dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements Experiments on MEG data were made possible thanks to MNE [103,74], as well as the scientific Python ecosystem: Matplotlib [104], Scikit-learn [75], Numpy [105], Scipy [106] and PyTorch [65]. This work was supported by the  French   ANR-20-CHIA-0016  and the  European Research Council Starting  Grant  SLAB ERC-StG-676943  to AG, and by the  French   ANR-17-EURE-0017  and the  Fyssen Foundation  to JRK for his work at PSL. The authors would like to thank  Nicholas Tolley  and  Hubert Banville  for their feedback and suggestions on the early versions of this manuscript.
This constitutes a strong rationale for testing liver gene transfer as a therapeutic option for MSUD. Among the available gene-delivery platforms, adeno-associated virus (AAV) vectors are the most suitable for liver gene transfer  11. AAV liver gene therapy achieved a major milestone with the proof of safety and long-term efficacy in a clinical trial for hemophilia B  12. Inborn errors of metabolism are good candidates for AAV gene therapy  13. Proof of concept of efficacy was obtained in mice for urea cycle disorders  [14] [15] [16] [17]  , organic acidemias  18  , phenylketonuria  19  , and others, and clinical trials of liver gene transfer are currently being conducted for a number of diseases, including ornithine transcarbamylase deficiency (OTC) (NCT02991144), glycogen storage disease type 1a (NCT03517085), mucopolysaccharidosis type VI (MPSVI) (NCT03173521), and Pompe disease (NCT03533673 and NCT04093349).
The blood sample of a Bckdha -/-mice was insufficient for analysis. c Vector genome copy number (VGCN), d human BCKDHA (hBCKDHA) mRNA(Bckdha -/-injected at 10 13 vg/kg n = 7, Bckdha -/-injected at 10 14 vg/kg n = 2, controls: Bckdha -/-non injected and sacrificed at 1 week n = 2), and e BCKDHA protein levels in the liver, heart, and brain in mice sacrificed around 4 weeks after neonatal injection (Bckdha -/-injected at 10 13 vg/kg n = 7, Bckdha -/-injected at 10 14 vg/kg n = 2, controls: Bckdha -/-n = 1 non-injected and sacrificed at 1 week + 1 non-injected Bckdha +/+ sacrificed at 4 weeks); analyses were performed in triplicate. The antibody detected both the human BCKDHA and the murine BCKDHA proteins. All data are shown as mean ± SD. Source data are provided as a Source data file. animals were in good health when they were sacrificed at age 12 weeks at the end of the study.
d Vector genome copy number (VGCN), e human BCKDHA (hBCKDHA) mRNA, and f BCKDHA protein levels in the liver, heart, and brain in mice sacrificed 6 months after neonatal injection (Bckdha -/-injected n = 5, controls: Bckdha -/-n = 1 non-injected and sacrificed at 1 week + 1 non-injected Bckdha +/+ sacrificed at 6 months for western blots). Analyses were performed in triplicate. The antibody detected both the human BCKDHA and the murine BCKDHA proteins. g Kaplan-Meier curves for all injected Bckdha -/-mice (n = 9) (blue line) during the first 6 months. The red dash line represents the maximum survival of untreated Bckdha -/-mice. All data are shown as mean ± SD. Source data are provided as a Source data file. Fig. 5  Long-term partial rescue of severe MSUD phenotype in Bckdha -/-mice injected with AAV8-hAAT-hBCKDHA at 10 14 vg/kg.
a Weight curves for males (Bckdha -/-n = 4, Bckdha +/-n = 6, Bckdha +/+ n = 2) and b females (Bckdha -/-n = 2, Bckdha +/-n = 5 Bckdha +/+ n = 4). c Leucine and alloisoleucine concentrations in plasma until age 6 months (Bckdha -/-n = 6, Bckdha +/-n = 11, Bckdha +/+ n = 6, all injected). One Bckdha -/-male mouse died on day 28 and was not included for the points >day 28. All data are shown as mean ± SD. Source data are provided as a Source data file. markedly at age one month, i. e. , 7 ± 5% activity relative to agematched controls (Fig. S11 ). Finally, as the two cassettes (EF1α and hAAT) differed in one intron (iEF1α vs. iSV40, respectively), we wanted to confirm in vitro data showing that in Huh7 cells, there was no overt difference in terms of BCKDHA protein expression between the original hAAT driven construct containing the SV40 intron, iSV40 and an additional hAAT driven construct containing the EF1α intron, iEF1α.
