dataesr/scientific-paragraphs-categorization huggingface dataset:
Selection of physical activity levels (PALs) PAL values for children and adolescents were derived from measurements of TEE and REE. These values vary considerably according to lifestyle, geographic habitat and socioeconomic conditions, and inter-individual coefficients of variability as high as ± 34 %  (Torun, 2001)  have been reported. As indicated in the FAO/WHO/UNU report (  2004 ), most studies were carried out on random or convenient samples, and therefore may not have captured the full range of potential physical activity. SACN (2011)  derived PAL values from a dataset of all published DLW studies in children aged over one year, including those studies assembled by  Torun (2005)  and other studies published until 2006. Among these studies, seven were from Sweden, six from the UK and two from the Netherlands.
Key data on which to base dietary reference values (DRVs) The Panel decided to define only one DRV for energy, namely the AR, and to use the factorial method based on REE x PAL to obtain the average energy requirements for adults, children and adolescents. For infants, TEE was derived by regression equations based on DLW measurements. The additional energy requirements associated with growth during infancy, childhood and adolescence, and with pregnancy, were accounted for by estimates of the energy content of the newly-accrued tissue mass, as well as of the energy for its synthesis. For the additional energy requirement during lactation, milk energy output and energy mobilisation from tissue stores accumulated during pregnancy were taken into account. As explained in Section 5, different equations and/or databases can be used, and this would lead to a range of ARs for various situations (see Tables  6  and 9 ).
Calculation of resting energy expenditure (REE) Although several predictive equations may be appropriate for estimating REE of various populations (as outlined in  Sections 2. 4. and 5. 3. 1. ) , for practical reasons the Panel decided to calculate REE as a function of age, sex, body mass and height by means of only one set of equations, namely those of  Henry (2005). These equations were chosen because the underlying database is, at present, the most comprehensive as regards number of subjects, their nationalities and age groups. As described in Section 5. 3. 1. 1. , measured heights (obtained in nationally representative surveys of adults in 13 EU countries) and corresponding body masses to yield a BMI of 22 kg/m 2 were used to calculate REE (see Table  12 ). Because of a lack of anthropometric data from EU countries for age groups beyond 79 years of age, the Panel decided not to calculate AR for adults from 80 years onwards.
Infants Exclusive breastfeeding to the age of about six months with continued breastfeeding as part of a progressively varied diet after six months is nutritionally adequate for most healthy infants born at term (EFSA Panel on Dietetic Products Nutrition and Allergies (NDA), 2009). For infants during the first half year of life (until six months of age), energy requirements are considered to be equal to the energy supply from human milk. The Panel decided to use the equation for estimation of TEE derived from data of initially breast-fed infants (see  Section 5. 3. 2. 1. ). Energy requirements during infancy were estimated from TEE measured by the DLW method in healthy, full-term infants initially breast-fed for four months after birth and with adequate body mass, plus the energy needs for growth (Table  11 , the equivalent table using energy units in kcal is given in Appendix 15).
Calculation of resting energy expenditure (REE) Although, in principle, both the equations of  Schofield et al. (1985)  and  Henry (2005)  are considered as appropriate for the estimation of REE for children and adolescents, for practical reasons and because the results obtained with these equations are very similar, only the equations of  Henry (2005)  are applied for the estimation of REE values to calculate ARs for energy for children and adolescents as described in Section 5. 3. 3. 1. The Henry equations were chosen because their database comprises more subjects than the one underlying the Schofield equations (see Table  1 ). For the calculations, median body masses and heights from the  WHO Growth Standards (WHO Multicentre Growth Reference Study Group, 2006)  were used for children aged 1-2 years, and from harmonised growth curves of children in EU countries  (van Buuren et al.
CONCLUSIONS The Panel concludes that one DRV for energy, namely an AR, can be derived for adults, infants and children, and pregnant and lactating women. For infants, this is based on estimates of TEE determined with DLW studies, whereas for children and adults TEE is determined factorially from estimates of REE using the predictive equations of  Henry (2005)  and the energy needed for various levels of physical activity. For pregnant and lactating women, the additional energy needed for the deposition of newly formed tissue, and for milk output, is derived from data acquired with the DLW method, or from factorial estimates, respectively. Summary tables with the proposed AR expressed as kcal/day can be found in Appendix 16. The application of these values requires consideration of the objective, such as dietary assessment (for groups or individuals), dietary planning (for groups or individuals specifying also the goal for body mass: maintenance, increase, decrease), labelling dietary reference values, and development of foodbased dietary guidelines, and a need to define and characterise the target population (homogeneous, heterogeneous, in relation to age, sex, physical activity, body mass).
