All-Optical Control of Ultrafast Switching between the Hybridized Plasmonic Fields of Au Nanorod Dimer in fs-nm Scale with Dispersed Femtosecond Laser.

With the increasing demand for ultrafast communication and information processing in future optical chips, arbitrary manipulation of electromagnetic fields in the femtosecond-nanometer spatiotemporal scale has attracted great attention in integrated optics. Manipulation of the nanoscale light field in the real femtosecond temporal domain is challenging work. Here, we have demonstrated all-optical control of ultrafast switching between the hybridized plasmonic fields of a Au nanorod dimer in the fs-nm scale using a dispersed femtosecond laser and revealed the transformation process with ultrahigh spatiotemporal resolved technology via the combination of a pump-probe technique and photoemission electron microscopy (PEEM). The results show that we can actively and coherently control the transformation sequence and time (with the shortest temporal interval of around 15 fs) of the two hybridized modes in the Au nanorod dimer by tuning the dispersion of the laser pulse. The nanoscale light manipulation achieved by all-optical control may contribute to the design of high-speed miniaturized signal-processing systems.

Deciphering driver regulators of cell fate decisions from single-cell transcriptomics data with CEFCON.

Single-cell technologies enable the dynamic analyses of cell fate mapping. However, capturing the gene regulatory relationships and identifying the driver factors that control cell fate decisions are still challenging. We present CEFCON, a network-based framework that first uses a graph neural network with attention mechanism to infer a cell-lineage-specific gene regulatory network (GRN) from single-cell RNA-sequencing data, and then models cell fate dynamics through network control theory to identify driver regulators and the associated gene modules, revealing their critical biological processes related to cell states. Extensive benchmarking tests consistently demonstrated the superiority of CEFCON in GRN construction, driver regulator identification, and gene module identification over baseline methods. When applied to the mouse hematopoietic stem cell differentiation data, CEFCON successfully identified driver regulators for three developmental lineages, which offered useful insights into their differentiation from a network control perspective. Overall, CEFCON provides a valuable tool for studying the underlying mechanisms of cell fate decisions from single-cell RNA-seq data.

Impact of the geometry of the excitation structure on optical skyrmion.

Optical skyrmions have attracted great attention for the potential applications in novel information storage and communication. It is of great significance to get insight into the generation of optical skyrmions by surface waves. Here, we have paid greater emphasis on the influence of the geometry of the coupling structure on the formation of optical skyrmions. Optical skyrmions are constructed from the superposition of the interfering surface plasmons excited by polygon trenches on Ag film. The results show the field texture of optical skyrmions is mainly determined by the excitation structure, with distinct properties revealed with various closed and non-closed geometries. Moreover, the ratio between the electric field strengths of the optical skyrmions can be larger than 4 between the optimized and unoptimized coupling structures. The pattern of the optical skyrmion shows a strong dependence on the excitation structure, implying the significant role in skyrmion topology it plays.

Molecular characterization of extracellular vesicles derived from follicular fluid of women with and without PCOS: integrating analysis of differential miRNAs and proteins reveals vital molecules involving in PCOS.

PURPOSE: To elucidate the characterization of extracellular vesicles (EVs) in the follicular fluid-derived extracellular vesicles (FF-EVs) and discover critical molecules and signaling pathways associating with the etiology and pathobiology of PCOS, the differentially expressed miRNAs (DEmiRNAs) and differentially expressed proteins profiles (DEPs) were initially explored and combinedly analyzed. METHODS: First, the miRNA and protein expression profiles of FF-EVs in PCOS patients and control patients were compared by RNA-sequencing and tandem mass tagging (TMT) proteomic methods. Subsequently, Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes were used to analyze the biological function of target genes of DEmiRNAs and DEPs. Finally, to discover the functional miRNA-target gene-protein interaction pairs involved in PCOS, DEmiRs target gene datasets and DEPs datasets were used integratedly. RESULTS: A total of 6 DEmiRNAs and 32 DEPs were identified in FF-EVs in patients with PCOS. Bioinformatics analysis revealed that DEmiRNAs target genes are mainly involved in thiamine metabolism, insulin secretion, GnRH, and Apelin signaling pathway, which are closely related to the occurrence of PCOS. DEPs also closely related to hormone metabolism processes such as steroid hormone biosynthesis. In the analysis integrating DEmiRNAs target genes and DEPs, two molecules, GRAMD1B and STPLC2, attracted our attention that are closely associated with cholesterol transport and ceramide biosynthesis, respectively. CONCLUSION: Dysregulated miRNAs and proteins in FF-EVs, mainly involving in hormone metabolism, insulin secretion, neurotransmitters regulation, adipokine expression, and secretion, may be closely related to PCOS. The effects of GRAMD1B and STPLC2 on PCOS deserve further study.

Full-length ribosome density prediction by a multi-input and multi-output model.

