An expedited model for identifying potential patients with periodic leg movements.

Periodic leg movements during sleep (PLMS) may have crucial consequences in adults. This study aimed to identify baseline characteristics, symptoms, or questionnaires that could help to identify sleep-disordered breathing patients with significant PLMS. Patients aged 20-80 years who underwent polysomnography for assessing sleep disturbance were included. Various factors such as sex, age, body measurements, symptoms, apnea-hypopnea index (AHI), and sleep quality scales were analysed to determine the presence of PLMS. The study included 1480 patients with a mean age of 46.4 ± 13.4 years, among whom 110 (7.4%) had significant PLMS with a PLM index of 15 or higher. There were no significant differences observed in terms of sex or BMI between patients with and without significant PLMS. However, the odds ratios (OR) for PLMS were 4.33, 4.41, and 4.23 in patients who were aged over 50 years, had insomnia, or had an ESS score of less than 10, respectively. Notably, the OR increased up to 67.89 times in patients who presented with all three risk factors. Our analysis identified significant risk factors for PLMS: age over 50, self-reported insomnia, and lower daytime sleepiness levels. These findings aid in identifying potential PLMS patients, facilitating confirmatory examinations and managing associated comorbidities.

Detection of preceding sleep apnea using ECG spectrogram during CPAP titration night: A novel machine-learning and bag-of-features framework.

Obstructive sleep apnea (OSA) has a heavy health-related burden on patients and the healthcare system. Continuous positive airway pressure (CPAP) is effective in treating OSA, but adherence to it is often inadequate. A promising solution is to detect sleep apnea events in advance, and to adjust the pressure accordingly, which could improve the long-term use of CPAP treatment. The use of CPAP titration data may reflect a similar response of patients to therapy at home. Our study aimed to develop a machine-learning algorithm using retrospective electrocardiogram (ECG) data and CPAP titration to forecast sleep apnea events before they happen. We employed a support vector machine (SVM), k-nearest neighbour (KNN), decision tree (DT), and linear discriminative analysis (LDA) to detect sleep apnea events 30-90 s in advance. Preprocessed 30 s segments were time-frequency transformed to spectrograms using continuous wavelet transform, followed by feature generation using the bag-of-features technique. Specific frequency bands of 0.5-50 Hz, 0.8-10 Hz, and 8-50 Hz were also extracted to detect the most detected band. Our results indicated that SVM outperformed KNN, LDA, and DT across frequency bands and leading time segments. The 8-50 Hz frequency band gave the best accuracy of 98.2%, and a F1-score of 0.93. Segments 60 s before sleep events seemed to exhibit better performance than other pre-OSA segments. Our findings demonstrate the feasibility of detecting sleep apnea events in advance using only a single-lead ECG signal at CPAP titration, making our proposed framework a novel and promising approach to managing obstructive sleep apnea at home.

Directional emission of nanoscale chiral sources modified by gap plasmons.

Efficient manipulation of the emission direction of a chiral nanoscale light source is significant for information transmission and on-chip information processing. Here, we propose a scheme to control the directionality of nanoscale chiral light sources based on gap plasmons. The gap plasmon mode formed by a gold nanorod and a silver nanowire realizes the highly directional emission of chiral light sources. Based on the optical spin-locked light propagation, the hybrid structure enables the directional coupling of chiral emission to achieve a contrast ratio of 99.5%. The emission direction can be manipulated by tailoring the configuration of the structure, such as the positions, aspect ratios, and orientation of the nanorod. Besides, a great local field enhancement exists for highly enhanced emission rates within the nanogap. This chiral nanoscale light source manipulation scheme provides a way for chiral valleytronics and integrated photonics.

Impact of lifetime air pollution exposure patterns on the risk of chronic disease.

