dataesr/scientific-paragraphs-categorization huggingface dataset:
Disconnectome morphospace component mapping In the next level, we brought the score prediction results back to the neuroimaging space to explore the neuroanatomical patterns leading to symptoms. The rst dataset was split in half (2 X 666 disconnectomes maps) to assess reproducibility. Latent patterns of predicted neuropsychological performances were statistically associated with brain disconnections maps of the two halves of the rst dataset using voxelwise linear regressions. In doing so we obtained two sets of maps of brain disconnection for each neuropsychological score (see example in gure 2d-f and all maps together with their full discussion in Supplementary Material -Section C). We were able to produce a comprehensive atlas of the brain disconnections associated with neuropsychological test scores and the statistical comparison of the two sets of maps indicated a good level of reproducibility (Pearson R = 0.
Accuracy in predicting neuropsychological score at 1-year after stroke To assess the accuracy of the predictions, data derived from a third independent dataset (20 stroke patients withheld from the original dataset 14 ; see Supplementary Table  1 ) were projected into the morphospace. From there, equations derived from the composite morphospace were applied to predicted individual neuropsychological scores. Prediction accuracy was assessed as the difference between the observed and predicted scores, normalized by the maximum score (i. e. , normalized prediction error; Figure  5 ). The pro le of neuropsychological scores for single patients was predicted with an average accuracy of 84. 3 ± 5. 6 % while each test was predicted individually with an average accuracy of 83. 9 ± 7 %. Overall, the prediction of two-thirds (N=65) of the tests was replicated in this third independent dataset with an accuracy >80% (Supplementary Table  4 ).
Disconnection Symptoms Discovery web application To make this resource and method available for the clinical-research community, we deployed an interactive web application platform called Disconnectome Symptoms Discovery -DSD (http://disconnectomestudio. bcblab. com). The DSD requires the input of brain lesions converted to disconnection maps and returns the expected 1-year neuropsychological scores for individual disconnectome maps (see the DSD user guide in the Supplementary Material Section E). The DSD tool prediction model relies on the databases presented in this study that can be updated on-demand with new neuropsychological assessments and patients' disconnectomes.
Discussion Applying state-of-the-art data embedding methods we succeeded in combining complementary databases of stroke patients and produced an atlas of neuropsychological scores associated with brain disconnections. This atlas applied to an out-of-sample dataset accurately predicted 65 neuropsychological scores with an accuracy of over 80%. An openly available web application, the disconnectome symptoms discoverer (DSD) capitalises on our methods and provides new anatomical insights into cognitive symptoms for researchers and clinicians. Similar patterns of stroke-induced white matter disconnections were distributed close by in the embedding space comparably to other research elds using UMAP methods  31  , e. g. , single-cell genetic transcriptomes  34, 35. Therefore, the disconnectome morphospace acted as a reference to quickly import and summarize new stroke disconnections.
First, some neuropsychological scores are more reliable than others in assessing performances  37. Second, plasticity and interindividual variability might interact with recovery  11, [38] [39] [40]. Third, the disconnectome model may not capture all the variance of brain injuries. Indeed, hypoperfusion  41  and hypometabolism  42  factors as well as acute imaging changes such as pseudonormalization  43  are not included. Besides these limitations, the disconnectome symptoms discoverer (DSD) web application is a free and user-friendly web browser tool that only requires an internet connection. Instant software access and automatic updates make the word-wide-web the ideal media for clinical translations. The application of the DSD results can help personalized prognosis. Further, while our predictions were validated in an outof-sample dataset, the DSD web application allows for a wider validation with crowdsourcing usage, through new dataset implementation.
Methods across the HCP participants. As a result, for each stroke patient a map of probability, ranging from 0 to 1, was obtained to quantify lesion disconnections. Spatial embedding. Dimensionality reduction of patients' disconnectome was obtained using the UMAP method  31. A non-linear embedding method that distributes data variability along major axes. Dataset 1 3-dimensional disconnectome maps were vectorised and imported as features of the embedding methods. As UMAP parameters, an approximation of 15 neighbours and a minimum 0. 1 Euclidean distance was set to obtain a two-dimensional embedding of dataset 1. A space locally connected as Riemannian manifold that we addressed in the paper as disconnectome morphospace. The UMAP embedding transformation was stored as a Python object, using the Pickle library, to apply the same lowdimensional transformation further when new patients are imported into the model.
