dataesr/scientific-paragraphs-categorization huggingface dataset:
same:hasAuthority owl:DatatypeProperty Authority of the resource. same:hasIteration owl:DatatypeProperty Iteration of the equivalence relationships algorithm during which the named graph was generated. same:hasNamespace owl:DatatypeProperty Namespace of the resource. same:statementInDataset rdf:ObjectProperty Points to the void:Dataset that a statement is a part of. same:votingType owl:ObjectProperty rdf:type of the resource determined after voting.
see VoiD documentation on "discovering": https://www. w3. org/TR/void/#well-known. 25 https://www. w3. org/TR/2012/REC-owl2-syntax-20121211/#Inverse- Functional Object Properties 26 https://www. w3. org/TR/2012/REC-owl2-syntax-20121211/ #Functional Object Properties 27 http://lod. sztaki. hu/sparql 28 http://aims. fao. org/aos/agrontology#isPartOfSubvocabulary 29 the endpoint http://linkeddata. ge. imati. cnr. it:8890/sparql in- cludes the EARTh -Enviromental Applications Refer- ence THesaurus-and ThIST -Italian Thesaurus of Earth Sciences-datasets).
Transfected cells were stained with a specific CBX4 primary antibody and the number and area of CBX4-containing Pc bodies were examined in more than 100 cells per condition (Figure  S6 ). Using confocal microscopy and image analysis with Fiji software (Figure  7A , B), we observed that Pc bodies were significantly larger and more abundant in cells expressing SALL1 or SALL1ΔSUMO than in cells expressing β-Gal. No significant differences in the number of bodies were observed between cells expressing SALL1 and SALL1ΔSUMO. However, the area of the Pc bodies was significantly smaller in SALL1ΔSUMO compared to SALL1 transfected cells. These results revealed that SALL1 SUMOylation status does not influence the increase in the number of Pc bodies, but it may influence their size. Finally, since SALL1 increases CBX4 protein levels, as well as the size and number of Pc bodies, and increased formation of Pc bodies may lead to stronger transcriptional repression of several PRC1 target genes  (Gonzalez et al.
ACKNOWLEDGEMENTS R. B. , M. S. R. , A. C. O. V. , J. D. S. and O. B. -G. acknowledge funding by the grant  765445-EU (UbiCODE Program ). R. B. acknowledges funding by grants  BFU2017-84653-P  ( MINECO/FEDER, EU ),  SEV-2016-0644  ( Severo Ochoa Excellence Program ),  SAF2017-90900-REDT (UBIRed Program ) and  IT1165-19  ( Basque Country Government). Additional support was provided by the  Department of Industry, Tourism, and Trade of the Basque Country Government (Elkartek Research Programs ) and by the  Innovation Technology Department of the Bizkaia County. A. M. A. acknowledges CIBERehd. We thank  Laura Bozal-Basterra  and  Arkaitz Carracedo  ( CIC bioGUNE ) for their help in data analysis.
Data collection Data were recorded by the intensivists in each ICU. The variables reported in the tables and figures were abstracted from the medical charts and electronic reports. Causes of immunosuppression included solid tumours, haematological malignancies, solid organ transplantation, long-term immunosuppressive therapy (i. e. , high-dose steroids (> 1 mg/kg whatever the duration) or any immunosuppressant for more than 3 months), and HIV infection  [10]. Obesity was defined as previously reported  [11]. The SOFA score was calculated within 24 h of ICU admission  [12]. Regards to renal data, day 1 serum creatinine was collected as well as urine output within the first 24 h. To note that some of the patients included from the Saint Louis hospital were already reported elsewhere  [13].
Validity of proportional hazards assumption, influence of outliers, and linearity in relationship between the log hazard and the covariates were carefully checked. Final model was the selected model including frailty effect on center. Survival curve were plotted using Kaplan Meier curves and influence of AKI on outcome was compared using log-rank test. All tests were two-sided, and P values less than 0. 05 were considered statistically significant. Analyses were done using R software version 3. 6. 2 (https:// www. rproje ct. org), including survival, lme4, lmerTest, givitiR packages.
Docking parameters were set to 0. 35 Å as grid space and different points for all six targets (DNA-1, 90 × 90 × 82; DNA-2, 67 × 84 × 90; pre-miR21, 100 × 130 × 130; BAG-3, 125 × 160 × 125; BcL-X L, 120 × 140 × 100; caspase-3, 250 × 231 × 248). A total of 100 LGA runs were performed for each ligand/receptor system. All docking calculations were carried out using Autdock 4. 26 software package. The results of the most favorable free energy binding were selected. The exploration of the ligand interactions with the proposed targets was performed using LigPlot + software  [41 , 42].
