dataesr/scientific-paragraphs-categorization huggingface dataset:
RNA extraction and microarray analysis. For each condition, we used four independently FACS samples, to provide four biological replicates. Total RNA was extracted using NucleoSpin RNA XS kit (Macherey-Nagel). Quantity and quality of RNA extractions were analyzed using Agilent RNA 6000 Pico kit (Agilent). Labeled RNAs (Liqa Kit, Agilent) were then hybridized onto Agilent whole-mouse genome microarray chips. Data were normalized and analyzed using the R statistical open tool (R Manuals; RRID:OMICS_01764). We used a Student's t test and Benjamini-Hochberg test to identify the differentially expressed genes between two conditions (cutoffs: p Ͻ 0. 001 and q Ͻ 0. 01, respectively). Gene expression levels and unsupervised hierarchical clustering were visualized using MultiExperiment Viewer version 4.
6. 0 open software (TM4 Microarray Software Suite: TIGR MultiExperiment Viewer; RRID: nif-0000-10486). The gene ontology enrichment analyses were performed using GOrilla open software. Ariadne Genomics-Pathway Studio software was used to select genes of interest. A full list of genes was deposited in NCBI GEO (Gene Expression Omnibus; RRID:nif-0000-00142; accession number: GSE48872). Immunostaining. For immunohistochemistry, animals were perfused with 4% PFA in PBS 1ϫ. The brains were dissected, and cryoprotected in PBS 1ϫ and sucrose 15% at 4°C overnight, frozen embedded in gelatin 7% (gelatin porcine skin, Merck), sucrose 15%, PBS 1ϫ; and 14 m serial coronal cryostat sections were saved. Other brain samples were dissected, and maintained in PBS 1ϫ in 20 m serial coronal vibratome sections.
The slides were treated for 10 min with 100% ethanol at Ϫ20°C, and, after saturation in PBS 1ϫ, 0. 3% Triton X-100, and 10% horse serum for 1 h at RT, primary antibodies were incubated overnight at 4°C in PBS 1ϫ, 0. 3% Triton X-100, and 5% horse serum. After washing, Alexa Fluor-conjugated and biotinylated secondary antibodies were incubated for 1. 5 h at RT. Nuclei were stained with Hoechst solution (1 g/ml), and sections were mounted in Fluoromount-G (CliniSciences). For immunocytochemistry, cells were fixed with 4% PFA for 15 min at RT, and staining was performed as described above, without the ethanol step. Staining was observed using a fluorescence microscope Zeiss Imager. Pictures were acquired with an AxioCam camera and analyzed using ImageJ software (ImageJ; RRID:nif-0000-30467). For quantification of Ccl2 and Il1␤ expression on coronal sections of demyelinated brains, only demyelinating areas, selected by the absence of or low myelin basic protein (MBP) staining, were quantified.
Activated aOPCs have increased expression of genes associated with innate immune system function To identify which changes in gene expression that occur with activation might account for functional changes, we identified all genes significantly differentially expressed between activated and nonactivated aOPCs. This selection was performed using Student's t test and Benjamini-Hochberg test (cutoff: p Ͻ 0. 001 and q Ͻ 0. 03; Tables  1, 2 ). This resulted in the selection of 839 differentially expressed genes, which were then classified according to gene ontology (Fig. 5a ). We then compared our data with a previously published database of gene expression occurring during remyelination of toxin-induced demyelination of adult rat CNS white matter  (Huang et al. , 2011) , reasoning that genes with differential expression in both isolated activated aOPCs and in remyelinating lesions were likely to have functional significance.
Specifically, to identify putative genes responsible for the en-hanced migration of activated aOPCs, we compared our activated aOPC profile with genes that had decreased expression in the remyelination model from the stage of 14 d postlesion (dpl), when recruited OPCs are undergoing differentiation, compared with 5 dpl, when OPC recruitment is maximal. One hundred nineteen genes were differentially expressed in both databases (cutoff: q Ͻ 0. 05 used for both databases; Fig. 5b, c ; Table  3 ). Using Ariadne Genomics-Pathway Studio to identify likely genes interactions within these 119 genes, we selected the following group of 7 interacting genes: IL1␤, CCL2, P2RY2, DDIT3, PTK2, HSPB2, and SMAD7. Among these seven genes, the expression of IL1␤, CCL2, P2RY2, and DDIT3 was increased, whereas the expression of PTK2, HSPB2, and SMAD7 was decreased in activated aOPCs compared with nonactivated aOPCs (Fig.
