dataesr/scientific-paragraphs-categorization huggingface dataset:
Each of them represents a different layer of a multiplex biological network, and each layer has its own meaning, size, relevance and bias. For instance, the network of pathways is derived from information provided by several pathway databases containing heavily curated expert knowledge rather than large-scale datasets prone to noise. Conversely, the network of correlations computed from RNA-seq expression data contains a lot of links, some of which are undoubtedly noise. Furthermore, the different experimental approaches sourcing the interaction data capture different parts of the interaction space, therefore leading to incomplete networks. It is estimated for instance, that only 20% of the human protein-protein interaction space has been currently deciphered  (Menche et al. , 2015). Similarly, curated interactions in pathway databases are limited to the proteins for which such information exists, letting aside a potential wealth of data yet to be deciphered.
Funding This work was supported by the  Centre National de la Recherche Scientifique (PEPS BMI IMFMG)  and the A* MIDEX project  (no  ANR-11-IDEX-0001-02 ) funded by the '' Investissements d'Avenir'' French Government program , managed by the  French National Research Agency (ANR). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
This work presents a methodology to evaluate the reconstruction of the Computer-Aided Design (CAD) model in reverse engineering of an aspherical lens. A glass made lens is used as reference part for the measurements. The reconstruction of the CAD model is explained and the analysis of the deviations between data points measured on the lens surface and theoretical CAD model is presented. The theoretical model was developed through the measurement of data points on the profile of the part using a Coordinate Measuring Machine (CMM) Cantilever type having a probing uncertainty of 3. 2 µm. A 10 th order stepwise polynomial regression model was fitted to the data profile with Matlab software and the parameters K and R of the lens were determined by analyzing the aspherical theoretical equations.
Recently, three fitting algorithms were investigated to evaluate aspherical surfaces form large-volume datasets. The methods of Limited memory-Broyden-Fletcher-Goldfarb-Shanno (L-BFGS), Levenberg-Marquardt (LM) and Iterative Closest Point (ICP) variant were applied and their speed of convergence and robustness was compared  [11]. Selection of the measurement instrument and fitting algorithm are determinant of the uncertainty in designed lens. This work presents a methodology to carry out reconstruction of CAD model in reverse engineering of an aspherical lens. A physical lens prototype was used as reference part to take the measurements. The reconstruction of the CAD model is explained and the analysis of the deviations between lens surface data points and theoretical CAD model is presented.
Web of Conferences The measurement strategy is described as follows: i) measurement of the contour circle of the lens taking 30 points after aligning with the machine basis, to determine the circle center; the process was repeated with 5 new measurements of this circle, taking 3 points each time and determining the center to compare with the first measurement. ii) extraction of the orthogonal axis (Z) from the center of the circle, with the CMM software. iii) determination of the top surface point of the lens by moving the CMM probe, locked at the x and y axes as determined by the lens circle center. iv) measurement of the lens top surface with the probe to determine the top point, changing the origin of the coordinate system to this point. This strategy is presented in Fig. The data points were fitted to a curve using an algorithm built in MatLab software.
The variability produced changes and uncertainty in bi coefficients that propagated to the aspherical constants of the lens. The three-dimensional (3D) CAD model of the surface was developed with Catia software. The model was built by inserting the lens equation with determined parameters K and R to initially fit a 2D line and thus extrude in 3D space to obtain the surface. An analysis of the CAD model was carried out by measuring the lens prototype and taking a cloud of points to compare. It was used the same Cantilever CMM. The cloud of points were compared with the CAD model and the deviations were determined and saved as txt data files. Alignement was accomplished by using the lens external circle of CAD model.
Results and discussion A group of 51 points obtained of the aspherical profile was determined with the CMM and the coordinates x, y and z were saved in a file txt format. The coordinates of x and z-axis are used to fit the regression model, since the y coordinates were fixed (locked y-axis). Despite of this, y presented a small variation and was considered to determine the s parameter in the model, as squared root of x squared plus y squared. The results were fitted using the stepwise polynomial regression method with the algorithm developed in MatLab, showed in Table  1. The terms were consecutively introduced in the model, as x 2 , x 4 , x 6 , x 8 and x 10 , followed by an evaluation of the regression fitness. This evaluation was done by t tests of the coefficients and by F Snedecor test over regression.