Long-term exposure to air pollution can lead to cardiovascular disease, metabolic syndrome, and chronic respiratory disease. However, from a lifetime perspective, the critical period of air pollution exposure in terms of health risk is unknown. This study aimed to evaluate the impact of air pollution exposure at different life stages. The study participants were recruited from community centers in Northern Taiwan between October 2018 and April 2021. Their annual averages for fine particulate matter (PM2.5) exposure were derived from a national visibility database. Lifetime PM2.5 exposures were determined using residential address information and were separated into three stages (<20, 20-40, and >40 years). We employed exponentially weighted moving averages, applying different weights to the aforementioned life stages to simulate various weighting distribution patterns. Regression models were implemented to examine associations between weighting distributions and disease risk. We applied a random forest model to compare the relative importance of the three exposure life stages. We also compared model performance by evaluating the accuracy and F1 scores (the harmonic mean of precision and recall) of late-stage (>40 years) and lifetime exposure models. Models with 89% weighting on late-stage exposure showed significant associations between PM2.5 exposure and metabolic syndrome, hypertension, diabetes, and cardiovascular disease, but not gout or osteoarthritis. Lifetime exposure models showed higher precision, accuracy, and F1 scores for metabolic syndrome, hypertension, diabetes, and cardiovascular disease, whereas late-stage models showed lower performance metrics for these outcomes. We conclude that exposure to high-level PM2.5 after 40 years of age may increase the risk of metabolic syndrome, hypertension, diabetes, and cardiovascular disease. However, models considering lifetime exposure showed higher precision, accuracy, and F1 scores and lower equal error rates than models incorporating only late-stage exposures. Future studies regarding long-term air pollution modelling are required considering lifelong exposure pattern. .1.

Effects of inspiratory muscle training on blood pressure- and sleep-related outcomes in patients with obstructive sleep apnea: a meta-analysis of randomized controlled trials.

PURPOSE: Obstructive sleep apnea (OSA) is frequently accompanied by hypertension, resulting in cardiovascular comorbidities. Continuous positive airway pressure is a standard therapy for OSA but has poor adherence. Inspiratory muscle training (IMT) may reduce airway collapsibility and sympathetic output, which may decrease OSA severity and blood pressure. In this meta-analysis of randomized controlled trials (RCTs), we evaluated the efficacy of IMT in patients with OSA. METHODS: We searched PubMed, EMBASE, Cochrane Library, Web of Science, and ClinicalTrials.gov databases for relevant RCTs published before November 2022. RESULTS: Seven RCTs with a total of 160 patients with OSA were included. Compared with the control group, the IMT group exhibited significantly lower systolic and diastolic blood pressure (mean difference [MD]: - 10.77 and - 4.58 mmHg, respectively), plasma catecholamine levels (MD: - 128.64 pg/mL), Pittsburgh Sleep Quality Index (MD: - 3.06), and Epworth Sleepiness Scale score (MD: - 4.37). No significant between-group differences were observed in the apnea-hypopnea index, forced vital capacity (FVC), ratio of forced expiratory volume in 1 s to FVC, or adverse effects. The data indicate comprehensive evidence regarding the efficacy of IMT for OSA. However, the level of certainty (LOC) remains low. CONCLUSION: IMT improved blood pressure- and sleep-related outcomes without causing adverse effects and may thus be a reasonable option for lowering blood pressure in patients with OSA. However, additional studies with larger sample sizes and rigorous study designs are warranted to increase the LOC.

Associations of overnight changes in body composition with positional obstructive sleep apnea.

PURPOSE: Body composition is considered to be associated with obstructive sleep apnea (OSA) severity. This cross-sectional study aimed to examine associations of overnight body composition changes with positional OSA. METHODS: The body composition of patients diagnosed with non-positional and positional OSA was measured before and after overnight polysomnography. Odds ratios (ORs) of outcome variables between the case (positional OSA) and reference (non-positional OSA) groups were examined for associations with sleep-related parameters and with changes in body composition by a logistic regression analysis. RESULTS: Among 1584 patients with OSA, we used 1056 patients with non-positional OSA as the reference group. We found that a 1-unit increase in overnight changes of total fat percentage and total fat mass were associated with 1.076-fold increased OR (95% confidence interval (CI): 1.014, 1.142) and 1.096-fold increased OR (95% CI: 1.010, 1.189) of positional OSA, respectively (all p < 0.05). Additionally, a 1-unit increase in overnight changes of lower limb fat percentage and upper limb fat mass were associated with 1.043-fold increased OR (95% CI: 1.004, 1.084) and 2.638-fold increased OR (95% CI: 1.313, 5.302) of positional OSA, respectively (all p < 0.05). We observed that a 1-unit increase in overnight changes of trunk fat percentage and trunk fat mass were associated with 1.056-fold increased OR (95% CI: 1.008, 1.106) and 1.150-fold increased OR (95% CI: 1.016, 1.301) of positional OSA, respectively (all p < 0.05). CONCLUSION: Our findings indicated that nocturnal changes in the body's composition, especially total fat mass, total fat percentage, lower limb fat percentage, upper limb fat mass, trunk fat percentage, and trunk fat mass, may be associated with increased odds ratio of positional OSA compared with non-positional OSA.

