dataesr/scientific-paragraphs-categorization huggingface dataset:
Persistency and bioaccumulation potential of detected compounds The compounds detected in our study were screened for persistency and bioaccumulation potential, representing hazard criteria in EU REACH regulation (REGULATION (EC)  No 1907 No /2006)). Due to a lack of consistent databases on experimental data, the predicted half-life time (DT50 pred ) as indicator for persistency and the predicted bioconcentration factor (BCF pred ) as indicator for the bioaccumulation potential were applied, both calculated with OPERA  (Mansouri et al. , 2018). According to this approach, 49% of all detected compounds and 90% of the top 30 compounds by the MEC95 (Fig. 2 ), respectively, have a predicted BCF pred below 10 represented by score 5 for the lowest priority regarding the bioconcentration potential.
Availability of toxicity data for detected compounds Environmental risk assessment of contamination strongly relies on reliable toxicity data. Experimental PNEC values from the NORMAN Ecotoxicology Database, based on experimental endpoints (incl. regulatory-adopted EQS values or (ad-hoc) proposals), were available for 43% of the detected target compounds (Fig. S5 , Table  S7 ), while PNECs of the remaining compounds were predicted (55%) or not available (2%). Fractions of 47% of the top 30 compounds by MEC95 are covered by experimental PNECs (Fig. 2 ). In the case of HU ch for 108 of the 366 detected chemicals (29%) chronic SSDs were available and considered sufficiently reliable (Fig. S5 , Table  S7 ). This corresponds to 17% of the top 30 MEC95 compounds (Fig. 2 ). In the largest experimental toxicity database (US EPA ECOTOX DB), experimental EC 50 values needed for the TU calculation for all three BQEs were only available for 60 of the 366 detected compounds (16%).
For 48 compounds (13%) there were measured values for two BQEs, and for 50 compounds (14%) there were measured values for only one BQE. For 208 compounds (57%) no measured toxicity data was available (Fig. S5 , Table  S7 ). This implies that for 70% of the total of 1098 assessments (366 detects multiplied by 3 BQEs), only predicted effect concentrations based on QSARs for baseline toxicity have been used, neglecting specific effects. For the top 30 compounds ranked by their MEC95, only one compound (diclofenac) was supported by experimental EC values for all BQEs, for 17% of the compounds two BQEs were covered and for 7% only for one BQE experimental data could be found (Fig. 2 ).
e. 33-to 8400-fold (Fig. 9 ). The assessment of chemicals according to RQ is based on PNECs which have been selected based on expert judgement, considering the availability of toxicity data to any endpoint and in any organism, also considering uncertainty represented by respective AFs. This approach is considered to be protective for the individual compounds and provides incentives to improve data quality, especially in case of predicted acute toxicity values using an AF of 1000. For 16 out of the 30 top priority compounds ranked by RQ95, no experimental toxicity data for the BQEs are available in the ECOTOX database (Fig. 4 ). In fact, half of the compounds identified as risk drivers were based on predicted PNECs that involve an AF of 1000, among them the top-ranked compounds vancomycin and HMMM.
Acknowledgements This project has received funding from the  NORMAN network. The QExactive Plus LC-HRMS  used in this study is part of the  major infrastructure initiative CITEPro (Chemicals in the Terrestrial Environment Profiler ) and was funded by the  Helmholtz Association. The conclusions expressed in this paper represent the expert judgement of the authors, but not necessarily the opinion of their affiliation. Finally, we would like to thank all WWTP operators and people involved in the sampling and laboratory work, especially  Jörg Ahlheim  and  Margit Petre.
At the time of the data collection, the latest fiscal year business report publicly available on the MHLW website was for fiscal year 2017 (21). We collected data on all the clinical research papers of each hospital from the fiscal year 2017 business report on the MHLW website. All the papers had been published in journals from June 2013 to March 2017 (21). The following categories were noted: type of clinical trial (e. g. , drug development or medical device), study design (e. g. , randomized controlled trial or pilot study), and publishing options (e. g. , open access, Web of Science). This information is summarized in Supplementary material. Data on the research funding status for these papers were gathered based on their Acknowledgments and any funding information section. Research funding status was classified as (1) non-funded, (2) public institution-funded, and (3) industryfunded.
