dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, this work has received funding from the  European Union Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement number  603502  ( EU  project  DAC-CIWA: dynamics-aerosol-chemistry-cloud interactions in West Africa ). The authors would also like to address a special thanks to the pilots and ight crew from Sare for their enthusiasm and support during the measurement campaigns aboard the ATR 42 aircra. In addition, the authors are very grateful to  Eric Hamonou  for his logistical help in organizing the ChArMEx/SAFMED campaign,  S. Chevallier ,  C. Gaimoz ,  S. Triquet  and  N. Grand  from  LISA  for their technical support for the implementation of trace gas instrumentation during the MEGAPOLI and SAFMED campaigns.
Since numbers of imaged and active neurons were substantially different across mice, we subsampled our datasets to compare decoding performance of equally-sized populations across different mice. Exposure to EE resulted in an increase in decoding accuracy compared to mice in RC (Fig. 1B, D, E ), reflecting an increase in spatial information content in the DG. While multiple exposures to a single context are known to increase hippocampal spatial information within that same context  37, 38  , our findings show that 2-weeks of EE housing can increase spatial information in future exposures to a novel context.
, 2015) because it allows us to assess population-wide effects more accurately than decoding performance  44  and because it allows us to express the sensitivity also in terms of discrimination thresholds. First, to test the contribution of noise correlations to spatial information encoding in the DG, we created surrogate datasets where we shuffled the trial order independently for each neuron. The response of each neuron is given by the response at the same location but on a different trial/timepoint. Therefore, noise correlations are destroyed, but the tuning of individual neurons is preserved. Our results show that spatial information in the shuffled datasets was slightly increased although this difference was not significant (Fig. 3A-D ), indicating that noise correlations have no effect on decoding accuracy in the DG or are may be even detrimental.
S5 ). Although tuning trended higher in EE mice, this difference was not significant with our sample size. Importantly, in this control experiment no immature (<6 weeks-old) ABNs were labeled, since their axons had not reached CA3 at the time of the viral injection. This confirms that the changes in neuronal activity leading to improved spatial coding occur in the mature granule cells, and not only in the immature ABNs. In our analysis of the factors that contribute to spatial encoding, we found that noise correlations between neurons had no effect on spatial information content or were even detrimental to it (Fig  3A-D ), although spatial information in the shuffled datasets was not significantly different. This suggests that noise correlations might limit the amount of spatial information encoded by the DG, similar to recent results in CA1  46  , although one caveat is that we imaged a relatively low number of active cells when compared with the entirety of the DG coding space, as DG activity is very sparse.
Data from the treadmill was acquired using digitizer hardware (National Instruments) and the ThorSync software (Thorlabs). The treadmill and microscope setting were completely novel to the mice, which were allowed to sit still or freely move and explore the treadmill during initial setup, but were required to walk during imaging, as the treadmill was rotated. Throughout the imaging session, the mice remained in the dark, in an enclosed box built around the microscope.
Analysis of calcium imaging data Calcium imaging fluorescence intensity data was extracted from movies using the Suite2p opensource software suite  79. Briefly, all movies were registered for motion correction, the cell contours of active cells were detected and calcium traces extracted for each cell. Movement (rotary encoder) and texture (RFID) data were matched with the corresponding imaging frames. The position of the mouse on the treadmill was determined by calculating the cumulative sum of the treadmill rotation signal for every frame of the calcium imaging movie. To decode the spatial position from the neuronal population activity, we used a logistic regression model  43  , trained on 75% of the data and tested using the remaining 25%, cross-validating using 10 random splits of the data to account for overfitting.
Statistical analyses A non-parametric two-tailed Mann-Whitney test was used to compare between mouse groups. When pooling cell data from different mice into a single experimental group, significance testing was done using a muti-level bootstrapped approach that took data nesting into account, as follows. To assess a significant difference between two experimental conditions (e. g. EE and RC), the null distribution was constructed for each condition as follows: The data from both conditions was combined into one group. The animal and data value were sampled in each condition, a total equivalent to the number of data values within the condition. The mean was then calculated. This process was repeated for each condition and the difference between the null distributions generated for each group was calculated.
