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In this article, a common-mode noise filter with a wideband suppressing and dual-band high absorbing common mode filter, which is called absorptive common-mode filter, is proposed. The filter is constructed by a reflective-type common-mode filter, a phase matching, and an absorber. It is composed of three layers with 14.98 mm × 9.585 mm. The broadband common-mode noise suppression can be achieved by positioning multiple transmission zeros. By using dual-band phase matching and an absorber, the dual-band absorption can be realized. The frequencies are in the range of 2.4 to 2.5 GHz and 5.17 to 5.33 GHz, respectively. The positioning of transmission zeros and absorption points at those frequencies leads to a high absorption efficiency. The simulation result shows that the common-mode suppression, which is below −10 dB, is from 2 to 11 GHz. The fractional bandwidth is 138%. The differential-mode signal keeps integrity in the range from DC to 12 GHz. The absorption efficiencies in the two frequency sections are 97% and 96%, respectively. The experiment results show only slight differences from the simulation results.

Background: Sympathetic and parasympathetic nerve remodeling play an important role in cardiac function after myocardial ischemia (MI) injury. Increasing evidence indicates that electroacupuncture (EA) can regulate cardiac function by modulating the autonomic nervous system (ANS), but little is known about its effectiveness on neural remodeling post-MI. Objectives: To investigate the role of EA in ANS remodeling post-MI. Methods: Adult male C57/BL6 mice were equally divided into the Control (Ctrl), MI and EA groups after generating the MI model by ligating the left anterior descending (LAD) coronary artery. Echocardiography and 2,3,5-triphenyltetrazolium (TTC) staining were employed to evaluate cardiac function and infarct size after EA treatment for five consecutive days. Serum norepinephrine (NE) levels were measured by ELISA to quantify sympathetic activation. Then, ANS remodeling was detected by immunohistochemistry (IHC), RT-qPCR, and Western blotting. Results: Our preliminary findings showed that EA increased ejection fraction and fractional shortening and reduced infarct area after MI injury. Serum NE levels in the EA group were significantly decreased compared with those in the MI group. IHC staining results demonstrated that the density of growth associated protein (GAP)43 and tyrosine hydroxylase (TH) positive nerve fibers in the EA group were decreased with increased choline acetyltransferase (CHAT) and vesicular acetylcholine transporter (VACHT). Meanwhile, the results verified that mRNA and protein expression of GAP43 and TH were significantly inhibited by EA treatment in the MI mice, accompanied by elevated CHAT and VACHT. Conclusions: EA treatment could improve cardiac function and reduce infarct size by modulating sympathetic and parasympathetic nerve remodeling post-MI, thus helping the cardiac ANS reach a new balance to try to protect the heart from further possible injury.

BACKGROUD Follicular dendritic cell (FDC) sarcomas are rare neoplasms that occur predominantly in the lymph nodes. They can also occur extranodally. Extranodal FDC sarcomas most commonly present as solitary masses. Inflammatory pseudotumor (IPT)-like FDC sarcomas, a subcategory of FDC sarcomas, are rarer than other sarcoma subtypes. They are composed of spindle or ovoid neoplastic cells and exhibit an admixture of plasma cells and prominent lymphoplasmacytic infiltration. Paraneoplastic pemphigus (PNP), also known as paraneoplastic autoimmune multiorgan syndrome, is a rare autoimmune bullous disease that is associated with underlying neoplasms. PNP has a high mortality, and its early diagnosis is usually difficult. CASE SUMMARY We describe a 27-year-old woman who presented with stomatitis, conjunctivitis, and skin blisters and erosions as her first symptoms of PNP with an intra-abdominal IPT-like FDC sarcoma. The patient underwent surgical tumor resection and received tapering oral corticosteroid treatment. She showed no recurrence at the 1-year follow-up. CONCLUSION IPT-like FDC sarcomas are rare underlying neoplasms that have an uncommon association with PNP. PNP-associated FDC sarcomas predominantly occur in intra-abdominal sites and suggest a poor prognosis. Surgical resection is an essential and effective treatment for PNP and primary and recurrent FDC sarcomas.

To evaluate hospital mortality and associated risk factors for acute exacerbations of idiopathic pulmonary fibrosis (AEIPF). Emphases were put on incidence and impact of extra-pulmonary organ failures. Patients diagnosed with AEIPF from July 2014 to September 2018 were enrolled. Clinical data were collected. Acute physiology and chronic health evaluation II (APACHE II) and simplified acute physiological score II (SAPS II) were calculated. Extra-pulmonary organ failures were diagnosed upon criteria of sequential organ failure assessment (SOFA). Forty-five patients with AEIPF were included. Eighteen patients (40.0%) developed extra-pulmonary organ failures, and 25 patients (55.6%) died during hospitalization. Serum C-reactive protein (CRP) (p = 0.001), SAPS II (p = 0.004), SOFA (p = 0.001) were higher, whereas arterial oxygen pressure (PaO2)/ fractional inspired oxygen (FiO2) (p = 0.001) was lower in non-survivors than survivors. More non-survivors developed extra-pulmonary organ failures than survivors (p = 0.002). After adjustment, elevated serum CRP (OR 1.038, p = 0.049) and extra-pulmonary organ failure (OR 13.126, p = 0.016) were independent predictors of hospital mortality in AEIPF. AEIPF had high hospital mortality and occurrence of extra-pulmonary organ failure was common. Elevated serum CRP and extra-pulmonary organ failure had predictive values for mortality.

The Aerial Internet-of-Things (Aerial-IoT) systems, deploying sensors on high-altitude platforms, e.g., drones, parachutes, and aircrafts, are a crucial monitor due to its agile maneuverability and augmentation of observation, collection, and communication. As such, the measurement accuracy and requirements of Aerial-IoT are far beyond the ability of general commercial-off-the-shelf sensors, especially in the high-altitude environment, where environmental factors (air pressure, temperature, humidity, wind movement, etc.) tend to change rapidly and lead to highly deviated readings. In this article, we tackle this challenge. First, we introduce our designed measurement system for Aerial-IoT. Then, to compensate for the low data quality and calibrate the deviation data from sensors, we take into account the inherent correlations and interaction between sensor data and environmental factors, and construct a data calibration model, called data calibration based on the neural network (DC-NN). Finally, to illustrate the effectiveness of our system, we carry out a real-world implementation by deploying sensors on the surface of parachutes in a dynamic airdrop environment. Extensive experiments on temperature-humidity-material-tensile-testing (THMTT) and high-altitude airdrop are conducted to show the significant improvements of our proposed DC-NN model.

