dataesr/scientific-paragraphs-categorization huggingface dataset:
This technique could be in principle applied to all the Virgo cluster members cataloged in the NGVS to select a complete sample of galaxies with a high spin parameter. The large number of objects (3689), however, makes this approach prohibitive given the heavy data reduction procedure and the computational time necessary for the fits. Past works clearly link galaxies selected to be LSB to large spin parameters  (Boissier et al. 2003a (Boissier et al. , 2016)). For dwarf galaxies, however, the surface brightness itself does not necessarily indicates high spin parameters. To distinguish especially extended galaxies from regular dwarfs, different selection criteria have been proposed in the literature (such as the Lim et al. 2020 criteria adopted in the main body of the paper). van Dokkum et al.
5. 3 and Sect. 6. 6 with redder galaxies and longer t rps in the center. The best-fit values (linear regression) and the Spearman correlation coefficients are given in Table  B. 1. Eight galaxies are detected in HI by ALFALFA (one in common with the UDG and diffuse galaxy sample). Distribution of the V C , λ, and t rps parameters for the sample selected using a surface-brightness and size cut similar to that of  Koda et al. (2015). The median values corresponding to each parameter are shown within each panel. NGVS 3543   Notes. (1) ID of the source. (2) Last detected radius in the g band above 3σ sky level. (3-7) u, g, r, i, and z band magnitudes. (8-9) GALEX NUV and FUV magnitudes. The upper limits (3σ) in the broadband magnitudes are denoted with > symbol. The sources with no GALEX UV data are marked with the "-" symbol.
Acknowledgements. We are grateful to the whole CFHT team who assisted us in the preparation and in the execution of the observations and in the calibration and data reduction:  Todd Burdullis ,  Daniel Devost ,  Bill Mahoney ,  Nadine Manset ,  Andreea Petric ,  Simon Prunet ,  Kanoa Withington. We acknowledge financial support from " Programme National de Cosmologie and Galaxies" (PNCG)  funded by  CNRS/INSU-IN2P3-INP ,  CEA  and  CNES, France , and from  "Projet International de Coopération Scientifique" (PICS)  with  Canada  funded by the  CNRS, France. J. and K. M. are also grateful for the support from the  Polish National Science Centre  via grant  UMO-2018/30/E/ST9/00082. A. L. is supported by  Fondazione Cariplo , grant No  2018-2329. Studies on UDGs are gratefully supported within a  French-Austrian cooperation project  under grants no.
All synthesized compounds and reference ligands were analytically characterized by 1 H NMR, 13 C NMR, MS, and HPLC purity determination. All data are shown in S1-S5 in the Supplementary Materials. The chemical stability of two CRBN targeting PROTACs (CRBN_1b and CRBN_1e as examples) was tested under assay condition using HPLC. We used a non-enzymatic stability test in assay medium at 37 • C (see S6, Supplementary Materials) and observed that both CRBN PROTACs tested were stable at 6 h (100. 0% and 91. 6%) and moderately stable at 24 h (91. 6% and 65. 5%). This is in agreement with the reported stability of other CRBN-based degraders  [20, 21].
Predicted AUC ∆t were determined based on predicted individual PK profiles for each tissue, while observed AUC ∆t were calculated using the linear trapezoidal rule (Phoenix WinNonlin version 6. 2; Certara USA, Inc. , Princeton, NJ, USA). A minimal PBPK modelling approach was used to describe the concentration-time profiles of metronidazole in plasma and CNS. In each patient, two individual unbound fraction values (fu) of metronidazole were calculated as the ratio of metronidazole concentrations in ultrafiltrates to corresponding total plasma concentrations. The mean value was used to convert total concentrations into unbound concentrations. The blood to plasma (B/P) ratio of metronidazole was estimated using the software tools PK-Sim (version 10. 0, Open Systems Pharmacology, Bayer Technology Services, Leverkusen, Germany) based on fu, log P and haematocrit, as defined by Rodgers and Rowland [45] (Table  4 ) and used to convert measured unbound plasma concentrations into unbound blood concentrations in the model.