Amplification conditions were 95 °C for 10 min then 40 cycles of 95 °C for 30 s, 62 °C for 30 s, 72 °C for 1 min. Vector genome copy number analysis. DNA was extracted from tissues homogenates using DNeasy Blood & Tissue kit (Qiagen, Germany) and quantified. Vector genome copy number was determined by qPCR using 100 ng of DNA (assay with Nanodrop, version 1. 6. 198). Primers annealed to bGH polya signal region of the expression cassette sequence: forward: 5′-gccactcccactgtcctttc-3′; reverse, 5′-cccagcatgcctgctattgt-3′. Data were recorded with QuantStudio (version 5. 2). RNA extraction and expression. Total RNA was extracted from snap-frozen tissue homogenates using the RNeasy mini kit (Qiagen, Germany). RNA was quantified and 0. 8 mg (quantified with Nanodrop, version 1. 6. 198) was retrotranscribed to cDNA using the QuantiTect Reverse Transcription Kit (Qiagen, Germany).
RT-minus reactions were performed as a negative control. For hBCKDHA mRNA and mice Bckdha expression analyses, the qPCR on cDNA was performed using Sybergreen and primers annealing specifically on the WT hBCKDHA transgene sequence (hBCKDHA forward, 5′-cccacctctgagcagtatcgc-3′; hBCKDHA reverse, 5′-caaacacatcattaccatccacgc-3′) and the mice Bckdha sequence respectively (Bckdha forward, 5′-gatgcctgttcactacggct-3′, Bckdha reverse, 5′cacaatccggttggcattgg-3′). Mouse Gapdh gene transcripts were used to normalize Bckdha or hBCKDHA expression (Gapdh forward, 5′-ggcattgtggaagggctcat-3′; Gapdh reverse, 5′-gtcttctgggtggcagtgat-3′). Data were recorded with QuantStudio (version 5. 2). Western blot. Snap frozen mouse tissues were weighted, homogenized with RIPA buffer supplemented with protease inhibitors, sonicated using the Bioruptor system (Diagenode, Belgium), and centrifuged 20 min at 20,000 × g to collect the supernatant.
2. The polyclonal anti-BCKDHA antibody recognized both the mouse BCKDHA and human BCKDHA proteins. For quantification, the hBCKDHA and mouse BCKDHA protein expression was normalized to GAPDH or actin (anti-actin, mouse polyclonal; ProteinTech; cat. no. 20536-I-AP; WB 1:1. 000) protein expression. The same protocol was used for the detection and quantification of phosphorylated BCKDHA (Se293) using the Anti-human phosphoBCKDHA antibody (Rabbit polyclonal; Abcam; cat. no. ab200577; lot GR369441; WB 1:2000). For BCKDHB detection, the polyclonal anti-BCKDHB antibody (Rabbit polyclonal; Abcam; cat. no. ab201225; lot GR3244895; WB 1:200) was used and protein expression was normalized to GAPDH protein expression. Uncropped and unprocessed scans of western blots are provided in the Source data file.