We showed that this increased migration corresponds to a chemokinetic effect, with an increased percentage of mobile adult OPCs, without change in the speed of migration. Therefore, aOPCs acquire increased migration and differentiation capacity, both parameters being crucially needed after a de-myelinating insult, to reach the demyelinating area and initiate the regenerative process during a window of time when axonal damage is still reversible. In the cuprizone model, where demyelination and remyelination often occur contemporaneously, it is hazardous to correlate mRNA gene expression changes to a particular stage of the regenerative process. Therefore, to select genes of interest, we took advantage of a gene expression database obtained in a different experimental model, allowing us to distinguish the separate stages of spontaneous remyelination  (Huang et al.
, 2011). In this study, mRNAs were extracted from microdissected lesions that included different cell populations. Our strategy has therefore been to compare our aOPC-specific database and the "early repair database" (14 vs 5 dpl), and to select genes differentially expressed in both databases, which resulted in the identification of 119 genes. These were further analyzed to identify interacting genes, from which emerged two genes of the innate immune system, IL1␤ and CCL2. We showed that both Ccl2 and Il1␤ influenced the motility of aOPCs through Ccr2 and Il1r1 receptors, respectively. In contrast, no effect on differentiation was detected, suggesting that, among the acquired capacities of activated OPCs, increased expression of Ccl2 and Il1␤ were contributing only to the increased migration.
Quantitative omics data can be mapped on top of a pre-defined biological network layout. Currently, most of the pathway databases (such as KEGG  [2] , Reactome  [3] ) provides such a possibility, using simple data visualization tools. Omics data visualization tools using networks are constantly improved and become more elaborated  [4]. For example, VANTED tool  [5]  creates a classification tree according to the KEGG pathway hierarchy and shows a biological network with omics data as barplots or pie-charts attached to the nodes which allows to visualize more complex data than by simple node coloring. NaviCell  [6]  and related pathway database Atlas of Cancer Signalling Network (ACSN) together with standard heat maps and barplots provide more flexible data visualization tools such as glyphs (symbols with configurable shape, size and color) and map staining (using the network background for visualization)  [7].
An interesting approach for data visualization using biological networks was developed in NetGestalt online tool  [8]  which uses a NetSAM R package to create modules by hierarchical ordering of the network in one dimension and visualizes high-throughput data accordingly to a chosen track as a combination of barplots and heat maps. Omics data are used to identify overexpressed or enriched subnetworks. For example, in  [9]  expression data were combined with network information in order to identify under-or overexpressed subnetworks in Huntington's disease and breast cancer. Inspired by this method, several Cytoscape plug-ins were developed and applied to various omics data in order to find connected sub-components where most of the genes are differentially expressed or co-expressed  [10, 11].
A recent review on integrating molecular profiles with networks in order to find "network modules" can be found in  [12]. Using projection of the high-throughput data into the basis of functions smooth on a biological network graph was suggested in  [13]. Recently, biological networks were used to regularize the genome-wide mutational landscapes (which are sparse) in cancer, using network smoothing methods  [14]. However, none of the methods cited above had a purpose to visualize highthroughput data by computing a specific network layout based on the omics data themselves, which would combine both network structure and the data from the network node attributes. Some of the existing Cytoscape layout algorithms (such as Group Attributes Layout) allow using the values of single node attributes, but this possibility is currently under-developed.
Implementation DeDaL is a simplified Cytoscape 3 app implemented in Java language. For computing linear and non-linear principal manifolds, DeDaL uses VDAOEngine Java library, developed by AZ (http://bioinfo-out. curie. fr/projects/elmap/). For computing the eigenvectors of a symmetric Laplacian matrix, Colt library has been used (http://acs. lbl. gov/ACSSoftware/colt/). Internal graph implementation is re-used from BiNoM Cytoscape plugin  [15, 16, 17]. The source code of DeDaL is available at http://bioinfo-out. curie. fr/projects/dedal. Producing data-driven network layouts Data-driven network layout (DDL) is produced by DeDaL by positioning the nodes of the network according to their projection from the multidimensional data space of associated numerical vectors into some 2D space. DeDaL implements three algorithms for performing this dimension reduction: (1) Projection onto a plane of two selected principal components; (2) Projection onto a non-linear 2D surface approximating the multidimensional data distribution, i.
