Scalable gradients enable Hamiltonian Monte Carlo sampling for phylodynamic inference under episodic birth-death-sampling models.

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.

Scalable gradients enable Hamiltonian Monte Carlo sampling for phylodynamic inference under episodic birth-death-sampling models.

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.

Size Effects of Atomically Precise Gold Nanoclusters in Catalysis.

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.

A Metal-Organic Framework Thin Film for Selective Mg2+ Transport.

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. 202 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  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 0.05 mA cm-2 . The film was effective in blocking solvent molecules and counterions from crossing over for extended period of time.

Using a Multi-Shelled Hollow Metal-Organic Framework as a Host to Switch the Guest-to-Host and Guest-to-Guest Interactions.

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.