dataesr/scientific-paragraphs-categorization huggingface dataset:
Participants 903 participants living in the US aged 18 and above (395 men, 489 women, mean age = 27 years, SD = 8. 0 years, range = 18-66 years, mean number of years of formal education = 16. 1 years, 315 currently living in cities, 588 currently living outside cities) were recruited using the Prolific database  (www. prolific. co, 2023)  and reimbursed for their time. Ethical approval was obtained from the University College London Review Board conforming to Helsinki's Declaration. All participants provided informed written consent. We first removed 94 participants who reported that they did not currently drive (12% of the sample), and then removed an additional 17 participants who selected 'other' for gender given that there were too few participants in this category to run the mediation analysis.
We next identified outliers using Mahalanobis' Distance, a method shown to have high sensitivity and specificity and a minimal change in bias in simulated and real datasets based on questionnaire data when removing outliers compared to other methods  (Curran, 2016; Zijlstra et al. , 2011) (Supplementary Materials). 97 participants were removed as outliers. This resulted in a final sample size of 694 participants (291 men, 403 women, mean age = 26. 8 years, SD = 7. 4 years, range = 18-52 years, mean number of years of formal education = 16. 2 years, 232 currently living in cities, 462 currently living outside cities). Demographic information for the final sample is summarised in Table  1. Data analysis was completed using R studio  (version 1. 4. 1564)  and Python (version 3. 9. 12).
Conclusion Overall, our findings indicate that early life experiences, such as driving, may be important in predicting future navigation ability. Accordingly, this research will serve as a platform for future research looking into how actively engaging in such activities in early life may improve one's ability to navigate in complex environments and provide a greater understanding as to what drives the wide variation in individual differences in navigation ability. the SHQ system according to the criteria and requirements of a specific project decided by the experimenter. Access to the portal will be granted for non-commercial purposes. Future publications based on this dataset should add 'Sea Hero Quest Project' as a co-author.
Statistics Categorical variables were expressed as percentages. Cronbach's alpha coefficient was used to determine the internal consistency of the assessment tool after each round. Cronbach's alpha varies between 0 e 1, with 1 corresponding to 100% consistency, and demonstrates how closely related a set of test items is as a group. Consensus in the final round 4 was defined as Cronbach's alpha > 0. 80. 25  IBM SPSS Statistics version 25. 0 (IBM Corp. , Armonk, NY, USA) was used for statistical analysis.
Inside the channel flows the liquid metal Galinstan. The microfluidic channel crosses the cavity at its center, where the electric field of the fundamental resonant mode reaches its highest magnitude. A detailed view of the microfluidic channel with Galinstan through the waveguide is displayed in Fig. 1(c ). The meniscus position l of Galinstan in the channel is called here "the level of Galinstan. " When this level is 0 mm, it means that the microchannel placed inside the cavity is empty; when the level equals the height of the waveguide (i. e. , when l = 3. 556 mm), the channel inside the waveguide structure is completely filled with Galinstan. In Fig. 2  (blue color), the simulated magnitude of the transmission coefficient S 21 at 23. 8 GHz of the two-port cavity is displayed as a function of the level of Galinstan (simulation results are obtained from the finite element method provided by the software Ansys HFSS).
0 mm). When the micro-positioning table displaces the cavity along the channel, the level of Galinstan increases. We note that the uncertainty of the waveguide thickness is of ±20 µm, and the curvature in the meniscus of the liquid metal induced an uncertainty of 10 µm on the level l of the Galinstan. Therefore, the level of Galinstan is estimated with a precision of ±30 µm during the displacement of the cavity along the (static) channel. The measured magnitude of the transmission coefficient as a function of the level of Galinstan is displayed in Fig. 2  (red color). The difference between the (lossless) simulation results and experimental data does not exceed 5 dB. We obtain a measured full dynamic range |S 21 | = 33 dB, and consequently, a measured sensitivity |S 21 |/ l of 9. 1 dB/mm. Between 0.
