dataesr/scientific-paragraphs-categorization huggingface dataset:
Applying a similar strategy with TB strains collected from 2019 onward will likely allow unraveling and measuring the impact of the COVID-19 epidemics on TB, facilitating therefore local decision-making and prioritization of aid. TEMPEST1. 5 51. To assess the robustness of our root-to-tip regression, we performed a permutation test of 5000 replicates using the LMPERM Package 2. 1. 0 52 in R. As a complementary assessment of the temporal signal in the data, date randomization was performed on our datasets using the TIPDATINGBEAST 1. 1. 0 R package  53. Sampling dates of the genomes were randomly shuffled five times, and date-randomized datasets were analyzed with BEAST V2. 3. 2 using the same parameters as the original ones. For all simulations, there was no overlap between the substitution rates 95% HPD intervals between the real data and the randomized data, suggesting that the data contain sufficient temporal structure and spread.
Statistical analysis, THD success index computation and modeling The THD success index was computed as described elsewhere  17  based on the matrix of genetic distances between isolates (SNPs counts). User-defined parameters were a mutation rate of 1. 1 × 10 -7 mutation per site per year, an effective genome size (number of positions retained for SNP calling) of 3. 97 × 10 6 and a time scale of 10 y, consistent with the relatively fast pace of expansion of W148 suggested by demographic analyses (Fig. 2c ). To account for variations in the sampling effort between countries in our dataset, the THD computations were corrected for sampling bias. The THD reflects both the abundance and genetic proximity of isolates in a group of interest, so that an unbalanced sampling coverage between groups can falsely increase THD in groups with higher coverage.
Consider the following example, where two genetically distinct groups A and B have similar demographic history such that their true average THD values are equal, and their THD estimates depend on their respective sample sizes. If, due to unbalanced sampling effort, individuals in A and B were sampled with a probability (sampling coverage) of 1. 0 and 0. 5, respectively, we expect THD estimates in A to be higher than in B due only to sampling bias. Because each THD value is an average of densities, this sampling bias can be corrected for by using a weighted average, with weights inversely proportional to sampling coverage. In our example, assigning weights of 1 to individuals in A and weights of 2 to individuals in B would restore the balance between A and B. Based on this rationale, we estimated the sampling coverage for each country in our dataset (Supplementary Data 3) to assign weights to isolates during THD computations.
The THD success index was then compared between groups using Mann-Whitney U-test or used as the response variable in LMMs. All constructed LMMs were controlled for population structure and country-specific variations (see details below). The significance of LMM coefficients was assessed using package LMERTEST v3. 1-3. All THD computations used R software version 4. 0. 2. Detailed model outputs can be found in the Supplementary Note. R scripts and data for all THD models and figure generation can be found at https://github. com/rasigadelab/xdrtb_thd and at the Zenodo repository at https://zenodo. org/record/6826135 under DOI 10. 5281/zenodo. 6826135. Correction for population structure in THD models is used to detect potential predictors independent of vertical inheritance  18. Correction for population structure was conducted using principal component analysis  57.
Acknowledgements We thank the  Institut Convergences Migrations  for travel grants to T. W. Parts of the work were funded by the  German Ministry of Education and Research (BMBF)  for the  German Center of Infection Research (DZIF)  and the  Leibniz Science Campus of Evolutionary Medicine of the Lung (Evolung). We would also like to thank  Ulrich Nübel ,  Qin Yi  and  Emilie Rousseau  for constructive comments and help on the paper.
A detailed overview of the sampling per country and estimates for the regional prevalence of W148 strains are given in Supplementary Data 3, and numbers of MDR/XDR isolates per country are displayed in Supplementary Fig. 10. The global dataset entails 537 newly sequenced genomes, plus another 194 publicly available W148 genomes (see Supplementary Data 1). WGS was performed on all isolates using Illumina Technology (MiSeq and HiSeq 2500) with Nextera XT library preparation kits as instructed by the manufacturer (Illumina, San Diego, CA, USA). The raw data (fastq files) were submitted to the European nucleotide archive (accession numbers are given in Supplementary Data 1).
