dataesr/scientific-paragraphs-categorization huggingface dataset:
Added value of this study We analysed data from the standardised, population-based Demographic and Health Surveys to identify the magnitude of the effect of wealth-related and education-related inequalities on uptake of HIV testing in 16 sub-Saharan African countries. We also investigated how this effect changed over time, by comparing data from surveys done before and after 2008 (when international recommendations to expand providerinitiated opt-out testing were released, and by when antiretroviral therapy in sub-Saharan Africa had been scaled up). We report both relative and absolute inequalities based on indicators that are widely used for the quantification and comparison of socioeconomic gradients in health, and also calculated mean overall estimates for the 16 countries included. In the most recent surveys (ie, those done after 2008), we noted a general trend of inequalities that disfavoured poor and less educated people (ie, these groups were less likely to have undergone an HIV test in the previous 12 months).
Data In the DHS, each included household was classified as rural or urban according to nationally defined boundaries. There are many ways to measure socio economic position in lowincome and middleincome countries, and each method has both strengths and limitations. Assetbased measures and education are commonly used comple mentarily and are often highly correlated, although they rely on different theoretical bases. 13 Individual socio demographic characteristics collected as part of the DHS included age, level of school attended (ie, none, primary, or secondary or higher) and marital status (ie, married or cohabiting, single, or widowed or separated). Household wealth was assessed with the DHS's wealth index-a composite measure of living standards that is based on the household's assets (eg, televisions, refrigerators) and characteristics (eg, type of water access, type of flooring).
Results The 16 subSaharan African countries included in the analyses were Cameroon, Côte d'Ivoire, DR Congo, Ethiopia, Guinea, Kenya, Lesotho, Liberia, Malawi, Mali, Niger, Rwanda, Sierra Leone, Tanzania, Zambia, and Zimbabwe. The earlier surveys were done between 2003 and 2008, and the later surveys were done between 2008-09 and 2016, with the intersurvey period ranging from 5 years to 11 years across countries (tables 1-4). 90-100% of women and girls approached participated, and 82-100% of men and boys (tables 1-4). Overall, data were collected from 537 784 people, 354 431 female participants and 183 353 male participants. In the surveys done after 2008, most participants in most countries were living in rural areas (except for Cameroon, Côte d'Ivoire, and Liberia) and most were married or cohabiting (except for male participants in Cameroon and Lesotho; tables 1-4).
Overreporting of HIV testing uptake might also have contributed to the findings in some countries (eg, among female participants in Lesotho and Zimbabwe) that poorer people had higher uptake than wealthier people. Contextual factors such as the communitylevel stigma towards people with HIV could also affect the validity of selfreported HIV testing, although evidence is scant about the probable direction of such a bias. Our research work relied on data collected up to 2016. Thus, it might not capture the most recent changes in HIV testing patterns in response to UNAIDS' 909090 objective. Another limitation was the heterogeneity noted in the results of the metaanalyses, which prevented us from generalising our results beyond the subset of countries that we included in our analysis (appendix 2 p 24).
We examine the gender wage gap in Austria from 2005 to 2017 using data from EU-SILC. The raw wage gap declined from 18. 6 log points in 2005 to 14. 9 log points in 2017. We use standard decomposition techniques that correct for differences in the distributions of human capital and other variables between men and women. All calculated decompositions indicate that the unexplained part of the gender wage gap decreased substantially over the last ten years. The decrease of the unexplained gender wage gap between the largest gap in this period (  2006 ) and the most recent gap (2017) ranges from 3. 7 log points to 8. 5 log points depending on the decomposition approach. Using the approach developed by  Neumark (1988) , the corrected wage gap shrank from 8. 7 log points in 2005 (8. 8 log points in 2006) to 5.
, 2013). However, there is no study for Austria that uses a consistent source of data to analyze such development over a longer time. Earlier work used dierent data or dierent empirical methods. See, for example,  Zweimüller and Winter-Ebmer (1994) ,  Pointner and Stiglbauer (2010) , Bundesministerium für Frauen (2010),  Grandner and Gstach (2015)  or  Christl and Köppl-Turyna (2019). This makes an assessment of the gender wage gap's evolution over time dicult. We provide an analysis of the development of the gender wage gap in Austria for the period 2005 to 2017, using data from the Austrian EU-SILC  (Statistik Austria, 2018). The EU-SILC is the only long-term, yearly survey which is currently available for Austria. It provides a range of personal characteristics and job-specic information which allows us to contrast changes over time, using a unied and consistent approach.
