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The growth, development, and survival of fish, especially in the early stages of development, is influenced by a complex of environmental factors, among which temperature is one of the most important. Although the physiological effects of environmental stress in fish have been extensively studied, the molecular mechanisms are poorly understood. However, recent advances in transcriptomic techniques have facilitated the study of the molecular mechanisms of environmental stress responses in aquatic species. Here, we aimed to elucidate the effects of breeding temperatures (21, 24, 27, and 30 °C) on the growth and nutrient metabolism in the early developmental stage of Platax teira, using transcriptomic techniques. Transcriptomic analysis identified 5492, 6937, and 4246 differentially expressed genes (DEGs) in the 21 vs. 24 °C, 27 vs. 24 °C, and 30 vs. 24 °C comparisons, respectively, most of which were involved in cell processes, single organism, metabolism, catalytic activity, and cell part, based on gene ontology (GO) functional annotations. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that the DEGs were mainly enriched in pathways related to metabolism of matter and energy, protein digestion and absorption, and glucose and lipid metabolism. Additionally, the expression of genes related to energy, lipid, and glucose metabolism in the fish liver was upregulated under a low-temperature condition (21 °C), although increasing the temperature within the acceptable threshold improved nutrient metabolism and growth in the fish. Meanwhile, nutrient metabolism and growth were suppressed by an extremely high temperature (30 °C) owing to oxidative stress. Overall, it was shown that nutrient metabolism pathways were involved in thermal stress responses in P. teira, and the optimal breeding temperature range was 24–27 °C. Through transcriptomics, the regulatory mechanism of larval development in P. teira under different growth temperatures was elucidated, with the goal of establishing a theoretical basis for industrial breeding.

The golden pompano (Trachinotus ovatus) is one of the most economically valuable marine fishes in South China. Streptococcus agalactiae, an infectious Gram-positive bacterium that is highly destructive for golden pompano culture, has recently caused massive losses to the golden pompano industry. This study aimed to investigate the dynamic immune response of golden pompano to S. agalactiae infection, using RNA-seq analysis at two different time points after infection. Abundances of differentially expressed genes (DEGs) gradually increased in the liver and spleen 48–120 h post-infection, whereas those in the head kidney were lower at 120 h than at 48 h. Pathway enrichment analysis of DEGs revealed that genes related to the complement system were continuously transcribed between 48 and 120 h. Metabolic and immune-regulation-related pathways were highly enriched in the liver 48 h after infection. Transcriptome analysis was verified using quantitative PCR for eight genes with similar expression trends. This study revealed the inflammatory response of golden pompano after S. agalactiae infection, including inflammation-related chemokines and signaling pathways. Our findings provide a theoretical basis for studying S. agalactiae resistance in golden pompano and provide a reliable resource for the genetic breeding of fish.

This study aimed to investigate the intoxication mechanism of golden pompano (Trachinotus ovatus) exposed to high ammonia levels and the effects on the immune and antioxidant mechanisms of gills. Juvenile golden pompano was exposed to ammonia (total ammonia: 26.9 mg/L) to induce 96 h of ammonia stress, and a 96 h recovery experiment was performed after poisoning. Then, we evaluated hematological parameters, the histological structure and the expression of related genes. In this experiment, continuous exposure to high levels of ammonia led to a significant increase in plasma alkaline phosphatase (ALP), acid phosphatase (ACP) and lactate dehydrogenase (LDH) levels (P

In VANET,sending beacon messages reliably played a vital role in cooperative awareness and collision avoidance,and changes of vehicle density in real traffic environment had a direct impact on channel load and will cause the channel congestion to a certain degree,thus causing inefficiency of cooperative security applications.Aiming at this issue,a adaptive method based on channel congestion detection to control message sending rate was designed.First of all,an interference model that can accurately reflect the current state of the channel was established.Secondly,according to the Shannon information theory and network utility maximization theory,the optimal transmission speed of the current node in the physical layer was calculated.Finally,according to the mismatch of transmission rate and transmission queue length,the next moment's cost function of the channel congestion degree by monitoring the message queue was established to avoid channel congestion and queue overflow.The simulation experiment shows that the scheme effectively reduces communication transmission delay and avoids channel congestion,ensuring that the data be sent reliably.

An application oriented cross-layer multi-channel(AOCM)MAC protocol was proposed for VANET.The AOCM protocol could derive the optimal control channel(CCH)interval to ensure the timely and reliable transmission of safety messages on the CCH,based on real-time statistics and prediction for different types of sages coming from upper application layer.In the service channels(SCH),the proposed protocol relies on a reservation mechanism for con-tention-free transmission,so as to improve the throughput of SCH.The proposed protocol was suitable for both saturation and unsaturation conditions,and has low computational complexity and high scalability.Extensive simulation results demonstrate the superiority of the AOCM protocol compared with other existing protocols.

Cooperative vehicle safety systems rely on periodic broadcast of each vehicle's state information to track neighbors' positions and therefore to predict potential collisions. The most pressing challenge in such systems is to maintain real-time tracking accuracy while avoiding channel congestion. Previous work on congestion control in CVSSs primarily focuses on the effort of individual vehicle to adjust the contention window based on its local channel status, which may cause the channel resource to be monopolized by a small number of vehicles. As a result, unfairness issue arises. To overcome this problem, in this paper, we present a game-based channel access approach for contention window adjustment. In this approach, a utility function that defines the relationship between vehicle tracking accuracy and throughput is firstly proposed. Then, based on the utility function, a non-cooperative dynamic game model with a penalty function is established. In order to implement the game model on each vehicle, a distributed channel access algorithm is proposed. This algorithm adjusts the contention window for each vehicle, and hence, guides each vehicle to the Nash equilibrium. Simulation results confirm that the gamebased channel access approach can guarantee a fair share of channel resources while achieving the optimal tracking performance for vehicles. [ABSTRACT FROM AUTHOR]