Translational Biomarkers in Aging and Dementia (TRIAD) study, Alzheimer's and Families (ALFA) study, and BioCogBank Paris Lariboisière cohort IMPORTANCE Glial fibrillary acidic protein (GFAP) is a marker of reactive astrogliosis that increases in the cerebrospinal fluid (CSF) and blood of individuals with Alzheimer disease (AD). However, it is not known whether there are differences in blood GFAP levels across the entire AD continuum and whether its performance is similar to that of CSF GFAP.OBJECTIVE To evaluate plasma GFAP levels throughout the entire AD continuum, from preclinical AD to AD dementia, compared with CSF GFAP.

Background Although the distribution of Giardia duodenalis genotypes in humans has been increasingly reported in recent years, data on possible differences in pathogen transmission between age groups and virulence between genotypes are scarce. The purpose of this study is to investigate the genetic diversity of G. duodenalis in humans in Spain and compare the distribution of G. duodenalis assemblages A and B between children and adults and clinical presentations between the two genotypes. Methods In the present study, 125 microscopy-positive fecal samples were collected from humans in Spain over a 7-year period. PCR and sequence analyses of the triosephosphate isomerase, β-giardin and glutamate dehydrogenase genes were used to identify the multilocus genotypes of G. duodenalis . Results Sequence analysis of three genetic loci identified both G. duodenalis assemblages A (29) and B (66), with co-infections of the two in two patients. Among the sequences obtained in this study, four multilocus genotypes (MLGs) of the sub-assemblage AII were observed within assemblage A. In contrast, 19 MLGs were detected within assemblage B due to the high sequence diversity at each locus. One MLG, however, was found in 51.9% (27/52) of assemblage B samples. Children were more commonly infected by assemblage B (44/53 or 83%) than adults (22/42 or 52.4%; χ 2 = 10.371, df = 1, P = 0.001). Asymptomatic infection was more common in patients with assemblage A (4/29 or 13.8%) than in those with assemblage B (1/66 or 1.5%; χ 2 = 6.091, df = 1, P = 0.029), and the frequency of abdominal pain occurrence was higher in assemblage B patients (65/66 or 98.5%) than assemblage A patients (25/29 or 86.2%; χ 2 = 6.091, df = 1, P = 0.029). Conclusions These results illustrate the existence of differences in genotype distribution between children and adults and clinical presentations between G. duodenalis genotypes. They are useful in understanding the transmission of G. duodenalis in humans in Spain.

Summary Science gateways provide UIs and high‐level services to access and manage applications and data collections on distributed resources. They facilitate users to perform data analysis on distributed computing infrastructures without getting involved into the technical details. The e‐BioInfra Gateway is a science gateway for biomedical data analysis on a national grid infrastructure, which has been successfully adopted for neuroscience research. This paper describes the motivation, requirements, and design of a new generation of e‐BioInfra Gateway, which is based on the grid and cloud user support environment (also known as WS‐PGRADE/gUSE framework) and supports heterogeneous infrastructures. The new gateway has been designed to have additional data and meta‐data management facilities to access and manage (biomedical) data servers, and to provide data‐centric user interaction. We have implemented and deployed the new gateway for the computational neuroscience research community of the Academic Medical Center of the University of Amsterdam. This paper presents the system architecture of the new gateway, highlights the improvements that have been achieved, discusses the choices that we have made, and reflects on those based on initial user feedback. Copyright © 2014 John Wiley & Sons, Ltd.

-We present a simple but yet effective algorithm to speed up the codebook search in a 11. THE ALGORITHM vector quantizatjon scheme when a MSE criterium is used, A considerable reduction in the number ofThe algorithm we present reduces the encoding complexity operations is achieved.hi^ algorithm was when the minimum Square error is used to evaluate the originally designed for image vector quantization in similarity between the signal vector and the codeword. This is which the samples of the image signal (pixels) are the most widely used distortion measure in speech and image positive, although it can be used with any positive-The distortion between the vector X = (XI, . . . , XN) and negative signal with only minor modifications.coding due to its good results and simplicity. I. INTRODUCI-IONThe vector quantization scheme has proven to be very effective in speech and image coding [l], where a number of samples are grouped together forming a so called vector. Each vector X is then treated as an unbreakable unit during the encodingldecoding operation.In image vector quantization, the image is divided into small rectangular blocks typically of size 3x3 or 4x4 pixels or similar. Although in this case X has the structure of a matrix rather than of a vector we prefer to call it vector in a general sense as this is the most common terminology. Let X be an input vector from the original signal and let Y represent a codeword or reproduction vector. The codebook contains all the available codewords and the quantizer replaces the input vector by the codeword that is more similar to X. If the usual minimum square error is used as the measure of similarity, the quantizer searches for the codeword located at the minimum error from the input vector. Other distortion measures are also used, although will not be considered in this work. In order to assign to X the best codeword of the codebook, an exhaustive search (also called full search) is required. That means that every codeword has to be examined to ensure that the best will be selected. When the number of codewords is large this process requires a very important computational load. Several algorithms [2,3,4] have been designed to reduce the computational load of the encoding process wilh the same goal as the one presented here, Le. to reduce the computational I IYI l2 can be previously calculated and stored to be used every time the distortion between a vector and a codeword has to be computed. On the other hand, there is no need to compute llX1I2 as it depends only on the input vector X and for a given vector it is a constant that does not affect the nearest neighbor selection.The term 2xnyn depends on both the vector and the codeword and cannot be precalculated as before. However, if all the vector components are positive, Le., xn 2 0 and yn 2 0 then it is obvious that (3) IEEE Log Number 9400893.