dataesr/scientific-paragraphs-categorization huggingface dataset:
But according to other disciplinary points of view, the digital humanities are not able to propose a complete data analysis process, as the statistician or the geomatician does with his tools and specific disciplinary competences, which are more related to the hard sciences 4. In the context of this article, the authors voluntarily consider the expression "digital humanities" in its broadest sense, in order to take into account the diversity of perceived or experienced realities: it is the exploitation of digital tools in the intellectual process of production and analysis of humanities and social sciences, an alliance whose objective is to optimize the potential of exploitation and valorization of scientific data. In no way does this article claim to give a firm and immutable definition of the field of digital humanities, but rather aims to engage in a reflection on the practices that have emerged over the past several years.
This initiative is coordinated by the two authors of this article in collaboration with Julien Cavero (MOM). It has numerous partners and a significant network (EUR Translitterae, ANR-10-IDEX-0001-02 PSL* and ANR-17-EURE-0025 future investment programme, GTHN Working Group, CNRS laboratories, etc. ). 3  The artist Saint-Oma, whose two drawings are reproduced in this article, graphically declines the digital humanities and the portraits of digital humanists. These meaningful drawings contribute to embodying digital humanities by tracing the contours of professions in deep mutation. Gathered in the portfolio of the Digit_Hum Initiative, these graphic traces offer a look at digital humanities and their actors, between knowledge and know-how (https://digithum. huma-num. fr/portfolio/saintoma/ consulted on 09/07/2021).
4 We thank Damien Cartron, sociologist at the Maurice Halbwachs Center (CNRS-ENS-EHESS), for giving us his opinion on this question. At first glance, the humanities -tradition of arts, letters and books -seem diametrically opposed to digital, computer and network technology. Things are somewhat different for some social science disciplines (economics, sociology), whose production and analysis of quantitative data rely on methods and tools involving computer science. However, whether in the humanities or the social sciences, digital humanities are creating a deep interdisciplinary dialogue 5 on the challenges of digital technology and are renewing the methodology for collecting, analyzing, enhancing and publishing data 6. This "zone of exchange" 7 does not just involve using new tools for pre-existing research practices.
Interactions of this sort were already well established in disciplinary fields like geography or archaeology and in recent years they have strongly developed in fields like history and philosophy. This context means it is not a simple task to identify the actors of a digital humanities project. This was the subject of two Digit_Hum study days, in 2015, "Paysage et structuration des humanités numériques à l'ENS" (Landscape and structuring of digital humanities at the ENS) and in 2017, "Data deluge: quelles compétences pour quelles 5 [Schmitt, E. , 2017] p. 25, distinguishes between collaboration and interdisciplinarity: "(…) interdisciplinarity combined with computing requires computing to be considered as a scientific discipline without which there can be collaboration projects between actors but no dialogue between disciplines.
67 researchers, teachers, engineers, technicians, library staff and students were identified as being involved in activities linked to the digital humanities (structuring and development of tools, enrichment, visualisation, etc. ). The CNRS is by far the most highly represented organisation (over two thirds), with laboratories which mainly belong to firstly Literature and Languages departments and secondly Sciences of Antiquity departments 12. The research units with the greatest presence in the survey bring together researchers who extensively use digital humanities tools for their research projects. These include databases, visualisation tools, geographic information systems, automatic data processing, online publication of digital corpora and so forth. These teams also have engineers whose work is directly related to computer science and digital technology (computer specialist, database engineer, data production and processing engineer, etc.
)  13. Processing the responses to the survey questionnaires also provided us with an enhanced understanding of how diverse and polymorphous are the functions and professions exercised by the actors in the digital humanities, the skills deployed, and the tools manipulated (Figure  2 ). The respondents are anthropologists, archaeologists, historians, philosophers (research and teaching professions). They are also cartographers, specialized in ancient sources, in linguistics or in data processing (research support professions). Computer science, scientific and technical information (archivists, librarians, documentalists) and publishers are also represented. It is clear from this survey that for most of them, digital humanities have profoundly changed their working methods and practices, on the one hand by reinforcing collaborative work, and on the other hand by facilitating access to sources, automatic processing, and the development of collections and data.
