Levitated electromechanics: all-electrical cooling of charged nano- and micro-particles

We show how charged levitated nano- and micro-particles can be cooled by interfacing them with an $RLC$ circuit. All-electrical levitation and cooling is applicable to a wide range of particle sizes and materials, and will enable state-of-the-art force sensing within an electrically networked system. Exploring the cooling limits in the presence of realistic noise we find that the quantum regime of particle motion can be reached in cryogenic environments both for passive resistive cooling and for an active feedback scheme, paving the way to levitated quantum electromechanics.Comment: Manuscript: 16 pages, 5 figures. Supplementary material: 3 pages 2 figure

Investigation of topographical stability of the concave and convex Self-Organizing Map variant

We investigate, by a systematic numerical study, the parameter dependence of the stability of the Kohonen Self-Organizing Map and the Zheng and Greenleaf concave and convex learning with respect to different input distributions, input and output dimensions

PCA Beyond The Concept of Manifolds: Principal Trees, Metro Maps, and Elastic Cubic Complexes

Multidimensional data distributions can have complex topologies and variable local dimensions. To approximate complex data, we propose a new type of low-dimensional ``principal object'': a principal cubic complex. This complex is a generalization of linear and non-linear principal manifolds and includes them as a particular case. To construct such an object, we combine a method of topological grammars with the minimization of an elastic energy defined for its embedment into multidimensional data space. The whole complex is presented as a system of nodes and springs and as a product of one-dimensional continua (represented by graphs), and the grammars describe how these continua transform during the process of optimal complex construction. The simplest case of a topological grammar (``add a node'', ``bisect an edge'') is equivalent to the construction of ``principal trees'', an object useful in many practical applications. We demonstrate how it can be applied to the analysis of bacterial genomes and for visualization of cDNA microarray data using the ``metro map'' representation. The preprint is supplemented by animation: ``How the topological grammar constructs branching principal components (AnimatedBranchingPCA.gif)''.Comment: 19 pages, 8 figure

BLProt: prediction of bioluminescent proteins based on support vector machine and relieff feature selection

<p>Abstract</p> <p>Background</p> <p>Bioluminescence is a process in which light is emitted by a living organism. Most creatures that emit light are sea creatures, but some insects, plants, fungi etc, also emit light. The biotechnological application of bioluminescence has become routine and is considered essential for many medical and general technological advances. Identification of bioluminescent proteins is more challenging due to their poor similarity in sequence. So far, no specific method has been reported to identify bioluminescent proteins from primary sequence.</p> <p>Results</p> <p>In this paper, we propose a novel predictive method that uses a Support Vector Machine (SVM) and physicochemical properties to predict bioluminescent proteins. BLProt was trained using a dataset consisting of 300 bioluminescent proteins and 300 non-bioluminescent proteins, and evaluated by an independent set of 141 bioluminescent proteins and 18202 non-bioluminescent proteins. To identify the most prominent features, we carried out feature selection with three different filter approaches, ReliefF, infogain, and mRMR. We selected five different feature subsets by decreasing the number of features, and the performance of each feature subset was evaluated.</p> <p>Conclusion</p> <p>BLProt achieves 80% accuracy from training (5 fold cross-validations) and 80.06% accuracy from testing. The performance of BLProt was compared with BLAST and HMM. High prediction accuracy and successful prediction of hypothetical proteins suggests that BLProt can be a useful approach to identify bioluminescent proteins from sequence information, irrespective of their sequence similarity. The BLProt software is available at <url>http://www.inb.uni-luebeck.de/tools-demos/bioluminescent%20protein/BLProt</url></p

Optimizing the procedure of grain nutrient predictions in barley via hyperspectral imaging

