dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical Analysis To determine the effect of age on cortical thickness in each of the 34 regions, we used generalized additive mixed models (GAMMs; mgcv package in  R Wood 2006 ) with a cubic spline smoothing terms for age. Sex and scanner site were included as fixed effects. GAMM fitting is well suited for estimating non-linear trajectories-such as thickness estimates across the lifespan-and is robust to changes in modeled parameters such as age range  (Storsve et al. 2014; Fjell et al. 2015; Vandekar et al. 2015). To adjust for repeated measures and participant kinship, random effects for participant ID and family ID were included. We then tested the association between age and the NR3C1-and NR3C2-Thickness Similarity with outliers defined as greater than 3 standard deviations (SDs) from the mean.
To fit the lifespan trajectories, we again used a GAMM with sex and scanner site included as fixed effects and participant ID and family ID as random effects. The same procedure was used to test the association between age and NR3C2-Thickness Similarity. We compared NR3C1-Thickness Similarity with NR3C2-Thickness Similarity by 1) overall mean and 2) rate of change with age using first derivatives of the GAMM functions (gratia package in R), in early (<28 years; peak in NR3C2-Thickness Similarity) and late life (>68 years; initial-decline in NR3C1-Thickness Similarity). To determine significance of these comparisons, we used bootstrapped test statistics (10 000 iterations) where a Gene-Thickness Similarity label was randomly assigned to each participant prior to calculating the test statistics.
Due to limited overlap in sampled regions, NR3C1 and NR3C2 expression from all 5 gene expression databases were scaled and pooled by cerebral lobe (frontal, parietal, temporal, and occipital). Within each of the lobes, we used linear mixed effect models with fixed effects for age and sex as well as random effects for donor ID. All statistical analyses were performed using R v3. 5. 1 (https:// www. r-project. org/), and reported significance values (P < 0. 05) are FDR corrected.
Lifespan Variation in NR3C1 and NR3C2 Expression A total of 502 donor brains (66. 93% male) were included in this analysis. Donor age ranged from 4 to 102 years (mean = 47. 25, SD = 20. 19). Lobar NR3C1 expression did not change with age (Fig. 3a ). For NR3C2, only the frontal lobe displayed age-related increases in expression, which appear to be confined to the early years (Fig. 3b ). Additional analysis of age-related changes in gene expression was conducted for each sampled region of the 5 databases (Supplementary Tables  S8  and S9 ). None of the cortical regions sampled displayed age-related changes in NR3C1 expression. Expression of NR3C2 increased with age in 5 sampled regions and decreased with age in 2 regions.
Oxidative stress, denoted by the cellular accumulation of free-radical by-products, is closely linked to aging  (Lee et al. 2000; Terman and Brunk 2006). Moreover, neuro-inflammation is strongly associated with aging  (Lee et al. 2000). These are 2 late life biological processes that may alter the strength of association between the profile of cortical thickness and the profile of NR3C1 (or NR3C2). Therefore, senescence is linked to disruption of many biological processes, beyond the dysregulation of the HPA axis, all of which may impact the patterned changes in cortical thickness. Here we used gene expression data, sampled from different regions of the human cerebral cortex and deposited in 5 separate databases with over 500 post-mortem donor brains, to show that the expression of NR3C1 and, with a few exceptions, also of NR3C2 do not change with age.
This rules out the potential confounding effect of age-related changes in gene expression driving observed variations in NR3C1-or NR3C2-Thickness Similarity with age. Previous studies have shown an increase in NR3C1 mRNA expression in cerebral cortex with age  (Perlman et al. 2007; Sinclair et al. 2011). Our results may vary due to differences in sample size, age range, and brain regions sampled. Additionally, unlike previous studies, age was modeled as continuous variable in our analysis. Also, increase in NR3C1 and/or NR3C2 expression with age would likely result in a lifespan strengthening of both the NR3C1-and NR3C2-Thickness Similarity, which was not observed. We acknowledge that there are various methods used for mapping gene expression data to a brain atlas space. Arnatkevic˘i ūt ė et al.