Data We use data from the Austrian European Union Statistics on Income and Living Conditions (EU-SILC) covering the years 2005 to 2017  (Statistik Austria, 2018). EU-SILC is an annual survey of about 6,000 households with about 14,000 persons. The survey is a combined cross-sectional and longitudinal survey where each year about a quarter of observations are dropped from the survey, while a similar number of observations is added. Each quarter of the sample remains in the survey for four years. The survey collects data on income, poverty, social exclusion, housing, labor, education, and health at the household and individual levels. Our empirical analysis uses wage regressions that also control for sample selection using a Heckman procedure  (Heckman, 1979). We include persons aged 20-60 years who work in the public or the private sector and analyze the wage of their main job.
We repeat some of our analyses also for the private sector separately and show the results in the Appendices A (tables) and B (gures). EU-SILC does not provide an hourly wage. We calculate the hourly gross wage by dividing the usual monthly earnings (including overtime and bonuses) by the number of usual hours in paid work and deate all wage data to prices of 2014, using the Consumer Price Index (CPI)  (Statistik Austria, 2019b). To control for dierent probabilities of working in the wage regressions, we add non-working persons to our sample. Table  1  shows the average gross hourly wages for men and women, 20052017. Wages are deated using the CPI with the base year in 2014  (Statistik Austria, 2019b).
Because the correlation between the unemployment rate and the raw wage gap is stronger than with the corrected wage gap, the rst explanation seems more plausible. This would be consistent with evidence by  Weichselbaumer and Winter-Ebmer (2007)  and  Weber and Zulehner (2014). 12 When we restrict our sample to private-sector workers, we observe a similar pattern. See Table  A. 9 in Appendix A. (Zweimüller and Winter-Ebmer, 1994)  and showed a rather large gap of more than 30 log points. In addition,  Zweimüller and Winter-Ebmer (1994)  used net wages which usually result in a smaller wage gap due to progressive taxation. Hourly wage data were not regularly collected in Austria and thus there was no further study until the mid-1990s. From then onwards, several studies produced estimates of the gender wage gap.
These, however, used dierent data sets (Mikrozensus, EU-SILC or tax data) or dierent econometric methods (inclusion of an indicator for women in OLS regressions or dierent variants of Blinder-Oaxaca decompositions). Despite these dierences, it becomes quite apparent that the raw and the corrected gender wage gap declined over time. 13 Several studies made comparisons over time and provide more than one data point. The other studies are represented by one point in the graph.
Summary and conclusions We examined the gender wage gap in Austria using EU-SILC data from 2005 to 2017. Using standard decomposition techniques, we decompose the gender wage gap over time. We nd that the raw wage gap declined from 18. 6 (20. 7) log points in  2005 (2006)  to 15. 0 log points in 2017. Controlling for observed dierences between women and men in human capital, occupation, and other explanatory variables, we nd that the unexplained part of the gender wage gap decreased substantially over the last ten years. The decrease of the unexplained gender wage gap between the largest gap in this period (  2006 ) and the most recent gap (2017) ranges from 3. 7 log points to 8. 5 log points depending on the decomposition approach. Using the method by  Neumark (1988) , we nd that the unexplained gender wage gap shrank from 8.
However, the dierence in the raw gender wage gap is still large due to dierences in observed and unobserved characteristics between men and women. Labor market experience, occupation and industrial segregation, and labor market attachment are still important aspects where men and women dier, which results in average wage dierences. A Appendix: Tables  Table A. Notes: Austrian EU-SILC data 20052017  (Statistik Austria, 2018). Gross hourly wages in Euros, deated using the CPI (base year is 2014). Based on EU-SILC data 2005-2017. Means of personal characteristics for men and women in  2007, 2009, 2011, 2013, 2015 and 2017. Standard deviations in parentheses.
