dataesr/scientific-paragraphs-categorization huggingface dataset:
); (2) it is processed and acquired in the mind; and (3) it may be subjected to formalization. Therefore, the parameterization of context for specialized knowledge representation should be approached from a multidisciplinary perspective. Specialized knowledge can be represented in a variety of formats (i. e. , ontologies, vocabularies, thesauri, controlled languages, databases, etc. ) that may or may not support context, because knowledge resources are conceived for very different purposes (i. e. , classification, reasoning, knowledge acquisition, standardization, harmonization, information retrieval, machine, or human translation, etc. ). More specifically, terminological knowledge bases (TKBs) generally describe the concepts and terms of specialized knowledge domains for users with linguistic and/or cognitive needs.
Contextual data extracted from the enTenTen12 general English corpus in Sketch Engine show that dissipate is often used unaccusatively. In other words, the first argument is not made explicit. The most frequent meanings of dissipate in general language are ii (2) and iv (3): (2) To cause to spread thin or scatter and gradually vanish (i) Temperature (e. g. , warmth, heat As can be seen in the case of sense (2), the dissipated entity is most frequently related to temperature, weather, sensory perception, and emotions, whereas in sense (3), it is generally wealth or financial resources. However, in specialized contexts, the meaning of dissipate does not really correspond to any of these possibilities. The reason for this is the semantic content of domain-specific arguments, which interact with the base meaning of dissipate and model it to create a new sense that is apt for scientific contexts.
In EcoLexicon, various context parameters have been explored. Their specification has materialized in various modules representing different contextual aspects, ranging from term variation, collocations or knowledge-rich contexts, to dynamic conceptual networks, flexible definitional templates, or conceptually enriched graphical resources. However, much still remains to be done, especially with regards to the interdependence between all modules and the transition from local to global constraints. In the future, users will be provided with different types of information selected according to context. For this to be possible, context in all of its facets must be accounted for in a systematic and principled way.
The liver performs many essential metabolic functions, which can be studied using computational models of hepatocytes. Here we present HepatoDyn, a highly detailed dynamic model of hepatocyte metabolism. HepatoDyn includes a large metabolic network, highly detailed kinetic laws, and is capable of dynamically simulating the redox and energy metabolism of hepatocytes. Furthermore, the model was coupled to the module for isotopic label propagation of the software package IsoDyn, allowing HepatoDyn to integrate data derived from 13 C based experiments. As an example of dynamical simulations applied to hepatocytes, we studied the effects of high fructose concentrations on hepatocyte metabolism by integrating data from experiments in which rat hepatocytes were incubated with 20 mM glucose supplemented with either 3 mM or 20 mM fructose.
In this work, we present HepatoDyn (Hepatocyte Dynamics) a model of hepatocyte core metabolism capable of simulating the redox (NAD/NADH, NADP/NADPH, etc. ) and energy (ATP/ADP/AMP, etc. ) dynamics. The model includes glycolysis, gluconeogenesis, glycogen metabolism, the pentose phosphate pathway, the Krebs cycle and fatty acid metabolism as well as reactions associated with energy and redox metabolism (respiratory chain, malate/aspartate shuttle, glycerol phosphate shuttle, etc. ). To our knowledge, no model of such size capable of dynamic redox and energy metabolism simulations exists in the literature. Furthermore, the model was coupled to the module for isotopic label propagation of the software package IsoDyn  [25, 26]. This enables HepatoDyn to integrate data from 13 C based experiments to assist in the parametrization process, regardless of whether experimental measurements correspond to an isotopic steady state.
Electron impact ionization was used to characterize the isotopologue fractions of C1-C4 (m/z 242) and C3-C6 (m/z 187) glycogen glucose fragments, as well as C2-C4 (m/z 152) and C2-C5 (m/z 198) glutamate fragments. Spectral data were corrected using regression analysis to extract natural 13 C enrichment from results  [41]. Measurement of 13 C label distribution determined the different relative distribution percentages of the isotopologues, m0 (without any 13 C labels), m1 (with one 13 C), m2 (with two 13 C), etc.
