dataesr/scientific-paragraphs-categorization huggingface dataset:
The human brain is shaped through evolution and learning to group elements in a useful way to solve the tasks it faces. As mentioned, (un)crowding can be seen as a probe into this grouping strategy. Hence, we expect that training CapsNets on more naturalistic tasks such as ImageNet may lead to grouping strategies similar to humans and may therefore naturally equip the networks with priors that explain (un)crowding results. At the moment, however, CapsNets have not been trained on such difficult tasks because the routing by agreement algorithm is computationally too expensive. The approach we took in this contribution goes beyond the standard use of deep learning in neuroscience, in which large ffCNN are fitted on large datasets to model behavior. To our knowledge, there is little research on the specific kinds of computations that ffCNNs cannot do and, importantly, why they cannot do it.
We do not suggest that CapsNets are the only implementation of grouping and segmentation. We only suggest that grouping and segmentation is important and CapsNets are one potential way in which grouping could be solved. Further work is needed to show how the brain implements it. Regardless of the specific implementation, a focus on recurrent grouping and segmentation offers a fresh way of conceptualizing global visual processing and has important implications far beyond crowding, for neuroscience and computational science in general. Harnessing the power and flexibility offered by deep learning approaches beyond classic ffCNNs will be crucial to understand and model these complex processes. We suggest that important new avenues of research will open up when complex deep networks such as CapsNets can be scaled to deal with large complex datasets, and this contribution is an example of this promising approach.
Methods The code to reproduce our results is available at https://github. com/adriendoerig/Capsule-networksas-recurrent-models-of-grouping-and-segmentation. All models were implemented in Python 3. 6, using the high-level estimator API of Tensorflow 1. 10. 0. Computations were run on a GPU (NVIDIA GeForce GTX 1070). We used the same basic network architecture in all experiments, motivated by the following rationale (Figure  6a ). After training, ideally, primary capsules detect the individual shapes present in the input image, and the secondary capsules group and segment these shapes through recurrent routing by agreement. The network can only group shapes together because it was taught during training that specific shapes form a group. To match this rationale, we set the primary capsules' receptive field sizes to roughly match the size of one shape.
Experiment 1: Modelling Human data for experiment 1 is based on  (29). We trained CapsNets with the above architecture to solve a vernier offset discrimination task and classify groups of identical shapes. The loss function included a term for shape type classification, a term for vernier offset discrimination, a term for the number of shapes in the image, a term for reconstructing the input based on the network output, and a term localizing the stimulus position in the image (see equations 1-6). The shape repetition, reconstruction and location loss terms are used to stabilize performance but are not essential for producing (un)crowding (see supplementary material). Each loss term was scaled so that none of the terms dominated the others. For the shape type classification loss, we implemented the same margin loss as in  (2).
All loss terms decreased during training (see supplementary material). The training dataset included vernier stimuli and six different shape types (Figure  7a ). Shapes were presented in groups of one, three or five shapes of the same type. The group was roughly centered in the middle of the image. We trained N=10 networks and averaged their performance. Importantly, the network was never exposed to (un)crowding stimuli during training. Therefore, the network could not trivially learn when to (un)crowd by overfitting on the training dataset. This situation is similar for humans: they know about shapes and verniers, but their visual system has never been trained on (un)crowding stimuli. After training, we tested the vernier discrimination performance on (un)crowding stimuli (Figure  7b ), and obtained reconstructions of the input images.
Procedure Observers were instructed to fixate a fixation cross during the trial. After each response, the screen remained blank for a maximum period of 3 s during which the observer was required to make a response on vernier offset discrimination by pressing one of two push buttons. The screen was blank for 500 ms between response and the next trial. An adaptive staircase procedure (PEST; 67) was used to determine the vernier offset for which observers reached 75% correct responses. Thresholds were determined after fitting a cumulative Gaussian to the data using probit and likelihood analyses. In order to avoid large vernier offsets, we restricted the PEST procedure to not exceed 33. 3' i. e. twice the starting value of 16. 66'. Each condition was presented in separate blocks of 80 trials.
