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Idiopathic inflammatory myopathy (IIM) in pregnancy is uncommon but may result in complications for both mother and the fetus.In this systematic review, we summarized the current literature investigating outcomes of pregnancy related to the dermatomyositis/polymyositis (DM/PM) process.We searched PubMed, Embase, Cochrane Library, and Web of Science databases and included 61 studies reporting the disease course, pregnancy outcomes, and management of both pregnancy and DM/PM in the final analysis.The specific information of 221 pregnancies was extracted and these pregnancies were divided into three distinct forms: pregnancies after disease onset (n = 159), pregnancies with new disease onset (n = 37), and pregnancies followed by postpartum onset (n = 25). In most cases, DM/PM disease activity remained stable or improved throughout pregnancy (80.2%) and the postpartum period (83.9%). Active DM/PM during pregnancy significantly increased the risk of stillbirth or neonatal death (12% vs. 1%, P = .005) and preterm birth (34.7% vs. 11%, P .001). The rates of other poor outcomes (total fetal loss, low birth weight, and intrauterine growth retardation) were also increased in pregnancies with active disease. Mainstay treatments for active DM/PM during pregnancy are glucocorticoids and intravenous immunoglobins.The present results underline the importance of good control of myopathy in optimizing the pregnancy outcomes of women with DM/PM.

Objectives: To evaluate the role and therapeutic value of homocysteine (hcy)-inducible endoplasmic reticulum stress (ERS) protein with ubiquitin like domain 1 (Herpud1) in hcy-induced calcific aortic valve disease (CAVD). Background: The morbidity and mortality rates of calcific aortic valve disease (CAVD) remain high while treatment options are limited.Methods: In vivo , we use the low-density lipoprotein receptor (LDLR) and Herpud1 double knockout (LDLR-/-/Herpud1-/-) mice assessment of aortic valve calcification lesions, ERS activation, autophagy, and osteogenic differentiation of aortic valve interstitial cells (AVICs). We used siRNA to knock down Herpud1 in cultured Primary AVICs were isolated from mice to further validate our findings. Results: Herpud1 was highly expressed in calcified human and mouse aortic valves as well as primary aortic valve interstitial cells (AVICs). Herpud1 deficiency inhibited hcy-induced CAVD in vitro and in vivo. Herpud1 silencing activated cell autophagy, which subsequently inhibited hcy-induced osteogenic differentiation of AVICs. Hcy increased Herpud1 expression through the ERS pathway and promoted CAVD progression. ERS inhibitor 4-phenyl butyric acid (4-PBA) significantly attenuated aortic valve calcification in high methionine diet–fed low-density lipoprotein receptor-/- (LDLR-/-) mice by suppressing ERS and subsequent Herpud1 biosynthesis. Conclusions: These findings identify a previously unknown mechanism of Herpud1 upregulation in CAVD progression, suggesting that Herpud1 silencing or inhibition is a viable therapeutic strategy for arresting CAVD progression.

Bacillus subtilis A-5 has the capabilities of high-molecular-weight γ-PGA production, antagonism to plant pathogenic fungi, and salt/alkaline tolerance. This multifunctional bacterium has great potential for enhancing soil fertility and plant security in agricultural ecosystem. The genome size of B. subtilis A-5 was 4,190,775 bp, containing 1 Chr and 2 plasmids (pA and pB) with 43.37% guanine-cytosine content and 4605 coding sequences. The γ-PGA synthase gene cluster was predicted to consist of pgsBCA and factor (pgsE). The γ-PGA-degrading enzymes were mainly pgdS, GGT, and cwlO. Nine gene clusters producing secondary metabolite substances, namely, four unknown function gene clusters and five antibiotic synthesis gene clusters (surfactin, fengycin, bacillibactin, subtilosin_A, and bacilysin), were predicted in the genome of B. subtilis A-5 using antiSMASH. In addition, B. subtilis A-5 contained genes related to carbohydrate and protein decomposition, proline synthesis, pyruvate kinase, and stress-resistant proteins. This affords significant insights into the survival and application of B. subtilis A-5 in adverse agricultural environmental conditions.

Inborn errors of immunity (IEIs) unveil regulatory pathways of human immunity. We describe a new IEI caused by mutations in the GTPase of the immune-associated protein 6 (GIMAP6) gene in patients with infections, lymphoproliferation, autoimmunity, and multiorgan vasculitis. Patients and Gimap6−/− mice show defects in autophagy, redox regulation, and polyunsaturated fatty acid (PUFA)–containing lipids. We find that GIMAP6 complexes with GABARAPL2 and GIMAP7 to regulate GTPase activity. Also, GIMAP6 is induced by IFN-γ and plays a critical role in antibacterial immunity. Finally, we observed that Gimap6−/− mice died prematurely from microangiopathic glomerulosclerosis most likely due to GIMAP6 deficiency in kidney endothelial cells.

Rosacea is a chronic inflammatory disease characterized by facial flushing, erythema, telangiectasia, papules, and pustules. Its pathogenesis has not been fully understood. In 2017, the global ROSacea COnsensus (ROSCO) panel updated the diagnosis, classification, and assessment of rosacea. Phenotype-based treatments and long-term managements have also been recommended. Murine models are a powerful tool in unveiling and dissecting the mechanisms of human diseases. Here, we summarized murine models of rosacea developed or used in previous research, including LL-37 intradermal injection model, KLK-5-induced inflammation model, croton oil inflammation model, 12-O-Tetradecanoylphorbol-13-acetate inflammation model, arachidonic acid inflammation model, RTX-induced vasodilation model, and UVB-induced model. LL-37 injection model has become the most intensively used model in rosacea research. Each model could show the pathophysiological and clinical features of rosacea to some extent. However, no model can show the full picture of the characteristics of rosacea. Improving existed murine models, developing new murine models, and applying them to pathogenesis and treatment research on rosacea are highly warranted in the future.

