dataesr/scientific-paragraphs-categorization huggingface dataset:
While valuable for assessing the quality of tracking, location and intensity forecast, this approach does not answer the question of whether the algorithm really detected the phenomena of interest (e. g. , a thunderstorm, fog, etc. ). Although desirable, comparative verification of various nowcasting methods across national borders is not easily accomplishable, due to issues mentioned in Section 3. 1 as well as specific choices of threshold. Some forecast demonstration projects run during Olympic games also included nowcasting systems, which were then applied to the same area using the same input datasets.
Currently, the main information source on European severe weather is the METEOALARM portal (METEOALARM, 2021), as well as individual portals maintained by the NHMS. However, across borders, there are large inconsistencies in defining the warning levels, because of differences in criteria, requirements, forecasting capabilities, availability of observations or experience. There have been several activities in the past to fill these gaps and to create unified nowcasting products for large domains, for example, INCA-CE  (Wang et al. , 2017)  or OPERA  (Saltikoff et al. , 2019). Despite being successful, it was also shown that merging information from several, qualitatively different observational sources or NWP/nowcasting products is a difficult task. To provide common nowcasting information at sufficiently high resolution will above all require highquality observational databases consisting of a large number of frequently-updated inputs (on the order of minutes).
ACKNOWLEDGEMENTS The authors would like to acknowledge contributions and comments from the following individuals:  Mateusz Barczyk ,  Claire Bartholomew ,  Evelyn Cusack ,  Paul Davies ,  Eoghan Harney ,  Simon Keogh ,  Thomas Kratzsch ,  Ingo Meirold-Mautner ,  Paolo Ruti ,  Joachim Saalmüller ,  Lothar Schüller ,  André Simon ,  Jacqueline Sugier ,  Roope Tervo ,  Sreerekha Thonipparambil  and two anonymous reviewers.
F I G U R E 5 Summary of the EUCOS gap analysis for basic atmospheric variables (Temperature T, wind w, humidity q, pressure P, and integrated water vapour iwv) for nowcasting/very short-range forecasting and aeronautical meteorology applications over land and ocean areas. The colour of each cell indicates the achievement on average over 2018 of the: (yellow) minimum requirement; (green) breakthrough requirement; and (blue) goal requirement as set in the WMO OSCAR database. The (grey) cells represent gaps in the EUCOS observing networks; the (blank) cells represent areas where there is no registered requirement.
Microarray Analysis For the microarray analysis, RNA from 3 independent experiments was extracted as described in "RNA extraction and qPCR". Labeling and hybridization to Affimetrix Mouse Gene 1. 0ST microarrays were performed at UCL Genomics at the Institute of Child Health. Normalization and statistical analysis were performed using Robust Multichip Average and then imported into Partek. Data can be accessed with the accession number GSE93725. The Babelomics data analysis platform  [21]  was used to analyze the predicted miRNA targeting those significantly up-regulated by a 1. 2-fold change in Dicer -/-ESCs (p < 0. 05). The bioinformatics platform DAVID  [22]  was used to obtain gene ontology terms from that gene list. , pH 7. 4) before incubating with 4 µM Fura-2, AM (Invitrogen, Waltham, MA, USA) in HBSS for 30 min in the dark, followed by 3 washes in HBSS.
Image acquisition was carried out using a Nikon UV microscope and Andor1 software. Emission after excitation at 340 nm and 380 nm was recorded every second. Ratios were then calculated at every time point for at least 30 cells in the specified field. Cytoplasmic Ca 2+ levels were determined using the method of  [23]. ECSs were loaded with Fura-2, AM in Ca 2+ -free HBSS with 1 mM EGTA. The basal fluorescence 340/380 nm emission ratio was measured followed by the addition of 3 mM Ca 2+ and 1 mM ionomycin (final concentrations) to obtain the maximum fluorescence ratio (Rmax). Finally, 1 mM EGTA (final concentration) was added to obtain the minimum fluorescence ratio (Rmin). Ca 2+ levels were calculated using the following equation: [Ca 2+ ] nM = Ca 2+ Kd × (R -Rmin)/ (Rmax -R) × Sf2/Sb2 , where Ca 2+ Kd in the cytosol is 225 nM, Sf2 is the 380 emission value from the basal fluorescence measurement above, Sb2 is the 380 emission value from the maximum fluorescence measurement above, and R is the average of the 340/380 emission ratios from the cells under the designated condition in an individual experiment.
