On-line measurement of COD and nitrate in water against stochastic background interference based on ultraviolet-visible spectroscopy and physics-informed multi-task learning.

Traditional ultraviolet-visible spectroscopic quantitative analytical methods face challenges in simultaneous and long-term accurate measurement of chemical oxygen demand (COD) and nitrate due to spectral overlap and the interference from stochastic background caused by turbidity and chromaticity in water. Addressing these limitations, a compact dual optical path spectrum detection sensor is introduced, and a novel ultraviolet-visible spectroscopic quantitative analysis model based on physics-informed multi-task learning (PI-MTL) is designed. Incorporating a physics-informed block, the PI-MTL model integrates pre-existing physical knowledge for enhanced feature extraction specific to each task.

Increased and sex-differentiated medial prefrontal cortex activation during the MATRICS Consensus Cognitive Battery in schizophrenia: A fNIRS study.

Executive impairment in schizophrenia is common, but the mechanism remains unclear. This is the first study to use simultaneously functional near-infrared spectroscopy (fNIRS) to monitor the hemodynamic response in schizophrenia during the MATRICS Consensus Cognitive Battery (MCCB). Here, we monitored relative changes in oxyhemoglobin concentration in the medial prefrontal cortex (mPFC) during Trail Making Test, Symbol Coding Test and Mazes Test of the MCCB in 63 patients (29 females) with schizophrenia and 32 healthy controls (15 females).

Classification of Benign-Malignant Thyroid Nodules Based on Hyperspectral Technology.

In recent years, the incidence of thyroid cancer has rapidly increased. To address the issue of the inefficient diagnosis of thyroid cancer during surgery, we propose a rapid method for the diagnosis of benign and malignant thyroid nodules based on hyperspectral technology. Firstly, using our self-developed thyroid nodule hyperspectral acquisition system, data for a large number of diverse thyroid nodule samples were obtained, providing a foundation for subsequent diagnosis.

The role of eye movement signals in non-invasive brain-computer interface typing system.

Brain-Computer Interfaces (BCIs) have shown great potential in providing communication and control for individuals with severe motor disabilities. However, traditional BCIs that rely on electroencephalography (EEG) signals suffer from low information transfer rates and high variability across users. Recently, eye movement signals have emerged as a promising alternative due to their high accuracy and robustness.

Classification of self-limited epilepsy with centrotemporal spikes by classical machine learning and deep learning based on electroencephalogram data.

Electroencephalogram (EEG) has been widely utilized as a valuable assessment tool for diagnosing epilepsy in hospital settings. However, clinical diagnosis of patients with self-limited epilepsy with centrotemporal spikes (SeLECTS) is challenging due to the presence of similar abnormal discharges in EEG displays compared to other types of epilepsy (non-SeLECTS) patients. To assist the diagnostic process of epilepsy, a comprehensive classification study utilizing machine learning or deep learning techniques is proposed.

Tetherless Optical Neuromodulation: Wavelength from Orange-red to Mid-infrared.

Optogenetics, a technique that employs light for neuromodulation, has revolutionized the study of neural mechanisms and the treatment of neurological disorders due to its high spatiotemporal resolution and cell-type specificity. However, visible light, particularly blue and green light, commonly used in conventional optogenetics, has limited penetration in biological tissue. This limitation necessitates the implantation of optical fibers for light delivery, especially in deep brain regions, leading to tissue damage and experimental constraints.

Spectral encoder to extract the efficient features of Raman spectra for reliable and precise quantitative analysis.

Raman spectroscopy has become a powerful analytical tool highly demanded in many applications such as microorganism sample analysis, food quality control, environmental science, and pharmaceutical analysis, owing to its non-invasiveness, simplicity, rapidity and ease of use. Among them, quantitative research using Raman spectroscopy is a crucial application field of spectral analysis. However, the entire process of quantitative modeling largely relies on the extraction of effective spectral features, particularly for measurements on complex samples or in environments with poor spectral signal quality.

A Snapshot Multi-Spectral Demosaicing Method for Multi-Spectral Filter Array Images Based on Channel Attention Network.

Multi-spectral imaging technologies have made great progress in the past few decades. The development of snapshot cameras equipped with a specific multi-spectral filter array (MSFA) allow dynamic scenes to be captured on a miniaturized platform across multiple spectral bands, opening up extensive applications in quantitative and visualized analysis. However, a snapshot camera based on MSFA captures a single band per pixel; thus, the other spectral band components of pixels are all missed.

Rapid Determination of Positive-Negative Bacterial Infection Based on Micro-Hyperspectral Technology.

To meet the demand for rapid bacterial detection in clinical practice, this study proposed a joint determination model based on spectral database matching combined with a deep learning model for the determination of positive-negative bacterial infection in directly smeared urine samples. Based on a dataset of 8124 urine samples, a standard hyperspectral database of common bacteria and impurities was established. This database, combined with an automated single-target extraction, was used to perform spectral matching for single bacterial targets in directly smeared data.

CMID: Crossmodal Image Denoising via Pixel-Wise Deep Reinforcement Learning.

Removing noise from acquired images is a crucial step in various image processing and computer vision tasks. However, the existing methods primarily focus on removing specific noise and ignore the ability to work across modalities, resulting in limited generalization performance. Inspired by the iterative procedure of image processing used by professionals, we propose a pixel-wise crossmodal image-denoising method based on deep reinforcement learning to effectively handle noise across modalities.

Event-related potentials reveal visual episodic memory deficits in patients with temporal lobe epilepsy.

Objective: Temporal lobe epilepsy (TLE) patients usually suffer from impaired episodic memory (EM), but its underlying electrophysiologic mechanism and impacted cognitive performance are unclear. We aim to investigate the association between episodic memory reserve and physiological measures of memory workload in TLE patients using Event-related potentials (ERP).

