dataesr/scientific-paragraphs-categorization huggingface dataset:
5-87. 8%) like in the HTS data. The Tubificidae and Spionidae (Malacoceros) are also present in both datasets, but their abundance is much higher in the HTS data. In contrast, the nematodes show reverse pattern, being much more abundant in morphological counts (9. 1-33. 3%) than in the DNA/RNA samples from the AZE stations. This high congruence between morphological and HTS data in AZE stations is clearly less pronounced in the distant samples. The morphological inventories of these samples are largely dominated by molluscs (31. 3-39. 3%) and ophiuroids (10. 7-14. 7). Both groups are represented by few OTUs, which relative abundance is generally low (except Corbula gibba). Several taxonomic groups represented by the OTUs common in distant stations, such as Hydrozoa or Acoelomorpha, are absent from morphotaxonomic inventories.
4 ). Interestingly, the station 10, situated on the other side of the fish farm, at distance of 76 m, shows ITI and AMBI values similar to the distant stations, presumably because this station was oriented perpendicularly to the direction of the residual current, corresponding to the main depositionary axis of the fish farm. The correlation between values inferred from morphological and molecular data is very high for both ITI (DNA: R 2 = 0. 866, RNA: R 2 = 0. 974) and AMBI indices (H-AMBI DNA: R 2 = 0. 821, RNA: R 2 = 0. 898; S-AMBI DNA: R 2 = 0. 899, RNA: R 2 = 0. 855; M-AMBI DNA: R 2 = 0. 811, RNA: R 2 = 0. 868) (Fig. 5 ). In the case of AZE stations, the values of ITI and AMBI indices inferred from DNA/RNA data are higher than those based on morphological analyses. This difference is particularly pronounced in the case of station 5 (50 m off the cage), with ITI, H-AMBI, S-AMBI and M-AMBI values inferred from DNA being 30.
Moreover, the metabarcoding will allow extending the range of potential bio-indicators to meiofauna  40, 41  and protists  21, 42. Compared to the morphological approach, metabarcoding provides a more holistic view of the metazoan taxonomic diversity, regardless of the size and developmental stage. Our HTS data not only include the macrofaunal species that dominate in the morphological samples, but also small-sized (<1 mm) species, extending the scope of analysis to the much broader meiofaunal diversity. In fact, taxa such as harpacticoid copepods, ostracods and many minor groups (gastrotriches, kinorhynchans, rotifers) are currently not included in the morphology-based bioassessments. Yet, some of them (kinorhynchans, turbellarians, ostracods) have been shown to be good indicators for assessing the impact of finfish  43  and shellfish farming  44.
The main shortcomings involve the incompleteness of reference sequence databases as well as the fragmented knowledge on meiofaunal autecology. Despite considerable barcoding efforts, reference sequences are still very rare for benthic meiofaunal species. Given the prevalence of meiofauna in molecular assemblages, it is crucial to further describe potential meiofaunal bioindicator taxa not only through their genetic identification but also to specify their ecological values. Indeed, only half of the taxa detected with HTS could be ascribed to an ecological group and a unique ecological value is often assigned to an entire meiofaunal phylum. For instance, the nematodes form a hyper diverse group  48  , but all are ascribed to the same AMBI ecological group. Given the immense phylogenetic diversity of meiofaunal groups, the relevance of these values and thus of the inferred indices is doubtful.
It is also important that molecular databases include more than one gene. At present, most of benthic metazoans are represented either by 18S rRNA gene or COI gene. Although these markers have different advantages and offer different taxonomic resolutions, both suffer similar limitations related to database incompleteness and primer specificities, and ideally should be coupled as in a recent comparative study  49. We chose the V4 fragment of the 18S because it is shorter and easier to PCR amplify and sequence using Illumina technology. However, the resolution of the V4 region is limited for species-level assignments and has been shown to provide less accurate diversity estimates than COI  31. Nevertheless, some key species could only be detected using the 18S marker, as Malacoceros fulginosus, for which there is no reference COI sequence.
