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Finn E. Brian W. Fiona A. Kristin K. Select your desired journals and corridors below. You will need to select a minimum of one corridor. What are corridors? This survey has a total exposure time of 1. Twenty-eight sources are firmly detected, and 10 are detected with low significance; 8 of the 38 sources are expected to be active galactic nuclei.

The three brightest sources were previously identified as a low-mass X-ray binary, high-mass X-ray binary, and pulsar wind nebula. Based on their X-ray properties and multiwavelength counterparts, we identify the likely nature of the other sources as two colliding wind binaries, three pulsar wind nebulae, a black hole binary, and a plurality of cataclysmic variables CVs.

This temperature difference may indicate that the Norma region has a lower fraction of intermediate polars relative to other types of CVs compared to the Galactic center. Hard X-ray observations of the Galaxy can be used to identify compact stellar remnants—white dwarfs WDs , neutron stars NSs , and black holes BHs —and probe stellar evolution in different environments.

While a number of sensitive surveys of Galactic regions e. During the first two years of its science mission, NuSTAR performed surveys of the Galactic center GC and the Norma spiral arm in order to compare the X-ray populations in these regions of the Galaxy, which differ with regard to their star formation history and stellar density. The Norma region was targeted because its stellar populations are younger than those in the GC but older than those in the young Carina and Orion star-forming regions observed by Chandra F14 and references therein.

An additional goal of this survey was to identify low-luminosity high-mass X-ray binaries HMXBs falling below the sensitivity limits of previous surveys in order to constrain the faint end of the HMXB luminosity function; the evolutionary state of the Norma arm and the large number of OB associations along this line of sight Bodaghee et al.

Distinguishing between these types of sources is not possible based on Chandra data alone, especially since most of the Norma X-ray sources have low photon statistics. Descriptions of our source detection technique, aperture photometry, and spectral analysis are found in Sections 4 , 5 , and 5.

Figure 1. The pattern of stray light contamination is well understood and can be carefully predicted, 22 while the patterns of ghost rays are more challenging to model Koglin et al. Therefore, rather than observing the whole region surveyed by Chandra , we performed simulations of stray light contamination and focused our observations on three areas of the sky that would be least affected by stray light.

Even in these "cleaner" areas, at least one of the FPMs was often affected by stray light, so exposure times for more contaminated observations were lengthened to compensate for the fact that we would not be able to combine data from both modules. Seven additional pointings were specifically made at the locations of some of the brightest NARCS sources found to be hard in the Chandra band and for which optical or infrared spectra have been obtained Rahoui et al.

Corral-Santana et al. Although the sources in the first mini-survey King et al. The contamination in observations A, A, B, and was so extensive that these observations were not included in our analysis. The analysis of these Chandra observations and the details of the spectral extraction are provided in F For reference, we provide information about all of these relevant archival Chandra observations in Table 2.

Furthermore, in this study, we make use of Chandra observations that were triggered to follow up four transient sources discovered by NuSTAR. These Chandra observations were used to constrain their soft X-ray spectra and better localize their positions so as to be able to search for optical and infrared counterparts.

These times vary significantly because some of these sources were obvious in the raw images, while others required mosaicking and careful photometric analysis to determine that they were significant detections. We made exposure maps with and without vignetting corrections to be used in different parts of our analysis. Next, we cleaned the event files of stray light contamination by filtering out X-ray events in stray light—affected regions.

We also excised the most significant ghost rays from observations from the first mini-survey, defining the ghost ray pattern regions in the same way as B This observation helped to characterize the outburst duration of this transient T14 , but it was so extensively contaminated by ghost rays that it was not included in our analysis.

Finally, a few observations show additional contamination features, such as sharp streaks listed in Table 1 , which were also removed. This source localization was done independently for each FPM of each observation and was used to apply translational shifts to event files and exposure maps.

In performing astrometric corrections, we limited ourselves to using sources with net counts in each individual observation and FPM and located on-axis. For on-axis sources with this number of counts, we expect the statistical error on the centroid to be based on simulations B. Grefenstette , personal communication. Table 4 lists the applied boresight shifts and the bright sources used for astrometric correction.

We were only able to apply these astrometric corrections to 23 out of 60 observations 43 out of modules due to the dearth of bright X-ray sources in our survey. Our inability to astrometrically correct all the observations does not significantly impact the results of our photometric and spectral analysis, since the radii of the source regions we use are significantly larger than the expected shifts.

