dataesr/scientific-paragraphs-categorization huggingface dataset:
25 mg kg -1 intraperitoneally on alternative days, DM-NG-aPC-Aza) treatment for further 6 weeks. Mice were killed and plaque morphology was analysed as previously described  5. A group of HFD mice was randomly assigned to (a) persistent hyperlipidaemia (HFD-adeno LacZ) or (b) normalization of blood lipids for 6 weeks after 16 weeks of hyperlipidaemia by re-expressing human ApoE in the liver (HFD-adeno hApoE)  54. Mice were randomly allocated to different groups. All animal experiments were performed using 6-12 mice per group and data from all mice was included in the study to ensure biological reproducibility of in vivo data. Atherosclerotic plaque morphology was analysed using the Image Pro Plus software from Media Cybernetics as previously described  5. Briefly, plaque characteristics were determined as described in the following section (please see also Supplementary Fig.
Lipid rich areas of the aortae were analysed by a blinded investigator using the Image Pro Plus software as described previously  5, 56. MOVAT's stain was performed on frozen sections of brachiocephalic arteries. Frozen sections (4 µm) were fixed in Bouin's solution at 50 °C for 10 min and stained with 5% sodium thiosulfate for 5 min, 1% alcian blue for 15 min, alkaline alcohol for 10 min, Movat's Weigert's solution for 20 min, crocein scarlet acid/fuchsin solution for 1 min, 5 % phosphotungstic acid for 5 min and 1% acetic acid for 5 min. Between every staining step the tissue sections were washed with tap water and distilled water. Afterwards they were dehydrated in 95% and 100% ethanol for 1 min and stained in alcohol saffron for 8 min. Brachiocephalic arteries were washed in 100% ethanol for 1 min, moved to Xylol for 10 min and covered with cytoseal mounting medium.
After washing nuclear counterstaining was conducted using mounting medium with DAPI. Images were visualized, captured, and analysed using a fluorescence microscope. All histological analyses were performed by two independent blinded investigators. Immunohistochemistry and immunofluorescence images were captured with an Olympus Bx43-Microscope (Olympus, Hamburg, Germany). The Image J software was used for image analysis. To determine co-localization of fluorescently detected proteins we determined a correlation index (I corr ). I corr reflects the automatically computed fraction (plug in available: https://sites. google. com/site/ colocalizationcolormap/home) of positively correlated pixels in an image and is used to quantify colocalized fluorescent signals  58. Isolation and culture of bone marrow-derived macrophages.
The study complied with the Declaration of Helsinki and all patients entered the study according to the guidelines of the local ethics committees after giving informed consent (Ethic-Committee-No: 92/09). Statistical analysis. The data are summarized as mean ± standard error of the mean (SEM). Statistical analyses were performed with Student t-test (two-sided), Mann-Whitney U test, or analysis of variance (ANOVA), as appropriate. Post hoc comparisons of ANOVA were corrected with the method of Bonferroni, as indicated in figure legends. The Kolmogorov-Smirnov test or D'Agostino-Pearson normality test was used to determine whether the data is consistent with Gaussian distribution. StatistiXL (www. statistixl. com) and Prism 5 (www. graphpad. com) software were used for statistical analyses. Values of P < 0.
Acknowledgements We thank  Kathrin Deneser ,  Julia Judin ,  Juliane Friedrich ,  René Rudat , and  Rumiya Makarova  for excellent technical support. This work was supported by  grants of the 'Deutsche Forschungsgemeinschaft'  ( IS-67/5-3 ,  IS-67/8-1, IS-67/11-1 ,  CRC854/B26 ,  CRC1118/B07N ,  RTG2408/P7&P9  to B. I. ,  RTG2408/P5 ,  SH 849/1-2  to K. S. , KO  5736/1-1  to S. K. , and  SFB 1118  to PPN), of the ' Stiftung Pathobiochemie und Molekulare Diagnostik ' (SPMD to K. S. ), the  National Institute of Health  (TACTIC;  UM1-HL120877  to C. T. E. ), and by a  DAAD  scholarships to M. M. A.
