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Plant microRNAs (miRNAs) associate with ARGONAUTE1 (AGO1) to direct post-transcriptional gene silencing and regulate numerous biological processes. Although AGO1 predominantly binds miRNAs in vivo, it also associates with endogenous small interfering RNAs (siRNAs). It is unclear whether the miRNA/siRNA balance affects miRNA activities. Here we report that FIERY1 (FRY1), which is involved in 5'-3' RNA degradation, regulates miRNA abundance and function by suppressing the biogenesis of ribosomal RNA-derived siRNAs (risiRNAs). In mutants of FRY1 and the nuclear 5'-3' exonuclease genes XRN2 and XRN3, we find that a large number of 21-nt risiRNAs are generated through an endogenous siRNA biogenesis pathway. The production of risiRNAs correlates with pre-rRNA processing defects in these mutants. We also show that these risiRNAs are loaded into AGO1, causing reduced loading of miRNAs. This study reveals a previously unknown link between rRNA processing and miRNA accumulation.

AIM: To compare the optic disc microvessel density(MVD)with different degrees of myopia and to evaluate the association of MVD with axial length(AL)and retinal nerve fibre layer thickness(RNFL)by using optical coherence tomography angiography(OCTA).METHODS: Prospective and pathological control study. A total of 94 eyes [31 eyes with mild myopia(SE: +0.50D to -0.50D), 32 eyes with low and moderate myopia(-0.75D≤SE

The growing demand for real-time ultraviolet (UV) monitoring calls for a simple, rapid, and low-cost strategy to prepare UV photodetectors (PDs). We prepare a wearable real-time UV PD based on hydrogenated titanium dioxide film synthesized by radio frequency atmospheric pressure plasma. The conductivity of our hydrogenated titanium dioxide is improved to 10.2 S cm−1, nine orders of magnitude higher than that of pristine titanium dioxide after 10 min plasma treatment. Plasma hydrogenation disrupts the surface crystal structure, introducing oxygen vacancies (OVs) that create self-doped titanium(III) and titanium(II) species. First-principles calculations indicate that the OVs raise the Fermi level of TiO2 and distort the lattice locally. Our optimized film has a distinctive periodic switching characteristic under intermittent illumination; its responsivity is good from 280 to 400 nm, peaking at 632.35 mA W−1 at 365 nm. The fabricated wearable sensor based on the optimized film effectively monitors the daily variation of ambient UV intensity in three typical weather types, transferring its data to a smartphone via Wi-Fi. [ABSTRACT FROM AUTHOR]

Our previous study found that the aerial parts of Chinese liquorice (Glycyrrhiza uralensis Fisch.) had pharmacological effects against chronic non-bacterial prostatitis in rats, however the pharmacologically active compounds remain unclear. Here, a method based on UPLC-Q-Exactive Orbitrap-MS was established to qualitatively analyse the flavonoid glycosides rich fraction extracted from the aerial part of G. uralensis Fisch., after pretreatment with n-butanol and enrichment using AB-8 macroporous resin. Using both positive and negative ion modes, 52 compounds were identified or tentatively characterised by comparison with standards and the literature: 40 flavonoids, 8 organic acids, 2 chromones, 1 coumarin, and 1 phenylethanoid glycoside. This study provides not only an approach to enrich flavonoid glycosides but also a methodology for quickly determining the relevant bioactive components in the aerial parts of G. uralensis Fisch. [ABSTRACT FROM AUTHOR]

Summary: Cracks can develop in high‐rise buildings because of long‐term environmental changes and extreme loading events such as strong winds or earthquakes. Although deep learning‐based identification methods can efficiently identify cracks, the accuracy of crack identification in high‐rise buildings needs to be improved due to the lack of crack datasets specifically related to high‐rise structures. Moreover, the number of available images of cracks in high‐rise is limited. To this end, this paper establishes an intelligent crack identification method based on a photorealistic synthetic modeling technique. First, a computer graphics (CG) model of a high‐rise building with assumed damage is constructed. Subsequently, the CG model is utilized to generate a dataset that includes photorealistic images of the high‐rise building as well as corresponding labels for various components and types of damage. The generated dataset is then used to train a DeepLabv3 + neural network for structural component and damage identification, followed by validation by employing images of both synthetic and full‐scale high‐rise buildings. The trained network can accurately identify different components in images of the full‐scale, high‐rise building and identify cracks that are intentionally synthesized in those images. The results show that the synthetic dataset generated by the CG model not only allows for fast and efficient labeling for the purpose of neural network training but also outperforms methods that do not consider any application‐specific context in crack identification. [ABSTRACT FROM AUTHOR]