Statistical Analysis Statistical analysis of data was performed using Graphpad Prism (Graphpad Software, version 9, San Diego, CA, USA). Differences in Ca 2+ levels and protein expression were analyzed using a two-way ANOVA. Analysis of Annexin V time-course experiments, where multiple wells from two experimental groups were treated and analyzed at set times, was carried out using a non-repeated-measures two-way ANOVA, followed by the Fisher's exact Least Significant difference posthoc test. When analyzing relative data such as qPCR and luciferase assay, where control cells had a value of 1, a Wilcoxon Signed Rank test was performed. All data were plotted as the mean value with error bars displaying SEM. For all cases, p-values of less than 0. 05 were accepted as significant.
Therefore, any cells that had escaped Dicer deletion that could have a selective growth advantage  [6]  did not overgrow the population during the timeframe of our analysis. To further validate the loss of miRNA function in this system, we examined the expression by qPCR of Rbl2, Lats2, and Cdk1a, whose miRNA regulation was previously shown to be important in the differentiation and cell cycle regulation of ESCs  [7, 8, 12]. These genes were all found to be similarly upregulated upon Dicer deletion, as has been reported in the above studies (Figure  1D ). To gain a further understanding of the immediate effects of miRNA depletion, we performed gene expression arrays and found 1065 genes that were significantly upregulated (p < 0. 05) by 1. 2-fold or more (Table  S1 ). We then analyzed the 3 UTRs of these genes using Babelomics software (version 5) to determine the miRNAs that had an enrichment in target sites within these sequences.
(C) Dicer -/-ESCs expressed higher levels of the ER Ca 2+ transporter genes Itpr2 (p < 0. 0001) and Atp2a1 (encoding SERCA1) (p = 0. 0212), as measured by qPCR. Likewise, the protein level of Itpr2 as measured in Western blots was increased (p = 0. 0116). SERCA1 levels also appeared to be increased. However, the weak detection by the antibody gave variable results such that the difference was not significant (p = 0. 0632). qPCR and Western data are each from three independent experiments, * p < 0. 05, **** p < 0. 0001. One possible explanation for the miRNA-mediated buffering of Ca 2+ levels in the cytoplasm could be through their regulation of Ca 2+ transporters in the ER. Ca 2+ release from the ER is mediated by the stimulation of ryanodine and inositol trisphosphate (IP 3 ) receptors in the ER membrane, whereas import into the ER is mediated by SERCA, as stated above.
(C) The effect of representative miRNAs of the indicated miRNA seed families on these reporters was analyzed in HEK293T cells using miRNA mimics. The relative luciferase activity normalized to a control non-specific mimic is shown in the bar graphs. The only case of miRNA suppression was for miR-92 of the Itpr1 3 UTR reporter (p = 0. 0010). All other miRNAs either had no effect or, in some cases, enhanced the expression of the reporters presumably through indirect effects. Data are from 12 independent experiments each with the exception of miR-96 on pGL3 cont n = 11 and pGL3 Itpr2 3 UTR n = 7. (D) The miRNA regulation of Itpr2 appeared to be mediated by transcriptional regulation. The relative increase in the primary transcript levels (as measured by qPCR primers to the first intron) (p < 0.
0001) was reflected in the increase in the mature transcript levels, as measured by qPCR using primers to the indicated exon sequences (p < 0. 0001). This was not the case for Atp2a1 as there was no increase in its primary transcript. Data are from three independent experiments. (E) Expression of two known transcriptional repressors of Itpr2, Bach1, and Rxra is not regulated by miRNAs in ESCs. Mature mRNA levels were measured by qPCR using primers spanning adjacent exons. Data are from three independent experiments, * p < 0. 05, ** p < 0. 01, *** p < 0. 001.
