Speech Dereverberation Based on Multi-Channel Linear Prediction

Room reverberation can severely degrade the auditory quality and intelligibility of the speech signals received by distant microphones in an enclosed environment. In recent years, various dereverberation algorithms have been developed to tackle this problem, such as beamforming and inverse filtering of the room transfer function. However, this kind of methods relies heavily on the precise estimation of either the direction of arrival (DOA) or room acoustic characteristics. Thus, their performance is very much limited. A more promising category of dereverberation algorithms has been developed based on multi-channel linear predictor (MCLP). This idea was first proposed in time domain where speech signal is highly correlated in a short period of time. To ensure a good suppression of the reverberation, the prediction filter length is required to be longer than the reverberation time. As a result, the complexity of this algorithm is often unacceptable because of large covariance matrix calculation. To overcome this disadvantage, this thesis focuses on the MCLP dereverberation methods performed in the short-time Fourier transform (STFT) domain. Recently, the weighted prediction error (WPE) algorithm has been developed and widely applied to speech dereverberation. In WPE algorithm, MCLP is used in the STFT domain to estimate the late reverberation components from previous frames of the reverberant speech. The enhanced speech is obtained by subtracting the late reverberation from the reverberant speech. Each STFT coefficient is assumed to be independent and obeys Gaussian distribution. A maximum likelihood (ML) problem is formulated in each frequency bin to calculate the predictor coefficients. In this thesis, the original WPE algorithm is improved in two aspects. First, two advanced statistical models, generalized Gaussian distribution (GGD) and Laplacian distribution, are employed instead of the classic Gaussian distribution. Both of them are shown to give better modeling of the histogram of the clean speech. Second, we focus on improving the estimation of the variances of the STFT coefficients of the desired signal. In the original WPE algorithm, the variances are estimated in each frequency bin independently without considering the cross-frequency correlation. Thus, we integrate the nonnegative matrix factorization (NMF) into the WPE algorithm to refine the estimation of the variances and hence obtain a better dereverberation performance. Another category of MCLP based dereverberation algorithm has been proposed in literature by exploiting the sparsity of the STFT coefficients of the desired signal for calculating the predictor coefficients. In this thesis, we also investigate an efficient algorithm based on the maximization of the group sparsity of desired signal using mixed norms. Inspired by the idea of sparse linear predictor (SLP), we propose to include a sparse constraint for the predictor coefficients in order to further improve the dereverberation performance. A weighting parameter is also introduced to achieve a trade-off between the sparsity of the desired signal and the predictor coefficients. Computer simulation of the proposed dereverberation algorithms is conducted. Our experimental results show that the proposed algorithms can significantly improve the quality of reverberant speech signal under different reverberation times. Subjective evaluation also gives a more intuitive demonstration of the enhanced speech intelligibility. Performance comparison also shows that our algorithms outperform some of the state-of-the-art dereverberation techniques

Tumor-promoting properties of miR-8084 in breast cancer through enhancing proliferation, suppressing apoptosis and inducing epithelial–mesenchymal transition

Abstract Background Breast cancer is one of the most frequent malignancies and the second leading cause of cancer-related mortality in women. MicroRNAs play a key role in breast cancer development and progression. microRNA(miR)-8084 has been observed an aberrant expression in breast cancer. However, the functions and regulatory axes of miR-8084, particularly in breast cancer, were not entirely clear. Methods miR-8084 expression in breast cancer were investigated in a GEO dataset by in silico analysis and in 42 paired tumor tissues by qPCR. The effects of deregulation of miR-8084 on breast cancer cell proliferation, migration and invasion in vitro and tumorigenicity in vivo were examined by colony-formation assay, wound healing assay, transwell assay and nude mouse subcutaneous tumor formation model. The target gene of miR-8084 were predicted by TargetScan and miRDB, and confirmed by luciferase reporter system. The roles of miR-8084 in the breast cancer cell proliferation, apoptosis and epithelial–mesenchymal transition (EMT) were investigated by MTS, FACS and associated-marker detection by western blot. Results miR-8084 is significantly up-regulated in both serum and malignant tissues from the source of breast cancer patients. miR-8084 promotes the proliferation of breast cancer cells by activating ERK1/2 and AKT. Meanwhile miR-8084 inhibits apoptosis by decreasing p53-BAX related pathway. miR-8084 also enhances migration and invasion by inducing EMT. Moreover, the tumor suppressor ING2 is a potential target of miR-8084, and miR-8084 regulatory axes contribute to pro-tumor effect, at least partially through regulating ING2. Conclusion Our results strongly suggest that miR-8084 functions as an oncogene that promotes the development and progression of breast cancer, and miR-8084 is a potential new diagnostic marker and therapeutic target of breast cancer

Modular Genetic Design of Multi-domain Functional Amyloids: Insights into Self-assembly and Functional Properties

© The Royal Society of Chemistry. Engineering functional amyloids through a modular genetic strategy represents new opportunities for creating multifunctional molecular materials with tailored structures and performance. Despite important advances, how fusion modules affect the self-assembly and functional properties of amyloids remains elusive. Here, using Escherichia coli curli as a model system, we systematically studied the effect of flanking domains on the structures, assembly kinetics and functions of amyloids. The designed amyloids were composed of E. coli biofilm protein CsgA (as amyloidogenic cores) and one or two flanking domains, consisting of chitin-binding domains (CBDs) from Bacillus circulans chitinase, and/or mussel foot proteins (Mfps). Incorporation of fusion domains did not disrupt the typical β-sheet structures, but indeed affected assembly rate, morphology, and stiffness of resultant fibrils. Consequently, the CsgA-fusion fibrils, particularly those containing three domains, were much shorter than the CsgA-only fibrils. Furthermore, the stiffness of the resultant fibrils was heavily affected by the structural feature of fusion domains, with β-sheet-containing domains tending to increase the Young's modulus while random coil domains decreasing the Young's modulus. In addition, fibrils containing CBD domains showed higher chitin-binding activity compared to their CBD-free counterparts. The CBD-CsgA-Mfp3 construct exhibited significantly lower binding activity than Mfp5-CsgA-CBD due to inappropriate folding of the CBD domain in the former construct, in agreement with results based upon molecular dynamics modeling. Our study provides new insights into the assembly and functional properties of designer amyloid proteins with increasing complex domain structures and lays the foundation for the future design of functional amyloid-based structures and molecular materials

Diverse Supramolecular Nanofiber Networks Assembled by Functional Low-Complexity Domains

Self-assembling supramolecular nanofibers, common in the natural world, are of fundamental interest and technical importance to both nanotechnology and materials science. Despite important advances, synthetic nanofibers still lack the structural and functional diversity of biological molecules, and the controlled assembly of one type of molecule into a variety of fibrous structures with wide-ranging functional attributes remains challenging. Here, we harness the low-complexity (LC) sequence domain of fused in sarcoma (FUS) protein, an essential cellular nuclear protein with slow kinetics of amyloid fiber assembly, to construct random copolymer-like, multiblock, and self-sorted supramolecular fibrous networks with distinct structural features and fluorescent functionalities. We demonstrate the utilities of these networks in the templated, spatially controlled assembly of ligand-decorated gold nanoparticles, quantum dots, nanorods, DNA origami, and hybrid structures. Owing to the distinguishable nanoarchitectures of these nanofibers, this assembly is structure-dependent. By coupling a modular genetic strategy with kinetically controlled complex supramolecular self-assembly, we demonstrate that a single type of protein molecule can be used to engineer diverse one-dimensional supramolecular nanostructures with distinct functionalities