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This is an article in the field of metallurgical engineering. Mangane-zinc spinel ferrite has been widely used in various fields due to its advantages of high permeability， high frequency and low loss. In order to make full use of Fe, Zn, Mn and other elements in electric arc furnace dust (EAFD)， Mn-Zn spinel ferrite was prepared by solid phase sintering method with alkali leaching zinc-containing EAFD as raw materials and MnSO4·H2O added. The effects of temperature， mass ratio (RZ/M) and pretreatment on the synthesis and magnetic properties of Mn-Zn ferrite were investigated by XRD, SEM-EDS and VSM. The results show that with the increase of reaction temperature，the speed of inter-grain merging in the Mn-Zn ferrite increases， the magnetic saturation induction and crystal grain size increase， and the coercivity decreases. When RZ/M changes to 1∶1.0，the magnetic saturation induction， coercivity and grain size increase. After pretreatment，the content of SiO2 in the EAFD decreases，and the magnetic saturation strength of the synthesized manganese-zinc ferrite increases. Therefore, after pretreatment with 2 mol/L alkali solution， the performance of manganzn-spinel ferrite synthesized at the condition of mass ratio 1∶1.0 and calcination temperature 1 100 ℃ is the best. Its saturation magnetic induction intensity (Ms) is 17.902 emu/g, and coercivity (Hc) is 3.21 kA/m.

Abstract The aim of this study was to identify key genes and investigate the immunological mechanisms of atopic dermatitis (AD) at the molecular level via bioinformatics analysis. Gene expression profiles (GSE32924, GSE107361, GSE121212, and GSE230200) were obtained for screening common differentially expressed genes (co-DEGs) from the gene expression omnibus database. Functional enrichment analysis, protein–protein interaction network and module construction, and identification of common hub genes were performed. Hub genes were validated using receiver operating characteristic curve analysis based on GSE130588 and GSE16161. NetworkAnalyst was used to detect microRNAs (miRNAs) and transcription factors (TFs) associated with the hub genes. The immune cell infiltration was analyzed using the CIBERSORT algorithm to further analyze the correlation between hub genes and immune cells. A total of 146 co-DEGs were obtained, showing significant enrichment in cytokine–cytokine receptor interaction and JAK-STAT signaling pathway. Seven hub genes were identified by Cytoscape and validated with external datasets. Subsequent prediction of miRNAs and TFs targeting these hub genes revealed their regulatory roles. Analysis of immune cell infiltration and correlation revealed a significant positive correlation between CCL22 expression and the number of dendritic cells activated. The identified hub genes represent potential diagnostic and therapeutic targets in the immunological pathogenesis of AD.

Abstract Background Therapeutic approaches that combine conventional photodynamic therapy (PDT) with gas therapy (GT) to sensitize PDT are an attractive strategy, but the molecular structure design of the complex lacks effective guiding strategies. Results Herein, we have developed a nanoplatforms Cy-NMNO@SiO2 based on mesoporous silica materials loaded NIR-activatable small-molecule fluorescent probe Cy-NMNO for the synergistic treatment of photodynamic therapy/gas therapy (PDT/GT) in antibacterial and skin cancer. The theoretical calculation results showed that the low dissociation of N-NO in Cy-NMNO enabled it to dissociate effectively under NIR light irradiation, which is conducive to produce Cy and NO. Cy showed better 1O2 generation performance than Cy-NMNO. The cytotoxicity of Cy-NMNO obtained via the synergistic effect of GT and PDT synergistically enhances the effect of photodynamic therapy, thus achieving more effective tumor treatment and sterilization than conventional PDT. Moreover, the nanoplatforms Cy-NMNO@SiO2 realized efficient drug loading and drug delivery. Conclusions This work not only offers a promising approach for PDT-GT synergistic drug delivery system, but also provides a valuable reference for the design of its drug molecules. Graphical Abstract

This is an article in the field of metallurgical engineering. In order to strengthen the effective separation and recovery of zinc and iron resources in zinc containing electric arc furnace dust(EAFD), and reduce the consumption of carbon reducing agent, the idea and method of calcified carbothermal reduction roasting with high basicity burden prepared from EAFD was proposed. Thermodynamic calculation and test study were combined, the main phase transformation during the calcified carbothermal reduction roasting process of EAFD was analyzed to explore calcified carbothermal reduction reaction behavior and path. It is shown that the reduction of ZnFe2O4 to Fe0.85-xZnxO inhibits the reduction and volatilization of zinc when the molar ratio of carbon to oxygen is less than 0.6 and the temperature is lower than 1000 ℃. While the calcification of ZnFe2O4 and Fe0.85-xZnxO to Ca2Fe2O5 by adding CaO can promote the reduction of zinc and Ca2Fe2O5 will be further reduced. When the temperature is lower than 1100 ℃ and nc/no< 1.0, the reaction path of calcified carbothermal reduction roasting is ZnFe2O4 + CaO → Ca2Fe2O5 + ZnO → Ca2Fe2O5 + Zn (g) and Fe0.85-xZnxO + CaO → Ca2Fe2O5 + ZnO + FeO → Ca2Fe2O5 + Fe + Zn (g). Both of these reactions can promote the release of zinc. The results show that CaO can promote zinc volatilization in the range of nc/no 0.4～1.2 and calcination temperature 1000～1100 ℃. The dezincification rate of calcified carbon thermal reduction roasting at nc/no = 1.0 is close to that at nc/no = 1.2, both of which are about 90%, so the consumption of carbon reducing agent can be reduced and energy consumption can be saved.

The building sector is one of the largest energy user and carbon emitters globally. To increase the utilization rate of renewable energy and reduce carbon dioxide emissions, the optimal technical scheme of active public institutions and coupled utilization of renewable energy is studied. In this study, the energy consumption of three types of public institutions in various regions of China was simulated by using DeST building energy consumption software, combined with energy conversion efficiency and data released by the National Bureau of Statistics, and the total energy demand and total energy supply of public institutions were predicted using the load density method. Based on the coupling mechanism of the MARKAL model, the optimal proportion of renewable energy in the energy supply of public buildings in different regions is determined. Through the study of the number of public institutions in various regions of China, energy consumption characteristics, construction area, and other related data, the reverse energy flow method is creatively proposed, and the active and renewable energy coupling algorithm from the energy demand side of public institutions to the energy supply side is established. The results show that the central region has the highest utilization rate of renewable energy in the public sector, reaching 36.18%. The use of renewable energy in public buildings in hot summer and warm winter zones decreased to 35.08%, and it was 12.82% in cold zones. By 2025, the proportion of renewable energy resources in China is expected to reach 29.2%. The energy coupling model and algorithm constructed in this paper can provide a basis for the coupling macro configuration of renewable energy in public institutions in China.

