dataesr/scientific-paragraphs-categorization huggingface dataset:
Detection performance In this study, we use an expanded dataset of ~ 600,000 images for testing, which allows us to consider the performance of the machine learning algorithm under a wider range of atmospheric conditions, signal characteristics and land cover types than was previously possible. In this section, we consider the effects that these factors have on detection. First, we calculate a formal detection threshold using the time-series results, and then we explore the effects that atmospheric conditions, coherence and deformation characteristics have on detectability. We use contrasting regions as case studies: we consider atmospheric effects in Asia, coherence in the Chilean Andes and signal characteristics in Afar, Ethiopia.
Detection threshold We use the time series results to estimate the detection threshold of the CNN. We plot the output probability, P, plotted against maximum displacement for each step of the time series (Fig. 9 ) and fit a sigmoidal curve, defined as f(x) = (1 + e -a(x-b) ) following  Anantrasirichai et al. (2019a). The transition between P∼0 (undetectable) and P∼1 (detectable) occurs over a narrow range and corresponds to the minimum value of displacement that the CNN can identify. The detection threshold for the whole dataset is 5. 9 cm, equivalent to a rate of 1. 2 cm/year over the 5-year study period. However, it is important to note that this estimate is based on time-series generated for volcanoes where deformation has been detected, and the thresholds might be significantly higher elsewhere.
Interferometric coherence Next, we consider the case of the Chilean Andes where high relief and seasonal snow cover represent challenging conditions for automated systems. Furthermore, this is an environment that was not well-represented in the original training or testing datasets of  Anantrasirichai et al. (2018 Anantrasirichai et al. ( , 2019b)). We focus on 3 rapidly uplifting volcanoes with similar deformation rates and footprints: Laguna del Maule (2162 m) was uplifting at a rate of 20 cm/year, Domuyo (4702 m) was uplifting at a rate of 15 cm/year and Nevados de Chillan (3180 m) began uplifting at a rate of ~ 10-12 cm/year in July 2019  (Astort et al. 2022). Each of these volcanoes is covered by ~ 1400 interferograms, but the number of detections is very different: Laguna del Maule was flagged 61 times, Domuyo 63 times and Nevados de Chillan just once.
Discussion and conclusions Satellite systems are ideally suited for global environmental monitoring, and several pilot studies have shown the potential for using machine learning approaches for characterising and detecting volcanic deformation  (Anantrasirichai et al. 2019a (Anantrasirichai et al. , 2019b;; Bountos et al. 2022 Bountos et al. , 2021;; Gaddes et al. 2019; Sun et al. 2020 ). This study is the first to demonstrate the powerful combination of automatically processed satellite data and machine learning on a large global dataset: we process and analyse more than half-a-million images acquired by Sentinel-1 in a 5-year period and covering > 1000 volcanoes. This enables us to test the model on a much wider range of land cover, atmospheric conditions and signal characteristics than previously possible.
(2018). Similar minor effusive eruptions in September 2017 and June 2018 may have caused a decrease in rate of uplift. We also identify deformation near Nevados de Casiri in Peru associated with a seismic swarm reported by the Institute Geofisico de Peru  (Antayhua et al. 2021). Although designed for detecting volcanic deformation, the machine learning algorithm is also capable of identifying non-volcanic signals, such as those at Kverkfjöll, Fujisan and Rinjani, and has considerable potential in other applications  (Anantrasirichai et al. 2020). By testing machine learning algorithms on a more representative global dataset, we are able to make recommendations for the development of global volcano monitoring systems. We find that the performance of the machine learning algorithm is primarily limited by the quality of the available data, with poor coherence and slow signals being particularly challenging.
Open research The InSAR data are available at http:// comet. nerc. ac. uk/ COMET-LiCS-portal/. Machine Learning Codes are available at: https:// doi. org/ 10. 5281/ zenodo. 55508 15. The machine learning outputs (filenames and probabilities) are included as a supplementary datafile and is also available at https:// doi. org/ 10. 5281/ zenodo. 71993 56. Datacubes for the 20 volcanoes studied in depth are available at https:// doi. org/ 10. 5281/ zenodo. 72134 07.
