dataesr/scientific-paragraphs-categorization huggingface dataset:
, USA) and implanted in the NAcSh of TrpV1-Cre::ChR2-EYFP mice (n = 5; n = 3 lacking ChR2-EYFP expression and used as negative controls) using stereotaxic coordinates: AP + 1. 1, ML ± -0. 52, and DV -4. 0 mm under deep anesthesia (isofluorane with air (0,5-2 L/min, 1-3% v/v). Light stimulation was generated by a 473 nm MBL-III-473-100 mW laser (CNI Lasers, China) giving five subsequent light pulses, separated by 30 seconds at 40 Hz (5 ms pulses for 1 s, 4 mW output) whereupon glutamate release was recorded by the microelectrode maintained at a constant potential of + 0. 7 V. To control for possible light-induced artifacts, the protocol was repeated at a constant potential of + 0. 25 V and these amplitudes were subtracted from the amplitude at + 0. 7 V for each experiment. Raw data was analyzed using FAST analysis 6.
Acknowledgements The authors thank  Professor José Alfredo Mendez ,  Instituto de Física, Universidad Autónoma de San Luis Potosí, Mexico , for expert technical assistance; the  Uppsala Array Platform at Uppsala University  for performing the microarray analysis;  Professors Lars Olsson  and  Nils-Göran Larsson ,  Karolinska Institutet , for generously sharing the Dat-Cre transgenic mice; the  Image platform at Institut de la Vision (Paris, France)  for performing slide scanning;  Professor Karl Deisseroth  at  Stanford University  for generously sharing optogenetics virus;  Dr. Nadine Schweizer ,  Uppsala University , for providing the illustration for Fig. 5a;  Drs Maria Papathanou  and  Gian Pietro Serra ,  Uppsala University , and members of the  Mackenzie lab  for constructive input. This work was supported by grants from the  Swedish Research Council  ( Vetenskapsrådet 2013-4657 ,  2014-3804 ),  Uppsala University , the  Swedish Brain Foundation (Hjärnfonden) ,  Parkinsonfonden , the  Hållsten Research Foundation , and the foundations of Major Gösta Lind, Åhlén and  Åke Wiberg.
Nomenclature and other important information about precursor hairpin sequences, and experimentally identified mature miRNA sequences, are available online in a database called miRBase (http://www. mirbase. org/). In October 2016, miRBase had a total of 28,645 entries of hairpin precursor miRNAs profiled in a vast range of plant and animal species. For Homo sapiens we counted 1881 precursors and 2588 mature miRNAs. In addition, miRBase provides links to databases for validated miRNA targets (TarBase) and miRNA target prediction (DIANA-microT, microRNA. org, miRDB, RNA22, TargetMiner, PicTar-vertebrates) for in silico studies. Other in silico tools are progressively becoming available for functional miRNA analysis. For example, MAGIA83 (miRNA And Genes Integrated Analysis) (http://gencomp. bio.
unipd. it/magia/start/) integrates expression profiles of miRNA and mRNA to investigate more deeply into biological networks and pathways. Online databases, such as miR2disease86 (http://www. mir2disease. org/), can display miRNA expression profiles in human disease scenarios. Ingenuity Pathway Analysis (IPA) is a commonly used tool to identify mRNAs as targets for miRNAs in silico. miRNAs are expressed not only in tissues and cells, but also in all body fluids tested: milk, plasma, urine, saliva, tears, seminal fluid, amniotic fluid  [4] , and umbilical cord blood  [30]. RNA molecules were once considered to be very unstable due to the presence of ribonucleases (RNases), which are abundant and ubiquitous to neutralize viral and bacterial nucleic acids. However, RNA molecules are now known to circulate in a "safe and stable" manner in body fluids  [4].
Experiment Methods Participants. Fourteen individuals (6 men and 8 women; mean age 24. 4 ± 2. 7) took part in this experiment. None of the individuals reported having hearing problems. They all provided written informed consent to participate in the study. The Institutional Review Board of the French Institute of Medical Research and Health ethically approved this specific study prior to the experiment (opinion n°14-151). All participants were paid for their participation. Original and sketch sounds. 120 original sounds were used, equally divided into four categories: instruments, birds, vehicles, and voices (30 different sound exemplars in each category). These original sounds were selected in the Sound Ideas database (vehicle and bird sounds), in the Vienna Library database (instrument sounds), and in the RWC database (voice sounds).
