Synovia tissue-specific exosomes participate in the dual variation of the osteoarthritis microenvironment via miR-182.

Osteoarthritis (OA) is the most common type of joint disease and the leading cause of chronic disability among older adults. As an important component of the joint, synovium influences the inflammatory and degenerative process of OA. This study found that miRNA 182 (miR-182) in synovium-specific exosomes can modulate inflammation and apoptotic signaling. It also regulated different biological functions to promote the progression of OA. Experiments based on rat OA model and synovium samples from OA patients, we found that synovium-derived miR-182 regulates inflammatory response in the early stage of OA by regulating the expression level of forkhead box O-3 (FOXO3). However, the expression of miR-182 was significantly increased in synovial tissue of advanced OA, which was involved in the apoptotic signal of severe OA. These findings suggest that miR-182 may directly regulate OA progression by modulating FOXO3 production inflammation, and apoptosis.

Analysis of carbon emission drivers and multi-scenario projection of carbon peaks in the Yellow River Basin.

The Yellow River Basin is a key ecological barrier and commercial zone in China, as well as an essential source of energy, chemicals, raw materials, and fundamental industrial foundation, the achievement of its carbon peaking is of great significance for China's high-quality development. Based on this, we decomposed the influencing factors of carbon dioxide emissions in the Yellow River Basin using the LMDI method and predicted the carbon peaking in the Yellow River Basin under different scenarios using the STIRPAT model. The results show that (1) the energy intensity effect, economic activity effect and population effect play a positive role in promoting carbon emissions during 2005-2020. The largest effect on carbon emissions is the population size effect, with a contribution rate of 65.6%. (2) The STIRPAT model predicts that the peak of scenarios "M-L", "M-M" and "M-H" will occur in 2030 at the earliest. The "M-H" scenario is the best model for controlling carbon emissions while economic and social development in the Yellow River Basin. The results of this paper can provide a theoretical basis for the development of a reasonable carbon peak attainment path in the Yellow River Basin and help policy makers to develop a corresponding high-quality development path.

Effects of qufeng xuanfei formula in guinea pig model of airway hyperergy.

This work aimed to clarify the potential regulating effects of Qufeng Xuanfei formula (QFXF) on airway neurogenic inflammation and its underlying target signal pathway. Guinea pig model of airway hyperergy (AHR) was used. The relative susceptibility of major proteins to airway neurogenic inflammation was assessed using Western blot immunoassay followed by being separated by SDS-PAGE. Compared to the model group, QFXF of all concentrations effectively depressed the capsaicin enhanced cough in guinea pigs and the peak values of airway resistance significantly decreased. The results illustrated that QFXF alleviated cough symptom in guinea pigs and reduced airway neurogenic inflammation when compared to AHR model group. Airway inflammation and damage, as well as the levels of NGF, SP and c-Fos in QFXF decreased the most in the high-dose group. The mechanism of antitussive activity may be associated with reducing airway inflammation. QFXF displayed effect on chronic cough through reducing the levels of neuropeptides, attenuating airway inflammation and promoting recovery from disease to decrease the airway neuro sensitivity, suggesting that the potential mechanism may be related to Ras/ERK/c-Fos pathway.

Logical Topology Inference via CPGCN Joint Optimizing With Pedestrian Re-Id.

