dataesr/scientific-paragraphs-categorization huggingface dataset:
After this, we call the CASA task mstransform again to rebin the data to the final channel width of ∼2. 54 km s -1 for PHANGS-ALMA. This rebinning averages together an integer number of channels, typically 5-6 for This data set produces the noise spectrum seen in the top panel. Subsequent panels show the noise spectrum in the visibility data after regridding using CASA's mstransform task and using visstat to measure the noise spectrum. The regridding introduces patterns into the noise due to the uneven amount of independent data contributing to each output channel. The frequency of the variations is set by the aliasing between the new and original channel width. For example, increasing the channel size by a factor of 1. 05 creates a pattern with 20 channel periodicity. To see this, compare the second (1.
2× increase in channel width), fourth (1. 9×), and bottom (1. 05×) panels. In the bottom right of each panel we quote the channel-to-channel correlation of the data. This is the linear correlation coefficient between noise values in adjacent channels of individual spectra and would ideally remain zero under interpolation. The correlation is a function of the statistical independence of the data contributing to the new channel and the interpolation method used. Because cubic interpolation draws on many nearby channels the correlation induced between immediate neighbors is weaker. PHANGS-ALMA CO(2-1) data, and uses no interpo-lation. The rebinning factor is picked to ensure that the final channel width is as close as possible to the desired spectral resolution for that configuration without exceeding the specified value.
Next we combine all regridded and rebinned spectral windows for each target and spectral product into a single measurement set using CASA's concat task. We adopt this regrid-then-rebin approach in order to work around current limitations in CASA's spectral regridding capabilities, which we describe below. For the PHANGS-ALMA CO(2-1) data this procedure yields a final channel width, and so a final spectral resolution, near ∆v ≈ 2. 54 km s -1 for CO(2-1) and 13 CO(2-1), with minor variations from target to target. It yields a resolution near ∆v ≈ 6. 0 km s -1 for C 18 O(2-1), see Table  1. After combining the data, the user has the option of re-weighting the visibilities by the measured noise using CASA's statwt. This reweighting ensures self-consistent weights in each final line data set but risks introducing pathologies if real line or continuum emission contaminates the weight calculation.
For PHANGS-ALMA this step occurs after continuum subtraction, so the main danger is contamination by broad line emission. We do apply this procedure to PHANGS-ALMA. We used a 50 km s -1 -wide window at each edge of the final spectral window for re-weighting with statwt. This process excludes channels associated with the line itself from the weight calculation. The new weights reflect noise measured from channels far from the systemic velocity of the galaxy. Noise and spectral regridding in CASA: Our rebinning and regridding strategy introduces some frequency dependence into the noise in the final data products and also leads to some channel-to-channel correlation. While this is unfortunate, our strategy appears to reflect the best current option given the spectral regridding capabilities of CASA.
We expect that this situation will improve in future versions of CASA. Since it leaves an imprint on our data and likely affects a significant amount of already published ALMA data, we explain the effect here. The noise pattern arises from the interpolation carried out by CASA's mstransform task. mstransform can only regrid to larger channel widths than those in the input data. In the case where the output channel width is not an integer multiple of the input channel width, this regridding leads to a varying number of independent data points contributing to different output channels. We illustrate this effect in Figure  3. We begin with a pure noise, 100 channel visibility data set created using CASA's simalma. The nominal frequency and channel width are ∼230 GHz and 1 MHz, but do not matter for this exercise.
For example, consider gridding from a 1 km s -1 channel to a 1. 2 km s -1 channel. Every ∼5 output channels, the balance of independent input data will shift from 5-to-1 to 1-to-1 and then back. In addition to noise variation, the interpolation also affects the correlation between the intensity in successive channels. Because of the variable amount of input data per output channel, the interpolation both introduces channel-to-channel correlation and leads to variations in this channel-to-channel correlation. In optical terms, this processing broadens the line spread function of the data and leads to some dependence of the line spread function on frequency. Figure  3  notes the magnitude of the induced channel-to-channel correlation for each case. These issues reflect current limitations of CASA, and we expect that the situation will improve in the future.
