dataesr/scientific-paragraphs-categorization huggingface dataset:
Forecasts for future searches In Fig. 15  we show the 1σ uncertainty one would obtain with a sample of 24 DLA fields studied with MUSE (green shaded), showing that, with future observations, it may be possible to start discriminating between models. Datasets of this size are achievable in the near term with MUSE. Interestingly, if the underlying radial density profile were as low as predicted by the eagle simulation with urchin post-processing, the BOSS halo painting prescriptions could be ruled out. Some degeneracy will, however, exist between different DLA models and the clustering of the LAEs used in this cross-correlation, but it should be possible to break this degeneracy with surveys of LAEs that measure the largescale clustering properties (e. g. MUSE-Wide and HETDEX at higher luminosities;  Diener et al.
B0. 3 J0818+0720 No high confidence or candidate LAEs are detected in association to DLA J0818+0720. In  Fumagalli et al. (2015)  broadband counterpart candidates to DLA J0818+0720 were detected in the WFC3/UVIS F390W imaging. These objects fall very close to the quasars (0. 7 ± 0. 1 arcsec and 1. 5 ± 0. 1 arcsec), they appear to be compact, possibly stellar, in the WFC3 imaging. Even with the excellent seeing of the MUSE data these objects are too faint and too close to the quasar to have their spectra extracted cleanly.
Figure13. The cumulative distribution of LAEs around DLA sightlines for our MUSE observations (high confidence only, black) and two simulated datasets. N g al is the average number of bright LAEs within a radius (D L O S ) and a velocity window of 1000 km s -1 from a DLA. The DLAs used in calculating the simulated profiles come from eagle with urchin post-processing (blue, dotted) and the painted halos applied to both the eagle 100 Mpc (green, dot-dahsed) and galics (red dashed) data. The 1σ error in the profile from the observations is estimated via jackknife resampling (grey shaded), and, at small scales where there are no detections, the 66. 7% upper limit is shown (black arrow). The grey hatched regions indicate the limits of scales probed in the MUSE observations, between 1 arcsec (of the order of the seeing) and 0.
ACKNOWLEDGEMENTS We would like to thank  S. McAlpine  and  P. Norberg  for helpful discussions, and  R. A. Jorgenson  for providing the spectrum of J1220+0921. RM, MF and TT acknowledge support by the  Science and Technology Facilities Council  [grant number  ST/P000541/1 ]. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  [grant agreement No  757535 ]. TT additionally acknowledges the  Interuniversity Attraction Poles Programme  initiated by the  Belgian Science Policy Office  [ AP P7/08 CHARM ]. TG is grateful to the  LABEX Lyon Institute of Origins  [ ANR-10-LABX-0066 ] of the  Université de Lyon  for its financial support within the  programme 'Investissements d'Avenir'  [ ANR-11-IDEX-0007 ] of the French government operated by the  National Research Agency (ANR).
TG additionally acknowledges support from the  European Research Council  under grant agreement ERC-stg-757258 (TRIPLE). JXP acknowledges support from the  National Science Foundation (NSF)  grants  AST-1010004  and  AST-1412981. This work is based on observations collected at the  European Organisation for Astronomical Research in the Southern Hemisphere  under  ESO  programme  ID 095. A-0051  and  096. A-0022. Some of ancillary data presented herein were obtained at the  W. M. Keck Observatory and Magellan Telescopes at Las Campanas Observatory. This research made use of Astropy, a community-developed core Python package for Astronomy (Astropy Collaboration et al. 2013). For access to the codes and advanced data products used in this work, please contact the authors or visit http://www. michelefumagalli.
The momentum of scRNA-seq data sets prompts for simple and powerful tools exploring their meaningful signatures. Here we present Single-Cell Signature Explorer (https://sites. google. com/site/fredsoftwares/products/singlecell-signature-explorer), the first method for high throughput scoring at single cell level of any gene set-based signature and visualization across t-SNE. By scanning data sets for single or combined signatures, it quantitatively and qualitatively maps any multi-gene feature, exemplified here with signatures of cell lineages, biological hallmarks and metabolic pathways in large scRNAseq datasets of human PBMC, lung cancer and adult testis.