2015a), the Virgo observatory  (Acernese et al. 2014) , and the KAGRA detector  (Akutsu et al. 2020) , and with the potential to be expanded and adapted to several other detection efforts. The core analysis tools, described in detail in what follows, are heavily inspired by the LIGO, Virgo, and KAGRA Collaboration (LVK) stochastic analysis code, stochastic. m. The latter consists of a set of MATLAB scripts easily parallelizable on a high-throughput computing cluster, and has been used in LVK data analysis for the past data acquisition runs  (Abbott et al. 2007 (Abbott et al. , 2009 (Abbott et al. , 2017 (Abbott et al. , 2019 (Abbott et al. , 2021)). These include the three observing runs: O1 (2015 September-2016 January), O2 (2016 November-2017 August), and O3 (2019 April-2020 March), performed with Advanced LIGO Hanford and Livingston and Advanced Virgo  Acernese et al.
(2014)  for parts of O2 and O3. Data from Virgo have been included in stochastic analyses as of the latest observing run. The analysis consists in the calculation of an optimal statistic (Allen & Romano 1999) from the data of multiple interferometers, which is directly related to the amplitude of the GWB signal. A notable change throughout the years of stochastic GW analyses has been the constant shift toward Bayesian parameter estimation  (Mandic et al. 2012; Abbott et al. 2021 ). To date, there is no preferred stochastic parameter estimation software, and different groups have employed private scripts. To extend the scope of the stochastic search beyond the optimal statistic, we include a parameter estimation module in pygwb, based on the Bilby package (Ashton et al. 2019), which allows the user to test both predefined and user-defined models and obtain posterior distributions on the parameters of interest.
The steady inflow of ever-improving GW data open for analysis  (LIGO Scientific Collaboration et al. 2021 ) has been a catalyst for open-source GW data analysis codebase development. By adopting the Python language and focusing on userfriendliness, flexibility, and portability, we intend to introduce stochastic searches to the wider GW community. Detecting a GWB with ground-based interferometers will be a community effort, and we expect search pipelines to evolve along the way. The format and structure of pygwb facilitates this evolution, and conversely the package is suitable for beginners approaching GWB data analysis for the first time. This paper is structured as follows. In Section 2, concepts related to the characterization and detection methods of a GWB are reviewed. A detailed overview of the individual modules that make up the pygwb package follows in Section 3, outlining the steps of LVK stochastic analyses.
Several manager objects that store relevant data and handle the analysis internally are described in Section 4. The built-in pygwb pipeline, which combines individual modules and performs the search for an isotropic GWB, is presented in Section 5. To test the capabilities of the pipeline, mock data sets with a variety of simulated signals are analyzed in Section 6. 1. To conclude, results from the analysis of the third LVK collaboration observing run, O3, are presented and compared with collaboration results in Section 6. 2.
We then describe the built-in parameter estimation module pe, based on Bilby (Ashton et al. 2019), a Python-based Bayesian inference library widely used in GW data analysis. Finally, we present the simulator module, which includes different mock data injection techniques for GWB study and detection validation. A schematic of the pygwb package is presented in Figure  1. This includes the manager objects Interferometer, Baseline, and Network, presented in Section 4.
e. , excluded from the analysis. In previous isotropic GWB searches  (Abbott et al. 2009 (Abbott et al. , 2017 (Abbott et al. , 2019 (Abbott et al. , 2021)) , the input data are high-pass-filtered using a 16th-order Butterworth filter with a specific knee frequency. A 16th-order Butterworth filter is built by first computing its transfer function (in zero-pole-gain form) using the scipy library and then filtering the data with the relevant gwpy Time-Series method. The design of the high-pass filter is fixed in the module, only allowing the user to specify the knee frequency. The default value of the knee frequency is 11 Hz, which was chosen to avoid spectral leakage from the noise power spectrum below 20 Hz (Abbott et al. 2021). See Figure  2  for an example of data before and after preprocessing.
2021). Gating is performed internally by pygwb by multiplying the data by an inverse Planck-taper window  (McKechan et al. 2010). Time periods around samples in the whitened data that have an absolute value above a chosen threshold are marked for gating independently for each interferometer. The width of the gate must be sufficiently large to remove the entirety of the relevant glitch. The required width may change, based on the data quality of the specific data in the analysis, and hence must be empirically determined. The tapering length of the window must also be sufficiently long to minimize the addition of artifacts by the gating; 0. 25 s is found to be sufficient  (Davis & Walker 2022). This technique is generically beneficial for the analysis of data that are non-Gaussian, such as real GW detector data.
