Search

Abstract Background Aortic dissection (AD) is a macrovascular disease which is pathologically characterized by aortic media degeneration.This experiment aims to explore how iron deficiency (ID) affects the function of vascular smooth muscle cell (VSMC) and participates in the occurrence and development of AD by regulating gene expression. Methods The relationship between iron and AD was proved by Western-blot (WB) and immunostaining experiments in human and animals. Transcriptomic sequencing explored the transcription factors that were altered downstream. WB, flow cytometry and immunofluorescence were used to demonstrate whether ID affected HIF1 expression through oxygen transport. HIF1 signaling pathway and phenotypic transformation indexes were detected in cell experiments. The use of the specific HIF1 inhibitor PX478 further demonstrated that ID worked by regulating HIF1. Results The survival period of ID mice was significantly shortened and the pathological staining results were the worst. Transcriptomic sequencing indicated that HIF1 was closely related to ID and the experimental results indicated that ID might regulate HIF1 expression by affecting oxygen balance. HIF1 activation regulates the phenotypic transformation of VSMC and participates in the occurrence and development of AD in vivo and in vitro.PX478, the inhibition of HIF1, can improve ID-induced AD exacerbation.

BackgroundPLAC8 has been identified in the progression of various cancers by inducing tumorigenesis, immune response, chemotherapy resistance and metastasis. Nevertheless, the precise biological function of PLAC8 in renal cancer remains unknown.MethodsWe obtained the expression profile and associated clinical characteristics of patients diagnosed with clear cell renal cell carcinoma (ccRCC) from The Cancer Genome Atlas database. The biological behavior of specific cell lines was detected using Cell Counting Kit-8 (CCK-8), colony formation, and 5-ethynyl-2’-deoxyuridine (EdU) assay. A prognostic model was constructed based on PLAC8-related molecules through a machine-learning algorithm.ResultsWe observed overexpression of PLAC8 in ccRCC patients. In addition, PLAC8 has been identified as being linked to unfavorable clinical characteristics and adverse prognosis outcomes. Biological enrichment analysis revealed the potential involvement of PLAC8 in cell cycle checkpoints, mitotic phase transformation, immunotherapy-predicted and reactive oxygen species (ROS) related pathways. In addition, immune analyses showed that PLAC8 was involved in remodeling the tumor microenvironment (TME) and affecting the effect of immunotherapy in ccRCC patients. In vitro experiments demonstrated a significant reduction in the proliferation, invasion and migration of renal cancer cells following the knockdown of PLAC8. Finally, LASSO logistics regression was applied to construct a prognosis model, which presented a favorable prediction ability on the prognosis of ccRCC.ConclusionOur results implied that PLAC8 may be a novel immunotherapy biomarker of ccRCC, which is a crucial molecule in remodeling the cancer microenvironment. PLAC8 can predict immunotherapy response and is expected to guide precise treatment.

Objective Hospital management and medical treatment changed during the coronavirus disease 2019 (COVID-19) pandemic. This study investigated the impact of the COVID-19 pandemic on patients with bladder cancer. Methods In this multicenter retrospective study, we collected information from the electronic medical records of outpatients who underwent cystoscopy and inpatients with confirmed bladder cancer in three hospitals in Nanjing (two province-level and one county-level hospitals) in 2019 and 2020. Patients’ home addresses, treatment methods, length of stay, and pathology were compared between the periods. Results In total, 4048 outpatients and 1242 inpatients were included. The average number of cystoscopies decreased significantly during the lockdown. In province-level hospitals, the number of cystoscopies increased gradually as the pandemic was brought under control but remained lower than that in 2019, whereas the number grew in 2020 in county-level hospitals. The rates of recurrence and radical cystectomy were higher in 2020 than in 2019. No significant difference in the pathological grade was observed. More patients who underwent radical cystectomy were diagnosed with muscle-invasive bladder cancer during the 2020 lockdown. Conclusion The pandemic severely affected patients with bladder cancer, mainly in their choice of institution and treatment.

Abstract This study aimed to compare the peri- and postoperative outcomes of patients treated with conventional versus robot-assisted laparoendoscopic single-site radical prostatectomy (C-LESS-RP vs. R-LESS-RP). Data of patients with prostate cancer (106 who underwent C-LESS-RP, 124 underwent R-LESS-RP) were retrospectively collected and analyzed. All operations were performed by the same surgeon from January 8, 2018, to January 6, 2021, in the same institution. Information on clinical characteristics and perioperative outcomes was obtained from records at the medical institution. Postoperative outcomes were acquired from follow-up. Intergroup differences were retrospectively analyzed and compared. All patients had similar clinical characteristics in meaningful aspects. The perioperative outcomes were better with R-LESS-RP than with C-LESS-RP in terms of operation time (120 min vs. 150 min, p

