dataesr/scientific-paragraphs-categorization huggingface dataset:
e. in the kidney) are not necessarily straightforward. Another specificity of PRYNT has been to work on improving the PPI network. To build PRYNT PPI network, we chose to work with STRING database, as it is a well-known, recognized comprehensive database of PPI based on experimental evidence as well as interactions predicted by comparative genomics and text mining. To limit the risk of false prediction, we decided to only select PPI with highest confidence. Two major drawbacks that we identified in this network and using such settings were that a lot of the input information was missing and that the network was massively structured into clique sub-graphs. Instead of using the raw PPI network, we hence decided to contextualize then network by adding the deregulated proteins from the input data, regardless of their confidence, and by grouping the cliques.
Urinary proteomic datasets. Study by Bakun et al. 5  (ADPKD1) analyzed urine protein composition from 30 ADPKD patients and 30 healthy volunteers identifying 155 differentially abundant proteins (Supplementary Table  S1 ). Study by Rauniyar et al. 9  (ADPKD2) compared 14 urine samples from ADPKD patients to 18 normal controls and identified 69 significantly deregulated proteins (Supplementary Table  S2 ). Lacroix et al. 7  (UPJ1) explored the urinary proteome of newborns with (n = 8) or without (n = 10) UPJ and discovered 174 differentially abundant proteins (Supplementary Table  S3 ). Chen et al. 6  (UPJ2) analyzed the proteome of urine from 23 infants with UPJ and 23 controls and identified 175 proteins with different urinary abundance between the two groups (Supplementary Table  S4 ). PRYNT algorithm.
2 ). After removing duplicates and self-linked interactions, we obtained 353643 interactions between 6391 proteins. The raw PPI network was contextualized by adding the deregulated urinary proteins regardless of their confidence level and by removing cliques (Fig. 2 ). Clique calculation. Applied to a PPI network, a clique is defined as a group of proteins that all interact with each other. Interactions in those cliques in the PRYNT network were considering as undirected, as there is no published method using directionality in cliques. We took into account the maximal cliques of the network, using the R igraph package  46. In the raw PPI network, 3569 proteins of the 6391 were included in 265 cliques, each clique containing on average 13. 5 proteins. We grouped proteins that were part of cliques and selected for each clique the protein candidate with best ranking following prioritization.
Prioritization based on experimental results (Exp). For prioritization based on experimental results, differentially abundant proteins from the four proteomics datasets were ranked based on their p-value (from smallest to largest). Prioritization based on URA algorithm. Prioritization based on URA algorithm was performed using IPA software (content version release date 2017-12-07). This analysis examines how many known targets of each upstream regulator are present in the experimental dataset. Disease candidates (limited to proteins) were ranked based on the overlap p-value. The overlap p-value, calculated using Fisher's Exact Test, measures whether there is a statistically significant overlap between the experimental dataset and the known targets that are under control of the upstream regulator.
List of reference disease candidates. Disease candidates already referenced to be associated to ADPKD and UPJ were collected from Comparative Toxicogenomics Database (CTDbase) (http://ctdba se. org)  47  using the R package CTDquerier  48. CTDbase contains curated and inferred disease candidates. Curated candidates are extracted from the published literature by CTD curators or are derived from the OMIM database. The majority of disease candidates are inferred thought the association with a 'drug and chemicals' element (according to the MeSH definition  49  ). A disease candidate is associated to the disease if a chemical compound or drug has an effect on the disease and on the expression of the gene. The more chemicals or drugs are associated with the disease and the disease candidate, the stronger is the association.
A positive difference was expected to be associated with specific approach while a negative difference was expected to be in favor of a lack of specificity. For ADPKD datasets, specific precision AUC was calculated based on prioritization of reference ADPKD candidates, and non-specific AUC was calculated based on prioritization of reference UPJ candidates. Conversely, specific precision AUC for UPJ datasets was calculated based on prioritization of reference UPJ candidates, and non-specific AUC was calculated based on prioritization of reference ADPKD candidates. Overall specificity. Overall specificity was assessed by ranking precision AUC for reference specific disease candidates and precision AUC of reference candidates from the 80 non-specific diseases. Specific prioritization method was expected to be associated with specific AUC being in the top ranked AUCs.
