Machine Learning Modeling of Aerobic Biodegradation for Azo Dyes and Hexavalent Chromium

Interdisciplinary Overview of Lipopeptide and Protein-Containing Biosurfactants

Biosurfactants are amphipathic molecules capable of lowering interfacial and superficial tensions. Produced by living organisms, these compounds act the same as chemical surfactants but with a series of improvements, the most notable being biodegradability. Biosurfactants have a wide diversity of categories. Within these, lipopeptides are some of the more abundant and widely known. Protein-containing biosurfactants are much less studied and could be an interesting and valuable alternative. The harsh temperature, pH, and salinity conditions that target organisms can sustain need to be understood for better implementation. Here, we will explore biotechnological applications via lipopeptide and protein-containing biosurfactants. Also, we discuss their natural role and the organisms that produce them, taking a glimpse into the possibilities of research via meta-omics and machine learning.

Bioremediation of Endocrine Disrupting Chemicals- Advancements and Challenges

Endocrine Disrupting Chemicals (EDCs), major group of recalcitrant compounds, poses a serious threat to the health and future of millions of human beings, and other flora and fauna for years to come. A close analysis of various xenobiotics undermines the fact that EDC is structurally diverse chemical compounds generated as a part of anthropogenic advancements as well as part of their degradation. Regardless of such structural diversity, EDC is common in their ultimate drastic effect of impeding the proper functioning of the endocrinal system, basic physiologic systems, resulting in deregulated growth, malformations, and cancerous outcomes in animals as well as humans. The current review outlines an overview of various EDCs, their toxic effects on the ecosystem and its inhabitants. Conventional remediation methods such as physico-chemical methods and enzymatic approaches have been put into action as some form of mitigation measures. However, the last decade has seen the hunt for newer technologies and methodologies at an accelerated pace. Genetically engineered microbial degradation, gene editing strategies, metabolic and protein engineering, and in-silico predictive approaches - modern day's additions to our armamentarium in combating the EDCs are addressed. These additions have greater acceptance socially with lesser dissonance owing to reduced toxic by-products, lower health trepidations, better degradation, and ultimately the prevention of bioaccumulation. The positive impact of such new approaches on controlling the menace of EDCs has been outlaid. This review will shed light on sources of EDCs, their impact, significance, and the different remediation and bioremediation approaches, with a special emphasis on the recent trends and perspectives in using sustainable approaches for bioremediation of EDCs. Strict regulations to prevent the release of estrogenic chemicals to the ecosystem, adoption of combinatorial methods to remove EDC and prevalent use of bioremediation techniques should be followed in all future endeavors to combat EDC pollution. Moreover, the proper development, growth and functioning of future living forms relies on their non-exposure to EDCs, thus remediation of such chemicals present even in nano-concentrations should be addressed gravely.

Waterlogging Resistance Evaluation Index and Photosynthesis Characteristics Selection: Using Machine Learning Methods to Judge Poplar’s Waterlogging Resistance

Flood disasters are the major natural disaster that affects the growth of agriculture and forestry crops. Due to rapid growth and strong waterlogging resistance characteristics, many studies have explained the waterlogging resistance mechanism of poplar from different perspectives. However, there is no accurate method to define the evaluation index of waterlogging resistance. In addition, there is also a lack of research on predicting the waterlogging resistance of poplars. Based on the changes of poplar biomass and seedling height, the evaluation index of poplar resistance to waterlogging was well determined, and the characteristics of photosynthesis were used to predict the waterlogging resistance of poplars. First, four methods of hierarchical clustering, lasso, stepwise regression and all-subsets regression were used to extract the photosynthesis characteristics. After that, the support vector regression model of poplar resistance to waterlogging was established by using the characteristic parameters of photosynthesis. Finally, the results show that the SVR model based on Stepwise regression and Lasso method has high precision. On the test set, the coefficient of determination (R2) was 0.8581 and 0.8492, the mean square error (MSE) was 0.0104 and 0.0341, and the mean relative error (MRE) was 9.78% and 9.85%, respectively. Therefore, using the characteristic parameters of photosynthesis to predict the waterlogging resistance of poplars is feasible.

Trends in predictive biodegradation for sustainable mitigation of environmental pollutants: Recent progress and future outlook

The feasibility of in-silico techniques, together with the computational framework, has been applied to predictive bioremediation aiming to clean-up contaminants, toxicity evaluation, and possibilities for the degradation of complex recalcitrant compounds. Emerging contaminants from different industries have posed a significant hazard to the environment and public health. Given current bioremediation strategies, it is often a failure or inadequate for sustainable mitigation of hazardous pollutants. However, clear-cut vital information about biodegradation is quite incomplete from a conventional remediation techniques perspective. Lacking complete information on bio-transformed compounds leads to seeking alternative methods. Only scarce information about the transformed products and toxicity profile is available in the published literature. To fulfill this literature gap, various computational or in-silico technologies have emerged as alternating techniques, which are being recognized as in-silico approaches for bioremediation. Molecular docking, molecular dynamics simulation, and biodegradation pathways predictions are the vital part of predictive biodegradation, including the Quantitative Structure-Activity Relationship (QSAR), Quantitative structure-biodegradation relationship (QSBR) model system. Furthermore, machine learning (ML), artificial neural network (ANN), genetic algorithm (GA) based programs offer simultaneous biodegradation prediction along with toxicity and environmental fate prediction. Herein, we spotlight the feasibility of in-silico remediation approaches for various persistent, recalcitrant contaminants while traditional bioremediation fails to mitigate such pollutants. Such could be addressed by exploiting described model systems and algorithm-based programs. Furthermore, recent advances in QSAR modeling, algorithm, and dedicated biodegradation prediction system have been summarized with unique attributes.