dataesr/scientific-paragraphs-categorization huggingface dataset:
These structural differences, and the presence of a related peptide in Conus planorbis with only one residue different to Pl168 (Y20N mutation; GenBank: ADJ67509. 1), suggests that Pl168 could represent a new structural class and possibly a new sub-family of conotoxins. Extensive mutational studies have been done on a range of α-conotoxins, which have identified residues important for bioactivity, including a recent study on MilIA, the first conotoxin isolated from Conus milneedwardsi  [38]. Interestingly, mutations in MilIA allowed elucidation of residues important in switching between muscle and neuronal nAChRs preference. However, the α-conotoxin family is large with diverse inter-cysteine loop sequences and there does not appear to be a common pharmacophore. The lack of activity of Pl168 against a range of nAChRs suggests that the overall three-dimensional structural and side-chain orientations are not optimal for interaction with the mammalian nAChRs tested in this study.
Background: To assess whether genetically determined quantitative and qualitative HDL characteristics were independently associated with coronary artery disease (CAD). Methods: We designed a two-sample multivariate Mendelian randomization study with available genome-wide association summary data. We identified genetic variants associated with HDL cholesterol and apolipoprotein A-I levels, HDL size, particle levels, and lipid content to define our genetic instrumental variables in one sample (Kettunen et al. study, n = 24,925)  and analyzed their association with CAD risk in a different study (CARDIoGRAMplusC4D, n = 184,305). We validated these results by defining our genetic variables in another database (METSIM, n = 8372) and studied their relationship with CAD in the CARDIoGRAMplusC4D dataset.
Study design and data sources We designed a two-sample MR study using aggregated summary data  [10]  from three published meta-analyses of genome-wide association studies. The main analysis was based on data from Kettunen et al. [13]  (n = 24,925) and the CARDIoGRAMplusC4D consortium [14] (n = 184,305), and the validation analysis used the METSIM  [15]  (n = 8372) and the CARDIoGRAMplusC4D datasets. Regarding the GWASs on lipoprotein traits, we used the Kettunen et al. and the METSIM studies because these are the only ones with publicly available summary data. Regarding CARDIoGRAMplusC4D, we selected it because it had several quality characteristics that made it stand out among all GWAS on CAD with publicly available summary results: a large number of total participants (n = 184,305), a large number of CAD cases (n = 60,801), a large number of genetic variants analyzed (8.
Mendelian randomization analyses Based on the adjusted gene variant effects and their standard errors computed as previously described, we performed the MR analysis using the Generalized Summary-data-based Mendelian Randomization method  [18]. The genetic variants to be considered were selected with the following criteria: 1) strong association with the lipid traits of interest (p-value<5•10 -8 ); 2) not in linkage disequilibrium (R 2 < 0. 01, using the 1000 Genome project datahttp://www. 1000genomes. org/ phase-3-structural-variant-datasetas reference  [19] ); and 3) a minor allele frequency ≥ 0. 05. As an additional approach to exclude potential pleiotropy, we also removed the variants with a significant result in the HEIDI-outlier test (p-value<0. 01). Finally, we explored and confirmed the exclusion of potential pleiotropic effects using Egger regressions  [20].
Selected genetic variants We identified genetic variants significantly associated with 13 lipoprotein characteristics in the data published by Kettunen et al. and with 8 lipoprotein traits in the METSIM data. The number of genetic variants for each lipoprotein trait ranged from 6 (level of medium-sized HDL particles) to 22 (ApoA-I). Genetic variants included in the analyses and their unadjusted and adjusted effects are listed in Supplementary Excel File 1. We observed high inverse genetic correlations (correlation coefficient ≤ -0. 50) between triglyceride and HDL-C levels. Conversely, we observed very high direct genetic correlations (correlation coefficient ≥ 0. 70) between the cholesterol content in each HDL size subpopulation and HDL-C concentrations, between HDL mean diameter and the level of very large HDL particles, and between ApoA-I and the level of very large HDL particles and their cholesterol content (Supplementary Fig.
