dataesr/scientific-paragraphs-categorization huggingface dataset:
Dynamical Models and Inference From Homotypic Growth Curves We describe the exponential phase 1 dynamics as dL dt = Lr L,1 , dW dt = Wr W,1 , where L (loser) and W (winner) are the population sizes of 168 and 4T07, respectively, and r L,1 and r W,1 are the respective growth rates. In phase 2, we assume a density-dependent competitive Lotka-Volterra model, parameterized in terms of intrinsic growth rates r L,2 and r W,2 and interaction terms a, b, c and d: dL dt = L(r L,2 + aL + bW), dW dt = W(r W,2 + cL + dW). In the homotypic case, terms bW and cL vanish and the phase 2 model is equivalent to logistic growth. We combine the two models and fit to the normalized time-lapse data for the homotypic growth curves using least-squares with R package deSolve  (Soetaert et al. , 2010)  to infer the values of r L,1 , r W,1 , r L,2 , r W,2 , a, and d.
W L = (W i -W f ) /W i × 100 (1) For membrane observation by confocal laser scanning microscopy (CLSM), fluorescent membranes were immobilized on glass slides and let to incubate in artificial saliva at 37 °C under agitation during 30 min, 3 h, 6 h or 24 h. The degradation was stopped by rinsing with acetate buffer until CLSM observation with LSM710 Confocal Microscope (Carl Zeiss SAS, France). All images were analyzed using Carl Zeiss Zen Softwares and Image J Software.
5% Sodium Deoxycholate, SDS (0. 1%) EDTA (1 mM) and a protease inhibitor cocktail (1%) after homogenization using scissors. They were then disrupted using a bead beater (2 × 5 min, 30 Hz, 4 °C) (TissueLyser II, Qiagen, Ger-many) and by sonication (2 min, 60 Hz), followed by centrifugation at 10 0 0 0 rpm for 10 min at 4 °C. Total protein concentration in supernatants was then determined using BCA protein assay kit. Interleukin 1 beta (IL-1 β), IL-6 and Tumor Necrosis Factor alpha (TNF-α) were simultaneously quantified in each sample by electroluminescence. Data were determined as the mean of two replicates and three mice per condition.
Mucosal presentation and penetration of the cargo protein The mucosal penetration of OVA AF647 was tracked by confocal microscopy. Even though the signal intensity 2 min after administration of OVA AF647 in liquid form was visible at the mucosal surface at high magnification (40x objective, zoom 2, data not shown), no signal was detectable in the selected conditions of image acquisition (40x objective) ( Fig. 7  ). Therefore, no control of liquid OVA AF647 application was performed at the longer time points. On the contrary, a clear staining of OVA AF647 was observed at the  mucosal surface after administration by the patch. OVA AF647 entered the keratinized layer 10 min after patch administration and was observed in the epithelium among nucleated cells (red arrows, Fig. 7  ). The patch was not detectable 30 min after administration but OVA AF647 was slightly detectable deeply in the mucosa (white arrows, Fig.
Acknowledgements The authors would like to thank  Thomas Barré  from the An-iCan plateform for his assistance and PHENOCAN for imaging devices [grant number  ANR -11-EQPX-0035  PHENOCAN]; all the technical assistance provided at the  Plateau de Biologie Expérimentale de la Souris, Lyon ;  Jérôme Sohier  for assistance with DMA experiments; PLATIM for confocal microscopy; and  Fabienne Jospin  and  Stéphane Paul  for providing the Ho-1u-1 cells and technical assistance. This work was supported by the  Agence Nationale de la Recherche contre le Sida et les Hépatites virales ( ANRS )  [grant number  ECTZ60600  ]; the  European Union 's  Horizon 2020 research and innovation programme  [grant agreement number  751061  ];  Sidaction  [grant number  11623  ] and the  Agence Nationale de la Recherche ( ANR )  [grant number  192974  ].
This will rely on the reliability of the model; thus, it is essential to be able to correctly evaluate a large dataset. Consequently, a properly estimated correlation model will showcase the complex relationship between protein properties and ingredient functionalities. This paper aims to compare two different data analytical tools, Pearson's correlation analysis and Principal Component Analysis (PCA), in their assessment of a large data set. These advanced and relevant statistical tools were chosen for their diverse nature in explaining relationships between variables: one through covariance (PCA) and the other through correlation. Despite being advanced, they can now be easily used through available software and thus are relevant to both industries and researchers working on large data sets  [8, 9].