As in the Giordano et al. 's study  [15] , the sound set was characterized in terms of pitch, HNR and spectral centroid. Ten different instruments were selected: celesta, bassoon, flute, harp, clarinet, marimba, oboe, trumpet, cello, and vibraphone. Each instrument was played at 3 different pitches (F4, G#4, and B4), leading to 30 instrument sounds. For the voices, 5 different vowels were chosen (/a/, /e/, /i/, /o/, and /u/), each sung by a male tenor singer or a female alto singer. Vowels were sung at the same pitches as the instrument sounds (F4, G#4, and B4). Their Harmonic-to-Noise Ratio (HNR) was estimated using Praat software  [29]. The HNR measures the ratio of the periodic and aperiodic (noisy) components of a signal. The mean (± standard deviation, SD) HNR value for the voices was 22.
Finally, the 840 (720 sketches and 120 original) stimuli were normalized by the root-meansquare level. Examples of original sounds and sketches, from each category, are represented in Fig  2. Apparatus. The stimuli were presented diotically through a Sennheiser HD 250 Linear II headphone, connected to a RME Fireface 800 digital-to-analog converter, at a 16 kHz sampling rate. They were presented at around 68 dBA. The experimental session was run using a Matlab R2008b interface on an Apple Mac Pro. The participants were tested individually in a doublewalled IAC sound-proof booth. They provided their response using the computer mouse, by clicking on the corresponding button on a computer screen. Procedure. A four-alternative forced choice (4-AFC) paradigm was used. On each trial, participants heard a single sound, which could be either an instrument, a bird, a vehicle or a voice sound.
Sounds for the training were not included in the main experiment stimulus dataset. At the end of the main experiment, a control block was run on the original stimuli alone, to ensure that the original sounds were well recognized. The order of presentation of the 120 original stimuli was randomized within a unique block of 120 stimuli. The four original sounds that were used to generate the sketches sounds of the first training session were also presented at the beginning of the second block, as a small training. The total experiment lasted about two hours. Statistical analyses: signal detection model. To evaluate performance, the d' statistic of signal detection theory (SDT) was used. However, because the original theory has been developed for tasks with only 2 possible responses (yes/no, or 2-AFC; see  [31] ), we had to extend the theory to a 4-AFC case.
The four d' scores, corresponding to the four categories, were the variables. Both models were fitted thanks to OpenBUGS programs. They were run for each dataset with 4000 burnins and 16000 iterations. With this amount of iterations, the Monte Carlo errors were less than 5% of the posterior standard deviation, which is the criterion suggested for convergence (cf. [32] ). All the statistical tests (repeated-measures ANOVA) were conducted on these d' scores. Chance level corresponds to a d' of 0, while near perfect recognition (here, proportion correct of 97%) corresponds to a d' of 3. 2.
Using the parallel dataset formed by Simple English Wikipedia (SWKP) 1  and traditional English Wikipedia (EWKP) 2  , further work has focused on developing machine learning approaches to sentence simplification. Zhu et al. (2010)  constructed a parallel Wikipedia corpus (PWKP) of 108,016/114,924 complex/simple sentences by aligning sentences from EWKP and SWKP and used the resulting bitext to train a simplification model inspired by syntax-based machine translation  (Yamada and Knight, 2001). Their simplification model encodes the probabilities for four rewriting operations on the parse tree of an input sentences namely, substitution, reordering, splitting and deletion. It is combined with a language model to improve grammaticality and the decoder translates sentences into simpler ones by greedily selecting the output sentence with highest probability.
