dataesr/scientific-paragraphs-categorization huggingface dataset:
The H1 proportion of T--tests over the set of measured transcripts was estimated using B Storey method : (1 - 2 × mean { if(p i > 0. 5) then 1 else 0} probe set i:1. 55K )  (21). Anova models were used for multigroup comparison. To control for multiple testing we measured the local false discovery rate. miRNome data: Differential expression (DE) analysis is performed using likelihood ratio tests (LRT) based on a negative binomial model for gene--level read counts. We used the Bioconductor R package edgeR  (22)  to fit a negative binomial model to gene--level read counts and perform likelihood ratio tests of DE. edgeR was used on the original counts by passing an offset to the generalized linear model. This method was also used on normalized data. Finally, a Wilcoxon test was performed. The results of these three methods were used to select differentially expressed microRNAs.
Genome data: we used moderate T--tests to identify differential aberrations between groups of samples, using limma R package. The H1 proportion of T--tests over the set of measured transcripts was estimated using B Storey method : (1 -2 × mean { if(p i > 0. 5) then 1 else 0} probe set i:1. 55K )  (21). Because of the large number of probes, multiple test correction was not performed but we used the sensitivity and the specificity related to each probe to select the probes characterizing the R1 subgroup.
3--Signaling pathway analysis To identify biological features associated with unsupervised samples partitions, 17306 pathways collected from KEGG, GO, MSigDB, SMD and Biocarta (and related genes) were tested. Four methods were used to compare gene sets with sample groups: • GSA (23): R package GSA • globaltest (24) R package globaltest • SAM--GS (25): original R code (available at http://www. ualberta. ca/~yyasui/SAM--GS/SAM--GS%20code. txt) • Tuckey approach (algorithm described in the table 4 from (26): original R code Each method gave a p--value (based on Monte--Carlo simulations). The lower the p--value, the more the genes in the gene set that are differentially expressed between the sample groups. To aggregate the results of the four methods, given a list of gene sets (pathways/ GO terms/…), we first sorted the list of gene sets for each method according to p--value, and then we calculated for each gene set the geometric mean rank across the four methods.
The final order is based on this mean rank. For the miRNome data, genes used in the signaling pathway analysis are the targets predicted simultaneously by mirTarget2. 0  (27)  and miRanda  (28). 4--Survival analysis Survival time was calculated from the date of surgical resection. Patients who were lost to follow--up or alive at the time of the study were treated as censored events. Survival curves were calculated according to the Kaplan--Meier method (function Surv, R package survival, V2. 29) and differences between curves were assessed using the log--rank test (function survdiff, R package survival). To find clinical criteria related to metastasis--free survival, overall survival or disease--free survival, we built univariate and multivariate cox models (function coxph, R package survival) and we selected clinical criteria on the score test p--value of the cox models.
The coxph function was also used to calculate adjusted models (using the parameter strata). Events related to metastasis--free survival included the metastasis development whatever the body localization (Bone, CNS, kidney, lung…). Events related to disease--free survival included death events associated with the disease (metastasis and/or loco--regional relapse) contrarily to the overall survival that did not distinguish the death cause. 5--Classifier building Methylome data: the training set was composed of our 84 samples analyzed by the Illumina HumanMethylation27K arrays and the validation set was our independent data set composed of 101 samples analyzed by the Illumina HumanMethylation450K arrays. A centroid based predictor was build using the differentially methylated genes between the studied groups.
Validation of omic data by alternative technologies 1. Gene expression assay Total RNA were extracted from frozen tumor tissues using DNA/RNA allprep minikits (Qiagen, France), according to the manufacturer's instructions. The integrity of extracted RNA was verified on an Agilent 2100 Bioanalyser (Agilent Technologies, Palo Alto, CA). RNA concentrations were measured using a ND--1000 NanoDrop spectrophotometer (Labtech, Palaiseau, France). 0. 5 µg of extracted RNA was used for cDNA synthesis using the iScript™ cDNA Synthesis Kit (Bio--Rad), according to the manufacturer's instructions. One µl of diluted cDNA corresponding to either 5 ng or 1. 25 ng of reverse transcribed RNA, was analyzed with SyberGreen (Roche, Meylan, France), in duplicate, using the LightCycler 480 real--time PCR system c).
