dataesr/scientific-paragraphs-categorization huggingface dataset:
5 brains, we used qPCR and confirmed the differential expression of some genes consistently found to be up-regulated by T3 including Dbp, Dio3, Klf9, Mgp, Mme, Sned1, and Thrb (Figure  5E ). We also confirmed up-regulation of genes with important developmental roles including Igf1, Igf2, H19, and Meg3 (Figure  5E ). Overall, these results show that the fetal brain is sensitive to T3 as early as embryonic age E13. 5. We used DAVID and IPA to analyze the ontology and biological functions of DEGs at each gestational age. To avoid bias due to differences in the number of genes entered into these algorithms, we used 588 DEGs at E13. 5 (adjusted P < 0. 05) and 599 DEGs at E18. 5 (non-adjusted P < 0. 01). For each dataset statistically enriched terms with a FDR < 0. 0001 are listed in the Supplementary Data.
Selected enriched terms identified by DAVID are shown for E13. 5 and E18. 5 DEGs in Tables  1, 2 , respectively. There were enriched biological themes common for both sets of DEGs showing comparable statistical significance, including "neurogenesis, " "methylation, " extracellular matrix, " "alternative splicing, " "activator, " "repressor, " "differentiation. " Enrichment terms including "glycoprotein" and "EGF-like" were more significant or specific for DEGs at E18. 5. Other enrichment terms were much more significant or specific for DEGs at E13. 5, including "phosphoprotein, " "developmental protein, " "transcriptional regulation, " "DNA binding, " "nucleus, " chromosome, " "homeobox, " and "cell cycle" (Tables  1, 2 ). Ingenuity pathway analysis analysis of the two DEGs datasets indicated enrichment in a number of canonical pathways, some of which were common (Supplementary Data).
, 2006; Kukkonen, 2003; Nathan et al. , 2017; Rahimi, 2017). This study takes advantage of the enhanced source apportionment obtained in the companion paper  (Borlaza et al. , 2021) , revealing the fine-scale spatiotemporal characteristics of PM sources within a medium-size city area (Grenoble basin), specifically in three different urban environments (background, hyper-centre, and peri-urban typologies). Here, the main drivers of OP are first attributed to PM sources (resolved by PMF) using a classical MLR analysis. Second, the possible advantages of MLP analysis are also evaluated to compare MLP prediction of OP activity with MLR prediction. In summary, by taking the opportunity of this unique database on PM chemistry and OP, we aim to investigate mainly two innovative questions. 1. Is there variability in the OP activity within a mediumsized urban area, and can this be related to the variability of the contributions of the emission sources? 2.
Synthesis of the methodology used for PM 10 source apportionment The source apportionment performed on this dataset has been described in detail in the companion paper  (Borlaza et al. , 2021). In brief, the PMF methodology used the EPA PMF5. 0 software (US EPA,  Norris et al. , 2014)  and closely follows the parameterization used in previous works by our group  (Favez et al. , 2017; Waked et al. , 2014; Weber et al. , 2019 Weber et al. , , 2021) )  with a few relevant modifications. The input variables used were mass concentration and uncertainty levels of PM 10 and its chemical composition (a total of 35 variables) including OC, EC, ions, elements, and some organic markers (MSA, levoglucosan, mannosan, poly-ols, pinic acid, 3-MBTCA, phthalic acid, and cellulose). The associated uncertainties were calculated based on a method proposed by  Gianini et al.
Implementation of the MLP As an initial step, a rescaling process is applied to both the input and output layers to eliminate potential bias due to the range of variance within the dataset  (Gardner and Dorling, 1998). Each variable is standardized by subtracting the mean observed value and then divided by the standard deviation. The daily contributions of the PM sources obtained from the PMF were fed in the input layer to the hidden layer. The MLP analysis was performed for each site using the OP v from each assay (OP DTT v , OP AA v , and OP DCFH v ) as multiple variables in the output layer (see Fig. 2 ), making a set of nine independent studies. At each node (or neuron), the information given by the input neurons is condensed into a unique value and propagated to the next layer. For instance, the MLP described in Fig.
