dataesr/scientific-paragraphs-categorization huggingface dataset:
(2016). Further investigations will be carried out to check the suitability of this technique for different locations and the Moon's illumination conditions. It should be highlighted that in this study the authors only intend to correct the AOD retrieval at nighttime affected by these biases as much as possible. It is out of the scope of the present study to propose and perform corrections on the ROLO USGS model. This issue is beginning to be addressed thoroughly by several groups. An example is the Global Space-based Inter-Calibration System (GSICS) Lunar Observation Dataset (GLOD), which is a collaborative effort to use the Moon as a common and unique calibration reference at the international level. This database includes Moon observations from several space organizations such as the European Space Agency, the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), the National Centre for Space Studies (CNES), the Japan Aerospace Exploration Agency, the National Satellite Meteorological Center from the China Meteorological Administration (NSMC-CMA), or the National Aeronautics and Space Administration, among others.
Acknowledgements. This work has been developed within the framework of the activities of the  World Meteorological Organization (WMO) Commission for Instruments and Methods of Observations (CIMO) Izaña  test bed for aerosols and water vapor remote sensing instruments. AERONET Sun photometers at Izaña have been calibrated within the  AERONET Europe TNA , supported by the  European Union 's  Horizon 2020 research and innovation program  under grant agreement no. 654109  ( ACTRIS-2 ). The authors also thank AERONET team for their support. The  GAW-Precision Filter Radiometer network  for AOD at WMO-GAW global observatories has been implemented by the  World Optical Depth Research and Calibration Center (WORCC). Edited by: Piet Stammes Reviewed by: two anonymous referees.
Big Data is "things that one can do at a large scale that cannot be done at a small one". Analyzing flows of news events that happen worldwide falls in the scope of Big Data. Twitter has emerged as a valuable source of information where users post their thoughts on news events at a huge scale. At the same time traditional media channels also produce huge amount of data. This paper presents means to compare the propagation of the same news topic through Twitter and news articles, both important yet varied sources. We present visual means based on maps to make it possible to visualize the flow of information at different level of temporal granularity. We also provide an example and how the flow can be interpreted.
On the other hand, Britons may be more aware of events happening in India than Polish because more people have links with India in UK. In certain cases, various regions are not aware about an incident due to the lack of technical advancement in that area or because authorities try to hide or minimize an event. By analyzing the information spreading, an automatic system could alarm people or decision makers (NGOs, politicians, marketers, etc. ) and help them understanding situations. Collecting data and finding such patterns can enable understanding opinion differences and similarities amongst various countries and maybe to prevent problem by an adequate communication. Traditional news channels and social media are two different and complementary sources of information. They differ in many aspects mostly related to the type of information they deliver and the writers/readers that contribute.
On the other hand, taking journalism into account, it could be interesting to know precisely which areas news channels cover. It is likely that a news channel covers events happening in its original country, however, what other countries do they cover and on what basis do they cover those areas and give more importance to them are important issues that our research tackles. The results can be helpful in predicting foreign policies that will shape the world, understanding biases of nations which will further help people who migrate for jobs and studies too. This is part of what is called the spatial thinking and citizenship. In this paper, we focus on means to visualize the information spreading. We have considered two main sources from which the data will be exploited and analyzed: One of them is Twitter which is a social networking site.
This work succeeded in identifying trending events and showed such detection tasks are feasible. Recognizing that newsprint differs substantially from social media both in content and velocity, the research community began experimenting with new social media sources like blogs, but real gains came when micro-blogging platforms began their rise in popularity. One of the most well-known works in detecting events from microblog streams is  Sakaki et al. (2010)  paper on detecting earthquakes in Japan using Twitter. Sakaki et al. show that not only can one detect earthquakes on Twitter but also that it can be done simply by tracking frequencies of earthquake related tokens. Surprisingly, this approach can outperform geological earthquake detection tools since digital data propagates faster than tremor waves in the Earth crust.
