dataesr/scientific-paragraphs-categorization huggingface dataset:
The bootstrap analysis suggest that results are fairly robust against statistical noise (except FL-MOC; see also Fig. 6 ). The rightmost column in Fig. 9  shows the SSA difference as a function of (AERONET) AOD. To ensure the largest possible range in AOD values, Inversion L1. 5 instead of L2. 0 is used. Especially at lower AOD, this dataset will have larger errors in AAOD and SSA than L2. 0. Interestingly, as AOD increases, all satellite products seem to agree better with AERONET (for FL-MOC, the bin with largest AOD values is affected by a very low data count). This is of course https://doi. org/10. 5194/acp-21-6895-2021 Atmos. Chem. Phys. , 21, 6895-6917, 2021 as one would expect. For smaller AOD, there is increasingly more spread, although the difference distribution remains fairly unbiased.
Results for FL-MOC may be a statistical fluke due to the low data count. POLDER-GRASP-M shows quite high correlations for AOD (0. 86) and AAOD (0. 6) with reasonable slopes but has a very low correlation with AERONET for SSA (0. 41), but this seems to depend strongly on sampling as discussed at the start of this section. In addition, it appears to systematically underestimate SSA at low AOD. Yet another aspect to this dataset (not visible in any of the analysis shown) is that it appears to have a hard SSA cut-off as SSA values larger than 0. 99 do not occur. A profound problem is the paucity of data. Even for POLDER-GRASP-M, we can only evaluate its performance (against AERONET) for less than 0. 006 % of the total number of available observations. Is this sufficient to make meaningful statements about the performance of a product at large? In  Schutgens et al.
Not only that, but the mean difference in SSA for the first sampling is 2. 6 times as large (-0. 043 vs. -0. 017) as for the third sampling. As POLDER-SRON is biased high and POLDER-GRASP-M is biased low vs. AERONET, the corollary to this is of course that at least one of the products has a larger bias vs. the truth globally than can be seen in the AERONET observations. Conversely this suggests that the AERONET Inversion dataset does not allow a truly global evaluation of satellite datasets: it provides a subsample with skewed statistics of SSA errors. Incidentally, it is the temporal subsampling enforced by collocation with AERONET observations that causes the largest shift in the difference distribution (POLDER measurements over AERONET sites show an SSA distribution similar to the global dataset).
Conclusions In this study, we evaluate several remote sensing datasets of AAOD and SSA from a variety of sensors (CALIOP on CALIPSO, OMI on Aura, POLDER on PARASOL), in preparation of an AEROCOM model evaluation. This is the The evaluation of the products (daily aggregates over 1 • ×1 • ) is done through comparison with AERONET Direct-Sun (AOD) and Inversion (AAOD and SSA) observations. To minimize sampling issues, satellite products and AERONET data are collocated in time and space within 3 h and 1 •. One interesting finding is that AAOD evaluation requires a tighter temporal collocation criterion than AOD, with steep declines in correlation found for temporal collocation after 3 h or more. We interpret this to be due to absorbing aerosol primarily being found in plumes. While we do not explore this further, this high temporal variability in observed AAOD may affect model evaluation as well.
S1 ). Identifying the cause of this bias may lead to substantial improvements of both products (or at least one of them). Based on a comparison with AERONET data, we suggest that cloud contamination is a possible candidate. Throughout the paper, we have given examples of how limited sampling of observations (especially AERONET) constrains our ability to understand the true error statistics of satellite AAOD and SSA. The most prominent example is a much reduced systematic difference (-0. 017) between POLDER-GRASP-M and POLDER-SRON SSA as seen in an evaluation with AERONET Inversion L2. 0 observations, as compared to the global satellite dataset (-0. 04). This suggest that biases inferred from an AERONET evaluation will be smaller than those actually present in the satellite products. To increase available SSA observations, one could use Inversion L1.
5 data (which includes SSA at low AOD) and sample them to L2. 0 AOD measurements (which, unlike SSA, exist at low AOD), thereby benefitting from the better L2. 0 cloud screening. Especially if follow-up studies can show that inversion errors at individual sites behave as random errors (amenable to temporal averaging) and not systematic biases, such an intermediate product might be very useful. This paper is one part of a two-part study into the use of satellite AAOD and SSA for aerosol model evaluation. In the companion paper, we use the datasets introduced in the current paper to evaluate AEROCOM (Aerosol Comparisons between Observations and Models) models. It turns out that robust and consistent evaluation of the models is possible, notwithstanding the biases in the satellite data we have detailed in the current paper.
