dataesr/scientific-paragraphs-categorization huggingface dataset:
The CD56 dim compartment contained 3 uniformly GZMB + clusters and 2 clusters that additionally had up to 20% GZMK + NK cells (Figures  1G-H , J, Supplemental Figure  1D ). The conventional cytotoxic CD56 dim cluster was characterized by cytotoxicity markers (PRF1, FGFBP2, NKG7, GNLY, CST7, PRF1 and GZMB) and CX3CR1, while the transitional/recently activated NK cluster combined intermediate expression of these genes with the expression of immediate early genes (e. g. DUSP2, NR4A2 and FOSL2) (Figure  1G , Supplemental Figure  1D )  11, 26, 27, 28. The terminal NK cluster was transcriptionally similar to the conventional cytotoxic CD56 dim cluster but had increased average expression of several cytotoxicity genes including FCGR3A and PRF1 (Supplemental Figures  1D-E ). In line with other NK cell datasets  26, 27, 28  , transcription of CD57 (B3GAT1), the functionally distinguishing marker for terminally differentiated NK cells, was too low to differentiate conventional cytotoxic NK cells from terminal NK cells (Supplemental Figure  1E ).
Cell cycle analysis confirmed increased expression of G2M and S phase transcripts in this latter cluster (Figure  1K ). The developmental trajectory of MM BM NK cells was mapped by pseudotime analysis and revealed, when assigning the naïve GZMK + CD56 bright NK cell cluster as starting point, that trajectories progress via the transitional/active NK cell cluster and conventional cytotoxic CD56 dim NK cells into the direction of either the IFN-responsive or terminal and proliferating GZMB + CD56 dim clusters (Figure  1L ). This validated the existence of a transitional NK cell cluster in MM BM, in line with previous studies  11, 26, 27, 28. Calculation of a module score based on the gene expression of helper ILC  29, 30  did not identify helper ILCs in our dataset (Supplemental Figure  1I ), which is likely due the exclusion of NKG7 low cells during in silico pre-processing  31.
Altered frequencies of CD56 dim and CD56 bright NK cells in NDMM The conventional CD56 dim cluster is a major cluster of CD56 dim NK cells. This raised the possibility that the reduction in this cluster might be reflected in the CD56 dim to CD56 bright ratio as traditionally determined by flow cytometry. To explore this possibility, we first evaluated the distribution of total CD56 dim versus total CD56 bright cells in our single-cell transcriptomics dataset (Figures  2I-K ). A subset of NDMM patients had an NK cell distribution that was very similar to controls (those with 'high cytotoxicity', hereafter depicted as 'normal'), while there was a subgroup of patients with a skewed NK cell repertoire caused by a relative loss of cytotoxic cells and a gain of cytokine-producing CD56 bright NK cells (hereafter depicted as 'skewed').
BM NK cell alterations during treatment Treatment can severely impact the tumor microenvironment. To assess how MM treatment affects the altered BM NK cell landscape in NDMM we analyzed this compartment throughout treatment and disease relapse. Datasets were generated from 19,009 NK cells from 27 patients during post-induction, post-consolidation and maintenance therapy, as well as at relapse (Figure  3A-B , therapy regimen and clinical trial design summarized in Supplemental Figure  2 )  36. Previous studies described a decrease of total and CD38 + NK cells after daratumumab treatment  18. So to understand the effects of anti-CD38 antibody-containing myeloma treatment on BM NK cells, we first analyzed NK cell composition in patients who were treated with or without daratumumab during induction and consolidation therapy or during maintenance (see Supplemental Figure  2  for trial design) compared to patients who did not receive daratumumab.
Yet, the physiological function of these IFN-responsive NK cells, and their contribution to MM pathobiology, remains to be elucidated. In general, data on NK cells from relapsed/refractory MM (RRMM) patients is limited, yet previously reported gene expression profiles indicated increased activation and reduced negative regulation of NK cell activation, suggesting more chronic NK cell activation in the BM of RRMM patients. In functional assays, PB NK cells from RRMM are relatively unresponsive to elotuzumab  46  , which is in line with the inferior responses to therapeutic antibodies in vitro we describe for NDMM patients with persistent cytotoxic NK cell reduction. In a study of RRMM patients receiving daratumumab monotherapy, reduced frequencies of cytotoxic GZMB + CD56 dim NK cells were found in patients with primary and acquired daratumumab resistance and associated with inferior PFS and OS  18  , further highlighting that alterations in BM NK cell subset composition can contribute to difference in response to therapeutic antibodies in MM.