Translation elongation is regulated by a series of complicated mechanisms in both prokaryotes and eukaryotes. Although recent advance in ribosome profiling techniques has enabled one to capture the genome-wide ribosome footprints along transcripts at codon resolution, the regulatory codes of elongation dynamics are still not fully understood. Most of the existing computational approaches for modeling translation elongation from ribosome profiling data mainly focus on local contextual patterns, while ignoring the continuity of the elongation process and relations between ribosome densities of remote codons. Modeling the translation elongation process in full-length coding sequence (CDS) level has not been studied to the best of our knowledge. In this paper, we developed a deep learning based approach with a multi-input and multi-output framework, named RiboMIMO, for modeling the ribosome density distributions of full-length mRNA CDS regions. Through considering the underlying correlations in translation efficiency among neighboring and remote codons and extracting hidden features from the input full-length coding sequence, RiboMIMO can greatly outperform the state-of-the-art baseline approaches and accurately predict the ribosome density distributions along the whole mRNA CDS regions. In addition, RiboMIMO explores the contributions of individual input codons to the predictions of output ribosome densities, which thus can help reveal important biological factors influencing the translation elongation process. The analyses, based on our interpretable metric named codon impact score, not only identified several patterns consistent with the previously-published literatures, but also for the first time (to the best of our knowledge) revealed that the codons located at a long distance from the ribosomal A site may also have an association on the translation elongation rate. This finding of long-range impact on translation elongation velocity may shed new light on the regulatory mechanisms of protein synthesis. Overall, these results indicated that RiboMIMO can provide a useful tool for studying the regulation of translation elongation in the range of full-length CDS.

A machine learning-based framework for modeling transcription elongation.

RNA polymerase II (Pol II) generally pauses at certain positions along gene bodies, thereby interrupting the transcription elongation process, which is often coupled with various important biological functions, such as precursor mRNA splicing and gene expression regulation. Characterizing the transcriptional elongation dynamics can thus help us understand many essential biological processes in eukaryotic cells. However, experimentally measuring Pol II elongation rates is generally time and resource consuming. We developed PEPMAN (polymerase II elongation pausing modeling through attention-based deep neural network), a deep learning-based model that accurately predicts Pol II pausing sites based on the native elongating transcript sequencing (NET-seq) data. Through fully taking advantage of the attention mechanism, PEPMAN is able to decipher important sequence features underlying Pol II pausing. More importantly, we demonstrated that the analyses of the PEPMAN-predicted results around various types of alternative splicing sites can provide useful clues into understanding the cotranscriptional splicing events. In addition, associating the PEPMAN prediction results with different epigenetic features can help reveal important factors related to the transcription elongation process. All these results demonstrated that PEPMAN can provide a useful and effective tool for modeling transcription elongation and understanding the related biological factors from available high-throughput sequencing data.

Glycerin/NaOH Aqueous Solution as a Green Solvent System for Dissolution of Cellulose.

Dissolving cellulose in water-based green solvent systems is highly desired for further industrial applications. The green solvent glycerin-which contains hydrogen-bonding acceptors-was used together with NaOH and water to dissolve cellulose. This mixed aqueous solution of NaOH and glycerin was employed as the new green solvent system for three celluloses with different degree of polymerization. FTIR (Fourier-transform infrared), XRD (X-ray diffractometer) and TGA (thermogravimetric analysis) were used to characterize the difference between cellulose before and after regenerated by HCl. A UbbeloHde viscometer was used to measure the molecule weight of three different kinds of cellulose with the polymerization degree of 550, 600 and 1120. This solvent system is useful to dissolve cellulose with averaged molecule weight up to 2.08 × 105 g/mol.

Distinct spatiotemporal imaging of femtosecond surface plasmon polaritons assisted with the opening of the two-color quantum pathway effect.

Accurately capturing the spatiotemporal information of surface plasmon polaritons (SPPs) is the basis for expanding SPP applications. Here, we report spatio-temporal evolution imaging of femtosecond SPPs launched from a rectangular trench in silver film with a 400-nm light pulse assisted femtosecond laser interferometric time-resolved (ITR) photoemission electron microscopy. It is found that introducing the 400nm light pulse in the spatially separated near-infrared (NIR) laser pump-probe ITR scheme enables distinct spatiotemporal imaging of the femtosecond SPPs with a weak probe pulse in the ITR scheme, which is free from the risk of sample damage due to the required high monochromatic field for a clear photoelectron image as well as the entangled interference fringe (between the SPPs and probe pulse) in the usual spatially overlapped pump-probe ITR scheme. The demonstrated great improvement of the visibility of the SPPs spatiotemporal image with an additional 400nm light pulse scheme facilitates further analysis of the femtosecond SPPs, and carrier wavelength (785nm), group velocity (0.94C) and phase velocity (0.98C) of SPPs are extracted from the distinct spatio-temporal evolution images of SPPs. Furthermore, the modulation of photoemission induced by the quantum pathway interference effect in the 400nm-assisted scheme is proposed to play a major role in the distinct visualization for SPPs. The probabilities of electrons in different quantum pathways are obtained quantitatively through fitting the experimental results with the quantum pathway interference model. The probability that electrons emit through the quantum pathway allows us to quantitatively analyze the contribution to electron emission from the different quantum pathways. These findings pave a way for the spatiotemporal imaging of the near-infrared light-induced SPPs, such as the communication wave band using PEEM.

