dataesr/scientific-paragraphs-categorization huggingface dataset:
In combination with the low Ag concentration (1 mM), this means that the EXAFS data are rather noisy; above the edge, the counts per second are 1-2 × 10 4 , compared to a background of 5 × 10 3 counts below the edge. EXAFS analysis was done using VIPER,  28  and the Athena and Artemis 29 software packages. Scattering phases and amplitudes were calculated using FEFF for a number of different compounds, including Ag and Ag 2 S, coordinates of which were obtained from the Inorganic Crystal Structure Database  30  (numbers 44387 and 44507, respectively), as well as Ag 29 (BDT) 12 (TPP) 4 ,  31  with TPP (triphenylphosphine) ligands removed and BDT (1,3-benzenedithiol) replaced by LA. To ensure reasonable C-C and C-H bond lengths and angles in the newly added LA ligands, the geometry of the ligands was optimized using molecular mechanics (UFF force field, steepest descent algorithm) in Avogadro.
32 The positions of Ag and S atoms were fixed so that the core structure remained unchanged. The Journal of Physical Chemistry C Article One Ag-Ag and one Ag-S path were used in the fit. Typical fit parameters for as-synthesized clusters were: k = 3. 0-12. 5 Å -1 , R = 1. 0-3. 2 Å, using a Hanning window (dk = 1 Å -1 ) for the Fourier transformation. Fitting was done using k = 3 weighting in VIPER and k = 1, 2, 3 weighting in Artemis. The compound from which the scattering path was calculated had a minor effect on the fit, as had the fitting software and parameters. Clusters during synthesis were measured over a shorter k-range, k = 3. 0-8. 15 Å -1 , and R = 1. 0-3. 45 Å. To determine coordination numbers during synthesis, a number of parameters were fixed to that of as-synthesized clusters, such as bond lengths or Debye-Waller factors, or both, of the two scattering paths.
To monitor sample changes, we also recorded UV-vis absorption spectroscopy by pumping the solution through a Hellma 3-in-1 flow cuvette (product no. 176-766-15-40, 2 mm path length) which was built into the circuit just before the capillary. The excitation source was an Ocean Optics DH-2000-BAL lamp equipped with an Ocean Optics FVA-UV fiber optic variable attenuator, and transmitted light was detected using an Ocean Optics Maya 2000 Pro spectrometer. The attenuator was positioned between the lamp and flow cuvette to decrease the intensity of UV-vis light, which was necessary to avoid saturation of the spectrometer. By recording a blank and dark spectrum, the absorbance of the sample was immediately calculated by the software (SpectraSuite). A schematic figure of the setup is shown in Figure  1.
See Figure  S1  for the effect of purification on the mass spectrum. Purification took 5-10 min, after which the sample was measured for around 15 min to collect sufficient scans. Mass spectra are shown in Figure  3. The clusters are observed as [Ag 29 (LA) 12  3 --(y + x)H + + xNa + ] (3+y)-, in overall charge states (3 + y)-= 3-, 4-, and 5-. Thus, multiple ion signals are observed in each charge state due to the association with Na + ions. Our mass spectrometry data confirm the results of optical spectroscopy, namely that the Ag 29 cluster appears early in the synthesis and that its concentration (signal/noise ratio) increases with time. For a number of the spectra (140, 208, and 298 min), the intensities of Na + adducts appear to show a bidisperse distribution and additional species are present.
Pointed end dynamics were monitored by anchoring gelsolin-capped barbed ends to the coverslip surface, using biotinylated gelsolin and a neutravidin-decorated surface. Microscopy and Data Analysis. Images were acquired via epifluorescence or TIRF microscopy (ILAS2, Roper Scientific, now Gataca systems) on a Nikon TiE inverted microscope equipped with a 60× oil-immersion objective, either with an Evolve EMCCD camera (Photometrics) controlled by Metamorph or with an Orca-Flash2. 8 camera (Hamamatsu) controlled by Micromanager. Images were analyzed using ImageJ. Elongation or depolymerization rates (Figure  4 ) were determined on individual filaments, and median values were reported. We considered that each actin subunit contributed 2. 7 nm to the filament length. For the quantification of severing (Figure  3 ), uncapping (Figure  5C ), and rescue (Figure  5E ,F), survival fractions were determined, following a Kaplan-Meier algorithm.
