dataesr/scientific-paragraphs-categorization huggingface dataset:
Particulate matter formation from incomplete combustion The large amount of particles observed at 800 °C may be explained by the incomplete combustion of the biomass. Soot, elemental carbon and tars are visually observed on the collections stage of the ELPI. The combustion of softwoods (as spruce) is well-known and numerous literature data are available to explain that they largely contribute to give tars and soot, contrary to hardwoods. The formation of products as CO and Polycyclic Aromatic Hydrocarbons (PAH) during an incomplete combustion is favored. PAH mainly constitute tars and are defined as soot precursors  (Eberle et al. , 2017). The high number of PM2. 5 for hydrolysis lignin at 800 °C may be attributed to the incomplete combustion of this biofuel, mainly composed of aromatic polymers as lignin, as deduced from the lignocellulosic composition of this material.
After stabilization, the voltage was varied from 0. 1 to 30 V for each flow rate. Each point displayed on Figure  8  corresponds to an average of 10 signal measurements taken within 2 min interval at each flow rate and voltage. The repeatability is calculated individually for each point and corresponds to the standard deviation of the 10 measurements. The relative uncertainty of the µPID signal (U/Signal) varies from 7. 5% to 0. 1% at low and high voltages respectively. signal measurements taken within 2 min interval at each flow rate and voltage. The repeatability is calculated individually for each point and corresponds to the standard deviation of the 10 measurements. The relative uncertainty of the µPID signal ( / ) varies from 7. 5% to 0. 1% at low and high voltages respectively. The three flow rate datasets (0.
In order to compare the performance of the three channels fabricated, Figure  9  presents the current signal for nitrogen and toluene 100 ppm for each of the three channels. The gas sample was injected at 20 mL/min and 30 V was applied on the electrodes. The current was measured after signal stabilization and five measurements were taken, the values displayed on Figure  9  are the average. The same procedure was adopted to calculate the uncertainties of this data. However, the repeatability corresponds to the average of the five measurements taken after stabilization. The values of uncertainties are given in the table of Figure  9  and the highest yielded value was 1. 9%. The three flow rate datasets (0. 5, 10 and 20 mL/min) present similar curve shape and the signal increases with flow rate.
54056 Å) radiation. For high-quality patterns, the lower level of the pulse-height discriminator was increased to remove a high background from Fe fluorescence and data collected in the 2θ-range 10-135 • for a total time of ~20 h. Structural analysis was performed by the Rietveld method, as implemented in the FullProf program (https://www. ill. eu/sites/fullprof/php/downloads. html, Grenoble, France)  [14].
6 Synthetic transition metal nitrite compounds, especially those of Fe and Cu, are of interest in relation to the mechanism of the activity of nitrite reductase enzymes. 7, 8 However, as a ligand, nitrite has not been studied in as much detail as its fully oxidized nitrate congener (a CCDC search on 26/4/2019 gave 2106 entries for Cu-ONO 2 , but only 141 for Cu-ONO and 34 for Cu-NO 2 (Cambridge Structural Database version 5. 0, update Feb. 2019)), possibly because of its instabilitynitrite is often either oxidized to nitrate or reduced to NO, even under mild conditions. Neither has the function of a Cu I/II -NO 2 center in CuNIR been fully understood; while multiple model studies have pointed to redox-induced release of N 2 O and NO from the Cu I/II centers of CuNIR, the order of events (i.
Potentials are reported vs. Fc + /Fc. Single crystal X-ray diffraction data were collected on a Bruker D8 QUEST CMOS system equipped with a TRIUMPH curved-crystal monochromator and a Mo-Kα fine-focus X-ray tube with graphite monochromated Mo-Kα radiation (λ = 0. 71073 Å) at ambient or low temperature using the APEX3 or APEX2 suite. 22 Frames were integrated with the Bruker SAINT software package using a narrow-frame algorithm. Absorption effects were corrected using the multi-scan method (SADABS). 23 Structures were solved by intrinsic phasing methods with ShelXT  24  and refined with ShelXL using full-matrix least-squares minimization Using Olex2. 25 All nonhydrogen atoms were refined anisotropically. Hydrogen atoms positions were calculated and fixed by HFIX with their thermal ellipsoids riding on those of their carbon atoms.
