Applications of functional dyes in biomedicine and life sciences

Functional dyes are useful tool in Biomedical and Life Sciences. In chapter 2 and 3, we introduced a general and simple design concept using unnatural amino acids (UAAs) as flexible scaffold to link organic fluorophore covalently to a photostabilizer on an arbitrary biomolecular target using only NHS- or click chemistry. With this method, chemically distinct fluorophore species that could not be studied before can be improved through intramolecular photostabilization as “self-healing” fluorophores. . In chapter 4, we developed novel photoswitchable aminoglycosides, that we can call “Opto-Drugs”, which can resist aminoglycoside-modifying enzymes while simultaneously demonstrating selective antibiotic activities upon visible light light irradiation. In chapter 5, we generate an artificial photoresponsive system based on genetically encoded aptameric RNA that can respond to photoisomerizable, cell-permeable small molecules

Being and Becoming: the Implications of Different Conceptualizations of Children and Childhood in Education

How to conceptualize children and childhood are intrinsic to the ways of understanding education. The ‘becoming’ child is seen as an opposite or negative form of adults while the ‘being’ child is recognized as a social actor with the ability as well as rights to actively participate in its own life and in those of others. However, in addition to the problems of each discourse, such a dyad also leads to a separation of fundamental education factors. With reference to the Interpretative Reproduction Model and the concept of time, this paper discusses an alternative perspective on children and childhood that views the child as the ‘human becoming and being’ concurrently, coupled with its associated implication on education. By doing so, the author hopes to provide a platform facilitating adult professionals, pre-service or in-service teachers, to reflect upon their attitudes towards students and the ways of understanding education

Factors Influencing Behavioral Intention Towards MOOC Platform of Jingdezhen Vocational University of Art Students Majoring in Art and Design in Jiangxi Province, China

The objective of this investigation was to analyze the behavioral intention of students at the Jingdezhen Vocational University of Art who majoring in art and design majors to use the MOOC platform. It was carried out by the researchers using quantitative research techniques. Based on the Theory of Reason and Action (TRA), the Technology Acceptance Model (TAM), and the Unified Theory of Technology Acceptance and Use (UTAUT), this study develops a conceptual framework. Seven potential variables were chosen to assess the validity of the research tool using project-goal consistency and passed the internal consistency test: self-efficacy, perceived ease of use, perceived usefulness, attitude, performance expectancy, subjective norm, and behavioral intention. The reliability was evaluated by Cronbach α coefficient through the pilot test. In addition, the sampling strategy was multi-stage sampling. In the course of the study, a face-to-face questionnaire was distributed to 500 professional undergraduates majoring in art and design with MOOC platform experience at the School of Ceramic Art and Design and the School of Digital Art of Jingdezhen Art Vocational University. As statistical analysis tools, confirmatory factor analysis and structural equation modeling were applied in this research to advance the influence on data, matrix accuracy, basic variables, hypothetical support, and path coefficients. The results revealed that all the hypotheses are suggested, and the subjective norm was the most influential factor that affected art and design majors' behavioral intention to use the MOOC platform

Spectral Unsupervised Domain Adaptation for Visual Recognition

Unsupervised domain adaptation (UDA) aims to learn a well-performed model in an unlabeled target domain by leveraging labeled data from one or multiple related source domains. It remains a great challenge due to 1) the lack of annotations in the target domain and 2) the rich discrepancy between the distributions of source and target data. We propose Spectral UDA (SUDA), an efficient yet effective UDA technique that works in the spectral space and is generic across different visual recognition tasks in detection, classification and segmentation. SUDA addresses UDA challenges from two perspectives. First, it mitigates inter-domain discrepancies by a spectrum transformer (ST) that maps source and target images into spectral space and learns to enhance domain-invariant spectra while suppressing domain-variant spectra simultaneously. To this end, we design novel adversarial multi-head spectrum attention that leverages contextual information to identify domain-variant and domain-invariant spectra effectively. Second, it mitigates the lack of annotations in target domain by introducing multi-view spectral learning which aims to learn comprehensive yet confident target representations by maximizing the mutual information among multiple ST augmentations capturing different spectral views of each target sample. Extensive experiments over different visual tasks (e.g., detection, classification and segmentation) show that SUDA achieves superior accuracy and it is also complementary with state-of-the-art UDA methods with consistent performance boosts but little extra computation