dataesr/scientific-paragraphs-categorization huggingface dataset:
Data analysis To assess the spatio-temporal variations in abundance present within the species of each group, their population throughout the period of study were segregated into the four ecological sectors of the lagoon season wise. These were then plotted to find variations, if any, both spatially and seasonally. Principal component analysis (PCA) is performed using BR^software, with the average plankton abundance and the environmental parameters of the corresponding period to address the causes and reasons of spatio-temporal patterns. The values were pretreated with log transformation to minimize the effect of variations of different units of measurement in case of environmental parameters. With the most influential environmental parameter determined from PCA, the parameters most explaining the abundance of major groups of microphytoplankton and microzooplankton species were determined through multivariate statistical approach using canonical correspondence analysis (CCA), using CANOCO software.
Acknowledgements This work was funded by the  Chilika Development Authority (CDA) ,  India , under  World Bank  sponsored project BPost-restoration assessment of the ecology and fisheries diversity of Chilika Lake,^executed by the  ICAR-Central Inland Fisheries Research Institute, Kolkata. The first author is thankful to  Prof. A. P. Sharma , former Director,  ICAR-CIFRI  and the  Chilika Development Authority, Odisha  for granting permission to pursue Doctoral degree under the project, publishing the data and facilities provided to carry out the study. Author is also grateful to the other team members of the project for their help in collection and analysis of data.
Introduction Assessing the environmental risk of chemical compounds requires environmental quality standards (EQS) such as PNECs, RACs and MAC-EQS under the ECHA, EFSA PPR and WFP regulatory 1 frameworks respectively  (EFSA PPR, 2013; ECHA, 2017). Derivation of EQS results from the combination of assessment factors with toxicity endpoints mainly derivated from estimated or measured exposure response of a set of target species to that chemical compound  (EFSA PPR, 2013; Isigonis et al. , 2015; Syberg and Hansen, 2016; ECHA, 2017). Deriving reliable toxicity endpoints is challenging and the subject matter is very controversial  (Laskowski, 1995; Jager, 2011). Today, Environmental Risk Assessment (ERA) rests on fitting classical dose-response models to quantitative toxicity test data. For acute effect assessment, such data are collected from standard toxicity tests, from which the 50% lethal or effective concentration (LC 50 or EC 50 ) is generally estimated at the end of the exposure duration, meaning that the monitoring of observations over time is not fully exploited.
In the present study, we explore how scrutinizing uncertainties helps to provide recommendations on the experimental design and the characteristics of toxicity endpoints used for EQS, while maximizing their reliability. We first give an overview of TKTD models with a focus on GUTS  (Jager et al. , 2011). Handling GUTS models within the R-package morse  (Baudrot et al. , 2018a ) is then illustrated with five example data sets. Then, we explore how a variety of experimental designs influence the uncertainties in derived LC(x, t) and M F (x, t). Finally, we provide a set of recommendations on the use of TKTD models for ERA, based on their added-value and the way the uncertainty may be handled under the Bayesian framework.
The "Profile type" is the type of exposure profiles (constant or time-variable), "Data points" refers to the number of data points in the data set, "Nbr profiles" is the number of profiles in the dataset "N init " is the initial number of individuals in profile, "Nbr days" is the number of days for each experiment, and "Time points per profile" is the number of time points for each time series (each constant profiles consisted in 5 time-points).
Implementation and Bayesian inference GUTS models were implemented within a Bayesian framework through JAGS  (Plummer, 2016)  by using the R-package morse  (Baudrot et al. , 2018a). The Bayesian inference methods, choice of priors and parameterisation of MCMC process have previously been fully explained  (Delignette-Muller et al. , 2017; Baudrot et al. , 2018c,a). The joint posterior distribution of parameters was used to predict survival curve under tested and untested exposure profiles, for the calculation of LC(x, t) and M F (x, t), and for the computing of goodness-of-fit measures (see hereinafter). The use of the joint posterior distribution allow to quantify the uncertainty around all these predictions, and therefore the computing of their median and their 95% credible intervals as follow: under a specific exposure profile, we simulated the survival rate over time for every joint posterior parameter set ; then at each time point of the time series, we computed 0.