qRT--PCR data were analyzed using LightCycler® 480 software. Ct levels were normalized to the average Ct values of 2 internal controls • 95°C: 5 min • 95°C: 30 sec 62-->52°C: 30 sec. 72°C: 1 min è 10 cycles with a gradual decrease of hybridization Tm: 1. 0°C per cycle. • 95°C: 30 sec 52°C: 30 sec 72°C: 1 min è 30 cycles. • 72°C: 5 min PCR products were analyzed by High--Resolution Melting (HRM) in a LC480 thermocycler (Roche, Meylan, France). Alternatively, DNA was purified with the MinElute® PCR Purification Kit (Qiagen, France) digested with 10U of TaqI (Invitrogen, Life technologies, France) for one hour at 65°C. 10 µl samples were resolved by electrophoresis on 3% agarose gels.
Data collection and analysis In cell experiments are performed using an epifluorescence microscope. The movies are acquired on an inverted Zeiss Axiovert 100 equipped with a 100Â Zeiss objective (NA 1. 40) and an EMCCD camera (Andor, Neo sCMOS 2560 Â 2160, pixel size 6. 5 mm). Movies are acquired at 100 frames per second. In vitro experiments are performed on the same customized set-up using an epifluorescence microscope for the study of the interaction and a total internal reflection fluorescence microscope (TIRFM) with a 63 Â objective (Olympus, NA 1. 49), a laser diode as light source (532 nm, 40 mW) and a CCD camera (Watek, 756 Â 512 pixel) for the observations of the CyProt:DNA:QD motion. These experiments are performed with an acquisition rate of 25 frames per second. The analysis of the trajectories is performed using two different algorithms for the in cell and in vitro experiments.
The in cell experiments are tracked using a customized version of the MTT algorithm  (35)  adapted to our purpose. For the in vitro experiments, the tracking is performed with a version of particle tracking algorithm developed by Crocker et al. (36)  and adapted for MATLAB by Daniel Claire (Georgetown University) and Eric Dufresne (Yale University). We estimate the pointing accuracy of the tracking by following a QD stuck on the coverslip for 1000 frames and measuring the standard deviation of its position. We find that the error is $30 nm.
The resulting spectra are then analyzed via the Mascot Software created with Proteome Discoverer (version: 1. 2. 0. 92, Thermo Scientific) using the SwissProt Homo sapiens Protein Database (18 April 2012, 20 255 sequences). The resulting Mascot result files are loaded into the myProMS (37) server for further processing. In myProMS, we fix the estimated false discovery rate (FDR) of all peptide and protein identifications to <1%, by automatically filtering on peptide length, mass error and Mascot score of all peptide identifications.
Before each motility experiment, an image of the microtubules on the surface was acquired by RICM. We observed a large fraction (60%) of immobile CyProt:DNA:QD complexes, which are either bound to the microtubules or stuck to the surface. Conversely, 40% of the CyProt:DNA:QD complexes exhibited a clear movement (over a movie of 2 min). This ATP-based activity supports the hypothesis that the DNA fragments are actively transported along the microtubules by dynein or kinesin molecular motors. Among the 40% of moving spots, we distinguished three behaviors: (i) directional motion, typical of molecular motors (50%), (ii) confined diffusion (20%) and (iii) bidirectional motion, probably because of a competition of two or more molecular motors (30%). Examples of trajectories for each behavior are showed in Supplementary Data (Supplementary Figures  S1-S4 ).
ACKNOWLEDGEMENTS The authors thank  Maxime Deforet  for his help with single-particle tracking and  Wolfgang Faigle  for his initial contribution to MS experiments. They also thank  Aure´lien Roux ,  Fabien Montel  and  Karine Guevorkian  for the useful discussions and critical reading of the manuscript. They thank  Maria-Isabel Yuseff  for having kindly read and corrected the manuscript. The authors greatly acknowledge the  Nikon Imaging Centre  and the imaging platform  Pict-IBiSA at Institut Curie-CNRS.
