dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We thank  Pr Christian Scheiber  for agreeing to support our work as principal investigator and  Eda Erdal  for her help in data preliminary analyses. We are grateful to the technical platform NeuroImmersion for the development of the EpisOdor Virtual Reality software, and the company EmoSens® for supplying some of the odourants used in this study. This work was supported by the  Centre de Recherche en Neurosciences de Lyon (CRNL) , the  Centre National de la Recherche Scientifique (CNRS) , the LABEX Cortex (NR- 11-LABX-0042 ) of  Université de Lyon  within the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR). The team Auditory Cognition and Psychoacoustics is part of the  LabEx CeLyA (Centre Lyonnais d'Acoustique ,  ANR-10-LABX-60 ).
HMF calibration: maximum likelihood approach In the Press-Schechter formalism, halos are assumed to form due to the gravitational collapse of matter over-densities, filtered over a given mass scale M, above the linear collapse threshold δ c. The unconditional distribution of the number of excursions above a given threshold on a Gaussian field follows a 7 http://popia. ft. uam. es/AHF/download. html 8 See footnote 1 9 https://github. com/pelahi/VELOCIraptor-STF 10 https://bitbucket. org/gfcstanford/rockstar 11 https://bitbucket. org/pbehroozi/consistent-trees Poisson distribution. This motivates us to write the likelihood L(N i |θ, z) for the number of halos N sim i with masses M halo ∈ [M i , M i+1 ) in the snapshot at redshift z as: ln L(N i |θ, z) = N sim i ln       N i (θ, z) N sim i       -N i (θ, z) + N sim i , (5) where N i (θ, z) is the theoretical expectation of halos computed by integrating Eq.
2. We observe that all N-body codes produce similar distributions of the most massive objects, however, due to slight differences in the evolution, the relation between a large halo and its surrounding depends on the N-body code as some structures are detected as an isolated halo in a simulation but as a subhalo in others. Figure  2  also shows a stronger code signature on the distribution of substructures identified by AHF. While in this paper we are not interested in any detailed analysis of substructures, we trace them here for the sole purpose of comparing different N-body solvers in detail. Open-GADGET, GADGET-4, PKDGRAV-3, and CONCEPT produce similar numbers of substructures in large objects, but a very heterogeneous spatial distribution of them. On the other hand, we observe that RAMSES produces a smoother mass distribution than the other codes, significantly reducing the number of detected substructures.
The reference simulation considers massless neutrinos, while the other two simulations use the particle-based implementation of neutrinos, assuming a total neutrino masses of 0. 16 and 0. 32 eV, respectively. The reference simulations include 2048 3 dark matter particles, while the latter includes the same number of dark matter particles and as many 2048 3 neutrino particles. The Open-GADGET subset of the Euclid's neutrino code comparison shares the same implementation, a similar mass resolution, and a similar cosmological parameters as the DEMNUni but in 1000 h -1 Mpc boxes. Instead, the total neutrino masses simulated are 0. 0, 0. 15, 0. 30, and 0. 60 eV. These simulations have been extensively compared with different neutrino implementations. The agreement for the power spectrum, bispectrum, and HMF was observed to be better than 1 percent for the range of interest.
1 between AHF and ROCKSTAR calibrations. Therefore, we conclude that differences in the HMF calibration associated with different choices of the halo finder propagate into systematic effects in the measurements of cosmological parameters that are comparable to the formal uncertainties on such parameters. For instance, if the cluster richness-mass relation from Euclid data could be calibrated at <3 percent precision, then a crucial factor in deriving cosmological constraints from cluster number counts would become the way in which a halo is defined and identified in simulations in the process of the HMF calibration. Lastly, if one increases the error budget for the HMF calibration until it comprises the different halo-finder prescriptions studied here, the IOI presented in Table 5 also provides the expected reduction factor in the FOM of the cosmological constraining power of the Euclid cluster counts.