and ẟh). A sphere can then be plotted. The center of the sphere corresponds to the solubility parameters of naringin. The radius of the sphere is determined experimentally by testing the solubility of naringin in different solvents. Effective and suitable solvents for solubilization of naringin are those within the sphere. Solvents that are not appropriate for the solubilization of naringin will therefore be outside the sphere. The sphere is drawn using the Matlab 2015b program. The distance Ri between the naringin (center of the sphere) and every solvent is calculated as follows  (Li, Fabiano-Tixier, Ginies, & Chemat, 2014) : Ri 2 = 4 [ẟd (A)-ẟd (B)] 2 + [(ẟp (A)-ẟp(B]) 2 + [(ẟh (A)-ẟh (B)] 2 (8) Where A refers to the solute (naringin) and B to the solvent.
Statistical analysis All experiments and measurements were repeated at least three times. One-way analysis of variance (ANOVA) was used for statistical analysis of the data using SPSS (Statistical Package for the Social Sciences) version 24. 0. ANOVA is a parametric statistical test, used to compare three or more mean values, in order to check if the differences are significant  (Granato, de Araújo Calado, & Jarvis, 2014). In order to specify exactly the pairs of means that differ statistically, the post hoc Fisher's Least Significant Difference (LSD) was conducted for all the analysis. For each analysis, significance level of 5% was assumed. The error bars presented on the figures correspond to the standard deviations.
, 1978). In 2004, guidance for the application of a tiered TTC approach for chemicals present in the diet was published by  Kroes et al. , (2004). Several classes of chemicals are excluded from TTC, including metals, proteins, steroids, nanomaterials, potent hormones, suspected bioaccumulation potential, and high potency carcinogens  (SCCSP, 2012). There have been multiple investigations  (Lapenna and Worth, 2011; Tluczkiewicz et al. , 2011; EFSA/WHO 2016)  for potential revisions of the classification scheme; however, the decision tree has so far withstood these. The Threshold of Regulation approach was originally intended for substances potentially migrating into foods from food packaging. The underlying toxicological datasets broadly were limited only by the availability of data and not by a consideration of the type of chemical or its intended use (first for carcinogens and later for non-cancer endpoints).
In 2016, a more favorable opinion of the SCCS was published (SCCS, 2016) based on work expanding the chemical domain by bringing in cosmetic associated chemicals (ingredients and contaminants) from the COSMOS database  (Yang et al. , 2017). This work was carried out by the EU and CosEU-funded COSMOS project (http://www. cosmostox. eu). Since the TTC is based on toxicological data from oral exposure studies in animals expressed as an administered dose, the appropriate TTC level is compared to a human external exposure estimate. There are times in a safety assessment where the internal exposure is more meaningful, for instance linking the internal exposure to the cellular concentration leading to the occurrence of adverse effect. An 'internal TTC' (iTTC; i. e. , a TTC based on plasma concentration) has been suggested by several groups  (Bessems, 2009; Coecke et al.
, 2013; Hartung 2017; Partosh et al. , 2015)  as a possible evolution of the externally-based TTC that could be a useful approach in the development of non-animal methods and as a tool to further refine the use of TTC and expand its applicability. If an iTTC database could be established to derive iTTC thresholds, then human plasma concentrations (estimated or measured) for a given exposure scenario could be compared to these iTTC thresholds. The iTTC thresholds would provide conservative hazard-based values that could be utilized in exposure-based safety assessments in the context of: (1) refinement of de minimis exposure levels for dermal exposures; (2) metabolism-based structure-activity relationship (SAR) assessments; (3) low level aggregate exposures from different dose routes, or; (4) in vitro biological assays.