Both observational and experimental studies have more consistently found an inverse relationship between the number of HDL particles and cardiovascular risk, compared to HDL-C levels  [7]. Similarly, we observed that the concentrations of HDL particles of all sizes were inversely related to CAD risk, although only the genetically determined levels of very large HDLs were significantly associated with it in the main analysis. Unfortunately, we could not validate these results due to the lack of valid genetic variants in the METSIM study. Our results are mechanistically plausible and highlight novel potential therapeutic targets in cardiovascular disease, since genetic variants individually associated with HDL qualitative traits and CAD in our data were located within several HDL-related genes or gene clusters.
Fifth, the statistical power of our analyses was limited for some of the traits of interest. Finally, in the validation analysis we could not generate genetic instrumental variables for some of the lipoprotein traits. However, our study has several methodological strengths. First, our results are based in MR, a useful approach to explore the causality of the association between biomarkers and specific diseases. Second, we included two independent MR analyses to validate the results initially observed. Finally, the validity of the genetic variants for HDL-C, LDL-C and triglyceride levels initially generated was confirmed, supporting the validity of these datasets for the analysis of other genetic variants.
Acknowledgements We thank  Elaine M. Lilly, PhD , for her critical reading and revision of the English text. Data were downloaded from: www. computationalmedicine. fi/data#NMR_GWAS(Kettunen study), http:// csg. sph. umich. edu/boehnke/public/metsim-2017-lipoproteins/(METSIM study), www. cardiogramplusc4d. org(CARDIoGRAMplusC4D), http://csg. sph. umich. edu/abecasis/public/lipids2013/( Global Lipid Genetic Consortium ), http://www. 1000genomes. org/phase-3-structural-variant-dataset(1000 Genome).
Funding sources This work was supported by the  Instituto de Salud Carlos III-European Regional Development Fund  [grant numbers  CD17/00122 ,  IFI14/00007 ,  PI18/00017 ], the  Medical Research Council  [grant numbers  MR/K501256/1 ,  MR/N013468/1 ], the  Spanish Ministry of Economy and Competitiveness  [grant numbers  BES-2014-069718 ,  SAF2015-71653-R] , the  European Union 's  Horizon 2020 Research and Innovation Program  [grant number  796216 ], and the  Government of Catalonia  through the  Agency for Management of University and Research Grants  [ 2017 SGR 222 ]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. CIBER Enfermedades Cardiovasculares (CIBERCV) ,  Epidemiología y Salud Pública (CIBERESP) , and  Fisiopatología de la Obesidad y Nutrición (CIBEROBN)  are initiatives of the  Instituto de Salud Carlos III, Madrid, Spain , and financed by the  European Regional Development Fund.
Study design ACROSTUDY is an open-label, global, non-interventional, longitudinal, post-marketing surveillance study that included patients with acromegaly who were treated with PEG. All collected patient data were from routine clinical practice. All patients gave their informed consent for the study. ACROSTUDY included 2221 patients who were treated with PEG from 14 countries, between 2004 and 2018, and provided long-term follow-up for safety and treatment outcomes. In France, the study was not approved by a medical ethical or institutional review board as not deemed necessary by the French law for observational noninterventional studies on humans at the time of initiation. However, formal approval of participants was obtained for using patients data for study purposes and data handling was performed according to national laws.
The second objective was to compare the efficacy of the three regimens in terms of IGF-I normalisation, dose and tolerance. To understand and define the treatment choice rationale, we compared M to PB and M to SB. The patient characteristics and treatment data were compared between baseline and after PEG initiation for each treatment regimen. After PEG initiation, the first and the last IGF-I levels of each treatment regimen were analysed as well as the PEG posology at the time where these IGF-I levels were assessed. Baseline was defined as the period before PEG initiation. After PEG initiation, the first and the last IGF-I levels of each treatment regimen were analysed as well as the PEG posology at the time where these IGF-I levels were assessed. For SB, data after PEG treatment and before SA introduction were also considered.
Statistical analysis The full analysis set included all French patients enroled in ACROSTUDY. Data are reported as mean ± standard deviation or median with first and third quartiles (Q1, Q3) according to the distribution or percentages, as appropriate. IGF-I and GH levels were log-transformed, and results are expressed as geometric means. As the three patient groups were not defined by a randomisation process, betweengroup comparisons were considered exploratory. Continuous variables were analysed using the Student's t test or Mann-Whitney U test for independent samples and the paired t test or sign test for paired samples. Chi-square and Fisher's exact tests were used to compare categorical variables between groups, and a McNemar's test was used for paired samples. The time needed to achieve first normal IGF-I level was described using Kaplan-Meier estimates and compared using a log-rank test.