Subsequently, to have positive coordinates with a zero origin, the maximum negative dataset 1 UMAP values across dimensions were added to shift the coordinate scales (Umap 1 and Umap 2). Relationship to neuropsychological scores. Statistical correlations between patient localisation in the disconnectome morphospace and neuropsychological scores were conducted. Before the multiple regression formula, UMAP coordinates were converted into a 2D nifti image (260x260 matrix, 0. 05 mm pixel size), and a Gaussian kernel spatial smoothing of 1 mm was applied (using FSL libraries https://fsl. fmrib. ox. ac. uk/fsl/fslwiki/). This step was conducted to consider the uncertainty of UMAP coordinates and obtaining a spatial distribution of patient localisation in the disconnectome morphospace. Pixel-wise Pearson correlations between the patient probability of localisation and neuropsychological scores were conducted with iterative loops in Python (python numpy.
corrcoef). Medium effect size correlation results only were considered informative (). Subsequently, since multiple clusters of voxels survived the threshold, a principal component analysis (PCA) has been run to compress the patient coordinate distribution variability. Three main principal components have been considered (Python sklearn. decomposition. PCA). Subsequently, patients' principal components have been entered, as dependent variables, in the multiple regression model (Python sklearn. linear_model. LinearRegression) to predict neuropsychological scores: White matter atlas of neuropsychological components. In order to create a white matter atlas of the evaluated neuropsychological assessments, white matter disconnectomes (dataset 1) were correlated with patients' PCA scores, evaluated by running the prediction model on the dataset 1.
The former disconnectome data were used in de ning the UMAP space, whereas the latter model weights as variables of the multiple regression model to predict long term neuropsychological symptoms. Using randomise (FSL libraries) a generalized voxel-based linear regression model was run, with disconnectome maps as independent variables and PCA scores as dependent variables. To address the result of replicability this procedure was repeated twice, splitting the dataset 1 into two halves of n=666 subjects each. The randomise T-maps obtained were used to calculate the correspondent effect size maps (f 2 , python code reported in http://www. bcblab. com/BCB/Coding/Coding. html). For each neuropsychological score three principal component scores were evaluated and the maximum effect size across the components was considered.
Subsequently, the highest effect size across neuropsychological assessments was reported in the white matter atlas summary map (FSL libraries nd_the_biggest function). The replicability of the neuropsychology white matter atlas was quanti ed by means of Pearson correlations between the two summary maps. DSD web application development. The DSD web was built using the Django framework (https://www. djangoproject. com). This web framework allows database manipulation and is Pythonbased. The DSD frontend was created with standard Javascript and css templates; whereas the backend is hosted in a DigitalOcean webserver (https://www. digitalocean. com). Gunicorn and Ngnix are used for the web application live production. Visualization. visualisation of the results was performed using Trackvis (http://trackvis.
Data availability. All the neuropsychological score map used for de ning the white matter atlas of neuropsychological components are freely available at https://neurovault. org/collections/11260/. The raw dataset imported in the BCBtoolkit software to calculate individual patient disconnectomes is available at https://www. humanconnectome. org (7 T diffusion data). In addition, processed data are available on request to the corresponding author. Code availability. The code used in the analyses is available as part of the BCBtoolkit package http://toolkit. bcblab. com and the DSD web application http://disconnectomestudio. bcblab. com. Any additional information is available on request to L. T.
The combination of structural (inter-dye distances) and dynamic (kinetic connectivity and exchange rates) information enables identification of a consistent model. (B) Study of structure and dynamics of chromatin fibers. Multimodal FRET-study of structure and dynamics of chromatin fibers using three FRET labeling positions (DA1-3) for two pairs of dyes with distinct Förster distances (Eq. 3 bellow). Prior structural information provided by X-ray crystallography of tetra-nucleosome(top, right PDB ID: 1ZBB  (Schalch et al. , 2005) ) and cryo-electron microscopy (top, left  (Song et al. , 2014) ) is combined with the structural and dynamic information obtained by FRET experiments on immobilized molecules measured by total internal reflection (TIRF) microscopy and on freely diffusing molecules by confocal microscopy  (Kilic et al.
Another challenge is the kinSoftChallenge2019 (http://www. kinsoftchallenge. com, Schmid et al, in preparation) that aims to evaluate existing tools for extracting kinetic information from single-molecule time trajectories (for time-binned or intensity-based measurements of immobilized molecules). This challenge aims to: (1) demonstrate the ability of smFRET-based kinetic analyses to accurately extract dynamic information from smFRET data; (2) provide the singlemolecule (FRET) community a way to judge the different software tools out there; and (3) assess and communicate analysis procedures to yield reliable kinetic data and/or FRET state models. In the first part of this study, synthetic data was evaluated. In the second part of the study, experimental smFRET data are currently being evaluated.