With upcoming extensions of Illumina sequencing read lengths, it will be possible to sequence the full COI barcode, which might be more informative for future metazoan-based metabarcoding. Alternatively, improvements are being proposed towards the design of new primers targeting shorter COI fragments  50  or new amplification strategies  51. However, the COI marker is a protein-coding gene and thus remains less suitable than the 18S rDNA marker for the design of universal primers  40. Moreover, the use of COI is hampered by difficulties in assigning higher taxonomic level to those sequences that lack close correspondence in the reference database  52. Finally, it is necessary to expand the dimension of DNA sequence databases by gathering knowledge on gene copy numbers and polymorphisms. With this new information on intra-genomic variation in hand, it will be possible to refine sequence taxonomic assignments and quantitative ecological inferences.
Acknowledgments The authors thank  Florian Gschwend  for interesting discussion and  Marco Sigovini  for his helpful advices. We thank the crew of the SAMS research vessel  Seol Mara  and  SAMS  for hosting this research and to  Scottish Sea Farms  for access to the fish farm site. This study was supported by the  ASSEMBLE program  ( EU FP  grant agreement no. 227799 ), the  Swiss National Science Foundation  (grants  31003A-140766  and  316030_150817  to JP), and  G & L Claraz Donation.
DNA was purified from frozen samples and analysed on a GAIIx DNA sequencer (Illumina, San Diego, CA, USA). Low-depth wholegenome sequencing data were converted to DNA copy number plots with the CNAnorm package  (Gusnanto et al, 2012)  in R statistical software (R Core Team, 2013) as described by  (Wood et al, 2010). Base quality, mapping quality, read depth and tumour cell content are summarised in Supplementary Table  6 , and further details of the bioinformatics approaches are given in the Supplementary Methods file.
A total of 108 patients were thus assessable and eligible for the primary efficacy end point. Two patients in the anastrozole arm and six in the fulvestrant arm did not undergo surgery (see the Supplementary Material for details); both patients in the anastrozole arm and three in the fulvestrant arm had a biopsy at 6 months. Pathological response, ER, PR, Ki67 and PEPI score. Pathological response data at 6 months for the primary tumour were available for 92 patients (Table  2 ). Pathological reports were not evaluable for eight patients. No complete pathological responses were observed (Sataloff 'Tumour A';  Sataloff et al, 1995). Pathological responses greater than 50% (Sataloff 'Tumour B') were observed in 43% of patients in the anastrozole arm and in 25% of patients in the fulvestrant arm.
In five cases, two independent biopsies were tested before treatment and in four cases two different parts of the surgical specimen were tested after treatment. In the remaining cases, single samples were tested before and after treatment. Next-generation sequencing was performed at low depth and reads were counted in moving windows to estimate DNA copy number. Hierarchical clustering was used to detect broad patterns in the data (Figure  3 ; the Treeview data files are provided as Supplementary Data to permit examination of the heatmap at high resolution). As expected for ER þ breast cancer, many tumours had minimally rearranged 'simplex' copy number profiles with gains of 1q/16p and loss of 16q. Apart from H06 and H13, all samples from the same tumour clustered together. Overall, the clustering shows that the differences between tumours were in general much greater than the differences before and after treatment of the same tumour.
Protocol approval. The study was approved by the South-West France and Overseas Departments, Committee for Protection of Research Subjects (Comitede Protection des Personnes, Sud-Ouest et Outre-Mer III, 2007/57) and conducted according to the Declaration of Helsinki, EU clinical trials directives (2001/20/CE) and Good Clinical Practice guidelines. The clinicaltrials. gov registry number is NCT0087858. Patients gave signed informed consent to participate in the study and to allow the use of their tumour samples for research including genomic analysis. Samples from the trial were stored in the Bergonie Institute Tumour Bank in compliance with French laws on tumour banks (authorisation no.
ACKNOWLEDGEMENTS We thank  Jone Iriondo  and  Pippa McKelvie-Sebileau  of the Institut Bergoniefor editorial assistance. This work was supported by  INCA  (plbio  2010-216  and  INCa-DGOS-Inserm   6046 );  La Ligue Contre le Cancer  (Equipe Labellisee 2011);  Yorkshire Cancer Research Campaign  ( L341PA ); La Fondation Widmer Contre le Cancer; AstraZeneca contributed to this study with an  Educational Grant  to  Institut Bergonie  ´and by providing Anastrozole (ARIMIDEX) and fulvestrant (FASLODEX). The funding sources had no role in the design of the study; the collection, analysis or interpretation of the data; or in the writing of the manuscript The study complies with current French laws.
