dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis For longitudinal survival analysis, neurons that died during the imaging interval were assigned a survival time (the period between transfection and their disappearance from an image). These event times were used to generate exponential cumulative survival curves in JMP statistical software. Survival curves describe the risk of death for single cells in the group being longitudinally imaged. To determine differences in the survival curves, they were then analyzed for statistical significance by the log-rank test as described  (Moruno-Manchon et al. , 2017; Moruno Manchon et al. , 2015; Moruno-Manchon et al. , 2018). To compare differences across two groups, the groups were analyzed with Student's t-test. Differences across multiple groups were analyzed with one-way ANOVA.
1b-d ), changed its conformation compared with hTfR1 (Fig. 3d ). In the case of hTfR1  28  , Glu294 from this loop forms a salt bridge with Lys169 of GP1 MACV (Fig. 3e ), and in the case of sAD an alternative salt bridge with Lys169 is formed with Glu340 from αII-2 instead (Fig. 3e ). Overall, sAD mostly preserves the native structure of the TfR1-apical domain and shows remarkably broad reactivity with GP1s from NW mammarenaviruses. showing the structure of the complex between sAD and GP1 MACV , in blue and white, respectively, superimposed on the hTfR1/GP1 MACV complex (PDB ID: 3KAS), colored orange and gray, respectively. The long loop that connects strands βII-6 and βII-7 changes position in sAD compared to hTfR1 and is highlighted in green (sAD). This loop in hTfR1 originally included residues 301-326 (pink), which were eliminated from sAD.
Crystals were then successively cryo-protected using 20% (v/v) MPD in reservoir solution before flash cooling in liquid nitrogen. Data collection, structure solution, and refinement. X-ray diffraction data were collected at the European Synchrotron Radiation Facility (ESRF) beamlines ID30B using a Pilatus 6M-F detector at 100 K. Data to 2. 7 Å in a tetragonal space group were collected. HKL2000  45  was used to index, integrate, and scale the data. Phaser  46  was used to obtain a molecular replacement solution using the structure of GP1 MACV in complex with the apical domain of hTfR1 (PDB: 3KAS) as a search model. The crystal belonged to a tetragonal P4 3 22 space group and contained four sAD/GP1 complexes in the ASU. The model was manually fitted into electron density maps using Coot 47 and refined using Phenix Refine 46 in an iterative fashion.
Acknowledgements The genes encoding the GPC from WWAV and MACV were a kind gift of  Dr. Hyeryun Choe. Purified H-ferritin was a kind gift of  Dr. Beatrice Vallone. Ron Diskin  is the incumbent of the Tauro career development chair in biomedical research. We are grateful to  Gotthard Guillaume  at the  ESRF, Grenoble, France  for providing assistance in using beamline ID30B. We thank  Dr. Yosef Scolnik  for his kind help in recording CD spectra,  Dr. Emmanuel Levy  for granting us access to his confocal microscope,  Mr. Daniel J. Deer  for technical support, and  Dr. Deborah Fass  for providing critical comments and suggestions. We thank  Miles Carroll  and  Roger Hewson  ( Public Health England, Porton Down, UK ) for providing the Machupo virus, Carvallo strain. BSL-4 experiments using MACV were supported by the  Pasteur-Weizmann joint research program  collaborative research grant  S-CR18069-02  to M.
M. and R. D. BSL-4 operations of the  Galveston National Laboratory and Department of Microbiology  and Immunology are supported by  NIAID/NIH  Grant No. UC7AI094660  to T. W. G. The lab of R. D. is supported by research grants from the  Enoch Foundation , from the  Abramson Family Center for Young Scientists , from  Ms. Rudolfine Steindling , and from the  I-CORE Program of the Planning and Budgeting Committee  and  The Israel Science Foundation  (grants no. 1775/12  and  682/16 ).
Statistical analysis Study participants were selected by consecutive sampling and described only once, at their first visit, but acceptability rates were calculated on the total number of proposed viral tests, each proposal counting as an individual testing opportunity. Only the first individual visit was included in the analytical sample, which represents the denominator for all prevalence values. The acceptability of the testing strategy was evaluated by statistical analysis of questionnaires and of activity data in each center. Nominal variables were presented as counts and their corresponding percentages. All statistical analyses were carried out using STATA (version 13. 1; StataCorp, College station, TX).