Objective: Vitamin D deficiency was associated with CTD-ILD and reduced lung function. We sought to confirm that lower Vitamin D level would be related to shorter survival times. Results: The CTD-ILD patients had lower Vitamin D level(P

A broadband common-mode filter composed of three layers of a printed circuit board (PCB) with $\mathbf{5.2} \text{mm} \times 17.6 \text{mm}$ area is proposed. Multiple transmission zeros can be created by aT-type resonator on the second PCB layer with a differential pair on the first PCB layer. The broadband common-mode noise suppression can be achieved by positioning transmission zeros appropriately. ANSYS HFSS is used to verify the design of proposed filter and the simulation result shows the common-mode noise is suppressed over 10 dB from 2 GHz to 10.5 GHz with 136% fractional bandwidth. The differential signal keeps good integrity in the frequency range from DC to 12 GHz.

Four types of CSRR DGS RCMF (Complementary Split Ring Resonator Defected Ground Structure Reflective Common-Mode Filter) simulations are presented. Their equivalent circuits are also presented. Their performance results are also shown and analyzed. ANSYS HFSS is used to verify these performances. The dimension of the proposed filters is 11.0 mm x 11.0 mm. The best result is achieved by D _ CSRR DGS RCMF with the resonance frequency of the transmission zero at 2.44 GHz, the transmission zero depth is -26.0 dB, the -5 dB bandwidth is 1.8 GHz, and the -10 dB bandwidth is 500 MHz.

Many famous attacks take web browsers as transmission channels to make the target computer infected by malwares, such as watering hole and domain name hijacking. In order to protect the data transmission, the SSL/TLS protocol has been widely used to defeat various hijacking attacks. However, the existence of such encryption protection makes the security software and devices confront with the difficulty of analyzing the encrypted malicious traffic at endpoints. In order to better solve this kind of situation, this paper proposes a new efficient and transparent method for large-scale automated TLS traffic analysis, named as hyper TLS traffic analysis (HTTA). It extracts multiple types of valuable data from the target system in the hyper mode and then correlates them to decrypt the network packets in real time, so that overall data correlation analysis can be performed on the target. Additionally, we propose an aided reverse engineering method to support the analysis, which can rapidly identify the target data in different versions of the program. The proposed method can be applied to the endpoints and cloud platforms; there are no trust risk of certificates and no influence on the target programs. Finally, the real experimental results show that the method is feasible and effective for the analysis, which leads to the lower runtime overhead compared with other methods. It covers all the popular browser programs with good adaptability and can be applied to the large-scale analysis.

Genetic factors were identified to be associated with the development of idiopathic pulmonary fibrosis (IPF). We aimed to investigate associations between mucin 5B (MUC5B) and telomerase reverse transcriptase (TERT) polymorphisms and telomere length (TL) with honeycombing extent and survival in a Chinese IPF cohort. Seventy-nine patients diagnosed with IPF were enrolled. The honeycombing extents in high resolution CT scan (HRCT) were quantitatively scored and defined as mild (50%) upon the honeycombing extents involving the total lung. We tested five single-nucleotide polymorphisms [rs35705950, rs868903 in MUC5B, rs2736100, rs2853676 in TERT and rs1881984 in Telomerase RNA Gene (TERC) and TLs in peripheral blood leucocytes, and evaluated their associations with radiographic extent and survival in IPF. The minor allele frequencies (MAF) were significantly greater for MUC5B rs868903 (P = 0.042) and TERT rs2853676 (P = 0.041) in IPF than those in healthy controls. CT/CC genotype of MUC5B rs868903 (p = 0.045) and short TLs (p = 0.035) were correlated with the more extensive honeycombing opacities in HRCT. After adjustment for age, sex, and smoking status, MUC5B rs868903 polymorphism was the significant gene risk factors for reduced survival (p = 0.044) in IPF. MUC5B promoter rs868903 polymorphism and TLs were associated with radiographic extent and survival in a Chinese IPF cohort. These findings suggested a genetic clue for exploring the underlying molecular basis and pathogenesis of IPF.

Can physical concepts and laws emerge in a neural network as it learns to predict the observation data of physical systems? As a benchmark and a proof-of-principle study of this possibility, here we show an introspective learning architecture that can automatically develop the concept of the quantum wave function and discover the Schrodinger equation from simulated experimental data of the potential-to-density mappings of a quantum particle. This introspective learning architecture contains a machine translator to perform the potential to density mapping, and a knowledge distiller auto-encoder to extract the essential information and its update law from the hidden states of the translator, which turns out to be the quantum wave function and the Schrodinger equation. We envision that our introspective learning architecture can enable machine learning to discover new physics in the future.

Background. Simple obesity has become a global risk to health of human beings. Acupuncture, as one of traditional Chinese medicine therapies, has been widely used in obesity treatment in recent years. However, the individual heterogeneity which makes acupuncture’s efficiency unstable leads to some controversy. So more evidence-based results are necessary to judge the effectiveness of acupuncture in treatment of simple obesity. Compared with clinical trials, animal experiments are controllable, and the underlying mechanism is more likely to be explored. Last but not the least, more and more experimental studies on acupuncture for animal obesity have been published. Therefore, we conducted the systematic review and meta-analysis to evaluate the effectiveness of acupuncture in treating simple obesity in animal experiments. Methods. Randomized Controlled Trials (RCTs) on acupuncture for simple obesity animal models were searched from six databases: PubMed, MEDLINE, CNKI, VIP, WanFang Date, and CMB from inception to February 2017 and updated on April 12, 2019. RevMan 5.3 software was used for meta-analysis. Treatment effects were summarized as relative risk (RR) and Standard mean difference (SMD) with 95% of confidence interval (CI). Results. A total of 108 trials involving 5731 rats were included. Meta-analysis showed that acupuncture had better effect on reducing weight (SMD -2.60, 95%CI: -2.93 to -2.26, p Conclusions. Our review provided positive evidence of acupuncture for simple obesity. Unfortunately, none of the firm conclusions can be drawn due to methodological flaws, high heterogeneity, and publication bias. More high-quality trials are needed in future to get objective conclusions.

