dataesr/scientific-paragraphs-categorization huggingface dataset:
g. the Milky Way), with clear non-axisymmetric features such as a bar and spiral arms, this is not the case. However, combining the Stäckelfudge method with an axisymmetric potential that is well fitted to observed data still allows us to estimate the instantaneous axisymmetric actions and to extract the main features and substructure of the underlying kinematics in the Galaxy. Essentially, any substructure that appears in this action space is likely due to non-axisymmetric or time-varying components of the gravitational potential, whether the actions are accurate or not. We used the AGAMA code to compute the instantaneous set of actions, J = (J R , J φ , J Z ), in three different axisymmetric potentials: (a) Cautun20, (b) McMillan17, and (c) MWPotential2014  (Cautun et al. 2020; McMillan 2017; Bovy 2015, respectively).
Kinematics of the Cepheids We here considered the kinematics of the young Cepheids from GD22. Plotting their azimuthal velocity with respect to the galactocentric radius (i. e. the rotation curve), we note a gap in the azimuthal velocity distribution at approximately R = 13 kpc (see Fig. 1a ). This gap has a negative slope with respect to R at about 12 kpc to 14 kpc. However, plotting the angular momentum of the Cepheids (Fig. 1b ) shows that this gap is apparently at a fixed value of L Z (=V φ R). In Fig. 2  we show the 1D distribution in L Z for the Cepheid sample, where the gap can quite clearly be identified in this space. To estimate the precise location and its width, we performed a kernel density estimate (KDE) using the sklearn package  (Pedregosa et al. 2011). We used a Gaussian kernel, setting the bandwidth following  Scott (1992) , and performed the KDE on 1000 bootstrapped samples.
Kinematics of the RGB and RV samples The Cepheid sample has revealed an interesting feature in the L Z distribution in Fig. 2 , suggesting the presence of a resonance. We now study the RV and RGB datasets to gain a manifold increase in density (from 1000 s to millions of stars), allowing us to explore the distribution in action space where we expect to see the signature of the resonances more clearly. In Fig. 3  we plot the distribution of the two datasets in the ( √ J R , L Z ) plane for the four different axisymmetric potentials listed in Sect. 2. We normalised both axes by the z-component of angular momentum at the Sun, L Z, = 2081. 6 km s -1 kpc. We recovered the large-scale diagonal features (ridges) seen in this space by T21. (Unlike T21, we chose to present our results using √ J R and not J R itself, as it enhances the features slightly).
Compared to T21, we have the benefit of adding more data in the outer disc thanks to Gaia DR3. Keeping Fig. 2  in mind, we are interested in probing the region around L Z = 2950 km s -1 kpc (or ∼1. 42 L Z, ). However, it is clear from Fig. 3  that even with the added coverage, the density in action space falls off sharply beyond this L Z value. Nevertheless, no clear gap in the distribution is seen. Thus, we again followed T21 and show in by N(V R < 0)/N tot , that is, the fraction of sources moving in towards the Galactic centre. This has two immediate effects: first, it makes the diagonal features stand out dramatically, and second, we are now able to observe features for the entire extent of our datasets. Our Fig. 4  can be directly compared to Figs. 1, 11 and 12 in T21, who were limited in coverage out to about L Z /L Z, = 1.
Here we colour-code the distribution by the fraction of stars moving inward (V R < 0). The black curves mark the expected OLR of the bar at 1. 08 L Z, and the Cepheid gap at 1. 42 L Z,. The diagonal ridges are much clearer in this space than in Fig. 3. We report a previously unseen new overdensity of outward-moving stars just beyond the Cepheid gap. This is marked by black arrows. orientation was flipped in action space. Notwithstanding this open question regarding the correct orientation of the red-blue feature at resonances, we continue to refer to it as the OLR because our predicted location for the OLR marks a boundary in this space. The location of the second curve is quite interesting for two reasons. First, inwards of the second curve is the dataset that was mapped by T21. In particular, they were the first to show the ridge just inside of this curve.
