Correction of Error of Airborne Anemometers Caused by Self-Excited Air Turbulence.

An airborne anemometer, which monitors wind on the basis of Meteorological Multi-rotor UAVs (Unmanned Aerial Vehicles), is important for the prevention of catastrophe. However, its performance will be affected by the self-excited air turbulence generated by UAV rotors. In this paper, for the purpose of the correction of an error, we developed a method for the elimination of the influence of air turbulence on wind speed measurement. The corresponding correction model is obtained according to the CFD (Computational Fluid Dynamics) simulation of a six-rotor UAV which is carried out with the sliding grid method and the S-A turbulence model. Then, the model is applied to the developed prototype by adding the angle of attack compensation model of the airborne anemometer. It is shown by the actual application that the airborne anemometer can maintain the original measurement accuracy at different ascent speeds.

Less polar ginsenosides have better protective effects on mice infected by Listeria monocytogenes.

Listeria monocytogenes widely exists in the natural environment and does great harm, which can cause worldwide public safety problem. Infection with L. monocytogenes can cause rapid death of Kupffer cell (KCs) in liver tissue and liver damage. American ginseng saponins is a natural compound in plants, which has great potential in inhibiting L. monocytogenes infection. Therefore, American ginseng stem-leaf saponins (AGS) and American ginseng heat-transformed saponins (HTS) were used as raw materials to study their bacteriostatic experiments in vivo and in vitro. In this experiment, female Kunming mice were randomly divided into five groups: control group, negative group, AGS group, HTS group (10 mg/kg/day in an equal volume via gastric administration) and penicillin group, each group containing six mice. Profiles AGS and HTS components were evaluated by high-performance liquid chromatography (HPLC) analysis. The bacteriostatic effect of AGS and HTS on L. monocytogenes was evaluated by inhibition zone test, minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC). The bacteriostatic effect of AGS and HTS pretreatment on mice infected with L. monocytogenes were studies by animal experimental. The results showed that the content of polar saponins in AGS was 0.81 ± 0.003 mg/mg, less polar saponins was 0.08 ± 0.02 mg/mg, the content of polar saponins in HTS was 0.10 ± 0.01 mg/mg, less polar saponins was 0.76 ± 0.02 mg/mg. The in vitro bacteriostatic diameter of HTS (16.6 ± 0.8 mm) is large than that of AGS (10.2 ± 1.2 mm). AGS and HTS pretreatment could reduce the colony numbers in the livers of mice infected with Listeria monocytogenes. The levels of alanine aminotransferase (ALT), IL-1ß, IL-6, TNF-α and IFN-γ in the livers of mice in the pretreatment group were significantly lower than those in the negative group. There were obvious leukoplakia, calcification and other liver damage on the liver surface in the negative control group, and obvious inflammatory cell infiltration in HE sections. AGS and HTS pretreatment can reduce liver injury caused by L. monocytogenes and protect the liver. Compared with AGS, HTS has higher content of less polar saponins and better bacteriostatic effect in vitro. The count of bacterial in liver tissue of HTS group was significantly lower, the survival rate was significantly higher than that of AGS group. Less polar saponins had better bacteriostatic effect. Collectively, less polar saponins pretreatment has a protective effect on mice infected with L. monocytogenes, to which alleviated liver damage, improved anti-inflammatory ability and immunity of the body, protected liver may contribute.

Early Sepsis Prediction Using Ensemble Learning With Deep Features and Artificial Features Extracted From Clinical Electronic Health Records.