The beads were washed with five volumes of binding buffer and the peptide-interacting proteins were released by competitive elution with 500 µL of elution buffer containing an excess of biotin (2 mM) for 1 h at 4 °C under stirring. Mag-2 putative protein interactors were fractionated by SDS-PAGE and the protein bands from both the sample and the control lanes were excised from the gel and subjected to in situ hydrolysis with trypsin. The resulting peptide mixtures were directly analyzed by Liquid Chromatography/Tandem Mass Spectrometry (LC-MS/ MS) using an LTQ Orbitrap XLOrbitrap mass spectrometer (Thermo Fisher Scientific, Bremen, Germany) and the obtained data were used to search for a non-redundant protein database using an inhouse version of the Mascot software, leading to the identification of the putative AMP protein interactors.
Primary structure of BamA was validate by mass mapping using MALDI-TOF and circular dichroism (CD) analysis was performed to verify the correct folding of the protein, using a JASCO J-715 spectropolarimeter equipped with a Peltier thermostatic cell holder (Model PTC-348WI) in 1 cm optical path-length quartz cell. CD spectra were acquired in the range 190-250 nm, performing three accumulations for each measure, a scanning speed of 100 nm min -1 and data pitch of 0. 2 nm.
Binding experiment Fluorescence experiments were performed on a Fluoromax-4 spectrofluorometer from Horiba Scientific, using 1 cm optical path-length quartz cell under controlled temperature conditions (Peltier control system at 20 °C). Titrations were carried out in 20 mM Tris-HCl pH 8. 4, 0. 2% triton. The intrinsic fluorescence intensity of BamA P5 at a concentration of 3. 7 × 10 -6 M was monitored at 280 nm (slit 4 nm) and the emission was monitored at 308 nm (slit 4 nm) without and in the presence of increasing concentrations of Mag-2 peptide (from 2 to 18. 64 µM). All experiments were performed in duplicate. The change in the fluorescence intensity of the reaction set was fit into "one sitespecific binding" equation of GraphPad Prism 5 (GraphPad Software).
LC-MRM/MS analysis The identification of peptides performed by full scan LC-MS/ MS analysis was useful to select the best peptides of OmpA and OmpF to be monitored by LC-MS/MS in Multiple Reaction Monitoring (MRM) ion mode. Skyline software (3. 7, 64-bit version MacCoss Lab Software, University of Washington, United States) was used for the selection of the best precursor ion-product ion transitions and instrumental parameters like collision energy (CE), dwell time and cone voltage. Peptides with zero missed cleavages were considered and the best two to five transitions per peptide were selected from the top ranked y-and b-fragments. As a result, 18 peptides for OmpA and OmpF target proteins were selected, and 140 transitions were monitored during a single analysis. Table  1  summarizes the amino acid sequence for the selected peptides, the m/z of precursor ion and product ions and the corresponding Collision energy (CE) values to set up the MRM-MS method.
, 2014). The purity of the protein was assessed by SDS-PAGE and its primary structure validated by peptide mapping with MALDI mass spectrometry. Circular dichroism analyses were also carried out to verify the correct folding of the protein. Figure  3  shows the corresponding CD spectrum displaying the expected ratio of αhelix and β-sheets secondary structures. Molecular docking analysis of the complex between BamAp5 and Mag-2 was then performed suggesting again the formation of a stable complex with no relevant differences on the thermodynamics and kinetic parameters with the previous data with the entire BamA protein.
Binding experiments The binding of the Mag-2 peptide to BamA P5 protein was investigated by fluorescence assays in order to monitor changes in the tertiary structure of the protein. As BamAp5 has a large number of aromatic residues resulting in a high value of fluorescence intensity at 280 nm, the intrinsic fluorescence of BamA P5 was recorded upon incubation with increasing concentrations of Mag-2 avoiding the use of specific fluorescence tag/labels. The set of emission spectra of BamA P5 at various concentration of Mag-2 is shown in Figure  4. BamA P5 fluorescence decreased at increasing concentrations of the peptide confirming the occurrence of a peptide-protein interaction. Analysis of fluorescence data allowed us to calculate the dissociation constant of the complex, Kd value of 3. 5 ± 0.
