dataesr/scientific-paragraphs-categorization huggingface dataset:
Using the NIH Image J software (cell counter plugin), the NeuN(+)/TH(+) cells were counted by two different persons with no knowledge of the animal groups. Split-cell counting errors were corrected using the formula of Abercrombie  (32) , where N = n[t/(t + d)] (N = total number of cells; n = number of cells counted; t = section thickness; and d = cell diameter), and this correction factor was 0. 65. Data are expressed as mean [NeuN(+)/TH(+) in left and right VTA] ± SEM.
TH Fiber Density in NAc The TH protein content in the dopaminergic nerve terminals of the NAc was estimated by anatomical densitometric analysis of TH immunolabeled sections. The TH fibers density was quantified at three arbitrary levels along the rostrocaudal axis of the NAc (Bregma 2. 20, 1. 70, and 1. 20 mm) (Figure  6B ). Briefly, digitized picture comprising the whole striatum and NAc obtained using ×40 magnification of a NanoZoomer-XR Digital slide scanner C12000 (Hamamatsu, Japan) were obtained. For a given NAc, a line was drawn around the whole nucleus to define the area of optical density (OD) measurement (Figure  6B ). The obtained value was normalized with the OD value measured from a circular zone drawn on the corpus callosum (a region not stained for TH immunochemistry) of the same section using NIH Image J software.
We used a specifically designed TLDA made to cover different gene families relevant to plasticity and regulation of food intake. Each custom card was configured as 2 × 4-sample loading lines containing 2 × 48 reaction chambers (reference: 96a). A 92-gene set (Table  S1  in Supplementary Material) and four housekeeping genes (18S, Gapdh, Polr2a, and Ppia) were studied. Real-time PCR was carried out using Life Technologies TaqMan reagents and run on ABI Prism 7900HT sequence detection system (Applied Biosystems, Foster City, CA, USA). Raw fluorescence data were collected through the PCR using the SDS 2. 3 software (Applied Biosystems, Foster City, CA, USA), which further generated threshold cycles Ct with automatic determination of both baseline and threshold. After filtering using ThermoFisher cloud App (ThermoFisher, USA) to discriminate aberrant PCR runs, the assays per sample were n = 6 (n = 5 for WD group at P25).
The data were then analyzed with ThermoFisher Cloud App (ThermoFisher, USA) for relative quantitation. Relative quantitation of gene expression (RQ) was based on the comparative Ct method using the equation RQ = 2 -ΔΔCt , where ΔΔCt for one gene target was its own Ct variation subtracted from a calibrator sample and normalized with an endogenous control. Precisely, we determined the most stable housekeeping gene using geNorm algorithm (ThermoFisher Cloud App RQ, ThermoFisher, USA). Among the four housekeeping genes, Gapdh was defined as the endogenous control for NAc and hypothalamus, and Ppia for VTA and this was true for all samples from the three time periods analyzed. Graphic representation of genes expression was manually designed to assign one color for a 10% increment of gene expression relative to the CD group.
Cell cycle analysis After UVA treatment, HaCaT cells were harvested and fixed with 5 ml of ice-cold 70% ethanol and 5 mM butylated hydroxytoluene (BHT) and 5 mM indomethacin to prevent lipid peroxidation. For cell cycle analysis, the cell samples were prepared by Leung et al. [29]  method with slight modifications. The fixed cells were centrifuged at 800 Â g for 5 min at 4 C and washed with PBS. Then, the cells were incubated with propidium iodide (PI) staining solution containing PI (50 lg/ml), RNase (10 lg/ml), Tris-base (0. 01 M), NaCl (10 mM), and Nonidet P-40 (0. 1%) (Sigma-Aldrich, St Louis, MO). The stained cells were incubated in the dark for 30 min at room temperature and thereafter, DNA content was determined by flow cytometry (Beckman Coulter, Brea, CA) and analyzed using FlowJo software (TreeStar, Ashland, OR).