In summary, our data highlight the dynamic alterations of the BM NK cell compartment, including a persistent loss of cytotoxic BM NK cells in MM patients at diagnosis, during treatment and at disease relapse. Whether BM NK cell composition affects clinical outcomes and therapy responses, specifically in the context of daratumumab-containing therapy, warrants further study in a larger cohort. NK cell profiling could hold the potential of informing on clinical impact of immune-based therapies, which would be especially relevant in those therapeutic interventions where ADCC is the major mode-of-action.
Methods Cryopreserved bone marrow aspirates from 6 NDMM, 21 post-therapy and 6 RRMM patients were thawed and sorted into CD45 + CD38 -CD34 -CD235a -CD71 -and CD45 + CD38 + CD34 -CD235a -CD71 -immune fractions and processed for single-cell RNA sequencing according to the 10X Genomics protocol as previously described  22. This dataset was supplemented with data from 5 controls and 13 additional NDMM patients that were previously generated in our lab  22  , resulting in a total single-cell dataset of 51 individuals, including 19 NDMM patients and 6 RRMM patients. As the majority of NK cells are CD38  +47, 48  (Supplemental Figure  1A -B, Supplemental Figure  6A-B ) and CD38 high NK cells have increased cytotoxic function against cancer cells 37 , CD38 + NK cells were identified in silico by transcription of KLRF1, KLRD1, GNLY and NKG7 (Supplemental Figure  1 ) as previously described  26.
The singlecell NK cell dataset is available for interactive exploration at https://www. bmbrowser. org/. Demographics and clinical characteristics of MM patients and controls are described in Supplemental Table  1  and the study workflow is shown in Figure  1A. Additional experimental details are included in Supplemental Methods. All statistical analyses were performed using GraphPad Prism 7 software. Mann-Whitney U test was used to compare groups, and significance was set at P≤0. 05. All graphs show median unless otherwise indicated in the figure legends.
(H) Violin plots of marker genes characterizing the seven clusters depicted in figure 1C. (I) Transcription of marker genes characterizing CD56 bright NK cell clusters. (J) Transcription of marker genes characterizing CD56 dim NK cell clusters. (K) Transcription of cell cycle associated genes in the CD38 + NK cell dataset. (L) Monocle 3 pseudotime analysis of NK cells. '1' represents the selected starting point of the trajectory analysis. The data in (F) is depicted as median. Significance was calculated using the Mann-Whitney U test (two-tailed); NS, P>0. 05; **P≤0. 01.
Methods Participants. Twenty-four volunteer participants (mean age = 33, age-range = 25-65) took part in the experiment. They were organized in three groups of eight participants of different expertise: professional sound engineers (mean age = 31, age-range = 25-33; seven men, one woman), professional conductors (mean age = 37, age-range = 30-60, eight men) and non-experts (mean age = 33, age-range = 25-65; four men, four woman). The number of participants corresponds to the sufficient number of evaluations of the dataset that provides robust Figure  1. Schematic view of the methodology used to investigate the mental representations associated with specific sound concepts for different populations. (A) We collected ratings on an orchestral sound dataset using four sound concepts from three participant groups with the Best-Worst Scaling methods.
(B) Using these ratings, we computed consistency metrics and measured similarities and differences between groups and between concepts. (C) We extracted acoustic features (e. g. , spectral centroid, attack slope) and meta features (i. e. , instrument, playing technique) from the sound dataset. (D) We trained a tree-based model and assessed the most important features for the prediction of the model. and consistent BWS scores according to the reference studies  29, 30. The non-expert group only included participants who reported no amateur nor professional practice related to sound or music (less than 2 years of music practice). All participants reported normal hearing and had no history of audiological or neurological disorders. The protocol (ID: 2021-76) was approved according to Helsinki Declaration by the Ethics Committee of Institut Européen d'Administration des Affaires (INSEAD).
