dataesr/scientific-paragraphs-categorization huggingface dataset:
It may indicate the specificity of the Chernevaya taiga microbiome, and its importance for the features of this biotope. The reported study was funded by Russian Scientific Foundation (grant ID 19-16-00049). Total RNA was isolated from plant leaves, ribosomal RNA was removed and libraries for Illumina technology were prepared. Sequencing was done in MiSeq and NovaSeq machines, always for 2 × 150 nt. Data were processed using Geneious and CLC Genomics Workbench, pipeline included removing duplicated reads, de novo assembly, contigs search by TBLASTX for plant viruses using a local database. Reads were then mapped back to contigs, coverage was calculated. In the case of novel viruses, mapped reads were checked by BLAST. The alignment of virus sequences with relative viruses was done using MUSCLE.
A novel member of the family Phenuiviridae (single-stranded negative RNA genome) was found in orchids Neotinea, tentative name Neotinea-associated coguvirus. A novel member of the family Chrysoviridae (double-stranded RNA genome) was found in orchids Restrepia, tentative name Restrepia-associated alphachrysovirus. Conclusions Intuitive tools for analysis of high-throughput sequencing data like Geneious and CLC Genomics Workbench were successfully used to process large datasets obtained from Illumina sequencers. Genomes of four novel viruses from ornamental plants were assembled and annotated.
P8 Installing and searching BLAST databases in a data science framework  Graham Alvare 1 , Abiel Roche-Lima 2 , Brian Fristensky 3* 1 Access Norwest Co-op Community Health, Winnipeg, Canada ; 2  RCMI Program ,  Medical Science Campus, University of Puerto Rico ,  Puerto Rico ; 3  Department of Plant Science, University of Manitoba, Winnipeg ,  Canada Correspondence: Brian Fristensky -brian. fristensky@umanitoba. caBMC Bioinformatics 2020, 21(Suppl 20): P8 Data science embodies a pipeline of processes: acquisition, cleaning and organization of data, quality control and assurance, validation, and downstream visualization and analytics. Because of the overwhelming number of tools for each of these steps, the challenge is often making them work in concert to facilitate a thorough and insightful analysis.
Sequences are retrieved to a new blprotein object for downstream analysis. For example, proteins aligned using mafft would pop up in a blpalign object. Compared to web applications, which show only a single step in a full-screen window, BioLegato objects can be arrayed on the screen to give a more global view of the pipeline. BioLegato simplifies experimentation with the data at every step. Because output of each step appears in a new BioLegato object, there are no dead ends. Output from one step can be used directly as input for subsequent steps because BioLegato automates otherwise tedious and error-prone tasks like file format conversion. We call this process ad hoc pipelining. Ad hoc pipelining enables the user to learn from each step before going to the next. We also describe blastdbkit, a Python script run from  BioLegato , for downloading and managing BLAST databases on the users's computer.
Together, BioLegato applications provide a seamless point and click pipeline for sequence database searches, within the context of the larger BIRCH system. New programs can be added to any BioLegato application by creating a file using BioLegato's PCD language [2], which specifies parameters to be set and a shell command to run the program. In this way, the core BIRCH functions can be integrated with locally-installed bioinformatics software.
Results We collected nine previously published metagenomics datasets obtained in the course of either in vivo (human, mice, rats, piglets), or in vitro fermentation studies and containing both 16S rRNA sequencing data and SCFA metabolomic measurements to investigate if the predicted metabolic potentials (calculated as CPI values) correlate with measured metabolite concentrations. Each 16S dataset was analyzed using the DADA2 plugin from QIIME2. The obtained ASVs were annotated using a multi-taxonomic assignment approach based on similarity to 16S sequences from the RDP database. The Phenotype Profiler tool was applied to the annotated ASV abundance tables to calculate CPI values for SCFAs production in each sample. The obtained CPI values showed the absence of correlation with experimentally measured concentrations of butyrate and propionate in fecal samples from five in vivo studies, which can be explained by highly efficient absorption of SCFAs in the large intestine.
