Mechanisms of Synaptic Development and Premature Aging in Drosophila: A Dissertation

Development and aging, two fundamental aspects of life, remain key biological processes that researchers try to understand. Drosophila melanogaster, thanks to its various merits, serves as an excellent model to study both of these processes. This thesis includes two parts. In the first part, I discuss our finding that the presynaptic neuron controls a retrograde signaling pathway by releasing essential components via exosomes. During synaptic development, postsynaptic cells send retrograde signals to adjust the activity and growth of presynaptic cells. It remains unclear what the mechanism is which triggers the release of retrograde signals; and how presynaptic cells are involved in this signaling event. The first part of this thesis demonstrates that a retrograde signal mediated by Synaptotagmin4 (Syt4) depends on the anterograde delivery of Syt4 protein from the presynaptic neuron to the muscle compartment likely through exosomes. This trans-synaptic transfer of Syt4 is required for the retrograde control of activity-dependent synaptic growth at the Drosophila larval neuromuscular junction. In the second part of this thesis, I talk about our discovery that the disruption of nuclear envelope (NE) budding, a novel RNA export pathway, is linked to the loss of mitochondrial integrity and premature aging in Drosophila. We demonstrate that several transcripts, which are essential for mitochondrial integrity and function, use NE-budding for nuclear export. Transgenic Drosophila expressing a LamC mutation modeling progeroid syndrome (PS), a premature aging disorder in humans, displays accelerated aging-related phenotypes including progressive mitochondrial degeneration as well as decreased levels of a specific mitochondrial transcript which is normally enriched at NE-budding site. The PS-modeled LamC mutants exhibit abnormal lamina organization that likely disrupts the egress of these RNAs via NE-budding. These results connect defective RNA export through NE-budding to progressive loss of mitochondrial integrity and premature aging in Drosophila

Learning on the correct class for domain inverse problems of gravimetry

We consider end-to-end learning approaches for inverse problems of gravimetry. Due to ill-posedness of the inverse gravimetry, the reliability of learning approaches is questionable. To deal with this problem, we propose the strategy of learning on the correct class. The well-posedness theorems are employed when designing the neural-network architecture and constructing the training set. Given the density-contrast function as a priori information, the domain of mass can be uniquely determined under certain constrains, and the domain inverse problem is a correct class of the inverse gravimetry. Under this correct class, we design the neural network for learning by mimicking the level-set formulation for the inverse gravimetry. Numerical examples illustrate that the method is able to recover mass models with non-constant density contrast

Research on the CSR Reporting Disclosure Practice in China – Critical Evaluate the Different Gap between the BP and CNPC

The corporate social responsibility (CSR) is becoming more and more important in today’s business, because the CSR shows the corporate culture, reputation and accountability to the public; so the CSR Reporting is the best way to disclose the corporate non-financial issues to the public. Nowadays, the CSR Reporting is still a new subject and the report’s purpose is not familiar to the users in China, only large enterprise publishes CSR reports every year and the report disclosure practice is still in a very poor situation. In this dissertation, I am going to investigate the CSR Reporting leading-edge disclosure practice and the report guidelines, in order to gain the different gaps between the leading-edge organization and Chinese organization’s disclosure practice. The BP and CNPC will recognize as the research cases in this dissertation

Video-Helpful Multimodal Machine Translation

Existing multimodal machine translation (MMT) datasets consist of images and video captions or instructional video subtitles, which rarely contain linguistic ambiguity, making visual information ineffective in generating appropriate translations. Recent work has constructed an ambiguous subtitles dataset to alleviate this problem but is still limited to the problem that videos do not necessarily contribute to disambiguation. We introduce EVA (Extensive training set and Video-helpful evaluation set for Ambiguous subtitles translation), an MMT dataset containing 852k Japanese-English (Ja-En) parallel subtitle pairs, 520k Chinese-English (Zh-En) parallel subtitle pairs, and corresponding video clips collected from movies and TV episodes. In addition to the extensive training set, EVA contains a video-helpful evaluation set in which subtitles are ambiguous, and videos are guaranteed helpful for disambiguation. Furthermore, we propose SAFA, an MMT model based on the Selective Attention model with two novel methods: Frame attention loss and Ambiguity augmentation, aiming to use videos in EVA for disambiguation fully. Experiments on EVA show that visual information and the proposed methods can boost translation performance, and our model performs significantly better than existing MMT models. The EVA dataset and the SAFA model are available at: https://github.com/ku-nlp/video-helpful-MMT.git.Comment: Accepted by EMNLP 2023 Main Conference (long paper