dataesr/scientific-paragraphs-categorization huggingface dataset:
Details of mesoscopic model The work presented here builds on our previous mesoscale filament modeling  (32) , which is implemented using the software MATLAB 2018b (The MathWorks, Natick, MA). Proteins are represented as rigid ellipsoids. Protein subunit size and filament geometry are measured from Protein Data Bank (PDB): 3J8I (actin) and 3J0S (cofilactin)  (34, 35). Protein interface areas are estimated by the buried solvent-accessible surface area between adjacent molecules in the structural models. Filaments are 100 nm in length to balance between physiologically relevant lengths and computational simplicity. The boundary conditions for each type of deformation were chosen so that the result should apply for a small segment within a longer filament. The (minor) effects of this choice are explored further in Figs.
(A) A snapshot is given of the most strained longitudinal interfaces of a compressed actin filament and (B) the corresponding structure of the actin filament (PDB: 2ZWH) oriented with the pointed end to the left. The highest strain in bent filaments is between subdomain 2 and subdomain 1 of the adjacent monomers. This region spatially corresponds to the location of the D-loop. Interface disruption can be caused by (C) uneven application of force on an interface; (D) protein (cofilin) binding, leading to a structural change; or (E) chemical modification of amino acids (as by MICAL). To see this figure in color, go online. the interface energy at actin-cofilactin boundaries (Fig. 7  D; (48)). Similarly, the actin-destabilizing enzyme MICAL acts by reversibly oxidizing methionine residues in the D-loop of F actin monomers to inhibit these contacts (Fig.
In a previous work  [4]  a parametric investigation has been carried out tuning the model with TCV experimental data from ohmic discharge #51333. In this paper we propose an analytical derivation of the free parameter of the system, represented by the coefficient of self-saturation of turbulence Δω, via the scaling of the global confinement inspired by Eich et al. [5]  and Goldston  [6]. In this way we would like to give a less constraining closure than a crude case-by-case tuning of the free parameters, and recover experimental evidence of the dependence of λ SOL on typical machine parameters like major and minor radius of the tokamak and poloidal magnetic field. The model closure and the scan will be better explained in the second and third sections. The fourth and final section will be devoted to some preliminary results obtained by implementing such reduced model into the 2D transport code So-lEdge2D, thus simulating magnetic configuration and physics of a real tokamak study-case.
Acknowledgements This work was granted access to the HPC resources of CINES, under the allocation 2018-A0040510482 made by  GENCI  and to the  HPC resources of Aix-Marseille University  financed by the project  Equip@ Meso  ( ANR-10-EQPX-29-01 ). The project leading to this publication ( TOP  project) has received funding from  Excellence Initiative of Aix-Marseille University -A*MIDEX, a french Investissements dAvenir programme. This project has received funding from the  European Unions   Horizon 2020 research and innovation programme  under grant agreement number  633053  for the project  WP17-ENR-CEA-08. The views and opinions expressed herein do not necessarily reflect those of the  European Commission. This work has been carried out within the framework of the  EUROfusion Consortium  and has received funding from the  Euratom  research and training programme  2014-2018  under grant agreement No  633053.
GROMOS The Groningen Molecular Simulation (GROMOS) package and its affiliated force field were developed in the 1980s and are continuously being improved. Many researchers investigated cellulosic materials with this force field and found acceptable agreement with experimental results. Yu et al. [68]  used the GROMOS force field to study the stability and solvation of cellulose fragments, where the simulation well represented the conformations of disaccharides. Kulasinski et al. [27]  carried out a comparative study of crystalline, paracrystalline, and amorphous celluloses, and achieved an agreement with available experiments in multiple aspects, such as the mechanical modulus and cell parameters of cellulose crystal. Bergenstrahle et al. [69]  simulated crystalline cellulose at different temperatures, and found reasonable agreement with experimental data for crystal density, corresponding packing, chain conformation, thermal expansion, etc.