Demographic characteristics We analysed data from 312 patients who were enroled from the French centres of ACROSTUDY. The main demographic characteristics of the patients are given in Table  1. By definition, all patients were being treated with PEG when they were included in this study. The number (%) of patients in the three groups: M, PB and SB cohorts, for this descriptive analysis was: 167 (53. 5%), 88 (28. 2%) and 57 (18. 3%), respectively. In terms of previous therapy of the 312 patients enroled in this analysis, 238 (76. 3%) had undergone surgery, 83 (26. 6%) had undergone irradiation and 293 (93. 9%) had been treated by SAs before inclusion in ACROSTUDY. There were no clinical differences between the three groups of patients in terms of past treatment history. The proportion of patients with concomitant treatment with DA was 24% (n = 75) in the whole cohort and 18.
Compliance with ethical standards Conflict of interest  P. Ca. has served as a consultant and speaker for  Ipsen ,  Novartis  and  Pfizer , and as an advisory board member for Ipsen. B. D. has served on advisory boards  Pfizer ,  Novartis , and was speaker for  Novartis, Ipsen ,  Pfizer. H. L. has received payment for lectures ( MSD, Novo-Nordisk, Novartis ) and travel grants ( Novartis, Ipsen ,  Pfizer , Sanofi). G. R. has received unrestricted research grants from  Ipsen  and  Novartis , G. R. has served as an investigator (principal or coordinator) for clinical trials funded by  Novartis ,  Pfizer ,  Ipsen, Chiasm. He gave lectures for Ipsen ,  Novartis  and  Pfizer. C. C-. R. has served as an investigator (principal or coordinator) for clinical trials funded by  Novartis ,  Pfizer  and  Ipsen , she is an advisory board member for Novartis, and gave lectures for Ipsen and  Novartis.
T. B. received research grants from  Ipsen ,  Pfizer  and speaker fees from  Advanz ,  Ipsen ,  Novartis ,  Pfizer , Strongbridge; he was clinical trial investigator for  Novartis, Strongbridge. P. Ch. has received unrestricted research and educational grants from  Ipsen ,  Novartis ,  Novo-Nordisk  and  Pfizer ; he has served as an investigator (principal or coordinator) for clinical trials funded by  Novartis ,  Pfizer ,  Ipsen , Italpharmaco, Antisense and  Prolor Biotech ; he is an advisory board member for  Ipsen ,  Novartis ,  Pfizer  and Tiburio; he gave lectures for  Ipsen ,  Novartis  and  Pfizer. All the fees and honoraria are paid to his institution. R. H. , C. G. and Y. B. are Pfizer employees. The other authors declare that they have no conflict of interest.
Hence, a combination of different 'omics' levels is likely to improve the identification of mode of action related information that may finally be used to understand population responses as part of a risk assessment. Therefore, the scientific community has developed numerous tools that combine omics data  (Pinu et al. , 2019) , mainly designed for integrative pathway investigation but rarely provide outputs usable to derive thresholds or points of departure for chemical effects. Combining complex responses at different biological levels represents a challenge because different experimental designs are often used for each level (e. g. exposure time, number of replicates), the way they are measured (e. g. different extraction methods, chemical-analytical tools, pre-processing of data) and the nature of the data (e.
3 mL from the lower lipophilic chloroform/methanol phase were transferred into 3 mL reaction glass vials. Samples were dried at 40 °C under a nitrogen stream for a maximum of 3 h. Compounds from hydrophilic extracts were methoximated using 200 μL MOXsolution for 120 min at 80 °C. Compounds from the lipophilic extracts were esterified with 300 μL methanolic HCl for 240 min at 80 °C. Afterwards, both fractions were dried under a stream of nitrogen at 40 °C and silylated in 100 μL of MSTFA for 20 min at 90 °C. Metabolites were analyzed using an Agilent 6890 N gas chromatograph coupled to a quadrupole 5975 mass spectrometric detector (GC/MS) guided by the Agilent ChemStation software (G1712DA, Rev. 02. 00). From GC/MS analysis, peaks were deconvoluted and identified by using AMDIS with a matching to the mass spectra (match factor > 90 %) against an inhouse library, NIST database, Golm Metabolome Database and Fiehn library.