Resolving Geolocations To resolve the locations of the tweet, we used the Carmen tool. (Dredze et al. , 2013. ). Carmel resolves locations using three primary methods i. e. looking up directly at the "Place" object, use reverse geocoding APIs to find the name of place from latitude and longitude co-ordinates and third look-up the location from user profile from the four methods described by Gonzalez and co-workers  (Gonzalez et al. , 2012 , Oussalah et al. , 2012). The locations obtained were in a tiered format starting from Earth and followed stepwise to country, then state, county and finally the city. From the above, we extracted only the country so as to keep the place data from tweets consistent with those from the place data for news channels (discussed in Section 3. 2).
Collecting Information Alchemy API by IBM has provided a Watson Platform, that uses natural language processing and machine learning to reveal insights from large amount of unstructured data. The API provided by Watson platform is: https://gatewaya. watsonplatform. net/calls/data/GetNews There are various parameters associated with the API which are as follows: apikey: This is a 40 digit unique key allocated to every registered user, which enables data retrieval. outputMode: It returns the data in desired API output format such as XML, JSON. start/end: It determines the time (in UTC seconds) of the beginning/ending of the query duration, from when one wants the data. count: Maximum number of articles to be retrieved. q. enriched. url. title: The API streams the URLs and searches for programmer defined keywords in the title of articles; for our project we defined the keyword as "tunisia".
Whereas the twitter data was very widely spread over a period of time, thus we chose to cluster it on the basis of hour. Based on the above plots, the observations of the data are as follows: Inferences have been made based on the visual plots on the world map. The inferences are as follows: The event occurred in Tunisia located in northern region of Africa, however there is hardly any tweet originating from this continent even though it was expected that the proximity of this event would have triggered a burst of tweets from this region. One of the factors to which this could be attributed to is that there might be other more popular forms of micro-blogging sites which have an upperhand over twitter. This can form the basis of a different study altogether. United States of America has closely been following the event since its advent.
It contributed to nearly 50% of the tweets and news channel reporting. Here, a classic example of news channels shaping the opinions of the masses has been witnessed as both the tweets and news-channels have been actively following this event. Russia has been indifferent to this event in terms of both tweets and news channel reporting as only 5 tweets were found to originate from this region and no news reporting of the same was found during the advent of the event. However the news channels started participating in the news when the government of Tunisia actively implemented steps to combat terrorism (i. e. end of December) India has contributed to the twitter data (20%) about the event however no news channel reporting was found from this country. Hence it can be concluded that today twitter has emerged as a powerful micro-blogging site to express one's views and the news channels are not the only factor that shapes the human awareness to an event.
CONCLUSION In this paper, we have mentioned two sources of news dissemination, Social Media and News Channels. We have presented methodologies from which we can extract current news events or an event that has occurred in the past on the basis of specific keywords. Using Twitter API, we can retrieve those tweets that have specific query terms. The Meta-data that we get from the tweets enables us to get the time and the location (using Carmen tool) of the user. We use the same query terms in retrieving the URL's of top news channels. For this purpose, we have used Alchemy API by IBM. We can get the time field and location of the URL's. Using the data from Twitter and News Channels we can group the places according to the time of hosting and plot it on Visualization Tool, Tableau. Hence, this application is useful in understanding the relation between various countries and how an event occurring in a country affects another country.
Raw sequence data generated in this study are available at the Gene Expression Omnibus (GEO) data repository (Accession number GSE101524). mRNA reads were aligned to the C. elegans N2 reference genome (release WB235. 85) (http://www. ensembl. org/ Caenorhabditis_elegans/Info/Index). After read count on gene features (FeatureCounts software, http://subread. sourceforge. net/), quantitative analysis of differential expression between conditions was performed by following the SARTools pipeline (https://github. com/PF2-pasteur-fr/ SARTools) (see Materials and Methods for details). Gene expression data were normalized by a negative binomial distribution model by using DESeq2 and EdgeR implementations and compared in pairs to analyze the effect of the two variables of this study: diet (E. coli and B.
