dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank  Chris N. Parkhurst  and  Wenbiao B. Gan  for providing the Cx3cr1 creERT2 knock-in mice, the  Cell and Tissue Imaging Facility of the Institut du Fer à Moulin  (namely  Theano Eirinopoulou ,  Mythili Savariradjane  and  Xavier Marquès ), where all image acquisitions and analyses have been performed, and the staff of the IFM animal facility (namely  Baptiste Lecomte ,  Gaël Grannec ,  François Baudon ,  Anna-Sophia Mourenco ,  Emma Courteau  and  Eloise Marsan ). We warmly thank  Patricia Gaspar ,  Ludmilla Lokmane ,  Sonia Garel ,  Véronique Fabre  and  Jean Christophe Poncer  for the discussion and revision. This work has been supported by grants from the  Agence Nationale de la Recherche  ( ANR-17-CE16-0008 ,  ANR-11-IDEX-0004-02 ), the  Fondation pour la Recherche Médicale  (Equipe FRM  DEQ2014039529 ) and the  Fédération pour la Recherche sur le Cerveau  ( FRC-2019-19F10 ).
Experimental data showed that 500 pulses at 1 Hz induced 57% LTD and 100 pulses at 100 Hz led to 191% LTP in hippocampal CA1 pyramidal neurons in rats  (Pousinha et al. , 2017). We investigated the influence of the partial and full blockade of GluN2B-NMDAR on synaptic plasticity outcome using frequencydependent stimulation and STDP protocols (Figure  7 ). First, presynaptic input was stimulated at 100 Hz for 1 s (LTP protocol), and the normalized EPSP to the pre-LTP baseline value was estimated. The blockade of the GluN2B-NMDAR synaptic conductance, leaving 0. 3 fraction its active baseline value, resulted in the decrease of LTP from 190% to 164%, while the full blockade of the GluN2B-NMDAR led to LTD, the decrease to 90% of somatic EPSP. The impairment of GluN2B-NMDAR did not affect LTD leaving it to 63% (Figure  7A ).
Recruitment and randomization Volunteers aged between 60 and 80 years were recruited between June and December 2021 through social media advertisements and email databases. Volunteers interested in the study visited a website page with information about the trial and a link to complete a screening questionnaire. This questionnaire assessed for selfreported memory problems; medication use; medical or psychiatric disorder history; alcohol, nicotine, and other drug use; and herbal and nutraceutical intake. To assess the severity of depressive and anxiety symptoms, respondents completed the 4-item Patient Health Questionnaire (PHQ-4)  (Kroenke et al. , 2009)  and the Geriatric Depression Scale -Short Form (GDS-SF)  (Van Marwijk et al. , 1995). If assessed as likely eligible, a telephone interview was conducted where participants were asked questions to confirm their eligibility and to obtain further demographic information.
This included the completion of the telephone-administered Montreal Cognitive Assessment-Blind Version (MoCA-BV), which is a validated and commonly used clinician-administered assessment for MCI  (Wittich et al. , 2010). The MoCA-BV contains the same items as the original MoCA, except questions requiring visual abilities have been removed. A score of 13 to 18 on the MoCA-BV falls within the MCI range. Eligible and consenting participants were then allocated randomly to one of two groups (Memophenol™ or placebo; 1:1 ratio). To ensure the concealment of sequencing, a randomization calculator 1  was used with the randomization structure involving 14 permuted blocks, with 10 participants per block. The randomization sequence was established by a researcher not directly involved in volunteer recruitment, and bottle codes were held by the study sponsor and revealed after all data were collected.
Polyphenol food intake To assess for polyphenol intake, participants completed a food diary for 3 days before their baseline, week 12, and week 24 assessments. Consumed foods and liquids were entered into Nutrilog, 2  and the mean daily polyphenol intake was estimated based on the following food databases: Nuttab 2019 -Release 1, Mc Cance, Nuttab 2010, and USDA.
Data from participants were included in the location learning analyses if data were obtained at week 12 [last observation carried forward from week 12 for missing values]. As the location learning displacement scores were not normally distributed, the data were winsorized, where scores greater than 3 standard deviations from the mean were replaced with the highest score that fell below the criterion of 3 standard deviations from the mean. All results were analyzed using SPSS (version 28; IBM, Armonk, NY) using a value of p of ≤0. 05. Due to the exploratory nature of this trial, there was no adjustment to the value of p for multiple testing.
