dataesr/scientific-paragraphs-categorization huggingface dataset:
To investigate the number of usable tracks and determine the causes of data loss, we analyzed the available soundings and the quality flags representing sounding quality from 2014 to 2019. We focused on the OCO-2 nadir and glint modes, excluding the target mode from this study due to its limited sampling area. The OCO-2 X CO2 soundings are subject to a two-level quality inspection (L1 and L2). Only soundings that meet all selection criteria in the inspection are marked as high-quality soundings in the L2 OCO-2 quality flags (QF = 0). Soundings failing to meet one or more criteria are marked as low-quality soundings (QF = 1). A detailed introduction of the two-level quality inspection can be found in Section Text S2.
The 25 tracks, referred to as all-data tracks, contain all high-and lowquality soundings (i. e. , quality flags are not considered yet). Only 8 out of 25 tracks fit the above criteria, referred to as high-quality tracks, if just high-quality soundings are considered. Detailed information about the selected tracks is shown in Fig. S1. Due to the lack of ground-based XCO 2 data, we ensured the robustness of the results by comparing the OCO-2 data with outputs of three flux quantification methods, WRF-Chem, X-STILT, and the FCSI method. The outputs of WRF and X-STILT are XCO 2 based on ODIAC, while the FCSI method output is CO 2ff emissions. We compared the ratios of XCO 2 from OCO-2 to XCO 2 from WRF and X-STILT and the ratios of CO 2ff emissions from the FCSI method to CO 2ff emissions from the ODIAC product in Section 3.
The meteorology from Global Data Assimilation System at 0. 5 • grid spacing (GDAS0p5,  Rolph et al. , 2017)  is used to drive X-STILT. The sum of the convolution of gridded footprints and ODIAC emissions represent the CO 2 enhancement from fossil fuel emissions, as sampled by air parcels arriving at the OCO-2 sounding locations. To have the same representation of the WRF-Chem model setup, only contributions from the nearfield land (30. 88-32. 155 • N, 73. 55-75. 04 • E) were included in the X CO2ff calculation.
Global view of high-quality OCO-2 tracks To understand the potential of OCO-2 satellite data on detecting an urban CO 2ff emission trend, the global data availability must be quantified. Thus, we scanned all the OCO-2 soundings from 2014 to 2019 and calculated how many are high-quality soundings, as marked by quality flags on 1 • x 1 • grids (Fig. 1 ). Note that the OCO-2 soundings are subject to a two-level quality inspection (L1 and L2). The ratios of high-quality soundings for each level are shown in Fig. S3. The ratios used in Fig. 1  were obtained by multiplying the ratios in L1 and L2. Globally, about 10. 82% of soundings are marked as high-quality by OCO-2 quality flags, while the high-quality sounding ratio in the low-and mid-latitudes (< 60 • ) is 14. 61%. The global mean ratio over land (11.
Further investigation shows that the data loss in OCO-2 measurements is mainly due to cloud and aerosol followed by topography (see Section Text S3). We only considered the target and glint modes in our study. The numbers of soundings in the same-size boxes over different cities are almost equal because the soundings are evenly distributed along the tracks (for both nadir and glint modes). Assuming that the ratio of useable tracks is similar to the fraction of high-quality soundings, on average 3-4 tracks per city per year are valid for CO 2ff emission inversion, given the 16-day OCO-2 revisit period. The ratios of high-quality soundings near cities in North America and Africa are higher than for any other continent. Over North America, Europe, and Asia the ratios of high-quality soundings gradually decrease as the box size increases.
Comparison of modeled and observed local X CO2ff enhancement over Lahore The X CO2ff enhancement is defined as the enhancement in X CO2 due to local fossil fuel emissions. Fig. 3  shows two sample comparisons between modeled and observed X CO2ff enhancements. On Apr 2, 2017, the OCO-2 1-s averaged data showed a 0. 68 ppm X CO2ff enhancement peak at 31. 57 • E latitude. WRF-Chem captured a similar 0. 58 ppm peak, but the position shifted slightly south. X-STILT showed a 0. 29 ppm peak, much lower than OCO-2 and WRF-Chem, located between the OCO-2 and WRF-Chem peaks. On Jan 15, 2018, OCO-2 1-s averaged data showed a 0. 22 ppm peak at 31. 43 • E latitude. WRF-Chem also shows a peak at that position, but the enhancement is about 1. 77 times higher than the OCO-2 peak at 0. 39 ppm. X-STILT showed a similar 0.
