dataesr/scientific-paragraphs-categorization huggingface dataset:
The reader is referred to  van Haarlem et al. (2013)  for full details of the LOFAR receiving system. The raw data for this observation can be obtained from the LOFAR long-term archive (lta. lofar. eu); observation ID L169059 under project "IPS". We first illustrate the data in a more traditional sense. Figure  1  shows time series' at three discrete observing frequencies of the data taken by LOFAR station CS002, at the centre of the core, and their associated power spectra. The power spectra show a fairly typical shape for intensity scintillation: An initial flat section at the lowest spectral frequencies represents scattering from larger-scale density structures which are close enough to the observer that the scattered waves have not had the space to fully interfere to develop a full intensity scintillation pattern; the turnover (often termed the "Fresnel Knee") indicates the largest density scales for which the intensity scintillation pattern has fully formed; this is followed by a power-law in the spectra illustrating the cascade from larger to smaller density scales, which is cut off in these spectra by white noise due to the receiving system (the flat section covering high spectral frequencies).
Scintillation Pattern Flow The core area of LOFAR contains 24 stations within an area with a diameter of ∼3. 5 km. When viewing dynamic spectra from each of these stations it is clear that the scintillation pattern is mobile over the core (i. e. , temporal shifts in the scintillation pattern are clear between stations) but does not necessarily evolve significantly. Therefore, the flow of the scintillation pattern over the core stations may be viewed directly by simply plotting the intensity received, for a single subband, by each station on a map of geographical station locations, for data from successive time steps. A movie (CasA 20130818 NL. mp4) of the scintillation pattern flow through the observation is published as an online supplement to this article. The result, for 12 example time steps, is displayed in Figure  5 , where a band of higher intensities can be seen to progress from north-west to south-east over the core.
The wave pattern is strongest over the first two hours shown (18:00 -20:00 UT) but then weakens considerably by the start of the LOFAR observation, although it remains evident. STEC from the observations of both stations appears well-correlated, with the Bruxelles dataset lagging behind that of Dentergem. Since Dentergem lies to the WNW of Bruxelles, this suggests a strong westerly component in the direction of travel, which could correspond with the secondary velocity seen by LOFAR. Figure  15  shows hourly plots of the overall geographical distribution of the STEC residuals calculated for all satellite passes seen within each hour by the GNSS stations used. The patterns shown in Figure  15  suggest a spatially and temporally varying propagation of MSTID wavefronts with components along the NE-SW as well as the NW-SE directions.
Furthermore, the examples shown in Figure  15  also indicate the presence of smaller-scale ionisation structures in proximity to the wavefronts of the MSTIDs. This suggests that the scintillation seen by LOFAR is likely associated with the perpendicular propagation of two MSTIDs. However, the STEC variations here are also seen to fade by the start of the LOFAR observation. A further illustration looks at the overall power spectral densities for the STEC residuals on all satellite-receiver pairs considered here over the hourly periods 20:00 UT to 21:00] UT and 21:00 UT to 22:00 UT (Figure  16 ). The earlier hour is chosen alongside the hour covering the LOFAR data period as this better displays the components seen in the spectra The temporal fre- quencies f can be converted into spatial scales L by assuming a given velocity V REL for the motion of the ionospheric structures across a GNSS raypath.
The elevations of the radio source at the start and the end of the first hour of observation were used to establish the range of Fresnel scales for each altitude. The frequencies were chosen to match Figure  1. Table  1  shows that the Fresnel length ranges between ∼1 km and ∼5 km and therefore the plasma structures causing the variations in signal intensity are likely to have a spatial scale size between ∼100 m and ∼5 km. The velocities calculated from the LOFAR data indicate that such structures would take tens of seconds to pass through the source-to-receiver line and the intensity variations in the observed signal occur on a similar timescale.
Further Discussion Geomagnetic activity was low in the mid-latitudes at the time, so enhanced activity was unlikely to be the direct cause of the scintillation observed. However, a weak sub-storm was seen at high latitudes and this reached its peak at the time of the start of the observation. An analysis of GNSS and ionosonde data reveals the presence of an MSTID travelling in the north-west to south-east direction. The larger-scale nature of this TID, and its direction of travel, are strongly consistent with the primary velocity and F-region scattering altitudes seen in the LOFAR observation. It is possible that this TID was caused by the geomagnetic activity at high latitude, but this is not confirmed. Simultaneously, an MSTID is also present travelling in a north-east to south-west direction which would most likely be associated with an atmospheric gravity wave propagating up from the neutral atmosphere.
