dataesr/scientific-paragraphs-categorization huggingface dataset:
Data and empirical approach From 2013 to 2018, the Aid Attitudes Tracker (AAT) conducted surveys of public attitudes towards, and engagement with global poverty in France, Germany, Great Britain and the United States. 2  The AAT is the first large, cross-national panel survey of how donor publics engage with global poverty  (Clarke et al. 2013). Fieldwork was conducted using YouGov's online panels with weighted nationally representative samples (n = c. 6000-8000 respondents per country). The 10-wave panel was fielded twice-annually, starting in November 2013, with subsequent waves at six-month intervals. One of the distinctive features of AAT is its focus on behavioural engagement, including measurement of the 18 actions (Table  1 ), through which we study public engagement with global poverty.
The assumption is that of a "linear" journey, from no or low-cost actions (Totally Disengaged) to higher-cost and frequency actions (Fully Engaged). We used the data to generate whole (engagement) sequences for each individual respondent who had completed more than one consecutive wave, resulting in 49,359 individual sequences (of which 21,483 were unique combinations of segments). We took every individuals' engagement journey and grouped them into clusters that share similar journey patterns over time. We used optimal matching to calculate sequences that were more and less similar to one another by calculating the amount of additions, deletions, substitutions to transform one sequence into another  (Abbott and Forrest 1986). Again, we use TraMineR for optimal matching  (Studer and Ritschard 2016)  and the distance matrix generated in a standard cluster analysis, identifying seven longitudinal clusters, shown in Figure  5.
Discussion and conclusions This article has introduced an innovative new datasetthe Aid Attitudes Trackerthat offers novel insights into how the public in major donor countries take action to engage with global poverty and sustainable development. Using AAT data, we segment the French, German, British and US publics into one of five engagement groups, highlighting that engagement is best understood as not one, but many publics. The motivating question for this research is: do we see evidence of an "engagement journey"? Do people start off from no or minimal activity, rise to the top, and stay there? While we hear such assumptions in common narratives, our data suggests that this is not the case. We show that there are changes in levels of engagement, but that people become more and less engaged at both the aggregate and individual level.
4. The transition rates are calculated using TraMineR  (Gabadinho et al. 2011). 5. We used the K-Medoids algorithm which seeks to find the best partition of the data in a predefined number of groups by minimising the (weighted) sum of distance to the medoid of each group. The medoid is defined as the observation that minimise the sum of distance to the other observations of this group. We examined all the possible clustering solutions from 2 to 20 clusters and using substantive interpretation as well 10 statistical measures of the quality of the partitioning available in Studer's Weighted Cluster package  (Studer 2013 ). 6. For two different respondents their first wave could have been Wave 1 or Wave 7 depending on when they joined the panelwe assume no difference between the two. We also removed gaps in the data, so if an individual responded in Wave 7 and Wave 9 that would be shown in Figure  6  as Wave 1 and Wave 2.
Acknowledgments: We thank the entire  OSIRIS-REx Team  for making the encounter with Bennu possible. For fig. S5, we thank  D. Rachford  and  IMV Minerals  for providing the SAP105 sample, and  S. Mertzman  of  Franklin and Marshall College  for confirming the composition of the SAP105 sample. We thank  Robert J. Burmeister  for assistance with graphic design of the figures. Funding: D. N. D, K. N. B. , K. J. W. , P. H. S. , D. R. G. , E. B. B. , R. L. B. , T. B. , H. C. , B. E. C. , A. S. , K. B. , C. A. B. , W. F. B. , J. I. B. , H. C. C. , C. Y. D. D. , K. L. E. , V. E. H. C. W. H. , E. S. H. , E. R. J. , H. H. K. , L. L. C. , L. L. , J. Y. L. , M. C. N, J. N. , N. A. P. ,  B. Ri , A. J. R. , N. K. S. , A. A. S. , C. W. V. W. , and D. S. L. were supported by  NASA  under Contract  NNM10AA11C  issued through the  New Frontiers Program.