The disease is multifactorial where genetic factors have a certain contribution. Numerous genetic polymorphisms are believed to increase the risk of IS. The advent of genome-wide genotyping caused a wave of genome-wide association studies (GWAS). At least ten candidate-genes associated with IS were previously described. Here we present the first round findings of single nucleotide polymorphisms (SNPs) associated with the development of IS in individuals of the Eastern Slavic ancestry. The results were obtained with GWAS and machine learning (ML) approaches. The case and control groups consisted of 1051 and 421 individuals, correspondingly. They were genotyped with several types of DNA-microarrays. Upon meeting the requirement of quality control, we obtained about 82,000 autosomal SNPs in the merged dataset.
Plant virome analysis in search for new viruses: experience from CRI Prague, Czech Republic It may indicate the specificity of the Chernevaya taiga microbiome, and its importance for the features of this biotope. The reported study was funded by Russian Scientific Foundation (grant ID 19-16-00049). Total RNA was isolated from plant leaves, ribosomal RNA was removed and libraries for Illumina technology were prepared. Sequencing was done in MiSeq and NovaSeq machines, always for 2 × 150 nt. Data were processed using Geneious and CLC Genomics Workbench, pipeline included removing duplicated reads, de novo assembly, contigs search by TBLASTX for plant viruses using a local database. Reads were then mapped back to contigs, coverage was calculated. In the case of novel viruses, mapped reads were checked by BLAST.
The virus branched separately from recognized genera of the family, its designation into a genus is not certain. A novel member of the family Phenuiviridae (single-stranded negative RNA genome) was found in orchids Neotinea, tentative name Neotinea-associated coguvirus. A novel member of the family Chrysoviridae (double-stranded RNA genome) was found in orchids Restrepia, tentative name Restrepia-associated alphachrysovirus. Conclusions Intuitive tools for analysis of high-throughput sequencing data like Geneious and CLC Genomics Workbench were successfully used to process large datasets obtained from Illumina sequencers. Genomes of four novel viruses from ornamental plants were assembled and annotated. P6 P5 Petr Komínek*, Marcela Komínková Plant Virology and Phytoplasmatology Research Group, Crop Research Institute, 16106 Prague, Czech Republic Correspondencwwe: Petr Komínek -kominek@vurv.
For instance, once built, electric connection is rarely compatible with the conservation of the roof sealing. To the knowledge of the authors, only the following works can be mentioned. Palla et al. (2009a)  studied an instrumented portion (170 m 2 ) of a green roof in Genoa (Italy) under the Mediterranean climate. This pilot site was equipped to monitor the different components of the water balance with a meteorological station for rainfall, several time domain reflectometry probes installed horizontally along a vertical profile for retention in the substrate, and triangular weir and tipping-bucket devices to follow the outflowing discharge. Hakimdavar et al. (2016)  used the data collected on three full-scale extensive green roofs in New York City (USA) to validate a modelling approach based on the Soil Water Apportioning Method (SWAM).
Rainfall measurement Local rainfall has been analysed with the help of an optical disdrometer: Campbell Scientific ® PWS100. This device is made of two receivers and a transmitter generating four laser sheets. By analysing the signals received from the light re-55 fracted by each drop passing through the 40 cm 2 sampling area, the drop size and velocity are estimated. A rain rate can then be derived. Disdrometers are now considered to be a reliable rainfall measurement instrument  (de Moraes Frasson et al. , 2011; Gires et al. , 2016; Thurai et al. , 2011). The de-60 vice has been installed since September 2013 on the roof of the École des Ponts ParisTech building (see Fig. 1 ). This disdrometer and its corresponding data have already been presented in detail in a previous data paper  (Gires et al.
Consequently, a ubiquitous wireless TDR sensor network has been implemented on the ENPC Blue Green Wave to measure both water content and temperature. For this purpose 32 CWS665 wireless TDR sensors (produced by Campbell Scientific ® ) were initially installed. The data were collected by four CWB100 wireless bases, able to each store the data of eight sensors. Then the data were transferred to a CR6 data logger from Campbell Scientific ®. The initial selected time step was 1 min. It appeared that this first configuration was responsible for many gaps in the time series due to interference between the different TDR sensors and the bases. To avoid this problem, only 16 TDR sensors were used, all of them connected to the same CWB100 base. For this same reason of possible interferences between the sensors, the time interval was enlarged to 4 min.
