Prediction of Sleep Apnea Events Using a CNN-Transformer Network and Contactless Breathing Vibration Signals.

It is estimated that globally 425 million subjects have moderate to severe obstructive sleep apnea (OSA). The accurate prediction of sleep apnea events can offer insight into the development of treatment therapies. However, research related to this prediction is currently limited. We developed a covert framework for the prediction of sleep apnea events based on low-frequency breathing-induced vibrations obtained from piezoelectric sensors. A CNN-transformer network was utilized to efficiently extract local and global features from respiratory vibration signals for accurate prediction. Our study involved overnight recordings of 105 subjects. In five-fold cross-validation, we achieved an accuracy of 85.9% and an F1 score of 85.8%, which are 3.5% and 5.3% higher than the best-performed classical model, respectively. Additionally, in leave-one-out cross-validation, 2.3% and 3.8% improvements are observed, respectively. Our proposed CNN-transformer model is effective in the prediction of sleep apnea events. Our framework can thus provide a new perspective for improving OSA treatment modes and clinical management.

Contactless screening for sleep apnea with breathing vibration signals based on modified U-Net.

BACKGROUND: Obstructive sleep apnea (OSA) is a chronic sleep disorder characterized by frequent cessations or reductions of breathing during sleep. Polysomnography (PSG) is a definitive diagnostic tool for OSA. The costly and obtrusive nature of PSG and poor access to sleep clinics have created a demand for accurate home-based screening devices. METHODS: This paper proposes a novel OSA screening method based solely on breathing vibration signals with a modified U-Net, allowing patients to be tested at home. Sleep recordings over a whole night are collected in a contactless manner, and sleep apnea-hypopnea events are labeled by a deep neural network. The apnea-hypopnea index (AHI) calculated from events estimation is then used to screen for the apnea. The performance of the model is tested by event-based analysis and comparing the estimated AHI with the manually obtained values. RESULTS: The accuracy and sensitivity of sleep apnea events detection are 97.5% and 76.4%, respectively. The mean absolute error of AHI estimation for the patients is 3.0 events/hour. The correlation between the ground truth AHI and predicted AHI shows an R2 of 0.95. In addition, 88.9% of all participants are classified into correct AHI categories. CONCLUSIONS: The proposed scheme has great potential as a simple screening tool for sleep apnea. It can accurately detect potential OSA and help the patients to be referred for differential diagnosis of home sleep apnea test (HSAT) or polysomnographic evaluation.

Changes in Consumer Purchasing Behavior of Cultural and Creative Products During the COVID-19 Pandemic.

The COVID-19 pandemic that began in 2020 has led the changes of many business models, including rapid growth in e-commerce that had made the change of consumers' shopping behavior from physical to online shopping. This study takes Taiwan's cultural and creative industry as an example to understand the relationship between the development of e-commerce and the design of commodity packaging during COVID-19, as well as the changes in consumer consumption patterns during the epidemic period. After exploring more than a dozen cultural and creative brand operators in the target research area using qualitative research methods, this study found that the epidemic has indeed affected the sales of some cultural and creative brands, but through the change of packaging design, consumers' visual perception stimulates the sales of goods and re-established pay attention to the cultural and creative brands when add some new packaging designs, and bring positive effects on e-commerce sales to the brand. Also, the good packaging design brought some more attention and procurement of goods, and also brings the effect of quality improvement.

Zuogui Wan slowed senescence of bone marrow mesenchymal stem cells by suppressing Wnt/ß-catenin signaling.