Using both the PWKP corpus developed by  Zhu et al. (2010)  and the edit history of simple Wikipedia,  Woodsend and Lapata (2011)  learn a quasi synchronous grammar  (Smith and Eisner, 2006)  describing a loose alignment between parse trees of complex and of simple sentences. Following  Dras (1999) , they then generate all possible rewrites for a source tree and use integer linear programming to select the most appropriate simplification. They evaluate their model on the same dataset used by  Zhu et al. (2010)  namely, an aligned corpus of 100/131 EWKP/SWKP sentences. Wubben et al. (2012), Coster and Kauchak (2011)  and  Xu et al. (2016)  saw simplification as a monolingual translation task where the complex sentence is the source and the simpler one is the target. To account for deletions, reordering and substitution, Coster and Kauchak (2011) trained a phrase based machine translation system on the PWKP corpus while modifying the word alignment output by GIZA++ in Moses to allow for null phrasal alignments.
UNSUP In terms of numbers of splits, three systems often simplify by splitting namely Zhu (80 splits),  Woodsend (63)  and UNSUP (49). Interestingly, Narayan, trained on the parallel corpus of Wikipedia and Simplified Wikipedia splits less of-S1 Complex. This array distributes data across multiple disks, but the array is seen by the computer user and operating system as one single disk. Zhu. This array sells data across multiple disks but the array is seen. ten (10 splits vs 49 for UNSUP) and less well (2. 09 average score versus 2. 37 for UNSUP). This is unsurprising as the proportion of splits in SWKP was reported in  (Narayan and Gardent, 2014)  to be a low 6%. In contrast, the set of observations we use to learn the splitting probability is the set of all sequences of thematic role sets derived from the DRSs of the SWKP corpus.
Examples from the Test Set Table  6  shows some examples from the evaluation dataset which were selected to illustrate the workings of our approach and to help interpret the results in  Table 2, 4 and 5. S1 and S2 and S3  show examples of contextaware unsupervised lexical substitutions which are nicely performed by our system. In S1, The array distributes data is correctly simplified to The array moves data whereas Zhu's system incorrectly simplifies this clause to The array sells data. Similarly, in S2, our system correctly simplifies Papers on simulation of artificial selection to Papers on models of selection while the other systems either do not simplify or simplify to Papers on feeling. For splitting, the examples show two types of splitting performed by our approach namely, splitting of coordinated sentences (S1) and splitting between a main and a relative clause (S2,S3).
Acknowledgements We are grateful to  Zhemin Zhu ,  Kristian Woodsend  and  Sander Wubben  for sharing their data. We would like to thank our annotators for participating in our human evaluation experiments and to anonymous reviewers for their insightful comments. This research was supported by an  EP-SRC  grant ( EP/L02411X/1 ) and an  EU   H2020 grant  ( 688139/H2020-ICT-2015 ; SUMMA). We also thank the  French National Research Agency  for funding the research presented in this paper in the context of the  WebNLG  project.
Software and equipment Manuscript in  preparation [v1. 1. 4; Wed Nov 4 11:42:55 2015; #3bfb82e]  Eye position and pupil size were recorded with an Eyelink 1000 (SR Research, Mississauga, Canada, ON), a video-based eye tracker sampling at 1000 Hz. Stimuli were presented on a 19" Nokia Multigraph 446Xpro monitor (1024 x 768 px, 120 Hz). Stimulus presentation was controlled with OpenSesame  (Mathôt, Schreij, & Theeuwes, 2012)  and PsychoPy  (Peirce, 2007).
Eye-movement results Manuscript  On about 15% of trials, despite that we had instructed participants to maintain central fixation, gaze deviated considerably from the display center; trials with such gaze errors were discarded for the main (pupil-size and behavioral) analyses. However, we used the full, untrimmed data to test whether and when the eyes were captured by the memory-match probe. For each 10 ms period, we conducted an LME with horizontal gaze position as dependent measure, Memory-Match-Probe Position (Left, Right) as fixed effect, and by-participant random intercept and slopes. As shown in Figure  2a , the eyes were sometimes captured by the memorymatch probe at the start of the retention interval, but there was hardly any capture later in time (230 -449 ms; criterion: t > 2 for at least 200 ms, cf.
Acknowledgements The research leading to these results has received funding from the  People Programme  ( Marie Curie Actions ) of the European Union's  Seventh Framework Programme  ( FP7/2007-2013)  under  REA  grant agreement n°  622738  awarded to Sebastiaan Mathôt. In addition, this research was funded by a  VIDI Grant   452-13-008  from the  Netherlands Organization for Scientific Research  to  Stefan Van der Stigchel.