Abstract Introduction Gestational diabetes mellitus (GDM), a metabolism-related pregnancy complication, is significantly associated with an increased risk of macrosomia. We hypothesized that maternal circulating metabolic biomarkers differed between women with GDM and macrosomia (GDM-M) and women with GDM and normal neonatal weight (GDM-N), and had good prediction performance for GDM-M. Methods Plasma samples from 44 GDM-M and 44 GDM-N were analyzed using Olink Proseek multiplex metabolism assay targeting 92 biomarkers. Combined different clinical characteristics and Olink markers, LASSO regression was used to optimize variable selection, and Logistic regression was applied to build a predictive model. Nomogram was developed based on the selected variables visually. Receiver operating characteristic (ROC) curve, calibration plot, and clinical impact curve were used to validate the model. Results We found 4 metabolism-related biomarkers differing between groups [CLUL1 (Clusterin-like protein 1), VCAN (Versican core protein), FCRL1 (Fc receptor-like protein 1), RNASE3 (Eosinophil cationic protein), FDR

Abstract Background Anillin is a F‐actin binding protein (ANLN) mainly involved in the process of cytokinesis and known to be dysregulated in diverse cancers. However, the role of ANLN in pan‐cancer prognosis and tumor immunity remains unclear. Methods Gene expression profiles of 31 solid tumors were downloaded from The Cancer Genome Atlas (TCGA) database. ANLN mRNA and protein expression were quantified using quantitative real‐time PCR (qRT‐PCR) and immunohistochemistry (IHC). Protein expression of ANLN was further confirmed in Human Protein Atlas (HPA) database. Cox regression and Kaplan–Meier analysis were utilized to assess the prognostic value of ANLN in pan‐cancer. The correlation between ANLN and different immune gene markers and infiltration cells was analyzed via ESTIMATE and CIBERSORT. A BLCA immunotherapy cohort: IMvigor (210) was used to confirm the role of ANLN in immune response. Results ANLN upregulation was detected in 21 types of cancers and was associated with poor overall survival (OS), disease‐free interval (DFI), and progression‐free interval (PFI) in most cancers except in THYM (Thymoma). Additionally, correlation analysis revealed a significantly positive association between ANLN expression and tumor mutation burden (TMB), microsatellite instability (MSI), immune cells infiltration. and immune checkpoint genes in various cancers. The BLCA immunotherapy cohort confirmed that patients with higher ANLN level had better immune responses and longer OS. Conclusion ANLN may serve as a prognostic biomarker for pan‐cancer. ANLN upregulation is associated with higher TMB, MSI, and immune cell infiltration in multiple types of tumors, shedding new light for cancer treatment.

BackgroundImmunogenic cell death (ICD) is considered a particular cell death modality of regulated cell death (RCD) and plays a significant role in various cancers. The connection between kidney renal clear cell carcinoma (KIRC) and ICD remains to be thoroughly explored.MethodsWe conducted a variety of bioinformatics analyses using R software, including cluster analysis, prognostic analysis, enrichment analysis and immune infiltration analysis. In addition, we performed Quantitative Real-time PCR to evaluate RNA levels of specific ICD genes. The proliferation was measured through Cell Counting Kit-8 (CCK-8) assay and colony-formation assay in RCC cell lines. ResultsWe determined two ICD subtypes through consensus clustering analysis. The two subtypes showed significantly different clinical outcomes, genomic alterations and tumor immune microenvironment. Moreover, we constructed the ICD prognostic signature based on TF, FOXP3, LY96, SLC7A11, HSP90AA1, UCN, IFNB1 and TLR3 and calculated the risk score for each patient. Kaplan-Meier survival analysis and ROC curve demonstrated that patients in the high-risk group had significantly poorer prognosis compared with the low-risk group. We then validated the signature through external cohort and further evaluated the relation between the signature and clinical features, tumor immune microenvironment and immunotherapy response. Given its critical role in ICD, we conducted further analysis on LY96. Our results indicated that downregulation of LY96 inhibited the proliferation ability of RCC cells.ConclusionsOur research revealed the underlying function of ICD in KIRC and screened out a potential biomarker, which provided a novel insight into individualized immunotherapy in KIRC.

As an emerging distributed technology, blockchain has begun to penetrate into many fields such as finance, healthcare, supply chain, intelligent transportation. However, the interoperability and value exchange between different independent blockchain systems is restricting the expansion of blockchain. In this paper, a notary group-based cross-chain interaction model is proposed to achieve the interoperability between different blockchains. Firstly, a notary election mechanism is proposed to choose one notary from the notary group to act as a bridge for cross-chain transactions. Secondly, a margin pool is introduced to limit the misconduct of the elected notary and ensure the value transfer between the involved blockchains. Moreover, a reputation based incentive mechanism is used to encourage members of the notary group to participate in cross-chain transactions. Ethereum-based experiments demonstrate that the proposed mechanism can provide an acceptable performance for cross-chain transactions and provide a higher security level than ordinary cross-chain mechanisms.

