Lessons to be Learned from Natural Control of HIV – Future Directions, Therapeutic, and Preventive Implications

Accumulating data generated from persons who naturally control HIV without the need for antiretroviral treatment has led to significant insights into the possible mechanisms of durable control of AIDS virus infection. At the center of this control is the HIV-specific CD8 T cell response, and the basis for this CD8-mediated control is gradually being revealed. Genome wide association studies coupled with HLA sequence data implicate the nature of the HLA-viral peptide interaction as the major genetic factor modulating durable control of HIV, but host genetic factors account for only around 20% of the variability in control. Other factors including specific functional characteristics of the TCR clonotypes generated in vivo, targeting of vulnerable regions of the virus that lead to fitness impairing mutations, immune exhaustion, and host restriction factors that limit HIV replication all have been shown to additionally contribute to control. Moreover, emerging data indicate that the CD8+ T cell response may be critical for attempts to purge virus infected cells following activation of the latent reservoir, and thus lessons learned from elite controllers (ECs) are likely to impact the eradication agenda. On-going efforts are also needed to understand and address the role of immune activation in disease progression, as it becomes increasingly clear that durable immune control in ECs comes at a cost. Taken together, the research achievements in the attempt to unlock the mechanisms behind natural control of HIV will continue to be an important source of insights and ideas in the continuous search after an effective HIV vaccine, and for the attempts to achieve a sterilizing or functional cure in HIV positive patients with progressive infection

Robustness against Power is PSPACE-complete

Power is a RISC architecture developed by IBM, Freescale, and several other companies and implemented in a series of POWER processors. The architecture features a relaxed memory model providing very weak guarantees with respect to the ordering and atomicity of memory accesses. Due to these weaknesses, some programs that are correct under sequential consistency (SC) show undesirable effects when run under Power. We call these programs not robust against the Power memory model. Formally, a program is robust if every computation under Power has the same data and control dependencies as some SC computation. Our contribution is a decision procedure for robustness of concurrent programs against the Power memory model. It is based on three ideas. First, we reformulate robustness in terms of the acyclicity of a happens-before relation. Second, we prove that among the computations with cyclic happens-before relation there is one in a certain normal form. Finally, we reduce the existence of such a normal-form computation to a language emptiness problem. Altogether, this yields a PSPACE algorithm for checking robustness against Power. We complement it by a matching lower bound to show PSPACE-completeness

Obesity Prevalence and Dietary Intake of Antioxidants in Native American Adolescents

Antioxidants are well known for possessing anti-inflammatory properties, which can reduce the risk of chronic disease and obesity. However, very little research has been done to examine antioxidant intake among adolescent minority populations such as Native American adolescents. Our study examined the significance of antioxidant intake among Native American adolescents at an urban residential high school in Southern California. Our study population consisted of 183 male and female Native American adolescents, 14-18 years of age, representing 43 tribes from across the United States. Students' primary source of meals was provided by the school food service. Based on the BMI calculations, the rate of obesity within our population was 38% for males and 40% for females, more than two-fold the national rate indicated by NHANESIII data. We used the Harvard School of Public Health Youth/Adolescent Questionnaire (HSPH YAQ), a semi-quantitative food frequency questionnaire, to examine antioxidant nutrient intake and evaluate the differences in the intake between normal and obese weight students. Statistical analysis of the results showed that intakes of vitamins C, E, and lycopene were the antioxidant nutrients found to be significantly different between normal and obese weight students and intakes of these nutrients were found to be higher among normal weight students (p-values = 0.02451, 0.00847, and 0.04928, respectively). These results suggest that dietary intake of antioxidants could be increased among Native American adolescents. Further research is needed to confirm our findings and identify effective ways for school food service to incorporate antioxidant rich foods into school menus

A subgraph isomorphism algorithm and its application to biochemical data

BackgroundGraphs can represent biological networks at the molecular, protein, or species level. An important query is to find all matches of a pattern graph to a target graph. Accomplishing this is inherently difficult (NP-complete) and the efficiency of heuristic algorithms for the problem may depend upon the input graphs. The common aim of existing algorithms is to eliminate unsuccessful mappings as early as and as inexpensively as possible.ResultsWe propose a new subgraph isomorphism algorithm which applies a search strategy to significantly reduce the search space without using any complex pruning rules or domain reduction procedures. We compare our method with the most recent and efficient subgraph isomorphism algorithms (VFlib, LAD, and our C++ implementation of FocusSearch which was originally distributed in Modula2) on synthetic, molecules, and interaction networks data. We show a significant reduction in the running time of our approach compared with these other excellent methods and show that our algorithm scales well as memory demands increase.ConclusionsSubgraph isomorphism algorithms are intensively used by biochemical tools. Our analysis gives a comprehensive comparison of different software approaches to subgraph isomorphism highlighting their weaknesses and strengths. This will help researchers make a rational choice among methods depending on their application. We also distribute an open-source package including our system and our own C++ implementation of FocusSearch together with all the used datasets (http://ferrolab.dmi.unict.it/ri.html). In future work, our findings may be extended to approximate subgraph isomorphism algorithms

