dataesr/scientific-paragraphs-categorization huggingface dataset:
Design of the simulator The simulator has been developed in the GNU R language, generally used for data processing and statistical analysis. A user manual and a description of the R language can be found on the website http://www. r-project. org/. The main interest of such a language remains in that it is distributed under a GNU GPL license that does R routines in an open source program, available on various platforms (i. e. , GNU/Linux, FreeBSD, NetBSD, OpenBSD, Mac OS and Windows). The simulator is composed of three main blocks (Fig. 2 ): an input block that contains the different elements mandatory for the processing, a processing block where the user can choose which algorithm to use, and an output block containing the different results of the simulation. As inputs, this simulator requires the receiver coordinates, the satellite ephemeris and a set of optional environmental parameters such as a DEM in order to take the possible masking of the terrestrial topography into account, as well as adaptive mapping functions to integrate atmospheric delays and bending effects.
GNSS orbit parameters The simulations are based on the determination of the positions of the specular reflection points, once the receiver and the satellite positions are known. Satellite coordinates can be obtained from the International GNSS Service (IGS) ephemeris final products, which provide GNSS orbit and clock offset data with a temporal resolution of 15 min in the SP3 format for the past epochs, or are derived from the Keplerian parameters (semi-major axis, inclination, and argument of perigee) to predict GNSS satellite positions. Ephemeris products are available on the IGS website (http://igs. org/), and Keplerian parameters, e. g. , at http: //www. navcen. uscg. gov.
Acknowledgements. This work was funded by  CNES  in the framework of the  TOSCA  project " Hydrologie, Océanographie par Réflectométrie GNSS (HORG) " and by the  RTRA STAE foundation in the framework of the "Potentialités de la Réflectométrie GNSS In Situ et Mobile (PRISM) " project. Nicolas Roussel  is supported by a PhD granted from the  French Ministère de l'Enseignement Supérieur et de la Recherche (MESR). Edited by:  R. Marsh.
The transformation principle of the NEF proposes that the connection weight matrix between two neural populations can compute a non-linear function, and can be factored into two significantly smaller matrices. By using these factors instead of full connection weight matrices, NEF-designed models are more computationally efficient, which allows Nengo to run large-scale neural models on low-cost commodity hardware. In order to make Nengo more simple, extensible, and fast, we have rewritten Nengo 2. 0 from scratch in Python, leveraging NumPy  (Oliphant, 2007)  for manipulating large amounts of data. While NumPy is its only dependency, Nengo contains optional extensions for plotting if Matplotlib is available  (Hunter, 2007)  and for interactive exploration if IPython is available  (Pérez and Granger, 2007).
Since Nengo only depends on one third-party library, it is easy to integrate Nengo models in arbitrary CPython programs, opening up possibilities for using neurally implemented algorithms in web services, games, and other applications. Nengo 2. 0 has a simple object model, which makes it easy to document, test, and modify. Model creation and simulation are decoupled, allowing for models to be run with other simulators as drop-in replacements for Nengo 2. 0's platform-independent reference simulator. To date, we have implemented one other simulator that uses PyOpenCL  (Klöckner et al. , 2012)  to take advantage of a GPU or multicore CPU. The OpenCL simulator can simulate large models on the scale of Spaun at least 50 times faster than Nengo 1. 4 using inexpensive commodity hardware. In all, Nengo 2.
NENGO SIMULATORS Decoupling model creation and simulation has been done previously by PyNN  (Davison et al. , 2008). In PyNN, the same Python script can be used to run a model on four different simulators. Nengo follows this programming model by decoupling neural model creation and simulation, which enables Nengo simulators to allocate memory and schedule computations in the most efficient ways possible. Simulators are given a Model as an argument; this Model is a static symbolic description. The simulator can take the model description and build whatever data structures are best suited to implement the simulation. We have implemented a platform-independent reference simulator as an example for simulator designers. This simulator is not a specification; any object that accepts a Nengo Model as an argument is considered a Nengo simulator.