Exporting the pre-processed data The results of pre-processing the data for a given network can be exported to a file. Actually, two files are created: one in a simple tab-delimited format suitable for further analysis in most statistical software packages and another file in the ". dat" format, suitable for analysis in ViDaExpert multidimensional data visualization tool  [22]. This possibility can be used, for example, for network smoothing of an expression dataset for further application in any machine learning algorithm (clustering, classification). For this purpose, DeDaL can be also used in a command line mode (see examples at the web-site).
Using The Cancer Genome Atlas transcriptome data and Human Protein Reference Database network We used The Cancer Genome Atlas (TCGA) transcriptomic dataset for breast cancer (548 patients)  [24]  and Human Reference Protein Database (HRPD) database as a source of protein-protein interaction network. Firstly, as an example of a small subnetwork, we selected proteins involved in Fanconi DNA repair pathway  [26]  as it is defined in Atlas of Cancer Signaling Network (ACSN, http://acsn. curie. fr). For node coloring, we mapped the value of the t-test computed for the gene expression difference between the basal-like (one of the molecular subtypes of breast cancer, significantly contributing to the intertumoral variability) and non basal-like breast tumours. We've imported the TCGA data in Cytoscape and applied DeDaL for the transcription levels of the genes in the subnetwork (Figure  1 ).
The strength of genetic interactions is characterized by an epistatic score which quantifies deviation from a simple multiplicative model. In the global network of genetic interactions, each gene can be characterized by its epistatic profile, which is a vector of epistatic scores with all other genes  [28]. It is shown that the genes with similar epistatic profiles tend to have similar cellular functions. We applied DeDaL to create a DDL layout for a group of yeast genes involved in DNA repair and replication. The genetic interactions between these genes and the epistatic profiles (computed only with respect to this group of genes) were used from  [28]. The definitions of DNA repair pathways were taken from KEGG database  [2]. Figure  4  shows the difference between application of the standard organic layout for this small network of genetic interactions and PCA-based DDL (computed here without applying data matrix double-centering to take into account tendencies of genes to interact with smaller or larger number of other genes).
Acknowledgements We acknowledge  Eric Viara  and  Eric Bonnet  for their help in implementing DeDaL and  Loredana Martignetti  for helping analysing the data. All authors are members of the team " Computational Systems Biology of Cancer ". The work is supported by  ITMO Cancer SysBio program , ( INVADE  project) and, the grant " Projet Incitatif et Collaboratif: Computational Systems Biology Approach for Cancer " from  Institut Curie  and by  Institut National de la Santé et de la Recherche Médicale  ( U900  budget).
5 μM of primers W001 (5'-AGAGTTTGATCMTGGCTC) and W002 (5'-GNTACCTTGTTACGACTT)  [16] , 200 μM dNTP and 1U Phusion polymerase (New England Biolabs). The PCR conditions were as follows: denaturation step at 95°C for 5 min, followed by 30 cycles of denaturation at 95°C for 30 s, annealing at 50°C for 30 s, and extension at 72°C for 1 min 30 s. A final extension step was performed for 10 min at 72°C. Sequencing of the PCR product was performed by LGC Genomics (Berlin, Germany) using the same primers. Sequences were further compared to Genbank Database using BLAST. Identification was achieved at specie level for similarity higher that 98%. When similarity search gave the same score with several species, isolates were identified at the genus level. The 22 LAB strains retained for PFGE analysis (see results) have been registered in the collection of the CIRM-BIA Biological Resource Center (Rennes, France).
After this incubation, the supernatant was collected and stored at -20°C until use. Concentration of IL-8 in the supernatant was measured by ELISA as previously described  [22]. All samples were analyzed in duplicate in two independent assays (a total of four data points). IL-8 production by PS cells alone was used as a reference to assess the effect of LAB alone, whereas IL-8 production by E. coli-stimulated PS cells was used as a reference to assess the effect of LAB in the presence of E. coli. Data were normalized relative to these references.
