Clinical Prevalence of Enhanced Vestibulo-Ocular Reflex Responses on Video Head Impulse Test.

OBJECTIVE: To measure and analyze the clinical and epidemiological characteristics of patients and healthy controls with enhanced eye velocity responses as well as evaluate their relationship with endolymphatic hydrops related diseases. STUDY DESIGN: Cross-sectional clinical study. SETTING: Tertiary hospital. PARTICIPANTS: Three hundred sixty three participants allocated to patients (310) and healthy control (53) groups were collected on first time visit to neurotology unit. INTERVENTION: Diagnostic. MAIN OUTCOME MEASURES: Video head impulse test records, clinical diagnose, and variables and demographic data were used to get cross tables, a general linear model, diagnostic epidemiological parameters, and machine learning variable importance evaluation methods. RESULTS: All the statistical tests revealed a significant association between enhanced vestibulo-ocular reflex (VOR) and diagnostic categories (p < 0.001). Chi-squared residual and machine learning analyses showed Menière's disease as the main associated diagnostic category, whereas the lowest residuals and gain values were found in the control group. Enhanced VOR as a diagnostic sign of Menière's disease had a sensitivity of 42.59% and a specificity of 86.32%, with an odds ratio of 4.68 (p < 0.001). CONCLUSION: There is a significantly higher prevalence of enhanced VOR responses in patients with Menière's disease, central origin vertigo, otosclerosis, and vestibular migraine than in those with other neurotologic diseases and controls. Our study found that enhanced VOR are not pathognomonic of hydrops-related diseases and the diagnosis should not solely be based on these and instead take into context other clinical and examination findings.

Core-Shell Palladium/MOF Platforms as Diffusion-Controlled Nanoreactors in Living Cells and Tissue Models.

Translating the potential of transition metal catalysis to biological and living environments promises to have a profound impact in chemical biology and biomedicine. A major challenge in the field is the creation of metal-based catalysts that remain active over time. Here, we demonstrate that embedding a reactive metallic core within a microporous metal-organic framework-based cloak preserves the catalytic site from passivation and deactivation, while allowing a suitable diffusion of the reactants. Specifically, we report the fabrication of nanoreactors composed of a palladium nanocube core and a nanometric imidazolate framework, which behave as robust, long-lasting nanoreactors capable of removing propargylic groups from phenol-derived pro-fluorophores in biological milieu and inside living cells. These heterogeneous catalysts can be reused within the same cells, promoting the chemical transformation of recurrent batches of reactants. We also report the assembly of tissue-like 3D spheroids containing the nanoreactors and demonstrate that they can perform the reactions in a repeated manner.

Plasmonic Cell-Derived Nanocomposites for Light-Controlled Cargo Release inside Living Cells.

Translating the potential of thermoplasmonics to cell-derived nanomaterials offers exciting opportunities to fabricate beyond state-of-art artificial biomimetic nanocomposites that upon illumination perform active tasks such as delivery of cargo in complex, dynamic media such as the cytosol of cells. Cell-derived nanoparticles have shown stunning potential to implement cell-specific functions, such as long blood circulation or targeting capabilities, into advanced drug delivery nanosystems. The biomimicry nanotechnology has now advanced to offer new and exciting opportunities to improve the commonly poor in vivo performance of most current nanomedicines, including evading the immune system and targeting specific tissues such as tumors, the latest remaining among the most wanted breakthroughs in nanomedicine. However, the use of cell-derived nanocomposites as stimulus-controlled drug delivery agents remains virtually unexplored. This study reports the fabrication of a plasmonic cell-derived nanocomposite by integrating near-infrared active gold nanorods in its structure. As a proof of concept, the plasmonic nanomembranes are loaded with cell non-permeant antibodies, which upon near-infrared stimulation can be released from the plasmonic nanomembranes into the cytosol of living cells, without impairing cell viability or the antibodies' function. These results set the stage for the development of photoactive cell-derived nanocarriers, which in addition to cell-specific functions promise straightforward access to spatiotemporal-controlled intracellular delivery of antibodies.