Z stacks were acquired using a piezo stage NanoScan Z100 (Prior Scientific). The system was controlled using MetaMorph software. Fresh frozen sections of human colon were immunolabeled using a similar procedure and analyzed on an LSM780 confocal microscope (Zeiss) using a 40× 1. 1 NA water objective. All experimental and control images of the IF or phase-contrast data were collected using identical imaging settings, and images were merged using Fiji software tools (ImageJ; National Institutes of Health;  Schindelin et al. , 2012). For FasL-binding assay, cells were incubated with FasLcoated beads or uncoated beads (as a control) in DMEM and 0. 1% BSA for 30 min at 37°C and 5% CO 2. After extensive washing to remove unbound beads, cells were fixed with a solution of 3. 7% formaldehyde in PBS, and phase-contrast pictures of each well were performed using a 10× objective.
Specific enrichment quantification Fas specific enrichment at cell-cell junctions or Fas and caspase recruitment at FasL-coated beads were quantified using the same the process with Fiji software. Briefly, reference regions, either cell-cell junctions (labeled by E-cadherin, actin, and Dlg1) or bead contours were defined using intensity-based thresholds. The intensity of the marker of interest, endogenous Fas, FasGFP, or cleaved caspase-8, was measured inside those predefined contours (cell-cell junctions or beads). Then, the intensity of the marker of interest, endogenous Fas, FasGFP, or cleaved caspase-8, was measured outside those predefined contours to measure the basal level of expression. Finally, the enrichment factor was calculated by the ratio of the signal of interest inside/outside of the junctions or beads.
DuoLink in situ PLA To detect the interaction between Fas and Dlg1 or α-catenin, we used the DuoLink in situ PLA (Olink Bioscience) according to the manufacturer's protocol. HCT15 or SW480 cells were seeded on poly-l-lysine (40 µg/ml)-coated microscope slides. Cells were fixed with 4% PFA and permeabilized with 0. 5% Triton X-100. Cells were immunolabeled with primary antibodies: anti-Fas, anti-Dlg1, or anti-α-catenin overnight at 8°C. The secondary antibodies (PLA probes supplied in the DuoLink kit) were incubated for 1 h at 37°C. Images were collected on an inverted Axio-Observer Z1 microscope (Zeiss) equipped with a 63× 1. 4 NA Plan Apochromat DIC III oil objective lens. Images were acquired with a monochrome EMC CD iXON+897 camera (Andor Technology) controlled with MetaMorph 7. 8 software.
Mass spectroscopy analysis Following peptide pulldown (see above), protein extracts were loaded and stacked on a NuPAGE gel (Life Technologies). Stained bands were submitted to an in-gel trypsin digestion. Peptide extracts were reconstituted with 0. 1% trifluoracetic acid in 4% acetonitrile and analyzed by liquid chromatography (LC)-tandem mass spectrometry (MS/MS) using an Orbitrap Fusion Lumos Tribrid mass spectrometer (Thermo Fisher Scientific) online with an Ultimate 3000RSLCnano chromatography system (Thermo Fisher Scientific). Protein identification and quantification were processed using the MaxQuant computational proteomics platform (version 1. 5. 3. 8) using the human subset of the SwissProt database  (date 2018. 01; 20,244 entries). The statistical analysis was done with Perseus program (version 1.
Acknowledgments We are grateful to  Lawrence Banks ,  Jon Lane , and  Pascal Schneider  for providing us with expression vectors (human rat Dlg1, human GFP-Fas, and human Fas, respectively), to the  Institute de Biologie Valrose  facilities (Histology-S. Rekima and Cytometry-A. loubat), and to  Sébastien Huault  for technical assistance. This work was supported by institutional funding from  Inserm ,  Centre National de la Recherche Scientifique , and by grants from the  Association pour la Recherche sur le Cancer  and the  LAB EX SIG NAL IFE program  ( ANR-11-LABX-0028-01 ). J. -P. Borg 's laboratory is supported by  La Ligue Nationale Contre le Cancer  ( Label Ligue ) and  Ruban Rose Award. J. -P. Borg  is a scholar of  Institut Universitaire de France. Proteomic analyses were done using the mass spectrometry facility of  Aix Marseille Université Proteomics labeled "Plateforme technologique de l'Université Aix-Marseille ," supported by  Infrastructures Biologie Santé et Agronomie , the  Cancéropôle PACA , the  Provence-Alpes-Côte d'Azur Region , the  Institut Paoli-Calmettes , and the  Centre de Recherche en Cancérologie de Marseille.
