dataesr/scientific-paragraphs-categorization huggingface dataset:
These targeted analyses use the same data quality products as nontargeted searches with similar search techniques. Targeted searches using matched filtering rely upon CBC data quality products, while unmodeled targeted searches use burst data quality products. The segments for each category of veto used in analyses of LIGO data are available via GWOSC  [14, 15, 16]. Veto segemnts for CBC and Burst searches are provided separately. Alongside the categories discussed in this work, segments corresponding to time periods where hardware injections are underway are also released. To measure the effects of data quality vetoes on astrophysical searches, the PyCBC search pipeline  [115]  was run on O3 data with and without using category 2 data quality vetoes to remove data. Data from both LHO and LLO recorded between April 18, 2019 and April 26, 2019 was used in this comparison.
In both cases, the PyCBC pipeline was ran with the exact same configuration as used in  [4] , but without including category 2 vetoes for one analysis. During this analysis period, the only category 2 vetoes that existed were at LLO, so all additional discussion will focus on the impact of these vetoes on the LLO data or the combined LLO and LHO analysis. These category 2 vetoes specifically vetoed noise that was due to acoustic from thunder and vibrations from a camera shutter that was inadvertently left operating on a timer while the detector was in observing mode. Category 2 vetoes are designed to remove a higher fraction of triggers than the fraction of time that is removed. Using these two PyCBC analyses of the same time period, we can directly test if this is true. All triggers recorded by PyCBC are ranked based on the recovered matched filter SNR and multiple signal consistency tests.
This ranking is referred to as the "ranking statistic. " After binning triggers based on the recovered ranking statistic, we compare the total number of triggers before and after the inclusion of category 2 data quality vetoes. This comparison, both in terms of the total number of triggers and the fractional differences in the number of triggers, is shown in figure  9. Only 0. 78% of data was removed by category 2 vetoes during this analysis period, but over 50% of triggers were removed for some ranking statistic bins. Considering all triggers with a ranking statistic above 6. 5, 42% of triggers were removed. The large difference in the fraction of time removed by the data quality flags and the fraction of triggers removed shows that the data quality flags are removing time periods that produce a large number of triggers in the PyCBC search, as designed.
The specific ranking statistic bins that have the highest fraction of triggers removed by the category 2 vetoes during this analysis are consistent with the amplitude of the noise that was vetoed. The data quality issues present during this analysis were particularly problematic for the analysis because they were loud enough to create significant background triggers but quiet enough to not be easily rejected by signal consistency checks. In addition to considering the total number of triggers identified by PyCBC, we also compare the sensitivity of the PyCBC search to astrophysical signals before and after the inclusion of category 2 data quality flags. The sensitivity of PyCBC is assessed by analyzing a large number of simulated signals with the analysis pipeline, and measuring the efficiency with respect to distance at which these signals are identified by the pipeline.
The distance at which the pipeline recovers 50% of simulated signals is referred to as the "sensitive distance. " The volume of a sphere with this distance as a radius, multipled by the total amount of time analyzed is called the volume-time. The measured volume-time of an analysis is the metric that most directly estimates the total number of real gravitational-wave signals that a pipeline could detect. We measure the volume-time of PyCBC before and after the inclusion of category 2 data quality flags. The ratio of these measured volume-time at two different significance levels, is shown in Figure  10. Since different regions of the parameter space are more susceptible to instrumental artifacts, the sensitivity measurement is broken up into several chirp mass bins. Overall, there does appear to be a modest increase in the measured volume-time of PyCBC after including data quality flags.
likely due to improvements to the PyCBC pipeline  [141, 78]. The impact on the volume-time of other CBC searches is expected to be comparable to the impact on PyCBC. In addition to data quality flags, the iDQ  [129]  timeseries was used as a part of the GstLAL search pipeline in O3  [116, 142]. The generation of the higher latency iDQ dataset was identical to that of low latency iDQ, but the iDQ model was retrained using the full dataset available. Investigations into the impact of inclusion of iDQ information into the GstLAL search pipeline have also shown increases in search sensitivity  [142].
