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Purpose: To investigate the mechanism of action of galangin (GAL) against indomethacin (Indo)-induced gastric injury. Methods: Ten micromolar GAL with 2 mM Indo or 0.5 mM L-NAME or 2 mM propargylglycine were co-administered to human gastric mucosal epithelial cells GES-1 for 24 h. Cell viability assay and reactive oxygen species (ROS) test were conducted with commercial kits. Protein expressions of constitutive and inducible nitric oxide synthase (cNOS and iNOS, respectively), and cystathionine-γ-lyase (CSE) in GSE-1 cells were investigated by western blot. After 100 - 300 mg/kg, GAL was daily administered to rats for 5 days, and 30 mg/kg Indo was administered to induce gastric injury. Protein expressions of endothelial NOS (eNOS), iNOS, CSE, nuclear factor (NF)-κB, PKC, Akt, phospho-eNOS (Ser-1177), and phospho-eNOS (Thr-495) were investigated by western blot. Results: Galangin ameliorated the proliferative inhibitions of Indo, propargylglycine, and L-NAME on GSE-1 cells (p < 0.05), rapidly scavenged ROS, and increased cNOS protein expressions (p < 0.05). Galangin (300 mg/kg) inhibited protein expressions of iNOS (p < 0.01), NF-κB, Akt, and PKC (p < 0.05), inhibited the Ser-1177 and Thr 495 phosphorylation of eNOS, elevated eNOS and CSE protein expressions (p < 0.001). Conclusion: Galangin protects gastric mucosa from Indo by scavenging ROS and inhibiting protein expressions of Akt and PKC. The inhibition leads to Ser-1177 and Thr 495 dephosphorylation of eNOS, thereby prolonging eNOS activity. Besides, GAL inhibits iNOS-produced NO by inhibiting NF-κB to ameliorate the inflammatory reaction induced by Indo.

Physical-layer key generation (PKG) based on wireless channels is a lightweight technique to establish secure keys between legitimate communication nodes. Recently, intelligent reflecting surfaces (IRSs) have been leveraged to enhance the performance of PKG in terms of secret key rate (SKR), as it can reconfigure the wireless propagation environment and introduce more channel randomness. In this paper, we investigate an IRS-assisted PKG system, taking into account the channel spatial correlation at both the base station (BS) and the IRS. Based on the considered system model, the closed-form expression of SKR is derived analytically considering correlated eavesdropping channels. Aiming to maximise the SKR, a joint design problem of the BS precoding matrix and the IRS phase shift vector is formulated. To address this high-dimensional non-convex optimisation problem, we propose a novel unsupervised deep neural network (DNN)-based algorithm with a simple structure. Different from most previous works that adopt iterative optimisation to solve the problem, the proposed DNN-based algorithm directly obtains the BS precoding and IRS phase shifts as the output of the DNN. Simulation results reveal that the proposed DNN-based algorithm outperforms the benchmark methods with regard to SKR., This paper has been submitted to IEEE Transactions for possible publications. arXiv admin note: substantial text overlap with arXiv:2301.08179

Background With advances in massive parallel sequencing (MPS) technology, whole-genome sequencing (WGS) has gradually evolved into the first-tier diagnostic test for genetic disorders. However, deployment practice and pipeline testing for clinical WGS are lacking. Methods In this study, we introduced a whole WGS pipeline for genetic disorders, which included the entire process from obtaining a sample to clinical reporting. All samples that underwent WGS were constructed using polymerase chain reaction (PCR)-free library preparation protocols and sequenced on the MGISEQ-2000 platform. Bioinformatics pipelines were developed for the simultaneous detection of various types of variants, including single nucleotide variants (SNVs), insertions and deletions (indels), copy number variants (CNVs) and balanced rearrangements, mitochondrial (MT) variants, and other complex variants such as repeat expansion, pseudogenes and absence of heterozygosity (AOH). A semiautomatic pipeline was developed for the interpretation of potential SNVs and CNVs. Forty-five samples (including 14 positive commercially available samples, 23 laboratory-held positive cell lines and 8 clinical cases) with known variants were used to validate the whole pipeline. Results In this study, a whole WGS pipeline for genetic disorders was developed and optimized. Forty-five samples with known variants (6 with SNVs and Indels, 3 with MT variants, 5 with aneuploidies, 1 with triploidy, 23 with CNVs, 5 with balanced rearrangements, 2 with repeat expansions, 1 with AOHs, and 1 with exon 7–8 deletion of SMN1 gene) validated the effectiveness of our pipeline. Conclusions This study has been piloted in test development, optimization, and validation of the WGS pipeline for genetic disorders. A set of best practices were recommended using our pipeline, along with a dataset of positive samples for benchmarking.

