analysis (CASA) system has been used as input to distinct algorithms to identify kinematic subpopulations. In contrast, the images of the trajectories were depicted only as examples of the patterns of motility in each subpopulation. Here, python code was written to reconstruct the images of trajectories, from their coordinates, then the images of trajectories were used as input to a machine learning clustering algorithm of classification, and the subpopulations were described statistically by the motility parameters. Finally, the images of trajectories in each subpopulation were displayed in a way we called Pollock plots.
4 software  [10]  at 60 frames per second (60 Hz) and 640 x 480 pixels. The image sequences were analyzed with ImageJ version 1. 50d software  [11]  and the CASA_bgm plugin  [12]. We adjusted the settings of the plugin to use with boar sperm  [13]. Briefly, the particle area was adjusted to 7-80 μm, sperm were considered motile if the average velocity path (VAP) >15 μm/s. Similarly to  [14] , the trajectory of each sperm identified and recorded in each field was visually assessed before the track sequence analysis. This consideration was carried out to eliminate possible debris and to diminish the risk of including unclear tracks in the analysis. The values for each of the following motility parameters were recorded for each sperm analyzed: average velocity path (VAP, μm/s), curvilinear velocity (VCL, μm/s), straight line velocity (VSL, μm/s), beat cross frequency (BCF, Hz), linearity (LIN, VSL/VCL), straightness coefficient (STR, VSL/VAP), amplitude of lateral head displacement (ALH, μm), and wobble (WOB, VAP/VCL).
| A computational model to analyze individual images of sperm trajectories (novel approach) The CASA_bgm plugin generates a data sheet with the kinematic values of each sperm, and displays an image where are described the trajectories of all sperm analyzed in a sequence of video  [12]. Additionally, it is possible to save the coordinates of each sperm analyzed. To automate the construction of the datasets for coordinates of sperm and individual kinetic data, we wrote a python script that takes the data from the results sheet as input, then constructed two csv files: one for the coordinates and other for the motility parameters (the python script was named log_motility_data-6. py, and is available at doi:10. 7910/DVN/YI3N4Q). The coordinates file was used as the input of other python scripts, which converted the coordinates to individual images.
The images of trajectories were generated as png images by using matplotlib version 3. 6. We used the image module Pillow Imaging Library to convert each trajectory into binary vector; This module has the option "L" with which the pixels are mapped to their nearest limit either black (0) or white (256)  [15]. We did verify that data were binary, and set up all values as 1; thus, our input data was a set of binary vectors with values either 0 or 1. Thereupon, the individual images were stored into a folder (the script was named trajah-6-full-0. ipynb, and it is available as a Jupyter notebook at doi:10.
| Construction of the datasets and master dataframe The option to print the coordinates of each sperm, of the CASA_bgm plugin  [12] , provided the data in a way complicated to follow. Thus, we modified the code of the plugin. The new version of the BGM plugin permits correctly obtaining the coordinates of each spermatozoon detected, along with kinematic data for each sperm (this plugin was named "CASA_RA," and is available at doi:10. 7910/DVN/YI3N4Q). Two files were constructed with results of the CASA_RA: one for coordinates (named "traj" file, containing 1,634,950 rows) and other for the motility parameters (named "motility parameters," containing data acquisition and computational model in Figure  1 ). The DBSCAN algorithm was discarded because tuning epsilon (perhaps its most important parameter) and running the algorithm through our dataset was time-consuming.
In general, six different images of trajectories were identified; thus, the number of clusters was adjusted to six in the settings of the agglomerative algorithm, because it matches the criteria of technicians. The resulting clustering was consistent (Figure  2 ). After the clustering, the python script created a new column with the identifiers of the clusters (subpopulations) for each row in the F I G U R E 1 Acquisition of data and computational model for clustering of images of trajectories. Boar sperm were treated with different concentrations of ketanserin, then sequences of images were acquired by using a computer-assisted sperm analysis system (pictures enclosed by the red lines). From the sequences of images there were obtained the coordinates and the motility parameters of each sperm (both in a results sheet).
