UNCOVERING THE BIOPHYSICAL MECHANISMS OF HISTONE COMPLEX ASSEMBLY

At the most basic level, inheritance in living beings occurs by passing the genomic information such as the DNA sequences from the parent generation to the offspring generation. Hence, it is a fundamental goal for every generation to efficiently express the genomic information and safely pass it on to the next generation. In human and other eukaryotic species, this mission is mediated via chromatin, a macromolecule with intricate hierarchical structure. The fundamental unit of chromatin is called a nucleosome, a complex of histone proteins wrapped around with DNA. To carry out diverse biological functions such as transcription and DNA replication, the DNA-protein complex must dynamically transition between more compact, closed states and more accessible, open ones. To fully understand the chromatin structure and dynamics, it is essential to comprehend the basic structural unit of chromatin, nucleosome. In this dissertation, I present my doctoral research in the exploration of the nucleosome dynamics problem, focusing on the assembly process of histone proteins. From histone monomer to dimer, then to tetramer, octamer, and nucleosome, I used different computational modeling theories and techniques, together with different experimental collaborations, to investigate the overall thermodynamics and specific mechanistic details of nucleosome dynamics at different levels. My work has shed light on the fundamental principles governing the histone protein folding and histone complex assembly, in particular, highlighting similarities and differences between the canonical and variant CENP-A histones

Analyzing Responses of Mouse Olfactory Sensory Neurons Using the Air-phase Electroolfactogram Recording

Animals depend on olfaction for many critical behaviors, such as finding food sources, avoiding predators, and identifying conspecifics for mating and other social interactions. The electroolfactogram (EOG) recording is an informative, easy to conduct, and reliable method to assay olfactory function at the level of the olfactory epithelium. Since the 1956 description of the EOG by Ottoson in frogs1, the EOG recording has been applied in many vertebrates including salamanders, rabbits, rats, mice, and humans (reviewed by Scott and Scott-Johnson, 2002, ref. 2). The recent advances in genetic modification in mice have rekindled interest in recording the EOG for physiological characterization of olfactory function in knock-out and knock-in mice. EOG recordings have been successfully applied to demonstrate the central role of olfactory signal transduction components3-8, and more recently to characterize the contribution of certain regulatory mechanisms to OSN responses9-12

Towards Zero-Shot Personalized Table-to-Text Generation with Contrastive Persona Distillation

Existing neural methods have shown great potentials towards generating informative text from structured tabular data as well as maintaining high content fidelity. However, few of them shed light on generating personalized expressions, which often requires well-aligned persona-table-text datasets that are difficult to obtain. To overcome these obstacles, we explore personalized table-to-text generation under a zero-shot setting, by assuming no well-aligned persona-table-text triples are required during training. To this end, we firstly collect a set of unpaired persona information and then propose a semi-supervised approach with contrastive persona distillation (S2P-CPD) to generate personalized context. Specifically, tabular data and persona information are firstly represented as latent variables separately. Then, we devise a latent space fusion technique to distill persona information into the table representation. Besides, a contrastive-based discriminator is employed to guarantee the style consistency between the generated context and its corresponding persona. Experimental results on two benchmarks demonstrate S2P-CPD's ability on keeping both content fidelity and personalized expressions.Comment: Accepted by ICASSP 202

AliCHI: A Large-scale Multi-modal Dataset and Automated Evaluation Tool for Human-like Dialogue Systems

A well-designed interactive human-like dialogue system is expected to take actions (e.g. smiling) and respond in a pattern similar to humans. However, due to the limitation of single-modality (only speech) or small volume of currently public datasets, most dialogue systems can only respond in speech and cannot take human-like actions. In this work, we build a large-scale multi-modal dataset of human-to-human conversation in a face-to-face fashion, with fine-grained annotations. The raw data in video format contains 635 dialogue sessions, being collected from 200 participants on designed topics and lasting 52 hours in total. Moreover, we manually annotated the verbal and non-verbal behaviors in each dialogue session on their start/end timestamp. Furthermore, we developed a corresponding evaluation tool for human-like dialogue systems to automatically evaluates the accuracy of two basic tasks, turn-taking prediction, and backchannel prediction, on both time and content. We have opened the data, the tools will be released at the conference