dataesr/scientific-paragraphs-categorization huggingface dataset:
The actual theoretically clean quantities that can be extracted from experiment and on which we will focus from now on, must be composed of the integrated quantities in Eq. (  8 ): P 1 bin ≡ A (2) T bin = bin dq 2 c 1 (q 2 ) bin dq 2 c 4 (q 2 ) = c 1 bin c 4 bin , (10) P 2 bin = bin dq 2 c 2 (q 2 ) bin dq 2 c 4 (q 2 ) = c 2 bin c 4 bin , (11) P 3 bin = bin dq 2 c 3 (q 2 ) bin dq 2 c 4 (q 2 ) = c 3 bin c 4 bin. ( 12 ) Other observables are accessible to the current LHCb data set by means of similar partial angular analyses. Three observables related to P 4 , P 5 and P 6 (see Ref. [32] ) could be extracted by the LHCb collaboration in the near future. This means that, without actually performing a full angular analysis, the LHCb collaboration could be able to provide measurements of the complete set of 8 observables that describe the full distribution in the massless approximation (six of them being theoretically clean  [32] ).
These uncertainties will improve very significantly in the near future in forthcoming analyses of larger data sets by the LHCb collaboration. In order to illustrate the very large impact that moderately precise measurements of the clean observables will have on New Physics, we consider the constraints on C 7 , C 7 by hypothetical measurements of P 1  [2,4. 3] , P 2  [2,4. 3] , P 4  [2,4. 3] and P 5  [2,4. 3]  with central values at their SM points, and experimental uncertainties of σ exp = 0. 10. It is important to emphasise that these errors are not only realistic but also achievable with the current statistics. The result of this exercise is presented in Fig. 14 , where the individual constraints in the C 7 -C 7 plane (corresponding to Scenario A) from these four observables are shown together with the combined 68.
Acknowledgments It is a pleasure to thank Nicola Serra and Thomas Blake for helpful discussions on experimental issues. We also acknowledge fruitful discussions with Rafel Escribano and Federico Mescia. J. M. enjoys financial support from FPA2011-25948, SGR2009-00894. J. V. is supported in part by ICREA-Academia funds and FPA2011-25948. A J 8 in terms of P i observables and the Q observable In Section 2 we have provided the explicit expressions for the coefficients of the distribution in terms of the observables of the basis, neglecting mass terms and scalar contributions. 6 n this case, one finds very simple and compact expressions for all these coefficients (see Eqs. (  14 )) with the exception of J 8. The reason is that J 8 , in the absence of scalar contributions, is not an independent quantity (exactly like J 1s = 3J 2s and J 1c = -J 2c in the massless case) and deserves a separate discussion.
© 2013 Davì et al. ; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons. org/licenses/by/2. 0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons. org/publicdomain/zero/1. 0/) applies to the data made available in this article, unless otherwise stated.
Cetaceans in Europe are protected by many international and national regulations (e. g. European Union Habitats Directive, Marine Strategy Framework Directive and Common Fisheries Policy; US Endangered Species Act and Marine Mammal Protection Act; the OSPAR Convention; ASCOBANS). Several parameters can be measured to provide relevant information on cetacean population status: absolute abundance, relative abundance, distribution, demographic parameters and health status. Most of these parameters (absolute and relative abundance and distribution) require extensive data to be collected at sea, generally at high costs. However, the efficiency of monitoring plans is also based on cost-effectiveness, and the development of low-cost indicators for assessing cetacean population status is of great interest.
In several European countries, marine mammal strandings have been recorded for decades. Stranding data held in national data-bases jointly constitute one of the largest datasets about cetaceans in European waters. Thanks to their fairly low running costs, national stranding schemes have developed in most European countries and cover extensive spatial (1000 s km coastline) and temporal (several decades) ranges that are consistent with the characteristics of cetacean populations (extensive population home ranges, low recovery rates). During the last 20 years cetacean stranding networks have been aimed at contributing monitoring strategies by collecting data on inter-alia spatio-temporal patterns of occurrence, cause of death, health status, ecological traits and population structure  [27] [28] [29] [30] [31] [32].
