dataesr/scientific-paragraphs-categorization huggingface dataset:
These two components describe differences in shape as deviations from an average configuration of landmarks. When sample sizes were smaller than the number of shape variables, a restricted shape representation, i. e. , a set of principal components (relative warps) derived from the shape variables, was used. These relative warps were used as input into a discriminant analysis. The significance in the discriminant analysis was evaluated using the values of Wilks and Mahalanobis distances. These values were evaluated using non-parametric tests based on permutations (5. 000 runs). The Bonferroni correction was used for multiple comparisons. The percentage of phenotypic similarity was calculated using the cross-check test of discriminant analysis. In this test, each individual in the post-treatment population is removed and reinserted into the analysis and they are later assigned into a group according to their phenetic similarity (PAD software).
The numbers of articles with the word resistance in the title for the 10, 25, 50 and 100 most cited articles, over the four successive periods, were 5, 8, 12 and 21, respectively. Publications on analgesic and anti-inflammatory drugs continue to be well represented in the latest 10-year period  (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) , comprising 9 of the top 50 and 20 of the top 100. Further information on the rapid development of veterinary pharmacology has been gleaned from another database, the Web of Knowledge (WOK). This gives coverage of 23 000 academic and science journals, 110 000 conference proceed-ings and many textbooks. This source indicates that veterinary pharmacology became 'visible' in terms of published research output only in the mid-sixties.
From 1964 to 1995, the number of publications increased slowly and progressively; then in the late 1990s and beyond, there was a surge with a sixfold increase from 1997 to 2007 (358 versus 2291 publications Fig. 3 ). This was due to both a general increase in the number of pharmacology publications and a specific increase in our field with a percentage of veterinary publications that has increased from 1995. The WOK database indicates that there were three periods of interest for veterinary pharmacology: 1950-1965, when there was a very low level of relative activity (<0. 2% of all pharmacology publications); followed by the next 25 years  when the ratio was of the order of of all publications in pharmacology; and a third period 1995-2010 when the ratio was in the range 3-4% (Table  3 ). Figure  4  presents the H-index of publications in veterinary pharmacology, obtained from the WOK database.
Reproduction management and data collection Oestrus was detected once every 48 hours by teasing with a stallion in early morning. Mares detected in oestrus were examined then clinically by rectal palpation and ultrasound to verify the follicular status of the mare. Matings or inseminations were performed at 48 h-intervals until ovulation was detected. Pregnancy detection was performed by ultrasound examination at days 15 post ovulation. Mares diagnosed pregnant were reexamined at days 30 post ovulation to confirm pregnancy. Teasing and matings were performed by a single team of three people. Rectal palpation, artificial insemination and pregnancy diagnosis were made by a single veterinarian. No medication or hormonal treatment was used for these mares. Teasing, ultrasound examinations, veterinary treatments, artificial inseminations and matings were monitored for all the mares.
For each ultrasound examination, the follicular and the uterus status of the mare were noted. Were excluded from this analysis the breeding data of the mares which were not clinically healthy (lameness, sickness, injury during the stay…), which left the stud before they had been diagnosed for pregnancy (owner's decision) and those which were not bred further due to the decision of a new owner (sold before breeding diagnosis). Breeding data for 102 oestrus cycles corresponding to 32 experimental mares and 38 standard feeding pattern mares were thus kept for statistical analysis. In total, 32 mares were pregnant after the first service, and therefore only 35 were submitted to a second (or more) service (3 SFP mares failed to show oestrus and were thus not bred). Mares were bred with 9 stallions housed in the facility, 55 were mated naturally and 12 inseminated with fresh semen according to the owner's decision (Table  1 ).