Predicting Fatigue-Associated Aberrant Driving Behaviors Using a Dynamic Weighted Moving Average Model With a Long Short-Term Memory Network Based on Heart Rate Variability.

OBJECTIVE: This study proposed a moving average (MA) approach to dynamically process heart rate variability (HRV) and developed aberrant driving behavior (ADB) prediction models by using long short-term memory (LSTM) networks. BACKGROUND: Fatigue-associated ADBs have traffic safety implications. Numerous models to predict such acts based on physiological responses have been developed but are still in embryonic stages. METHOD: This study recorded the data of 20 commercial bus drivers during their routine tasks on four consecutive days and subsequently asked them to complete questionnaires, including subjective sleep quality, driver behavior questionnaire and the Karolinska Sleepiness Scale. Driving behaviors and corresponding HRV were determined using a navigational mobile application and a wristwatch. The dynamic-weighted MA (DWMA) and exponential-weighted MA were used to process HRV in 5-min intervals. The data were independently separated for training and testing. Models were trained with 10-fold cross-validation strategy, their accuracies were evaluated, and Shapley additive explanation (SHAP) values were used to determine feature importance. RESULTS: Significant increases in the standard deviation of NN intervals (SDNN), root mean square of successive heartbeat interval differences (RMSSD), and normalized spectrum of high frequency (nHF) were observed in the pre-event stage. The DWMA-based model exhibited the highest accuracy for both driver types (urban: 84.41%; highway: 80.56%). The SDNN, RMSSD, and nHF demonstrated relatively high SHAP values. CONCLUSION: HRV metrics can serve as indicators of mental fatigue. DWMA-based LSTM could predict the occurrence of the level of fatigue associated with ADBs. APPLICATION: The established models can be used in realistic driving scenarios.

Association of air pollution exposure with exercise-induced oxygen desaturation in COPD.

BACKGROUND: There is a link between exposure to air pollution and the increased prevalence of chronic obstructive pulmonary disease (COPD) and declining pulmonary function, but the association with O2 desaturation during exercise in COPD patients with emphysema is unclear. Our aims were to estimate the prevalence of O2 desaturation during exercise in patients with COPD, and determine the association of exposure to air pollution with exercise-induced desaturation (EID), the degree of emphysema, and dynamic hyperinflation (DH). METHODS: We assessed the effects of 10-year prior to the HRCT assessment and 7 days prior to the six-minute walking test exposure to particulate matter with an aerodynamic diameter of < 10 µm (PM10) or of < 2.5 µM (PM2.5), nitrogen dioxide (NO2), and ozone (O3) in patients with emphysema in this retrospective cohort study. EID was defined as a nadir standard pulse oximetry (SpO2) level of < 90% or a delta (△)SpO2 level of ≥ 4%. Ambient air pollutant (PM2.5, PM10, O3, and NO2) data were obtained from Taiwan Environmental Protection Administration (EPA) air-monitoring stations, usually within 10 km to each participant's home address. RESULTS: We recruited 141 subjects with emphysema. 41.1% of patients with emphysema exhibited EID, and patients with EID had more dyspnea, worse lung function, more severe emphysema, more frequent acute exacerbations, managed a shorter walking distance, had DH, and greater long-term exposure to air pollution than those without EID. We observed that levels of 10-year concentrations of PM10, PM2.5, and NO2 were significantly associated with EID, PM10 and PM2.5 were associated with the severity of emphysema, and associated with DH in patients with emphysema. In contrast, short-term exposure did not have any effect on patients. CONCLUSION: Long-term exposure to ambient PM10, PM2.5 and NO2, but not O3, was associated with EID.

