Random Tag Research Articles

Scalable and Popularity-Based Secure Deduplication Schemes With Fully Random Tags

Scalable and Popularity-Based Secure Deduplication Schemes With Fully Random Tags

Exploring transient global transcriptional changes induced by ascorbic acid revealed via atKAS-seq profiling.

Transcription initiates the formation of single-stranded DNA (ssDNA) regions within the genome, delineating transcription bubbles, a highly dynamic genomic process. Kethoxal-assisted single-stranded DNA sequencing (KAS-seq) utilizing N3-kethoxal has emerged as a potent tool for mapping specific guanine positions in ssDNA on a genome-wide scale. However, the original KAS-seq method required the costly Accel-NGS Methyl-seq DNA library kit. This study introduces an optimized iteration of the KAS-seq technique, referred to as adapter-tagged KAS-seq (atKAS-seq), incorporating an adapter tagging strategy. This modification involves integrating sequencing adapters via complementary strand synthesis using random N9 tagging. Additionally, by harnessing the potential of ascorbic acid (ASC), recognized for inducing global epigenetic changes, we employed the atKAS-seq methodology to elucidate critical pathways influenced by short-term, high-dose ASC treatment. Our findings underscore that atKAS-seq enables rapid and precise analyses of transcription dynamics and enhancer activities concurrently. This method offers a streamlined, cost-efficient, and low-input approach, affirming its utility in probing intricate genomic regulatory mechanisms.

Analysis of Guzheng Music Style Transformation Based on Generative Adversarial Networks

With the emergence of more and more new music styles, Guzheng, a traditional national musical instrument that has been handed down for thousands of years, also faces new challenges. However, with the development of the times and aesthetics, composers have created various modern Guzheng music, thus injecting new vitality into the development of Guzheng. In order to study the mutual conversion between Guzheng music and other music, this paper proposes a Guzheng music conversion model based on Star generative adversarial networks (GAN), which can convert any length of music and realize the music conversion between various styles. Based on the idea of star GAN, this model uses Mel spectrum of music and random target style tags to train a general generator to generate a specified music style. It is found that the model proposed in this paper has better conversion effect between similar styles of music; that is, when Guzheng music is converted into pop and flute, the effect is the best, the average accuracy is 89.62% and 90.32%, respectively, and the cosine similarity is also 15.8 and 20.5.

Privacy protection domain-user integra tag deduplication in cloud data server

<span>The cloud with strong storage management has recently developed in the big data world which can confirm the data integrity and keep just a single data duplicate. Many cloud auditing storage techniques have been developed to overcome the data deduplication (DD) problem, but they are vulnerable and can't resist brute force attacks (BFA). There is some privacy leakage problem that occurred in the present method. In this article, an original strategy called domain-user integra tag (DUIT) has been presented which comprises inter and intra deduplication with file tag and symmetric encryption key. The DUIT has two phases, the first one is random tag generation for Intra deduplication and the other is random ciphertext (CT) generation for encryption. The benefit of the DUIT is the security of individual user’s files would not reveal to people in general, hence we proved that the DUIT is protected from the BFA. Finally, an experiment has conducted in Linux processor and C program software. The outcome of DUIT demonstrates that our method has reduced the computation cost (CC) by 27% and 35% and searching complexity (SC) by 10% and 26% related with the previous methods. It is decided that the DUIT achieves the low CC and SC.</span><br /><div style="mso-element: comment-list;"><div style="mso-element: comment;"><div id="_com_1" class="msocomtxt"><!--[if !supportAnnotations]--></div><!--[endif]--></div></div>

Chemical identification and indexing in PubMed full-text articles using deep learning and heuristics.

The identification of chemicals in articles has attracted a large interest in the biomedical scientific community, given its importance in drug development research. Most of previous research have focused on PubMed abstracts, and further investigation using full-text documents is required because these contain additional valuable information that must be explored. The manual expert task of indexing Medical Subject Headings (MeSH) terms to these articles later helps researchers find the most relevant publications for their ongoing work. The BioCreative VII NLM-Chem track fostered the development of systems for chemical identification and indexing in PubMed full-text articles. Chemical identification consisted in identifying the chemical mentions and linking these to unique MeSH identifiers. This manuscript describes our participation system and the post-challenge improvements we made. We propose a three-stage pipeline that individually performs chemical mention detection, entity normalization and indexing. Regarding chemical identification, we adopted a deep-learning solution that utilizes the PubMedBERT contextualized embeddings followed by a multilayer perceptron and a conditional random field tagging layer. For the normalization approach, we use a sieve-based dictionary filtering followed by a deep-learning similarity search strategy. Finally, for the indexing we developed rules for identifying the more relevant MeSH codes for each article. During the challenge, our system obtained the best official results in the normalization and indexing tasks despite the lower performance in the chemical mention recognition task. In a post-contest phase we boosted our results by improving our named entity recognition model with additional techniques. The final system achieved 0.8731, 0.8275 and 0.4849 in the chemical identification, normalization and indexing tasks, respectively. The code to reproduce our experiments and run the pipeline is publicly available. Database URL https://github.com/bioinformatics-ua/biocreativeVII_track2

Random peptides rich in small and disorder-promoting amino acids are less likely to be harmful.