(  2019 ) have reviewed a number of approaches (including the approach employed here) used for relating gene expression data from the Allen Human Brain Atlas to various neuroimaging datasets. As pointed out by Arnatkevic˘i ūt ė et al. (  2019 ), while valid, our original approach did not include probes unassigned to an annotated gene and did not filter probe expression based on their intensity. On the other hand, we applied strict consistency criteria (profile similarity across donors and atlases), which have minimized possible technical sources of noise in the expression data.
Conclusions The aim of this study was to determine the association between corticosteroids and cortical thickness across the lifespan. This was accomplished by using a large sample of participants in conjunction with human post-mortem gene expression data from several publicly available databases. The 2 corticosteroid receptors may be involved in shaping the profile of cortical thickness early in the lifespan. In midlife, there is a relative maintenance of a strong association between cortical thickness and corticosteroid receptor expression across the cerebral cortex. In late life, this association deteriorates. The results suggest that corticosteroids may influence brain structure to a varying degree throughout life and, as such, may contribute to age-and stress-related variations in cognitive functioning and mental health.
Bottom: NR3C2-Thickness Similarity by age modeled in each study separately. Study-specific statistics can be found in Supplementary TableS7. HRC, Brazilian High Risk Cohort for Psychiatric Disorders; SYS, Saguenay Youth Study; BIL_GIN, Brain Imaging of Lateralization by the Groupe d'Imagerie Fonctionnelle; LCBC, Lifespan Changes in Brain and Cognition; HCP, Human Connectome Project; SHIP&TREND, Study of Health in Pomerania and SHIP-TREND; DLBS, Dallas Lifespan Brain Study; RS, Rotterdam Study; SALD, Southwest University Adult Lifespan Dataset; MAS, Memory and Aging Study; FHS, Framingham Heart Study; ASPS_Fam, Austrian Stroke Prevention Study Family Cohort; OATS, Older Australian Twin Study; OASIS, Open Access Series of Imaging Studies; 3C_Dijon, The 3 City Study -Dijon location; LBC1936, Lothian Birth Cohort 1936.
9 years a monotonic decrease in NR3C2-Thickness Similarity occurs. Bottom: NR3C2-Thickness Similarity by age modeled in each study separately. Study-specific statistics can be found in Supplementary TableS7. HRC, Brazilian High Risk Cohort for Psychiatric Disorders; SYS, Saguenay Youth Study; BIL_GIN, Brain Imaging of Lateralization by the Groupe d'Imagerie Fonctionnelle; LCBC, Lifespan Changes in Brain and Cognition; HCP, Human Connectome Project; SHIP&TREND, Study of Health in Pomerania and SHIP-TREND; DLBS, Dallas Lifespan Brain Study; RS, Rotterdam Study; SALD, Southwest University Adult Lifespan Dataset; MAS, Memory and Aging Study; FHS, Framingham Heart Study; ASPS_Fam, Austrian Stroke Prevention Study Family Cohort; OATS, Older Australian Twin Study; OASIS, Open Access Series of Imaging Studies; 3C_Dijon, The 3 City Study -Dijon location; LBC1936, Lothian Birth Cohort 1936.
