dataesr/scientific-paragraphs-categorization huggingface dataset:
After surveying alternate ICP implementations, an Code Sample 3. Example of Python script to replay and visualize logs from the SherpaTT rover. ICP algorithm from the open-source libpointmatcher library was implemented and produced good results over large numbers of input images. The first variant of the 2D matching approach works as presented in Table  3. The second variant extends the first variant to improve subsequent pose estimates while satisfying constraints on 3D pose estimate constraints from the projection matrix. In the third variant, both optimization problems are solved numerically using the Ceres library (Table  4 ). The two approaches, each with three variants, of 3D reconstruction have been tested on PNG format image sets captured by replaying ROS bag files that were recorded by a mobile computing platform developed for easy handheld operation and easy mounting on mobile robots: the Handheld Central Rover Unit (HCRU) depicted in Figure  3.
The bag files were captured in field trials where a mobile robot ran a circular trajectory around a stationary object, capturing one image per second. Figure  4  shows the object in a sample input image, and Figure  5  shows a reconstructed 3D point cloud after 10 image pairs have been processed by the SparseRegistrationFromStereo DFPC. Figure  5  shows a view of the reconstructed point cloud in CDFF-Dev's 3D visualizer. Using CDFF-Dev's tools, the logged data can be replayed, the relevant data given as input to the DFPC, and the resulting data fusion product displayed in an interactive 3D visualization software as the data are replayed. In addition to point clouds, the visualizer tool can also display coordinate frames (two frames, connected by a red line, are visible in Figure  5 ), trajectories, maps, and meshes loaded from URDF.
We conducted an off-line evaluation of these experiments with CDFF-Dev. We will now describe two experiments and show how CDFF-Dev can be used to analyze the result. In the first experiment, SherpaTT starts driving at a workshop toward a road to enter the desert. We compare the wheel odometry of SherpaTT with the D-GPS position, which represents ground truth in this experiment. We visualize a simplified model of SherpaTT at the pose estimate derived from wheel odometry, the trajectory of Sher-paTT according to wheel odometry and D-GPS, and a ground truth map obtained from a drone. Optionally, we can show the joint movements of SherpaTT. A simple DFN implemented in Python will compare both position estimates and print the error between both to the terminal, but it could also easily be stored or plotted.
The visualization is shown in Figure  8. The log files and the GeoTIFF file for the ground truth map have been uploaded to Zenodo (https://zenodo. org/record/2575416). The script can be found in the CDFF-Dev repository (https://gitlab. com/ h2020src/og3/cdff_dev/blob/master/examples/morocco/ wheel_odometry_with_ground_truth. py). In the second experiment, SherpaTT drives in a circle around a box. The stereo cameras of the HCRU are used to make a 3D reconstruction of the environment. The result is shown in Figure  9. The log files have been uploaded to Zenodo (https://zenodo. org/record/2576885). The script can be found in the CDFF repository CDFF-Dev repository(https://gitlab. com/h2020src/og3/cdff_dev/blob/master/ examples/morocco/reconstruction3d. py). (3) Experiments performed in Morocco with the Mana and Minnie rovers: A great amount of data was acquired during the test campaign in Morocco with the help of two additional rovers, provided by   LAAS-CNRS: Mana, equipped with a lidar, and Minnie, which is focused on visual data, with its three stereo benches.
Discussion The CDFF is presented as an intermediate software component between libraries from perception and robotic control frameworks. It is independent of the robotic middleware (e. g. ROS) and is not pursuing its replacement. In fact, it is intended to be used as a repository of solutions which can be easily picked up and placed in the target system which might use any robotic middleware. Thus, the use of the CDFF is in principle compatible with any existing robotic framework known to us. In addition to this, it has been designed to allow for the testing of data fusion solutions on sensor data obtained from robots reducing as maximum as much as possible the middleware software overhead. The data fusion implementations provided in the framework have been tested online and off-line. The CDFF software will be maintained in the following years by researchers and industry consortia in the context   of EU-Funded projects which ambitions include the use and enhancement of the framework.
