dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Handling Because of the very well documented vast increase in the overall absolute number of psychological publications in the 20th century (which can be modeled by way of exponential smoothing  [9] ), the absolute frequencies found for professional comments and replies must be relativized by the total number of publications documented in the database for each year of publication. This is similar to findings in the other sciences: Behrens and Luksch  [10] , for example, showed a similar increase in literature published in the field of mathematics between 1868 and 2010, which can be modeled by exponential or linear functions. These increased rates reflect the growth of the research communities and resources and have been-in addition-strongly intensified in the last decades by digitalization technologies that enable more efficient submission, communication, and publication systems via the Internet, and shorter production times.
For reasons of parsimony, I chose to forego a visual presentations of time trends depicting the rates of comments and replies in the subdisciplines, and not only because of the large number of necessary figures (22 subdisciplines x 2 databases = 44 figures). The time trends are diverse and could not be clustered to more or less similar curves. The presentation of selected "prototypical" time trends is not meaningful, because there are no such prototypical trends with the exception of the general time trend of the percentage of all comments and replies presented in Figure  1  (see above), to which most of the subdiscipline-specific trends are more or less similar.
DISCUSSION AND CONCLUSION Integration of the presented results to the research on scientific discourse practice is difficult, because this state of research is sparse. However, first of all, it should be emphasized that, in contrast to other sciences, very low percentage of critical citations in psychological publications (less than 1%  [1] ) is not confirmed by the percentage of publications classified as written scientific comments and replies, in fact it is almost tripled (PsycINFO: 2. 8%) or doubled (PSYNDEX: 2. 2%), respectively. Unfortunately, these rates for psychological publications cannot be compared with other sciences up till now, because there are neither data nor databases which allow the kind of scientometric analyses with comparable thesaurus index terms presented here. Of course, we frequently find critical and discursive statements in the sections "Introduction" and "Conclusions and Discussion" of many published articles.
Notes:a With reference to all publications documented in the databases denoted by the publications years. b Documentations of publications with the PsycINFO and PSYNDEX classification codes (CC) = 2* or 3* or 4* in the Thesaurus of Psychological Index Terms ([8], retrieval, e. g. , from http://www. apa. org/pubs/databases/psycinfo/index. aspx).
General practitioners provided all previous medical treatments. Cats with recheck examinations were included in part 2 of the study. Multivariate statistical analysis was performed to assess the relationship between the different parameters. results: One hundred four cats met the requirements for part 1 and 56 cats for part 2 of the study. Data collected from patients' record included patient history, viral testing results, extent and severity of oral lesions, extent of teeth extraction. Signalment, history, preoperative treatment, and severity of caudal and alveolar stomatitis score were not associated with FCV load (P > 0. 05). Presence of lingual ulcers was significantly correlated with FCV load (P = 0. 0325). Clinical cure (32. 1%) or very significant improvement (19. 6%) was achieved in 51.
statistical analysis Both descriptive and analytical statistical analyses were per formed with a commercial software (Statgraphics Centurion version XVI. II, DYNACENTRIX, Neuilly sur Seine, France). The statistical influence of categorical factors FCV load on the severity of FCGS lesions was analyzed. The relationship between those factors and oral lesions or lymph node enlargement was assessed using a simple linear regression, respectively. The statistical influ ence of those factors on lingual ulceration was evaluated using a logistic regression. The evolution of clinical cure rates over time depending on several risk factors [FCV load, GCSIS, and alveolar stomatitis intensity score (ASIS)] at initial assessment was analyzed by means of Kaplan-Meier method and Cox model. A threshold value of α = 0.
acKnOWleDgMenTs The authors are grateful to Véronique Privat, DVM, VetPharma conseil, Orsay, France, for the statistical analysis. The authors thank Corine BoucrautBaralon, DVM, Scanelis Veterinary laboratory for the technical support. Conflict of Interest Statement: PH and ID are employed at ADVETIA Veterinary Specialty Center. The reviewer PP and handling editor declared their shared affiliation.