Since this approach can be used to investigate the possibility of interaction between TPA and biological targets via charge complementarity, our results reinforce its electron-accepting property, which is related to its high electrophilic character. Thus, a molecular docking approach was applied to obtain better insights about anti-osteosarcoma activity of TPA. These studies were performed of TPA with DNAs (PDB: 1BNA and 1ZEW)  [53 , 54]  , a miRNA (pre-miR21,PDB: 5UZT)  [55]  and three proteins, BAG-3(PDB: 1UK5)  [56]  , Bcl-X L (PDB: 1R2D)  [57]  and caspase-3 (PDB: 3DEI)  [58]  , since these proteins present key roles on OS pathology  [13] [14] [15] [16] [17]. Molecular docking was carried out using Autodock 4. 26 software. After the preparation of ligand and receptors, the parameters were set and 100 LGA runs were performed.
Acknowledgments This work was supported by the  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior -Brazil (CAPES)  -Finance Code 001. M. The authors thank to  LAME (Laboratório Multiusuário de Espectroscopia) and LaReMN (Laboratório Multiusuário de Ressonância Magnética Nuclear) of Universidade Federal Fluminense , and  INTO  ( Instituto Nacional de Traumatologia e Ortopedia ) for the use of their facilities. Thanks are due to  Prof. Olivier Gros  from the  laboratoire YSYEB (University of Antilles ) for the kind donation of dsDNA.
Main grade 3-4 toxicities were: thrombocytopenia, neutropenia, increased bilirubin, increased ALT and asthenia. In the sunitinib group, the median PFS and OS were 9. 05 [5. 81;11. 63] and 25. 0 [13. 5;36. 8] months, respectively. In the placebo group, the median PFS and OS were 5. 51 [4. 14;7. 79] and  20. 5 [15. 1;30. 6] months, respectively. Conclusions: TACE plus sunitinib in the first-line therapy for patients with HCC not suitable for surgical resection was feasible. ClinicalTrials.
Results are presented as means (standard deviation), median and range or percentages, and 95% confidence intervals (CI) when applicable. Median treatment follow up is calculated according to the ''reverse Kaplan Meier'' method. Survival curves were estimated using the Kaplan-Meier methods. Median and time to events at different time-points were done for PFS and OS with their 95% CI. Data analyses were performed using SAS (Version 9. 4). The study has been stopped after the Phase II due to new strategy of treatments in HCC. All authors had access to the study data and had reviewed and approved the final manuscript.
So far, five drugs, all with antiangiogenic properties, evaluated in a phase III clinical setting have demonstrated positive outcomes in first-line (sorafenib, lenvatinib) or second line (regorafenib, cabozantinib, ramucirumab) treatment of advanced HCC  [8, 28]. Recently, positive results of the phase III trial IMbrave150 comparing atezolizumabbevacizumab with sorafenib were announced. Studies testing the combination with TACE of antiangiogenics with immune checkpoint inhibitors are being launched: The EMERALD-1A study evaluating TACE in combination with durvalumab and bevacizumab therapy is currently recruiting patients with locoregional HCC (ClinicalTrials. gov Identifier: NCT03778957). A study testing the combination of lenvatinib and pembrolizumab with TACE is also planned. The results of the PRODIGE 16 trial confirm that the combination of an antiangiogenic drugs with TACE is feasible in a multicentric setting, and justify the continuation of research in this field.
Acknowledgements The authors acknowledge all sub-investigators and the clinical staff at each hospital for their active participation and contribution to the well-conduct of this study. We thank the FFCD operational team (data manager and CRAs) with  Carole Monterymard ,  Fadil Masskouri ,  Marie Moreau ,  Caroline Choine ,  Florence Guiliani ,  Nouredine Lasmi ,  Guillaume Arnould ,  Nathan Guiet ,  Morgane Maury-Nègre ,  Hicham Fattouh ,  Nicolas Le Provost ,  Jérémie Bez , and  Cécile Girault. We thank also la ''Ligue contre le cancer''.