This can be explained by the inability of GNSS to detect smaller amplitudes in STEC residuals, as the noise floor is encountered for observations with pierce points above the core LOFAR stations (Figures  15  and 16 ). The scale sizes of plasma structures calculated for the LOFAR data indicate that these are an order of magnitude lower than those estimated from GNSS STEC. Smaller ionisation scales developing, for example, through the Perkins instability could induce scintillation on the VHF radio frequencies received by LOFAR but not on the L-band frequencies of GNSS. Hence, scintillation from these mid-latitude smaller-scale ionisation structures, formed through the Perkins instability in conjuction with the presence of TIDs, is likely to be what is detected through LOFAR.
Conclusions and Outlook This paper presents the results from one of the first observations of ionospheric scintillation taken using LOFAR, of the strong natural radio source Cassiopeia A taken overnight on 18-19 August 2013. The observation exhibited moderately strong scattering effects in dynamic spectra of intensity received across an observing bandwidth of 10-80 MHz. Delay-Doppler spectra from the first hour of observation showed two discrete parabolic arcs, one with a steep and variable curvature and the other with a shallow and static curvature, indicating that the scintillation was the result of scattering through two distinct layers in the ionosphere. A cross-correlation analysis of the data received by stations in the LOFAR core reveals two different velocities in the scintillation pattern: A primary velocity of ∼20-40 m s -1 is observed travelling in a north-west to south-east direction, which is associated with the primary parabolic arc and altitudes of the scattering layer varying in the range ∼200-700 km.
A secondary velocity of ∼110 m s -1 is observed travelling in a north-east to south-west direction, which is associated with the secondary arc and a much lower scattering altitude of ∼60-70 km. The latter velocity is associated with a secondary "bump" seen at higher spectral frequencies in power spectra calculated from time series' of intensities, indicating that it is more strongly associated with smaller-scale structure in the ionosphere. GNSS and ionosonde data from the time suggest the presence of two MSTIDs travelling in perpendicular directions. The F-region scattering altitudes calculated from the LOFAR primary scintillation arc and primary velocity, and the larger density scales associated with this, suggest that this is associated with a larger-scale TID seen in GNSS data potentially resulting from high-latitude geomagnetic activity.
With a far greater range of frequencies (multi-octave if the LOFAR high-band is also used) and fine sampling both across the frequency band and in time, LOFAR observations offer a wider sensitivity than that available to GNSS measurements. The analysis techniques shown in this paper also demonstrate that LOFAR can observe ionospheric structures at different altitudes simultaneously; a capability not commonly available for GNSS observations. It also complements these measurements by probing potentially different scintillation regimes to those observed by GNSS. Since this observation was taken, many more have been carried out under a number of projects, recording ionospheric scintillation data at times when the telescope would otherwise be idle. These demonstrate a wide range of scintillation conditions over LOFAR, some of which are seen only very occasionally and perhaps by only one or two of the international stations, illustrating the value to be had by monitoring the ionosphere at these frequencies.
A Design Study, LOFAR4SpaceWeather (LOFAR4SW -funded from the European Communitys Horizon 2020 Programme H2020 INFRADEV-2017-1 under grant agreement 777442) currently underway will design a possible upgrade to LOFAR to enable, amongst other space weather observations, ionospheric monitoring in parallel with the regular radio astronomy observations. Such a design, if implemented, would enable a full statistical study of ionospheric scintillation at these frequencies, alongside the advances in scintillation modelling and our understanding of the ionospheric conditions causing it which can be gleaned in focussed studies such as that presented here.
