Magnetic polarizability of hadrons from lattice QCD

The impact of dissolved oxygen on sulfate radical-induced oxidation of organic micro-pollutants: A theoretical study

Sulfate radical (SO₄^.-)-induced oxidation is an important technology in advanced oxidation processes (AOPs) for the removal of pollutants. To date, few studies have assessed the effects of dissolved oxygen (DO) on the SO₄^.--induced oxidation of organic micro-pollutants. In the present work, a quantum chemical calculation was used to investigate the influence of the external oxygen molecule on the Gibbs free energy (G_pollutant) and HOMO-LUMO gap (ΔE) of 15 organic micro-pollutants representing four chemical categories. Several thermodynamic and statistical models were combined with the data from the quantum chemical calculation to illustrate the impact of DO on the oxidation of organic micro-pollutants by SO₄^.-. Results indicated that the external oxygen molecule increased G_pollutant of all studied chemicals, which implies DO has the potential to decrease the energy barrier of the SO₄^.--induced oxidation and shift the chemical equilibrium of the reaction towards the side of products. From the perspective of kinetics, DO can accelerate the oxidation by decreasing ΔE of organic micro-pollutants. In addition, changes of G_pollutant and ΔE of the SO₄^.--induced oxidation were both significantly different between open-chain and aromatic chemicals, and these differences were partially attributed to the difference of polarizability of these two types of chemicals. Furthermore, we revealed that all changes of G_pollutant and ΔE induced by DO were dependent on the DO content. Our study emphasizes the significance of DO on the oxidation of organic micro-pollutants by SO₄^.-, and also provides a theoretical method to study the effect of components in wastewater on removal of organic pollutants in AOPs.

Prediction of HIV-1 protease cleavage site using a combination of sequence, structural, and physicochemical features

Background

The human immunodeficiency virus type 1 (HIV-1) aspartic protease is an important enzyme owing to its imperative part in viral development and a causative agent of deadliest disease known as acquired immune deficiency syndrome (AIDS). Development of HIV-1 protease inhibitors can help understand the specificity of substrates which can restrain the replication of HIV-1, thus antagonize AIDS. However, experimental methods in identification of HIV-1 protease cleavage sites are generally time-consuming and labor-intensive. Therefore, using computational methods to predict cleavage sites has become highly desirable.

Results

In this study, we propose a prediction method in which sequence, structural, and physicochemical features are incorporated in various machine learning algorithms. Then, a bidirectional stepwise selection algorithm is incorporated in feature selection to identify discriminative features. Further, only the selected features are calculated by various encoding schemes and used as input for decision trees, logistic regression, and artificial neural networks. Moreover, a more rigorous three-way data split procedure is applied to evaluate the objective performance of cleavage site prediction. Four benchmark datasets collected from previous studies are used to evaluate the predictive performance.

Conclusions

Experiment results showed that combinations of sequence, structure, and physicochemical features performed better than single feature type for identification of HIV-1 protease cleavage sites. In addition, incorporation of stepwise feature selection is effective to identify interpretable biological features to depict specificity of the substrates. Moreover, artificial neural networks perform significantly better than the other two classifiers. Finally, the proposed method achieved 80.0% ~ 97.4% in accuracy and 0.815 ~ 0.995 evaluated by independent test sets in a three-way data split procedure.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1337-6) contains supplementary material, which is available to authorized users.

The Influence of the Aromatic Character in the Gas Chromatography Elution Order: The Case of Polycyclic Aromatic Hydrocarbons

A link between the aromatic character of polycyclic aromatic hydrocarbons and gas chromatography elution order in columns with a polysiloxane backbone in the stationary phase is reported for the first time. The aromatic character was calculated using a method that combines the π-Sextet Rule and the Pauling Ring Bond Orders to allow the establishment of the location and migration of aromatic sextets in PAH structures. One GC column with a polysiloxane - like backbone (Rxi-PAH) and three GC columns with a polysiloxane backbone (DB-5, SE-52, and LC-50), were used for the analysis. According to the results of this study, within an isomer group, PAHs that contain a lower number of rings affected by the aromatic sextets tend to elute earlier than PAHs that contain a higher number of rings affected by the aromatic sextets. The PAHs that follow the calculated elution order are 88 % in the Rxi-PAH column, 88 % in the DB-5 column, 93 % in the SE-52 column, and 85% in the LC-50 column. It is expected that future analyses with other aromatic compounds in GC columns with a polysiloxane backbone in the stationary phase will follow a GC elution order that agrees with the aromatic character of the molecules.