Introduction GIS software and the field of data visualization have known a significant growth in recent years, especially with the emergence of phenomena such as the democratization of personal computers, their increased and miniaturized computing power, and more recently with the web 2. 0 phenomena  (Bakis & Valentin, 2010; Mericskay, 2011). It has also benefited from the emerging of websites such as Google maps or GeoPortail, that simplified the map creation process for ordinary users. The softwares of GIS and data visualization domains are part of the mapping, though not focused on representation. In addition, solutions focusing more clearly on the creation of maps exist but are aging and / or proprietary softwares  (Cabal 1500, WinCarto, Phil-Carto, Cartes et données, etc. ). With Magrit, we propose to take advantage of the new data visualization technologies in order to make available a thematic mapping tool that strictly respects methods and rules of graphic semiology through the enhancement of the processes used to represent the information.
Findings on the cartographer mapping toolbox The software toolbox available to an individual, researcher, student or practitioner who wishes to produce maps may include various tools targeted at specific practices. However, it is possible to note several trends in mapping practices, including the mapping of thematic maps in GIS software and the ability to easily access various cartographic representations on the web. In order to differentiate geomatics and cartography learnings, thematic and statistical cartography are currently taught in several universities with PhilCarto (e. g. Paris Diderot University). It is a proprietary software (meaning that its sources are not accessible), available only as an installer for Microsoft Windows and whose development seems to have been recently stopped (there would be no more upgrades or new versions).
Choosing the web and the Opensource Developing an opensource software was an obvious choice since the beginning of the project and made sense considering tools previously developed by the members of the team. That is why the code is placed under a free license, CeCILL version 2. 1, whose terms are compatible with the GPL (General Public License). This choice has not been pressured by the tools used (even though many software libraries used are placed under free licenses called "permissive", meaning that they can be included to proprietary products or commercial), but symbolizes the expression of a conviction. Indeed, the free model allows the transparency of the code and promotes the creation of a community, as it is possible to take part in the project and to report bugs brought through the Magrit repository page on the platform Github.
Moreover, this choice allows the use of modern libraries in terms of data-visualization such as d3. js  (Bostock, 2016)  on the client side and, on the server side, libraries benefiting from a rapid development because of their popularity or solidly installed, such as GDAL and proj. 4 regarding geographic transformations. Besides, the application was written to be easily translated and is already available in two languages, English and French. The user documentation is also written in French and English. In addition to the GitHub page, an online research blog has been opened. It is the channel that will be used the most to disseminate tutorials, information concerning the release of new versions and to support the project. Relying upon the network can be a constraint in places where the Internet connection is poor or non-existent and where access to a free thematic mapping tool could be useful (especially in Africa).
By the same token, the project was the opportunity to publish, under permissive licenses, two packages on PyPI (Python Package Index, the reference repository of Python additional packages), one allowing to realize smoothed maps (smoomapy) and the second implementing the Jenks' "natural breaks" algorithm (jenkspy) and a javascript plugin (loc-i18next) to the translation framework used, i18next, was also released on NPM (Node Package Manager, the reference repository of packages for nodes. js).
5 ). Inspired by both GIS software (QGis, ArcGis) and vector creation software (Inkscape, Adobe Illustrator), the application hierarchizes the information into "layers". The latter thus represent a coherent set of entities that can be represented together, in a manner analogous to a layer in a GIS software or a layer in a vector drawing software. Fig. 5. Menu for managing layer order and parameters. • Finally, several layout elements are provided to the user (see description in section 3. 2). The map size can hence be defined (height and width in screen pixels) as well as the background color. Various dressing elements (title, scale, legend) can be added and parameterized (Fig. 6 , see description in section 2. 2). The basemap can also be deeply modified by the choice and the customization of a projection (deforming, move projection center, rotating, etc.
