dataesr/scientific-paragraphs-categorization huggingface dataset:
After standard quality control, we had a total of 167,440 SNPs available for analyses. Principal components derived from SNP genotypes revealed a weak substructure among genotypes (Supplementary Fig. 3 ). Environmental data consisted of 106 environmental covariates (EC) generated using a crop model developed by Arvalis 8. The crop model computes EC values based on predicted growth stages and weather records (temperature, radiation, rainfall). The output of the model consists of ECs describing critical temperatures, radiation, and water availability for eight distinct phases of crop phenology. A complete list of the ECs used is presented in Supplementary Table  1. Further details about how the ECs were derived are provided in the "Methods" section and in Soenen et al. 8. The ECs used in this study were used before to characterize French wheat-growing environments  9  and to predict regional wheat yield in France  10.
4 ) and 28 wheat lines (x i ; i = 1,…,28) for which we have extensive records from Arvalis' network of trials. For each of the selected locations, we collected 16 years (2000-2015) of historical weather data. We used a crop model to derive environmental covariates (w ijk ) for each year-location for which we had weather data. Using the BGLR software package  16  , we fitted model TGW-G×W to all the data from past field trials. The software was used to collect 100,000 samples from the posterior distribution of the parameters of model TGW-G×W. These samples were thinned at an interval of 5, producing 20,000 samples (θ s ; s = 1,…, 20K). We then evaluated the prediction function (f(•) in Fig. 4 ; see "Methods" for details) for every genotype, location, year, and sample of the model parameters.
Moreover, we do not recommend simulating phenotypes for untested cultivars, unless such varieties are closely related to the ones used to train the models. Historical weather data, the third input in the simulation platform, should be readily accessible for locations with (or with nearby) weather stations. In this study, we applied the framework using historical weather data from individual locations. However, there are multiple potentially useful variants of this approach that are worth mentioning. First, if the goal is to breed or to produce agronomic recommendations for large growing regions, one could simulate at various locations and aggregate the simulated data at the regional level. Second, in this study, we used a long series of historical weather data. However, the same framework could be used as a tool for sensitivity analyses.
Methods Experimental data. Genotypes for each of 481 French-registered varieties were obtained using the Axiom high-density genotyping platform (Affymetrix Inc. , Santa Clara, CA) containing 420K (K = 1,000) SNPs 7 and generated within the Breedwheat project (ANR-10-BTR-03). Only SNPs with minor allele frequency >0. 05, with a calling rate >0. 8, and with <10% of heterozygous loci, were used in the analyses. A total of 167,440 SNPs fulfilled this criterion. The remaining missing genotypes were imputed to the mean (i. e. , two times the allele frequency at the SNP). Phenotype data originated in post-registration evaluation trials carried out each year by Arvalis for newly registered varieties. The trial network included 242 locations and a total of 752 year-locations. Each year-location had one (unreplicated) yield trial in which cultivars were arranged in a randomized complete block design with a plot size of 12 m².
Sowing dates and densities were adjusted at each location to represent the usual practices at each location. Trials were managed to reduce any biotic or abiotic factor that may reduce grain yield potential (optimal nitrogen fertilization, weed, insect, and disease control). Plots were harvested at maturity using a combine harvester, and the grain yield of each plot was adjusted to 15% moisture. A single check was used across all trials; however, since cultivars were tested at multiple year-locations, many genotypes provided strong connections between trials. To quantify this, we created a connectivity index (T ij(r) ) that counted the number of trials connected to trial ij through at least r genotypes (r = 1,…,5). The average values of these indices in the data set were T 2 ð Þ ¼ 573:3, T 3 ð Þ ¼ 473:3, T 4 ð Þ ¼ 402:3, and T 5 ð Þ ¼ 339:8.
Histograms of the distribution of these indices are presented in Supplementary Fig. 1. Environmental covariates were chosen based on physiological knowledge of bread wheat response to environmental factors (water, radiation, temperature) at different periods of the crop cycle (see Supplementary Table  1  for a list of ECs). Weather data were gathered from weather stations located within 10 km of the trial. The crop cycle was divided into phases between sowing, emergence, the beginning of stem elongation, meiosis, heading, anthesis, milky, and maturity corresponding to growth stages (GS) 00, 10, 30, 39, 55, 65, 75, 92  29. The GS dates were simulated using an ecophysiological model based on the daily accumulation of thermal time, possibly modified by vernalization and photoperiod factors  [30] [31] [32].