Patients and Procedure The sample consisted of patients enrolled in the Scleroderma Patient-centered Intervention Network (SPIN) Cohort  (Kwakkenbos et al. , 2013)   Internet upon enrollment and subsequently every three months  (Kwakkenbos et al. , 2013). SSc patients in the SPIN Cohort who had complete data at their baseline assessment for all BCSS items were included in the present analyses. The SPIN Cohort study was approved by the Research Ethics Committee of the Jewish General Hospital, Montréal, Canada and by the research ethics committees of each participating center.
The magnitude of this difference was interpreted using Cohen's effect size suggestions, with ≤. 20 SD indicating small,. 50 SD indicating moderate, and. 80 SD indicating large differences  (Cohen, 1988). CFA and DIF analyses were conducted using MPlus, Version 7. Cronbach's alpha was computed for the BCSS to assess internal consistency reliability. Construct validity was assessed by calculating Pearson's bivariate correlations of the BCSS total score with the Brief-SWAP subscale scores, PROMIS-29v2 depression and pain domain scores, and BFNE-II and SIAS-6 total scores. These analyses were conducted using SPSS, Version 20. Because some SSc patients from the SPIN Cohort could have been included in the Canadian Scleroderma Research Group (CSRG) Registry sample used in the original BCSS validation study, we also calculated the maximum possible patient overlap between the two samples based on sites that contribute data to both cohorts.
Sample Characteristics Of 943 patients, 897 had complete BCSS item data and were included in present analyses. The majority of the sample was female (N = 782, 87%) and White (N = 774, 86%). Mean age was 55. 4 years (SD = 12. 1), and 41% of patients had diffuse SSc, which is a rate similar to that seen among SSc patients more generally  (Mayes, 2005). See Table  1  for a breakdown of all sociodemographic and disease characteristics. There were 214 patients (24%) from CSRG sites whose diagnosis was before the date of the initial validation study using CSRG data and who could have possibly been in both cohorts.
RSM for the prediction of DPP-IV IC 50 as a function of hydrolysis parameters of MPI The RSM curves were generated within the boundaries of the DOE with a multilinear regression (MLR) model (Equation  1 , complete model) developed using Matlab (version R2015b, MathWorks, Inc, Natick, MA, USA) as previously described  (Nongonierma et al. , 2017). =  0 +  1  +  2  +  3  +  4  2 +  5  2 +  6  2 +  7  ×  +  8  ×  +  9  ×  +  (Equation  1 ) With Y, the DPP-IV IC 50 value; β 0 to β 9 : the coefficients of the MLR model; T (temperature), E:S (enzyme to substrate ratio) and t (time): the centred and reduced (z-centred) parameters of the experimental design and ε: the residual of the model. The complete model (Equation  1 ) was simplified (simplified model) by taking into account the main parameters (T, ES and t) and their interactions having a p-value < 0.
Peptide identification by liquid chromatography tandem mass spectrometry (LC-MS/MS) Peptide identification in H4, H11, H16 and the SGID of H16 (H16_CorPP) and MPI synthetic peptides (Ala-Leu, Phe-Leu, Leu-Leu/Ile-Leu/Leu-Ile/Ile-Ile, Leu-Pro-Leu, Val-Leu, Trp-Gln, Trp-Lys, Trp-Tyr, Tyr-Leu/Tyr-Ile) were used to spike hydrolysate samples, which were subsequently analysed using the same LC-MS/MS conditions in order to confirm the software identification (data not shown). All other amino acid sequences generated by PEAKS Studio software were manually checked in order to  (1)  verify that the peptide sequence corresponded with the same amino acid sequence in the main milk proteins and (  2 ) control the peptides fragmentation data as ion series continuity and major peaks identified as b or y ions.
Statistical Analysis All the data are presented as the means ± SEM, and the results were all analyzed using STATISTICA 10. 0 (StatSoft) or GraphPad Prism 5 software (GraphPad Software), applying either a Student's t test (unpaired or paired, 2-tailed), or a 1-way or 2-way ANOVA, followed by the appropriate posthoc tests (Bonferroni or Dunnett's tests). The independent variables were MA (between groups), paw, or drug treatment (between groups) (supplementary Table  1 ). A Fisher's exact test was used to evaluate the incidence of burst activity. Significance was accepted at P <. 05.