In this paper we study the performance of several state-of-the-art sequence-tosequence models applied to generation of short company descriptions. The models are evaluated on a newly created and publicly available company dataset that has been collected from Wikipedia. The dataset consists of around 51K company descriptions that can be used for both concept-to-text and text-to-text generation tasks. Automatic metrics and human evaluation scores computed on the generated company descriptions show promising results despite the difficulty of the task as the dataset (like most available datasets) has not been originally designed for machine learning. In addition, we perform correlation analysis between automatic metrics and human evaluations and show that certain automatic metrics are more correlated to human judgments.
In the literature, there has been few work related to our objective. For instance, in  (Lebret et al. , 2016) , authors used a neural model to generate short biographical summaries from structured data (concepts) using a dataset collected from Wikipedia. Similarly, in  (Chisholm et al. , 2017)  a sequenceto-sequence autoencoder with attention was used to generate biographies from Wikipedia. However, both of these work concentrate on generating mainly one sentence summaries, while our aim is to be able to generate longer summaries. In addition, since their generated summaries are extremely short, the information in the summaries is almost always is guaranteed to be present in the concepts, while in our dataset this is not the case. Finally, most Wikipedia biographies have very similar and repetitive writing styles which makes it easier for models to learn them, but in the case of company descriptions, the task is more challenging since most summaries are written in a different style.
The contribution of the paper can be summarized as follow: • a collection of a realistic dataset for conceptto-text and text-to-text task that is made available to the community; • the implementation and evaluation of several E2E models for the two tasks; • an evaluation by naive human subjects and a comparison with the automatic metrics. The paper starts by describing the dataset collection in Section 2 and then the seq2seq methods in Section 3. Corpus-based experiments and human evaluation are described in Section 4 and 5 respectively. The paper ends with a short discussion of the findings and an outlook for further work.
Data source Information about companies are typically be found in national company registers. However, they are not always accessible and are difficult to crawl. dbpedia. org also contains a semantically rich set of information about companies. However, the amount of companies is too small for a machine learning approach. A place were a large number of company descriptions can be found is Wikipedia. Wikipedia is a rich source of different types of data tackling a variety of topics, and the way articles are written in this source can be used for the task we address in this paper. As shown in Figure  1 , an article contains an abstract followed by a table of content and then a body text. The abstract is a brief summary of the entire article containing the important ideas in the body text. Moreover, the top right side is the infobox (to be taken as MR), a panel containing semi-structured data about the company described in the article.
Data collection method The method to collect data was first to build a list of company name/id. This was performed from a dump of English Wikipedia (enwiki-20170820-pages-articles. xml. bz2) from which articles containing the terms "company" or "companies" in the "Category" section were retained. However a large number of articles were actually not company descriptions. Hence, we made use of the infobox attributes such as Founded, Founder, Products, Industry, Headquarters, etc. which, in accordance to Wikipedia guidelines, should be found in company descriptions in Wikipedia. Then, articles which did not contain at least two company attributes in the infobox were dismissed from the list. At the end, we ended up with 64553 company links. The articles were then retrieved using the Wikipedia API.
For the infobox part, each attribute-value pair was represented as a sequence of string attribute  [value]. Each attribute value which could contain a list was divided into at most 5 attributes  (e. g. , attribute1 [value1] , attribute2 [value2]. attribute5 [value5]) using simple regex expression. Hence a string like "Founder=[David Hyams and Lloyd Spencer]" was converted into "founder1[David Hyams], founder2[Lloyd Spencer]". At the end, the infobox is composed of 41 attributes with 4. 5 attributes per article in average. The abstracts of the final dataset of 51k companies presents a vocabulary of size 158464 words.
Dataset characteristics At the end of the process, although the dataset is faithful to the information found in Wikipedia, the dataset is not ideal for machine learning since the abstract, the body and the infobox are only loosely correlated. For instance, Figure  2  shows an abstract which is not based on information provided in the body text. Moreover, Figure  3  shows Figure  2 : Body text information is not correlated with the summary that most of the abstract length is between 1 to 5 sentences while the body text size is much more spread with a peak at 1 sentence. The Pearson's correlation between abstract and body length (sentences) is very low r = 0. 275 even when the body data of size 1 is removed (r = 0. 327). In short, the problem found in the dataset can be summarized as follow.
