dataesr/scientific-paragraphs-categorization huggingface dataset:
A. baumannii was over-represented in the metagenomic datasets from host-associated environments (chi-square test, P < 0. 0001), biofilms (P < 0. 001, same test) and soil (P < 0. 0001, same test). This fits previous observations using classical identification methods  (Houang et al. , 2001; Vangnai and Petchkroh, 2007; Hamouda et al. , 2011; Rafei et al. , 2015). In the oral samples of the HMP, 86% of the Acinetobacter peptides were from A. baumannii.
Methodological limitations and implications Other methods have tried to identify genus-specific sequences in metagenomics data. Methods such as PhymmBL, Kraken or Metaphlan, aim to assess the taxonomic distribution of metagenomic datasets by matching them against a subset of genomic markers  (Brady and Salzberg, 2009; Segata et al. , 2012; Wood and Salzberg, 2014). These methods can process large amounts of information with good accuracy for known taxa, because they look for highly similar hits against either a small set of universal markers (in the case of PhymmBL) or a subset of species-discriminant markers (in the case of Kraken and Metaphlan). However, these methods do not place the sequence in a phylogenetic scenario and do not provide precise information on the evolutionary distance between the environmental taxa and the references.
It is interesting to observe that many of the novel lineages of Acinetobacter had not been observed before, in spite of previous projects aiming at sequencing all known species in the genus  (Touchon et al. , 2014) , and recent efforts to identify novel species  (Krizova et al. , 2015; Maixnerova et al. , 2015; Nemec et al. , 2015; Sedo et al. , 2016). Importantly, these potentially novel lineages were generally found in metagenomic datasets from environments similar to those of their close relatives. Nevertheless, we observed some disagreements between previous literature and our results on the distribution of Acinetobacter species in environments. Some of these may result from the coarse-grained classification of metagenomic samples into environmental categories, which is essential to attain sufficient statistical power to test our hypotheses, but may have resulted in some over-simplifications.
However, a careful analysis of the most striking discrepancies suggests different reasons. For example, A. baumannii has often been described as present in human-associated environments and in biofilms in dry inert surfaces  (Peleg et al. , 2012; McConnell et al. , 2013). This makes it particularly well-adapted to clinical settings and may explain its success as a nosocomial pathogen. We consistently found A. baumannii in household surfaces and other environments classified as biofilms. However, this species was not systematically found in all biofilm-associated datasets, probably due to the characteristics of the sampled environments. We have also identified it in soil samples. While previous association between the A. baumannii and the soil has been reported using cultivation-based approaches  (Houang et al.
, 2001; Hrenovic et al. , 2014) , the difficulty in reproducing these studies by others has led to suggestions that they resulted from methodological artifacts  (Peleg et al. , 2008). In our work, most A. baumannii were identified in the samples from humans and soil. Different metagenomic datasets consistently showed small abundances of A. baumannii in soil samples of different geographic locations, such as USA, UK or France  (Delmont et al. , 2012; Fierer et al. , 2013). These discrepancies between cultivation-based methods and metagenomics are not new. For instance, Benitez-Paez and colleagues described a completely different bacterial composition in oral samples when they compared classical isolation methods to a metagenomic approach  (Benı ´tez-P aez et al. , 2013). These results highlight the interest of complementing classical isolation methods  (Browne et al.
The diversification of the genus and the emergence of nosocomial lineages We consistently found Acinetobacter in four major types of environments. Several taxa were frequently found in more than one environment, and sporadically in others, suggesting they have the ability to colonize multiple environments transiently or permanently. This may have facilitated their environmental diversification. Closely related taxa tend to inhabit closely related environments. This not only applies to the lineages present in the original dataset used to build the phylogenetic tree, but also the new lineages predicted by EPA. However, there were exceptions to this trend, including some sharp transitions that led to the rapid diversification of clades I and II. In fact, branches close to the origin of clades I and II are among those showing larger differences in their environmental distribution, compared to their close relatives.