Area Under the Curve (AuC) -values were obtained for 224 metabolites. Then, we applied the following procedure: i) removing of metabolites for which the proportion of missing data (no detections) across all the samples is higher than 50 percent, ii) retrieving of missing values data using half minimum method (i. e. half of the minimum value found for a metabolite across all samples), iii) log10-transformation of AuCvalues.
The microarrays were washed and scanned, using an Agilent DNA Microarray scanner, and fluorescence intensities were evaluated using the Agilent Feature Extraction software. Genome information (cDNA sequences) have been deposited in NCBI's Sequence Read Archive and are accessible through the SRA accession number PRJNA498405 (https://www. ncbi. nlm. nih. gov/sra/PRJNA498405). The fluorescence data have been deposited in NCBI's Gene Expression Omnibus and are accessible via GEO Series accession number GSE122159 (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc= GSE122159). Fluorescence raw data were finally log2 transformed.
Metabolome and transcriptome dataset Our aim at this working step was 1) to identify the responsive elements of the transcriptome (= probes or gene transcripts) and the metabolome (=metabolites) to triclosan, 2) to derive their response trends (e. g. increase, decrease, U-shape, bell-shape) along the concentration gradient and 3) to calculate their effect concentrations as benchmark dose (BMD) based on modelled response curves. Both, the transcriptomics and metabolomics datasets were analysed by the DRomics tool  (Larras et al. , 2018) , designed to handle' omics' datasets obtained in a concentration-response framework (Fig. 1 ). Data were analysed using the DRomics R package version 2. 0-1 (Delignette-Muller et al. , 2019). In the first step (import and pre-treatment), the transcriptome dataset was normalized between arrays using the cyclic loess method.
Functional analysis Because the main interest of this study was to work on concentration-dependent contigs, only the sequences of the 3177 contigs remaining after the DRomics analysis were blasted against the NCBI nr database (used on the 15th July 2019, taxa investigated: chlorophytes, cyanobacteria, diatoms and Arabidopsis; e-value = 0. 001) using the DIAMOND tool  (Buchfink et al. , 2015). A NCBI code was obtained for 1612 contigs. Enzyme number was retrieved for 507 contigs via the UNIPROT tool (The UniProt Consortium, 2019). A Kyoto Encyclopedia of Genes and Genomes (KEGG)  (Kanehisa, 2000)  code was also retrieved but 443 contigs were finally associated to KEGG pathways relevant for microalgae. Annotation of the 68 metabolites selected by DRomics was performed manually against the KEGG database and 31 metabolites were found with annotations.
A1 ). We modeled the response with a Hill model to omit the hormesis and capture only the inhibition phase. For each molecular level, a cumulative sensitivity distribution (CSD) of effects concentrations (here, BMD-1SD) was built on 1/ all annotated metabolites or contigs, and 2/ all non-annotated metabolites or contigs (Fig. 2 ). For both molecular levels, a major proportion of items were not annotated according to the KEGG database (54. 4 and 84 %, respectively), however, non-annotated metabolites and contigs exhibited BMD-1SD frequency distributions comparable to those of the annotated ones. Effect concentrations from metabolites (here, BMD-1SD values) for triclosan exposure were found in a wide range of exposure concentration, from 0. 11 to 7 μg/L. The effect concentrations for transcript abundance showed a comparable range from 0.
Discussion The management and interpretation of stress responses at different molecular levels represents an issue faced by multiple research areas (e. g. food risk, medicine, ecological risk). This is demonstrated by the recent rising of works providing recommendations as how to better support multi-omics studies  (Beale et al. , 2016; Haas et al. , 2017; Misra et al. , 2019; Pinu et al. , 2019). In our study, we used the power of the concentration-response framework which allowed us the combination of multi-omics levels and enabled a quantitative comparison of dynamic molecular responses in cumulative sensitivity distributions. We especially paid attention to manage the data of those two molecular levels regarding their nature and the way they were obtained. The transcriptome and the metabolome datasets were both analyzed by the same statistical workflow (Fig.