4-Harbour Porpoise Stranding Data Harbour porpoise stranding time series were compiled from six European countries: Denmark, Germany, the Netherlands, Belgium, United Kingdom and France. Date and location of strandings, as well as cause of death were collected. Live stranding events were not considered as the stranding location is not entirely determined by drift conditions but even by active swimming of animals. United Kingdom stranding network. The stranding network in the United Kingdom is one of the oldest organisations in Europe that collects data on stranded cetaceans. The collaborative UK Cetacean Strandings Investigation Program (CSIP, www. ukstrandings. org) as it is now known is a consortium of partner organizations funded by Department of Environment, Food and Rural Affairs and the UK Devolved Administrations.
Partner organizations are the Zoological Society of London, Scottish Agricultural College (Inverness), the Natural History Museum and Marine Environmental Monitoring. In Cornwall, strandings data is collected by the Cornwall Wildlife Trust Marine Strandings Network and necropsies are carried out by the Animal Health and Veterinary Laboratories Agency (Truro). The CSIP is collectively tasked with recording information on all cetaceans, marine turtles and basking sharks that strand around UK shores each year and with the routine investigation of causes of mortality through necropsy of suitable strandings. Experienced pathologists and biologists carry out systematic necropsies of selected stranded cetaceans following a standardized protocol. Danish stranding network. The Danish stranding network is run by the Danish Nature Agency in collaboration with the Fisheries and Maritime Museum and the Zoological Museum, Natural History Museum of Denmark.
The central digital database is kept by Naturalis Biodiversity Center (formerly called the National Museum of Natural History Naturalis) in Leiden. Data and photographs are made visible on the internet (www. walvisstrandingen. nl). Belgian stranding network. Strandings were collected in Belgium since 1970's, but the dedicated and government supported network was established in 1990. It is organised and centralised by the Management Unit of the North Sea Mathematical Models (MUMM), department of the Royal Belgian Institute of Natural Sciences (RBINS). MUMM maintains, in cooperation with the University of Liege, a single database which can partly be consulted online. French stranding network. The French stranding network is co-ordinated by the Joint Service Unit PELAGIS, UMS 3462, University of La Rochelle-CNRS, dedicated to monitoring marine mammal and seabird populations, as a continuation of the monitoring programmes formerly known as the Centre de Recherche sur les Mammiferes Marins (CRMM).
1-General This work is an entirely novel approach to analysing and interpreting small cetacean stranding data that is aimed at taking drift, the major confounding factor involved in the stranding process, into account when analysing stranding data sets. Firstly we constructed stranding time series expected under the assumption that dead small cetacean be uniformly distributed in time and space and therefore that spatio-temporal patterns in stranding frequency would entirely result from drift conditions. The exposure to strandings on the coast of 8 large European subareas was shown to vary by a factor of 1 to 7 depending on the orientation of the coast relative to prevailing winds. Secondly, expected small cetacean stranding time series and seasonal patterns were compared to observed harbour porpoise stranding data on the assumption that any deviation from the null hypothesis would help disentangle the complexity of observed stranding records and express their biological component, i.
The improvement of the knowledge of the relationship between strandings and cetaceans at sea constitutes a major step in the use of strandings as indicators of atsea populations. Monitoring stranding data could provide maps of the likely origin of porpoises at sea, to identify high mortality areas and incidences with human activities  [26]. Stranding data will never provide abundance estimates, but the cost of dedicated surveys at European scale is too high to provide cetacean monitoring data. The combined use of both tools would be relevant in the development of an efficient monitoring strategy, notably in context of the Conservation Plan for harbour porpoises in the North Sea, carried out by ASCOBANS. Finally, the use of stranding anomaly as an indicator of cetacean mortality could become an additional efficient tool for environmental watch and to detect variations in strandings according to changes of abundance or mortality of cetaceans at sea.
Acknowledgments The authors would like to thank all persons involved in the different national stranding networks which provided data series for this work. -PELAGIS Observatory thanks all members of the French stranding network for their continuous effort in collecting stranding data. PELAGIS Observatory is funded by  Ministere de l'Ecologie, du Developpement Durable et de l'Energie , and by  Communauted'Agglomeration de la Ville de La Rochelle , with support of  Region Poitou-Charentes  and the  European Union. The PhD thesis of H. Peltier was supported by the  Centre National de la Recherche Scientifique  and by  Region Poitou-Charentes. -The  UK Cetacean Strandings Investigation Programme  is funded by  Defra in England  and the  Devolved Administrations in Scotland and Wales. -The Belgian strandings intervention network is supported by the  Royal Belgian Institute of Natural Sciences  and by the  Belgian Science Policy  -Data from  Schleswig-Holstein , Germany were collected partly funded by the  German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety , the  German Federal Ministry for Education and Research  and the  Ministry of Environment, Nature Conservation and Agriculture of Schleswig-Holstein.