Flow cytometry and histopathology Before staining with different antibody mixes, isolated cells were pre-incubated on ice for at least 20 min with the 2. 4 G2 mAb to block Fc receptors. Multiparameter FACS analysis was performed using a FACSCanto system (BD Biosciences). FACS data were analyzed using FlowJo software (Tree Star, Inc. ). For each experiment, control mice were included to define the proper gates. Antibodies used for identifying each cell population are indicated in Figure  S7. Dead cell populations were removed from analysis with LIVE/DEAD Fixable Aqua Dead Cell Stain (Molecular Probes) using AmCyan (Canto) channel. Blood was stained directly with the antibodies and lysed with BD lysing buffer (BD, Biosciences). All samples were fixed in paraphormaldehyde 3. 2% after staining and diluted in PBS prior acquisition.
Cytokine measurement The levels of IL-2, IL-4, IL-6, IFN-c, TNF-a, IL-17a, IL-10 were measured by CBA Mouse Th1/Th2/Th17 Cytokine Kit (BD, Biosciences) according to the manufacturer's specifications in sera of PMN-depleted, Genista and WT B. abortus infected mice. The data were analyzed using FCAP Array software from BD. A pool of normal sera from C57BL/6 mice was used for background estimation.
Data collection Data were simultaneously collected along 5 fixed transects (around 13 km for each; Fig. 1B ), walked simultaneously on the entire diurnal period of the day, from 3 March, 1996 to 3 November, 1996 (see  [35]  for more details). These 5 transects cover 29. 5% of the population's range. Data were sampled both during weekends (n = 15) and other weekdays (n = 13) to ensure that all conditions of human disturbances were included. These transects were not selected randomly but chosen to sample the environmental diversity in term of habitat types and human pressures (tourism and hunting) over the study area and to offer the best observational conditions (e. g. , offering the largest viewpoints on open areas commonly used by mouflon when feeding;  [35] ). Spatial coordinates (based on a 1256125 m grid size), group size and group composition in age-sex classes were recorded for each group encountered as well as the habitat used (4 habitats: forest, rock, moorlands and meadow/pasture -our analyses were restricted to the two latter habitats, see ''Study area and population'').
We finally computed buffers (i. e. , circles centered on group coordinates) to obtain estimates of landscape visibility experienced by each mouflon within its theoretical ''home range''. Buffer size was computed as the average home range of 46 mouflon (males = 294 ha, i. e. , circles with a radius of 968 m; females = 178 ha, i. e. , circles with a radius of 753 m; Marchand et al. unpublished data based on 16 males and 30 females) trapped and fitted with Lotek GPS collars 3300S (revision 2; Lotek Engineering Inc. , Carp, Ontario, Canada). Estimates of landscape visibility within males and females buffer corresponded to the average of visible pixels from each pixel included in the buffer.
Data analysis We performed sex-specific analysis and tested our hypothesis (see Table  1  for details) using a logistic regression (generalized linear model with binomial distribution and logit link) with vigilance status as the binary dependent variable. We tested for two-way interactions between habitat visibility and group size  [13]  and between hunting period and protected/unprotected areas. During model selection, we concurrently assessed the effect of variables including characteristics of the animals in the group (''Repro comp. F'' and ''Age comp. '') against the related variables of the focal animal (''Repro'' and ''Age''; see Table  1  for details). Because we only included factors for which we had 30 data for each modality, we only used age as a factor with two levels for females (''Age'', see Table  1 ) and not as a three levels factor (''Age comp.
The predicted probabilities were associated with the level of ''females with lamb'' and ''no WR'', and for the average group size (A) or the average visibility (B). Observed proportions were computed from a subset of the data including adult females with lamb observed outside the WR, and for the full ranges of group size (A) and habitat visibility values (B). doi:10. 1371/journal. pone. 0082960. g003 to detect than adults and thus less constrained to invest in vigilance and/or (iii) their greater nutritional needs and, thereby, an increase in foraging in detriment of vigilance  [31]. This last hypothesis is the most plausible to explain our results as juveniles, i. e. , yearlings in our case, have acquired. 1 yr of experience, have relatively large body size compared to adults, but still have high energy requirements.
