dataesr/scientific-paragraphs-categorization huggingface dataset:
However, these have remained largely unexplored. In the present study, we expand on the existing work by providing a detailed characterization of the behavioural output of the C. intestinalis larvae to complement the growing insights into the structure and function of this prototype chordate nervous system. To achieve this, we collected a large dataset of recordings of free-swimming C. intestinalis larvae on a custom IR-illuminated and temperature-controlled set-up (Fig. 1b ). We analysed the recordings using the automated tracking software ToxTrac  27  and custom written Python scripts to obtain positional data and descriptors like speed and its variability, turning angles, and path complexity (Fig. 1c, e ). To describe behaviour both accurately and objectively, we used unsupervised clustering methods to identify distinct behavioural clusters in our dataset, ultimately arriving at a quantifiable distribution of detectable behavioural components.
In green are the 25 frames before the current time-point and in red the 25 frames later. used to record at different ages. For modafinil experiments, we first transferred the animals to a dish containing DMSO or modafinil and then immediately transferred them to the arena that also contained DMSO or modafinil (2 or 20 mg/l). We used data from animals with age range between 2 and 8 hours post hatching to obtain the plots presented in Figs 2-4 and 7. For Fig. 6 , we used animals with age range between 2 and 8 hours post hatching for the 14 °C group while we used 0 to 3 hours post-hatching animals for the 18 °C group. For Fig. 5 , we collected data from animals aged 0 to 8 hours post hatching. We list the n-values per age in Table  S5. We would like to note that Figs 3, 6 and 7 share the same control data.
Video conversion and analysis. Following acquisition the videos were analysed using the program ToxTrac  27  which tracked the position of the animal as the centre of its detected shape. Prior to analysing, all frames for each video were enhanced with Contrast Limited Adaptive Histogram Equalization (CLAHE) with a clip limit of 1 and a tile grid size of 50 × 50 pixels. After histogram equalization, noise was reduced with a median blur with a tile grid size of 5 × 5 pixels. To input bright-background videos into the ToxTrac software, we inverted all frames by subtracting from a true white frame of equal size. Within the ToxTrac software, the ID algorithm used in our study was 2TCM sel. by Hist (MEE). We imaged approximately 710 animals and obtained around 1000 traces for further analysis and quantification.
The amount of thigmotaxis is quantified in two measures: "Total Time spent in Outer zone" (TTO) and "Total Distance travelled in Outer zone" (TDO), as utilised for zebrafish previously  31. As an alternative measure for thigmotaxis we have used Median distance from the arena centre. Supplemental Fig. 9  displays median distance measurements for the data contained in Figs 2-4, 6 and 7. All data analysis was performed with Python 3. 5 using the numpy, pandas, scipy, scikit-learn and matplotlib libraries. We would like to note that for speeds under 500 µm/s, 180° points shown on the polar plots may be a result of a π/2 imprecision on the larval orientation. We excluded animals that were immobile and hence indistinguishable from dead from further analysis by excluding all traces where the maximal displacement from the starting position was less than one body-length (comprising approximately 10% of all examined traces).
To this end, we performed unsupervised clustering of a minimal feature-set that describes the behaviour of the larvae. We created the feature set as follows: For each used recording, the velocity vectors (ρ, θ) were calculated from coordinates 5 frames apart. Values ρ, Δρ and Δθ were used as measures for speed, acceleration and turns respectively. For each point the mean of a sliding window of [-25: +25] frames was used to include information of past and future movement. This resulted in a dataset of three features and ca. 1. 8 million observations. We next used an agglomerative clustering algorithm with ward-linkage to cluster the data set. While data distribution was not very discretised, we identified the point, at which further increasing the number of clusters would no longer reduce the total distance of all points to their respective cluster centre notably, which was at twelve clusters (Supplemental Fig.