The average peak areas from the different replicates were recorded for each peptide from OmpA and OmpF and their statistical relevance was confirmed by t-test (p < 0. 05). The results of the quantitative evaluation of MRM/MS data are shown in Figure  8  where a clear decrease in the amount of both Omp and OmpF with increasing time of Mag-2 incubation was observed, confirming the results of the Western Blot experiments.
Finally, we tested whether the BamA-Mag-2 interaction might affect the biological functions of the BAM complex using two different and complementary approaches. In both cases a quantitative evaluation of the total amount of specific OMPs in E. coli cells was performed in the presence and in the absence of Mag-2. First, the amount of OmpA produced by E. coli cells following incubation with a subMIC concentration of Mag-2 was estimated by Western Blot using a specific antiOmpA antibody. The presence of the peptide clearly affected the production of OmpA as a gradual decrease in the amount of the protein compared with untreated cells could easily be observed. These data were confirmed by a more sophisticated experiment using tandem mass spectrometry in MRM scan mode. Several specific peptides from both OmpA and OmpF were selected and their mass transitions from the molecular ion to individual fragment ions were predicted using the Skyline software.
To ensure adequate opacification of the entire arterial system, bolus tracking was used (with a region of interest placed in the aortic arch) with a 7-second delay. Raw data were reconstructed using 1-mm contiguous slices for subsequent analysis. All anonymized MRA and CTA images were jointly analyzed by a vascular medicine specialist (M. L. -S. ) and a vascular radiologist (I. R. ) using a detailed preestablished case report form, with adjudication of any differences by a third FMD specialist (A. P. ). All 3 specialists were unaware of the participant's clinical status (healthy control vs SCAD), the number of controls, and any other demographic or disease-related characteristics. Magnetic resonance angiography and CTA from the same patient were unlinked and analyzed independently.
Statistical Analysis All analysis was performed using SPSS, version 21. 0 (IBM Corp). Continuous variables are expressed as mean (SD) or median (IQR) according to their distribution; categorical variables are expressed as counts and percentage. Data were analyzed with the Kolmogorov-Smirnov test to determine their distribution. Continuous variables were analyzed using the unpaired t test if distribution was gaussian or the Mann-Whitney test in case of nongaussian distribution. Categorical variables were compared using the χ 2 test. Statistical significance was set at 2-sided P value <. 05 for all analysis.
Chi-squared correlations and Binary logistic regression For all CVD´s positive patients, we did statistical correlations on each individual comorbidity, as they related to death for each group. Next, we built a binary logistic regression model to determine de amount of influence each comorbidity and a positive COVID-19 diagnosis would have on CVD´s patients mortality. All statistical analyzes including binary logistic regression, Pearson's chi-squared and an R ratio of 0. 05, were done using IBM SPSS Statistics for Windows, version 23. 0 (IBM Corp. , Armonk, NY, USA). Parallel set was further made in RStudio v. 1. 3. 1093 with R v. 4. 0. 3. The following packages were used: reshape2 v. 1. 4. 4, tidyverse v. 1. 3. 0, ggforce v. 0. 3. 3. These packages can be downloaded from CRAN (https://cran.
Finally, we integrated all 3 comorbidities into the subpopulation of death patients. From these results, we observed the highest mortality in the COVID-19 positive group, which was also positive for both diabetes and hypertension but negative for obesity (n = 631). The next 2 groups with the highest mortalities were also positive for COVID-19, negative for diabetes, negative for obesity; the group positive for hypertension had 570 recorded deaths and the group negative for hypertension had 420 deaths. The least number of deaths recorded overall were in the patients with all conditions negative (n = 58). Interestingly, through the course of the pandemic, the use of personal protective equipment, safe distancing, and newly developed vaccines have helped mediate the deadly effects of COVID-19, therefore considering these measures took time to take full effect we took the data from Table  4  A.