1b ) and this failed to cause MN death, despite the presence of copious amount of chromogranin, which upon interacting with mutSOD1 was reported to cause MN death  23. Thus, these results allowed us to discount SOD1 protein as the likely toxic factor in our in vitro mouse mutSOD1 model of ALS. Proteomic profile of ACM from mutSOD1 does not reflect an A1 phenotype. To identify potential toxic factors released by mouse mutSOD1 astrocytes, we started by performing differential liquid chromatography with tandem mass spectrometry (LC-MS/ MS)-based proteomics on Q-column-eluted ACMs of NTg vs. mutSOD1 astrocytes, as previously described  24  , except that protein identification was performed with MaxQuant using UniProt database. Analysis of three biological replicates revealed a list of 86 proteins in the Q-column-eluted ACMs (Supplementary Table  1 ).
In this case, the VIPER analysis had already been performed, leading to identification of NF-κB1 as the key MR protein  8. Finally, the individual probabilities from steps 2 to 4 are integrated to achieve a final prioritization of ligand-receptor interactions (Fig. 2 ). With the information about the Q-column-eluted mutSOD1 ACM proteome in hand, we sought to identify the receptor(s) that most likely transduce the MN death signal. For this subsequent step, we used the PrePPI database  4  to build a protein-protein interaction (PPI) network involving the candidate ligands, thus obtaining a probability-ranked list of putative ligand-receptor interactions (Supplementary Table  2 ). Next, we reasoned that if the toxic activity was mediated by one or more surface receptors, we should be able to assess their differential VIPER-inferred activity, in the presence of mutSOD1 vs.
Next, we mined the scientific literature and primary PPI databases  [29] [30] [31]  and found that of these three putative protein-protein interaction, only the APP-DR6 interaction was validated and confirmed by functional assays, albeit not in an ALS-relevant context  [32] [33] [34]  and was thus selected for further investigations. Therefore, a value of the SEARCHIN pipeline is the ability to reduce a potential list of tens of thousands of potential interactions between all over-abundant ligands produced by one compartment and all receptors in the other compartment to a ranked list, from which candidates for experimental validation can be effectively selected, based either on additional knowledge (e. g. , prior validation of the ligand-receptor interaction) or simply starting from the most statistically significant one.
All of the aforementioned investigations, not only confirmed the usual poor yield of candidate-based strategies aimed at elucidating the nature of the cell-to-cell communication, but also provided a solid rationale to try using an unbiased approach that combined ACM proteomics with MN functional genomics in a multistep bioinformatics process to address this challenging question. As such, we thought to utilize SEARCHIN as a proofof-principle to show that meaningful biology can be identified via an integrative approach, rather than provide a comprehensive benchmark of the methodologies that include testing a variety of parameters, algorithms or assessing the impact of alternative evidence integration methodologies. Incidentally, in developing SEARCHIN, we realized that effective gold-standard databases for astrocyte-MN-specific interactions are lacking and, to our knowledge, there are no alternative methodologies to prioritize the cell-to-cell interactions that mediate a specific phenotype.
Representative images are shown. c ChAT stained neurons in each ventral horn hemi-section were manually counted and quantified. Data are means ± SEM of n = 4 independent experiments and were analyzed by one-way ANOVA (F (2,9) = 43. 85; P < 0. 0001) followed by a Neuman-Keuls post hoc test: ****P < 0. 0001 NTg+sh-scram vs mutSOD1+sh-scram (d = 10. 43) and NTg+sh-scram vs mutSOD1+shDR6 (d = 5. 34); *P = 0. 034 mutSOD1+sh-scram vs mutSOD1 +shDR6 (d = 1. 63). d Survival of NTg mice (n = 10) and Tg mutSOD1 mice injected with sh-scram (n = 13) or shDR6 (n = 13) over time. Data were analyzed by Kaplan-Meier estimator with Log rank test which revealed statistical significant difference in survival between NTg vs Tg mutSOD1 mice injected with shscram or shDR6 (P < 0. 0001, df = 2). However, log rank test revealed no statistical significant difference in survival between Tg mutSOD1 mice injected with sh-scram compared to Tg mutSOD1 mice injected with shDR6.
e Loaded grid assay was performed in NTg (n = 10 mice) and Tg mutSOD1 mice injected with sh-scram (n = 19 mice) or shDR6 (n = 16 mice) over the course of the disease. Data were analyzed by non-linear regression curve fit using least sum-ofsquares method and revealed that shDR6-injected Tg mutSOD1 mice motor performance is significantly different than sh-scram injected Tg mutSOD1 mice (F (4,234) = 11. 41; P < 0. 0001). f Tibialis anterior muscle from NTg mice (black) and Tg mutSOD1 mice injected with sh-scram (red) or shDR6 (blue) was processed and incubated with α-bungarotoxin-594, anti-neurofilament and anti-synaptophysin antibodies. Representative images are shown. Scale bar: 50 μm. g Quantitative assessment of muscle innervation was done by counting at least 50 neuromuscular junctions (NMJs) by first imaging α-bungarotoxin-594 and then confirming innervation using anti-neurofilament.