2 (65-83) 42. 0 LBC1936 d 636 72. 7 (71-74) 52. 8 Gene Expression Databases Source Donors (n) Median Age (range) Sex (%Male) Allen Human Brain Atlas 6 42. 5 (24-57) 83. 3 BrainCloud 206 36. 3 (4-78) 68. 5 BrainEAC 134 57. 9 (16-102) 73. 9 BrainSpan 14 20. 2 (4-40) 57. 1 GTEx 142 57. 9 (25-80) 69. 7 HRC, Brazilian High Risk Cohort for Psychiatric Disorders; SYS, Saguenay Youth Study; BIL&GIN, Brain Imaging of Lateralization by the Groupe d'Imagerie Fonctionnelle; LCBC, Lifespan Changes in Brain and Cognition; HCP, Human Connectome Project; SHIP&TREND, Study of Health in Pomerania and SHIP-TREND; DLBS, Dallas Lifespan Brain Study; RS, Rotterdam Study; SALD, Southwest University Adult Lifespan Dataset; MAS, Memory and Aging Study; FHS, Framingham Heart Study; ASPS_Fam, Austrian Stroke Prevention Study Family Cohort; OATS, Older Australian Twin Study; OASIS, Open Access Series of Imaging Studies; 3C_Dijon, The 3 City Study-Dijon location; LBC1936, Lothian Birth Cohort.
Produced cDNAs were used to analyze transcript levels using real-time PCR in a Biorad CFX96 Real-time System and using Biorad iTaq Universal SYBR Green Supermix in 20 µl reactions for 40 cycles (95°C for 10 s, 60°C for 20 s, 72°C for 30 s followed by fluorescence measurement at 75°C to reduce the influence of primer dimer on quantification). Primers for Nft3 and Runx3 were designed using Primer3 Blast (NCBI). Primers for Nft3, Ntrk3 (TRKC), Isl1 and Runx3 were designed using Primer3 Blast (NCBI). Gapdh was used as a reference gene. A full list of primers used is given below. Negative controls did not amplify. Data were analyzed using Bio-Rad CFX manager. Gapdh, AACTCCCACT-CTTCCACCTTC and GATAGGGCCTCTCTTGCTCAG; Nft3, ATAAAA-TTCGTGTGCTTGCCTTCC and GAGAGCCCAATCACAAAACAAGG; Runx3, ACCAAGTGGCGAGATTTAACGA and ACGGTGACTTTAAT-GGCTCGG; Isl1, AAAAGAAGCATTATGATGAAGCAA and CATGTC-TCTCCGGACTAGCAG; Ntrk3, TGATCCTCGTGGATGGGCAG and CTTCACCAGCAGGTTGGCTCC.
Funding This work was supported by  StratNeuro  ( 2013-0237 ), the  Vetenskapsrådet  ( 2012-04708 ), a  Karolinska Institute doctoral  grant the  Knut och Alice Wallenbergs Stiftelse (Wallenberg Academy Fellow) , the  Hjärnfonden  ( FO2014-0048 ), the  Karolinska Institutet  (Faculty Funded Career Position) and the  Ragnar Söderbergs stiftelse  ( M48/12 ) (Ragnar Söderberg Fellow in Medicine), and by a  Ming Wai Lau research grant. F. L. is a Ragnar Söderberg fellow in Medicine, a Wallenberg Academy Fellow in Medicine and a Ming Wai Lau Center investigator. Deposited in PMC for immediate release.
We will then address the problem of explaining the relationship between neocortical arealisation and folding. To better understand neocortical folding and mechanical morphogenesis, we will briefly introduce the theory of mechanical elasticity and growth. We have developed a website with interactive mechanical simulations illustrating these principles, which can be accessed at http://neuroanatomy. github. io/growth. Finally, we review recent results highlighting the effect of mechanics on the developing brain tissue and discuss a mechanical morphogenesis hypothesis for the development and evolution of neocortical organisation, its predictions, and experimental approaches that should allow us to test them.
Grey level: logarithm of the deformation (Jacobian of the deformation tensor). Black: log(J)=-0. 007, white: log(J)=-0. 053, bulk modulus ‫,05=ܭ‬ shear modulus ߤ=1. An interactive simulation illustrating the formation of a residual stress gradient due to growth can be accessed at http://neuroanatomy. github. io/growth.
The inner core is overall dilated, with a deformation that decreases towards the centre of the ring. Grey level: logarithm of the deformation (Jacobian of the deformation tensor). Black: log(J)=-0. 406, white: log(J)=-0. 398, bulk modulus ‫,001=ܭ‬ shear modulus ߤ=100. An interactive simulation illustrating buckling induced by growth can be accessed at http://neuroanatomy. github. io/growth.
