dataesr/scientific-paragraphs-categorization huggingface dataset:
Analysis of 42 genes The expression of 42 genes ( plus two endogenous control genes) was analyzed using qPCR with the custom TaqMan Array Fast Plate (StepOnePlus Fast Real-Time PCR system, Applied Biosystems). The RQ gene expression data were analyzed using Data Assist Software (Applied Biosystems). A two-sample, two-tailed Student's t test comparing the ⌬Ct values of the two groups was performed and a p value was calculated. The p value was adjusted with the Benjamini-Hochberg method (Benjamini and Hochberg, 1995), and pairs with p Ͻ 0. 05 were considered as the statistically significant pairs. ␤-Actin and hypoxanthine ribosyltransferase (HPRT) were used to normalize the data. Data from nonexposed groups were considered as the reference. A gene was considered in Data Assist Software significantly upregulated or downregulated if both following conditions were present: (1) the value for RQ was Ͼ1.
Images were acquired with a CCD-based system (chemiDoc TM XRSϩ, Bio-Rad Laboratories) and bands quantified with ImageLab software (Bio-Rad Laboratories). The signal intensities of the GFP band of exposed mice (n ϭ 3) was compared with that of control mice (n ϭ 3). Relative quantification of GFP protein level was performed after normalization to ␤-actin used as a loading control (see Fig. 6A1 ). Data were normalized to control mice GFP band [set to 1 arbitrary unit (a. u. )]. Statistical analysis (two-sample, two-tailed homoscedastic t tests) was performed using the built-in macro in Excel.
Oxygenated Ringer's solution was continuously perfused at room temperature. The dendritic knobs of OSNs were visualized through an upright microscope (Olympus BX51WI) equipped with an Olympus DP72 camera and a 40ϫ water-immersion objective (numerical aperture, 0. 8). An extra 2ϫ magnification was achieved by a magnifying lens in the light path. The GFP-labeled cells were visualized under fluorescent illumination. Superimposition of the fluorescent and bright-field images allowed identification of the fluorescent cells under bright field, which directed the recording pipettes. Electrophysiological recordings were controlled by an EPC-10 USB amplifier combined with Patchmaster software (HEKA Electronic). Perforated patch clamp was performed on the dendritic knobs by including 260 M nystatin in the recording pipette, which was filled with the following solution (in mM): 70 KCl, 53 KOH, 30 methanesulfonic acid, 5 EGTA, 10 HEPES, 70 sucrose, pH 7.
EOG voltage signals were recorded using an XtraCell two-channel amplifier (Dipsi) used in a DC current-clamp configuration (I ϭ 0), low-pass Bessel filtered at 1 kHz, and digitized at a rate of 2 kHz using a Digidata 1322a analog-to-digital converter (Molecular Devices) interfaced to a Pentium PC and Pclamp 9. 2 software (Molecular Devices). A reference Ag/AgCl electrode was placed on the frontal bone overlaying the OB. Recordings were made with glass micropipettes of 4 -5 M⍀ filled with a saline solution. EOGs were recorded from two separated positions localized centrally on the septum. These positions gave robust, reproducible, and long-lasting EOG recordings ranging from 10 to 17 mV when stimulated with the mixture of odorants. Odorant-free air stimulation (with mineral oil) always produced signals of ϳ1 mV amplitude.
Analyses were performed using Clampfit 9. 2 (Molecular Devices). We measured the peak amplitude, onset slope (from 10 to 90%), fast-decay slopes (from 100 to 50%), and slow-decay slopes(from 40 to 10%). Data shown in Results represent the amplitude of response to lyral minus the amplitude with mineral oil alone. Statistical analysis (two-sample, two-tailed heteroscedastic t tests) was performed using the built-in macro in Excel.
We therefore performed a relative semiquantification of MOR23 expression using GFP as a reporter of MOR23 expression level. On immunoblots, we compared the signal intensities of the GFP band of exposed mice (n ϭ 3) with those of control mice (n ϭ 3). Relative quantification of GFP protein level was performed after normalization to ␤-actin used as a loading control (Fig. 6A1 ). Data were nor- malized to control mice GFP band (set to 1 a. u. ). We observed no difference in protein expression level (1 Ϯ 0. 07 a. u. for control vs 0. 95 Ϯ 0. 08 a. u. for exposed mice; p ϭ 0. 64; Fig. 6A2 ). We then performed a comparative analysis of mRNA levels for the MOR23 gene by reverse-transcription qPCR. To avoid genomic DNA amplification, we designed primers specific for exon-exon junctions (Table  1 ).
