dataesr/scientific-paragraphs-categorization huggingface dataset:
Main steps of the M2M pipeline and associated tools. The software's main pipeline (m2m workflow) takes as inputs a collection of annotated genomes that can be reference genomes or metagenomics-assembled genomes. The first step of M2M consists in reconstructing metabolic networks with Pathway Tools (step 0). This first step can be bypassed and GSMNs can be directly loaded in M2M. The resulting metabolic networks are analysed to identify individual (step 1) and collective (step 2) metabolic capabilities. The added-value of cooperation is calculated (step 3) and used as a metabolic objective to compute a minimal community and key species (step 4). Optionally, one can customise the metabolic targets for community reduction. The pipeline without GSMN reconstruction can be called with m2m metacom, and each step can also be called independently (m2m iscope, m2m cscope, m2m addedvalue, m2m mincom).
b. Description of key species. Community reduction performed at step 4 can lead to multiple equivalent communities. M2M provides one minimal community and efficiently computes the full set of species that occur in all minimal communities, without the need for a full enumeration, thanks to solving heuristics. It is possible to distinguish the species occurring in every minimal community (essential symbionts), from those occurring in some (alternative symbionts). Altogether, these two groups form the key species. A proportion of genes were randomly removed from all or a random subset of the 913 rumen MAGs: 2% from all genomes (2pc100), 5% from 80% of the genomes (5pc80), 5% from all genomes (5pc100) and 10% from 70% of the genomes (10pc70). M2M pipeline was ran on these four datasets and comparison was made with respect to the initial non-altered dataset of MAGs (original).
Subfigures a. to e each represent one piece of information computed by M2M and compared between the five experiments. a. Set of producible compounds by all metabolic networks in a cooperative system (community scope); supervenn representation. Each dataset of metabolic networks obtained from the original or degraded genomes is represented horizontally, with a unique colour. The right panel of the supervenn diagram indicates the number of metabolites in the community scope of the corresponding dataset. Vertical overlaps between sets represent intersections (e. g groups of metabolites retrieved in several datasets) whose size is indicated on the X axis. For example, there is a set of 37 metabolites that are producible in the original dataset only, and a set of 5 metabolites predicted as producible in all datasets but the one where 70% of genomes were 10%-degraded.
Each category of metabolites predicted as newly producible in the gut was defined as a target set for community selection among the 1,520 GSMNs from the gut microbiota reference genomes dataset. For each metabolic group, key species and the full enumeration of all minimal communities were computed. Association graphs were built to associate members that are found together in at least one minimal community among the enumeration. These graphs were compressed as power graphs to identify patterns of associations and groups of equivalence within key species. Power graphs a. , b. , c. , d. , e. , f. , g. were generated for the sets of lipids, aminoacids and derivatives, carboxy-acids, sugar derivatives, aromatic compounds, and coenzyme A derivative compounds respectively. Node colour describes the phylum associated to the GSMN.
Robustness analysis of GSMN reconstruction with MAGs MAGs from the rumen dataset were degraded by randomly removing contigs. The following degradations were tested: removal of 2% of genes in all MAGs, removal of 5% of genes in 80% of MAGs, removal of 5% of genes in all MAGs, removal of 10% of genes in 70% of MAGs. Table  1  summarises the characteristics of the genomes and GSMNs for all experiments. The average gene loss in genomes is similar to the average gene loss in metabolic networks. However, the average loss of metabolites and reactions is lower than the genetic loss: it increases more slowly than the loss of genes. For instance, the 2-percent degradation of MAGs leads to a nearly 2 percent decrease in reaction numbers in GSMNs. However, the 10-percent degradation in 70% of genomes (average gene loss of 7% in the initial community) only leads to a 5% decrease in reaction numbers.
can process existing metabolic networks in SBML format or proposes the automatic reconstruction of non-curated metabolic networks (m2m recon). As a multi-processing solution, it facilitates the treatment of hundreds or thousands of genomes that can be retrieved from metagenomic experiments. The underlying GSMN reconstruction software is Pathway Tools (Karp et al. , 2016) , a graphical user interface (GUI) based software suite for the generation of individual GSMNs, called Pathway/Genome Databases (PGDBs). Typically, a PGDB is obtained from an annotated genome using PathoLogic, the software prediction component of Pathway Tools, and curated afterwards. We developed Mpwt 1 (Multiprocessing Pathway Tools), a command-line Python wrapper for Pathway Tools. Mpwt and M2M i) format the genomic inputs, ii) automate the reconstruction step by initialising a PathoLogic environment for each genome, and iii) extract and converts the resulting GSMNs in PGDB and SBML (Hucka et al.