7 ). Using the clustered dataset, we trained a K-Nearest-Neighbours classifier and classified all collected traces. We found that apart from cluster 0 (accounting only for ca. 0. 05% of the data) which turned out to result from instances where there were insufficient datapoints in the window for averaging, we could assign a relevant descriptor to each cluster according to its biological interpretation. We did this by inspecting the original videos and superimposing corresponding cluster data. When unable to resolve a biological difference between two clusters, we assigned them the same descriptive name. We list the behavioural modes in approximate order of speed of movement: 01 and 02 -Inactive 1 and 2, 03 -Small twitches, 04 -Large twitches, 05-Collision or Deceleration event, 06 and 07 -Mode change 1 and 2, 08 -Slow active swimming, 09 -Medium active swimming, 10 and 11 -Fast swimming 1 and 2.
(see Figure. 1g for some example traces of different modes, Supplementary Video 1-5, for an illustration of the speed and turn values present in the different clusters see data in Supplemental Fig. 2 ). statistics. Whenever statistical significance was tested, we used nonparametric tests, since the datasets compared contained non-normally distributed data (See Supplemental Table  2  for results of Shapiro-Wilk normality tests), namely Levene's test for equality of variances, Kruskal-Wallis analysis of variance and Mann-Whitney U test. All p values stated are using Mann-Whitney U if not otherwise stated. To compare the similarity of distributions in the scatterplots of speed vs turn values presented in this paper we compared the sorted Mahalanobis distances of these distributions (presented in Supplemental Fig.
This may result in avoiding collisions and possibly in coordinated behaviour. Our data demonstrates that the presence of two or three animals in the arena can alter an individual's behavioural metrics, including an enhancement of thigmotaxis in the presence of multiple individuals. In light of previous work performed in C. intestinalis, it will be interesting to determine if and how C. intestinalis larvae achieve coordinated movement, especially in the presence of sensory stimuli. The combination of our hardware with the ToxTrac software  27  , which allows for solving occlusions events when tracking multiple animals  70  , is a suitable platform for future experiments. Our current automated image based tracking approach is relying on marking each animal with a centroid rather than segmenting out the entire shape of the animal in order to generate an outline or a skeleton.
We therefore are lacking postural information that would enrich our dataset significantly. This presents an important next step towards obtaining a complete ethogram of C. intestinalis larval behaviour. Furthermore, we are testing our animals in an open field arena that is suitable for recording a relatively small number of animals, possibly in a setting that is relatively distant to the natural ecological niche of the larvae. Numerous experimenters who are trying to obtain high quality tracking data in a controlled environment face this problem  3. We envision that in the future the use of larger arenas and the ability to deliver multiple sensory stimuli, reaping the benefits of the open architecture of the behavioural setup, will allow us to study other ecologically relevant behaviours such as settlement behaviour and metamorphosis more closely.
Acknowledgements We would like to thank  Felicia Keulder-Stenevik  for animal collection,  Patrick Steinmetz  and  Marion Lebouvier  for reagents and experimental advice related to neutral lipid analysis. We would like to thank  Mie Wong  and  Kushal Kolar  for comments on the manuscript. M. C. would like to thank  Lionel Christiaen ,  Alberto Stolfi  and  Florian Razy-Krajka  for introducing him to Ciona intestinalis experimental methods.
Data Availability We have deposited the designs for the behavioural setup components that were 3D printed here: https://github. com/ChatzigeorgiouGroup/Rudolf-Dondorp-2018/tree/master/3d%20Cad%20files. The dataset used for this study is available here: https://doi. org/10. 5281/zenodo. 1298978. The analysis code is located in our GitHub repository: https://github. com/ChatzigeorgiouGroup/Rudolf-Dondorp-2018/tree/master/Code.
Search of information sources A search for eligible documents was conducted using the eight following bibliographic databases: Medline, Global Index Medicus, Popline, Cochrane library, Excerpta Medica database (EMBASE), Iris, The European Library, and Africabib. The search strategy was designed to identify documents including: both concepts of community participation and public health surveillance, or terms denoting CBS. Tailored search requests were used to select documents from each bibliographic database. As an example, the search request used for Medline using Pubmed on the  28   Additional searches were also conducted using the Google search engine on the worldwide web, where the 50 first results of each of four search requests were screened for suitability. Detailed search requests and search results from each database and the world-wide web are presented in the S1 Table.
