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Ultralight dark matter (ULDM) is an interesting alternative to the cold dark matter (CDM) paradigm. Due to the extremely low mass of the constituent particle ($\sim 10^{-22}$ eV), ULDM can exhibit quantum effects up to kiloparsec scales. In particular, runaway collapse in the centres of ULDM halos is prevented by quantum pressure, providing a possible resolution to the 'core-cusp problem' of CDM. However, the the detailed relationship between the ULDM core mass and that of the overall halo is poorly understood. We simulate the collapse of both spherical and aspherical isolated ULDM overdensities using AxioNyx, finding that the central cores of collapsed halos undergo sustained oscillatory behaviour which affects both their peak density and overall morphology. The variability in core morphology increases with the asphericity of the initial overdensity and remnants of initial asphericity persist long after collapse. Furthermore, the peak central densities are higher in spherical configurations. Consequently, astrophysically realistic halos may exhibit substantial departures from theoretical core-halo profiles and we would expect a significant variance of the properties of halos with the same mass., Comment: 11 pages, 11 figures

Background Short-course, rifamycin-based regimens could facilitate scale-up of tuberculosis preventive therapy (TPT), but it is unclear how stringently tuberculosis (TB) disease should be ruled out before TPT use. Methods We developed a state-transition model of a TPT intervention among two TPT-eligible cohorts: adults newly diagnosed with HIV in South Africa (PWH) and TB household contacts in Pakistan (HHCs). We modeled two TPT regimens—4 months of rifampicin [4R] or 6 months of isoniazid [6H]—comparing each to a reference of no intervention. Before initiating TPT, TB disease was excluded either through symptom-only screening or with additional radiographic screening that could detect subclinical TB but might limit access to the TPT intervention. TPT’s potential curative effects on both latent and subclinical TB were modeled, as were both acquisitions of resistance and prevention of drug-resistant disease. Although all eligible individuals received the screening and/or TPT interventions, the modeled TB outcomes comprised only those with latent or subclinical TB that would have progressed to symptomatic disease if untreated. Results When prescribed after only symptom-based TB screening (such that individuals with subclinical TB were included among TPT recipients), 4R averted 45 active (i.e., symptomatic) TB cases (95% uncertainty range 24–79 cases or 40–89% of progressions to active TB) per 1000 PWH [17 (9–29, 43–94%) per 1000 HHCs]; 6H averted 37 (19–66, 52–73%) active TB cases among PWH [13 (7–23, 53–75%) among HHCs]. With this symptom-only screening, for each net rifampicin resistance case added by 4R, 12 (3–102) active TB cases were averted among PWH (37 [9–580] among HHCs); isoniazid-resistant TB was also reduced. Similarly, 6H after symptom-only screening increased isoniazid resistance while reducing overall and rifampicin-resistant active TB. Screening for subclinical TB before TPT eliminated this net increase in resistance to the TPT drug; however, if the screening requirement reduced TPT access by more than 10% (the estimated threshold for 4R among HHCs) to 30% (for 6H among PWH), it was likely to reduce the intervention’s overall TB prevention impact. Conclusions All modeled TPT strategies prevent TB relative to no intervention, and differences between TPT regimens or between screening approaches are small relative to uncertainty in the outcomes of any given strategy. If most TPT-eligible individuals can be screened for subclinical TB, then pairing such screening with rifamycin-based TPT maximizes active TB prevention and does not increase rifampicin resistance. Where subclinical TB cannot be routinely excluded without substantially reducing TPT access, the choice of TPT regimen requires weighing 4R’s efficacy advantages (as well as its greater safety and shorter duration that we did not directly model) against the consequences of rifampicin resistance in a small fraction of recipients.

Additional file 1:. S-1. Model details. S-2. Model calibration. S-3. Modeling the effects of the one-time intervention. S-4. Modeling the effects of medium-term health system strengthening. S-5. Sensitivity analyses. Figure S-1. Comparison of model simulations, and calibration targets. Figure S-2. Sensitivity analyses of the secondary outcome, TB cases averted over 10 years by a combined intervention of a one-time campaign plus health system strengthening. Figure S-3. Comparing the impact of curing LTBI versus TB disease. Figure S-4. The impact of a one-time intervention (without health system strengthening) when the intervention was targeted to the high-risk population. Figure S-5. The impact of a one-time intervention (without health system strengthening), with shorter duration of early LTBI. Figure S-6. The impact of the full intervention when preventive therapy is limited to recent infections. Table S-1. Model parameters. Table S-2. Model parameters for one-time intervention.

