dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We acknowledge  Aur elie Chauveau  for preparation of the libraries,  Elodie Marquand  and  Aur elie Canaguier  for data processing, two anonymous referees and  Dennis Lavrov  for their corrections, and  David Marsh  for English corrections. This work was funded by the  Fondation de France  (funds from the  Fondation Ars Cuttoli & Paul Appell , project name :  EVOLUTION ET PHYSIOLOGIE DES PLANTES QUI SE NOURRISSENT DE CHAMPIGNONS (MYCOHETEROTROPHIE) ) and the National Science Center (Poland; grant no. 2015/18/A/NZ8/00149 ). We thank the  Commissariat a l' Energie Atomique et aux Energies Alternatives -Institut de G enomique/Centre National de G enotypage  for conducting the DNA quality control and for giving the INRA-EPGV group access to their  Illumina Sequencing Platform.
This project has been deposited at GenBank under the accession  MH590345-MH590357 ; at  NCBI BioProject  under the accession  PRJNA484137 ; at  NCBI SRA  under the accession SRP160956; at  TreeBASE  under the accession 24919; at the GitHub repository https://github. com/flallema/Neottieae_plastomes.
Trimethylation on H3K27 mediated by Polycomb Repressive Complex 2 (PRC2) is required to control gene repression programs involved in development, regulation of tissue homeostasis or maintenance and lineage specification of stem cells. In Drosophila, the PRC2 catalytic subunit is the single protein E(z), while in mammals this function is fulfilled by two proteins, Ezh1 and Ezh2. Based on database searches, we propose that Ezh1 arose from an Ezh2 gene duplication that has occurred in the common ancestor to elasmobranchs and bony vertebrates. Expression studies in zebrafish using in situ hybridization and RT-PCR followed by the sequencing of the amplicon revealed that ezh1 mRNAs are maternally deposited. Then, ezh1 transcripts are ubiquitously distributed in the entire embryo at 24 hpf and become more restricted to anterior part of the embryo at later developmental stages.
The difference in the lethality timing between zygotic and maternal-zygotic ezh2 mutants outlines the key role played by ezh2 maternal products in zebrafish development. However, the fact that MZezh2 hu5670/hu5670 mutants develop a normal body plan raises the possibility that ezh1 could contribute to early embryogenesis in absence of ezh2 function. Using searches in vertebrate genomic and transcriptomic databases, we first show here that ezh1 arises from the duplication of the ezh2 gene in the common ancestor to elasmobranchs and bony vertebrates. On the other hand, we conducted a study of ezh1 expression and function during zebrafish development. Using the TALEN technology, we generated an ezh1 loss-of-function zebrafish line. This line is viable and fertile indicating that Ezh1 is dispensable in zebrafish.
S1 ). These substitutions, A544T, A569T, I631T, I650L, C668S and M705V are located in different regions of the SET domain (Fig. 1B, C ) and will be used to distinguish Ezh1 from Ezh2 in Blast searches in databases. Furthermore, we identify two additional amino-acid substitutions specific to zebrafish and medaka Ezh1 SET domains after α4, the changes K661R and V662I (Fig. 1B ,C and Supplementary Fig. S1 ). To identify Ezh1 from teleost genomes, the sequences of the SET domains from human and zebrafish Ezh1 proteins were used in independent TBLASTN searches against genome databases (NCBI, https://blast. ncbi. nlm. nih. gov/Blast. cgi; Ensembl, http://www. ensembl. org/Multi/Tools/Blast?db = core; EFish Genomics, https:// efishgenomics. integrativebiology. msu. edu/blast_search/) for 25 teleost species (Supplementary Table  S1 ).