Biochemical characterization. Plasma concentrations of amino acids were measured with liquid chromatography coupled to tandem mass spectrometry (UPLC-MS/MS). For accurate quantification, a stable isotope internal standard for the same structure for each metabolite (all purchased from Eurisotop, Saint Aubin, France) was added to the sample before protein precipitation. Samples were first derivatized using the AccQ Tag TM Ultra (Waters Corporation, Milford, MA, USA) according to manufacturer recommendations. Amino acid separation was performed with an Acquity™ UPLC system using a CORTECS™ UPLC C18 column (1. 6 µm, 2. 1 × 150 mm) coupled to microTQS™ tandem mass spectrometer operating with the software MassLinks (version 4. 2, Waters Corporation, Milford, MA, USA). Internal labeled standards for quantification were labeled on all carbons and nitrogen(s).
Enzymatic activity measurement was performed at 30 °C after the addition of 20 µL of 50 mM 2-ketoisovaleric acid (final concentration 1 mM; pH of the final assay = 7) by monitoring NADH production at 340 nm (with the software Kinetics version 3. 00). Protein concentrations of the tissue extract were assessed with a BCA assay. The activity was normalized to proteins and expressed in arbitrary units. Pathology studies. Organs were fixed overnight in 4% paraformaldehyde. For conventional bright-field light microscopy, organs were embedded in paraffin. Sections were stained with hematoxylin and Eosin. Numerisation of sections was performed with the software NDP. view2 (version 2. 7. 41). Statistical analysis. All the data shown are reported as means ± standard deviation (SD). The statistical analyses were performed using the R software (version 3.
Acknowledgements This work was supported by  "DIM" (Domaine d'Intérêt Majeur ) of the  Paris Ile-de-France Region  grant to C. P. , M. S. -S. , V. G. , C. G. , A. R. , C. O. , M. C. , and M. S. The authors are indebted to the AAV platform and the animal facility of  Imagine Institute , the histology and microscopy platforms of  Institut Necker Enfants Malades , the mass spectrometry platform, and the  Centre d'Investigation Clinique of the Biotherapy Department, Necker Hospital. We thank  Mrs Aurore Besse ,  Aline Huguet , and  Hélène Benyamine  for their help in training for injections. We also thank  Mrs. Dominique Farabos  and  Dr. Antonin Lamazière  ( Saint-Antoine University Hospital, Paris , mass spectrometry platform).
Animals and phenotypes In this study, we did not perform any experiments on animals; thus, no ethical approval was required. Data from Holstein cows were collected from herds in northwestern France that were enrolled in JD control plans. Two datasets were merged and analyzed. The first dataset was obtained from the PARADIGM project, which contained reliable MAP statuses that were deduced from repeated and concordant serum ELISA and fecal PCR results for 4100 cows. More details can be found in Sanchez et al. [10]. The second dataset, which constituted the majority of the overall dataset, included MAP statuses of 243,274 cows that were deduced from serological tests that have been routinely recorded since 2015. Cows for which all the tests were negative were considered non-infected while cows with at least one positive test were considered infected.
Of the entire dataset of 247,374 cows, about 92% were non-infected (n = 228,337) and about 8% were infected (n = 19,037) by MAP. These cows originated from 15,476 herds, of which 5637 had at least one infected cow. All serum samples were analyzed with one of two ELISA kits: Idexx Paratuberculosis Screening Ab Test (Idexx, Montpellier, France) for ~ 75% of the cows and Idvet ID Screen ® Paratuberculosis Indirect (Idvet, Montpellier, France) for ~ 25% of the cows. The PARADIGM dataset had already been cleaned and filtered; on the second dataset, we applied filters as follows. The S/P threshold values (sample optical density over positive control optical density) recommended by the manufacturer were used to distinguish between infected and non-infected cows. However, cows presenting intermediate S/P values, i.
e. , between 10 and 50, were considered to be of uncertain status and were excluded from the analyses (17. 6%). We kept only the cows that were more than 24 months old and that had at least one calving. We also excluded non-infected cows that were less than 3 years old to eliminate individuals that could potentially still be in the latency period. After applying these filters, 119,992 non-infected cows and 18,025 infected cows remained in the dataset. Then, to maximize the probability of exposure to MAP and to estimate the effect of the contemporary group, we kept only birth herd × birth-year combinations with at least one infected and one non-infected cow. We considered the birth herd regardless of the herd in which the animals were located on the day of the test. These filters resulted in a drastically reduced dataset, which ultimately contained 56,766 Holstein cows from 3114 herds.
