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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.

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.

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.

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

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.

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.

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.

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.

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

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.