1, potentially contains the last 5 exons of a gene coding for an Ezh1 SET domain (Supplementary Fig. S3 ). It is then likely that LOC109924829 and LOC109922287 correspond to two parts of the whale shark ezh1 gene. Alignment of the whale shark Ezh1 SET domain with other Ezh1 and Ezh2 SET domains reveals that the Ezh1 SET domain from the whale shark does not contain the K661R and V662I substitutions specific to the Ezh1 SET domains of the ray-finned fish. However, it possesses the Ezh1-specific substitutions I631T, C668S and M705V, but not I650L (Supplementary Fig. S3  and 1D ). The little skate (Leucoraja erinacea) is the second elasmobranch for which genomic and transcriptomic sequences are available in databases  41, 42. Using human and zebrafish Ezh1 sequences in a TBLASN search in the Skatebase (http://skatebase.
org), we identified a partial transcript from the little skate (LS-transcriptB2-ctg80080) that codes for a SET domain highly related to Ezh1. As for the whale shark, the Ezh1 SET domain of the little skate possesses the Ezh1-specific substitutions C668S and M705V, but not I650L nor the ray-finned fish-specific K661R and V662I changes (Supplementary Fig. S3 ). In contrast, we failed to detect ezh1 in a jawless vertebrate, the sea lamprey (Petromyzon marinus) while the ezh2 gene could be found (ENSPMAG00000004401), suggesting that ezh2 is more ancient and that ezh1 might have arisen from the duplication of ezh2. The elephant shark (Callorhinchus milii) is the only holocephalan having its genome sequenced  43. Using TBLASTN searches, we also failed to identify an ezh1 gene in the elephant shark genome while ezh2 could be found (NCBI Gene ID: 103187005).
Methods Database searches and analyses. Using the sequences of human and zebrafish Ezh1 and Ezh2 SET domains as search queries, TBLASTN analyses were performed using the NCBI (https://blast. ncbi. nlm. nih. gov/ Blast. cgi), the Ensembl (http://www. ensembl. org/Multi/Tools/Blast?db = core), the EFish Genomics (https:// efishgenomics. integrativebiology. msu. edu/blast_search/) or the Skatebase (http://skatebase. org/skateblast-skate-base%E2%80%8B/) Genome Servers. Amino acid sequences of protein domains of various species were obtained from SMART database (http://smart. embl-heidelberg. de/). Multiple sequence alignments were performed using Clustal Omega (https://www. ebi. ac. uk/Tools/msa/clustalo/) and 3D-structure analysis of EZH2 SET obtained from the Protein Data Bank (https://www. rcsb.
Acknowledgements We are grateful to  Shaghayegh Hasanpour  for helpful discussions and her drawings of fish in Fig. 2. This work was supported by the  Inserm , the  University of Lille  and the  GIP Cancéropôle Nord-Ouest. BD was supported by a fellowship from the  Région Hauts-de-France ,  Nord-Pas de Calais, Picardie  and the  University of Lille.
(2007)  with today data using up-to-date databases. However, Takemoto et al. (  2007 ) used only a fraction of all available enzymes from the genome and taking into account all the enzymes we fail to reproduce the main results. Therefore, we introduce three robust measures on directed representations of graphs, which lead to similar results regardless of the method of network reconstruction. We show that the size of the largest strongly connected component, the flow hierarchy and the Laplacian spectrum are strongly correlated to the environmental conditions. Conclusions: We found a significant negative correlation between the size of the largest strongly connected component (a cycle) and the optimal growth temperature of the considered prokaryotes. This relationship holds true for the spectrum, high temperature being associated with lower eigenvalues.
Background All living organisms rely on chemical reactions to exist, and the set of these life-sustaining chemical transformations is defined as metabolism. Because these reactions are mostly catalysed-accelerated-by enzymes, the transformation of organic molecules (substrates) into other chemicals (products) can directly be mapped by the enzyme set. *Correspondence: adela@nicoweb. com Sorbonne University, INSERM, NutriOmics F75013, France. , 91, blvd. de l'Hôpital, 75013 Paris, France The development of metabolic databases such as KEGG  [1]  linking enzymes to their reaction pairsubstrates/products-allows us to explore the structure of metabolism in general, and to investigate the structures of the metabolic graphs of particular organisms  [2]. Flow is an inherent concept of metabolic reactions, going from substrates to products which then become substrates for other reactions.