Checking each shifted and unshifted image by eye and comparing the locations of NuSTAR sources with their Chandra counterparts in shifted and unshifted mosaic images, we confirm that these boresight shifts constitute an improvement over the original NuSTAR positions. We reprojected the event files of each observation onto a common tangent point and merged all the observations and both FPMs together to maximize the photon statistics. We used the exposure maps without vignetting corrections when we calculated the source significance and net counts, since these calculations require comparing the exposure depth in the source and background region apertures and the background is dominated by nonfocused emission.

Instead, when calculating sensitivity curves Section 6. When calculating the source fluxes, vignetting corrections are taken into account through the ancillary response file ARF. This technique, which we refer to as the "trial map" technique, is described in detail by Hong et al. As a result of NuSTAR 's point-spread function PSF being larger and its background being higher and more complex compared to other focusing X-ray telescopes, such as Chandra and XMM-Newton , the utility of typical detection algorithms, such as wavdetect Freeman et al.

One way of dealing with this problem is to add an additional level of screening to the results of conventional algorithms, calculating the significance of detections by independent means and setting a significance detection threshold. The trial map technique is more direct, skipping over the initial step of using a detection algorithm such as wavdetect.

To make a trial map, for each sky pixel, we calculate the probability of acquiring more than the total observed counts within a source region due to a random background fluctuation. For each pixel, the source and background regions are defined as a circle and an annulus, respectively, centered on that pixel. The mean background counts expected within the source region are estimated from the counts in the background region scaled by the ratio of the areas and exposure values of the source and background regions.

Using background regions that are symmetric around the central pixel helps to account for spatial variations of the background. In making trial maps, we plot the inverse of the random chance probability, which is the number of random trials required to produce the observed counts simply by random background fluctuations, such that brighter sources with higher significance have higher values in the maps. The source region sizes we used are slightly larger than those used in the analysis of the NuSTAR GC survey, since the smaller sizes are especially suited for picking out relatively bright sources in areas of diffuse emission, but in the Norma region there is no evident diffuse emission apart from stray light and ghost rays.

Figure 2. The colors are scaled by the logarithmic trial map values. When considering how to set detection thresholds for our trial maps, we excluded the observations from the first mini-survey and observation , since they have significantly higher levels of stray light and ghost ray contamination than the rest of the survey; in the remainder of this paper, we will refer to this subset of observations as the "clean" sample.

Figure 3. The x -axis is shown in a double logarithmic scale. Following the procedure described in Hong et al. Figure 4. Fluxes of sources in the gray region are uncorrelated with the trial map values and used to set the detection threshold, which is shown by the red horizontal line. For a source to be considered for the final catalog, we require that it exceed the detection threshold in at least two trial maps.

If all 18 trial maps were independent of each other, the expected number of false sources would be equal to , where is the number of NARCS sources included in a NuSTAR counterpart search, is a binomial coefficient, and p is the fraction of false sources to be rejected in each map Hong et al. However, the trial maps are not completely independent, given that their energy ranges overlap.

Having established detection thresholds for each trial map, we first search for any Chandra sources detected by NuSTAR. We consider all NARCS sources that exceed the detection threshold in at least two trial maps as tier 1 candidate sources.

We then inspect all the candidate sources. Uncertainties for tier 2 sources are not provided, since the positions of these sources are simply set to the Chandra positions. This value is the number of random trials required to produce the observed counts from a random background fluctuation. For extended sources, this is the maximum trial map value within of the listed source location. There are 18 trial maps using six different energy bands and three different PSF enclosure fractions.

We calculated the statistical error by performing Gaussian fits to histograms of the spatial count distributions in the x - and y -directions in a pixel image cutout centered on the source position. For the systematic uncertainty, we assumed the nominal 8'' astrometric accuracy Harrison et al. Table 5 provides information about the detection, position, and Chandra counterparts of all NNR sources. For photometry and spectral extraction, we used circular source regions and, whenever possible, annular background regions centered on the source positions provided in Table 5.

Using aperture regions that are symmetric about the source position helps to compensate for this nonuniformity. The default background regions are annuli with inner radii and outer radii. In order to prevent contamination to the background from other sources, it is preferable for background regions not to extend within of any tier 1 source.

Furthermore, when a source is located close to the edge of the FOV, using an annular background region may not sample a statistically large enough number of background counts. Finally, although we removed the most significant patches of stray light and ghost ray contamination from the NuSTAR observations, nonuniform low-level contamination remained.

