dataesr/scientific-paragraphs-categorization huggingface dataset:
Unlike the permutation testing described above, MVPA does not rely on single channel waveform data (i. e. , a single averaged channel for each ROI), but considers fine differences in the pattern of activity elicited by periodic and nonperiodic faces across all channels simultaneously. We used an MPVA procedure for spatiotemporal decoding in EEG introduced by Bode and colleagues (The Decision Decoding Toolbox, available at http://ddtbox. github. io/DDTBOX/; Bode and Stahl 2014; Bode et al. 2016). Using the lowpass filtered data in which the image presentation frequency of 12 Hz was preserved (i. e. , not yet filtered out), we extracted epochs corresponding to periodic faces and nonperiodic faces (-166. 66ms to 500ms) for each participant. We used non-overlapping spatiotemporal analysis time-windows of 20 ms, each containing 10 data points for each of the 128 channels.
We calculated classification accuracy at each time-window by averaging across these 50 analyses, and compared each resulting value to an empirical chance distribution corresponding to that time-window. This reference distribution was generated using the exact same classification procedure with randomly shuffled labels for each iteration (i. e. , each face epoch randomly labelled as either periodic or nonperiodic). Collated across participants, classification accuracies for the permutated data form a reference distribution at each time point, the mean of which we compared to the real classification accuracy using a paired t-test (34 time points, significance criterion = p <. 05, Bonferroni corrected). This method is considered stricter than testing against theoretical chance accuracy of 50%  (Bode and Stahl 2014; Bode et al.
A complementary question was whether a classifier trained on periodic face responses would generalize well to nonperiodic face responses. To this end, we trained a second SVM classifier on spatiotemporal patterns of activation elicited by face and object images in the periodic condition (-166ms to 500ms, see Methods for details). We then tested classifier performance using novel data drawn from either the periodic condition (i. e. , within-condition decoding), or the nonperiodic condition (i. e. , cross-condition decoding). Here classifier decoding accuracy for the within-condition analysis acts as a baseline, reflecting the degree to which the response to faces can be reliably distinguished from the response to objects. Indeed, within-condition decoding of face and object responses was significantly higher than empirical chance from ~137 ms to ~352 ms following stimulus onset, with peak decoding at ~274 ms post onset (Figure  9A , blue lines).
A geographic information system (GIS) database can rarely be up to date in an active city. Buildings are demolished or constructed and not always updated in GIS. Constructions enclosing stores may change colors and store names often as well. Technology widely available among users would help updating these geographic systems. In fact, collaborative approaches are becoming increasingly used following the Volunteered geographic information (VGI) movement  (Goodchild, 2007). Websites such as OpenStreetMap. org and maps. google. com allow any user to add information into their database in order to make updates easier and more efficient. On the other side, many users publish their pictures on online image sharing websites like flickr, picasa or facebook. These photos may represent places in the city and can help, when well explored, updating geographic databases.
We can find such methods in  (Ventura et al. , 2014)  where authors created a mobile phone application that directly sends the first two images to reconstruct the 3D environment on a server. The result will then be compared with a 3D point cloud database including building textures. The point cloud is built using accurate GPS equipments and image acquisition technologies. (Zamir and Shah, 2010)  and  (Yazawa et al. , 2009)  uses image matching with street view images to find the best geolocalization. (Zamir and Shah, 2010)  method downloads images from Google Street View and compares SIFT descriptors  (Lowe, 2004)  with the ones in the query image. SIFT descriptors are indexed using a tree that will be queried using the nearest-neighbor method. Zamir worked also on semantic data in  (Zamir et al.
DATA COLLECTION PROCESS In the previous section we have presented some existing methods for urban images geolocalization. Unluckily none of those can work in a fully automatic and unsupervised way for any image. We thus present a system aiming at taking a 2D image with prior uncertain GPS position and refine its pose. This system combines visual information existing in the image with semantic information extracted from the image. Some other information may also be available such as camera intrinsic parameters saved in EXIF headers or users comments or tags assigned to the image retrieved from social networks. We compare these information elements to collaborative databases (as OpenStreetMap for example or in our case Nominatim  (OpenStreetMap, 2017) ) in order to get the most recent geographic information available.