Modelling: To model the results of experiment 2, we trained CapsNets to solve a vernier offset discrimination task and classify verniers, cuboids, scrambled cuboids and lines. The loss function included the shape type loss, the vernier offset loss and the reconstruction loss from experiment 1 (Eqs. 2, 3, and 5):   =  ℎ   ℎ  +       +      (7)  The training dataset included vernier stimuli and one of three different shape types (lines, cuboids, scrambled cuboids; Figure  8a ). The scrambled cuboids were included to make the task harder, and to prevent the network from classifying cuboids simply based on the number of pixels in the image. Similarly, the lines were randomly presented in a group of 2, 4, 6 or 8 to prevent the network from classifying cuboids simply based on the number of pixels in the image.
Both, cuboids and shuffled cuboids were always presented in groups of two facing each another. The distance between the shapes was varied randomly between one to six pixels. As before, (un)crowding stimuli were never presented during training. Therefore, the network could not trivially learn when to (un)crowd by overfitting on the training dataset. After training, we tested the vernier discrimination performance for (un)crowding stimuli (Figure  8b ) while varying the number of recurrent routing by agreement iterations from 1 to 8 iterations. We trained 50 networks with the same hyperparameters and averaged their performance. Some networks were excluded because vernier discrimination performance for both line and cuboid flankers was at ceiling (>=95%) or floor (<=55%). Qualitatively, these exclusions do not change our results.
Z1 fluorescence microscope equipped with a AxioCam Mrm camera (Zeiss Gmbh, Göttingen, Germany) using the X10 objective, with a 134 ms exposure time at maximal intensity. Photographs were analyzed using the Zen software. The fluorescence signal was quantified using a specific ImageJ  (Rasband, 1997 (Rasband, -2018) )  macro developed for the EASZY assay. The Fluorescence image AnalysiS Tool (FAST) macro is freely available at https://imagej. net/FAST/. For each picture, the integrated density was measured. It corresponds to the sum of the gray-values of all pixels, above 290 defined as background value, within the region of interest.
Data analysis 2. 4. 1. Quantification of relative estrogenic potency (REP) of steroidal hormones and estrogenic activity of water samples (EEQ) The statistical analysis was performed on fold-induction data relative to negative (solvent) control. A Kruskal-Wallis test was performed for the analysis of the variance first, followed by a Dunn's post-hoc test. If the response was significantly increased compared to the negative control, the sample was defined as an active sample. Statistical analyses were performed using GraphPad Prism 5. 00 (GraphPad Software, San Diego California USA). Concentration-response curves were modelled using log-transformed data. The Regtox 7. 0. 6 Microsoft Excel TM macro (http://www. normalesup. org/~vindimian/fr_index. html) uses the Hill equation model and allows calculation of EC 50 -values.
Linear regression analysis and logistic regression The linear regression between log EEQ in vivo and log EEQ in vitro was performed using GraphPad Prism 5. 00 (GraphPad Software, San Diego California USA). A t-test of Pearson's correlation coefficient was performed to analyze the statistical significance of the correlation. Logistic regression was used to investigate the relation between the in vitro EEQs and the activity of the samples after exposure of transgenic embryos zebrafish, coded as a categorical variable with only two possible outcomes, activity or inactivity. The active samples were defined as described previously. Logistic regression was performed using function glm in R 3. 3. 1  (Team, 2016). The optimal cut-off with the maximal sensitivity (or true positive rate) and specificity (or true negative rate) was selected using package ROCR  (Sing et al.
Acknowledgments This study was supported by  AQUAREF , the  NORMAN network  for emerging pollutants, the  French Agency for Biodiversity (AFB) , the  P181-DRC50  of the  French Ministry of Ecology  and the project  SOLUTIONS  ( European Union Seventh Framework Programme   FP7-ENV-2013-two-stage Collaborative project  under grant agreement number  603437 ). SB was supported by the  German Federal Ministry of Environment. The authors would like to express their thanks to  Cléo Tebby  ( Models for Ecotoxicology and Toxicology, INERIS ) for her help on Logistic regression analysis using R. and to  Beate I. Escher  of  Helmholtz Center for Environmental Research (UFZ, Leipzig ) for helpful discussion and her review of an earlier version of the manuscript.
