dataesr/scientific-paragraphs-categorization huggingface dataset:
Patients without response or progression assessments at the date of database lock were excluded from the analysis based on these data. PFS, OS, and ORR were assessed overall and within subgroups: according to the lines of treatment (L2, L3, and L4 +), age groups (< 80 years old versus ≥ 80-years old), frailty (frail versus non frail), prior exposure to lenalidomide, time interval between last lenalidomide and IXA-Rd (≤ 12 months versus > 12 months), renal failure based on creatinine clearance at initiation (> 50 ml/min, 30-50 ml/ min, ≤ 30 ml/min), autologous stem cell transplantation (ASCT), comorbidities (Charlson score), and cytogenetic abnormalities at baseline (standard risk (SR) versus high (HR) defined as del(17p) and/or t(4;14) and/or t(14;16)). A total of 500 patients were expected to participate in the study.
The initial conditions are evolved using linear theory to z = 99  (Zel'dovich 1970 ) , before we start following the evolution of dark matter, stars, and gas to z = 0 with the adaptive mesh refinement code RAMSES  (Teyssier 2002 ). We follow the dynamics of collisionless particles (dark matter and stars) using a multiscale particle-mesh solver  (Guillet & Teyssier 2011 ) , while fluid dynamics are solved with an HLLC Riemann solver  (Toro, Spruce & Speares 1994 )  assuming an ideal gas equation of state with adiabatic index γ = 5/3. Our Lagrangian refinement strategy allows us to reach a spatial resolution of 20 pc throughout the galaxy's ISM  (Agertz et al. 2021 ). We complement this hydrodynamical setup with an e xtensiv e galaxy formation model described in detail in  Agertz et al. ( 2021 ) , which we briefly summarize now.
AC K N OW L E D G E M E N T S We would like to thank the referee for a constructive review that impro v ed the quality of the manuscript. MR would like to thank  Vasily Belokurov ,  Alyson Brooks ,  Amina Helmi ,  Kathryn Johnston ,  Harley Katz  and  Sarah Pearson  for insightful discussions and comments during the construction of this work. MR is supported by the  Beecroft Fellowship  funded by  Adrian Beecroft. OA and FR acknowledge support from the  Knut and Alice Wallenberg Foundation , the  Swedish Research Council  (grant  2019-04659 ) and the  Royal Physiographic Society in Lund. TS is supported by a  CIERA   Postdoctoral Fellowship. AP is supported by the  Royal Society. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No.
818085  GMGalaxies. We acknowledge  PRACE  for awarding us access to  Joliot-Curie  at  GENCI/CEA ,  France  to perform the simulations presented in this work. Computations presented in this work were in part performed on resources provided by the  Swedish National Infrastructure for Computing (SNIC)  at the Tetralith supercomputer, part of the  National Supercomputer Centre, Link öping Uni versity. De velopment w ork w as carried out on facilities supported by the  Research Capital Investment Fund (RCIF)  provided by  UKRI  and partially funded by the  UCL Cosmoparticle Initiative. We thank the developers of GENETIC (Stopyra et al. 2021 ),  JUPYTER  ( Ragan-Kelley  et al. 2014 ), MATPLOTLIB (Hunter 2007 ),  NUMPY  (van der Walt, Colbert & Varoquaux 2011 ), PYNBODY (Pontzen et al. 2013 ), SCIPY (Virtanen et al.
Several authors have shown the efficacy of the Janus kinase (JAK) inhibitors tofacitinib or ruxolitinib in pulmonary and cutaneous sarcoidosis refractory to other drugs  [168]. We report an observation of paradoxical sarcoidosis panuveitis induced by adalimumab in the context of rheumatoid arthritis. Although the uveitis and inflammatory rheumatism remained active upon discontinuation of anti-TNFα, the ocular inflammation and mediastino-hilar adenopathy disappeared with tofacitinib treatment  [169]. Tofacitinib is currently being tested in non-infectious uveitis (NCT03580343).
lsst. org/about/timeline). The catalogs created by the Vera C. Rubin surveys, according to  [7] , will increase the total number of small bodies in the Solar System that are known by a factor of 10-100 times, with ≃ 1 million more asteroids expected to be discovered in the first year of operations alone. New methods for classifying large populations of likely resonant asteroids are urgently needed to deal with the huge influx of expected new data. In  [8] , we introduced methods based on Artificial Neural Networks (ANN) to automatically classify images of asteroids' resonant arguments. More recently,  [9]  used Convolutional Neural Networks (CNN) and their optimizations, for classifying large images databases. The main goal of this work is to obtain machine learning and deep learning models for g-type non-linear secular resonances, to more speedily classify the large new asteroidal population that is expected to be detected soon.
