dataesr/scientific-paragraphs-categorization huggingface dataset:
Study approval All experiments were conducted in conformity with the principles set forth in the Helsinki declaration and approved by the Ethics Committee for Biomedical Research (CERB) of Ibn Rochd University Hospital Center, under the approval code  (28/ 15). The free and informed consent form was signed by all subjects participating in this study. Medical records containing clinical and pathological data (age, stage, grade and histological and molecular subtypes) were obtained from the hospital pathology department. METABRIC patients are anonymous and their data are publicly available. The authors of the original publication have obtained free informed consent from all participants (57), therefore, this part of the present study was exempt from Institutional Review Board approval requirements.
Consequently, the transcript-level study illustrated the association between A2AR and hormone receptor status with unfavorable prognosis (ER-and PR-) (Figures  3C, D ). In contrast, analysis of HER2 status (Figure  3E ) showed no significant difference in expression between groups. Ki-67 is another distinct parameter considered for decades as a prognostic marker related to disease aggressiveness  (58). In order to evaluate A2AR expression according to the tumor proliferation index, we stratified our cohort into two groups, Ki-67 low (≤20%) and Ki-67 high (>20%). However, although Ki-67 high tumors seem to exhibit a strong A2AR transcript level trend (Figure  3F ), the difference is not statistically significant. The large-scale METABRIC dataset was also investigated to support the transcriptomic findings from our cohort.
To this end, microarray expression data from 1904 patients with primary invasive breast carcinoma were explored. Patient clinicalpathological parameters are described in (Supplementary Table  1 ). Analysis of public data showed that A2AR is linked to ductal, lobular and mixed histological subtypes (Figure  4A ). In accordance with the experimental cohort, High-grade tumors displayed increased A2AR expression (Figure  4B ). As illustrated in (Figure  4D ), the molecular subtyping of the METABRIC dataset included two additional subgroups (Normal and Claudin-low). In addition to its adverse prognosis, the latter represents a distinctly aggressive subgroup, related to stemness characteristics, downregulation of major cell junction components and activation of the EMT process during   tumor progression  (59, 60).
A2AR is closely linked to the biological processes underlying tumorigenesis and breast cancer progression After shedding light on the clinical and prognostic relevance of A2AR in breast cancer, we attempted to assess its probable involvement in tumor pathogenesis. In this regard, we performed Gene Set Enrichment Analysis (GSEA) to decipher the biological functions and mechanisms implicated in cancer development and progression. According to the Normalized Enrichment Score (NES), analysis of three human molecular signature databases (Hallmark, Curated and Ontology) revealed that the A2AR high phenotype is mainly concentrated in a panoply of gene sets related to oncogenesis and tumor progression (Figure  8C ). As illustrated in (Figures  8A, B ), the A2AR is linked to the invasive breast cancer signature, oncogenic and angiogenic signaling pathways (Myc, VEGF and IL6-JAK-STAT3) as well as proliferation, metastasis, hypoxia, adhesion and cell cycle processes (Rac1 GTPASE cycle).
A2AR tends to be prominently expressed on Tregs and exhausted CD8+ T cells To decipher A2AR-expressing cells in the TME, we used the Tumor Immune Single-cell Hub 2 (TISCH2) database. For this purpose, three breast cancer datasets; BRCA_EMTAB8107 (Figures  11A, B ), BRCA_GSE114727_10X (Figures  11C, D ) and BRCA_Alex (Figures  11E, F ), were analyzed. As a first result, A2AR seems to be expressed more in immune cells than in malignant and stromal cells. Subsequently, major lineage data showed that among the different cell populations analyzed, A2AR tends to be prominently expressed on Tregs and exhausted CD8+ T cells. These findings further underscore the potential contribution of A2AR to the immunosuppressive process.
Acknowledgments The authors' sincere gratitude goes to patients who agreed to be included and participate in this study. We would like to thank all medical staff of the  Mohamed VI Oncology Center , mainly gynecological surgeons  Pr M. Benhessou  and  Pr M. Ennachit. We also appreciate the contribution of the  Department of Pathological Anatomy team , notably  R. Sairi , for her assistance in the access and collection of patients' clinicopathological records. Our deepest appreciation is extended to  Dr. S. Stitou , from the  Pathological Anatomy Laboratory , for her technical guidance and valuable advice.
