dataesr/scientific-paragraphs-categorization huggingface dataset:
An independent run of molecular dynamics was performed in order to improve the statistics at the interfaces and to compute realistic interactions. The system was built with the CHARMM-GUI/Quick MD simulator module  62, 63. Only the interfaces (head/head, coiled-coil/head) of the model were relaxed in a box containing explicit water (TIP3P) and salt (150 mM KCl) in the CHARMM36m 66 force field. 10 ns of simulation was performed in GROMACS (version 2018. 3)  67. A final run of real space refinement was performed against map 1. This method, combining steps of molecular dynamics minimization at the interfaces of complexes and final steps of refinement allows to compute realistic interactions for the side chains that may not be fully defined in density. The strength of this calculation routine is to avoid bias due to molecular dynamics simulations which are independent to refinement in the map and focused on the interfaces.
Acknowledgements This work was funded by grants  NIH   RM1GM131981-01  (J. A. S. and A. H. ),  ANR-21-CE11-0022-01  (A. H. ), and  NIH   R01GM33289  (J. A. S. ). N. N. acknowledges postdoctoral funding from  AHA  ( 908934 ) and  Stanford MCHRI  ( 1220552-140-DHPEU ). D. P. acknowledges postdoctoral funding from  AHA.
Data availability The atomic model is available in the PDB  72  under the code 8ACT [PDB DOI]. The cryo-EM maps Map 1 and Map 2 are available in the EMDB database  73  under the accession numbers EMD-15353 and EMD-15354 respectively. The homology model 5TBY is available in the PDB. The homology model MS03 is available for download on the website of Jim Spudich's lab (http://spudlab. stanford. edu/homology-models/). The homology model MA1 is available for download as Supplementary Data 2 in  27.
Summary Background: Normative modeling is a statistical approach to quantify the degree to which a particular individual-level measure deviates from the pattern observed in a normative reference population. When applied to human brain morphometric measures it has the potential to inform about the significance of normative deviations for health and disease. Normative models can be implemented using a variety of algorithms that have not been systematically appraised. Methods: To address this gap, eight algorithms were compared in terms of performance and computational efficiency using brain regional morphometric data from 37,407 healthy individuals (53% female; aged 3-90 years) collated from 87 international MRI datasets. Performance was assessed with the mean absolute error (MAE) and computational efficiency was inferred from central processing unit (CPU) time.
The algorithms evaluated were Ordinary Least Squares Regression (OLSR), Bayesian Linear Regression (BLR), Generalized Additive Models for Location, Scale, and Shape (GAMLSS), Parametric Lambda, Mu, Sigma (LMS), Gaussian Process Regression (GPR), Warped Bayesian Linear Regression (WBLG), Hierarchical Bayesian Regression (HBR), and Multivariable Fractional Polynomial Regression (MFPR). Model optimization involved testing nine covariate combinations pertaining to acquisition features, parcellation software versions, and global neuroimaging measures (i. e. , total intracranial volume, mean cortical thickness, and mean cortical surface area). Findings: Statistical comparisons across models at PFDR<0•05 indicated that the MFPR-derived sex-and regionspecific models with nonlinear polynomials for age and linear effects of global measures had superior predictive accuracy; the range of the MAE of the models of regional subcortical volumes was 70-520 mm  3  and the corresponding ranges for regional cortical thickness and regional cortical surface area were 0•09-0•26 mm and 24-560 mm 2 , respectively.
Evidence before this study Normative reference values of neuroimaging measures of brain structure and function have great potential as clinical and research tools, but the models used to generate these values must be methodologically robust. We searched electronic databases for articles published in English between Jan 1, 2018, and Jan 31, 2023, using combinations of words or terms that included "normative modeling", OR "growth curves" OR "centile curves" AND terms referring to specific morphometric features. Although multiple studies employed normative models of brain morphometry, we identified a critical knowledge gap in the paucity of benchmarking statistical methods and sensitivity testing for key parameters that may influence model performance.
Samples We collated de-identified data from 87 datasets from Europe, Australia, USA, South Africa, and East Asia (appendix 1 p 2; appendix 2). Data use aligned with the policies of the ENIGMA Lifespan Working Group (https://enigma. ini. usc. edu/ongoing/enigma-lifespan/), and the policies of individual studies and national repositories. Based on the information provided in each dataset, data were further selected to include highquality neuroimaging measures (appendix 1 p 3) from participants that were free of psychiatric, medical, and neurological morbidity and cognitive impairment at the time of scanning. Only scans acquired at baseline were included from datasets with multiple scanning assessments.