Spectral The role of the spectral module is to compute, for each time segment of duration T, the discrete frequency-domain In the blue squares, we show the manager objects of the code that handle the analysis internally. These manager objects query (red arrows) different modules for specific objects, calculations, or quantities (rounded bubbles), imported (gray arrows) by either internal (i. e. , within pygwb) or external modules (i. e. , outside of pygwb). Internal modules are indicated in red, while external modules are indicated in green. quantities C IJ,f , P I,f , and P J,f used in Equations (  5 ) and (6). The one-sided cross-and auto-PSDs C IJ and P I , respectively, of a single segment are defined as * = = C T s s P T s 2 , 2 , 1 1 IJ f I f J f I f I f , , , , , 2 ˜˜|˜| ( ) where s f ˜are DFTs of s(t k ), defined by å º d p = - - s st e , 1 2 f t T t k i mt T 0 2 k k ˜( ) ( ) where f = mδf, with m a natural number between 0 and 1/(2 δt δf ), and δf is the desired frequency resolution, chosen such that 1/(2δtδf ) is an integer.
g. , Section IIIB of  Ain et al. 2015). To start, we construct the estimate of the GWB in a single segment t. As detailed in Section 2, the GWB search is often framed in terms of constructing a point estimate for Ω GW ( f ref ), the energy density of the GWB at the specific frequency f ref , assuming a PL for the GWB with spectral index α. We refer to the estimator of this quantity as W a , ref ˆin general, and for a single time segment of data, it can be constructed using a weighted average over the individual frequency bin estimators W f ˆand σ f ,  Note. Most of these choices reflect defaults chosen in the past when analyzing LIGO and Virgo data. Notably, the default start and end times for the analysis are not meaningful and represent placeholders for the user-defined times. A default initialization file is included in the package with meaningful start and end times present in the O3 open data set.
f f f k IJ f f k IJ f f I J f ref, odd, odd, 2 2 odd 2 2 even, even, 2 2 even 2 2 odd, 2 even, 2 , 2 ( ) ( ) ( ) T he even and odd estimators for each frequency bin are defined as in Equations (  15 ) and (16), except applied to individual binby-bin estimators calculated at each time segment. As discussed above, these expressions have been normalized, such that s s W = å W å - - , 2 7 f f f f f ref ref, ref, 2 ref, 2 ˆˆ( ) å s s = - -. 2 8 f f ref 2 ref, 2 1 ⎡ ⎣ ⎢ ⎤ ⎦ ⎥ ( ) The postprocessing module implements the above expressions to estimate W f ref, ˆand σ ref,f at a fixed α, and returns them in form of an OmegaSpectrum object, which subclasses the classic gwpy. FrequencySeries and adds two key attributes: the spectral index α and the reference frequency f ref at which the spectrum is calculated.
Delta-sigma Cut In general, the noise level in ground-based detectors changes slowly on timescales of tens of minutes to hours. The variance s GW 2 (see Equation (  6 )) associated to each segment is an indicator of that level of noise, which typically changes at roughly the percent level from one data segment to the next. However, there are occasional very loud disturbances to the detectors, such as glitches, which violate the Gaussianity of the noise. Autogating procedures are in place, as explained in Section 3. 1, to remove loud glitches from the data; however, the procedure does not remove all nonstationarities. To avoid biases due to these noise events, an automated technique to exclude them from the analysis has been developed  (Abbott et al. 2007). To this end, the pygwb package includes the delta-sigma cut module, which flags specific segments to be cut from the analyzed set.
The Δσ cut calculation is performed assuming different spectral indices α, as each PL is sensitive to a different frequency band (see Figure  5 ). The union of all the segments flagged for each α is taken, leading to a full list of segments to discard from the analysis. The default choice of α values in the delta-sigma cut module is α = { -5, 0, 3}, as this adequately covers most of the frequency band of LVK searches, from 20-1726 Hz (Abbott et al. 2021), at current sensitivity. These may be easily modified by the user. This would be especially recommended if the search were carried out over a different set of frequencies, or for data from detectors with a spectral sensitivity different than that for Advanced LIGO, Advanced Virgo, or KAGRA. Often, the value of α = 5 is also considered, and was employed in the most recent LVK isotropic search (Abbott et al.
