dataesr/scientific-paragraphs-categorization huggingface dataset:
We trained the models using the first dataset and tested their precision on the second one to check their ability to generalize to unseen data. As a final remark, we indicate with t the time bin of activity, i. e. x i (t) indicate the activity of the zone i in a time range [tδt, (t + 1)δt]. Pseudo-log-likelihood maximization. The Boltzmann probability is defined over the whole time series of all the zones, i. e. From this, it is straightforward to define the conditional probability of one-time step x(t) concerning all the previous ones: Using equation (  8 ), we can define the Pseudo-Log-Likelihood as: Here, using Eq. (  16 ) and substituting the functional form of the two total probabilities, we obtain with Substituting in eq. (  17 ), we get: (13) z = x σ , ( 14 ) z i (t) = x i (t) σ i (t δt ) , (15) P x(t), x(t -1),.
Data availability The data has been collected via the Enjoy APIs, scraping the information through a client account. We obtained information about the area of service (i. e. , the limit where people can start and end their ride), the location of vehicles, and the points of interest (POIs) related to the service (such as fuel stations and reserved parking) from the API endpoints. The endpoints used have base URL https://enjoy. eni. com/ajax/ and the following endpoints: retrieve_areas for the area, retrieve_vehicles for cars, and retrieve_pois for points of interests. Through the scraping procedure, we collected a series of events that are divided into two categories: parking events and travel events of each Enjoy car we could observe. Each event comes with information such as the car's number plate, the time at which the event occurred (with the precision of seconds), and the latitude and longitude of the parking spot in the case of parking events.
The light from the Si 3 N 4 waveguide was directly coupled into a gas cell and the transmitted spectrum was collected by an optical spectrum analyzer where clear dips showing an excellent agreement with the HITRAN database were measured. In  [201] , this source was further optimized by controlling the pump wavelength and the waveguide geometry. The spectral coverage of a single DW could be significantly enhanced, reaching a 10 dB bandwidth of 1000 nm around 3. 5 μm, without sacrificing efficiency. As such the MIR source from one Si 3 N 4 waveguide was used for simultaneous and discrete detection of multiple gas-phase molecules in a single gas cell: acetylene, methane, and ethane were detected with a simple direct-absorption spectroscopy setup (Figure  19(a)-(c) ). The coherence of a DW generated in Si 3 N 4 was also exploited in a dual-comb experiment, where both combs were generated in integrated waveguides, the dispersion of which was engineered based on a coupled structure to flatten and extend the integrated dispersion on the long wavelength side  [202].
Research funding: We acknowledge the support of the  European Research Council  ( ERC-2017-CoG 771647 PISSARRO ), the  International Associated Laboratory in Photonics between France and Australia (LIA ALPhFA) , the  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie Actions  (Grant No ECLAUSion, 801512), the  Agence Nationale de la Recherche (ANR)  (Grant No. MIR-SiCOMB ,  ANR-17-CE24-0028 ;  MIRthFUL ,  ANR-21-CE24-0005 ).
INTRODUCTION Time perception is notoriously labile and in uenced by numerous factors ranging from basic physiological states such as arousal, to cognitive factors such as one's temporal perspective, emotion or cognitive load. Time is so fundamental to human lives that an altered sense of it often indexes an individual's well-being  (Holman & Grisham, 2020). During the Covid-19 pandemic, a consortium of researchers collected a series of online tests and questionnaires in several countries, in and out of lockdown, to preserve a quantitative historical record of subjective temporalities and potential covariates  (Chaumon et al. , 2022). Using the Blursday database, the present study asks whether time perception is parametrically altered when varying participants' task load during the time interval to be estimated.
Wearden et al. , 2014 ; J. H. Wearden, 2015). Passage of time (PoT) judgements consist in asking participants to rate how fast time felts to be passing using a Likert or a visualanalog-scale. The distinctiveness of PoT with duration estimations has been observed for durations in the order of minutes (Droit-Volet et al. , 2017). Whether PoT judgments are covariates or radically differ from typical duration estimations and temporal expectation quanti cations remains quite uncharted although a recent study using virtual reality suggests that duration and passage time estimates may capture distinct cognitive processes  (Jording et al. , 2022). In the present study, we tested a series of working hypotheses, replicating and largely extending previous literature. Using the online large-scale Blursday database  (Chaumon et al.