OBJECTIVE Acute exacerbation of idiopathic pulmonary fibrosis (AE-IPF) is of concern because of its propensity for rapid deterioration and high mortality. Its aetiology and mechanism are still unclear. The aims of this study were to clarify the pathophysiology differences between AE-IPF and stable IPF (S-IPF) by comparing the serum levels of various cytokines and chemokines in the two groups and to identify those involvement in the occurrence of acute exacerbation and associated with mortality. METHODS The study included 28 patients with AE-IPF, 32 patients with S-IPF, and 18 healthy control subjects. We measured the serum cytokine and chemokine levels in all cases by multiplex assay. Serum levels of cytokines and chemokines were compared between AE-IPF and S-IPF subjects. Logistic regression analysis was applied to identify the ability of these variables to predict acute exacerbation. Kaplan-Meier curves were used to analyse survival and Cox proportional hazard regression was used to identify predictors of survival. RESULTS Levels of several cytokines and chemokines were significantly higher in both patient groups with IPF (with the exception of interleukin-2 [IL-2], chemokine cc-motif ligand 3, and RANTES [regulation upon activation normal T-cell express sequence]) than in healthy controls. Serum IL-1β (p = 0.008) and interferon (IFN)-γ (p = 0.007) levels tended to be higher in patients with AE-IPF than in those with S-IPF. The concentration of chemokine cc-motif ligand (CCL) 2 was significantly higher in bronchoalveolar lavage fluid than in serum (p = 0.001). Higher C-reactive protein, lactate dehydrogenase, percent forced vital capacity, percent diffusing capacity of the lung for carbon monoxide, and IFN-γ values in the patients with IPF were correlated with acute exacerbation status, with respective odds ratios of 1.241 (p = 0.011), 1.050 (p = 0.004), 1.043 (p = 0.001), 0.927 (p = 0.014), and 0.929 (p = 0.020). Acute exacerbation status was associated with an increased risk of mortality (hazard ratio 0.107, 95% confidence interval 0.036-0.314; p

Background Idiopathic pulmonary fibrosis (IPF) is an age-related fatal disease with an unknown etiology. Increased oxidative stress and mitochondrial dysfunction are thought to be involved in its pathogenesis. However, the effect of the AluYb8MUTYH polymorphism on IPF is not known. Results The mean age of onset for IPF in patients homozygous for the AluYb8MUTYH variant (P/P) was 66.5 years old, which was significantly earlier than that in patients with the wild-type (A/A, 70.45 years old). For the 97 male IPF patients with lung function data, the FVC% of the P/P patients was lower than that of the wild-type (A/A) or heterozygous (A/P) patients. The laboratory analysis indicated that an increased mtDNA content and impaired mitochondrial quality control were associated with the P/P genotype. We also confirmed that AluYb8 insertion into MUTYH caused decreased MUTYH1 expression in lung tissues. Methods We compared the lung function of IPF patients and observed the mtDNA content, mtDNA integrity and molecular expression of mitochondrial quality control among subjects with different AluYb8MUTYH genotypes. Additionally, immunoblotting and a reporter gene system were used to test whether altered mitochondrial MUTYH1 expression was linked to AluYb8MUTYH. Conclusions The AluYb8 insertion polymorphism in MUTYH impairs mtDNA stability and affects the age of onset of IPF.

Real-life data on guselkumab in psoriasis are limited and not available in China hitherto. This study aimed to evaluate the short-term effectiveness and safety of guselkumab in patients with psoriasis under Chinese real-life conditions and to explore the effect of guselkumab on CD4+ CD25+ Foxp3+ regulatory T cells (Tregs). A Chinese prospective and real-life study involving patients with psoriasis in Dermatology Hospital of Southern Medical University, Guangzhou, China from April to September 2020 was conducted. A total of 45 patients with psoriasis were finally enrolled in the study. Psoriasis Area Severity Index (PASI) 90 and 100 responses at week 16 were achieved by 88.6% and 45.5% of patients, respectively. The analysis of PASI response in different subgroups showed no statistically significant difference. Univariate logistic regression analysis revealed that at week 16, none of the variables were associated with decreasing PASI 90 response, whereas age at onset of disease was a predictor of PASI 100 response. Dynamic detection of CD4+ CD25+ Foxp3+ Tregs frequency from peripheral blood suggested a stable maintained trend in terms of guselkumab treatment duration. No severe adverse events occurred during the follow-up period. This study confirmed the short-term effectiveness and safety of guselkumab, as well as its good tolerance against psoriasis, in the Chinese population. Guselkumab treatment maintains levels of Tregs in patients with psoriasis.

In this paper, we have proposed a self-powered wireless wearable health data tracking system based on an inertial rotary electromagnetic energy harvester. The whole system can be divided into three parts. The first part is the charging circuit. The energy harvester, which generate 1.65 V Root Mean Square (RMS) voltage and 32.15 mW power at the frequency of one foot while running (1 Hz), could convert kinetic energy of human motion into electrical energy to charge the Li-ion battery. The second part contains an Arduino Microcontroller Unit (MCU) and two sensors. These two sensors could monitor the temperature and the blood oxygen saturation (which can be used in calculation of heart rate) information of the feet and toes, respectively. In the last part, A Bluetooth module is applied to communicate with the MCU and send the health data to the cloud. Meanwhile, this data could be displayed on the smartphone. We also demonstrated the workflow of the whole system. With the energy harvester, the energy from human motion is used to drive sensors and the function of health monitoring is realized.