The deep gap occurs between the spiral arms generated by the interaction as they wind up slowly after the event, but the depth of the transition is slowly filled in by orbit migration. This gap remains for a few rotation periods and could reasonably account for what is seen here. However, essentially all Sgr orbit studies to date indicate that the disc transit occurred much farther out than the observed Cepheid gap (e. g. Laporte et al. 2019). Gaia continues to reveal a surprising amount of structure in the phase space of stars. With each data release, the coverage of the Milky Way disc increases, which has led to the discovery of new kinematic features at each step. The improved quality of the Gaia data across the disc allows for a better discrimination between the non-axisymmetric features responsible for resonances, and potentially to distinguish them from transient features that are excited by satellite interactions, which should show significant variation at different azimuthal angles.
Acknowledgements. We thank the anonymous referee for their comments and suggestions. R. D. thanks  Ortwin Gerhard  for useful discussions. S. K. acknowledges  Tomás Ruiz-Lara  and  Eduardo Balbinot  for preparations of the extended radial velocity catalogue, and  Eugene Vasiliev  for useful discussion on actions. R. D. , V. R. , G. C. and T. M. are supported in part by the  Italian Space Agency (ASI)  through contract  2018-24-HH. 0  and its addendum  2018-24-HH. 1-2022  to the  National Institute for Astrophysics  (INAF). S. K. , T. C. G. and A. C. G. acknowledge support from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No. 101004110. R. D. and S. K. acknowledge  Ricky Smart  and  Luciano Nicastro  for use of the computing resources under the GLORIA-project, funded by the  European Union   7th Framework Programme  under grant agreement n.
283783. P. R. acknowledges support from the  University of Barcelona , via a  Margarita Salas grant  (NextGen-erationEU). T. T. G. acknowledges partial financial support from the  Australian Research Council (ARC)  through an  Australian Laureate Fellowship  awarded to JBH. We acknowledge the use of the  National Computational Infrastructure (NCI)  which is supported by the  Australian Government , and accessed through the  Sydney Informatics Hub (SIH)   HPC Allocation Scheme 2022  (PI: TTG; CI: JBH). L. C. acknowledges funding from the  Chilean Agencia Nacional de Investigación y Desarrollo (ANID)  through  Fondo Nacional de Desarrollo Científico y Tecnológico (FONDECYT) Regular  Project  1210992. This work presents results from the  European Space Agency (ESA)  space mission Gaia. Gaia data are being processed by the  Gaia Data Processing and Analysis Consortium (DPAC).
Funding for the DPAC is provided by national institutions, in particular the institutions participating in the Gaia MultiLateral Agreement (MLA). The Gaia mission website is https://www. cosmos. esa. int/gaia. The Gaia archive website is https://archives. esac. esa. int/gaia. This work has used the following software products: TOPCAT, STIL, and STILTS (Taylor 2005(Taylor , 2006));  Matplotlib  (Hunter 2007);  IPython  (Pérez & Granger 2007); Astropy, a communitydeveloped core Python package for Astronomy (Astropy Collaboration 2018); and Pynbody (https://github. com/pynbody/pynbody).
Consumer testing Samples were evaluated for Experiment 1 according to Honfozo et al. 1  Overall liking data were collected from 113 consumers, 18 to 70 years old, including 54. 9% males. In addition, the 3point "just about right" (JAR) test (1 = Too weak; 2 = "JAR: just about right" and 3 = Too strong) was performed on crumbliness, easiness to break and sweetness.
rotundata were in the right. Furthermore, the D. alata TDa 1510043 and TDa 1515030 clones grown at Ubiaja were close to the acceptance threshold in texture attributes and related biophysical parameters. Both clones were the only D. alata located close to the D. rotundata varieties. 10970010, ja, Downloaded from https://onlinelibrary. wiley. com/doi/10. 1002/jsfa. 12589 by CIRAD -DGDRS -DIST, Wiley Online Library on [14/04/2023]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License.
ACKNOWLEDGMENTS The authors are grateful to  Léa Ollier  and  Pham and Thi Ha Giang (CIRAD)  and the grant opportunity  INV-008567  (formerly OPP1178942):  Breeding RTB Products for End User Preferences (RTBfoods) , to the  French Agricultural Research Centre for International 10970010 , ja, Downloaded from https://onlinelibrary. wiley. com/doi/10. 1002/jsfa. 12589 by  CIRAD -DGDRS -DIST, Wiley Online Library on  [ 14/04/2023 ]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions)on  Wiley Online Library  for rules of use; OA articles are governed by the applicable Creative Commons License Development (CIRAD), Montpellier, France, by the  Bill & Melinda Gates Foundation  (BMGF): https://rtbfoods. cirad. frThe editorial comments by as well as the final proofreading of the manuscripts by  Clair Hershey  improved greatly the quality of this manuscript.