Genome data We analysed the 133 complete genomes of 29 validly named species and 8 genomic species of Acinetobacter analysed in  (Touchon et al. , 2014)  (Supporting Information Table  S1 ). The places of isolation of the bacteria were retrieved from the same reference or from the literature. We also analysed the 2644 complete genomes available in GenBank RefSeq (Supporting Information Table  S2 , last accessed November 2013). At the end of the study, we retrieved from RefSeq 29 novel genomes of Acinetobacter (not identified as A. baumannii), that were published after the work of  (Touchon et al. , 2014)  (last accessed October 2015). We only used genomes that mentioned the isolation site of the strain (Supporting Information Table  S3 ). The 16 genomes that lacked annotation were annotated using prodigal v.
Identification of protein families over-represented in clades I to III We used hmmsearch from HMMer v. 3. 1. 2 (Eddy, 2011)  to search for the best hit (e-value < 10 25 ) of each protein profile in the eggNOG database v. 4. 0  (Powell et al. , 2013). Protein profiles were annotated using the functional information of the best eggNOG hit. Of them, 40% of the proteins were not assigned to any eggnog category and were discarded from further analyses. We then compared the abundances of the different eggNOG categories in each clade relative to the whole genus' pan-genome. The over-representation of protein families was assessed statistically using the Pearson Chi square test with Benjamini-Hochberg correction for multiple tests  (Benjamini and Hochberg, 1995). Two pan-genome protein families were considered to be in a relation of gene order conservation when the respective genes co-localized (less than 5 CDS apart) in all the genomes where the two families were present.
Metagenomics data We analysed 2568 metagenomic datasets from 126 independent locations with relevant meta-data retrieved from MG-RAST  (Meyer et al. , 2008). These sets represent a broad diversity of host-associated and environmental ecosystems. They contain 6 3 10 11 metagenomic fragments (6 Terabytes of data). We only retrieved the datasets with multiple samples (to be able to assess the diversity within a sequencing project). We ignored datasets obtained by procedures involving amplification, because they may generate genomic and metagenomic coverage biases, produce chimeric contaminant sequences and over or under-estimate the abundance of certain taxa  (D zunkov a et al. , 2014; Marine et al. , 2014). We grouped the datasets in 8 major environmental categories and 39 sub-categories (Supporting Information Table  S4 ).
We searched for ORFs in the metagenomic data using FragGe-neScan v. 1. 17 using the options to operate with fragmented data  (Rho et al. , 2010). We only kept fragments of size higher than 35 amino acids, based on the distribution of false positives and negatives observed during the methodological validation (see Simulated metagenomic fragments). Fragments were queried against the core-genome HMM profiles using hmmsearch. Significant hits were kept for the EPA analysis. For more details, see Supporting Information.
These peptides were thus considered to be outgroups and were removed from further analysis. The others were incorporated in the multiple alignments using the addfragments algorithm of MAFFT v. 7. 153b  (Katoh et al. , 2002). They were then placed on the phylogenetic tree using the EPA included in RAxML v. 8. 1. 2 (Berger et al. , 2011) , with the '-f v' option and the same evolutionary model used in the phylogenetic reconstruction. For more information about the validation of EPA, see Supporting Information. The pipeline scripts are available at https://gitlab. pasteur. fr/gem/Core-Genome-EPA. The number of assignations per metagenome sample was correlated with the size of the metagenomic dataset (Spearman's q 5 0. 68, P < 0. 0001). Hence, we divided the abundance of the hits from a specific dataset by the total number of peptides in the metagenomic dataset.
Community analysis of the Acinetobacter selected peptides We determined the distribution of the peptides placed by the EPA for each environment in each phylogenetic branch of the reference tree of the Acinetobacter genus. We then assessed similarities and differences between these environmental distributions across the branches of the tree. For this, we computed Bray-Curtis (BC) and KL dissimilarity matrices using the vegan R-package and the functions defined in Faust and colleagues  (Oksanen et al. , 2008; Faust et al. , 2012). These two measurements are often used to quantify the differences between datasets, either by comparing the compositional dissimilarity between sites (BC, in this case environmental composition between branches) or by comparing the probability distributions derived from the observed frequencies in each dataset (KL)  (Gorelick and Bertram, 2010).
