Visualizing Structures in Confocal Microscopy Datasets Through Clusterization: A Case Study on Bile Ducts

Aiming at a better result from previous works, we employed some heuristics found in the literature to determine the appropriate parameters for the clustering. We proposed our methodology by adding some steps to be performed before the clustering phase: one step for pre-processing the volumetric dataset and another to analyzing candidate features to guide the clustering. In this latter aspect, we provide an interesting contribution: we have explored the gradient magnitude as a feature that allowed to extract relevant information from the density-based spatial clustering. Besides the fact that DBSCAN allows easy detection of noise points, an interesting result for both datasets was that the first and largest cluster found as significant for the visualization represents the structure of interest. In the red channel, this cluster represents the most prominent vessels, while in the green channel, the peribiliary glands were made more evident.Abstract—Three-dimensional datasets from biological tissues have increased with the evolution of confocal microscopy. Hepatology researchers have used confocal microscopy for investigating the microanatomy of bile ducts. Bile ducts are complex tubular tissues consisting of many juxtaposed microstructures with distinct characteristics. Since confocal images are difficult to segment because of the noise introduced during the specimen preparation, traditional quantitative analyses used in medical datasets are difficult to perform on confocal microscopy data and require extensive user intervention. Thus, the visual exploration and analysis of bile ducts pose a challenge in hepatology research, requiring different methods. This paper investigates the application of unsupervised machine learning to extract relevant structures from confocal microscopy datasets representing bile ducts. Our approach consists of pre-processing, clustering, and 3D visualization. For clustering, we explore the density-based spatial clustering for applications with noise (DBSCAN) algorithm, using gradient information for guiding the clustering. We obtained a better visualization of the most prominent vessels and internal structures.info:eu-repo/semantics/publishedVersio

A Multilayer Interval Type-2 Fuzzy Extreme Learning Machine for the recognition of walking activities and gait events using wearable sensors

In this paper, a novel Multilayer Interval Type-2 Fuzzy Extreme Learning Machine (ML-IT2-FELM) for the recognition of walking activities and Gait events is presented. The ML-IT2-FELM uses a hierarchical learning scheme that consists of multiple layers of IT2 Fuzzy Autoencoders (FAEs), followed by a final classification layer based on an IT2-FELM architecture. The core building block in the ML-IT2-FELM is the IT2-FELM, which is a generalised model of the Interval Type-2 Radial Basis Function Neural Network (IT2-RBFNN) and that is functionally equivalent to a class of simplified IT2 Fuzzy Logic Systems (FLSs). Each FAE in the ML-IT2-FELM employs an output layer with a direct-defuzzification process based on the Nie-Tan algorithm, while the IT2-FELM classifier includes a Karnik-Mendel type-reduction method (KM). Real data was collected using three inertial measurements units attached to the thigh, shank and foot of twelve healthy participants. The validation of the ML-IT2-FELM method is performed with two different experiments. The first experiment involves the recognition of three different walking activities: Level-Ground Walking (LGW), Ramp Ascent (RA) and Ramp Descent (RD). The second experiment consists of the recognition of stance and swing phases during the gait cycle. In addition, to compare the efficiency of the ML-IT2-FELM with other ML fuzzy methodologies, a kernel-based ML-IT2-FELM that is inspired by kernel learning and called KML-IT2-FELM is also implemented. The results from the recognition of walking activities and gait events achieved an average accuracy of 99.98% and 99.84% with a decision time of 290.4ms and 105ms, respectively, by the ML-IT2-FELM, while the KML-IT2-FELM achieved an average accuracy of 99.98% and 99.93% with a decision time of 191.9ms and 94ms. The experiments demonstrate that the ML-IT2-FELM is not only an effective Fuzzy Logic-based approach in the presence of sensor noise, but also a fast extreme learning machine for the recognition of different walking activities

Prevalence and Penetrance of BRCA1 and BRCA2 Germline Mutations in Colombian Breast Cancer Patients

9 páginasPathogenic BRCA1/2 germline mutations confer high risks of breast and ovarian cancer to women of European ancestry. Characterization of BRCA1/2 mutations in other ethnic groups is also medically important. We comprehensively screened 68 Colombian breast/ovarian cancer families for small-range mutations, 221 families for large-genomic rearrangements, and 1,022 unselected breast cancer cases for Colombian founder mutations in BRCA1/2. The risk of cancer among relatives of mutation carriers and the mutation penetrance were estimated by survival analysis. Identified BRCA2 mutations included 6310delGA and the recurrent 1991del4 mutations. A novel large BRCA2 deletion was found in 0.9% of the screened families. Among unselected breast cancer cases, 3.3% tested positive for BRCA1/3450del4, 2.2% for BRCA1/A1708E, 1.1% for BRCA2/3034del4, and 0.4% for BRCA2/1991del4. Female relatives of carriers of BRCA1/2 founder mutations showed a 5.90 times higher risk of breast cancer, when the woman herself carried a BRCA1 mutation compared to a non-carrier (95% CI 2.01–17.3). The estimated cumulative risk of breast cancer by age 70 years for BRCA1 mutations carriers was 14% (95% CI 5–38) compared to 3% for the general Colombian population (relative risk of breast cancer 4.05). Together with known founder mutations, reported novel variants may ease a cost-effective BRCA1/2 screening in women with Colombian ancestry