Mapping of reads, SNP filtering and genotypic drug resistance prediction Fastq files/reads were mapped to the M. tuberculosis H37Rv genome (GenBank ID: NC_000962. 3) with the MTBseq pipeline as described earlier  46. Briefly, variants were called with a minimum coverage of 10 reads and at least 75% allele frequency. After exclusion of drug resistance associated genes, repetitive regions and non-informative/non discriminating SNPs, the remaining positions that matched the abovementioned threshold levels in at least 95% of all isolates were considered as valid and used for all isolates in a concatenated sequence alignment. To determine strain genotypic drug resistances, we first considered all mutations in 28 genes implicated in resistance against firstand second-line drugs as well as rpoA and rpoC associated with compensatory effects (Supplementary Data 4).
In the context of this study, we only considered a mutation in either rpoA, rpoB, or rpoC, as putative compensatory mutation when it co-occurred with a RIF resistance conferring mutation. We excluded known phylogenetic variants for the resistance prediction according to ref. 47. Rationales and references for mutations considered as resistance conferring are given in Supplementary Data 4. Homoplasy was analyzed with HomoplasyFinder (accessed at 26. 10. 2020, https://github. com/JosephCrispell/homoplasyFinder) as described earlier  48. As input, we used the sequence alignment described above and reintroduced SNPs in genes implicated in drug resistance and compensatory effects, and a maximum-likelihood tree based on a sequence alignment excluding genes associated with drug resistance and compensatory evolution (see below).
Time-dependent phylogenetic and phylogeographic reconstruction Phylogenetic relationships were reconstructed using the maximumlikelihood approach implemented in PHYML 3. 412  49. The robustness of the maximum-likelihood tree topology was assessed with bootstrapping analyses of 1000 pseudoreplicated datasets. Phylogenies were rooted with the midpoint rooting option using FIGTREE software v1. 4 and with the reference M. tuberculosis strain H37Rv, both resulting in the same topology. The profile of drug resistances for each strain and information of compensatory mutations were plotted on the maximum-likelihood tree using ITOL  50. Linear regression analysis of the root-to-tip distances against sampling time was performed using Linear mixed models (LMMs) using THD indices as the response variable had the following structure [Eq.
The IberArray and PYROPE backbone deployments (2010-2013) The seismological component of the TOPOIBERIA project  (2006) (2007) (2008) (2009) (2010) , the IberArray seismic network (http:// iberarray. ictja. csic. es/), covered the Iberian Peninsula in three successive deployments, from south to north. This initiative was a strong impulse to motivate the PYROPE project, a temporary deployment of broadband stations in the French side, synchronized with the beginning of the third IberArray deployment, north of Iberia. The main objective of the PYROPE project was to improve the seismic ray coverage in the Pyrenean domain by deploying a dense temporary array of seismological stations to collect high quality broadband data  (Chevrot et al. , 2014).
, 2018). A final development has been to also implement the injection of an incident plane wave computed with the frequency-wavenumber (FK) method  (Monteiller et al. , 2021). When the regional domain is small (less than several hundreds of kilometres), the effects of the curvature of the wavefront and of the Earth can be neglected, and FK provides an acceptable approximation of the incident wavefield. All these numerical tools are now gathered in RegHyM, a complete package to compute synthetic seismograms in 3-D regional models that is now available and distributed to the scientific community (https://gitlab. com/ Seismic_Imaging/RegHyM).
Improving ray coverage in local earthquake tomography with deep learning approaches Another important source of information comes from the regional Pyrenean seismicity  (Sylvander et al. , 2021)  which produces P and S waves that are recorded by nearby seismic stations. Whereas these waves are routinely picked on permanent stations to build catalogues of seismicity, from temporary deployments are rarely exploited and most of the time absent of seismic bulletins. The manual picking of P and S waves is indeed very time consuming, even for a seasoned analyst. However, recent studies have found that deep neural networks can be trained to perform this routine analysis, with results that are on par with human picks (e. g. ,  Ross et al. , 2018; Zhu and Beroza, 2019). This opens important new perspectives for building complete and extensive catalogues of P and S travel time picks from the exploitation of PYROPE and IberArray datasets that could be exploited for local earthquake tomography.