A new network architecture of SCN (service customized networking) was systematically expounded, which provided a novel network underlying capability and usage method for Internet applications.TCP/IP network architecture served the application with “best effort” quality of service, while SCN converted the subject-object relationship between the application and network that allowed the application to “on-demand customize” the QoS of networks.From the perspective of applications, three major connotations “declarable”, “fine-grained” and “end-to-end” were excavated to deduce the overall design of SCN, where a concrete SCN architecture and a feasible SCN system realization were present.In future, SCN can be applied in scenarios of man-machine-things, such as remote industrial control, and augment reality, to provide a novel and ideal means for NaaS (network as a service).

The popularization of intelligent healthcare devices and big data analytics significantly boosts the development of Smart Healthcare Networks (SHNs). To enhance the precision of diagnosis, different participants in SHNs share health data that contain sensitive information. Therefore, the data exchange process raises privacy concerns, especially when the integration of health data from multiple sources (linkage attack) results in further leakage. Linkage attack is a type of dominant attack in the privacy domain, which can leverage various data sources for private data mining. Furthermore, adversaries launch poisoning attacks to falsify the health data, which leads to misdiagnosing or even physical damage. To protect private health data, we propose a personalized differential privacy model based on the trust levels among users. The trust is evaluated by a defined community density, while the corresponding privacy protection level is mapped to controllable randomized noise constrained by differential privacy. To avoid linkage attacks in personalized differential privacy, we design a noise correlation decoupling mechanism using a Markov stochastic process. In addition, we build the community model on a blockchain, which can mitigate the risk of poisoning attacks during differentially private data transmission over SHNs. Extensive experiments and analysis on real-world datasets have testified the proposed model, and achieved better performance compared with existing research from perspectives of privacy protection and effectiveness.

The time of a satellite navigation system is primarily generated by the main control station of the ground system. Consequently, when ground stations fail, there is a risk to the continuous provision of time services to the equipment and users. Furthermore, the anticipated launch of additional satellites will further strain the satellite–ground link. Next-generation satellite navigation systems will rely on time deviation measurements from inter-satellite links to independently establish and maintain a space-based time reference, enhancing the system’s reliability and robustness. The increasing number of low-Earth-orbit satellite navigation constellations provides ample resources for establishing a space-based time reference. However, this also introduces challenges, including extensive time scale computations, increased link noise, and low clock resource utilization. To address these issues, this paper proposes a Distributed Kalman Plus Weight (D-KPW) algorithm, which combines the benefits of Kalman filtering and the weighted average algorithm, balancing the performance with computational resources. Furthermore, an adaptive clock control algorithm, D-KPW (Control), is developed to account for both the short-term and long-term frequency stability of the time reference. The experimental results demonstrate that the frequency stability of the time reference established by the D-KPW (Control) algorithm reaches 7.40×10−15 and 2.30×10−15 for sampling intervals of 1000 s and 1,000,000 s, respectively, outperforming traditional algorithms such as ALGOS. The 20-day prediction error of the time reference is 1.55 ns. Compared to traditional algorithms such as AT1, ALGOS, Kalman, and D-KPW, the accuracy improves by 65%, 65%, 66%, and 67%, respectively.

Abstract Background This study investigated the role of apoptosis-related genes in thoracic aortic aneurysms (TAA) and provided more insights into TAA's pathogenesis and molecular mechanisms. Material/methods Two gene expression datasets (GSE9106 and GSE26155) were retrieved from the Gene Expression Omnibus (GEO) database. Apoptosis-related genes were obtained from the KEGG apoptosis pathway (hsa04210). Differentially expressed apoptosis-related genes were identified by performing differential expression analysis using limma for TAA blood and tissue samples. GO and KEGG enrichment analysis of the differentially expressed apoptosis genes was performed using the Metascape web tool. The miRNA-mRNA regulatory network was reconstructed using the ENCORI and miRDB databases, and functional enrichment analysis was performed on the related miRNAs using the miEAA tool. The correlation between the expression levels of differentially expressed apoptosis-related genes and genes involved in immune infiltration in TAA was calculated using the CIBERSORT algorithm. The apoptosis modification patterns mediated by differentially expressed apoptosis-related genes were systematically assessed in TAA samples. Results A total of 9 differentially-expressed apoptosis-related genes were identified in TAA samples compared with normal samples. 150 miRNAs and 6 mRNAs regulatory networks were reconstructed using the ENCORI and miRDB databases. Immune infiltration analysis revealed that the GZMB had the strongest positive correlation with activated NK cells and the DFFA presented the strongest positive correlation with T cells follicular helper. 3 distinct apoptosis modification patterns mediated by 9 differentially-expressed apoptosis-related genes were identified. They differ in immune characteristics and drug sensitivity, and their biological functions in these subtypes were further studied. Conclusions This study identified key apoptosis-related genes related to TAA and evaluated the modification patterns of key apoptosis genes in TAA, providing insights into potential targets and mechanisms of TAA pathogenesis and progression.

Federated learning for edge computing is a promising solution in the data booming era, which leverages the computation ability of each edge device to train local models and only shares the model gradients to the central server. However, the frequently transmitted local gradients could also leak the participants’ private data. To protect the privacy of local training data, lots of cryptographic-based Privacy-Preserving Federated Learning (PPFL) schemes have been proposed. However, due to the constrained resource nature of mobile devices and complex cryptographic operations, traditional PPFL schemes fail to provide efficient data confidentiality and lightweight integrity verification simultaneously. To tackle this problem, we propose a Verifiable Privacy-preserving Federated Learning scheme (VPFL) for edge computing systems to prevent local gradients from leaking over the transmission stage. Firstly, we combine the Distributed Selective Stochastic Gradient Descent (DSSGD) method with Paillier homomorphic cryptosystem to achieve the distributed encryption functionality, so as to reduce the computation cost of the complex cryptosystem. Secondly, we further present an online/offline signature method to realize the lightweight gradients integrity verification, where the offline part can be securely outsourced to the edge server. Comprehensive security analysis demonstrates the proposed VPFL can achieve data confidentiality, authentication, and integrity. At last, we evaluate both communication overhead and computation cost of the proposed VPFL scheme, the experimental results have shown VPFL has low computation costs and communication overheads while maintaining high training accuracy.