• Metronidazole quantification Metronidazole concentrations in HBSS were determined by high-pressure liquid chromatography with UV detection, as previously described for plasma samples  [30]. The chromatographic system consisted of an XTERRA MS C18 (3. 9 mm × 150 mm; 5 µm) column, a SHIMADZU HPLC chromatography system equipped with two LC-20ADxr pumps, a SIL-20ACHT autosampler and an SPD-20A UV detector set at 310 nm (Shimadzu France, Marne-La-Vallée, France). The data were recorded and analysed on a LabSolutions acquisition station v5. 98. The mobile phase consisted of a solution of 0. 01M KH2PO4 mixed with acetonitrile (86/14 (vol/vol)) at a flow rate of 0. 4 mL/min. A total of 25 microliters of either calibration standard, QC or samples were mixed with 50 µL of the internal standard mixture at a concentration of 2.
Recently, TAVR for a failed surgical bioprosthetic aortic valve [TAVR-valve-in-valve (ViV)] has emerged as an attractive option for patients who are at an increased risk for a surgical redo; although, according to a recent meta-analysis, it may be associated with a higher incidence of severe PPM as compared to redo-SAVR  (6, 7). Indeed, over 30% of TAVR-ViV procedures in the Society of Thoracic Surgeons (STS)/the American College of Cardiology, the Transcatheter Valve Therapy (TVT), and the Valve-in-Valve International Database (VIVID) Registries resulted in an elevated postprocedural transvalvular gradient  (4, 6). Although, rarely, transcatheter aortic valves can also degenerate  (8) : the TRANSIT international project collected the largest series of patients with a degenerated TAVR treated by means of a second TAVR (TAV-in-TAVR) and, consistently with a previous smaller registry, showed acceptable procedural and 1-year outcomes  (9, 10).
Materials and methods The TRANSIT-PPM project is an investigatorinitiated international multicenter registry, including consecutive patients undergoing TAVR for a degenerated transcatheter aortic valve (ClinicalTrials. gov Identifier: NCT04500964). We evaluated cases performed with supraannular self-expanding (S-SE) (CoreValve, Evolut R, and Evolut PRO) and intra-annular balloon-expandable (BE) transcatheter heart valves (THVs) (Edwards SAPIEN, SAPIEN XT, and SAPIEN S3). Data concerning procedural results and echocardiographic parameters after each TAVR were collected. Data concerning the last available follow-up were also collected. This study was approved by an institutional review committee and the subjects gave informed consent.
Results Because of the sensitive nature of the data collected for this study, requests to access the dataset from qualified researchers trained in human subject confidentiality protocols may be sent to the corresponding author. The TRANSIT project is an investigator-initiated registry that started collecting data in January 2020 (ClinicalTrials. gov Identifier: NCT04500964). A group of 28 centers took part in the project: 22 in Europe, 4 in North America, 1 in South America, and 1 in the Middle East. Among a total number of about 40,000 procedures performed since 2008, 155 cases of TAV-in-TAVR were eventually included in the TRANSIT-PPM study. Of these, 73 (47%) cases presented a degenerated supra-annular self-expanding valve, while 82 (53%) cases had a degenerated balloonexpandable device.
Fiji (ImageJ software) was subsequently used to quantify the bacterial burden, reflected by the corresponding fluorescent pixel counts (FPC) (15) at 48, 72 and 96 hpA. Our results confirm that the different doses were properly administered into the larvae, as judged by the correlation between the infectious inoculum (low, moderate, high) and the FPC at different time points postamputation (Figures  1C-E ). To address whether M. marinum infection influences caudal fin fold regeneration, we next evaluated the fin regenerative outgrowth by measuring the length and the area of the regenerating tissue from the initial amputation position to the new distal fin edge. Neither the size nor the morphology of the regenerating fin was affected with the low dose at the early phase of regeneration i. e.
Imaging of Larvae and Quantification For imaging, larvae were anesthetized in 0. 016% Tricaine, immobilized in 35 mm glass bottom dishes (FluoroDish ™ , World Precision Instruments) using 1% low melting point agarose (Sigma-Aldrich) and covered with a small volume of fish water containing tricaine. Epifluorescence microscopy was used to quantify bacterial loads and measure the length and the area of regrowth and performed with a Zeiss Axio Zoom. V16 equipped with an Axiocam503 monochrome (Zeiss) camera. Fluorescent pixel count (FCP) determination, which reflects the bacterial loads, was performed with Fiji (ImageJ Software), using the 'Analyze particles' function  (15). Regenerative fin growth length was evaluated by measuring the distance between the amputation plane and the edge of the fin in the median plane with Fiji.