Results Characteristics of the study population, dietary habits and exercise performance. In total, 40 male endurance cross-country runners completed the study. Participants' characteristics including age, weight, body mass index (BMI), dietary habits and sports performance data obtained in the exercise session are described in Table  1. The exercise session consisted of a treadmill test and running 1 km at maximum speed (see "Material and methods" Fecal pH and ammonia. Fecal pH and ammonia were determined in samples collected before and after the exercise session. No changes in pH (pre = 7. 59 ± 0. 48 post = 7. 54 ± 0. 39; p = 0. 337) were detected after the exercise session, but the concentration of fecal ammonia was significantly greater post-exercise (pre = 13. 46 ± 7. 25 mmol/L; post = 16.
Metabolomic serum profile. Metabolomics analyses were performed using liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) with a combination of two complementary chromatographic methods: reversed-phase chromatography (C18 chromatographic column [C18(+)] and Hydrophilic Interaction Liquid Chromatography [HILIC(-)], for the analysis of hydrophobic and polar metabolites in the positive and the negative ionization modes, respectively). Using an untargeted approach, we detected a total of 3195 and 1600 metabolite features using C18(+) and HILIC(-) conditions, respectively. Among those metabolite features, 101 from the C18(+) and 159 from the HILIC(-) analysis accurately matched the mass and retention time of metabolites included in our chemical database (30 of which were common in both analyses).
3A, B ). Complementary to the panel of metabolites identified from the multivariate model, univariate analysis was applied using pairwise comparisons (T1 vs T2) of individual metabolites (Wilcoxon p-values with Benjamini-Hochberg correction). Under these conditions, up to 85 annotated metabolites were significantly different and were subjected to additional tandem mass spectrometry (MS/MS) experiments for identity confirmation (Supplementary Table  S1 ). The 85 metabolites were then analyzed by MetaboAnalyst (with the KEGG database  14  ) to identify potential discriminately per- turbed metabolic pathways before and after the session of acute exercise (T1 vs T2). Results showed that alanine, aspartate and glutamate metabolism, aminoacyl-tRNA biosynthesis and arginine biosynthesis were the three most significantly modified pathways (Fig.
Metabolomic fecal profile. A similar experimental set-up was used for the fecal analysis. A total of 10,866 and 6795 metabolite features were detected under C18(+) and HILIC(-) LC-HRMS conditions, respectively. Firstly, lists of 176 and 335 metabolite features were obtained following annotation with our chemical database and using the C18(+) and HILIC(-) datasets, respectively (88 of which were common). The score plot of the principal component analysis (PCA) and PLS-DA did not reveal any distinction, showing that the two groups did not cluster separately (data not shown). However, 50 annotated metabolites were significantly different when comparing T1 and T2 groups using univariate statistical analysis, and were further confirmed by MS/MS analysis (Supplementary Table  S2 ). Further correction of the p-values for multiple hypotheses using the Benjamini-Hochberg method narrowed down this list to 12 relevant metabolites (Table  2 ).
9/17), and all procedures were in accordance with the 1964 Helsinki Declaration and its later amendments. Written informed consent was obtained from all the volunteers. The study was registered in Clinicaltrials. gov with the accession number NCT04244149. Dietary habits. Dietary pattern characterization of the participants was carried out using a validated food frequency questionnaire with 93 food items  40  and three 24-h dietary recalls (two weekdays and one weekend day). Data from the questionnaires were analyzed using Dietsource software 3. 0 (Novartis, Barcelona, Spain) to obtain the total energy ingested (in kcals) of proteins, fat, carbohydrates and fiber. Exercise protocol. Body weight and height were measured with a scale and a height meter (Asimed T2, Barcelona, Spain). BMI was calculated by dividing the weight by the square of the height.
Quality control samples were obtained by pooling aliquots of each sample and these were injected every 5-10 samples throughout the analysis for further data normalization/ standardization. Untargeted metabolomics experiments were performed by liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) using a combination of two complementary chromatographic methods  41, 42  , consisting of reversed-phase chromatography (C18 chromatographic column) and hydrophilic interaction chromatography (HILIC) for the analysis of hydrophobic and polar metabolites, respectively. LC-HRMS experiments were conducted on an Ultimate 3000 chromatographic system (Thermo Fisher Scientific, Courtaboeuf, France) coupled to an Exactive mass spectrometer from Thermo Fisher Scientific fitted with an electrospray ionization source and operating in the positive and negative ion modes for C18 and HILIC separations, respectively (designated as C18(+) and HILIC(-), respectively).