Various receptor methodologies have been developed in the last decades to investigate the geographical origins of atmospheric pollution, based either on wind data or on backtrajectory analyses. To date, only few software packages exist to make use of one or the other approach. We present here ZeFir, an Igorbased package specifically designed to achieve a comprehensive geographical origin analysis using a single statistical tool. ZeFir puts the emphasis on a user-friendly experience in order to facilitate and speed up working time. Key parameters can be easily controlled, and unique innovative features bring geographical origins work to another level.
Both approaches are complementary, but are rarely performed together, mainly because they require specific scripts or programs, when they exist and/or are available. Indeed, the widespread use of a given methodology is essentially driven by its potential to solve a given problem, and also by the user-friendliness of the tool. For example, one of the reasons why Positive Matrix Factorization (PMF) analyses are nowadays regularly performed for source apportionment studies are partly linked to the development of user-friendly interfaces, like US EPA PMF software  (Norris et al. , 2008)  or SourceFinder  (SoFi, Canonaco et al. , 2013). In the case of geographical origin works, several receptor-based methodologies have been developed in the past decades such as Non-parametric Wind Regression (NWR,  Henry et al.
, 2009) , Conditional Bivariate Probability Function (CBPF, Uria-Tellaetxe and Carslaw, 2014) for wind analyses; Potential Source Contribution Function (PSCF,  Polissar et al. , 2001) , or clusters for trajectory analyses. They all have their own pros and cons, and no single approach can be brought out as the one that should always be used. This emphasizes the need of tools providing the whole diversity of such methodologies; and because such tools serve science, it is critical to make them the most user friendly as possible. For instance, Openair  (Carslaw and Ropkins, 2012 ) is a powerful Rpackage that offers, among others, the possibility to perform trajectory and wind analysis; but the lack of Graphical User Interface (GUI) requires the user to dig into the functions and get accustomed to R coding.
Conversely, TrajStat  (Wang et al. , 2009)  is a standalone program that is Geographical Information System (GIS)-based, but is able to perform trajectory analysis only. There is therefore a gap to fill with a GUI solution to perform both, wind and trajectory analyses. This paper aims at describing a new Igor-based tool, named ZeFir, allowing to perform a comprehensive investigation of the geographical origins of air pollutants. The philosophy of ZeFir is to give users the possibility to interactively explore various methodologies for wind and trajectory analyses. NWR, Sustained Wind Incidence Method (SWIM), PSCF, Concentration-Weighted Trajectory (CWT) or Concentration Field (CF) can be easily performed from user-friendly panels, where some innovative features are implemented.
Background on Igor and ZeFir structure Igor Pro (http://www. wavemetrics. com) is a data-analysis software, combining point-and-click interactions and programming. Because Igor is a licensed and proprietary software package, the choice of distributing ZeFir under this environment could, at first sight, constitute a major limitation. This choice has been constrained by two main reasons: -First, the "hard science" behind ZeFir is easily programmable (i. e. not computationally intensive), and can therefore be implemented into an open-source software package, such as Openair. However, much more work would need to be done to build a GUI, which is essential to facilitate the use of such tool. Igor already benefits from a whole workspace to store, visualize, edit, and plot the data. Unlike some open-source software applications (e.
g. R or Python), graphs are not "frozen" objects, which the user can easily interact with. Each point-and-click operation is linked to a displayed line code, which greatly simplifies script coding, especially for complex graphs and GUIs. A built-in debugger eventually makes the code more robust. The "Igor procedure" format makes ZeFir portable to any computer with an up-to-date version of Igor Pro 6 installed. -Second, Igor is already well implemented within the atmospheric science community, notably through a growing interest in the aerosol mass spectrometer measurements (ACSM/AMS). Then, because i) the existing source apportionment userfriendly tools are unfortunately not open-source, but are compiled standalone programs -e. g. , EPA PMF toolkit -, or Igor procedures, such as SoFi or PET (PMF Evaluation Tool -  Ulbrich et al.
