Silver nanoprism-loaded eggshell membrane: a facile platform for in situ SERS monitoring of catalytic reactions

We reported the fabrication of an in situ surface-enhanced Raman scattering (SERS) monitoring platform, comprised of a porous eggshell membrane (ESM) bioscaffold loaded with Ag nanoprism via an electrostatic self-assembly approach. The localized surface plasmon resonance (LSPR) property of silver nanoprism leads to the blue color of the treated ESMs. UV-vis diffuse reflectance spectroscopy, scanning electron microscope (SEM), X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS) measurements were employed to observe the microstructure and surface property of Ag nanoprisms on the ESMs. The silver nanoprism-loaded eggshell membrane (AgNP@ESM) exhibited strong catalytic activity for the reduction of 4-nitrophenol by sodium borohydride (NaBH4) and it can be easily recovered and reused for more than six cycles. Significantly, the composites also display excellent SERS efficiency, allowing the in situ SERS monitoring of molecular transformation in heterogeneous catalysis. The results indicate that the AgNP@ESM biocomposite can achieve both SERS and catalytic functionalities simultaneously in a single entity with high performance, which promotes the potential applications of ESM modified with functional materials

Genome dynamics and diversity of Shigella species, the etiologic agents of bacillary dysentery

The Shigella bacteria cause bacillary dysentery, which remains a significant threat to public health. The genus status and species classification appear no longer valid, as compelling evidence indicates that Shigella, as well as enteroinvasive Escherichia coli, are derived from multiple origins of E.coli and form a single pathovar. Nevertheless, Shigella dysenteriae serotype 1 causes deadly epidemics but Shigella boydii is restricted to the Indian subcontinent, while Shigella flexneri and Shigella sonnei are prevalent in developing and developed countries respectively. To begin to explain these distinctive epidemiological and pathological features at the genome level, we have carried out comparative genomics on four representative strains. Each of the Shigella genomes includes a virulence plasmid that encodes conserved primary virulence determinants. The Shigella chromosomes share most of their genes with that of E.coli K12 strain MG1655, but each has over 200 pseudogenes, 300∼700 copies of insertion sequence (IS) elements, and numerous deletions, insertions, translocations and inversions. There is extensive diversity of putative virulence genes, mostly acquired via bacteriophage-mediated lateral gene transfer. Hence, via convergent evolution involving gain and loss of functions, through bacteriophage-mediated gene acquisition, IS-mediated DNA rearrangements and formation of pseudogenes, the Shigella spp. became highly specific human pathogens with variable epidemiological and pathological features

Learning a gradient-free Riemannian optimizer on tangent spaces

A principal way of addressing constrained optimization problems is to model them as problems on Riemannian manifolds. Recently, Riemannian meta-optimization provides a promising way for solving constrained optimization problems by learning optimizers on Riemannian manifolds in a data-driven fashion, making it possible to design task-specific constrained optimizers. A close look at the Riemannian meta-optimization reveals that learning optimizers on Riemannian manifolds needs to differentiate through the nonlinear Riemannian optimization, which is complex and computationally expensive. In this paper, we propose a simple yet efficient Riemannian meta-optimization method that learns to optimize on tangent spaces of manifolds. In doing so, we present a gradient-free optimizer on tangent spaces, which takes parameters of the model along with the training data as inputs, and generates the updated parameters directly. As a result, the constrained optimization is transformed from Riemannian manifolds to tangent spaces where complex Riemannian operations (e.g., retraction operations) are removed from the optimizer, and learning the optimizer does not need to differentiate through the Riemannian optimization. We empirically show that our method brings efficient learning of the optimizer, while enjoying a good optimization trajectory in a data-driven manner