3-Nitrobenzanthrone promotes malignant transformation in human lung epithelial cells through the epiregulin-signaling pathway.

Exposure to environmental and occupational contaminants leads to lung cancer. 3-Nitrobenzanthrone (3-nitro-7H-benz[de]anthracen-7-one, 3-NBA) is a potential carcinogen in ambient air or diesel particulate matter. Studies have revealed that short-term exposure to 3-NBA induces cell death, reactive oxygen species activation, and DNA adduct formation and damage. However, details of the mechanism by which chronic exposure to 3-NBA influences lung carcinogenesis remain largely unknown. In this study, human lung epithelial BEAS-2B cells were continuously exposed to 0-10-µM 3-NBA for 6 months. NanoString analysis was conducted to evaluate gene expression in the cells, revealing that 3-NBA-mediated transformation results in a distinct gene expression signature including carbon cancer metabolism, metastasis, and angiogenesis. Alterations in tumor-promoting genes such as EREG (epiregulin), SOX9, E-cadherin, TWIST, and IL-6 were involved in epithelial cell aggressiveness. Kaplan-Meier plotter analyses indicated that increased EREG and IL-6 expressions in early-stage lung cancer cells are correlated with poor survival. In vivo xenografts on 3-NBA-transformed cells exhibited prominent tumor formation and metastasis. EREG knockout cells exposed to 3-NBA for a short period exhibited high apoptosis and low colony formation. By contrast, overexpression of EREG in 3-NBA-transformed cells markedly activated the PI3K/AKT and MEK/ERK signaling pathways, resulting in tumorigenicity. Furthermore, elevated IL-6 and EREG expressions synergistically led to STAT3 signaling activation, resulting in clonogenic cell survival and migration. Taken together, chronic exposure of human lung epithelial cells to 3-NBA leads to malignant transformation, in which the EREG signaling pathway plays a pivotal mediating role. • Short-term exposure of lung epithelial cells to 3-NBA can lead to ROS production and cell apoptosis. • Long-term chronic exposure to 3-NBA upregulates the levels of tumor-promoting genes such as EREG and IL-6. • Increased EREG expression in 3-NBA-transformed cells markedly contributes to tumorigenesis through PI3K/AKT and MEK/ERK activation and synergistically enhances the IL-6/STAT3 signaling pathway, which promotes tumorigenicity.

Elevated tau and ß-amyloid in the serum of fibromyalgia patients.

OBJECTIVE: Fibromyalgia (FM) is a chronic widespread pain syndrome. Although its mechanism remains relatively unknown, accelerated neurodegeneration in the brain has been reported in patients with FM. Sleep disturbance can increase the risk of neurocognitive disorders, which are associated with tau and beta-amyloid (Aß) protein accumulation. We hypothesize neurodegeneration in patients with FM may be associated with sleep disturbance. METHODS: In this case-control study, we analyzed serum tau and Aß levels and their association with symptom profiles for patients with FM, by recruiting 22 patients with FM and 22 age-matched healthy participants. The visual analog scale, Fibromyalgia Impact Questionnaire, pressure pain threshold test, Pittsburgh Sleep Quality Index (PSQI), Beck Depression Inventory-II, Beck Anxiety Inventory, and serum tau and beta-amyloid-42 (Aß-42) levels were recorded. The Mann-Whitney test was conducted to compare questionnaire and protein level results between the groups. Pearson correlation test was conducted to investigate the correlation of questionnaire scores with tau and Aß-42 levels in patients with FM. The significance level was set at P < .05. RESULTS: Serum tau and Aß-42 levels were significantly higher in patients with FM than in controls. A positive correlation between serum tau levels and PSQI scores was observed in patients with FM (r = 0.476, P = .025). We found that only sleep disturbance in patients with FM was significantly associated with higher serum tau levels among all symptom scores. CONCLUSIONS: We suggest sleep disturbance may play a vital role in the pathomechanism of accelerated neurodegeneration in FM.

Screening for Obstructive Sleep Apnea Risk by Using Machine Learning Approaches and Anthropometric Features.