The network The NETSARC+ network includes 26 expert sarcoma centers, organizing multidisciplinary tumor board (MDTB) with sarcoma specialized pathologist(s), radiologist(s), surgeon(s), radiation oncologist(s), medical oncologist(s), molecular biologist(s), orthopedist(s), and pediatrician(s)  [1, 29, 30]. All sarcoma or suspected sarcoma patient cases presented to the MDTB of these centers are recorded in the NETSARC+ database, by a dedicated team of clinical research assistant (CRAs), supervised by three Coordinating centers (Centre Leon Bérard, Institut Gustave Roussy, Institut Bergonié). Patient files may be presented to the MDTB before any diagnostic procedure, before initial biopsy, before primary surgery, after primary surgery, at relapse, and/or in case of inclusion in a clinical trial.
The NETSARC database and the CONTICABASE The NETSARC database exhaustively describes the incident and prevalent population of all sarcoma patients in France, using a cross comparison of the pathological review database (pathology review of sarcoma is mandatory in France) and the clinical database. NETSARC database includes information on the diagnostic and initial treatment procedures, and patients' outcome including survival and relapse but this database includes a limited set of data, on purpose, describing patients and tumors characteristics, surgery, relapse and survival  [29] [30] [31]. For the present study, the database was extracted on January 2023 for the period from January 2010 to December 2022. The CONTICABASE is a sub-set of NETSARC+, which includes all patients managed since the initial diagnosis in one of the NETSARC+ centers.
It contains more detailed information on the nature of treatment, responses, and follow-up, as well as molecular characteristics. It is extensively used for publications of the group  [30, 31]. The 116 patients included during the same period within the Conticabase were extracted and all 12 patients with documented systemic treatment were analyzed. These databases were approved by the French Ethic Committee and Agency in charge of non-interventional trials: the "Comite´Consultatif sur le Traitement de l′Information en matière de Recherche dans le domaine de la Sante´(CCTIRS: number of approval 09. 594)" and the "Commission. Nationale Informatique et Liberte´(CNIL: number of approval 909510)". As part of being managed in the network and to have their data added to the NETSARC registry, the patients have the opportunity to opt-out for the analysis of their data in national and academic research programs.
Statistical analyses The categorical data were summarized by the frequencies and percentages, and the continuous covariates were summarized with median, range and numbers of observations. The statistical test used for comparison was a chi-square (or a Fisher) test for categorical covariates, without adjustment for multiple comparisons. The diagnostic date was the date of first histological diagnosis (biopsy or first surgery). Overall survival (OS) was calculated from the date of diagnosis to the date of last follow-up or death. Event-free survival (EFS) was computed from the date of diagnosis to the date of the last follow-up or the date of the first local or metastatic relapse, progression or death, whichever came first. Progression-free survival (PFS) was computed from the date of diagnosis of metastasis to the date of the last follow-up or the date of local or distant progression or death, whichever came first.
Factors included in the multivariate model were identified by a backward selection procedure which entails including all the covariates in the model and removing those whose p-value is higher than 0⋅10 one at a time. At each step of the model, all included variables were tested and removed if they were no longer associated with the outcome considering a 5% type one error (p-value ≥0⋅05). Therapeutic parameters (immediate surgery yes/no; immediate medical treatment yes/no) were subsequently introduced in the model along with independent clinical prognostic factors. All statistical tests were two-sided. All statistical analyses were performed using SPSS (version 22. 0).
An intriguing observation was that the rate of R0 surgery was significantly lower in SEF, even in reference centers, and even after re-excision. This points to a different local and distant progression pattern of SEF with more infiltrative and locally invasive pattern. This is also supported by the observation that R0 surgery is associated with a less favorable long-term outcome in SEF as compared to LGFMS, with a higher proportion of local and distant relapses. In this series, both SEF and LGFMS were found to have a longer PFS and OS in advanced phase as compared to other sarcomas in the NET-SARC database, and as generally reported. While expected considering the low grade nature of LGFMS, this is more surprising for SEF reported to be a more aggressive tumor with higher risk of progression  [17].
With the ERN EURACAN, the Tracking study (https://clinicaltr ials. gov/ct2/show/NCT04921553) enables to collect information on the response to systemic treatments. In conclusion, LGFMS and SEF, while sharing close hallmark molecular alterations, have a very different clinical presentation, natural history, and therapeutic outcome. They are not indolent diseases in the long term, and should be rigorously managed at diagnosis, following guidelines for sarcomas. Diagnostic and therapeutic management must be conducted in sarcoma reference centers. A limited yet detectable activity of systemic treatments is observed in some patients. These data will also be of interest to be merged in larger international cohorts, possibly using federated learning such as in the the Starter program (https://euracan.
