Combining hydrogen peroxide addition with sunlight regulation to control algal blooms

The concentration, light conditions during treatment, and the number of hydrogen peroxide (H2O2) additions as well as the H2O2 treatment combined with subsequent shading to control algal blooms were studied in the field (Lake Dianchi, China). The cyanobacterial stress and injury due to H2O2 were dose dependent, and the control effectiveness and degradation of H2O2 were better and faster under full light than under shading. However, H2O2 was only able to control a bloom for a short time, so it may have promoted the recovery of algae and allowed the biomass to rebound due to the growth of eukaryotic algae. A second addition of H2O2 at the same dose had no obvious effect on algal control in the short term, suggesting that a higher concentration or a delayed addition should be considered, but these alternative strategies are not recommended so that the integrity of the aquatic ecosystem is maintained and algal growth is not promoted. Moreover, shading (85%) after H2O2 addition significantly reduced the algal biomass during the enclosure test, no restoration was observed for nearly a month, and the proportion of eukaryotic algae declined. It can be inferred that algal blooms can be controlled by applying a high degree of shading after treatment with H2O2.</p

Secured and Cooperative Publish/Subscribe Scheme in Autonomous Vehicular Networks

In order to save computing power yet enhance safety, there is a strong intention for autonomous vehicles (AVs) in future to drive collaboratively by sharing sensory data and computing results among neighbors. However, the intense collaborative computing and data transmissions among unknown others will inevitably introduce severe security concerns. Aiming at addressing security concerns in future AVs, in this paper, we develop SPAD, a secured framework to forbid free-riders and {promote trustworthy data dissemination} in collaborative autonomous driving. Specifically, we first introduce a publish/subscribe framework for inter-vehicle data transmissions{. To defend against free-riding attacks,} we formulate the interactions between publisher AVs and subscriber AVs as a vehicular publish/subscribe game, {and incentivize AVs to deliver high-quality data by analyzing the Stackelberg equilibrium of the game. We also design a reputation evaluation mechanism in the game} to identify malicious AVs {in disseminating fake information}. {Furthermore, for} lack of sufficient knowledge on parameters of {the} network model and user cost model {in dynamic game scenarios}, a two-tier reinforcement learning based algorithm with hotbooting is developed to obtain the optimal {strategies of subscriber AVs and publisher AVs with free-rider prevention}. Extensive simulations are conducted, and the results validate that our SPAD can effectively {prevent free-riders and enhance the dependability of disseminated contents,} compared with conventional schemes

Collaborative Honeypot Defense in UAV Networks: A Learning-Based Game Approach

The proliferation of unmanned aerial vehicles (UAVs) opens up new opportunities for on-demand service provisioning anywhere and anytime, but also exposes UAVs to a variety of cyber threats. Low/medium interaction honeypots offer a promising lightweight defense for actively protecting mobile Internet of things, particularly UAV networks. While previous research has primarily focused on honeypot system design and attack pattern recognition, the incentive issue for motivating UAV's participation (e.g., sharing trapped attack data in honeypots) to collaboratively resist distributed and sophisticated attacks remains unexplored. This paper proposes a novel game-theoretical collaborative defense approach to address optimal, fair, and feasible incentive design, in the presence of network dynamics and UAVs' multi-dimensional private information (e.g., valid defense data (VDD) volume, communication delay, and UAV cost). Specifically, we first develop a honeypot game between UAVs and the network operator under both partial and complete information asymmetry scenarios. The optimal VDD-reward contract design problem with partial information asymmetry is then solved using a contract-theoretic approach that ensures budget feasibility, truthfulness, fairness, and computational efficiency. In addition, under complete information asymmetry, we devise a distributed reinforcement learning algorithm to dynamically design optimal contracts for distinct types of UAVs in the time-varying UAV network. Extensive simulations demonstrate that the proposed scheme can motivate UAV's cooperation in VDD sharing and improve defensive effectiveness, compared with conventional schemes.Comment: Accepted Aug. 28, 2023 by IEEE Transactions on Information Forensics & Security. arXiv admin note: text overlap with arXiv:2209.1381

