Yeast adaptation and survival under acute exposure to lethal ethanol stress

The ability to respond to stress is universal in all domains of life. Failure to properly execute the stress response compromises the fitness of the organism. Several key stress pathways are conserved from unicellular organisms to higher eukaryotes, so knowledge of how these pathways operate in model organisms is crucial for understanding stress-related diseases and aging in humans. The mechanisms of stress tolerance have been well-studied in the budding yeast Saccharomyces cerevisiae. Yeast respond to diverse stresses by initiating both general and stress-specific responses that generally protect the cells during and after the stress exposure. While previous work has revealed mechanistic insights on adaptation and survival under mild and long-term exposure to stress, how they cope with acute exposure to lethal stress is not well understood. Here, we combined transcriptional profiling, fitness profiling, and laboratory evolution to investigate how S. cerevisiae survive acute exposure to lethal ethanol stress. By using high throughput methods such as RNA-seq and barcode sequencing of the pooled yeast deletion library, we were able to discover and characterize both existing and novel pathways that yeast utilize to adapt to and survive ethanol stress. We found both ethanol-specific and as well general stress response mechanisms. We were also able to evolve a strain of ethanol under lethal ethanol stress to exhibit a survival of at least an order of magnitude greater than the parental wild-type strain. Additionally, this evolved strain exhibited cross protection to other stresses without compromising bulk growth rate. We found that this strain adapted its global expression levels to a post-stress state, making it more robust to various stresses even under optimal growth conditions

Solving a class of zero-sum stopping game with regime switching

This paper studies a class of zero-sum stopping game in a regime switching model. A verification theorem as a sufficient criterion for Nash equilibriums is established based on a set of variational inequalities (VIs). Under an appropriate regularity condition for solutions to the VIs, a suitable system of algebraic equations is derived via the so-called smooth-fit principle. Explicit Nash equilibrium stopping rules of threshold-type for the two players and the corresponding value function of the game in closed form are obtained. Numerical experiments are reported to demonstrate the dependence of the threshold levels on various model parameters. A reduction to the case with no regime switching is also presented as a comparison

Motion Planning for Mobile Robots

This chapter introduces two kinds of motion path planning algorithms for mobile robots or unmanned ground vehicles (UGV). First, we present an approach of trajectory planning for UGV or mobile robot under the existence of moving obstacles by using improved artificial potential field method. Then, we propose an I-RRT* algorithm for motion planning, which combines the environment with obstacle constraints, vehicle constraints, and kinematic constraints. All the simulation results and the experiments show that two kinds of algorithm are effective for practical use

An exact solution of spherical mean-field plus orbit-dependent non-separable pairing model with two non-degenerate j-orbits

An exact solution of nuclear spherical mean-field plus orbit-dependent non-separable pairing model with two non-degenerate j-orbits is presented. The extended one-variable Heine-Stieltjes polynomials associated to the Bethe ansatz equations of the solution are determined, of which the sets of the zeros give the solution of the model, and can be determined relatively easily. A comparison of the solution to that of the standard pairing interaction with constant interaction strength among pairs in any orbit is made. It is shown that the overlaps of eigenstates of the model with those of the standard pairing model are always large, especially for the ground and the first excited state. However, the quantum phase crossover in the non-separable pairing model cannot be accounted for by the standard pairing interaction.Comment: 5 pages, 1 figure, LaTe

Are you in a Masquerade? Exploring the Behavior and Impact of Large Language Model Driven Social Bots in Online Social Networks

As the capabilities of Large Language Models (LLMs) emerge, they not only assist in accomplishing traditional tasks within more efficient paradigms but also stimulate the evolution of social bots. Researchers have begun exploring the implementation of LLMs as the driving core of social bots, enabling more efficient and user-friendly completion of tasks like profile completion, social behavior decision-making, and social content generation. However, there is currently a lack of systematic research on the behavioral characteristics of LLMs-driven social bots and their impact on social networks. We have curated data from Chirper, a Twitter-like social network populated by LLMs-driven social bots and embarked on an exploratory study. Our findings indicate that: (1) LLMs-driven social bots possess enhanced individual-level camouflage while exhibiting certain collective characteristics; (2) these bots have the ability to exert influence on online communities through toxic behaviors; (3) existing detection methods are applicable to the activity environment of LLMs-driven social bots but may be subject to certain limitations in effectiveness. Moreover, we have organized the data collected in our study into the Masquerade-23 dataset, which we have publicly released, thus addressing the data void in the subfield of LLMs-driven social bots behavior datasets. Our research outcomes provide primary insights for the research and governance of LLMs-driven social bots within the research community.Comment: 18 pages, 7 figure

Design-Based Causal Inference with Missing Outcomes: Missingness Mechanisms, Imputation-Assisted Randomization Tests, and Covariate Adjustment

Design-based causal inference is one of the most widely used frameworks for testing causal null hypotheses or inferring about causal parameters from experimental or observational data. The most significant merit of design-based causal inference is that its statistical validity only comes from the study design (e.g., randomization design) and does not require assuming any outcome-generating distributions or models. Although immune to model misspecification, design-based causal inference can still suffer from other data challenges, among which missingness in outcomes is a significant one. However, compared with model-based causal inference, outcome missingness in design-based causal inference is much less studied, largely due to the challenge that design-based causal inference does not assume any outcome distributions/models and, therefore, cannot directly adopt any existing model-based approaches for missing data. To fill this gap, we systematically study the missing outcomes problem in design-based causal inference. First, we use the potential outcomes framework to clarify the minimal assumption (concerning the outcome missingness mechanism) needed for conducting finite-population-exact randomization tests for the null effect (i.e., Fisher's sharp null) and that needed for constructing finite-population-exact confidence sets with missing outcomes. Second, we propose a general framework called ``imputation and re-imputation" for conducting finite-population-exact randomization tests in design-based causal studies with missing outcomes. Our framework can incorporate any existing outcome imputation algorithms and meanwhile guarantee finite-population-exact type-I error rate control. Third, we extend our framework to conduct covariate adjustment in an exact randomization test with missing outcomes and to construct finite-population-exact confidence sets with missing outcomes

Tracing the Shadows of Gothic Bach: An Overview of J.S. Bach’s Keyboard Music in American Horror Films

The Toccata and Fugue in D Minor, BWV 565, and the Goldberg Variations, BWV 988, have been by far the most prominent of J.S. Bach’s works to evoke evil and horror on screen. This “Horror Bach” association remains exclusively a subculture phenomenon in pop culture (almost universally outside of the classical music world). A historical overview of this cultural phenomenon suggests three factors that made these pieces become horror film music: 1) the popularity of the pieces, 2) the choice of instrumentation, and 3) popular cultural factors. This doctoral document also introduces this pop culture phenomenon of Bach to classical keyboardists, providing them with the information necessary to create “Horror-Bach” concert programs