The Radio frequency (RF) fingerprinting technique makes highly secure device authentication possible for future networks by exploiting hardware imperfections introduced during manufacturing. Although this technique has received considerable attention over the past few years, RF fingerprinting still faces great challenges of channel-variation-induced data distribution drifts between the training phase and the test phase. To address this fundamental challenge and support model training and testing at the edge, we propose a federated RF fingerprinting algorithm with a novel strategy called model transfer and adaptation (MTA). The proposed algorithm introduces dense connectivity among convolutional layers into RF fingerprinting to enhance learning accuracy and reduce model complexity. Besides, we implement the proposed algorithm in the context of federated learning, making our algorithm communication efficient and privacy-preserved. To further conquer the data mismatch challenge, we transfer the learned model from one channel condition and adapt it to other channel conditions with only a limited amount of information, leading to highly accurate predictions under environmental drifts. Experimental results on real-world datasets demonstrate that the proposed algorithm is model-agnostic and also signal-irrelevant. Compared with state-of-the-art RF fingerprinting algorithms, our algorithm can improve prediction performance considerably with a performance gain of up to 15\%., This paper was accepted by IEEE INFOCOM 2023 Workshop (DeepWireless)

Yellowhorn tea (YT) is traditionally used as a lipid-lowering beverage in Mongolian minorities. However, the pharmacological effects of YT extract and its specific metabolic changes in hyperlipidemia models are not fully understood. The aim of this study was to identify biomarkers using untargeted metabolomics techniques and to investigate the mechanisms underlying the changes in metabolic pathways associated with lipid lowering, anti-inflammation and anti-oxidant in hyperlipidemic mice. A high-fat diet (HFD)-induced hyperlipidemic mouse model was established. YT extract was administered as oral gavage at 0.15, 0.3, and 0.6 g/kg doses for 10 weeks. HFD-induced hyperlipidemia and the therapeutic effect of YT extract were evaluated based on histopathology and by assessing blood lipid levels. Liver inflammatory factors and oxidative stress indices were determined using enzyme-linked immunosorbent assays. Liver metabolites were evaluated using untargeted metabolomics. Biochemical and histological examinations showed that YT extract significantly reduced body-weight gain (p < 0.01) and fat deposition in tissues. YT extract significantly reduced the levels of serum and liver triglyceride and total cholesterol; inflammatory factors [interleukin (IL)-6, IL-1β, and tumor necrosis factor-α]; malondialdehyde; and leptin (p < 0.05) in hyperlipidemic mice. YT extract also significantly increased the levels of oxidative stress indicators (superoxide dismutase, catalase, and glutathione peroxidase) and adiponectin. Metabolomics studies revealed several endogenous molecules were altered by the high-fat diet and recovery following intervention with YT extract. The metabolites that were significantly different in the liver after YT intake included citicoline, acetylcholine, pyridoxine, and NAD. Pathway analysis indicated that YT extract ameliorated HFD-induced hyperlipidemia in mice via three major metabolic pathways, namely, glycerophospholipid metabolism, vitamin B6 metabolism, and nicotinate and nicotinamide metabolism. This study demonstrates YT extract has profound effects on the alleviation of HFD-induced hyperlipidemia, inflammation and oxidative stress.

Objective. To analyze the intestinal microbiota and H2S levels in patients with HT. Methods. Twenty euthyroid HT patients and twenty healthy control individuals were recruited. Fecal samples were collected, and the microbiota was examined using 16S RNA gene sequencing. We also collected serum samples to examine the H2S levels. Results. Compared with patients with HT, the ACE and Chao indices were significantly lower in healthy controls (P=0.04, 0.03, respectively). The microbial composition of the HT group differed significantly from that of the healthy group. We observed a significant increase in the proportions of Bacteroides, Fusobacterium, Sutterella, and Veillonella in patients with HT (P

Physical-layer key generation (PKG) based on wireless channels is a lightweight technique to establish secure keys between legitimate communication nodes. Recently, intelligent reflecting surfaces (IRSs) have been leveraged to enhance the performance of PKG in terms of secret key rate (SKR), as it can reconfigure the wireless propagation environment and introduce more channel randomness. In this paper, we investigate an IRS-assisted PKG system, taking into account the channel spatial correlation at both the base station (BS) and the IRS. Based on the considered system model, the closed form expression of SKR is derived analytically. Aiming to maximize the SKR, a joint design problem of the BS precoding matrix and the IRS reflecting coefficient vector is formulated. To address this high-dimensional non-convex optimization problem, we propose a novel unsupervised deep neural network (DNN) based algorithm with a simple structure. Different from most previous works that adopt the iterative optimization to solve the problem, the proposed DNN based algorithm directly obtains the BS precoding and IRS phase shifts as the output of the DNN. Simulation results reveal that the proposed DNN-based algorithm outperforms the benchmark methods with regard to SKR., Comment: Accepted by ICC 2023