Given a article a =< s, b, i > where s is the abstract b the body text, and i the infobox, the following problems exist: • s is not guaranteed to be built from b. • s and i does not always contains the same information. • s, i and b vary greatly in terms of size and none of the size is correlated. Often, one or two of the sections are empty. • there is only one version of s. • there is no information about what was the objective of the writer(s) when producing s. However, despites these problems, we believe this dataset represents a valuable resource since it represents the kind of data that can be found in real situations and that End2End systems must deal with in order to make a significant impact in society. The dataset is available for download 1.
This can appear when the word to predict is infrequent in the training set and therefore has a poor word embedding making it close to a lot of other words. One way to deal with this problem would be to increase the training dataset but this is not always possible. Furthermore, any of such kind of systems will be likely to meet new unseen words such as company name and founder. Hence, a method to copy and past input word to the output has been developed in the translation domain and applied in the summarization community. The approach we have adopted is based on the Pointer-Generator Network  (See et al. , 2017)  which computes a generation probability p gen ∈ [0, 1]. This value evaluates the probability of 'generating' a word based on the vocabulary known by the model, versus copying a word from the source.
concept-to-text approaches The task of generating company description from a set of attribute-value pairs can be exemplified as going from the sequence However, the Wikipedia company dataset that was collected contains a rather low rate of attribute-value pairs per article. Indeed, as mentioned in section 2. 3 the target descriptions were often written using pieces of information that were not present in the infobox section. For this reason, an attribute-value augmentation step was considered using Natural Language Understanding (NLU) technique. Thus, the concept-to-text was done in two processing steps: NLU : extraction of attribute-value pair from textual company descriptions NLG : generation of company descriptions from a set of attribute-value pairs The strategy employed to deal with the NLU part was to first create a reversed dataset where the company abstract is considered as the source sequence and the infobox attribute-value pairs as the target sequence, using a 5-fold cross-validation.
More specifically, at each of the 5 turns, a 4/5 of the data was used to train a seq2seq character NLU model to infer the missing attributes in the unseen 1/5 of the data. During training, since ground truth was only partially available, inferred attribute-value pair was classified correct if: • it corresponds to an attribute-value pair in the infobox ; • it corresponds to an attribute-value pair whose similarity in the input text is high. The similarity was computed using "difflib" library 2  of Python, which is an extension of the Ratcliff and Obershelp algorithm  (Ratcliff and Metzener, 1988). Using this method the dataset went from 304475 total attributes to 328682 in the augmented dataset. This is this augmented dataset that was used in all the experiments. For the NLG method, a basic seq2seq model with attention was used without any preprocessing.
Dataset formating The original dataset has been through a limited amount of preprocessing for machine learning. For the C2T approaches, the dataset presented in Section 2 is filtered to contain only companies having abstracts of at least 7 words and at most 105 words. As a result of this process, 43681 companies are retained. Finally the dataset is partitioned to learning (35384), dev(3929) and test(  4368 ) sets. For the T2T approaches, the dataset is filtered at first to keep only the companies having abstracts with less than 105 tokens and bodies greater than 100 tokens while having the size of the abstract smaller than the size of the body text. As a result, 28034 are kept. The dataset is then splitted into three sets: training (21309), dev (2357) and test  (4368). In all the experiment the test set of 4368 companies is the same.
In order to verify this, we conducted few tests using our C2T char model on the E2E challenge data without any parameter tunning. The results showed that when only a single reference is counted, our model was able to achieve a score of 0. 29 and 0. 47 for BLEU and ROUGE-L respectively. However, when multiple references were included, the scores increased to 0. 51 and 0. 61 for BLEU and ROUGE-L. This clearly shows that the E2E challenge dataset is closer to the ideal case for machine learning than our case. Thus our results should not be directly compared with the E2E challenge. However, we also computed the F1 of ROUGE 1, 2 and L score using the pyrouge package 5  and compared to recent summarization methods 6. In that case C2T+pg exhibits the best results for ROUGE-1 (. 3346) ROUGE-2 (. 1701) and ROUGE-L (.