Target DNA deletion frequencies were measured by dsRed-directed flow cytometry at 3 days post-transduction. Data are shown as mean ± SEM of three independent biological replicates. Significant differences between the indicated datasets were determined by two-tailed Student's t tests; **p < 0. 01, *p < 0. 05; p > 0. 05 was considered non-significant (ns). (D) Representative flow cytometry dot plots of HeLa. dsRed TS. An. TS cells transfected and transduced with the specified reagents. (E-G) Characterization of gene editing outcomes through amplicon deep sequencing. The precise deletion and imprecise editing read frequencies within the total edited read counts and the cumulative precise deletion and imprecise editing read frequencies within the total read counts in reporter cells exposed to the indicated gene editing reagents are shown in (F) and (G), respectively.
The next-generation sequencing analysis was performed on genomic DNA from two independent biological replicates ($50,000 pairedend reads per sample). Significant differences between the indicated datasets were determined by two-way ANOVA; *p < 0. 05, ****p < 0. 0001. (H) Multiplexing DNA editing precision in reporter cells. gRNA set-specific and cumulative (inset) precision index plot corresponding to the ratios between precise and imprecise editing read frequencies in HeLa. dsRed TS. An. TS cells transfected with gRNA sets 1 through 4 and transduced or co-transduced with the indicated AdVPs. upon site-specific cleavage at intron 50 and exon 51, respectively (Figure 2A, top panel). Targeted SA elimination is expected to induce exon skipping during pre-mRNA processing resulting in reading frame restoration and ensuing Becker-like dystrophin expression in muscle cells from 13%-14% of DMD patients.
The transducing unit titers of purified AdVP stocks were determined through qPCR assays using iQ SYBR Green Supermix (Bio-Rad, cat. no. L010171C) and primers targeting the AdVPs packaging signal (c) listed in Table  S5. Three 3-fold serial dilutions of the vector genomes extracted from the purified AdVP stocks using the DNeasy Blood & Tissue Kits (QIAGEN, cat. no. 69506) were diluted 1:100 and employed as qPCR templates. Eight 10-fold serial dilutions of a linearized parental plasmid stock containing 1 Â 10 7 GC mL À1 were used as qPCR templates to generate a standard curve. The qPCR primers, cycling conditions and reaction components are specified in Tables  S5  and S6. Data analysis was performed by using the Bio-Rad CFX Manager 3. 1 software and the titers were calculated based on the Ct values of standard curves and extracted AdVP genome dilutions.
SaC9::SpC9. dgRNA D51 was essentially carried out as described previously. 56 In brief, 80 mL of purified AdVPs were treated with 8 mL of 10 mg mL À1 DNase I (Sigma-Aldrich, cat. no. 10104159001) at 37 C for 30 min. Next, 2. 4 mL of 0. 5 M EDTA (pH 8. 0), 6 mL of 10% (w/v) sodium dodecyl sulfate (SDS), and 1. 5 mL of 20 mg mL À1 proteinase K (Thermo Fisher Scientific, cat. no. EO0491) were added to inactivate the DNase I activity. Following an incubation at 55 C for 1 h, vector DNA isolation was completed by using the QIAEX II Gel Extraction Kit (QIAGEN, cat. no. 20021) according to the manufacturer's instructions. The isolated vector genomes were then subjected to restriction enzyme fragment analysis by using the Gel-Doc XR+ system (Bio-Rad) and the Image Lab 6. 0. 1 software (Bio-Rad). Parental AdVP plasmid clones digested with the same restriction enzymes applied to vector genomes served as molecular weight references.
Flow cytometry The frequencies of edited HeLa. dsRed TS. An. TS cells were determined by reporter-directed flow cytometry using a BD LSR II flow cytometer (BD Biosciences). In brief, after large-volume PBS washes and trypsin treatments, the reporter cells were collected by centrifugation at 300 Â g for 5 min and cell pellets were resuspended in PBS containing 0. 5% bovine serum albumin (BSA) and 2 mM EDTA (pH 8. 0) (FACS buffer). Mock-transduced cells served as control to establish background fluorescence levels. A minimum of 10,000 single live cells were acquired per sample and the resulting data were analyzed with the aid of the FlowJo 10. 6. 0 software (TreeStar).
no. B7002S) and 5 U of T7EI (New England Bio-labs, cat. no. M0302). Next, after 15-min incubations at 37 C, the DNA samples were subjected to electrophoresis through 2% (w/v) agarose gels in 1Â Tris-acetate-EDTA buffer. The resulting ethidium bromide-stained DNA species were then detected by using a Molecular Imager Gel-DocTM XR+ system (Bio-Rad) and analyzed via the Image Lab 6. 0. 1 software (Bio-Rad).
