dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2019) , but this was beyond the scope of the current work. For our study, we also used different methodologies, each with distinct assumptions and considerations that need to be taken into account, to address the two different aspects of distribution and cover. For example, the exact regions and data points used differed between methodologies. Because the ensemble model used presence data and environmental layers for the full Arctic range, whereas the Random Forest models were restricted to abundance data that were collected in situ from sites in the study area accessible by divers, the latter did not include data from areas with high ice thickness. In contrast, data for the ensemble models included museum records from CANA that specifically sampled from under the ice. In our study, the importance of different environmental layers varied depending on the modeling approach.
, 2013). We endeavored to minimize this effect by only using occurrence points from Arctic populations (see above), although some models nonetheless had low performance. This discrepancy could be related to the relationship between distribution extent and niche ranges, since better predictions are expected from species with narrow niches  (Nyström Sandman et al. , 2013). Another explanation could be related to the diversity of algorithms and base assumptions for each model included in the ensemble method. The data included in each model (occurrence data points and number of pseudo-absences/background) was the same for all algorithms to standardize inputs. However, the number of absences, pseudo-absences, or background data that are appropriate for inclusion in a given model has a degree of uncertainty since pseudo-absences do not correspond to true absences, and the assumptions behind each model can influence outcomes  (Sillero, 2011).
ACKNOWLEDGMENTS We thank  K. Shoenrock ,  I. Hendriks ,  M. Ronowicz , and  G. Saunders  for sharing species occurrence information. We also thank  Pauline Fortin  for helping to collate museum species data and  Gabrielle Martineau  for helping to extract species identification and cover data from video recordings, local people and organizations for providing sampling support:  D. Kaludjak ,  K. Lindell ,  A. Williams , and  J. Williams  ( community of Iqaluit );  C. Alaku ,  C. Kadjulik ,  K. Ningiurluut ,  K. Okituk ,  C. Okituk ,  L. Yuliusie , and  A. Keatainak  (community of Salluit);  A. Allianaq ,  T. R. Avingaq ,  G. Illupaalik ,  J. Kukkik ,  A. Kutiq , and  T. A. Taqqaugaq  ( communities of Igloolik and Hall Beach );  Mittimatalik, Igloolik ,  Hall Beach , and  Amaruq Hunters  and Trappers; the  Qaqqalik Landholding Corporation ;  Churchill Northern Studies Center ;  Nunavut Research Institute  and the  Nunavut Fisheries Association , and the people that helped in the field: professional divers and people in charge of logistics and who helped taking samples and analyzing in the lab.
Out of the 58 children who participated the second (i. e. , fMRI) session, 14 were excluded from the fMRI analyses because of incomplete data acquisition (n = 7) or excessive motion in the scanner (n = 7) (see criteria below). Therefore, our fMRI sample consisted of 44 children who had at least one run of data analyzable in both the word and digit adaptation tasks. Children had a full-scale IQ comprised between 83 and 135 (mean = 112, standard deviation [SD] = 11), as measured by the NEMI-2 test  37. A previous study  58  explored the relation between brain activity and home numeracy practices in the same sample of participants. Demographic information about children and parents can be found in Table  2. All children and parents were native French speakers. Eighty-nine percent of the parents were mothers.
Justification of fMRI sample size The relation between children's literacy skills and home literacy practices typically ranges between r = 0. 40 to r = 0. 60  30, 60. This range is in line with two previous studies that investigated the relation between home literacy practices and language-related (phonological processing and story listening) brain activity in young children. Specifically, Powers et al. 30  and Romeo et al. 28  (n = 36) found effect sizes of this relation ranging from r = 0. 40 and r = 0. 58 (see Table  5  in Powers et al. ;  30  and openly available dataset in Romeo et al. 28  ). Considering these estimates, power analyses using G* Power version 3. 1  61  indicate that our final fMRI sample size of n = 44 would provide at least 88% power to detect correlations between home literacy practices and reading-related activity in the expected direction (α = 0.