Study sites and climatic data The study sites were two evergreen Mediterranean monitored forests in Southern France where CO 2 , water vapour and energy fluxes are measured using the Eddy covariance technique  (Baldocchi, 2003). Both sites are included in FLUXNET (http://fluxnet. ornl. gov/). The first site, Fontblanche (43. 2 • N, 5. 7 • E; 420 m), is a mixed stand where Pinus halepensis Mill. dominates the open-top canopy layer reaching about 12 m; Quercus ilex L. forms a lower canopy layer, reaching about 6 m, and there is a sparse shrub understory which includes Quercus coccifera L. (Simioni et al. , 2013). The second site, Puechabon (43. 4 • N, 3. 4 • E; 270 m), is a dense coppice in which overstorey is dominated by Q. ilex with a density of around 6000 stems ha -1  (Rambal et al. , 2004; Limousin et al.
, 2012). Both forests grow on rocky and shallow soils that have a low retention capacity and are of Jurassic limestone origin. The climate is Mediterranean, with a water stress period in summer, cold or mild winters and most precipitation occurring between September and May. Meteorological data were obtained from the neighbouring stations of St. Martin de Londres (for Puechabon) and Aubagne (for Fontblanche). According to those data Puechabon is colder and receives more precipitation than Fontblanche (Table  1 ). Meteorological data showed a decrease in total rainfall since the 1970s in Puechabon but no trend in Fontblanche. Both sites exhibit a positive trend in temperatures more evident for the maximum values (Fig. A1 ). We assumed that GPP (gross primary productivity) is driven by the top pine and/or oak layers and that the percentage of LAI related to the understory shrub layer will behave like that of the oak species (evergreen, shrubby).
The model We used MAIDEN  (Misson, 2004) , a stand productivity mechanistic model driven by a number of functions and parameters representing different processes. The model inputs are precipitation, maximum and minimum temperature, and [CO 2 ] with a daily time step. This model has been previously implemented for monospecific forests, including two oaks and one pine species, using dendroecological chronologies of growth and, when available, stand transpiration estimates from sap-flow sensors  (Misson et al. , 2004; Gaucherel et al. , 2008). However, the model has never been compared to actual CO 2 flux data to ensure that it provides unbiased estimates of forest productivity. In this study, the model was further developed to match ground-based observations and generalize model use by modifying the photosynthesis and allocation modules (including the different phenophases) in relation to climatic drivers.
Eddy covariance CO 2 flux and dendrochronological data The process-based model was calibrated using daily gross primary productivity (GPP), dendrochronological data and inventory data. To develop the model, those functions used to model daily stand photosynthesis (i. e. Eq. 1 to 9) were first calibrated against GPP values. GPP estimates were obtained from half-hourly net CO 2 flux measurements (NEP). GPP was obtained as the difference between measured net ecosystem productivity and calculated ecosystem respiration  (Reichstein et al. , 2005). Negative GPP values were corrected following  Schaefer et al. (2012). Half-hourly GPP data were integrated to obtain daily estimates for the period 2001-2013 (Puechabon, methods detailed in  Allard et al. , 2008)  and 2008-2012 (Fontblanche, Table  1 ).
In the second step, those functions used to model how carbon assimilated and/or storage is allocated to the growth of the tree stem (i. e. Eqs. 10 and 11) were developed using calculated annual stem biomass increment time series. Stem biomass increment chronologies were built combining dendroecological data and forest inventory data collected at each site. We built one chronology for Q. ilex in Puechabon, a second for Q. ilex in Fontblanche and a third one for P. halepensis at Fontblanche (Fig. 2 ). For pines, two perpendicular cores were extracted using an increment borer from 25 trees in fall 2013, whereas for oaks we used cross sections. In Fontblanche, 15 oak stems were felled and basal sections collected in spring 2014. A total of 17 oak stems from Puechabon were logged in 2005 and 2008.