59 0. 7 0. 8 798 1674 798 783 17 17 15 12 FP* FP FP FP* Erte Ale Lake dataset Ayelu 0. 34 1006 9 FP Atmos Dabbahu 0. 6 1178 9 TP 3. 9 cm/year Tat Ali 0. 7 810 9 FP* Lake Nabro 0. 63 1299 6 TP 3. 1 cm/year Borawli 0. 74 2213 5 FP Dallol 0. 54 1026 4 FP -3. 3 cm/year Dama Ali 0. 55 782 4 FP Dabbayra 0. 44 859 3 FP Ma Alalta 0. 57 798 3 FP Mousa Alli 0. 67 783 3 FP Adwa 0. 37 776 2 FP* Atmos Alid 0. 63 638 2 FP Gada Ale 0. 56 1122 2 TP -1. 9 cm/year Gufa 0. 84 783 2 FP Gabillema 0. 67 782 1 TN Manda-Inakir 0. 57 1251 1 TN Yangudi 0. 32 1151 1 TN Groppo 0. 35 415 0 TN Manda Gargori 0. 38 1415 0 TN Manda Hararo 0. 6 1092 0.
Acknowledgements. This study was partly funded by the  WSL Institute for Snow and Avalanche Research SLF  and by the  Swiss Federal Office for the Environment (FOEN). Part of this work was carried out at the  Centre des Études   de la Neige (MétéoFrance)  under  SNSF Postdoc. Mobility  grant  P500PN_202741. We would like to thank  Kalliopi Koutantou  for providing UAV-LiDAR data and for many informal chats during fieldwork, which sparked the idea for this research. We thank  Christian Ginzler  for giving us access to Planetscope data. We are grateful to  Rebecca Mott  and  Jan Magnusson  from the  Operational Snow Hydrological Service at SLF  for their support on matters related to the meteorological input and the model framework and to  Richard Essery  for his help with FSM2-related questions.
1b ). As expected for proteoforms with different cellular functions, we observed that peptides mapping to a single gene symbol often formed groups with distinct thermal stability patterns. In fact, grouping of peptides by thermal stability reflected annotated proteoforms to some extent (Supplementary Figure  2b, c ). We thus exploited clustering of similar peptide melting profiles by developing a method to assign peptides to different proteoforms without relying on their annotation (Fig. 2a ). To do so, we filtered our dataset to contain only peptides that had been identified and quantified in at least two cell lines and computed pairwise similarities between all melting curves of peptides mapping to the same gene symbol. Then, for each gene symbol, a fully connected graph was constructed based on respective peptide similarities, and clusters were detected using the Leiden algorithm  28.
2e ). Thus, our method successfully detected the TMPO alpha and beta isoforms solely by considering the melting profiles of the peptides across cell lines mapping to the respective gene symbol. In another example, we identified two proteoforms of the Zinc phosphodiesterase ELAC2 (Fig. 2f ), an enzyme known to localize to the nucleus and to mitochondria  31. While ELAC2_2, comprising an unmodified peptide covering serine 199 (S199), showed a profile similar to the median peptide signal per gene symbol, ELAC2_1 displayed a pattern (Fig. 2g ) reminiscent of a differentially melting proteoform phosphorylated on S199 that we observed in a previous study using a phosphoTPP experiment  22. To corroborate ELAC2_1 as pS199 phospho-proteoform of ELAC2, we queried our dataset against the human database, this time including phosphorylation as a modification.
Proteoform coaggregation analysis reveals differential protein-protein interactions across cell lines Melting curves of interacting proteins (protein-protein interactions, PPI) or complex members have been shown to often coincide, a feature attributed to coaggregation of the respective interactors  19. Recently, we have exploited this concept to test for differential coaggregation of protein interactors between two conditions  40. Here, we adapted this approach to a robust multi-group comparison (Fig. 4a ) to detect differential proteoform-proteoform interactions (PFPFI). across the profiled cALL cell lines using an extended PPI annotation of STRING-database  41. In total we tested 2,901 PFPFI, which showed coaggregation in at least one of the cell lines, for differential coaggregation across cell lines.
Proteins were digested overnight with 1 to 40 Lys-C (Wako Chemicals GmbH, Neuss, Germany)-to-protein ratio. Consecutively, trypsin (Thermo Fisher Scientific, Waltham, MA) was added at a 1 to 70 enzyme-to-protein ratio for an eight hours incubation at 37°C. Finally, the same amount of trypsin was added one more time for a second overnight incubation. Resulting peptides were labeled by 16-plex TMTpro-tags (TMTpro, Thermo Fisher Scientific, Waltham, MA, USA) using the same amount of respective label for each sample. Eight melting points of two randomly selected cell lines were combined in each TMT 16-plex set. The protein amounts were adjusted to contain the same total protein amount for all cell lines throughout the TMT sets. An overview of the sets is given in Supplementary Data 1. Labeling was performed according to manufacturer's instructions but with 2 h instead of 1 h incubation prior to quenching the TMT labeling reaction.
