dataesr/scientific-paragraphs-categorization huggingface dataset:
Data analysis We measured the level of blur that participants attributed to the Sample to judge the Target and the Sample as equally blurred (Matched Blur Level, MBL). Data were analysed using R software (R Core Team, 2019) and lme4 package  (Bates, Mächler et al. , 2015)  to perform a linear mixed-effects analysis of the effect of signal Reliability (high vs. low), Target-Sample pair-type (identical vs. different), and Congruence between the Sample object and its context (congruent vs. incongruent) in a 2 × 2 × 2 within-subject design. The signal Reliability, the Target-Sample pair-type, the congruence of the Sample and their interaction terms were introduced into the model as fixed effects. The intercepts for subjects, as well as the random slopes per participant for the effect of signal Reliability, Target-Sample pair-type, Sample congruence and their interactions were specified as random effects.
The p-values were obtained by Satterthwaite approximation with the lmerTest package  (Kuznetsova et al. , 2017). The significance threshold was set at 0. 025 (0. 05/2 = 0. 025) after Bonferroni correction for the two non-orthogonal tests performed (the Sample congruence effect in the low and high reliability conditions for the different Target-Sample pair-types). Effect sizes were estimated using Cohen's d z  (Lakens, 2013).
Statistical analysis Data are represented as mean values and standard errors of the mean (SEM). For each chart, the number of independent experiments (n) is given in the legend of the figure. Statistical analysis was performed with GraphPad Prism software 9. 1. Statistical significance was evaluated using the one-sample t-test, which compares the mean of the sample with a hypothetical mean, or the unpaired Student's t-test when appropriate. The Gaussian distribution of the values was confirmed for each group before running any test where this hypothesis is assumed. Statistical significance is indicated as *P < 0. 05, **P < 0. 01, ***P < 0. 001, and ****P < 0. 0001.
Acknowledgements We appreciate the support of the  Swiss National Fund  ( 310030_179235  and  310030_212504 ), the  Swiss Cancer League  ( KFS-4398-02-2018  and  KFS-5402-08-2021 ) and the  Swiss National Centre for Competence in Research "Kidney. CH"  ( N-403-06-26   HCP  project grant) to CS. MS was supported by the  internal postdoc funding program  of the  University of Zurich  ( Forschungskredit UZH Postdoc 2019 ). We thank the  Laboratory Animal Services Center (LASC)  for animal care and the  UZH Irchel Flow Cytometry core facility. We acknowledge the support from the  SKINTEGRITY. CH collaborative research program. Open access funding provided by  Universitat Zurich.
Data availability The datasets produced in this study are available in the following databases: • RNA-Seq data: ArrayExpress E-MTAB-12082 (https://www. ebi. ac. uk/biostudies/arrayexpress/studies/E-MTAB-12082). • Targeted Metabolomics: Metabolights MTBLS6863 (https://www. ebi. ac. uk/metabolights/MTBLS6863/descriptors). Expanded View for this article is available online.
Contents This is a citation summary for Opioid receptors in the Guide to Pharmacology database (GtoPdb). It exists purely as an adjunct to the database to facilitate the recognition of citations to and from the database by citation analyzers. Readers will almost certainly want to visit the relevant sections of the database which are given here under database links. GtoPdb is an expert-driven guide to pharmacological targets and the substances that act on them. GtoPdb is a reference work which is most usefully represented as an on-line database. As in any publication this work should be appropriately cited, and the papers it cites should also be recognized. This document provides a citation for the relevant parts of the database, and also provides a reference list for the research cited by those parts.
Within the European Space Agency's (ESA) Space Weather Service Network, the development of performance indicators for the Global Navigation Satellite System (GNSS) has been identified as essential to meet the growing needs of end users for Space Weather information in the field of navigation. This requires a targeted analysis of Space Weather-related disturbances of technical systems and services in the field of satellite-based navigation, considering the growing requirements of all the different users in this domain. The goal of the Space Weather Impact on GNSS Performance: Application Development (SWIGPAD) project is to develop GNSS performance indicators (GPI) based on Space Weather data provided by European research institutes through the ESA Space Weather Service Portal (available at https://swe.
SWE-CRD-TIO-USR-02 This user group includes users of GNSS single frequency services with average accuracy, using integrity. The most popular example of this kind of service is the EGNOS. Using a wide area network of GNSS receivers, it provides an additional data stream that is transmitted to the users, e. g. aircrafts during landing approaches, via geostationary satellites. The transmitted data includes an esti- The SWIGPAD application provides a tool that enables the users to extract VTEC/GIVE information in formats tailored for their own services and to get information on the time evolution of disturbed periods.