Determination of the Step Retreat Rate in Solution and Air. Line Intercept Method. To determine the step retreat rate, we employed the commonly adopted line intercept method (Figure  1a ) with the AFM data obtained as deflection images. In this method, the translation of a step along a predetermined interception line is monitored over time. The (average) rate of translation (step retreat) was then taken as a measure for the material removal rate from the surface. For each sample, up to six intercept lines were defined, originating from an assumed stationary point (e. g. , a deep etch pit nucleation point) and traversing the sample along arbitrary directions. The intersection points between these lines and steps were recorded for consecutively scanned images as a distance d relative to the stationary point.
■ ASSOCIATED CONTENT * S Supporting Information The Supporting Information is available free of charge on the ACS Publications website at DOI: 10. 1021/acsearthspacechem. 8b00120. Statistical treatment of resulting data in the frequency domain (PDF) Supplementary video showing a sequence of step movement around islands (Figure  2a , Figure  3 ) on the surface of a fluoranthene crystal, which constitutes the most complex topography (AVI).
Methods The causes of death database is composed of vital registration (VR), verbal autopsy (VA), registry, survey, police, and surveillance data. GBD 2017 added ten VA studies, 127 country-years of VR data, 502 cancer-registry country-years, and an additional surveillance country-year. Expansions of the GBD cause of death hierarchy resulted in 18 additional causes estimated for GBD 2017. Newly available data led to subnational estimates for five additional countries-Ethiopia, Iran, New Zealand, Norway, and Russia. Deaths assigned International Classification of Diseases (ICD) codes for non-specific, implausible, or intermediate causes of death were reassigned to underlying causes by redistribution algorithms that were incorporated into uncertainty estimation. We used statistical modelling tools developed for GBD, including the Cause of Death Ensemble model (CODEm), to generate cause fractions and causespecific death rates for each location, year, age, and sex.
All subnational estimates for these countries were incorporated into model development and evaluation as part of GBD 2017. To meet data use requirements, in this publication we present all subnational estimates excluding those pending publication (Brazil, India, Japan, Kenya, Mexico, Sweden, the UK, and the USA); because of space constraints these selected subnational results are presented in appendix 2. Subnational estimates for countries with populations larger than 200 million (measured with our most recent year of published estimates) that have not yet been published elsewhere are presented wherever estimates are illustrated with maps but are not included in data tables. The complete cause-specific estimation results include the years 1980 through to 2017, and are available for exploration by an online data visualisation tool.
Cause of death data The GBD cause of death database consists of VR and VA data; survey and census data for injuries and maternal mortality; surveillance data for maternal mortality and child death; cancer registries; and police records for interpersonal violence and road injuries. Self-harm estimates incorporate VR data and are based on ICD categorisation as described in appendix 1 (section 7). In this iteration of GBD, ten new VA studies and 127 new country-years of VR data were added at the country level. 502 new cancer-registry country-years were added, as was one additional new surveillance countryyear. Data sources comprising the GBD cause of death database can be reviewed on the Global Health Data Exchange website. Multiple factors can influence changes between GBD studies in estimates for a given cause-location-year, including the quality of a country's data system (as represented by the GBD star rating system) and the addition of more recent data.
7). We excluded all data sources with more than 50% of deaths assigned to major garbage codes (those at Level 1 or Level 2 of the GBD hierarchy) in any locationyear to mitigate the potential for bias from these sources. The proportion of VR data assigned to major garbage code categories for each location-year is shown, with supporting detail, in appendix 1 (section 7). New to GBD 2017, the uncertainty around redistribution methods was also estimated. Additional details for this process are provided in appendix 1 (section 2. 7). Because mortality due to HIV/AIDS is sometimes coded to other causes of death such as tuberculosis, meningitis, or toxoplasmosis, we also corrected the cause of death assignment to HIV/AIDS for peak epidemic times. Tuberculosis deaths can be misclassified as pneumonia deaths in children in locations with a high tuberculosis burden.
