dataesr/scientific-paragraphs-categorization huggingface dataset:
Participating centers This was an international, multicenter, retrospective observational, open-label study involving seven endoscopy units from India, Egypt, Poland, United Kingdom, France, Romania, and Pakistan. The data has been collected by high-volume endoscopy centers, performing in the region of 1000 diagnostic and interventional EUS procedures per year, including EUS-guided biopsy. In all centers, the evaluation was performed by an expert endosonographer who was defined as having performed at least 1000 hepatopancreaticobiliary (HPB) EUS procedures.
Endosonography of SPN All patients were referred to EUS evaluation due to the non-metastatic, growing locally pancreatic tumor recognized in computed tomography for establishing the diagnosis. Information on EUS, images, EUS-guided biopsy including the number of passes, type of needle and fluid biochemistry analysis (amylase, CA 19. 9 and mucin stain) from cystic component were recorded using a collective database. In all cases, surgical resection was the treatment of choice, providing definitive histological SPN confirmation. The study was conducted and carried out in accordance with the Helsinki declaration as revised in 1989. Based on the anonymized data collection, the Institutional Review Board of Pomeranian Medical University in Szczecin granted approval. The study was conducted in the line with the STROBE guidelines.
Statistical analysis Data management and analysis were performed using Statistical Package for Social Sciences (SPSS) version 28. Numerical data was summarized using mean and standard deviations or medians and/or ranges as appropriate. Categorical data was summarized as numbers and percentages. Estimates of the frequency were done using the numbers and percentages. Numerical data was explored for normality using Kolmogrov-Smirnov test and Shapiro-Wilk test. Chi square or Fisher's tests were used to compare between the independent groups with respect to categorical data as appropriate. Comparisons between two groups for normally distributed numeric variables were done using the Student's t-test while for non-normally distributed numeric variables, comparisons were done by Mann-Whitney test.
Technical aspects of EUS-guided biopsy, therapeutic strategy: EUS guided biopsy was performed using three different types of needle: FNA in 67 (63. 2%), FNB in 37 (34. 9%), Trucut in 2 (1. 9%) lesions. For the later, the size 18G and 22G were selected. The 22G and 19G size was mainly selected (94. 0%) for the FNA needle type, and 22G for the FNB needle (89. 2%). The mean number of passes was 2 and 3 for FNA and FNB needle respectively. Data regarding the needle type used are presented in Table  5.
To test for curvilinear effects, linear ( x ij ) and quadratic terms ( x 2 ij ) were employed for BMI and non-HDL-C with the subscripts i and j representing individuals and variables, respectively. As a robustness check and for comparative purposes, we also employed models in which BMI and non-HDL-Cis divided into categories. Sample sizes for each predictor are given in SI Appendix, Table  S3. Data, Materials, and Software Availability. All relevant computer code for variable definitions and statistical analysis has been deposited in  [Github]  (https://github. com/hkaplan/tsimane-brain-volume). Individual-level data are stored in the Tsimane Health and Life History Project (THLHP) Data Repository, and are available through restricted access for ethical reasons. THLHP's highest priority is the safeguarding of human subjects and minimization of risk to study participants.
anth. ucsb. edu/data. html). Requests for individual-level data will require institutional IRB approval (even if exempt) and will be reviewed by an Advisory Council composed of tribal leaders, tribal community members, Bolivian scientists, and the THLHP leadership. A similar structure exists for the Moseten data. The study authors and the Tsimane leadership are committed to open science and are available to assist interested investigators in preparing data access requests. ACKNOWLEDGMENTS. We thank the Tsimane and Moseten greatly for participating and Tsimane Health and Life History Project personnel for collecting and coding data. We also thank the tribal leadership organizations that helped guide this research: Gran Consejo Tsimane, Consejo Regional Tsimane Moseten and the Organization of the Moseten Indigenous People.
pnas. org the  NIH/National Institute on Aging  ( R01AG054442  and  RF1AG054442 ) and the  NSF  ( 1748282 ). J. S. acknowledges IAST funding from the  French National Research Agency (ANR)  under the Investments for the  Future (Investissements d'Avenir) program , grant  ANR-17-EURE-0010. Funding sources had no role in research conduct, study design, or article preparation.