Confirmation of the state-of-the art results of Takemoto et al. (2007) In the study conducted by  [11] , undirected substrate graphs were constructed using KEGG metabolic pathways for 113 prokaryotes from four different growth temperature classes (hyperthermophiles, thermophiles, mesophiles, and psychrophiles). They considered several properties of undirected graphs, and analysed the correlation between the graph properties and the optimal growth temperatures of the organisms. We implemented and tested three measures from their study: edge density, maximum likelihood estimate of degree exponent, and average clustering coefficient. Since more than ten years have gone by, the databases have evolved significantly. We focused on directed graphs, and we reconstructed the metabolic networks without using pathways directly (see Methods).
Discussion We have observed that prokaryotic metabolic network properties can correlate with environmental phenotypes, namely with the optimal growth temperature in our study. First of all, we confirmed the results of  [11]  that dates more than ten years back: a negative correlation between edge density and optimal growth temperature and between average clustering coefficient and optimal growth temperature, and a positive correlation between maximum likelihood estimate of degree exponent and optimal growth temperature. This clearly shows the robustness of the data from the KEGG database, even though some data has been modified, and some new data has been added. On another hand, the results of our experiments also illustrate the validity of the results of  [11]. Although the amount of species in our experiments is doubled compared to the number of prokaryotic species in  [11] , the trends are still the same.
These results hold for a particular subset of known metabolic data of the species. We consider important to take all available data (enzymes) into account. However, in the case where all enzymes associated to the species in KEGG are taken into account, these results do not hold anymore: they change sign of the correlation. The origins of these inversions are still unclear, but we believe that it could be due to biases in KEGG for non-mesophilic species (especially for thermophilic species). We believe a directed network representation is more appropriate to model metabolism, so we looked for directed topological properties that were robust for the different reconstruction protocols. We tested directed graph structural properties related to cycles (largest strongly connected component and flow hierarchy) and to connectivity and flow (Laplacian spectrum).
Other directed graph topological properties were tested, with some having significant correlations with optimal growth temperature for both reconstructions, such as the fraction of nodes with an in-degree of 0 (startingpoint nodes, input metabolites) or the fraction with an out-degree of 0 (end-point nodes, output metabolites), having both positive correlations with temperature (data not shown), or also the number of some of the triads among the 16 possible triads in a directed network also show significant correlations, positive and negative (data not shown). All of this shows the clear link between the immediate environment and the metabolism of a given species, and can be looked into in different contexts and environments. To integrate directed graphs and bring it a step further, an interesting future research avenue would be to study the differences, or complementarities between community graphs and single organism graphs, as well as differences in their largest strongly connected components and other directed structural properties.
An important point has to however be made on the direction of chemical reactions. In this work, we fixed the directions of reactions as found in the KEGG database. However, there might be some chemical reactions happening in the opposite direction than the one fixed in KEGG. We believe that it may be interesting to infer directionality of reactions, since homeostasis is extremely important for organisms, and it is regulated, e. g. , by enzymes. There is a need to study this problem, for example through thermodynamics, and it would be promising to study the flux of our graphs, in particular from observational data to investigate the dynamics of the biomass.
Conclusions We have reproduced the results of  [11] , and we state that the results mostly hold even with the evolution of the KEGG database, and even while significantly increasing the number of species in the data set. We have found a positive correlation between the degree exponent estimate and optimal growth temperature, and a negative correlation between the edge density and the temperature and between average clustering coefficient and the temperature. We have noticed that when we include all KEGG enzymes we could find for a species into metabolic networks, and not only the enzymes from KEGG pathways, the results do not hold anymore. We propose three directed graph measures, namely, the size of the largest strongly connected component, the flow hierarchy, and the Laplacian spectrum. We have shown that these measures are robust for all considered graphs, and they correlate respectively negatively, positively and negatively to the optimal growth temperature.
Prokaryotic species Our data set contains 228 prokaryotic species where 100 species are from the databased used by  [11]. We decided to increase the number of species in our experiments, since the number of bacteria in three growth temperature classes was too small (1 psychrophile, 9 hyperthermophiles, and 9 thermophiles). We added 52 species from the Bacterial Diversity metadatabase (BacDive)  [20] , chosen according to their growth temperature class. We also added 76 mesophilic species from the Human Pan-Microbe Communities (HPMC) database  [21]. We obtained the following distribution over four growth temperature classes (from hot to cold): 19 hyperthermophiles (HT), 35 thermophiles (T), 158 mesophiles (M), and 16 psychrophiles (P). Mesophiles are the most well-studied species, thus biasing databases towards these species and explaining the temperature distribution of our species.