ETHNOPHARMACOLOGICAL RELEVANCE: In traditional Chinese medicine (TCM), Zuogui Wan (ZGW) is a classical prescription for senile disorders and delay aging. Modern studies show that ZGW promotes central nerve cell regeneration, prevents and cures osteoporosis, enhances the body's antioxidant capacity, regulates the body's immune function, and promotes mesenchymal stem cells (MSCs) proliferation. AIM OF THE STUDY: It has been shown that MSCs aging is closely associated with organism's aging and age-related disorders. The study aimed to define the effects of ZGW on the aging bone marrow mesenchymal stem cells (BMSCs) and to identify the mechanisms of ZGW delaying BMSCs senescence. MATERIALS AND METHODS: Network pharmacology analysis combined with GEO data mining, molecular docking and experimental validation were used to evaluate the mechanisms by which ZGW delays MSCs senescence (MSCS). LC-MS was used for quality control analysis of ZGW. RESULTS: PPI network analysis revealed that EGF, TNF, JUN, MMPs, IL-6, MAPK8, and MYC are components of the core PPI network. GO and KEGG analyses revealed that oxidative stress, regulation of response to DNA damage stimuli, and Wnt signaling were significantly enriched. GEO database validation also indicated that Wnt signaling closely correlated with MSCs aging. Molecular docking analysis of the top-13 active components in the "ZGW-Targets-MSCS" network indicated that most components have strong affinity for key proteins in Wnt signaling, suggesting that modulation of Wnt signaling is an important mechanism of ZGW activity against MSCS. Further experimental validation found that ZGW indeed regulates Wnt signaling and suppresses the expression of age-related factors to enhance cell proliferation, ameliorate DNA damage, and reduce senescence-related secretory phenotype (SASP) secretion, thereby maintaining multidirectional differentiation of rat BMSCs. Similar results were obtained using the Wnt inhibitor, XAV-939. CONCLUSIONS: Together, our data show that ZGW slows BMSCs aging by suppressing Wnt signaling.

Efficient J Peak Detection From Ballistocardiogram Using Lightweight Convolutional Neural Network.

Ballistocardiagram (BCG) is a non-contact and non-invasive technique to obtain physiological information with the potential to monitor Cardio Vascular Disease (CVD) at home. Accurate detection of J-peak is the key to get critical indicators from BCG signals. With the development of deep learning methods, many researches have applied convolution neural network (CNN) and recurrent neural network (RNN) based models in J-peak detection. However, these deep learning methods have limitations in inference speed and model complexity. To improve the computational efficiency and memory utilization, we propose a robust lightweight neural network model, called JwaveNet. Moreover, in the preprocessing stage, J-peaks are re-modeled by a new transformation method based on their physiological meaning, which has been proven to increase performance. In our experiment, BCG signals, including four different sleeping positions, were collected from 24 subjects with synchronous electrocardiogram (ECG) signals. The experiment results have shown that our lightweight model greatly reduces latency and model size compared to other baseline models with high detecting accuracy.

Unobtrusive Inter-beat Interval Estimation from Multichannel Ballistocardiogram Signal Using Kalman Filter.

Unobtrusively detecting inter-beat interval (IBI) from ballistocardiogram (BCG) is useful for monitoring cardiac activity at home, especially for calculating heart rate variability (HRV), the critical indicator to evaluate heart health. Compared to single-sensor system in most studies, this research used a bed-embedded 9 by 2 array sensors system to improve measurement coverage and precision of IBI estimation. Based on this system, we proposed a mode-switch based algorithm to solve the problem on array sensor signal selection and multichannel data fusion using linear regression model and Kalman filter. In addition, a peak detection algorithm was designed to estimate IBI from each channel signal. The algorithm was validated by approximately 48 hours BCG recordings captured from 24 subjects with different sleeping positions. A mean absolute error of 31ms at 83% average coverage was obtained by the proposed method, which has proven to be a promising candidate for IBI estimation from BCG signal on multichannel array sensors system.

Carbon-Based Electrocatalysts Derived From Biomass for Oxygen Reduction Reaction: A Minireview.

Oxygen reduction reaction (ORR) electrocatalysts derived from biomass have become one of the research focuses in hetero-catalysis due to their low cost, high performance, and reproducibility properties. Related researches are of great significance for the development of next-generation fuel cells and metal-air batteries. Herein, the preparation methods of various biomass-derived catalysts and their performance in alkaline, neutral, and acidic media are summarized. This review clarifies the research progress of biomass carbon-based electrocatalysts for ORR in acidic, alkaline and neutral media, and discusses the future development trends. This minireview can give us an important enlightenment to practical application in the future.

