TRAF3 loss-of-function reveals the noncanonical NF-κB pathway as a therapeutic target in diffuse large B cell lymphoma.

Here, we report recurrent focal deletions of the chr14q32.31-32 locus, including TRAF3, a negative regulator of NF-κB signaling, in de novo diffuse large B cell lymphoma (DLBCL) (24/324 cases). Integrative analysis revealed an association between TRAF3 copy number loss with accumulation of NIK, the central noncanonical (NC) NF-κB kinase, and increased NC NF-κB pathway activity. Accordingly, TRAF3 genetic ablation in isogenic DLBCL model systems caused upregulation of NIK and enhanced NC NF-κB downstream signaling. Knockdown or pharmacological inhibition of NIK in TRAF3-deficient cells differentially impaired their proliferation and survival, suggesting an acquired onco-addiction to NC NF-κB. TRAF3 ablation also led to exacerbated secretion of the immunosuppressive cytokine IL-10. Coculturing of TRAF3-deficient DLBCL cells with CD8+ T cells impaired the induction of Granzyme B and interferon (IFN) γ, which were restored following neutralization of IL-10. Our findings corroborate a direct relationship between TRAF3 genetic alterations and NC NF-κB activation, and highlight NIK as a potential therapeutic target in a defined subset of DLBCL.

Learning to Learn Functions.

Humans can learn complex functional relationships between variables from small amounts of data. In doing so, they draw on prior expectations about the form of these relationships. In three experiments, we show that people learn to adjust these expectations through experience, learning about the likely forms of the functions they will encounter. Previous work has used Gaussian processes-a statistical framework that extends Bayesian nonparametric approaches to regression-to model human function learning. We build on this work, modeling the process of learning to learn functions as a form of hierarchical Bayesian inference about the Gaussian process hyperparameters.

Time to diagnosis and treatment of pulmonary tuberculosis in indigenous peoples: a systematic review.

BACKGROUND: Time to diagnosis and treatment is a major factor in determining the likelihood of tuberculosis (TB) transmission and is an important area of intervention to reduce the reservoir of TB infection and prevent disease and mortality. Although Indigenous peoples experience an elevated incidence of TB, prior systematic reviews have not focused on this group. We summarize and report findings related to time to diagnosis and treatment of pulmonary TB (PTB) among Indigenous peoples, globally. METHODS: A Systematic review was performed using Ovid and PubMed databases. Articles or abstracts estimating time to diagnosis, or treatment of PTB among Indigenous peoples were included with no restriction on sample size with publication dates restricted up to 2019. Studies that focused on outbreaks, solely extrapulmonary TB alone in non-Indigenous populations were excluded. Literature was assessed using the Hawker checklist. Registration Protocol (PROSPERO): CRD42018102463. RESULTS: Twenty-four studies were selected after initial assessment of 2021 records. These included Indigenous groups from five of six geographical regions outlined by the World Health Organization (all except the European Region). The range of time to treatment (24-240 days), and patient delay (20 days-2.5 years) were highly variable across studies and, in at least 60% of the studies, longer in Indigenous compared to non-Indigenous peoples. Risk factors associated with longer patient delays included poor awareness of TB, type of health provider first seen, and self-treatment. CONCLUSION: Time to diagnosis and treatment estimates for Indigenous peoples are generally within previously reported ranges from other systematic reviews focusing on the general population. However among literature examined in this systematic review that stratified by Indigenous and non-Indigenous peoples, patient delay and time to treatment were longer compared to non-Indigenous populations in over half of the studies. Studies included were sparse and highlight an overall gap in literature important to interrupting transmission and preventing new TB cases among Indigenous peoples. Although, risk factors unique to Indigenous populations were not identified, further investigation is needed as social determinants of health among studies conducted in medium and high incidence countries may be shared across both population groups. Trial registration N/a.

Mathematical modeling of the dynamics of COVID-19 variants of concern: Asymptotic and finite-time perspectives.

The COVID-19 pandemic has seen multiple waves, in part due to the implementation and relaxation of social distancing measures by the public health authorities around the world, and also caused by the emergence of new variants of concern (VOCs) of the SARS-Cov-2 virus. As the COVID-19 pandemic is expected to transition into an endemic state, how to manage outbreaks caused by newly emerging VOCs has become one of the primary public health issues. Using mathematical modeling tools, we investigated the dynamics of VOCs, both in a general theoretical framework and based on observations from public health data of past COVID-19 waves, with the objective of understanding key factors that determine the dominance and coexistence of VOCs. Our results show that the transmissibility advantage of a new VOC is a main factor for it to become dominant. Additionally, our modeling study indicates that the initial number of people infected with the new VOC plays an important role in determining the size of the epidemic. Our results also support the evidence that public health measures targeting the newly emerging VOC taken in the early phase of its spread can limit the size of the epidemic caused by the new VOC (Wu et al., 2139Wu, Scarabel, Majeed, Bragazzi, & Orbinski, ; Wu et al., 2021).