Background: Lipids are known to accumulate abnormally in the alveoli and circulate during pulmonary alveolar proteinosis (PAP). However, the relationship between lipid ratios and PAP is not clear. In this study, we investigated the lipid ratios in PAP patients and explored the relationships between lipid ratios and the severity of PAP.Methods: A total of 122 PAP patients were diagnosed and divided the mild- moderate PAP group (n = 61) and the severe PAP group (n = 61) according to the value of disease severity score (DSS). One hundred thirty healthy volunteers were classified as the control group. Routine blood examination and pulmonary function tests were performed and lipid profile were measured.Results: Compared with the control group, patients with PAP had significantly higher TG, TC/HDL-C, TG/HDL-C, and non-HDL-C, while lower HDL-C (all P < 0.05). Patients with the severe PAP had higher TC, TG, LDL-C, TC/HDL-C, and non-HDL-C, while lower HDL-C than patients with the mild- moderate PAP (all P < 0.05). Binary logistic regression analysis indicated that TC/HDL-C (OR = 2.322, 95% CI 1.621–3.713, P = 0.024) and non-HDL-C (OR = 1.797, 95% CI 1.239–3.109, P = 0.036) were all significantly correlated with the severity of PAP after adjustment for other risk factors. The AUC value of TC/HDL-C for predicting the severity of PAP was larger than that of non-HDL-C. The AUROC for TC/HDL-C was 0.741 (0.654–0.828), and the optimal cut-off point for TC/HDL-C was 5.05 (sensitivity: 73.6%, specificity: 68.1%).Conclusions: Lipid ratios, including TC-HDL-C and non-HDL-C, were independent risk factors for the severity of PAP. TC/HDL-C is a promising biomarker for the severity of PAP.

This study is about the manufacturing of a personified automatic robotic lawn mower with image recognition. The system structure is that the platform above the crawler tracks is combined with the lawn mower, steering motor, slide rail, and webcam to achieve the purpose of personification. Crawler tracks with a strong grip and good ability to adapt to terrain are selected as a moving vehicle to simulate human feet. In addition, a lawn mower mechanism is designed to simulate the left and right swing of human mowing to promote efficiency and innovation, and then human eyes are replaced by Webcam to identify obstacles. A human-machine interface is added so that through the mobile phone remote operation, users can choose a slow mode, inching mode, and obstacle avoidance mode on the human-machine interface. When the length of both sides of the rectangular area is input to the program, the automatic robotic lawn mower will complete the instruction according to the specified path. The chip of a Digital Signal Processor (DSP) TMS320F2808 is used as the core controller, and Raspberry Pi is used as image recognition and human-machine interface design. This robot can reduce labor costs and improve the efficiency of mowing by remote control. In addition to the use as an automatic mower on farms, this study concept can also be used in the lawn maintenance of golf courses and school playgrounds.

With the rapid development of the Internet, security issues in cyberspace have received more and more attention, and network security situation awareness has become a research focus. This paper proposes a network security situation awareness framework based on situation fusion, which decomposes the network security situation into two parts: the host security situation and the network attack situation. First, the host asset information and network topology information are used to calculate the weight vector of all hosts to make the weight setting more reasonable. Then using CVSS to evaluate the host security situation value. Meanwhile, security events are extracted from the alarm information of the intrusion detection system, and we designed threat downgrading rules and escalation rules based on system environment matching and the attacker’s willingness, so as to calculate the threat of network attacks, and ultimately integrated into the overall network security situation value. The results of the case analysis show that the framework proposed in this paper can quantify the security situation better.

With the popularity of unmanned aerial vehicles (UAVs) and the development of the network technology, the UAV network has become a new research hotspot. However, due to various factors such as terrain, transmission distance, and hacker attacks, UAV network needs to reconstruct the network architecture and restore data according to the actual situation. The blockchain technology can be applied to the UAV network for its high reliability and distributed characteristics. Therefore, this paper proposes a blockchain-based adaptive reconstruction technology framework for UAV networks. Also, to solve possible data damage or missing problems in UAV networks, a data recovery and update approach as well as the corresponding algorithms are given. Furthermore, this paper uses the blockchain to record the behavior of the UAV nodes in the UAV network, and proposes an algorithm to check whether the UAV is malicious or malfunctioning. So that the ground control station (GCS) can reconstruct the UAV network communication link.

With the emergency of the Internet of Things in all walks of life, its security problems are getting more attention. Different devices need to access users’ data from different platforms. However, as different platforms are developed based on different security architectures, when the security policies of different platforms are supposed to be composed, it will result in new vulnerabilities and bring security risks. To address this problem, a new cross-domain access control framework based on OAuth 2.0 is proposed in this paper. The framework realizes secure and flexible management of authentication and authorization of cross-platform access control. The security domain token is used for entities to access and share the resources of each security domain, which solves the cross-domain security problem. The proposed approach is formally modeled in Coq theorem prover, and the results show that the proposed access control mechanism satisfies the security properties.

Aiming at the main problems of terminal data storage, long service delay and transmission security of (WBAN) in wireless body area network, combined with fog computing distributed computing model, this paper proposes an intelligent medical service framework in wireless body area network, which is divided into intelligent medical terminal layer, fog computing central layer and cloud computing data storage layer, and proposes a real-time encrypted transmission scheme for data transmission between each layer. Security analysis shows that the framework and scheme can reduce the cost and delay, while ensuring the security and dispersion of data transmission between layers.

Background: An unmet need for more effective and affordable kinase inhibitors remains in patients with progressive radioactive iodine-refractory differentiated thyroid cancer (RAIR-DTC) in China, w...

To observe the influence of acupuncture on microcirculation perfusion of the pericardium meridian and heart in acute myocardial ischemia (AMI) rats and evaluate whether acupuncture can simultaneously affect the meridians and corresponding viscera. Additionally, acupoints at different meridians were compared and whether they exert the same effects was discussed.Totally 32 Sprague-Dawley rats were subjected to left anterior descending (LAD) ligation to develop an AMI model. Rats were divided into 4 groups, including AMI, acupuncture Neiguan (PC 6), Lieque (LU 7) and Qiansanli (LI 10) groups (n=8). Eight rats received only thoracotomy (sham-operated group). The rats in the acupuncture groups received manual acupuncture at PC 6, LU 7 and LI 10 acupoints for 15 min, respectively. The microcirculation perfusion of pericardium meridian and heart was monitored by laser speckle perfusion imager (LSPI) before, during and after acupuncture manipulation for 15 min. Subsequently, the perfusion unit (PU) was calculated and analyzed by PSI System.After LAD, compared to pre-acupuncture stage, the heart microcirculation perfusion (HMP) in the AMI group decreased continuously at during-acupuncture (P0.05) and post-acupuncture stages (P0.05), and the pericardium meridian microcirculation perfusion (PMP) showed no significant differences at 3 stages (P0.05). Compared to pre-acupuncture stage, the PMP and HMP in PC 6 group significantly increased during acupuncture manipulation (both P0.05), and PMP decreased obviously after acupuncture (P0.05). The PMP in the LU 7 and LI 10 groups were slightly elevated (both P0.05); however, they were significantly reduced after acupuncture manipulation (both P0.05). Additionally, HMP of LI 10 group was decreased significantly during acupuncture, especially compared to pre-acupuncture stage (P0.05).Acupuncture at PC 6 obviously increased the PMP and HMP in AMI rats, and the effects were superior to at LU 7 and LI 10 acupoints. It was further confirmed that acupuncture promoted qi and blood circulation, indicating that acupoint specificity exists and features a meridian-propagated effect.