Data Retrieval And Preprocessing Layer The first module goal in our data retrieval and preprocessing layer is to retrieve images. Images retrieved from online databases, as shown in Figure  2. The images should follow user requirements, ie. they may be related to a specific time (before or after a certain date) and/or a specific search zone (e. g. a specific city, or a delimited zone by a rectangular boundary box). Images retrieved from online sources should be also be retrieved with their metadata. Additional information like hashtags and comments may be retrieved too because it may evince geographical information. We included in our first layer an EXIF data extractor module that will provide information about camera parameters and some uncertain but helpful GPS data used to restrict the search zone for future image geolocalization.
Semantic Data On the other hand, an image may also contain semantic information. Extracting semantic information like text can disclose geographic information. In fact, a restaurant logo text such as "McDonald's", "Burger King", etc. , can be compared with the list of restaurants in the city. It is the same for any text in the picture that tells a store front name, road names, buildings number, etc. We use the Google Vision API (Google, 2017) in order to detect text in the image. The API returns the detected text in the image. We believe that comparing the detected text with a collaborative mapping project will provide up to date information about city places. In our system, we use the OpenStreetMap. org website and its Nominatim API (OpenStreetMap, 2017) that returns a list of geolocations and the building shapes when available.
If we would like to do a better text recognition, we should first perform a text detection and only send to the recognition process the part of the image that includes text. We then should introduce an autocomplete or a correction API for improving text quality. This would help to complete missing or occluded letters when detecting text. We then explore the geolocalization of images using only semantic data. Two causes affect the results in this module. The first cause is that some of the semantic data detected previously are wrong or not relevant for a good geolocation detection (e. g. an adds banner may present a text that is not relevant to the location, text detection may find text where none exists). The second cause is when the text sent to the API with is relevant but the place is not available on the Nominatim Places API, thus no results can be returned.
This module will be completed with an automatic pose verification by automatically comparing Google street views to the base image. Some examples are presented in 14, our photos on the left side of the image and Google Street View photos on the right side of the image. We can find that many SIFT descriptors where matched in both photos. Thereby, the pose used to download the Google Street View image that has the biggest number of matching descriptors will be retained as the most accurate one. Finally, we need to implement a weighted decision model to take into account the importance of every type of data, especially if the case of contradictory information. Figure  14. Geolocalization detection using the whole system.
As we cannot directly extract the body rotation frequency ω b from our data, the motor frequency ω m is difficult to access from freely swimming cells. However, in our data sets, we encountered several examples of cells that got stuck to the cover slip but still maintained a fully operational rotary bundle performing sequences of transitions between the different bundle configurations. In these cases, the cell body did not rotate (ω b = 0), so that the rotation frequency of the motors equals the rotation frequency of the filaments, ω m = ω f , which could be directly measured. From the recordings of surface attached cells, we estimated a motor rotation frequency of 200 Hz in the push/pull mode and 50 Hz in the wrapped mode. We furthermore note that movement in all three swimming modes was from time to time interrupted by stops in the motor rotation.
8W optical output power, 470 nm). Images were recorded on a PC running the Hokawo software (Hamamatsu) onto a SSD RAID. Up to a frame rate of 100 fps, a full field of view (2048 × 2048 px) was possible. For higher frame rates the field of view had to be reduced by half for each 100 fps speed increase. Because of the high frame rates and sometime low densities of swimmers, recorded image stacks were processed with a custom ImageJ 41 macro to extract only parts which contained swimmers. Our macro computed the grouped maximum projection for a batch of stacks. Each stack was divided into sub-sequences of 300 images each. Of those, the maximum intensity projection was computed, where moving cells will leave bright streaks in the projection of each sub-sequence. Those episodes were cropped in space and time to produce sub-stacks for further analysis.
In this manner it was possible to reduce the large amount of data generated for visual inspection of all swimmers that could be recorded. Sub-stacks were then cataloged by swimming mode and observed transitions to obtain a complete picture of the motility pattern. Note that in most cases, also the cell body is slightly marked by the fluorescent dye, see the examples in Fig. 1. We attribute this to heterogeneities in the polysaccharide shell of the cells, due to which the dye can access proteins in the outer cell membrane to a degree that varies from cell to cell. For several episodes of swimming in pushing, pulling, and wrapped mode mean speeds of propagation were computed by measuring the path length from the maximum intensity projection and noting beginning and end frames. The bundle rotation frequency of wrapped swimmers was measured using ImageJ's orthogonal views  41.
