dataesr/scientific-paragraphs-categorization huggingface dataset:
Mediation analysis Given previous research linking response rates to context sensitivity  (Byrom et al. , 2015)  and depression  (Blanco et al. , 2012) , and the preceding results that suggest a drug effect on levels of responding and sensitivity to the context, we explored the links between drug treatment, BDI, p(R) and perceived context sensitivity using a moderated mediation analysis. Mediation analysis uses regression to work out the causal or directional relation between factors. In order to do this, we calculated a raw difference score, which is a measure of control that contrasts sensitivity to the context with the response, such that positive values represent less perceived control (context minus response rating). These data were then entered into a conditional mediation model  (Preacher, Rucker, & Hayes, 2007)  using PROCESS in SPSS (version 20).
Acknowledgments This study was supported by the  P1vital CNS Experimental Medicine Consortium  (members  AstraZeneca ,  GlaxoSmithKline ,  Lundbeck, Organon  (a subsidiary of Merck) and  Pfizer ). This work was also supported by the  ESRC  (http://www. esrc. ac. uk) grant number:  RES-062-23-2525  awarded to  Dr. R. M. Msetfi  and  Dr. R. A. Murphy.
Characterization methods Electrochemical experiments were carried out using AUTOLAB PGSTAT30 potentiostat driven by Nova. 10 software. Classical three electrodes setup was used with mercury sulfate electrode as reference electrode (MSE = 640 mV/SHE), platinum wire as counterelectrode and modified stainless steel substrate as working electrode (0. 79 cm 2 ). Electrolyte was composed of 5 mM in Potassium hexacyanoferrate (II) with 0. 2 M in sodium sulfate (background salt) in pure water. Cyclic voltammograms were realized at 100 mV/s. XPS was used to measure elemental composition in order to determine oxidation state of elements. Spectra were acquired using monochromatized Al Ka radiation (1486. 6 eV). X-ray radiation source operated in vacuum of 3 Â 10 À9 mbar. Binding energies of cores levels were calibrated in function of the C1 s binding energy set at 285.
2 eV, a characteristic energy of alkyl moieties. Deconvolutions were made using mixed Gaussian-Lorentzian curves (80% of Gaussian character). Polarization Modulation Infrared Reflection Absorption Spectroscopy data were collected from a Bruker VERTEX 70 PMA 50 equipped with a liquid-nitrogen-cooled mercury-cadmiumtelluride (MCT) detector and a photoelastic modular PMA: ZnSe. The infrared light, reaching the sample surface at an angle of 75°, was modulated between s-and p-polarizations at a frequency of 50 kHz. Signals generated from each polarization (Rs and Rp) were detected simultaneously by a lock-in amplifier and used to calculate the differential surface reflectivity DR/R = (RpÀRs)/(Rp + Rs). The spectra were taken by collecting 250 scans with spectral resolution of 2 cm À1.
The stained cells were analyzed by flow cytometry (Accuri, BDbioscience) with CFlow Sampler software (BD). For stimulation experiments, 2 × 10 5 cells per well were cultured for 48 h (37 • C, 10% CO 2 ) in DMEM medium in P24 plates pre-coated with anti-CD3/CD28 antibodies (4 μg/mL each; eBioscience) or phorbol 12-myristate 13-acetate (PMA)/ionomycin (cell stimulation cocktail, 1×, ebioscience). Culture supernatant was frozen at -80 • C until processing.
Bifidobacterium. lactis CNCM I-2494 Modulates CD3 + /CD4 + T-Cell Populations in Spleen and MLNs by Increasing T Helper (Th) Profile 2 To study further the mechanism by which B. lactis CNCM-I2494 exerts protective function, T-cells from spleen and MLN were isolated and analyzed by flow cytometry. DNBS-treated mice showed lower CD3 + /CD4 + T-cell percentages in spleen (Figure  4A ) than the control group and higher CD3 + /CD4 + cell percentages in MLN (Figure  5A ; p < 0. 05). B. lactis CNCM-I-2494 tends to reduce the CD3 + /CD4 + decrease in spleen (Figure  4A ) and significantly control CD3 + /CD4 + increase in MLN (p < 0. 05; Figure  5A ). No variations were observed in CD3 + /CD8 + T-cell percentages in spleen or MLN (data not shown). As variations in CD4 + T-cell populations were found, MLN and spleen cells were cultured after isolation in the presence of two different stimulators during 48 h: CD28 + /CD3 + to specifically stimulate lymphocytes and PMA/IO to stimulate all the cells present in the organ disaggregate.