Deep learning-based physical-layer secret key generation (PKG) has been used to overcome the imperfect uplink/downlink channel reciprocity in frequency division duplexing (FDD) orthogonal frequency division multiplexing (OFDM) systems. However, existing efforts have focused on key generation for users in a specific environment where the training samples and test samples obey the same distribution, which is unrealistic for real world applications. This paper formulates the PKG problem in multiple environments as a learning-based problem by learning the knowledge such as data and models from known environments to generate keys quickly and efficiently in multiple new environments. Specifically, we propose deep transfer learning (DTL) and meta-learning-based channel feature mapping algorithms for key generation. The two algorithms use different training methods to pre-train the model in the known environments, and then quickly adapt and deploy the model to new environments. Simulation results show that compared with the methods without adaptation, the DTL and meta-learning algorithms both can improve the performance of generated keys. In addition, the complexity analysis shows that the meta-learning algorithm can achieve better performance than the DTL algorithm with less time, lower CPU and GPU resources.

Personal sound zone systems require to know the acoustic transfer functions (ATFs) between the loudspeakers and matching microphones, which in general have to be measured in advance. As the measurement is affected by such factors as sensors' position mismatches and environmental changes, deviation of the measured ATF-s from the true ones is unavoidable and such perturbation may lead to significant performance degradation in sound zone production. In this work, a method is developed for robust acoustic contrast control, which simultaneously considers the norm-bounded uncertainties and a positive semidefinite constraint to ensure the optimization problem to have a solution. Since the problem is non-convex, the polynomial time difference-of-convex functions (POTDC) algorithm is adopted to identify the optimal solution. Simulations are performed to validate the effectiveness of the proposed method and it demonstrates a performance superior to other existing robust control methods.

The physical-layer secret key generation has emerged as a promising solution for establishing cryptographic keys by leveraging reciprocal and time-varying wireless channels. However, existing approaches suffer from low key generation rates and vulnerabilities under various attacks in slowly varying environments. We propose a new physical-layer secret key generation approach with channel obfuscation, which improves the dynamic property of channel parameters based on random filtering and random antenna scheduling. Our approach makes one party obfuscate the channel to allow the legitimate party to obtain similar dynamic channel parameters yet prevents a third party from inferring the obfuscation information. Our approach allows more random bits to be extracted from the obfuscated channel parameters by a joint design of the K-L transform and adaptive quantization. A testbed implementation shows that our approach, compared to the existing ones that we evaluate, performs the best in generating high entropy bits at a fast rate and a high-security level in slowly varying environments. Specifically, our approach can achieve a significantly faster secret bit generation rate at about $67$ bit/pkt, and the key sequences can pass the randomness tests of the NIST test suite.

Maternal overnutrition can affect fetal growth and development, thus increasing susceptibility to obesity and diabetes in later life of the offspring. Placenta is the central organ connecting the developing fetus with the maternal environment. It is indicated placental fatty acid metabolism plays an essential role in affecting the outcome of the pregnancy and fetus. However, the role of placental fatty acid β-oxidation (FAO) in maternal overnutrition affecting glucose metabolism in the offspring remains unclear. In this study, C57BL/6J female mice were fed with normal chow or high-fat diet before and during pregnancy and lactation. The placenta and fetal liver were collected at gestation day 18.5, and the offspring's liver was collected at weaning. FAO-related genes and AMP-activated protein kinase (AMPK) signaling pathway were examined both in the placenta and in the human JEG-3 trophoblast cells. FAO-related genes were further examined in the liver of the fetuses and in the offspring at weaning. We found that dams fed with high-fat diet showed higher fasting blood glucose, impaired glucose tolerance at gestation day 14.5 and higher serum total cholesterol (T-CHO) at gestation day 18.5. The placental weight and lipid deposition were significantly increased in maternal high-fat diet group. At weaning, the offspring mice of high-fat diet group exhibited higher body weight, impaired glucose tolerance, insulin resistance and increased serum T-CHO, compared with control group. We further found that maternal high-fat diet downregulated mRNA and protein expressions of carnitine palmitoyltransferase 2 (CPT2), a key enzyme in FAO, by suppressing the AMPK/Sirt1/PGC1α signaling pathway in the placenta. In JEG-3 cells, protein expressions of CPT2 and CPT1b were both downregulated by suppressing the AMPK/Sirt1/PGC1α signaling pathway under glucolipotoxic condition, but were later restored by the AMPK agonist 5-aminoimidazole-4-carboxyamide ribonucleoside (AICAR). However, there was no difference in CPT2 and CPT1 gene expression in the liver of fetuses and offspring at weaning age. In conclusion, maternal high-fat diet can impair gene expression involved in FAO in the placenta by downregulating the AMPK signaling pathway, and can cause glucose and lipid dysfunction of offspring at weaning, indicating that placental FAO may play a crucial role in regulating maternal overnutrition and metabolic health in the offspring.

