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With the massive deployment of the Internet of things (IoT) devices, many data analysis applications emerge for the large amount of data accumulated by IoT. Federated learning (FedL) on IoT devices is an appealing mode to train a precise data analysis model. However, existing federated learning schemes either take expensive computation costs (e.g., public key cryptographic operations) or a large number of interactions among participants. Obviously, these schemes are unsuitable for IoT devices due to the limited computational and communication resources. In this work, we propose a lightweight privacy-preserving federated learning scheme for IoT devices. To protect the privacy of individual local data, we add masks to intervening parameters. An effective secret sharing scheme is adopted to ensure that masks can be eliminated accurately. Considering that FedL involves multiple iterations and mask generation for each iteration costs a large number of interactions among users for privacy guarantee, we also design a secure mask reusing mechanism for large-scale FedL tasks. We prove that our scheme is secure against the honest-but-curious model. In addition, we also expand our scheme to deal with the collusion attack. Extensive experiments on real IoT devices demonstrate the accuracy and efficiency of our work.

Nowadays, online spamming has already been a remarkable threat to contents security of Internet of Things (IoT). Due to constant technical progress, online spamming activities have been more and more concealed. This brings much fuzziness to spammer detection scenarios, yielding the issue of fuzzy detection of spammers. Although existing detection techniques for spammers utilized idea of deep learning, they still ignore to release power of label spaces. As real nature about a user may be usually fuzzy, but the label annotated for a user is always certain. To remedy such gap, this paper proposes a label smoothing-based fuzzy detection method for spammers (Fuz-Spam). First of all, deep representation is still utilized to deeply fuse features, which acts as the foundation of neural computing. On this basis, generative adversarial learning is introduced to transform previous label spaces into distributed forms. In addition, two groups of experiments are carried out on two real-world datasets for evaluation. The results demonstrate that the Fuz-Spam improves identification efficiency about 10% to 20% than previous ones, and that the Fuz-Spam is endowed with proper stability.

In the last decade, a number of severe urban power outages have been caused by extreme natural disasters, e.g., hurricanes, snowstorms and earthquakes, which highlights the need for rethinking current planning principles of urban energy systems and expanding the classical reliability-oriented view. In addition to being reliable to low-impact and high-probability outages, power system should also have high level of resilience to withstand high-impact and low-probability (HILP) events. Compared with power system, multi-energy systems (MESs) have advantages in improving resilience through energy shifting across multiple energy sectors, a variety of generalized energy storage resources and thermal inertia of heat/cooling loads. This paper proposes a resilience-oriented planning method to determine optimal configuration of distribution level MES, e.g., urban energy supply systems, considering comprehensive impacts from supply, network and demand sides in MES. Impacts of energy shifting at supply side, pipe storage at network side and thermal inertia at demand side are described in the same linear modeling framework using energy hub (EH) model. Generalized energy storage resources including centralized and distributed energy storage devices, pipe network storage and building heat capacity are all modeled into centralized energy storage to facilitate an efficient configuration planning of MES.

Identification of transmit-antenna number is of importance in cognitive Internet of Things with multiple-input multiple-output (MIMO). Previous studies on transmit-antenna number detection only consider Gaussian noise and ignore impulsive interference. In the practical wireless communication, impulsive interference may exist due to low-frequency atmospheric noise, multiple access and electromagnetic disturbance. Such interference can usually be modeled as symmetric alpha stable (SαS), which cause the performance degradation of conventional algorithms based on Gaussian model. In this paper, we present a novel scheme to detect the transmit-antenna number for MIMO systems in cognitive Internet of Things, assuming that signals are corrupted by both SαS interference and Gaussian noise. We first introduce a new approach to characterize the generalized correlation matrix, and provide its bound with SαS interference. Then, the discriminating feature vector is constructed by utilizing the higher-order moments (HOM) of eigenvalues of the generalized correlation matrix. Finally, an advanced clustering algorithm is employed to detect the transmit-antenna number, using the cluster where the minimum eigenvalue is located. The proposed algorithm avoids the need for a priori information about the transmitted signals, such as coding mode, modulation type and pilot patterns. Simulation experiments demonstrate the feasibility of the proposed transmit-antenna number detection scheme in MIMO systems with Gaussian noise and SαS interference.

Internet of Things (IoT) is vulnerable to various cyber attacks due to the massive deployment of IoT devices and the openness of wireless environments. In this paper, taking IoT devices as the network resources competed between an attacker and a defender, we study the modeling and analysis of network resource competition in an attack-defense game. The attacker and defender inject different competition strength in each IoT device as their strategies. As a result, the security state of each IoT device will change, which is captured by differential equations. To study the interaction between the attacker and defender and the evolution of the system security states, a zero-sum differential game is formulated by modeling the competition of IoT devices. To achieve the equilibrium of the formulated differential game, optimal control theory is employed to solve the optimization problems of players. Further, a Gauss-Seidel-like implicit finite-difference method is utilized to obtain the saddle point strategy. Finally, numerical results are provided to demonstrate the evolution of network resource competition between the attacker and defender. The results show that our formulated model can effectively and accurately characterize the evolution of the system security states with strategic interactions between the attacker and defender.

With the increase of mining depth of coal seam, the impact pressure has severely restricted the coal mine safe and efficient mining, drilling pressure is a kind of through the construction of borehole in the coal seam, achieve the purpose of partial discharge, pressure relief effect is very obvious, the larger the aperture effect of pressure, the better, by comparing the 150 mm in diameter and 300 mm diameter CT detection construction surrounding rock displacement change. Compare the pressure relief effect of 150 mm drilling and 300 mm drilling.

As a lightweight, flexible and high-performance operating system virtualization, containers are used to speedup the big data platform. However, due to the imperfection of resource isolation mechanism and property of shared kernel, the meltdown and spectre attacks can lead to the information leakage of kernel space and co-resident containers. In this paper, a noise-resilient and real-time detection system, named ContainerGuard, is proposed to detect meltdown and spectre attacks in container-based big data platform. ContainerGuard uses a non-intrusive manner to collect lifecycle multivariate time-series performance events data of processes in containers, then uses ensemble of variational autoencoders as generative neural networks to learn the robust representations of normal patterns. Therefore, ContainerGuard meets the urgent need for information protection in container-based big data platform. Our evaluations using real-world datasets show that ContainerGuard achieves excellent detection performance, only introduces about 4.5% of running performance overhead to the platform.