2. Based on this database, the IoM derived prediction equations of TEE by nonlinear regression analysis taking into account age, sex, height, body mass, and a physical activity constant. Four physical activity constants were defined as equivalents to a range of PAL values appropriate for sedentary, low active, active, and very active lifestyles. Individual PAL values were determined by dividing the measured individual TEE values by the measured or predicted individual REE values, and PAL values less than 1. 0 or greater than 2. 5 were omitted.
For the prediction of REE with the equations of Harris-Benedict (1919), Schofield et al. (1985), Mifflin et al. (1990), Müller et al. (2004) and Henry (2005) (see Section 2. 4. 1. ), individual data from 36,469 subjects were used (for details of the database and calculation see Appendices 9 and 11). Figure 1 illustrates the median REE values according to age group and sex obtained with the respective equations. Predicted REE decreases with age for both sexes. For women, the Mifflin predictive equation predicted lowest values in all age groups ≥30 years. For men, there is no equation that always predicted the lowest values. For both sexes, the Harris-Benedict equation predicted highest values for ages 18-29 and 30-39 years, whereas Schofield predicted highest values for ages 40-49 and 50-59 years.
PREDICTIVE EQUATIONS FOR REE IN ADULTS Author Year Number of subjects Age range or mean BMI range or mean Remarks on large database Sex Statistics and cross validation REE predictive equations APPENDIX 2: Harris and (1919) 239 27 (M) - - 136 M M: r=0. 86, CL=±211 kcal a M: BM x 13. 7516 + HTCM x 5. 0033 -AGE x 6. 755+66. 473 Benedict ( a) 31 (F) 103 F F: r=0. 77, CL=±212 kcal F: BM x 9. 5634 + HTCM x 1. 8496 -AGE x 4. 6756+655. 0955 (kcal/day) (USA) Schofield et al. (b) (body mass) (1985) 7,173, including children n=4,814 >18 y mean BMIs of the 6 groups: n=3,388 Italians (47 %), n=615 tropical residents, n=322 Indians; 4,809 M M: r=0. 65, SE=0. 64, n=2879 M: r=0. 60, SE=0. 70, n=646 M: AGE 18-30 y: 0. 063 x BM + 2. 896 M: AGE 30-60 y: 0. 048 x BM + 3. 653 (MJ/day) (about 11,000 values, between 21 and 24 114 published studies, most European and North M: r=0.
Age range or mean BMI range or mean Remarks on large database Sex Statistics and cross validation REE predictive equations Author Year Number of APPENDIX 3B: subjects (2004) 2,528 5-91 y Mean BMI: - 1,027 M, Development: R 2 =0. 71, SEE=0. 77. 0. 05192 x FFM + 0. 04036 x FM + 0. 869 x SEX -0. 01181 x AGE + Müller et al. (b) (development 27 1,501 F (development: Cross-validation in sub- 2. 992 (FFM (d) , MJ/day) of equation in sub- 388 M, 658 F), population 2: n=1,059 (410 M, 649 F) e : r=0. 83 (Germany) population 1: n=1,046) (2004) 2,528 5-91 y Mean BMI: - 1,027 M, Development: R 2 =0. 54, SEE=0. 78, BMI > 18. 5 to 25: 0. 0455 x FFM + 0. 0278 x FM + 0. 879 x SEX - (development 27 1,501 F (development: (n=444, for BMI > 18. 5 to 25). 0. 01291 x AGE + 3. 634 of equation in 388 M, 658 F), Cross-validation in sub- sub- population 2: r=0.