With the rise of artificial intelligence, deep learning has become the main research method of pedestrian recognition re-identification (re-id). However, most of the existing researches usually just determine the retrieval order based on the geographical location of cameras, which ignore the spatio-temporal logic characteristics of pedestrian flow. Furthermore, most of these methods rely on common object detection to detect and match pedestrians directly, which will separate the logical connection between videos from different cameras. In this research, a novel pedestrian re-identification model assisted by logical topological inference is proposed, which includes: 1) a joint optimization mechanism of pedestrian re-identification and multicamera logical topology inference, which makes the multicamera logical topology provides the retrieval order and the confidence for re-identification. And meanwhile, the results of pedestrian re-identification as a feedback modify logical topological inference; 2) a dynamic spatio-temporal information driving logical topology inference method via conditional probability graph convolution network (CPGCN) with random forest-based transition activation mechanism (RF-TAM) is proposed, which focuses on the pedestrian's walking direction at different moments; and 3) a pedestrian group cluster graph convolution network (GC-GCN) is designed to measure the correlation between embedded pedestrian features. Some experimental analyses and real scene experiments on datasets CUHK-SYSU, PRW, SLP, and UJS-reID indicate that the designed model can achieve a better logical topology inference with an accuracy of 87.3% and achieve the top-1 accuracy of 77.4% and the mAP accuracy of 74.3% for pedestrian re-identification.

Recent advances in cell-based and cell-free therapeutic approaches for sarcopenia.

Sarcopenia is a progressive loss of muscle mass and function that is connected with increased hospital expenditures, falls, fractures, and mortality. Although muscle loss has been related to aging, injury, hormonal imbalances, and diseases such as malignancies, chronic obstructive pulmonary disease, heart failure, and kidney failure, the underlying pathogenic mechanisms of sarcopenia are unclear. Exercise-based interventions and multimodal strategies are currently being considered as potential therapeutic approaches to prevent or treat these diseases. Although drug therapy research is ongoing, no drug has yet been proven to have a substantial safety and clinical value to be the first drug therapy to be licensed for sarcopenia. To better understand the molecular alterations underlying sarcopenia and effective treatments, we review leading research and available findings from the systemic change to the muscle-specific microenvironment. Furthermore, we explore possible mechanisms of sarcopenia and provide new knowledge for the development of novel cell-free and cell-based therapeutics. This review will assist researchers in developing better therapies to improve muscle health in the elderly.

Using Machine Learning Methods to Provision Virtual Sensors in Sensor-Cloud.

The advent of sensor-cloud technology alleviates the limitations of traditional wireless sensor networks (WSNs) in terms of energy, storage, and computing, which has tremendous potential in various agricultural internet of things (IoT) applications. In the sensor-cloud environment, virtual sensor provisioning is an essential task. It chooses physical sensors to create virtual sensors in response to the users' requests. Considering the capricious meteorological environment of the outdoors, this paper presents an measurements similarity-based virtual-sensor provisioning scheme by taking advantage of machine learning in data analysis. First, to distinguish the changing trends, we classified all the physical sensors into several categories using historical data. Then, the k-means clustering algorithm was exploited for each class to cluster the physical sensors with high similarity. Finally, one representative physical sensor from each cluster was selected to create the corresponding virtual sensors. The experimental results show the reformation of our scheme with respect to energy efficiency, network lifetime, and data accuracy compared with the benchmark schemes.

Efficacy and Safety of Traditional Chinese Medicine in Idiopathic Pulmonary Fibrosis: A Meta-Analysis.