Participants engaged in the initial n-back sequence were not aware that subsequent timing judgements would be required: hence, the initial sequence in the study provided a measure of retrospective duration and PoT judgments. Subsequent sequences provided data for prospective duration and PoT judgments. This approach provides a unique 2 x 2 intra-individual design of four measures of time estimations. Additionally we could test in a single experimental protocol the predictions of a foundational meta-analysis on the differential effects of cognitive load in prospective and retrospective duration judgments  (Block et al. , 2010). Last, as the data were collected during the Covid-19 pandemic, we explored how lockdowns affected participants' experiences of time at the scale of a few seconds to minutes, both prospectively and retrospectively, and under controlled cognitive load.
Timing errors were de ned as the difference between participants' duration estimation and the veridical duration. We separately computed the z-scores (the distance of the value from the mean expressed in standard deviation) of the duration estimates distributions obtained following the 45 s and 90 s n-back sequences within each participant. Timing errors above or below two standard deviations (i. e. , |zscore| > 2) away from the mean of the distribution were discarded. This procedure resulted in the exclusion of about ~ 3% of the total number of data points (28 out of 873) for the retrospective trials and for the prospective trials (363 out of 12204). For the control session, it resulted in the exclusion of 5% (8 out of 157) and ~ 3% (89 out of 2931) of the retrospective trials and the prospective trials, respectively.
45, CI = [1. 10, 1. 90]). Table  4  Results of the models that best explains the variance of the PoT in prospective (clmm, 14,633 obs. ) and retrospective (polr, 987 obs. ) direction, and globally by combining these two conditions (lme, 15620 obs. ). Data from SC and S1 were included. Last, we asked whether duration estimation and PoT estimates were related. For this, we tested the rDE quanti ed in the duration estimations as a predictor of the PoT judgments for all trials (Table  4 ). We found that participants who overestimated the duration (rDE > 1) rated the passage of time as passing "Slow" or "Very Slow" (Fig. 5 ): for every unit increase of rDE, the odds of answering "Very Slow" was 51% higher (OR = 0. 49, CI = [0. 45, 0. 53]). Additionally, we found a signi cant interaction between the rDE and the Direction: for every one unit increase in rDE, participants were approximately 3.
Acknowledgments: We acknowledge all researchers from the consortium that have enabled the collection of the Blursday database:  Federico Alvarez Igarzábal ,  Fuat Balci ,  Nicola Cellini ,  Arnaud D'Argembeau ,  Elisa M. Gallego Hiroyasu ,  Anne Giersch ,  Simon Grondin ,  Claude Gron er ,  Sophie K. Herbst ,  André Klarsfeld ,  Rodrigo Laje ,  Sebastian Kübel ,  Ljubica Jovanović ,  Elisa Lannelongue ,  Esteban Durán Mendoza-Durán ,  Luigi Micillo ,  Giovanna Mioni ,  Ramya Mudumba ,  Pier-Alexander Rioux ,  Serife Leman Runyun ,  Ignacio Spiousas ,  Narayanan Srinivasan ,  Shogo Sugiyama ,  Rania Tachmatzidou ,  Vassilis Thanopoulos ,  Argiro Vatakis ,  Anna Wagelmans ,  Marc Wittmann ,  Yuko Yotsumoto. We thank  Bernadette Martins  ( CEA, NeuroSpin ) for help with ethical aspects in setting up the study.
For example, CD83 (encoding (C) Volcano plot showing differentially expressed genes calculated using DEseq2 (padj < 0. 05, FC > 1. 5) between AS patients and HCs in CD4 + T cells, CD8 + T cells, and monocytes. Genes in AS-associated GWAS regions are purple. Red genes are upregulated and blue downregulated in AS patients. CD4 + T cells had 122 differentially expressed genes, CD8 + T cells 299 genes, and monocytes 300 genes. (D) Cell type specificity of differentially expressed genes; numbers of differentially expressed genes are given. (E) Examples of differential gene expression at CD83 and TNFSF14; **padj < 0. 01, ***padj < 10 À7 (from DEseq2). (F) Enriched pathways in the Reactome database (FDR < 0. 01 from XGR output) from significant differentially expressed genes in each cell type. Dot size represents percentages of genes represented in that pathway, and colors represent cell types.