In the corresponding manuscript  51  , we showed that >80% of the predictions made by PrePPI using this analysis were confirmed experimentally. In future studies, it will thus be interesting to determine whether different choice of parameters might produce even deeper biological insights into the cell-to-cell communication in models of human diseases, a possibility that cannot be assessed at this stage, owing to experimental complexity, lack of alternative methodologies, and lack of effective gold-standard datasets. At last, it should be remembered that we used an evidence integration approach, as our ultimate goal was to achieve a global score that represents the balanced integration of all the available evidences, rather than being dominated by a specific evidence source (e. g. , PrePPI or VIPER).
Loaded grid score ¼ 10 t1 ð Þ þ 20 t2 ð Þ þ 30 t3 ð Þ þ 40ðt4Þ weight of mouse where t 1 , t 2 , t 3 , and t 4 represent the seconds the mouse held on to weights 10, 20, 30, and 40 g, respectively. Bioinformatics analysis. The SEARCHIN pipeline implements a systematic and unbiased genome-wide approach to the identification of cross-compartment ligand-receptor pairs responsible for transducing signals from one cellular compartment to activate or inhibit MR proteins in the other. Specifically, the proof-ofconcept SEARCHIN implementation described in this manuscript is aimed at identifying the ligand-receptor interactions that mediate transduction of death signals that determine MN demise when exposed to culture media from mutSOD1 astrocytes. The full source code used to run the experiment discussed in the manuscript, and a comprehensive tutorial on how to use the methodology and how the datasets were built.
The source code is available under the GNU GPLv3 license at the following URL: http://www. github. com/Califano-lab/SEARCHIN. The tutorial is also available at the same link. The pipeline is conceptually summarized as follows. Ligands binding partner predictions. To prioritize ligand-receptor binding interactions, we leveraged a large collection of PPIs inferred by the PrePPI algorithm  4, 51. PrePPI integrates 3D protein structural information as well as other sources of information, such as Gene Ontology and experimentally determined interactions. The current version of the PrePPI database contains 1,545,710 pairs of interacting proteins that are based on the human proteome, considered as high confidence predictions (probability > 0. 5). To generate a null model, we used the full collection of potential PPI pairs that PrePPI may infer (i.
e. , 203,931,823 based on 20,259 proteins). Specifically, we generated a data driven empirical null model for each candidate interaction, by integrating the PrePPI scores from (a) binding partners of a ligand identified by MS, and (b) proteins predicted to interact with a differentially active receptor inferred by VIPER. In brief, for each possible interaction between two proteins P 1 and P 2 , we built an empirical null model using the collection of all the PrePPI scores between P 1 and all its putative binding partners present in PrePPI database (i. e. , 20259 protein coding genes); the same was done for P 2. The p value was generated with the empirical p value method that calculates p values from a set of observed test statistics (see ref. 67  ). This approach enabled helped us correct any bias owing to the number and specificity of individual PPIs, thus providing a more robust biological rationale for the empirical null model.
We used an open source implementation of a graph database (i. e. , the Neo4J Open Source Community Edition 3. 2. 5, http://neo4j. org/) to build a PPIs interactome from PrePPI inferences and the Cypher Query Language to access it programmatically through Python 3. 6 scripts. An empirical p value was computed using the package q value-2. 8. 0 in the R environment version 3. 4. 1. This step of the SEARCHIN pipeline generated a list of 1478 candidate PPIs (Supplementary Table  2 ). To combine MS data (i. e. , proteins from rat ACM) with PrePPI (i. e. , human proteome), we used the programmatically accessible web services provided through BioMart  68  for biological identifier conversion (e. g. , to translate orthologous genes between organisms or gene identifiers and their protein products). Membrane receptors inferred activity.
