dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2006 ). Yet, MRI-based alignment to a standard brain is more accurate as it takes intracranial structures into account (e. g. brain, ventricles). Whichever coregistration method is used, visual inspection of the alignment is highly recommended. Wrong or inaccurate definition of the landmarks (e. g. swapping left and right preauricular points) leads to incorrect results and affects subsequent source modeling. Similarly, automatic surface based alignment procedures may show small numerical errors despite obvious misalignments. Accuracy of MEG-MRI registration is important as head models are often based on the anatomical MRI and coregistration errors can therefore propagate into the head model and lead-field computations, which ultimately affects the accuracy of source reconstructions. Finally, it is preferred that individual MRIs are acquired after the MEG session, as MRI scanning may often lead to a considerable increase of 'magnetic noise' from the participant and may negatively affect MEG data quality.
• Head shape: system used for the digitization of the head shape. • Head movements: Were head movements recorded? What was the maximum movement? If continuous recording is not available, the maximum difference between head positions before and after the run should be stated. • Position of the participant: Was the participant in seated or supine position? How was the head of the participant positioned in the MEG helmet? • For external stimulation and recording devices: State delays with respect to MEG triggers. For example a photodiode can be used to determine visual stimulus onset. Were the data corrected for the delay? Coregistration: Describe the coregistration procedure. Were individual anatomical MRIs used? In case of surface-based matching, the typical number of digitized points should be stated, the optimization algorithm and its adjustable parameters, and a performance measure describing the goodness of fit.
Background An enduring tradition of MEG signal analysisinherited from decades of EEG researchis to analyze brain responses that are evoked by a stimulus or an action, by averaging the data about each eventdefined as an epochacross trials. The underlying assumption is that there are consistent brain responses time-locked and 'phase-locked' to a specific event (e. g. , a motor act or a presentation of a stimulus). Hence, it is straightforward to increase the signal-to-noise ratio of these responses by averaging epochs across trials, under the assumption that the rest of the data is uncorrelated noise, i. e. , inconsistent in time or phase with respect to the event of interest. This simple practice has permitted a vast amount of research using so-called event-related potentials (ERP) in EEG, or event-related fields (ERF) in MEG  (Handy, 2005; Niedermeyer and Silva, 2004).
The offset can be efficiently eliminated using a high-pass filter with a low cut-off frequency (b 0. 3 Hz, with at least 100-dB DC attenuation) applied on the continuous raw MEG traces. Applying such filter on shorter epoched data would certainly cause edge effects and uncontrolled contamination of signals and is therefore not recommended. Another approach is to subtract the average value of the data across specific time intervals from the signal of each channel. The interval is defined as the baseline, and it should not contain event-related fields, while being close in time to the event occurrence. The baseline duration must be long enough for an accurate estimate of the DC value at each sensor. The required number of samples depends on the noise in the data so a range of values around baseline duration of about 100 samples may be explored.
Reporting General • Provide exact details of the preprocessing steps, e. g. filtering, as application of filters can alter the outcome of connectivity analysis  (Florin et al. , 2010). • Describe procedures that were used to deal with artifacts such as the heartbeat signal or eyeblinks. • Report on the amount of data used to estimate connectivity. Several connectivity measures such as coherence or phase-locking value are biased, where the amount of bias depends on the amount of data (e. g. trials) used to estimate connectivity. Address this potential bias problem if the amount of data in two experimental conditions is different  (Maris et al. , 2007). • Discuss the interpretation of the results taking potential effects of electromagnetic field spread into account. This is particularly important for sensor-level analysis.
