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With the increasing data traffic in the fifth generation communication system (5G), the 5G new radio extended to unlicensed bands (5G NR-U) has become a promising approach to relieve the heavy pressure on the cellular system. To achieve the efficient coexistence with the WiFi system and improve the efficiency of temporal, spectral and spatial resource utilization, we firstly divide the transmission space into two subspaces by leveraging spatial reuse, where the data transmitted by cellular user equipments (UEs) falls into one subspace and the data transmitted by Internet of Things (IoT) devices coexisting with WiFi users through power control is in the other subspace. Then, the coexistence among cellular UEs, IoT devices, and WiFi users is formulated as an optimization model with the aim of maximizing the cellular system throughput via the joint power and subchannel allocation under the interference constraint. Although the resulting optimization problem is a mixed integer nonlinear programmming, we decompose it into two subproblems and develop an alternating iterative approach to effectively solve them. Also, the closed-form allocations of the power and subchannels are obtained. Simulation results confirm that the proposed scheme can improve the cellular system performance and guarantee the coexistence in the unlicensed band.

Nondipping heart rate (HR), defined as a night/day HR ratio0.90, has been associated with increased mortality in epidemiologic studies. However, its prognostic value in stage 5 chronic kidney disease (CKD5) patients and the effects of parathyroidectomy (PTX) on nondipping HR remain unknown.This case-control study of 162 healthy controls and 502 CKD5 patients was performed between 2011 and 2018, in which CKD5 patients were further divided into non-PTX (n = 186) and severe secondary hyperparathyroidism (SHPT) with PTX (n = 316) subgroups. Each participant underwent 24-hour Holter monitoring for HR ratio. Mortality was followed up in CKD5 patients (median time: 46.0 months).The HR ratio in CKD5 patients was higher than in controls (0.92 ± 0.08 vs 0.81 ± 0.08, P.001), associated with a 44% increase in mortality risk per 0.1 increment (hazard ratio, 1.44; 95% CI: 1.02-2.03; P =.04), and was positively related to serum intact parathyroid hormone levels (P.001). PTX reversed nondipping HR in SHPT patients (n = 50, median time: 6.3 months, P.001). Survival probabilities for PTX (n = 294) were better than non-PTX (n = 47) (hazard ratio, 0.31; 95% CI: 0.14-0.67; P.01) in SHPT patients (serum intact parathyroid hormone500.0 pg/mL).CKD5 patients displayed a nondipping HR pattern, which is a prognostic marker of all-cause mortality. PTX for SHPT patients was associated with a reversal in nondipping HR ratio, which may mediate a better outcome.

The facial caricature shows the distinct characteristics of a person via exaggerations of both shape and appearance. This paper presents a novel framework that automatically generates vivid facial caricatures by encoding personalized semantic information. To this end, we first design a part-based scheme for geometry warping, which composes local semantic deformation into a global warping field, equipped with sufficient warping freedom of different facial components. Second, under the scheme of Part-based Warping, we design a photo-to-caricature translation network called PbWarpGAN, and adopt several novel losses to capture the personalized characteristics of each input face and preserve its identity better. Third, based on PbWarpGAN, we develop a user-friendly interface by introducing an attention scheme on each facial component, allowing ordinary users to adjust the automatically generated caricature by PbWarpGAN according to their preference conveniently. Experimental results show that our PbWarpGAN is more effective in capturing personalized characteristics than counterparts, and provides an efficient tool for caricature designing application.

Cloud storage has become a primary industry in remote data management service but also attracts security concerns, where the best available approach for preventing data disclosure is encryption. Among them the public key encryption with keyword search (PKSE) is considered to be a promising technique, since clients can efficiently search over encrypted data files. That is, a client first generates a search token when to query data files, the cloud server uses the search token to proceed the query over encrypted data files. However, a serious attack is raised when PKSE meets cloud. Formally speaking, the cloud server can learn the information of a newly added encrypted data file containing the keyword that previously queried by using the search tokens it has received, and can further discover the privacy information. To address this issue, we propose a forward secure public key searchable encryption scheme, in which a cloud server cannot learn any information about a newly added encrypted data file containing the keyword that previously queried. To better understand the design principle, we introduce a framework for constructing forward secure public key searchable encryption schemes based on attribute-based searchable encryption. Finally, the experiments show our scheme is efficient.

Fulfilling the diverse communication requirements of heterogeneous users is envisioned to be one of the key challenges in cognitive radio-enabled IoT networks. To this end, we propose a novel scheme focused on multi-tier prioritized and heterogeneous regime of users and reservation-based resources allocation. The proposed scheme manipulates the secondary users’ (SUs’) heterogeneity based on their priorities for improving their capacities and blocking probabilities. Besides, the scheme systematically deals with SUs’ dropping for bringing fairness in the service provisioning to SUs. Furthermore, the scheme allows primary users (PUs) to follow an organized approach when accessing channels for mitigating the number of handoffs. Additionally, we propose a dynamic channel reservation algorithm for PUs to enhance spectrum utilization. We leverage the continuous-time Markov chain for system modeling and consider channel failures for making the analysis more realistic. Results confirm the effectiveness of our scheme as compared to the state-of-the-art.

The explosively increasing traffic demand in the fifth generation (5 G) mobile communication system has made the limited licensed spectrum quite competitive. Such status quo motivates the development of new radio (NR) based access to the unlicensed band, which is a promising solution for system performance enhancement. In this paper, we focus on the coexistence between the NR system and the WiFi system based on the duty cycle mechanism. Firstly, an optimization model is formulated to maximize the throughput of the NR system in the unlicensed band (NR-U) via the joint bandwidth and transmission opportunity allocation under the fairness requirement. Then, to solve this optimization problem, we convert it into a Markov decision process with continuous action space, based on which a deep deterministic policy gradient (DDPG) based coexistence algorithm is proposed. At last, simulation results show that the proposed algorithm can ensure the coexistence between the NR-U system and the WiFi system from three aspects. Firstly, the throughput improvements of the NR-U system under different fairness thresholds and different number of user equipments and access points are illustrated. Secondly, the proposed algorithm adapts to the dynamic situations of varying fairness thresholds and channel conditions. Thirdly, the effectiveness of the proposed algorithm is validated with at least 5.01% throughput improvement by comparing with proportional scheme, average scheme and Q-learning scheme.