Jasmonate Induces the Expression of DAO1 and DAO2 in a COI1-Dependent Manner We have previously shown that ARI is controlled by a complex crosstalk involving auxin signaling and JA homeostasis  [22]. To identify novel players acting downstream of JA with a potential role in ARI, we screened the publicly available JA-related transcriptomic datasets. Among several candidates, we found the recently characterized DAO1 and its closely related paralog DAO2  [16] [17] [18]. These two genes were differentially expressed in several JA-related transcriptome profiling experiments  [25] [26] [27]  and seem to be specifically induced by exogenously applied JA, as indicated by the Arabidopsis eFP browser  [28]  (Figure  S1 ). Because JA is one of the primary mediators of mechanical wounding, we also searched in the recent publicly available wounding-related transcriptomic datasets  [29] , and interestingly we found that wounding rapidly induced DAO1 (i.
e. , within 10 min), peaking within 30 min and with sustained upregulation for 12 h at the wounding site of an Arabidopsis leaf explant  [29]. These data suggest that DAO genes play an important role at the crossroads of auxin and JA interaction during JA and wounding-mediated ARI. To confirm that JA induces DAO1 and DAO2, we first quantified the relative transcript amount, using qRT-PCR, of these two genes in wild-type Arabidopsis seedlings (Col-0 ecotype) treated for 1 h with different doses of JA. Both DAO1 and DAO2 were upregulated in a dose-dependent manner (Figure  1A ), consistent with the published transcriptomic datasets  [28]. Next, we investigated whether JA-mediated DAO1 and DAO2 induction requires a functional COI1 receptor. We quantified the relative transcript amount of DAO1 and DAO2 in both the wild type and the weak-allele mutant coi1-16 treated either with 50 µM JA or mock solution.
Interestingly, we found one G-box-like (AACGTG) motif within 396 base pairs (bp) upstream of the translation start codon of DAO1 (Figure  1D ). We also found two canonical G-box (CACGTG, CACATG) motifs, respectively, within 356 bp and 556 bp upstream of the translation start codon of DAO2 (Figure  1D ). Although direct experimental evidence such as ChIP and/or EMSA would be required, these data suggest that MYC2 may directly regulate the expression of DAO1 and DAO2. We next quantified the relative transcript amount of these two genes in the wild type and the jin1-2/myc2 loss of function mutant treated with different JA concentrations for one hour. As shown in Figure  1E ,F, JA induced the expression of DAO1 and DAO2 in the wild type and jin1-2 loss of function mutant in a similar manner. These data suggest either that JA acts independently of MYC2 or that MYC2 acts redundantly with MYC3 and MYC4 to control the expression of these two genes.
In the etiolated hypocotyl, their induction by ARF6 and ARF8 causes a depletion of the JA pool and the subsequent induction of ARI  [22]. We showed that JA inhibits ARI through the master regulator MYC2 transcription factor in a COI1-dependent manner  [22]  (Figure  5 ). This is in line with physiological approaches showing that continuous JA or MeJA applications inhibit AR formation in Bupleurum kaoi  [36] , Petunia hybrida leafy cuttings  [37] , and Arabidopsis leaf explants  [29]. Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 10 of 16  [22]  (Figure  5 ). This is in line with physiological approaches showing that continuous JA or MeJA applications inhibit AR formation in Bupleurum kaoi  [36] , Petunia hybrida leafy cuttings  [37] , and Arabidopsis leaf explants  [29]. By searching the publicly available JA-related transcriptomic datasets, we identified novel components from the auxin catabolism machinery acting downstream of JA.
These data are in agreement with the fact that JA rapidly induced DAO1 expression within 5 min of treatment. Besides inducing DAO1 expression, wounding has also been shown to enhance both the abundance of the auxin transporter ATP-BINDING CASSETTE B19 and the IAA biosynthesis genes, leading to a local increase in free By searching the publicly available JA-related transcriptomic datasets, we identified novel components from the auxin catabolism machinery acting downstream of JA. We found that the enzymes DAO1 and DAO2 are consistently induced by exogenously applied JA. We experimentally confirmed this observation and found that these two genes are, indeed, transcriptionally regulated by COI1-dependent JA signaling and, possibly, are downstream targets of MYC transcription factors. A number of reports indicated that MYC2, the master regulator, acts redundantly with MYC3, MYC4  [38] , and also with MYC5  [39]  to control JA-mediated transcriptional cascades.
