dataesr/scientific-paragraphs-categorization huggingface dataset:
Moreover, using the same technique to analyze transcripts in A. marginale-infected IDE8 tick cells  [15, 16]  and A. marginale-infected R. microplus demonstrated that more differentially regulated transcripts were identified in vivo (Table  1 ), suggesting that in vitro models should be used with caution. In any case, the lack of genomic information for almost all tick species leads to difficulties in data analysis. The analysis of mRNA expression levels is undoubtedly an effective method to identify tick gene expression during TBP infection, but the level of mRNA and the concentration of corresponding proteins only have a correlative, rather than a causative, association. Therefore, the quantities of translated proteins in ticks in response to TBP infection should also be assessed.
Acknowledgments The contents of this publication are the sole responsibility of the authors and don't necessarily reflect the views of the  European Commission. We are grateful to the '' Tiques et Maladies aTiques'' working group  ( REID -Reseau Ecologie des Interactions Durables ) for many stimulating discussions. We also acknowledge  M. Vayssier-Taussat  and  Sara Moutailler  for their critical reading of the manuscript.
Here we used a unique, extensive dataset of biological data on sprat collected in the Baltic Sea during autumn international acoustic surveys in 1985-2009. Using descriptive spatial statistics, our objectives were 1) to investigate the temporal changes in the distribution of the sprat population and its body condition as indicator of spatial density-dependence, 2) to investigate whether the strength of the spatial density-dependence varied in time, and 3) to investigate whether the patchiness of the sprat distribution and condition varied in time. We also discussed possible explanations for the observed spatial patterns in light of the current knowledge on the Baltic hydrology and predator-prey interactions.
Data Sprat densities. Time-series of sprat abundance (total number, ages 1+) by ICES statistical rectangle (0. 5u in latitude and 1u in longitude) were extracted from the ICES BAD1 international database  [20]  and survey reports stored at the former Swedish Board of Fisheries, Institute of Marine Research  [19]. These data are based on hydroacoustic surveys regularly performed in September-October in the open areas of the central Baltic Sea (the Baltic Proper, corresponding to the ICES Subdivisions (SDs)  [25] [26] [27] [28] [29]  and are regularly used as tuning indices for the assessment of the Baltic sprat stock  [13]. Sprat density (number/squared nautical mile) was calculated yearly for the whole survey area (mean yearly density), and for each ICES rectangle, for the period 1985-2009.
Removing sequentially every data value on the map allowed for constructing a statistical distribution for the centres of gravity and an associated confidence interval. The estimates for the centre of gravity coordinates were obtained from the median of each yearly statistical distribution. The time series of coordinates obtained for the density were then plotted against those for condition to investigate the link between their trajectories. This approach allowed summarizing the patterns of density and condition over space and to follow their evolution over time. Under the effect of limited available resource we may expect a negative spatial density-dependence between density and condition, which would result in centres of gravity moving in opposite directions. Correlations over space. In order to interpret the movement of the centres of gravity over space, and to explore the intensity of the relationship between density and condition over time, the following approach was used.
These advanced diagnostic technologies are making important contributions to the research and development of new AD medicines. Additionally, appropriate use criteria for amyloid-detecting PET radiopharmaceuticals have been proposed jointly by the Society of Nuclear Medicine and the Alzheimer's Association  [13]. If upcoming trials and analyses of public databases confirm the validity of one or more of these technologies as an early diagnostic tool with a satisfactory level of sensitivity and specificity, payers should consider coverage in the research setting. However, at this time, given the limited resources of European Union health-care systems and the unknown magnitude of the risk of false-positive cases, we do not endorse the use of advanced diagnostic tools to screen the general population for AD.