Data used As described in the previous section the performance indicator is based on scintillation indices S4, ROTI and σ ϕ as well as TEC and Kp. These are crucial parameters to consider when assessing the impact of space weather on GNSS users  [ 1 , 5 ]. The Earth's ionosphere contains free electrons that can delay the propagation of GNSS signals. TEC quantifies the total number of electrons along the signal path between the satellite and receiver which introduces a delay in the signal arrival time, affecting the accuracy of position calculations. By monitoring TEC, GNSS users can account for ionospheric delays and correct for them in their positioning solutions. Additionally, space weather events, such as solar flares or geomagnetic storms (indicated by Kp Index), can cause disturbances in the ionosphere, resulting in irregularities in the TEC distribution.
These irregularities can also lead to signal scintillation, which refers to rapid and random fluctuations in the amplitude and phase of GNSS signals. Severe scintillation can disrupt the GNSS signal tracking and degrade positioning accuracy. Finally, sudden changes in TEC, particularly during space weather events, can also affect receiver performance, leading to loss of signal lock or extended time for signal acquisition. Therefore, monitoring TEC and incorporating TEC products into the GPI application helps to assess the GNSS error budget due to ionospheric disturbances, which is one of the major sources of error related to GNSS positioning. To compute the TEC performance indicator, at least one of the following data sources is required: To process the ROTI based performance indicator, one of the following data sources is required: • RTIM ROTI maps (Northern Europe) as shown in Fig.
Two participants were excluded, one due to failure to produce temporal intervals varying monotonically with actual intervals and the other due to failure to comply with instructions to control the gamepad to report the interval estimations. The remaining 43 participants (24 females, age = 24. 8 ± 4. 4 years old) participated in all three conditions. In both experiments, participants were naive as to the purpose of the study and reported normal or corrected-to-normal vision, normal audition and no neurological history. All participants provided informed consent before participation and received a payment for their participation. Procedures were approved by the ethics committee (CEEI/IRB Comité d'Evaluation Ethique de l'Inserm, n°21-772, IRB00003888, IORG0003254, FWA00005831)) and adhered to the ethical standards of the Declaration of Helsinki except for registration in a database.
The light bulb was presented at either of two taskirrelevant distances from participants (45 cm or 4. 5 m). As this manipulation, which was outside the scope of the present study, did not bring any difference in temporal binding, data were pooled together for analysis. Both Experiment 1 and Experiment 2 were programmed using the unity platform (Unity 2018. 4. 22f1 Personal) and Microsoft Visual Studio 2019.
Funding This study was supported by the  ANR  grant  DEC-SPACE  ( ANR-21-CE28-0001 ) to A. F and M. V, the  Impulsion  grant from  IDEXLYON  to M. V, the post-doctoral fellowship from  Fondation Fyssen  to G. K, and has been performed within the framework of the  LABEX CORTEX  ( ANR-11-LABX-0042 ).
Genome Wide Association Study of NETs plasma levels The present study relies on a subsample of 657 individuals (372 VT cases and 285 controls) with both NETs measurements and imputed genetic data. All single nucleotide polymorphisms (SNPs) with minor allele frequency (MAF) greater than 0. 01 and imputation quality score greater than 0. 3 were tested for association with NETs plasma levels. As shown in the next section, 3 different statistical models were deployed. In all, associations were tested on imputed allelic doses and adjusted for potential confounders that is age, sex, smoking, case-control status and the four first principal components derived from genome wide genotype data  [36] [37] [38]. The standard genome-wide statistical threshold of 5x10 -8 was used to consider SNPs as significantly associated with NETs plasma levels.
Statistical modeling for GWAS analysis of NETs plasma levels Since we were interested in identifying genetic factors that influence mean NETs plasma levels, any statistical approach that treats independently the zeros mass and the distribution of positive values, such as the two-part, ZIP and ZINB models, was deemed not adapted to our application. As a consequence, only three models were compared in this study: the Tobit, Compound Poisson-Gamma, and Negative Binomial models. The Poisson model was not investigated because NETs plasma data presented a large overdispersion (see Results section), a situation where Negative Binomial model is preferable. In this study, we aimed to identify the most suitable model for semicontinuous data in order to conduct a GWAS on NETs and highlight the difference between the three models.
