dataesr/scientific-paragraphs-categorization huggingface dataset:
4d ; Protein Data Bank (PDB) accession 2UV8) and Pichia pastoris  32  (Extended Data Fig. 8c ; Electron Microscopy Data Bank (EMDB) accession EMD-12139). The ACP shuttles the growing acyl-chain between the different catalytic sites, and its localization has been suggested in connection with the activity of the whole multi-enzyme complex  31, [33] [34] [35] [36]. Here, maps of the S. pombe FAS complex in exponentially growing cells revealed a specific, native state, ACP site (Fig. 4d , Extended Data Fig. 8 , and Supplementary Note 2). For ribosomes, the particle averages and numbers derived from DeePiCt predictions are comparable to the ground truth annotations (Supplementary Tables  8  and 9 , Supplementary Note 2, and Supplementary Figs. 7  and 8 ). 3D refinement of the ribosomal particles resulted in subtomogram averages with nominal resolutions of 11 Å (ground truth) and 15 Å (DeePiCt predictions) after multi-particle refinement in M  22  (Fig.
DeePiCt-predicted ribosomes reveal functional subpopulations The large number of particles localized by DeePiCt in the defocus dataset in a high-throughput manner allows for examining subpopulations of functionally distinct complexes. Focused classification of all DeePiCt-predicted ribosomes on the head of the 40S small subunit revealed a subset with additional densities close to the head and at the exit tunnel (Fig. 4i  and Supplementary Fig. 9 ). This class was also detected in the VPP and defocus ground truth datasets (Supplementary Tables  7 8 9 ), resolving densities for P-and E-site tRNAs in the latter (Extended Data Fig. 9a-e ). The ribosome-bound ATPase eEF3 from S. cerevisiae  37  fitted well into the additional head density (CC 0. 8972, EMDB accession EMD-12062; Fig. 4i  and Extended Data Fig.
9a-c ). During translation, this eukaryotic elongation factor facilitates binding of a new tRNA to the A-site via the ternary aminoacyl-tRNA-eEF1A-GTP complex  37. Focused classification of all DeePiCt-predicted ribosomes at the ribosomal exit tunnel provided an average fitting the S. cerevisiae ribosome  38  (CC 0. 9657, EMDB accession EMD-1667) with the rRNA expansion segment ES27L in a particular configuration  39  connecting to an additional density close to the ribosomal exit tunnel (CC 0. 7938, PDB accession 3IZD; Fig. 4j  and Supplementary Fig. 10 ). ES27L plays a role in translation fidelity. Enzymes, such as the methionine aminopeptidase (MetAP) that co-translationally processes the nascent peptide chain  40  , nuclear export factor Arx1, which is released during ribosomal 60S maturation in S.
cerevisiae  41  , and its human homologue Ebp1, a translation regulator  42, 43  , bind at locations of the observed extra density. The binding factors recruit the flexible rRNA scaffold ES27L and cover the ribosomal exit tunnel with their MetAP-like folds. This structural class was also detected in the defocus and VPP ground truth datasets (Extended Data Fig. 9f -j and Supplementary Tables  7 8 9 ). Thus, the large number of particles obtained with DeePiCt predictions combined with structural analysis revealed functional subpopulations of ribosomes.
Trained networks can be readily applied to other species As a demonstration of the domain generalization potential of our workflow across species and the ease of applying pre-trained models, we predicted ribosomes, membranes, and cytoskeletal filaments (actin and microtubules) in a published VPP HeLa cell dataset  45  (Fig. 5 ), for which we manually generated a mask (using Amira  49  ) of the cytoplasmic volume, to exclude the nucleus. For evaluation, we computed the voxel-F1 score using the publicly available annotations (EMDB accession EMD-11992 (ref. 45 )). Using the ribosome and membrane networks trained on the ten VPP tomograms from S. pombe described above resulted in a voxel-F1 of 0. 55 for ribosomes and a voxel-F1 of 0. 18 for membranes (Extended Data Fig. 10a ). However, the available expert membrane segmentation for the dataset covered only ER membranes.
