dataesr/scientific-paragraphs-categorization huggingface dataset:
The resulting 31 APPtg and 32 TAUtg modules were each summarized by the first principal component, known as module eigengenes (MEs). Next, Fisher's exact test with BY P-value adjustment was used to determine whether a list of cell type-specific genes overlap significantly with genes in a module. More than half of all modules (APPtg: 16/31; TAUtg: 20/32) show significant overlap with a specific cell type (P adj < 0. 05), particularly with the neuron and interneuron gene sets (APPtg: 10/31; TAUtg: 11/32; see Appendix Figs  S2  and S3 ). Gene set enrichment analysis of GWAS genes from  Marioni et al (2018)  at different P-value cut-offs among the WGCNA-derived modules in APPtg or TAUtg mice was performed using Fisher's exact test with BY P-value adjustment. Functional annotations of the modules were performed using first GOrilla  (Eden et al, 2009)  and when no significant enrichment could be found, using DAVID (see Datasets EV2 and EV3  (Huang et al, 2009b,a) ).
GO categories were deemed significant if the FDR-corrected P-value (GOrilla) or Benjamini-based P < 0. 05 (DAVID). To search for potential regulators in each module, we ran i-cisTarget (Imrichovaet al, 2015) which predicts transcription factor motifs (position weight matrices) and experimental data tracks (e. g. , ENCODE, Roadmap Epigenomics Project) that are enriched in the input set of regions (i. e. , genomic regions for each gene within the module). The default setting collected over 23,588 features across all the databases available in i-cisTarget. Only regulators that were also expressed within the same WGCNA module were considered. Top regulators were selected based on the maximum normalized enrichment score for feature enrichment.
Gene set overlap assessment Overlap between the GWAS P < 0. 001 gene set (n = 314), all genes significantly differentially expressed within the APPtg age*genotype interaction comparison (n = 798), genes within the APPtg-Blue module (n = 4,236), and the microglia-specific gene set (n = 435) were assessed using SuperExactTest  (Wang et al, 2015; version 1. 0. 4) , which calculates, based on combinatorial theory, the statistical probability of finding an over-representation of genes within the intersection of multiple sets, compared to random expectation. P-values were BY-adjusted (see Appendix Table  S3 ). Empirical P-values associated with predicted changes in cell type fraction We predict changes in cell type fractions based on shifts in the mean (per gene z-normalized across samples) expression of a wide panel of genes associated with a specific cell type by  Zeisel et al (2015) , see  Fig EV1).
Acknowledgements We thank  Veronique Hendrickx  and  Jonas Verwaeren  for animal husbandry and  Carlo Sala Frigerio  and  Tom Jaspers  for discussions. We thank in particular  Prof. Mathias Jucker  for making the line APP/PS1 available for our research and for providing us with an additional batch of aged APP/PS1 mice on very short notice. Confocal microscopy equipment was acquired through a Hercules Type  1 AKUL/09/037  to  Wim Annaert. Annerieke Sierksma  and  Bart De Strooper  are supported by the  Opening the Future campaign of the Leuven Universitair Fonds (LUF)  and the  Alzheimer Research Foundation  ( SAO-FRA; P16017 ). Renzo Mancuso is a recipient of a postdoctoral fellowship from the  Alzheimer's Association USA. Nicola Fattorelli  is an  Fonds voor Wetenschappelijk Onderzoek (FWO) fellow  ( 1139520N ).
Evgenia Salta is an  FWO fellow  ( 12A5316N ) and supported by the  Alzheimer's Association  ( AARF-16-442853 ). Work in the  De Strooper laboratory  was supported by the  FWO  (grant no. G0C9219N ),  KU Leuven ,  VIB , and a  Methusalem grant  from  KU Leuven  and the  Flemish Government , the " Geneeskundige Stichting Koningin Elisabeth ", the  MRC , the  Alzheimer Society, and Alzheimer Research UK. Bart De Strooper  is holder of the  Bax-Vanluffelen Chair for Alzheimer's Disease. Luc Buée  is supported by grant  ANR-16-COEN-0007.