In these cases, we adopted a circle with a radius for the background region and placed it in as ideal a location as possible following these criteria:. For a given source, background aperture regions were defined for each observation and FPM individually, since stray light and ghost ray contamination and the fraction of the default annular background that lies on a given detector vary depending on the observation and the module.

Thus, the exposure value at the location of a source in the mosaicked exposure map may be higher than the effective exposure for the source based only on observations used for photometric analysis; the latter effective exposure is the value reported in Table 5. Table 6 provides the results of our aperture photometry and includes flags that indicate which sources required modified background regions.

Values in the top bottom row for each entry are based on using source aperture regions with small large radii. All other table column values are based on using small aperture regions. See Section 5. These sources are only separated by and thus contaminate each other's default background regions, although they do not suffer from any additional background problems. Therefore, since annular background regions are preferable for minimizing the vignetting effect, we simply redefined their background regions as an annulus with an inner radius and outer radius centered between the two sources.

Due to their proximity, the photometric and spectral properties of these sources as derived from the radius circular apertures are less reliable than those from the radius apertures. Having defined aperture regions, we extracted the source and background counts for each source in each observation.

We then calculated the expected number of background counts in each source region by multiplying the counts in the background region by the ratio , where and are the areas in units of pixels and and are the exposures without vignetting corrections of the source and background regions, respectively. Then, the net source counts were calculated by subtracting the total expected background counts in the source region from the total source counts.

In Section 5. However, for all sources, we also derived fluxes in a model-independent way, since the spectral fitting of faint sources is prone to significant uncertainty. For each source and background region in each observation and module, we used nuproducts to extract a list of photon counts as a function of energy and generate both an ARF and a response matrix file RMF ; the ARFs are scaled by the PSF energy fraction enclosed by the aperture region. The estimated background contribution, scaled from the photon flux measured in the background region, was subtracted.

These photon flux measurements assume a quantum efficiency of 1; this is a decent approximation for the NuSTAR CdZnTe detectors, which have a quantum efficiency of 0. These measurements are presented in Table 6. Fluxes derived using the two different source region sizes are in agreement with one another, except for three sources that are located in regions of diffuse emission or ghost rays and thus do not appear as exactly point-like.

Comparing the model-independent fluxes with those we derived from spectral modeling see Section 5. As a result, accurate background subtraction is more important when using the larger aperture regions, and it is not surprising that our crude subtraction method, which assumes a spectrally flat background, for the model-independent fluxes leads to discrepancies with the spectral fluxes.

NuSTAR 's high time resolution allows us to characterize the timing properties of detected sources over a range of timescales. To characterize the source variability on timescales, we used the Kolmogorov—Smirnov KS statistic to compare the temporal distributions of X-ray events extracted from source and background apertures in the energy band.

The background light curve acts as a model for the count rate variations expected in the source region due to the background. The maximal difference between the two cumulative normalized light curves gives the probability that they are drawn from the same distribution, i. Any source with a KS statistic lower than in any observation is flagged as short-term variable by an "s" in Table 6. For each source, we ran the KS test independently for each of the observations in which it was covered.

Since the KS test was applied times in total, the adopted threshold corresponds to spurious detection. We identified two sources as variable using the KS test. Figure 5. The light curve exhibits evident short-term variability.

The bottom panel displays the light curve extracted from the background aperture region. The blue dashed lines in the top two panels show the mean background count rate scaled by the source region area. Sources were flagged as long-term variable with an "l" in Table 6 if their photon flux differed by based on their flux measured uncertainties; given the number of flux comparisons performed, this threshold should result in spurious detection. Since we performed these joint fits for 24 sources, we would expect as many as 2 spurious detections of variability.

But we made the criterion more stringent by requiring that, for a source to be considered variable between the Chandra and NuSTAR observations, its Chandra and NuSTAR normalizations must be inconsistent regardless of which of three different spectral models is adopted. We searched for a periodic signal from the NuSTAR sources with sufficient counts to detect a coherent timing signal, determined as follows. The ability to detect pulsations depends strongly on the source and background counts and the number of search trials.

For a sinusoidal signal, the aperture counts source plus background necessary to detect a signal of pulsed fraction f p is , where S is the power associated with the single trial false detection probability of a test signal S is distributed as with two degrees of freedom van der Klis In practice, for a blind search, we need to take into account the number of frequencies tested, , when T span is the data span and , the effective NuSTAR Nyquist frequency.