Statistical Analyses Graph Pad Prism 6. 0 Software (San Diego, CA, USA) was used to generate the graphs and to analyze the data. Specific statistical tests were used to evaluate the significance of differences between the groups. Graphs show the mean ± standard error of the mean (SEM). Student's t-tests were performed to compare the significance levels of two groups with n>30. The Mann-Whitney test was performed to compare the significance levels of two groups with n<30. The ANOVA test was performed to compare more than two groups with n>30. The Kruskal-Wallis ANOVA was performed to compare more than two groups with n<30, except for the Figure  5C  for which a Mann-Whitney one-tail test was applied.
FUNDING This work was supported by  INSERM  and the  University of Montpellier. We also thank  Foundation for Research in Rheumatology (FOREUM; SEN-OA)  and  "Agence Nationale de Recherche"  ( ANR-18-CE18-0010 ) for financial support of this project. MJ received a post-doctoral fellowship granted by  Labex EpiGenMed , an  «Investissements d'avenir» program  ( ANR-10-LABX-12-01 ).
Subsequently, infected cells were washed three times with MB medium, incubated for the time indicated at 25°C, and mCherry fluorescence was measured every 2 days using a microtiter plate reader (Synergy H1, Biotek). To assess CFUs, D. discoideum amoebae were infected (MOI 1) with mCherry-producing L. pneumophila JR32 or ΔicmT grown for 21 h, diluted in MB medium, centrifuged and incubated for 1 h at 25°C. The infected cells were washed three times with MB medium and incubated for the time indicated at 25°C. The infected amoebae were lysed for 10 min with 0. 8% saponin (47036, Sigma-Aldrich). Subsequently, serial dilutions were plated every 2 days on CYE agar plated containing Cam (5 μg/ml) and incubated for 3 days at 37°C. CFUs were counted using an automated colony counter (CounterMat Flash 4000, IUL Instruments, CounterMat software) and the number of CFUs (per ml) was calculated.
2) software (Amnis). Infected amoeba containing one intracellular L. pneumophila bacterium were gated and assessed for colocalization of GFP and mCherry (host) with mPlum produced by the bacteria. The software computes the IFC colocalization score (bright detail similarity), which is the log-transformed Pearson's correlation coefficient of the localized bright spots with a radius of 3 pixels or less in two images and is used to quantify relative enrichment of a marker on the LCV. Data analysis was performed using GraphPad Prism.
1% acetic acid over 82 min. The LC was coupled online to a LTQ Orbitrap Elite mass spectrometer (Thermo Fisher, Bremen, Germany) with a spray voltage of 2. 5 kV. After a survey scan in the Orbitrap (r = 60,000), MS/MS data were recorded for the twenty most intensive precursor ions in the linear ion trap. Singly charged ions were not considered for MS/MS analysis. The lock mass option was enabled throughout all analyzes. After mass spectrometric measurement, database search against a database of Dictyostelium discoideum and Legionella pneumophila Philadelphia downloaded Uniprot on 14/10/2019 (25,478 and 3,024 entries, respectively) as well as label-free quantification (LFQ) was performed using MaxQuant (version 1. 6. b +: co-localization in L. pneumophila-infected cells; -: no co-localization in uninfected cells.
Acknowledgements We would like to thank  Markus Maniak  for providing the deletion construct for osbH,  Leoni Swart ,  Xiaoli Ma ,  Deise Schäfer , and  Iris Hube  for help with cloning, and  Sebastian Grund  for technical support with the preparation of MS samples. Work in the group of H. H. was supported by the  Swiss National Science Foundation  (SNF;  31003A_175557 ,  310030_207826 ). Work in the group of D. B. was supported by the  Federal Ministry of Education and Research (BMBF ; grant  031A410B ). Work in the group of C. B. was supported by the  DFG  and the SFB944 (grant  SFB 944/3-P25 ). Work in the group of F. L. was supported by the  Région Occitanie , the  Centre National de la Recherche Scientifique (CNRS)  and the  University of Montpellier (UM). The authors declare no conflict of interest.
The temperatures of the vaporizer and detector were 250 • C and 300 • C, respectively. Aliquot (1. 0 µL) of the esterified extract was injected into the chromatograph device. All FAs were identified by comparing the retention times of the methyl esters of the samples with the FA standards using the SupelcoTM Component Mix (cat. 18919; Supelco, Bellefonte, PA, USA) and rumenic acid c9, t11 CLA (cat. 05632; Sigma-Aldrich Corporation, St. Louis, MO, USA). The percentages of fatty acids were obtained using the Chromquest 4. 1 software (Thermo Electron, Milan, Italy). The FA results were expressed as area percentages (feeds) and as mg/100 g of meat.