The issue could be addressed by using the fftshift option in mstransform but that option was not functioning as intended in the versions of CASA that we used. Alternatively, the effect could be mitigated by allowing mstransform to oversample the line spread function (i. e. , to move to smaller channel width). In this case, heavily oversampled data could be convolved with an appropriate kernel to produce an even amount of independent data per final, coarser output channel. This functionality is also currently not available. Regridding in the pipeline: To minimize the effects of the interpolation scheme, the pipeline picks an output channel size that leads to only slow noise variations, i. e. , a much more "stretched out" version of the last panel in Figure  3. During the initial regridding step we increase the common channel size by a small factor, ≈ 3×10 -4 , compared to the largest channel in any input data set.
This will lead to slow noise variations on scales of 1,000 channels. After this regridding, we rebin the data. The magnitude of this effect is damped out by the rebinning that follows our regridding. At this stage, many independent channels are averaged together to form each final output channel, e. g. , for PHANGS-ALMA CO(2-1) we rebin by a factor of 5-6. As a result of this rebinning the fractional difference in the amount of independent data in a final output channel varies only modestly across our data. Still, this effect is enough to induce gradual noise variations with magnitude of ∼10% and corresponding variations in the channel-to-channel covariance. These algorithm-induced variations combine with real receiver temperature variations and atmospheric effects to yield the final frequency dependence of the noise in our data cubes (see Section 7).
After this flagging, we combine all of these line-free measurement sets using the CASA task concat. At this stage, as for spectral line imaging, the user has the option to re-weight the combined data set according to the measured rms noise in the visibility data using the CASA statwt. This option runs the risk of down-weighting regions with bright emission. For PHANGS-ALMA, the signal-to-noise in the continuum is extremely low, and the scatter in amplitude for individual u-v data will be determined mainly by the noise in the data. Therefore, we did apply this option for PHANGS-ALMA. Finally, we use the CASA task split to collapse each spectral window in this continuum-only data set to have only a single channel. This step dramatically reduces the overall volume of the measurement set. For PHANGS-ALMA even after this averaging, the fractional bandwidth of the widest continuum channels is modest, 1%, and bandwidth smearing is not a large concern given the low signal-to-noise of the continuum.
Throughout this process, we force frequent major cycles. During a major cycle, the model is projected into u-v space and subtracted from the visibility data. The residual u-v data are then imaged and used in the next deconvolution step. By comparing the model and data in the u-v plane, we minimize the impact of CASA's assumption that the synthesized beam, i. e. , the interferometric response to a point source, does not vary as a function of position on the sky. In actuality, the synthesized beam can vary across a large mosaic. This variation can introduce minor inconsistencies during the image-plane deconvolution performed in the minor cycles. By frequently projecting back into u-v space, these inconsistencies are mostly corrected. More generally, the major cycle represents a direct comparison between data and model.
When setting these thresholds, the noise level is estimated from the data based on the median absolute value of the residual image. The noise estimated in this way changes relatively little during the course of deconvolution. This procedure has proven robust. It runs with minimal human intervention across all of PHANGS-ALMA and many other line emission maps. It also works well with many VLA 21-cm H I data sets, though we note a few caveats below. In our view, the key choices were: • Use multi-scale clean with no clean mask or a very non-restrictive clean mask and a relatively high signal-to-noise threshold. • Force many major cycles. • Clean deep with a carefully directed single-scale clean, adopting a low signal-to-noise threshold. • Direct this single-scale clean by applying automated masking to the current deconvolved image, rather than, e.
Indeed, our first-pass imaging for the PHANGS-ALMA targets without any clean masks yielded almost the same results as the final imaging run. By contrast, supplying an over-restrictive mask often biases the deconvolution and can lead to divergence during multi-scale cleaning. We illustrate the procedure for one galaxy in Figures  4  and 5. Figure  4  shows the deconvolution of the 7-m data for that galaxy. Figure  5  shows the combined de-convolution of the 12-m and 7-m data. Both figures show snapshots of a 20-channel "slab", i. e. , an integral across twenty velocity channels, in one PHANGS-ALMA galaxy. Because the integral extends across the slab, the signal-to-noise of these images is improved by a factor of ∼5 compared to the individual channel maps themselves. Thus, these visualizations show a very aggressive stretch that could bring out artifacts not necessarily visible in individual channel maps.