Background and Aims We explored the associations among fruit consumption, physical activity and their dose-response relationship with all-cause and cardiovascular disease (CVD) mortality in type 2 diabetic patients. Methods and Results We prospectively followed 20,340 community-dwelling type 2 diabetic patients aged 21-94 years. Information on diets and physical activity was collected using standardized questionnaires. All-cause and CVD mortality were assessed. Hazard ratios (HRs) for all-cause mortality were estimated with Cox regression models, and HRs for CVD mortality were derived from a competing risk model. Restricted cubic spline regression was used to analyse dose-response relationships. We identified 1,362 deaths during 79,844 person-years. Compared to non-consumption, fruit consumption >42.9 g/d was inversely associated with all-cause mortality (HR 0.76; 95% CI 0.64-0.88), CVD mortality (HR 0.69, 0.51-0.94) and stroke mortality (HR 0.57, 0.36-0.89), but not with heart disease mortality (HR 0.93, 0.56-1.52). The HRs comparing the top vs bottom physical activity quartiles were 0.44 (0.37-0.53) for all-cause mortality, 0.46 (0.33-0.64) for CVD mortality, 0.46 (0.29-0.74) for stroke mortality and 0.51 (0.29-0.88) for heart disease mortality. Lower fruit consumption combined with a lower physical activity level was associated with a greater mortality risk. A nonlinear threshold of 80 g fruit/day was identified, all-cause mortality risk was reduced by approximately 24% at this value. A physical activity threshold of 8 metabolic equivalents (MET) h/day was also identified, after which the risk of mortality did not decrease. Conclusions Fruit consumption and physical activity may reduce all-cause, CVD and stroke mortality in type 2 diabetic patients.

Background Janus kinase (JAK) inhibitors have shown encouraging results in the treatment of alopecia areata (AA), an autoimmune form of hair loss, in small, uncontrolled studies and case reports. Objective To conduct a biopsy sub-study during the randomized, double-blind, placebo-controlled first 24 weeks of a phase 2a clinical trial that evaluated the efficacy and safety of ritlecitinib, an inhibitor of JAK3 and the tyrosine kinase expressed in hepatocellular carcinoma (TEC) kinase family, and brepocitinib, an inhibitor of tyrosine kinase 2 (TYK2)/JAK1 in the treatment of AA. Methods Change in biomarkers in lesional scalp biopsies between baseline and weeks 12 and 24 was an exploratory endpoint, and 46 patients participated from the ritlecitinib (n = 18), brepocitinib (n = 16), and placebo (n = 12) groups. Correlations of biomarkers with hair regrowth, measured using the Severity of Alopecia Tool (SALT) score, were also evaluated. Results At week 24, both ritlecitinib and brepocitinib demonstrated improvement exceeding 100% in the lesional scalp transcriptome toward a nonlesional profile. At week 12, the improvements in scalp tissue were greater with brepocitinib than ritlecitinib; however, at week 24, the improvements were greater with ritlecitinib. Conclusions For both ritlecitinib and brepocitinib, improvement in the SALT scores was positively associated with expression of Th1 markers and negatively associated with expression of hair keratins. Larger, long-term clinical trials are warranted.

Myocardial infarction (MI), as one of the leading causes of global death, urgently needs effective therapies. Recently, hydrogen sulfide (H2S) has been regarded as a promising therapeutic agent for MI, while its spatiotemporally controlled delivery remains a major issue limiting clinical translation. To address this limitation, we designed and synthesized a novel H2S donor (HSD-R) that can produce H2S and emit fluorescence in response to reactive oxygen species (ROS) highly expressed at diseased sites. HSD-R can specifically target mitochondria and provide red fluorescence to visualize and quantify H2S release in vitro and in vivo. Therapeutically, HSD-R significantly promoted the reconstruction of cardiac structure and function in a rat MI model. Mechanistically, myocardial protection is achieved by reducing cardiomyocyte apoptosis, attenuating local inflammation, and promoting angiogenesis. Furthermore, inhibition of typical pro-apoptotic genes (Bid, Apaf-1, and p53) played an important role in the anti-apoptotic effect of HSD-R to achieve cardioprotection, which were identified as new therapeutic targets of H2S against myocardial ischemia injury. This ROS-responsive, self-immolative, and fluorescent H2S donor can serve as a new theranostic agent for MI and other ischemic diseases., Graphical abstract Image 1, Highlights • A reactive oxygen species-responsive and self-reporting H2S donor (HSD-R) is developed for controlled H2S delivery. • HSD-R shows desirable fluorescence for imaging H2S release upon triggering by reactive oxygen species. • HSD-R displays significant cardioprotective effects in rats. • HSD-R exhibits multiple biological activities including anti-apoptotic, anti-inflammatory, and pro-angiogenic effects. • Anti-apoptotic activity of HSD-R is due to inhibiting the expression of several pro-apoptotic factors.

Frontal fibrosing alopecia (FFA) is a progressive, scarring alopecia of the frontotemporal scalp that poses a substantial burden on quality of life. Large-scale global profiling of FFA is lacking, preventing the development of effective therapeutics.To characterize FFA compared to normal and alopecia areata using broad molecular profiling and to identify biomarkers linked to disease severity.This cross-sectional study assessed 33,118 genes in scalp using RNA sequencing and 350 proteins in serum using OLINK high-throughput proteomics. Disease biomarkers were also correlated with clinical severity and a fibrosis gene set.Genes differentially expressed in lesional FFA included markers related to Th1 (IFNγ/CXCL9/CXCL10), T-cell activation (CD2/CD3/CCL19/ICOS), fibrosis (CXCR3/FGF14/FGF22/VIM/FN1), T-regulatory (FOXP3/TGFB1/TGFB3), and Janus kinase/JAK (JAK3/STAT1/STAT4) (Fold changes [FCH]1.5, FDR.05 for all). Only one protein, ADM, was differentially expressed in FFA serum compared to normal (FCH1.3, FDR.05). Significant correlations were found between scalp biomarkers (IL-36RN/IL-25) and FFA severity, as well as between JAK/STAT and fibrosis gene-sets (r.6; P .05).This study was limited by a small sample size and predominantly female FFA patients.Our data characterize FFA as an inflammatory condition limited to scalp, involving Th1/JAK skewing, with associated fibrosis and elevated T-regulatory markers, suggesting the potential for disease reversibility with JAK/STAT inhibition.

High penetration of renewable energy brings seasonal electricity imbalance in the power system and results in considerable energy curtailment. Such large scale curtailed electricity could be used to produce hydrogen via Power-to-Hydrogen (P2H) technology. The introduction of P2H would largely affect the configuration of both power grid and the hydrogen supply chain (HSC). This paper proposes a coordinated planning model of power system generation and transmission (GT) as well as HSC with transportable seasonal hydrogen storage. A co-planning model is put forward to investigate the optimal configuration of infrastructures in the GT-HSC and their coupling during operation. The model is formulated as LP model so that the year-round energy balance of seasonal hydrogen storage with daily resolution could be considered. Case studies on 5-bus PJM system and Northwest China HRP-38 system demonstrate the effectiveness of the proposed model. Results of the case study also illustrate the benefits of incorporating GT with HSC to reduce the renewable energy curtailment and bring down the energy supply cost.

Rationale: The development of novel therapies for idiopathic pulmonary fibrosis (IPF) has brought increased attention to the population burden of disease. However, little is known about the epidemi...