Acknowledgements We thank all study participants, as well as all non-medical staff from the three centers. We also acknowledge their efforts in data collection. Prof. S Pol  has received consulting and lecturing fees from  Bristol-Myers Squibb ,  Janssen ,  Gilead ,  Roche ,  MSD, MYR_Pharma, Shionogi, Biotest and Abbvie , and grants from  Bristol-Myers Squibb ,  Gilead ,  Roche  and  MSD , none related to the  ANRS CUBE study. The other authors declared no competing interest.
Data collection and definitions Data were collected by the local investigator for each participating centre, using patient record files. Baseline patients' characteristics before pregnancy included age, parity, primary cardiac diagnosis and concomitant cardiac or valvular disease, prior interventions, cardiovascular risk factors, New York Heart Association (NYHA) functional classification, cardiac medication and if available, echocardiographic parameters. The International Monetary Fund classification was used to define a participating country as emerging or advanced.
Statistical analysis Baseline characteristics and outcomes were compared between women with prior hypertension and without prior hypertension, as well as between repaired and unrepaired CoA and between women with and without bicuspid aortic valve (BAV). Additionally, the outcomes of MACE and hypertensive disorders were evaluated after dividing the cohort in three risk profiles: (1) no prior hypertension and repaired CoA, (2) prior hypertension or unrepaired CoA and (3) both prior hypertension and unrepaired CoA. Normality of continuous data was examined using QQ-plots and subsequently data are presented as mean and SD, or as median and IQR. Differences between the groups were calculated using Student's t-tests, or Mann-Whitney U tests for skewed data. Categorical data are presented as percentages and compared using χ 2 tests.
Univariate logistic regression was used to identify associations with MACE or hypertensive disorders. The number of events (n=13) did not allow for multivariate analysis. Missing data were handled with multiple imputation. A two-sided p value <0. 05 was considered to be statistically significant. There were no corrections for multiple testing performed. All analyses and imputations were performed using IBM SPSS Statistics V. 25. 0 (IBM).
Study design A prospective, open, nonrandomized PK study was performed at the National Transplant Center in Norway, Oslo University Hospital -Rikshospitalet, from December 2015 to May 2017. Renal transplant recipients older than 18 years using twice-daily Tac (Prograf; Astellas Pharma Ltd. , Chertsey, UK) without concomitant drugs known to interact with Tac PK were included. The study was conducted in accordance with ethical principles in the Declaration of Helsinki, guidelines for Good Clinical Practice, and was approved by the Norwegian Medicine Agency (EudraCT number: 2015-004734-10) and the local ethic committee (reference number 2015/2098). All patients received verbal and written information and signed an informed consent before entering the study.
AUC determined by limited sampling strategies Different LSS were used to predict individual Tac AUC using a previously developed and validated nonparametric population PK model as Bayesian estimator. 21, 22 The model was adapted to also handle the flatter real-life nonfasting and evening-time PK profiles obtained in the present study (see Supplementary Material). The makeAUC function in the Pmetrics package for R (linear model) was used to calculate model-derived AUC 0-tau values over respective dose interval. 23 The predictive performance for AUC determination when using the Bayesian estimator derived from the adapted model, used in combination with different LSS, was evaluated in a validation dataset, not previously used for developing the adapted model, by comparing the different LSS-derived AUCs with respective trapezoidal determined AUCs.
The LSS tested included the validated sampling times of 0, 1, and 3 hours, as previously published  12  and single trough concentrations (C 0 for AUC 0-12 and C 12 for AUC 12-24 ). In addition, the multiple model optimal sample time function (MMopt) in Pmetrics,  23  weighted for AUC, was used to determine the best LSS using three optimal sampling times for the real-life nonfasting and evening PK-profiles. The MMopt function in the Pmetrics package for R was used to determine the sampling times that minimize the risk of misrepresenting the patients as the wrong set of support points in the model (i. e. , estimating the wrong set of individual PK parameters). 24.