0640 x BM + 2. 84 M: AGE 30-60 y: 0. 0485 x BM + 3. 67 M: AGE ≥60 y: 0. 0565 x BM + 2. 04 F: BM x 7. 18 + 795 BMI ≥30: 0. 05685 x FFM + 0. 04022 x FM + 0. 808 x SEX -0. 01402 x AGE + 2. 818 9. 99 x BM + 6. 25 x HTCM -4. 92 x AGE + 166 x SEX -161 616. 93 -14. 9 x AGE + 35. 12 x BM + 19. 83 x HTCM -271. 88 x ETHNICITY M: 53. 284 x BM + 20. 957 x HTCM -23. 859 x AGE + 487 F: 46. 322 x BM + 15. 744 x HTCM -16. 66 x AGE + 944 0. 047 x BM -0. 01452 x AGE + 1. 009 x SEX + 3. 21 M: AGE 18-30 y: 0. 0669 x BM + 2. 28 M: AGE 30-60 y: 0. 0592 x BM + 2. 48 M: AGE ≥60 y: 0. 0563 x BM + 2. 15 BMI >18. 5 to 25: 0. 02219 x BM +0. 02118 x HTCM -0. 01191 x M: AGE 60-70 y: 0. 0543 x BM + 2. 37 (Oxford Müller et al. (b) (BMI (f) , MJ/day) (Germany) Database) (development of equation in sub-population 1: n=1,046) 27 1,501 F (development: 388 M, 658 F) 4,702 F (4,708) F: r=0.
The disambiguation of named entities is a challenge in many fields such as scientometrics, social networks, record linkage, citation analysis, semantic web. etc. The names ambiguities can arise from misspelling, typographical or OCR mistakes, abbreviations, omissions. Therefore, the search of names of persons or of organizations is difficult as soon as a single name might appear in many different forms. This paper proposes two approaches to disambiguate on the affiliations of authors of scientific papers in bibliographic databases: the first way considers that a training dataset is available, and uses a Naive Bayes model. The second way assumes that there is no learning resource, and uses a semi-supervised approach, mixing soft-clustering and Bayesian learning. The results are encouraging and the approach is already partially applied in a scientific survey department.
Introduction In bibliographic databases, affiliations of authors are of paramount importance. Hence, they permit to the laboratories or institutes to get national and even international visibility, as well as they consequently provide authors with scientific caution. We cannot discuss the issue of affiliations without talking of "Shanghai ranking" which aims at evaluating universities. Our purpose here is not to feed up the controversy (Van Raan 2005)  (Liu, Cheng and Liu 2005) , but to point out that the management of affiliation plays an important role in the calculation of universities "performance". Moed  (Moed 2005 ) reports some problems with author's names and also institutions: "Authors from the same institution, or even from the same department, may not indicate their institutional affiliations in the same way".
Depending on the country, it is not always clear how to name a laboratory with respect to its supervisory authorities. The affiliation is also important information to disambiguate author names in bibliographic databases. In this context, Wang points out that: "as the amount of available information increases, problem caused by misspelling, spelling difference, and name or affiliation change also become worse. " (Wang and al. 2012). A standardization of data in bibliographic databases is thus necessary to carry out informetrics studies, but it is not a trivial task: the practice, whatever it is intentional or not, of omitting institutional affiliations, or giving incomplete or wrong information is not uncommon  (Hood and Wilson 2003). Each year, the CNRS (the French National Center of Scientific Research) has to spot its laboratories affiliations among the French ones.
State of the art and discussion As part of bibliometric analyzes, the authors affiliations can produce statistics by laboratories as well as by institutes or universities. However, such analyses often face with problems of high variability and heterogeneity of naming: a single laboratory name may thus appear in several different ways if the authors use different abbreviations, incomplete or misspelled words (typing mistakes, spelling. ). In addition, some universities can have several names (for example University Pierre and Marie Curie = University Paris VI). This problem is known for long but still persists nowadays. In the 1990s, De Bruin et al. (De Bruin and Moed 1990 ) point out the problem of variability of the author addresses in databases such as SCI (Science Citation Index). They highlight the case of countries like Germany or France where the heterogeneity of data is particularly important.
French et al. (French & Powell and Schulman 2000)  supply an authority file after a cleaning step (name of country, zip codes, states, expansion of abbreviations, acronyms. ) and then use a clustering based on an "edit distance". Recent approaches also address the problem by the single use of NLP methods, like in  (Galvez and Moya-Anegn 2006). The terms data cleaning, data scrubbing, data standardization, data disambiguation, data homogenization and also entity resolution are used to refer to the tasks of transforming source data into clean or normalized data for loading in databases or linking with other data sets or computing statistical indexes (bibliometric analysis for example). If as we have seen, these problems are essential in bibliometrics, but they are also recurrent in many other areas where the heterogeneity of data is an important problem.
