dataesr/scientific-paragraphs-categorization huggingface dataset:
Several filters can then be combined using Boolean "AND" operators. The GLIDER web portal also supports Web Map Services (WMS). This allows the web portal users to contextualize observations by adding map layers from external services, e. g. , representing meteorological and oceanographic data. During a survey, NIVA servers monitor the Kognifai database time series and automatically perform quality control (QC) services. The QC services collect the new data added to the database and perform a set of regionally dependent QC tests. A flag indicating the quality of each data point is written back to the database. The QC tests and algorithms follow the International Copernicus. eu standard  [61]. The web portal provides an option to filter and view only good-quality data flagged during the QC process.
Due to bandwidth and cost limitations, echo sounder and hydrophone data were not transmitted to the time series database via satellite link but stored on-board the gliders. These data files were retrieved at the end of the survey and uploaded to Microsoft Azure Blob Storage. Through the brushing and linking functionality, the GLIDER portal supports selecting relevant bulk data for download, which can be readily incorporated into models. For example, MET Norway uses hydrographic data provided by the gliders for near-realtime assimilation in their ocean model  [59]. The GLIDER web portal also supports Web Map Services (WMS). This allows the web portal users to contextualize observations by adding map layers from external services, e. g. , representing meteorological and oceanographic data. During a survey, NIVA servers monitor the Kognifai database time series and automatically perform quality control (QC) services.
The QC services collect the new data added to the database and perform a set of regionally dependent QC tests. A flag indicating the quality of each data point is written back to the database. The QC tests and algorithms follow the International Copernicus. eu standard  [61]. The web portal provides an option to filter and view only good-quality data flagged during the QC process. Due to bandwidth and cost limitations, echo sounder and hydrophone data were not transmitted to the time series database via satellite link but stored on-board the gliders.
GLIDER Environmental Data Recordings The three gliders effectively recorded data across planned transects in the LoVe region. All data were uploaded into the GLIDER data management platform where scientists could access and download the data for further analysis. For instance, a subset of environmental data recorded by the Wave Glider showed temporal variation and trends in surface fluorescence, temperature, salinity and oxygen during the summer (June-July) (Figure  5 ). These data were further employed to explore relations between environmental parameters and the spatiotemporal distribution of zooplankton and cetaceans, as well as to improve regional ocean models (see below). gion. All data were uploaded into the GLIDER data management platform where scientists could access and download the data for further analysis.
Echo Sounder Mapping of Shallow Zooplankton Layers The Wave Glider and the Sailbuoy successfully collected good quality echo-sounder data along the Norwegian coast from 66. 5 N to 70. 9 N, from March to September 2018. The data allowed for mapping of the spatiotemporal and vertical distribution and density of zooplankton down to approximately 100 m depth. Using the WBT Mini echo sounder on the Sailbuoy, dense spring zooplankton concentrations were detected throughout the deployment period (Figure  6 ).
Echo Sounder Mapping of Shallow Zooplankton Layers The Wave Glider and the Sailbuoy successfully collected good quality echo-sounder data along the Norwegian coast from 66. 5 N to 70. 9 N, from March to September 2018. The data allowed for mapping of the spatiotemporal and vertical distribution and density of zooplankton down to approximately 100 m depth. Using the WBT Mini echo sounder on the Sailbuoy, dense spring zooplankton concentrations were detected throughout the deployment period (Figure  6 ). In early spring (April), when copepods ascend from overwintering depths, weak layers of zooplankton were registered from approximately 20-30 m depth. Later in the season (June), a persistent high-density layer of zooplankton was observed in the upper 10-40 m. In late summer (July-August), the zooplankton layers were distributed across a wider depth range, exhibiting diel vertical migration patterns  [49].
Discussion In this paper, we presented results from the GLIDER project in which two surface and one subsurface vehicle were deployed to collect ecological and environmental data throughout the LoVe region, from March to September 2018. In these months, the gliders recorded data during a period of increased biological activity starting prior to the onset of the spring phytoplankton bloom, zooplankton overwintering ascendance and reproduction, and cetacean migrations. Previously, four operational factors were identified as limiting the use of ocean observatories in ecological and fisheries applications: "(1) limited spatial coverage, (2) limited integration of multiple types of technologies, (3) limitations in the experimental design for in situ studies, and (4) potential unpredicted bias in monitoring outcomes due to the infrastructure's presence and functioning footprint"  [29].
