Molecular tools for recording and intervention of neuronal activity.

Observing the activity of neural networks is critical for the identification of learning and memory processes, as well as abnormal activities of neural circuits in disease, particularly for the purpose of tracking disease progression. Methodologies for describing the activity history of neural networks using molecular biology techniques first utilized genes expressed by active neurons, followed by the application of recently developed techniques including optogenetics and incorporation of insights garnered from other disciplines, including chemistry and physics. In this review, we will discuss ways in which molecular biological techniques used to describe the activity of neural networks have evolved along with the potential for future development.

Prediction of Sleep Stages Via Deep Learning Using Smartphone Audio Recordings in Home Environments: Model Development and Validation.

Background: The growing public interest and awareness regarding the significance of sleep is driving the demand for sleep monitoring at home. In addition to various commercially available wearable and nearable devices, sound-based sleep staging via deep learning is emerging as a decent alternative for their convenience and potential accuracy. However, sound-based sleep staging has only been studied using in-laboratory sound data.

End-to-End Sleep Staging Using Nocturnal Sounds from Microphone Chips for Mobile Devices.

Purpose: Nocturnal sounds contain numerous information and are easily obtainable by a non-contact manner. Sleep staging using nocturnal sounds recorded from common mobile devices may allow daily at-home sleep tracking. The objective of this study is to introduce an end-to-end (sound-to-sleep stages) deep learning model for sound-based sleep staging designed to work with audio from microphone chips, which are essential in mobile devices such as modern smartphones.

Confidence-Based Framework Using Deep Learning for Automated Sleep Stage Scoring.

Study Objectives: Automated sleep stage scoring is not yet vigorously used in practice because of the black-box nature and the risk of wrong predictions. The objective of this study was to introduce a confidence-based framework to detect the possibly wrong predictions that would inform clinicians about which epochs would require a manual review and investigate the potential to improve accuracy for automated sleep stage scoring.

Methods: We used 702 polysomnography studies from a local clinical dataset (SNUBH dataset) and 2804 from an open dataset (SHHS dataset) for experiments.

Synthesis of Mesoporous α-FeO Nanoparticles by Non-ionic Soft Template and Their Applications to Heavy Oil Upgrading.

This paper reports the synthetic route of 3-D network shape α-FeO from aqueous solutions of iron precursor using a non-ionic polymeric soft-template, Pluronic P123. During the synthesis of α-FeO, particle sizes, crystal phases and morphologies were significantly influenced by pH, concentrations of precursor and template. The unique shape of worm-like hematite was obtained only when a starting solution was prepared by a weakly basic pH condition and a very specific composition of constituents.

Capping with multivalent surfactants for zeolite nanocrystal synthesis.

Multiammonium surfactants exhibited a remarkable capping effect for zeolite synthesis in the forms of nanoparticles, nanorods, and nanosponges in cases where common monovalent surfactants failed. A nanorod-shaped mordenite zeolite synthesized in this manner showed significantly enhanced catalytic lifetimes in acid-catalyzed cumene synthesis reactions.

Directing zeolite structures into hierarchically nanoporous architectures.

Crystalline mesoporous molecular sieves have long been sought as solid acid catalysts for organic reactions involving large molecules. We synthesized a series of mesoporous molecular sieves that possess crystalline microporous walls with zeolitelike frameworks, extending the application of zeolites to the mesoporous range of 2 to 50 nanometers. Hexagonally ordered or disordered mesopores are generated by surfactant aggregates, whereas multiple cationic moieties in the surfactant head groups direct the crystallization of microporous aluminosilicate frameworks.