ID3RSNet: cross-subject driver drowsiness detection from raw single-channel EEG with an interpretable residual shrinkage network.

Accurate monitoring of drowsy driving through electroencephalography (EEG) can effectively reduce traffic accidents. Developing a calibration-free drowsiness detection system with single-channel EEG alone is very challenging due to the non-stationarity of EEG signals, the heterogeneity among different individuals, and the relatively parsimonious compared to multi-channel EEG. Although deep learning-based approaches can effectively decode EEG signals, most deep learning models lack interpretability due to their black-box nature.

Linking microRNA to metabolic reprogramming and gut microbiota in the pathogenesis of colorectal cancer (Review).

Colorectal cancer (CRC), an emerging public health concern, is one of the leading causes of cancer morbidity and mortality worldwide. An increasing body of evidence shows that dysfunction in metabolic reprogramming is a crucial characteristic of CRC progression. Specifically, metabolic reprogramming abnormalities in glucose, glutamine and lipid metabolism provide the tumour with energy and nutrients to support its rapid cell proliferation and survival.

Overview of High-Dynamic-Range Image Quality Assessment.

In recent years, the High-Dynamic-Range (HDR) image has gained widespread popularity across various domains, such as the security, multimedia, and biomedical fields, owing to its ability to deliver an authentic visual experience. However, the extensive dynamic range and rich detail in HDR images present challenges in assessing their quality. Therefore, current efforts involve constructing subjective databases and proposing objective quality assessment metrics to achieve an efficient HDR Image Quality Assessment (IQA).

GRESS: Grouping Belief-Based Deep Contrastive Subspace Clustering.

The self-expressive coefficient plays a crucial role in the self-expressiveness-based subspace clustering method. To enhance the precision of the self-expressive coefficient, we propose a novel deep subspace clustering method, named grouping belief-based deep contrastive subspace clustering (GRESS), which integrates the clustering information and higher-order relationship into the coefficient matrix. Specifically, we develop a deep contrastive subspace clustering module to enhance the learning of both self-expressive coefficients and cluster representations simultaneously.

Neural Network-Based Knowledge Transfer for Multitask Optimization.

Knowledge transfer (KT) is crucial for optimizing tasks in evolutionary multitask optimization (EMTO). However, most existing KT methods can only achieve superficial KT but lack the ability to deeply mine the similarities or relationships among different tasks. This limitation may result in negative transfer, thereby degrading the KT performance.

SRNSD: Structure-Regularized Night-Time Self-Supervised Monocular Depth Estimation for Outdoor Scenes.

Deep CNNs have achieved impressive improvements for night-time self-supervised depth estimation form a monocular image. However, the performance degrades considerably compared to day-time depth estimation due to significant domain gaps, low visibility, and varying illuminations between day and night images. To address these challenges, we propose a novel night-time self-supervised monocular depth estimation framework with structure regularization, i.

Decomposition-Estimation-Reconstruction: An Automatic and Accurate Neuron Extraction Paradigm.

The extraction of spatiotemporal neuron activity from calcium imaging videos plays a crucial role in unraveling the coding properties of neurons. While existing neuron extraction approaches have shown promising results, disturbing and scattering background and unused depth still impede their performance. To address these limitations, we develop an automatic and accurate neuron extraction paradigm, dubbed as decomposition-estimation-reconstruction (DER), consisting of D-procedure, E-procedure, and R-procedure.

Image Quality Assessment: Measuring Perceptual Degradation via Distribution Measures in Deep Feature Spaces.

This study aims to develop advanced and training-free full-reference image quality assessment (FR-IQA) models based on deep neural networks. Specifically, we investigate measures that allow us to perceptually compare deep network features and reveal their underlying factors. We find that distribution measures enjoy advanced perceptual awareness and test the Wasserstein distance (WSD), Jensen-Shannon divergence (JSD), and symmetric Kullback-Leibler divergence (SKLD) measures when comparing deep features acquired from various pretrained deep networks, including the Visual Geometry Group (VGG) network, SqueezeNet, MobileNet, and EfficientNet.

MRI of Soft-Tissue Tumors: What to Include in the Report.

MRI serves as a critical step in the workup, local staging, and treatment planning of extremity soft-tissue masses. For the radiologist to meaningfully contribute to the management of soft-tissue masses, they need to provide a detailed list of descriptors of the lesion outlined in an organized report. While it is occasionally possible to use MRI to provide a diagnosis for patients with a mass, it is more often used to help with determining the differential diagnosis and planning of biopsies, surgery, radiation treatment, and chemotherapy (when provided).