MATERIAL AND METHODS For all analyses, data from Cancer Incidence in Five Continents (CI5) Vol. X  (18)  and GLOBOCAN 2012 (1) were used. Both databases are governed by the International Agency for Research on Cancer but differ in their underlying data and purpose. CI5 provides high quality data on the incidence of cancer from cancer registries around the world and its Vol. X contains information from 290 cancer registries in 68 countries about cancers diagnosed from 2003 to 2007. The GLOBOCAN project provides estimates of the incidence of, mortality and prevalence from major cancer types at the national level, for 184 countries of the world, for the year 2012. These estimates are based on most recent data available and further details are provided elsewhere  (19). Using both data sources, age-specific and age-standardized (world population, 1960) incidence rates were estimated for esophageal cancer by histological subtype (EAC/ESCC) and gastric cancer by topographical subsite (CGC/NCGC) in 2012.
The exact method and the resulting incidence rates have been described in more detail in Arnold et al (3) and Colquhoun et al  (4). In brief, sex-and age-specific (<65; ≥65 years) proportions of EAC/ ESCC were computed for all countries included in CI5X except for those with no cases of EAC/ESCC in one of four substrata (male, female; <65, ≥65 years). Similarly, the proportions of CGC (C16. 0) and NCGC (C16. [1] [2] [3] [4] [5] [6]  cases out of all gastric cancers with known topography (C16. 0-6) were calculated for each country included in CI5X and stratified by sex and the same broad age group, provided there were two or more cases of CGC and NCGC within each sex and age group stratification. Where data were available from multiple regional registries within a country, cases and populations were pooled to obtain national proportions.
In the past decades, there have been programs to increase awareness and implement early detection in these countries. This has led to increased cancer diagnosis, especially of small lesions, and hence increased cancer incidence  (25). Such activities may obscure the apparent relationship with other risk factors and we therefore conducted a sensitivity analysis, excluding China, Japan and Republic of Korea. Given that tobacco smoking is another important risk factor for both esophageal and gastric cancer, the interaction between obesity prevalence and smoking was assessed for each cancer by reclassifying the included countries according to tertiles of obesity prevalence combined with tertiles of lung cancer mortality in 2012 obtained from the GLOBOCAN database (1), as a proxy for past smoking patterns (Supplementary Tables  2a  and 2b ).
Strengths & Limitations To ensure the validity of our approach, we used only high quality data (including only registries that have been selected for CI5) to estimate incidence rates of esophageal and gastric cancers. This was done by applying country-, age-and sex-specific proportions of esophageal and gastric cancers by subtype/-site obtained from CI5X to GLOBOCAN 2012 estimates. Hence, these rates represent estimates of the true incidence within each country and should be interpreted with caution. We only included countries represented in CI5X in our analyses, resulting in a selection of 50 mostly high-income countries, which may not be representative on the global level. As a consequence, the obesity groupings and the respective incidence rates depend on the countries included and can potentially change if other countries were included.
Seven measures were evaluated: one parametric test (circular Watson-Williams test), and three distinct measures of phase opposition (phase bifurcation index, phase opposition sum, and phase opposition product) combined with two procedures for non-parametric statistical testing (permutation, or a combination of z-score and permutation). While these are obviously not the only existing or conceivable measures, they have all been used in recent studies. All tested methods performed adequately on a previously published dataset  (Busch et al. , 2009). On a variety of artificially constructed datasets, no single measure was found to surpass all others, but instead the suitability of each measure was contingent on several experimental factors: the time, frequency, and depth of oscillatory phase modulation; the absolute and relative amplitudes of post-stimulus event-related potentials for the two trial groups; the absolute and relative trial numbers for the two groups; and the number of permutations used for non-parametric testing.