A wideband millimeter-wave magneto-electric dipole-like array with low-profile is presented in this letter. The unit is fed by twin lines located on the same dielectric plate as the shorted patches, making the antenna have a low profile (0.13 M). Furthermore, by using the butterfly patch and staggered arrangement, the impedance bandwidth of the designed unit can cover 23.946.75 GHz (64.7%), and the broadside gain is 5.51.1 dBi. Then, a power divider based on the printed ridge gap waveguide (PRGW) is used to excite the 14 array. Finally, the designed array is manufactured and tested. The measured results are in good agreement with the simulated ones, showing that the working bandwidth of the array combined with the feed network can cover 24.540 GHz (48%), while the broadside gain is 7.7312.65 dBi.

With the prevalence of smart appliances, smart meters, and Internet of Things (IoT) devices in smart grids, artificial intelligence (AI) built on the rich IoT big data enables various energy data analysis applications and brings intelligent and personalized energy services for users. In conventional AI of Things (AIoT) paradigms, a wealth of individual energy data distributed across users’ IoT devices needs to be migrated to a central storage (e.g., cloud or edge device) for knowledge extraction, which may impose severe privacy violation and data misuse risks. Federated learning, as an appealing privacy-preserving AI paradigm, enables energy data owners (EDOs) to cooperatively train a shared AI model without revealing the local energy data. Nevertheless, potential security and efficiency concerns still impede the deployment of federated-learning-based AIoT services in smart grids due to the low-quality shared local models, non-independently and identically distributed (non-IID) data distributions, and unpredictable communication delays. In this article, we propose a secure and efficient federated-learning-enabled AIoT scheme for private energy data sharing in smart grids with edge-cloud collaboration. Specifically, we first introduce an edge-cloud-assisted federated learning framework for communication-efficient and privacy-preserving energy data sharing of users in smart grids. Then, by considering non-IID effects, we design a local data evaluation mechanism in federated learning and formulate two optimization problems for EDOs and energy service providers. Furthermore, due to the lack of knowledge of multidimensional user private information in practical scenarios, a two-layer deep reinforcement-learning-based incentive algorithm is developed to promote EDOs’ participation and high-quality model contribution. Extensive simulation results show that the proposed scheme can effectively stimulate EDOs to share high-quality local model updates and improve the communication efficiency.

For the integrated energy system (IES) formed by a cluster of energy hubs (EHs), the outputs of EHs and system parameters, which greatly influence the security performance, should be properly adjusted. In this article, an event-triggered distributed hybrid control scheme is proposed to achieve security and economic operation for the IES. First, the EH output control is designed based on the features of energy networks as well as the containment and consensus algorithms. According to the control of outputs, the electricity and heat load power can be accurately shared without knowing the network parameters. Second, the pressure is bounded within an acceptable range, and the frequency is reverted to the reference value by implementing the proposed control method. Third, an event-triggered communication strategy is employed to design the corresponding protocols, resulting in reduced communication cost. Finally, the control of devices based on the equal incremental principle is proposed to achieve the minimal economic cost for each EH with considering energy prices. The results of numerical case studies are presented to validate the performance of the proposed control method.

BACKGROUND: The morphological alterations of small pulmonary vessels measured by computed tomography (CT) is increasingly used in evaluation of suspected pulmonary hypertension (PH). OBJECTIVE: To investigate the significance alterations of quantitative assessment of small pulmonary vessels on chest CT in distinguishing different types of PH and their severity. METHODS: We retrospectively analyzed a dataset of 120 healthy controls (HCs) and 91 PH patients, including 34 patients with connective tissue diseases-related PH (CTD-PH), 26 patients with idiopathic pulmonary arterial hypertension (iPAH), and 31 patients with chronic obstructive pulmonary disease-related PH (COPD-PH). The CTD-PH patients were divided into mild to moderate PH (CTD-LM-PH) group (n = 17) and severe PH (CTD-S-PH) group (n = 17). A total of 53 CTD patients without PH (CTD-nPH) were enrolled for comparison with the CTD-PH. We measured the cross-sectional area of small pulmonary vessels

The increasing integration of variable renewable energy increases the demand for a power system’s frequency regulation resources. The decreasing share of controllable power generators makes the frequency regulation resources insufficient. Simultaneously, distributed energy resources (DER) on the distribution network side, such as storage devices, electric vehicles, and controllable building energy systems, have excellent potential to provide frequency support. However, power system dispatchers cannot control these resources in the same manner as conventional power sources. These sources have power output uncertainties and cannot directly access power system communication networks. This paper proposes a cloud-edge collaboration and wireless communication coordination framework to facilitate DER frequency regulations. The cloud balances the power uncertainty of controllability in edges together with computing burden, wire, and wireless communication loads. Also, the edges adjust storage’s power outputs to achieve frequency regulations and covering PV power fluctuations. A numerical case study demonstrates the effectiveness of the proposed method. The solution holds 95.5% frequency regulation adjustable power capacity of theoretical maximum on average. Meanwhile, the computing costs reduce by 36.3%, and wire communication only operates in 0.35% of a day as a supplement (reduces by 99.65%).

Tone mapping operators (TMOs) can compress the range of high dynamic range (HDR) images so that they can be displayed normally on the low dynamic range (LDR) devices. Recent TMOs based on deep neural networks can produce impressive results, but there are still some shortcomings. On the one hand, their supervised learning procedure requires a high-quality paired dataset which is hard to be accessed. On the other hand, they are too slow and heavy to meet the needs of practical applications. This paper proposes a real-time deep semi-supervised learning TMO to solve the above problems. The proposed method learns in a semi-supervised manner by combining the adversarial loss, cycle consistency loss, and the pixel-wise loss. The first two can simulate the image distributions in the real world from the unpaired LDR data and the latter can learn the guidance of paired LDR labels. In this way, the proposed method only requires HDR sources, unpaired high-quality LDR images, and a few well tone-mapped HDR-LDR pairs as training data. Furthermore, the proposed method divides tone mapping into luminance mapping and saturation adjustment and then processes them simultaneously. By this strategy, we can reconstruct each component more precisely. Based on the aforementioned improvements, we propose a lightweight tone mapping network that is efficient in tone mapping task (up to 5000x parameters-saving and 27x time-saving compared to the learning-based TMOs). Both quantitative and qualitative results demonstrate that the proposed method performs favorable against state-of-the-art TMOs.

Background The symptoms that patients with major depressive disorder (MDD) experience are the dominant contributing factors to its heavy disease burden. This study sought to identify key symptoms leading to disability in patients with MDD. Methods Subjects consisted of patients who had a 12-month MDD diagnosis based on the China Mental Health Survey (CMHS). World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) was used to assess the degree of disability. The associations between depressive symptoms and disability were analyzed using a linear regression and logistic regression with a complex sampling design. Results Of the 32,552 community residents, 655 patients were diagnosed with 12-month MDD. The disability rate due to MDD was 1.06% (95% CI: 0.85%–1.28%) among adults in Chinese community and 50.7% (95% CI: 44.3%–57.1%) among MDD patients. Depression was associated with all functional losses measured by the WHODAS. Feelings of worthlessness in life or inappropriate guilt, and psychomotor agitation or retardation were the key symptoms related to disability. Economic status, co-morbidity of physical diseases or anxiety disorders were correlates of disability scores. Limitations The disability rate might be underestimated due to the exclusion of MDD patients living in hospitals. The effect of treatments on disability was excluded. Conclusions Psychological symptoms, not somatic symptoms, contribute to disability in MDD patients. Disability worsens when physical diseases or anxiety disorders are present. More attention could be paid to psychological symptoms, physical diseases, and anxiety disorders in MDD patients with disabilities.