Background: Food frequency questionnaires (FFQs) are used to estimate the usual food and nutrient intakes over a period of time. Such estimates can suffer from measurement errors, either due to bias induced by respondent's answers or to errors induced by the structure of the questionnaire (e. g. , using a limited number of food items and an aggregated food database with average portion sizes). The "structural validation" presented in this study aims to isolate and quantify the impact of the inherent structure of a FFQ on the estimation of food and nutrient intakes, independently of respondent's perception of the questionnaire. Methods: A semi-quantitative FFQ (n = 94 items, including 50 items with questions on portion sizes) and an associated aggregated food composition database (named the item-composition database) were developed, based on the self-reported weekly dietary records of 1918 adults (18-79 years-old) in the French Individual and National Dietary Survey 2 (INCA2), and the French CIQUAL 2013 food-composition database of all the foods (n = 1342 foods) declared as consumed in the population.
Reference intakes of foods ("REF_FOOD") and nutrients ("REF_NUT") were calculated for each adult using the food-composition database and the amounts of foods self-r eported in his/her dietary record. Then, answers to the FFQ were simulated for each adult based on his/her self-reported dietary record. "FFQ_FOOD" and "FFQ_NUT" intakes were estimated using the simulated answers and the item-composition database. Measurement errors (in %), spearman correlations and cross-classification were used to compare "REF_FOOD" with "FFQ_FOOD" and "REF_NUT" with "FFQ_NUT". results: Compared to "REF_NUT," "FFQ_NUT" total quantity and total energy intake were underestimated on average by 198 g/day and 666 kJ/day, respectively. "FFQ_FOOD" intakes were well estimated for starches, underestimated for most of the subgroups, and overestimated for some subgroups, in particular vegetables.
This study focused on the adult population (n = 2,624). Individuals who completed the report less than 7 days were excluded as well as under-reporters identified using Black equations  (19) , leading to a final sample of 1,863 individuals (1,111 women and 752 men). Individual socioeconomic variables were collected using a self-reported questionnaire and a face-to-face questionnaire. Food intakes were collected using a 7-day record in which each individual reported all foods and beverages consumed at home or outside on seven consecutive days, during three meals and three snacking occasions. "REF_FOOD" intakes were the amount in g/ day of each individual food consumed by each individual from their dietary record. The CIQUAL 2013 French food composition database  (20)  was used to estimate "REF_NUT" intakes.
Development of the FFQ The FFQ used in this study was designed to assess food and nutrient intakes of adults during the previous month. The list of items was developed by experts, according to the nutritional content of foods and the type of foods (raw or cook, liquid, etc. ) using data from the national dietary survey INCA2. Each item of the FFQ is a combination of individual foods (e. g. , the item "fatty fish" is the combination of "cooked salmon, " "cooked trout, " "sardine in vegetal oil, " and others) from the list of foods consumed in the INCA2 study. The quantitative French FFQ contains 94 items. Portion sizes are requested for 50 items using units (one egg, two eggs), manufacture's containers (one can of soft drink, etc. ), or household measures (one teaspoon, two teaspoons, etc. ).
consumed these foods as a side dish as well as a main dish. For breads, frequencies and portion sizes are requested by moment of consumption. Portion sizes are not requested for 44 items, for which there are no simple units or household measures, or, for which portion sizes varied slightly in the population of interest (e. g. , yogurt). Frequencies and portion sizes of beer, wine, and strong alcohol are also requested. An additional question asks whether the respondent adds salt in his/her plate for each meal. Sex-specific item-composition databases were developed as follows. The nutritional composition of each item was derived from a wider list of corresponding foods from the food composition database associated with the INCA2 survey. The list of foods used to derive the nutrient content of each item was selected, according to the number of foods related to each item, and the frequency of consumption of each food, avoiding to take into account too peculiar and rarely consumed foods.
Pairwise comparisons were performed between "REF_NUT, " "ITEM_NUT, " and "FFQ_NUT" intakes using mixed generalized linear model with repeated measures, first to identify the impact on nutrient intakes of using the item-composition database by comparing "REF_NUT" and "ITEM_NUT" intakes and then to identify the impact of the use of average portion sizes by comparing "ITEM_NUT" with "FFQ_NUT" intakes. Measurement errors in nutrient intakes were assessed for each nutrient by calculating mean variations in absolute values between "ITEM_NUT" or "FFQ_NUT" and "REF_NUT" intakes, in percentage of "REF_NUT" intakes. For each nutrient, measurement errors were compared, using generalized linear models, between individuals with over-and underestimations identified as described earlier. Variations (in %) between "FFQ_NUT" and "REF_NUT" energy and macronutrient intakes were plotted against deciles of "FFQ_NUT" intakes.
