Advances, challenges, and prospects of electroencephalography-based biomarkers for psychiatric disorders: a narrative review.

Owing to a lack of appropriate biomarkers for accurate diagnosis and treatment, psychiatric disorders cause significant distress and functional impairment, leading to social and economic losses. Biomarkers are essential for diagnosing, predicting, treating, and monitoring various diseases. However, their absence in psychiatry is linked to the complex structure of the brain and the lack of direct monitoring modalities.

Using Deep Learning Techniques as an Attempt to Create the Most Cost-Effective Screening Tool for Cognitive Decline.

Objective: This study aimed to use deep learning (DL) to develop a cost-effective and accessible screening tool to improve the detection of cognitive decline, a precursor of Alzheimer's disease (AD). This study integrating a comprehensive battery of neuropsychological tests adjusted for individual demographic variables such as age, sex, and education level.

Methods: A total of 2,863 subjects with subjective cognitive complaints who underwent a comprehensive neuropsychological assessment were included.

The Complexity of Borderline Personality Disorder: Network Analysis of Personality Factors and Defense Styles in the Context of Borderline Personality Organization.

Objective: Borderline personality disorder (BPD) is known to share characteristics with a variety of personality disorders (PDs) and exhibits diverse patterns of defense mechanisms. To enhance our understanding of BPD, it's crucial to shift our focus from traditional categorical diagnostics to the dimensional traits shared with other PDs, as the borderline personality organization (BPO) model suggests. This approach illuminates the nuanced spectrum of BPD characteristics, offering deeper insights into its complexity.

Bidirectional connectivity alterations in schizophrenia: a multivariate, machine-learning approach.

Objective: It is well known that altered functional connectivity is a robust neuroimaging marker of schizophrenia. However, there is inconsistency in the direction of alterations, i.e.

Fine-Grained Motion Recognition in At-Home Fitness Monitoring with Smartwatch: A Comparative Analysis of Explainable Deep Neural Networks.

The squat is a multi-joint exercise widely used for everyday at-home fitness. Focusing on the fine-grained classification of squat motions, we propose a smartwatch-based wearable system that can recognize subtle motion differences. For data collection, 52 participants were asked to perform one correct squat and five incorrect squats with three different arm postures (straight arm, crossed arm, and hands on waist).

Understanding insomnia as systemic disease.

Sleep plays a critical role in homeostasis of the body and mind. Insomnia is a disease that causes disturbances in the initiation and maintenance of sleep. Insomnia is known to affect not only the sleep process itself but also an individual's cognitive function and emotional regulation during the daytime.

Aberrant EEG signal variability at a specific temporal scale in major depressive disorder.

Objective: Signal variability is linked to irregularities in time series caused by intrinsic nonlinearities of the neural system and can be measured on variable temporal scales over short time series. By measuring refined complex multiscale permutation entropy (RCMPE) from resting-state electroencephalography (EEG) data, we investigated the presence of a specific range of time scales characterizing major depressive disorder (MDD).

Method: We used an EEG dataset acquired from 22 MDD patients and 22 healthy controls in the eyes-closed (EC) and eyes-open (EO) states available on the PRED + CT website.

Psychiatric understanding and treatment of patients with amputations.

Amputation changes the lives of patients and their families. Consequently, the patient must adapt to altered body function and image. During this adaptation process, psychological problems, such as depression, anxiety, and posttraumatic stress disorder, can occur.

The psychological impact of the coronavirus disease pandemic on hospital workers in Daegu, South Korea.

Objective: This study aimed to assess the immediate stress and psychological impact experienced by healthcare workers and other personnel during the Coronavirus disease (COVID-19) pandemic.

Method: The sample consisted of 2554 hospital workers (i.e.

Characteristics of Individuals Who Converted to Dementia during a 5-Year Follow-Up.

Introduction: Early diagnosis of dementia is important; however, the diagnostic criteria for the preclinical stage of dementia, including biomarkers and pathophysiological markers, are not suitable for application in patients in real-world clinical settings. One potential noninvasive method to predict the risk of dementia conversion is the neuropsychological test. Therefore, in this study, we examined the results of various assessments, such as comprehensive neuropsychological tests, and the daily function of participants who were evaluated periodically for 5 years.

Self-Organized Freestanding One-Dimensional Au Nanoparticle Arrays.

One-dimensional Au nanoparticle arrays encapsulated within freestanding SiO nanowires are fabricated by thermal oxidation of Au-coated Si nanowires with controlled diameter and surface modulation. The nanoparticle diameter is determined by the Si nanowire diameter and Au film thickness, while the interparticle spacing is independently controlled by the Si nanowire modulation. The optical absorption of randomly oriented Au nanoparticle arrays exhibits a strong plasmonic response at 550 nm.

Broadband and wide field-of-view plasmonic metasurface-enabled waveplates.

Quasi two-dimensional metasurfaces composed of subwavelength nanoresonator arrays can dramatically alter the properties of light in an ultra-thin planar geometry, enabling new optical functions such as anomalous reflection and refraction, polarization filtering, and wavefront modulation. However, previous metasurface-based nanostructures suffer from low efficiency, narrow bandwidth and/or limited field-of-view due to their operation near the plasmonic resonance. Here we demonstrate plasmonic metasurface-based nanostructures for high-efficiency, angle-insensitive polarization transformation over a broad octave-spanning bandwidth.

Near-ideal optical metamaterial absorbers with super-octave bandwidth.

Nanostructured optical coatings with tailored spectral absorption properties are of interest for a wide range of applications such as spectroscopy, emissivity control, and solar energy harvesting. Optical metamaterial absorbers have been demonstrated with a variety of customized single band, multiple band, polarization, and angular configurations. However, metamaterials that provide near unity absorptivity with super-octave bandwidth over a specified optical wavelength range have not yet been demonstrated experimentally.

Tailoring dispersion for broadband low-loss optical metamaterials using deep-subwavelength Inclusions.

Metamaterials have the potential to create optical devices with new and diverse functionalities based on novel wave phenomena. Most practical optical systems require that the device properties be tightly controlled over a broad wavelength range. However, optical metamaterials are inherently dispersive, which limits operational bandwidths and leads to high absorption losses.

Low-loss impedance-matched optical metamaterials with zero-phase delay.

Metamaterials have dramatically expanded the range of available optical properties, enabling an array of new devices such as superlenses, perfect absorbers, and ultrafast switches. Most research has focused on demonstrating negative- and high-index metamaterials at terahertz and optical wavelengths. However, far less emphasis has been placed on low-loss near-zero-index metamaterials that exhibit unique properties including quasi-infinite phase velocity and infinite wavelength.

Conformal dual-band near-perfectly absorbing mid-infrared metamaterial coating.

Metamaterials offer a new approach to create surface coatings with highly customizable electromagnetic absorption from the microwave to the optical regimes. Thus far, efficient metamaterial absorbers have been demonstrated at microwave frequencies, with recent efforts aimed at much shorter terahertz and infrared wavelengths. The present infrared absorbers have been constructed from arrays of nanoscale metal resonators with simple circular or cross-shaped geometries, which provide a single band response.

Synthesizing low loss negative index metamaterial stacks for the mid-infrared using genetic algorithms.

Negative index metamaterial designs for the mid-infrared with low absorption and impedance mismatch losses are presented. A robust genetic algorithm is employed to optimize the flexible metamaterial structure for targeted refractive index and impedance values. A new figure of merit is introduced to evaluate the impedance match of the metamaterial to free space.