Data and software availability All RNA-seq data have been submitted to the GEO database under accession number GSE110741 and GSE142267; other data are available in the Expanded View Datasets or the Appendix Tables. The software tools used for this study include Tophat2 (version 2. 0. 7, Trapnell et al, 2009) , available from https://ccb. jhu. edu/softwa re/tophat/index. shtml; Subread/Featurecounts  (Liao et al, 2014) , available from http://subread. sourceforge. net/; Pandas Python Data Analysis Library, available from http://pandas. pydata. org/; Limma/ Linear Models for Microarray Data  (Ritchie et al, 2015) , available from https://bioconductor. org/packages/release/bioc/html/limma. html; Gene Set Enrichment Analysis  (Mootha et al, 2003; Subramanian et al, 2005) , available from http://software.
broadinstitute. org/ gsea/index. jsp; WGCNA package in R (Langfelder & Horvath, 2008), available from https://cran. r-project. org/web/packages/WGCNA/ index. html; dynamicTreeCut package (Langfelder et al, 2008), available from https://cran. r-project. org/web/packages/dynamicTree Cut/index. html; Gene Ontology enrichment with GOrilla  (Eden et al, 2009) , available from http://cbl-gorilla. cs. technion. ac. il/; DAVID (Huang et al, 2009a,b), available from https://david. ncifcrf. gov/ home. jsp; i-cisTarget (Imrichovaet al, 2015), available from https:// gbiomed. kuleuven. be/apps/lcb/i-cisTarget/; SuperExactTest  (Wang et al, 2015) , available from https://cran. r-project. org/web/package s/SuperExactTest/index. html; StatsModels for Python available from http://www. statsmodels. org/0. 8.
Crucially, our model implies that the sequence in which options are sampled will have an impact on the final choice: if the best option is encountered earlier in the series, it will win more covert comparisons, which should further boost its subjective value and hence the likelihood that this option will be selected in the end, as well as the confidence that this option was indeed the best. We compared our model to alternative models, derived from pure economic decision theory (softmax function of option values) or memory accounts (additive bias depending on serial position). We also tested more subtle alternatives, such as the progressive elimination of losing options in covert choices, instead of value updating. These models were compared on the basis of behavioral choice data collected in a novel task requiring participants to sample a set of 3 to 6 goods displayed on the screen, then selecting their favorite option in the presented set and, finally, reporting their confidence on a rating scale.
Participants Three groups of participants (sample size: 30, 29, 33; gender: 18/12, 13/16, 21/12 female/male; age ± SEM: 24. 3 ± 0. 6, 24. 8 ± 0. 9, 25. 1 ± 0. 7, respectively) were recruited from a volunteer database (the RISC, a local online cognitive science information relay platform for subject recruitment in the region of Paris, France) to participate in experiments 1 to 3. The recruitment criterion were healthy subjects between 20 to 40 years old, right-handed, no history or current treatment of neuropsychological disorders, and no particular dietary constraints. The sample size was based on educated guess rather than formal power calculation, as we could not know in advance whether the effect of serial position would be present or not. A posteriori, given the observed size of the bonus parameter and its standard deviation across participants, a formal estimation indicated that a group of n = 20 participants would have been sufficient to test the effect with significance at 0.
Stimuli and apparatus We used a total of 430 items in Exp 1 and 180 items in Exp 2 & 3 which were presented as standardized photos (400 x 300 pixels) on a computer screen. To avoid boredom, we selected items from different categories: processed food (sweet and salty snacks), unprocessed food (vegetables and fruits), films, music albums and readings (newspapers or magazines). The five categories were used in Exp 1, only three in Exp 2 and 3 (unprocessed food and reading categories were discarded). The majority of food items were from the French INSEAD food database, the other items were created in the lab. Food products were photographed frontally over a black background, with some of the contents displayed in front of the packaging. Items from the music category (album cover with name of singer as it is popularized in the French market) and from the reading category (journal or magazine logo over a black background) were recycled from previous studies.