ACKNOWLEDGEMENTS We would like to thank  Jake Jordan ,  Roland Ferger ,  Elizabeth Wood ,  Maria Gulinello  and  Sacha Sokoloski  for technical advice, assistance and discussions. M. A. F. was supported by a  Fulbright Scholarship. M. A. F. and K. D. M. were funded by The  Einstein Training Program in Stem Cell Research  from the  Empire State Stem Cell Fund  through  New York State Department of Health  Contract  C34874GG. J. T. G. acknowledges the support of the  Whitehall Foundation  (Research Grant  2019-05-71 ) and the  National Institutes of Health  ( NINDS R01NS125252-01A1 ). R. C. C. acknowledges the support of the  National Institutes of Health  ( NEI R01EY030578 ,  NIDA RF1DA056400 ). This article is dedicated to the memory of  Dr. Paul S. Frenette  (1965-2021) who provided invaluable encouragement and support for this project.
Data and code availability The analysis code used in this study is openly available at https://github. com/GoncalvesLab/Frechou-et-al-Neurogenesis. [All the imaging data used in this study, consisting of calcium traces for every active cell and the position of the mouse on the treadmill, will be made openly available on a repository site before final publication. Due to their large size, raw calcium imaging movies will be made available upon request. ].
MNase-gel analysis The MNase-gel analysis was performed as follows. Mildly digested MNase resistant nucleosomal DNA was separated by electrophoresis in a 1. 3 % agarose gel. After migration, the gel was colored with Ethidium Bromide (0. 5µg/mL) and imaged in a Gbox bioImager (Syngene UK). The resulting image was analysed using a simple image analysis routine developed in-house in Matlab to calculate the size of digested nucleosomal DNA relative to Molecular Weight standards (1 kb+ DNA (Thermo Fischer Scientific)). Following gel imaging, the DNA was transferred on a nitrocellulose membrane by capillarity using the Southern blot protocol described above. Membranes were hybridized with a 147 bp α 32P -labelled probed and exposed against a phosphor plate for 24-48 hours. Phosphor plates were scanned using a Typhoon FLA 9500 scanner at 200 µm resolution (GE Healthcare).
3. 5. 1)  [41, 42]  allowing at most 2 alignments per read (-k 2) and fragments length ranging from 100 to 200 bp for 167 and 197 strains, and from 120 to 250 bp for 237 strains (-I and -X parameters). By choosing a reference genome with two repeats and 65 bp of an additional 601, a large proportion of the reads of interest should be able to map twice. For this reason we allowed 2 hits per read using Bowtie2. Concordant reads were specifically selected using samtools (version 1. 10). Reads presenting multiple hits on the repeat region were filtered to select the most 5 ′ alignment using a python script. Coverage per base-pair was determined in both the whole-genome (WG) and the 601-repeats in each individual library, to calculate the enrichment factor in the repeats (see Supplementary Figure  S6 ).
RNA-seq analysis We sequenced RNA from the three 601-strain and the wild-type stain during exponential growth phase in YPAD media. Reads were pseudoaligned to the reference protein-coding cDNA collection of the S. cerevisiae strain S288C via Kallisto  [43]  (version 0. 46. 2) in the single-end mode. The YMR262W cDNA sequence of the fasta files used to create index was replaced by the sequence of four 601 repeats of the appropriate design when aligning the three 601 strains. For each of the four biological replicates cpm (counts-per-million) values for every gene were calculated with the edgeR (version 3. 30. 3) package of Bioconductor  [44]. cpm values were normalized using edgeR to correct for sample-specific variation typically introduced by differences in library size. To verify that 601 repeats do not disturb the global transcriptome we calculated the log2(cpm) value of every gene (except the ones for which all samples displayed less than 1 cpm ) to determine expression fold change between the wild-type strain and the three 601 strains (see Supplementary Table  S4  (polyA-RNA) and Supplementary Table  S5  (total RNAs)).
