dataesr/scientific-paragraphs-categorization huggingface dataset:
This paper proposes a new finite-element modelling of a recent layerwise model for multilayered plates. This layerwise model is built from a specific 3D stress-field expansion along the thickness direction and involves, in particular, interlaminar transverse shear and out-of-plane stresses as generalized stresses. Its main feature is that 3D equilibrium equations and free-edge boundary conditions are directly taken into account into the stress-based construction of the model. A dual displacement-based finite-element discretization is implemented using the FEniCS software package and a remeshing strategy is proposed based on a novel error indicator. The error indicator is built based on the 3D stress field directly deduced from the layerwise generalized stresses and compared to a reconstructed stress field based on the model generalized displacements.
Finite element discretization A finite-element discretization of the SCLS1 model has been proposed in  [41]  using the MPFEAP in-house software described in  [36]. In our numerical study, the SCLS1 multilayered plate model has been implemented in the open-source finite element FEniCS package  [42, 43]. The FEniCs Project is a collection of free and open-source software components with the common goal to enable automated solutions of differential equations. The components provide scientific computing tools for working with computational meshes, finite element variational formulations of ordinary and partial differential equations, and numerical linear algebra. We therefore benefit from FEniCS high-level domain-specific language for implementing the variational formulation associated with the SCLS1 model.
This article investigates the long-term impact of historical missionary activity on HIV in sub-Saharan Africa. On the one hand, missionaries were the first to invest in modern medicine in the region. On the other hand, Christianity influenced sexual beliefs and behaviors that affect the risk of contagion. We build a new geocoded dataset locating Protestant and Catholic missions in the early 20th century, as well as the health facilities they invested in, that we combine with individual-level DHS data. With these data, we can address separately these two channels, within regions close to historical missionary settlements. First, we show that proximity to historical missionary health facilities decreases the likelihood of HIV; persistence in healthcare provision and safer sexual behaviors in the region explain this result.
To do so, we have completed a novel dataset of early Protestant and Catholic mission settlements in sub-Saharan Africa, that also accounts for their health facilities. For Protestant missions, we rely on an original dataset released with a previous article  (Cagé and Rueda, 2016). The Protestant data was compiled using the 1903 Geography and Atlas of Christian Missions  (Beach, ed, 1903). We complement these data with an entirely new dataset on Catholic missions and their health facilities. This dataset was compiled from the 1929 Atlas Hierarchicus  (Streit, 1929) , a publication from the Vatican's Congregation for the Propagation of Faith (Sacra Congregatio de Propaganda Fide). For each settlement, we documented its coordinates, the geographic and historical characteristics, as well as whether there was an investment in health facilities.
Because there is a twenty-year gap between the two sources, we complement the data on health facilities for former British colonies by geocoding all the health facilities reported in the British Blue Books up to 1920. We merge these data with contemporary individual-level geocoded data from the Demographic and Health Survey compiled by USAID (DHS). The DHS are standardized nationally representative surveys in developing countries that collect biomarker data on HIV. Our econometric analysis deals with two forms of selection. First, historical and geographical characteristics might have determined mission station location, preventing the comparison between regions close and far from these settlements. Early European missionaries, either Protestant or Catholic, did choose to locate in geographically favored areas that ultimately urbanized  (Johnson, 1967; Nunn, 2010; Akyeampong et al.
Moreover, we document the role played by sexual behaviors (see e. g. DellaVigna and Ferrara, 2015). 12 he rest of the paper is organized as follows. Section 2 presents a brief historical background on missionary activity in sub-Saharan Africa, the early investments in modern medicine and the focus of a number of religious groups on abstinence until marriage. Section 3 describes the new dataset we build for this paper and presents summary statistics. In Section 4, we provide empirical evidence on the long-term impact of missionary activity on the likelihood of HIV, and disentangle the positive effect of health investments and the negative effect of the focus on abstinence. Section 5 presents a number of robustness checks. The endogenous selection of missions into health investments is extensively discussed.