Films were selected among the most popular ones in France during the past 10 years, according to an online film database, and the corresponding images were created in the lab from the French version of movie posters. Although we intended to present familiar items, it remained possible that a participant never heard of a particular film or music album. In this case, the participant was asked to evaluate its likeability on the basis of previous experience with items of a similar kind. All experimental stimuli were presented with Matlab_R2017a (https://www. mathworks. com/) running Psychophysics Toolbox-3  [26]  (http://psychtoolbox. org) and additional custom scripts. The experiment was run on a 20-inch touch screen Windows-based PC with a resolution of 1920 � 1080.
We then grouped the ranked items into choice trials in a way that balanced the position of the best item in the sequence (the highest initial rating compared to other items of a same trial). Note that rankings were only used to control the position of the best item in the sequence, never for data analysis or computational modeling, which instead were based on likeability ratings (i. e. , cardinal and not ordinal values). There were 72 trials per choice session. Choice trials began with a fixation cross (2 seconds), followed by six equally distanced empty squares (three on the upper half and three on the lower half of the screen). Then, the sequential sampling phase started. At each step of the sequence, a new option was displayed at a random location among the six placeholder squares. Participants sampled three to six options before they were asked to make a choice.
Given that in Exp 1, different trial lengths did not result in different predictions or observations, we removed most of the trials with six options. Two such trials were kept per repetition, to avoid participants anticipating the end of the sequence, but were not included in the analyses. These catch trials were composed of items with the six lowest and six highest ratings, because extreme ratings might be bounded by the scale and thus less accurately estimated. We further removed the reading and unprocessed food categories, reducing the set of items to three categories only. Each of the items was presented six instead of four times in the corresponding choice session, while ensuring that they were paired with different items at each repetition. In total, there were 60 items in a rating session, for a total of 84 choice trials (72 in data analysis) per session.
This modified model was labeled H2. 1, to make the link with H1, as it also incorporates a notion of primacy bias (applied to the first option only). In H3 (pruning model), the sequence order affects selection probabilities by eliminating options instead of updating values, after each pairwise covert comparison. At every step, the probability of keeping the current best versus the new option is calculated as in (1), through a softmax function of their values, which remain equal to initial ratings. Therefore, the global likelihood that an option is eventually chosen can be estimated by the multiplication of all conditional probabilities along the corresponding branch in a probability tree diagram. To identify differential predictions of models H0 to H3, we simulated datasets under Exp 1 design.
Ratings were generated using a uniform distribution ranging from 0 to 100, slightly left-skewed towards 0, as was observed in a pilot study showing a subset of stimuli to an independent sample of subjects. The inverse temperature was extracted at first from a pilot experiment with similar designs, and then readjusted with the inverse temperature parameter fitted on choice data collected in Exp 1. The other parameters (λ and δ) were varied to examine their effects on choices. Trial-by-trial selection probabilities were generated for every simulated subject and averaged over 200 simulated datasets of 30 subjects. A critical difference across models was identified in the relationship between the probability of selecting the best option and its position in the sequence (  In H0 (null model), option sampling is neutral, such that choice probabilities are given by a softmax function applied to initial ratings, as in standard decision theory, using one free parameter β (inverse temperature).
Statistical analyses All analyses were run with Matlab_R2018b (www. mathworks. com). Effect sizes are provided as means and standard errors of the mean (SEM). Model-free analyses used linear regressions at the individual level, followed by two-tailed t-tests on regression coefficients, at the group level. Computational models were inverted using a Variational Bayes approach under the Laplace approximation. Model inversions were implemented using Matlab VBAtoolbox (available at http://mbb-team. github. io/VBA-toolbox/). This iterative algorithm provides a free-energy approximation for the model evidence, which represents a natural trade-off between model accuracy (goodness of fit) and complexity (degrees of freedom). Additionally, the algorithm provides an estimate of the posterior density over the free parameters, starting with Gaussian priors.
As the outcome of covert comparisons is probabilistic, the figure illustrates only one of many possible trees. B) Results of model simulations (under Exp 1 setup). Graphs show simulated choices as the probability of selecting the best option (y axis), depending on its serial position in the sampling sequence (x-axis), which was varied across trials. The same softmax function is used in all models to derive choice probabilities from option values, with an inverse temperature β = 0. 081, corresponding to the posterior estimates obtained from fitting the best model (H2. 1) to choice data collected in Exp 1. Each of the plots is an average over 200 simulated datasets of 30 subjects implementing the corresponding model, for various (color-coded) number of options. Shaded areas indicate the average inter-participant SEM, across all datasets.