Proteins are the workhorses of the cell, yet they carry great potential for harm via misfolding and aggregation. Despite the dangers, proteins are sometimes born de novo from noncoding DNA. Proteins are more likely to be born from noncoding regions that produce peptides that do little to no harm when translated than from regions that produce harmful peptides. To investigate which newborn proteins are most likely to “first, do no harm,” we estimate fitnesses from an experiment that competed Escherichia coli lineages that each expressed a unique random peptide. A variety of peptide metrics significantly predict lineage fitness, but this predictive power stems from simple amino acid frequencies rather than the ordering of amino acids. Amino acids that are smaller and that promote intrinsic structural disorder have more benign fitness effects. We validate that the amino acids that indicate benign effects in random peptides expressed in E. coli also do so in an independent data set of random N-terminal tags in which it is possible to control for expression level. The same amino acids are also enriched in young animal proteins.

Time Efficient Tag Searching in Large-Scale RFID Systems: A Compact Exclusive Validation Method

RFID technology has been widely applied in a range of applications such as inventory control, warehouse management and supply chain logistics. Many practical applications need to search a given set of tags (called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">wanted tags</i> ) to determine which of them are present in the system, which is usually called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tag searching</i> . Existing tag searching protocols suffer performance bottleneck and the time efficiency and need to be improved. The bottleneck stems from two factors. First, the existing methods validate the wanted tags in a random way due to the randomness of the hash function, which unavoidably generate many useless slots. Second, in order to achieve the predefined reliability requirement, the existing methods have to repeatedly validate target tags multiple times. In this paper, we design new tag searching techniques of compact exclusive validation that break through the existing performance bottleneck from two aspects. First, our protocols avoid slot waste. Different from random tag validation, our protocols validate tags orderly by mapping the wanted tags and the slots in a one-to-one manner, which makes the reader be able to validate tags in every slot. Second, our protocols avoid repeated tag responding. By combining two lightweight indicators, we rapidly filter out non-wanted tags so that there is no interference when validating the wanted tags. Hence, we need to validate each wanted tag only once, which avoids redundant validation and greatly improves time efficiency. Our protocols work with the assumption that the rough number of tags in the system can be obtained by using existing estimation algorithms, but they do not need to know exactly which tags are in the system. Theoretical analysis illustrates our methods achieve linear time complexity. Extensive experimental results show that, our best protocol can improve the time efficiency by up to 81 percent when compared with the-state-of-art solution.

Budgeted influence and earned benefit maximization with tags in social networks

Influence Maximization Problem aims at identifying a limited number of highly influential users who will be working for diffusion agents to maximize the influence. In case of Budgeted Influence Maximization (BIM), the users of the network have a cost and influential user selection needs to be done within a given budget. In case of Earned Benefit Maximization (EBM) Problem, a set of target users along with their benefit value is given and the aim is to choose highly influential users within an allocated budget to maximize the earned benefit. In this paper, we study the BIM and EBM Problem under the tag-specific edge probability setting, which means instead of a single edge probability a set of probability values (each one for a specific context e.g., ‘games,’ ‘academics,’ etc.) per edge is given. The aim is to identify the influential tags and users for maximizing the influence and earned benefit. Considering the realistic fact that different tags have a different impact on different communities of a social network, we propose two solution methodologies and one pruning technique. A detailed analysis of all the solution approaches has been done. An extensive set of experiments have been carried out with three benchmark datasets. From the experiments, we observe that the proposed solution approaches outperform baseline methods (e.g., random node-random tag, high-degree node–high-frequency tag, high-degree node–high-frequency tag with community). For the tag-based BIM Problem the improvement is upto \(8\%\) in terms of number of influenced nodes and for the tag-based EBM Problem the improvement is upto \(15\%\) in terms of earned benefit.