Protestant missionary investments Regarding Protestant missions, we originally compiled the data of missionary investments and their locations in a previous article  (Cagé and Rueda, 2016). We constructed the mission-level data and geocoded the maps of sub-Saharan African regions from the Geography and Atlas of Christian Missions  (Beach, ed, 1903). The maps locate all the Protestant mission stations in 1903 (an example of these maps is provided in Figure  2b ). As opposed to other available geographic datasets of Protestant missions  (Akyeampong et al. , eds, 2014, Chap. 16 ), the one generated for this article contains detailed information for each mission settlement. In the Geography and Atlas of Christian Missions, each mission station is uniquely identified in a statistical appendix, which provides information on the mission's size (number of students, of missionaries, etc.
Catholic missionary investments This paper complements the data from  Cagé and Rueda (2016)  with new information on Catholic missionary activity. We exploit the information from the 1929 Atlas Hierarchicus, which is the official publication of the Vatican (the Fide service) that inventories Catholic activities worldwide and locates all the Catholic missionary stations in the world on a series of maps  (Streit, 1929). 21 An example of these maps is provided in Figure  2a. The Atlas also gives some statistical information about the investments at the mission level, although it is less detailed than the Protestants' Geography and Atlas of Christian Missions. Notwithstanding this limitation, it does report the location of missionary hospitals; which is enough for the scope of our study. Our sample of sub-Saharan African missions includes a total of 536 Catholic missions out of which 36 had a hospital.
Contemporary data Our analysis relies on the individual-level data from the Demographic and Health Surveys (DHS) distributed by USAID. The DHS are standardized nationally representative surveys in develop-ing countries. Women and men answer questions related to their anthropometrics, living conditions, health, sexual reproduction behaviors, and attitudes and beliefs regarding different health outcomes. Since the 2000s, the DHS also collect biomarker data on Sexually Transmitted Diseases (STDs). Biomarkers are objective physical or biologic measures of health conditions. Using field-friendly technologies, the DHS surveyors can collect biomarker data on HIV. 23 Hence, our measure of HIV infection is based on actual HIV contamination information, not on self-reported surveys where the mean rates of HIV infection are likely to be severe underestimates.
24 The DHS data also provides information on anemia for women; just like for HIV, biomarker testing is used for anemia, making them comparable. We use anemia in Section 5 to show that our result are specific to STDs (i. e. not driven by poorer health conditions in general). We use the DHS data for sub-Saharan African countries where we have at least one missionary hospital reported, and where both GPS information and biomarker data on HIV are provided. Figure 4a  shows the locations of these clusters. There is a total of 344,212 individuals, distributed in 8,498 clusters distributed among 17 countries: Burkina Faso, Burundi, the Democratic Republic of Congo, Cameroon, Ethiopia, Gabon, Guinea, Kenya, Liberia, Lesotho, Malawi, Rwanda, Senegal, Swaziland, Uganda, Zambia, and Zimbabwe. The information is collected in several rounds, from 2003 to 2014.
The surveys are not systematically carried for all countries every year, so each country appears 1 to 3 times in the sample. 25   The DHS now also releases geospatial covariates for all the clusters for which it provides GPS coordinates. We rely on these data to obtain geocovariates that are standard in the literature. For each location, we observe land characteristics (drought episodes, aridity, rainfall, temperature, malaria ecology, etc. ), average night-time luminosity, and proximity to water sources, among others. Importantly, this source gives information on the average travel time to a major urban center. This variable captures urbanization and also connectedness to the infrastructure network, which is of particular interest to us, as Oster (2010) shows that road density is an important determinant of HIV prevalence.
Selection on unobservables compared to selection on observables Using the insights from Oster (2016), we check the likelihood that our results are driven by the bias due to unobservables. This method, which we implement using her Stata module psacalc, assesses how large the bias due to unobservables should be, compared to that on observables in order to explain away the results. This ratio is called δ. Using the R-max value of 0. 13 (30% higher than our estimated R-squared), we find that selection in unobservables would need to represent 50% to 150% of selection in observables to explain away the results. When adding country and year fixed effects as unrelated controls, the estimated δ is always larger than one. The results are reported in the appendix Table  C. 11.
