The well-coordinated linkage between acidogenicity and aciduricity via insoluble glucans on the surface of Streptococcus mutans.

Streptococcus mutans is considered the principal cariogenic bacterium for dental caries. Despite the recognition of their importance for cariogenesis, the possible coordination among S. mutans' main virulence factors, including glucan production, acidogenicity and aciduricity, has been less well studied. In the present study, using S. mutans strains with surface-displayed pH-sensitive pHluorin, we revealed sucrose availability- and Gtf functionality-dependent proton accumulation on S. mutans surface. Consistent with this, using a pH-sensitive dye, we demonstrated that both in vivo cell-produced and in vitro enzymatically synthesized insoluble glucans displayed proton-concentrating ability. Global transcriptomics revealed proton accumulation triggers the up-regulation of genes encoding functions involved in acid tolerance response in a glucan-dependent manner. Our data suggested that this proton enrichment around S. mutans could pre-condition the bacterium for acid-stress. Consistent with this hypothesis, we found S. mutans strains defective in glucan production were more acid sensitive. Our study revealed for the first time that insoluble glucans is likely an essential factor linking acidogenicity with aciduricity. The coordination of these key virulence factors could provide new insights on how S. mutans may have become a major cariogenic pathogen

Finding disease-specific coordinated functions by multi-function genes: Insight into the coordination mechanisms in diseases

AbstractWe developed an approach using multi-function disease genes to find function pairs whose co-deregulation might induce a disease. Analyzing cancer genes, we found many cancer-specific coordinated function pairs co-deregulated by dysfunction of multi-function genes and other molecular changes in cancer. Studying two subtypes of cardiomyopathy, we found they show certain consistency at the functional coordination level. Our approach can also provide important information for finding novel disease genes as well as their mechanisms in diseases

SPar: estimating stellar parameters from multi-band photometries with empirical stellar libraries

Modern large-scale photometric surveys have provided us with multi-band photometries of billions of stars. Determining the stellar atmospheric parameters, such as the effective temperature (\teff) and metallicities (\feh), absolute magnitudes ($M_{G}$), distances ($d$) and reddening values (\ebr) is fundamental to study the stellar populations, structure, kinematics and chemistry of the Galaxy. This work constructed an empirical stellar library which maps the stellar parameters to multi-band photometries from a dataset with Gaia parallaxes, LAMOST atmospheric parameters, and optical to near-infrared photometry from several photometric surveys. Based on the stellar library, we developed a new algorithm, SPar (\textbf{S}tellar \textbf{P}arameters from multib\textbf{a}nd photomet\textbf{r}y), which fits the multi-band stellar photometries to derive the stellar parameters (\teff, \feh, $M_G$, $d$ and \ebr) of the individual stars. The algorithm is applied to the multi-band photometric measurements of a sample of stars selected from the SMSS survey, which have stellar parameters derived from the spectroscopic surveys. The stellar parameters derived from multi-band photometries by our algorithm are in good agreement with those from the spectroscopic surveys. The typical differences between our results and the literature values are 170\,K for \teff, 0.23\,dex for \feh, 0.13\,mag for $M_G$ and 0.05\,mag for \ebr. The algorithm proved to be robust and effective and will be applied to the data of future large-scale photometric surveys such as the Mephisto and CSST surveys.Comment: 16 pages, 10 figures, Accepted by The Astronomical Journal on 7/8/202

Rethinking Closed-loop Training for Autonomous Driving

Recent advances in high-fidelity simulators have enabled closed-loop training of autonomous driving agents, potentially solving the distribution shift in training v.s. deployment and allowing training to be scaled both safely and cheaply. However, there is a lack of understanding of how to build effective training benchmarks for closed-loop training. In this work, we present the first empirical study which analyzes the effects of different training benchmark designs on the success of learning agents, such as how to design traffic scenarios and scale training environments. Furthermore, we show that many popular RL algorithms cannot achieve satisfactory performance in the context of autonomous driving, as they lack long-term planning and take an extremely long time to train. To address these issues, we propose trajectory value learning (TRAVL), an RL-based driving agent that performs planning with multistep look-ahead and exploits cheaply generated imagined data for efficient learning. Our experiments show that TRAVL can learn much faster and produce safer maneuvers compared to all the baselines. For more information, visit the project website: https://waabi.ai/research/travlComment: ECCV 202

Research on multi-energy cooperative participation of grid frequency inertia response control strategy for energy storage type doubly-fed wind turbine considering wind speed disturbance

With the proposal of carbon peaking and carbon neutralization, the penetration rate of wind power generation continues to increase. This paper focuses on the problem that doubly fed induction wind turbines are vulnerable to input “source” disturbances and have weak frequency modulation ability, which reduces the stability of the power grid. Based on the structural model of energy storage system embedded in doubly fed wind power generation system, it is compared the ability of super capacitor energy storage and releasing rotor kinetic energy to provide inertia response power and energy, and the feasibility of multi-energy coordinated inertia response is analyzed. Based on the inertia time constant of conventional synchronous generator set, the inertia time constant and actual inertia constant of energy storage doubly fed wind power generation system under variable wind speed are defined. An extended state observer is used to estimate the change of captured mechanical power caused by the change of wind speed, and a control strategy for doubly fed induction generator with super capacitor to participate in power grid frequency regulation is designed. Finally, considering the aggregation of wind power and the difference of the state of charge during the operation of distributed energy storage, the 3*3*3 wind farm model is established using Matlab/Simulink simulation software. The feasibility and advantages of the frequency modulation control strategy proposed in this paper are verified by building a power grid frequency modulation simulation involving wind farms and traditional generators