Early warning indicators via latent stochastic dynamical systems.

Detecting early warning indicators for abrupt dynamical transitions in complex systems or high-dimensional observation data are essential in many real-world applications, such as brain diseases, natural disasters, and engineering reliability. To this end, we develop a novel approach: the directed anisotropic diffusion map that captures the latent evolutionary dynamics in the low-dimensional manifold. Then three effective warning signals (Onsager-Machlup indicator, sample entropy indicator, and transition probability indicator) are derived through the latent coordinates and the latent stochastic dynamical systems.

Learning effective dynamics from data-driven stochastic systems.

Multiscale stochastic dynamical systems have been widely adopted to a variety of scientific and engineering problems due to their capability of depicting complex phenomena in many real-world applications. This work is devoted to investigating the effective dynamics for slow-fast stochastic dynamical systems. Given observation data on a short-term period satisfying some unknown slow-fast stochastic systems, we propose a novel algorithm, including a neural network called Auto-SDE, to learn an invariant slow manifold.

Self-Referenced Surface-Enhanced Raman Scattering Nanosubstrate for the Quantitative Detection of Neurotransmitters.

Quantitative, label-free detection of neurotransmitters is of vital importance to the diagnosis and treatment of neurologic diseases. The surface-enhanced Raman scattering (SERS) effect has great application prospects in the field of biosensing and bioimaging because of its unique nondestructive testing and its capability of being used in molecular fingerprint identification. However, the quantitative SERS analysis of neurotransmitters is still a great challenge because of the poor reproducibility of the SERS-active sites, as well as the small Raman cross-section and low physiological concentration of neurotransmitter molecules.

Recent Advances of DNA Nanostructure-Based Cell Membrane Engineering.

Materials that can regulate the composition and structure of the cell membrane to fabricate engineered cells with defined functions are in high demand. Compared with other biomolecules, DNA has unique advantages in cell membrane engineering due to its excellent programmability and biocompatibility. Especially, the near-atomic scale precision of DNA nanostructures facilitates the investigation of structure-property relations on the cell membrane.

Attachment and Survival of O157:H7 on In-Shell Hazelnuts.

The multistate () O157:H7 outbreak associated with in-shell hazelnuts highlights the pathogen's ability to involve non-traditional vehicles in foodborne infections. Furthermore, it underscores significant gaps in our knowledge of pathogen survivability and persistence on nuts. Therefore, this study investigated the ability of O157:H7 to attach and survive on in-shell hazelnuts.

Green synthesis and evaluation of an iron-based metal-organic framework MIL-88B for efficient decontamination of arsenate from water.

Iron-containing metal-organic frameworks (MOFs) have gradually emerged as environmentally benign alternatives for reducing the levels of environmental contamination because of their advantages, such as readily obtained raw materials with low cost, nontoxic metal source with good biocompatibility, and distinguished physicochemical features e.g., high porosity, framework flexibility, and semiconductor properties.