dataesr/scientific-paragraphs-categorization huggingface dataset:
In this cluster, seven interactions are predicted as involving short linear motifs and are, therefore, potentially transient. The RN169-DDB1 interaction, however, is predicted by homology (26. 0% sequence identity) with two subunits found in the structure of the yeast spliceosome (PDB code 3JB9). The DDB1-DCAF7 interaction is predicted both by the short linear motif analysis and homology: sequence identity <20% with subunits present in seven PDB structures (codes 6DNH, 6FBS, 6FUW, 6BLY, 6BM0, 6F9N and 6EOJ). Importantly, in the whole graph, all the interactions are confirmed by experimental data from BioGRID. Despite the integration of data from BioGRID, PDB and ELM, no direct link could be found between Dyrk1A and the fibrinogen chains. Nevertheless, the additional nodes resulting from the BioGRID analysis, allowed establishing an indirect link.
However, this is not the case for the other interaction between the fibronectin and Dyrk1A, as the latter is found in the nucleus. Thus, the connection with intermediates such as DDB1 and DCAF7 seems realistic, as they are found in both the nucleus and cytoplasm. Finally, the fact that DDB1 and the fibronectin are linked through Myc is an issue. Indeed, this protein is described as exclusive to the nucleus and the computational analysis could not identify any intermediates. A possibility supported by experimental data from BioGRID could be that the Myc-fibronectin interaction would be indirect, passing through the von Hippel-Lindau disease tumour suppressor (VHL), which is in the cytoplasm, membrane and nucleus, simultaneously. This hypothesis of an undetected interactant linking the Dyrk1A and fibrinogen levels would be consistent with the fact that there was no significant difference in protein levels, for the present co-IP experiment, between healthy and DS LCLs (Wilcoxon rank-sum test, with an α error of 5%).
4 ± 1. 4 μg/mL versus 5. 1 ± 1 μg/ mL (n = 6 for each) (p < 0. 04)) without difference in fibronectin content (data not shown). These results suggest that increased plasma fibrinogen levels would be due to increased hepatic production, the increased plasma fibronectin level being rather due to its increased half time in mice overexpressing Dyrk1A. Even if the liver is the source of at least 98% of the circulating fibrinogen, expression, synthesis, and secretion have been in vitro described in a variety of non-hepatic cells in response to inflammatory mediators. 28 We therefore quantified plasma CRP and IL-6 levels, CRP in plasma increasing during acute phase response to tissue injury or inflammatory stimuli especially IL-6. No difference was found for plasma CRP (4. 2 ± 0. 2 ng/mL versus 4.
| Platelet number is decreased in mice overexpressing Dyrk1A To analyse the putative link between thrombocytopenia and overexpression of Dyrk1A, blood was collected and analysed for platelet count (Figure  3 ). Mice overexpressing Dyrk1A (TgDyrk1A mice) have a significantly lower platelet number compared to control (WT) mice (Figure  3 ). However, no difference was found for the volume F I G U R E 2 Comparison of platelet fibrinogen and control protein levels obtained from control mice (WT) and mice over-expressing Dyrk1A (TgDyrk1A). Platelet lysates were prepared and Filamin A (FlnA), α-Fibrinogen (Fg), β-Actin, or 14-3-3 z was determined by western blotting. Immunoreactive bands were visualized by chemiluminescence and quantified using a luminescent image analyser system. Data represent mean ± SD (n = 3) and results are expressed as fold increase relative to controls (β-Actin, or 14-3-3 z).
Funding information Agence Nationale de la Recherche , Grant/ Award Number:  ANR-18-CE16-0020 DYRKDOWN );  Fondation Jerome Lejeune , Grant/Award Number: grant number  2017a-1634  ' Agence Nationale de la Recherche '  program  ( ANR-18-CE16-0020 DYRKDOWN ). We thank  Claire Dumoulin  for technical assistance.
Stimuli For the behavioral and functional MRI studies, video clips of angry and happy facial expressions by four Japanese women and four Japanese men were used as dynamic facial expression stimuli. These stimuli were selected from our video database of facial expressions of emotion, which included expressions by 65 Japanese models  (35). The stimulus models looked straight ahead and were unfamiliar to the participants. These specific stimulus expressions were selected because they represented theoretically appropriate facial expressions, as confirmed by coding analyses performed by a trained coder using the Facial Action Coding System  (36)  and the Facial Action Coding System Affect Interpretation Dictionary  (37). The speeds of dynamic changes in these expressions were within the natural range of the observers  (38) , and the stimuli were validated in several previous behavioral and functional MRI studies.
