dataesr/scientific-paragraphs-categorization huggingface dataset:
2,1,2,2 Passive dataset In the SN database, we extracted the 2005 serotyping data for the 5 main commodities common to the active surveillance and added sheep, ducks, other poultry and sea products. The "sea products" category gathered data on fish, shellfish and seafood, the "other poultry" category gathered data on geese, guinea fowls, pheasants, quails and pigeons. We only included strains collected at the farm, on production animals and their environment, and at the abattoir, on whole carcasses. Strains relative to breeding animals, to diseased animals or for which the origin was unclear were excluded. For cattle, an overrepresentation of pathogenic strains has been identified at the farm level. As a consequence, for this commodity, we only included the strains collected at the abattoir.
, 2000). All the routine tested antimicrobials common to the human and animal databases were included: amoxicillin, chloramphenicol, ceftazidime, gentamicin, kanamycin, nalidixic acid, streptomycin, sulfonamides, sulfamethoxazoletrimethoprim and tetracycline. For each antimicrobial, the strains were classified either as susceptible or as not susceptible when intermediate or resistant. The subtypes were defined through Multiple Correspondence Analysis (MCA) and mixed classification  (Berge et al. , 2003)  applied on the antimicrobial profiles obtained.
As defined by  Hald et al. , (2004)  the type-dependant factor (q i ) summarizes the characteristics of the type (such as survivability, virulence and pathogenicity) which determine its capacity to cause an infection and the source-dependant factor (a j ) summarizes the characteristics of the source (such as physical properties, preparation methods and processing procedures) which determine its capacity to act as a vehicle for Salmonella  (Hald et al. , 2004). Since the model is overparameterized, it is necessary to introduce information on some parameters, and we used the adapted parameterization recommended in  David et al. (2012). The type-dependant parameters relative to specific types (types only present in a unique food-animal source) other than Enteritidis and Typhimurium were set to the following databased value:     j ij i i i i p o o q 1 (2) where o i is the observed number of cases due to type i and p jj is the prevalence in the sources.
It should be noted that    j i j ij p p p because the type i is only present in its unique source j. We thus used the percentage of human cases per point of prevalence in the source, as an indicator of the capacity of that particular type to cause an infection. The prior distributions for the other type-dependant parameters and the source-dependant parameters are uniform distributions with 0 as the lower value and an upper value defined to encompass all possible values for the parameter. This parameterization requires the availability of at least as many specific types not corresponding to Enteritidis and Typhimurium as there are different sources, which was the case for both datasets. Since not all S. Typhimurium and S. Enteritidis isolates from human cases were subtyped, an allocation step was necessary to extrapolate from the observed subtypes' distribution among the subtyped strains to the strains that were not subtyped.
Gamma distributions were used to reflect the uncertainty distributions for the proportions of cases per subtype. The proportions used for the allocation within a serotype are as follows:  i i i o Gam m a o Gam m a ) 1 ; ( ) 1 ; ( (3) where o i is the observed number of cases of subtype i  (Hald et al. , 2004). The directed acyclic graph (DAG) relative to the Bayesian model is presented in Figure  1. It is divided into three parts to take into account the differences between the types. A general part represents the model common to all types that are neither SE or ST subtypes, nor specific types, and the two other parts represent the specificities linked to the specific types (parameterization) and to SE and ST subtypes (allocation). When considering the passive dataset, since prevalences were not available, proportions were used instead.
Analyses We first focused on the impact of two data characteristics that are part of the differences between the two types of surveillance on the attribution results: the indicator used to measure the sources contamination (proportions versus prevalences) and the number of sources included. We then studied the impact of the type of surveillance globally on the attribution results. To study the impact of the indicator, we used the active dataset for which both prevalences and proportions were available to measure the sources contamination. The model was applied using both indicators alternatively. To measure a potential effect of the indicator, we compared first the sources ranking and second, for each source, the posterior means of the attribution estimates and their 95% credibility intervals (95% CIs).