CHARMM Chemistry at Harvard Macromolecular Mechanics (CHARMM)  [75]  is one of the widely used softwares and force fields for modeling biomolecules, lipids, and membranes  [76]  and includes united atom (CHARMM 19), all-atom (CHARMM 22), and polarizable  [77]  parameterization for a wide range of biomolecules. Reiling and colleagues employed the CHARMM 22 force field to obtain the physical properties of cellulose Iα, cellulose Iβ, and cellulose II. They validated their method by comparing the simulated density and Young's modulus to experimental results  [78]. In a comparative study, CHARMM35, GLYCAM06, and GROMOS 45a4 force fields were employed for modeling crystalline Iβ cellulose. The CHARMM force field was successful in modeling the crystalline cellulose configuration and the results were in an acceptable agreement with experimental results  [79].
Solvers There are numerous software packages developed incorporating several molecular dynamics integration algorithms, and methods to post-process the simulation results. The LAMMPS  [83] , GROMACS [84], NAMD  [85] , Materials Studio, CHARMM  [86] , and AMBER [87] are some of the well-known software packages developed especially for the simulation of biomaterials. Most of these software products include a set of different force fields such as OPLS, Gromos, CHARMM, PCFF, CVFF, AMBER, and COMPASS. Almost all of these packages are capable of performing multithreading, GPU acceleration, and parallel computation with conventional integration methods such as velocity Verlet and leap-frog, and different methods of long-range calculation, energy minimization, free-energy calculation, temperature, and pressure control.
Many studies have compared different MD software products using different test systems, benchmarks, and hardware  [88, 89]. Comparing GROMACS, LAMMPS, CHARMM, and NAMD with five protein systems from 20,000 to 3 million atoms as test systems found GROMACS to be faster for all of the tested systems sizes while GROMACS, LAMMPS, and NAMD showed similar scaling behavior for larger systems  [89]. Wikipedia  [90]  has a detailed article comparing around 30 different programs applicable to different tasks of molecular mechanics modeling. Due to the excellent flexibility of LAMMPS and GROMACS, we mainly used these two packages for our calculations along with Material Studio and Packmol for building molecules and initial structures. While we find GROMACS faster and more compatible with the available force field parameter data banks such as SwissParam  [91]  and Automated Topology Builder  [92] , LAMMPS is more flexible and, since it is able to do hybrid MD and GCMC calculations, it was used for calculation of chemical potential for the determination of adsorption/desorption isotherms.
Building an Amorphous System There are different methods for building an amorphous system. Here we present two common methods: a Monte Carlo method and a relaxation method. We discuss first the Monte Carlo method, which has been implemented in some commercial software like Material Studio 8. 0. The degrees of freedom, such as position, orientation and any torsion, of each repeating unit of the polymer are assigned one by one. One has to reserve a number of candidate values for the coming degree of freedom. To determine which value to pick, each candidate Forests 2019, 10, 628 12 of 27 value will be subject to a number of geometrical checks, such as whether the associated segment will have close contacts, and/or ring spearing with the formerly placed segments. Then, the Boltzmann weight w i of each candidate that survives the geometrical check is calculated by: w i ∼ exp (- E i k B T ) (6) where k B is the Boltzmann constant, T is the absolute temperature, and E i , the energy change caused by the trial segment insertion.
Water Molecules There is a number of different water models used in molecular simulations. The GROMOS force field is designed to work with single-point charge (SPC) or SPC/Extended (SPC/E). In terms of the obtained saturated vapor pressure at room temperature of 300 K, SPC (~4400 Pa) is closer to experiment (~3600 Pa) than SPC/E (~1017 Pa)  [103]. However, SPC/E outperforms SPC model in terms of density and diffusion constant  [104]. There is no consensus on the best water model regarding sorption. Currently both water modules are popular in the sorption community.
