Similarity Search Results Research Articles

SEAnet: A Deep Learning Architecture for Data Series Similarity Search

A key operation for massive data series collection analysis is similarity search. According to recent studies, SAX-based indexes offer state-of-the-art performance for similarity search tasks. However, their performance lags under high-frequency, weakly correlated, excessively noisy, or other dataset-specific properties. In this work, we propose Deep Embedding Approximation (DEA), a novel family of data series summarization techniques based on deep neural networks. Moreover, we describe SEAnet, a novel architecture especially designed for learning DEA, that introduces the Sum of Squares preservation property into the deep network design. We further enhance SEAnet with SEAtrans encoder. Finally, we propose novel sampling strategies, SEAsam and SEAsamE, that allow SEAnet to effectively train on massive datasets. Comprehensive experiments on 7 diverse synthetic and real datasets verify the advantages of DEA learned using SEAnet in providing high-quality data series summarizations and similarity search results.

Authenticating q-Gram-Based Similarity Search Results for Outsourced String Databases

Approximate string searches have been widely applied in many fields, such as bioinformatics, text retrieval, search engines, and location-based services (LBS). However, the approximate string search results from third-party servers may be incorrect due to the possibility of malicious third parties or compromised servers. In this paper, we design an authenticated index structure (AIS) for string databases, which is based on the Merkle hash tree (MHT) method and the q-gram inverted index. Our AIS can facilitate verification object (VO) construction for approximate string searches with edit distance thresholds. We design an efficient algorithm named GS2 for VO construction at the server side and search result verification at the user side. We also introduce an optimization method called GS2-opt that can reduce VO size dramatically. Finally, we conduct extensive experiments on real datasets to show that our proposed methods are efficient and promising.

Pseudomonas aegrilactucae sp. nov. and Pseudomonas morbosilactucae sp. nov., pathogens causing bacterial rot of lettuce in Japan.

Phytopathogenic bacterial strains (MAFF 301350T, MAFF 302030T and MAFF 302046), isolated from lettuce (Lactuca sativa var. capitata) with bacterial rot disease in Japan, were subjected to polyphasic characterization to determine their taxonomic affiliations. The cells were Gram-reaction-negative, aerobic, non-spore-forming, motile with polar flagella and rod-shaped. The results of similarity searches and phylogenetic analyses based on the 16S rRNA gene sequences, as well as the analysis results of the cellular fatty acid composition and genomic DNA G+C content indicated that these strains belong to the genus Pseudomonas . Phylogenetic analyses using the rpoD gene sequences and phylogenomic analyses of the whole genome sequences grouped them into the Pseudomonas putida group (MAFF 301350T) and the Pseudomonas fluorescens group (MAFF 302030T and MAFF 302046), but the phylogenetic positions of the strains did not match those of any known Pseudomonas species. The average nucleotide identity and digital DNA–DNA hybridization values between the strains and their closely related species were lower than the thresholds for prokaryotic species delineation (95–96 and 70 %, respectively). Phenotypic characteristics, pathogenicity toward lettuce, cellular fatty acid composition and whole-cell MALDI-TOF mass spectrometry profiles could differentiate the strains from their closest relatives. The phenotypic, chemotaxonomic and genotypic data obtained in this study showed that the strains represent two novel species of the genus Pseudomonas , Pseudomonas aegrilactucae sp. nov. for MAFF 301350T and Pseudomonas morbosilactucae sp. nov. for MAFF 302030T and MAFF 302046. The respective type strains are MAFF 301350T (= ICMP 23989T) and MAFF 302030T (= ICMP 24377T).

Isolation and Molecular Identification of a Siderophore Producing Bacterium and its Antagonistic Effect against Fusarium oxysporum f. sp. cubense Tropical Race 4

Bananas are one of the world’s most consumed fruits. Developing countries in the Global South depend on bananas for food security and livelihoods. Still, the banana industry also drives a multinational trade worth billions of US dollars. In addition, banana plants also hold cultural and religious significances in many Asian countries. However, banana production faces several challenges, and one of the major issues is the Fusarium wilt disease caused by the fungus Fusarium oxysporum f. sp. cubense Tropical Race 4 (Foc TR4). This disease threatens numerous banana cultivars, including the Cavendish, the most traded banana cultivar. Therefore, the objective of this study was to find effective measures to control the spread of this disease through antagonistic soil bacteria. This study isolated 14 fluorescent, siderophore-producing bacteria with in vitro inhibition rates of 21.73-50.38% against Foc TR4 from the soil surrounding banana plants. Most of the isolates were identified as Pseudomonas spp. via 16S rRNA sequencing, and phylogenetic analysis based on 16s rRNA sequences showed clustering of isolates congruent with the results of similarity searches. Three isolates that exhibited relatively higher antagonistic activity against Fusarium oxysporum f. sp. cubense compared to other isolates were JBAA132 (50.38%), K2B131 (46.28%), and KTP231 (45.38%). Isolate JBAA132 displayed differences in biochemical characteristics compared to its closest match (Pseudomonas aeruginosa type strain DSM50071). It also demonstrated nitrogen-fixing and phosphate solubilising properties common to plant growth promoters. Therefore, isolate JBAA132 may be further explored as a potential biocontrol agent in curbing the spread of Foc TR4.

