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Recent observational studies aiming to quantify the molecular cloud lifecycle require the use of known 'reference time-scales' to turn the relative durations of different phases of the star formation process into absolute time-scales. We previously constrained the characteristic emission time-scales of different star formation rate (SFR) tracers, as a function of the SFR surface density and metallicity. However, we omitted the effects of dust extinction. Here, we extend our suite of SFR tracer emission time-scales by accounting for extinction, using synthetic emission maps of a high-resolution hydrodynamical simulation of an isolated, Milky-Way-like disc galaxy. The stellar feedback included in the simulation is inefficient compared to observations, implying that it represents a limiting case in which the duration of embedded star formation (and the corresponding effect of extinction) is overestimated. Across our experiments, we find that extinction mostly decreases the SFR tracer emission time-scale, changing the time-scales by factors of 0.04-1.74, depending on the gas column density. UV filters are more strongly affected than H$\alpha$ filters. We provide the limiting correction factors as a function of the gas column density and flux sensitivity limit for a wide variety of SFR tracers. Applying these factors to observational characterisations of the molecular cloud lifecycle produces changes that broadly fall within the quoted uncertainties, except at high kpc-scale gas surface densities ($\Sigma_{\rm g}\gtrsim20~{\mathrm{M_{\odot}\,pc^{-2}}}$). Under those conditions, correcting for extinction may decrease the measured molecular cloud lifetimes and feedback time-scales, which further strengthens previous conclusions that molecular clouds live for a dynamical time and are dispersed by early, pre-supernova feedback., Comment: 14 pages, 10 figures, 2 tables; published in MNRAS

It remains a major challenge to derive a theory of cloud-scale ($\lesssim100$ pc) star formation and feedback, describing how galaxies convert gas into stars as a function of the galactic environment. Progress has been hampered by a lack of robust empirical constraints on the giant molecular cloud (GMC) lifecycle. We address this problem by systematically applying a new statistical method for measuring the evolutionary timeline of the GMC lifecycle, star formation, and feedback to a sample of nine nearby disc galaxies, observed as part of the PHANGS-ALMA survey. We measure the spatially-resolved ($\sim100$ pc) CO-to-H$\alpha$ flux ratio and find a universal de-correlation between molecular gas and young stars on GMC scales, allowing us to quantify the underlying evolutionary timeline. GMC lifetimes are short, typically 10-30 Myr, and exhibit environmental variation, between and within galaxies. At kpc-scale molecular gas surface densities $\Sigma_{\rm H_2}\geqslant8$M$_{\odot}$pc$^{-2}$, the GMC lifetime correlates with time-scales for galactic dynamical processes, whereas at $\Sigma_{\rm H_2}\leqslant8$M$_{\odot}$pc$^{-2}$ GMCs decouple from galactic dynamics and live for an internal dynamical time-scale. After a long inert phase without massive star formation traced by H$\alpha$ (75-90% of the cloud lifetime), GMCs disperse within just 1-5 Myr once massive stars emerge. The dispersal is most likely due to early stellar feedback, causing GMCs to achieve integrated star formation efficiencies of 4-10% These results show that galactic star formation is governed by cloud-scale, environmentally-dependent, dynamical processes driving rapid evolutionary cycling. GMCs and HII regions are the fundamental units undergoing these lifecycles, with mean separations of 100-300 pc in star-forming discs. Future work should characterise the multi-scale physics and mass flows driving these lifecycles., Comment: 39 pages, 14 figures; resubmitted to MNRAS after a favourable referee report (November 6, 2019)

Direct comparisons between galaxy simulations and observations that both reach scales < 100 pc are strong tools to investigate the cloud-scale physics of star formation and feedback in nearby galaxies. Here we carry out such a comparison for hydrodynamical simulations of a Milky Way-like galaxy, including stochastic star formation, HII region and supernova feedback, and chemical post-processing at 8 pc resolution. Our simulation shows excellent agreement with almost all kpc-scale and larger observables, including total star formation rates, radial profiles of CO, HI, and star formation through the galactic disc, mass ratios of the ISM components, both whole-galaxy and resolved Kennicutt-Schmidt relations, and giant molecular cloud properties. However, we find that our simulation does not reproduce the observed de-correlation between tracers of gas and star formation on < 100 pc scales, known as the star formation 'uncertainty principle', which indicates that observed clouds undergo rapid evolutionary lifecycles. We conclude that the discrepancy is driven by insufficiently-strong pre-supernova feedback in our simulation, which does not disperse the surrounding gas completely, leaving star formation tracer emission too strongly associated with molecular gas tracer emission, inconsistent with observations. This result implies that the cloud-scale de-correlation of gas and star formation is a fundamental test for feedback prescriptions in galaxy simulations, one that can fail even in simulations that reproduce all other macroscopic properties of star-forming galaxies., Comment: 13 pages, 10 figures, accepted for publication in MNRAS