e. ௩ ே is the fraction of fold changes without missing values of either peptide. Obtained graphs were then used for community detection using the Leiden algorithm  28  , however only gene symbols for which at least 10 peptides were identified and with at least two peptides per sample were subjected to this analysis (a detected community had to be supported by at least 5 peptides to be accepted). Peptides mapping to gene symbols for which these criteria were not fulfilled were grouped to single proteoforms, peptides mapping to gene symbols which were included in the community detection were assigned to proteoform groups if the modularity of the detected communities was higher than 0. Modularity was computed using the function modularity() of the igraph R package  62. Through the assignment of peptides to communities, proteoforms for each gene symbol were created.
Differential melting curve analysis All proteoforms detected in at least 10 cell lines were fitted by a sigmoid function for each cell line individually. The sigmoid was fit using the NPARC R package implementation which is defined as: ݂ሺܶሻ ൌ ଵ ି ଵ ା ௫ሺ ି ೌ ሻ ‫‬ , (4) where ܶ represents the temperature, ‫‬ the plateau, and ܽ and ܾ are parameters affecting the slope and inflection point of the curve  32, 63. Fits for individual cell lines (alternative model for the NPARC method) were accepted if they had a residual standard deviation of ‫ݏ‬ ௦ ൏ 0. where the degrees of freedom ݀ ଵ ൌ ߭ ଵ െ ߭ and ݀ ଶ ൌ ‫‬ െ ߭ , with ‫‬ , ߭ and ߭ ଵ representing the number of observations for protein ݅, the number of parameters of the null and alternative model, respectively  32. Proteoforms with an F-statistic above the 90% percentile were considered significantly different across cell lines.
Proteoform thermal stability and drug sensitivity correlation analysis Proteoform thermal stabilities were associated with drug sensitivity scores (DSS) by performing correlation analyses between the area under the melting curves of proteoforms found to differ across cell lines (90% percentile of observed F-statistics obtained from the NPARC analysis) with the DSS of the respective cell lines for all drugs with a minimal effect (DSS < 6 for at least one cell line) using the R package limma  46, 64. Results obtained for all proteoforms and drugs were jointly adjusted for multiple testing using the method by Benjamini and Hochberg  65. Proteoform-DSS associations with an adjusted p-value of less than 0. 1 were considered significant.
Gene Ontology enrichment Gene Ontology enrichment was performed using the R Bioconductor package clusterprofiler  68. CETSA temperature range analysis COG-355 and ALL-PO cells were cultured as previously described. Cell suspensions were then centrifuged at 300g for 5 min, the supernatant media was discarded and the cells were washed twice with Hank's Balanced Salt Solution (HBSS, Gibco/Life Technologies). Pelleted cells were resuspended in HBSS and 75µL cell suspension were aliquoted to 0. 2 mL tubes. Samples were then heated in a temperature range of 37-70°C in a Veriti Thermal Cycler (Applied Biosystems/Thermo Fisher Scientific) for 3 min, followed by 3 min cooling at room temperature and immediate snap-freezing in liquid nitrogen. The cells were then lysed by three repeated freeze-thawing cycles and centrifuged at 21000 x g for 40 min at 4.
Acknowledgements This study was supported by grants from the  Swedish Childhood Cancer Foundation  ( R. J. , grant reference  TJ2016-0035 ,  PR2016-0019  and  PR2019-0025 ; M. S. ,  TJ2019-0023 ), the  Swedish Research Council  ( R. J. , grant reference  2017-01653 ), Felix Mindus Contribution to Leukemia research (R. J. ),  Dr Åke Olsson Foundation for Hematological Research (R. J. , grant reference  2017-00437  and  2021-00130 ),  Cancer Society Stockholm  and the  King Gustaf V Jubilee Fund.
Acknowledgements We thank the entire  MMS team and instrument PIs  for data access and support. This work was supported by the  Swedish National Space Agency (SNSA) , grants  128/17  and  176/15 , and the  Swedish Research Council  grant  2016-05507. A. D. is supported by the  Ministry of Science and Higher Education of the Russian Federation  under agreement  075-15-2021-583. J. F. D. is supported by  NASA  Grant  80NSSC19K0396  and  NSF  Grant  PHY2109083. The French involvement (SCM instruments) in MMS is supported by  CNES  and  CNRS.