Of these cows, 42,829 were non-infected (~ 75%) and 13,937 were infected (~ 25%). The pedigree was traced over four generations and contained 161,253 animals, including 12,431 genotyped individuals among which, 4031 cows had MAP statuses (2787 non-infected and 1244 infected). The 12,431 genotyped animals had previously been genotyped with different versions of the 50K SNP Beadchip, including the EuroGMD Beadchip currently used for genomic selection (https:// www. eurog enomi cs. com/ actua lites/ theeurog-md:-a-unique-genot yping-micro array-for-cattl e-. html). In total, 53,469 SNPs passed all quality control filters (individual call rate > 95%; SNP call rate > 90%; minor allele frequency (MAF) > 1%; genotype frequencies in Hardy-Weinberg equilibrium with P > 10 -4 ). Missing genotypes were imputed using the FImpute software  [11]  as part of the French routine system of evaluation (covering more than one million French Holstein animals with genotypes), with a reference population comprising about 35,000 genotyped bulls used for artificial insemination.
Breeding values Model (1) was applied to the hybrid single-step (HSS) model, proposed by Fernando et al. [13] , and implemented in the HSSGBLUP software that is used in the French bovine evaluation. We defined six unknown parent groups depending on the birth year of the animals  (≤ 2008, 2009-2010, 2011-2012, 2013-2014, 2015-2016, and 2017-2018). Coherence between pedigree and genotypes was obtained by fitting a J vector  [14, 15]  for each unknown parent group. This method directly provides estimates of SNP effects and single-step estimated breeding values (ssEBV) of non-genotyped animals. The ssEBV of the genotyped animals were obtained from genotypes (1) y = Xβ + Za + e, and SNP effects. ssEBV were expressed in genetic standard deviation ( σ a ) units by dividing predictions obtained on the raw scale by the genetic standard deviation estimated with Model (1) and were centered around a base population consisting of the genotyped cows born from 2015 to 2018.
Reliability of genomic predictions Model (1) was applied to a truncated dataset (training) that included the phenotypes of the 41,774 cows born before 2015  (31, 115  non-infected and 10,659 infected), of which 3118 had genotypes (2069 non-infected and 1049 infected). The reliability of ssEBV for the animals without phenotypes or progeny was estimated in a validation (VAL) population of 907 cows born between 2015 and 2018 that had both genotypes and MAP statuses (715 non-infected and 192 infected cows). Reliability was estimated by r 2 /h 2 , where r is the correlation in the VAL population between MAP status (1 or 0, adjusted for non-genetic effects estimated with Model (1) on the complete dataset) and GP (calculated using SNP effects estimated from the training dataset).
Single-step GWAS To detect QTL for resistance to JD, we performed a single-step GWAS (ssGWAS)  [17, 18]  from the SNP effects estimated with the HSSGBLUP software. We split the genome into non-overlapping, adjacent 1-Mb regions and calculated the percentage of ssEBV variance ( %GV i ) accounted for by the i th genomic region as: where n is the sample size, σ 2 α is the variance in ssEBV, and a i = M i s i is the vector of genomic values for the i th region with M i the matrix of SNP content in region i and s i is the vector of estimated effects of the SNPs in the region. We also applied the same approach to whole chromosomes to obtain the %GV c explained by each chromosome. Genomic regions with %GV > 0. 05% were annotated with FAANGMine v1. 1 (https:// faang mine. elsik lab. misso uri. edu), which was developed by the Functional Annotation of ANimal Genomes initiative  [19]  and integrates the ARS-UCD1.
