Anthracyclines are potent antineoplastic agents associated with cardiotoxicity, which may lead to congestive heart failure, causing impairment of autonomic cardiovascular function as assessed by heart rate variability (HRV). This decreases survival rates. This study aimed to determine whether music therapy intervention improves autonomic function in anthracycline-treated breast cancer patients, and if so, whether such improvements persist after cessation of the intervention. Participants were 12 women with breast cancer who had undergone mastectomy or breast-conserving treatment and adjuvant chemotherapy; they attended 8 weekly music therapy sessions, each lasting 2 hours. Electrocardiogram traces (5 minutes) for HRV analysis were recorded 4 times: prior to the first music session, T1; after the fourth music session, T2; after the eighth music session, T3; and 4 weeks after the completion of music therapy, T4. HRV parameters were subjected to a nonparametric Friedman test on the differences between T1 and T2, T3, and T4. The standard deviation of normal intervals and the total power of HRV parameters, related to global autonomic function, were significantly higher at T3 than at T1. The root-mean-square differences of successive normal R-R intervals and high-frequency (HF) HRV parameters, related to parasympathetic activity, were significantly increased, but no change was seen in the LF/HF ratio of HRV parameters (which is related to sympathetic activity) during the music therapy. Global autonomic function and parasympathetic activity had not changed significantly at T4 relative to T1. The authors provide preliminary evidence of the benefits of music therapy for anthracycline-treated breast cancer survivors.

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This paper focuses on machine learning based voice conversion (VC) techniques for improving the speech intelligibility of surgical patients who have had parts of their articulators removed. Because of the removal of parts of the articulator, a patient's speech may be distorted and difficult to understand. To overcome this problem, VC methods can be applied to convert the distorted speech such that it is clear and more intelligible. To design an effective VC method, two key points must be considered: 1) the amount of training data may be limited (because speaking for a long time is usually difficult for postoperative patients); 2) rapid conversion is desirable (for better communication). We propose a novel joint dictionary learning based non-negative matrix factorization (JD-NMF) algorithm. Compared to conventional VC techniques, JD-NMF can perform VC efficiently and effectively with only a small amount of training data. The experimental results demonstrate that the proposed JD-NMF method not only achieves notably higher short-time objective intelligibility (STOI) scores (a standardized objective intelligibility evaluation metric) than those obtained using the original unconverted speech but is also significantly more efficient and effective than a conventional exemplar-based NMF VC method. The proposed JD-NMF method may outperform the state-of-the-art exemplar-based NMF VC method in terms of STOI scores under the desired scenario. We confirmed the advantages of the proposed joint training criterion for the NMF-based VC. Moreover, we verified that the proposed JD-NMF can effectively improve the speech intelligibility scores of oral surgery patients. This paper focuses on machine learning based voice conversion (VC) techniques for improving the speech intelligibility of surgical patients who have had parts of their articulators removed. Because of the removal of parts of the articulator, a patient's speech may be distorted and difficult to understand. To overcome this problem, VC methods can be applied to convert the distorted speech such that it is clear and more intelligible. To design an effective VC method, two key points must be considered: 1) the amount of training data may be limited (because speaking for a long time is usually difficult for postoperative patients); 2) rapid conversion is desirable (for better communication). We propose a novel joint dictionary learning based non-negative matrix factorization (JD-NMF) algorithm. Compared to conventional VC techniques, JD-NMF can perform VC efficiently and effectively with only a small amount of training data. The experimental results demonstrate that the proposed JD-NMF method not only achieves notably higher short-time objective intelligibility (STOI) scores (a standardized objective intelligibility evaluation metric) than those obtained using the original unconverted speech but is also significantly more efficient and effective than a conventional exemplar-based NMF VC method. The proposed JD-NMF method may outperform the state-of-the-art exemplar-based NM...

Ti[O(CH2)4OCHCH2]4, used for the ring‐opening polymerization (ROP) of ε‐caprolactone, was synthesized through the ester‐exchange reaction of titanium n‐propoxide and 1,4‐butanediol vinyl ether, and its chemical structure was confirmed by nuclear magnetic resonance (1H NMR) and thermogravimetric analysis (TGA). The mechanism and kinetics of Ti[O(CH2)4OCHCH2]4‐initiated bulk polymerization of ε‐caprolactone were investigated. The results demonstrate that Ti[O (CH2)4OCHCH2]4‐initiated polymerization of ε‐caprolactone proceeds through the coordination‐insertion mechanism, and all the four alkoxide arms in Ti[O (CH2)4OCHCH2]4 share a similar activity in initiating ROP of ε‐caprolactone. The polymerization process can be well predicted by the obtained kinetic parameters, and the activation energy is 106 KJ/mol. Then, the rheological method was employed to investigate the feasibility of producing the crosslinked poly(ε‐caprolactone)‐poly (n‐butyl acrylate) network by using Ti[O(CH2)4OCHCH2]4 as the ROP initiator. The tensile test demonstrates that the in situ generated crosslinked PCL‐PBA network in PMMA matrix provides the possibility of ameliorating the tensile properties of PMMA. © 2008 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem 46: 7773–7784, 2008