Simulated signals from the merger of two non-spinning 1. 4M components were injected near the loud glitch at GPS time 1253878751 in gravitational wave strain data from LLO. Data from the surrounding 1024 seconds was used to calculate the power spectral estimate of the data. Each injection was then recovered using the PyCBC single-detector ranking statistic  [115] , before and after mitigating the glitch with the previously described gating method. The result of this study is shown in figure  11. The recovered ranking is consistent with that expected with Gaussian noise after gating the loud glitch. Without gating, the simulated signal is recovered at a much lower ranking statistic than expected, even for time periods where the signal does not overlap the glitch. When the simulated signal overlaps the glitch, the recovered ranking statistic is reduced due to signal-consistency tests that are used by PyCBC to reject instrumental artifacts.
The presence of the loud glitch also biases the estimate of the power spectral density, which reduces the recovered ranking statistic during the entire period considered. In O2 and O3, a subset of instrumental issues that were correlated with high amplitude transient noise were indicated to CBC searches using gating windows rather than data quality vetoes. While most of these glitches were sub-second in duration, they sometimes manifested at a high enough rate that consecutive gating windows were constructed, leading to several seconds of data being set to zero. To avoid underestimating the steady state noise in the detector, any gating window longer than 3 seconds in duration was considered a category 2 veto. In addition, several search pipelines implemented a complementary automatic gating procedure that constructed gating windows to remove transient noise above a set threshold on the whitened data amplitude  [115, 116].
Unvetoed noise sources Despite the wealth of information available from the LIGO summary pages and various automated algorithms to correlate noise with auxiliary channels, several classes of glitches have persisted in the data with insufficient clues to remove them all. These glitches can be categorized based on their time-frequency evolution. Three main types of glitches that have been harmful to transient searches are short duration transients, scattered light, and extremely loud glitches  [3, 75]. The two main classes of short duration transients that impact the searches are blips and tomte glitches. Blip glitches (see section 4. 2. 1) are particularly bad for searches for high mass binary black holes and generic transients  [13, 145, 146]. The short duration makes it difficult to differentiate a short astrophysical signal from a blip.
The combined impact of these unvetoed noise sources varies by analysis pipeline. As an example, we conducted a short study on the impact of unvetoed glitches on a CBC search for gravitational wave events coincident with externally detected gamma-ray burst (GRB) events. This study found that of high SNR injections in the LLO gravitational wave strain data that were not found by the search pipeline, approximately 35% were missed due to contamination by one of the categories of noise sources listed above. These missed injections are tests of the pipeline's ability to detect a simulated (injected) signal within the data, and missed high SNR injections indicate that the analysis's ability to detect gravitational waves is negatively affected by these noise sources. The noise directly reduces the effective astrophysical distance of gravitational waves to which the search is sensitive, typically defined for this type of targetted search as the distance at which 90% of injections are successfully recovered  [139].
These data quality products were collated as a part of the Data Quality Report (DQR)  [54] , introduced in section 3. 2. 4. Even including the time required for the rapid response team to review data quality products and make release decisions, significant candidates were usually announced less than 30 minutes after they were identified by the end of O2  [124]. Updates to automatically generated alerts in O3 were delivered on a similar timescale. The median reponse time of the rapid response team in O3 was 35 minutes. Based on the decision of the rapid response team, each candidate in Gravitational-Wave Candidate Event Database was assigned the "ADVOK" label, indicating that there was not evidence to support retraction of the candidate, or the "ADVNO" label, indicating that there was evidence to support retraction.