Acknowledgements. This paper is based on data obtained with the  International LOFAR Telescope (ILT)  under project code "IPS". LOFAR (van Haarlem et al. , 2013) is the  Low Frequency Array designed  and constructed by  ASTRON. It has observing, data processing, and data storage facilities in several countries, that are owned by various parties (each with their own funding sources), and that are collectively operated by the ILT foundation under a joint scientific policy. The ILT resources have benefitted from the following recent major funding sources:  CNRS-INSU, Observatoire de Paris and Universit d'Orlans, France ;  BMBF ,  MIWF-NRW, MPG, Germany ;  Science Foundation Ireland (SFI) ,  Department of Business, Enterprise and Innovation (DBEI) , Ireland; NWO, The Netherlands; The  Science and Technology Facilities Council, UK; Ministry of Science and Higher Education, Poland.
The work carried out at the  University of Bath  was supported by the  Natural Environment Research Council  [Grant Number  NE/R009082/1 ] and by the  European Space Agency/Thales Alenia Space Italy  [ H2020-MOM-TASI-016-00002 ]. We thank  Tromsø Geophysical Observatory, UiT the Arctic University of Norway , for providing the lyr, bjn, nor, tro, rvk, and kar magnetometer data. The Kp index and the Chilton ionosonde data were obtained from the  U. K. Solar System Data Centre  at the  Rutherford Appleton Laboratory. Part of the research leading to these results has received funding from the  European Community 's  Horizon 2020 Programme   H2020-INFRADEV-2017-1under  grant agreement  777442.
The maximum spatial scale at which emission can be recovered by ALMA is approximately 10. 6. The polarization data include 8. 0 GHz of wide-band dust continuum ranging in frequency from ∼ 223-227 GHz and ∼ 239-243 GHz, with a mean frequency of 233 GHz (1. 3 mm). Each 2 GHz spectral window (with 1. 875 GHz of usable bandwidth) was divided into 64 channels with widths of 31. 25 MHz. The flux and bandpass calibrator was J1107-4449; the polarization calibrator was J1256-0547; and the phase calibrator was J1206-6138. These flux/bandpass and phase calibrators were chosen automatically by querying the ALMA source catalog when the project was executed. The polarization calibrator was chosen by hand due to its high polarization fraction. The observatory's flux monitoring program has determined that at Band 6, ALMA's systematic fluxcalibration accuracy is ∼ 10%.
See  Nagai et al. (2016)  for a detailed discussion of the ALMA polarization system. The systematic uncertainty in on-axis linear polarization observations with ALMA is 0. 03% (corresponding to a minimum detectible polarization of 0. 1%). We produced the dust continuum images using the Common Astronomy Software Applications (CASA,  Mc-Mullin et al. 2007 ) tasks TCLEAN and CLEAN. For all of the figures below that show both IRS1 and IRS2 in the same image, we constructed a two-point mosaic of the sources using TCLEAN, centered at (α J2000 = 12:01:35. 202, δ J2000 = -65:08:48. 64), half way between the two sources. While we mainly analyze the lower (∼ 1. 0) resolution, robust = 2. 0 images, we also made higher resolution images Briggs weighting parameters of robust = 0. 5 and -0. 5, yielding approximate resolutions of 0.
This paper makes use of the following ALMA data: ADS/JAO. ALMA# 2013. 1. 00518. S and ADS/JAO. ALMA# 2017. 1. 00655. S. ALMA is a partnership of  ESO  (representing its member states),  NSF (USA)  and  NINS (Japan) , together with  NRC (Canada) ,  MOST  and  ASIAA (Taiwan) , and  KASI (Republic of Korea) , in cooperation with the  Republic of Chile. The Joint ALMA Observatory is operated by  ESO ,  AUI/NRAO  and  NAOJ. The  National Radio Astronomy Observatory  is a facility of the National Science Foundation operated under cooperative agreement by  Associated Universities, Inc. Facilities:  ALMA. Software: APLpy , an open-source plotting package for Python hosted at http://aplpy. github. com( Robitaille & Bressert  2012). CASA (McMullin et al. 2007). Astropy (Astropy Collaboration et al. 2018).
