dataesr/scientific-paragraphs-categorization huggingface dataset:
Mutation descriptions have also been standardised within GtoPdb. Future releases will link drugs to diseases via the clinical data tab (Figure  7 ) and provide new target-disease-drug navigation options. This will not only allow users to browse and search using disease names but also enable us to present disease pages containing lists of associated targets and ligands. We also intend to review our listings of single nucleotide polymorphism (SNP) variants, many of which are disease-associated.
WEBSITE FEATURES The following description includes some basic aspects for context, but focuses on the most important features added since the previous report. We have improved our help documentation and tutorials. This now includes a substantial set of frequently asked questions (FAQs) at http://www. guidetopharmacology. org/faq. jsp) that inform users on new features and data types. Enhancements have been made to the search tools to improve user experience of the website. The quick search box at the top right of every page and the advanced search pages for targets and ligands now include autocomplete functionality for target, target family and ligand names. Users are able to click on the matched name and go directly to the corresponding database page. We have also added support for the recognition of special characters such as Greek letters found in target names (e.
g. ␦ opioid receptor). Our ligand structure search tool uses the JavaScript chemical editor Marvin JS (ChemAxon Limited, Hungary), which replaces the Java applet version and offers cross-platform compatibility including for tablets and mobile devices. Searches now cover more database fields which allows, for example, searches by disease name to retrieve associated targets and ligands. As well as providing a variety of ways to search the database (e. g. name, keyword, database identifier or ligand structure), users can browse target and ligand lists according to their biological or chemical classification. To deal with the increasing size of the database and intersecting classifications for some targets (e. g. EC 3. 4 and protease) we have introduced a hierarchical organisation. Targets are grouped into families and subfamilies and visualised as a navigable HTML tree with expandable and collapsible nodes (see Figure  1  for example).
Each family has a linked database page including an overview, background reading and details of subfamilies or individual family member proteins. Alternatively, users may browse lists of ligands organised by chemical class or drug approval status. We have in-  troduced a new category of labelled ligands for those with radioactive incorporation or a fluorescent moiety. Labelled ligands are also indicated within bioactivity data tables using a new symbol. We have also added two other new symbols to bioactivity tables to indicate where the ligand is an approved drug, and (as described above) where the target can be considered the primary data-supported target of that ligand. Furthermore, the information curated in support of new interactions has been expanded to include affinity data and details of the assay used, accessible in the bioactivity table by clicking on the arrow at the right (e.
g. see the entry for ligand 'example 20 (WO2010128058)' in Figure  4 ). Our grant mandate to curate the MMOAs of approved drugs and clinical candidates has led to the introduction of various new features on the ligand pages. A new 'clinical data' tab provides summaries of clinical use, MMOA, as well as absorption, distribution, metabolism and excretion (ADME) data (Figure  7 ). Drug approval status is indicated along with the FDA and European Medicines Agency (EMA) first approval dates (a small number of drugs approved only in Japan are also included). ical structure matches to many databases and chemical vendors  (28).
CONNECTIVITY COLLABORATIONS We manually curate out-links to other databases that we judge as having utility for a significant fraction of users. This applies both for navigation and computational mining across linked data. For this reason, we continually review out-links and monitor the status of reciprocal in-links (but note there may also be in-links of which we are unaware). We also maintain a tradition of collaborative networking with most of these resources, with inter-team contacts often initiated at conferences and/or NC-IUPHAR meetings. A selection of those collaborative interactions that have had direct technical consequences for connectivity and with whom we have arranged reciprocity, is outlined in Table  5  (more of these are pending and we are open to new engagements). The overriding principle of collaborative crossreferencing is complementarity.
The expansion of our interactions with GPCRDB during 2014/15 exemplifies this, since both resources have had historically overlapping engagement with the human GPCR repertoire  (29, 30). This has now evolved into collaborative strategic curatorial divergence, while at the same time offering users differential features for the 365 human Swiss-Prot IDs we have in common. In general, this is manifested by quantitative ligand mapping and major clinical variant collation on the GtoPdb side, complemented by the emphasis on sequence/structure relationships on the GPCRDB side, which includes data on engineered substitution variants. In addition, we are in the process of harmonising both our web services to make it easier for users to make entity and data joins between the two resources.