3132) on the test set. These results are comparable to the abstractive method of (Nallapati et al. , 2016) (ROUGE-1=. 3546, ROUGE-2=. 1330, ROUGE-L=. 3265) and the pointer generation approach of  (See et al. , 2017)  (ROUGE-1=. 3644, ROUGE-2=. 1728, ROUGE-L=. 3342). However, they were both tested on the CNN/Daily Mail test set, for which no problem of content mismatch between documents and summaries were reported while it is a difficulty of our dataset. Furthermore, the difference with the E2E challenge is important since our dataset contains a large vocabulary, a large number of named entities and only one -not always reliable-reference summary. The few number of reference summaries give fewer opportunity for the models output to match ngrams in the references than when multiple references are available.
It is a known problem of the Wikipedia dataset and the systems were not able to do better than the reference. Nonredundancy metric gives a more contrasted view of the systems. C2T+pg was judged to be the least repetitive after the reference, while C2T char to be the most repetitive. Regarding semantic correctness, C2T+pg is clearly above the others again including the reference. Same observation can be made for grammatical correctness. These results of human evaluation were compared to those of the automatic metrics (excluding the reference one). The correlation matrix is given in Figure  4. It can be seen that among automatic metrics, METEOR, ROUGE-L and CIDEr are highly correlated. When it comes to human vs automatic metrics, it is obvious that CIDEr has a highest correlation with semantic and grammar.
This obviously poses a limitation on the models to learn to generate all the information given in the infobox. Then when it comes to our models, it can be seen that the C2T char manages to generate all the infobox information but it has repetition problem. The T2T+pg, on the other hand, is not so behind when it comes to information coverage, however this models suffers from the problem of hallucination as it can be seen in its last sentence. Finally the C2T+pg manages to generate a correct but too short sentence which is lacking some information of the infobox. Some weakness of the current C2T approaches may be due to the NLU model. A possible future work might be to deal with the weakness of the database and to perform more joined learning of NLU/NLG and to evaluate models on the real company database provided by the company that we are working with on a research project.
To specifically focus on U7-E53 off-targets, we performed in silico filtrations of all transcripts containing a predictive degenerated binding site for the antisense sequence harbored in the U7-E53, using the alignment tools miRanda and Blast for splicing analysis and miRanda alone for gene analysis. RNA-seq results and in silico predictions were subsequently compared with the final set of off-targets candidates, most likely specific for the U7-E53 (Figure  2 ). Moreover, experimental validations were performed through qRT-PCR or nested RT-PCR on the off-target candidates identified through genome-wide analysis. For these final validations, some supplemental controls were included to confirm the U7-E53-related specificity of the off-target events. These controls were generated through the transduction of human primary myotubes and hepatocytes with the rAAV3b-U7-Scramble (Scr) vector, which carries a scrambled sequence instead of the U7-E53 antisense sequence.
Differentially expressed genes were identified using DESeq2. Junctional read dataset was analyzed with DEX-seq to quantify the differentially used junctions. Alignment tools miRanda and Blast enable in silico predictions of U7-E53 binding sites. Off-target gene candidates with a splicing modification or a differential expression were defined after comparing the in silico results and DEX-seq or DEseq2 results, respectively. Experimental validations with samples transduced using a rAAV3b-U7-E53 or a rAAV3b-U7-Scramble vector enabled the final identification of U7-E53-specific offtarget events. ure 3B). Albeit not strictly quantitative, the skipping of exon 53 was weaker in hepatocytes than in myotubes samples, consistent with the weaker U7-E53 RNA levels in these cells. Of note, we also assessed the ratios of DMD transcripts versus U7-E53 RNA expressed in both cell types by evaluating their relative RPKM (reads per kilobase per million mapped reads) values in each sample (Table  S2 ).