Transcriptomic insights unveil the crucial roles of cytochromes, NADH, and pili in Ag(I) reduction by Geobacter sulfurreducens.

Silver (Ag) is a pivotal transition metal with applications in multiple industries, necessitating efficient recovery techniques. Despite various proposed methods for silver recovery from wastewaters, challenges persist especially for low concentrations. In this context, bioreduction by bacteria like Geobacter sulfurreducens, offers a promising approach by converting Ag(I) to Ag nanoparticles. To reveal the mechanisms driving microbial Ag(I) reduction, we conducted transcriptional profiling of G. sulfurreducens under Ag(I)-reducing condition. Integrated transcriptomic and protein-protein interaction network analyses identified significant transcriptional shifts, predominantly linked to c-type cytochromes, NADH, and pili. When compared to a pilus-deficient strain, the wild-type strain exhibited distinct cytochrome gene expressions, implying specialized functional roles. Additionally, despite a down-regulation in NADH dehydrogenase genes, we observed up-regulation of specific downstream cytochrome genes, highlighting NADH's potential role as an electron donor in the Ag(I) reduction process. Intriguingly, our findings also highlight the significant influence of pili on the morphology of the resulting Ag nanoparticles. The presence of pili led to the formation of smaller and more crystallized Ag nanoparticles. Overall, our findings underscore the intricate interplay of cytochromes, NADH, and pili in Ag(I) reduction. Such insights suggest potential strategies for further enhancing microbial Ag(I) reduction.

Application of artificial intelligence-based methods in bioelectrochemical systems: Recent progress and future perspectives.

Bioelectrochemical Systems (BESs) leverage microbial metabolic processes to either produce electricity by degrading organic matter or consume electricity to assist metabolism, and can be used for various applications such as energy production, wastewater treatment, and bioremediation. Given the intricate mechanisms of BESs, the application of artificial intelligence (AI)-based methods have been proposed to enhance the performance of BESs due to their capability to identify patterns and gain insights through data analysis. This review focuses on the analysis and comparison of AI algorithms commonly used in BESs, including artificial neural network (ANN), genetic programming (GP), fuzzy logic (FL), support vector regression (SVR), and adaptive neural fuzzy inference system (ANFIS). These algorithms have different features, such as ANN's simple network structure, GP's use in the training process, FL's human-like thought process, SVR's high prediction accuracy and robustness, and ANFIS's combination of ANN and FL features. The AI-based methods have been applied in BESs to predict microbial communities, products or substrates, and reactor performance, which can provide valuable information and improve system efficiency. Limitations of AI-based methods for predicting and optimizing BESs and recommendations for future development are also discussed. This review demonstrates the potential of AI-based methods in optimizing BESs and provides valuable information for the future development of this field.

From epidemics to information propagation: striking differences in structurally similar adaptive network models.

The continuous-time adaptive susceptible-infected-susceptible (ASIS) epidemic model and the adaptive information diffusion (AID) model are two adaptive spreading processes on networks, in which a link in the network changes depending on the infectious state of its end nodes, but in opposite ways: (i) In the ASIS model a link is removed between two nodes if exactly one of the nodes is infected to suppress the epidemic, while a link is created in the AID model to speed up the information diffusion; (ii) a link is created between two susceptible nodes in the ASIS model to strengthen the healthy part of the network, while a link is broken in the AID model due to the lack of interest in informationless nodes. The ASIS and AID models may be considered as first-order models for cascades in real-world networks. While the ASIS model has been exploited in the literature, we show that the AID model is realistic by obtaining a good fit with Facebook data. Contrary to the common belief and intuition for such similar models, we show that the ASIS and AID models exhibit different but not opposite properties. Most remarkably, a unique metastable state always exists in the ASIS model, while there an hourglass-shaped region of instability in the AID model. Moreover, the epidemic threshold is a linear function in the effective link-breaking rate in the AID model, while it is almost constant but noisy in the AID model.

Epidemic threshold and topological structure of susceptible-infectious-susceptible epidemics in adaptive networks.

The interplay between disease dynamics on a network and the dynamics of the structure of that network characterizes many real-world systems of contacts. A continuous-time adaptive susceptible-infectious-susceptible (ASIS) model is introduced in order to investigate this interaction, where a susceptible node avoids infections by breaking its links to its infected neighbors while it enhances the connections with other susceptible nodes by creating links to them. When the initial topology of the network is a complete graph, an exact solution to the average metastable-state fraction of infected nodes is derived without resorting to any mean-field approximation. A linear scaling law of the epidemic threshold τ(c) as a function of the effective link-breaking rate ω is found. Furthermore, the bifurcation nature of the metastable fraction of infected nodes of the ASIS model is explained. The metastable-state topology shows high connectivity and low modularity in two regions of the τ,ω plane for any effective infection rate τ>τ(c): (i) a "strongly adaptive" region with very high ω and (ii) a "weakly adaptive" region with very low ω. These two regions are separated from the other half-open elliptical-like regions of low connectivity and high modularity in a contour-line-like way. Our results indicate that the adaptation of the topology in response to disease dynamics suppresses the infection, while it promotes the network evolution towards a topology that exhibits assortative mixing, modularity, and a binomial-like degree distribution.