dataesr/scientific-paragraphs-categorization huggingface dataset:
The overall agreement between stations is very good at the altitude range where the lidar data are of highest quality. The most outstanding site is Dumont d'Urville, which besides being at an extreme location also has a limited number of collocations in our data selection. The top part (above 40 km) shows the largest divergence and increasing standard deviations which can be attributed to the increasing contribution of the a priori in the SCIAMACHY data. The differences are normalised to set the variance to unity. As the transformation is linear, the distribution shapes are preserved. Figure  5  shows the Pearson linear correlation of the normalised relative differences between the 28 altitude bins. The correlation ranges between -0. 09 and 0. 95 (offdiagonal). It can be seen that differences at low altitudes are hardly correlated to those at higher altitudes and that at higher altitudes similar differences are found over a larger range of nearby altitudes.
Training of the SOM The normalised data were used to train a SOM. Here we used the SOM toolbox for MATLAB version 2. 0 beta by Alhoniemi, Himberg, Parhankangas and Vesanto available at http://www. cis. hut. fi/projects/somtoolbox. The selforganising map was set up as a lattice grid of 46 by 75 hexagonal neurons. The dimensions were chosen to theoretically allow an average of four input vectors to map onto a single neuron, which was chosen as a trade-off between complexity and over-simplification. Different ratios determine the level of detail that can be studied, but the principles remain the same. In the case of a greatly extended geographical input space, one could additionally go for a high-resolution representation with more neurons than input vectors  (Skupin and Esperbé, 2011). The training was done in two phases.
Cluster-wise correlation hunting Section 3. 4 has presented the correlations for the full set of explanatory variables and the SOM's component planes, providing a global overview. More complex relations might be obtained when examining the relations inside clusters. It is possible that some parts of the data set respond to different variables (local relations) or behave in an opposite manner, which then do not show up as a significant correlation in the global analysis. We have repeated the correlation analysis for the three clusters created from the codebook vectors in the previous section. Figure  12  shows the linear correlations between the codebook vectors and the mapped EVs for the three clusters. We can see that some details are very distinct for the different clusters. For instance, the dependence on latitude at higher altitudes appears to be much lower for the first cluster in comparison to the other two clusters.
Acknowledgements. The authors would like to acknowledge financial support from the  European Space Agency (ESA)  through the  VALID-2  project and the  Netherlands Space Office (NSO)  through the  NL-SCIAvisie  project led by  Ilse Aben  ( Netherlands Institute for Space Research (SRON) ). We would also like to thank the two anonymous reviewers for their feedback. Edited by:  M. Weber.
These genomes were also completely devoid of capsid proteins. Evidence that unknown life forms populated the microbial world, i. e. microbes still unsequenced to date, including possibly some microbial dark matter (e. g. microbes that are especially difficult to grow in lab cultures) was ever growing. Interestingly, just like many other environments, the human microbiome also presented an exciting share of unknown organisms  [27, 28]. Comparing predicted genes from over 1 million shotgun reads to an extended reference database, Kurokawa et al. found that the sequences from the majority of gut microbes were highly divergent from previously known sequences  [29]. Most of the organisms represented by these sequences were uncharacterized at the genus level. In 2010, Qin et al. investigated the diversity of fecal samples of 124 European adults and reported that only 31-48.
8 % of the reads from their studies (plus those from two former studies with comparable methodology but smaller sampling size) could be assigned to 194 public human gut bacterial genomes, while 7. 6-21. 2 % could be assigned to the bacterial genomes from Genbank  [30]. Numerous completely novel gene families without known functions, amounting together to three quarters of the gene families of the gut microbiome, were also reported  [30]. One year later, Arumugam et al. analyzed metagenomes from 39 individuals and estimated that around 16. 5 % of the reads were likely to belong to yet unknown genera  [31]. Finally, in 2012, the use of 212 different culture conditions revealed novel species and genera in the gut microbiome, including the largest bacterial genome and the largest virus reported in that environment, demonstrating that metagenomic studies still underestimate microbial diversity within the gut  [8].
