Research on the Physics-Intelligence Hybrid Theory Based Dynamic Scenario Library Generation for Automated Vehicles.

The testing and evaluation system has been the key technology and security with its necessity in the development and deployment of maturing automated vehicles. In this research, the physics-intelligence hybrid theory-based dynamic scenario library generation method is proposed to improve system performance, in particular, the testing efficiency and accuracy for automated vehicles. A general framework of the dynamic scenario library generation is established. Then, the parameterized scenario based on the dimension optimization method is specified to obtain the effective scenario element set. Long-tail functions for performance testing of specific ODD are constructed as optimization boundaries and critical scenario searching methods are proposed based on the node optimization and sample expansion methods for the low-dimensional scenario library generation and the reinforcement learning for the high-dimensional one, respectively. The scenario library generation method is evaluated with the naturalistic driving data (NDD) of the intelligent electric vehicle in the field test. Results show better efficient and accuracy performances compared with the ideal testing library and the NDD, respectively, in both low- and high-dimensional scenarios.

Combining sequence and network information to enhance protein-protein interaction prediction.

BACKGROUND: Protein-protein interactions (PPIs) are of great importance in cellular systems of organisms, since they are the basis of cellular structure and function and many essential cellular processes are related to that. Most proteins perform their functions by interacting with other proteins, so predicting PPIs accurately is crucial for understanding cell physiology. RESULTS: Recently, graph convolutional networks (GCNs) have been proposed to capture the graph structure information and generate representations for nodes in the graph. In our paper, we use GCNs to learn the position information of proteins in the PPIs networks graph, which can reflect the properties of proteins to some extent. Combining amino acid sequence information and position information makes a stronger representation for protein, which improves the accuracy of PPIs prediction. CONCLUSION: In previous research methods, most of them only used protein amino acid sequence as input information to make predictions, without considering the structural information of PPIs networks graph. We first time combine amino acid sequence information and position information to make representations for proteins. The experimental results indicate that our method has strong competitiveness compared with several sequence-based methods.

Using deep reinforcement learning to speed up collective cell migration.

BACKGROUND: Collective cell migration is a significant and complex phenomenon that affects many basic biological processes. The coordination between leader cell and follower cell affects the rate of collective cell migration. However, there are still very few papers on the impacts of the stimulus signal released by the leader on the follower. Tracking cell movement using 3D time-lapse microscopy images provides an unprecedented opportunity to systematically study and analyze collective cell migration. RESULTS: Recently, deep reinforcement learning algorithms have become very popular. In our paper, we also use this method to train the number of cells and control signals. By experimenting with single-follower cell and multi-follower cells, it is concluded that the number of stimulation signals is proportional to the rate of collective movement of the cells. Such research provides a more diverse approach and approach to studying biological problems. CONCLUSION: Traditional research methods are always based on real-life scenarios, but as the number of cells grows exponentially, the research process is too time consuming. Agent-based modeling is a robust framework that approximates cells to isotropic, elastic, and sticky objects. In this paper, an agent-based modeling framework is used to establish a simulation platform for simulating collective cell migration. The goal of the platform is to build a biomimetic environment to demonstrate the importance of stimuli between the leading and following cells.

Attention-based recurrent neural network for influenza epidemic prediction.

BACKGROUND: Influenza is an infectious respiratory disease that can cause serious public health hazard. Due to its huge threat to the society, precise real-time forecasting of influenza outbreaks is of great value to our public. RESULTS: In this paper, we propose a new deep neural network structure that forecasts a real-time influenza-like illness rate (ILI%) in Guangzhou, China. Long short-term memory (LSTM) neural networks is applied to precisely forecast accurateness due to the long-term attribute and diversity of influenza epidemic data. We devise a multi-channel LSTM neural network that can draw multiple information from different types of inputs. We also add attention mechanism to improve forecasting accuracy. By using this structure, we are able to deal with relationships between multiple inputs more appropriately. Our model fully consider the information in the data set, targetedly solving practical problems of the Guangzhou influenza epidemic forecasting. CONCLUSION: We assess the performance of our model by comparing it with different neural network structures and other state-of-the-art methods. The experimental results indicate that our model has strong competitiveness and can provide effective real-time influenza epidemic forecasting.

The Subjective Well-Being of Elderly Migrants in Dongguan: The Role of Residential Environment.

To examine the association between community and individual-level residential environment in relation to subjective well-being (SWB) amongst 470 elderly migrants in China, this community-based survey was conducted. The manner and extent to which the SWB of these elderly migrants is influenced by their residential environment was the main area of focus. The Scale of Happiness of the Memorial University of Newfoundland was used to assess SWB. SWB was found to be associated significantly with environmental factors such as social cohesion, closeness to the nearest facility of recreation, the density of recreation facilities, financial facilities, and health facilities. The health facility density (B = 0.026, p < 0.001) and recreation facility density (B = 0.032, p < 0.001) had positive associations with SWB, while financial facility density (B = −0.035, p < 0.001) had a negative association. The primary determinants of SWB for elderly migrants ranged from individual to environmental factors. Through the enhancement of the accessibility to healthcare facilities in their new homes, in addition to promoting recreational activities and social services, the SWB amongst elderly migrants could be enhanced further.

Functional analysis of luxS in Streptococcus suis reveals a key role in biofilm formation and virulence.

Streptococcus suis (SS) is an important pathogen of pigs, responsible for diverse diseases in swine and human. LuxS has been reported to play critical roles in both regulating various behaviors and interspecies quorum sensing in a large spectrum of bacteria. In this study, the luxS deletion mutant of SS was constructed using homologous recombination and its biofilm formation, hemolytic activity, cell adherence, virulence and expression of virulence factors were evaluated. Compared to the parental strain, the biofilm formation and hemolytic activity were significantly decreased in the luxS mutant. The addition of synthetic autoinducer 2 could complement the deficiencies of biofilm production in the mutant strain. Furthermore, its adherence to the HEp-2 cell line was dramatically decreased by 51% compared to the parental strain. Expressions of the known virulence genes gdh, cps, mrp, gapdh, sly, fbps and ef in the mutant strain were decreased by 0.66, 0.61, 0.45, 0.48, 0.29, 0.57 and 0.38, respectively, as quantified by real-time PCR. In a zebrafish infection model, the 50% lethal dose of the mutant strain was increased up to 10-fold. The findings demonstrated that the luxS gene deletion resulted in a significant decrease of bacterial biofilm formation, cell adhesion, hemolytic activity and transcription levels of many virulence genes in SS, and these factors may be associated with the attenuation of virulence in zebrafish. These results suggest that luxS may be involved in the interruption of bacterial communication and biofilm formation that contribute to the virulence of the bacterium.