The future sixth generation (6G) wireless communication networks will face the challenges of large-scale connections green communication. To meet these requirements, cognitive ambient backscatter communication (C-AmBC) has been proposed as a new spectrum paradigm for the green Internet-of-Things (IoT) with stringent energy and spectrum constraints, in which the backscatter device (BD) can achieve communications by simultaneously sharing both spectrum and radio-frequency (RF) sources. However, due to the broadcasting nature of wireless communication channels, BD is vulnerable to eavesdropping from unlicensed eavesdroppers. To address this, this paper proposes a framework of C-AmBC networks in the presence of an unlicensed eavesdropper. Specifically, we investigate the reliability and security of the proposed framework by invoking the outage probability (OP) and intercept probability (IP) with analytical derivations. In addition, the asymptotic behaviors are conducted for the OP in the high signal-to-noise ratio (SNR) regime and IP in the high main-to-eavesdropper ratio (MER) regime. Extensive analytical and computer simulated performance evaluation results show that: 1) when the considered system is under high SNR, the OP of the legitimate user and BD tends to be a non-zero fixed constant, indicating that the existence of error floors for the diversity orders; 2) the performance trade-off of reliability and security can be optimized by adjusting various parameters of the considered system; 3) with the increase of MER, the security of the legitimate user increases, while that of BD decreases.

The prevailing coronavirus disease-19 (COVID-19) caused by a novel severe acute respiratory syndrome coronavirus (SARS-CoV-2) has presented some neurological manifestations including hyposmia, hypogeusia, headache, stroke, encephalitis, Guillain–Barre syndrome, and some neuropsychiatric disorders. Although several cell types in the brain express angiotensin-converting enzyme-2 (ACE2), the main SARS-CoV-2 receptor, and other related proteins, it remains unclear whether the observed neurological manifestations are attributed to virus invasion into the brain or just comorbidities caused by dysregulation of systemic factors. Here, we briefly review the neurological manifestations of SARS-CoV-2, summarize recent evidence for the potential neurotropism of SARS-CoV-2, and discuss the potential mechanisms of COVID-19-associated neurological diseases.

Objective: The objective of this study is to investigate the association between the serum sclerostin, the coronary artery calcification (CAC), and patient outcomes in maintenance dialysis patients. Methods: We performed a prospective cohort study of 65 maintenance dialysis patients in 2014, including 39 patients on peritoneal dialysis and 26 on hemodialysis, and followed up for 5 years. Parameters of mineral metabolism including bone-specific alkaline phosphatase, fibroblast growth factor 23, sclerostin, and other biochemical factors were determined at the baseline. Meanwhile, the CAC score was analyzed by cardiac computed tomography. Results: Serum sclerostin in hemodialysis patients was significantly higher than that in peritoneal dialysis patients (632.35 ± 369.18 vs. 228.85 ± 188.92, p < 0.001). The patients with CAC were older, receiving hemodialysis, lower Kt/V, and had longer dialysis vintage, as well as higher levels of serum 25-(OH)-vit D and sclerostin. In multivariate logistic regression analysis, older age and lower Kt/V were risk factors for CAC. The area under the receiver operating characteristic curves for prediction of CAC by sclerostin was 0.74 (95% confidence interval 0.605–0.878, p = 0.03), and the cutoff value of sclerostin is 217.55 pg/mL with the sensitivity 0.829 and specificity 0.619. After 5 years of follow-up, 51 patients survived. The patients in the survival group had significantly lower age, sclerostin levels, and low CAC scores than the nonsurvival group. Old age (≥60 years, p < 0.001) and high CAC score (≥50 Agatston unit, p = 0.031) were significant risk factors for the patient survival. Conclusions: Sclerostin is significantly elevated in dialysis patients with CAC. But sclerostin is not a risk factor for CAC. After 5 years of follow-up, patients in the survival group are younger and have lower sclerostin levels and CAC scores. But sclerostin levels are not independent risk factors for high mortality in dialysis patients.

Prior reports have examined the relationship between obstructive sleep apnea (OSA) and the mortality rate of lung cancer. However, the findings remain controversial. The present meta-analysis was performed to assess the relationship between OSA and increased risk of mortality in patients with lung cancer. PubMed, Web of Science, and Embase were systematically searched for the correlative studies. Data were analyzed and pooled to evaluate odds ratios (ORs) of lung cancer mortality related to OSA. From 249 identified studies, 3 met inclusion criteria and were analyzed, including 67 patients with lung cancer and comorbid OSA and 45 patients with lung cancer and no OSA. The meta-analysis indicated that OSA was not significantly correlated with mortality rate in lung cancer (OR = 2.005, 95% CI = 0.703 to 5.715, z = 1.30, p = 0.193). There was no significant publication bias according to Begg’s tests (p = 0.296) and Egger’s tests (p = 0.097). This meta-analysis suggests that OSA is not significantly correlated with the mortality rate in lung cancer.

The current study aimed to assess the dietary salt intake in patients with CKD in Jiangsu province and investigate the relationship of urinary sodium excretion with blood pressure. A total of 800 patients with CKD stages 1–4 were recruited. All enrolled patients were asked to collect complete 24‐h urine specimen. At the same time, patient's demographic and laboratory data were recorded. The mean age was 47.45 ± 15.25 years old, including 423 men and 377 women. There was no significant difference in urinary sodium excretion among different stages of CKD (p = .748). This study revealed that the median urinary sodium excretion of all patients was 127.20 mmol/d (IQR 91.03–172.06), corresponding to a salt intake of 7.4 g/d. Among them, only 167 (20.9%) cases had salt intake