All observations are assigned to a spatio-temporal grid box according to their times and geolocations. Once all observations have been assigned, observations are averaged by grid box. It is possible to require a minimum number of observations to calculate an average. Finally, all grid boxes that contain observations are used to construct a list of super-observations as in Fig. A2. Only times and geolocations with aggregated observations are retained. As the original L2 dataset was homogeneous, so is the resulting L3 dataset. Station data are similarly aggregated over 1 • ×1 • ×30 min. Point observations will suffer from spatial representativeness issues  (Sayer et al. , 2010; Virtanen et al. , 2018; Schutgens et al. , 2016b) , but the representativity of AERONET sites for 1 • ×1 • grid boxes is fairly well understood  (Schutgens, 2020 ; see also  Sect.
2. 1. 5). These aggregated L3 AERONET data will also be called super-observations. Different datasets of super-observations can be collocated in a very similar way. Again a regular spatio-temporal grid is defined as in Fig. A1  but now with grid boxes of larger temporal extent (typically 1 • ×1 • ×3 h). Because this temporal extent is short compared to satellite revisit times, either a single satellite super-observation or none is assigned to each grid box. A single AERONET site however may contribute up to six super-observations per grid box (in which case they are averaged). After two or more datasets are aggregated individually, only grid boxes that contain data for both datasets will be used to construct two lists of aggregated data as in Fig. A2. Those two lists will have identical size and ordering of times and geolocations and are called collocated datasets.
By choosing a larger temporal extent of the grid box, the collocation criterion can be relaxed. As the super-observations are on a regular spatio-temporal grid and collocation requires further aggregation to another regular but coarser grid, the whole procedure is very fast. It is possible to collocate seven products from afternoon platforms over 3 years using an IDL (Interactive Data Language)  code and a single processing core in just 30 min  (Schutgens et al. , 2020). This greatly facilitates sensitivity studies. Starting from super-observations, a 3-year average can easily be constructed by once more performing an aggregation operation but now with a grid box of 1 • × 1 • × 3 year. If two collocated datasets are aggregated in this fashion, their 3-year average can be compared with minimal representation errors.
FigureA1. A regular spatio-temporal grid in time, longitude and latitude. Such a grid is used for the aggregation operation that is at the heart of the collocation procedure used in this paper. Grid boxes may either contain data or be empty. Note that data may refer to any combination of observations, e. g. AOD at multiple wavelengths or AOD and AAOD at 550 nm. However, the dataset is homogenous. Reproduced from Watson-Parris et al. (2016).
Acknowledgements. We thank the principal investigator(s) and coinvestigator(s) and their staff for establishing and maintaining the many AERONET sites used in this investigation. The figures in this paper were prepared using  David W. Fanning 's Coyote Library for IDL. Nick Schutgens thanks  Tom Eck ,  Greg Schuster  and  Kostas Tsigaridis  for insightful discussions on the use of AERONET observations. We would also like to thank four anonymous reviewers for attentive reading of our manuscript and many useful comments. Financial support. This research has been supported by the  Nederlandse Organisatie voor Wetenschappelijk Onderzoek  (grant no. VICI 016. 160. 324 ). Review statement. This paper was edited by  Matthias Tesche  and reviewed by four anonymous referees.
1a, b ). C-terminal to the beta strands is a short loop followed by an alpha helix which is contributed by the I domain (α2; aa 63-73). The orientation of the C-terminal alpha helix differs between MEF2 (PDB codes 1EGW, 1C7U, 1TQE, 3KOV) and SRF-like MADS TFs (PDB code 1HBX), with SEP3 MI resembling the MEF2 structure. Overall, SEP3 MI adopts the MADS/MEF2 fold, as predicted for type II plant MADS TFs. Each SEP3 MI monomer dimerises via extensive contacts between the pairs of alpha helices and beta strands from each partner, with dimerisation required for DNA binding. In the structure, all dimers were formed by crystallographic symmetry, with dimerisation burying over 25% of the total surface area of the molecule (Fig. 1 )  34. The dimer interface includes hydrophobic interactions between residues in the N-terminal DNA-binding alpha helices, Leu28 and Leu35, and pairs of salt bridges between Glu34 and Arg24 (Fig.
3 ). We used a highly stringent peak calling for each replicate and peaks were kept only when present in all replicates. This yielded 6347 peaks for SEP3-AG and 3552 peaks for SEP3-AG IAP1. These peak lists were merged and the binding intensities for the two complexes were compared for all resulting peaks. This profiling indicates that while most peaks have a similar binding intensity, several hundred exhibited at least a twofold difference in binding intensity between SEP3-AG and SEP3-AG IAP1 (Fig. 4a, b ). The same profiling between replicas for a given dataset demonstrated no such variability in binding intensity (Supplementary Fig. 3 ). The top 15% sequences (1073 sequences for each complex) displaying the most extreme binding difference were used to search for specific DNA patterns that potentially code for SEP3-AG and SEP3-AG IAP1 differential response.
