dataesr/scientific-paragraphs-categorization huggingface dataset:
Retrieving potential drugs Based on a list of 40 input genes identified with their gene symbol, we leverage the DrugBank  (27)  dataset publicly available as linked data through the Bio2RDF (28) SPARQL endpoint (https://bio2rdf. org/sparql). For each of the candidate gene targets, the principle consists of generating a graph pattern that links drugs to gene names through the DrugBank target and gene-name edges. These patterns are implemented in a SPARQL query that is executed online through the Bio2RDF endpoint. In this query, we filter only drugs with an "approved" status. These 39 gene targets lead to one approved drug. Augmenting drug targets pyBRAvo is then launched to assemble a GRN with all query expansion features (suffixes, synonyms, complex decomposition) with a single recursion depth and excluding the PC miRTarBase and MSigDB data sources.
In this experiment, we showed that running pyBRAvo provides a list of candidate drugs with an indirect effect on the biomass production. Although an evaluation of the biological or clinical relevance of the retrieved potential drugs was beyond the scope of this work, we provide the indication of each of the candidate drugs in the Jupyter notebook available online (https://github. com /pyBRAvo/pyBRAvo/blob/master/drugbank-usecase/Noteb ook/drugbank-metabolism. ipynb). We believe that GRNs and associated metrics such as centrality could provide interesting perspectives when prioritizing drugs in in silico screening applications. This experiment can be reproduced based on the online Jupyter notebook.
System implementation pyBRAvo was designed as a Python module to be used through an application programming interface in any Python script or directly in Command Line Interfaces. The proposed use cases were aimed at facilitating integration and fine-tuning of pyBRAvo in bioinformatics tools and workflows. pyBRAvo is also provided with several Jupyter notebooks to reproduce the experiments presented here as well as for training purposes. pyBRAvo source code and notebooks are available at https://github. com/pyBRAvo/pyBRAvo.
It is thus relevant to link a gene with its mRNA and the associated protein to obtain a complete reconstruction when querying the data sources. Furthermore, the reconstruction benefits from several improvements, namely, complex decomposition, label expansions, synonym expansions and network unification, that allow a deeper exploration of the knowledge base. It proves that the use of entity names together with these improvements enables going far beyond the simple use of gene or protein identifiers as shown in Figure  3. In principle, pyBRAvo shares some characteristics with pyPath, a method that relies on the OmniPath database to reconstruct SNs on a large scale. In a comparison at a coarse-grained scale, the two sets of results appear comparable with respect to the size and the number of edges.
Nevertheless, a deeper inspection of the results shows that several artifacts appear in the pyPath reconstruction, such as the large number of cycles, cliques and 4-and 3-size motifs, possibly resulting from protein complexes management in Omnipath (see Table  4 ). It is highly necessary to reduce such artifact edges when the obtained networks are used as input to build predictive models in both logical and probabilistic frameworks. We observe that PyPath extracted graphs tend to increase the graph connectivity. This signaling flow, carefully detailed in the BioPAX content, is kept when using pyBRAvo to extract the graph. In addition, these predictive models have to be considered very carefully. Indeed, most of the knowledge aggregated in the databases results from independent experiments.
It is crucial to introduce a validation step by testing the model with experimental data, for instance, before using it in predictive approaches. Nevertheless, we strongly believe that adding such an automatic reconstruction step prior to the prediction step will permit more rapid design of large-scale predictive models while limiting errors due to manual data curation tasks. By leveraging Semantic Web technologies and the BioPAX ontology, pyBRAvo is able to mine any biological network knowledge expressed with commonly accepted pathway terms, concepts and relations. It could additionally be enriched using other external RDF datasets, such as DrugBank or DisGenet  (29) , with an adapted querying process. This can largely enrich the construction of systems biology models based on disease descriptions for instance.
In addition, Semantic Web technologies enable performing complex queries on multiple data sources without the need to locally host possibly massive datasets. Assembling the vast amount of knowledge that is now publicly available is challenging. pyBRAvo has been designed with a particular focus on data and knowledge reuse. Through this work, we wish to encourage the Systems Biology community to combine their efforts toward developing machine actionable and causality-oriented biological resources, leveraging community standards, and ensuring rich and shared semantics.