, 1994; Hochreiter, 1998; Hochreiter and Schmidhuber, 1997)  tested to obtain the optimal weights in both the input and output layers  (Slini et al. , 2006; Vakili et al. , 2015). The various MLP architectures tested are summarized in Sect. S3 in the Supplement. The dataset was partitioned into (1) the training set accounting for 80 % and (2) the testing set accounting for 20 % of the dataset. For each of the nine studies, the training set contains data points that were used to train the MLP, while the testing set is an independent set of data points used to monitor errors during the training step. During the training step, the MLP is continually developed and refined until the weighting values between the nodes accurately predict the outcome (i. e. minimal possible errors). To prevent the model from over-fitting, a set of stopping rules is applied to terminate the training of the MLP when any of these scenarios occur, such as (1) there being no decrease in prediction error for more than one step, (2) the maximum training time being reached (15 min), (3) the minimum relative change in the training error being reached (0.
0001), and (4) the minimum relative change in the training error ratio being reached (0. 001). A maximum of 1000 data passes (epochs) are stored in memory until this step is completed. Using the results obtained in the training step, the results are validated in the testing step to check the performance of the network by assessing its forecasting capability on data points outside the training set. The MLP neural network analysis was performed using IBM SPSS Statistics for Windows, version 20 (IBM Corp. , Armonk, NY, USA).
Demonstration of the non-linear behaviour of sources using the MLP models Since MLP analysis should account for the interactions between PM 10 sources, the non-linear atmospheric dynamics causing possible synergistic or antagonistic effects on the OP activity can be captured. To visualize such possible nonlinear behaviour, the MLP models obtained were applied on a set of dummy datasets. Each dummy dataset consists of the same mass contributions (from PMF analysis) of each source (in µg m -3 ) as in the original dataset but setting one source (n) to zero. This modelled OP using a dummy dataset (MLP n ) is subtracted to the modelled OP by the original MLP model (MLP) (containing all source contributions). This difference represents a source-specific OP contribution, and their summation (MLP sum ) is described in Eq.
(3): MLP sum = MLP n. (3) For example, if the biomass burning source contribution was set to zero in the dummy dataset (MLP n=biomass burning ), then (MLP -MLP n=biomass burning ) represents the MLP-modelled OP contribution of the biomass burning source. Assuming there are completely no synergistic or antagonistic effects between PM 10 sources, then the original MLP-modelled OP contributions should be equal to the sum of all source-specific OP contributions (MLP = MLP sum ). In cases where MLP > MLP sum , then synergistic effects are highlighted between some PM 10 sources, resulting in an increased MLPmodelled OP activity. Conversely, MLP < MLP sum highlights antagonistic effects between some PM 10 sources, resulting in a decreased MLP-modelled OP activity.
It is important to note that, although there are other more complex architectures, we limited our tests to a rudimentary MLP architecture that is deemed sufficient and appropriate based on the type of input and output dataset of this study. Clearly, there is room for further exploration in the direction of using MLP for predicting OP from PM sources. To our knowledge, this is the first attempt to use MLP in apportioning OP from PM sources and may serve as a baseline for future applications of MLP in PM toxicity.
The non-linearity of OP contributions of PM 10 sources based on MLP analysis With some interactions between PM 10 sources resulting in synergistic or antagonistic effects on the OP activity, it is deemed essential to look closer into this potential non-linear aspect to understand better the oxidizing capacity of PM 10 sources. To demonstrate this non-linearity, the MLP models were applied to dummy datasets leading to source-specific OP v. The total source-specific OP v (MLP sum ; see Sect. 2. 4. 3) was compared to the original MLP-modelled OP v as presented in Fig. 8  for OP DTT v in the UH site (see Sect. S7 for similar figures for the UB and PU sites). The data points below the 1 : 1 line show an overall synergistic effect between PM 10 sources on OP v , while data points above the 1 : 1 line show an overall antagonistic effect between PM 10 sources on OP v.