Objective: To develop predictive models for contrast induced acute kidney injury (CI-AKI) among acute myocardial infarction (AMI) patients treated invasively. Methods: Patients with AMI who underwent angiography therapy were enrolled and randomly divided into training cohort (75%) and validation cohort (25%). Machine learning algorithms were used to construct predictive models for CI-AKI. The predictive models were tested in a validation cohort. Results: A total of 1,495 patients with AMI were included. Of all the patients, 226 (15.1%) cases developed CI-AKI. In the validation cohort, Random Forest (RF) model with top 15 variables reached an area under the curve (AUC) of 0.82 (95% CI: 0.76-0.87), while the best logistic model had an AUC of 0.69 (95% CI: 0.62-0.76). ACEF (age, creatinine, and ejection fraction) model reached an AUC of 0.62 (95% CI: 0.53-0.71). RF model with top 15 variables achieved a high recall rate of 71.9% and an accuracy of 73.5% in the validation group. Random Forest model significantly outperformed logistic regression in every comparison. Conclusions: Machine learning algorithms especially Random Forest algorithm improves the accuracy of risk stratifying patients with AMI and should be used to accurately identify the risk of CI-AKI in AMI patients.

Objective\ud The trajectories of pulse pressure (PP) might affect the prognosis of malignant hypertensive nephropathy (MHN). We aimed to describe the association between PP trajectories and the future risk of end-stage renal disease and to identify and compare the associated patient characteristics of any distinct trajectory patterns in MHN patients.\ud \ud Methods\ud Patients with newly diagnosed biopsy-proven MHN 2010–2015 were included. Latent class growth analysis was applied to the PP measured over 3 years prior to biopsy to identify distinct trajectories. Concurrent systolic blood pressure, diastolic blood pressure, plasma creatinine, and 24-h urine protein measurements for each trajectory group were modelled using generalized estimating equations. The risk of end-stage renal disease (with kidney replacement therapy as a proxy) was estimated using Logistic regression.\ud \ud Results\ud Two hundred three patients were included (median-age 34 years, and 19.7% female). A two-group cubic model was optimal, with trajectories distinguished by the rate of PP and absolute level at final measurement. Trajectory Group-1 (n = 84) was characterized by ‘first-increased-then-decreased’ PP and trajectory Group-2 (n = 119) was characterized by ‘first-decreased-then-increased’ PP over 3 years prior to biopsy. Systolic and diastolic blood pressures, plasma creatinine, and 24-h urine protein were differed by the trajectory group. Baseline characteristics differed substantially between trajectory groups. Compared with Group-1, Group-2 had a 66% greater risk of developing into end-stage renal disease in the subsequent 3 years.\ud \ud Conclusions\ud Two distinct 3-year trajectories for PP exist with MHN. Early introduction of intensive antihypertensive treatment might delay the development of end-stage renal disease among patients with malignant hypertension.

BackgroundNumerous studies showed that insulin resistance (IR) was associated with cancer risk. However, few studies investigated the relationship between IR and non-small cell lung cancer (NSCLC). The aim of this study is to explore the association of triglyceride glucose (TyG) index, a simple surrogate marker of IR, with NSCLC risk.Methods791 histologically confirmed NSCLC cases and 787 controls were enrolled in the present study. Fasting blood glucose and triglyceride were measured. The TyG index was calculated as ln [fasting triglycerides (mg/dl) ×fasting glucose (mg/dl)/2]. Logistic regression analysis was performed to estimate the relationship between NSCLC risk and the TyG index.ResultsThe TyG index was significantly higher in patients with NSCLC than that in controls (8.42 ± 0.55 vs 8.00 ± 0.45, P < 0.01). Logistic regression analysis showed that the TyG index (OR = 3.651, 95%CI 2.461–5.417, P < 0.001) was independently associated with NSCLC risk after adjusting for conventional risk factors. In addition, a continuous rise in the incidence of NSCLC was observed along the tertiles of the TyG index (29.4 vs 53.8 vs 67.2%, P < 0.001). However, there were no differences of the TyG index in different pathological or TNM stages. In receiver operating characteristic (ROC) curve analysis, the optimal cut-off level for the TyG index to predict incident NSCLC was 8.18, and the area under the ROC curve (AUROC) was 0.713(95% CI 0.688–0.738).ConclusionsThe TyG index is significantly correlated with NSCLC risk, and it may be suitable as a predictor for NSCLC.

In this work we train a neural network to identify impurities in the experimental images obtained by the scanning tunneling microscope measurements. The neural network is first trained with large number of simulated data and then the trained neural network is applied to identify a set of experimental images taken at different voltages. We use the convolutional neural network to extract features from the images and also implement the attention mechanism to capture the correlations between images taken at different voltages. We note that the simulated data can capture the universal Friedel oscillation but cannot properly describe the non-universal physics short-range physics nearby an impurity, as well as noises in the experimental data. And we emphasize that the key of this approach is to properly deal these differences between simulated data and experimental data. Here we show that even by including uncorrelated white noises in the simulated data, the performance of neural network on experimental data can be significantly improved. To prevent the neural network from learning unphysical short-range physics, we also develop another method to evaluate the confidence of the neural network prediction on experimental data and to add this confidence measure into the loss function. We show that adding such an extra loss function can also improve the performance on experimental data. Our research can inspire future similar applications of machine learning on experimental data analysis., 5 page, 5 figures

Multi-focus image fusion consists in the integration of the focus regions of multiple source images into a single image. At present, there are still some common problems in image fusion methods, such as block artifacts, artificial edges, halo effects, and contrast reduction. To address these problems, a novel, to the best of our knowledge, multi-focus image fusion method using energy of Laplacian and a deep neural network (DNN) is proposed in this paper. The DNN is composed of multiple denoising autoencoders and a classifier. The Laplacian energy operator can effectively extract the focus information of source images, and the trained DNN model can establish a valid mapping relationship between source images and a focus map according to the extracted focus information. First, the Laplacian energy operator is used to perform focus measurement for two source images to obtain the corresponding focus information maps. Then, the sliding window technology is used to sequentially obtain the windows from the corresponding focus information map, and all of the windows are fed back to the trained DNN model to obtain a focus map. After binary segmentation and small region filtering, a final decision map with good consistency is obtained. Finally, according to the weights provided by the final decision map, multiple source images are fused to obtain a final fusion image. Experimental results demonstrate that the proposed fusion method is superior to other existing ones in terms of subjective visual effects and objective quantitative evaluation.