D. rotundata were in the right. Furthermore, the D. alata TDa 1510043 and TDa 1515030 clones grown at Ubiaja were close to the acceptance threshold in texture attributes and related biophysical parameters. Both clones were the only D. alata located close to the D. rotundata varieties. 10970010, ja, Downloaded from https://onlinelibrary. wiley. com/doi/10. 1002/jsfa. 12589 by CIRAD -DGDRS -DIST, Wiley Online Library on [14/04/2023]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License.
DESIGN, SETTING, AND PARTICIPANTS This study was a retrospective pooled analysis of 3772 patients from 14 multicenter CheckMate clinical trials across 8 tumor types. Patients with advanced cancers received nivolumab, 3 mg/kg (n = 2746); nivolumab, 3 mg/kg, plus ipilimumab, 1 mg/kg (n = 713); or nivolumab, 1 mg/kg, plus ipilimumab, 3 mg/kg (n = 313). Baseline BMI was categorized as normal weight or underweight (<25), overweight (25 to <30), or obese (Ն30) according to World Health Organization criteria. The studies began patient enrollment between February 9, 2012, and May 21, 2015, and patients were followed up to database lock on May 1, 2019. Data analysis was conducted from May 1 to September 1, 2019. INTERVENTIONS Nivolumab, 3 mg/kg; nivolumab, 3 mg/kg, plus ipilimumab, 1 mg/kg; and nivolumab, 1 mg/kg, plus ipilimumab, 3 mg/kg.
Methods Safety data for this pooled analysis were from a total of 3772 patients with advanced cancers treated with weight-based nivolumab monotherapy (n = 2746) or nivolumab plus ipilimumab combination therapy (nivolumab, 3 mg/kg, plus ipilimumab, 1 mg/kg [NIVO3 + IPI1] [n = 713] or nivolumab, 1 mg/kg, plus ipilimumab, 3 mg/kg [NIVO1 + IPI3] [n = 313]) in 14 Check-Mate trials across 8 tumor types (ClinicalTrials. gov Identifiers: NCT01472081, NCT01592370, NCT01642004, NCT01658878, NCT01668784, NCT01673867, NCT01721772, NCT01844505, NCT02041533, NCT02060188, NCT02105636, NCT02181738, NCT02231749, and NCT02387996) (eTable 1 in the Supplement). Included studies were the most extensive data sets available with published primary data at the time of analysis. Data from patients receiving NIVO3 were included from 12 trials across 8 tumor types (CheckMate 017, 025, 026, 039, and 040 [all expansion cohorts]; 057, 066, 067, 141, and 142 [all microsatellite instability-high/mismatch repair deficient colorectal cancer per local laboratory; 205 [cohorts A, B, and C]; and 275),  [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34]  data from patients receiving NIVO3 + IPI1 were included from 3 trials across 2 tumor types (CheckMate 016, 142 [all microsatellite instability-high/mismatch repair deficient colorectal cancer], and 214),  [35] [36] [37]  and data from patients receiving NIVO1 + IPI3 were from 1 trial in 1 tumor type (CheckMate 067) (eTable 1 in the Supplement).
26 Patient enrollment began between February 9, 2012, and May 21, 2015, and patients were followed up to database lock on May 1, 2019. Data analysis was conducted from May 1 to September 1, 2019. Follow-up was defined as the time between randomization date (or first dose date for nonrandomized studies) and last known alive date (for participants who were alive) or death. Instiutional review board approval was not required because the study was a retrospective analysis of data from published clinical trials. Safety was defined as the incidence of any-grade and grade.
Baseline BMI was categorized according to World Health Organization criteria as normal weight or underweight (<25), overweight (25 to <30), or obese (≥30). 38 Because of the small number of patients in the underweight category (n = 160), data were grouped with the normal category; sensitivity analyses showed no differences in safety outcomes between underweight and normal categories. For the CheckMate 016, 025, and 214 trials, the Karnofsky performance status was recalculated to the Eastern Cooperative Oncology Group (ECOG) performance status scale. Cumulative doses of nivolumab and ipilimumab per kilogram of BW from the initiation of study treatment to study drug discontinuation or date of last follow-up were calculated for each BMI category. Incidence of irAEs was reported by BMI category for each treatment cohort.
