RESUMO
Birth-death models play a key role in phylodynamic analysis for their interpretation in terms of key epidemiological parameters. In particular, models with piecewise-constant rates varying at different epochs in time, to which we refer as episodic birth-death-sampling (EBDS) models, are valuable for their reflection of changing transmission dynamics over time. A challenge, however, that persists with current time-varying model inference procedures is their lack of computational efficiency. This limitation hinders the full utilization of these models in large-scale phylodynamic analyses, especially when dealing with high-dimensional parameter vectors that exhibit strong correlations. We present here a linear-time algorithm to compute the gradient of the birth-death model sampling density with respect to all time-varying parameters, and we implement this algorithm within a gradient-based Hamiltonian Monte Carlo (HMC) sampler to alleviate the computational burden of conducting inference under a wide variety of structures of, as well as priors for, EBDS processes. We assess this approach using three different real world data examples, including the HIV epidemic in Odesa, Ukraine, seasonal influenza A/H3N2 virus dynamics in New York state, America, and Ebola outbreak in West Africa. HMC sampling exhibits a substantial efficiency boost, delivering a 10- to 200-fold increase in minimum effective sample size per unit-time, in comparison to a Metropolis-Hastings-based approach. Additionally, we show the robustness of our implementation in both allowing for flexible prior choices and in modeling the transmission dynamics of various pathogens by accurately capturing the changing trend of viral effective reproductive number.
Assuntos
Epidemias , Doença pelo Vírus Ebola , Influenza Humana , Humanos , Vírus da Influenza A Subtipo H3N2 , Algoritmos , Influenza Humana/epidemiologia , Doença pelo Vírus Ebola/epidemiologia , Método de Monte CarloRESUMO
The incompatibility between the anode and the cathode chemistry limits the used of Mg as an anode. This issue may be addressed by separating the anolyte and the catholyte with a membrane that only allows for Mg2+ transport. Mg-MOF-74 thin films were used as the separator for this purpose. It was shown to meet the needs of low-resistance, selective Mg2+ transport. The uniform MOF thin films supported on Au substrate with thicknesses down to ca. 202â nm showed an intrinsic resistance as low as 6.4â Ω cm2 , with the normalized room-temperature ionic conductivity of ca. 3.17×10-6 â S cm-1 . When synthesized directly onto a porous anodized aluminum oxide (AAO) support, the resulting films were used as a standalone membrane to permit stable, low-overpotential Mg striping and plating for over 100 cycles at a current density of 0.05â mA cm-2 . The film was effective in blocking solvent molecules and counterions from crossing over for extended period of time.
RESUMO
A bio-inspired design of using metal-organic framework (MOF) microcrystals with well-defined multi-shelled hollow structures was used as a matrix to host multiple guests including molecules and nanoparticles at separated locations to form a hierarchical material, mimicking biological structures. The interactions such as energy transfer (ET) between different guests are regulated by precisely fixing them in the MOF shells or encapsulating them in the cavities between the MOF shells. The proof-of-concept design is demonstrated by hosting chromophore molecules including rhodamine 6G (R6G) and 7-amino-4-(trifluoromethyl)coumarin (C-151), as well as metal nanoparticles (Pd NPs) into the multi-shelled hollow zeolitic imidazolate framework-8 (ZIF-8). We could selectively establish or diminish the guest-to-framework and guest-to-guest ET. This work provides a platform to construct complex multifunctional materials, especially those need precise separation control of multi-components.
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The global mpox epidemic in 2022 was likely caused by transmission of mpox virus (MPXV) through sexual contact networks, with New York City (NYC) experiencing the first and largest outbreak in the United States. By performing a phylogeographic and epidemiological analysis of MPXV, we identify at least 200 introductions of MPXV into NYC and 84 leading to onward transmission. Through a comparative analysis with human immunodeficiency virus (HIV) in NYC, we find that both MPXV and HIV genomic cluster sizes are best fit by scale-free distributions and that people in MPXV clusters are more likely to have previously received an HIV diagnosis (odds ratio=1.58; p =0.012) and be a member of a recently growing HIV transmission cluster, indicating overlapping sexual contact networks. We then model the transmission of MPXV through sexual contact networks and show that highly connected individuals would be disproportionately infected at the start of an epidemic, thereby likely resulting in the exhaustion of the most densely connected parts of the sexual network. This dynamic explains the rapid expansion and decline of the NYC outbreak, as well as the estimated cumulative incidence of less than 2% within high-risk populations. By synthesizing the genomic epidemiology of MPXV and HIV with epidemic modeling, we demonstrate that MPXV transmission dynamics can be understood by general principles of sexually transmitted pathogens.
RESUMO
Birth-death models play a key role in phylodynamic analysis for their interpretation in terms of key epidemiological parameters. In particular, models with piecewise-constant rates varying at different epochs in time, to which we refer as episodic birth-death-sampling (EBDS) models, are valuable for their reflection of changing transmission dynamics over time. A challenge, however, that persists with current time-varying model inference procedures is their lack of computational efficiency. This limitation hinders the full utilization of these models in large-scale phylodynamic analyses, especially when dealing with high-dimensional parameter vectors that exhibit strong correlations. We present here a linear-time algorithm to compute the gradient of the birth-death model sampling density with respect to all time-varying parameters, and we implement this algorithm within a gradient-based Hamiltonian Monte Carlo (HMC) sampler to alleviate the computational burden of conducting inference under a wide variety of structures of, as well as priors for, EBDS processes. We assess this approach using three different real world data examples, including the HIV epidemic in Odesa, Ukraine, seasonal influenza A/H3N2 virus dynamics in New York state, America, and Ebola outbreak in West Africa. HMC sampling exhibits a substantial efficiency boost, delivering a 10- to 200-fold increase in minimum effective sample size per unit-time, in comparison to a Metropolis-Hastings-based approach. Additionally, we show the robustness of our implementation in both allowing for flexible prior choices and in modeling the transmission dynamics of various pathogens by accurately capturing the changing trend of viral effective reproductive number.
RESUMO
The emergence of ligand-protected, atomically precise gold nanoclusters (NCs) in recent years has attracted broad interest in catalysis due to their well-defined atomic structures and intriguing properties. Especially, the precise formulas of NCs provide an opportunity to study the size effects at the atomic level without complications by the polydispersity in conventional nanoparticles that obscures the relationship between the size/structure and properties. Herein, we summarize the catalytic size effects of atomically precise, thioate-protected gold NCs in the range of tens to hundreds of metal atoms. The catalytic reactions include electrochemical catalysis, photocatalysis, and thermocatalysis. With the precise sizes and structures, the fundamentals underlying the size effects are analyzed, such as the surface area, electronic properties, and active sites. In the catalytic reactions, one or more factors may exert catalytic effects simultaneously, hence leading to different catalytic-activity trends with the size change of NCs. The summary of literature work disentangles the underlying fundamental mechanisms and provides insights into the size effects. Future studies will lead to further understanding of the size effects and shed light on the catalytic active sites and ultimately promote catalyst design at the atomic level.