Interestingly, the highest COVID-19 positive cases seen were in males 51 to 60 years (n = 1668) and males 61 to 70 years (n = 1768). Finally, the highest total death count recorded was in males ages 61 to 70 (n = 570), males aged 71 to 80 years (n = 655), and older males aged 81+ (n =459). Finally, we correlated individual comorbidities to death, demonstrating high correlations, as all 3 comorbidities had p-values of < 0. 01 (Supplemental Table  2 ). Parallel set visualization of the data is shown in Figure  2. Figure  3  represents total deaths individually and combined comorbidities by Venn diagram, as presented in Table  4A.
Variables For our studyt, data from 33 banks operating in the Indian economy have been taken into consideration for the period of 10 years, starting from 2010 to 2019. The data of secondary nature has been retrieved from the CMIE Prowess database. Table  1  includes the variables used in the current study, their measurements, and the referenced studies. The dependent variable is equity dividend divided by net worth (eqdiv_nw). The main independent variables of interest include an index to measure transparency and information disclosures (td) and a shareholder activism index (sha). In addition, a square term td 2 is included to test whether a non-linear relationship exists between td and eqdiv_nw. Similarly, an interaction term (td x sha) is included in the study to test if sha moderates the relationship between the td and eqdiv_nw.
Tumor Samples Our data set included clinicopathological and gene expression data of clinical GIST samples from 15 public data sets that we collected from the National Center for Biotechnol-ogy Information (NCBI)/Genbank GEO, ArrayExpress databases, and authors' websites (Table  S1 ). The samples had been profiled using commercial or homemade whole-genome DNA microarrays. The pooled data set contained 309 clinical samples. The study was approved by our institutional board. We also analyzed cancer cell lines data from the Dependency Map (DepMap) portal (https://depmap. org/portal; accessed on 14 December 2021) to compare the RPPA-based protein vs. RNA-seq-based mRNA expression of CSPG4.
Gene Expression Data Analysis A pre-analytic processing of data was done. In a first step, we normalized each data set separately: a quantile normalization was applied to the already processed non-Affymetrix data, and Robust Multichip Average (RMA) with non-parametric quantile algorithm to the raw Affymetrix data. That was done in R using Bioconductor and associated packages. In a second step, we mapped hybridization probes represented across the different technological platforms, and when multiple probes mapped to the same GeneID, we kept the most variant probe in a given data set. Next, we corrected the 15 studies for batch effects using z-score normalization. Briefly, for each CSPG4 expression value in each study separately, the value was transformed by subtracting the mean of the gene in that dataset divided by its standard deviation in the GIST samples.
All variables with a p-value inferior to 5% in univariate analysis were included in the multivariate analysis. When the time to follow-up was available, the 5-year DFS was estimated using the Kaplan-Meier curves; and curves were compared using the log-rank test. A logistic regression analysis using the lm function tested the correlations of molecular variables with "CSPG4-high" vs. "CSPG4-low" groups (R's package). All statistical tests were two-sided and the significance threshold was 5%. All analyses were done with the survival package (version 2. 43) from R software (version 3. 5. 2).
In particular, whereas recent advances in synchrotron-based X-ray reflectivity (XRR), atomic force microscopy (AFM), and other techniques now enable direct observations of the atomic-scale structure of the hydration layers at the mineral-bulk liquid water interfaces  [26] [27] [28] [29]  , equivalent characterization of water in thin adsorbed films remains arduous, notably because of the difficulty of precisely controlling film thickness in the presence of adventitious carbon  [30] [31] [32]  , formation of reactive radicals  [33] [34] [35]  , or capillary interactions  36- 38. Consequently, knowledge of the nanoscale properties of water in thin adsorbed films remains comparatively limited. The experimental database consists largely of bulk measurements of thermodynamic properties as a function of film thickness, notably based on adsorption isotherms and calorimetry measurements  [39] [40] [41] [42] [43] [44].
5 ns of equilibration in the NVT ensemble. Then, hydration states below 3 ML were generated by removing randomly selected water molecules from the 3 ML system, with each water removal being followed by 0. 5 ns of equilibration and 0. 5 ns of simulation before the next water removal step. Finally, each state was simulated for an additional 2. 5 ns in the NVT ensemble for data production. All simulations were carried out at 298 K using a Nose-Hoover thermostat. Equations of motion were solved using the Verlet algorithm with a 1 fs time step for all atoms except for a single plane of interlayer K ions at the mid-plane of the phlogopite slab that was kept immobile and water molecules kept rigid using the SHAKE algorithm  81. For calculations of water chemical potential and relative humidity (refer to following paragraphs), a selected set of states was simulated for 40 ns.