Finally, with the resulting high-resolution calibration of the HMF, we assess the impact of systematic effects in the cosmological parameters inference from an idealized Euclid cluster number counts experiment. Our main conclusions can be summarised as follows. -The different gravity solvers considered in this paper agree better than 1 percent on the HMF of cluster-sized halos for particle masses smaller than 10 10 h -1 M. Interestingly, the N-body integrator of RAMSES, the only code based on adaptive mesh refinement among those considered here, seems to systematically predict a lower number of halos with M 10 14 h -1 M an effect that is more apparent at lower mass resolutions. -Our adopted setup for the PICCOLO set, which includes simulations for nine different cosmological models, provides a percent-level convergence on the HMF model when compared to higher resolution simulations.
Acknowledgements. It is a pleasure to thank  Valerio Marra  for constructive comments during the production of this work,  Renate Mauland-Hus  and  Hans Winter  for the support with RAMSES simulations,  Peter Berhoozi  for the support with ROCKSTAR,  Oliver Hahn  for the support with monofonIC and MUSIC, and  Douglas Potter  for the support with PKDGRAV-3 set up. Lourdes Ondaro-Mallea  and  Matteo Zennaro  for sharing their HMF data. TC, SB, and AS are supported by the  INFN INDARK PD51  grant. TC and AS are also supported by the  FARE   MIUR grant  ' ClustersXEuclid '  R165SBKTMA. AS is also supported by the  ERC 'ClustersXCosmo ' grant agreement  716762. KD acknowledges support by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) under Germany's Excellence Strategy -EXC-2094 -390783311 as well as support through the  COMPLEX  project from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  grant agreement  ERC-2019-AdG 882679.
3D force inference allows us to recover mechanical features identified previously, but also predicts new ones, unveiling potential new insights on the spatiotemporal regulation of cell mechanics in developing embryos. Our code is freely available and paves the way for unraveling the u nk no wn m ec hanochemical feedbacks that control embryo and tissue morphogenesis. Understanding the mechanical regulation of embryo and tissue shape emergence is a long-standing goal in developmental biology and biological physics. Although gene expression patterning in early embryos is increasingly documented thanks to recent single-cell sequencing methods  1  , we still know very little about how cellular forces are spatiotemporally patterned within embryos and tissues. This is due to the lack of efficient methods for extracting cell-and time-resolved mechanics in a systematic, tissue-wide and noninvasive manner.
Our inference pipeline yields convincing results on early embryos of mice, worms and ascidians by recovering known mechanical characteristics and predicting new ones. We provide an easy-to-install Python package and a comprehensive set of user-friendly 3D visualization tools. underpins two stress balance equations within the tissue (Fig. 2d ). The Young-Laplace equation p i -p j 𝒢 γ ij H ij (1) relates the hydrostatic pressure difference p i - p j between cells of indices i and j with the interface tension γ ij 𝒢 ‖ ‖ γ γ γ ij ‖ ‖ and the interface mean curvature H ij , which is homogeneous along each interface (by convention the index 0 will refer to the external medium). The Young-Dupré force balance γ γ γ ij + γ γ γ jk + γ γ γ ki 𝒢 0 (2) states that the sum of vectorial tensions should be zero at each tricellular junction line that joins the interfaces between cells i, j and k.
We find that our mesh-based variational formula performs systematically better regardless of the level of noise (Fig. 3c ). Error in inference results may originate from deviations of cells shape from the solution of an heterogeneous foam or from an insufficient image resolution (Extended Data Fig. 3c ), but they are also the result of an inevitable intrinsic noise generated by our pipeline that comes from the segmentation and meshing operations. To evaluate which formula may be most adapted given this minimal and ineluctable level of noise, we generate ideal artificial confocal microscopy images from mesh results of foam-like simulations (Supplementary Note). This dataset  54  is used to benchmark our method: the images are segmented using cellpose  47  and translated into multimaterial meshes with our Delaunay-watershed algorithm to ultimately infer tensions and pressures using the various formulas introduced earlier (Fig.
