dataesr/scientific-paragraphs-categorization huggingface dataset:
Non-identifiable sequences are still informative because the broad coverage of the existing COI library allows classification, at least to a higher taxonomic level, of species that have not yet been barcoded. The detection of such species in faecal samples is valuable to pinpoint where and when specimen sampling is likely to help discovering putative new species. The BOLD database is growing at an increasing rate and its limitations are expected to lessen with time. COI, or a mini-barcode internal to COI (e. g. Hajibabaei et al. , 2006) , is therefore the ideal candidate for the biodiversity capsule approach. Other possible sources of biodiversity overestimation include secondary prey detection where DNA adheres to or is retained within the gut-contents of the predated items  (Sheppard et al.
Potential Effect of Polyphenols in Neurodegenerative Diseases The role of oxidative stress in age-related dementia and in stroke pathophysiology is crucial; therefore the use of antioxidant plant derivatives is under investigation. In the studies by C. Sutalangka et al. and W. Kirisattayakul et al. the neuroprotective effect of Moringa oleifera, a plant possessing potent antioxidant activity, was assessed in animal models of age-related dementia and focal stroke. Their data showed that M. oleifera leaves extract is neuroprotectant and cognitive enhancer partly via the decreased oxidative stress and the enhanced cholinergic function. Moreover they showed that all extract doses decreased infarction volume in both cortex and subcortex. The protective effect of low extract doses in all areas occurs mainly via the decreased oxidative stress.
Examples of such models applied to ecological problems include the foraging behavior of mouse lemurs  (Schliehe-Diecks et al. 2012) , the spawning success of shovelnose sturgeon  (Holan et al. 2009) , atsea behavior of Manx Shearwater  (Dean et al. 2012) , and diving behavior in Macaroni Penguins  (Hart et al. 2010). HMMs assume that observations will depend on a finite number of underlying unobservable states  (MacDonald and Zucchini 2009). Accordingly, each individual observation is assumed to be drawn from one of several distributions, each corresponding to a ''hidden'' state. In the case of animal behavior, one might assume that an individual has resting and feeding states, each associated with distinct distributions in the data recorded by a bio-logging device. The underlying principle is analogous to an independent or discrete mixture model  (Welsh et al.
Data pre-processing For the HMMs in this study, data were assembled into a two-dimensional space where each axis represented a summary metric calculated from dive profiles that had been separated into temporal bins. Our method involved three stages of data pre-processing. First, raw dive data were divided and compressed to a time series of these summary metrics. Temporal binning and metrics were chosen to capture the variation of patterns in the dive profile, representing relevant dynamics and temporal scale to the behaviors we wished to examine. These summary metrics were then log-transformed to approximate a mixed-normal distribution, and finally arranged as a multivariate time series. It is important to match the temporal scale of observations to the temporal scale of the behaviors that are the focus of the investigation and the specific questions being asked.
Sensitivity of this 3-h interval was also examined by summarizing the data across a range of time bins, with no discernable improvement for higher resolutions in the interpretation of the time series for the purposes of our example application (see Appendix A). Summary metrics were calculated from each 3-h subsection to form the observations from which the HMM was estimated. Any number of summary metrics can be chosen to form the multivariate arrangement of observations, but in this study a two-dimensional observation model was chosen. Median temperature was selected as a measure of habitat that was an alternative to absolute depth, and the standard deviation of depth experienced (a proxy for amplitude in the dive profile) was chosen to quantify variation in movement through the water column.
Real-world data Having examined the success of using multivariate HMMs on simulated time series, we then applied this method to real-world data. Time series were chosen from six individuals across two species of tropical tuna: three Thunnus albacares (yellowfin tuna, see Plate 1), and three T. obesus (bigeye tuna) tagged and released in the Western and Central Pacific Ocean on separate occasions spanning February 2007 to December 2011. The vertical behavior of these species has been described in numerous previous studies (e. g. , Ohta and Kakuma 2004,  Dagorn et al. 2006 , Schaefer et al. 2007 ), exhibiting some differences in their evolved strategies to exploit prey through the water column  (Dagorn 2000). We have chosen these species in part because of the need to incorporate quantitative measures of their behavior in models for natural resource management, but also because across species and size classes, they exhibit behaviors that can be difficult to distinguish, some being constant and others variable  (Schaefer et al.