As individuals tire they fall back towards the cod end. How fast a fish will get tired, and whether it can outswim the gear is species and size dependent. The amount caught per area swept may also depend on the time of day because this can influence how close to the seabed the fish are found  (Kotwicki et al. 2009 ). To ensure that catch rates can be compared across years, much is therefore done to standardise the trawling operation, the gear and the procedures for sampling and for analysing the catch (e. g. ,  Miller 2013). In some parts of the world standardised bottom trawl surveys have now been conducted for more than 50 years and some of the resulting data are publicly available or available upon request. ICES provides online access to a database with trawl survey data from the north eastern Atlantic (www.
ices. dk/marine-data/dataportals/Pages/DATRAS. aspx) and similar databases are available for other areas such as the Eastern Bering Sea and Gulf of Alaska (www. afsc. noaa. gov/RACE/ groundfish/survey_data). Additional data can be downloaded from international data portals such as OBIS (Table  6. 2), but much data still reside in the custody of national fisheries research institutions. These data constitute a so far underutilised source of information on the distribution, abundance, and diversity of marine fishes on the world's continental shelves.
The Continuous Plankton Recorder (CPR) The Continuous Plankton Reorder (CPR) survey is the longest sustained and geographically most extensive marine biological survey in the world, covering *1000 taxa over multi-decadal periods since 1931  (Edwards et al. 2010). It samples phytoplankton and zooplankton in oceans and shelf seas using ships of opportunity from *30 different shipping companies, at monthly intervals on *50 trans-ocean routes. In this way the survey autonomously collects biological and physical data from ships covering *20,000 km of the ocean per month, ranging from the Arctic to the Southern Ocean. The survey is operated by the Sir Alister Hardy Foundation for Ocean Science (SAHFOS), an internationally funded charity with a wide consortium of stakeholders. Since the first tow of a CPR more than 6 million nautical miles of sea have been sampled and over 100 million data entries have been recorded.
Plankton are collected on a band of silk and subsequently visually identified by experts. Additionally, over the last decade the CPRs have been equipped with modern chemical and physical sensors as well as molecular probes. The database and sample archive together provide a resource that can be utilised in a wide range of environmental, ecological and fisheries related research, e. g. , molecular analyses of marine pathogens, modelling for forecasting and data for incorporation in new approaches to ecosystem and fishery management. In 2011 SAHFOS, along with 12 other research organisations using the CPR from around the world formed a Global Alliance of CPR surveys (GACs) with the aim of developing new surveys and a global database, and producing a global ocean status report  (Edwards et al.
2012). This global network of CPR surveys now routinely monitors the North Sea, North Atlantic, Arctic, North Pacific and Southern Ocean. New surveys are underway in Australian, New Zealand, Japanese and South African waters with a Brazilian and an Indian Ocean survey under development. These surveys provide coverage of large parts of the world's oceans but many gaps still exist particularly in the South Atlantic, Indian and Pacific Oceans. This global network also brings together the expertise of approximately 60 plankton specialists, scientists and technicians from 14 laboratories around the world. Working together, centralising the database and working in close partnership with the maritime shipping industry, this global network of CPR surveys with its low costs and new technologies makes the CPR an ideal tool for an expanded and comprehensive marine biological sampling programme.
In volunteer programs, assessments are generally restricted to indicator species and more rapid estimates of variables such as benthic cover, and lower levels of replication are accepted than in intermediate and expert monitoring. Though variable in quality and coverage, the resulting data can be invaluable in broad scale scientific assessments of reef status  (Bruno and Selig 2007). The urgency for accurate and reliable monitoring of coral reefs, that can serve both national (local) and international (global) needs is high, due to the poor performance of coral reef targets in the mid-term assessment of Aichi Target performance (GBO 2014). The GCRMN is developing with involvement from GEO BON and GOOS to become a mature observation network (UNESCO 2012), to better report on global targets (Aichi Target 10 on climate-sensitive ecosystems, and 14 on Oceans), and to feed into management, such as through the IUCN Red Lists of species and ecosystems.