06 cm) corresponds to an additional offset due to the elevation of the sensor By studying both water level variations, a relationship between the water level measured in the first unit (H 1 ) and the outflow routing to the second unit Q 2 (and related to H 2 ) has been established (see Fig. 4 ). Finally, the total discharge reaching the first unit and collecting the downstream rainfall can be assessed by the following equation depending only on H 1 : Q 1 = Q 2 + dH 1 dt ⇥ A1 = f (H 1 ) + dH 1 dt ⇥ A1, ( 12 ) where Q 1 is the discharge reaching the first unit and Q 2 the second unit; A1 = 33. 2 m 2 is floor area of the first unit. Finally, discharge data were recorded with a time step of 30 s for the sensor implemented in the conduit and 15 s for the one in the storage unit. 2. 3 Available output, data processing, and period of study As already presented in detail in  Gires et al.
(2018) , precipitation data are collected in real time and stored through daily files. Here, these files for a 30 s time step rain rate have been gathered with the help of a Python script to create a long time series covering the whole period of study. Each line contains the time step expressed as YYYY-MM-DD HH:MM:SS and the corresponding rainfall intensity (mm h 1 ) separated by a comma. Water content and water level data inside the pipe are collected and stored every night on the HM&Co server in two different files. For this purpose, the Loggernet software produced by Campbell Scientific ® has been used. It supports programming, communication, and data retrieval between data loggers and a PC. Concerning the water level file, each line corresponds to a time step for which the following information is recorded (in each line, these values are separated by a comma): exact definition of the time step expressed as YYYY-MM-DD HH:MM:SS, -item number, voltage indicator to ensure the quality of the measurement (it should be close to 12 V), internal temperature of the data-logger box, unused data coming from a non-operational sensor, water level measured inside the pipe (U in Eq.
6, expressed in millivolts), unused data coming from a non-operational sensor, Earth Syst. Sci. Data, 12, 1-11, 2020 www. earth-syst-sci-data. net/12/1/2020/ -unused data coming from a non-operational sensor. A similar format has been chosen for volumetric water content data (note that names of the 16 volumetric water content (VWC) sensors are indicated in the header and are also reported in Fig. 1 ): exact definition of the time step expressed in YYYY-MM-DD HH:MM:SS, -item number, voltage indicator to ensure the quality of the measurement (it should be close to 12 V), internal temperature of the data-logger box, volumetric water content (expressed as k a ) for the 16 TDR sensors, -STT_B3: summary transfer time for basis, which is related to the total time required for collecting information from all the sensors that are collected to that base.
Water level data inside the storage units have been collected by using the open-source Arduino Uno microcontroller board that works in the offline regime. This Arduino system was chosen because the storage unit was instrumented a few 20 months after the conduit and because the distance was too long to make a connection between the storage unit and the existing data logger. Data are continuously stored on the 64 MB memory card implemented in the board and copied manually to the HM&Co server once per week. Data contain the following information (in each line, these values are separated by a space): item number, voltage values for the first storage unit -U 1 (in millivolts), voltage values for the second storage unit -U 2 (in millivolts), exact definition of the time step expressed in YYYY-MM-DD HH:MM:SS.
Presentation of the available data set This data set presented in detail in the next section contains the following files: a rainfall file, 2018_0219-0507_Data_rainfall. csv; a water content file, 2018_0219-0507_VWC. csv; a file of the water level inside the pipe, 2018_0219-0507_Data_discharge. csv; a file of the water level in the storage, 2018_0219-0507_Data_Arduino. csv; a Python script to select the data, transform the raw data in physical measurements, and carry out some initial analysis. In detail, the Python script is structured as follows. -Time period selection. This part could be changed to select a study time period by choosing an initial and final date. -Data selection and transformation. The data corresponding to this time period are selected in the different files. Electric signals measured by the water level sensors are converted in water level (by using Eqs.