38 ppm peak as WRF-Chem, but located at 31. 22 • E latitude. It is notable that the modeled peak enhancement and position are sensitive to the modeled wind field. For example, the WRF-Chem and X-STILT peaks on Jan 15, 2018, not overlapping but show a similar enhancement (Fig. 3d ), implying that the wind directions slightly differ in the two models. The comparison of 25 all-data tracks is shown in Fig. S9  and the comparison of the 8 high-quality tracks is shown in Fig. S10. Overall, the OCO-2 data in the all-data tracks showed greater variations of X CO2ff and greater discrepancies with the two models than the high-quality tracks. The X CO2 enhancement peaks of the two models are more consistent with each other than with those in the OCO-2 data, in terms of both position and magnitude. The first example (Fig.
Evaluation of OCO-2 quality flags at the city-level The OCO-2 quality flags were originally designed for global-scale studies. Although there are some TCCON sites located in urban areas (http://www. tccon. caltech. edu/site-locations/index. html), its effectiveness at city scale has not been fully evaluated. Because the soundings filtered out by the quality flags in the L1 product have no corresponding X CO2 values, we could only examine the X CO2 soundings in the L2 data. Due to the lack of ground-based X CO2 data, we evaluated the relevance of quality flags in L2 products based on the three methods. This multimodel evaluation is independent of model-specific assumptions, especially the meteorological conditions: WRF driven by ERA-5, X-STILT driven by GDAS, and FCSI with ECMWF HRES. Fig. 5  shows the satellite/bottom-up-emissions (OCO-2/ODIAC) ratios for the 25 all-data tracks (in red) and 8 high-quality tracks (in blue).
We compared the ratios rather than the differences because the CO 2ff emission scaling factors are based on the satellite/bottom-up-emissions ratios. For simplicity, Fig. 5  is a boxplot without outliers (see Fig. S11  for outliers). The ratios of the high-quality group converge closer to 1 compared to the all-data group. The high-quality tracks produce relatively few outliers while the 25-track ratios show large outliers (>10; Fig. S11 ). For high-quality tracks, WRF-Chem and X-STILT have similar reported error bars but the error bar of the FCSI method is greater. All 3 models consistently suggest that the median ratios are greater than 1, which implies that the ODIAC slightly underestimates the CO 2ff emissions over Lahore. The possible reasons for underestimation could be unreported sources or inaccurate emission factors over fast-growing areas like Lahore.
For the FCSI method, the mean of high-quality cases is not similar to the other methods, but the spread is much smaller than the all-data estimate. Based on the reduced spreads of high-quality tracks, we conclude that the OCO-2 quality flags are useful filters to identify low-quality OCO-2 retrievals producing unlikely emission values over Lahore. Hence, quality flags should be considered in cityscale studies. The larger uncertainties of the FCSI method might be caused by the variability in the surface footprint, which is difficult to accurately determine due to uncertainties in the identification of the city plume and vertical distribution of CO 2 (with well-mixed conditions assumed in the wind calculation). The high-quality track ratios from X-STILT seem to converge more (smaller range) than WRF-Chem.
Weekday scaling factors were obtained from the Temporal Improvements for Modeling Emissions by Scaling (TIMES) model  (Nassar et al. , 2013). In future studies, these adjustments should be based on economic activity data (e. g. , energy production/consumption, traffic, industrial activities) instead of a prescribed climatology of fossil fuel emission. The trend in the posterior emission baseline case is about 734 kt Carbon per year (or 6. 7%) independent of prior uncertainty. However, the emission trend decreases as the level of confidence in the OCO-2 data (and the WRF-Chem model) increases (i. e. , observation uncertainty decreases) (cf. Table  1 ). Based on the Mann-Kendall test  (Lei et al. , 2018 (Lei et al. , , 2019)) , we evaluated the trend significant probability at various prior uncertainty levels.
We calculated the possibility of p-values less than 0. 05 from 10,000 Monte Carlo simulations of the Mann-Kendall upward trend test. The possibility significantly increases as prior uncertainty decreases, while significantly decreasing as observation uncertainty decreases. We conclude here that for the 8 high-quality tracks, the trend is mostly driven by prior emissions while the track-to-track variability is too large to identify the positive trend based solely on OCO-2 data. To understand the effect of the number of valid satellite tracks, we also ran idealized simulations of p-values for 25 high-quality tracks (with the same dates as the 25 all-data tracks) assuming that posterior emissions and uncertainties are equal to the prior. The probability of detecting the positive trend with 25 tracks increases to 98.