A Divergent Articulavirus in an Australian Gecko Identified Using Meta-Transcriptomics and Protein Structure Comparisons.

The discovery of highly divergent RNA viruses is compromised by their limited sequence similarity to known viruses. Evolutionary information obtained from protein structural modelling offers a powerful approach to detect distantly related viruses based on the conservation of tertiary structures in key proteins such as the RNA-dependent RNA polymerase (RdRp). We utilised a template-based approach for protein structure prediction from amino acid sequences to identify distant evolutionary relationships among viruses detected in meta-transcriptomic sequencing data from Australian wildlife. The best predicted protein structural model was compared with the results of similarity searches against protein databases. Using this combination of meta-transcriptomics and protein structure prediction we identified the RdRp (PB1) gene segment of a divergent negative-sense RNA virus, denoted Lauta virus (LTAV), in a native Australian gecko (Gehyra lauta). The presence of this virus was confirmed by PCR and Sanger sequencing. Phylogenetic analysis revealed that Lauta virus likely represents a newly described genus within the family Amnoonviridae, order Articulavirales, that is most closely related to the fish virus Tilapia tilapinevirus (TiLV). These findings provide important insights into the evolution of negative-sense RNA viruses and structural conservation of the viral replicase among members of the order Articulavirales.

Personalization in Collaborative Fusion based Enterprise Information retrieval

Due to data spread across various heterogeneous data stores, information retrieval in Enterprise data stores is always challenging compared to web based retrieval systems. We have proposed a collaborative fusion based information retrieval in [1] using the observation on similar users tends to prefer similar search results. The solution applied three dimensions of user similarity, document similarity and user to documents affinity to a collaborative information fusion based retrieval. The work also proposed active feedback based search result revision to get highly relevant results. But the work did not have any provision for personalization and could not handle the cold start problems. Without consideration for cold start problems, the user to document affinity cannot be modeled accurately as the result, the collaborative fusion process is affected. In this work, we improve our earlier solution of collaborative fusion based information retrieval with consideration for user personalization and solution for cold start problems. The solution is based on query refinement using the information hidden in enterprise messaging systems. A user profile is built as vector of concepts using the information in enterprise messaging systems and this user profile concept vector is used to refine the query in way to personalize the results and avoid cold start problems. Compared to approach in [1], the proposed query refinement based personalization is able to increase the relevancy accuracy by 10% as obtained from experimental results.

DNA barcoding identified selected ornamental fishes in Murti river of East India

Nomenclature for the nameless is one of the successful events after the invention of the DNA barcoding technique in biodiversity research. The ornamental fish species are frequently hunted from the east and northeast India and trafficked with various elusive names and high demanding values. Such illegitimate trading stimulated the severe threats on the native freshwater ecosystems and their indigenous biodiversity. Both traditional taxonomy and DNA barcoding technique successfully identified 11 ornamental fish species from a small riverine system, Murti river; linked up with three protected areas in the northern part of West Bengal. To test the efficacy of DNA barcode data for species identification, the generated sequences were subjected to similarity search results, and Neighbour-Joining tree clustering and genetic divergences. The mean genetic divergence was 21.3% and the interspecific genetic distance was ranging from 17.8% to 28.7% in the studied dataset. The detected high intraspecific genetic divergence in Opsarius barna (14.1%) and Channa gachua (6.4%) in the present dataset suggested further genetic investigation from their known distributions.