Aiming at the fact that the impact of quarantine on network virus propagation has not been considered in existing dynamical models, a network virus paopagation model with dynamical quarantine was proposed on the basis of the simple epidemic SI model. The basic idea of the model is that the suspicious node is quaranteened immediately as soon as the network intrusion detection system finds the suspicious behavior of node. Then, the isolated node is released after a short of period. Furthermore, by using stochastic probability theory, under the assumption that the activity of network nodes follows a Poisson distribution with different degrees of activity, a mathematical expression is obtained for the number of network nodes that may be isolated at any one time. Finally, numerical experiments simulate the change trend of the speed and scale of network virus spread over time with different values of isolation parameters. Simulation results show that quarantine measures can effectively delay the spread of network viruses, and quarantine time has a significant impact on the speed and scale of network viruses. When quarantine time exceeds 0.5 minutes, the scale of network virus spread will be reduced by about 72%.

Abstract Based on a limited number of studies, a random urine protein‐creatinine ratio (uPCR) value of ≥ 0.3 indicates abnormal proteinuria in preeclampsia with renal damage. However, current guidelines do not recommend a reasonable diagnostic threshold of uPCR for severe preeclampsia with renal damage. Furthermore, the correlation between the uPCR value and clinical adverse outcomes remains poorly understood. The aim of the present study was to evaluate the value of uPCR in the diagnosis of significant proteinuria and to assess its correlation with adverse pregnancy outcomes in preeclampsia characterized by renal damage. In all, 1837 women were enrolled in this retrospective cohort study. Eventually, 961 women were enrolled under the exclusion criteria. First, the authors found that uPCR and 24‐hour proteinuria showed a significant association (r = 0.901). The optimal threshold of uPCR for diagnosing preeclampsia was 0.295, and for diagnosing severe preeclampsia the cut‐off was 0.625. Meanwhile, the adjusted odds ratio per 1 unit increase in ln (uPCR) was 1.679 (95% confidence interval [CI]:1.142–2.469) for severe adverse perinatal outcomes; 1.456 (95% CI: 1.242–1.705) for small for gestational age; 1.380 (95% CI: 1.051–1.811) for severe small for gestational age; 1.672 (95% CI: 1.210–2.310) for very early preterm birth; 1.989 (95% CI 1.726–2.293) for severe hypertension; and 2.279 (95% CI 1.906–2.724) for preterm birth. This study indicated that there was a significant and positive correlation between uPCR and 24‐hour urine protein. For neonatal and maternal adverse outcomes, uPCR is an independent predictor of prognosis.

Previous studies primarily focused on the single metal exposure and one-sided glucose metabolism disordered states, leading to conflicting results. Herein, we combined diabetes and prediabetes as abnormal glucose metabolism (AGM) to describe the effect of metal mixture exposure on it. Eligible data were obtained from the National Health and Nutrition Examination Survey (NHANES) 2015–2016. In the generalized linear model (GLM), Cd (OR: 1.060, 95 %CI: 1.032–1.089, P value

Background. Post-renal transplant patients have a high likelihood of developing renal cancer. However, the underlying biological mechanisms behind the development of renal cancer in post-kidney transplant patients remain to be elucidated. Therefore, this study aimed to investigate the underlying biological mechanism behind the development of renal cell carcinoma in post-renal transplant patients. Methods. Next-generation sequencing data and corresponding clinical information of patients with clear cell renal cell carcinoma (ccRCC) were obtained from The Cancer Genome Atlas Program (TCGA) database. The microarray data of kidney transplant patients with or without rejection response was obtained from the Gene Expression Omnibus (GEO) database. In addition, statistical analysis was conducted in R software. Results. We identified 55 upregulated genes in the transplant patients with rejection from the GEO datasets (GSE48581, GSE36059, and GSE98320). Furthermore, we conducted bioinformatics analyses, which showed that all of these genes were upregulated in ccRCC tissue. Moreover, a prognosis model was constructed based on four rejection-related genes, including PLAC8, CSTA, AIM2, and LYZ. The prognosis model showed excellent performance in prognosis prediction in a ccRCC cohort. In addition, the machine learning algorithms identified 19 rejection-related genes, including PLAC8, involved in ccRCC occurrence. Finally, the PLAC8 was selected for further research, including its clinical and biological role. Conclusion. In all, our study provides novel insight into the transition from the rejection of renal transplant to renal cancer. Meanwhile, PLAC8 could be a potential biomarker for ccRCC diagnosis and prognosis in post-kidney transplant patients.

Prostate adenocarcinoma (PRAD) is a common malignancy with increasing morbidity and mortality. Kinetochore scaffold 1 (KNL1) has been reported to be involved in tumor progression and prognosis in other tumors, but its role in PRAD has not been reported in detail. KNL1 expression analysis, clinicopathological parameters analysis, prognostic correlation analysis, molecular interaction network and functional abdominal muscle analysis and immune infiltration analysis by using multiple online databases and downloaded expression profile. The results suggest that KNL1 is highly expressed in PRAD, which is associated with worse prognosis in PRAD patients. KnL1-related genes are highly enriched in mitotic function, which is considered to be highly related to the development of cancer. Finally, KNL1 expression is associated with a variety of tumor infiltrating immune cells, especially Treg and Th2 cells. In conclusion, our findings provide preliminary evidence that KNL1 may be an independent prognostic predictor of PRAD and is associated with immune infiltration.

