Search for: All records

Anaerobic digestion (AD) is a well-established waste-to-value technology commonly used at water resource recovery facilities (WRRFs), generating biogas from organic waste. However, the generated biogas is typically used only for heat and electricity generation due to contaminants, while the nutrient-rich AD effluent requires further treatment before environmental release. Methanotroph-microalgae cocultures have recently emerged as promising candidates for integrated biogas valorization and nutrient recovery. Although the choice of the coculture pairs is one of the most important factors that determine the performance of the application, there have not been any results on the comparison or screening of different coculture pairs for a desired application. To expedite the screening of methanotroph-microalgae cocultures for optimal performance, we developed a cost-effective screening system consisting of nine parallel bioreactors. The compact design of the system allows it to fit in a fume hood, and enables the simultaneous evaluation of multiple species with triplicates under uniformly controlled conditions. The system was applied to screen seven methanotrophs, five microalgae, and six methanotroph-microalgae coculture pairs on a diluted AD effluent from a local WRRF. To systematically assess the growth performance of different monocultures and cocultures, mathematical models that describe the microbial growth under batch cultivation were developed to determine the maximum growth rate, delay time, and carrying capacity from growth data, allowing for consistent and systematic assessment of different species, as well as the identification of the coculture pairs with synergistic and inhibitory interactions. The developed experimental system and modeling approach enabled expedited strain screening and unbiased assessment for integrated biogas valorization and nutrient recovery. Specifically, the cost of each bioreactor system in S3 is less than 5% of commercially available bioreactor system (such as Bioflo 120), while the screening throughput of S3 is 9 times that of a single bioreactor system. In addition, the identified synergistic cocultures demonstrate potential for scalable biogas valorization and nutrient recovery in wastewater treatment. 

For many infectious diseases, including malaria and COVID-19, the host may experience more than one episode of infection, where reinfection occurs due to waning immunity. In this paper, we propose a new age-structured epidemic model to investigate the dynamics of such diseases with multiple infections. The model is based on a system of partial differential equations that describes the interplay between completely susceptible individuals, temporarily immune individuals, and infected individuals at different stages. The model incorporates both time and age-dependent variables and parameters. We derive the basic reproduction number and conduct rigorous analyses on the equilibrium solutions and their stability properties. Specifically, we study the global asymptotic stability of the disease-free equilibrium and obtain the explicit conditions for the occurrence of a backward bifurcation. Our findings could provide useful insights into the effects of disease prevention and intervention strategies such as vaccination campaigns. 

Deep unfolding networks have gained increasing attention in the field of compressed sensing (CS) owing to their theoretical interpretability and superior reconstruction performance. However, most existing deep unfolding methods often face the following issues: (1) they learn directly from single-channel images, leading to a simple feature representation that does not fully capture complex features; and (2) they treat various image components uniformly, ignoring the characteristics of different components. To address these issues, we propose a novel wavelet-domain deep unfolding framework named WTDUN, which operates directly on the multi-scale wavelet sub-bands. Our method utilizes the intrinsic sparsity and multi-scale structure of wavelet coefficients to achieve a tree-structured sampling and reconstruction, effectively capturing and highlighting the most important features within images. Specifically, the design of tree-structured reconstruction aims to capture the inter-dependencies among the multi-scale sub-bands, enabling the identification of both fine and coarse features, which can lead to a marked improvement in reconstruction quality. Furthermore, a wavelet domain adaptive sampling method is proposed to greatly improve the sampling capability, which is realized by assigning measurements to each wavelet sub-band based on its importance. Unlike pure deep learning methods that treat all components uniformly, our method introduces a targeted focus on important sub-bands, considering their energy and sparsity. This targeted strategy lets us capture key information more efficiently while discarding less important information, resulting in a more effective and detailed reconstruction. Extensive experimental results on various datasets validate the superior performance of our proposed method. 

Aim: Metabolic interactions within a microbial community play a key role in determining the structure, function, and composition of the community. However, due to the complexity and intractability of natural microbiomes, limited knowledge is available on interspecies interactions within a community. In this work, using a binary synthetic microbiome, a methanotroph-photoautotroph (M-P) coculture, as the model system, we examined different genome-scale metabolic modeling (GEM) approaches to gain a better understanding of the metabolic interactions within the coculture, how they contribute to the enhanced growth observed in the coculture, and how they evolve over time. Methods: Using batch growth data of the model M-P coculture, we compared three GEM approaches for microbial communities. Two of the methods are existing approaches: SteadyCom, a steady state GEM, and dynamic flux balance analysis (DFBA) Lab, a dynamic GEM. We also proposed an improved dynamic GEM approach, DynamiCom, for the M-P coculture. Results: SteadyCom can predict the metabolic interactions within the coculture but not their dynamic evolutions; DFBA Lab can predict the dynamics of the coculture but cannot identify interspecies interactions. DynamiCom was able to identify the cross-fed metabolite within the coculture, as well as predict the evolution of the interspecies interactions over time. Conclusion: A new dynamic GEM approach, DynamiCom, was developed for a model M-P coculture. Constrained by the predictions from a validated kinetic model, DynamiCom consistently predicted the top metabolites being exchanged in the M-P coculture, as well as the establishment of the mutualistic N-exchange between the methanotroph and cyanobacteria. The interspecies interactions and their dynamic evolution predicted by DynamiCom are supported by ample evidence in the literature on methanotroph, cyanobacteria, and other cyanobacteria-heterotroph cocultures.