The seedlings were etiolated in the dark until the hypocotyls reached approximatively 6 mm long, then they were moved to long-day conditions (16 h light at 22 • C and 8 h dark at 17 • C, 130-135 µmol/m 2 /s and 65% relative humidity). The number of primordia as well as the number of emerged ARs were scored under a binocular stereomicroscope seven days after moving the seedlings to the light. The number of lateral roots was scored from scanned plates the same day. The primary root length was measured using ImageJ software (version number, manufacturer, city, state abbreviation, country)  [49]. The lateral root density was calculated as a ratio between lateral root number and primary root length. At least 30 seedlings were used for each measurement. Three independent biological replicates were included in each experiment.
CRISPR-Cas9 Vector Construction, Plant Transformation, and Genotyping To generate the dao1-1dao2C double mutant, the DAO2 gene was CRISPRed in a dao1-1 loss of function mutant background. Two guide RNAs (DAO2_gRNA_F GTCATTCCAACAATAGACTTGG and DAO2_gRNA_R TTAGCGGAGAGCTACGGAGTGG) were designed to target the first and second exons including the intron of the DAO2 gene (Figure  S2 ). The gRNAs were designed using a combination of software available online: http://www. rgenome. net/cas-designer/, http://crispr. hzau. edu. cn/cgi-bin/CRISPR2/CRISPR and http://crispor. tefor. net/. The best set of gRNAs was selected based on the efficiency and possible no off-targets. The two gRNAs were assembled into the binary vector pHEE401E using the Golden Gate cloning method as described by  [50, 51]. Agrobacterium-mediated floral dip was used to transform the CRISPR-Cas9 construct into the dao1-1 background.
Quantitative RT-PCR (qRT-PCR) Transcript levels were assessed by qRT-PCR, in assays with triplicate reaction mixtures (final volume, 20 µL) containing 5 µL of cDNA, 0. 5 µM of both forward and reverse primers, and 1× LightCycler 480 SYBR Green I Master (Roche, Indianapolis, IN, USA), and quantitative PCR was performed with a LightCycler 480 (Roche) according to the manufacturer's instructions. A melting curve analysis was added to each PCR program. The sequences of primers used for all target genes are presented in Table  S1. The crossing threshold (CT) values for each sample were acquired with the LightCycler 480 software (version number, Roche) using the second derivative maximum method. All quantifications were repeated with at least two independent biological replicates. The relative transcript amount was calculated as described by  [34].
To test this idea, we superposed the backbone of the bisubstrate analog consisting of α-tubulin residues 38-41 from the αTAT1 cocrystal structure (PDB ID code 4PK3) with representatives of the clusters from the metainference ensemble. Not surprisingly, most arrangements generated severe clashes between αTAT1 and the globular domain of α-tubulin (SI Appendix, Fig. S4 ). However, the model from cluster 2 had only a single severe clash (between αTAT1:R74 and α-tubulin:G57) and displayed reasonable surface complementarity with αTAT1, positioning αK40 directly into the catalytic groove (Fig. 3C, i ). Here, αK40 remains within a close proximity of αTAT1:D157 (Fig. 3C , ii-iv), which coordinates an essential hydrogen-bond network at the enzyme-tubulin interface and when mutated decreases acetylation by ∼96% and ∼92% for free tubulin and within assembled MTs, respectively  (26).
Micrographs were collected using a Titan Krios microscope (Thermo Fisher Scientific) operated at an accelerating voltage of 300 kV. All cryo-EM images were recorded on a K2 Summit direct electron detector (Gatan), at a nominal magnification of 22,500×, corresponding to a calibrated pixel size of 1. 07 Å. The camera was operated in superresolution mode, with a dose rate of ∼2 e -per pixel per s on the detector. We used a total exposure time of 4 s, corresponding to a total dose of 25 electrons/ Å 2 on the specimen. The data were collected semiautomatically using the SerialEM software suite  (46). Image Processing. Stacks of dose-fractionated image frames were aligned using the UCSF MotionCor2 software  (47). MT segments were manually selected from the drift-corrected images (acetylated dataset, 205 images; deacetylated MT dataset, 476 images) using the APPION image processing suite  (48).