Data charting process and data items We developed a data collection form using the LimeSurvey software  [19]  to systematically list terms and definitions used to characterize CBS, identify and summarize guidance documents and recommendations available for its implementation and operation, and map the details of any past and existing examples of in-country CBS implementations. The variables collected are listed in Fig 1. The same two reviewers independently filled the data collection form for each included document. Any discrepancy in the collected information was resolved by discussion and consensus.
Descriptions of past and existing CBS systems One hundred fourteen documents (85%) mentioned a past or existing in country-CBS system. From these, 79 unique CBS systems were identified. The data collected for each unique CBS system is displayed in the S2 Table. Missing information on CBS systems. For each type of variable, the percentage of missing data was (n = 79 unique CBS systems): country 0%; start year 20%; end/ongoing year 22%; coverage 9%; setting 25%; purpose 28%; scope 0%; data collection actor 0%; data collection method 37%; data reporting method 53%, and frequency 24%; report recipient type 28%, and level 32%; performance indicators 75%. Country of implementation. CBS systems were identified in 42 countries (see  Fig 3). Operation period. Ninety-two percent of identified CBS systems were established after 1980 (n = 58/63), with an upsurge notable in the period from 2001 to 2010 (45%, n = 28/63).
Limitations The main limitation of included documents was the inconsistent manner in which information on CBS systems was available, with a lot of missing information for several aspects of the systems. One explanation is our broad inclusion criteria which included documents that did not have a main focus on the description of a CBS system, but merely mentioned its existence. We tried as much as possible to correct this limitation by consolidating all available information for each specific system from several documents. For this scoping review, we strove to apply best standards with double-blind screening and data collection, discrepancies being solved through consensus. We tried to be as sensitive as possible using tailored search algorithms to each bibliographic database, specific terms to search the worldwide web, screening the references of each included document, removing any time limits, and looking at publications in four languages (English, French, Portuguese and Spanish).
Acknowledgments We thank  Lisa Stevens  for reviewing the paper and  Pierre Nabeth and Sebastien Cognat  for their strategic oversight. 2019  World Health Organization. Licensee Public Library of Science. This is an open access article distributed under the  Creative Commons Attribution IGO License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. http://creativecommons. org/licenses/by/3. 0/igo/. In any use of this article, there should be no suggestion that WHO endorses any specific organization, products or services. The use of the WHO logo is not permitted. This notice should be preserved along with the article's original URL.
Fast video data recorded from 2008 to 2012 in full-tungsten ASDEX Upgrade have been analyzed with the TRACE algorithm developed to automatically detect and track dust particles. The purpose of the work presented in this paper is to complete earlier study of the influence of various discharge conditions on dust rates with an investigation of dust motion under most remarkable conditions. The 3D trajectories of intrinsic dust particles have been confronted to simulations carried out with the DUCAD (Dust Characterization And Dynamics) code, based on the Orbital Motion Limited (OML) approach for dust/plasma interaction modelling. The motion of micrometric spherical dust grains is found to be largely dominated by inertia in the considered experimental conditions. The influence of Vertical Displacement Events (VDEs) and of various heating scenarios on dust areas of formation and trajectories is investigated.
The use of fast imaging is however not only devoted to benchmark dust transport codes. Recently, we have used fast imaging to carry out a statistical survey of the influence of various discharge parameters on dust rates in ASDEX Upgrade (AUG)  [15]. In the present contribution, we make use of the yet unexploited spatial and temporal information in order to investigate open questions raised by this previous investigation. The main purpose of this contribution is to better identify how plasma conditions and heating scenario influence dust formation areas and transport. After introducing the experimental analysis techniques used to carry out our study and the DUCAD code used for modelling  [16] , we present the main results derived from the analysis of intrinsic dust 3D trajectories. Then we present our observations on the influence on dust spatial distribution and trajectories of vertical displacement events  (VDE)  and heating scenarios based on Neutral Beam Injection (NBI), before conducting a more detailed investigation on the influence of the use of Ion Cyclotron Resonance Heating (ICRH).
