dataesr/scientific-paragraphs-categorization huggingface dataset:
B. Upper view from the extracellular side. The protein is shown in Newcartoon drawing method. Transmembrane a-helices (M1-M4) are in blue, extracelluar b-sheets are shown in red, and coils in yellow. (PDF) Figure S5 Chemical structures of ivermectin and emamectin. Sites involved in H-bond interactions with CrGlua as suggested by the docking assays are indicated. M2 and M3 belong to one subunit whilst M1 is in the neighbouring subunit. Images are from the PubChem (http://pubchem. ncbi. nlm. nih. gov/) database. (PDF)Table S1 Primers used in PCR reactions at various stages of the cloning of C. rogercresseyi GluCla receptor. (PDF).
Acknowledgments We are grateful to  Rodrigo Balladares  ( Sernapesca, Aysen, Chile ),  Guillermo Carcamo  ( Universidad Austral, Valdivia, Chile ) and the late  Rodrigo Scheihing  ( CECs, Valdivia, Chile ) for kindly providing us with Caligus rogercresseyi specimens. Camila Navas  provided invaluable help with the docking analysis.
A: Vista lateral view. B. Upper view from the extracellular side. The protein is shown in Newcartoon drawing method. Transmembrane a-helices (M1-M4) are in blue, extracelluar b-sheets are shown in red, and coils in yellow. (PDF) Figure S5 Chemical structures of ivermectin and emamectin. Sites involved in H-bond interactions with CrGlua as suggested by the docking assays are indicated. M2 and M3 belong to one subunit whilst M1 is in the neighbouring subunit. Images are from the PubChem (http://pubchem. ncbi. nlm. nih. gov/) database. (PDF)Table S1 Primers used in PCR reactions at various stages of the cloning of C. rogercresseyi GluCla receptor. (PDF).
Bioinformatics analysis All sequences obtained in this study were subjected to blast searching on the NCBI website: http://www. ncbi. nlm. gov/blast/ cgi, using the program BLASTn. A search for the presence of putative signal peptides, and the cleavage site in the N-terminal part of the protein, was carried out using the software tools PrediSi (http://www. predisi. de/home. html) and SignalP (http://www. cbs. dtu. dk/services/SignalP/). Phylogenetic analysis was performed using the software MEGA 5. 2. The different genes comprising the rap-1 locus were analysed by the ''neighbor-joining'' method based on genetic distances. A tree was also constructed by including RAP-1 sequences from other species of Babesia spp. , such as B. gibsoni, B. caballi, B. ovis, B. bovis, B. orientalis and B. divergens, in order to show the relationships between the isolates in our study and relate them to other RAP-1 proteins.
Nucleotide accession numbers All sequences have been deposited in GenBank with the following accession numbers: KJ205322 (rap-1a61-2 and IG1. 1), KJ205323 (rap-1a67-1 and IG1. 2), KJ205324 (rap-1a61-3), KJ205326 (rap-1a67-2 and IG3), KJ205325 (rap-1b and IG2), and KJ205327 (rap-1c) for the Babesia sp. BQ1 Ningxian isolate; KJ205328 (rap-1a61-2 and IG1. 1), KJ205329 (rap-1a61-3), KJ205330 (rap-1a67-1), KJ205332 (rap-1a67-2 and IG3), KJ205331 (rap-1b and IG2), and KJ205333 (rap-1c) for the Babesia sp. Tianzhu isolate; KJ205334 (rap-1a1 and IG1), KJ205335 (rap-1a2), KJ205336 (rap-1b and IG2), KJ205337 (rap-1a3 and IG3), and KJ205338 (partial rap-1c) for the Babesia sp. Hebei isolate.
The identities of the isolates which had provided the genomic DNA sent on an FTA card from China were confirmed by re-sequencing the 18S rDNA gene. The sequencing results were consistent with previously published data  (Liu et al. , 2007). Phylogenetic studies based on this gene placed Babesia sp. Hebei on a separate branch from Babesia sp. Tianzhu, Babesia sp. BQ1 Lintan and Ningxian  (Lack et al. , 2012; Gou et al. , 2013). This phylogenetic separation was confirmed by sequencing the rap-1 locus, which is almost identical (about 99. 9%) over about 31 Kb in Babesia sp. Tianzhu, Babesia sp. BQ1 Ningxian and Lintan (Table  4 ). In contrast, the rap-1 genes and intergenic regions in Babesia sp. Hebei clearly differs from the three other isolates, displaying on average 78% similarity at the nucleotide level (including IG regions).
