dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments This work was supported by a grant from the  French National Research Agency (ANR , project  Adapt-Ome ,  ANR-13-BSV7-0012-01 ). Most of the bioinformatics analyses were performed on the CBGP HPC computational platform (Montferrier-sur-Lez, France, https://www6. montpellier. inrae. fr/cbgp_ eng/Platforms/Computing-platform ). We are grateful to the  bioinformatics platform ABiMS (Roscoff, France , http://abims. sb-roscoff. fr) for providing computational resources.
For the past 20 years, the recombination detection program (RDP) project has focused on the development of a fast, flexible, and easy to use Windows-based recombination analysis tool. Whereas previous versions of this tool have relied on considerable user-mediated verification of detected recombination events, the latest iteration, RDP5, is automated enough that it can be integrated within analysis pipelines and run without any user input. The main innovation enabling this degree of automation is the implementation of statistical tests to identify recombination signals that could be attributable to evolutionary processes other than recombination. The additional analysis time required for these tests has been offset by algorithmic improvements throughout the program such that, relative to RDP4, RDP5 will still run up to five times faster and be capable of analyzing alignments containing twice as many sequences (up to 5000) that are five times longer (up to 50 million sites).
For users wanting to remove signals of recombination from their datasets before using them for downstream phylogenetics-based molecular evolution analyses, RDP5 can disassemble detected recombinant sequences into their constituent parts and output a variety of different recombination-free datasets in an array of different alignment formats. For users that are interested in exploring the recombination history of their datasets, all the manual verification, data management and data visualization components of RDP5 have been extensively updated to minimize the amount of time needed by users to individually verify and refine the program's interpretation of each of the individual recombination events that it detects.
Introduction Recombination and genome component reassortment are processes that strongly impact the evolution of many virus species. The patterns of nucleotide sequence variation created in virus genomic sequence datasets by these processes (which are hereafter collectively referred to as recombination) can seriously undermine the accuracy of phylogenetics-based molecular evolution analyses  (Schierup and Hein 2000a,b; Scheffler, Martin, and Seoighe 2006; Arenas and Posada 2010). It is therefore frequently desirable to test nucleotide sequence datasets for evidence of recombination and, when such evidence is found, take steps to minimize the impacts of detected recombination events on downstream analyses of these datasets. Of the many available computer programs for analyzing recombination (see http://bioinf.
man. ac. uk/robertson/recombina tion/programs. shtml; Martin et al. 2011) , recombination detection program (RDP) is presently one of the most commonly used. Successive versions of RDP have applied an expanding array of recombination event detection, recombination breakpoint demarcation, and recombinant sequence identification methods, all applied in unison, to yield detailed descriptions of how recombination may have impacted the evolution of any given set of aligned nucleotide sequences  (Martin et al. 2015). The accuracy of these descriptions, however, frequently depended on the amount of effort users were willing to put into exploring the many similarly plausible ways in which the detected patterns of recombination may have arisen. A guiding principle during the development of RDP5, the latest version of the RDP series, has therefore been a minimization of the amount of time that users need to invest in detecting and removing signals of recombination from nucleotide sequence datasets.
Generation of recombination-free datasets RDP5 can take as input an aligned nucleotide sequence dataset, automatically identify and characterize individual recombination events that are evident within that dataset, and output modifications of the input alignment within which all signals of detectable recombination have been removed. The different types of modified recombination-free multiple sequence datasets that RDP5 can output include alignments where: (1) recombinant sequences have been removed; (2) fragments of sequence derived through recombination have been removed; (3), recombinant sequences are split up into their constituent parts; and (4) the input alignment is divided into multiple different gene/genome sub-region alignments based on the locations of detected recombination breakpoints (Supplementary Fig.
