dataesr/scientific-paragraphs-categorization huggingface dataset:
Thus, an accurate estimate of their contribution is necessary to improve N recommendations. In sugar beet trials receiving no mineral fertiliser (T0), the differences in crop N uptake were attributed to differences in the net N mineralisation of organic products (dataset 2). N uptake by sugar beets in the different treatments ranged from 120 to 280 kg N ha -1 (Figure  4 ). Comparatively, the N supply simulated by AzoFert ® ranged from 110 to 260 kg N ha -1. The observed difference between the measured and estimated N supply was, on average, -5 kg N ha -1. Although the simulated and measured N supply was similar for the control plots and the plots with only vinasse, AzoFert ® tended to overestimate the N supply in the plots with both catch crops and vinasse. Under culture conditions with organic amendments, the r 2 coefficient reached 0.
45, and the RMSE and MG were 5. 47 and 1. 00 kg N ha -1 , respectively. Under conditions with organic amendments, soil mineral N availability often leads to a substantial contribution of organic sources (such as catch crops and organic products) to the N nutrition of main crops. Thus, an accurate estimate of their contribution is necessary to improve N recommendations. In sugar beet trials receiving no mineral fertiliser (T0), the differences in crop N uptake were attributed to differences in the net N mineralisation of organic products (dataset 2). N uptake by sugar beets in the different treatments ranged from 120 to 280 kg N ha -1 (Figure  4 ). Comparatively, the N supply simulated by AzoFert ® ranged from 110 to 260 kg N ha -1. The observed difference between the measured and estimated N supply was, on average, -5 kg N ha -1.
Residual N at Crop Harvest Based on AzoFert ® The residual N at crop harvest is an important criterion for determining the risk of nitrate leaching during the intercropping period  [33]. The residual mineral N at harvest calculated using AzoFert ® was evaluated in trials conducted on winter and spring crops as well as field vegetables. The trials included increasing N doses, with measures of yield and residual mineral N in the soil at harvest (dataset 3). To compare the various treatments, the results were normalised using the yield and residual soil mineral N of the control treatments as a reference. The residual soil mineral N rapidly increased under an excessive fertiliser N dose, i. e. , greater than the dose that resulted in optimal yield (Figure  5 ); the increase was equal to 4 kg and 6 kg of N per 10 kg N ha -1 of extra fertiliser N applied to winter and spring crops, respectively.
The residual N at crop harvest is an important criterion for determining the risk of nitrate leaching during the intercropping period  [33]. The residual mineral N at harvest calculated using AzoFert ® was evaluated in trials conducted on winter and spring crops as well as field vegetables. The trials included increasing N doses, with measures of yield and residual mineral N in the soil at harvest (dataset 3). To compare the various treatments, the results were normalised using the yield and residual soil mineral N of the control treatments as a reference. The residual soil mineral N rapidly increased under an excessive fertiliser N dose, i. e. , greater than the dose that resulted in optimal yield (Figure  5 ); the increase was equal to 4 kg and 6 kg of N per 10 kg N ha -1 of extra fertiliser N applied to winter and spring crops, respectively.
The evolution of recommended N rates after the release of AzoFert ® is a consequence of its use. From 1993-2004, prior to AzoFert ® development, a static balance sheet (Azobil method), Reference  [34]  was used to recommend N doses for specific crops in a given area. In 2005, AzoFert ® software The evolution of recommended N rates after the release of AzoFert ® is a consequence of its use. From 1993-2004, prior to AzoFert ® development, a static balance sheet (Azobil method), Reference  [34]  was used to recommend N doses for specific crops in a given area. In 2005, AzoFert ® software replaced Azobil. Table  3  shows the evolution of the mean recommended N doses for three main crops in a large number of fields in northern France. From 2005 to 2015, the number of recommendations using AzoFert ® increased.
