dataesr/scientific-paragraphs-categorization huggingface dataset:
Spectral data analysis The dataset consisted of 10 spectra/cell collected from five cells in three independent biological replicates for each treatment, at 25 and 50 µM, after 5 min and 18 h. Intensities per channels of each spectrum were averaged per cell and normalized to 1 before statistical analysis with R software V4. 0. 0 (GNU GPLv2, R Core Team) and RStudio V 1. 2. 5001 (AGPLv3, RStudio) using the ChemoSpec package V5. 2. 12. A distant matrix was established for each dataset applying the Pearson's correlation coefficient, and a robust principal component analysis (PCA) was performed. The same procedure was used with solutions of pure compounds to analyze 10 spectra/acquisition in triplicate.
The end plate offset was set at 500 V, capillary voltage at 2500 V, nebulizer at 2 Bar, dry gas at 8 L. min -1 and dry temperature at 200°C. The transfer time was set at 20-70µs and MS/MS collision energy at 80-120% with a timing of 50-50% for both parameters. The MS/MS cycle time was set to 3 seconds, absolute threshold to 816 cts and active exclusion was used with an exclusion threshold at 3 spectra, release after 1 min and precursor ion was reconsidered if the ratio current intensity/previous intensity was higher than 5. Raw data were processed in MetaboScape 4. 0 software (Bruker): molecular.
ACKNOWLEDGMENTS This work was supported by the  French National Centre for Scientific Research  and the  University of Strasbourg. We thank  Dr. Gilles Ulrich  ( Institut de Chimie des Procédés pour l'Énergie, l'Environnement et la Santé, Strasbourg ) for access to the spectrofluorometer to establish reference spectra and quantum yields. We thank to  Pr. Thomas J. Bach  ( Institut de Biologie Moléculaire des Plantes, Strasbourg ) for sharing collection of phenolic compounds and to  Dr. Dimitri Heintz  for access to mass spectrometry equipments (metabolomics facility, IBMP). We are also grateful to  Stephanie Riché  and  Dr. Sridevi Ramanoudjame  ( Laboratoire d'Innovation Thérapeutique, Strasbourg ) for excellent technical assistance to purify vismione H. We acknowledge helpful criticism and discussion with  Dr.
Proteomic Study Proteomic analyses were performed as previously described  [19]. Peptides were identified and quantified by HR/AM LC-MS/MS on an Orbitrap Tribrid Fusion Lumos as described  [47]. The relative abundances of 5593 putative O-GlcNAcylated proteins were evaluated by label-free quantification within Proteome Discoverer from MS1 intensities (Table  S6 ). Profiles of abundances were analyzed using RStudio software (RStudio 3. 12. 0, RStudio Team (2020). RStudio: Integrated Development for R. RStudio, PBC, Boston, MA, USA) and protein-protein networks were analyzed using STRING database (v11)  [48].
Statistical Analyses Results were expressed as an average ± SEM of n different rats. Analyses of Western blots were expressed in relation to the average of the stain free and then reduced to the average of the control samples (D84 or CTRL). For D28-D84 Western blots, data were analyzed by a Mann-Whitney test. For CTRL-LPS-LPS+R-NButGT Western blots, physiological and biological parameters and PRISM score, data were analyzed by a Kruskal-Wallis test. Survival analysis is presented using a Kaplan-Meyer curve and was evaluated using a Mantel-Cox test. A value of p < 0. 05 was considered significant. All statistical calculations and graphs (except those performed with R software) were performed using GraphPad PRISM software (version 7. 00).
Study design Volunteers were screened until 30 adult participants were enrolled in a randomized, open-labeled, crossover study following guidelines for appropriate sample size selection in pilot studies  (35) (36) (37) (38) (39) (40). In chronological order of inclusion, participants were assigned a sequence of turmeric products. Randomization was carried using SAS software version 9. 3 (SAS Institute) and a Latin square design. Inclusion and exclusion criteria are detailed in Supplemental Table  1. In addition, to ensure the health status of the volunteers, blood chemistry was tested at the screening visit (glycemia, alanine aminotransferase, aspartate aminotransferase, γ -glutamyl transpeptidase, urea, creatinine, and complete blood count) and pregnancy was tested for nonmenopausal women (β-HCG, Beta human chorionic gonadotropin).