Introduction Dermatologists play essential roles in providing dermatology consultations to inpatients admitted to hospital for care in another speciality ward. Data on dermatology consultations provided to inpatients admitted to general surgery wards are limited. The aim of this study was to analyze the reasons for and diagnoses of consultations provided by dermatologists to hospitalized patients in a general surgery ward and compare the provisional diagnoses by surgical residents and the final diagnoses by dermatologists. Methods Electronic health records were retrieved for patients admitted to a general surgery ward who received dermatology consultations while inpatients in Peking Union Medical College Hospital between 1 September 2015 and 31 August 2020. Sex, age, surgical diagnosis, reason for dermatology consultation, provisional diagnosis by surgical residents, and final diagnosis by dermatologists were reviewed. Results A total of 262 dermatology consultations for 251 patients (n = 251, 123 women and 128 men) were identified, of whom 240 (95.6%) required only one consultation and 11 (4.4%) required two. Dermatology consultations were classified into three categories: preoperative consultation (n = 45, 17.9%), postoperative consultation (n = 65, 25.9%), and consultation unrelated to general surgery diseases or treatments (including surgery) (n = 141, 56.2%). For consultations falling in the category preoperative consultation, common reasons for the consultation were: to evaluate whether the current treatment plan for previously diagnosed skin diseases needed to be changed; to evaluate and manage skin problems that emerged after admission; to evaluate syphilis; and to evaluate whether previously diagnosed skin diseases would affect surgical incision or wound healing. Drug eruption, dermatitis/eczema, infectious skin disorders, and urticaria were the most common skin diseases in the hospitalized general surgery patients. Only 32 (12.7%) provisional diagnoses were made, of which 25 (78.1%) were correct and seven (21.9%) were incorrect. Surgical residents mainly had difficulty distinguishing herpes zoster, drug eruption, and infectious skin disorders from dermatitis/eczema. Conclusion Our results facilitate the understanding of inpatient dermatology consultations in general surgery wards and may help in the design of future educational materials and/or management guidelines.

Hanlin Zhang,* Keyun Tang,* Yuanzhuo Wang, Rouyu Fang, Qiuning Sun Department of Dermatology, Peking Union Medical College Hospital, Beijing, People’s Republic of China*These authors contributed equally to this workCorrespondence: Qiuning Sun Tel +86-10-69156114Fax +86-10-69156114Email doctorjenny1@163.comBackground: Rosacea is a chronic skin disorder characterized by erythema, flushing, telangiectasia, papules and pustules, phymatous changes, and ocular involvement. The aim of this study was to examine all published research articles in which botulinum toxin was used to treat rosacea and to evaluate the efficacy and safety of this treatment.Methods: PubMed, Embase, Cochrane Library, and Web of Science database were used to identify articles eligible for systematic review on March 26, 2021. Prospective or retrospective studies which directly used botulinum toxin to treat rosacea were included and reviewed.Results: Nine studies were included in this systematic review. Two were randomized controlled trials, graded as Level 2 for the quality of evidence. The total number of participants was 130, and the number of participants in each study ranged from 1 to 25. The improvement was observed in all studies in signs and symptoms compared with baseline. Adverse events were transitory and self-limited.Conclusion: Botulinum toxin could have overall satisfying efficacy and safety in the treatment of rosacea, though limited by small sample size, imperfect study design, and short follow-up visits.Keywords: botulinum toxin, rosacea, systematic review

Object recognition of hyperspectral remote sensing images based on machine learning is widely applied in many industries. However, the efficiency of the training and recognizing process of object recognition on hyperspectral remote sensing images is a critical issue since it involves complex matrix operations and large scale training data sets, especially for resource-constrained devices. One solution is to outsource the heavy workload of object recognition on hyperspectral remote sensing images to a cloud server. Nonetheless, it may bring some security problems when the cloud server is untrustworthy. Therefore, how to enable resource-constrained devices to securely and efficiently accomplish the training and recognizing process of object recognition on hyperspectral remote sensing images is of significant importance. In this article, we propose a secure and efficient scheme to outsource the object recognition on hyperspectral remote sensing images to the untrustworthy cloud server. The proposed scheme can protect the privacy of the computation input and output. Also, we develop an effective verification approach in our scheme that can detect the misbehavior of cloud server with the optimal probability 1. The theoretical analysis and experimental results indicate that our proposed scheme is secure and efficient.

Verifiable Searchable Symmetric Encryption, as an important cloud security technique, allows users to retrieve the encrypted data from the cloud through keywords and verify the validity of the returned results. Dynamic update for cloud data is one of the most common and fundamental requirements for data owners in such schemes. To the best of our knowledge, the existing verifiable SSE schemes supporting data dynamic update are all based on asymmetric-key cryptography verification, which involves time-consuming operations. The overhead of verification may become a significant burden due to the sheer amount of cloud data. Therefore, how to achieve keyword search over dynamic encrypted cloud data with efficient verification is a critical unsolved problem. To address this problem, we explore achieving keyword search over dynamic encrypted cloud data with symmetric-key based verification and propose a practical scheme in this paper. In order to support the efficient verification of dynamic data, we design a novel Accumulative Authentication Tag ( AAT ) based on the symmetric-key cryptography to generate an authentication tag for each keyword. Benefiting from the accumulation property of our designed AAT , the authentication tag can be conveniently updated when dynamic operations on cloud data occur. In order to achieve efficient data update, we design a new secure index composed by a search table ST based on the orthogonal list and a verification list VL containing AATs. Owing to the connectivity and the flexibility of ST, the update efficiency can be significantly improved. The security analysis and the performance evaluation results show that the proposed scheme is secure and efficient.