Measurement 2. 5. 1. EMG EMG was used to monitor the muscles of the corrugator supercilii (related to brow-lowering actions, prototypical of angry facial expressions) and the zygomatic major (related to lip-cornerpulling actions, prototypical of happy facial expressions). These muscles were selected as indices of facial mimicry because several previous studies employing facial EMG indicated that observation of angry and happy facial expressions induced corrugator supercilii and zygomatic major muscle activities, respectively [e. g. ,  (42) ]. The Ag/AgCl electrodes were placed according to established guidelines  (43, 44). A ground electrode was placed on the forehead. The data were amplified, filtered online (band pass: 20-400 Hz), and sampled at 1,000 Hz using an EMG-025 amplifier (Harada Electronic Industry, Sapporo, Japan) and the PowerLab 16/35 data acquisition system and LabChart Pro v8.
Data analyses 2. 6. 1. Subjective ratings The valence and arousal ratings were analyzed separately. The ratings of dynamic angry expression, dynamic happy expression, and dynamic mosaic stimuli were averaged separately for each participant. Composite scores were calculated by averaging valence ratings for angry expressions × -1 and valence ratings for happy expressions to represent valence and averaging arousal ratings for angry and happy expressions to represent arousal. The composite scores were analyzed in terms of the difference between YG and controls using Crawford and Howell's modified t-test (31, 46) (onetailed) implemented using the Singlims_ES function  (47). This test is a modified version of the two-sample t-test in which the target single case is treated as a sample of n = 1 and does not contribute to estimating the within-group variance  (31).
EMG EMG data were analyzed using the Psychophysiological Analysis Software 3. 3 (Computational Neuroscience Laboratory of the Salk Institute, La Jolla, CA, USA) implemented in MATLAB 2020a (MathWorks, Natick, MA, USA). The data were sampled for 3,500 ms in each trial, including pre-stimulus baseline data for 1,000 ms (during observation of the fixation point) and the data for 2,500 ms after stimulus onset. The time window of the post-stimulus period was the same as that of a previous study that detected facial EMG activity in response to dynamic facial expressions  (26). For each trial, the differences in the mean absolute amplitudes between the pre-and post-stimulus periods were calculated as the EMG data. As in the subjective rating analysis, the mean EMG activity was calculated for each condition of each participant, excluding data beyond the total mean > 3 SD or < -3 SD as artifacts.
Functional MRI Functional and structural MRI analyses were performed using the statistical parametric mapping package SPM12, 1  implemented in MATLAB R2020a (MathWorks, Natick, MA, USA). As a pre-processing step, functional images of each run were first realigned with the first scan as a reference to correct for head motion. The realignment parameters revealed only a small (< 2 mm) motion correction. All functional images were corrected for slice timing, and then the functional images were coregistered to the anatomical image. Next, all anatomical and functional images were normalized to Montreal Neurological Institute (MNI) space using the anatomical image-based unified segmentation-spatial normalization approach  (48). Finally, the spatially normalized functional images were resampled to a voxel size of 2 × 2 × 2 mm and smoothed with an isotropic Gaussian kernel of 8-mm fullwidth at half-maximum (FWHM) to compensate for anatomical variability among participants.
22 µm filters. V-ATPase mediated proton uptake: Proton uptake experiments were performed as previously described  [33]. Briefly, rat brains were homogenized in 0. 32 M sucrose, 10 mM HEPES, pH 7. 4, and 0. 2 mM EGTA in the presence of protease inhibitor cocktail, and the post-nuclear supernatant was layered onto a 0. 8 M sucrose cushion and centrifuged 20 min at 257,000× g. Synaptosomal pellet was resuspended in hypotonic buffer (10 mM Tris-HCl pH 8. 5, 1 mM PMSF) and centrifuged 20 min at 40,000× g. The synaptic vesicle-enriched supernatant was adjusted to 10 mM Tris-HCl pH 8. 5, 60 mM sucrose, 140 mM KCl, 2 mM MgCl 2 , and 50 µM EGTA (assay buffer). Synaptic vesicles were preincubated with 2 µM acridine orange (AO) before triggering proton uptake by the addition of 500 µM Mg-ATP. ATP-dependent proton transport was monitored, using a Biotek Sirius HT injector plate reader, by the quenching of AO fluorescence at 525 nm with data acquisition every 15 s.
