dataesr/scientific-paragraphs-categorization huggingface dataset:
Historical Data Collection from TransCelerate Member Companies Companies were asked to complete a data collection tool without using trial identifiers (e. g. , trial name) so that data could be aggregated blindly. The data contribution was voluntary. Key trial characteristics captured for each trial were therapeutic area, phase, number of participants (categorized as less than 20, 21-50, 51-200, 201-1000, or more than 1000), route of drug administration, trial design, and when the trial was completed (within last five years vs. more than five years ago). Trial characteristics that were not available were left blank. Along with trial characteristics, this tool collected QTL parameters measured in each trial and their units and values. Each record (row) within the tool represented one parameter per trial.
If a trial was assessed for multiple parameters, multiple lines were completed with the required information. As this was historical data collection, the parameters could be assessed after trial completion and were not used for monitoring the trial actively. Data collection tools completed by each responding member company were shared with the TransCelerate Project Manager, an independent consultant retained by TransCelerate to oversee the QTL initiative. For all planned aggregations, dataset balancing exercise was performed to prevent results being disproportionally driven by one member company. For this purpose, the Project Manager (1) calculated the percentage of data from each member company; (2) defined the number of study records that need to be removed from the sets coming from top-contributing member companies to achieve a balanced dataset; and (3) assigned a random number to every record in a company dataset and removed records starting with the smallest random number and moving higher, until the number of records defined in step 2 had been removed.
External Partner Data Collection Clinical trials targeting specific indications were selected from the Medidata Enterprise Data Store (MEDS), comprised of 22,000 + historical clinical trials, for de-identified aggregate analyses. In order to identify differences in parameter values between different therapeutic areas, Alzheimer's disease (AD) and oncology trials were compared. Selection of these indications was based on the fact that AD and oncology trials differ significantly and on the availability of the data. A Medidata statistician performed the analysis based on authors' instructions. The information on protocol deviations (PDs) was retrieved from the disposition dataset and represents the disposition-related protocol deviations. Aggregations were conducted without formal statistical analyses or hypotheses testing.
Historical Data Collection from TransCelerate Member Companies Seven TransCelerate member companies provided historical data from a total of 294 trials on parameters previously proposed in the TransCelerate QTL Framework  [2]. Each company provided data on 4-119 trials. The trials were predominantly late phase (72% Phase 2 or 3) and represented a wide range of therapeutic areas. The details of the trials are presented in Table  1. The companies provided data on 2-10 parameters. The number of companies providing data on each parameter is presented in Table  2. In all further aggregations at data contributed by at least five member companies are presented. This criterion was met for five parameters. These aggregated data for parameters and trial subsets are presented in Table  3. The lowest mean value for the whole dataset was collected for the percentage of lost to follow-up trial participants and highest for the percentage of trial participants with premature discontinuation of investigational drug.
External Partner Data Analysis As comparisons between different therapeutic areas and other trial characteristics were limited in the member company historical data collection, this was addressed using an alternate source of data. Medidata provided values for three parameters in 45 AD and oncology historical trials. The information on classification of all PDs was incomplete in the available datasets; therefore, the percentage of all patients with disposition-related PDs was assessed. This analysis did not show any striking differences between the AD and oncology trials in any of the three analyzed parameters. However, there was a somewhat higher mean percentage of patients withdrawing consent in the oncology trials (Table  4 ). Also, trial characteristics (phase, design, and time of completion) did not appear to correlate with historical parameter values within the oncology dataset (Table  5 ).
Discussion Historical data analyses, together with medical and statistical characteristics of a trial, were previously proposed as factors to be considered in setting QTLs  [3]. Many sponsors conduct historical data analyses using data from their own completed trials in order to generate benchmark data for setting QTLs in new trials. This activity may be difficult or impossible when a sponsor has a small portfolio of trials or when an organization is entering a new therapeutic area. This article shares benchmark data on parameters proposed as the basis for QTLs and summarizes the availability of public domain data that could be helpful in determining the historical values of the parameters. For the purpose of this study, we collected historical data from several TransCelerate member companies.