For example, AzoFert ® can be used in low-input or even organic systems and, depending on the source of soil N supply, can recommend no mineral fertilisation. The high-performance computer design of the software is related to its structure and modularity. The software can easily be used to (i) improve the formalisms that describe the processes and calculations of the different items of the balance sheet; (ii) modify the parameterisation in the domain of settings, catalogues and grids; and (iii) permit the implementation of new processes, such as by estimating N loss by denitrification and including new references for the parameters and input variables. The multi-local evaluation of AzoFert ® and its use by laboratories and extension services in several regions have generated adaptations in the software parameters to the regional diversity of agricultural conditions, such as those encountered in the loamy soils of Picardie, the calcareous soils of Champagne, and the soils of Belgium  [32]  and Switzerland  [1].
Acknowledgments: This work was funded by  INRA  ( Institut National de la Recherche Agronomique, France ),  LDAR (Laboratoire Départemental d'Analyses et de Recherche de l'Aisne)  and  ITB (Institut Technique de la Betterave) , who also provided the numerous datasets that were used to evaluate the model. The authors thank  B. Mary ,  B. Nicolardot ,  V. Parnaudeau , the partners of the  RMT Fertilisation & Environment, France , for fruitful scientific exchanges on the formalisms of soil and crop processes, and  C. Le Roux ,  B. Verbeque , and  V. Ruas  for their early involvement in the testing and improvement of the first versions of the software. We also thank  A. Dupont  for his contribution to the software programming. TEREOS and VIVESCIA are acknowledged for their contribution to the datasets for sugar beet and cereal crops.
DeCoSTAR is a software that aims at reconstructing the organization of ancestral genes or genomes in the form of sets of neighborhood relations (adjacencies) between pairs of ancestral genes or gene domains. It can also improve the assembly of fragmented genomes by proposing evolutionary-induced adjacencies between scaffolding fragments. Ancestral genes or domains are deduced from reconciled phylogenetic trees under an evolutionary model that considers gains, losses, speciations, duplications, and transfers as possible events for gene evolution. Reconciliations are either given as input or computed with the ecceTERA package, into which DeCoSTAR is integrated. DeCoSTAR computes adjacency evolutionary scenarios using a scoring scheme based on a weighted sum of adjacency gains and breakages. Solutions, both optimal and near-optimal, are sampled according to the Boltzmann-Gibbs distribution centered around parsimonious solutions, and statistical supports on ancestral and extant adjacencies are provided.
2014). We describe a software, DeCoSTAR, which reconstructs putative ancestral states of adjacencies, for example, ancestral domain structures of a modular protein, as well as chromosome organizations of whole ancestral genomes, or fusion/ fission histories or modular genes, when genes have complex histories made of gain, duplication, transfer, speciation, and loss events. The input of DeCoSTAR consists in a species tree, a set of extant gene families-each in the form of one or several gene trees-and extant adjacencies between pairs of extant genes. The gene trees and the species tree follow the reconciliation framework that is described by  Jacox et al. (2016). (Reconciled gene trees are rooted gene trees whose nodes are associated to an evolutionary event, such as speciation, gene loss, gene duplication, or lateral gene transfer, and to a position in the species tree.
Features and Implementation DeCoSTAR supersedes (with the exception of the ability of DeClone  [Chauve et al. 2015]  to compute the exact expectation of the frequency of a property of interest using a variant of the inside-outside algorithm) and combines all the features of DeCo (Berard et al. 2012), DeCoLT  (Patterson et al. 2013) , DeClone  (Chauve et al. 2015) , and ART-DeCo  (Anselmetti et al. 2015). The generalization of all these methods offers novel capabilities, including the Boltzman-Gibbs sampling of ancestral adjacencies in the presence of transfers from error-prone/partial genome assemblies. The integration with the software package ecceTERA dedicated to reconciliations  (Jacox et al. 2016 ) adds novel features, such as the possibility of taking unrooted gene trees or undated species trees as input.
