Novel Players in Telomere Maintenance and Beyond

Telomeres are specialized nucleoprotein caps at the end of linear chromosomes, critical for genome stability. A major function of telomeres is to distinguish chromosome ends from ends of double strand breaks. A second function is to counteract incomplete end-replication via telomerase extension. POT1 (Protection of Telomere 1) is a highly conserved telomere protein known for its essential role in chromosome end-protection and end-replication. Arabidopsis thaliana encodes three POT1 paralogs, POT1a, POT1b, and POT1c. AtPOT1a promotes telomerase processivity and therefore is required for telomere length homeostasis. The functions of AtPOT1b and AtPOT1c are less understood. In this dissertation, I characterized the function of POT1b at telomeres. In contrast to POT1a, I found that POT1b is dispensable for telomere length maintenance and serves as a negative regulator of telomerase. In addition, I tested the hypothesis that TER2/POT1b works in concert with Ku to stabilize the blunt-ended telomeres. Further characterization of POT1b using biochemical and genetic approaches revealed several unexpected features. First, unlike POT1a, which is primarily localized to the nucleus, POT1b accumulates in the cytoplasm, where its binding partner TER2 also resides. This observation suggests a potential regulatory pathway for TER2 RNP via subcellular trafficking. In addition, I found that early development of POT1b mutants is significantly delayed, indicating that POT1b has a novel role in plant development. Together, these studies provide insights into the role of AtPOT1b in telomere biology and expand our understanding of POT1 protein function and evolution. In addition to these studies of POT1 proteins, I examined the role of chromosome remodeler DDM1 (Deficient in DNA Methylation 1) in telomere length maintenance. I showed that plants deficient in DDM1 suffer from abrupt telomere shortening in the sixth generation of the deficiency due to deletional recombination. This telomere rapid deletion (TRD) coincides with increased transposon activation and increased DNA damage sensitivity at the root apical meristem, suggesting that TRD may serve as a mechanism to stimulate programmed cell death, thereby eliminating stem cells with massive DNA damage. These studies open a new avenue for telomere function in promoting genome integrity

A08: Effects of Participation in Sports Clubs Activity on College Students’ Perceived Stress and Well-Being

Purpose: Mandated social distancing to prevent the spread of COVID-19 pandemic has brought more anxiety and stress to college students. The primary purpose of this study was to examine whether college students\u27 participation in sports club activities can reduce anxiety and stress. The secondary purpose was to compare the effects of different types of sport clubs. Methods: The sample consisted of 242 college students (143 males; mean age=22.63 years old) in an academically prestigious university. They were voluntarily enrolled in either team sports clubs, such as volleyball, football, baseball, and softball, etc. (n=96), or individual sports clubs, such as squash, cycling, mountaineering, etc. (n=146). They responded to validated scales to assess perceived stress (Sheldon Cohen, 1983) and well-being (Diener & Biswas-Diener, 2009). Self-compiled questionnaires on motivation to join sports clubs and basic information on club organization activities were collected. All surveys were conducted in October 2021. Results: A considerable proportion of students (34.7%) participated in sports clubs to reduce academic pressure. Most of them (72.26%) have already recognized the physical and mental health benefits of physical activity. Significant decreases were observed for perceived stress in both groups: team sports group (ΔM = -0.76, p \u3c 0.01), and individual sports group (ΔM= -0.77, p \u3c 0.01). A significant increase in well-being was observed in two types of courses led by the team sports group (ΔM=1.55, p \u3c 0.01) followed by the individual sports group (ΔM=1.34, p \u3c 0.01). Individual sports clubs have a more pronounced effect on reducing negative emotions than team sports clubs (ΔM = -2.01, p \u3c 0.05). Conclusion: Participation in both team sports clubs and individual sports clubs reduced perceived stress and increased well-being. Individual sports clubs had more decreases in negative emotions compared to team sports clubs

FastLog: An End-to-End Method to Efficiently Generate and Insert Logging Statements

Logs play a crucial role in modern software systems, serving as a means for developers to record essential information for future software maintenance. As the performance of these log-based maintenance tasks heavily relies on the quality of logging statements, various works have been proposed to assist developers in writing appropriate logging statements. However, these works either only support developers in partial sub-tasks of this whole activity; or perform with a relatively high time cost and may introduce unwanted modifications. To address their limitations, we propose FastLog, which can support the complete logging statement generation and insertion activity, in a very speedy manner. Specifically, given a program method, FastLog first predicts the insertion position in the finest token level, and then generates a complete logging statement to insert. We further use text splitting for long input texts to improve the accuracy of predicting where to insert logging statements. A comprehensive empirical analysis shows that our method outperforms the state-of-the-art approach in both efficiency and output quality, which reveals its great potential and practicality in current real-time intelligent development environments.Comment: accepted by ISSTA 202

Unpacking the Ethical Value Alignment in Big Models

Big models have greatly advanced AI's ability to understand, generate, and manipulate information and content, enabling numerous applications. However, as these models become increasingly integrated into everyday life, their inherent ethical values and potential biases pose unforeseen risks to society. This paper provides an overview of the risks and challenges associated with big models, surveys existing AI ethics guidelines, and examines the ethical implications arising from the limitations of these models. Taking a normative ethics perspective, we propose a reassessment of recent normative guidelines, highlighting the importance of collaborative efforts in academia to establish a unified and universal AI ethics framework. Furthermore, we investigate the moral inclinations of current mainstream LLMs using the Moral Foundation theory, analyze existing alignment algorithms, and outline the unique challenges encountered in aligning ethical values within them. To address these challenges, we introduce a novel conceptual paradigm for aligning the ethical values of big models and discuss promising research directions for alignment criteria, evaluation, and method, representing an initial step towards the interdisciplinary construction of the ethically aligned AI This paper is a modified English version of our Chinese paper https://crad.ict.ac.cn/cn/article/doi/10.7544/issn1000-1239.202330553, intended to help non-Chinese native speakers better understand our work