In particular, public and private stakeholders should work together to conduct and finance the long-term follow-up necessary for end point validation. Although acknowledging that no tool is perfect, we believe that innovative technologies and statistical/economic models should be used to track the connection between reimbursement end points and long-term outcomes. In particular, we support the use of broad-scale registries, electronic medical records, and other databases to monitor downstream outcomes. Investing in the collection of these data ultimately will assist all stakeholders. For example, these data could serve as baseline comparators for a novel DMT to demonstrate cost savings attributable to a shortening of the severe stage of AD. Alternatively, they could be used to anticipate expenditures associated with the slowing of progression and lengthening of stages of the disease.
In this study, we report the results of French passive surveillance of bat rabies from 1989 to 2013. We analysed the epidemiologic patterns of EBLV-1 in serotines from 1989-2012 using diagnostic laboratory data. Environmental (seasonal, annual, and geographical) and biological (sex) factors that could potentially affect the occurrence of rabies cases in serotines, were analysed in an attempt to improve knowledge of the dynamics of EBLV-1 infection. In addition, in the present study we undertook the phylogenetic analysis of the partial nucleoprotein gene from the collection of EBLV-1 samples with referenced sequences (Gen-Bank database) isolated in Europe, to better understand the circulation of the two lineages of EBLV-1 in the country.
Data Source and laboratory diagnosis methods The data analysed in the study included the passive bat rabies surveillance data collected from 1989 to 2013 by ANSES's Nancy Laboratory for Rabies and Wildlife, which is one of the two laboratories involved in rabies diagnosis in France. The national passive surveillance network is based on the testing of sick bats, bats suspected of having rabies (showing clinical signs or abnormal behaviour) or bats found dead (all species), for the detection of lyssavirus infections  [16]. All dead bats found in human environments as well as sick and suspicious bats with no human contact were submitted for rabies testing at ANSES's Nancy laboratory. The data included the diagnostic results, information on the location where the bat was isolated, date of submission, morphological identification of the bat  [17]  and the name of the collector (local veterinary services or bat handler from the French national bat conservation network).
5 ml of PCR products were separated on a 2% agarose gel then purified on Nucleospin Extract II columns (Macherey Nagel, France) and sequenced in both directions by Beckman Coulter Genomics (Takeley, Essex, United Kingdom). Consensus sequences were obtained by aligning the forward and reverse sequences with Vector NTI software (version 10. 3. 1). Duplicate sequences with a 100% match were removed from the dataset (n = 15). The N gene analysis was constituted of 52 sequences (390 nucleotides, positions 71 to 460; nucleotide numbering is based on the Pasteur virus genome NC_001542) including 32 serotine isolates from France, one serotine isolate from Luxembourg, the French BBLV isolate (KC169985), 13 representative EBLV-1 sequences (Spain, Germany, Poland, Netherlands, Russia, Ukraine, Denmark), 5 representative EBLV-2 strains (United Kingdom, Switzerland, Germany, Finland, Netherlands), the German BBLV (JF311903) and one referenced sequence (WCBV (EF614258)).
Acknowledgments We would like to thank the  SFEPM group (SocieteFrancaise pour l'Etude et la Protection des Mammife  `res, groupe Chiropteres), and especially the bat network workers, for their collaboration in the passive and active surveillance of bat rabies. The authors would also like to thank  Audrey Tapiero  ( Federation des Conservatoires d'Espaces Naturels, Besancon, France ) for her coordination of the national bat surveillance plan. We gratefully acknowledge the expert technical support of the diagnostics team ( Estelle Litaize ,  Valere Brogat) , the molecular biology team ( Melanie Biarnais ,  Jean Luc Schereffer ,  Carine Peytavin de Garam ),  Nathalie Stroucken  for her contribution to the administrative coordination of the bat surveillance network and  Marie Jose  ´Duchene for her work on the bibliography.
River flow data Daily river flows at 38 gauging stations across France are analysed in this study. Data have been extracted from the national HYDRO database (http://www. hydro. eaufrance. fr/). These stations have been selected so that measurements cover at least 70 yr. Series at all selected gauging stations start before 1940, and their median length is 94 yr. Gauging stations where too many values are missing have been discarded. When less than three-quarters of daily values are available during a given civil year (season), the corresponding annual (seasonal) mean is considered as missing. At most, 6 % of years are missing for the selected stations with that definition. The missing values as previously defined in the selected series are filled by the corresponding long-term climatological average.
