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Obesity is a major risk factor for morbidity and mortality because it has a close relationship to metabolic illnesses, such as diabetes, cardiovascular diseases, and some types of cancer. With no drugs available, the mainstay of obesity management remains lifestyle changes with exercise and dietary modifications. In light of the tremendous disease burden and unmet therapeutics, fresh perspectives on pathophysiology and drug discovery are needed. The development of epigenetics provides a compelling justification for how environmental, lifestyle, and other risk factors contribute to the pathogenesis of obesity. Furthermore, epigenetic dysregulations can be restored, and it has been reported that certain natural products obtained from plants, such as tea polyphenols, ellagic acid, urolithins, curcumin, genistein, isothiocyanates, and citrus isoflavonoids, were shown to inhibit weight gain. These substances have great antioxidant potential and are of great interest because they can also modify epigenetic mechanisms. Therefore, understanding epigenetic modifications to target the primary cause of obesity and the epigenetic mechanisms of anti‐obesity effects with certain phytochemicals can prove rational strategies to prevent the disease and develop novel therapeutic interventions. Thus, the current review aimed to summarize the epigenetic mechanisms and advances in therapies for obesity based on natural products to provide evidence for the development of several potential anti‐obesity drug targets. XXXX

Unmanned aerial vehicle (UAV) swarms are widely applied in various fields, including military and civilian domains. However, due to the scarcity of spectrum resources, UAV swarm clustering technology has emerged as an effective method for achieving spectrum sharing among UAV swarms. This paper introduces a distributed heterogeneous multi-agent deep reinforcement learning algorithm, named HMDRL-UC, which is specifically designed to address the cluster-based spectrum sharing problem in heterogeneous UAV swarms. Heterogeneous UAV swarms consist of two types of UAVs: cluster head (CH) and cluster member (CM). Each UAV is equipped with an intelligent agent to execute the deep reinforcement learning (DRL) algorithm. Correspondingly, the HMDRL-UC consists of two parts: multi-agent proximal policy optimization for cluster head (MAPPO-H) and independent proximal policy optimization for cluster member (IPPO-M). The MAPPO-H enables the CHs to decide cluster selection and moving position, while CMs utilize IPPO-M to cluster autonomously under the condition of certain partial channel distribution information (CDI). Adequate experimental evidence has confirmed that the HMDRL-UC algorithm proposed in this paper is not only capable of managing dynamic drone swarm scenarios in the presence of partial CDI, but also has a clear advantage over the other existing three algorithms in terms of average throughput, intra-cluster communication delay, and minimum signal-to-noise ratio (SNR).

PurposeEffects of aeration on distribution and release potential of organic phosphorus in sediments are of great significance. The aim of this study was to investigate effects of fine bubble aeration at the sediment‒water interface on species distributions of organic phosphorus and related microbial activities in the sediments from Nanfei River, a seriously polluted urban river in Hefei City, Anhui Province, China.Materials and methodsA simulation experiment with a precision oxygen distribution system with fine bubble was applied and the sediments in system were taken out at intervals to test various indicators, mainly including the contents of phosphorus, species distribution of organic phosphorus, microbial biomass (MBC), and alkaline phosphatase activity (APA), as well as the number of phosphate solubilizing bacteria.ResultsThe results showed that the content of dissolved organic carbon (DOC) increased in the sediments, along with the decrease of pH and the significant increase of oxidation–reduction potential (ORP). The content of total phosphorus and inorganic phosphorus in the surface sediments generally presented a downward trend when the content of organic phosphorus increased first and then decreased during aeration. The variation trends of liable organic phosphorus and moderately labile organic phosphorus were similar to organic phosphorus, while the content of non-labile organic phosphorus was slightly decreased. In addition, the MBC and APA increased. The number of organic phosphorus mineralizing bacteria (OPB) increased while that of inorganic phosphorus solubilizing bacteria (IPB) decreased, and the number of OPB was significantly correlated to APA, which corresponds to the mineralization mechanism of organic phosphorus.ConclusionsThere were two stages of phosphorus transformation in the surface sediments during the aeration treatment: the stabilization of inorganic phosphorus in the early stage and the mineralization of organic phosphorus latterly, which means an increased risk of phosphorus release into the water in the late stage. Therefore, the fine bubble aeration treatment at the sediment‒water interface applied to controlling the internal pollution of water bodies should be considered, especially focusing on the strict control of the aeration time. Overall, the present study can provide scientific guidance for in situ remediation of heavily polluted sediments.