For this reason, we visually analyzed the resonant arguments of the asteroids' candidates under the effect of the Yarkovsky force. This analysis confirmed the librating status for 132 numbered and 78 multi-opposition asteroids, respectively. The bottom panels of figure  (1)  show the distribution of these objects in the proper (a, sin (i)) and (a, g) planes. Librating asteroids are only found in the outer main belt, and have values of proper g close to that of the resonance center, 3g 6 -2g 5 = 76. 215 "/yr, as expected. The analysis of the 2ν 6ν 5 and 3ν 6 -2ν 5 secular resonances produced two reliable databases of asteroids' resonant status in the proper (a, e, sin (i)) domain, and one data set of images of 2ν 6ν 5 resonant arguments, with their labels. The analysis of these datasets using imbalanced classification methods and Convolutionary Neural Networks (CNN) is reported in section  (1)  of the Supplementary Materials.
Identification of resonant asteroid groups Asteroids in librating states of the 2ν 6 -ν 5 resonance are mostly located in the central belt (1337), with 21 scattered asteroids in the outer main belt. We first applied HCM and the stalactite diagram method to the databases. Plots of the dependence of the number of family members as a function of the velocity cutoff are shown in figure  (2)  of the Supplementary Materials (2). Our results are shown in the left panel of figure  (2)  and summarized in the Supplementary Table  (7). Overall, we identified 8 different asteroid groups, whose orbital distribution in the (a, sin (i)) plane is displayed in the left panel of figure  (2). All these groups are part of the larger  [2]  Astraea family, well known for its interaction with the 2ν 6ν 5 secular resonance.
Their proper (a, sin (i)) orbital distribution is displayed in figure  (5)  of the Supplementary Materials  (2). Physical properties of the newly identified groups and of local background, like taxonomies, colors, albedo, Gaia spectra, and behavior in the (G 1 , G 2 ) phase curve parameter space were revised according to the latest results in available databases. The methods and results are discussed in the Supplementary Material (3). We found that most groups interacting with the 2ν 6ν 5 resonance are likely to be compatible with an S-complex composition. The 3ν 6 -2ν 5 groups are mostly compatible with a C-complex taxonomy, but the current data are insufficient to quantify the level of any background contamination.
Multi-opposition asteroids were added to the core of the numbered objects using machine learning methods optimized through the use of genetic algorithms  [10]. The physical properties of the newly identified groups, like albedos and taxonomies, were then revised using currently available databases. Time-reversal numerical methods, like the Backward Integration Method (BIM,  [19] ) and the Close Encounters Method (CEM, [20]), were then applied to the newly identified groups to find possible young asteroid families, i. e, families with ages of 7 My or less. We identified the (5507) Niijima family in the 2ν 6 -ν 5 resonance, and the (170776) (2004 CA95) family in the 3ν 6 -2ν 5 , which are the first young asteroid families ever to be found in such resonant configurations. After eliminating likely interlopers, the families' ages were found to be 4.
The process is then repeated for decreasing distance cutoff values. The results are depicted in a plane with the distance cutoff on the y-axis and the memberships of the families on the x-axis. See  [24]  for further information on each of these steps and processes. Families determined with this approach can then be tested in an extended proper element database including all asteroids in the regions, to see how quickly they may merge with other local families, as listed in surveys like those of  [32]  and  [66]. Finally, machine learning methods optimized by the use of genetic algorithms can be used to attach multi-opposition asteroids to the core families identified among the numbered population of librating bodies. Interested readers can find more details about both these approaches in  [38].