The relative agreements between "REF_NUT" and "FFQ_NUT" intakes were assessed using cross-classification and weighted Kappa coefficients. Weighted Kappa coefficients (one per nutrient) were plotted in descending order. For each nutrient, the association between "REF_NUT" intakes and the two other estimates was also tested using Spearman correlation coefficient. All analyses were adjusted on "REF_NUT" total energy intakes, age and gender, and were performed with SAS Version 9. 4. An α level of 1% was used for all statistical tests. resUlTs comparison between "reF_FOOD" and "FFQ_FOOD" intakes On average, "FFQ_FOOD" total food intake was lower than "REF_FOOD" total food intake (-198 g/day), with a measurement error of 10. 7% (Table  1 ). "FFQ_FOOD" total food intake was considered as underestimated for 56.
Impact on Nutrient Intakes Estimates of Using the Item-Composition Database For all nutrients except free sugars (% energy), "ITEM_NUT" intakes were not significantly different from "REF_NUT" intakes (Table  2 ). Variations in absolute values between "ITEM_NUT" and "REF_NUT" intakes (in %) ranged from 1. 2% for water to 42. 5% for EPA. The highest measurement error (above 15%) were found for EPA (42. 4%), DHA (39. 7%), vitamin A (36. 4%), free sugars (in % energy) (33%), vitamin B-12 (27. 8%), vitamin D (23. 1%), α-linolenic acids (20. 3%), vitamin C (18. 9%), copper (18. 3%), and iodine (16. 8%).
DiscUssiOn This paper describes a new method for validating a FFQ, independently to the bias induced by respondent's answers. This method was named "structural validation, " because it aims to assess the impact-on food and nutrient intakes estimates-of the inherent structure of a FFQ, especially the impact of using an aggregated food database and of using average and/or standard portion sizes. In this paper, the method was applied to a French medium-length quantitative FFQ. Results indicated an overall good structural validity, although an overall tendency to underestimate most of food groups, subgroups, and nutrient intakes was noticed. Overestimation was observed for certain food groups such as vegetables and sweet drinks, as well as certain nutrients such as free sugars. However, it was noticeable that, for some food groups, intakes were correctly estimated, notably for starches.
The use of an aggregated food database can lead to measurement errors due to the dilution of the nutritional information of specific foods explaining between-person variance in nutrient intakes. But, less the aggregation is, longer the questionnaire will be. In a review, Cade et al. found that the number of food items in existing FFQs ranged from 5 to 350. There is currently no consensus about the optimal length of a questionnaire (2). Whereas the accuracy was greater using less aggregation of foods  (22, 26) , a food list of more than 100 items induced overestimation  (6, 27, 28). In this study, the length was closed to the median identified by Cade (median length at 79 items). Without taking into account the respondent's perception, this study showed that the use of aggregated food items did not impact the estimation of most of the nutrients except free sugars.
67 to 1) compared with the range 0. 5-0. 8 proposed by Willett et al. (30). Finally, the comparison of results obtained in this paper with other validation studies is difficult because of different statistical methods and because we did not consider respondents' bias. Nevertheless, this study pointed out that the inherent structure of the questionnaire (use of average portion sizes and of an aggregated food database) induced on average an underestimation of nutritional and food intakes. This study presents limitations. First, methods based on self-reporting of food consumption, as the self-reported weekly dietary record used in this study, are prone to multiple bias, but, they are still widely used in epidemiological research. The design of a FFQ must be chosen according to the target population, which determines the source of the data to use, to build the questionnaire.
The novelty of this study was to explore the impact on food and nutrient intakes estimates of the inherent structure of a FFQ. Usually, validation is done by comparing food and nutrient intakes estimated from the FFQ and a reference method (i. e. , 24 h-recall or dietary record), completed by the same respondent under the same period. The reference method is supposed to quantify the same measure (i. e. , food intakes) and should be independent of the FFQ, to avoid an interdependence of errors  (36). However, measurement errors in validity measurement can also be attributable to the reference method. A better option would be to validate the nutrient intakes questionnaire estimates against biomarkers, but it is often too expensive and difficult to implement. This new "structural validation" method provides a first insight into validity of a FFQ by decomposing the measurement error according to its source (the use of an aggregated food database or the estimation of food quantity), independently of respondent induced bias and possible correlation errors.