These fatty acids included nine SFAs with relative concentrations higher than 0•05%: myristic acid (14:0), coeffi cient of variation 9•4%; pentadecanoic acid (15:0), 11•9%; palmitic acid (16:0), 1•6%; heptadecanoic acid (17:0), 4•2%; stearic acid (18:0), 2•0%; arachidic acid (20:0), 15•3%; behenic acid (22:0), 10•3%; tricosanoic acid (23:0), 18•9%; and lignoceric acid (24:0), 14•7%. We used human and equine plasma (Sera Laboratories International, West Sussex, UK) for quality control. 23 eight and height were measured by trained professionals using standardised protocols and were used to calculate BMI (in kg/m²). Waist circumference was 0•37 (0•11) 0•39 (0•11) 0•36 (0•10) 0•38 (0•11) 0•38 (0•11) 0•37 (0•11) 0•36 (0•11) 0•37 (0•11) 0•36 (0•11) 0•37 (0•11) Pentadecanoic acid (15:0) 0•22 (0•06) 0•21 (0•07) 0•22 (0•07) 0•19 (0•07) 0•22 (0•06) 0•23 (0•07) 0•21 (0•07) 0•20 (0•06) 0•21 (0•07) 0•21 (0•06 0•20 (0•06) Palmitic acid (16:0) 29•88 (1•72) 30•09 (1•69) 30•22 (1•58) 30•32 (1•51) 29•96 (1•75) 30•22 (1•65) 30•09 (1•68) 29•80 (1•67) 30•10 (1•67) 29•93 (1•74) 30•21 (1•67) Heptadecanoic acid (17:0) 0•42 (0•08) 0•41 (0•09) 0•41 (0•09) 0•39 (0•09) 0•43 (0•09) 0•42 (0•09) 0•41 (0•09) 0•40 (0•10) 0•41 (0•09) 0•41 (0•08) 0•40 (0•09) Stearic acid (18:0) 13•99 (1•45) 14•11 (1•35) 14•22 (1•24) 14•07 (1•14) 14•15 1•44) 13•83 (1•31) 14•21 (1•29) 14•72 (1•30) 14•12 (1•34) 14•05 (1•47) 14•22 (1•35) Arachidic acid (20:0) 0•13 (0•04) 0•13 (0•04) 0•14 (0•04) 0•13 (0•04) 0•13 (0•04) 0•13 (0•04) 0•13 (0•04) 0•13 (0•03) 0•13 (0•04) 0•13 (0•04) 0•13 (0•04) Behenic acid (22:0) 0•23 (0•08) 0•23 (0•07) 0•24 (0•10) 0•23 (0•08) 0•24 (0•08) 0•23 (0•07) 0•24 (0•09) 0•23 (0•06) 0•24 (0•08) 0•23 (0•07) 0•23 (0•07) Tricosanoic acid (23:0) 0•10 (0•04) 0•11 (0•05) 0•11 (0•06) 0•10 (0•05) 0•11 (0•05) 0•11 (0•05) 0•11 (0•05) 0•11 (0•04) 0•11 (0•05) 0•10 (0•04) 0•10 (0•05) Lignoceric (24:0) 0•23 (0•06) 0•23 (0•06) 0•23 (0•08) 0•23 (0•07) 0•23 (0•07) 0•23 (0•06) 0•23 (0•07) 0•22 (0•05) 0•23 (0•07) 0•22 (0•06) 0•22 (0•06) SFA group 1 † 44•23 (1•09) 44•57 (1•21) 44•84 (1•26) 44•76 (1•12) 44•50 (1•27) 44•44 (1•22) 44•66 (1•20) 44•88 (1•20) 44•59 (1•22) 44•34 (1•12) 44•80 (1•20) SFA group 2 ‡ 0•63 (0•11) 0•62 (0•14) 0•63 (0•14) 0•58 (0•13) 0•65 (0•13) 0•65 (0•13) 0•61 (0•13) 0•60 (0•13) 0•63 (0•13) 0•62 (0•11) 0•60 (0•14) SFA group 3 § 0•70 (0•19) 0•70 (0•18) 0•72 (0•24) 0•69 (0•20) 0•71 (0•20) 0•71 (0•19) 0•70 (0•22) 0•69 (0•16) 0•70 (0•20) 0•69 (0•18) 0•68 (0•19) Ratio of 16:1(n-7) to 16:0 0•01 (0•01) 0•02 (0•01) 0•02 (0•01) 0•02 (0•01) 0•02 (0•01) 0•02 (0•01) 0•02 (0•01) 0•02 (0•01) 0•02 (0•01) 0•01 (0•01) 0•02 (0•01) Ratio of 18:1(n-9) to 18:0 0•70 (0•15) 0•71 (0•16) 0•70 (0•16) 0•72 (0•16) 0•70 (0•16) 0•73 (0•16) 0•70 (0•16) 0•66 (0•16) 0•71 (0•16) 0•70 (0•15) 0•70 (0•16) Data are mean (SD).