WAIC and LOO-CV Information criteria as WAIC and LOO-CV are common measures of predictive precision also used to compare models. The WAIC is the sum of the log predictive density computed for every points, to which a bias is added to take into account the number of parameters. The LOO-CV use the log predictive density estimated from a training subset and applied it on another one  (Gelman et al. , 2013). Both WAIC and LOO-CV were computed with the R-package bayesplot  (Gabry and Mahr, 2017).
Variation in the number of pulses in exposure profiles A first step has been to explore the effect of the number of pulses (9, 6 and 3 pulses of one day each) along a period of 20 days with the same cumulative amount of contaminant in external concentration after the 20 days (Figure  4  and Supplementary Material). From a conservative approach for ERA, whatever the model, GUTS-RED-SD or GUTS-RED-IT, it seems better to have few pulses of high amplitude than frequent pulses of low amplitude. Indeed, the survival rate over time with only 3 high pulses is lower than the survival rate under frequent lower exposure. This is confirmed in Supplementary Material for Malathion and Propiconazole data sets. With GUTS mechanistic models, the higher is the pulse, the higher is the scaled internal concentration and so is the damage.
2 ). Data are assessed at baseline (T0), 6 months (T2), 12 months (T3), 18 months (T4), and 24 months (T5) via self-report measures and three one-week assessment periods (T0, T3, T5) when participants wear accelerometers for one-week periods each. Within the Pre-vOP-PAP-I, the main computer-assisted face-to-face intervention takes place at T1 (one week following T0), additional action control and booster intervention phases take place between week 1 and week 4, between week 25 and week 28, and between week 50 and week 53 following baseline (T0), using computer-assisted phone-based interventions and paper-pencil activity calendars (i. e. , action control intervention, see below). Initial self-report assessments and the computer-assisted face-to-face intervention are done at the study center at Charité -Universitätsmedizin Berlin.
Masking Regarding study staff conducting the PrevOP-PAP intervention sessions, allocation of participants to intervention or control conditions cannot be masked. Study staff know participants' allocation at the beginning of the face-to-face computer-assisted intervention session (T1). Moreover, allocation to study condition cannot be masked for participants, as upon inclusion, they are provided a-priori information on the existence of different PrevOP-PAP conditions and randomized allocation to one of them. Analyses of data will be conducted by PrevOP-PAP study staff and will not be masked.
Data collection and entry Data are collected over a 2-year period (i. e. , participants remain in the study for 2 years) at 6 measurement points (or periods) in time (T0 to T5) by trained study personnel (see Fig. 3 ). Inter-measurement intervals range from daily to 6 months, depending on the assessment in question. Data are assessed with self-report questionnaires and objective measures (e. g. , accelerometers, body weight). Participants schedule assessment sessions at the study center at Charité -Universitätsmedizin Berlin. During assessment sessions, they are handed a questionnaire booklet, asked to complete it during the session and instructed to turn to study staff with questions (T0, T2, T3, T4, T5; T1 is the main intervention session with additional assessments). At T0, T3, and T5 participants undergo further medical examinations and are handed and instructed to wear accelerometers for eight days.
At the end of T0, T3, and T5 periods, i. e. , following each one-week accelerometer assessment, participants are additionally asked to complete a brief self-report measure on their daily PA during the past week. Completed study materials and accelerometers are either directly returned to study personnel or mailed to the health psychology lab at Freie Universität Berlin. Data entry will be conducted by trained PrevOP-PAP study personnel. Quality management of data entry will include guidance by a data entry manual, regular plausibility checks, and double entry of portions of the assessed data.
Secondary outcomes Physical activity (PA) Daily PA (in minutes) is measured with tri-axial accelerometer devices (ActiGraph, GT3X) providing information on activity counts. During each assessment period (see Fig. 3 ), participants are instructed to wear devices strapped around their hips for 8 days, during their waking hours. As devices do not have screens, participants are unable to monitor their PA data on their own. Following assessment periods, data are downloaded from the devices by trained study staff. Using a software program, activity data are then grouped into minutes of PA of different intensities; i. e. , light activity, moderate to vigorous physical activity (MVPA), and sedentary behaviors. Data are to be used if participants have worn accelerometers on at least 4 days for at least 10 h a day.
Acknowledgements Authors wish to thank the team of student research assistants of PrevOP-PAP:  Lisa Bosch ,  Sally Di Maio ,  Eva Marie Keinert ,  Patrick Klaiber ,  Theresa Reschke ,  Annekathrin Teichmann , and  Luisa Wirth. Authors also wish to thank the team of PrevOP-MMT:  Gabriele Armbrecht ,  Hendrikje Börst ,  Nadine Christen ,  Tim Felsenberg ,  Martina Kratzsch ,  Felix Müller , and Frank Touby.
