dataesr/scientific-paragraphs-categorization huggingface dataset:
Source data to generate Figure 4a is provided as Figure 3-source data 1, file 2; Figure 4-figure supplement 1 shows the proportion of phylogenies best fitting different species richness dynamics for a reduced dataset including only trees with more than 20 species (N=99), or with a sampling fraction over 20% (N=137). The online version of this article includes the following source data and figure supplement(s) for figure 4: Source data 1. Provides the data to construct Figure 4b and c.
(c) Correspondence analysis for the pooled dataset showing the association between species richness dynamics (represented by red arrows) and the environmental drivers (blue arrows). If the angle between two arrows is acute, then there is a strong association between the corresponding variables. Figure 5-source data 1 provides the data to construct this figure. Figure 5-figure supplement 1 shows the proportion of phylogenies best fitting different paleoenvironmental models based on the most supported and second most supported model. Results are also reported for a reduced dataset including only trees with more than 20 species (N=99), or with a sampling fraction over 20% (N=137). Figure 5-figure supplement 2 shows the comparison of diversification results based on different paleotemerature curves.
Acknowledgements We are grateful to the three reviewers and the editors, for many insightful comments that helped improve the quality of our work. We thank all researchers who shared their published data through databases or with us directly ( Drs Arevalo ,  Martins ,  Fortes Santos, Simon, Lohmann, Mendoza, Swenson, Erkens, van der Meijden , and Freitas). Drs J Muñoz ,  J Lobo ,  I Sanmartín ,  S Louca ,  P Manzano , and  M Godefroid  for invaluable comments on the manuscript and/or analyses. This work was funded by an  'Investissements d'Avenir ' grant managed by the  Agence Nationale de la Recherche  ( CEBA , ref  ANR-10-LABX-25-01 ) and the  ANR   GAARAnti  project ( ANR-17-CE31-0009 ). ASM was also supported by the  Atracción de Talento CAM program  ( 2019-T1/AMB-12648 ),  Juan de la Cierva  grant ( IJCI-2017-32301 ), and Grant  PID2020-120145GA-I00  funded by  MCIN/AEI/ 10.
13039/501100011033  and by the  European Union. AA is supported by the  Swedish Research Council  ( 2019-05191 ), and the  Royal Botanic Gardens, Kew. OAPE is funded by the  Swiss Orchid Foundation  and the  Sainsbury Orchid Fellowship  at the  Royal Botanic Gardens, Kew. GC is funded by a  Natural Environment Research Council   Independent Research Fellowship  ( NE/S014470/1 ). RR is funded by the  Spanish Ministry of Science  ( PID2019-108109GB-I00 ,  AEI/FEDER). This is contribution ISEM 2022-270 of the  Institut des Sciences de l'Evolution de Montpellier.
Grant reference number Author Agence Nationale de la ANR-10-LABX-25-01 Andrea S Meseguer Recherche Alice Michel Pierre-Henri Fabre Pierre-Olivier Antoine Frédéric Delsuc Fabien L Condamine Agence Nationale de la ANR-17-CE31-0009 Pierre-Henri Fabre Recherche Pierre-Olivier Antoine Frédéric Delsuc Fabien L Condamine Ministerio de Ciencia e PID2020-120145GA-I00 Andrea S Meseguer Innovación Comunidad Autonoma 2019-T1/AMB-12648 Andrea S Meseguer de Madrid, Atraccion de Talento Ministerio de Ciencia e PID2019-108109GB-I00 Ricarda Riina Innovación Swedish Research Council 2019-05191 Alexandre Antonelli Natural Environment NE/S014470/1 Guillaume Chomicki Research Council Swiss Orchid Foundation Oscar A Pérez Escobar Ministerio de Ciencia e IJCI-2017-32301 Andrea S Meseguer Innovación.
Collected Data We collected data on 40 autotransplants, from 6 November 2017 to 7 February 2022 (i. e. , 151 anastomoses: 76 venous and 75 arterial). Age, weight, and the rank of the ewe were recorded. This last quantitative variable, reflecting the learning curve, was divided into two groups: the first 10 ewes versus the last 30, according to a receiver operating characteristic (ROC) curve. The different operative times were collected, and especially the time of dissection of the uterus, and the time of cold and warm ischemia. Next, for each vessel, we collected anatomic data, the caliber and length of the free pedicle, noted the primary operator who performed the anastomoses, the technique used (separate stitches or continuous suture), and finally whether catch stitches were required to make the anastomoses airtight.