Acknowledgments The authors thank the members of the  Genome Structure and Instability unit  for discussions and  Dr Romain Koszul  for providing assistance with sequencing. The authors also thank the four anonymous reviewers for their insightful comments on the original manuscript. This work was supported by a grant from the french national research agency (MuSeq,  ANR-15-CE12-0015 ),  Sorbonne Université  ( CONVERGENCE -CVG1110 ) and by core funding from  CNRS ,  INSERM  and  MNHN  to the Structure and  Genome instability unit. French Museum of National History  to the  Genome Structure and Instability unit. AL was supported by a doctoral fellowship from the  National Museum of Natural History. Work in the group of VC is part of " Institut Pierre-Gilles de Gennes" ("Investissements d'Avenir" program   ANR-10-IDEX-0001-02 PSL  and  ANR-10-LABX-31 ) and the  Qlife Institute of Convergence (PSL Univesité ).
Pathway analyses We then used ConsensusPathDB to test whether 42 proteins of the 127 biomarkers that were not bound to any cell and were associated with frontotemporal dementia, Alzheimer's or Parkinson's disease play a role in pathways leading to amyloid precursor protein, tau protein or α-synuclein that characterize these diseases. These analyses showed that all of the proteins shared a common pathway and were within only zero to two molecules distance from these proteins, providing additional support for the link between proteins and dementia-causing diseases (Extended Data Figs. 6-10). To identify other biological processes that may be regulated by the 127 biomarkers, we performed analyses based on Kyoto Encyclopedia of Genes and Genomes (KEGG), ClueGO and ConsensusPathDB databases using the 78 biomarkers that were plasma proteins or receptors on a cell and thus had an ID applicable for analysis.
PRS and HLA analysis To further study autoimmunity and the combined effects of the 127 biomarkers, we created an MR-based polygenetic risk score (MR-PRS) using SNPs associated with biomarkers and then performed CD4 cell count may increase the risk of dementias  [11] [12] [13] [14] [15]  , these datasets are relatively small and captured only a limited number of biomarkers. A Mendelian randomization (MR) approach that uses large Genome-Wide Association Study (GWAS) libraries for unconfounded genetic proxies for biomarkers would allow a more comprehensive examination of the immune system and BBB biomarkers. This method enables the integrated use of data from multiple independent studies, testing of causality (although under strong assumptions), and has informed drug development  16. As such, MR based on GWAS libraries is appealing in regard to explorative analyses of potential therapeutic targets for dementia-causing diseases.
Certain HLA alleles increase risk for autoimmune diseases, including type 1 diabetes and rheumatoid arthritis  18, 19. We therefore ran an  To study BBB-and immune system-related biology, biomarkers and drug targets for dementiacausing diseases, we conducted six separate studies. Study 1 used MR and MR-Base database to explore how BBB and immune system-related biomarkers associate with dementia-causing diseases. This hypothesis-generating study identified 127 biomarkers associated with dementia-causing diseases, many related to BBB, inflammation and self-tolerance, suggesting that inflammatory and autoimmune processes may play a role in these diseases. Study 2 is a pathway analysis on the associations of study 1. Providing additional support for the autoimmune hypothesis, the analysis showed that the biomarkers are enriched in several autoimmune-related biological processes and share pathways with amyloid-β, tau and α-synuclein proteins that characterize dementia-causing diseases.
The numbers of individuals using TNF-α inhibitors or developing Parkinson's disease were too small for subgroup analyses. To allow an additional analysis for rare medications, we searched the Open Targets database for medications that modify the levels of the 127 biomarkers. This search identified 64 drugs (mostly monoclonal antibodies) that target 18 of the 127 biomarkers, suggesting that these may also have potential for repurposing in the treatment of dementiacausing diseases (Supplementary Data 7).
Strengths and limitations Combining multiple lines of research allowed us to examine the role of immune system and BBB biomarkers in the etiology of dementiacausing diseases and to identify potential new drug targets and opportunities to repurpose existing medications for these diseases. The use of an MR approach across 1,827 biomarkers contributed to the evaluation of causality  16. The findings were summarized with KEGG and ClueGO analyses, both pointing to autoimmune processes. ConsensusPathDB, one of the most comprehensive collections of databases on molecular pathways and interactions  89  , linked the biomarkers to proteinopathies in dementia-causing diseases. Plasma protein analyses allowed us to  PRS and HLA analyses used data from the FinnGen study, with a sample size of 340,000 providing sufficient statistical power.