The physics of star formation and the deposition of mass, momentum, and energy into the interstellar medium by massive stars (`feedback') are the main uncertainties in modern cosmological simulations of galaxy formation and evolution. These processes determine the properties of galaxies, but are poorly understood on the $\lesssim$100 pc scale of individual giant molecular clouds (GMCs) resolved in modern galaxy formation simulations. The key question is why the timescale for depleting molecular gas through star formation in galaxies ($t_{\rm dep}\approx 2$ Gyr) exceeds the dynamical timescale of GMCs by two orders of magnitude. Either most of a GMC's mass is converted into stars over many dynamical times, or only a small fraction turns into stars before the GMC is dispersed on a dynamical timescale. Here we report our observation that molecular gas and star formation are spatially de-correlated on GMC scales in the nearby flocculent spiral galaxy NGC300, contrary to their tight correlation on galactic scales. We demonstrate that this de-correlation implies rapid evolutionary cycling between GMCs, star formation, and feedback. We apply a novel statistical method to quantify the evolutionary timeline and find that star formation is regulated by efficient stellar feedback, driving GMC dispersal on short timescales (~1.5 Myr) due to radiation and stellar winds, prior to supernova explosions. This feedback limits GMC lifetimes to about one dynamical timescale (~10 Myr), with integrated star formation efficiencies of only 2-3%. Our findings reveal that galaxies consist of building blocks undergoing vigorous, feedback-driven lifecycles, that vary with the galactic environment and collectively define how galaxies form stars. Systematic applications of this multi-scale analysis to large galaxy samples will provide key input for a predictive, bottom-up theory of galaxy formation and evolution., Comment: Published in the May 23 issue of Nature; this is the authors' version before final edits; low-resolution versions of the Supplementary Videos are available as ancillary files in the arXiv source (see the link in the top right of this page), whereas the full-resolution versions are available with the Nature publication

Diffuse emission is observed in galaxies in many tracers across the electromagnetic spectrum, including tracers of star formation, such as H$\alpha$ and ultraviolet (UV), and tracers of gas mass, such as carbon monoxide (CO) transition lines and the 21-cm line of atomic hydrogen (HI). Its treatment is key to extracting meaningful information from observations such as cloud-scale star formation rates. Finally, studying diffuse emission can reveal information about the physical processes taking place in the ISM, such as chemical transitions and the nature of stellar feedback (through the photon escape fraction). We present a physically-motivated method for decomposing astronomical images containing both diffuse emission and compact regions of interest, such as HII regions or molecular clouds, into diffuse and compact component images through filtering in Fourier space. We have previously presented a statistical method for constraining the evolutionary timeline of star formation and mean separation length between compact star forming regions with galaxy-scale observations. We demonstrate how these measurements are biased by the presence of diffuse emission in tracer maps and that by using the mean separation length as a critical length scale to separate diffuse emission from compact emission, we are able to filter out this diffuse emission, thus removing its biasing effect. Furthermore, this method provides, without the need for interferometry or ancillary spectral data, a measurement of the diffuse emission fraction in input tracer maps and decomposed diffuse and compact emission maps for further analysis., Comment: 23 pages, 20 figures and 7 tables. Submitted to MNRAS (14th of September 2018)

We recently presented a new statistical method to constrain the physics of star formation and feedback on the cloud scale by reconstructing the underlying evolutionary timeline. However, by itself this new method only recovers the relative durations of different evolutionary phases. To enable observational applications, it therefore requires knowledge of an absolute 'reference time-scale' to convert relative time-scales into absolute values. The logical choice for this reference time-scale is the duration over which the star formation rate (SFR) tracer is visible because it can be characterised using stellar population synthesis (SPS) models. In this paper, we calibrate this reference time-scale using synthetic emission maps of several SFR tracers, generated by combining the output from a hydrodynamical disc galaxy simulation with the SPS model SLUG2. We apply our statistical method to obtain self-consistent measurements of each tracer's reference time-scale. These include H${\alpha}$ and 12 ultraviolet (UV) filters (from GALEX, Swift, and HST), which cover a wavelength range 150-350 nm. At solar metallicity, the measured reference time-scales of H${\alpha}$ are ${4.32^{+0.09}_{-0.23}}$ Myr with continuum subtraction, and 6-16 Myr without, where the time-scale increases with filter width. For the UV filters we find 17-33 Myr, nearly monotonically increasing with wavelength. The characteristic time-scale decreases towards higher metallicities, as well as to lower star formation rate surface densities, owing to stellar initial mass function sampling effects. We provide fitting functions for the reference time-scale as a function of metallicity, filter width, or wavelength, to enable observational applications of our statistical method across a wide variety of galaxies., Comment: 24 pages, 18 figures, 7 tables (including Appendices); published in MNRAS