Nevertheless, the quality of the acoustic records in the upper layers did vary with weather conditions as bubble entrainment during periods of strong wind and high wave action disturbed the signal mainly in the upper four meters, but on occasions even down to 25 m. In addition, the acoustic data provided by the Sailbuoy were recorded without introducing light pollution. Recently, it was shown that biological surveys performed in the dark by illuminated ships, may introduce biases on biological sampling, bioacoustic surveys, and possibly stock assessments of commercial and non-commercial species, due to among others, disruption of species diel vertical migration and strong light-escape responses  [70] [71] [72]. This becomes especially problematic in the Arctic polar night, characterized by low irradiance levels day and night.
The novel Seaglider ® -integrated hydrophone used in this study was able to record cetacean vocalizations. The production of time series datasets during periods of unknown animal distribution contributes to the growing body of knowledge on marine mammal distribution in the Norwegian sea outside traditional summer survey periods. Although it is generally known that sperm whales are often found along the continental slope, this fact had not yet been documented in the Norwegian Sea and outside summer months. Furthermore, recordings of humpback whale occurrence beyond the months previously reported in the literature for low-latitude migration shows that not only can gliders equipped with passive acoustic equipment document species distribution, but they can also address gaps in areas and periods of the year for which there is a severe lack of information  [44, 73].
The GLIDER project demonstrated that a suite of autonomous vehicles can collect physical oceanographic parameters alongside biological data across multiple ecosystem components in the coastal Arctic. The productive waters of the LoVe region are shared by ecological important low (phytoplankton and zooplankton) and high (marine mammals) trophic groups and an increasing number of human stakeholders. High spatial and temporal resolution data collected with a small environmental footprint and made available by the GLIDER data management platform make it possible to capture ecosystem dynamics, important towards the realization of an ecosystem-based management approach in the region and elsewhere. The integration between the current underwater observatory system in LoVe and gliders should be prioritized to form an ecosystem observatory module network able to provide unique marine research and monitoring capabilities.
To facilitate further exploration of our model, we provide a graphical user interface (GUI) [https://doi. org/10. 5281/zenodo. 4631141]. The GUI allows The TS from (a) at select times. c Normalized intensity fluctuations at the TS for CTD-RNAP2 (red circles), Ser5ph-RNAP2 (green squares), and mRNA (blue diamonds) vs. time. Times of minimal mRNA (less than 0. 20 arb. units) are marked with dashed gray lines (t 1-4 ). d The average normalized intensity of each signal surrounding times of minimal mRNA. Both the Ser5ph-RNAP2 and CTD-RNAP2 signals have deep minima well below the steady-state value (dashed horizontal line). Solid connecting lines show the Gaussian fit, and solid vertical lines mark the minima with a lighter shadow depicting the S. E. M. from the Gaussian fit. n = 40 events from 13 of 20 cells.
Data are presented as mean values ± S. E. M. When the same analysis is performed at 100 random time points, no obvious minima are seen (gray diamonds). e Cropped 50frame (50 min total) moving-average image of the TS in (a) and the fitted center position for mRNA (blue), Ser5ph-(green), and CTD-RNAP2 (red), n = 126 events from 13 of 20 cells. f 50-frame moving-average XY position of each signal at the TS in (a) over time. Note the mRNA signal was used as the reference signal within the crop. g The distance between each signal in (f) over time: Ser5ph-RNAP2 to mRNA (cyan circles; (1)), CTD-RNAP2 to mRNA (Purple squares; (2)), and Ser5ph-RNAP2 to CTD-RNAP2 (orange diamonds; (3)). h The distribution of distances measured as in (g) at all TSs in all cells analyzed (sampled every 10 min). The line in the middle of each box represents the mean.