BACKGROUND: Intravascular optical coherence tomography (IVOCT) enables detailed plaque characterisation in-vivo, but visual assessment is time-consuming and subjective.AIMS: This study aims to develop and validate an automatic framework for IVOCT plaque characterisation using artificial intelligence (AI).METHODS: IVOCT pullbacks from 5 international centres were analysed in a corelab, annotating basic plaque components, inflammatory markers and other structures. A deep convolutional network with encoding-decoding architecture and pseudo-3D input was developed and trained using hybrid loss. The proposed network was integrated into commercial software to be externally validated on additional IVOCT pullbacks from three international corelabs, taking the consensus among corelabs as reference.RESULTS: Annotated images from 509 pullbacks (391 patients) were divided into 10,517 and 1,156 cross-sections for the training and testing datasets, respectively. Dice coefficient of the model was 0.906 for fibrous plaque, 0.848 for calcium and 0.772 for lipid in the testing dataset. Excellent agreement in plaque burden quantification was observed between the model and manual measurements (R2=0.98). In the external validation, the software correctly identified 518 out of 598 plaque regions from 300 IVOCT cross-sections, with a diagnostic accuracy of 97.6%[95%CI:93.4%-99.3%] in fibrous plaque, 90.5%[95%CI:85.2%-94.1%] in lipid and 88.5%[95%CI:82.4%-92.7%] in calcium. The median time required for analysis was 21.4 (18.6-25.0) seconds per pullback.CONCLUSIONS: A novel AI framework for automatic plaque characterisation in IVOCT was developed, providing excellent diagnostic accuracy in both internal and external validation. This model might reduce subjectivity in image interpretation and facilitate IVOCT quantification of plaque composition, with potential applications in research and IVOCT-guided PCI.

OBJECTIVES This study aimed to compare the effect of atorvastatin 60 (AT60) mg to that of rosuvastatin 10 (RT10) mg on the morphological changes in lipid-rich plaques (LRPs) and plaque volume, using serial optical coherence tomography (OCT) and intravascular ultrasound imaging (IVUS). BACKGROUND Intensive lipid lowering therapy by statin provides more clinical benefit compared to that of moderate lipid lowering therapy. METHODS Fifty patients who underwent OCT and IVUS at baseline, 6, and 12 months were grouped by statin therapy into the AT60 mg (n = 27) and RT10 mg (n = 23) groups. The relationships between absolute and percentage changes in biomarkers and fibrous cap thickness (FCT) during follow-up were investigated using a simple regression analysis. RESULTS At 6 months, the mean low-density lipoprotein cholesterol level reduced from 113.5 to 65.5 mg/dl (AT60 mg group) and 100.2 to 72.2 mg/dl (RT10 mg groups). A continuous increase in FCT from baseline to 12 months was observed in both groups (p

BACKGROUND: Coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has rapidly spread throughout the world. In this study, we aimed to identify the risk factors for severe COVID-19 to improve treatment guidelines. METHODS: A multicenter, cross-sectional study was conducted on 313 patients hospitalized with COVID-19. Patients were classified into two groups based on disease severity (nonsevere and severe) according to initial clinical presentation. Laboratory test results and epidemiological and clinical characteristics were analyzed using descriptive statistics. Univariate and multivariate logistic regression models were used to detect potential risk factors associated with severe COVID-19. RESULTS: A total of 289 patients (197 nonsevere and 92 severe cases) with a median age of 45.0 (33.0, 61.0) years were included in this study, and 53.3% (154/289) were male. Fever (192/286, 67.1%) and cough (170/289, 58.8%) were commonly observed, followed by sore throat (49/289, 17.0%). Multivariate logistic regression analysis suggested that patients who were aged ≥ 65 years (OR: 2.725, 95% confidence interval [CI]: 1.317-5.636; P = 0.007), were male (OR: 1.878, 95% CI: 1.002-3.520, P = 0.049), had comorbid diabetes (OR: 3.314, 95% CI: 1.126-9.758, P = 0.030), cough (OR: 3.427, 95% CI: 1.752-6.706, P 

Integrated energy systems and multi‐energy micro‐grids are complex multi‐agent collaborative optimization projects. According to the operating structure and characteristics of the multi‐energy system, considering the externality, economy and other factors, a scientific and effective comprehensive evaluation are important decision supports to ensure its implementation and promote its development. Therefore, this paper proposes a comprehensive benefit evaluation model of a multi‐energy micro‐grid based on the matter‐element extension model. With the research trends and practical requirements of multi‐energy micro‐grid benefit evaluation, an evaluation index system covering energy efficiency indicators, economic indicators, reliability indicators, and renewable energy consumption indicators has been constructed, and uncertainties have been resolved in cloud model and fuzzy set theory. Based on the advantages of the performance evaluation information, the system operation scenarios in the process of planning scheme formation are fully considered, and then a matter‐element‐extensible multi‐attribute decision‐making model is proposed to evaluate the comprehensive benefits of multi‐energy micro‐grids. Finally, the feasibility of the above method is verified by comparing the comprehensive benefit evaluation results of the multi‐energy micro‐grid under different planning scenarios. In addition, a certain decision‐making reference is provided for the formation of a multi‐energy micro‐grid configuration scheme.

Commercial specifications for a new biotherapeutic product are a critical component of the product's overall control strategy that ensures safety and efficacy. This paper describes strategies for setting commercial specifications as proposed by a consortium of industry development scientists. The specifications for some attributes are guided by compendia and regulatory guidance. For other product quality attributes (PQAs), product knowledge and the understanding of attribute criticality built throughout product development should drive specification setting. The foundation of PQA knowledge is an understanding of potential patient impact through an assessment of potency, PK, immunogenicity and safety. In addition to PQA knowledge, the ability of the manufacturing process to consistently meet specifications, typically assessed through statistical analyses, is an important consideration in the specification-setting process. Setting acceptance criteria that are unnecessarily narrow can impact the ability to supply product or prohibit consideration of future convenient dosage forms. Patient-centric specifications enable appropriate control over higher risk PQAs to ensure product quality for the patient, and flexibility for lower risk PQAs for a sustainable supply chain. This paper captures common strategic approaches for setting specifications for standard biotherapeutic products such as monoclonal antibodies and includes considerations for ensuring specifications are patient centric.

