Pop Music Highlighter: Marking the Emotion Keypoints

The goal of music highlight extraction is to get a short consecutive segment of a piece of music that provides an effective representation of the whole piece. In a previous work, we introduced an attention-based convolutional recurrent neural network that uses music emotion classification as a surrogate task for music highlight extraction, for Pop songs. The rationale behind that approach is that the highlight of a song is usually the most emotional part. This paper extends our previous work in the following two aspects. First, methodology-wise we experiment with a new architecture that does not need any recurrent layers, making the training process faster. Moreover, we compare a late-fusion variant and an early-fusion variant to study which one better exploits the attention mechanism. Second, we conduct and report an extensive set of experiments comparing the proposed attention-based methods against a heuristic energy-based method, a structural repetition-based method, and a few other simple feature-based methods for this task. Due to the lack of public-domain labeled data for highlight extraction, following our previous work we use the RWC POP 100-song data set to evaluate how the detected highlights overlap with any chorus sections of the songs. The experiments demonstrate the effectiveness of our methods over competing methods. For reproducibility, we open source the code and pre-trained model at https://github.com/remyhuang/pop-music-highlighter/.Comment: Transactions of the ISMIR vol. 1, no.

Revisiting the problem of audio-based hit song prediction using convolutional neural networks

Being able to predict whether a song can be a hit has impor- tant applications in the music industry. Although it is true that the popularity of a song can be greatly affected by exter- nal factors such as social and commercial influences, to which degree audio features computed from musical signals (whom we regard as internal factors) can predict song popularity is an interesting research question on its own. Motivated by the recent success of deep learning techniques, we attempt to ex- tend previous work on hit song prediction by jointly learning the audio features and prediction models using deep learning. Specifically, we experiment with a convolutional neural net- work model that takes the primitive mel-spectrogram as the input for feature learning, a more advanced JYnet model that uses an external song dataset for supervised pre-training and auto-tagging, and the combination of these two models. We also consider the inception model to characterize audio infor- mation in different scales. Our experiments suggest that deep structures are indeed more accurate than shallow structures in predicting the popularity of either Chinese or Western Pop songs in Taiwan. We also use the tags predicted by JYnet to gain insights into the result of different models.Comment: To appear in the proceedings of 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP

Liquid biopsy genotyping in lung cancer: ready for clinical utility?

Liquid biopsy is a blood test that detects evidence of cancer cells or tumor DNA in the circulation. Despite complicated collection methods and the requirement for technique-dependent platforms, it has generated substantial interest due, in part, to its potential to detect driver oncogenes such as epidermal growth factor receptor (EGFR) mutants in lung cancer. This technology is advancing rapidly and is being incorporated into numerous EGFR tyrosine kinase inhibitor (EGFR-TKI) development programs. It appears ready for integration into clinical care. Recent studies have demonstrated that biological fluids such as saliva and urine can also be used for detecting EGFR mutant DNA through application other user-friendly techniques. This review focuses on the clinical application of liquid biopsies to lung cancer genotyping, including EGFR and other targets of genotype-directed therapy and compares multiple platforms used for liquid biopsy

Human parvovirus B19 nonstructural protein NS1 enhanced the expression of cleavage of 70 kDa U1-snRNP autoantigen

<p>Abstract</p> <p>Background</p> <p>Human parvovirus B19 (B19) is known to induce apoptosis that has been associated with a variety of autoimmune disorders. Although we have previously reported that B19 non-structural protein (NS1) induces mitochondrial-dependent apoptosis in COS-7 cells, the precise mechanism of B19-NS1 in developing autoimmunity is still obscure.</p> <p>Methods</p> <p>To further examine the effect of B19-NS1 in presence of autoantigens, COS-7 cells were transfected with pEGFP, pEGFP-B19-NS1 and pEGFP-NS1K334E, a mutant form of B19-NS1, and detected the expressions of autoantigens by various autoantibodies against Sm, U1 small nuclear ribonucleoprotein (U1-snRNP), SSA/Ro, SSB/La, Scl-70, Jo-1, Ku, and centromere protein (CENP) A/B by using Immunoblotting.</p> <p>Results</p> <p>Significantly increased apoptosis was detected in COS-7 cells transfected with pEGFP-B19-NS1 compared to those transfected with pEGFP. Meanwhile, the apoptotic 70 kDa U1-snRNP protein in COS-7 cells transfected with pEGFP-B19-NS1 is cleaved by caspase-3 and converted into a specific 40 kDa product, which were recognized by anti-U1-snRNP autoantibody. In contrast, significantly decreased apoptosis and cleaved 40 kDa product were observed in COS-7 cells transfected with pEGFP-NS1K334E compared to those transfected with pEGFP-B19-NS1.</p> <p>Conclusions</p> <p>These findings suggested crucial association of B19-NS1 in development of autoimmunity by inducing apoptosis and specific cleavage of 70 kDa U1-snRNP.</p

Proteomic analysis of rhein-induced cyt: ER stress mediates cell death in breast cancer cells

Rhein is a natural product purified from herbal plants such as Rheum palmatum, which has been shown to have anti-angiogenesis and anti-tumor metastasis properties. However, the biological effects of rhein on the behavior of breast cancers are not completely elucidated. To evaluate whether rhein might be useful in the treatment of breast cancer and its cytotoxic mechanism, we analyzed the impact of rhein treatment on differential protein expression as well as redox regulation in a non-invasive breast cancer cell line, MCF-7, and an invasive breast cancer cell line, MDA-MB-231, using lysine- and cysteine-labeling two-dimensional difference gel electrophoresis (2D-DIGE) combined with MALDI-TOF/TOF mass spectrometry. This proteomic study revealed that 73 proteins were significantly changed in protein expression; while 9 proteins were significantly altered in thiol reactivity in both MCF-7 and MDA-MB-231 cells. The results also demonstrated that rhein-induced cytotoxicity in breast cancer cells mostly involves dysregulation of cytoskeleton regulation, protein folding, the glycolysis pathway and transcription control. A further study also indicated that rhein promotes misfolding of cellular proteins as well as unbalancing of the cellular redox status leading to ER-stress. Our work shows that the current proteomic strategy offers a high-through-put platform to study the molecular mechanisms of rhein-induced cytotoxicity in breast cancer cells. The identified differentially expressed proteins might be further evaluated as potential targets in breast cancer therapy