Patients Thirty-one stable renal transplant recipients (74% men) were prospectively enrolled in the study between 13 and 54 days post-transplant. Demographic data and patient characteristics are presented in Table  1 , and were considered representative of our kidney transplant population. All included patients, except one, received concomitant MMF. Four (13%) were immunological high-risk patients and three patients (10%) were CYP3A5 expressers (all CYP3A5*1/*3). No overall difference in Tac PK was observed between CYP3A5 expressers and nonexpressers. All patients performed 2 successive 12-hour PK investigations (morning and evening doses), and 14 patients (45%) also repeated these PK investigations within 7-28 days (median 14 days; Figure  1 ). A total of ninety 12-hour PK profiles: 45 from the morning dose and 45 from the successive evening dose were obtained in the present study.
CP was 0. 854 (95% CI: 0. 607-1. 0), which indicates that < 15% of the predicted AUCs had an error greater than ±15%. Using MMopt sampling times (1, 3, and 6 hours postdose) for the slow-absorption profiles (fasting evening dose) resulted in predicted AUC 12-24 of accepted agreement (Table  5 ) in the validation dataset (n = 3). A single trough concentration did not predict neither AUC 0-12 nor AUC 12-24 within the acceptance limit. Real-life nonfasting dose administration. The LSS with samples obtained at 0, 1, and 3 hours postdose or a single trough concentration did not show acceptable agreement for real-life nonfasting AUC predictions (Table  5 ). Using the MMopt determined sampling times (1, 3, and 6 hours postdose) for predictions of both AUC 0-12 and AUC 12-24 showed overall better agreement with trapezoidal AUC 0- 12 and AUC 12-24 compared with LSS 0, 1, and 3 hours for fasting conditions: CCC was 0.
946 (95% CI: 0. 897-0. 995), Reference AUC calculated using the trapezoidal method. Bold type indicates better agreement than the prespecified boundaries: CCC ≥ 0. 9, TDI ≤ 15, and CP ≥ 0. 85. AUC, area under the concentration vs. time curve; AUC 0-12 , area under the concentration vs. time curve after the morning dose; AUC 12-24 , area under the concentration vs. time curve after the evening dose; CCC, concordance correlation coefficient; CI, confidence interval; CP, coverage probability index; LSS, limited sampling strategy; NA, not available (too few samples -see Supplementary Digital Content 1); TDI, total deviation index. a AUC calculated using the previous developed population PK model. b AUC calculated in the validation dataset using the adapted version of the previous developed population PK model.
Based on the current data, we evaluated potentially clinical applicable sampling strategies for predictions of AUC following the evening dose (AUC 12-24 ; data not shown). Samples closest to the MMopt sampling times (1, 3, and 6 hours) were tested, but samples during sleep were avoided (between 11 pm and 7 am). In this regard, the best strategy with the highest agreement in C-statistics was to use samples 1, 2, and 10 hours after the evening dose (e. g. , at 10 pm and 11 pm and again at 7 am the next morning when utilizing a 9 am to 9 pm dosing scheme (CCC was 0. 895 (95% CI: 0. 795-0. 995), TDI 16. 6% (95% CI: 12. 0-21. 1), and CP 0. 816 (95% CI: 0. 620-1. 0)). Once-daily Tac is suggested to increase adherence. 36, 37 9] [40]  Most of the patients at our transplant center take their Tac dose without respecting the ±2-hour fasting rule, and as clearly shown in the present study, the high peaks following administration of the twice-daily Tac formulation will then be avoided.
Day And Night Tacrolimus Pharmacokinetics Gustavsen et al. this study is performed in the early post-transplant phase, and because Tac PK change during the first 6-12 months after transplantation,  15, 35, 41  the results from the present study should be extrapolated with care to the long-term follow-up situation. Second, even though the validation metrics of the adapted population PK model were convincing, relatively few PK-profiles of the different dose scenarios were included in the development and validation datasets (see Supplementary Material), so the results have to be interpreted with caution. The numbers of patients with complete dual data, performing both fasting and real-life nonfasting investigations, are very low (n = 6). Finally, only the immediate-release formulation of Tac was investigated.