Achieving Efficient and Privacy-Preserving Multi-Domain Big Data Deduplication in Cloud

Secure data deduplication, as it can eliminate redundancies over encrypted data, has been widely developed in cloud storage to reduce storage space and communication overheads. Among them, the convergent encryption has been extensively adopted. However, it is vulnerable to brute-force attacks that can determine which plaintext in a message space corresponds to a given ciphertext. Many existing schemes have to sacrifice efficiency to resist brute-force attacks, especially for cross-domain deduplication, which is inevitably contrary to practical applications. Moreover, few existing schemes consider protecting the message equality information (i.e., whether two different ciphertexts correspond to an identical plaintext). To address the above challenges, in this paper, we propose an efficient and privacy-preserving big data deduplication scheme for a two-level multi-domain architecture. Specifically, by generating a random tag and a constant number of random ciphertexts for each data, our scheme not only ensures data confidentiality under multi-domain deduplication but also resists brute-force attacks. By allowing only the agent and cloud service provider to perform intra-deduplication and inter-deduplication, respectively, our scheme can protect the message equality information from disclosure as much as possible. Detailed security analysis shows that our scheme achieves privacy-preservation for both data content and the message equality information and data integrity while resisting brute-force attacks. Furthermore, extensive simulations demonstrate that our scheme significantly outperforms the existing competing schemes, especially the computational cost and the time complexity of the duplicate search.

An Efficient Protocol for the Tag-information Sampling Problem in RFID Systems

Given a population S of N tags in an RFID system, the tag-information sampling problem is to randomly choose K distinct tags from S to form a subset T, and then inform each tag in T of a unique integer from {1,2,⋯ ,K}. This is a fundamental problem in many real-time analysis applications of RFID systems, as it enables rapidly selecting a small random tag subset T from a large tag population S and collecting the tag-information from T for analyzing purposes. However, existing protocols for this problem are far from satisfactory due to high communication costs. This paper aims at solving this problem by using a small communication cost. First, a lower bound on communication cost, denoted by Clb, is obtained for the tag-information sampling problem. Then, a protocol Ps is proposed for solving the studied problem, and is proved to have a communication cost within a factor of 2 of the lower bound Clb. Lastly, extensive simulation results not only verify the theoretical properties of the proposed protocol but also demonstrate its advantages in comparison with the state-of-art protocols.

Hybrid-cloud management in secure multi-domain environments

In this paper, we've proposed a secure multi-domain deduplication scheme supporting data deduplication, both intra-deduplication & inter-deduplication by generating a random convergent key and therefore a random tag, which supports a bilinear pairing technique to make sure of data confidentiality. It must produce a series of random cipher-texts to make sure that the outsourced encrypted data are often correctly decrypted by users with ownership. Concerning the safety issues existing on clouds described by the cloud security alliance to avoid an equivalent, we have applied several security enforcements. CoT paradigm provides managing VMs and containers in both Edge and Cloud layers in a secure and versatile fashion becomes very compelling. To provide security, convergent encryption is employed which permits the deduplication of encrypted data, secure cross-domain deduplication schemes are also evaluated. We have followed CSA guidance by which we will ensure security, privacy, availability of services, resource access control, vulnerability mitigation, and privacy of the audited user data.

Simultaneous absolute quantification and sequencing of fish environmental DNA in a mesocosm by quantitative sequencing technique

The combination of high-throughput sequencing technology and environmental DNA (eDNA) analysis has the potential to be a powerful tool for comprehensive, non-invasive monitoring of species in the environment. To understand the correlation between the abundance of eDNA and that of species in natural environments, we have to obtain quantitative eDNA data, usually via individual assays for each species. The recently developed quantitative sequencing (qSeq) technique enables simultaneous phylogenetic identification and quantification of individual species by counting random tags added to the 5′ end of the target sequence during the first DNA synthesis. Here, we applied qSeq to eDNA analysis to test its effectiveness in biodiversity monitoring. eDNA was extracted from water samples taken over 4 days from aquaria containing five fish species (Hemigrammocypris neglectus, Candidia temminckii, Oryzias latipes, Rhinogobius flumineus, and Misgurnus anguillicaudatus), and quantified by qSeq and microfluidic digital PCR (dPCR) using a TaqMan probe. The eDNA abundance quantified by qSeq was consistent with that quantified by dPCR for each fish species at each sampling time. The correlation coefficients between qSeq and dPCR were 0.643, 0.859, and 0.786 for H. neglectus, O. latipes, and M. anguillicaudatus, respectively, indicating that qSeq accurately quantifies fish eDNA.