Division of the Dataset for the Computational Studies The dataset used here includes 47 chemically diverse compounds (most of them with the corresponding experimentally determined logBB values), which were taken from the literature  [50] , including the tested triterpenoid saponins. The chemical structures of the investigated saponins are presented in Table  2. The dataset was separately divided into training and test sets in a random manner for each of the newly constructed models. In total, a random division of the whole dataset was made several times. The lowest value of the mean square error of the leave-ten-out cross-validation (i. e. , the adjusted mean square error of leave-ten-out cross-validation (adjusted MSECV)) process decided between inclusion in either the training or test set.
Another commonly used approach is the linear free energy relationship (LFER), suggested by Abraham, which is based on parameters such as hydrogen-bond acidity (A) and basicity (B), polarizability (S), molar refraction (E), and the McGowan volume (V) of a solute. In our research, we used both of the above-mentioned theories. Therefore, the most important physicochemical descriptors, as well as the LFER parameters, were calculated and are presented here in Table  4  (ACD/Percepta software). On the basis of the LFER parameters, the following model (Equation (  1 )) was generated: logBB = -0. 118 -0. 11 A -1. 174 B -0. 176 S -0. 242 E + 1. 195 V (1) In the above equation, n = 40, R 2 CV = 80. 80%, R 2 pred = 76. 80%, and S = 0. 432, where n is the number of compounds, R 2  CV is the cross-validated coefficient of determination, R 2 pred is the predicted R 2 value, and S is the standard deviation.
In the next model (Equation (  2 )), we decided to correlate BBB penetration with the lipophilic properties (logP ow ), excess molar refraction (E), and hydrogen-bonding potential, expressed as the difference between the n-octanol/water and cyclohexane/water logP values (∆logP)  [52] [53] [54]. Thus, we built the following model: logBB = -0. 114 -0. 098 ∆logP + 0. 278 logP ow + 0. 218 E (2) In the above equation, n = 40, R 2 CV = 78. 25%, R 2 pred = 74. 02%, and S = 0. 436. On the basis of Equation (  2 ), the logBB values were calculated (logBB pred ) and then they were correlated and compared with the experimental logBB values (logBB exp ), taking into account the training and the test sets (Figure  1 ). descriptors, as well as the LFER parameters, were calculated and are presented here in Table  4  (ACD/Percepta software).
25%, R 2 pred = 74. 02%, and S = 0. 436. On the basis of Equation (  2 ), the logBB values were calculated (logBBpred) and then they were correlated and compared with the experimental logBB values (logBBexp), taking into account the training and the test sets (Figure  1 ). According to Figure  1 , it can be stated that both tools used, i. e. , the ACD/Percepta software and the newly established QSAR (Equation (  2 )), can be recognized as good predictive models for assessing the ability of the tested compounds to penetrate through the blood-brain barrier. According to Figure  1 , it can be stated that both tools used, i. e. , the ACD/Percepta software and the newly established QSAR (Equation (  2 )), can be recognized as good predictive models for assessing the ability of the tested compounds to penetrate through the blood-brain barrier.
In our case, the response space concerns the BBB permeation of compounds. The AD was evaluated to confirm the reliability and predictability of the model expressed in Equation (2). Different approaches have been developed for statistically-based QSARs. In our experiment, we used a distance-based method, which can be used to separate regions of varying density by imposing cut-off values. The distance from a query data point to a dataset was calculated via this approach. If there is a criterion for the distance to be below a defined threshold, the decision as to whether a data point is close to the dataset can be made  [56]. The model space can be represented by the descriptor matrix (X), which is the two-dimensional matrix comprised of n chemicals (rows) and k variables (columns). A measure of the distance of the chemical from the centroid of X can define the leverage of a chemical (h i ).
The leverages of all chemicals in the dataset were generated here by manipulating X according to the following equation  [56]  (Equation (  3 )): h i = (X T X) -1 x i T (3) where i = 1 to n, x i is the row vector of the descriptors, T is the matrix/vector transposed, and i and X are the variable matrices deduced from the training set variable values. The critical leverage value (h*) can be generally obtained using the following equation (Equation (  4 )): h* = 3(k + 1)/n ( 4 ) where k is the number of predictor variables and n is the number of training compounds  [56]. To identify both the chemicals that are outside the AD and the leverage points which could destabilize the model, the Williams plot can be used. The plot is presented in Figure  2. The standardized residual equals the value of a residual divided by an estimate of its standard deviation.