Importantly, we find no statistically significant relationship between proximity to missions that invested in health and the number of years of schooling. It is thus unlikely that all of our effect would be driven by human capital accumulation. Non-sexually transmitted diseases and access to healthcare This section explores the possibility that our results could be unspecific to STDs, and capturing other factors that would correlate with systematically worse health around regions where missions settled, in particular Protestant ones. The DHS do not provide test results for other STDs, but do report other health-related outcomes, mostly for women. The DHS female dataset indeed includes results for anemia tests and a few anthropometric measures. Table  C. 16 and C. 17 in the online Appendix report the results of estimating equation (1) when the dependent variable is a binary variable equal to one if the respondent is either anemic or has stunted growth, and to zero otherwise.
Channels Taken together, the results of this article show that historical missionary activity has heterogeneous effects on HIV today. In this section, we discuss the channels that could explain the relationship between missionary presence, missionary health investments and HIV prevalence. First, we aim at getting a clearer understanding of the persistent effect of missionary health investments on HIV prevalence. To do so, we explore the possibility of persistence in infrastructure in the medium term by using the location of the colonial hospitals (we collect this information until 1926 from the British colonial statistical records -the so-called Blue Books) and the available geocoded infrastructure data from the DHS. Furthermore, we follow  Calvi and Mantovanelli (2018)  and analyze whether there is persistence in health habits, and in particular sexual behaviors.
The numbers are larger, albeit no statistically significantly so, around missions with health facilities. [Table  5  about here. ] Second, Table  6  records the results from the same analysis ran in Table  3 , but replacing the distance to missionary health stations with the distance to any health center (missionary or colonial). Because the data on the location of colonial hospitals comes from the British Blue Books, the sample is restricted to former British colonies. Columns (1) and (  2 ) show that the results are similar to those in our baseline regression. The results for Catholic health stations are not significant, which can be due to the sample restriction. [Table  6  about here. ] Infrastructure in the long run The DHS conducts a survey called "Service Provision Assessment" (SPA) that provides an overview of countries' health service delivery, such as the range of services (child health, family planning, etc.
Data reduction The data have been reduced with version v. 0. 6. 082 of the APERO SPIRou data reduction software  (Cook et al, 2020, in prep. ). APERO first combines the sub-exposures at the read-out level, correcting for the non-linearity in the pixel-by-pixel response. The 1D spectral fluxes are optimally extracted following  Horne (1986). The individual spectral orders are processed and saved separately, providing a 2D frame with about 4088 spectral pixels for 48 orders. SPIRou uses two optical fibers to collect light from the two images formed by a Wollaston prism. For pure spectroscopy, APERO merges the spectra of the two beams, whereas for polarimetry the fluxes of the two channels are individually saved for later polarimetric analysis. APERO also calculates for each channel a blaze function from daytime flat-field exposures.
The shifted template CCF m pv `∆vq is calculated by cubic interpolation. We Article number, page 6 of 23 also measured RVs by fitting a Gaussian to each CCF i , which is the most usual method. That gives similar results but shows stronger systematics correlated with the air mass of the observations, and we therefore adopt the CCF matching (CM) method in our analysis. In yet another processing alternative, we apply a median filter (MF) to the CCF time series before calculating RVs through template matching, using a 3 x 3 window along the time and velocity axes. The RV data measured by SPIRou are presented in Appendix A.
Results and Discussion Our   The final fit parameters are presented in Table  3. We obtain a fitted obliquity angle of λ " ´0. 3 `17. 8 ´15. 0 degrees and 5. 1-m s ´1 and 11. 5-m s ´1 dispersions for the SPIRou (masked data excluded) and iSHELL residuals. This result confirms that the planet is on a prograde orbit and that the orbital and rotation spins are closely aligned. Fig. 4  shows this final fit model to the RV data for both instruments. Since iSHELL only observed a partial transit of AU Mic and no out-of-transit baseline, its data alone do not constrain a full RM model independently of SPIRou, but the two data sets are fully mutually compatible. The agreement between the data sets from these two different instruments Article number, page 10 of 23 using independent techniques for data analysis is remarkable and shows that both instruments are stable and can provide RVs with precisions of a few m s ´1 for an active star.
