dataesr/scientific-paragraphs-categorization huggingface dataset:
ACA products are mid/upper tropospheric CO 2 and CH 4 mixing ratios from IASI  (Crevoisier et al. , 2009a (Crevoisier et al. , , 2009b (Crevoisier et al. , , 2013)) , upper tropospheric / stratospheric vertical CH 4 profiles from MIPAS  (Laeng et al. , 2015) , stratospheric CH 4 and CO 2 profiles from SCIAMACHY solar occultation observations  (Noël et al. , 2012 (Noël et al. , , 2016) )  and stratospheric CO 2 profiles from ACE-FTS  (Foucher et al. , 2009). An overview about the GHG-CCI ECAs and corresponding data products is given in Tab. 1 for XCO 2 and in Tab. 2 for XCH 4. As can be seen, there are two algorithms for each data product. For example, there are two algorithms for XCO 2 from SCIAMACHY and two algorithms for XCO 2 from GOSAT, resulting in four XCO 2 products generated independently with different algorithms.
Acknowledgements This study has been funded by  ESA  via the  GHG-CCI project of ESA's Climate Change Initiative  (CCI). We thank  ESA/DLR  for providing us with SCIAMACHY Level 1 data products and JAXA for GOSAT Level 1B data. We also thank  ESA  for making these GOSAT products available via the ESA Third Party Mission archive. We also thank the TCCON team (in particular  P. Wennberg ,  CalTech ) for providing TCCON data (obtained via http://tccon. ornl. gov), the  European MACC/CAMS  projects for providing CO 2 model fields (obtained from http://macc. copernicus-atmosphere. eu/) and  NOAA/ESRL  for providing CarbonTracker data (obtained via http://www. esrl. noaa. gov/gmd/ccgg/carbontracker/).
, 2016. hatcomparisons with global models as well as CO 2 flux inversion results using CRDP3 XCO 2 (and XCH 4 ) are also presented and discussed inChevallier et al. , 2016. The European MACC (Monitoring of Atmospheric Composition Change) / CAMS (Copernicus Atmospheric Monitoring System) project global atmospheric CO 2 reanalysis data product, version v14r2, has been obtained from the MACC/CAMS website (http://macc. copernicusatmosphere. eu/catalogue/->http://apps. ecmwf. int/datasets/data/macc-ghg-inversions/,access:23-Feb-2016). analysed winds for flux inversion. The MACC inversion product also contains the 4-D CO 2 field that is associated to the inverted surface fluxes through the LMDZ transport model. These 4-D fields have been used for this study. Satellite XCO 2 observations have not been assimilated in MACCv14r2.
pneumophila infection, we have established a novel imaging method that allows for single-cell analysis of L. pneumophila-infected macrophages in real-time. This enables the identification of subtle and transient host-pathogen interactions, which may be overlooked in traditional methods that extrapolate from only a small number of isolated data points, or only analyze cells at the population level. With this technique, we now show that L. pneumophila is able to replicate and induce normal macrophage killing rates in the absence of SidF, as well as BNIP3 and BCL-RAMBO. Consistent with this, loss of SidF, BNIP3, or BCL-RAMBO did not affect L. pneumophila lung infections in mice. Moreover, we show that mitochondrial apoptosis itself is not essential for the induction of host macrophage cell death, nor for bacterial replication.
Live-Cell Imaging To Determine Macrophage Viability To follow Legionella infection in real-time using live-cell imaging, macrophages (2. 5 × 10 5 cells/mL) were seeded into 96-well tissue culture-treated plates. Before infection, BMDMs were stained with 1 µM Cell Tracker Green (CTG) (Invitrogen) for 20 min in serum-free RPMI 1640. Medium was then replaced with RPMI 1640 supplemented with 15% FBS and 10% L-cellconditioned medium containing 50 nM tetramethylrhodamine (TMRM) and 600 nM Draq7 (Abcam). Cells were infected with L. pneumophila strains at a MOI of 10. In some experiments, the CellEvent Casaspase-3/7 detection reagent (Invitrogen) was added to measure caspase activity by time-lapse imaging. Before imaging, 50 µL of mineral oil (Sigma) was added to each well to prevent evaporation. Experiments were performed on a Leica AF6000 LX epifluorescence microscope equipped with an incubator chamber set at 37 • C + 5% CO 2 and an inverted, fully-motorized stage driven by Leica Advanced Suite Application software.