The total subcohort of 15 919 people includes 755 people with incident type 2 diabetes as per the design of a case-cohort study. The 15 164 non-cases represent the subcohort minus the 755 incident cases of type 2 diabetes in this subcohort; this approach enables a comparison of non-cases with cases of type 2 diabetes. *110 individuals in the subcohort had missing BMI values, so BMI data are available for only 15 809 people. †SFA group 1=sum of 14:0, 16:0, and 18:0. ‡SFA group 2=sum of 15:0 and 17:0. §SFA group 3=sum of 20:0, 22:0, 23:0, and 24:0. SFA=saturated fatty acid. measured by staff , with the exception of French participants and a subset of participants from Oxford, UK, who self-reported their measurements, and in Umeå, Sweden, where this parameter was not recorded. We used lifestyle question naires to assess demographics, smoking status, medical history, and education level.
Physical activity was assessed by a validated questionnaire from which a four-point ordinal category of activity was derived. We assessed habitual diet during the past 12 months at baseline using country-specifi c validated food frequency questionnaires or diet histories. Total energy and nutrient intakes were based on the standardised EPIC Nutrient Database. 24 HbA1 C was measured in the erythrocyte fraction from samples stored at -196°C using the Tosoh-G8 HPLC analyser (Tosoh Bioscience, Japan) at Stichting Huisartsen Laboratorium (Etten-Leur, Netherlands).
Systematic review We searched PubMed for relevant articles using terms related to the exposure ("saturated fatty acids" or "saturated fat" or "myristic" or "palmitic" or "stearic" or "myristate" or "palmitate" or "stearate" or "dairy" or "ruminant" or "odd-numbered" or "odd numbered" or "odd chain" or "odd-chain" or "long chain" or "long-chain"), the outcome ("diabetes"), and the prospective study design ("cohort" or "nested"). We reviewed articles resulting from these searches, relevant references cited in those articles, and our own reference databases. Our search showed that past evidence was inconclusive, based on prospective studies with small sample size in which the number of type 2 diabetes events ranged from 34 to 673, and only a small number of individual saturated fatty acids were studied.
Contributors SJS and NGF had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. AK was responsible for the measurement of fatty acids, LW and KS did the fatty acid measurements, SJS did the statistical analyses, and NGF drafted the report. Working group members helped to improve the report (AK, SJS, FI, JK, MBS, FC, JMH, MG, JWB, GJvW, CL, and NJW). NJW is the co-ordinator of the InterAct project. All authors contributed to interpretation of data, critically revised the article for important intellectual content, and approved the fi nal version.
