Hydrocarbon Desaturation in Cyanobacterial Thylakoid Membranes Is Linked With Acclimation to Suboptimal Growth Temperatures

The ability to produce medium chain length aliphatic hydrocarbons is strictly conserved in all photosynthetic cyanobacteria, but the molecular function and biological significance of these compounds still remain poorly understood. This study gives a detailed view to the changes in intracellular hydrocarbon chain saturation in response to different growth temperatures and osmotic stress, and the associated physiological effects in the model cyanobacterium Synechocystis sp. PCC 6803. We show that the ratio between the representative hydrocarbons, saturated heptadecane and desaturated heptadecene, is reduced upon transition from 38°C toward 15°C, while the total content is not much altered. In parallel, it appears that in the hydrocarbon-deficient ∆ado (aldehyde deformylating oxygenase) mutant, phenotypic and metabolic changes become more evident under suboptimal temperatures. These include hindered growth, accumulation of polyhydroxybutyrate, altered pigment profile, restricted phycobilisome movement, and ultimately reduced CO2 uptake and oxygen evolution in the ∆ado strain as compared to Synechocystis wild type. The hydrocarbons are present in relatively low amounts and expected to interact with other nonpolar cellular components, including the hydrophobic part of the membrane lipids. We hypothesize that the function of the aliphatic chains is specifically associated with local fluidity effects of the thylakoid membrane, which may be required for the optimal movement of the integral components of the photosynthetic machinery. The findings support earlier studies and expand our understanding of the biological role of aliphatic hydrocarbons in acclimation to low temperature in cyanobacteria and link the proposed role in the thylakoid membrane to changes in photosynthetic performance, central carbon metabolism, and cell growth, which need to be effectively fine-tuned under alternating conditions in nature.

Train Bi-Control Problem on Riemannian Setting

This article refers to the optimization of the energy consumption of guided traction rails, such as those used for electric trains (including subway electric units), railcars, locomotives, and trams, in a Riemannian framework. The proposed optimization strategy takes into account the compliance time drive and aims at improving the transport system for given operation conditions. Our study has five targets: (1) improving the optimal control techniques; (2) establishing a strategy for the operating conditions of the vehicle; (3) formulating and solving additional problems of optimal movement; (4) improving automatic systems for vehicle traction to optimize energy consumption in a Riemannian context; (5) formulating and solving a problem of maximizing the profit of the train. Some significant figures and formulas obtained by Maple procedures clarify the problems.

Optimal movement behaviors: correlates and associations with anxiety symptoms among Chinese university students

Background The Canadian 24-Hour Movement Guidelines for Adults was released in 2020. There is a dearth of evidence on the association between adherence to the 24 h movement guidelines and health indicators. This study aims to (a) explore the associations between potential correlates and meeting the 24 h movement guidelines using a sample of Chinese university students; and (b) examine if meeting 24 h movement guidelines is associated with the severity of anxiety symptoms. Methods Cross-sectional findings are based on 1846 Chinese university students (mean age = 20.7 years, 64.0% female). Movement behaviors (physical activity, sedentary behavior, and sleep duration), possible correlates, and anxiety symptoms were measured through self-reported online questionnaires. Logistic regression models were performed to examine the associations. Results We found that male students and those who had a mother with a master’s degree or above, more close friends and higher perceived family affluence were more likely to meet the overall 24 h guidelines. Meeting all 24 h movement guidelines presented the lower odds for severe anxiety symptoms than those meeting fewer recommendations in the 24 h movement guidelines. Conclusions As one of the first to examine the correlates of adherence to the 24 h movement guidelines and the relationship between anxiety symptoms and meeting the guidelines among Chinese university students, our findings contribute to the growing body of evidence linking movement behaviors, psychosocial correlates, and heath indicators. Schools and health providers can encourage movement behaviors that follow the guidelines on campus.

Research on the Multi-Robot Cooperative Pursuit Strategy Based on the Zero-Sum Game and Surrounding Points Adjustment

Making full use of the cooperation of multi-robots can improve the success rate of apursuit task. Therefore, this paper proposes a multi-robot cooperative pursuit strategy based on the zero-sum game and surrounding points adjustment. First, a mathematical description of the multi-robot pursuit problem is constructed, and the zero-sum game model is established considering the cooperation of the pursuit robots and the confrontation between the pursuit robots and the escape robot. By solving the game model, the optimal movement strategies of the pursuit robots and the escape robot are obtained. Then, the position adjustment method of the pursuit robots is studied based on the Hungarian algorithm, and the pursuit robots are controlled to surround the escape robot. Based on this, a multi-robot cooperative pursuit strategy is proposed that divides the pursuit process into two stages: pursuit robot position adjustment and game pursuit. Finally, the correctness and effectiveness of the multi-robot cooperative pursuit strategy are verified with simulation experiments. The multi-robot cooperative pursuit strategy allows the pursuit robots to capture the escape robot successfully without conflicts among the pursuit robots. It can be seen from the documented simulation experiments that the success rate of the pursuit task using the strategy proposed in this paper is 100%.

Motor learning by selection in visual working memory

Motor adaptation maintains movement accuracy over the lifetime. Saccadic eye movements have been used successfully to study the mechanisms and neural basis of adaptation. Using behaviorally irrelevant targets, it has been shown that saccade adaptation is driven by errors only in a brief temporal interval after movement completion. However, under natural conditions, eye movements are used to extract information from behaviorally relevant objects and to guide actions manipulating these objects. In this case, the action outcome often becomes apparent only long after movement completion, outside the supposed temporal window of error evaluation. Here, we show that saccade adaptation can be driven by error signals long after the movement when using behaviorally relevant targets. Adaptation occurred when a task-relevant target appeared two seconds after the saccade, or when a retro-cue indicated which of two targets, stored in visual working memory, was task-relevant. Our results emphasize the important role of visual working memory for optimal movement control.

Automated planning of the optimal movement trajectories of mobile mechatronic devices

The compatible work is considered and the obtained results of the known algorithms generating unobstructed trajectories of different length and smoothness are investigated. Their operation is performed within the framework of the developed software product LSTr. The use of the analyzed set of these algorithms on the set of considered sections of the generated trajectories according to the obtained results allows a differentiated approach to the use of different algorithms on different sections of trajectories, determined by the accepted criteria of length and / or smoothness. The scientific novelty of the work and its practical significance in this area of research are determined.

An Approach Towards Intelligent Traffic Environment Using Machine Learning Algorithms

To make an optimal movement of vehicles and to reduce the accident rate, the government has installed traffic lights at almost every intersection. Traffic lights are intended to decrease congestion. However, the dynamic nature of traffic movement causes congestion always. This congestion leads to increased waiting times for every vehicle. In this chapter, two machine learning-based approaches used to improve in the congested traffic environment. The first part of the work is Deep-Learning based traffic signaling, which identifies the congestion on all sides of the intersection with the help of image processing techniques. By analyzing the congestion, the algorithm proposes dynamic green-light times rather than the traditional fixed lighting system. In the second part, a Q-learning-based approach has been suggested in which an agent decides the state of the traffic light based on a cumulative reward. Further, these algorithms have been tested on different traffic simulated environments using SUMO, and detailed analysis has been carried out.