Skyline query is very useful in decision-making systems, WSN and so on. As a variation of skyline query, skyline-join query can return the results from multiple datasets. However, incomplete datasets are a frequent phenomenon due to the widespread use of automated information extraction and aggregation. Existing methods for dealing with incomplete data, such as probability, data padding can solve the problem, but cannot effectively reflect the real situation and are lack of integrality. Therefore, in this paper, in order to reflect the situation more accuracy and more user-centric, we research the problem of skyline-join query over incomplete datasets with crowdsourcing, named CrowdSJ. The crowdsourcing-based skyline-join query processing problem over incomplete datasets is divided into two situations. One is the skyline-join query only involves the unknown crowdsourcing attribute and the join attribute, named Partial Skyline-Join with Crowdsourcing (PSJCrowd). The other one is the skyline-join query involves all the attributes, named All Skyline-Join with Crowdsourcing (ASJCrowd). For PSJCrowd, first, we filter the known dataset. Then, we present the level-preference-tree-index, and propose the partial skyline-join with crowdsourcing algorithm. For ASJCrowd, first, we filter the known dataset too. Second, we build a level-preference-tree-index based on the known attributes of the incomplete dataset. Third, we propose the skyline-join with crowdsourcing on single dataset algorithm, CrowdSJ-single, to filter the dataset containing unknown attributes. Then, we build a global level-preference-tree-index based on the known attributes of the incomplete dataset and the complete dataset. We propose the skyline-join with crowdsourcing on multiple datasets algorithm, CrowdSJ-multiple. We filter the linked tuples based on the global level-preference-tree-index and the results of each round of crowdsourcing. Numerous experiments on synthetic and real datasets demonstrate that our algorithms are efficient and effective.

Chemical fertilizer reduction combined with novel and green agricultural inputs has become an important practice to improve microecological health in agricultural production. Given the close linkages between rhizosphere processes and plant nutrition and productivity, understanding how fertilization impacts this critical zone is highly important for optimizing plant-soil interactions and crop fitness for agricultural sustainability. Here, by using a pot experimental system, we demonstrated that nitrogen fertilizer reduction and microbial agent application promoted plant fitness and altered the microbial community structure in the rhizosphere soil with the following treatments: no fertilization, CK; conventional chemical fertilizer, CF; 30% reduced nitrogen fertilizer, N; 30% reduced nitrogen fertilizer with pure γ-PGA, PGA; 30% reduced nitrogen fertilizer with

Brown adipose tissue (BAT) is the primary site of adaptive thermogenesis, which is involved in energy expenditure and has received much attention in the field of obesity treatment. By screening a small-molecule compound library of drugs approved by the Food and Drug Administration, pantothenic acid was identified as being able to significantly upregulate the expression of uncoupling protein 1 (UCP1), a key thermogenic protein found in BAT. Pantothenate (PA) treatment decreased adiposity, reversed hepatic steatosis, and improved glucose homeostasis by increasing energy expenditure in C57BL/6J mice fed a high-fat diet. PA also significantly increased BAT activity and induced beige adipocytes formation. Mechanistically, the beneficial effects were mediated by UCP1 because PA treatment was unable to ameliorate obesity in UCP1 knockout mice. In conclusion, we identified PA as an effective BAT activator that can prevent obesity and may represent a promising strategy for the clinical treatment of obesity and related metabolic diseases.

Polycystic ovary syndrome (PCOS) is a common metabolic and endocrine disease affecting women of reproductive age. Due to its complex aetiology, there is no currently effective cure for PCOS. Brown adipose tissue (BAT) activity is significantly decreased in PCOS patients, and BAT activation has beneficial effects in animal models of PCOS. Here, we investigated the effect of ginsenoside compound K (CK) in an animal model of PCOS and its mechanism of BAT activation.Primary brown adipocytes, Db/Db mice and dehydroepiandrosterone (DHEA)-induced PCOS rats were used. The core body temperature, oxygen consumption, energy metabolism related gene and protein expression were assessed to identify the effect of CK on overall energy metabolism. Oestrous cycle, serum sex hormone, ovarian steroidogenic enzyme gene expression and ovarian morphology were also evaluated following CK treatment.Our results indicated that CK treatment could significantly protect against body weight gain in Db/Db mice via BAT activation. Furthermore, we found that CK treatment could normalize hyperandrogenism, oestrous cyclicity, normalize steroidogenic enzyme expression and decrease the number of cystic follicles in PCOS rats. Interestingly, as a potential endocrine intermediate, C-X-C motif chemokine ligand-14 protein (CXCL14) was significantly up-regulated following CK administration. In addition, exogenous CXC14 supplementation was found to reverse DHEA-induced PCOS in a phenotypically similar manner to CK treatment.In summary, CK treatment significantly activates BAT, increases CXCL14 expression and ameliorates PCOS. These findings suggest that CK might be a potential drug candidate for PCOS treatment.

In individuals with traumatic shoulder instability, there is a loss of proprioception. This paper reviews the academic literature on shoulder instability and functional impairment in recent years and combines it with clinical practice experience to summarize several techniques of proprioceptive regeneration following traumatic shoulder instability. Many issues were discovered, including a lack of literature on the role of sensory input, a lack of basic proprioceptive research, insufficient sample size in proprioceptive research, and a lack of systematic and standardized standards for the evaluation and training of proprioception in clinical practice, among others. In the future, we will need to better understand the mechanism of proprioception and conduct research on various groups of people, with a focus on discussing the optimal intensity, frequency, and duration of various training methods, as well as implementing proprioceptive training in stages throughout the rehabilitation process. The reestablishment of shoulder joint function, the restoration of proprioception, and the enhancement of daily activities are all critical.