As the code is open source and Python-based, our flexible 3D architecture could further be expanded by implementing variations, such as ResNet encoders, atrous convolutions, class-normalization, positioning DeePiCt to serve as a tester for deep-learning techniques. A major bottleneck in the field of supervised machine learning is the availability of expert curated training data. Here, we provide an experimental cryo-ET dataset of 20 S. pombe tomograms under two microscopy acquisition settings (VPP and defocus), together with high-quality comprehensive annotations of ribosomes and FAS, membranes, organelle, and cytosol segmentations. This constitutes the first gold-standard dataset in the field that is large enough for model training, which will enable benchmarking of current methods and spur the development of future computational tools for unbiased data mining in cryo-ET data.
Our workflow allows easy adaptation to the segmentations of other structures, as demonstrated by the application of cytoskeleton segmentation networks. The networks show high-quality performance for microtubules in the HeLa cell dataset after training on data with broad orientation sampling of the filaments, producing segmentations that can be used for subsequent structural analysis. Actin predictions revealed a low F1 score and therefore likely require more training data, and sampling different orientations of the structural features, to improve performance. Altogether, the application of multiple segmentation networks to the HeLa cell dataset revealed that DeePiCt models trained on datasets from different microscopes, species, and conditions lead to reasonably good results in high-quality data.
Although more in-depth analysis is needed to study the limitations for the applicability of DeePiCt networks on other datasets, the results presented here constitute the first step towards conducting large-scale quantitative analyses for structural biology using cryo-ET on cells from different laboratories and publicly available datasets. In this sense, the generated ground truth annotations in this study provide the community with a resource to improve and further develop cryo-ET object segmentation and detection tools, to ultimately enable broad exploration of particles in their cellular context. Together with the trained networks and the flexibility of the DeePiCt workflow, the software harbors great potential for quantitative cryo-ET studies in the future.
Cryo-ET Autogrids with lamellae were loaded into a Titan Krios (Thermo Fisher Scientific) such that the axis of the pre-tilt introduced by FIB milling was aligned perpendicular to the tilt axis of the microscope  53. Cryo-ET acquisition parameters are summarized in Supplementary Tables  7 8 9. Tomograms were acquired on a K2 Summit direct detection camera (Gatan) operating in dose fractionation mode utilizing a Quantum post-column energy filter operated at zero-loss (Gatan). A calibrated pixel size of 3. 45 Å was used for the NPC defocus* data and 3. 37 Å for the remaining datasets. Up to 14 tilt series were collected on a single lamella in low dose mode using SerialEM  54. Starting from the lamella pre-tilt, images were acquired in 2° increments within a range of +58° to -40° using a dose-symmetric tilt scheme  55  with a constant electron dose per tilt image.
Tomogram reconstruction Tilt movie frames were aligned using a SerialEM plugin. Tilt series were filtered according to the accumulated electron dose by Fourier cropping using the mtffilter function in etomo (IMOD/BETA4. 10. 12  58  ), and sorted by tilt angle using a python script. Four-times-binned tilt images were aligned in etomo (IMOD/4. 9. 4)  58  using patch tracking (typical residual error 0. 291-0. 569 pixels) and tomograms were reconstructed via weighted back projection. Tomogram thicknesses ranged between 80-310 nm.
Ground truth annotation for organelles, cytoplasm, and membranes In VPP and defocus datasets, organelles (mitochondria, vesicle, tube, ER, nuclear envelope, nucleus, vacuole, lipid droplet, Golgi apparatus, and vesicular body; Supplementary Table  4 ) and cytosol were annotated. Each compartment was identified through a unique numerical label to allow for selection of specific subsets of compartments. Segmentations of ten VPP tomograms were performed manually in Amira  49  , and used to train a 2D CNN. Using this trained CNN, we predicted in ten defocus tomograms pre-processed with the spectrum equalization filter, and manually corrected the segmentations. Membrane annotations were performed on ten VPP tomograms and five defocus tomograms. Initially, five VPP tomograms were annotated using Amira by manual segmentation on every two to three slices and subsequently interpolated.
