Hyper-IgD and Periodic Fever Syndrome: a New MVK Mutation (p.R277G) Associated with a Severe Phenotype

Hyperimmunoglobulinemia D and periodic fever syndrome (HIDS; MIM#260920) is a rare recessively-inherited autoinflammatory condition caused bymutations in the MVK gene, which encodes for mevalonate kinase, an essential enzyme in the isoprenoid pathway. HIDS is clinically characterized by recurrent episodes of fever and inflammation. Herewe report on the case of a 2 year-old Portuguese boy with recurrent episodes of fever, malaise, massive cervical lymphadenopathy and hepatosplenomegaly since the age of 12 months. Rash, arthralgia, abdominal pain and diarrhea were also seen occasionally. During attacks a vigorous acute-phase response was detected, including elevated erythrocyte sedimentation rate, C-reactive protein, serum amyloid A and leukocytosis. Clinical and laboratory improvement was seen between attacks. Despite normal serum IgD level, HIDS was clinically suspected. Mutational MVK analysis revealed the homozygous genotype with the novel p.Arg277Gly (p.R277G) mutation, while the healthy non consanguineous parents were heterozygous. Short nonsteroidal anti-inflammatory drugs and corticosteroid courses were given during attacks with poor benefits, where as anakinra showed positive responses only at high doses. The p.R277Gmutation here described is a novel missense MVK mutation, and it has been detected in this casewith a severe HIDS phenotype. Further studies are needed to evaluate a co-relation genotype, enzyme activity and phenotype, and to define the best therapeutic strategies.

A parameter-free approach for solving combinatorial optimization problems through biased randomization of efficient heuristics

This paper discusses the use of probabilistic or randomized algorithms for solving combinatorial optimization problems. Our approach employs non-uniform probability distributions to add a biased random behavior to classical heuristics so a large set of alternative good solutions can be quickly obtained in a natural way and without complex conguration processes. This procedure is especially useful in problems where properties such as non-smoothness or non-convexity lead to a highly irregular solution space, for which the traditional optimization methods, both of exact and approximate nature, may fail to reach their full potential. The results obtained are promising enough to suggest that randomizing classical heuristics is a powerful method that can be successfully applied in a variety of cases.

Abnormal expression of CD54 in mixed reactions of mononuclear cells from hyper-IgE syndrome patients

Hyper-IgE syndrome (HIES) is a rare multisystem disorder characterized by increased susceptibility to infections associated with heterogeneous immunologic and non-immunologic abnormalities. Most patients consistently exhibit defective antigen-induced-T cell activation, that could be partly due to altered costimulation involving accessory molecules; however, the expression of these molecules has never been documented in HIES. Therefore, we investigated the expression of CD11a, CD28, CD40, CD54, CD80, CD86, and CD154 in peripheral blood mononuclear cells from six patients and six healthy controls by flow cytometry after autologous and mixed allogeneic reactions. Only the allogeneic stimuli induced significant proliferative responses and interleukin 2 and interferon gamma production in both groups. Most accessory molecules showed similar expression between patients and controls with the exception of CD54, being expressed at lower levels in HIES patients regardless of the type of stimulus used. Decreased expression of CD54 could partly explain the deficient T cell activation to specific recall antigens in HIES patients, and might be responsible for their higher susceptibility to infections with defined types of microorganisms.

Incorporating waiting time in competitive location models: formulations and heuristics

In this paper we propose a metaheuristic to solve a new version of the Maximum Capture Problem. In the original MCP, market capture is obtained by lower traveling distances or lower traveling time, in this new version not only the traveling time but also the waiting time will affect the market share. This problem is hard to solve using standard optimization techniques. Metaheuristics are shown to offer accurate results within acceptable computing times.

Adaptive approach heuristics for the generalized assignment problem

The Generalized Assignment Problem consists in assigning a setof tasks to a set of agents with minimum cost. Each agent hasa limited amount of a single resource and each task must beassigned to one and only one agent, requiring a certain amountof the resource of the agent. We present new metaheuristics forthe generalized assignment problem based on hybrid approaches.One metaheuristic is a MAX-MIN Ant System (MMAS), an improvedversion of the Ant System, which was recently proposed byStutzle and Hoos to combinatorial optimization problems, and itcan be seen has an adaptive sampling algorithm that takes inconsideration the experience gathered in earlier iterations ofthe algorithm. Moreover, the latter heuristic is combined withlocal search and tabu search heuristics to improve the search.A greedy randomized adaptive search heuristic (GRASP) is alsoproposed. Several neighborhoods are studied, including one basedon ejection chains that produces good moves withoutincreasing the computational effort. We present computationalresults of the comparative performance, followed by concludingremarks and ideas on future research in generalized assignmentrelated problems.

Consumer choice in competitive location models: Formulations and heuristics

A new direction of research in Competitive Location theory incorporatestheories of Consumer Choice Behavior in its models. Following thisdirection, this paper studies the importance of consumer behavior withrespect to distance or transportation costs in the optimality oflocations obtained by traditional Competitive Location models. To dothis, it considers different ways of defining a key parameter in thebasic Maximum Capture model (MAXCAP). This parameter will reflectvarious ways of taking into account distance based on several ConsumerChoice Behavior theories. The optimal locations and the deviation indemand captured when the optimal locations of the other models are usedinstead of the true ones, are computed for each model. A metaheuristicbased on GRASP and Tabu search procedure is presented to solve all themodels. Computational experience and an application to 55-node networkare also presented.

Incorporating waiting time in competitive location models: Formulations and heuristics

In this paper we propose a metaheuristic to solve a new version of the Maximum CaptureProblem. In the original MCP, market capture is obtained by lower traveling distances or lowertraveling time, in this new version not only the traveling time but also the waiting time willaffect the market share. This problem is hard to solve using standard optimization techniques.Metaheuristics are shown to offer accurate results within acceptable computing times.