1% Triton X-100 in TBS, the specimens were incubated with fluorochrome-conjugated secondary antibodies diluted in blocking solution for 1 h in the dark at RT (Table  S9 ). Finally, after three 10-min washes with 0. 1% Triton X-100 in TBS, ProLong Diamond Antifade Mountant reagent containing DAPI (Thermo Fisher Scientific, cat. no. P36971) was used for mounting the specimens. Immunofluorescence microscopy images were acquired by using an upright Leica SP8 confocal microscope equipped with Leica hybrid detectors HyD. All images were analyzed through the LAS X (Leica Microsystems) and ImageJ (US National Institutes of Health) software packages.
The cumulative precise deletion and imprecise editing read frequencies within the total read counts in DMD. 1. dgRNA D51 exposed to three MOIs of the indicated AdVPs are shown in (D) (left and right graph, respectively). The next-generation sequencing data were derived from four biological replicates with $50,000 paired-end reads analyzed per sample. (E) Representative indel profiles in DMD. 1. dgRNA D51 myoblasts transduced with the indicated AdVPs at a total MOI of 100 GCs cell À1. The frequencies, types, and distributions of unintended indel "footprints" detected within the DMD target region are plotted. MOI, multiplicity of infection; GCs cell À1 , genome copies per cell. Significant differences between the indicated datasets were determined by two-way ANOVA; ****p < 0. 0001, ***p < 0.
ACKNOWLEDGMENTS This project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Skłodowska-Curie  grant agreement no. 765269  ( IMGENE -Improving Genome Editing Efficiency ). This work was also supported by the  Prinses Beatrix Spierfonds  (W. OR21-01 ) and the  Dutch Duchenne Parent Project  ( 17. 012 ). Authors of this paper are members of the European Reference Network -Neuromuscular diseases (ERN EURO-NMD).
Sediment cores, chronology, and macrocharcoal In October 2018, a field campaign organized by the University of Bern in collaboration with the Tanzania Fisheries Research Institute (TAFIRI) recovered sediment cores from four locations along a transect near the Shirati Bay (Fig. 1 ). The coring was performed with the UWITEC Niederreiter coring system of the Institute of Plant Sciences at University of Bern, which was adapted to reach seaworthy conditions with the aid of eight pontoons made of aluminium instead of rubber and special sea anchors. This paper relies on data from three cores (Fig. 1  a, b): Core LVC18_S1 (hereafter LV1) located at 01 06,914 0 S, 33 55,146 0 E at a water column depth of 37 m; core LVC18_S2 (LV2) located at 01 07,850 0 S, 33 56,780 0 E at a water column depth of 22.
Statistical analyses The number of charcoal particles was resampled on a regular time bin, every 40 years based on the average accumulation rate. Then, we compared the three cores with a nonparametric twosample ManneWhitney test and the distribution of the charcoal influx (CHAR), as shown in boxplots (Fig. 5f ). For each CHAR time series, we used a generalized additive model (GAM) to model and identify zones of similar trends. Additionally, to assess the trend change and highlight the phases of high, medium, or low values of CHAR, for each site we conducted a Bayesian change point analysis with the bcp v 4. 0. 5 package in R  (Erdman and Emerson, 2007). The results between sites showed slight variations, but this was expected because the age uncertainty varies between models. Therefore, we decided to use only the change points of LV4, because their age uncertainty is the smallest of the three cores included in this study.
Pollen zonation and ordination To determine the local pollen assemblage zones (LPAZ), we used the optimal partitioning approach with minimum sum of squares and the broken stick model  (Bennett, 1996; Birks and Gordon, 1985) , and the subzones in zone 1 were based on visual criteria. Then, we conducted a principal component analysis (PCA) using Canoco 5. 10 (ter Braak and Smilauer, 2018) to seek similarities in samples between sites and correlations with temperature records in the area and with our charcoal records. Because our response data are compositional and have a short length of the gradient (sd ¼ 1. 5), we applied a linear ordination method on the squareroot-transformed pollen percentage data  (ter Braak and Prentice, 1988). We grouped the samples and represented groups by colour according to their LPAZ and by figure according to their sites of origin (Fig.
Acknowledgments The authors  Yunu en TemoltzineLoranca ,  Giulia Wienhues , and  Moritz Muschick  were supported by  SNSF Sinergia  grant number  183566  awarded to  Ole Seehausen ,  Martin Grosjean ,  Tom Gilbert , and  Blake Matthews. The coring in Lake Victoria was financed by the strategic pool of the  Faculty of Natural Sciences of University of Bern  (grant to  Ole Seehausen ,  Anna Sapfo Malaspinas ,  Willy Tinner ,  Oliver Heiri ,  Martin Grosjean , and  Flavio Anselmetti ) and the  Institute of Plant Sciences of University of Bern. We gratefully acknowledge the coring leaders  Petra Boltshauser-Kaltenrieder ,  Moritz Muschick ,  Salome Mwaiko , and  Willi Tanner  and the coring team members  Sandra Brügger  and  Alexander Bolland. We thank  Edith Vogel  and  Gary Salazar  for their contribution to the 14 C analysis.