Single Cell experiments Data for 4k and 8k PBMC obtained by 10x Genomics 3' chemistry V2 were downloaded from 10x Genomics website (https://support. 10xgenomics. com/singlecell-gene-expression/data sets/2. 1. 0/pbmc8k, https://support. 10xgenomics. com/single-cell-geneexpression/data sets/2. 1. 0/pbmc4k). Data for 19k of lung tumor and normal adjacent cells (4) obtained by 10x Genomics 3' chemistry V2 were downloaded from Array Express E-MAAB-6149 and E-MTAB-6653. The human spermatogenesis data (5) obtained by 10x Genomics 3' chemistry V2 were downloaded from GEO data set GSE120508. The 60k melanoma data set obtained by MARS platform  (6)  were downloaded from GEO data set GSE12313.
Noise reduction of data sets Single cell transcriptomic technology is powerful to explore gene expression at the single cell level, but may yield scRNAseq datasets in which technical noise can hinder biological variability. This technical noise may arise from both gene sampling fluctuations and cell-to-cell variations in sequencing efficiency  (8). To reduce such technical noise from raw UMI data c 2019 The Author(s) This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons. org/licenses/ by-nc/2. 0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. sets, we applied scRNAseq DataSet Noise Reductor (https://sites. google.
com/site/fredsoftwares/products/singlecell-signature-explorer). Briefly, this algorithm successively applies median normalisation and Freeman-Tuckey transform to the raw UMI count of each gene and single cell from any data set such as to stabilize its technical noise at a comparable level between genes and across cells  (9). A detailed description of scRNAseq DataSet Noise Reductor results is provided in the supplemental methods. This section shows that Single-Cell Signature Explorer indifferently processes scRNAseq datasets of either raw UMI data, noisereduced UMI data, or UMI data normalized by Seurat. Noise reduction is optional, under the responsibility of users and in any case a preliminary to Single-Cell Signature Explorer. Whatever the signature, Single-Cell Signature Explorer produces similar t-SNE images from these various types of UMI data, but requires considerably longer computing time for the noise-reduced data.
Single-Cell Signature Explorer Single-Cell Signature Explorer is a package of four successive tools dedicated to high throughput signature exploration in single-cell analysis: 1. Single-Cell Signature Scorer computes for each cell a signature score. These four softwares have been developed with usability, low memory usage and performances in mind. They require no complex command lines or computing skills, they can be used on a laptop but also scale up very well on powerful workstations for fast computations : results are obtained within seconds in function of the gene set size. Single-Cell Signature Scorer This algorithm computes in each single cell transcriptome a score for any each gene set from a database. More than 17,000 curated gene sets from MSigDB  (3, 10)  and Reactome  (11) , as well as additional userdefined gene sets can be computed by the software.
Each gene set is a list of HUGO gene symbols in a text file format labeled by the name of the gene set. The database can also be implemented by additional custom pathways composed of text files for user-defined list of HUGO gene symbols. Scores were calculated as follows : The score of the gene set GS x in cell C j was computed as the sum of all UMI for all the GS x genes expressed in C j , divided by the sum of all UMI expressed by C j : score of cell C j for gene set GS x = k i=1 U M I GSx i n i=1 U M I C j i ×100 , where U M I GSx i are the UMI of the k genes from GS x found in the cell C j. We also implemented the possibility to subtract the contribution of a gene when it is preceded by a "-" sign in the gene list. Single-Cell Signature Scorer was developed in Go language v1. 11. 1 (https://golang.
org/). The software is multi-threaded to take full advantage of multicore processors. Single-Cell Signature Scorer can compute 13 million scores (1000 gene sets for 13300) cells in less than 4'30" on a bi-Xeon E5-2687w-v3 workstation. Single-Cell Signature Scorer was compiled for GNU Linux and Microsoft c Windows TM 64 bits, and can be compiled for any platform using crosscompilation feature of Go. Single-Cell Signature Merger This very fast tool, written in Go (https://golang. org/), yields a table merging for each cell the signature score and the t-SNE coordinates. This software takes as input the score tables created by Single-Cell Signature Scorer and t-SNE coordinates produced by Seurat  (7)  to produce tables compatibles with the Single-Cell Signature Viewer (see below). This software is multi-threaded and can merge in parallel a set of score tables with a t-SNE coordinates table.
