How Can Subsampling Reduce Complexity in Sequential MCMC Methods and Deal with Big Data in Target Tracking?

Target tracking faces the challenge in coping with large volumes of data which requires efficient methods for real time applications. The complexity considered in this paper is when there is a large number of measurements which are required to be processed at each time step. Sequential Markov chain Monte Carlo (MCMC) has been shown to be a promising approach to target tracking in complex environments, especially when dealing with clutter. However, a large number of measurements usually results in large processing requirements. This paper goes beyond the current state-of-the-art and presents a novel Sequential MCMC approach that can overcome this challenge through adaptively subsampling the set of measurements. Instead of using the whole large volume of available data, the proposed algorithm performs a trade off between the number of measurements to be used and the desired accuracy of the estimates to be obtained in the presence of clutter. We show results with large improvements in processing time, more than 40 % with a negligible loss in tracking performance, compared with the solution without subsampling

Optimized focusing ion optics for an ultracold deterministic single ion source targeting nm resolution

Using a segmented ion trap with mK laser-cooled ions we have realised a novel single ion source which can deterministically deliver a wide range of ion species, isotopes or ionic molecules [Schnitzler et al., Phys. Rev. Lett. 102, 070501 (2009)]. Experimental data is discussed in detail and compared with numerical simulations of ion trajectories. For the novel ion source we investigate numerically the influence of various extraction parameters on fluctuations in velocity and position of the beam. We present specialized ion optics and show from numerical simulations that nm resolution is achievable. The Paul trap, which is used as a single ion source, together with the presented ion optics, constitutes a promising candidate for a deterministic ion implantation method for applications in solid state quantum computing or classical nano-electronic devices.Comment: 17 pages, 13 figures (including 2 color figures), submitted to special issue of Journal of Modern Optics - Conference Proceedings of PQE-200

Short-term effects of amelogenin gene splice products A+4 and A-4 implanted in the exposed rat molar pulp

In order to study the short-time effects of two bioactive low-molecular amelogenins A+4 and A-4, half-moon cavities were prepared in the mesial aspect of the first maxillary molars, and after pulp exposure, agarose beads alone (controls) or beads soaked in A+4 or A-4 (experimental) were implanted into the pulp. After 1, 3 or 7 days, the rats were killed and the teeth studied by immunohistochemistry. Cell proliferation was studied by PCNA labeling, positive at 3 days, but decreasing at day 7 for A+4, whilst constantly high between 3 and 7 days for A-4. The differentiation toward the osteo/odontoblast lineage shown by RP59 labeling was more apparent for A-4 compared with A+4. Osteopontin-positive cells were alike at days 3 and 7 for A-4. In contrast, for A+4, the weak labeling detected at day 3 became stronger at day 7. Dentin sialoprotein (DSP), an in vivo odontoblast marker, was not detectable until day 7 where a few cells became DSP positive after A-4 stimulation, but not for A+4. These results suggest that A +/- 4 promote the proliferation of some pulp cells. Some of them further differentiate into osteoblast-like progenitors, the effects being more precocious for A-4 (day 3) compared with A+4 (day 7). The present data suggest that A +/- 4 promote early recruitment of osteogenic progenitors, and evidence functional differences between A+4 and A-4