Models' comparison The three aforementioned tested models were applied to NETs data while adjusting for age, sex, smoking and case-control status. The fit of these models to NETs data were assessed in two ways. First, we computed the Root Mean Square Error (RMSE) of each tested model defined as: ‫ܧܵܯܴ‬ ൌ ඩ 1 ܰ ሺ‫ݕ‬ ො െ ‫ݕ‬ ሻ ଶ ே ୀଵ where N is the sample size, ‫ݕ‬ ො the prediction of the ݅ ௧ individual according to its covariates provided by a given modeling approach and ‫ݕ‬ the observed value. Instead of predicting ‫ݕ‬ ො by ॱሺܻ |ܺ, ߚ መ ሻ that cannot be equal to zero, we used simulated predictions. For each studied model, a random value was generated for each individual according to its explanatory variables and the estimated model parameters. This process was repeated 1,000 times and the mean of RMSEs over the 1000 replicates was reported.
As the Tobit model predicts negative values that are not observed in our semicontinuous outcome, these were imputed at zero to calculate the corresponding RMSE. Second, we graphically assessed the fit of models predictions using Quantile-Quantile plot (QQplot) of the observations and predictions for each tested model. Gamma models) to a deviation from their underlying distribution. To assess the robustness to outliers, each simulated dataset was also analysed after the exclusion of individuals with simulated trait higher than 0. 5, a threshold corresponding on average to the exclusion of 3% of individuals with the highest NETs levels.
GWAS analysis on NETs plasma levels A total of 9,670,724 autosomal SNPs were tested for association with NETs plasma levels using the   S1 ) when data distribution fit the one here observed for NETs plasma levels in the FARIVE study. Type I error was rather well controlled in absence of extreme values (Supplementary Table  S2 ). Of note, these simulations also show that the Compound Poisson-Gamma model generally controls the nominal type I error. All these observations add support for the use of the Compound Poisson-Gamma model for the GWAS analysis of NETs plasma levels. The corresponding Manhattan plot shown in Figure  4  revealed one genome-wide significant locus. The lead SNP rs57502213 is a deletion of two nucleotides (TC), mapping to the miR-155 hosting gene (MIR155HG). This variant had a MAF of ~7%, exhibited a good imputation quality (Rsq=0.
Fundings GM and D-AT are supported by the  EPIDEMIOM-VT Senior Chair  from the  University of Bordeaux initiative of excellence IdEX. The FARIVE study was supported by grants from the  Fondation pour la Recherche Médicale , the  Program Hospitalier de recherche Clinique  ( PHRC 20 002 ;  PHRC2009   RENOVA-TV ), the  Fondation de France , and the  Leducq Foundation. FARIVE genetic data were funded by the  GENMED Laboratory of Excellence on Medical Genomics  [ ANR-10-LABX-0013 ], a research program managed by the  National Research Agency (ANR)  as part of the  French Investment  for the Future.
Since the SPR band position depends on the thickness of the capping molecule shell, we obtained different SPR positions for the same gold core size. 40 To confirm the capacity of these gGNP to be used for PTT, an in vitro study was performed to measure the hyperthermia induced by laser irradiation. A temperature increase of 14 and 20 °C was observed for an irradiation of 0. 2 and 0. 6 W/cm 2 respectively (Figure  S1G, H ). B, C ) Transcription of the SLC2A1, SLC2A2, SLC2A3, SLC2A4, FLT1, and KDR genes coding respectively for Glut-1, Glut-2, Glut-3, Glut-4, VEGFR1, and VEGFR2 proteins in healthy skin and primary melanomas. Box plots are presented on a linear scale for the cohorts of patients GSE46517 (n = 39) (B) and GSE114445 (n = 22) (C). Both cohorts are sourced from historical data sets  49, 50  *P < 0,05; ***P < 0,001; ****P < 0,0001 evaluated by the Welch t test.
* Represents the statistical difference with blue and green curves, # with black curve. (D) Relative gGNP fluorescence in SK-MEL-28 cells incubated for 30 min in media with or without glucose containing gGNP@G at 3 nM. that glucose transporters, GLUT, and VEGFR1/2 are good cancer markers, we analyzed the expression of GLUT1-4 and VEGFR1/2 encoding genes in tumors of patients with melanoma. We extracted the probe sets for these genes available in two cohorts GSE46517  49  and GSE114445  50  with more than 22 specimens consisting of at least 16 samples of melanomas in a single platform. Levels of GLUT1-4 and VEGFR1/2 gene expression were compared between healthy skin and melanoma specimens. We found that, at least, one GLUT gene -GLUT1 and GLUT3 in GSE46517, GLUT3 in GSE114445-and one VEGFR gene -VEGFR1 in GSE46517 and VEGFR2 in GSE114445-was significantly upregulated in melanomas specimens, compared to healthy skin (Figure  1B, C ).