). The remaining parameters of the kinetic model were assigned based on an extensive literature search, completed with data from Brenda  [45]  and UniProt  [46]  databases. The fitting algorithm, a variant of the basic simulated annealing algorithm  [47] , seeks the set of m parameters (E z ) that minimizes the objective function. The objective function (X 2 ) is the square deviation between the n experimentally measured values (Y i ) and simulated values (Z i ) for both isotopologue fractions and total metabolite concentrations, normalized by the experimental standard deviation (σ i ). To prevent a bias generated by very low standard deviations, a minimum threshold of 0. 01 was used. Additionally, parameter sets where any metabolite reached concentrations greater than 50 mM were discarded.
The kinetic model, fully parametrized, can be found in SBML format in the Supplementary Material (S1 XML and S2 XML). In addition, HepatoDyn is capable of simulating the propagation of 13 C from isotopically labelled substrates to metabolic intermediaries and products. This allows HepatoDyn to integrate isotopologue enrichment measurements from 13 C based experiments greatly enhancing the predictive capabilities of the model. HepatoDyn is provided in the Supplementary Material as a C++ program (S1 Software).
CF was supported by  European commission as part of the Biohealth Computing Erasmus Mundus Program , by " La Caixa" foundation as part of the program  " Becas de la Caixa para estudios de doctorado en universidades españolas " and by an  internship grant  funded by  "Centre National de la Recherche Scientifique ". MC was supported by  MINECO-European Commission FEDER  funds -" Una manera de hacer Europa " ( SAF2014-56059-R ), by the  European Commission  (Etherpaths,  FP7-KBBE-222639 ) and by the  Agència de Gestió d'Ajuts  for indicating flux distributions. Universitaris i de Recerca (AGAUR) -Generalitat de Catalunya  ( 2014SGR1017 ). MC acknowledges the support received through the prize " ICREA Academia"  for excellence in research, funded by  ICREA foundation -Generalitat de Catalunya.
The approach followed by A4Cloud is interdisciplinary and includes legal and regulatory, socio-economic and technical aspects. This paper describes the outcomes of the workshop held during the IFIP summer school 2015 on the topic of accountability for data protection in the cloud. At the time of this workshop some of the A4Cloud prototypes were in a mature enough state to be demonstrated. We chose different kinds of such prototypes to be shown at the workshop. The chosen tools offered complementary functionalities that will be also described in this paper. On the one hand, we concentrated on the Cloud Offering Advisory Tool (COAT) and Data Protection Impact Assessment Tool (DPIAT) that facilitate users in finding and assessing a suitable cloud provider that may fulfill their needs. On the other hand, we also considered tools that aid users having control over their data, such as the Data Track (DT) tool or the Accountability-PrimeLife Policy Language (A-PPL), that is the reference language for representation of accountability policies.
These policies can be defined by using the Data Protection Policies Tool (DPPT) and enforced by the associated engine. The structure of the paper is as follows. Section 2 provides an overview of the A4Cloud project, whereas the following sections provide insights on specific tools demonstrated within the workshop. Thus, Section 3 describes tools for facilitating users' choice of providers: in particular, COAT and DPIAT. Section 4 describes some control and transparency tools, namely DPPT and A-PPL Engine. The feedback given by the participants in the workshop is analysed in Section 5. Finally, Section 6 concludes the paper.
Overview of the Accountability Project The goal of the A4Cloud project is to provide an increased basis for trustworthiness in cloud computing by devising methods and tools, through which cloud stakeholders can be made accountable for the privacy and confidentiality of personal data held in the cloud. These methods and tools combine risk analysis, policy enforcement, monitoring and compliance auditing. They will contribute to the governance of cloud activities, providing transparency and assisting legal, regulatory and socio-economic policy enforcement. The A4Cloud tools are grouped into different categories depending on their functionality or their intended usage. They are classified as follows  [3] : -Preventive tools are those that aid mitigation of the consequences of any unauthorised action.
