State-Insensitive Trapping of Alkaline-Earth Atoms in a Nanofiber-Based Optical Dipole Trap

Neutral atoms trapped in the evanescent optical potentials of nanotapered optical fibers are a promising platform for developing quantum technologies and exploring fundamental science, such as quantum networks and quantum electrodynamics. Building on the successful advancements with trapped alkali atoms, here we demonstrate a state-insensitive optical dipole trap for strontium-88, an alkaline-earth atom, using the evanescent fields of a nanotapered optical fiber. Leveraging the low laser-cooling temperatures of $\sim\!\!1~\mu$K readily achievable with strontium, we demonstrate trapping in record low trap depths corresponding to $\sim\!\!3~\mu$K. Further, employing a double magic wavelength trapping scheme, we realize state-insensitive trapping on the kilohertz-wide 5s^{2}\;^{1}\!S_{0}-5s5p\;^{3}\!P_{1,|m|=1} cooling transition, which we verify by performing near-surface high-resolution spectroscopy of the atomic transition. This allows us to experimentally find and verify the state insensitivity of the trap nearby a theoretically predicted magic wavelength of 435.827(25) nm. Given the non-magnetic ground state and low collisional scattering length of strontium-88, this work also lays the foundation for developing versatile and robust matter-wave atomtronic circuits over nanophotonic waveguides.Comment: 14 pages, 15 figure

Examination of effects of GSK3β phosphorylation, β-catenin phosphorylation, and β-catenin degradation on kinetics of Wnt signaling pathway using computational method

<p>Abstract</p> <p>Background</p> <p>Recent experiments have explored effects of activities of kinases other than the well-studied GSK3β, in wnt pathway signaling, particularly at the level of β-catenin. It has also been found that the kinase PKA attenuates β-catenin degradation. However, the effects of these kinases on the level and degradation of β-catenin and the resulting downstream transcription activity remain to be clarified. Furthermore, the effect of GSK3β phosphorylation on the β-catenin level has not been examined computationally. In the present study, the effects of phosphorylation of GSK3β and of phosphorylations and degradation of β-catenin on the kinetics of the wnt signaling pathway were examined computationally.</p> <p>Methods</p> <p>The well-known computational Lee-Heinrich kinetic model of the wnt pathway was modified to include these effects. The rate laws of reactions in the modified model were solved numerically to examine these effects on β-catenin level.</p> <p>Results</p> <p>The computations showed that the β-catenin level is almost linearly proportional to the phosphorylation activity of GSK3β. The dependence of β-catenin level on the phosphorylation and degradation of free β-catenin and downstream TCF activity can be analyzed with an approximate, simple function of kinetic parameters for added reaction steps associated with effects examined, rationalizing the experimental results.</p> <p>Conclusion</p> <p>The phosphorylations of β-catenin by kinases other than GSK3β involve free unphorphorylated β-catenin rather than GSK3β-phosphorylated β-catenin*. In order to account for the observed enhancement of TCF activity, the β-catenin dephosphorylation step is essential, and the kinetic parameters of β-catenin phosphorylation and degradation need to meet a condition described in the main text. These findings should be useful for future experiments.</p

ODAM Expression Inhibits Human Breast Cancer Tumorigenesis

We have posited that Odontogenic Ameloblast Associated Protein (ODAM) serves as a novel prognostic biomarker in breast cancer and now have investigated its potential role in regulating tumor growth and metastasis. Human breast cancer MDA-MB-231 cells were transfected with a recombinant ODAM plasmid construct (or, as a control, the plasmid vector alone). ODAM expression increased adhesion and apoptosis of the transfected MDA-MB-231 cells and suppressed their growth rate, migratory activity, and capability to invade extracellular matrix-coated membranes. Implantation of such cells into mouse mammary fat pads resulted in significantly smaller tumors than occurred in animals that received control cells; furthermore, ODAM-expressing cells, when injected intravenously into mice, failed to metastasize, whereas the control-transfected counterparts produced extensive lung lesions. Our finding that induction of ODAM expression in human breast cancer cells markedly inhibited their neoplastic properties provides further evidence for the regulatory role of this molecule in tumorigenesis and, consequently, is of potential clinical import

Heterogeneous Delays in Neural Networks

We investigate heterogeneous coupling delays in complex networks of excitable elements described by the FitzHugh-Nagumo model. The effects of discrete as well as of uni- and bimodal continuous distributions are studied with a focus on different topologies, i.e., regular, small-world, and random networks. In the case of two discrete delay times resonance effects play a major role: Depending on the ratio of the delay times, various characteristic spiking scenarios, such as coherent or asynchronous spiking, arise. For continuous delay distributions different dynamical patterns emerge depending on the width of the distribution. For small distribution widths, we find highly synchronized spiking, while for intermediate widths only spiking with low degree of synchrony persists, which is associated with traveling disruptions, partial amplitude death, or subnetwork synchronization, depending sensitively on the network topology. If the inhomogeneity of the coupling delays becomes too large, global amplitude death is induced