The chromatogram shows that AIV delivers the highest peak area, found using the ImageJ program (peak area of 7451 u), followed by AI (peak area of 4113 u), and AIII (peak area of 6556 u), and, therefore, it can be recognized as the most neuroactive compound among the tested compounds (Figure  4 ). The results of in silico and in vitro studies allow astragaloside IV to be selected as the most promising compound with potential procognitive properties. Therefore, the authors have planned its isolation from aqueous methanolic extract of Astragalus mongholicus roots.
Isolation of Astragaloside IV The isolation of astragaloside IV from aqueous methanolic extract of the roots was performed by semi-preparative HPLC chromatography. For this purpose, 52 fractions were collected in a single-cycle separation process. The compositions of the obtained fractions were analyzed using high performance liquid chromatography, coupled with mass spectrometry (HPLC-MS) to provide The chromatogram shows that AIV delivers the highest peak area, found using the ImageJ program (peak area of 7451 u), followed by AI (peak area of 4113 u), and AIII (peak area of 6556 u), and, therefore, it can be recognized as the most neuroactive compound among the tested compounds (Figure  4 ). The results of in silico and in vitro studies allow astragaloside IV to be selected as the most promising compound with potential procognitive properties.
The logBB value was calculated here based on the experimentally obtained concentrations of astragaloside IV in the brains and plasma of mice after the analyses of the two groups of animals that received 12. 5 and 25 mg/kg b. w. of astragaloside IV. The obtained collective value of the logBB is equal to 0. 49 ± 0. 03 here. Moreover, a comparison of logBB values for AIV predicted by our model with the values experimentally obtained and those determined using ACD/Percepta software has been made (Figure  7 ). The closest logBB value to the value obtained experimentally for the mouse brain tissues is that obtained from the Equation (2) (the absolute error is equal to 0. 056). Therefore, it can be seen that the second model corresponds to the value obtained in vivo. Based on the analysis of variance (Table  5 ), Equation (1) was not used for further consideration.
Moreover, a comparison of logBB values for AIV predicted by our model with the values experimentally obtained and those determined using ACD/Percepta software has been made (Figure  7 ). The closest logBB value to the value obtained experimentally for the mouse brain tissues is that obtained from the Equation (2) (the absolute error is equal to 0. 056). Therefore, it can be seen that the second model corresponds to the value obtained in vivo. Based on the analysis of variance (Table  5 ), Equation (1) was not used for further consideration.
Computer Programs Within in silico studies, ACD/Percepta software (version 2012, Advanced Chemistry Development, Inc. , Toronto, ON, Canada) was used. Statistical analysis of the obtained results was made using the Minitab 18 Statistical Software (Minitab Inc. , State College, PA, USA). For studying the TLC-bioautography chromatogram, ImageJ software (1. 48v, Wayne Rasband, National Institutes of Health, Maryland, USA) was used.
9%). The detection and visualization of the obtained chromatograms was done under UV light at a wavelength of 254 nm and was then archived using a Reprostar 3 video camera with the WinCats software (version 1. 4. 4, Camag, Muttenz, Switzerland). The chromatogram was carefully studied with the help of an image processing program (ImageJ software; 1. 48v, Wayne Rasband, National Institutes of Health, Maryland, USA) and the peak areas were recorded for the studied astragalosides. The spots were comparable as each compound was applied at a concentration of 1 mg/mL (4 µL) at a band of 8 mm.
Isolation of the Selected Active Natural Product Semi-preparative HPLC was used to isolate and purify the most active saponin in the extract. For this purpose, the Vp liquid chromatographic system was used equipped with an LC 10AT pump, an SPD 10A UV-VIS detector, an SCL 10A system controller, a CTO-10 AS chromatographic oven, and a Rheodyne injector valve with 20 µL loop (Shimadzu, Kyoto, Japan). This system utilizes the Class-Vp computer program to control the hardware and acquire and store data, as well as determine retention times. The analysis was carried out at a wavelength of 254 nm and at a temperature of 20 • C. The isolation of the individual components was carried out with the Kromasil 100-10-C-18 column (250 mm × 4. 6 mm, 10 µm; Akzo Nobel, Bohus, Sweden) and a mobile phase composed of acetonitrile and water.