To identify genomic regions that are involved in the genetic determinism underlying resistance to JD, we applied a multi-SNP GWAS approach that analyzed 161,253 animals in the pedigree, including 56,766 with phenotypes and 12,431 with genotypes. Previous studies have demonstrated equivalence between GBLUP and classical single-marker GWAS, e. g. ,  [25] , and furthermore, it has been shown that the detection of QTL via GBLUP can be extended to single-step GBLUP, also referred to as ssGWAS  [17, 18]. In recent years, ssGWAS approaches, which have the advantage of estimating all SNP effects jointly, have been applied in multiple studies to identify QTL for different traits, e. g. ,  [26]. When applied to resistance to JD in our Holstein cattle dataset, it highlighted regions of the genome located on chromosomes 1,  3, 5, 6, 7, 19, 20, 21, 23, 25 , and 27, which account for the largest GV percentage in this trait (> 0.
Conclusions Using a single-step genomic evaluation applied to a large dataset of Holstein cows, we confirm the genetic determinism that underlies host resistance to paratuberculosis. Overall, the heritability estimate for resistance to this disease was 0. 14 and we identified various regions on the genome that account for a substantial part of the ssEBV variance in resistance to paratuberculosis in Holsteins, with the largest contributions due to regions located on chromosomes 23, 5, 1, and 3. In these regions, association analyses at the sequence level allowed the identification of individual genes and gene variants that are responsible for resistance to paratuberculosis. Genomic predictions for this trait were found to have a satisfactory reliability (0. 55), which means that such predictions could be of great interest to identify cows at high risk of infection and to highlight resistant bulls for inclusion in mating plans.
Application We illustrate our theoretical findings simulating scenarios of mortality improvement depending on level versus rate changes in the Gompertz mortality model  (Gompertz, 1825). This model captures an exponential change of the force of mortality µ(x) = α • e β x over age x, where α is the baseline level of mortality and β the rate of aging. The R code  (R Core Team, 2021)  to reproduce all the figures and results discussed in the following will be publicly available upon acceptance.
Acknowledgements This work was partially funded by the  European Research Council  (Grant  ERC-2019-AdG-884328 ) (J. M. A. and F. V. ). J. M. A. also acknowledges funding from the  British Academy 's  Newton International Fellowship (Grant   NIFBA19/190679 ) and the  Leverhulme Trust Large Centre , and F. V. from the  Juan de la Cierva Programme ,  Spanish Ministry of Science and Innovation  (Grant  IJC2019-039144-I ).
Aging is associated with cognitive changes, with strong variations across individuals. One way to characterize this individual variability is to use techniques such as magnetoencephalography (MEG) to measure the dynamics of neural synchronization between brain regions, and the variability of this connectivity over time. Indeed, few studies have focused on fluctuations in the dynamics of brain networks over time and their evolution with age. We therefore characterize aging effects on MEG phase synchrony in healthy young and older adults from the Cam-CAN database. Age-related changes were observed, with an increase in the variability of brain synchronization, as well as a reversal of the direction of information transfer in the default mode network (DMN), in the delta frequency band. These changes in functional connectivity were associated with cognitive decline.
Impaired stability of brain network dynamics could lead to the neurocognitive changes observed with advancing age  (Voytek & Knight, 2015). The directionality of connectivity between neuronal oscillations may also play a role in the transmission of neuronal communications. Therefore, changes in dynamic connectivity would take place in order to maintain cognitive performance, while failure to make these changes would lead to cognitive decline  (Ariza et al. , 2015). Here, we investigated the stability and variability of resting brain networks' synchrony over time in young and older healthy participants from the Cam-CAN (Cambridge Centre for Ageing and Neuroscience) database (e. g. ,  Shafto et al. , 2014; Taylor et al. , 2017). This database includes multimodal neuroimaging data (MEG, f/MRI) as well as cognitive performance assessment in each individual.
