Nanoscale probing of electron-regulated structural transitions in silk proteins by near-field IR imaging and nano-spectroscopy

Silk protein fibres produced by silkworms and spiders are renowned for their unparalleled mechanical strength and extensibility arising from their high-β-sheet crystal contents as natural materials. Investigation of β-sheet-oriented conformational transitions in silk proteins at the nanoscale remains a challenge using conventional imaging techniques given their limitations in chemical sensitivity or limited spatial resolution. Here, we report on electron-regulated nanoscale polymorphic transitions in silk proteins revealed by near-field infrared imaging and nano-spectroscopy at resolutions approaching the molecular level. The ability to locally probe nanoscale protein structural transitions combined with nanometre-precision electron-beam lithography offers us the capability to finely control the structure of silk proteins in two and three dimensions. Our work paves the way for unlocking essential nanoscopic protein structures and critical conditions for electron-induced conformational transitions, offering new rules to design protein-based nanoarchitectures.National Science Foundation (U.S.) (1563422)National Science Foundation (U.S.) (1562915

Exploring the relationship between response time sequence in scale answering process and severity of insomnia: a machine learning approach

Objectives: The study aims to investigate the relationship between insomnia and response time. Additionally, it aims to develop a machine learning model to predict the presence of insomnia in participants using response time data. Methods: A mobile application was designed to administer scale tests and collect response time data from 2729 participants. The relationship between symptom severity and response time was explored, and a machine learning model was developed to predict the presence of insomnia. Results: The result revealed a statistically significant difference (p<.001) in the total response time between participants with or without insomnia symptoms. A correlation was observed between the severity of specific insomnia aspects and response times at the individual questions level. The machine learning model demonstrated a high predictive accuracy of 0.743 in predicting insomnia symptoms based on response time data. Conclusions: These findings highlight the potential utility of response time data to evaluate cognitive and psychological measures, demonstrating the effectiveness of using response time as a diagnostic tool in the assessment of insomnia

CARDIAC FUNCTION OF BASKETBALL PLAYERS UNDER STRESS TRAINING

ABSTRACT Introduction: Basketball can enhance the physical fitness of young people, promote the growth and development of their bodies, and improve health and athletic ability. Objective: To explore the characteristics of basketball players’ cardiac response to increasing load training. Methods: By analyzing 12 juvenile male amateur basketball training athletes, when performing incremental load exercises on the treadmill, using a 12-lead electrocardiograph to record the electrocardiogram, HR, and blood pressure responses for each level of exercise. Results: The mean heart rate of the basketball players before movement was 82.45± 11.44 bpm, slightly higher than the heart rate at rest. Depending on the exercise load, the blood pressure should increase by 5 to 12 mmHg. Under different load training conditions, each level of blood pressure in the Bruce treadmill test procedure should increase 12.5 ~ 44mmHg. The basketball player’s systolic pressure increased by 2.25 ~ 15.7mmHg, diastolic pressure increased by 0.43 to 11.37 mmHg. Conclusions: In basketball players, the psychological stress is less than that of the average person performing the same exercise. The strong ability to adapt to exercise under incremental load training, the contractility of the ventricular muscles and the development of the heart are good. Level of evidence II; Therapeutic studies - investigation of treatment results.</jats:p

Functional Nanomaterials: From Structures to Biomedical Applications

In recent decades, a number of functional nanomaterials have attracted a great amount of attention and exhibited excellent performance for biomedical and pharmaceutical applications [...

Construction of a novel ratiometric near-infrared fluorescent probe for SO₂ derivatives and its application for biological imaging

A novel near-infrared ratiometric fluorescent probe for detecting SO2 derivatives was developed and used for fluorescence imaging in living cells.</p

Short-Term Electricity Demand Forecasting for DanceSport Activities

This paper introduces a novel hybrid deep learning-based approach for short-term electricity demand forecasting in dance sport activities. Traditional deep learning methods often overlook important spatial dependencies and key features like trend and seasonal patterns. To address these limitations, we propose a model that combines Transformer for temporal feature extraction and Graph Neural Networks for spatial feature extraction, enabling prediction based on spatial-temporal features. Additionally, we employ the decomposition techniques to extract seasonal and trend features from dance sports data. By integrating early fusion (feature-level fusion) and late fusion (score-level fusion) strategies, our model achieves superior performance, outperforming baseline methods by over 4% on benchmark datasets. Additionally, we conduct the ablation study to comprehensively analyze the impact of each module on prediction accuracy, providing valuable insights into the contribution of spatial, temporal, seasonal and trend features to the overall forecasting performance