Experimental techniques The experimental results presented in this contribution are based on the analysis of 2425 discharges recorded on ASDEX Upgrade in between 2008 and 2012 (i. e. discharges 23174 to 28695). Video data recorded by up to 3 fast cameras (Phantom v7. 1, Phantom v7. 3 and Photron SA1. 1) have been analyzed automatically with the TRACE code, according to the procedure presented in  [15]. Single fast camera measurements have been mostly used to evaluate dust rates under various discharge conditions in  [15]. In the present contribution, these measurements are also used to assess dust formation or remobilization areas and to estimate the direction of their motion. For a given camera position, lines of sight (LOS) may vary significantly due to the use of different lenses or different optical setups.
Recalibration procedure is more difficult after the replacement of Plasma Facing Components (PFCs), which may cause the disappearance of several reference points, as for instance after the installation of RMP coils after shot 25945. In the end, in order to recalibrate 2008 and 2012 views, up to 11 iterations with intermediate views have been realized. Finally, recalibration is sometimes not possible for sets of tens of videos because PFCs are not sufficiently visible to identify point pairs. The corresponding data are therefore not taken into account in our analysis. In addition to single camera measurements, two series of stereoscopic camera measurements were also carried out, in 2010 (shots 26023-26067) and 2012 (shots 28581 -28695). The aim of stereoscopic measurements was to reconstruct dust motion in 3D and to compare experimental trajectories with dedicated modelling, in order to infer some dust particle's properties such as their sizes.
DUCAD transport code Modelling of 3D dust trajectories has been carried out with the DUCAD (Dust Characterization And Dynamics) code  [16] , inspired by DUSTT  [2] , DTOKS  [5]  and MIGRAINe  [10]  codes, in an attempt to retrieve dust size from the comparison between simulated and experimental trajectories. DUCAD integrates the Orbital Motion Limited (OML)  [18]  approach for modelling dust/plasma interactions: in order to calculate currents to the dust grain, the latter is considered as a perfectly spherical probe embedded in plasma. Hence, collection of electrons and ions by the grain are described as an orbital problem. DUCAD focuses on four different observables: dust charge number, temperature, mass evolution and dynamics by solving the corresponding differential equations. Dust charging is described by the differential equation: dQ d /dt = ∑ I i + I e + I th + I see where Q d = Ze is the dust grain charge, I i and I e the ion and electron currents to the grain, I th is the thermionic emission from the grain and I see the secondary electron emission.
A more realistic determination of T -should be based on a diffusion law. In the simulations presented in this paper the time step was set equal to 50 microseconds, i. e. small enough to provide stable results while keeping the computation time reasonable. This procedure differs from the one used in  [8]  where it is considered that the exposed surface is in thermal equilibrium with the impinging plasma heat flux. Strictly speaking, it is shown in  [16]  that the effective time for achieving thermal equilibrium is of the order of some milliseconds meaning that dust grains with velocities 10 -100 m/s hardly approach thermal equilibrium before migrating to a different part of the reactor vessel with different plasma parameters and hence, different equilibrium temperature. More details on the calculation and implementation of forces in the DUCAD code, as well as the code itself are provided in reference  [16].
Results and discussion As pointed out in  [15] , dust rates deduced from fast video measurements are significantly lower in full-W ASDEX Upgrade than in tokamaks with carbon PFCs, with an average rate of 1. 35 dust particle per second, while 52% of the shots appear as free of visible moving dust. In reference  [15] , disruptive shots, which exhibit the largest dust rates, were investigated separately from non-disruptive shots. In the present paper, both disruptive and non-disruptive shots are included in the same data set in order to improve statistics on dust origin and direction, but only dust trajectories detected at least 0. 1 second before disruption are kept in order to ensure that they are not caused by disruption. Stereoscopic camera measurements were originally conducted with the aim to investigate intrinsic dust motion under various discharge conditions.