, 2009) , and ii) the geographical origin work is complementary to source apportionment, the only way in order to have an integrated solution avoiding untimely copy-and-paste (or export-import) between a program to another was constrained to develop an Igor based procedure. ZeFir structure is illustrated in Fig. 1. "Concentration" covers a large variety of data type: i) data already stored or produced within Igor experiments (e. g. aerosol mass spectrometer chemical composition, or outputs from SoFi or PET); or ii) external data which can be manually imported, such individual atmospheric chemical species, or outputs from other source apportionment software applications. These can be manually imported by copyand-paste operations or dedicated built-in panels permitting the import of various types of files (e.
Wind analysis Wind analysis in ZeFir is performed by NWR. Originally developed by  Henry et al. (2009) , NWR couples ambient concentrations with co-located measurements of wind direction and speed. This approach can be simplified as a weighing average of the data at each predictive (q, u) couple (respectively representing predictive wind direction and speed), where the weighing coefficients are determined through Gaussian-like functions; the global idea is to give weight to concentration values associated to wind direction and speed relatively close to (q, u). One methodological improvement of NWR, called SWIM (Sustained Wind Incidence Method) consists in taking wind direction and speed standard deviations into account instead of constant smoothing parameters  (Vedantham et al. , 2012). This allows to dynamically "downweight" data associated with high standard deviations.
Fig. 3  illustrates the localization, around Paris, of such facilities, and the receptor site. The three wind analyses results are all in accordance with this map, as most of incinerators are located in the North to East sector. More specifically, SWIM-2 highlights hotspots in the N-NNE and NE-to-E sectors at wind speeds of about 20 and 18 km/h, respectively. This contrasts with NWR and CBPF, which give rather homogeneous concentrations between N and E over a large range of wind speeds. The benefit of SWIM-2 in this example is that data associated with unstable wind conditions were effectively downweighted. But it is noteworthy that SWIM approaches should not i) be always best suited, since each dataset is different, and ii) always meet the user's needs.
, 1998). The use of different wind field files surely helps to downweight these uncertainties, but it is, in the case of ZeFir, the user's responsibility to calculate his own backtrajectories, as long as HYSPLIT is used. The retrieval of backtrajectories from different dispersion models may also be a good alternative, and future versions of ZeFir will allow the import of backtrajectory files from other models than HYSPLIT. Another indirect source of uncertainties is the temporal resolution of the input dataset. In the case of low time-resolution measurements (e. g. daily filter sampling), only one backtrajectory is associated to the concentration point, leading to poor temporal representativeness. A built-in interactive solution is available in ZeFir, and consists in enlarging the size of the input data in order to consider more trajectories, leading to more representative results.
Fig. 5  illustrates the utility of such an approach: particulate sodium ion concentrations have been determined from 24-h PM 10 high-volume sampling carried out every third day from April 2015 to March 2016 in Metz, France. Using the regular dataset, results indicate that sodium may have a rather continental origin with emission zones in the in-lands. This is in contradiction with the expected pattern, since, given the location of the sampling site, it should mainly originate from sea salt. However, when enlarging the dataset to take backtrajectory at þ3, þ6, þ9, þ12, þ15, þ18 and þ 21 h into account, the oceanic pattern is much more highlighted and thus in  accordance with what is expected. To date, no existing tool proposes this feature. A limitation of PSCF-based approaches is the need to settle a weighing function in order to downweight cells associated with low residence time, usually observable as "trailing effects".