Metabolite separations were performed using a Hypersil GOLD C18 1. 9 µm, 2. 1 × 150 mm column maintained at 30 °C (Thermo Fisher Scientific) or a Sequant ZIC-pHILIC 5 µm, 2. 1 × 150 mm column at 15 °C (Merck, Darmstadt, Germany), operated under gradient elution conditions as described  41. Metabolomic data processing, metabolite annotation and metabolic pathway analysis. The raw LC-HRMS data were first converted into mzXML files using MSConvert (ProteoWizard), and further data processing (e. g. , normalization, scaling, filtering) and statistical treatments were performed using the Workflow-4Metabolomics (W4M) platform  43. Metabolite annotation was first realized using our spectral database according to accurately measured masses and chromatographic retention times  41. Our chemical database initially included ~ 1000 metabolites mainly of human origin, but was then broadened by the addition of commercially available bacterial metabolites and other molecules described in the literature  44, 45.
Confirmation of metabolite annotation was then accomplished by running additional LC-MS/MS experiments using a Dionex Ultimate chromatographic system combined with a Q-Exactive mass spectrometer (Thermo Fisher Scientific) under nonresonant collision-induced dissociation conditions using higher-energy C-trap dissociation. Obtained MS/MS spectra were both manually and automatically matched using MS-DIAL software  46  to the spectra included in our in-house spectral database, as described  47. To be identified, ions had to match at least 2 orthogonal criteria (accurately measured mass, isotopic pattern, MS/MS spectrum and retention time) to those of an authentic chemical standard analyzed under the same analytical conditions, as proposed by the Metabolomics Standards Initiative  48. The identified metabolites were imported into the free online web-based platform MetaboAnalyst 4.
0 49 for metabolic pathway enrichment. Thus, the annotated m/z features with a Wilcoxon p-value < 0. 005 (BH-critical value) were imported as KEGG numbers using the appropriate Homo sapiens pathway library. The interpretation of the results was performed after considering data with a p-value < 0. 05 and an impact value > 0. 1. pH and ammonia determination in stool samples. The pH was measured using a basic pH meter (Crisom, Hach Lange, Barcelona, Spain) according to the method described by Dai and Karring  50. Ammonia content was determined using a high-performance ammonia-selective ion electrode (Orion, Thermofisher Scientific, Waltham, MA). Stool (100 mg) was dissolved in 5 mL of MilliQ water; the mixture was vortexed for 2 min and then sonicated (Ultrasons, Selecta, Barcelona, Spain) for 10 min.
A volume of 100 µL 1 M NaOH was added to the sample and ammonia was immediately measured. A standard curve was made from serial dilutions of ammonium chloride from 0. 1 M, according to the manufacturer's instructions. Bacterial DNA extraction and bioinformatics. Bacterial DNA was extracted using the E. Z. N. A. Kit (Omega-Biotek, Norcross, GA) and a bead homogenizer (Bullet Blender Storm, Next Advance, Averill Park, NY). A DNA fragment comprising the V3 and V4 hypervariable regions of the 16 s rRNA gene was amplified for sequencing analysis  6. Amplicons were sequenced on the MiSeq Illumina platform (Illumina, San Diego, CA). Sequence results were analyzed using Quantitative Insights into Microbial Ecology (QIIME2) v2019. 7  51  and were processed with DADA2 for quality control  52. The classify-sklearn method was used for taxonomy assignment  53  with an in-house customized classifier based on the SILVA reference database  54, 55.
To construct the customized reference database, we extracted the sequences according to our primers (forward primer sequence: CCT ACG GGNGGC WGC AG, reverse primer sequence: GAC TAC HVGGG TAT CTA ATC C) from the SILVA 132 database clustered at 99% identity  56. We trained the classifier using our tailored reference reads and SILVA 7-levels for reference taxonomy, including the species probability (weights) likely to be observed for human stool (downloaded from https ://githu b. com/BenKa ehler /ready towea r)  56, 57.
Statistical analysis. For metabolomics data, multivariate analyses were used to identify molecular features that discriminate metabolite differences in athletes before and after a session of acute exercise. PCA and PLS-DA were performed using the W4M platform and were used to identify features with discriminative power and to maximize variation between the two groups (before and after the test). Also, permutation tests (200 cycles) were conducted to assess the robustness of the PLS-DA model when using a small sample size  58. The significance of the discriminant metabolites from the two groups was defined by a statistically significant threshold of VIP, which was derived from the PLS-DA model. A value of VIP > 1. 0 was considered sufficient for group discrimination  59. The univariate data analyses included a Wilcoxon signed-rank test, corrected for multiple testing by the Benjamini-Hochberg procedure, to assess the statistical significance of each compound.