METHODS Protocol approval. The study was approved by the South-West France and Overseas Departments, Committee for Protection of Research Subjects (Comitede Protection des Personnes, Sud-Ouest et Outre-Mer III, 2007/57) and conducted according to the Declaration of Helsinki, EU clinical trials directives (2001/20/CE) and Good Clinical Practice guidelines. The clinicaltrials. gov registry number is NCT0087858. Patients gave signed informed consent to participate in the study and to allow the use of their tumour samples for research including genomic analysis. Samples from the trial were stored in the Bergonie Institute Tumour Bank in compliance with French laws on tumour banks (authorisation no.
Phoenix NoC's Architecture Phoenix is a distributed fault-tolerant architecture implemented over a NoC-based MPSoC platform. It comprises hardware part placed on each NoC router and software part (i. e. OsPhoenix) placed into the operating system of each PE  [5] , which is composed of a processormemory pair  [4]. In addition, Phoenix NoC employs a direct 2D mesh topology consisting of routers using bidirectional links to connect with other routers and PEs. The NoC employs tables for source routing decisions, and the OsPhoenix implements routing algorithms to fill the Routing Table  according  to the position of PEs and faulty links. Figure  1  shows the Phoenix's router architecture, which includes mechanisms for packet routing and fault-tolerance. The basic packet routing mechanism of Phoenix's router encompasses four set of components described next: (i) four bidirectional ports (NORTH, SOUTH, EAST and WEST) dedicated to interconnect routers, and a bidirectional port (LOCAL) that enables the communication between the router and its local PE.
Then, the Fault Monitor detects whether the link is faulty or not, sets this information on the Faulty Table, and informs this procedure to the Fault Control Machine that sends the Faulty Table  to  the OsPhoenix (TR_FAULT_TAB). The static mechanism sets only the Level 1, changing the status link from "not verified" to "operating properly" or "with permanent fault". In order to accomplish the link test, each bidirectional link contains a HE and a HD to perform a dynamic strategy similar to the one used in  [6] , which identifies fault tendencies based on thresholds of ACKs and NACKs. The HD, placed on the input of the buffer, receives the data and the redundancy bits that were encoded by the HE in the adjacent router. The HD module can correct one bit flip and detect at most two faults in a data flit.
Thus, the module informs the communication status by the counters NE, CE and DE, which performs the Level 2 of the fault model. The HE module generates the redundancy bits according to the flit that will be transmitted. FPM assumes a fault tendency or a permanent fault on link measuring the density of ACKCs or NACKs, respectively, in a given window of time. This fault tendency information is propagated to the OsPhoenix that may perform high-level decisions to accomplish the fault-tolerance mechanism.
Event generators and simulation framework Corrections for the instrumental effects in the photon measurement and the estimation of systematic uncertainties are performed based on simulated events using various tunes of PYTHIA and PHOJET event generators. The PYTHIA event generator combines perturbative QCD and phenomenologically motivated models. PYTHIA employs several tunable parameters, which result in different tunes of the event generator, such as PYTHIA 6. 4  [8]  tune D6T  [15] , Perugia-0  [16] , and ATLAS-CSC  [17] ). The PHO-JET 1. 2 generator  [9, 10]  is based on a two-component approach, that describes high-energy collisions as an interplay of soft and hard components. The response of the PMD to the produced particles is studied using PYTHIA and PHOJET event generators. The AliRoot  [18]  software package, which includes a detailed information of ALICE apparatus, has been utilized.
Particle transport of the generated particles from the event generators have been simulated using the GEANT-3  [19]  software package, and stored in terms of energy depositions and positions of the hits. The deposited energy is converted to ADC values using the energy loss conversion relation to treat the simulated data on a similar footing as the experimental data. The MC description of the cluster sizes has been validated using test beam data. The distribution of cluster sizes for photon candidates was found to reproduce well the measured distribution, even in the tail region.
Performance of the PMD The performance of the PMD has been studied by exposing the detector modules to pion and electron beams at energies ranging from 1 to 6 GeV at the CERN Proton Synchrotron. Two modules were mounted back to back on a movable stand. A gap was maintained between the modules to place different thickness of lead converters for the pre-shower study. Data were readout by using same front-end electronics and data acquisition as in the real experiment. Special trigger combinations, using scintillator paddles and Cherenkov counters, were configured for pion and electron beams separately. Data were collected for various combinations of beam energy, converter thickness and detector operating voltage. The responses of the modules to pion and electron beams have been simulated by using GEANT-3 code, to understand the observed data.