The age and diameter distributions of the studied forests are depicted in Fig. A2. All samples were processed using standard dendrochronological methods  (Fritts, 1976). Annual growth (RW) was measured using a stereomicroscope and a moving table connected to a computer. RW cross-dating was visually and statistically verified. RW estimates were transformed to basal area increments (BAI, cm 2 yr -1 ). Mean BAI chronologies were obtained by averaging individual tree BAI time series. In Fontblanche, BAI during the period 1987-1995 was standardized relative to the mean calculated after excluding that period (Fig. 2 ). BAI data were standardized because we did not find a climatic explanation for the abrupt growth peak observed in Fontblanche during that period (Fig. 2 ). Therefore, we assumed that it had been caused by a release event (i.
e. reduction in competition) produced by the death of neighbours as a consequence of winter frost during  1985 and 1987 (Vennetier, personal communication, 2014)). These two frosts were reflected by the presence of characteristic frost rings in most individuals from Fontblanche. To scale BAI chronologies to the same units as annual stem biomass (which is an output of the model), we used plot inventory data collected around the flux towers at the two sites. Inventory data included stem diameter for all trees and tree height collected for a subsample every 2 years during 2007-2011 in Fontblanche as well as annual diameter estimates for the period 1986-2011 for Puechabon. Individual annual biomass increments were estimated by subtracting the stem biomass of one year from that of the next; then, stand stem biomass increments (SBIs, g C m -2 yr -1 ) were calculated by integrating plot data.
Acknowledgements. G. Gea-Izquierdo was funded by the  Labex OT-Med  (no. ANR-11-LABEX-0061 ) from the " Investissements d'Avenir" program  of the  French National Research Agency  through the A* MIDEX  project (no. ANR-11-IDEX-0001-02 ). Federation de Recherche FR3098 ECCOREV, the labex IRDHEI and OHM-BMP also supported the study. We are grateful to  Roland Huc  for sharing data from Fontblanche. Edited by:  Akihiko Ito.
Invariom partitioning and notation are used to estimate anisotropic hydrogen displacements for incorporation in crystallographic refinement models. Optimized structures of the generalized invariom database and their frequency computations provide the information required: frequencies are converted to internal atomic displacements and combined with the results of a TLS (translation-libration-screw) fit of experimental non-hydrogen anisotropic displacement parameters to estimate those of H atoms. Comparison with TLS+ONIOM and neutron diffraction results for four example structures where high-resolution X-ray and neutron data are available show that electron density transferability rules established in the invariom approach are also suitable for streamlining the transfer of atomic vibrations.
Introduction Improving the accuracy of structural information derived from conventional single-crystal X-ray diffraction (XRD) experiments has been the initial aim for transferring aspherical scattering factors  (Brock et al. , 1991)  and it remains the central aim of the generalized invariom database (GID;  Dittrich et al. , 2013). Deriving molecular properties from aspherical electron density is another important and closely related aim, since such properties can only be as accurate as the best possible set of coordinates that can be refined from a given data set. Anisotropic displacement parameters (ADPs) of atoms and their electron density distribution (EDD) are correlated  (Hirshfeld, 1976). It has hence been shown to be beneficial to incorporate hydrogen ADPs for obtaining accurate EDDs by least-squares refinement of multipole parameters against highresolution data  (Madsen et al.
, 2004). Since this must also hold true for parameters derived from conventional data sets, our recent efforts concern the estimation of temperature-dependent hydrogen ADPs (H-ADPs) for selected applications in structure determinations with the independent atom model (IAM) as well as refinement with aspherical scattering factors. This includes charge density (CD) studies and refinements of data sets of normal resolution (sin = ¼ 0:6 A ˚À1 or ISSN 1600-5767 d ¼ 0:84 A ˚) using scattering-factor databases, of which the GID is one. 1  Scattering of H atoms is limited in reciprocal space owing to their comparably low scattering contribution and their missing core density. XRD therefore has limitations in locating their positions and displacements accurately  (Cooper et al. , 2010) , a situation that has already been improved considerably by scattering factor databases  (Dittrich et al.