None of the approaches used caused an improvement by more than 0. 4%. The database of images for the 2ν 6ν 5 resonant argument was both large enough and reliable enough to sustain the use of more advanced image classification methods. Here we apply the techniques described in the recent work from  [44]. In this study, three Convolutional Neural Networks models (CNN), the VGG  [45] , Inception  [46]  the Residual Network or ResNet  [47] , were applied to two data sets of resonant arguments of the ν 6 and M1:2 resonances using validation sets and series of regularization techniques like data augmentation (DA), dropout (DO), and batch normalization (BN), to predict the labels of thousands of images. The effects of overfitting, which occur when the CNN model learns all the minute aspects of the training test and performs exceptionally on this data but fails to transfer this information to other databases, were decreased by the application of regularization techniques.
[44]  selected the three best-performing models less affected by overfitting, and then applied them to larger databases with several thousands of images. The use of the accuracy and F-beta measures allowed for the selection of the approach that performed the best. Accuracy is the fraction of correctly predicted labels, while F-beta is a score created to account for both Precision, or the percentage of correctly predicted labels for the positive class, and Recall, or the percentage of correctly predicted labels for the positive class out of all potentially positive predictions. Interested readers can find more details on these metrics and the methods used for selecting the best-performing CNN model in  [44]. Here, we applied the same approaches to the data set of images for the 2ν 6ν 5 resonant arguments using a set of 13052 images for training (all numbered asteroids up to designation (523134)), a set of 2108 images for (all numbered asteroids with designations between (523134) and (600000)) and 71 asteroids for the test (numbered asteroids with designations larger than (600000)).
Our results are summarized in table (A5). The bestperforming model was the Inception one, followed by the VGG, and the VGG corrected by data augmentation and dropout. Figure  (A1)  shows the behavior of the cross-entropy loss (see  [44] , equation (3) for its definition) and accuracy for the Inception model. The model starts to overfit the data at its sixth epoch and had a accuracy at this epoch equal to 0. 79. We saved the three best models and applied them to a larger database of 6400 images of resonant arguments of multi-opposition asteroids in the 2ν 6 -ν 5 resonance. Our results are shown in table (A6) of the Supplementary Materials. For this data set, the best-performing model was the VGG, corrected by data augmentation and dropout. This architecture was saved for future modelizations of the 2ν 6ν 5 resonances and will be made available to the research community.
imcce. fr/webservices/ssodnet/, accessed on February 14th, 2023), a web service from the Institut de mécanique céleste et de calcul des éphémérides (IMCCE), which contains a compilation of data from over 3,000 peer-reviewed publications on minor bodies  [49] , including those published in the surveys of  [50] ,  [51] , and  [52] ; the Sloan Digital Sky Survey-Moving Object Catalog data (SDSS-MOC4;  [53] ) used with the taxonomical method of  [54]  and  [55] ; and albedo data in the WISE and NEOWISE, AKARI, or IRAS databases  [56] [57] [58] , among other sources. All the relevant papers from which data and methodologies have been used in this work are listed in the appendix (B). For albedo data, where an object had multiple albedo measurements in the WISE and NEOWISE data, we chose the one with the smallest listed diameter uncertainty, as this will be the strongest metric for the quality of the physical property fit used to derive albedo from infrared data.
We further search for reflectance spectra in the Gaia DR3 data  [59]  and in a compilation of ≃ 7000 spectra created by  [60]  in their work on asteroid taxonomy. Taxonomical clasfor reflectance spectra in Gaia DR3 can be obtained using classy (https://github. com/maxmahlke/classy/), a classification algorithm that can handle missing data and includes the visual albedo, when possible. Before classifying the Gaia spectra, we remove reflectance values that carry the photometric flags 1 and 2, indicating low quality. We further remove data points with a signal-to-noise ratio lower than 10. The systematic reddening towards the ultraviolet is corrected following  [61]. Finally,  [62]  showed that asteroids with different taxonomic classes display different behaviors in the (G 1 , G 2 ) phase curve parameter space.
