Understanding The Complexity of Human Structural Genomic Variation Through Multiple Whole Genome Sequencing Platforms

Genomic structural variants (SVs) are major sources of genome diversity and closely related to human health, as indicated by numerous studies. In spite of the recent advances in sequencing technology and discovery methodology, there are still considerable amounts of variants in the genome that are partially or completely misinterpreted. This thesis has mainly focused on comprehensively interpreting the structural variants in human genomes by accurately defining the locations and formats of variants with the application of different sequencing platforms. To accomplish this goal, I developed a randomized iterative approach to define all types of SVs, which has shown superior performance in accurately defining complex variants. Next, I built a recurrence based validation pipeline to systematically validate SVs with long read sequences. I conclude with a systematic integration of SVs in multiple individuals discovered by various short read based detecting algorithms, with supportive evidence from orthogonal technologies, which presents to date the most comprehensive SV map in the human genome and the best current technologies allow us to do.PhDBioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/138462/1/xuefzhao_1.pd

A Communication Theory Perspective on Prompting Engineering Methods for Large Language Models

The springing up of Large Language Models (LLMs) has shifted the community from single-task-orientated natural language processing (NLP) research to a holistic end-to-end multi-task learning paradigm. Along this line of research endeavors in the area, LLM-based prompting methods have attracted much attention, partially due to the technological advantages brought by prompt engineering (PE) as well as the underlying NLP principles disclosed by various prompting methods. Traditional supervised learning usually requires training a model based on labeled data and then making predictions. In contrast, PE methods directly use the powerful capabilities of existing LLMs (i.e., GPT-3 and GPT-4) via composing appropriate prompts, especially under few-shot or zero-shot scenarios. Facing the abundance of studies related to the prompting and the ever-evolving nature of this field, this article aims to (i) illustrate a novel perspective to review existing PE methods, within the well-established communication theory framework; (ii) facilitate a better/deeper understanding of developing trends of existing PE methods used in four typical tasks; (iii) shed light on promising research directions for future PE methods

Three photosynthetic patterns characterized by cluster analysis of gas exchange data in two rice populations

AbstractPlant photosynthetic rate is affected by stomatal status and internal CO2 carboxylation. Understanding which process determines photosynthetic rate is essential for developing strategies for breeding crops with high photosynthetic efficiency. In this study, we identified different physiological patterns of photosynthetic rate in two different rice populations. Photosynthetic gas exchange parameters were measured during the flowering stage in two rice populations. Clustering and correlation analyses were performed on the resulting data. Five or six groups were defined by K-means clustering according to differences in net photosynthetic rates (Pn). According to differences in stomatal conductance (gs) and carboxylation efficiency (CE), each group was clustered into three subgroups characterized by physiological patterns stomatal pattern, carboxylation pattern, and intermediate pattern. Pn was significantly correlated with gs (r=0.810) and CE (r=0.531). Pn was also significantly correlated with gs and CE in the three physiological patterns. The correlation coefficients were highest in the stomatal pattern (0.905 and 0.957) and lowest in the carboxylation pattern (0.825 and 0.859). Higher correlation coefficients between Pn and gs or CE in the three physiological patterns indicate that clustering is very important for understanding factors limiting rice photosynthesis

Association between platelet distribution width and serum uric acid in Chinese population

© 2019 International Union of Biochemistry and Molecular Biology Platelet distribution width (PDW) is a simple and inexpensive parameter, which could predict activation of coagulation efficiently. And it has been confirmed to have a significant role in many diseases. We aimed to explore the association between PDW and hyperuricemia in a large Chinese cohort. This cross-sectional study recruited 61,091 ostensible healthy participants (29,259 males and 31,832 females) after implementing exclusion criteria. Clinical data of the enrolled population included anthropometric measurements and serum parameters. Database was sorted by gender, and the association between PDW and hyperuricemia was analyzed after dividing PDW into quartiles. Crude and adjusted odds ratios of PDW for hyperuricemia with 95% confidence intervals were analyzed using binary logistic regression models. We found no significant difference in PDW values between the genders. Males showed significantly higher incidence of hyperuricemia than females. From binary logistic regression models, significant hyperuricemia risks only were demonstrated in PDW quartiles 2 and 3 in males (P < 0.05). This study displayed close association between PDW and hyperuricemia as a risk factor. It is meaningful to use PDW as a clinical risk predictor for hyperuricemia in males. © 2019 BioFactors, 45(3):326–334, 2019