To study the impact of the number of sources included, we used the passive dataset, in which 4 additional sources were available in addition to the five main sources. We then estimated the model, considering alternatively the entire dataset (9 sources) and only the five main sources. The impact of the number of sources was compared based on the total number of attributable cases, the sources ranking and for each source, the posterior mean of the source attribution estimates and the corresponding 95% CI. To study the global impact of the type of surveillance (sources contamination) on the attribution results, we applied the model alternatively to each of the datasets. The results were compared based on the number of attributable cases, on the ranking of the sources, on the posterior means and 95% CIs of the source attribution estimates for each source, on the source-and type-dependant parameters estimates and on the number of cases with unknown source.
Description of the datasets A total of 9,076 domestic sporadic confirmed human cases were included in the study. Enteritidis and Typhimurium were the most frequent serotypes, representing respectively 35% and 39% of the cases. The subtyping resulted in 9 subtypes both for Enteritidis and Typhimurium. Some of those subtypes were just human or food-animal sources related. The main characteristics of the subtypes are summarized in Tables  1  and 2.
Active dataset Among the 9,076 included cases, 5,938 were attributable to the five considered food sources, spread between 28 serotypes, 5 Typhimurium subtypes and 3 Enteritidis subtypes. The overall observed prevalences in the sources were 30. 8% for layers, 18. 2% for pigs, 13. 8% for turkeys, 8. 2% for broilers and 2. 4% for cattle. The types proportions among the human cases and in the sources are presented in Table  3a. Enteritidis and Typhimurium represented over 75% (47. 1 % and 30. 4%, respectively) of the attributable human cases. The human cases were mostly associated to SE-multiS (35. 2%), ST1 (17. 5%), SE1 (10. 4%), ST4 (6. 5%) and Infantis (2. 8%). Only 2 types were observed in cattle, Indiana and Mbandaka, the latter representing 62. 5% of all cattle strains. Derby was the most frequently observed type both in turkeys and pigs.
Enteritidis was mainly observed in the layers source (7. 9%) and Typhimurium, if ubiquitous, was mostly associated with pigs (38. 7%) and to a lesser extent layers (14. 3%) and broilers (13. 4%). This dataset comprised 15 specific types (Table  4 ), including one Typhimurium subtype and one Enteritidis subtype. All sources but cattle were represented among the specific types, pigs and layers being the sources that comprised the highest number of specific types.
Passive dataset In total 6,527 human cases were attributable to the 9 included sources, spread between 37 serotypes, 5 Typhimurium subtypes and 4 Enteritidis subtypes. The number of strains included in the study was 617 for layers, 1,273 for broilers, 796 for turkeys, 213 for pigs, 71 for cattle, 104 for sheep, 40 for the sea products, 3,113 for ducks and 722 for other poultry. The distribution of types among the human cases and in the sources is presented in Table  3b. Serotypes Enteritidis and Typhimurium represented over 75% (47. 6% and 27. 7%, respectively) of the attributable cases. The five main types were almost the same as in the active dataset  SE-multiS (32. 1%), ST1 (15. 9%), SE1 (9. 4%), ST4 (5. 9%) and SE3 (4. 7%). Poultry commodities were characterised by a high number of subtypes.
5%) and sea products (15. 0%). This dataset comprised 14 specific types (Table  4 ), including 2 Enteritidis subtypes. As for the active dataset, all sources except cattle were represented among the specific types and pigs and layers were the most frequently concerned, according to the number of specific types and the number of associated human cases. There were 4 specific types in common with the active dataset: SE2, Oranienburg and Havana, linked to layers; and Goldcoast, linked to pigs. Finally, as expected, the types observed differed according to the dataset leading to a difference in the number of attributable cases, which was 10% higher in the passive dataset. When considering the 5 common food-animal species, the types' distribution differed between the two datasets for a given species, which was confirmed by Fisher exact tests.