Query vs reference scans for recombination In addition to the fully exploratory recombination analysis modes found in previous versions of the program  (Martin et al. 2015) , RDP5 also includes a new highly automated 'query vs reference' analysis mode such as that found in the programs REGA  (de Oliveira et al. 2005)  and jpHMM  (Schultz et al. 2006). Unlike with the default fully exploratory recombination analysis mode where every sequence is tested for evidence of recombination, the query vs reference mode will test a user-defined set of query sequences for evidence that they originated through recombination between a user-defined set of reference sequences. Such an analysis mode is well suited to analyzing patterns of recombination between two or more groups of viruses that have only recently had the opportunity to start recombining with one another: such as in an individual patient that has been infected with two distinct variants of a virus  (Sheward et al.
Automated sequence annotation Given an accessible internet connection, RDP5 will automatically annotate the genomic features of input virus sequence datasets using the curated NCBI virus reference sequence database (https://www. ncbi. nlm. nih. gov/genome/viruses/). Annotation is useful in the context of generating recombination-free datasets because it enables RDP5 to output gene sequence alignments that are suitable for downstream codon-focused analyses of natural selection. The gene sequence alignments that RDP5 produces can account for variation in the positions of gene start and stop codons, intron splicing (wherever intron donor and acceptor sites are annotated in the NCBI reference sequence records), portions of genes that are expressed in two or more different open reading frames (these regions can be excluded) and real and/or artifactual frame-shift mutations (partial codons can be removed).
Improved computational performance Despite the additional tests that RDP5 carries out during the characterization of detected recombination events, it is still able to analyze any given dataset between two and five times faster than RDP4 could (Supplementary Table  S1 ). To achieve this, RDP5 implements a multitude of algorithmic improvements such as multi-core CPU-level parallelization and intensive use of lookup tables for bootstrap, permutation, likelihood and probability calculations. Additionally, while previous versions of RDP were restricted to using 2 GB of available RAM (which is standard for 32-Bit Windows programs), RDP5 can utilize 4 GB of RAM even under 32-bit Windows operating systems. This, together with improvements in memory management, means that RDP5 can analyze datasets that contain up to twice as many sequences that are each five times longer than those which RDP4 could manage.
Operational limits RDP5 will work on computers with Windows 7/Vista/8/10 operating systems and can also be installed to run under Windows 7 emulators running on computers with MacOSx and UNIX operating systems. RDP5 can be used to productively analyze datasets containing up to 400 million nucleotides within 24 h on a standard 8core 2. 5 GHz processor with >4 GB of RAM. Such datasets might, for example, consist of 120 3. 0-Mb-long bacterial genome sequences, or 4000 10-kb-long viral genome sequences. With default program settings, RDP5 can analyze 100 10-kb-long sequences in under 5 min on a standard desktop computer.
Availability RDP5 is available for free download from http://web. cbio. uct. ac. za/darren/rdp. html. It is distributed with an extensive manual that contains (1) detailed descriptions of the various methods implemented in the program; (2) a step-by-step guide describing how to create and analyze datasets for recombination detection; (3) instructions on how to run completely automated analyses from the command-line using RDP5CL; and (4) information on how to run RDP5 on Mac and Linux computers.
On the other hand, simulation tools are widely used to model buildings in order to predict their indoor air quality and energy consumption. These expectations have to be reliable. Essentially, the simulation results must reflect the ambiguity effects associated with the design parameters, construction quality, building uses and climatic conditions. Hence, building models need to be validated. In this context, numerous research works have been performed to validate models developed using MATEC Web of Conferences 307,  01025 (2020)  https://doi. org/10. 1051/matecconf/202030701025 ICOME'17 and ICOME  '18  building energy simulation tools  [8, 9]. For instance, Bontemps and Mora  [10]  developed their model using Modelica and validated it by comparing the predicted results with measured data regarding room temperature and heating power.