Acknowledgements. The authors wish to recognize and acknowledge the very significant cultural role and reverence that the summit of Maunakea has always had within the indigenous Hawaiian community. We are most fortunate to have the opportunity to conduct observations from this mountain. We acknowledge funding from the  French National Research Agency (ANR)  under contract number  ANR-18-CE31-0019  (SPlaSH) and also in the framework of the  Investissements dAvenir program  ( ANR-15-IDEX-02 ), through the funding of the " Origin of Life " project of the  Univ. Grenoble-Alpes. We also acknowledge funding from the  European Research Council (ERC)  under the  H2020 research & innovation programme  (grant agreement  740651  NewWorlds). SPIRou  project is funded by the  IDEX initiative at UFTMP ,  UPS , the  DIM-ACAV programme in Region Ile de France , the  MIDEX initiative atAMU , the  Labex @ OSUG2020  programme,  UGA ,  INSU/CNRS ,  CFI , CFHT, LNA, CAUP and DIAS.
, 2019; Geyer et al. , 2016a Geyer et al. , , 2016b Geyer et al. , , 2017 Geyer et al. , , 2019;; Liu et al. , 2015; Niu et al. , 2019; Wewer Albrechtsen et al. , 2018). Increasingly, MS-based proteomics is also entering regulated clinical and diagnostic environments  (He, 2019; Mischak et al. , 2012; Crutchfield et al. , 2016). It has the potential to yield complex and predictive biomarker signatures that support clinical decision making, as well as to enable the prediction of patient trajectories via machine learning methods with datasets of sufficient depth and size  (Ahadi et al. , 2020). However, in clinical practice, its potential is yet to be completely realized. For routine applications, MS-based proteomic methods must combine precision, reproducibility, and robustness with low cost and high throughput, such that results can be routinely compared within and between clinical studies and laboratories  (Geyer et al.
In order to achieve a sufficiently fast mass spectrometric duty cycle, we used a q-TOF instrument with a very fast sampling rate  (Schilling et al. , 2017)  and applied SWATH-MS, a DIA method specifically developed to minimize stochastic elements in data acquisition  (Gillet et al. , 2012; Ludwig et al. , 2018). To record sufficient data points per chromatographic peak, we optimized the method for duty cycles of 700 ms and scan a precursor mass range of m/z 450-850 using 25 windows with variable window size (Table  S6 ) and with 25-ms accumulation time. In order to deconvolute the complex data recorded, we built on our recent developments of DIA-NN software, that includes several algorithms that boost the number of true positive precursor identifications in the short-gradient DIA-MS runs. DIA-NN can handle complex short-gradient data as it contains algorithms that correct for signal interferences and uses deep neural networks to assign confidence scores to peptide-spectrum matches and identify true positive signals  (Demichev et al.
(B) Data processing (red boxes): the analysis of the highly complex short-gradient DIA data is achieved with an optimized version (1. 7. 10) of DIA-NN  (Demichev et al. , 2020). DIA-NN is based on neural networks to enable confident peptide identification with fast gradients and achieves a throughput of >2,000 samples/day on a conventional PC. First, a spectral library is automatically ''refined'' using the dataset in question: only detectable peptide precursors are retained, and their reference spectra and retention times are replaced with empirically observed. Reanalysis with this refined library is then followed by batch correction and, finally, protein quantification using MaxLFQ  (Cox et al. , 2014). Abbreviations: ABC, ammonium bicarbonate; DTT, dithiothreitol; IAA, iodoacetamide; FA, formic acid.