Methods: A change detection task with image stimuli assesses visual episodic memory.

An eye tracking investigation of attention mechanism in driving behavior under emotional issues and cognitive load.

Emotions have specific effects on behavior. At present, studies are increasingly interested in how emotions affect driving behavior. We designed the experiment by combing driving tasks and eye tracking.

Temporal transformer-spatial graph convolutional network: an intelligent classification model for anti N-methyl-D-aspartate receptor encephalitis based on electroencephalogram signal.

Encephalitis is a disease typically caused by viral infections or autoimmunity. The most common type of autoimmune encephalitis is anti-N-methyl-D-aspartate receptor (NMDAR) antibody-mediated, known as anti-NMDA receptor encephalitis, which is a rare disease. Specific EEG patterns, including "extreme delta brush" (EDB), have been reported in patients with anti-NMDA receptor encephalitis.

Deep neural network: As the novel pipelines in multiple preprocessing for Raman spectroscopy.

Raman spectroscopy is a kind of vibrational method that can rapidly and non-invasively gives chemical structural information with the Raman spectrometer. Despite its technical advantages, in practical application scenarios, Raman spectroscopy often suffers from interference, such as noises and baseline drifts, resulting in the inability to acquire high-quality Raman spectroscopy signals, which brings challenges to subsequent spectral analysis. The commonly applied spectral preprocessing methods, such as Savitzky-Golay smooth and wavelet transform, can only perform corresponding single-item processing and require manual intervention to carry out a series of tedious trial parameters.

Automatic Seizure Detection and Prediction Based on Brain Connectivity Features and a CNNs Meet Transformers Classifier.

(1) Background: Epilepsy is a neurological disorder that causes repeated seizures. Since electroencephalogram (EEG) patterns differ in different states (inter-ictal, pre-ictal, and ictal), a seizure can be detected and predicted by extracting various features. However, the brain connectivity network, a two-dimensional feature, is rarely studied.

Recent advances of lanthanide nanomaterials in Tumor NIR fluorescence detection and treatment.

Lanthanide nanomaterials have garnered significant attention from researchers among the main near-infrared (NIR) fluorescent nanomaterials due to their excellent chemical and fluorescence stability, narrow emission band, adjustable luminescence color, and long lifetime. In recent years, with the preparation, functional modification, and fluorescence improvement of lanthanide materials, great progress has been made in their application in the biomedical field. This review focuses on the latest progress of lanthanide nanomaterials in tumor diagnosis and treatment, as well as the interaction mechanism between fluorescence and biological tissues.

Adaptive convolution kernel network for change detection in hyperspectral images.

Feature extraction is a key step in hyperspectral image change detection. However, many targets with great various sizes, such as narrow paths, wide rivers, and large tracts of cultivated land, can appear in a satellite remote sensing image at the same time, which will increase the difficulty of feature extraction. In addition, the phenomenon that the number of changed pixels is much less than unchanged pixels will lead to class imbalance and affect the accuracy of change detection.

Joint Texture Search and Histogram Redistribution for Hyperspectral Image Quality Improvement.

Due to optical noise, electrical noise, and compression error, data hyperspectral remote sensing equipment is inevitably contaminated by various noises, which seriously affect the applications of hyperspectral data. Therefore, it is of great significance to enhance hyperspectral imaging data quality. To guarantee the spectral accuracy during data processing, band-wise algorithms are not suitable for hyperspectral data.

Rapid Identification of Infectious Pathogens at the Single-Cell Level via Combining Hyperspectral Microscopic Images and Deep Learning.

Identifying infectious pathogens quickly and accurately is significant for patients and doctors. Identifying single bacterial strains is significant in eliminating culture and speeding up diagnosis. We present an advanced optical method for the rapid detection of infectious (including common and uncommon) pathogens by combining hyperspectral microscopic imaging and deep learning.

Calligraphy and Painting Identification 3D-CNN Model Based on Hyperspectral Image MNF Dimensionality Reduction.

As a kind of cultural art, calligraphy and painting are not only an important part of traditional culture but also has important value of art collection and trade. The existence of forgeries has seriously affected the fair trade, protection, and inheritance of calligraphy and painting. There is an urgent need for the efficient, accurate, and intelligent technical identification method.

Transformer-based factorized encoder for classification of pneumoconiosis on 3D CT images.

In the past decade, deep learning methods have been implemented in the medical image fields and have achieved good performance. Recently, deep learning algorithms have been successful in the evaluation of diagnosis on lung images. Although chest radiography (CR) is the standard data modality for diagnosing pneumoconiosis, computed tomography (CT) typically provides more details of the lesions in the lung.

An Attention-Based CoT-ResNet With Channel Shuffle Mechanism for Classification of Alzheimer's Disease Levels.

Detection of early morphological changes in the brain and early diagnosis are important for Alzheimer's disease (AD), and high-resolution magnetic resonance imaging (MRI) can be used to help diagnose and predict the disease. In this paper, we proposed two improved ResNet algorithms that introduced the Contextual Transformer (CoT) module, group convolution, and Channel Shuffle mechanism into the traditional ResNet residual blocks. The CoT module is used to replace the 3 × 3 convolution in the residual block to enhance the feature extraction capability of the residual block, while the Channel Shuffle mechanism is used to reorganize the feature maps of different groups in the input layer to improve the communication between the feature maps from different groups.

A Deep-Learning Based System for Rapid Genus Identification of Pathogens under Hyperspectral Microscopic Images.

Infectious diseases have always been a major threat to the survival of humanity. Additionally, they bring an enormous economic burden to society. The conventional methods for bacteria identification are expensive, time-consuming and laborious.