Data quality for persistent gravitational wave searches Searches for persistent gravitational wave sources include those for quasimonochromatic signals from rapidly rotating neutron stars as well as stochastic backgrounds due to astrophysical or cosmological sources. Many searches have been carried out using initial LIGO/Virgo data and using Advanced LIGO/Virgo data, though no persistent signals have yet been confidently detected  [160, 161, 162, 163, 164, 165, 166]. Persistent gravitational wave searches are impacted by different types of detector noise, usually by noise sources that are persistent as well. Spectral artifacts in detector data, narrow in frequency and with long-term coherence (called "lines"), pose significant challenges in analyses for persistent, narrowband gravitational wave signals  [11].
Lines are typically caused by external disturbances (e. g. , 60 Hz power mains, suspension resonances, electronic/magnetic coupling, etc. ) that appear as artifacts in the main gravitational wave strain time series. Broadband artifacts in detector data typically do not degrade searches for persistent narrowband gravitational wave searches, but coupling of magnetic fields to the gravitational wave channel may degrade broadband gravitational wave searches  [99]  (see section 4. 2. 7). Additionally, frequent, large-amplitude transient glitches (section 4. 2. 2) can impact analyses for persistent gravitational waves by degrading power spectral density estimation used in these searches (see section 6. 1). Narrowband spectral artifacts in detector data can degrade searches in different ways, either obscuring or mimicking putative astrophysical signals; analogous to transient noise obscuring or mimicking transient astrophysical signals.
This is a difficult, iterative process because low-level spectral artifacts require long stretches of data in order to be visible above the noise (this is essentially analogous to the long stretches of data that persistent gravitational wave signal detection analyses use). Sometimes lines may be spaced equally distant from each other in frequency and are caused by the same source; these are referred to as "combs", where the spacing is often due to some periodic effect. Nearly all combs identified in the ASD data begin at a certain harmonic number and extend up to a cutoff harmonic, i. e. , most combs do not span the entire senstive frequency band, though this is not universal. The specific details of a comb are complex and depend on factors such as the sensitivity of the detector, the source of the noise artifact, and how the noise couples into the detector data.
The first two aLIGO observing runs yielded highly sensitive data, enough for detecting multiple transient gravitational wave signals from compact binary mergers, but not yet sensitive enough for detection of persistent gravitational wave signals. An overview of the spectral artifacts from the first two observing runs may be found in  [11]. Lists of spectral artifacts are also available via GWOSC  [14, 15, 16]. In summary, the first observing run was found to have many lines and combs while the second observing run had fewer lines and combs, though still quite many that are problematic and many that remain unidentified  [167, 168]. One of the most prolific and problematic spectral artifacts was a comb of lines, spaced 1 Hz apart, that polluted the majority of the LHO and LLO spectra in O1. This was traced to blinking light emitting diodes (LEDs) in the LIGO data acquisition and timing system electronics and was largely mitigated in O2 (though not entirely eliminated).
Acknowledgments The authors would like to thank members of the Virgo and KAGRA detector characterization groups who have contributed to an environment of open collaboration that enabled this work. The authors gratefully acknowledge the support of the  United States National Science Foundation  ( NSF ) for the construction and operation of the  LIGO Laboratory  and  Advanced LIGO  as well as the  Science and Technology Facilities Council (STFC)  of the United Kingdom, and the  Max-Planck-Society  ( MPS ) for support of the construction of Advanced LIGO. Additional support for Advanced LIGO was provided by the  Australian Research Council. LIGO was constructed by the  California Institute of Technology and Massachusetts Institute of Technology  with funding from the  National Science Foundation , and operates under cooperative agreement  PHY-1764464.
Regarding (X, Y) drifts, the region of interest (ROI) should remain centred to avoid significant cropping after the final post mortem alignment of images greatly misaligned. At this stage, it is then only question of keeping the ROI in the field of view at the chosen magnification. This is a particularly critical point when the sample is rotated during the slice-and-view process as it is performed to mix the tomography approach with a crystallographic analysis by Electron Backscatter Electron Diffraction (EBSD) as performed by Zaefferer et al. [33]. Drift corrections can be run using a cross-correlation based alignment of a fixed fiducial marker milled on the top surface  [32]  as proposed in Atlas software developed by Fibics company for Zeiss FIB/SEM instruments or of part of the ROI itself.