IFT52 depletion reduces centrosome clustering dynamics in late G2 and mitosis Given that IFT proteins depletion results in a reduced clustering of supernumerary centrosomes and a reduced HSET motor recovery on microtubules following photobleaching, we next assessed the impact of IFT proteins depletion on the dynamics of centrosomes. We took advantage of the fact that the doxycycline-inducible Plk4 RPE-1 cells (used in Fig  1 ) also carried a centrin-GFP marker  [34] , and we imaged these cells with a high temporal resolution. In this experiment, the centrin signal corresponds to centrosomes made of a pair of centrioles (Fig  EV3B ). Using Imaris software, we established the 3-dimensional trajectories of centrosomes over time and extracted parameters on their dynamics. These parameters included individual centrosome speed and inter-centrosomal distance.
Microscopy and image analysis Epifluorescence images were acquired with a Leica DM6000 Microscope (objective: 63× Plan Apo 1. 4 NA) equipped with a CoolSNAP HQ2 Camera and controlled by MetaMorph (Molecular Devices). Confocal images were acquired with a Zeiss LSM780 microscope (objective: 63× Plan Apo 1. 4 NA) controlled by ZEN software (Zeiss); with a spinning disk confocal microscope, a Nikon Ti Eclipse coupled to a Yokogawa CSU-X1 spinning disk head and an EMCCD iXon Ultra camera (objectives: 60× Plan Apo 1. 4 NA and 100× Plan Apo 1. 45 NA), controlled by the Andor iQ3 software (Andor); or with a Dragonfly spinning disk microscope (Andor) equipped with iXon 888 Life EMCCD Andor camera (100× Plan Apo 1. 45 NA) and controlled by Fusion software (Andor, Oxford instruments). Live-cell imaging for quantification of multipolar anaphases was performed, overnight at 37°C in a CO 2 -controlled atmosphere, using an inverted Olympus IX83 microscope equipped with a Zyla 4.
2 MP sCMOS camera (objective: 40× LUCPLFLN 0. 6NA RC2, air) and controlled by MetaMorph (Molecular Devices) on cells seeded in 12-well plates. Images were acquired every 5 min on three plans with 8-lm Z interval. DNA was visualized with the H2B-EGFP signal or with SiR-DNA probe (Spirochrome) at 500 nM added 1 h before imaging begins. Image processing and analysis (cropping, rotating, brightness, contrast adjustment, color combining, and measurements) were performed using Fiji (ImageJ). Live imaging of centrosome clustering dynamics was performed, overnight at 37°C in a CO 2 -controlled atmosphere, using spinning disk confocal microscope, a Nikon Ti Eclipse coupled to a Yokogawa CSU X1 spinning disk head and an EMCCD iXon Ultra camera (objective: 40× Plan Apo 1. 3 NA) controlled by the Andor iQ3 software (Andor) on cells seeded on l-Slide 8 Well (ibiTreat, Ibidi).
Images were acquired every 2 min on 23 plans with 0. 5-lm Z interval. DNA was visualized using SiR-DNA probe (Spirochrome) at 500 nM and centrosomes with centrin-GFP signal. Image segmentation and centrosome tracking were performed using Imaris (XT module, Bitplane). Individual cell drift and rotation were corrected based on the DNA mass trajectories. Spots were created for each individual centrosome using the spot-tracking tool, and various parameters on centrosome spot dynamics were generated using the XT module.
FRAP experiments were performed using the 488 nm laser line controlled by ZEN software (Zeiss). Images were acquired on a single plan every 460 ms, and 10 measurements were carried out before the bleaching event. The bleached zone was a fixed area represented by the green square in Fig 3I. FRAP analysis was performed, using ImageJ, only in cells where microtubules remain stable in focus during the acquisition time in the bleached zone. Background fluorescence was measured from a zone outside the spindle, and observational photobleaching was measured from an area equivalent to the FRAP zone in a zone not submitted to FRAP. Photobleaching and background normalization were calculated using Excel software (Microsoft) and applied to the mean fluorescence intensity values in the FRAP zone.