Participants We analysed data from 46 young (29 women and 17 men; aged 22-29 years) and 46 older healthy adults (29 women and 17 men; aged 60-69 years; see participant demographics characteristic in Table  1 ). Participants were selected from the Cam-CAN database (e. g. ,  Shafto et al. , 2014; Taylor et al. , 2017) , in line with demographic characteristics of individuals recruited in previous work (e. g. ,  Coquelet et al. , 2017; Hinault et al. , 2020). All participants were right-handed, showed normal cognitive functioning (Montreal Cognitive Assessment (MoCA) score >26;  Nasreddine. et al. , 2005) , and no neurological or psychiatric condition.
MEG and structural MRI data acquisition Resting brain activity was measured for 10 minutes (sampling rate: 1kHz, bandpass filter: 0. 03-330 Hz) with a 306-channel MEG system. Participants' 3D-T1 MRI images were acquired on a 32-channel 3T MRI scanner. The following parameters were used: repetition time = 2250 ms; echo time = 2. 99 ms; inversion time = 900 ms; flip angle = 9 degrees; field of view = 256 mm x 240 mm x 192 mm; voxel size = 1 mm; GRAPPA acceleration factor = 2; acquisition time = 4 minutes and 32 seconds.
Data pre-processing The Elekta Neuromag MaxFilter 2. 2 has been applied to all MEG data (temporal signal space separation (tSSS): 0. 98 correlation, 10s window; bad channel correction: ON; motion correction: OFF; 50Hz+harmonics (mains) notch). Afterwards, artifact rejection, filtering (0. 3-100 Hz bandpass), temporal segmentation into epochs, averaging and source estimation were performed using Brainstorm  (Tadel et al. , 2011). In addition, physiological artefacts (e. g. blinks, saccades) were identified and removed using spatial space projection of the signal. In order to improve the accuracy of the source reconstruction, the FreeSurfer  (Fischl, 2012) , software was used to generate cortical surfaces and automatically segment them from the cortical structures from each participant's T1-weighted anatomical MRI.
Statistical tests Permutation analyses were performed in Brainstorm  (Tadel et al. , 2011) , using methods originally implemented in Fieldtrip  (Maris & Oostenveld et al. , 2011). Both toolboxes support open access and scripts are available online. To assess differences between age groups in demographic and functional connectivity variables, t-tests and ANOVAs were applied using Jamovi software (https://www. jamovi. org/; version 1. 6. 23). Functional data (PLV, TE) were analyzed using 2 (age group: young/old) x 4 (networks: DMN, SN, FPL, and FPR) x 6 (frequency bands: delta, theta, alpha, beta, gamma1, gamma2) repeated-measures ANOVAs to determine which network and frequency band showed the greatest young/old changes. The Greenhouse-Geisser epsilon correction was used where necessary. Original degrees of freedom and corrected p-values are reported.
Age-related differences in cognitive performance The main behavioral and demographic data from the Cam-CAN database are summarized in Table  1. Relative to younger individuals, older adults showed lower scores in the MMSE (p=0. 013), VSTM (p<0. 001), Cattell (p<0. 001) and hotel test (p=0. 018 for number of rooms; and p=0. 005 for time) scores. For the hotel test, a decrease in the rate of correct answers was observed (p=0. 018). A significant increase in response time for the hotel test was also observed in older individuals (p=0. 005).
Discussion Our main objective was to investigate changes in the stability and variability of brain communication dynamics with age and the relationship of these changes with age-related cognitive changes. Our connectome-based approach, based on MEG data in healthy young and older participants from the Cam-CAN database, allowed us to investigate changes of connectivity dynamics with aging. Two time-resolved connectivity aspects were studied: the stability of synchronized communications over time, and directed connectivity. Brain activity was studied at rest, as previous work suggested a link between the activity of specific networks at rest and cognitive abilities (e. g. Nashiro et al. , 2017). In this study, we first showed an increased variability of phase synchrony over time with age, especially in the delta frequency band.