The present study aims to compare seven variants of phase opposition measures that have been used in previous published studies. By applying all measures to the same experimental datasets, and by systematically varying key experimental parameters in artificially constructed datasets, we can gain insight about the relative merit of each phase opposition measure, and the conditions under which it should (or should not) be applied. We hasten to note that a number of other measures might already exist and that many more could also be conceived for similar purposes-in other words, this comparison is not intended to be exhaustive, but it should at least contribute to organizing a significant portion of the existing literature.
Experimental Assumptions In all following analyses, we shall work on (real or artificial) experimental datasets for which two possible outcomes A and B have been recorded in otherwise identical trials, resulting in two trial groups A and B. (It is worth noting, however, that both parametric and non-parametric measures of phase The table depicts four situations in which 200 trials were simulated. For each trial, a phase value was randomly assigned from a uniform distribution between 0 and 2π. Thus, the overall ITC is expected to be near-zero; but due to the relatively small sample size (n = 200), the observed ITC all can in fact vary between near-zero (left column) and somewhat higher values above 0. 1 (right column). In each simulation, the 200 trials were divided into two groups A and B with equal probability (50%).
We further assume that stimulus onset (t = 0) for every trial occurs in a temporally unpredictable manner (e. g. , because of randomized inter-trial intervals). This assumption ensures that we consider truly ongoing brain activity: even if certain brain states fluctuate rhythmically, each phase of this fluctuation would be equally likely to be sampled at stimulus onset. In other words, for every frequency, the pre-stimulus phases 2 The Watson-Williams test included in the Matlab Circular Statistics toolbox  (Berens, 2009)  can already handle more than 2 trial groups. The PBI, POP, and POS measures can be extended to n trial groups (n > 2) by inserting additional terms in the corresponding Equations (5-7). A detailed analysis of these measures' behavior in experiments producing three or more trial outcomes is unfortunately beyond the scope of the present study.
Consequently, the purpose of our phase opposition analyses is to reveal the oscillatory frequency involved. That is, we place ourselves in the situation of an experimenter trying to determine whether task outcome is rhythmically modulated by any oscillatory signal, and if yes, at what frequency. Phase opposition must therefore be evaluated for all time and frequency points ("time-frequency" analysis). Although we do not explore or simulate "control" datasets in which no phase modulation is present, the likelihood of detecting phase opposition at an incorrect time and/or frequency can be taken as a measure of false alarm rate or baseline (chancelevel) performance for our procedures. (Note finally that, in specific cases, experimenters may have strong a priori hypothesis about the exact rhythmic frequency involved; in such cases, which we do not address here, other analyses procedures may be warranted).
When multiple datasets are recorded for a given experimental situation (e. g. , multiple subjects in a given experiment), we assume that the rhythmic modulation that we aim to reveal occurs around the same time and frequency for all datasets. However, the exact phase value favoring outcome A vs. B may or may not be the same across datasets/subjects. (Different reasons may justify this assumption: various conduction delays resulting in shifts of optimal phases; differences in cortical folding resulting in signal polarity reversals, etc. For all these reasons, while the absolute phase of local cortical oscillations at the level of a given neuronal ensemble is obviously important for its operation, we generally consider the absolute phase recorded at the scalp to be irrelevant, and focus instead on the relative phase between the two trial outcomes).
Circular Watson-Williams test The circular Watson-Williams test is a two-sample test for equal means, equivalent to a two-sample t-test for linear data. It assumes that each set of phases to be compared follows a von Mises circular distribution, and that the two distributions share a common concentration parameter κ. For our purposes, we adapted the implementation provided in the Matlab "circStat" (circular statistics) toolbox  (Berens, 2009) , in order to process multi-dimensional datasets (time-frequency matrices). This function implements the procedure described by  Zar (1999) , where the test statistic F defined as: F = 1 + 3 8κ (n -2) n A ITC A + n B ITC B -nITC all n -n A ITC A -n B ITC B ( 4 ) follows a F distribution with (1, n -2) degrees of freedom. Since this parametric test statistic can be directly related to the corresponding p-value (by means of the F cumulative distribution function), no further statistical analysis was required for this test.