ACKNOWLEDGMENT We thank  Dr. Stefano Azzini  for recording the transmission spectra of the ITO based cavities and  Dr. Thibault Chervy  for performing the transfer matrix calculations. This work was financially supported by  EC  through the  ERC  project  SUPRAFUNCTION  ( GA-257305 ) and the  Marie Sklodowska-Curie ETN projects iSwitch  (GA No. 642196 ) and the  SYNCHRONICS  ( GA 643238 ), the  ANR Equipex Union  ( ANR-10-EQPX-52-01 ), the  Labex projects CSC  ( ANR-10-LABX-0026 CSC ) and  NIE  ( ANR-11-LABX-0058 NIE ) within the  Investissement d'Avenir program   ANR-10-IDEX-0002-02  and the  International Center for Frontier Research in Chemistry (icFRC).
Results In-silico prediction of annealing blocker and PNA blocker specificity. Annealing blockers were designed as dual priming oligonucleotides with the 5′ segment overlapping the universal amplification primer and the 3′ segment specific to the non-desired 18s rRNA gene sequence. For the V4 hypervariable region, annealing blockers were designed that separately target A. coluzzii and mammal sequences respectively, whereas for the V9 region, an annealing blocker was designed targeting A. coluzzii, while for mammalian sequences the mammal blocker of the Earth Microbiome Project (EMP) was used. The blockers were evaluated in silico to determine the fraction of matching 18s rRNA gene sequences among Anopheles and Mammalia references in the Silva 119 database (match was defined as a weighted score <1.
0, see Methods). For the V9 region blocker (Fig. 1A ), both segments matched greater than 75% of Anopheles 18s rRNA gene sequences, a level similar to that of the V9-Forward universal eukaryotic amplification primer. In contrast, the 3′ segment of the EMP mammal blocker matched at most 50% of the mammalian sequences from the Silva 119 database. In the V4 region (Fig. 1B ), the mammal and Anopheles blockers matched most of their reference sequences from the Silva 119 database, except for the 5′ segment of Anopheles blocker, which matched only 29% of Anopheles reference sequences in Silva 119, all of them corresponding to A. gambiae. PNA blocker design was carried out by mapping all possible 17 mers contained in the reference sequence of the hypervariable regions V4 and V9 of A. gambiae 18s rRNA gene against the full set of 18s rRNA gene sequences from the Silva database version 119, allowing at most one mismatch.
A total of 5,052,260 paired-end MiSeq reads were generated for V4 amplicons, with an average of 157,883. 125 paired-ends reads per sample after the demultiplexing step. Of these, 4,137,310 (81. 89% of the total) full-length V4 amplicons were reconstructed after quality filtering and joining steps (129,290. 94 amplicons per sample on average), and clustered into 119 Operational Taxonomic Units (OTUs) of a minimum size of 5 reads from which the taxonomic profiles of the samples across all conditions (with/without PNA blockers or PCR annealing blockers) were determined by sequence similarity against the 18s rRNA gene subdivision of Silva database version 119. As expected, mosquito was the predominant taxonomic group in V4 amplicons from samples amplified without any blocker, ranging from >99% of the amplicons in larval samples to 80-97% of the amplicons in human and animal fed samples (Fig.
3 ). The lower percentage of mosquito reads in human and animal fed samples is a consequence of the amplification of sequences derived from the bloodmeal. The efficiency of PCR annealing blockers was negligible, with the corresponding samples showing a taxonomic profile similar to the samples without any blocker, dominated by mosquito sequences. The annealing blocking primers did shift the reaction threshold cycle (Ct), indicating interference with PCR amplification, but data below indicate that this effect was probably non-specific. As a general rule, fairly permissive hybridization conditions are preferred for microbiome profiling, so increasing PCR stringency in an effort to improve annealing blocker performance would in turn  In order to design PNA blockers specific to Anopheles, but allowing efficient amplification of other eukaryotic organisms, the Silva eukaryotic database was queried with mosquito candidate sequences to identify those with the smallest number of non-mosquito hits.