Abstract Background Previous studies have not investigated the association between medical insurance and instrumental activity of daily living (IADL) disability. To fulfill this research gap, this study aims to explore the association between Urban and Rural Resident Basic Medical Insurance (URRBMI) and IADL disability among middle-aged and older adults in China. Methods The data of this study were sourced from the 2018 wave of China Health and Retirement Longitudinal Study (CHARLS). Logit regression models were used to analyze the association between URRBMI and odds of suffering from IADL disability. Furthermore, we used IV-Probit regression model to address the potential endogeneity problem. Moreover, propensity score matching and generalized random forest model were employed to conduct robustness checks. Results The logit regression results reveal that URRBMI participation was significantly related to reduced odds of suffering from IADL disability by 39.86% after adjusting for the control variables (p

Abstract Publish/subscribe(pub/sub) systems are widely used in large-scale messaging systems due to their asynchronous and decoupled nature. With the population of pub/sub cloud services, the privacy protection problem of pub/sub systems has started to emerge, and events and subscriptions are exposed when executing event matching on untrustworthy cloud brokers. However, as the number of subscriptions increases, the effectiveness of the previous confidentiality protection approaches declines drastically. In this paper, we propose SBM (scalable blind matching), an effective confidentiality protection scheme for pub/sub systems. To the best of our knowledge, SBM is the first scheme that applies order-preserving encryption algorithm to protect the system’s confidentiality and ensure its scalability. In this scheme, SBM-I is highly effective in subscription matching but is unable to achieve ideal security IND-OCPA, whereas SBM-II is suggested to ensure system security and SGX is used to reduce interaction and boost ciphertext matching performance. The experiment demonstrates that this method has better matching performance compared to others: the average matching time of SBM-I is 3–4 orders of magnitude faster than the matching algorithm MP and SGX-based algorithm SCBR when the number of subscriptions is 500,000, and the average matching time of SBM-II is 40 times faster than MP and 24 times than SCBR.

Textual Emotion Analysis (TEA) aims to extract and analyze user emotional states in texts. Various Deep Learning (DL) methods have developed rapidly, and they have proven to be successful in many fields such as audio, image, and natural language processing. This trend has drawn increasing researchers away from traditional machine learning to DL for their scientific research. In this paper, we provide an overview of TEA based on DL methods. After introducing a background for emotion analysis that includes defining emotion, emotion classification methods, and application domains of emotion analysis, we summarize DL technology, and the word/sentence representation learning method. We then categorize existing TEA methods based on text structures and linguistic types: text-oriented monolingual methods, text conversations-oriented monolingual methods, text-oriented cross-linguistic methods, and emoji-oriented cross-linguistic methods. We close by discussing emotion analysis challenges and future research trends. We hope that our survey will assist readers in understanding the relationship between TEA and DL methods while also improving TEA development.

Variations in winter sea ice concentrations in the Barents-Kara seas have been linked with extreme heat events over Eurasia in following spring over the past two decades, suggest statistical analyses of observational data.

IntroductionSchizophrenia is a severe and chronic psychiatric disorder with hereditary risk up to 80% as previous studies indicated. Several researches have demonstrated a significant association between schizophrenia and microduplications that overlap the vasoactive intestinal peptide receptor 2 gene (VIPR2).MethodsTo further investigate potential causal VIPR2 gene variants, all exons and un-translated portions of the VIPR2 gene were sequenced using amplicon targeted resequencing in 1804 Chinese Han patients with schizophrenia and 996 healthy counterparts in the present study.ResultsNineteen rare non-synonymous mutations and 1 frameshift deletion was identified for schizophrenia, among which 5 variants have never been reported so far. Frequencies of rare non-synonymous mutations were significantly different between the two groups. Specifically, the non-synonymous mutation rs78564798 (Pallele = 0.006) as well as two rare variations in the VIPR2 gene’s introns (rs372544903, Pallele = 0.026 and a novel mutation, chr7:159034078, GRCh38, Pallele = 0.048) were significantly associated with schizophrenia.DiscussionOur findings add new evidence that the functional and probable causative variants of VIPR2 gene may play an important role in susceptibility to schizophrenia. Further studies on validations of VIPR2’s function in the etiology of schizophrenia are warranted.

Abstract Background The aim of this study was to draw a comprehensive mutational landscape of nasopharyngeal carcinoma (NPC) tumors and identify the prognostic factors for distant metastasis-free survival (DMFS). Methods A total of forty primary nonkeratinizing NPC patients underwent targeted next-generation sequencing of 450 cancer-relevant genes. Analysis of these sequencing and clinical data was performed comprehensively. Univariate Cox regression analysis and multivariate Lasso-Cox regression analyses were performed to identify factors that predict distant metastasis and construct a risk score model, and seventy percent of patients were randomly selected from among the samples as a validation cohort. A receiver operating characteristic (ROC) curve and Harrell’s concordance index (C-index) were used to investigate whether the risk score was superior to the TNM stage in predicting the survival of patients. The survival of patients was determined by Kaplan–Meier curves and log-rank tests. Results The twenty most frequently mutated genes were identified, such as KMT2D, CYLD, and TP53 et al. Their mutation frequencies of them were compared with those of the COSMIC database and cBioPortal database. N stage, tumor mutational burden (TMB), PIK3CA, and SF3B1 were identified as predictors to build the risk score model. The risk score model showed a higher AUC and C-index than the TNM stage model, regardless of the training cohort or validation cohort. Moreover, this study found that patients with tumors harboring PI3K/AKT or RAS pathway mutations have worse DMFS than their wild-type counterparts. Conclusions In this study, we drew a mutational landscape of NPC tumors and established a novel four predictor-based prognostic model, which had much better predictive capacity than TNM stage.

Abstract Deep neural networks are widely adopted powerful tools for perceptual tasks. However, recent research indicated that they are easily fooled by adversarial examples, which are produced by adding imperceptible adversarial perturbations to clean examples. Here the steganalysis rich model (SRM) is utilized to generate noise feature maps, and they are combined with RGB images to discover the difference between adversarial examples and clean examples. In particular, a two‐stream pseudo‐siamese network that fuses the subtle difference in RGB images with the noise inconsistency in noise features is proposed. The proposed method has strong detection capability and transferability, and can be combined with any model without modifying its architecture or training procedure. The extensive empirical experiments show that, compared with the state‐of‐the‐art detection methods, the proposed approach achieves excellent performance in distinguishing adversarial samples generated by popular attack methods on different real datasets. Moreover, this method has good generalization, it trained by a specific adversary can defend against other adversaries effectively.