Journal-to-database connectivity We have three initiatives in this area. The first of these is the production of the Concise Guide to PHARMACOLOGY (CGTP), published online as a series of PDF documents (and in HTML) at two-yearly intervals as a supplement in the British Journal of Pharmacology (BJP). CGTP provides succinct overviews of families of drug targets in the form of a desktop reference guide. The first of these appeared in 2013  (31) , with the second due for publication in November 2015. Thus, targets and ligands specified in the CGTP articles online are hyperlinked directly to the database records for users to navigate. To achieve this, the GtoPdb team and the CGTP editors collaborate with the Wiley publishers on what is, in effect, the automatic converting of (pre-tagged) sections of the database directly into the online CGTP PDF documents.
The second initiative, also a collaboration with the BJP, involves marking-up tables of links (ToLs) for both regular papers and reviews  (32). An example is a recent invited review on epigenetic pathway targets for the treatment of disease, which can be viewed at http://onlinelibrary. wiley. com/doi/10. 1111/bph. 12848/epdf (the ToLs are on the second page)  (33). This exemplifies a 'virtuous circle' from our special relationship with the BJP and NC-IUPHAR. The invited review provided the curatorial starting point for the capture of new ligands and targets to populate the database and these were consequently surfaced as ToLs in the article. The third journal-to-database initiative is a logical extension of the previous two  (32). This involves an updated version of the BJP instructions-to-authors that now includes recommendations on resolving the molecular identities of targets and ligands at the submission stage.
Another challenge we want to address concerns the search space, formal representation and rendering (i. e. to provide informative visualisations) for our 1981 peptide ligands. These are too small for BLAST-type peptide searches and too large for Tanimoto-based small molecule searching. In addition, many have post-translational and/or synthetic chemical modifications. This means the linear primary sequence we include is incomplete as a structural specification (although we use IUPAC nomenclature for some modifications if sufficiently detailed in the papers). We have been testing algorithmic approaches that can ameliorate some of these problems, in particular HELM  (37)  and Sugar & Splice (NextMove Software, Cambridge, UK) and look forward to the launch of PubChem Biologicals towards the end of 2015.
Our content of targets with quantitative ligand interactions constitutes a de facto druggable genome. The difference is that our 1228 target interactions are supported by data rather than possible chemical modulation being merely inferred via transitive extrapolation. So where might the upper limit be that we could expect to achieve with our stringent but successful curation model? One source of data to address this question is Swiss-Prot where key sources of curated chemistry-to-protein mappings, including our own, can be compared. The result is shown in Figure  8. The union of the four sources covers 18% of the human proteome. However, caveats (many of which are detailed in a 2013 database comparison study  (21) ) indicate this figure should be considered a maximum count. The proportion that would match our own criteria for quantitative mapping is difficult to estimate, since the chemistry-to-protein curation strategies and source selections for each database diverge considerably.
This is manifest in the relatively high unique content of 1147 (31% of the union). While they converge as a four-way intersect for only 490 proteins (13. 5% of the union), concordance between at least two sources (i. e. the non-unique proportion) expands to 2456. Notwithstanding, a capture goal of 2000-2500 data-supported tar-D1066 Nucleic Acids Research, 2016, Vol. 44, Database issue gets for GtoPdb seems plausible. This number is particularly relevant to the 'Illuminating the Druggable Genome (IDG) Program' recently launched by the National Institutes of Health (NIH) (https://commonfund. nih. gov/idg/ index). This is designed to expand our understanding (and drug targeting possibilities) of thinly annotated GPCRs, NHRs, ion channels and kinases. This specifically applies to 'orphans' within those classes hitherto without good chemical probes for function.