As a novelty, it also fully handles gene orientations whenever available, and provides statistical supports of ancestral adjacencies by sampling among optimal solutions. DeCoSTAR is a C ++ program requiring the Bio ++ library  (Gueguen et al. 2013 ) and the Boost library (BOOST 2003) to be installed. It is a command-line program whose various options and input can be specified on the command line or given in a parameter file. It handles newick format for trees and recPhyloXML  (Gence 2016 ) format for trees and reconciliations. A detailed documentation of DeCoSTAR options, input and output formats is available in the supplementary material (Supplementary Material online) and is included within the distributed version of the software.
Each propagation rule is translated into a specific term in a dynamic programming equation for the reconstruction of ancestral states. The complete set of rules (19 rules, whose combinations cover all the cases encountered by the algorithm) implemented in DeCoSTAR is the result of a complete rewriting of a combination of rules taken in the previous softwares, aggregating them in more general rules. For comparison DeCoLT  (Patterson et al. 2013 ), a less general algorithm, used a set of 23 rules. For two genes a and b, we note c 1 (a,b) and c 0 (a,b) the cost of, respectively, having an adjacency and having no adjacency between a and b. We call a 1 and a 2 (respectively, b 1 and b 2 ) the children of a (respectively b) (NB: if a [respectively b] only has one child, then a 2 [respectively b 2 ] does not exist).
Protein domain families were aligned using MUSCLE  (Edgar 2004 ) and their trees were inferred using RAxML  (Stamatakis 2014 ) with the appropriate model (inferred using the RAxML perl helper script for finding the best protein substitution model). The adjacencies used as input reflect neighborhood relationship between domains of the same extant protein. There are in average 5,278 proteins per extant species in the input data set with an average protein size of 2. 030 domains. DeCoSTAR was used to infer ancestral adjacencies forming an average of 4,977 proteins per ancestral species, for an average protein size of 2. 188 domains. As with the validation on the Anopheles species data set, some ancestral protein domains have been erroneously inferred with more than two neighbors, leading to the presence of some nonlinear proteins in the ancestral species.
A Fusion-Fission History in Actinobacteria Adjacencies can be used to denote the fact that two genes are fused into one. To illustrate this, we use a set of three gene families from the HOGENOM database  (Penel et al. 2009 ) that we, respectively, call A, B, and C. In all Actinobacteria present in HOGENOM, the A and B genes are always present together, but never with C genes. Furthermore, in a profile alignment, A and B both align on disjoint, consecutive regions of C, covering nearly 98% of its length. We use this signal as the marker that A and B genes fused in order to give C genes. To reconstruct the history of this system, we manually cut each C gene into its parts that, respectively, aligned with A and B, added them to the alignment of the family with whom they aligned and put an adjacency between the newly formed gene so that we could account for the fact that they fused.
Conclusion There exists an extensive set of bioinformatics tools aiming at reconstructing the history of an evolutionary unit, as a gene or FIG. 2. -Density of the distribution of the degree of all genes inferred by DeCoSTAR on the 18 Anopheles data set. The degree of an extant or ancestral gene is the sum of the weights of all adjacencies containing this gene. For extant genomes in the input (black line), this value can only be 0, 1, or 2. For genomes in the output, extant (red line) or ancestral (green line), all values are possible because adjacencies have scores between 0 and 1, and a gene can belong to an arbitrary number of adjacencies. The difference between the black and red lines are due to the scaffolding: genes with 0 or 1 neighbor are linked to other genes as an output of DeCoSTAR.
In ancestral genomes, some genes have degree three or slightly more. a domain or a gene concatenate. But they all make the assumptions that, inside a unit, all sites have the same history, and that two units are independent. The inter or intra unit organization is rarely modeled, with the effect of missing an evolutionary view on what the living is essentially made of: organization and interaction. Here, we propose to depict this interaction in the form of adjacencies between units, where the units can be genes, gene domains, or parts of genes having different histories like in the case of fusions or fissions. We present a software-DeCoSTAR-that generalizes several algorithms published by our group, is easy to install and to use, allows a wide range of genomic events such as duplications, transfers, losses, rearrangements, and can deal with poorly assembled genomes.