Obstructive sleep apnea (OSA) is a global health concern and is typically diagnosed using in-laboratory polysomnography (PSG). However, PSG is highly time-consuming and labor-intensive. We, therefore, developed machine learning models based on easily accessed anthropometric features to screen for the risk of moderate to severe and severe OSA. We enrolled 3503 patients from Taiwan and determined their PSG parameters and anthropometric features. Subsequently, we compared the mean values among patients with different OSA severity and considered correlations among all participants. We developed models based on the following machine learning approaches: logistic regression, k-nearest neighbors, naïve Bayes, random forest (RF), support vector machine, and XGBoost. Collected data were first independently split into two data sets (training and validation: 80%; testing: 20%). Thereafter, we adopted the model with the highest accuracy in the training and validation stage to predict the testing set. We explored the importance of each feature in the OSA risk screening by calculating the Shapley values of each input variable. The RF model achieved the highest accuracy for moderate to severe (84.74%) and severe (72.61%) OSA. The level of visceral fat was found to be a predominant feature in the risk screening models of OSA with the aforementioned levels of severity. Our machine learning models can be employed to screen for OSA risk in the populations in Taiwan and in those with similar craniofacial structures.

Impaired lnc-IL7R modulatory mechanism of Toll-like receptors is associated with an exacerbator phenotype of chronic obstructive pulmonary disease.

Patients with chronic obstructive pulmonary disease (COPD) are susceptible to bacterial infections, which worsen lung inflammation and contribute to lung function decline and acute exacerbation. Long noncoding (lnc) RNAs are emerging regulators of inflammation with unknown clinical relevance. Herein, we report that levels of the Toll-like receptor (TLR)-related lnc interleukin (IL) 7 receptor (IL7R) were significantly reduced in peripheral blood mononuclear cells from patients with COPD compared with those from normal controls, and the levels were correlated with pulmonary function. Moreover lnc-IL7R levels were reduced in lavaged alveolar macrophages and primary human small airway epithelial cells (HSAEpCs) from patients with COPD. Lnc-IL7R knockdown in primary human macrophages, HSAEpCs, and human pulmonary microvascular endothelial cells (HPMECs) significantly augmented the induction of proinflammatory mediators after TLR2/4 activation. By contrast, lnc-IL7R overexpression attenuated inflammation after TLR2/4 activation. Similar results with lnc-IL7R-mediated inflammation were observed in COPD HSAEpCs. Mechanistically, lnc-IL7R mediated a repressive chromatin state of the proinflammatory gene promoter as a result of decreased acetylation (H3K9ac) and increased methylation (H3K9me3 and H3K27me3). Plasma lnc-IL7R levels were reduced in patients with COPD who experienced more acute exacerbation in the previous year. Notably, patients with lower lnc-IL7R levels in the subsequent year had increased exacerbation risk. Low lnc-IL7R expression in COPD may augment TLR2/4-mediated inflammation and be associated with acute exacerbation.

Three bufadienolides induce cell death in the human lung cancer cell line CL1-5 mainly through autophagy.

The effects of 3 bufadienolides, namely kalantuboside B, kalantuboside A, and bryotoxin C, isolated from Kalanchoe tubiflora (Harvey) were evaluated and characterized in CL1-5 highly metastatic human lung cancer cells. In contrast to their apoptosis-promoting activity in other cancer cells, these bufadienolides only slight or did not induce apoptosis in CL1-5 cancer cells. Instead, they activated an autophagy pathway, as indicated by increased autophagosome formation. Autophagy induced by these bufadienolides was demonstrated to be linked to the down-regulation of p-mTOR and the up-regulation of LC3-II, ATG5, ATG7, and Beclin-1. Our findings revealed an autophagy as the alternative mechanism of drug action by bufadienolides in CL1-5 lung cancer cells and provided evidence that bufadienolides are a potential therapeutic strategy for highly metastatic human lung cancer.

Subjective sleep quality and association with depression syndrome, chronic diseases and health-related physical fitness in the middle-aged and elderly.