The medication analyses in 120,000 FinnGen participants relied on the IPW method, which is proposed to provide more reliable causal estimates than traditional survival analyses of observational data  90. Our study also has limitations. Rather than having a single dataset with complete information, we used an approach in which separate analyses were performed in separate cohorts. This heterogeneity in study samples and assessment of biomarkers and outcomes is a potential source of inconsistent results, but simultaneously, convergent findings across different studies and methodological approaches support the robustness and generalizability of the results. Although we explored 1,827 biomarkers related to the BBB and immune system, we may have missed some biomarkers due to limited numbers of immune system-and BBB-related biomarkers captured by our free text field searches or lack of SNPs available.
MR The SNPs for biomarkers and outcomes were searched from the MR-Base database  91. Immune system and BBB search terms were identified using identifiers of cell types, receptors, proteins, metabolites and genes. Identifiers were searched from the literature  4, 7, 9, 10, 86, 88, [92] [93] [94]  and the UniProt database  95  using the search terms 'immune' and 'blood brain barrier'. A complete list is available in Supplementary Data 1 and 2. Outcomes were diseases causing dementia, including the following conditions: all types of Alzheimer's disease, Parkinson's disease, vascular dementia, frontotemporal dementia, dementia in general and progression of dementia. Cognitive performance was chosen as an intermediate outcome. Additional SNPs for sensitivity analyses were searched from full summary statistics of three additional plasma-proteome-wide studies  [96] [97] [98].
Pathway analyses We used KEGG pathway analysis with Generally Applicable Gene-set Enrichment  100  to study the effect of biomarkers on validated pathways. We used MR Wald ratios or IVW betas and P values as input for expression ratios. Gene Ontology term enrichment analyses were done with ClueGO v. 2. 5. 8 (ref. 101 ) in Cytoscape v. 3. 7. 2 (ref. 102 ). In the hypergeometric test, we used 78 of the 127 biomarkers that were plasma proteins or receptors on a cell and thus had an ID applicable for these analyses as input, and all immune system-and BBB-related proteins from UniProt  95  as background and a correction for 5% FDR. The shortest interaction path analyses were done with ConsensusPathDB 89 , a web-based analysis tool containing a range of biomedical databases. ConsensusPathDB was used to decipher potential common pathways between biomarkers and amyloid-β, tau protein and α-synuclein.
Plasma protein analyses Plasma protein measurements in the Whitehall II study were available for 6,545 individuals of whom 310 developed dementia  7, [103] [104] [105]. The participants were linked to the National Health Service (NHS) Hospital Episode Statistics (HES) database and the UK national mortality register using individual NHS identification numbers for linkage  103. The NHS provides almost complete health care coverage for all individuals legally resident in the UK. We defined incident dementia using the WHO International Classification of Diseases, revision 10 (ICD-10) codes F00, F01, F03, G30 and G31 and ICD-9 codes 290. 0-290. 4, 331. 0-331. 2, 331. 82 and 331. 9. We also conducted informant interviews and checked participants' medications at each screening (in 1996-1998, 2011-2013 and 2016-2017) for dementia-related medication.
The specificity of aptamer reagents is good and has been confirmed in several ways  7, 108, 109. Median intra-and interassay coefficients of variation for SomaScan v. 4 are ~5% and assay sensitivity is comparable to that of typical immunoassays, with a median lower limit of detection in the femtomolar range. In the Whitehall II study, standard self-administered questionnaires provided data on age and sex. Using DNA extracted from whole blood, a standard PCR assay determined APOE genotype using the salting-out method  110, 111. Two blinded independent observers read the genotype, and any discrepancies were resolved by repeating the PCR analysis. In Whitehall II analyses, we studied the eight proteins associated with all-cause dementia in MR analyses. Dementia subtype data were not available in Whitehall.