A python script was wrote to split the Results sheet into two sheets, which contained the Motility parameters and the coordinates, respectively. Each sheet of results was iteratively added to a new dataframe. Then, the coordinates from each sperm analyzed were used to construct the individual images of the trajectories; which in turn served as input for the agglomerative algorithm. The individual images of trajectories before and after the clustering are actually a grouped representation of the images constructed from the coordinates. The clustered trajectories (subpopulations) were linked with their associated motility parameters to make the statistical description. [Color figure can be viewed at wileyonlinelibrary. com] original master dataframe. Finally, the statistical description of subpopulations of images of trajectories, in each treatment with ketanserin and time, was made with subsets of the data of motility parameters.
This is due to the algorithm does not look for images but for multiple dimensions given by the pixels composing images of trajectories. Unsupervised learning uses unlabeled input data to find the underlying structure and relationship in a dataset  [23] , and this is a powerful tool for association and clustering tasks  [24]. Since hierarchical clustering is an unsupervised algorithm, the correct output labels are unknown; therefore the performance of clustering cannot be directly measured  [23]. Actually, the performance depends on whether the clusters capture the trends in the data  [25]. These data trends were solved by the technicians, who generally discerned in general six patterns in images of trajectories. The named patterns of motility are the graphical representation of the trajectories of sperm observed during a given period.
Although the dimension time was absent in the analysis of the images of F I G U R E 3 The ketanserin induced changes in the trajectories of sperm. The dataset of images of trajectories served as input for the agglomerative hierarchical algorithm; then, six subpopulations were identified. Identifiers of time of incubation and treatment with ketanserin were used to subsetting the data of sperm in each subpopulation. "Pollock" plots of trajectories of sperm in subpopulation 2, in the distinct time and concentration of ketanserin are indicated. Sperm in subpopulation 2 presented higher values of velocities, and the trajectories in 1000 nM of ketanserin at 30 min look curved like those observed at any concentration of ketanserin at 0 min trajectories, it was partially recovered when the values of the motility parameters were associated with the images of trajectories.
20,805 rows, 11,394 at time 0 and 9411at time 30; both datasets are available at doi:10. 7910/DVN/JDJVBT). The construction of the files was tedious and time-consuming; thus, we wrote a python script to automatize the process and to construct the files iteratively (the python script, log_motility_data-6. py, and instructions of use are available at doi:10. 7910/DVN/YI3N4Q). The files of motility parameters and trajectories were taken by other python script to construct an instance of the class dataframe called "master dataframe. " In the master dataframe the rows corresponded to individual sperm; two columns contained string data for the different concentrations of ketanserin and time, respectively; eight columns contained float values for each of the eight motility parameters evaluated; finally, one column contained the lists of the coordinates.
Microscopic data We obtained 12 muco-perichondral aps which were analyzed under an optical microscope at x25 magni cation. The pedicle width was 13. 33 mm (± 4. 18 mm) (Table. 2). The septal branch of the anterior ethmoidal artery was identi ed in all aps. It was unique and presented a red coloration at least at its origin. Its origin was not included in the ap in only 1 case. This branch ran tangential to the skull base, at 3. 67 mm (± 1. 03 mm) under it, and then joined an anastomotic network with no reproducible pattern extending over the entire ap surface. This network was mainly colored in red (anterior ethmoidal artery dependency) in 2 cases (Fig. 9a ) and it was mainly colored in black (sphenopalatine artery dependency) in 8 cases (Fig. 9b ). The oor mucosa was always found colored in black (sphenopalatine artery dependency).