Acknowledgements This study was supported financially by a grant from the  European Union cooperation  project  PIROVAC  ( KBBE-3-245145 ) and the  « Région Pays de Loire » (France). The authors would like to thank  Profs. Hong Yin  and  Guiquan Guan  from  Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural sciences , for their assistance to kindly send genomic DNA from China to France for this research.
Tooling The aforementioned approach has been applied with TTool  [12]. TTool is an open-source toolkit that supports several UML/SysML profiles, including TURTLE  [3]  , DIPLODOCUS  [4] , and AVATAR  [27, 18]. TTool includes diagramming facilities, formal and non-formal code generators, model simulators, modelcheckers and analysis tools. DIPLODOCUS is focused on the design space exploration of complex embedded systems. TURTLE is now deprecated and replaced with AVATAR. the latter targets the design of embedded applications. The main strength of TTool is to hide knowledge of the underlying simulation or formal proofs techniques, thus offering a press-button approach to perform safety or security proofs. We have used the SysML-Sec profile as a part of the AVATAR profile to build attack trees.
On the other hand, since we obtain an OWL described document, we can integrate our SysMLsec models with any other ontology based applications such as integrating the security requirement knowledge with security resource annotating approaches like  [11] , or providing input to the ontology based risk analysis approaches  [3]  in order to compute risk metrics. In this paper, we refer to SQWRL (Semantic Query Web Language)  [16]  as a query language because of its concise, readable, and semantically robust semantic. SQWRL is a SWRL-based  [7]  query language that can be used to query OWL ontologies and provided in Protégé 4. 2 beta version  [23]. To retrieve knowledge from OWL ontology, SQWRL provide SQL-like operation. The form of rule is antecedent → consequent In this rule an antecedent part is referred to as the body, and a consequent part is referred to as the head.
Choice of the models At each phase of the key generation it is important to use models for elliptic curves that offer the fastest formulas for doubling, addition, isogeny computation and evaluation, etc. To measure efficiency, we count the number of elementary operations in F p 2 : we write I; M; S for the costs of one inversion, multiplication and squaring respectively, and we make the assumption S Ä M Ä I. We neglect additions, subtractions and comparisons. We refer to the Explicit Formulas Database (EFD)  [4]  for operation counts of elliptic point addition, doubling, etc. in various models and coordinate systems. Contrary to the convention taken in the EFD, we count multiplications by constants (other than small integers) as ordinary multiplications. Any curve used in our cryptosystem has group structure.
Implementation results and example To evaluate the performance of our schemes, we implemented the key exchange protocol in the computer algebra system Sage  [40]  using a mixed C/Cython/Sage architecture. This allows us to access the large palette of number theoretic algorithms distributed with Sage, while still permitting very efficient code in C/Cython for the critical parts such as the algorithms of Section 4. 2. The source code can be downloaded from De Feo's web page. Arithmetic in F p 2 is written in C. We use the library GMP for arithmetic modulo p. The field F p 2 is implemented as F p 2 OEX=. X 2 C1/ (this requires p D 3 mod 4); using this representation, one multiplication in F p 2 requires three multiplications in F p , one squaring requires two multiplications in F p , and one inversion requires one inversion, two squarings, and two multiplications in F p.
Our experiments show that, for the sizes we are interested in, I D 10M and S D 0:8M. The computation of the optimal strategies as described in Section 4. 2. 2 is done in pure Python, using a dynamic programming algorithm. We implemented the key exchange algorithm in C for `D 2; 3, and in Cython for any `. Our experiments show that, in the C implementation, the ratio between scalar multiplications and isogeny evaluations is about 2, which is consistent with the predictions made in Table  1. We used this ratio to tune the optimal strategies for `D 2; 3: starting from 768 bits, a gain over the balanced strategy, comprised between 1% and 3%, starts getting noticeable. The performances of 3-isogenies are comparable to those of 2-isogenies, thus contradicting the prediction made by Table  2 ; this is explained by the operations not accounted for in Table  2 , such as the computation of the isogenies and of the isogenous curves (3-isogenies gain a factor of about log 3 2 on these operations).