The particle lists were cleaned for duplicates by applying elliptic distance constraints to the coordinates (Supplementary Note 1 and Supplementary Tables  2  and 3 ). Template matching for ribosomes was performed with pyTOM 60 by a 3D cross-correlation search over 1,944 Euler angle combinations using the large subunit of S. cerevisiae 80S ribosome map (EMDB accession EMD-3228 (ref. 61 )) scaled to the corresponding pixel size, and a spherical mask (diameter 337 Å). The 2,000-3,000 highest cross correlation scores were manually revised in Gaussian-filtered tomograms (σ = 3) with tom_chooser (pyTOM toolbox  60  ). For FAS in the VPP dataset template matching with a published S. cerevisiae FAS 33 map (EMDB accession EMD-1623) as reference failed.
Voxel-level representation of ground truth For ribosome and FAS, the lists of coordinates from the ground truth were used to paste spherical masks (with radii of 10. 78 nm for ribosome and 13. 48 nm for FAS; Code Availability). For the NPCs, a subunit mask obtained by prior 3D averaging in novaSTA (https://doi. org/10. 5281/ zenodo. 3973623) was pasted at each of the eight subunit locations. The ribosome, FAS, membrane, organelle and cytosol masks in defocus and VPP along with the list of coordinates and respective tomograms are used in the subsequent training and performance analysis, and are available from the Electron Microscopy Public Image Archive (EMPIAR; Data Availability).
Final subtomogram averages of each particle class were obtained by 3D refinement and post-processing, filtered to their respective resolutions determined by FSC of two independently refined half maps at a cut-off of 0. 143. Details of particle numbers and resolutions for each subtomogram average are summarized in Supplementary Tables  7 8 9. Visualization and calculation of cross-correlations (CC) between different maps and models, was performed with the UCSF ChimeraX package  68.
6d-f ). The nuclear envelope predictions, which were used as 'region mask' in the 'contact' mode during post-processing of the NPC prediction, were achieved with a 3D CNN trained on 18 manually annotated tomograms, and which were uniformly distributed across the three dataset splits. For organelle and cytosol segmentation evaluation, model performance was evaluated on the voxel-level for the post-processed 2D CNN predictions produced by the individually trained model of each cross-validation fold, using 5,000 voxels picked randomly from each tomogram of the respective test set of cross-validation fold. Precision and recall (Supplementary Note 1 and Supplementary Fig. 4 ) were computed at threshold values varying from 0 to 1 on the picked voxels to compute the area under the precision-recall curves (AURPC) for each tomogram.
Pre-processing of input data by spectrum equalization. a, Input defocus tomogram with low SNR. b, Target VPP tomogram (one example from n = 10 tomograms) with high SNR. c, Output showing the tomogram in a after spectrum matching. d, Amplitude spectra of target, input and output tomograms, as well as per-frequency (1/Å) scaling factors of the rotational equalization (EQ) kernel (y-axis cropped). https://doi. org/10. 1038/s41592-022-01746-2 Details of the construction of ground truth for ribosome and FAS in VPP and defocus tomograms. a,b, Contributions of the steps applied during the construction of ribosome ground truth in the VPP (a) and defocus (b) datasets (n = 10 tomograms each.
All densities show the same interface between ribosome and ER membrane. o,p, Overlay of ribosomes within 25 nm distance to mitochondria (green) with mito-bound ribosomes from DeePiCt predictions in defocus (o, gray), and VPP gt data (p, gray). All densities show the same interface between ribosome and mitochondria membrane. q,r, Comparisons between ER-bound (orange) and mitochondria-bound (mitobound, green) subtomogram averages in defocus gt (q) and VPP gt (r) data reveal an angular offset of the membrane interfaces of 35° and 33°, respectively. See next page for caption. https://doi. org/10. 1038/s41592-022-01746-Domain generalization of different cellular structures and species. a, Performance results of five DeePiCt networks trained on a variety of cell types for the segmentation of different cellular structures used for predictions in a HeLa cell dataset 45.
Left: plot of performance scores when employing the spectrum equalization (+EQ) filter in both the training and the HeLa cell data. The results correspond to segmentations of actin filaments and microtubules (MT) (trained on 4 RPE1 tomograms; pink), ribosomes (yellowgreen) and membranes (aquamarine) trained on 10 S. pombe VPP tomograms, MT (trained on 11C. elegans tomograms; blue) and actin filaments (trained on 2 RPE1 and 3 MEF 3T3 tomograms with actin filaments at different orientations; light purple). Right: performance for ribosomes and membranes without employing the equalization filter (-EQ) in the HeLa cell dataset. b, RPE1 training data examples (left and middle, tomograms on top, ground truth annotations at the bottom) and predictions (right) of the actin filament and MT models in the HeLa cell dataset.