In computing N , we must allow for the reduced sensitivity of the search due to background contamination in the source aperture N b ; the minimum detectable pulse fraction is then increased by. We computed the detectability in individual observations for each source in our sample and considered those suitable for a pulsar search, with at the level.

For each source, we evaluated the power at each frequency oversampling by a factor of 2 using the unbinned test statistic Buccheri et al. We repeated our search for an additional combination of energy ranges , , , and and aperture size and. For all these searches, no significant signals were detected. For NNR 5 and 8, we can constrain the pulsed fraction of X-ray emission to be and , respectively, at the confidence.

The archival Chandra observation see Table 2 , which provides additional coverage of NNR 19 was also subjected to the same analysis. The statistical uncertainties of the Chandra positions were calculated using the parameterization in Equation 5 of Hong et al. The Chandra positions and uncertainties are reported in Table 8. The Chandra follow-up observations of NNR 19, 20, and 25 are shown in Figure 6 , where green circles indicate the NuSTAR source positions and magenta circles show the locations of the nearest Chandra sources.

Figure 6. The NuSTAR and Chandra positional uncertainties are provided in Tables 5 and 8 and are approximately and 0 7, respectively, for all three sources. Furthermore, this Chandra source was detected in in Chandra ObsID but undetected in in ObsID ; the fact that this Chandra source is a transient boosts the probability that it is the counterpart of NNR In order to extract photometric and spectral information for each Chandra counterpart, we defined source aperture regions as circles with radii and background regions as annuli with inner radii and outer radii.

For each source in each Chandra observation, we calculated the net 0. Since spectral fitting can be unreliable or impractical for faint sources, we used hardness ratios and quantile values Hong et al. In order to reduce the level of background contamination and prevent the hardness ratios and quantile values from being skewed toward the values of the NuSTAR background, we opted to use the aperture regions with smaller radii to derive these spectral parameters.

The hardness ratio for each source is calculated as , where H is the counts in the hard 10—20 keV band and S is the counts in the soft 3—10 keV band. While hardness ratios are the most widely used proxy for spectral hardness of faint X-ray sources, they are subject to selection effects associated with having to choose two particular energy bands, and they do not yield meaningful information for sources that have zero net counts in one of the two energy bands.

The latter energies were combined into a single quantile ratio QR , which is a measure of how broad or peaked the spectrum is and is defined as , where is the lower bound of the energy band: 3 keV for NuSTAR and 0. The gridlines in the figure indicate where a source with a particular blackbody, bremsstrahlung, or power-law spectrum would fall in the NuSTAR quantile space.

Figure 7. Quantile diagrams showing the quantile ratio on the y -axis and the median energy on the x -axis or median energy "normalized" by the Chandra 0. Comparing the positions of sources in the quantile diagrams to the spectral model gridlines provides a rough measurement of their spectral parameters. The Chandra quantiles are very sensitive to the amount of absorption suffered by a source, while the NuSTAR quantiles are more useful for separating sources with different spectral slopes. Grids representing absorbed bremsstrahlung, blackbody, and power-law models are shown in blue, green, and orange, respectively.

Red primarily vertical lines represent values of the photon index and 4 from right to left. Blue primarily horizontal lines represent values of the hydrogen column density , 0. Finally, we did not adopt the NARCS catalog quantile values for the extended sources because they were derived using aperture regions whose position and extent were determined by eye and that removed embedded point sources not distinguishable with NuSTAR.

As can be seen in Figure 7 , the Chandra quantiles can easily differentiate between foreground sources and those subject to high levels of absorption due to gas along the line of sight. Since these surveys have 0 5 resolution, the interstellar values we derive are averages over 0.

Thus, the sources whose X-ray spectra show column densities in excess of these values may be located behind dense molecular clouds or suffer from additional absorption due to gas or dust local to the X-ray source. The NuSTAR quantiles are not particularly sensitive to but instead are able to separate sources with intrinsically soft and hard spectra, regardless of their level of absorption.

Thus, the combination of quantile values in the Chandra and NuSTAR bands allows us to learn a fair amount about the spectral properties of sources that are too faint for spectral fitting and provide a check on spectral fitting results that can depend on the choice of binning for low photon statistics. We also included a cross-normalization constant between NuSTAR FPMA and FPMB in our models; for most sources, due to limited photon statistics, the errors on this normalization constant are large, and the constant is consistent with 1.