A new quantitative 16S rRNA amplicon sequencing method

In recent years, 16S rRNA amplicon sequencing technology has been widely used to study human gut microbiota and to detect unknown pathogens in clinical samples. However, its resolution to bacterial population can only reach the relative abundance of genus level, and different factors affect the final bacterial profile, such as sample concentrations, PCR cycle numbers and amplification primers. In order to solve these problems, we developed a quantitative 16S rRNA amplicon sequencing method by combining random tag and internal marker method. The new methods improved the accuracy of human gut microbiota, reduced the impact of experimental operation on the results, and improved the comparability between sequencing and other molecular biological methods.

Replacing Linguists with Dummies: A Serious Need for Trivial Baselines in Multi-Task Neural Machine Translation

AbstractRecent developments in machine translation experiment with the idea that a model can improve the translation quality by performing multiple tasks, e.g., translating from source to target and also labeling each source word with syntactic information. The intuition is that the network would generalize knowledge over the multiple tasks, improving the translation performance, especially in low resource conditions. We devised an experiment that casts doubt on this intuition. We perform similar experiments in both multi-decoder and interleaving setups that label each target word either with a syntactic tag or a completely random tag. Surprisingly, we show that the model performs nearly as well on uncorrelated random tags as on true syntactic tags. We hint some possible explanations of this behavior.The main message from our article is that experimental results with deep neural networks should always be complemented with trivial baselines to document that the observed gain is not due to some unrelated properties of the system or training effects. True confidence in where the gains come from will probably remain problematic anyway.

Exploring the diversity of Poaceae-infecting mastreviruses on Reunion Island using a viral metagenomics-based approach

Mostly found in Africa and its surrounding islands, African streak viruses (AfSV) represent the largest group of known mastreviruses. Of the thirteen AfSV species that are known to infect either cultivated or wild Poaceae plant species, six have been identified on Reunion Island. To better characterize AfSV diversity on this island, we undertook a survey of a small agroecosystem using a new metagenomics-based approach involving rolling circle amplification with random PCR amplification tagging (RCA-RA-PCR), high-throughput sequencing (Illumina HiSeq) and the mastrevirus reads classification using phylogenetic placement. Mastreviruses that likely belong to three new species were discovered and full genome sequences of these were determined by Sanger sequencing. The geminivirus-focused metagenomics approach we applied in this study was useful in both the detection of known and novel mastreviruses. The results confirm that Reunion Island is indeed a hotspot of AfSV diversity and that many of the mastrevirus species have likely been introduced multiple times. Applying a similar approach in other natural and agricultural environments should yield sufficient detail on the composition and diversity of geminivirus communities to precipitate major advances in our understanding of the ecology and the evolutionary history of this important group of viruses.

Secure Deduplication Based on Rabin Fingerprinting over Wireless Sensing Data in Cloud Computing

The rapid advancements in the Internet of Things (IoT) and cloud computing technologies have significantly promoted the collection and sharing of various data. In order to reduce the communication cost and the storage overhead, it is necessary to exploit data deduplication mechanisms. However, existing data deduplication technologies still suffer security and efficiency drawbacks. In this paper, we propose two secure data deduplication schemes based on Rabin fingerprinting over wireless sensing data in cloud computing. The first scheme is based on deterministic tags and the other one adopts random tags. The proposed schemes realize data deduplication before the data is outsourced to the cloud storage server, and hence both the communication cost and the computation cost are reduced. In particular, variable-size block-level deduplication is enabled based on the technique of Rabin fingerprinting which generates data blocks based on the content of the data. Before outsourcing data to the cloud, users encrypt the data based on convergent encryption technologies, which protects the data from being accessed by unauthorized users. Our security analysis shows that the proposed schemes are secure against offline brute-force dictionary attacks. In addition, the random tag makes the second scheme more reliable. Extensive experimental results indicate that the proposed data deduplication schemes are efficient in terms of the deduplication rate, the system operation time, and the tag generation time.

Collaborative Reader Code Division Multiple Access in the Harmonic RFID System

Reading reliability is a critical aspect in the ultrahigh-frequency radio frequency identification (RFID) system when a large amount of tags are in the reading zone with complex multi-path interferences. The read failure incidences can be caused not only by the random tag position and orientation but also by the RF shadowing of other objects and tags, both of which can misalign the main lobes of the tag and reader antennas. To increase the system coverage and total read yield in practical indoor ambient, a possible solution is to employ multiple readers with overlapping reading zones. However, when the tags are within the reading zones of multiple readers, the conventional tag singulation protocol will suffer serious reader-to-reader collision (R2RC) unless the conflicting readers can agree on proper collaborative time or space division schemes. The uncertain delay of the time division multiple access and the lack of definitive time sequence can further affect the reading process, especially when the RFID system is employed for applications requiring real-time accessing, tracking, and sensing. In this paper, we introduce a collaborative reader code division multiple access (CDMA) protocol in the harmonic RFID system, which provides synchronous tag access for all readers within range. The tag can distinguish the downlink commands from multiple readers simultaneously and then formulate the response accordingly. The proposed reader CDMA is demonstrated in a simple experimental prototype to verify R2RC resolution and read yield improvement. The tworeader collaborative CDMA scheme achieved below 10−5 in the read failure rate when individual readers had 0.05 – 0.2 failure rates in the complex multi-path scenarios due to rich channel exploitation and anti-correlation in the failure incidences.