HIV-associated kidney disease is common but data on the pathology spectrum of kidney biopsy in China is lacking. This study aimed to illustrate the clinical presentation, laboratory findings and pathological spectrum of different subtypes of HIV-associated kidney disease in China.Eighteen HIV patients with renal biopsy indications at the Peking Union Medical College Hospital from January 2002 to October 2021 were retrospectively enrolled. All had CD4 counts and HIV viral load measurements. Renal biopsies were examined with light microscopy, immunofluorescence, and electron microscopy. Shapiro-Wilk test was used to test whether the data was normally distributed. The data is presented as medians (interquartile range), number (%), or means (±SD) according to their distribution.Seventeen patients had glomerular disease, and one patient had interstitial nephritis. Membranous nephropathy was present in eight patients (47.1%), and IgA nephropathy in four patients (23.5%). The difference in urine protein and serum albumin before and after treatment was statistically significant and no deaths or dialysis were observed to the end of follow-up.This study found that classic HIV-associated nephropathy (HIVAN) was uncommon in Chinese HIV patients. HIV immune complex kidney (HIVICK) disease, such as membranous or IgA nephropathy, was more common, and associated with better prognosis. Antiretroviral therapy, ACE inhibitors, and angiotensin II receptor blockers were effective in decreasing proteinuria and preserving renal function. The use of corticosteroids and immunosuppressive agents seems safe. However, the nephrotoxic effect of antiretroviral agents and other medications should be carefully monitored.

As a key smart transportation service, public vehicle systems are intended to improve traffic efficiency and vehicle occupancy ratios, and to reduce the number of vehicles on roads, by inducing travelers to share rides with others. Despite the clear logic behind this service, achieving a viable model for matching multiple riders to vehicles with low latency and high satisfaction remains an open issue. In this paper, we propose an Edge Computing based Public Vehicle (ECPV) system to improve traffic efficiency and vehicle occupancy ratios by scheduling ridesharing among travelers and reduce the delay of decision making by leveraging edge computing. Particularly, by introducing a metric of traveler satisfaction jointly considering travel time, distance, and costs (i.e., charge), we formalize public vehicle scheduling problem as an optimization problem with maximizing traveler satisfaction as objective to reduce travel time and improve traffic efficiency. Further, as achieving globally optimal matching of rides and travelers is time consuming, to reduce the delay of decision making (i.e., response to ride-traveler pairs matching) and improve real time ridesharing service and traveler satisfaction, an edge computing based ride request transmission mechanism and a tree based heuristic matching mechanism are proposed to effectively exchange ride requests and select appropriate vehicles for mostof ride requests on edge devices. Also, our ECPV system considers driverless cars as public vehicles to remove the subjective influence of drivers and improve the efficiency of vehicle scheduling, and introduces a graph partition based depot placement mechanism to determine vehicleparking locations and balance vehicle distribution across system. Through extensive performance evaluations, our experimental results show that our proposed ECPV system can effectively match ride requests to public vehicles, reduce travel times and distances for all trips, reducecharges to travelers, and improve vehicle occupancy.

A fair contract signing scheme ensures that contract signing participants can fairly exchange the digital signatures of the contract, which has a wide range of applications on the Internet. Verifiable Encrypted Signature (VES) can be used as a fair exchange mechanism for digital signatures, in which it requires a centralized Trusted Third Party (TTP) as an adjudicator. However, there are many security challenges with the centralized TTP. For example, it may disclose the contents of the contract, collude with others, or even occurs a service interruption. To address those problems, in this paper, we propose a two-party fair contract signing scheme based on Ethereum smart contract, which allows participants to fairly perform the contract signing procedures on the blockchain. Specifically, we propose a modified VES scheme in which no centralized adjudicator is needed. Then we design the fair contract signing scheme based on the modified VES scheme. We leverage the Ethereum smart contract technology to ensure fairness, which has the characteristics of decentralization, verifiability, autonomy, efficiency, and tampering resistant. The theoretical analysis and experimental results show that our scheme is secure and feasible.

White adipose tissue (WAT) browning may have beneficial effects for treating metabolic syndrome. miRNA are important regulators of the differentiation, development, and function of brown and beige adipocytes. Here, we found that the cold-inducible miRNA17-92 cluster is enriched in brown adipose tissue (BAT) compared with WAT. Overexpression of the miR17-92 cluster in C3H10T1/2 cells, a mouse mesenchymal stem cell line, enhanced the thermogenic capacity of adipocytes. Furthermore, we observed a significant reduction in adiposity in adipose tissue-specific miR17-92 cluster transgenic (TG) mice. This finding is partly explained by dramatic increases in white fat browning and energy expenditure. Interestingly, the miR17-92 cluster stimulated WAT browning without altering BAT activity in mice. In addition, when we removed the intrascapular BAT (iBAT), the TG mice could maintain their body temperature well under cold exposure. At the molecular level, we found that the miR17-92 cluster targets Rb1, a beige cell repressor in WAT. The present study reveals a critical role for the miR17-92 cluster in regulating WAT browning. These results may be helpful for better understanding the function of beige fat, which could compensate for the lack of BAT in humans, and may open new avenues for combatting metabolic syndrome.