Value FULCRA: Mapping Large Language Models to the Multidimensional Spectrum of Basic Human Values

The rapid advancement of Large Language Models (LLMs) has attracted much attention to value alignment for their responsible development. However, how to define values in this context remains a largely unexplored question. Existing work mainly follows the Helpful, Honest, Harmless principle and specifies values as risk criteria formulated in the AI community, e.g., fairness and privacy protection, suffering from poor clarity, adaptability and transparency. Inspired by basic values in humanity and social science across cultures, this work proposes a novel basic value alignment paradigm and introduces a value space spanned by basic value dimensions. All LLMs' behaviors can be mapped into the space by identifying the underlying values, possessing the potential to address the three challenges. To foster future research, we apply the representative Schwartz's Theory of Basic Values as an initialized example and construct FULCRA, a dataset consisting of 5k (LLM output, value vector) pairs. Our extensive analysis of FULCRA reveals the underlying relation between basic values and LLMs' behaviors, demonstrating that our approach not only covers existing mainstream risks but also anticipates possibly unidentified ones. Additionally, we present an initial implementation of the basic value evaluation and alignment, paving the way for future research in this line

An innovative approach for testing bioinformatics programs using metamorphic testing

Background: Recent advances in experimental and computational technologies have fueled the development of many sophisticated bioinformatics programs. The correctness of such programs is crucial as incorrectly computed results may lead to wrong biological conclusion or misguide downstream experimentation. Common software testing procedures involve executing the target program with a set of test inputs and then verifying the correctness of the test outputs. However, due to the complexity of many bioinformatics programs, it is often difficult to verify the correctness of the test outputs. Therefore our ability to perform systematic software testing is greatly hindered

From Instructions to Intrinsic Human Values -- A Survey of Alignment Goals for Big Models

Big models, exemplified by Large Language Models (LLMs), are models typically pre-trained on massive data and comprised of enormous parameters, which not only obtain significantly improved performance across diverse tasks but also present emergent capabilities absent in smaller models. However, the growing intertwining of big models with everyday human lives poses potential risks and might cause serious social harm. Therefore, many efforts have been made to align LLMs with humans to make them better follow user instructions and satisfy human preferences. Nevertheless, `what to align with' has not been fully discussed, and inappropriate alignment goals might even backfire. In this paper, we conduct a comprehensive survey of different alignment goals in existing work and trace their evolution paths to help identify the most essential goal. Particularly, we investigate related works from two perspectives: the definition of alignment goals and alignment evaluation. Our analysis encompasses three distinct levels of alignment goals and reveals a goal transformation from fundamental abilities to value orientation, indicating the potential of intrinsic human values as the alignment goal for enhanced LLMs. Based on such results, we further discuss the challenges of achieving such intrinsic value alignment and provide a collection of available resources for future research on the alignment of big models.Comment: 20 pages, 5 figure

A Comprehensive Empirical Investigation on Failure Clustering in Parallel Debugging

The clustering technique has attracted a lot of attention as a promising strategy for parallel debugging in multi-fault scenarios, this heuristic approach (i.e., failure indexing or fault isolation) enables developers to perform multiple debugging tasks simultaneously through dividing failed test cases into several disjoint groups. When using statement ranking representation to model failures for better clustering, several factors influence clustering effectiveness, including the risk evaluation formula (REF), the number of faults (NOF), the fault type (FT), and the number of successful test cases paired with one individual failed test case (NSP1F). In this paper, we present the first comprehensive empirical study of how these four factors influence clustering effectiveness. We conduct extensive controlled experiments on 1060 faulty versions of 228 simulated faults and 141 real faults, and the results reveal that: 1) GP19 is highly competitive across all REFs, 2) clustering effectiveness decreases as NOF increases, 3) higher clustering effectiveness is easier to achieve when a program contains only predicate faults, and 4) clustering effectiveness remains when the scale of NSP1F is reduced to 20%

Unified Detoxifying and Debiasing in Language Generation via Inference-time Adaptive Optimization

Warning: this paper contains model outputs exhibiting offensiveness and biases. Recently pre-trained language models (PLMs) have prospered in various natural language generation (NLG) tasks due to their ability to generate fairly fluent text. Nevertheless, these models are observed to capture and reproduce harmful contents in training corpora, typically toxic language and social biases, raising severe moral issues. Prior works on ethical NLG tackle detoxifying and debiasing separately, which is problematic since we find debiased models still exhibit toxicity while detoxified ones even exacerbate biases. To address such a challenge, we propose the first unified framework of detoxifying and debiasing called UDDIA, which jointly formalizes these two problems as rectifying the output space. We theoretically interpret our framework as learning a text distribution mixing weighted attributes. Besides, UDDIA conducts adaptive optimization of only a few parameters during decoding based on a parameter-efficient tuning schema without any training data. This leads to minimal generation quality loss and improved rectification performance with acceptable computational cost. Experimental results demonstrate that compared to several strong baselines, UDDIA achieves debiasing and detoxifying simultaneously and better balances efficiency and effectiveness, taking a further step towards practical ethical NLG.Comment: Work in Progress. Preprin