Acknowledgements The experiments were performed on the imaging platform Montpellier Ressources Imagerie (MRI), Montpellier, member of the national infrastructure  France-BioImaging  supported by the  French National Research Agency  ( ANR-10-INSB-04 , "Investments for the future"). We particularly thank  S. De Rossi  for his assistance regarding image analysis,  M. Boyer  for her help with cytometry, and  C. Hassen-Kodja  for his help with data analysis. We thank the  Montpellier Genomic Collection Platform  ( S. Fromont  and F. Lionneton). We would like to thank  D. Xirodimas  for critical reading of the manuscript. We also thank  A. Holland ,  D. Fachinetti ,  S. Diez ,  S. Doxsey , and  C. Janke  for reagents and advice. We thank the  IRCM Cell Culture Unit  ( Grant INCa-DGOS-Inserm 6045 ) for cancer cell lines.
This work was supported by  Projet Fondation   ARC grants  ( PJA 20151203324  and  PJA 20171206441  to  BV ),  ANR   JCJC IRMM grant  ( ANR-18-CE11-0025-01  to  BV ),  ANR   "Chaire d'excellence" CilMitoCyst grant  ( ANR-12-CHEX-005  to BD), the  Marie Curie Career Integration  Grant (CilMitoPatho to B. D. ), the  Fondation pour la Recherche Médicale (Partenariat Fondation Schlumberger pour l'Education et la Recherche  to B. D. ) and the  CNRS (BV, CA , and BD).
Ray tracing software systems are commonly used to analyze the optics of solar energy devices, since they allow to predict the energy gains of devices in real conditions, and also to compare them with other systems constantly emerging in the market. However, the available open-source packages apply excessive simplifications to the model of light-matter interaction, making that the optical behaviour of the systems can not be properly characterized, which in turn implies disagreements between physical experiments and computer simulations. We present here the open source python package OTSun, which applies the Fresnel equations in their most general form, without further simplifications, and is suitable for the simulation of both solar-thermal and photovoltaic systems. The geometrical objects used in this package are created using the parametric 3D modeler FreeCAD, which is also a free and open source program and allows for the construction of arbitrary geometries that can be analyzed with OTSun.
These, and other software capabilities, make OTSun extremely flexible and accurate for the optical analysis of solar devices with arbitrary geometry. Additionally, OTSun has a companion webtool, OTSunWebApp, that allows for the usage of certain features of the package without the need to install anything locally. We also show here two numerical experiments that we performed in order to validate the model and implementation: The analysis of the optical efficiency of a Linear Fresnel Reflector (with moving objects), and of a second surface mirror (with variable wavelengths). In each case, the numerical computations had deviations of less than 0. 25% from reference models (either computed with another program or with exact formulas).
With this method, several interesting parameters, such as the optical efficiency, flux irradiation and charge carrier generation, can be accurately determined. It is important to mention that rigorous optical models are needed to elucidate the optical behavior of a solar optical system. Focusing on solar thermal applications, a review of existing optical simulation tools used for Solar Central Receiver Systems (SCRS) was recently presented in  [3]. In this review, a total of 18 different software bundles have been analyzed. At present, only three of them, Tonatiuh  [4] , HOpS  [5]  and SolTrace  [6] , are licensed as open source. Another recent study focused in solar thermal applications was realized in the context of IEA/SHC Task 49 "Solar Heat Integration in Industrial Processes"  [7] , where a round-robin ray tracing software comparison for Linear Focusing Solar Collectors (LFSC) was done.
In this study, the main differences between the software packages under study were found in how they model the angular dependency of the optical properties of the materials. As a consequence, different modeling options by different software tools produced different values of optical efficiency. The main reason is that they implement simplified models of optical Fresnel equations, instead of implementing them in its generic form. On the photovoltaic applications side, optical simulation tools based on MCFRT for solar cells are also used  [8]. While there are many proprietary software packages, for example, CROWM  [9] , actually, to the best knowledge of the authors, Scientrace  [10]  is the only opensource ray tracing software focused on photovoltaic cells  [11]. It should be noted that some of the commercial software packages can be used for both solar-thermal and photovoltaic cells technologies, but no open source software has been found that applies to both technologies.