Electrochemical measurements of catecholamine secretion were performed using 5 µm diameter carbon-fibre electrodes (ALA Scientific, Farmingdale, NY, USA) held at a potential of +650 mV compared with the reference electrode (Ag/AgCl) and approached closely to the transfected cells essentially as described previously  [47]. Amperometric recordings were performed with an AMU130 (Radiometer Analytical, Villeurbanne, France) amplifier, sampled at 5 kHz, and digitally low-pass filtered at 1 kHz. Analysis of amperometric recordings was performed as described previously  [48] , allowing automatic spike detection and extraction of spike parameters. The number of amperometric spikes was counted as the total number of spikes with an amplitude >5 pA. Data were analysed using SigmaPlot 13 software. In the figure legends, n represents the number of cells analysed.
Statistical significance has been assessed using t-test as data fulfilled requirements for parametric tests. Catecholamine content measurement: Chromaffin cells were incubated for 30 min with 0. 5 µM of Neurosensor 510 (NS510) at 37 • C and then washed to remove excess sensor before fixation. Labelled cells were visualized using a Leica SP5II confocal microscope and quantification of NS510 signal was performed in individual cells.
The three SNARE proteins (devoid of any TM domains) were co-expressed in bacteria and SDS-resistant purified SNARE complexes were obtained (Figure  S3 ). SNARE complexes were immobilized on ELISA plates, and binding of V0c was assayed with or without preincubation with complexin. As shown in Figure1D, V0c interacts with the SNARE complex in the presence of complexin. In summary, V0c interacts with complexin through  We then used SPR to identify the domain of V0c that interacts with V0d. As in Figure  2B , purified recombinant V0c bound stably to immobilized V0d1 and no apparent dissociation, in accordance with previous data showing that V0d st partitions with V0 in detergent-resistant membranes  [11]. The specificity interaction was corroborated by the absence of V0c binding when it had be incubated with an excess of V0d1 (Figure  2B ).
Acknowledgements We thank the research team  US2B of Nantes  and more specifically  Jean-Bernard Pouvreau  for providing the broomrape seeds. We thank the company  RAGT  for providing seeds of the oilseed rape cultivars. We thank members of the  Rhizo team of the Microbial Ecology unit at University Lyon 1  for their help in enumeration of broomrapes bound to B. napus roots in greenhouse. We are most grateful to  PLATIN ' ( Plateau d'Isotopie de Normandie) core facility  for all element and isotope analysis used in this study. The platform ''Serre'' of  FR BioEEnViS (University Lyon  1) was used to carry out this work.
Funding This research and TL were supported by a grant from the  French national research agency  " Ecophyto Maturation " ( ANR-19-ECOM-0002 WeedsBiocontrol ) and by a maturation grant from the  Pulsalys Technology Transfer Acceleration Company. JV was supported by the  Swiss National Centre of Competence in Research (NCCR) Microbiomes  (no. 51NF40_180575 ).
Cryo-electron tomograms capture a wealth of structural information on the molecular constituents of cells and tissues. We present DeePiCt (deep picker in context), an open-source deep-learning framework for supervised segmentation and macromolecular complex localization in cryo-electron tomography. To train and benchmark DeePiCt on experimental data, we comprehensively annotated 20 tomograms of Schizosaccharomyces pombe for ribosomes, fatty acid synthases, membranes, nuclear pore complexes, organelles, and cytosol. By comparing DeePiCt to state-of-the-art approaches on this dataset, we show its unique ability to identify low-abundance and low-density complexes. We use DeePiCt to study compositionally distinct subpopulations of cellular ribosomes, with emphasis on their contextual association with mitochondria and the endoplasmic reticulum.
Finally, applying pre-trained networks to a HeLa cell tomogram demonstrates that DeePiCt achieves high-quality predictions in unseen datasets from different biological species in a matter of minutes. T he c om pr eh en sively annotated experimental data and pre-trained networks are provided for immediate use by the community. Cryo-electron tomography (cryo-ET) produces three-dimensional (3D) snapshots of cellular landscapes at molecular resolution, making it possible to investigate structural and functional states of macromolecular complexes in their native environment, and to unveil how different macromolecular populations interact with cellular structures 1. With improved instrumentation, sample preparation protocols, and automation, high-quality in-cell cryo-ET data are generated at increasingly high-throughput 2-4.