Background: Last December 2019, a cluster of viral pneumonia cases identified as coronavirus disease 2019 (COVID-19), was reported in Wuhan, China. We aimed to explore the frequencies of nasal symptoms in patients with COVID-19, including loss of smell and taste, as well as their presentation as the first symptom of the disease and their association with the severity of COVID-19.Methods: In this retrospective study, 1,206 laboratory-confirmed COVID-19 patients were included and followed-up by telephone call one month after discharged from Tongji Hospital, Wuhan. Demographic data, laboratory values, comorbidities, symptoms, and numerical rating scale scores (0-10) of nasal symptoms were extracted from the hospital medical records, and confirmed or reevaluated by the telephone follow-up. Results: From COVID-19 patients (N = 1,172) completing follow-up, 199 (17%) subjects had severe COVID-19 and 342 (29.2%) reported nasal symptoms. The most common nasal symptom was loss of taste (20.6%, median score = 6), while 11.4% had loss of smell (median score = 5). The incidence of nasal symptom including loss of smell and loss of taste as the first onset symptom was Conclusions: In the Wuhan COVID-19 cohort, only 1 out of 10 hospital admitted patients had loss of smell while 1 out 5 reported loss of taste which was associated to severity of COVID-19. Most patients recovered smell and taste dysfunctions in 2 weeks.

Previous studies have indicated that cerebral arterial morphology is linked to aging and some cerebrovascular diseases. However, the mechanisms of morphological changes remain unclear. This study evaluated age-related positional changes in the basilar artery (BA) bifurcation based on longitudinal computed tomography angiography (CTA) data. This retrospective study evaluated clinical and imaging data from 72 subjects who underwent two CTA scans between July 2011 and August 2019. Three-dimensional (3D) models were reconstructed for each subject based on the two CTA scans with the longest separating interval. Skull landmarks were used to fuse the two models, and the fused model was used to evaluate positional changes in the BA bifurcation. Univariable and multivariable analyses were used to identify variables that were correlated to BA bifurcation shifting. Pearson’s correlation test was used to analyze the correlation between the shifting distance and change in the BA bifurcation angle. Significant differences between aneurysm and non-aneurysm cases were observed in terms of sex (p = 0.004), CTA scan interval (p = 0.023), and BA bifurcation shifting distance (p = 0.007). Multivariable linear regression analysis revealed that the BA bifurcation shifting distance was significantly correlated with the CTA scan interval (p = 0.038) and the presence of aneurysms (p

We demonstrate that the directionality of far-field second harmonic (SH) emission generated from individual gold nanosphere can be flexibly engineered by manipulating three-dimensional (3D) focal polarization orientation of the excitation field, which is explained by the coherent interference between SH dipolar and quadrupolar emission modes. The SH dipolar emission mode is independent of the polarization direction of the fundamental field whereas the evolution of the focal polarization orientation can dramatically modify the quadrupolar emission pattern. Therefore, the resultant SH emission pattern has a polarization-dependent behavior and side scattering almost perpendicular to the propagation direction of the incident light can be observed under a specific condition. Our findings provide a novel degree of freedom for all-optical control of directional nonlinear scattering from single nanoantenna, thereby opening new possibilities for future potential applications.

Automatic modulation classification (AMC), which aims to blindly identify the modulation type of an incoming signal at the receiver in wireless communication systems, is a fundamental signal processing technique in the physical layer to improve the spectrum utilization efficiency. Motivated by deep learning (DL) high-impact success in many informatics domains, including radio signal processing for communications, numerous recent AMC methods exploiting deep networks have been proposed to overcome the existing drawbacks of traditional approaches. DL is capable of learning the underlying characteristics of radio signals effectively for modulation pattern recognition, which in turn improves the modulation classification performance under the presence of channel impairments. In this work, we first provide the fundamental concepts of various architectures, such as neural networks, recurrent neural networks, long short-term memory, and convolutional neural networks as the necessary background. We then convey a comprehensive study of DL for AMC in wireless communications, where technical analysis is deliberated in the perspective of state-of-the-art deep architectures. Remarkably, several sophisticated structures and advanced designs of convolutional neural networks are investigated for different data types of sequential radio signals, spectrum images, and constellation images to deal with various channel impairments. Finally, we discuss some primary research challenges and potential future directions in the area of DL for modulation classification.

The new effects induced by light in materials have important potential applications in optoelectronic multifunctional electronic devices. In this work, double perovskite oxide Bi2FeCrO6 (BFCO) thin films were prepared by the sol–gel method, and the structural, optical, and electrical transport properties were investigated in detail. Good and stable resistive switching (RS) behavior with a large ROFF/RON ratio (>60) at a small voltage of 0.5 V can be realized. Interestingly, optical modulation conversion of the RS behavior and a negative differential resistance (NDR) effect induced by light were found in the Au/BFCO/FTO devices. Furthermore, the mechanisms of the RS behavior, NDR effect, and carrier dynamics induced by light for the Au/BFCO/FTO devices were discussed; the formation of oxygen vacancies was proposed to explain the good and stable RS performance, and the effective separation of photogenerated carriers can result in the NDR effect, which is further confirmed by transient absorption spectroscopy. The conversion of RS memory and the NDR effect have potential applications in optical modulation multifunctional electronic devices, electronic information, and optical quantum computers.