Brain morphometry Acquisition protocols and scanner vendors varied across datasets (appendix 2). Morphometric feature extraction from whole-brain T1-weighted images was implemented using the standard pipelines in the FreeSurfer image analysis suite (http://surfer. nmr. mgh. harvard. edu/) (appendix 2) to yield global measures of total intracranial volume (ICV), mean cortical thickness, and mean surface area, and regional measures of cortical thickness (N=68) and cortical surface area (N=68) based on the Desikan-Killiany atlas (https://surfer. nmr. mgh. harvard. edu/fswiki/CorticalParcellation) and 14 subcortical volumetric measures based on the Aseg atlas (https://freesurfer. net/fswiki/SubcorticalSegmentation). Sex-specific normative models were developed separately for each of the 150 regional morphometry measures to accommodate sex differences in brain morphometry.
Optimization of normative models The procedures used to generate optimized sex-specific models for each brain morphometric measure are illustrated in figure  1  and consisted of the following steps: (I) Data preparation: Sex-specific subsamples of the study sample were randomly split into a training subset (80%) and a test subset (20%) stratified by scanning site. In each subset, data were mean-centered after extreme values in each site-dataset were identified and removed using the interquartile range (IQR) method. (II) Algorithm selection: The data for each morphometric measure were analyzed with the following algorithms: (1) Ordinary Least Squares Regression (OLSR) (implemented using the "lm" function in R): This is a linear regression model that aims to minimize the sum of squared differences between the observed and predicted values; (2) Bayesian Linear Regression (BLR) (implemented using the "stan" package in R): This is a linear model in which the outcome variable and the model parameters are assumed to be drawn from a probability distribution; (3) Generalized Additive Models for Location, Scale, and Shape (GAMLSS): This framework can model heteroskedasticity, non-linear effects of variables, and hierarchical structure of the data.
This algorithm was implemented using the "caret" package in R following prior recommendations;  17  (4) Parametric Lambda (λ), Mu (μ), Sigma (σ) (LMS) method: This subclass of GAMLSS assumes that the outcome variable follows the Box-Cox Cole and Green distribution. This algorithm was implemented using the "gamlss" package in R;  15  (5) Gaussian Process Regression (GPR): This is a nonparametric regression model that follows Bayesian principles, and was implemented using the "kernlab" package in R and the "sigest" function for estimating the hyperparameter sigma; (6) Warped Bayesian Linear Regression (WBLR):  18  This framework is based on Bayesian linear regression with likelihood warping and was implemented using the "PCNtoolkit" (https://github. com/amarquand/PCNtoolkit) in Python following authors' recommendations; (7) Hierarchical Bayesian Regression (HBR):  10, 12  This approach also uses Bayesian principles and is considered particularly useful when variance from multiple hierarchical levels are present, including the scanning protocol or site effects.
This algorithm was implemented using the "PCNtoolkit" (https://github. com/amarquand/PCNtoolkit) in Python following authors' recommendations; (8) Multivariable Fractional Polynomial Regression (MFPR): This algorithm enables the determination of the functional form of a predictor variable by testing a broad family of shapes and multiple turning points while simultaneously providing a good fit at the extremes of the covariates. This algorithm was implemented using the "mfp" package in R and the closed test procedure (known as RA2) was used to select the most appropriate fractional polynomial. For the HBR, WBLR, GPR, and GAMLSS, site effects were modeled as random effects as recommended by prior literature. 10, 17, 19  For all other algorithms, site-harmonization used ComBat-GAM  20  as this approach has the potential to generate models without having to recalculate model parameters for each unseen dataset.
We focused on these algorithms as they have been applied to other large normative datasets. 1, 13, 17, 19  The MAE, RMSE, and CPU time for each of the sex-and region-specific model derived from these five algorithms are shown in appendix 1 (pp 13-14) and appendix 6. Pairwise statistical comparison of the models from each algorithm indicated superior accuracy at PFDR<0•05 for the optimal MFPR-derived models. We illustrate these findings in figure  6  and appendix 1 (p 12) using the left thalamic volume and left medial orbitofrontal cortical thickness and surface area as exemplars. In addition to having better accuracy, the MFPR-derived models remained the most computationally efficient with CPU times of less than a second.