Immunohistochemical (IHC) analysis for microenvironment-related biomarkers, included T-cell subsets (identified by CD8, CD4, CD3, PD1, and FOXP3) and macrophages (CD68, CD163), performed on tissue microarrays with semiautomated quantification by image analysis. Copy number aberrations (CNA) analysis was performed using the R-script QDNAseq (v1. 12. 0) and Gistic (v2. 0) from 50-bp single read shallow whole genome sequencing data. 10 argeted next-generation sequencing panel for mutations (369 target genes) used for mutation and translocation analysis was conducted on 150-bp paired-end data of a 3 Mb SeqCapEZ capture panel (Roche NimbleGen, Madison, WI; order ID 43712) both sequenced on a HiSeq 4000 (Illumina, San Diego, CA) 10 (see Suppl. Methods for more details). Patient's clinical characteristics were summarized with descriptive statistics (median (range) for quantitative and frequency (percent) for qualitative variables) and compared using Chi-Square test and Mann-Whitney test.
The IHC biomarker score used in the analysis was the average from 2 cores. Given the multiple tests performed, Bonferroni FWER correction was applied for the IHC biomarkers so the 2-sided α level was 0. 05/7 = 0. 007. Fisher exact test was used for frequencies of mutations and translocations. For comparisons between copy number regions, P values and false discovery rates (FDR) were calculated with comparative genomic hybridization test, which implements a Wilcoxon rank sum test. P values were not corrected for multiple testing, and FDR was controlled at 10% level for mutations and translocations. Statistical analysis was performed using various strategies in R (version 3. 5. 1) and SAS (SAS software version 9. 4). The study and protocols to obtain human archival tissues and patient data were approved by the local ethical committee of the VU University Medical Center, Amsterdam (FWA00017598) and for all collaborating centers and complied with the Code for Proper Secondary Use of Human Tissue in the Netherlands (http://www.
ACKNOWLEDGMENTS The authors thank all pathologists and pathology laboratories for providing tissue materials and data about patients who have been under their care. Matias Mendeville ,  Nathalie J. Hijmering , and  Jurriaan Janssen  for their help with performing the experiments and analysis and interpreted of the data. Prof Dr Anton Hagenbeek , the deceased founding father of the  Lunenburg Lymphoma Biomarker Consortium. We thank the  Hartwig Medical Foundation  ( Amsterdam, the Netherlands ) for generating, analyzing, and providing access to reference whole genome sequencing data of the Netherlands population.
SOURCES OF FUNDING This study was supported by the  Dutch Cancer Society  grants  KWF 2015-7925  and by unrestricted grants from: van  Vlissingen Lymfoom Fonds ,  Genentech/Roche ,  GlaxoSmithKline ,  Pfizer Pharma, Teva, Pharmaceuticals/Cephalon ,  Millennium Pharmaceuticals Inc and Celgene. Haematological Malignancy Research Network  is funded by  Cancer Research UK , grant numbers 29685; and  Blood Cancer UK , grant number  1503. GTLdV, DdJ, BY, and MJK received funding from the  Dutch Cancer Society  grants  KWF 2015-7925. WBCS, EvD, CL-J, and DM-J were funded by unrestricted grants from: van  Vlissingen Lymfoom Fonds ,  Genentech/ Roche ,  GlaxoSmithKline ,  Pfizer Pharma, Teva, Pharmaceuticals/Cephalon, Millennium Pharmaceuticals Inc  and Celgene. AGS was funded by  Cancer Research UK  and  Blood Cancer UK.
| Study design The S-LAM (Survival of Acute Myeloblastic Leukaemia) project is a retrospective longitudinal study including all incident AML cases diagnosed from 01 January 2012 to 31 December 2016 in the three French population-based registries specialised in haematological malignancy (Côted'Or, Basse-Normandie and Gironde; around 3,625,400 inhabitants). For each patient, in addition to the core data (age, sex, place of residence, medical history, type of haematological cancer, medical follow-up, treatment, sources of information, last date of follow-up and vital status), we collected information on biological and molecular analyses, dates of occurrence of each event in the care pathway, including the various medical consultations and associated dates, and patient clinical evolution. The end point of patient follow-up was set at 1 January 2021.