BACKGROUND: As a complex phenomenon, sleep quality is difficult to objectively define and measure, and multiple factors related to sleep quality, such as age, lifestyle, physical activity, and physical fitness, feature prominently in older adult populations. The aim of the present study was to evaluate subjective sleep quality using the Pittsburgh Sleep Quality Index (PSQI) and to associate sleep quality with health-related physical fitness factors, depressive symptoms, and the number of chronic diseases in the middle-aged and elderly. METHODS: We enrolled a total of 283 middle-aged and elderly participants from a rehabilitation clinic or health examination department. The PSQI was used to evaluate sleep quality. The health-related fitness assessment included anthropometric and physical fitness parameters. Depressive symptoms were measured with the Center for Epidemiologic Studies Depression Scale (CES-D) short form. Data were analyzed with SPSS 18.0, and descriptive statistics and logistic regression analysis were used for the analyses. RESULTS: Overall, 27.9% of participants in this study demonstrated bad sleepers (with a PSQI score of > 5), 10.2% of study participants frequently used sleep medication to help them fall asleep, and 6.0% reported having significant depressive symptoms (with a CES-D score of ≥10). There are two major findings: (1) depression symptoms, the number of chronic diseases, self-rated health, and arthritis were significantly associated with a poor sleep quality, and (2) the 2-min step test was associated with longer sleep latency. These results confirmed that the 2-min step was associated with a longer sleep latency among the health-related physical fitness items. CONCLUSIONS: Our study found that depressive syndrome, chronic disease numbers, a poor self-rated health status, and arthritis were the main risk factors that influenced subjective sleep quality.

Suppressor of variegation 3-9 homologue 1 impairment and neutrophil-skewed systemic inflammation are associated with comorbidities in COPD.

BACKGROUND: Systemic manifestations and comorbidities are characteristics of chronic obstructive pulmonary disease (COPD) and are probably due to systemic inflammation. The histone methyltransferase SUV39H1 controls the Th1/Th2 balance. We previously reported that reduced SUV39H1 expression contributed to abnormal inflammation in COPD. Here, we aimed to determine whether impaired SUV39H1 expression in COPD patients associated with neutrophilic/eosinophilic inflammation responses and comorbidities. METHODS: A total of 213 COPD patients and 13 healthy controls were recruited from the Shuang Ho Hospital, Taipei Medical University. SUV39H1 levels in peripheral blood mononuclear cells (PBMCs) from 13 healthy and 30 COPD participants were measured by immunoblotting. We classified the patients into two groups based on low (fold change, FC < 0.5) and high SUV39H1 expression (FC ≥ 0.5) compared to normal controls. Clinical outcomes including neutrophil or eosinophil counts associated with SUV39H1-related inflammation were evaluated by Chi square analyses or Mann-Whitney U test. The correlations between the percentage of neutrophils and number of COPD comorbidities or Charlson Comorbidity Index (CCI) scores were performed by Spearman's rank analysis. RESULTS: Low SUV39H1 expression group had high neutrophil counts relative to high SUV39H1expression group. In the COPD cohort, the high comorbidity group (≥ 2 comorbidities) had higher counts of whole white blood cell (WBC) and neutrophil, and lower proportion of eosinophil and eosinophil/neutrophil, as compared with low comorbidity group (0 and 1 comorbidities). The quantity of neutrophils was associated with COPD comorbidities (Spearman's r = 0.388, p < 0.001), but not with CCI scores. We also found that the high comorbidity group had more exacerbations per year compared with low comorbidity group (1.5 vs. 0.9 average exacerbations, p = 0.005). However, there were no significant differences between groups with these non-frequent (0-1 exacerbation) and frequent exacerbations per year (> 1 exacerbation) in numbers of WBC and proportion of neutrophils, eosinophils or eosinophil/neutrophil. Finally, patients with high comorbidities had lower SUV39H1 levels in their PBMCs than did those with low comorbidities. CONCLUSION: Blood neutrophil counts are associated with comorbidities in COPD patients. Impaired SUV39H1 expression in PBMCs from COPD patients are correlated with neutrophilic inflammation and comorbidities.

Comparison of Hospital-Based and Home-Based Obstructive Sleep Apnoea Severity Measurements with a Single-Lead Electrocardiogram Patch.