The fit with our objective is clear. However, it remains to be seen, when and what data will surface that could be of use for curatorial expansion of the druggable genome within GtoPdb. We plan to add enhanced query building functionality to the website allowing users to paste in lists of identifiers to retrieve targets and ligands, to choose their selection of output fields and build customised downloads. This will be accompanied by development of new browsing options and alternative entrance portals presenting a subset of the data but linked to the main database and designed for specific target audiences. One such example would combine information on targets, diseases and drugs relevant to immunology with tools to access pharmacological data. Furthermore, we are exploring options for providing access to our data in Resource Description Framework (RDF) format, which can be readily integrated in semantic web projects such as Open-PHACTS  (38).
GtoPdb is available online at http://www. guidetopharmacology. org under the Open Data Commons Open Database License (ODbL) (http://opendatacommons. org/licenses/odbl/), and its contents are licensed under the Creative Commons Attribution-ShareAlike 3. 0 Unported license (http://creativecommons. org/licenses/by-sa/3. 0/). Information on linking to our pages is provided at http://www. guidetopharmacology. org/linking. jsp. W e aim for three database public releases per year: the statistics quoted in this paper are from release 2015. 2 (i. e. August 2015). The number of entries we deprecate between releases is low, but in rare instances an entry revision could result in a dead link in a past release. Our downloadable files include all target lists, NC-IUPHAR nomenclature, synonyms, genetic information, protein identifiers and other database accessions.
Ligand downloads include isomeric SMILES and InChI strings that can be used to generate structuredata (SD) files. We can be contacted regarding other file formats or some of the custom data slices specified in recent slide presentations (enquiries@guidetopharmacology. org). Users can also download our UniProtKB and HGNC cross-links. A simple PubChem query ('IUPHAR/BPS Guide to PHARMACOLOGY'[SourceName]) will retrieve our entire CID content (those wishing to source our local database links for these should use the corresponding SID query). The PubChem records should be synced within approximately two weeks of our release date but note it may take a little longer for all pre-computed relationships to be fully indexed. To further facilitate distribution, we have developed an application program interface (API) in the form of REST web services to provide computational access to the data.
This uses JavaScript Object Notation (JSON) as a lightweight data-interchange format that is simple for humans to read and write as well as for machines to parse and generate. JSON can be readily integrated into other websites using JavaScript. In the past, we have made an SQL dump file for download. This remains available but in response to user requests we have added a MySQL (Oracle Corporation, Redwood Shores, CA, USA) version migrated from PostgreSQL (http://www. postgresql. org/). This was created using MySQL Community Server version 5. 6 on Windows, and the migration conducted with MySQL Workbench 6. 2. Note that usage requires UTF-8 4-byte support using the utf8mb4 character set. We also plan to enhance our Entity Relationship Diagram for advanced users. Since our 2014 publication, we have noted that our content has been integrated into various academic resources including CARLSBAD  (39)  and ChemProt 2.
0  (40). In addition, we have also been informed of incorporation into some pharmaceutical company knowledgebases, such as the AstraZeneca internal Chemistry Connect system (Dr Plamen Petrov, personal communication)  (41). We would ask groups (academic or commercial) interested in incorporating our data into their own resources, to contact us at the outset of their integration process so that we can assist with any technical issues that might arise on our side. The retirement of IUPHAR-DB (the precursor of GtoPdb) over two years ago  (42)  still produces global persistence and propagation problems. Redirects have been applied wherever possible, but users need to be circumspect if they come across secondary sources that still include IUPHAR-DB identifiers (if you notify us we can contact the parties concerned about substituting GtoPdb links).
ACKNOWLEDGEMENTS The authors wish to thank all members of  NC-IUPHAR  for their continued support (http://www. guidetopharmacology. org/nciuphar. jsp#membership). This includes the following members who are not already authors listed on this manuscript:  T. I. Bonner ,  E. A. Bruford ,  A. Christopoulos ,  J. A. Cidlowski ,  C. T. Dollery ,  S. Enna ,  K. Kaibuchi ,  Y. Kanai ,  R. R. Neubig ,  E. H. Ohlstein ,  A. N. Phipps  and  U. Ruegg. We also thank the global network of NC-IUPHAR subcommittees and all the CGTP contributors (a full list of subcommittee members and contributors can be viewed at http://www. guidetopharmacology. org/GRAC/ ContributorListForward). We thank  V. Divincova  for administrative support. In addition to our primary funding, we are grateful for sponsorship from the  American Society for Pharmacology and Experimental Therapeutics (AS-PET) ,  Laboratoires Servier  and  The University of Edinburgh.