PRSs and IPW analyses FinnGen Data Freeze 8 comprises 339,233 individuals and represents approximately 7% of the adult Finnish population. FinnGen is a collection of prospective epidemiological and disease-based cohorts and hospital biobank samples that links genotypes by unique national personal identification numbers to nationwide health registries, including national hospital discharge (available from 1968 onwards), death (1969), cancer (1953) and medication reimbursement (1964) and purchase (1995) registries. The registry-based follow-up ended on 31 December 2020. Alzheimer's disease was defined with ICD-10 codes under F00 and G30, ICD-9 codes under 3310, ICD-8 code under 29010 and medication purchase Anatomical Therapeutic Chemical (ATC) code N06D; vascular dementia with ICD-10 codes under F01 and ICD-9 codes under 4378; and Parkinson's disease with ICD-10 codes under G20, ICD-9 codes under 3320 A, ICD-8 code under 34200 and medication reimbursement code 110.
FinnGen samples were genotyped with Illumina and Affymetrix (Thermo Fisher Scientific) arrays. Genotype calls were made with Gen-Call or zCall (for Illumina) and the AxiomGT1 algorithm (for Affymetrix data). Individuals with ambiguous gender, high genotype missingness (>5%), excess heterozygosity (±4 s. d. ) or non-Finnish ancestry were excluded, as well as all variants with high missingness (>2%), low Hardy-Weinberg equilibrium (P < 1 × 10 -6 ) and minor allele count <3. Array data prephasing was carried out with Eagle 2. 3. 5 (ref. 113 ) with the number of conditioning haplotypes set at 20,000. Genotype imputation was done using the population-specific SISu v. 3 imputation reference with 3,775 high-coverage (25-30×), whole-genome sequences in Finns, described in detail at https://doi. org/10.
17504/protocols. io. xbgfijw. We constructed PRSs from SNPs associated with the 127 biomarkers identified from MR analyses. To ensure comparability of the SNPs, we used only studies that included participants of European ancestry from the MR-Base database; in this database, data are harmonized. To ensure interoperability, PRSs were designed to be outcome specific by creating a separate PRS for each outcome from the pool of SNPs for biomarkers associated with the outcome of interest. SNPs were LD pruned, with clumping cutoff R 2 = 0. 01 and a 500-kb window with the R package TwoSampleMR. The final PRS contained only SNPs available in FinnGen genotypes. Final scores were determined with PLINK v. 2. 00aLM3, by calculating the SNP biomarker beta-weighted sum of risk alleles for each SNP. PRSs were scaled to zero mean and one-unit variance.
IPW analyses used the weighted FinnGen data to estimate the causal effect of each medication compared to no use of the medication studied. For an RCT that did not report P values, these were estimated using a method described by Altman and Bland  115. APOE alleles in FinnGen were inferred based on genotype (rs7412 with minor allele frequency (MAF) 0. 054 in Finns, INFO 0. 997; rs429358, MAF 0. 18, INFO 0. 999). We used R (4. 1. 2) for these analyses.
HLA analyses These analyses were done in FinnGen using HLA alleles and imputed with high accuracy using a Finnish-specific reference panel, as previously described in detail  116. After filtering based on an HLA carrier frequency of ≥0. 01 and posterior probability of ≥0. 6, we assessed the association between HLA alleles and dementias and autoimmune diseases using logistic regression adjusted for birth year, sex and the first ten principal components of ancestry. Patients and control participants in FinnGen provided informed consent for biobank research, based on the Finnish Biobank Act. Separate research cohorts, with data collected before the Finnish Biobank Act came into effect (in September 2013) and before the start of FinnGen (August 2017), were based on study-specific consents and were later transferred to the Finnish biobanks after ethical approval by Fimea (Finnish Medicines Agency), the National Supervisory Authority for Welfare and Health.
Statistics and reproducibility To study BBB-and immune system-related biology, biomarkers and drug targets for dementia-causing diseases, we conducted six separate studies, the designs and data of which are described in Fig. 1  and Table  1. No statistical methods were used to predetermine sample sizes; instead, these were determined based on available data. Study 1 used the freely available MR-Base GWAS catalog and MR to examine associations between a range of biomarkers and dementia-causing diseases. The details are described in Github  117  , and the data used in these analyses are provided in Zenodo  118. Study 2 examined the pathways regulated by the biomarkers identified in study 1 using publicly available KEGG, ClueGO and ConsensusPathDB databases, Cytoscape and web-based analysis tools.