Common and rare variant association analyses in amyotrophic lateral sclerosis identify 15 risk loci with distinct genetic architectures and neuron-specific biology

A cross-ancestry genome-wide association meta-analysis of amyotrophic lateral sclerosis (ALS) including 29,612 patients with ALS and 122,656 controls identifies 15 risk loci with distinct genetic architectures and neuron-specific biology. Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with a lifetime risk of one in 350 people and an unmet need for disease-modifying therapies. We conducted a cross-ancestry genome-wide association study (GWAS) including 29,612 patients with ALS and 122,656 controls, which identified 15 risk loci. When combined with 8,953 individuals with whole-genome sequencing (6,538 patients, 2,415 controls) and a large cortex-derived expression quantitative trait locus (eQTL) dataset (MetaBrain), analyses revealed locus-specific genetic architectures in which we prioritized genes either through rare variants, short tandem repeats or regulatory effects. ALS-associated risk loci were shared with multiple traits within the neurodegenerative spectrum but with distinct enrichment patterns across brain regions and cell types. Of the environmental and lifestyle risk factors obtained from the literature, Mendelian randomization analyses indicated a causal role for high cholesterol levels. The combination of all ALS-associated signals reveals a role for perturbations in vesicle-mediated transport and autophagy and provides evidence for cell-autonomous disease initiation in glutamatergic neurons

Common and rare variant association analyses in amyotrophic lateral sclerosis identify 15 risk loci with distinct genetic architectures and neuron-specific biology

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with a lifetime risk of one in 350 people and an unmet need for disease-modifying therapies. We conducted a cross-ancestry genome-wide association study (GWAS) including 29,612 patients with ALS and 122,656 controls, which identified 15 risk loci. When combined with 8,953 individuals with whole-genome sequencing (6,538 patients, 2,415 controls) and a large cortex-derived expression quantitative trait locus (eQTL) dataset (MetaBrain), analyses revealed locus-specific genetic architectures in which we prioritized genes either through rare variants, short tandem repeats or regulatory effects. ALS-associated risk loci were shared with multiple traits within the neurodegenerative spectrum but with distinct enrichment patterns across brain regions and cell types. Of the environmental and lifestyle risk factors obtained from the literature, Mendelian randomization analyses indicated a causal role for high cholesterol levels. The combination of all ALS-associated signals reveals a role for perturbations in vesicle-mediated transport and autophagy and provides evidence for cell-autonomous disease initiation in glutamatergic neurons

A hybrid level-based learning swarm algorithm with mutation operator for solving large-scale cardinality-constrained portfolio optimization problems

We propose a hybrid variant of the level-based learning swarm optimizer (LLSO) for solving large-scale portfolio optimization problems. This solver fills the gap due to the inadequacy of the particle swarm optimization algorithm for high-dimensional instances. We aim to extend the classical mean-variance formulation by maximizing a modified version of the Sharpe ratio subject to cardinality, box, and budget constraints. The algorithm involves a projection operator to deal with these three constraints simultaneously. Further, we implicitly control transaction costs thanks to a rebalancing constraint handled by a suitable exact penalty function. In addition, we develop an ad hoc mutation operator to modify candidate exemplars in the highest level of the swarm. The experimental results, using three large-scale data sets, show that including this procedure improves the accuracy of the solutions. Then, a comparison with other variants of the LLSO algorithm and two state-of-the-art swarm optimizers points out the outstanding performance of the proposed solver in terms of exploration capabilities and solution quality. Finally, we assess the profitability of the portfolio allocation strategy in the last five years using an investable pool of 1119 constituents from the MSCI World Index

The gigavision camera

We study a new image device called gigavision camera [1] or the gigapixel digital film camera [2]. The main differences between a conventional and a gigavision camera are that the pixels of th

On pixel detection threshold in the gigavision camera

Circuits and SystemsElectrical Engineering, Mathematics and Computer Scienc

Induction nitriding of grade 5 titanium alloy: process optimization

Conventional nitriding treatment on Ti alloys is a challenging technological process because of both Ti high affinity with N, that produces a barrier for the diffusion of gas in the bulk of material, and the need to perform the treatment at high temperatures, thus producing a degradation of the microstructure obtained by previous conventional heat treatment. In this work, the induction heating has been obtained in a nitrogen rich environment at atmospheric pressure in order to nitride Ti-6Al-4V alloy, thus reducing the microstructural alteration in the bulk of material caused by the treatment temperature of conventional nitriding. In this case the aim was to preserve, in the core, the solution and aging treatment of the material that was previously performed. The nitriding number cycles has been varied in order to produce treatments with different thicknesses and possibly different properties. Post-treatment surface morphologies and cross section microstructures have been studied by means of Stylus Profilometry and Scanning Electron Microscopy, in order to evaluate possible modifications of the original surface caused by the diffusion of N. The mechanical properties of the different treatments have been also evaluated using Vickers micro-hardness tests at different loading conditions