Acknowledgements We gratefully acknowledge  Nathalie Villa-Vialaneix, PhD  ( Unité MIA-T, INRA, Toulouse, France ) and  Sebastien Dejean, PhD  ( Université Toulouse III Paul Sabatier, Institut de Mathématiques, Toulouse, France ) for their support and input during this research. This work was supported in part by a grant from the  "Fondation pour la Recherche Médicale " (grant number  DEQ20170336759 ). FB was supported by a research grant from the  Toulouse University  and Occitanie region. JPS and JK were supported in part by funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No  764474.
Data availability All data analyzed during this study are included in Bakun et al. 5  (ADPKD1) (Supplementary Table  S1 ); Rauniyar et al. 9  (ADPKD2) (Supplementary Table  S2 ); Lacroix et al. 7  (UPJ1) (Supplementary Table  S3 ); Chen et al. 6  (UPJ2) (Supplementary Table  S4 ). Data are also available on GitHub at: https ://githu b. com/Boiza rd/PRYNT / tree/maste r/datas.
Fortunately, both of these impediments appear to have been resolved by the recent development of a peroxidase variant, dAPEX2, with enhanced enzymatic activity  10. dAPEX2 is derived from soybean ascorbate peroxidase and was shown to successfully deposit EM-visible DAB reaction products in tissue sections at up to 200 microns depth as visualized after sectioning by transmission EM  10. These data suggest that dAPEX2 may be compatible with blockface volume EM methods that require homogeneous DAB reagent infiltration into thick tissue samples prior to sectioning. Here we demonstrated the multiplex imaging of synaptic contacts between long-range excitatory inputs and genetically-defined target neurons using dAPEX2 and volume EM in laboratory mice. We showed that mitochondrial matrix and endoplasmic reticulum-tagged dAPEX2 can be reliably detected in thick tissue samples by blockface EM methods and used to systematically identify pre-and postsynaptic contacts in the resulting 3D isotropic imaging datasets.
ACC projections target specific neuron sub-types in dPAG Having shown that the multiplex dAPEX2/DAB method could be used to reliably identify axons and dendrites by volume EM we turned to applying the method to identify the synaptic targets of long-range ACC projections in dPAG. Initially, selected axons containing labeled mitochondria (N = 2) and dendrites containing labeled ER (N = 24) were fully traced in one representative isotropic FIBSEM sample (Sample 3; see Table  1  and Fig   S3 ). Labeling was confirmed as reliable in this dataset as the mean pixel value of labeled mitochondria differed significantly from that of mitochondria contained in nearby putative unlabeled axons (N = 2, 8, and 4 mitochondria/axon, P-value = 3. 55x10 -13 ; Fig. 2d ). The majority of labeled dendrites (N = 17/24) were aspiny (Fig.
Serial blockface electron microscopy (SBEM) Flat-embedded PAG sections were mounted on a pin stub using silver conductive epoxy resin (section parallel to stub surface) and trimmed to the right size around the region-of-interest. The sample was then imaged with a Zeiss GeminiSEM 450 equipped with a Gatan 3View system and controlled by the open-source software package SBEMimage  43. To reduce charging artifacts we used a focal charge compensation device (Zeiss). Images were taken at 1. 5kV/300pA and 1. 6 µs dwell time with a pixel size of 10 x 10 nm and 40 nm cutting thickness.
Data analysis and statistics Aligned, scaled, and LUT inverted tiff stacks were opened using Fiji  44  and a binary mask was imposed by thresholding pixel values using the threshold Fiji function. Pixel threshold was selected based on one manually seleted reference stained mitochondrion per sample. Mitochondria average pixel value was extracted by selecting one representative 2D plane for each analyzed mitochondrion, using the ROI manager function in Fiji  44. Processes containing stained mitochondria and ER were traced and categorized manually. 3D reconstruction of selected processes was carried out using 3DMOD (http://bio3d. colorado. edu/imod/). Data visualization and statistics were done using custom Python scripts (Python Software Foundation). No statistical methods were used to predetermine sample sizes.
