Upright and Crawling Locomotion and Its Transition for a Wheel-Legged Robot.

To face the challenge of adapting to complex terrains and environments, we develop a novel wheel-legged robot that can switch motion modes to adapt to different environments. The robot can perform efficient and stable upright balanced locomotion on flat roads and flexible crawling in low and narrow passages. For passing through low and narrow passages, we propose a crawling motion control strategy and methods for transitioning between locomotion modes of wheel-legged robots. In practical applications, the smooth transition between the two motion modes is challenging. By optimizing the gravity work of the body, the optimal trajectory of the center of mass (CoM) for the transition from standing to crawling is obtained. By constructing and solving an optimization problem regarding the posture and motion trajectories of the underactuated model, the robot achieves a smooth transition from crawling to standing. In experiments, the wheel-legged robot successfully transitioned between the crawling mode and the upright balanced moving mode and flexibly passed a low and narrow passage. Consequently, the effectiveness of the control strategies and algorithms proposed in this paper are verified by experiments.

Genome-scale metabolic network model of Eriocheir sinensis icrab4665 and nutritional requirement analysis.

BACKGROUND: Genome-scale metabolic network models (GEMs) provide an efficient platform for the comprehensive analysis the physical and biochemical functions of organisms due to their systematic perspective on the study of metabolic processes. Eriocheir sinensis is an important economic species cultivated on a large scale because it is delicious and nutritious and has a high economic value. Feed improvement is one of the important methods to improve the yield of E. sinensis and control water pollution caused by the inadequate absorption of feed. RESULTS: In this study, a GEM of E. sinensis, icrab4665, was reconstructed based on the transcriptome sequencing, combined with KEGG database, literature and experimental data. The icrab4665 comprised 4665 unigenes, 2060 reactions and 1891 metabolites, which were distributed in 12 metabolic subsystems and 113 metabolic pathways. The model was used to predict the optimal nutrient requirements of E. sinensis in feed, and suggestions for feed improvement were put forward based on the simulation results. The simulation results showed that arginine, methionine, isoleucine and phenylalanine had more active metabolism in E. sinensis. It was suggested that the amount of these essential amino acids should be proportionally higher than that of other amino acids in the feed to ensure the amino acid metabolism of E. sinensis. On the basis of the simulation results, we further suggested increasing the amount of linoleic acid, EPA and DHA in the feed to ensure the intake of essential fatty acids for the growth of E. sinensis and promote the accumulation of cell substances. In addition, the amounts of zinc and selenium in the feed were also suggested to be properly increased to ensure the basic metabolism and growth demand of E. sinensis. CONCLUSION: The largest GEM of E. sinensis was reconstructed and suggestions were provide for the improvement of feed contents based on the model simulation. This study promoted the exploration of feed optimization for aquatic crustaceans from in vivo and in silico. The results provided guidance for improving the feed proportion for E. sinensis, which is of great significance to improve its yield and economic value.

The Protein-Protein Interaction Network of Litopenaeus vannamei Haemocytes.

Protein-protein interaction networks (PINs) have been constructed in various organisms and utilized to conduct evolutionary analyses and functional predictions. Litopenaeus vannamei is a high-valued commercial aquaculture species with an uncharacterized interactome. With the development of RNA-seq techniques and systems biology, it is possible to obtain genome-wide transcriptional information for L. vannamei and construct a systematic network based on these data. In this work, based on the RNA-seq of haemocytes we constructed the first L. vannamei PIN including 4,858 proteins and 104,187 interactions. The PIN constructed here is the first large-scale PIN for shrimp. The confidence scores of interactions in the PIN were evaluated on the basis of sequence homology and genetic relationships. The immune-specific sub-network was extracted from global PIN, and more than a third of proteins were found in signaling pathways in the sub-network, which indicates an inseparable relationship between signaling processes and immune mechanisms. Six selected signaling pathways were constructed at different age groups based on evolutionary analyses. Furthermore, we showed that the functions of the pathways' proteins were associated with their evolutionary history based on the evolutionary analyses combining with protein functional analyses. In addition, the functions of 1,955 unclassified proteins which were associated with 3,191 unigenes were assigned using the PIN, which account for approximately 70.3 and 44.9% of the previously unclassified proteins and unigenes in the network, respectively. The annotation of unclassified proteins and unigenes based on the PIN provides new candidates for further functional studies. The immune-specific sub-network and the pathways extracted from the PIN provide a novel information source for studying of immune mechanisms and disease resistances in shrimp.