5 and 11) and then in discharge by using the Manning-Strickler equation (Eq. 6) for the pipe and Eq. (  12 ) for the storage unit. In order to smooth the erratic 15 s signal produced by storage unit measurements, the computed discharge data are averaged on a moving window, whose number of time steps can be modified. Dielectric constants measured by the 16 TDR sensors are converted in water content by using the Topp equation (Eq. 3). -Representation of the computed data. -Computation of runoff coefficients. Runoff coefficient is the ratio between the total amount of precipitation (computed by multiplying the rain depth by the corresponding contributive area) and the total volume of water flowing through the monitored pipe or the storage unit. This value ranging from 0 % to 100 % illustrates the capacity of the green roof to retain rainwater.
Conclusions This paper presents the data collected by several devices devoted to the assessment of the water balance of a particular green roof located close to Paris. The data set made available for research purposes contains three types of data, representing the relevant components of the water balance during a rainfall event: rainfall, water content in the substrate, and the discharge flowing out of the infrastructure. These data were collected during 78 d between February and May 2018. These measurements are useful to study the capacity of such 20 vegetated structures to store rainwater and act as a stormwater management tool. They could also be useful to develop and validate some appropriate modelling approaches  (Stovin et al. , 2013; Versini et al. , 2016). This data set is available for download free of charge from the following web page: https://doi.
org/10. 5281/zenodo. 3687775 (Versini et al. , 2019b). It is provided by the Hydrology, Meteorology, and Complexity laboratory of École des Ponts ParisTech (HM&Co-ENPC). The following references should be cited for every use of the data:  Versini et al. (2019a Versini et al. ( , 2020)). Research focused on the assessment of ecosystem services provided by Blue Green Solutions is continuing at HM&Co-ENPC, and particularly on the BGW. The monitoring set-up has been recently extended to the energy balance component measurement (radiation balance, conduction, sensitive and latent heat flux) and particularly to the evapotranspiration flux. Such data will be particularly useful to study the ability of Blue Green Solutions to mitigate urban heat islands (but also to assess their retention potential during dry periods).
Acknowledgements. This work was initiated during the Climate-KIC-funded  Blue Green Dream  project (http://bgd. org. uk/, last access:  22 April 2020 ). It has also been supported by the  Academic Chair "Hydrology for Resilient Cities ", a partnership between  École des Ponts ParisTech  and the  Veolia group , and the  ANR   EVNATURB  project dealing with the evaluation of ecosystem performance for renaturing urban environments.
A dedicated software was built to provide multimodal visualization and an annotation tool. In this paper, we propose a 2-fold contribution with a study of the temporal organization of QS of preterm newborns, and the description of a new software tool for video and signal visualization and sleep stage annotation. The temporal organization of QS is described according to the percentage of time spent in QS and the average duration of intervals (ADI) spent in QS which have been studied as a function of GA and PMA. The development of a configurable software for multimodal signal visualization and annotation named ViSiAnnoT (Video Signal Annotation Tool) allows to visualize as many signals and videos as wanted and handles modalities that are not initially necessarily synchronized with each other.
Ethical The data used in this study was part of the database of the Digi-NewB observational cohort (NCT02863978, Horizon 2020 European project GA-689260). This cohort prospectively included newborn infants hospitalized in the neonatal units of six University hospitals in the western region of France (University Hospitals of Rennes, Angers, Nantes, Brest, Poitiers, and Tours) in 2017-2019. The collection of the data was carried out after approval by the ethics committee (CPP Ouest IV 34/16), national agency for the safety of medicines and health products (Authorization number: 2016062400181) and informed parental consent. Digi-NewB project aimed at improving clinical intervention in neonatal units by developing a new generation of non-invasive perinatal health monitoring based on multiparametric digital representation of clinically relevant functions.