The TF binding sites were modelled using position weight matrices (PWM), TF flexible models (TFFM) and k-mer set memory (KSM) analysis. Due to the highly similar binding modes of the MADS family TFs, only KSM was sensitive enough to differentiate the MADS heterocomplexes (Fig. 4c, d )  42. For the KSM, pipeline, KMAC and KSM tools from the GEM package  43  were used to search for clusters of short (4-20 bp) overlapping sequences (with allowances and penalties for gaps and mismatches) that are over-represented in the 600 sequences bound best by either SEP3-AG or SEP3-AG IAP1. This analysis yielded 55 clusters for SEP3-AG and 22 clusters for SEP3-AG IAP1 and served as k-mer-based models for prediction analysis. With AUC of 0. 79 for SEP3-AG and 0. 84 for SEP3-AG IAP1 , the k-mer-based models were highly predictive.
In order to determine whether these observed in vitro differences in binding were potentially relevant in vivo, we examined published ChIP-seq datasets. Previous studies have shown that the in vitro binding of SEP3-AG relative to SEP3-AP1 in SELEX-seq correlates with the in vivo binding intensity of AG relative to AP1 in ChIP-seq for the 1500 most enriched SEP3 ChIP-seq peaks (Fig. 4f )  42. This result suggested that specific sequence patterns detected in vitro were able to at least weakly discriminate sequences either more likely to be bound by SEP3-AG or by SEP3-AP1 in the Arabidopsis genome. Applying the same analysis to our seq-DAP-seq data, we found a similar weak positive correlation between the SEP3-AG binding affinity relative to that of SEP3-AG IAP1 and the corresponding ChIPseq derived in vivo binding, suggesting that SEP3-AG IAP1 behaves more similarly to SEP3-AP1 than to SEP3-AG (Fig.
d Predictive power of TFBS models. Models are built using 600 sequences best bound by each of the two heterocomplexes and are searched against 1073 SEP3-AG (orange) and 1073 SEP3-AG IAP1 (blue) specific regions, defined as the top 15% of sequences that are most strongly bound by one complex relative to the other. Matrix-based models (PWM and TFFM) are not able to differentiate SEP3-AG and SEP3-AG IAP1 binding, whereas k-mer-based analysis is able to better predict binding for the respective datasets. e SEP3-AG favours intersite spacings of 47 and 57 bp based on SEP3-AG-specific regions. SEP3-AG IAP1 favours intersite spacings of 25 and 34 bp based on SEP3-AG IAP1I specific regions. f Top, published SELEX-seq for SEP3-AP1 and SEP3-AG  78  comparing the normalised score ratios (SEP3-AG/SEP3-AP1) for SELEX-seq and score ratios (AG/AP1) ChIP-seq at 1500 SEP3 best bound loci in ChIP-seq show a positive correlation, suggesting that SEP3-AP1 and SEP-AG bind different sequences in vivo and that in vitro binding is able to differentiate bound sequences that are more SEP3-AP1-like versus SEP3-AG-like.
The protein was dialysed against 25 mM Tris, pH 8. 0, 300 mM NaCl and 1 mM TCEP. The protein was then applied to a heparin column to remove any bound DNA using a salt gradient from 300 mM to 2 M NaCl. The protein was dialysed overnight in the presence of TEV protease (10:1) to cleave the His-MBP tag at 4 °C. The protein was then passed over a Ni-NTA column to deplete the His-MBP and any uncleaved protein. A second heparin column was run to remove any His-MBP and to obtain pure SEP3 MI. The protein was concentrated to ~6 mg/ml and used for crystallisation trials. SEP MI mutant constructs were generated using the QuikChange (Agilent) protocol according to the manufacturer's instructions. Primers used for mutagenesis are given in Supplementary Table  3. Protein crystallisation, data collection and refinement.
SEP3 MI at a concentration of 6 mg/ml was mixed at a 1:1 ratio with potassium sodium tartrate tetrahydrate (0. 2 M), bis-tris propane (0. 1 M, pH 7. 5) and 20% PEG 3350. The protein crystallised over 20 days at 4 °C forming a diamond shaped single crystal. Fifteen percent glycerol was used as a cryoprotectant and the crystal flash frozen in liquid N 2. Diffraction data were collected at 100 K at the European Synchrotron Radiation Facility, Grenoble, France, on ID29 at a wavelength of 1. 0 Å. Indexing was performed using MXCube  56  and the default optimised oscillation range and collection parameters used for data collection. The dataset was integrated and scaled using the programs XDS and XSCALE  57. The structure of the MEF2 dimer (PDB code 3KOV) was used as a search model. Molecular replacement was performed using Phaser  58.