Acknowledgments Funding for the InterAct project was provided by the  EU   FP6  programme (grant number  LSHM_CT_2006_037197 ). Additionally, InterAct investigators acknowledge funding from the following sources:  Medical Research Council Epidemiology Unit  (grants  MC_UU_12015/1  and  MC_UU_12015/5 ),  Medical Research Council Human Nutrition Research  (grant  MC_UD99999906 ), and  Cambridge Lipidomics Biomarker Research Initiative  (grant  G0800783 ) (to AK, LW, KS, and JLG);  Cancer Research UK  (to FLC and TJK);  Health Research Fund  of the  Spanish Ministry of Health  (to JMH and MJT);  Murcia Regional Government  (no. 6236 , to JMH);  Regional Government of Navarre  (to MG); the  Dutch Ministry of Public Health, Welfare and Sports, Netherlands Cancer Registry, LK Research Funds, Dutch Prevention Funds, Dutch ZON ,  World Cancer Research Fund , and  Statistics Netherlands  (to JWJB, DLvdA, AMWS, and YTvdS);  Swedish Research Council  (to PWF and PMN);  Novo Nordisk, Swedish Diabetes Association , and the  Swedish Heart-Lung Foundation  (to PWF);  German Cancer Aid  and  German Ministry of Research  (to RK);  Medical Research Council UK and Cancer Research UK  (to KTK) ;  Danish Cancer Society  (to KO and AT);  Asturias Regional Government  (to JRQ); The  Västerboten County Council  (to OR);  AIRE-ONLUS Ragusa, AVIS-Ragusa , and  Sicilian Regional Government  (to RT); and  Imperial College Biomedical Research Centre  (to ER).
Experimental Procedures Rats were randomly separated into 5 groups (D0, AL, H0, H1 and H4) of 6 individuals (no significant difference of initial body weight was found between these groups: p-value = 0. 26). Each rat was identified and housed individually throughout the protocol. The group D0 was sacrificed on the first day of the experiment (as described below), so the initial biometric data of the rats are available, including body weight, body length, white adipose tissue mass, brown adipose tissue mass, muscles and organs weights (see Table  1 ). At the end of the experiment (i. e. 8 weeks) the other rats were sacrificed to obtain the same data. Blood samples were collected at the same time for further analyses. The total body lipid content can easily and accurately be predicted from the gravimetric determination of the retroperitoneal fat deposits  [38].
Individual data is freely available upon request. Biochemical analysis. Plasma ghrelin and leptin assays were performed using immunoassays (acylated rat/mouse ghrelin #A05117 and rat/mouse leptin EIA #A05176, Cayman, SpiBio, Montigny le Bretonneux, France) according to the manufacturer's recommendations. The detection limit and intra-assay coefficient of variation for ghrelin were 0. 2 pg. mL {1 and 11%, respectively. The detection limit and intra-assay coefficient of variation for leptin were 50 pg. mL {1 and 4%, respectively. Blood glucose was measured using an automatic glucose monitor (Optium Xceed, Abbott, Rungis, France). All assays were performed at least in duplicate (see Table  2 ). Individual data is freely available upon request.
The evolution of the amount of available food a (in kJ) depends on the input of food in the system f (usually a given amount each day) and the consumption c. The available food a satisfies da dt ~f (t){c(a,h): ð3Þ In order to describe variations in appetite, the model should take into account the evolution of factors influencing hunger. As previously mentioned, we focus on leptin, glucose and ghrelin concentration. The total plasma leptin l (in ng) is assumed to be produced proportionally to the fat mass  [27] , dl dt ~c2 S{c 1 l: ð4Þ Soleus (g) 235+48 248+37 172+33 203+24 183+45 EDL (g) 110+37 165+44 193+18 188+12 192+51 Each column gives biometric data for the 5 groups: for all groups except D0 (group sacrificed on the first day of the experiment), data have been obtained at the end of the experiments.
Parameter Estimation System (1)-(  8 ) use 21 parameters whose values are essential to the relevance of the simulation results. Amongst these, the 4  parameters of hormone production and degradation are taken from the literature (see Table  4  for a summary of units and origins of parameters of the model). Food-relative parameters depend on the composition of the chow diet. To estimate the 12 remaining parameters, we used the final fat mass and the evolution of body weight of each individual rat from groups AL and H1. Parameter values were obtained by minimizing the residual sum of squares (RSS) of observed data compared to simulation results using the Nelder-Mead algorithm  [45]. We then used these parameter values to test the predictive capacity of our model against data from groups H0 and H4.