During the period up to formal diagnosis of AML, patients may consult several clinical units and undergo various additional examinations, leading to rather diverse care pathways. Several factors, including clinical symptoms, age, patient geographical location, and other socioeconomic factors influence this. 15 Our data show that advanced age remains a limitation for access to the specialised haematology unit, as observed in patients with the AML-NOS subtype (median age = 84 vs. 73 years on average; 37% access to the SHU vs. 69% on average; OR = 0. 10, 95% CI, 0. 01-0. 51). Lack of referral of these older patients to a specialised haematology unit resulted in less access to cytogenetic analysis (39% vs. 80% on average) thus potentially explaining their low access to curative treatment (18% vs.
The Minamata Convention, a global and legally binding treaty that entered into force in 2017, aims to protect human health and the environment from harmful mercury (Hg) effects by reducing anthropogenic Hg emissions and environmental levels. The Conference of the Parties is to periodically evaluate the Convention's effectiveness, starting in 2023, using existing monitoring data and observed trends. Monitoring atmospheric Hg levels has been proposed as a key indicator. However, data gaps exist, especially in the Southern Hemisphere. Here, we present over a decade of atmospheric Hg monitoring data at Amsterdam Island (37. 80°S, 77. 55°E), in the remote southern Indian Ocean. Datasets include gaseous elemental and oxidised Hg species ambient air concentrations from either active/continuous or passive/discrete acquisition methods, and annual total Hg wet deposition fluxes.
icos-cp. eu) and, since 2012, the Global Observation System for Mercury (GOS 4 M; http://www. gos4m. org/)  7, 10, 12, 13, 22. In an effort to support the effectiveness evaluation of the Minamata Convention, we report here Hg datasets recommended in Tier 1, i. e. , ambient air GEM concentrations and total mercury (THg) wet deposition fluxes. Our datasets of oxidised Hg species (referred to as Reactive Mercury; see Fig. 1 ) are also being shared with the community to promote scientific progress and better understanding of the Hg cycle. Early subsets of these observations have already been described in the literature, as detailed below: (1) For GEM active/continuous measurements, the period covered was 2012 to 2017  7, 10, 12, 13  , with no subsequent publication available for datasets collected from 2018 onwards.
(2) For GEM passive/discrete measurements, the range was from November 2018 to November 2021  24  , with no further description available for datasets collected since then. (3) In terms of RM active/continuous measurements, data from 2012 and 2013 were previously discussed  10  ; however, there is no additional description of datasets collected afterwards. (4) As for RM active/ discrete measurements, no description of this dataset has been presented up to the present date. (5) Lastly, wet deposition fluxes from 2013 to 2019 were published  22, 25  , without additional details provided for datasets collected from 2020 onwards. In addition to presenting unpublished datasets collected in recent years, this data descriptor provides the first comprehensive overview of all Hg measurements performed at this site since 2012.
Data records Our datasets are available under Creative Commons Attribution 4. 0 International (CC-BY 4. 0) Licence from the GMOS-FR AERIS website (https://gmos. aeris-data. fr/ last access: 13/03/2023). As summarised in Table  2 , level 1 and 2 data products are available on the AERIS website for active/continuous GEM measurements  60, 61  , discrete GEM measurements  62  , RGM/PBM2. 5 active/continuous measurements  60  , RM active measurements  63  , and THg wet deposition fluxes  64. Tables 3-6 summarise the list of attributes for each qualified and downloadable dataset. Level 1 data represent quality-checked datasets in their original time resolution. Level 2 data, when available, are modified quality-checked data products. The GEM level 2 dataset provides hourly averaged GEM concentrations calculated from quality-controlled level 1 GEM data (5-or 15-min time resolution) when the hourly recovery rate exceeds 50% (i.
Once NBase consecutive no changes have been registered, the first such reading is considered to be the end of the peak and a baseline condition is consequently flagged. LSB (Least Significant Bit) is the smallest level that an A/D can convert. Delimited and standardized (FAIR) dataset ediƟon (data + metadata) Step 0 Data acquisiƟon and extracƟon GEM -GOM -PBM2. 5 raw data from in-situ acƟve instruments Step 5 PublicaƟon and diffusion Fig. 4  Data processing workflow for GEM, GOM, and PBM2. 5 active/continuous measurements (adapted from Magand et al. 66  ). Only level 1 and 2 datasets are publicly available on the GMOS-FR AERIS website. collected during the corresponding year. It is important to note that the periods considered for each annual flux do not always strictly correspond to a calendar year starting on January 1 st and ending on December 31 th due to logistical constraints and depending on rainfall events.