After blocking in 3% BSA at room temperature for 1 h, the blots were incubated with mouse monoclonal PLK1 antibody (abcam, UK) at 10 μ g/mL overnight on ice. The blots were then incubated with the secondary antibody, horseradish peroxidase linked anti-mouse antibody (Cell Signalling Technology, USA) at 1:1000 dilution for 1 h at room temperature. The specifi c bands were detected using chemiluminescent kits (Immun-Star small 2013, 9, No. 21, 3610-3619 Chemiluminescent Kit, BioRad, UK) and imaged and quantitatively analysed using ChemiDoc MP imaging system and Image Lab software (BioRad, UK). Cells treated with cationic liposome (DOTAP/ cholesterol)/siPLK1 complexes at 4. 3:1 mass ratio was used as a positive control following the same protocol. GAPDH was used as an internal reference (house-keeping) gene.
Acknowledgements This work was supported by  CNRS  ( PICS 2010 Program ),  University of Trieste and Italian Ministry of Education MIUR  (cofi n Prot. 2010N3T9M4 and Firb  RBAP11C58Y ). Partial funding from  Biotechnology and Biological Sciences Research Council  ( BB/J008656/1 ) is also acknowledged. A. B. is grateful to Università italo-francese/ Université franco-italienne for supporting her international PhD (VINCI program 2008). We are also thankful to  C. Fabbro  for her help on TGA measurements.
Individuals were also genotyped with an AFLP combination using the following selective primers: PstI primer + A (5 0 -GACTGA GTACGTGCAGA-3 0 ) and MspI primer + AA (5 0 -CGATG AGTCCTGAGCGGAA3 0 -). Both microsatellite and AFLP amplifications were performed according to a previously described protocol  [20, 24]. PCR products were scored against a fluorescently labelled size standard (400HD-rox for microsatellite and GS500-TAMRA for AFLP) in an ABI 3130 automated sequencer using Genemapper 4. 0 software (Applied Biosystems, Foster City, CA, USA). Only fragments between 70 and 395 bp were scored. The presence or absence of a polymorphic marker was binary coded. The whole dataset composed of 11 SSR and 79 AFLP polymorphisms is available through the DRYAD data archive system (see the availability of supporting data).
Data analysis Standard diversity indices, AMOVA and PCoA Because samples were collected during the asexual stages of the V. inaequalis life cycle, isolates with exactly the same alleles at all loci were removed using Arlequin software, v. 3. 11  [29]  (clone-corrected dataset). A genetic distance matrix was created from the dataset under Genalex 6. 1  [30]. An analysis of molecular variance (AMOVA) and a principal coordinates analysis (PCoA) were performed using this genetic distance matrix. The average gene diversity (H d ;  [31] ) and the average number of alleles (A) were estimated from clone-corrected datasets using Arlequin  [29].
Testing for IBD patterns The detection of IBD was performed using two methods. The first method is implemented in SPAGeDI software, v. 1. 2  [45] , and the second one uses variograms of gene diversity  [46]. In an IBD pattern, kinship is expected to linearly decrease with geographic distance when the demes are connected according to a one-dimensional stepping stone (1D-SS) model, and to linearly decrease with the natural logarithm of distance according to a two-dimensional stepping stone (2D-SS) model  [47, 48]. Under an IBD model, an autocorrelogram shows a decreasing curve of the mean kinship between strains when the distance increases, whereas in a variogram of gene diversity, the curve represents the strain differences and is therefore expected to increase with distance. The gradual decrease of the relationship with distance between strains can be explained by limited migration abilities.
[46]. ⌢ Y l r ð Þ ¼ X k X a<b χ r ð Þ ab 2n r z lka À z lkb ð Þ 2 Ninety-five percent confidence intervals were estimated by manually analysing 100 different permuted datasets and observing the variation range of ⌢ Y 1 gene diversity for each r class. For each of the 100 datasets, we performed permutations of individuals: 500 within the virRvi6 subpopulation and 1000 within the avrRvi6 subpopulation.