Methods to adjust for this potential mis classi fication are described in detail in appendix 1 (section 3. 3). Mortality rates from dementia and Parkinson's disease reported in VR systems cannot be reconciled with observed trends in prevalence and excess mortality-a disparity that can be attributed to variation in death certification practices for these causes across countries and over time. 17 For GBD 2017, we sought to address this known bias by using details from multiple cause of death data. For GBD 2017, multiple cause of death data were available to investigators only for the USA, where recent years show improved use of previously underutilised codes such as dementia. Statistical models of these USA data were used to reclassify deaths from other GBD causes and garbage codes to dementia and Parkinson's disease according to the pattern of intermediate and immediate causes observed in the most recent years.
Model results were applied to all countries. A similar reallocation pro cess was used for atrial fibrillation deaths misclassified as deaths due to heart failure or thromboembolic events. A detailed description of these redistribution procedures and the manner in which they were applied to all countries is available in section 2 of appendix 1. This reallocation is illustrated in appendix 1 (section 7). For the first time in GBD 2017, we separately estimated deaths from diabetes by type. Deaths due to diabetes can be reported in VR and VA data as type 1, type 2, or unspecified. Two data manipulation steps were necessary. First, we assumed all deaths reported in individuals younger than 15 years were type 1 regardless of the original code assignment. Second, we redistributed unspecified diabetes deaths on the basis of a regression in which the true proportions of type 1 and type 2 deaths by age-sex-location-year are a function of the proportion of unspecified deaths, age, the age-standardised prevalence of obesity, and an interaction term for age and obesity prevalence.
Data completeness assessment Completeness of VR data was assessed by location-year, and sources with less than 50% completeness were excluded. We multiplied the estimated all-cause mor tality for each age-sex-location-year by the cause fraction for the corresponding age-sex-location-year to adjust all included sources to 100% completeness. VA and VR data availability and completeness are shown for each location-year in appendix 1 (section 7). To further char acterise the quality of data available in each country, the GBD study rated each location-year from 1980 to 2017 on a level of 0 to 5 stars according to methods previously described. 18 atings convey an overall measure of the reliability of cause of death estimates for each location-year but do not directly affect the estimation process.
Various ensemble models with different weighting parameters were created from the combination of these models, with the highest weights assigned to models with the best out-of-sample prediction error for trends and levels, as detailed in appendix 1 (section 7). Model performance of these ensembles was assessed against the root-mean squared error (RMSE) of the ensemble model predictions of the log of the age-specific death rates for a cause, assessed with the same 15% of the data. The ensemble model performing best was subsequently selected and assessed against the other 15% of the data withheld from the statistical model building. CODEm was run independently by sex for each cause of death. A separate model was run for countries with 4-star or greater VR systems to avert uncertainty inflation from more heterogeneous data.
Cause of death estimation with alternative estimation strategies Alternative estimation strategies were used to model a subset of causes of death with unique epidemiology, large changes in reporting over time, or particularly limited data availability, including HIV/AIDS, malaria, chronic kidney disease, cirrhosis, liver cancer, meningitis, de mentia, and atrial fibrillation. Alternative strategies included prevalence-based models, incidence and case fatality models, and sub-cause proportion models as described in appendix 1 (section 7). Mortalityincidence ratio models based on registry data were used to estimate mortality from 32 cancers (appendix 1 section 3. 3). Negative-binomial models were used for eight causes of death with typically low death counts or causes that typically have no deaths in countries with a high Socio-demographic Index (SDI), including ascariasis, cystic echinococcosis, cysticer cosis, diphtheria, iodine deficiency, other intestinal infectious diseases, schistosomiasis, and varicella and herpes zoster virus.
