Lichen 1. Solo fungal and algal partners

A review of biowaste remediation and valorization for environmental sustainability: Artificial intelligence approach

Biowaste remediation and valorization for environmental sustainability focuses on prevention rather than cleanup of waste generation by applying the fundamental recovery concept through biowaste-to-bioenergy conversion systems - an appropriate approach in a circular bioeconomy. Biomass waste (biowaste) is discarded organic materials made of biomass (e.g., agriculture waste and algal residue). Biowaste is widely studied as one of the potential feedstocks in the biowaste valorization process due to its being abundantly available. In terms of practical implementations, feedstock variability from biowaste, conversion costs and supply chain stability prevent the widespread usage of bioenergy products. Biowaste remediation and valorization have used artificial intelligence (AI), a newly developed idea, to overcome these difficulties. This report analyzed 118 works that applied various AI algorithms to biowaste remediation and valorization-related research published between 2007 and 2022. Four common AI types are utilized in biowaste remediation and valorization: neural networks, Bayesian networks, decision tree, and multivariate regression. The neural network is the most frequent AI for prediction models, the Bayesian network is utilized for probabilistic graphical models, and the decision tree is trusted for providing tools to assist decision-making. Meanwhile, multivariate regression is employed to identify the relationship between experimental variables. AI is a remarkably effective tool in predicting data, which is reportedly better than the conventional approach owing to its characteristics of time-saving and high accuracy. The challenge and future work in biowaste remediation and valorization are briefly discussed to maximize the model's performance.

Trebouxia lynnae sp. nov. (Former Trebouxia sp. TR9): Biology and Biogeography of an Epitome Lichen Symbiotic Microalga

Two microalgal species, Trebouxia jamesii and Trebouxia sp. TR9, were detected as the main photobionts coexisting in the thalli of the lichen Ramalina farinacea. Trebouxia sp. TR9 emerged as a new taxon in lichen symbioses and was successfully isolated and propagated in in vitro culture and thoroughly investigated. Several years of research have confirmed the taxon Trebouxia sp. TR9 to be a model/reference organism for studying mycobiont−photobiont association patterns in lichen symbioses. Trebouxia sp. TR9 is the first symbiotic, lichen-forming microalga for which an exhaustive characterization of cellular ultrastructure, physiological traits, genetic and genomic diversity is available. The cellular ultrastructure was studied by light, electron and confocal microscopy; physiological traits were studied as responses to different abiotic stresses. The genetic diversity was previously analyzed at both the nuclear and organelle levels by using chloroplast, mitochondrial, and nuclear genome data, and a multiplicity of phylogenetic analyses were carried out to study its intraspecific diversity at a biogeographical level and its specificity association patterns with the mycobiont. Here, Trebouxia sp. TR9 is formally described by applying an integrative taxonomic approach and is presented to science as Trebouxia lynnae, in honor of Lynn Margulis, who was the primary modern proponent for the significance of symbiosis in evolution. The complete set of analyses that were carried out for its characterization is provided.