Acknowledgements We thank the  EMBL Rome Laboratory Animal Facility, Genetic & Viral Engineering Facility, Microscopy Facility , and  Roberto Voci  and  Valerio Rossi  for support with animal husbandry and management. We thank the  EMBL Heidelberg Electron Microscopy Core Facility  for supporting volume EM experiments and  Dr. Yannick Schwab  and  Dr. Santiago Rompani  for useful comments and advice,  Oleksandr Radomskyi  for assistance with data analysis, preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this this version posted December 14, 2021. ; https://doi. org/10. 1101/2021. 12. 13. 472405  doi: bioRxiv preprint.
Data availability All FIBSEM datasets (samples 3 to control 8; Table  S1 ) were deposited as tiff files after preprocessing in the Electron Microscopy Public Image Archive (EMPIAR; EMPIAR-10883)  48  and uploaded to the interactive viewer MoBIE  49  (https://github. com/mobie/mobie-viewer-fiji) with bookmarks directing the viewer to all figure captions. MoBIE projects can be visualized with the MoBIE Fiji 44 plugin (https://imagej. net/plugins/mobie) under MoBIE>Open>Open Published MoBIE Project with the name 'PAG-dAPEX2-FIBSEM'. TEM, SBEM, and raw FIBSEM data is available upon reasonable request. preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.
In recent years, advances in fluorescence techniques have greatly increased the availability of high-precision trajectories of single molecules in living systems  [35] , producing an increasing drive to develop methods for quantifying anomalous diffusion  [16, 25, 32, [36] [37] [38] [39]. Furthermore, the recent blossoming of machine learning has promoted the accessibility of new powerful tools for data analysis  [40]  and further widened the palette of available methods  [33, [41] [42] [43]. Some of the novel approaches have already delivered new insights into anomalous diffusion in different scenarios  [44] [45] [46]. This recent increase of available methods performing similar tasks requires an objective assessment on a common reference dataset to define the state of the art and guide end users in the optimal choice of characterization tools for each specific application.
To assess the performance in quantifying anomalous diffusion, we have therefore run an open competition, the Anomalous Diffusion (AnDi) Challenge, divided in three different tasks: anomalous exponent inference, model classification, and trajectory segmentation, each for 1D, 2D, and 3D trajectories. The performance of submitted methods (Supplementary Note 1) was assessed with common metrics on simulated datasets with trajectory length and signal-tonoise level reproducing realistic experimental conditions (Methods, "Structure of the datasets"). The submitted methods were also compared on the blind analysis of experimental trajectories (Supplementary Note 2).
Datasets and ground-truth Details of the datasets generation are provided in Methods ("Structure of the datasets" and "Theoretical models"). The challenge datasets contained 10 4 trajectories of variable length simulated according to five anomalous diffusion models, both ergodic and nonergodic (Fig. 1c ): ATTM (weakly non-ergodic), a motion with random changes of diffusion coefficient in time  [14] ; CTRW (weakly non-ergodic), a motion undergoing local trapping with a wide distribution of waiting times  [11] ; FBM (ergodic), a motion with longrange correlated steps, often used to describe viscoelastic effects  [12] ; LW (ultra-weakly non-ergodic), a motion displaying irregular jumps with constant velocity, often associated with animal foraging strategies  [13] ; and SBM (weakly non-ergodic), a motion whose diffusion coefficient features deterministic time-dependent changes  [15].
Trajectories were simulated with random anomalous diffusion exponents corresponding to subdiffusive (0 < α < 1, all models except LW), Brownian (α = 1, all models), or superdiffusive behavior (1 < α ≤ 2, all models except CTRW and ATTM). To mimic the localization precision of experiments, trajectories were corrupted with Gaussian noise corresponding to three different levels of signal-to-noise-ratio (SNR). An open-source Python package (https://github. com/ AnDiChallenge/ANDI_datasets) was developed to generate the data. Examples of trajectories for various exponents and models are presented in Fig. 1c.
Analysis of experimental data The datasets provided to the participants for the scoring of the methods participating in T1 and T2 also included experimental trajectories of mRNA molecules in bacterial cells, telomeres in the cell nucleus, proteins in the cell membrane and cytoplasm, single atoms in an optical trap, and tracer particles in cell cytoplasm and stirring liquid, from previously published works. For these trajectories, no objective ground truth is available besides the interpretation given in the literature. Therefore, it is not possible to assess their absolute errors and they were not included in the scoring. However, we found it interesting to carry out a comparative analysis of the predictions blindly provided by the 5 top-scoring challenge participants in each task. Out of the whole dataset, we discuss the results for 4 representative experiments  [20, 38, [47] [48] [49]  for the inference of α (Fig.
