Determination of Polycyclic Aromatic Hydrocarbons and Their Methylated Derivatives in Sewage Sludge from Northeastern China: Occurrence,Profiles and Toxicity Evaluation

This paper assesses the occurrence, distribution, source, and toxicity of polycyclic aromatic hydrocarbons (PAHs), and their methylated form (Me-PAHs) in sewage sludge from 10 WWTPs in Northeastern China was noted. The concentrations of ∑PAHs, ∑Me-PAHs ranged from 567 to 5040 and 48.1 to 479ng.g−1dw, which is greater than the safety limit for sludge in agriculture in China. High and low molecular weight 4 and 2-ring PAHs and Me-PAHs in sludge were prevalent. The flux of sludge PAHs and Me-PAHs released from ten WWTPs, in Heilongjiang province, was calculated to be over 100 kg/year. Principal component analysis (PCA), diagnostic ratios and positive matrix factorization (PMF) determined a similar mixed pyrogenic and petrogenic source of sewage sludge. The average values of Benzo[a]pyrene was below the safe value of 600ng.g−1 dependent on an incremental lifetime cancer risk ILCR of 10⁻⁶. Sludge is an important source for the transfer of pollutants into the environment, such as PAHs and Me-PAHs. Consequently, greater consideration should be given to its widespread occurrence.     

Two Intercalation Mechanisms of Oxazole Yellow Dimer (YOYO-1) into DNA

The oxazole yellow dye, YOYO-1 (a symmetric homodimer), is a commonly used molecule for staining DNA. We applied the brightness analysis to study the intercalation of YOYO-1 into the DNA. We distinguished two binding modes of the dye to dsDNA: mono-intercalation and bis-intercalation. Bis-intercalation consists of two consecutive mono-intercalation steps, characterised by two distinct equilibrium constants (with the average number of base pair per binding site equals 3.5): K1=3.36±0.43×107M−1 and K2=1.90±0.61×105M−1, respectively. Mono-intercalation dominates at high concentrations of YOYO-1. Bis-intercalation occurs at low concentrations.     

Free Energies of Hydrated Halide Anions: High Through-Put Computations on Clusters to Treat Rough Energy-Landscapes

With a longer-term goal of addressing the comparative behavior of the aqueous halides F−, Cl−, Br−, and I− on the basis of quasi-chemical theory (QCT), here we study structures and free energies of hydration clusters for those anions. We confirm that energetically optimal (H2O)nX clusters, with X = Cl−, Br−, and I−, exhibit surface hydration structures. Computed free energies, based on optimized surface hydration structures utilizing a harmonic approximation, typically (but not always) disagree with experimental free energies. To remedy the harmonic approximation, we utilize single-point electronic structure calculations on cluster geometries sampled from an AIMD (ab initio molecular dynamics) simulation stream. This rough-landscape procedure is broadly satisfactory and suggests unfavorable ligand crowding as the physical effect addressed. Nevertheless, this procedure can break down when n≳4, with the characteristic discrepancy resulting from a relaxed definition of clustering in the identification of (H2O)nX clusters, including ramified structures natural in physical cluster theories. With ramified structures, the central equation for the present rough-landscape approach can acquire some inconsistency. Extension of these physical cluster theories in the direction of QCT should remedy that issue, and should be the next step in this research direction.     

Spectroscopic properties and bond dissociation energies of PbX,PbX±, PbX2 and PbX2± (X ​= ​F,Cl, Br,I)

Ab initio calculations have been performed to investigate some of the spectroscopic properties, like geometry, frequency, electron affinity, ionization potential and finally adiabatic bond dissociation energies (BDEs) of lead monohalides, lead dihalides and their ions viz. PbX, PbX^±, PbX₂, $\mathrm{P}\mathrm{b}{\mathrm{X}}_{\mathrm{2}}^{\pm }$ (X ?= ?F, Cl, Br, I) in their ground state at the QCISD(T)//MP2 level of theory using correlation consistent basis sets. For the validation of MP2 optimized geometry and frequency, we further obtained geometry and frequency of all the neutral and ionic systems using QCISD(T) method with the same basis sets. The BDEs of PbX₂ molecules are calculated using the BDEs of $\mathrm{P}\mathrm{b}{\mathrm{X}}_{\mathrm{2}}^{\pm }$ ions and taking ionization potential and electron affinity of various systems. The calculated values are found in good agreement with the available data. Most of the data for ionic systems are reported first time in literature.     