Combined data for background evaluation Background composition was evaluated by combining data from background hydrocasts performed both during MAYOBS4 (MAY04-HY03) and MAYOBS15 (MAY15-HY09) (Fig. 1 ). We defined the background as an envelope that integrates the variations of water masses and their movements through the seasons. The background reference for turbidity was determined by first combining the turbidity data from both hydrocasts recorded at 1 Hz by the CTD. Then averaging over 40 values so that the standard deviation for depth did not exceed 10 m and that a signal frequency of 1/40 Hz was obtained. For the chemical parameters studied, the background reference was determined by first Table  1  Most striking features of the different lava flows and sampling sites, including site location, sampling date, visual observations from the submersible interactive camera system (SCAMPI, Ifremer) and main physicochemical characteristics.
Hilton et al. (1998)  have shown that the degassing of an alkalic magma may explain CO 2 / 3 He ratios (measured in fluids) higher than the initial magma ratio due to the solubility behavior of CO 2 and He in magmas of different chemistry. In low-SiO 2 alkali basalts, the solubility of CO 2 is significantly higher than in tholeiites, while helium solubility decreases with decreasing SiO 2. Thus, volatiles residual after a degassing event will have higher CO 2 / 3 He. A possible origin for excess carbon is Profiles of H 2 , CO 2 and CH 4 were measured at the summit and above the northern flank of the volcano. Pure-CO 2 density changes with the water column depth at the volcano was calculated using Span and Wagner's equation of state  (Span and Wagner, 1996). Standard-seawater density profile was calculated from CTD data recorded at both sites and Archer's thermodynamic model for NaCl  (Archer, 1992).
Temperature profile was extracted from CTD data recorded at both sites. 3D representation of the volcano edifice was made using 30 m-resolution bathymetry (vertical exaggeration x2), with counter lines at 50 m intervals (Geo-Ocean (Ifremer), 2022). the contribution (i. e. contamination) of carbonaceous sediments being a prominent source of CO 2 under the appropriate T-P-f O2 conditions  (Marty et al. , 1989). However, even if the contribution of crustal gases was more elevated for July 2019 samples than for the October 2020 samples, we do not observe a significant change in CO 2 / 3 He values between those both periods. Therefore we attribute the excess in CO 2 relative to 3 He to an enrichment of CO 2 originating likely from carbonates and organic matter of the thick sediment cover between the main volcanic layer, below the new volcano, and the top of the crust (2.
However, the volcano does lie above ~2. 2-2. 5 km thick sediment cover  (Masquelet et al. , 2022b) , which depending on the magma ascent rate and conditions, may release enriched methane mature fluids of thermogenic signatures  (Etiope and Sherwood Lollar, 2013; Pegler and Ferguson, 2021). With the current dataset, it is not possible to favor an abiotic over a thermogenic origin of CH 4.
Acknowledgments We thank the crew members of the  R/V Marion Dufresne (TAAF/ IFREMER/LDA)  and its  Captain A. Eyssautier. We thank  N. Feuillet ,  Y. Fouquet ,  E. Rinnert  and  I. Thinon  the chief scientists of the MAYOBS01 cruise (May 2019, doi: 10. 17600/18001217), the  MAYOBS04  cruise (July 2019, doi: 10. 17600/18001238 ), and the  MAYOBS15  cruise (July 2019, doi: 10. 17600/18001745 ). We also thank all the scientists involved. We thank  Jürgen Sültenfuß  from the  Institute of Environmental Physics/Section of Oceanography at the University of Bremen  who performed all helium and neon isotopic analyses. We thank  Bart van der Haven  from Isolab b. v. who carried out the carbon isotope analyses.
However, the significantly lower densities in AGB stars, compared to novae, mean that it is very unlikely that the 25 Al(p,γ) reaction is active in these environments. To assess the astrophysical implications of the present work, we have performed a series of nova-outburst simulations using the hydrodynamic, Lagrangian, timeimplicit code SHIVA  [52, 53]. Energy generation by nuclear reactions is calculated using a network of 120 nuclear species from 1 H to 48 Ti linked through 630 nuclear processes. Reaction rates are from the STARLIB database  [54] , and we have considered accreting 1. 15-, 1. 25-and 1. 35-M ⊙ white dwarfs (with initial luminosity 10 -2 L ⊙ and mass-accretion rate 2 × 10 -10 M ⊙ per year). Nucleosynthetic yields of Mg -Si isotopes obtained using the current 25 Al(p, γ) 26 Si reaction rate are displayed in Table  III.