The first TransCelerate publication on QTLs was released in 2017  [3]. It included examples of parameters: percentage of ineligible participants, percentage of premature treatment discontinuations, and percentage of participants lost to follow-up. These are some of the same parameters for which the most responding member companies provided data for this publication. The data collection period for the article was short, and the member companies were encouraged to share data that were readily available. Therefore, the parameters most represented in the collected historical dataset may reflect the ease of data collection or frequency of use in QTL implementation projects. The parameters proposed by Bhagat et al. [2]  can be grouped into those that revolve around missing data (including those due to withdrawal of consent) and those that pertain to PDs.
In addition to calculating historical benchmarks for parameters that may provide some basis for QTLs, it may be important to understand how various trial characteristics affect the parameter values. In the data collected from the TransCelerate member companies, the only notable difference within the dataset based on study characteristics was observed for the percentage trial participants with premature discontinuation of investigational drug. The mean parameter value was higher for larger trials. This finding is difficult to interpret without analyses of other trial characteristics. It can be hypothesized that larger trials usually represent later phases of development with longer treatment periods and more real-life settings making premature drug discontinuations more prevalent. Alternatively, the size of a trial may correlate with the therapeutic area, e.
More studies and analyses are needed to answer the question. The data presented in this article may, however, make the planning and hypotheses formulation for such work easier. The analyses presented in this article have some limitations. As this was the first look at historical data in the context of parameters explored by TransCelerate, conclusions were limited by the lack of hypothesis testing. The survey methodologies, the large variance of trials for which Trans-Celerate member companies provided data, Medidata dataset being limited to only AD and oncology trials altogether limited comparisons between different therapeutic areas, drug administration routes, and other trial characteristics. While the authors attempted to analyze trials representative of the types for which parameters proposed by Bhagat et al.
Conclusion The first TransCelerate publication on QTLs included examples of parameters  [2]. For this paper, we collected historical data on these parameters from clinical trials conducted by TransCelerate member companies and Alzheimer's disease and oncology trials in a dataset provided by Medidata and shared benchmark data on parameters that may serve as the basis for QTLs. We did not provide a definitive answer to the question whether trial characteristics have a significant impact on historical values of parameters proposed for QTLs. More studies and analyses are needed to answer the question.
Acknowledgements We acknowledge contributions from  Jonathan Rowe, PhD  for contributions to the concepts;  Karma Tarap  for contributions to the data collection; Acorn AI, by  Medidata , a  Dassault Systèmes company , for data comparing Alzheimer's disease and oncology trials and on subsets of oncology trials;  Leslie Wehr  and  Frances McNierney  for project management support; and  Frances Pu, PhD , for medical writing.
The nucleus was stained with 4′, 6diamidino-2-phenylindole (DAPI). Immunofluorescence images were visualized using a laser scanning confocal microscope at 63x magnification (Leica Microsystems, Cambridge, United Kingdom). Staining intensity and cell diameter were calculated using Zen software (Carl Zeiss, Germany2. 7). Detection of coronin and IBA-1 protein in microglial cells was also determined by western blots.
All antibodies were purchased from Cell Signaling Technology (Danvers, MA, United States). Protein signals were detected using an ECL Detect Kit and visualized using Chemidoc MP imaging system (Bio-Rad, Hercules, CA). Image J software (NIH, United States) was utilized to quantify the bands and the ratio of phosphorylated ERK1/2 relative to total ERK1/2 was acquired and compared to control untreated microglial cells.
Systems Biology and Subnetwork Enrichment Interactome Analysis For the interactome analysis, the Elsevier's Pathway Studio version 10. 0 (https://www. elsevier. com/solutions/pathwaystudio-biological-research) was used to establish the relationships among the different validated proteins relevant to microglial-PKall interaction using the ResNet database. Proteome interactome network was generated using "direct interaction" algorithm to map cellular processes and interactions among the genes of the altered proteins. For the brain and circulation deduced interactions, "Subnetwork Enrichment Analysis" (SNEA) algorithm was selected to extract statistically significant altered functional pathways pertaining to the validated proteins. SNEA utilizes Fisher's statistical test to determine if there are nonrandom associations between two categorical variables organized by specific relationships.