The Impact of Quarantine and Medical Resources on the Control of COVID-19 in Wuhan based on a Household Model.

In order to understand how Wuhan curbed the COVID-19 outbreak in 2020, we build a network transmission model of 123 dimensions incorporating the impact of quarantine and medical resources as well as household transmission. Using our new model, the final infection size of Wuhan is predicted to be 50,662 (95%CI: 46,234, 55,493), and the epidemic would last until April 25 (95%CI: April 23, April 29), which are consistent with the actual situation. It is shown that quarantining close contacts greatly reduces the final size and shorten the epidemic duration. The opening of Fangcang shelter hospitals reduces the final size by about 17,000. Had the number of hospital beds been sufficient when the lockdown started, the number of deaths would have been reduced by at least 54.26%. We also investigate the distribution of infectious individuals in unquarantined households of different sizes. The high-risk households are those with size from two to four before the peak time, while the households with only one member have the highest risk after the peak time. Our findings provide a reference for the prevention, mitigation and control of COVID-19 in other cities of the world.

Nonpharmaceutical interventions contribute to the control of COVID-19 in China based on a pairwise model.

Nonpharmaceutical interventions (NPIs), particularly contact tracing isolation and household quarantine, play a vital role in effectively bringing the Coronavirus Disease 2019 (COVID-19) under control in China. The pairwise model, has an inherent advantage in characterizing those two NPIs than the classical well-mixed models. Therefore, in this paper, we devised a pairwise epidemic model with NPIs to analyze COVID-19 outbreak in China by using confirmed cases during February 3rd-22nd, 2020. By explicitly incorporating contact tracing isolation and family clusters caused by household quarantine, our model provided a good fit to the trajectory of COVID-19 infections. We calculated the reproduction number R = 1.345 (95% CI: 1.230 - 1.460) for Hubei province and R = 1.217 (95% CI: 1.207 - 1.227) for China (except Hubei). We also estimated the peak time of infections, the epidemic duration and the final size, which are basically consistent with real observation. We indicated by simulation that the traced high-risk contacts from incubated to susceptible decrease under NPIs, regardless of infected cases. The sensitivity analysis showed that reducing the exposure of the susceptible and increasing the clustering coefficient bolster COVID-19 control. With the enforcement of household quarantine, the reproduction number R and the epidemic prevalence declined effectively. Furthermore, we obtained the resumption time of work and production in China (except Hubei) on 10th March and in Hubei at the end of April 2020, respectively, which is broadly in line with the actual time. Our results may provide some potential lessons from China on the control of COVID-19 for other parts of the world.

Epidemic models with discrete state structures.

The state of an infectious disease can represent the degree of infectivity of infected individuals, or susceptibility of susceptible individuals, or immunity of recovered individuals, or a combination of these measures. When the disease progression is long such as for HIV, individuals often experience switches among different states. We derive an epidemic model in which infected individuals have a discrete set of states of infectivity and can switch among different states. The model also incorporates a general incidence form in which new infections are distributed among different disease states. We discuss the importance of the transmission-transfer network for infectious diseases. Under the assumption that the transmission-transfer network is strongly connected, we establish that the basic reproduction number R 0 is a sharp threshold parameter: if R 0 ≤ 1 , the disease-free equilibrium is globally asymptotically stable and the disease always dies out; if R 0 > 1 , the disease-free equilibrium is unstable, the system is uniformly persistent and initial outbreaks lead to persistent disease infection. For a restricted class of incidence functions, we prove that there is a unique endemic equilibrium and it is globally asymptotically stable when R 0 > 1 . Furthermore, we discuss the impact of different state structures on R 0 , on the distribution of the disease states at the unique endemic equilibrium, and on disease control and preventions. Implications to the COVID-19 pandemic are also discussed.

Modeling the Effects of Latency Reversing Drugs During HIV-1 and SIV Brain Infection with Implications for the "Shock and Kill" Strategy.

