dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments The authors would like to thank  Dr Mireille Drouet  and  Dr Helène Pauthier  and the  HLA laboratory of Limoges (University hospital)  for their technical assistance,  Zeinab Daher Abdi  and  Fathia Mouzarine  for collecting the data. The authors thank  David P. Figgitt PhD ,  Content Ed Net , for providing editorial assistance.
We have recently devised a technique for color editing and 3D volume rendering of OCT-A images. The images used abSTraCT Purpose: To describe an innovative imaging technique of color editing and 3D volume rendering of optical coherence tomography-angiography (OCT-A) images. Methods: Each vascular layer acquired by OCT-A (AngioVue, Optovue) is colored and edited with the GNU Image Manipulation Program. Then a 3D volume rendering of the assessed retinal area is possible, using Adobe After Effect software (Adobe Systems). results: The combined color editing and 3D volume rendering of optical coherence tomography angiography images allow the creation of a short movie displaying the 3D image from chosen angles. Conclusions: Image editing and 3D volume rendering is a rather simple technique, which, besides its aesthetic prerequisites, also facilitates simultaneous, distinct visualization of the segmented retinal layers.
TECHNOLOGY @ POiNT Of CarE to exemplify the technique in this article were acquired with AngioVue. The OCT-A software automatically generates four segmentations: (i) the superficial capillary plexus (SCP); (ii) the deep capillary plexus (DCP): (iii) the outer retina layer (ORL) and (iv) the choriocapillary layer (CL). The SCP is located between the internal limiting membrane and the posterior part of the inner plexiform layer, while the DCP is situated in the inner nuclear layer  (2). The ORL, normally avascular, is delimited by the outer boundary of the outer plexiform layer and the retinal pigment epithelium, and the CL is located from the Bruch's membrane to 10-20 μm above. OCT-A allows the user to save separate images corresponding to each of the four segmentations. Image editing is generated using these separate images, epitomizing the four automatic segmentations.
results Using the color selection mode we can select and extract various vascular networks at various depths (e. g. , SCP, DCP, type 1 or 2 neovascularization) as image layers and then colorize them (Fig. 1 ). Once the separate images corresponding to all available segmentations have been edited and colorized, a superposition is possible, generating an intuitive image of both the inner retinal microvascularization and the eventual presence of choroidal neovascularization (CNV) (Fig. 2 ). Furthermore, a 3D volume rendering of the assessed retinal area is possible using Adobe After Effect (AAE) software (Adobe Systems). AAE is an application that gener- This new type of imaging is depth-enhanced, allowing a detailed visualization of retinal and choroidal microvascularization at different depth levels.
Multiple slabs -generated either manually or automatically, using the software segmentation, or manual segmentation level -are available on all OCT-A machines and prototypes (AngioVue, Optovue; HRA 2, Heidelberg Engineering; DRI OCT Triton, Swept Source OCT, Topcon; OCT Spectral RS-3000 Advance, Nidek SA; Cirrus An-gioPlex OCT Angiography, Carl Zeiss Meditec, Inc. ). ates digital visual effects, motion graphics, and image compositing. The different colored image layers, extracted with GIMP, can be spatially displaced and/or rearranged. Using the "Camera Mode" of the AAE software, we are then able to create a short movie displaying the 3D image from the chosen angles (Fig. 3  and movie in Supplementary Video 1 -Neovascularization. Available online at ophthalmology. pointofcarejournals. com).
This paper presents mosquito mapper: an android phone application created with the goal of giving science-driven citizens the means to monitor mosquito populations in an urban environment. Mosquito mapper allows the recording of mosquito encounters as well as conditions surrounding the encounter. It also features a rudimentary identification tool. The goal of the application is to create a database and construct a map of the encounters free to consult for citizens and scientists. Such database constitutes a necessary first step for the development of useful management strategies addressing potential human health threats induced by mosquitoes. The citizen scientist may voluntarily provide other additional information on the circumstances of the encounter that may contain scientifically useful information.
2014). This endeavour, however, comes with limitations regarding the accuracy of the acquired data  (Dickinson et al. 2010; Conrad and Hilchey 2011). These inaccuracies originate from human use but also directly from the monitoring devices but can be tempered with statistical tools on large dataset. (Cohn 2008). Citizen scientists may provide information at a scale virtually impossible to obtain from regular collection and therefore help the scientific community tackle usually difficult questions  (Conrad and Hilchey 2011). Here, we suggest using citizen science to tackle the growing threats posed by mosquitoes. Mosquitoes typically constitute a nuisance in urban environments, a nuisance that may become a threat due to globalisation and climate change as diseases carried by mosquito may spread significantly faster in big centre of urbanisation  (Gubler and Clark 1995; Brown et al.