H-MRS Acquisition MR data were acquired on 3-Tesla Siemens Tim Trio MRI (Siemens, Erlangen, Germany), using 32-channel head coil. Each exam included the acquisition of sagittal 3D T 1 -weighted MP-RAGE anatomical images (repetition time/echo time = 2300/ 2. 9 ms, inversion time = 900 ms, flip angle = 9u, averages = 1, FOV = 25662406192 mm, matrix = 25662406160) covering the entire brain. Anatomical images guided the placement of a 20 mm isotropic voxel on leg and foot regions of right PMC. Both water suppressed (averages = 192) and unsuppressed (averages = 8) spectra were localized by point resolved spectroscopy (PRESS, repetition time/ echo time = 2000/135 ms, voxel size = 8 cm 3 ). Data processing included lineshape correction and removal of residual unsuppressed water signal  [10, 12]. Resultant metabolite spectra were fitted in the time domain incorporating a template of prior knowledge of metabolite lineshapes including N-acetylaspartate (NAA) and creatine (Cr).
Statistical Analysis Comparisons between participants separated into two groups based on NAA/Cr in cPMC (#1. 17 or. 1. 17) were performed using two-sided Mann-Whitney U-test or Chi-square test, as appropriate. Multivariate linear regressions were used to examine the association between lower vitamin D status (independent variable) and NAA/Cr in cPMC (dependent variable), while adjusting for clinical characteristics, neuroimaging measures, and serum measures. P-values,0. 05 were considered significant. All statistics were performed using SPSS (v19. 0, IBM Corporation, Chicago, IL).
Acknowledgments The authors have listed everyone who contributed significantly to the work in the Acknowledgments section. Permission has been obtained from all persons named in the Acknowledgments section. 2  Susan W Muir, PhD , and  Karen Gopaul, MSc , from the  'Gait and Brain Lab', Lawson Health Research Institute, the University of Western Ontario, London, Ontario, Canada  for their help in the participants' assessment and data gathering. There was no compensation for this contribution. 2  Irene B Gulka, MD , from the  Department of Radiology, London Health Sciences Centre, London, Ontario, Canada , for her kind advice. There was no compensation for this contribution. 2  Izabella Kowalczyk ,  MSc , and  Sandy Goncalves, MSc , from the  Robarts Research Institute, Department of Medical Biophysics, Schulich School of Medicine and Dentistry , the  University of Western Ontario, London ,  Ontario ,  Canada , for their kind advice.
This project was supported by the  Canadian Institute of Health and Research (CIHR)  (Principal investigator:  Manuel Montero-Odasso ; grant number  211220 ). Dr. Annweiler is supported by a grant from the  Canadian Institutes for Health and Research -Institute of Aging (CIHR-IA) , and holds a research grant from  Servier Institute in France. Dr Montero-Odasso  is supported by grants from  CIHR-IA; Drummond Foundation; Physician Services Incorporated Foundation of Canada (PSI) ;  Ontario Ministry of Research and Innovation ; and by  Western University Department of Medicine Program of Experimental Medicine (POEM)   Research Award. He is the first recipient of  Schulich Clinician-Scientist Award and of CIHR New Investigator Award. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
These data can reveal when and where the fish were located as well as when and where they were being exploited. The data can then be compared in time and space and used in models with other data (e. g. fishing effort, fish sizes and ages) to estimate whether populations are stable, declining or increasing. When provided with this knowledge, fishery managers can make decisions on future quotas and identify possible conservation actions (e. g. minimum size limits, implementation of closed areas or seasons for fishing) that can reduce the risk of stock collapses and local extinctions.
These data will help with studies into understanding the causes of fluctuations in abundance and distribution of these species and contribute to the wider objectives of a large European Union project (EURO-BASIN) investigating how climate variability and fisheries affect food webs and biogeochemical fluxes in the off-shelf regions of the northeastern Atlantic Ocean. Additional brief backgrounds of the species biology and fisheries are given in the Methods section. The survey and catch data reported here have been submitted to the PANGAEA website.