Acknowledgements United Kingdom personnel were supported by the  Science and Technologies Facilities Council (STFC). J. H. acknowledges support at the under  UKRI Future Leaders Fellowship   MR/T0222641/1. J. J. acknowledges support from  Spanish MINECO  Grant No. PID2020-117252GB-I00 , the  E. U. FEDER  funds, and  AGAUR / Generalitat de Catalunya  Grant No. SGR-661/2017. This research used resources of ANL's ATLAS facility, which is a  DOE Office of Science User Facility. Research at UNC, TUNL  and ORL is supported by grants  DEFG02-94-ER40834  ( UNC )  DEFG02-94-ER41043  ( TUNL ) and  DE-AC05-00OR22725  ( ORNL ),  US Department of Energy, Office of Science, Office of Nuclear Physics. This research was also supported by the  U. S. Department of Energy, Office of Sciences, Office of Nuclear Physics , under the contract number  DE-AC02-06CH11357.
In an orthotopic glioma rat model, Dufort et al. demonstrated the selective accumulation of AGuIX in high-grade glioma as well as the potential survival benefits when combined with standard of care TMZ and radiotherapy  [13]. The first-in-human phase I NANO-RAD (NCT02820454) evaluated the tolerance and the maximum tolerated dose of the intravenous injection of AGuIX nanoparticles, (15, 30, 50, 75 or 100 mg/kg) in combination with whole brain radiotherapy in patients with multiple brain metastases not suitable for stereotactic radiotherapy (from primary cancers of melanoma type n = 6, non-small cell lung cancer n = 6, colon cancer n = 1, and breast cancer n = 2). No doselimiting toxic effects were observed up to AGuIX 100 mg/ kg. This clinical trial demonstrated a good tolerance of the intravenous injection of AGuIX nanoparticles, and a rapid elimination by the kidney in correspondence with preclinical results.
Among the 29 SAEs, four were considered possibly related to AGuIX and were delayed effects of RT, possibly enhanced by AGuIX  [14]. Moreover, this clinical trial also demonstrated (i) the selective accumulation of AGuIX in brain metastases from different types of primary tumors due to the EPR effect, with a persistence in the tumor lasting few days after injection, (ii) a dose-correlated uptake of AGuIX in brain metastases, (iii) a dose-correlated tumor volume reduction and (iv) a clinical benefit for 13 patients over 14 when combination of AGuIX with radiotherapy  [15]. Four clinical phase Ib and/or II trials are currently underway with AGuIX, with more than 100 patients already injected with AGuIX nanoparticles. The NANO-RAD 2 (NCT03818386) and the NANOBRAINMETS (NCT04899908) trials are randomized phase II trials evaluating the efficacy of the combination of intravenous injections of AGuIX nanoparticles (100 mg/kg) with whole brain radiotherapy or stereotactic radiosurgery/radiotherapy, respectively, in the treatment of brain metastases.
The NANOSMART trial (NCT04789486) is a phase I/II trial evaluating the safety and the efficacy AGuIX in combination with stereotactic magnetic resonance-guided adaptive radiation therapy for centrally located lung tumors and locally advanced pancreatic cancers, and the NANOCOL trial (NCT03308604) is a phase Ib evaluating AGuIX in combination with radiochemotherapy in advanced cervical cancer. According to preclinical and clinical results, the AGuIX nanoparticles could be a promising approach to increase the effectiveness of the standard of care (TMZ and RT) in GBM patients. Our phase I/II trial aims to test the combination of AGuIX nanoparticles with radio-chemotherapy and concomitant TMZ in patients with newly diagnosed GBM with incomplete resection.
The randomization sequence is known only to the statistician. For the experimental arm, a single-stage Fleming design will be used. While the design does not allow for a formal comparison between the two arms, it may reinforce the value of the conclusions on the potential efficacy of the experimental treatment by validating the hypothesis on the control arm. An interim analysis will be performed without interruption of inclusions for the independent data monitoring committee (IDMC) review, after 20 patients are treated in the experimental arm at the recommended dose (i. e. approximately 30 patients in phase II). A maximum of sixty-six patients are expected to be enrolled: 12 patients in phase I, of which (minimum) 6 are treated at the recommended dose, 34 additional patients in the phase II experimental arm and 20 patients in the phase II reference arm.