CASA version: tclean refers to the latest CLEAN algorithm implementation available in CASA. This task evolved significantly over the course of the PHANGS-ALMA project. For the "v2 pipeline" and v4 PHANGS-ALMA data release associated with this paper, we imaged the data using tclean in its serial (i. e. , nonparallel) mode in CASA version 5. 4. 0. PHANGS-ALMA CO(2-1) imaging summary: Table  2  and Figures 7, 8 , and 9 summarize our application of this procedure to image the PHANGS-ALMA CO(2-1) data. They report the minimum, maximum, median, and 16 th -84 th percentile range of key quantities including the properties of the synthesized beam, the area imaged, and the noise and dynamic range achieved in the cubes. For PHANGS-ALMA we imaged both the 7-m array data and the combined 12-m+7-m array data.
Imaging Most of the inputs to tclean are tunable parameters in the pipeline. By default, PHANGS-ALMA uses the following imaging parameters: 1. Cell size. We use the ALMA observatorydeveloped analysisutils package to estimate the size of the synthesized beam based on the u-v coverage of the data. Then the pipeline picks a cell size that is both a round number, e. g. , 0. 05 or 0. 2 , and oversamples the synthesized beam by a factor of 4 along the minor axis and more along the major axis. As shown in Table  2  and Figure 7 , for PHANGS-ALMA we place 4-7 pixels along the beam minor axis and 6-10 pixels across the beam major axis. 2. Image size. The pipeline chooses an image size with a linear extent > 20% larger than the field of view of the data themselves. We choose an image size in pixels that matches the recommendations for best performance using CASA's Fast Fourier Transform (FFT) algorithm, i.
This is simply a conventional peak temperature (sometimes referred to as "moment 8") map constructed from a cube that has been convolved spectrally with a matched-line width filter. 5. We have compared the imaging results for different arrays and vetted the performance of our pipeline using simulated observations with known input. These tests show that, after the inclusion of total power data, the pipeline does an excellent job of recovering known input. They also show that the 12-m+7-m imaging performs significantly better than 7-m only imaging in many cases, even after matching the resolution of the output images. The differences, which are explored in detail in the appendix, are a function of signal-to-noise and source structure. The PHANGS-ALMA pipeline has so far been applied successfully to roughly 1,000 individual measurement sets and is publicly available on github.
Relative calibration between scheme anchors the amplitude calibration of the total power data to interferometric observations with the 7-m array, and so to the ALMA calibrator database. In principle this should yield stable, high quality calibration. To check this, we assessed the internal consistency of the PHANGS-ALMA total power observations. Over the course of our ALMA Large Program, we observed the same targets repeatedly, and the individual observations are already deep enough that they detect most galaxies at high significance. This allows us to compare how the line brightness towards the same target varies when measured on different days. The magnitude of these variations gives us an upper limit to the stability of the ALMA total power flux calibration. For this test, we selected a subset of six galaxies.
We did not find any convincing indication that the slopes depend on the difference between the observing dates of the EB pairs. This suggests that the calibration uncertainties affecting different EBs are mutually uncorrelated. We interpret this scatter as indicative of the stability of the ALMA total power calibration. It will not reflect underlying uncertainties in the ALMA calibrator database, but most other uncertainties should be captured by this test. Assuming that calibration uncertainties affecting different EBs are uncorrelated, the uncertainty in individual EBs is about √ 2 times lower than the measured 0. 03 dex ≈ 3% difference between two independent EBs. The final total power cube for each galaxy typically results from averaging several EBs. If the calibration uncertainty is uncorrelated, this would reduce the uncertainty from 3% by an additional √ N factor, where N is the number of EBs.