Obstructive sleep apnoea (OSA) is a global health concern, and polysomnography (PSG) is the gold standard for assessing OSA severity. However, the sleep parameters of home-based and in-laboratory PSG vary because of environmental factors, and the magnitude of these discrepancies remains unclear. We enrolled 125 Taiwanese patients who underwent PSG while wearing a single-lead electrocardiogram patch (RootiRx). After the PSG, all participants were instructed to continue wearing the RootiRx over three subsequent nights. Scores on OSA indices-namely, the apnoea-hypopnea index, chest effort index (CEI), cyclic variation of heart rate index (CVHRI), and combined CVHRI and CEI (Rx index), were determined. The patients were divided into three groups based on PSG-determined OSA severity. The variables (various severity groups and environmental measurements) were subjected to mean comparisons, and their correlations were examined by Pearson's correlation coefficient. The hospital-based CVHRI, CEI, and Rx index differed significantly among the severity groups. All three groups exhibited a significantly lower percentage of supine sleep time in the home-based assessment, compared with the hospital-based assessment. The percentage of supine sleep time (∆Supine%) exhibited a significant but weak to moderate positive correlation with each of the OSA indices. A significant but weak-to-moderate correlation between the ∆Supine% and ∆Rx index was still observed among the patients with high sleep efficiency (≥80%), who could reduce the effect of short sleep duration, leading to underestimation of the patients' OSA severity. The high supine percentage of sleep may cause OSA indices' overestimation in the hospital-based examination. Sleep recording at home with patch-type wearable devices may aid in accurate OSA diagnosis.

Ramsey interferometry through coherent A2Πu-X2Σg+-B2Σu+ coupling and population transfer in N2+ air laser.

Motivated by the hot debate on the mechanism of laser-like emission at 391 nm from N2 gas irradiated by a strong 800 nm pump laser and a weak 400 nm seed laser, we theoretically study the temporal profile, optical gain, and modulation of the 391 nm signal from N2+. Our calculation sheds light on the long standing controversy on whether population inversion is indispensable for optical gain and show the Ramsey fringes of the emission intensity at 391 nm formed by additionally injecting another 800 nm pump or 400 nm seed, which provides strong evidence for the coherence driven modulation of transition dipole moment and population transfer between the A2Πu(ν=2)-X2Σg+ states and the B2Σu+(ν=0)-X2Σg+ states. Our results show that the 391 nm optical gain is susceptible to the population inversion within N2+ states manipulated by the Ramsey technique and thus clearly reveal their symbiosis. This study reveals not only the physical picture of producing N2+ population inversion but also versatile control of the N2+ air laser.

Influence of non-equilibrium electron dynamics on photoluminescence of metallic nanostructures.

A microscopic model is still strongly needed to understand the intrinsic photoluminescence (iPL) of metallic nanostructures. In this paper, a phenomenological model concerning the electron dynamics at the excited states, including the electron-phonon (e-p) and electron-electron (e-e) interactions, is developed. This model shows that the dynamics of non-equilibrium electrons at the excited states influence the iPL features significantly. Two main aspects determine the iPL process of metallic nanostructures: the photonic density of states relating to the Purcell effect caused by the surface plasmon resonances, and the electrons transition factor. This model takes into account the contribution of the e-p and e-e interactions to the dynamic electron distribution. The decay process of the non-thermal electrons at the excited states helps understanding most of the iPL features of metallic nanostructures. The calculated and experimental results coincide well regarding the spectral shape, temperature-dependent anti-Stokes emission, and nonlinear behaviors, and time-resolved spectra. The results presented in this paper provide a concise, intuitive, and comprehensive understanding of the iPL of metallic nanostructures.

Save Muscle Information-Unfiltered EEG Signal Helps Distinguish Sleep Stages.

Based on the well-established biopotential theory, we hypothesize that the high frequency spectral information, like that higher than 100Hz, of the EEG signal recorded in the off-the-shelf EEG sensor contains muscle tone information. We show that an existing automatic sleep stage annotation algorithm can be improved by taking this information into account. This result suggests that if possible, we should sample the EEG signal with a high sampling rate, and preserve as much spectral information as possible.

Nuclear Quantum Memory and Time Sequencing of a Single γ Photon.

We propose a technique for γ photon quantum memory through a Doppler frequency comb, produced by a set of resonantly absorbing nuclear targets that move with different velocities. It provides a reliable storage, an on-demand generation, and a time sequencing of a single γ photon. This scheme presents the first γ-photon-nuclear-ensemble interface opening a new direction of research in quantum information science.