The key object of the notch module is the StochNotch-List, which is a list of StochNotch objects. A Stoch-Notch object represents a physical noise line that has been identified and needs to be removed from the data analysis. The object has a minimum and maximum frequency, indicating the contaminated frequency region. Furthermore, it also comes with a descriptive string that allows the user to keep track of the reason why the line was notched. All the different Stoch-Notch objects for a certain analysis are then stored in the StochNotchList, which contains the entire list of lines to be notched from the analysis. The notch mask used to apply a set of notches within the analysis is constructed conservatively, such that any frequency that has overlapping frequency content with the noise lines defined in the StochNotchList will be removed when applying the notch mask.
e. , α = 0) estimate W f IJ ref, 0, ˆto be the measured GWB spectrum in each frequency bin, and plug it into Equation (33). Note that it is necessary for the input spectra used in parameter estimation to be unweighted, as any other value would constitute a model choice and bias results. Within the pygwb package, we include the pe module to perform parameter estimation as an integral part of the analysis, which naturally follows the computation of the optimal estimate of the GWB. This is a notable improvement compared to previous LVK analyses, where data products and parameter estimation were handled independently by packages in different programming languages. Furthermore, the pe module is a simple and user-friendly toolkit for any model builder to constrain their physical models with GW data.
2019) to perform parameter space exploration, and employ the sampler dynesty by default  (Speagle 2020). The user has flexibility in choosing the sampler as well as the sampler settings. Child classes in the pe module inherit attributes and methods from the GWBModel class. Each child class represents a single GWB model, and combined they form a catalog of available GWB models that may be probed with GW data. The inheritance structure of the module makes it straightforward to expand the catalog, allowing users of the pygwb package to add their own Ω M ( f|Θ) models. The flexibility of the pe module allows the user to combine several GWB models defined within the module. A particularly useful application of this is the modeling of a GWB in the presence of correlated magnetic noise, as discussed in  Meyers et al.
Manager Objects pygwb counts three manager objects the user can interface with: Interferometer, Baseline, and Network, which are defined in the detector, baseline, and network modules, respectively. Each object is in charge of storing and saving relevant data, and handles data analysis internally. The manager objects are designed such that the user need never call a method from a module directly, but rather will invoke the manager, which queries the relevant module to perform the calculation. For details on how to use these objects, see the complete set of tutorials in the pygwb online documentation  (Renzini et al. 2023).
Detector The detector module is designed to organize the data products related to a GW detector and provides functions to create and process its internal data. It is formally defined as a subclass of the Bilby Interferometer class  (Ashton et al. 2019). In what follows, we describe the additional features we have developed, and refer the reader to the Bilby documentation for the built-in properties inherited from the parent class. By default, the Interferometer object is initialized by taking geometrical information of a GW detector, such as latitude, longitude, and elevation, as arguments. from pygwb import detector my_detector = detector. Interferometer ( * args, * * kwargs) While the above method allows the user to customize the detector's specification, one can initialize the object based on existing GW detectors, as done in bilbyʼs Interferometer class, by calling the get_empty_interferometer method.
Once initialized, this object provides several ways to read in and process time-series data, all of which internally call the preprocessing module, using information such as a channel name to query the data or pointing to a numpy array or a gwpy object directly. Additionally, the Interferometer object includes processing methods relying on the 7. Example of injection using the simulator module. The noise PSD (orange) and the injected signal (red) are clearly discernible as parts of the PSD of the simulated data (blue). spectral module described in Section 3. 2 to calculate naive and averaged spectrograms from the stored time-series data. A pair of Interferometer objects can be used to initialize a Baseline object, as described below. These are then imported as attributes of the Baseline object and store data products specific to each detector.
Baseline The Baseline module is by design the core of the pygwb stochastic analysis. Its main role is to manage the crosscorrelation between Interferometer data products, combine these into a single cross-spectrum, which represents the point estimate of the analysis, and calculate the associated error, as introduced in Section 2. The standard initialization of a Baseline object simply requires a pair of Interferometer objects. from pygwb import baseline H1H2_baseline = baseline. Baseline("H1-H2," H1, H2) Here, H1 and H2 are Interferometer objects. It is also possible to load a previously stored Baseline object in pickle format by calling the relevant class method. The data loaded into the Interferometer objects are automatically imported into the Baseline object upon initialization. The Baseline object relies on the spectral module to calculate cross-correlations between the data streams, following the methodology shown in Section 3.