Progressive Ataxia with Elevated Alpha-Fetoprotein: Diagnostic Issues and Review of the Literature

Background: Ataxias represent a challenging group of disorders due to significant clinical overlap. Here, we present a patient with early-onset progressive ataxia, polyneuropathy and discuss how elevation of alpha fetoprotein (AFP) narrows the differential diagnosis. Case report: Ataxia, polyneuropathy, and mild elevation of AFP are features compatible with ataxia with oculomotor apraxia type 2 (AOA2) but also with ataxia with oculomotor apraxia type 4 (AOA4). A genetic analysis demonstrated biallelic mutations in senataxin (SETX), confirming the diagnosis of AOA2. Discussion: Mild elevation of AFP is found in patients with AOA2 and AOA4, and higher levels are commonly seen in ataxia-telangiectasia. AFP is a useful diagnostic tool but not a biomarker for disease progression in AOA2

The composition of the protosolar disk and the formation conditions for comets

Conditions in the protosolar nebula have left their mark in the composition of cometary volatiles, thought to be some of the most pristine material in the solar system. Cometary compositions represent the end point of processing that began in the parent molecular cloud core and continued through the collapse of that core to form the protosun and the solar nebula, and finally during the evolution of the solar nebula itself as the cometary bodies were accreting. Disentangling the effects of the various epochs on the final composition of a comet is complicated. But comets are not the only source of information about the solar nebula. Protostellar disks around young stars similar to the protosun provide a way of investigating the evolution of disks similar to the solar nebula while they are in the process of evolving to form their own solar systems. In this way we can learn about the physical and chemical conditions under which comets formed, and about the types of dynamical processing that shaped the solar system we see today. This paper summarizes some recent contributions to our understanding of both cometary volatiles and the composition, structure and evolution of protostellar disks.Comment: To appear in Space Science Reviews. The final publication is available at Springer via http://dx.doi.org/10.1007/s11214-015-0167-

A nodes reduction procedure for RBFNDDA through histogram

This paper presents a two-stage learning algorithm to reduce the hidden nodes of a radial basis function network (RBFN). The first stage involves the construction of an RBFN using the dynamic decay adjustment (DDA) and the second stage involves the use of a modified histogram algorithm (HIST) to reduce hidden neurons. DDA enables the RBFN to perform constructive learning without pre-defining the number of hidden nodes. The learning process of DDA is fast but it tends to generate a large network architecture as a result of its greedy insertion behavior. Therefore, an RBFNDDA-HIST is proposed to reduce the nodes. The proposed RBFNDDA-HIST is tested with three benchmark medical datasets. The experimental results show that the accuracy of the RBFNDDA-HIST is compatible with to that of RBFNDDA but with less number of nodes. This proposed network is favorable in a real environment because the computation cost can be reduced

LACAIS: Learning Automata based Cooperative Artificial Immune System for Function Optimization

Abstract. Artificial Immune System (AIS) is taken into account from evolutionary algorithms that have been inspired from defensive mechanism of complex natural immune system. For using this algorithm like other evolutionary algorithms, it should be regulated many parameters, which usually they confront researchers with difficulties. Also another weakness of AIS especially in multimodal problems is trapping in local minima. In basic method, mutation rate changes as only and most important factor results in convergence rate changes and falling in local optima. This paper presented two hybrid algorithm using learning automata to improve the performance of AIS. In the first algorithm entitled LA-AIS has been used one learning automata for tuning the hypermutation rate of AIS and also creating a balance between the process of global and local search. In the second algorithm entitled LA-CAIS has been used two learning automata for cooperative antibodies in the evolution process. Experimental results on several standard functions have shown that the two proposed method are superior to some AIS versions