With the development of Internet of Things (IoT) and 5G, edge computing, as a new computing paradigm, has been widely popularized in academia and industry. Due to the distributed architecture and being close to the user, edge computing can faster respond to the IoT device’s request and provide a better quality of service for IoT applications. An important application of edge computing is to outsource the complicated computation task to the nearby edge nodes. Modular exponentiation is widely considered as one of the most common and expensive operations in cryptographic protocols. As far as we know, all secure outsourcing algorithms of modular exponentiation are based on the centralized cloud server, but not based on multiple edge nodes. In this article, we propose the first secure and distributed outsourcing algorithm for modular exponentiation (fixed base and variable exponent) under the multiple noncolluding edge node model. In our algorithm, the exponent is divided into a certain number of parts. In addition, we propose another secure and distributed outsourcing algorithm of modular exponentiation (variable base and variable exponent). The user can protect the privacy in the process of outsourcing and detect the invalid results from edge nodes with high probability. Finally, we provide the experimental evaluation to support that our proposed algorithms are efficient on both the user side and the edge node side.

Many articles involving human brain banks have been published. Bibliometric analysis can determine the history of the development of research and future research trends in a specific field. Three independent researchers retrieved and reviewed articles from the Web of Science database using the following strategy: “TS = (((brain OR cerebral) AND (bank* OR biobank*)) OR brainbank*).” The top 100 most-cited articles were identified and listed in descending order by total citations. Web of Science was used to identify ten recent articles describing bank construction. GeenMedical ( https://www.geenmedical.com/ ) was used to identify ten recent articles from journals with an impact factor (IF) > 20. The top 100 most-cited articles citing human brain banks were published between 1991 and 2017. Fifty-two percent of the articles focused on a specific type of neurodegenerative disease, and 16% discussed the construction and development of human brain banks. Articles using brain tissue had more total and annual citations than those on bank construction. Ten articles with high IFs were published from 2017 to 2019, and they were primarily studies using novel research techniques such RNA sequencing and genome-wide association studies. Most studies were published in journals specializing in neurology or neuroscience such as Movement Disorders (10%), and had been conducted in the United States (52%) by neurologists (62%). The top 100 most-cited articles and recent publications citing human brain banks and their bibliometric characteristics were identified and analyzed, which may serve as a useful reference and pave the way for further research.

Rivest–Shamir–Adleman (RSA) is one of the widely deployed public-key algorithms. Yet, its decryption facet is very time consuming for resource-constrained Internet-of-Thing (IoT) devices, as it is based on the modular exponentiation of a large number. Although several variants of RSA have been designed to accelerate decryption, the outcomes have been far from satisfactory. Therefore, it is of imminent importance to investigate how to securely outsource RSA decryption to computational powerful parties as an alternative solution. In this article, we introduce the first efficient and secure outsourcing scheme for RSA decryption in IoT. Though RSA decryption is achieved via modular exponentiation, existing secure outsourcing schemes for modular exponentiation either assume the modulus to be prime and are not applicable to RSA or incur massive computation costs and are heavy laden in practice. To address these issues, we have designed our scheme based on the Chinese remainder theorem (CRT). In our scheme, the private keys (including the exponent and the modulus) and the plaintext are concealed concurrently, and the proposed scheme is highly efficient for both client and cloud. In addition, our scheme enables the client to detect any misbehavior of the cloud server with a probability of 99.17%. To validate the effectiveness of our proposed scheme, we provide rigorous proofs of security and verifiability, as well as efficiency analysis. The effectiveness and efficiency of our scheme are further confirmed based on experimental results.

In this paper, we propose a Multitype-users Welfare Equilibrium based Real-Time Pricing scheme to determine effective electricity prices in smart grid to achieve welfare equilibrium between the utility company and customers, in which three types of customers (i.e., residential-users, commercial-users and industrial-users) are considered. Via introducing value functions related to energy consumption, optimization problems on welfare equilibrium between customers and the utility company are formalized. In addition, a greedy-based task scheduling mechanism is proposed for industrial-users to reduce electricity cost and improve the efficiency of industrial production. Due to the welfare contradictions between customers and the utility company and to achieve welfare equilibrium and determine effective real-time electricity prices, an optimal energy consumption mechanism is proposed for customers to determine the optimal energy consumption and a quantum-behaved particle swarm optimization (QPSO)-based solution is proposed for the utility company to determine the effective electricity prices. Through extensive performance evaluation, our data shows that the QPSO-based solution can determine the real-time electricity price faster than existing solutions and our proposed real-time pricing scheme can reduce load fluctuation, achieve load balance, improve welfare of customers, as well as achieve welfare equilibrium between customers and the utility company. Additionally, our proposed pricing scheme can also achieve greater welfare for both customers and the utility company in comparison with other pricing schemes.

The supercritical H2O/CO2 mixture is the working fluid to drive a turbine in a novel power generation system with coal gasified in supercritical water. This system is promising because of zero pollution emission in contrast to the conventional coal-fired power plant. Heat transfer coefficients of the supercritical H2O/CO2 mixtures are important to design heat transfer devices in this system, which is similar to the role of heat transfer to supercritical water in conventional systems. However, heat transfer to supercritical mixtures has received less attention. Here, we show that the supercritical mixtures with H2O being one of the components, have similar convection heat transfer behavior to supercritical pure fluids for temperatures and pressures above the critical point of H2O. This phenomenon was demonstrated from two aspects. Firstly, the forced convection heat transfer coefficients of supercritical mixtures were numerically calculated using the simulation model for supercritical pure fluids and using the thermophysical properties (density, heat capacity, thermal conductivity and viscosity) of supercritical mixtures as input. The calculated results agree well with experimental data in the supercritical region. Secondly, the calculated results also agree well with the correlations for supercritical pure fluids. The mechanisms were investigated by molecular dynamics simulations on the diffusion of supercritical mixtures. These results lay the foundation for predicting convection heat transfer coefficients of supercritical mixtures and for designing heat transfer devices with supercritical mixtures as heat transfer fluids.