, 2005)  and Hirshfeld atom refinement  (Jayatilaka & Dittrich, 2008; Capelli et al. , 2014) , as comparisons with results from neutron diffraction have shown. These developments allow free refinement of hydrogen parameters but require good low-order data  (Orben & Dittrich, 2014). Several authors have proposed improved or alternative hydrogen treatment in XRD, mainly for CD work. With respect to positional parameters  Hoser et al. (2009)  recommend to only use low-order reflections from a high-resolution data set to determine the X-H directions, and then elongate to average neutron diffraction values  (Allen & Bruno, 2010) , whereas we advocate the use of calculated positions and X-H distances from the invariom database  (Schu ¨rmann et al. , 2012) , a procedure also applicable to data sets of low quality.
Computed frequencies can subsequently be converted into internal atomic displacements, which are again combined with a TLS analysis 2 after appropriate scaling  (Scott & Radom, 1996). Last but not least, full periodic computations, implemented in the SHADE3 server, may also provide H-ADPs  (Madsen et al. , 2013; Madsen & Hoser, 2014). However, all these approaches have disadvan-tages: estimates derived from diffraction data do not take into account temperature dependence of the internal contribution of atomic vibration; 3  neutron data for the SHADE2 approach are not available for rare bonding environments; theoretical studies require high computational costs and are thus unsuited for conventional structure determinations. This is why we introduce a new approach based on the invariom database, combined with a new freely available TLS analysis program.
Our approach relies on the geometry-optimized model compounds in the invariom database. 4 It covers a wide range of chemical environments in organic chemistry  (Dittrich et al. , 2013)  and now also facilitates aspherical-atom refinements of coordination compounds  (Dittrich et al. , 2015). Earlier work is here extended by providing functionality to estimate H-ADPs relying on the established empirical rules of partitioning electron density with invarioms. The rules already allow one to separate and reconstruct molecular EDDs from fragments and now also provide estimates of the internal modes of vibration of a particular chemical environment. Estimation of H-ADPs thus allows further improvement of all those structures where chemical environments are covered by scattering factors of the GID.
Moreover, estimated H-ADPs increase the choices in handling three common situations: (a) data of low quality can be better evaluated by reducing the number of refined parameters; (b) high-quality data of comparably low resolution are available; or (c) refinement of H-atom positions becomes an option when aspherical scattering factors and ADPs are kept fixed, thereby reaching better agreement with results from neutron diffraction and bond-length predictions of quantum chemistry. Central to this work is the underlying development of a new segmented-body  (Schomaker & Trueblood, 1998 ) TLS refinement program called APD-Toolkit (anisotropic proton displacement toolkit), which is introduced here.
1 Four scattering-factor databases currently exist: the 'supramolecular-synthon based fragments approach' (SBFA;  Hathwar et al. , 2011) , the 'experimental library multipolar atom model' (ELMAM2;  Zarychta et al. , 2007; Domagała et al. , 2012)  (both based on high-resolution experiments), the 'generalized invariom database' (GID;  Dittrich et al. , 2006 Dittrich et al. , , 2013) )  and the 'University at Buffalo Databank' (UBDB2011;  Dominiak et al. , 2007; Jarzembska & Dominiak, 2012)  (the latter two based on theoretical DFT computations). All four rely on the established Hansen/Coppens multipole model  (Hansen & Coppens, 1978)  and can successfully be used to improve the accuracy and precision of least-squares structure refinements. 2 In the context of this manuscript the term 'TLS analysis' is used for a postrefinement analysis of the atomic displacement parameters determined by the refinement program.
Workflow of the program The APD-Toolkit carries out the following steps: (1) Determination of invariom names of all atoms. (2) Calculation of internal displacement parameters from GAUSSIAN  (Frisch et al. , 2013)  output files and caching of results 5 for subsequent applications. (3) Transformation of internal ADPs to the crystal coordinate system. (4) Calculation of the difference between observed and calculated internal ADPs for all non-H atoms to remove contamination of the TLS parameters with internal ADPs. (5) Determination of a suitable segmentation model for the segmented rigid-body analysis. (6) Computation of a physically meaningful set of TLS+ARG (attached rigid group) parameters describing ADP differences. (7) Computation of external ADPs for all H atoms based on the TLS+ARG parameters and the atomic coordinates.