Image Processing The gels were scanned and the images were imported into the ImageMaster Platinum software for comparisons (contrasts) of the images between treatments and to obtain information such as the number of spots per gel, % matching (correspondence between the protein spots in the gels), isoelectric point (pI), molecular weight (MW), and spot volume. The gel correspondence (matching) of each sample (three technical replicates) was higher than 95%, i. e. , 95% of the spots were present in the technical replicates, indicating a good reproducibility. For the image comparison, one reference gel per treatment was selected  [27] , which contained the largest number of most well-defined spots. The reference gel of the treatment was compared with each gel used for the other treatments.
Tryptic Digestion of Protein Spots and Identification of Proteins by ESI-MS/MS Protein spots from the experimental groups (control vs. WDG) were selected based on their MW and pI obtained by image analysis, cut out (fragments of approximately 1 mm 3 ), and prepared according to the method described in  [28]. In brief, the sediments were transferred to microtubes and submitted to the following four steps: (1) removal of the dye; (2) reduction and alkylation; (3) trypsin digestion (Trypsin Gold Mass Spectrometry, Promega, Madison, WI, USA); and (4) elution of peptides extracted from the gel. Subsequently, mass spectra of the peptides were obtained by analyzing the aliquots of the solutions using a nanoACQUITY UPLC-Xevo TQ-MS System (Waters, Manchester, UK). The proteins of the Bos taurus genome were identified in the UniProt database (UniProtKB/Swiss-Prot, available online: www.
Bioinformatics Procedures Bioinformatic analyses were conducted for the classification of differentially expressed proteins in the muscle tissues from the animals of the control vs. WDG treatment group in terms of biological processes (BPs), the molecular function (MF), and cellular components (CC). For this purpose, the accession numbers of the proteins identified by ESI/MS/MS were entered into the UniProt database (available online: www. uniprot. org, accessed on 1 August 2022), and their FASTA sequences were extracted. After this step, the proteins were analyzed using the OMICSBOX v. 2. 0 (available online: https://www. biobam. com/ omicsbox/, accessed on 1 August 2022) and Blast2GO tools  [29]. Additionally, the interactions between the proteins identified in the treatments were analyzed using the open-source STRING 11.
0 platform (available online: https://stringdb. org/, accessed on 1 August 2022). The same list of proteins whose expression differed between the experimental groups were used in these analyses  [30]. The minimum required interaction score was set at 0. 900 (highest confidence), and no more than 20 interactions were allowed in the database search. Subsequently, further bioinformatic analyses were performed, following the procedures described by Gagaoua et al. [31] , using the Metascape ® platform. Briefly, gene identifiers were converted using Uniprot Retrieve/ID mapping. Thus, key information regarding the proteins (gene names (GN)) and its relationships with the carcass and meat quality traits described in the current study were annotated for each treatment (control and WDG). These procedures aimed to compare the two protein lists so as to better understand the common and divergent molecular signatures.
Hierarchical heatmap clustering was also carried out using enriched GO terms analyzed by Metascape ® (available online: https://metascape. org/, accessed on 1 August 2022). Additionally, the ProteQTL tool, included in ProteINSIDE (available online: http://www. proteinside. org/, accessed on 1 August 2022), was used for the rapid search of carcass and meat quality quantitative trait loci (QTL) among the list of putative biomarkers. ProteQTL interrogates a public library of published QTLs in Animal QTL Database (available online https://www. animalgenome. org/QTLdb, accessed on 1 August 2022) that contains cattle QTLs and association data collected from published scientific articles.
Data Analysis The data were submitted to analysis of variance (ANOVA) by the F test using the GLM procedure of the Statistical Analysis System (SAS, version 9. 1, Cary, NC, USA). Means were compared by the Tukey test and a p value < 0. 05 was adopted as the critical probability. The design was completely randomized, and the following model was used: Yij = µ + ti + ε ij where Yij is the observed value of the experimental unit referring to treatment i in repetition j; µ is the general effect of the mean; t is the treatment effect (diet); and ε is the experimental error. Regarding the proteome data, the protein spot volume was imported into ImageMaster Platinum software and the mean and standard deviation were calculated for the selected spots. The images were compared between treatments by matching the spots in terms of their distribution, volume, relative intensity, pI, and MW.