The industrial internet of things (IIoT) is a key pillar of the intelligent society, integrating traditional industry with modern information technology to improve production efficiency and quality. However, the IIoT also faces serious challenges from advanced persistent threats (APTs), a stealthy and persistent method of attack that can cause enormous losses and damages. In this paper, we give the definition and development of APTs. Furthermore, we examine the types of APT attacks that each layer of the four-layer IIoT reference architecture may face and review existing defense techniques. Next, we use several models to model and analyze APT activities in IIoT to identify their inherent characteristics and patterns. Finally, based on a thorough discussion of IIoT security issues, we propose some open research topics and directions.

A 71-year-old female with a dual-chamber pacemaker presented to our hospital complaining of repeated chest pain. She was diagnosed with unstable angina. On day 7, the patient suddenly suffered cardiopulmonary arrest due to an inferior ST segment elevation myocardial infarction (STEMI). Pacemaker lost capture was suspected and was later confirmed by a pacemaker check with a high pacing threshold and a low sensing parameter. Emergency coronary angiography revealed that a large filling defect remained due to an extensive thrombus in the proximal left circumflex (LCX) with thrombolysis in myocardial infarction (TIMI) grade 2 flow, and then a repeat thrombus aspiration was performed. After reperfusion, the parameters of the right ventricular lead were gradually returned. We conclude that the loss of the right ventricular lead pacing occurred in this case of acute coronary syndrome (ACS) induced by an LCX thrombus due to an LCX supplying the right ventricular septal.

ABSTRACT The connection between gestational diabetes mellitus (GDM) and the offspring’s development, such as obesity, is well established. Emerging evidence indicates that the microbiota of the neonate's meconium is associated with maternal GDM status. To explore whether the association between GDM and infant body mass index (BMI) in early childhood is affected by the meconium microbiota, we recruited 120 mothers (60 healthy women and 60 with GDM) and their newborns from the Women’s Hospital of Nanjing Medical University. Meconium of 120 neonates was collected within a few hours after birth and sequenced using 16S rRNA sequencing analysis. Children’s BMI was measured at 12 months of age. The results revealed that infants born to mothers with GDM had increased BMI Z-scores at 12 months old and that the β-diversity of their meconium microbiota was reduced. Several genera were observed to be significantly different between the GDM and control groups. The genus Burkholderia-Caballeronia-Paraburkholderia and an untitled genus in the family Enterobacteriaceae enriched in neonates born to healthy mothers were found to be negatively associated with infant BMI by using regression analysis. A coabundance group depleted in the GDM group was correlated negatively with 12-month BMI and mediated 21.65% of the association between GDM and infant BMI by mediation analyses. This study provided evidence for the associations among maternal GDM, the meconium microbiota, and infant BMI. Maternal GDM was demonstrated to affect infant BMI, mediated by the gut microbiome. Gut microbiome interventions might represent a novel technique to decrease the risk of GDM-induced childhood obesity. IMPORTANCE Using 16S rRNA sequencing analysis, regression analysis and mediation analysis were used to explore whether maternal gestational diabetes mellitus (GDM) changed the function and composition of the meconium microbiota and whether this explained the GDM-induced alterations of infant body mass index (BMI). This study showed that gut microbiome dysbiosis induced by maternal GDM might play an important role in the increased infant BMI during the first 12 months of life. Therefore, gut microbiome interventions might represent a novel technique to decrease the risk of GDM-induced childhood obesity.

Abstract Background Light-chain cardiac amyloidosis (AL-CA) has been highly valued in developed countries, but in developing countries, the recognition and diagnosis of this condition is still limited. There are currently few reports on a large number of Chinese patients with AL-CA. The present study aimed to report real-world clinical characteristics and prognosis of AL-CA in China. Methods and results Consecutive patients with AL-CA diagnosed at the Second Xiangya Hospital of Central South University between June 2012 and September 2020 were reviewed. A total of 170 patients with AL-CA have been recruited, whose mean ages were 60.81 ± 10.46. 70.59% of the patients were male. They were from eight provinces in southern China, 55.7% were referred patients, and 37.3% had been misdiagnosed previously. 64 (37.6%) patients received chemotherapy. The median survival time for patients with AL-CA was 8.00 months, and survival time for patients who received chemotherapy was 13.00 months, which was significantly longer than that of patients with palliative treatment (13.00 vs 6.00, p = 0.004). Conclusions Although clinicians have improved their understanding of AL-CA in recent years, the prognosis of AL-CA is still poor, and the misdiagnosis rate and missed diagnosis rate are still very high in China. It is imperative to improve the recognition and early diagnosis of this condition, which may require multidisciplinary collaboration among cardiologists, hematologists and nephrologists.