Solving quadratic congruences is a widely applied operation in cryptographic protocols to ensure the data secrecy in the Internet of Things (IoT). Yet it requires unaffordable computation resource for resource-constrained IoT devices when bulk of this type of operations need to be performed. How to efficiently and effectively solve quadratic congruences on IoT devices becomes a challenging issue. To address this problem, in this article, we propose two practical and secure outsourcing algorithms for solving quadratic congruences. Our proposed algorithms enable the IoT devices to outsource the heavy computation of solving quadratic congruences to a single cloud server, and therefore, achieve high efficiency for IoT devices. Meanwhile, we obscure the input and the output so that the outsourcing process does not leak the privacy of the computation, and the IoT devices in our algorithms can detect any misbehavior of the cloud server with a probability of 1. In addition, we take the Rabin encryption algorithm as an example to show how our proposed algorithms can be applied to IoT applications. The theoretical analysis and experimental results support the fact that our proposed algorithms are secure and efficient.

Cloud computing gives resource-constrained clients great conveniences to outsource exorbitant computations to a public cloud. The extended Euclidean algorithm with large-scale polynomials over finite fields is fundamental and widespread in computer science and cryptography, yet it is computationally overloaded for quantities of lightweight devices emerged with the dawn of internet of things (IoT). In this respect, we design an efficient outsourcing algorithm that enables a lightweight client to achieve this heavy computation with the assistance of a remote cloud server. By comprehensively employing the variable substitution, random polynomial-based blind technique and unimodular matrix transformation , our algorithm achieves the goals of cheating resistance, privacy preservation , and high efficiency. Concretely, our algorithm is based on single untrusted program model which avoids the too strong security assumption between multiple servers, and it enables the client to detect the cloud’s misbehaviors with (optimal) probability 1. Also, Thorough theoretical analysis indicates that the algorithm provides robust input/output privacy. Moreover, the algorithm only needs one round interaction between the client and the cloud. Strict theoretical analysis and extensive experimental evaluations demonstrate our algorithm’s practical efficiency and effectiveness. Finally, an important application of our algorithm on securely outsourcing the expensive key generation step of NTRU cryptosystem is given.

Modeling individual and crowd behaviors is the basis for evacuation research, which is essential to reduce casualties under emergency conditions in large venues. This paper focuses on the pedestrian evacuation of large-scale venues with multiple exits, and proposes an improved pedestrian evacuation model which uses a novel dual-strategy adaptive particle swarm optimization algorithm with affinity propagation clustering. Compared with the traditional models, the proposed model is demonstrated to have a better performance in simulating pedestrians' herding behavior in panic situations, especially when not familiar with the environment, as i) individual heterogeneity is considered and affinity propagation clustering is used in population division to simulate the process of people spontaneously gathering into swarms; ii) A dual-strategy updating scheme is designed to balance the cognition difference between the leaders and the agents in the swarm; and iii) Adaptive control of parameters is used to simulate the human-surrounding interaction and psychological fluctuates when the evacuees are moving towards exits. Numerical examples, which simulated the evacuation of a rectangle venue with multiple exits, demonstrate the effectiveness and practicality of the proposed model. Moreover, the influences of pedestrian velocity, characteristics of exits, and leader movement on evacuation are analyzed. Experimental results show that the movement of leaders is different from other evacuees and the parameters of doors, such as width, quantity, and location all have a great influence on evacuation time.

Finding min-cut is a fundamental operation in graph theory. It has been widely used in many applications such as image segmentation and network partition. However, solving the min-cut problem is time-consuming for resource-constrained devices, especially when the graph is large and dense. In this paper, we explore how to securely solve the min-cut problem in the cloud environment, and propose two secure outsourcing schemes for the min-cut of undirected edge-weighted graphs. The first scheme is based on two non-colluded untrusted cloud servers, and the second one is under the single untrusted cloud server model. In the designed schemes, we develop a new technique that contains merging vertices and edges, inserting fake vertices and edges, shuffling vertices and randomizing weights of edges to protect the privacy of graphs. In order to realize the checkability of result from a single untrusted cloud server, we design a novel verification mechanism by outsourcing the min-cut of two related graphs. Besides, we also provide security analysis and the experimental evaluation. To the best of our knowledge, it is the first research on secure outsourcing of graph algorithm.

Background Great progress has been made in understanding the epigenetic regulation of the immune response. However, the influence of RNA N6-methyladenosine (m6A) regulation on the tumor microenvironment (TME) of melanoma is still unknown. Methods We comprehensively evaluated 790 samples of melanoma based on 21 m6A regulator genes and associated these regulators with infiltration characteristics in the TME. We used the m6A score to assess the m6A modification pattern and response to immunotherapy. Results Three distinct m6A modification patterns were identified among 790 melanoma samples, and the patterns correspond to different clinical outcomes. The characteristics of the three modification patterns mainly included three phenotypes: immune inflamed, immune desert and immune excluded. We found that the m6A modification pattern could be applied to assess genetic variations, immune infiltration and prognosis. Based on the m6A score, melanoma patients could be classified into high and low m6A score subgroups. Stromal activation was observed in the high m6A score subgroup, indicating the immune-excluded phenotype. Patients in the low m6A score subgroup had prolonged overall survival and enhanced immune infiltration. Further analysis showed that the low m6A score correlated with a high tumor mutation burden, and better response to anti-PD-1/PD-L1 therapy, which indicated that the low m6A score subgroup might have therapeutic advantages and better clinical effects. Conclusion Our research revealed that m6A modification plays an essential role in shaping TME complexity and diversity of melanoma. Evaluation of m6A modification patterns could provide more information on TME infiltration characteristics and new ideas for immunotherapies.