For similar reasons, we bounded the prior of the bias parameter in the primacy model (H1) to be positive, because we did not observe any trend in the data that would reflect a recency bias. To examine whether these models can be distinguished through Bayesian comparison, we conducted a model recovery analysis. Obviously, recovery success depends on computational parameters, since for instance, model H1 with a null λ parameter is nothing but H0. What we intended to check is whether a winning model, with an exceedance probability over 0. 95, could be confused with another in the model space, given the observed choice data. We therefore used the observed likeability ratings, and computational parameters fitted on our choice data, to simulate virtual groups of participants. Bayesian model comparison was then conducted on simulated data in the exact same manner as with observed data.
24 ± 0. 047, β = 0. 082 ± 0. 0065; Exp 2: β c = 0. 22 ± 0. 049, β = 0. 10 ± 0. 009). However, the bonus parameters were in the same range as with the deterministic model H2. 1, and significantly different from zero in both experiments (Exp 1: δ = 0. 57 ± 0. 13; Exp 2: δ = 0. 41 ± 0. 14). Thus, even if using a softmax function for covert choice is theoretically more grounded, our choice dataset was not sensitive enough to benefit from this additional parameter. In any case, the impact of covert pairwise comparisons (the bonus added to the winning option and subtracted from the losing option) was significant in both the deterministic and probabilistic version of model H2. 1. Across-trial effects. Lastly, we assessed whether the shift in preference, captured with the bonus parameter in model H2.
Image acquisition and analysis. Whole-slide images were acquired on an Axio Scan. Z1 (Zeiss). Image analysis and cell counts were obtained manually in the HALO software (Indica Labs Inc. ). Analysis thresholds were applied before images were taken. Brightness and contrast were subsequently modified to facilitate signal visibility. A schematic image of each analyzed coronal brain section was created using Adobe Illustrator CS5 (Adobe Systems Software). For each POMC neuron, its distance to the ventricle and the subpopulations it belonged to was extracted from these schematic images by a custom-written Python script (Graham N. Stratton).
POMC neurons with specific neurotransmitter profiles have distinct spatial distribution and molecular fingerprints POMC neurons are molecularly diverse  (Campbell et al. , 2017; Henry et al. , 2015; Lam et al. , 2017)  and include different subtypes, also based on the neurotransmitter they produce  (Dicken et al. , 2012; Hentges et al. , 2004; Hentges et al. , 2009; Wittmann et al. , 2013). Accordingly, triple fluorescent in situ hybridization (FISH) analysis in brains of adult C57BL/6J mice revealed different hypothalamic POMC subpopulations depending on whether or not POMC cells expressed the GABA synthesizing enzyme GAD67 and/or the glutamatergic marker vesicular glutamate transporter 2 (vGlut2, Figure  1A ). Different POMC subpopulations also   S2 ). mTOR and associated proteins such as rptor and rictor  (Saxton and Sabatini, 2017) , as well as CB1R were detected in all POMC subpopulations (see GEO: GSE74672).
had specific spatial distribution, as assessed by analyzing their rostro-caudal distribution and the distance from the third ventricle(Figures 1B, 1C and S1A in Supplemental Material). POMC/glutamatergic neurons were generally furthest from the ventricle and had a rostrocaudal decrease, while POMC/GABAergic neurons were closest to the ventricle and had a rostro-caudal increase(Figures 1B, 1C and S1A). POMC GABA/glutamatergic cells and POMC cells negative for the 2 neurotransmitters were somewhat in between these two populations(Figures 1B, 1C and S1A). To then align POMC neurons to GABA, glutamate or mixed neurotransmitters expression, we analyzed publicly available single-cell RNA sequencing data of POMC neurons from the hypothalamus of C57BL/6N mice [GEO accession number: GSE74672, (Romanov et al.