Thus, wherever one looks in naturefrom hot springs to human guts-, novel microbial lineages from various taxonomic levels are being discovered through environmental sequencing. However, a general question remains to be addressed: are we missing novel lineages in the microbial dark universe by relying on standard phylogenetic and BLAST methods? If so, how can we probe that universe using alternative sensitive approaches? Indeed, studies of microbial dark matter using metagenomic data raise significant methodological challenges for comparative analyses. A first difficulty comes from the size of the dataset. For instance, in 2012, Lynch et al. devised a strategy to analyze 6. 5 million assembled paired-end Illumina reads of 16S rRNA from Arctic tundra soil samples  [32] , in which they found 3 very divergent bacterial lineages.
To handle this large amount of data, the authors relied first on sequence similarity networks. Nodes in these networks corresponded to abundant environmental 16S rRNA sequences and reference sequences from a database with 2,000,000 curated assigned sequences, connected by edges when they presented > 90 % identity and matched over > = 80 % of their hit length. In such networks, candidate new lineages are typically unconnected to reference sequences, because they are highly divergent  [32]. This study had a strong methodological implication: novel comparative strategies and tools are critical for the screening of the microbial dark matter, because reconstructing a tree of every 16S rRNA sequences was no longer a tractable default option (or at least became a real challenge). Comparative strategies targeting novel lineages however need to be designed with the specific goal of enhancing the exploratory power of microbial diversity analyses, which could also benefit microbial dark matter analyses.
We designed a multi-marker sampling strategy, relying on sequence similarity network to identify (i) well-distributed gene families in cultured prokaryotes and (ii) their distant environmental homologs, characterized by their topological distance to known sequences in these graphs and their significant sequence divergence (>40 %) from their closest published homolog present in the NCBI database in July 2013. We applied these tools to environmental samples, with a particular focus on the human gut microbiome, because these data are amongst the most abundant and cleanest (and therefore most convenient) publicly available, and because establishing the gut microbiome composition is one of the major challenges of the 21st century.
It is noteworthy that the gene families meeting this criterion included genes coding for ribosomal proteins (Table  1 ). By contrast to direct BLAST searches  [36] , we used two rounds of BLAST searches against a local environmental database, comprising 9,865,367 predicted ORFs from about 33 million sequences from 236 metagenomic samples, to search for homologs of the cultured sequences of these conserved families. The first round of BLAST defined a set of environmental homologs with significant hits to known cultured sequences. In the second round, these environmental homologs were used as seeds to look for their own environmental homologs. Using this protocol we retrieved 309,245 sequences. We then applied a stringent criterion to preserve homology relationships between sequences  [37] , imposing a minimal alignment coverage of > = 80 % between any pair of sequences with > 30 % identity, eventually retaining 131,162 environmental sequences.
All these environmental homologs were then compared against the whole NCBI nr database (July 2013) to look for their closest published relatives (CPR), not only in our networks, but also among all the publicly available sequences from cultured organisms and viruses, defining for each environmental sequence an identity to its CPR (Fig. 1a ). 22. 5 % of these environmental sequences had > =95 % identity to their CPRs. However, 44. 3 % of the environmental sequences were more divergent, presenting only 60 %-95 % identity to known sequences. Finally, one third of the environmental homologs (33. 2 %) showed even less similarity to CPRs than this threshold, thus being more distant from their cultured homologs than homolog pairs from different prokaryotic domains were (on average) for these gene families.