LORENZ ATTRACTOR NETWORK While the communication channel exemplifies the representation and transformation principles of the NEF, the Lorenz attractor exemplifies the dynamics principle. Many models in theoretical neuroscience are based on attractor networks  (Amit, 1992; Deco and Rolls, 2003). The NEF has been used in the past to implement many different types of attractor networks by recurrently connecting ensembles with functions that implement dynamical systems  (Eliasmith, 2005). Figure  5  depicts a Nengo implementation of the Lorenz chaotic attractor with a single ensemble composed of 2000 leaky integrate-and-fire neurons. We have implemented the Lorenz attractor in PyNN for benchmarking purposes (code not shown; the PyNN script is ∼100 lines long, while the Nengo script in Figure  5  is 20 lines long).
BENCHMARKS While benchmark models are not indicative of performance on all models, increasing simulation speed was a primary goal of Nengo 2. 0. To validate that performance has improved, we ran the models described in section 5 for various numbers of neurons and dimensions for each ensemble. The communication channel and Lorenz attractor are small models that demonstrate the principles of the NEF. Their small size enables us to write PyNN scripts that implement roughly the same functionality with Brian  (Goodman and Brette, 2008) , NEURON  (Hines et al. , 2009) , and NEST  (Eppler et al. , 2009)   2. We ran each parameter set five times on the same machine, and plot the mean time elapsed in Figure  7. In most cases, the coefficient of variation for the five sample times is well below 0. 1, except for two outliers with coefficients of 0.
CONCLUSION Nengo 2. 0 is the next generation of Nengo. It has been rewritten from scratch, but can already simulate most models that have been built using Nengo 1. 4. It does this with 11% as many lines of code as its predecessor, and interacts seamlessly with other scientific Python tools. While the reference simulator is simple and easy to understand, the OpenCL simulator is extremely fast; it can simulate circular convolution models 50-200 times faster than Nengo 1. 4, which itself is faster than alternative simulators on simpler models. This makes the creation and simulation of models that are many times larger than Spaun tractable with current hardware. These models will further test the NEF as a theory of neural computation; Nengo makes those models accessible to anyone with a modern computer.
ACKNOWLEDGMENTS We thank  Bryan Tripp  for editing this paper, contributing to Nengo and the Nengo OpenCL simulator and for creating Nengo 1. 4, which has been indispensable for the past 6 years, and will continue to be used for many years to come. We thank  Peter Blouw  and  Brent Komer , who have contributed to Nengo by providing examples, unit tests, and bugfixes. Finally, we thank the two reviewers who have provided constructive feedback on this paper.
A gene expression atlas is an essential resource to quantify and understand the multiscale processes of embryogenesis in time and space. The automated reconstruction of a prototypic 4D atlas for vertebrate early embryos, using multicolor fluorescence in situ hybridization with nuclear counterstain, requires dedicated computational strategies. To this goal, we designed an original methodological framework implemented in a software tool called Match-IT. With only minimal human supervision, our system is able to gather gene expression patterns observed in different analyzed embryos with phenotypic variability and map them onto a series of common 3D templates over time, creating a 4D atlas. This framework was used to construct an atlas composed of 6 gene expression templates from a cohort of zebrafish early embryos spanning 6 developmental stages from 4 to 6.
3 hpf (hours post fertilization). They included 53 specimens, 181,415 detected cell nuclei and the segmentation of 98 gene expression patterns observed in 3D for 9 different genes. In addition, an interactive visualization software, Atlas-IT, was developed to inspect, supervise and analyze the atlas. Match-IT and Atlas-IT, including user manuals, representative datasets and video tutorials, are publicly and freely available online. We also propose computational methods and tools for the quantitative assessment of the gene expression templates at the cellular scale, with the identification, visualization and analysis of coexpression patterns, synexpression groups and their dynamics through developmental stages.
However, moving forward from databases of gene expression that contain average values at low spatiotemporal resolutions-such as those obtained from DNA microarrays available for most model organisms-to a dynamic, cell-based 4D atlas is a major paradigm shift that requires the development of appropriate methods and tools. In this context, the design and implementation of automated image analysis strategies to build a gene expression atlas with resolution at the cellular scale is an important methodological bottleneck towards greater biological insights  [8] ,  [9]. The task of assembling imaging data from cohorts of individuals, or analyzed embryos, onto a series of 3D prototypes, or templates (one per developmental stage), can be approached by finding a spatial correspondence between individuals based on registration methods, a technique used in medical imaging  [10].