Steps 4 and 5 -testing phase and test-retest reliability The testing phase of the BDC-Q was carried out among 985 GPs from the Alsace and Rhone-Alpes regions of France. They were recruited through regional professional organisation mailing lists. Surveys were web-based and gathered socio-demographic characteristics. The questionnaire items were displayed randomly to avoid response contamination bias  [27]. We prevented missing data by forcing a response to all items. We used principal component analysis followed by internal consistency testing to organise the items into descriptive dimensions. Test retest reliability was conducted with a convenience sub-sample of 40 GPs in the region of Lyon (France). They were asked to respond to the survey again after a 14-day interval.
Acknowledgements The authors thank all the participants for their contribution towards this study. We warmly acknowledge the contribution as data collectors of Alban Glangetas (AG),  Carole Champet  (CC),  Leslie Llorente  (LL),  Noémie Lebret , as well as  Amir Moussa , administrative assistant. We are grateful to all the members of the  Primary Care Unit of the University of Geneva  for their insights and to  Jennifer Hasselgard-Rowe  and  Joshua Arancino-Gamper  for their help in language editing.
Thus, from the number of cpm mm -2 measured on the remains of resin, the efficiency of the detector is about 36%. This value is slightly lower than the one calculated by Samarati et al. 41  , which is about 50% for  14  C, with another setup of PIM-MPGD device. The MPGDA based on PIM-MPGD device, has revealed to be an efficient technique for producing  14  C-PMMA autoradiographs. The linear response of the PIM-MPGD device facilitates porosity calculations and produces reliable results, especially when a large range of porosities is investigated. SPA shows linear response in a range of activities depending on the exposure time, thus leading to tedious work on optimization of exposure time, whereas in the BeaQuant system the user is able, through the Beamage software, to control directly in real time the evolution of the activity on the autoradiograph during the acquisition.
Therefore, since no potential saturation is possible (and provided that exposure time is sufficient) each acquisition performed can be subjected to analysis. Note however that the maximal activity supported by the acquisition electronic of the BeaQuant is 30000 cps. In this study, porosity calculation depending of exposure time was not performed. Nevertheless, a new feature provided by the Beamage software will allow to produce different autoradiographs at different exposure times over the total duration of acquisition. It will be possible to follow properties such as porosity or fracture density depending on the exposure time and thus to determine appropriate exposure time for the property investigated. Regarding thin structures such as cleavages, it has been observed that the EA may require longer exposure times.
After 3 days of exposure, the IPs were scanned using a FUJI-FLA 5000 digital scanner with a resolution of 1200 dpi and a 16-bit image depth to get the autoradiographs of the impregnated rock samples. calibration and porosity calculation with SpA. A calibration of the phosphor screen is needed in order to convert the grey values of each pixel into porosity values. This has been performed using several PMMA standards of known  14  C activities covering a range from 1 to 280 kBq. mL -1. In addition, the impregnation solution activity was taken from the 14 C-PMMA around samples and have been used in the calculation of the porosities. Mean grey values for each standard have been extracted using the ImageJ software  50  and corresponding optical densities have been calculated using the equation: =             OD I I log (1) 10 0 Where OD is the optical density; I the intensity of the pixel (grey value 0-65536) and I 0 the mean intensity of the background measured on 5 different locations of the autoradiograph.
Therefore, detector efficiency ε can be calculated using the following equation: ∆ϕ ϕ ∆ρ ρ ∆ρ ρ ∆ ∆ ∆ ∆ = + + + +       -       + - ⁎ ⁎ A A k k B D D D D D B D D D 1 1 (9) ϕ β = ⋅ ⋅ N N 100 (10) ε = ⋅ ⋅ N N F 100 ( 11 ) a E 0 where No is the number of pulses (cpm mm -2 ) measured by MPGD from 14 C-PMMA resin; Na is the total number of beta particles emitted per mm² and per minute in the volume of thickness Rmax, and was calculated from the activity of the 14 C-PMMA resin. F E [0-1] is the fraction of beta particles actually emitted up to the surface, and was determined by a Geant4 simulation run described above. The energy distribution of  14  C used in the simulations come from the database provided in the Rad Toolbox toolkit developed by Oak Ridge National Laboratory  54. Geant4.
