Modified Friedmann equations via conformal Bohm -- De Broglie gravity

We use an alternative interpretation of quantum mechanics, based on the Bohmian trajectory approach, and show that the quantum effects can be included in the classical equation of motion via a conformal transformation on the background metric. We apply this method to the Robertson-Walker metric to derive a modified version of Friedmann's equations for a Universe consisting of scalar, spin-zero, massive particles. These modified equations include additional terms that result from the non-local nature of matter and appear as an acceleration in the expansion of the Universe. We see that the same effect may also be present in the case of an inhomogeneous expansion.Comment: Accepted for publication in Ap

Fast nonadiabatic dynamics of many-body quantum systems

Modeling many-body quantum systems with strong interactions is one of the core challenges of modern physics. A range of methods has been developed to approach this task, each with its own idiosyncrasies, approximations, and realm of applicability. However, there remain many problems that are intractable for existing methods. In particular, many approaches face a huge computational barrier when modeling large numbers of coupled electrons and ions at finite temperature. Here, we address this shortfall with a new approach to modeling many-body quantum systems. On the basis of the Bohmian trajectory formalism, our new method treats the full particle dynamics with a considerable increase in computational speed. As a result, we are able to perform large-scale simulations of coupled electron-ion systems without using the adiabatic Born-Oppenheimer approximation

Properties of purified enzymes induced by pathogenic drug-resistant mutants of herpes simplex virus. Evidence for virus variants expressing normal DNA polymerase and altered thymidine kinase

The DNA polymerases and thymidine kinases induced by three drug-resistant mutants of herpes simplex virus type 1 (S1, Tr7, and B3) and their common parent strain, SC16, have been purified and their properties compared. No significant differences were seen in the affinities of the polymerases for TTP and dGTP, or for the triphosphates of 9-(2-hydroxyethyloxymethyl)guanine (acyclovir) or (E)-5-(2-bromovinyl)-2'-deoxyuridine (BVdU) (drugs used in their isolation). In contrast all three mutants induced abnormal thymidine kinases. Those induced by the acyclovir-resistant mutants, S1 and Tr7, showed reduced affinities for thymidine, acyclovir, and also BVdU. Thymidine kinase induced by the BVdU-resistant mutant B3 showed reduced affinity for BVdU, but its affinities for thymidine and acyclovir were similar to those of the wild type enzyme. Thus, it appears that these variants of herpes simplex virus express altered thymidine kinases with impaired ability to phosphorylate particular nucleoside analogue drugs and these characteristics probably account for the drug resistance of the viruses. This strategy for resistance is important as it may result in variants with undiminished pathogenicity

Stability analysis of mixtures of mutagenetic trees

<p>Abstract</p> <p>Background</p> <p>Mixture models of mutagenetic trees are evolutionary models that capture several pathways of ordered accumulation of genetic events observed in different subsets of patients. They were used to model HIV progression by accumulation of resistance mutations in the viral genome under drug pressure and cancer progression by accumulation of chromosomal aberrations in tumor cells. From the mixture models a genetic progression score (GPS) can be derived that estimates the genetic status of single patients according to the corresponding progression along the tree models. GPS values were shown to have predictive power for estimating drug resistance in HIV or the survival time in cancer. Still, the reliability of the exact values of such complex markers derived from graphical models can be questioned.</p> <p>Results</p> <p>In a simulation study, we analyzed various aspects of the stability of estimated mutagenetic trees mixture models. It turned out that the induced probabilistic distributions and the tree topologies are recovered with high precision by an EM-like learning algorithm. However, only for models with just one major model component, also GPS values of single patients can be reliably estimated.</p> <p>Conclusion</p> <p>It is encouraging that the estimation process of mutagenetic trees mixture models can be performed with high confidence regarding induced probability distributions and the general shape of the tree topologies. For a model with only one major disease progression process, even genetic progression scores for single patients can be reliably estimated. However, for models with more than one relevant component, alternative measures should be introduced for estimating the stage of disease progression.</p

Mapping HIV-1 Vaccine Induced T-Cell Responses: Bias towards Less-Conserved Regions and Potential Impact on Vaccine Efficacy in the Step Study