Harmful Algal Blooms (HAB) Proliferation of microalgae in marine or brackish waters can cause massive fish kills, contaminate seafood with toxins, and alter ecosystems in ways that humans perceive as harmful. These phenomena are referred to as harmful algal blooms (HAB). Data on the distribution of toxic and harmful microalgae are collected through national surveillance programmes aimed at protecting public health, wild and cultured fish and shellfish, and bathing water quality. Sampling methods include plankton net hauls, water samples and molecular tools to detect species or genus-specific algal toxins in fish and shellfish. Benthic HAB species are collected from sediment, corals, seaweed or standardised screens. The detection of HAB species is challenging as many are difficult or impossible to identify even by using a light microscope.
The challenge of maintaining a consistent microalgal taxonomy is addressed in the IOC Taxonomic Reference List of Toxic Plankton Algae within the World Register of Marine Species  (Moestrup et al. 2009 ). The Intergovernmental Oceanographic Commission (IOC) of UNESCO has for two decades facilitated research to improve observations of harmful algae, provided training opportunities for their improved monitoring, as well as supported regional and global networks for knowledge and data sharing. The provision of method manuals and guides is central to observations of HAB species. Global data on HAB species occurrences and their impacts are stored in the Harmful Algae Event Data Base (HAEDAT) in OBIS (Table  6. 2). This international compiling and sharing of HAB data was initiated in the 1980s and is now accelerating and will provide the basis for a 'Global HAB Status Report' with the aims of compiling an overview of HAB events and their societal impacts; providing a worldwide appraisal of the occurrence of toxin-producing microalgae; and assessing the status and probability of change in HAB frequencies, intensities, and distribution resulting from environmental changes at the local and global scale.
Thus WoRMS (see Sect. 6. 4. 1) is developing a standardised approach to apply biological and ecological traits to marine species  (Costello et al. 2015a). A necessary step in organizing marine biodiversity data in integrated information systems is the development of appropriate thesauri and classification systems, as well as implementing quality control and feedback mechanisms. When integrating quantitative and qualitative natural history and distributional data, the use of both authoritative taxonomic and geographical hierarchical schema is essential. Here we introduce the leading taxonomic and geographic standards databases for the marine environment (Table  6. 2).
World Register of Marine Species (WoRMS) WoRMS is an open-access online database that provides an authoritative and comprehensive list of names of all marine organisms, including information on higher classification, synonymy, images and links to other information  (Costello et al. 2013c). It currently contains over 240,000 accepted species names  (Boxshall et al. 2015). While highest priority goes to valid names, other names in use are included so that this register is a guide to interpret taxonomic literature. Automated and sea depth (sublittoral, offshore), with substratum (rock and grades of sediment), and the exposure of rocky habitats to wave action. These factors distinguish biotopes at the upper levels of the habitat classification. Within the matrix the characteristic species of the communities occurring in the habitats are indicated.
Adapted from  Costello and Emblow (2005)  tools allow users to upload their species lists and match and classify their names against WoRMS. WoRMS makes use of the Aphia infrastructure which is designed to capture taxonomic and related data and information  (Vandepitte et al. 2015a). WoRMS was a development from the European Register of Marine Species (ERMS)  (Costello 2000; Costello et al. 2001) , and thus its content is controlled by an Editorial Board of taxonomic and thematic experts who elect a governing and steering committee, and invite colleagues to assist them. A permanent host institution provides professional computational support for the database, including monthly archiving. As of January 2016, there were 393 editors from 273 institutions in 50 countries actively involved in the management and quality control of the WoRMS content.
Marine Regions Marine Regions (www. marineregions. org) hierarchically organises over 30,000 geographic areas from national and global marine gazetteers and databases  (Claus et al. 2014 ). It contains spatial information of 264 different physical (e. g. , sandbank, seamount, island, bay) and administrative (e. g. , Exclusive Economic Zones, Marine Protected Area, Fisheries Zones or Biogeographic Regions) kinds of places. Both marine (e. g. , seamounts, canyons, guyots, fracture zones, banks, ridges, basins) and coastal features (e. g. , bays, fjords, cliffs, lagoons, beaches) are included. In order to preserve the identity of the marine geographic objects from the database, and to name and locate the geographic resources on the web, each geographic object is allocated a Marine Region Identifier, or MRGID.