Multiple distinct small RNAs originate from the same microRNA precursors

Abstract Background MicroRNAs (miRNAs), which originate from precursor transcripts with stem-loop structures, are essential gene expression regulators in eukaryotes. Results We report 19 miRNA precursors in Arabidopsis that can yield multiple distinct miRNA-like RNAs in addition to miRNAs and miRNA*s. These miRNA precursor-derived miRNA-like RNAs are often arranged in phase and form duplexes with an approximately two-nucleotide 3'-end overhang. Their production depends on the same biogenesis pathway as their sibling miRNAs and does not require RNA-dependent RNA polymerases or RNA polymerase IV. These miRNA-like RNAs are methylated, and many of them are associated with Argonaute proteins. Some of the miRNA-like RNAs are differentially expressed in response to bacterial challenges, and some are more abundant than the cognate miRNAs. Computational and expression analyses demonstrate that some of these miRNA-like RNAs are potentially functional and they target protein-coding genes for silencing. The function of some of these miRNA-like RNAs was further supported by their target cleavage products from the published small RNA degradome data. Our systematic examination of public small-RNA deep sequencing data from four additional plant species (Oryza sativa, Physcomitrella patens, Medicago truncatula and Populus trichocarpa) and four animals (Homo sapiens, Mus musculus, Caenorhabditis elegans and Drosophila) shows that such miRNA-like RNAs exist broadly in eukaryotes. Conclusions We demonstrate that multiple miRNAs could derive from miRNA precursors by sequential processing of Dicer or Dicer-like proteins. Our results suggest that the pool of miRNAs is larger than was previously recognized, and miRNA-mediated gene regulation may be broader and more complex than previously thought

Hardware-algorithm collaborative computing with photonic spiking neuron chip based on integrated Fabry-P\'erot laser with saturable absorber

Photonic neuromorphic computing has emerged as a promising avenue toward building a low-latency and energy-efficient non-von-Neuman computing system. Photonic spiking neural network (PSNN) exploits brain-like spatiotemporal processing to realize high-performance neuromorphic computing. However, the nonlinear computation of PSNN remains a significant challenging. Here, we proposed and fabricated a photonic spiking neuron chip based on an integrated Fabry-P\'erot laser with a saturable absorber (FP-SA) for the first time. The nonlinear neuron-like dynamics including temporal integration, threshold and spike generation, refractory period, and cascadability were experimentally demonstrated, which offers an indispensable fundamental building block to construct the PSNN hardware. Furthermore, we proposed time-multiplexed spike encoding to realize functional PSNN far beyond the hardware integration scale limit. PSNNs with single/cascaded photonic spiking neurons were experimentally demonstrated to realize hardware-algorithm collaborative computing, showing capability in performing classification tasks with supervised learning algorithm, which paves the way for multi-layer PSNN for solving complex tasks.Comment: 10 pages, 8 figure

Bacteria-responsive microRNAs regulate plant innate immunity by modulating plant hormone networks

MicroRNAs (miRNAs) are key regulators of gene expression in development and stress responses in most eukaryotes. We globally profiled plant miRNAs in response to infection of bacterial pathogen Pseudomonas syringae pv. tomato (Pst). We sequenced 13 small-RNA libraries constructed from Arabidopsis at 6 and 14 h post infection of non-pathogenic, virulent and avirulent strains of Pst. We identified 15, 27 and 20 miRNA families being differentially expressed upon Pst DC3000 hrcC, Pst DC3000 EV and Pst DC3000 avrRpt2 infections, respectively. In particular, a group of bacteria-regulated miRNAs targets protein-coding genes that are involved in plant hormone biosynthesis and signaling pathways, including those in auxin, abscisic acid, and jasmonic acid pathways. Our results suggest important roles of miRNAs in plant defense signaling by regulating and fine-tuning multiple plant hormone pathways. In addition, we compared the results from sequencing-based profiling of a small set of miRNAs with the results from small RNA Northern blot and that from miRNA quantitative RT-PCR. Our results showed that although the deep-sequencing profiling results are highly reproducible across technical and biological replicates, the results from deep sequencing may not always be consistent with the results from Northern blot or miRNA quantitative RT-PCR. We discussed the procedural differences between these techniques that may cause the inconsistency

Dynamical Analysis of a Stochastic Multispecies Turbidostat Model

A stochastic turbidostat system in which the dilution rate is subject to white noise is investigated in this paper. First of all, sufficient conditions of the competitive exclusion among microorganisms are obtained by employing the techniques of stochastic analysis. Furthermore, the results demonstrate that the competition among microorganisms and stochastic disturbance will affect the dynamical behaviors of microorganisms. Finally, the theoretical results obtained in this contribution are illustrated by numerical simulations

Existence and persistence of positive solution for a stochastic turbidostat model

Abstract A novel stochastic turbidostat model is investigated in this paper. The stochasticity in the model comes from the maximal growth rate influenced by white noise. Firstly, the existence and uniqueness of the positive solution for the system are demonstrated. Secondly, we analyze the persistence in mean and stochastic persistence of the system, respectively. Sufficient conditions about the extinction of the microorganism are obtained. Finally, numerical simulation results are given to support the theoretical conclusions

Development Characteristics and Formation Mechanism of Nanoparticles in the Ductile Shear Zone of the Red River Fault

Nanoparticles in the ductile shear zones of faults are thought to be closely related to fault activity and the seismogenic mechanism. Using scanning electron microscopy (SEM), nanoparticles with a variety of morphological characteristics were found in mylonite, gneiss, and schist from the ductile shear zones of the Red River Fault. The nanoparticle morphology is dominated by rods, spherulites, massive forms, lamellae and film-like shapes. Energy spectrum analysis showed that the nanoparticles were mainly composed of silicate minerals, while a few contained carbonate minerals. Nanoparticles follow certain spatial distribution rules in ductile shear zones. Near the main fault plane of the Red River Fault Zone, the nanoparticles are dispersed, brittle and spherulitic, with a small particle diameter. Those far from the main fault plane are agglomerated and plastic, with rod-like, massive, lamellar and film-like shapes, and a relatively large particle diameter. According to nanoparticle development characteristics and the theory of microscopic deformation, a study on the formation mechanism of nanoparticles in the ductile shear zones of the Red River Fault was carried out. The rock minerals are the first to experience granulation, through intergranular movement under strong strain, generating brittle spherulitic particles, which eventually loosen and disperse. In the later stages, through recrystallization, nanoparticle crystals may become large or develop various morphologies