ACKNOWLEDGMENTS We gratefully acknowledge a prompt and constructive review by the anonymous referee during a difficult time. This work also benefited immensely from helpful discussions with  Crystal Brogan ,  Jeffrey Mangum , and the  North American ALMA Science Center  and  European Southern Observatory  staff, including  Dirk Petry. The computing and software infrastructure used to process PHANGS-ALMA and develop this pipeline at  OSU  was built and supported by  David Will , who for years was the most supportive and welcoming person in a supportive and welcoming department. He will be sorely missed. This work was carried out as part of the PHANGS collaboration. The work of A. K. L. , J. S. , and  D. U. is partially supported by the  National Science Foundation (NSF)  under Grants No. 1615105 ,  1615109 , and  1653300 , as well as by the  National Aeronautics and Space Administration (NASA)  under ADAP grants  NNX16AF48G  and  NNX17AF39G.
Full length glycosylated SARS-CoV-2 Spike protein (S-protein). The preparation of this system has been described elsewhere  (6)  and experimentally validated by cryo-electron tomography  (7). In brief, the system contained four membrane-embedded S-protein trimers assembled from available structures and de novo models for the missing parts. S-protein head was modelled based on PDB ID: 6VSB (8) with one RBD domain in an open conformation. The stalk connecting S-protein head to the membrane was modelled de novo as two trimeric coiled coils  (9). The TMD as well as the cytosolic domain were modelled de novo. Glycans were modelled according to glycoproteomics data (10) (Supplemental Figure  S6  for glycan composition).
Quantification of protein solvent accessible surface area (SASA) To quantify the impact of glycans on protein surface accessibility, per-residue SASA was calculated in parallel for unglycosylated and glycosylated proteins (SASAnogly and SASAgly, respectively) where SASAgly is the accessible surface area of the protein in the presence of glycans. Calculations were performed with GROMACS gmx sasa and custom Python scripts (see code description and availability below). In case of multiple glycan conformers, SASAgly was calculated and averaged over all conformations. Shielding was calculated as a relative SASA:.
Methodology and user scripts A library of conformers of 11 distinct glycans (up to 40 000 conformers per glycan) was generated and is publicly available on Zenodo (https://zenodo. org/record/5083355). Each conformer contains structural information about the glycan and a tripeptide to which glycan was attached. For convenience, trajectories sampled at 100 ps and 1 ns are provided (GlycoSHIELD time step). The library has an open character and additional glycans species as well as more extensive sets of conformers will be made available in the future. Users are also encouraged to share glycan+tripeptide trajectories from existing glycoprotein simulations. For every glycosylation site, users select a glycan to be grafted from the available library. GlycoSHIELD aligns every glycan conformer using the backbone atoms of its tripeptide anchor onto the corresponding residues on the protein, i.
GlycoSHIELD output consists of glycan frames successfully grafted onto the static protein for each probed glycosylation site which are exported in. XTC and. PDB formats. In case of multiple glycosylation sites, the script "GlycoTRAJ. py" may be used to merge this information into a single master trajectory with all grafted glycans. Typically, the smallest number of glycan frames grafted across all glycosylation sites of the protein can be selected, thus generating a "trajectory" with static protein and mobile glycans.
Code description and availability Source code and tutorial are available on GitHub (https://github. com/GlycoSHIELD-MD/GlycoSHIELD-0. 1). Code for GlycoSHIELD installation via conda (e. g. , « conda install GlycoSHIELD ») is in preparation. GlycoSHIELD. As an input, user provides a static structure file (PDB format) or series of structures (PDB or GROMACS XTC format). Glycosylation sites and glycans to be grafted are provided by users as batch input files as follows: the first column defines protein chain symbol as defined in PDB, followed by comma-separated glycosylation site residue number together with its two flanking residues, third column defines the tripeptide residue numbers as defined in the glycan conformer library (typically 1,2,3). Fourth and fifth column provide path to the glycan conformer reference structure (PDB) and trajectory files (XTC), respectively.
e. , a trajectory). Each of the frames will be grafted separately. --dryrun -does not produce grafted structures, but informs about the number of accepted glycan conformers per site and per protein conformer. Useful, GlycoSASA. Shielding calculation requires a set of glycan trajectories generated by the GlycoSHIELD script (see above), each containing the same, static protein conformer, and a mobile glycan. Script syntax is as follows: The output consists of a text file that assigns a shielding value to each residue. These values multiplied by 100 are also written into the b-factor column of the PDB file. Residues that are not visible to the probe at all are further marked by 0 in the occupancy column of the PDB file. --ignorewarn -does not stop the grafting if no glycan conformers not clashing with the protein were found.