Discussion The present study undertook a comparative evaluation of eight algorithms commonly used for normative modeling using morphometric data (i. e. , 14 regional subcortical volumes, 68 measures of cortical thickness, and 68 measures of cortical surface area) from a multisite sample of 37,407 healthy individuals. Models based on the MFPR algorithm with nonlinear fractional polynomials of age and linear global neuroimaging measures emerged as optimal based on their performance and computational efficiency, the latter being an important consideration when analyzing large datasets. These models were robust to variations in sample composition with respect to age and their performance plateaued at sample sizes of ≈ 3,000. Establishing a minimum sample size requirement for normative models of brain morphometry provides a useful point of reference when evaluating prior literature or designing new studies.
A recent publication that used a smaller sample (569 healthy participants) and a narrower age range (6-40 years) suggested the HBR with site as an explanatory variable may be superior to ComBat-based site harmonization for the normative modeling of brain morphometry. 10  We found no support for this assertion, as in this study MFPR-derived models using Combat-GAM for site harmonization outperformed HBR-derived models with site as a random effect. An additional advantage of using ComBat-GAM is that it removes the requirement for calibration and model parameter adaptation every time the model is applied to data from a new site. In the HBR models, by contrast, pre-trained parameters can be used for new data if they originate from one of the sites in the training dataset  10  or under the assumption that the variation accounted for by an unseen site should align with that of the sites in the training dataset.
In later life, samples of healthy individuals are likely to include those that are more resilient to mortality and morbidity. The present study did not include an exhaustive list of potential explanatory variables. It could be argued that the inclusion of other variables, such as childhood adversity, premature birth, or socioeconomic status that are known to influence brain morphometry,  29, 30  could have further improved model performance. Exploring this possibility further could be best achieved within the context of single large-scale studies where such variables would be consistently recorded in all participants. On the other hand, the inclusion of multiple explanatory variables in the normative model itself could restrict its applicability to those datasets where all such features were assessed.
In conclusion, this study presents a detailed evaluation of the comparative performance of the key eight algorithms used for normative modeling and of the influence of key parameters pertaining to site effects, covariates, sample size, and sample composition with respect to age on model accuracy and robustness. (1) The study sample was stratified by sex and split into training (80%) and testing (20%) datasets, followed by outlier removal, and mean-centering. (2) Normative models were generated using eight different algorithms and compared in terms of accuracy and computational efficiency; (3) Explanatory variables were added to the optimal algorithm to identify the appropriate combination for optimal model performance. ICV=intracranial volume; IQR=interquartile range.
Acknowledgments We thank  Dr. Andre F. Marquand  and  Dr. Seyed Mostafa Kia , both from  Radboud University, the Netherlands , for their guidance with the HBR models. This work was supported by the computational resources and staff expertise provided by the  Advanced Research Computing at the University of British Columbia  and by the  Scientific Computing  at the  Icahn School of Medicine at Mount Sinai  (supported by the  Clinical and Translational Science Awards  grant  UL1TR004419  from the  National Center for Advancing Translational Sciences).
021. 00; Norway: Research Council of Norway: 223273; South and Eastern Norway Regional Health Authority: 2017-112, 2019-107, 2014-097, 2013-054; Russian Federation: Russian Foundation for Basic Research: 20-013-00748; Spain: Fundación Instituto de Investigación Marqués de Valdecilla: API07/011, NCT02534363, NCT0235832; Instituto de Salud Carlos III: PI14/00918, PI14/00639, PI060507, PI050427, PI020499; Sweden: Swedish Research Council: 523-2014-3467, 2017-00949, 521-2014-3487, K2007-62X-15077-04-1, K2008-62P-20597-01-3, K2010-62X-15078-07-2, K2012-61X-15078-09-3; Knut and Alice Wallenberg Foundation; UK: Medical Research Council: G0500092; USA: National Institutes of Health/Mental Health/Aging/ Child Health and Human Development/ Drug Abuse/National Center for Advancing Translational Sciences: UL1 TR000153; U24RR025736-01,.