For the three brightest sources, which have been carefully analyzed in other papers, we adopted simplified versions of the best-fitting models found in King et al. For the other tier 1 sources, we fitted absorbed power-law, bremsstrahlung, and collisionally ionized models; we employed the tbabs absorption model with solar abundances from Wilms et al.

When Fe line emission was clearly visible between 6. Thus, measurements of the Fe line parameters should be interpreted as the average energy of the Fe line complex and the combined equivalent width of the Fe lines. If Fe line emission was not evident, the source spectrum was first fit without a Gaussian component. Then, having determined which of the three spectral models best fit the spectrum, a Gaussian component was added in order to place constraints on the strength of Fe line emission that may not be visible due to poor photon statistics.

The central energy of this Gaussian component was constrained to be between 6. The results of our spectral analysis can be found in Table 9 , and the spectra and fit residuals are shown in Figure 8 and the Appendix. As can be seen, spectra with NuSTAR counts cannot place strong constraints on the spectral parameters. However, we nonetheless include these results in order to be able to compare nonparametric fluxes with spectrally derived fluxes and as a reference to aid the design of future NuSTAR surveys.

Figure 8. Additional spectra are shown in the Appendix. Spectral analysis results can be found in Table 9. For all other sources, we present fits using power-law PL , bremsstrahlung BR , and collisionally ionized apec models AP. The best-fitting model for each source is written in italics. The constant is set to 1. For sources with insufficient photon statistics, this ratio is set to 1.

In cases where a Fe line is clearly visible in the spectrum, a Gaussian line G is included in the model; otherwise, we provide the results of the best-fit models without a Gaussian line and an upper limit to the Fe line equivalent width derived by adding a Gaussian component as described in Section 5.

F, fusion protein; HN, hemagglutinin-neuraminidase; SH, small hydrophobic. The date of the most recent genotype A clinical sample is indicated, excluding samples that closely resemble a mumps vaccine strain. A World map indicating number of SH sequences in our data set from each of 15 regions; the 4 circled global regions represent the 4 regions from which we resampled input for migration analyses see Materials and methods for details regarding geographic and temporal resampling of sequences.

B Tree with the highest clade credibility across all trees generated on resampled input from 4 global regions. Branch line thickness corresponds to posterior support for ancestry indicated by branch color. C Migration between the 4 global regions shown in panel A. Each plot shows a posterior probability density, taken across resampled input, of the fraction of all reconstructed migrations that occur to the destination indicated in upper right from each of the other 3 sources.

D Migration between the 4 global regions shown in panel A. Shading of each migration route indicates its statistical support quantified with BF in explaining the diffusion of mumps virus. E Average proportion of geographic ancestry of samples in each of the 4 global regions labeled from each of the 4 regions colored , going back 5 years from sample collection.

F Average proportion of Europe in geographic ancestry of US samples, and vice-versa. Shaded regions are pointwise percentile bands 2. BF, Bayes factor; SH, small hydrophobic. HPD, highest posterior density. Demographic information of all mumps cases in Massachusetts between and , and the subset of these included in this study. A Marginal likelihoods estimated in 6 models: combinations of 3 coalescent tree priors constant size population, exponential growth population, and Skygrid and 2 clock models strict clock and uncorrelated relaxed clock with log-normal distribution.

Estimates are with PS and SS. The BF are calculated against the model with constant size population and a strict clock. Influenzavirus B is the only segmented virus listed, and we identify 51 reads mapping to 6 of the 8 segments: in order, 2, 6, 0, 0, 8, 10, 4, 21 reads to each segment. List of SH and HN sequences and corresponding metadata used in analysis, as well as SH sequences available for resampling. HN, hemagglutinin-neuraminidase; SH, small hydrophobic. We thank A. Matthews and S.

Winnicki for management and guidance; I. Shlyakhter, S. Weingarten-Gabbay, S. Ye, C. Tomkins-Tinch, and other members of the Sabeti Laboratory for discussions and reading of the manuscript; J. Hall, P. Patel, E. Buzby, K. Chen, and F. Halpern-Smith for mumps diagnosis and laboratory support; A. Osinski, C. Brandeburg, H. Johnson, J. Cohen, K. Royce, M. Popstefanija, N.

Harrington, R. Hernandez, and J. Leaf for case management and epidemiological investigation; T. Salit for sharing reagents. We are indebted to mumps patients and clinical and epidemiological teams for making this work possible. The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention, the National Institute of General Medical Sciences, the National Institute of Allergy and Infectious Diseases, or the National Institutes of Health.