RNF4 accumulated in the SCA7 patients, both in the cytoplasm and in the nucleus of the Purkinje cells (k, l ). We analyzed n=20 Purkinje cells for each patient: cells in which nuclei were not visible were not taken into account. GCL, granular cell layer; ML, molecular layer; PCL, Purkinje cell layer. Scale bars: 20 μm. (B) Model for mutant ATXN7 degradation via the proteasome. We propose this model, which summarizes data from this study and our previously published results on polyQ-ATXN7 degradation by PML clastosomes and their stimulation by IFN-beta treatment  (Janer et al. , 2006; Chort et al. , 2013). First, mutant ATXN7 is modified by SUMO2 by addition of poly-SUMO2/3 chains. This modification promotes the recruitment of ATXN7 to PML clastosomes, where it is ubiquitinated by the SUMO-dependent ubiquitin ligase RNF4 for its final degradation by the proteasome.
Densitometry was carried out using ImageJ software (NIH). The pellet was analyzed in a filter retardation assay (SR fraction): samples (40 µg) were boiled for 5 min in 2% SDS buffer and filtered on a BRL dot-blot filtration unit through a cellulose acetate membrane (Schleicher and Schuell, 0. 2 µm pore size) equilibrated with 0. 1% SDS  (Sittler et al. , 1998; Wanker et al. , 1999).
Acknowledgements We thank  V. Lallemand  and  Prof. H. de Théfor  fruitful scientific discussions;  M. Ruberg  and  Y. Trottier  for critical reading of the manuscript;  F. Melchior  for advice and for the anti-SUMO1 and -SUMO2 antibodies; and  J. Palvimo  for the anti-RNF4 antibody. A. W. thanks  F. Melchior  for general support. We are indebted to  Prof. H. Zoghbi  ( Baylor College of Medicine, Houston, TX, USA ) for the  Atxn7 100Q/5Q  mice. We thank the  Cellular Imaging Platform (ICM, Paris, France) ,  Dr A. Dauphin , for advice on confocal imaging. We are also grateful to  IgenSeq Platform  (ICM), in particular to  D. Bouteiller  and  J. Garrigue , for technical assistance.
Funding This study was supported by grants from  Agence Nationale de la Recherche  ( ANR-07-MRAR-025-01  to A. S. ;  ANR-10-IAIHU-06  to the  Brain and Spine Institute, Paris) ,  Association Française contre les Myopathies  (to A. B. and long-term fellowship to S. A. ), Connaitre les Syndrômes Ceŕebelleux (short-term fellowships to S. A. and M. M. ) and  Ecole Pratique des Hautes Etudes  (short-term fellowship to M. M. ).
The body temperature was monitored and maintained at 36. 5 ± 0. 5 °C using a rectal probe + heating pad; FHC (Bowdoin, ME) for fUS and TPLSM recordings, SA Instruments, Stony Brook, New York, for BOLD. Mice were supplemented with 29% O 2 throughout the experiments. Odour stimulation. All the experiments performed with the different imaging techniques were conducted with no blinding and following the same stimulation protocol and materials (Teflon odour tubing and nose cone). A home built olfactometer based on the design of the Rinberg lab (https://www. janelia. org/openscience/olfactometer) and controlled by a custom Labview software was used to deliver the odours. The odour and exhaust lines were equilibrated at the start of each experiment. In order to ensure that the exact same odour concentrations were used across techniques, the odour concentration was calibrated before every experiment by measuring the odour puffs at all the odour concentrations tested with a photo-ionization detector that measures a calibrated voltage change/concentration (miniPID 200B, Aurora Scientific, Aurora, Canada).