Participant demographics and study materials Forty-two volunteers were screened, 6 declined to participate, 6 were disqualified due to health considerations, and 30 were enrolled in the study (Figure  3 ). All 30 of the participants (14 men and 16 women) completed the study, and no serious adverse events were reported after randomization. The mean age of the participants was 33. 6 ± 6. 8 y, the mean BMI was 22. 1 ± 2. 1 kg/m 2 , and all blood chemistry and physical examination values were normal (Table  2 ). The daily dosages of specific curcuminoids in each formulation were determined using UHPLC-tandem MS. In all cases, the total curcuminoid concentrations were in accordance with the nominal values reported on the product labels (Table  1 ). All visits for all participants (n = 30) and 1648/1650 plasma time points were included in the analyses on the ITT population (see Supplemental Methods for kinetics missing data handling).
Acknowledgments We thank  Pascale Bellenger  and  Christophe Reynaud  for their technical assistance in plasma analyses. The authors' responsibilities were as follows-PF-B and AM: designed the study; SLB-B and HC: performed document preparation and authorities' submissions; SLB-B: performed and supervised the conduct of the trial; MT: developed LC-MS quantification method and supervised blind plasma analyses; CM and AD: verified the data, analyzed the data, and performed the statistical analyses; and all authors: contributed to writing and revising the manuscript, and read and approved the final manuscript.
RESULTS Lactonase SsoPox W263I modulates C. violaceum proteome Proteomes of CV 12472 with and without treatment by the QQ lactonase SsoPox W263I were analyzed. The use of a statistical PLS-DA analysis revealed a significant impact of the enzyme on the proteome of CV 12472 as the control and treated groups were found distinctly separated on the first component (15. 2% of the total variance) (Fig. 1a ). A total of 889 out of the 4 397 (i. e. , 20. 2%) proteins of CV 12472 were detected and identified (Uniprot database) (Supplementary Data 1). These 889 proteins were clustered by biological functions according to the GO database using the DAVID bioinformatic tool  22  (Fig. 2a ). This method allowed to assign of biological functions to 82. 6% of this set of proteins. Proteins were mainly involved in metabolic processes (31%), followed by translation (8.
SsoPox W263I changes the metabolomic profile of C. violaceum To further associate proteome modifications to molecular changes, metabolomic investigations were undertaken. LC-(+)-ESI-MS metabolomic analysis enabled the identification of a primary dataset of 8 496 m/z features from raw LC-MS data, which was further refined and reduced down to a final dataset of 406 metabolites thanks to three filtration steps (Supplementary Table  1 ). The concentration of several metabolites was found to be significantly different between enzymatically treated and control samples (Fig. 3a ). PLS-DA analysis revealed two distinct groups formed by treated and control samples, separated on the first component (75. 8% of the total variance) (Fig. 1b ), thus indicating a significant effect of the enzymatic treatment on the metabolome of CV 12472.
The PLS-DA model showed high predictive accuracy and relevance with R 2 = 0. 94 and Q 2 = 0. 84. This analysis was used to determine the variable importance in projection (VIP) score values, indicating the importance of each m/z feature in the discrimination between treated and control samples. Twenty-one m/z features with the higher VIP scores (VIP score >1. 5) were selected (Fig. 3a ), and the corresponding molecular formulae were determined based on accurate mass measurement, isotopic patterns, and MS/MS data analysis. In most cases, a putative identification was proposed (Supplementary Table  2 ). Global Natural Products Social (GNPS) molecular networking workflow was further used to organize the whole MS/MS metabolomic dataset so as to obtain a global view of the different groups of chemical compounds (called "clusters") containing VIPs (Fig.
Transfer collision low energy was set to 5 V, trap collision low energy was set to 4 V. The high energy ramp was applied from 4 to 5 V for the trap collision and from 19 to 45 V for the transfer collision enabling fragmentation of the ions after ion mobility cell and before the time-of-flight (TOF) MS. The On-column sample load was 800 ng. Each sample was injected in duplicate. The files acquired were imported into Progenesis QI software v. 2. 0 (Nonlinear Dynamics, Newcastle, UK) for label-free quantification analysis. The data were automatically aligned and normalized. Processing parameters were 150 counts for the low energy threshold, 30 counts for the elevated energy threshold. The database used to identify peptides contains the protein sequences of C. violaceum ATCC 12472 (UniProt, 08/ 05/2018, 4397 sequences).