Beta release Mosquito Mapper is readily available on the android store (https://play. google. com/store/apps/details?id=com. sciencetogether. perecastor. mosquitomapper&hl=en). The project is currently in a beta version. The source code for the application is accessible on GitHub (https://github. com/ Layninou/MosquitoMapper). The upcoming companion website is accessible at this address (https://layninou. github. io/ScienceTogether/index. html#!/mosquitomapper) but we demand the readers' indulgence here as it is largely a work in progress. The intended use of the application, that we call an "experiment", follows this scheme: the user opens the application, chooses whether they want to locate or identify an encounter, then follows through the chosen activity. At the end of one activity, the user is asked whether they want to follow with the other option (locate a mosquito if the user just identified one and viceversa).
A guide presenting the application workflow is found in Fig. 1. At the end of the experiment all data associated with the unique experiment ID is confirmed in our database. Data are stored in a JSON objects (Java-Script Object Notation) divided in three branches for questionnaire and location, identification, and pictures. Mosquito Mapper opens with a page displaying three buttons: "locate", "identify" button, and "information" (Fig. 1 ). The information button lists the contributors to the project, acknowledges helpers and supplies contact information. The "identify" button brings a user through a short identification key. Finally, the "locate" button brings up the current user's location followed by a short questionnaire. All information provided by users populate the JSON file stored in a database.
2  The determination process consist of three consecutive questions. The first one ensures the encountered animal is indeed a mosquito. The second one helps determining the subfamily of the encounter while the third question aims at determining the sex of the encountered mosquito which could clutter our database. To reduce the size of images, these are modified locally using a BLOB (Binary Large Object) procedure. This procedure transforms the bitmap image taken with the camera into a bytes object. The BLOB is then sent directly to the server. Image quality is decreased to a quarter of the original picture. Mosquitoes can be hard to capture on photograph. Especially outside at night when mosquitoes are most active and the lack of external light will affect the quality of image. We recommend taking pictures when the user feel that he or she can take a clear focused picture.
First look at the data We have so far gathered very limited data, most by unknown users around the globe. Despite the app being developed for Berlin, circumstances have prevented us to make a satisfactory field test. The dynamic nature of phone application ensures that more useful data will be collected with time. You can find all the existing data on the website currently under construction at this address: https://layninou. github. io/ScienceTogether/index. html#!/ mosquitomapper. On this page, after a short presentation of the app we first show a world map of localisation. It is possible to zoom in and out of the map to focus on a particular place. Then we display the time at which mosquitoes are recorded in a histogram. More questionnaire replies follow and finally the results of identifications are given.
We will include regular notifications in the final release (see planned features) as a way to mitigate this issue. In its present state, the application relies on the willingness of participants to provide data as well as the introduction of new users. Inaccuracies also arise because of the inherent difference between the existing data and the true temporal and spatial distribution. This can be overcome through the use of dedicated statistical methods  (Fithian et al. 2015). The accuracy of our data will strongly depend on the frequency and the amount of recorded encounters. The use of our application will probably remain limited to the city of Berlin were we have the capacity to advertise our work on a regular basis to an interested audience. The use of Mosquito Mapper on larger scale, however useful it may be, would rely on time investment from other people.
Developing a smartphone application for big data comes with a lot of uncertainties. Before such endeavour is widely adopted, there is a great possibility that our work produces little useful studies. However, because such application can remain dormant at little to no cost, we believe it offers scientists a great opportunity to study mosquitoes in a way that can be both cheap and efficient. In fact, the strengths and limits described above all rely on the amount of users and their willingness to provide accurate and regular data. All limits eventually turn into strength once a critical amount of users is reached. As such, the value of an application such as Mosquito Mapper can only increase with time, as even low amount of data generated per unit of time will compound into appreciable quantities.
The data collected through the application serve several purposes. For the scientist, GPS data together with the number of recorded encounters plus their time frame can be used to generate dynamic maps of mosquito populations and to predict changes of mosquito populations' sizes for modelling purposes. The pictures constitute a repository that anyone could use to create species distribution maps or compare phenotypes within the same species. Each experiment can then be put in a temporal framework. Ways of using the data that we have not thought of or that do not exist yet may emerge as people use the application. Because of this possibility, we offer free access to all the data collected for anyone. For the citizen the two main positive outcomes of Mosquito Mapper are the entertaining part of being engaged in citizen science and the future outcome of the database.
For example, the predictive maps of mosquito presence could serve as an information tool at the same level as the weather forecast, so that people are informed where not to go if they want to minimise the probability of a mosquito encounter. Finally, the data could be used by municipalities to initiate public health actions in order to reduce mosquito presence. Few other Android applications have the same goal as Mosquito Mapper. We found three similar educational apps: Caza Mosquitos, ZanzaMapp and Mosquito Alert. Caza Mosquitos is currently only available is Spanish, ZanzaMapp in Italian while Mosquito Alert is available in Spanish and English. The applications are very similar in capabilities and goals. Mosquito Mapper is currently available in English, German, French, Spanish and Portuguese.