Acknowledgments: We would like to thank the  Brazilian National Research Council (CNPq , grants  301577/2017-0 ,  153683/2018-0 ). Z. K. acknowledges support from the  Serbian Academy of Sciences and Arts  through the project  F-187. F. R. is grateful for the support from  CNPq , grant  306009/2019-6. R. C. D. thanks to the  São Paulo Research Foundation (FAPESP , grant  2016/024561-0 ) and the  Financier of Studies and Projects (Finep , grant  0527/18 ). G. C. acknowledges support from the  Coordination for the Improvement of Higher Level Personnel (CAPES , Finance Code 001). M. V. F. L. is thankful for the support from  FAPESP , grant  2022/14241-9. We are grateful for the use of data from the Asteroid Dynamics Site (AstDyS) (https://newton. spacedys. com/astdys/, [17]). This publication also makes use of data products from the Wide-field Infrared Survey Explorer (WISE) and Near-Earth Objects (NEOWISE), which are a joint project of the  University of California, Los Angeles , and the  Jet Propulsion Laboratory/California Institute of Technology , funded by the  National Aeronautics and Space Administration.
Data availability: Data on labels for asteroids affected by the 2ν 6ν 5 and 3ν 6 -2ν 5 resonances and our best-performing CNN model weigths, as well as a set of images of resonant angles for multi-opposition asteroids affected by the 2ν 6ν 5 resonance are available at a MASB (https://drive. google. com/drive/folders/1QmMsiwMXU7MtnIltBlmwMF -pam1aQTVY?usp=sharing) and at a figshare repositories (https://figshare. com/s/09f313b16a11e07b9e31, DOI: https://doi. org/10. 6084/m9. figshare. 22324012). The data that support the plots within this paper and other findings of this study are either available in the paper and in its supplementary materials tables, or are available from the corresponding author upon reasonable request.
However, no systematic evaluation of REE patterns of basement rocks from the central and southern Black Forest has been conducted so far. The largest dataset of REE abundances of granites comprises data from 12 intrusions  (Emmermann et al. , 1975) , which lacks, however, the full range of REEs. A comprehensive overview of mineral, thermal and mine waters from the entire Black Forest area on the other hand is given by  Göb et al. (2013). The majority of previous studies are based on extensive comparisons of a large number of different hydrothermal veins across the entire Black Forest district but are limited with respect to the number of investigated samples per vein. For example,  Walter et al. (2018b)  determined the Sm / Nd ages of 21 fluorite-bearing veins on the basis of 25 samples. In addition,  Walter et al.
Raw data evaluation was performed using the software GLITTER  (van Achterbergh et al. , 2000). All measurements were performed as point analyses with varying numbers of measurement spots per sample depending on zoning, intergrown crystallites or internal heterogeneities. The trace element patterns are presented as REY patterns after  Bau and Dulski (1995)  normalized to PAAS after McLennan (1989) (Table  1 ). All patterns from the single analysis are classified individually into distinct groups based on manual inspection, followed by geochemical discrimination and λ coefficients after O'Neill (2016). The λ coefficients are directly related to the shape of their respective REE pattern, thus allowing them to be used as a mathematical discrimination approach. They represent the polynomial fit coefficients to the shape of REE patterns if algebraically rearranged into an orthogonal form (O'Neill, 2016): ln ([REE] /[REE] PAAS ) = λ 0 + λ 1 f orth 1 + λ 2 f orth 2 +.
This difference in colour is observed in additional samples that contain subgrains classified as either group I or group III. The relative order between group III and I in the zoned fluorite is   , and λ coefficients (a-c) using the mathematical approach of O'Neill  (2016). The overlap between groups III and I on the one hand, as well as VI and VII on the other, is resolved by a hybrid discrimination plot (d). Note that the samples comprising group V are collected from the Finstergrund deposit but have been left separated due to unclear attachment to either the Finstergrund or Tannenboden suite as explained in the text. Each data point refers to a single analysis. in agreement with the order observable in the sequence presented before. Figure  9  shows a second example for concentric fluorite zoning with group II dominating from the core outwards except for the outermost zone classified as group V as the rim.
Acknowledgements. Many thanks to the volunteers of the mining association " Bergmannsverein Finstergrund Wieden  e. V. ", as well as the local authority, for access permission and safe guidance during the sample campaigns in the Finstergrund and Tannenboden deposits. We further thank the chair of the geochemistry section of the  Institute of Earth and Environmental Sciences at the University of Freiburg ,  Wolfgang Siebel , for financial support and  Helene Brätz  from the  Geozentrum Nordbayern  at the  Friedrich-Alexander University  for performing the LA-ICP-MS analysis. The technical staff of the former institution,  Melanie Schrage  and  Gordon Mette , are thanked for assistance in sample preparation. The reviews of three anonymous reviewers helped to improve the quality of this paper.