LC-ESI-MS and MS/MS data acquisition Samples were prepared by solubilizing the dry extracts in 1 ml of LC-MS grade methanol. Six medium blanks were also prepared using the same protocol but without bacteria. Four quality control samples (QCs) were prepared by mixing an aliquot of all the samples. Samples and medium blanks were randomly injected, and a QC was injected every three samples. At the beginning of the injection sequence, two analytical blanks (only methanol) and a QC were injected before the samples. Samples were analyzed by UPLC-ESI-QToF-MS in positive mode. The UPLC-MS instrumentation consisted of a Dionex Ultimate 3000 Rapid Separation (Thermo Fisher Scientific) chromatographic system coupled with a QToF Impact II mass spectrometer (Bruker Daltonics, Mannheim, Germany). The separations were carried out on a reverse-phase column (150 × 2.
Metabolomic data processing and analysis LC-MS raw data were converted into ". netCDF" files using DataAnalysis v. 4. 3 (Bruker, Mannheim, Germany) and processed with MZmine v. 2. 53  67. All steps and parameters used to obtain the final data matrix are listed in Supplementary Table  1. In a second time, the data were submitted to two filtering steps using an in-house script on R and final manual filtering. Filtering consisted in removing successively experimental and analytical bias according to signal/noise ratio (using blanks) and coefficient of variation (using QCs). For multivariate analyses, the resulting data matrix was log 10 -transformed, mean-centered, and analyzed using MetaboAnalyst 4. 5 online  68. Data were analyzed using Principal Component Analysis (PCA) and Partial Least Squares (PLS-DA) discriminant analysis.
Feature annotation via molecular networking approach LC-MS/MS raw data (samples and standards) were converted into ". mzXML" files using DataAnalysis. The Internet platform GNPS (http:// gnps. ucsd. edu) was used to create molecular networking of related compounds based on the similarity of their MS/MS fragmentation patterns  23. Similar chemical structures share similar fragmentation pathways in mass spectrometry leading to identical fragments and losses  69. The parameters used were: Precursor Ion Mass Tolerance (Da): 0. 02, Fragment Ion Mass Tolerance (Da): 0. 02, Minimum Cosine Score: 0. 62, Minimum Matched Fragment Ions: 4 and Network TopK: 10. Data were imported and treated offline (Cystoscape v 3. 7. 0). Two methods were used for the annotation of a specific m/z feature: (1) Searching the most probable molecular formula with DataAnalysis software with the "Smartformula" package, (2) Using online databases, such as PubChem (https://pubchem.
ncbi. nlm. nih. gov), Lipidmaps (http://www. lipidmaps. org), CEU mass mediator (http://ceumass. eps. uspceu. es), Metlin (https://metlin. scripps. edu)  70 , or SIRIUS 4. 4. 17  71. Finally, a careful analysis of the MS/MS fragmentation pattern in comparison with the literature data was performed to confirm the putative annotation.
Multi-omic data processing Proteomic and metabolomic data matrices were processed by regularized Canonical Correlation Analysis (rCCA)  [34] [35] [36]  with the R package MixOmics following the protocol found on the website http://mixomics. org/. The shrinkage method was used to adjust the regularization parameters λ1 and λ2. A cut-off value of 0. 5 was chosen to create a molecular network with the function write. graph() under the format ". graphml". The resulting network was exported and treated using Cytoscape v. 3. 7. 0.
ACKNOWLEDGEMENTS This work was financially supported by the  French Ministry of Higher Education, Research and Innovation , the  University of Toulon  (Ph. D. grant of N. Carriot), the  Direction Générale de l'Armement (DGA)  (Ph. D. grant of S. Mion), and  Investissements d'avenir program  (Méditerranée Infection  10-IAHU-03 ) of the  French Agence Nationale de la Recherche (ANR). This work also received support from the  ANR   Astrid research program  (RESSAC) from the  DGA. The authors wish to thank  Dr B. Paix  ( Toulon University, MAPIEM ) for the helpful discussions concerning multi-omics analyses,  Dr S. Greff  ( Aix-Marseille University, IMBE ) for the acquisition of LC-MS data,  Prof. J-L. Mège  for providing macrophages, and  P. Decloquement  and  S. Azza  for the acquisition of proteomic data.