2009 , Robert et al. 2012 , Matsumoto et al. 2013). A data set of bio-logging data from these species has been collected by the Pacific Tuna Tagging Programme (PTTP). The behavioral time series used in this study are taken from this database, and were recorded by archival tags surgically implanted in tropical tuna following the methods outlined in  Schaefer et al. (2007). A variety of electronic tag devices were used, and each time series is summarized in Table  1.
However, states estimated with large variance-covariance and lowpersistence transition probabilities should be interpreted with particular caution. In the application for tropical tuna species, our aim was to examine the way in which real-world continuous dive data can be described and classified using multivariate-normal HMMs, and to determine if the results were consistent with known ecology. Consideration of our 3-h temporal scale is critical when comparing our results to previous studies. At this scale, we do not identify fine-grained changes in movement (e. g. ,  Sims et al. 2008 , Humphries et al. 2010) , individual dives (e. g. ,  Dagorn et al. 2006) , or longer-term composite dive profiles (e. g. ,  Schaefer et al. 2007, Wilson and Block 2009). Rather, we aim to describe behaviors that occur at the scale of feeding and fishing events.
ACKNOWLEDGMENTS The authors would like to thank the  PNG National Fisheries Authority , the  New Zealand Agency for International Development , the 9th  European Development Fund  ( SciCoFish Project ), the  Republic of Korea , and the  Pelagic Fisheries Research Project  for supporting this work. This work was funded by the  Lenfest Ocean Program  and the  Institute for Complex Systems Simulation , through  EPSRC Doctoral Training Centre grant  ( EP/G03690X/1 ).
Results and discussion A set of C-cinnamoyl glycosides (Figure  1 ) was synthesized as outlined in Scheme 1 and described previously by us  11. C-cinnamoyl glycosides 1-4 have been prepared by aldol condensation of b-C-glucosyl and b-C-galactosyl ketones with 3-hydroxy or 4-hydroxybenzaldehyde at room temperature in the presence of pyrrolidine as catalyst. The O-acetate protecting groups of the carbohydrate moiety were next removed using triethylamine in methanol/water to afford the deprotected C-glycosides 5-8. The inhibitory activity of the C-glycosides 1-8 against human CA I and CA II and the purified pathogen b-CAs, bsCA I and bsCA II, are listed in Table  1. These inhibition data were acquired using a stopped flow assay that monitors the physiological reaction, that is, the CA-catalyzed hydration of CO 2.
ACKNOWLEDGMENTS We are very grateful to  A. Ephrussi ,  P. Lasko ,  P. MacDonald , M. and  H. Siomi , and  C. Smibert  for their gifts of fly stocks and antibodies;  J. Ko  ¨nig and  J. Ule  for their advice on iCLIP;  S. Bullock  for his support and advice on CRISPR-Cas9 editing; and  A. C. Meunier  for technical assistance. This work was supported by the  CNRS UPR1142  to M. S. and S. C. ; the  ANR Blanche  ( ANR-06-BLAN-0343  and  ANR-2010-BLAN-1201 01 ), the  Fondation pour la Recherche Medicale (FRM)  (Equipe FRM 2007, Equipe FRM 2013  DEQ20130326534 , and Projets Innovants  ING20101221078 ), and the  Fondation pour la Recherche sur le Cancer  ( ARC Libre 2009 ; no. 3192 ) to M. S. ; the  Slovenian Research Agency  ( P2-0209  and  J7-5460 ) to T. C. ; and the  ANR JCJC  ( ESSOR-JCJC-1604 01 ) to S.