3 ). For particle localization, a list of coordinates is generated from the clusters' centroids. For segmentations of continuous structures, such as cellular filaments, coordinates can be sampled along the segmentation centerline at a chosen spacing and exported; the particle orientations and structural features can then be obtained by subtomogram analysis in external software (for example, Warp  21  , M  22  , RELION  23  , Dynamo  24  , or EMAN2  25  ). For more details on the method, including post-processing and performance evaluation (Supplementary Fig. 4 ), we refer to Supplementary Note 1 and our Github repository (https://github. com/ZauggGroup/DeePiCt).
Generation of ground truth annotations in S. pombe Developing and benchmarking deep neural networks require ground truth non-synthetic dataset. To this end, we created a comprehensive annotation of cellular features in tomograms acquired from wild-type S. pombe (Methods), representing diverse structures, particle sizes, and abundances. We devised an iterative workflow combining template matching, DeePiCt, and manual picking, to annotate ribosomes, fatty acid synthases (FAS), membranes, organelles, and the cytoplasm in ten tomograms acquired by combining defocus and a Volta potential phase plate (VPP) and ten defocus-only tomograms (defocus) (Fig. 2  and Supplementary Tables  2 3 4 ). For annotating the nuclear pore complex (NPC), an additional dataset of 127 tomograms (denoted by defocus*) featuring ~354 NPCs  26  was used to ensure enough training data for this large, low abundance (on average three per tomogram), and and incomplete angular sampling), such data mining of 3D cryo-ET volumes remains a major bottleneck.
A range of available semi-automated methods for segmentation of cellular structures and localization of macromolecular complexes (from here on, particles) in cryo-ET datasets are broadly classified as template-based and template-free approaches. Template Matching 7 is a commonly applied computational approach and is based on a point-wise numerical computation of a similarity coefficient (cross correlation) to a known template of the complex in question. It is accurate in the localization of large structures, but fails at identifying smaller or less dense particles  8  , and is computationally intensive. Current template-free methods using classical image processing are designed for, and thus limited to, specific molecular configurations in which particles are associated with large cellular structures such as membranes or microtubules  [9] [10] [11].
Both these methods typically require manual inspection and are therefore laborious and time-consuming. The advent of deep-learning methods, particularly convolutional neural networks (CNNs)  12  , enabled more general and automated approaches for segmentation and particle localization in cryo-ET. The first of such approaches was a two-dimensional (2D) CNN that performs semantic segmentation of large structures, such as ribosomes or membranes  13. However, its 2D nature makes it less suitable for particle localization, where probing the 3D structure becomes beneficial. More recently, DeepFinder, a fully supervised 3D CNN method that is based on the U-Net architecture  14  for multi-class semantic segmentation, has positioned itself as the state-of-the-art in automated particle localization in both simulated and real cryo-ET datasets  15, 16.
Remaining limitations include the localization of less prevalent particles, and the interpretation of the obtained predictions within their cellular context. Here, we present DeePiCt (deep picker in context), an open-source software that synergizes supervised convolutional networks for segmentation of cellular compartments (organelles or cytosol) and structures (membranes or cytoskeletal filaments), and localization of particles. We generated a set of comprehensively expert-annotated tomograms acquired on cryo-focused ion beam (cryo-FIB) lamellae of wild-type S. pombe for training and benchmarking of our method, which we openly provide to overcome the critical limitations arising from the absence of publicly available annotated experimental datasets. From here on, we refer to the term 'network' as the deep-learning algorithm itself and 'model' as the algorithm once already trained.
3d ,f, and Supplementary Tables  3  and 6 ). Overall, the final manual picking was crucial to generate a comprehensive ground truth annotation for FAS and, in defocus data, also for ribosomes, highlighting the value of our carefully curated annotations. Total numbers of ribosomes in both acquisition types were comparable, while fewer FAS particles were detected in the defocus dataset likely owing to the lower SNR despite the application of an equalization filter (Extended Data Fig. 3 , Supplementary Tables  2  and 3 , and Supplementary Note 2). Nevertheless, our annotations of fully assembled FAS in both dataset are in agreement with levels of FAS-α and FAS-β quantified by previous proteomics studies  27, 28  (Extended Data Fig. 4 ; Methods). To assess the quality of the obtained ground truth annotations for ribosome and FAS, we performed structural 3D classification and refinement (Supplementary Note 2, Supplementary Figs.