Single-Cell Signature Viewer This tool was developed to display the gene set scores computed by Single-Cell Signature Scorer on a t-SNE map. It takes as input a table score merged with t-SNE coordinates. Single-Cell Signature Viewer was written in R  (12)  with the package Shiny  (13)  to draw in real time a colored score scale of signatures selected from a drop-down list on t-SNE maps. Since this scaling is sensitive to outliers, the viewer draws a density distribution of scores and provides a color scale cursor allowing users to prune such potential outliers. Single-Cell Signature Combiner This tool was developed to display the combination of two gene sets scores previously computed by Single-Cell Signature Scorer on a t-SNE map. User must select two signatures from two drop-down lists. Before combining the two corresponding score sets, they are normalized between [0-1] to be comparable.
The operators available to compute the combination are [-+], thus user selects to add or subtract two signatures prior to visualize the resulting score on one t-SNE map. Single-Cell Signature Combiner takes as input a table score merged with t-SNE coordinates. Single-Cell Signature Combiner was written in R  (12)  with the package Shiny  (13)  to draw in real time on t-SNE maps a colored score scale of two signatures selected from the drop-down lists. As above, since this scaling is sensitive to potential outliers, the viewer draws a density distribution of scores and provides a color scale cursor allowing the user to prune such outliers. Code availability Single-Cell Signature Explorer was developed using the high performance cross-platform Go language v1. 11. 1 (https://golang. org/) and the t-SNE viewers were developed using the cross-platform R (12) language with the package Shiny  (13).
Software Benchmarks We compared Single-Cell Signature Explorer with AUCell  (14) , Seurat CellCycleScore  (7) , and GSVA/ssGSEA  (15, 16). Their respective computation efficiency and display of the results on the t-SNE map were evaluated. Signature Scores computation speed. Benchmarks were performed on a Linux Xubuntu 18. 10 workstation with two processors Xeon E5-2687w-v3 and 128Go RAM. KEGG database was generated from MSigDB v6. 2 (http://www. broad. mit. edu/gsea/msigdb/index. jsp). For Single-Cell Signature Scorer, KEGG database was downloaded from the software web site where additional, ready-to-use databases of 17,000 gene sets for Human, Mouse, Rat, Zebra Fish, and Macaccus, obtained from MSigDB v6. 2 are also available (https://sites. google. com/site/fredsoftwares/products/databases).
RESULTS AND DISCUSSION Although GSEA measures the relative enrichment of functionally defined sets of genes, it cannot characterize the gene signature from an isolated transcriptome, while the related ssGSEA  (15) , GSVA  (16)  and AutoCompare SES  (17)  achieve this by scoring the intrinsic enrichment of pre-defined gene sets in any single sample transcriptome. Nevertheless, both algorithms rely on statistics inappropriate for single cell transcriptomes mostly composed of zero values, and where the missing genes of each cell vary extensively across the entire scRNA-seq data set. To address these issues with minimal computing time, here we introduce Single-Cell Signature Explorer, a package of four softwares dedicated to high throughput signature exploration in single-cell transcriptome analysis.
The score of the gene set GS i in cell C j is computed as the sum of all UMI for all the GS i genes expressed in C j , divided by the sum of all UMI expressed by C j. Using this, the scores of each single cell for thousands gene sets (e. g. 17,000 gene sets from the Molecular Signatures Database (MSigDB) (http://www. broad. mit. edu/gsea/msigdb/index. jsp) are computed within few minutes, and these scores are then visualized across entire scRNA-seq data sets. As a first example, the scRNA-seq datasets of 4k and 8k PBMC from one healthy donor were downloaded (https://support. 10xgenomics. com/single-cell-geneexpression/datasets), integrated, processed and the resulting t-SNE was plotted. In parallel, each of these 12k single cells was individually scored for each of the 17,000 MSIgDB gene sets.