The data collected made it possible to represent the institutional network of the digital humanities at the ENS in 2018. It also clearly shows the extreme diversity of collaboration projects with other French, European and other international organisations in the framework of digital humanities activities at the ENS (Figure  3 ). This survey 15  contributes to a better understanding of the role of the engineer in the humanities and social sciences. Research support functions have long been considered as accessories which serve researchers or professors but they are becoming an integral part of the research process in the context of digital humanities. The research assistant of the past supported researchers in their daily work but these have now been replaced by real autonomous collaborators who contribute their own expertise, technicality, tools, methods, language, vocabulary and even grammar to research.
For example, in the context of statistical data processing or the setting up of databases, engineers need to use their professional know-how and mastery of best practices to support researchers. They guarantee the interoperability, durability and reuse of research data by transmitting their knowledge of models and languages. What knowledge and skills should this "dialogue engineer" who acts as the link between academic disciplines and professional techniques possess to effectively respond to the requirements of the changing research world? Is a data scientist for the humanities possible 21 ? Are specific digital humanities positions desirable in large research organisations like the CNRS? Although the position of a digital humanities engineer does not exist yet in higher education and research job profiles, more and more laboratories or human science centres wish to recruit "digital humanities" engineers to work on digital project management.
Research organisations have yet to fully identify or define a job type for these professions which are often seen as "rare birds". A new profile is required for an enhanced engineer working at the crossroads between humanistic and technical cultures. Digital humanities engineers are recruited to advise and support HSS researchers in the use of digital technologies and take part in the life and structuring of HSS research in the digital environment within institutions and establishments. They help drive scientific activities by organising training courses and scientific events, helping to set up projects and carrying out scientific and technological monitoring. They develop new tools and bring researchers involved in the digital humanities together. Finally, they provide the interface between research teams and the national and international mechanisms that have been created over the last ten years such as the Digital Scientific Library (BSN)  22  , the CLEO 23 and Huma-Num 24 initially and more recently DARIAH  25   Between the tradition and the future of the humanities, this new kind of engineer must also be specialized in a number of advanced information analysis processes (data mining, text mining, statistics, 3D modeling, etc.
Purpose To evaluate the use of preimplantation genetic testing (PGT) and live birth rates (LBR) in the USA from 2014 to 2017 and to understand how PGT is being used at a clinic and state level. Methods This study accessed SART data for 2014 to 2017 to determine LBR and the CDC for years 2016 and 2017 to identify PGT usage. Primary cycles included only the first embryo transfer within 1 year of an oocyte retrieval; subsequent cycles included transfers occurring after the first transfer or beyond 1 year of oocyte retrieval. Results In the SART data, the number of primary PGT cycles showed a significant monotonic annual increase from 18,805 in 2014 to 54,442 in 2017 (P = 0. 042) and subsequent PGT cycles in these years increased from 2946 to 14,361 (P = 0. 01). There was a significant difference in primary PGT cycle use by age, where younger women had a greater percentage of PGT treatment cycles than older women.
Introduction After the first two reports of world data on PGT in the 1990s  [1, 2] , the ESHRE PGT Consortium was established in 1997 to collect global PGT data  [3]. The outcome of individual embryos following an ART treatment can be tracked from start to finish in the newer databases  [4]  and data are reported by indication, including the PGT subgroups (PGT for monogenic diseases (PGT-M), PGT for structural abnormalities (PGT-SR), and PGT for aneuploidy (PGT-A)  [5]. This gives a very rich picture of the use of PGT. However, a considerable limitation of these data is that it only represents a small proportion of the global PGT performed. The latest ESHRE PGT Consortium paper contains data from only 2 US clinics  [4]. In the USA, PGT data are collected and reported by the Society for Assisted Reproductive Technology (SART) and also reported by the Centers for Disease Control and Prevention (CDC).