Objective: To evaluate the efficacy and safety of traditional Chinese medicine (TCM) on lung function and quality of life of idiopathic pulmonary fibrosis (IPF) patients by meta-analysis. Methods: Randomized controlled trials (RCTs) related to TCM and IPF were searched on PubMed, EMBASE Cochrane Library, ClinicalTrials, China National Knowledge Infrastructure (CNKI), Wanfang Database, Chin VIP Information (VIP), and Chinese Biomedical Database (CBM) until December 2018. Standard mean difference (SMD) and 95% CI were calculated for the measurements related to lung function (FEV1/FVC, FVC%, FEV1%, TLC%, DLCO% or DLCO, and VC%) and other parameters (PO2, 6MWD, and SGRQ) when comparing TCM treatment to the control group. Relative risk (RR) and 95% CI of adverse events (AEs) were calculated to assess the safety of TCM. Results: A total of 40 RCTs comparing TCM to western medicine (WM) and involving 3194 IPF patients were eligible for the meta-analysis. The pooled results showed that TCM treatment improved significantly PO2 (SMD = 0.80, 95% CI 0.54 to 1.06, p < 0.001), FEV1% (SMD = 0.57, 95% CI 0.42 to 0.71, p < 0.001), DLCO% (SMD = 0.38, 95% CI 0.28 to 0.48, p < 0.001), 6MWD (SMD = 0.70, 95% CI 0.56 to 0.84, p < 0.001) and other measurements and reduced SGRQ scores (SMD = −0.51, 95% CI −0.70 to −0.22, p < 0.001). Subgroup analysis of different study durations (3 months, ≥ 6 months) and comparison models (TCM vs. WM, TCM + WM vs. WM or TCM vs. placebo) showed similar results. No significant difference of risk of AEs was observed between both groups (RR = 0.66, 95% CI: 0.27­1.60, p=0.352). There was no obvious publication bias, and the pooled results were stable according to sensitivity analysis. Conclusion: To the best of our knowledge, the present study had the largest sample size. Our results indicated that TCM treatment may help provide benefit to the lung function, exercise capacity, and quality of life of IPF patients, alone or combined with WM, when compared to WM. More rigorous RCTs were needed in the future.

3VSR: Three Valued Secure Routing for Vehicular Ad Hoc Networks using Sensing Logic in Adversarial Environment.

Today IoT integrate thousands of inter networks and sensing devices e.g., vehicular networks, which are considered to be challenging due to its high speed and network dynamics. The goal of future vehicular networks is to improve road safety, promote commercial or infotainment products and to reduce the traffic accidents. All these applications are based on the information exchange among nodes, so not only reliable data delivery but also the authenticity and credibility of the data itself are prerequisite. To cope with the aforementioned problem, trust management come up as promising candidate to conduct node's transaction and interaction management, which requires distributed mobile nodes cooperation for achieving design goals. In this paper, we propose a trust-based routing protocol i.e., 3VSR (Three Valued Secure Routing), which extends the widely used AODV (Ad hoc On-demand Distance Vector) routing protocol and employs the idea of Sensing Logic-based trust model to enhance the security solution of VANET (Vehicular Ad-Hoc Network). The existing routing protocol are mostly based on key or signature-based schemes, which off course increases computation overhead. In our proposed 3VSR, trust among entities is updated frequently by means of opinion derived from sensing logic due to vehicles random topologies. In 3VSR the theoretical capabilities are based on Dirichlet distribution by considering prior and posterior uncertainty of the said event. Also by using trust recommendation message exchange, nodes are able to reduce computation and routing overhead. The simulated results shows that the proposed scheme is secure and practical.

Privacy-Preserving Location-Based Service Scheme for Mobile Sensing Data.

With the wide use of mobile sensing application, more and more location-embedded data are collected and stored in mobile clouds, such as iCloud, Samsung cloud, etc. Using these data, the cloud service provider (CSP) can provide location-based service (LBS) for users. However, the mobile cloud is untrustworthy. The privacy concerns force the sensitive locations to be stored on the mobile cloud in an encrypted form. However, this brings a great challenge to utilize these data to provide efficient LBS. To solve this problem, we propose a privacy-preserving LBS scheme for mobile sensing data, based on the RSA (for Rivest, Shamir and Adleman) algorithm and ciphertext policy attribute-based encryption (CP-ABE) scheme. The mobile cloud can perform location distance computing and comparison efficiently for authorized users, without location privacy leakage. In the end, theoretical security analysis and experimental evaluation demonstrate that our scheme is secure against the chosen plaintext attack (CPA) and efficient enough for practical applications in terms of user side computation overhead.

Secure Nearest Neighbor Query on Crowd-Sensing Data.