Analytical techniques The technical setup for NCS analyses using purge and trap technology is extensively described in Fourel et al 2014  3  and is illustrated in figure  1. The system we used is an Elementar Pyrocube that consists of a combustion oven using tungsten oxide as an oxidant operating at 1150°C, then a reduction oven filled with reduced copper operating at 850°C connected to a chemical water trap filled with sicapent dryer. This is similar to traditional EA setup. After the water trap, the analyte gases are no longer separated on a packed GC column but enter the original purge and trap module. N 2 is not trapped and is then introduced first in the source of the IRMS. CO 2 and SO 2 are then trapped respectively in 2 separate traps. The CO 2 trap is then heated at 110°C to release CO 2 followed by the SO 2 trap which is then heated at 220°C.
Analyte gases are then introduced in the source of an IsoPrime IRMS through an open split in parallel with pulses of N 2 , CO 2 and SO 2 calibration gases. The resulting chromatogram is shown in figure  3. Figure  3  : Chromatograms for a typical NCS analysis using a Pyrocube purge and trap system comparing the SO2 peak shape from a) conventional EA setup isotopic traces with b) purge and trap EA setup isotopic traces. SO2 chromatographic peaks from masses 64 and 66 show significant improvement on the purge and trap chromatogram. C) shows the TCD traces measured from the Pyrocube software allowing the determinations of N, C and C compositions. The sample analysed here is our working calibrated material: rubeanic acid.
Stable isotope analyses of human tissues can generate significant information regarding diets of ancient Egyptians. For instance, it can indicate the various proportions of plant and animal proteins, the proportions of terrestrial or aquatic food or the proportions of C3 and C4 plants. During this study  5  we have determined 13 C values of hairs from ancient Egyptians. Those data are coherent with diets consisting of low amounts of C4 plant-derived food products as well as for inhabitants who lived in the Nile valley or oases despite different agricultural techniques. EA-IRMS 13 C values of hair are also coherent with 13 C determinations obtained from apatite carbonates analyzed by using acid digestion techniques. The lower 13 C values obtained for Coptic mummies might be explained by the consumption of olive oil introduced in Egypt during the Roman period.
Moreover, 15 N and 34 S values of human and animal tissues from the studied area have been represented in figure  6. 15  N variations recorded in antelopes and gazelles are interpreted as resulting more from aridity than a specific diet. Thus, the use of 15 N alone may not be sufficient for reconstructing paleo-diets but it has to be coupled with another proxy such as 34 S. Indeed, combinations of 15 N and 34 S obtained from various human and animal samples tend to illustrate that the consumption of fish such as the Nile perch remained limited as indicated by the data obtained from hairs of Coptic mummies. This study appears as a good example of the importance of the multiisotopic approach in the field of archeology.
In this paper, we draw attention that a large part of idle MRs results in considerable energy losses. Thousands of WMNs worldwide jointly aggravate this problem, while raising serious worry about the energy wastage that will happen in the future. In response to this problem, we first propose an optimal approach called Optimal Energy Efficiency Management Design (OEEMD) by formulating the problem as a Mixed Integer Linear Program (MILP) model to investigate the power consumption in WMN. As this problem is known to be NPhard, we then propose an efficient heuristic algorithm called Green Energy Efficiency Management Design (GEEMD) for wireless mesh networks. GEEMD uses a sleep mechanism to power on/off underutilized MRs, based on the traffic load and solar energy generation. For the performance evaluation, we used OEEMD and GEEMD to study the performance of WMN deployment scenario in the university and campus area of Tlemcen city in Algeria over dynamic traffic profiles.