Hyperspectral imaging enables researchers and plant breeders to analyze various traits of interest like nutritional value in high throughput. In order to achieve this, the optimal design of a reliable calibration model, linking the measured spectra with the investigated traits, is necessary. In the present study we investigated the impact of different regression models, calibration set sizes and calibration set compositions on prediction performance. For this purpose, we analyzed concentrations of six globally relevant grain nutrients of the wild barley population HEB-YIELD as case study. The data comprised 1,593 plots, grown in 2015 and 2016 at the locations Dundee and Halle, which have been entirely analyzed through traditional laboratory methods and hyperspectral imaging. The results indicated that a linear regression model based on partial least squares outperformed neural networks in this particular data modelling task. There existed a positive relationship between the number of samples in a calibration model and prediction performance, with a local optimum at a calibration set size of ~40% of the total data. The inclusion of samples from several years and locations could clearly improve the predictions of the investigated nutrient traits at small calibration set sizes. It should be stated that the expansion of calibration models with additional samples is only useful as long as they are able to increase trait variability. Models obtained in a certain environment were only to a limited extent transferable to other environments. They should therefore be successively upgraded with new calibration data to enable a reliable prediction of the desired traits. The presented results will assist the design and conceptualization of future hyperspectral imaging projects in order to achieve reliable predictions. It will in general help to establish practical applications of hyperspectral imaging systems, for instance in plant breeding concepts

Characterization of K-Complexes and Slow Wave Activity in a Neural Mass Model

NREM sleep is characterized by two hallmarks, namely K-complexes (KCs) during sleep stage N2 and cortical slow oscillations (SOs) during sleep stage N3. While the underlying dynamics on the neuronal level is well known and can be easily measured, the resulting behavior on the macroscopic population level remains unclear. On the basis of an extended neural mass model of the cortex, we suggest a new interpretation of the mechanisms responsible for the generation of KCs and SOs. As the cortex transitions from wake to deep sleep, in our model it approaches an oscillatory regime via a Hopf bifurcation. Importantly, there is a canard phenomenon arising from a homoclinic bifurcation, whose orbit determines the shape of large amplitude SOs. A KC corresponds to a single excursion along the homoclinic orbit, while SOs are noise-driven oscillations around a stable focus. The model generates both time series and spectra that strikingly resemble real electroencephalogram data and points out possible differences between the different stages of natural sleep

Inferring Market Structure from Customer Response to Competing and Complementary Products

We consider customer influences on market structure, arguing that market structure should explain the extent to which any given set of market offerings are substitutes or complements. We describe recent additions to the market structure analysis literature and identify promising directions for new research in market structure analysis. Impressive advances in data collection, statistical methodology and information technology provide unique opportunities for researchers to build market structure tools that can assist “real-time” marketing decision-making.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46981/1/11002_2004_Article_5088105.pd

Chronic oxytocin-driven alternative splicing of Crfr2α induces anxiety

The neuropeptide oxytocin (OXT) has generated considerable interest as potential treatment for psychiatric disorders, including anxiety and autism spectrum disorders. However, the behavioral and molecular consequences associated with chronic OXT treatment and chronic receptor (OXTR) activation have scarcely been studied, despite the potential therapeutic long-term use of intranasal OXT. Here, we reveal that chronic OXT treatment over two weeks increased anxiety-like behavior in rats, with higher sensitivity in females, contrasting the well-known anxiolytic effect of acute OXT. The increase in anxiety was transient and waned 5 days after the infusion has ended. The behavioral effects of chronic OXT were paralleled by activation of an intracellular signaling pathway, which ultimately led to alternative splicing of hypothalamic corticotropin-releasing factor receptor 2α (Crfr2α), an important modulator of anxiety. In detail, chronic OXT shifted the splicing ratio from the anxiolytic membrane-bound (mCRFR2α) form of CRFR2α towards the soluble CRFR2α (sCRFR2α) form. Experimental induction of alternative splicing mimicked the anxiogenic effects of chronic OXT, while sCRFR2α-knock down reduced anxiety-related behavior of male rats. Furthermore, chronic OXT treatment triggered the release of sCRFR2α into the cerebrospinal fluid with sCRFR2α levels positively correlating with anxiety-like behavior. In summary, we revealed that the shifted splicing ratio towards expression of the anxiogenic sCRFR2α underlies the adverse effects of chronic OXT treatment on anxiety