(2009) , using a wireless sensor network as the control channel, where a broadcast mechanism is available. This protocol is based on a previous work on control-mechanism protocols called PULSE  (Birari and Iyer, 2005). The control operation is based on three messages that the readers broadcast only when reading tags, which reduces energy consumption. The readers contend for the right to use the RFID data channel; only one reader wins and takes the channel until the reading is finished. When contending, the readers backoff a randomly selected time, and the first to have its timer expired wins. To deal with the exposed and hidden-node problems, the readers are expected to have control over the power of the control channel radio and adjust it such that R control ≥ R data. These assumptions would mean the need of additional investment on a wireless sensor network and a very specific radio-power control hardware feature perhaps not available in all RFID readers.
The tag-to-tag collisions within the reading area of a RFID reader are assumed to be solved by the RFID EPC Class1 Gen2 standard. The proposed distributed simple RFID reader anti-collision protocol, DiSim, is briefly described in Figure  5  with a finite state machine model. This proposal deals with the coexistence of RFID readers with different capacities and power, some static and some mobile. It does not require any centralized software service, neither additional hardware from the already exiting in commercial RFID readers. It also does not need to track the location of the readers in the area. A priority mechanism is embedded in DiSim to ensure reading fairness and absence of starvation. Additionally, as DiSim is based on status notifications over the IoT channel, it avoids constantly probing the RFID frequency periodically to contend for the medium with the neighbor readers.
Contrary to the ETSI standard LBT method, we consider the fact that the RFID medium is shared and has to be orderly coordinated between the readers, similar to the way a single resource is shared in a queuing mechanism. The queue is thought as a G/G/1/N model, where the N capacity determines the blocking stage of the queue. 6. Experimentation 6. 1 Simulation I evaluate the simple protocol in a homemade Python model simulation using discreteevent framework Simpy  [11]. I simulate a mixed reader scenario as described in Section 1. The simulation parameters are summarized in Table  I. To understand the real value of the DiSim proposal Both both DiSim and RFID standard ETSI anticollisions protocols are simulated. The area considered for the simulation is a square of 100 Â 100 m 2 , which is an average size for an industrial warehouse area.
New IoT proximity service Finally, from a security point of view, the effect of RFID jammers that try to keep the air busy and cause a denial of service of the RFID tags can be examined. This kind of disturbance affects the overall performance of the system and may be taken into consideration to define a countermeasure for this kind of attacks. Future work considers experimentation in a real warehouse equipped with heterogeneous RFID readers and real time analysis of RFID reading efficiency also combined with indoor localization and navigation for warehouse mobile robots. 10. Wifi Alliance Video Presenting Wifi-Aware. www. youtube. com/watch?v=xwSYPqqhTsQ& feature=youtu. be 11. Simpy Python library: As we are concerned by controlling the reader collision problem, in particular the heterogeneous readers installed in same area, we selected a set of parameters to compare between the standard ETSI approach and our DiSim approach.
Gap-filling a metabolic network: A very sensitive approach Metabolic knowledge is crucial to understand physiology and biotic interactions. Supported by an unprecedented rise of sequencing technologies, the last decade saw the increasing understanding of metabolic capacities using genomic knowledge. In particular, in 2010, Thiele and Palsson  [1]  described a general protocol enabling the reconstruction of high-quality metabolic networks, and several approaches have since been proposed to automate this process  [2] [3] [4]. These methods rely primarily on two distinct steps. First, they provide automatic reconstructions of networks, called draft metabolic networks  [5, 6] , and in a second step fill the gaps of the draft networks. To this end, reference databases of metabolic reactions are used to check whether adding reactions to networks allows compounds of interest to be produced from given growth media.