In particular, there is still a substantial number of highly divergent microbial genes in the gut. However, other environments harbor proportionally more of these divergent genes than the gut (Fig. 1a ). Indeed, while non gut environmental sequences represent only 17 % of the ). The median of the distribution of the distance to a CPR lies around 40 % for homologs from other environments than the gut while it lies around 72 % for homologs from gut samples. This difference is likely due to the Human Microbiome Project, which has produced relatively many highly divergent sequences from gut samples. Strong sequencing efforts focusing on non-gut environments would help identify more hard-tofind, highly divergent environmental homologs. This contrasted observation a posteriori justifies the choice of gut samples as a major part of our dataset, and makes it obvious that massive metagenomic analyses are a relevant way to reveal more as yet unknown genetic diversity.
By contrast, sequences from other environmental samples are especially numerous in class 2 homologs. In particular, in this dataset, marine (Botany Bay), cold (Antarctica Aquatic) and hot (Hot Aloa) water environments proved very rich in these most divergent environmental homologs. This difference in relative network positions demonstrates the impact of the strong sequencing effort targeting the human gut microbiome that revealed some (but by no means all) microbial diversity in the environment. Moreover, the structure of our graphs (Figs. 2  and 3 ) also allowed us to distinguish groups of divergent environmental homologs, whose identity to CPRs was lower than 60 %, defining 562 tight clusters (network cliques with exclusively highly divergent environmental content) in the graphs of 69 gene families under study, including ribosomal proteins.
These expectedly short alignments were used to reconstruct 289 maximum likelihood unrooted trees for 65 gene families. Some of these divergent environmental homologs Sequences from each of these cliques were aligned, and then their average amino acid identity and dN/dS were computed, and shown in abscissa. Proportion of corresponding cliques is given in ordinate branch within the archaeal and bacterial clans for some genes, and some also branch outside the archaeal and bacterial clans  [41]  (Fig. 5 ). Such a phylogenetic position is compatible with major deep branching divisions. Overall, this analysis further confirms the large phylogenetic depth within groups of environmental homologs detected in the network, and their significant phylogenetic distance to sequences present in cultured lineages in our dataset.
Of course, the detection of orthologs in larger datasets using networks (currently something we have not achieved) could in principles also greatly help to this endeavour. The potential discovery of novel groups would be of major importance in microbiology and evolutionary biology. Just as the discovery of Archaea spawned an entire research field  [45] , one can imagine that significant progresses may also result from the discovery of major lineages and remarkable variants of important gene families within the microbial dark matter. As a follow up of this proof of concept analysis, we encourage larger comparative studies, with expanded datasets, including more sequences from cultured organisms and from more environments.
Constitution of the datasets The initial dataset comprised 564,448 protein sequences from 116 prokaryotic genomes (54 Archaea and 70 Bacteria, sampled in order to cover prokaryotic diversity as in  [33] ), 7 eukaryotic genomes (Oryza sativa, Paramecium tetraurelia, Saccharomyces cerevisiae, Entamoeba histolytica and the nucleomorphs of Guillardia theta, Bigelowiella natans and Hemiselmis andersenii) and various MGE (plasmids and viruses). In addition to this, we used MetaGeneAnnotator  [46]  to predict 9,456,237 protein sequences longer than 50 amino acids, from 31,779,190 contigs/reads, issued from 236 samples of microbial metagenomics (excluding projects of viral and plasmid metagenomics, see Additional file 1: Table  S1  for a detailed list).
Selection of gene families with a strong ancient genealogical signal We reconstructed the gene network of the initial dataset, defining significant similarity for sequences with a BLAST e-value < 1e-5 and >30 % identity, inferred as the BLAST identity multiplied by hit coverage for the shortest sequence, as in  [47]. We computed the conductance  [48]  of each Domain in each connected component of this network, estimated by the ratio of external edges (from nodes within the Domain to nodes outside the Domain) over internal edges (between nodes within the Domain only). We found 86 connected components with a conductance lower than 0. 4 for two Domains, i. e. in which sequences were clearly structured in at least 2 conserved groups corresponding to 2 Domains of Life between which there was no or little evidence of LGT in the graph.