In disaster areas, a large amount of data (e.g., rescue commands, road damage, and rescue experience) should be delivered among ground rescuing vehicles for safe driving and efficient rescue. When communication infrastructures are destroyed by disasters, unmanned aerial vehicles (UAVs) can be employed to perform immediate rescue missions in destroyed areas and assist data sharing for ground Internet of vehicles (IoV). However, in such UAV-assisted IoV under disaster situation, there exist potential security threats on data sharing among vehicles and UAVs because of the untrusted network environment, unreliable misbehavior tracing, and low-quality shared data. To address these issues, in this paper, we develop a lightweight vehicular blockchain-enabled secure (LVBS) data sharing framework in UAV-aided IoV for disaster rescue. Firstly, we propose a novel UAV and blockchain-assisted collaborative aerial-ground network architecture in disaster areas. Secondly, we develop a credit-based consensus algorithm in the lightweight vehicular blockchain to securely and immutably trace nodes' behaviors and record data transactions with improved efficiency and security in reaching consensus. Thirdly, since each node has no explicit knowledge of the whole network, we develop reinforcement learning-based algorithms to optimally schedule the pricing and quality of data sharing strategies for both data contributor and data consumer via trial and error. Finally, extensive simulations are conducted, which demonstrate that LVBS can effectively improve the security of consensus phase and promote high-quality data sharing.

Target detection in a high-frequency surface-wave radar (HFSWR) system is a challenging task because radar returns are strongly polluted by various sources of interference. To address the detection problem, this letter presents an automatic detection method based on multidirection dictionary learning for HFSWR. First, we perform clutter identification and statistical analysis to become aware of the time-varying clutter environment. The analysis of real data shows that the clutter in an HFSWR system has spatial correlations and geometric directions. Second, motivated by this information, we design a multidirection dictionary learning-based constant false alarm rate (MDDL-CFAR) detector in which the spatial and geometric direction information is well represented by multidirectional dictionaries. The MDDL-CFAR can simultaneously learn dictionaries and estimate clutter statistics to adaptively set the detection threshold. Experimental results on HFSWR data sets demonstrate the effectiveness of the proposed detection method.

The coronavirus disease 2019 (COVID-19) pandemic is a global public health event. Wuhan used to be the epicenter of China and finally controlled the outbreak through city lockdown and many other policies. However, the pandemic and the prevention strategies had a huge impact on the medical care procedures for breast cancer patients, leading to the delay or interruption of anticancer therapies. To better serve breast cancer patients under the premise of epidemic control, many strategies have been proposed and optimized in our center. One of the most important parts of these strategies is the promotion of telemedicine, including online consultation, online prescription, and drug mailing services. In keeping with the city and hospital policies, we have also introduced stricter ward management policies and more precise care. Here, we collected the diagnosis and treatment process of breast cancer patients in our center during the COVID-19 pandemic, which was found to be correlated to a reduction in chemotherapy-related myelosuppression and hepatic dysfunction, hoping to provide a reference for other cancer centers that may suffer from the similar situation. MicroAbstract The COVID-19 pandemic has a huge impact on the medical care procedures for breast cancer patients. Here we share our experience in the management of breast cancer patients during the pandemic in Wuhan, and it is found to be correlated to a reduction in chemotherapy-related myelosuppression and hepatic dysfunction, which may provide a reference for other centers.

The high penetration of variable renewable energy requires flexibility from both the generation and demand sides. This raises the necessity of modeling stochastic and flexible energy resources in power system operation. However, some distributed energy resources have both stochasticity and flexibility, e.g., prosumers with distributed photovoltaics and energy storage, and plug-in electric vehicles with stochastic charging behavior and demand response capability. Such partly controllable participants pose challenges to modeling the aggregate behavior of large numbers of entities in power system operation. This paper proposes a new perspective on the aggregate modeling of such energy resources in power system operation. Specifically, a unified controllability-uncontrollability-decomposed model for various energy resources is established by modeling the controllable and uncontrollable parts of energy resources separately. Such decomposition enables straightforward aggregate modeling of massive energy resources with different controllability by integrating their controllable components using linking constraints and uncontrollable components using dependent discrete convolution. Furthermore, a two-stage stochastic unit commitment model based on the proposed model for power system operation is established. The proposed model is tested using a three-bus system and real-size Qinghai provincial power grid. The result shows that this model is able to characterize with high accuracy the aggregate behavior of massive energy resources with different controllability so that their flexibility can be fully explored.

The linear frequency‐modulated frequency agile radar (FAR) can be used for synthesizing high‐range‐resolution profiles (HRRP), and also has excellent electronic counter‐countermeasures performance. Similar to the Newtonalized orthogonal matching pursuit (NOMP) for line spectral estimation problem, the NOMP for the FAR radar termed as NOMP‐FAR is designed to process each coarse range bin to extract the HRRP and velocities of multiple targets, including the guide for determining the oversampling factor and the stopping criterion. In addition, it is shown that the target will cause false alarm in the nearby coarse range bins, and a post‐processing algorithm is proposed to suppress the ghost targets. Numerical simulations are conducted to demonstrate the effectiveness of NOMP‐FAR.

The development of deep learning has brought new prospects into the field of hyperspectral classification, and the classification ability of this method for the classification of hyperspectral images (HSIs) has been continuously improved. However, there are still some problems that must be solved; for example, the spectral-spatial features of HSIs are not effectively extracted, the labeled samples in the data set are limited, and the number of samples in different categories is imbalanced. To facilitate the progress of hyperspectral classification, a spectral-spatial fractal residual convolutional neural network with data balance augmentation is proposed here. In this network, a data balance augmentation approach that can solve the problems of limited labeled data and imbalanced categories is proposed. In addition, the spectral-spatial residual module is proposed to learn the spectral-spatial information and alleviate the problem of model degradation effectively. In addition, the spectral-spatial focal structure, which can guarantee the integrity of the information, is introduced. Moreover, the spectral-spatial dimensional transformation module, which can reduce the size and number of hyperspectral feature maps without losing the fine features, is presented. In particular, the proposed network has a strong ability to classify the categories that have a small number of samples and reaches the state-of-the-art level for three benchmark data sets.