Besides, the NMR measurements were carried out in a buffer at high ionic strength yet essentially devoid of sodium or potassium ions (HEPES/tri-ethyl ammonium [TEA]). TEA is a bulky organic cation that has little chance to compete with sodium for its binding site. As shown in Figure  1 , we measured a negligible effect of non-specific binding to empty nanodiscs under such conditions. We then titrated the apo receptor in lipid nanodiscs with increasing amounts of sodium chloride and measured the sodium R 2 and R 1 relaxation rates for each concentration. Fitting R 2 -R 1 as a function of sodium chloride concentration by a single-site binding equation (see STAR Methods) provided a K d value of 43. 5 ± 10. 7 mM (Figure  1 ). These data show that sodium binds to GHSR, in a transient but specific manner, with a dissociation constant in the 40 mM range.
23 Na-NMR measurements  23  Na-NMR spectra were acquired on a 500-MHz Bruker Avance spectrometer, using a 5 mm broadband probe, with deuterium lock, that was tuned to the  23  Na frequency of 132. 256 MHz. The temperature of the sample was maintained at 278K ± 0. 1 K with a Bruker variable temperature unit and calibrated from a methanol reference sample. The 200 mL samples were prepared in 3 mm NMR tubes. The 23 Na p/2 pulses had a length of 13 ms and were applied at the exact sodium resonance frequency. The dwell time was 49 ms, and 8k data point were acquired, for a total acquisition time of 400 ms (i. e. , above 10xT 1 ). An additional inter-scan delay was set to 500 ms for the T 1 measurements, in order to allow for a complete recovery of magnetization. The number of scans was 6144 for the lower sodium concentration (3 mM), giving a signal to noise ratio superior to 100.
Molecular dynamics The ghrelin receptor was retrieved from its available X-ray structure describing an antagonist-bound state (PDB: 6KO5)  13  and submitted to MD simulations using GROMACS v. 2020. 4  27 under the CHARMM36m force field. 35 The native sequence of GHSR was used, replacing K130  3. 39  and Q188 in the structure with a threonine and an asparagine, respectively. The missing segment G293-I300 7. 30 (EL3) was built with MODELLER v9. 19  36  and the final GHSR model was chosen by selecting the one with the best DOPE score among 100 models generated. The system was built with the CHARMM-GUI web-server 37 by embedding the receptor in a membrane of 80x80 A ˚2 composed of POPC and POPG in percentages of 80 and 20, respectively. The system was solvated, neutralized and then NaCl was added until reaching a concentration of 0.
40 The production phase was run in the NPT ensemble over 1 ms with a time-step of 2 fs using the Nose-Hoover thermostat  41, 42  and Parrinello-Rahman barostat. 43 Five independent simulations were run to verify reproducibility. Data were collected every 50 ps. Analyses were performed using homemade scripts implemented in VMD. 25  The same protocol was employed for the simulations of the dopamine D2 receptor (PDB: 6VMS),  44  removing all other proteins or small molecules and adding C443 lipidation prior to build the system. Molecular dynamics data are freely available at https://mycore. core-cloud. net/index. php/s/cRUxvdjvCn4O5I6.
ACKNOWLEDGMENTS We thank  Julie Kniazeff  ( IBMM, Montpellier ) for a critical reading of the manuscript and for very fruitful discussions. This work was supported by  CNRS ,  Universitede Montpellier, Universitede Toulouse UPS , the  Agence Nationale de la Recherche  ( ANR-17-CE11-0011 ,  ANR-17-CE17-0022 , and  ANR-20-CE92-0028 ), and the  Fondation pour la Recherche Medicale (E ´quipe FRM   EQ202103012736 ). We thank  CAMPUS FRANCE  for promoting the  French-Brazilian collaboration via CAPES-COFECUB  project number  Ph-C882/17. We also thank  GENCI  ( Grand E ´quipement National de Calcul Intensif ) and  TGCC/IDRIS  for having selected us for the '' Great Challenge '' phases of both the IRENE JOLIOT-CURIE and JEAN-ZAY supercomputers.
Statistical comparisons Statistical analyses were done using Student t test, analysis of variance (ANOVA) or repeatedmeasure ANOVA (RMANOVA) with GraphPad (Prism, versions 6. 02 and 8). ANOVAs were followed by post-hoc comparisons of the group means with Tukey HSD test for one-way ANOVA and Bonferroni-Dunn multiple-comparisons for RMANOVA. Significance criterion was set to α =. 05. Results are reported as mean ± SEM (bars on figures denote SEM). All datasets passed D'Agostino & Pearson normality test. Detailed procedures are found in Supplemental information.