Additional file 1: Supplemental methods, Tables S1-S6, and Figures S1-S5. Supplemental methods ��� Additional details of primary human subjects data collection, estimation of model parameters, state-transition model, and approach to probabilistic parameter sampling. Table S1. Parameter estimates, sizes of latent and subclinical progressor populations. Table S2. Parameter estimates, preventive therapy efficacy and related parameters. Table S3. Parameter estimates, preventive therapy resistance acquisition. Table S4. Parameter estimates, TB treatment outcomes. Table S5. Composition of cohorts with respect to initial drug resistance and age or CD4 count. Table S6. Correspondence between baseline culture-positive TB prevalence and 3-month clinical TB incidence in cohort of people with newly diagnosed HIV and a negative TB symptom screen in Kwa-Zulu Natal. Fig S1. ��� Projected outcomes of 4R or 6H, each compared to no TPT, among a cohort of 1000 all-age Household contacts. Fig S2. Head-to-head comparison of TB outcomes after TPT, comparing 4R regimen to 6H. Fig S3. Effect of reduced access on the impact of subclinical TB screening prior to TPT, among HHC cohort. Fig S4. Sensitivity of key results to individual parameters. Fig S5 ��� Projected outcomes of 4R or 6H, each compared to no TPT, among 1000 household contacts, when TPT is only considered for the 17% of contacts who are under age 5

Additional file 1. Supplemental Results.

Detailed methodology for cost analyses, simulation model development, and sensitivity analyses. (DOCX 2.10 mb)

While dark matter accounts for approximately 85% of the matter content of the observable universe, its fundamental nature is one of the most important mysteries of modern physics. Many different theories of dark matter have been proposed. We are studying ultra-light dark matter, a scenario in which the dark matter particle is far lighter than any particle currently known to have a non-zero mass. The behaviour of ultra-light dark matter is governed by the “Schroedinger-Poisson equations”. We will introduce PyUltraLight, the code we have developed at the University of Auckland to solve this system of coupled partial differential equations. PyUltraLight is written in Python and can run inside a Jupyter notebook; it is currently the only freely shared, open-source Schroedinger-Poisson solver available to astrophysicists. We are now using PyUltraLight to study galaxy formation and galaxy-galaxy interactions in a universe with ultra-light dark matter. One of the main challenges we are now facing is the need to simultaneously simulate large physical volumes while resolving dynamics on small scales, e.g. in the central regions of the galaxies or the detailed dynamics of galactic mergers. Increasing spatial resolution across the whole simulation is computationally expensive and we are developing a simulation tool that uses adaptive mesh refinement (AMR), a standard technique used to tackle problems where a significant proportion of the simulation volume is empty. AMR codes allow the resolution to be increased only in the locations of greatest interest, on the basis of specified refinement criteria. We will describe the numerical methods we are now developing to simulate dark matter dynamics and discuss some recent results and future research directions. ABOUT THE AUTHOR(S) Emily Kendall is a PhD student under Richard Easther at the University of Auckland. Mateja Gosenca is a Postdoctoral researcher in the cosmology group at the University of Auckland. Both are working on the theory and computational modelling of ultra-light dark matter.

Experimental models of tuberculosis are rarely interrogated for consistency with observed clinical outcomes at the population level. We developed a mathematical framework of tuberculosis to describe individual-level rates of disease progression and recovery that are consistent with reported clinical outcomes., Background Substantial individual heterogeneity exists in the clinical manifestations and duration of active tuberculosis. We sought to link the individual-level characteristics of tuberculosis disease to observed population-level outcomes. Methods We developed an individual-based, stochastic model of tuberculosis disease in a hypothetical cohort of patients with smear-positive tuberculosis. We conceptualized the disease process as consisting of 2 states—progression and recovery—including transitions between the 2. We then used a Bayesian process to calibrate the model to clinical data from the prechemotherapy era, thus identifying the rates of progression and recovery (and probabilities of transition) consistent with observed population-level clinical outcomes. Results Observed outcomes are consistent with slow rates of disease progression (median doubling time: 84 days, 95% uncertainty range 62–104) and a low, but nonzero, probability of transition from disease progression to recovery (median 16% per year, 95% uncertainty range 11%–21%). Other individual-level dynamics were less influential in determining observed outcomes. Conclusions This simplified model identifies individual-level dynamics—including a long doubling time and low probability of immune recovery—that recapitulate population-level clinical outcomes of untreated tuberculosis patients. This framework may facilitate better understanding of the population-level impact of interventions acting at the individual host level.

Background Mathematical models are increasingly used to understand the dynamics of infectious diseases, including “chronic” infections with long generation times. Such models include features that are obscure to most clinicians and decision-makers. Methods Using a model of a hypothetical active case-finding intervention for tuberculosis in India as an example, we illustrate the effects on model results of different choices for model structure, input parameters, and calibration process. Results Using the same underlying data, different transmission models produced different estimates of the projected intervention impact on tuberculosis incidence by 2030 with different corresponding uncertainty ranges. We illustrate the reasons for these differences and present a simple guide for clinicians and decision-makers to evaluate models of infectious diseases. Conclusions Mathematical models of chronic infectious diseases must be understood to properly inform policy decisions. Improved communication between modelers and consumers is critical if model results are to improve the health of populations.

Additional file 1: Text S1. Expert consultation.

Supplemental Material. Contains supplementary methods including a complete description of the model, and supplementary results tables and figures. (DOCX 234 kb)

Additional file 2: Text S2. Model equations.

Additional file 4: Table S1. Summary of cost per death or infectious person-day averted of results presented in the main text.

Additional file 2: Text S2. Model equations.

Additional file 4: Table S1. Summary of cost per death or infectious person-day averted of results presented in the main text.

Additional file 1: Text S1. Expert consultation.