Jožef Stefan Institute

About: Jožef Stefan Institute is a facility organization based out in Ljubljana, Slovenia. It is known for research contribution in the topics: Liquid crystal & Dielectric. The organization has 3828 authors who have published 12614 publications receiving 291025 citations.

Palatable leptoquark scenarios for lepton flavor violation in exclusive b → sℓ 1 ℓ 2 modes

Abstract: We examine various scenarios that involve a light $$ \mathcal{O} $$ (1 TeV) leptoquark state and select those which are compatible with the current experimental values for ℬ(B s → μμ), $$ \mathrm{\mathcal{B}}{\left(B\to K\mu \mu \right)}_{\mathrm{large}-{q}^2},{R}_K={\mathrm{\mathcal{B}}}^{\prime}\left(B\to K\mu \mu \right)/{\mathrm{\mathcal{B}}}^{\prime}\left(B\to Kee\right) $$ , and which lead to predictions consistent with other experimental data. We show that two such scenarios are phenomenologically plausible, namely the one with a doublet of scalar leptoquarks of hypercharge 1/6, and the one with a triplet of vector leptoquarks of hypercharge 2/3. We also argue that a model with a singlet scalar leptoquark of hypercharge 1/3 is not viable. Using the present experimental data as constraints, it is shown that the exclusive lepton flavor violating decays, ℬ(B s → μτ), ℬ(B → Kμτ) and ℬ(B → K ∗ μτ), can be as large as $$ \mathcal{O}\left(1{0}^{-5}\right) $$ .

A comprehensive review on plasmonic-based biosensors used in viral diagnostics.

Abstract: The proliferation and transmission of viruses has become a threat to worldwide biosecurity, as exemplified by the current COVID-19 pandemic. Early diagnosis of viral infection and disease control have always been critical. Virus detection can be achieved based on various plasmonic phenomena, including propagating surface plasmon resonance (SPR), localized SPR, surface-enhanced Raman scattering, surface-enhanced fluorescence and surface-enhanced infrared absorption spectroscopy. The present review covers all available information on plasmonic-based virus detection, and collected data on these sensors based on several parameters. These data will assist the audience in advancing research and development of a new generation of versatile virus biosensors.

Predicting gene function using hierarchical multi-label decision tree ensembles

Abstract: S. cerevisiae, A. thaliana and M. musculus are well-studied organisms in biology and the sequencing of their genomes was completed many years ago. It is still a challenge, however, to develop methods that assign biological functions to the ORFs in these genomes automatically. Different machine learning methods have been proposed to this end, but it remains unclear which method is to be preferred in terms of predictive performance, efficiency and usability. We study the use of decision tree based models for predicting the multiple functions of ORFs. First, we describe an algorithm for learning hierarchical multi-label decision trees. These can simultaneously predict all the functions of an ORF, while respecting a given hierarchy of gene functions (such as FunCat or GO). We present new results obtained with this algorithm, showing that the trees found by it exhibit clearly better predictive performance than the trees found by previously described methods. Nevertheless, the predictive performance of individual trees is lower than that of some recently proposed statistical learning methods. We show that ensembles of such trees are more accurate than single trees and are competitive with state-of-the-art statistical learning and functional linkage methods. Moreover, the ensemble method is computationally efficient and easy to use. Our results suggest that decision tree based methods are a state-of-the-art, efficient and easy-to-use approach to ORF function prediction.