Experimental techniques The X-ray powder diffraction data were collected from an X'Pert MPD diffractometer (Panalytical AXS) with a goniometer radius of 240 mm and using Cu Kα radiation (λ = 0. 15418 nm). The average particle size was calculated from the line broadening using the Debye-Scherrer formula, D = Kλ/β•cos(θ), where D is the average crystallite size, K is the shape factor taken as 0. 9, λ is the X-ray wavelength, β is the full width at half maximum of the Bragg reflection in radians, and θ is the diffraction angle. The transmission electron microscopy images were taken by placing a drop of the particles dispersed in water onto a carbon film-supported copper grid, and the size and the shape of the particles were determined using a Philips CM20 instrument operating at 200 kV. The DLS measurements were performed on a ZEN 3600 Zetasizer Nano ZS.
Kleijn et al. (2013)  documented wild bee species frequently recorded as visitors to crop flowers (making up at least 5% of the visitor community) from 42 studies across Europe. Their list contains just 41 bee species. From this list, we selected six species known to exist in England that were very commonly recorded visiting flowers of crops grown in England. These species, and the criteria for their selection are shown in Table  2. Other species of bee that are common, generalised species in England, such as for example, Bombus hortorum (Linnaeus), are not included because they were only recorded in a limited number of datasets by  Kleijn et al. (2013). Of course, a focus on a few currently abundant and widespread species -those likely to be delivering the pollination service to crops -may not provide appropriate resources for species that are actually declining.
A floral unit can be a single flower or an inflorescence for species with tightly clustered flowers, such as those in the family Asteraceae or Dipsacaceae. Two sources of data exist on the volumes of pollen produced per floral unit of particular plant species. In the published literature,  Muller et al. (2006)  reported pollen volumes for the 16 plant species on which their specialised solitary bees were feeding. More recently, a dataset of pollen volumes per flower for 153 native UK plant species has been developed (see Materials and methods, File S1). Both datasets are summarised in Table  S6  of File S1. For the purposes of crude calculation, here we have used an average value of pollen per floral unit from these datasets (see Table  5 , assumption 5, for discussion of the implications of this).
Discussion From evidence available at the time when the policy opportunity arose, it was possible to draw tentative upper and lower quantitative bounds on the demand for floral resources by a set of common, widespread, pollinating bee species. As noted by  Muller et al. (2006) , these flower requirements are surprisingly high. They are calculated for only six bee species, at densities estimated from real farmed landscapes. Flower-visiting insect communities that have been documented always have substantially more species than this. For example, the five English plant-insect visitor network datasets reported in  Carvalheiro et al. (2014)  include between 16 and 243 flower-visiting species that are potentially pollinators. When compared against floral resources likely to be provided by the key agri-environment options included as nectar and pollen sources in the Wild Pollinator and Farmland Wildlife Package, it was shown that elements of the Package for arable/mixed farms are sufficient to supply a basic set of pollinating bee species with enough pollen to feed their larvae at the lowest estimates only, taking the minimum values for estimated parameters where a range was available and assuming the bees are either polylectic or will consume pollen from one or more of the flowering species provided.
Acknowledgements We would like to thank  Richard Comont  of the  Bumblebee Conservation Trust  for providing bumblebee phenology data,  Helen Thompson  for sharing bumblebee colony size data,  Richard Pywell ,  Matt Heard ,  Jeroen Everaars ,  Jeff Ollerton ,  Pete Brotherton ,  Gary Kass ,  Phil Grice ,  Jane Memmott  and  Bill Kunin  for valuable comments on the methods and text. L. V. D. was funded by  NERC  ( NE/K015419/1 ), C. C. by  Defra and Natural England  (flower density data); M. B. and C. C. by  BBSRC Defra ,  NERC , the  Scottish Government  and the  Wellcome Trust  under the  Insect Pollinators Initiative  ( BB/I000925/1  and  Agriland -BB/H014934/1 ). M. G. and J. P. are  Natural England  staff, directly involved in delivering the Countryside Stewardship scheme. L. V. D. designed the study, compiled and analysed the data, and wrote the paper.