Abstract In massive multiple‐input multiple‐output (MIMO) systems, most of the existing detection work mainly assumes that the channel is the additive white Gaussian noise (AWGN). However, this assumption is difficult to apply to practical communication scenarios. To this end, this paper proposes a message passing detection (MPD) algorithm with a convolutional neural network (CNN) (denoted as iterative MPD‐CNN structure) under correlated noise channels, which is helpful to solve the issue of detection performance degradation in non‐ideal AWGN channels. Firstly, the MPD algorithm based on the channel hardening phenomenon is used to initially estimate the transmitted signal, and then the CNN is concatenated to remove the estimation error for obtaining more accurate channel noise, which provides a beneficial noise distribution for the MPD algorithm. Finally, the theoretical analysis and simulation results show that the proposed iterative MPD‐CNN structure can improve the detection performance in conditions of correlated noise channels and fewer antennas. Compared with the traditional MPD algorithm, its detection performance is more superior.

In this paper, a nonlinear activation function (NAF) is proposed to constructed three recurrent neural network (RNN) models (Simple RNN (SRNN) model, Long Short-term Memory (LSTM) model and Gated Recurrent Unit (GRU) model) for sentiment classification. The Internet Movie Database (IMDB) sentiment classification experiment results demonstrate that the three RNN models using the NAF achieve better accuracy and lower loss values compared with other commonly used activation functions (AF), such as ReLU, SELU etc. Moreover, in terms of dynamic problems solving, a fixed-time convergent recurrent neural network (FTCRNN) model with the NAF is constructed. Additionally, the fixed-time convergence property of the FTCRNN model is strictly validated and the upper bound convergence time formula of the FTCRNN model is obtained. Furthermore, the numerical simulation results of dynamic Sylvester equation (DSE) solving using the FTCRNN model indicate that the neural state solutions of the FTCRNN model quickly converge to the theoretical solutions of DSE problems whether there are noises or not. Ultimately, the FTCRNN model is also utilized to realize trajectory tracking of robot manipulator and electric circuit currents computation for the further validation of its accurateness and robustness, and the corresponding results further validate its superior performance and widespread applicability.

Cloud-fog-edge computing especially cloud computing is providing a variety of services in many areas around the world and plays a vital role in cyber-physical-social systems (CPSS). Particularly, virtualization is one of main enabling technologies of cloud computing and realizes the dynamic deployment of computing tasks through the migration of virtual machines (VMs), so how to secure the virtual environment in the cloud is very crucial. The purpose of this paper is to address the issue of malware propagation among VMs under the infrastructure as a service (IaaS) architecture. Firstly, a dynamical propagation model is proposed to explore the important factors affecting the spread of malware, especially the impact of installing antivirus software in VMs. On this basis, a theoretical analysis for this model is investigated by means of differential dynamics, from which it is able to understand the dissemination behavior of malware under an infected cloud environment. Finally, some numerical simulations are conducted to verify the applicability and effectiveness of the proposed model.

Background and Aims: Patients with light-chain cardiac amyloidosis (AL-CA) are characterized by high levels of serum carbohydrate antigen 125 (CA 125). However, studies have not explored the correlation between CA 125 and AL-CA. The aim of this study was to explore the clinical implications of an increase in CA 125 in patients with AL-CA.Methods and Results: A total of 95 patients diagnosed with AL-CA at the Second Xiangya Hospital were enrolled in this study. Out of the 95 patients with AL-CA, 57 (60%) patients had elevated serum CA 125 levels. The mean age was 59.7 ± 10.0 years with 44 (77.2%) men in the high serum CA 125 group, and 61.8 ± 9.6 years with 28 (73.7%) men in the normal group. Patients with high CA 125 showed higher rates of polyserositis (79.3% vs. 60.5%, p = 0.03), higher levels of hemoglobin (117.4 ± 21.9 g/L vs. 106.08 ± 25.1 g/L, p = 0.03), serum potassium (4.11 ± 0.47 mmol/L vs. 3.97 ± 0.40 mmol/L, p = 0.049), low-density lipoprotein-cholesterol (3.0 ± 1.6 mmol/L vs. 2.3 ± 1.10 mmol/L, p = 0.01), and cardiac troponin T (96.0 pg/mL vs. 91.9 pg/mL, p = 0.005). The median overall survival times for patients with high or normal serum CA 125 were 5 and 25 months, respectively (p = 0.045). Multivariate Cox hazard analysis showed that treatment without chemotherapy (HR 1.694, 95% CI 1.121–2.562, p = 0.012) and CA 125 (HR 1.002, 95% CI 1.000–1.004, p = 0.020) was correlated with high all-cause mortality. The time-dependent receiver operating characteristic (t-ROC) curve showed that the prediction accuracy of CA 125 was not inferior to that of cardiac troponin T, N-terminal pro-B-type natriuretic peptide (NT-proBNP), and lactate dehydrogenase (LDH) based on the area under the curve.Conclusions: CA 125 is a novel prognostic predictor. High serum CA 125 values are correlated with low overall survival, and the accuracy of predicting prognosis is similar to that of traditional biomarkers in AL-CA.