Upscaling Molecular simulations are powerful to characterize material properties and reveal the mechanism at the molecular level. However, the high resources demanded and the time-consuming nature of molecular simulation still impose limitations on both time and length scales. Therefore, if one is interested in the hygromechanical behavior at a higher scale, upscaling is generally needed. All the results obtained from atomistic simulation, such as moisture sorption isotherms, swelling and moisture-dependency of mechanical properties for all the different components and composite systems form an integrated dataset which can be upscaled using a continuum approach in order to provide material properties. In this approach, the swelling coefficients and mechanical properties, and their dependence on moisture content are fed as data into classical continuum constitutive laws and the governing conservation laws can be solved using a finite-element approach and solver.
Throughout the HIV-1 replication cycle, complex host-pathogen interactions take place in the infected cell, leading to the production of new virions. The virus modulates the host cellular machinery in order to support its life cycle, while counteracting intracellular defense mechanisms. We investigated the dynamic host response to HIV-1 infection by systematically measuring transcriptomic, proteomic, and phosphoproteomic expression changes in infected and uninfected SupT1 CD4+ T cells at five time points of the viral replication process. By means of a Gaussian mixed-effects model implemented in the new R/Bioconductor package TMixClust, we clustered host genes based on their temporal expression patterns. We identified a proteo-transcriptomic gene expression signature of 388 host genes specific for HIV-1 replication.
Proteo-transcriptomic temporal analysis of the host cell upon HIV-1 infection. Differential expression analysis between Mock and HIVeGFP-infected conditions was performed initially regardless of the time point (cf. Methods) and resulted in 1506 differentially expressed genes, 415 differentially expressed proteins, and 157 differentially expressed phosphorylation sites, corresponding to 125 proteins with a differential phosphorylation status (Supplementary Fig. S5 , Supplementary File S2). Using a Gaussian mixed-effects models framework, we modeled the changes in expression of the differentially expressed genes through time and clustered the genes at each omics level based on their temporal profiles (cf. Methods). We implemented the model in the dedicated R/Bioconductor package TMixClust. For each data type (RNA, protein, and phosphoprotein), stability and silhouette analyses indicated that the number of Gaussian model components, or clusters, that provided the optimal segregation with the largest average silhouette width was two (Supplementary Figs  S6-S9 ).
Only 26. 5% of the genes detected at the RNA level were also measured successfully at the protein level, and only 4% were quantified at all three omics levels. Furthermore, the phosphoproteomic measurements contained a large amount of missing values and noise, and consequently could not be used for identifying proteo-transcriptomic expression signatures of infection. Furthermore, because phosphoprotein measurements needed to be adjusted by their protein expression in order to remove an existing abundance bias in SILAC data  49  , a large number of phosphoproteins could not be assessed due to lack of quantification at the protein level. Alternatives to SILAC, such as tandem mass tagging (TMT), could be attractive as they may yield a better coverage and especially less missing data in time series  19.
Peptides were separated on a reversed-phase Acclaim Pepmap nanocolumn (75 μm ID × 25 cm, 2. 0 μm, 100 Å, Dionex) with a gradient from 5 to 45% acetonitrile in 0. 1% formic acid in 120 min). Full MS survey scans were performed at 60,000 resolution. All survey scans were internally calibrated using the 445. 1200 background ion mass. In data-dependent acquisition controlled by Xcalibur 2. 1 software (Thermo Fisher), the twenty most intense multiply charged precursor ions detected in the full MS survey scan were selected for Collision-Induced Dissociation (CID) fragmentation in the LTQ linear trap with an isolation window of 3. 0 m/z in multi-stage activation mode (exciting the precursor and the neutral loss simultaneously) and then dynamically excluded from further selection during 120 s. Bioinformatics data analysis and statistical modeling of time series omics data.