Green Layer-by-Layer Assembly of Porphyrin/G-Quadruplex-Based Near-Infrared Nanocomposite Photosensitizer with High Biocompatibility and Bioavailability.

A simple and green layer-by-layer assembly strategy is developed for the preparation of a highly bioavailable nanocomposite photosensitizer by assembling near-infrared (NIR) light-sensitive porphyrin/G-quadruplex complexes on the surface of a highly biocompatible nanoparticle that is prepared via Zn2+-assisted coordination self-assembly of an amphiphilic amino acid. After being efficiently delivered to the target site and internalized into tumor cells via enhanced permeability and retention effect and interactions between aptamers and tumor markers, the as-prepared nanoassembly can be directly used as an NIR light-responsive photosensitizer for tumor photodynamic therapy (PDT) since the porphyrin/G-quadruplex complexes are exposed on the nanoassembly surface and kept in an active state. It can also disassemble under the synergistic stimuli of an acidic pH environment and overexpressed glutathione, leasing more efficient porphyrin/G-quadruplex composite photosensitizers while reducing the interference caused by glutathione-dependent 1O2 consumption. Since the nanoassembly can work no matter if it is disassembled or not, the compulsory requirement for in vivo photosensitizer release is eliminated, thus resulting in the great improvement of the bioavailability of the photosensitizer. The PDT applications of the nanoassembly were well demonstrated in both in vitro cell and in vivo animal experiments.

Spatial- and energy-resolved photoemission electron from plasmonic nanoparticles in multiphoton regime.

Spatial-resolved photoelectron spectra have been observed from plasmonic metallic nanostructure and flat metal surface by a combination of time-of-flight photoemission electron microscope and femtosecond laser oscillator. The photoemission's main contribution is at localized 'hot spots,' where the plasmonic effect dominates and multiphoton photoemission is confirmed as the responsible mechanism for emission in both samples. Photoelectron spectra from hot spots exponentially decay in high energy regimes, smearing out the Fermi edge in Au flat surface. This phenomenon is explained by the emergence of above threshold photoemission that is induced by plasmonic effect; other competing mechanisms are ruled out. It is the first time that we have observed the emergence of high kinetic energy photoelectron in weak field region around 'hot spot.' We attribute the emergence of high kinetic energy photoelectron to the drifting of the liberated electron from plasmonic hot spot and driven by the gradient of plasmonic field.

Ultrafast switching of photoemission electron through quantum pathways interference in metallic nanostructure.

The localized photoemission electron originating from the plasmonic "hot spots" in a metallic bowtie nanostructure can be separately switched on and off by adjusting the relative time delay between two orthogonally polarized laser pulses. The demonstrated femtosecond timing, nanometric spatial switching of multiphoton photoemission results from the interference of quantum pathways. Energy resolved measurement of the photoemission electrons further shows that the quantum pathway interference mechanism applies to control all the liberated electrons. The experimental results also show that the probability of electron emission through the quantum pathways from a plasmonic hot spot is determined by the localized emission response to the two incident laser pulses. These findings are of importance for controlling photoemission in ultrahigh spatiotemporal resolution in metallic plasmonic nanostructures.

Recurrence network analysis of the synchronous EEG time series in normal and epileptic brains.

We sought to analyze the dynamic properties of brain electrical activity from healthy volunteers and epilepsy patients using recurrence networks. Phase-space trajectories of synchronous electroencephalogram signals were obtained through embedding dimension in phase-space reconstruction based on the distance set of space points. The recurrence matrix calculated from phase-space trajectories was identified with the adjacency matrix of a complex network. Then, we applied measures to characterize the complex network to this recurrence network. A detailed analysis revealed the following: (1) The recurrence networks of normal brains exhibited a sparser connectivity and smaller clustering coefficient compared with that of epileptic brains; (2) the small-world property existed in both normal and epileptic brains consistent with the previous empirical studies of structural and functional brain networks; and (3) the assortative property of the recurrence network was found by computing the assortative coefficients; their values increased from normal to epileptic brain which accurately suggested the difference of the states. These universal and non-universal characteristics of recurrence networks might help clearly understand the underlying neurodynamics of the brain and provide an efficient tool for clinical diagnosis.