dataesr/scientific-paragraphs-categorization huggingface dataset:
The task does not consist of inferring the location of the target, but rather to infer an action that may provide relevant pixels at the center of fixation, allowing to identify the target's category. By comparison with the external display (see A), the action is processed from log-polar coefficients, representing a focal sample of the total visual field. Controlling the clutter and reducing the contrast of the digit allows modulating the task's difficulty. AIM  (Bruce & Tsotsos, 2009)  and SUN  (Zhang, Tong, Marks, Shan, & Cottrell, 2008)  models. Recently, the saliency approach was updated using deep learning to estimate saliency maps over large databases of natural images  (Kummerer, Wallis, Gatys, & Bethge, 2017). While efficient at predicting the probability of fixation, these methods miss an essential component in the action-perception loop: They operate on the full image while the retina operates on the nonuniform, foveated sampling of visual space (see Figure  1C ).
, 2019). Each intermediate layer's output is rectified, and the network output uses a sigmoid operator to predict the likelihood of detecting each of the 10 digits. The index of one of the 10 output neurons with maximum probability provides the image category. It is first trained over the (centered) MNIST data set after approximately 20 training epochs. This strategy achieves an average 98. 7% accuracy in the center on the validation data set  (Lecun et al. , 1998). To achieve an even more generic "What" pathway, a specific data set is constructed to train the network. It is made of randomly shifted digits overlaid on a randomly generated noisy background, as defined above. Both the shift and the contrast relative to the background noise make the task more difficult than the original MNIST categorization.
(B) Finally, we scanned parameters of the deep learning neural network. We observed that accuracy quickly converged after approximately 25 epochs (Epochs_adam). We then tested different values for the dimension of respectively the first (Dim1) and second (Dim2) hidden layers, showing weak changes in accuracy. (C) The accuracy also changes with the architecture of the foveated input, as shown here by changing the number N_azimuth of azimuth directions, which are sampled in visual space. This shows a compromise between a rough azimuth representation and a large precision, which necessitates a longer training phase, such that the optimal number is around 24 azimuth directions. scripts for reproducing the figures and exploring the results to the full range of parameters are available at https://github.
Acknowledgments Commercial relationships: none. Corresponding author:  Laurent U. Perrinet. Email: laurent. perrinet@univ-amu. fr. Address:  Institut de Neurosciences de la Timone  ( UMR 7289 )  Aix Marseille Université ,  CNRS Faculté de Médecine -Bâtiment Neurosciences ,  27 ,  Bd Jean Moulin ,  Marseille ,  PACA 13385 Marseille Cedex 05.
Numerical integration We numerically evaluated the dimensionless 3D density (Eq. [21]) and mass (Eq. [24]) profiles by peforming the numerical integrations in cells 50 × 100 of [log n, log(r/R e )], with log 0. 5 ≤ log n ≤ 1 and -3 ≤ log(r/R e ) ≤ 3. Numerical calculations were done with Python's scipy. integrate. quad. For both density and mass profiles, we split the numerical integration in two, i. e. b a f (X) dX = X crit a f (X) dX + b X crit f (X) dX , (25) where exp -b n X 1/n crit = 10 -9 and a ≤ X crit ≤ b. We used a relative tolerance of epsrel = 10 -4 and limit = 1000 in both integrals. If X crit  [a, b] , we also used epsrel = 10 -4 and limit = 1000, but for a single integral from a to b. We performed our analysis using either the highly accurate approximations for b n of  Ciotti & Bertin (1999, hereafter, CB)  or the exact (numerical) solutions of Eq.
(  2 ). We noticed that the difference between these two approaches was negligible (see Sect. 3). We then fit two-dimensional polynomials to both log ρ LGM (x, n)/ ρ(x, n) and log M LGM (x, n)/ M(x, n) , for geometrically spaced x and n, i. e. writing log f LGM / f = - k i=0 k-i j=0 a i j log i x log j n (26) with polynomial orders 2 ≤ k ≤ 12. For this, we used Python's package numpy. linalg. lstsq. We found the smallest residuals for order 10 polynomials when using both the b n approximation of CB and b n by numerically solving Eq. (  2 ). The coefficients are provided in Tables B. 1, B. 2, B. 3 and B. 4 in Appendix B. In the rest of the paper, we present the results relative to the CB approximation, since it is a simpler and more used model, and also because our order 10 polynomial fits remarkably well the exact b n case.
