Metamaterials proposed as perfect magnetoelectrics

Magnetoelectric susceptibility of a metamaterial built from split ring resonators have been investigated both experimentally and within an equivalent circuit model. The absolute values have been shown to exceed by two orders of magnitude that of classical magnetoelectric materials. The metamaterial investigated reaches the theoretically predicted value of the magnetoelectric susceptibility which is equal to the geometric average of the electric and magnetic susceptibilities.Comment: 5 pages, 3 figure

Increased markers of cardiac vagal activity in leucine-rich repeat kinase 2-associated Parkinson's disease.

PurposeCardiac autonomic dysfunction manifests as reduced heart rate variability (HRV) in idiopathic Parkinson's disease (PD), but no significant reduction has been found in PD patients who carry the LRRK2 mutation. Novel HRV features have not been investigated in these individuals. We aimed to assess cardiac autonomic modulation through standard and novel approaches to HRV analysis in individuals who carry the LRRK2 G2019S mutation.MethodsShort-term electrocardiograms were recorded in 14 LRRK2-associated PD patients, 25 LRRK2-non-manifesting carriers, 32 related non-carriers, 20 idiopathic PD patients, and 27 healthy controls. HRV measures were compared using regression modeling, controlling for age, sex, mean heart rate, and disease duration. Discriminant analysis highlighted the feature combination that best distinguished LRRK2-associated PD from controls.ResultsBeat-to-beat and global HRV measures were significantly increased in LRRK2-associated PD patients compared with controls (e.g., deceleration capacity of heart rate: p = 0.006) and idiopathic PD patients (e.g., 8th standardized moment of the interbeat interval distribution: p = 0.0003), respectively. LRRK2-associated PD patients also showed significantly increased irregularity of heart rate dynamics, as quantified by Rényi entropy, when compared with controls (p = 0.002) and idiopathic PD patients (p = 0.0004). Ordinal pattern statistics permitted the identification of LRRK2-associated PD individuals with 93% sensitivity and 93% specificity. Consistent results were found in a subgroup of LRRK2-non-manifesting carriers when compared with controls.ConclusionsIncreased beat-to-beat HRV in LRRK2 G2019S mutation carriers compared with controls and idiopathic PD patients may indicate augmented cardiac autonomic cholinergic activity, suggesting early impairment of central vagal feedback loops in LRRK2-associated PD

Effects of doping and epitaxy on optical behavior of NaNbO3 films

Cube-on-cube epitaxy of perovskite sub-cell of Pr-doped and undoped NaNbO3 is obtained in 130-nm-thick films on top of (La0.18Sr0.82)(Al0.59Ta0.41)O3 (001) substrates. Experimental studies show that the edge of optical absorption red-shifts and some interband transitions change in the films compared to crystals. Bright red luminescence is achieved at room-temperature under ultraviolet excitation in the Pr-doped film. An interband mechanism of luminescence excitation is detected in the film, which is in contrast to the intervalence charge transfer mechanism in the crystal. The results are discussed in terms of epitaxially induced changes of crystal symmetry and ferroelectric polarization in the films. It is suggested that the band structure and interband transitions in NaNbO3and the transition probabilities in the Pr ions can be significantly modified by these changes.Peer reviewe

Inducing Probabilistic Grammars by Bayesian Model Merging

We describe a framework for inducing probabilistic grammars from corpora of positive samples. First, samples are {\em incorporated} by adding ad-hoc rules to a working grammar; subsequently, elements of the model (such as states or nonterminals) are {\em merged} to achieve generalization and a more compact representation. The choice of what to merge and when to stop is governed by the Bayesian posterior probability of the grammar given the data, which formalizes a trade-off between a close fit to the data and a default preference for simpler models (`Occam's Razor'). The general scheme is illustrated using three types of probabilistic grammars: Hidden Markov models, class-based $n$-grams, and stochastic context-free grammars.Comment: To appear in Grammatical Inference and Applications, Second International Colloquium on Grammatical Inference; Springer Verlag, 1994. 13 page

Detection and diversity of a putative novel heterogeneous polymorphic proline-glycine repeat (Pgr) protein in the footrot pathogen Dichelobacter nodosus

Dichelobacter nodosus, a Gram-negative anaerobic bacterium, is the essential causative agent of footrot in sheep. Currently, depending on the clinical presentation in the field, footrot is described as benign or virulent; D. nodosus strains have also been classified as benign or virulent, but this designation is not always consistent with clinical disease. The aim of this study was to determine the diversity of the pgr gene, which encodes a putative proline-glycine repeat protein (Pgr). The pgr gene was present in all 100 isolates of D. nodosus that were examined and, based on sequence analysis had two variants, pgrA and pgrB. In pgrA, there were two coding tandem repeat regions, R1 and R2: different strains had variable numbers of repeats within these regions. The R1 and R2 were absent from pgrB. Both variants were present in strains from Australia, Sweden and the UK, however, only pgrB was detected in isolates from Western Australia. The pgrA gene was detected in D. nodosus from tissue samples from two flocks in the UK with virulent footrot and only pgrB from a flock with no virulent or benign footrot for >10 years. Bioinformatic analysis of the putative PgrA protein indicated that it contained a collagen-like cell surface anchor motif. These results suggest that the pgr gene may be a useful molecular marker for epidemiological studies

Search algorithms as a framework for the optimization of drug combinations

Combination therapies are often needed for effective clinical outcomes in the management of complex diseases, but presently they are generally based on empirical clinical experience. Here we suggest a novel application of search algorithms, originally developed for digital communication, modified to optimize combinations of therapeutic interventions. In biological experiments measuring the restoration of the decline with age in heart function and exercise capacity in Drosophila melanogaster, we found that search algorithms correctly identified optimal combinations of four drugs with only one third of the tests performed in a fully factorial search. In experiments identifying combinations of three doses of up to six drugs for selective killing of human cancer cells, search algorithms resulted in a highly significant enrichment of selective combinations compared with random searches. In simulations using a network model of cell death, we found that the search algorithms identified the optimal combinations of 6-9 interventions in 80-90% of tests, compared with 15-30% for an equivalent random search. These findings suggest that modified search algorithms from information theory have the potential to enhance the discovery of novel therapeutic drug combinations. This report also helps to frame a biomedical problem that will benefit from an interdisciplinary effort and suggests a general strategy for its solution.Comment: 36 pages, 10 figures, revised versio