Data availability MMS data were available at https://lasp. colorado. edu/mms/sdc/public. The data can be found in the following directories: mms#/edp/brst/l2/dce/ for electric fields, mms#/fgm/ brst/l2/ for the background magnetic field, mms#/scm/brst/l2/scb/ for the fluctuating magnetic field, mms#/fpi/brst/l2/des-moms/ for the background electron moments, mms#/ fpi/brst/l2/des-qmoms/ for the highest resolution electron moments, and mms#/fpi/brst/l2/ dis-qmoms/ for the highest resolution ion moments. Source data required to generate the figures in this paper can be found at https://github. com/danbgraham/anomres  44 and are available on request. The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
2a, b ). This transformation is concomitant with the development of the endocardial cavity, the aortae and their connection through the first branchial arch arteries (Fig. 2c, d ). Morphometric staging of heart tube formation. We next aimed the temporal ordering of the specimen collection. The examination of the collection of datasets reflected high morphological heterogeneity of the developing heart within groups of embryos at an apparently similar developmental stage, as judged by somite number, head folds and foregut pocket shapes. The morphological heterogeneity affects both general conformation features, such as proportions, degree of dorsal closure or degree of looping, and local morphological aspects, such as non-stereotyped presence of bulges of different sizes and distribution. To tackle the problem of staging despite this variability, we implemented different morphometric strategies.
While dorsal closure appeared predominantly associated with looping and, therefore, was seen in all specimens in group 10+ and most specimens in group 9, we also found some hearts already closed in group 8 (E41) and group 6 (E29), while some specimens at stage 9 were clearly advanced in looping but quite delayed in dorsal closure (E44 and E48; Supplementary Figs. 3 4 5 6 ). In agreement with these observations, dorsal gap correlation with d1/d2 was quite noisy (Fig. 3e ), indicating that the degree of dorsal closure is not a reliable parameter for staging. Finally, we wanted to obtain a real-time calibration of the staging system generated. For this, we analyzed 3D+t datasets previously generated in our lab that capture heart tube formation in vivo  10. The limitations of the live-imaging setup did not allow us to measure d1/d2 or h/w; however, we established that the proportion of the ventral protrusion of the primitive chamber to its lateral extension was measurable in live imaging and correlated reasonably well with d1/d2 (Fig.
For large specimens, a tile scan was performed, and tiles were stitched using Leica LAS X 3. 5. 2. 18963 software. For the Nodal-mutant and control series, 16-bit images were acquired with a Zeiss Z. 1 light-sheet microscope and a ×20/1. 0 objective. Image visualization. Visualization of image stacks was performed in ImageJ 1. 53f (ref. 33 ) and ITK-SNAP 3. 8. 0. Image processing. Raw images were deconvolved with 'Huygens Professional version 19. 10' to compensate for the blurring and noise inherent to the optical system. Deconvolved images were exported in tagged image file format, rectified for axial orientation and saved in NIFTI format. All the image processing steps described hereafter were implemented in Python 3. 8 (ref. 34 ) making use of different libraries for each specific process.
The library NiBabel, version 3. 1. 1 (ref. 35 ), was used for reading and writing NIFTI images. Images read in this format are represented as array-like structures, defined as an instance of the class ndarray, which is part of the scientific computing package NumPy  36. The library used for processing the segmentation images (Fig. 1f ) was scikit-image, version 0. 16. 2 (ref. 37 ). Image segmentation. Segmentations were performed using ImageJ in a scaleddown version of the images by applying a diminishing scale factor equal to 2 in each of the three axes. This resulted in lighter files, with xy pixel sizes of 0. 76 µm-0. 98 µm and a z step of 0. 98 µm-4 µm, depending on the specimen. The reduced resolution did not introduce loss of accuracy in the segmentation at the tissue level, because the segmented tissue layers are approximately 20 µm thick.
We then performed expert manual delimitation of each of the tissues composing the pericardial cavity and the myocardium and correction of failures detected in the automatic segmentation. For this task, both the mGFP signal, which labels all mesodermal cell membranes, and the Tomato signal, which labels all cell bodies, were used. The segmentation of the forming aortae and the foregut pocket lumen was done using Mask R-CNN  39. The network was trained for the 2D detection of both, the forming aortic lumen and the closed contour defined by the foregut endoderm (as is shown in Fig. 1d ). The elaborated dataset was composed of 351 slices taken from random specimens and random planes. In each slice, the contour of every different instance was annotated using ImageJ's ROI manager. The dataset was divided 80/20 for training and validation, respectively.