Statistical Analysis Four analyses were performed: clinico-pathologic variables associated with successful arterial and venous anastomoses were determined intraoperatively and at 4th postoperative day, respectively. Categorical variables were expressed as n (%), and were compared by applying chi-square test (or Fisher's test if the sample size was too small). Quantitative data were expressed as mean ± standard deviation (SE), and were compared using Student's t test or a Wilcoxon test in case of non-parametric distribution. Parametric distribution was tested using the Kolmogorov-Smirnov test. Receiver operating characteristic (ROC) analyses of the significant quantitative factors were made to define threshold values. p values lower than 0. 05 were considered as significant. Significant variables that were identified by univariate analyses were entered into a multivariate logistic regression model in order to determine the variables that were independently associated with successful vascular anastomoses.
Biologic Parameters We collected data from 12 lactate samples at T1 (after reperfusion) and 11 at T2, 6 h after completion of the anastomoses. The crude levels are presented in Table  4. We then compared the average failure versus the average success according to the six sampling times. The maximum lactate level occurred at T2, i. e. , 6 h after reperfusion. The results are presented in Figure  5  and in Supplementary Table  S3. By comparing T2 to T1, ewes from the failed uterine autotransplants group had significantly increased lactate levels compared to ewes from the successful autotransplant group (3. 58 ± 2. 03 mmol/L versus 1. 39 ± 0. 8 mmol/L, respectively, p = 0. 03). ROC analysis showed that an increase in lactate levels that was lower than 2. 72 mmol/L predicted successful uterine autotransplant, with an 85.
Acknowledgments: We warmly thank SAAJ,  INRAE ,  2022 ,  Livestock Physiology-Reproduction and Animal Diets Experimental Facility , https://doi. org/10. 17180/MAQZ-V844,access on 2 september 2022, and in particular  Patrice Congar ,  Aurélien Raynaud ,  Jean-François Alkombre , and  Gwendoline Morin. We thank very much the Foch foundation and the foundation pour la recherche médicale. We also thank  Polly Gobin ,  Simon Jarjoura , and  Helene Le Gal.
The discretization of the spatial operators in the Ginzburg-Landau model is handled with second-order P2 continuous elements using the code FreeFEM  (Hecht 2012) , the source code used here having been adapted from  Sipp, de Pando & Schmid (2020). The mesh is uniform with x = 0. 05 and extends over x ∈ [-30, 100], well enough for discretizing the regions where the instability term is positive (μ(x) > 0 roughly for |x| ≤ 20). The optimal forcings/responses were obtained using Arpack (Lehoucq,  Sorensen & Yang 1998) combined with a direct LU-solver  (Amestoy et al. 2019)  for the matrix inverses. In order to obtain a model whose dynamics varies periodically, we choose to make μ 0 time-dependent in the following manner: μ 0 (t) = μ0 + A μ 0 sin(ω 0 t -π/2), (3. 4) where μ0 is the average value, A μ 0 is the amplitude and ω 0 is the frequency for the oscillation of μ 0 (t).
Flow around a squared-section cylinder at Re = 22 000 In this section, we apply the tools presented previously to the turbulent flow around a squared-section cylinder, at Reynolds number Re = U ∞ D/ν = 22 000, where D is the cylinder's diameter and U ∞ the incoming uniform velocity. These two reference scales are used to non-dimensionalize all quantities in the following. We recall that this flow field has two clear distinct features: first, a low-frequency periodic VS motion; and second, high-frequency modes in the fin shear layers arising from the KH instability mechanisms (see figure  1 ). The dataset used throughout the paper came from a direst numerical simulation run, generated by the FastS code, developed by ONERA, which is a highly optimized solver for high-performance computing clusters, solving the three-dimensional compressible Navier-Stokes equations  (Dandois, Mary & Brion 2018).
The code is run at a low inflow Mach number, M = 0. 1, to be close to an incompressible flow regime. The spatial discretization used in the solver corresponds to a second-order accurate finite-volume method based on a modification of the AUSM+(P) scheme  (Mary & Sagaut 2002). The time integration is handled with a second-order-accurate backward scheme of Gear, with a time step of 3. 3 × 10 -4. The size of the simulated time window (after an initial transitory phase was convected away) was of around 300 time units, corresponding to approximately 40 VS periods. The spatial domain for the direct numerical simulation consists of a circle of diameter 100. This domain is discretized with a mesh built by extruding a two-dimensional mesh, of around 255 × 10 3 cells, clustered around the cylinder, along four diameters in the span, discretized with 960 equally spaced planes.