Ethical approval These databases and the work on these databases were approved by the French Ethic Committee and Agency in charge of non-interventional trials: the "Comite´Consultatif sur le Traitement de l′Information en matière de Recherche dans le domaine de la Sante´(CCTIRS: number of approval 09. 594)" and the "CommissionNationale Informatique et Lib-erte´(CNIL: number of approval 909510)".
Funding NetSARC (INCA &  DGOS)  and  RREPS (INCA & DGOS) ,  RESOS  ( INCA & DGOS ) and  LYRICAN+  ( INCa-DGOS-INSERM -ITMO cancer_18003 ),  LabEx DEvweCAN  ( ANR-10-LABX-0061 ),  Eurosarc  ( FP7-278742 ),  EURACAN  ( EC 739521 ),  La Ligue contre le Cancer, Association DAM's ,  la Fondation ARC, Infosarcome, InterSARC (INCA) ,  Ligue de L′Ain contre le Cancer , funded the study. The funders had no role in the study design, data collection, data analysis, data interpretation, or writing of the report.
For endothelial expression, staining was evaluated in capillaries, venules, and small arteries. In a subset of cases to prove the p-S6RP expression in the specific cell types, multiplex immunofluorescence with Opal 3-Plex Detection Kits (Akoya Biosciences) with the following primary antibodies was performed according to the manufacturer's protocol: anti-CD68/anti-AE1AE3/anti-p-S6RP (for co-localization of p-S6RP with macrophages and epithelial cells) and anti-CD31/anti-p-S6RP (for co-localization of p-S6RP with endothelial cells) (data not shown).
Statistical analysis The data in the study is presented as the median (interquartile range) and compared using Fisher's exact test, Pearson's Chi-square test or Wilcoxon rank sum test, depending on the type of variable. The dataset was complete, so no missing data imputation was performed. The sensitivity, specificity, and other performance parameters were calculated and reported using the report ROC R package. 18  The agreements were assessed using Cohen's Kappa. ROC plots were generated using the pROC R package. 19  A total score of ≥7 was chosen as suggestive of AMR diagnosis and was considered, in addition to C4d value, to evaluate the change in AMR levels of certainty. This cut-off was also used for overall survival and CLAD-free survival analyses. The improvement in AMR levels of certainty was assessed by comparing "probable" to "definite" and "possible" to "probable.
" Significance was set at p < 0. 05, and R 4. 2. 2 was used for analysis and plotting. 20  To investigate the relationship between the p-S6RP score and the overall survival or CLAD-free survival, we conducted a survival analysis using Kaplan-Meyer (KM) curves (logrank test) and the Cox proportional hazards regression model. A total score of ≥7 was chosen as suggestive of AMR diagnosis and used as cut-off value for prognostic stratification. Covariates included in the Cox regression were age, sex, biopsy timing (i. e. , the time intercurred from the transplant to the biopsy), native disease, donor age, donor sex, donor smoking status, type of transplant (mono or bilateral), and ischemia time. Since donor data were not always available due to privacy laws, we had about 9% of missing data on our dataset.
Acknowledgments We thank the study participants and the staff of the  Thoracic Surgery, Respiratory, Lung Function Testing  departments from the  University Hospital of Bordeaux (Bordeaux, France) ,  Isabelle Goasdoue ,  Isabelle Bernis ,  Natacha Robert ,  Virginie Niel , and  Marine Servat  from the clinical investigation center for technical assistance, and  Atika Zouine  and  Vincent Pitard  for technical assistance at the  Flow cytometry facility  ( CNRS UMS 3427 ,  INSERM US 005, Univ. Bordeaux ,  F-33000 Bordeaux, France ),  Christel Poujol ,  Magali Mondin ,  Sébastien Marais  and  Fabrice Cordelières  for help with imaging and image analysis et the  Bordeaux Imaging Centre (BIC; Bordeaux, France). Microscopy was performed at  BIC , a service unit of the  CNRS-INSERM and Bordeaux University , a member of the national BioImaging infrastructure of France supported by the  French National Research Agency  ( ANR-10-INBS-04 ).