Inherent Adaptivity of Alzheimer Peptides to Crowded Environments

Amyloid β (Aβ) is the major constituent in senile plaques of Alzheimer's disease in which peptides initially undergo structural conversions to form elongated fibrils. The impact of crowding on the fibrillation pathways of Aβ₄₀ and Aβ₄₂, the most common peptide isoforms are studied. PEG and Ficoll are used as model crowders to mimic a macromolecular enriched surrounding. The fibrillar growth is monitored with the help of ThT-fluorescence assays in order to extract two rates describing primary and secondary processes of nucleation and growth. Techniques as fluorescence correlation spectroscopy and analytical ultracentrifugation are used to discuss oligomeric states; fibril morphologies are investigated using negative-staining transmission electron microscopy. While excluded volume effects imposed by macromolecular crowding are expected to always increase rates of intermolecular interactions and structural conversion, a vast variety of effects are found depending on the peptide, the crowder, or ionic strength of the solution. While investigations of the obtained rates with respect to a reactant-occluded model are capable to display specific surface interactions with the crowder, the employment of crystallization-like models reveal the crowder-induced entropic gain with $\mathrm{\Delta }\mathrm{\Delta }{G}_{\text{fib}}^{\text{crow}}=-116\pm 21k$J mol⁻¹ per volume fraction of the crowder.     

The Relationship between the IC50 Values and the Apparent Inhibition Constant in the Study of Inhibitors of Tyrosinase Diphenolase Activity Helps Confirm the Mechanism of Inhibition

Tyrosinase is the enzyme involved in melanization and is also responsible for the browning of fruits and vegetables. Control of its activity can be carried out using inhibitors, which is interesting in terms of quantitatively understanding the action of these regulators. In the study of the inhibition of the diphenolase activity of tyrosinase, it is intriguing to know the strength and type of inhibition. The strength is indicated by the value of the inhibition constant(s), and the type can be, in a first approximation: competitive, non-competitive, uncompetitive and mixed. In this work, it is proposed to calculate the degree of inhibition (iD), varying the concentration of inhibitor to a fixed concentration of substrate, L-dopa (D). The non-linear regression adjustment of iD with respect to the initial inhibitor concentration [I]0 allows for the calculation of the inhibitor concentration necessary to inhibit the activity by 50%, at a given substrate concentration (IC₅₀), thus avoiding making interpolations between different values of iD. The analytical expression of the IC₅₀, for the different types of inhibition, are related to the apparent inhibition constant (KIapp). Therefore, this parameter can be used: (a) To classify a series of inhibitors of an enzyme by their power. Determining these values at a fixed substrate concentration, the lower IC₅₀, the more potent the inhibitor. (b) Checking an inhibitor for which the type and the inhibition constant have been determined (using the usual methods), must confirm the IC₅₀ value according to the corresponding analytical expression. (c) The type and strength of an inhibitor can be analysed from the study of the variation in iD and IC₅₀ with substrate concentration. The dependence of IC₅₀ on the substrate concentration allows us to distinguish between non-competitive inhibition (iD does not depend on [D]0) and the rest. In the case of competitive inhibition, this dependence of iD on [D]0 leads to an ambiguity between competitive inhibition and type 1 mixed inhibition. This is solved by adjusting the data to the possible equations; in the case of a competitive inhibitor, the calculation of KI1app is carried out from the IC₅₀ expression. The same occurs with uncompetitive inhibition and type 2 mixed inhibition. The representation of iD vs. n, with n=[D]0/KmD, allows us to distinguish between them. A hyperbolic iD vs. n representation that passes through the origin of coordinates is a characteristic of uncompetitive inhibition; the calculation of KI2app is immediate from the IC₅₀ value. In the case of mixed inhibitors, the values of the apparent inhibition constant of meta-tyrosinase (Em) and oxy-tyrosinase (Eox), KI1app and the apparent inhibition constant of metatyrosinase/Dopa complexes (EmD) and oxytyrosinase/Dopa (EoxD), KI2app are obtained from the dependence of iD vs. n, and the results obtained must comply with the IC₅₀ value.     

Deuterium Isotope Effects on Acid-Base Equilibrium of Organic Compounds

Deuterium isotope effects on acid–base equilibrium have been investigated using a combined path integral and free-energy perturbation simulation method. To understand the origin of the linear free-energy relationship of ΔpKa=pKaD2O−pKaH2O versus pKaH2O, we examined two theoretical models for computing the deuterium isotope effects. In Model 1, only the intrinsic isotope exchange effect of the acid itself in water was included by replacing the titratable protons with deuterons. Here, the dominant contribution is due to the difference in zero-point energy between the two isotopologues. In Model 2, the medium isotope effects are considered, in which the free energy change as a result of replacing H₂O by D₂O in solute–solvent hydrogen-bonding complexes is determined. Although the average ΔpKa change from Model 1 was found to be in reasonable agreement with the experimental average result, the pKaH2O dependence of the solvent isotope effects is absent. A linear free-energy relationship is obtained by including the medium effect in Model 2, and the main factor is due to solvent isotope effects in the anion–water complexes. The present study highlights the significant roles of both the intrinsic isotope exchange effect and the medium solvent isotope effect.