As a typical example a dynamic procedure for live shift correction was developed  [30]  on a Carl Zeiss instrument by using the manufacturer API (application programming interface) and the 'MultiStackReg' plugin  [31]  in the Fiji  [32]  open source software. Such corrections using either the ROI microstructure or artificial markers as a reference essentially rely on cross-correlation techniques, which are also the most common basis of post mortem alignments, a second and complementary strategy to correct ideally any residual drift remaining after the acquisition  [11, 32, 34, 35] ; they will thus be discussed in the next section. As for Z drift, an ideally constant slicing layer thickness is expected by cross-correlating a fixed reference marker on the top surface and adjusting beam shifts.
Post mortem alignment procedures and their limitations A post mortem alignment is indeed required even if no obvious external source has produced severe drifts, and even if on-line corrections have been run: in general, drifts may occur at a spatial scale making it very delicate to run efficient and fast on-line corrections. post-mortem (X, Y) alignment usually consists in operating translations by applying cross-correlation-based operations to successive images. Several softwares can be used, like Amira ® (Visualization Sciences Group, FEI, Burlington, MA, USA), Avizo ® (Visualization Sciences Group, FEI, Burlington, MA, USA), Aphelion ® (ADCIS, Saint-Contest, France), IDL ® (Exelis, McLean, VA, USA), IMOD  [39] , plugin 'StackReg'  [40]  or 'MultiStackReg' in Fiji package. To do so, several approaches have been utilised.
This is due to the relatively flat topography visible on Figure  5d , which does not allow discerning easily close FIB/MEX profiles. The situation gets better for the rest of the series which shows that there is a monotonous variation of BM(i) as a function of i. Although monotonous, this variation is however not perfectly linear as it is expected if the slice thickness was constant. It is indeed frequent that fluctuations occur during a FIB/SEM tomography experiment, which induces some variation of this thickness. Moreover, the linear regression of these data exhibits a slope of 0. 6, which means that the average interval between MEX profiles matching successive FIB profiles is 0. 6, meaning 6 MEX profiles every 10 FIB slices in average. Since the pixel size in SEM images used to obtain the MEX profiles is 10 nm, this indicates that the mean value of INC STYLE is indeed 6 nm instead of the 10 nm targeted as reported in Table  2.
From a statistical point of view, it is then quite meaningful to re-analyse the data (that is performing again the matching procedure of the whole FIB series) by adding the constraint that the slice thickness is actually constant. Details of this analysis are reported in the SI file, see Figure  SI-2 , and the result is illustrated by the continuous line 'Match with a constrained slice thickness' in Figure  7. Note that this linear variation is obviously close to the linear approximation of the initial matches. The FIB slice thickness is then about 5. 75 nm instead of the 10 nm value expected according to the setting of the INC STYLE parameter. From this complete alignment using the surface topography, we can perform the volume reconstruction and check the lateral side of this volume where line markers were produced prior to the slice-and-view acquisition.
Comparing the topographic alignment with other alignment methods We have already demonstrated that direct cross-correlation based alignments on the microstructure are not accurate (see section 3. 2). Nevertheless, it is still worth comparing these methods with the topographic alignment described above. Additional alignments were thus performed by cross-correlation using the plugin 'MultiStackReg' in the Fiji software for three different areas: 1) the two parallel upper lines shown in Figure  8  but when seen edge-on from the viewing Z direction as in Figure  6 , 2) the interface between the deposited W and C layers and the sample top surface and 3) the centre of the ROI. Figure  9  provides the comparison between reconstructed volumes using all approaches (see also Figure SI-3 of the S. I.