The objective of Web-based expert epidemic intelligence systems is to detect health threats. The Global Health Security Initiative (GHSI) Early Alerting and Reporting (EAR) project was launched to assess the feasibility and opportunity for pooling epidemic intelligence data from seven expert systems. EAR participants completed a qualitative survey to document epidemic intelligence strategies and to assess perceptions regarding the systems performance. Timeliness and sensitivity were rated highly illustrating the value of the systems for epidemic intelligence. Weaknesses identified included representativeness, completeness and flexibility. These findings were corroborated by the quantitative analysis performed on signals potentially related to influenza A/H5N1 events occurring in March 2010.
Quantitative analysis: Database and indicator analyses Raw data. Despite intrinsic differences, expert systems operate in similar ways. They search the Internet to detect information potentially relevant for epidemic intelligence purpos- Databases. Two different databases were constituted: The first one (''prospective'') aimed at assessing the event detection process under close to real life conditions (i. e. , detecting pertinent signals potentially relevant for the study among a large volume of raw signals). The second (''retrospective'') database aimed at assessing systems' theoretical performances. ''Prospective'' database: Detection rate, Effective Positive Predictive Value (EPPV), F1-score. All raw signals detected between March 1 and March 31, 2010 that potentially referred to an A/H5N1 event (human cases and epizootics) were considered for the analysis.
Raw signals were automatically collected (through prospective specifically designed queries), provided directly by the systems (i. e. datasets), or collected manually (through retrospective ad hoc queries). From these data, each signal captured by the systems was reviewed and classified as: detected or inadequately detected. A detected (DET) event was defined as the first report mentioning a human case or an epizootic detected by a system in March 2010 and before the reporting of this event by WHO/OIE on their respective websites. An inadequately detected (XDET)) was defined as a signal initially tagged A/H5N1, but after verification was found to be not related to the occurrence of confirmed A/H5N1 cases, or an event previously detected by the same system (i. e. duplicate), or an A/ H5N1 case report detected by a system after or on the same day as the reporting of this event by WHO/OIE.
A not-detected (NDET) event was defined as an event reported by WHO/OIE but not detected by the system in March 2010. True negative events could not be considered because it is not possible to determine the total number of reports issued on the Internet nor those events discovered but not published by systems. The detection rate (DR) was defined as the ability of a system to detect confirmed A/ H5N1 cases before their reporting by WHO/OIE (DR = DET/ (DET+NDET)). Effective Predictive Positive Value (EPPV) was defined as the probability for the system to timely detect confirmed A/H5N1 cases among all reports (EPPV = DET/(DET+XDET)). The F1-score is the harmonic mean of DR and EPPV, weighted equally [16] F1 = 2*(EPPV*DR)/(EPPV+DR). ''Retrospective'' database: Sensitivity and timeliness. For each event included in the gold standard (i.
Individual spider graphs tend to have relatively similar surfaces, except for the less utilized systems (,5 users). Quantitative analysis: A/H5N1 data Detection rate, positive predictive value & F1-score (Table  3 ). A total of 1,154 signals potentially relating to A/ H5N1 events were collected. For the same period, 29 A/H5N1 events were reported by WHO (14 events) or OIE (15 events) and were included in the gold standard. In regard to large differences in terms of intrinsic systems' characteristics, interface designs, database storage or extraction capacities it has not been possible to implement a homogenous data collection procedure across the seven systems. For one system (GPHIN), the system design did not allow the extraction or collection of data in a format compatible with this analysis and as such 366 signals were excluded from the analysis.
As of July 30, 2010, six datasets were collected from the six other systems for a total of 788 signals. Three of these datasets were collected prospectively and three were collected retrospectively. For the six systems, the overall detection rate (DR) ranged from 31% to 38%, from 29% to 57% for human cases and from 20% to 40% for epizootics. Differences in DR were observed between human cases and epizootic events (the largest being 57% for human cases versus 20% for epizootics). For the combined system (pooled from six systems), the DR increased to 72% overall, to 93% for human cases and to 53% for epizootics. Overall EPPV ranged from 3% to 24% and the F1-score ranged from 6% to 27%. The overall EPPV and F1-scores of the combined system were 3% and 5%, respectively. Sensitivity and Timeliness.