Data and methods We use the ISAS20_ARGO dataset containing Argo and Deep-Argo temperature and salinity profiles on the period 2002-2020. Vertical profiles are available on 187 standard depth levels between 0 and 5500 m depth, with higher vertical resolution close to the surface (2-5 m) and coarser resolution in the deep ocean (100 m), resulting from the interpolation performed with the ISAS procedure  (Gaillard et al. , 2016). We select data in the depth range 0-2000 m as only recent floats provides measurement below 2000 m. In the first part of this study (section 3), we will use the gridded seasonal climatology of temperature and salinity fields, which is the result of an optimal interpolation applied on the profile set (see for a full description of the interpolation method  Gaillard et al.
e. the UOP. Stratification peaks are detected in smoothed N 2 profiles using the Python algorithm scipy. signal. find_peaks with the following parameters. Peaks are detected when their amplitude exceeds 2s, with s being the vertical standard deviation of the stratification profile N 2. The minimum prominence of peaks must be larger than s, the prominence being the elevation between the peak and the surrounding troughs. In practice, we limit the evaluation of the peak prominence within a window of 250 m centered around the peak instead of using the full profile. This allows more efficient computation. The minimum distance between peaks is set to 25 m, which corresponds to a rough guess for the transition layer/UOP thickness (e. g. ,  Johnston and Rudnick, 2009). Below this distance, the two peaks are considered to be only one wide peak with the largest peak setting the amplitude.
The results are smoothed using a diffusive Gaussian filter with a 500 km scale  (Grooms et al. , 2021; Loose et al. , 2022). In this study, we mainly present the gridded UOP climatology for winter and summer, respectively defined as January-February-March (JFM) and July-August-September (JAS) for the northern hemisphere, and the opposite way for the southern hemisphere. We add spring (March-April-May, MAM in the northern hemisphere) and autumn (October-November-December, OND in the northern hemisphere) for the UOSI. The extended monthly climatology is shown in Supplementary materials. The corresponding dataset is made widely available (https://www. seanoe. org/data/00798/91020/), including the UOP gridded fields and the UOP characteristics evaluated on each profile.
Whilst the global climatology showed mainly the UOP median values computed in 2°×2°bins, regional statistics are computed in this section by taking all the hydrographic profiles within each of the regions. In these regions, we found that  February and August are well representative of the minimum and maximum positions of the UOP for the summer and winter seasons, so that these months will be used to illustrate some of our results. We also added information on the permanent ocean pycnocline (POP) depth using the dataset produced by  Feucher et al. (2019). This dataset will help to discuss the relative position of the UOP and the POP.
In winter, this minimum disappears suggesting that the mode water layer may be influenced by air-sea exchanges. The POP values taken from the climatology of  Feucher et al. (2019) , shown with black dotted lines in Figure  10 , seem consistent with our dataset since the POP is located on a local maximum of the median stratification profile (i. e. , KE, NPESTMW, GS, RP, SPESTMW). In these regions, the winter UOP is always shallower than the POP, suggesting that the UOP does not always merge with the POP in winter. In all the regions of study, the UOP is located below the MLD (blue and orange dash lines), except in winter in the Labrador Sea (LS). The depth separating the MLD and the UOP is usually small in summer, but it may slightly increase in winter (e. g. , KE, NPESTMW, AL). In the Labrador Sea, the median winter MLD (∼250 m) is found to be much deeper than the median winter UOP depth (∼150 m).
The UOSI is generally minimum in spring and associated with a maximum in relative variability during the same season. A focus on a few regions has shown that restratification happens generally in late winter/ Percentage of winter profiles whose UOP merges with the permanent pycnocline taken from  (Feucher et al. , 2019). early spring, but this transition is not homogeneous between regions: as for the depth-intensity seasonal cycle, restratification may occur abruptly or in a smoother way. The UOP climatology only includes data from ARGO floats for the moment but should be improved by including additional data from other observing systems such those on marine mammals. Moreover, we only provide a monthly climatology of the dataset, but a monthly-varying dataset could be produced using an optimal interpolation as done for the ISAS gridded dataset.