SUMMARYThe deleterious potential to generate oxidative stress and damage is a fundamental challenge to metabolism. The oxidative stress response transcription factor, SKN-1/NRF2, can sense and respond to changes in metabolic state, although the mechanism and physiological consequences of this remain unknown. To explore this connection, we performed a genetic screen in C. elegans targeting amino acid catabolism and identified multiple metabolic pathways as regulators of SKN-1 activity. We found that genetic perturbation of the conserved amidohydrolase T12A2.1/amdh-1 activates a unique subset of SKN-1 regulated detoxification genes. Interestingly, this transcriptional program is independent of canonical P38-MAPK signaling components but requires the GATA transcription factor ELT-3, nuclear hormone receptor NHR-49, and mediator complex subunit MDT-15. This activation of SKN-1 is dependent on upstream histidine catabolism genes HALY-1 and Y51H4A.7/UROC-1 and may occur through accumulation of a catabolite, 4-imidazolone-5-propanoate (IP). Triggering SKN-1 activation results in a physiological trade off of increased oxidative stress resistance but decreased survival to heat stress. Together, our data suggest that SKN-1 is a key surveillance factor which senses and responds to metabolic perturbations to influence physiology and stress resistance.

This work investigated the effects of the aging process on the performance of asphalt binder under multiple environmental conditions based on multitechnique experimental results. The aging ...

Objectives: Frailty might be useful to identify older drivers who are at risk for traffic crashes. We aim to examine the association between pre-frailty/frailty defined by the Kihon Checklist (KCL) and the involvement of traffic crashes and clarify whether some domains of the KCL are associated with traffic crashes. Methods: This cross-sectional study analyzed data from 2,208 Japanese community-dwelling older drivers aged ��� 65 years in Kasama City, who participated in our postal survey in November 2019. A self-reported history of traffic crashes was used to divide participants into non-crash-involved and crash-involved groups. Results: A total of 192 (8.7%) participants had been involved in traffic crashes in the past year. The crash-involved group was found to have gained more body mass index, driven more frequently, and scored higher on the total KCL score than the non-crash-involved group (all P < .05). Binary logistic regression analyses showed that after adjusting for age, sex, education, driving frequency, and driving distance, pre-frailty (OR = 1.52, 95% CI: 1.10���2.10) was more significantly associated with traffic crashes as compared to robustness. Those who had impairment in the oral domain (OR = 1.57, 95% CI: 1.09���2.27) and memory domain (OR = 1.38, 95% CI: 1.01���1.90) were also more likely to be involved in traffic crashes. Conclusion: The results suggest that identifying pre-frailty may play an important pole in crash prevention. Additionally, more attention should be given to older drivers with oral dysfunction and cognitive impairment.

Identifying combustion regimes is important for understanding combustion phenomena and the structure of flames. This study proposes a combustion regime identification (CRI) method based on rotated principal component analysis (PCA), clustering analysis and the back-propagation neural network (BPNN) method. The methodology is tested with large-eddy simulation (LES) data of two turbulent non-premixed flames. The rotated PCA computes the principal components of instantaneous multivariate data obtained in LES, including temperature, and mass fractions of chemical species. The frame front results detected using the clustering analysis do not rely on any threshold, indicating the quantitative characteristic given by the unsupervised machine learning provides a perspective towards objective and reliable CRI. The training and the subsequent application of the BPNN rely on the clustering results. Five combustion regimes, including environmental air region, co-flow region, combustion zone, preheat zone and fuel stream are well detected by the BPNN, with an accuracy of more than 98% using 5 scalars as input data. Results showed the computational cost of the trained supervised machine learning was low, and the accuracy was quite satisfactory. For instance, even using the combined data of CH4-T, the method could achieve an accuracy of more than 95% for the entire flame. The methodology is a practical method to identify combustion regime, and can provide support for further analysis of the flame characteristics, e.g., flame lift-off height, flame thickness, etc.

Acute myocardial infarction (AMI) is the most severe form of coronary heart disease caused by ischemia and hypoxia. The study is aimed at investigating the role of neuropeptides and the mechanism of electroacupuncture (EA) in acute myocardial infarction (AMI) treatment. Compared with the normal population, a significant increase in substance P (SP) was observed in the serum of patients with AMI. PGI2 expression was increased in the SP-treated AMI mouse model, and TXA2 expression was decreased. And PI3K pathway-related genes, including Pik3ca, Akt, and Mtor, were upregulated in myocardial tissue of SP-treated AMI patients. Human cardiomyocyte cell lines (HCM) treated with SP increased mRNA and protein expression of PI3K pathway-related genes (Pik3ca, Pik3cb, Akt, and Mtor). Compared to MI control and EA-treated MI rat models, Myd88, MTOR, Akt1, Sp, and Irak1 were differentially expressed, consistent with in vivo and in vitro studies. EA treatment significantly enriched PI3K/AKT signaling pathway genes within MI-associated differentially expressed genes (DEGs) according to Kyoto Encyclopedia of Genes and Genomes (KEGG). Furthermore, it was confirmed by molecular docking analysis that PIK3CA, AKT1, and mTOR form stable dockings with neuropeptide SP. PI3K/AKT pathway activity may be affected directly or indirectly by EA via SP, which corrects the PGI2/TXA2 metabolic imbalance in AMI. MI treatment is now better understood as a result of this finding.