Rounds of BLAST searches to gather environmental homologs We BLASTed the 10,673 nuclei sequences against the 9,456,237 predicted environmental ORFs (TBLASTN, e-value threshold = 1e-5, 500 hits). Then, the 232,660 retrieved environmental ORFs were used as seeds for a second round of BLASTN (to avoid possible detection problems caused by frameshifts, e-value threshold = 1e-5, 500 hits) against the predicted environmental ORFs to look for their own homologs in the environment. This protocol yielded a dataset with a total of 309,245 sequences (298,578 environmental ones) to reconstruct a sequence network with nuclei and environmental sequences.
Analyses of the exploratory similarity networks All the environmental sequences from each of the 82 connected components were BLASTed against the July 2013 release of Genbank nr database (BLASTP, e-value threshold 1e-5) to quantify the identity percentage between each of these environmental ORFs and its closest sequence in nr, called closest published relative (CPR). Thus, comparison was made against all eukaryotic and prokaryotic sequences deposited at this date. CPR were computed relatively to the shortest sequence, as BLAST identity multiplied by hit coverage for the shortest sequence. Moreover, the topological distance of each of these environmental ORFs to a reference genome from the three Domains of life was estimated by the number of edges separating them in the graph. Class1 sequences were identified as environmental ORFs directly connected to a nucleus sequence.
Extraction and analysis of highly divergent closely related environmental sequences Maximal cliques (totally connected subnetworks) of environmental sequences whose identity to a CPR was lower than 60 % were identified in the previous 82 components using MACE  [49] , yielding 562 subsets of closely related environmental sequences. Each of these subsets was aligned with MUSCLE v3. 8  [50] , then tested for potential recombination with RDP3  [39]. Corresponding nucleotide alignments were also analyzed with yn00 software from the PAML v4. 7 package  [51, 52]. For each clique, average dN/dS ratio was computed as the average of pairwise omega parameters. MUSCLE alignments were used to compute average residue identity, as the mean of pairwise identity percentages for each clique. All scripts used to measure conductances and network distances, and to extract largest maximal cliques in our networks are available from PL upon request.
As a follow up of this proof of concept 441 analysis, it would next be interesting to perform larger comparative studies, with 442 expanded datasets, including more sequences from cultured organisms and from more 443 environments, this new dataset might identify other ancient, divergent, rarely 444 transferred between domains gene families. Sorry, is that essential and what is it supposed to mean? theories on the origins of eukaryotes [41] -Woese did not revolutionize theories for eukaryote origin, he (co-)discovered archae(bacteri)a and introduced the concept of microbial phylogeny. Author's response: typos have been corrected and the manuscript has been edited in accordance with the reviewer's points. Reviewer's report 3 (James McInerney, University of Manchester, U. K. ) Summary : This is an interesting paper that demonstrates quite clearly that environmental sequence space is not well known and that future work should aim to explore this.
Acknowledgements This work was supported by the  European Research Council   FP7  [ 2007-2013 Grant Agreement n°615274 ] to E. B, and S. H. was supported by a  Genome Canada/Genome Quebec  research grant (Genorem) awarded to  M. St-Arnaud  and  M. Hijri. We thank  R. M. Burian ,  D. Bhattacharya  and  J. W. Leigh  for critical reading.
Nitrogen indicators The typology presented in Figure  3  results from the analysis of a database of 1464 environmental indicators issued from 112 methods or reviews on indicators. The database was created by  Rosnoblet et al. (2006)  and extended and analyzed by  Schneller et al. (2013b). The whole variability presented in Figure  3  ranges from causal indicators based on management data, like the manure storage capacity, to measured effect indicators of water quality with in-between effect predictive indicators based on operational models. The category "nitrogen balance" is the most popular with more than 30 proposals. Those could be differentiated between farm-gate, soil surface and soil system budgets  (Oenema et al. , 2003). Although nitrogen balance is recognized as the most commonly used indicator to assess nitrogen management  (Langeveld et al.