This paper discusses the information management and service of open scientific data in university library in the big data era and analyze the open channels and application and service method of data information resource for the university library to get scientific data and information resources.

In this work, a novel enzymatic glucose photoelectrochemical biosensor was successfully fabricated based on rutile/anatase TiO2 (R/A-TiO2) heterostructure which was composed of anatase nanowires and rutile nanorods. Compared to a rutile nanorod electrode, an obvious enhanced photocurrent was observed on R/A-TiO2 electrode in photoelectrochemical test. Excellent photoeletrochemical performance was obtained due to these follow points: higher optical absorptivity for the larger surface area, better separation of the photogenerated carriers related to the specific R/A-TiO2 heterostructure and faster electrical transmission stemmed from the unique conductive structure. This R/A-TiO2 heterostructure array was applied to glucose detection by loading glucose oxidase on R/A-TiO2 electrode. A remarkable performance with a 5.71 μA mM−1 cm−2 sensitivity, 1–20 mM linear range and a 0.019 mM glucose detection limit (S/N = 3) were obtained in the glucose detection.

To quantify the purine concentrations of the acupoints along the pericardium and nonpericardium meridians under healthy and myocardial ischemia conditions to investigate the relationship between acupoint purine change and body functional status in rats. A total of 70 rats underwent an operation for myocardial ischemia, while 40 of them survived. They were randomly assigned to the following 5 subgroups: Neiguan (PC 6), Quze (PC 3), Tianquan (PC 2), Quchi (LI 11), and Jianyu (LI 15). Simultaneously, another 40 healthy rats were also randomized into the same 5 subgroups as the control group. The tissue fluids at the acupoints were collected by microdialysis for 30 min. Subsequently, the concentration of adenosine triphosphate (ATP), adenosine diphosphate (ADP), adenosine monophosphate (AMP), and adenosine (ADO) were quantified using the high-performance liquid chromatography method. Compared with the healthy group, the ADO at PC 6 (P=0.012), PC 3 (P=0.038), PC 2 (P=0.024), and LI 15 (P=0.042) obviously increased in the model group, while no significant difference was observed at LI 11 (P=0.201). However, ATP, ADP, and AMP manifested no significant changes in these areas, except for ATP at LI 15 (P=0.036). Myocardial ischemia could induce an increase in ADO at acupoints of the upper arm and shoulder area, suggesting that the body functional status could affect the responsiveness of acupoints. The status of these acupoints could be pathogenically activated by disease, and distribution following some specific courses.

In order to reduce the energy consumption in the cloud data center, it is necessary to make reasonable scheduling of resources in the cloud. The accurate prediction for cloud computing load can be very helpful for resource scheduling to minimize the energy consumption. In this paper, a cloud load prediction model based on weighted wavelet support vector machine(WWSVM) is proposed to predict the host load sequence in the cloud data center. The model combines the wavelet transform and support vector machine to combine the advantages of them, and assigns weight to the sample, which reflects the importance of different sample points and improves the accuracy of load prediction. In order to find the optimal combination of the parameters, we proposed a parameter optimization algorithm based on particle swarm optimization(PSO). Finally, based on the WWSVM model, a load prediction algorithm is proposed for cloud computing using PSO-based weighted support vector machine. The Google cloud computing data set is used to verify the algorithm proposed in this paper by experiments. The experiment results indicate that comparing with the wavelet support vector machine, autoregressive integrated moving average, adaptive network-based fuzzy inference system and tuned support vector regression, the proposed algorithm is superior to the other four prediction algorithms in prediction accuracy and efficiency.

This paper is concerned with a partially observed linear-quadratic game problem driven by forward–backward stochastic differential equations where the forward diffusion coefficients do not contain control variables and the control domains are not necessarily convex. The drift term of the observation equation is linear with respect to the state, and there is correlated noise between the state and the observation equation. By virtue of the classical spike variational method and the backward separation technique, we derive a necessary and a sufficient condition of the stochastic differential game problem. Then we obtain filtering equations and present a feedback representation form of the equilibrium point through Riccati equations. As a practical application, we solve a partial information investment problem involving g-expectation as a convex risk measurement and give the numerical simulation to show the explicit investment strategy and illustrate some reasonable phenomena influenced by common financial parameters.

Community discovery is a leading research topic in social network analysis. In this paper, we present an ego-network probabilistic graphical model (ENPGM) which encodes users’ feature similarities and the causal dependencies between users’ profiles, communities, and ego networks. The model comprises three parts: a profile similarity probabilistic graph, social circle vector, and relationship probabilistic vector. Using Bayesian networks, the profile similarity probabilistic graph considers information about both the features of individuals and network structures with low memory usage. The social circle vector is proposed to describe both the alters belonging to a community and the features causing the community to emerge. The relationship probabilistic vector represents the probability that an ego network forms when given a set of user profiles and a set of circles. We then propose a parameter-learning algorithm and the ego-network probabilistic criterion (ENPC) for extracting communities from ego networks with some missing feature values. The ENPC score balances both the positive and negative impacts of social circles on the probabilities of forming an ego network. Experimental results using Facebook, Twitter, and Google+ datasets indicate that the ENPGM and community learning algorithms can predict social circles with similar quality to the ground-truth communities.