Case study examples for some of these possible uses are presented in Supplementary File 1. One of the primary constraints to developing an iTTC database has been the significant resource investment that it would take to complete this work. Partosch et al. , (2015)  attempted to derive an iTTC by adjusting the external No Observable Adverse Effect Levels (NOAELs) of substances from three databases by in silico estimates. Limited details are provided regarding the calculations of internal exposure made in  Partosch et al (2015) ; however, the authors do state that the NOAELs defined from administered dose were multiplied by in silico estimates for human oral bioavailability. The oral bioavailability prediction method used by  Partosch et al. , (2015)  takes into account passive oral absorption but does not consider the possible impact of active transport (either influx or efflux), pre-systemic metabolism, systemic metabolism and clearance or any of the important factors that would impact internal exposure levels to target tissues after passive uptake from the GI tract.
iTTC preliminary framework A proposed approach for deriving iTTCs based on the concentration of a chemical in blood is shown in Figure  1. The approach starts with identifying an existing TTC dataset which contains chemical specific NOAELs expressed as an external dose in mg/kg/day. For each of the chemicals within the dataset, it will be necessary to identify chemical-specific ADME data through a multi-tier approach of literature searching, using in silico estimation tools and generating empirical data. Chemical-specific PBPK modeling will then be conducted to convert the chemical-specific external exposures from the TTC dataset of chemicals to an internal blood concentration (Css, AUC, or C max ) for each chemical. Css has most commonly been used for comparison to exposure estimates of daily aggregated dose  (Wetmore et al.
Chemical Databases for the iTTC project The first step in the framework is to identify suitable TTC datasets on which the iTTC database would be based. Ideally, the data should be transparent and publicly available. Of the multiple databases that have been published, the  Munro et al. , (1996)  non-cancer TTC database is one of the more widely known and cited. This database consists of 613 chemicals, representing a range of industrial chemicals, pharmaceuticals, food substances and environmental, agricultural and consumer chemicals likely to be encountered in commerce. The database contains data from multiple species (rat, mouse, rabbit and hamster) and all data are derived from oral exposure studies. For each of the chemicals, the database identifies a sub-chronic or chronic NOAEL. A second TTC database that was prioritized is the COSMOS database (553 chemicals), which was developed through a joint European Commission-CosEu funded project, "Integrated in silico models for the prediction of human repeated dose toxicity of cosmetics to optimise safety" "Safety Evaluation Ultimately Replacing Animal Testing" (SEURAT-1, COSMOS)  (Cronin et al.
, 2012). The COSMOS TTC database is a non-cancer TTC dataset enhanced with cosmetic and packaging materials. It contains chemicals that have been tested in oral exposure studies in rat, mouse, dog, primate or rabbit and it contains NOAELs from studies of varying durations 28 days or longer  (Yang et al. , 2017). The Munro and COSMOS TTC databases have been combined, and the current version (version 2. 0) is located on the COSMOS website at https://cosmosdb. eu. The third relevant database that was selected by the Working Group is the Research Institute of Fragrance Materials (RIFM) database (1355 chemicals), which is composed of flavor and fragrance materials. RIFM follows the same approach as COSMOS and Munro and defines thresholds for each chemical included in the database. One of the unique features of the RIFM dataset is that it includes data from chronic, sub-chronic reproductive and developmental toxicity studies for a large number of chemicals in Cramer Class II, which is a Cramer Class that has historically been underrepresented in TTC databases.
The combination of the RIFM dataset with the Munro and COSMOS databases results in more than 1000 unique chemicals. The diverse chemical dataset that is derived by combining the Munro, COSMOS and RIFM databases introduces a practical challenge for the iTTC work related to the resources required to generate new in vitro ADME data and complete PBPK modeling for such a large set of chemicals. In some cases, it may be impractical to generate new in vitro ADME data for a chemical due to issues with chemical availability, cost, stability, solubility or toxicity. To narrow down the number of chemicals in the final iTTC workflow to a more manageable number that can be used in a strategic targeted assessment, the chemical space will be assessed according to descriptors related to chemical structure, as well as ADME and pharmacokinetics (PK) to ensure that the final iTTC chemical dataset contains a distribution of chemicals covering a broad chemical and PK space.