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related mortality worldwide. HCC cells possess biological characteristics of high invasion and metastasis. In this respect, to prevent cancer cell invasion and metastasis and early active intervention, we herein screened through the TCGA database for further prognostic analysis including overall survival and disease-free survival . The Kaplan-Meier curve suggested that Cyclin-Dependent Kinase 4 (CDK4) might be an independent prognostic factor for HCC. Moreover, we performed mRNA expression analysis to measure CDK4 levels in normal liver tissues and HCC tissues, and immunohistochemistry analysis to detect protein level of CDK4 in Non-tumor tissue and HCC tissues . Our findings indicated that the expression of CDK4 was significantly higher in tumor tissues compared with Non-tumor tissue in HCC, which increased from HCC stage 1 to 3. Furthermore, the results of transwell-assay indicated that knocking down CDK4 significantly suppresses the invasion and migration of HCC cells, and the results of bioinformatics analysis revealed that genes closely associated with CDK4 are potentially worthy of further investigation. Additionally, the results of Western Blot indicated CDK4 regulates epithelial mesenchymal transition in HCC,and CDK4 appears to regulate EMT and HCC progression via the Wnt/β-catenin pathway. Collectively, this study found the key target gene through bioinformatic analysis and further functional validation through cell experiments. In particular, CDK4 is anticipated to become a crucial hub gene to snipe the metastasis of cancer cells in HCC. Abbreviations: Hepatocellular carcinoma (HCC);Cyclin-Dependent Kinase 4(CDK4);Genomic Data Commons (GDC); genes; EC, Endometrial cancer; GEO, gene expression omnibus; GO, Gene Ontology; GSEA, Gene set enrichment analysis; KEGG, Database; TCGA, The Cancer Genome Atlas; TSGs, tumor suppressor genes;epithelial mesenchymal transition (EMT).

Covert wireless communications aim to hide the existence of transmission behavior from watchful adversaries to enhance security. In this paper, we propose a spectrum mask based covert communication strategy in Internet of Things (IoT) networks, where the overt channels are leveraged to enhance the covertness. Specifically, the legitimate IoT transmitter superimposes its own message on the overt channel to avoid being detected by the warden. We assume that proper Gaussian signaling (PGS) is adopted at the overt channel, and improper Gaussian signaling (IGS) is adopted at the legitimate transmitter to improve the covert transmission performance. To maximize the covert rate of the legitimate IoT system, a joint transmit power and IGS factor optimization problem is formulated under the constraints of covertness requirement. The metric of minimum error detection probability, that represents the worst-case for the legitimate transmitter, is utilized to measure the covertness. By exploiting the piece-wise monotonic properties of the objective function and the constraints, we derive the optimal transmit power and IGS factor pairs in both the IGS and PGS schemes. Finally, extensive numerical results are presented to demonstrate that the IGS scheme can improve the covert rate compared to the PGS scheme under a given covertness constraint.

The ultra-wideband near-infrared (NIR) emission of Bi-doped glass and fiber makes it play an increasingly important role in laser technology and optical communication system. However, challenge remains to grasp the original of NIR emission and manipulate the valence state of bismuth for efficient Bi-doped fiber lasers and amplifiers. In this article, we present a facile method to control the valence of bismuth by using one kind of porous host, the nano-porous silica glass (NPSG). Bi-doped silicate glass with novel NIR emission was obtained in NPSG based on glass phase separation technology (GPST). The valence state and NIR luminescence of bismuth can be controlled by sintering of NPSGs impregnated with Al 3 + and P 5 + ions in inert atmosphere. Three kinds of bismuth active centers (BACs) with their own fluorescence characteristics are formed in NPSG. Al can disperse bismuth active centers (BACs) to enhance O band NIR emission; P can promote the reduction of bismuth in glass to produce up-conversion and S+C+L+U band NIR emission. This work pushes us to take a big step towards understanding the truth of NIR emission of bismuth. The GPST may serve as key technology for efficient Bi-doped fiber lasers and amplifiers.

Fine-texture Image Retrieval, a special case in Content Based Image Retrieval, has high potential application value in many fields. However, there are very few researches focus on this special case. Due to the difference between fine-texture images and common images, general retrieval methods for common images are difficult to apply to fine-texture image retrieval. It is also a challenging issue with several obstacles: variety and complexity of appearance, as well as high requirements for retrieval accuracy. To address this issue, this study proposes a novel approach, called deep multi-view hashing (DMVH), to learn enhanced hash codes for efficient fine-texture image retrieval. We propose to use the first few layers of a deep convolutional neural network for fine-texture image presentation. To avoid overfitting, we employ L0Linear layer instead of the commonly used Linear layer in all the fully connected layers. Then we introduce a pairwise quantified similarity computed on the semantic labels. And the learning process of the model is guided by multiple labels of images. This study takes fabric as an example, and builds a dataset called MFT-fabric-v1, to validate the effectiveness of the DMVH method. On this data set, the proposed model achieved a performance with a mAP value of o.8735. The area under the pr curve is larger than other comparison methods. The experimental results demonstrate that the proposed method outperforms the state-of-the-art on the MFT-fabric-v1 dataset.

In the past 37 years, human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) has undergone various major transmission routes in China, with the world most complex co-circulating HIV-1 subtypes, even the prevalence is still low. In response to the first epidemic outbreak of HIV in injecting drug users and the second one by illegal commercial blood collection, China issued the Anti-Drug Law and launched the Blood Donation Act and nationwide nucleic acid testing, which has avoided 98,232 to 211,200 estimated infections and almost ended the blood product-related infection. China has been providing free antiretroviral therapy (ART) since 2003, which covered >80% of the identified patients and achieved a viral suppression rate of 91%. To bend the curve of increasing the disease burden of HIV and finally end the epidemic, China should consider constraining HIV spread through sexual transmission, narrowing the gaps in identifying HIV cases, and the long-term effectiveness and safety of ART in the future.

One of the most common malignant tumors of the digestive tract is emergency colorectal cancer. In recent years, both morbidity and mortality rates, particularly in our country, are getting higher and higher. At present, diagnosis of colorectal cancer, specifically in the emergency department of a hospital, is based on the doctor's pathological diagnosis, and it is heavily dependent on the doctor's clinical experience. The doctor's workload is heavy, and misdiagnosis events occur from time to time. Therefore, computer-aided diagnosis technology is desperately needed for colorectal pathological images to assist pathologists in reducing their workload, improve the efficiency of diagnosis, and eliminate misdiagnosis. To address these issues, a gland segmentation of emergency colorectal pathology images and diagnosis of benign and malignant pathology is presented in this paper. Initially, a multifeatured auxiliary diagnosis is designed to enable diagnosis of benign and malignant diagnosis of emergency colorectal pathology. The proposed algorithm constructs an SVM-enabled pathological diagnosis model which is based on contour, color, and texture features. Additionally, their combination is used for pathological benign and malignant pathological diagnosis of two types of data sets D1 (original pathological image dataset) and D2 (dataset that has undergone glandular segmentation) diagnosis. Experimental results show that the proposed pathological diagnosis model has higher diagnostic accuracy on D2. Among these datasets, SVM based on the multifeature fusion of contour and texture achieved the highest diagnostic accuracy rate, i.e., 83.75%, which confirms that traditional image processing methods have limitations. Diagnosing benign and malignant colorectal pathology in an emergency is more difficult and must be treated on a priority basis. Finally, an emergency colorectal pathology diagnosis method, which is based on deep convolutional neural networks such as CIFAR and VGG, is proposed. After configuring and training process of the two networks, trained CIFAR and VGG network models are applied to the diagnosis of both datasets, i.e., D1 and D2, respectively.