The C. elegans transcriptome is strongly influenced by the diet To understand the effect of the diet (E. coli versus B. subtilis) on the C. elegans transcriptome, we performed pairwise comparisons of gene expression datasets for worms grown at the same temperature on each diet. We identified specific sets of genes that showed a statistically significant higher expression when the nematodes were fed on E. coli compared to B. subtilis at the three tested temperatures, 15ºC, 20ºC, and 25ºC (Figure  7A  and Supplementary Table  12 ). In the same way, we identified those genes showing a significantly higher expression on a B. subtilis diet at the three tested temperatures (Figure  7B , Supplementary Table  13 ). Functional analysis of the 68 genes significantly more expressed in worms fed on E.
However, we found that for worms maintained at 15ºC or at 25ºC gene down-regulation was a more prevalent phenomenon than gene up-regulation. Down-regulated genes were grouped as either "false hot" or "false cold" genes. The presence of up-and down-regulated groups of genes suggests that both, transcription induction and well as transcription repression play a role in the adaptation of worms to physiological temperature variations. We validated the activation/repression of our transcriptomic dataset by RT-qPCR and fluorescent reporters demonstrating that at least some of the temperature responsive genes of our study could be used as in vivo "biological thermometer" biomarkers. Thus, genes from our data set might temperatures shows that 68 genes exhibit significantly higher expression on an E.
Bioinformatic analysis The quality of RNA-seq results was firstly assessed using FastQC (http://www. bioinformatics. babraham. ac. uk/ projects/fastqc/). The raw reads were trimmed, filtered with a Phred quality score of at least 25 and all adapters removed with Trimmomatic software  [14]. Clean reads were aligned versus the N2 Caernohabditis elegans reference genome (release WBcel235. 85, http://www. ensembl. org/Caenorhabditis_ elegans/Info/Index) by using Tophat2  [15]  with default parameters. Resulting alignment files were quality assessed with Qualimap2  [16]  and sorted and indexed with Samtools software  [17]. After read count on gene features with the FeatureCounts tool  [18] , quantitative differential expression analysis between conditions was performed both by DESeq2  [19]  and edgeR  [20]  implementations, to compare all groups in pairs to analyze the effect of the two variables of this study: food sources (E.
coli diet vs B. subtilis diet) and growth temperatures (15ºC vs 20ºC vs 25ºC). Both of them, implemented as R Bioconductor packages, perform read-count normalization by following a negative binomial distribution model. In order to automate this process and facilitate all group combination analysis, the SARTools pipeline  [21]  was used. The resulting data was obtained as an. html file and. csv tables, including density count distribution analysis, pairwise scatter plots, cluster dendrograms, Principal Component Analysis (PCoA) plots, size factor estimations, dispersion plots and MA-and volcano plots. Resulting tables, including raw counts, normalized counts, Fold-Change estimation and dispersion data for each of the analysis methods (DESeq2 and edgeR) were annotated with additional data from Biomart (https://bioconductor.
org/packages/release/bioc/html/biomaRt. html), WormBase (http://www. wormbase. org) and org. Ce. eg. db (https:// bioconductor. org/packages/release/data/annotation/html/ org. Ce. eg. db. html) databases. Final tables also include the associated gene name, Ensembl Transcript and protein information, GO Term ID and names, EntrezID, UniprotTrEMBL information and Human ortolog ID and gene name data. Normalized values for each gene were graphically represented into barplots for all samples under different temperature and feeding conditions. To control the False Discovery Rate (FDR), p-values were amended by Benjamini-Hochberg (BH) multiple testing correction  [22]. Those features showing corrected p-values below a 0. 05 threshold were considered as significantly up-or down-regulated genes. In order to reinforce downstream analysis and discard false-positive over/under-expressed genes, common up-and down-regulated features were extracted from DESeq2 and edgeR tables.