Semantic Multi-Classifier Systems for the Analysis of Gene Expression Profiles

The analysis of biomolecular data from high-throughput screens is typically characterized by the high dimensionality of the measured profiles. Development of diagnostic tools for this kind of data, such as gene expression profiles, is often coupled to an interest of users in obtaining interpretable and low-dimensional classification models; as this facilitates the generation of biological hypotheses on possible causes of a categorization. Purely data driven classification models are limited in this regard. These models only allow for interpreting the data in terms of marker combinations, often gene expression levels, and rarely bridge the gap to higher-level explanations such as molecular signaling pathways. Here, we incorporate into the classification process, additionally to the expression profile data, different data sources that functionally organize these individual gene expression measurements into groups. The members of such a group of measurements share a common property or characterize a more abstract biological concept. These feature subgroups are then used for the generation of individual classifiers. From the set of these classifiers, subsets are combined to a multi-classifier system. Analysing which individual classifiers, and thus which biological concepts such as pathways or ontology terms, are important for classification, make it possible to generate hypotheses about the distinguishing characteristics of the classes on a functional level

Small-Size Resonant Photoacoustic Cell of Inclined Geometry for Gas Detection

A photoacoustic cell intended for laser detection of trace gases is represented. The cell is adapted so as to enhance the gas-detection performance and, simultaneously, to reduce the cell size. The cell design provides an efficient cancellation of the window background (a parasite response due to absorption of laser beam in the cell windows) and acoustic isolation from the environment for an acoustic resonance of the cell. The useful photoacoustic response from a detected gas, window background and noise are analyzed in demonstration experiments as functions of the modulation frequency for a prototype cell with the internal volume ~ 0.5 cm^3. The minimal detectable absorption for the prototype is estimated to be ~ 1.2 10^{-8} cm^{-1} W Hz^{-1/2}.Comment: 11 pages, 5 figure

A highly efficient multi-core algorithm for clustering extremely large datasets

<p>Abstract</p> <p>Background</p> <p>In recent years, the demand for computational power in computational biology has increased due to rapidly growing data sets from microarray and other high-throughput technologies. This demand is likely to increase. Standard algorithms for analyzing data, such as cluster algorithms, need to be parallelized for fast processing. Unfortunately, most approaches for parallelizing algorithms largely rely on network communication protocols connecting and requiring multiple computers. One answer to this problem is to utilize the intrinsic capabilities in current multi-core hardware to distribute the tasks among the different cores of one computer.</p> <p>Results</p> <p>We introduce a multi-core parallelization of the k-means and k-modes cluster algorithms based on the design principles of transactional memory for clustering gene expression microarray type data and categorial SNP data. Our new shared memory parallel algorithms show to be highly efficient. We demonstrate their computational power and show their utility in cluster stability and sensitivity analysis employing repeated runs with slightly changed parameters. Computation speed of our Java based algorithm was increased by a factor of 10 for large data sets while preserving computational accuracy compared to single-core implementations and a recently published network based parallelization.</p> <p>Conclusions</p> <p>Most desktop computers and even notebooks provide at least dual-core processors. Our multi-core algorithms show that using modern algorithmic concepts, parallelization makes it possible to perform even such laborious tasks as cluster sensitivity and cluster number estimation on the laboratory computer.</p

Predicting disease progression in behavioral variant frontotemporal dementia

Introduction: The behavioral variant of frontotemporal dementia (bvFTD) is a rare neurodegenerative disease. Reliable predictors of disease progression have not been sufficiently identified. We investigated multivariate magnetic resonance imaging (MRI) biomarker profiles for their predictive value of individual decline. Methods: One hundred five bvFTD patients were recruited from the German frontotemporal lobar degeneration (FTLD) consortium study. After defining two groups ("fast progressors" vs. "slow progressors"), we investigated the predictive value of MR brain volumes for disease progression rates performing exhaustive screenings with multivariate classification models. Results: We identified areas that predict disease progression rate within 1 year. Prediction measures revealed an overall accuracy of 80% across our 50 top classification models. Especially the pallidum, middle temporal gyrus, inferior frontal gyrus, cingulate gyrus, middle orbitofrontal gyrus, and insula occurred in these models. Discussion: Based on the revealed marker combinations an individual prognosis seems to be feasible. This might be used in clinical studies on an individualized progression model

Tumorbiologie des Oropharynxkarzinoms

Hintergrund Oropharynxkarzinome (OPSCC) unterscheiden sich abhängig von noxenbasierter oder durch humane Papillomaviren (HPV) getriebener Ätiologie in klinischen Faktoren und der Prognose. Zugrunde liegend sind molekulare Unterschiede der Tumorbiologie. Ziel der Arbeit Ziel war die Darstellung wichtiger molekularbiologischer Charakteristika der Genetik, Epigenetik und Immunologie von OPSCC. Material und Methoden Es handelt sich um eine Übersichtsarbeit zu einer Auswahl molekularbiologischer Faktoren der Tumorbiologie von OPSCC aus Genetik, Epigenetik und Immunologie. Ergebnisse Genetische Veränderungen und deren Auswirkungen auf Kanzerogenese und Tumorbiologie werden in zunehmender Tiefe verstanden. Epigenetische Phänomene ergänzen funktionelle Zusammenhänge. Die epigenetischen Regulationsmechanismen der Gene sind komplex. Daher besteht in diesem Feld weiterhin großer Forschungsbedarf. Immunologische Aspekte der Molekularbiologie gewinnen im Kontext der aktuellen Entwicklungen in der Immunonkologie an Bedeutung. Schlussfolgerung Die Tumorbiologie von Oropharynxkarzinomen unterscheidet sich v. a. bezüglich des HPV-Status. Zusätzlich werden HPV-unabhängige Subgruppen genetisch, epigenetisch und immunologisch zunehmend charakterisiert. Aus diesen Erkenntnissen können logische Grundprinzipien neuer Therapiekonzepte abgeleitet werden