OBJECTIVES: Sepsis is caused by infection and subsequent overreaction of immune system and will severely threaten human life. The early prediction is important for the treatment of sepsis. This report aims to develop an early prediction method for sepsis 6 hours ahead on the basis of clinical electronic health records. DATA SOURCES: Challenge data are released by PhysioNet/Computing in Cardiology Challenge 2019 and obtained from ICU patients in three separate hospital systems. Part of the data from two datasets, including 40,336 subjects, are publicly available, and the remaining are used as hidden test set. A normalized utility score defined by the organizing committee is used for model performance evaluation. STUDY SELECTION: The supervised machine learning is applied to tackle this challenge. Specifically, we establish the prediction model under the framework of ensemble learning by integrating the artificial features based on clinical prior knowledge of sepsis with deep features automatically extracted by long short-term memory neural network. DATA EXTRACTION: Forty clinical variables, including eight vital signs, 26 laboratory values, and six demographics, were measured and recorded once an hour for each individual, and the binary label (0 or 1) was simultaneously provided for each item. DATA SYNTHESIS: The proposed model was evaluated by 30-fold cross-validation. The sensitivity, specificity, and normalized utility score were 0.641 ± 0.022, 0.844 ± 0.007, and 0.401 ± 0.019 on publicly available datasets, respectively. The final normalized utility score our team (UCAS_DataMiner) has obtained was 0.313 on full hidden test set (0.406, 0.373, and -0.215 on test set A, B, and C, respectively). CONCLUSIONS: We realized a 6-hour ahead early-onset prediction of sepsis on the basis of clinical electronic health record by ensemble learning. The results indicated the proposed model functioned well in the early prediction of sepsis. In particular, ensemble learning had a significant (p < 0.01) improvement than any single model in performance.

Synthesis, characteristics and medical applications of plant nanomaterials.

MAIN CONCLUSION: The recent preparations of metal nanoparticles using plant extracts as reducing agents are summarized here. The synthesis and characterization of plant-metal nanomaterials and the progress in antibacterial and anti-inflammatory medical applications are detailed, providing a new vision for plant-based medical applications. The medical application of plant-metal nanoparticles is becoming a research hotspot. Compared with traditional preparation methods, the synthesis of plant-metal nanoparticles is less toxic and more eco-friendly, increasing application potential. Highly efficient plant-metal nanoparticles are usually smaller than 100 nm. This review describes the synthesis, characterization and bioactivities of gold- and silver-plant nanoparticles as examples and clearly explained their antibacterial and anticancer mechanisms. An analysis of actual cases shows that the synthetic method and type of plant extract affect the activities of the products.

High Accuracy and Miniature 2-D Wind Sensor for Boundary Layer Meteorological Observation.

Wind speed and direction are important parameters in meteorological observation. A solid wind sensor is needed with a small quadcopter for boundary layer meteorological observation. In this paper, the principle of a cylindrical two-dimensional wind sensor is reported and the data from wind tunnel experiments are analyzed. A model is proposed to describe the distribution of the pressure difference across a diameter of a cylinder, and the wind sensor is fabricated with MEMS (Micro-Electro-Mechanical System) differential pressure sensors. The wind sensor cylinder has a small size with a diameter of 30 mm and a height of 80 mm. In wind tunnel tests in the range of 1 to 40 m/s, the relative speed measuring errors and the direction measuring errors of the prototype are no more than ± (0.2 + 0.03 V) m/s (V is standard wind speed) and 5°, respectively. An inclination angle model is proposed to correct the influence of tilt angle on the quadcopter platform, the wind sensor can maintain the original wind speed and direction measurement accuracy within the 30° inclination range after compensation.

A Flexible Interdigital Electrode Used in Skin Penetration Promotion and Evaluation with Electroporation and Reverse Iontophoresis Synergistically.

Skin penetration is related to efficiencies of drug delivery or ISF extraction. Normally, the macro-electrode is employed in skin permeability promotion and evaluation, which has the disadvantages of easily causing skin damage when using electroporation or reverse iontophoresis by alone; furthermore, it has large measurement error, low sensitivity, and difficulty in integration. To resolve these issues, this paper presents a flexible interdigital microelectrode for evaluating skin penetration by sensing impedance and a method of synergistical combination of electroporation and reverse iontophoresis to promote skin penetration. First, a flexible interdigital microelectrode was designed with a minimal configuration circuit of electroporation and reverse iontophoresis for future wearable application. Due to the variation of the skin impedance correlated with many factors, relative changes of it were recorded at the end of supply, different voltage, or constant current, times, and duration. It is found that the better results can be obtained by using electroporation for 5 min then reverse iontophoresis for 12 min. By synergistically using electroporation and reverse iontophoresis, the penetration of skin is promoted. The results tested in vivo suggest that the developed microelectrode can be applied to evaluate and promote the skin penetration and the designed method promises to leave the skin without damage. The electrode and the method may be beneficial for designing noninvasive glucose sensors.