Acknowledgements. The authors would like to kindly thank the dedicated efforts of many people from  Atmo-AuRA  at the sampling sites and in the lab at  IGE  ( Anthony Vella ,  Claire Vérin ,  Céline Voiron ,  Rhabira El Azzouzi , and  Armelle Crouzet ) for collecting and analysing the samples, respectively. The authors would also like to thank the  Fondation Université Grenoble Alpes (FUGA)  for supporting this research. Financial support. This work is supported by the  French National Research Agency  in the framework of the  Investissements d'avenir programme  ( ANR-15-IDEX-02 ), for the  MobilAir programme and ANR Get OP Stand OP  ( ANR-19-CE34-0002-01 ), and the  Fondation Université Grenoble Alpes (FUGA)  through the  Predict'Air programme. It also received support from the  QAMECS  programme funded by  ADEME  (convention  1662C0029 ) and from  LC-SQA  and the  French Ministry of Environment  for part of the analyses for the Les Frênes site within the  CARA programme.
Chemical analysis of the Air-O-Sol facility at IGE was made possible with the funding of some of the equipment by the  Labex OSUG@2020  ( ANR10 LABX56 ). The PhD of Samuël Weber is funded by  ENS Paris. The internship of T Cañete is taking place within the  Erasmus exchange programme. Review statement. This paper was edited by  James Allan  and reviewed by two anonymous referees.
Animal relocation data and study areas The data used for this study were obtained from the EURODEER database of the EUROMAMMALS initiative (www. eurom ammals. org), a collaborative science project that stores and manages spatial data of European mammals from across Europe. We initially considered data from 344 individuals fitted with GPS collars between 2004 and 2015, for a total of 2,264,497 locations. Intervals between consecutive locations spanned between 15 min and 12 h. The collared individuals were from six different populations in mountainous or sub-mountainous areas in the Alps and central Europe (  Despite being different in many aspects, the selected areas are characterised by a mountainous topography, the presence of natural or semi-natural areas, and a limited proportion of agricultural areas.
Movement data management and classification of movement patterns From the initial dataset, we retained only the individuals with a fix interval of up to 5 h. Between January and March, movements are generally limited within the winter ranges. As such, we selected the GPS trajectories with at least 10 months of data from a start date between 1 and 31 st March to an end date between the 1st and 31st January of the following year. These dates were chosen based on Peters et al. [50]  and Peters et al. [49]  to ensure that we captured migration and dispersal events, and to maximise sample size. Each GPS trajectory was then truncated by discarding locations falling outside of our specified date window. In order to classify movement patterns, we used the Net Squared Displacement method (NSD-  [11] ).
The NSD is calculated as the squared geographical distance of each GPS location from a first, reference location. In this case, we ensured that the first location fell in an animal's winter range. The NSD method fits several non-linear models to the observed NSD values, each representing a movement mode (i. e. resident, migrant, disperser). The best model, which classifies the movement pattern of each individual, is then selected based on the Akaike Information Criteria (AIC)  [11]. The NSD analyses were conducted using the 'migrateR' package Spitz et al. [64]-parameters available in Additional file 1) in R 3. 5. 1 (R Development Core Team 2013). Animal trajectories were also spatially investigated using SeqScan, a spatio-temporal clustering algorithm available in the QGIS plugin 'MigrO' which has previously proved effective for the analysis of movement patterns from GPS trajectories  [21, 22].
The use and comparison of multiple methods is recommended for a more accurate classification of movement patterns, especially in species like roe deer with a wide range of movement behaviours  [12]. If the classification was ambiguous using the NSD method and SeqScan, the trajectory was discarded. Because we were interested in comparing step selection among different movement modes, for migration and dispersal, we only retained the trajectory of the transience phase (i. e. the segment of trajectory during the period of migration and dispersal). To do this, we identified the start and end dates of these events from the NSD plot using the 'locator()' function in the 'graphics' R package and we used these dates to truncate the trajectories. As such, spring migration trajectories start from the time when an animal leaves its winter range to the time it settles in the summer range.
e. the least common multiple would be 60 h, which is much larger than the scale we wanted to focus on). Following this process, we retained a total of 95 trajectories from 79 individuals, which represent the final dataset used in the model (Italy: Val Rendena (n = 10 individuals), Monte Bondone (n = 9); Germany: Bavarian Forest (n = 26), Hegau (n = 2); Switzerland: Bernese Oberland (n = 31); Austria: Leoben (n = 1)). The re-sampling of the trajectories was carried out using the R package 'adehabitatLT'  [14].