Such data would make the zebrafish the first vertebrate model amenable to a systemic study. However, building 3D templates of gene expression for the zebrafish blastula and gastrula stages is especially problematic due to the lack of morphological landmarks required for the registration of patterns  [20] ,  [21]. We provide a methodology to construct, visualize and analyze a gene expression atlas composed of templates at various stages of vertebrate early development. We designed, implemented and now deliver two computational frameworks, Match-IT and Atlas-IT, to support the automatic mapping of 3D gene expression patterns from different individuals (the analyzed embryos) onto common reference specimens (the templates) with resolution at the cellular scale. This ''virtual multiplexing'' procedure  [14]  overcomes the limited number of gene products that can be jointly stained and measured in a single specimen.
Match-IT was used to produce the prototypic cartography of 9 gene expression patterns imaged from 3D double fluorescent in situ hybridization at 6 developmental stages (Table  S1 , Movie S1, Figs. S1, S2, S3, S4, S5, S6, S7). Atlas-IT was designed to interactively visualize gene coexpression patterns and their dynamics. We validated our 4D atlas construction methodology by an automated quantitative assessment of gene patterns' similarity and overlap through time. Analytical tools, such as clustering, were designed to identify morphogenetic domains and gene synexpression groups, i. e. groups of genes sharing the same spatiotemporal expression patterns. The proposed spatiotemporal atlas of zebrafish blastula and early gastrula preserves the information of the cell as the gene expressing unit, providing means for the integration of genetic and cellular data unavailable so far.
Match-IT: A workflow to build a gene expression atlas We designed a computational framework (Fig. 1 ), going from image acquisition to image data analysis, to perform the mapping of different stained gene expression patterns onto a common prototypic model at each developmental stage (Fig. S8 ), thus creating a series of 3D templates of gene expression with resolution at the cellular scale. The processing workflow consisted of embryo staining, image data acquisition (Materials and Methods), nuclear center detection, gene pattern segmentation, mapping of the analyzed embryos onto a template at each stage, and selection of template cells positive for the expression of specific genes. This methodology was designed to document at a sufficient spatial and temporal resolution the gene expression dynamics underlying the formation of the Spemann organizer and the embryonic axis of zebrafish early embryos.
To this end, we imaged the dorsal side of fluorescently stained embryos with cellular resolution from fixed specimens about every 30 min from 4 to 6. 3 hpf. The resulting 6 templates comprised a stencil of in toto 3D images of the template specimens (Fig. 2a ) at different stages, and mappings of the partial 3D views of the analyzed embryos (Fig. 2b ). In order to integrate 3D data into one template, our novel Match-IT tool (Software S1 and User Guide S1) performed the segmentation of gene expression domains, the mapping of analyzed.
Author Summary We propose a workflow to map the expression domains of multiple genes onto a series of 3D templates, or ''atlas'', during early embryogenesis. It was applied to the zebrafish at different stages between 4 and 6. 3 hpf, generating 6 templates. Our system overcomes the lack of significant morphological landmarks in early development by relying on the expression of a reference gene (goosecoid, gsc) and nuclear staining to guide the registration of the analyzed genes. The proposed method also successfully maps gene domains from partially imaged embryos, thus allowing greater microscope magnification and cellular resolution. By using the workflow to construct a spatiotemporal database of zebrafish, we opened the way to a systematic analysis of vertebrate embryogenesis. The atlas database, together with the mapping software (Match-IT), a custommade visualization platform (Atlas-IT), and step-by-step user guides are available from the Supplementary Material.