4c ) and calculated the intersection with the foregut endoderm mesh. Three-dimensional reconstruction. After the 2D segmentations were complete, we used the marching cubes algorithm with the library PyMCubes, version 0. 1. 2, to obtain 3D reconstructions. The resulting meshes were then saved as polygon file format. The meshes were handled in Python by making use of the library Trimesh, version 3. 9. 19, which was used to filter the meshes, thus creating smooth surfaces. As part of the postprocessing steps, meshes for each of the tissues segmented were first simplified using a simplify quadratic decimation algorithm provided by the   40  ), to a number of faces equal to 10,000. Then, meshes were checked to make sure that they are closed surface of genus 0 by using Trimesh 3. 9. 19 and visual inspection using Meshlab 2020.
1a ). We list here the name of the embryo, its corresponding staging group and what makes the dataset exceptional. • E21, group 5: unable to reconstruct the somatic or paraxial mesoderm of this embryo. • E22, group 5: unable to image the most dorsal part of the foregut endoderm and the aortic lumen in that region. • E26, group 6: unable to fully reconstruct the foregut endoderm. • E37, group 8: only myocardium and the endocardial lumen could be reconstructed. • E41, group 8: unable to segment the splanchnic and somatic mesoderm. • E51, outlier: unable to segment the foregut endoderm and the aorta. Midsurface extraction. The meshes were first isotropically remeshed  41  to a 2-µm edge length and smoothed  42. Then, the skeleton of the mesh was derived using ref. 15  and smoothed to eliminate spikes and/or high-frequency details.
4a (i) and Extended Data Fig. 4c-f ). We provide a software package that implements these procedures, including the vertex-to-vertex mapping tool (SurfaceMapComputation) and a visualization tool (ViewMap) that displays a rigid registration of the shapes. This allows for the adjustment of the landmarks used, the addition of new landmarks and/or trying different initialization algorithms for finding the best mapping between two shapes. The software takes as input parameters the two shapes to be compared, a minimum of four landmarks, number of iterations and a choice of algorithms for the initialization of the map. The initialization algorithm can be automatically selected by the software or manually selected from those described in refs. 44, 45. Shape averaging. We selected one reference shape per group (Fig.
Acknowledgements We thank members of the  Torres laboratory  for fruitful discussions and advice on this work. Microscopy was conducted at the  Microscopy & Dynamic Imaging,CNIC, ICTS-ReDib , co-funded by  MCIN/AEI /10. 13039/501100011033andFEDER   Una manera de hacer Europa " (# ICTS-2018-04-CNIC-16 ). We are grateful to  F. Sánchez-Cabo  for helpful advice on the statistics. I. E. acknowledges attendance to the Computational Image Analysis (CIAN) course at the  MBL ,  Woods Hole , MA (funded by  NIH  grant  R25 GM103792 ). We thank  S. Bernheim ,  T. Holm Bønnelykke ,  L. Guillemot  and  M. Franco  for technical assistance and the Imaging platform and the LEAT animal facility of the  SFR Necker. The following provided funding for this study: grant  PGC2018-096486-B-I00  from the  Spanish Ministerio de Ciencia e Innovación  to M.
T. , grant  H2020-MSCA-ITN-2016-722427  from the  EU   Horizon 2020 program  to M. T. ,  S2017/BMD-3875  from the  Comunidad de Madrid  to M. T. , predoctoral fellowship  BFU2015-71519-P  from the  Spanish Ministry of Economy, Industry and Competitiveness (MEIC)  to I. E. , partial funding from the  German Research Foundation  within the  Gottfried Wilhelm Leibniz program  the  Excellence Strategy of the Federal Government  and the Länder to L. K. , support from the core funding from the  Institut Pasteur  to S. M. M. and state fundings from the  Agence Nationale de la Recherche  under the ' Investissements d'avenir' program  ( ANR-10-IAHU-01 ) to S. M. M. S. M. M. is an INSERM research director. The CNIC is supported by the  Ministerio de Ciencia e Innovación  and the  Pro CNIC Foundation and is a Severo Ochoa Center of Excellence  ( CEX2020-001041-S ).
Data availability Datasets with the original segmentations, the processed 3D models and the 3D+t models are available at 'Mendeley Data' at https://data. mendeley. com/datasets/ t828xhg66k/1. We recommend opening with the open-source software 'Paraview' (www. paraview. org). Instructions for visualizing the 3D images and videos in ParaView with custom selection of tissues, coloring and viewpoint are provided with these datasets. Datasets with the source confocal images and original segmentations are available at the IDR repository at https://doi. org/10. 17867/10000174.