Privacy-Preserving Content-Aware Search Based on Two-Level Index

Nowadays, cloud computing is used more and more widely, more and more people prefer to using cloud server to store data. So, how to encrypt the data efficiently is an important problem. The search efficiency of existed search schemes decreases as the index increases. For solving this problem, we build the two-level index. Simultaneously, for improving the semantic information, the central word expansion is combined. The purpose of privacy-preserving content-aware search by using the two-level index (CKESS) is that the first matching is performed by using the extended central words, then calculate the similarity between the trapdoor and the secondary index, finally return the results in turn. Through experiments and analysis, it is proved that our proposed schemes can resist multiple threat models and the schemes are secure and efficient

Generative text steganography method based on emotional expression in semantic space

Aiming at the problems that “over optimizing” the quality of steganographic text and lack of constraints on the semantic expression of the generated steganographic text in existing generative text steganography methods, a generative text steganography method was proposed based on emotional expression in semantic space.In order to make use of the scene fusion provided by the new media platform to obtain many camouflage scenes, the focus was how to use the unsupervised extraction model to extract the emotional expression combination candidate set from the original data set, then sort the candidate set of emotional expression combinations based on the improved bipartite graph sorting algorithm to obtain the emotional expression combination set, map them to the semantic space, and then implement embedding secret information while generating the user’s opinions based on the emotion expression combinations.Experimental results show that, compared with the existing generative text steganography methods in semantic space, the product reviews generated by the proposed method have a minimum perplexity of 10.536, and have a strong correlation with the chosen product, which can further guarantee the cognitive concealment of steganographic texts.At the same time, the proposed method can also be effectively used in the field of secure and confidential communication, and can avoid the senders being traced and analyzed

The open banking era:An optimal model for the emergency fund

The COVID-19 outbreak has negatively impacted the income of many bank users. Many users without emergency funds had difficulty coping with this unexpected event and had to use credit or apply to the government for bailout funds. Therefore, it is necessary to develop spending plans and deposit plans based on transaction data of users to assist them in saving sufficient emergency funds to cope with unexpected events. In this paper, an emergency fund model is proposed, and two optimization algorithms are applied to solve the optimal solution of the model. Secondly, an early warning mechanism is proposed, i.e. an unexpected prevention index and a consumption index are proposed to measure the ability of users to cope with unexpected events and the reasonableness of their expenditure respectively, which provides early warning to users. Finally, the model is experimented with real bank users and the performance of the model is analysed. The experiments show that compared to the no-planning scenario, the model helps users to save more emergency funds to cope with unexpected events, furthermore, the proposed model is real-time and sensitive.</p

Guanxintai Exerts Protective Effects on Ischemic Cardiomyocytes by Mitigating Oxidative Stress

Oxidative stress participates in numerous myocardial pathophysiological processes and is considered a therapeutic target for myocardial ischemia and heart failure. Guanxintai (GXT), a traditional Chinese medicine, is commonly used to treat cardiovascular disease on account of its numerous beneficial physiological activities, such as dilating coronary arteries, inhibiting platelet aggregation, and reducing the serum lipid content. However, the antioxidative properties of GXT and potential underlying mechanisms remain to be established. In the present study, we investigated the protective effects of GXT on ischemic cardiomyocytes and the associated antioxidative mechanisms, both in vivo and in vitro. Notably, GXT treatment reduced the degree of cardiomyocyte injury, myocardial apoptosis, and fibrosis and partially improved cardiac function after myocardial infarction. Furthermore, GXT suppressed the level of ROS as well as expression of NADPH oxidase (NOX) and phospho-p38 mitogen-activated protein kinase (MAPK) proteins. Our results collectively suggest that the protective effects of GXT on ischemic cardiomyocytes are exerted through its antioxidative activity of NOX inhibition