The scRNA-seq data sets of 19k cells from 20 tumor and 4 matched normal adjacent biopsies from 6 lung cancer patients(4) were downloaded from Array Express E-MTAB-6149 and E-MTAB-6653, processed, integrated and their t-SNE was plotted. Each of these 19k single cells was also scored for the MSigDB gene sets as above. Visualizing scores for a series of lineage-defining gene sets identified the cell types of each cluster (Supplementary Figure  2 ) consistent with single gene expressions (4). The myeloid cells from either the tumor or the adjacent tissue were further gated in parallel analyses, and their scores for the above metabolic signatures were visualized. This evidenced the higher glycolytic signature of a fraction of intra-tumoral myeloid cells relative to those from adjacent lung tissue, while all cells scored quite similarly for the other pathways.
Furthermore, the lung cancer cells also displayed this glycolytic bias (Supplementary Figure  3 ). Hence, scoring any single signature across the t-SNE of a scRNA-Seq data set allows to visualize its imprint at the single cell level, as exemplified above with cell identities or biological hallmarks. Further, this strategy enabled the exploration of two signatures combined through simple subtraction or addition of their scores, and projecting the result on the t-SNE map. This was illustrated using the human testis cell atlas scRNASeq data set of 6. 5 k cells involved in adult spermatogenesis  (5). This publicly available data set was downloaded, processed as above and its partitioning into 16 clusters was projected onto a t-SNE of the germ cells pseudo-time maturation trajectory. Both cluster identities  (5)  and positions on the trajectory (Figure  2a ) were consistent with the molecular transitions underlying this development.
This was consistent with the above-depicted decrease of mitochondria in maturing spermatozoids (Supplementary Figure  4a ) and the progressive catabolic switch of glucose metabolism evidenced by the combined score for "GO Glucose metabolic process" gene set minus "GO Glucose catabolic process" gene set (Supplementary Figure  4b ). Thus Single-Cell Signature Explorer allows to visualize combinations of 2 distinct signatures, yielding newer informative displays, possibly completing those from single gene set-based signatures. The recently published algorithms Seurat's Cell CycleScore module  (7) , and AUCell (  14 ) can also compute the enrichment scores of gene set-based signatures from single cell transcriptomes. With the above-depicted healthy PBMC, lung adenocarcinoma tumors, and human adult testis data sets, we compared Single-Cell Signature Explorer to these latter and to GSVA/ssGSEA on criteria including high throughput performance, computing time, and display of the results (Supplementary Table  2  and Supplementary Figure  5 ).
CONCLUSION Functionally meaningful displays of gene set-based signature enrichment are essential to understand t-SNE maps of complex cell samples, but so far, no current method perform this rapidly for a plethora of signatures at the single cell level across large scRNAseq data sets. By quickly delineating multi-gene features such as cell lineage or metabolic pathways with Single-Cell Signature Explorer in lung tumours and normal human blood and testis, we showed that gene setbased signatures outperform single genes and provide a straightforward visualization of sample's hallmarks. Within few minutes from any computer, this new method provides users with thousands of gene set-based signatures for thousands of single cells, and the immediate visualization in t-SNE of any of these single signatures or combination of signatures.
On the other hand, wearables are often used for everyday life monitoring. However, wearing sensors is constraining for stroke patients who already have difficulties getting dressed for example. Wearables are more adapted for monitoring during rehabilitation sessions where a therapist can help the patient to place the sensors. New platforms based on self-contained objects embedding sensors and displays allow for conceiving new approaches for stroke monitoring and rehabilitation by collecting objective data during rehabilitation sessions. These objects communicate with each other, are connected to the Internet and favor connectivity and interoperability. This paper presents an ecosystem of smart objects, called SpECTRUM, inspired by the current rehabilitation tools observed during rehabilitation sessions and the Action Research Arm Test protocol.