Finally, the photothermal effects in B16F1 cells were evaluated by flow cytometry after treatment with Pd-PLGA NPs and NIR irradiation (Figure  S9 , Supporting Information). We observed that under subcytotoxic doses (1 mg mL -1 ) no statistically significant changes on cell cycle were observed when treating the cells with just the NPs, just the NIR light (at the required irradiances to generate optical hyperthermia) or with the combination of both nanoparticles and NIR light. No changes in apoptosis (early, late apoptosis, and necrosis) compared to untreated controls were observed either. Finally, no caspase 3 activation was detected by flow cytometry (data not shown). All in all, those results highlight the benign character of our NPs on the nontreated cells. After performing optical hyperthermia with those NPs on the metastatic melanoma cell line, all dead cells affected by the transduced heat were detached from the wells (being adherent cells when alive) and those dead cells were removed in the washing followed during the trypsin treatment used prior cell cytometry analysis.
Physico-Chemical Characterization of the NPs: Morphological characterization of the NPs was preliminarily performed using a T20-FEI Tecnai TEM and an Inspect F-50SEM (FEI, Holland), with the NPs previously coated with a carbon layer. Aberration corrected scanning transmission electron microscopy (Cs-corrected STEM) images were acquired using a high angle annular dark field detector in a FEI XFEG TITAN electron microscope operated at 300 kV equipped with a CESCOR Cs-probe corrector from CEOS. Electron cryo-tomography was acquired in a FEI Tecnai F-20 working at 200 kV and -170 °C using a GATAN cryotomographic holder. The acquisition was made in TEM mode with the GATAN acquisition software. The tilt angles were -67° to +67° with a 2° angular step and Saxton scheme. The reconstruction was made using Tomviz software to the alignment of the images and TomoJ for the reconstruction (OS-SART-30 iterations).
Then, Pd-PLGA NPs were added to the cells at different concentrations (0. 02-2 mg mL -1 ) in supplemented medium and incubated for 24 h at 37 °C and 5% CO 2. After incubation, cells were washed twice with Dulbecco's Phosphate Buffered Saline (DPBS; Biowest, France) and the Blue Cell Viability reagent (Abnova, Taiwan) was added (10% v/v in supplemented medium) and incubated for 4h at 37 °C and 5% CO 2 to elucidate the cytotoxicity of Pd-PLGA NPs based on cell metabolism effects. Then, cell viability was determined by fluorescence reading (535/590 nm ex/em; Synergy HT Microplate Reader, Biotek, USA). Viability percentages were calculated by the interpolation of the emission data obtained from treated and control samples (100% viability). To validate the photothermal effects of Pd-PLGA NPs mediated by NIR irradiation, B16F1 cells and macrophages were seeded in 24-well plates (B16F1 40 000 cells per well, macrophages 500 000 cells per well).
Then, DNA was stained by adding a solution of 50 µg mL -1 propidium iodide and 100 µg mL -1 RNase A in PBS to be then analyzed by flow cytometry. Finally, the activation of Caspase-3 was analyzed by flow cytometry after fixing cells with 4% PFA and incubated with a FITClabeled antibody against the active form of caspase-3 (Pharmingen) diluted in 0. 1% saponin in PBS. After washing, cells were resuspended in 1% PFA and analyzed by flow cytometry. All the experiments were evaluated in a FACSARIA BD equipment with the FACSDIVA BD software (Cell Separation and Cytometry Unit, CIBA, IIS Aragon, Spain). In these assays, negative controls were also run to compare the basal level of cells to that obtained after treatment, and the control samples without the cells were also analyzed to test a potential NP interference with the methodology.
Acknowledgements L. U. and C. Y. contributed equally to this work. The authors thank financial support from the  ERC   Consolidator Grant program  (No. ERC-2013-CoG-614715 , NANOHEDONISM). CIBER-BBN  is an initiative funded by the  VI National R&D&i Plan   2008-2011  financed by the  Instituto de Salud Carlos III  with the assistance of the  European Regional Development Fund. The (HR)TEM and STEM-EDS studies were conducted at the  Laboratorio de Microscopias Avanzadas ,  Universidad de Zaragoza, Spain. G. M. thanks the support from the  Miguel Servet Program  (No. MS19/00092 ;  Instituto de Salud Carlos III ). V. S. acknowledges the financial support of  Ministerio de Ciencia, Innovación y Universidades, Programa Retos Investigación , Proyecto >REF:  RTI2018-099019-A-I00. R. A. acknowledges funding from the  Spanish MICINN  (project grant  PID2019-104739GB-100/AEI/10.