BACKGROUND Whether interferon (IFN)-α therapy is better than nucleos(t)ide analogs (NAs) in the prevention of adverse outcomes, including hepatocellular carcinoma (HCC) in patients with chronic hepatitis B (CHB) is still uncertain or controversial. This study aimed to compare the cumulative incidence of adverse outcomes in patients with CHB on IFN-α- and NA-based therapies. METHODS This was a retrospective study of patients with CHB on antivirals. Patients treated with IFN-α (IFN-α or peginterferon-α) with or without NAs were defined as the IFN-α group, and those only receiving NAs were defined as the NAs group. Propensity score matching (PSM) was used to minimize baseline bias. Cox regression models were performed to select possible factors related to adverse outcomes development. RESULTS All 1247 patients were divided into the IFN-α (n = 877) and NAs (n = 370) groups. 26patients (20 and 6 in the NAs and IFN-α groups) developed adverse outcomes (decompensated cirrhosis, liver failure, HCC, liver transplantation and deaths) during a median follow-up of 5.2 years. The cumulative adverse outcomes occurrence at 10 years was significantly lower in the IFN-α group than in the NAs group in all (1.1% vs. 11.9%, P

As a key component of building management and security, occupancy inference through smart sensing has attracted a lot of research attention for nearly two decades. Recently, a cutting edge technique visible light sensing (VLS) that utilizes the LED luminaires as light sensors has shown its promising application potentials in occupancy inference as it piggybacks on pervasive lighting infrastructure without extra equipment deployment. Although existing inference algorithms based on the VLS data set can achieve high accuracy, the performance degrades when the occupants are moving. This paper focuses on the occupancy inference issue and presents an ensemble learning algorithm to improve the inference accuracy. We use heterogeneous learning algorithms to generate diverse learners. Consequently, we adopt forward sequential pruning to enhance the ensemble that pursues inference error minimization. We conduct extensive experiments based on the field data. The experiment results show that the proposed algorithm is able to improve inference accuracy, especially for highly dynamic occupancy data set. Published version

To address the problem of detecting malicious codes in malware and extracting the corresponding evidences in mobile devices, we construct a consortium blockchain framework, which is composed of a detecting consortium chain shared by test members and a public chain shared by users. Specifically, in view of different malware families in Android-based system, we perform feature modeling by utilizing statistical analysis method, so as to extract malware family features, including software package feature, permission and application feature, and function call feature. Moreover, for reducing false-positive rate and improving the detecting ability of malware variants, we design a multi-feature detection method of Android-based system for detecting and classifying malware. In addition, we establish a fact-base of distributed Android malicious codes by blockchain technology. The experimental results show that, compared with the previously published algorithms, the new proposed method can achieve higher detection accuracy in limited time with lower false-positive and false-negative rates.

ATP-binding cassette A3 (ABCA3) is a phospholipid carrier that is mainly expressed in the alveolar epithelium. Biallelic mutations of ABCA3 has been associated with fatal respiratory distress syndrome and interstitial lung disease (ILD) in children. However, whether variations in ABCA3 have a role in the development of adult ILD, including idiopathic pulmonary fibrosis (IPF), remains to be addressed. In this study, we screened for germline variants of ABCA3 by exons-sequencing in 30 patients with sporadic IPF and in 30 matched healthy controls. Eleven missense variants, predominantly in heterozygous, were found in 13 of these patients, but only two missenses in 2 healthy controls. We then selected four of the detected missense variants (p.L39V, p.S828F, p.V968M and p.G1205R) to performed cohort analysis in 1,024 ILD patients, containing 250 IPF and 774 connective tissue disease-ILD (CTD-ILD) patients, and 1,054 healthy individuals. Our results showed that the allele frequency of p.G1205R, but not p.L39V, was significantly higher in ILD patients than in healthy controls. However, no additional subject carrying the variant p.S828F or p.V968M was detected in the cohort analysis. These results indicate that the heterozygous ABCA3 gene variants may contribute to susceptibility to diseases in the Chinese population.

Cloud computing is growing in popularity among computing paradigms, but in practice the energy consumption of the cloud data centers is very high. In this paper, we build the resource model of the cloud data center and the dynamic power model of the physical machine, and then propose a three-dimensional virtual resource scheduling method for energy saving in cloud computing (TVRSM), in which the process of virtual resource scheduling are divided into three stages: virtual resource allocation, virtual resource scheduling and virtual resource optimization. For the different objective of each stage, we design three different algorithms, respectively. The simulation results prove that the TVRSM is able to efficiently allocate and manage the virtual resources in the cloud data center. And compared with other traditional algorithms, the TVRSM can effectively reduce the energy consumption of the cloud data center and significantly minimize the amount of violations of Service Level Agreement (SLA). We propose a three-dimensional virtual resource (VR) scheduling method.We present a bin packing problem based heuristics VR allocation algorithm.We demonstrate a multi-dimensional power-aware based VR scheduling algorithm.We put forward a VR optimization algorithm to reduce energy consumption.The simulation results demonstrate the effectiveness of the proposed method.

In this paper, a model-driven software collaborative modeling method is proposed, and a software collaborative modeling framework on model assembly and model updating is established. The composability of models, the integrity of model assembly and the consistency of model updating are studied from two aspects: model assembly mechanism and model updating mechanism. The collaborative modeling protocol is defined, and six collaborative relationship stereotypes are introduced to add collaborative semantics to the model based on the extensible mechanism of UML. The algorithm of model assembly and integrity checking is proposed and the collaborative modeling of multiple clients is implemented. The algorithm of model updating and consistency checking is proposed to solve the problem of model updating in collaborative modeling.

With the widespread use of mobile embedded devices in the Internet of Things, mobile office, and edge computing, security issues are becoming more and more serious. Remote attestation, one of the mobile security solutions, is a process of verifying the identity and integrity status of the remote computing device, through which the challenger determines whether the platform is trusted by discovering an unknown fingerprint. The remote attestation on the mobile terminal faces many security challenges presently because there is a lack of trusted roots, devices are heterogeneous, and hardware resources are strictly limited. To ARM’s mobile platform, we propose a mobile remote attestation scheme based on ARM TrustZone (TZ-MRAS), which uses the highest security authority of TrustZone to implement trusted attestation service. Compared with the existing mobile remote attestation scheme, it has the advantages of wide application, easy deployment, and low cost. To defend against the time-of-check-to-time-of-use (TOC-TOU) attack, we propose a probe-based dynamic integrity measurement model, ProbeIMA, which can dynamically detect unknown fingerprints that generate during kernel and process execution. Finally, according to the characteristics of the improved dynamic measurement model, that is, the ProbeIMA will expand the scale of the measurement dataset, an optimized stored measurement log construction algorithm based on the locality principle (LPSML) is proposed, which has the advantages of shortening the length of the authentication path and improving the verification efficiency of the platform configuration. As a proof of concept, we implemented a prototype for each service and made experimental evaluations. The experimental results show the proposed scheme has higher security and efficiency than some existing schemes.