, 2003 (Hucka et al. , , 2018) )  formats using the PADMet library (Aite et al. , 2018). Mpwt handles three types of genomic inputs (Genbank, Generic Feature Format (GFF) or PathoLogic format) that must contain GO-terms and EC-numbers annotations necessary for Pathway Tools. These annotations are for example found in the Genbank files generated by Prokka (Seemann, 2014). In addition, we specifically developed Emapper2gbk, a Python package dedicated to the connection between the Eggnog-mapper annotation tool (Huerta-Cepas et al. , 2017)  and Mpwt in order to generate these inputs.
Figure1. Overview of the M2M pipeline. a. Main steps of the M2M pipeline and associated tools. The software's main pipeline (m2m workflow) takes as inputs a collection of annotated genomes that can be reference genomes or metagenomics-assembled genomes. The first step of M2M consists in reconstructing metabolic networks with Pathway Tools (step 0). This first step can be bypassed and GSMNs can be directly loaded in M2M. The resulting metabolic networks are analysed to identify individual (step 1) and collective (step 2) metabolic capabilities. The added-value of cooperation is calculated (step 3) and used as a metabolic objective to compute a minimal community and key species (step 4). Optionally, one can customise the metabolic targets for community reduction. The pipeline without GSMN reconstruction can be called with m2m metacom, and each step can also be called independently (m2m iscope, m2m cscope, m2m addedvalue, m2m mincom).
1038/nmeth. 3176. Burgunter-Delamare B, KleinJan H, Frioux C, Fremy E, Wagner M, Corre E, Salver AL, Leroux C, Leblanc C, Boyen C, Siegel A, Dittami SM. Metabolic complementarity between a brown alga and associated cultivable bacteria provide indications of beneficial interactions. bioRxiv. 2019; https://www. biorxiv. org/content/early/2019/10/ 22/813683, doi: 10. 1101/813683. Carlström CI, Field CM, Bortfeld-Miller M, Müller B, Sunagawa S, Vorholt JA. Synthetic microbiota reveal priority effects and keystone strains in the Arabidopsis phyllosphere. Nature Ecology & Evolution. 2019; 3(10):1445-1454. doi: 10. 1038/s41559-019-0994-z. Caspi R, Billington R, Keseler IM, Kothari A, Krummenacker M, Midford PE, Ong WK, Paley S, Subhraveti P, Karp PD. The MetaCyc database of metabolic pathways and enzymes -a 2019 update.
The right panel of the supervenn diagram indicates the number of metabolites in the community scope of the corresponding dataset. Vertical overlaps between sets represent intersections (e. g groups of metabolites retrieved in several datasets) whose size is indicated on the X axis. For example, there is a set of 37 metabolites that are producible in the original dataset only, and a set of 5 metabolites predicted as producible in all datasets but the one where 70% of genomes were 10%-degraded. A full superimposition of all the coloured bars would indicate a complete stability of the community scope between datasets. b. Comparison of the cooperation potential between the five experiments. c. Comparison of key species that gather essential symbionts (d. ) and alternative symbionts (e. ).
Figure3. Power graph analysis of predicted microbial associations within communities for the human gut dataset. Each category of metabolites predicted as newly producible in the gut was defined as a target set for community selection among the 1,520 GSMNs from the gut microbiota reference genomes dataset. For each metabolic group, key species and the full enumeration of all minimal communities were computed. Association graphs were built to associate members that are found together in at least one minimal community among the enumeration. These graphs were compressed as power graphs to identify patterns of associations and groups of equivalence within key species. Power graphs a. , b. , c. , d. , e. , f. , g. were generated for the sets of lipids, aminoacids and derivatives, carboxy-acids, sugar derivatives, aromatic compounds, and coenzyme A derivative compounds respectively.