Specifically, we retained 22 instruments with different playing techniques, e. g. , sul ponticello, multiphonics, flatterzunge. Thus, the dataset included 140 string instrument sounds, 172 woodwind sounds, 102 brass sounds, 67 keyboard sounds, 16 harp sounds, 9 guitar sounds and 14 accordion sounds (see Supplementary information for the full report of instruments and playing techniques). To ensure that the stimuli covered the full spectral range, while controlling for harmonic interactions, we selected instrumental samples playing over several octaves of Cs ranging from C1 (32. 70 Hz) to C8 (4186. 01 Hz) with different dynamics. The loudness of the sound samples was equalized following the EBU norm on loudness (R-128) with the ffmpeg library (https:// pypi. org/ proje ct/ ffmpegpython/). The sounds of the dataset were 5.
Data analysis. Analysis of behavioral data. To measure whether the mental representations of specific sound concepts are shared between populations, we computed compliance scores-an individual measure of inter-participant consistency. Specifically, compliance is the proportion of matching duels of sounds between participant choices and means scores computed with the BWS scoring algorithm. For instance, if a participant from the sound engineer group answered that sound A > sound B because he or she chose sound A as 'best' in the trial [A, B, C, D], then, that participant's compliance will increase if the BWS score of sound A is indeed greater than the one of sound B for the sound engineer group. In other words, a consistent group will have a higher average compliance score than a less consistent group.
Random responses from a participant in the experiment would result in a compliance score of 50%. We tested for the influence of the concept and the group of participants on compliance with two Kruskall-Wallis tests, because of the non-normality of the data distributions. We performed a non-paired test for the influence of the concept because the ' concept' variable did not have a clear paired nature due to its computation (i. e. , compliance is calculated for each participant and depends on the mean score obtained for each group). As post hoc tests, we used Mann-Whitney U tests to measure the significance of differences between concepts and between groups. We measured intra-participant consistency by comparing test and retest trials. To do this, we calculated the proportion of duels of sounds with identical results both in test and retest trials.
Feature analysis. In this section, we detail the analyses we led to explain the BWS scores associated with each concept and each population (Fig. 1C, D ). First, we trained a machine learning (ML) model on a regression task for predicting scores of brightness, roundness, warmth, and roughness based on static (i. e. , collapsed over time) acoustic features. Second, we evaluated the contribution of all features to the BWS score of each sound with Explainable Artificial Intelligence (XAI)  36  -a process that aims to give sense to the learning/predicting process of an ML model. We extracted spectral and spectro-temporal features (median value and interquartile ratio) with the Librosa library  37  , and temporal features with the Python version of the timbre toolbox  38  (see Fig. 1C ). We computed a.
Overall, we found that roughness was the concept with the highest accuracy scores (R SE 2 = 0. 80; R CN 2 = 0. 70; R NE 2 = 0. 74), followed by roundness (R SE 2 = 0. 74; R CN 2 = 0. 63; R NE 2 = 0. 66) warmth (R SE 2 = 0. 72; R CN 2 = 0. 67; R NE 2 = 0. 67), and brightness (R SE 2 = 0. 70; R CN 2 = 0. 52; R NE 2 = 0. 53). Moreover, sound engineers' scores were predicted with more accuracy than the two other populations. Although some accuracy scores are low (e. g. , R CN 2 = 0. 52 and R CN 2 = 0. 53 for brightness), previous studies have shown that the interpretability offered by the SHAP library and a model created via XGBoost remains valid even for low predictive accuracy  45. Moreover, note that the importance of the contribution of these features remained mainly unchanged regardless of the dataset split performed before training.
Discussion In the present study, we investigated the mental representations of four sound concepts, namely, brightness, warmth, roundness, and roughness within groups of sound engineers, conductors, and non-expert participants. To do this, we used a dataset of orchestral sounds showcasing a great diversity of instrument timbres and playing techniques that participants rated on the four sound concepts using Best-Worst Scaling. To our knowledge, this investigation is the first to reveal and acoustically explain similarities and discrepancies in the mental representations of sound concepts between participant groups of different expertise based on acoustic portraits. The results in terms of concept relations and acoustic portraits echo many findings of previous sound semantics research. First, we found that the spectral centroid is unanimously the principal feature of warmth and roundness  10, 25  and that expert participants, also associated it with brightness  10, 14, 16, 24, 25.