Whole blood methylome-derived features to discriminate endocrine hypertension

Background: Arterial hypertension represents a worldwide health burden and a major risk factor for cardiovascular morbidity and mortality. Hypertension can be primary (primary hypertension, PHT), or secondary to endocrine disorders (endocrine hypertension, EHT), such as Cushing's syndrome (CS), primary aldosteronism (PA), and pheochromocytoma/paraganglioma (PPGL). Diagnosis of EHT is currently based on hormone assays. Efficient detection remains challenging, but is crucial to properly orientate patients for diagnostic confirmation and specific treatment. More accurate biomarkers would help in the diagnostic pathway. We hypothesized that each type of endocrine hypertension could be associated with a specific blood DNA methylation signature, which could be used for disease discrimination. To identify such markers, we aimed at exploring the methylome profiles in a cohort of 255 patients with hypertension, either PHT (n = 42) or EHT (n = 213), and at identifying specific discriminating signatures using machine learning approaches. Results: Unsupervised classification of samples showed discrimination of PHT from EHT. CS patients clustered separately from all other patients, whereas PA and PPGL showed an overall overlap. Global methylation was decreased in the CS group compared to PHT. Supervised comparison with PHT identified differentially methylated CpG sites for each type of endocrine hypertension, showing a diffuse genomic location. Among the most differentially methylated genes, FKBP5 was identified in the CS group. Using four different machine learning methods—Lasso (Least Absolute Shrinkage and Selection Operator), Logistic Regression, Random Forest, and Support Vector Machine—predictive models for each type of endocrine hypertension were built on training cohorts (80% of samples for each hypertension type) and estimated on validation cohorts (20% of samples for each hypertension type). Balanced accuracies ranged from 0.55 to 0.74 for predicting EHT, 0.85 to 0.95 for predicting CS, 0.66 to 0.88 for predicting PA, and 0.70 to 0.83 for predicting PPGL. Conclusions: The blood DNA methylome can discriminate endocrine hypertension, with methylation signatures for each type of endocrine disorder

Rare coding variants in PLCG2, ABI3, and TREM2 implicate microglial-mediated innate immunity in Alzheimer's disease

We identified rare coding variants associated with Alzheimer’s disease (AD) in a 3-stage case-control study of 85,133 subjects. In stage 1, 34,174 samples were genotyped using a whole-exome microarray. In stage 2, we tested associated variants (P<1×10-4) in 35,962 independent samples using de novo genotyping and imputed genotypes. In stage 3, an additional 14,997 samples were used to test the most significant stage 2 associations (P<5×10-8) using imputed genotypes. We observed 3 novel genome-wide significant (GWS) AD associated non-synonymous variants; a protective variant in PLCG2 (rs72824905/p.P522R, P=5.38×10-10, OR=0.68, MAFcases=0.0059, MAFcontrols=0.0093), a risk variant in ABI3 (rs616338/p.S209F, P=4.56×10-10, OR=1.43, MAFcases=0.011, MAFcontrols=0.008), and a novel GWS variant in TREM2 (rs143332484/p.R62H, P=1.55×10-14, OR=1.67, MAFcases=0.0143, MAFcontrols=0.0089), a known AD susceptibility gene. These protein-coding changes are in genes highly expressed in microglia and highlight an immune-related protein-protein interaction network enriched for previously identified AD risk genes. These genetic findings provide additional evidence that the microglia-mediated innate immune response contributes directly to AD development

Genetically elevated high-density lipoprotein cholesterol through the cholesteryl ester transfer protein gene does not associate with risk of Alzheimer's disease

Introduction: There is conflicting evidence whether high-density lipoprotein cholesterol (HDL-C) is a risk factor for Alzheimer's disease (AD) and dementia. Genetic variation in the cholesteryl ester transfer protein (CETP) locus is associated with altered HDL-C. We aimed to assess AD risk by genetically predicted HDL-C. Methods: Ten single nucleotide polymorphisms within the CETP locus predicting HDL-C were applied to the International Genomics of Alzheimer's Project (IGAP) exome chip stage 1 results in up 16,097 late onset AD cases and 18,077 cognitively normal elderly controls. We performed instrumental variables analysis using inverse variance weighting, weighted median, and MR-Egger. Results: Based on 10 single nucleotide polymorphisms distinctly predicting HDL-C in the CETP locus, we found that HDL-C was not associated with risk of AD (P > .7). Discussion: Our study does not support the role of HDL-C on risk of AD through HDL-C altered by CETP. This study does not rule out other mechanisms by which HDL-C affects risk of AD