Selection of Milk Samples In a cohort study, a total of 128 unpasteurized sheep-milk samples, including 47 BTM samples from 41 flocks and 81 individual milk samples from a MAP-infected flock (MIF) were collected from different farms in Sardinia, Italy. These samples came from animals that were recently tested by other MAP-diagnostic assays, and corresponding data was published in detail in another work (Tables  S1  and S2 )  [31]. Accordingly, the milk samples were distributed into two groups of negative-and MAP-positive controls, based on the positivity/negativity status of the studied animals tested by milk qPCR, commercial milk ELISA, serum ELISA, and fecal PCR. Specifically, the specimens belonging to animals that were found positive by at least one test were considered MAP-positive, whereas the healthy milk samples came from animals that showed no positivity by any of the mentioned techniques (Table  1 ).
| INTRODUCTION Pharmacovigilance is defined by the World Health Organisation (WHO) as "the science and activities relating to the detection, assessment, understanding and prevention of adverse effects or any other drug-related problems". 1 It is conducted throughout the life cycle of medicinal products, starting with the first administration in humans and usually ending when the product is no longer marketed in any country. 2, 3 Pharmacovigilance is performed on safety data originating from many different sources and countries. 3 It includes a continuous evaluation of all available, global data (i. e. , non-clinical and clinical data, including solicited and spontaneous reports). In addition, data for special patient populations is evaluated (e. g. , patients with hepatic or renal impairment, pregnant women, children and the elderly).
Technical issues with obtaining good quality samples for safety monitoring need to be addressed at the time of planning a paediatric study. 76 [79]  The collection of safety data in a paediatric study is, on one side, informed by the paediatric safety specification (e. g. , identified and potential risks and missing information) and, on the other side, by a more general approach of collecting AEs, regardless of severity, seriousness, expectedness or causality. so-called DMEs, will be reviewed whilst considering how they would present in children. 18, 71 AEs may include abnormal test results. If an AE is reported without any test result, it might be relevant to document whether it has been reported by parents/care takers and whether the child has been reviewed by a paediatric HCP. As for adults, every effort should be made to obtain the final diagnosis which should then be added to the Case Report Form (CRF) and coding should ensure the AE is coded to the diagnosis and not kept separate.
80 For example, if the initial AE was feeding difficulties and the final diagnosis was pneumonia, the CRF needs to be updated to ensure the symptom of feeding difficulties is coded under the term of pneumonia. From a pharmacovigilance perspective, feeding difficulties in a child can be due to a broad range of childhood diseases. 6] [87]  The evaluation of seriousness of paediatric AEs should follow standard seriousness criteria. 81  is useful to remember that the quality of ICSRs is key and will help escalate new information in a timely manner. Therefore, investigator training should include the current understanding of the paediatric safety specification and what should be included in a case report explaining why it is important. CRFs with auto-populated fields from the CRF database such as the name of the reporting investigator, the age, sex, weight, height, relevant medical history, co-medications, start date of study drug, dose and frequency will help investigators to provide all the relevant information in the initial report and reduce the number of follow-up requests from the sponsor or health authorities.
| Safety data analysis in paediatric studies The The causality of paediatric safety data can be assessed using a structured approach such as the considerations proposed by Austin Bradford Hill. 63, 64, 82, 84 Supporting Information Table  S2  provides examples of questions for the assessment of causality of paediatric ICSRs and aggregate data based on the Bradford Hill points to consider. Further details on paediatric signal evaluation are described in Section 2. 7. 2.
High-quality spontaneous reports are key for identifying new paediatric safety signals. Therefore, particular attention needs to be paid to the information included in paediatric ICSRs and follow-up may help in providing a high-quality report. 105 Table  4  lists examples of points to consider for paediatric case reports. 18, 81, 106  for adults, calculating the incidence of paediatric ADRs based on spontaneous reports is not possible, because the number of children exposed to the drug of interest is not known. In addition, efforts for the estimation of a denominator are hampered by the fact that databases recording dispensed drugs frequently code data as defined daily dose (DDD), which is the estimated daily maintenance dose for adults. 107 Since children may have been prescribed a smaller or larger dose and prescribing practices vary considerably between HCPs, it is currently difficult to derive reliable numbers for children.