The fluorescence map was obtained by measuring the emission spectra at excitation wavelengths from 250 to 450 nm at 5 nm intervals. The emission spectra were recorded from 300 to 550 nm at 2 nm intervals, with a dwell time of 0. 05 s/nm. Slit widths of 5 nm were used for both excitations and emissions, and the iris was set to 100. Rayleigh scattering was removed  [19]. The three-way array spectral map obtained was further decomposed by PARAFAC in MATLAB 2017b (Mathworks, Natick, MA, United States) into three matrices: a score matrix, an excitation loading matrix and an emission loading matrix. The landscape was then divided into two areas: one in the protein region, ranging from 250 to 300 nm in excitation and 325 to 360 nm in emission  [20, 21] , and one for the higher region, with excitation between 305 and 450 nm and emission between 362 and 550 nm (Figure  1 ).
05 s/nm. Slit widths of 5 nm were used both excitations and emissions, and the iris was set to 100. Rayleigh scattering wa moved  [19]. The three-way array spectral map obtained was further decomposed by P AFAC in MATLAB 2017b (Mathworks, Natick, MA, United States) into three matric score matrix, an excitation loading matrix and an emission loading matrix. The lands was then divided into two areas: one in the protein region, ranging from 250 to 300 n excitation and 325 to 360 nm in emission  [20, 21] , and one for the higher region, with tation between 305 and 450 nm and emission between 362 and 550 nm (Figure  1 ). A th component PARAFAC model was sufficient for modelling the protein region (PR1-3). region is in the range of amino acid residues in proteins (tryptophan, tyrosine and nylalanine)  [20] [21] [22].
Results and Discussion The foam and emulsion properties (capacity and stability) of the 37 ingredients (1 fava bean initial concentrate + 24 modified concentrates + 12 process replicates) at pH utilization 4 and 7 were evaluated against the following attributes: (i) nitrogen solubility, indicating the solubility of proteins; (ii) absolute zeta potential, representing the protein surface charge; (iii) fluorescence PARAFAC components derived from the protein region (PR1-3); and (iv) fluorescence PARAFAC components derived from the non-protein region (NPR1-7). This resulted in a data set of 74 observations by 26 variables. First of all, it must be taken into consideration that in this study, both PR and NPR signals were considered. It is likely that both these types of signals contribute to the observed functional properties.
(L. ) B20053 Spain * A. part 90. 00 € TT-S3 Aromazone 4 T. vulgaris L. 19HE0236/5 Spain F. top 55. 00 € TT-S4 Aromazone 4 T. vulgaris L. 20HE0081/1 France * F. top 88. 00 € TT-S5 Bioflore 7 T. vulgaris L. IL1812 France * F. top 178. 00 € TT-S6 Botanica 6 T. vulgaris L. LSB103116 Spain * Flower 74. 60 € TT-S7 Valnet 8 T. vulgaris L. 18K027E France * F. top 157. 40 € TT-S8 Puressentiel 9 T. zygis Loefl. (L. ) unreadable France * A. part 170. 00 € Order place/website: 1-drugstore; 2-onatera. com; 3-voshuiles. com; 4-aroma-zone. com; 5-bioflore. be; 6-lecomptoirdessences. be; 7-kissplanet. shop; 8-docteurvalnet. com; 9-puressentiel. com. F. top-flowering top, A. part-aerial part, ns-not specified. * Organic (AB mark and European label).
Effect of Hyperglycemic Condition and Polyphenols on the Production of Permeability Markers on Cerebral Endothelial Cells The tight junctions occludin, claudin-5, ZO-1 and ZO-2 play a critical role for maintaining the restrictive paracellular permeability of the BBB. We assessed the impact of high glucose concentration on the endothelial permeability to FITC-Dextran and the expression of genes encoding tight junctions. The results show that the hyperglycemic condition led to an increased FITC-Dextran permeability reaching 127. 01 ± 1. 30% (Figure  6A ). Moreover, RT-qPCR data demonstrate that high glucose caused a down-regulation of the expression of genes coding for occludin (Figure  6B ), claudin-5 (Figure  6C ), ZO-1 (Figure  6D ) and ZO-2 (Figure  6E ). Importantly, all polyphenol-rich plant extracts and pure polyphenols counteracted the deleterious effect of hyperglycemic condition on FITC-Dextran permeability and claudin-5 gene expression.
