Investigating the Motivation Factors of Food Choice During the Transition of High School into College Life among College Students Attending Western Kentucky University

Most individuals with chronic diseases, such as cardiovascular disease, stroke, cancer, and type 2 diabetes, were diagnosed in their late adulthood. The fact that these chronic diseases is a consequence of long-term unhealthy behaviors is often ignored. The unhealthy behaviors are often traced back to the young adulthood (age 18-25). Some young adults may participate in unhealthy behaviors, such as unhealthy diet, under the perception that they are “still young”. However, it is often overlooked that once a habit is established, it is difficult to eliminate or modify it. Furthermore, the awareness that the development of the chronic disease is a gradual progress is deficient. This enhances the perception that doing unhealthy behaviors is benign to the “young body”. Additionally, individuals in this age group start to live independently. Their existing behaviors may change due to the changes in the available resources. Lack of capability to cope with the transition from living at home to living independently has been shown to contribute to an unhealthy diet, especially among college students. Given that unhealthy diet behaviors in young adulthood often remains over the lifetime, there is a need in identifying the factors that motivate the food choices during the transition from high school into college life. The findings of this research suggest that the campus environment is not conducive to a healthy diet. When compared to the students who live on-campus, students who live offcampus (either live with or without family) reported a better dietary quality

Synthetic epidermal growth factor receptor (EGFR)-mitochondria desired axles-based split green-fluorescent-protein (GFP) could screen for the signaling molecules that overcome the drug resistance to tyrosine kinase inhibitor (TKI)

The epidermal growth factor receptor (EGFR) pathway, involving in cancer cell migration, proliferation, and survival, attracts lots of attention of cancer biologists for seeking therapeutic targets. Tyrosine kinase inhibitor (TKI)-resistance of small cell lung cancer and cancer stem cells, the sub-population with EGFR mutations, has been associated with frustrating outcomes for anti-EGFR-based therapy. Methods & Results With our synthetic EGFR-mutant axles that enlightened mitochondria, the small-cell lung cancer CL1-0 cell line interestingly revealed good correlation of the activated EGFR or spontaneously activated EGFR mutant T790M/L858R with high energy-demanding status. The facts implied that EGFR signaling might induce mitochondria proliferation to meet cellular energy demand by an unknown mechanism. The activated EGFR resulted in elevated MMP7 expression and further induced mitochondria proliferation in multiple cell lines. Therefore, enzymatically dead mutant MMP7 N-GFP fusion protein could be used as baits to screen for the putative substrates that modulate signals transduction from EGFR to mitochondria proliferation. Conclusion This synthetic cellular model platform could screen for a variety of mitochondria-targeting molecules, such as mitochondria ATP synthetase inhibitor, namely compound X, in lung cancer cells in cooperation with Gefitinib, a widely used TKI, to see whether it may increase the efficacy of Gefitinib on the resistant cells by cutting off energy supply in mitochondria

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