Acknowledgements M. H. , V. W. , and C. B. thank the research training group GRK 1558 funded by  Deutsche Forschungsgemeinschaft  for financial support and  Holger Stark  ( TU Berlin ) for discussions. M. K. was supported by a grant ( TH831 6-1 ) from the  Deutsche Forschungsgemeinschaft  to K. T. R. G. and F. P. gratefully acknowledge support from the  Agence Nationale de la Recherche  via Grant No. ANR-15-CE30-0002-01. R. G. has received funding from the  People Programme  ( Marie Curie Actions ) of the European Union's  Seventh Framework Programme  ( FP7/2007-2013)  under  REA  grant agreement n. PCOFUND-GA-2013-609102, through the  PRESTIGE programme  coordinated by  Campus France.
The high combined viscosity of the solvent and polymer allowed retention of the original array pattern for times long enough to permit sample loading on an Axio A1 inverted microscope (Zeiss, Oberkochen, Germany) where the dissolving of the polymer took place simultaneously with application of a magnetic field and imaging. Field application took place using mounted coaxial coils (SBEA, Vitry, Fr coils, Paris, France), with mu-metal cores (length: 40 mm, diameter: 26-88 mm, 750 spires) and imaging with a Neo CMOS high speed camera (Andor Technology, Belfast, UK). The coils were powered by a bipolar operational power supply amplifier 6 A/36 V (Kepco, Flushing, NY, USA) and controlled by LabVIEW software (National Instruments, Austin, TX, USA). A ×40 (Zeiss, NA = 0. 65, Oberkochen, Germany) objective was used for microscopic imaging.
, 2013)  and national forest inventory data  (Tum, Buchhorn, G€ unther & Haller, 2011). The radar remote sensing based biomass product takes into account above-as well as belowground vegetation carbon stocks, applying information on wood density and allometric relationships (derived from forest inventory databases) to estimates of stem volume from remote sensing. A corresponding uncertainty estimate integrates the uncertainty of the underlying remote sensing and inventory data. The high accuracy (r² =. 70-. 90) of this biomass product with respect to upscaled forest inventory biomass has been demonstrated at regional scales  (Thurner et al. , 2014). k ¼ NPP Biomass : (1.
Absence of recurrence was defined as no event with a minimum follow-up of 6 months. Median follow-up was 11. 3 months (range: (1. 3-117)). The Kaplan-Meier method was used to estimate recurrence-free survival (RFS), and curves were compared using the Log-rank test. Univariate and multivariate analysis were performed using Cox proportional hazard model with 95% confidence intervals (CI). All tests were set at the significance level of Po0. 05. Statistical analyses were performed using the IBM SPSS Statistics software (release 19).
Detection of viral and cellular antigens Indirect immunofluorescence (IIF) was performed as described  [31]. Secondary antibodies were either anti-mouse IgG, or anti-chicken IgG conjugated to Alexa Fluor 1 488 or 594 (Invitrogen-France). Polymerised actin (F-actin) was detected using Alexa Fluor 1 594 Phalloidin (Invitrogen-France) and cell nuclei were stained by Hoechst 33342 (Invitrogen-France). Preparations were viewed under a Zeiss Axiovert 200 M microscope, and de-blurred images or optical sections were obtained by using the Zeiss ApoTome structured illumination microscopy (SIM) system. Photographs were obtained with a Zeiss digital camera AxioCam MRm, using Axiovision software. Images were compiled for publication with Adobe Photoshop CS6.
Western blot analysis Cells were lysed in sample buffer and proteins were resolved on SDS-polyacrylamide gels  [41] , transferred to nitrocellulose membranes, and blots were processed essentially as described  [31]. Bound antibodies were detected by using Supersignal West Pico Chemiluminescent substrate (Pierce protein biology products-Thermoscientific). Chemiluminescence signal was acquired using a Fusion FX7 system and images were processed with Adobe Photoshop CS6 software.
Transmission electron microscopy ESCDL-1 cells at passage 25 were grown in Nunc UpCell 6-well Multidishes (Nunc UpCell surface-Thermoscientific), infected with 2. 9x10 4 PFU per well of vBAC20 virus at passage 2 after transfection on ESCDL-1 or mock-infected. Monolayers were lifted from the dish as specified in the manufacturer's instructions, immediately fixed and processed as described  [9]. High-resolution images resulting from the merge of several contiguous images were obtained by using the Adobe Photoshop CS6 software.
