24 research outputs found
New insights into the genetic etiology of Alzheimer's disease and related dementias
Characterization of the genetic landscape of Alzheimer's disease (AD) and related dementias (ADD) provides a unique opportunity for a better understanding of the associated pathophysiological processes. We performed a two-stage genome-wide association study totaling 111,326 clinically diagnosed/'proxy' AD cases and 677,663 controls. We found 75 risk loci, of which 42 were new at the time of analysis. Pathway enrichment analyses confirmed the involvement of amyloid/tau pathways and highlighted microglia implication. Gene prioritization in the new loci identified 31 genes that were suggestive of new genetically associated processes, including the tumor necrosis factor alpha pathway through the linear ubiquitin chain assembly complex. We also built a new genetic risk score associated with the risk of future AD/dementia or progression from mild cognitive impairment to AD/dementia. The improvement in prediction led to a 1.6- to 1.9-fold increase in AD risk from the lowest to the highest decile, in addition to effects of age and the APOE ε4 allele
Improvements to Supervised EM Learning of Shared Kernel Models by Feature Space Partitioning
Expectation maximisation (EM) is usually thought of as an unsupervised
learning method for estimating the parameters of a mixture distribution,
however it can also be used for supervised learning when class labels are
available. As such, EM has been applied to train neural nets including the
probabilistic radial basis function (PRBF) network or shared kernel (SK) model.
This paper addresses two major shortcomings of previous work in this area: the
lack of rigour in the derivation of the EM training algorithm; and the
computational complexity of the technique, which has limited it to low
dimensional data sets. We first present a detailed derivation of EM for the
Gaussian shared kernel model PRBF classifier, making use of data association
theory to obtain the complete data likelihood, Baum's auxiliary function (the
E-step) and its subsequent maximisation (M-step). To reduce complexity of the
resulting SKEM algorithm, we partition the feature space into
non-overlapping subsets of variables. The resulting product decomposition of
the joint data likelihood, which is exact when the feature partitions are
independent, allows the SKEM to be implemented in parallel and at times
lower complexity. The operation of the partitioned SKEM algorithm is
demonstrated on the MNIST data set and compared with its non-partitioned
counterpart. It eventuates that improved performance at reduced complexity is
achievable. Comparisons with standard classification algorithms are provided on
a number of other benchmark data sets.Comment: 29 pages, 5 figures, 1 table. arXiv admin note: text overlap with
arXiv:2205.0904
Development of an ombrotrophic peat bog (low ash) reference material for the determination of elemental concentrations
Given the increasing interest in using peat bogs as archives of atmospheric metal deposition, the lack of validated sample preparation methods and suitable certified reference materials has hindered not only the quality assurance of the generated analytical data but also the interpretation and comparison of peat core metal profiles from different laboratories in the international community. Reference materials play an important role in the evaluation of the accuracy of analytical results and are essential parts of good laboratory practice. An ombrotrophic peat bog reference material has been developed by 14 laboratories from nine countries in an inter-laboratory comparison between February and October 2002. The material has been characterised for both acid-extractable and total concentrations of a range of elements, including Al, As, Ca, Cd, Cr, Cu, Fe, Hg, Mg, Mn, Na, Ni, P, Pb, Ti, V and Zn. The steps involved in the production of the reference material (i.e. collection and preparation, homogeneity and stability studies, and certification) are described in detail