To date, the few FFQs which have been developed in French are either not recent  (37, 38) , longer than we needed  (6, 29, 38, 39) , or designed for a specific population or food group  (40) (41) (42)  and often not freely accessible. The FFQ presented in this study will be a useful tool to assess the usual food and nutrient intakes of French individuals. In a near future, a web-based version of this questionnaire will be used for French adults. Such tool will enable to assess easily the habitual diet of individuals to be used in ongoing studies focusing on monitoring usual behavior. Webbased versions have shown similar accuracy when compared to printed version  (5, 43). They were also recognized to facilitate the collection of data (immediate storage), to reduce errors via automatic control, and are less burdensome for respondent than paper versions  (44, 45).
3  and S4 ) were performed on 2-ID-D beamline of Advanced Photon Source synchrotron (Argonne National Laboratory, Chicago, USA). Cells were located using a phase-contrast optical microscope. All measurements were conducted at room temperature, under a He atmosphere using a 6. 8 keV monochomatic X-ray incident beam focused to 0. 2 μm diameter. The fluorescence signal was detected with an integration time of 2 s per pixel, with a 200 nm pixel size, at 90° to the incident beam using a Vortex EM single element silicon drift detector. Images analyses were performed by using MAPS software from APS. Due to the last washing of cells with NaCl 0. 9 % before cryofixation and freezedrying, some NaCl crystals were present on silicon nitride membranes. The strong signal of Cl was then subtracted from other element signals.
ACKNOWLEDGMENT We thank  TGE RENARD  ( FR 3443 , CNRS) and UMR8601 for access to the EPR spectrometer for the EPR experiments and  Dr. Boucher  for useful discussions,  Institut Curie (Orsay)  for free access to cryofixation and freeze-drying facilities and  Dr. Guerquin-Kern  and  Dr. Marco  for useful discussions and help in cryofixed samples sample preparation,  Dr. Grimaud  for access to potentiostat, and  APS committee for beamtime. Use of the Advanced Photon Source , an  Office of Science User Facility  operated for the  U. S. Department of Energy (DOE) Office of Science by Argonne National Laboratory , was supported by the  U. S. DOE  under Contract No. DE-AC02-06CH11357.
Funding Sources Use of the  Advanced Photon Source , an  Office of Science User Facility  operated for the  U. S. Department of Energy (DOE) Office of Science by Argonne National Laboratory , was supported by the  U. S. DOE  under Contract No. DE-AC02-06CH11357. ENS,  CNRS  ( UMR7203 ),  Université Pierre et Marie Curie Paris 6, ANR (METABACT, TAKE CARE ,  MAGIC-ANR-15-CE07-0027  projects),  Fondation pour la Recherche Médicale ,  PSL-Inocellchem , are acknowledged for financial support. ENS is gratefully acknowledged for A. S. B. 's fellowship and  ENS-Cachan  for E. M. 's fellowship. Philippe Seksik declares consulting fees from  Abbvie ,  Merck-MSD and Biocodex , grants from  Biocodex , sponsored travel from  Merck-MSD and Takeda.
Literature search strategy A review was conducted using Medline and Scopus search that includes all original research articles written in English, published since January 1990, on the relationship between inflammatory markers and the consumption of dairy products in humans. A first Medline search was conducted on February 13, 2013. A search of the Scopus database was also conducted on June 18, 2013 and the entries not identified in Medline were included into the evaluation. Medline and Scopus were searched again on December 10, 2013 to identify and include additional articles published until November 30, 2013. The search strategies were as follows: Medline search strategy. (milk OR cheese OR yog Ã OR dair Ã ) AND inflam Ã NOT ("breast milk" NOT "human milk") NOT review ) AND TITLE-ABS-KEY(inflam Ã ) AND NOT TITLE-ABS-KEY("breast milk" not "human milk")) AND DOCTYPE(ar)) AND (humans)) AND (inflammation) AND (LIMIT-TO(LANGUAGE,"English")).