5a-d ) and the classification of the underlying model (Fig. 5e-h ). The results obtained by all methods are shown in Figs. S21 -S28. The first dataset includes trajectories of mRNA molecules inside live E. coli cells from the work by Golding and Cox  [47]  (Fig. 5a ). Together with Ref. [50] , these data provide one of the first evidences of subdiffusion in cellular systems. These experiments have generated a lively discussion about their underlying diffusion model (mainly between FBM and CTRW) and ergodicity [21,  [51] [52] [53]. All top-ranking methods provided distributions of exponents centered (median between 0. 75 and 0. 81) around the value estimated in the original publication (α = 0. 77) with variable width (st. dev. between 0. 04 and 0. 18) (Fig. 5e ). However, the methods agreed in classifying the large majority (between 74% and 100%) of trajectories as ATTM (Fig.
5i ). This classification confirms the occurrence of ergodicity breaking, since both CTRW and ATTM are compatible with non-ergodic behavior and both have power-law waiting-time distribution. The preference toward ATTM might arise because of its varying diffusivity that better accounts for heterogeneity due to the biological environment or to variable noise. The second dataset of experiments includes trajectories of telomeres in the nucleus of mammalian cells  [38, 48]  (Fig. 5b ). It was previously shown that their TA-MSD features a FBM-like subdiffusive scaling for short and intermediate times with a mean exponent α ∼ 0. 5, approaching a linear behavior (α ∼ 1. 0) at longer timescales  [48]. Also in this case, the classification methods largely agree and associate most of the trajectories to FBM (between 65% and 85%) (Fig.
5j ). However, the determination of the exponent often produces a bimodal distribution with median values between 0. 61 and 0. 75 (Fig. 5f ). Likely, the methods are not able to pick up the crossovers between diffusion regimes and rather assign an average exponent to each trajectory. The analysis of these experiments deserves further methodological effort, since heterogeneous diffusion is emerging as a key feature of random motion in the biological environment  [54]. The third dataset consists of trajectories recorded for receptors diffusing in the plasma membrane of mammalian cells (Fig. 5c ). In the original work [20], the TA-MSD was found to scale roughly linearly, whereas the EA-MSD showed subdiffusion with α ∼ 0. 84; this nonergodicity was attributed to a temporal change of diffusivity and associated to ATTM.
Once more, the classification methods largely confirmed previous results. A large percent of trajectories were attributed to the two models with time-dependent diffusion coefficient, namely the ATTM (between 57% and 71%) and the SBM (between 22% and 33%) (Fig. 5k ). Moreover, inference methods consistently detected a large heterogeneity in α, including both sub-and superdiffusion, with a slightly subdiffusive overall value, median between 0. 86 and 0. 95 (Fig. 5g ), in agreement to the original study  [20]. To demonstrate the applicability of these methods beyond biological systems and at different spatio-temporal scales, we included a dataset with trajectories obtained for single atoms moving in a 1D periodic potential and interacting with a near-resonant light field that acts as a thermal bath  [49]  (Fig.
From the blind analysis of various experimental datasets, we observed that the top methods, although based on different principles, lead to very similar results. This is encouraging as it points to an objective underlying reality of the anomalous diffusion phenomena and its mechanisms, which can be measured experimentally and has now been underpinned by the results of the AnDi challenge. Importantly, the results provided by the challenge methods were also in line with the conclusions of previous studies  [20, 38, [47] [48] [49] , further reinforcing their reliability. Interestingly, while the original works required a combination of several estimators, including ensemble averages, the challenge methods were able to provide compatible predictions in a one-shot analysis and with no prior knowledge about the experimental conditions.