Material and methods taxon sampling and data collection. Porpoise tissue samples from 56 live-biopsies, bycaught, or stranded animals (Table  1  and Table  S1 ) were stored in salt-saturated 20% DMSO or 95% Ethanol and stored at -20 °C until analyses. All samples were collected under appropriate Marine Mammal Protection Act permits within the US, or appropriate permits elsewhere, following the relevant guidelines and regulations, and transferred internationally under CITES permit. Genomic DNA was extracted from tissues using the PureGene or DNeasy Tissue kits (Qiagen), following the manufacturer's recommendations. DNA quality and quantity were assessed on an agarose gel stained with ethidium bromide, as well as using a Qubit-v3 fluorometer. Genomic libraries for 44 porpoise samples including three spectacled porpoises, three Burmeister's porpoises, 12 vaquita, six Dall's porpoises, three East Asian finless porpoises, two Yangtze finless, one Indo-Pacific finless and 14 North Pacific harbor porpoises.
Data cleaning. The quality of the reads was first evaluated for each sample using FastQC v. 0. 11. 5  45. Trimmomatic v. 0. 36  46  was used to remove low quality regions, overrepresented sequences and Illumina adapters. Dif-Table  1. Taxon sample size (n) and descriptive statistics for the shotgun sequencing and mitochondrial assembly per species and mitochondrial lineage. The statistics include the total number of reads before and after filtering (R b and R a ), the sequencing coverage depth, the size of the mitochondrial assembly (in basepairs), and the GC content in percent (%GC). The mean value and the standard deviation are shown. Mitogenome assembly. Porpoises mitogenome assemblies were reconstructed using two different approaches. First, we used Geneious v. 8. 1. 8  47 to perform a direct read mapping to the reference mitogenome of the harbor porpoise (accession number AJ554063  48  ).
We used default settings except the minimum mapping quality set to 20 and the number of iterations set to 5. This step was followed by a reconstruction of the consensus sequences (Table  S2 ). The second approach implemented in MITOBIM 49 is a hybrid approach combining a baiting and iterative elongation procedure to perform a de-novo assembly of the mitogenome (see details in Text S2). We visually compared the assemblies provided by the two methods in Geneious to assess and resolve inconsistencies (Text S2 and Table  S2 ). In addition to the 57 assembled individuals, we retrieved six porpoise mitogenomes from Genbank (Table  S2 ). We also added eight complete mitogenomes from four outgroup species: one narwhal (Monodon monoceros)  48  , three bottlenose dolphins  6  , one Burrunan dolphin (Tursiops australis)  6  and three orcas  50.
Sequences alignments. We performed the alignment of the 71 mitogenomes with MUSCLE 51 using default settings in Geneious. A highly repetitive region of 226 bps in the D-loop was excluded from the final alignment (from position 15,508 to 15,734) because it was poorly assembled, and included many gaps and ambiguities. We manually annotated the protein-coding genes (CDS), tRNA and rRNA of the final alignment based on a published harbor porpoise mitogenome  48. Contrary to the remaining CDSs, ND6 is transcribed from the opposite strand  52. Therefore, to assign the codon positions in this gene, we wrote a custom script to reverse complement ND6 in the inputs of all the analyses that separates coding and non-coding regions of the mitogenomes. This led to a 17 bps longer alignment due to the overlapping position of ND5 and ND6.
For the Bayesian inference, a total of 1 × 10 6 MCMC iterations was run after a burn-in of 1 × 10 5 steps, recording values with a thinning of 2,000. We performed ten independent replicates and checked the consistency of the results. Runs were considered to have properly explored the parameter space if the effective sample sizes (ESS) for each parameter was greater than 200 and by visually inspecting the trace plot for each parameter using Tracer v1. 6  56. We assessed the statistical robustness and the reliability of the Bayesian tree topology using the posterior probability at each node. Finally, the four phylogenetic trees were rooted with the eight outgroup sequences and plotted using the R package ggtree v1. 4  57.
