dataesr/scientific-paragraphs-categorization huggingface dataset:
ω 2 m = (S 4 - d 2 3 4 )Γ 2 m 1 ,m 2 + (c 2 + d1d3 2 )Γm 1 ,m 2 - d 2 1 4 (12) Fig. 2. Distributions of the five sorted eigenvalues of M(θn). For the sake of comparison between PNP and P-loss, the bold line indicates the eigenvalues of the identity matrix (see Equation  6 ). σm = d3 2 Γm 1 ,m 2 - d1 2 (13) Km(u, t) = y m u δ(t) sin( πm1u1 l ) sin πm2u2 lα ( 14 ) where Γm 1 ,m 2 = π 2 m 2 1 /l 2 + π 2 m 2 2 /(lα) 2 , and y m u is the m th coefficient associated to the eigenfunction sin(πmu/l) that decomposes yu(u). Without losing connections to the acoustical manufacturing of the drum yet better relating g's input with perceptual dimensions, we reparametrize the PDE parameters {S, c, d1, d3, α} into θ = {log ω1, τ1, log p, log D, α}, detailed in Section 3. 4 of  [19]. We prescribe sonically-plausible ranges for each parameter in θ, normalize them between -1 and 1, uniformly sample in the hyper-dimensional cube, and obtain a dataset of 100k percussive sounds sampled at 22050 HZ.
although very well adapted to brain study, Magnetic Resonance Imaging (MRI) remains limited by the facilities and capabilities required to acquire data, especially for non-human primates. addressing the data gaps resulting from these limitations requires making data more accessible and open. In contempt of the regular use of Saimiri sciureus in neuroscience research, in vivo diffusion has yet to be openly available for this species. Here we built and made openly available a unique new resource consisting of a high-resolution, multishell diffusion-weighted dataset in the anesthetized Saimiri sciureus. the data were acquired on 11 individuals with an 11. 7 T MRI scanner (isotropic resolution of 400 µm 3 ). this paper presents an overview of our dataset and illustrates some of its possible use through example analyses.
Since DWI sequences can be expensive to implement and challenging to process, several groups offered open access to datasets in humans  31  , macaques  32, 33  , mice and other species  [34] [35] [36] [37]  to fuel progress in neurosciences. However, the absence of a living squirrel monkey DWI dataset hampers the progress of comparative models and prevents us from building comprehensive and accurate evolutionary trees of the brain. Therefore, this paper summarizes the parameters and open access details to the first diffusion-weighted imaging dataset of the living squirrel monkey. We also provide codes, when available, preliminary results, and perspectives for future projects using this new resource.
A total number of 8 non-diffusion weighted volumes were also acquired. The whole scanning time for this multi-shelled diffusion acquisition was 3h04 min per animal. Preprocessing. Raw data were denoised, reoriented using ExploreDTI (www. exploredti. com), and the three b-values were merged (Fig. 1 ). A brain mask was generated and manually corrected with BrainSuite21a (www. brainsuite. org). Manual editing mainly consisted of extending the mask to the missing areas (due to a signal drop) or eroding the masked cerebrospinal fluid. Eddy currents-induced distortions and movement were corrected using the eddy tool from FSL. tractography. StarTrack (http://www. mr-startrack. com), together with Matlab, performed deterministic tractography analysis on multishell diffusion-weighted images by means of the damped Richardson Lucy Spherical deconvolution algorithm  39, 40  (Fig.
Models fitting. DWI can shed light on advanced microstructural information through specific models. For instance, neurite orientation dispersion and density imaging (NODDI) estimates the complexity of dendrites and axons based on diffusion MRI data  20  (Fig. 1 ). It relies on three sub-model for intracellular, extra-cellular and cerebrospinal fluid (CSF) compartments. NODDI analyses provide maps of isometric volume fraction in CSF, intracellular volume fraction, and orientation dispersion (OD). The latter is a surrogate for neurite dispersion and synaptic complexity. Additionally, ActiveAx is a model used to calculate an orientationally invariant index of axon diameter and density  41. Accordingly, we applied both models to the multishell dataset (B300, B1000, and B2000) after eddy current correction.