These tools include assessing risk, selection of providers or identification of appropriate policies to mitigate the risks. These tools are the Data Protection Impact Assessment Tool (DPIAT), Cloud Offerings Advisory Tool (COAT), Accountability Lab (AccLab), the Data Protection Policies Tool (DPPT), Accountable Primelife Policy Engine (A-PPL Engine) and Assertion Tool (AT). -Detective tools are those that monitor for and detect policy violations. The corresponding A4Cloud tools are the Audit Agent System (AAS), Data Transfer Monitoring Tool (DTMT), Data Track (DT) and Transparency Log (TL). -Corrective tools are those designed to mitigate the consequences of any incident occuring. They are the Incident Management (IMT) and the Remediation Tool (RT). Among the preventive tools we are going to concentrate in this paper on those designed for facilitating the choice of a cloud provider.
Furthermore, there are lines drawn from the service to any other downstream services used by the service provider to provide the service. In Figure  6 , Spotify is as an example using Visa (presumably to accept creditcard payments for subscriptions). Exercising Control For compatible services, the Data Track enables the user to request access to, correction of, or deletion of their personal data stored remotely at services. In A4Cloud, a DT-compatible service is provided by the A-PPL Engine (presented in Section 4. 2). Outside A4Cloud, the vast majority of conventional services do not provide data subjects with online APIs to excercise control over their personal data.
Privacy Policy Definition and Enforcement Implementation of privacy policies can be thought of as a two phase process, as shown in Figure  7. Tasks carried out in the first phase have the objective to formally specify the policies that describe the different aspects of the practices related to the cloud service provision. During this phase different types of artifacts are designed and produced. For privacy policy statements that can be technically enforced by means of software tools, appropriate technical representation of the statements need to be generated. The specific language for the technical representation of the policies is tied to specific components tasked with the policy enforcement. The A4Cloud Project has designed i) a language, called A-PPL, whose expression capabilities address the need for accountability related policies; ii) an engine, the A-PPL Engine, which is able to translate into actions policies In the following subsections we will describe the tools used in the policy definition and the policy enforcement phases, respectively DPPT and the A-PPL Engine, along with the artifacts produced and used.
Depending on the types of events that can be captured by the components deployed for audit and monitoring purposes, sending of notifications can be automated. DPPT GUI allows the provider to configure the A-PPL Engine for notifications sending. The GUI presents a set of options for the type of events, which reflect the capabilities of the audit and monitoring tools developed within A4Cloud. Examples of event types are: data personal data deletion, data transfer policy violation, access denied. Specific type of events that affect personal data, such as data leakage or data loss, assume particular relevance and are specified in a separate section of the GUI. The information required to be provided for the generation of the corrisponding A-PPL policy element includes: the type of event to notify about, the relevant stakeholder identifier to be notified, the communication channel (basically e-mail or post), the address and the timeframe.
Policy Enforcement: A-PPL Engine Organizational cloud customers usually assume the role of data controller, thus they are held accountable for the way cloud services respond to many regulations, including the EU Data Protection Directive  [11]. Appropriate policies mitigate risks to data protection as they clarify in which way obligations regarding personal data are carried out. In particular, machine-readable policy languages, such as A-PPL  [12, 13]  make organizations accountable, ensure that obligations to protect personal data and data subjects' rights are fulfilled by all who store and process the data, irrespective of where that processing occurs. In the cloud, mechanisms to automatically enforce organizational and data governance policies are fundamental for compliance management. Using the A4Cloud policy enforcement tools, cloud providers can offer more transparency about the data handling.
A-PPL Engine High Level Architecture The A-PPL engine has been created as an extension of the PPL engine in order to enforce the new A-PPL language policies. The first step was to extend the PPL XML schema with the new obligations defined in A-PPL. Many PPL engine functionalities have been simplified since the notion of sticky policy is no longer used. The new A-PPL engine is a simplified version of the PPL engine. Many PPL engine functionalities have been removed since they are no longer needed in A-PPL: the matching engine is no longer used since the notions of sticky policy and data handling preferences do not exist in the A-PPL language. A new module called Policy Administration Point (PAP) is responsible for storing, updating and deleting PIIs and their policies in the database. The access control is performed in the Policy Decision point (PDP) module which uses the Heras XACML engine  [14]  exactly the way it was implemented in PPL.