Microwave amplification with nanomechanical resonators

Sensitive measurement of electrical signals is at the heart of modern science and technology. According to quantum mechanics, any detector or amplifier is required to add a certain amount of noise to the signal, equaling at best the energy of quantum fluctuations. The quantum limit of added noise has nearly been reached with superconducting devices which take advantage of nonlinearities in Josephson junctions. Here, we introduce a new paradigm of amplification of microwave signals with the help of a mechanical oscillator. By relying on the radiation pressure force on a nanomechanical resonator, we provide an experimental demonstration and an analytical description of how the injection of microwaves induces coherent stimulated emission and signal amplification. This scheme, based on two linear oscillators, has the advantage of being conceptually and practically simpler than the Josephson junction devices, and, at the same time, has a high potential to reach quantum limited operation. With a measured signal amplification of 25 decibels and the addition of 20 quanta of noise, we anticipate near quantum-limited mechanical microwave amplification is feasible in various applications involving integrated electrical circuits.Comment: Main text + supplementary information. 14 pages, 3 figures (main text), 18 pages, 6 figures (supplementary information

Non-Gaussianity from isocurvature perturbations

We develop a formalism to study non-Gaussianity in both curvature and isocurvature perturbations. It is shown that non-Gaussianity in the isocurvature perturbation between dark matter and photons leaves distinct signatures in the CMB temperature fluctuations, which may be confirmed in future experiments, or possibly, even in the currently available observational data. As an explicit example, we consider the QCD axion and show that it can actually induce sizable non-Gaussianity for the inflationary scale, H_{inf} = O(10^9 - 10^{11})GeV.Comment: 24 pages, 6 figures; references added; version to appear in JCA

Mechanisms of Psychological Distress following War in the Former Yugoslavia: The Role of Interpersonal Sensitivity

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.This study was funded by a grant from the European Commission, contract number INCO-CT-2004-509176. AN was supported by a Clinical Early Career Research Fellowship (113295) and a Project Grant (104288

Dark Matter Spin-Dependent Limits for WIMP Interactions on 19-F by PICASSO

The PICASSO experiment at SNOLAB reports new results for spin-dependent WIMP interactions on $^{19}$F using the superheated droplet technique. A new generation of detectors and new features which enable background discrimination via the rejection of non-particle induced events are described. First results are presented for a subset of two detectors with target masses of $^{19}$F of 65 g and 69 g respectively and a total exposure of 13.75 $\pm$ 0.48 kgd. No dark matter signal was found and for WIMP masses around 24 GeV/c$^2$ new limits have been obtained on the spin-dependent cross section on $^{19}$F of $\sigma_F$ = 13.9 pb (90% C.L.) which can be converted into cross section limits on protons and neutrons of $\sigma_p$ = 0.16 pb and $\sigma_n$ = 2.60 pb respectively (90% C.L). The obtained limits on protons restrict recent interpretations of the DAMA/LIBRA annual modulations in terms of spin-dependent interactions.Comment: Revised version, accepted for publication in Phys. Lett. B, 20 pages, 7 figure

Decoupling property of the supersymmetric Higgs sector with four doublets

In supersymmetric standard models with multi Higgs doublet fields, selfcoupling constants in the Higgs potential come only from the D-terms at the tree level. We investigate the decoupling property of additional two heavier Higgs doublet fields in the supersymmetric standard model with four Higgs doublets. In particular, we study how they can modify the predictions on the quantities well predicted in the minimal supersymmetric standard model (MSSM), when the extra doublet fields are rather heavy to be measured at collider experiments. The B-term mixing between these extra heavy Higgs bosons and the relatively light MSSM-like Higgs bosons can significantly change the predictions in the MSSM such as on the masses of MSSM-like Higgs bosons as well as the mixing angle for the two light CP-even scalar states. We first give formulae for deviations in the observables of the MSSM in the decoupling region for the extra two doublet fields. We then examine possible deviations in the Higgs sector numerically, and discuss their phenomenological implications.Comment: 26 pages, 24 figures, text sligtly modified,version to appear in Journal of High Energy Physic