The proliferation of powerful facial recognition systems poses a serious threat to user privacy. Attackers could train highly accurate facial recognition models using public data on social platforms. Therefore, recent works have proposed image pre-processing techniques to protect user privacy. Without affecting people's normal viewing, these techniques add special noises into images, so that it would be difficult for attackers to train models with high accuracy. However, existing protection techniques are mainly designed for image data protection, and they cannot be directly applied for video data because of high computational overhead. In this paper, we propose an efficient protection method for video privacy that exploits unique features of video protection to eliminate computation redundancy for computational acceleration. The evaluation results under various benchmarks demonstrate that our method significantly outperforms the traditional methods by reducing computation overhead by 35.5%.

ObjectiveA systematic review and meta-analysis was performed to evaluate the prevalence of suicide ideation among HIV/AIDS patients in China.MethodsSystematic search of CNKI, Wanfang, China biology medicine database, Weipu, EMBASE, Web of science and PubMed for studies related to the suicide ideation of HIV/AIDS patients. The incidence of suicide ideation of HIV / AIDS patients in China was investigated by meta-analysis.ResultsA total of 16 studies were included (n = 6,174). The incidence of suicidal ideation in HIV/AIDS patients was 30.6% (95%CI: 21.4–39.9%). The results of subgroup analysis showed that the incidence of suicidal ideation in male was 36.1%, which was higher than that in female (32.8%), homosexual patients (39.7%) higher than heterosexual patients (27.1%), 2013–2021 survey (35.2%) higher than 2003–2012 survey (26.5%), the unmarried patients (39.6%) were higher than the married patients (34.5%), the patients diagnosed >1 year (28.4%) were higher than the patients diagnosed 400 cells/ul group (19.8%).ConclusionThe incidence of suicide ideation in HIV/AIDS patients in China is relatively high.

Background and aimImplantable cardiac monitors (ICM) can facilitate the detection of asymptomatic atrial fibrillation episodes. We performed a systematic review and meta-analysis to investigate whether ICM can prevent stroke in patients with prior stroke and risk factors for stroke.MethodsThis study included randomized controlled trials comparing ICM with conventional (non-ICM) external cardiac monitoring in patients with prior stroke and risk factors for stroke. We searched Medline, Embase, and CENTRAL from inception until January 5, 2022, without language restriction. Quantitative pooling of the data was undertaken using a random-effects model. The primary outcome was ischemic stroke at the longest follow-up.ResultsFour trials comprising 7237 patients were included. ICM was significantly associated with decreased risk of ischemic stroke (RR 0.76; 95% CI, 0.59-0.97; moderate-quality evidence) in patients with prior stroke and risk factors for stroke. ICM was associated with higher detection of atrial fibrillation (RR 4.21, 95% CI 2.26-7.85) and use of oral anticoagulants (RR 2.29, 95% CI 2.07-2.55).ConclusionsICM results in a significantly lower risk of ischemic stroke than conventional (non-ICM) external cardiac monitoring in patients with prior stroke and risk factors for stroke. Due to the clinical heterogeneity of study population and limited related studies, more trials were needed to furtherly explore the topic in patients with prior stroke or high risk of stroke.

The East Asian trough (EAT) is an important member of the East Asian winter monsoon system, profoundly influencing the local climate in winter. In this study, we report the phase-shift mode of the monthly EAT variations from December to February based on the extended empirical orthogonal function (EEOF) method. Associated with the phase-shift mode are the noticeable opposite air temperature anomalies over East Asia between December and February, consistent with the recently reported warm early winter and cold late winter (or vice versa). Possible mechanism analysis indicates that the EAT phase-shift mode is closely linked with the anomalous North Atlantic Oscillation (NAO). By exciting a zonal Rossby wave train in December, an anomalous NAO could lead to significantly simultaneous changes in the EAT. However, in January, the NAO-excited Rossby wave train could hardly reach East Asia and has a weak influence on the EAT. In contrast, anomalous NAO in January can significantly influence Arctic Sea ice, causing significant sea ice anomalies over the Barents-Kara (BK) Sea. The BK Sea ice anomalies can persist to the following February, which further excites a Rossby wave train propagating to East Asia, leading to the opposite anomalous EAT in February relative to that in December. Therefore, through the exciting Rossby wave train in December and its resultant BK Sea ice anomalies in February, the NAO contributes to the phase shift of the anomalous EAT from December to February.

The blockchain-enabled smart logistics market is expected to grow worth USD 1620 billion and at a compound annual growth rate of 62.4%. Smart logistics ensures intelligence infrastructure, logistics automation, real-time analysis of supply chain data synchronization of the logistics process, cost transparency, unbroken shipment tracking all the way down to the transportation route, etc. In the smart logistics domain, significant advancement and growth of the Internet of Things (IoT) sensors are evident. However, the connectivity of IoT systems, including Tactile Internet, without proper safeguards creates vulnerabilities that can still be deliberately or inadvertently cause disruption. In view of this, we primarily notice two key issues. Firstly, the logistics domain can be compromised by a variety of natural or man-made activities, which eventually affect the overall network security. Secondly, there are thousands of entities in the supply chain network that use extensive machine-learning algorithms in many scenarios, and they require high-power computational resources. From these two challenges, we note that the first concern can be addressed by adding blockchain to IoT logistic networks. The second issue can be addressed using 6G. This will support 1-μs latency communications, support seamless computing at the edges of networks, and autonomously predict the best optimal location for edge computing. Motivated by this, we have highlighted motivational examples to show the necessity to integrate 6G and blockchain in smart logistic networks. Then, we have proposed a 6G and blockchain-enabled smart logistic high-level framework. We have presented the key intrinsic issues of this framework mainly from the security and resource management context. In this paper, recent state-of-the-art advances in blockchain enabled next-generation smart logistic networks are analyzed. We have also examined why 6G and not 5G would be compatible with the smart network. We have introduced five different use cases of blockchain technology in smart logistics. Later, this paper discusses some important concerns that blockchain in smart logistics might face. We have also provided potential solutions to tackle these concerns.