Quiescent (Quiesc) states: Wk, low/weak enhancer; Int, introns; Quies, quiescent. (C) Significance of disease-specific ChromHMM segment frequency for each state in each cell type, calculated using permutation analysis (ns, not significant). Only monocytes contained significantly over-represented states. (D) MCA showing overall distribution of ChromHMM states within each cell type comparing AS patients and HCs. The monocyte plot dotted line at x = 0 delineates AS and HC samples. (E) Enriched pathways in the Reactome database (FDR < 0. 01, XGR output) performed on genes linked to promoter-like states, transcribed regions, or enhancerlike states in monocytes. Dot size represents percentage of genes represented in that pathway. (F) Visualization of differential ChromHMM regions (vertical arrows) at (i) the promoter of ELOVL3 and (ii) enhancer of TLE3, with (iii) magnified region of the TLE3 differential enhancer.
A B C (legend on next page) located between CCR5 and CCRL2, but this locus has not been fine mapped to establish a functional variant and modulated gene in AS. Consequently, we interrogated the whole monocyte-specific topologically associating domain (TAD) and discovered several regions with disease-specific chromatin signatures. We found evidence of an intergenic region between XCR1 and CCR1 that has an enhancer-like profile (ChromHMM state 10, EnhA) in AS patients with a promoter-like profile (ChromHMM state 3, FlnkU) in HCs, consistent with observed differences in eRNA and H3K27ac between cases and controls. Analysis of chromatin interaction data shows that this region is involved in DNA looping to CCR1, CCR2, CCR5, and CCRL2 but not with CCR3. Looping events correlate with expression of the cognate genes in monocytes.
This SNP lies at chr2p15 and overlaps a putative enhancer that forms a looping interaction with B3GNT2 identified by publicly available PCHi-C (Figure  5D ). rs4672505 is associated with AS, Crohn disease, and psoriasis  10  and is an eQTL for B3GNT2. 45 e of disease-specific functional genomic datasets enhances drug target discovery in AS The final aim of our study was to prioritize new therapeutic targets in AS. We previously developed Pi, a genetics-led approach that annotates GWASs with functional genomic data to prioritize therapeutic targets across a range of immune-mediated diseases. 33, 34 We modified the underlying algorithm of Pi to include our new AS-specific functional genomic datasets (Figure  6A ; STAR Methods), the algorithm previously having been limited to non-disease-context functional genomics data, and assessed whether this inclusion increased the power to identify potential therapeutic targets.
In the original algorithm, we used diseasespecific genetic associations to define seed (core) genes, including (1) nearby genes (nGene) using genomic proximity and organization, (2) expression-associated genes (eGene) (B) Enrichment of ATAC, H3K4me3, H3K27ac, and eRNA peaks at AS-associated GWAS loci  9  in CD4 + T cells, CD8 + T cells, and monocytes; GWAS association p value thresholds are indicated. Error bars represent 95% confidence interval of the OR (from GARFIELD  42  ). ***p < 0. 001, **p < 0. 01, *p < 0. 05. (C) Visualization of multiple epigenomic datasets at chr3p21 (top, chr3:45890000-46600000; bottom, chr3:46090000-46200000). PCHiC looping events  38  that intersect regions of differential chromatin are indicated in red. Selected gene transcripts from the Ensembl database are shown.
ChromHMM is shown for 4 AS patients and 4 HCs with colors as in Figure  3B. The lead AS-associated SNP rs1001007 10 is shown. Representative ATAC, H3K4me3, and H3K27ac tracks (RPKM) and total RNA tracks (log2 count) are shown for AS patients (orange) and HCs (blue), with called peaks marked by gray boxes and differential peaks shown in red (upregulated in AS) or blue (downregulated in AS). See also Figure  S4. integrating eQTL datasets, and (3) conformation genes (cGene) using PCHi-C datasets. Here, we added five types of AS-specific functional genomic predictors using data from this study (denoted RNA, eRNA, H3K27ac, H3K4me3, and ATAC). The AS-specific expression predictor (RNA) was generated based on differential gene expression, and AS-specific epigenomic predictors (ATAC, H3K4me3, H3K27ac, and eRNA) were prepared on the basis of differential peaks linked to genes as above.