(A) The full-length sequence of the V9 hypervariable region of A. gambiae was split in silico into all possible 17mers, and each 17mer was queried against the full length 18s rRNA gene sequences from the Silva 119 database for all non-mosquito eukaryotes. The number of matches was graphed (y-axis), limiting the representation to a maximum of 100 hits. The location of the selected current PNA blockers is indicated (red vertical line). (B) As in A, but for V4 region PNA oligonucleotide candidates. hinder the ability to achieve high resolution of the eukaryotic microbiome. Attempts to amplify with annealing blockers using the proofreading enzyme, KAPA polymerase, as used for PNA blockers, yielded no amplification, likely due to interference of the annealing blocker C3 spacer region with KAPA amplification by an unknown mechanism.
In contrast, in the larval sample, a similar decrease of mosquito reads in PNA blocker samples is instead correlated with an increase in reads from microbial eukaryotic lineages (Fig. 3 ). For example, reads belonging to the Pseudoperkinsidae lineage of protist parasites 38 increased to 48. 2% of the amplicons in the larval sample with 7. 5 μM PNA, as compared to less than 1% of the amplicons without PNA blocker. The second most abundant group in the PNA-treated larval sample corresponds to Unassigned amplicons, with no taxonomic assignment by sequence similarity against the Silva 119 database. These Unassigned amplicons, 26% of sequences in the larval sample with 7. 5 μM PNA, indicate the limitation of current reference databases, and highlight the need for additional phylogenetic placement methods for more precise classification  39.
7 ). In human bloodmeal and animal bloodmeal samples, the progressive replacement of mosquito host by mammal sequences with increasing PNA concentration explains the patterns of evenness observed. Rarefaction analyses of V9 and V4 amplicon datasets yields similar results, showing that the eukaryotic community found in larval samples amplified with PNA blockers is the most phylogenetically diverse of the three sources of samples analysed (Figure  S1 ). However, the presence of PNA blocker does not affect the relative fractions of individual OTUs and other taxonomic subdivisions of the eukaryotic microbial community recovered in larval samples (chi-square p = 0. 92 for V4 and p = 1 for V9, Figure  S2 ). These results indicate that the increase in evenness due to PNA blocker observed in the alpha diversity analysis is the consequence of the efficient blocking of mosquito host 18s rRNA gene amplification, allowing emergence of the eukaryotic microbial community signal, without effect on the relative proportions of the lineages in the larval community.
The V4-region PNA blocker displays the highest blocking efficiency, reducing the fraction of host DNA amplification by 46. 0x in the animal-fed adult mosquito sample (from 82. 8% to 1. 8% of mosquito amplicons at 7. 5 μM concentration of V4-PNA blocker) and by 4. 8x in the larval sample (from 99. 5% to 20. 8% of mosquito amplicons at 7. 5 μM concentration of the PNA blocker). The efficiency of the V9 PNA blocker is lower in comparison, probably due to the smaller size of the V9 region, leading to higher amplification of host DNA. However, in both cases amplification of the eukaryotic microbiome was substantially increased in the larval sample, as evidenced by the sequencing depth of the samples after filtering out mammalian and insect sequences. There are slight differences in the OTU catalogues generated from the two different amplicons (see Figs  3  and 4 ), which is likely the result of the smaller V9 amplicon (153 bp on average) providing less information content than the longer V4 amplicon (409 bp on average), perhaps combined with the relative immaturity of the databases.
Recently, a phylogenetic analysis including all complete 18s rRNA gene sequences available in GenBank revealed a large and previously hidden diversity of non-fungal unicellular opisthokonts including ichthyosporeans  43. More data from field samples would be necessary to evaluate the presence of this group in natural mosquito populations, but the predominance in our larval sample points to these taxa as a likely important component of the mosquito eukaryotic microbiome. Similarly, the large fraction of Unassigned sequences observed in the 7. 5 μM PNA larval sample is consistent with other surveys of eukaryotic microbial environmental diversity based on 18s rRNA gene sequences, where a large fraction of observed sequences could not be assigned to any described species, suggesting in some cases even the presence of new eukaryotic kingdoms  44, 45.
Blocking primers for Anopheles (Anoph_V4_block) and mammals (Mammal_V4_block) targeting the V4 region of the 18s rRNA gene were designed in a similar way, in this case overlapping with the universal V4 reverse primer (Figure  S4 ). The specificity of the 5′ and 3′ segments of all blocking primers was evaluated with the analyze_primers. py program of the PrimerProspector package  53  against reference 18s rRNA gene sequences from version 119 of the Silva database  54. This program computes a local alignment for the input primer(s) against target sequences and determines a weighted score based on the number of mismatches and gaps, giving large penalties to gaps and mismatches at the 3′ end of the primer. The program taxonomic_coverage. py of the PrimerProspector package was used to extract the fraction of Anopheles and mammal sequences in Silva 119 database matched by each blocking primer segment (with match defined as a weighted score < 1.