We expect that this will encourage other laboratories to generate, map, visualize and analyze new gene expression datasets. embryos onto a common reference specimen and the identification of positive cells (Fig. 1  and Movie S2), eventually delivering a 3D database that summarized the genetic profile of single cells. Nuclear center detection. Nuclear center detection was an important preliminary step to (1) compute a common referential for all the specimens that will guide the first coarse mapping, (2) keep image registration within the boundaries of a ''nuclear mask'' around the embryo, and (3) quantitatively analyze gene expression domains from intracellular locations only, without taking into account extracellular space where staining is weaker. Additionally, detecting the nuclei has the advantage that it allows working at the cell level: cell clustering and cell entropy have a biological meaning, whereas working at the voxel level, although theoretically possible, does not have this biological significance.
2e ) the optimal values were subsequently chosen and validated by an expert through comparison of the raw data with the candidate cell positions from different runs. A quantitative evaluation of this strategy performed on one dataset by comparing the detected centers with 689 manually labeled nuclei produced an error rate of 4% (Fig. S9a-d ). This error detection rate was considered acceptable to assign positive gene expression at the cellular level and was shown to be robust against possible variations in the parameter choices by the expert (Fig. S9e ). Gene pattern segmentation. Our segmentation of the gene expression domains first required supervision and selection by a biologist of the lower image intensity values that best defined the  domain features. Match-IT then used these parameters to perform a thresholding operation followed by morphological image processing (see Materials and Methods).
S12 ). The rigid transformation preserved original gene patterns, making it possible to go back to the raw data for visualization and validation/correction with the Match-IT software at every step of the processing pipeline. After the final step, the average manual offset needed to adjust the mapping of analyzed embryos onto the template was 13 mm (i. e. approximately one cell row) and a 3u rotation. Positive template cell selection. Finally, the selection of positive template cells (Fig. S13 ) was performed using the same rule described for the identification of positive cells in the analyzed embryos. The number N i of cells positive for the expression of gene i in an analyzed embryo differed from the number M i of cells selected as positive in the template after the mapping procedure. Independently from the fluctuations in the mapping procedure mentioned above, this difference was interpreted as resulting from individual variations in terms of internuclear distance and embryo shape, which can reflect staging misalignments (Fig.
S14 ). Atlas-IT: A visualization tool for a gene expression atlas Analysis of the 3D templates produced by Match-IT required dedicated visualization tools to test hypotheses and derive biological insights. The available software kits did not fulfill our requirements, either because they were too specific for a given model organism (such as PointCloudXplore  [26]  for Drosophila) or because they were too generic as visualization and processing tools (such as Icy  [27] , Vaa3D  [28] , or CellProfiler  [29] ) and did not allow displaying selections of individual cellular positions or querying a template for coexpression domains with resolution at the cellular scale. For these reasons, we designed, developed and deliver here the Atlas-IT interactive visualization interface (Fig. 4a  and Software S2 and User Guide S2) to explore 4D atlas resources.
With Match-IT, we added two new tbx16 datasets, tbx16 b and tbx16 c , to the already existing tbx16 a expression in the atlas at 6. 3 hpf (Fig. S20a ). The mean distance from the complete outer surface of each individual tbx16 j domain to the closest boundary point of tbx16 mean remained under one cell diameter (Fig. S20b ). Moreover, the histogram of distances between border points of tbx16 j and tbx16 mean confirmed that most of the expression contours lay within two cell rows from each other (Fig. S20c ). Note that this quality measure was an upper bound of the registration quality reflecting both the mapping variability and the intrinsic inter-embryo variability. Additionally, we confirmed that the spatial relationships between every gene and the gsc j patterns in the analyzed embryos were the same in each template with respect to the gsc mean domain.
Discussion We have designed, developed and delivered the Match-IT and Atlas-IT software tools dedicated to the reconstruction, analysis and visualization of a 4D atlas of gene expression in zebrafish early embryogenesis. The atlas comprises 6 different time points between 4 and 6. 3 hpf, gathering data for 9 gene patterns into 6 different 3D templates. So far, the only known method delivered for the reconstruction of gene expression atlases in the zebrafish was designed by Ronneberger et al. [21]  for the brain and at late developmental stages, when a large number of morphological landmarks could already be recognized. Given the complexity of building a zebrafish brain atlas at late stages, the authors imposed strong constraints on the data in terms of staining protocols and imaging. Our own atlasing strategy was designed to map partial 3D volumes onto whole embryos chosen as templates.
