Photon trains and lasing : The periodically pumped quantum dot

We propose to pump semiconductor quantum dots with surface acoustic waves which deliver an alternating periodic sequence of electrons and holes. In combination with a good optical cavity such regular pumping could entail anti-bunching and sub-Poissonian photon statistics. In the bad-cavity limit a train of equally spaced photons would arise.Comment: RevTex, 5 pages, 1 figur

Acoustically driven storage of light in a quantum well

The strong piezoelectric fields accompanying a surface acoustic wave on a semiconductor quantum well structure are employed to dissociate optically generated excitons and efficiently trap the created electron hole pairs in the moving lateral potential superlattice of the sound wave. The resulting spatial separation of the photogenerated ambipolar charges leads to an increase of the radiative lifetime by orders of magnitude as compared to the unperturbed excitons. External and deliberate screening of the lateral piezoelectric fields triggers radiative recombination after very long storage times at a remote location on the sample.Comment: 4 PostScript figures included, Physical Review Letters, in pres

Enhanced sequential carrier capture into individual quantum dots and quantum posts controlled by surface acoustic waves

Individual self-assembled Quantum Dots and Quantum Posts are studied under the influence of a surface acoustic wave. In optical experiments we observe an acoustically induced switching of the occupancy of the nanostructures along with an overall increase of the emission intensity. For Quantum Posts, switching occurs continuously from predominantely charged excitons (dissimilar number of electrons and holes) to neutral excitons (same number of electrons and holes) and is independent on whether the surface acoustic wave amplitude is increased or decreased. For quantum dots, switching is non-monotonic and shows a pronounced hysteresis on the amplitude sweep direction. Moreover, emission of positively charged and neutral excitons is observed at high surface acoustic wave amplitudes. These findings are explained by carrier trapping and localization in the thin and disordered two-dimensional wetting layer on top of which Quantum Dots nucleate. This limitation can be overcome for Quantum Posts where acoustically induced charge transport is highly efficient in a wide lateral Matrix-Quantum Well.Comment: 11 pages, 5 figure

Degradation of the Disease-Associated Prion Protein by a Serine Protease from Lichens

The disease-associated prion protein (PrPTSE), the probable etiological agent of the transmissible spongiform encephalopathies (TSEs), is resistant to degradation and can persist in the environment. Lichens, mutualistic symbioses containing fungi, algae, bacteria and occasionally cyanobacteria, are ubiquitous in the environment and have evolved unique biological activities allowing their survival in challenging ecological niches. We investigated PrPTSE inactivation by lichens and found acetone extracts of three lichen species (Parmelia sulcata, Cladonia rangiferina and Lobaria pulmonaria) have the ability to degrade prion protein (PrP) from TSE-infected hamsters, mice and deer. Immunoblots measuring PrP levels and protein misfolding cyclic amplification indicated at least two logs of reductions in PrPTSE. Degradative activity was not found in closely related lichen species or in algae or a cyanobacterium that inhabit lichens. Degradation was blocked by Pefabloc SC, a serine protease inhibitor, but not inhibitors of other proteases or enzymes. Additionally, we found that PrP levels in PrPTSE-enriched preps or infected brain homogenates are also reduced following exposure to freshly-collected P. sulcata or an aqueous extract of the lichen. Our findings indicate that these lichen extracts efficiently degrade PrPTSE and suggest that some lichens could have potential to inactivate TSE infectivity on the landscape or be a source for agents to degrade prions. Further work to clone and characterize the protease, assess its effect on TSE infectivity and determine which organism or organisms present in lichens produce or influence the protease activity is warranted

Stokes' Drift of linear Defects

A linear defect, viz. an elastic string, diffusing on a planar substrate traversed by a travelling wave experiences a drag known as Stokes' drift. In the limit of an infinitely long string, such a mechanism is shown to be characterized by a sharp threshold that depends on the wave parameters, the string damping constant and the substrate temperature. Moreover, the onset of the Stokes' drift is signaled by an excess diffusion of the string center of mass, while the dispersion of the drifting string around its center of mass may grow anomalous.Comment: 14 pages, no figures, to be published in Phys.Rev.

Bridging the gap between systems biology and medicine

Systems biology has matured considerably as a discipline over the last decade, yet some of the key challenges separating current research efforts in systems biology and clinically useful results are only now becoming apparent. As these gaps are better defined, the new discipline of systems medicine is emerging as a translational extension of systems biology. How is systems medicine defined? What are relevant ontologies for systems medicine? What are the key theoretic and methodologic challenges facing computational disease modeling? How are inaccurate and incomplete data, and uncertain biologic knowledge best synthesized in useful computational models? Does network analysis provide clinically useful insight? We discuss the outstanding difficulties in translating a rapidly growing body of data into knowledge usable at the bedside. Although core-specific challenges are best met by specialized groups, it appears fundamental that such efforts should be guided by a roadmap for systems medicine drafted by a coalition of scientists from the clinical, experimental, computational, and theoretic domains

Testing for Differentially-Expressed MicroRNAs with Errors-in-Variables Nonparametric Regression

MicroRNA is a set of small RNA molecules mediating gene expression at post-transcriptional/translational levels. Most of well-established high throughput discovery platforms, such as microarray, real time quantitative PCR, and sequencing, have been adapted to study microRNA in various human diseases. The total number of microRNAs in humans is approximately 1,800, which challenges some analytical methodologies requiring a large number of entries. Unlike messenger RNA, the majority of microRNA (60%) maintains relatively low abundance in the cells. When analyzed using microarray, the signals of these low-expressed microRNAs are influenced by other non-specific signals including the background noise. It is crucial to distinguish the true microRNA signals from measurement errors in microRNA array data analysis. In this study, we propose a novel measurement error model-based normalization method and differentially-expressed microRNA detection method for microRNA profiling data acquired from locked nucleic acids (LNA) microRNA array. Compared with some existing methods, the proposed method significantly improves the detection among low-expressed microRNAs when assessed by quantitative real-time PCR assay

Hierarchical information clustering by means of topologically embedded graphs

We introduce a graph-theoretic approach to extract clusters and hierarchies in complex data-sets in an unsupervised and deterministic manner, without the use of any prior information. This is achieved by building topologically embedded networks containing the subset of most significant links and analyzing the network structure. For a planar embedding, this method provides both the intra-cluster hierarchy, which describes the way clusters are composed, and the inter-cluster hierarchy which describes how clusters gather together. We discuss performance, robustness and reliability of this method by first investigating several artificial data-sets, finding that it can outperform significantly other established approaches. Then we show that our method can successfully differentiate meaningful clusters and hierarchies in a variety of real data-sets. In particular, we find that the application to gene expression patterns of lymphoma samples uncovers biologically significant groups of genes which play key-roles in diagnosis, prognosis and treatment of some of the most relevant human lymphoid malignancies.Comment: 33 Pages, 18 Figures, 5 Table