Background: Increased catabolism has recently been recognized as a clinical manifestation of amyotrophic lateral sclerosis (ALS). The hypothalamic systems have been shown to be involved in the metabolic dysfunction in ALS, but the exact extent of hypothalamic circuit alterations in ALS is yet to be determined. Here we explored the integrity of large-scale cortico-hypothalamic circuits involved in energy homeostasis in murine models and in ALS patients. Methods: The rAAV2-based large-scale projection mapping and image analysis pipeline based on Wholebrain and Ilastik software suites were used to identify and quantify projections from the forebrain to the lateral hypothalamus in the SOD1(G93A) ALS mouse model (hypermetabolic) and the Fus ΔNLS ALS mouse model (normo-metabolic). 3 T diffusion tensor imaging (DTI)-magnetic resonance imaging (MRI) was performed on 83 ALS and 65 control cases to investigate cortical projections to the lateral hypothalamus (LHA) in ALS.
All mice expressing mSOD were routinely tested for motor impairment and euthanized in case of overt motor disability. Since male and female mice differ substantially in progression rates of clinical and biological manifestation of motoneuron disease (e. g. [35] ), the present study focused only on male mice. The exploratory nature of this study prevented a formal calculation of the cohort size based on power calculations; we therefore estimated the group size (n = 3-6) based on the previously obtained dataset for motor cortex  [26].
Immunohistochemistry The mSOD mice were sacrificed at postnatal day 40 (P40, injection at P25) or at P110 (injection at P95). The Fus mice were injected at P255 and sacrificed at P270. For immunohistochemistry, the mice were transcardially perfused with PBS and then 4% PFA in PBS; after 18 h of post-fixation in 4% PFA, brains were cryoprotected in 30% sucrose/PBS, embedded in optimal cutting temperature compound and serially sectioned using a Leica CM1950 cryostat into 70-μm sections from AP + 2. 6 mm to -3. 0 mm as previously reported  [26]. Immunostaining for SOD1 (Anti SOD1, Sigma-Aldrich Chemie GmbH, Taufkirchen, Germany; HPA001401, 1:500), misfolded SOD1 (Misfolded SOD1 (B8H10), MédiMabs, Montréal, Canada; MM-0070-p, 1: 250) and Fus (Anti Fus, Sigma-Aldrich Chemie GmbH, HPA008784, 1:300) was performed.
Neuron mapping A multiple-software approach was devised to analyze the images and properly parcellate and quantify projections from cortex to LHA (Fig. 1a ). Anatomical annotation -First, a custom Fiji [39] macro allowed to crop single brain sections out of whole microscopic slide images while appending serial numbers. Hands-on registration of coronal brain sections was performed by means of WholeBrain package in R  [40] , laying its foundations on the Allen Brain Mouse Reference Atlas (ABA, version 2011  [41] ). For tilted sections (as compared to the original ABA coronal planes) and biological inter-individual brain differences, a series of easily recognizable anatomical landmarks were chosen to appropriately map the ROIs along the anterior-posterior axis (Table  S1 ). The stereotactic coordinates were determined with openbrainmap.
The WholeBrain dataset was also used to derive an independent anatomical annotation in the Allen Brain Atlas (backward warp transform field [atlas plate to section], 6. 1; forward warp transformation field [section to atlas plate] 6. 2) and 3D reconstructions (6. 3). b Training of the ilastik cell density counting suite. Accuracy improvement of the classifier according to the number of images used for training. The increasing need of computing power is indicated by the time needed to process 60 images. Accuracy was calculated by comparing randomly selected and manually analyzed images. A final accuracy of 94% was achieved with the use of 250 images after adaptation of parameters for the Random Regression Forest.
MRI acquisition for hypothalamic volumetry and hypothalamus volume quantification The T1w MRI data for 72 patients with ALS and 43 controls were acquired using a 3. 0 T MRI scanner (Allegra Siemens Medical, Erlangen, Germany) with a T1weighted magnetisation-prepared gradient echo image (MPRAGE) sequence (192 sagittal slices, no gap, voxel size 1. 0 × 1. 1 × 1. 0 mm 3 , matrix 256 × 192 × 256, TE = 4. 7 ms, TR = 2200 ms). The MPRAGE images were used for manual delineation of the hypothalamus in the coronal plane using a previously reported landmark-based procedure (Tensor Imaging and Fiber Tracking software, TIFT  [19] ). Briefly, a three-step processing pipeline was used: (1) rigid brain normalisation along the anterior commissure (AC)-posterior commissure (PC) axis; (2) spatial upsampling into a study-specific grid (in-plane resolution 62.