This work has been partially funded by the  European Commission  through the  FP7/2007-2013 project A4Cloud  under grant agreement number  317550. The first author is supported by the  Ministery of Economy of Spain  through the  Young Researchers Programme : project  PRECISE  ( TIN2014-54427-JIN ). The authors would like to thank  Saul Formoso  for taking notes during the workshop and all the members of A4Cloud involved in the development of the tools.
Repeated elements and gene content. The larger genome size of C. geophilum compared with its close relatives is explained by its high content (81%) in repeated sequences, mainly consisting of transposable elements (TE; Fig. 2a , Supplementary Data 2). Many plant pathogenic fungi show an increased content in TE, particularly (hemi)biotrophic ones  15  and this trend is even more striking for symbiotic mycorrhizal fungi (Supplementary Fig. 2 ). There is considerably less variation in gene count than genome size, the former ranging from 9,739 to 14,748 in Dothideomycetes with Mytilinidiales at the upper margin (Fig. 2b , Supplementary Data 3). C. geophilum has 2,176 unique genes within 1,422 gene families; the largest families, which hold 5 to 30 members (63 families), contain small (o300 amino acids), mostly functionally unknown and often truncated proteins that show no match to the Pfam domain database  16  (76% of families).
geophilum and either Suillus granulatus or Rhizopogon roseolus, two common ECM fungi associated to pines  27. We then measured the differential expression of Scots pine genes in the respective mycorrhizas using oligoarrays. Although 8% of these genes were differentially regulated in mycorrhizal compared with NM roots (Po0. 05; posterior probability of differential expression 28 PPDE40. 97, Supplementary Data 12), the expression of 1% significantly differed in ECM root tips challenged by C. geophilum compared with the other two symbionts (Fig. 6  and Supplementary Data 13). Several interesting patterns emerged from these data: (1) Genes for membrane transporters, such as a the SWEET sugar exporters ( (2) A K þ efflux transporter SKOR, which is induced by stressors and reactive oxygen species mediating K þ export in xylem vessels and which is thought to be involved in stress signalling  29  , was expressed 18.
In addition, LFPE (ligation-free paired end) mate pair fragments were generated using the 5500 SOLiD Mate-Paired Library Construction Kit (SOLiD) as described for C. geophilum. The purified fragments were ligated with Illumina adaptors and amplified using eight cycles of PCR with Illumina primers (Illumina) to generate the final library. Quantification and sequencing were performed as described above for C. geophilum. Genomic data from the two libraries were filtered and assembled with AllPathsLG  49. Glonium stellatum genome sequencing and assembly was performed within the AFTOL project using a single short insert library. For this library, 1. 0 mg of genomic DNA was sheared using the Bioruptor XL (Diagenode) and size-selected for 400 bp through agarose gel isolation. The fragments were treated with end-repair, A-tailing and ligation of Illumina compatible adaptors (IDT, Inc) using the NEBNext kits (New England BioLabs).
The genome assemblies of the three fungi were each annotated using the JGI Annotation Pipeline, which detects and masks repeats and transposable elements, predicts genes, characterizes each conceptually translated protein, chooses a best gene model at each locus to provide a filtered working set, clusters the filtered sets into draft gene families and creates a JGI Genome Portal with tools for public access and community-driven curation of the annotation  14, 52. For G. stellatum, which was not sequenced and assembled at JGI, an additional ab initio gene prediction that was performed within the AFTOL-project with AUGUSTUS 53 2. 3. 1 was included in the JGI annotation pipeline. Phylogenomic placement of Cenococcum geophilum. For phylogenomic placement, we selected additional 48 species (Supplementary Data 1) of which the genomes were available on the basis of the following criteria: all published Dothideomycetes, one to five members of other ascomycetous orders and eight Basidiomycota from five orders selected to cover major orders as well as based on the diversity of lifestyles (biotrophic/hemibiotrophic/necrotrophic pathogens, white rot/brown rot/litter/aquatic saprotrophs and ericoid/ECM species).