One limitation of the current study is the low sample size of each socio-professional category (N = 8), which restricts, for example, our ability to investigate gender differences in the data. However, it is important to note that the BWS method we used is different from other studies using traditional inferential statistical methods. Indeed, the BWS algorithm computes a ranking of the dataset for each attribute and group, which requires an optimal number of annotations for convergence. Based on previous research  29  , a sample size of eight is an optimal trade-off between data collected and sorting algorithm precision. Hence, despite the low sample size, our results showed high consistency within groups (see "Behavioral results") and stable weights of acoustic features across different dataset splits during the acoustic analyses.
Conclusion With this work, we assessed the impact of sound expertise of three groups of participants on their mental representations of metaphorical sound concepts. To do so, we acoustically explained brightness, roundness, warmth, and roughness according to the evaluation of a sound dataset on these terms by sound engineers, conductors, and non-experts of sound. Surprisingly, the term most used in the expert domains (brightness) is much less consensual than the least used term (roughness). Furthermore, we went deep into the acoustic descriptions of the concepts revealing the existing relationships between concepts according to the ratings of each group of participants. For example, we studied the subtle specifics of roundness and warmth, which are spectrally very similar, for all participants, but also for each group.
With this work, we bring a fine understanding of the technical vocabulary of sound, as well as an ergonomic methodology based on Best-Worst Scaling that can be applied in the future in crowdsourcing contexts, paving the way for the study of other complex sound concepts (e. g. , richness, fullness) as perceived by other populations (e. g. , brass instrument player vs string instrument player), but also on other issues (e. g. , voice identity, sound dataset validation).
At the surface, the model is driven by atmospheric analyses and forecasts obtained from the European Centre for Medium-Range Weather Forecasts-Integrated Forecast System (ECMWF-IFS) at 3-hr resolution. The model jointly assimilates along-track satellite altimetry data, satellite sea surface temperature (from Operational Sea Surface Temperature and Ice Analysis, OSTIA), sea-ice concentration (from the Satellite Application Facility on Ocean and Sea Ice, OSI SAF), and in situ temperature and salinity profiles (from the CORA3. 3 database) using a reduced-order Kalman filter with a 3-D multivariate modal decomposition of the background error and a 7-day assimilation cycle  (Lellouche et al. , 2018). During the assimilation procedure, a particular treatment is applied to areas potentially covered with sea ice: all observations errors in the multivariate SEEK filter increase linearly (less weight in the analysis) with the decrease of the SST from -1°C to -1.
Further Evaluation Over the Northern Nordic Seas An additional evaluation of the model over the Northern Nordic Seas was performed comparing simulations to Argo float profiles (assimilated by the model) present in the area. There are 7,762 Argo floats profiles from the Coriolis data center (only good quality flags are retained) from 2008 to 2020, located between 70 and 84°N and between -30 and 30°E (Figure  2a ). The number of profiles increases from 2014 onward, with no seasonal bias (Figure  2b ). Highest profile densities are found in the center of the Greenland Sea Gyre and east of the Mohn and Knipovitch Ridges; there are very few profiles north of 79°N because of sea ice (Figure  2a ). Daily PSY4 fields were colocated in time and space (closest grid cell) with the in situ profiles. Profiles from PSY4 modeled data were linearly interpolated to 1 m vertical resolution to ease model-observations comparisons.
Summary and Concluding Remarks We examined the evolution of wintertime convection over the Greenland Sea between 2008 and 2020 through the Mercator Ocean Physical System (PSY4). The model showed a good ability in depicting the Northern Nordic Seas hydrographic properties over the period (Figure  5 ). Although model Mixed Layer Depths (MLDs) in the JFMA (during the convection period) are positively biased, their temporal evolution is well represented and the correlation coefficient between modeled and observed MLDs in winter within the convective area is of 0. 89 (Figures  4  and 5 ). The model documents a large variability of winter convection in the Greenland Sea, both in the Greenland and Boreas Basins. The main results are as follows: 1. The maximum depth of deep convection decreased in recent years (Figures 5e, 8c Argo data clearly support results 1, 2 about deep convection (Figure  5 , Figure  S3  in Supporting Information S1), 4 on hydrographic changes (Figure  5 , Figure  S5  in Supporting Information S1).