Implementing traceability using particle randomness-based textile printed tags

This article introduces a random particle-based traceability tag for textiles. The proposed tag not only act as a unique signature for the corresponding textile product but also possess the features such as easy to manufacture and hard to copy. It seeks applications in brand authentication and traceability in textile and clothing (T&C) supply chain. A prototype has been developed by screen printing process, in which micron-scale particles were mixed with the printing paste and printed on cotton fabrics to attain required randomness. To encode the randomness, the image of the developed tag was taken and analyzed using image processing. The randomness of the particles acts as a product key or unique signature which is required to decode the tag. Finally, washing and abrasion resistance tests were conducted to check the durability of the printed tag.

Single molecule counting and assessment of random molecular tagging errors with transposable giga-scale error-correcting barcodes

BackgroundRNA-Seq measures gene expression by counting sequence reads belonging to unique cDNA fragments. Molecular barcodes commonly in the form of random nucleotides were recently introduced to improve gene expression measures by detecting amplification duplicates, but are susceptible to errors generated during PCR and sequencing. This results in false positive counts, leading to inaccurate transcriptome quantification especially at low input and single-cell RNA amounts where the total number of molecules present is minuscule.To address this issue, we demonstrated the systematic identification of molecular species using transposable error-correcting barcodes that are exponentially expanded to tens of billions of unique labels.ResultsWe experimentally showed random-mer molecular barcodes suffer from substantial and persistent errors that are difficult to resolve. To assess our method’s performance, we applied it to the analysis of known reference RNA standards. By including an inline random-mer molecular barcode, we systematically characterized the presence of sequence errors in random-mer molecular barcodes. We observed that such errors are extensive and become more dominant at low input amounts.ConclusionsWe described the first study to use transposable molecular barcodes and its use for studying random-mer molecular barcode errors. Extensive errors found in random-mer molecular barcodes may warrant the use of error correcting barcodes for transcriptome analysis as input amounts decrease.

Application of Stochastic Labeling with Random-Sequence Barcodes for Simultaneous Quantification and Sequencing of Environmental 16S rRNA Genes.

Next-generation sequencing (NGS) is a powerful tool for analyzing environmental DNA and provides the comprehensive molecular view of microbial communities. For obtaining the copy number of particular sequences in the NGS library, however, additional quantitative analysis as quantitative PCR (qPCR) or digital PCR (dPCR) is required. Furthermore, number of sequences in a sequence library does not always reflect the original copy number of a target gene because of biases caused by PCR amplification, making it difficult to convert the proportion of particular sequences in the NGS library to the copy number using the mass of input DNA. To address this issue, we applied stochastic labeling approach with random-tag sequences and developed a NGS-based quantification protocol, which enables simultaneous sequencing and quantification of the targeted DNA. This quantitative sequencing (qSeq) is initiated from single-primer extension (SPE) using a primer with random tag adjacent to the 5’ end of target-specific sequence. During SPE, each DNA molecule is stochastically labeled with the random tag. Subsequently, first-round PCR is conducted, specifically targeting the SPE product, followed by second-round PCR to index for NGS. The number of random tags is only determined during the SPE step and is therefore not affected by the two rounds of PCR that may introduce amplification biases. In the case of 16S rRNA genes, after NGS sequencing and taxonomic classification, the absolute number of target phylotypes 16S rRNA gene can be estimated by Poisson statistics by counting random tags incorporated at the end of sequence. To test the feasibility of this approach, the 16S rRNA gene of Sulfolobus tokodaii was subjected to qSeq, which resulted in accurate quantification of 5.0 × 103 to 5.0 × 104 copies of the 16S rRNA gene. Furthermore, qSeq was applied to mock microbial communities and environmental samples, and the results were comparable to those obtained using digital PCR and relative abundance based on a standard sequence library. We demonstrated that the qSeq protocol proposed here is advantageous for providing less-biased absolute copy numbers of each target DNA with NGS sequencing at one time. By this new experiment scheme in microbial ecology, microbial community compositions can be explored in more quantitative manner, thus expanding our knowledge of microbial ecosystems in natural environments.