Statistical Analysis From a given dataset, we computed seven time-frequency maps of p-values, corresponding to seven distinct ways of evaluating phase opposition, as illustrated in Figure  2. The electrophysiological signal recorded on every trial was subjected to a time-frequency transform ("timefreq" function from the Matlab EEGlab toolbox, using the "wavelet" option, and frequencies increasing logarithmically from 2 to 50 Hz while the number of cycles in each wavelet increases linearly from 2 to 15 cycles). At each time and frequency point, the values ITC A , ITC B , and ITC all were computed, and from these values, four distinct time-frequency maps were obtained. One map directly contained the p-values resulting from the circular Watson-Williams test (Equation  4 ); this corresponds to the procedure employed for example by  Baumgarten et al.
Permutation Test For a given dataset, the trial assignment to group A or B was randomly permuted a number of times (n perm ; here n perm = 1000, except when stated otherwise). The phase opposition measures PBI, POP, and POS were recomputed after every permutation. For each of these three measures, at each time-frequency point, the final p-value assigned was the proportion of permutations that yielded a higher measure than in the original dataset, or 1/(2n perm ), whichever was highest. (As in other statistical inference tests, the p-value therefore reflects the likelihood that an experiment-in this case, a permutation-would provide equal or higher phase opposition if the null hypothesis-no phase opposition-was true). Note that low numbers of permutations limit the range of p-values that can be obtained, which can prove problematic, for example when a correction for multiple comparisons across time and frequency points is needed (e.
Permutation + z-score Test To circumvent this problem, we have suggested a streamlined procedure  (Drewes and VanRullen, 2011; Dugue et al. , 2011 Dugue et al. , , 2015;; Inyutina et al. , 2015; McLelland et al. , 2016; Sherman et al. , 2016) , in which a relatively low number of permutations is used to characterize the mean and standard deviation of the null-hypothesis distribution. The true value from the original dataset is then compared against the null-hypothesis distribution by means of a z-score. In other words, for each of the three phase opposition measures (PBI, POP, POS), for each timefrequency point, the difference between the original dataset and the mean of all permutations was expressed in units of standard deviation (across all permutations). The resulting time-frequency map of p-values was obtained by means of the normal cumulative distribution function.
Test Performance on Real Experimental Dataset The above statistical analysis procedure describes how the seven p-value maps of phase opposition are derived from a given dataset (Figure  2 ). It remains to be seen how each of these seven measures will perform in situations where the "ground truth" is known (or assumed to be known). Is a significant phase opposition detected at the proper time and frequency point? Are there other, spuriously significant phase opposition values detected at erroneous time-frequency points? We first addressed these questions using a previously published experimental dataset  (Busch et al. , 2009)  for which the existence of phase opposition around 7 Hz and 120 ms pre-stimulus could be assumed as "ground truth" (insofar as previous publication can be considered a mark of reliability).
p-value Combination As the experiment had been performed for multiple observers (N = 12), each of whom contributed one dataset for the To determine the statistical significance of these measures, two distinct non-parametric procedures were applied. The first consisted in randomly permuting the trial labels (assignment of outcome A vs. B), and for each permutation recalculating POS, POP, and PBI. The ranking (percentile) of the original dataset values against these null-hypothesis distributions could be used as a p-value (right hand-side, green time-frequency maps). Alternately, the null-hypothesis distributions could be summarized by their mean and standard deviation (across permutations), the values of the original dataset could then be expressed as a standardized z-score against the null distribution, and a p-value assigned using the normal cumulative distribution function (right hand-side, red time-frequency maps).