RNA-Seq data processing. Illumina adapter sequences were removed from RNA-Seq reads using Cutadapt v1. 7. 1  51. Reads were then filtered using Prinseq-lite  52  which removed their poly-A and low-quality (quality score less than 6) boundaries and kept for further analysis only the reads longer than 30 nucleotides and with a mean quality score larger than 20. The FastQC software  53  was used for quality assessment of the datasets. Sequencing reads were aligned using STAR v2. 5  54  to the GRCh38 Human Genome Assembly concatenated to the HIVeGFP/VSV-G HIV genome sequence (Supplementary File S11). A standard way to construct the concatenated human/HIV genome is to add HIV as an extra chromosome to the human genome. This allows for a correct and unbiased mapping of the reads  18. The number of reads aligned to each gene was retrieved at each time point with HTSeq-count v0.
Principal component analysis of the gene expression read counts in all the RNA-Seq samples was used to initially explore the gene expression profiles for HIV-infected and Mock-treated samples over time. Mass Spectrometry SILAC data processing. Mass spectrometry data from protein and phosphoprotein experiments were analyzed and quantified using MaxQuant v1. 3. 0. 5 (2013 release). The human subset of the release 2013_07 of the UNIPROTkb database was used (www. uniprot. org). A separate database was constructed, containing all sequences of HIV proteins and used in parallel. Cleavage specificity was Trypsin (cleavage after K, R) with two missed cleavages. Initial mass tolerances were of 4. 5 particles per million (ppm) (after recalibration) for the precursor and 0. 5 Da for CID tandem mass spectra.
Protein and phosphopeptide identifications were filtered at 1% false discovery rate (FDR) established by MaxQuant against a reversed sequence database. Common contaminants and hits against reverse sequences were filtered out. Light/Heavy (L/H) ratios were normalized and the log 2 (HIV/Mock) fold changes at all time points were calculated. Only proteins with measurements supported by more than 2 peptides at a minimum of two consecutive time points were considered for further analysis. As suggested in  49  , the phosphoprotein ratios were normalized by their corresponding protein ratios in order to remove the bias in the phosphoprotein measurements introduced by the protein relative abundance. Therefore, only phosphoproteins with available protein measurements were considered for the analysis.
Based on their log 2 (HIV/ Mock) time series profiles, differentially expressed genes were clustered using a Gaussian mixed-effects model as introduced in  60  (see the Supplementary Information for a detailed description of the model). The clustering model was implemented in the R/Bioconductor package TMixClust and is available at https://bioconductor. org/ packages/release/bioc/html/TMixClust. html. Of note, we could not assess the variability of individual time point measurements due to the limited number of replicates. However, since measurements at consecutive time points are considered to be correlated, an increased number of time points is usually preferred over the availability of replicates  61. Our statistical model is designed to capture smoothness of expression between time points and time series with highly variable behavior between consecutive time points would yield an unstable clustering result and would consequently be discarded.
Acknowledgements We would like to thank  Hannes Röst  for his useful feedback and comments on the analysis of SILAC protein data. We thank  Antonio Rausell  for valuable discussions. This work was done in part while  Monica Golumbeanu  and  Niko Beerenwinkel  were visiting the  Simons Institute for the Theory of Computing in Berkeley, CA. The research leading to these results has received funding from the  Swiss National Science Foundation  grants no. 130699 ,  146579  and  166412 , and from the  European Union 's  Seventh Framework Program  FP7/2007-2013/under grant agreement  n°305762/Hit Hidden HIV. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Data Availability The raw RNA-Seq time series data have been deposited in NCBI's Gene Expression Omnibus  65  and are accessible through GEO Series accession number GSE100587 (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?ac-c=GSE100587). The raw proteomic and phosphoproteomic data have been deposited on the ProteomeXchange database  66  on the PRIDE repository  67  and are accessible through the accession number PXD005810. The source code and user manual for the Bioconductor package TMixClust is available at https://bioconductor. org/packages/ release/bioc/html/TMixClust. html. The source code and data of the PEACHi2 R/Shiny resource is available on GitHub at https://github. com/cbg-ethz/PEACHi2.