Our Python 3 codes are available at https://github. com/eduardo-vitral/Vitral_Mamon2020a along with coefficients of Tables B. 1 and B. 2. These results will be useful in future mass-orbit modeling analyses of low-mass spherical systems, as we are preparing for globular clusters  (Vitral & Mamon, in prep. ). (A. 1) The ratio of half-mass radius to scale radius r h /a is given by  In Eq. (A. 2) for NFW, c = r max /a, where r max is the maximum allowed radius (because, contrary to all other models discussed here, the NFW model has logarithmically divergent mass). Also, for Einasto, P (-1) (a, y) is the inverse regularized lower incomplete gamma function, i. e. x = P (-1) (a, y) r h a =                              1 + 2 1/3 / √ 3 (Plummer) , satisfies γ(a, x)/Γ(a) = y.
Pharmacokinetic analysis The pharmacokinetic analyses were performed by least-squares regression analysis using WinNonlin ® software as previously described  (Leghait et al. , 2009). The time course of fipronil concentrations were fitted using a monoexponential equation with a first-order absorption phase and clearance defined as a time-dependent variable to account for by the dramatic increase in fipronil elimination rate between the first and the last administrations. The data were weighted by the inverse of the fitted values. The terminal half-life (t1/2), the time (Tmax) at the maximal concentration (Cmax), the area under the concentration curve vs. time (AUC) from time zero to the last detectable concentration and the apparent clearance were determined.
After completing the hybridization procedure, microarrays were scanned on a Genepix 4000B® scanner and signal was quantified using Agilent Feature Extraction Software v9. 5. The raw and processed data, together with details of the experimental procedure and data analysis are available in the Gene Expression Omnibus (GO) database under the accession number GSE75275. All details of microarray data filtering, normalization and quality controls are described in GSE75275. Data were analysed under R (v. 2. 6. 1, www. r-project. org) using packages from Bioconductor (www. bioconductor. org). Briefly, only spots passing a series of quality controls on 8 out of 10 microarrays were analysed and replicated probes were summarized by their median intensity. Data were normalized within (loess) and between arrays (Aquantile) using R limma package.
Microarray data were analysed for gene expression changes using a separate channel analysis as described in limma user guide and GSE75275. We selected 228 probes corresponding to differentially expressed genes (raw p<0. 005 and q-value <0. 39), used hierarchical clustering to represent the data as a heatmap and analysed the enrichment of Gene Ontology biological processes among the upregulated and downregulated genes using the GOstats R package. qPCR RNA samples (2µg) were converted to cDNA using the High Capacity Reverse Transcription kit (Applied Biosystems). Real time qPCR amplifications were performed using the SYBR Green PCR Master Mix® (Applied Biosystems) on an ABI7900HT Real-Time PCR system (Applied Biosystems). Oligonucleotides primer sequences are described in table 1. Based on data from the literature on bovine thyroid gland indicating that TATA-Box-binding protein (TBP) is one of the most stably expressed housekeeping genes in this tissue  (Lisowski et al.
Statistical analysis The effect of the treatment on the time course of body weight was analysed using a two way ANOVA with the time of measure, the treatment and their interaction as fixed effect factor and the animal nested in the treatment as random effect factor. For endocrine parameters and qPCR mRNA expression, variance homogeneity was tested and the effect of the treatment was analysed using an unpaired t-test using Systat 12® software for homogenous or non-homogenous variance. For TT4, as all available data consistently showed decreased total T4 in response to fipronil, a unilateral test was used to analyze this hormone.
Main experiment: effects of fipronil treatment on thyroid transcriptome We studied gene expression in the thyroid gland of 10 control and 10 fipronil exposed-rats using Agilent whole rat genome microarrays. By contrast with what was observed in the liver, fipronil had a relatively limited impact on the transcriptome of the thyroid gland in our conditions. In order to rule out technical issues with the microarray analysis, we also verified that consistent fold-changes (Fig  3A ) and low signal variability  (Fig 3B)  were observed for Agilent spiked-in control RNAs. Given the fact that RNA expression / cDNA data from thyroid gland cells are scarce in databases and that it is likely that the first generation of Agilent "whole rat genome" microarrays used in this study only represents a fraction of the transcripts that are expressed in this highly specialized tissue, we accepted a higher chance of false positives (larger q-value=0.
