Search for: All records

Abstract Molecular simulations are an important tool for research in physics, chemistry, and biology. The capabilities of simulations can be greatly expanded by providing access to advanced sampling methods and techniques that permit calculation of the relevant underlying free energy landscapes. In this sense, software that can be seamlessly adapted to a broad range of complex systems is essential. Building on past efforts to provide open-source community-supported software for advanced sampling, we introduce PySAGES, a Python implementation of the Software Suite for Advanced General Ensemble Simulations (SSAGES) that provides full GPU support for massively parallel applications of enhanced sampling methods such as adaptive biasing forces, harmonic bias, or forward flux sampling in the context of molecular dynamics simulations. By providing an intuitive interface that facilitates the management of a system’s configuration, the inclusion of new collective variables, and the implementation of sophisticated free energy-based sampling methods, the PySAGES library serves as a general platform for the development and implementation of emerging simulation techniques. The capabilities, core features, and computational performance of this tool are demonstrated with clear and concise examples pertaining to different classes of molecular systems. We anticipate that PySAGES will provide the scientific community with a robust and easily accessible platform to accelerate simulations, improve sampling, and enable facile estimation of free energies for a wide range of materials and processes. 

Since the early 1990s, single-molecule detection in solution at room temperature has enabled direct observation of single biomolecules at work in real time and under physiological conditions, providing insights into complex biological systems that the traditional ensemble methods cannot offer. In particular, recent advances in single-molecule tracking techniques allow researchers to follow individual biomolecules in their native environments for a timescale of seconds to minutes, revealing not only the distinct pathways these biomolecules take for downstream signaling but also their roles in supporting life. In this review, we discuss various single-molecule tracking and imaging techniques developed to date, with an emphasis on advanced three-dimensional (3D) tracking systems that not only achieve ultrahigh spatiotemporal resolution but also provide sufficient working depths suitable for tracking single molecules in 3D tissue models. We then summarize the observables that can be extracted from the trajectory data. Methods to perform single-molecule clustering analysis and future directions are also discussed. 

A PEM fuel cell with a hydrophobically treated cathode catalyst layer (CL) demonstrates ∼220% peak power increase with humidified air at 70 °C. To understand the reasons of the increase, a mathematical model was developed focusing on the oxygen-water two-phase transport phenomena in the CL. It suggests the treatment affects the CL in two ways. First, the interface of the ionomer layer exposed to the gas pores becomes more hydrophobic, facilitating less liquid water coverage and faster water drainage from the CL and resulting in better performance at high current densities. Second, it also affects the hydration level in the ionomer phase resulting in higher oxygen concentration in the ionomer phase on and in the catalyst agglomerates, leading to higher performance over the whole polarization curve. The properties having significant influence on the model fitting the experimental data are the capillary pressure property of the CL, the hydrophobic ionomer ratio in the catalyst agglomerate, and the oxygen solubility/diffusivity in the Nafion® phases. With this experimentally verified model, additional case studies combining the hydrophobic gas diffusion material with the hydrophobic CL demonstrate that the membrane’s self-humidification (zero-net-water flux) and peak power enhancement (∼15%) can be reached simultaneously, providing direction for the future materials development. 

Bayesian regression is performed to infer parameters of thermodynamic binding models from isothermal titration calorimetry measurements in which the titrant is an enantiomeric mixture. For some measurements the posterior density is multimodal, indicating that additional data with a different protocol are required to uniquely determine the parameters. Models of increasing complexity—two-component binding, racemic mixture, and enantiomeric mixture—are compared using model selection criteria. To precisely estimate one of these criteria, the Bayes factor, a variation of bridge sampling is developed. 

iOS is one of the most valuable targets for security researchers. Unfortunately, studying the internals of this operating system is notoriously hard, due to the closed nature of the iOS ecosystem and the absence of easily-accessible analysis tools. To address this issue, we developed TruEMU, which we present in this talk. TruEMU is the first open-source, extensible, whole-system iOS emulator. Compared to the few available alternatives, TruEMU enables complete iOS kernel emulation, including emulation of the SecureROM and the USB kernel stack. More importantly, TruEMU is completely free and open-source, and it is based on the well-known and highly extensible emulator QEMU. This talk will start by presenting the challenges and the solutions we devised to reverse engineer current iOS boot code and kernel code, and explain how to provide adequate support in QEMU. Then, to showcase TruEMU's usefulness and capabilities, we will demonstrate how it can completely boot modern iOS images, including iOS 14 and the latest iOS 15, and how it can properly run different user-space components, such as launchd, restored, etc. Later, we will showcase two promising ways to use TruEMU as an iOS vulnerability research platform. Specifically, we will demonstrate how to use TruEMU to enable coverage-based fuzzing of the iOS kernel USB stack. Further, we will show how TruEMU provides a platform to implement coverage-based, syscall-level fuzzing. This platform enables security researchers to automatically explore multiple attack surfaces of iOS. In sum, building a complete emulator for iOS is a daunting task. Many features (i.e., many peripherals) still need to be implemented to allow a complete emulation of a modern iOS device. We hope this talk will also bootstrap a large community involvement in this project that will progressively shed more light on the obscure corners of iOS security.