Combination antiretroviral therapy (cART) has greatly increased life expectancy for human immunodeficiency virus-1 (HIV-1)-infected patients. Even given the remarkable success of cART, the virus persists in many different cells and tissues. The presence of viral reservoirs represents a major obstacle to HIV-1 eradication. These viral reservoirs contain latently infected long-lived cells. The "Shock and Kill" therapeutic strategy aims to reactivate latently infected cells by latency reversing agents (LRAs) and kill these reactivated cells by strategies involving the host immune system. The brain is a natural anatomical reservoir for HIV-1 infection. Brain macrophages, including microglia and perivascular macrophages, display productive HIV-1 infection. A mathematical model was used to analyze the dynamics of latently and productively infected brain macrophages during viral infection and this mathematical model enabled prediction of the effects of LRAs applied to the "Shock and Kill" strategy in the brain. The model was calibrated using reported data from simian immunodeficiency virus (SIV) studies. Our model produces the overarching observation that effective cART can suppress productively infected brain macrophages but leaves a residual latent viral reservoir in brain macrophages. In addition, our model demonstrates that there exists a parameter regime wherein the "Shock and Kill" strategy can be safe and effective for SIV infection in the brain. The results indicate that the "Shock and Kill" strategy can restrict brain viral RNA burden associated with severe neuroinflammation and can lead to the eradication of the latent reservoir of brain macrophages.

Modeling Rabies Transmission in Spatially Heterogeneous Environments via [Formula: see text]-diffusion.

Rabies among dogs remains a considerable risk to humans and constitutes a serious public health concern in many parts of the world. Conventional mathematical models for rabies typically assume homogeneous environments, with a standard diffusion term for the population of rabid animals. It has recently been recognized, however, that spatial heterogeneity plays an important role in determining spatial patterns of rabies and the cost-effectiveness of vaccinations. In this paper, we develop a spatially heterogeneous dog rabies model by using the [Formula: see text]-diffusion equation, where [Formula: see text] reflects the way individual dogs make movement decisions in the underlying random walk. We numerically investigate the dynamics of the model in three diffusion cases: homogeneous, city-wild, and Gaussian-type. We find that the initial conditions affect whether traveling waves or epizootic waves can be observed. However, different initial conditions have little impact on steady-state solutions. An "active" interface is observed between city and wild regions, with a "ridge" on the city side and a "valley" on the wild side for the infectious dog population. In addition, the progressing speed of epizootic waves changes in heterogeneous environments. It is impossible to eliminate rabies in the entire spatial domain if vaccination is focused only in the city region or only in the wild region. When a seasonal transmission is incorporated, the dog population size approaches a positive time-periodic spatially heterogeneous state eventually.

On the basic reproduction number in semi-Markov switching networks.

Basic reproduction number R0 in network epidemic dynamics is studied in the case of stochastic regime-switching networks. For generality, the dependence between successive networks is considered to follow a continuous time semi-Markov chain. R0 is the weighted average of the basic reproduction numbers of deterministic subnetworks. Its position with respect to 1 can determine epidemic persistence or extinction in theories and simulations.

Why is it difficult to accurately predict the COVID-19 epidemic?

Since the COVID-19 outbreak in Wuhan City in December of 2019, numerous model predictions on the COVID-19 epidemics in Wuhan and other parts of China have been reported. These model predictions have shown a wide range of variations. In our study, we demonstrate that nonidentifiability in model calibrations using the confirmed-case data is the main reason for such wide variations. Using the Akaike Information Criterion (AIC) for model selection, we show that an SIR model performs much better than an SEIR model in representing the information contained in the confirmed-case data. This indicates that predictions using more complex models may not be more reliable compared to using a simpler model. We present our model predictions for the COVID-19 epidemic in Wuhan after the lockdown and quarantine of the city on January 23, 2020. We also report our results of modeling the impacts of the strict quarantine measures undertaken in the city after February 7 on the time course of the epidemic, and modeling the potential of a second outbreak after the return-to-work in the city.

TMEM30A loss-of-function mutations drive lymphomagenesis and confer therapeutically exploitable vulnerability in B-cell lymphoma.