The transition between the two extreme pictures should clarify the mechanism responsible for the formation of such a source. In this context, two contributions are presented in this workshop, using the same EPJ Web of Conferences data set, used to study central collisions of Xe + Sn from E/A = 8 to 25 MeV. D. Gruyer et al. [8]  developed a new chronometer to extract the evolution of the fragment emission time in this system. The second part will be presented in the next section. A global evolution of the reaction products and fusion cross section will be presented as a function of the bombarding energy. The third section will focus on the investigation of the symmetry energy term in the EOS.
X-ray diffraction Phase purity, homogeneity, and crystal properties were determined by powder X-ray diffraction (XRD) at room temperature using a MPDPro Panalytical diffractometer equipped with CuKa (1. 54056 A ˚) radiation. An additional powder XRD data for the powder calcined at 900 °C for 4 h was collected at ambient temperature in the range 10-70 in the 2h domain with a 0. 02 step and a 600 s per step counting time.
Acknowledgments This study has been supported by the  Tunisian Ministry of Scientific Research and Technology. This research was also supported by the  Centre National de la Recherche Scientifique ,  CNRS. Laure Vendier  is acknowledged for PXRD measurements. The authors are grateful to the  ''Service Commun de Microscopie Electronique de L'UniversitePaul Sabatier'' (TEMSCAN). Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creativecommons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Methods The methods described here represent the data categorisation, filtering and analysis that took place during the Red List assessments and the meta-analyses conducted on the assessment output. According to the most up-to-date phylogeny  (Teng et al. , 2009)  there are 16 species of anguillid eel, three of which have been proposed by some researchers as having two subspecies each (Anguilla australis, Anguilla bengalensis and Anguilla bicolor). Species-specific data describing the distribution, population trends, ecology and habitat type, use and trade, threats and conservation, where available, were collated from as complete a geographic range as possible for each species. In accordance with IUCN Red List protocol, a relative threat of extinction is determined on a categorical scale (Fig.
Data analysis Because of their broad geographic range and the potential for relatively large populations, anguillid eel species were evaluated predominantly under Criteria A 2  that quantifies extinction risk based on a reduction in population size over a period of 10 years or three generations, whichever proves longer. It should be noted that in relation to anguillids, 'population' specifically refers to abundance of the continental phase (yellow eels) while recruitment relates to arriving glass eels/elvers, and escapement to departing silver eels. Available metrics for the separate life-stages were examined individually but ultimately pooled in order to apply the Red List criteria. While escapement is the nearest proxy for a spawning stock, there are few data sets that accurately estimate this for any species and as such changes in recruitment and population metrics and what affect their declines might have on escapement, were considered in pooling the data.
While such estimates are not perfect, future assessments will be able to incorporate up to date data to improve them. To explore the current state of anguillid population data globally, species data sets were ranked on a categorical, linear scale in order to prioritise those most suitable for assessing population change using the Red List Categories and Criteria. The simplified scale ranged from effort-based metrics of silver eel spawning and escapement as the best proxy for the spawning stock, to non-effort landings data for glass eel recruitment and trade data (see Table  A1  for details on the full data ranking). Where catch per unit effort (CPUE) and fisheries independent monitoring (FIM) data were rare (e. g. for most tropical species), glass eel landings data were used as a proxy for recruitment.
We acknowledge the potential ambiguity of assigning a linear scale to the circular life history of anguillids, however, for illustrative purposes (see Fig. 2 ), we feel this classification provided a suitable summary. The IUCN Criterion A calculator was used as a guide to inform expert opinion on the qualitative components of the assessments. The Criterion A calculator is designed to identify declines, at constant exponential rates, in the different sub-populations, and to estimate the current population levels and relative percentage change over three generations. As population-wide data were incomplete for all species, this quantitative method was used not for automatically identifying the rate of population change or directly assigning a category, but only for guiding the discussion to identify the final category.