Experimental tasks We adapted from Experiment 2B of Perrachione et al. 31  a task in which a series of words were passively presented in blocks at the center of the screen (see Fig. 1b ). As in Perrachione et al. 31  , words were monosyllabic nouns of three to five letters in length (mode = 4). The only difference between our task and that of Perrachione et al. 31  was that our stimuli were in French instead of English. Words were thematically varied and equated for frequency using the CHACQFAM database in http://www. lexique. org/ shiny/openlexicon/. In another version of the task that served as a control in the present study, words were replaced by Arabic digits ranging from 1 to 8. Word and digit adaptation tasks were each composed of two runs. In each of these runs, participants were presented with adaptation and no-adaptation blocks.
fMRI data acquisition Images were collected using a Siemens Prisma 3 T MRI scanner with a transmit body coil and a 64-channel receiver head-neck coil (Siemens Healthcare, Erlangen, Germany) at the CERMEP Imagerie du vivant in Lyon, France. The BOLD signal was measured with a susceptibility-weighted single-shot EPI sequence. Imaging parameters were as follows: TR = 2000 ms, TE = 24 ms, flip angle = 80°, matrix size = 128 × 120, field of view = 220 × 206 mm, slice thickness = 3 mm (0. 48 mm gap), number of slices = 32. A high-resolution T1-weighted whole-brain anatomical volume was also collected for each participant. Parameters were as follows: TR = 3500 ms, TE = 2. 24 ms, flip angle = 8°, matrix size = 256 × 256, field of view = 224 × 224 mm, slice thickness = 0. 9 mm, number of slices = 192.
fMRI data preprocessing Images were analyzed with SPM12 (Wellcome Department of Cognitive Neurology, London, UK). The first four images of each run were discarded to allow for T1 equilibration effects. Functional images were corrected for slice acquisition delays and spatially realigned to the first image of the first run to correct for head movements. Realigned images were smoothed with a Gaussian filter (4 mm × 4 mm × 7 mm full-width at half maximum). Using ArtRepair (https://cibsr. stanford. edu/tools/human-brain-project/artrepairsoftware. html), functional volumes with a global mean intensity greater than three standard deviations from the average of the run or a volume-tovolume motion greater than 2 mm were identified as outliers and substituted by the interpolation of the two nearest non-repaired volumes  28.
fMRI data analysis Statistical analysis of fMRI data was performed according to the GLM. Brain activity associated with periods of adaptation and no-adaptation was modeled as epochs with onsets time-locked to the beginning of each block and a duration of 9. 6 s. All epochs were convolved with a canonical hemodynamic response function. The time-series data were high-pass filtered (1/128 Hz), and serial correlations were corrected using an autoregressive AR(1) model. For each participant, the word adaptation effect was identified by subtracting activity associated with adaptation blocks from activity associated with no-adaptation blocks. Individual contrasts were then submitted to one-sample t-tests across all participants. First, we whole-brain voxelwise approach to evaluate whether the overall brain network involved in word adaptation was similar to that found in Perrachione et al.
Mediation analyses were then performed to test whether, in regions for which there was a relation between home literacy practices and word adaptation, there was a mediating effect of vocabulary. These analyses were performed using the M3 toolbox in Matlab (https://github. com/ canlab/MediationToolbox 67 ). Briefly, a mediation estimates the degree to which a variable (e. g. , M) can explain the relation between two other variables (e. g. , X and Y). Here, X was defined as the frequency of home literacy practices. Y was defined as the word adaptation effect. M was defined as vocabulary. Path a reflected the relation between home literacy practices and vocabulary. Path b reflected the relation between vocabulary and word adaptation, controlling for home literacy practices. The total relationship between frequency of home literacy practices and word adaptation (including direct and indirect effects) is reflected in path c, while the direct effect of the relationship between frequency of home literacy practices and word adaptation (controlling for vocabulary) is reflected in path c′.
DATA AVAILABILITY The task, electronic version of the questionnaire (in French), behavioral data, and individual MRI data are available from Zenodo (https://doi. org/10. 5281/ zenodo. 5112814). The ROI images and the whole-brain unthresholded p-maps corresponding to Fig. 4a  and Fig. 7  are available from NeuroVault (https://identifiers. org/neurovault. collection:9453).
Both aircraft and tower [CO 2 ] data show that the CASA ensemble net ecosystem exchange (NEE) of CO 2 is biased high (NEE too positive) and identify the maximum light use efficiency E max a key parameter that drives the spread of the CASA ensemble in summer 2016. These findings are verified with flux-measurements. The direct comparison of the CASA flux ensemble with flux-measurements confirms missing sink processes in CASA. Separating the daytime and nighttime flux, we discover that the underestimated net uptake results from missing sink processes that result in overestimation of respiration. NEE biases are smaller in the CT2017 posterior biogenic fluxes, which assimilate observed [CO 2 ]. Flux tower analyses reveal an unrealistic overestimation of nighttime respiration in CT2017 which we attribute to limited flexibility in the inversion strategy.