Transmembrane protein 30A (TMEM30A) maintains the asymmetric distribution of phosphatidylserine, an integral component of the cell membrane and 'eat-me' signal recognized by macrophages. Integrative genomic and transcriptomic analysis of diffuse large B-cell lymphoma (DLBCL) from the British Columbia population-based registry uncovered recurrent biallelic TMEM30A loss-of-function mutations, which were associated with a favorable outcome and uniquely observed in DLBCL. Using TMEM30A-knockout systems, increased accumulation of chemotherapy drugs was observed in TMEM30A-knockout cell lines and TMEM30A-mutated primary cells, explaining the improved treatment outcome. Furthermore, we found increased tumor-associated macrophages and an enhanced effect of anti-CD47 blockade limiting tumor growth in TMEM30A-knockout models. By contrast, we show that TMEM30A loss-of-function increases B-cell signaling following antigen stimulation-a mechanism conferring selective advantage during B-cell lymphoma development. Our data highlight a multifaceted role for TMEM30A in B-cell lymphomagenesis, and characterize intrinsic and extrinsic vulnerabilities of cancer cells that can be therapeutically exploited.

A Deterministic Model for Q Fever Transmission Dynamics within Dairy Cattle Herds: Using Sensitivity Analysis and Optimal Controls.

This paper presents a differential equation model which describes a possible transmission route for Q fever dynamics in cattle herds. The model seeks to ascertain epidemiological and theoretical inferences in understanding how to avert an outbreak of Q fever in dairy cattle herds (livestock). To prove the stability of the model's equilibria, we use a matrix-theoretic method and a Lyapunov function which establishes the local and global asymptotic behaviour of the model. We introduce time-dependent vaccination, environmental hygiene, and culling and then solve for optimal strategies. The optimal control strategies are necessary management practices that may increase animal health in a Coxiella burnetii-induced environment and may also reduce the transmission of the disease from livestock into the human population. The sensitivity analysis presents the relative importance of the various generic parameters in the model. We hope that the description of the results and the optimality trajectories provides some guidelines for animal owners and veterinary officers on how to effectively minimize the bacteria and control cost before/during an outbreak.

Single-Cell Transcriptome Analysis Reveals Disease-Defining T-cell Subsets in the Tumor Microenvironment of Classic Hodgkin Lymphoma.

Hodgkin lymphoma is characterized by an extensively dominant tumor microenvironment (TME) composed of different types of noncancerous immune cells with rare malignant cells. Characterization of the cellular components and their spatial relationship is crucial to understanding cross-talk and therapeutic targeting in the TME. We performed single-cell RNA sequencing of more than 127,000 cells from 22 Hodgkin lymphoma tissue specimens and 5 reactive lymph nodes, profiling for the first time the phenotype of the Hodgkin lymphoma-specific immune microenvironment at single-cell resolution. Single-cell expression profiling identified a novel Hodgkin lymphoma-associated subset of T cells with prominent expression of the inhibitory receptor LAG3, and functional analyses established this LAG3+ T-cell population as a mediator of immunosuppression. Multiplexed spatial assessment of immune cells in the microenvironment also revealed increased LAG3+ T cells in the direct vicinity of MHC class II-deficient tumor cells. Our findings provide novel insights into TME biology and suggest new approaches to immune-checkpoint targeting in Hodgkin lymphoma. SIGNIFICANCE: We provide detailed functional and spatial characteristics of immune cells in classic Hodgkin lymphoma at single-cell resolution. Specifically, we identified a regulatory T-cell-like immunosuppressive subset of LAG3+ T cells contributing to the immune-escape phenotype. Our insights aid in the development of novel biomarkers and combination treatment strategies targeting immune checkpoints.See related commentary by Fisher and Oh, p. 342.This article is highlighted in the In This Issue feature, p. 327.

R0 and sensitivity analysis of a predator-prey model with seasonality and maturation delay.

Coexistence and seasonal fluctuations of predator and prey populations are common and well documented in ecology. Under what conditions can predators coexist with prey in a seasonally changing environment? What factors drive long-term population cycles of some predator and prey species? To answer these questions, we investigate an improved predator-prey model based on the Rosenzweig-MacArthur [1] model. Our model incorporates seasonality and a predator maturation delay, leading to a system of periodic differential equations with a time delay. We define the basic reproduction ratio R0 and show that it is a threshold parameter determining whether the predators can coexist with the prey. We show that if R0 < 1, then the prey population has seasonal variations and the predator population goes extinct. If R0 > 1, then the prey and the predators coexist and fluctuate seasonally. As an example, we study a Daphnia-algae system and explore possible mechanisms for seasonal population cycles. Our numerical simulations indicate that seasonal Daphnia-algae cycles are attributed to seasonality rather than Daphnia maturation delay or Daphnia-algae interaction. The Daphnia maturation delay, the amplitude of algae growth rate and the amplitude of the carrying capacity are found to affect the amplitude of cycles and average population levels. Our sensitivity analysis shows that R0 is most sensitive to Daphnia death rate.

Global dynamics of a discrete age-structured SIR epidemic model with applications to measles vaccination strategies.

