WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing

Self-supervised learning (SSL) achieves great success in speech recognition, while limited exploration has been attempted for other speech processing tasks. As speech signal contains multi-faceted information including speaker identity, paralinguistics, spoken content, etc., learning universal representations for all speech tasks is challenging. To tackle the problem, we propose a new pre-trained model, WavLM, to solve full-stack downstream speech tasks. WavLM jointly learns masked speech prediction and denoising in pre-training. By this means, WavLM does not only keep the speech content modeling capability by the masked speech prediction, but also improves the potential to non-ASR tasks by the speech denoising. In addition, WavLM employs gated relative position bias for the Transformer structure to better capture the sequence ordering of input speech. We also scale up the training dataset from 60k hours to 94k hours. WavLM Large achieves state-of-the-art performance on the SUPERB benchmark, and brings significant improvements for various speech processing tasks on their representative benchmarks. The code and pre-trained models are available at https://aka.ms/wavlm.Comment: Submitted to the Journal of Selected Topics in Signal Processing (JSTSP

Multicolor-Encoded Reconfigurable DNA Nanostructures Enable Multiplexed Sensing of Intracellular MicroRNAs in Living Cells

Despite the widespread utilization of gold nanoparticles and graphene for in vivo applications, complex steps for the preparation and functionalization of these nanomaterials are commonly required. In addition, the cytotoxicity of such materials is currently still under debate. In this work, by taking the significant advantages of DNA in terms of biocompatibility, nontoxicity, and controllability as building blocks for DNA nanostructures, we describe the construction of a reconfigurable, multicolor-encoded DNA nanostructure for multiplexed monitoring of intracellular microRNAs (miRNAs) in living cells. The DNA nanostructure nanoprobes containing two fluorescently quenched hairpins can be obtained by simple thermal annealing of four ssDNA oligonucleotides. The presence of the target miRNAs can unfold the hairpin structures and recover fluorescent emissions at distinct wavelengths to achieve multiplexed detection of miRNAs. Importantly, the DNA nanostructure nanoprobes exhibit significantly improved stability over conventional DNA molecular beacon probes in cell lysates and can steadily enter cells to realize simultaneous detection of two types of intracellular miRNAs. The demonstration of the self-assembled DNA nanostructures for intracellular sensing thus offers great potential application of these nanoprobes for imaging, drug delivery and cancer therapy in vivo

Experimental Study of Influence of Different Parameters on Flow Field Structures Around an Airfoil Covered with Rough Ice

Rough ice can change the leading edge of airfoil and affect the aerodynamic characteristics. Studying the influence of rough ice caused by supercooled water droplets can provide reference for anti-icing design of aircrafts. A detailed experimental study was conducted to measure the flow field structure of an airfoil model with rough ice in a low-speed wind tunnel by using particle image velocimetry. The parameters include Reynolds number, roughness of rough ice, and angle of attack. The results show that with the increase of Reynolds number, the range and value of spanwise vorticity at the wake of the airfoil with ice increased, while the normalized Reynolds stress decreased slightly. The presence of rough ice reduced the airflow velocity near the airfoil, increased the vorticity of wake, and seriously affected the shear stress distribution. Compared with the clean airfoil, the rough ice caused the air flow to separate earlier and the velocity in the separation bubble fluctuated more violently

Ultrasensitive Assay for Telomerase Activity via Self-Enhanced Electrochemiluminescent Ruthenium Complex Doped Metal–Organic Frameworks with High Emission Efficiency

Here, an ultrasensitive “off–on” electrochemiluminescence (ECL) biosensor was proposed for the determination of telomerase activity by using a self-enhanced ruthenium polyethylenimine (Ru–PEI) complex doped zeolitic imidazolate framework-8 (Ru–PEI@ZIF-8) with high ECL efficiency as an ECL indicator and an enzyme-assisted DNA cycle amplification strategy. The Ru–PEI@ZIF-8 nanocomposites were synthesized by self-enhanced Ru–PEI complex doping during the growth of zeolitic imidazolate framework-8 (ZIF-8), which presented high ECL efficiency and excellent stability. Furthermore, owing to the porosity of Ru–PEI@ZIF-8, the self-enhanced Ru–PEI complex in the outer layer and inner layer of self-enhanced Ru–PEI@ZIF-8 could be excited by electrons causing the utilization ratio of the self-enhanced ECL materials to be remarkably increased. To further improve the sensitivity of the proposed biosensor, the telomerase activity signal was converted into the trigger DNA signal which was further amplified by an enzyme-assisted DNA recycle–amplification strategy. The proposed ECL biosensor presented great performance for telomerase activity detection from 5 × 10¹ to 10⁶ Hela cells with a detection limit of 11 cells. Moreover, this method was applied in the detection of telomerase activity from cancer cells treated with an anticancer drug, which indicated the proposed method held potential application value as an evaluation tool in anticancer drug screening