To protect core functions, applications often utilize the countermeasure techniques such as antidebugging to avoid analysis by outsiders, especially the malware. Dynamic binary instrumentation is commonly used in the analysis of binary programs. However, it can be easily detected and has stability and applicability problems as it involves program rewriting and just-in-time compilation. This paper proposes a new lightweight analysis method for binary programs with the assistance of hardware features and the operating system kernel, named BAHK, which can automatically analyze the target program by stealth and has wide applicability. With the support of underlying infrastructures, this paper designs several optimization strategies and specific analysis approaches at instruction level to reduce the impact of fine-grained analysis on the performance of target program so that it can be well applied in practice. The experimental results show that the proposed method has good stealthiness, low memory consumption, and positive user experience. In some cases, it shows better analysis performance than the traditional dynamic binary instrumentation method. Finally, the real case studies further show its feasibility and effectiveness.

The complexity and diversity of cloud computing bring about cloud faults, which affect the quality of services. Existing fault detection methods suffer problems such as low efficiency and low accuracy. In order to improve the reliability of the cloud data center, a fault detection algorithm based on weighted one-class support vector machine (WOCSVM) is proposed to detect and identify the host faults in the cloud data center. Specifically, first, we conduct correlation analysis among monitoring metrics and select key ones for reducing the complexity. Second, for imbalanced monitoring dataset, one-class support vector machine is used to detect and identify host faults, and a weight allocation strategy is proposed to assign weights to the samples, which describes the importance of different sample points in order to improve detection accuracy on potential faults. Finally, for the purpose of increasing the accuracy further, the parameters are set via a parameter optimization algorithm based on quantum-behaved particle swarm optimization (QPSO). Furthermore, experiments by comprising with similar algorithms, demonstrate the superiority of our algorithm under different classification indicators.

Background: Early stages of lung cancer have a better prognosis than later stages. The accuracy of current lung cancer early detection methods is relatively poor. We firstly try to use circRNAs of platelets enable blood-based early detection of lung cancer. Methods: We did a three-stage study that included healthy controls, benign lung nodule, and patients with diagnosed lung cancer from two hospitals in China. We used High-throughput Illumina sequencing analysis to profile circRNAs expression in the 8 patients with lung cancer and 8 non-cancer controls (including healthy controls and benign lung nodule patients). Using a training cohort of patients with lung cancer and non-cancer controls, we built a circRNAs classifier to detect lung cancer. Then, the classifiers’ ability was validated in two independent cohorts of patients and non-cancer controls. We used the sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) to evaluate diagnostic performance. Findings: Between Aug 1, 2018, and September 31, 2019, we recruited 88 participants to the training cohort, and 694 and 605 participants to the two independent validation cohorts. A signature comprising 23 circRNAs was identified by RNA-Seq analysis in the discovery stage. Then, four circRNAs (CD274, TIMP1, FLNA, ITGA2B) was found to have the possibility to serve as biomarkers for LC detection in the training stage by qRT-PCR. These selected four circRNAs from platelets was identified and subsequently assessed in the two independent validation cohorts. The area under the curve (AUC) for the five circRNAs from platelets (Pcs) were 0.975 in the validation cohort. In the following blinded test including the platelets from benign pulmonary nodule, LC patients and LC patients after surgery, Pcs correctly discriminated the patients with 90.6% sensitivity and 96.3% specificity. Interpretation: CircRNAs from platelets are very promising biomarkers for early stages of lung cancer diagnosis with very high sensitivity and specificity. This novel technology could serve as a Non-invasive, highly accurate diagnostic tool for lung nodule carriers that difficult to distinguish malignance from benign ones. Funding Statement: This work was supported by grants from National Natural Science Foundation of China (81572262), Jiangsu Province’s Key provincial Talents Program (ZDRCA2016028), 333 high class Talented Man Project (BRA2016516) and The Natural Science Foundation of the Jiangsu Higher Education Institution of China (18KJB320006). Declaration of Interests: None declared. Ethics Approval Statement: The study was approved by institutional ethics committee in each study center.

Aiming at the problem of host load forecasting in mobile cloud computing, the Long Short Term Memory networks (LSTM) is introduced, which is suitable for the complex and long-time series data of the cloud environment and a load forecasting algorithm based on Glowworm Swarm Optimization LSTM neural network is proposed. Specifically, we build a mobile cloud load forecasting model using LSTM neural network, and the Glowworm Swarm Optimization Algorithm (GSO) is used to search for the optimal LSTM parameters based on the research and analysis of host load data in the mobile cloud computing data center. Finally, the simulation experiments are implemented and similar prediction algorithms are compared. The experimental results show that the prediction algorithms proposed in this paper are superior to similar prediction algorithms in prediction accuracy.

Aiming at the problem of insufficient memory in the real-time processing of SAR imaging on embedded TX2, this paper studies a design optimization scheme of fixed-point imaging algorithm on TX2. In order to ensure the accuracy, this paper only performs fixed-point processing on the FFT. According to the characteristics of the algorithm, the memory multiplexing method is adopted to effectively reduce the occupation of the work area buffer. A large number of registers are used to improve computing performance. The results show that the fixed-point FFT processing scheme can achieve a hundred times faster than the Intel Xeon CPU, and the memory footprint is less.

Grounding electrode line impedance supervision (ELIS) method is used to detect and deal with faults of electrode line in UHVDC project. The reliability of ELIS depends on the difference between the normal impedance and fault impedance. Presently, there is no theoretical analysis about the fault impedance of electrode line, which makes it difficult to evaluate the adaptability of ELIS. This paper studies the fault impedance characteristic under different fault conditions by means of compound mode network. The influence of fault resistance and fault distance on fault impedance is also discussed. Then the adaptability analysis of ELIS is carreid out. The analysis results indicate that the ELIS has dead zone in some special fault points.