It has also been observed that the signals from a charged hadrons affect on average ∼1. 1 cells, whereas signals from 3 GeV electrons affect 11 cells. Thus, compared to the charged pions, the electrons deposit larger amounts of energies and affect larger number of cells. Mean energy depositions in the detector modules have been obtained from both experimental data and simulations. Fig  2  shows the relationship between the mean energy depositions, obtained from the simulated data (in keV) and the experimental data (in ADC) for pion and electron beams of different energies. A linear fit gives the conversion relation from the simulated to experimental data. This relation has been used in the analysis chain to convert the keV scale of the simulated data to total ADC of the experimental data. During the data taking period at the LHC, all the modules of PMD are operated at -1300 V, where the efficiency for charged pions is ∼90%.
The average photon multiplicities for the collisions at 0. 9 TeV are 4. 46 ± 0. 02 (stat) ±0. 20 (sys), for 2. 76 TeV collisions are 6. 12 ± 0. 01 (stat) ±0. 47 (sys), and for 7 TeV collisions are 7. 19 ± 0. 01 (stat) ±0. 45 (sys). The relative increase in average photon multiplicity for 2. 76 and 7 TeV energies with respect to that of the 0. 9 TeV are 37. 2% ± 0. 3% (stat) ± 8. 8% (sys) and 61. 2% ± 0. 3% (stat) ± 7. 6% (sys) for inelastic collisions. The multiplicity distributions are compared to the photon multiplicities obtained from PHOJET, PYTHIA Perugia-0, PYTHIA Perugia-2011, and PYTHIA ATLAS-CSC. The bottom panel for each collision energy shows the ratios of experimental data to the event generators. PHOJET explains the data at 0. 9 TeV, but overestimates the data at all other energies.
Acknowledgements The ALICE Collaboration would like to thank all its engineers and technicians for their invaluable contributions to the construction of the experiment and the CERN accelerator teams for the outstanding performance of the LHC complex. The ALICE Collaboration gratefully acknowledges the resources and support provided by all Grid centres and the  Worldwide LHC Computing Grid  (WLCG) collaboration. The ALICE Collaboration acknowledges the following funding agencies for their support in building and running the ALICE detector:  State Committee of Science, World Federation of Scientists (WFS)  and  Swiss Fonds Kidagan ,  Armenia ,  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) ,  Financiadora de Estudos e Projetos (FINEP) ,  Fundacão de Amparo à Pesquisa do Estado de São Paulo (FAPESP) ;  National Natural Science Foundation of China (NSFC) , the  Chinese Ministry of Education (CMOE)  and the  Ministry of Science and Technology of China (MSTC) ;  Ministry of Education and Youth of the Czech Republic ;  Danish Natural Science Research Council , the  Carlsberg Foundation  and the  Danish National Research Foundation ; The  European Research Council  under the European Community's  Seventh Framework Programme ;  Helsinki Institute of Physics  and the  Academy of Finland ;  French CNRS-IN2P3 , the  'Region Pays de Loire', 'Region Alsace', 'Region Auvergne'  and  CEA, France ;  German BMBF  and the  Helmholtz Association ;  General Secretariat for Research and Technology, Ministry of Development, Greece ;  Hungarian OTKA  and  National Office for Research and Technology (NKTH) ;  Department of Atomic Energy and Department of Science and Technology of the Government of India ;  Istituto Nazionale di Fisica Nucleare (INFN)  and  Centro Fermi -Museo Storico della Fisica e Centro Studi e Ricerche "Enrico Fermi", Italy ;  MEXT   Grant-in-Aid for Specially Promoted Research, Japan ;  Joint Institute for Nuclear Research, Dubna ;  National Research Foundation of Korea (NRF) ;  CONACYT, DGAPA, México, ALFA-EC  and the  EPLANET Program  ( European Particle Physics Latin American Network) Stichting voor Fundamenteel Onderzoek der Materie (FOM)  and the  Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO) ,  Netherlands ;  Research Council of Norway (NFR) ;  Polish Ministry of Science and Higher Education ;  National Science Centre, Poland ;  Ministry of National Education/Institute for Atomic Physics  and  CNCS-UEFISCDI -Romania ;  Ministry of Education and Science of Russian Federation, Russian Academy of Sciences, Russian Federal Agency of Atomic Energy, Russian Federal Agency for Science and Innovations and The Russian Foun-.