Since it operates in 2D rather than 3D, it requires little memory (<1GB at the default batch size) and training (~15 min to train on 10 tomograms using a Nvidia V100 GPU; Methods). Hyperparameter tuning for the 3D CNN. For the 3D CNN, we characterized the effect of the adjustable hyperparameters (Methods) using a cross-validation approach for ribosome, FAS, and membranes in the VPP dataset, and for NPC in the defocus* dataset. To measure performance, we integrated the predictions of each target structure with an appropriate 'region mask' to remove false positives outside of expected regions (Supplementary Fig. 3 ): for ribosome and FAS, we used a cytosol prediction from the 2D CNN in 'intersection' mode; for membrane segmentation, we used the predicted cytosol from the 2D CNN in 'contact' mode; for NPCs, we used a 3D CNN prediction of the nuclear envelope in 'contact' mode.
In all experiments, we used 4× binned tomograms with inter-voxel spacing of ~13. 5 Å (Methods), sufficient for the detection of ~30-nm diameter particles investigated here. The binning can be optimized to detect different structures of interest. Lower binning retains higher-resolution information, but at the cost of higher noise and decreased physical receptive field of the network. The latter could be compensated with a higher depth, thus requiring more training data and possibly amplifying the well-known 'vanishing gradient effect' associated with deeper architectures  29. Higher binning may be sufficient for larger structures such as organelles. Performance of DeePiCt in the same-domain setting. Using the optimized networks, we analyzed the performance of the DeePiCt workflow in the same-domain setting (that is, training and testing in the same dataset type) using threefold cross-validation (Fig.
3b  and Extended Data Fig. 7a-c ). In VPP, it achieved a performance F1 score between 0. 68 and 0. 80 (median 0. 79) for ribosomes, between 0. 21 and 0. 64 (0. 46) for FAS, and a voxel-F1 between 0. 58 and 0. 90 (0. 71) for membranes. For the NPC segmentation in defocus* (Fig. 3c ), the performance depended on the quality of the tomograms, 23% of which were assessed to be high quality on the basis of lamella thickness and tilt-series alignment error (Methods; median voxel-F1 of 0. 47 versus 0. 19 for high and lower quality, respectively), even if the network was trained on the full dataset. Comparative analysis between DeePiCt single-class versus multi-label networks showed that single-class networks provide better results (Extended Data Fig. 6 ), even when employing loss functions that account for class imbalance, such as Generalized Dice, in multi-label networks training (Supplementary Fig.
i, Combined ground truth annotation of ribosomes, FAS, membranes, and NPC (green cylindrical mask, manual annotation). j,k, Relative contributions of the DeePiCt rounds for ribosome (j) and FAS (k) identification are plotted across ten VPP and ten defocus S. pombe tomograms: contribution of step 1 (yellow: TM and manual annotation for ribosomes; green: manual annotation for FAS; salmon: initial annotations not detected by DeePiCt), step 2 (blue); and step 3 (pink bars). l,m, Subtomogram averages of all ribosomes, exclusively DeePiCt-detected (TPs new; blue) and all FAS particles in VPP and defocus ground truth. We benchmarked DeePiCt against DeepFinder and template matching for localizing ribosomes and FAS in the VPP dataset (Fig. 3d, e ). Following the suggestions by the authors  15    comparably to DeePiCt (median F1 0.
83 versus 0. 79). For FAS localization, DeepFinder performed significantly worse than DeePiCt (0. 11 versus 0. 46; t-test P = 0. 007; Fig. 3d ). Single-class DeepFinder networks for FAS failed to localize any particles. DeePiCt required ~17 h for training versus ~3 h for DeepFinder, while predicting and post-processing is equally fast for both (~500 clusters per minute; Methods). In contrast to DeepFinder, the trained networks of DeePiCt (models) for all structures mentioned in this work are open source and publicly available (Code Availability). Template matching performance was measured by comparing the top 2,000 cross correlation peaks per tomogram of the raw output for ribosome and the top 1,000 peaks for FAS. It required several hours for detection in each dataset and showed worse performance than both deep-learning methods for the localization of ribosome, and completely failing for FAS (Fig.