At present, malware is still a major security threat to computer networks. However, only a fraction of users with some security consciousness take security measures to protect computers on their own initiative, and others who know the current situation through social networks usually follow suit. This phenomenon is referred to as conformity psychology. It is obvious that more users will take countermeasures to prevent computers from being infected if the malware spreads to a certain extent. This paper proposes a deterministic nonlinear SEIQR propagation model to investigate the impact of conformity psychology on malware propagation. Both the local and global stabilities of malware-free equilibrium are proven while the existence and local stability of endemic equilibrium is proven by using the central manifold theory. Additionally, some numerical examples and simulation experiments based on two network datasets are performed to verify the theoretical analysis results. Finally, the sensitivity analysis of system parameters is carried out.

Abstract Compressed sensing (CS) is a signal processing framework, which reconstructs a signal from a small set of random measurements obtained by measurement matrices. Due to the strong randomness of measurement matrices, the reconstruction performance is unstable. Additionally, current reconstruction algorithms are relatively independent of the compressed sampling process and have high time complexity. To this end, a deep learning based stacked sparse denoising autoencoder compressed sensing (SSDAE_CS) model, which mainly consists of an encoder sub-network and a decoder sub-network, is proposed and analyzed in this paper. Instead of traditional linear measurements, a multiple nonlinear measurements encoder sub-network is trained to obtain measurements. Meanwhile, a trained decoder sub-network solves the CS recovery problem by learning the structure features within the training data. Specifically, the two sub-networks are integrated into SSDAE_CS model through end-to-end training for strengthening the connection between the two processes, and their parameters are jointly trained to improve the overall performance of CS. Finally, experimental results demonstrate that the proposed method significantly outperforms state-of-the-art methods in terms of reconstruction performance, time cost, and denoising ability. Most importantly, the proposed model shows excellent reconstruction performance in the case of a few measurements.

Online social networks have become extremely important in daily life and can be used to influence lives in dramatic ways. Two issues are the veracity and provenance of posted information, including rumors. There is a need for methods for tracing rumors (or any piece of information) to their most likely source in such networks. We consider the detection problem of single rumor source based on observed snapshots based on the susceptible-exposed-infected-recovered (SEIR) model. According to the SEIR model, all nodes in the network are formulated into four possible states: susceptible (S), exposed (E), infected (I), and recovered (R). Given an observed snapshot in the network, from which we can know the relevant graph topology and all infected nodes, but where nodes in susceptible, exposed, or recovered status cannot be distinguished, the purpose of our research is to identify the rumor source based on the observed snapshot and graph topology. We propose the concept of the optimal infection process and derive an estimator for the rumor source based on this optimal infection process. Subsequently, we prove that this estimator matches the rumor source with a high probability. The effectiveness of the proposed scheme is validated using experiments based on regular tree networks with different degrees. We further evaluate the performance of our scheme on two well-known synthetic complex networks and four real-world networks; the results suggest that our proposed scheme outperforms the traditional rumor centrality heuristics. The performance analysis on computational complexity demonstrates that our scheme has advantages in efficiency compared with other rumor centrality heuristics used in rumor detection methods.

Critical limb ischemia (CLI) is a severe complication of diabetes mellitus that occurs without effective therapy. Excessive reactive oxygen species (ROS) production and oxidative stress play critical roles in the development of diabetic cardiovascular complications. N-acetylcysteine (NAC) reduces ischemia-induced ROS production. The present study aimed to investigate the effect of NAC on the recovery of ischemic limb in an experimental model of type-2 diabetes. TALLYHO/JngJ diabetic and SWR/J non-diabetic mice were used for developing a CLI model. For NAC treatment, mice received NAC (1 mg/mL) in their drinking water for 24 h before initiating CLI, and continuously for the duration of the experiment. Blood flow, mechanical function, histology, expression of antioxidant enzymes including superoxide dismutase (SOD)-1, SOD-3, glutathione peroxidase (Gpx)-1, catalase, and phosphorylated insulin receptor substrate (IRS)-1, Akt, and eNOS in ischemic limb were evaluated in vivo or ex vivo. Body weight, blood glucose, plasma advanced glycation end-products (AGEs), plasma insulin, insulin resistance index, and plasma TNF-a were also evaluated during the experiment. NAC treatment effectively attenuated ROS production with preserved expressions of SOD-1, Gpx-1, catalase, phosphorylated Akt, and eNOS, and enhanced the recovery of blood flow and function of the diabetic ischemic limb. NAC treatment also significantly decreased the levels of phosphorylated IRS-1 (Ser307) expression and plasma TNF-α in diabetic mice without significant changes in blood glucose and AGEs levels. In conclusion, NAC treatment enhanced the recovery of blood flow and mechanical function in ischemic limbs in T2D mice in association with improved tissue redox/inflammatory status and insulin resistance.