The sample preparation was done in four 96-well plates and the experiment involved 409 non-blank injections. For interrogating the raw data, we made use of a high-quality spectral library  (Bruderer et al. , 2019)  that was refined with DIA-NN based on the actual data (Figure  1B ). Upon protein extraction and batch effect correction (STAR Methods), we assessed the robustness and consistency of protein identification achieved and herein illustrate the key quality parameters. The high-flow LC setup yielded exceptional retention time stability across the whole experiment (Figure  3A ). As expected, due to the short gradients, the total proteomic depth (total number of peptides quantified) is lower than achieved with MS workflows that use pre-fractionation and longer gradients with lower flow rates and have a slower duty cycle to scan over a larger mass range.
We consistently identified around 3,000 peptide precursors (i. e. , peptides ionized to a specific charge; Figure  3B ) and 200 unique proteins (i. e. , gene products identified with specific proteotypic peptides; Figure  3C ) across all 409 proteome acquisitions. In total, we detected 311 protein groups, out of which 245 uniquely identified proteins were measured with 87% data completeness and with at least five peptides. Among these, 182 unique proteins were quantified with 99% data completeness (Figure  3D ). (C) A tryptic digest of K562 human cell lines was separated with a 20-min linear gradient ramping from 3% ACN 0. 1% FA to 36% ACN, 0. 1% FA on high-flow (800 mL/min; C18 column 50 mm 3 2. 1, column length). Peak widths at FWHM of the eluting peptides were compared to a 20-min micro-flow run (5 mL/min; 15-cm column;  Demichev et al.
We further examined in detail the ''serum'' cluster of points and did not detect any bias between different sample preparation plates (Figure  3F ). Finally, we evaluated the quantification precision by calculating the CV of protein quantities across the studies. Median values obtained are 5. 4% for repeat reference sample injections (''QC'') after instrument drift correction, with high abundant proteins being measured with a less than 2% CV of protein quantities (Figure  S2B ). Serum controls and plasma controls were measured with 7. 6% and 7. 3% CV, reflecting the precision of the entire workflow including sample preparation, acquisition, and data analysis. These values are much lower than the biological variation detected across the randomly chosen GS participants; when this biological variation is expressed as a CV value, it corresponds to a variation of 25.
Moreover, it is difficult to assemble a control cohort that is matched for the key confounding factors, like age. Nonetheless, our proteomic platform yielded only slightly inferior values compared to the ideal-case scenario of the GS study. In the exploratory cohort, which included 104 serum samples obtained from the 31 of the earliest COVID-19 patients, we quantified 297 protein groups among which 229 unique proteins were detected with 75% data completeness. We surmise that this somewhat lower data completeness was caused by the massive changes in levels of a number of proteins upon severe SARS-CoV-2 infection as well as by the decrease in total serum protein content, also observed previously in patients hospitalized in ICU units  (Nie et al. , 2020). To account for this biological limitation, we applied very strict filtering to the dataset, namely, we only tested for differential abundance of proteins which had at least five different peptide precursors identified at least in one of the acquisitions.
We identified 37 protein groups with either increasing or decreasing levels, depending on the severity of the disease (0. 05 significance, multiple testing corrected, Theil-Sen test against the WHO severity score; see STAR Methods for testing methodology) (Figure  S7 ). Next, to validate the biomarkers, we processed the validation cohort (Table  S1 ) and recorded 96 proteomes in triplicates for 15 healthy volunteers and 17 COVID-19 patients (Table  S1 ). The experiment quantified 319 protein groups among which 248 unique proteins were detected with 85% data completeness. Despite being conducted on a different matrix (citrate plasma), this independent study confirmed 27 of the protein groups with either increasing or decreasing levels (A1BG, ACTB;ACTG1, ALB, APOA1, APOC1, C1R, C1S, C8A, CD14, CFB, CFH, CFI [complement factor I], CRP, FGA, FGB, FGG [fibrinogen alpha, beta, and gamma chains], GSN, HP, ITIH3 [inter-alpha-trypsin inhibitor heavy chain 3], ITIH4 [interalpha-trypsin inhibitor heavy chain 4], LBP, LGALS3BP, LRG1, SAA1 [serum amyloid A1], SAA1;SAA2 [serum amyloid A1 and A2 protein group], SERPINA10, TF; 0.