BackgroundThough acute kidney injury (AKI) in the context of aneurysmal subarachnoid hemorrhage (aSAH) worsens short-term outcomes, its impact on long-term survival is unknown.AimWe aimed to evaluate the association between long-term mortality and AKI during hospitalization for aSAH.MethodsThis was a retrospective study of patients who survived >12 months after aSAH. All patients were evaluated at West China Hospital, Sichuan University, between December 2013 and June 2019. The minimum follow-up time was over 1 year. the maximum follow-up time was about 7.3 years. AKI was defined by the KDIGO (The Kidney Disease Improving Global Outcomes) guidelines, which stratifies patients into three stages of severity. The primary outcome was long-term mortality, which was analyzed with Kaplan-Meier curves and Cox proportional hazards models.ResultsDuring this study period, 238 (9.2%) patients had AKI among 2,592 patients with aSAH. We confirmed that AKI during care for aSAH significantly increased long-term mortality (median 4.3 years of follow-up) and that risk increased with the severity of the kidney failure, with an adjusted hazard ratio (HR) of 2.08 (95% CI 1.49–2.89) for stage 1 AKI, 2.15 (95% CI 1.05–4.43) for stage 2 AKI, and 2.66 (95% CI 1.08–6.53) for stage 3 AKI compared with patients without AKI. Among patients with an AKI episode, those with renal recovery still had increased long-term mortality (HR 1.96; 95% CI 1.40–2.74) compared with patients without AKI but had better long-term outcomes than those without renal recovery (HR 0.51, 95% CI 0.27–0.97).ConclusionsAmong 12-month survivors of aSAH, AKI during their initial hospitalization for aSAH was associated with increased long-term mortality, even for patients who had normal renal function at the time of hospital discharge. Longer, multidisciplinary post-discharge follow-up may be warranted for these patients.

Due to the increasing cyber-attacks, various Intrusion Detection Systems (IDSs) have been proposed to identify network anomalies. Most existing machine learning-based IDSs learn patterns from the features extracted from network traffic flows, and the deep learning-based approaches can learn data distribution features from the raw data to differentiate normal and anomalous network flows. Although having been used in the real world widely, the above methods are vulnerable to some types of attacks. In this paper, we propose a novel attack framework, Anti-Intrusion Detection AutoEncoder (AIDAE), to generate features to disable the IDS. In the proposed framework, an encoder transforms features into a latent space, and multiple decoders reconstruct the continuous and discrete features, respectively. Additionally, a generative adversarial network is used to learn the flexible prior distribution of the latent space. The correlation between continuous and discrete features can be kept by using the proposed training scheme. Experiments conducted on NSL-KDD, UNSW-NB15, and CICIDS2017 datasets show that the generated features indeed degrade the detection performance of existing IDSs dramatically.

ObjectivesSerum neurofilament light chain (NfL) is a biomarker for neuroaxonal damage, and S100B is a blood marker for cerebral damage. In the present study, we investigated the relationship between serum NfL and S100B levels, severity, and outcomes in patients with aneurysmal subarachnoid hemorrhage (aSAH).MethodsWe prospectively recruited aSAH patients and healthy controls between January 2016 and January 2021. Clinical results included mortality and poor outcomes (modified Rankin scale score of 3-6) after 6 months. The ultrasensitive Simoa technique was used to evaluate NfL levels in the blood, and ELISA was used to detect S100B.ResultsA total of 91 patients and 25 healthy controls were included in the study, with a death rate of 15.4%. The group of aSAH patients had significantly higher serum levels of NfL and S100B (P < 0.01). Furthermore, the levels of NfL and S100B increased when the Hunt-Hess, World Federation of Neurological Surgeons (WFNS), and Fisher grades increased (P < 0.01). Serum NfL and S100B levels were linked to poor prognoses and low survival rates. The blood levels of NfL and S100B were found to be an independent predictor related to 6-month mortality in multivariable analysis. Additionally, the areas under the curves for NfL and S100B levels in serum were 0.959 and 0.912, respectively; the clinical diagnostic critical thresholds were 14.275 and 26.54 pg/ml, respectively; sensitivities were 0.947 and 0.921, and specificities were 0.849 and 0.811.ConclusionsThe NfL and S100B values for aSAH patients within 12 days of admission were considerably associated with Hunt-Hess grade, WFNS, and Fisher grade. The higher the grade, the higher the NfL and S100B value, and the poorer the prognosis. Serum NfL and S100B values could be feasible biomarkers to predict the clinical prognosis of patients with aSAH.

Evolutionary information is essential for the protein annotation. The number of homologs of a protein retrieved is correlated with the annotations related to the protein structure or function. With the continuous increase in the number of available sequences, fast and effective homology detection methods are particularly important. To increase the efficiency of homology detection, a novel method named CONVERT is proposed in this paper. This method regards homology detection as a translation task and presents a concept of representative protein. Representative proteins are not real proteins. A representative protein corresponds to a protein family, it contains the characteristics of the family. Our method employs the seq2seq model to establish the many-to-one relationship between proteins and representative proteins. Based on the many-to-one relationship, CONVERT converts protein sequences into fixed-length numerical representations, so as to increase the efficiency of homology detection by using numerical comparison instead of sequence alignment. For alignment results, our method adopts ranking to obtain a sorted list. We evaluate the proposed method on two benchmark datasets. The experimental results show that the performances of our method are comparable with the state-of-the-art methods. Meanwhile, our method is ultra-fast and can obtain results in hundreds of milliseconds.

Due to the COVID-19 epidemic, face-to-face team working has changed into distanced work from home. Modern, decentralized digital communication environments are changing with the availability of new technologies and the development of new real-world applications, which lead to novel challenges in security and privacy protection. 5G/6G mobile applications, the smart Internet of Things (IoT) devices, big data applications, and cloud systems are developing to better meet new requirements. Mobile–cloud architecture is emerging as 5G /6G mobile IoT devices are generating large volumes of data which need cloud infrastructure to process. Many IoT systems and cloud systems are decentralized, and new security and privacy protection solutions are emerging in decentralized networks. The increasing interdependence of IT solutions accepted by society has led to a sharp increase in data. As a result, the chances of data leakage or privacy infringement also increase, along with the need for new solutions for digital security and privacy protection.

Due to uncertainties and time-varying parameters in design, manufacturing, and commissioning, many structural systems often exhibit uncertain and dynamic properties. These systems need time-dependent reliability analysis to help effectively estimate the safe state during their lifecycle. However, one of the challenging issues in doing so lies in computational efficiency. This paper develops an active learning Kriging technique to improve the computational efficiency of time-dependent reliability analysis. The Kriging model is employed first as a response surface to fit the extreme value response of time-dependent limit state functions. The most probable point of the Kriging response surface is then determined by solving an optimization problem in terms of a cumulative distribution function. Further, an adaptive iterative algorithm is developed to prepare the sampling points for updating the Kriging model based on an adaptive uniform design. Monte Carlo simulations are thus performed to facilitate evaluation using the final generated Kriging response surface. Several case studies are undertaken to test and validate the effectiveness of the proposed method and to demonstrate its applicability to engineering problems.