[Social network analysis of traditional Chinese medicine on treatment of constipation].

The methods of literature metrology and data mining were used to study the research topics and social network analysis of traditional Chinese medicine for constipation. The major Chinese databases were searched to include the research studies of traditional Chinese medicine for constipation. BICOMS analysis software was used to extract and collect the main information and produce co-occurrence Matrix; gCLUTO software was used for cluster analysis. Data analysis was conducted by using SPSS 19.0 software. The results showed that the number of studies on traditional Chinese medicine for constipation was constantly increased, with two literature volume peaks respectively in 2003 and 2006. Related studies have been published in 31 provinces, autonomous regions and municipalities have published, but the studies in developed areas were more than those in developing areas. There was little cooperation between research institutions and the authors, especially the cooperation between different areas. At present, the research field of Chinese medicine for constipation is divided into five research topics. In terms of specific traditional Chinese medicine, angelica sinensis is in the core position. The results showed regional imbalance in the number of studies on Chinese medicine treatment for constipation, as well as little cooperation between researchers and research institutions. The research topics mainly focused on the evaluation of clinical efficacy, but the research on optimizing the prescriptions was still not enough, so the future researchers shall pay more attention to the studies of constipation prescriptions with Angelica sinensis as the core herb.

[Protective Effects of Angelica sinensis Volatile Oil on Atherosclerosis in Hyperlipidemia Mice].

Objective: To study the protective effects of Angelica sinensis volatile oil on atherosclerosis in hyperlipidemia mice. Methods: 60 mice were randomly divided into normal control group, model group, fluvastatin group, and high-, medium- and low-dose groups of Angelica sinensis volatile oil. Normal control group were fed with normal diet, the other groups were fed with high fat diet, and treated orally Vitamin D3 (100 million IU/kg) daily for 42 d. At the 14th day after modeling, fluvastatin group were orally administrated fluvastatin (6.7 mg /kg), and high-, medium- and low-doses of Angelica sinensis volatile oil groups were orally administrated Angelica sinensis volatile oil (40, 20, 10 mg /kg) for 28 d, and the normal control group and model group were administrated equal volume normal saline. The activity state, body weight and the levels of TC, TG, HDL-C and LDL-C in serum were measured. The atherosclerosis indexes (AI1, AI2), coronary heart index (R-CHR) were calculated. After the mice were killed, the heart, liver and abdominal aortas were taken. The mass of the heart and liver were measured, and the organ indexes were calculated; the tissues were fixed by formalin, embedded in paraffin, sliced, HE stained, and the histopathology changes were observed by microscope. Results: Compared with normal control group, the body weight of mice in the model group were decreased (P＜0.01), and the heart, liver indexes were significantly increased (P＜0.05), the levels TC, TG, LDL-C and HDL-C in serum and AI1, AI2 and R-CHR were significantly increased after modeling 42 d (P < 0.01). Compared with the model group, the mice body weight were significantly increased, and the heart, liver index were significantly decreased (P＜0.05) in the high-, middle-dose group of Angelica sinensis volatile oil groups; the TC, TG and LDL-C levels were significantly decreased in low-dose group (P＜0.05 or P＜0.01); AI1 and R-CHR were significantly decreased (P＜0.05 or P＜0.01) in all Angelica sinensis volatile oil groups, but the AI2 in the high-dose group of Angelica sinensis volatile oil was significantly decreased (P＜0.05). The histopathology results showed that Angelica sinensis volatile oil could relieve the fatty degeneration of hepatic cells and the injury of thoracic aortic intimae, and myocardial fibrosis, which could inhibit the formation of atherosclerotic plaque. Conclusion: The certain protective effects of Angelica sinensis volatile oil are determinated on atherosclerosis in hyperlipidemia mice.