The training examples show 2 of the RPE1 tomograms 50  used for this model. The performance of actin and microtubule predictions in the HeLa cell dataset is poor (voxel-F1 = 0. 09 for actin and 0. 26 for MT; a left) despite the highquality training data (left), likely due to the high unidirectional orientation of the filaments in the training data. c, Predictions in the HeLa cell dataset (left) of MT model trained with C. elegans tomograms (voxel-F1 = 0. 83). Coordinates (middle) were extracted from the binary prediction map (left) using custom scripts (Code Availability). The inset shows a zoom into the boxed area. A 3D microtubule average (right, cyan) at 39 Å resolution was generated using Dynamo 24. 2D slices through the volume show the xy plane (top) with the microtubule protofilaments labeled, and the xz plane (bottom) with the 25 nm microtubule and 17 nm lumen diameter indicated.
Acknowledgements We thank  J. Zagoriy  and  W. Hagen  for technical support, EMBL IT and especially  T. Hoffmann  for computational support,  C. Haering  for kindly providing the S. pombe and  J. B. Woodruff  for the C. elegans. I. d. T. -T. and M. T. -N. were supported by a fellowship from the  EMBL   Interdisciplinary Postdoctoral Program  ( EI3POD ) under  Marie Skłodowska-Curie Actions COFUND. D. W. C. C. was supported by the  Boehringer Ingelheim Fonds PhD fellowship  and the  Croucher Scholarship for Doctoral Study. J. M. and J. B. Z. acknowledge funding from the  EMBL. J. M. acknowledges funding from the  European Research Council   starting grant  ( 3DCellPhase -760067 ).
Code availability The DeePiCt code is assembled as two Snakemake pipelines (2D CNN and 3D CNN); the 3D CNN is implemented in the Pytorch framework while the 2D CNN is implemented in Keras, both in Python 3. The code, trained models, link to the Google Colab notebook, including custom scripts for the extraction of particle coordinates from filaments are available in the GitHub repository https://github. com/ZauggGroup/ DeePiCt.
1 |. 3 | 13 Pre-processing of input data by spectrum equalization. a, Input defocus tomogram with low SNR. b, Target VPP tomogram (one example from n = 10 tomograms) with high SNR. c, Output showing the tomogram in a after spectrum matching. d, Amplitude spectra of target, input and output tomograms, as well as per-frequency (1/Å) scaling factors of the rotational equalization (EQ) kernel (y-axis cropped). https://doi. org/10. 1038/s41592-022-01746-2 Details of the construction of ground truth for ribosome and FAS in VPP and defocus tomograms. a,b, Contributions of the steps applied during the construction of ribosome ground truth in the VPP (a) and defocus (b) datasets (n = 10 tomograms each.
g. Gamelon et al. 2012; Imperio et al. 2010; Ruiz-Rodríguez et al. 2022). However, hunting statistics can be influenced by many factors not always measured in the datasets (such as regional hunting traditions and hunting regulations, hunting pressure, hunting ground size, hunting area characteristics, and hunters  ' availability, training, and engagement). When these factors are not taken into account, it makes it difficult to directly compare datasets across territories (e. g. Bosch et al. 2012; Vajas et al. 2021 ). However, when hunting statistics are recorded under a rigorous and systematic way, including relevant variables related to hunting effort (e. g. surface of the hunting area), they can be used as simple indicators of relative population abundance, even achieving estimations of population density  (Artelle et al.
Material and methods Spain is composed of 17 Autonomous Communities (AC hereafter; NUTS2 level), 15 of which are mainland communities composed of 47 provinces (NUTS3 level) where hunting legislation and management policies may be shared. Under the umbrella of the national legislation "Ley 1/1970, de 4 de Abril de Caza" (see  Martinez-Jauregui et al. 2011) , each AC has its own hunting regulations and data collection methodology. We distributed a questionnaire (see Hunting Questionnaire in Supplemental information) to mainland Spain AC governmental hunting agencies, except for Basque Country, which had the questionnaire distributed to each of its three provinces (NUTS3, i. e. Araba, Bizkaia, Gipuzkoa), which operated independently in terms of hunting management and hunting statistics data compilation (regions hereafter, a total of 17).