Statistical Analysis Descriptive analysis was conducted on each group and summary statistics were reported in terms of mean ± SE. Normality of the data was assessed using the Shapiro-Wilk test for normality. Comparison between the different groups was conducted using the nonparametric Kruskal-Wallis test and Bonferroni adjusted p-values were reported to account for multiple comparisons. In addition, Mann-Whitney U test was conducted when comparison was done between two groups only. Significance was considered at p < 0. 05 and statistical analyses were conducted using the SPSS (Statistical Package for the Social Sciences) software.
ACKNOWLEDGMENTS The authors would like to thank  Dr. Gerry Shaw, Ph. D. and  Encor biotechnology Inc. Gainesville, FL, United States (https:// encorbio. com/),for generously providing the murine N9 microglial cells, and the coronin and IBA-1 antibodies and  Miss Ghina Tleys (AUB)  for assistance in characterizing the SAR 405 in macrophages.
Samples were placed onto carbon conductive tape and coated with about 20 nm of carbon. EDS analyses were performed with a QUANTAX-XFlash6 detector with a peak resolution of 129eV, from Bruker. EDS was performed to confirm the presence of colloidal silicas on the surface of host powders. Water sorption isotherms were performed at 20°C in 3 Flex from Micromeritics. Before scanning, samples were vacuum dried for ten hours at 15°C. The true density analysisof powder was performed in an AccuPyc 1330 Helium pycnometer from Micromeritics. All data measurements are the average of ten measurements obtained from one powder sample.
Uniaxial compaction, packed bed conditions Compressibility studies in packed bed conditions were performed using an FT4 powder rheometer from Freeman Technology ©. The samples were poured into a borosilicate split vessel. The test started with a mechanical conditioning step by establishing uniform stress in the powder bed (Fig. 2 , left). When the conditioning cycle is over, the vessel splits allowing a precise volume of 10 mL to be attained. The analysis was achieved by applying increasing levels of force directly onto the granular media with a vented piston, allowing the trapped air within the powder to escape, and by measuring volume changes depending on the applied load (Fig. 2 , right). During the test, the changes in volume for a given applied normal stress were collected. In order to determine the specific energy supplied to the granular media an external data treatment must be carried out.
In fact, FT4 software determines the energy supplied to the powder by using a trapezoidal numerical integration. Nevertheless, this data treatment lacks of physical meaning as it integrates all height changes, even the non-drecreasing ones. As the height sensor is extremely sensitive, the energy provided to the system is calculated as time-dependent and so, aberrant values can be obtained. In order to give a mechanical sense to the experimental data, the specific energy supplied to the system was determined with Matlab ® by discretizing the normal stress values as function of the packed bed height evolution. To do so, it is necessary to identify the areas where the normal stress is kept constant by the derivative of force with respect to time (Fig. 3. a in red). Then, to determine an average force, and to be able to relate it to displacement values, we selected around 20 data values in the red zone, where non-evolution was found.
(1998). In our case, we estimated that when adding 0. 5 or 2% w/w of colloidal silica, the interaction between the grains is governed by silica-to-silica interactions. This rather rough estimation supports our results and could explain why no differences were found when increasing the amount of NPs added to the    and 16 ). The mathematical approach also suggests that to cover the surface with 0. 5% w/w, the adsorbates should measure 300 nm. Nonetheless, Fig. 19  shows that binary mixtures with SIPERNAT ® D10 generated a high coverage of particles with monodisperse fine adsorbates of around 50 nm. When possible, surface coverage was also estimated using SEM micrographs using ImageJ software (Table  4 ). The number of adsorbates was determined in different regions of Avicel's surface of binary mixtures.
