Structural Study of Disease Relevant ABC Transporters-Cystic Fibrosis Transmembrane Conductance Regulator and ABCA4

ATP-binding cassette (ABC) transporters are primary transporters that utilize the energy from ATP binding and hydrolysis to transport substrates across membrane against their concentration gradients [1]. Structurally, canonical ABC transporters consist of four subunits— two transmembrane domains (TMDs) which form the substrate transport pathway and two nucleotide binding domains (NBDs) which dimerize upon ATP binding to provide energy for substrate transport. Most mammalian ABC transporters are exporters, with three exceptions: SUR—a regulatory protein for the KATP channel, cystic fibrosis transmembrane conductance regulator (CFTR)—an chloride channel and ABCA4 (aka the Rim protein and ABCR)—an retinylidene-PE importer [1-3]. In addition to their unique functional properties, both CFTR and ABCA4 are very important in human health. Mutations in CFTR cause cystic fibrosis, a lethal disease with a prevalence of 1 in 2,500 in Caucasian populations [4, 5]. Over 800 mutations have been identified in ABCA4 to associate with various types of retinal disease [6], including the Stargardt disease (also known as juvenile macular degeneration), the most common form of inherited macular degeneration [7, 8]. To understand how those two proteins function, I mainly took a structural approach to capture the structures of CFTR and ABCA4 in different functional states. In correlating the structures with functional data, we now have a deeper mechanistic understanding of these two important ABC transporters. The function of CFTR is regulated by ATP and phosphorylation. Once phosphorylated, ATP binding opens the CFTR channel and ATP hydrolysis closes it [9]. First, we determined by cryo-electron microscopy (cryo-EM), the structure of dephosphorylated human CFTR in the absence of ATP (Chapter 2). With this structure, in conjunction with the functional studies performed by our collaborator (Prof. Laszlo Csanady and Prof. David C. Gadsby), we were able to propose a mechanism of how phosphorylation regulates CFTR. In addition, we identified a structural feature distinguishing CFTR from all other ABC transporters, which likely forms the structural basis for CFTR’s channel function. Next, we determined the structure of CFTR in the phosphorylated, ATP-bound state (Chapter 3 and 4). By comparing the ATP-free and -bound structures, we identified the nature of conformational changes that lead to channel opening. These structures also allow us to map many disease-causing mutants and explain how they lead to the malfunctioning of CFTR. To understand how small molecules, called potentiators, interact with CFTR and increase its open probability, we determined the structures of CFTR in complex with 2 potentiators— ivacaftor and GLPG1837 (Chapter 5). Interestingly, both small molecules bind to the same pocket inside the transmembrane region. These studies identified a hotspot on CFTR for rational drug design. Finally, I also studied ABCA4, the only known importer in mammalian ABC transporters. To understand how ABCA4 functions, I determined the structures of ABCA4 in the absence and presence of ATP (Chapter 6). Based on these structures, we propose a rudimentary transport mechanism for ABCA4. Future work will be carried out in the Chen lab to test this model

Constraints on Asymmetric Dark Matter Self Annihilation Cross Sections in Non-standard Cosmological Scenarios

We investigate the relic abundance of asymmetric Dark Matter in the non-standard cosmological scenarios when the annihilation cross section includes the self annihilation. Here we discuss the kination model and brane world cosmology. When the self annihilation is permitted for asymmetric dark matter, there is possibility of washing out the pre-existed asymmetry. We find the constraints on the cross section to avoid the complete washing out of the asymmetry in the non-standard cosmological scenarios. The enhanced cosmic expansion rate causes the freeze out point of wash-out to be earlier. The larger self annihilation cross sections are allowed to exist in kination model and brane world cosmology.Comment: 10 pages, 6 figure

How to tackle an emerging topic? Combining strong and weak labels for Covid news NER