Additional comparisons between these approaches using more tracks and over different cities are required in the future. The next generation Orbiting Carbon Observatory satellite, OCO-3, was launched in May 2019, providing a novel viewing mode dedicated to denser data collection over cities. The Snapshot Area Map (SAM) mode of OCO-3 yields a raster of data by scanning wider areas over cities compared to OCO-2, reducing the possibility that the tracks miss the plumes from cities. The Bayesian inversion framework should be adjusted to fit the wider spatial coverage. OCO-2 continues to collect observations even after the launch of OCO-3, allowing us to study the difference between the two satellites and whether the SAM mode improves CO 2ff emission inversion. In addition, more CO 2 monitoring satellites (e.
Conclusions In this study, we examined the number of valid OCO-2 tracks over the 70 most populated cities globally by examining the ratios of highquality soundings. About 17% of OCO-2 soundings are marked as high-quality by the current flagging procedure. Given the 16-day OCO-2 revisit period, this produces 3-4 valid tracks per year per city on average. The occurrence of sounding selection criteria shows that cloud blockage and aerosol contamination are the two main causes of data loss. The OCO-2 quality flags were originally designed for global-scale studies. We for the first time evaluated their effectiveness at the city level. We selected Lahore in Pakistan as a case study and compared satellite/bottom-up-emissions ratios from WRF-Chem, X-STILT, and the FCSI method using 25 all-data tracks and 8 high-quality tracks.
The ratios of the high-quality group with significantly lower uncertainties converge closer to 1 compared to the all-data group. Thus, OCO-2 quality flags defined at the global level are useful filters for removing low-quality OCO-2 retrievals in city-scale studies. All three models consistently suggest that the ratio medians are greater than 1, implying that the ODIAC product may be slightly underestimating CO 2ff emissions over Lahore. We estimated the posterior (optimal) CO 2ff emissions over Lahore using a Bayesian inversion system at various prior and observation uncertainty levels and adjusted posterior emissions by monthly and weekday scaling factors. The prior CO 2ff emissions from ODIAC suggest that the Lahore 2014-2019 trend was about 646 kt C/year (i. e. , an annual 5. 9% increase), while the baseline of posterior CO 2ff emission trend was about 734 kt C/year (i.
Acknowledgments  R. Lei  and  S. Feng  have been funded jointly by the  NASA  grants # 80NSSC18K1313  (subcontracted from the  Universities Space Research Association  # 05783-01  to  Penn State ) and # 80NSSC19k0093. S. Feng  is also supported by  NASA  grant # 80HQTR21T0070  at  Pacific Northwest National Laboratory  (PNNL). PNNL is operated for DOE by  Battelle Memorial Institute  under contract  DE-AC06-76RLO 1830. T. Lauvaux  has been supported by the  French research program Make Our Planet Great Again  (project  CIUDAD ). A. Danjou  has been supported by the  PhD fellowship program of Commissariat à l'Energie Atomique et aux Energies Alternatives. G. Broquet  has been supported by the  French National Space Agency (CNES)  as part of the  TOSCA program  ( OCO-3   City  project). D. Wu  and  J.
Data availability The following data in this study is available from public sources: NCAR Upper Air Database: https://rda. ucar. edu/datasets/ds370. 1/ NCEP ADP Global Surface Observational Weather Data: https://rda. ucar. edu/datasets/ds461. 0/ CO 2ff emission from ODIAC: http://db. cger. nies. go. jp/dataset/ODIA C/DL_odiac2019. html TIMES temporal scaling factors: https://cdiac. ess-dive. lbl. gov/ftp/ Nassar_Emissions_Scale_Factors/ MsTMIP Biogenic CO2 Fluxes: https://nacp. ornl. gov/MsTMIP_products. shtml#datasets OCO2_L2_Lite_FP: https://disc. gsfc. nasa. gov/datasets/OCO2_L2_Lite _FP_9r/summary ERA5 climate reanalysis: https://climate. copernicus. eu/climate-r eanalysis GDAS0p5: ftp://arlftp. arlhq. noaa. gov/archives/gdas0p5/ All analysis code will be made available on request.
Evolutionary history of endosymbiosis in the Hyalomma genus We further examined the evolutionary history of Hyalomma species and their associations with FLE and Midichloria through a bibliographic survey. Out of 10 Hyalomma species with sufficient published data, all were found infected by FLE, and 9 by Midichloria (Supplementary file 1d). We next reconstructed Hyalomma phylogeny using mitochondrial DNA sequences of the cytochrome c oxidase I (cox1) gene available on Genbank (Figure  1 -figure supplement 2). Mapping of FLE and Midichloria onto the Hyalomma phylogenetic tree showed that co-infection by FLE and Midichloria is widespread across all Hyalomma lineages (Figure  1 -figure supplement 3). However, FLE and Midichloria co-infection was not detected in one species, H. asiaticum, nested within the Hyalomma phylogeny among co-infected species.