Source contamination indicator: proportion vs prevalence Based on the active dataset, using proportions instead of prevalence as indicator for sources contamination gave the same ranking of the sources (Figure  2 ), layers being ranked number one source, and cattle ranked last. The numbers of cases attributed to each source differed significantly only for broilers. Using prevalence versus proportion led to a significant decrease in the attribution posterior means of 58. 3% (427 cases attributed versus 178) for broilers and to a non-significant decrease of 24. 9% (3,060 cases attributed versus 2,297) for layers. For the three other food-sources, a non-significant increase of 26. 9% for pigs, 12. 0% for cattle and 57% for turkeys was observed.
Increasing sources' number Based on the passive dataset, when including 4 supplementary sources to the 5 initial sources, the total number of attributable cases increased slightly, leading to 1. 1% newly attributed cases. Moreover, even though the ranking of the 5 common sources remained unchanged, 25. 5% of the cases initially attributed to one of the 5 common sources were reattributed to one of the 4 additional sources (Figure  3 ). Thus, the number of attributed cases decreased by 46. 6% for cattle, 39. 9% for pigs, 24. 3% for broilers and 19. 0% for layers, turkeys being the only exception with an increase of 2. 8%. This difference was significant only for pigs (1,728 attributed cases; 95%CI 1,3452,094 versus 1,039; 95%CI 7,84. 91,310).
Impact of the type of surveillance on the attribution results The model was applied separately to the active and passive datasets, with prevalence and proportion as source contamination indicator and 5 and 9 sources included, respectively. To assess the adequacy of the model, estimated numbers of attributed cases per type and observed numbers of cases were compared. As can be seen in Figure  4 , the model fit was satisfactory for both datasets. To be able to compare the estimated number of cases, the observed numbers of cases per Enteritidis and Typhimurium subtype were replaced by deterministically allocated numbers (the subtypes' distribution observed among the subtyped cases was deterministically applied to allocate the cases with unknown subtype). As expected, the estimated total number of cases was higher with the passive dataset than the passive dataset: 7,122 attributable cases (95% CI: 6,719-7,528) versus 5,746 (95% CI: 5,307-6,172), respectively.
However, for the passive dataset, the estimated number was overestimated with regard to the 6,527 attributable cases. With the active dataset (Figure  5 ), the top ranked source of infection was layers (eggs) to which 53. 3% (95% CI, 46. 060. 0) of the estimated cases were attributed corresponding to 3,060 attributed cases, followed by pigs, to which 25. 7% (95% CI, 20. 731. 1) of the cases were attributed (1,477 attributed cases). The third ranked sources were broilers and turkeys, which did not differ significantly, representing 7. 4% and 12. 4% of the cases respectively (95% CI, 5. 69. 5 and 6. 919. 2) corresponding to 427 and 714 attributed cases, respectively. Cattle was a minor source, comprising only 1. 2% of the cases (69 attributed cases). The cases whose origin remained unknown, were as numerous as the expected number of cases attributed to layers.
Using the passive dataset led, in term of attributed cases, to comparable results concerning layers (2,328 versus 3,060 for active dataset), pigs (1,039 versus 1,477), turkeys (336 versus 714) and cattle (133 versus 69). However, for broilers the number of attributed cases was significantly higher (1,410 versus 427) so that this source was comparable to pigs (1,410; 95% CI, 1,0261,821 versus 1,039; 95% CI, 7851,310) in importance. The ranking of the sources indicated layers as the most important source of infection, broilers and pigs as second most important sources, all the other sources (turkeys, cattle and the four additional) following without distinction (as the expected numbers of attributed cases were not significantly different). As for the results obtained with the active dataset, the cases of unknown origin were as numerous as the cases attributed to layers.
The source-dependant parameter estimates for the five common sources were quite similar between the two datasets, except for cattle (Table  5 ). Ranking of the sources based on their ability as vectors of Salmonella could not be concluded from the active dataset because of the wide posterior credibility interval related to cattle (first ranking). However, with the passive dataset, the highest source-dependant parameter posterior means were relative to ducks (1. 37) and other poultry (1. 06). Unlike the source-dependant parameters, the type-dependant parameters estimates were systematically different, mostly significantly, between the two datasets; estimates for the passive dataset were lower. The only exception was serotype Stourbridge for which the estimates were coherent for both datasets.