ipk-gatersleben. de/misa/download/ misa. pl) with default parameters was used to identify mono-to hexanucleotide simple sequence repeat (SSR) motifs using default parameters. Tandem repeats finder  60  was used to identify minisatellites (motif between 7 to 100 bp) and satellites (motif above 100 bp) with the following parameters: 2, 7, 7, 80, 10, 50, 500 (Supplementary Data 2). Annotation of carbohydrate active enzyme (CAZyme) genes was performed using the CAZy database (www. cazy. org) annotation pipeline  61. To assess differences in the numbers of genes present within CAZy families among phylogenetic groups or lifestyles, we applied non-parametric median tests in SPSS (version 23, IBM) with false discovery rate (FDR) correction. The secretome was identified using a custom pipeline as outlined in Kohler et al.
1. Proteins predicted to contain a signal-peptide, but not predicted to localize within the endoplasmic reticulum or mitochondria, and displaying less than one transmembrane domain were considered as secreted proteins. They were classified into four functional categories: CAZymes, lipases, proteases and small-secreted proteins (SSP; proteins less than 300 amino-acids long with no CAZyme, protease or lipase domains). The secreted proteases were identified in the genome using the MEROPS peptide database (http://merops. sanger. ac. uk) and the secreted lipases using the Lipase engineering database (www. led. uni-stuttgart. de). Genes and gene clusters involved in secondary metabolism were predicted using a custom pipeline based on the SMURF method  20. Conserved protein domains were predicted using PFAM version 27 (ref.
php?manual=RNA_Seq_analysis. html) and as described before 1. To identify differentially regulated transcripts in mycorrhizal tissues compared with free-living mycelium, the test of Baggerly et al. 63  implemented in CLC Genomic Workbench and the Benjamini and Hochberg multiple-hypothesis testing to correct for FDR was applied to the data. If not otherwise indicated, transcripts with Z5 fold change and a FDR-corrected P value o0. 05 were kept for further analysis. To assess whether symbiosis-regulated transcripts were either conserved among fungal species or lineage-specific, their protein sequences were queried against the proteins of 50 fungal genomes (Supplementary Data 1) using BLASTP as indicated in Kohler et al. 1. The distributions of the symbiosis-regulated transcripts within each cluster were quantified according to their putative function as CAZymes, SSPs, with KOG classification or without KOG.
The enrichment of secreted proteins and SSPs in symbiosis-regulated transcript clusters was assessed by comparing their percentages in symbiosis-regulated transcript clusters to their relative percentage in the total gene repertoire and applying a Fisher's exact test. The data were visualized and hierarchically clustered by the binary distance metric and ward clustering for the analysis of five genomes (Fig. 4 ) and the euclidean distance metric and the average-linkage clustering method for the 50 fungal genome comparison (Supplementary Fig. 6 ) using R (package HeatPlus). Functional analyses of aquaporins in Cenococcum geophilum. For heterologous expression of selected aquaporins in Xenopus laevis oocytes, total RNA was isolated from C. geophilum/P. sylvestris ECM root tips using the NucleoSpin RNA Plant kit (-NAGEL GmbH, Du ¨ren, Germany) with a DNAse step according to the manufacturer's instructions.
First-strand cDNA was synthesized in a total volume of 20 ml, containing 50 pmol oligo-d(T)18-primer (Eurofins-MWG-Operon, Ebersberg, Germany), 0. 5 ml RiboLock RNase inhibitor (40 U ml À 1 ) and 200 U RevertAid Premium Reverse Transcriptase (Thermo Fisher Scientific, Waltham, MA, USA) according to the manufacturer's instructions. The coding regions of AQP genes were PCR amplified in a total volume of 20 ml from 0. 2 ml first-strand cDNA using a proof-reading DNA polymerase (Phusion, New England Biolabs, Beverly, MA, USA) according to the manufacturer's instructions with the gene-specific primers (Eurofins) named by the JGI protein IDs (Cenge3:690706, Cenge3:604158, Cenge3:647346 Supplementary Data 15). The coding regions of the selected AQPs were PCR amplified using a proof-reading DNA polymerase (Phusion, New England Biolabs) using the gene-specific forward primer and the pJET1.