For the Israeli sample, reads were trimmed using Cutadapt (v1. 13)  (Martin, 2011)  and contaminants' reads were removed through a mapping against Coliphage phi-X174 (GenBank: NC_001422) and Staphylococcus aureus (NC_010079) genomes with BWA (v0. 7. 17-r1188) (option mem) and SAMtools (v1. 10)  (Li and Durbin, 2009). Remaining reads were further assembled using SPAdes (v3. 11. 1)  (Bankevich et al. , 2012)  with default parameters. These 'raw' assembled scaffolds were compared to different reference genomes separately (FLE of O. moubata FLEOm, M. mitochondrii, and R. aeschlimannii; Supplementary file 1b) to discard unspecific scaffolds using RaGOO (v1. 02)  (Alonge et al. , 2019) , and, for each reference genome separately, the assignation of the remaining scaffolds ('filtered' assembly) was confirmed using the online NCBI BLAST tool (https:// blast.
, 2020)  on each genome, including newly obtained and reference genomes of FLE and Midichloria (Supplementary file 1b). The FLE and Midichloria data sets were filtered excluding pseudogenes from the subsequent analyses. Clusters of orthologous groups of proteins (COGs) were predicted using the NCBI pipeline  (Galperin et al. , 2015)  and COGs presence was then plotted with an in-house R script. Then, two different data sets were created: (i) a FLE data set including the three newly sequenced FLE-Hmar genomes and other published FLE genomes; and (ii) a Midichloria data set including the three newly sequenced Midi-Hmar genomes and M. mitochondrii. For each data set, SCOs were identified using OrthoFinder (v2. 3. 12) (Emms and Kelly, 2019) , then SCO lists were retrieved on a R environment (v3.
6. 2) to build Venn diagrams using the 'VennDiagram' R package  (Chen and Boutros, 2011). To check if whether B-vitamin biosynthesis genes of other origin than FLE, Midichloria, and Rickettsia, we used the following procedure: we predicted the genes present in the preliminary assemblies of the three tick metagenomes using prodigal (using the setting '-p meta') and annotated them with GhostKOALA  (Kanehisa et al. , 2016). Indeed, for biotin, we checked for the presence of the bioA, bioD, bioC, bioH, bioF, and bioB genes searching for 'K00833, K01935, K02169, K02170, K00652, and K01012' sequences in the annotations, setting a cutoff to exclude contigs shorter than 500 bp or with a kmer coverage below 4. The hits above cutoff were manually examined with BLAST for functional and taxonomic classification.
Phylogenomics The FLE, Midichloria, and Rickettsia data sets were filtered excluding the pseudogene sequences and were then used for the phylogenetic analyses. For each symbiont data set, SCOs were identified using OrthoFinder (v2. 3. 12) (Emms and Kelly, 2019)  and then aligned with MUSCLE (v3. 8. 31)  (Edgar, 2004). Poorly aligned positions and divergent regions were removed by Gblocks (v0. 91b)  (Castresana, 2000)  prior to concatenation of individual alignments using an in-house script. Finally, the most suitable evolutionary model was determined using modeltest-ng (v0. 1. 5) (Darriba et al. , 2020)  according to the Akaike information criterion and maximum likelihood (ML) trees were inferred using RAxML (v8. 2. 9)  (Stamatakis, 2014)  with 1000 bootstrap replicates.
Biotin operon phylogeny and structure Nucleic and protein sequences of the six genes of the biotin biosynthesis pathway were retrieved using BLASTn, BLASTp, and tBLASTx on all Midichloria genomes. Sequences from described streamlined biotin operons and sequences from biotin genes from other symbionts (retrieved from GenBank) were included in the data set (details in Supplementary file 1c). All protein sequences of the six biotin genes were manually concatenated and aligned with MAFFT (v7. 450)  (Katoh et al. , 2002)  and ambiguous positions were removed using trimAl (v1. 2rev59)  (Capella-Gutiérrez et al. , 2009). The appropriate substitution model, and then ML-tree, were determined as described above. Meanwhile, 16S rRNA sequences from all organisms included in this data set were retrieved from GenBank and aligned with MEGA software.