SpECTRUM is composed of three objects: (i) a jack to monitor fingers grasping and the input of each finger during grasping, (ii) a cube to monitor the global hand motor functions during manipulation exercises and (iii) a wrist band to monitor the arm motor functions during the ring tree exercise consisting in moving rings along horizontal shafts. The jack, the cube and the wrist band provide relevant and measurable information on the motor functions of the hand and the arm of the patient. These objects are also able to monitor the appearance and evolution of the patient's tremors during exercises. Moreover, we developed an Android application that ensures data management including real-time data visualization and access to data history. With this monitoring platform, the therapists will have better knowledge of the patient's motor functions level and could assess degradation or an improvement of the patient's state thanks to the data history.
Then, we present the design choices based on the results of the previous study of a mobile application to record and to perform data visualization collected by the objects in Section 7. A second preliminary study has been carried out with health care professionals to collect feedback on possible improvements on the visualization interfaces (Section 7. 2). Section 8 presents the results of tests performed with three stroke patients in order to assess the technical reliability of the SpECTRUM ecosystem. Finally, we conducted a preliminary study involving nine stroke patients who are asked to assess the usability and the acceptability of the SpECTRUM ecosystem (Section 9).
In addition, the cube allows for following the evolution of the global pressure of the bi-digital or tri-digital pliers on the object during the manipulation -unlike the jack which is intended for a more precise follow-up of the dexterity. Third, we decided to use a smart wrist band to monitor the arm activity of stroke patients during the rings tree exercise revealed in the observational study. Indeed, collecting data from the rings by adding sensors on them is not conceivable because of their size and shape. The smart wrist band is able to monitor the movements of the patient's arm as well as the patient's tremors during the exercise. Finally, we designed a visualization interface in order to present the patient's motor data to the therapists in an easy and fast, understandable way. The visualization tool allows one to easily collect, record and visualize data in real-time on a tablet and visualize previous records.
The exercise is based on the rings tree exercise and consists in moving the rings one by one from the Level 1 to the Level 2 by passing them behind the back and moving the rings from the Level 2 to the Level 3 by passing them behind the head. This exercise solicits the shoulders, the neck and the back and involves proprioception. No participant mentioned concerns about the transmission of collected data as the data will be transferred from the devices to a tablet or a computer through local wireless network and secure connection. They all mentioned that the collected data is not critically personal such as medical information. None of the patients involved in this experiment figured out potential problems with the SpECTRUM ecosystem either in terms of design or in terms of functionalities.
Conclusion and perspectives The paper presents the conception and development of the SpECTRUM ecosystem, including a smart jack, a smart cube and a smart watch intended for monitoring the arm and hand motor activity of stroke patients during rehabilitation sessions. The ecosystem design is based on an observational study performed in 3 rehabilitation centers with the aim of instrumenting objects currently used during rehabilitation sessions. The SpECTRUM ecosystem also provides a visualization interface for the data collected to help occupational therapists evaluate the evolution of motor functions of patients during rehabilitation sessions. The arm and hand motor activities are assessed by the therapist based on quantitative information (the grasping force on the jack or the cube, the fingers' position on the jack and the movements of the watch).
Moreover, appearance and evolution of tremors can be assessed by the therapist based on the information provided by each instrumented object of the ecosystem. The rehabilitation program can be adapted by the therapists according to the patient's recovery state. After the implementation of the first prototype of the SpECTRUM objects, a preliminary study has been carried out with health care professionals in order to collect feedback on possible improvements on the objects' design and functionalities. It evidences that the objects' design has been validated by the health care professionals and some features need to be removed. Only the sensors' placement on the jack needs to be reviewed (i. e. the sensors have to be aligned with the edges of the jack) in order to facilitate the grasping for the patient.
Based on these results, a mobile application for recording and visualizing the collect data has been developed. A second preliminary study has been carried out in order to collect feedback on possible improvements on the visualization interfaces. The results show that the visualization interfaces are relevant, but need several improvements. Functionalities, such as pressure goals or pressure curve reproduction, have to be added to the next update. Afterwards, we performed pre-tests with stroke patients in order to assess the reliability of the collected data. The results show that the cube and the watch data are reliable. The data collected by the jack are not always reliable when the patients have a weak finger strength. This result implies that the jack has to be used by patients with strong finger strength or spasticity.