2 dB)  (ICES, 2011; Pedersen et al. , 2011) , but the abundance estimates in the PANGAEA database are based on the previously used function. To estimate the total abundance of fish, the unit area abundance for each statistical square was multiplied by the number of square nautical miles in each statistical rectangle and then summed for all the statistical rectangles within defined subareas and over the total area. Biomass was calculated by multiplying abundance in numbers by the average weight of the fish in each statistical rectangle then summing all rectangles within defined subareas and over the total area. The Norwegian BEAM software  (Totland and Godø, 2001)  was used to make estimates of total biomass and numbers of individuals by age and length in the whole survey area and within different subareas.
2. 3 The pelagic survey in the Bay of Biscay (PELGAS) Acoustic surveys were carried out every year in the Bay of Biscay in May from 2000 to 2012 (except 2001) onboard the research vessel Thalassa, which is equipped with a Simrad EK60 echo sounder operating at five frequencies  (18, 38, 70, 120 and 200 kHz; 7  • beam angle at -3 dB and 1. 024 ms pulse length for all frequencies) at 6 m depth on the fixed vessel keel. Only the data collected at 38 kHz were used here. The survey protocol for acoustic data collection has been stable since 2000. Systematic parallel transects (12 nmi distance) perpendicular to the French coast were carried out. The survey covered the continental shelf from 20 m depth to the shelf break about 200 m (in certain years more offshore). Acoustic data were only collected during daytime.
During night the pelagic target species are usually dispersed and found close to the sea surface, and therefore they "disappear" in the blind layer of the echo sounder, which extends between the surface and 8 m depth. The calibration method has been stable over time. Acoustic data were acquired with the Movies+ and Hermes software and archived in the international hydroacoustic data format (HAC) with a -100 dB threshold applied. The identification of species and size classes comprising fish echo traces heavily depends on identification via trawl hauls performed by R/V Thalassa using a pelagic trawl (two doors; headline: 76 m; foot rope: 70 m). Echograms were scrutinised in real time and trawl hauls were performed as often as possible. The criteria for performing an identification haul included observation of numerous fish echotraces over several elementary distance sampling units (EDSUs) or of very dense fish echotraces in one EDSU, changes in the echotrace characteristics (morphology, density or position in the water column), and observation of an echotrace type fished on previous transects but never fished on the current transect.
Bluefin tuna catch data in northern European waters (ICES areas II-VII) Bluefin tuna catch data were extracted from the ICES catch databases for the time periods  1906-1949 and 1950-2010. The catch database versions used in this report were those available on the ICES website (http://www. ices. dk/marine-data/dataset-collections/Pages/ Fish-catch-and-stock-assessment. aspx) and downloaded on 21 January 2014. The data in the ICES catch databases are derived from ICES Bulletin Statistiques; these data are annual statistical reports containing fishery data for northeastern Atlantic countries  (Lassen et al. , 2012). The data contained in those reports have been entered by ICES into the existing catch database. The data contained in the ICES catch database are resolved by species, year, country and sea region (known as ICES areas).
For this report, we were most interested in the sea areas north of the Bay of Biscay, because this was the former summer foraging habitat until its disappearance in the 1970s and because catches in this region have undergone large fluctuations (see above) whose reasons remain unclear  (Tiews, 1978; Mather et al. , 1995; MacKenzie and Myers, 2007; Fromentin, 2009). We extracted data by year, country and region and made time series plots of the data to illustrate and compare temporal- spatial trends and to identify which countries and/or regions had the largest catches. We are also aware of some historical catch data which are not presently included in the ICES catch databases; these data have been identified in various fishery reports, museum records and scientific literature (MacKenzie and  Myers, 2007) , and are included in the database described here and available at the PANGAEA website.
The data sources not included in the ICES catch database are identified with notes in the online data files. The ICES areas considered are only part of the entire spatial domain over which the bluefin tuna fishery is managed, which covers the North Atlantic east of 45 • longitude and includes the Mediterranean and Black seas  (ICCAT, 2012). Catches for these areas (i. e. northeastern Atlantic and Mediterranean, including the Black Sea) are available from the International Commission for the Conservation of Atlantic Tunas (ICCAT), which is the agency responsible for management of bluefin tuna in the Atlantic and Mediterranean. We extracted and plotted the ICCAT catch data (total international landings) to enable comparison of the total international landings in the entire stock management area with the ICES data for the northern region.
