Parallel attention recursive generalization transformer for image super-resolution.

Transformer architectures have demonstrated remarkable performance in image super-resolution (SR). However, existing Transformer-based models generally suffer from insufficient local feature modeling, weak feature representation capabilities, and unreasonable loss function design, especially when reconstructing high-resolution (HR) images, where the restoration of fine details is poor. To address these issues, we propose a novel SR model, Parallel Attention Recursive Generalization Transformer (PARGT) in this study, which can effectively capture the fine-grained interactions between local features of the image and other regions, resulting in clearer and more coherent generated details.

Hippocampal MEF2C phosphorylation mediates synaptic plasticity in lead-induced learning and memory impairments.

Lead (Pb), an ancient metallic environmental pollutant, has received widespread attention and has been strictly controlled, but current findings have found the hazards of Pb to be much higher than previously estimated. Pb exposure can lead to memory impairment. However, the underlying molecular mechanisms have not been fully elucidated.

Interplay and long-lasting effects of maternal low-level Pb, Hg, and Cd exposures on offspring cognition.

Lead (Pb), mercury (Hg), and cadmium (Cd) are prevalent and persistent environmental contaminants, causing detrimental effects on millions of individuals worldwide. Our previous research demonstrated that early-life exposure to low-level Pb, Hg, and Cd mixtures may lead to cognitive impairments. However, the association and interaction among low levels of Pb, Hg, or Cd exposure remains unclear.

High-level feature-guided attention optimized neural network for neonatal lateral ventricular dilatation prediction.

Background: Periventricular-intraventricular hemorrhage can lead to posthemorrhagic ventricular dilatation or even posthemorrhagic hydrocephalus if not detected promptly. Sequential cranial ultrasound scans are typically used for their diagnoses. Nonetheless, manual image audit has numerous disadvantages.

Automatic measurement of fetal anterior neck lower jaw angle in nuchal translucency scans.

This study aims at suggesting an end-to-end algorithm based on a U-net-optimized generative adversarial network to predict anterior neck lower jaw angles (ANLJA), which are employed to define fetal head posture (FHP) during nuchal translucency (NT) measurement. We prospectively collected 720 FHP images (half hyperextension and half normal posture) and regarded manual measurement as the gold standard. Seventy percent of the FHP images (half hyperextension and half normal posture) were used to fit models, and the rest to evaluate them in the hyperextension group, normal posture group (NPG), and total group.

Co-exposure to low-dose lead, cadmium, and mercury promotes memory deficits in rats: Insights from the dynamics of dendritic spine pruning in brain development.

Lead (Pb), cadmium (Cd), and mercury (Hg) are environmentally toxic heavy metals that can be simultaneously detected at low levels in the blood of the general population. Although our previous studies have demonstrated neurodevelopmental toxicity upon co-exposure to these heavy metals at these low levels, the precise mechanisms remain largely unknown. Dendritic spines are the structural foundation of memory and undergo significant dynamic changes during development.

Insight into the effect of a heavy metal mixture on neurological damage in rats through combined serum metabolomic and brain proteomic analyses.

The heavy metals lead (Pb), cadmium (Cd), and mercury (Hg) that cause neurocognitive impairment have been extensively studied. These elements typically do not exist alone in the environment; they are often found with other heavy metals and can enter the body through various routes, thereby impacting health. Our previous research showed that low Pb, Cd, and Hg levels cause neurobehavioral impairments in weaning and adult rats.

Disentangled Dynamic Deviation Transformer Networks for Multivariate Time Series Anomaly Detection.

Graph neural networks have been widely used by multivariate time series-based anomaly detection algorithms to model the dependencies of system sensors. Previous studies have focused on learning the fixed dependency patterns between sensors. However, they ignore that the inter-sensor and temporal dependencies of time series are highly nonlinear and dynamic, leading to inevitable false alarms.

Assessing electrocardiogram changes after ischemic stroke with artificial intelligence.

Objective: Ischemic stroke (IS) with subsequent cerebrocardiac syndrome (CCS) has a poor prognosis. We aimed to investigate electrocardiogram (ECG) changes after IS with artificial intelligence (AI).

Methods: We collected ECGs from a healthy population and patients with IS, and then analyzed participant demographics and ECG parameters to identify abnormal features in post-IS ECGs.

Deep Self-Taught Hashing for Image Retrieval.

Hashing algorithm has been widely used to speed up image retrieval due to its compact binary code and fast distance calculation. The combination with deep learning boosts the performance of hashing by learning accurate representations and complicated hashing functions. So far, the most striking success in deep hashing have mostly involved discriminative models, which require labels.