MRI acquisition of DTI datasets DTI scanning was performed in 83 ALS patients (58 ± 14 years; 49 males; ALS Functional Rating Scale-Revised, 40 ± 6; disease duration, 19 ± 16 months) and 64 controls (52 ± 11 years, 33 males) according to a standardized protocol (for details see  [45] ). The DTI data were acquired on a 3. 0 T head scanner (Allegra, Siemens Medical, Erlangen, Germany). The standardized DTI scanning protocol was as follows: 49 gradient directions (b = 1000 s/mm 2 ) including one b = 0 gradient direction, 52 slices, 96 × 128 voxels in-plane, slice thickness 2. 2 mm, in-plane voxel size 2. 2 mm × 2. 2 mm, echo time 85 ms, and repetition time 7600 ms. All participating patients and controls provided written informed consent for the study according to institutional guidelines. The study was approved by the Ethical Committee of the University of Ulm (reference #19/12).
DTI dataset analysis The TIFT software  [46]  was used for DTI data processing and statistical analysis, as previously described  [45]. In brief, stereotaxic normalization to the Montreal Neurological Institute space was performed iteratively using study-specific templates. To map the white matter microstructure, fractional anisotropy (FA) maps were calculated from the stereotaxically normalized DTI data sets of all subjects. A Gaussian filter of 8-mm full-width at half maximum was applied for smoothing of FA maps for a good balance between sensitivity and specificity  [47]  and the FA maps were corrected for the covariate age. Tractwise FA statistics was performed by comparing the FA values between the two subject groups in a given tract system (Student's t-test)  [48] , focusing on the following tract systems: a tract from orbitofrontal regions to the hypothalamus (orbitofrontal-hypothalamic tract), a tract from the hypothalamus to the insula (insularhypothalamic tract), the cingulate-hypothalamic tract, and the corticospinal tract as a reference.
Statistical analysis Statistical analysis was performed with the GraphPad8 software suite. Comparison of neuronal counts (absolute or normalized value) was performed by two-way ANOVA (genotype x structure design) using Sidak's post-hoc multiple comparisons correction; corrected P values are provided. Normalized counts were obtained by dividing the absolute number of neurons in each structure by the total number of neurons counted in the same brain and multiplying the ratio by 50,000 (50 k normalization). To compensate for the atrophied LHA, the individual LHA volume of each mouse was divided by the average LHA volume of wild-type (WT) mice (2. 75 mm 3 ), and this ratio was multiplied with the absolute number of neurons counted in each area (LHA atrophy compensation). Comparison of the total neuronal counts was performed with the two-tailed unpaired Student's t-test.
1a, 2 ). Single-structure outlines (cortical layers, subcortical structures, e. g. Fig. 1a, 4. 1) were extracted using a dedicated R script, then size-corrected and reoverlapped onto the original image in Fiji; the corresponding area in the original image was then cropped (Fig. 1a, 4. 2), resulting in the unpacking of the original single-section into several-dozen cropped pictures (each image was coded to be identified later), resulting in > 100,000 pictures per brain. To identify and count the number of neurons in each fragment, the ilastik software suite was employed. Simple foreground/background segmentations were generated by resorting to the pixel classification workflow, which allowed automatic clearance of most of the brain autofluorescence background in Fiji (Fig. 1a , 4. 6). This step improved the discrimination of few ZsGreen+ neurons in ROIs containing many more dendrite stretches and artifacts, minimizing the chance of generating artifactual counts and reducing the computational burden placed on ilastik cell density classifier.
The latter was trained with 250 randomly selected ROI images that were annotated by a human operator; the accuracy of the software reached 86% after training with 100 images and achieved a peak of 94% (Fig. 1b ) by improving the performance of the Random Regression Forest algorithm with 150 additional images and tuning its parameters (trees number and depth; see Methods). The whole procedure was then sequentially implemented so that the neuronal counts for each picture and every section were annotated and logged together with their anatomical location within the brain hemisphere as well as with the rostro-caudal serial number of the section itself.