The QS resolvent results We now present the results of the QS resolvent analysis. Those results were produced by discretizing the linear Navier-Stokes equations (4. 3a-c) with the finite-element method in the open-source software FreeFEM++  (Hecht 2012 ). The refinement of the mesh was similar to the spatial discretization of a longitudinal plane in the direct numerical simulation. Moreover, since the focus of the present work is on the spanwise-averaged fields, we only looked at spanwise-invariant modes. To deal with high-Reynolds-number flows, we employ a second-order streamline-upwind Petrov-Galerkin method  (Brooks & Hughes 1982; Franceschini, Sipp & Marquet 2020). The resolvent modes are obtained by solving the eigenvalue problem (2. 17) using ARPACK, interfaced with FreeFEM++. We recall that the inner product used for the energy gain definition (γ ) is the same as that used for the SPOD and corresponds to the integration of the velocity fields over Ω (see green windows in figure  15b ).
Accepted manuscript / Final version When analyzing the 3D structure of pVHL, three distinct pVHL interaction interfaces have been described engaging in exclusive interactions  [7]. The α domain binds elongins and Cullin2 while the β sheet domain of pVHL extending from residues 63 to 157 binds and participates to the polyubiquitination and ultimately targets HIF-α for proteasomal degradation  [8]. Among the 1230 mutations listed in the VHL database (http://umd. be/VHL) some mutants still remain biochemically uncharacterized  [3]. Referred families diagnosed with P81S mutation linked to low penetrance Type 1 von Hippel-Lindau disease and a mild phenotype  [9]   [10]. A previous study investigating defects Type 2C associated mutant protein demonstrated at the molecular level the deleterious role involvement of pVHL L188V , pVHL P81S/L188V , pVHL V84L.
Wound healing assay Cells were cultured at 37°C in a humidified atmosphere containing 5% CO2 until confluence in culture-inserts 2 wells (Ibidi-Cell in focus; Clinisciences, Nanterre, France). Cells were treated or not with 10µg/ml mitomycin C (Sigma-Aldrich). Cell migration was then recorded every 15 min for 24 hours using an Axiovert 200M microscope equipped with a LD Plan-Neofluar 20×/0. 4 Ph2 lens and an AxioCam MRm camera under the control of the ZEN 2012 software (Zeiss). The wound area was measured using the Simple PCI6 software (Hamamatsu Photonics, Massy, France). The analysis of cell migration during the assay was performed with the MRI Wound Healing Tool (http://dev. mri. cnrs. fr/projects/imagejmacros/wiki/Wound_Healing_Tool) using Image J software.
Discussion In this study, we described the workup of a family carrying a germline missense mutation c. 241C>T, p. Pro81Ser (P81S) with carriers' members : a 37-year-old adult diagnosed with a ccRCC and some family members carrying the mutations who were asymptomatic. Moreover, the P81S in the VHL gene has been identified in 4 other French families in the PREDIR molecular genetics laboratories network (https://predir. org/View/accueil. aspx). One proband had a ccRCC at a young age (28 years), 3 others had pheochromocytoma or paraganglioma at older ages (68 and 70 years for two of them, age unspecified for the third case). Other proline mutations have been reported in databases as well, including Proline in positions 2, 86, 97 and converted in non-polar residue (Leu, Ala), polar (Ser) or basic amino acid residues (Arg, His).
Data collection For each patient, the Parker functional score  [21, 23, 24]  was determined at emergency room admission. A telephone interview was conducted to determine the patient's status at last follow-up and to ask whether there had been a contralateral fracture. The other data were taken from the pre-anaesthesia visit records and included the main risk factors for bilateral hip fractures identified in the literature, i. e. , age, sex, ASA score  [25] , comorbidities, visual disturbances  [26] , neurological abnormalities  [16] , dementia  [26] , alcohol abuse, and use of psychoactive agents  [26, 27].
Statistical analysis An unconditional logistic regression model was built (nonparametric analysis). To identify risk factors for a second hip fracture, we compared the log-likelihoods of nested models (likelihood ratio test). Variables for which the likelihood ratio test produced a level of significance below 10% and for which the relative frequency of the least frequent modality was not less than 10% (categorical variables) were selected for the multivariate analysis. For the multivariate model, we entered the variables for which the likelihood ratio test produced a level of significance of less than 5%. All the statistical analyses were done using SPSS 18. 0 software (SPSS Inc. Chicago, IL, USA).