, 2006; Gurney et al. , 2002; Philip et al. , 2019; Schuh et al. , 2019). Flux priors are commonly obtained from the simulated net ecosystem exchange (NEE) from terrestrial biosphere models (TBMs). TBMs (a "bottom up" approach) simulate surface CO 2 fluxes from site level to global scale as they integrate ecological and meteorological drivers  (Fung et al. , 1987). Flux priors in atmospheric inversions are commonly obtained from the NEE simulated by TBMs. TBMs also simulate the land carbon component in Earth system models used for climate projections. Joint assimilation systems have also been developed to benefit from both ecosystem measurements and atmospheric mole fractions  (Kaminski et al. , 2002). The optimization procedure relies on an adjoint model of the biogeochemical processes such as the Biosphere Energy-Transfer Hydrology model included in the Carbon Cycle Data Assimilation System at large scales  (Knorr, 2000).
Global inversions usually optimize CO 2 fluxes on monthly to annual timescales at the grid scale in the magnitude of a few degrees by a few degrees. Higher spatiotemporal resolutions in the prior are often not altered. As the resolution of inversion system improves and the need for accurate flux data at high resolution increase, evaluating the higher-resolution spatial and temporal structure of TBMs is increasingly important. TBMs with more accurate, high-resolution surface biogenic CO 2 fluxes will improve our diagnosis, attribution, and projection of terrestrial carbon dynamics, and improve "top-down" analysis systems that rely on TBMs for a priori surface flux estimation. Several multiple-model intercomparison projects have been conducted to characterize or synthesize current understanding of land-atmosphere carbon exchange and inform the uncertainty or confidence surrounding projections of future exchange and feedbacks with the climate system, such as the Multi-Scale Synthesis and Terrestrial Model Intercomparison Project  (Huntzinger et al.
This merits deep exploration of just one modeling framework but with a perturbed parameter ensemble. T opt , and temperature response of respiration Q 10 -on the basis of the CASA biome types. Those parameters were chosen as a result of a sensitivity test of simulated biogenic CO 2 fluxes to a series of CASA parameters. Furthermore, the range of E max values was determined at the ecosystem level by comparison with eddy-covariance measurements of net CO 2 flux. Zhou, Williams, Lauvaux, Davis, et al. (2020)  illustrated that the pruned L2 ensemble has good agreement with the flux data and outperforms many other TBMs at diurnal and annual scales, while overestimating model errors as represented by comparison with flux measurements. Atmospheric [CO 2 ] measurements provide another opportunity for evaluating modeled biogenic CO 2 fluxes, as evidenced by the fact that continental [CO 2 ] gradients are mainly attributed to the biosphere (e.
Here we use CASA  (Potter et al. , 1993)  for demonstration and adopt the Eulerian transport model WRF-Chem  (Grell et al. , 2005; Skamarock et al. , 2008)  to serve this objective. In this study, we employ both ground-based and aircraft in situ  [CO 2  ] data for the model evaluations. The NASA-funded Atmospheric Carbon and Transport (ACT)-America project was designed to improve the CO 2 and methane (CH 4 ) flux estimates by reducing transport and flux uncertainties. Two aircraft measured atmospheric CO 2 , CH 4 , and other gas species over Mid-Atlantic, Mid-West, and Southern Gulf regions in fair and frontal weather regimes. Typical flights encompassed 4-6 h of midday conditions, encompassing 400-800 km in the horizontal and altitudes ranging from 300 to 9,000 m above ground level. Two aircraft flew together, collecting two to four vertical levels (level legs), often stacked one above the other, and typically about 8-12 vertical profiles per flight day.
The third objective is to explore to what degree the aircraft [CO 2 ] measurements and tower measurements yield consistent evaluations of the modeled fluxes. Merging these two [CO 2 ] observing systems lends more confidence to our conclusions regarding the modeled fluxes and provides a test of the two observing systems. The three objectives of this study are addressed through comparing the WRF-Chem [CO 2 ] simulated using the CASA L2 flux ensemble members to the airborne and tower-based [CO 2 ] data for the ACT-America summer 2016 campaign period (July 18 to August 28, 2021). The WRF-Chem model setup and methods are described in Section 2. All the data used in this work are described in Section 3. The modeled and observed [CO 2 ], CASA parameter constraints, and causes of the model errors are illustrated in Section 4.
In addition to aircraft [CO 2 ] measurements, we also use a subset of the ground-based [CO 2 ] tower measurements from the NOAA ObsPack GlobalViewPlus package (Cooperative Global Atmospheric Data Integration Project, 2019; see Section 3. 2). We create footprints for the towers by releasing particles at 21 UTC backward in time over two-week period using the nudged transport from July 18 to August 28, 2016 across 23 different tower sites in the US. The selection of 21 UTC limits the tower observations to well-mixed ABL conditions, minimizing model transport uncertainty.