We hope this effort might be extended in the future to reach a larger consensus. To this aim, we have built an interactive tool (http://andi-challenge. org/interactive-tool/) where datasets and results of the challenge are stored; new methods can undergo an automated benchmarking according to the challenge rules and compare their scores with those of other participants. Therefore, the challenge is permanently open and performance improvements are continuously updated. d ). e-h, Histograms of the estimation of the anomalous diffusion exponent αp predicted by top teams for trajectories from experimental datasets. Gray areas correspond to the results of baseline method TA-MSD. Dashed lines indicate the original estimations of α provided by Refs. [47]  (e),  [38, 48]  (f ), [20] (g), and  [49]  (h).
i-l, Histograms of the diffusion model predicted by top teams for trajectories from experimental datasets. Dashed boxes indicate the original classifications provided by Refs. [47]  (i),  [38, 48]  (j), [20] (k), and  [49]  (l). We show predictions obtained by the top 5 teams for the corresponding subtask. For the last dataset, we further selected the teams based on their performance on short (L ∼ 10) trajectories. All results for the analysis of the experimental data are presented in Suppl. Figs. S21-S28.
Organization of the challenge We ran the Anomalous Diffusion (AnDi) challenge as a time-limited competition from March 1, 2020, to November 1, 2020. The competition was hosted on the Codalab platform (https://competitions. codalab. org/competitions/23601) and divided in three phases (Development, Validation, and Challenge). The competition has later been converted to an open challenge, continuously accepting new submissions. Datasets, methods, list of participants, and results of the AnDi Challenge are available at http://andi-challenge. org. Software for simulation and analysis is hosted on the competition GitHub repository https://github. com/AnDiChallenge.
Structure of the datasets Simulated datasets were composed of synthetic trajectories generated according to five different mathematical models: annealed transient time motion (ATTM)  [14] , continuous-time random walk (CTRW)  [11] , fractional Brownian motion (FBM)  [12] , Lévy walk (LW)  [13] , and scaled Brownian motion (SBM)  [15]. We considered trajectories with anomalous diffusion exponents in the range α ∈ [0. 05, 2]. Exponents were restricted to α > 0. 05 because smaller exponents produce practically immobile trajectories. Note that CTRW and ATTM are strictly subdiffusive (α ≤ 1), LW is superdiffusive (α ≥ 1), FBM cannot have ballistic behavior (α < 2), whereas SBM covers the whole exponent range. Each dataset contained 10 4 trajectories. All trajectories were first generated with a length L = 1000.
A different dataset was generated for each task to ensure the proper balance of the feature to be determined. Therefore, the dataset for T1 had a balanced distribution of anomalous exponents but not of diffusion models, whereas the dataset for T2 was balanced with respect to the diffusion models. For T3, trajectories were obtained by concatenating trajectories simulated for all models and exponents. Each tra-jectory had a random changepoint at a discrete index t GT ∈  [1, 199]  corresponding to a change at least in one of the two features (α and diffusion model). An example of such kind of trajectories is presented in Fig. 1d. Three levels of noise were used to corrupt trajectories, corresponding to σ noise = 0. 1, 0. 5, 1. The SNR was calculated as SNR = σ D /σ noise. Due to the previous standardization, the SNR levels thus were SNR = 1, 2, 10.
Theoretical models In this section, we present a brief introduction to the concepts of anomalous diffusion and ergodicity breaking. We provide theoretical insights about the anomalous diffusion models considered in the AnDi challenge, as well as the description of the pseudocode used for simulations. Finally, we describe how to extend the algorithms to simulate the diffusion models in two and three dimensions. The Python implementation of all the algorithms described below is available at https: //github. com/AnDiChallenge/ANDI_datasets.
e. , 1 < α < 2, superdiffusive) and one where the noise is negatively correlated (0 < H < 1/2, i. e. , 0 < α < 1, subdiffusive). For H = 1/2 (α = 1) the noise is uncorrelated, hence the FBM converges to Brownian motion. For a d-dimensional FBM, the corresponding position vector has zero mean, x(t) = 0, the EA-MSD is x(t) 2 = 2dK H t 2H , the autocorrelation is x(t 1 )x(t 2 ) = dK H (t 2H 1 + t 2H 2 -|t 1 -t 2 | 2H ), and the fGN reads ξ fGn,i (t 1 )ξ fGn,j (t 2 ) = 2K H H(2H -1)|t 1 -t 2 | 2H-2 δ ij + 4K H H|t 1 -t 2 | 2H-1 δ(t 1 -t 2 )δ ij , (6) where i, j in the subindex of the fGN denotes a different cartesian coordinate. Various numerical approaches have been proposed to solve the FBM generalized Langevin equation exactly. Here, we use the Davies-Harte method [70] and the Hosking method  [71]  via the FBM Python package(https: //pypi.