Acknowledgments We are grateful to the  GIEC , the SIVOM and interns of the  Office National de la Chasse et de la Faune Sauvage  for collecting data (G. Dalery,  B. Descaves ,  Y. Gilbert ,  S. Lemmet  and  C. Vuiton ), with a special thanks to  K. Martinetto  for her invaluable help and considerable involvement in this project. We thank  C. Togo  and 3 anonymous reviewers for their constructive comments on the previous version of the manuscript,  C. Kourkgy  for the final read-through and  P. Marchand  for his great help with GPS data. P. Blanchard is at the lab EDB, part of the  ''Laboratoire d'Excellence'' (LABEX)  entitled  TULIP  ( ANR -10-LABX-41 ).
Before the acoustical analyses, we selected the recordings with a low background noise. All together 1513 calls (data are available on request by contacting the corresponding author) were analysed from 93 recordings made on 84 different piglets belonging to 34 litters (Table  1 ). We identified 34 males and 28 females. Identity of the remaining 22 piglets was not determined because we did not want to disturb the behaviour of the animals (e. g. , during fighting, huddling or nursing). Each piglet was recorded only in a single situation with the exception of 9 piglets that were recorded in two different situations. The duration of the situations varied naturally. For instance ''surprise'' situation was logically very short lasting (1s) and duration of ''nursing situations'', ''fighting'', ''huddling'' and ''reunion'' depended on the naturally occurring sequence of mother-young interactions (2-15s).
As we looked for distinctive classes we focused only on the parameters that showed multimodal distributions in the subsequent analyses  [24, 31]. We further checked for pair-wise correlations between variables. Only one of the variables from a Table  1. Description of the situations of recordings and data collected. A stockperson hold the piglet between his legs, while the veterinarian performed the castration surgery in a standard way without anaesthesia. Calls were recorded both during the ''holding'' phase and the ''cutting'' phase. The situation when a sow traps a piglet under her body was simulated through restraining a laterally lying piglet by both hands against the floor. For detailed description of the methods see  [51]. Nursing situations BN Before nursing when a piglet came at the head of the sow before nursing  [20].
15 5 SU Surprised* when an isolated piglet was surprised by the arrival of a person. After the isolation period, the experimenter suddenly appeared above the pen silently (previously used by  [18] ). pair with a high correlation coefficient (. 0. 9) has been retained in the analysis. We preferred to exclude highly correlated variables rather than reduce the dimensionality of the dataset by Principal Component Analysis for example, to ensure easier and more straightforward interpretability of the results. The 8 variables finally used for cluster analysis were: -peak frequency; frequency with the highest amplitude; derived from spectrum of entire call (pf), -frequency that divides the spectrum into two frequency intervals of equal energy; derived from spectrum of entire call; amplitude is integrated over the whole frequency range of spectrum and split by frequency into equal halves (q50), -mean Wiener entropy of the call; ratio of the geometric mean to the arithmetic mean of the spectrum; as Wiener entropy intrinsically increases with bandwidth of the sound, we scaled the value reported by SASLab to q50 (ent).
Statistical Analyses All statistical analyses were carried out in R  [32]. We used the k-means clustering method to see whether the calls would fit into several call types. We had no prior expectations about number of call types (i. e. number of clusters). Therefore we ran cluster analyses assuming 2-15 clusters and subsequently judged the validity of obtained cluster solutions based on the EtaK and PreK values  [7] ; EtaK = 1-(WSS K /WSS 1 ) and PreK = 1-(WSS K /WSS K-1 ). EtaK reflects the relative reduction in withincluster variance for a solution with a given number of clusters (WSS K ) compared to variance of the unpartitioned dataset (1cluster solution, WSS 1 ). PreK reflects relative improvement in the reduction of variance for a given solution (WSS K ) compared to previous solution (WSS K-1 ).