0). The sequences of all oligonucleotides used in the current study are given in Table  S1. Peptide nucleic acid (PNA) design. Candidate PNA sequences for blocking A. gambiae 18s amplification were identified using a described methodology  36. We in-silico fragmented the sequences for two hypervariable regions of the 18s rRNA gene of A. gambiae, V4 (541 nt; extracted from SilvaID: AAAB01003942) and V9 (170 nt; extracted from SilvaID: AAAB01003343), into 16 nt-overlapping k-mers of 17 nt length using the splitter program from the EMBOSS package  55. We queried the resulting kmers for matches against the representative set of 18s rRNA gene sequences from the Silva database version 119 using bowtie  56  allowing at most one mismatch (~6%), which is the described threshold for efficient amplification blocking  46.
PNA sequence candidates targeting V4 or V9 hypervariable regions were identified from the set of A. gambiae-specific kmers according to bowtie alignments. The previous species taxon, A. gambiae, was recently divided into the species A. gambiae and A. coluzzii  2. The Silva 119 database 18s rRNA gene entry for the taxon is currently still referenced as A. gambiae, without distinguishing between the two new species. To confirm the applicability of the Silva database entry for both species, we used vcf files from the Anopheles gambiae 1000 Genomes Consortium project  57  to extract the V4 and V9 region from whole-genome sequences of both A. gambiae and A. coluzzii individuals, and sequences were aligned using Jalview (http://www. jalview. org/). Identity over the current PNA oligonucleotide regions was 100% between A.
Sequence analysis. Paired-end reads from MiSeq sequencing were quality trimmed and joined with Pandaseq  59. Joined amplicons were clustered into Operational Taxonomic Units (OTUs) at a 97% similarity level using the pick_open_reference_otus. py script of QIIME version 1. 9. 1  60. This script carries out an open-reference OTU picking strategy by first comparing against the 18s subdivision of the 119 release of Silva database and secondly clustering remaining sequences de novo  61. Only those OTUs containing more than 5 sequences were retained for subsequent analysis. Taxonomic assignment to the final OTUs was carried out with assign_taxonomy. py script of QIIME using UCLUST as classifier against the 18s subdivision of 119 release of Silva database. Rarefaction, alpha diversity, and OTU frequency changes were compared across samples and analyzed with QIIME and PhyloSeq 62.
To compare the two blocking strategies (annealing blockers and PNA blockers) for species diversity and richness, an alpha-diversity analysis was carried out over 100 subsamplings of size 1000 of the initial datasets, over which the average number of observed species (richness) and the Shannon index (evenness) were estimated for each trial using the Phyloseq R package  62. This approach estimated the species abundance of the real population while standardizing sampling effort. In order to evaluate if different concentrations of PNA blocker affect the composition of the eukaryotic microbiome in larval samples, mosquito and mammal OTUs were excluded from the OTU table, and chi-square tests were carried out over the filtered OTU table with the chisq. test function in R. Chi-square tests were carried out at different levels of the taxonomic classification according with UCLUST results.
Figure2. Identification of PNA oligonucleotide blocker candidates for the V9 region of Anopheles. In order to design PNA blockers specific to Anopheles, but allowing efficient amplification of other eukaryotic organisms, the Silva eukaryotic database was queried with mosquito candidate sequences to identify those with the smallest number of non-mosquito hits. (A) The full-length sequence of the V9 hypervariable region of A. gambiae was split in silico into all possible 17mers, and each 17mer was queried against the full length 18s rRNA gene sequences from the Silva 119 database for all non-mosquito eukaryotes. The number of matches was graphed (y-axis), limiting the representation to a maximum of 100 hits. The location of the selected current PNA blockers is indicated (red vertical line). (B) As in A, but for V4 region PNA oligonucleotide candidates.