The CRAN packages eVenn (https://CRAN. R-project. org/package = eVenn) and pheatmap (https://CRAN. R-project. org/package = pheatmap) were used to graphically represent Venn diagrams and heatmap plots showing these common features. Gene Ontology enrichment analysis was performed for common up/down regulated genes by using clusterProfiler package  [23]  through its enrichGO tool. This tool uses a hypergeometric BH model to obtain adjusted q-values. Each GO category (Biological Process -BP-, Molecular Function -MF, and Cellular Component -CC-) was represented in barplots, showing its relative abundance and associated q-value. Similarly, KEGG and Reactome pathway analysis was conducted by clusterProfiler (enrichKEGG) and ReactomePA  [24]  tools. The KEGG pathway maps were obtained with Pathview package  [25].
As these authors stated, the reason for these discrepancies remain unclear, although it is likely to be due to a sum of causes, such as inaccurate instrument calibration, possible systematic errors in the ROLO model, instrumental problems and/or uncertainties in nighttime AOD calculation. This work is based on all of the previous results to improve the AOD retrieval at nighttime by selecting a set of clean and stable nighttime conditions at Izaña in which daytime AOD data could be considered a good proxy for nocturnal AOD. Clean and stable conditions of days used in this study have been ensured using AERONET daytime data at the station and micropulse lidar version 3 (MPL-3) atmospheric vertical profiles extracted from a nearby coastal station. The main aim of this study is to identify the error sources, thereby trying to experimentally fix some of the problems currently affecting Moon photometry.
AOD differences at nighttime ( AOD fit ) are defined through the comparison of the nighttime AOD estimated from linear interpolation using AERONET daylight data (AOD night,interp ) and the AOD obtained directly from nocturnal CE318-T measurements using Eq. (1) (AOD night ). Then, AOD fit can be obtained by means the following expression: AOD fit = AOD night,interp. -AOD night (4) These differences are displayed in Fig. 3a  with asterisks. We observe a clear dependence with the phase angle (g), increasing considerably for higher g values. This dependence is more evident for higher wavelengths and seems somewhat asymmetric (higher differences for positive g, namely, after full Moon, especially for the 1020 nm channel), which is in agreement with the results obtained in Sect. 3. 1. We have found the best fit for this dependency (δ g ) to be a secondwww.
atmos-meas-tech. net/10/3007/2017/ Atmos. Meas. Tech. , 10, 3007-3019, 2017 order polynomial (Eq. 3), as it is displayed in Fig. 3a  with solid lines. δ g (λ) = a 0 (λ) + a 1 (λ) • g + a 2 (λ) • g 2 (5) The coefficients a i in Eq. (  3 ) are calculated for each channel centered at λ. In addition, the presence of a nocturnal cycle on AOD fit is also evident from Fig. 3a. These results are also in agreement with those found by  Barreto et al. (2016) , who found higher AOD discrepancies with respect to daytime AERONET data for higher phase angles, and a faint nocturnal AOD cycle. The increasing uncertainty in AOD with g and the asymmetry of this dependence within the Moon cycle can be attributed, at least partially, to the existence of some residuals on the ROLO model, according to the results presented in Sect.
We used standardized residuals vs. fitted-value plots, residual distribution, variance overdispersion, and log-likelihood information to select the most appropriate transformation for each model. Finally, even if there was not always significant variations over time (i. e. , Figure  2b ), for the sake of clarity and consistency, we extracted predictions for each LME over time along with 95% confidence intervals, which we report here as our main results (Figure  2 ). We interpreted treatments with nonoverlapping confidence intervals as significantly different. As complementary information, between treatment differences are reported as mean ± standard deviation in text. The statistical model tables can be readily reproduced by using the provided data and R-script (https:// github. com/harveye/Directional_flow).