| Variant interpretation Variants interpretation was aided using ALAMUT (Interactive Bio-Software), which includes splice site analysis tools (SpliceSiteFinder, MaxEntScan), protein-function prediction tools (SIFT, Polyphen 2. 0), and links to different databases (ClinVar, LOVD). Only truncating variants were considered in this study: nonsense, frameshift, and canonical splice-site variants that can be considered to be loss-of-function variants. The frequency of truncating variants in the general population was determined using gnomAD, which aggregates whole-exome and wholegenome sequencing data from approximately 140 000 individuals. Missense variants were not included due to unknown impact on function.
| Statistical analyses For each of the 11 genes, the frequency of variants observed in our dataset was compared to that of the gnomAD population in order to highlight differentially abundant variants. This comparison was made for our entire population, as well as for the HBOC and digestive cases separately. Due to low-expected frequencies, the exact binomial test was preferred to an approximate test for proportions. A modified (for discrete test statistics) Benjamini-Yekutieli multitesting correction (under P-value dependence) was applied to the raw P-values obtained from the whole study (n = 33). The tests were bilateral, and an adjusted P <. 05 was considered statistically significant, indicating evidence against the hypothesis of equality of proportions. Statistical analysis was performed using R software, v.
Additionally,  Robertson et al. (2014)  showed that the activity can Article number, page 1 of 21 arXiv:2005. 10105v2 [astro-ph. EP] 21 May 2020 A&A proofs: manuscript no. Hojjatpanah-et-al-arxiv  masquerade as a planet located inside the habitable zone of M dwarfs. It has further been shown that the stellar photometric variability is correlated with the RV jitter (e. g. ,  Aigrain et al. 2012; Bastien et al. 2014; Oshagh et al. 2017 ). Defining such relations as precisely as possible is crucial in optimizing the telescope time invested in the RV follow-up of upcoming planetary candidates from ongoing and upcoming missions such as TESS  (Ricker et al. 2014)  and PLATO  (Rauer et al. 2014)  with high-resolution spectrographs, such as HARPS  (Mayor et al. 2003)  and ESPRESSO  (Pepe et al. 2014).
This paper is organized as following: we explain the target selection and data reduction in Sect. 2. The derivation of stellar parameters is described in Sect. 3. In Sect. 4, we analyze the correlation between RV jitter and photometric peak-to-peak variation. The application of our obtained result on TESS planet candidate host stars is presented in Sect. 6 by identifying the potential targets which will have the lowest RV jitter based on their light curve photometric variability. Finally, conclusions are drawn in Sect. 7.
Light curve: TESS We obtained the light-curves of all 171 stars from MAST. MAST contains simple aperture photometry (SAP_flux)  (Morris et al. 2017)  as well as presearch data conditioning (PDCSAP_flux). Most of the targets (96 %) were observed in only one TESS sector. For stars with light-curves in two or more consecutive sectors, we merged all available light-curves. We used quality-flag 7 as suggested by the TESS Data Product review, also recently used and tested in  (Vida et al. 2019). We used SAP_flux which optimizes the aperture for the best signal-to-noise for the each target  (Bryson et al. 2017 ) and also the calibrated pixels in order 7 101010111111: https://outerspace. stsci. edu/display/ TESS/2. 0+-+Data+Product+Overview to perform a summation of the flux. The SAP light curves provided by the TESS pipeline are also background corrected.
HARPS RVs For the 171 stars selected in Section 2 we collected the available HARPS data through ESO archive 8. The data were reduced using the official HARPS pipeline (DRS), that delivers the RV and full width at half maximum (FWHM) measured on each Cross-Correlation Function (CCF). We determined the significant outliers of the RV time-series by applying a sigmaclipping of three standard deviations, and removed the few measurements that show an RV error larger than 9 ms -1 as an admittedly arbitrary value. This threshold was set for being significantly larger than the median RV error (∼ 0. 80 ms -1 ) for the whole sample. After the nightly binning of the data, in total, over 3971 spectra were collected and 81 RV measurements (∼ 2%) were removed based on the previous outlier and photon noise criteria.