Data analysis Statistical analysis will be realized on modified intentionto-treat (mITT) population, i. e. patients who received at least 1 injection of AGuIX nanoparticles. The primary endpoint of phase I, the RP2D of AGuIX nanoparticles, will be determined as the highest dose tested for which the percentage of dose-limiting toxicities (DLT) is less than 33%. DLT percentage will be calculated for each tested dose. Progression-free survival, progression-free survival rate at 6 months and 12 months, and overall survival at 6 months and 12 months will be estimated by the Kaplan-Meier method in both arms. The primary endpoint of phase II, 6-month PFS in the experimental arm, will be calculated with its 90% CI, and the result will be interpreted according to the Fleming method. Missing data will not be replaced.
Data management and monitoring The data collected for the study will be recorded in an eCRF (Ennov Clinical software) by each investigating center. The staff with access to the data will be the investigators, the clinical research associates, the project leaders and the biostatisticians. They are authorized professionals and are subject to professional secrecy. The investigator will ensure the accuracy, completeness, and consistency of the pseudonymized patient data recorded and of the provision of answers to data queries. Regular monitoring reviews will be performed by a clinical research associate mandated by the sponsor. The objectives will be to ensure the proper conduct of the study in each center, the recording of the data generated in writing, their documentation, recording and reporting, in accordance with the legislative and regulatory provisions in force.
Conclusion: Our results suggest that emotion dysregulation is a major difficulty of autistic adults without intellectual disability eligible for dialectical behavior therapy, and this is especially the case for autistic females. There seem to be different sex-specific factors involved in emotion dysregulation found in autistic adults, which highlight the need to target-specific domains (e. g. alexithymia) in the treatment of emotion dysregulation in autistic females. ClinicalTrials. gov.
Participants The data used in this study were collected from 2019 to 2022 at the Psychiatric Unit of the University Hospital of Strasbourg, as part of a larger study on ED and dialectical behavior therapy (DBT). 54  Participants were enrolled because they presented with self-reported ED and were eligible for DBT. There were 85 participants originally recruited, with various primary conditions, that is, ASD, BPD, bipolar disorder (BD), ADHD and eating disorders. Given the goals of the study, we focused on a subset of emotionally dysregulated participants (N = 74), excluding patients with disorders other than ASD or BPD. Due to the small sample size of males with BPD in our study (N = 7), only females with BPD were included in the analysis. The final sample of 74 participants was categorized into three groups: women with a diagnosis of ASD (ASDf; N = 28; age, M = 29.
In Senegal, the first four complete SARS-CoV-2 genome sequences were obtained from the first four positive cases at the Pasteur Institute in Dakar. All sequences are deposited in the Global Influenza Data Sharing Initiative (GISAID) database under accession numbers EPI_ISL_418206-9  [12]. Subsequently, genomic surveillance of SARS-CoV-2 in Senegal was intensified to monitor the evolution of the virus. This led to the identification of several VOCs  [13]. A study that focused on the mutational analysis of the Spike protein in 2020-2021 revealed new combinations of mutations and evidence of local diversification  [14]. Significant lineage diversification was also reported by another study during the first and second waves of the epidemic  [15]. A comprehensive molecular characterization of the number and distribution of SNPs across the entire genome of Senegalese SARS-CoV-2 strains has been poorly explored.
This study was conducted using amplicon-based sequencing following the ARTIC protocol; sequencing based on a metagenomic approach using probe enrichment (xGen) and focused on spike protein mutations was also conducted. Prior to the present study, there were little data on COVID-19 from urban areas of Senegal. Thus, by applying the Illumina COVIDSeq protocol to Senegalese samples for the first time, we aimed to unveil the genetic epidemiology of Senegalese SARS-CoV-2 strains using nasopharyngeal samples collected in an urban environment during the second wave of the epidemic-which was considered the most deadly in Senegal, with a case-fatality rate of 3. 16%  [16] -through SNP characterization and analysis of the phylogenetic distribution.
Consensus Sequences An in-house pipeline, inspired by the Illumina analysis pipeline but with some modifications, was used to assemble the Illumina sequencing data into consensus genomes  [19]. The raw data were demultiplexed into fastq files. Using trimmomatic (v 0. 39), we performed quality control of the fastq files and adaptor trimming of raw reads  [20]. Minimap2 (version 2. 24) was used to map the fastq files back to the SARS-CoV-2 reference genome indexed with GenBank accession no. NC_045512. 2. Subsequently, the mapped genomes were converted to BAM files, sorted and indexed. Duplicate sequences and primers were removed using samtools (version 1. 15. 1). Using an in-house script written in the Python programming language, consensus genomes were generated using Sam2consensus (https://github.