It can occur within a file or a database, but also when combining information from heterogeneous sources (e. g. record linkage). Erhard Rahm  (Rahm and Do 2000)  classifies data quality problems encountered in data cleaning tasks (fig. 2 ). In our case, we can assimilate the multi-source to a bibliographic database reporting papers from journals of different publishers. The approach presented by Fellegi and Sunter  (Fellegi and Sunter 1969)  is the reference widely used in record linkage to identify the same entities in different datafiles. It is based on calculating similarity scores between two records. Generalizations of this method for more than two data files have been recently proposed  (Sadinle, Hall and Fienberg 2010)   (Sadinle and Frienberg 2012). Ventura  (Ventura & Nugent and Fuchs 2012)  mixes disambiguation and record linkage algorithms, using Random Forest, and applies this methodology to a case study of inventors referenced in USPTO patents in the optoelectronics field.
He points out the transitivity issue that we'll discuss in the conclusion. Alternatively, a only few authors propose approaches based on unsupervised algorithms. This is the case of Niu  (Niu, Wu and Shi 2006)  which presents a new method for entity disambiguation using textual information and interobject relationship to evaluate similarity. Entities are author names, and interobject relationship is related to coauthorship network. The novel methodology developed by Aswani for unification of authors names use web mining to get full names and find publications pages  (Aswani, Bontcheva and Cunningham 2006 ). As we see, applications are numerous, be it in bibliographic databases, in datawarehouses, in multiple record linkage, in semantic web, or also in semantic digital libraries as shown in  (Jiang and al.
2011 ). To conclude lets quote the standardization actions conducted through the International Standard Name Identifier (ISNI) and the Virtual International Authority File (VIAF). The aim of ISNI (http://www. isni. org) is to identify international public identities of individuals or communities and to provide tools for disambiguation. VIAF (http://viaf. org/) is a research project of OCLC (Online Computer Library Center) which aims to align lists of authorities (including proper names) to form an international reference database. Do not forget to mention the Oyster software (Open sYSTem Entity Resolution): is an open-source software developed by Talburt in the ERIQ Research Center (Entity Resolution and Information Quality at the University of Arkansas). OYSTER is an entities resolution system using XML scripts  (Zhou and al.
Datasets We used three different datasets: a first dataset with 10 057 French affiliations (noted hereafter A1), a second small dataset of 150 Lorraine affiliations (French region) (noted A2), and a last dataset with 2266 French affiliations extracted from WOS and SCI (noted A3). All those datasets have been preprocessed by splitting the affiliations into words using space as separator (any punctuation, including dash, is firstly removed). Given the difficulty we had to cut affiliations into words in the datasets A1 and A2 (dash, sometimes missing space. ), we have then used a second splitting technique based on n-grams after converting affiliations in string without spaces or punctuation. Supervised learning is applied on dataset A1, and semi-supervised one is applied on the two other datasets.
Supervised learning The dataset A1 was split into training dataset and test dataset successively represented by the words of affiliations and by the n-grams. The figure  5  shows the distribution of the affiliations in the 53 resulting classes (test + train) and thus highlights the fact that the resulting classification is highly unbalanced. The classification results on the dataset A1 are presented in Table  1. Because of the problem of individualization affiliations into words in dataset A1, the results obtained with n-grams appear slightly better, with an optimum of Recall of 0. 92. It should also be noted that a systematic lookup of the analysis results of NB which got a very high probability, whilst being in contradiction with the expected (i. e. human labeled) results, permits us to prove that the manual labeling of the test dataset was sometimes wrong (the model was giving the right answer in all that cases!).
Semi-supervised classification The datasets A2 and A3 were used for this experiment. Those datasets are split into train and test datasets with the methodology described in the former section detailing our approach. The figure 6 reports the distribution of affiliations in the resulting classes (A2: 19 classes; A3: 10 classes). It highlights that the smaller dataset (A2) is highly unbalanced, whilst the bigger one (A3) is homogeneous. The exploitation of this two different kinds of distribution is intentional Hence it permits to highlight the impact of data configuration on our results. With this kind of data, K-means approaches produce low quality results (Table  2 ). It is probably due to the bad representation of data. As we discuss in the last section of this paper, a vector space representation gathering scientific content and the address should certainly improve the clustering results.
