Metagenomic Analysis of Bacteria, Fungi, Bacteriophages, and Helminths in the Gut of Giant Pandas

To obtain full details of gut microbiota, including bacteria, fungi, bacteriophages, and helminths, in giant pandas (GPs), we created a comprehensive microbial genome database and used metagenomic sequences to align against the database. We delineated a detailed and different gut microbiota structures of GPs. A total of 680 species of bacteria, 198 fungi, 185 bacteriophages, and 45 helminths were found. Compared with 16S rRNA sequencing, the dominant bacterium phyla not only included Proteobacteria, Firmicutes, Bacteroidetes, and Actinobacteria but also Cyanobacteria and other eight phyla. Aside from Ascomycota, Basidiomycota, and Glomeromycota, Mucoromycota, and Microsporidia were the dominant fungi phyla. The bacteriophages were predominantly dsDNA Myoviridae, Siphoviridae, Podoviridae, ssDNA Inoviridae, and Microviridae. For helminths, phylum Nematoda was the dominant. In addition to previously described parasites, another 44 species of helminths were found in GPs. Also, differences in abundance of microbiota were found between the captive, semiwild, and wild GPs. A total of 1,739 genes encoding cellulase, β-glucosidase, and cellulose β-1,4-cellobiosidase were responsible for the metabolism of cellulose, and 128,707 putative glycoside hydrolase genes were found in bacteria/fungi. Taken together, the results indicated not only bacteria but also fungi, bacteriophages, and helminths were diverse in gut of giant pandas, which provided basis for the further identification of role of gut microbiota. Besides, metagenomics revealed that the bacteria/fungi in gut of GPs harbor the ability of cellulose and hemicellulose degradation

Energy -efficient mobile robots

Mobile robots can be used in many applications, such as carpet cleaning, search and rescue, exploration, and entertainment. Robots usually carry limited energy and thus energy conservation is important. To our best knowledge, this is the first research project to study energy-efficient mobile robots in a systematic way. In this thesis, I first develop power models for two different types of robots, and then focus on five related problems: motion planning, exploration, fleet size, deployment, and sensor network maintenance. The first two problems consider single-robot systems, and the latter three focus on multi-robot systems. Corresponding to the five individual problems, my contributions are the following. (1) My research compares energy consumptions of three different routes and presents my method of energy-efficient motion planning. (2) This thesis investigates an orientation-based exploration algorithm that can reduce repeated coverage and save traveling distance and energy consumption. (3) I study the fleet size problem to determine the number of robots for pickup and delivery tasks under energy and time constraints. (4) This study develops three robot deployment strategies to minimize the deployment overhead and use fewer robots to cover an area. (5) This thesis designs three different algorithms for coordinating robots with sensor networks to automate sensor network maintenance and consider motion and communication overhead

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Energy-efficient mobile robot exploration

Abstract — Mobile robots can be used in many applications, including exploration in an unknown area. Robots usually carry limited energy so energy conservation is vital. This paper presents an approach for energy-efficient robot exploration. Our approach determines the next target for the robot to visit based upon orientation information. The robot plans the path between the current position to the next target in an energy-efficient way. Our method reduces repeated coverage, a common problem for most existing utility-based target selecting methods. We conduct simulations for both random and structured environments, and compare our method with a utility-based method that chooses the middle cell from the widest opening. Results show that our method can reduce energy consumption by 42 % and traveling distance by 41%. I

Energy Efficiency in Robotics Software: A Systematic Literature Review

Nowadays, robots are widely used in many areas of our lifes, such as autonomous storage, self-driving vehicles, drones, industrial automation, etc. Energy is a critical factor for robotic systems, especially for mobile robots where energy is a finite resource (e.g., surveillance autonomous rovers). Since software is becoming the central focus of modern robotic systems, it is important to understand how it influences the energy consumption of the entire system. However, there is no systematic study of the state of the art in energy efficiency of robotics software that could guide research or practitioners in finding solutions and tools to develop robotic systems with energy efficiency in mind the goal of this paper is to present a review of existing research on energy efficiency in robotics software. Specifically, we investigate on (i) the used metrics for energy efficiency, (ii) the application domains within the robotics area covered by research on energy efficiency, (iii) the identified major energy consumers within a robotic system, (iv) how existing approaches are evaluated, (v) the used energy models, (vi) the techniques supporting the development of energy-efficient robotics software, and (vii) which quality attributes tend to be traded off when dealing with energy efficiency in robotics. We also provide a replication package to assess, extend, and/or replicate the study the results of this work can guide researchers and practitioners in robotics and software engineering in better reasoning and contributing to energy-efficient robotics software

Predictive computational modeling of the mucosal immune responses during Helicobacter pylori infection.

T helper (Th) cells play a major role in the immune response and pathology at the gastric mucosa during Helicobacter pylori infection. There is a limited mechanistic understanding regarding the contributions of CD4+ T cell subsets to gastritis development during H. pylori colonization. We used two computational approaches: ordinary differential equation (ODE)-based and agent-based modeling (ABM) to study the mechanisms underlying cellular immune responses to H. pylori and how CD4+ T cell subsets influenced initiation, progression and outcome of disease. To calibrate the model, in vivo experimentation was performed by infecting C57BL/6 mice intragastrically with H. pylori and assaying immune cell subsets in the stomach and gastric lymph nodes (GLN) on days 0, 7, 14, 30 and 60 post-infection. Our computational model reproduced the dynamics of effector and regulatory pathways in the gastric lamina propria (LP) in silico. Simulation results show the induction of a Th17 response and a dominant Th1 response, together with a regulatory response characterized by high levels of mucosal Treg) cells. We also investigated the potential role of peroxisome proliferator-activated receptor γ (PPARγ) activation on the modulation of host responses to H. pylori by using loss-of-function approaches. Specifically, in silico results showed a predominance of Th1 and Th17 cells in the stomach of the cell-specific PPARγ knockout system when compared to the wild-type simulation. Spatio-temporal, object-oriented ABM approaches suggested similar dynamics in induction of host responses showing analogous T cell distributions to ODE modeling and facilitated tracking lesion formation. In addition, sensitivity analysis predicted a crucial contribution of Th1 and Th17 effector responses as mediators of histopathological changes in the gastric mucosa during chronic stages of infection, which were experimentally validated in mice. These integrated immunoinformatics approaches characterized the induction of mucosal effector and regulatory pathways controlled by PPARγ during H. pylori infection affecting disease outcomes