Supplementary Materials: The following supporting information can be downloaded at: https:// www. mdpi. com/article/10. 3390/foods11203233/s1. Table  S1 : Composition of the experimental diets. Figure  S1 : Classification of proteins identified in the muscle tissue (Longissimus thoracis) of feedlot-finished F1 Angus-Nellore bulls fed on diets without the inclusion of wet corn distiller grains (control). Proteins were separated by 2D-PAGE and identified by mass spectrometry (ESI-MS/MS). The OMICSBOX software was used to classify the proteins according to the biological process (BP), molecular function (MF), and cellular component (CC). Figure  S2 : Classification of proteins identified in the muscle tissue (Longissimus thoracis) of feedlot-finished F1 Angus-Nellore bulls fed on diets with the inclusion of wet corn distiller grains (WDG).
Acknowledgments: We thank  Cargill Animal Nutrition  for their support regarding the study design and acquisition of feed resources from the experimental feedlot at  São Paulo State University (UNESP). We also thank  Maria Antonia Ladalardo  ( USP/ESALQ ) for her supervision of the fatty acid profile assays. Mohammed Gagaoua further acknowledges the support of the  Marie Sklodowska-Curie  grant agreement No. 713654  under the project number  MF20180029.
Solubility tests were done in a test tube using 1-2 mg of polymer and 1 mL of the selected solvent, and placed under magnetic stirring for 24 h. After that, if the polymer was not soluble, it was heated at the solvent's boiling point. The density (ρ) of membranes was determined using a top-loading electronic XS105 Dual Range Mettler Toledo balance coupled with a density kit based on Archimedes' principle. The samples were sequentially weighed in air and into high-purity isooctane at room temperature. The density was calculated from Equation (1). ρ = ρ liquid × w airw liquid w air  (1)  where ρ liquid is the density of isooctane, w air is the weight of the sample in air, and w liquid is its weight when submerged in isooctane. Four density measurements were made for each sample. The fractional free volume (FFV) of membranes was calculated from their density data using the following Equation (2): FFV = V e -1.
3V w V e (2) where V e is the specific volume of the membrane and V w is the polymer's van der Waals volume that was calculated from the Biovia Materials Studio program  [60]. A 20-unit polymer structure was built using the Polymer Builder and the Atom Volumes and Surface algorithms of the Materials Studio program. The polymer structure was optimized by using the Compass force field. The crosslinking of the copolymers was checked by determining their gel fractions. The gel fraction, Gf(%), of film samples was estimated using Equation (3): Gf(%) = 100 × W final W inicial (3) where W initial is the weight of crosslinked sample and W final is the weight of the crosslinked sample after extracting the soluble fraction. W final was determined by immersing the copolymer samples in DMAc at room temperature for 48 h and at 60 Mechanical properties were measured using an MTS Synergie-200 testing machine equipped with a 100 N load at room temperature.
Therefore, all impedance spectra were acquired in a modified 3-electrode set-up, where the Pt coaxial to the working electrode was used as the counter-electrode and a second Pt-wire of another sample was used as the reference electrode. Impedance spectra were acquired using Gamry Instruments 600 potentiostat during the entire experiment at more or less regular intervals, mostly depending on the possibility of obtaining points at frequencies as low as 15 µHz. Three specimens were exposed during the experiment, two treated with LSP and one as-received, with the latter designated as the untreated specimen further in the text. However, one of the LSP-treated specimens was short-circuited since the start of the experiment and no meaningful data could be obtained. The impedance spectra were measured in the corrosion potential with an amplitude of 15 mV in the frequency range 100 kHz-15 µHz whenever possible; in case of excessive scatter in the low-frequency domain, the measurement was terminated earlier.
Electrochemical Impedance Spectroscopy Measurements All impedance spectra were evaluated using the ZView software version 3. 5 h by Scribner. The measured impedance spectra were all modelled using the impedance of the equivalent circuit shown in Figure  6. In this circuit, R s represents the resistance of the environment. The time constant R ox -CPE ox , apparent at high frequencies, represents the response of the oxide layer; as the oxides that grow on stainless steels are semiconductors, the measured resistance R ox ranges from about 2 to 3 orders of magnitude lower than the charge transfer resistance R ct found in low frequencies. Therefore, the response of the oxide layer was only found in a short interval of frequencies, between approximately 500 and 50 Hz. Furthermore, the software algorithm was unable to properly evaluate this time constant, finding it impossible to discern it from the much larger arc formed by the R ct -CPE dl time constant at lower frequencies; hence, this time constant was manually evaluated.