Sleep apnea assessment using declination duration-based global metrics from unobtrusive fiber optic sensors.

OBJECTIVE: Sufficient sleep helps to restore the immune, nervous and cardiovascular systems, but is sometimes disturbed by sleep apnea (SA). The early diagnosis of sleep apnea is beneficial for the prevention of diseases. Polysomnography (PSG) recording provides comprehensive data for such assessment, but is not suitable for use at home due to discomfort during measurement and the difficulty of identification. This study proposes an unobtrusive measurement process by placing fiber optic sensors (FOSs) in a pillow (head-neck) or a bed mattress (thoracic-dorsal). APPROACH: We test two approaches: drop degrees from the baseline to validate the capability of catching respiratory drops, and linear regression models based on a new global measure, the percentage of the total duration of respiratory declination (PTDRD), to estimate the hand-scored apnea/hypopnea index (AHI). MAIN RESULTS: Based on data recorded from 63 adults, the drop degrees derived from respiratory signals exhibited statistical differences among central sleep apnea (CSA), obstructive sleep apnea (OSA) and normal breathing. The regression models based on the PTDRDs derived from head-neck FOS and thoracic-dorsal FOS also achieved good agreement with manually scored AHIs in Bland-Altman plots as well as oronasal airflow and thoracic wall movement. SIGNIFICANCE: The aforementioned performance demonstrates the capability of the FOS measurement and the efficacy of the PTDRD metrics for SA assessment.

Sponge-like N-doped carbon materials with Co-based nanoparticles derived from biomass as highly efficient electrocatalysts for the oxygen reduction reaction in alkaline media.

The development of highly efficient and low-cost catalysts towards Oxygen Reduction Reaction (ORR) is of significance for renewable energy technologies such as proton-exchange membrane fuel cells and metal-air batteries. This study is to utilize the biomass of soybean straw as the supporting carbon materials to prepare nitrogen and cobalt dual-doped porous biocarbon electrocatalysts (CoNASS) possessing high content of N (1.92%), embedding cobalt nanoparticles and sponge-like structure with high specific surface area (1185.00 m2 g-1) as well as appropriate pore diameter (∼2.17 nm). Meantime, CoNASS exhibits a good electrocatalytic activity with a half-wave potential of 0.786 V (vs. RHE), comparable to a half-wave potential of 0.827 V (vs. RHE) for the commercial Pt/C. The detections of electrochemical kinetics show the electron transfer number of CoNASS is in the range of 3.84-3.92, which indicates 4-electron pathway dominantly occurs in ORR. And the limiting diffusion current density of CoNASS at 1600 rpm is around 5.8 mA cm-2 slightly higher than that of the benchmark Pt/C (5.6 mA cm-2). This work opens a new avenue to utilize soybean straw, one of agriculture waste of large quantity, to prepare high efficient and low-cost catalysts for ORR.

RecQL4-Aurora B kinase axis is essential for cellular proliferation, cell cycle progression, and mitotic integrity.