Thanks to rapidly increasing computing power, Computational Fluid Dynamics (CFD) has become a powerful tool in engineering design and optimization. With adequate numerical methods, CFD provides accurate, cheap and quick results compared to a full-scale experimental study  [8] [9]. The aim of our study is to use CFD in order to provide an accurate description of the flow and therefore the best conditions for an optimization. Different commercial CFD package can be used, such as ANSYS CFX and Fluent  [5] [6]. In this study, we will use ANSYS Fluent for simulations of vertical turbines. We will introduce background related to HKT, define the different parameters and more adequate turbulence models. Finally, simulations with different parameters, geometries and configurations will be conducted.
Mesh The mesh is made using Meshing software integrated in Ansys Workbench. Geometry already shared in geometry bodies. We create sizing on each edge, inflations around the walls of the blades and the axis of rotation and we apply the meshing method of faces to create a mesh in quadrilaterals (figure  1. d). Finally, we add the named selections for boundaries and the interface between rotating and fixed part  [16]. Once the mesh created, we import it under Fluent and adjust the various parameters before starting the computation: First to create an interface between the rotating part and the fixed part: "Mesh Interface". Then we can proceed as: -Unsteady regime, -We choose the turbulence model,  - (2eqn) in "Model" and SST in "  - model". -In "Cell Zone Conditions", we put the desired rotation for the rotating zone by checking the "Mesh Motion" box and choose the axis of rotation and the angular velocity.
Radiopharmacokinetic modelling ICRP Publication 137  [9]  reports new biokinetic data of 223 Ra and its progeny. Figure  1   Ra and its progeny, and according to ICRP 137  [9] , each biokinetic model of the nuclides in the radium decay chain (including 223 Ra) was expanded for the five complementary compartments mentioned above; all transfer rates were taken from ICRP 137  [9]. For fraction. The masses of the above mentioned organs and tissues were taken from ICRP  [13]. The transfer rate from the blood compartment to the Other Soft Tissue was reduced accordingly. Fig. 1  Systemic model of 223 Ra, according to ICRP 137 (2017)  [9]  Implementation of the complete compartmental model in SAAM II software The biokinetics of 223 Ra and its progeny were described by a system of first-order linear ordinary differential equations, which were numerically solved by using the commercial software package SAAM II  [16].
Organ absorbed and equivalent doses The importance of patient dosimetry in radionuclide therapy and its limitations have been recognized by many authors and reviewed by Lassmann and Eberlein  [27]. The present work is reporting recent developments combining new biokinetic and dosimetric modelling. Ra progeny to bone lesion-absorbed doses was found in the study of Murray et al. [25]. The calculated dose coefficients for bone endosteum, red marrow and colon were lower than clinical data reported by Yoshida et al. [5]  and Chittenden et al. [7]. The authors of these studies evaluated clinical imaging data, and derived the cumulated activities through region of interests (ROIs). For the dosimetry, the dosimetric tool OLINDA/EXM  [28]  was used, software based on the MIRD algorithm and SAFs derived on mathematical phantoms.
We based our method on interviews with key informants at international, national and local levels. Information was collected during meetings involving one, two or many of the following actor categories: REDD+ experts, scientists, policy entrepreneurs, donors and technical partners. Data collection on the case studies was completed in person by the first author of this paper from May to August 2014 across the three field sites; a couple of update processes were made after this period of field work. Additionally, based also on our experience in these study areas, we formulated hypotheses and analyzed results from the field. The substantial experience of the first author as a 'scientific expert' with institutions; at both local and international levels, was useful for identifying the correct roles of many of the organizations involved in REDD+ processes and their influence on the production and dissemination of knowledge within climate management.
Acknowledgements The authors would like to acknowledge the insightful comments of anonymous colleagues on earlier drafts of this manuscript. Data were collected via the  Congo Basin Forest and Climate Change Adaptation and Mitigation (COBAM)  project, within the PACEBco programs, funded by the  African Development Bank (AfDB)  and  Economic Commission of Central African States (ECCAS). The funding partners that have supported this research include the  Norwegian Agency for Development Cooperation (Norad) , the  International Climate Initiative (IKI)  of the  German Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety (BMUB)  and the  CGIAR Research Program on Forests, Trees and Agroforestry (CRP-FTA) , with financial support from  CGIAR Fund donors. The views expressed here are those of the authors and do not necessarily reflect the views or policies of any organization referred to in this paper.