Nearest neighbor queries are fundamental in location-based services, and secure nearest neighbor queries mainly focus on how to securely and quickly retrieve the nearest neighbor in the outsourced cloud server. However, the previous big data system structure has changed because of the crowd-sensing data. On the one hand, sensing data terminals as the data owner are numerous and mistrustful, while, on the other hand, in most cases, the terminals find it difficult to finish many safety operation due to computation and storage capability constraints. In light of they Multi Owners and Multi Users (MOMU) situation in the crowd-sensing data cloud environment, this paper presents a secure nearest neighbor query scheme based on the proxy server architecture, which is constructed by protocols of secure two-party computation and secure Voronoi diagram algorithm. It not only preserves the data confidentiality and query privacy but also effectively resists the collusion between the cloud server and the data owners or users. Finally, extensive theoretical and experimental evaluations are presented to show that our proposed scheme achieves a superior balance between the security and query performance compared to other schemes.

High capacity data hiding scheme based on (7, 4) Hamming code.

Aiming to embed large amount of data while minimize the sum of costs of all changed pixels, a novel high capacity data hiding scheme based on (7, 4) Hamming code is realized by a family of algorithms. Firstly, n (n = 1, 2, 3) cover pixels are assigned to one set according to the payload. Then, 128 binary strings of length seven are divided into eight sets according to the syndrome of every binary string. Binary strings that share the same syndrome are classified into one set. Finally, a binary string in a certain set determined by the data to be embedded is chosen to modify some of the least significant bits of the n cover pixels. The experimental results demonstrate that the image quality of the proposed method with high embedding payload is superior to those of the related schemes.

EPPS: Efficient and Privacy-Preserving Personal Health Information Sharing in Mobile Healthcare Social Networks.

Mobile healthcare social networks (MHSNs) have emerged as a promising next-generation healthcare system, which will significantly improve the quality of life. However, there are many security and privacy concerns before personal health information (PHI) is shared with other parities. To ensure patients' full control over their PHI, we propose a fine-grained and scalable data access control scheme based on attribute-based encryption (ABE). Besides, policies themselves for PHI sharing may be sensitive and may reveal information about underlying PHI or about data owners or recipients. In our scheme, we let each attribute contain an attribute name and its value and adopt the Bloom filter to efficiently check attributes before decryption. Thus, the data privacy and policy privacy can be preserved in our proposed scheme. Moreover, considering the fact that the computational cost grows with the complexity of the access policy and the limitation of the resource and energy in a smart phone, we outsource ABE decryption to the cloud while preventing the cloud from learning anything about the content and access policy. The security and performance analysis is carried out to demonstrate that our proposed scheme can achieve fine-grained access policies for PHI sharing in MHSNs.

Secure Data Aggregation with Fully Homomorphic Encryption in Large-Scale Wireless Sensor Networks.

With the rapid development of wireless communication technology, sensor technology, information acquisition and processing technology, sensor networks will finally have a deep influence on all aspects of people's lives. The battery resources of sensor nodes should be managed efficiently in order to prolong network lifetime in large-scale wireless sensor networks (LWSNs). Data aggregation represents an important method to remove redundancy as well as unnecessary data transmission and hence cut down the energy used in communication. As sensor nodes are deployed in hostile environments, the security of the sensitive information such as confidentiality and integrity should be considered. This paper proposes Fully homomorphic Encryption based Secure data Aggregation (FESA) in LWSNs which can protect end-to-end data confidentiality and support arbitrary aggregation operations over encrypted data. In addition, by utilizing message authentication codes (MACs), this scheme can also verify data integrity during data aggregation and forwarding processes so that false data can be detected as early as possible. Although the FHE increase the computation overhead due to its large public key size, simulation results show that it is implementable in LWSNs and performs well. Compared with other protocols, the transmitted data and network overhead are reduced in our scheme.

Nitrate-nitrogen reduction by established tree and pasture buffer strips associated with a cattle feedlot effluent disposal area near Armidale, NSW Australia.