The emerging 6G network will make it possible for cybertwin, which relies deeply on the low latency and powerful computation provided by the edge network. To this end, the convergence of computing and network has been attached great importance. Most existing work study either placing edge servers or deploying user plane functions (UPFs), seldom considers the two processes jointly. In this article, we study how to minimize the latency with cost limitation by means of jointly deploying edge servers and UPFs in 6G scenario. We have shown that the problem is NP-hard. Then, we simplify the problem by analyzing the placement relationship between edge servers and UPFs and prune the solution space of the problem. To solve the problem effectively, a UPF and edge server placement algorithm is proposed. Massive experiments are conducted based on real-world data set and an edge core network emulator. The evaluation results show that our algorithm outperforms the benchmark algorithms.

OBJECTIVE To investigate whether anterior selective fusion (ASF) could save more distal fusion segments compared with posterior approach in the treatment of Lenke type 5 adolescent idiopathic scoliosis with long term follow-up. METHODS A retrospective cohort study. From 2008 to 2011, 22 AIS girls with Lenke type 5 who underwent ASF or posterior selective fusion (PSF) with more than 8-year follow-up, were extracted from the database. 13 girls in the ASF group had an average age of 14.3 ± 1.3 years and Risser sign of 3.3 ± 1.1; 9 PSF girls had an average age of 16.2 ± 3.6 years and Risser sign of 3.8 ± 1.5. The radiographic outcome was compared between groups preoperatively, 6-month postoperatively, 8-year postoperatively and at last follow-up (>8 years). RESULTS The average follow-up duration was 8.7 ± 0.4 (ASF) and 8.8 ± 0.5 (PSF) years, respectively. There was no significant difference at baseline in age, Risser sign and preoperative curve pattern in the coronal and sagittal plane between the groups (P > 0.05). The ASF group had significantly shorter fusion segments (5.1 ± 0.6 vs. 7.0 ± 1.3) and decreased upper instrumented vertebra (UIV) (T11 ± 0.8 vs. T10 ± 0.8) than the PSF (P 0.05), which suggested that ASF could shorten the fusion segments by lowering UIV. The distal compensatory curve in the ASF group (9.0° ± 3.9°) was significantly larger than in the PSF group (3.3° ± 2.4°, P = 0.003), despite of no significant difference in the incidence of coronal imbalance (P > 0.05), indicating that both two approaches could obtain satisfactory correction in the coronal plane. In the sagittal plane, PSF patients had significantly larger lumbar lordosis (LL, 59.1° ± 10.5°), thoracic kyphosis (TK, 37.2° ± 13.3°) and proximal junctional angle (PJA, 13.3° ± 6.1°) at the last follow-up than the ASF (LL: 43.4° ± 9.4°; TK: 20.7° ± 8.4°; PJA: 4.7° ± 3.4°; P 0.05). After controlling for age, Risser sign, and radiographic parameters related to the primary curve pattern, shorter fusion segments and more distal reserved segments still remained significant in the ASF group with greater Risser sign (P

Summary Background In China, depressive disorders have been estimated to be the second leading cause of years lived with disability. However, nationally representative epidemiological data for depressive disorders, in particular use of mental health services by adults with these disorders, are unavailable in China. The present study, part of the China Mental Health Survey, 2012–15, aims to describe the socioeconomic characteristics and the use of mental health services in people with depressive disorders in China. Methods The China Mental Health Survey was a cross-sectional epidemiological survey of mental disorders in a multistage clustered-area probability sample of adults of Chinese nationality (≥18 years) from 157 nationwide representative population-based disease surveillance points in 31 provinces across China. Trained investigators interviewed the participants with the Composite International Diagnostic Interview 3.0 to ascertain the presence of lifetime and 12-month depressive disorders according to DSM-IV criteria, including major depressive disorder, dysthymic disorder, and depressive disorder not otherwise specified. Participants with 12-month depressive disorders were asked whether they received any treatment for their emotional problems during the past 12 months and, if so, the specific types of treatment providers. The Sheehan Disability Scale (SDS) was used to assess impairments associated with 12-month depressive symptoms. Data-quality control procedures included logic check by computers, sequential recording check, and phone-call check by the quality controllers, and reinterview check by the psychiatrists. Data were weighted according to the age–sex–residence distribution data from China's 2010 census population survey to adjust for differential probabilities of selection and differential response, as well as to post-stratify the sample to match the population distribution. Findings 28 140 respondents (12 537 [44·6%] men and 15 603 [55·4%] women) completed the survey between July 22, 2013, and March 5, 2015. Ethnicity data (Han or non-Han) were collected for only a subsample. Prevalence of any depressive disorders was higher in women than men (lifetime prevalence odds ratio [OR] 1·44 [95% CI 1·20–1·72] and 12-month prevalence OR 1·41 [1·12–1·78]), in unemployed people than employed people (lifetime OR 2·38 [95% CI 1·68–3·38] and 12-month OR 2·80 [95% CI 1·88–4·18]), and in people who were separated, widowed, or divorced compared with those who were married or cohabiting (lifetime OR 1·87 [95% CI 1·39–2·51] and 12-month OR 1·85 [95% CI 1·40–2·46]). Overall, 574 (weighted % 75·9%) of 744 people with 12-month depressive disorders had role impairment of any SDS domain: 439 (83·6%) of 534 respondents with major depressive disorder, 207 (79·8%) of 254 respondents with dysthymic disorder, and 122 (59·9%) of 189 respondents with depressive disorder not otherwise specified. Only an estimated 84 (weighted % 9·5%) of 1007 participants with 12-month depressive disorders were treated in any treatment sector: 38 (3·6%) in speciality mental health, 20 (1·5%) in general medical, two (0·3%) in human services, and 21 (2·7%) in complementary and alternative medicine. Only 12 (0·5%) of 1007 participants with depressive disorders were treated adequately. Interpretation Depressive disorders in China were more prevalent in women than men, unemployed people than employed, and those who were separated, widowed, or divorced than people who were married or cohabiting. Most people with depressive disorders reported social impairment. Treatment rates were very low, and few people received adequate treatment. National programmes are needed to remove barriers to availability, accessibility, and acceptability of care for depression in China. Funding National Health Commission and Ministry of Science and Technology of People's Republic of China. Translation For the Chinese translation of the abstract see Supplementary Materials section.

