Indole at low concentration helps exponentially growing Escherichia coli survive at high temperature.

A culture of stationary phase Escherichia coli cells has been reported to produce copious indole when exposed to high temperature (50°C), and this response has been proposed to aid survival. We reinvestigated this phenomenon and found that indole production under these conditions is probably not a direct response to heat stress. Rather, E. coli produces indole when growth is prevented, irrespective of whether this is due to heat stress, antibiotic treatment or the removal of nutrients. Moreover, 300μM indole produced at 50°C does not improve the viability of heat stressed cells. Interestingly, a much lower concentration of indole (20 μM) improves the survival of an indole-negative strain (ΔtnaA) when heat stressed during exponential growth. In addition we have shown that the distribution of tryptophanase, the enzyme responsible for indole synthesis, is highly heterogeneous among cells in a population, except during the transition between exponential and stationary phases. The observation that, despite the presence of the tryptophanase, very little indole is produced during early exponential phase suggests that there is post-translational regulation of the enzyme

Smaller Sensitivity of Precipitation to Surface Temperature under Massive Atmospheres

Precipitation and its response to forcings is an important aspect of planetary climate system. In this study, we examine the strength of precipitation in the experiments with different atmospheric masses and their response to surface warming, using three global atmospheric general circulation models (GCMs) and one regional cloud-resolving model (CRM). We find that precipitation is weaker when atmospheric mass is larger for a given surface temperature. Furthermore, the increasing rate of precipitation with increasing surface temperature under a larger atmospheric mass is smaller than that under a smaller atmospheric mass. These behaviors can be understood based on atmospheric or surface energy balance. Atmospheric mass influences Rayleigh scattering, multiple scattering in the atmosphere, pressure broadening, lapse rate, and thereby precipitation strength. These results have important implications on the climate and habitability of early Earth, early Mars, and exoplanets with oceans

Improved Best-of-Both-Worlds Guarantees for Multi-Armed Bandits: FTRL with General Regularizers and Multiple Optimal Arms

We study the problem of designing adaptive multi-armed bandit algorithms that perform optimally in both the stochastic setting and the adversarial setting simultaneously (often known as a best-of-both-world guarantee). A line of recent works shows that when configured and analyzed properly, the Follow-the-Regularized-Leader (FTRL) algorithm, originally designed for the adversarial setting, can in fact optimally adapt to the stochastic setting as well. Such results, however, critically rely on an assumption that there exists one unique optimal arm. Recently, Ito (2021) took the first step to remove such an undesirable uniqueness assumption for one particular FTRL algorithm with the $\frac{1}{2}$-Tsallis entropy regularizer. In this work, we significantly improve and generalize this result, showing that uniqueness is unnecessary for FTRL with a broad family of regularizers and a new learning rate schedule. For some regularizers, our regret bounds also improve upon prior results even when uniqueness holds. We further provide an application of our results to the decoupled exploration and exploitation problem, demonstrating that our techniques are broadly applicable.Comment: Update the camera-ready version for NeurIPS 202

Achieving Near-Optimal Regret for Bandit Algorithms with Uniform Last-Iterate Guarantee

Existing performance measures for bandit algorithms such as regret, PAC bounds, or uniform-PAC (Dann et al., 2017), typically evaluate the cumulative performance, while allowing the play of an arbitrarily bad arm at any finite time t. Such a behavior can be highly detrimental in high-stakes applications. This paper introduces a stronger performance measure, the uniform last-iterate (ULI) guarantee, capturing both cumulative and instantaneous performance of bandit algorithms. Specifically, ULI characterizes the instantaneous performance since it ensures that the per-round regret of the played arm is bounded by a function, monotonically decreasing w.r.t. (large) round t, preventing revisits to bad arms when sufficient samples are available. We demonstrate that a near-optimal ULI guarantee directly implies near-optimal cumulative performance across aforementioned performance measures. To examine the achievability of ULI in the finite arm setting, we first provide two positive results that some elimination-based algorithms and high-probability adversarial algorithms with stronger analysis or additional designs, can attain near-optimal ULI guarantees. Then, we also provide a negative result, indicating that optimistic algorithms cannot achieve a near-optimal ULI guarantee. Finally, we propose an efficient algorithm for linear bandits with infinitely many arms, which achieves the ULI guarantee, given access to an optimization oracle