GC-MS was associated with 1 H NMR and 13 C NMR to obtain a complete photodegradation mechanism. Before UV irradiation, two chromatogram peaks were obtained, due to the two fenvalerate isomers. Under irradiation, both peaks decreased, and new peaks appeared, corresponding to photoproduct formation. After a 12-to 13-h irradiation, 99. 39 % of fenvalerate was degraded with a mean rate constant of 0. 305 h -1. The chemical structure of the formed photoproducts was identified, either by using the National Institute of Standards and Technology (NIST) mass spectral database or by interpreting the mass spectra. Finally, a detailed mechanism was proposed for fenvalerate photodegradation.
Study of fenvalerate photolysis by 1 H NMR in CD3CN The fenvalerate molecular structure, carrying the proton chemical shifts, evaluated with the Chem 1 H NMR Estimation software is given in Figure  1 , which served as a reference to identify the probable cleavage sites of bonds and photoproducts formed during the fenvalerate photolysis under UV irradiation. We remember that 1 H NMR spectroscopy was also applied by Yassine et al. (2018) to study the photodecomposition of novel oral anticoagulants dabigatran, rivaroxaban, and apixaban in different aqueous matrices. The 1 H NMR spectra of fenvalerate in CD3CN (γ = 58. 8 μg ml -1 ), performed during its photolysis with the solar simulator (xenon lamp) at different irradiation times, are presented in Figure  2. Fenvalerate solutions were irradiated for 18 hours, and their irradiation produced important variations of proton signal intensity.
202 These fenvalerate photoproducts were photodegraded and tentatively identified by means of their mass spectra (MS) or by using the NIST MS database (NIST 08, Software Version 2. 0f, 2008; Standard Reference Data Program). The identification of potential transformation products was based on the absence in blanks and controls, % NIST match, and retention time. T a b l e 3  Twelve of the eighteen formed photoproducts were clearly identified. They are listed in Table  3  with their retention times, MS fragments, and chemical structures. In contrast, six other fenvalerate photoproducts were not found in the NIST MS database. Therefore, their identification was mainly based on MS interpretation and MS fragmentation pathways (Fig. 9A-D ). By means of MS interpretation, it was possible to identify these photoproducts whose structures are summarized in Table  4.
GC-MS detection and identification of fourteen photoproducts formed during fenvalerate photolysis, using the NIST MS database Comparing our work to literature data, we found that some fenvalerate photoproducts were already identified, such as: 1-chloro-4-isobutyl benzene, 3-phenoxybenzaldehyde, 4-phenoxybenzaldehyde, 3-phenoxyphenylacetonitrile, 4-phenoxyphenylacetonitrile, 3-phenoxybenzoic acid, and 4-phenoxybenzoic acid. Two cyanohydrin isomers were also found in the literature, cyano-3phenoxyphenylmethanol  19  and cyano-4-phenoxyphenylmethanol. 32 Some of these fenvalerate photoproducts were determined in various studies, such as in n-hexane and methanol/water (50:50 V/V) by GC-MS,  19  in acetonitrile by UV/ozone-GC-MS,  33  by vacuum UV photoionization aerosol time-of-flight mass spectrometer (VUV-ATOF-MS) and NMR and MS analysis,  34  and by Fouriertransform infrared spectroscopy (FTIR), followed by GC-MS.
To establish the role and effects of these photoproducts, we studied the fenvalerate photolysis by 1 H NMR,  13  C NMR, and GC-MS to better understand the environmental fate of this insecticide. The combination of these three methods allowed us to establish the formation of eighteen fenvalerate photoproducts obtained under simulated sunlight irradiation and propose a probable mechanism for the photodegradation of this insecticide. Also, we were able to determine four homolytic cleavage sites, yielding the formation of intermediate radicals. Moreover, we formally identified twelve fenvalerate photoproducts using the NIST database and, afterwards, the other remaining photoproducts through mass spectra interpretation. The results of this work demonstrated the interest of combining the three methods to identify and possibly quantify the fenvalerate photoproducts formed under sunlight irradiation.
The following variables were extracted from the EORP registry database: body mass index (BMI)categorized in two groups as overweight (BMI≥25 Kg/m 2 ) or obese (BMI≥30 Kg/m 2 ); hypertension (HT); and diabetes mellitus (DM). BMI was calculated from height and weight registered in the "physical characteristics" section of the CRF. Although no specific definition for HT or DM was provided, the investigators were expected to follow current guidelines (8),(  9 ), (10).