Deep Feature Statistics Mapping for Generalized Screen Content Image Quality Assessment.

The statistical regularities of natural images, referred to as natural scene statistics, play an important role in no-reference image quality assessment. However, it has been widely acknowledged that screen content images (SCIs), which are typically computer generated, do not hold such statistics. Here we make the first attempt to learn the statistics of SCIs, based upon which the quality of SCIs can be effectively determined.

A Novel Multiobjective Genetic Programming Approach to High-Dimensional Data Classification.

The development of data sensing technology has generated a vast amount of high-dimensional data, posing great challenges for machine learning models. Over the past decades, despite demonstrating its effectiveness in data classification, genetic programming (GP) has still encountered three major challenges when dealing with high-dimensional data: 1) solution diversity; 2) multiclass imbalance; and 3) large feature space. In this article, we have developed a problem-specific multiobjective GP framework (PS-MOGP) for handling classification tasks with high-dimensional data.

IGF-2-mediated hypoglycemia: a case series and review of the medical therapies for refractory hypoglycemia.

Summary: Non-islet cell tumour hypoglycemia (NICTH), typically mediated by insulin-like growth factor 2 (IGF-2), is a rare but highly morbid paraneoplastic syndrome associated with tumours of mesenchymal or epithelial origin. Outside of dextrose administration and dietary modification which provide transient relief of hypoglycemia, resection of the underlying tumour is the only known cure for NICTH. Available medical therapies to manage hypoglycemia include glucocorticoids, recombinant growth hormone, and pasireotide.

Surgical outcomes of intraocular lens iris suture fixation in eyes with residual capsule support.

Purpose: To evaluate the safety and refractive outcomes of eyes after intraocular lens (IOL) iris suture fixation (ISF).

Setting: Private practice, Los Angeles, California.

Design: Nonrandomized and unmasked retrospective chart review.

Virtual idol marketing: Benefits, risks, and an integrated framework of the emerging marketing field.

The marketing practice involved with virtual idols became popular, leading to the emergence of virtual idol marketing. However, there is a lack of scientific understanding of this emerging marketing field. To promote a fundamental understanding of virtual idol marketing, this study clarifies the conceptual boundary of virtual idols and provides meaningful insights into the definitions, benefits, and risks of virtual idol marketing.

pSK41/pGO1-family conjugative plasmids of Staphylococcus aureus encode a cryptic repressor of replication.

The majority of large multiresistance plasmids of Staphylococcus aureus utilise a RepA_N-type replication initiation protein, the expression of which is regulated by a small antisense RNA (RNAI) that overlaps the rep mRNA leader. The pSK41/pGO1-family of conjugative plasmids additionally possess a small (86 codon) divergently transcribed ORF (orf86) located upstream of the rep locus. The product of pSK41 orf86 was predicted to have a helix-turn-helix motif suggestive of a likely function in transcriptional repression.

KepSalinst: Using Peripheral Points to Delineate Salient Instances.

Salient instance segmentation (SIS) is an emerging field that evolves from salient object detection (SOD), aiming at identifying individual salient instances using segmentation maps. Inspired by the success of dynamic convolutions in segmentation tasks, this article introduces a keypoints-based SIS network (KepSalinst). It employs multiple keypoints, that is, the center and several peripheral points of an instance, as effective geometrical guidance for dynamic convolutions.

Increasing densities of Pacific crown-of-thorns starfish (Acanthaster cf. solaris) at Lizard Island, northern Great Barrier Reef, resolved using a novel survey method.

Recurrent population irruptions of Pacific crown-of-thorns starfish (CoTS, Acanthaster cf. solaris) are among the foremost causes of coral mortality on Australia's Great Barrier Reef (GBR). Early intervention during the initiation of new population irruptions represents the best opportunity to effectively manage this threat.

Factors associated with health-seeking for urinary incontinence in Hong Kong Chinese women: a cross-sectional study.

Introduction And Hypothesis: To explore levels of urinary incontinence (UI) knowledge among Hong Kong Chinese women and the factors affecting patients' help-seeking behavior.

Methods: Chinese women with age ≥ 40 years who attended General Out-patient Clinics between May 1 and June 30, 2022, were invited to complete the questionnaire. The questionnaire consisted of four sections: (1) demographic data, (2) knowledge of UI (UI quiz), (3) severity of UI (UDI-6) and impairment of quality of life (QOL) (IIQ-7), and (4) barriers to seeking medical help.