Long precipitation and temperature data set In order to study the local climate variations concomitant with river flow variations in France, different precipitation and temperature data sets are considered. For precipitation, a large set of monthly homogenized time series aggregated by department to form 51 time series that sample a large portion of France, from Météo-France, is used (HPS -homogenized precipitation series;  Moisselin et al. , 2002). HPS data are available from 1900 to 2000. In this work, HPS is seen as the reference data set for multi-decadal variations in precipitation. Unfortunately, this high-quality data set does not cover the beginning of the 21st century. A second precipitation data set, that covers this period, from the Global Precipitation Climatology Centre (GPCC), is also used (full data reanalysis from 1901 to 2011;  Rudolf and Schneider, 2005).
This monthly gridded data set with a resolution of 0. 5 • is not adjusted to study long-term variability. Most notably, the stations used to estimate precipitation in each grid cell vary in time. Using GPCC in this study therefore requires caution and it is a priori less suitable than HPS to study low-frequency variability. Precipitation from the 20th Century Reanalysis project (20CR in the following;  Compo et al. , 2011)  is also analysed. This data set covers the 1871-2010 period, with a daily time step and a spatial resolution of roughly 2 • × 2 •. The only observation assimilated by the 20CR system is surface pressure (SP). Observed sea surface temperature, sea ice cover, time-varying global mean CO 2 and volcanic aerosol concentration, as well as incoming solar radiation, are used as forcing in the atmospheric model of the 20CR system.
Link between multi-decadal variations in river flows and precipitation To assess whether multi-decadal variations in spring precipitation and river flows are in phase over the entire available periods, correlations are computed between detrended, low-pass-filtered spring precipitation averaged over France (GPCC data) and detrended, low-pass-filtered spring river flows at each gauging station (Fig. 6a ). GPCC data are used here as they cover a longer period than HPS. The use of GPCC data is supported by the fact that on average over France, GPCC remains very close to our reference data set, HPS, for their common period as shown later in Fig. 7. Very high significant correlations are obtained for the great majority of the gauging stations. Note that the average of precipitation over France is used here because the poor spatial sampling of HPS, the low resolution of GPCC and 20CR, and their potential inaccuracies at the grid cell level (see discussion in Sect.
Role of large-scale circulation Based on the previous analyses, and considering the straightforward physical link between precipitation and river flows, it appears highly likely that multi-decadal fluctuations in spring river flows are driven to a large extent by largescale precipitation variations over France. Assessing the robustness of the multi-decadal signal seen in spring precipitation and understanding its physical causes are therefore crucial. Averaged and detrended precipitation over France in spring from three data sets (GPCC, HPS and 20CR) are depicted in Fig. 7a. Very similar multi-decadal signals are visible for all three data sets. GPCC and HPS data are not independent, as some stations used in GPCC are also used in HPS. However, the statistical treatment of raw data for the two data sets is largely different.
HPS is a priori more adapted to the study of low-frequency variations because of the homogenization procedure applied. The extremely small differences seen between multi-decadal precipitation variations in GPCC and HPS show the robustness of those features to the statistical pre-treatment of precipitation data and to the exact number and location of stations. Moreover, it shows that even if it is not homogenized, GPCC data can be used to estimate averaged precipitation over France, as it has the advantage of being available for a longer period compared to HPS. Spring precipitation over France in 20CR exhibits a temporal pattern that is very similar to the one seen in HPS and GPCC, even if some limited differences exist, especially at the beginning of the record. This consistency with direct observations (GPCC, HPS) is noteworthy, as 20CR provides an indirect and completely independent estimation of precipitation.