Another global feature was that the 95% CIs were narrower for Enteritidis and Typhimurium subtypes in the passive dataset. When considering the 10 top ranked type-dependant parameter estimates for each dataset (Table  6 ), 6 types appeared in both lists: SE1, SE-multiS, Stourbridge, Havana, Oranienburg and Napoli. However, only the parameter for Stourbridge had the same value, the calculated value in the active dataset (5. 40) being in accordance with the estimate in the passive dataset (5. 41; 95% CI, 2. 4210. 72). For serotype Napoli, if the values were different, the ranking was the same in both cases (10 th ). Heidelberg, ST2 and SE2 ranked high in the active dataset but low in the passive dataset. Finally, all Enteritidis subtypes but one were in the top ten for each dataset, whereas for Typhimurium only ST2, a subtype that gathers multi-resistant strains resistant to nalidixic acid, was present in the top ten of the active dataset.
Discussion The model estimates appeared to be robust to a deviation from the required quality of the data, due to a passive design of the sources data collection (but with a national coverage ensured), and to the consequent use of proportions instead of prevalences for the contamination indicator, but sensitive to the number of sources included. The results confirmed layers as the top source for Salmonella and were globally coherent for both datasets except for broilers. This was probably linked to the higher frequency in this source of Typhimurium and Enteritidis subtypes in the passive dataset (27. 4% of the strains globally versus 5. 1% in the active dataset). In both configurations, the number of cases with unknown source represented about a third of all cases and with the passive dataset, the 4 additional sources contributed to a non-negligible proportion of all cases (around a quarter of all cases).
Because source-and type-dependant parameters measured different quantities according to the dataset and because of the wide posterior credibility intervals, those parameters were difficult to interpret. However, it can be extrapolated from the few convergent results that birds appear to be a good vehicle for Salmonella and that Enteritidis has a high capacity for infection, whichever the subtype, as do some other serotypes such as Stourbridge, Havana, Oranienburg and Napoli. When considering the active dataset complemented by the passive dataset for the 4 additional sources, relative importance of the sources deduced from the posterior estimations were in accordance with those from Denmark  (Hald et al. , 2004; Havelaar et al. , 2007) : layers were the most important source, followed by pigs and broilers, turkeys, ducks and cattle, which were not significantly different.
The significant difference observed in broilers between the two datasets could be related to the higher frequency of Typhimurium and Enteritidis among the strains in the passive dataset, the source-dependant factor being similar between both datasets. Another possibility is that some samples corresponding to abattoir data for broilers may correspond to spent hens, despite our efforts to discriminate these (many strains have been excluded because of incomplete or unclear information). Such a difference in the results obtained for Salmonella with active and passive data, especially for broilers (compared to pigs), has been recently shown for antimicrobial resistance  (Mather et al. , 2009). It would be interesting to further explore the reasons for this difference, which seems to be specific to broilers.
Plants  (Brandl, 2006; Elviss et al. , 2009)  and other non-animal products  (Kirk et al. , 2008)  have been implicated as a source for salmonellosis. Finally, imported products, which represent from 15% (eggs) to 60% (lamb) of the national consumption according to the foodanimals products are a potential major source for the human cases but could not be taken into account in this analysis. Because of this large category of cases with unknown sources, the attribution estimates, although valuable for studying the methodological differences between passive versus active data, have to be interpreted with caution regarding their representativeness and accuracy. Direct comparison between the source-dependant parameters for the two datasets was difficult. The contamination indicator was prevalence in one case and proportion in the other case, so that the source-dependant parameter did not measure the same quantity, which is also true for the type-dependant parameter.