In both sets of data, PGT cannot be separated into PGT-A, M, or SR. In the UK, PGT data are collected by the Human Fertilisation and Embryology Association (HFEA) and are separated into PGT-A and PGT-M/SR. A recent analysis of the UK HFEA data and US SART data for 2014-2016 showed that in the UK, PGT was used in less than 2% of cycles, while in the USA, it was used in 21% of cycles  [6]. Over these 3 years, 94,935 cycles using PGT were performed in the USA which is more than all the ESHRE PGD Consortium cycles collected since 1997. The present study investigated the utilization and live birth rates of PGT in the USA among different age groups, as well as the use of PGT at both clinic and state levels.
Data collection Data were drawn from the US national reports of SART for 2014 to 2017 and the CDC for 2016 and 2017. Both data sources were accessed online to investigate the use of PGT for autologous cycles and so institutional review board (IRB) approval was not required. Because the CDC combined reporting for autologous and oocyte donor cycles prior to 2016, the CDC analysis was restricted to 2016 and 2017 for the purpose of this study. Due to the differences in inclusion criteria for cycles between the two databases, these two data sources were not directly compared. Individual analyses were performed using each database, where SART data were evaluated for trends in overall ART and PGT use for autologous cycles, while the CDC data were evaluated for trends in the use of PGT across individual clinics and states.
SART PGT usage is available from SART in the SART CORS registry from 2003 to 2017 (www. sart. org). However, for the purpose of this project, only cycles from 2014 to 2017 were analysed. SART CORS collects data from more than 95% of clinics offering fertility services in the US (www. sart. org). The interactivity of the SART database allowed for filters to be applied to accurately differentiate between those cycles that used PGT from those that did not. Live birth rates were determined for PGT versus non-PGT cycles, where live birth rate was defined as the percentage of oocyte retrievals or embryo transfer procedures that led to the birth of at least one living child  [7]. Both fresh and frozen autologous cycles were included and defined as either primary or subsequent. A primary cycle was counted when the first embryo transfer procedure occurred within a year of the oocyte retrieval.
CDC The CDC Fertility Clinic Success Rates Reports included data from all reporting SART clinics, as well as clinics that were not SART members (https://www. cdc. gov/art/reports/2016/ fertility-clinic. html). The CDC report included about 98% of ART cycles in the USA  [8]. Although CDC data were available from 2004, we only included data from 2016 and 2017 because prior to 2016, PGT usage was only reported for fresh, autologous oocyte cycles. In 2016, the reports were restructured to include both autologous and donor oocyte cycles together, except for cycles started with the intention of banking oocytes and cycles that involved experimental procedures  [9]. Due to the PDF format of the CDC report, the information could not be filtered. Therefore, live birth rates, age profiles, and information on the number of embryos in a cycle could not be assessed.
Statistical analysis For trends over time, the non-parametric Kendall's tau correlation coefficient was determined on the SART data to determine if these trends were significant. To identify differences between percentages in age groups and 4 years of interest, a repeated-measures analysis of variance (ANOVA) was, if significant, followed by the post hoc Bonferroni tests for pairwise comparisons. The latter adjusted the P values to avoid spuriously significant results arising from multiple comparisons. The assumptions of the ANOVA were verified by a study of the residuals. Chi-squared tests were used to assess associations between years (2016 and 2017) and percentage use of PGT using the CDC data. Results were classified as significant when a P value less than 0. 05 was obtained. SPSS (IBM Corp.
Some studies have shown that transferring a thawed embryo results in a significantly higher live birth rate  [11, 22, 23]  but a recent multicentre randomised controlled trial has shown no benefit  [24]. Some clinics may offer to freeze all cycles in association with non-PGT and PGT. Perhaps the most significant result shown in this study is that the LBR by age for subsequent cycles by PGT showed no evidence of an association between age and LBR, especially for the data for 2017. In the Theobald study, the same result was seen for the HFEA data but not the SART data when considering years 2014-2016. This finding agrees with the hypothesis that PGT-A is used to balance out LBRs across age groups  [25].