EEG-based emotion classification using spiking neural networks

A novel method of using the spiking neural networks (SNNs) and the electroencephalograph (EEG) processing techniques to recognize emotion states is proposed in this paper. Three algorithms including discrete wavelet transform (DWT), variance and fast Fourier transform (FFT) are employed to extract the EEG signals, which are further taken by the SNN for the emotion classification. Two datasets, i.e., DEAP and SEED, are used to validate the proposed method. For the former dataset, the emotional states include arousal, valence, dominance and liking where each state is denoted as either high or low status. For the latter dataset, the emotional states are divided into three categories (negative, positive and neutral). Experimental results show that by using the variance data processing technique and SNN, the emotion states of arousal, valence, dominance and liking can be classified with accuracies of 74%, 78%, 80% and 86.27% for the DEAP dataset, and an overall accuracy is 96.67% for the SEED dataset, which outperform the FFT and DWT processing methods. In the meantime, this work achieves a better emotion classification performance than the benchmarking approaches, and also demonstrates the advantages of using SNN for the emotion state classifications

Long-lived magmatic evolution and mineralization resulted in formation of the giant Cuonadong Sn-W-Be polymetallic deposit, southern Tibet

The Cuonadong Sn-W-Be polymetallic deposit is the first Cenozoic leucogranite-related rare-metal deposit with giant metallogenic potential in the Himalayan orogen. However, controlling factors for the supernormal enrichment of beryllium, tin and tungsten in this deposit remain vague. In this study, we carried out systematic geochronological, whole-rock geochemical, and Sr-Nd isotopic analysis for the Cuonadong leucogranites, as well as detailed ore-forming geochronological analysis. The monazite U-Th-Pb, cassiterite U-Pb and muscovite Ar-Ar dating results, together with previously reported geochronological data, indicate that the major Cuonadong leucogranites (including, from old to young, weakly-oriented two-mica, two-mica granite and muscovite) were formed during ∼21-15 Ma, whereas the Sn-W-Be mineralization mainly occurred at ∼18-14 Ma. The Cuonadong leucogranites show strong peraluminous (A/CNK=1.09-1.22) features, and have high SiO2 (71.62-75.97 wt.%) and Al2O3 (14.04-16.09 wt.%) and low MgO (0.07-0.33 wt.%), MnO (0.01-0.15 wt.%) and total Fe2O3 (0.36-1.01 wt.%) contents, and are enriched in large ion lithophile elements (e.g., Rb, U, K, and Pb). These geochemical features together with enriched Sr-Nd isotopes (εNd(t) = -15.7 to -11.7; (87Sr/86Sr)i=0.71957-0.76313) indicate that the Cuonadong leucogranites belong to S-type granite and were derived from muscovite-induced dehydration melting of metapelites of the Higher Himalayan Crystalline Sequence. Perceptible linear variations of some major elements (e.g., Na2O, K2O, MnO, Fe2O3T, TiO2 and A/CNK) with increasing Rb/Sr ratios suggest these leucogranites experienced different degrees of evolution. Quantitative simulation calculations based on the whole-rock Rb, Sr, and Ba contents imply that the Cuonadong leucogranites experienced increasingly-strong fractional crystallization of plagioclase, K-feldspar and biotite from the weakly-oriented two-mica granite to two-mica granite and muscovite granite. Importantly, intense fractional crystallization leaded to notable enrichment of Sn, W and Be, although these elements are not obviously high in the relatively primitive magma for the Cuonadong leucogranites. Significantly, evident REE tetrad effects and deviation of twin-element pair ratios (K/Rb, K/Ba, Zr/Hf, Nb/Ta, and Y/Ho) from the chondritic values demonstrate that intense interaction between melts and F-rich aqueous fluids occurred during magmatic evolution. This implies that the Cuonadong leucogranites were derived from a volatile-rich magmatic system. The abundant volatiles probably remarkably facilitated and extended the fractional crystallization though lowering the solidus and viscosity of the magma. Thus, we propose that long-lived crystal fractionation (∼21-15 Ma) and mineralization (∼18-14 Ma) collectively leaded to supernormal enrichment of Sn, W, and Be in the Cuonadong Sn-W-Be polymetallic deposit. In contrast, the enrichment of Sn, W, and Be during the partial melting was insignificant.publishedVersio

Research progress of novel bio-denitrification technology in deep wastewater treatment