This approach was substantially slower than the registration via WholeBrain (> 500 h for a whole dataset). The individually cropped images were then segmented with the Cell Density Counting module in ilastik (since the accuracy and reproducibility of this step had already been established and quantified). In the manually annotated dataset, the sum of neurons projecting from the 28 areas to the LHA was not significantly different between mSOD1 and WT (Fig. S4b ). First, we considered absolute neuronal counts: the comparison between the two genotypes (ipsilateral hemisphere) revealed a statistically significant difference (two-way ANOVA, F 1,112 = 6. 281 0; P = 0. 013 6), further post-hoc comparison (Sydak's) revealed a significant expansion of projections from the ORBl/vl + AI in mSOD1 mice (P = 0.
035 2) (Fig. S4c ). Upon 50 k normalization we also detected a significant effect of genotype (two-way ANOVA, F 1,112 = 23. 780 0; P < 0. 000 1) with post-hoc revealing significant differences in ORBl/vl + AI (P < 0. 000 1), ILA (P = 0. 000 1) and PIR (P < 0. 000 1) (Fig. S4d ). However, no significant effect of genotype was found on the absolute counts of neurons projecting from the contralateral hemisphere (Fig. S5a ), or in any area of interest (Fig. S5b ), but upon 50 k-normalization a statistically significant loss of projections from the PL + ORBm was identified (P < 0. 000 1), together with a trend toward increase of projections from the ORBl/vl + AI (P = 0. 072) (Fig. S5c ). Thus, the manually registered dataset displayed substantial similarities with the WholeBrain-registered dataset (expansion of projections from the ORBl/vl + AI) but also a major discrepancy: increased projection from the ILA to LHA in the manual dataset and no difference in MOs projections, whereas the opposite was detected by WholeBrain.
There was an effect of mSOD1 transgene on the absolute projecting neurons from the 28 ipsilateral areas (two-way ANOVA: F 1,112 = 12. 550 0; P = 0. 006) but not on the 50 k normalized dataset (twoway ANOVA: F 1,112 = 1. 838 0; P = 0. 177 9), and in both cases the post-hoc comparison did not reveal any significant difference in any of the considered structures between mSOD1 and WT groups (Fig. 3c, d ). Likewise, no statistical difference was found on the sum of projections from the contralateral hemisphere in absolute counts (Fig. S6a ) and the significant increase in projections from ACA (P = 0. 007 8; Fig. S6b ) was not confirmed upon normalization for 50 k projecting neurons (Fig. S6c ). Indeed, no differences were found in the 3D reconstructions either (Fig. S6d ). In all, these findings suggest that the alterations of ORBl/vl + AI projection to the LHA arise during disease progression.
9, df = 113. 0; P < 0. 000 1; Fig. 6a, b ). Then, five white-matter tracts were investigated: the orbitofrontal-hypothalamic tract, the insular-hypothalamic tract, the cingulate-hypothalamic tract (Fig. 7 ) and, as a reference, the corticospinal tract that is known to be substantially altered in ALS patients  [45]  and the cerebellar peduncle as control for jitter artifacts. The former three tracts converging on the hypothalamus were selected because they most closely match (when accounting for the different anatomy) the structures investigated in the mouse model and have reproducible and unequivocal identification in DTI datasets. We identified a significant decrease of FA of the orbitofrontal-hypothalamic tract in Fig. 4  Significant misfolded SOD1 burden in the ORBl/vl + AI in mSOD1 mice.
Discussion In this study, we provide converging evidence from ALS mouse model and patients of the involvement of largescale projections to LHA in ALS, in particular the disruption of connections from the orbitofrontal cortex and/or the AI to the LHA. This effect is independent of systematic biases introduced by software or human operators in the registration, does not appear to be an intrinsic developmental defect, and is not detected in an independent ALS model devoid of metabolic phenotypes. The findings in the murine model display striking similarity to the disruption of the orbitofrontal-hypothalamic tract identified in a DTI-MRI dataset of ALS patients. Hypermetabolism in ALS has been repeatedly reported in a substantial subset of patients  [9] [10] [11] [12] [13]  as well as in some, but not all, ALS murine models  [14].
Acknowledgements We thank the colleagues of the  German Center for Neurodegenerative Diseases (DZNE)-Ulm  for helpful comments. We are grateful to  Sruthi Sankari Krishnamurthy  and to  Jelena Scekic-Zahirovic  for the critical revision of the final draft of the manuscript, and to  Thomas Lenk  and  Florian olde Heuvel  for the technical assistance. We thank the  BruenstlMedizin NeuroNanoMed Zentrum (Beirut)  for sponsoring the  Praktikum of Rami Saad.
