Natural enzymes are highly efficient catalysts with strong substrate specificity, making them ideal for biomedical applications. However, they often face issues such as variability, high costs, challenging preparation processes, and difficulties in large-scale production. This has led to significant efforts in developing effective nanoenzymes and exploring their application potential. In recent years, carbon dots (CDs) have gained attention due to their strong fluorescence, excellent biocompatibility, and low cytotoxicity. Cationic CDs, which possess a positively charged surface, have shown the ability to mimic natural enzyme applications. The positive charge on the surfaces of these nanomaterials significantly influences their fluorescence, biological activity, and interactions with other biomolecules. Therefore, understanding how surface charge affects the performance of CDs is crucial for enhancing their usability. Considerable progress has been made in the design, synthesis, and mechanistic research of enzyme-like cationic CDs, as well as their advanced applications. This article reviews the latest research on the design structure, catalytic mechanisms, biosensing capabilities, and biomedical applications of enzyme-like cationic CDs. First, we review the synthesis strategies for cationic CDs and how surface charge influences their physical and chemical properties. Next, we highlight various applications of these cationic CDs, demonstrating their use in areas such as detection, biomedical applications (including antibacterial agents, gene carriers, and therapeutic agents), catalysis, and more. Finally, we discuss the challenges and obstacles faced in the development of cationic CDs and look forward to exploring new applications in the future.

A pesky fish may be the culprit behind bleached tropical coral off the coast of the Florida Keys, according to research from the University of Georgia.

Accurate and efficient traffic speed prediction is crucial for improving road safety and efficiency. With the emerging deep learning and extensive traffic data, data-driven methods are widely adopted to achieve this task with increasingly complicated structures and progressively deeper layers of neural networks. Despite the design of the models, they aim to optimize the overall average performance without discriminating against different traffic states. However, the fact is that predicting the traffic speed under congestion is normally more important than the one under free flow since the downstream tasks, such as traffic control and optimization, are more interested in congestion rather than free flow. Unfortunately, most of the state-of-the-art (SOTA) models do not differentiate the traffic states during training and evaluation. To this end, we first comprehensively study the performance of the SOTA models under different speed regimes to illustrate the low accuracy of low-speed prediction. We further propose and design a novel Congestion-Aware Sparse Attention transformer (CASAformer) to enhance the prediction performance under low-speed traffic conditions. Specifically, the CASA layer emphasizes the congestion data and reduces the impact of free-flow data. Moreover, we adopt a new congestion adaptive loss function for training to make the model learn more from the congestion data. Extensive experiments on real-world datasets show that our CASAformer outperforms the SOTA models for predicting speed under 40 mph in all prediction horizons.

Attention detection using electroencephalogram (EEG) signals has become a popular topic. However, there seems to be a notable gap in the literature regarding comprehensive and systematic reviews of machine learning methods for attention detection using EEG signals. Therefore, this survey outlines recent advances in EEG-based attention detection within the past five years, with a primary focus on auditory attention detection (AAD) and attention level classification. First, researchers provide a brief overview of commonly used paradigms, preprocessing techniques, and artifact-handling methods, as well as listing accessible datasets used in these studies. Next, researchers summarize the machine learning methods for classification in this field and divide them into two categories: traditional machine learning methods and deep learning methods. Researchers also analyze the most frequently used methods and discuss the factors influencing each technique’s performance and applicability. Finally, researchers discuss the existing challenges and future trends in this field.

Analysis of data from the Family Financial Survey, included in the Spanish Statistical Plan, shows that parents aged 50-75 living together with children over 30 in the family home has an adverse effect on the well-being of mothers, especially if they are adult male sons.

In Southern European countries, more than 40% of adults aged 25-34 live at home with their parents and the average age of leaving the family home is 29.8 years. Apart from economic aspects, the cultural contexts of Mediterranean countries seem to explain these results, as they are traditionally characterised by stronger family ties and a less equal division of household labour.

Chloride toxicity is a growing threat to salt-sensitive crops, causing oxidative stress, membrane damage, and cell death.

Sterols are beneficial plant metabolites with proven human health effects. In rapeseed (Brassica napus), enhancing sterol content could add significant nutritional value to edible oils.

Researchers have developed an improved vacuum-infiltration system that overcomes long-standing barriers in tomato genetic engineering.