05) and a significance level of 0. 05 was required for a variable to stay in the model (SLSTAY=0. 05). No interaction was tested. To verify that the models were optimal, Hosmer and Lemeshow Goodness-of-Fit test and percent concordant were calculated. A two-sided p<0. 05 was considered statistically significant. All analyses were performed using SAS statistical software version 9. The study cohort comprised 1 739 adult patients with HCM; 648 patients (37%) were hypertensive, 176 (10%) were diabetic (type I: 13 and type II: 163), and 1043 (60%) were overweight (n=683, 39%) or obese (n=360 21%). Seventy-five (4%) were both hypertensive and diabetic, 147 (9%) both hypertensive and obese, 13 (1%) diabetic and obese and 47 (3%) had all three risk factors; 816 patients (47%) had none of these three risk factors reported.
Risk factors and prognosis As this was a cross-sectional analysis of the EORP registry, it is not possible to determine the influence of common risk factors on outcomes in HCM. Nevertheless, as in the general population (  16 ), a history of stroke was more frequent in patients with hypertension and diabetes and AF, particularly in older patients with diabetes  (17). In addition, LV systolic function was more severely impaired in genotype-positive hypertensive patients and genotype-positive obese patients, a relevant finding as evolution to systolic dysfunction is an ominous prognostic feature in HCM that occurs in 5-10% of patients. (18). These observations highlight the need for stricter control of these risk factors in HCM patients. Non-diabetic patients had more ICDs implanted at baseline; recent data reporting higher 15-year mortality (4), has shown no differences in sudden cardiac death rate or appropriate ICD therapy in diabetic patients with HCM.
Limitations In common with other registry-based studies with voluntary participation, selection bias can be present due to inclusion of more severe/symptomatic patients from referral centres and the cohort might not be fully representative of the overall European HCM patient population. Also in common with other registry-based studies, each centre was expected to follow contemporary guidelines for the correct definition of HT and DM, which always has the potential to introduce a level of heterogeneity in data collection. The cross-sectional nature of the data prevents further assessment of the prognostic impact of the analysed risk factors. Ethnicity or coronary artery disease data were not available, as these parameters were not included as part of the study design and as such not systematically collected on the registry's CRF.
Acknowledgments EORP  Oversight Committee , The Registry Executive Committee of the  EURObservational Research Programme (EORP). Data collection was conducted by the EORP department from the  ESC  by  Rachid Mir Hassaine  as Clinical Project Manager,  Emanuela Fiorucci ,  Myriam Glemot  and  Patti-Ann McNeill  as Project Officers,  Marème Konté  and  Sebastien Authier  as Data Managers. Statistical analyses were performed by  Cécile Laroche. Overall activities were coordinated and supervised by  Doctor Aldo P. Maggioni  (EORP Scientific Coordinator).
We find evidence for a weak, but highly variable jet component at mid-infrared wavelengths. We also report the long-term optical light curve of the source and find that the quiescent ¢ i -band magnitude is 21. 39 ± 0. 15 mag. Furthermore, we discuss how previous estimates of the system parameters of the source are based on various incorrect assumptions, and so are likely to be inaccurate. By comparing our GRS 1716-249 data set to those of other outbursting black hole X-ray binaries, we find that while GRS 1716-249 shows similar X-ray behavior, it is noticeably optically fainter, if the literature distance of 2. 4 kpc is adopted. Using several lines of reasoning, we argue that the source distance is further than previously assumed in the literature, likely within 4-17 kpc, with a most likely range of ∼4-8 kpc.
LCO Optical Data We monitored GRS 1716-249 during its 2016-2017 outburst extensively with the Las Cumbres Observatory (LCO) between 2017 January 28 and October 21 (MJD 57781-58046). Observations were made using the 1 m LCO telescopes at Siding Spring Observatory, Australia, Cerro Tololo Inter-American Observatory, Chile, and the South African Astronomical Observatory (SAAO), South Africa, as well as the 2 m Faulkes Telescopes at Haleakala Observatory, Maui, Hawai'i, USA and Siding Spring Observatory, Australia. The source was also monitored during quiescence, before and after the 2016-2017 outburst, for 11 yr since 2006 February, as part of an ongoing monitoring campaign of ∼50 low-mass X-ray binaries (LMXB) coordinated by the Faulkes Telescope Project  (Lewis et al. 2008; Lewis 2018). Imaging data were primarily taken in the Sloan Digital Sky Survey (SDSS) g′, r′, i′, and the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) Y-band filters, with some data also taken in Bessel B and V bands.