Detection of Polyphenols at the Intracellular Level or Membrane-Bound to Cerebral Endothelial Cells in Normoglycemic or Hyperglycemic Condition There is still a lack of data regarding the molecular mechanisms involved in the uptake of polyphenols by cells. Both Pgp and BCRP are main efflux transporters regulating the BBB paracellular permeability. To determine whether these transporters could be implicated in the interaction between polyphenols and cerebral endothelial cells, bEnd3 cells were exposed to pure quercetin, caffeic, chlorogenic or gallic acids in normoglycemic or hyperglycemic condition, in the presence or not of Pgp or BCRP specific inhibitor. Then, polyphenols present at the intracellular level or membrane-bound to cells were extracted and identified by mass spectrometry. Data show that quercetin, its methylated metabolite isorhamnetin produced by cells and caffeic acid were present at the intracellular level while chlorogenic and gallic acids were not detected in cells (Table  1 ).
Peak intensities were plotted against the corresponding standard concentrations used to build the calibration curves. Intracellular and membranebound concentrations of polyphenols measured were expressed as nM of quercetin, caffeic, chlorogenic or gallic acids. Isorhamnetin levels were expressed as nM of quercetin equivalent. Data are means ± SEM of three independent experiments (three cellular passages). *: p < 0. 05 and **: p < 0. 01 as compared to NG; # : p < 0. 05, ## : p < 0. 01 as compared to HG. nd: not detected.
Polyphenols were identified according to their accurate mass, retention time and MS/MS analysis. Data were acquired by XCalibur 4. 0 software (Thermo Fisher Scientific Inc. , Les Ulis, France). For quantitation, a mix of standard solutions of quercetin, caffeic, ferulic, chlorogenic, gallic and syringic acids was prepared in methanol with concentrations ranging 0. 005-10 µM. In parallel, two quality control samples were analyzed in duplicate within each batch, together with two blank samples containing methanol. The intensity of peaks was plotted against the corresponding standard concentrations to establish the calibration curves. Internal standard recovery was determined similarly. Calibration curves were built according to the method published  [20].
Fungal susceptibility to auranofin under menadione-induced oxidative stress The activity of ARF on fungal growth was also determined under oxidative stress. We first evaluated the effect of menadione on the growth of five randomly selected isolates representing each species. A sublethal dose of menadione (SDM) was defined as the highest concentration causing no decrease in fungal growth. SDM was estimated at 31. 25 µM for the five tested isolates. The activity of ARF at the concentration range used above (15. 625 ng/ml to 16 μg/ml) was then studied in the presence of SDM for all the isolates except S. minutisporum isolates. For each isolate, two concentration-growth curves were generated (ARF alone and ARF + SDM). Data from these curves were then grouped by species, and fitted to a four-parameter logistic model defined in the equation: E ¼ E min þ E max À E min ð Þ =ð1 þ 10 LogIC 50 À X ð Þslope where X is the log 10 of ARF concentration, and E is the percentage of fungal growth observed at X.
The four parameters are: E min and E max corresponding to the minimal and maximal growth (%), respectively; the slope (Hill coefficient) describing the curve steepness; and the IC 50 corresponding to the 50% inhibitory concentration. The shift in the IC 50 of ARF between the control curve (ARF alone) and the SDM-treated curve (ARF + SDM) was accepted only when it was ± four-fold the scale. For some species, the three-parameter logistic model (with slope fixed at -1) fitted better; thus, it was chosen over the four-parameter model. The coefficient of determination (R 2 ) was used to assess the goodness of fit, and the deviation from models was checked using the run test. Modeling was performed using the GraphPad Prism software (version 5).
Analysis of Benzophenone Benzophenone content of lipid nanoparticles was determined by high-performance liquid chromatography (HPLC), using an Agilent 1260 Infinity model equipped with diode array detector (Agilent Tech, Germany). Separation was carried out using a HiCrom C18 column (5 μm, 4. 6 × 250 mm) (HiCrom, UK) at 25°C. Liquid chromatography was performed by isocratic elution at a flow rate of 1. 0 mL/min. The mobile phase composition was acetonitrile:distilled water (80:20 v/v) and 20 μL of each sample was injected into the system to detect BP content at 252 nm. The peak areas were integrated by HPCORE ChemStation software (Agilent Tech, Germany). The determination method of BP was fully validated in accordance with the International Council on Harmonization Guidelines, ICH Q2(R1), and linearity, accuracy and recovery, precision, specificity, detection/quantitation limits were analyzed  (Guideline, 1994).