We used the ARACNe algorithm 1 to reverseengineer a membrane protein-specific regulatory network from 437 mouse brain expression profiles (GSE10415). As regulators with fewer than 25 targets were shown to reduce inference accuracy, as comprehensively benchmarked in ref. 2  , only plasma membrane proteins, annotated in the Gene Ontology  69  database as "signal transduction activity" (GO:0005886 and GO:0007165), with ≥25 target genes (among 16,589 possible ones) were included in the network, for a total of 873 murine receptor and 376,672 transcriptional interactions. The plasma membrane interactome is available through FigShare at the following permanent URL: https:// doi. org/10. 6084/m9. figshare. 6387671. The VIPER algorithm 2 was used to infer changes in plasma membrane signaling protein activity based on the mutSOD1 gene expression signature and mouse brain plasma membrane regulatory model.
Because the inferred activity is based on the transcriptional programs induced by the treatment, the algorithm will identify the specific receptors as well as other regulatory proteins associated with similar transcriptional programs. We used the list of differentially active receptors, based on the rank-ordered p values (from the most to least significant) as another source of evidence to further prioritize the candidate paracrine interactions. The list of candidate receptors is shown in Supplementary Table  3. NF-κB1 modulators inference. The CINDy algorithm  3  was used to predict the most likely modulators of NF-κB1 activity among the membrane receptor proteins. We used gene expression profiles from 437 mouse brain samples (GSE10415) as input to CINDy. The null model was generated using the number of triplets (i.
e. , receptor-TF-target) identified by the algorithm for NF-κB1. Specifically, only the triplets that had a Conditional Mutual Information (CMI) greater than or equal to the average CMI of the data set were considered. The list of candidate modulators is shown in Supplementary Table  4. Scores integration. To combine the scores from the different sources of evidence, we used the Robust Rank Aggregation method for meta-analysis  27. From the R package RobustRankAggreg version 1. 1, we used the function "aggregateRanks" with method "RRA". The final list of ranked ligand-receptor pairs is shown in Table  1. For validation, we selected the first pair, i. e. , APP/TNFRSF21, with the highest "experimental score" as reported in PrePPI DB, whose receptor was also shown to be differentially activated.
SEARCHIN code and implementation. Although the present manuscript provides the outline of the algorithm, its full source code is available at the following URL: https://github. com/califano-lab/SEARCHIN. Treatment of ACM. ACM were passed through Q or S columns (Pierce Strong Anion Exchange Spin column, Thermo Scientific #90011; Cation Exchange Spin column, Thermo Scientific #90009). Sample flow-through from the column after passing the sample was discarded. Molecules bound to the column were eluted by increasing concentrations of NaCl (0. 5, 1, and 2 M respectively). The eluate was desalted using spin columns (Amicon Ultra-15 Centrifugal Filter Unit with Ultracel-3 membrane, Millipore, Cat. # UFC900396). The elute was brought up to original volume and supplemented with HS, B27, P/S, L-glutamine, and βmercaptoethanol and then applied to primary SC MN cultures.
Cell counting and statistics. Results correspond to independent experiments and each experiment is the average of 3-6 coverslips or 96-wells per time point and per condition. For mouse ES-MN number with co-culture, MN number was assessed by automatic quantification using the Metamorph Imaging System software (Universal Imaging Corporation, Downingtown, PA). For primary cultures, MNs were counted manually using the Nikon Epifluorescent microscope. The investigator counting the MN numbers was always blind to the treatment conditions. Differences between means were analyzed by a two-tailed Student's t test. Photoshop v19 was used for preparing figures. NIH Fiji ImageJ was used for measuring intensity for RNAscope analysis. Differences among means were analyzed by oneor two-way analysis of variance (ANOVA) with the different types of astrocytes, MNs or treatments as independent factors.
When the ANOVA showed significant differences, pair-wise comparisons between means were tested using Sidak post hoc testing, unless stated otherwise. In all analyses, the null hypothesis was rejected at the 0. 05 level. All statistical analyses were performed using SigmaPlot for Windows (version 12. 0; Jandel Corp. , San Rafael, CA) and software GraphPad Prism version 7 (San Diego, CA). Loaded grid test data were fitted by a non-linear iterative least squares method provided by the software GraphPad Prism. Then, the two different curves were compared by the extra sum-of-squares F test  70. Survival statistics was performed by Kaplan-Meyer analysis also with GraphPad Prism. Whenever suitable, statistical results include 95% confidence interval and the Cohen's d effect size.