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However, they also raise some concerns that will need to be addressed in a revision. Of particular note, Reviewers 2 and 3 both believe that further evidence that vaccine escape is not contributing to the outbreak is needed. Reviewer 2 and the Academic Editor also believe that the scope of the results and title should be tempered to reflect the region where samples were obtained.

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Thank you again for your submission to our journal. We hope that our editorial process has been constructive thus far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments. Wohl et al. While I didn't have access to previous reviews and author responses, this version reads very well and tells a very interesting and complete story.

The analyses all seem appropriate and the figures look nice and are mostly clear. I highly recommend this for publication and provide only a couple minor comments about the figures. It wasn't until I read the main text did I really understand figure 2 and I typically look at all of the figures before reading.

There is a lot of important info buried in the legend, especially to explain B and C. Could the authors add a label to show that the left panels are estimates made from epi data and the right panels include genomic data? If this is clear from the start, the figure would be much clearer and more powerful. Given the space in the main text, I would recommend brining some of the very nice and informative supplemental figures to the main text.

The authors can choose, but I would recommend S1, S4 but update the title to make it clear that this was used to investigate vaccine escape, then perhaps simplify it to showcase the main points , and S9. Using whole genome viral data, they are able to reconstruct the spread of mumps between neighboring communities including several universities in the region and connect these local outbreaks to larger-scale transmission patterns in the United States.

They also explore whether mumps has undergone antigenic evolution to escape vaccine-based immunity, but do not find any genetic changes that can be correlated with vaccination patterns. Thus authors conclusions about mumps circulation in the larger U. If anything there is a fair amount of clustering in the phylogeny by region and lineages sampled in MA and NE one year are most closely related to lineages sampled in the those regions the previous year.

In light of this, maybe it would be better to plot the frequency of each mutation among sampled viruses over time and look if any mutations in antigenic regions have dramatically increased in frequency? The authors generate approximately new genomes of mumps virus circulating during an unusually large epidemic in and around Boston universities. They show that the virus consists of multiple widely circulating lineages.

They highlight the increased resolution into transmission. Using the SH gene, they compare their sample to viruses circulating globally, and make the case for a persistent US-European lineage causing most cases. They demonstrate that the virus has an effective reproductive number RE well above one, and that control measures need to focus on blocking local transmission.

A lot of work went into this impressively comprehensive analysis. This study is well executed. The case that there is a lot of local transmission is convincing, and the finding of multiple closely related lineages circulating is surprising. The authors also address the question of whether the virus has mutated to escape vaccine, and conclude that waning immunity is a more likely explanation for persistent transmission.

I was less convinced by the robustness of this section of the paper. I was not convinced about the power of the dN dS analysis to detect escape from the vaccine selection pressure. At least, it was not clearly motivated what assumptions or hypotheses were being tested.

Perhaps more could be made of the data on time since vaccination, comparing negative and positive samples, and also formally testing whether subtitutions are associated with time since vaccination. The data sharing statement should refer both to the sequences and to the associated metadata; currently the authors only propose to share sequences. Introductory paragraph, lines How sure are the authors that temporally changing patterns of reporting might not affect these observations?

Line Does this include a household? Line 60, and repeated several times later. The number of genomes is variously here , line 96 , and Line and Figure S1. Please check to make this consistent, or specify if slightly different samples were used for different analyses. The authors generated genomes from samples. This is relevant information for readers interested in assessing the sequencing method used here.

It is also possibly relevant as a sampling bias to consider in the discussion. Lines It rather seems to be a conclusion of the partial genes Figure 3. To conclude this, you need to place the diversity in your sample in the context of the full global diversity, which you can only do with the SH gene.

If you agree, then it seems you may need to present Figure 3 before Figure 1. To me, this was the most opaque section of the paper. Why would vaccine selection produce higher dN than non-vaccine immunological selection? Is the study powered for this comparison? In terms of the substitutions, the authors conclude that most mutations fixed here were already present in an ancestral sample Iowa , and thus that immune escape is unlikely lines The authors should make explicit the assumption of some kind of additive model here: why is not plausible that one of the two mutations identified, and , could alone be responsible for vaccine escape?

Without further wet-lab neutralisation assays, it must be hard to tell. Overall, I can see why the authors did the analyses, the question is interesting. But the conclusions seem vague at this stage. This is a convincing analysis, and really highlights the power of the whole genome data. The authors use well-established in-house viral genomics methods and publicly available phylogenetic software. Stochastic model.