Complexity of molecular responses Metabolite and contigs responses to triclosan exposure showed a remarkable complexity in terms of variety of (i) response trends, (ii) signal intensities and (iii) sensitivities. This response diversity was found at both molecular levels as well as within individual pathways. From the initial set of metabolites (n = 224) and contigs (n = 21495) measured in this study, 30 % of each were significantly impaired by the biocide. Also, almost all pathway classes of the basic metabolism (according to the KEGG-database) of the chlorophyte were disturbed with a high coverage of the underlying pathways, indicating severe and unspecific impacts on the global metabolism of the chlorophyte or even compensatory responses. To disentangle these complex response patterns, new approaches (e.
Acknowledgments We thank the  European Commission  for the financial support ( MicroERA  project,  H2020-MSCA-IF-2015  (Grant number: 705149)). This work has been also financially supported by the " Ecosphère continentale et côtière" (EC2CO) interdisciplinary program  from the  Centre National de la Recherche Scientifique (CNRS, France) , as a part of the  DROMADERE  project. We additionally thank  Janet Krüger  and  Nicole Schweiger  for support in the laboratory.
Morphological Characterization of the Particles Scanning electron microscopy was conducted on an Inspect F50 (FEI Company, Hillsboro, OR, USA) at an accelerating voltage of 20 kV. The microparticles samples were coated with gold before analysis. Electro-sprayed phase-change microparticles were observed via optical microscopy (Motic BA410, Barcelona, Spain) coupled to a Moticam 5 Digital Camera (Barcelona, Spain), controlled by the software Motic image plus 3. 0 which was used to analyze the microparticles. Particles diameters were measured from the OM and SEM micrographs using the Image J software. Size distributions were obtained from a minimum of 200 random measurements. The obtained data were statistically analyzed in a spreadsheet (Microsoft Excel 2011, Microsoft Company, Redmond, WA, USA) to calculate the mean diameter as well as the particle size distribution.
Thermal Analyses of the Samples The thermal behavior of n-hexadecane, n-eicosane, PCL microparticles and mPCM was recorded using a DSC 3+ (Mettler Toledo, Columbus, OH, USA) piloted on PC with STARe software. The sample space was purged with nitrogen flow during the experiments. The onset temperatures of solid-liquid and liquid-solid transitions (T onset ), end temperatures of phase transitions (T end ), and maximum temperatures of melting (T m ) and crystallization (T c ), as well as melting (∆H m ) and crystallization (∆H C ) enthalpies of each transitions were determined. 3-8 mg of microcapsules (or n-alkanes) were placed in an aluminum pan and placed in DSC apparatus, heated from -20 • C to 80 • C at 5 • C•min -1 , held constant at 80 • C for 10 min, and then cooled to -20 • C at the same scanning rate, and held for 10 min.
Sample Label Latent Heat (J•g -1 ) Differential Scanning Calorimetry was also used to determine the effect of solvent and core compounds on the thermal properties of PCL microparticles. All data were determined from the second run, and the first run was used to clear the thermal history. The DSC thermograms (Figures  4  and 5 ) show single melting peaks with maximum melting start and end temperatures of 54. 9 and 56. 4 • C for ethyl acetate solution, which increase slightly to 55. 6 and 57. 6 • C for chloroform. The latent heat of fusion associated with these transitions is approximately 57. 0 and 59. 5 J•g -1 , which corresponds to a crystallinity degree of 40. 8% and 42. 7%, respectively. The presence of n-alkanes in the microparticles does not affect the melting temperatures of the PCL, but at high n-hexadecane content, there is a slight decrease in crystallization temperatures, while n-eicosane acts as a nucleating agent of the PCL, and therefore the crystallization temperatures increase.
The data available in the literature on Bentheimer and Berea sandstones were used as input parameters for the interpola1on module (using finite elements) that was developed by di Chiara Roupert (2009). Then, for each rock sample, the three-phase data that are TD-compa1ble will be determined from a global capillary pressure func1on and a global mobility func1on, which depend on the satura1on and overall global pressure level p. Note that the boundary condi1ons (of the ternary diagram) for the global capillary pressure and global mobility must be consistent with the three sets of two-phase data ("step 2" and "step 3" in Table  1 )  (di Chiara Roupert et al. , 2010a). Note, in "step 2", one has to respect the so-called TD-compa1bility condi1on. The three sets of two-phase data built up on the edges of the ternary diagram have to sa1sfy this condi1on.