Pulmonary route of drug delivery has drawn significant attention due to the limitations associated with conventional routes and available treatment options. Drugs administered through pulmonary route has been an important research area that focuses on to developing effective therapeutic interventions for asthma, chronic obstructive pulmonary disease, tuberculosis, lung cancer etc. The intravenous route has been a natural route of delivery of proteins and peptides but associated with several issues including high cost, needle-phobia, pain, sterility issues etc. These issues might be addressed by the pulmonary administration of macromolecules to achieving an effective delivery and efficacious therapeutic impact. Efforts have been made to develop novel drug delivery systems (NDDS) such as nanoparticles, microparticles, liposomes and their engineered versions, polymerosomes, micelles etc to achieving targeted and sustained delivery of drug(s) through pulmonary route. Further, novel approaches such as polymer-drug conjugates, mucoadhesive particles and mucus penetrating particles have attracted significant attention due to their unique features for an effective delivery of drugs. Also, use of semi flourinated alkanes is in use for improvising the pulmonary delivery of lipophilic drugs. Present review focuses on to unravel the mechanism of pulmonary absorption of drugs for major pulmonary diseases. It summarizes the development of interventional approaches using various particulate and vesicular drug delivery systems. In essence, the orchestrated attempt presents an inflammatory narrative on the advancements in the field of pulmonary drug delivery.

Intelligent vehicles are advancing at a fast speed with the improvement of automation and connectivity, which opens up new possibilities for different cyber-attacks, including in-vehicle attacks (e.g., hijacking attacks) and vehicle-to-everything communicationattacks (e.g., data theft). These problems are becoming increasingly serious with the development of 4G LTE and 5G communication technologies. Although many efforts are made to improve the resilience to cyber attacks, there are still many unsolved challenges. This paper first identifies some major security attacks on intelligent connected vehicles. Then, we investigate and summarize the available defences against these attacks and classify them into four categories: cryptography, network security, software vulnerability detection, and malware detection. Remaining challenges and future directions for preventing attacks on intelligent vehicle systems have been discussed as well.

Abstract Background The Reelin (RELN) gene encodes the protein reelin, which is a large extracellular matrix glycoprotein that plays a key role in brain development. Additionally, this protein may be involved in memory formation, neurotransmission, and synaptic plasticity, which have been shown to be disrupted in schizophrenia (SCZ). A decreasing trend in the expression of RELN mRNA in the brain and peripheral blood of SCZ patients has been observed. There is a need to determine whether changes in RELN mRNA expression in SCZ patients are the result of long-term antipsychotic treatment rather than the etiological characteristics of schizophrenia. The expression levels of RELN mRNA in the peripheral blood of 48 healthy controls and 30 SCZ patients before and after 12-weeks of treatment were measured using quantitative real-time PCR. Results The expression levels of RELN mRNA in the SCZ group were significantly lower than that of healthy controls; however, after 12-weeks of antipsychotic treatment, RELN mRNA levels were significantly increased in the SCZ group. Conclusion The up-regulation of RELN mRNA expression was current in SCZ patients after antipsychotic treatment, suggesting that the changes in RELN mRNA expression were related to the effect of the antipsychotic treatment.

With the deployment of more and more 5g networks, the limitations of 5g networks have been found, which undoubtedly promotes the exploratory research of 6G networks as the next generation solutions. These investigations include the fundamental security and privacy problems associated with 6G technologies. Therefore, in order to consolidate and solidify this foundational research as a basis for future investigations, we have prepared a survey on the status quo of 6G security and privacy. The survey begins with a historical review of previous networking technologies and how they have informed the current trends in 6G networking. We then discuss four key aspects of 6G networks – real-time intelligent edge computing, distributed artificial intelligence, intelligent radio, and 3D intercoms – and some promising emerging technologies in each area, along with the relevant security and privacy issues. The survey concludes with a report on the potential use of 6G. Some of the references used in this paper along and further details of several points raised can be found at: security-privacyin5g-6g.github.io.

Abstract Background To identify the spatial patterns of regional lymph node failure of locally advanced hypopharyngeal squamous cell carcinoma (SCC) after first-line treatment with surgery and/or intensity-modulated radiotherapy (IMRT). Methods We retrospectively obtained the clinicopathological characters of 123 hypopharyngeal SCC patients, and investigated the patterns of regional lymph node failure. Univariate and multivariate logistic regression were used to determine the risk factors of regional lymph node failure. Results Forty patients (32.5% of total patients) were suffered regional lymph node failure. In these patients, the ipsilateral neck level II nodal failure account for 55.0% (22/40) followed by level III 30.0% (12/40), level VIb 15.0% (6/40), level VII 15.0% (6/40), and level IV 5.0% (2/40). In addition, 17.5% (7/40) patients suffered contralateral neck level II nodal failure and 7.5% (3/40) patients suffered level III nodal failure. The common failure levels were the II (7/46, 15.2%), III (4/46, 8.7%), VIb (4/46, 8.7%), and VII (5/46, 10.9%) for treatment by surgery. The lymph node recurrence and persistent disease at levels II (19/77, 24.7%) and III (10/77, 13.0%) remained the major cause of failure following curative intent of IMRT. The postoperative radiation significantly decreased the risk of regional lymph node failure (OR = 0.082, 95% CI: 0.007–1.000, P = 0.049); and the radiologic extranodal extension significantly increased the risk of regional lymph node failure (OR = 11.07, 95% CI: 2.870–42.69, P

Distributed machine learning (DML) can realize massive dataset training when no single node can work out the accurate results within an acceptable time. However, this will inevitably expose more potential targets to attackers compared with the non-distributed environment. In this paper, we classify DML into basic-DML and semi-DML. In basic-DML, the center server dispatches learning tasks to distributed machines and aggregates their learning results. While in semi-DML, the center server further devotes resources into dataset learning in addition to its duty in basic-DML. We firstly put forward a novel data poison detection scheme for basic-DML, which utilizes a cross-learning mechanism to find out the poisoned data. We prove that the proposed cross-learning mechanism would generate training loops, based on which a mathematical model is established to find the optimal number of training loops. Then, for semi-DML, we present an improved data poison detection scheme to provide better learning protection with the aid of the central resource. To efficiently utilize the system resources, an optimal resource allocation approach is developed. Simulation results show that the proposed scheme can significantly improve the accuracy of the final model by up to 20% for support vector machine and 60% for logistic regression in the basic-DML scenario. Moreover, in the semi-DML scenario, the improved data poison detection scheme with optimal resource allocation can decrease the wasted resources for 20-100%.