PolarLight is a compact soft X-ray polarimeter onboard a CubeSat, which was launched into a low-Earth orbit on October 29, 2018. In March 2019, PolarLight started full operation, and since then, regular observations with the Crab nebula, Sco X-1, and background regions have been conducted. Here we report the operation, calibration, and performance of PolarLight in the orbit. Based on these, we discuss how one can run a low-cost, shared CubeSat for space astronomy, and how CubeSats can play a role in modern space astronomy for technical demonstration, science observations, and student training., Comment: accepted for publication in Advances in Space Research

Lignite is difficult to obtain highly efficient indexes by conventional flotation due to its poor surface hydrophobicity. Although the modification of flotation reagents has made some progress in improving the flotation performance, they all remain at the stage in the laboratory. Here, we proposed to improve the flotation performance by hydrothermal treatment dewatering (HTD) for lignite. Combined with the 13C NMR and FT-IR analysis, the impact of the HTD process on lignite’s chemical structural evolution and flotation performance was investigated. The results showed that the HTD process is an effective means for dehydration and deoxygenation to increase lignite quality and the metamorphic degree of coal. The content of oxygen-containing functional groups generally decreases during the HTD process, especially carboxyl acid and ether groups. Therefore, surface properties and wettability of HTD coal samples were changed, and the contact angle gradually increases with the HTD temperature increased, which enhance the hydrophobicity and decrease hydrophilia of the lignite surface. The HTD process effectively improves the flotation performance of lignite during the conventional flotation operation condition, even if the coal pulp after HTD was directly used as a flotation feedstock. Specifically, the coal oil and capryl alcohol were used as collector and forming agents, respectively, and the pulp concentration was adjusted to 60 g/L. The yield of the cleaned coal increased from 12.14% of the raw coal to 55.58% of HTD310, and combustible matter recovery increased from 13.83% of the raw coal to 65.17% of HTD310 by raw coal basis.

Owing to their potential mobility and agility, unmanned aerial vehicles (UAVs) have captured predominant interests in sustaining 5G wireless communication and beyond. In this paper, we scrutinize the downlink transmission of UAV-enabled wireless backhaul networks in which non-orthogonal multiple access is incorporated to boost up the massive connectivity and high spectra efficiency. More precisely, our aim is to maximize the worst ground user’s achievable rate by optimizing bandwidth allocation, UAV’s power allocation and placement. The formulated problem is non-convex and not easy to solve optimally. Consequently, to deal with the complexity and non-convexity of our problem, we develop a path following procedure and generate a less-onerous algorithm that is iteratively run till convergence. The simulation results are executed to validate not only the effectiveness, but also the convergence of the proposed method. In addition, a comparison with other alternative schemes is depicted to divulge the outperformance of our proposed algorithm.

Introduction Circulating intact parathyroid hormone (iPTH) levels include full-length (1-84) PTH and long C-PTH fragments, but primarily (7-84) PTH, which have been reported to have antagonistic effects on the bones and kidneys. However, their effects on the cardiovascular system remain unclear. In this study, the relationships between the plasma PTH fragments levels and heart rate variability (HRV) in stage 5 chronic kidney disease (CKD5) patients are explored. Furthermore, the effects of parathyroidectomy (PTX) on the above indices are investigated. Methods In this cross-sectional study, 164 healthy controls and 354 CKD5 patients, including 208 secondary hyperparathyroidism (SHPT) subgroup with PTX, were enrolled. Circulating (7-84) PTH levels were calculated by subtracting plasma (1-84) PTH levels from iPTH levels. The HRV parameters were measured using a 24-hour Holter. Results The baseline levels of plasma iPTH, (1-84) PTH, and (7-84) PTH in the CKD5 patients were 930.40 (160.65, 1792.50) pg/mL, 448.60 (99.62, 850.45) pg/mL, and 468.20 (54.22, 922.55) pg/mL, respectively. In the CKD5 patients, plasma (1-84) PTH levels were independently correlated with the standard deviation of the normal-to-normal R-R intervals (SDNN) and the standard deviation of the five-minute average of the normal R-R intervals (SDANN). With a median follow up time of 6.50 months after PTX in the SHPT patients (n = 30), improved SDNN and SDANN markers were related with decreased (1-84) PTH levels. Furthermore, an improved SDNN was related with decreased (7-84) PTH levels. Conclusions The CKD5 patients’ baseline (1-84) PTH levels were correlated with the SDNN and SDANN. After PTX, an improved SDNN was related with decreased (1-84) PTH and (7-84) PTH levels, while improved SDANN was related with decreased (1-84) PTH levels. No antagonistic effects of (1-84) PTH and (7-84) PTH on HRV were found in the CKD5 patients.

Improving the utilization efficiency of renewable energy sources (RES) is an important task for the development of an integrated energy system (IES). To address this challenge, this paper proposes a novel multi-objective interval optimization framework for the energy hub (EH) planning problem from the perspective of the source load synergy, while considering the impacts of both supply- and demand-side uncertainties. For this aim, based on an in-depth analysis of the adjustable characteristics of various loads in EH and their effect on RES absorption, an interval model is first established to describe the responsiveness of users’ load demand to real-time energy price variations and its associated uncertainties. In view of the natural contradiction between the system’s economic and environmental benefits, a multi-objective interval optimization model for the EH planning problem is developed, wherein the minimization of the system’s economic costs and the maximization of the RES utilization rate are considered as the dual objectives to be optimized simultaneously. Moreover, this study takes into account the uncertainties of RES availability and demand-side behaviors by using interval numbers and properly considering their impacts in the context of long-term planning. According to the features of the proposed model, the interval order relation and possible degree method are jointly used to transform the model into a deterministic optimization problem first, and then an improved non-dominant sorting genetic algorithm is used to derive the optimal solution to the problem. The results show that the proposed method can effectively improve the economy of EH and the utilization efficiency of RES and flexibly meet different planning requirements, giving better engineering practicability.

Background This study aims to evaluate specific risk factors influencing prognosis of HIV-infected patients with toxoplasma encephalitis (TE) in order to develop a prognostic risk scoring system for them. Methods This is a six-center retrospective study of hospitalized HIV/TE patients. Data including six-week mortality after diagnosis, baseline characteristics, clinical features, laboratory tests and radiological characteristics of eligible patients were assimilated for risk model establishing. Results In this study, the six-week mortality among 94 retrospective cases was 11.7% (11/94). Seven specific risk factors, viz. time from symptom onset to presentation, fever, dizziness, CD4+ T-cell counts, memory deficits, patchy brain lesions, and disorders of consciousness were calculated to be statistically associated with mortality. A criterion value of ‘9’ was selected as the optimal cut-off value of the established model. The AUC of the ROC curve of this scoring model was 0.976 (p Conclusions The developed scoring system was established with simple risk factors, which also allows expeditious implementation of accurate prognostication, and appropriate therapeutic interventions in HIV-infected patients with TE.