Being able to train Named Entity Recognition (NER) models for emerging topics is crucial for many real-world applications especially in the medical domain where new topics are continuously evolving out of the scope of existing models and datasets. For a realistic evaluation setup, we introduce a novel COVID-19 news NER dataset (COVIDNEWS-NER) and release 3000 entries of hand annotated strongly labelled sentences and 13000 auto-generated weakly labelled sentences. Besides the dataset, we propose CONTROSTER, a recipe to strategically combine weak and strong labels in improving NER in an emerging topic through transfer learning. We show the effectiveness of CONTROSTER on COVIDNEWS-NER while providing analysis on combining weak and strong labels for training. Our key findings are: (1) Using weak data to formulate an initial backbone before tuning on strong data outperforms methods trained on only strong or weak data. (2) A combination of out-of-domain and in-domain weak label training is crucial and can overcome saturation when being training on weak labels from a single source.Comment: AACL-IJCNLP 202

Sharpness-Aware Minimization with Dynamic Reweighting

Deep neural networks are often overparameterized and may not easily achieve model generalization. Adversarial training has shown effectiveness in improving generalization by regularizing the change of loss on top of adversarially chosen perturbations. The recently proposed sharpness-aware minimization (SAM) algorithm conducts adversarial weight perturbation, encouraging the model to converge to a flat minima. SAM finds a common adversarial weight perturbation per-batch. Although per-instance adversarial weight perturbations are stronger adversaries and they can potentially lead to better generalization performance, their computational cost is very high and thus it is impossible to use per-instance perturbations efficiently in SAM. In this paper, we tackle this efficiency bottleneck and propose sharpness-aware minimization with dynamic reweighting ({\delta}-SAM). Our theoretical analysis motivates that it is possible to approach the stronger, per-instance adversarial weight perturbations using reweighted per-batch weight perturbations. {\delta}-SAM dynamically reweights perturbation within each batch according to the theoretically principled weighting factors, serving as a good approximation to per-instance perturbation. Experiments on various natural language understanding tasks demonstrate the effectiveness of {\delta}-SAM

Reranking Overgenerated Responses for End-to-End Task-Oriented Dialogue Systems

End-to-end (E2E) task-oriented dialogue (ToD) systems are prone to fall into the so-called 'likelihood trap', resulting in generated responses which are dull, repetitive, and often inconsistent with dialogue history. Comparing ranked lists of multiple generated responses against the 'gold response' (from training data) reveals a wide diversity in response quality, with many good responses placed lower in the ranked list. The main challenge, addressed in this work, is then how to reach beyond greedily generated system responses, that is, how to obtain and select such high-quality responses from the list of overgenerated responses at inference without availability of the gold response. To this end, we propose a simple yet effective reranking method which aims to select high-quality items from the lists of responses initially overgenerated by the system. The idea is to use any sequence-level (similarity) scoring function to divide the semantic space of responses into high-scoring versus low-scoring partitions. At training, the high-scoring partition comprises all generated responses whose similarity to the gold response is higher than the similarity of the greedy response to the gold response. At inference, the aim is to estimate the probability that each overgenerated response belongs to the high-scoring partition, given only previous dialogue history. We validate the robustness and versatility of our proposed method on the standard MultiWOZ dataset: our methods improve a state-of-the-art E2E ToD system by 2.4 BLEU, 3.2 ROUGE, and 2.8 METEOR scores, achieving new peak results. Additional experiments on the BiTOD dataset and human evaluation further ascertain the generalisability and effectiveness of the proposed framework.Comment: 22 pages, 10 figure

Visual Pivoting for (Unsupervised) Entity Alignment

This work studies the use of visual semantic representations to align entities in heterogeneous knowledge graphs (KGs). Images are natural components of many existing KGs. By combining visual knowledge with other auxiliary information, we show that the proposed new approach, EVA, creates a holistic entity representation that provides strong signals for cross-graph entity alignment. Besides, previous entity alignment methods require human labelled seed alignment, restricting availability. EVA provides a completely unsupervised solution by leveraging the visual similarity of entities to create an initial seed dictionary (visual pivots). Experiments on benchmark data sets DBP15k and DWY15k show that EVA offers state-of-the-art performance on both monolingual and cross-lingual entity alignment tasks. Furthermore, we discover that images are particularly useful to align long-tail KG entities, which inherently lack the structural contexts necessary for capturing the correspondences.Comment: To appear at AAAI-202