Limitations There are several limitations to this study. One of the main limitations is that PGT is a rapidly evolving area. From the data we analysed, it was not possible to determine the stage of biopsy or the method of diagnosis, something which the ESHRE PGT Consortium data does achieve. Also, with the SART data used in this study, it was not possible to differentiate between PGT-A, PGT-M, and PGT-SR. We have assumed that the vast majority of PGT cycles in the USA will be for PGT-A, as has been the case since 1997 in the ESHRE PGD Consortium data. Another limitation in the SART data is the lack of clarity in the subsequent cycle data. It is unclear whether the patient's age is reflective of the time of her oocyte retrieval or the time of cycle start. For this contribution, it was assumed that the age reflected the age at the time of oocyte retrieval.
Results Identification of the different cell populations in the regenerating caudal fin fold by scRNA-seq. Caudal fin fold amputation in 3 days post fertilization (dpf) larvae induces a robust regeneration of the missing tissues within 3 days through the formation of the blastema at 24 h post-amputation (hpA). To determine the cellular and transcriptomic profiles of intact (uninjured) and regenerating caudal fin fold samples at 24 hpA, we used scRNAseq and the Chromium system (10X Genomics platform). This system employs the microdroplet technology to isolate individual cells, followed by next-generation sequencing, analysis and visualization of the single-cell datasets with the Cloud software (Fig. 1a ). The two-dimensional (2D) distribution profiles obtained by Uniform Manifold Approximation and Projection (UMAP) revealed seven distinct cell clusters (K-mean = 7) in the regenerating fin fold (Fig.
1b ). Then, we generated the lists of the top 100 upregulated transcripts (filtered by FDR < 0. 05 and ranked by fold change) in each cluster of the regenerating fin sample (Fig. 1b  and Supplementary Data 1). Hierarchical clustering based on these differentially expressed genes showed specific signatures in each cluster (Fig. 1c ). We used well-structured lists of markers to assign a cell identity to each cluster. We assessed the robustness of these markers by reviewing the literature (Supplementary Data 2). In the regenerating fin sample, we focused on cluster 7 because it included cells that expressed neuron and glia markers, such as CD59, tfap2 and egr2 (Supplementary Data 1). In cells of this cluster, hmx3a (a gene encoding a protein predicted to have sequence-specific DNA binding activity) and irg1l (a gene encoding a protein involved in the inflammatory response to wounding) also were upregulated, suggesting that this cell population might specifically respond to amputation (Fig.
Clusters 2 and 3 were also enriched in extracellular matrix protein-encoding genes (col5a2a, col1a2a). In cluster 1 that included epidermis cells, cldne, evpla and agr1 were strongly expressed. However, we could not formally identify cells in cluster 2 because they did not specifically express any typical cell/tissue marker. We hypothesized that this cluster comprised epidermis cells because they expressed lgals1l1 or krt91. Moreover, we used the Cell Ranger software to aggregate and compare the expression data in the amputated fin and in the intact control (Supplementary Fig. 1a ). The comprehensive list of the 100 differentially expressed genes is presented in Supplementary Data 3. Then, we used marker genes (Supplementary Data 2) and the UMAP method to distinguish cell subpopulations in the uninjured and injured samples.
This analysis showed that cell clusters were quite comparable between conditions. This is in line with a similar study carried out in Xenopus laevis  24  showing that wound epidermis is not a novel cell state, but a redeployment of the embryonic apical epidermal ridge. As our study relied on developing zebrafish larvae and on the basis of the similitudes between clusters of the two studies, we propose the same hypothesis. The most striking difference was the strong increase of the myeloid cell cluster in the injured condition compared with uninjured samples. This result was expected because myeloid cells are recruited after amputation (Supplementary Fig. 1c ). Finally, analysis of the uninjured sample datasets (Supplementary Fig. 1d ) revealed the presence of another glia/neuron subpopulation type in cluster 6 and some muscle markers in cluster 4, indicating light contamination from the distal trunk muscle cells in this sample (Supplementary Fig.