RNA Sequencing RNA was extracted from heart tissue using TRIzol (Life Technology) and the RNeasy Mini Kit (Qiagen), according to manufacturer's instructions. 3'RNAseq libraries were prepared from 500 ng of RNA per sample using the Lexogen QuantSeq 3' mRNA-Seq Library Prep Kit FWD for Illumina and pooled for reduced run variability. Libraries were sequenced with single-end 86 bps on an Illumina NextSeq500 sequencer (Cornell Genomics Facility). All computations were performed in the R statistical environment (version >4. 2. 0; https://www. R-project. org/). Raw sequence reads were processed using ShortRead package (version 1. 54. 0)  (Morgan et al. , 2009). Trimmed reads were aligned to the mouse genome assembly GRCm38. 94 using the Rsubread package (version 2. 10. 5)  (Liao et al. , 2019)  with default parameter settings.
The Rsubread::featureCounts function was used to assign mapped stranded sequencing reads to genes and count features. The Limma package (version 3. 52. 1)  (Ritchie et al. , 2015)  was used to obtain normalized and variance stabilized counts and to calculate differential gene expression. Pathway analysis for all gene expression data was performed with the gprofiler2  (Kolberg et al. , 2020)  and clusterProfiler packages  (Wu et al. , 2021) , using the GO Biological Process and KEGG databases. A false discovery rate corrected p value of < 0. 05 was used to determine significance. Pathways shown in the figures were condensed using the simplify function from the clusterProfiler package  (Wu et al. , 2021)  to merge terms with more than 40% overlapping annotated genes.
Data analysis The number of animals (biological replicates) for transcriptomics and metabolomics performed at 18 months was 3/sex/genotype for FXN G127V and KIKO-700 and 4/genotype for YG8-800. Biological replicates for enzymatic assays and qPCR were 5/sex/genotype (except FXN G127V males, which were n = 3) at 6 months. Statistical analyses were performed using GraphPad Prism (GraphPad Software version 9. 1. 1). Two-group comparisons were analyzed by the two-tailed t-test. Differences were considered statistically significant with a p value < 0. 05. Data are expressed as the mean ± SEM. Pathway analysis was performed using MetaboAnalyst using the hypergeometric test and relative-betweenness centrality. Joint pathway analysis was performed with hypergeometric test with degree centrality topology applied and these data integrated based on pathway level combined p values for all pathways.
Participants Ten TGA patients admitted to the emergency department of Caen University Hospital (France) were recruited, over five years, by expert neurologists (Table  2 ) according to the Hodges and Warlow criteria  1. Neurological examination (except regarding memory impairment) was normal. Individual TGA contexts are detailed in the Supplementary data 1. Ten healthy control subjects matched for age and education were recruited. All participants gave their written informed consent to participate to this study, approved by the local ethic committee.
Statistical analyses Psychological and neuropsychological profile. R 4. 0. 3 (R Core Team, 2020) and RStudio 1. 4. 1106 (RStudio Team, 2019) softwares were used for psychological and neuropsychological data analysis. Standard descriptive statistics were used, as well as nonparametric Mann Whitney U tests to compare patients and controls (stats 4. 0. 3 package). Individual Z-scores were also calculated based on the performance of the control subjects and were considered as impaired for a Z-score inferior at -1. 85 or superior at + 1. 85 depending on the variables (adjusted threshold for a small sample size, degree of freedom equal to 8 for N=10).
Discussion The brevity of TGA makes it very difficult to study its pathophysiological mechanisms at an early stage, which explains the scarcity of neuroimaging studies conducted in these patients and the fact that they are limited either to case studies or to group studies where patients have been studied after the acute phase. In that context, our study provides novel data that has been scrupulously acquired over the past 10 years, all under the same conditions and having the same clinical, neuropsychological and neuroimaging examinations. Using this rare dataset of FDG-PET data collected in a group of ten TGA patients (three in acute and seven in recovery phase), the present study aimed at investigating the contribution of glucose metabolism measure to the study of pathophysiological mechanisms underlying amnesia and anxiety in TGA.