Oxidative deterioration of vegetable oils is of great importance in the food industry. In China, vegetable oils produced via thermal pretreatment are popular owing to their strong oil flavor and enhanced yield. Here, we review: (i) the currently employed thermal treatment methods of oilseeds before oil extraction; (ii) effects of thermal treatments on the physicochemical properties, contents of minor lipid components, and oxidative stability of vegetable oils; and (iii) Maillard model systems that are related to oil and oilseed chemistry. Among the thermal pretreatment technologies, microwave and infrared radiations are promising, but these are not performed on the same large production scales as roasting. For most oilseeds, thermal treatments increase the yield of extracted oil and content of minor lipid compounds in the oil, such as polyphenols, tocopherols, and phytosterols. In addition, some Maillard reaction products (MRPs) generated by heating oilseeds have been extracted. The presence of both minor lipids and MRPs in the oil confers improved oxidative stability. However, the mechanism or relationship between thermal treatment and oxidative stability is yet to be clearly elucidated because vegetable oil oxidation is dependent on variables such as unsaturation, concentration and types of minor lipid components, MRPs, and the potential synergistic effects of these components. Recently, several Maillard reaction models related to thermally treated oilseeds have been established, suggesting that MRPs play a critical role during oxidation. However, comprehensive identification of antioxidants and the mechanism by which they inhibit oxidation are lacking. Future research can be performed to establish models that would help elucidate the antioxidative mechanisms of MRPs for more oilseeds. Using these models, it will be possible to predict the oil quality after processing, based on the presence of MRPs and oil chemistry.

PURPOSE To investigate the clinical value of transrectal ultrasound in judging the Gross Target Volume (GTV) of cervical cancer (CC). METHODS A total of 196 CC patients admitted to the Department of Radiotherapy, China-Japan Union Hospital, Jilin University, from January 2016 to June 2019 were selected as the study subjects. The GTVs before and after applicator insertion were determined by transrectal ultrasound and compared with those judged by MRI. RESULTS All 196 patients were successfully undergoing applicator insertion according to the pretreatment plan. The GTV doses reached the clinical requirements during treatment. There was no significant difference between the GTVs judged by MRI and ultrasound before insertion in terms of upper/lower diameter (MRI Before 1 vs. Ultrasound Before 1) (MB1 vs. UB1), left/right diameter (MB2 vs. UB2), or ventral/dorsal diameter (MB3 vs. UB3), and the intragroup correlation coefficients (ICC) were 0.59, 0.77, and 0.66, respectively; moreover, there was no significant difference between the GTVs judged by MRI and ultrasound after insertion in terms of MRI After one vs. Ultrasound After one (MA1 vs. UA1), MA2 vs. UA2, and MA3 vs. UA3, and the ICC values were 0.62, 0.79, and 0.76, respectively. CONCLUSIONS Transrectal ultrasound can satisfactorily determine the GTV of CC and has certain value in brachytherapy for CC.

Objective To evaluate the long-term consequences of COVID-19 survivors one year after recovery, and to identify the risk factors associated with abnormal patterns in chest imaging manifestations or impaired lung function. Methods COVID-19 patients were recruited and prospectively followed up with symptoms, health-related quality of life, psychological questionnaires, 6-minute walking test, chest computed tomography (CT), pulmonary function tests, and blood tests. Multivariable logistic regression models were used to evaluate the association between the clinical characteristics and chest CT abnormalities or pulmonary function. Results Ninety-four patients with COVID-19 were recruited between January 16 and February 6, 2021. Muscle fatigue and insomnia were the most common symptoms. Chest CT scans were abnormal in 71.28% of participants. The results of multivariable regression showed an increased odds in age. Ten patients had diffusing capacity of the lung for carbon monoxide (DLCO) impairment. Urea nitrogen concentration on admission was significantly associated with impaired DLCO. IgG levels and neutralizing activity were significantly lower compared with those in the early phase. Conclusions One year after hospitalization for COVID-19, a cohort of survivors were mainly troubled with muscle fatigue and insomnia. Pulmonary structural abnormalities and pulmonary diffusion capacities were highly prevalent in surviving COVID-19 patients. It is necessary to intervene in the main target population for long-term recovery.

Owing to the flexible mobility and convenient deployment, unmanned aerial vehicle (UAV) aided networks can facilitate many applications. However, the open accessibility of wireless communication brings a great risk of privacy to UAV-based networks. Thus, in this paper, we propose a UAV-aided covert communication scheme assisted by a multi-antenna jammer to maximize the transmission rate between a ground transmitter and a UAV receiver against several randomly distributed wardens. The transmitter adopts the maximum ratio transmission, while the jammer zero-forces its transmitted signal at the UAV to disturb the monitoring at wardens without interfering the legitimate transmission. First, we analyze the detection performance and derive the optimal threshold for each warden to minimize its detection outage probability (DOP). Then, with the worst situation in which all wardens set their respective optimal thresholds to achieve the minimum global DOP, the location and the transmit power of the jammer are optimized to maximize the DOP. The location of UAV and the transmit power of the ground transmitter are also optimized to maximize the transmission rate with the minimum DOP requirement satisfied. Numerical results are provided to demonstrate the effectiveness of the proposed UAV-aided covert communication scheme.

In response to the needs of physical health data management in the context of the Internet of Everything, this article first uses cloud computing, big data, mobile Internet and other technologies to build a physical health smart management system. When the system is deployed, edge nodes are introduced in each data collection area, and the system is composed of data collection, transmission, and query and analysis modules. Secondly, it uses convolutional neural network to learn features from body measurement data unsupervised. Then, based on the Gaussian mixture distribution, a three-level physical fitness assessment model was established. Finally, input the learned features into the evaluation model to get the result of physical fitness evaluation. The results show that the system not only has a better response to the family, but also can reduce operating costs and improve work efficiency. Moreover, the algorithm in this paper is not affected by individual physical fitness assessment methods and results, and provides new ideas and methods for physical fitness assessment.

Introduction Symptomatic juvenile idiopathic arthritis (sJIA) is an autoinflammatory disease, and monocytes/macrophages play an important role. However, which macrophage subtype plays a major role in different stages of sJIA is still unclear. This study aimed to explore macrophage subtypes in different stages of sJIA. Methods Twenty-two children with sJIA who were followed up at Shanghai Children’s Hospital from January 2018 to December 2020 were enrolled in this study. sJIA children were divided into an activity group (n = 12) and an inactivity group (n = 10). In the activity group, subjects with newly diagnosed sJIA and untreated were included; in the inactivity group, subjects with inactive sJIA meeting the 2011 ACR criteria for sJIA were recruited. Ten children with orthostatic proteinuria served as controls. Peripheral blood was collected. Flow cytometry was performed to detect macrophage subtypes: M1 (CD14+CD86+CD80+), M2a (CD14+CD206+CD301+), M2b (CD14+CD206+CD86+) and M2c (CD14+CD206+CD163+), and the contents of cytokines were also examined, including interleukins (IL) (IL-1β, IL-2, IL-4, IL-5, IL-6, IL-8, IL-10 and IL-17), interferon-α, interferon-γ, and tumor necrosis-α. Results M1 marker CD80 and M2 marker CD163, CD301 were highly expressed in children with active sJIA. The majority of macrophages were M1 and M2a in the activity group (P

Recent neuroimaging studies have identified alterations in activity and connectivity among many brain regions as potential biomarkers for panic disorder. However, the functional connectome of panic disorder is not well understood. Therefore, a graph-theoretical approach was applied in this study to construct functional networks of patients and healthy controls in order to discover topological changes in panic disorder. 31 patients and 33 age and sex matched healthy controls underwent resting-state functional magnetic resonance imaging. Brain networks for each participant were structured using nodes from the Anatomical Automatic Labeling template and edges from connectivity matrices. Then, topological organizations of networks were calculated. Network-based statistical analysis was conducted, and global and nodal properties were compared between patients and controls. Unlike controls, patients with panic disorder displayed a small-world network. Patients also revealed decreased nodal efficiency in right superior frontal gyrus (SFG), middle frontal gyrus (MFG), right superior temporal gyrus (STG), and left middle temporal gyrus (MTG). Decreased functional connectivity was found in panic disorder between right MTG and extensive temporal regions. Among these disrupted regions, the decreased nodal efficiency of SFG showed a positive correlation with clinical symptoms while nodal betweenness centrality in angular gyrus showed a negative correlation. Our results indicated decreased function of global and regional information transmission in panic disorder and emphasized the role of temporal regions in its pathology.