Model building was performed using Coot 59 and all refinements were carried out in Refmac  60. The structure quality was assessed using MolProbity. Data collection and refinement statistics are given in Table  1. The structure is deposited under PDB code 7NB0. In vitro pull-down assays. The following constructs from type I and type II MADS TFs are cloned into pTnT vector for in vitro pull-down assays. Type I MADS TFs include AGL61 M (63-122)-5Myc, AGL61 MI (63-155)-5MyC, PHE M (1-60)-3FLAG, PHE MI (1-95)-3FLAG, AGL80 M (1-61)-3FLAG and AGL80 MI (1-88)-3FLAG, and type II MADS TFs include SEP3 M (1-57)-3FLAG, SEP3 MI (1-90)-3FLAG, AG M (1-73)-5MyC, AG MI (1-107)-5MyC, AG(ΔN) M (17-73)-5MyC, AG (ΔN) MI (17-107)-5MyC, AP1 M (1-57)-5MyC and AP1 MI (1-92)-5MyC. A list of primers used for cloning is provided in Supplementary Table  4.
5). The eluted DNA fragments were PCR amplified using Illumina TruSeq primers for 20 cycles, and purified by AMPure XP beads (Beckman). The libraries were quantified by qPCR, pooled and sequenced on Illumina HiSeq (Genewiz) with specification of pairedend sequencing of 150 cycles. Each library obtained 10-20 million reads. The seq-DAP-seq was performed in triplicate. Seq-DAP-seq data analysis. Reads processing and peak calling for SEP3-AG and SEP3-AG IAP1 were performed as previously described  31. Briefly, reads were checked using FastQC (http://www. bioinformatics. babraham. ac. uk/projects/fastqc/ ) and adaptor sequences removed with NGmerge 62 and mapped with bowtie25  63  onto the TAIR10 version of the A. thaliana genome (www. arabidopsis. org), devoid of the mitochondrial and the chloroplast genomes.
The duplicated reads were removed using the samtools rmdup program  64. The resulting alignment files for each sample were input to MACS2  65  to call peaks using the input DNA as control. Consensus peaks between replicates were defined using MSPC  66  (P value cutoff = 10 -4 ) for each experiment. Each consensus peak was scanned for possible subpeaks, split into several peaks if needed and the peak widths were then re-sized to ±200 bp at both side of the peak maximal height. For all the resulting peaks, coverage was computed as the mean of the normalised read coverage for each replicate. This normalised coverage defines the binding intensity of a heterocomplex at a bound region. Comparison between SEP3-AG and SEP3-AG IAP1 was performed as follows. SEP3-AG or SEP3-AG IAP1 was merged according to the following procedure: peaks were considered common if at least 80% of two peaks overlapped with <50% of either peak non-overlapping.
31. PWM, TFFM and KSM were obtained as follows. For each experiment, PWM, TFFM and KSM models were reconstructed out of the 600 best peaks (judged according to their averaged coverage). PWM were generated by the meme-suite, using meme-chip  69  with options -meme-minw 16, -mememaxw 16, -meme-nmotifs 1 -meme-pal. TFFM were generated using the TFFMframework package and the PWM obtained in the meme-chip output  70. KSM is a recently developed TF binding motif representation that consists of a set of aligned k-mers that are over-represented at TF binding sites. KSM were generated using the KMAC tool  43  with options set to search k-mers, with size ranging from 4 to 20 bp in a 300 bp sliding windows, that are enriched compare to TF unbound sequences. TF binding sites were predicted by searching the PWM, TFFM and KSM models against TF bound sequences and unbound sequences.
Acknowledgements This project received support from the  ANR  ( ANR-16-CE92-0023 ) and GRAL, a programme from the  Chemistry and Biology Health Graduate School of the University Grenoble Alpes  ( ANR-17-EURE-0003 ). This work benefited from access to the  MX-Grenoble , an Instruct-ERIC centre within the  Grenoble Partnership for Structural Biology. Financial support was provided by  Instruct-ERIC  ( PID 13317 ) and  FRISBI  ( ANR-10-INBS-05-02 ). R. V. -L. and C. S. were supported by the  Deutsche Forschungsgemeinschaft  ( DFG  project number  408478570 ).