Bootstrapping Multi-view Representations for Fake News Detection

Previous researches on multimedia fake news detection include a series of complex feature extraction and fusion networks to gather useful information from the news. However, how cross-modal consistency relates to the fidelity of news and how features from different modalities affect the decision-making are still open questions. This paper presents a novel scheme of Bootstrapping Multi-view Representations (BMR) for fake news detection. Given a multi-modal news, we extract representations respectively from the views of the text, the image pattern and the image semantics. Improved Multi-gate Mixture-of-Expert networks (iMMoE) are proposed for feature refinement and fusion. Representations from each view are separately used to coarsely predict the fidelity of the whole news, and the multimodal representations are able to predict the cross-modal consistency. With the prediction scores, we reweigh each view of the representations and bootstrap them for fake news detection. Extensive experiments conducted on typical fake news detection datasets prove that the proposed BMR outperforms state-of-the-art schemes.Comment: Authors are from Fudan University, China. Under Revie

Game Theoretic Resource Allocation in Media Cloud With Mobile Social Users

Due to the rapid increases in both the population of mobile social users and the demand for quality of experience (QoE), providing mobile social users with satisfied multimedia services has become an important issue. Media cloud has been shown to be an efficient solution to resolve the above issue, by allowing mobile social users to connect to it through a group of distributed brokers. However, as the resource in media cloud is limited, how to allocate resource among media cloud, brokers, and mobile social users becomes a new challenge. Therefore, in this paper, we propose a game theoretic resource allocation scheme for media cloud to allocate resource to mobile social users though brokers. First, a framework of resource allocation among media cloud, brokers, and mobile social users is presented. Media cloud can dynamically determine the price of the resource and allocate its resource to brokers. A mobile social user can select his broker to connect to the media cloud by adjusting the strategy to achieve the maximum revenue, based on the social features in the community. Next, we formulate the interactions among media cloud, brokers, and mobile social users by a four-stage Stackelberg game. In addition, through the backward induction method, we propose an iterative algorithm to implement the proposed scheme and obtain the Stackelberg equilibrium. Finally, simulation results show that each player in the game can obtain the optimal strategy where the Stackelberg equilibrium exists stably

Oral microbiota of periodontal health and disease and their changes after nonsurgical periodontal therapy

This study examined the microbial diversity and community assembly of oral microbiota in periodontal health and disease and after nonsurgical periodontal treatment. The V4 region of 16S rRNA gene from DNA of 238 saliva and subgingival samples of 21 healthy and 48 diseased subjects was amplified and sequenced. Among 1979 OTUs identified, 28 were overabundant in diseased plaque. Six of these taxa were also overabundant in diseased saliva. Twelve OTUs were overabundant in healthy plaque. There was a trend for disease-associated taxa to decrease and health-associated taxa to increase after treatment with notable variations among individual sites. Network analysis revealed modularity of the microbial communities and identified several health- and disease-specific modules. Ecological drift was a major factor that governed community turnovers in both plaque and saliva. Dispersal limitation and homogeneous selection affected the community assembly in plaque, with the additional contribution of homogenizing dispersal for plaque within individuals. Homogeneous selection and dispersal limitation played important roles, respectively, in healthy saliva and diseased pre-treatment saliva between individuals. Our results revealed distinctions in both taxa and assembly processes of oral microbiota between periodontal health and disease. Furthermore, the community assembly analysis has identified potentially effective approaches for managing periodontitis

Effects of sugarcane variety and nitrogen application level on the quality and aerobic stability of sugarcane tops silage

To better understand the effects of sugarcane variety and nitrogen application level on silage, we analyzed the fermentation quality, microbial community dynamics, and aerobic exposure of sugarcane tops silage from three sugarcane varieties (B9, C22, and T11) treated with three levels of nitrogen (0, 150, and 300 kg/ha urea). After 132 days of silage, the sugarcane tops silage produced from variety B9, with strong nitrogen fixation ability, treated with nitrogen had the highest crude protein (CP) contents, pH, and yeast counts (P &lt; 0.05), as well as the lowest Clostridium counts (P &lt; 0.05), and the CP increased with increasing N application level (P &lt; 0.05). In contrast, the sugarcane tops silage produced from variety C22, with poor nitrogen fixation ability, treated with 150 kg/ha nitrogen had the highest lactic acid bacteria (LAB) counts, dry matter (DM), organic matter (OM) and lactic acid (LA) contents (P &lt; 0.05), as well as the lowest acid detergent fiber (ADF) and neutral detergent fiber (NDF) contents (P &lt; 0.05). However, these results were not present in the sugarcane tops silage produced from variety T11, with no nitrogen fixation ability, whether it was treated with nitrogen or not; although the silage was treated with 300 kg/ha nitrogen, the ammonia-N (AN) content was the lowest (P &lt; 0.05). After 14 days of aerobic exposure, Bacillus abundance increased in the sugarcane tops silage produced from variety C22 treated with 150 kg/ha nitrogen and from varieties C22 and B9 treated with 300 kg/ha nitrogen, while Monascus abundance increased in the sugarcane tops silage produced from varieties B9 and C22 treated with 300 kg/ha nitrogen and from variety B9 treated with 150 kg/ha nitrogen. However, correlation analysis showed that Monascus was positively correlated with Bacillus irrespective of nitrogen level and sugarcane variety. Our results indicated that sugarcane variety C22, with poor nitrogen fixation ability, treated with 150 kg/ha nitrogen produced the highest sugarcane tops silage quality and inhibited the proliferation of harmful microorganisms during spoilage