We benchmarked the performance of the Pi algorithm with and without the AS-specific functional genomic predictors to prioritize currently approved drug targets for AS versus simulated negative targets (STAR Methods). This showed that inclusion of disease-specific data improved the predictive power (area under the curve [AUC] = 0. 869) compared with the original prediction (AUC = 0. 822) and the state-of-the-art approach (Open Targets,  49  including text mining [AUC = 0. 761] and genetic associations [AUC = 0. 582] from the Open Targets Genetics Portal 50 ) (Figure  6B ). As expected, combined use of predictors performed much better than each predictor alone (Figures  6B  and S15A ). Among the top 1% of prioritized genes (of >17,000 ranked genes) were the known AS drug targets IL23R, JAK2, and TYK2 (Figure  S15B ).
This variant has been associated previously with differential abundance of B3GNT2, a poly-N-acetyllactosamine synthase in whole blood. 45 B3GNT2 is upregulated in T cells on activation, and a recent CRISPR screen showed evidence that this enzyme is important in modulating T cell activation in the setting of cancer. 57 Our findings suggest that reduced expression of B3GNT2 in individuals with the AS risk allele is likely caused by reduced chromatin openness at this locus. This may impact higher-order chromatin structures, such as the looping event identified between a distal enhancer and B3GNT2 (Figure  5 ). The JASPAR 2022  58  database of TF binding profiles predicts that STAT1 binds to this site, a TF that plays an important role in transcriptional activation in the immune system. 59 Further work, such as genomic editing and functional assays, will be needed to identify the function of these or other unknown TFs at this locus.
Limitations of the study This study used disease-relevant immune cell subsets isolated from PBMCs of AS patients and HCs. One limitation of our study (C) Prioritized target pathways (FDR < 0. 05) based on KEGG pathway enrichment analysis using the top 1% prioritized genes. Dot size represents percentages of genes represented in that pathway. (D) Identification of pathway crosstalk; that is, a network of highly prioritized and interconnecting genes defined by KEGG interaction data, with nodes segmented by predictor/evidence types. (E) Table summarizing the prioritization and evidence information associated with pathway crosstalk genes. Colored boxes underneath each predictor type represent the datasets in support. Existing therapeutic targets are indicated for a AS and b other autoimmune conditions.
Therapeutic target prioritisation Using our previously established Pi pipeline,  33  we first prepared three types of genomic predictors taking as inputs GWAS summary data in AS  9  and the knowledge of protein interactions  116  including: (i) nearby genes (the nGene predictor) using genomic proximity and organisation  117  (ii) expression-associated genes (the eGene predictor) integrating eQTL datasets,  [118] [119] [120] [121] [122]  and (iii) conformation genes (the cGene predictor) using PCHi-C datasets. 38 Next, we modified the Pi algorithm to prepare additional five types of genomic predictors using disease-specific functional genomic datasets arising from this study, including (iv) the AS-specific expression predictor (the RNA predictor) generated based on differential gene expression between AS patients and HC, and (v) AS-specific epigenomic predictors (the ATAC, eRNA, H3K4me3 and H3K27ac predictors) on the basis of differential peaks (linked to genes via genomic proximity or PCHi-C looping events) between AS patients and HC.
The knowledge of protein interactions was obtained from the STRING database,  116  and only the genes located in chromosomes 1-22 were considered; this corresponded to a total of 17,249 genes prioritised (and ranked by priority rating). The performance benchmarking (including how to define clinical proof-ofconcept targets and simulate negative targets) and comparisons with naı ¨ve prioritisation and Open Targets  49, 50  were the same as previously described  33, 34  and uses Area Under the Curve (AUC) as a global measure of performance. We carried out pathway enrichment analysis of the top 1% prioritised genes using KEGG pathways  123  with the enrichments measured by Z-score and FDR (one-sided Fisher's exact test). Using the algorithm originally proposed in dnet,  98  we performed pathway crosstalk analysis to identify a subset of gene interactions (merged from KEGG pathways) that contained highly prioritised and interconnecting genes.
01, ***padj < 10 À7 (from DEseq2). (F) Enriched pathways in the Reactome database (FDR < 0. 01 from XGR output) from significant differentially expressed genes in each cell type. Dot size represents percentages of genes represented in that pathway, and colors represent cell types. (G) CXC subfamily of the Kyoto Encyclopedia of Genes and Genomes (KEGG) ''cytokine-cytokine reception interaction'' pathway colored by gene expression log 2 FC. Significantly differentially expressed genes are marked by asterisks. The p value of CXC family subset over-representation is shown below for each cell type, calculated by chi-squared test with Yates' correction. See also FigureS1and TableS3.
