Microstructural and Mössbauer properties of low temperature synthesized Ni-Cd-Al ferrite nanoparticles

We report the influence of Al3+ doping on the microstructural and Mössbauer properties of ferrite nanoparticles of basic composition Ni0.2Cd0.3Fe2.5 - xAlxO4 (0.0 ≤ x ≤ 0.5) prepared through simple sol-gel method. X-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive X-ray, transmission electron microscopy (TEM), Fourier transformation infrared (FTIR), and Mössbauer spectroscopy techniques were used to investigate the structural, chemical, and Mössbauer properties of the grown nanoparticles. XRD results confirm that all the samples are single-phase cubic spinel in structure excluding the presence of any secondary phase corresponding to any structure. SEM micrographs show the synthesized nanoparticles are agglomerated but spherical in shape. The average crystallite size of the grown nanoparticles was calculated through Scherrer formula and confirmed by TEM and was found between 2 and 8 nm (± 1). FTIR results show the presence of two vibrational bands corresponding to tetrahedral and octahedral sites. Mössbauer spectroscopy shows that all the samples exhibit superparamagnetism, and the quadrupole interaction increases with the substitution of Al3+ ions

Taking Multiple Infections of Cells and Recombination into Account Leads to Small Within-Host Effective-Population-Size Estimates of HIV-1

Whether HIV-1 evolution in infected individuals is dominated by deterministic or stochastic effects remains unclear because current estimates of the effective population size of HIV-1 in vivo, Ne, are widely varying. Models assuming HIV-1 evolution to be neutral estimate Ne∼102–104, smaller than the inverse mutation rate of HIV-1 (∼105), implying the predominance of stochastic forces. In contrast, a model that includes selection estimates Ne>105, suggesting that deterministic forces would hold sway. The consequent uncertainty in the nature of HIV-1 evolution compromises our ability to describe disease progression and outcomes of therapy. We perform detailed bit-string simulations of viral evolution that consider large genome lengths and incorporate the key evolutionary processes underlying the genomic diversification of HIV-1 in infected individuals, namely, mutation, multiple infections of cells, recombination, selection, and epistatic interactions between multiple loci. Our simulations describe quantitatively the evolution of HIV-1 diversity and divergence in patients. From comparisons of our simulations with patient data, we estimate Ne∼103–104, implying predominantly stochastic evolution. Interestingly, we find that Ne and the viral generation time are correlated with the disease progression time, presenting a route to a priori prediction of disease progression in patients. Further, we show that the previous estimate of Ne>105 reduces as the frequencies of multiple infections of cells and recombination assumed increase. Our simulations with Ne∼103–104 may be employed to estimate markers of disease progression and outcomes of therapy that depend on the evolution of viral diversity and divergence

Accurately Measuring Recombination between Closely Related HIV-1 Genomes

Retroviral recombination is thought to play an important role in the generation of immune escape and multiple drug resistance by shuffling pre-existing mutations in the viral population. Current estimates of HIV-1 recombination rates are derived from measurements within reporter gene sequences or genetically divergent HIV sequences. These measurements do not mimic the recombination occurring in vivo, between closely related genomes. Additionally, the methods used to measure recombination make a variety of assumptions about the underlying process, and often fail to account adequately for issues such as co-infection of cells or the possibility of multiple template switches between recombination sites. We have developed a HIV-1 marker system by making a small number of codon modifications in gag which allow recombination to be measured over various lengths between closely related viral genomes. We have developed statistical tools to measure recombination rates that can compensate for the possibility of multiple template switches. Our results show that when multiple template switches are ignored the error is substantial, particularly when recombination rates are high, or the genomic distance is large. We demonstrate that this system is applicable to other studies to accurately measure the recombination rate and show that recombination does not occur randomly within the HIV genome