Human RecQL4 helicase plays critical roles in the maintenance of genomic stability. Mutations in RecQL4 helicase results in three clinically related autosomal recessive disorders: Rothmund-Thomson syndrome (RTS), RAPADILINO, and Baller-Gerold syndrome. In addition to several premature aging features, RTS patients are characterized by aneuploidy involving either loss or gain of a single chromosome. Chromosome mosaicism and isochromosomes involving chromosomes 2, 7, and 8 have been reported in RecQL4-deficient RTS patients, but the precise role of RecQL4 in chromosome segregation/stability remains to be elucidated. Here, we demonstrate that RecQL4 physically and functionally interacts with Aurora B kinase (AURKB) and stabilizes its expression by inhibiting its ubiquitination process. Our study indicates that the N-terminus of RecQL4 interacts with the catalytic domain of AURKB. Strikingly, RecQL4 suppression reduces the expression of AURKB leading to mitotic irregularities and apoptotic cell death. RecQL4 suppression increases the proportion of cells at the G2/M phase followed by an extensive cell death, presumably owing to the accumulation of mitotic irregularities. Both these defects (accumulation of cells at G2/M phase and an improper mitotic exit to sub-G1) are complemented by the ectopic expression of AURKB. Finally, evidence is provided for the requirement of both human telomerase reverse transcriptase and RecQL4 for stable immortalization and longevity of RTS fibroblasts. Collectively, our study suggests that the RecQL4-AURKB axis is essential for cellular proliferation, cell cycle progression, and mitotic stability in human cells.

Metabolomics Reveals Dynamic Metabolic Changes Associated with Age in Early Childhood.

OBJECTIVES: A detailed understanding of the metabolic processes governing rapid growth in early life is still lacking. The aim of this study was to investigate the age-related metabolic changes in healthy children throughout early childhood. METHODS: Healthy children from a birth cohort were enrolled in this study from birth through 4 years of age. Urinary metabolites were assessed at 6 months, and 1, 2, 3, and 4 yr of age by using 1H-nuclear magnetic resonance (NMR) spectroscopy coupled with multivariate statistical analysis including principal components analysis (PCA) and partial least-squares discriminant analysis (PLS-DA). Metabolic pathway analysis was performed using the MetPA web tool. RESULTS: A total of 105 urine samples from 30 healthy children were collected and analyzed. Metabolites contributing to the discrimination between age groups were identified by using supervised PLS-DA (Q2 = 0.60; R2 = 0.66). A significantly higher urinary trimethylamine N-oxide (TMAO) and betaine level was found in children aged 6 months. Urinary glycine and glutamine levels declined significantly after 6 months of age and there was a concomitant compensatory increase in urinary creatine and creatinine. Metabolic pathway analysis using MetPA revealed similar nitrogen metabolism associated energy production across all ages assessed. Pathways associated with amino acid metabolism were significantly different between infants aged 6 months and 1 year, whereas pathways associated with carbohydrate metabolism were significantly different between children at ages 2 and 3 years. CONCLUSIONS: Urine metabolomics ideally represents dynamic metabolic changes across age. Urinary metabolic profiles change significantly within the first year of life, which can potentially provide crucial information about infant nutrition and growth.

Osteoporosis prevention behaviors practiced among youth in Taichung City, Taiwan.

OBJECTIVE: To examine demographic characteristics, knowledge, and attitudes of adolescents on osteoprotective practices in Taichung City, Taiwan. METHODS: Using convenience sampling, 329 students from randomly selected high schools and colleges were recruited to respond to a 58-item questionnaire. RESULTS: Adolescents had significantly higher overall osteoprotective behavior practice scores than those of young adults (P<0.001). Those self-described as healthy had significantly higher osteoprotective behavior scores. Younger participants had significantly higher scores regarding osteoprotective behaviors. CONCLUSIONS: Young people in Taiwan have moderate knowledge about osteoporosis and do practice osteoprotective behaviors, but infrequently. Osteoporosis knowledge and attitudes are not associated with osteoprotective behaviors.

[The relationship between sleep/awake rhythm development and caregiving activities].

Ensuring proper central nerve system (CNS) development is the core issue in premature infant care. Because an infant's sleep pattern (or sleep state) manifests his or her CNS integration, recognizing infant sleep patterns is essential for caregivers involved with developmental care. This article aims to explore the development of biological rhythms and infant sleep/awake states, factors influencing sleep states, and the effectiveness of various interventions to promote infant sleep. Five major factors that influence infant sleep states include noise, light, environmental temperature, physical contact with caregivers, and body position. Accordingly, we propose interventions to improve infant sleep quality to facilitate the growth of premature infants.