Classifier Subset Selection for the Stacked Generalization Method Applied to Emotion Recognition in Speech.

In this paper, a new supervised classification paradigm, called classifier subset selection for stacked generalization (CSS stacking), is presented to deal with speech emotion recognition. The new approach consists of an improvement of a bi-level multi-classifier system known as stacking generalization by means of an integration of an estimation of distribution algorithm (EDA) in the first layer to select the optimal subset from the standard base classifiers. The good performance of the proposed new paradigm was demonstrated over different configurations and datasets. First, several CSS stacking classifiers were constructed on the RekEmozio dataset, using some specific standard base classifiers and a total of 123 spectral, quality and prosodic features computed using in-house feature extraction algorithms. These initial CSS stacking classifiers were compared to other multi-classifier systems and the employed standard classifiers built on the same set of speech features. Then, new CSS stacking classifiers were built on RekEmozio using a different set of both acoustic parameters (extended version of the Geneva Minimalistic Acoustic Parameter Set (eGeMAPS)) and standard classifiers and employing the best meta-classifier of the initial experiments. The performance of these two CSS stacking classifiers was evaluated and compared. Finally, the new paradigm was tested on the well-known Berlin Emotional Speech database. We compared the performance of single, standard stacking and CSS stacking systems using the same parametrization of the second phase. All of the classifications were performed at the categorical level, including the six primary emotions plus the neutral one.

Feature selection for speech emotion recognition in Spanish and Basque: on the use of machine learning to improve human-computer interaction.

Study of emotions in human-computer interaction is a growing research area. This paper shows an attempt to select the most significant features for emotion recognition in spoken Basque and Spanish Languages using different methods for feature selection. RekEmozio database was used as the experimental data set. Several Machine Learning paradigms were used for the emotion classification task. Experiments were executed in three phases, using different sets of features as classification variables in each phase. Moreover, feature subset selection was applied at each phase in order to seek for the most relevant feature subset. The three phases approach was selected to check the validity of the proposed approach. Achieved results show that an instance-based learning algorithm using feature subset selection techniques based on evolutionary algorithms is the best Machine Learning paradigm in automatic emotion recognition, with all different feature sets, obtaining a mean of 80,05% emotion recognition rate in Basque and a 74,82% in Spanish. In order to check the goodness of the proposed process, a greedy searching approach (FSS-Forward) has been applied and a comparison between them is provided. Based on achieved results, a set of most relevant non-speaker dependent features is proposed for both languages and new perspectives are suggested.

Elemental analyses of soil and sediment fused with lithium borate using isotope dilution laser ablation-inductively coupled plasma-mass spectrometry.

Quantitative analysis using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) remains challenging primarily due to the lack of appropriate reference materials available for the wide variety of samples of interest and to elemental fractionation effects. Isotopic dilution mass spectrometry (IDMS) is becoming the methodology of choice to address these issues because the different isotopes of an element represent near-perfect internal standards. In this work, we investigated the lithium borate fusion of powdered solid samples, including soils, sediments, rock mine waste and a meteorite, as a strategy to homogenously distribute, i.e. equilibrate the elements and the added isotopically enriched standards. A comparison of this methodology using two pulsed laser ablation systems (ArF* excimer and Nd:YAG) with different wavelengths as well as two ICP-MS instruments (quadrupole and double-focusing sector field) was performed. Emphasis was put on using standard equipment to show the potential of the proposed strategy for its application in routine laboratories. Cr, Zn, Ba, Sr and Pb were successfully determined by LA-ICP-IDMS in six Standard Reference Materials (SRMs) representing different matrices of environmental interest. Experimental results showed the SRM fused glasses exhibited a low level of heterogeneity (intra- and inter-sample) for both natural abundance and isotopically enriched samples (RSD <3%, n=3, 1σ). A good agreement between experimental results and the certified values was also observed.