ETGP: Top-K Geography-Text P/S Approach without Threshold

Social media are more and more popular. Subsequently, geography-text data has caused wide attention. Different from the traditional publish/subscribe (P/S), geography-text data is published and subscribed in the form of dynamic data flow in the mobile network. The difference raises higher demands for facility. However, previous top-k geography-text P/S approaches want to set a set of thresholds. A user should take time to set a threshold for each subscription, which is not facile enough. The threshold yields many weaknesses to users. Therefore, we herein propose an efficient top-k geography-text P/S approach that excludes the threshold, called ETGP. Our approach does not need users to set any threshold. Subsequently, the ETGP returns the highest score results to the subscriber without setting a threshold. Therefore, our approach can lessen redundant computations, promote the query integrity rate, and make P/S system easier for the user to use. Comprehensive experiments prove the efficiency of the proposed approach with high facility

An Efficient Feature Extraction Scheme for Mobile Anti-Shake in Augmented Reality

In recent years, augmented reality on mobile devices has become popular. Mobile shakes are the most typical type of interference in mobile augmented reality. To negate such interference, anti-shake is an urgent requirement. To enhance anti-shake efficiency, we propose an efficient feature extraction scheme for mobile anti-shake in augmented reality. The scheme directly detects corners to avoid the non-extreme constraint such that the efficiency of feature extraction is improved. Meanwhile, the scheme only updates the added corners during mobile shakes, which improves the accuracy of feature extraction. In the experiments, the memory consumption of existing methods is almost double compared to that in our scheme. Further, the runtime of our scheme is only half of the runtime of the existing methods. The experimental results demonstrate that our scheme performs better than the existing classic methods on mobile anti-shake in terms of memory consumption, efficiency, and accuracy

Maximum Recommendation in Geo-social Network for Business

Most of existing methods do not consider the maximum recommendation issue. Meanwhile, the methods also do not consider the negative influence in recommendation model. These two shortcomings limit further application of the recommendation system. In another word, the shortcomings not only decrease the recommendation effect but also increase the recommendation cost in the business. To remove the shortcomings, we propose a Maximum Recommendation scheme in Geo-social network for business (called as MRG). On the one hand, we identify k nodes with maximum recommendation according to the expected paid node number k. On the other hand, we exclude the negative node from the geo-social network. Based on the above innovation, we effectively increase the recommendation effect and decrease the company\u27s recommendation cost. Meanwhile, MRG considers the negative influence to enhance the recommendation efficiency. Experimental results show that our scheme has better performance than most of the existing methods in the maximum recommendation field

Reversible Watermarking Using Prediction-Error Expansion and Extreme Learning Machine

Currently, the research for reversible watermarking focuses on the decreasing of image distortion. Aiming at this issue, this paper presents an improvement method to lower the embedding distortion based on the prediction-error expansion (PE) technique. Firstly, the extreme learning machine (ELM) with good generalization ability is utilized to enhance the prediction accuracy for image pixel value during the watermarking embedding, and the lower prediction error results in the reduction of image distortion. Moreover, an optimization operation for strengthening the performance of ELM is taken to further lessen the embedding distortion. With two popular predictors, that is, median edge detector (MED) predictor and gradient-adjusted predictor (GAP), the experimental results for the classical images and Kodak image set indicate that the proposed scheme achieves improvement for the lowering of image distortion compared with the classical PE scheme proposed by Thodi et al. and outperforms the improvement method presented by Coltuc and other existing approaches

An Efficient Top-k Query Scheme Based on Multilayer Grouping

The top-k query is to find the k data that has the highest scores from a candidate dataset. Sorting is a common method to find out top-k results. However, most of existing methods are not efficient enough. To remove this issue, we propose an efficient top-k query scheme based on multilayer grouping. First, we find the reference item by computing the average score of the candidate dataset. Second, we group the candidate dataset into three datasets: winner set, middle set and loser set based on the reference item. Third, we further group the winner set to the second-layer three datasets according to k value. And so on, until the data number of winner set is close to k value. Meanwhile, if k value is larger than the data number of winner set, we directly return the winner set to the user as a part of top-k results almost without sorting. In this case, we also return the top results with the highest scores from the middle set almost without sorting. Based on above innovations, we almost minimize the sorting. Experimental results show that our scheme significantly outperforms the current classical method on the performance of memory consumption and top-k query

Genome Characterization and Potential Risk Assessment of the Novel SARS-CoV-2 Variant Omicron (B.1.1.529)

