Hongchun Qu

Hesperidin ameliorates behavioral impairments and neuropathology of transgenic APP/PS1 mice.

In addition to cognitive impairments, deficits in non-cognitive behaviors are also common neurological sequelae in Alzheimers disease and its animal models. Hesperidin, a flavanone glycoside found abundantly in citrus fruits, was orally given (100 mg/kg body weight) to 5-month-old transgenic APP/PS1 mice, a mouse model of cerebral amyloidosis for Alzheimers disease. After a relatively short-term treatment of 10 days, hesperidin significantly restored deficits in non-cognitive nesting ability and social interaction. Further immunohistochemical analysis showed significantly attenuated β-amyloid deposition, plaque associated APP expression, microglial activation and TGF-β immunoreactivity in brains of APP/PS1 mice, which suggests that ameliorated behavioral impairments might be attributable to reduced Aβ deposition and attenuated neuro-inflammatory reaction. Additionally, efficient anti-inflammatory effects of hesperidin were confirmed in vitro. Our findings suggest that hesperidin might be a potential candidate for the treatment of AD or even other neurodegenerative diseases.

Simulation of carbon-based model for virtual plants as complex adaptive system

Abstract This research presented a teleonomic-based simulation approach to virtual plants integrating the technology of intelligent agent as well as the knowledge of plant physiology and morphology. Plant is represented as the individual metamers and root agents with both functional and geometrical structure. The development of plant is achieved by the flush growth of metamer and root agents controlled by their internal physiological status and external environment. The eggplant based simulation results show that simple rules and actions (internal carbon allocation among organs, dynamic carbon reserve/mobilization, carbon transport in parallel using a discrete pressure-flow paradigm and child agent position choosing for maximum light interception, etc.) executed by agents can cause the complex adaptive behaviors on the whole plant level: carbon partitioning among metamers and roots, carbon reserve dynamics, architecture and biomass adaptation to environmental heterogeneity and the phototropism, etc. This phenomenon manifest that the virtual plant simulated in presented approach can be viewed as a complex adaptive system.

AN INTELLIGENT LEARNING APPROACH TO L-GRAMMAR EXTRACTION FROM IMAGE SEQUENCES OF REAL PLANTS

In this paper, we propose an automatic analyzing and transforming approach to L-system grammar extraction from real plants. Instead of using manually designed rules and cumbersome parameters, our method establishes the relationship between L-system grammars and the iterative trend of botanical entities, which reflect the endogenous factors that caused the plant branching process. To realize this goal, we use a digital camera to take multiple images of unfoliaged (leafless) plants and capture the topological and geometrical data of plant entities using image processing methods. The data then stored into specific data structures. A Hidden Markov based statistical model is then employed to reveal the hidden relations of plant entities which have been classified into categories based on their statistical properties extracted by a classic EM algorithm, the hidden relations have been integrated into the target L-system as grammars. Results show that our method is capable of automatically generating L-grammars for a given unfoliaged plant no matter what branching type it is belongs to.

A spatially explicit agent-based simulation platform for investigating effects of shared pollination service on ecological communities

Abstract The alarming reports from around the world on pollinator population declines made the understanding of the effects of shared pollination service on biodiversity into one of the most urgent goals in nature conservation, both for the scientists and managers. The classic field-based methodology which is commonly used in such studies, has three major problems which limit the researchers’ ability to further understand the nature of plant-pollinator dynamics: (1) Natural systems do not allow for a full factorial controlled studies of specific characteristics and traits of both plants and pollinators, because of many confounding effects which are usually unknown. Furthermore, (2) Many of the interactions between plants and pollinators are indirect, via their reciprocal effect on shared pollination services and therefore difficult to detect in the field. Finally, and (3) though plant composition and abundance may be manipulated in the field, it is almost impossible to manipulate pollinator populations, strongly restricting researchers’ ability to thoroughly understand the specific pollinator characteristics which created the observed effects. Therefore, simulation tools are needed that can address this complexity on one hand, and allow to identify potential research directions for targeted experiments on the other hand. Here, we present EcoSimInGrid, a spatially explicit agent-based simulator for investigating effects of shared pollination services on plant communities. EcoSimInGrid can be used to represent complex spatio-temporal interactions among ecological entities of different trophic levels, to investigate effects of plant traits, spatial distribution and pollinator behavior on shared pollination services and to analyze the relative effects of shared pollination and habitat productivity in shaping community diversity. Features like capability to model large ecosystems with tens of thousands of plants and pollinators, graphical user interface, flexible parameter configuration, comprehensive data output and fast speed parallel computing make EcoSimInGrid a welcome addition to the ecological modeling world.