[Therapeutic effects of ethanol extracts of Angelica sinensis and Astragalus mongholicus in diabetic gastroparesis rats].

OBJECTIVE: To observe therapeutic effects of the ethanol extract of Angelica sinensis and Astragalus mongholicus (1:2) in diabetic gastroparesis (DG) rats. METHODS: Diabetic rats were induced by intraperitoneal injection of alloxan (200 mg/kg). DG was based on gastrointestinal motility index and the character of stool in diabetic rats. The metformin and cisapride mixed solution (containing met-formin 175 mg/kg and cisapride 3.5 mg/kg)was intragastric administrated in the positive control group, the ethanol extract of Angelica sinensis and Astragalus mongholicus was intragastric treated in the high dose (10. 5 g/kg) and low dose(5. 2 g/kg) group rats, 1/d for 42 consecutive days. The body weight, fasting blood glucose (FBG) value and 24 h intake and drinking amount were detected at interval of 14 days. The gastrointestinal propulsion index, motilin (MOT), glucagon (GLG) and gastrin (GAS) contents in the blood were detected after the last administration. And then the gastric antrum smooth muscle cells and interstitial cells of Cajal were observed in microscope. RESULTS: The general state and stool had been improved,and the body weight and 24 h intake were significantly increased, and 24 h drinking in the high and low dose groups at 28 d of administration were reduced. At the 42th day,the body weight and 24 h in- take were increased, 24 h drinking water and FBG were reduced, and the MOT, GAS and GLG contents were decreased in the high dose group. Gastric mucosa and gastric smooth muscle tissue morphology were significantly improved. CONCLUSION: The therapeutic effect of ethanol extracts of Angelica sinensis and Astragalus mongholicus was obvious in DG rats. Its mechanism of action was associated with FBG, MOT and GAS levels decreasing.

A Novel Approach for the Detection and Severity Grading of Chronic Obstructive Pulmonary Disease Based on Transformed Volumetric Capnography.

Chronic Obstructive Pulmonary Disease (COPD), as the third leading cause of death worldwide, is a major global health issue. The early detection and grading of COPD are pivotal for effective treatment. Traditional spirometry tests, requiring considerable physical effort and strict adherence to quality standards, pose challenges in COPD diagnosis. Volumetric capnography (VCap), which can be performed during natural breathing without requiring additional compliance, presents a promising alternative tool. In this study, the dataset comprised 279 subjects with normal pulmonary function and 148 patients diagnosed with COPD. We introduced a novel quantitative analysis method for VCap. Volumetric capnograms were converted into two-dimensional grayscale images through the application of Gramian Angular Field (GAF) transformation. Subsequently, a multi-scale convolutional neural network, CapnoNet, was conducted to extract features and facilitate classification. To improve CapnoNet's performance, two data augmentation techniques were implemented. The proposed model exhibited a detection accuracy for COPD of 95.83%, with precision, recall, and F1 measures of 95.21%, 95.70%, and 95.45%, respectively. In the task of grading the severity of COPD, the model attained an accuracy of 96.36%, complemented by precision, recall, and F1 scores of 88.49%, 89.99%, and 89.15%, respectively. This work provides a new perspective for the quantitative analysis of volumetric capnography and demonstrates the strong performance of the proposed CapnoNet in the diagnosis and grading of COPD. It offers direction and an effective solution for the clinical application of capnography.

Reliable automatic sleep stage classification based on hybrid intelligence.