Our current results support this hypothesis by showing that thyroglobulin gene expression is increased. In addition a strong trend toward an increase expression of the Slc5a5 gene (25%, p=0. 06) was also observed. An upregulation of several genes with documented functions in cell/tissue dynamics was also evidenced that could be related to the hyperplasia described in toxicological non peer-reviewed survey of fipronil and one experimental study. In particular, we confirmed by RT-qPCR the upregulations upon fipronil exposure of Areg and Ptgs1 by 3. 4-and 1. 8-fold, respectively. Concerning fipronil exposure, our data are in agreement with data obtained by us and others in the rat and clearly show that repeated administrations of fipronil lead to an induction of its own metabolism/elimination resulting in a very limited exposure of the animals to fipronil itself toward the end of the experiment contrasting with an important exposure to fipronil main metabolite, fipronil sulfone  (Leghait et al.
Acknowledgements The authors thank  Dr Marlène Lacroix  and  Amatsigroup laboratories  for their technical support and supervision of fipronil and fipronil sulfone assay,  Sylvie Puel  for performing the assays. This work was funded by  French National Institute for Agronomical Research (INRA) ,  French Ministry of Ecology and Sustainable Development :  Programme National de Recherche sur les perturbateurs endocriniens  ( PNRPE 0000442 ) and PhD grant from  SEVAB Doctoral School, Toulouse, France. The authors declare that there are no conflicts of interest.
5 3 62. 5 3 125 nm. Each exposure depends on the overall fluorophore intensity, but commonly we expose for tens of milliseconds using laser light at 10-100 mW/cm 2 illumination power. Because the sample remains cryo-protected at all times during data collection, devitrification or crystalline ice formation is avoided (Figures  S1J-S1P ). A typical dataset is collected within an average time of 3-5 min capturing a field of view (FOV) of 64 3 64 mm in all z slices required. Point spread functions for reconstruction purposes at cryogenic temperatures are generated before data collection using 175 nm PS-Speck nanobeads (Thermo-Fischer Scientific) at emission wavelengths of 440, 525, 605, and 647 nm. Beamline and X-ray microscopy setup for Soft X-ray tomography (Cryo-SXT) The full field transmission X-ray microscope (TXM) (UltraXRM-S220C, Carl Zeiss X-ray Microscopy Inc.
For non-destructive SXT imaging at beamline B24, samples need to be embedded in vitreous ice and mounted on flat EM grids to immobilize them and preserve them against radiation and heat damage before exposure to X-ray radiation. Gold (legend continued on next page) nanospheres, 100-250 nm in diameter, are traditionally included in sample preparation just before vitrification to provide highly absorbent points of reference (fiducials) for image alignment and subsequent data reconstruction. 3D imaging data are collected as a series of projections at defined regular angular steps generating tilt series of up to 140 in total with projections collected typically with 0. 5-1 s X-ray exposures per image. The images are subsequently processed into tomograms by using IMOD  (Kremer et al. , 1996)  through a fully automated, in silico pipeline that runs concurrently with data collection.
Samples are placed in standard Linkam cryoholders at the cry-oSIM that is initially used to generate a brightfield transmission 2D mosaic of the grid holder structure, and ROIs are annotated on this mosaic as data collection takes places. 3D-SIM data are collected through the sample and along the z axis (4-12 mm depending on cell thickness). We have not observed any thawing of vitreous samples during data collection under the above-mentioned standard conditions and samples that have been used in cryoSIM imaging do not appear structurally different from other samples at the TXM. After SIM imaging, samples could also be used for cryo-electron tomography through a focused ion beam milling step  (Medeiros et al. , 2018) , however, we have yet to fully explore this potential. After cryoSIM data collection, grids are recovered from the Linkam holders and stored until they can be loaded into TXM holders (either conventional cryo-holders or autogrid TXM holders, the design for which was a kind gift by the Mistral beamline at the Spanish synchrotron, ALBA [https://intranet.
cells. es/ Beamlines/XM]). Once at the TXM, grids are examined and mapped with visible light to produce a new mosaic at the sample orientation (moving from one holder to the other results in random sample rotation for each microscope). The mosaics are then aligned with a semi-automated plugin at the beamline and the established ROIs from SIM data collection are recovered to allow X-ray data collection in the same regions. The samples are initially evaluated for susceptibility to radiation damage and uniformity of vitrification (semi-crystalline ice areas can be readily identified in X-ray projections). Next, a data collection strategy is devised on the basis of trial exposures in areas away from established ROIs in conjunction with the imaging (C) Imaging beam profile recorded at a scintillator 0.