Video and Signal Annotation Tool The use of a software tool for multimodal visualization and annotation eases the process of manual scoring of QS periods. For this study, we have built a software tool which allow to display video and physiological signals in a synchronized way, while integrating a tool for annotating temporal events. There are only a very few software tools that allow a multimodal visualization, such as RTGraph (24) (physiological signals), CAPTIV (25) (video, motion curve, audio, and hypnogram) or Natus NeuroWorks (EEG, video)  (26). Nevertheless, they do not integrate an annotation tool. We can find some annotation tools in the domain of affect computing, such as FEELTRACE  (27) , CARMA  (28) , or ANNEMO  (29). The drawback is that they are designed to visualize only audio-visual recordings, so we cannot combine it with the visualization of physiological signals.
Regarding software distribution, RTGraph (  24 ) is the only one being open source. Open source distribution has many advantages such as: being available to everyone, not depending on a third party software requiring a license and allowing collaborative work for improving the software and adding features. To the best of our knowledge, there is no software tool that meet all of our requirements. So, we developed a software tool for multimodal signal visualization and annotation: Video and Signal Annotation Tool (ViSiAnnoT). It has been used in this study in order to score QS periods. The main advantage of ViSiAnnoT is to provide an annotation tool that eases the scoring process in a research study. The first main feature was to provide a combined video and signal multimodal visualizer. Several synchronized videos can be displayed, and it is possible to display together several signals regularly sampled or not on multiple or single plots.
An example of ViSiAnnoT visualization of non-regularly sampled signal is shown in Figure  1  which presents the visualization of RR intervals time series (cardiac cycle lengths) extracted from the ECG together with video and other plots. The supported formats for video files are those supported by OpenCV  (30). The supported formats for signal files are txt, mat (Matlab) and h5. The software is designed to work off-line, meaning that the signals must be extracted beforehand and cannot be retrieved directly from the acquisition system. The second main feature of ViSiAnnoT is the management of long recordings. In the context of this study, each recording was split into files of 30 min length, so we integrated a user interaction that allows to navigate easily from one file to another with buttons "next" and "previous.
Thus, it can easily adapt to a wide range of applications where it is needed to annotate temporal events. ViSiAnnoT mainly relies on PyQt5 package (Python binding for Qt)  (31)  and PyQtGraph package  (32). For someone with background knowledge in these packages and in object-oriented programming, it should be quite easy to add new features. So, we chose to distribute ViSiAnnoT as an open-source software, so that the user can adapt it to his particular needs. Compared to commercial system, ViSiAnnoT most likely has poorer performances and provides less features, but it is easy to use, freely available and is highly configurable. The software is currently in the beta phase.
Statistical Analysis Statistical analysis was performed using Statistica 13. 2 (StatSoft Inc. ) and SAS 9. 4 (SAS Institute). All variables were tested for normal distribution using the Shapiro-Wilk normality test. Variables were analyzed by paired or unpaired Student's t-test, or Wilcoxon w-test and Mann-Whitney u-test as appropriate. We first tested if characteristics of QS (i. e. , ADI and % time spent in QS) were correlated with maturation measured by PMA. The % of QS was calculated on the whole usable recording after removing the NOK periods from the denominator. Correlations between data were calculated considering repeated data. Then we tried to get an index which could be clinically useful to estimate sleep maturation in terms of post-menstrual equivalent age (expressed in estimated weeks of PMA) using ADI, % of time spent in QS and GA as independent variables.
The results of R 2 is presented for the primary model of multiple linear regression analyses for estimation of PMA using percentage of time spent in QS and ADI spent in QS as independent variables. The R 2 values presented between brackets corresponds to the estimation of PMA in adding GA, which was correlated with PMA (r = 0. 66), to the primary model. thanks to the ViSiAnnoT software tool for signal visualization and annotation. The American Academy of Sleep Medicine have recently proposed rules for sleep scoring in full-term infants from birth to 2 months of age based on eye status, chin EMG, behavior and EEG but the criteria used require invasive procedures that are not applicable for monitoring of sleep maturation in clinical practice  (37). Some authors such as Prechtl, Anders or Brazelton  (6, 7, 33)  have proposed non-invasive behavioral evaluation to scale sleep but the proposed criteria have not been uniformly applied across the neonatal studies.