Characteristics of the metabolic networks built for the gut and the rumen datasets. a. Distribution of the number of metabolic compounds in GSMNs reconstructed for the gut dataset (purple) and the rumen dataset (green). b. Distribution of the number of metabolic reactions. c. Distribution of the number of complete pathways according to the MetaCyc database. d. Distribution of the number of genes included into the GSMNs. e. Distribution of the number of reactions associated to genes. f. Principal component analysis of the GSMNs reconstructions based on the previous characteristics (a. to e. ). g. Distribution of the number of genes (not necessarily related to metabolism) in the initial genomes/MAGs. h. Individual metabolic potentials (scopes) for the gut bacteria, dotted line represents the number of seeds (nutrients) used in the algorithm.
Acknowledgments The authors acknowledge the  GenOuest bioinformatics core facility  for providing the computing infrastructure. This research was supported in part by the  NBI Computing infrastructure for Science (CiS) group. FH and CF's salaries have been funded by the  BBSRC   Institute Strategic Programme Gut Microbes and Health   BB/r012490/1 , its constituent project  BBS/e/F/000Pr10355. AB's, CF,and MA's salaries have been funded by the  ANR  project  IDEALG  ( ANR-10-BTBR-04 ) " Investissements d' Avenir, Biotechnologies-Bioressources ". The authors acknowledge  P. Karp ,  S. Paley ,  M. Krummenacker ,  R. Billington ,  A. Kothari  from the  Bioinformatics Research Group of SRI International  for their help regarding Pathway Tools. The authors also thank  Lucas Bourneuf  for his help on power graph analyses,  Yann Le Cunff  for his help regarding statistical analyses, and  Samuel Blanquart  and  David Sherman  for their useful comments on the manuscript.
M2M can process existing metabolic networks in SBML format or proposes the automatic reconstruction of non-curated metabolic networks (m2m recon). As a multi-processing solution, it facilitates the treatment of hundreds or thousands of genomes that can be retrieved from metagenomic experiments. The underlying GSMN reconstruction software is Pathway Tools (Karp et al. , 2016) , a graphical user interface (GUI) based software suite for the generation of individual GSMNs, called Pathway/Genome Databases (PGDBs). Typically, a PGDB is obtained from an annotated genome using PathoLogic, the software prediction component of Pathway Tools, and curated afterwards. We developed Mpwt 1 (Multiprocessing Pathway Tools), a command-line Python wrapper for Pathway Tools. Mpwt and M2M i) format the genomic inputs, ii) automate the reconstruction step by initialising a PathoLogic environment for each genome, and iii) extract and converts the resulting GSMNs in PGDB and SBML (Hucka et al.
M2M answers to the upscaling limitation of individual GSMN reconstruction with Pathway Tools by automating this task using the Mpwt wrapper. GSMNs in SBML format obtained from other platforms such as Kbase Arkin et al. (2018), ModelSEED (Henry et al. , 2010b; Seaver et al. , 2020) or CarveMe (Machado et al. , 2018), can also be used as inputs to M2M for all metabolic analyses. For instance, we used highly curated models from AGORA in the application of M2M to metagenomic datasets. The above reconstruction platforms already implement solution to facilitate the treatment of large genomic collections. There is no universal (Popp and Centler, 2020)metabolic networks reconstructed from reference genomes and MAGs display similar characteristics and that M2M modelling predictions are robust to missing genes in the original genomes.
Pour tous les tests statistiques appliqués, l'hypothèse nulle a été rejetée au seuil de signification de P ≤ 0,05. Les traitements statistiques ont été effectués à l'aide du package Statistica 6. 0 et SPSS 16. 0. Pour comparer les rythmes de la croissance radiale des arbres dépéris et sains, les séries maîtresses de chaque station ont été reportées sur le même graphique dont les années figurent en abscisses et l'épaisseur des cernes en ordonnées. Une analyse en composantes principales (ACP) a été réalisée pour étudier la variabilité spatio-temporelle des épaisseurs des cernes des différentes séries chronologiques. En un premier temps, les observations (années) sont projetées dans les différents axes factoriels afin de déceler la variabilité temporelle des épaisseurs des cernes. En un second temps, les variables exprimées par les populations sont projetées afin de déceler la variabilité spatiale  (Fritts, 1976).