The expected calcium signal was calculated by interpolating the values of odour concentration vs. calcium on the dose-response curves. The dose-response curve for each mouse was made by fitting the average calcium response amplitudes at the tested odour concentrations with a cubic interpolation function (Matlab, interp1 function with 'pchip'). BOLD acquisition. The MR acquisitions were performed on a horizontally oriented 17. 2T small animal MRI scanner (Biospec, Bruker Biospin, Etlingen, Germany) using a custom built single loop surface coil (1 cm ID). For positioning the targeted field-of-view (FOV), multislice fast low-angle shot (FLASH) images were acquired. Good B0 homogeneity was ensured through automatic iterative FASTMAP methods (ParaVision 5. 1), followed by a MAPSHIM correction  54, 55  in the region of interest.
fMRI data were acquired using a 2D FLASH sequence with the following acquisition parameters: flip angle = 30°, field of view = 0. 84 × 0. 84 cm 2 , in plane resolution = 110 × 130 µm  2  , number of slices = 3, slice thickness = 500 µm, echo time = 6 ms, repetition time = 70 ms, number of repetitions = 50, acquisition time = 3 min and 44 s. Frames were acquired every 4. 48 s. At the end of the fMRI session, high-resolution anatomical images of the same slices were acquired using a Rapid Acquisition with relaxation enhancement (RARE) pulse sequence: field of view = 0. 64 × 0. 64 cm 2 , in plane resolution = 50 × 50 µm 2 , echo time = 8 ms, repetition time = 2500 ms, RARE acceleration factor = 8, number of averages = 32, acquisition time = 21 min 20 s. For BOLD-fMRI recordings, a thin layer of Kwik-Cast (WPI, Sarasota, Florida) was put over the PMP window to avoid air susceptibility artifacts.
BOLD data analyses. All analyses were performed with custom-made software developed in Matlab (MathWorks, Inc. , Natick, Massachusetts) and SPM12 (Wellcome Trust Centre for Neuroimaging, http://www. fil. ion. ucl. ac. uk/spm/ software/spm12/) fMRI suite. For analysis of the global signal, a mask of the OB was made by thresholding the averaged T2* signal across the time series. This analysis had minimal post processing (e. g. no spatial smoothing). Data realignment was performed using a 3-order polynomial interpolation and all trials at a given odour concentration were averaged voxel by voxel. The BOLD activation maps (percentage) show the average of four frames after odour onset subtracted from a baseline period of nine frames before odour delivery for each voxel. As in Fig. 3b, c , the AUC maxima (up to four frames (18 s) following the odour onset) was used to quantify BOLD responses.
FUS data acquisition and post processing. fUS imaging was performed as previously described  28. Ultrasound-coupling gel was placed between the window and the linear ultrasound probe (15 MHz central frequency, 128 elements; Vermon; Tours, France). The transducer was connected to an ultrafast ultrasound scanner (AixplorerTM, SuperSonic Imagine; Aix en-Provence, France). Programming of custom transmit/receive ultrasound sequences was done in Matlab, using softwarebased architecture of the scanner. The mice bulbs were insonified with a succession of ultrasound plane waves and the backscattered echoes were recorded and beamformed to produce an echographic image for each transmission. To increase the SNR of each echographic image taken at 500 Hz, the echographic images were compounded by transmitting several tilted plane waves and adding their backscattered echoes.
The Doppler signal of each spatial voxel was obtained by the incoherent temporal mean of the blood signal. The increase in Power Doppler signal was measured in individual voxels, which were 100 × 110 μm 2 in plane size with a slice thickness of 200 μm. The background image upon which the ΔPD/PD signals are imposed in Fig. 4  (tissue signal) shows the first 40 components of the singular value decomposition filter  43. These components account for the slowest variation of Power Doppler signal, representing the tissue. FUS data analyses. All analyses were performed with custom made software developed in Matlab. Post-processed data resulted in 500 Hz frame rate time series. OB masks were made based on the activation map at the highest odour concentration, which triggered activation within the entire OB coronal section we were recording from.
