A deeper understanding of the biological events occurring when bioprocess parameters changed will be of great value in improving the monoclonal antibodies (mAbs) production. Design of experiment (DoE) was applied to i...A deeper understanding of the biological events occurring when bioprocess parameters changed will be of great value in improving the monoclonal antibodies (mAbs) production. Design of experiment (DoE) was applied to investigate the effect of process parameters (pH, temperature shift and dissolve oxygen (DO)) on protein titer. The key metabolites connecting the critical process parameters (CPPs) with monoclonal antibody production were identified by different chemometrics tools. Finally, the biological events of marker metabolites relating with titer improvement were concluded. pH and temperature shift were identified as CPPs that affect the target protein titer. A series of metabolites influenced by the altered CPPs and correlated with protein titer were screened by principal component analysis (PCA) and Pearson' correlation test. The marker metabolites and their pathways linking CPPs to target protein titer in different culture phases were summarized. Metabolomics and chemometrics are promising data-driven tools to shine light into the biological black box between the bioprocess parameters and process performance.展开更多
Using approximation technique, we introduce the concepts of canonical extension and symmetrio integral for jump process and obtain some results in the chaotic form.
A novel algorithm for voice conversion is proposed in this paper. The mapping function of spectral vectors of the source and target speakers is calculated by the Canonical Correlation Analysis (CCA) estimation based o...A novel algorithm for voice conversion is proposed in this paper. The mapping function of spectral vectors of the source and target speakers is calculated by the Canonical Correlation Analysis (CCA) estimation based on Gaussian mixture models. Since the spectral envelope feature remains a majority of second order statistical information contained in speech after Linear Prediction Coding (LPC) analysis, the CCA method is more suitable for spectral conversion than Minimum Mean Square Error (MMSE) because CCA explicitly considers the variance of each component of the spectral vectors during conversion procedure. Both objective evaluations and subjective listening tests are conducted. The experimental results demonstrate that the proposed scheme can achieve better per- formance than the previous method which uses MMSE estimation criterion.展开更多
Voice conversion algorithm aims to provide high level of similarity to the target voice with an acceptable level of quality.The main object of this paper was to build a nonlinear relationship between the parameters fo...Voice conversion algorithm aims to provide high level of similarity to the target voice with an acceptable level of quality.The main object of this paper was to build a nonlinear relationship between the parameters for the acoustical features of source and target speaker using Non-Linear Canonical Correlation Analysis(NLCCA) based on jointed Gaussian mixture model.Speaker indi-viduality transformation was achieved mainly by altering vocal tract characteristics represented by Line Spectral Frequencies(LSF).To obtain the transformed speech which sounded more like the target voices,prosody modification is involved through residual prediction.Both objective and subjective evaluations were conducted.The experimental results demonstrated that our proposed algorithm was effective and outperformed the conventional conversion method utilized by the Minimum Mean Square Error(MMSE) estimation.展开更多
基金Supported by the Science and Technology Development Program of Zhejiang Province(2017C03003)
文摘A deeper understanding of the biological events occurring when bioprocess parameters changed will be of great value in improving the monoclonal antibodies (mAbs) production. Design of experiment (DoE) was applied to investigate the effect of process parameters (pH, temperature shift and dissolve oxygen (DO)) on protein titer. The key metabolites connecting the critical process parameters (CPPs) with monoclonal antibody production were identified by different chemometrics tools. Finally, the biological events of marker metabolites relating with titer improvement were concluded. pH and temperature shift were identified as CPPs that affect the target protein titer. A series of metabolites influenced by the altered CPPs and correlated with protein titer were screened by principal component analysis (PCA) and Pearson' correlation test. The marker metabolites and their pathways linking CPPs to target protein titer in different culture phases were summarized. Metabolomics and chemometrics are promising data-driven tools to shine light into the biological black box between the bioprocess parameters and process performance.
基金Supported in part by National Natural Science Foundation of China.
文摘Using approximation technique, we introduce the concepts of canonical extension and symmetrio integral for jump process and obtain some results in the chaotic form.
文摘A novel algorithm for voice conversion is proposed in this paper. The mapping function of spectral vectors of the source and target speakers is calculated by the Canonical Correlation Analysis (CCA) estimation based on Gaussian mixture models. Since the spectral envelope feature remains a majority of second order statistical information contained in speech after Linear Prediction Coding (LPC) analysis, the CCA method is more suitable for spectral conversion than Minimum Mean Square Error (MMSE) because CCA explicitly considers the variance of each component of the spectral vectors during conversion procedure. Both objective evaluations and subjective listening tests are conducted. The experimental results demonstrate that the proposed scheme can achieve better per- formance than the previous method which uses MMSE estimation criterion.
基金Supported by the National High Technology Research and Development Program of China (863 Program,No.2006AA010102)
文摘Voice conversion algorithm aims to provide high level of similarity to the target voice with an acceptable level of quality.The main object of this paper was to build a nonlinear relationship between the parameters for the acoustical features of source and target speaker using Non-Linear Canonical Correlation Analysis(NLCCA) based on jointed Gaussian mixture model.Speaker indi-viduality transformation was achieved mainly by altering vocal tract characteristics represented by Line Spectral Frequencies(LSF).To obtain the transformed speech which sounded more like the target voices,prosody modification is involved through residual prediction.Both objective and subjective evaluations were conducted.The experimental results demonstrated that our proposed algorithm was effective and outperformed the conventional conversion method utilized by the Minimum Mean Square Error(MMSE) estimation.