Cyber-Physical-Social System (CPSS) provides users secure and high-quality mobile service applications to share and exchange data in the cyberspace and physical world. With the explosive growth of data, it is necessary to introduce cloud storage service, which allows devices frequently resort to the cloud for data storage and sharing, into CPSS. In this paper, we propose a data storage and sharing scheme for CPSS with the help of cloud storage service. Since data integrity assurance is an inevitable problem in cloud storage, we first design a secure and efficient data storage scheme based on the technology of public auditing and bilinear map, which also ensures the security of the verification. In order to meet the real-time and reliability requirements of the CPSS, the rewards of timeliness incentive and effectiveness incentive are considered in the scheme. Secondly, based on the proposed storage scheme and ElGamal encryption, we propose a lightweight access model for users to access the final data processed by cloud server. We formally prove the security of the proposed scheme, and conduct performance evaluation to validate its high efficiency. The experimental results show that the proposed scheme has lower overheads in communication and access as compared to the technique CDS.

The Internet has a significant impact on people’s daily lives and work. Recent studies claim that Artificial Intelligence (AI) has resulted in advances of many scientific and tech- nological fields, that is, AI-based medicine, AI-based trans- portation, and AI-based finance. The era of AI is upon us. As one of the biggest concerns, security is of significance to the development of a sustainable, resilient, and prosperous Internet ecosystem. However, cybersecurity still faces many challenging issues such as intrusion detection, privacy pro- tection, proactive defense, anomalous behaviors, advanced threat detection, and so on. In addition, many threat variations emerge and spread continuously. AI-assisted self-adaptable approaches are expected to deal with these security issues. A joint consideration of the interweaving nature between AI and cybersecurity is a key factor for driving future secure Internet.

The current COVID-19 pandemic and its uncertainty have given rise to various myths and rumours. These myths spread incredibly fast through social media, which has caused massive panic in society. In this paper, we comprehensively examined the prevailing myths related to COVID-19 in regard to the diffusion of myths, people's engagement with myths and people's subjective emotions to myths. First, we classified the myths into five categories: spread of infection, preventive measures, detection measures, treatment and miscellaneous. We collected the tweets about each category of myths from 1 January to 7 July in the year 2020. We found that the vast majority of the myth tweets were about the spread of the infection. Next, we fitted myths spreading with the SIR epidemic model and calculated the basic reproduction number$R_0$ for each category of myths. We observed that the myths about the spread of infection and preventive measures propagated faster than other categories of myths, and more miscellaneous myths raised and quickly spread from later June 2020. We further analyzed people's emotions evoked by each category of myths and found that fear was the strongest emotion in all categories of myths and around 64% of the collected tweets expressed the emotion of fear. The study in this paper provides insights for authorities and governments to understand the myths during the eruption of the pandemic, and hence enable targeted and feasible measures to demystify the most concerned myths in due time.

With the emergence of intelligent terminals, the Internet of Vehicles (IoV) has been drawing great attention by taking advantage of mobile communication technologies. However, high computation complexity, collaboration communication overhead and limited network bandwidths bring severe challenges to the provision of latency-sensitive IoV services. To overcome these problems, we design a cloud-edge cooperative content-delivery strategy in asymmetrical IoV environments to minimize network latency by providing optimal computing, caching and communication resource allocation. We abstract the joint allocation issue of heterogeneous resources as a queuing theory-based latency minimization objective. Next, a new deep reinforcement learning (DRL) scheme works in each network node to achieve optimal content caching and request routing on the basis of the perceptive request history and network state. Extensive simulations show that our proposed strategy has lower network latency compared with the current solutions in the cloud-edge collaboration system and converges fast under different scenarios.

Phytophthora nicotianae causes black shank, a serious soil-borne disease, in tobacco. In this study, the Bacillus strain GUMT319 was isolated from the rhizosphere of healthy tobacco plants grown in a field in Guizhou with a high incidence of tobacco black shank. Genome sequencing revealed that GUMT319 contained a single circular chromosome 3,940,023 bp in length, with 4,053 predicted genes and an average GC content of 46.6%. Based on phylogenomic analyses, GUMT319 was designated as Bacillus velezensis. The genome of GUMT319 contained more than 60 genes and 13 gene clusters that have previously been found to be active in antifungal mechanisms, biofilm formation, and chemotaxis motility. Additionally, confocal laser scanning microscopy and scanning electron microscopy showed that GUMT319 formed a spatially organized biofilm in vivo. In addition, lauric acid negatively regulated biofilm formation. This is the first study to report that nicotine in tobacco root exudates was a chemoattractant for biocontrol Bacillus strains. In this study, we identified new interactions between beneficial microorganisms and tobacco roots in the rhizosphere. Moreover, dual culture tests in vitro showed that GUMT319 inhibited the growth of P. nicotianae and also displayed inhibitory effects against eight other plant pathogens, namely, Colletotrichum scovillei, Colletotrichum capsici, Fusarium carminascens, Sclerotinia sclerotiorum, Alternaria alternata, Phomopsis sp., Phyllosticta sorghina, and Exserohilum turcicum. Furthermore, GUMT319 exhibited > 70% control efficiency against tobacco black shank in field experiments conducted in 2018–2020. Thus, GUMT319 was more effective in controlling the incidence of tobacco black shank than other treatments including fungicide application. Overall, these results suggested that GUMT319 (B. velezensis) could be used as a potential biocontrol agent against tobacco black shank.

The combined effect of the Pacific–Japan (PJ) pattern and Mediterranean–northern Eurasia (MnE) pattern on East Asian surface air temperature (SAT) during summer is investigated using the Japanese 55-year reanalysis and Climatic Research Unit SAT data over the period of 1958–2016. The results show that the combination of the two patterns in different phases can result in different SAT anomalies. During the in-phase PJ-MnE years, the overlapping of opposite signs of the atmospheric circulations associated with the PJ and MnE patterns results in weak atmospheric circulation and SAT anomalies in central East Asia; during these years, the significant SAT anomalies are over northern East Asia. In contrast, during the out-of-phase PJ-MnE years, the overlapping of the same signs of the atmospheric circulations associated with the PJ and MnE patterns leads to significant atmospheric circulation and SAT anomalies in central East Asia and northern Asia. The analysis in this study indicates that to better understand and predict the variability of East Asian summer SATs, the combined effect of the PJ and MnE patterns should be taken into account.