However, this time, c was determined by the values of a corresponding to Ad libitum food and h is given by  (7). As R is supposed to be constant over group AL, the system used here was composed by all equations except  (8). Finally, those parameters relative to the rate of energy expenditure R, were estimated in a third step, using experimental data from hypocaloric group H1. We then used the whole system of equations and parameter values previously estimated for AL. Parameter estimation is detailed as follows: Step 1. Only equations (  1 ) and (  2 ) are used. The input of the system is the experimentally determined consumed food c for group AL. We assume R is equal to R 0 when Ad libitum food is available. As we have a value for c, it is not necessary to describe the variations of hunger and hormones so the only dynamical variables of the subsystem are S and W.
Predictions Following the estimation procedure (see previous paragraphs), the model was tested with the patterns of food input corresponding to the two other groups of rats (H0 and H4) to evaluate its predictive capacity. Parameter values determined for groups AL and H1 were used. As all the rats were supposed to be similar (same origin and age), we used the same parameter values for each group. If another group (H0 or H4) was chosen at step 3 of the estimation procedure instead of H1, the set of parameters associated with R was different. However the RSS for each set of parameters were close from one another. Hence, the simulated data will be better for the chosen group than it will for the other groups. The data from each group could be fitted individually to have better results but this would suppress the predictive capacity of the model.
Statistical analysis Reported values are expressed as means ± standard deviation for quantitative variables or numbers and percentages for qualitative variables. Statistical analysis was performed with SAS statistical software V9. 1 (SAS Institute, Cary, NC). Our statistical plan included two distinct analyses. In the first step of the study, biological parameters were compared between the four groups using a one-way analysis of variance or the Kruskal-Wallis Test, when needed. In case of a statistically significant difference, pairwise comparisons were performed using the Student's t test with Bonferroni correction. In the second step of the study, biological parameters were compared between the two LPS groups -with and without selenite administration -using the Student's t test or the Wilcoxon rank sum test when needed.
Acknowledgements The authors would like to thank the veterinary and laboratory staffs of the  French Army Biomedical Research Institute (IRBA)  for their very kind welcome and for their attention to the animals. The authors are also extremely grateful to the interest and support of the direction of IRBA for this approach of control of sepsis and inflammation using oxidant selenocompound. They should also thank  Ms. Jeannette de Vigan  ( US ) and  Dr. Bénédicte Forceville  ( Australia ) for kind assistance in written English.
To define whether the observed differences may be related to genetic variations between the two SFB isolates, we determined the genome sequence of strain SFB-mouse-NL. DNA was isolated from fecal material from SFB-monocolonized mice using a modified version of the protocol described in Morita et al. (10). The DNA was sequenced using Illumina paired-end sequencing technology. The sequence reads were filtered using CLC and BOWTIE  (11)  to eliminate mouse genome sequences, and they were assembled using CLC, ABySS  (12) , and SOAPdenovo  (13) , yielding 40 scaffolds Ͼ1,000 bp with high BLASTn scoring to existing SFB genome sequences. Finishing was performed using GAP4 of the Staden package (  14 ) through iterative selection of read pair mapping to scaffold extremities and independent de novo assembly of these reads by SOAP.
Following strips equilibration, second dimension horizontal electrophoresis was performed on a HPE TM flat-top tower (Serva) according to the manufacturers' instructions using plastic-backed gels and buffers provided by Serva. The two 11 cm strips corresponding to the same subject (one pool of three samples after control stimulation, one pool of three samples after C18:1 stimulation) were placed adjacently on one nonfluorescent large gel with a 12. 5% acrylamide concentration. Gels were fixed in a solution containing 15% ethanol and 1% citric acid, and stained with ProteoStain (Serva). Gel images were acquired on a GS-800 densitometer (Bio-Rad) and images were analyzed using Samespots software (NonLinear Dynamics). Spot volumes were normalized within one gel and transformed into log values.