Sleep staging is a vital aspect of sleep assessment, serving as a critical tool for evaluating the quality of sleep and identifying sleep disorders. Manual sleep staging is a laborious process, while automatic sleep staging is seldom utilized in clinical practice due to issues related to the inadequate accuracy and interpretability of classification results in automatic sleep staging models. In this work, a hybrid intelligent model is presented for automatic sleep staging, which integrates data intelligence and knowledge intelligence, to attain a balance between accuracy, interpretability, and generalizability in the sleep stage classification. Specifically, it is built on any combination of typical electroencephalography (EEG) and electrooculography (EOG) channels, including a temporal fully convolutional network based on the U-Net architecture and a multi-task feature mapping structure. The experimental results show that, compared to current interpretable automatic sleep staging models, our model achieves a Macro-F1 score of 0.804 on the ISRUC dataset and 0.780 on the Sleep-EDFx dataset. Moreover, we use knowledge intelligence to address issues of excessive jumps and unreasonable sleep stage transitions in the coarse sleep graphs obtained by the model. We also explore the different ways knowledge intelligence affects coarse sleep graphs by combining different sleep graph correction methods. Our research can offer convenient support for sleep physicians, indicating its significant potential in improving the efficiency of clinical sleep staging.

The application of impulse oscillometry system based on machine learning algorithm in the diagnosis of chronic obstructive pulmonary disease.

Objective. Diagnosing chronic obstructive pulmonary disease (COPD) using impulse oscillometry (IOS) is challenging due to the high level of clinical expertise it demands from doctors, which limits the clinical application of IOS in screening. The primary aim of this study is to develop a COPD diagnostic model based on machine learning algorithms using IOS test results.Approach. Feature selection was conducted to identify the optimal subset of features from the original feature set, which significantly enhanced the classifier's performance. Additionally, secondary features area of reactance (AX) were derived from the original features based on clinical theory, further enhancing the performance of the classifier. The performance of the model was analyzed and validated using various classifiers and hyperparameter settings to identify the optimal classifier. We collected 528 clinical data examples from the China-Japan Friendship Hospital for training and validating the model.Main results. The proposed model achieved reasonably accurate diagnostic results in the clinical data (accuracy = 0.920, specificity = 0.941, precision = 0.875, recall = 0.875).Significance. The results of this study demonstrate that the proposed classifier model, feature selection method, and derived secondary feature AX provide significant auxiliary support in reducing the requirement for clinical experience in COPD diagnosis using IOS.

Unrestricted molecular motions enable mild photothermy for recurrence-resistant FLASH antitumor radiotherapy.

Ultrahigh dose-rate (FLASH) radiotherapy is an emerging technology with excellent therapeutic effects and low biological toxicity. However, tumor recurrence largely impede the effectiveness of FLASH therapy. Overcoming tumor recurrence is crucial for practical FLASH applications. Here, we prepared an agarose-based thermosensitive hydrogel containing a mild photothermal agent (TPE-BBT) and a glutaminase inhibitor (CB-839). Within nanoparticles, TPE-BBT exhibits aggregation-induced emission peaked at 900 nm, while the unrestricted molecular motions endow TPE-BBT with a mild photothermy generation ability. The balanced photothermal effect and photoluminescence are ideal for phototheranostics. Upon 660-nm laser irradiation, the temperature-rising effect softens and hydrolyzes the hydrogel to release TPE-BBT and CB-839 into the tumor site for concurrent mild photothermal therapy and chemotherapy, jointly inhibiting homologous recombination repair of DNA. The enhanced FLASH radiotherapy efficiently kills the tumor tissue without recurrence and obvious systematic toxicity. This work deciphers the unrestricted molecular motions in bright organic fluorophores as a source of photothermy, and provides novel recurrence-resistant radiotherapy without adverse side effects.

A Fast Calibration Method for Pneumotachograph with a 3L Syringe.

The pneumotachograph (PNT), a commonly used flowmeter in pulmonary function diagnostic equipment, is the required frequency calibration to maintain high accuracy. Aiming to simplify calibration steps, we developed a fast calibration system with a commercially available 3L syringe to provide a real output flow waveform. The acquisition of the real output flow waveform is based on the reliable measurement of in-cylinder pressure and the real-time detection of plunger speed. To improve the calibration accuracy, the tapping position for measuring in-cylinder pressure was optimized by CFD dynamic-mesh updating technique. The plunger speed was obtained by tracking the handle of the plunger with a smart terminal. Then, the real output flow was corrected using a compensation model equation. The calibration system was verified by the pulmonary waveform generator that the accuracy satisfied the requirements for respiratory flow measurement according to ATS standardization. The experimental results suggest that the developed method promises the fast calibration of PNT.