Resource requirements of each project. 2D X-ray mosaics are acquired at all ROIs covering single grid squares (imaging of 7 3 7 adjacent FOV at 16 3 16 mm will image a whole grid square in a standard EM grid) and these are used to decide on data collection areas. 3D X-ray data are collected as a tilt series and relayed in realtime to an automated pipeline by using IMOD Batchruntomo  (Mastronarde and Held, 2017) , and provided a sample has good contrast and fiducials (ideally >3 and well dispersed in the FOV), the data are reconstructed to tomograms by using IM-OD's weighted back projection, serial iterative reconstruction, and patch tracking options. The tomograms can then be used to further refine data collection strategy. In silico correlation of CryoSIM and CryoSXT data using eC-CLEM Processed data from both high-end microscopes can be imported in eC-CLEM  (Paul-Gilloteaux et al.
, 2017)  initially for a preliminary in silico 2D relocation of all related datasets and thereafter for the refined 3D correlation of associated features (Figure  5 ). During this process, only rigid transformations are used to link the data (only rotation, translation, and isotropic scaling; the latter usually negligible). The main advantage of this approach is that imaging data are correlated in 3D without any local warping (Video S1). We have assessed the accuracy of our in silico correlative workflow by using test cryoSIM and cryoSXT data collected on U2OS cells that were stained with Mitotracker-Red and fiducialized by using 150 nm Characteristic cellular features are identified by arrows: red for nuclear envelope, purple for mitochondria, green for MVBs, blue for the contact area between two adjacent cells, and yellow for reovirus-carbon-rich structures within vesicles.
, 2012). We mapped the time course of reovirus localization in U2OS-Gal3 cells by transfecting them with Rab7-GFP (late endosomal marker) and subsequently infecting with fluorescently labeled T3D reovirus. Live-cell spinning-disc confocal microscopy was used to follow virus entry, the association of the fluorescent virus with the late Rab7-marked endosome, and the appearance of localized Gal3 upon endosomal rupture process. Our data show that within 1 h after infection (AI), more than 60% of virus particles are associated with Rab7-positive endosomes (Figures  6A  and 6B ). Importantly, by 2 h AI, 60% of Gal3-positive vesicles are associated with virus particles (Figure  6C ). U2OS-Gal3 cells were then seeded on EM gold grids and infected with high titers of Alexa488-labeled T3D reovirus (see STAR Methods).
Grids with infected cells were lightly blotted to remove excess media and plunged frozen into liquid nitrogen-cooled liquid ethane at different time points post-exposure to the virus (T = 0, 1, 2, and 4 h). All grids were first mapped through conventional cryo-microscopy in accordance with the workflow previously discussed (Figures S4A-S4C), and selected ROIs were 3D imaged first by using the cryoSIM (see STAR Methods). The resulting super-resolution imaging data extended the confocal study by capturing small Gal3-positive vesicles that are also positive for reovirus at 1 h AI (Figures  6D-6G ), and these structures are seen to further increase in number, size, and signal intensity by 2 h AI, gradually forming large vesicle-like perinuclear structures (up to 3 mm diameter) by 4 h AI (Figures 6H-6K and S4D-S4H).
'' Hence, the above mentioned domed structures could be the result of the accumulation of viral waste products because of the large numbers of particles intracellularly or it could represent a pathway for viral escape and/or release and can be the subject for further studies in the future. Once all data were collected, we used eC-CLEM (Paul-Gilloteaux et al. , 2017) to align in 3D all associated imaging that (C) A schematic of the operational workflow that incorporates cryoSIM and cryoSXT as well as other imaging applications and potential imaging partnerships (hashed lines denote methods and correlative paths currently under development). (G) 3D superposition of previous (bottom of column) with X-ray tomogram at ROI (top; lateral resolution of imaging beyond 60 nm; measured resolution at the B24 transmission X-ray microscope with the 40 nm objective).