Vegetated buffer strips have been recognized as an important element in overall agro-ecosystem management to reduce the delivery of non-point source pollutants from agricultural land to inland water systems. A buffer strip experiment consisting of two tree species (Eucalyptus camaldulensis and Casuarina cunninghamiana) with two planting densities and a pasture treatment was conducted to determine the effectiveness of NO(3)-N removal from a cattle feedlot effluent disposal area at Tullimba near Armidale, NSW Australia. Different management methods were applied for the buffers where grass and weeds were mowed 2-3 times during the second and third years and were not managed during the rest experimental years for the tree buffer, while grass was harvested 1-3 times per year for the pasture buffer. The differences between tree species and planting density significantly affected tree growth, but the growth difference did not significantly affect their capacities to reduce NO(3)-N in soil surface runoff and groundwater. On average for all the tree and pasture treatments, the buffer strips reduced NO(3)-N concentration by 8.5%, 14.7% and 14.4% for the surface runoff, shallow and deep groundwater respectively. The tree and pasture buffer strips were not significantly different in NO(3)-N reduction for both shallow and deep groundwater while the pasture buffer strips reduced significantly more NO(3)-N concentration in surface runoff than the tree buffer strips. Both buffer strips reduced more than 50% of surface runoff volume indicating that both the tree and pasture buffer strips were efficient at removing water and nutrients, mostly through a significant reduction in soil surface runoff volume.

Patrol detection for replica attacks on wireless sensor networks.

Replica attack is a critical concern in the security of wireless sensor networks. We employ mobile nodes as patrollers to detect replicas distributed in different zones in a network, in which a basic patrol detection protocol and two detection algorithms for stationary and mobile modes are presented. Then we perform security analysis to discuss the defense strategies against the possible attacks on the proposed detection protocol. Moreover, we show the advantages of the proposed protocol by discussing and comparing the communication cost and detection probability with some existing methods.

Behavior of pentacene initial nucleation on various dielectrics and its effect on carrier transport in organic field-effect transistor.

The influence of dielectric surface energy on the initial nucleation and the growth of pentacene films as well as the electrical properties of the pentacene-based field-effect transistors are investigated. We have examined a range of organic and inorganic dielectrics with different surface energies, such as polycarbonate/SiO2, polystyrene/SiO2, and PMMA/SiO2 bi-layered dielectrics and also the bare SiO2 dielectric. Atomic force microscopy measurements of sub-monolayer and thick pentacene films indicated that the growth of pentacene film was in Stranski-Kranstanow growth mode on all the dielectrics. However, the initial nucleation density and the size of the first-layered pentacene islands deposited on different dielectrics are drastically influenced by the dielectric surface energy. With the increasing of the surface energy, the nucleation density increased and thus the average size of pentacene islands for the first mono-layer deposition decreased. The performance of fabricated pentacene-based thin film transistors was found to be highly related to nucleation density and the island size of deposited Pentacene film, and it had no relationship to the final particle size of the thick pentacene film. The field effect mobility of the thin film transistor could be achieved as high as 1.38 cm2Ns with on/off ratio over 3 x 10(7) on the PS/SiO2 where the lowest surface energy existed among all the dielectrics. For comparison, the values of mobility and on/off ratio were 0.42 cm2Ns and 1 x 10(6) for thin film transistor deposited directly on bare SiO2 having the highest surface energy.

[Research and application advances on vegetative filter strip].

Water resources have been increasingly polluted, while vegetative filter strip (VFS) is accepted as one of the best management practices to reduce water body pollution, especially non-point source pollution. A vegetative filter strip is a vegetated strip of land, which separates the runoff and pollutant contributing areas from surface water bodies and allows the runoff and associated pollutants to be attenuated before reaching surface waters via infiltration, adsorption, uptake, decay, filtering, and deposition. In this paper, the research and application advances on VFS were reviewed, including the history, structure, mechanisms of pollution control, design, management, and cost effectiveness of VFS. The studies on VFS in China started recently, and very few results of experiments and applications had been achieved. In order to efficiently promote and use this ecological technique in China, more studies on VFS are needed to obtain relative experimental data and results.