The same analysis was performed on cutaneous squamous cell carcinomas, using cohorts GSE32979  51  and GSE53462  52  (Figure  S2 ), and on cutaneous basal cell carcinomas using cohort GSE53462  53  (Figure  S3 ). The same expression pattern was observed: overexpression of GLUT1 and/or GLUT3 and overexpression of VEGFR1 and/or VEGFR2 encoding genes in tumors cells compared to healthy skin. Therefore, we decided to coat our gGNPs with glucose (gGNP@G) to, first, stabilize them and, second, to target GLUT1/3. Then, we developed another gGNP@G candidate also able to target VEGFR1/2 by functionalizing gGNP@G with a VEGF macrocyclic mimic, CBOP11 peptide, gGNP@ G@CBOP11. The stabilization of gGNPs with glucose and their functionalization with CBOP11 peptide were confirmed in our previous study by combining different techniques: Raman spectroscopy, X-ray photoelectron spectroscopy, dynamic light scattering, and zeta potential.
Briefly, to boiling solution (24. 75 mL) of disodium citrate, 0. 25 mL (25. 4 mM) chloroauric acid was added. According to Kumar' model prediction, the molar ratio of sodium citrate solution to HAuCl4 was set to 2. 5. 80 The solution mixture was stirred continuously for 15 min. After that, the solution was allowed to cool down to room temperature under stirring and then, was centrifuged. The precipitate was washed with ultrapure water. 81 icroarray Gene Expression Data Extraction. Two data sets with more than 20 samples in a single microarray platform tested for gene expression in melanomas (GSE46517  49  and GSE114445  50  ) were identified from the literature. Data were extracted and analyzed as previously described. 82 In a nutshell, there are URLs for individual data sets (data series as referred to on GEO) on the GEO website and for each data series, a link permits the access to the Series Matrix Files which contain the expression values for each gene (probeset) and each sample.
Microarray gene expression data were retrieved from the data matrices deposited to GEO by the original authors. Data normalization methods were mentioned under each data series on GEO. Once the gene expression matrices were successfully obtained, expression values were extracted for GLUT1-4 and VEGFR1/2 encoding genes. Cell Lines and siRNA Transfections. SK-MEL-28 cells were purchased from ATCC and were grown in Dulbecco's modified Eagle's medium (DMEM) (4. 5 g/L) (Gibco) supplemented with 1% sodium pyruvate (Gibco), 1% Glutamax (Gibco), and 10% heat-inactivated FBS (Eurobio -CVFSVF00-0 U) at 5% CO 2 at 37 °C. Primary epidermal melanocytes, normal, human, adult (HEMa) (PCS-200-013), were purchased from ATCC and were grown according to the manufacturer's recommendations: in dermal cell basal medium (PCS-200-030) supplemented with adult melanocyte growth kit (PCS-200-042) at 5% CO 2 at 37 °C.
4 N. A. objective (Zeiss). The following excitation/emission couples were acquired: 405-460/50 for DAPI/HCS, 488-535/50 for Tf-AF488, 561-535/50 for GNP, and 640-700/75 for AF647. Microscopy images were processed and quantified with ICY software  83  (http://icy. bioimageanalysis. org). Colocalization quantification was performed on confocal images using the distance analysis plugin in ICY software. GNP and Tf-AF647 endocytosis were quantified with ICY software using "HK-Means" and "Active Contours" plugins to automatically detect cell boundaries and "Spot Detector" plugin to measure the number of GNP and Tf-AF647 spots within the detected cells. The total intensity of GNP and Tf-AF647 spots was normalized to the mean value of the time 0 (Figures  2C  and 5C ), to the 0 g/L of glucose condition (Figure  2D ) or to the not transfected samples (Figure  2F, H ).
Transmission Electron Microscopy. Cells incubated with GNP were fixed in 2. 5% glutaraldehyde (Sigma-Aldrich) in PHEM buffer 1× for 1 h at room temperature. Fixed samples were washed three times by the addition of fresh 0. 1 M PHEM buffer and postfixed in 1% osmium (EMS) in 0. 1 M PHEM buffer for 1 h. Samples were gradually dehydrated in ethanol (Sigma-Aldrich) series from 50 to 100%. Samples were embedded in epoxy resin (EMS), followed by polymerization for 48 h at 60 °C. Ultrathin sections (70 nm) were cut with a Leica UC7 microtome and stained with 4% uranyl acetate for 45 min followed by lead citrate for 10 min. Sections were observed using a TEM Thermo Fisher Tecnai T12 at 120 kV. Flow Cytometry and Data Analysis. Incubated cells were detached using PBS supplemented with EDTA (Invitrogen) or trypsin (Sigma -59427C) diluted in PBS to a final concentration of 1% for 5 min at 37 °C, and then neutralized with PBS supplemented with 5% FBS.