Label-free optical metabolic imaging of adipose tissues for prediabetes diagnosis.

Rationale: Prediabetes can be reversed through lifestyle intervention, but its main pathologic hallmark, insulin resistance (IR), cannot be detected as conveniently as blood glucose testing. In consequence, the diagnosis of prediabetes is often delayed until patients have hyperglycemia. Therefore, developing a less invasive diagnostic method for rapid IR evaluation will contribute to the prognosis of prediabetes. Adipose tissue is an endocrine organ that plays a crucial role in the development and progression of prediabetes. Label-free visualizing the prediabetic microenvironment of adipose tissues provides a less invasive alternative for the characterization of IR and inflammatory pathology. Methods: Here, we successfully identified the differentiable features of prediabetic adipose tissues by employing the metabolic imaging of three endogenous fluorophores NAD(P)H, FAD, and lipofuscin-like pigments. Results: We discovered that 1040-nm excited lipofuscin-like autofluorescence could mark the location of macrophages. This unique feature helps separate the metabolic fluorescence signals of macrophages from those of adipocytes. In prediabetes fat tissues with IR, we found only adipocytes exhibited a low redox ratio of metabolic fluorescence and high free NAD(P)H fraction a1. This differential signature disappears for mice who quit the high-fat diet or high-fat-high-sucrose diet and recover from IR. When mice have diabetic hyperglycemia and inflamed fat tissues, both adipocytes and macrophages possess this kind of metabolic change. As confirmed with RNA-seq analysis and histopathology evidence, the change in adipocyte's metabolic fluorescence could be an indicator or risk factor of prediabetic IR. Conclusion: Our study provides an innovative approach to diagnosing prediabetes, which sheds light on the strategy for diabetes prevention.

MicroRNAs: Novel players in the diagnosis and treatment of cancer cachexia (Review).

Cachexia denotes a complex metabolic syndrome featuring severe loss of weight, fatigue and anorexia. In total, 50-80% of patients suffering from advanced cancer are diagnosed with cancer cachexia, which contributes to 40% of cancer-associated mortalities. MicroRNAs (miRNAs) are non-coding RNAs capable of regulating gene expression. Dysregulated miRNA expression has been observed in muscle tissue, adipose tissue and blood samples from patients with cancer cachexia compared with that of samples from patients with cancer without cachexia or healthy controls. In addition, miRNAs promote and maintain the malignant state of systemic inflammation, while inflammation contributes to cancer cachexia. The present review discusses the role of miRNAs in the progression of cancer cachexia, and assess their diagnostic value and potential therapeutic value.

Polyimide-Based High-Performance Film Bulk Acoustic Resonator Humidity Sensor and Its Application in Real-Time Human Respiration Monitoring.

Respiration monitoring is vital for human health assessment. Humidity sensing is a promising way to establish a relationship between human respiration and electrical signal. This paper presents a polyimide-based film bulk acoustic resonator (PI-FBAR) humidity sensor operating in resonant frequency and reflection coefficient S11 dual-parameter with high sensitivity and stability, and it is applied in real-time human respiration monitoring for the first time. Both these two parameters can be used to sense different breathing conditions, such as normal breathing and deep breathing, and breathing with different rates such as normal breathing, slow breathing, apnea, and fast breathing. Experimental results also indicate that the proposed humidity sensor has potential applications in predicting the fitness of individual and in the medical field for detecting body fluids loss and daily water intake warning. The respiratory rates measured by our proposed PI-FBAR humidity sensor operating in frequency mode and S11 mode have Pearson correlation of up to 0.975 and 0.982 with that measured by the clinical monitor, respectively. Bland-Altman method analysis results further revealed that both S11 and frequency response are in good agreement with clinical monitor. The proposed sensor combines the advantages of non-invasiveness, high sensitivity and high stability, and it has great potential in human health monitoring.