We investigate an SIR epidemic model with discrete age groups to understand the transmission dynamics of an infectious disease in a host population with an age structure. We derive the basic reproduction number R0 and show that it is a sharp threshold parameter. If R0≤1, the disease-free equilibrium E0 is globally stable. If R0>1,E0 is unstable, the model is uniformly persistent, and an endemic equilibrium exists. The global stability of the endemic equilibrium when R0>1 is established under a sufficient condition. The model is then used to analyze the measles data in India and evaluate the effectiveness of several vaccination strategies for the control of measles epidemics in India.

Modeling brain lentiviral infections during antiretroviral therapy in AIDS.

Understanding HIV-1 replication and latency in different reservoirs is an ongoing challenge in the care of patients with HIV/AIDS. A mathematical model was created to describe and predict the viral dynamics of HIV-1 and SIV infection within the brain during effective combination antiretroviral therapy (cART). The mathematical model was formulated based on the biology of lentiviral infection of brain macrophages and used to describe the dynamics of transmission and progression of lentiviral infection in brain. Based on previous reports quantifying total viral DNA levels in brain from HIV-1 and SIV infections, estimates of integrated proviral DNA burden were made, which were used to calibrate the mathematical model predicting viral accrual in brain macrophages from primary infection. The annual rate at which susceptible brain macrophages become HIV-1 infected was estimated to be 2.90×10-7-4.87×10-6 per year for cART-treated HIV/AIDS patients without comorbid neurological disorders. The transmission rate for SIV infection among untreated macaques was estimated to be 5.30×10-6-1.37×10-5 per year. An improvement in cART effectiveness (1.6-48%) would suppress HIV-1 infection in patients without neurological disorders. Among patients with advanced disease, a substantial improvement in cART effectiveness (70%) would eradicate HIV-1 provirus from the brain within 3-32 (interquartile range 3-9) years in patients without neurological disorders, whereas 4-51 (interquartile range 4-16) years of efficacious cART would be required for HIV/AIDS patients with comorbid neurological disorders. HIV-1 and SIV provirus burdens in the brain increase over time. A moderately efficacious antiretroviral therapy regimen could eradicate HIV-1 infection in the brain that was dependent on brain macrophage lifespan and the presence of neurological comorbidity.

Joint effects of mitosis and intracellular delay on viral dynamics: two-parameter bifurcation analysis.

To understand joint effects of logistic growth in target cells and intracellular delay on viral dynamics in vivo, we carry out two-parameter bifurcation analysis of an in-host model that describes infections of many viruses including HIV-I, HBV and HTLV-I. The bifurcation parameters are the mitosis rate r of the target cells and an intracellular delay τ in the incidence of viral infection. We describe the stability region of the chronic-infection equilibrium E* in the two-dimensional (r, τ) parameter space, as well as the global Hopf bifurcation curves as each of τ and r varies. Our analysis shows that, while both τ and r can destabilize E* and cause Hopf bifurcations, they do behave differently. The intracellular delay τ can cause Hopf bifurcations only when r is positive and sufficiently large, while r can cause Hopf bifurcations even when τ = 0. Intracellular delay τ can cause stability switches in E* while r does not.

Stable and transient periodic oscillations in a mathematical model for CTL response to HTLV-I infection.

The cytotoxic T lymphocyte (CTL) response to the infection of CD4+ T cells by human T cell leukemia virus type I (HTLV-I) has previously been modelled using standard response functions, with relatively simple dynamical outcomes. In this paper, we investigate the consequences of a more general CTL response and show that a sigmoidal response function gives rise to complex behaviours previously unobserved. Multiple equilibria are shown to exist and none of the equilibria is a global attractor during the chronic infection phase. Coexistence of local attractors with their own basin of attractions is the norm. In addition, both stable and unstable periodic oscillations can be created through Hopf bifurcations. We show that transient periodic oscillations occur when a saddle-type periodic solution exists. As a consequence, transient periodic oscillations can be robust and observable. Implications of our findings to the dynamics of CTL response to HTLV-I infections in vivo and pathogenesis of HAM/TSP are discussed.

Modeling the effects of carriers on transmission dynamics of infectious diseases.

An S-Ic-I-R epidemic model is investigated for infectious diseases that can be transmitted through carriers, infected individuals who are contagious but do not show any disease symptoms. Mathematical analysis is carried out that completely determines the global dynamics of the model. The impacts of disease carriers on the transmission dynamics are discussed through the basic reproduction number and through numerical simulations.