Genome sequencing Genomic DNA of L. brevis 1595, L. casei 1542, L. lactis 1596, and L. plantarum 1610 and 1612 was extracted and purified as described above. Genome sequencing, assembly and annotation were performed as previously described  [20]. Briefly, paired-end sequencing was performed using HiSeq sequencing system (Illumina, San Diego, USA). Sequencing statistics are included in S1 Table. Coding sequence (CDS) detection was performed with the Glimmer3 software application  [24]. Gene products were subjected to protein location prediction using the SurfG software package  [25]. Genomes were further screened using the CD-search tool of the Conserved Domain Database for the presence of specific domains  [26]. In particular, all genomes were screened for the presence of domains involved in binding to mucin, collagen and fibronectin: pfam06458 (MucBP, mucin-binding protein domain), cl05785 (MucBP superfamily), pfam5737 (Collagen_bind, collagen-binding domain), pfam05738 (Cna-B, Cna protein B-type domain;this domain is found in the Staphylococcus aureus collagen-binding surface protein, but does not mediate collagen binding), cl15753 (collagenBindB superfamily), cl05349 (col-lagen_bind superfamily), pfam07299 (FBP, fibronectin-binding domain), pfam05833 (FbpA, fibronectin-binding protein A N-terminus), cl06363 (FBP superfamily), pfam00497 (SBP-bac-3; bacterial extracellular solute binding proteins, family 3).
Systematic analysis of the conserved domain content of proteins annotated as Internalin was also included. In addition, all genomes were screened for the presence of genes potentially encoding antibiotic resistance using the annotation tool of the Antibiotic Resistance Genes Database (ARDB; http://ardb. cbcb. umd. edu/blast/genome. shtml). Genome sequences have been deposited at DDBJ/EMBL/GenBank under the accession numbers LDEI00000000 (Lactobacillus brevis CIR-M-BIA 1595), LDEJ00000000 (Lactobacillus casei CIRM-BIA 1542), LDEK00000000 (Lactococcus lactis CIRM-BIA 1596), LDEL00000000 (Lactobacillus plantarum CIRM-BIA 1610), LDEM00000000 (Lactobacillus plantarum CIRM-BIA 1612).
Acknowledgments We deeply thank  Mikaël Carpeza  for primary analysis of the PP242 effect in C2C12 cells. We are grateful to  Dr. A. Ferreiro  for critical review of our manuscript, and  Dr. H. Le Stunff  for valuable advice for LC3 staining and autophagy analysis. We thank  Pr. M. Kobb ,  University of Texas, USA  for PAK1 constructs, Pr. S. Narumiya ,  Kyoto University, Japan  for mDia constructs,  Dr. K. J. Irani , the  John Hopkins Hospital, USA  for Rac1 constructs,  Dr. K. Matsumoto ,.
The LE TGA remains relatively constant at around 9% regardless of the [silica] 0 (see Figure  5b ). It is perhaps worth emphasizing the difference between LE TGA and EE DCP. The former parameter is calculated from experimental TGA data and represents the proportion of silica that is encapsulated within the vesicles relative to [silica] 0. In contrast, EE DCP is calculated by combining the DCP and SAXS data. SAXS is used to determine an accurate weight-average vesicle diameter, vesicle membrane thickness, and vesicle lumen volume. The numerator term is the mean number of silica nanoparticles per vesicle (determined by using the SAXS diameter to calculate the precise vesicle density required to correct the raw DCP data), while the denominator is calculated by multiplying the [silica] 0 by the total vesicle lumen volume divided by the total volume of the solution.
Since interparticle interactions are expected for silica particles occupying the vesicle lumen, a hardsphere interaction structure factor based on the Percus-Yevick approximation  47  was introduced into the model for population 2: = S q S q R f ( ) ( , , ) 2 P Y P Y PY (10) where R PY is the interaction radius and f PY is an effective hardsphere volume fraction. The model was incorporated in Irena SAS macros for Igor Pro software,  48  and numerical integration of eqs 1, 6, and 9 was used for data fitting. Accordingly, use of population 1 alone was sufficient for satisfactory data fits to SAXS patterns obtained for empty G 58 H 250 diblock copolymer vesicles synthesized in the absence of any silica nanoparticles. Use of the vesicle model (l = 1 in eq 1 and ξ l = ξ sol in eq 3) produced a reasonably good fit over 7 orders of magnitude of X-ray scattering intensity (see Figure  6 ).