Excessive nitrogen emissions are a major contributor to water pollution, posing a threat not only to the environment but also to human health. Therefore, achieving deep denitrification of wastewater is of significant importance. Traditional biological denitrification methods have some drawbacks, including long processing times, substantial land requirements, high energy consumption, and high investment and operational costs. In contrast, the novel bio-denitrification technology reduces the traditional processing time and lowers operational and maintenance costs while improving denitrification efficiency. This technology falls within the category of environmentally friendly, low-energy deep denitrification methods. This paper introduces several innovative bio-denitrification technologies and their combinations, conducts a comparative analysis of their denitrification efficiency across various wastewater types, and concludes by outlining the future prospects for the development of these novel bio-denitrification technologies

In Silico and In Vivo Studies on the Mechanisms of Chinese Medicine Formula (Gegen Qinlian Decoction) in the Treatment of Ulcerative Colitis

Ulcerative colitis (UC) is a chronic inflammatory bowel disease, and Gegen Qinlian Decoction (GQD), a Chinese botanical formula, has exhibited beneficial efficacy against UC. However, the mechanisms underlying the effect of GQD still remain to be elucidated. In this study, network pharmacology approach and molecular docking in silico were applied to uncover the potential multicomponent synergetic effect and molecular mechanisms. The targets of ingredients in GQD were obtained from Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and Bioinformatics Analysis Tool for Molecular mechANism of TCM (BATMAN-TCM) database, while the UC targets were retrieved from Genecards, therapeutic target database (TTD) and Online Mendelian Inheritance in Man (OMIM) database. The topological parameters of Protein-Protein Interaction (PPI) data were used to screen the hub targets in the network. The possible mechanisms were investigated with gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Molecular docking was used to verify the binding affinity between the active compounds and hub targets. Network pharmacology analysis successfully identified 77 candidate compounds and 56 potential targets. The targets were further mapped to 20 related pathways to construct a compound-target-pathway network and an integrated network of GQD treating UC. Among these pathways, PI3K-AKT, HIF-1, VEGF, Ras, and TNF signaling pathways may exert important effects in the treatment of UC via inflammation suppression and anti-carcinogenesis. In the animal experiment, treatment with GQD and sulfasalazine (SASP) both ameliorated inflammation in UC. The proinflammatory cytokines (TNF-α, IL-1β, and IL-6) induced by UC were significantly decreased by GQD and SASP. Moreover, the protein expression of EGFR, PI3K, and phosphorylation of AKT were reduced after GQD and SASP treatment, and there was no significance between the GQD group and SASP group. Our study systematically dissected the molecular mechanisms of GQD on the treatment of UC using network pharmacology, as well as uncovered the therapeutic effects of GQD against UC through ameliorating inflammation via downregulating EGFR/PI3K/AKT signaling pathway and the pro-inflammatory cytokines such as TNF-α, IL-1β and IL-6

Global systematic review with meta-analysis shows that warming effects on terrestrial plant biomass allocation are influenced by precipitation and mycorrhizal association

Biomass allocation in plants is fundamental for understanding and predicting terrestrial carbon storage. Yet, our knowledge regarding warming effects on root: shoot ratio (R/S) remains limited. Here, we present a meta-analysis encompassing more than 300 studies and including angiosperms and gymnosperms as well as different biomes (cropland, desert, forest, grassland, tundra, and wetland). The meta-analysis shows that average warming of 2.50 °C (median = 2 °C) significantly increases biomass allocation to roots with a mean increase of 8.1% in R/S. Two factors associate significantly with this response to warming: mean annual precipitation and the type of mycorrhizal fungi associated with plants. Warming-induced allocation to roots is greater in drier habitats when compared to shoots (+15.1% in R/S), while lower in wetter habitats (+4.9% in R/S). This R/S pattern is more frequent in plants associated with arbuscular mycorrhizal fungi, compared to ectomycorrhizal fungi. These results show that precipitation variability and mycorrhizal association can affect terrestrial carbon dynamics by influencing biomass allocation strategies in a warmer world, suggesting that climate change could influence belowground C sequestration