Note that for practical purposes, ActiveAx estimations focused on the corpus callosum's mid-section (delineated manually by the authors). Fig. 1  Overview of the experimental workflow. The top frame summarizes preprocessing steps from raw data to eddy-corrected multishell. The bottom frame outlines analyses applied to the preprocessed images. Eddycorrected images are used to generate maps and compute tractography. Maps of 10 individuals were used to create average templates  46. Note that the 11th individual data were acquired after the template creation and were not voluntarily excluded from the template creation.
Data Records The present dataset is stored on Figshare 42 and a dedicated website, which will be regularly updated with additional data and analyses (http://saimiri. bcblab. com). We also opened a forum topic for questions and answers (https://neurostars. org/t/squirrel-monkey-brain-mapping/24110). The dataset comprise 11 folders, one per subject. Each subject folder contains one subfolder per b-value (2 000, 1 000, 300) and one subfolder for the B0 map registered to the diffusion data set. B-values folders comprise the raw image in. nii format, the b-vecs file in. txt format, and metadata in a. json file. B0 map folder includes B0 maps in. nii format and metadata in a. json file.
technical Validation We present here the first DWI open database of living squirrel monkeys' brains. To prompt the quality of our data we assessed the details of small anatomical structures, the quality of tractography dissections, the clarity of advanced multishell diffusion maps and the reproduction of classical expected brain patterns. Figure  2  shows that despite the high resolution of the dataset, the signal was strong enough to make anatomical structures visible at the cortical and subcortical levels. Fractional anisotropy maps sharply delineated fine white matter anatomical landmarks such as the distinction between the extreme and external capsule, stripes in the internal capsule and the micro-and macrogyric architecture of the dentate nucleus  43. Figure  3  shows the result of deterministic whole-brain tractography and examples of track dissection in a single subject.
A similar gradient, posterior-anterior this time, seems to exist in the occipital lobes, albeit, to our knowledge, less documented. The multishell parameter of our acquisition also allows for using ActiveAx to estimate mean axonal density and diameter. Figure  6  is an example of ActiveAx analysis focusing on a sagittal mid-section of the corpus callosum and demonstrating a dorsoventral gradient of axonal diameter with thicker axons located dorsally. Importantly, DWI-derived estimation of the axonal diameter profile received a lot of criticism  55. Accordingly the values estimated by AxtiveAx were higher than standard values reported post-mortem in other species  56. While postmortem shrinking can affect values, future investigation on our dataset will allow us to provide true postmortem estimation of the axonal diameter in the squirrel monkey.
Usage Notes Our in vivo diffusion-weighted imaging dataset of living squirrel monkeys is available on Figshare and a dedicated website regularly updated with additional data and analyses (http://saimiri. bcblab. com). We also opened a forum topic for questions and answers (https://neurostars. org/t/squirrel-monkey-brain-mapping/24110). This dataset provides future perspectives for anatomists working on brain evolution, who will be able to peer further into the past of primate brain evolution. It will benefit neuroscientists who wish to investigate behavioral models or anatomical systems not available otherwise and neuroimagers who would like to test their new algorithms. This resource will also permit the refinement of evolutionary mechanisms modeling and potentially allow for identifying future therapeutic targets.
acknowledgements This work was supported by the  European Union 's  Horizon 2020 research and innovation program  under the  European Research Council (ERC)  Consolidator grant agreement N°. 818521 (MTdS, DISCONNECTOME). This study was conducted in the framework of the  University of Bordeaux 's  IdEx "Investments for the Future" program RRI "IMPACT " which received financial support from the  French government. We thank the primate facility staff for their valuable help caring for and preparing the animals for the procedures.
Study design The present study is a post hoc analysis of the MANCTRA-1 study, conducted in 150 centres in Europe, Asia, Africa, South America and Oceania  [24, 25]. Ethical approval of the MANCTRA-1 study and subsequent post hoc analyses was granted by the Institutional Review Board of the University of Cagliari (Italy) (PROT. P. G. / 2021/5410-31/03/2021) and local boards of the participating centres. This study was conducted under the principles of the Declaration of Helsinki and was developed and presented according to Strengthening the Reporting of Observational Studies in Epidemiology (STROBE, ClinicalTrials. Gov NCT04747990)  [26]. A retrospective analysis was performed on all consecutive patients hospitalised between January 2019 and December 2020 with a diagnosis of IPN associated with biliary pancreatitis.