Genomic and Phylogenetic Analyses The obtained consensus whole genome sequences were mapped against the SARS-CoV-2 Wuhan-Hu-1/2019 reference genome sequence (Genbank: MN908947) and then analyzed using the Nextclade (v1. 14. 1) web application  [21]  for mutation detection and determination of genomic locations of SNPs. In addition, mutations were assessed using the CoVsurver mutations web application available on the GISAID website  [22]. To construct the initial maximum likelihood phylogenetic tree, the obtained genomic sequences were aligned against the reference sequence of SARS-CoV-2 (GenBank accession no. NC_045512. 2) using a multiple sequence alignment program (MAFFT v7. 505). From the obtained alignment, a phylogenetic tree was reconstructed using IQ-TREE2 with the GTR+I+G model and 1000 ultrafast bootstrap repeats.
The generated tree was stored in treefile format. This file was then uploaded to the online software Interactive Tree Of Life (iTOL)  [23]  for tree visualization and annotation. For genetic lineage assignment, we used the two commonly used dynamic classification systems: Nextclade web tool (v1. 14. 1) and Phylogenetic Assignment of Named Global Outbreak Lineages (PANGOLIN) software v1. 1. 14 (https://github. com/hCoV-2019/pangolin (accessed on 15 July 2022)  [24]. SNP distribution and density were determined using a set of Excel formulas packaged by us: Density (SNPs/1000 nt) = (Number of SNPs × 1000 nt) ÷ Length of genomic region. A second in-house Python script was used during the consensus sequence analysis to analyze the different SARS-CoV-2 genotypes obtained from these Senegalese SARS-CoV-2 sequences.
Lineage Distribution, Clades, and Phylogeny The median number of raw reads generated from the 379 sequenced strains was 2,961,812, with a minimum of 254,768 reads and a maximum of 6,712,590 reads (Supplementary Data S4). After genomic analysis, all sequences with more than 50% missing nucleotides were excluded from analysis. In total, 291 genotypable sequences were obtained from the 379 sequenced samples. These sequences had an average depth of 16,163X (403X-48,157X) and an average genome coverage of 92% (Genomic coverage range of 50-100%). For each sample, the total number of raw reads, total number of mapped reads, sequencing depth and coverage are listed in Supplementary Data S4. Following quality control, 291 genomes, including 12 strains collected in July 2020 and 279 strains collected in March and April 2021, were included in the analysis.
The 291 genomes have been deposited in the public GISAID database (accession nos. EPI_ISL_13307473-557, EPI_ISL_13710664-722, EPI_ISL_16941819-97, EPI_ISL_16942089-145, EPI_ISL_16987269-76, EPI_ISL_1827867, EPI_ISL_1855408, EPI_ISL_13325688 and EPI_ISL_16941818) (Supplementary Data S1). These sequences were then subjected to phylogenetic analysis. The 291 sequences were grouped, according to the PANGOLIN classification, into 16 SARS-CoV-2 lineages (Figure  1A ). Of these, B. 1B ). The initial maximum likelihood phylogenetic tree constructed with the 291 sequences and rooted with the Wuhan reference sequence indexed with GenBank accession no. NC_045512. 2 confirmed the clustering of the analyzed sequences into these main groups (Figure  2 ).
1. 14 isolate's genome was characterized by 33 substitutions compared with the SARS-CoV-2 Wuhan-Hu-1/2019 (Genbank: MN908947) reference genome (Supplementary Data S3). The most frequent of these substitutions was the replacement of C with U, which occurred 12 times. These single nucleotide variations resulted in 22 non-   3D ). The most frequent non-synonymous mutation (D614G) was driven by the SNP at position 23,403 changing adenosine to a guanosine (A23403G). This amino acid change occurred in 276/291 (94. 8%) of the SARS-CoV-2 genomes. The single nucleotide variation at position 3037 (C3037T) resulted in an amino acid change (P314L) in ORF1b that was the second most common non-synonymous mutation in the analyzed sequences (257/291, 88. 3%). A total of 35 amino acid deletions were also found.