In vitro drug release studies and release kinetics of BP In vitro drug release studies of uncoated SLN, 1layer SLN and 2layers SLN were investigated using cellulose acetate dialysis bag with a 12-14000 kDa molecular weight cut-off (Spectra/Por, Spectrum Laboratories, Inc. , CA). 2 mL of samples were dropped into the dialysis bag and immersed in 25 mL of 0. 01 M, pH 7. 4 phosphate buffer at 37°C as dissolution medium. Release studies for each SLN formulations were performed in triplicate for 8h and the release medium was completely replaced after 3rd h. At predetermined time intervals, aliquots were withdrawn and directly analyzed by HPLC at 252 nm. Afterwards, various kinetic equations were applied on in vitro release data of coated and uncoated SLN in order to clarify the release mechanism and the effect of coating on release profile.
The cells were seeded in 96-well plates and treated with 1, 20, 50 and 100 µg/mL of the uncoated and coated SLNs for 24h. The cells were then treated with MTT solution (5 mg/mL) and incubated for 2 h. The formazan crystals were dissolved in DMSO and the absorbance was recorded at 540 nm by spectrophotometer (Thermo, Germany). Statistical analyses were performed using GraphPad Prism 6. 0 version (GraphPad Software Inc. ). The results are expressed as the mean±SD of three independent experiments and one-way ANOVA test were applied for multiple comparisons. The differences are * p<0. 05, ** p<0. 01 and # p<0. 0001 compared to control.
ACKNOWLEDGMENTS This study was supported by  Ankara University Scientific Research Projects Coordination , ( 13L3336003 ) and " 2017 -Sejour Scientifique de Haut Niveau -SSHN" scholarships program  of the  Embassy of France in Turkey. The authors would like to thank  Eric Leroy  (ICMPE,  CNRS UMR 7182 ) and  Jean-Michel Guignier  ( IMPMC, Sorbonne University ) for TEM experiments.
Adverse Outcome Pathways (AOP) provide structured frameworks for the systematic organization of research data and knowledge. The AOP framework follows a set of key principles that allow for broad application across diverse disciplines related to human health, including toxicology, pharmacology, virology and medical research. The COVID-19 pandemic engages a great number of scientists world-wide and data is increasing with exponential speed. Diligent data management strategies are employed but approaches for systematically organizing the data-derived information and knowledge are lacking. We believe AOPs can play an important role in improving interpretation and efficient application of scientific understanding of COVID-19. Here, we outline a newly initiated effort, the CIAO project (https://www.
INTRODUCTION The coronavirus disease 2019 (COVID-19) pandemic engages a large number of scientists across the world. Many countries have adapted and re-assigned their research funding schemes, and established expert teams engaged in studying and providing information about the outbreak. Literature and data are emerging quickly and have been made openly available to support reuse. A single-word search on "COVID-19" in PubMed generates a result of 63,481 papers published this year (accessed October 15th, 2020). Several life science data management infrastructures have acted quickly to provide a range of services, such as databases, software tools, and cloud storage, for handling and analyzing the data. For example, the European intergovernmental organization ELIXIR supports COVID-19 data management with a wide variety of services (https://elixir-europe.
org/services/covid-19), and national branch infrastructures have appeared, including the COVID-19 Data Portal in Sweden (https://covid19dataportal. se/) or the Portuguese inter-institutional initiative (https://colife. eu/ en/). In the US, the National COVID Cohort Collaborative (N3C) is coordinating the collection and analysis of clinical, laboratory, and diagnostic data (https://ncats. nih. gov/n3c), including data generated by the National Center for Advancing Translational Sciences (NCATS; https://opendata. ncats. nih. gov/covid19/). Interesting artificial intelligence-driven data compilation initiatives, as well as efforts to harmonize terminology (i. e. , ontology developments) are also arising  (1) ; https://github. com/CIDO-ontology/WCO/. These enormous worldwide joint research efforts on one specific disease provide us with a unique opportunity to map and understand human biology and the response to virus infection in-depth.