The overall vesicle radius, R v = R out + 2R g , was calculated to be 145. 5 nm (Table  S1 ), which is consistent with both TEM observations (Figure  2 ) and DLS data (Table  1 ). The R g of the G 58 corona block was determined to be 2. 3 nm from fitting of the G 58 H 250 SAXS pattern. 38 This experimental value is comparable to a theoretical estimate: the projected contour length of a single GMA monomer is 0. 255 nm (two carbon bonds in all-trans conformation), the total contour length of a G 58 block, L PGMA = 58 × 0. 255 nm = 14. 79 nm, and the Kuhn length of 1. 53 nm, based on the literature value for poly(methyl methacrylate),  49  result in an estimated R g of (  14. 79 × 1. 53/ 6) 1/2 or 1. 94 nm. The SAXS data fit suggested that the hydrophobic PHPMA component of the vesicle membrane was solvated, x sol = 0.
* S Supporting Information The Supporting Information is available free of charge on the ACS Publications website at DOI: 10. 1021/jacs. 5b10415. Full details of synthetic protocols and characterization techniques, DCP, TGA, and fluorometer calculations, DMF GPC traces, 1 H NMR spectra, additional TEM images, TGA traces, [silica] s data, SAXS patterns and data fits for the silica sols and a control sample, summary table of SAXS parameters, DLS data, fluorescence emission spectra and calibration plot, and [BSA] s data (PDF).
The hypothesis that the lesioned hemisphere would have reduced tDCS-evoked hemodynamic response was evaluated in 14 stroke survivors (age group 42-73 years), i. e. , the NIRS-tDCS stroke study at the Institute of Neurosciences, Kolkata. Subjects were recruited after ethics approval and experiments were conducted after taking informed consent, conforming to the Declaration of Helsinki. Participants were seated in a quiet room and their eyes were open and fixated on a point on the wall in front of them during the entire experiment. The total testing time was roughly 14 minutes. NIRS was performed for two minutes on the F3 site followed by anodal tDCS for three minutes. After anodal tDCS, NIRS was performed again to measure the hemodynamic changes as a result of anodal tDCS. Therefore, the NIRS data was collected for 2 minutes pre-and post-tDCS.
The same process was repeated with the F4 site. Analog arterial pressure data from the operating room hemodynamic monitor and the NIRS signals were both sampled at 60 Hz. These signals were time integrated as non overlapping 10sec mean values, which is equivalent to a non-overlapping moving average filter with a 10sec time window re-sampled at 0. 1 Hz. The objective was to eliminate high frequency noise from the respiratory and pulse frequencies thus allowing detection of oscillations and transients below 0. 05 Hz. The signals were further high pass filtered with a cut off at 0. 003Hz to remove slow drifts. The change in the signal to noise ratio (SNR), i. e. the ratio between signal power and noise power in the overall NIR signal, between the on and off phases of the anodal tDCS was investigated.
The background noise is the recording when the device was not interfaced with the skin. A paired t-test (MATLAB function ''ttest'') was performed to find any significant difference in the SNR values between the on and off phases of the anodal tDCS in order to confirm haemodynamics response to tDCS in 15 healthy subjects (age group of 20-30 years). Then, 14 patients with established and acute ischemic stroke (<1 month) localized to a single hemisphere (10 male and 4 females from age 42 to 73) were recruited to study any difference in the hemodynamic response of the lesioned and non-lesioned hemisphere during perturbation with anodal tDCS. A paired t-test (MATLAB function ''ttest'') was performed to find any significant differences.
C. DEVELOPMENT OF THE EEG DEVICE The EEG Front-End (EEG-FE) from Texas Instruments (Texas Instruments, Inc. ) that is based on ADS1299an eight-channel, 24-bit, low-power; integrated analog front-end designed for EEG applications -was used in this study. The motherboard allowed the EEG-FE to be connected to a battery-operated laptop computer via an available USB port. Due to the extremely low input-referred noise of the ADS1299, it allows for direct interface to EEG biopotential signals. With a typical noise of 1µVP-P and a 70Hz input bandwidth, this was a 77% reduction in currently existing medical analog Front-Ends. The graphical user interface (see Figure  3 ) was developed in LabVIEW (short for Laboratory Virtual Instrument Engineering Workbench) which is a system-design platform and development environment for a visual programming language from National Instruments (National Instruments, USA).
At Cz, the surface Laplacian was computed  [30]  which provided the magnitude of the radial (transcranial) current flow leaving (sinks) or entering (sources) the scalp. From EEG recordings before and after anodal tDCS, the average experimental power spectrum was analyzed from 0. 25Hz to 50Hz for 25 successive 4s artifact-free epochs (i. e. , 100sec immediately before and 100sec immediately after anodal tDCS) using Welch's averaged, modified periodogram spectral estimation method (MATLAB function ''spectrum. welch'').