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  1. Abstract Bacterial natural product biosynthetic genes, canonically clustered, have been increasingly found to rely on hidden enzymes encoded elsewhere in the genome for completion of biosynthesis. The study and application of lanthipeptides are frequently hindered by unclustered protease genes required for final maturation. Here, we establish a global correlation network bridging the gap between lanthipeptide precursors and hidden proteases. Applying our analysis to 161,954 bacterial genomes, we establish 5209 correlations between precursors and hidden proteases, with 91 prioritized. We use network predictions and co-expression analysis to reveal a previously missing protease for the maturation of class I lanthipeptide paenilan. Wemore »further discover widely distributed bacterial M16B metallopeptidases of previously unclear biological function as a new family of lanthipeptide proteases. We show the involvement of a pair of bifunctional M16B proteases in the production of previously unreported class III lanthipeptides with high substrate specificity. Together, these results demonstrate the strength of our correlational networking approach to the discovery of hidden lanthipeptide proteases and potentially other missing enzymes for natural products biosynthesis.« less
    Free, publicly-accessible full text available December 1, 2023
  2. Abstract Filtering facepiece respirators (FFRs) provide effective protection against diseases spread through airborne infectious droplets and particles. The widespread use of FFRs during the COVID-19 pandemic has not only led to supply shortages, but the disposal of single-use facemasks also threatens the environment with a new kind of plastic pollution. While limited reuse of filtering facepiece respirators has been permitted as a crisis capacity strategy, there are currently no standard test methods available for decontamination before their repeated use. The decontamination of respirators can compromise the structural and functional integrity by reducing the filtration efficiency and breathability. Digital segmentation ofmore »X-ray microcomputed tomography (microCT) scans of the meltblown nonwoven layers of a specific N95 respirator model (Venus-4400) after treatment with one and five cycles of liquid hydrogen peroxide, ultraviolet radiation, moist heat, and aqueous soap solution enabled us to perform filtration simulations of decontaminated respirators. The computed filtration efficiencies for 0.3 µm particles agreed well with experimental measurements, and the distribution of particle penetration depths was correlated with the structural changes resulting from decontamination. The combination of X-ray microCT imaging with numerical simulations thus provides a strategy for quantitative evaluation of the effectiveness of decontamination treatments for a specific respirator model.« less
    Free, publicly-accessible full text available December 1, 2023
  3. Abstract Optical manipulation of coherent phonon frequency in two-dimensional (2D) materials could advance the development of ultrafast phononics in atomic-thin platforms. However, conventional approaches for such control are limited to doping, strain, structural or thermal engineering. Here, we report the experimental observation of strong laser-polarization control of coherent phonon frequency through time-resolved pump-probe spectroscopic study of van der Waals (vdW) materials Fe 3 GeTe 2 . When the polarization of the pumping laser with tilted incidence is swept between in-plane and out-of-plane orientations, the frequencies of excited phonons can be monotonically tuned by as large as 3% (~100 GHz). Our first-principlesmore »calculations suggest the strong planar and vertical inter-atomic interaction asymmetry in layered materials accounts for the observed polarization-dependent phonon frequencies, as in-plane/out-of-plane polarization modifies the restoring force of the lattice vibration differently. Our work provides insightful understanding of the coherent phonon dynamics in layered vdW materials and opens up new avenues to optically manipulating coherent phonons.« less
    Free, publicly-accessible full text available December 1, 2023
  4. Abstract Driven by the big data science, material informatics has attracted enormous research interests recently along with many recognized achievements. To acquire knowledge of materials by previous experience, both feature descriptors and databases are essential for training machine learning (ML) models with high accuracy. In this regard, the electronic charge density ρ ( r ), which in principle determines the properties of materials at their ground state, can be considered as one of the most appropriate descriptors. However, the systematic electronic charge density ρ ( r ) database of inorganic materials is still in its infancy due to the difficultiesmore »in collecting raw data in experiment and the expensive first-principles based computational cost in theory. Herein, a real space electronic charge density ρ ( r ) database of 17,418 cubic inorganic materials is constructed by performing high-throughput density functional theory calculations. The displayed ρ ( r ) patterns show good agreements with those reported in previous studies, which validates our computations. Further statistical analysis reveals that it possesses abundant and diverse data, which could accelerate ρ ( r ) related machine learning studies. Moreover, the electronic charge density database will also assists chemical bonding identifications and promotes new crystal discovery in experiments.« less
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