Model Predictive Control of Linear Systems over Networks with State and Input Quantizations

Although there have been a lot of works about the synthesis and analysis of networked control systems (NCSs) with data quantization, most of the results are developed for the case of considering the quantizer only existing in one of the transmission links (either from the sensor to the controller link or from the controller to the actuator link). This paper investigates the synthesis approaches of model predictive control (MPC) for NCS subject to data quantizations in both links. Firstly, a novel model to describe the state and input quantizations of the NCS is addressed by extending the sector bound approach. Further, from the new model, two synthesis approaches of MPC are developed: one parameterizes the infinite horizon control moves into a single state feedback law and the other into a free control move followed by the single state feedback law. Finally, the stability results that explicitly consider the satisfaction of input and state constraints are presented. A numerical example is given to illustrate the effectiveness of the proposed MPC.

Automata-based L-Grammar extraction from multiple images for virtual plants

L-system (Lindenmayer system) and its application have been one of the most famous and powerful tools for virtual plant modelling. But it is really hard to develop L-grammar manually for a given plant depending only on imagination or experience. For bridging this gap, a novel automatic L-grammar extraction approach is presented in this work. Initially, image processing as well as pattern recognition methods are employed to recover morphological and geometrical information for growth units and metamers. And then, these data are further analyzed using Markovian methods and acted as parameters for bidimensional hierarchical automata (BHA) to describe plant branching structure. Finally, the L-grammar has been extracted by means of the transformation from BHA to L-system. Experimental results show that our approach can extract L grammar for unfoliaged tree effectively.

Constrained off-line synthesis approach of model predictive control for networked control systems with network-induced delays.

This paper investigates the off-line synthesis approach of model predictive control (MPC) for a class of networked control systems (NCSs) with network-induced delays. A new augmented model which can be readily applied to time-varying control law, is proposed to describe the NCS where bounded deterministic network-induced delays may occur in both sensor to controller (S-A) and controller to actuator (C-A) links. Based on this augmented model, a sufficient condition of the closed-loop stability is derived by applying the Lyapunov method. The off-line synthesis approach of model predictive control is addressed using the stability results of the system, which explicitly considers the satisfaction of input and state constraints. Numerical example is given to illustrate the effectiveness of the proposed method.

Orange tree simulation under heterogeneous environment using agent-based model ORASIM

Abstract This paper presented an agent-based functional–structural model ORASIM for orange tree growth simulation. In ORASIM, detailed geometry, carbon/water acquisitions and expenses, as well as their dynamics are integrated into individual metamer/root agents. The nested-list of metamer/root agents forms a growing, three-dimensional orange tree structure. After model parameterization and validation using field data of orange tree growth, main features of tree functioning, i.e., morphological and physiological responses to environmental heterogeneity on different time scales have been investigated. It demonstrated that, using ORASIM, the phenotypic plasticity can be fully resulted from interactions between agents. Meanwhile, the output of ORASIM shows a good agreement for the characters of shape, branch pattern and other physiological features between the simulation and the real growth orange trees.

Virtual EP: a simulator of multi-agents-based virtual plant growth in response to environmental heterogeneity

This paper presents VirtualEP, a novel simulator for eggplant growth, integrating Agent-Based Modelling technology and existing knowledge of plant physiology. VirtualEP simulates the growth and development of eggplant as an evolution of a dynamic branching network whose nodes are represented by Autonomous Virtual Organs (AVOs). The AVO possesses inbuilt data structure, states and functional rules so that it can autonomously perform physiological procedures (eg photosynthesis, nutrient uptake, storage, mobilization and respiration, etc) to respond to environmental heterogeneity. A discrete implementation of pressure-flow paradigm is incorporated to simulate carbon, water and nitrogen transport and allocation among AVOs. Simulation results demonstrate that VirtualEP can effectively deal with global nutrients allocation, growth in response to variation of air temperature, solar radiation as well as water and nitrogen stress. Moreover, VirtualEP can also provide vivid 3D visualization of these features.

A Lightweight Intrusion Detection Method Based on Fuzzy Clustering Algorithm for Wireless Sensor Networks

For resource-constrained wireless sensor networks (WSNs), designing a lightweight intrusion detection technology has been a hot and difficult issue. In this paper, we proposed a lightweight intrusion detection method that was able to directly map the network status into sensor monitoring data received by base station, so that base station can sense the abnormal changes in the network. Our method is highlighted by the fusion of fuzzy c-means algorithm, one-class SVM, and sliding window procedure to effectively differentiate network attacks from abnormal data. Finally, the proposed method was tested on the wireless sensor network simulation software EXata and in real applications. The results showed that the intrusion detection method in this paper could effectively identify whether the abnormal data came from a network attack or just a noise. In addition, extra energy consumption can be avoided in all sensor monitoring nodes of the sensor network where our method has been deployed.