Under these conditions, the model predicts a build-up in RNAP2 at the 3 0 end of the gene because processing takes longer than elongation. This buildup is consistent with our ChIP data in Fig. 1b. The estimated 4 min processing time is also consistent with an earlier estimate at this HIV-1 locus  20  , although such relatively long processing may not be representative of other genes. Similarly, the RNAP2 initiation and promoter escape rates we quantified are consistent with earlier reports, taking between a minute and a few minutes  27, 49. Finally, we also detected bursts in transcription that result in convoys of RNAP2, as previously reported  20  , and consistent with widespread bursting observed across the genome  38, 52. The global agreement between studies suggests some convergence in the field, particularly given the uniqueness of our dataset, which is based on fluctuations of both MS2  21  and RNAP2 Fab signals  27.
93 mW for 488 and 561 nm, respectively. To minimize photobleaching, the 488 nm laser fired once every ten frames (to track the TS), while the 561 nm laser fired every frame (for tracking individual H2B). Single-molecule tracks were identified using TrackMate 3. 8 with the following parameters: LoG Detector; Estimated Blob Diameter: 5. 0; Pixel Threshold: 100; Sub-Pixel Localization: Enabled; Simple LAP Tracker; Linking Max Distance: 3 pixels; Gap-Closing Max Distance: 2 pixels, Gap-closing Max-Frame Gap: 1 frame. Custom Mathematica code was used to calculate the average Euclidean displacement for each track longer than 5 frames. Tracks were plotted with a blue-purple color distribution based upon their average Euclidean displacement. The transcription site was identified using TrackMate and plotted in red.
3d  (bottom panel, purple distribution) was computed. Quantifying signal intensities at the transcription site from live-cell imaging movies. Images were pre-processed using either Fiji 70 or custom-written batch processing Mathematica code (Wolfram Research 11. 1. 1) to create 2D maximum intensity projections from 3D movies. Using Mathematica code, the 3D images were corrected for photobleaching and laser fluctuations, z-stack by z-stack, by dividing the movie by the mean intensity of the whole cell or the nucleus in each channel. The offset between the two cameras was registered using a built-in Mathematica routine FindGeometricTransform, which finds a transformation function that aligned the best-fitted positions of 100 nm diameter Tetraspeck beads evenly distributed across the image field of view.
2D maximum projections and 3D image sequences from the images corrected for bleaching and laser fluctuations were then analyzed with a custom-written code in Mathematica to detect and track the transcription site. Briefly, thresholds were selected in each channel to visualize spots at the transcription site and a bandpass filter was used to highlight just the transcription site in the mRNA channel. The resulting image was binarized and used to create two masks for each time point: one marking the transcription site (TS mask: a mask semi-manually thresholded to cover just the transcription site within the image) and one marking the BG (mask: a ring of width one pixel that surrounds the transcription site and is separated from the site by two pixels). The built-in Mathematica routine ComponentMeasurements-IntensityCentroid was used to find the coordinates of the transcription site in XY through time.
The normalized intensity (as in Fig. 2c ) was calculated by dividing Raw-Int Ch by the average 95% intensity from all transcription sites. Occasionally, normalized intensities for CTD-RNAP2 and Ser5ph-RNAP2 dip below zero. This can be caused either by RNAP2 signals being temporally depleted at the transcription site relative to the BG or by bright signals in the BG due to nearby transcription in the local vicinity. To display transcription sites over time (as in Figs. 1c  and 2b  and Supplementary Movie 1), 3-time point moving-average trims from the "best z" were created in each channel (showing CTD-RNAP2, Ser5ph-RNAP2, mRNA, and the merge). Each trim was centered on the intensity centroid of the mRNA. Covariance analysis in Supplementary Figs. 1f  and 3g. To test for covariance between intensity signals from control spots and the transcription site, signal covariance was calculated using the "cov" function in MATLAB.
For quantification of bleed-through, the covariance was calculated between all possible pairs of raw intensities (CTD-mRNA, CTD-Ser5ph, and Ser5ph-mRNA) in normal vs. bleedthrough control conditions. For quantification of signals off-target, the covariance was calculated between all possible pairs of normalized intensities on-target at the transcription site vs. off-target at a random site p1. Significance was calculated using the Mann-Whitney U test. Analysis of minima signals in Fig. 2d. The local minima in the mRNA signal of each cell were detected using the "islocalmin" function in MATLAB. The cells that exhibited minimas below a threshold (normalized intensity ≤ 0. 20 arb. units) were selected by the algorithm. Then seven-time points before and after the mRNA valley were considered, including the minimum, in each channel.