Remerciements Les auteurs remercient tous les cadres  de la direction du Parc national de Theniet El Had pour l 'assistance durant le travail de terrain, le  Dr F. Dahlia  pour l'analyse des données sous SPSS et trois relecteurs anonymes pour leurs remarques et critiques constructives. Ce travail rend hommage à feu  Dr Mahand Messaoudene , directeur de recherche à l' Institut national de recherche forestière d'Algérie, initiateur des travaux sur le dépérissement du cèdre de l'Atlas en Algérie.
The SAIEUS software available at www. NLDFT. com was used. To further characterize textural and surface chemistry of the ACs surfaces, water vapor adsorption/desorption isotherms were measured using the same apparatus. About 200 mg of AC previously outgassed at 150 °C during 3h were used. For obtaining the isotherms, the samples were kept at constant temperature of 20 °C for relative humidity ranging from 0 to 96%. Elemental analysis using Thermo Fisher Flash 2000 CHNS analyzer was carried out to assess the carbon, hydrogen, nitrogen and sulfur composition of the samples. The proximate analysis was carried out using a Pyris Diamond TG/DTA analyzer from PerkinElmer. The moisture content and the volatile content were determined as the weight loss at 110 and 900 °C respectively when heating about 5 mg AC in N2 flow (40 ml min -1 , 10 °C min -1 ).
Chemical composition The chemical composition of the ACs was determined by combining the CHNS elemental analysis with temperature dependent weight loss measurements (proximate analysis), ICP-OES chemical analysis and EDX results. The data obtained are summarized in Table  1  (elemental and proximate analysis results) and   [27]. The three AC powders show a predominant basic character, given the greater content of basic sites as compared to acidic sites. The total amount of basic sites increases in the order: 207C < AP4 < STIX. These results are also reflected in the obtained values of the point of zero charge (pH value at which a solid submerged in an electrolyte exhibits zero net electrical charge on the surface). Attempts for determining the nature of oxygen-bearing surface groups were not fruitful due to their low amount.
The anionic mechanism requires strong basic which might be present in STIX and 207C but not in AP4. Looking at the chemical composition of the ACs, there seems to be a relationship between the degree of polymerization and the amount of alkali metals (K, Na): the higher the quantity of alkali metals, the higher the D4 fraction polymerized as revealed by the capacity loss. Alkali metals could appear as a marker of strong basic sites. They could also be identified as the specific hydrophilic sites revealed by H2O adsorption. Similar loss of capacity is observed upon thermal treatments and during co-adsorption of D4 and H2O. Semi-quantitative assessments were further derived from FTIR data of desorption experiments. The normalized evolution of the intensity of the bands at 1092 cm -1 (characteristic of D4 gas) and at 1035 cm -1 (characteristic of D3 gas) during thermal treatment after the adsorption step over STIX, AP4 and 207C at 20 °C is reported in Fig.
Conclusion In this paper, the intrinsic reliability performances of all-solid-state microbatteries were evaluated. The analysis of experimental irreversible capacity loss data has revealed that the capacity fade of batteries was accelerated by various environmental and operational factors, such as temperature, depth of charge, and discharge rate. Therefore, a reliability model based on an exponential function has been proposed to fit the aging data. This exponential law, assumed to describe a process of continuous loss of active material within the LiCoO 2 positive electrode, has been observed to be very suitable for the case of all-solid state battery system. Indeed, this exponential law makes it possible to overcome the insufficient justification of the use of a function defined by the square root of time, or by the square root of the number of cycles, generally deployed for modeling the diffusion phenomena within the specific SEI layer of conventional battery technologies.