Acknowledgements This work received financial support to KDV from the  European Commission ,  Horizon 2020 Infrastructures # 731060 Infravec2 ;  European Research Council , Support for frontier research, Advanced Grant # 323173  AnoPath; and  French Laboratoire d'Excellence  " Integrative Biology of Emerging Infectious Diseases " # ANR-10-LABX-62-IBEID , to MMR from  National Institutes of Health ,  NIAID  # AI121587 , to DMG from the  Minnesota Partnership for Biotechnology and Medical Genomics  (MNP IF # 14. 09 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
prime Anopheles V4; 5 prime Mammalia V4; 3 prime Mammalia V4; 5 prime Universal V4 Reverse Fraction of Mammalia sequences Silva119 Figure 1. In silico evaluation of target specificity for the annealing blockers of Anopheles and Mammalia. The y-axis represents the relative fraction of 18s rRNA gene sequences present in version 119 of the Silva database that match the primer indicated on the x axis, with match defined as a weighted score <1. 0 (See Methods). (A) Specificity for V9 region annealing blockers. (B) As in A, but for the V4 region annealing blockers. As positive controls, the reference universal eukaryotic PCR amplification primers V9-Forward and V4-Reverse primers were also queried against the Silva database (right bar in each frame).
Firms and employment We use panel data for employment and firm locations from two different sources published by the Mexican National Institute of Statistics (inegi). 3  The first source is the Economic Census for the years 1986, 1989, 1994, 1999, 2004, 2009, and 2014, which provides employment figures and breakdown by broad sector of activity, as well as total income at the level of municipalities and for formal businesses. The second source of data is the Directory of Economic Units (denue)  4  for the years 2004, 2009 and 2014. It is an exhaustive dataset which contains detailed microgeographic information on all formal establishments, including the 6-digit naics classification, the number of employees of the establishment, turnover, the municipality identifier, and exact geographic coordinates.
For the year 2014, the database contains information on approximately 3,000,000 establishments throughout the country. Note that although the denue database has finer geographic identifiers than the Economic Census (actual coordinates vs. municipality of location) and a finer industry identifier (6-digit naisc vs. broad sectors of activity), it covers less years than the Economic Census. In the analysis, we use either one, as appropriate. Table  1  presents changes in the distribution of formal jobs in Mexico between 1999 and 2014 according to Economic Censuses. As can be seen, the bulk of jobs are in services, commerce and manufacturing. Although employment has increased in all sectors over the period, the greatest increases have occurred for commerce and services jobs, which relative shares have gone up while the relative shares of all the other sectors have gone down.
The decrease in the relative share of manufacturing employment from 30 t0 24 percent is particularly noticeable. In 2014, more people were actually employed in the commerce sector than in manufacturing, showing that Mexico is following a trend of tertiarization similar to that of developed economies in previous decades. Figure  1  plots the location of establishments in the denue database in 2004 and 2014 for all firms (top panel) and for manufacturing firms (bottom panel) respectively. 5  It reveals a high concentration of economic activity in the center of the country and in the surroundings of Mexico City, with a greater concentration of manufacturing firms than for the other sectors, but no visually noticeable change over the past decade.
Roads In this paper, we focus on the later waves of road construction and improvement that occurred since the mid-eighties. To have a consistent measure of road extent and road types over time, we constructed a geo-referenced database for the period 1985-2016. This was done by importing historical road type and road extent information from the American Automobile Association (aaa) paper maps into the 2014 road geometry published by DeLorme. Because the DeLorme road geometry is network-enabled and topologically correct, it allows for clean travel time computations needed to measure access to markets (see below). Using this fixed geometry, we then imported information on road type and extent for the reference years or the nearest available year before which we have firm data in the denue and in the Economic Censuses (1986, 1994, 1999, 2004, 2009 and 2014), leaving us with a road panel for the years 1985, 1993, 1999, 2004, 2008 and 2016.
The regression sample is a balanced panel of 2,094 localities with employment data for the years 1986, 1994, 1999, 2004, 2009, and 2014. The specialization and concentration indices are calculated at the establishment and locality levels. Since the latter micro-geographic data only span the 2004-2014 period, we will therefore restrict our analysis on specialization to this time period. Y m,t = β M M m,t + β int C M m,t I m,t + β C C m,t + α t + µ m + m,t (3) In addition to the 'market access' specification, we also estimate an 'infrastructure model' where we substitute market access with our measure of efficient roads within a fixed radius surrounding a locality's centroid. In that model, we also incluse a measure of access to international markets (measured by the minimum time or travel cost to a port or port of entry into the u.