(C) Significance of disease-specific ChromHMM segment frequency for each state in each cell type, calculated using permutation analysis (ns, not significant). Only monocytes contained significantly over-represented states. (D) MCA showing overall distribution of ChromHMM states within each cell type comparing AS patients and HCs. The monocyte plot dotted line at x = 0 delineates AS and HC samples. (E) Enriched pathways in the Reactome database (FDR < 0. 01, XGR output) performed on genes linked to promoter-like states, transcribed regions, or enhancerlike states in monocytes. Dot size represents percentage of genes represented in that pathway. (F) Visualization of differential ChromHMM regions (vertical arrows) at (i) the promoter of ELOVL3 and (ii) enhancer of TLE3, with (iii) magnified region of the TLE3 differential enhancer.
Selected gene transcripts from the Ensembl database are shown. ChromHMM is shown for 4 AS patients and 4 HCs with colors as in Figure3B. The lead AS-associated SNP rs1001007 10 is shown. Representative ATAC, H3K4me3, and H3K27ac tracks (RPKM) and total RNA tracks (log2 count) are shown for AS patients (orange) and HCs (blue), with called peaks marked by gray boxes and differential peaks shown in red (upregulated in AS) or blue (downregulated in AS). See also FigureS4.
Detailed methods are provided in the online version of this paper and include the following: Data and code availability d Sequence level RNA-seq, ATAC-seq, ChIPm fastq and genotype data have been deposited at the European Genome-Phenome Archive (EGA); access is managed by a Data Access Committee. Count data for RNA-seq, ATAC-seq, ChIPm and eRNA (raw and normalised), Capture-C mean pile-up tracks and PeakY scores, and ChromHMM data are deposited at Zenodo. Accession numbers are listed in the key resources table. This paper analyses existing, publicly available data (GWAS data, PCHiC data and eQTL). The accession numbers for the datasets are listed in the key resources table. d All original code for the ICeCAP pipeline has been deposited at GitHub and Zenodo and is publicly available as of the date of publication.
ACKNOWLEDGMENTS This research was supported by  Versus Arthritis  (program grant  20773  to J. C. K. ),  Janssen Oxford Translational fellowships  (to J. C. K. ),  Wellcome  grant  106130/Z/14/ZR  (to  Prof. Doug Higgs ), and a  Wellcome Trust  grant ( 090532/Z/09/Z ) to core facilities at the  Wellcome Center for Human Genetics. Computation was supported by  Wellcome Trust   Core Award  grant  203141/Z/16/Z  with funding from the  NIHR Oxford BRC. The study received support from the  National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC)  (to J. C. K. , B. P. W. , and P. B. ). The views expressed are those of the author(s) and not necessarily those of the  NHS , the  NIHR , or the Department of Health. J. C. K. is supported by a  Wellcome Trust   Investigator Award  ( 204969/Z/16/Z ).
K. S. E. and A. C. B. are supported by  Janssen Oxford Translational Fellowships. C. J. C. was supported by funds from  NDORMS, University of Oxford , and  Oxford University Bridging Salary Support. C. D. is supported by  Versus Arthritis  PhD studentship 22198. F. C. was supported by research grants from  FOREUM Foundation for Research in Rheumatology, SocieteFrancaise de Radiologie, Fondation pour la Recherche Medicale  ( FRM SPE201803005058 ), and  Fondation Bettencourt Schueller. A. L. L. was supported by  Psoriasis UK  PhD studentship 200314. A. S. was supported by  Arthritis Research UK  PhD studentship MP/20331. H. F. is supported by the  National Natural Science Foundation of China  ( 32170663 ) and  Shanghai Pujiang Program  ( 21PJ1409600 ). P. Z. is supported by the  Chinese Academy of Medical Sciences (CAMS) Innovation 537 Fund for Medical Science  ( 2018-I2M-2-002 ).
Introduction The basic characteristics of natural proteins are the ability to fold into compact three-dimensional structures, to carry out chemical reactions, and to adapt as conditions of selection fluctuate. To understand how these properties are encoded in the amino acid sequence, a powerful approach is statistical inference from datasets of homologous sequences -the study of evolutionary constraints on and between amino acids. In different implementations, this approach has led to the successful prediction of protein tertiary structure contacts  [1] [2] [3] [4]  , protein-protein interactions  [5] [6] [7]  , mutational effects  [8] [9] [10] [11]  , and even the design of synthetic proteins that fold and function in a manner indistinguishable from their natural counterparts  [12] [13] [14].