Integrated step selection analysis In order to assess the impact of spatial variation in resources, topography, and roads on roe deer movement, we conducted an integrated step selection analysis (iSSA-  [4] ). iSSA jointly estimates resource selection and animal movement parameters (e. g. step length), by relaxing the implicit assumption that these are independent  [4]. In iSSA, each observed animal step (i. e. , movement between two consecutive GPS fixes) is compared to a set of random steps (i. e. , that animal could have taken) using conditional logistic regression. In this study, we matched each observed step with 10 random steps, computed using distances sampled from a gamma distribution fitted to the empirical step length distribution and random turning angles, using the R package 'amt' [62] (see Additional file 1 for the step length and turning angle distributions used to generate random steps).
Models were fitted using the R package 'coxme'  [65, 70]. We also attempted to account for variation among individuals by fitting random slopes using multiple approaches described by Muff et al. [45] , Craiu et al. [20] , and Therneau [65] unfortunately failing due to convergence issues (see Additional file 1 for the R code with the attempts to run these models and the section "Caveats and Potential Biases" in the Discussion). The importance of each explanatory variable in predicting habitat selection was assessed by removing one variable at a time from the full model, which included all predictors, and recording the change in AIC value for each sub-model (here called ΔAIC REMOVED ). The most important variables were identified as those that, when removed, caused the highest increase in AIC (i.
[20] , but this was not applicable as the values of several variables in our model remained constant within all strata of at least one cluster. This is the main limitation of the two-step approach  [20, 28, 45]. The R code used to run all the above-mentioned models is provided in Additional file 1. Including mixed effects in conditional logistic regressions remains challenging and is a developing topic in iSSA  [28]. The use of random slopes has been recommended to fully account for inter-individual heterogeneity  [45]. Indeed, random-intercept-only models like the one presented here cannot account for among-individual variation in the regression slopes. Nonetheless, while we think that individual variation is important, the main aim of this study was to investigate how roads and other environmental factors can affect roe deer movement at the population level.
Acknowledgments This work has been partly supported by  EPSRC (UK)  under grant  EP/N021037/1 , a SEPnet PhD scholarship for D. A. Duffy, the  French ANR  ( Project OPTOSi , No. ANR-12-BS03-002 ) and by the  French "Investment for the Future" program (EquipEx EXTRA , No. ANR-11-EQPX-0016 ).
Nomenclature and Data Processing Clumped isotope compositions are reported as an excess abundance of the CO 2 isotopologue of cardinal mass 47 (dominantly the isotopologues 13 C 18 O 16 O) compared to a stochastic distribution according to the formula:   47 47* 47 Δ / 1 R R where R 47 is the ratio of the abundances of the set of minor isotopologues with mass 47 (mostly ). The stochastic ratio R 47* is calculated using the measured abundance of 13 C and 18 O and measured or calculated abundance of 17O in the sample  (Affek & Eiler, 2006). According to the IUPAC guidelines the formula does not include the factor 1,000  (Coplen, 2011 ; though Δ 47 is commonly reported in units of per mil, which implies multiplication by a factor of 1,000). Also, we omit here the classically included terms involving R 45 * and R 46 *, which are assumed to be zero by definition when computing δ 13 C and δ 18 O, and in practice never exceed ±0.
Instrumentation The reported data were produced with a variety of preparation systems including custom built (13 laboratories) and commercial systems (11 laboratories; Protium MS IBEX, ThermoFisher Scientific Kiel IV device and Nu Instruments Nucarb). Reaction temperatures were generally 90 °C for "large-sample" custom preparation systems and 70 °C for the Kiel and the NuCarb. Four mass spectrometer types were used: Thermo Fisher Scientific MAT253 and 253Plus, Nu Instruments Perspective, and Elementar Isoprime 100. All participants contributed results they considered to be of "publication-grade" quality, based on their existing quality-control procedures.