No selection signature was detected within each differentiated subpopulation To check whether the loci are neutral or targeted by natural selection, the distribution of F ST against the probability to be under selective pressure was simulated with BayeScan software  [32]. Considering the two previously detected subpopulations, the posterior probability revealed no marker plotted outside the 95% confidence interval constructed (data not shown). No SSR and AFLP loci were subject to selection, but instead exhibited a moderate to high level of neutral differentiation (mean F ST ± SE = 0. 179 ± 0. 057). All markers were then considered as neutral and were used for all subsequent analyses. No evidence of additional genetic structure related to the host Considering these two subpopulations, molecular diversity indices revealed a higher diversity in the avrRvi6 than in the virRvi6 subpopulation (Additional file 1: Table  S4 ).
Each dataset was then reanalysed under a clustering programme (STRUCTURE) to assess host-specificity within each subpopulation. Concerning the avrRvi6 subpopulation, application of Evanno's method indicated two clusters. Individual assignments to the two clusters indicated that this genetic structure was not correlated to the host species (Additional file 2: Figure  S1a ). Subsequently, the avrRvi6 subpopulation was analysed by another clustering method (TESS), taking the spatial distribution of samples within the orchard into account. Simulations with Kmax = 2 for the avrRvi6 subpopulation indicated a spatial structure (Figure  3a ). The posterior probability of an individual belonging to cluster 1 gradually increased with distance (Figure  3a ). Sampling of strains on M. baccata trees along a line transect (the yellow diamonds in ranks 10 and 11; Figure  1 ) allowed us to validate the observed pattern of spatial structure (Figure  3a ).
Detection of an IBD pattern within each subpopulation The presence of an IBD pattern was then tested within each subpopulation using two methods based on autocorrelograms  [47]  and variograms of gene diversity  [46]. Correlograms based on kinship analyses showed a significant IBD pattern across the avrRvi6 subpopulation  4a ). A non-significant IBD pattern (p = 0. 085) was detected among the virRvi6 strains ⌢ b ¼ À0:0307 AE 0:0080; r 2 ¼ 0:0157 (Figure  4b ) and between avrRvi6 vs. virRvi6 individual pair comparisons (p = 0. 99; Figure  4c ) Moreover, we observed a global increase of gene diversity with distance (Additional file 4: Figures  S3a  and b ). For the two subpopulations, gene diversity significantly increased between the first and second distance classes. The low gene diversity observed within each first class was never observed after random permutations of the dataset, which indicated a strong and fine-scale IBD pattern.
Acknowledgements The authors wish to thank  Sabine Bartsch  and  Jürgen Egerer  for technical assistance on Rvi6 marker analysis, and  Cyril Dutech  and  Fabien Halkett  for their helpful suggestions. We are grateful to two anonymous reviewers for their detailed comments on earlier versions of this paper. We acknowledge Laurence Hibrand-Saint Oyant, responsible for the ANAN platform of the SFR QUASAV, where genotyping was performed. Thibault Leroy  was supported by a PhD fellowship from the  General Council of the Maine & Loire Department , and the  COSAVE programme , funded by the  Regional Council of the Pays de La Loire.
Four complementary datasets were gained. Survey 1: Coverage by dominant primary producers at the ≥ 1 cm level from 1993 to 2002. This survey was done using the line intercept transect (LIT) method, thus only ≥ 1 cm mats have been recorded (Table  1 ). In this study, the primary producers were divided into 4 major groups: (a) the Cyanophytes, (b) the turf algae, (c) the encrusting corallines, and (d) the macroalgae. Marine phanerogams, abundant in the north of Saint-Gilles, were limited to a few patches of Syringodium isoetifolium, one to several meters in diameter, in the coral zone at Site-P, and were not recorded on the LITs. Cyanobacteria. They generally occurred as episodic blooms on the Saint-Gilles La Saline reef. As a result, they were generally underestimated by the LIT method. Turf algae.