1 ). Data on herring distribution and aggregation patterns from the IESNS survey from 2004 to 2012, are provided in PANGAEA. Examples of the data on herring distribution from this survey are shown in Fig. 6. For a more comprehensive review of the distribution of NSS herring, see  Utne et al. (2012). There are various sources of uncertainty related to acoustic estimates for pelagic fish stocks. For NSS herring, inadequate coverage of the distribution area and active vessel avoidance by the herring are the two most important factors contributing to uncertainty in the acoustic abundance estimate  (Løland et al. , 2007).
Bluefin tuna catches -temporal, spatial and national distributions The There is a notable omission of data from the version of the ICES catch database downloaded for this work. The data omitted are those from Norway. These data are, however, present in the original ICES Bulletins Statistiques and have been reported earlier in the literature (e. g. MacKenzie and Myers, 2007) , and they are also included in the ICCAT database. The omitted landings are substantial because Norway had the largest catches of all countries in the region considered in this report until the fishery in this area declined in the mid-1960s to early 1970s. Moreover, Norway was the first country in this region to report its landings to  ICES (starting in 1927; ICES, 1903 ICES, -1972)). The Norwegian landings which are in the ICES , but which are omitted from the ICES catch database, are those for 1927-1949 for areas II, III and IV.
Aside from the omission of early officially reported Norwegian landings, there are additional landings by Norway  (Tangen, 1999)  and a few other countries from the early decades of the 20th century which are not included in the ICES  or the present version of the ICES catch database. The earliest bluefin tuna catches noted (1907) were those by a French herring boat fishing at Dogger Bank in the southern North Sea. Several more years of French tuna catches in this area are available in French fishery reports (Statistiques de Pêches Maritimes; MacKenzie and  Myers, 2007). Norwegian, Swedish and Danish fishermen also caught bluefin tuna in the Norwegian Sea and Skagerrak-Kattegat in the 1910s-1920s, and thus before these governments began reporting the landings to ICES (Fig. 12 ). In comparison with available catch data from ICCAT, the ICES data compilation presented here covers a longer time period and has higher spatial resolution (e.
g. to regional sea level); however its geographic range is limited to waters of northern Europe, whereas the ICCAT database contains data from countries of southern Europe, including the Mediterranean Sea. A summary of historical bluefin tuna catches in the northwestern European waters with maps of catch locations is presented in  LeGall (1927). The vast majority of the catches (96 % by weight in the whole time period 1906-2010) were taken in areas II-IV, and the rest were in areas VI-VII. Norway, Denmark, Germany, Sweden and France were responsible for 99. 8 % of the total reported landings during 1906-2010; Norway's share was largest (73 %), followed by Denmark (11 %, Fig. 13 ).
Acknowledgements. We thank  Øyvind Tangen  for preparing IESNS data on NSS herring and blue whiting, and acknowledge all the people who went to sea and collected these valuable observations. We thank all countries which have provided data to ICES and ICCAT, as well as both organisations for maintaining catch databases. Paul J. B. Hart  and  David Miller  are acknowledged for valuable comments on a previous version of the paper. This work received funding from the  European Union Seventh Framework Programme  project  EURO-BASIN (ENV. 2010. 2. 2. 1-1 ) under grant agreement no. 264933. Edited by:  G. Melvin.
This thesis introduces a novel video and audio analysis framework, able to meet the above mentioned needs by analyzing the low-level data acquired by video and audio peripherals and generating high-level information, in terms of events the human operator is interested in. Audio and video information, complementary for their nature, are used and properly combined in order to face different situations: in fact, a video analysis system is not feasible for every context, although it is able to provide detailed information about the scene; think, as an example, to a public toilette where it is obvious impossible to install a camera or to environments with too many obstacles, where a full covering would result in a large expense. Different typologies of events can be recognized by the proposed system.
No systems able to integrate audio and video information for security purposes have been proposed; furthermore, the analysis of behaviors through video as well as audio inspection is a very young field, and the scientific community is very active since a definitive solution to the problem has not been found yet. This thesis aims to bridge over these considerations: it introduces a novel and efficient system for analyzing behaviors through a proper combination of video and audio analysis, by joining together different knowledge and skills, ranging from pattern recognition and computer vision to databases and signal processing.