The Gblocks program with default parameters was used to obtain nonambiguous sequence alignments. Phylogenetic analyses were performed with MEGA software, that is, determination of the appropriate substitution model using Akaike information criterion, ML-tree inference. Clade robustness was assessed by bootstrap analysis using 1000 replicates. Contigs carrying the streamlined biotin operon were retrieved from newly obtained and reference genomes of different intracellular bacteria. Contigs were annotated using Prokka (v1. 13. 1)  (Seemann, 2014)  with default parameters. Prokka GenBank outputs were imported in R (v3. 6. 2) and analyzed using the 'genoplotR' R package  (Guy, 2010). Sequences of ribH and folD genes from bacteria carrying the streamlined biotin operon and from other bacteria were secondarily separately analyzed.
Bibliographic survey and phylogeny of Hyalomma To access the associations between Hyalomma species with FLE and Midichloria, we performed an extensive bibliographic survey of published studies. For Hyalomma species for which presence/ absence of FLE and Midichloria is documented, we retrieved cytochrome c oxidase I (cox1) gene sequences from GenBank and further performed an ML phylogenetic analysis with MEGA software as described above.
Statistical analyses Statistical analyses were carried out using the R statistical package (v3. 6. 2) and the 'lme4' R package  (Bates et al. , 2015). The FLE load was analyzed using linear mixed-effects models on a data set containing loads measured in ovaries from adult engorged ticks. The normality of the data distribution was tested through a Shapiro-Wilk test with a 95% confidence interval. Outliers were removed from data sets. To build models, Midichloria load was fitted as a fixed explanatory variable. The best-fitted model was determined with the following procedure: the maximal model, including all higher-order interactions, was simplified by sequentially eliminating interactions and non-significant terms to establish a minimal model. A likelihood ratio test (using a chi-square distribution and a p-value cutoff of 0.
Acknowledgements The authors are grateful to  Tal Azagi  for tick collection and extraction and  Inbar Plaschkes  for initial sequence analysis of the Israeli sample, to  Justine Grillet ,  Valentina Serra , and  Catherine Hartley  for technical support during molecular analyses. This work benefited from (1) an international 'Joint Research Projects' grant (EVOSYM) co-managed by the  Ministry of Science, Technology and Space (Israel)  and the  Centre National de la Recherche Scientifique (CNRS, France) , from (2) The  Israel Science Foundation  ( ISF Grant  no. 1074/18 ), from (3) the  Human Frontier Science Programme Grant   RGY0075/2017  to DS, and the  Italian Ministry of Education, University and Research (MIUR) :  Dipartimenti di Eccellenza Programme  ( 2018-2022 ) - Department of Biology and Biotechnology 'L.
Data and code availability The bacterial genomes obtained in this study were deposited in European Nucleotide Archive (ENA) under the project PRJEB44779 and accession numbers GCA_910592745 (FLE-Hmar-ES), GCA_910592815 (FLE-Hmar-IL), GCA_910592705 (FLE-Hmar-IT), GCA_910592695 (Midi-Hmar-ES), GCA_910592725 (Midi-Hmar-IL), GCA_910592865 (Midi-Hmar-IT), and GCA_910592765 (RAES-Hmar-IT). Metagenome reads were deposited in ENA under accession numbers ERR7255744 (Hmar-ES), ERR7255745 (Hmar-IL), and ERR7255743 (Hmar-IT). Command lines used for the different genomic analyses are available on GitHub (https:// github. com/ mariebuysse/ Hmar-2021; Buysse et al. , 2021).
Data availability The bacterial genomes obtained in this study were deposited in European Nulceotide Archive (ENA) under the project PRJEB44779 and accession numbers GCA_910592745 (FLE-Hmar-ES), GCA_910592815 (FLE-Hmar-IL), GCA_910592705 (FLE-Hmar-IT), GCA_910592695 (Midi-Hmar-ES), GCA_910592725 (Midi-Hmar-IL), GCA_910592865 (Midi-Hmar-IT) and GCA_910592765 (RAES-Hmar-IT). Metagenome reads were deposited in ENA under accession numbers ERR7255744 (Hmar-ES), ERR7255745 (Hmar-IL) and ERR7255743 (Hmar-IT). Command lines used for the different genomic analyses are available on GitHub (https:// github. com/ mariebuysse/ Hmar-2021; copy archived at swh:1:rev:a459aeecb87d3fae17cf6ab8317654feadb62dba).
We show the expressiveness of LFIT for this specific problem and propose a scheme that can be applicable to other domains. In order to check the ability to cope with other domains no matter the machine learning paradigm used, we have done a preliminary test of the expressiveness of LFIT, feeding it with a real dataset about adult incomes taken from the US census, in which we consider the income level as a function of the rest of attributes to verify if LFIT can provide logical theory to support and explain to what extent higher incomes are biased by gender and ethnicity.