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.

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.

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.

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.

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.