Microarray analysis The cES cells and CESCs were subjected to RNA extraction after exposure to 5mM HMBA and infection by a cell-sorted inoculum of vBAC20UL49GFP, as described  [9]. A biological replicate was produced for each condition, including the non-treated cells and the HMBA treated non-infected cells as controls, leading to the testing of 12 different independent samples. RNA preparation, labelling and microarray (4x44k GE chicken V2 slides (#G2519F-26441, Agilent)) hybridizations were performed as described  [42]. Each of the 12 samples were randomly chosen and hybridized, resulting in a total of 12 hybridized pairs of samples. Raw data acquisition and analysis was also performed with a corrected p-value chosen as significant when < 0. 05  [42]. The expression values and the identification of differentially expressed genes were done with R program and Limma library using a separate channel analysis of 2 color data.
Differentially expressed genes were then filtered with log fold change ratios (Log (FC) between pairs of conditions of less than -2 and more than 2. Probes sequences from the filtered gene lists were submitted to the DAVID GO analysis tool (https://david. ncifcrf. gov/) and after the probe ID conversion, the GO analysis was performed with a p-value of 0. 05 as a limit to define cluster of genes with predicted functions. The R package FactoMineR was used to perform PCA on all 44k probes using the NCBI GEO (http://www. ncbi. nlm. nih. gov/geo/) datasets GSE61221 for the CEF-1, GSE38168 for the CEF-2, GSE47191 for the DF1 cells.
ESCDL1 RNAseq analysis All the RNAseq analyses were performed by Helixio (http://www. helixio. com/). One μg of total RNA from ESCDL-1 at passage 31 from the initial cES cells was used to prepare the library using the TruSeq R Stranded mRNA sample preparation kit (Illumina). The average sizes, estimated by Bioanalyser (Agilent) and molarities, estimated by Qubit (Life Technologies) were of 268bp, 277 bp and 267 bp and of 157 nM, 202 nM and 94. 5 nM for CEF, cES cells and ESCDL-1 respectively. The paired-end sequencing was performed on the NextSeq 500 sequencer (Illumina) and controlled by the SeqencingAnalysis viewer software (Illumina). The quality control of the 'Sample-ID. fastq files was performed with the FasQC software (Babraham Institute). The total number of reads was of 85093694, 108943912 and 87951932 and the percentage of aligned reads reached 90.
8, 82. 9 and 82. 2 for CEF, cES cells and ESCDL-1 respectively. The alignment was performed on the chicken genome (http://www. ensembl. org/ Gallus_gallus/Info/Index, Galgal4) using the Hisat, subread and R (DEseq2) package  [43]. The String software (http://string-db. org/) was used to identify putative networks between proteins in order to better define cellular phenotypes  [44].
QRT-PCR analysis Quantitative real time PCR was performed using StepOne Plus TM Real Time PCR system (Applied Biosystems) as described  [42]. All oligonucleotides (provided by Eurogentec) were designed with Primer 3 (S1 Table) from sequences extracted from the chicken genome (http://www. ensembl. org/Gallus_gallus/Info/Index, Galgal4). Two independent samples were run each in triplicate and the StepOne Plus TM software provides the RQ value for each one with the RSP17 (X07257) as internal control using the ΔΔCt method.
Those few pathways identified emphasize one of the main HMBA effects, which is change in cell morphology and phenotypic markers. Using the datasets of primary fibroblasts (CEF) from two different origins, of DF1 fibroblastic cell line and of those CESCs and cES cells, treated and non-treated, a PCA analysis clearly established that the HMBA treatment resulted in a profile for the treated cES cells that resembled neither to CEF nor to DF1 cell line profiles (Fig  2B). Interestingly, despite the very different origin of both cell types, when comparing both CESCs and cES treated versus non-treated cells, 96 probe ID (corresponding to 56 unique gene symbol) were found to be differentially expressed in both cell types, with more downregulated (72) than up-regulated genes  (18). Six genes (CDKN2A, TMP200C/LOC421054, ARAP3, LOC422654, CADPS and CR385173) were found to be up-regulated in treated cES cells, but down-regulated in treated CESCs cells, and Histone genes (LOC770022/ HIST2H3L1, HIST1H46L4, HIST1H46L5), genes encoding transmembrane proteins (LOC421054/TMEM200C, TMEFF2, ASGP/MUC4), transferase proteins (MGAT3, HS6ST3) and few other ones (MT3, TECRG1L, LOC428714) were found to be up-regulated in both cell types (S2 File).