As an important part of linear perspective, vanishing points (VPs) provide useful clues for mapping objects from 2D photos to 3D space. Existing methods are mainly focused on extracting structural features such as lines or contours and then clustering these features to detect VPs. However, these techniques suffer from ambiguous information due to the large number of line segments and contours detected in outdoor environments. In this paper, we present a new convolutional neural network (CNN) to detect dominant VPs in natural scenes, i.e., the Dominant Vanishing Point detection Network (D-VPnet). The key component of our method is the feature line-segment proposal unit (FLPU), which can be directly utilized to predict the location of the dominant VP. Moreover, the model also uses the two main parallel lines as an assistant to determine the position of the dominant VP. The proposed method was tested using a public dataset and a Parallel Line based Vanishing Point (PLVP) dataset. The experimental results suggest that the detection accuracy of our approach outperforms those of state-of-the-art methods under various conditions in real-time, achieving rates of 115fps., Comment: 18 pages, 6 figures, under review

In a multi-energy collaboration system, cooling, heating, electricity, and other energy components are coupled to complement each other. Through multi-energy coordination and cooperation, they can significantly improve their individual operating efficiency and overall economic benefits. Demand response, as a multi-energy supply and demand balance method, can further improve system flexibility and economy. Therefore, a multi-energy cooperative system optimization model has been proposed, which is driven by price-based demand response to determine the impact of power-demand response on the optimal operating mode of a multi-energy cooperative system. The main components of the multi-energy collaborative system have been analyzed. The multi-energy coupling characteristics have been identified based on the energy hub model. Using market elasticity as a basis, a price-based demand response model has been built. The model has been optimized to minimize daily operating cost of the multi-energy collaborative system. Using data from an actual situation, the model has been verified, and we have shown that the adoption of price-based demand response measures can significantly improve the economy of multi-energy collaborative systems.

We develop a bilevel model, which captures strategic decision making by plug-in electric vehicle (PEV) owners, to optimize the design of a PEV charging station with distributed energy resources. The upper level of the model determines the optimal configuration of the station and pricing schemes, whereas the lower level captures charging decisions by PEV owners. A robust formulation is employed to capture uncertain wholesale energy prices, renewable resource availability, and PEV flows. The resulting bilevel robust optimization model is transformed into an equivalent single-level optimization problem by replacing the lower level problem with Karush–Kuhn–Tucker optimality conditions. A column-and-constraint-generation algorithm is used to solve the resultant single-level problem. Results from a realistic case study and a parameter analysis demonstrate the effectiveness of the proposed model in capturing the impacts of uncertainty and self-interested behavior by PEV owners.

BACKGROUND: The impacts of chronic airway diseases on coronavirus disease 2019 (COVID-19) are far from understood. OBJECTIVE: To explore the influence of asthma and chronic obstructive pulmonary disease (COPD) comorbidity on disease expression and outcomes, and the potential underlying mechanisms in COVID-19 patients. METHODS: A total of 961 hospitalized COVID-19 patients with a definite clinical outcome (death or discharge) were retrospectively enrolled. Demographic and clinical information were extracted from the medical records. Lung tissue sections from patients suffering from lung cancer were used for immunohistochemistry study of angiotensin-converting enzyme II (ACE2) expression. BEAS-2B cell line was stimulated with various cytokines. RESULTS: In this cohort, 21 subjects (2.2%) had COPD and 22 (2.3%) had asthma. After adjusting for confounding factors, COPD patients had higher risk of developing severe illness (OR: 23.433; 95% CI 1.525-360.135; P

Uveal melanoma (UM) is a rare ocular tumor. The loss of BRCA1-associated protein 1 (BAP1) and the aberrant activation of G protein subunit alpha q (GNAQ)/G protein subunit alpha 11 (GNA11) contribute to the frequent metastasis of UM. Thus far, limited molecular-targeted therapies have been developed for the clinical treatment of UM. However, an increasing number of studies have revealed the close relationship between the ubiquitin proteasome system (UPS) and the malignancy of UM. UPS consists of a three-enzyme cascade, i.e. ubiquitin-activating enzymes (E1s); ubiquitin-conjugating enzymes (E2s); and ubiquitin-protein ligases (E3s), as well as 26S proteasome and deubiquitinases (DUBs), which work coordinately to dictate the fate of intracellular proteins through regulating ubiquitination, thus influencing cell viability. Due to the critical role of UPS in tumors, we here provide an overview of the crosstalk between UPS and the malignancy of UM, discuss the current UPS-targeted therapies in UM and highlight its potential in developing novel regimens for UM.

Mobile edge computing (MEC) has been envisioned as a promising technology for enhancing the computational capacities of mobile devices, by enabling computational task offloading. In this article, we employ massive multiple-input multiple-output methods to facilitate offloading in MEC. Our objective is to minimize the maximum delay for offloading and computing among the users, which requires a joint allocation of wireless and computational resources. Both perfect and imperfect channel state information (CSI) are considered. Under perfect CSI, we derive a semi-closed-form solution for the formulated problem. Under imperfect CSI, since the formulated problem is non-convex, we transform it into a convex one using a successive convex approximation technique and propose an iterative algorithm to solve it. Presented numerical results show the benefits of having a large number of antennas at the base station, and the necessity of performing joint radio and computational resource allocation.

Background Pneumocystis pneumonia (PCP) is a common acquired immune deficiency syndrome (AIDS)-related opportunistic infection. Recent reports estimate that more than 400,000 patients with human immunodeficiency virus (HIV) develop PCP each year globally. However, the timing of antiretroviral therapy (ART) initiation for HIV-infected patients with PCP is still controversial, and the benefits and risks of early initiation of ART are not completely clear. We thus designed this study in order to determine the optimal timing for ART initiation for HIV-positive patients with moderate to severe PCP. Methods This study will be an open-label, multi-centre, prospective randomised controlled trial. A total of 200 subjects will be randomly assigned to an early ART initiation group (≤14 days after PCP diagnosis) and a deferred ART initiation group (>14 days after PCP diagnosis) at a 1:1 ratio. All subjects will be followed up for 48 weeks after starting ART. The primary endpoint is incidence of disease progression (including new or relapsing opportunistic infections and death) at week 48. The secondary endpoints are the changes in CD4 counts from baseline at weeks 12, 24 and 48; the degree of virological suppression (HIV RNA Discussion We hope that the results of this study will reveal the optimal timing for initiation of ART in HIV-infected patients with moderate to severe PCP. Trial registration This trial was registered as one of the 12 trials under the name of a general project at chictr.org.cn on February 1, 2019. The registration number of the general project is ChiCTR1900021195.