5 and 25 mg/kg. The doses of astragaloside were chosen based on the literature data  [89] , as well as preliminary studies. The blood samples collected from the animals delivered plasma that was immediately deep frozen and kept for quantitative analysis. Then, the mice were decapitated 1 h after the last injection and the brains were collected and homogenized in a plastic Eppendorff vial kept on ice. The homogenates of the brain and plasma samples were further vortexed for 10 min with 200 µL of 50% ethanol. The Eppendorf vials were further centrifuged at 3500 RPM for 5 min and the supernatant was filtered through a nylon syringe filter with a diameter of 0. 22 µm to the autosampler vials with glass inserts. The analytical standard of astragaloside IV was dissolved in 50% ethanol and dissolved into 10 different concentrations within the range of 0.
Author Contributions: The study was designed by K. S. and W. K. -K. The analytical part was performed by K. S. (semi-preparative HPLC, postmortem studies, QSAR studies) and W. K. -K. (HPLC-MS analysis, acetylcholinesterase inhibitory activity test, postmortem studies). Data were analyzed by both authors. The manuscript was written by K. S. and W. K. -K. All authors have read and agreed to the published version of the manuscript.
Methanogen cultures require hydrogen produced by fermentative bacteria such as Bacteroides thetaiotaomicron (biological method). We developed an alternative method for hydrogen production using iron filings and acetic acid with the aim of cultivating methanogens more efficiently and more quickly (chemical method). We developed this new method with a reference strain of Methanobrevibacter oralis, compared the method to the biological reference method with a reference strain of Methanobrevibacter smithii and finally applied the method to 50 saliva samples. Methanogen colonies counted using ImageJ software were identified using epifluorescence optical microscopy, real-time PCR and PCR sequencing. For cultures containing the pure strains of M. oralis and M. smithii, colonies appeared three days postinoculation with the chemical method versus nine days with the biological method.
Colonies were enumerated at day 3, day 6 and day 9 postinoculation by focusing the image of the entire 5-cm plate on a black background using a portable camera without flash (Lenovo L38011, Beijing, China). Numbered images were analyzed and counted using ImageJ version 8 (https://imagej. nih. gov/ij/download. html) (Wayne Rasband, Java, National Institutes of Health, USA) as follows: each image was imported into ImageJ software and analyzed in a first step using the blue, diamidino phenylindole filter to normalized the image. In a second step, the normalized image was analyzed using the green fluorescein isothiocyanate filter and saved in the Joint Photographic Experts Group format. We standardized the images using the elliptical selection tabulation of the software. We then proceeded to manually count the enlarged colonies using the magnifying glass and the multipoint tabulation of the software.
The addition of antioxidants in the SAB medium makes this culture medium reductive. Then, the pH and redox potential of distilled water enriched with iron filings and acetic acid (chemical method) were measured at 5. 23, 5. 16 and 5. 08 units and +109. 77 mV, +108. 6 mV and +112. 7 mV, respectively (Table  2 ). These values were 5. 8, 5. 25, and 5. 24 units and +113. 8 mV, +113. 8 mV and +114. 5 mV at day 3, day 6 and day 9 for the SAB culture medium incubated with B. thetaiotaomicron, respectively (Table  2 ). These data indicated that the cultivation of B. thetaiotaomicron in the SAB medium releases fermenting compounds such as CO 2 , hydrogen and acetate, which could oxidize the SAB medium  (26). Then, we measured these values in the Petri dishes placed in the upper compartment of the double-chamber system, which hosted the methanogen culture (Fig.