Image data acquisition As an input, our methodology used 3D images acquired by confocal laser scanning microscopy from fixed zebrafish embryos with fluorescent staining of gene expression patterns and DAPI counterstain to highlight cell nuclei. Image acquisition was performed with a Leica SP2 two-photon (for DAPI) and confocal laser scanning upright microscope with a Leica objective HCX APO 20X/0,5W U-V-I or HCX APO 10X/0,3. Embryos were mounted in a teflon mold at the bottom of a 3 cm Petri dish filled with 16PBS, 01% twin 20, and maintained properly oriented with 1% agarose. The nuclei and gsc expression domains were systematically revealed in all the analyzed embryos and templates, and used to compute the gene expression mappings. In addition to the reference gene, gsc, each analyzed embryo was stained for the expression of another gene of interest.
Algorithmic details of Match-IT and Atlas-IT The Match-IT custom-made code was implemented in ITK and Matlab, including the MathWorks package ''geom3D'' redistributed under a BSD license. A public release of this software, together with sample datasets and a user guide, accompanies the publication of this article, http://bioemergences. iscpif. fr/documents/ MatchIT. zip. The segmentation of the gene expression patterns in each analyzed embryo was carried out by a thresholding operation supervised by a biologist to best define the domain features. This operation was followed by ''morphological closing''  [39] , a mathematical transformation based on a spherical structuring element the size of a typical cell diameter (i. e. internuclear distance). Finally, a converse ''morphological opening'' operation left only the largest connected pattern.
The embryo shapes were weighted by the inverse distance function to the external blastoderm contour (i. e. half the average internuclear distance away from the outermost nuclear layer). The Atlas-IT custom-made visualization platform was implemented in Processing. A public release of this software, together with sample datasets and a user guide, accompanies the publication of this article, http://bioemergences. iscpif. fr/documents/AtlasIT. zip Analysis tools for spatiotemporal gene expression atlases 3D clustering of cells according to their gene expression profiles. Template cells at one given developmental stage were grouped according to the similarity of their gene expression profiles using a hierarchical clustering scheme. For a given observed time t, we associated a gene expression vector to each of the detected M cells: c c i ~(g i,1 ,:::,g i,N ), where N~9 is the number of genes under study, g i,j is 1 if the i-th cell expressed the j-th gene, and is 0 otherwise.
Then, a weighted pair group method with averaging (WPGMA) was performed on these vectors based on the Euclidean distance (Fig. 5 ). This method was implemented with the Statistics toolbox of Matlab. 3D+time clustering of genes according to their spatiotemporal regions of expression. Here the analysis involved the totality of cells across all acquisition times. For each of the N~9 genes under study, we associated a spatiotemporal expression vector: g g j ~(c j,1 ,:::,c j,P ), where P is the total number of cells observed across all the observation times: P~P 6 k~1 M k ~55,759, with M k being the number of cells observed at the k-th time of acquisition. For the j-th gene, the i-th coordinate of that vector, c j,i , was set to 1 if the i-th cell expressed that gene, and 0 otherwise. Clustering the genes according to their associated region of expression vectors allowed identifying synexpression groups  [41].
We used the same WPGMA algorithm based on the Euclidean distance and Matlab implementation (Fig. S23 ). Shannon entropy of gene expression. We used information theory to measure an ''entropy'' for gene expression. We consider that the gene expression c c i observed in the i-th cell (1ƒiƒM, where M is the number of cells) is the value taken by a discrete random variable G i among all possible N-uples (g i,1 ,:::,g i,N ) of 0's and 1's, i. e. all the integers in the interval ½0,2 N {1. Assuming that the random variables G 1 ,:::,G M are independent and identically distributed, e. g. with the same law as a given random variable G, their common entropy is: H(G)~{ P 2 N {1 k~0 p k log 2 p k , with the usual conventions that p k denotes the probability of event G~k and we set 0 log 2 0~0, where 2 was chosen as the base in order to express the result in bits.