Acknowledgments Our thanks to the entire EAR working group for their participation and fruitful discussion raised by this manuscript. We gratefully acknowledge GHSI which supports the project and allow countries and institutions to provide human and financial supports for this project. * EAR  project core members:  Ray R. Arthur  ( CDC ),  Philippe Barboza (InVS) ,  Mike Barker  (HPA),  John Brownstein  ( Children's Hospital Boston ),  Catherine Chow  (CDC),  Nigel Collier  ( National Institute of Informatics ),  Ricardo Cortes  ( Ministry of Health of Mexico ),  Silvia Declich  (ISS),  Maria Grazia Dente  (ISS),  Pamela S. Diaz (CDC), Brian Doherty (JRC) ,  Christian Herzog (RKI) ,  Mike Hiley  (HPA),  Nigel Lightfoot  (HPA),  Jens Linge (JRC) ,  Lawrence C. Madoff  (ProMED-mail),  Jas Mantero  (ECDC),  Abla Mawudeku  (PHAC),  Noele P.
In this paper, we address the problem of object segmentation in multiple views or videos when two or more viewpoints of the same scene are available. We propose a new approach that propagates segmentation coherence information in both space and time, hence allowing evidences in one image to be shared over the complete set. To this aim the segmentation is cast as a single efficient labeling problem over space and time with graph cuts. In contrast to most existing multi-view segmentation methods that rely on some form of dense reconstruction, ours only requires a sparse 3D sampling to propagate information between viewpoints. The approach is thoroughly evaluated on standard multiview datasets, as well as on videos. With static views, results compete with state of the art methods but they are achieved with significantly fewer viewpoints.
Indeed, it then becomes difficult to rely Work sponsored by the OSEO-funded Quaero Programme, and partially sponsored by European Commission FP7 Project React. on shared appearance models of the object between views while parts of the background seen from several viewpoints will present similar aspects. In that respect, the MVOS problem significantly differs from the object cosegmentation problem  [21, 14] , which assumes shared appearance models for the foreground but different backgrounds. In most applications where viewpoints see a single scene and object, calibration is available or computable using off the shelf tools such as Bundler  [23]. This includes static camera setups such as performance capture studios  [11] , static camera networks used for surveillance, or even crowd sourced data of a single shape such as a monument  [23].
Experimental protocol We implemented our approach using publicly available software for superpixel segmentation (SLIC  [1] ) and using Kolmogorov's s-t mincut implementation  [4]. We use superpixel sizes of 30-50 pixels to ensure oversegmentation, obtaining around 2000 superpixels per image. For appearance models, we run K-means on texture and color values, to quantize texture and color into respectively 60 and 150 "words". The region of interest is computed by keeping only 3D samples in the common visibility domain, i. e. which project inside all views. We randomly generate 100k 3D samples for all tests. The only free parameters in the method, λ 1 , λ 2 and λ 3 were respectively set to 2. 0 , 4. 0 and 0. 05 for all datasets. No particular sensitivity was observed to these settings. Initialization of the algorithm was very weak, by setting H F i to the statistics of the projection region of the common visibility domain of all views, which is quite large on all datasets, only eliminating about 25% of pixels on outer regions of the image.
Qualitative results To validate our approach, we run our implementation on a dozen challenging datasets. Note that among the existing literature on the subject, few or no MVOS datasets are made publicly available, making comparisons difficult. We obtained datasets from two state of the art approaches: COUCH, BEAR, CAR, CHAIR1 from  [16]  which we use for qualitative and quantitative evaluation, BUSTE from  [17]  and PLANT 1  which we use for qualitative evaluation. The figures from 6 to 8 show the results for our methods on the various datasets. We show the graph cut result on superpixels at convergence and the final segmentation at pixel level. We illustrate the resilience of the algorithm in particular with low numbers of viewpoints on all the datasets. Very good results are obtained with only 3 widespread viewpoints (such as Fig.
Input images Superpixels Segmentation For the 8 views result, the table is seen by all the views. With 13 views some cameras eliminate parts of the table and it is thus classified as background. Finally with all the views, the black elements in background appear close and similar to the black base. Thus only the head is identified as foreground in this case. In complex scenarios, such as in Fig. 8 , approaches relying only on color  [9]  fail to segment foreground objects, where our approach benefits from a more complex appearance model. Fig. 7  shows that what is considered as foreground object depends on the viewpoints. For the first ex-Input Our method Djelouah  [9]  Figure  8. Results on PLANT dataset (3 views) with qualitative comparison with  [9]. Our method benefits from a richer appearance model and also from intra-image consistency constraints.