The authors wish to make the following corrections to their paper  [1]. In the original publication, there was a mistake in Table  1  as published. While editing Table  1 , there has been a row offset for the "PDB ID and Structural Insights" column. Moreover, the final lysine residue was missing on the BsrG sequence. Consequently, the size, the charge and the GRAVY index have been modified. The corrected Table  1  appears below. The authors apologize for any inconvenience caused and state that the scientific conclusions are unaffected. The original publication has also been updated. Table  1. Overview of membrane-associated type I toxins from toxin-antitoxin systems for which insights into their mechanism of action have been published. Predicted or experimentally determined (according to respective PDB file) α-helix are highlighted in orange and β-sheet in yellow.
When the structure has not been experimentally determined, α-helix have been predicted with Jpred4 (http://www. compbio. dundee. ac. uk/jpred/index. html, accessed on 5 April 2021)  [27]. Transmembrane domains are delimited by lipid representation surrounding each sequence and have been predicted with TMPRED (https://embnet. vital-it. ch/software/TMPRED_form. html, accessed on 5 April 2021). Boxes colored in blue correspond to the toxins inducing morphological changes as a primary detected effect, the green one for toxins inducing membrane perturbations as a primary detected effect and the grey one is for toxins with dual effects.
aas) Amino Acids Sequence with the α-Helical Transmembrane Domain PDB ID and Structural Insights Toxins. 2021, x FOR PEER REVIEW 2 Bacteria Species Toxins Size (Charge GRAVY Index HokB 49 2. 13 0. 59 TisB 29 0. 98 1. 62 LdrA 35 Molecular modeling 2. 22 0. 76 E. coli LdrD 35 PDB: 5LBJ 1. 22 0. 69 IbsC 19 0. 97 2. 44 ZorO 29 -0. 02 1. 40 ShoB 26 2. 94 1. 59 DinQ 27 Molecular modeling -0. 02 1. 79 E. faecalis Fst 33 PDB: 2KV5 -3. 02 0. 42 B. subtilis BsrG 38 1. 98 1. 00 L. rhamnosus Lpt 29 Molecular modeling 1. 22 0. 82 PDB: 6GIG + H. pylori AapA1 30 Molecular 7. 18 0. 15 modeling PDB: 4B19 + SprA1 30 Molecular 3. 18 1. 00 modeling S. aureus SprG131 31 PDB: 7NS1 1. 98 1. 39 SprG144 44 Molecular modeling 4. 97 1. 01.
Recent developments of innovative strategies to study metabolites from complex matrices such as the organization of the spectral information using Global Natural Product Social Molecular Networking (GNPS)have also been very useful. This latter methodology is capable of simultaneously visualizing the chemical space from non-targeted mass spectrometry data and identifying compounds through mass spectral matching  [13]. Efforts to identify metabolites of Stryphnodendron species have been intense and increasing due to the promising biological activities reported  [3, [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29]. Tannins have been found to be their major components, particularly in the "barbatimao" species, and are likely responsible for the reported antimicrobial activities  [30, 31].
Stryphnodendron Pulcherrimum Extract Characterization by UHPLC-MS/MS The hydro-acetonic extract was cleaned up using a C-18 SPE cartridge to remove highly lipophilic compounds such as chlorophylls. It was subjected to UHPLC-MS/MS in the negative ion (NI) mode to favor the ionization of phenolic compounds known to be abundant in plants from the genus. This high-resolution MS analysis using a Qtof analyzer provided nearly accurate molecular masses (MS 1 ) and the corresponding fragmentation patterns (MS 2 ) of the molecular ions  [32]. To obtain a molecular network, the MS data were treated using MZmine software  [33]  and then, the data MS 1 and MS 2 were uploaded to the GNPS platform  [34]. The molecular network (MN) obtained consisted in 284 features (Figure  1 ). The resulting MN was then subjected to dereplication against experimental MS 2 data from the GNPS databases, and in silico fragmentation spectra created from a large database of natural products, followed by a re-ranking of the putative identities based on taxonomy  [35, 36].