However, the immense amounts of information and data are difficult to organize, integrate, and evaluate in order to develop a comprehensive understanding of the disease and apply the knowledge in real-world practice. The Adverse Outcome Pathway (AOP) framework provides a muchneeded common basis and infrastructure for collaboration across various disciplines. The aim of this Perspective is to provide an overview of the goals in a newly initiated effort to apply the AOP framework for systematic organization of COVID-19 research.
WHY ADVERSE OUTCOME PATHWAYS FOR COVID-19? The AOP framework was developed to provide a means for interdisciplinary and systematic gathering, organization, and review of highly variable types of available data and information across multiple levels of biological organization. An AOP organizes information pertaining to a biological process describing a sequence of causally connected biological events that lead to and are essential for the rise of an adverse outcome, such as that caused by chemical exposure or disease. One of the main aims of the framework is to provide mechanistic understanding of health effects allowing for development of mechanism-based and increasingly human-relevant (i. e. , ideally animal-free) model systems for safety testing, diagnostics and drug development  (2). The AOP framework was formalized by the Organisation for Economic Cooperation and Development (OECD) in 2012  (3, 4) , when an AOP Development Programme was introduced and the AOP-knowledgebase (AOP-KB), including the AOP-Wiki (https://aopwiki.
Here we describe the aims and objectives of the European Commission-initiated project CIAO, which broadly covers the aspects outlined above by "modeling the pathogenesis of COVID-19 using the Adverse Outcome Pathway framework" (www. ciao-covid. net/). The project is coordinated by the European Joint Research Center (JRC) together with partners from Humane Society International, Physicians Committee for Responsible Medicine, the OECD Environmental Health and Safety Division, and scientists from the Karolinska Institute and Research Triangle Institute International, with the overarching goal to provide a structured way of organizing and translating scientific evidence into simplified and trusted messages supporting efficient political actions. The main objectives of the CIAO project are (i) to allow for interdisciplinary collaboration toward better understanding of the disease through establishing a unique overview of the complete COVID-19 disease process(es), (ii) to foster crowdsourcing, and (iii) to establish a workflow and infrastructure for systematic handling of data in potential future health crises.
AOPs provide a basis for harmonized communication and collaboration toward common goals and can be seen as providing a simplified "front page" overview of KEs representing "abstracts" of in-depth research, which is maintained within the framework in the form of detailed descriptions, reference to literature and links to curated biological pathway databases  (25). Common goals for multidisciplinary teams can for example be to elucidate KE-related mechanisms, to fully describe KERs, to develop or provide context for KE-targeted alternative model systems and methods for safety testing, diagnostics, and drug development, or to have a scientific basis upon which to take decisions and develop regulation for diverse clinical strategies. Thus, AOP-supported collaborative efforts can be expected to provide a broader overview of COVID-19 and help identify both knowledge ready for real-world application and areas in need of further research, as described in detail in the next section (What can AOPs do for COVID-19 research).
WHAT CAN AOPs DO FOR COVID-19 RESEARCH? AOPs do not produce new data, but rather provide a platform, not only for organizing and integrating the abundant available relevant data and information from any related field, but also for interconnecting scientists and experts from various fields. This holistic and collaborative approach facilitates curation of data, discussions, and an improved knowledge exchange between people that work and research on the same problem but in different fields. In addition, AOPs provide insight into and synthesis of prior knowledge gained within other fields of research, such as toxicology, where the framework has been applied and utilized for the purpose of better understanding chemicaland nanomaterial-associated adverse effects. A wide variety of AOPs covering lung, cardiovascular, liver, and neurological dysfunction, as well as hub KEs describing inflammation and oxidative stress have been developed and all provide a basis and starting point for unraveling effects associated with COVID-19  (5, 21, 29).
In addition, systematic processes for cross-disciplinary application of information and AOPs/KEs developed within chemical toxicity research to nanotoxicology have been described and provide further substance for widening the application of AOPs to COVID-19  (30). Although AOPs may not produce new data per se, interlinking existing data and information across biological levels from molecular up to population level may provide insights and new knowledge that are not evident when data are interpreted in isolation. This supports a better understanding of the underlying pathophysiological processes and an improved basis for interpreting and identifying underlying reasons for the various clinical courses of the disease. For example, there may be turning points in the natural history of the disease, which, when identified, might allow for therapeutic intervention prior to clinical manifestation  (31).