Artificial Datasets Creation To determine and compare the sensitivity of each phase opposition measure in a variety of well-controlled situations, we also created artificial datasets, for which the parameters of oscillatory modulation could be precisely ascertained (Figure  3 ). Just like with real experimental data, an artificial dataset was made of n trials, each of which had a (simulated) electrophysiological signal associated with an experimental outcome A or B. The electrophysiological data of each trial was The signals were initialized with white noise, then band-pass filtered at a critical frequency f. The Hilbert transform extracted the oscillatory phase value at a critical time t. This phase was used to determine the experimental outcome for that trial: outcome A was randomly assigned with a mean probability p A that was modulated as a cosine function of the phase angle.
An ERP (with slightly randomized latency, duration and amplitudes for both P1 and N1 components, as illustrated by the shaded areas representing standard deviation across trials), whose overall amplitude depended on the trial outcome A or B, was added to the original white noise signal, to produce the final artificial dataset. randomly initialized with white noise 5. This signal was bandpass filtered around a specific frequency f (different for each simulation), and a Hilbert transform converted the resulting oscillatory waveform into complex values. The phase angle was extracted at a critical time point t (different for each simulation), and the trial outcome was decided on the basis of this phase value. The probability of outcome A was a cosine function of this phase angle 6 : 5 White noise comprises equal power at all frequencies.
Test Performance on Artificial Datasets: Statistical Power The statistical power of a test is the proportion of experiments that would return a positive test result, given that an effect was indeed present. For a given set of parameters, we created 100 distinct artificial datasets, and then for each phase opposition measure, we evaluated the proportion of datasets yielding a successful statistical result. Based on the time t and frequency f of phase modulation implanted into the artificial datasets (see Figure  3 ), we defined a target time-frequency region [centered on point (t, f), with a tolerance of ±400 ms and ±2 logarithmic frequency steps]. An experiment was classified as a "hit" if a statistically significant pre-stimulus phase opposition was detected inside the target region; a "false alarm" occurred if significant pre-stimulus phase opposition was found outside of this region.
The hit rate was thus defined as the proportion of datasets with a hit (out of 100 datasets), and the false alarm rate as the proportion of datasets with a false alarm. As previously (Section AUC), the hit and false alarm rates were contingent on the choice of statistical threshold. Thus, we varied the threshold systematically, and constructed the entire ROC curve; the statistical power was defined as the area under this ROC curve. Statistical power varies between 0 and 1. A statistical power value of 0. 8, for example, would indicate that 80% of experiments could detect a significant phase opposition around the correct time and frequency (without also detecting it at incorrect times and/or frequencies); this value (or a higher one) is typically considered acceptable. Note also that chance level (the statistical power of a test with zero sensitivity) is not 0.
RESULTS Real Experimental Dataset  (Busch et al. , 2009)  First, it appears important to verify that all the statistical measures compared here share, at least, the ability to identify phase opposition in a real experimental situation, where the perceptual outcome is known to be modulated by ongoing oscillatory phase. To this end, we re-analyzed a previously published dataset  (Busch et al. , 2009). In that experiment, a brief peripheral target at detection threshold was presented on every trial, after a randomized inter-trial interval, such that observers reported only about half of the targets (hits) but missed the other half (misses). The phase bifurcation index (PBI) applied to this dataset (electrode Fz) had revealed significant phase opposition between these two trial groups at 7 Hz, peaking 120 ms before stimulus onset.
We then explored the performance of other phase opposition measures on this same dataset (Figure  4 ). The seven time-frequency maps of p-values shown in Figure  4A -C all demonstrated significant (p < 0. 0002) phase opposition around the same time (-120 ms) and frequency (7 Hz) as in the original study  (Busch et al. , 2009). For six of these seven measures, the resulting p-values were robust to an FDR correction for multiple comparisons using alpha = 0. 05  (Benjamini and Hochberg, 1995). It is worth noting that the last remaining measure was the PBI coupled with a standard permutation test (Figure  4B ), precisely the one employed in the original paper. This indicates that our comparison did not unwittingly favor the initially applied measure. (In the original study, the PBI-values were first combined across observers before applying the permutation test, resulting in slightly different p-values, which were in fact robust to FDRcorrection; here, p-values were first obtained for each observer and subsequently combined across observers, for consistency with the procedure applied to the circular Watson-Williams test).