Background The aim of statistical analysis is to quantify the reliability of features that are estimated from the data, in the presence of noise (where noise could refer here to noise in the MEG recording (see Data preprocessing section), but also, for example, to inter-subject variability). This quantification typically takes the shape of a probability (p-value), a likelihood (e. g. when comparing models) or a confidence interval. Usually, the quantification is followed by an inferential step, e. g. a decision to reject or accept a particular experimental hypothesis, or the acceptance of a particular generative model of the data. Essentially, many steps in advanced MEG data analysis pipelines carry uncertainty that may warrant quantification, for example to assess the effect of particular parameter values on the quality of a source reconstruction.
g. when the location of the head relative to MEG sensors differ across participants). Projection onto a common source space would address this issue through well-established techniques for spatial normalization  (Ashburner and Friston, 1997; Fischl et al. , 1999)  although realignment of the data could also be done in sensor space  (Knösche, 2002; Taulu and Simola, 2006; Taulu et al. , 2004 ). • When the source orientations vary substantially over subjects, for example in cortical regions like V1 it is likely that the same sensors even see polarity reversals over subjects. • For multimodal datasetsfor example, data from magnetometers, gradiometers and EEGfor which source space statistics after their combined localization  (Molins et al. , 2008; Henson et al. , 2009)  are likely to be more sensitive than statistics on each sensor-type alone.
Non-parametric methods can be computationally expensive, though this is becoming less of an issue these days. • Both RFT and non-parametric approaches to the MCP offer the ability to make inferences about specific features of test statistics: for example, not just the "size" of a statistic in each point in a space (or "peak" of a statistical field), but also its "extent". Extent-level correction can be more sensitive than a height level correction at the cost of localizing power (e. g. Singh et al. , 2003). This is why it is often used in fMRI, but its relevance to some MEG applications, such as sensor-time volumes, is questionable in cases like magnetometer data with their bipolar (non-contiguous) fields. • Decide in advance whether the scientific question concerns the identification of spatiotemporal patterns or the localization and mapping of effects in space/time.
The polycrystalline silicon layer was etched by a 20 second immersion in a HNO 3 :H 2 O:HF 5052051 mixture. Chrome-gold Ohmic contacts were provided on the back sides of all wafers by evaporation (10 nm Cr, followed by 100 nm Au). Electrical measurements. Current/voltage (I/V) data were obtained using a Keithley 4200S Semiconductor Characterisation System and a Signatone probe station. Measurements were two-terminal measurements, with the Ohmic contact grounded and a potential applied to the top contact. Unless otherwise stated, measurements were conducted in an open laboratory in ambient conditions. No current compliance was set for the measurements of quantum conductance; consequently, care was taken to ensure that the measurement regime was well away from the hard breakdown limit of the devices.
Low temperature measurements were conducted by immersing samples into liquid nitrogen. Conductance was defined as I/V, and the histogram shown in figure 2(b) was constructed from a measurement of ,1,000 abrupt steps in I/V. Seventy individual devices were measured, for each of which a number of I/V curves was recorded. Some curves yielded more steps than others, but data from all curves were aggregated in order to generate the histogram shown in figure  2. Example curves from nine different devices are shown in the supplementary information to give an indication of the device-to-device variation in conductance jumps.
(Lepidoptera, Pieridae) sampled from the center to the periphery of Marseille. We utilize established and novel environmental correlation approaches to scan genetic variation for evidence of selection. We find significant morphological differences in urban populations, as well as weak genetic structure and decreased genetic diversity in urban versus non-urban sites. However, environmental correlation tests provide little support for selection in our dataset. Our comparison of different methods and allele frequency clines suggests that loci identified as significant are false positives. Although there is some indication that selection may be acting on wing size in urban butterflies, genetic analyses suggest P. rapae are undergoing neutral drift.
Sampling and Environmental Data The collection of butterfly samples includes a total of 219 P. rapae individuals from 41 sites along a 100 km transect, extending from the center of Marseille northeast through fields and shrubland/forest (Figure  1 , plotted using Google Earth software). Genetic data consists of 552 AFLP markers amplified from an EcoRI/MseI digest with three primer pairs (EACT-MCAG, EAAC-MCAC and EACT-MCTG). PCR products were labeled with FAM and electrophoresis was performed on a capillary sequencer with a ROX-500 size standard. A panel of 13 individuals was genotyped twice and loci were discarded if errors occurred in more than two individuals. The remaining 366 markers were used in downstream analyses. We measured wing size variation among the sampled male butterflies, as females represented a small proportion of our total sample size (26%).