As detailed in table 3, the results are very good for the dataset A3, but significantly lower for the dataset A2, where recall is average. This is due to the large number of classes as compared to the small size of the dataset and to the strong imbalance of these classes. Indeed, in this case the exploited clustering method is becoming blind to small classes. The use of OKM clustering can slightly improve results, especially in the case of unbalanced data: this is probably due to the different overlapping strategies exploited by the methods, AKM being impacted by the use of a threshold for assignments to clusters. It must be noted that a small number of affiliations cannot be classified by these approaches. These are very short addresses containing only few discriminant information. Whatever the improvements that can be done, these "poor" affiliations may still be rejected.
Conclusions and discussions The results we obtained with our approaches for affiliations disambiguation are very encouraging, both in the supervised learning context and in the semi-supervised one. Our experiments also allow us to show that our methods provide significant assistance for correcting the results of human labeled affiliations. However, it is clear that we must achieve more experiments to conclude on the overall relevance of the methodology. Hence, there still remain a few weaknesses in our methodology. Those are related to the exploited clustering methods in the case of very unbalanced classes. We thus plan to conduct tests with other clustering methods and to implement data balancing techniques. The data used here are only affiliations CNRS laboratories (except for the small dataset "Lorraine"), because the study was done for a steering support department of CNRS.
Having more diverse data might change the results. For this purpose, we are going to create a benchmark dataset of labeled international addresses which could be accessible by anyone who would test his disambiguation method. In a near future, we propose to test the IGNGF algorithm developed by Lamirel  (Lamirel et al. 2011)  in conjunction with variable selection techniques based on the optimization of the feature F-measure. Another important point would be to exploit a learning method that should be able to learn with character strings of variable length. It would be necessary to develop an automatic method that could highlight the conflicting cases. Future works should also take into account the xml structure to consider separately the cities, street names, the laboratories names. Another way is to consider the scientific content of documents, such as titles and abstracts of articles published.
Establishing a large HTLV-1 sequence database is thus essential for any study of the epidemiology and pathogenicity of HTLV-1c subtype. The aim of the present study was therefore to describe the HTLV-1 genotypes infecting Indigenous central Australian residents and to correlate the results of the HTLV-1 nucleotide sequence variability with the geographic origin of the individuals living within this vast region of approximately 1,000,000 km 2.
Studied populations and data collection Our work was performed using HTLV-1 isolates obtained from a large series of patients who were initially enrolled to HTLV-1 pathogenesis studies between October 2007 and August 2010  [22]. Plasma and peripheral blood buffy coat (PBBC) samples were obtained from 23 HTLV-1 infected patients who presented to Alice Springs Hospital, predominantly with bronchiectasis. Presumed place of origin was determined from language group and/ or place of residence in infancy (figure  1 ). Numerous Indigenous languages are spoken in this region; however, for the purpose of the present study, these were divided into two groups according to the predominant geographic areas in which they are spoken; i) Northern (comprising the Ngarrkic language groups) and ii) Southern (comprising both Arandic and Western Desert language groups) (table  1 ).
The LTR-gag primers are the following: Enh280: 59-TGACGACAACCCCTCACCTCAA-39 and R2380: 59-GTCCGGAAAGGGAGGCGTATTAG-39 corresponding to nucleotides 258 to 279 and 2,377 to 2,399 respectively of the prototype ATK-1 sequence (Genbank: J02029). The Px-LTR primers are F6501: 59-CTTAACTGGGACCTTGGCCTCT-CAC-39 (nt 6,476 to 6,500) and 3VLTRext: 59-CGCAGTTCAG-GAGGCACCRMA-39 (nt 8,741 to 8,761) (figure  2A ). To obtain the 522-bp fragment of the gp21 env gene, 1 mg of DNA from the Aus-GN strain was subjected to 2 series of PCR as previously described  [27]. Two additional series of PCR using pro-pol and pol-env primers were necessary to obtain the entire genome amplification of the four Australian proviral sequences (Aus-CS, Aus-NR, Aus-DF and Aus-GM). The pro-pol primers are F2279: 59-GGAGCAGACAT-GACAGTCCTTCC-39 (nt 2,254 to 2,276) and R5005: 59-GGCGGCTATTAAGACCAGGAAGC-39 (nt 5,002 to 5,024).