(2013). After picking all the stations of the network and associating the arrival time picks that were compatible with local events, a catalog of 1,624 earthquakes was obtained. From this catalog, we selected well recorded earthquakes for the tomographic inversion that met the following criteria: number of P arrival times ≥8, number of S arrival times ≥2, and azimuthal gap 200°. This resulted in a dataset of 996 earthquakes with 87,122 P arrival times, and 72,445 S arrival times. The size of the model region was a rectangle of 130 km Â 115 km in the E-W and the N-S directions, respectively (see Fig. 15 ). This dataset produces a high-resolution model inside the region covered by the dense station deployment (e. g. , Fig. 15a ), but the ray-path sampling falls off quickly. To increase the data coverage both laterally and in depth, we also considered a model for a larger region, using arrival time data from the large-N experiment and from a regional dataset of earthquakes in the western Pyrénées recorded by permanent stations of the monitoring networks and also by the temporary stations described in Sections 3.
1 and 3. 2. Using the same selection criteria, we obtained a dataset consisting of 4,175 earthquakes recorded on 553 stations, for a total of 103,314 P arrival times, and 94,512 S arrival times. The size of the regional model is a rectangle of 255 km Â 220 km in the E-W and the N-S directory respectively (between 2. 5°W to 0. 5°E longitude and 42°N to 44°N in latitude, see Fig. 16a ). For the tomographic inversion we used the method of  Benz et al. (1996)  as modified by  Tryggvason et al. (2002)  in order to include also S arrival times. This method performs an iterative joint inversion for velocity structure and earthquake relocations. Images of the resulting local and regional models are shown in Figures  15  and 16 , respectively. The local model images in detail the shallow structure of the region, with main structures trending WNW-ESE (Fig.
Density structure of the lithosphere From a compilation of geological and geophysical information a 3-D model of crustal density was built with the Geomodeller software from the BRGM  (Wehr et al. , 2018). The final model fits the observed Bouguer anomalies within 3. 5 mGal. Figure  18  shows the Bouguer anomalies corrected for the contribution of the variations of crustal thickness and of sediments. Strong positive residual anomalies are observed beneath the North Pyrenean Zone in the western and central Pyrénées. In the eastern Pyrénées, where the crustal thickness is around 30 km, a broad and/or deep low-density anomaly is required to explain gravity data, suggesting that this part of the range is underlaid with a hot and buoyant lithosphere that provides some dynamic support to topography.
Second, the local and regional seismicity recorded by temporary stations should allow us to improve dramatically the catalogues of P and S picks for local earthquake tomography. Since picking manually all the P and S arrivals on these datasets would be extremely time consuming, we will need to turn to semi-supervised approaches relying on deep neural networks. With these new complete catalogs, we should be able to improve significantly the quality of the images at upper and mid crustal levels. Additionally, the Maupasacq deployment allowed us to demonstrate the potential of passive approaches to image the deep architecture of sedimentary basins (see  Lehujeur et al. , 2021, this special issue). For example, the velocity lineaments seen with passive methods (Fig. 15a ) are also in very good agreement with the kilometric-scale folds in the Mauléon Basin and with the major thrusts such as the North Pyrenean Front  (Lehujeur et al.
Absorbance was measured directly by spectrophotometry at 450 nm corrected to 630 nm, using a SPEC-TROstarNano2. 10 plate reader and MARS Data Analysis Software (BMG LabTech ® ). e toxicity assessment deter- mined the PM 10 concentrations to be used for the mechanistic studies. It was agreed not to exceed 10 µg/cm 2 , a concentration resulting in less than 30% cell death. is choice was also made for this concentration to remain as realistic as possible regarding the concentrations to which humans could be exposed.
e production of ROS was measured in HPAEC by spectrofluorimetry (probe CM-H 2 DCFDA) after 4 hours of exposure. Fluorescence intensity was determined using a plate reader (FLUOstar Omega 2. 10 and MARS Data Analysis Software 2. 30 R3 (BMG LabTech ® ) at an excitation wavelength of 485 nm and an emission of 520 nm. e results were expressed as a percentage increase in ROS compared to control cells.
e assay of IL-6 cytokines in culture supernatants was performed by the enzyme-linked immunosorbent assay (ELISA) technique using DuoSet kits (R&D Systems ® ), a SPECTROstarNano2. 10 plate reader, and MARS 2. 41 Data Analysis Software (BMG LabTech ® ). To relate the IL-6 cyto- kine level (ELISA) to the protein levels, a Lowry test was performed in the culture supernatants for each condition, using the Biorad ® DC Protein assay reagent package kit. Absorbance was measured using a SPECTROstarNano2. 10 plate reader 2. 41 data analysis software (LabTech ® ) at 750 nm. For each test, the results are expressed as means ± the standard error of the mean (SEM). Each experiment was performed independently three times (n indicates the number of experiments, n � 3). IL-6 was assayed in the culture supernatant of HPAEC cells after 24 hours of intoxication at different concentrations of PM 10 (1.
