A Novel Obstacle Traversal Method for Multiple Robotic Fish Based on Cross-Modal Variational Autoencoders and Imitation Learning.

Precision control of multiple robotic fish visual navigation in complex underwater environments has long been a challenging issue in the field of underwater robotics. To address this problem, this paper proposes a multi-robot fish obstacle traversal technique based on the combination of cross-modal variational autoencoder (CM-VAE) and imitation learning. Firstly, the overall framework of the robotic fish control system is introduced, where the first-person view of the robotic fish is encoded into a low-dimensional latent space using CM-VAE, and then different latent features in the space are mapped to the velocity commands of the robotic fish through imitation learning. Finally, to validate the effectiveness of the proposed method, experiments are conducted on linear, S-shaped, and circular gate frame trajectories with both single and multiple robotic fish. Analysis reveals that the visual navigation method proposed in this paper can stably traverse various types of gate frame trajectories. Compared to end-to-end learning and purely unsupervised image reconstruction, the proposed control strategy demonstrates superior performance, offering a new solution for the intelligent navigation of robotic fish in complex environments.

Trajectory Tracking and Obstacle Avoidance of Robotic Fish Based on Nonlinear Model Predictive Control.

The attainment of accurate motion control for robotic fish inside intricate underwater environments continues to be a substantial obstacle within the realm of underwater robotics. This paper presents a proposed algorithm for trajectory tracking and obstacle avoidance planning in robotic fish, utilizing nonlinear model predictive control (NMPC). This methodology facilitates the implementation of optimization-based control in real-time, utilizing the present state and environmental data to effectively regulate the movements of the robotic fish with a high degree of agility. To begin with, a dynamic model of the robotic fish, incorporating accelerations, is formulated inside the framework of the world coordinate system. The last step involves providing a detailed explanation of the NMPC algorithm and developing obstacle avoidance and objective functions for the fish in water. This will enable the design of an NMPC controller that incorporates control restrictions. In order to assess the efficacy of the proposed approach, a comparative analysis is conducted between the NMPC algorithm and the pure pursuit (PP) algorithm in terms of trajectory tracking. This comparison serves to affirm the accuracy of the NMPC algorithm in effectively tracking trajectories. Moreover, a comparative analysis between the NMPC algorithm and the dynamic window approach (DWA) method in the context of obstacle avoidance planning highlights the superior resilience of the NMPC algorithm in this domain. The proposed strategy, which utilizes NMPC, demonstrates a viable alternative for achieving precise trajectory tracking and efficient obstacle avoidance planning in the context of robotic fish motion control within intricate surroundings. This method exhibits considerable potential for practical implementation and future application.

A Path-Planning Method Based on Improved Soft Actor-Critic Algorithm for Mobile Robots.

The path planning problem has gained more attention due to the gradual popularization of mobile robots. The utilization of reinforcement learning techniques facilitates the ability of mobile robots to successfully navigate through an environment containing obstacles and effectively plan their path. This is achieved by the robots' interaction with the environment, even in situations when the environment is unfamiliar. Consequently, we provide a refined deep reinforcement learning algorithm that builds upon the soft actor-critic (SAC) algorithm, incorporating the concept of maximum entropy for the purpose of path planning. The objective of this strategy is to mitigate the constraints inherent in conventional reinforcement learning, enhance the efficacy of the learning process, and accommodate intricate situations. In the context of reinforcement learning, two significant issues arise: inadequate incentives and inefficient sample use during the training phase. To address these challenges, the hindsight experience replay (HER) mechanism has been presented as a potential solution. The HER mechanism aims to enhance algorithm performance by effectively reusing past experiences. Through the utilization of simulation studies, it can be demonstrated that the enhanced algorithm exhibits superior performance in comparison with the pre-existing method.

Prioritization of risks posed by synthetic chemicals manufactured in China toward humans and the environment via persistence, bioaccumulation, mobility and toxicity properties.

Over a third of the global chemical production and sales occurred in China, which make effective assessment and management for chemicals produced by China's chemical industry essential not just for China but for the world. Here, we systematical assessed the persistence (P), bioaccumulation (B), mobility (M) and toxicity (T) potency properties for the chemicals listed in Inventory of Existing Chemical Substances of China (IECSC) via experimental data retrieved from large scale databases and in silico data generated with well-established models. Potential PBT, PMT and PB&MT substances were identified. High risk potentials were highlighted for groups of synthetic intermediates, raw materials, as well as a series of biocides. The potential PBT and PMT synthetic intermediates and/or raw materials unique to the IECSC were dominated with organofluorines, for example, the intermediates used as electronic light-emitting materials. Meanwhile, the biocides unique to the IECSC were mainly organochlorines. Some conventional classes of insecticides, such as organochlorines and pyrethroids, were classified as being of high concern. We further identified a group of PB&MT substances that were considered to be both "bioaccumulative" and "mobile". Their properties and common substructures for several major clusters were characterized. The present results prioritized groups of substances with high potentials to cause adverse effects to the environment and humans, many of which have not yet been fully recognized.