Questionnaires were designed with the purpose of collecting information about data collected in every region. This encompassed not just the statistics on hunted animals, but also general aspects of the hunting activity, such as the hunting grounds or management units, the number of animals seen during the hunting activity, the number of animals taken, carcass management, and data management. The questionnaire had a total of 20 questions (Table  1 ) regarding the type of data available about big game hunting, and answers were coded as recorded/not recorded. Questionnaires covered different aspects of the information collected on big game species present in each region. The animals that were considered to be big game for the purposes of the questionnaire included all hunting species of wild ungulates: wild boar (Sus scrofa), red deer (Cervus elaphus), roe deer (Capreolus capreolus), fallow deer (Dama dama), Iberian wild goat (Capra pyrenaica), Pyrenean Chamois (Rupicapra pyrenaica), mouflon (Ovis musimon), and Barbary sheep (Ammotragus lervia).
In certain regions, the specific types or range of data collected varied depending on the game species. In such instances, governmental hunting agencies were requested to provide information that could be generalised and applied broadly, focusing on the records collected for the most widely distributed big game species in Spain, namely wild boar, red deer, and roe deer. We distributed questionnaires in 2018-2019 to governmental hunting agencies, and we offered support via email and telephone. The information collected was updated in 2021, so this paper reflects the situation in that year. The responses from these questionnaires were compared to the responses from forms provided to hunters or managers of hunting grounds for reporting the results of hunts conducted by each region's governmental hunting agencies.
This allowed for a comparison between the information gathered through the questionnaires and the data recorded through the official reporting system used by governmental hunting agencies in each region. However, in many cases, it is not mandatory to provide all the hunting statistics, and numerous hunting results are submitted without filling in all the variables requested by the regional hunting agencies. Consequently, the data collected through the questionnaires represent the best available data in Spain. We first used descriptive statistics to identify differences among regions in the big game statistics data collection methodologies. We used multivariate statistics using a hierarchical clustering analysis with the packages "stats", "cluster"  (Maechler et al. 2022) , and "factoextra" (Kassambara and Mundt 2020) using R software (R Core Team 2021) to select the number of groups with similar data collection frames.
Note that the 3 provinces [NUTS3] belonging to the Basque Country [NUTS2] are marked with *. For Catalonia, information is also detailed separately for the Wild Boar Monitoring Program Although the main problem for comparing data across regions is that different information is collected, the resolution is also not equivalent and different methodologies for data collection were used (Martinez-Jauregui et al. 2011). However, despite these comparability issues, overall Spanish regions have relatively complete data collection frameworks (Fig. 1 ) in terms of the number of parameters collected when compared to other European countries. The number of hunted animals at the hunting ground and season levels was collected in all regions (ENETWILD-consortium et al.
In Catalonia, the Monitoring Program of Wild Boar populations in Catalonia  (Rosell et al. 2021 ) collects data at the event level in a network of 22 sites. On the right bottom, map C of mainland Spain, showing regions according to cluster (regions belonging to cluster 1 in grey, and cluster 2 regions in yellow) ◂ questionnaire results presented here represent, at best, the data collected by each region. The hunting modalities of "drive hunting with dogs" and "battue" are predominantly used for harvesting wild boar, as well as other species such as red deer, roe deer (in Northern Spain), fallow deer, and mouflon. In these modalities, the number of animals observed during hunting events is frequently recorded by hunters. It is noteworthy that in most regions, the recorded observations typically refer only to individuals of the same species being hunted.
2021). This program has monitored wild boar populations for more than 20 years at the regional scale. The objective is to quantify the demographic trends of wild boar populations utilising the expertise of a network of collaborators consisting of managers, technicians, and hunters. The program consists of a network of 22 sites (observatories) which represent different bioclimatic regions of Catalonia. Each observatory collects detailed data on all wild boar hunts. The same methodology has been maintained over time, allowing for comparison of results and the exploration of population parameters through time. Data collected are of high temporal (collected for each wild boar drive hunt or event) and spatial resolutions with registered information about: date, hunting area, the number of hunters and dogs, the number of wild boars observed and hunted, and the sex and weight of each hunted animal.