Statistical Analyses. Differences between the means of HPAEC control and PM 10 -exposed cells were evaluated using different tests: an ANOVA parametric test followed by a multiple comparison test with Dunnett's correction. Statistical analyses and graphs were performed with GraphPad Prism 5. 0 software. Differences were considered significant when p < 0. 05( * ), p < 0. 01( * * ), and p < 0. 001( * * * ).
Discussion e PM 10 collected each of the three districts in the current study led to a concentration-dependent decrease in cell viability. Our results are consistent with those of Dieme et al. , who had shown the ability of particles collected in Dakar (Senegal) to alter mitochondrial metabolism  [27]. Faso ). e HPAEC cells were seeded for 24 hours and then treated with different concentrations of PM 10 (1. 5, 10, 25, and 50 µg/cm 2 ) for 4 hours. Intracellular ROS production was evaluated by the CM-H2DCFDA probe and measured by spectrofluorimetric method. e percentage increase in ROS is calculated relative to control cells. Data are expressed as means ±SEM of three independent experiments n � 3 (three wells per concentration). e statistical test used is a one-way ANOVA followed by a multiple comparison with Dunnett's correction, * * * p < 0.
Figure3: Secretion of the proinflammatory cytokine IL-6 in HPAEC cells after 24 hours of exposure to PM 10 from districts 2 (a), 3 (b), and 4 (c) of Ouagadougou (Burkina Faso). HPAEC cells were seeded for 24 hours and then treated with different concentrations of PM10 (1, 5, and 10 µg/cm2) for 24 hours. e IL-6 concentration (pg/mL) was determined by using the ELISA technique and measured by spectrophotometry. is concentration was then related to total protein levels for each well (pg/mg). Data are expressed as the mean ± SEM of three independent experiments n � 3 (three wells/concentration). e test used is a one-way ANOVA followed by a multiple comparison with Dunnett's correction * p < 0. 05: Significance level compared to control cells.
Acknowledgments e authors would like to thank  Dr Isdine LAOUROU ,  Prof. Edmond CREPPY , and  Ophelie GERMANDE  for their material support. e authors also thank the work grant of the  French Embassy  for a living allowance to start the experiment. In addition, the authors thank CERBA, the  Laboratory of Applied Hygiene Toxicology  ( University of Bordeaux ), the  Cardiothoracic Research Center of Bordeaux (CRCTB)   INSERM-U1045 , and the  Institute of Public Health of Epidemiology and Development (University of Bordeaux ) for all the laboratory analyses, support, and the opportunity to carry out this project.
Organic aerosol (OA) is a key component of total submicron particulate matter (PM 1 ), and comprehensive knowledge of OA sources across Europe is crucial to mitigate PM 1 levels. Europe has a well-established air quality research infrastructure from which yearlong datasets using 21 aerosol chemical speciation monitors (ACSMs) and 1 aerosol mass spectrometer (AMS) were gathered during 2013-2019. It includes 9 non-urban and 13 urban sites. This study developed a state-of-the-art source apportionment protocol to analyse long-term OA mass spectrum data by applying the most advanced source apportionment strategies (i. e. , rolling PMF, ME-2, and bootstrap). This harmonised protocol was followed strictly for all 22 datasets, making the source apportionment results more comparable. In addition, it enables quantification of the most common OA components such as hydrocarbon-like OA (HOA), biomass burning OA (BBOA), cooking-like OA (COA), more oxidised-oxygenated OA (MO-OOA), and less oxidised-oxygenated OA (LO-OOA).
Typically, OA sources are identified using the receptor model positive matrix factorisation (PMF) on data from an Aerodyne aerosol mass spectrometer (AMS;  (Jayne et al. , 2000) ). Many studies have reported a broad spatial overview of OA sources, providing the chemical composition of the bulk non-refractory PM 1 and, more recently, also PM 2. 5  (Elser et al. , 2016; Xu et al. , 2017). Zhang et al. (2007)  first reviewed the use of factor analysis to investigate OA sources in urban and rural/ remote sites in the Northern Hemisphere. Jimenez et al. (2009)  provided an overview of PM 1 chemical composition and OA sources worldwide (including eight European sites), while  Ng et al. (2010)  provided a big picture of OA sources over the Northern Hemisphere in various environments (including 43 AMS datasets).