Funding: The project was funded by : the " Agence Nationale de la Recherche"  ( ANR-21-CE18-0001-01 ) (ID) the  "Region Nouvelle Aquitaine" (ID)  ( AAPR2022A-2021-16982910  and  AAPR2022I-2021-16983510 ) the " Departement Sciences et Technologies" of the Bordeaux University (ID).
We leverage the large RNA-seq dataset from the Genotype-Tissue Expression (GTEx) project  (40)  , encompassing post-mortem tissue samples from hundreds of donors aged from 20 to 70 years, with a pipeline for gene expression profiling with an optimized temporal resolution. voyAGEr allows to investigate aging from two perspectives: (i) gene-centric -how each gene's tissue-specific expression progresses with age; (ii) tissue-centric -how tissue-specific transcriptomes change with age. Additionally, voyAGEr enables the examination of modules of co-expressed genes altered with age in 4 tissues (brain, skeletal muscle, heart (left ventricle), whole blood), namely their enrichment in specific cell types, biological pathways, and association with diseases. We therefore expect voyAGEr to become a valuable support tool for researchers aiming to uncover the molecular mechanisms underlying human aging.
Enrichment The user can thus explore the biological functions of the set of genes underlying each peak of transcriptomic changes by assessing their enrichment in manually curated pathways from the Reactome database  (47). voyAGEr performs Gene Set Enrichment Analysis (GSEA)  (48)  and the user can visualize heatmaps displaying the evolution over age of the resulting normalized enrichment score (NES, reflecting the degree to which a pathway is over-or under-represented in a subset of genes) for the given tissue, effect (Age, Sex or Age&Sex) and all or some user-specified Reactome pathways (Figure  4A ). To reduce redundancy and facilitate the understanding of their biological relevance, we clustered those pathways into families that also include Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways  (49)  and Gene Ontology (GO) Biological Processes of level 3  (50).
Modules of co-expressed genes voyAGEr also allows functional analyses of modules of co-expressed genes (i. e. genes with highly correlated expression across samples, defined by weighted correlation network analysis  (51)  ) altered with age. Genes in the same module are likely to be coregulated and share biological functions or associations with phenotypical or pathological traits  (52). Those modules may also act as markers of core transcriptional features of cellular activity and identity  (53). Concretely, voyAGEr enables the user to visualize how the expression of modules of genes that are associated with a specific cell type or disease progresses over age in a specific tissue. In the example of Figure  5B , the "midnight blue" module, gathering 11 genes co-expressed in the heart's left ventricle, shows significant enrichment in fibroblast markers, namely collagens.
Tissue-specific assessment of age-associated progression of modules of co-expressed genes A. Heatmap of eigengene expression for all the inferred modules of co-expressed genes in the heart's left ventricle over age. B. Scatter plot (above) of eigengene expression over age, in all left ventricle samples, for a selected module of 11 genes. The eigengene expression is derived from the 1 st component of the single value decomposition of its genes' expression. This module was analysed, with Fisher's tests, for enrichment in cell types, based on markers from the literature (Skelly  (84)  , Cui  (83)  and He  (82)  , surnames of the first authors of the studies the markers are retrieved from) and found to be associated with fibroblasts, as revealed by the TreeMap below (where each rectangle's area and darkness are proportional to the significance of its association with a cell type and its color linked to the markers' source study).
C. Heatmap of association of the modules with five selected diseases, computed with the DOSE package  (57). After selecting one tissue of interest, the user has, for each module, access to four levels of information: (i) Expression: its eigengene expression progression over age; (ii) Cell types: its enrichment in specific cell types, based on marker genes found literature, (iii) Pathways: its enrichment in Reactome pathways; (iv) Diseases: its enrichment in disease markers, based on gene-disease associations from DisGeNET  (55) ,  (56)  , calculated with both the DOSE package  (57)  and with Fisher's tests (Figure  5C ). By default, for each tissue, results are displayed in the form of heatmaps showing how all modules evolve with age (Figure  5A ). The user has also the possibility to select a module of interest and see its eigengene progression over age in a scatter plot (Figure  5B ), lists of its association with diseases and pathways ordered by significance, and a TreeMap for its cell type enrichment (Figure  5B ).