The generation of the clustered heatmap was performed using the Canberra distance metric and normalizing data, adding a pseudocount of 1 before log10 conversion. To detect changes in microbiota, samples in T1 and T2 were compared using the q2-longitudinal method  60. First, a feature volatility analysis was performed to explore the data. This type of analysis uses a supervised learning regressor to predict a continuous variable (time in this case) as a function of feature composition (bacterial taxa). Based on volatility analysis, some bacterial taxa were selected for subsequent analyses according to their importance and cumulative average change. Selected taxa were analyzed using a linear mixed-effects model to detect significant bacterial taxa. Associations between different variables were studied with Spearman´s correlation and multivariate association by linear models (MaAsLin), an additive general linear model to find associations between metadata and bacterial abundance  61.
Some miRNAs are located in RNA precursors (pri-miRNAs) annotated as long non-coding (lncRNAs) due to absence of long open reading frames (ORFs). However, recent studies have shown that some lnc pri-miRNAs encode peptides called miPEPs (miRNA-encoded peptides). Initially discovered in plants, three miPEPs have also been identified in humans. Herein, we found that a dozen human pri-miRNAs potentially encode miPEPs, as revealed by ribosome profiling and proteomic databases survey. So far, the only known function of plant miPEPs is to enhance the transcription of their own pri-miRNAs, thereby increasing the level and activity of their associated miRNAs and downregulating the expression of their target genes. To date, in humans, only miPEP133 was shown to promote a positive autoregulatory loop.
The Pri-miR-155 and Pri-miR-497 Transcripts Are Translatable in Hela Cells The plant pri-miRNAs were shown to contain translatable sORFs by fusing miPEP ORFs with the GUS (β-glucuronidase) reporter gene  [7]. First, we investigated whether human pri-miRNAs are translatable. To avoid difficulties of interpretation with proteincoding genes, we focused on pri-miRNAs (MIRHG for miRNA host gene) identified as lncRNAs in UCSC Genome Browser. Using ribosome profiling as a first indication of translation marks, we analyzed the 48 promising candidates with GWIPS-viz (Genome Wide Information on Protein Synthesis visualized), an online genome browser for viewing ribosome profiling data  [17]  (http://gwips. ucc. ie (accessed on 4 March 2021)). We found that among the 48 candidates, 32 MIRHGs contained potentially translated sORFs, highlighted by ribosome profiling marks (Table  S1 ), suggesting that translatable sORFs within pri-miRNAs are widespread in humans.
Discussion Some miRNAs are located in RNA precursors (pri-miRNAs) annotated as lncRNAs, as they lack long ORFs and were thought to be unable to encode proteins. However, recent studies have shown that a few lnc pri-miRNAs encode small peptides called miPEPs (miRNA-encoded peptides). Initially discovered in plants  [7] , miPEPs encoded by the lncRNAs MIR22HG (pri-miRNA-22)  [11] , MIR155HG (pri-miR-155)  [15] , and MIR34AHG (pri-miR34a)  [16]  have also been identified in humans. Analysis of the 48 lnc MIRHGs identified in UCSC Genome Browser with the GWIPS-viz genome browser  [17]  revealed that 32 of them contain sORFs exhibiting ribosome profiling marks (Table  S1 ). Moreover, using OpenProt (an online database containing predicted ORFs longer than 30 codons supported by ribosome profiling and mass spectrometry studies from several species, tissues, and cell lines (www.
Immunoblotting and Antibodies Cells were lysed in NuPAGE®LDS Sample Buffer (Novex, Life Technologies, Carlsbad, CA, USA), sonicated, and boiled at 96 • C for 3 min. Proteins were separated on 4-15% SDS-PAGE gels (BioRad, Hercules, CA, USA) and transferred to polyvinylidene difluoride membrane (Immobilon-P, Millipore, Burlington, MA, USA). Membranes were blocked in TBS-T (Tris Buffered Saline with 0. 1% Tween 20) containing 5% non-fat dry milk, blotted with antibodies overnight at 4 • C, washed, then incubated at room temperature with HRP-conjugated secondary antibodies (Jackson ImmunoResearch, Ely, UK). After washes, detection was achieved with chemiluminescence detection reagents (Clarity TM Western ECL substrate from Bio-Rad). Image acquisitions and quantifications of immunoblots were performed with a Fusion Solo X chemiluminescence imaging system using the Evolution-Capture software (Vilber Lourmat, Marne La Vallée, France) or with a ChemiDoc Touch imaging system using the Image Lab Touch software (BioRad).