Electronic database searches An extensive electronic search of relevant literature until December 10, 2022, with no language restrictions, was performed using the following databases: Cochrane Library's Controlled Trials Registry and Database of Systematic Reviews, PubMed/MEDLINE of the United States National Library of Medicine, Google Scholar, Excerpta Medica Database (Embase), and Scopus. The keywords used were "Randomized Controlled Trials, " "Clinical Controlled Trials, " "inguinal hernia, " "hernia repair, " "hernioplasty, " "herniorrhaphy, " "laparoscopic hernioplasty, " "reduction, " "transection, " "dissection, " and "ligation" "total extraperitoneal, " "transabdominal preperitoneal, " "seroma, " and "morbidity. " We manually checked the reference lists of articles obtained for eligible clinical trials.
Summary of findings Two authors independently assessed the evidence of the primary outcomes. We used The Grading of Recommendations Assessment, Development, and Evaluation (GRADE)  [19]. We considered the study limitations in terms of the constancy of effect, imprecision, indirectness, and publication bias. We assessed the certainty of the evidence as high, moderate, low, or very low. We used GRADEpro GDT software to prepare a summary of the findings tables.
Samples were then warmed to 37˚C in a fluorometer, and the fluorescence signal was monitored over 10 min. "+pH" indicates when buffers at pH 5. 0 or 7. 4 were added to trigger virus fusion. Data were normalized to those at the time point 0. The red line shows the virus fusion-specific R18 release at pH *5. 0. RU, relative unit. (H) R18-TOSV penetration into A549 cells at MOI 10 was recorded in real time for 90 min using the protocol in D, and triton X-100 was added at the end to induce the dequenching of all R18 molecules associated with bound and internalized virions. The data show the ratio between the fluorescence resulting from viral fusion and that associated with all virions in the cells. https://doi. org/10. 1371/journal. ppat. 1011562. g006 brief, R18-labeled virions were first allowed to attach to A549 cells on ice, and viral fusion was then triggered by the addition of buffer at pH *5.
The level of identity and similarity in amino acids supports the idea that the Gc glycoproteins of TOSV and RVFV resemble each other structurally. To further test this possibility, we utilized AlphaFold to predict the structure of the TOSV Gc ectodomain based on the information available for the RVFV Gc structure (S1 File). A bent conformation was obtained by comparison with intact RVFV virions (Protein Data Bank [PDB], 6F9F)  [16] , likely corresponding to the canonical pre-fusion orientation of the glycoprotein (Fig  7B ). The Gc AlphaFold prediction exhibited domains I, II, and III typical of its phenuivirus orthologs and other viral class-II fusion proteins. The acid-activated conformation of TOSV Gc (S2 File) was predicted using the X-ray post-fusion structure of RVFV Gc (PDB, 6EGT)  [30]  (Fig  7C ).
Cryo-electron microscopy Sucrose gradient-purified virus particles were washed in a buffer containing 10 mM HEPES, 150 mM NaCl, 1 mM EDTA, pH *7. 3, pelleted by ultracentrifugation, and fixed with 4% paraformaldehyde. Subsequently, 2. 5 μL of the fixed virion solution was applied to degassed Quantifoil R2/2 Cu grids that were discharged at 30 mA for 2 min before sample application. The sample was vitrified in liquid ethane using a Leica EM GP2 plunge freezer at 4˚C and 90% humidity, and sensor blotting from the reverse side for 3 sec. Data were acquired using Seri-alEM software on a Thermo Fisher Scientific Glacios transmission electron microscope operated at 200 kV and equipped with a Falcon 3 direct electron detector. Before data acquisition, the microscope was adjusted by a comma-free alignment in SerialEM, and the gain reference was determined.
Regions of interest were identified in low-magnification setups. For high-resolution data acquisition, the nominal magnification was 73,000, resulting in a pixel spacing of 2. 019 Å. The camera was operated in linear mode with a dose rate of 16 e-/s/pixel. The total dose was 19. 6 e-/Å 2 and was divided into 22 dose-fractions that were aligned and gain-corrected in SerialEM. Cryo-EM micrographs were analyzed using ImageJ v1. 53c (NIH). The length and width of a viral particle were determined by measuring the largest and smallest distances between peaks in density profiles or membranes on the opposite side of the viral particle.