PoS(LHCP2018)167 background due to the interaction of the particles with the vertex locator RF foil, in the example of LHCb. A p-value is assigned to the hypothesis that a secondary vertex originates from material interaction. Figure  6 : Expected (open dots with 1σ and 2σ bands) and observed (full dots) cross-section times branching fraction upper limits at 95% confidence level, as a function of the LLP mass, in the RPV-mSUGRA (left) and simplified topologies (right) approaches  [14]. Two approaches to search for massive LLP decaying semileptonically into SM particles have been followed: a RP violating mSUGRA neutralino type, in a mass range in 23 -198 GeV/c 2 ; less model-dependent simplified topologies in the mass range 25 -80 GeV/c 2. The run 1 dataset (3 fb -1 ) was used, the trigger being based on a large P T muon and a displaced vertex in the lifetime range 5 -100 ps.
We observed that Ad5 contacts regions enriched for FOXA and CEBP binding motifs. While we do not know whether FOXA controls Ad5 transcription, it has been shown that it recruits factors required for transcriptional activation by E1A  39. It will be interesting to further analyze whether FOXA and interacting partners modulate Ad5 transcription in PHH. Viral-host genomes interactions in HBV-infected PHH may also influence the expression of cellular genes, as suggested by the Histone modification marks data were from the Encode project generated using hepatic HepG2 cells (Encode Project:GSE29611)  15  enrichment of CGIs associated with deregulated genes in the set of HBV contacts. Similarly, the set of genes whose TSS are enriched in Ad5 contacts are upregulated in Ad5 infected arrested human fibroblasts, suggesting a causal relationship between viral contacts and gene expression changes.
5% Triton X-100, 10% FBS in 1× PBS] for 30 min. Cells were washed 3 times with 0. 1% Tween 20 in PBS and incubated for 2 h at RT with the primary antibody. After three washes in PBS 0. 1% Tween 20, cells were incubated with a secondary antibody coupled to Alexa 488 for 45 min at RT. Coverslips were mounted with Vectashield (Vector Laboratories) supplemented with diamidino phenyl indol (DAPI) for nuclear staining. Fluorescent images were acquired on an Axio Observer. Z1 microscope with an Apotome Camera with Plan-Apochromate 63x/N. A. 1. 40. Images were acquired with AxioVision software (Carl Zeiss, Germany). Hi-C library preparation. Hi-C was carried out as described previously  12  with minor modifications. Five million PHH cells were collected and incubated for 10 min at RT in culture medium containing 1% formaldehyde.
Hi-C and capture Hi-C data analysis. PCR duplicates for each Hi-C library were filtered using the random tags present on the custom oligonucleotides  56. Read-pairs were aligned independently against the reference human genome (NCBI hg19) and the virus genomes using Bowtie 2, using an iterative procedure  57  (very sensitive mode, mapping quality > 30). The HBV and adenovirus sequences used were HBVayw genotype D (https://www. ncbi. nlm. nih. gov/nuccore/V01460. 1) and adenovirus C JX173077. 1 Human adenovirus C strain human/ARG/A8649/2005/2 [P2H2F2], respectively. To generate contact maps, each mapped position was assigned to a BglII restriction fragment (RF). Non-informative events such as self-circularized restriction fragments or uncut adjacents RFs were discarded by taking into account the read-pairs relative directions and the distribution of the different configurations  11, 58.
The correlation matrix was then generated and the eigen values and first eigen vector were computed. The latter vector allows the compartment A or B attribution as previously shown in  57. Positive and negative eigen values were arbitrarily chosen to correspond to the A and B compartments, respectively. This was done using a positive Pearson correlation coefficient with a signal of histone marks associated with active chromatin (H3K4Me3). For the computing and display of the contact signal profile of virus genome along human chromosomes, we assigned each pairs of read-pairs involving virus and human chromosome to the corresponding 400 kb bins. For HBV profile, Hi-C and CHi-C data were merged. Histone mark enrichment plot. Histone modification ChIP-seq datasets were recovered from publicly available databases, and the corresponding reads are aligned along the NCBI hg19 genome using Bowtie.