Congenital adrenal hyperplasia (CAH) is one of the most prevalent, and potentially severe, genetic inborn errors of steroid synthesis directly affecting metabolism. Most patients are diagnosed and treated at an early age. There have been very limited reports of adults with CAH-associated adrenal myelolipomas. We aimed to analyze two families with CAH-associated giant adrenal myelolipomas caused by defects in CYP21A2 and CYP17A1 genes. A total of 14 individuals from two unrelated families were identified with either CYP21A2 or CYP17A1 mutations. Of note, five patients were found with adrenal myelolipomas. Total DNA isolated from the peripheral blood of the two probands was screened for potential mutations in the following susceptibility genes of CAH: CYP21A2, CYP11B1, CYP17A1, HSD17B3, HSD3B2, ARMC5, and STAR using target capture-based deep sequencing; and Sanger sequencing was conducted for the family members to detect the potential mutations. The following results were obtained. In family 1, molecular genetics sequencing revealed a compound heterozygous mutation (c.293-13C>G/c.518T>A, p.I173N) in CYP12A2 in the patient and his brother. In family 2, all three female patients with adrenal myelolipomas were found to have a compound heterozygous mutation (c.1118A>T, p.H373L/c.1459_1467del9, p.D487_F489del) in CYP17A1. To avoid giant CAH-associated adrenal myelolipomas in adults, it is important to identify CAH early so that appropriate treatment can be initiated to interrupt the chronic adrenal hyperstimulation resulting from increased ACTH. Genetic testing and counseling could be useful in CAH.

By incorporating the security awareness of computer users into the susceptible-infected-susceptible (SIS) model, this study proposes a new malware propagation model, named the SID model, where D compartment denotes the group of nodes with user awareness. Through qualitative analysis, the basic reproductive number R0 is given. Furthermore, it is proved that the virus-free equilibrium is globally asymptotically stable if R0 is less than one, whereas the viral equilibrium is globally asymptotically stable if R0 is greater than one. Then, some numerical examples are given to demonstrate the analytical results. Finally, we put forward some efficient control measures according to the theoretical and experimental analysis.

Tensor compression algorithms play an important role in the processing of multidimensional signals. In previous work, tensor data structures are usually destroyed by vectorization operations, resulting in information loss and new noise. To this end, this article proposes a tensor compression algorithm using Tucker decomposition and dictionary dimensionality reduction, which mainly includes three parts: tensor dictionary representation, dictionary preprocessing, and dictionary update. Specifically, the tensor is respectively performed by the sparse representation and Tucker decomposition, from which one can obtain the dictionary, sparse coefficient, and core tensor. Furthermore, the sparse representation can be obtained through the relationship between sparse coefficient and core tensor. In addition, the dimensionality of the input tensor is reduced by using the concentrated dictionary learning. Finally, some experiments show that, compared with other algorithms, the proposed algorithm has obvious advantages in preserving the original data information and denoising ability.

Abstract Traditional Chinese medicine (TCM) has a combined therapeutic result in cancer treatment by integrating holistic and local therapeutical effects, by which TCM can enhance the curative effect and reduce the side effect. In this study, we analyzed the effect of CFF‐1 (alcohol extract from an anticancer compound Chinese medicine) on prostate cancer (PCa) cell lines and studied in detail the mechanism of cell death induced by CFF‐1 in vitro and in vivo. From our data, we found for the first time that CFF‐1 obviously arrested cell cycle in G1 phase, decreased cell viability and then increased nuclear rupture in a dose‐dependent manner and finally resulted in apoptosis in prostate cancer cells. In molecular level, our data showed that CFF‐1 induced inhibition of EGFR auto‐phosphorylation and inactivation of EGFR. Disruption of EGFR activity in turn suppressed downstream PI3K/AKT and Raf/Erk signal pathways, resulted in the decrease of p‐FOXO1 (Ser256) and regulated the expression of apoptosis‐related and cycle‐related genes. Moreover, CFF‐1 markedly induced cell autophagy through inhibiting PI3K/AKT/mTOR pathway and then up‐regulating Beclin‐1 and LC‐3II and down‐regulating phosphorylation of p70S6K. In vivo, CFF‐1‐treated group exhibited a significant decrease in tumor volume compared with the negative control group in subcutaneous xenograft tumor in nude mice via inhibiting EGFR‐related signal pathways. Thus, bio‐functions of Chinese medicine CFF‐1 in inducing PCa cell growth inhibition, autophagy, and apoptosis suggested that CFF‐1 had the clinical potential to treat patients with prostate cancer.