LDA can be used on nominal variables, mainly as a black-box classification algorithm, even though the LDA assumptions are often violated in such cases. We checked carefully that the classification gave meaningful results (i. e. the call was assigned into the situation with highest proportion of corresponding call type in case of single call; the ''averaged'' call was assigned into the situation with the most similar call type proportions in case of multiple calls available for classification, see below). Results of LDA were cross-validated using the holdout method  [33]. Holdout method was performed as follows. The whole dataset was first split into two halves. One half of calls was used for determination of discriminant function (calibration dataset). Discriminant function was then applied to the other half calls (validation dataset) to obtain the classification of calls into situations.
To evaluate classification success, we compared the number of obtained correct classifications with the distribution of correct classifications by chance on permuted dataset (1000 permutations)  [34]. This approach has been suggested for groups with unequal sample sizes  [35]. As several calls from a single piglet were often used and different piglets and piglet numbers were recorded in each situation, we used pDFA  [36]  to see whether the results remain significant when individuality is accounted for. We used agglomerative hierarchical cluster analysis to investigate acoustic similarity between situations. This method successively links the most similar objects into larger groups. Average values of acoustic parameters or call type proportions were used as input data. Euclidean distances were used as measurement of distance between the pair of situations and Ward's criterion was the linkage criterion.
Many studies found opposite relationship trends between valence and pitch  [11]. This might come from the fact that researchers focused on a limited range of situations or call types. However, inspection of our scatterplots did not show clear support for such parabolic distribution of data points. The proportions of call types were linked or tended to be linked to the valence except for LFs. The long HF calls and especially the HFs type calls were associated with a more negative valence, while shorter and lower pitched LFm and LFt calls corresponded to a less negative valence. One limitation of our results is that the emotional negativity was assessed by human experts and not obtained from observational studies with the piglets. Indeed, the experts might not have captured the emotional negativity of some situations as it is perceived by the piglets in reality.
Materials and methods Six major application stores (Apple, Google Play, Blackberry Mobile Market, Nokia Ovi, Samsung and Microsoft Windows Marketplace) were searched for smartphone applications specifically targeted to radiology using the terms radiology, radiation, x-ray(s), computed tomography/CT, magnetic resonance imaging/MRI, ultrasound, nuclear medicine, fluoroscopy, mammography and mammogram. The searches were conducted by a single author between 18-30 June 2012. Radiotherapy, veterinary and dental radiology applications as well as non-English language applications, games, wallpaper and applications under construction were excluded. Data was recorded on the source of the application, application name, ratings, number of ratings and cost [prices in US dollars and British pounds were converted to euros using the exchange rate on 31 April 2013 ($1=€0.
7779, £1=€1. 1853 )]. Details regarding application content and medical input were obtained from the associated application description and any relevant websites. Applications were categorised as "teaching" (flashcards, questions, revision guides/notes, textbooks, cases, tutorials/ talks, etc. ), "reference" (guidelines, cancer staging, radiology positioning guides, scanning protocols, glossaries, calculators including radiation dose calculators, convertors, cost comparison, etc. ), "viewing software", "Hospital Information System" (HIS) (patient data including imaging reports), "patient education" and "others" (marketing, magazines, journal access, etc. ). Food and Drug Administration (FDA) approval for viewing software applications was ascertained from applicationrelated publicity material and the FDA 510(k) premarket notification database  [14].
Whilst the software of the mobile PACS applications is very similar or identical to existing PACS systems, the relevant hardware is clearly different (smartphones versus reporting workstation), and this potentially affects the safety of the smartphone application. With these hardware limitations in mind both the Mobile MIM and ResolutionMD applications have clear disclaimers stating they should only be used when there is no access to a workstation, as they are not intended to replace full workstations. The Medical Devices Directive (MDD)  [18]  ensures safety and performance of medical devices throughout the EU, with each member state having its own regulatory framework. However there is currently no consensus between member states over the definition of a medical device and which medical applications should be considered medical devices, leading to uncertainty among application developers and healthcare providers.