Fenofibrate, which is metabolized to the PPARα agonist fenofibric acid, is a widely used systemic treatment to lower plasma lipid concentrations. Fenofibrate has successfully proven its protective effect in patients with DR  (27, 28). It has also been shown to reduce systemic inflammation in patients with NPDR, suggesting a potential role of circulating lipids or lipid metabolism in the chronic inflammatory status of patients with T2DM  (64). Fenofibric acid and GW6471, a PPARα antagonist, did not regulate the expression of the DR prototypical cytokine Statistics. GraphPad Prism 8 (GraphPad Software) was used for all graphical representations and for all but the RNA-Seq statistical analysis. A detailed explanation of the specific statistical choices made is provided in the Supplemental Methods.
A P value of less than 0. 05 was considered significant. Study approval. Plasma samples from patients with T2DM and healthy donors were collected from volunteer donors at the Institute of Ophthalmology Foundation Conde de Valenciana (Mexico City, Mexico), in accordance with Declaration of Helsinki principles and with the approval of the Investigation Committee (registry 13 CI 09 015 261, protocol number CI-051-2015); the Research Ethics Committee (registry 13 CEI 09 015 095, protocol number CEI-2015-11-05); and the Biosafety Committee (registry 13 CB 31 050 269, protocol number CB-051-11-2015) (all in Mexico City, Mexico). Individuals provided written informed consent prior to their participation in the study. Data and materials availability. Experimental materials are available upon request with no restrictions.
The RNA-Seq raw fastq files and count files were deposited in the NCBI's Gene Expression Omnibus (GEO) database (GEO GSE239512). Values for all data points in graphs can be found in the Supplemental Supporting Data Values file. Address correspondence to: Guillaume Blot or Xavier Guillonneau, Institut de la Vision 17, rue Moreau 75012, Paris. Phone: 33153462654; Email: guillaume_blot@outlook. com (GB); Email: xavier. guillonneau@inserm. fr (XG). onist  (77). Overall, this suggests that PPAR antagonists could be used locally in addition to systemic PPARα agonist treatment to inhibit the pathological differentiation of local and infiltrating cells in the retina in DR. Study limitations. In this study, we focused on identifying the major class of lipids present in the blood of our cohort of patients.
Acknowledgments Funding for this study was provided by  INSERM  (to FS and XG); the  Agence Nationale pour la Recherche Labex Lifesenses  ( ANR-10-LABEX-65 , to XG);  IHU FOReSIGHT  ( ANR-18-IAHU-01 , to XG);  UNADEV  ( INSERM-UNADEV-22UU146-00 , to XG); and  ED394 -Sorbonne University  (to GB and HCM, salary). We wish to thank the cell culturing facility and  Stéphane Fouquet  of the imaging facility at the  Institut de la Vision  and  Wassila Carpentier  and  Maureen Hayibor ,  Pierre Duquesne ,  Antoine Auvergne , and  Mariana Garcia , who provided technical assistance. Certain elements of the schemes were adapted from  Servier Medical Art , licensed under a Creative Commons Attribution 3. 0 Unported License (https://creativecommons. org/licenses/by/3. 0/). The figure color codes are based on the Vibrant qualitative color scheme of Paul Tol (https://personal.
FT-IR Spectroscopy To further characterize the Moringa leaves polysaccharide, FT-IR spectroscopy was performed. Figure  4  displays all peaks and bands associated with polysaccharides. All typical peaks and bands associated with polysaccharides were reported. The MLWSP spectrum exhibited eight typical signals of polysaccharides at wavenumbers of approximately 3400, 2920, 2840, 1620,1560, 1400, 1100, and 1000. The broad absorption peak at approximately 3400 cm -1 is equivalent to intermolecular or intramolecular induced stretching of hydroxyl (OH) groups, which are always described as a characteristic of polysaccharides  2. 1. 6. Scanning Electron Microscopy (SEM) Figure  5  gives the SEM images of the WSMLP. Our data showed that the MLWSP is composed of 50 to 500 µm geometric shaped units with irregular, rough, and/or rocky surfaces.