Interatomic interactions were resolved in real space up to 12 Å. Long-range Coulomb interactions beyond 12 Å were resolved in reciprocal space using the particleparticle/particle-mesh (PPPM) technique with an accuracy of 99. 99%. Simulations were carried out on the Cori supercomputer at the National Energy Research Scientific Computing Center (NERSC) using the code LAMMPS  91. Two energetic properties were computed for each hydration state: the differential enthalpy H ads (N) and free energy G ads (N) associated with the adsorption of a single water molecule to a surface at a given hydration state N. The adsorption enthalpy was determined from the potential energy of the system under the assumption that internal energy U is mostly due to intermolecular potential energies and that the P∆V term is negligibly small at near-ambient pressures  54  , such that H ads (N)  dE pot /dN at any water content N, where E pot is the total potential energy due to intermolecular interactions predicted by our simulations.
C. Materials corporation, IL, USA) was cleaved using tape in ultrapure water to minimize the splitting force. The freshly cleaved crystal was then mounted in an X-ray thin-film cell where aqueous solutions (9 mM MCl, with M = Na, K, or Cs) were injected and allowed to react for at least 30 min. The excess solution was drained, and a several-micrometers-thick solution film was maintained on top of the sample. The sample cell was mounted in a Newport 6-circle diffractometer for X-ray experiments. A monochromatic X-ray beam (at 14. 0 keV) reflected from the sample surface was collected using a Pilatus 100K area detector as a function of momentum transfer q, defined as q = 4π (2θ/2)/λ wh 2θ h g w h d d reflected X-ray b m d λ h w g h f h X-rays (0. 886 Å). The experimental and data analysis methodologies have been described in previous studies  28,   95.
Briefly, XRR data were analyzed using a structural model consisting of the phlogopite mica structure with the top three 2:1 layers (~30 Å in depth) allowed to relax vertically, the interfacial solution profile, and bulk water. The bulk mica structure was obtained from the single-crystal X-ray diffraction analysis of the F phlogopite (data obtained at Diamond I19). A mica model with structural relaxation allowed in the top four 2:1 layers was also tested but showed no substantial improvement in the quality of fit based on the χ 2 criterion defined in the next paragraph. In addition, the vertical relaxation remains small and mostly limited to the topmost 2:1 layer (Fig. S2 ) as expected from the less distorted hexagonal cavities in phlogopite mica (trioctahedral) as compared to muscovite mica (dioctahedral) 28,72,73, 96.
While the peak shift observed for the K system is in qualitative agreement with Adapa et al. 56  , their predictions for the Na ion densities indicate significant peak shift and do not exhibit different surface species. Comparison Between MD and XRR Results. The validity of the interfacial solution structures obtained by MD simulations was tested by comparing experimental XRR data with the patterns calculated from the MD-derived profiles (Fig. 3 ). The calculation was conducted for the Na, K, and Cs systems determined using the highest water loading (9 ML). The experimental data were collected with F phlogopite (001) in 9 mM alkali metal chloride solutions. The comparison between the measured and calculated XRR data shows generally good agreement, indicating that the MDderived profiles well represent the overall solution structure in the experimental systems, including the location of adsorbed cations and primary hydration layers.
These distinct regimes in undersaturated conditions suggest that hygroscopic surfaces provided by minerals such as mica, as examined here, can significantly impact heterogeneous reactivity and photochemistry in conditions relevant to atmospheric sciences  5, [113] [114] [115] [116]. Finally, we note that the predicted d ΔH ads vs. film thickness described above deviate from the only existing experimental dataset on the enthalpy of adsorption of water on mica (for the muscovite mica system), wh d m mum ΔH ads was reported near 1 ML  39. Ou d f m mum ΔH ads near 0 ML film thickness, however, is consistent with previous simulation results for mica  54  and with experimental and simulation results for other surfaces  42, 117, 118. I w h h u f ΔH ads presented above, our pred d u f ΔG ads are most negative in the case of the dry surface and increase towards the value for bulk water (-26.