This limitation is to be overcome by the accumulation of transcriptomes of human tissues in public databases, promising a gradually increase in accuracy and age resolution with which human transcriptomic aging can be profiled. Similarly, the accumulation of single-cell transcriptomes in public databases is yielding better gene markers for a growing diversity of human cell types, increasing the usefulness of exploiting bulk transcriptomes in studying the impact of aging on the cellular composition of human tissues, for which the co-expression module approach in voyAGEr provides a proof-of-concept. As voyAGEr is open source, it is envisaged as an ever-evolving resource, that can be adapted by researchers to accommodate the aforementioned new data and expanded to provide new functionalities (e.
g. integration with perturbagen data for the inference of molecular causes of observed gene expression alterations or small molecules for therapeutic purposes  (60) ,  (61)  ). As an in silico approach with no experimental validation for its results, voyAGEr is meant to be a discovery tool, supporting biologists of aging in the exploration of a large transcriptomic dataset, thereby generating, refining or preliminary testing hypotheses that will pave the way for subsequent experimental work. It can be an entry point for projects aiming at better understanding the tissue-and sex-specific transcriptional alterations underlying human aging, to be followed by more targeted studies focusing on the functional roles of the most promising markers identified therein in the physiology of aging. Those marker genes can contribute to the development of more robust and cross-tissue gene signatures of aging  (62)  and the expansion of age-related gene databases  (57) ,  (63).
Moreover, the observed diverse and asynchronous changes in gene expression between tissues over the human adult life provides potentially relevant information for the design of accurate diagnostic tools and personalized therapies. On one hand, finding those changes' association with specific disorders could have a prognostic value by enabling the identification of their onset before their clinical symptoms' manifestation  (36). On the other hand, computational screening of databases of genetic and pharmacologically induced human transcriptomic changes could help to infer their molecular causes and shed light on candidate drugs to delay their effects  (60) ,(  61 ),(  64 ),  (65).
Development's platform Data analysis was performed in R (version 3. 6. 3) and the application developed with R package Shiny  (66). voyAGEr's outputs are plots and tables, generated with R packages highcharter  (67)  and DT  (68)  , respectively, that can easily be downloaded in standard formats (png, jpeg, and pdf for the plots; xls and csv for the tables). voyAGEr was deployed using Docker Compose and ShinyProxy 2. 6. 0 in a Linux virtual machine (64GB RAM, 16 CPU threads and 200GB SSD) running in our institutional computing cluster. The source code for voyAGEr is available at GitHub (https://github. com /nunolbmorais/voyAGEr).
Read count data pre-processing We downloaded the matrix with the RNA-seq read counts for each gene in each GTEx v7 sample from the project's data portal (https://www. gtexportal. org/)  (40). We then preprocessed the read count data for each tissue separately. We started by filtering out the uninformative genes with very low expression across samples. For that purpose, we normalised read counts for library size, to account for the differences in sequencing depth between samples, by transforming them into counts per million (CPM) with function cpm from R package edgeR  (69). Only genes with at least 1 CPM in at least 40% of the samples were kept for analysis (the number of genes analysed for each tissue can be found in Supplementary Table  1 ). Read counts for those kept genes were used to calculate normalization factors to scale the raw library sizes, using function calcNormFactors from edgeR that implements the trimmed mean of M-values  (70)  , and normalised accordingly and subsequently log-transformed with the voom function  (71)  from package limma  (72)  , which also estimates mean-variance relationships to be used in linear modelling (v.
ShARP-LM To model the changes in gene expression with age, we developed the Shifting Age Range Pipeline for Linear Modelling (ShARP-LM). For each tissue, we fitted linear models to the gene expression of samples from donors with ages within windows with a range of 16 years shifted through consecutive years of age (i. e. in a sliding window with window size = 16 and step size = 1 years of age). As samples at the ends of the dataset's age range would be thereby involved in fewer linear models, we made the window size gradually increase from 11 to 16 years when starting from the "youngest" samples and decrease from 16 to 11 years when reaching the "oldest" (Supplementary Figure  5 ).