Sort by
Treatment of Water Contaminated with Polycyclic Aromatic Hydrocarbons (PAHs): A Review of Various Techniques, Constraints, and Field Procedures

Clean water is vital in the creation of energy and sustenance of life. However, the pollution of water and the absence of potable water are global problems resulting from agricultural and industrial activities. We have witnessed significant growth in the pollution of water by organic compounds like PAH. Experts have made an effort to establish favorable techniques for the treatment of PAH polluted water. These techniques are either thermal, biological, physical or chemical. Bioremediation, chemical oxidation, solid-phase extraction, coagulation, photocatalytic degradation and adsorption using graphenes, mesoporous silica and agricultural wastes are techniques that are already in use in the field treatment of PAHs while electrokinetic remediation and nanoremediation are still in their developmental stage. Several reviews on the treatment of sediments and soils contaminated with PAHs have been published, but only a few reviews center mainly on the removal of PAHs in water. Therefore, this review aims to provide information on the techniques used in the treatment of water contaminated with PAHs. Techniques that are already in use and those that are in their developmental stage were reviewed. The successes of these methods, limitations, constraints and field procedures were analyzed and this will help to inform decision making.

Open Access
Relevant
Development of Predictive Model for Helper T Lymphocyte Epitope Binding to HLADRB1* 01:01

Epitopes are essential peptides for immune system stimulation, such as governing helper T lymphocyte (HTL) activation via antigen presentation and recognition. Current predictive models for epitope selection mainly rely on the antigen presentation, although HTLs only recognize 50% of the presented peptides. Thus, we developed a HTL epitope predictor which involves the antigen recognition step. The predictor is specific for epitopes presented by Human Leukocyte Allele (HLA)-DRB1*01:01, which is protective against developing multiple sclerosis and association with autoimmune diseases. As the data set, we used binding register of immunogenic and non-immunogenic HTL peptides related to HLA-DRB1*01:01. The binding registers were obtained from consensus results of two current HLA-binder predictors. Amino acid descriptors were extracted from the binding registers and subjected to random forest algorithm. A threshold optimization were applied to overcome data set imbalance class. In addition, descriptors were screened by using a recursive feature elimination to enhance the model performance. The obtained model shows that the hydrophobicity, steric, and electrostatic properties of epitopes, mainly at center of binding registers, are important for the TCR recognition as well as the HTL epitopes predictive model. The model complements current HLA-DRB1*01:01-binder prediction methods to screen immunogenic HTL epitopes.

Open Access
Relevant