LQR Control and Optimization for Trajectory Tracking of Biomimetic Robotic Fish Based on Unreal Engine.

A realistic and visible dynamic simulation platform can significantly facilitate research on underwater robots. This paper uses the Unreal Engine to generate a scene that resembles real ocean environments, before building a visual dynamic simulation platform in conjunction with the Air-Sim system. On this basis, the trajectory tracking of a biomimetic robotic fish is simulated and assessed. More specifically, we propose a particle swarm optimization algorithm-based control strategy to optimize the discrete linear quadratic regulator controller for the trajectory tracking problem, as well as tracking and controlling discrete trajectories with misaligned time series through introducing a dynamic time warping algorithm. Simulation analyses of the biomimetic robotic fish following a straight line, a circular curve without mutation, and a four-leaf clover curve with mutation are carried out. The obtained results verify the feasibility and effectiveness of the proposed control strategy.

Reduced Transcriptome Analysis of Zebrafish Embryos Prioritizes Environmental Compounds with Adverse Cardiovascular Activities.

Cardiovascular diseases are the leading cause of premature death in humans and remain a global public health challenge. While age, sex, family history, and false nutrition make a contribution, our understanding of compounds acting as cardiovascular disruptors is far from complete. Here, we aim to identify cardiovascular disruptors via a reduced transcriptome atlas (RTA) approach, which integrates large-scale transcriptome data sets of zebrafish and compiles a specific gene panel related to cardiovascular diseases. Among 767 gene expression profiles covering 81 environmental compounds, 11 priority compounds are identified with the greatest effects on the cardiovascular system at the transcriptional level. Among them, metals (AgNO3, Ag nanoparticles, arsenic) and pesticides/biocides (linuron, methylparaben, triclosan, and trimethylchlorotin) are identified with the most significant effects. Distinct transcriptional signatures are further identified by the percentage values, indicating that different physiological endpoints exist among prioritized compounds. In addition, cardiovascular dysregulations are experimentally confirmed for the prioritized compounds via alterations of cardiovascular physiology and lipid profiles of zebrafish. The accuracy rate of experimental verification reaches up to 62.9%. The web-based RTA analysis tool, Cardionet, for rapid cardiovascular disruptor discovery was further provided at http://www.envh.sjtu.edu.cn/cardionet.jsp. Our integrative approach yields an efficient platform to discover novel cardiovascular-disrupting chemicals in the environment.

A dual action small molecule enhances azoles and overcomes resistance through co-targeting Pdr5 and Vma1.

Fungal infection threatens human health worldwide due to the limited arsenal of antifungals and the rapid emergence of resistance. Epidermal growth factor receptor (EGFR) is demonstrated to mediate epithelial cell endocytosis of the leading human fungal pathogen, Candida albicans. However, whether EGFR inhibitors act on fungal cells remains unknown. Here, we discovered that the specific EGFR inhibitor osimertinib mesylate (OSI) potentiates azole efficacy against diverse fungal pathogens and overcomes azole resistance. Mechanistic investigation revealed a conserved activity of OSI by promoting intracellular fluconazole accumulation via inhibiting Pdr5 and disrupting V-ATPase function via targeting Vma1 at serine 274, eventually leading to inactivation of the global regulator TOR. Evaluation of the in vivo efficacy and toxicity of OSI demonstrated its potential clinical application in impeding fluconazole resistance. Thus, the identification of OSI as a dual action antifungal with co-targeting activity proposes a potentially effective therapeutic strategy to treat life-threatening fungal infection and overcome antifungal resistance.

Environmental chemicals affect circadian rhythms: An underexplored effect influencing health and fitness in animals and humans.

Circadian rhythms control the life of virtually all organisms. They regulate numerous aspects ranging from cellular processes to reproduction and behavior. Besides the light-dark cycle, there are additional environmental factors that regulate the circadian rhythms in animals as well as humans. Here, we outline the circadian rhythm system and considers zebrafish (Danio rerio) as a representative vertebrate organism. We characterize multiple physiological processes, which are affected by circadian rhythm disrupting compounds (circadian disrupters). We focus on and summarize 40 natural and anthropogenic environmental circadian disrupters in fish. They can be divided into six major categories: steroid hormones, metals, pesticides and biocides, polychlorinated biphenyls, neuroactive drugs and other compounds such as cyanobacterial toxins and bisphenol A. Steroid hormones as well as metals are most studied. Especially for progestins and glucocorticoids, circadian dysregulation was demonstrated in zebrafish on the molecular and physiological level, which comprise mainly behavioral alterations. Our review summarizes the current state of knowledge on circadian disrupters, highlights their risks to fish and identifies knowledge gaps in animals and humans. While most studies focus on transcriptional and behavioral alterations, additional effects and consequences are underexplored. Forthcoming studies should explore, which additional environmental circadian disrupters exist. They should clarify the underlying molecular mechanisms and aim to better understand the consequences for physiological processes.