All analyses were conducted with R 3. 1. 2 (R Core Team 2016), using the "bemovi" package  (Pennekamp et al. , 2015)  for video analyses, the "nlme" package for statistical modelling  (Pinheiro et al. , 2016) , and the "car"  (Fox & Weisberg, 2011)  and "MASS"  (Venables & Ripley, 2002)  packages to identify proper variable transformations for statistical analyses. All data and the main R-script to reproduce the results can be downloaded at https://github. com/harveye/Directional_flow.
Images were captured while the plant was rotating at constant rate (20 rpm) using a brushless motor (Rexroth, Germany). Step 2: Image segmentation For side view images, pixels corresponding to the plant were segmented from those of the background by combing two threshold algorithms. This was performed by using a 'mean shift' method  [25, 26]  and a thresholding using Python  [27] /OpenCV libraries (Open Source Computer Vision Library, http://opencv. org). In a first step, a mean image was calculated for each plant and day by pooling the twelve side view images acquired each 30° (Fig. 1a, b ). The values of red, green and blue intensities in each pixel of each individual side image were then divided by those corresponding to pixels of the mean image (Fig. 1b ) and a threshold was applied on the result of this division to extract plant pixels.
3c ). The second step consisted in measuring stem width as a function of position along the stem. We first applied a distance transform algorithm from Python/OpenCV  [35]  on segmented images. We computed in this way, for each pixel of the image, the distance to the nearest boundary pixel (Fig. 3d ). This image was then superimposed onto the extracted stem skeleton (Fig. 3e ). The stem width (in pixels) was then determined along the stem skeleton as two times the corresponding distance transform pixel value (Fig. 3e ). The resulting 'width curve' presented alternations of low values corresponding to internodes and of peaks corresponding to leaf starting points (Fig. 3f  ). Knowing that the primary ear is located in the upper half of the stem  [32]  we have estimated a reference internode width in the lower half of the stem and use it to detect the position of thinner internodes in the upper half.
4d ). Step 6: Analysis of ear and silk images RGB images (2048 × 2448 pixels) of ears and silks (Fig. 4d ) were then analysed using two different methods. First, total pixels corresponding to the plant were extracted from background by applying a thresholding based on HSV colour space. In a second method, pixels corresponding to silks were extracted from the previous step using a random forest classification method based on colour (Gaussian smoothing of 5 px) and texture (structure tensor eigenvalues of 1. 6 px) (Ilastik software, version 1. 1. 7) [37]. We used for that a machine learning procedure based on a training set of contrasting ear images involving plants of different genotypes at different ear and silk developmental stages (Additional file 3). Finally, the time courses of pixels corresponding to silk bundles were individually fitted for each plant using the R scripts  [30]  'segmented' package  [38] , and the maximum rates of silk growth and duration of silk growth were extracted.
Availability of data and materials The source code and examples are available on Github (https://github. com/ openalea/eartrack) under an Open Source license (CeCILL-C). It has been integrated as a reusable package in the OpenAlea platform  [54, 55]. User and developer documentation is also available at http://eartrack. readthedocs. io. A subset of whole plant images is available at https://zenodo. org/ record/1002675 and a subset of ear images, Ilastik project and outputs are available at https://zenodo. org/record/1002173. It requires Python 2. 7 and OpenCV libraries.
The unparalleled photometric data obtained by NASA's Kepler Space Telescope has led to an improved understanding of stellar structure and evolution -in particular for solar-like oscillators in this context. Binary stars are fascinating objects. Because they were formed together, binary systems provide a set of two stars with very well constrained parameters. Those can be used to study properties and physical processes, such as the stellar rotation, dynamics and rotational mixing of elements and allows us to learn from the differences we find between the two components. In this work, we discussed a detailed study of the binary system KIC 9163796, discovered through Kepler photometry. The ground-based follow-up spectroscopy showed that this system is a double-lined spectroscopic binary, with a mass ratio close to unity.