The type-dependant parameters were almost systematically different when using one or the other dataset, even when (rarely) the ranking was coherent. However, the source-dependant parameter estimates were in good agreement between both datasets, except for cattle. Hald et al. (2004, p267)  underlined that these factors were only multiplication factors that helped arrive at the most probable solution given the observed data. This was actually the case in our study, although it was difficult to draw any firm conclusion on the posterior distributions, especially for the type-dependant parameter. The type-and source-parameters (q i and a j ) are conceived as 'black boxes', and they allow differences between types and between sources to be taken into account, which is of primary importance  (Blaser and Newman, 1982; D'Aoust, 1989; Sarwari et al.
Our aim in this work was to study the behaviour of the Hald model according to the data quality. So we adapted the parameterization while keeping the original outlines of the model. The parameterisation of the model relied on specific types for which the type-dependant parameter was set to a data-based value. Only a few of those types were common to both datasets. This raises the question of their definition. For now they are defined on an observational basis, but biological and ecological criteria could be used to refine the choice of those types and introduce complementary information to calculate the value of the associated type-dependant parameters and thus avoid any negative impact on the attribution results. We may have taken into account the data generating process in the model by putting some priors on the prevalence of types, as has been recommended by Muellner et al,.
The sampling protocol of the active dataset was detailed enough to define those priors, but this would have introduced greater uncertainty on the results and would have led to a different structure of the model. Moreover, we couldn't have used this model structure for the passive dataset, as the sampling protocols are not known in this system. In France, thanks to the European regulatory framework and to long running laboratorybased surveillance systems (the NRC began its activities in 1947 and the SN in 1997), all the data required for the chosen approach were available and the typing methods were harmonized for all datasets, as a consequence of the on-going cooperation for outbreaks investigations. Besides, we disposed for the sources both of active and passive surveillance data, an ideal situation to assess the relative advantages of using the data generated by those two surveillance systems in the Bayesian microbial subtyping attribution approach.
Because the data were fragmented between several actors, gathering all those data required a large scope collaboration, which is a key element to ensure the success of such projects  (Batz et al. , 2005; ICMSF, 2006). However, we had to deal with some insufficiencies in the data. The human data were retrieved through voluntary participation of private and public laboratories, and thus were neither exhaustive nor perfectly representative of all cases. Moreover, the information on travel was scarce. The NRC relies on a stable and nationwide network of public and private laboratories, which is estimated to have good representativeness (a national survey conducted in 2008 concluded that the NRC covered 66. 3% of all laboratory confirmed cases). Therefore, no major bias should have occurred. Another problem was that, due to logistical constraints, only a fraction of the human cases of serotype Enteritidis and Typhimurium were subtyped.
As a consequence, it is likely that less prevalent subtypes have not been observed in the small sample of cases tested. Furthermore, around 96% of the Enteritidis and Typhimurium cases had to be reallocated (through Gamma distributions) in the model, which led to wide credibility intervals. This can be corrected in future studies by changing the subtyping method (for example a method based on CRISPR polymorphisms that makes it logistically possible to routinely test a high number of strains). Regarding the sources contamination, the representativeness of the passive surveillance data cannot be assessed. In the active dataset the sampling point varies from one commodity to another. Poultry were tested at the farm, pigs were tested at the slaughterhouse, which could have led for the latter to underestimation of the prevalence but overestimation of the variety of serovars compared to what could have been observed at the farm level  (Beloeil et al.
, 2004). Also for the active dataset, there is a lack of time consistency between the food-sources data and the human cases data, the sources data being posterior to the cases data that they should explain. We could not avoid this potential bias in this exploratory study. This is a further reason to consider the attribution results with caution. Such a problem should not occur in future studies because the main sources (layers, broilers, turkeys and pigs) are now monitored on a yearly basis as required by the European Zoonosis Directive  (David et al. , 2011). Finally, the food consumption data were recovered through interviews of people aged more than 3 years, whereas the cases were of all ages. Because it is known that the risk factors can be different for young children  (Bellido Blasco et al.