We focused on the 2 extreme BMI categories and calculated odds ratios (ORs) and 95% CIs for the occurrence of any-grade and grade 3 or 4 irAEs for patients with obesity vs normal weight or underweight BMI. These analyses were descriptive. Breslow-Day testing was conducted to determine data homogeneity. Some variability was identified across the NIVO3 studies, but the data remained homogeneous overall; NIVO3 + IPI1 studies were homogeneous, and NIVO1 + IPI3 comprised a single study, so Breslow-Day testing was not conducted. Formal statistical tests and multivariable analyses were not performed. We further examined the association between BMI category and incidence of irAEs within predefined subgroups (age, ECOG performance status, geographic region, sex, smoking status, and tumor type). Odds ratios were not calculated for subgroups with fewer than 10 patients per treatment group.
Discussion ] [5] [6] [7] [8] [9] [10] [11]  Studies on the BMI-safety association using clinical trial data have shown heterogeneous  b Baseline ECOG PS was not available for 2 patients. c Odds ratios for occurrence of any-grade and grade 3 or 4 irAEs were not calculated for 176 patients in the Asia subgroup because fewer than 10 patients were categorized as having obesity. Asia was not included in the "rest of world" analysis. d  An OR could not be calculated for occurrence of any-grade irAEs in the CRC subgroup because all patients categorized as having obesity had at least 1 any-grade irAE. results, with some studies indicating no association  7  and others suggesting an inc reased risk. 2 0, 2 2 Similarly, meta-analyses  12, 21  have demonstrated heterogeneous results. Our study analyzed primary trial data in which irAEs were uniformly defined across the trials and included 2746 patients receiving nivolumab monotherapy and 1026 patients treated with combination therapy.
Limitations This analysis is subject to several limitations because of its retrospective pooled-data design, the use of descriptive univariable analyses, and the heterogeneity of the included studies in terms of study designs, tumor types, treatment settings, and patient demographic and clinical characteristics. However, homogeneity testing showed that the overall populations were homogeneous, and associations between BMI and incidence of irAEs were similar across all subgroups except for increased incidence of grade 3 or 4 irAEs with obesity among female patients. The irAE incidence data used for this analysis were limited to prospectively collected data for specific irAEs included in the case report form used during the conduct of each study, as described in the Methods. Because knowledge of ICI-related irAEs has continued to evolve since these studies were designed, data were not available for some irAEs that were identified more recently.
Population All included patients had suffered an ischemic stroke. Population demographics are shown in Table  1. There were 42% females, mean age was 62. 8 (standard deviation 15. 0) years. Admission NIHSS scores and follow-up mRS scores were available for 2,234 and 1,871 patients, respectively. Exhaustive ordinal mRS scale (0-6) data were available for 871 patients. Median NIHSS was 3 (IQR: 1-6), good functional outcome was achieved by 72. 5% of patients.
The second most detrimental clinical trait for brain aging in our sample was diabetes, which also emerged as a signi cant condition accelerating brain aging in previous work. 24 Identifying potentially modi able factors impacting brain health yields relevance for prevention interventions. 25 RBA and brain age could furthermore be used as follow-up brain health biomarkers. Several sources of data and methods have been developed to calculate brain age. The majority of them leveraged either voxel-wise information 2 , grey and white matter volumes 8 , or template parcellated volumes  17  data to predict brain age. To our knowledge, radiomics were never assessed as an approach to predict brain age. While in previous work brain age is mostly predicted by volumetric information, radiomics description encompass information that goes beyond volume, characterizing the shape and the texture of an image; consequently, radiomics can potentially capture more information.
10, 28, 29 We deliberately chose to tackle the challenge of leveraging clinical imaging and a lighter methodological framework, with the idea of developing models that are more interpretable and may have a better chance of translating brain age biomarkers to routine clinical care. This work has several limitations. First, we could not benchmark our method against other published methods like BrainAGE or Brain age delta 2,17 , as we did not have T1-weighted images, further work could evaluate our method on reference datasets if they include T2-FLAIR imaging. Second, we could not explore the relationship between T2-FLAIR radiomics derived brain age biomarkers and more detailed outcome metrics such as cognitive or language outcomes. Future studies could indeed study the relationship between radiomics brain age and cognitive reserve in stroke patients.