Doing this, a group of fragments that has been detected a lot of times in a Hi-C library will also have more chances to be picked in the random positions generations respecting the difference of detectability between genomic regions inherent to a Hi-C experiment. The mean of 100 realizations was computed to estimate the expected strength of contact for genomic positions of random group. The log2 ratio between the virus contact strength and the random positions strength is computed and plotted for five histone marks. The dataset used in this work correspond to the following references on the short-read archive (SRA) database: SRR227563 for H3K4me3, SRR227466 for H3K4me2, SRR227575 for H3K27ac, SRR227598 for H3K27me3, SRR568329 for H3K9me3, and SRR227552 for input. Co-localization analysis. We developed a bootstrap strategy to determine whether the proportion of contacts made by the virus and overlapping a biological set of genomic positions was significant.
The pair-end reads for which one read of the pair contacts the virus and the other read the human genome were processed as follow. First, 7 kb windows centered on the start of each read mapped on the human genome were selected and for each window, the number of positions of various categories of functional elements was computed (lamins, TSS, Enhancers, CpG, replication origins, TADs borders, and integration of HBV in non-tumoral cells). The positions of lamins (track: laminB1Lads), TSS (track: refGe-ne_hg19_TSS, 7 kb window centered on the positions), CpG (track: cpgIslandExt), origins of replication (file used: wa. HepG2. rep-1. J7. hg19. pks. bed, 20 kb window centered on the position) were retrieved from USCS database on the hg19 assembly. Enhancers positions were extracted from the ENCODE project (https:// www.
TADs boundaries were determined with the HiCseg software  59  based on a block-wise segmentation model. The software was run on SCN normalized contact map of each chromosome (bin: 100 kb) with the following parameters: "P" for Poisson distribution, "Dplus" for the extended block-diagonal model and the maximal number of change-points was set to the size of the matrice divided by five. The positions of HBV integration within the genome of non-tumoral cells were retrieved from the work of  16. The duplicates in the biological group positions were removed using R "unique" function. CGIs associated with highly and poorly expressed genes were established as follows: the list of transcript expressions was sorted according to their values in FPKM. Then, the 5000 first elements and the 5000 last elements were retained.
Duplicated genes were removed. CGIs were associated to their corresponding genes with windows ± 5 kb surrounding the TSS which gives a total of 3698 CGI and 2213 CGI for highly expressed genes and poorly expressed genes, respectively. The group of deregulated genes includes genes whose log2 ratio of expression was above +2. 0 (upregulated) or below -2. 0 (downregulated). This procedure allows us to have large groups, which was necessary to have significance. The number of overlaps with the set of positions of a group of genomic features was computed with the function "findOverlaps" from the R package GenomicRanges. The null models were built as followed: genomic positions of all the contacts from a Hi-C library were loaded into memory. From this set of genomic positions, a group of positions with the same number of virus contacts were randomly picked with replacement.
TSS/CGI genes in contact with the viral genome were analyzed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) tool (v6. 8)  60. Gene enrichment was evaluated by using Agilent CpG island gene list provided by the database as background. Motif enrichment analysis. To identify recognition binding sequences of known transcription factors in regions enriched in contacts with the virus, we binned the contact signal of Ad5 at 2 kb resolution. We computed the Hi-C coverage at the same resolution. Peaks of enriched contacts for Ad5 were defined as the ones with a ratio of contact signal over the Hi-C coverage above five, resulting in 2976 sequences. The genomic positions of the corresponding bins along the hg19 genome were used as input for the HOMER software  61  with default parameters (hg19 ER_MotifOutput/ -size 200 -mask).
Transcriptomics data from GSE32340 study (GSE32340_RPKM_mock_e1a_samples. txt) were used to determine highly and poorly expressed as well as up-and downregulated genes with E1A protein infection. RNAseq analysis of PHH was used to group genes according to their expression levels, with 3249 highly expressed genes corresponding to genes whose expression is above 20 RPKM (Reads Per Kilobase per Million mapped reads). 20,141 poorly expressed genes were pooled together, corresponding to genes whose expression levels are below 0. 5 RPKM. Genes up-and downregulated during Ad5 infection were defined as those displaying a log2 fold change superior to +1 or inferior to -1, respectively, in the transcriptomics data  32. Genes up-and downregulated with E1A protein infection were defined as those displaying a log2 fold change superior to +1 or inferior to -1, respectively.
