Designing a bioremediator: mechanistic models guide cellular and molecular specialization

Recent applications of AI to environmental disciplines: A review

The rapid development and efficiency of Artificial Intelligence (AI) tools have made them increasingly popular in various fields and research domains. The environmental discipline is now experiencing an exponential interest in harnessing the potential of AI over the past decade. We have reviewed the latest applications of AI tools in the environmental disciplines, highlighting the opportunities they present and discussing their advantages and disadvantages in this field. After the emergence of deep learning algorithms in 2010, interest in using AI tools for environmental tasks has grown exponentially. Among the studied articles, over 65 % of environmental tasks that demonstrate interest in using AI tools initially relied on conventional statistical and mathematical models. Using AI tools can greatly benefit the areas of environmental science and engineering. One of the main advantages of utilizing AI tools is their ability to analyze and process large amounts of data efficiently. Recently, the European Union established a European supercomputing ecosystem program to advance science and enhance the quality of life for its citizens. Nine of these projects prioritize environmental and sustainable goals. Despite the benefits of AI, it is still in its early stages of development, which comes with environmental concerns. The amount of power consumed and the time required to train an AI model can greatly affect the carbon emissions it produces, exacerbating the challenges posed by climate change. Efforts are currently underway to develop AI technology that is environmentally sustainable, minimizes energy consumption, and has a low carbon footprint. Selecting the appropriate AI model architecture can reduce energy consumption by almost 90 %. The main finding suggests that collaboration between environmental and AI professionals becomes crucial in leveraging the full potential of AI in addressing pressing environmental challenges.

Experimental-theoretical study of laccase as a detoxifier of aflatoxins

We investigate laccase-mediated detoxification of aflatoxins, fungal carcinogenic food contaminants. Our experimental comparison between two aflatoxins with similar structures (AFB₁ and AFG₂) shows significant differences in laccase-mediated detoxification. A multi-scale modeling approach (Docking, Molecular Dynamics, and Density Functional Theory) identifies the highly substrate-specific changes required to improve laccase detoxifying performance. We employ a large-scale density functional theory-based approach, involving more than 7000 atoms, to identify the amino acid residues that determine the affinity of laccase for aflatoxins. From this study we conclude: (1) AFB₁ is more challenging to degrade, to the point of complete degradation stalling; (2) AFG₂ is easier to degrade by laccase due to its lack of side products and favorable binding dynamics; and (3) ample opportunities to optimize laccase for aflatoxin degradation exist, especially via mutations leading to π-π stacking. This study identifies a way to optimize laccase for aflatoxin bioremediation and, more generally, contributes to the research efforts aimed at rational enzyme optimization.

Microbial co-culturing strategies for the production high value compounds, a reliable framework towards sustainable biorefinery implementation - an overview

The microbial co-cultures or consortia are a natural set of microorganisms formed from different species or the same species but different strains, in which members can interact with each other. The co-culture systems have wide variety of technological applications such as the production of foods, treatment of wastewater, removal of toxic substances, environmental recovery, and all these without the need to work in sterile conditions. Therefore, the need of understanding communication mechanisms between cell-to-cell within co-culture will allow to construct and to program their biological behavior from the use of complex substrates to produce biocompounds. The technology of co-culture systems enables the development of biorefinery platforms to obtain biofuels, and high value compounds through biomass transformation by sustainable process. This review focuses on understanding the roles of consortia microbial to design and built co-culture systems to produce high value compounds in terms a sustainable biorefinery.

Controlling evolutionary dynamics to optimize microbial bioremediation

Some microbes have a fascinating ability to degrade compounds that are toxic for humans in a process called bioremediation. Although these traits help microbes survive the toxins, carrying them can be costly if the benefit of detoxification is shared by all surrounding microbes, whether they detoxify or not. Detoxification can thereby be seen as a public goods game, where nondegrading mutants can sweep through the population and collapse bioremediation. Here, we constructed an evolutionary game theoretical model to optimize bioremediation in a chemostat initially containing "cooperating" (detoxifying) microbes. We consider two types of mutants: "cheaters" that do not detoxify, and mutants that become resistant to the toxin through private mechanisms that do not benefit others. By manipulating the concentration and flow rate of a toxin into the chemostat, we identified conditions where cooperators can exclude cheaters that differ in their private resistance. However, eventually, cheaters are bound to invade. To overcome this inevitable outcome and maximize detoxification efficiency, cooperators can be periodically reinoculated into the population. Our study investigates the outcome of an evolutionary game combining both public and private goods and demonstrates how environmental parameters can be used to control evolutionary dynamics in practical applications.

The Potential Diagnostic Value of Exosomal Long Noncoding RNAs in Solid Tumors: A Meta-Analysis and Systematic Review

Background

Exosomes are defined as small membranous vesicles. After RNA content was discovered in exosomes, they emerged as a novel approach for the treatment and diagnosis of cancer. Long noncoding RNAs (lncRNA), a kind of specific RNA transcript, have been reported to function as tumor growth, metastasis, invasion, and prognosis by regulating the tumor microenvironment in exosomes. This study aims at exploring the potential diagnostic of exosomal lncRNA in solid tumors.

Methods

A meta-analysis conducted from January 2000 to October 2019 identified publications in the English language. We searched all relevant English literature from the Web of Science, EMBASE, and PubMed databases through October 1, 2019. The articles were strictly screened by our criteria and critiqued using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.

Results

There were 28 studies with 19 articles (4017 patients) identified, including studies on gastric cancer, laryngeal squamous cell carcinoma, colorectal cancer, cholangiocarcinoma, breast cancer, esophageal squamous cell carcinoma, hepatocellular carcinoma, nonsmall cell lung cancer, and prostate cancer. A meta-analysis showed that the combined value of sensitivity in 29 studies was 0.74 (95% confidence interval [CI], 0.7-0.78), and the combined value of specificity in the studies was 0.81 (95% CI, 0.78-0.83). This suggests the high diagnostic efficacy of liquid exosomes in cancer patients. It is statistically insignificant in terms of sex, ethnicity, and year. The diagnostic power of urinary system tumors was found to be higher than that of digestive system tumors by several subgroup analyses.

Conclusions

We performed a meta-analysis and literature review of 28 studies that included 4017 patients with 10 malignant cancer types. Mechanistically, our study demonstrated that lncRNAs in exosomes could be a promising bioindicator for the diagnosis and prognosis of solid tumors. INPLASY Registration Number: INPLASY202060083.