Strategy for PBPK model simulation verification in absence of in vivo data-The iTTC project will utilize PBPK modeling to estimate the internal exposure that is associated with the external dose (NOAEL) from a toxicity study. Application of PBPK models typically involves a verification step which compares the simulated data from the PBPK model to in vivo data, so that the model accuracy can be evaluated  (Clark et al. , 2004; McLanahan et al. , 2012). In the case of the iTTC project, many of the chemicals in the database will lack the existing PK data that would typically be used for model verification, and as such, the Working Group discussed possible strategies to evaluate the iTTC PBPK modeling workflow performance in the absence of PK data. One possible strategy for evaluating the accuracy and uncertainties in the PBPK modeling workflow is to utilize anchoring data similar to approaches published by  Yoon et al.
Prior to conducting k-means clustering and PCA, structural accuracy of each chemical was determined by using SciFinder (American Chemical Society) and confirming concordance between the chemical name, CAS registration number and structure. Additionally, chemicals with incompletely defined structures or multiple components were excluded from the dataset since the in silico modeling approaches that will be utilized in the project can only be applied to chemicals with a single defined structure. Combining the  Munro et al. , (1996) , COSMOS and RIFM databases and applying the exclusion criteria mentioned above (e. g. removing incompletely defined structures) resulted in approximately 1250 unique chemicals which could be included in the iTTC project. For each chemical, continuous molecular descriptors were calculated using the CORINA Symphony Descriptors Community Edition V2 (Molecular Networks GmbH, Germany and Altamira, LLC, USA) with SMILES strings as input; the presence or absence of relevant structural features was described using ToxPrint fingerprints (v2.
0 r7. 11) implemented in the ChemoTyper software Version 1. 0 (Revision 14605M) (Molecular Networks GmbH, Germany and Altamira, LLC, USA); and physicochemical and ADME descriptors were estimated using ADMET Predictor 8. 5 (Simulations Plus Inc. , Lancaster CA), Advanced Chemistry Development, Inc. (ACD/ Percepta, version 1. 3, Advanced Chemistry Development, Inc. , Toronto, ON, Canada, www. acdlabs. com, 2017)  and OPERA  (Mansouri et al. , 2016 and 2018). An average value was used when an ADME descriptor could be estimated by more than one software package. Additional descriptors utilized in the assessment included Cramer Class (obtained from COSMOS website; for RIFM chemicals it was determined using Toxtree followed by expert review), ECCS class and expected clearance pathway (obtained using  Varma et al.
, (2015)  approach and in silico inputs for permeability that were estimated from ADMET Predictor and ACD), passive permeability potential (obtained using  Smith et al. , (2014)  approach) and likelihood for being a P-glycoprotein substrate (obtained using ADMET Predictor). Table  1  provides a summary of select descriptors for the iTTC chemical dataset, while Supplementary File 2 contains a list of all of the chemical specific descriptors, the k-means clustering group and the principle component (PC) values for PC 1 -PC 10. The freely available program, CheS-Mapper (v2. 6. 6) (Gutlein et al. , 2012)  was used to visualize the PCA. The data matrix was cleaned to remove collinear (i. e. identical) or redundant (i. e. lacking relevant information) variables. A PCA using the significant Corina Symphony, ToxPrint and in silico ADME descriptors was performed (Fig.
Results from the literature search (see next section for details) will also be integrated into the chemical selection process so that the existing PK and ADME data can be maximized and to highlight where additional in vitro data may be required. 2. 6. 2 Literature search-In the months following the iTTC workshop, an automated workflow was developed and is being utilized to locate relevant PK and ADME data for the iTTC chemicals. The workflow is implemented in Spyder using the Bio-python Entrez package to find articles with the specified compound, species, and either metabolism, pharmacokinetics, or clearance in PubMed. The search string that was used to search the title and abstract of articles via PubMed was: ({compound/synonym}) AND ("{species}" OR "-{species}" OR "{species_plural}") AND (metabolism OR pharmacokinetic* OR clearance).
First, a list of synonyms was curated for each compound by searching the Medical Subject Heading (MeSH) database with its CASRN. Then, PubMed was searched iteratively using each compound's synonyms to attain the articles that matched the search string criteria. The search was performed with respect to a specific species in which NOEL data was collected. A secondary search was then performed to create a term frequency matrix for each article using keywords geared towards prioritizing papers that have IVIVE and/or pharmacokinetics data. The hypothesis was that papers having a higher frequency of our specified terms would be more relevant for PBPK modeling. Thus, to prioritize the importance of an article, a score was calculated that summed the most important terms from the list. Manual review of the literature and extraction of the relevant data is ongoing.