Related to this, we mention here some proprietary packages that apply Fresnel equations: OptiCAD  [12] , ZEMAX  [13] , ASAP [14], TracePro [15], and COMSOL  [16]. In all of them, the optical efficiency and radiance flux can be determined, among others parameters. However, we want to emphasize the importance of having open source software, since this allows for the inspection, modification, and implementation of new capabilities by the scientific community, which gives transparency and added value to the tool. In this setting, our contribution with the development of OTSun is twofold. On the one hand, we cover the absence of open source software that is focused on both solar technologies (thermal and photovoltaic). On the second hand, we implement the Fresnel equations in their most general form, without further simplifications, avoiding the aforementioned problem reported in  [7].
The OTSun package uses FreeCAD  [17] , a free 3D CAD system, in order to model the geometry of the optical systems to be analyzed, and also uses its API to compute the intersections of rays with the scene. FreeCAD is open sourced and permits to work with open file formats such as STEP, STL, DXF, and OBJ. OTSun is a python package, open sourced under the MIT license. It can be obtained from the public repository https://github. com/bielcardona/OTSun, and can also be installed directly from the Python Package Index (PyPI) by calling pip install otsun. We also remark the existence of the web tool OTSunWebApp, publicly available at http:// otsun. uib. es, which has been developed to allow for making experiments without having to install anything. Some tutorials for this tool can be found in  [18].
The aim of this paper is to give a full overview of the OTSun package. In Section Background, we give a brief account of the theory of geometrical optics and describe the algorithm we have used as a basis for our model. Section Implementation gives an overall view of the implementation of the different modules in the OTSun package. In Section The Model validation, we exhibit some experiments we have performed to validate our models and implementations. We conclude this manuscript with some final remarks in Section Conclusion.
Implementation The python package OTSun (imported as otsun) is composed by different modules, some of them collecting helper functions that implement both mathematical and optical methods, and other ones built around each of the main classes that define the functionality of the package. To ease the reading of this section, where we shall be dealing with classes and instances of those classes, whenever a class (say Scene or LightSource) is considered, the downcase version of their identifiers (scene and light_source in our example) will indicate instances of those classes. Except for those classes defined by FreeCAD (like Part. Face or Base. Vector), we refer to the corresponding section in this manuscript for the definition and initialization options for each of these classes. For instance, in Section The Experiment class we explain how to create an experiment (an instance of the Experiment class), giving an scene and a light_source, which are instances of Scene and LightSource, described in Sections The Scene class and The LightSource class, respectively.
A typical use of the package involves the creation of an experiment, which specifies the solar optics experiment to be run, defined by certain data that includes a scene and a light_source. The scene holds the data of all the different objects that interact with light rays, included in a FreeCAD document  [17] , and where each of them has a material associated describing its optical behaviour. Eventually, some objects may have movements, implemented by a multi_tracking, and in such a case these elements will be moved to maximize the absorbed energy. When the experiment is run, the light_source creates rays, which interact with the scene until they either leave the scene or are absorbed, and in this last case the collected energy (among other data) is stored for future analysis. We comment now the main classes that have been implemented, together with its basic functionality.
materials[obj] will be the Material instance mat such that mat. name is mat_name. For instance, the file ParabolicTrough. FCStd in the public repository https://github. com/bielcardona/OTSunSuppMat (see also Section The MultiTracking class) contains a model prepared to analyze a parabolic trough collector. The parabolic mirror (in blue) is made by extruding a parabolic segment, and its label is Parabolic_reflector(Mir1). It means that when imported with OTSun, it will have an associated material whose parameter name is Mir1. In turn, this material has to be properly defined (see Section The Material class) so that it behaves as a mirror. Other elements in this model are the central cylindrical surface (in red), labeled Cylindrical_absorber(Abs1), and its covering (in green), labeled Tube_glass(Glass1); hence, materials named Abs1 and Glass1 have to be defined as an absorber surface and as a transparent volume material, respectively (see Section The Material class).