The significance of the NI values was evaluated using a G-tests in DnaSP. Furthermore, the distribution of NI values for each lineage were compared among each other using a PERMANOVA with the R package RVAideMemoire v. 0. 9-77  65. Pairwise comparisons were assessed with a permutation tests and were adjusted for multiple comparisons using the false rate discovery method  66. The PERMANOVA and pairwise comparisons were conducted using 9,999 permutations. The neutral theory predicts that the efficacy of purifying selection increases with Ne  67. Under these assumptions, Ne is expected to be proportional to NI  68, 69. To test this hypothesis, we assessed the correlation between values of NIs and π derived by rarefaction as a proxy of Ne. However, MK-test is also known to be impacted by demographic changes in some specific cases.
More specifically, we first fitted the YN98 codon model using the BPPML program  73  , then we mapped the estimated d N /d S values onto the branches of the ML tree using the program MAPNH of the TESTNH package v1. 3. 0  74. Extreme d N /d S ratio (> 3) are often due to branches with very few substitution (d N or d S )  72  and were discarded. We compared the distribution of d N /d S among species (i. e. , across all the branches) using a PERMANOVA. Finally, the estimated ratios were correlated with π values obtained by rarefaction using a Pearson's correlation tests in R  75. To do so, we pooled the signal from each lineage as a single data point as suggested by Figuet et al. 76. We considered the intraspecific and interspecific lineages, except for those where no non-synonymous substitutions were observed (ex.
4. 3 using Bayes Factors  83. We thus conducted ten independent CONST runs using 10 8 MCMC iterations after a burn-in of 10%, logging every 3,000 iterations. We assessed the proper mixing of the MCMC and ensured ESS were greater than 200. We then used the Path sampler package in BEAST v2. 4. 3 to compute the log of marginal likelihood (logML) of each run for both BSP and CONST. We set the number of steps to 100 and used 10 8 MCMC iterations after a burn-in of 10%. Bayes Factors were computed as twice the difference between the log of the marginal likelihoods (i. e. 2[LogML BSP -LogML CONST ]) and were performed for pairwise comparisons between BSP and CONST runs. As recommended, Bayes Factors greater than 6 were considered as a strong evidence to reject the null hypothesis (i. e. CONST)  83.
Material and Methods 147 participants (108 females and 39 males; age: 21. 0 years (SD 2. 0 y. ), body mass index: 21. 7 kgm -2 (SD 2. 6 kgm -2 ) voluntarily participated in the experiment after signing a statement of informed consent pertaining to the experimental procedure as required by the Helsinki declaration. Data were collected (figure  1 ) in two sessions: one when the IMU (Opal, APDM) was located at the wrist and the second one when the IMU was located at the ankle. All sensors were calibrated before each session to avoid sensor drift or offsets according to the manufacturer's procedure. The sample rate was 128 Hz. Participants were asked to maintain a standing posture for 2 minutes, followed by self-pace walking (466 m), followed by another 2 minutes standing, followed by a self-pace run over (700 m).
18) t #w! "!## = m m 6 (eq. 19) To quantify the similarity of the inter-subject of the quantile curves, the coefficient of multi correlation (CMC) on the quantile functions d & ef of all parameters for all subjects and for each physical activity and sensor's positions was computed in order to analyze the inter-subject reliability of this analysis (Kabada 1989). CMC scores the similarity between curves by a value between 0 (very different) to 1 (very similar) (Kabada 1989). t Finally, the relationship between data reductions MEAN and RMS and MET values of the subject was investigated by a linear regression model with a robust linear least mean square method. The relevance of the linear regression model by the coefficient of determination R 2 was reported  (Draper 1998). All computation and statistics analyses were performed in Matlab (R2016a, The Mathworks Inc, Natick, MA).
, 2016; Cacciottolo et al. , 2017; Chen et al. , 2017; Jung et al. , 2015; Chang et al. , 2014; Carey et al. , 2018) , and even fewer studies have investigated the effects of air pollution exposure on the risk of AD or VaD  (Grande et al. , 2020; Jung et al. , 2015; Carey et al. , 2018). Moreover, these studies present important methodological limitations. Indeed, most of them relied on population-based health administrative databases for the diagnosis of dementia, a method that is prone to classification bias  (Chen et al. , 2017; Jung et al. , 2015; Chang et al. , 2014; Carey et al. , 2018). The rare longitudinal population-based studies with active search of dementia cases and validated diagnosis concerned only women  (Cacciottolo et al. , 2017)  and small samples  (Grande et al. , 2020; Oudin et al.