(R)-5-hydroxy-1,7-diphenyl-3-heptanone (DPHC) from the natural plant Alpinia officinarum has been reported to have antioxidation and antidiabetic effects. In this study, the therapeutic effect and molecular mechanism of DPHC on type 2 diabetes mellitus (T2DM) were investigated based on the regulation of oxidative stress and insulin resistance (IR) in vivo and in vitro. In vivo, the fasting blood glucose (FBG) level of db/db mice was significantly reduced with improved glucose tolerance and insulin sensitivity after 8 weeks of treatment with DPHC. In vitro, DPHC ameliorated IR because of its increasing glucose consumption and glucose uptake of IR-HepG2 cells induced by high glucose. In addition, in vitro and in vivo experiments showed that DPHC could regulate the antioxidant enzyme levels including superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GSH-Px), thereby reducing the occurrence of oxidative stress and improving insulin resistance. Western blotting and polymerase chain reaction results showed that DPHC could promote the expressions of nuclear factor erythroid 2-related factor 2 (Nrf2), the heme oxygenase-1 (HO-1), protein kinase B (AKT), and glucose transporter type 4 (GLUT4), and reduced the phosphorylation levels of c-Jun N-terminal kinase (JNK) and insulin receptor substrate-1 (IRS-1) on Ser307 both in vivo and in vitro. These findings verified that DPHC has the potential to relieve oxidative stress and IR to cure T2DM by activating Nrf2/ARE signaling pathway in db/db mice and IR-HepG2 cells.

Radio frequency fingerprint identification (RFFI) is an emerging device authentication technique, which exploits the hardware characteristics of the RF front-end as device identifiers. RFFI is implemented in the wireless receiver and acts to extract the transmitter impairments and then perform classification. The receiver hardware impairments will actually interfere with the feature extraction process, but its effect and mitigation have not been comprehensively studied. In this paper, we propose a receiver-agnostic RFFI system that is not sensitive to the changes in receiver characteristics; it is implemented by employing adversarial training to learn the receiver-independent features. Moreover, when there are multiple receivers, this functionality can perform collaborative inference to enhance classification accuracy. Finally, we show how it is possible to leverage fine-tuning for further improvement with fewer collected signals. To validate the approach, we have conducted extensive experimental evaluation by applying the approach to a LoRaWAN case study involving ten LoRa devices and 20 software-defined radio (SDR) receivers. The results show that receiver-agnostic training enables the trained neural network to become robust to changes in receiver characteristics. The collaborative inference improves classification accuracy by up to 20% beyond a single-receiver RFFI system and fine-tuning can bring a 40% improvement for under-performing receivers.

Water and fertilizer integration technology can effectively improve nutrient utilization efficiency. However, the existing water and fertilizer machines have some shortcomings, such as huge cost, single fertilizer injection, need for cleaning water and so on, which hinder the development of water and fertilizer integration technology. Aiming at the problems of precise and low-cost compounding of compound fertilizer at the local farm, the water and fertilizer integrated intelligent irrigation and fertilization system were taken as the research object. In this research, new concept of an intelligent sensing system was proposed， and accurate proportioning system of water and fertilizer concentration was constructed and implemented. Firstly, a fast on-line method of intelligent sensing model of water and fertilizer was established based on a series of concentration gradient compound fertilizer solutions. The conductivity values of these formulated solutions were tested by contactless conductivity detection electrodes. Subsequently, the data analysis algorithms were discussed and compared to fit regression model. Based on the intelligent sensing model of water and fertilizer , the framework structure of in-situ intelligent sensing and accurate proportioning system of water and fertilizer concentration was designed, and the working principle of the system was also explained. The system proposed includs a first-level water and fertilizer concentration intelligent perception model building subsystem and a second-level water and fertilizer accurate proportioning subsystem. The first-level subsystem was designed as a portable device, which mainly included a precise pump for quantitative dosing, a large-range online conductivity sensor, a plastic bucket and supporting control and model building software. The second-level subsystem was designed as a dynamic and precise fertilizer distribution device. The effectiveness of the system was verified by three types of water intelligent fertilizer application so as to guide the in-situ water and fertilizer concentration ratio. The testing results showed that the second-order polynomial fitting curve under regularization conditions was the best model to express the relationship between the conductivity and the concentration of water and fertilizer, and the correlation coefficients R2 was higher than 0.999. Combined with the proportion of each index of compound fertilizer, the concentration of each index of compound fertilizer that the user cares about can be obtained according to this model. The results of three types of water intelligent fertilizer application showed that the conductivity of natural water had an effect on the water and fertilizer system, and the relative deviation was more than 0.1. The online water and fertilizer perception and ratio system proposed in this research realized the elimination of the interference of the local water conductivity on the accuracy of the ratio of water and fertilizer, and the accurate calculation of compound fertilizer was achieved through model calculation. This system has a simple structure and accurate ratio, low cost， and can be easily combined with the existing water and fertilizer integrated machine or artificial fertilizer system. The system could be widely used in facility agriculture, orchard cultivation and field cash crop cultivation, et al.