Quantitative and Comparative results To illustrate the strength of the approach and for the purpose of comparison, we use the same protocol as  [16] , computing accuracy as the proportion of correctly labeled pixels (Fig. 9 ). We evaluate here the sensitivity of our approach to the number of viewpoints and the quality of the segmentation result compared to state of the art approaches  [9, 16, 25] , by randomly picking 10 viewpoint subsets for a given tested number of viewpoints and averaging results. Clearly Fig. 9  shows that our approach exhibits very little sensitivity to the number of viewpoints and achieves excellent segmentation results even with only 3 widespread viewpoints. Let us emphasize the excellent performance of the algorithm on CAR and CHAIR1 datasets, despite the very low number of viewpoints used and the challenging nature of color ambiguities in the datasets.
Video segmentation results In the case of video sequences, our framework has the ability to propagate multi-view segmentation evidences over time. It also enables to propagate temporal evidences from a given viewpoint to other viewpoints, e. g. static background or moving foreground. They can help resolve local segmentation ambiguities in few views in time or space. In order to demonstrate these principles, we evaluated the approach with two datasets DANCERS and HALF-PIPE from  [11]  and  [12]  respectively (more avaible as supplemen-.
Input images With temporal No temporal constraints constraints tal material 2  ). The first consists of 8 cameras in an indoor setup whereas the second is captured with 4 handheld cameras in a challenging outdoor environment. Fig. 10  shows segmentation results with and without temporal consistency. Results on the DANCERS sequence (first row) show how temporal evidences help resolving background ambiguities. This is achieved by taking advantage of pixels with static values when building the background model. With the HALF-PIPE video dataset (Fig. 10  second and third rows), we experiment propagation in time and space of user inputs. In this dataset, the complex nature of the environment, the handheld cameras in general motion and non-static backgrounds, and the few, widespread viewpoints make the segmentation very challenging.
Conclusion We have presented a new approach to solve the MVOS problem based on iterated joint graph cuts. To our knowledge we propose the first unified solution dealing with intra-view, inter-view, and temporal cues in a multi-view image and video segmentation context, into a single consistent MRF model. The approach is shown to cope with a low number of widespread viewpoints, many times achieving state of the art quality with only three wide baseline views. The algorithm has been demonstrated on very challenging datasets, including MVOS segmentation with videos from four moving handheld cameras. We believe that the framework is a solid basis to explore more complex multi-view.
In this work, we compare vertical column density of water vapour measured at Observatoire de Haute-Provence, Southern France (  5 • 42 E, +43 • 55 N). Data were obtained by three satellite sensors, GOME, GOME 2 and SCIAMACHY, and by two groundbased spectrometers, Elodie and SAOZ. These five instruments are able to measure total column density of water vapour in the visible and have different principles of observation. All these instruments reproduce the total column water vapour with good accuracy. The mean difference between the satellite measurements, ground-based measurements, and between both types, are quantified. The diurnal cycle of water vapour above the station and its variability with latitude have been investigated. The differences between these data sets are due sometimes to the differences in the time of the measurements, or to the differences in the geometry of observations, or also due to both effects.
Promising infrared techniques (e. g. MERIS, IASI) could be used in the future, however, the differences about techniques, characteristic, and algorithms will be a next step used beyond the scope of this study. This article is structured as follows: Sect. 2 presents a description of these instruments; Sect. 3 describes the method used to select the satellite measurements above OHP considered for comparisons; Sect. 4 presents the results and the comparison between the different data sets, as well as a quantification of their differences; Sect. 5 outlines the interpretation of the differences followed by Sect. 6, the conclusion and final remarks. SAOZ  (Pommereau and Goutail, 1988 ) is a ground-based zenith-sky UV-visible spectrometer. Water vapour total columns are obtained from twilight measurements at 592 nm using the differential optical spectroscopy method, averaged over 30-40 individual measurements taken from 86 • SZA (Solar Zenith Angle) up to 91 • SZA.