(Reich et al. , 2006)s expressing Pomc transcripts in the hypothalamus through public databases on in situ mRNA hybridization(Allen Brain Atlas, Mouse Brain, experiments 2493 and 2494)and noted highly specific Pomc expression in the ARC. This validates that Pomc-expressing neurons in our single-cell RNA-seq dataset originate from the ARC, and can be used for subtype deconvolution. For the validity of our following analysis, we checked cells for outliers using Principal Component Analysis to exclude those neurons coming to the dataset because of the contamination/ectopic Pomc gene transcription. Finally, we performed differential gene expression profiling of Pomc + neurons by using Genepattern 3. 9. 8(Reichet al. , 2006); which was not certified by peer review) is the author/funder. All rights reserved.
Acknowledgements We thank the animal, genotyping and bioinformatics platforms of the  INSERM   U1215 , funded by  INSERM  and  Labex Brain , for animal care, mouse lines management and genotyping. We thank the analytical chemistry facility of the  INSERM   U1215 , funded by  INSERM , for endocannabinoids quantification, and  UT Southwestern  and IGBMC for POMC-CreER T2 mice. The microscopy was done in the  Bordeaux Imaging Center , a service unit of the  CNRS-INSERM and Bordeaux University , member of the national infrastructure  France BioImaging  supported by the  Labex Brain. The help of F. Cordelières,  C. Poujol ,  S. Marais  and  P. Mascalchi  ( University of Bordeaux ) is acknowledged. We thank the biochemistry and biophysics platform of Bordeaux NeuroCampus, supported by the  Labex Brain , and  Dr.
Evaluation of WIN effect on mIPSC on PVN parvocellular neurons of POMC-CB1 mice (Reich et al. , 2006)s expressing Pomc transcripts in the hypothalamus through public databases on in situ mRNA hybridization(Allen Brain Atlas, Mouse Brain, experiments 2493 and 2494)and noted highly specific Pomc expression in the ARC. This validates that Pomc-expressing neurons in our single-cell RNA-seq dataset originate from the ARC, and can be used for subtype deconvolution. For the validity of our following analysis, we checked cells for outliers using Principal Component Analysis to exclude those neurons coming to the dataset because of the contamination/ectopic Pomc gene transcription. Finally, we performed differential gene expression profiling of Pomc + neurons by using Genepattern 3. 9. 8(Reichet al.
Data were then rendered into heatmaps (HeatMapImage module) for maximum visual clarity. The Database for Annotation, Visualization and Integrated Discovery (DAVID) Bioinformatics Resources 6. 7 was used to identify the genes that had statistically significant differences in expression among the different POMC neuron subpopulations(Huang da et al. , 2009). To interpret our data, we used the «Functional annotation chart» tool. This tool associates gene ID with a biological term which belongs to one out of the 40 annotation categories available in DAVID, including Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. This extended annotation coverage increases the analytic power by allowing investigators to analyze their genes from many different biological aspects in a single space.
Statistical Analysis From the results of this event (women and men final), we obtained the lap times (in s) and lap positioning. Women and men performances were tested separately. Data in the text are given as mean values ± standard deviation (SD). The variability was tested for lap time and position variation (position range of each rider position) using coefficients of variation (%). Statistical analyses were conducted using Statistica v8. 0 (StatSoft Inc. , Tulsa, USA). P < 0. 05 was set as the level of statistical significance for all tests. Because sphericity and normal distribution of the data were verified by using Mauchly and Shapiro-Wilk tests, parametric statistics were used for lap time. A one-way analysis of variances (ANOVA) was used to test differences between laps. The main factor (lap number) was used as a repeated measure.
Image Analysis Immunostainings were quantified using the semi-automated CellInsight NXT High Content Screening (HCS) platform (Thermo Fisher Scientific) and Studio Cell Analysis Software (Thermo Fisher Scientific), as previously described  (de Lucia et al. , 2020; Smeeth et al. , in press ). This platform relies on light intensity thresholds, which identify DAPI (wavelength 386) or secondary antibody fluorescence (wavelengths 488 and 555). These thresholds, combined with other parameters based on cell size and shape, identify cells stained by each antibody in an unbiased way and enable semi-automated quantification of immunocytochemical stains. Threshold settings were set by an author blinded to exposure/concentration and parameters were kept constant across experiments. For further details on the protocols and parameters used see Supplementary Material.