Parameter Estimation for Patient Enrollment in Clinical Trials

In this paper, we study the Poisson-gamma model for recruitment time in clinical trials. We proved several properties of this model that match our intuitions from a reliability perspective, did simulations on this model, and used different optimization methods to estimate the parameters. Although the behaviors of the optimization methods were unfavorable and unstable, we identified certain conditions and provided potential explanations for this phenomenon and further insights into the Poisson-gamma model

The Effect of Indium Concentration on the Structure and Properties of Zirconium Based Intermetallics: First-Principles Calculations

The phase stability, mechanical, electronic, and thermodynamic properties of In-Zr compounds have been explored using the first-principles calculation based on density functional theory (DFT). The calculated formation enthalpies show that these compounds are all thermodynamically stable. Information on electronic structure indicates that they possess metallic characteristics and there is a common hybridization between In-p and Zr-d states near the Fermi level. Elastic properties have been taken into consideration. The calculated results on the ratio of the bulk to shear modulus (B/G) validate that InZr3 has the strongest deformation resistance. The increase of indium content results in the breakout of a linear decrease of the bulk modulus and Young’s modulus. The calculated theoretical hardness of α-In3Zr is higher than the other In-Zr compounds

The Effects of Weather on Passenger Flow of Urban Rail Transit

Predicting passenger flow on urban rail transit is important for the planning, design and decision-making of rail transit. Weather is an important factor that affects the passenger flow of rail transit by changing the travel mode choice of urban residents. This study aims to explore the influence of weather on urban rail transit ridership, taking four cities in China as examples, Beijing, Shanghai, Guangzhou and Chengdu. To determine the weather effect on daily ridership rate, the three models were proposed with different combinations of the factors of temperature and weather type, using linear regression method.   The large quantities of data were applied to validate the developed models.  The results show that in Guangzhou, the daily ridership rate of rail transit increases with increasing temperature. In Chengdu, the ridership rate increases in rainy days compared to sunny days. While, in Beijing and Shanghai, the ridership rate increases in light rainfall and heavy rainfall (except moderate rainfall) compared to sunny days. The research findings are important to understand the impact of weather on passenger flow of urban rail transit. The findings can provide effective strategies to rail transit operators to deal with the fluctuation in daily passenger flow

Development of a computational-experimental model to predict glioma response to radiation treatment

Radiation is essential to malignant glioma and glioblastoma treatment. However, the prognosis of glioblastoma remains poor with a median survival of 15 months. This is partly due to the heterogeneous radiosensitivity among patients. If we have a mechanism-based model that can make dynamic predictions, it has the potential to guide and optimize the treatment on a patient-specific basis. The purpose of this dissertation is to develop and validate a computational-experimental model that explicitly incorporates underlying radiobiology as well as making accurate predictions of the radiation response of glioma cells. Specifically, we first propose a mathematical model to a single dose of radiation that incorporates DNA repair and cell death pathways and validate it under eight different doses from 2 Gy to 16 Gy via microscopy in vitro. We then extend this model to fractionated treatment and validate it with six different fractionation schemes using total doses of either 16 Gy or 20 Gy. Finally, we propose a data assimilation framework that will individualize the prediction based on the observations of individual replicates, which further improves the prediction accuracy. We present a full story of how developing a mechanism-based experiment-driven mathematical model can assist us in characterizing and predicting radiation response, which could eventually, be used to optimize the treatment schedule.Biomedical Engineerin