However, as observed for the microarray analysis, on the 574 genes only 328 (less than 2/3) could be taken into account for the GO analysis, mainly due to the insufficient chicken genome annotation. No significant differences were found with a similar analysis performed with the 1172 gene differentially expressed by a FC > 4. The analysis of the protein-protein interactions of this first gene cluster (76 genes with an enrichment fold of 3. 09 GO:0044421~extracellular region part), allowed the identification of a few nodes by using the String software. A first one was centred on IL-6 as a main cytokine linked to several growth factors, another one on CTGF in close relationship with a third one centred on SPARC, different collagens and the TIMP proteins, reinforcing the importance of extracellular matrix for the ESCDL-1 phenotype (S3A Fig).
ESCDL-1 are permissive to cell-adapted and "wild type" Mardiviruses To monitor the susceptibility of ESCDL-1 to different GaHV-2, we used viruses that could be restored from corresponding BAC transfections in this cell-line. As it was shown previously that vBAC20UL17mRFP did not differ greatly from its parental vBAC20 virus in its replication (data not shown), we transfected the BAC20UL17mRFP (5. 4 μg) in 4. 5 x10 6 of ESCDL-1 at passage 28 (Table  1 ). Viral plaques typical of MDV infection were visualised by day 4 post transfection and reached a total of 250 PFU on 2 wells in a six-well plate, 7 days after transfection. From this initial step (T1), vBAC20UL17mRFP was serially passaged and viral production over the first 12 passages was quantified by plaque titration (Table  1 ). Viral titres were in the range of 10 5 from P3 on, occasionally reaching 10 6 PFU/ml, and, when titrations were performed in parallel on CESC and ESCDL-1, no statistically significant differences could be observed.
For vBACRB-1BUL17mRFP titres at passage 2 on ESCDL-1 reached 3. 35±0. 42 × 10 5 PFU/ml and the titre of vUL47-mRFP at the same passage was 1. 2±0. 23 × 10 5 PFU/ml. The replication of HVT (MeHV-1) was also assessed on ESCDL-1 by using a first passage of FC126 virus on CESC as an inoculum, at a split ratio of 1/23. A second passage was performed on ESCDL-1 (at a ratio of 1/7) and titrated. The titres progressed from 3. 5 (± 0. 4) × 10 4 in the inoculum to 1. 61 (± 0. 14) × 10 5 for passage 2 on primary CESCs and 9. 7 (± 0. 62) × 10 4 in ESCDL-1 to reach 1. 73 (± 0. 17) × 10 5 for passage 3 (corresponding to the second passage in ESCDL-1). The susceptibility of ESCDL-1 cells for GaHV-1 (ILTV) was also tested, in comparison with the LMH cells, and ESCDL-1 did not support ILTV replication (data not shown).
5 vs 34 to 40%) and three times higher for intracytoplasmic C capsids (32. 8 vs 10%) in MDV infected cells compared to HSV  [64, 65]. Interestingly, several images suggesting difficulties in the maturation of a tegument were recorded together with frequent accumulation of C capsids in the cytoplasm. This is also reminiscent of secondary envelopment blockade associated in HSV-1 with deletion of tegument or glycoprotein genes (for a review see  [66] ). Whether this blockade is relieved in FFE remains to be ascertained, but the differential expression of several tegument proteins  [67]  might be indicative of the progression of MDV toward a morphogenesis process yielding infectious cell-free particles. However the sole over-expression of UL48 in a BAC context  [67]  or the expression of UL48 in ESCDL-1 (data not shown) did not relieve the impairment in viral morphogenesis in cell culture.
Acknowledgments We are grateful to  Sonia Georgeault  for her expert technical assistance in the preparation of TEM samples,  Dr Atsushi Miyawaki  for providing the plasmid Venus/pCS2,  Dr D. Dhouailly  for providing the E10 monoclonal antibody, and  Clement Mazoyer  and  Frantz Bouhallier  for their help in the microarray analysis. We also thank  Keith Jarosinski  for his dedicated help in proofreading and style adjustments.