2) and TCS SP8 (Leica Application Suite V3. 5) equipped with an HCXPL APO 40x/1. 25-0. 75 oil and an HC PL APO 0. 70 ∞ (infinity) 20x objective (Leica). The mCherry signal was excited with a 560 nm laser, and GFP with a 490 nm laser. Datasets were analysed using Fiji Software (ImageJ 1. 52p)  58. Cell isolation, library preparation and data processing of 10x Genomics Chromium scRNA-seq data. Approximately, 150 cut and uncut caudal samples were collected and dissociated into a single-cell suspension. Cell viability and aggregation were tested prior to proceeding with the 10X Genomics protocol for 3' transcript capture and single-cell library preparation. The concentration of freshly dissociated cells was adjusted to 700-800 cells/μl in PBS aiming to capture 4000 cells by the 10X Genomics device.
Briefly, the manufacturer's protocol (Chromium™ Single Cell 3' Reagent kit v3. 1) was followed to prepare single-cell libraries for Illumina sequencing. Libraries quantification was performed using the Fragment Analyzer system (NGS High Sensitivity Kit) and qPCR (ROCHE, Light Cycler 480). Sequencing was performed in paired-end mode with an S1 flow cell (28/8/87 cycles) using a NovaSeq 6000 sequencer (Illumina) at the MGX core facility of Montpellier. We used cell ranger mkfastq and cellranger count pipelines from the Cell Ranger Single Cell software by 10x Genomics (http://10xgenomics. com) for the initial quality control, sample demultiplexing, mapping, and quantification of raw sequencing data. Quality controls for scRNA-seq are provided (Supplementary Data 5). Raw scRNA-seq data were processed using the Cell Ranger software (Cell Ranger v3.
1. 0) provided by 10X Genomics with the default options. Briefly, files were first converted to the fastq format, and then sequences were aligned to the Danio rerio reference genome (danRer11) to generate single-cell feature counts using the standard 10x Genomics CellRanger Count pipeline with default parameters. The Cloud software (version 3. 1. 1) was used to visualize and analyze the results obtained with Cell Ranger. The Uniform Manifold Approximation and Projection (UMAP) dimensional reduction technique was used to visualize data. Output graphing allowed the visualization of cell cluster identity and marker gene expression. Morpholino injections, Drug treatments. For foxd3 (NM_131290) knock down experiments, morpholino antisense oligonucleotides (Gene Tools) against the ATG site were used (MOfoxd3): 5' TGCTGCTGGAGCAACCCAAGGTAAG 3'.
Caudal fin fold regeneration was monitored by measuring the fin fold growth length from the transected plan (end of the notochord) up to the most proximal end of the fin fold with the Fiji software. Macrophages and cell proliferation in the wound region were measured directly on images acquired by microscopy using the indicated reporter lines and staining. Morphological changes of foxd3 + rcn3 + cells were assessed with the Fiji software using the circularity and roundness plugins. These plugins are an extended version of the ImageJ Measure command that calculates object circularity using the formula: circularity = 4pi(area/perimeter^2). A circularity value of 1. 0 indicates a perfect circle. Values approaching 0. 0 indicate an increasingly elongated polygon. As roundness = 4area/(πmajor_axis^2), roundness is more relative to the area of the object compared with the main axis.
It could be considered as opposite to the elongation factor, whereas circularity refers to the object shape compared to a perfect circle. Graphs show the mean ± standard error of the mean (SEM). The Mann-Whitney test was performed to test the significance of the data presented in all the figures except for Fig. 3a  for which Kruskal-Wallis ANOVA with the Dunn's post-hoc test was performed to test the significance of the data using the GraphPad Prism 6 software (San Diego, CA, USA). Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.