Therefore, to reduce the number of free parameters required by the model, we fixed k off at 1000 min -1 such that each burst would be very short-lived on the time scale of the experimental measurements. This choice led to a simpler model but had no discernible effect on the fit of the model to the data. All codes, including GUI are available at Zenodo [https://doi. org/10. 5281/ zenodo. 4631141]. Model parameter search. Parameters were found using maximum likelihood estimation considering several data types as follows. First, errors in the measurement of the normalized auto-and cross-covariances were assumed to be normally distributed with the measured standard error, SEM G ðτÞ, such that their loglikelihood functions are written log L G ðθÞ ¼ C G À 1 2 The model was further constrained to match the mean and variance for the measured number of mRNA per transcription site as estimated in units of mature mRNA as calibrated using FISH-quant.
Assuming the central limit theorem, the log-likelihood of matching the observed sample mean was estimated as: log L μ ðθÞ ¼ C μ À 1 2 ∑ N t n¼1 ðG D ðτ n Þ À G M ðτ n ; θÞÞ ðμ D À μ M ðθÞÞ 2 SEM 2 D ; ð14Þ where μ D is the sample mean levels of mRNA from the data, μ M (θ) is the mean number of mRNA predicted by the model, and SEM D = 0. 93 is the standard error of mean level of mRNA from the data. Similarly, the log-likelihood of the measured variance, σ 2 D given the model was estimated as log L σ 2 ðθÞ ¼ C σ 2 À 1 2 ðσ 2 D À σ 2 M ðθÞÞ 2 SEM 2 σ 2 ; ð15Þ where σ 2 M ðθÞ is the mRNA variance predicted by the model, SEM σ 2 is the standard error for the mRNA variance, and C σ 2 is a constant that does not depend on the parameters. The standard error of the sample variance was estimated using a Gaussian approximation such that SEM σ 2 ¼ SEM 2 D ffiffiffiffiffiffiffiffiffiffiffiffi 2 N À 1 r ¼ 14: ð16Þ Under the assumption of independence between the different data types, the total log likelihood to match all data was the sum of the individual likelihoods log L Total ðθÞ ¼ log L G ðθÞ þ log L μ ðθÞ þ log L σ 2 ðθÞ: ð17Þ Maximum likelihood estimates were found using iterated rounds of MATLAB's "fminsearch" until convergence.
E. M. ) was calculated. Finally, to determine the time delays between CTD-RNAP2, Ser5ph-RNAP2, and mRNA, an inverse tanh fit was applied and weighted with respect to the variance of each signal. Software. All images were acquired with Micro-Manager software (1. 4. 22). Image pre-processing was made using ImageJ (2. 0. 0rc -67/1. 52e Java 1. 8. 0_66, 64bit). Images were analyzed with a custom Wolfram Mathematica (11. 1. 1) code. For the fast movies, tracking of the spots was performed using the ImageJ plugin, TrackMate (3. 8. 0). Final plots, modeling, and the GUI were made using MATLAB R2019b; Figures were assembled together using CorelDraw 2020 (64bit). Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Acknowledgements We thank all the members of the  Stasevich and Munsky labs  for helpful discussion and suggestions, especially  Luis Aguilera  for help with the spot minima analyses. T. J. S. , T. M. , and M. S. were supported by an award to T. J. S. from the  NIH  ( R35GM119728 ). B. M. and W. R. were supported by an award to B. M. from the  NIH  ( R35GM124747 ). L. S. F. Q. was supported by the  W. M. Keck Foundation. T. H. was supported by an award from  JSPS KAKENHI   JP17K17719. H. K. and T. H. were supported by an award to H. K. from  JSPS   KAKENHI JP17H01417  and  JP18H05527.