Tetraphenylethylene-doped covalent organic frameworks as a highly efficient aggregation-induced electrochemiluminescence emitter for ultrasensitive miRNA-21 analysis

MicroRNAs (miRNAs) as a well-known kind of cancer marker are closely associated with the formation and metastasis of tumors. Here, a novel tetraphenylethylene (TPE)-doped covalent organic frameworks (TPE-COFs) with strong aggregation-induced electrochemiluminescence (AIECL) response was synthesized and introduced to construct an ultrasensitive biosensor for the detection of miRNA-21. The strong aggregation-induced emission (AIE) response was obtained because the molecular motion of TPE was restricted by COFs which had the porosity and highly ordered topological structure. Meanwhile, the porous structure of COFs allowed TPE to react with electrochemiluminescence (ECL) coreactants more effectively. Furthermore, COFs significantly improved the electron transport efficiency of the entire ECL system. All of these endowed the TPE-COFs with superior AIECL performance. Then, a TPE-COFs based ECL resonance energy transfer (ECL-RET) system was constructed for ultrasensitive miRNA-21 biosensing with differential signal readout. The proposed assays exhibited excellent sensitivity with a wide dynamic range from 10 aM to 1 pM and a low detection limit of 2.18 aM. Therefore, these indicated that doping TPE in COFs was a creative way to develop functional COFs and provided an effective way for enhancing AIECL. Furthermore, this work boarded the application of AIECL in analytical chemistry

Ultrasensitive Cytosensor Based on Self-Enhanced Electrochemiluminescent Ruthenium-Silica Composite Nanoparticles for Efficient Drug Screening with Cell Apoptosis Monitoring

The self-enhanced electrochemiluminescence (ECL) with high sensitivity could be an effective method for anticancer drug screening with cell apoptosis monitoring. Here we reported an ultrasensitive ECL cytosensor for cell apoptosis monitoring by using self-enhanced electrochemiluminescent ruthenium–silica composite nanoparticles (Ru–N–SiNPs) labeled annexin V as signal probes. The Ru–N–SiNPs were first synthesized through simple hydrolysis of a novel precursor containing luminescent and intracoreactant groups in one molecule, which presented higher emission efficiency and enhanced ECL intensity due to the shorter electron-transfer path and less energy loss. Moreover, the as-proposed ECL cytosensor was successfully used to investigate efficiency of paclitaxel toward MDA-MB-231 breast cancer cell in the range from 1 nM to 200 nM with a detection limit of 0.3 nM and a correlation coefficient of 0.9917. The improved accuracy and excellent dynamic range revealed the potential applications in biomolecules diagnostics and cells detections, especially in living and complex systems

Table1_Application of yeast in plant-derived aroma formation from cigar filler leaves.PDF

Introduction: There are various degrees of defects of cigar filler leaves after air drying.Methods: In order to improve the quality and plant-derived aroma content of cigar filler leaves, nine aroma-producing yeasts were applied in artificially solid-state fermentation of cigar filler leaves in this study. The differences with various yeasts application were compared by chemical composition and GC-MS analysis.Results and discussion: The results showed that 120 volatile components were identified and quantified in cigar filler leaves after fermentation, including aldehydes (25 types), alcohols (24 types), ketones (20 types), esters (11 types), hydrocarbons (12 types), acids (4 types) and other substances (23 types). Based on the analysis of odor activity value (OAV), the OVA of fruity and floral aroma components were higher. It was found that floral aroma are the representative aroma types of cigar filler leaves treated with Clavispora lusitaniae, Cyberlindera fabianii, Saccharomycosis fibuligera and Zygosaccharomyces bailii R6. After being inoculated with Hanseniaspora uvarum J1, Hanseniaspora uvarum J4 and Pichia pastoris P3, the OAV of fruity aroma in cigar filler leaves was the highest, followed by tobacco aroma and woody aroma. The correlation between volatile components of cigar filler leaves with different yeasts was revealed after PCA analysis. It was concluded that the quality of cigar filler leaves was improved, and cigar filler leaves fermented with different yeasts showed different flavor.</p