One option used by several of the viewing applications is not storing any protected health information on the mobile device. Instead the user has to log in to a secure server and access the data via a secure, encrypted data transfer link. Further improvements in data security can be gained from applications utilising the HTLM5 markup language. In contrast to previous versions, HTLM5 has many commonly used applications integrated, such as video and audio elements. This makes it easier for users to access graphical and multimedia content directly via the web server without the need for additional plug-ins and subsequent data download onto a user's device. Whilst these options provide safe methods for managing confidential data, it obviously limits the use of the application to areas that have Internet access.
The speed of data transfer and download is a further limitation of mobile image review on smartphones. A study evaluating the OsiriX smartphone application showed that the time taken to transfer 85 5-mm axial slices of a CT of the abdomen and pelvis using an iPhone 3S was up to 51 s with a WiFi connection, over 7 min using a 3G connection and over 35 min using an EDGE network connection  [20]. The time taken for transferring thin (1-mm) slices to permit multiplanar formatting will be considerably longer. While smartphone and network technology continues to improve, with a corresponding increase in the rate of data transfer, the prolonged transfer times, particularly when no Wi-Fi connection is available, may currently limit the usefulness of smartphone viewing software applications. Other concerns regarding noise, distraction, infection control, and health and safety cautions have also been reported  [30, 31].
M13 tail incorporation was achieved in 8 cycles at 94°C (30 s) / 53°C (45 s) / 72°C (45 s), and a final extension at 72°C for 10 min. PCR products were pooled into sets of four loci with differently coloured labels and separated on a ABI Prism 3130 XL (LifeTechnologies, Darmstadt) capillary sequencer using a LIZ 500 size standard. Product sizes were scored using the GeneMarker v1. 90 software (SoftGenetics) and pairwise genetic differentiation between populations was calculated as Wright's fixation indices (F ST ) according to Weir & Cockerham  [32]. Pacific oysters are known to harbour substantial amounts of null alleles  [33]  that could bias any estimate of population differentiation. We therefore estimated the frequency of null alleles within our sample using the software freeNA  [34].
Frequencies of null alleles were small for all loci and populations (range: 0 -0. 06) except for locus Cgi-194 where estimates were higher within all oyster beds (range: 0. 05 -0. 15). We therefore excluded this locus from the analysis, and only report the corrected F ST values after removal of loci with high frequencies of null alleles in all populations. Genetic distance between individuals was calculated as geometric AMOVA distances: ∂ 2 ¼ X S s¼1 p sj -p sk 2 ½35; where distances between individual genotypes j,k are summed over S loci. Calculations were performed using the R package Gstudio.
Data analysis Assignment of reads to individual PCRs was done using modified python scripts from the cogent package. In short, within each raw read we looked for the presence of both primers ensuring complete sequencing of the PCR product. Afterwards, we identified individuals by determining combinations of MID tags allowing for a maximum hemming distance of one in each MID tag. After correct assignment of single reads to an individual oysters, we used the AmpliconNoise pipeline  [39]  to remove pyrosequencing and PCR noise and Perseus to remove chimeric sequences using default parameters except for alpha and beta values for false discovery detection in Perseus, which were set to -7. 5 and 0. 5, respectively. Reads were trimmed by cutting off their forward and reverse primers. We used scripts from the Qiime package  [40]  for the analysis of microbial diversity.
We used previously described genera of pathogenic bacteria in shellfish  [3]  and other marine organisms  [43]  to identify such potentially pathogenic bacteria. These included Arcobacter spp. , Citrobacter spp. , Corynebacterium spp. , Escherichia spp. , Halomonas spp. , Micrococcus spp. , Mycoplasma spp. , Photobacterium spp. , Pseudoalteromonas spp. , Pseudomonas spp. , Shewanella spp. , Staphylococcus spp. , Streptococcus spp. , Tenacibaculum spp. We used non-metric multidimensional scaling from the vegan R package to visualise distance matrices (Horn-Morisita distances, Wisconsin double square root transformation) between individual microbiomes. Statistical differences between treatments and oyster beds were analysed by means of multivariate permutational ANOVA (adonis function, Horn-Morisita distances) and comparisons between distance matrices were based on non-parametric Mantel tests or procrustes rotations of ordinations.