Data availability The source data underlying Figs. 1b, d, 2c, d, , 3a, b, d, f, 4c-f, and Supplementary Figs. 1b, c, 1f, 3b-d, f-h, 4a-c, 5a, b, 7, 8a-c, 10a-d are provided as a Source Data file. The raw images/movies were deposited to Figshare [https://doi. org/10. 6084/m9. figshare. 14187011]. All other data included in the manuscript and/or supplemental materials are available from the corresponding authors upon reasonable request. Source data are provided with this paper.
, 2015). Although existing tools are capable of matching a list of fragment ions to a protein sequence (ProSight Lite or MASH Explorer for instance)  (Fellers et al. , 2015; Wu et al. , 2020) , there is currently no computational tool allowing to visualize fragments arising from diverse MS/MS experiments on a unique fragmentation map without losing information on the contribution and the specificity of each experiment. Current tools provide a unique fragmentation map per MS/MS experiment, and thus their comparison, in particular when multiple parameters are assessed, is both difficult and time-consuming. Moreover, although the intensity of fragment ions can represent a precious source of information when interpretating MS/MS data, it is often absent of fragmentation maps. To circumvent these limitations, we introduce TDFragMapper, a novel software-tool that can display and combine pre-assigned fragment ions achieved from various MS/MS experiments on a unique protein sequence, keeping an easy access to the individual contribution of each experiment and to the intensity of deconvoluted fragment ions.
Material and Methods We analyzed a standard mixture of six proteins (Thermo Scientific Pierce Intact Protein Standard Mix) in LC-MS/MS on an Orbitrap Fusion™ Lumos™ mass spectrometer using multiple experimental conditions, as described in the Supplementary Material. Raw files were deconvoluted using FreeStyle TM (v. 1. 6. 75. 20) and the lists of deconvoluted ion masses were imported into ProSight Lite (v. 1. 4) in order to assign fragments. Lists of assigned fragments were then exported and used with deconvoluted data and the protein sequence as input for TDFragMapper. The software was programmed in C# with. NET Framework 4. 8 and requires a computer with Windows 10 or later, and at least 8 GB of RAM. To evaluate the individual contributions of key-parameters in TDP experiments, and rapidly assess the combination leading to the best sequence coverage, the software is organized in three major interfaces: an input interface, a filter interface, and a graphical visualization module.
Results & Discussion Displaying fragments obtained from different MS/MS experiments onto a unique linear protein sequence with a specific color code makes it possible to highlight at a glance the contribution of each experimental parameter. TDFragMapper can be used for example to spot the dependency of a protein sequence coverage on the selected precursor charge state when using a particular fragmentation method (Figure  1 , lower panel) or to assess the reproducibility of a particular fragmentation method across technical replicates. For instance, in Figure  1 , it is easy to visualize that the charge state leading to the best sequence coverage for the HCD fragmentation of thioredoxin is the +9 one. Moreover, the user can add extra layer of information to the fragmentation map, such as the position and the number of golden complementary pairs  (Kelleher et al.
BREC is based on pure statistics and is data-driven, implying that good input data quality remains a strong requirement. Therefore, a data pre-processing module (data quality control and cleaning) is provided. Experiments show that BREC handles different markers' density and distribution issues. Conclusions: BREC's heterochromatin boundaries have been validated with cytological equivalents experimentally generated on the fruit fly Drosophila melanogaster genome, for which BREC returns congruent corresponding values. Also, BREC's recombination rates have been compared with previously reported estimates. Based on the promising results, we believe our tool has the potential to help bring data science into the service of genome biology and evolution. We introduce BREC within an R-package and a Shiny web-based user-friendly application yielding a fast, easy-to-use, and broadly accessible resource.
melanogaster-specific. With the emerging NGS technologies, accessing whole chromosome sequences has become possible on a wide range of species. Therefore, we may expect an exponential increase in the markers number, requiring more adapted tools to handle such new scopes of data efficiently. Here, we propose a new Marey map-based method as an automated computational solution that aims to, firstly, identify heterochromatin boundaries (HCB) along chromosomes, secondly, estimate local recombination rates, and lastly, adjust recombination rates on chromosome along the chromosomal regions marked by the identified boundaries. Our proposed method, called BREC (heterochromatin Boundaries and RECombination rate estimates), is provided within an R-package and a Shiny web-based graphical user interface.