According to the dictionary of natural products (DNP, v. 29. 1,), 42 compounds have been reported in the genus Stryphnodendron. From them, 36 correspond to saponin derivatives, mostly stryphnosides, which have been reported in fruits from Stryphnodendron fissuratum  [37, 38]  and Stryphnodendron coriaceum  [39]. The remaining six compounds include polyphenolic derivatives from Stryphnodendron adstringens  [14, 15, 40]. Since the working sample is from leaves, not the fruits, an extensive literature research was conducted to create an in-house database (Table  S1 ). It included 74 compounds reported for the Stryphnodendron genus, in different parts of the plants [  [14] [15] [16] [17] 23, 24, 29, [40] [41] [42] [43] [44] [45] [46] [47]. Based on the molecular formula (MF) and heuristic filtering  [48] , this database was used to further identify compounds in the extract.
Structural Identification of Compounds Present in the Extract of S. pulcherrimum According to our annotation results, 30 out 49 compounds had a spectral match against the experimental database from GNPS, while the remaining 19 had a match against the in silico database. To corroborate the structural proposals for these 19 features, their individual fragmentation patterns were explained. Figure  3  display examples of fragmentation patterns for each structural group. Group 1 includes conjugates of gallic acid with sugars and small organic acids (Figure  4 ). Fragmentation patterns of these compounds are characterized by the ions of m/z 169 (gallate) and 125 (3,4,5-trihydroxybenzene, ion derived from the decarboxylation of the gallate)  [50, 51]. If a sugar unit is present, common losses of H2O and consecutive losses of C2H4O2 units from the residual hexose and the heterolytic cleavage of the sugar are observed  [52].
Structural Identification of Compounds Present in the Extract of S. pulcherrimum According to our annotation results, 30 out 49 compounds had a spectral match against the experimental database from GNPS, while the remaining 19 had a match against the in silico database. To corroborate the structural proposals for these 19 features, their individual fragmentation patterns were explained. Figure  3  display examples of fragmentation patterns for each structural group. Group 1 includes conjugates of gallic acid with sugars and small organic acids (Figure  4 ). Fragmentation patterns of these compounds are characterized by the ions of m/z 169 (gallate) and 125 (3,4,5-trihydroxybenzene, ion derived from the decarboxylation of the gallate)  [50, 51]. If a sugar unit is present, common losses of H 2 O and consecutive losses of C 2 H 4 O 2 units from the residual hexose and the heterolytic cleavage of the sugar are observed  [52].
Discussion Several Stryphnodendron species have been the focus of chemical studies to identify their chemical composition, and to assess the natural products responsible for the reported biological activities, such as anti-inflammatory and antimicrobial  [3, [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29]. The many uses of these plants in Brazilian folk medicine point to them as possible sources of bioactive compounds  [30, 31]. In this study, we aimed to comprehensively describe the composition of the leaves Stryphnodendron pulcherrimum based on the annotation of all metabolites detected in the hydro-acetonic extract by UHPLC-MS/MS. The combination of spectral organization through MN  [34]  and the search against both experimental and in silico spectral databases considerably reduces the time and resources for the putative identification of a considerable number of metabolites.
Combining the MN with matching of MS/MS against in silico databases  [35]  was possible re-weighting of the putative candidates based on the biological source and taxonomy  [36]. Also, filtering with MF assignment and manually verifying major fragmentation patterns allowed us to significantly increase the confidence of the metabolite annotation. All compounds reported here, mainly small phenolic conjugates and polyphenols, derive either from the upper stream of the shikimate, phenylpropanoid or phenylpropanoidacetate pathway. The MN presents two major clusters. The largest one is mainly constituted by compounds from G1 (Figure  4 ) which are derivatives and conjugates of gallic acid  (1). Note that gallic acid displays several biological activities and is considered a biomarker for the chemotaxonomy and differentiation of Stryphnodendron species because of its recurrence and concentration  [43, [64] [65] [66] [67].