Increasing discharge of plastic debris into aquatic ecosystems and the worsening ecological risks have received growing attention. Once released, plastic debris could serve as a new substrate for microbes in waters. The complex relationship between plastics and biofilms has aroused great interest. To confirm the hypothesis that the presence of plastic in water affects the composition of biofilm in natural state, in situ biofilm culture experiments were conducted in a lake for 40 days. The diversity of biofilm attached on natural (cobble stones (CS) and wood) and plastic substrates (Polyethylene terephthalate (PET) and Polymethyl methacrylate (PMMA)) were compared, and the community structure and composition were also analyzed. Results from high-throughput sequencing of 16S rRNA showed that the diversity and species richness of biofilm bacterial communities on natural substrate (observed species of 1353~1945, Simpson index of 0.977~0.989 and Shannon–Wiener diversity index of 7.42~8.60) were much higher than those on plastic substrates (observed species of 900~1146, Simpson index of 0.914~0.975 and Shannon–Wiener diversity index of 5.47~6.99). The NMDS analyses were used to confirm the taxonomic significance between different samples, and Anosim (p = 0.001, R = 0.892) and Adonis (p = 0.001, R = 808, F = 11.19) demonstrated that this classification was statistically rigorous. Different dominant bacterial communities were found on plastic and natural substrates. Alphaproteobacterial, Betaproteobacteria and Synechococcophycideae dominated on the plastic substrate, while Gammaproteobacteria, Phycisphaerae and Planctomycetia played the main role on the natural substrates. The bacterial community structure of the two substrates also showed significant difference which is consistent with previous studies using other polymer types. Our results shed light on the fact that plastic debris can serve as a new habitat for biofilm colonization, unlike natural substrates, pathogens and plastic-degrading microorganisms selectively attached to plastic substrates, which affected the bacterial community structure and composition in aquatic environment. This study provided a new insight into understanding the potential impacts of plastics serving as a new habitat for microbial communities in freshwater environments. Future research should focus on the potential impacts of plastic-attached biofilms in various aquatic environments and the whole life cycle of plastics (i.e., from plastic fragments to microplastics) and also microbial flock characteristics using microbial plastics in the natural environment should also be addressed.

Rapid urbanization has destroyed urban water systems and led to blackened and odorous rivers. The heavily polluted rivers are always facing eutrophication and heavy metal pollution, while the combined effects of these environmental factors on the microbial diversity and distribution of the river microbial communities have not been adequately reported, especially the archaeal communities. In this study, we investigated the community structure and microbial distribution of sediment archaeal communities from an urban blackened and odorous river basin of the Zhang river, in Nanling, China. Results showed that the archaeal community from the eight sediment sites have average values of Shannon and Chao1 at 3.4921 and 232.7202, respectively. The community diversity and richness were different among samples. Halobacterota and Euryarchaeota were the most abundant phylum and Crenarchaeota also took up a considerable amount of the archaeal community. To reveal the main environmental drivers of the distribution of archaeal communities in sediment, the environmental physicochemical factors (total nitrogen, total phosphorus, oxidation/reduction potential, nitrate nitrogen, ammonia nitrogen, pH and total organic carbon) and heavy metals (Cr, Ni, Cu, Zn, As, Cd, Pb and Hg) in sediment were determined. A redundancy analysis (RDA) revealed that Eh was the most prominent influencing factor, and As was the most influential heavy metal on the microbial distribution of archaeal communities. Furthermore, a variance partitioning analysis (VPA) was used to identify the impacts of physicochemical factors and heavy metals on the archaeal community distribution. Results showed that heavy metals have higher effects on archaeal community distribution than physicochemical factors. The present study suggested that the heavy metal pollution should be paid more attention in the microbial distribution in heavily polluted urban rivers, and also should be taken into consideration for improving the efficacies of ecological evaluation and remediation.

Ammonia (NH 3 ) is a harmful gas in livestock houses. So far, many researchers have demonstrated that NH 3 is detrimental to animal and human organs. Selenium (Se) is one of the essential trace elements in the body and has a good antioxidant effect. However, there was little conclusive evidence that Se alleviated NH 3 poisoning. To investigate the toxic mechanism of NH 3 on pig spleen and the antagonistic effect of L-selenomethionine, a porcine NH 3 -poisoning model and an L-selenomethionine intervention model were established in this study. Our results showed that NH 3 exposure increased the apoptosis rate, while L-selenomethionine supplementation alleviated the process of excessive apoptosis. Immunofluorescence staining, real-time quantitative polymerase chain reaction (qRT-PCR), and western blot results confirmed that exposure to NH 3 changed the expression levels of interleukin family factors, apoptosis, death receptor, and oxidative stress factors. Our study further confirmed that excessive NH 3 induced inflammatory response and mediated necroptosis leading to cell apoptosis by activating the Nrf2 signaling pathway. Excessive NH 3 could mediate spleen injury through oxidative stress-induced mitochondrial dynamics disorder. L-Selenomethionine could alleviate inflammation and abnormal apoptosis by inhibiting the IL-17/TNF-α/FADD axis. Our study would pave the way for comparative medicine and environmental toxicology.