It is well known that blink, yawn, and heart rate changes give clue about a human's mental state, such as drowsiness and fatigue. In this paper, image sequences, as the raw data, are captured from smart phones which serve as non-contact optical sensors. Video streams containing subject's facial region are analyzed to identify the physiological sources that are mixed in each image. We then propose a method to extract blood volume pulse and eye blink and yawn signals as multiple independent sources simultaneously by multi-channel second-order blind identification (SOBI) without any other sophisticated processing, such as eye and mouth localizations. An overall decision is made by analyzing the separated source signals in parallel to determine the driver's driving state. The robustness of the proposed method is tested under various illumination contexts and a variety of head motion modes. Experiments on 15 subjects show that the multi-channel SOBI presents a promising framework to accurately detect drowsiness by merging multi-physiological information in a less complex way.

Recent years have witnessed that the new mobility Intelligent Transportation System is booming, especially the development of Vehicular Ad Hoc Networks (VANETs). It brings convenience and a good experience for drivers. Unfortunately, VANETs are suffering from potential security and privacy issues due to the inherent openness of VANETs. In the past few years, to address security and privacy-preserving problems, many identity-based privacy-preserving authentication schemes have been proposed by researchers. However, we found that these schemes fail to meet the requirements of user privacy protection and are vulnerable to attacks or have high computational complexity. Hence, we focus on enhancing privacy-preserving via authentication and achieving better performance. In this paper, first, we describe the vulnerabilities of the previous scheme. Furthermore, to enhance privacy protection and achieve better performance, we propose an efficient privacy-preserving mutual authentication protocol for secure V2V communication in VANETs. Through security analysis and comparison, we formally demonstrate that our scheme can accomplish security goals under dynamic topographical scenario compared with the previous scheme. Finally, the efficiency of the scheme is showed by performance evaluation. The results of our proposed scheme are computationally efficient compared with the previously proposed privacy-preserving authentication scheme.

In dense Wireless Local Area Networks (WLANs), high-density Access Points (APs) bring severe interference that seriously affects the experience of users, resulting in lower throughput and poor connection quality. Due to the heavy computation workload raised by the sizable networking systems and the difficulty in estimating instantaneous Channel State Information (CSI), existing works are hard to solve interference problem. In this paper, we propose a Joint Power control and Channel allocation based on Reinforcement Learning (JPCRL) algorithm combining with statistical CSI to reduce interference adaptively. Firstly, we analyze the correlation between transmit power and channel, and formulate the interference optimization as a Mixed Integer Nonlinear Programming (MINLP) problem. Secondly, we use the statistical CSI method to take the power and channel state as the state and action space, the overall throughput increment as the reward function of Q-learning, and obtain the optimal joint optimization strategy through off-line training. Moreover, for the periodic reinforcement learning process leading to resource consumption, we design an event-driven mechanism of Q-learning, which triggers online learning to refresh the optimal policy by event-driven condition and the consumption of computing resources can be reduced. The evaluation results show that the proposed algorithm can effectively improve the throughput compared with the existing scheme.

As the sensor layer of Internet of Things (IOT), enormous amount of sensor nodes are densely deployed in a hostile environment to monitor and sense the changes in the physical space. Since sensor nodes are driven with limited power batteries, it is very difficult and expensive for wireless sensor networks (WSNs) to extend network lifetime. In order to achieve reliable data transmission in WSNs, energy efficient routing protocol is a crucial issue in extending the network lifetime of a network. However, traditional routing protocols usually propagate throughout the whole network to discover a reliable route or employ some cluster heads to undertake data transmission for other nodes, which both require large amount energy consumption. In this paper, to maximize the network lifetime of the WSN, we propose a novel energy efficient region source routing protocol (referred to ER-SR). In ER-SR, a distributed energy region algorithm is proposed to select the nodes with high residual energy in the network as source routing node dynamically. Then, the source routing nodes calculate the optimal source routing path for each common node, which enables partial nodes to participate in the routing process and balances the energy consumption of sensor nodes. Furthermore, to minimize the energy consumption of data transmission, we propose an effective distance-based ant colony optimization algorithm to search the global optimal transmission path for each node. Simulation results demonstrate that ER-SR exhibits higher energy efficiency, and has moderate performance improvements on network lifetime, packet delivery ratio, and delivery delay, compared with other routing protocols in WSNs.

Introduction: Income inequality between the richest 20% and the poorest 80% in the United States has been increasing over the past two decades. Emerging evidence indicates widening disparities between the two groups in cardiovascular disease prevalence as well. However, the mechanisms behind this trend remains unclear. This analysis examines whether a similar trend exists in the levels of biomarkers and risk factors of cardiovascular disease in the United States. Methods: We conducted a serial cross-sectional analysis of a nationally representative data from the National Health and Nutrition Examination Survey (NHANES) for participants age 20 or older between 1999 and 2018. We calculated trends in age-standardized means of body mass index (BMI), systolic blood pressure (SBP), and high-density lipoproteins (HDL) and the trend in prevalence of obesity, high SBP, and low HDL by income group. Results: This analysis included 49,764 participants. Age-standardized mean BMI increased every two years by an average of 0.15 kg/m 2 among the richest 20% and by an average of 0.21 kg/m 2 among the poorest 80%. Age-standardized mean SBP decreased every two years by an average of 0.13 mm Hg among the richest 20% and by an average of 0.10 mm Hg among the poorest 80%. Age-standardized mean HDL increased every two years by an average of 0.39 mg/dL among the richest 20% and by an average of 0.19 mg/dL among the poorest 80%. When adjusted for demographic factors and time, the richest 20% had lower mean BMI (OR = −0.67, 95% CI: −0.89, - 0.44), lower mean SBP (OR = −0.72, 95% CI: −1.24, −0.20), and higher mean HDL (OR = 3.04, 95% CI: 2.46, 3.62) compared to the poorest 80% Conclusion: There are increasing disparities in cardiovascular disease biomarkers by income in the US. Between 1999 and 2018, improvement in biomarkers overwhelmingly occurred among the richest 20%

The threats of caching poisoning attacks largely stimulate the deployment of DNSSEC. Being a strong but demanding cryptographical defense, DNSSEC has its universal adoption predicted to go through a lengthy transition. Thus the DNSSEC practitioners call for a secure yet lightweight solution to speed up DNSSEC deployment while offering an acceptable DNSSEC-like defense. This paper proposes a new On-Demand Defense (ODD) scheme against cache poisoning attacks, still using but lightly using DNSSEC. In the solution, DNS operates in DNSSEC-oblivious mode unless a potential attack is detected and triggers a switch to DNSSEC-aware mode. The modeling checking results demonstrate that only a small DNSSEC query load is needed by the ODD scheme to ensure a small enough cache poisoning success rate.