A similar SLP index (SLPI) is used in our study. As shown in Fig. 7a , there is a very good anti-correlation between low-pass-filtered SLPI and precipitation anomalies over France in spring. High and significant anti-correlations with precipitation series between -0. 86 and -0. 94 depending on the precipitation data set (and therefore on the exact period) are noted. A positive value of SLPI corresponds to positive SLP anomalies over central/western Europe and negative anomalies over the eastern Atlantic and is associated, as physically expected, with negative precipitation anomalies over France. Figure  7b  shows the correlation map between the low-pass-filtered and detrended SLPI index and HPS precipitation at each station in France in spring. The impact of SLPI on precipitation is large-scale and very consistent over France.
Acknowledgements. This work was supported by the  French National Research Agency (ANR)  in the framework of its JCJC programme (ECHO, decision no. ANR 2011 JS56 014 01 ). The authors thank  Laurent Terray ,  Christophe Cassou  and  Yohan Ruprich-Robert  for useful discussions about this work. The authors also acknowledge  Météo-France  ( Direction de la climatologie ) for providing the homogenized precipitation and temperature data sets, and  Eric Martin  for providing the SAFRAN-ISA-MODCOU data. Support for the Twentieth Century Reanalysis Project data set is provided by the  US Department of Energy, Office of Science, Innovative and Novel Computational Impact on Theory and Experiment (DOE INCITE ) programme; by the  Office of Biological and Environmental Research (BER) ; and by the  National Oceanic and Atmospheric Administration Climate Program Office.
The aim of this study: was firstly to describe the MMSE (Mini-Mental State Examination) score upon initial diagnosis of Alzheimer's disease and related disorders among the French population, according to age. Secondly, education, gender and place of residence were studied as factors potentially associated with delayed Alzheimer's disease diagnosis. Design: we conducted a cross sectional analysis of the French National Alzheimer database (BNA). Data from 2008 to 2012 were extracted. Patients were selected at the moment of their first diagnosis of AD (n = 39,451).
The aims of this study were therefore, first of all, to describe the MMSE score among the French population upon initial diagnosis of Alzheimer's disease and related disorders according to age. Secondly, factors associated with late Alzheimer's disease and related disorders diagnosis were studied. The French National Alzheimer database (BNA)  [5] , created within the Third National Plan for ''Alzheimer's disease and related disorders'' provided us with the opportunity to perform this study among a large sample of patients.
Methods The French National Alzheimer Database (Banque Nationale Alzheimer: BNA) The French National Database (known as ''BNA'') is part of the French National Plan 2008-2012 for Alzheimer's disease and related disorders. Activity and epidemiological follow up data are recorded nationwide within specialized memory centres. The national network currently includes 400 ''memory units'' (CMs Consultation Memoire) and 28 ''Memory Resources and Research Centres'' (CMRRs Centres Memoire, de Ressources et de Recherche) and volunteer private specialists (neurologists, geriatricians and psychiatrists). The French National Alzheimer database has been described in a previous article  [5]. Online information concerning the number of participating centres and patients is available at www. banque-nationale-alzheimer.
Selection of the study population In this cross-sectional study, patients aged between 60 and 100 years were selected at the time of their first diagnosis of Alzheimer's disease or related disorders. Only one record was selected for each patient. The selected record was the first within the year following the initial consultation in the memory centre, in order to avoid including patients recorded in the BNA with a prior diagnosis of Alzheimer's disease or related disorders and thereby selecting only patients with a newly established diagnosis. Patients with missing data for MMSE score, education, age, gender, or place of residence were excluded.
Selection of the studied variables The full list of items, with the possible choices for each one, and a glossary of definitions can be accessed on the CMRR-Nice website (http://www. cmrr-nice. fr/) or the National Alzheimer Plan 2008-2012 website (http://www. plan-alzheimer. gouv. fr/ mesure-no34. html). The CIMA (mandatory minimal information) currently displays 31 variables to be completed. For each procedure and each patient, this minimum data set must be transmitted to the BNA. The diagnosis procedure drawn up by the National Federation of CMRRs uses the ICD 10 classification. For this study, the following patient characteristics were extracted from the BNA: 1/age at the time of consultation; 2/education level categorized as follows: ''primary school or none (ages 3-11)'', ''secondary school -cycle 1'' (ages  [11] [12] [13] [14] , ''secondary schoolcycle 2'' (ages  [14] [15] [16] [17] [18]  and ''university level'' (age.