EEG-Based Emotion Recognition Using a 2D CNN with Different Kernels.

Emotion recognition is receiving significant attention in research on health care and Human-Computer Interaction (HCI). Due to the high correlation with emotion and the capability to affect deceptive external expressions such as voices and faces, Electroencephalogram (EEG) based emotion recognition methods have been globally accepted and widely applied. Recently, great improvements have been made in the development of machine learning for EEG-based emotion detection. However, there are still some major disadvantages in previous studies. Firstly, traditional machine learning methods require extracting features manually which is time-consuming and rely heavily on human experts. Secondly, to improve the model accuracies, many researchers used user-dependent models that lack generalization and universality. Moreover, there is still room for improvement in the recognition accuracies in most studies. Therefore, to overcome these shortcomings, an EEG-based novel deep neural network is proposed for emotion classification in this article. The proposed 2D CNN uses two convolutional kernels of different sizes to extract emotion-related features along both the time direction and the spatial direction. To verify the feasibility of the proposed model, the pubic emotion dataset DEAP is used in experiments. The results show accuracies of up to 99.99% and 99.98 for arousal and valence binary classification, respectively, which are encouraging for research and applications in the emotion recognition field.

Single-channel EEG sleep staging based on data augmentation and cross-subject discrepancy alleviation.

Automatic sleep stage classification is an effective technology compared to conventional artificial visual inspection in the field of sleep staging. Numerous algorithms based on machine learning and deep learning on single-channel electroencephalogram (EEG) have been proposed in recent years, however, category imbalance and cross-subject discrepancy are still the main factors restricting the accuracy of existing methods. This study proposed an innovative end-to-end neural network to solve these problems, specifically, four data augmentation methods were designed to eliminate category imbalance, and domain adaptation modules were designed for the alignment of marginal distribution, conditional distribution, and channel and spatial level distribution of feature maps, as well as the capture of transferable regions on the feature maps using a transfer attention mechanism. We conducted experiments on two publicly available datasets (Sleep-EDF Database Expanded, 2013 and 2018 version), Cohen's kappa coefficient (k) of 0.77 (Fpz-Cz) and 0.73 (Pz-Oz) were realized on the Sleep-EDF-2013 dataset, and a k of 0.75 (Fpz-Cz) and 0.68 (Pz-Oz) were realized on the Sleep-EDF-2018 dataset. An experiment was also conducted on the dataset drawn from the 2018 Physionet challenge, which containing people with sleep disorders, and a performance improvement was still found. Our comparative experiments with similar studies showed that our model was superior to most other studies, indicating our proposed EEG data augmentation and domain adaptation based cross-subject discrepancy alleviation approach is effective to improve the performance of automatic sleep staging.

Prediction of Pulmonary Function Parameters Based on a Combination Algorithm.

OBJECTIVE: Pulmonary function parameters play a pivotal role in the assessment of respiratory diseases. However, the accuracy of the existing methods for the prediction of pulmonary function parameters is low. This study proposes a combination algorithm to improve the accuracy of pulmonary function parameter prediction. METHODS: We first established a system to collect volumetric capnography and then processed the data with a combination algorithm to predict pulmonary function parameters. The algorithm consists of three main parts: a medical feature regression structure consisting of support vector machines (SVM) and extreme gradient boosting (XGBoost) algorithms, a sequence feature regression structure consisting of one-dimensional convolutional neural network (1D-CNN), and an error correction structure using improved K-nearest neighbor (KNN) algorithm. RESULTS: The root mean square error (RMSE) of the pulmonary function parameters predicted by the combination algorithm was less than 0.39L and the R2 was found to be greater than 0.85 through a ten-fold cross-validation experiment. CONCLUSION: Compared with the existing methods for predicting pulmonary function parameters, the present algorithm can achieve a higher accuracy rate. At the same time, this algorithm uses specific processing structures for different features, and the interpretability of the algorithm is ensured while mining the feature depth information.