Background Eosinophilic fasciitis (EF) is a rare disease characterized by inflammation of the fascia with immune system involvement. Failure to promptly diagnose and treat this disease can seriously affect the quality of life of patients. However, no clear and uniform criteria for diagnosis and treatment exist. Case summary In this paper, we report two cases of EF, both of which showed symmetrical limb swelling and rigidity, increased eosinophils in the peripheral blood and bone marrow, increased red blood cell sedimentation rate, increased antinuclear antibody titer, and pathological changes in the tissues such as eosinophil and lymphocyte infiltration. Both patients were treated with hormones and cyclosporine, and showed significant improvements in their conditions. Conclusion EF is an autoimmune disease causing swelling and sclerosis of the fascia and eosinophilia. It is diagnosable by magnetic resonance imaging, positron emission tomography-computed tomography, blood routine tests, and bone marrow puncture. Glucocorticoids and immunosuppressants are effective treatments.

Different from developed countries, there is a paucity of research examining how the Dietary Approaches to Stop Hypertension (DASH) and Mediterranean diets relate to lipids in less-developed ethnic minority regions (LEMR). A total of 83 081 participants from seven ethnic groups were retrieved from the baseline data of the China Multi-Ethnic Cohort study, which was conducted in less-developed Southwest China between May 2018 and September 2019. Multivariable linear regression models were then used to examine the associations of the DASH and alternative Mediterranean diet (AMED) scores, assessed by modified DASH score and AMED, as well as their components with total cholesterol (TC), LDL-cholesterol, HDL-cholesterol, TAG and TC/HDL-cholesterol. The DASH scores were negatively associated with TC, HDL-cholesterol and TAG. Comparing the highest quintiles with the lowest DASH scores, TC decreased 0·0708 (95 % CI −0·0923, −0·0493) mmol/l, HDL-cholesterol decreased 0·0380 (95 % CI −0·0462, −0·0299) mmol/l and TAG decreased 0·0668 (95 % CI −0·0994, −0·0341) mmol/l. The AMED scores were negatively associated with TC, LDL-cholesterol and HDL-cholesterol. Comparing the highest quintiles with the lowest AMED scores, TC decreased 0·0816 (95 % CI −0·1035, −0·0597) mmol/l, LDL-cholesterol decreased 0·0297 (95 % CI −0·0477, −0·0118) mmol/l and HDL-cholesterol decreased 0·0275 (95 % CI −0·0358, −0·0192) mmol/l. Although both the DASH diet and the Mediterranean diet were negatively associated with blood lipids, those associations showed different patterns in LEMR, particularly for TAG and HDL-cholesterol.

Brain tumor segmentation using MRI data remains challenging for some reasons. Hence, how to accurately segment the brain tumor is kept as a significant topic in the area of medical image segmentation. To follow the idea of “coarse-to-fine” in practice, this paper proposes an end-to-end two-stage generative adversarial neural network (ToStaGAN) to improve the braintumor is kept as a significant topic in the area of medical image segmentation. To follow the idea of “coarse-to-fine” in practice, this paper proposes an end-to-end two-stage generative adversarial neural network (ToStaGAN) to improve the brain tumor segmentation performance by making full use of semantic information in high-level.In the proposed ToStaGAN, the UNET network is adopted as the “coarse” generation network in the first stage, while a U-Shaped contextual autoencoder (ConEnDer), which consists of the proposed fine-grained extraction module (FEM) and dense skip connection, is proposed as the “fine” generation network in the second stage. To be more specific, the FEM is mainly used to effectively extract “fine-grained” features that plays a crucial role in semantic segmentation with only increasing a few parameters. Furthermore, it has the ability to obtain more diverse and abstract features. And by further utilizing the features extracted from the FEM and the coarse prediction map from the first stage, the ConEnDer holds the great potential to generate the optimized segmentation results. We then evaluate the performance of ToStaGAN on the BARATS 2015. Findings from the evaluations suggest that the proposed two-stage generation network achieves a better performance than the one-stage network; thus, also implying the effectiveness of the proposed ConEnder for “fine” segmentation. A comparative summary also shows that the ToStaGAN achieves a better segmentation performance in comparison to other competing approaches.

Fluorescent solid-state carbon dots have drawn significant attention because of their intriguing optical properties, e.g., broad absorption, stable fluorescence, etc. However, obtaining the fluorescent solid-state carbon dots with a long-wavelength emission is still a challenging problem due to the notorious aggregation-induced quenching problem. In this paper, a technically feasible and low-cost approach for preparing fluorescent solid-state carbon dots is presented. The carbon dots are encapsulated in the nanoporous glass using the solution-phase-doping method. The size of nanoporous glass is tuned to 4.95 nm by regulating the ratio of boron to silicon to match the prepared CDs. Owing to the physical isolation and chemical connection of the -C-O-Si functional groups, the CDs are dispersed uniformly into the nanochannels of nanoporous glass. In this way, the aggregation-induced quenching problem of carbon dots is solved. The experiment results show that the photoluminescence has the excitation-independent emission at 609 nm, i.e., the prepared solid-state carbon dots are with orange fluorescence. More importantly, the solid-state carbon dots preserve orange fluorescence after heating treatment (400 °C) in the ambient atmosphere.

The combination of wireless energy transfer (WET) and mobile edge computing (MEC) has been proposed to satisfy the energy supply and computation requirements of resource-constrained Internet of Things (IoT) devices. However, the energy transfer efficiency and task offloading rate cannot be guaranteed when wireless links between the hybrid access point (HAP) and IoT devices are hostile. To address this problem, this paper aims at utilizing the intelligent reflecting surfaces (IRS) technique to improve the efficiency of WET and task offloading. In particular, we investigate the total computation bits maximization problem for IRS-enhanced wireless powered MEC networks, by jointly optimizing the downlink/uplink phase beamforming of IRS, transmission power and time slot assignment used for WET and task offloading, and local computing frequencies of IoT devices. Furthermore, an iterative algorithm is presented to solve the non-convex non-linear optimization problem, while the optimal transmission power and time allocation, uplink phase beamforming matrixes and local computing frequencies are derived in closed-form expressions. Finally, extensive simulation results validate that our proposed IRS-enhanced wireless powered MEC strategy can achieve higher total computation rate as compared to existing baseline schemes.