However, these insulin analogs are typically more expensive than older prandial insulins such as RHI. 8] [19]  RHI is much less expensive than many rapid-acting insulin analogs,  17, 18  and trial data suggest that patients with T2D may be appropriately controlled on human insulin. 17, 20 Nonetheless, while rapidacting insulin products are approved for use in insulin pumps,  21, 22  RHI is not approved or recommended for pump use according to manufacturers' drug labeling. 22, 23  novel insulin delivery device (IDD) has been developed to deliver rapid-acting insulin over *3 days as a continuous infusion (basal dose) in addition to bolus doses on demand for adults with T2D (Fig. 1 ). The IDD's ability to deliver shortacting human insulin was also evaluated in this study. The aim of this exploratory pilot study was to assess the IDD performance, safety, effectiveness, and usability for providing rapid-acting or RHI over a total of 6 days in a clinic setting for adults with T2D.
Statistical analyses Since the study was exploratory, no formal sample size or statistical power calculation was performed. We summarized demographic and clinical characteristics of participants as well as glycemic and other outcomes using descriptive analyses, including frequencies and percentages for categorical variables and meanstandard deviation (SD) or median (range) for variables measured on the continuous or interval scale. Statistical comparisons of CGM or BGM data between the MDI and IDD study periods were made using linear mixed effect models to evaluate the period effects on glycemic measures. In the models, the participant was a random effect, and study period, time point, and insulin type were fixed effects. Two-way and three-way interactions between fixed effects were investigated; and interactions were removed from the model as no significant interactions were identified (all P ‡ 0.
05). Models were also used to compute estimated mean and corresponding two-sided 95% confidence intervals (CIs) for responses of interest by study period. Bootstrapping was used to compute two-sided 95% CIs for responses per day within each period because of the imbalance in the number of days per period. A similar model was used to compare participant-reported questionnaire results between the two insulin cohorts. Statistical significance was defined as P < 0. 05, and adjustments for multiple comparisons were made when appropriate using Tukey's method. Analyses were carried out using the R language for statistical computing (version 3. 2. 0 or newer, https://www. r-project. org).
Discussion In the context of this small pilot study, the use of the investigational IDD appeared to be safe and effective, delivering insulin as intended for adults with T2D. During the in-clinic IDD period, fasting BG, 24-h mean BG, and time in glycemic range were all significantly improved compared with values observed during the prior in-clinic period of MDI therapy, with no change in total daily insulin dose. Furthermore, while using the IDD, participants experienced significantly less time in the hyperglycemic range, with no difference in time spent in hypoglycemia, compared with the MDI period. The MAGE results were also improved during  a Fasting BG data were taken using the blood glucose meter from a single fingerstick value before breakfast. All other glycemic measures were recorded using the continuous glucose monitor.
Numerical computation of intensity asymmetries The system comprised of Equations (  23 ) to (25) constitutes a closed, semi-analytical form of the intensity power spectrum. The equilibrium quantities involved, as well as the turbulent quantities needed as inputs for the analytical model of turbulence, are all extracted from a 3D hydrodynamic simulation of the solar atmosphere computed using the CO 5 BOLD code, which, after horizontal and time average, we patched on top of a 1D solar model provided by the evolutionary code CESTAM (for more detail, see  Manchon et al. 2018). Once the intensity power spectrum is calculated, we fit the obtained synthetic line profiles using the following formula  (Nigam & Kosovichev 1998 ) P(ω) = H 0 (1 + Bx) 2 + B 2 1 + x 2 , ( 26 ) where x = 2(ω -ω 0 )/Γ ω 0 is the reduced pulsation frequency, ω 0 the angular eigenfrequency and Γ ω 0 the linewidth of the mode.
Observational datasets To compare the asymmetries predicted by the model with observations, we use the same observational data as for the velocity power spectrum in  Philidet et al. (2020). These data points were extracted from the spectrum analysis conducted by  Barban et al. (2004)  on observations made by the GONG network. We used these observations because they are particularly fitting for the study of asymmetry reversal: indeed, the GONG network provides with velocity and intensity measurements simultaneously. We recall that the spectral analysis of  Barban et al. (2004)  considers non-radial modes (15 < l < 50), whereas our model was developed for radial modes. However, the dependence of asymmetry on angular degree is very weak, and the asymmetry observations collapse to a slowly varying function of frequency, so long as l 100 (see e.