Six wing size characters were recorded, including forewing length (FL), forewing width (FW), forewing area (FA), hindwing length (HL), hindwing width (HW), and hindwing area (HA; Figure  S1 ). Digital images were scanned alongside a metric ruler and measurements were made in Adobe Photoshop CS3 software. Data on road width (var1) were obtained from MapInfo (MapInfo Corporation); in addition, the density and number of buildings in a radius of 150 m were combined as a composite measurement of urbanization (var2). Land cover category (var3) and percent tree cover (var4) were obtained from GlobalMap (NASA Earth Observatory). The free database WorldClim was used to extract 19 climatic variables representing temperature and precipitation averages measured during 1950-2000. To reduce cross-correlations among climatic variables, we identified 16 highly correlated (Pearson's r.
Genetic Diversity and Structure Genetic diversity was measured at each collecting site as the percentage of polymorphic loci and the expected heterozygosity or Nei's gene diversity (Hj), using the software Genalex v. 6. 41  [23]. Using the groups based on distance classes, we measured pairwise F ST and Nei's genetic distance. These analyses were conducted in the software AFLP-Surv  [24] , using a Bayesian method  [25]  developed for biallelic dominant markers  [26]. Pairwise F ST is typically robust to small deviations from demographic equilibrium and provides a measure of genetic differentiation among populations  [27]. Finally, all non-urban and urban populations Table  1. Morphological differences in male wing size along the urbanization gradient, with significant values in bold. were pooled into two groups to measure pairwise F ST and Nei's gene diversity.
To examine population structure graphically, we used principal coordinate analysis (PCoA) on all individuals, calculating independent coordinates using a standardized covariance matrix of genetic distance, and plotted the first two coordinates to identify spatial clusters. We then used the Bayesian clustering program Structure v. 2. 3. 3  [28]  to assign individuals to populations based on inferred allele frequencies. Using collection sites as prior information, we estimated membership coefficients in one to four clusters (K) while allowing for admixture and correlated allele frequencies across populations. We examined 20 runs at each K to determine the maximum log-likelihood, setting the burnin to 100,000 and the MCMC sampling to 500,000 steps. Finally, we tested for isolation by distance (IBD) using a Mantel test of the correlation between genetic distance among individuals and Euclidean geographic distance in Genalex.
The fixed effect of each variable was compared to a null model without the variable using a likelihood-ratio test to assess significance. All environmental correlation tests were corrected for multiple testing using a Dunn-S ˇidak correction (n = 9 variables 6366 loci) with the adjusted p-value of 1. 56610 25. For loci with significant correlations, we examined allele frequency clines along the Marseille transect using the hzar package in R. We plotted and estimated parameters for one-dimensional (1-D) clines, comparing this 1-D model to a null model of frequency changes independent of location using a likelihood ratio test.
While the intent of the GEE and LMM methods was to correct for underlying genetic correlations among sampled individuals, the subtle change in genetic structure might not be captured by these models. One solution to this problem is to evaluate the environmental correlation tests based on the output of multiple statistical models. In our dataset, we find little concordance among the different methods. We conclude that selection does not account for the observed genetic differences, but we must add the caveat that our sampling reflects only a small fraction of the P. rapae genome (expected coverage ,0. 1% for a typical butterfly genome of 400 Mb). Given the observed shifts in morphological traits, it is possible that evidence of adaptation might be found with more comprehensive coverage. The recent developments in next generation sequencing in non-model organisms  [38]  should increase genome coverage and improve future efforts to detect genetic adaptation in P.
Acknowledgments We thank  Marion Luchaire  for sampling butterflies,  Marie-He  ´lene Lizee for help in landscape analysis and the choice of sampling sites,  Delphine Rioux  and  Ludovic Gielly  for help with laboratory work, and  Olivier Franc  ¸ois for advice using LMM. We also thank the editor,  Dr. Nicolas Salamin , and the anonymous reviewers for their comments.