Results and discussion 3. 1. Similarity of ADPs from TLS+ONIOM and TLS+INV 7  estimates: similarity of U(2) from TLS+ONIOM and TLS+INV For initial validation, theoretical ADPs taken from the generalized invariom database were compared with those obtained from ONIOM computations  (Svensson et al. , 1996)  to assess the transferability of internal ADPs. Computations were performed with the B3LYP functional and the basis set combination 6-31G(d,p):3-21G, which has been shown to be a good compromise between computational requirements and the quality of results  (Dittrich et al. , 2012). Internal ADPs are not compared directly since the internal parts of the ADPs encompass different parts of the overall displacements. Instead the values of internal displacements were combined Illustration of the rigidity index: the NO 2 group attached to a molecule R is considered rigid if the average of out i is twice as large as the average value of in i.
5 Frequency information is extracted from all GAUSSIAN output files and stored in a database linking normal modes to the model compound including its geometry. This reduces the amount of data to store from several hundred gigabytes to about 100 MB. In a second step, when the method is applied to a structural model, the database is loaded and displacement parameters are calculated for all model compounds at the given temperature. The results are stored in a second database file now containing all internal ADPs at a certain temperature. The file is about 4 MB in size. During every subsequent application of the method it is checked whether the database already contains ADPs for the current temperature and ADPs are only recomputed if the method is applied at this temperature for the first time.
Comparison with results from neutron diffraction Estimated ADPs were compared with ADPs refined against neutron diffraction data to further validate the TLS+INV method. A set of four structures where both high-resolution X-ray data and neutron data are available were taken from the literature  (Cole et al. , 2002; Woin ´ska et al. , 2014; S ¸erb et al. , 2014; Madsen et al. , 2003). A scaling model was fitted to each neutron data set to bring both sets of ADPs onto the same scale  (Blessing, 1995). This was achieved by computing the set of parameters S 1 -S 7 in equation (  3 ) that minimize the difference between ADPs of equivalent atoms in both models. U scaled N ¼ U measured N S 1 þ S 2 S 5 S 6 S 5 S 3 S 7 S 6 S 7 S 4 0 @ 1 A : ð3Þ Modified TLS+INV results were then compared with those obtained with the SHADE server.
Usability The program APD-Toolkit was designed specifically to be easy to use. To demonstrate this a series of crystal structures were taken from the literature. The structures were re-refined with the invariom model and a subsequent TLS+INV treatment was applied. The TLS+INV application only requires one program call with output files from a previous refinement and no further input is needed. Currently SHELXL-style  (Sheldrick, 2008). res files, XD-style (http://xd. chem. buffalo. edu/ ). res files, CIFs and PDB files (http://www. rcsb. org/) are supported. In Table  8  the effect of including the estimated H-ADPs on R 1 (F) was investigated. Respective refinement models use the same number of parameters. Table  8  shows that the improvements in the R 1 (F) values are temperature dependent.
Conclusion and outlook The combination of segmented rigid-body analysis with information from geometry optimized model compounds allows one to rapidly estimate anisotropic hydrogen displacements from tabulated values in the TLS+INV approach introduced here. The invariom approach is thus being extended to predict not only aspherical scattering factors but also H-ADPs, all from one consistent model and notation. This is an important advantage over other scattering-factor databases. The program APD-Toolkit provides an easy to use way of estimating these displacement parameters with an accuracy comparable to the TLS+ONIOM method without the need for extensive computations upon application. The software is a standalone alternative to the SHADE server, is freely available for download (http://ewald.
The severe shortage of publicly shared data in the field also restricts the scope of large-scale analyses. It is possible to predict the structure of ecological networks, either using latent variables  (Rohr et al. 2010 , Eklöf et al. 2013)  or actual trait values  (Gravel et al. 2013). The calibration of these approaches require accessible data, not only about the interactions, but about the traits of the species involved. Comparing the efficiency of different methods is also facilitated if there is a homogeneous way of representing ecological interactions, and the associated metadata. In this paper, we 1) establish the need for a data specification serving as a common language among network ecologists, 2) describe this data specification, and 3) describe rmangal, a R package and companion database relying on this data specification.