Acknowledgments We acknowledge  Elodie Chatre  and the  Imaging Platform Platim, SFR Biosciences, Lyon , as well as  Vibor Laketa  and the  Infectious Diseases Imaging Platform (IDIP)  at the  Center for Integrative Infectious Disease Research (CIID) Heidelberg. The sand fly cell lines were supplied by the  Tick Cell Biobank at the University of Liverpool. F. K. M. S. acknowledges support from the  Scientific Service Units (SSUs)  of ISTA through resources provided by the  Electron Microscopy Facility (EMF).
This work was supported by  CellNetworks Research Group  funds and  Deutsche Forschungsgemeinschaft (DFG)  funding ( LO-2338/  fusion mechanisms. 3-1 ) and the  Agence Nationale de la Recherche (ANR)  funding (grant numbers  ANR-21-CE11-0012  and  ANR-22-CE15-0034 ), all awarded to P. -Y. L. This work was also supported by the  LABEX ECOFECT  ( ANR-11-LABX-0048 ) of  Universitede Lyon (UDL) , within the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) operated by the  ANR  and by the  RESPOND program  of the  UDL  (awarded to P. -Y. L). C. A. was supported by the  Chica and Heinz Schaller Research Group  funds,  NARSAD   2019  award, a  Fritz Thyssen Research Grant , and the  SFB1158-S02  grant. L. B-S. is supported by a  United Kingdom Biotechnology and Biological Sciences Research Council  grant ( BB/ P024270/1 ) and a  Wellcome Trust  grant ( 223743/ Z/21/Z ).
F. K. M. S acknowledges support from the Austrian Science Fund  (FWF,  P31445 ). J. K. received a salary from the  DFG  ( LO-2338/3-1 ) and then from the  ANR  ( ANR-11-LABX-0048 ). The salary of Z. M. U. was partially covered by the  DFG  ( LO-2338/3-1 ). S. K. received a salary from the  DFG  ( SFB1129 ). We are grateful to the  Chinese Scholarship Council  (CSC;  201904910701 ),  DAAD/ ANID  ( 57451854/62180003 ), the  Rufus A. Kellogg fellowship program  ( Amherst College, Massachusetts, USA)  for awarding fellowships to Q. X. , J. C. , and H. A. A. , respectively. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Using Fiji imaging processing software and home-made macros, three parameters were calculated: (1) the ratio of fluorescence intensity of image A to image B per liver area (F green/F blue), (2) the amount of lipid droplets per liver area, and (3) the surface occupied by lipid droplets per liver area. Based on these three parameters, a steatosis score was calculated. Each parameter was pondered by a specific coefficient determined empirically. Among the reference treatments tested, only the HFD-fed larvae showed a significant increase in the steatosis score calculated by our test (Figure  2 ), indicative of lipid accumulation in the liver. For ethanol exposed larvae, despite the large induction of the steatosis score, this was not significant. However, this therefore was in line with steatosis initiation following HFD feeding or ethanol consumption, as previously reported in literature  [35, 88, 94, 95].
Quantitative Analysis of Hepatic Lipid Fluorescence Signals To evaluate neutral lipid content, fluorescence images of zebrafish larvae stained with Nile red were acquired with a confocal fluorescence microscope LEICA TCS SP8 (Leica Microsystems, Wetzlar, Germany). A first image-characteristic of neutral lipid fluorescence-was taken under excitation at 488 nm using an argon ion laser with a photomultiplier tube (PMT) range of 500-580 nm, whereas a second image-insensitive to neutral lipids-was taken under excitation at 405 nm with a diode laser with a PMT range of 450-480 nm. Using Fiji imaging processing software (ImageJ, National Institutes of Health, Bethesda, MD, USA) with three developed macros (Supplementary Files S1-S3), liver area was delimited by user for each larva with the first one, and then, with the two others, three parameters were calculated: (1) Ratio: this is the value corresponding to fluorescence intensity of image A in liver area divided by the fluorescence intensity of image B in the liver area (F green /F blue ).
5 µM FCCP (carbonyl cyanide-p-trifluoromethoxyphenylhydrazone), 8 cycles (32 min); Phase 3: addition of 6. 25 mM NaN3 (sodium azide), 20 cycles (80 min). Using Wave software (version 2. 6. 0, Agilent Technologies), OCR levels were analyzed in order to obtain basal, maximal, and spare mitochondrial and non-mitochondrial respiration levels with at least 4 larvae per condition.