Summary and outlook Fast camera measurements recorded in 2425 discharges in the AUG tokamak have been analyzed with the aim to better understand the transport of intrinsic dust. Stereoscopic measurements supported by simulations with the DUCAD code have shown that dust motion is largely dominated by inertia for micrometric spherical W dust. It is found that only small grains of a few hundreds of nanometers in diameter or less, invisible to fast cameras, and non-spherical dust particles may experience noticeable influence of the plasma forces. The latter particles and dust grains experiencing collisions with PFCs are the only ones whose motion can significantly deviate from the inertial motion under the considered experimental conditions. The investigation of camera measurements during VDEs confirms that upward VDEs have a stronger impact on dust activity than downward VDEs, due to larger energy release to the PFCs.
A. Comparison and discussion To verify the effectiveness of our MediCompress approach, on database images (Fig. 4 ); we did a comparative study of the results with those obtained from lossless compression approaches in classical networks such as: SPIHT, Arithmetic, Huffman, Run-length encoding, Burrowswheeler transform Move-to-front encoding, Winrar and Winzip; Table  II presents     Based on the plots of the experimental results presented in Fig. 7  and Fig. 8 , we find that our approach (MediCompress) whose curve is in red color has a good Compression Ratio compared to the curves of approaches: SPIHT, Arithmetic, Huffman, Run-length encoding, Burrows-wheeler transform Move-to-front encoding, Winrar and Winzip. Table  III , Fig. 9  and Fig. 10  show that the MediCompress PSNR (dB) and MSE results have been satisfactory compared to the JPEG2000 standard implemented in OpenJPEG library, and the compressor without loss of the algorithm SPIHT implemented in QccPackSPIHT4 library.
We present version 10 of OrthoMaM, a database of orthologous mammalian markers. OrthoMaM is already 11 years old and since the outset it has kept on improving, providing alignments and phylogenetic trees of high-quality computed with state-of-the-art methods on up-to-date data. The main contribution of this version is the increase in the number of taxa: 116 mammalian genomes for 14,509 one-to-one orthologous genes. This has been made possible by the combination of genomic data deposited in Ensembl complemented by additional good-quality genomes only available in NCBI. Version 10 users will benefit from pipeline improvements and a completely redesigned web-interface.
OrthoMaM is a database of high-quality orthologous sequence alignments and phylogenetic trees from mammalian genomes. It has been, for instance, used for developing new molecular markers, inferring mammalian phylogenies, simulating sequences for testing alignment filtering methods, and studying the evolution of base composition in protein-coding sequences. Previous versions of our database only included mammalian genomes from Ensembl  (Ranwez et al. 2007; Douzery et al. 2014). With the progress of sequencing techniques, the number of genomes included in NCBI, but not yet in Ensembl, grows each year. This motivated us to totally rethink our database to include the annotated mammalian genomes available in NCBI only. To identify the core set of orthologous sequences to be used, we rely on the OrthoMaM v8 pipeline described in  Douzery et al.
(2014). Briefly, we isolate 1-to-1 orthologous genes among pairs of our pillar placental species (Homo-Mus, Homo-Canis, and Mus-Canis) by using Ensembl v91 annotations  (Zerbino et al. 2018 ). These clusters are then enriched by adding sequences of 69 additional mammals annotated in Ensembl v91 as 1-to-1 orthologues to the human gene, and turned into clusters of 1-to-1 orthologous CDSs by selecting the longest transcript of each gene. These proto-clusters are aligned using MAFFT at the amino acid level  (Katoh and Standley 2013). The CDSs of the 47 annotated mammal genomes available in NCBI  (Rigden and Fern andez 2017)  but not in Ensembl as of March 2018 are then used to enrich the proto-alignments. For each proto-alignment, an HMM profile is created via hmmbuild using default parameters of the HMMER toolkit  (Eddy 2011).