The accuracy of the mask was verified by alignment on MRI anatomical image from the same mouse (+/-100 μm rostral/caudal). The top voxels of this mask were confirmed to accurately report the surface of the OB with a B-mode image that detects the interface between CSF and PMP. The peak AUC value was selected within 20 s after odour delivery, similar to the BOLD analysis of the same parameter. A 0. 5 s moving average filter (Matlab 'smooth' function) was applied prior to peak finding because of the high frequency noise in the 500 Hz sampled fUS data. Similar to BOLD analyses, the signal intensity was averaged and then normalized to the maximal odour concentration for each mouse in order to calculate the input/output curves. For correlation analyses we calculated the Pearson correlation coefficient, r, between the local Power Doppler temporal signal computed from each spatial voxel of the fUS acquisition and either of two regressors: (1) a step function based on the odour application timing shifted by 1 s or (2) the time-course of the RBC velocity responses from the same mouse.
The RBC velocity response was interpolated (cubic interpolation, 'pchip' option in Matlab 'interp1' function) to get the same sampling frequency as fUS. A threshold obtained from the average +2 SD of the correlation values against at least 50 voxels in the acoustic gel layer of each mouse was applied to the Pearson based correlation maps to visualize and select active voxels for further analyses  25. Correlation-based fUS I/O (Fig. 7e, f ) response to odours were computed averaging the ΔPD/PD value of the active (supra-threshold) voxels in each mouse/odour concentration. SPM12 analyses of fUS data was performed as follows. fUS time-course recordings of the average of three odour applications per concentration were single voxel undersampled at 10 Hz with a cubic interpolation. Each frame was then converted to a NIfTI file.
The absence of movement was previously assessed using a custom made Matlab script. No realignment or spatial smoothing was performed with SPM. GLM analyses used regressors from the same mouse/concentration RBC velocity responses. For the time reliability analyses, the ΔPD/PD responses of three odour applications for each tested mouse in the 2 days of recordings were averaged in time. The average response and the correspondent standard deviations were reported in a histogram plot. The coefficients of variation (CV) across days and trials were also computed and added to the plot containing the TPLSM data in Supplementary Figure  1. The corresponding location of two-photon glomerulus and fUS voxels was obtained by first matching the surface vasculature from twophoton images to the wide-field images of the craniotomy, then by measuring the distance between this location and the edge of the craniotomy.
We then verified that the width of the fUS activation map was the same as the width of the craniotomy, and the glomerulus could be assigned to a voxel with a given error (see Supplementary Figure  2 ). We therefore decided to show the response time-course of both the central voxel and the six voxels centred on this voxel. TPLSM acquisition and data analysis. TPLSM imaging was performed using a custom-made acquisition system (National Instruments, Austin, Texas) and Lab-VIEW software (National Instruments, Austin, Texas). Femtosecond laser pulses were delivered by a Ti:sapphire laser (Mira by Coherent, Santa Clara, California, 120 fs pulse width, 76 MHz). An acoustic optical modulator (AA Optoelectronic, Orsay, France, MT110B50-A1. 5-IR-Hk) was used to custom-modulate the laser power. Femtosecond pulses were targeted on the sample with galvanometric mirrors (Cambridge Technology, Bedford, Massachusetts).
The power of individual segments of the broken line were modulated to optimize for differences in fluorescence of the plasma compared to GCaMP6f. Linescan acquisitions were repeated three times for each odour concentration, with the trials at different concentrations interleaved. Calcium data was interpolated (10 ms, cubic interpolation, 'pchip' option in Matlab 'interp1' function) and then trials at the same odour concentration were averaged. As with mesoscopic data, the peak AUC values were calculated from the average response at each odour concentration. Normalization was performed with respect to the highest odour concentration (35% ET). Time reliability assessment was performed similarly to fUS (see the 'Statistical test' section for quantification). In addition to the five mice used to generate the input-output curve in Fig.