The literature search will: (1) provide an opportunity to identify existing PK and ADME data for chemicals within the TTC dataset; (2) help prioritize which chemicals need more data generated for modeling; (3) help identify existing in vivo data to support verification of PBPK models; (4) help determine if metabolism of a chemical is activating, detoxifying or neutral, (5) identify gaps in coverage of the toxicities in the database of chemicals based on metabolism and PK properties. 2. 6. 3 PBPK model platform review-An initial review was conducted to determine the most suitable PBPK modeling platform for the iTTC project. The iTTC project will require rapid PBPK modeling for hundreds of chemicals to predict the plasma concentration associated with the daily oral dose in an animal toxicity study.
The toxicity studies for the iTTC chemicals were primarily conducted in rats, but also in mice, rabbits, dogs, hamster and monkey. The modeling approach will use IVIVE of metabolism and quantitative structure activity relationship (QSAR) prediction of tissue partitioning. As such, a platform is needed that includes a generic model for repeated oral dosing, capability to support metabolism IVIVE and QSAR prediction of partition coefficients, and a database of physiological parameters for multiple animal species. The ability for batch analysis is also necessary given the large number of chemicals included in the dataset. The PBPK model platform review first started with a broad review of the available PBPK model platforms followed by a filtering of the results to identify platforms most suitable for the current project.
Two commercially available (GastroPlus (https://www. simulations-plus. com/ software/gastroplus/) and Berkeley Madonna (https://berkeley-madonna. myshopify. com/)) and two open source (PLETHEM (http://www. scitovation. com/plethem. html) and PK-SIM (http://www. systems-biology. com/products/pk-sim. html)) PBPK model platforms were ultimately chosen for further review. These platforms were chosen for review for a number of different reasons including the fact that they had the relevant physiologies, can perform IVIVE and batch processing, and were accessible for use. The final PBPK modeling workflow for the iTTC project is still to be determined and will be guided by the outcomes from this review.
Conclusions and key decisions from the workshop The TTC concept has evolved over the last 50 years and a logical next step in the continued evolution of TTC is to develop this concept further by converting the external NOAEL values from in vivo animal studies to internal plasma concentrations. The discussion held during the two-day iTTC workshop highlighted the complexity of developing an iTTC database and how significantly more research is required beyond current attempts where NOAELs were only adjusted for by applying an in silico oral absorption adjustment factor  (Partosch et al. , 2015). The broad-spanning complex nature of the project requires expertise from multiple disciplines, and therefore, an iTTC Working Group was formed. The iTTC Working Group developed strategies for a number of challenging aspects of the project while others, such as predicting the impact of metabolism, remain open for further discussion.
Acknowledgments The authors would like to acknowledge and thank several individuals for their help and support with different aspects of this work. Kamel Mansouri  (formally at ScitoVation, currently at  Integrated Laboratory Systems, Inc. ) provided the estimates from  OPERA. Joan Fisher ,  Greg Dameron  and  Cathy Lester  (all at P&G) provided structure accuracy checks, ACD estimates and ECCS estimates, respectively. James Firman  ( Liverpool John Moores University ) provided quality control analysis for the chemical space analysis. Chantel Nicolas ,  Alina Efremenko  and  Melyssa Minto  (all at ScitoVation) provided support for the literature search strategy.
Density of states calculation. Following equilibration at a temperature T p , zero-temperature glasses are produced by instantaneously quenching equilibrium configurations to their inherent structures using the Fast Inertia Relaxation Engine algorithm  [53]. We then calculate the modes by diagonalizing the Hessian matrix using Intel Math Kernel Library (https://software. intel. com/en-us/mkl/) and ARPACK (http://www. caam. rice. edu/software/ARPACK/). We calculate all the normal modes for the 48000 particle systems, but only the low-frequency part of the spectrum in systems with N > 48000. We characterize the modes through the density of states D(ω) = , where e l,i is the polarization vector of particle i in mode l with frequency ω l. For a mode localized to one particle P (ω) = N -1 , and for an ideal plane wave P (ω) = 2/3.