Estimation of fatal discontinuities Fatal discontinuities are large changes in deaths due to unexpected spikes in injuries or epidemics-defined by GBD as more than one per million or more than   25 deaths-in a specific location-year. We classified fatal discontinuities as conflict and terrorism, major trans portation accidents, natural disasters, other forms of disaster such as large fires or the collapse of large buildings, or major outbreaks of infectious diseases. Data on fatal discontinuities came from VR data in the 75 countries with a 4-star or 5-star data quality rating for the interval of 1980-2017. For the remaining 120 countries with a rating of 3 stars or lower, we used alternative databases (appendix 1 section 7). Cholera and meningitis were estimated as fatal discontinuities to reduce the risk of underestimation for small-magnitude outbreaks caused by the smoothing of VR or VA data over time in CODEm.
Uncertainty analysis Uncertainty in our estimates was attributable to causespecific model specifications; varied availability of data by age, sex, location, or year; and variability of sample size within data sources. We quantified and propagated uncertainty into final estimates by calculating uncertainty intervals (UIs) for cause-specific estimation components based on 1000 draws from the posterior distribu tion of cause-specific mortality by age, sex, location, and year. 21 95% UIs were calculated with the 2•5th and 97•5th percentiles, and point estimates were calculated from the mean of the draws. Changes over time were considered statistically significant when the uncertainty interval of the percentage change over time did not cross zero.
Epidemiological transitions The from 1980 to 2017 this increase occurred most often at lower SDI levels, and even with YLL rates slowly decreasing during this time period. Low SDI countries had a decrease in the rate of YLLs due to self-harm, falling from 604•4 (499•7-712•0) per 100 000 in 1980 to 441•4 (410•9-479•2) per 100 000 in 2017, but still remained higher than in all other SDI quintiles. In general, the precision of estimates was lower at lower SDI levels, represented by the wider 95% UIs across causes, which reflects the availability of data for these locations. Despite increasing populations and changes in population age-structure, YLL rates decreased across the five leading Level 2 GBD causes of YLLs in all SDI quintiles (figure  6B ). Large decreases in YLL rates were estimated at low SDI levels for respiratory infections and tuberculosis (from 14 900 [95% UI 13 000-16 600] YLLs per 100 000 in 1980 to 4750 [4505-4990] YLLs per 100 000 in 2017).
Continuous quality improvement GBD 2017 has developed new methods to address the observation that some common causes of death are reported by surveillance systems as having implausible trends across time and varying widely between countries. 54, 55 7] [58] [59] [60]  A new analysis by GBD 2017 uses 35 years of person-level underlying and intermediate cause of death data from the USA-a database of 80•4 million deathsto better understand how physicians are choosing from a range of alternative diseases when selecting an underlying cause of death that was actually dementia. Countries should consider better use of important contextual data already being collected, such as intermediate and immediate causes of death, to improve the stability and robustness of their mortality surveillance systems.
Changes in health estimates between GBD 2016 and GBD 2017 Each iteration of GBD re-analyses the entire time series by use of newly available data sources from across all estimation years and continually improved methods. New data and modelling approaches effectively improve model validity and decrease uncertainty from various sources with the consequence that estimates for a given cause, location, and year might differ between GBD iterations. The magnitude of these differences between GBD 2017 and GBD 2016 is presented in figure  1. Below we discuss some specific data and methodology changes underlying distinct differences in estimation. A novel, integrated demographic assessment of population, fertility, and all-cause mortality was completed for GBD 2017. This development affected all causes because of the inclusion of population estimates in age-sex splitting algorithms, but most directly affected maternal and HIV/AIDS-related mortality estimates.
The GBD 2017 assess ment of the proportion of all-cause deaths due to maternal and neonatal disorders (3•7% [95% UI 3•5-3•8]) was similar to that of GBD 2016 (3•6% [3•4-3•8]), with the difference largely due to addition of new data and expanded subnational estimation. Additionally, GBD 2017 combined maternal and neonatal conditions as a category at Level 2 of the cause hierarchy; as a result, causes within this grouping are now separately reported at Level 3 (rather than Level 2), with neonatal conditions appearing for the first time as the second most common Level 3 source of YLLs. Access to additional data sources led to several differences in estimation, including the addition of 2778 deaths in children younger than 5 years attributable to the inclusion of additional VA data for Nigeria 61 -a highpopulation, high-burden location-in GBD 2017.