1 H-NMR analysis 100 lL aliquots were added to 550 lL of deuterium oxide (D 2 O) containing 0. 25 mM sodium trimethylsilyl-[2,2,3, 3-2 H 4 ]-1-propionate (TSP; as a chemical shift reference at 0 ppm). The samples were then centrifuged at 5000 g for 10 min and the supernatants placed in 5 mm NMR tubes. 1 H NMR spectra were obtained using a Bruker DRX-600 Avance NMR spectrometer operating at 600. 13 MHz for a 1 Hr e s onance frequency and an inverse detection 5 mm 1 H-13 C-15 N cryoprobe attached to a cryoplatform. Spectra were acquired at 300 K using the Carr-Purcell-Meiboom-Gill (CPMG) spin-echo pulse sequence with presaturation, as described previously  (Neyraud et al. , 2013)  using 512 transients. Finally, spectra were data reduced using AMIX  (version 3. 91. 11, Bruker, Germany)  to integrate 0.
01 ppm wide regions corresponding to the d 9. 0-0. 7 ppm region. The d 6. 5-4. 5 ppm region, which includes water resonance, was excluded. The signal relative to C18:1 was also excluded  (d 1. 355-1. 245 ppm). This region was determined comparing the 1 H NMR spectra of saliva without stimulation and with C18:1 stimulation. A total of 601 NMR buckets were included in the data matrices. Each integrated region was normalized to the total spectral area. the analyses were limited to some targeted analyses while the use of omics approaches like proteomics and metabolomics can provide a larger picture of the molecules secreted. Omics approaches may also be usefully applied to the field of nutrition. For example, a nutri-metabolomics study demonstrated that serum composition can be modified by a four-week nutritional intervention consisting of consuming tomato sauces varying in their lycopene content  (Bondia-Pons et al.
Data analysis was performed as in  Neyraud et al. (2013)  and consists in: 1) OSC filtering to remove variations not linked to the sensitivity (sensitivity was used as a corrective factor); 2) Mean-centering of filtered data; 3) selection of number of components by cross validation; 4) Evaluation of predictive capacity of the model (Q 2 parameter); and 5) assessment of model robustness by permutation test (200 permutations were done). Discriminant variables were selected using variable importance in the projection (VIP) with a threshold of 1. 5. The Kruskal-Wallis test was finally used to determine metabolites that differed significantly between the two groups, considering p < 0. 05 as the level for significance. Multivariate analysis was performed using SIMCA-P software (V13, Umetrics AB, Umea, Sweden).
Micelles characterization In order to find out what types of structures could be involved as an on-pathway, we have performed a time dependent electron microscopic analysis during polymerization of hamster rPrP. Few minutes after dilution into polymerization buffer, we observed spherical structures looking like rigid micelles (Fig. 1A  and Fig. S6  in Supporting Information). The size distribution fits well with a log normal distribution (Fig. 1B ). The size of these spheres was heterogeneous with a mean around 30 nm. A sphere of 30 nm diameter reaches a surface area of around 30 nm 2 , the rPrP have a diameter (thanks to Protein Workshop v1. 0 and PDB ID 1B10) of about 1. 5 to 2 nm thus the sphere contains about 1000 proteins on its surface. In order to ensure that these structures are formed of rPrP, we decided to label them with antibodies.
Number of micelles were counted directly on images and size of micelles as well as length and width of fibrils were measured using ImageJ software (http://rsbweb. nih. gov/ij/). Kinetics measurements of polymerization. Kinetics of amyloid formation was monitored in SpectraMax Gemini XS (Molecular Devices). Samples containing 0:1 to 1:2mg : ml {1 of the oxidized form of HaPrP90-231 (rPrP) were incubated in Buffer A, Buffer B or Buffer C (see  [10]  for more details on the buffers composition) upon continuous shaking at 1350 rpm in 96-well plates and in the presence of ThT (10mM). The kinetics was monitored by bottom-reading of fluorescence intensity using 445nm excitation and 485 and 500nm emission. Every set of measurements was performed in triplicates, and the results were averaged. Previously prepared amyloid were used to performed the seeding and the w/w per cent were calculated assuming that suspension was homogeneous.
This work has been supported by  ANR  grant  MADCOW  no. ANR-08-JCJC-0135-01  (France). EH has been partially supported by  FONDECYT  postdoctoral grant no. 3130318  (Chile). The authors thank  INRIA  for its financial support. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.