A p value < 0. 05 (two-tailed) was considered statistically significant. All the statistical analyses were performed using the Statistical Product and Service Solution (SPSS) 26. 0 software (IBM SPSS Statistics, I. B. M. Corp. , Armonk, NY, U. S. A. ) and Jamovi Computer Software (The Jamovi project (2022). Jamovi (Version 2. 3). Retrieved from https:// www. jamovi. org).
General characteristics of the cohort of patients Over the two-year study period (January 2019-December 2020), a total of 5275 patients were included in the MANC-TRA-1 database as they were admitted to any of the 150 participating general surgery, hepato-pancreato-biliary (HPB) surgery, gastroenterology or internal medicine departments for biliary AP; 4587 (87%) patients had mild AP, 490 (9. 3%) patients had moderately severe AP, and 198 patients had severe AP (3. 8%) according to the RAC determined within 72 h from the hospital admission  [24]. Figure  1  is the study flowchart. Over the same study period, 247 patients who developed IPN during the hospital stay met the inclusion criteria and were considered for the final post hoc analysis on IPN (Table  1 ).
Acknowledgements This study has been endorsed by: The World of data for the study; Drafting the study and revising it critically for important intellectual content; Final approval of the version to be published. DP: Conception and design of the  MANCTRA  project and the  MANCTRA-1 study ; Acted as statistical advisor and data management lead; analysed and interpreted data for the study; Drafted the study and revised it critically for important intellectual content; Final approval of the version to be published. SPBC: Conception and design of the  MANCTRA  project and the  MANCTRA-1 study ; Analysis and interpretation of data for the study; Drafting the study and revising it critically for important intellectual content; Final approval of the version to be published. FV: Conception and design of the  MANCTRA  project and the  MANCTRA-1 study ; Analysis and interpretation of data for the study; drafting and revising it critically for important intellectual content; Final approval of the version to be published.
GP: Conception and design of the  MANCTRA  project and the  MANCTRA-1 study ; Acquisition, Analysis, and interpretation of data for the study; Drafting the study and revising it critically for important intellectual content; Final approval of the version to be published. MO: Conception and design of the  MANCTRA  project and the  MANCTRA-1 study ; Acquisition, Analysis, and interpretation of data for the study; Drafting the study and revising it critically for important intellectual content; Final approval of the version to be published. DD: Conception and design of the  MANCTRA  project and the  MANCTRA-1 study ; Analysis and interpretation of data for the study; Drafting the study and revising it critically for important intellectual content; Final approval of the version to be published.
The MIMOSIS-1 beam telescope and data analysis To measure their detection efficiency and spatial resolution, the sensors were tested with a telescope composed of six planes of MIMOSIS-1 chips. The sensors were wire-bonded to a PCB and individually placed in a temperature stabilised dark chamber operating with a coolant temperature of 15 • C. The planes are 1. 5 cm distant. The two outer pairs of sensors were operated as reference planes and equipped with 60 μm thin sensors featuring a standard epitaxial layer for the sake of optimal spatial resolution. The two middle sensors were operated as Devices Under Test (DUT). All sensors were operated in a synchronised way. A scintillator hodoscope was available in case of trigger needs, but it was decided to operate the sensors in free streaming mode during the measurements summarised hereafter.
The data was processed with the TAF data analysis package  [8]. Hits from typically 5 consecutive frames were merged during the data analysis to reduce artefacts introduced by the few 100 ns time walk of the pixels  [5]  in a simple way. A reference track was considered as identified if it was detected by all four reference planes and a particle was considered as detected by the DUT if a hit was found within less than 100 μm from the hit projected from a reference track. The resolution was derived from the distance between the extrapolated beam particle hit position and the closest hit found in the DUT within the search window. The centre-of-gravity of the hit clusters was computed to improve the spatial resolution of all detector planes. We assume a resolution on the extrapolated hit position of 2.