These results also suggest that PLEXY has better prediction accuracy since both transcripts displaying robust significant expression changes were predicted by the PLEXY algorithm (Supplementary file 6). ENG is a type I transmembrane glycoprotein, part of the transforming growth factor beta (TGF-β) receptor complex and is primarily expressed with activated endothelial cells playing a major role in angiogenesis both in development and tumor progression  (Schoonderwoerd et al. , 2020). Since NRG3 is primarily expressed in the central nervous system and has been linked to psychiatric disorders in previous studies, we focused our attention exclusively on NRG3 in downstream experiments. Given that our in-silico predictions indicate that SNORD90 is primarily interacting with intronic regions of pre-NRG3, we next investigated that expression of pre-NRG3 in the same experiments detailed above; however, pre-NRG3 expression was not altered by SNORD90 over-expression or knock-down (Figure  3 -figure supplement 1S-T).
Participants resistant to at least two previous AD treatments or who had received electroconvulsive therapy in the 6 weeks before the study began were excluded. Other exclusion criteria included: MDE in bipolar disorder, presence of psychotic features, and recent substance use disorder. This clinical trial was approved by ethics boards of participating centers, and all participants provided written informed consent. https://www. clinicaltrials. gov/ (11984 A NCT00635219; 11918 A NCT00599911; 13267 A NCT01140906). For more details, please refer methods section of  Lopez et al. , 2017. Cohort 2 (N=236) was obtained in collaboration with the Canadian Biomarker Integration Network in Depression (CAN-BIND) and is composed of individuals diagnosed with MDD (N=153) and healthy controls (N=83). Patients and controls were recruited at six Canadian clinical centers.
Exclusion criteria included personal and family history of schizophrenia or bipolar disorder, or current substance dependence. Depressed patients were treated with escitalopram (10-20 mg per day), a selective serotonin reuptake inhibitor (SSRI), for eight weeks. Depression severity was assessed at baseline and after treatment by MADRS. This trial was approved by ethics boards of participating centers and all participants provided written informed consent. https://www. clinicaltrials. gov/ identifier NCT01655706. Registered 27 July 2012. For more details, please refer to  Kennedy et al. , 2019. Cohort 3 (N=166) was obtained at the Douglas Mental Health University Institute and is composed of healthy controls (N=28) and individuals diagnosed with MDD (N=138) who were enrolled in the community outpatient clinic at the Douglas Mental Health University Institute.
All sequencing data was extracted from FASTQ files and processed using CASAVA 1. 8+. Illumina adapter sequences were trimmed using the Fastx_toolkit, and additionally filtered by applying the following cut-offs:  (Brody and Gu, 2020)  Phred quality (Q) score higher than 30,  (Cipriani et al. , 2018)  reads between 15-40nt in length  (Harmer et al. , 2017) , adapter detection based on perfect-10nt match, and  (Ross and Renyi, 1969)  removing reads without detected adapters. Bowtie35 (John Hopkins University) was used to align reads to the human genome (GRCh37). Furthermore, Rfam database was used to map reads to known small nucleolar RNAs. Sequencing data was normalized with the Bioconductor-DESeq2 package.
For all cohorts, outliers were identified using boxplot methods with values above quartile 3 (Q3) +1. 5 interquartile range (IQR) or below quartile 1(Q1) -1. 5IQR (IQR = Q3-Q1). Shapiro-Wilk test and QQ plots were used to assess data normality. All above mentioned tools were apart of publicly available R package 'rstatix' (https://CRAN. R-project. org/package=rstatix) (Kassambara, 2020).
Quantification and statistical analysis Details related brain dissection and RNA extraction can be found at  Hervé et al. , 2017. RNA was reverse-transcribed using M-MLV Reverse Transcriptase (200 U/µL) (ThermoFisher) with random hexamers. Snord90 and Nrg3 were quantified by RT-PCR using SYBR green (Applied Biosystems). Reactions were run in triplicate using the QuantStudio 6 Flex System and data collected using Quant-Studio Real-Time PCR Software v1. 3. See Supplementary file 12 for a list of primer sequences used. One-way ANOVA was used as described above.
RNA-sequencing RNA was extracted from all brain samples using a combination of the miRNeasy Mini kit and the RNeasy MinElute Cleanup kit (Qiagen), with DNase treatment, and divided into small (<200 nt) and large (>200 nt) fractions. RNA quality, represented as RNA Integrity Number, was assessed using the Agilent 2200 Tapestation. Small RNA-seq libraries were prepared from the small RNA fraction, using the Illumina TruSeq Small RNA protocol following the manufacturer's instructions. Samples were sequenced at the McGill University and Genome Quebec Innovation Centre (Montreal, Canada) using the Illumina HiSeq2000 with 50nt single-end reads. All sequencing data were processed using CASAVA 1. 8 + (Illumina) and extracted from FASTQ files. The Fastx_toolkit was used to trim the Illumina adapter sequences.