Lastly, our dataset included patients from 2003 to 2011, who might not be representative of patients treated in modern stroke care settings, further work could evaluate our method on modern cohorts of patients treated with up-to-date reperfusion therapies. To conclude, in this cohort study of 4,163 ischemic stroke patients, using radiomics extracted from clinically acquired T2-FLAIR images, we derived relative brain age, a chronological-age-independent biomarker describing individual biological brain age. A higher relative brain age was linked to the presence of cardiovascular risk factors and worse post-stroke outcomes. Therefore, this newly radiomicsderived relative brain age may capture previously unidenti ed prognostic information. correlated with chronological age because of a regression dilution bias induced by the accumulation of random measurement errors, which can be encountered in radiomics analyses.
Participants We all stroke patients' neuroimaging data included in a large international multisite collaborative effort: the MRI-GENetics Interface Exploration (MRI-GENIE) study. Sites shared clinical, MRI imaging, and genetic data. Both study design, data collection protocols, and populations have been previously published. 12, 13 hics All participants or health care proxy provided signed informed consent. The MRI-GENIE project has been approved by the MGH Institutional Review Board (IRB, Protocol #: 2001P001186 and Protocol #: 2003P000836), as well as the ethics boards of the collaborating institutions.
Radiomic feature extraction Radiomic features were extracted using the open-source toolbox PyRadiomics V2. 2. 0 from brain parenchyma on T2-FLAIR. 16 Brie y, 760 features were extracted describing the shape, histogram, and texture of the brains. The list of the extraction parameters can be found at https://github. com/MBretzner/Brain-Age-Radiomics (uploaded upon acceptance).
Chronological Age, Brain Age, and Relative Brain Age Radiomics-derived predictions of patients' chronological ages were performed by an ElasticNet linear regression model in a 5-fold nested strati ed cross-validation scheme. First, the whole dataset was split into ve equivalent training and test samples (80/20%) in order to produce one single out-of-trainingsample age prediction for every patient. Then, for each of the ve train-test splits, feature selection was performed on the training set by an ElasticNet linear regression model in a 3-fold cross-validation scheme (inner loop). Selected features were subsequently entered into another ElasticNet model that was tted on the entire training set where its L1 and L2 hyperparameters were optimized, and then nally tested on the unseen test set (outer loop).
Ethics statement The described research adhered to the tenets of the Declaration of Helsinki. All patients were included in the CCK-CONE cohort study following informed consent. Ethics committee approval was obtained from the Institut National des Données de Santé (#255645) following approval by the CEREES (13 February 2020). The data protection process was in agreement with the reference methodology MR-004 and the conformity declaration was approved by the CNIL (23 March 2020). The cohort protocol is available on the Health Data Hub (https:// www. health-data-hub. fr/projets/observatoire-de-la-prise-encharge-des-patients-presentant-un-keratocone-du-centre-de-0).
Visual acuity was converted to LogMAR units before statistical analysis. Changes in quantitative variables between two timeframes were assessed with the paired t-test (ie, only eyes with available data for both timeframes were included). For quantitative variables, the unpaired t-test was used to compare the DALK group with the PK group, and analysis of variance was used to compare the three subgroups. Qualitative variables were analysed using the χ 2 test. As we aimed to show real-life data, we included fellow eyes of patients previously grafted. We analysed our data including either only primary cases (ie, first graft performed for each patient, n=436) or all cases (n=502). Similar comparison results were obtained with primary and overall cases (online supplemental table). Statistical analysis was performed using a software programme (Statistica V.
Analysis of QR descendant migration The final position of the QR descendant QR. pap was determined using widefield fluorescence microscopy at the late L1 larval stage (between 12 and 16 hours after hatching). In all experiments, transgenic animals expressing GFP in the seam cells and the Q lineage were used (transgene heIs63)  (Wildwater et al. , 2011). The position of QR. pap was determined by measuring the distance of QR. pap to the seam cells V1. p and V2. p. This distance was then normalized to the distance between V1. p and V2. p to determine the relative position of QR. pap to these cells. The average of two position measurements (one using V1. p, one using V2. p) was used in the analyses. Measurements were performed using the FIJI package  (Schindelin et al. , 2012).