A subgraph isomorphism algorithm and its application to biochemical data

BackgroundGraphs can represent biological networks at the molecular, protein, or species level. An important query is to find all matches of a pattern graph to a target graph. Accomplishing this is inherently difficult (NP-complete) and the efficiency of heuristic algorithms for the problem may depend upon the input graphs. The common aim of existing algorithms is to eliminate unsuccessful mappings as early as and as inexpensively as possible.ResultsWe propose a new subgraph isomorphism algorithm which applies a search strategy to significantly reduce the search space without using any complex pruning rules or domain reduction procedures. We compare our method with the most recent and efficient subgraph isomorphism algorithms (VFlib, LAD, and our C++ implementation of FocusSearch which was originally distributed in Modula2) on synthetic, molecules, and interaction networks data. We show a significant reduction in the running time of our approach compared with these other excellent methods and show that our algorithm scales well as memory demands increase.ConclusionsSubgraph isomorphism algorithms are intensively used by biochemical tools. Our analysis gives a comprehensive comparison of different software approaches to subgraph isomorphism highlighting their weaknesses and strengths. This will help researchers make a rational choice among methods depending on their application. We also distribute an open-source package including our system and our own C++ implementation of FocusSearch together with all the used datasets (http://ferrolab.dmi.unict.it/ri.html). In future work, our findings may be extended to approximate subgraph isomorphism algorithms

Predicting genome-wide redundancy using machine learning

<p>Abstract</p> <p>Background</p> <p>Gene duplication can lead to genetic redundancy, which masks the function of mutated genes in genetic analyses. Methods to increase sensitivity in identifying genetic redundancy can improve the efficiency of reverse genetics and lend insights into the evolutionary outcomes of gene duplication. Machine learning techniques are well suited to classifying gene family members into redundant and non-redundant gene pairs in model species where sufficient genetic and genomic data is available, such as <it>Arabidopsis thaliana</it>, the test case used here.</p> <p>Results</p> <p>Machine learning techniques that combine multiple attributes led to a dramatic improvement in predicting genetic redundancy over single trait classifiers alone, such as BLAST E-values or expression correlation. In withholding analysis, one of the methods used here, Support Vector Machines, was two-fold more precise than single attribute classifiers, reaching a level where the majority of redundant calls were correctly labeled. Using this higher confidence in identifying redundancy, machine learning predicts that about half of all genes in <it>Arabidopsis </it>showed the signature of predicted redundancy with at least one but typically less than three other family members. Interestingly, a large proportion of predicted redundant gene pairs were relatively old duplications (e.g., Ks > 1), suggesting that redundancy is stable over long evolutionary periods.</p> <p>Conclusions</p> <p>Machine learning predicts that most genes will have a functionally redundant paralog but will exhibit redundancy with relatively few genes within a family. The predictions and gene pair attributes for <it>Arabidopsis </it>provide a new resource for research in genetics and genome evolution. These techniques can now be applied to other organisms.</p

Obesity Prevalence and Dietary Intake of Antioxidants in Native American Adolescents

Antioxidants are well known for possessing anti-inflammatory properties, which can reduce the risk of chronic disease and obesity. However, very little research has been done to examine antioxidant intake among adolescent minority populations such as Native American adolescents. Our study examined the significance of antioxidant intake among Native American adolescents at an urban residential high school in Southern California. Our study population consisted of 183 male and female Native American adolescents, 14-18 years of age, representing 43 tribes from across the United States. Students’ primary source of meals was provided by the school food service. Based on the BMI calculations, the rate of obesity within our population was 38% for males and 40% for females, more than two-fold the national rate indicated by NHANESIII data. We used the Harvard School of Public Health Youth/Adolescent Questionnaire (HSPH YAQ), a semi-quantitative food frequency questionnaire, to examine antioxidant nutrient intake and evaluate the differences in the intake between normal and obese weight students. Statistical analysis of the results showed that intakes of vitamins C, E, and lycopene were the antioxidant nutrients found to be significantly different between normal and obese weight students and intakes of these nutrients were found to be higher among normal weight students (p-values = 0.02451, 0.00847, and 0.04928, respectively). These results suggest that dietary intake of antioxidants could be increased among Native American adolescents. Further research is needed to confirm our findings and identify effective ways for school food service to incorporate antioxidant rich foods into school menus

NetMatch: a Cytoscape plugin for searching biological networks.

Abstract Summary: NetMatch is a Cytoscape plugin which allows searching biological networks for subcomponents matching a given query. Queries may be approximate in the sense that certain parts of the subgraph-query may be left unspecified. To make the query creation process easy, a drawing tool is provided. Cytoscape is a bioinformatics software platform for the visualization and analysis of biological networks. Availability: The full package, a tutorial and associated examples are available at the following web sites: http://alpha.dmi.unict.it/~ctnyu/netmatch.html, http://baderlab.org/Software/NetMatch Contact: [email protected]

Partial Covering Arrays: Algorithms and Asymptotics

A covering array $\mathsf{CA}(N;t,k,v)$ is an $N\times k$ array with entries in $\{1, 2, \ldots , v\}$, for which every $N\times t$ subarray contains each $t$-tuple of $\{1, 2, \ldots , v\}^t$ among its rows. Covering arrays find application in interaction testing, including software and hardware testing, advanced materials development, and biological systems. A central question is to determine or bound $\mathsf{CAN}(t,k,v)$, the minimum number $N$ of rows of a $\mathsf{CA}(N;t,k,v)$. The well known bound $\mathsf{CAN}(t,k,v)=O((t-1)v^t\log k)$ is not too far from being asymptotically optimal. Sensible relaxations of the covering requirement arise when (1) the set $\{1, 2, \ldots , v\}^t$ need only be contained among the rows of at least $(1-\epsilon)\binom{k}{t}$ of the $N\times t$ subarrays and (2) the rows of every $N\times t$ subarray need only contain a (large) subset of $\{1, 2, \ldots , v\}^t$. In this paper, using probabilistic methods, significant improvements on the covering array upper bound are established for both relaxations, and for the conjunction of the two. In each case, a randomized algorithm constructs such arrays in expected polynomial time