Measurement In order to study students' thermal comfort in the Foyer, a multifunctional sensor (Figure  2 ) developed by IES (Institut d'Electronique et des Systèmes) was used to monitor the environmental parameters of the Foyer and to calculate and visualize in real-time the indices of thermal comfort (PMV and PPD)  [13]. The sensor measures the room and radiative temperature, air velocity, relative humidity, and the rate of carbon dioxide. Then it transmits the data using Wi-Fi to a work station. During data collection, the measurements made by the sensor are recorded as data frames in a text file. These data frames are directly used to plot the evolution of the different physical quantities during the series of PMV = (0. 303e -0. 036M + 0. 028) {(M -W) - 3. 05×10 -3 [5733 -6. 99 (M-W) -Pa] -0.
Modelling with Dymola The numerical model was developed using the Dymola ® graphical interface. Figure  3 (a) describes the interaction between sub-models and Figure  3(b)  shows the Dymola ® model. Both Modelica Buildings Library [14-16] and Modelica Standard Library have been used. Room model: MixedAir model of the Rooms package  [14]  is the main component of the model; it is used to illustrate a single room with an unlimited number of opaque constructions. It implies that the air inside the room is completely mixed and simulates all the heat transfer processes (convection, conduction, solar radiation and infrared radiation) within the room and through the building envelope. Hence, it takes the heat transfer through walls, ceiling, floor, and windows into consideration. The properties of the interior walls, ceiling, and floor are represented using the components OpaqueConstructions and SingleLayer of the HeatTransfer package.
And the thermal properties of exterior walls (double glazed walls) were presented using the component GlazingSystems from the HeatTransfer package. The glazed walls in the Foyer have no overhangs and side fins; however, they are equipped with roller blinds. Weather data: The ReaderTMY3 model of the BoundaryConditions package  [15]  was used to represent the outdoor weather conditions. In this model, the atmospheric pressure is a set parameter, while other parameters are obtained from the TMY3 file. Another possibility is to use a constant value or an input connector for some parameters (dry bulb temperature, relative humidity, infrared horizontal radiation). In our case, we used the DaySchedule model to define the outside temperature at the time of experimentation, and we fed it to the weather file using an input connector.
The past decades witnessed growing interest in polymeric closed-cell porous materials. They are lightweight, robust and find applications in many different sectors, like packaging, thermal insulation or cushioning. Numerical simulations are commonly used in the construction process to predict the behaviour of the products in application. So far, gas diffusion has often been neglected, as there are no tools available to include it. Therefore, this paper aims to present an algorithm to include the simulation of gas diffusion into a commercial finite element analysis (FEA) tool, at the example of MSC Marc. Using the similarity of thermal conductivity and gas diffusion, the thermal solver of the FEA program is exploited to perform the simulation of gas diffusion. Some first two-dimensional numerical simulations of porous closed-cell models under long-term load show promising results.
Numerical Method Fourier's law of thermal conduction and Fick's law share the same structure. Wadiak  [8]  proposed in 1985 to adapt existing FEM code to solve Fickian mass transport through polymers. Thermal conduction is already implemented in many FEA programs, so in MSC Marc. Exploiting the thermal solver for simulating gas diffusion is the idea behind this approach. For this purpose, both equations are compared, and the quantities of the mass transport are expressed in quantities of Fourier's law. Assuming constant density ρ, constant specific heat capacity c p and constant thermal conductivity k, as well as no thermal sources, the law of thermal conduction denotes ∂T ∂t = k ρc p ∇ 2 T (5) with temperature T. For a better comparison Fick's second law of diffusion is written here again, with Henry's law (4) already applied to substitute the concentration c by the pressure p: ∂p ∂t = DS S ∇ 2 p (6) Comparing both equations (  5 ), (  6 ) leads to the analogies as collected in table  3.
Using the analogy, a thermal analysis is set up to represent a gas diffusion set-up. Gas pressures for the diffusion process (4) are applied in the framework of temperatures and therefore called pseudo-temperature hereafter. The material's diffusion coefficient is linked to the thermal conductivity. At the left and right boundary of the domain the pseudo-temperature is set to ambient pressure. Zero thermal gas flux is prescribed at the top and bottom boundary because the gas must not penetrate these boundaries. A structural-thermal analysis can be run in MSC Marc and calculates the results. This is a big advantage, as two Marc-inherent solving capabilities can be used. No additional code has to be written for this purpose. However, to avoid non-physical effects, any influence from the temperature on the mechanical model has to be disabled.