Acknowledgments The measurements leading to these results have been performed at the  Test Beam Facility of DESY Hamburg (Germany) , a member of the  Helmholtz Association (HGF). The authors wish to thank the  DESY  test beam team as much as  Alexander Dierlamm  and co workers from  KIT  for their help in irradiating the sensors. This work has been funded and supported by the  German federal Ministry of Education and Research  ( BMBF 05P21RFFC2 ) , the  European network for developing new horizons for RIs (Eurizon)  and  HGS-HIRe for FAIR ,  HFHF  and  TANGERINE.
Image dataset and preprocessing From the van Hateren's database  [34] , we used for our tests a selection of 137 natural images that did not contain artificially created structures neither significant blur  [35]. We performed the same preprocessing stage described in  [36] : first, we rescaled all images separately between zero and one, then we normalized them by subtracting and dividing each pixel value by the image mean and standard deviation respectively. The resulting zero mean, unit variance images were then passed through a whitening filter in order to emulate the response of retinal ganglion cells. The images were finally rescaled such that they have a variance of 0. 1. This value serves as a baseline error, i. e. the mean square error (MSE) of a preprocessed image with an image with only zero pixel values (produced when all the coefficients of a neural code are zero).
CEL0 contained most of its data points near the origin with ISTA having them exclusively there. That region on the graph points to shapes that are either circular or slightly elongated. Visual inspection of the RFs near the origin indicates a distinctive difference between CEL0 and ISTA: CEL0 contains RFs that are reminiscent of the difference of Gaussian filter that typically models retinal and LGN cells (Fig 6A first row), but also found in V1  [33, 53] , while ISTA does not (Fig 6B first row). CEL0 also contains RFs with long widths either along the axis parallel or orthogonal to the enveloped grating irrespective of the We subsequently ask whether the degree of sparsity is the determining factor in the differentiation of a homogeneous set of RFs into a more variable one with RFs with different functions.
DP and IR acknowledge the support received by the  French National Research Agency (ANR)  through  Young Investigator  ( JCJC ) grant project ' Redundancy-free neuro-biological design of visual and auditory sensing' (RUBIN-VASE ). LUP  received funding from the  ANR  project ' Biomimetic agile aerial robots flying in real-life conditions' (AgileNeuRobot) , grant number  ANR-20-CE23-0021. LC acknowledges the support received from the  French National Centre for Scientific Research (CNRSreceived  support through  ANR   JCJC  project ' Task-adapted bilevel learning of flexible statistical models for imaging and vision' (TASKABILE) , grant number  ANR-22-CE48-0010. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
One explanation may be that most current maternity indicators derived from routinely collected hospital data are not always easy to understand. This is due mainly to the lack of clinical information contained in these medical-administrative databases and the absence of evidence for the best practice in specific obstetric situations  [18, 24]. Moreover, indicators derived from routine hospital data can lack validity. For example, "good" maternity wards are considered to be those with a global cesarean rate near the mean for the country. However, what is a good cesarean rate? We do not know; maternity units with low cesarean rates may have high neonatal morbidity and/or mortality, perhaps because their cesarean rates are too low  [25]. Several French obstetric guidelines do not provide indicators to assess their impact on the practice or appropriate outcome indicators  [26].
Literature Review The French steering committee established comprised nine specialists in obstetrics and/or public health, recognized for their expertise in quality indicators and/or the Delphi method. This committee reviewed the literature from January 2003 through December 2013. A PubMed search was conducted for the keywords "quality indicators" and "obstetrics". All reports on perinatal quality indicators issued by learned societies in France or abroad were also collected. A database of 318 references was identified. During face-to-face and telephone meetings, completed by email exchanges, the steering committee used the literature search results to establish an exhaustive list of potential quality indicators. The indicators to be chosen were to be collected and calculated from data in patient files, collected immediately intrapartum, during delivery, and early postpartum (in the delivery or operating unit, according to the type of delivery).