The different chemical species, both neutral (as Ar, H2…) and ionic (as Ar + , H + , e -…), inside the plasma plume depends on temperature which results from the balance between the electrical energy dissipated and the heat losses that occur in the plasma plume. The guesses of chemical composition and transport properties at different temperatures were carried out with the use of T&TWinner software which is feed by a huge database of chemical species. This software established the equilibrium onto minimization of Gibbs's free energy of Gibbs and Chapman-Enskog approach  [24]. is established onto minimization of Gibbs's free energy of Gibbs  [25]  and Chapman-Enskog approach [24] with the use of the database.
Experimental procedures and data analysis As illustrated in Fig. 1 , SHS spectroscopy is here based on a widely tunable (680 to 1300 nm) femtosecond laser (Insight X3 Spectra-Physics). The incident power is controlled thanks to an achromatic half-wave plate (Thorlabs, 690-1200 nm) associated with a vertical Glan-laser polarizer (Thorlabs). A near-infrared achromatic lens (Newport, f = 100 mm) mounted on a motorized translation stage is placed after a long pass filter (Thorlabs FELH0650) and is used to focus the incident beam at the center of a UV-fused quartz cuvette filled with the nanocrystal suspensions. The second harmonic signal is collected perpendicularly to the laser beam through two fused silica lenses (Thorlabs, f = 50 mm). An uncoated BK7 Dove prism (Thorlabs) is inserted between these two lenses so as to rotate the (horizontal) focal point image and to increase the signal  [27]  on the vertical entrance slit of a spectrometer (Andor Shamrock 193) coupled to a Peltier-cooled CCD camera (Andor iDus 401).
At the same time, relatively general and even contradictory hypotheses, guiding the feature selection phase of inductive models, can be confronted to the final results (this amounts, in certain respects, to hypothesis testing). Opportunities for this kind of spatial analyses have increased over the past decades with the ever-increasing size of available datasets and computing power (e. g. Batty, 2012). Such an approach usually requires a data-mining phase in order to gather data able to describe the situation of the targeted geographic space in the most exhaustive way with respect to the themes of investigations. Bayesian Network Clustering and Self-Organizing Maps under the Test of Indian. Cybergeo : European Journal of Geography , Systèmes, Modélisation, Géostatistiques Through the case study of urbanization in India, this paper will show the potential (and the limits) of inductive search of possible regionalizations of a large developing country.
) in the context of the urbanization process (urban density, insertion in urban macro-structures, etc. ), seen as a possible catalyst of sociodemographic modernity and economic development  (Cali and Menon, 2012). Indian regions experienced urban transformation following various patterns that defy a singular explanation (e. g. Denis and Marius-Gnanou, 2011; Raman et al. , 2015). Even from the specific point of view of our research, the exact geography of the spatial structures of a dual or of a multiple India remain to be ascertained. Is India a mosaic of small subspaces each in different stages (or typologies) of development or are vast regional spaces characterized by given levels of urbanization and socioeconomic development? To test these broad alternative hypotheses on the organization of Indian space and to formulate new hypotheses on the articulations of spatial structures in India, we thus resorted to AI based algorithms, allowing more freedom in knowledge discovery in databases.
A multi-stage clustering of Indian districts has been performed using Bayesian Networks  (Perez and Fusco, 2014)  and Self-Organizing Maps  (Fusco and Perez, 2015). Both approaches have proved to possess a strong capacity to inductively identify the main spatial structures of the Indian space as well as the ability to deal with incomplete dataset inductively s and with uncertainty issues. These results have been validated internally within each clustering procedure. Since validation between alternative clustering schemes implementing different methodologies and optimizing different quality functions is a difficult task  (Haldiki et al. , 2001) , the aim of this paper is to highlight the similarities between the protocols leading to such remarkable results (statistical parameters, latent factors, etc.
5 The paper is organized as follows. The "Theoretical Background, Methodology and Data" section describes the dataset used in this research and provides an overview of classical multivariate Hierarchical Clustering as well as Bayesian and Artificial Neural Network reasoning when used for clustering spatial units. "Implementing the Clustering Protocols" section details the three protocols developed for this research and highlights their similarities and differences, as well as the validation procedures. "Clustering Results" presents the statistical and geographical results. A final section concludes the paper by discussing the similarities and differences between the clustering protocols and the kind of results they can produce.