The key objective of the proposed work is to optimize network energy consumption by putting under-utilized MRs (Electrical Power Supply Mesh Routers ACMR and Green Energy Supply Mesh Routers GMR) to sleep state by following variation of traffic load and solar energy generation during the hour's intervals of the day. To do so, we first propose an optimal approach called Optimal Energy Efficiency Management Design (OEEMD) by formulating the problem as a Mixed Integer Linear Program (MILP) model to investigate the power consumption in WMN. As this problem is known to be NP-hard, we then propose an efficient heuristic algorithm called Green Energy Efficiency Management Design (GEEMD) for wireless mesh networks. GEEMD uses a sleep mechanism to power on/off underutilized MRs, based on the traffic load and solar energy generation.
OEEMD problem formulation WMN represents hierarchical access architecture with routers as the original traffic aggregation points. Gateways are the highest aggregation point to access network with the rest of the Internet. To achieve the above defined goal, we propose an optimal approach, called Optimal Energy Efficiency Management Design (OEEMD) by formulating the problem as a Mixed Integer Linear Program (MILP). As this problem is known to be NP-Hard, we then propose an efficient heuristic algorithm called Green Energy Efficiency Management Design (GEEMD), to solve the formulated MILP problem. Detailed descriptions of optimal approach are in the following section.
Conclusion In this paper, we draw attention that a large part of idle MRs results in considerable energy losses. Thousands of WMNs worldwide jointly aggravate this problem, while raising serious worry about the energy wastage that will happen in the future. In response to this problem, we first propose an optimal approach called Optimal Energy Efficiency Management Design (OEEMD) by formulating the problem as a Mixed Integer Linear Program (MILP) and developed a model that can act as a guideline on designing green WMN. As this problem is known to be NP-hard, we then propose an efficient heuristic algorithm called Green Energy Efficiency Management Design (GEEMD) for wireless mesh networks. The key objective of the proposed work is to optimize network energy consumption by putting under-utilized MRs (Electrical Power Supply Mesh Routers ACMR and Green Energy Supply Mesh Routers GMR) to sleep state by following variation of traffic load and solar energy generation during the hour's intervals of the day.
For the performance evaluation, we used OEEMD and GEEMD to study the performance of WMN deployment scenario in the university and campus area of Tlemcen city in Algeria over dynamic traffic profiles. Both approaches are used to set and find a good compromise between solar energy generation and energy consumption using a parameterized objective function. Through extensive simulations, illustrative numerical examples show that, with suitable design parameters, GEEMD achieves significant gains in terms of energy consumption; we demonstrated it can be easily implemented. We conclude that the Green Energy Efficiency Management Design techniques applied on WMN can be generalized so that they are also applicable to other access networks. Our next step is to evaluate the performance of GEEMD with the dartmouth/campus dataset of CRAWDAD that provides real localization of APs of the Dartmouth campus  [30].
TCRVb repertoire of wild-type and Nr2f6-deficient T lymphocytes prior or post-B16-OVA tumor challenge from naive or tumor dLN was investigated via clonality analysis of the individual Vb gene families of the CDR3 region. These data indicated unaltered clonal composition of the T cell compartment between wild-type and Nr2f6-deficient T cells in tumor-bearing mice  (Figures S2A and S2B). Furthermore, 75% of Nr2f6 À/À mice injected with the EG7 lymphoma cell line completely rejected EG7 tumors and remained tumor-free throughout the entire 10-month experimental period; in contrast, all Nr2f6 +/+ mice challenged with EG7 cells had to be sacrificed as early as day 21 post-injection because of their high tumor burden  (Figures 2C and 2D). Of note, comparing genetic NR2F6 deletion with blocking interaction of PD-1 with its ligand PD-L1 provides evidence that the observed superior anti-tumor immunity in Nr2f6 À/À mice is well comparable to the benefit of PD-L1 blocking therapy in wild-type mice (employing the established protocol with neutralizing Ab10F.