Spontaneous Activity of Contralateral LC Neurons Data from 57, 36, and 37 neurons were recorded from 19 control, 11 MA1W, and 17 MA4W rats, respectively, and to maintain the same experimental and recording conditions, whenever possible at least one control and one MA rat (MA1W or MA4W) were evaluated on the same day. The basal spontaneous LC activity was significantly lower in MA4W rats than in the controls (P <. 01: Figure  2D ), although no significant differences were observed when the MA1W rats were compared with the controls. While the burst activity tended to be weaker in the MA1W and MA4W rats (P =. 059 and P =. 056 vs control, respectively: Figure  2E ), the number of spikes per burst did not differ among the 3 experimental groups (Figure  2F ).
These data are consistent with the altered climbing behavior in the forced swimming test observed previously in MA4W rats  (Borges et al. , 2014) , possibly reflecting diminished noradrenaline availability in the face of a stressful situation  (Detke et al. , 1995; Cryan et al. , 2005). Interestingly, we also reported an increase in TH expression 4 weeks after chronic constriction injury, a model of neuropathic pain caused by nerve trauma. No significant changes in the spontaneous firing rate were detected after nerve constriction, although an increase in burst activity was evident  (Alba-Delgado et al. , 2013). These differences indicate that the origin of pain is decisive, suggesting that different and specific neuroplastic mechanisms are at play. We also evaluated the sensory-evoked activity of LC neurons by applying a noxious stimulation to the paw.
Acknowledgments We are very grateful to  Ms Raquel Rey-Brea ,  Mr José Antonio García Partida ,  Mr Jesus Gallego-Gamo ,  Ms Paula Reyes Perez ,  Mr Santiago Muñoz  and  Ms Elisa Galvão  for their excellent technical assistance. We thank the  UPV/EHU Advanced Research Facilities SGIker  for their collaboration. This work was supported by the  "Cátedra Externa del Dolor Fundación Grünenthal/Universidad de Cádiz ," which paid a grant to the first author; the " Cátedra em Medicina da Dor  from  Fundação Grünenthal-Portugal and Faculdade de Medicina da Universidade do Porto "; co-financed by the  Fondo Europeo de Desarrollo Regional ''Una manera de hacer Europa"  ( PI13/02659 ); the  ''Ministerio de Economía y Competitividad ", co-financed byFEDER ( SAF2015-68647-R (MINECO/FEDER) );  CIBERSAM G18 ; the '' Consejería de Economía, Innovación ,  Ciencia y Empleo de la Junta de Andalucía", Seville, Spain  ( CTS-510  and  Proyecto de Excelencia :  CTS-7748 ); 2015  NARSAD Young Investigator Grant  from the  Brain & Behavior Research Foundation ;  Fundación Española de Dolor  (travel fellowship granted to  Gisela Borges   -1536 ); and  Gobierno Vasco  ( IT747-13 ).
The 128-D descriptor of each keypoint in the first image is compared with that of the second image. The Euclidean distance is used as the similarity measurement of the two descriptors to locate the nearest matching keypoint. However, the SIFT algorithm usually generates hundreds to thousands of keypoints for each image. Hence, the SIFT features could be numerous in a large-scale image database. Moreover, the distance computation involves calculating the square root. Thus, image matching in the SIFT method for a largescale image database would be extremely time-consuming. To solve this problem, several binary SIFT (BSIFT) methods, which convert the SIFT descriptors to a binary representation, have been proposed in the last few years. The Hamming distance is used to measure the distance between two BSIFT descriptors, which takes advantage of bit-wise operations instead of the root mean square, and leads to a significant decrease in feature matching time.
Experiment We evaluated the proposed approach, MBR-SIFT, on a public dataset, the UKBench dataset  [18] , which contains 10,200 images from 2,550 object/scene groups. Each group consists of four images taken from different views or in different imaging conditions. To demonstrate the effectiveness of the improved similarity measurement, we also implemented another version called MBR-SIFT', which still used the Hamming distance instead of the improved Hamming distance for fine matching. Moreover, we also implemented the original SIFT, and Chen's  [15]  and Zhou's  [16]  methods to compare accuracy and efficiency. Moreover, several local binary features, such as CS-LBP  [19] , BRIEF  [20] , BRISK  [21] , and FREAK  [22] , have been proposed recently with promising performance in image matching, and we also implemented them to compare their potential with BSIFT in image matching.