The Daliao River watershed, an important industrial base of China, has been heavily influenced by anthropogenic activities. Microbes in sediments play an important role in recycling of organics and nutrients, and knowledge of the microbial composition and community structure in river sediments can help us to understand the contribution of microorganisms to environmental processes and their response to environmental perturbation. In this study, 11 surface sediment samples and 1 core sediment sample were collected from the Daliao River water system and its estuary, and their microbial abundance and community compositions were investigated using fluorescence in situ hybridization. Results showed that total cell numbers in surface sediments from different locations ranged from 4.2 × 10 8  to 16.2 × 10 8 cells cm −3 . Domain bacteria represented 58–82% with α-, β-, and γ-proteobacteria as the major subgroups summing up to 40%. Total cell numbers along the core sediment profile ranged from 7.9 × 10 8 to 20.1 × 10 8 cells cm −3 , with relatively higher total cell numbers in the upper (0–6 cm) and middle (21–30 cm) layers. In the core sediment, domain bacteria represented 62–85% and archaea 1.0–11.8%. α-, β- and γ-Proteobacteria were three major phylogenetic groups of bacteria in the core sediment also with γ-proteobacteria as the most abundant subgroup accounting for 9.8–40.8% of total cells.

Ammonia is a significant pollutant in the livestock houses and the atmospheric environment, and excessive ammonia would harm the health of livestock and breeders. Previous studies have shown that ammonia exposure could damage the tissue structure of the nervous system, but the molecular mechanism of ammonia-induced hypothalamus damage was still unclear. The purpose of this study was to determine the role of excessive ammonia in abnormal autophagy of pig hypothalamus and whether selenomethionine would have a mitigating effect on ammonia toxicity. Twenty-four 18-week pigs were randomly divided into four groups: the control group (C group), the selenium group (Se group), the ammonia + selenium group (A + Se group), and the ammonia group (A group). In our study, the expression levels of NF-κB, IL-1β, iNOS, TNF-α, IKK-α, p-IKK-α, Nrf2, ATG5, ATG 10, ATG 12, LC3 I/II, HSP60, HSP70, and HSP90 were increased after ammonia exposure; meanwhile, IFN-γ, IKB-α, p-IKB-α, Keap1, P62, mTOR, AKT, p-AKT, PI3K, SQSTM, and Beclin1 showed decreasing trends. The results indicated that excessive ammonia inhalation inhibited the AKT/mTOR pathway to acclerated autophagy through oxidative stress-mediated inflammation in the porcine hypothalamus. L-selenomethionine could alleviate hypothalamus injury induced by ammonia exposure.

Unmanned aerial vehicles (UAVs) serving as aerial base stations have attracted enormous attention in dense cellular network, disaster relief, sixth generation mobile networks, etc. However, the efficiency is obstructed by scarce spectrum resources, especially in massive UAV swarms. This paper investigates a graph neural network‐based spectrum resource optimisation algorithm to formulate the channel access and transmit power of UAVs with the consideration of both spectrum efficiency (SE) and energy efficiency (EE). We first construct a domain knowledge graph of UAV swarm (KG‐UAVs) to manage the multi‐source heterogeneous information and transform the multi‐objective optimisation problem into a knowledge graph completion problem. Then a novel attribute fusion graph attention transformer network (AFGATrN) is proposed to complete the missing part in KG‐UAVS, which consists of an attribute aware relational graph attention network encoder and a transformer based channel and power prediction decoder. Extensive simulation on both public and domain datasets demonstrates that, the proposed AFGATrN with a rapid convergence speed not only attains more practical spectrum resource allocation scheme with partial channel distribution information (CDI), but also significantly outperforms the other five existing algorithms in terms of the computation time and the trade‐off between the SE and EE performance of the UAVs. This paper investigates a graph neural network‐based spectrum resource optimisation algorithm to formulate the channel access and transmit power of unmanned aerial vehicles (UAVs) with the consideration of both spectrum efficiency and energy efficiency. We first construct a domain knowledge graph of UAV swarm (KG‐UAVs) to manage the multi‐source heterogeneous information and transform the multi‐objective optimisation problem into a knowledge graph completion problem. Then a novel attribute fusion graph attention transformer network is proposed to complete the missing part in KG‐UAVS, which consists of an attribute aware relational graph attention network encoder and a transformer based channel and power prediction decoder. Simulation results further confirm the superiority of our proposed algorithms on both public and domain datasets.

To address the security vulnerabilities inherent in cognitive radio technology amidst extensive control information and data exchanges, this paper introduces the use of unmanned aerial vehicle equipped with intelligent reflecting surfaces (UAV-IRS) as relay nodes to facilitate covert communications for cognitive users. Given the complexities and dynamics of the electromagnetic environment, which involve multiple interconnected and constantly changing communication resources, as well as high-dimensional data, we propose the NeuroEvolution of Augmenting Topologies based Resource Optimisation Algorithm Method (NEAT-ROAM). This advanced algorithm continuously optimizes and adapts the network structure in response to the external environment, ensuring robust and secure communication in dynamic and challenging scenarios. The algorithm not only provides superior covert communication performance but also exhibits fast convergence, which can be applied to the problem of assisting covert communication under mobile conditions using UAV-IRS.