The cloud-scale physics of star formation and feedback represent the main uncertainty in galaxy formation studies. Progress is hampered by the limited empirical constraints outside the restricted environment of the Local Group. In particular, the poorly-quantified time evolution of the molecular cloud lifecycle, star formation, and feedback obstructs robust predictions on the scales smaller than the disc scale height that are resolved in modern galaxy formation simulations. We present a new statistical method to derive the evolutionary timeline of molecular clouds and star-forming regions. By quantifying the excess or deficit of the gas-to-stellar flux ratio around peaks of gas or star formation tracer emission, we directly measure the relative rarity of these peaks, which allows us to derive their lifetimes. We present a step-by-step, quantitative description of the method and demonstrate its practical application. The method's accuracy is tested in nearly 300 experiments using simulated galaxy maps, showing that it is capable of constraining the molecular cloud lifetime and feedback time-scale to $<0.1$ dex precision. Access to the evolutionary timeline provides a variety of additional physical quantities, such as the cloud-scale star formation efficiency, the feedback outflow velocity, the mass loading factor, and the feedback energy or momentum coupling efficiencies to the ambient medium. We show that the results are robust for a wide variety of gas and star formation tracers, spatial resolutions, galaxy inclinations, and galaxy sizes. Finally, we demonstrate that our method can be applied out to high redshift ($z\lesssim4$) with a feasible time investment on current large-scale observatories. This is a major shift from previous studies that constrained the physics of star formation and feedback in the immediate vicinity of the Sun., Comment: 86 pages (including appendices), 30 figures, 16 tables; accepted by MNRAS (April 27, 2018). The method is summarised in Figure 2 and 3, with the main results shown in Figures 18, 26, and 27. For a cursory overview of the paper (of about 10 pages), please consult Sections 1, 2, 3.1, 4.4, 6, and 8. Figure 27 replaced with authors' original version

Determining phenotype from genetic data is a fundamental challenge. Influenza A viruses undergo rapid antigenic drift and identification of emerging antigenic variants is critical to the vaccine selection process. Using former seasonal influenza A(H1N1) viruses, hemagglutinin sequence and corresponding antigenic data were analyzed in combination with 3-D structural information. We attributed variation in hemagglutination inhibition to individual amino acid substitutions and quantified their antigenic impact, validating a subset experimentally using reverse genetics. Substitutions identified as low-impact were shown to be a critical component of influenza antigenic evolution and by including these, as well as the high-impact substitutions often focused on, the accuracy of predicting antigenic phenotypes of emerging viruses from genotype was doubled. The ability to quantify the phenotypic impact of specific amino acid substitutions should help refine techniques that predict the fitness and evolutionary success of variant viruses, leading to stronger theoretical foundations for selection of candidate vaccine viruses.

The sequence diversity of viral populations within individual hosts is the starting material for selection and subsequent evolution of RNA viruses such as foot-and-mouth disease virus (FMDV). Using next-generation sequencing (NGS) performed on a Genome Analyzer platform (Illumina), this study compared the viral populations within two bovine epithelial samples (foot lesions) from a single animal with the Inoculum used to initiate experimental infection. Genomic sequences were determined in duplicate sequencing runs, and the consensus sequence determined by NGS, for the Inoculum, was identical to that previously determined using the Sanger method. However, NGS reveals the fine polymorphic sub-structure of the viral population, from nucleotide variants present at just below 50% frequency to those present at fractions of 1%. Some of the higher frequency polymorphisms identified encoded changes within codons associated with heparan sulphate binding and were present in both feet lesions revealing intermediate stages in the evolution of a tissue-culture adapted virus replicating within a mammalian host. We identified 2,622, 1,434 and 1,703 polymorphisms in the Inoculum, and in the two foot lesions respectively: most of the substitutions occurred only in a small fraction of the population and represent the progeny from recent cellular replication prior to onset of any selective pressures. We estimated an upper limit for the genome-wide mutation rate of the virus within a cell to be 7.8 x 10-4 per nt. The greater depth of detection, achieved by NGS, demonstrates that this method is a powerful and valuable tool for the dissection of FMDV populations within-hosts.