ACKNOWLEDGEMENTS COG and GG benefit from Ph. D. grants from the  Fondation Méditerranée Infection, Marseille, France. This work was supported by the  French Government  under the  «Investissements d'avenir» (Investments for the Future) program  managed by the  Agence Nationale de la Recherche  ( ANR , fr:  National Agency for Research) , (reference: Méditerranée Infection  10-IAHU-03 ). This work was supported by  Région Sud (Provence Alpes Côte d'Azur)  and  European funding FEDER PRIMMI. certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
Concept-focused review to identify existing acceptability questionnaires A literature review was conducted to identify existing questionnaires that may be used to measure the key attributes in the revised draft conceptual model of acceptability. Searches and data extraction were performed in a similar manner to those for the concept-focused literature review (see the section "Conceptual literature review" for methodology). Abstracts were reviewed to identify studies reporting on the development or use of questionnaires to evaluate medicine acceptability in a pediatric population. The content of the identified questionnaires was then reviewed and compared against the draft conceptual model of acceptability, to evaluate the conceptual coverage of these questionnaires.
The CE and CD components of the interviews lasted approximately 60 min and 30 min, respectively (for a total of approximately 90 min per interview). Interviews were audiorecorded and transcribed. Each transcript was then anonymized to remove any potentially identifying information. Transcripts were coded using ATLAS. ti version 7. 0 (ATLAS. ti Scientific Software Development GmbH, Berlin, Germany) to facilitate analysis and interpretation. Coding was an iterative process, in which several researchers identified transcript text where a participant described a concept or topic relevant to the study objectives. Throughout the coding process, a 'codebook' was used to match the text to a code that best characterizes the concept or topic. Before coding began, researchers developed two "codebooks", one for patients and one for caregivers.
A codebook is a comprehensive list of all the codes used to characterize important segments of transcript text and is used as a guide to organize the content and meaning of actual participant language. The preliminary coding scheme was developed before coding and was modified as coders analyzed the transcripts. When a new concept or topic was identified during coding, a new code was created if no existing code applied to the identified concept or topic. Using the codebook, participants' perspectives on acceptability concepts, as well as their ability to understand and respond to the questionnaires, were classified and organized for further analysis. To facilitate data analysis, findings tables consisting of verbatim quotes were generated for both pediatric patients and caregivers. An acceptability attribute concept tracking matrix table was also developed to summarize the attributes reported during the CE component of the interviews, and this table included additional information such as how frequently each attribute was reported and whether the attribute was reported as being important to participants.
Data from the CE component of the interviews were analyzed by calculating the number of patients who expressed a given acceptability attribute concept during the interview. This provided a concept frequency count of the total number of unique participants who stated that they had experienced the attribute concept at least once over the course of their experience with a specific oral-administered medication. Data from the CD component of the interviews were qualitatively reviewed to describe: the ability of participants to understand the instructions, items, and response options of the questionnaires as intended; their ability to meaningfully respond to questionnaire items; and the relevance of the attribute concepts assessed by each item. Additionally, the data were used to indicate suggestions for rewording of the questionnaires, if any, as well as whether any attribute concepts were missing from them.
Cellular ATP Content and ATP Release Measurements In addition to potassium release, we investigated the ATP concentration inside and outside of cells after treatments. After DCEO, Oxy-Fr, EMIE, Elemicin, and carvacrol treatments, a negligible ATP level was detected outside of cells. The same result was observed for untreated cells (data not shown). On the contrary, ATP concentration inside the cells substantially decreases post-treatment as compared to the DMSO, erythromycin, and gentamicin treated cells (Figure  4 ). ATP level was detected outside of cells. The same result was observed for untreated cells (data not shown). On the contrary, ATP concentration inside the cells substantially decreases post-treatment as compared to the DMSO, erythromycin, and gentamicin treated cells (Figure  4 ).
Component Identification GC-FID and GC-MS analysis of essential oil and its fractions obtained by column chromatography were performed under the experimental conditions described previously  [9]. The identification was carried out by comparison of their mass spectra with those compiled in our laboratory-built spectral library, as well as by comparison of their retention indices with those of authentic samples or literature data  [42, 43]. The GC retention indices (RI) on the polar and non-polar columns were determined relative to the retention time of a series of n-alkanes with linear interpolation. Identification of the major components were ensured by 13 C-NMR, following a procedure developed and computerized in our laboratories and based on the comparison of 13 C-NMR chemical shifts of components in the mixture with those of pure compounds compiled in a laboratory-built library  [15].