Data Collection Round 1 data were collected from 11 December 2014 to 28 January 2015, and round 2 data from 25 March to 17 May 2015. The participants were asked to answer within four weeks for each round. No reminder was sent out during the first round, whereas two reminder emails were sent during the second. Only participants who completed the first-round electronic questionnaire received the URL to participate in the second round. During both rounds, the panelists were invited to rate individually and independently each quality indicator for its clinical relevance on a 9-point Likert scale from 1 (totally inappropriate/irrelevant) to 9 (totally appropriate/relevant), with 5 for no preference, indecision, etc. They were also invited to comment on each indicator. For the second round, the panelists received descriptive statistics of the distribution of the panel's scores for each indicator rated in the first round.
Acknowledgments: The authors would like to thank all obstetrician-gynecologists and midwives who participated in the Delphi process for their contribution to the study and their time in responding to the questionnaires. We acknowledge the CNSF and the  CNGOF  for their support and the  Audipog association  for its free logistic support. We acknowledge  Catherine Crenn-Hébert  ( Audipog, APHP, Hôpital Louis-Mourier ),  Jeanne Fresson  ( CHU de Nancy ),  Pascal Thibon  ( CHU de Caen ),  Sandra David-Tchouda  ( CHU de Grenoble ),  Marie-José D'Alché-Gautier  ( CHU de Caen ), and  Rym Boulkedid  ( APHP, Hôpital Robert Debré ) for their participation in the steering committee of the study.
The choice of these levels is due to the fact before the year 2000, temperature measurements were most often taken in the upper 700 m of the water column, but by about 2005 the Argo array had sufficient space-time sampling to yield an improved measurement of OHC down to a depth of 2000 m (von  Schuckmann et al. , 2016; Abraham et al. , 2013; Cheng et al. , 2022 Cheng et al. , , 2017)). The Copernicus Marine service provides marine data covering the global ocean and European seas, splitting them into six different regions. One of those, the Iberian-Biscay-Ireland (IBI) region, covers the northeastern Atlantic from the Canary Islands (26 • N) up to Ireland (55 • N), being limited by the 20 • W meridian and the continental margin. Among the variety of hydrographic processes taking place Published by Copernicus Publications.
25 < σ θ < 27. 45 kg m -3 , and salinity minima connected with Labrador Sea Water (LSW) underneath the isopycnal 27. 8 > σ θ kg m -3  (Talley and McCarney, 1982; Iorga and Lozier, 1999a, b; van Aken, 2000; Prieto et al. , 2013; de Pascual-Collar et al. , 2019). The density ranges for these water masses show considerable overlap, which allows both diapycnal and isopycnal mixing. Among the Copernicus Marine Service products are the Ocean Monitoring Indicators (OMIs), which are free downloadable trends and datasets covering the past quarter of a century. These are indicators that allow us to track the vital health signs of the ocean and changes in line with climate change. These OMIs are proposed to cover the blue ocean, e. g. the mean volume transport across sections from Reanalysis OMI (GLOBAL_OMI_WMHE_voltrp); the green ocean, e.
2 presents the datasets used, as well as the methodology applied, to compute the OHC in the IBI region. In Sect. 3 the time series of OHC averaged over the whole IBI region are discussed to provide a general view of the regional trends estimated. Section 4 is devoted to showing and discussing maps of trends computed in the same way as the ones provided in the GLO-OMI-trend product. Section 5 analyses the vertical profiles of OHC trends, studying the variability associated with different water masses. Section 6 summarises the availability of the data used in this article. Finally, main conclusions are summed up in Sect. 7.
In the present work the anomalies have been referenced to the monthly climatology computed between 1993 and 2019. Additionally, the OHC is presented for the typical depths of 700 and 2000 m, but also for the upper 150 m to analyse the OHC variability in the upper layer. In order to allow assessing uncertainties of results, different Copernicus products were used to provide multi-product estimations of OHC; therefore, all results were previously computed for a collection of Copernicus products and combined to give an ensemble mean and the standard deviation of the ensemble. Since the objective of this work is the analysis of OHC in the IBI region integrating results from the surface down to a maximum of 2000 m depth, this study has included all Copernicus Marine products that deliver gridded data on potential temperature with vertical coverage from the surface down to at least 2000 m.