Rare Earth Elements Lanthanum and Praseodymium Adversely Affect Neural and Cardiovascular Development in Zebrafish (Danio rerio).

Increasing rare earth element (REE) mining and refining activities have led to a considerable release of these substances into aquatic environment, yet the knowledge of their impacts on aquatic organisms is still limited. Here, we explored the developmental effects of 16 REEs (concentration ranged from 0.46 to 1000 mg/L) to zebrafish embryos and highlighted the adverse effects of lanthanum (La) and praseodymium (Pr). Among the multiple developmental parameters measured, the significant effects on swimming behavior and cardiac physiology were the most prominent. Transcriptomic analysis of La and Pr at concentrations of 1.1 to 10 mg/L revealed their rather uniform effects at molecular levels. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis revealed that among others, notch, glutamate, and serotonin signaling, as well as cardiac hypertrophy and cardiac muscle contraction, were significantly affected. These changes of neural signaling were consistent with behavior effects observed and supported by neurotransmitter changes and thus provide a reasonable molecular mechanistic explanation. Furthermore, increased DNA damage and apoptotic activity at high concentrations were observed, especially in the heart. They may contribute to explain the observed adverse morphological and physiological outcomes, such as pericardial edema. The effect concentrations observed in the present study were comparable to the concentrations of REE residues at highly contaminated sites (several mg/L), indicating ecotoxicological effects at environmentally relevant concentrations. Overall, the present data help to clarify the potential developmental toxicity of REEs that was not yet fully recognized and thus contribute to their environmental risk assessment.

Using a high-throughput zebrafish embryo screening approach to support environmental hazard ranking for cardiovascular agents.

Cardiovascular agents are among the most frequently prescribed pharmaceuticals worldwide. They are widely detected in aquatic ecosystems, while their ecotoxicological implications are rarely explored. Here, by the use of a new developed high-throughput zebrafish embryo screening approach, we systematically assessed the cardiovascular disruptive effects of 32 commonly used cardiovascular agents at environmental relevant concentrations and above (0.04, 0.2 and 1 µM). Multiple endpoints, including cardiac output, heart rate and blood flow, were quantified via customized video analysis approaches. Among the 32 agents, simvastatin and lovastatin exhibited the strongest toxicities to fish embryos, and the lethal doses were observed at 0.2 µM and 1 µM. Beta-blockers such as atenolol and metoprolol significantly decreased heart rates by up to 15% and 12% and increased blood flows by up to 14% and 14%, respectively, at concentrations as low as 0.04 µM. Several hypertension/hyperlipidemia medications such as pravastatin and enalapril led to significant inhibition of heart rates (up to 14% and 16% decreases, respectively) as well as slightly decreases of the cardiac outputs and blood flows. In addition, a tentative risk assessment clearly demonstrated that some compounds such as atenolol, metoprolol and bezafibrate pose considerable risks to aquatic organisms at environmental or slightly higher than surface water concentrations. Our results provided novel insights into understanding of the potential risks of cardiovascular agents and contributed to their environmental hazard ranking.

Angular distance constraints calibration for outdoor zoom camera.

Based on 2-D protractor property of camera, we proposed a flexible calibration method for zoom camera that used outdoors. It only requires the camera to observe control points once for given zooming settings, when there are several control points at infinity and known the angular distances. Under constraints of image points, the angular distance between their re-projecting vectors and the image of absolute conic (IAC), nonlinear optimization is used to solve parameters of IAC. Then IAC can be uniquely decomposed by the Cholesky factorization, and consequently the intrinsic parameters can be obtained. Towards the factors that affect the accuracy of the calibration, theoretical analysis and computer simulation are carried out respectively consequence in qualitative analysis and quantitative result. On the issues of inaccuracy of principal point, the zooming center is selected to improve the accuracy of calibration. Real data demonstrated the effectiveness of the techniques.

Global calibration of unleveled theodolite using angular distance constraints.

The theodolite is an important optical measurement instrument in application. Its global calibration, including position and orientation, is a prerequisite for measurement. Most global calibration methods require the theodolite to be leveled precisely, which is time-consuming and susceptible to error. We propose a global calibration method without leveling: it solves position results using the angular distance of control points by nonlinear optimization and then computes orientation parameters (rotation matrix) linearly based on position results. Furthermore, global calibration of multiple theodolites is also introduced. In addition, we introduced a method that can compute the dip direction and tilt angle by decomposing the rotation matrix. We evaluate the calibration algorithms on both computer simulation and real data experiments, demonstrating the effectiveness of the techniques.