Identifying these missing reactions constitutes the so-called gap-filling problem (n. b. considering the diversity of compound producibility and network consistency definitions, we here refer to a family of gap-filling problems rather than a single one)  [2]. Several approaches exist to solve gap-filling problems and to select, within databases, those reactions that must be added to the draft network to restore its consistency and a metabolic behavior. Reactions may be chosen to optimize a graph-based criterion  [7] , or to optimize a linear score modelling the quantitative metabolic production of the system, as in the GapFill tool  [8] , and its derivative fastGapFill  [9]. Some approaches also integrate complementary knowledge such as taxonomic information  [10]  or compartment modularity  [11].
More generally, the selection of reactions may be performed by optimizing a linear score modelling the consistency of a network with phenotypic knowledge, i. e. experimental flux data  [12]  or growth/no growth results  [13]. Lastly, some tools combine several of the previously mentioned approaches. An example of these is MIRAGE, which selects reactions in a database in order to maintain biomass producibility with respect to a score based on co-expression and taxonomic distance between the target species and the species for which enzymes were evidenced  [14]. The approach presented in  [15]  is similar although based on a different definition of producibility. In Table in S1 Table, we report the main characteristics of such methods in terms of required input data, the technological platform to be run, and examples of applications.
Euglena mutabilis is a photosynthetic protist and important primary producer in acidic aquatic environments. Despite the crucial role of E. mutabilis in these ecosystems and the fact that it has often been considered as an indicator species for acid mine drainages (AMDs), this organism has only been poorly described so far, in contrast to another species of the same genus, Euglena gracilis. The available data for E. mutabilis consists in assembled transcript sequences obtained from de novo transcriptomics and metabolomics experiments previously published in  [18]  and in  [19] , respectively. This sparse dataset prevented us from using most of the tools described above to construct a metabolic network: the absence of a sequenced genome for the Euglena genus and the fact that this genus is not closely related to any common model organism rendered taxonomy-based methods of network reconstruction unusable  [10, 11].
E. mutabilis is difficult to cultivate in controlled conditions and to obtain as clonal cultures, preventing the use of phenotype-based tools  [12, 13]. The family of tools  [8, 9, 14]  that could be used here are of functional nature. Another application for gap-filling methods has emerged as a tool for studying the coexistence of organisms living in communities. As an example, Candidatus Phaeomarinobacter ectocarpi is a symbiotic bacterium associated with Ectocarpus siliculosus. Its genome and a draft of its metabolism could be produced  [20]. Its host E. siliculosus has been studied for a longer time, and a functional metabolic network was reconstructed to explain the production of characteristic compounds of its metabolic profile  [21]. Additional transcriptomic datasets were used in this study to identify 1,125 internal or external compounds produced by at least one reaction of E.
This reaction is often copied from well-established model organisms and therefore cannot capture all of the characteristics of the studied organism, especially when dealing with extremophiles. Shortcomings in the determination of an adequate biomass reaction lead to a second problem, that is, the determination of the boundary compounds, dead-end metabolites, and cofactors in the system. These may be hard to characterize from experiments or literature, despite their strong potential impact on the capacity of the system to produce biomass according to stoichiometry-based formalisms  [8]. In particular, the score-based methods mentioned above depend on the stoichiometric balance of metabolic reactions, a criterion which may be prone to errors, especially with respect to cofactors and when using large-scale databases of metabolic reactions  [2].
Starting from given nutrients (e. g. growth medium), referred to as seeds, this tool computes their scope defined as all the metabolites that can be synthesized from them using a graph-based approach. For metabolic network gap-filling, a database of metabolic reactions is queried to look for minimal sets of reactions that can restore the observed bio-synthetic behaviour (i. e producibility of target metabolites). The Meneco tool was included in a pipeline implemented to construct EctoGEM, a metabolic network for the brown algal model E. siliculosus. The analysis of EctoGEM highlighted several interesting biochemical reactions, shedding light on the organization and evolution of some primary metabolic pathways of photosynthetic organisms  [21]. In the present work, the case for Meneco as an important tool for hypothesis generation is further supported by new observations related to a benchmark of networks on a model organism and two case studies.