1993, 1996, 2002, and   Encrusting corallines. On the Saint-Gilles La Saline reef flat, they cover dead coral skeletons, some of them forming rising knobs or pillars. They also cover bioclasts, forming rhodoliths in some back-reefs. They are recorded without taxonomic details in datasets 1 and 2. Macroalgae. When possible, they are identified at the species level or recorded as "mixed macroalgae" when too intimately mixed together. Epiphytes occurring on macroalgae were not taken into account in this survey. Survey 2: coverage of primary producers from 1987 to 2009, at the ≥ 1 cm level along two permanent transects Two permanent transects were established in 1987 in the coral zone (L-zone) at both  sites, and monitored in 1987 , 1993 , 1996 , 2002 , and also from 1998 by the Global Coral Reef Monitoring Network (GCRMN).
Data analysis Due to the multi-species nature of the data and the design of the survey, the most appropriate analyses were multivariate  (Clarke & Warwick 2001). To assess patterns in benthic data from all stations in all years, we used non-metric multidimensional scaling (MDS) and principal component analysis (PCA), and to assess temporal patterns in benthic variation, we used Kruskal-Wallis one-way analysis of variance by ranks: this non-parametric method tests equality of population medians among groups. It is identical to a one-way analysis of variance with the data replaced by their ranks and is an extension of the Mann-Whitney U test to 3 or more groups.
RESULTS Survey 1: The two sites,  T and P, between 1993 and 2002:  spatio-temporal variation of the 4 dominant groups (cyanophytes, turf algae, encrusting corallines and macroalgae) Distribution of the 4 groups of primary producers at both sites. From dataset 1, the primary producers appeared to be largely dominant at Site-P and inconspicuous at Site-T (T: 5. 2 + 11. 4 % of coverage, P: 61. 3 + 26. 6%). In the MDS treatment, we can see that bubbles of Cyanophytes, turf algae (including small turfs, damselfish turfs and outer reef turfs), encrusting corallines and macroalgae mostly occur on P-LITs (Fig. 1 ). Generalities and temporal variation. At both sites, turfs and macroalgae dominated and averaged 75 to 85% of primary producer coverage (Table  2 ). 3 T0 6 0   3 T0 9 0   3 T1 2 0   3 T1 5 0   3 T1 8 0   3 T2 1 0   3 T2 4 0   3 P 0 6 0   3 P 1 2 0   3 P 1 5 0   3 P 2 1 0   3 P 2 7 0   3 P 3 0 0   3 P 3 3 0   6 T0 3 0   6 T0 6 0   6 T0 9 0   6 T1 2 0   6 T1 5 0   6 T1 8 0 6 T2 1   (1993, 1996, 2002 means).
ACKNOWLEDGMENTS We thank  M. Daniel CHOUSSY  for his assistance during fieldwork. We also thank  Pr Terence J. DONE  for rereading this paper. This project was funded by the  « programme quadriennal laboratoire ECOMAR, université de la Reunion, Région Reunion » and the « Ministère de l'Enseignement Supérieur et de la Recherche  (MESR) ».
ChIP-Seq analysis was performed on basal and LPS-stimulated WKY BMDMs and used to complement the microarray results in order to identify primary JunD targets. ChIP-Seq and microarray analyses were also carried out in a Jund congenic strain (WKY. LCrgn2) known to have reduced JunD mRNA and protein levels  [12]. In this strain, the Jund locus was transferred from the Lewis strain into the WKY strain by back-crossing over nine generations. Genome-wide integration of all datasets identified primary JunD-target genes and a regulatory network involved in oxidative stress and IL-1β expression in macrophages leading to increases in mature IL-1β production in BMDMs and glomeruli from the WKY strain.
Fourteen peaks were successfully validated by ChIP-qPCR in WKY BMDMs with all the peaks showing at least two-fold enrichment for JunD above background (Additional file 1: Figure  S3A  and S3B ). The reduced level of JunD expression in the WKY. LCrgn2 strain was reflected in the lower levels of enrichment for the seven peaks analysed  (Additional file 1: Figure  S3C ). The majority of ChIP-Seq peaks in all the strains were located in intergenic regions (35-44% across the datasets) whilst the WKY BMDMs had a higher proportion of JunD peaks within the body of the gene (exon or intron) compared to WKY. LCrgn2 BMDMs in both the unstimulated and LPS stimulated states (Figure  4A ). The distance from the nearest gene to each peak was calculated and peaks were found to be preferentially located close to the TSS in both strains (Figure  4B ).