Linking lineage tracking with FUCCI remains challenging due to the complexity of integrating the needed algorithms. Here, we introduce CellMAPtracer, an open-source, free software tool that allows automated and supervised tracking of fluorescently labeled cells  [29]. CellMAPtracer is applicable for a variety of two-dimensional (2D) cell migration assays, such as random migration and directed migration. It is capable of combining automated tracking with manual curation. It provides basic motility analysis and categorized trajectory data for deeper trajectory investigation. CellMAPtracer allows users to trace and follow individual cells throughout the course of the live imaging. This can enable the user to visualize the tracks of the descended cells and their ancestor in an interactive multigeneration plot.
Then, cells were scanned at 10 min intervals over 3 days in Essen BioScience's IncuCyte S3 with objective lenses 10× (numerical aperture (NA): 0. 95; image resolution: 1. 24 µm/pixel) using both the phase channel (HD Phase imaging in gray values) and the green channel (emission wavelength: 524 nm; excitation wavelength: 460 nm; exposure time: 200 ms). Images were collected using a Basler Ace 1920-155 um camera with a complementary metal-oxide-semiconductor (CMOS) sensor. Cellular viability was assessed throughout the course of the scanning by comparing the phase cellular morphology between BT549 and GFP-BT549 cells. Data are available at https://doi. org/10. 5281/zenodo. 3878526 and https://doi. org/10. 5281/zenodo. 4179028 (Both accessed on 20 February 2021).
95; image resolution: 0. 26 µm/pixel) with a CMOS camera (Orca Flash V4. 0, Hamamatsu Photonics 430-8587, Japan) on a Zeiss AxioObserver. Z1 wide-field epifluorescence. Neither photobleaching nor phototoxicity was observed in cells imaged with this protocol. Images were collected at 20 min intervals for 18 h. Data are available at https://doi. org/10. 5281/zenodo. 4179316 (accessed on 20 February 2021).
Three-Channel FUCCI Time-Lapse Data Three-channel FUCCI time-lapse images of human telomerase reverse transcriptase (hTERT)-immortalized retinal pigment epithelial cells (RPE) were obtained from Dr. Katarzyna M. Kedziora and Prof. Jeanette Gowen Cook at the University of North Carolina, Chapel Hill, NC, USA. Data are available at https://doi. org/10. 5281/zenodo. 4179252 (accessed on 20 February 2021). According to the original study  (Grant et al. , 2018)  RPE1-hTert (RPE) cells were obtained from the American Type Culture Collection (ATCC). Fluorescently labeled PCNA and PIP-FUCCI cell-cycle indicators were introduced with lentiviral vectors (Addgene 118617 and 118616). The expression construct of PIP-FUCCI consists of human Cdt1 amino acids 1-17 including the PCNA-interacting protein (PIP) degron, SV40 nuclear localization signal, epitope tag, self-cleaving peptide, and human Geminin amino acids 1-110 including both the D box and the KEN motif.
The position with minimum distance is set as the new position of the tracked cell (Figure  S2 , Supplementary Materials). To save computational time, the local features are computed only within a fraction of the image defined by the interactive slider in the CellMAPtracer tracking window. MATLAB programmers can fully edit, extend, and add more tracking algorithms to CellMAPtracer. We designed CellMAPtracer to allow cell tracking to be done automatically and monitored in a stepwise manner (Figure  S3 , Supplementary Materials). Tracking errors can distort the cell trajectory results. For that reason, we designed the CellMAPtracer interface to include multiple features for importing, highlighting cell division, inspecting, and correcting existing tracked cells/nuclei to reach near 100% tracking accuracy.
Trajectory Analysis Basic trajectory analyses, including total distance, displacement, directionality, and speed, are automatically done by CellMAPtracer. Excel and many online tools can be used to visualize the statistical results through boxplots, scatter plots, and bar plots. A list of suggested online visualization tools is available on the CellMAPtrace readme page: https://github. com/ocbe-uio/CellMAPtracer (accessed on 20 February 2021). All the trajectory analyses in the study were carried out in the statistical programming language R  [36]. All the needed codes for the trajectory analysis with tutorials are available at the CellMAPtracer Wiki page: https://github. com/ocbe-uio/CellMAPtracer/wiki (accessed on 20 February 2021).