FUNCTIONAL ROLES FOR POST-TRANSLATIONAL MODIFICATIONS OF t-SNARES IN PLATELETS

Platelets affect vascular integrity by secreting a host of molecules that promote hemostasis and its sequela. Given its importance, it is critical to understand how platelet exocytosis is controlled. Post-translational modifications, such as phosphorylation and acylation, have been shown to affect signaling pathways and platelet function. In this dissertation, I focus on how these modifications affect the t-SNARE proteins, SNAP-23 and syntaxin-11, which are both required for platelet secretion. SNAP-23 is regulated by phosphorylation. Using a proteoliposome fusion assay, I demonstrate that purified IκB Kinase (IKK) phosphorylated SNAP-23, which increased the initial rates of SNARE-mediated liposome fusion. SNAP-23 mutants containing phosphomimetics showed enhanced initial fusion rates. These results, combined with previous work in vivo, confirm that SNAP-23 phosphorylation is involved in regulating membrane fusion, and that IKK-mediated signaling contributes to platelet exocytosis. To address the role(s) of acylation, I sought to determine how syntaxin-11 and SNAP-23 are associated with plasma membrane. Using metabolic labeling, I showed that both proteins contain thioester-linked acyl groups which turn over in resting cells. Mass spectrometry mapping showed that syntaxin-11 is modified on C275, 279, 280, 282, 283 and 285, while SNAP-23 is modified on C79, 80, 83, 85, and 87. To probe the effects of acylation, I measured ADP/ATP release from platelets treated with the acyl-transferase inhibitor, cerulenin, or the thioesterase inhibitor, palmostatin B. Cerulenin pretreatment inhibited t-SNARE acylation and platelet function while palmostatin B had no effect. Interestingly, pretreatment with palmostatin B blocked the inhibitory effects of cerulenin suggesting that maintaining the acylation state of platelet proteins is important for their function. Thus my work indicates that the enzymes controlling protein acylation could be valuable targets for modulating platelet exocytosis in vivo

DTMT: A Novel Deep Transition Architecture for Neural Machine Translation

Past years have witnessed rapid developments in Neural Machine Translation (NMT). Most recently, with advanced modeling and training techniques, the RNN-based NMT (RNMT) has shown its potential strength, even compared with the well-known Transformer (self-attentional) model. Although the RNMT model can possess very deep architectures through stacking layers, the transition depth between consecutive hidden states along the sequential axis is still shallow. In this paper, we further enhance the RNN-based NMT through increasing the transition depth between consecutive hidden states and build a novel Deep Transition RNN-based Architecture for Neural Machine Translation, named DTMT. This model enhances the hidden-to-hidden transition with multiple non-linear transformations, as well as maintains a linear transformation path throughout this deep transition by the well-designed linear transformation mechanism to alleviate the gradient vanishing problem. Experiments show that with the specially designed deep transition modules, our DTMT can achieve remarkable improvements on translation quality. Experimental results on Chinese->English translation task show that DTMT can outperform the Transformer model by +2.09 BLEU points and achieve the best results ever reported in the same dataset. On WMT14 English->German and English->French translation tasks, DTMT shows superior quality to the state-of-the-art NMT systems, including the Transformer and the RNMT+.Comment: Accepted at AAAI 2019. Code is available at: https://github.com/fandongmeng/DTMT_InDe

Analysis of Recovery Type A Posteriori Error Estimators for Mildly Structured Grids

Some recovery type error estimators for linear finite element method are analyzed under O(h1+alpha) (alpha greater than 0) regular grids. Superconvergence is established for recovered gradients by three different methods when solving general non-self-adjoint second-order elliptic equations. As a consequence, a posteriori error estimators based on those recovery methods are asymptotically exact