Acknowledgements We thank  Marc Tessier-Lavigne  for his support and critical comments on this manuscript and for providing reagents for assays on  DR6 ,  Genentech  for providing us with necessary reagents and mouse lines to study  DR6 ,  Lawrence S. Shapiro  for guidance with the liquid chromatography work,  Dimitar Nikolov  and  Jean-Pierre Julien  for generously providing recombinant E2 fragment and SOD1 recombinants and  Radhika Pradhan ,  Vernice Jackson-Lewis ,  James Caicedo  and  Norma Romero  for technical expertize. Drs. M. De Rosa  and  C. Doege  ( Molecular Biology/Molecular Genetics Core of the New York Nutrition Obesity Research Center  [ P30 DK26687-40 ]) provided technical support and advice regarding the RNAscope studies. S. P. is supported by the  Department of Defense  ( W81XWH-13-0416 ), the  National Institute of Health  ( NS107442 ,  NS117583 ,  NS111176 ,  AG064596 ), and  Project-ALS.
Data availability The authors declare that all the main data supporting the findings of this study are available within the article and its Supplementary Information files. All the data sets that were used as input for the study and the code to reproduce the manuscript findings are available at the following citable link: https://doi. org/10. 5281/zenodo. 4037259  71. The mass spectrometry proteomics data are available via ProteomeXchange at https://www. ebi. ac. uk/pride/ with the data set identifiers PXD021773.
2018; Edelman et al. 2019 ). However, the NMSC is still prohibitive for large datasets and relies on the user to choose the number of migration events based on a priori hypotheses  (Yu and Nakhleh 2015). Alternatively, sitebased summary statistics based on tree asymmetries (e. g. ,  Green et al. 2010; Pease and Hahn 4 2015) , or admixture proportions (e. g. ,  Reich et al. 2009; Martin et al. 2015; Malinsky et al. 2018 ) are simpler to implement, but offer less power for localizing the timing and number of introgression events when recurrent hybridization is layered across a phylogeny  (Malinsky et al. 2018). A combination of methods is thus most appropriate to infer a species tree that may have layered events of hybridization throughout time (e. g. ,  Kozak et al. 2018; Malinsky et al. 2018; Edelman et al.
Taxon Sampling and Exome Sequencing We generated new genome-wide resequencing data targeting 207,691 exonic and noncoding regions [totaling 61. 7 Megabases (Mb) total] from 14 hare species (30 individuals) and the outgroup pygmy rabbit (Brachylagus idahoensis; 2 individuals). We combined these data with published whole exomes from four snowshoe hares [Lepus americanus; NCBI Sequence Read Archive BioProject PRJNA420081 from  Jones et al. (2018 Jones et al. ( , 2020b)) ] and extracted data from the reference genome of the European rabbit (Oryctolagus cuniculus; OryCun2. 0; Carneiro et al. , 2014)  to use as a second outgroup. Our total sample of 15 hare species (34 individuals, 1 to 4 individuals per species) and 2 outgroup species (3 individuals) included species from all major regions of the Lepus native distribution: Africa (3 species), Africa and Eurasia (1 species), Eurasia (6 species) and North America (5 species, see Fig.
1  and Supplementary Table  S1  available on Dryad; doi:10. 5061/dryad. bzkh18967). Exome capture experiments were performed following the procedures outlined in  Jones et al. (2018)  and in the Supplementary Material and Methods. Briefly, we obtained samples as tissue or extracted DNA including samples from previous studies or through loans from collaborators (Supplementary Table  S1 ). Depending on the sample, genomic DNA was isolated using a saline extraction method  (Sambrook et al. 1989)  or DNeasy Blood & Tissue Kit (Qiagen) (Supplementary Table  S1  and Supplementary Materials and Methods). We prepared Illumina sequencing libraries for each sample following  Meyer and Kircher (2010)  with minor modifications [see Supplementary  Material and Methods and Jones et al. (2018) ]. Sequencing libraries were then enriched using NimbleGen SeqCap EZ v.