Acknowledgments We thank  IDRIS  for the allocation of HPC resources on the Jean Zay supercomputer (allocations # 2020-AP010711998  and # 2021-A0100712343  to CH). We thank the  Max Planck Computing  and  Data Facility  for providing computational resources. We thank the  French Institute for Bioinformatics (IFB)  for their support. MS is supported by a  Fonds zur Förderung der wissenschaftlichen Forschung Schrödinger fellowship  ( J4332-B28 ). Work in the laboratory of CH is supported by the  Agence Nationale de la Recherche  (grants # ANR-16-CE16-0009-01  and # ANR-21-CE16-xxxx-xx ) and the Foundation NRJ.
Despite numerous eruptions in the latest Holocene and efforts by several university and government groups to create and sustain a monitoring network, Ceboruco remains undermonitored, meaning that it is intermittently rather than continuously monitored by dedicated groups. With no consistent monitoring data available, we look at the geology and the eruptive history to inform our prior models. We estimate the probability of a magmatic eruption within the next time window (1 year) of ~ 0. 002. We show how the BET creates higher probabilities in the absence of monitoring data. That is, there is a cost in terms of higher probabilities and higher uncertainties for having not yet developed a sustained volcano monitoring network. We present absolute probability maps (unconditional in terms of eruption size and vent location) for a magmatic eruption at Ceboruco volcano.
2012; 2008). Here, we use the PyBetVH software  (Wild et al. 2019; Strehlow et al. 2017; Tonini et al. 2015) , an updated version of BET_VH, to conduct a PVHA for Ceboruco volcano (Mexico). We de ne a generic ET, that is, not calibrated by current monitoring or related observations, for Ceboruco. This ET includes hazards related to both magmatic and amagmatic unrest. We describe how PyBetVH is used to produce probability maps for the magmatic branch by merging information from the geological record (i. e. , past data), numerical simulations and expert-based weighting of the data (i. e. , prior models). An important reason to develop an ET for Ceboruco volcano is that the volcano has been quite active within the last ~1,000 yr, but has not erupted during the last century. Because of this hiatus in most recent activity, Ceboruco is less monitored than other active volcanoes in Mexico.
ii) Núñez-Cornú et al. (  2020 ), Rodríguez  Uribe et al. (2013)  and  Sánchez et al. (2009)  concluded that the seismic swarms at Ceboruco are dominated by volcano-tectonic (VT) and low-frequency (Lf) earthquakes and are associated with the faults in and around the edi ce; 3. increased degassing (i u ) -in ltration of abundant meteoric water from a passing hurricane may lead to an increased degassing due to the residual heat from cooling magma. From the analysis of two seismic datasets, Rodríguez  Uribe et al. (2013)  and  Sánchez et al. (2009)  con rmed that the Lf events recorded at Ceboruco are associated with movement of pressurized uids. Several hazardous phenomena have been associated with non-magmatic unrest episodes (i. e. , seismic and increased degassing) and are considered in our ET: rock slides, debris avalanches, debris ows, ballistics, ash fallout and increased fumarolic activity (Fig.
An advantage of PyBetVH is that the posterior probability calculation includes the distribution for the probabilities, in addition to the expected values, providing a more complete view of the uncertainty in the probability estimate than is otherwise possible. PyBetVH uses as input several text les in which the user speci es the values each parameter (e. g. , y, n, α and β) is taken in order to estimate the sample proportions and the prior distributions. The software and a step-by-step tutorial for building the input les are freely available at https://vhub. org/resources/betvh (Tonini et al. 2016 b)  or by request to the corresponding author. In the next section we describe the implementation of the BET for Ceboruco and the choice of model input data to estimate probabilities related to a magmatic eruption.