Metabolic network reconstruction There exist a number of ways to produce metabolic networks from chemical reactions. The nodes of such a metabolic reconstruction can be metabolites (small molecules, substrates, and products of the enzymes), or enzymes. We have built directed and undirected substrate graphs which are metabolite-centered graphs where each substrate is a node and is linked to each product of a metabolic reaction for a given species. Therefore, edges are enzymatic reactions linking substrates to products. Figure  5  sketches a metabolic reaction, a directed graph, and an undirected metabolic network. To build the metabolic graphs, we downloaded Ensembl or GenBank cDNA FASTA files for the 228 species. Figure  6  shows the reconstruction procedure. We retrieved gene labels from the FASTA files, see Fig.
6a. We then consulted the Kyoto Encyclopedia of Genes and Genomes (KEGG,  [22] ) database. With the KEGG code for a species and the gene labels, we found the species gene entries, and we extracted all enzyme commission codes (ECs) if the codes were found in complete form, i. e. , no hyphen was present in the code. This step is shown on Fig. 6b. We then extracted all substrates and products from the KEGG enzyme entries (Fig. 6c ), and we built directed and undirected substrate graphs, which is illustrated by Fig. 6d  and e. We excluded 13 species out of the 113 species from  [11]  because we could not find gene names in the cDNA FASTA files, or the gene names did not match to the KEGG species code, or the species entry (and code) in KEGG simply did not exist anymore. A common practice in metabolic network reconstruction is to exclude ubiquitous metabolites to make the network more relevant biologically, and because of the great impact on network structure.
Indeed, our main objective is to assess bacterial metabolic systems without any a priori knowledge, and therefore keep as much information as possible, which is why we keep all enzymes that can be deduced from the genome. This means that we have kept most inorganic compounds and generic reactions. We therefore may have less metabolic information regarding some nodes when considering generic reactions, but also more information as more data from the database is considered and as all substrates and products are included (pathways sometimes only show the main reactants and not all of them). For the directed reconstructed graphs, the default direction of the KEGG reaction was used, which is the direction of the catalytic reaction (substrates and products are specified). It is the direction in which the flow of biomass is expected.
Optimal growth temperatures For the species also considered by  [11] , we got the optimal growth temperatures from the supplementary material provided with their article. The data originally came from the Prokaryotic Growth Temperature Database (PGTdb)  [24]. The access to the PGTdb was not available since we started performing our experiments and later, so, the optimal growth temperature and the growth temperature classes for the rest of the species were taken from the BacDive database  [20]. For the species whose optimal growth temperature was given as an interval in the Bac-Dive database, we used the average value of the interval. For the species from the Human Pan-Microbe Communities (HPMC) database, the optimal growth temperature was fixed to 37°C.
Background: The recommendations of learned societies mention risk factors for the presence of multidrug resistant bacteria in hospital-acquired infections, but they do not propose a scoring system to guide empiric antibiotic therapy. Our study was aimed at developing a simple score for predicting "the presence of bacteria requiring carbapenem treatment" in ICU-acquired bloodstream infection and pneumonia. Methods: Between December 2011 and January 2015, we conducted a retrospective study using a prospectively collected French database of nosocomial infections in the polyvalent intensive care unit of a French university hospital. All patients with ICU-acquired bloodstream infection or pneumonia were included in the study. Bivariate and multivariate analyses were performed to develop the CarbaSCORE, and this score was internally validated.
Study population Between December 2011 and January 2015, we conducted a retrospective study using a prospectively collected French database of nosocomial infections in the 23-bed medical/surgical adult ICU of a French university hospital (Centre Hospitalo-Universitaire Félix Guyon, Saint-Denis). The study was approved by the Ethics Committee of the Centre Hospitalo-Universitaire de La Réunion (reference R15016). We included all patients with ICU-acquired bloodstream infection or pneumonia that occurred more than 2 days after admission and that were reported to the national network for the surveillance of ICU-acquired infections (REA-raisin network). Excluded from the analysis were patients under 18 years of age, patients with non-bacterial or undocumented infection, and patients whose incomplete records did not allow for analysis.