We estimated the CTF using CTFFIND4  (49)  and converted the segments to 84% overlapping boxes (512 × 512 pixels) for particle extraction. The remaining nonoverlapping region is set to 80 Å and corresponds to the tubulin dimer repeat (asymmetric unit). Consequently, there are ∼13 unique tubulin dimers per MT particle. To determine the initial global alignment parameters and PF number for each MT particle, raw particles were compared with 2D projections of low-pass filtered MT models (∼20 Å, 4°coarse angular step size) with 12, 13, 14, and 15 PFs (50) using the multireference alignment (MSA) feature of EMAN1  (51). Finally, 13-PF MT particles (acetylated dataset, 20,256 particles; deacetylated MT dataset, 29,396) were refined in FREALIGN, version 9. 11  (52, 53) , using pseudohelical symmetry to account for the presence of the seam.
To verify the location of the seam, we used the 40-Å shift approach to categorize MTs based on their azimuthal angle, as previously described  (28). Atomic Model Building and Coordinate Refinement. COOT (54) was used to build the missing polypeptides of the αK40 loop in α-tubulin, using the available PDB 3JAR as a starting model. Successively, all ab initio atomic models were iteratively refined with phenix. real_space_refine into EM maps sharpened with phenix. autosharpen (29, 55). For visual comparisons between states, potential density thresholds were interactively adjusted in Coot to maximize isocontour similarity around backbone atoms distant from the αK40 loop. For Figs. 1  and 2 , all densities are represented in Chimera at a threshold of 1. 1. MD Simulations. Code for map preparation, simulation execution, and analysis is available at https://github.
com/fraser-lab/plumed_em_md. To prepare the cryo-EM maps, we fitted the maps with a Gaussian mixture model (GMM) by applying a divide-and-conquer approach (34), using generate_gmm. py and convert_GMM2PLUMED. sh. Cross-correlations to the experimental maps were greater than 0. 99. All simulations were performed with GROMACS 2016. 5  (36)  and the PLUMED-ISDB module (56) of the PLUMED library (57) using the Charmm36-jul2017 forcefield  (58)  with patches for acetylated lysine (aly)  (59)  and the TIP3P water model. For the deacetylated simulations, the same starting model was used with a manual edit of the PDB to eliminate the acetylation (with all hydrogens replaced by GROMACS during model preparation). The initial model was minimized then equilibrated for 2 ns, using prep_plumed. py. MD simulations were performed on a metainference ensemble of eight replicas for an aggregate simulation time of 96 ns for each acetylation state, using prep_plumed2.
The DH energy is calculated between the following two groups of atoms, denoted as A and B in the formula above: (i ) all atoms in residue range 30-60 of chain A (α1-subunit) and (ii ) all atoms in residue range 200-380 of chain E (α2-subunit) in PDBs 6O2S and 602T. Residues not included in this range do not significantly contribute to the DH interaction energy between adjacent α-subunits. Parameters used in the calculation of the DH energy are as follows: temperature (T = 300 K), dielectric constant of solvent (e r = 80; water at room temperature), and ionic strength (I S = 0. 3-1 M). Accession Numbers. All electron density maps have been deposited in the Electron Microscopy Data Bank, www. ebi. ac. uk/pdbe/emdb/ (EMDB ID codes EMD-0612, EMD-0613, EMD-0614, and EMD-0615). Atomic models have been deposited in the Protein Data Bank, www.
ACKNOWLEDGMENTS. We thank  P. Grob  and  J. Fang  for cryo-EM data collection support;  A. Chintangal  and  P. Tobias  for computational support;  T. H. D. Nguyen ,  B. Greber ,  E. Kellogg ,  B. LaFrance ,  S. Howes , and  S. Pöpsel  for supportive discussions; and  A. Roll-Mecak  for helpful comments based on our preprint. We also acknowledge the  Berkeley Bay Area Cryo-EM Facility  and additional scientific resources at  University of California, Berkeley. J. S. F. was funded by the  University of California, San Francisco-University of California, Berkeley Sackler Faculty Exchange Program and National Institute of General Medical Sciences (NIGMS)  Grant  R01-GM123159. This work was funded through  NIGMS  Grants  R35-GM127018  and  P01-GM063210  (to E. N. ) and the  National Science Foundation  Grant  2016222703  and the  National Academy of Sciences National Research Council Ford Foundation  Grant (to L.