As the novel coronavirus SARS-CoV-2 spread around the world, multiple waves of variants emerged, thus leading to local or global population shifts during the pandemic. A new variant named Omicron (PANGO lineage B.1.1.529), which was first discovered in southern Africa, has recently been proposed by the World Health Organization to be a Variant of Concern. This variant carries an unusually large number of mutations, particularly on the spike protein and receptor binding domain, in contrast to other known major variants. Some mutation sites are associated with enhanced viral transmission, infectivity, and pathogenicity, thus enabling the virus to evade the immune protective barrier. Given that the emergence of the Omicron variant was accompanied by a sharp increase in infection cases in South Africa, the variant has the potential to trigger a new global epidemic peak. Therefore, continual attention and a rapid response are required to decrease the possible risks to public health

Efficient Q-Value Zero-Leakage Protection Scheme in SRS Regularly Publishing Private Data

Spontaneous Reporting System (SRS) has been widely established to collect adverse drug events. Thus, SRS promotes the detection and analysis of ADR (adverse drug reactions), such as the FDA Adverse Event Reporting System (FAERS). The SRS data needs to be provided to researchers. Meanwhile, the SRS data is publicly available to facilitate the study of ADR detection and analysis. In general, SRS data contains private information of some individual characteristics. Before the information is published, it is necessary to anonymize private information in the SRS data to prevent disclosure of individual privacy. There are many privacy protection methods. The most classic method for protecting SRS data is called as PPMS. However, in the real world, SRS data is growing dynamically and needs to be published regularly. In this case, PPMS has some shortcomings in the memory consumption, anonymity efficiency, data update and data security. To remove these shortcomings, we propose an Efficient Q-value Zero-leakage protection Scheme in SRS regularly publishing private data, called EQZS. EQZS can deal with almost all of potential attacks. Meanwhile, EQZS removes the shortcomings of PPMS. The experimental results show that our scheme EQZS solves the problem of privacy leakage in SRS regularly publishing private data. Meanwhile, EQZS significantly outperforms PPMS on the efficiency of memory consumption, privacy anonymity and data update

Characterization of an aspartate aminotransferase encoded by YPO0623 with frequent nonsense mutations in Yersinia pestis

Yersinia pestis, the causative agent of plague, is a genetically monomorphic bacterial pathogen that evolved from Yersinia pseudotuberculosis approximately 7,400 years ago. We observed unusually frequent mutations in Y. pestis YPO0623, mostly resulting in protein translation termination, which implies a strong natural selection. These mutations were found in all phylogenetic lineages of Y. pestis, and there was no apparent pattern in the spatial distribution of the mutant strains. Based on these findings, we aimed to investigate the biological function of YPO0623 and the reasons for its frequent mutation in Y. pestis. Our in vitro and in vivo assays revealed that the deletion of YPO0623 enhanced the growth of Y. pestis in nutrient-rich environments and led to increased tolerance to heat and cold shocks. With RNA-seq analysis, we also discovered that the deletion of YPO0623 resulted in the upregulation of genes associated with the type VI secretion system (T6SS) at 26°C, which probably plays a crucial role in the response of Y. pestis to environment fluctuations. Furthermore, bioinformatic analysis showed that YPO0623 has high homology with a PLP-dependent aspartate aminotransferase in Salmonella enterica, and the enzyme activity assays confirmed its aspartate aminotransferase activity. However, the enzyme activity of YPO0623 was significantly lower than that in other bacteria. These observations provide some insights into the underlying reasons for the high-frequency nonsense mutations in YPO0623, and further investigations are needed to determine the exact mechanism

Yersinia pestis and Plague: Some Knowns and Unknowns

Since its first identification in 1894 during the third pandemic in Hong Kong, there has been significant progress in understanding the lifestyle of Yersinia pestis , the pathogen that is responsible for plague. Although we now have some understanding of the pathogen’s physiology, genetics, genomics, evolution, gene regulation, pathogenesis and immunity, there are many unknown aspects of the pathogen and its disease development. Here, we focus on some of the knowns and unknowns related to Y. pestis and plague. We notably focus on some key Y. pestis physiologic and virulence traits that are important for its mammal-flea-mammal life cycle, but also its emergence from the enteropathogen, Yersinia pseudotuberculosis . Some aspects of the genetic diversity of Y. pestis , the distribution and ecology of plague, as well as the medical countermeasures to protect our population are also provided. Lastly, we present some biosafety and biosecurity information related to Y. pestis and plague