The branching process approach looks sensible. Table S1. Please include the time since vaccination for both the positive and negative samples. Could more not be made of a comparison between the two? This seems an orphan paragraph.

Could you add a sentence to place these findings in context? These are interesting findings on lack of diversity in host, though paragraph seems very speculative given the data. Thank you for submitting your revised Research Article entitled "Combining genomics and epidemiology to track mumps virus transmission in the United States" for publication in PLOS Biology.

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All research involving human participants must have been approved by the authors' Institutional Review Board IRB or an equivalent committee, and all clinical investigation must have been conducted according to the principles expressed in the Declaration of Helsinki. The authors' revision has improved an already good paper. But I still disagree with their major conclusion that widespread geographic dispersal must be common.

Specifically, the authors say that: "Given the modest sampling in this dataset from outside the Northeast, finding such wide geographic dispersal suggests that long-distance migration of the virus is common in the US". I hate to be difficult, but I really don't think this is the most parsimonious conclusion to be drawn from the phylogeny in Figure 1.

Rather I would say there is strong evidence for regional circulation e. After all, there is relatively strong clustering even from regions like the Midwest that were very under-sampled. Of course, more sampling from different regions may in fact reveal long-distance dispersal is common, I just don't feel that this should be assumed without evidence.

The authors have done a great job responding the comments. They have strengthened their conclusions on waning immunity. They have clarified both the motivations for the different linked analyses, and highlighted limitations where appropriate. This is an impressively comprehensive study with several interesting and to me, at least unexpected conclusions. The files will now enter our production system.

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Please be aware that this policy is in place both to ensure that any press coverage of your article is fully substantiated and to provide a direct link between such coverage and the published work. Please do not hesitate to contact me if I can provide any assistance during the production process. National Center for Biotechnology Information , U. PLoS Biol. Published online Feb Byrne , Formal analysis , Visualization , 1, 2 Rebecca J.

McNall , Data curation , 8 Rickey R. Hayden C. Stephen F. Joseph A. Lydia A. Katherine J. Christian B. Elizabeth H. Rebecca J. Rickey R. Daniel J. Paul A. Lawrence C. Nathan L. Bronwyn L. Yonatan H. Pardis C. Author information Article notes Copyright and License information Disclaimer. Received Jul 22; Accepted Jan 3.

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S2 Fig: Maximum likelihood tree, root-to-tip regression, and principal component analysis. S3 Fig: Phylogenetic tree colored by institution. S4 Fig: Amino acid substitution in the mumps virus genome. S6 Fig: Parameters used in epidemiological models. S7 Fig: Connection between epidemiological and genetic data. S8 Fig: Trees produced with single-gene and multigene sequences. S10 Fig: Additional analyses of global mumps spread using SH gene sequences.

S11 Fig: Skygrid reconstruction of population size. S1 Table: Sample metadata. S1 Data: Sample metadata. S2 Data: Single nucleotide polymorphisms. Attachment: Submitted filename: mumps-plosbio-response.

Attachment: Submitted filename: mumps-plosbio-response2. Abstract Unusually large outbreaks of mumps across the United States in and raised questions about the extent of mumps circulation and the relationship between these and prior outbreaks.

Introduction An unusually large number of mumps cases were reported in the United States in and , despite high rates of vaccination [ 1 , 2 ]. Results and discussion We generated whole mumps virus genomes from buccal swabs from patients who tested positive by polymerase chain reaction PCR for mumps virus Fig 1A , of which were from Massachusetts during the and outbreak Fig 1B , with 92 from Harvard, BU, or UMass in particular.

Open in a separate window. Fig 1. Massachusetts mumps outbreak overview. Table 1 Summary of samples and genomes. Fig 2. Epidemiological modeling and transmission reconstruction. Fig 3. Global spread of mumps virus based on SH gene sequences. Illumina library construction and sequencing cDNA synthesis was performed as described in previously published RNA-seq methods [ 38 ]. Hybrid capture Viral hybrid capture was performed as previously described [ 38 ] using 2 different probe sets.

Genome assembly We used viral-ngs version 1. Criteria for pooling across replicates We prepared one or more sequencing libraries from each sample and attempted to sequence and assemble a genome from each of these replicates. Visualization of coverage depth across genomes We plotted aggregate depth of coverage across the samples whose genomes were included in the final alignment S1C Fig as described in the work by Metsky and colleagues [ 49 ].