Context Graph-based Semi-Supervised Learning (G-SSL) is a modern important tool for classification. While Unsupervised Learning fully relies on the data structure and Supervised Learning demands extensive labeled examples, G-SSL combines limited tagged examples and the data structure to provide satisfactory results. This makes the field of G-SSL of utmost importance as nowadays large and structured datasets can be readily accessed in comparison to expert data which may be hard to obtain. Examples where G-SSL provide state of the art results are vast, ranging from classification of BitTorrent contents and users  [1] , text categorization  [2] , medical diagnosis  [3] , or zombie hunting under BGP protocol  [4]. Algorithmically, PageRank constitutes the reference tool in G-SSL. It has spurred a deluge of theory  [5, 6, 7, 8] , applications  [9, 10, 1, 4]  and implementations  [11, 12].
An algorithm for the estimation of the optimal γ Numerical experiments show that increasing γ can further decrease the generalized Cheeger ratio up to a point where it starts increasing. We show an example of this phenomenon in Figure  2a , displaying the evolution of h (γ) Sgt as a function of γ when S gt corresponds to a digit of the MNIST dataset. From the figure, it is evident that an optimal value appears, denoted γ * = arg min γ h (γ) Sgt , raising the question of how to find such value. Since the behavior of h (γ) Sgt depends on S gt , in practice, the derivative or a greedy search to find γ * cannot be employed since S gt is unknown. A second question that arises is whether the optimal value changes drastically or smoothly with changes in S gt. We perform the following test: for a given S gt (same MNIST digit), we remove some percentage of the nodes in S gt and record the optimal value on subsets of S gt.
Real world datasets Experimental setup and goals. In our following experiment, we assesses the performance of L γ -PageRank and Algorithm 1 on real world datasets. The experimental setup is as follows: graphs are build connecting the K-Nearest Neighbors (KNN) with distances computed via the Gaussian kernel, so that the weight between points x u and x v is given by W uv = exp{-x u -x v 2 2 σ 2 }. For each class, 2% of labeled points are randomly selected, L γ -PageRank is applied for a grid of µ values, partitions are retrieved via the sweep-cut, and the best performance, assessed in terms of MCC, is retained. Such procedure is repeated for 100 realization of labeled points, except for the MNIST on which 30 realizations only are employed. In all cases, classes are balanced in size and the graph construction parameters are selected to provide a good distribution of weights as follows: (a) MNIST  [26] : Images of handwritten digits (1 to 9).
From the entire dataset, 200 images of each digit are selected and used to build the graph with KNN = 10 and σ = 10 4 ; (b) Gender Images  [27] : Images of male and female subjects for gender recognition. From the entire dataset, 200 images of each gender are selected and used to build the graph with KNN = 60 and σ = 10 4. The large value of KNN is to avoid disconnected components; (c) BBC articles  [28] : Word frequency attributes from news media articles. From the entire dataset, 200 business and 200 entertainment articles are used to build the graph with KNN = 5 and σ = 50; and (d) Phoneme  [29] : Five attributes to discern nasal sounds from oral sounds. From the entire dataset, 200 oral and 200 nasal sounds are used to build the graph with KNN = 10 and σ = 2. Results and discussion. Table  2  shows the performance of L γ -PageRank on the classification of these real world datasets.
Clearly, the introduction of γ can significantly improve performance and, in general, the estimation γ performs close to the optimal value γ *. It can be seen that some datasets are more sensitive to γ than others. For instance, in the BBC articles we observe that a small change in γ, going from γ = 1 to γ * = 1. 1, increases performance, and going further to γ = 1. 3 and γ = 2 significantly worsens the classification. On the other hand, the MNIST dataset is less sensitive to γ, obtaining similar performances with larger variations in γ. It is important to stress that, thus far, we have assumed possession of the proper tuning of the diffusion rate (µ) that attains the best results. However, when working with real data, clusters may have intricate local structures, e. g. sub-clusters, that play an important role in the way information diffuses, and that can make more difficult the finding of the optimal diffusion rate µ.