NOAA ObsPack GlobalViewPlus [CO 2 ] Product We also use tower-based in situ [CO 2 ] data from the NOAA ObsPack GlobalViewPlus product (Cooperative Global Atmospheric Data Integration Project, 2019; see locations in Figure  1 ). Twenty-three tower locations FENG ET AL. (Table  3 ) from this data package were selected for investigations. The Ob-sPack product collects greenhouse gas data from providers around the globe and reformats the data into the ObsPack framework in support of carbon cycle modeling studies  (Masarie et al. , 2014). The [CO 2 ] data are first organized into hourly data, and then nearest point interpolation is applied to extract modeled [CO 2 ] at the tower locations.
Conclusions We evaluate the modeled [CO 2 ] biases associated with CASA TMB biogenic CO 2 flux ensemble members and the CT2017 posterior biogenic flux using aircraft and tower in situ [CO 2 ] jointly with eddy-covariance flux data from July 18 to August 28 in summer 2016. Aircraft and tower in situ [CO 2 ] were influenced by GL, EN, DB, and CR. While the mole fraction-based analyses revealed a systematic underestimation of carbon uptake by the balanced-biosphere CASA runs, the flux-based analyses identified a combined effect from an overestimation of respiration and under-estimation of productivity. The joint observational analysis yields strong confidence that these results span large spatial domains and multiple ecosystems due to the availability of long aircraft transects and a wide network of ground-based measurements.
The results from analyzing both mole fraction and flux model-data residuals were consistent. The systematic errors in CASA that span all parameter values suggest that missing processes cannot be properly simulated by adjusting the existing parameters. Analyses indicate that modeled [CO 2 ] biases are related to biome productivity; the models tend to be biased more for high productivity biomes (DB and CR) and biased less for GL and EN. In particular, the summer harvest sink absent from CASA seems to be responsible for large biases in the Midwest. Lastly, CT2017, an inversion product that is constrained by atmospheric [CO 2 ] data and has an imposed net biogenic carbon sink, shows better agreement with [CO 2 ] mole fraction data compared to CASA flux ensemble members. However, the flux-based analyses revealed that the diurnal variations of CT were unrealistically large.
We suggest that the scaling of the net daily fluxes in large-scale inversions must be further decomposed into day and night sub-components to reproduce the diel cycle of photosynthetic and ecosystem respiration processes. Lastly, our setup does not resolve the subgrid biome variability, which is a shortcoming at the scale. The CASA ensemble provides an alternative flux data set at 500 × 500 m. A sensitivity study with both sets of the CASA flux ensemble can be tested in future.
Data Availability Statement The CarbonTracker CT2017 product can be obtained from NOAA GML, Boulder, Colorado, USA from the website at http://carbontracker. noaa. gov. The L2 CASA ensemble suite used in this work can be found at https://doi. org/10. 3334/ORNLDAAC/1675. NCEP Real-Time 12-km SST are archived at ftp://polar. ncep. noaa. gov/pub/history/sst/ophi. The ensemble output will be made available from datacommons@psu at https://doi. org/10. 26208/z864-qk7. The Obspack CO 2 Global View Plus data package can be obtained from https://www. esrl. noaa. gov/gmd/ccgg/obspack/. The citations for individual [CO 2 ] towers are listed in Table  S3. AmeriFlux data are obtained from https://ameriflux. lbl. gov/, with citations given in Table  S2.
After three washing steps with 2%NGS-PBS-CMF, cells were incubated with secondary antibodies for 30 min at RT using goat antirabbit Alexa Fluor 568 (Invitrogen; dilution: 1/1000 in 2%NGS-PBS-CMF) for ECs. Nuclei were stained using Hoechst reagent (ref. B2883; Sigma Aldrich, Darmstadt, Germany) diluted by 1000 in the same secondary antibody solution. Finally, the cells were washed 3 times with PBS-CMF. Stained preparations were observed with the automated microscope ImageXpress Micro Confocal High-Content Imaging System (Molecular Devices, San Diego, CA, USA), using a blue DAPI filter for nuclei and green FITC filter for Z0-1, Claudin-5, Ve-Caherin and PECAM (CD31 + ). Images were processed using in MetaXpress software, version 6. 5. 2 (2018, Molecular Devices, LLC, San Jose, CA, USA).