SDS was supported by the  National Science Foundation  ( OISE-0965038 ). IW was supported by the  Department of Environment Technology and Society, Aix-Marseille University. SM was supported by the  Institut Universitaire de France. The project was funded by the  Fondation Francaise pour la Biodiversite  (call innovant). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Breast-milk Unfortunately, no studies have dealt on breast-milk consumption and mercury concentration in France. So, foreign data have to be necessarily studied. Breast-milk consumption data came from the study of  Butte et al. (2002). It concerned 1397 mother and infant pairs in Australia, USA, Canada and Europe (Finland, UK, Sweden…). The consumption of exclusively breast-milk consumers only was described. 24-hour or 48-hour test-weighing was mostly used to evaluate breast-milk intake. This variable followed a normal distribution (769 ± 54 mL/d)  (Butte et al. , 2002). The EFSA (2012a) has reviewed some European surveys about breast-milk contamination. We chose a recent study conducted from 2007 to 2009 on the Mediterranean area (Italy, Slovenia, Croatia and Greece)  (Miklavcic et al. , 2013).
Infant formula, standard and babies' food The survey of  Fantino and Gourmet (2008)  furnished a nutritional data of food items. It was conducted in 2005 on children aged 0 to 36 months. Overall, 706 French infants and toddlers, not breastfed, were included in the study. The sample representativeness of this French sub-population was based on region size, children's age, mothers' professional activity and family's socio-economic group. Parents recorded food quantities taken by children with an individual three-day report. Unfortunately, raw data for food consumption were not available. In fact, this study was commissioned by a private organisation (i. e. the French Syndicate of Children's Food -Syndicat Français des Aliments de l'Enfance). So, food intake was calculated by the following equation: FI ¼ %EI f Â DEI ð Þ =E f where FI: food intake (g/d); %EI f : percentage of energy intake attributable to food item f; DEI: total daily energy intake (kJ/d)  (Fantino and Gourmet, 2008 ) and E f : energy of food item i (kJ/g).
• UB (upper bound) assumption: LD is kept if concentration is inferior to LD and LQ replaces concentration if this one is included in LD and LQ. Concerning standard food, contamination data from EAT 2 (TDS -Total Diet Studyin France) from ANSES was used  (ANSES, 2011; Arnich et al. , 2012). Analyses were conducted in 2006 and total mercury was analysed in 1319 samples of food usually consumed by the French population. The French mercury levels were used for infants' foodstuffs with a low number of samples (ready-to-eat fruit-based, fruit juices and yogurt, cheese and milk-based dessert) in the EFSA report  (EFSA, 2012a). Total mercury mean for LB and UB assumptions for each food group were used for exposure calculation (Table  1 ).
Drinking water The level of mercury in drinking water was obtained by the EFSA's review in European foodstuffs (EFSA, 2012a). Analyses were conducted for 1637 samples. The mean of total mercury concentration was 0 and 0. 2 ng/g for LB and UB assumptions, respectively (Table  1 ). Drinking water includes bottled and tap water used not only for drinking, but also for cooking. Data in  Fantino and Gourmet (2008)  were not available to estimate French drinking water consumption. Currently, in Europe, only two studies deal with infant and toddler food consumption in Bulgaria and Italy. So, we chose the Italian National Consumption Food Survey  (Leclercq et al. , 2009). The National Research Institute for Food and Nutrition conducted it between 2005 and 2006. Finally, 52 children aged from 0 to 2 years old were included in the survey.
Soil and dust Stanek  et al. (2001)  proposed a distribution for soil and dust ingestion for the Monte Carlo simulation. This work was based on a 7-daymass-balance method using seven trace elements (Al, Ce, La, Nd, Si, Y and Zr). 64 American small children aged 1 to 4 years participated in this study. To assess soil and dust exposure separately, it is generally assumed that soil represents 45%, and dust 55% (US EPA, 2011). A loglogistic was the best fit-distribution (P50: 24 mg/d; P95: 91 mg/d) for this parameter. Due to the lack of French data on mercury contamination of soil and dust in France, it was necessary to choose foreign studies about these parameters. As for soil,  Ottesen et al. (2013)  measured the amount of mercury in the first 10 cm of soil, available for children. 2024 samples were analysed in Europe, including France  (Ottesen et al.