Although the role of shoe constructions on running injury and performance has been widely investigated, systematic reviews on the shoe construction effects on running biomechanics were rarely reported. Therefore, this review focuses on the relevant research studies examining the biomechanical effect of running shoe constructions on reducing running-related injury and optimising performance. Searches of five databases and Footwear Science from January 1994 to September 2018 for related biomechanical studies which investigated running footwear constructions yielded a total of 1260 articles. After duplications were removed and exclusion criteria applied to the titles, abstracts and full text, 63 studies remained and categorised into following constructions: (a) shoe lace, (b) midsole, (c) heel flare, (d) heel-toe drop, (e) minimalist shoes, (f) Masai Barefoot Technologies, (g) heel cup, (h) upper, and (i) bending stiffness. Some running shoe constructions positively affect athletic performance-related and injury-related variables: 1) increasing the stiffness of running shoes at the optimal range can benefit performance-related variables; 2) softer midsoles can reduce impact forces and loading rates; 3) thicker midsoles can provide better cushioning effects and attenuate shock during impacts but may also decrease plantar sensations of a foot; 4) minimalist shoes can improve running economy and increase the cross-sectional area and stiffness of Achilles tendon but it would increase the metatarsophalangeal and ankle joint loading compared to the conventional shoes. While shoe constructions can effectively influence running biomechanics, research on some constructions including shoe lace, heel flare, heel-toe drop, Masai Barefoot Technologies, heel cup, and upper requires further investigation before a viable scientific guideline can be made. Future research is also needed to develop standard testing protocols to determine the optimal stiffness, thickness, and heel–toe drop of running shoes to optimise performance-related variables and prevent running-related injuries.

Because the underwater acoustic communication network transmits data through the underwater acoustic wireless link, the Underwater Acoustic Communication Network is easy to suffer from the external artificial interference, in this paper, the detection algorithm of wormhole attack in Underwater Acoustic Communication Network based on Azimuth measurement technology is studied. The existence of wormhole attack is judged by Azimuth or distance outliers, and the security performance of underwater acoustic communication network is evaluated. The influence of different azimuth direction errors on the detection probability of wormhole attack is analyzed by simulation. The simulation results show that this method has a good detection effect for Underwater Acoustic Communication Network.

The data in the underwater acoustic channel (UAC) can be received through the hydrophone. If there are no encryption protection measures, the enemy can easily obtain sensitive information. According to the requirements of secure transmission of underwater network measurement and control secret information, a key generation method based on UAC response characteristics is proposed. Based on the short-term correlation of UAC, the amplitude and time delay characteristics of underwater acoustic channel multipath structure are extracted, the multi-path number with the strongest energy, the number of qualified sound rays and multipath number with maximum delay difference are quantized to generate the original key. Then, the inconsistent key is corrected by key agreement mechanism. The communication parties can generate encryption key in real time to ensure the confidentiality of information. Experimental results show that the short-term correlation of multi-path structure of UAC is good, which verifies the feasibility of key generation based on multi-path structure of UAC and the key generation scheme proposed in this paper can make the matching probability of underwater acoustic key generation not less than 80%.

Radio frequency fingerprint identification (RFFI) is an emerging device authentication technique that relies on intrinsic hardware characteristics of wireless devices. We designed an RFFI scheme for Long Range (LoRa) systems based on spectrogram and convolutional neural network (CNN). Specifically, we used spectrogram to represent the fine-grained time-frequency characteristics of LoRa signals. In addition, we revealed that the instantaneous carrier frequency offset (CFO) is drifting, which will result in misclassification and significantly compromise the system stability; we demonstrated CFO compensation is an effective mitigation. Finally, we designed a hybrid classifier that can adjust CNN outputs with the estimated CFO. The mean value of CFO remains relatively stable, hence it can be used to rule out CNN predictions whose estimated CFO falls out of the range. We performed experiments in real wireless environments using 20 LoRa devices under test (DUTs) and a Universal Software Radio Peripheral (USRP) N210 receiver. By comparing with the IQ-based and FFT-based RFFI schemes, our spectrogram-based scheme can reach the best classification accuracy, i.e., 97.61% for 20 LoRa DUTs., Comment: Accepted for publication in IEEE INFOCOM 2021