Even when using fiducial markers pre-machined on the lateral surfaces of the working area, some undesirable misalignments can subsist, which are indeed undetectable if no universal reference is available (the majority of the cases). Therefore, a post-mortem alignment is needed even if some alignment is performed in situ with the intention to roughly keep the ROI in the field-of-view. We have then demonstrated that using the topography of the sample surface itself, possibly reconstructed from a stereoscopic analysis (as, for example, permitted by commercial softwares such as MEX which was used in the present work), can be a good reference providing the mean of a correct alignment. To do so, we have developed a method consisting in aligning line profiles extracted from the SEM-FIB images stack with corresponding ones obtained from the 'MEX' surface reconstruction.
Some accidents at the surface may limit the accuracy with which these profiles can be re-associated to surface line profiles reconstructed by a stereophotogrammetry approach. For example, small open pores at the surface may lead to thickening of the deposited layer, giving rise to extracted profiles with some uncertainty in their height owing to their thickness; such difficulties were encountered near the middle of the top surface as seen in the micrographs shown in Figure  6. Another drawback of the method is that it is time consuming. Whereas the stereophotogrammetry analysis can be relatively fast (two or three SEM images to record before fibbing and a rapid reconstruction with a dedicated software such as MEX), the extraction of FIB profiles and their association to MEX line profiles requires a long post-treatment which should be optimized from a software point of view in order to allow its routine application.
Acknowledgments The CLYM (www. clym. fr) is gratefully acknowledged for the access to the FIB instrument. This work was partially funded by  Carl Zeiss S. A. S. in a PhD study. We thank  N. Hilairet  and  B. Reynard  from the  Laboratoire de Géologie de Lyon : Terre, Planètes ,  Environnement (LGL-TPE, UMR CNRS 5276 ENS Lyon ) for providing the mineral used in this study. S. Descartes  from the  Contact & Structure Mechanics laboratory (LaMCos ,  UMR CNRS 5259 ,  INSA Lyon)  is kindly acknowledged for the access to MEX software. This paper is dedicated to our dear colleague and co-author BVDM who was one of the responsible persons in charge of the FIB instrument at  CLYM  and who passed away from cancer disease on January 2, 2015 at the age of 42 ans.
Primary Sjögren's syndrome (pSS) is an autoimmune disease, associated with a high risk of lymphoma. Mounting evidence suggests that cardiovascular morbidity and mortality are higher in patients with pSS, although data are heterogeneous. The aim of this study was to assess whether pSS patients are at higher risk of hospitalisation for cardiovascular events (CVEs), venous thromboembolic events (VTEs), pulmonary hypertension (PH), and sleep apnoea syndrome (SAS). Through a nationwide population-based retrospective study using the French health insurance database, we selected new-onset pSS in-patients hospitalised between 2011 and 2018. We compared the incidence of CVEs (ischemic heart diseases (IHDs), strokes, and heart failure), SAS, VTEs, and PH with an age-and sex-matched (1:10) hospitalised control group.
Materials and Methods We performed a historical paired exposed/unexposed cohort study by analysing data from the French National Hospital discharge database (resulting from the "programme de médicalisation des systèmes d'information", PMSI). This database is a unique French database, covering more than 99% of the population and recording all hospital stays and their causes in private and public hospitals. The available data are age, sex, entry and discharge dates, diagnoses' codes associated with the hospitalisations (according to international classification of diseases (ICD-10), where the primary diagnosis is the main reason for the hospitalisation as entered by the physician), death during hospitalisation, and insurance coverage (general or special regimen involving some low-income individuals).
Data about immunological status (SSa or SSb (Sjögren's Syndrome associated antigen) antibody positivity), minor salivary gland biopsy results, disease activity (ESSDAI, EULAR Sjögren's Syndrome Disease Activity Index scores), targeted organs, or treatment strategies are not available. These data are anonymous and linkable for a given patient over the study period. We could access the data files for a 10-year period between 1 January 2009 and 31 December 2018. Studies on anonymised national health insurance database data are authorised by the National Commission of Information Technology and Liberty (CNIL), and no specific consent is needed.