Functional Gene Array-Based Ultrasensitive and Quantitative Detection of Microbial Populations in Complex Communities.

While functional gene arrays (FGAs) have greatly expanded our understanding of complex microbial systems, specificity, sensitivity, and quantitation challenges remain. We developed a new generation of FGA, GeoChip 5.0, using the Agilent platform. Two formats were created, a smaller format (GeoChip 5.0S), primarily covering carbon-, nitrogen-, sulfur-, and phosphorus-cycling genes and others providing ecological services, and a larger format (GeoChip 5.0M) containing the functional categories involved in biogeochemical cycling of C, N, S, and P and various metals, stress response, microbial defense, electron transport, plant growth promotion, virulence, gyrB, and fungus-, protozoan-, and virus-specific genes. GeoChip 5.0M contains 161,961 oligonucleotide probes covering &gt;365,000 genes of 1,447 gene families from broad, functionally divergent taxonomic groups, including bacteria (2,721 genera), archaea (101 genera), fungi (297 genera), protists (219 genera), and viruses (167 genera), mainly phages. Computational and experimental evaluation indicated that designed probes were highly specific and could detect as little as 0.05 ng of pure culture DNAs within a background of 1 μg community DNA (equivalent to 0.005% of the population). Additionally, strong quantitative linear relationships were observed between signal intensity and amount of pure genomic (∼99% of probes detected; r &gt; 0.9) or soil (∼97%; r &gt; 0.9) DNAs. Application of the GeoChip to a contaminated groundwater microbial community indicated that environmental contaminants (primarily heavy metals) had significant impacts on the biodiversity of the communities. This is the most comprehensive FGA to date, capable of directly linking microbial genes/populations to ecosystem functions.IMPORTANCE The rapid development of metagenomic technologies, including microarrays, over the past decade has greatly expanded our understanding of complex microbial systems. However, because of the ever-expanding number of novel microbial sequences discovered each year, developing a microarray that is representative of real microbial communities, is specific and sensitive, and provides quantitative information remains a challenge. The newly developed GeoChip 5.0 is the most comprehensive microarray available to date for examining the functional capabilities of microbial communities important to biogeochemistry, ecology, environmental sciences, and human health. The GeoChip 5 is highly specific, sensitive, and quantitative based on both computational and experimental assays. Use of the array on a contaminated groundwater sample provided novel insights on the impacts of environmental contaminants on groundwater microbial communities

Functional Ecological Gene Networks to Reveal the Changes Among Microbial Interactions Under Elevated Carbon Dioxide Conditions

Biodiversity and its responses to environmental changes is a central issue in ecology, and for society. Almost all microbial biodiversity researches focus on species richness and abundance but ignore the interactions among different microbial species/populations. However, determining the interactions and their relationships to environmental changes in microbial communities is a grand challenge, primarily due to the lack of information on the network structure among different microbial species/populations. Here, a novel random matrix theory (RMT)-based conceptual framework for identifying functional ecological gene networks (fEGNs) is developed with the high throughput functional gene array hybridization data from the grassland microbial communities in a long-term FACE (Free Air CO2 Enrichment) experiment. Both fEGNs under elevated CO2 (eCO2) and ambient CO2 (aCO2) possessed general characteristics of many complex systems such as scale-free, small-world, modular and hierarchical. However, the topological structure of the fEGNs is distinctly different between eCO2 and aCO2, suggesting that eCO2 dramatically altered the interactions among different microbial functional groups/populations. In addition, the changes in network structure were significantly correlated with soil carbon and nitrogen dynamics, and plant productivity, indicating the potential importance of network interactions in ecosystem functioning. Elucidating network interactions in microbial communities and their responses to environmental changes are fundamentally important for research in microbial ecology, systems microbiology, and global change