T cell directed HIV vaccines are based upon the induction of CD8+ T cell memory responses that would be effective in inhibiting infection and subsequent replication of an infecting HIV-1 strain, a process that requires a match or near-match between the epitope induced by vaccination and the infecting viral strain. We compared the frequency and specificity of the CTL epitope responses elicited by the replication-defective Ad5 gag/pol/nef vaccine used in the Step trial with the likelihood of encountering those epitopes among recently sequenced Clade B isolates of HIV-1. Among vaccinees with detectable 15-mer peptide pool ELISpot responses, there was a median of four (one Gag, one Nef and two Pol) CD8 epitopes per vaccinee detected by 9-mer peptide ELISpot assay. Importantly, frequency analysis of the mapped epitopes indicated that there was a significant skewing of the T cell response; variable epitopes were detected more frequently than would be expected from an unbiased sampling of the vaccine sequences. Correspondingly, the most highly conserved epitopes in Gag, Pol, and Nef (defined by presence in >80% of sequences currently in the Los Alamos database www.hiv.lanl.gov) were detected at a lower frequency than unbiased sampling, similar to the frequency reported for responses to natural infection, suggesting potential epitope masking of these responses. This may be a generic mechanism used by the virus in both contexts to escape effective T cell immune surveillance. The disappointing results of the Step trial raise the bar for future HIV vaccine candidates. This report highlights the bias towards less-conserved epitopes present in the same vaccine used in the Step trial. Development of vaccine strategies that can elicit a greater breadth of responses, and towards conserved regions of the genome in particular, are critical requirements for effective T-cell based vaccines against HIV-1

Cross-validated stepwise regression for identification of novel non-nucleoside reverse transcriptase inhibitor resistance associated mutations

<p>Abstract</p> <p>Background</p> <p>Linear regression models are used to quantitatively predict drug resistance, the phenotype, from the HIV-1 viral genotype. As new antiretroviral drugs become available, new resistance pathways emerge and the number of resistance associated mutations continues to increase. To accurately identify which drug options are left, the main goal of the modeling has been to maximize predictivity and not interpretability. However, we originally selected linear regression as the preferred method for its transparency as opposed to other techniques such as neural networks. Here, we apply a method to lower the complexity of these phenotype prediction models using a 3-fold cross-validated selection of mutations.</p> <p>Results</p> <p>Compared to standard stepwise regression we were able to reduce the number of mutations in the reverse transcriptase (RT) inhibitor models as well as the number of interaction terms accounting for synergistic and antagonistic effects. This reduction in complexity was most significant for the non-nucleoside reverse transcriptase inhibitor (NNRTI) models, while maintaining prediction accuracy and retaining virtually all known resistance associated mutations as first order terms in the models. Furthermore, for etravirine (ETR) a better performance was seen on two years of unseen data. By analyzing the phenotype prediction models we identified a list of forty novel NNRTI mutations, putatively associated with resistance. The resistance association of novel variants at known NNRTI resistance positions: 100, 101, 181, 190, 221 and of mutations at positions not previously linked with NNRTI resistance: 102, 139, 219, 241, 376 and 382 was confirmed by phenotyping site-directed mutants.</p> <p>Conclusions</p> <p>We successfully identified and validated novel NNRTI resistance associated mutations by developing parsimonious resistance prediction models in which repeated cross-validation within the stepwise regression was applied. Our model selection technique is computationally feasible for large data sets and provides an approach to the continued identification of resistance-causing mutations.</p

25 years of HIV-1 research – progress and perspectives

Twenty-five years after the discovery and isolation of the human immunodeficiency virus by French and American scientists, much progress has been made in basic research, clinical treatment, and public health prevention measures for acquired immunodeficiency syndrome. Here, we summarize, in brief, advances that have been achieved and provide some perspectives on future challenges

Comparison of HIV-1 Genotypic Resistance Test Interpretation Systems in Predicting Virological Outcomes Over Time

Background: Several decision support systems have been developed to interpret HIV-1 drug resistance genotyping results. This study compares the ability of the most commonly used systems (ANRS, Rega, and Stanford's HIVdb) to predict virological outcome at 12, 24, and 48 weeks. Methodology/Principal Findings: Included were 3763 treatment-change episodes (TCEs) for which a HIV-1 genotype was available at the time of changing treatment with at least one follow-up viral load measurement. Genotypic susceptibility scores for the active regimens were calculated using scores defined by each interpretation system. Using logistic regression, we determined the association between the genotypic susceptibility score and proportion of TCEs having an undetectable viral load (<50 copies/ml) at 12 (8-16) weeks (2152 TCEs), 24 (16-32) weeks (2570 TCEs), and 48 (44-52) weeks (1083 TCEs). The Area under the ROC curve was calculated using a 10-fold cross-validation to compare the different interpretation systems regarding the sensitivity and specificity for predicting undetectable viral load. The mean genotypic susceptibility score of the systems was slightly smaller for HIVdb, with 1.92±1.17, compared to Rega and ANRS, with 2.22±1.09 and 2.23±1.05, respectively. However, similar odds ratio's were found for the association between each-unit increase in genotypic susceptibility score and undetectable viral load at week 12; 1.6 [95% confidence interval 1.5-1.7] for HIVdb, 1.7 [1.5-1.8] for ANRS, and 1.7 [1.9-1.6] for Rega. Odds ratio's increased over time, but remained comparable (odds ratio's ranging between 1.9-2.1 at 24 weeks and 1.9-2.