Statistical analysis Using toxicology screens, each sample was separated into the following groups: MDD with presence of antidepressants (MDD +AD), MDD with presence of non-antidepressant drugs (MDD +nonAD drugs), MDD with negative toxicology screen (MDD), controls with non-antidepressant drugs (control +nonAD drugs), and controls with negative toxicology screens (control). No control samples were positive for antidepressant drugs. For this analysis we only investigated the expression for SNORD90 from this small RNA sequencing dataset. NRG3 was measured via qPCR from cDNA converted from the same RNA aliquot for each sample. See Supplementary file 12 for a list of primer sequences used. Analysis was performed in R using "rstatix" as mentioned above employing a one-way ANOVA. Shapiro-Wilk test and QQ plots were used to assess data normality.
Nuclei sorting and RNA extraction Labeled nuclei were passed through 40 μM filter caps to remove any remaining cellular debris and processed using the BDFACSAria III platform (BD Biosciences) according to technical specifications provided by the company. We used BD FACSDIVA Software (BD Biosciences) to first isolate single, intact nuclei based on DRAQ5 fluorescence at the 730/45 A filter (DRAQ5), and then to sort neuronal from non-neuronal nuclei based on fluorescence detected by the 585/42 filter (PE). Sorted nuclear fractions were collected in sheath fluid (1×phosphate-buffered saline (PBS)). Upon sort completion, 3 X volume Qiazol was immediately added to each fraction and stored at -80 °C until RNA extraction. RNA was extracted using Direct-zol RNA extraction kit (Zymo) following manufacturer instructions.
Cloning The Snord90 sequence was obtained from the Ensembl genome browser database (Ensemble ID ENSMUSG00000077756) (Supplementary file 4). The control sequence was generated by scrambling the Snord90 gene using siRNA wizard software (Invitrogen) (Supplementary file 4). Each fragment was designed to have KpnI and BamHI and a 10-nucleotide long overhang with the vector backbone at the 5' and 3' ends respectively. Each gene fragment was inserted into a KpnI with BamHI linearized pAAV-EF1a-eGFP-H1 backbone using Gibson Assembly (NEB) according to the manufacturer's protocol. This generated the following two vectors, pAAV-EF1a-eGFP-H1-Scramble and pAAV-EF1a-eGFP-H1-Snord90. All plasmids were checked for mutations by DNA sequencing.
Real-time PCR (RT-PCR) Total RNA was extracted from cells or tissue using the miRNeasy Mini Kit (Qiagen). cDNA was generated using the high-capacity cDNA RT kit with RNase inhibitor (Applied Biosystems) according to the supplied protocol. RT-PCR was performed according to the manufacturer's instructions using the QuantiFast SYBR green PCR Kit (Qiagen). RT-PCR data was collected on the QuantStudio 7 Flex Real-Time PCR System (Applied Biosystems). Absolute expression differences were calculated using the standard curve method. See Supplementary file 12 for a list of primer sequences used.
Behavioral tests For all behavioral tests, animals were brought into the room 30 min prior to the start of the test to habituate the animals to the test room. Open field (OF) test: The OF test was performed in a 50x50 cm light grey box, evenly illuminated with low light conditions (<10 lux). Mice were placed in the open field facing one of the walls and recorded for 6 min. Animals were tracked using ANY-maze software (Stoelting). Total distance traveled was used as measure of animal locomotion. Elevated plus maze (EPM): EPM apparatus was made of light grey material, and consists of four intersecting arms elevated approximately 30 cm above the floor. Splash test (Spl): Animals were placed in a novel cage containing fresh bedding material and were allowed to explore for 5 min. Each animal received two sprays of room temperature 10% sucrose water at the rear of their body.
Animals were recorded for 6 min under low-light conditions (<10 lux). The total groom time within the 3-6 min timeframe was manually scored using Solomon Coder software. Tail suspension test (TST): In the TST, animals were taped by their tales on a metal rod, approximately 30 cm above the ground, and illuminated with 30-35 lux. Animals were recorded for 6 min and struggle time was quantified using ANY-maze software. The total time an animal struggled within the 3-6 min timeframe was used as a measure of a depression-like emotional state. Z-scoring was used to integrate multiple behavioral tests as previously described  (Guilloux et al. , 2011). First, the z-score of each individual behavioral parameter was calculated. The final integrated score is associated with anxiety and depressive-like behaviors with a higher score indicating a higher emotional state, while a lower score indicating a lower emotional state.