MASC and DEXiPM have been developed to be implemented in research work to evaluate ex ante innovative cropping systems during the design phase and to help to select and improve most performant systems for experimentation. MASC is based on quantitative predictive effect indicators (see Fig. 2 ) and can also be used to evaluate actual cropping systems (ex post evaluation). DEXiPM is based on qualitative causal indicators which are aggregated to make qualitative predictive effect indicators. This makes the evaluation work much faster than in MASC in spite of higher number of basic indicators. For some methods like IDEA, aggregation is based on a sum of scores that is questioned by many authors  (Bockstaller et al. , 2008). MASC and DEXiPM decision trees are implemented thanks to the DEXi software  (Bohanec et al.
3 ),  Mertens and Huwe (2002)  handled uncertainty of data by implementing an approach based on fuzzy logic so that no unique value but an interval is given to the user. Many indicators are available at field and farm level as it comes out from the overview given in this article. However, for example, water quality indicators should be used at the scale of the water catchment or for a landscape. Emissions can be assessed at lower scale of cropping and farming systems. For indicator assessing emissions, results can be upscaled by aggregation of results obtained by calculation of an average value at higher scale weighted by the size or the number of entity at lower scale. Such aggregation at higher scale like a nation is not relevant for local impact, e. g. water quality, erosion, whereas it is possible for global impact, such as greenhouse gases.
2). D102, page 8 of 12 C. Bockstaller et al. : OCL 2015, 22  (1) D102  The relevance of composite aggregation (i. e. of indicators addressing totally different issues) is often discussed because of the loss of information but also due to the methodological problems it raises. A major problem is "adding apple and pear" in the case of composite indicators which can appear in scoring method  (Rigby et al. , 2001). Several method are available to avoid this problem like the normalization technique in monetary unit or physic unit, the multivariate approach, or decision trees based -or not -on fuzzy logic  (Bockstaller et al. , 2008). The DEXi software tool  (Bohanec et al. , 2008)  makes the design of decision tree (without fuzzy logic) quite easy but remains totally qualitative. For complex trees, special attention should be therefore put on the sensitivity of the aggregated indicator to the variability of basic ones  (Carpani et al.
Acknowledgements. The authors are grateful to the  GIS Grande Culture à Hautes performances Économiques et Environnementales  ( GIS GC-HP2E ,  French group of scientific interest  for arable crops), for its support of the work of  Chloé Schneller  and  Pauline Feschet. The authors want to thank  Chloé Schneller  for her work on the indicator database, and  Juliette Lairez  ( GIS Elevages Demain ) for her contribution to the reflection.
Its structure for tackling leading-order (LO) computations has indeed already proved to be very efficient at satisfying the needs of both the theoretical and experimental high energy physics communities. Generalizing this flexibility to the next-to-leading order (NLO) case is however not straightforward, essentially because of the necessity of specifying model-dependent counterterms, including those arising from the renormalization of the Lagrangian. Recent developments  [7]  in the FeynRules package  [8]  have allowed to overcome this main obstacle and paved the way to the full automation of NLO QCD predictions matched to parton showers for generic theories. We describe the details of this implementation by working through two specific cases and revisit some LHC phenomenology associated with stops and sgluons in the context of simplified models of new physics  [9, 10].
Employing state-of-the-art simulation techniques, we match matrix elements to the NLO in QCD to parton showers and present precision predictions for several kinematical observables after considering both the production and the decay of the new particles. In more detail, we make use of FeynRules to implement all possible couplings of the new fields to quarks and gluons and employ the NloCT program  [7]  to generate a UFO module  [11]  containing, in addition to tree-level model information, the ultraviolet and R 2 counterterms necessary whenever the loop integral numerators are computed in four dimensions, as in MadLoop  [12]  that uses the Ossola-Papadopoulos-Pittau (OPP) reduction formalism  [13]. This UFO library is then linked to the MadGraph5 aMC@NLO framework which is used, for the first time, for predictions in the context of new physics models featuring an extended colored sector.