The Role of Context in Processing Chinese Three-Character Verb-Object Metaphors: An Event-Related Potential Study.

Whether and how context plays a role in metaphor processing remains a controversial issue. One major theory on metaphor comprehension, the graded salience hypothesis (GSH) model, emphasizes salience as the key factor determining the precedence of semantic access. Using event-related potential technique, the present study examined Chinese metaphors to investigate whether the salient meaning is always processed first regardless of context. The experiment employed a Prime-Target-Probe paradigm. Three-character Chinese verb-object metaphors were used as the Target proceeded by one of the three contexts (the Prime): (1) metaphorical context priming the Target's metaphorical meaning, (2) literal context priming the Target's literal meaning, and (3) irrelevant context as the control condition. The Target was then followed by the Probe, which was always related to the Target (except in the filler condition). Forty participants were asked to judge whether the Target and the following Probe were semantically related. The N400 elicited by the Target showed no contextual effect. The N400 amplitude elicited by the Probe was smaller in the metaphorical priming condition compared with the literal priming condition, while the N400 in the irrelevant control condition was between the other two conditions, demonstrating a clear context effect. In addition, an unexpected P240 component also showed the similar graded pattern. Our results mostly support the GSH model, indicating that the salient meaning invariantly gets activated first before the activation of the nonsalient meaning at the lexical access stage. However, context does play a role in a parallel way either facilitating or suppressing this interpretation in the latter meaning integration stage.

Analyzing of Molecular Networks for Human Diseases and Drug Discovery.

Molecular networks represent the interactions and relations of genes/proteins, and also encode molecular mechanisms of biological processes, development and diseases. Among the molecular networks, protein-protein Interaction Networks (PINs) have become effective platforms for uncovering the molecular mechanisms of diseases and drug discovery. PINs have been constructed for various organisms and utilized to solve many biological problems. In human, most proteins present their complex functions by interactions with other proteins, and the sum of these interactions represents the human protein interactome. Especially in the research on human disease and drugs, as an emerging tool, the PIN provides a platform to systematically explore the molecular complexities of specific diseases and the references for drug design. In this review, we summarized the commonly used approaches to aid disease research and drug discovery with PINs, including the network topological analysis, identification of novel pathways, drug targets and sub-network biomarkers for diseases. With the development of bioinformatic techniques and biological networks, PINs will play an increasingly important role in human disease research and drug discovery.

The Genome-Scale Integrated Networks in Microorganisms.

The genome-scale cellular network has become a necessary tool in the systematic analysis of microbes. In a cell, there are several layers (i.e., types) of the molecular networks, for example, genome-scale metabolic network (GMN), transcriptional regulatory network (TRN), and signal transduction network (STN). It has been realized that the limitation and inaccuracy of the prediction exist just using only a single-layer network. Therefore, the integrated network constructed based on the networks of the three types attracts more interests. The function of a biological process in living cells is usually performed by the interaction of biological components. Therefore, it is necessary to integrate and analyze all the related components at the systems level for the comprehensively and correctly realizing the physiological function in living organisms. In this review, we discussed three representative genome-scale cellular networks: GMN, TRN, and STN, representing different levels (i.e., metabolism, gene regulation, and cellular signaling) of a cell's activities. Furthermore, we discussed the integration of the networks of the three types. With more understanding on the complexity of microbial cells, the development of integrated network has become an inevitable trend in analyzing genome-scale cellular networks of microorganisms.