Physical-layer key generation (PKG) establishes cryptographic keys from highly correlated measurements of wireless channels, which relies on reciprocal channel characteristics between uplink and downlink, is a promising wireless security technique for Internet of Things (IoT). However, it is challenging to extract common features in frequency division duplexing (FDD) systems as uplink and downlink transmissions operate at different frequency bands whose channel frequency responses are not reciprocal any more. Existing PKG methods for FDD systems have many limitations, i.e., high overhead and security problems. This paper proposes a novel PKG scheme that uses the feature mapping function between different frequency bands obtained by deep learning to make two users generate highly similar channel features in FDD systems. In particular, this is the first time to apply deep learning for PKG in FDD systems. We first prove the existence of the band feature mapping function for a given environment and a feedforward network with a single hidden layer can approximate the mapping function. Then a Key Generation neural Network (KGNet) is proposed for reciprocal channel feature construction, and a key generation scheme based on the KGNet is also proposed. Numerical results verify the excellent performance of the KGNet-based key generation scheme in terms of randomness, key generation ratio, and key error rate. Besides, the overhead analysis shows that the method proposed in this paper can be used for resource-contrained IoT devices in FDD systems., Comment: Accepted for publication in IEEE Internet of Things Journal

Chinese text research has always been a hot and difficult point in natural language processing. On the one hand, Chinese and English are very different in grammar, resulting in the uniqueness of Chinese word segmentation. On the other hand, Chinese part of speech tagging methods are also different from English. To some extent, the above two points hinder the research and application of natural language processing technology in Chinese text. The paper presents a word segmentation algorithm for Chinese text, and then introduces how to use vector space model to represent Chinese text. At the same time, the topic number k is given according to different criteria for measuring text clustering in order to build the Latent Dirichlet Allocation model base on Guangdong Province consumer complaint text. Through the model to assist the administrative departments to formulate corresponding policies, to grasp the key points of complaints accurately and quickly for the administrative personnel.

The physical-layer key generation is a lightweight technique to generate secret keys from wireless channels for resource-constrained Internet of things (IoT) applications. The security of key generation relies on spatial decorrelation, which assumes that eavesdroppers observe uncorrelated channel measurements when they are located over a half-wavelength away from legitimate users. Unfortunately, there is no experimental validation for communications environments when there are large-scale and small-scale fading effects. Furthermore, while the current key generation work mainly focuses on short-range communications techniques such as WiFi and ZigBee, the exploration with long-range communications, e.g., LoRa, is rather limited. This paper presents a LoRa-based key generation testbed and reveals a new colluding-eavesdropping attack that perceives and utilizes large-scale fading effects in key generation channels, by using multiple eavesdroppers circularly around a legitimate user. We formalized the attack and validated it through extensive experiments conducted under both indoor and outdoor environments. It is corroborated that the attack reduces secret key capacity when large-scale fading is predominant. We further investigated potential defenses by proposing a conditional entropy and high-pass filter-based countermeasure to estimate and eliminate large-scale fading associated components. The experimental results demonstrated that the countermeasure can significantly improve the key generation's security when there are both varying large-scale and small-scale fading effects. The key bits generated by legitimate users have a low key disagreement rate (KDR) and validated by the NIST randomness tests. On the other hand, eavesdroppers' average KDR is increased to 0.49, which is no better than a random guess., This is the replacement to the previous On the Colluding Attack to Compromise Physical Layer Key Generation Through a Large-Scale Fading Estimation article and the Colluding-Eavesdropping Attack and Countermeasure for LoRa-based Key Generation article. Added key disagreement rate (KDR) and randomness test analysis. Added key generation algorithm. Changed Title

This paper investigates the impact of a uniform circular array (UCA) in the context of wireless security via exposure region-based beamforming. An improvement is demonstrated for the security metric proposed in our previous paper, namely, the spatial secrecy outage probability (SSOP), by optimizing the configuration of the UCA. Our previous paper focused on formalizing the SSOP concept and exploring its applicability using a uniform linear array example. This paper proposes the UCA as a superior candidate because it is more robust against the effects of mutual coupling. The UCA's SSOP configuration is explored and a special expression is derived from the general expression for the first time, and a closed-form upper bound is then generated to facilitate analysis. By carefully designing the UCA structure particularly the radius, an SSOP optimization algorithm is derived and explored for mutual coupling. It is shown that the information leakage to eavesdroppers is reduced while the legitimate user's received signal quality is enhanced due to the use of beamforming.