Face alignment and cross-modal face transfer are two important tasks for automatic face analysis in computer vision. Over the years, they have been extensively studied. Recently, deep neural networks have attracted much research attention for both face alignment and face transfer. With the prevalence of the consumer depth sensor, depth-based face alignment and cross-modal (image and depth) are increasingly important. Different from existing RGB- image based tasks, the main challenge of depth-based tasks is the lack of annotated data. To address the challenge, we observe that these two tasks are closely related and their learning processes may benefit each other. This paper develops a joint multi-task learning algorithm for both depth-based face alignment and face transfer using the deep convolutional neural network (CNN). The proposed approach allows the CNN model to simultaneously share visual knowledge and information between two tasks. We use a dataset of 10,000 face depth images for validation. Our experiments show that the proposed approach outperforms state-of-the-art algorithms. The results also show that learning these two related tasks simultaneously improves the performance of each individual task.

The Chinese Government has issued numerous regulations that significantly affect the number of photovoltaic (PV) installations in the country and the subsidies for their use. This article summarizes the internal and external environment of China's PV industry and describes its future trends and prospects and also discusses a proposed rate-making process and renewable energy supervision.

This article proposes a new methodological framework to optimize the planning design of an electric vehicle (EV) charging station with renewable energy resources (RCS). Differing from extant studies, the proposed approach explicitly considers the strategic self-interested nature of EV users under a fully liberalized market environment and aims at fully exploiting the flexibility of EV charging loads to enhance the efficiency of renewable energy utilization. Such a problem has been formulated as a bilevel programming model with equilibrium constraints. In this formulation, the upper level problem determines the optimal configuration of the RCS and its operation/pricing schemes simultaneously to maximize the total profits of RCS owner, while the lower level models the strategic charging decisions of EV users in response to the provided pricing scheme of RCS owner. The resulting bilevel optimization model is reformulated into an equivalent single-level linear program, by replacing the lower level problem with Karush–Kuhn–Tucker conditions and linearizing the bilinear products via McCormick relaxation and Big-M technique. The simulation results from case studies demonstrate the effectiveness of the proposed methodology.

Background In countries in East Asia, the typical treatment for curable gastric cancer is gastrectomy with D2 lymphadenectomy. However, whether D2 lymphadenectomy is beneficial for high-risk N3 node disease remains controversial. We conducted a multi-institution retrospective study on patients with high-risk, locally advanced gastric cancer. To compare the rates of disease-free survival (DFS) and overall survival (OS) between radical D2-type gastric resection and lymphadenectomy and the more limited D1 type resection and lymphadenectomy. Methods From July 2010 to June 2015, 74 patients out of 949 who underwent curative-intent R0 surgery were selected in pairs to compare the survival outcomes between those who underwent radical D2 type (n=37) vs. the more limited D1 type (n=37) gastric resection and lymphadenectomy. Results The median DFS was 9.72 and 7.81 months for the D2 and D1 types, respectively (P=0.746), and the OS was 16.39 and 15.85 months for the D2 and D1 types, respectively (P=0.937). Conclusions No statistically significant differences in DFS and OS were noted between D1 and D2 procedures for those with N3 disease. Our results support the hypothesis that a novel multidisciplinary approach rather than a surgical approach alone is needed to improve the survival outcomes of high-risk patients with N3 gastric cancer.

The geometric design, meshing performance, and mechanical behavior of pure rolling helical gear drives are presented. Parametric equations for contact curves on the pinion and gear are determined by coordinate transformation of the active designed pure rolling meshing line for the whole cycle of meshing. Moreover, parametric equations for the tooth surfaces of helical gears with convex-to-convex meshing type are derived according to the motion of generatrices in the transverse section along the calculated contact curves. Then, the basic design parameters are analyzed and formulas for calculation of the geometric size are given. The meshing performance and mechanical behavior, including contact patterns, loaded function of transmission errors, and variation of stresses for two pitch angles of meshing are compared with those of a reference design of micro-geometry modified involute helical gears. Besides, the influence of basic design parameters on tooth contact analysis and stress analysis is studied. The analysis of the results shows that the proposed pure rolling helical gears have the advantage of reducing the relative sliding between tooth surfaces and the possibility of designing pure rolling helical gears with a small number of teeth, though the contact strength of the surfaces is impaired. However, if the appropriate design parameters and Hermite curve parameters for the fillets are properly evaluated as proposed here, the mechanical behavior of the proposed pure rolling helical gear drive, in terms of contact patterns and variation of bending stresses can be superior to that of the micro-geometry modified involute helical gear drives.

Photonic nanojets (PNJs) generated from a single microsphere illuminated by higher-order radially polarized (RP) beams are investigated. The effects of the size parameters of higher-order RP beams, the refractive index, and radius of the dielectric microsphere on the full width at half-maximum and peak intensity of the PNJ are numerically discussed and qualitatively interpreted. The results show that the minimal width of the PNJ can be obtained by optimally adjusting the size parameter. The PNJ beam waist becomes gradually narrower with increasing the radial mode number. As compared to the case of plane wave illumination, sharper PNJs are more easily generated when irradiated by a higher-order RP beam, even for microspheres with lower refractive indices or larger radii. Our findings can promote potential applications of PNJs in a variety of fields including super-resolution microscopy, nanolithography, and optical data storage.