Analysis of within- and between-sample variants We ran V-Phaser 2. Maximum likelihood estimation and root-to-tip regression We generated a maximum likelihood tree using the whole genome genotype G multiple sequence alignment. Relationship between epidemiological and genetic data We obtained detailed epidemiological data for samples shared by MDPH from the Massachusetts Virtual Epidemiologic Network MAVEN surveillance system, an integrated web-based disease surveillance and case management system [ 73 ].

Model of mumps transmission in a university setting We developed a stochastic model for mumps virus transmission accounting for the natural history of infection, vaccination status, and control measures implemented in response to the outbreak at Harvard. Inferring transmission dynamics The number of cases 71 and identification of multiple, distinct viral clades within Harvard suggested limited permeation of mumps after any introduction.

Transmission reconstruction using outbreaker We used the R package outbreaker version 1. Supporting information S1 Fig Sequencing results and predictors of outcome. TIF Click here for additional data file. S2 Fig Maximum likelihood tree, root-to-tip regression, and principal component analysis.

S3 Fig Phylogenetic tree colored by institution. S4 Fig Amino acid substitution in the mumps virus genome. S6 Fig Parameters used in epidemiological models. S7 Fig Connection between epidemiological and genetic data. S8 Fig Trees produced with single-gene and multigene sequences. S10 Fig Additional analyses of global mumps spread using SH gene sequences. S11 Fig Skygrid reconstruction of population size. S1 Table Sample metadata.

S1 Data Sample metadata. XLSX Click here for additional data file. S2 Data Single nucleotide polymorphisms. Acknowledgments We thank A. References 1. Centers for Disease Control and Prevention. Mumps Cases and Outbreaks. J Infect Dis. Recent resurgence of mumps in the United States. N Engl J Med. Vaccine waning and mumps re-emergence in the United States. Sci Transl Med.

National Notifiable Diseases Surveillance System. Mumps in prison: description of an outbreak in Manitoba, Canada. Can J Public Health. Mumps outbreak in Orthodox Jewish communities in the United States. Lancet Infect Dis. Genomic and epidemiological monitoring of yellow fever virus transmission potential.

Genomic insights into the — cholera epidemic in Yemen. Whole-genome sequencing and social-network analysis of a tuberculosis outbreak. Whole-genome analysis of human influenza A virus reveals multiple persistent lineages and reassortment among recent H3N2 viruses. A single mutation in the prM protein of Zika virus contributes to fetal microcephaly.

Evaluation of the potential impact of Ebola virus genomic drift on the efficacy of sequence-based candidate therapeutics. Tracking virus outbreaks in the twenty-first century. Nat Microbiol. Tracking a hospital outbreak of carbapenem-resistant Klebsiella pneumoniae with whole-genome sequencing.

Towards a genomics-informed, real-time, global pathogen surveillance system. Nat Rev Genet. Proposal for genetic characterisation of wild-type mumps strains: preliminary standardisation of the nomenclature. Arch Virol. World Health Organization. Mumps virus nomenclature update: Weekly Epidemiological Record. Clin Infect Dis. Mumps For Healthcare Providers. Moncla L BA. Multiple introductions of mumps virus into Washington State.

Am Acad Pediatrics; Differences in antigenic sites and other functional regions between genotype A and G mumps virus surface proteins. Sci Rep. Protective effects of glycoprotein-specific monoclonal antibodies on the course of experimental mumps virus meningoencephalitis. J Virol. Decreased humoral immunity to mumps in young adults immunized with MMR vaccine in childhood.

Recent mumps outbreaks in vaccinated populations: no evidence of immune escape. Antibody induced by immunization with the Jeryl Lynn mumps vaccine strain effectively neutralizes a heterologous wild-type mumps virus associated with a large outbreak. In: Centers for Disease Control and Prevention, editor. Manual for the Surveillance of Vaccine-Preventable Diseases. Identification of a new mumps virus lineage by nucleotide sequence analysis of the SH gene of ten different strains.

Genomic diversity of mumps virus and global distribution of the 12 genotypes. Rev Med Virol. Mumps Case Definition. Amplification and sequencing of variable regions in bacterial 23S ribosomal RNA genes with conserved primer sequences. Curr Microbiol. Enhanced methods for unbiased deep sequencing of Lassa and Ebola RNA viruses from clinical and biological samples.

Genome Biol. Capturing sequence diversity in metagenomes with comprehensive and scalable probe design. Nat Biotechnol. Kraken: ultrafast metagenomic sequence classification using exact alignments.

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