Background MiRNAs are involved in aberrant DNA methylation through regulation of DNA methyltransferases (DNMTs) in the pathogenesis and progression of glioblastomas (GBM). MiR-152-3p was down-expressed in human malignancies, and served as a tumor suppressor. Neurofibromatosis type 2 (NF2) was significantly decreased in GBM tissues with a high level of methylation. However, the link between miR-152-3p, DNMT1 and methylation of NF2 in GBM is not clearly established. This study was conducted to detect the mechanism between miR-152-3p, DNMT1 and NF2 in GBM. Methods The levels of DNMT1 and NF2 expression were studied by qRT-PCR, Western blot, immunofluorescence, and immumohistochemical staining. Methylation in the promoter region of NF2 was detected by methylation-specific PCR and bisulfate genomic sequencing PCR. Cell proliferation was examined by Cell-Counting Kit-8 and 5-ethynyl-2′-deoxyuridine assay, and cell invasion was evaluated by transwell assay. Flow cytomery and Hoechst staining were used to analyze cell apoptosis. A dual luciferase system was used to confirm the relationship between miR-152-3p and DNMT1. Results Methylation of NF2 and DNMT1 was markedly increased, and miR-152-3p was downregulated in GBM tissues and glioma cells. Both knockdown of DNMT1 and overexpression miR-152-3p showed that demethylation activated the expression of NF2. Furthermore, miR-152-3p directly targeted DNMT1. Both miR-152-3p overexpression and DNMT1 knockdown significantly induced cell apoptosis and inhibited invasive activity. This was also observed after NF2 overexpression. Conclusions These results indicated that miR-152-3p can inhibit glioma cell proliferation and invasion activities by decreasing DNMT1. The restoration of miR-152-3p may have therapeutic application in the treatment of GBM. Electronic supplementary material The online version of this article (doi:10.1186/s13046-017-0567-4) contains supplementary material, which is available to authorized users.

Fused Deposition Modeling (FDM) is an additive manufacturing process that can quickly produce geometrically complex parts through the melting, depositing and solidifying of thermoplastics, layer by layer. This paper proposes an algorithm of the filling mutational structure which is used to fill the mutational structure with a high density and the other regions with a low filling density. Through analyzing the area classification and geometric feature of the printing model, two-dimensional filling algorithm and three-dimensional filling algorithm are proposed respectively. By experimental verification, this filling algorithm not only improves the strength of the mutational structure but also saves the printing time and enhances the working efficiency.

Purpose: To investigate the lipid-lowering activity of two metabolites of galangin, namely, galangin-3-O-β-D-glucuronic acid (GG-1) and galangin-7-O-β-D-glucuronic acid (GG-2).Methods: Female Sprague-Dawley rats were orally administered with galangin. The two metabolites of galangin were isolated from urine sample and purified using Sephadex LH-20 and semi-preparative high performance liquid chromatography (HPLC). The structures of the metabolites were identified by analyzing spectroscopic data. Hypolipidemic activity was evaluated in HepG2 cells. The down- or upregulation of lipogenic genes was detected using real-time quantitative polymerase chain reaction (qPCR).Results: Both metabolites of galangin showed hypolipidemic activity. These activities are closely associated with the down-regulation of lipogenic genes such as SREBP-1a, SREBP-1c, and SREBP-2 transcription factors, and the downstream genes such as FAS, ACC, and HMGR were revealed by realtime qPCR data.Conclusion: The results show that both metabolites possess better lipid-lowering activities than galangin. These hypolipidemic activities are closely associated with inhibiting key genes or proteins that regulated the biosynthesis of both cholesterol and triglycerides.Keywords: Galangin, Galangin-3-O-β-D-glucuronic acid, Galangin-7-O-β- D-glucuronic acid, Hypolipidemic, Lipogenic genes, Metabolites

Epidemiological data have suggested an increased cancer rates in diabetic patients, for which the underlying mechanism is poorly understood. We studied whether high level of glucose (HG) treatment that mimic the hyperglycemic condition in diabetes mellitus is mutagenic. Mutagenesis studies were carried out at both hypoxanthine phosphoribosyltransferase (hprt) and thymidine kinase (tk) loci. Role of p53 in HG-induced mutagenesis was also investigated by using human lymphoblastoid cell lines derived from same donor but differs in p53 statuses; TK6 has wild-type p53, NH32 has null p53, and WTK1 has mutant p53 (ile237). In addition, we studied the influence of antioxidant treatment on HG-induced mutagenesis. Mutation fractions at both loci increased significantly in all three lines at 21 and 28 days after HG treatments. At tk locus, the increase of a class of mutants with normal growth rate is mainly responsible for the overall increased mutant fraction. Compared to TK6 cells, both NH32 and WTK1 cells showed an early onset of mutagenesis. Treatment of cells with antioxidant N-acetyl-L-cysteine partially reduced HG induced mutagenesis. This study is the first to indicate that HG is able to induce gene mutation which may be one of the important mechanisms of diabetes-associated carcinogenesis.