qPCR amplification was carried out in a CFX96 thermal cycler (Bio-Rad). Delta-Ct data was obtained from Bio-Rad CFX Manager software (Bio-Rad). Gene expression levels of the gene targets (Table  1 ) were calculated in base of the relative housekeeping gene RLP0 (ribosomal protein lateral stalk subunit P0) expression. Table  1. Homo sapiens DNA primers used to amplify the mRNA expression of: efflux transporters such as P-glycoprotein (P-gp), breast cancer resistance protein (BCRP) and multidrug resistance proteins (MRPs; subfamily of the ATP-binding cassette (ABC) transporters); influx transporters such as Monocarboxylate transporter 1 (MCT1) and Glucose transporter 1 (GLUT1); large molecule receptors like the receptor for advanced glycation end products (RAGE), low-density lipoprotein receptor (LDLR) and related proteins (LRP1 and LRP8), Scavenger receptor class B type 1 (SCARB1) and transferrin receptor (T-RF), as well as junctional proteins Claudin-5 (CLDN5) and Occludin (OCLN): in the BLEC monolayer from the miniaturized and original in vitro BBB models.
Target Gene AN * Primer F/R Primer Sequence P-GP ABCB1 NM_001348945 F CAGACAGCAGCTGACAGTCCAAGAACAGGACT R GCCTGGCAGCTGGAAGACAAATACACAAAATT BCRP ABCG2 NM_001348989 F TGGCTGTCATGGCTTGAGTA R GCCACGTGATTCTTCCACAA MRP1 ABCC1 NM_004996 F GTCCTTAAACAAGGAGGACACG R TCCTTGGAGGAGTACACAACCT MRP4 ABCC4 NM_005845 F ACCTTGCAAGAGCAGTGTATCA R TGTCTGCTAACTTCCGCATCTA MRP5 ABCC5 NR_135125 F CCCAGTCCTGGGTATAGAAGTG R CGAGTTCTCCTGAACTTGGAAT MCT1 SLC16A1 NM_001166496 F AAGGTATATTCCATGCCACCAC R GCTGATAGGACCTCCACCATAC GLUT1 SLC2A1 NM_006516 F CTTCTCCAACTGGACCTCAAAT R AGGAGCACAGTGAAGATGATGA RAGE AGER NM_001206929 F GAGTCCGTGTCTACCAGATTCC R ATCCAAGTGCCAGCTAAGAGTC LDLR LDLR NM_000527 F TTCATGGCTTCATGTACTGGAC R TTTTCAGTCACCAGCGAGTAGA LRP1 LRP1 NM_002332 F AATGAGTGTCTCAGCCGCAA R AACGGTTCCTCGTCAGTCAC LRP8 LRP8 NM_033300 F TGTTTTGCATAATCCAGCAATC R GGTCAACTGCATTTACCCTCTC SCARB1 SCARB1 NR_160416 F ATCCCCTTCTATCTCTCCGTCT R GTCGTTGTTGTTGAAGGTGATG T-FR T-fR NM_001313965 F ACTTCTTCCGTGCTACTTCCA R CCACTCTCATGACACGATCATT CLDN5 CLDN5 NM_003277 F GAGGCGTGCTCTACCTGTTTT R CACAGACGGGTCGTAAAACTC OCLN OCLN NM_002538 F GAGGCTATGGAACTTCCCTTTT R TAGCTACCAAAGCCACTTCCTC RPLP0 RPLP0 NM_053275 F CAGCTGATCAAGACTGGAGACA R CACTTCAGGGTTGTAGATGCTG * AN (Accession number).
Results were compared to unbound fraction ratios of CSF to plasma (Kp,uu,CSF) in humans taken from the dataset already published  [25]. Equation (2): In vitro Kp,uu,brain = (C0 -CD)/CD) × U (2) Equation (3): % Recovery (U) = (CD + CR) × 100)/C0 ( 3 ) where C0 is the concentration of the molecule in the donor compartment at time zero, CD and CR are the free drug concentration at 3 h in the donor compartment (luminal side) and in the receiver compartment (abluminal side), respectively. U is the percentage of the recovered amount of the unbound molecule total in the donor and receiver compartments at 3 h.
Statistical Analysis Results were expressed as the mean ± SD or SEM. The number of replicates (n), performed through independent experiments (N), were detailed in each experiment. For the analysis involving different groups, an unpaired t-test ormultiple comparison tests (ordinary one-way ANOVA) were used or multiple t-test when there were different conditions between groups. For correlation, a nonlinear regression analysis was used. The thresholds for statistical significance were set to * p-value < 0. 05 and ** p-value < 0. 01. All data was analyzed by the GraphPad Prism 8. 1 software (GraphPad Software, San José, CA, USA).