Chromospheric activity indicator log(R HK ) and v sin i We derived values for the S-index of each star to evaluate the chromospheric magnetic activity level and its evolution. We used the open source package ACT IN 11 (Gomes da Silva et al. 2018)  to calculate the S CaII index  (Vaughan et al. 1978; Duncan et al. 1991)  homogeneously for all individual spectra following the instructions in (Gomes da  Silva et al. 2011; Hojjatpanah et al. 2019, and references therein). We then calibrated S CaII to the Mt. Wilson scale using open source package pyrhk 12  and calculated the R HK chromospheric emission ratio as defined in  Noyes et al. (1984). For each star we created a log(R HK ) time-series and calculated the mean and peak-to-peak of variation of log(R HK ) to evaluate the variation of chromospheric activity during the observation span.
(2016)  which uses the Bayesian framework to evaluate the significance of the presence of a correlation. In each panel of Fig. 6  we also report the value of the ρ pe correlation coefficient and its posterior distribution. For data obtained within year 1 time span, we obtained the correlation coefficient ρ pe = 0. 989 ± 0. 005 (ρ sp = 0. 550 ± 0. 133) for the correlation between photometric variability and RV jitter. As the time lag between RV measurement and TESS observation increases (subsample year 2, year 3 and year 4), the correlation becomes less strong and less significant. In the Table. 1, we presented the values of the ρ pe and ρ sp  (Figueira et al. 2016). We also calculated the standard deviation and 95% Highest Posterior Density (HPD) Credible Interval as described in  Figueira et al.
This has an impact on the predictive ability of using one dataset to estimate the other: since several studies aimed at estimating the RV jitter using photometric measurements (e. g. ,  Oshagh et al. 2017; Bastien et al. 2014 ), here we note that such a prediction after more than 2 years of photometric observation can be imprecise since the correlation between RV jitter and peak-to-peak light curve variability decreases ( ρ pe ∼ 0. 8 at year 2 to ρ pe ∼ 0. 5 at year 3).
We used Markov-chain Monte Carlo (MCMC) sampling using the library emcee (Foreman-Mackey et al. 2013) to perform the fit and obtain the uncertainties of the fitted parameters; for details refer to Appendix. A. The best fits are overplotted in Fig. 8  for year 2 which is closest subsampling group to the TESS observations with fairly sufficient number of data points. The best fits are also overplotted in Fig. 9  for the all RV sampling. All fitted parameters are reported in the Table. A. 1. The best fit indicated with the Maximum a Posteriori (MAP). We compare the best fitted models using RMS of the residual (RMS resi ) and Bayesian information criterion (BIC). For all the subsamples and also for the whole sample the BIC values are similar between two models. However, since the piecewise function is penalized for the two extra parameters, BIC values for the single power law function has lower BIC than piecewise function, which suggests that the single power law function as a better model, but with a low significant evidence.
We also fitted the two models on the year 1, year 2, year 3, and year 4 subsampling (see Table. A. 1). The results show that for the year 2, which is the closest subsampling group to the TESS observations with fairly sufficient number of data points as well as a strong correlation, the piecewise power law also describes the data better than the single power law with lower RMS resi , however, the BIC is larger for the piecewise model because of the two extra parameters in this model. The correlation between RV-RMS and peak-to-peak of light curve variability of showed two regions separating with a knee point where the strength of the correlation changes. This might be due to the transition from the spot-dominated regime to the faculae-dominated regime. These two regimes have already been discussed in several studies  (Lockwood et al.
Materials and methods A retrospective review was performed using a multi-center, surgical research database compiled by 17 fellowship-trained shoulder surgeons between August 2007 and August 2016. Patients presented to clinic and were initially selected for either nonoperative or operative management by each surgeon at his/her respective site based on radiographic findings and evidence of fracture. In the case of nondisplaced fractures, a nonoperative management was trialed first. In cases of significant or worsening displacement, patients were referred to operative management. All patients over the age of 65 who underwent RTSA as a treatment for PHF were considered for inclusion in the study. Exclusion criteria included previous, same-side procedures to treat their PHF and lack of follow-up at or exceeding one year postoperatively.
Data analysis Data analysis was conducted using SPSS Ò (IBM, Armonk, NY, USA) to calculate descriptive statistics. Continuous data is described as means with ranges or standard deviations. Categorical data is described as counts and percentages. Means were compared between the two groups using a two-sided ttest. Univariate analysis was used to compare group and time interactions among patients. Statistical significance for all analyses was set at P < 0. 05.