Mercury speciation To evaluate health risk for each different mercury form, we applied a speciation factor used in the exposure equation. For breast-milk,  Miklavcic et al. (2013)  studied concentrations of THg and MeHg on 224 samples from Italian mothers. Parameters (P5: 16%, P50: 60% and P95: 100%) were used to adjust the distribution. In fish and seafood, the ratio of MeHg on THg was 93% in fish and 75% in seafood  (Clémens et al. , 2011). Due to the lack of data about MeHg in other food, we followed the recommendations of EFSA (2012a) (i. e. 100% IHg and 0% MeHg). Concerning air, elemental mercury represented 90 to 99% of THg and IHg less than 5%  (European Commission, 2003). As for soil and household dust, the mercury species breakdown was as follows: 0. 02% MeHg, 6-9% Hg0 and 85-88% IHg  (Morgan et al.
Exposure and risk assessment principle The Monte Carlo random simulations were performed using the @Risk 5. 7 software (Palisade Corp. ). This method is the most convenient and common in probabilistic exposure assessment in which parameters are described by a distribution. This technique is based on the execution by repeatedly selecting random values from each parameter distribution to derive a distribution of outcomes. The delivered exposure distributions were assessed according to the following general equation: WED m ¼ X k i¼0 P n j¼1 C ij Â L j Â SF j BW i Â DE i DT 0 @ 1 A where WED m : weekly exposure dose to each mercury species for each media m (food, soil and dust, air) (µg/kg bw/week), i: age classes of sub-populations of infants and toddlers (i = 0 to k, k is the number of different age class i), j: subgroups of media m (e.
g. food categories; indoor air; outdoor air; soil and dust) (j = 1 to n, n is the number of different media subgroup j), C ij is the consumption of media subgroup j for the age class i (g/week or m 3 /week), L j : level contamination of mercury species in media subcategory j (μg/g or μg/m 3 ), SF j : speciation factor for each mercury species in media subgroup j (unitless), BW i : body weight for age class i (kg bw), DE i : exposure duration of age class i (months or hours) and DT: duration of total exposure (36 months or 24 h for inhalation exposure). For each parameter, except for mercury concentration in food, values were adjusted to theoretical distributions with the Chi-squared goodness of fit test using the @Risk software. The probabilistic method was used to integrate inter-individual variability and parameter uncertainty (US EPA, 2001a).
• Scenario IF: infants, not breastfed, consumed "starting" infant formula from 0 to 5 months then "follow-on" infant formula from 6 to 36 months. Then, the non-cancer risk was evaluated by the Hazard Quotient (HQ), which was calculated by the ratio of the 95th percentile of WED on toxicological reference value (TRV). Moreover, the percentage of individuals exceeding the TRV was obtained by the @Risk software. For each form of mercury, TRVs were selected on a database of international organisations. These values were based on the principal toxicological observed effect (Table  2 ). To limit uncertainty about the TRV choice, we measured the health risk for each TRV. Elemental mercury induces mild tremor, memory disturbance and autonomic dysfunction (signs of central nervous toxicity) by inhalation  (RIVM, 2001; US EPA, 1995; WHO, 2003).
26 and 0. 28 µg/kg bw/week for LB and UB scenarios. P95 exposure was 1. 57 and 1. 62 µg/kg bw/week for LB and UB assumptions  (EFSA, 2012a). In this case, our average exposure was very close to these values (0. 26-0. 29 µg/kg bw/week above scenarios LB and UB). P95, in our study (0. 42-0. 45 µg/kg bw/week), was lower than the one found by the EFSA irrespective of the assumption (LB or UB) (Table  7 ). This difference was explained by the choice of contamination data and speciation factors used in each study. We compared also weekly exposure doses for methylmercury with bio-monitoring French data established by InVS in 255 children aged 3 to 5 years  (Fréry et al. , 2011). To achieve this comparison, we used the equation furnished by the US EPA (US EPA, 1997) to estimate mercury concentration in blood from calculated weekly exposure.