Chronic rhinosinusitis (CRS) is a highly heterogeneous disorder exhibiting considerable epidemiological, clinical, and immunopathological variations across patients with distinct ethnic backgrounds and in different geographical locations. Asian CRS patients present less eosinophilic and type 2 (T2) inflammation, but more prominent neutrophilic inflammation compared to patients in Western countries. Although several biologics targeting important elements of T2 inflammation, such as IL-4, IL-5, IL-13, and IgE, demonstrate promising benefit for Caucasian patients with recurrent nasal polyps, their efficacy in Asian patients remain poorly defined. The distinct endotypes in Asian patients warrant the identification and selection of patients who would benefit from T2 biologics in Asian countries. Additionally, developing novel treatments targeting neutrophilic, type 1, and type 3 inflammation may benefit approximately 50% of Asian CRS patients with non-T2 inflammation. In this review, we summarized and discussed recent progress in the study of Asian CRS endotypes in comparison to those in patients in Western countries, and the methods of identifying Asian patients with eosinophilic or T2 inflammation. T2 biologic treatment of Asian CRS patients, potential therapeutic candidates targeting non-T2 inflammation in Asian CRS patients, and the progress on developing other T2 biologics were discussed.

Speckle imposes obvious limitations on resolving capabilities of optical coherence tomography (OCT), while speckle-modulating OCT can efficiently reduce speckle arbitrarily. However, speckle-modulating OCT seriously reduces the imaging sensitivity and temporal resolution of the OCT system when reducing speckle. Here, we proposed a deep-learning-based speckle-modulating OCT, termed Sm-Net OCT, by deeply integrating conventional OCT setup and generative adversarial network trained with a customized large speckle-modulating OCT dataset containing massive speckle patterns. The customized large speckle-modulating OCT dataset was obtained from the aforementioned conventional OCT setup rebuilt into a speckle-modulating OCT and performed imaging using different scanning parameters. Experimental results demonstrated that the proposed Sm-Net OCT can effectively obtain high-quality OCT images without the electronic noise and speckle, and conquer the limitations of reducing the imaging sensitivity and temporal resolution which conventional speckle-modulating OCT has. The proposed Sm-Net OCT can significantly improve the adaptability and practicality capabilities of OCT imaging, and expand its application fields.

Recently, we have witnessed a boom in applications for 3D talking face generation. However, most existing 3D face generation methods can only generate 3D faces with a static head pose, which is inconsistent with how humans perceive faces. Only a few papers focus on head pose generation, but even these ignore the attribute of personality. In this paper, we propose a unified audio-driven approach to endow 3D talking faces with personalized pose dynamics. To achieve this goal, we establish an original person-specific dataset, providing corresponding head poses and face shapes for each video. Our framework is composed of two separate modules: PoseGAN and PGFace. Given an input audio, PoseGAN first produces a head pose sequence for the 3D head, and then, PGFace utilizes the audio and pose information to generate natural face models. With the combination of these two parts, a 3D talking head with dynamic head movement can be constructed. Experimental evidence indicates that our method can generate person-specific head pose sequences that are in sync with the input audio and that best match with the human experience of talking heads.

Calciphylaxis is a rare disease characterized histologically by microvessel calcification and microthrombosis, with high mortality and no proven therapy. We reported a severe uremic calciphylaxis patient with progressive skin ischemia, large areas of painful malodorous ulcers and mummified legs. Because of her rapid progression and refractory to conventional therapy, human amnion-derived mesenchymal stem cells (hAMSCs) treatment was approved. Establishment and release inspection of hAMSCs, efficacy and safety assessment including cytokines secretory ability, immunocompetence, tumorigenicity and genetics analysisin vitrowere introduced. We further performed acute and long-term hAMSC toxity evaluations in C57BL/6 mice/rats, abnormal immune response tests in C57BL/6 mice and tumorigenic tests in the neonatal NU nude mice. After pre-clinical research, she was treated by hAMSCs with intravenous and local intramuscular injection and external supernatants application to her ulcers. When followed up to 15 months, her blood-based markers of bone and mineral metabolism were improved, with regeneration of skin soft tissue and a more favorable profile of peripheral blood mononuclear cells. Skin biopsy after 1 month treatment showed vascular regeneration with mature non-calcified vessels within dermis and 20 months later re-epithelialization restored the integrity of damaged site. No infusion or local treatment related adverse events occurred. To the best of our knowledge, this is the first evidence for the clinical use of hAMSCs. These findings suggest hAMSCs warrant further investigation as a potential regenerative treatment for uremic calciphylaxis with effects of inhibiting vascular calcification, stimulating angiogenesis and myogenesis, anti-inflammatory and immune modulation, multi-differentiation, re-epithelialization and restorage of integrity.

Tumor-associated antigens (TAAs) have been tested in various clinical trials in cancer treatment but the patterns of specific T cell response to personalized TAA immunization remains to be fully understood. We report antigen-specific T cell responses in patients immunized with dendritic cell vaccines pulsed with personalized TAA panels. Tumor samples from patients were first analyzed to identify overexpressed TAAs. Autologous DCs were then transfected with pre-manufactured mRNAs encoding the full-length TAAs, overexpressed in the patients' tumors. Patients with glioblastoma multiforme (GBM) or advanced lung cancer received DC vaccines transfected with personalized TAA panels, in combination with low-dose cyclophosphamide, poly I:C, imiquimod and anti-PD-1 antibody. Antigen-specific T cell responses were measured. Safety and efficacy were evaluated. A total of ten patients were treated with DC vaccines transfected with personalized TAA panels containing 3-13 different TAAs. Among the seven patients tested for anti-TAA T cell responses, most of the TAAs induced antigen-specific CD4+ and/or CD8+ T cell responses, regardless of their expression levels in the tumor tissues. No Grade III/IV adverse events were observed among these patients. Furthermore, the treated patients were associated with favorable overall survival when compared to patients who received standard treatment in the same institution. Personalized TAA immunization-induced-specific CD4+ and CD8+ T cell responses without obvious autoimmune adverse events and was associated with favorable overall survival. These results support further studies on DC immunization with personalized TAA panels for combined immunotherapeutic regimens in solid tumor patients.Trial registration ClinicalTrials.gov, NCT02709616 (March, 2016), NCT02808364 (June 2016), NCT02808416 (June, 2016).