A summary of the outcome of this current assessment of the anguillid eels and the criteria met is given in Table  1. At the time of writing, the remaining three species are currently being assessed for an Australasian project. Substantially more CPUE and FIM data were available for the primarily temperate versus exclusively tropical eel species. Focusing on the three most studied temperate anguillids, data set quality and length, on average, fell short of their respective three generation length time frame (Fig. 2 ). Consequently, this forces us to use extrapolations and inferences to predict temporal trends in populations of different life-history stages, which although not ideal, is the only option currently available. Fig. 2  is a simplified illustration of the variation in data between species; however, as discussed below, the importance of continental recruitment and older stage eel metrics might vary between species in different region due to topography of the coastline.
three generation lengths). This species was thus assessed as EN due to an estimated reduction in the spawning population of approximately 50% during this time period, a result consistent with classification by the Japanese Environment Ministry. 3 FIM data for eels in East Asia is particularly sparse and consequently the discussion surrounding the assessment relied predominantly on fisheries catch data with some standardised CPUE from parts of its  range (e. g. Japan), where data were relatively more abundant. Improvements in the ranked data quality of this species will be essential for future accurate assessments of this species (Fig. 2 ). Long-term FIM and CPUE data were available for A. rostrata from the St. Lawrence River system to the Caribbean Gulf of Mexico and the Mississippi River system, although data from this southern portion of the distribution were sparse (see recent review by  Benchetrit and McCleave, 2015).
Knowledge gaps and recommendations One of the key roles and primary objectives of the ASSG was to highlight gaps in our knowledge that impede the implementation of eel conservation and management. The IUCN Red List process was a fundamental step towards achieving this objective. Analyses of data quality, presented here, promote the recommendation of a continuation and preservation of existing data sets to bring them up to the levels required for increasingly more robust measures of population change. Equally, there were a number of other key recommendations for the genus as a whole that we present here for the first time. Recommendations are in many ways linked and therefore are not listed in any order of importance.
Conclusion Anguillid eels face many threats throughout their long and complex life cycle. The commercial and cultural importance of the three primarily temperate eels (A. anguilla, A. rostrata and A. japonica) has added a political sensitivity to the listing of these species and because of a number of misconceptions about the IUCN Red List process, these assessments can often attract criticism. It is important to note that IUCN themselves state that automatically linking legislative response to a Red List category (e. g. banning trade) is an inappropriate use of the Red List  (IUCN, 2014). Here, we present a comprehensive review of the current data trends for anguillid eels, comparing threats with other 'high-profile', diadromous species and suggest priorities for future coordinated international eel conservation.
Acknowledgments We would like to thank the 29 assessment reviewers for contributing their expert knowledge and assistance during the review process and without whose help this work would not have been possible. We also thank  Suzanne Turnock ,  Peter Wood  and  Laurel Bennett  for their contribution to the workshop,  Caroline Pollock  and  Monika Böhm  for assistance with  Red Listing  and two anonymous reviewers for comments on earlier versions of the manuscript and the  Company of Fishmongers , the  Environment Agency , and  Synchronicity Earth  for financial support that made the work possible.
Imaging subsample Of the 72 participants included in the cognitive sample, 34 individuals aged 20-77 years, all right handed except for three who were ambidextrous, underwent structural T1-weighted MRI and [ 18 F]fluorodeoxyglucose positron emission tomography ( 18 F-FDG PET) scans. Descriptive data are reported in Table  1. The subsample did not differ from the original sample in terms of age, sex ratio or education level. The T1-, T2-and/or FLAIR-weighted MRI scans were normal for all participants, with no significant white-matter hyperintensities on the T2/FLAIRweighted images (see  Kalpouzos et al. , 2009a , for further details). As with the whole group, there was no significant correlation between age and the composite index of education level (r = -0. 11, p = 0. 53).