Cells were centrifuged (5 min, 1200 RPM, 4 °C), supernatants were removed, and the pellet was resuspended in PFA 4% diluted in PBS for 1 h at 4 °C. Cells were washed and finally run in an Attune NxT Flow Cytometer (Thermo Fisher Scientific) recording FSC, SSC, and YL2 channels. The geometrical mean fluorescence of intracellular GNP was measured in gated cells in the YL2 channel using flowjo software. Data were normalized to the GNP@G@CBOP11 at 30 min in SK-MEL-28 condition. In Vivo Experiments. Nanoparticles. gGNP@G and gGNP@G@CBOP11 were used at a concentration of 0. 15 or 0. 3 mg/mL. For experiment, nanoparticles were injected directly into the tumors with a volume corresponding to 20% of the tumor volume. Treatment of Reference. Anti-PD1 mAb at 5 mg/kg (ip injection every 5 days) was used as reference treatment.
Figure1. GNP design rational. (A) Comparison of tGNP and gGNP toxicity on healthy human melanocytes (PCS-200-013) using the LDH method and normalized to lysed cells (100%) and control cells (0%). Histograms represent the mean with SEM as error bars. (B,C) Transcription of the SLC2A1, SLC2A2, SLC2A3, SLC2A4, FLT1, and KDR genes coding respectively for Glut-1, Glut-2, Glut-3, Glut-4, VEGFR1, and VEGFR2 proteins in healthy skin and primary melanomas. Box plots are presented on a linear scale for the cohorts of patients GSE46517 (n = 39) (B) and GSE114445 (n = 22) (C). Both cohorts are sourced from historical data sets 49,50 *P < 0,05; ***P < 0,001; ****P < 0,0001 evaluated by the Welch t test.
We thank  Valérie Malardé  for technical help on the project. We thank ASSU 2000 for supporting NS. We are grateful for support for  UBI  equipment from the  French Government Programme Investissements d'Avenir France BioImaging (FBI ,  N°ANR-10-INSB-04-01 ) and the  French gouvernement (Agence Nationale de la Recherche )  Investissement d'Avenir programme, Laboratoire d'Excellence "Integrative Biology of Emerging Infectious Diseases"  ( ANR-10-LABX-62-IBEID ).
Wave Heights As demonstrated by  Ardhuin et al. (2010) , wave heights from global-scale wave models are most sensitive to parameters defining the swell dissipation, and any change to the wave breaking dissipation can have an impact on the wind-sea to swell transition and thus on the energy radiated into swell. We thus repeated the parameter adjustment procedure defined by  Alday et al. (2021) , using the distribution of wave heights measured by Jason-2 for the year 2011, as provided in the ESA Sea State Climate Change Initiative version 1 dataset  (Dodet et al. , 2022). We recall that, following  Leckler et al. (2013) , we parameterize swell dissipation due to air-sea friction  (Ardhuin et al. , 2009)  as a combination of viscous and turbulent terms with a transition at a Reynolds wavenumber Re c spread out over a range of values s 7 , in order to represent a Rayleigh distribution of wave heights  (Perignon et al.
, 2014; Stopa et al. , 2016). We ran the model with either T702 and the DIA or T707 and the GQM method. Both model runs are compared to the T475 which differs from the default ST4 by a small adjustment on the swell dissipation parameters  (Alday et al. , 2021). Here, the value of s 7 was reduced from 432,000 for T475 and T707-GQM to 360,000 for T702, and the swell dissipation factor was reduced from 0. 66 for T475 and T707-GQM to 0. 6 for T702. We also note that T475 and T702 use a wind-wave growth parameter β max = 1. 7 while T707-GQM uses β max = 1. 6, which is consistent with the general reduction of other source terms when replacing the DIA with an exact method  (Banner & Young, 1994). These adjustments were performed for the year 2011. We will now verify model results using independent data for the year 2007 that combines four different altimeters (GFO, Envisat, ERS-2 and Jason-1) provided in the ESA Sea State Climate Change Initiative version 1 dataset  (Dodet et al.