For conservation purposes, it is of upmost importance to study large spatial and taxonomic patterns of GD P across life-history, biogeographic and climatic contexts. Here, we synthetize published datasets to inform GD P (measured as expected heterozygosity inferred from nuclear markers) patterns across large spatial (worldwide) and taxonomic (across plants and animals) scales, and to identify the main life-history-related, biogeographic and climatic factors sustaining these patterns of GD P. Our study involves 8356 populations distributed across the globe and from 242 eudicot, 10 magnolid, 82 monocot, 50 pinopsida (hereafter pines), 51 amphibian, 36 reptile, 44 mollusc, 139 mammal, and 73 bird species (Fig. 1 , Tables  S1-S3 ). We quantify relations between life-history traits, biogeography, elevation, past and current climate and GD P.
e. decrease in gene flow). For each species, we obtained information on life-history traits and distribution ranges from iucn. org, utheria. org, animaldiversity. org, try-db. org, eol. org and the source papers used for collecting H E (Supplementary Data 3). To characterize animal life history, we focused on three life-history covariates (log10-transformed): life-time "Fecundity", average body "Size" and maximum "Longevity" (Supplementary Fig. 2 , Supplementary Methods). To reduce collinearity among these life-history traits, data were synthetized into two principal components (R package vegan), the first one (hereafter "PC_SizeLongevity") being positively associated with Size and Longevity and the second one (hereafter "PC_Fecundity") being positively associated with Fecundity (Supplementary Methods).
Acknowledgements M. B. and V. S. acknowledge a  Contrat de Recherche Laboratoires-Entreprises Région Midi-Pyrénées called TRAM-MIDPYR. H. D. K. holds a postdoctoral fellowship funded by  FWO (Research Foundation Flanders ,  12P6517N ). S. B. is financially supported by two grants from the  Agence National de la Recherche  ( TULIP 10-LABX-0041  and iBEF  ANR-18-CE02-0006 ).
Analysis of the distribution of cnf genes in a large collection of E. coli genomes At the start of this study, we mined large genomic datasets from EnteroBase to gain more insight into the distribution of the cnf1 gene and its close homologs in the population of E. coli  41. EnteroBase represents an integrated software environment widely used to define the population structure of several bacterial genera, including pathogens. Quantitative information on the collection of 141,234 E. coli genomes deposited in EnteroBase are reported in the supplementary figure  1. This collection, starting from 1900, aggregates genomes from strains collected worldwide, but mainly in Europe and North America, and from a wide range of sources but principally human isolates (Sup. Figure  1A, 1B, 1C ). Using a Hidden Markov Model (HMM) approach, coupled to amino acid pairwise distance calculation, we retrieved cnf-like positive strains and characterized each type of cnf sequence.
In total, we identified n=6,411 cnf-positive strains (4. 5% of all E. coli isolates) with a remarkable dominance of cnf1 (87. 8%, n=5,634), as compared to cnf2 (8. 6%, n=554) and cnf3 (3. 5%, n=223). These strains displayed only one CNF-like toxin encoding gene. The prevalent cnf1 gene in this genomic dataset was widely distributed among isolates of all origins but most notably in the groups denoted humans (5. 4% of n=48,518 human isolates) and companion animals (24. 1% of n=2,652 companion animal isolates) (Sup. Figure  1C ). We next studied the distribution of cnf1 among E. coli phylogenetic groups and sequence types (STs). The cnf1 gene is preferentially associated with isolates from the phylogroup B2, representing 24. 3% of n=22,305 retrieved genome sequences (Sup. Figure  1D ). We observed a tight association of cnf1 with the most frequently encountered ExPEC sequence types (STs) (Table  1 ).
We found a wide co-distribution of one combination of alleles of cnf1 (allele P1cnf1, 85,1%) and alpha-hemolysin encoding gene hlyA (allele P1hlyA, 77,2%) in E. coli ST131 clade A and C, whereas strains from clade B displayed a large range of combinations of various alleles (Sup. Figure  2A ). Together, our data point to a clonal expansion of worldwide disseminated ST131-H30 strains having the same allele of cnf1. Together, this prompted us to perform a clustering analysis of ST131-H30 strains according to their accessory gene contents. We generated a pan-genome matrix of 51,742 coding sequences from the n=3,981 strains of clade C. The dataset of accessory genes was built from n=7,678 sequences that were present in at least 50 and no more than 3,931 strains. We conducted a hierarchical clustering of strains according to the Ward's minimum variance-derived method  44  and retained 10 distinct accessory gene clusters.