Matching accuracy and efficiency To evaluate the accuracy performance of MBR-SIFT, the nine methods, CS-LBP, BRIEF, BRISK, FREAK, SIFT, Chen's method, Zhou's method, MBR-SIFT', and MBR-SIFT, were used in the matching experiments. We randomly selected 200 image pairs for the matching experiments. Some examples of image pairs with rotation, scale, viewport, lighting, and blur variance from the UKBench dataset are shown in Fig  4. We calculated recall and 1-precision under different values of distratio for the several image pairs. With the threshold distratio ranging from 0. 45 to 0. 85, with an interval of 0. 05 for the SIFT method, and from 0. 72 to 0. 88 with an interval of 0. 02 for the remaining methods, we achieved the average results shown in Fig  5. First, we compared five methods: SIFT, Chen's method, Zhou's method, MBR-SIFT', and MBR-SIFT.
Materials and methods Phenotypes, pedigree, and genotypes were simulated using the software QMSim version 1. 00  [16]. Two traits assuming low and moderate heritabilities were simulated: age at first calving (AFC; h 2 = 0. 12) and weight at 550 days (W550; h 2 = 0. 34). Heritabilities were based on real data estimates  [17] [18] [19] , and the phenotypic variance was assumed to be 1. 0. Ten replicates were performed for each trait and results were averaged among replicates.
Scenarios A total of 25 scenarios were tested for each trait (50 scenarios in total) with BLUP and ssGBLUP models, considering five proportions of paternity uncertainty and four strategies of scaling the G matrix to match to the A 22 matrix. These scenarios were proposed in order to evaluate the impact of disinformation (missing pedigree) in the A 22 matrix on the scale adjustment of the G matrix. In this sense, the A matrix was created assuming different proportions of multiple sires (0, 25, 50, 75, and 100%) in the genotyped animals. To evaluate the impact of scaling the G matrix on A 22 matrix under different situations with missing pedigree, four strategies of scaling the G matrix to match to the A 22 matrix were tested: S 1 -considering mean diagonal A 22 = mean diagonal G and mean-off diagonal A 22 = mean-off diagonal G matrix, this is the default option in the preGSf90 program to reduce the differences between the average diagonal and the average off-diagonal elements in the G and A 22 matrices  [12] ; S 2 -no scaling between the A 22 and G matrices, the elements of the G matrix were not adjusted for the elements of the A 22 matrix; S 3 -scaling only the animals which have known sire and dam, the elements of the G matrix were adjusted only considering the elements of the A 22 matrix of animals with both parents known; and S 4 -scaling only those animals which have one known parent, the elements of the G matrix were adjusted only considering the elements of the A 22 matrix of animals with at least one parent known.
5, 1. 5], and other 200 data points are computed using a sinusoidal input signal () = sin(2k/25)  [1, 5, 6]. The number of generations chosen is 500 and was iterated 50 times on a Pentium Core 2 Duo (1. 8 GHz CPU) and 2 GB memory personal computer in the same computing environment (MATLAB 2007a). The consequent parameters are chosen in [-10, 10] and the width of the premises parameters is given in [0, 5]. As those in  [1, 5, 6] , we select ( -1) and ( -2) as inputs in the aim to predict the nonlinear component ((-1), ( -2)) and 5 is the maximum number of rules. Table  2  gives the best results of 50 experimental trials. According to Table  2 , CS method gives the best results in terms of a number of rules (mean) fewer than the HUS method in  [18] , MSE (mean), and standard deviation (Std) in both training and testing stages compared with other methods.
Acknowledgments Mouse work was facilitated by  Christophe Joubert  and  Julien Tilliet  from the  Institut de Radiobiologie Cellulaire et Mol  éculaire/CEA animal facility. The authors thank  Julien Rucci  for his technical help in experiments and  Rima Haddad  and  Irina Naguibneva  for critically reading the manuscript. Damien Roos-Weil  and  Olivier Bernard  helped with genomic WES analyses. This work was supported by  INSERM ,  CEA ,  Universit é Paris Diderot, Universit é Paris Sud ,  Institut National du Cancer (INCA) ,  Canc érop ôle d'Ile de France, R égion Ile de France, Ligue Nationale contre le Cancer  (LNCC, équipe labellis ée), and the  Association Laurette Fugain and Fondation de France. X. C. and S. P. were fellows of INCA and LNCC.