Consequently, the transition from 'normal ageing' to 'dementia' may be less clear cut in the former and lead to a later presentation to clinical services (and lower MMSE at the time of diagnosis). Unfortunately, since we do not have any information in our database about the likely duration of symptoms or the severity of functional impairment at the time of diagnosis, it was not possible to adjust our analyses for these factors. It could not be excluded that if the analyses were adjusted for the severity of functional impairment, the score lines would have been parallel to the age axis. Our study confirmed the strong correlation between MMSE score and education. We established that low educational level and older age limited cognitive performance during the MMSE. The fact that the MMSE score depends on these two factors was shown in studies of the native American population in 1991 by Ganguli et al and in 1993 by Crum et al  [4, 9].
This study also has some limitations. First, the BNA is not, to date, a comprehensive dataset of all Alzheimer's disease patients in France and questions remain concerning the representativeness of the study. Moreover, some patients have not been included due to missing values on the inclusion criteria (e. g. diagnosis or date of first consultation). This could have biased the representativeness of our sample. A difference in age or gender between these not included patients and our selected population would therefore not be surprising. Finally, as expected, there was less patients over the very old ages (. 90 years). Thus, our estimates could be less precise for the very old patients. In conclusion, this study using the French National Alzheimer database indicated that, at the time of diagnosis, the level of cognitive performance in patients with a diagnosis of Alzheimer's disease and related disorders not only depends on age and education, but also on other factors such as gender and place of residence.
Acknowledgments BNA (Base Nationale Alzheimer DGOS -CHU Nice ) Measure 34 is subsidized by  DGOS  ( Direction Generale de l'Offre de Soins, France ), under the 2008-2012 National Alzheimer Plan, and by the IA association. We would like to thank  Cecile Balandier  (project officer, DGOS),  Philippe Mayer  ( CHU Nice director ), AndreGarrel (computer scientist,  CHU, Nice ),  Roland Chevrier  (BNA computer scientist) and  Amandine Gastaldi  ( Coordinator, Calliope ) for their tireless support.
Source attribution modelling A variety of methods to estimate the relative contribution of food-producing animal sources to human foodborne disease have been developed, including the microbial subtyping approach, comparative exposure assessment, epidemiological analyses of sporadic cases, analysis of data from outbreak investigations, and expert elicitations  (EFSA, 2008; Pires et al. , 2009). To assess the public health impact of setting new targets for the reduction of Salmonella in fattening turkeys, source attribution modelling using the microbial subtyping approach was applied (EFSA BIOHAZ Panel, 2012). The model is referred to as the 'Turkey-Target Salmonella Attribution Model' (TT-SAM). Detailed information can be found in the external report provided by the contractor (   (Threlfall et al.
5 for additional details) and can result in S. Enteritidis crossing the vitelline membrane before the egg is consumed. Whiting et al. (2000)  analysed the data presented in  Humphrey and Whitehead (1993). In that study, table eggs (collected within 2 hours post lay) were stored at 8-37 °C and at time intervals of three days, 10 eggs were removed. These eggs were aseptically broken and the albumen next to the yolk was inoculated with circa 500 cells of S. Enteritidis to determine when the albumen supported growth of S. Enteritidis and thus when the yolk membrane disintegrated (this is referred to as the Yolk Membrane Breakdown Time or YMT). The latter was (arbitrarily) defined when 20 % (2 out of 10) of eggs sampled at a certain storage time had S. Enteritidis counts greater than 10 4 CFU per egg after four days of storage at 20 °C.