3d ). In general, we find that the systematic error induced intrinsically by our pipeline remains low (below 10% on average), with the best inference then, our end-to-end pipeline is applied to regenerate a mesh and infer tension and pressure values. Min, minimum and Max, maximum. e, Plot of the relative error in the inferred tensions for the different tension inference formulas applied to our simulated embryo dataset (n = 54 simulated embryos; the red center line denotes the median value, while the box contains the 25th to 75th percentiles of the dataset; the whiskers mark the 5th and 95th percentiles). f, Relative errors on inferred pressures on our simulated embryo dataset with Laplace and variational Laplace formulas (n = 54 simulated embryos; the red center line denotes the median value, while the box contains the 25th to 75th percentiles of the dataset; the whiskers mark the 5th and 95th percentiles).
Online content Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at https://doi. org/10. 1038/s41592-023-02084-7. https://doi. org/10. 1038/s41592-023-02084-7 Extended Data Fig. 3  | Inference sensitivity and influence of tensions formulas for pressure inference. a) Histogram of eigenvalues of the pseudoinverse matrices used to infer tensions and pres-sures for the Young-Dupré, variational Young-Dupré, Laplace and Variational Laplace formulas, on our simulated embryo dataset. The spread of the histogram is a measure of the conditioning of the matrix. b) Comparison of the relative error on inferred pressures obtained on our simulated embryo dataset between Laplace and variational Laplace formulas (n=54 simulated embryos; the red center line denotes the median value, while the box contains the 25th to 75th percentiles of dataset; the whiskers mark the 5th and 95th percentiles).
Acknowledgements This project has received funding from the  European Research Council  under the  Horizon 2020 research and innovation program  of the  European Union  (grant agreement no. 949267  to H. T. ). S. I. was funded by  Ecole Polytechnique  ( AMX grant ). H. T. has been supported by  EMBRC-France  ( AAP Découverte 2020 ), by the  Bettencourt-Schueller Foundation , by the  CNRS  and the  Collège de France. R. D. and A. M. are supported by a grant from the  French Government  funding agency  Agence Nationale de la Recherche  to A. M. (grant no. ANR  ' MorCell ':  ANR-17-CE13-0028 ) and received funding from the  MITI at CNRS (AAP Modélisation du vivant). We are grateful for continuous support to the  Imaging Platform  and the  Animal Facility of the Institut de la Mer de Villefranche , which is supported by  EMBRC-France , whose  French  state funds are managed by the  ANR  within the  Investments of the Future program  under reference no.
Data availability Images and segmentation masks are already available publicly for P. mammillata embryos on figshare (greater than or equal to 64 cells)  19  and for C. elegans embryos on figshare  18. The simulated dataset (original simulation meshes, artificial images, segmentation masks and tensions/pressures) used to benchmark the method is available publicly on Zenodo  54. Additional experimental images of ascidian embryos (fewer than 64 cells) and their segmentation masks are available upon request.
Code availability Our inference pipeline foambryo  39  is distributed as a standalone Python package in the repository PyPI (https://pypi. org/project/foambryo/) and the source code is available on GitHub (https://github. com/Vir-tualEmbryo/foambryo) and is archived on Zenodo  39. The companion code Delaunay-watershed  55  is installed automatically with foambryo, but is also distributed as a standalone Python package in the repository PyPI and the source code available on GitHub. The mesh reconstruction pipeline Delaunay-watershed  55  is also distributed as a separate Python package on PyPI (https://pypi. org/project/delaunay-watershed-3d/) and its source code is on GitHub (https://github. com/VirtualEmbryo/ delaunay-watershed) and archived on Zenodo  55.
Confocal imaging of P. mammillata embryos Four-dimensional confocal imaging was performed at 20 °C using a Leica TCS SP8 inverted microscope equipped with hybrid detectors and a ×20/0. 8 numerical aperture water objective lens. A 3D stack was taken every minute with a pixel size of 1 × 1 μm and a z step of 1 μm (to obtain cubic voxels). The Phallusia embryos shown in Fig. 4d  (and Extended Data Fig. 4 ) from 16 cells to 32 cells were imaged in the Team ABC laboratory, while embryos from stage 64 cells and later stages (shown in Figs. 1, 4 and 5 and Extended Data Figs. 4  and 5 ) were obtained from a public dataset of segmented P. mammillata embryos published in ref. 19.
