This paper for the first time discusses a computational study of using magneto-electric (ME) nanoparticles to artificially stimulate the neural activity deep in the brain. The new technology provides a unique way to couple electric signals in the neural network to the magnetic dipoles in the nanoparticles with the purpose to enable a non-invasive approach. Simulations of the effect of ME nanoparticles for non-invasively stimulating the brain of a patient with Parkinson's Disease to bring the pulsed sequences of the electric field to the levels comparable to those of healthy people show that the optimized values for the concentration of the 20-nm nanoparticles (with the magneto-electric (ME) coefficient of 100 V cm−1 Oe−1 in the aqueous solution) is 3×106 particles/cc, and the frequency of the externally applied 300-Oe magnetic field is 80 Hz.

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Crowdsourcing labeling systems provide an efficient way to generate multiple inaccurate labels for given observations. If the competence level or the ``reputation,'' which can be explained as the probabilities of annotating the right label, for each crowdsourcing annotators is equal and biased to annotate the right label, majority voting (MV) is the optimal decision rule for merging the multiple labels into a single reliable one. However, in practice, the competence levels of annotators employed by the crowdsourcing labeling systems are often diverse very much. In these cases, weighted MV is more preferred. The weights should be determined by the competence levels. However, since the annotators are anonymous and the ground-truth labels are usually unknown, it is hard to compute the competence levels of the annotators directly. In this paper, we propose to learn the weights for weighted MV by exploiting the expertise of annotators. Specifically, we model the domain knowledge of different annotators with different distributions and treat the crowdsourcing problem as a domain adaptation problem. The annotators provide labels to the source domains and the target domain is assumed to be associated with the ground-truth labels. The weights are obtained by matching the source domains with the target domain. Although the target-domain labels are unknown, we prove that they could be estimated under mild conditions. Both theoretical and empirical analyses verify the effectiveness of the proposed method. Large performance gains are shown for specific data sets.

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An elevated level of lipoprotein (a) [Lp(a)] is a risk factor for CVD. Alirocumab, a monoclonal antibody to proprotein convertase subtilisin/kexin type 9, is reported to reduce Lp(a) levels. The relationship of Lp(a) reduction with apo(a) size polymorphism, phenotype, and dominance pattern and LDL cholesterol (LDL-C) reduction was evaluated in a pooled analysis of 155 hypercholesterolemic patients (75 with heterozygous familial hypercholesterolemia) from two clinical trials. Alirocumab significantly reduced total Lp(a) (pooled median: -21%, = 0.0001) and allele-specific apo(a), an Lp(a) level carried by the smaller (median: -18%, = 0.002) or the larger (median: -37%, = 0.0005) apo(a) isoform, at week 8 versus baseline. The percent reduction in Lp(a) level with alirocumab was similar across apo(a) phenotypes (single vs. double bands) and carriers and noncarriers of a small size apo(a) (≤22 kringles). The percent reduction in LDL-C correlated significantly with the percent reduction in Lp(a) level ( = 0.407, < 0.0001) and allele-specific apo(a) level associated with the smaller ( = 0.390, < 0.0001) or larger ( = 0.270, = 0.0183) apo(a) sizes. In conclusion, alirocumab-induced Lp(a) reduction was independent of apo(a) phenotypes and the presence or absence of a small size apo(a).