To that end, the coefficient of thermal expansion and the dependency of Young's modulus on the temperature are set to zero. The mechanical pressure has to be linked with the pseudo-temperature around the pores. As temperature and mechanical pressure are two different, but necessary quantities in the FEA, a parallel approach is chosen. On the one hand, to calculate the mechanically induced change of pressure ∆p mech due to change of volume ∆V in the pore. On the other hand, to obtain the diffusion-induced change ∆p diff from the thermal solver. Assuming isothermal compression, ideal gas law is applied to determine the pressure in a pore for the next p i+1 = p i + ∆p i diff + ∆p i mech = p i + ∆p i diff + ∆V i • p i V i. (7) Based on this idea, a Python script is written to set up and control a diffusion analysis in MSC Marc.
Discussion The method developed to simulate diffusion using a FEA program is successfully implemented in a Python script. The approach to employ the thermal solver proofs to be working. A stable code is developed and leads to quantitatively plausible results. For the simulation ideal gas law is used and isothermal compression is assumed. In that way, temperature dependencies are neglected. This is reasonable for long-term relaxation processes in polymer foam construction parts. However, in case of fast diffusion, e. g. for hydrogen storage in foams, thermal effects should be considered. As the diffusion coefficient is set to a constant value, its dependency on the concentration and local mechanical stress is neglected. It would be difficult to implement the latter, because there is no direct interaction between diffusive flux and mechanical stress during a time step.
program that computes the approximate p-values. From our perspective, it seems worth to trade off minor efficiency gains for the convenience of not having to store this larger amount of data, again emphasizing that such efficiency gains are negligible in the light of the remaining model uncertainty. The approximate p-value corresponding to the observed value of the test statistic τ is finally computed as p = F n i=0 φi τ i , ( 10 ) where φi are the coefficient estimates from equation (  9 ). This procedure to approximate p-values, as well as the critical-value predictions from equation (  8 ), is implemented in the Stata program described by  Kripfganz and Schneider (2018)  for both the F -statistic and the t-statistic. For example, consider a situation with k = 4 variables, T = 30 data points, q = 1 lag for each variable, and an unrestricted intercept.
As long as there are enough degrees of freedom available, additional lags of the variables can help to achieve this aim. Once a conclusion from the test is drawn, a more parsimonious model can be estimated along the lines of the Pesaran and Shin (1998) autoregressive distributed lag (ARDL) modelling approach. In the statistical software Stata, the ARDL and EC models can be estimated with the same program that computes the critical values and approximate p-values for the bounds test  (Kripfganz and Schneider, 2018). statistics and occurrence counts. The transformation is completely reversible, so that the original rounded 10 billion statistics per simulation design can be fully recovered. The resulting storage requirements are 40 GB, which decrease further to 8 GB when adding a conventional compression algorithm.
SPEN, the human ortholog of spen, is up-regulated in the substantia nigra pars compacta of PD patients. We first sought to determine whether SPEN (also named SHARP), the human ortholog of spen, had been reported to be expressed by human astrocytes and if its expression was differentially regulated in the context of PD. To this aim, we took advantage of a recent meta-analysis of microarray datasets obtained from substantia nigra samples derived from PD vs control subjects  26. From the list of differentially expressed genes (data supplement 1), we then performed a tissue enrichment analysis using the TargetMine webtool  27. We found that SPEN belongs to a set of 197 genes up-regulated in the SN of PD patients and previously shown to be expressed by human astrocytes  28  (data supplement 2).