Clumped Isotope Compositions of the ETH Anchor Materials The clumped isotope compositions of the four ETH reference materials relative to the CO 2 reference frame CDES were first reassessed based on new data provided by 10 laboratories that also provided HG and EG data measured during the same sessions as the ETH reference materials. The data were processed with the same Python script used for the carbonate data in order to avoid any differences in data processing (see Section 2. 4). Although, strictly speaking, 13 C-18 O clumping in carbonate represents a mass-63 anomaly, the clumped isotope composition of carbonate minerals is reported as Δ 47 , that is, as the mass-47 excess in the CO 2 produced by acid digestion of these minerals, including the respective temperature-dependent isotopic fractionation.
Data Processing, Correction, and Error Assessment It should be stressed that the InterCarb experiment, by design, is not intended to grade the analytical performance of individual laboratories. Each participating laboratory (or mass spectrometer, in the case of laboratories with several instruments) was thus randomly assigned an anonymous identifying number. Within each laboratory, analyses were grouped in different analytical sessions defined by the participants themselves. An analytical session is generally defined by a time in which the behavior of the analytical system (preparation system, source tuning, backgrounds, isotope scrambling in the source) is considered to be similar. The database record of each analysis consists of a laboratory identifier, a session identifier, an analysis identifier, the name of the analyzed sample, the mass spectrometer model, the acid reaction temperature, the mass of the reacted carbonate, and background-corrected δ 45 , δ 46 , and δ 47 values.
10. 1029/2020GC009588 To avoid artifacts arising from different calculation/standardization procedures, rounding errors, and 17 O correction parameters, raw data from all laboratories were processed by a single Python script (http://doi. org/10. 5281/zenodo. 4314448) based on data reduction, standardization and error propagation methods described in detail in the companion paper  (Daëron, 2021). Here we briefly summarize these calculations. Session-averaged, background-corrected δ 45 and δ 46 values for each of the three anchor samples were first used to calculate the bulk isotope composition of the working gas used in each session, based on (a) previously reported δ 13 C VPDB and δ 18 O VPDB values of ETH-1, ETH-2, and ETH-3  (Bernasconi et al. , 2018) , (b) the IUPAC 17 O correction parameters of  Brand et al.
InterCarb Results Results for the unknown carbonate samples were obtained from 25 mass spectrometers in 22 laboratories. The Δ 47 values of the four unknown samples were normalized to the new community-derived values of ETH-1, ETH-2, and ETH-3 of Table  1 , then averaged per individual analytical session and mass spectrometer (Tables  2  and 3 ). Mean Δ 47 values obtained for each sample in each mass spectrometer are shown in Figure  4. The details of each analytical session, including the number of samples and standards measured, the isotopic composition of the working standard, the scaling parameters and the internal reproducibilities (as 1SD) of the individual sessions are listed in Table  2. Some laboratories reported data for only a subset of the unknown samples, and both replication level and analytical reproducibility vary greatly from laboratory to laboratory (Table  2 ).
, 2012). With InterCarb, the nominal values of the ETH standards are robustly linked to the CDES, as they are now based on the average results of 10 laboratories, and are not only based on the values determined at ETH in 2013. Some laboratories may still want to continue measuring HG and EG to keep established laboratory procedures and/or to cover ranges in bulk compositions that require large extrapolations. However, the results of InterCarb, and the discussions in the literature (e. g. ,  Petersen et al. , 2019)  show that with the HG/EG approach there are still poorly understood interlaboratory discrepancies (as suggested by Figure  6 ) which are absent in the carbonate-based normalization (see Figure  5 ). For this reason, it is of paramount importance that several of the InterCarb reference carbonates are incorporated in the laboratory procedures to ensure interlaboratory data compatibility.
The use of matrix-matched reference materials is necessary so that delta values can be unambiguously compared on a like-for-like basis (see Meier-Augenstein & Schimmelmann, 2019 for a recent discussion). For laboratories using large sample common-acid bath methodologies and preferring gas-based data correction, we recommend that in addition to HG/EG a minimum of two of the InterCarb reference materials (or in-house standards with values calibrated to ETH standards) should be measured within the same analytical sessions as the samples and used in the data correction scheme. We recommend choosing two standards with a large difference in Δ 47 , for example, ETH-1 and ETH-3 or ETH-2 and IAEA-C2, depending on the bulk composition of the unknown samples (see Figure  1 ). In the case of samples with very low δ 47 values, we additionally recommend the use of MERCK.