Hence, open science practices as well as a platform for exchange of data and software would be beneficial. One question that remains to be addressed is whether a user-unbiased, thus, fully automated analysis of smFRET data is possible. Therein, the FRET community needs to address the question how to overcome user bias in data analysis. Akin to structural models in the protein data bank (PDB) based on NMR and/or crystallization data  (Rosato, Tejero and Montelione, 2013) , smFRET benchmark criteria must be defined and eventually fulfilled to classify the results of FRET-based inter-dye distance measurements and kinetics (i. e. transition detection and state identification) as reliable. As an example, single-molecule video analysis usually suffers from a limited number of molecules (potentially bad statistics) and a potential user-bias, meanwhile the so-called molecular-sorting (i.
In addition, and in line with the multitude of existing software presented here (see Table  1 ), the question of who will determine their accuracy and utility in yielding similar datasets (for simulations) and results (for analyses) is also raised. Accompanying this ponderation are the questions: (i) how is the value of results that differ from some expected consensus taken into consideration, (ii) are 'expected results' chosen objectively? While section 4. 1 (and the suggestions in this position paper) recommend certain guidelines, it is important to make sure that these stay as recommendations and will never evolve into 'requirements' disguised as good-practice guidelines. The proposition of a modular software platform using algorithms that are accepted by the smFRET community, although it could be useful for many, might be unrealistic, mainly because (i) there are currently many analysis and software approaches, with as of yet little agreement on the algorithms, procedures and parameter values used, and hence such community-wide acceptance is premature; and (ii) there are many smFRET practitioners who are de facto members of the community, but do not necessarily wish to officially be part of the newly formed FRET community (http://fret.
Open Science practices One of the cornerstones of the scientific method is the ability to reproduce experimental results. As experiments become more sophisticated, a clear description of experiments is crucial. Recent trends towards Open Science practices call for full transparency of the scientific process. Funding agencies embracing this philosophy (e. g. https://datascience. nih. gov/strategicplan) expect their grantees to publish in Open Access (OA) journals (and pay for the corresponding open access fees) or deposit manuscripts in repositories (e. g. Pubmed Central, arXiv, bioRxiv, ChemRxiv), deposit data (sometimes also raw data) in repositories (such as Zenodo, the Dryad Digital Repository, FigShare) as well as analyses codes (for instance in open notebook format in repositories such as GitHub), disseminate results and make them accessible for all.
As an example, for confocal-based FRET of freely-diffusing single molecules, many simulation software packages have been developed. Common to all approaches is the simulation of the Brownian motion of particles in a box and the evaluation of the fluorescence emission based on the excitation-emission profile, generally assumed to be Gaussian. This results in a trajectory of detected photons from each molecule as a function of time, and hence, as a function of its position at each moment. All photons from all times and all molecules together with an additional background process, form a dataset that simulates confocal-based smFRET experimental raw data. Then, the analysis of the simulated smFRET data can be performed using different procedures and tested against the known ground-truth. When such an approach is adopted, different teams could keep using their own analysis procedures yielding results that are comparable across different laboratories.
Currently, there are several simulation packages available: (i) PyBroMo (ii) module within the PAM software package, (iii) Burbulator, (iv) simFCS (https://www. lfd. uci. edu/globals/) and (v) a module in Fretica (https://www. bioc. uzh. ch/schuler/programs. html). For the simulation of immobilized single-molecules and single-molecule videos (SMV), there is a module in the software package MASH-FRET available  (Börner et al. , 2018). PyBroMo (https://github. com/tritemio/PyBroMo) is a Python-based software package for simulating freely-diffusing single-molecule fluorescence detection including Brownian motion and dynamical exchange currently between two-states. This approach has been employed in a few recent works  (Ingargiola, Lerner, et al. , 2017; Lerner, Ingargiola and Weiss, 2018; Hagai and Lerner, 2019).
Analogous approaches, including the full simulation of fluorescence lifetime and anisotropy information as well as conformational multi-state (up to 8 states) exchange dynamics, are implemented in the MATLAB-based PAM software package (https://www. cup. unimuenchen. de/pc/lamb/software/pam. html) and the LabView-based Burbulator software  (Kalinin et al. , 2010; Felekyan et al. , 2012; Dimura et al. , 2016; Barth, Voith von Voithenberg and Lamb, 2019)  (http://www. mpc. hhu. de/software/software-package. html), and have been applied to benchmark novel quantitative analysis methods to obtain structural and kinetic information. Fretica (https://www. bioc. uzh. ch/schuler/programs. html) enables the simulation of single-molecule multichannel-detection of immobilized molecules and mixtures of freely diffusing species, including dynamic exchange between an arbitrary number of internal (e.
g. conformational) states. The simulation of fluorescence lifetime and anisotropy information can be included  (König et al. , 2015; Zosel et al. , 2018). The Matlab-based MASH-FRET software package (https://rnafretools. github. io/MASH-FRET/) has been applied to evaluate transition detection and state identification algorithms used in particular for time-binned smFRET trajectories  (Hadzic et al. , 2018)  as well as for spot detection in single-molecule videos (SMV). To ensure simulations can be shared without the need for exchanging large files of simulated photon counts or single-molecule videos, the PyBroMo, PAM, Burbulator and MASH-FRET packages provide initialization files that contain the simulation parameters and the seed of the random number generator, so that identical trajectories can be produced  (Dimura et al.