Acknowledgments This work is a contribution to  H2020 Arctic Passion  project, Grant  101003472. We gratefully thank  Camila Artana  and  Marylou Athanase  for their help as well as  Jérôme Sirven  for early discussions. We deeply thank the two anonymous reviewers whose advice helped us improve the manuscript. Louise Abot  acknowledges funding from the  H2020 Arctic Passion project , ( 101003472 ) and  Léa Poli  from a  Sorbonne Université Ph. D. scholarship.
Data Availability Statement The authors confirm that all the data that support the findings of this study in the main text and Supporting Information S1 are openly available. This study has been conducted using E. U. Copernicus Marine Service Information. The ocean model data used for this study were produced by the global monitoring and forecasting centre operated by Mercator Ocean, and are available for download at https://marine. copernicus. eu. ERA-5 monthly averaged data on single levels are available on the Climate Copernicus Climate Change Service (C3S) Climate Data Store (CDS) website (Copernicus Climate Change Service, 2023). The Argo data were collected and made freely available by the International Argo Program and the national programs that contribute to it (https://argo. ucsd.
Computational methods All spin-polarized calculations were performed with Vienna Ab Initio Simulation Package (VASP)  [32]  using projector-augmented wave (PAW)  [33, 34]  pseudopotentials. The exchange-correlation interactions were described by the Perdew-Burke-Ernzerhof (PBE) functional with the generalized gradient approximation (GGA)  [35]. The dispersion interactions were included with the DFT-D3 approach  [36]. The plane wave cutoff energy was set to 500 eV. The Brillouin zone was sampled at gamma point  [37]  for geometry optimizations. The convergence criteria for energy and force were set to 1. 0 × 10 -5 eV and 0. 01 eV/Å, respectively. To get more accurate description of electronic structures for those systems of interest, the Heyd-Scuseria-Ernzerhof (HSE06)  [38]  hybrid functional was used for electronic structure calculations.
The European Beyond EPICA project aims to extract a continuous ice core of up to 1. 5 Ma, with a maximum age density of 20 kyr m -1 at Little Dome C (LDC). We present a 1D numerical model which calculates the age of the ice around Dome C. The model inverts for basal conditions and accounts either for melting or for a layer of stagnant ice above the bedrock. It is constrained by internal reflecting horizons traced in radargrams and dated using the EPICA Dome C (EDC) ice core age profile. We used three different radar datasets ranging from a 10 000 km 2 airborne survey down to 5 km long ground-based radar transects over LDC. We find that stagnant ice exists in many places, including above the LDC relief where the new Beyond EPICA drill site (BELDC) is located. The modelled thickness of this layer of stagnant ice roughly corresponds to the thickness of the basal unit observed in one of the radar surveys and in the autonomous phase-sensitive radio-echo sounder (ApRES) dataset.
(2021)  but with a different numerical scheme and optimisation method (see the Supplement). This approach will elucidate the spatial extent of this inferred stagnant ice layer and its impact on the age profile in the region. In this study, measurements made with an autonomous phase-sensitive radio-echo sounder (ApRES) show that the vertical velocity profile at LDC is better described when there is a layer of stagnant ice above the bedrock (Fig. 3 ). We also show observations of a basal unit in radar surveys, which could provide further evidence of a stagnant ice basal layer. We present a 1D numerical model which uses inverse methods to infer a layer of stagnant ice from the isochronal information and assess its relevance over a non-stagnant ice model. This model is applied to three radar datasets acquired in recent years, which cover the larger Dome C area.
Bayesian information criterion (BIC) The BIC is used to determine the relevance of one model relative to another  (Kass and Rafterty, 1995; Legrain et al   C bay = -2 ln L + K ln N iso , (6) where N iso is the number of isochrones used to constrain the model, and L is the maximum log-likelihood, defined here as σ R N iso. The comparison of the relevance of model i over model j is then calculated as C ij = C i -C j. (7) The dominance of model i over model j is weak if C ij < 2, positive if 2 < C ij < 6, strong if 6 < C ij < 10 and very strong if C ij > 10. 3 Datasets In this study we make use of three radar surveys collected in the LDC region, which were taken during the Antarctic field seasons from 2016-2020 and informed the selection of the location of the Beyond EPICA drill site (BELDC).