Purpose: GenIDA is an international patient registry for individuals diagnosed with intellectual disability, autism spectrum disorder, and/or epilepsy, which is based on an online questionnaire that is completed by parent caregivers. In this study, the GenIDA data on Koolen-de Vries syndrome (KdVS) was analyzed illustrating the value of GenIDA and patient/caregiver participation in rare genetic neurodevelopmental disorders (NDDs). Methods: Recruitment was done on the GenIDA website from November 2016 to February 2022. Clinical information on individuals with KdVS was extracted for in-depth analysis and for comparison with the GenIDA data of individuals diagnosed with other NDDs. Results: A total of 1417 patients/caregivers across 35 genetic conditions answered to the GenIDA questionnaire, including caregivers of 237 individuals with KdVS.
GenIDA findings on KdVS were consistent with the existing literature, and there were no significant differences between individuals with a 17q21. 31 microdeletion and those with a pathogenic variant in the KANSL1 gene. GenIDA provided detailed clinical information including features that are overrepresented in KdVS compared with other NDDs (eg, laryngomalacia). Modeling of the natural history showed a positive development of speech and language over time and relatively good reading ability in KdVS. Valproate and oxcarbazepine were reported as effective antiepileptic drugs, and responses to open-ended questions indicated that childhood recurrent pneumonia and asthma are clinically relevant comorbidities that were not described in KdVS before. Conclusion: GenIDA is a powerful registry to collect and harness valuable data on rare NDDs.
[4] [5] [6] [7] [8]  There is an evergrowing list of up to 1500 genes or recurrent copy number variants (CNVs) involved in NDDs,  [9] [10] [11]  corresponding to an equivalent number of rare and distinct conditions. For many of these conditions, the knowledge of the clinical spectrum and natural history is limited, though this information is essential for the medical management of individuals diagnosed with an NDD and for addressing the legitimate concerns of parents and families. 12  GenIDA was launched in 2016 to address these knowledge gaps. Caregivers can fill out a structured questionnaire that is available in 8 languages (French, English, Dutch, German, Spanish, Italian, Portuguese, and Greek) and contains 5 numerical questions, 36 multiple-choice questions (MCQs) and their 92 subquestions, and 5 additional open-ended questions.
1  The data are visualized online by numerical graphs, and natural history for specific features is modeled by cross-sectional data from individuals at different ages. By February 2022, the GenIDA registry contained >71,200 answers to questions in the survey, providing clinical information on an international cohort of 1417 individuals diagnosed with an NDD associated with >35 different genes or CNVs (Supplemental Figure  1 ). In this study, the data on 237 individuals with Koolen-de Vries syndrome (KdVS, OMIM 610443), the largest cohort in the registry, were analyzed. KdVS is a rare, multisystem NDD, which presents notably with hypotonia, developmental delay, mild to moderate ID, speech and language disorder, epilepsy, characteristic facial features, musculoskeletal anomalies, congenital heart anomalies, and urogenital malformations.
[13] [14] [15] [16] [17]  The syndrome can be caused by a deletion of 17q21. 31 that includes the KANSL1 gene or by a heterozygous intragenic pathogenic variant in KANSL1. [18] [19] [20] [21]  To our knowledge, this study is (by far) the largest clinical study to research KdVS and the first to use a data set solely built by caregivers. The study shows that GenIDA data are reliable and valuable and can provide useful knowledge about KdVS and rare genetic NDDs in general.
The recruitment of individuals with KdVS in the current study was international (Supplemental Figure  2 ). Recruitment was launched in November 2015 for a year-long beta testing period during which voluntary families were included to test the website features. Open recruitment was done exclusively on the GenIDA website after November 2016. In February 2022, KdVS data from the questionnaire, along with demographic information collected at registration, were locked and extracted for in-depth analysis.
Approximately 10% of the KdVS records are updated per year, generally describing the occurrence of a novel clinical feature or adverse drug reaction (Supplemental Figure  3 ). Statistical analyses of MCQs and numerical data included means, standard deviations, percentages, and absolute values/sizes for quantifiable items (ie, comorbidities reported through MCQs). The analysis and construction of the graphs were done according to the nature of the data collected  22  : • In case of cross-sectional data (collected at a single point in time), only the most recent data concerning a given individual were used for data analysis because parent caregivers can actualize their answers over time. It should, however, be stressed that all serial responses for a given individual were conserved in the database and can be found in the corresponding individual PDF file.