Developing the application to be more game-like implies various changes to the way the database is currently structured that would make the application more lightweight but would also consume more mobile data. A big issue of the program is that it consists of a presence-only dataset that is known to suffer from bias  (Blossey and Hunt-Joshi 2003; Fithian et al. 2015). Presence-absence datasets are more reliable. Therefore, we will incorporate notifications in the application to encourage users to report places and times when no encounter took place. The downside of such feature is that it may become annoying. The notification shall therefore only trigger rarely (once a week), with the possibility to turn the feature off. Finally, the notification feature may be used to spread timely information or help educate the citizens for example on the differences between mosquitoes and non-biting mites.
Ultimately, we wish to include more precise determination keys. The ability to use more complex keys may be limited to users reaching a certain "level", thus ensuring their willingness to participate fairly. The determination of mosquito species may be difficult and may vary wildly depending on geographical location. As a result, such feature may be limited to a handful of countries. If a research group or passionate citizens enjoy Mosquito Mapper and want to create an alternative version of this application for another pest or any other organism, the code available on Github contains all the necessary code to easily implement such ideas. These people are welcome to contact us if they need help in this endeavour.
The latter aspect allows addressing niche dynamics by highlighting the influence of atypical habitat conditions on species at a given time and/or space. Then, (3) the biological constraint exerted on the species subniche becomes observable within Euclidean space as the difference between the existing fundamental subniche and the realized subniche. We illustrate the decomposition of published OMI analyses, using spatial and temporal examples. The species assemblage's subniches are comparable to the same environmental gradient, producing a more accurate and precise description of the assemblage niche distribution under environmental change. The WitOMI calculations are available in the open-access R package "subniche.
Finally, the minimum convex polygon's contour of available SUs (black in Figs. 3  and 4 ) and of used SUs (blue and the purple dotted and dashed in Figs. 3  and 4 ) complete the realized niche and subniche breadth representation of species. The minimum convex polygons were drawn with the package "ade4" for R software  (Dray & Dufour, 2007). The species niche and subniche positions and their respective minimum convex polygons, relative to the origins, give us an idea about the habitat conditions used by species within the constraining habitat highlighted by the OMI analysis.
Temporal subniche dynamics The first data set investigated the hydraulic requirement of 57 invertebrate taxa (Merigoux & Doledec, 2004). Herein, instead of performing an OMI analysis for each season (i. e. , spring and autumn as done by authors) we performed one for the entire year. A total of 35 out of 57 taxa had significant OMI (Appendix S2; Table  S1 ). We selected the first two OMI axes, which represented 89% of the explained variability (Fig. 3A ), in order to represent the subniches. As depicted by Merigoux & Doledec (  2004 ), the first axis shows that FST hemisphere number and Froude number are the most influential hydraulic parameters on the species' realized niche (Figs. 3B  and 4C ). WitOMI were then calculated for spring and autumn for each of the 35 significant species. All WitOMI (WitOMIG and WitOMIG K ) were significant (Appendix S2; Table  S1 ).
Data Availability The following information was supplied regarding data availability: The data used are RDA files and are named "ardecheinv" and "drome. " The data, along with the WitOMI calculations as well as other computational tools, are available in the "subniche" package and can be downloaded for free on the CRAN repository http://cran. r-project. org or on Github (https://github. com/KarasiewiczStephane/WitOMI).
This work addresses the problem of grouping by genes long reads expressed in a whole transcriptome sequencing data set. Long read sequencing produces several thousands basepair long sequences, although showing high error rate in comparison to short reads. Long reads can cover full-length RNA transcripts and thus are of high interest to complete references. However, the literature is lacking tools to cluster such data de novo, in particular for Oxford Nanopore Technologies reads. As a consequence, we propose a novel algorithm based on community detection and its implementation. Since solution is meant to be reference-free (de novo), it is especially well-tailored for non model species. We demonstrate it performs well on a real mouse data set. When a reference is available, we show that it stands as an alternative to mapping.
This enables to retrieve the transcripts expressed by a gene, grouped in a cluster. Such clustering can be a component of a wider pipeline that aims at describing alternative variants or gene expression patterns. Such needs were already of concern in the past Figure 1: Clustering on several example cases. In eukaryotes, through transcription then splicing, exons (colored blocks) of genes are combined while introns are spliced to form RNA transcripts. Alternative events can produce variants with certain combination of exons, or part of exons. For basal gene expression as well as alternative events, all transcripts from a gene are expected to be found together in a cluster. In the complex case of families of genes, several copies of a paralog gene can express transcripts at the same time. If these transcripts share exon content and if the genes sequences have not diverged too much reads are in the same cluster.