Introduction and motivations The four-color theorem was the first major theorem proved with the assistance of a computer in 1976. In  [2]  the computer was merely checking thousands of cases via a dedicated program, thus completing an otherwise paper-and-pencil proof. In  [9]  the computer checked the whole formalized proof via a general-purpose and widely used software named Coq. Since then, other challenging (or just interesting) theorems have been likewise formalized and checked. In 2006 Coq was used by  [19] , which was generalized by  [13] , to formalize and check a result from game theory: Kuhn's existence of a Nash equilibrium (NE) in finite games in extensive form. Coq was also used to deal with some infinite games in extensive form  [15] , or with random Boolean games  [17]. Isabelle/HOL, another proof software, was used recently  [6]  to formalize and check a result in game theory for logic and computer science: the positional determinacy of parity games.
Corollary 11 Consider priority games with n vertices and m priorities. 1. Positional secure equilibria can be computed in O(n log(m)+7 ). 2. For fixed parameter m, there is an FPT-algorithm with runtime O(n 6 ) + h(m). PROOF In  [4]  the numbers are O(n log(m)+6 ) and O(n 5 ) + g(m). By the above complexity remark, they have to be multiplied by m, which cannot be greater than n since each vertex carries one priority. P 6 Coq formal setup for Theorem 9 We use the Coq proof assistant along with the SSReflect proof language and the MathComp library, 1  especially the following theories: fintype (finite types with decidable equality), finfun (functions over finite domains), finset (finite sets), and bigop (iterated operators). We also use the RelationClasses theory from Coq's standard library, to facilitate the reasoning on relations that are not in the scope of MathComp's framework.
Main definitions We chose to start the formalization by defining games and Nash equilibrium in the most general way. This general setting was not compulsory for mechanizing the proofs we focus on, but it allows one to get a wider game-theory library as a basis for further developments. First, we define strategies as follows, relying on the dependently-typed theory of Coq: Section Generic. Variables (Agt : Type) (Strat : Agt → Type). Definition strategy := ∀ a : Agt, Strat a. Agt and Strat represents the space (a term more appropriate than "set" in Coq) of agents and strategies, respectively, and strategy represents the (dependently-typed) space of strategy profiles (mapping each agent to its space of strategies). We then define game forms as follows: We then define what it means that a strategy profile is a Nash equilibrium (in the Coq code this notion is split into several definitions, but for the sake of readability we give below a syntactically equivalent definition, in one go): This means that each agent is stable (he or she has no incentive to change strategy assuming other agents keep their strategy).
g. , for an arbitrary number of players with arbitrary strategy spaces. We suspect that generalizing the Isabelle formalization to an arbitrary number of players would be notationally very heavy. Regarding the proof languages, we relied on an SSReflect proof style with a systematic use of forward-chaining in longer proofs, in order to put forth the structure of the Coq proofs. On the other hand, the Isabelle ISAR style allows for human-readable declarative proof code, but we hope to increase the degree of automation somewhat, not too much, to avoid distraction by too much detail. Also, our formalization work made us aware of the fact that we tend to write too terse paper-and-pencil proofs, and suggested some improvements in the paper-and-pencil formulation. Various generalizations of the result could be proven in both proof assistants, and some work remains to be done to benefit from the mutual insemination between theory (paper-and-pencil proofs) and practice (proof assistants) and between the two proof assistants.
I. 3. Contributions and outline From the previous section it is clear that an essential preprocessing step for solving a DDGP is to find a discretization order. We propose two formalizations of the discretization order problem (DOP) based on simple estimations of the quality of a discretization. The focus of the article is then on the exact solution of the problem with integer programming (IP). In this first study, we only solve the simpler version of the DOP. We develop two IP formulations and describe a cutting plane algorithm that achieves a significant speed-up of the solution time. The different approaches are tested numerically on a set of instances based on real protein conformations obtained from the Protein Data Bank (PDB)  [1]. Previous mathematical programming formulations were proposed in  [6]  where the problem is called Discretization Vertex Ordering Problem (DVOP), and in  [2]  where the adjacent predecessors should be contiguous, given raise to the Contiguous Trilateration Ordering problem (CTOP).