RNA extraction and q-PCR Total mRNA was isolated from 25 to 35 Drosophila heads following the RNeasy mini kit extraction protocol from Qiagen and reverse transcribed with oligo(dT)15 and the ImProm-II Reverse Transcription System (Promega) according to manufacturers' instructions. Quantitative PCR reactions were carried out on a StepOnePlus (Applied Biosystems) using FastStart Universal SYBER Green Master (Roche Applied Science). Primers set efficiency (E) was assessed using serial dilutions of cDNA preparations. Standard curves were used to determine the relationship between mRNA abundance and PCR cycle numbers (Ct) and to calculate relative quantity (Qr = E-Ct)  48. Values were normalized to Rp49 mRNA amount. Data from three independent experiments are presented as means ± standard deviation.
Image processing Images were acquired at the LYMIC-PLATIM -Imaging and Microscopy Core Facility of SFR Biosciences (UMS3444, ENS de Lyon, France). For PLIN1::GFP, Repo and BODIPY staining, images were acquired on a Leica LSM800 confocal microscope and analyzed with the ImageJ 36 software (see section below). For NRE-GFP, images were acquired on a Leica epifluorescence microscope. In each experiment, the mean GFP fluorescence of control brain (;Eaat1-GAL4/+;) was used to normalize the results. The NRE reporter is a synthetic construct with three copies of the SPS site (for Su(H) Paired Sites, binding sites for the Notch activity-dependent transcription factor, Su(H)) that is taken from the E(spl) regulatory region  34.
Automated Image analysis We developed an ImageJ macro (https://gitbio. ens-lyon. fr/dcluet/Lipid_Droplets) to identify fluorescent particles on confocal stacks using Drosophila brains stained with the lipid droplet dye BODIPY 493/503 (Molecular Probes, D-3922) or labelled with the glial specific marker Repo (immunlocalization of glial nuclei). The program requires ImageJ 36 v1. 49g or higher and is based on an iterative detection of the brightest particles followed by the removal of "doublets" of the same particle over the stack. Briefly, the program first identifies the tissue all along the stack. The particles will then be sought within the tissue all along the stack. The signal is first intensified using the Gaussian blur function and the maximum entropy treatment. The "Max-Entropy" threshold method  50  then allows the detection of the particles of interest.
ACKNOWLEDGMENTS This work was supported by the  French National Research Agency  award  ANR-12-BSV1-0019  to BM, « Projets développement technologique » SFR Biosciences to ND and BM. ARTHRO-TOOLS and PLATIM facilities. VG PhD thesis was supported by grants from  Fondation Servier ,  ENS  fond recherche and a  FRM  fellowship. MQ PhD thesis was supported by fellowships from the  French Ministry of Research and education and France Parkinson.
Yelmo is written in Fortran 2003, which provides continuity from previous code bases and supports the fact that clarity and readability of the code are important features. Like SICOPOLIS and other models, we have opted for "low-tech" solutions whenever possible, meaning that internally coded routines are preferred, and, thus, the external dependencies of the model are kept to a minimum. This ensures that the algorithms used remain accessible and easily changeable. Nonetheless, Yelmo has two key dependencies: the NetCDF library for convenient community-standard input/output capability and the Library of Iterative Solvers for linear systems (https://www. ssisc. org/lis/, last access: 2 May 2020) (Lis,  Nishida, 2010) , which is used for solving the elliptical SSA equations. The latter can be compiled with OpenMP parallelization active, which can speed up this computationally intensive step.
Multiple instances of the Yelmo object can therefore be defined in a program (e. g. , one Yelmo-object instance for Greenland and one for Antarctica), and each one will operate fully in isolation from the others. This is the model accounting, which is of a specific design built into Yelmo and is thus the only part not easily portable to other models. The model physics, meanwhile, consists of subroutines that are fully portable and, whenever possible, only rely on native data types (e. g. , scalar, vector and array). In other words, the specific, non-portable design structure of the Yelmo object does not contaminate the physics subroutines, since the necessary variables and parameters are always passed as arguments. This approach requires that all input and output to subroutines must be defined as arguments.