Data collection process Figure  1  shows the flow diagram with the five phases leading to the quantitative analysis of the 52 clinical studies. Seventy-eight study results were extracted from these clinical studies to measure the impact of dairy products on inflammation in humans. Phase 1. For phase 1, all studies identified by the search strategy were randomly split into six groups. Each group of studies was distributed to reviewers of one partner institution. Based on title and abstract, only studies that were clearly associated with inflammatory mediators and with the ingestion of dairy products (i. e. , milk, cheese, yoghurt, fermented milk, whey products, and other dairy foods) by humans, were kept for phase 2 of the review process. Studies investigating human milk and/or breastfeeding, were excluded.
Sustainability of effect over time-This line reports whether sustainability of the inflammatory effect over time was "investigated," "discussed," or "not discussed". A study result investigating and reporting a maintenance of the inflammatory effect after a washout phase of at least one week is labeled "yes". Dose-response-This line reports whether a dose-response relationship was investigated (yes) or not (no). If yes, a short description is presented. Bioavailability data-Label as "yes" if information is provided on bioavailability of dairy product components, otherwise label as "no". In cases where bioavailability data was obtained in the study (yes), a short presentation of the information is presented in the table. Biological plausibility-This line presents whether the mechanism of action by which the dairy constituents exert their anti-or pro-inflammatory effects was discussed or investigated.
Strengths and limitations of the IS The literature focusing on the impact of dairy products on inflammatory processes in humans revealed a very heterogeneous methodological landscape. The IS was therefore defined in order to take these limitations into account as follows: Inflammation is a complex phenomenon that cannot be described by a single biomarker  (Calder et al. , 2013). Indeed, more than fifty inflammatory markers were reported in the pool of the 52 human studies reviewed. The data consisted of cellular markers of inflammation and measures of tissue infiltration, but the majority of studies concentrated on a few soluble circulatory cytokines. Furthermore, the number of markers measured in each study varied from one to more than ten. These points all raised the issue of the weighting of each study result in this heterogeneous environment.
For the sake of simplicity, and to avoid over-interpreting the data, we decided to (i) rate each of the inflammatory markers listed in Table  1  at the same level and (ii) to increase the IS by one unit in cases in which changes in the concentration of more than one inflammatory markers were pointing in the same direction (see point 5 in Table  2 ). Note, however, that the IS was not upgraded by additional grades for studies in which more than two inflammatory markers were concordantly changed as this would have given too much weight to this criterion compared to the ten other criteria presented in Table  2. As milk is amenable to a wide range of technological transformations and important in human diets, a large spectrum of dairy products was investigated in the 52 reviewed studies. As each of these products may differently modulate inflammation, we addressed this issue by defining a limited range of product categories in which the data could be stratified and analyzed (low-fat vs.
high fat; fermented vs. non-fermented). The health status of the subjects enrolled in the 52 studies was quite diverse, reflecting the generic importance of inflammatory processes in modulating human health and disease. The clinical indications targeted by these studies were consequently heterogeneous and we therefore classified the study results according to a limited, but clinically meaningful, set of subject categories (HEALTH, MET, GIT, HYPER). Given the relative paucity of high-quality studies on the topic of dairy and inflammation, we chose an inclusive strategy which means that we considered all available publications on dairy and systemic inflammation, including randomized controlled trials, cross-over design trials and longitudinal cohort studies. This approach enabled us to analyze data from studies per se not considered in systemic reviews and we could thus provide a wide overview of studies dealing with dairy and inflammation.
Genetic Structure of L. lactis The development of next generation sequencing (NGS) technologies has provided numerous complete or draft lactococcal genomes. The sequence of the L. lactis subsp. lactis IL1403 strain was released in 2001  [36]. Of the 83 L. lactis sequences available on public databases, 50 were published in 2015 and 2016 (Table  1 ). However, a robust phylogenetic analysis of the species from the core genome derived from these data has not yet been conducted. Up to now, the extent of genetic diversity has been explored by Multi Locus Sequence Typing (MLST) studies. MLST is a powerful technique based on the sequencing of a limited number of genes in the core genome  [37]. It provides information on the population structure, the long-term epidemiology and the evolutionary history of the species.