The issue is to automatically group alternative transcripts in a same cluster (Figure  1 ); 2/ long reads currently suffer from difficult indel errors at high rate  [24, 22] ; 3/ all genes are not expressed at the same level in the cell  [20, 36, 37] , which leads to an heterogeneous coverage in reads of the different genes, then to communities of different sizes including small ones, which is a hurdle for community detection  [13]. Our proposal comes in two steps: first a graph of similarities between reads (see section 2. 1) is computed with a third-party tool and then our clustering scheme proposal is applied to the graph. We perform clustering on the graph to retrieve each community, i. e. gene's expressed transcripts (detailed in section 2. 2). In addition, we propose an implementation of the clustering algorithm in a tool dubbed CARNAC-LR (Clustering coefficient-based Acquisition of RNA Communities in Long Reads) inserted into a pipeline.
Results All experiments were run on a Linux distribution with 12 Intel Xeon 2. 50GHz processors and with 200 GB RAM available. First we compare our approach to well established community detection methods and demonstrate its relevance on long read application. Then we validate our method's results by comparing them with independent clusters obtained by mapping on a real size data set. In these two parts, reads from the brain mouse transcriptome were used in order to access a ground truth via a reference. Then we show that our approach can offer an alternative to the classical mapping approach even when a reference is available.
We highlight that the mapping procedure used for producing reference clusters for validation has its own limitations, thus the ground truth we refer to for the sake of clarity is in fact only partial. The growth of accession records in databases recently burst for transcripts obtained with short reads  [34]  but a laborious curation is needed to filter out false positive reconstructed variants that do not have enough support. This illustrates the need for new methods to access novel transcripts with full-length reads. Long reads enable to skip the transcript reconstruction step that is necessary with short reads, though difficult in particular when it involves assembly. Therefore, long reads constitute an interesting novel way to obtain reference transcripts. However, only a fraction of long reads are processed by mappers and analysis is made difficult because of the error rates.
In this context, our approach is shown to be an alternative approach to mapping for the identification of genes' transcripts. Non model species require such de novo approaches, thus two bioinformatics tools dedicated to them have emerged so far  [17, 26]. Both comprise a pipeline conceived to process Pacific Biosciences Isoseq  [16]  reads only and require high quality long reads. Thus they could not be used on the data presented here. On the other hand CARNAC-LR is a generic approach that is designed to be used regardless of TGS error profile and protocol. As a consequence it is the first method to perform de novo clustering on RNA reads from Oxford Nanopore. It takes reads early after their generation, without correction or filter. From the clusters, the expressed variants of each gene are obtained and related transcripts are identified, even when no reference is available.
We have also shown that our pipeline could be a complementary procedure when reads can be mapped to a reference. Thus it tends to retrieve some clusters missed by mapping and allows a efficient use of data. We have demonstrated a straightforward use case of our pipeline as a good proxy to access the expression levels by gene. ONT sequences have been shown to qualify for transcript quantification in  [33]. Moreover CARNAC-LR provides structured information that can be a sound input to other applications. For instance, a read correction step can be performed on each cluster instead of processing the whole data, in order to obtain high quality reference transcripts. We argue that particular instances such as paralog genes constitute research themes on their own and require specific developments to untangle each paralog contribution to the observed variants expression.
Reference material for validation 5. 2. 1 RNA MinION sequencing cDNA were prepared from 4 aliquots (250ng each) of mouse commercial total RNA (brain, Clontech, Cat# 636601 and 636603), according to the Oxford Nanopore Technologies (UK) protocol "1D cDNA by ligation (SQK-LSK108)". The data generated by MinION software (MinKNOWN, Metrichor) were stored and organized using a Hierarchical Data Format. FASTA reads were extracted from MinION HDF files using poretools  [27].
Acknowledgments The study has been partially supported by  ANR ASTER , contract  ANR-16-CE23-0001 , which provided the data. Authors would like to thank all members of ASTER as well as GenScale team members for the support and fruitful discussions, and  Gaëtan Benoit  and  Antoine Limasset  for their precious help on the implementation. Computations have been made possible thanks to the resources of the GenOuest infrastructures.
Liarokapis et al. (  2017 ) used Agisoft's PhotoScan®, a state-of-the-art commercially available software package, for 3D modelling and mapping of underwater archaeology sites. Agisoft's PhotoScan® handles all stages of the 3D reconstruction pipeline, including the dense multi-view stereo matching stage. It generally produces good results, especially when there are a lot of overlapping input images which provides a high-degree of redundancy. However, since the software is geared towards general purpose applications, it cannot be expected to perform optimally when applied to underwater images, especially compared with techniques specifically designed for the task. Moreover, the commercial nature of the software means that users only have limited control over certain parameters and workflows.