Males have a higher risk for cardiovascular diseases (CVDs) than females. Ambient fine particulate matter (PM) exposure increases CVD risk with increased reactive oxygen species (ROS) production and oxidative stress. Endothelial progenitor cells (EPCs) are important to vascular structure and function and can contribute to the development of CVDs. The aims of the present study were to determine if sex differences exist in the effect of PM exposure on circulating EPCs in mice and, if so, whether oxidative stress plays a role. Male and female C57BL/6 mice (8–10 weeks old) were exposed to PM or a vehicle control for six weeks. ELISA analysis showed that PM exposure substantially increased the serum levels of IL-6 and IL-1β in both males and females, but the concentrations were significantly higher in males. PM exposure only increased the serum levels of TNF-α in males. Flow cytometry analysis demonstrated that ROS production was significantly increased by PM treatment in males but not in females. Similarly, the level of circulating EPCs (CD34+/CD133+ and Sca-1+/Flk-1+) was significantly decreased by PM treatment in males but not in females. Antioxidants N-acetylcysteine (NAC) effectively prevented PM exposure-induced ROS and inflammatory cytokine production and restored circulating EPC levels in male mice. In sharp contrast, circulating EPC levels remained unchanged in female mice with PM exposure, an effect that was not altered by ovariectomy. In conclusion, PM exposure selectively decreased the circulating EPC population in male mice via increased oxidative stress without a significant impact on circulating EPCs in females independent of estrogen.

Security and efficiency are the two main challenges for designing a smart home system. In this paper, by incorporating Chinese remainder theorem (CRT) into the elliptic curve Diffie−Hellman (ECDH), a lightweight key agreement protocol for smart home systems is constructed. Firstly, one-way hash authentication is used to identify the sensor nodes instead of mutual authentication to reduce the authentication cost. Secondly, the CRT is introduced to enhance the security of the original ECDH key agreement. Security analysis showed that the proposed protocol can validate the data integrity and resist the replay attack, the man-in-middle attack, and other attacks. Performance analysis and experiments showed that the protocol achieves high security with low communication and computation costs, and can be implemented in smart home systems.

In reality, some computers have specific security classification. For the sake of safety and cost, the security level of computers will be upgraded with increasing of threats in networks. Here we assume that there exists a threshold value which determines when countermeasures should be taken to level up the security of a fraction of computers with low security level. And in some specific realistic environments the propagation network can be regarded as fully interconnected. Inspired by these facts, this paper presents a novel computer virus dynamics model considering the impact brought by security classification in full interconnection network. By using the theory of dynamic stability, the existence of equilibria and stability conditions is analysed and proved. And the above optimal threshold value is given analytically. Then, some numerical experiments are made to justify the model. Besides, some discussions and antivirus measures are given.

Vaccination is one of the most effective measures for suppressing the spread of computer virus, and the bilinear incidence rate assumption for the majority of previous models, which is a good first approximation of the general incidence rate, is in disagreement with the reality. In this paper, a new dynamical model with two kinds of generic nonlinear probabilities (incidence rate and vaccination probability) is established. An exhaustive mathematical analysis of this model shows that (a) there are two equilibria, virus-free equilibrium and viral equilibrium, and (b) the virus-free (or viral) equilibrium is globally asymptotically stable when the basic reproduction number is less (or greater) than unity. The analysis of the basic reproduction number is also included. Additionally, some numerical examples are given to illustrate the main results, from which it can be seen that the generic nonlinear vaccination is helpful to strengthen computer security.

Bst-2 (bone marrow stromal cell antigen 2) is a type II membrane protein, and it acts as a tetherin to inhibit virion releasing from infectious cells. Membrane type-1 matrix metalloproteinase (MT1-MMP) is a protease. It plays a pivotal role in cellular growth and migration by activating proMMP-2 into active MMP2. Our results here elaborate that MT1-MMP inhibits the tetherin activity of Bst-2 by interacting with Bst-2, and the cytoplasmic domains of both Bst-2 and MT1-MMP play critical roles within this interaction. Based on our experimental data, the assays for virion release and co-immunoprecipitation have clearly demonstrated that the activity of Bst-2 is markedly inhibited by MT1-MMP via their interaction; and both the N-terminal domain of Bst-2 and the C-terminal domain of MT1-MMP are important in the interaction. Immunostaining and Confocal Microscopy assay shows that MT1-MMP interacts with Bst-2 to form granular particles trafficking into cytoplasm from membrane and, finally, results in Bst-2 and MT1-MMP both being inhibited. In addition, mutant experiments elucidate that the N-terminal domain of Bst-2 is not only important in relating to the activity of Bst-2 itself, but is important for inhibiting the MT1-MMP/proMMP2/MMP2 pathway. These findings suggest that MT1-MMP is a novel inhibitor of Bst-2 in MT1-MMP expressed cell lines and also indicate that both the N-terminal domain of Bst-2 and the C-terminal domain of MT1-MMP are crucial in down-regulation.

This paper examines the propagation behavior of computer virus under human intervention. A dynamical model describing the spread of computer virus, under which a susceptible computer can become recovered directly and an infected computer can become susceptible directly, is proposed. Through a qualitative analysis of this model, it is found that the virus-free equilibrium is globally asymptotically stable when the basic reproduction number R0≤1, whereas the viral equilibrium is globally asymptotically stable if R0>1. Based on these results and a parameter analysis, some appropriate measures for eradicating the spread of computer virus across the Internet are recommended.