Online data deposition in well-documented file formats would therefore save a lot of headache to many laboratories. The reuse of smFRET data could also be performed online as part of the deposition of data in databases. To do so, the raw experimental data should be supplied in a universal data file format that could be easily read and scrutinized. Ideally, the file should store both raw data and sufficient metadata such that it will completely specify the measurement, setup and sample. Such a package (data and metadata) allows third parties to reanalyze the data in order to confirm published results or to benchmark new methods. Ideally, the metadata should be stored in a humanreadable text-based format, while space-efficient storage of the raw photon data should be ensured by lossless compression.
, 2016)  file format that connects the required metadata with the raw photon information in a single, space-efficient format that is suitable for sharing and long-term data archival. From the beginning, the development of the format has been done openly on GitHub (https://github. com/Photon-HDF5), encouraging comments and involvement from the community. The rich metadata included in Photon-HDF5 promotes best practices in documenting experimental details. To better communicate data transfer using the Photon-HDF5 file format, scripts for conversion of multiple different file formats (e. g. from Becker & Hickl and PicoQuant) into the Photon-HDF5 file format have been introduced. Photon-HDF5 might not be the ultimate format and might need further development, but its underlying philosophy is wellaligned with the proposed recommendation in this article.
File format for wide-field/TIRF camera-based acquisition: This type of data is acquired as a stack of images (e. g. TIFF). To extract time trajectories quite a bit of selection/filtering is needed in order to yield timed-binned photon lists (spot identification; D and A spot association; thresholding; time trajectory extraction; background subtraction, etc. ). A file format for TIRF-based smFRET (immobilized) measurements has been pro- posed  (Greenfeld et al. , 2015). Alternatively, human-readable plain text files -with an agreed format -work well for TIRF-based smFRET traces, since the amount of data is small, in particular when compared to the raw TIFF stacks. Such plain text files have been used in the kinSoftChallenge successfully, by several labs. Exchange file format for processed data: Currently, multiple software packages exist for the analysis of (sm)FRET experiments, each with its strengths and weaknesses.
While the software packages are often modular, the use of a certain analysis tool often requires that the data be reanalyzed in the given software. To address this situation, we recommend that exchange file formats be defined for different levels of processed data (e. g. burst analysis datasets, FCS curves, FRET efficiency histograms, TCSPC decays, photon counts for photon distribution analysis). In this way, researchers will be able to combine different software packages in a modular workflow tailored to their specific use case. Exchange file formats will also lower the barrier to adopt new analysis approaches, ensuring that the full capabilities of the tools available in the community can be implemented with minimal time investment and that the maximum amount of information (per the type of processed data) can be extracted from the experimental data.
In addition, if processed data is stored with publications, the use of agreed-upon file formats will enable researchers to reproduce the analysis with minimal effort. The deposition of processed data with publications will also be useful for the development of forward-modeling approaches that aim to reproduce the experimental information (fluorescence decays, FRET efficiency histograms) from the structural models or ensembles  (Köfinger et al. , 2019). To facilitate the exchange between different software packages and the adoption of exchange file formats, we propose that a command-line file conversion tool should be developed (https://github. com/Fluorescence-Tools/exchange-formats). However, we believe it is important to keep some unique identifier in each format exchange operation in order not to compromise the concept of raw data.
In this context, we strongly believe and recommend that the analysis codes should be open for the community to read as well as write and modify. To improve the inter-operation between methods and to establish convenient documentation protocols, it is essential to work in an open multivalent environment. Establishing a community-wide working group "Analysis software for FRET" in the context of the FRET community might assist in organizing and moderating this process. However, the product of this working group should be treated as 'soft recommendations' and should not, at any moment, impose new software developments or to use any of the already existing tools. For this goal, the use of the browser-based software such as "Jupyter notebooks", and/or other available workspaces, may serve as a potentially convenient platform for experts developing methods and depositing documented analysis procedures.
Such workspaces provide an interactive scripting environment by combining a rich document with code commands as well as code outputs (e. g. figures, tables, comments, equations) and explanations in a single web-based document. Such web-based workspace environments can be easily read and distributed over the internet as well as re-run, checked or modified and supports several programming languages including python, R, C++ and, to some extent, MATLAB. In the development stage, we suggest creating a separate library to implement all the core functions and steps for a given method. These functions can be called by a specific analysis workflow allowing the user to change parameters and explore results. With this approach, the library can be developed using modern software engineering approaches and tooling, thus minimizing the exchange of bugs and facilitating maintenance.