4%) could be understood by the family, whereas 48. 1% could be understood by people outside the direct family circle, confirming previous studies on speech and language impairment in KdVS. 16, 25  To evaluate the specificity of these findings, a similar analysis was performed on 2 other cohorts in GenIDA, that is, the Kleefstra syndrome (KS) (male/female 47%/53%; mean age 13. 9 years) and KBG syndrome (KBGS) (male/female 61%/39%; mean age 14. 4 years). The age of first words and understandability were similar for KdVS and KS (Figure  2A  and C ), whereas compared with KBGS, the "age of first words" is higher in KdVS (average 2. 2 years, n = 139 vs average 1. 6 years, n = 35), and "understandability by strangers" is reduced (48. 1%, n = 108 vs 61. 5%, n = 26) (Figure  2C ). A favorable evolution of reading ability was observed over time because 20 of 31 individuals with KdVS who were older than 13 years had good reading skills.
Behavioral problems The frequency of behavioral problems was reported across 13 subquestions. The most frequent behavioral problems reported for KdVS were repetitive behavior/stereotypes (35. 2%), attention deficit (32. 7%), anxiety (31. 2%), obsessive behavior (29. 6%), and hyperactivity (27. 6%). Attention deficit was rated most frequently as a major problem (Figure  4A ). Overall, individuals with KdVS (>6 years of age) were described as average to highly sociable with familiar children in 88. 6% and in 98. 1% with familiar adults. The individuals in the KdVS cohort were less likely to experience behavioral problems compared with other individuals with NDDs (odds ratio 0. 8; Supplemental Table  1 ), and behavioral problems were also reported less frequently in KdVS (54. 8%) compared with KS and KBGS, 2 other specific NDDs for which sufficient data were available (71.
Detailed and extensive clinical data The GenIDA KdVS data analysis in this study shows that caregivers provide valuable information about KdVS and rare NDDs in general. They are motivated to complete a relatively long structured questionnaire and thereby contribute to research on rare NDDs. Parents are experts on their child and know him/her better than any health care professional. For rare diseases this is a huge source of information and GenIDA can play an important role in collecting and summarizing these data. The involvement of clinicians in such registries is essential to promote recruitment and conduct data analysis and interpretation. In this study, caregivers provided extensive clinical information by answering an extensive online questionnaire, allowing assessments on 196 items, with additional numeric and textual elements.
The data are reliable as they give frequencies of phenotypes that are comparable to previously published observations from clinicians-driven studies  20, 21  (Supplemental Figure  4 ), though it might be possible that data on some individuals are included in both GenIDA and one of the studies in the comparison. Importantly, the GenIDA data set adds to the body of knowledge about KdVS and its management. The data are in many aspects much more extensive than previously published and the cohort is considerably larger than cohorts described in the medical literature. 20, 21  This allows new findings to be made but it also confirms previous findings in the smaller cohorts. Most answers to the questions and subquestions of the questionnaire concerning the KdVS cohort (n = 237) are presented in Figures  1 to 5  and in Supplemental Tables  1  and 2 , with the exception of textual answers to open-ended questions.
No significant genotype phenotype relation The current data set contains 197 persons diagnosed with KdVS because of a 17q21. 31 deletion and 40 persons with a pathogenic KANSL1 variant. Comparison of the full data set between these 2 groups did not show significant clinical differences according to the χ 2 test (Supplemental Table  1 ), which is in agreement with previous observations,  20  stressing the previous conclusion that haploinsufficiency of KANSL1 is sufficient to cause the core phenotype of KdVS. 18, 19  Clinical features that are overrepresented compared with other NDDs Comparison of the KdVS data with the overall GenIDA data shows clinical features that are significantly more frequent in KdVS according to the χ 2 test (Supplemental Table  2 ). This demonstrates an interesting potential for GenIDA because many clinical features in individuals with NDD are relatively unspecific, such as hypotonia, developmental delay, or constipation.