This paper expounds the important status and characteristics of Hydropower Engineering Geological Information (HEGI) in the "Digital Hydropower" by the knowledge system, further analysed its two critical parts: the modern engineering geological investigation technology and geographic information system, and analyses the specific contents and the combined methods, it paves the way for the work for realizing better hydropower engineering geological

The ability to exchange keys between users is vital in any wireless based security system. A key generation technique exploits the randomness of the wireless channel is a promising alternative to existing key distribution techniques, e.g., public key cryptography. In this paper a secure key generation scheme based on the subcarriers’ channel responses in orthogonal frequencydivision multiplexing (OFDM) systems is proposed. We first implement a time-variant multipath channel with its channel impulse response modelled as a wide sense stationary (WSS) uncorrelated scattering random process and demonstrate that each subcarrier’s channel response is also a WSS random process. We then define the X% coherence time as the time required to produce an X% correlation coefficient in the autocorrelation function (ACF) of each channel tap, and find that when all the channel taps have the same Doppler power spectrum, all subcarriers’ channel responses has the same ACF as the channel taps. The subcarrier’s channel response is then sampled every X% coherence time and quantized into key bits. All the key sequences’ randomness is tested using National Institute of Standards and Technology (NIST) statistical test suite and the results indicate that the commonly used sampling interval as 50% coherence time cannot guarantee the randomness of the key sequence.

Objective: To study the chemical constituents in leaves of Cebera manghas. Methods: Chemical constituents were isolated by using various column chromatography and the structures were elucidated on basis Of physicochemical constants and spectral data analysis. Results: Nine compounds were obtained including p-hydroxybenzaldehyde (1), benzamide (2), n-hexadecane acid monoglyceride (3), loliolide (4). B-sitosterol (5). Cerberin (6), neriifolin (7). cerleaside A (8). daucosterol (9). Conclusions: Compounds 1-4 are obtained from this genus for the first time.

There are two main categories of Distributed Denial of Service (DDoS) attacks that are capable of disrupting the daily operations of internet users and these are the low and high rate DDoS attacks. The detection and prevention of DDoS attacks is a very important aspect in network security in ensuring that the operations of businesses, communication, and educational facilities operate efficiently without disruptions. Over the years, many DDoS attacks detection systems have been proposed. These detection systems have focused more on obtaining high accuracy, reduction of false alarm rates and simplification of detection systems. However, less attention has been given to the computational costs of detection systems (processing power requirements and memory consumptions), early detection and flexibility in their deployment to support the different needs of networks and distributed monitoring approaches. The focus of this thesis is to investigate the use of a robust feature selection approach and machine learning classifiers to develop useful DDoS detection architectures for fast, effective, and efficient DDoS attacks detection to achieve high performance at low computational cost. To achieve this, a lightweight software architecture which is simple in design using minimal number of network flow features for distinguishing normal from DDoS attack network flows is proposed. The architecture is based on the Decision-Tree (DT) classifier and distinguishes DDoS attack from normal traffic network flows with a detection accuracy of over 99.9% when evaluated with up-to-date DDoS attack datasets. In addition, it can flexibly be deployed in a real-time network environment and at different network nodes to meet the needs of the network being monitored creating an avenue for distributed monitoring. Also, the use of minimal network flow features selected through a robust features selection approach results in a massive reduction in memory requirements when compared to traditional systems. Results from the software implementation of the architecture indicated that it uses just 7% processing power of a core of the detection system’s CPU in offline mode and provides no additional overhead to the monitored network. However, software applications for distinguishing normal from DDoS attack traffic are struggling to cope with the ever-increasing complexity and intensity of DDoS attack traffic. This increased workload ranges from the capturing and processing of millions of packets per second to classification of thousands of network flows per second which is evident in some of the most recent DDoS attacks faced by a variety of companies. To cope with this workload, a hardware accelerated hybrid network monitoring application is proposed. The proposed application is capable of fast network flows classification by leveraging the hardware parallel processing characteristics of a Field Programmable Gate Array (FPGA) whilst using a software application in the CPU for the network flow pre-processing required for classification. The hybrid system is capable of distinguishing DDoS attacks from normal network traffic flows with a detection accuracy of over 98% when deployed in a real-time environment under different network traffic conditions with detection in 1µs which is over thirty times faster than the software implementation of the architecture. The hardware accelerated application was implemented in the Zynq-7000 All Programmable SoCs ZedBoard which can monitor up to 1Gbps line rate. The evaluation results and findings from analysis of the experimental results of the hard ware accelerated application provide some important insights in improving the programmability, overall performance, scalability, and flexibility in deployment of the detection system across a network for accurate and early DDoS attack detection. In the final part of this thesis, the use of distributed network monitoring is explored with the implementation of the lightweight DDoS attacks detection architecture using Network Simulator 3 (NS-3). The systems are distributed at different parts of a network and results from the approach indicated that effective implementation of distributed network monitoring systems dramatically reduces the effect of DDoS attack to a minimal on the target network or network node.