On average, ∼95 million reads were generated per sample. Clean reads were aligned to the reference genome with Tophat2 63. The expression of lncRNAs and mRNA was assessed by Cuffdiff (http://cole-trapnell-lab. github. io/cufflinks/cuffdiff/ index. html). Square of Pearson Correlation Coefficient between the biological replicates was >0. 94. Differentially expressed genes had a q value < 0. 05. Enrichment pathways were analyzed using KEGG enrichment analysis. Antibodies and reagent. Anti-tubulin (Dilution 1/10,000 for western blot WB) was purchased from SIGMA (Catalog number T5168), Anti-beta actin (Dilution 1/ 10,000 for western blot WB) was purchased from SIGMA (Catalog number A5316), anti-E1A (dilution 1/1000 for WB and 1/200 for immunofluorescence IF) from Santa Cruz Biotechnology (Catalog number sc-58658, lot # G2611), anti-CGBP (Cfp1; dilution 1/1000 for WB) from Santa Cruz Biotechnology (Catalog number sc-136419, lot # H0510), HBc (Dilution 1/30,000 for WB and 1/500 for IF) was from DAKO (Catalog number B0586, lot # 1018412) and prediluted anti-HBs (no dilution) from sigma (Catalog number ab859, lot # GR3199714-3).
Western blot. Samples were resolved by SDS-PAGE and electro-transferred to nitrocellulose membranes. Blots were incubated with the indicated primary antibodies and then probed with Dye-conjugated secondary antibodies. Fluorescent immunoblot images were acquired and quantified by using an Odyssey scanner and Odyssey 3. 1 software (Li-CorBiosciences). Uncropped scans of blots are reported in the Supplementary Information as Supplementary Figure  16. ChIP-seq datasets used. Histone modification marks dataset were from the Encode project generated using hepatic HepG2 cells (GSE29611).
Acknowledgements We thank  C. Seeger  and  S. Urban  for kindly providing cell lines used in this study. We thank the  UTechS PBI (Imagopole)-DTPS/C2RT , part of the  France-BioImaging infrastructure network supported by the ANR  ( ANR-10-INSB-04 ; Investments for theFuture), for the use of the Zeiss widefield apotome microscope. This work was supported by funding to C. N. from the  Agence Nationale de la Recherche sur le Sida et les Hépatites Virales (ANRS)  and  Fondation pour la Recherche sur le Cancer (ARC). This research was also supported by funding to R. K. from the  European Research Council  under the 7th and  H2020 Framework Program  ( FP7/2007-2013 ,  ERC  grant agreement  260822  and  H2020 ERC grant  agreement  DLV-771813 ), and from  Agence Nationale pour la Recherche  (MeioRec  ANR-13-BSV6-0012 ).
OBSERVATIONS AND REDUCTION The four SMGs in our sample were observed with ALMA in Band 7 on 2015 November 9-14 for 22. 7 to 40. 7 min on source, using 44 to 47 antenna in extended configurations, with the longest baselines being ∼ 16. 2 km (2015. 1. 00456. S). The receivers were tuned such that one of the two spectral windows in the 7. 5 GHz sideband was centered to cover the expected frequency of the [CII] emission line. The FWHM of the ALMA primary beam is 18 at 345 GHz. For the three ALESS sources, the QSOs J0522-3627, J0334-4008, and J0348-2749 were used as bandpass, flux and phase calibrator, while the QSOs J0238+1636 and J0217-0820 were used for UDS 47. 0. We used the Common Astronomy Software Application (casa,  McMullin et al. 2007 ) version 4. 5. 3 to reduce the data for all four sources and version 4.
Figure5. Left: The 0. 03 resolution 345 GHz continuum map of UDS 47. 0 showing apparent clumps with sizes comparable to the synthesised beam (∼ 200 pc). Right: An example of a simulated 345 GHz continuum map of a smooth exponential disk observed with the same observational parameters as the data shown in the map to the left. Both maps show similar apparent structures, but the right-hand simulated map is known to be a smooth exponential disk. Analysis of the flux distribution of the pixels reveal that there is a 70% probability that the map of UDS 47. 0 is drawn from the simulated library of models. It is therefore not possible to rule out the hypothesis that UDS 47. 0 is a smooth disk from our observations, and so we suggest that the apparent structures identified by eye may be misleading.