Creating a new Ontology: a Modular Approach

Creating a new Ontology: a Modular ApproachComment: in Adrian Paschke, Albert Burger, Andrea Splendiani, M. Scott Marshall, Paolo Romano: Proceedings of the 3rd International Workshop on Semantic Web Applications and Tools for the Life Sciences, Berlin,Germany, December 8-10, 201

Isoperiodic deformations of the acoustic operator and periodic solutions of the Harry Dym equation

We consider the problem of describing the possible spectra of an acoustic operator with a periodic finite-gap density. We construct flows on the moduli space of algebraic Riemann surfaces that preserve the periods of the corresponding operator. By a suitable extension of the phase space, these equations can be written with quadratic irrationalities.Comment: 15 page

The effect of allophonic variability on L2 contrast perception: Evidence from perception of English vowels

Current frameworks of L2 phonetic acquisition remain largely underspecified with respect to the role of L1 allophonic variability in acquisition. Examining the role of L1 allophonic variability, the current study compared the perceptual discrimination of English /i-I/ and /E-æ/ by L1 Korean and L1 Mandarin speakers. Korean and Mandarin vowel inventories differ in that Mandarin employs significantly greater allophonic variation of the mid-region /E/ vowel. Results demonstrated worse perceptual accuracy by L1 Mandarin speakers for the /E-æ/ contrast than L1 Korean speakers. These results suggest that both L1 phonemic inventories and allophonic variation play a role in L2 phonetic acquisition

Protein Tracking by CNN-Based Candidate Pruning and Two-Step Linking with Bayesian Network

Protein trafficking plays a vital role in understanding many biological processes and disease. Automated tracking of protein vesicles is challenging due to their erratic behaviour, changing appearance, and visual clutter. In this paper we present a novel tracking approach which utilizes a two-step linking process that exploits a probabilistic graphical model to predict tracklet linkage. The vesicles are initially detected with help of a candidate selection process, where the candidates are identified by a multi-scale spot enhancing filter. Subsequently, these candidates are pruned and selected by a light weight convolutional neural network. At the linking stage, the tracklets are formed based on the distance and the detection assignment which is implemented via combinatorial optimization algorithm. Each tracklet is described by a number of parameters used to evaluate the probability of tracklets connection by the inference over the Bayesian network. The tracking results are presented for confocal fluorescence microscopy data of protein trafficking in epithelial cells. The proposed method achieves a root mean square error (RMSE) of 1.39 for the vesicle localisation and of 0.7 representing the degree of track matching with ground truth. The presented method is also evaluated against the state-of-the-art “Trackmate“ framework

Node-link and containment methods in ontology visualization

Computer Systems, Imagery and Medi

Multi-view ontology visualisation

Computer Systems, Imagery and Medi

Integration of Information from different ontologies

Computer Systems, Imagery and Medi

Single-Molecule Localization Microscopy Reconstruction Using Noise2Noise for Super-Resolution Imaging of Actin Filaments

Single-molecule localization microscopy (SMLM) is a super-resolution imaging technique developed to image structures smaller than the diffraction limit. This modality results in sparse and non-uniform sets of localized blinks that need to be reconstructed to obtain a super-resolution representation of a tissue. In this paper, we explore the use of the Noise2Noise (N2N) paradigm to reconstruct the SMLM images. Noise2Noise is an image denoising technique where a neural network is trained with only pairs of noisy realizations of the data instead of using pairs of noisy/clean images, as performed with Noise2Clean (N2C). Here we have adapted Noise2Noise to the 2D SMLM reconstruction problem, exploring different pair creation strategies (fixed and dynamic). The approach was applied to synthetic data and to real 2D SMLM data of actin filaments. This revealed that N2N can achieve reconstruction performances close to the Noise2Clean training strategy, without having access to the super-resolution images. This could open the way to further improvement in SMLM acquisition speed and reconstruction performance