Methods: The resulting REal Life EVidence AssessmeNt Tool (RELEVANT) was achieved through an extensive analysis of existing initiatives in this area. The first version was piloted among 9 raters across 6 articles; the revised, interim, version underwent extensive testing by 22 reviewers from the EAACI membership and REG collaborator group, leading to further revisions and tool finalisation. RELEVANT was validated through an analysis of real-life effectiveness studies identified via systematic review of Medline and Embase databases and relating to topics for which real-life studies may offer valuable evidence complementary to that from randomised controlled trials. The topics were selected through a vote among Task Force members and related to the influence of adherence, smoking, inhaler device and particle size on asthma treatment effectiveness.
Several study designs are available to reach this goal, including pragmatic randomized trials and observational studies, which can be prospective or historical, the latter relying on clinical cohorts, registries or databases  [1, 13]. Phase IV comparative effectiveness studies are crucial not only to clinicians, but also to other stakeholders such as guideline developers and healthcare policy makers  [14]. They can also generate new hypotheses to be tested in RCTs or other adapted study designs. In observational research as well as in RCTs, ensuring high-quality methodology is crucial to avoid biases that would compromise the reliability and validity of results  [15]. Following the GRADE methodology for evidence appraisal, RCTs are initially considered as providing high levels of evidence while observational studies are set at a lower level  [16, 17].
Methods The initial work comprised two simultaneous processes:  [1]  the development of a quality assessment tool through a synthesis of Task Force expertise and recommendations within existing relevant publications and  [2]  the selection of relevant research topics and identification of corresponding articles that would be used to test the tool. Once the tool was finalised (following pilot testing and iterative refinement), it was used to appraise the retrieved literature. Studies achieving sufficiently high quality scores were then assessed to determine the extent to which they offered novel data and insights that could influence clinical practice and inform guidelines. A Task Force activity schedule summary is presented in the Additional file 1: Table  S1.
Selection of topics and literature search A first set of 21 PICOT [(P)opulation selected for the study, (I)ntervention, (C)omparator, (O)utcome measures and (T)ime-duration] [41] questions was identified during a Task Force meeting. The questions were selected based on their relevance for asthma management, limited ability of available RCTs to provide robust answers and potential of observational research to address them. Discussions in a plenary session led to the prioritization of 9 PICOT questions. An online poll was conducted among REG members and EAACI representatives to prioritise the topics and 4 PICOT questions were finally selected to ensure feasibility of delivery. The literature search was conducted using the Medline and EMBASE bibliometric databases to identify asthma effectiveness studies.
Assessment of the literature Papers considered of sufficient quality were sent to at least two raters each with a reading grid (see Additional file 1: Table  S3 ) comprising the following items: summary of methods (studied population, intervention, outcomes, setting), summary of results (magnitude of differences/robustness) and possible remaining biases. The list of raters is available in the Additional file 1: Table  S4. Each paper was sent to a third rater when there Fig. 3  Flow diagram for inclusion/exclusion of articles was discordance in the conclusions from the two initial raters. For each PICOT question, a summary table was built, mentioning, for each article, the type of source (e. g. claims database or prospective cohort), the main conclusion, the corresponding level of evidence proposed based on GRADE assessment, the possible impact on clinical practice and whether similar evidence was available from RCTs.
Literature analysis A summary of the literature analysis is presented in Tables  3, 4  and 5. Study results are presented in a concise way due to space limitations and since more details would actually be out of the scope of the present manuscript. Nonetheless, the main limitation of currently available clinical or claims database studies was found to be the paucity or lack of detailed clinical data regarding:  [1]  asthma control as assessed by validated questionnaires,  [2]  severity as assessed by symptoms and lung function,  [3]  comorbidities, as evidenced by either symptoms suggestive of comorbidities such as rhinitis or gastro-oesophageal reflux disease, or physician diagnoses of comorbidities. As a consequence, authors used available proxy measures, including:  [1]  short-acting beta-agonist (SABA) use and exacerbations as markers of control;  [2]  inhaled corticosteroid (ICS) dose and add-on treatments for severity, and  [3]  prescribed therapies in place of comorbidity diagnoses.