In this paper, we introduce a new Eclipse-based IDE for teaching Java following the object-later approach. In particular, this IDE allows the programmer to write code in Java--, a smaller version of the Java language that does not include object-oriented features. For the implementation of this language we used Xtext, an Eclipse framework for implementing Domain Specific Languages; besides the compiler mechanisms, Xtext also allows to easily implement all the IDE tooling mechanisms in Eclipse. By using Xtext we were able to provide an implementation of Java--with all the powerful features available when using an IDE like Eclipse (including debugging, automatic building, and project wizards). With our implementation, it is also straightforward to create self-assessment exercises for students, which are integrated in Eclipse and JUnit.
INTRODUCTION Java--is an application that allows undergraduate students to learn programming by using a smaller version of Java without object-oriented features. This way, students can focus on the basic programming concepts without being distracted by complex constructs. The original implementation of Java--comes without any IDE; it provides a GUI, but without the advanced tooling mechanisms that are typical of an IDE like Eclipse. In this paper, we introduce a new implementation of Java--that includes a full-featured Eclipse-based IDE. Our IDE provides an Eclipse based editor with syntax highlighting, navigation, code completion and error markers, not to mention automatic integrated building and debugging. This way, the students will immediately become familiar with Eclipse. By using our implementation of the IDE for Java--, it is straightforward for the teachers to create exercise projects that students can use for selfassessment.
The teacher can rely on existing frameworks such as JUnit, which is already integrated in Eclipse. The Java--IDE is available as an open source project at https://github. com/Lorenzo Bettini/javamm. We also provide an Eclipse update site and preconfigured Eclipse distributions with Java--installed, for several architectures. In the following we will use the term Java--for denoting both the smaller version of the Java language and the application (with or without IDE) for writing code in this language.
Even though Java--allows the programmer to interact with a GUI, that includes some features, such as syntax coloring, typical of an Integrated Development Environment (in short, IDE), this application cannot be considered an IDE, since it lacks many other features, such as compiler and debugger integration, build automation, code completion, and easy navigation to definitions. On the other hand, all existing Java IDEs (such as Eclipse and Netbeans) require the programmer to deal, since the very beginning, with the object-oriented programming paradigm. That is why, in this paper, we propose a new Eclipse-based IDE (see Section 3), that has the same features of Java--, and, thus, allows the programmer to experiment with the basic programming concepts (without necessarily knowing any notion of object-oriented programming), and that, at the same time, has all the advantages of being an IDE (see Section 3.
Technology Used Developing a compiler and an IDE for a programming language is usually time consuming even when relying on a framework like Eclipse, which already provides typical IDE mechanisms. After implementing the compiler components like the parser and the validation, we then need to connect the compiler to the IDE components and this requires lot of manual programming. Xtext  (Itemis, 2015; Bettini, 2013)  is a modern language workbench (such as MPS  (Voelter, 2011)  and Spoofax  (Kats and Visser, 2010) ) that solves all the above issues in a uniform way: starting from a grammar definition Xtext generates not only a parser and an abstract syntax tree, but also all the typical Eclipse-based tooling features (e. g. , editor with syntax highlighting, code completion and static error markers).
Xtext comes with good defaults for all the above language implementation mechanisms and for the Eclipse integration; however, the language developer can customize all such mechanisms in a straightforward way. Thus, Xtext makes language implementation and its integration into Eclipse really easy  (Eysholdt and Behrens, 2010). For all the above reasons, we chose Xtext for the implementation of the Eclipse IDE presented in this paper. By relying on Xtext, our implementation provides all the typical Eclipse features: automatic building, error reporting, outline view, navigation to declarations (e. g. , variables, parameters, methods) and debugging, just to mention the main ones, mimicking the same features of Eclipse JDT (Java Development Tools). This means that Java--will make the transition to the complete Java language and its Eclipse integration easier for the students who already got familiar with our Java--IDE.