Data collection, variables and sources The data were extracted from the TB laboratory registers and TB treatment registers maintained in the PPM facilities. Of a total of 39 laboratories, respectively 25, 3, 7 and 4 were under PPM1, PPM2, PPM3 and PPM4. Data collection was undertaken during April-June 2016. The variables included type of PPM model, laboratory serial number, patient's name, age, sex, date of diagnosis and treatment start, smear grade, type of TB and treatment outcome (for definitions of treatment outcomes, see Table  2 ). The data were collected using a structured proforma. First, basic demographic data were extracted from the laboratory register. Each patient in the laboratory register was traced in the treatment registers of all the PPM facilities in Lahore using the laboratory name and serial number and the name, age and sex of the patient.
Public Health Action and starting treatment. 21, 22 We may thus have overestimated the extent of pre-treatment LTFU. There is no electronic database of patient records in the country and it was not feasible to manually review the treatment registers maintained in the hundreds of public sector facilities. Second, the results reflect the situation in only one city, and caution should be exercised before generalising the findings to the whole country. We found that pre-treatment LTFU was lowest among patients receiving care from GPs (PPM1). This might be due to the strong personal rapport of GPs with their patients, fewer TB patients per GP (∼1-2 per GP per quarter) and better documentation and follow-up. Pre-treatment LTFU was higher among patients with scanty or 1+ smear grade. This might be due to a perception of less severe disease and apparent well-being on the part of patients or providers.
Concept lattices, and thus, a fortiori, AOC-posets and AC-posets can be built for large datasets, e. g. in  (Wray et al. , 2016) , the authors mention a large dataset, the Rijksmuseum collection which contains 100,000 objects and 1,716 attributes, giving 994,967 concepts computed in 68ms in average with FCbO update algorithm. The computation of the groups can be a hard task, as pointed out in  (Ryssel et al. , 2011). Many FMs have a very large configuration set, as Video player FM from SPLOT, with 71 features and more than 1 billion configurations. We do not address these cases, as we more specifically address the contexts where the FMs have a reasonable number of configurations, which corresponds in particular to FMs coming from real-world product lines. Concerning product lines inducing a number of configurations not tractable by FCA, our approach could benefit from product line decomposition: dividing a feature model according to scopes, concerns or teams into less complex interdependent feature models.
Product comparison matrices (PCMs) studied in  (Bécan et al. , 2014)  give an overview of this type of dataset where many datasets could be investigated using our method: 75 PCMs, corresponding to 211 matrices that have 2 to 241 rows, and 3 to 51 possibly valued columns, with about 43% of the cells have boolean value (and the others should be translated into boolean features via FCA scaling  (Ganter and Wille, 1999) ).
The study presented in this paper investigates companies operating in the UK video-game industry with regard to their levels of survivability. Using a unique dataset of companies founded between 2009 and 2014, and combining elements and theories from the fields of Organisational Ecology and Industrial Organisation, the authors develop a set of hierarchical logistic regressions to explore and examine the effects of a range of variables such as industry concentration, market size and density on companies' survival rates. The analysis addresses locational dimension of the video-game industry is considered by introducing an extra regionally-related variable into the models, associated with the number of video-game university programmes locally available. In addition, companies are investigated with regard to their organisational type in order to identify potential effects associated with their intrinsic organisational structures.
Despite these impressive figures and the success of the UK videogame industry, however, there is a significant paucity of studies addressing and investigating issues and challenges faced by businesses operating within this industry. The aim of this paper is to contribute filling this gap by exploring and examining which factors have an impact on the survival rates of companies in the UK video-game industry. By developing their analysis on a unique dataset comprising information from videogames companies between 2009 and 2014, the authors depart from the traditional approaches used in the business management and entrepreneurship fields, and use a mixed approach with elements extracted from Organisational Ecology (OE) and Industrial Organisation (IO) theories to investigate locational dimensions alongside the diverse organisational types of newly founded companies operating in the industry.