Model validation We discuss here two experiments showing how the results obtained with OTSun are compared with those obtained using other software tools, hence providing a validation for both our model and our implementation. A third validation example, related to the optical behaviour of perovskite solar cells, can be found in  [2]. In each case, we have compared the results obtained by OTSun with a reference model using both the mean error (ME), so that we can determine if OTSun has an overall tendency to overestimate or underestimate the results, and the root mean square error (RMSE), to evaluate the global accuracy. The scripts and auxiliary files cited in this section can be found at https://github. com/bielcardona/OTSunSuppMat.
The files LFR. FCStd and LFR. tnh contain the implementation of this geometry in FreeCAD and Tonatiuh, respectively. Additionally, the file LFR_output. FCStd shows the output given by OTSun with the simulation of some rays with a transversal incidence angle equal to 45 degrees. Since the optics implemented in Tonatiuh is not based on the Fresnel equations in all its generality, we took the decision to use materials with constant optical properties, so that we can use materials with the same properties in both programs. The file validation1. py contains the specification of the optical materials used in this example, as well as the code used to compute the optical efficiency. In our computations (both with OTSun and Tonatiuh), we have simulated the emission of 100,000 sun rays per each sun position, using the Buie model approach  [21]  with a value of 0.
See the files mirror. FCStd and validation2. py for more details on this geometry and the script we have used to make the simulation with OTSun. Also, file mirror_output. FCStd shows the output produced by OTSun with some simulated rays drawn. The optical properties of this configuration can also be determined by the analytic transform matrix method (TMM), as exposed in  [23]  and implemented in  [24]. A comparison of the results obtained using these two methods is shown in Fig  4 , where the reflectances are plot as a function of the wavelength using two different incidence angles (θ = 45˚and θ = 80d egrees) and with parallel and perpendicular polarizations.
Conclusion Ray tracing software systems are commonly used to make optical analysis of solar energy devices. However, the available open source systems make excessive simplifications in the implementation of optics, causing limitations on their features. In this paper, we have  presented the open source package OTSun. It is a Monte Carlo ray tracing python library, where the optics is implemented using the Fresnel optics equations in their most general form. The optical phenomena implemented in OTSun allow for dependence on wavelength, incidence angle, light polarization, interference, ray attenuation, complex refractive index, source spectrum and direction of emission, among others. OTSun also implements tracking movements, so that objects move to track the sun or to reflect the rays onto a specific target.
But not only for them: It has been demonstrated that OTSun is a software tool suitable to analyze opto-mechanic problems, due to its flexibility and that the optics equations are implemented in its most general form. To this end, and as our future work, we have in mind to develop modules for FreeCAD to simplify the construction of optical elements such as lenses, collimators, filters, etc. , and solar energy devices such as LFRs, PTCs, solar power towers, PV cells, etc. In its present form, OTSun is exclusively an optics simulator. Even with this limitation, and for the case of solar cells, we have implement functions to obtain the photocurrent if the internal quantum efficiency is given as input. A future work consists in integrating a solver for the carrier transport equations, to evaluate the capabilities of power electricity conversion of solar cells.
The otsun package is available at (https://github. com/bielcardona/ OTSun). The scripts and auxiliary files used in the Model Validation section can be found at (https:// github. com/bielcardona/OTSunSuppMat). The complete documentation of the API can be found at (https://otsun. readthedocs. io). The web tool OTSunWebApp is available at (http://otsun. uib. es).
The capability of the simulation software motivates the engineers to analyze the stress in complex welded joints where ordinary mathematical expression may lack. However, unfortunately, the simulations were interpreted mechanically without considering the simulation parameters deeply. For this reason, this research aims to study the effect of simulation parameters on the stress concentration factor of square hollow sections structures using ANSYS workbench. In this context, two main meshing techniques (face split vs. slicing) and various behavior of materials (linear vs. nonlinear) having been considered. The outputs of the investigations revealed the superiority of the materials nonlinearity over linear behavior in terms of results acceptability comparing to their corresponding real-life ones.