In the thinner areas of the distal cytoplasmic region, several holes can be seen as part of the support film used for cell attachment (deep purple arrows). (B) Ortho-slices of (A), showing a representative portion of the volumetric data. (C) 3D rendering of the cryoSIM recorded structures displaying green fluorescence within and around the endolysosome. (D) 3D semi-transparent rendering of all cytoplasmic vesicles that contain red Galectin-3 fluorescence (data also collected on the cryoSIM) and are seen as an indication of viral presence in those vesicles. All images were generated with Chimera  (Pettersen et al. , 2004). d KEY RESOURCES.
Data and Code Availability Original imaging data referenced in the manuscript is deposited at the BioImage Archive (https://www. ebi. ac. uk/biostudies/ BioImages) and EMPIAR (https://www. ebi. ac. uk/pdbe/emdb/empiar/) is. The accession numbers for the data are EMPIAR: EM-PIAR-10412, EMPIAR-10413, EMPIAR-10414, EMPIAR-10415, EMPIAR-10416, EMPIAR-10417, EMPIAR-10418, EMPIAR-10419 and BioImage Archive: S-BIAD17, S-BIAD18, S-BIAD19 and S-BIAD20.
9 NA, 2 mm working distance). Emission path: Fluorescence signal from the sample is collected by the objective and transmitted back from the 45 mirror, through a 1:1 telescope and reflected by the silver-coated mirror upstream then separated from the excitation light by the primary dichroic. The emitted light is then reflected toward the cameras (Oxford Instuments, Andor iXon Ultra 897) by a broadband dielectric mirror and a final telescope which magnifies the image to optimize the camera pixel size to the optical resolution. There is a secondary dichroic, that splits the light before the detectors. Each detector has a dedicated filter wheel to allow selection of different emission channels. All hardware is controlled by two open source software packages, Cockpit and Python Microscope. These packages allow control of complex microscope systems in real time and provide a simple, user-friendly interface.
Cryo-SIM imaging and high-resolution data reconstruction Vitrified samples on grids were transferred to the cryoSIM and brightfield imaging was first employed to generate mosaics, where individual cells were evaluated based on cell location (likely to allow data collection on both this instrument and the TXM) and overall state (no obvious grid surface or cell sample disruption). Samples were then imaged in both green and red fluorescence to identify individual cells within the population that both expressed fluorescent Gal3 and were infected with fluorescent virus. 3D-SIM data were collected on a number of these representative cells (4 mock-infected controls, 8 at 1h after infection, 11 at 2 h after infection, 11 at 3 h after infection and 5 at 4 h after infection). Data were reconstructed with SoftWoRX 6.
5. 2 (GE Healthcare) using real optical transfer functions generated from 3D-SIM images of 175 nm single-color fluorescent beads (PS-Speck, Thermo Fisher Scientific) to produce super-resolution image stacks. Multi-channel images were aligned with Chromagnon  (Matsuda et al. , 2018). The raw and reconstructed data were analyzed in Fiji  (Schindelin et al. , 2012)  using SIMcheck  (Ball et al. , 2015)  to ensure the results were realistic and contained no artifacts. Cryo-soft X-ray Tomography and X-ray data reconstruction X-ray data were collected with an UltraXRM-S/L220c X-ray microscope (Carl Zeiss X-ray Microscopy, Inc. ) at beamline B24 (DLS) using 500 eV X-rays. This instrument is fitted with a capillary condenser, a 40nm zone plate objective (25 nm for the BSC-1 work) and a 1024B Pixis CCD camera (Princeton instruments).
All data were aligned and reconstructed automatically to tomograms using the in-house pipeline which employs Batchruntomo (Mastronarde and Held, 2017) via a number of scripts to allow parallel and near real-time processing. Specifically, three protocols for alignment and reconstruction are currently provided: weighted back projection (WBP), simultaneous iterative reconstruction techniques (SIRT) and automatic patch tracking. Batchruntomo  (Mastronarde and Held, 2017)  is part of the IMOD package (version 4. 9. 2)  (Kremer et al. , 1996). Where necessary, multiple adjacent sites were used for data collection for subsequent stitching and FOV expansion. Correlative cryo-SXT / cryo-SIM imaging Equivalent cryo-SIM and cryo-SXT datasets were overlaid with the multidimensional registration software eC-CLEM (Paul-Gilloteaux et al.