The global pandemic has many negative economic, social and health impacts, but the lock-downs also led to a reduction of traffic volume which resulted in lower NO2 levels in some areas. Our study made use of different air quality measurement techniques (in-situ, on-road, satellite remote sensing) to monitor long-term NO2 levels in Munich. While comparing NO2 levels associate with traffic volume before and after a lock-down, other influences based on meteorological parameters should be considered as well. In addition to traffic data we used records of wind, mixing layer height, temperature, humidity and other meteorological parameters to analyze the impact on measured pollution levels using a Generalized Additive Model (GAM) regression. Our long-term study using data between 2018 and 2021 shows that the dominating factor is wind speed, followed by traffic volume as the main factors for impacting NO2 levels, while absolute humidity and wind direction show less effects. We utilized those findings to find best suited time periods comparable to the lockdown time in terms of meteorological conditions. In order to focus on the traffic volume factor, we applied these findings to minimize other impact factors to evaluate the NO2 variability of different years comparing to the data from the lockdown periods. A significant reduction of the ground level NO2 concentrations in Munich during the early stage of the lockdown period in March 2020 could clearly be associated with a significant reduction of traffic volume.

The problem of elevated NO2 levels in cities has gained some attention in the public in recent years and has given rise to questions about the plausibility of banning diesel engines in cities, the meaning of exceedances of air quality limits and the effects of corona lock-downs on air quality to name a few. Urban air quality is typically monitored using a relatively small number of monitoring stations. Those in-situ measurements follow certain guidelines in terms of inlet height and location relative to streets, but the question remains how a limited number of point measurements can capture the spatial variability in cities. In this talk we present two measurement campaigns in Hong Kong and Munich where we utilized a combination of mobile in-situ and stationary remote sensing differential optical absorption spectroscopy (DOAS) instruments. We developed an algorithm to separate spatial and temporal patterns in order to generate pollution maps that represent average NO2 exposure. We use those maps to identify pollution hot spots and capture the weekly cycles of on-road NO2 levels and spatial dependency of long-term changes and we analyze how on-road measurements compare to monitoring station data and how the measurement height and distance to traffic emissions have to be considered when interpreting observed concentration patterns.

Geometrical optics approximation is a classic method for calculating the optical trapping force on particles whose sizes are larger than the wavelength of the trapping light. In this study, the effect of the lens misalignment on optical force was analyzed in the geometrical optics regime. We used geometrical optics to analyze the influence of off-axis placement and the tilt of the lens on the trapping position and stiffness in an optical trap. Numerical calculation results showed that lens tilting has a greater impact on the optical trap force than the off-axis misalignments, and both misalignments will couple with each other and cause a shift of the equilibrium point and the asymmetry of the optical trap stiffness in different ways. Our research revealed the asymmetry in optical traps caused by lens misalignment and can provide guidance for optimize lens placement in future experiments.

Psoriasis is a chronic and recurrent immune-related skin disease that often causes disfigurement and disability. Due to the visibility of lesions in patients and inadequate understanding of dermatology knowledge in the general public, patients with psoriasis often suffer from stigma in their daily lives, which has adverse effects on their mental health, quality of life, and therapeutic responses. This review summarized the frequently used questionnaires and scales to evaluate stigmatization in patients with psoriasis, and recent advances on this topic. Feelings of Stigmatization Questionnaire, Questionnaire on Experience with Skin Complaints, and 6-item Stigmatization Scale have been commonly used. The relationship between sociodemographic characteristics, disease-related variables, psychiatric disorders, quality of life, and stigmatization in patients with psoriasis has been thoroughly investigated with these questionnaires. Managing the stigmatization in patients with psoriasis needs cooperation among policymakers, dermatologists, psychologists, psychiatrists, researchers, and patients. Further studies can concentrate more on these existing topics, as well as other topics, including predictors of perceived stigmatization, stigmatization from non-patient groups, influence of biologics on stigmatization, and methods of coping with stigmatization.

Description, Cross-species genetic screens for mitochondrial regulators revealed EPS-8 as a link between actin stability and mitochondria., The dysfunction of mitochondria is associated with the physiological consequences of aging and many age-related diseases. Therefore, critical quality control mechanisms exist to protect mitochondrial functions, including the unfolded protein response of the mitochondria (UPRMT). However, it is still unclear how UPRMT is regulated in mammals with mechanistic discrepancies between previous studies. Here, we reasoned that a study of conserved mechanisms could provide a uniquely powerful way to reveal previously uncharacterized components of the mammalian UPRMT. We performed cross-species comparison of genetic requirements for survival under—and in response to—mitochondrial stress between karyotypically normal human stem cells and the nematode Caenorhabditis elegans. We identified a role for EPS-8/EPS8 (epidermal growth factor receptor pathway substrate 8), a signaling protein adaptor, in general mitochondrial homeostasis and UPRMT regulation through integrin-mediated remodeling of the actin cytoskeleton. This study also highlights the use of cross-species comparisons in genetic screens to interrogate cellular pathways.

Type 2 diabetes is currently experiencing an outbreak worldwide. GLP-1 effectively lowers blood glucose level as an emerging target for the treatment of type 2 diabetes. However, the application of GLP-1 is limited by short half-life and too expensive cost in clinic. In this study we employed the food-grade probiotics as delivery system to express human GLP-1 and its analogue. Recombinant lactococcus lactis could express GLP-1 and analogue in vitro and modified GLP-1 analogue was more resistant to DPP-4 degradation. Oral administration of GLP-1 analogue could reduce the fat mass. More importantly, GLP-1 analogue improved hyperglycemia and insulin resistance in Db/Db mouse although the insulin secretion is not observed in vitro. Our study demonstrates that lactococcus lactis genetically modified with single amino acid mutation could prolong half-life of GLP-1 and increase insulin sensitivity in Db/Db mouse model as an oral drug delivery system driving the development and innovation of drug therapy for type 2 diabetes.