PM 1 chemical composition and OA sources in Central Europe, including Switzerland, Germany, Austria, France, and Liechtenstein, have been described more specifically in  Lanz et al. (2010). Finally, an overview of the OA sources across Europe using 25 AMS datasets, combined with guidelines for source apportionment (SA) applications to AMS data, resulted from the work of  (Crippa et al. , 2014). However, the AMS requires labour-intensive maintenance, making it extremely difficult to run continuously over a long period. Most of the AMS datasets in previous studies only covered time periods up to a few months, and the seasonal variations of OA sources in Europe remain mostly unknown without consistent long-term SA studies. Therefore, a network of on-line chemical composition measurements has been built up within the well-established European Aerosol, Clouds and Trace gases Research InfraStructure (ACTRIS, http://actris.
eu), consisting of 1 AMS and 21 Aerodyne Aerosol Chemical Speciation Monitors (ACSM,  (Fröhlich et al. , 2013; Ng et al. , 2011b) ), the latter being a simpler and more robust version of the AMS. With these data, it is possible to address a critical knowledge gap in the literature, i. e. , the seasonal, diurnal, and spatial variabilities of OA sources across Europe. An important limitation of the conventional PMF applications for long-term datasets is that OA sources are assumed to be static over the entire sampling period, although the source profiles of OA have substantial seasonal variations  (Canonaco et al. , 2015; Crippa et al. , 2014; Zhang et al. , 2019). This study applies a novel SA technique, rolling PMF  (Canonaco et al. , 2021; Parworth et al. , 2015) , to account for the temporal variabilities of the factor profiles.
Instead of running PMF for the whole dataset, this work applies a smaller time window (e. g. , varying from 7 to 28 days), moving with daily steps over the entire dataset (typically around one year). This allows the model to gradually adapt the factor profiles across different observational periods. In addition, the rolling technique has been equipped with random input resampling and random variation of the constraints allowing for a quantitative estimate of the statistical and rotational uncertainties of the PMF solutions  (Canonaco et al. , 2021; Chen et al. , 2021). Meanwhile, rolling PMF can facilitate the analysis while handling the long-term ACSM/AMS datasets by saving computational time compared to conventional seasonal bootstrap PMF. The comparison of SA results from different sites is challenging, as they are not always equivalent due to the subjective decisions made by the analysts during the different PMF analysis steps, especially regarding the optimal number of factors, their identification, and validation.
Measurements and instrumentation This study is the main outcome of the Chemical On-Line cOmpoSition and Source Apportionment of fine aerosoL (COLOSSAL) project (https://www. costcolossal. eu/), based on measurements performed within ACTRIS. In total, 22 year-long datasets were used here from different countries via ACSM/AMS since 2013 (Fig. 1 ). This study includes data from 18 Q-ACSM (quadrupole ACSM,  (Ng et al. , 2011b) ), ToF-ACSM (Time-of-Flight ACSM  (Fröhlich et al. , 2013) ), and 1 C-ToF-AMS (compact time-of-flight AMS  (Drewnick et al. , 2005) ). Overall, the ACSM and AMS considered here apply similar techniques. Briefly, the air is passing through a critical orifice into an aerodynamic lens, where atmospheric aerosol is focused and accelerated (the smaller the aerodynamic size, the higher the velocity) into a vacuum chamber (10 -5 Torr) and impacts on the surface of a standard vaporiser heated at 600 • C.
The resulting vapours are then ionised by electron ionisation (70 eV), and these ions are further extracted into the mass analyzer. Compared to the AMS, the ACSM is more robust, affordable, and easier to operate, making it suitable for long-term monitoring purposes. However, it cannot measure the size-resolved chemical composition and its time and mass resolutions are poorer compared to the AMS. Most of the ACSMs deployed here participated in an inter-comparison activity conducted by the Aerosol Chemical Monitor Calibration Centre (ACMCC) at SIRTA (https://sirta. ipsl. fr/) and reported consistent results as long as proper calibrations were conducted  (Crenn et al. , 2015; Freney et al. , 2019). One of the objectives of the COLOSSAL project is to deliver a harmonised standard operating procedure (SOP) for ACSMs  (COLOSSAL, 2021) , and most of the datasets were collected by following this SOP.