Identification and quality evaluation of Tripterygium wilfordii

With the extensive clinical application of Tripterygium wilfordii, there are many counterfeit products on the market. Traditional technology can not effectively identify the authenticity of the traditional Chinese medicine. Therefore, a strategy of accurate identification and quality evaluation of Tripterygium based on DNA barcode and chemical fingerprint spectrum was established. Based on DNA barcode technology, HMMer annotation method of hidden Markov model and K2P model were used to analyze genetic distance.BLAST1, nearest distance and phylogenetic tree (NJ-tree) methods were used to assess the identification efficiency of the ITS2 barcode. The fingerprint of 27 T. wilfordii was established by UPLC-PDA method, and the similarity of the fingerprint of different sources was evaluated. The main components of T. wilfordii were determined by LC-MS/MS. The results revealed that the intraspecific genetic distances of T. wilfordii were lower than the interspecific genetic distances between T. wilfordii and its adulterants. The results of similarity search showed that ITS2 sequence was used to identify T. wilfordii and its adulterants. The clustering of T. wilfordii and its adulterants was clear in the tree of NJ cluster, and 12 of 27 samples were identified as true T. wilfordii.The chemical fingerprint spectrum research indicates that the feature one region can distinguish the false product of tripterygium glycosides more intuitively. The cluster analysis of HCA-thermal map showed that the contents of six active components of T. wilfordii from different habitats were significantly different, which could be used to evaluate the quality of T. wilfordii. This paper is of guiding significance for the accurate identification and quality evaluation of Tripterygium medicinal plants.

Context-aware pattern discovery for moving object trajectories

Abstract. Movement of point objects are highly sensitive to the underlying situations and conditions during the movement, which are known as contexts. Analyzing movement patterns, while accounting the contextual information, helps to better understand how point objects behave in various contexts and how contexts affect their trajectories. One potential solution for discovering moving objects patterns is analyzing the similarities of their trajectories. This article, therefore, contextualizes the similarity measure of trajectories by not only their spatial footprints but also a notion of internal and external contexts. The dynamic time warping (DTW) method is employed to assess the multi-dimensional similarities of trajectories. Then, the results of similarity searches are utilized in discovering the relative movement patterns of the moving point objects. Several experiments are conducted on real datasets that were obtained from commercial airplanes and the weather information during the flights. The results yielded the robustness of DTW method in quantifying the commonalities of trajectories and discovering movement patterns with 80 % accuracy. Moreover, the results revealed the importance of exploiting contextual information because it can enhance and restrict movements.

Drug Design, Synthesis and In Vitro Evaluation of Substituted Benzofurans as Hsp90 Inhibitors

Heat shock protein 90 is a molecular chaperone required for the stability and function of several client proteins that promote cancer cell growth and/or survival. Discovery of Hsp90 inhibitors has emerged as an attractive target of research in cancer therapeutics. Natural products like geldanamycin and radicicol are established Hsp90 inhibitors, but face limitations with toxicity and inactivity, by in vivo studies respectively. However, they lay the logical starting point for the design of novel synthetic or semi-synthetic congeners as Hsp90 inhibitors. In this article, the structure based drug design of substituted 2-aryl/heteroarylidene-6- hydroxybenzofuran-3(2H)-one analogues to optimize and mimic the pharmacophoric interactions of the valid Hsp90 inhibitor radicicolis focused. In silico docking study was performed by Surflex dock-Geom (SYBYL- X 1.2 drug discovery suite) and the designed ligands were chemically synthesized by conventional method using resorcinol and chlororesorcinol as starting materials. Two dimensional chemical similarity search was carried out to identify the chemical space of 'SY' series in comparison with reported Hsp90 inhibitors. The in vitro cell proliferation assay (resazurin reduction method) and proteomic investigation (DARTS) was carried out on whole cell lysate to evaluate anticancer activity. The chemical structures of all the synthesized compounds were confirmed by IR, 1H-NMR and Mass spectral analysis. The results of chemical similarity search show that SY series fit it in the chemical space defined by existing Hsp90 inhibitors. In vitro cell proliferation assay, against human melanoma and breast cancer cell lines, identified 'SY3' as the promising anticancer agent amongst the series. Docking studies, 2D chemical similarity search, resazurin reduction assay and qualitative proteomic analysis identify 'SY3'as a promising Hsp90 inhibitor amongst the series.

MULTI-DIMENSIONAL PATTERN DISCOVERY OF TRAJECTORIES USING CONTEXTUAL INFORMATION

Abstract. Movement of point objects are highly sensitive to the underlying situations and conditions during the movement, which are known as contexts. Analyzing movement patterns, while accounting the contextual information, helps to better understand how point objects behave in various contexts and how contexts affect their trajectories. One potential solution for discovering moving objects patterns is analyzing the similarities of their trajectories. This article, therefore, contextualizes the similarity measure of trajectories by not only their spatial footprints but also a notion of internal and external contexts. The dynamic time warping (DTW) method is employed to assess the multi-dimensional similarities of trajectories. Then, the results of similarity searches are utilized in discovering the relative movement patterns of the moving point objects. Several experiments are conducted on real datasets that were obtained from commercial airplanes and the weather information during the flights. The results yielded the robustness of DTW method in quantifying the commonalities of trajectories and discovering movement patterns with 80 % accuracy. Moreover, the results revealed the importance of exploiting contextual information because it can enhance and restrict movements.

Using SQL Databases for Sequence Similarity Searching and Analysis.

Relational databases can integrate diverse types of information and manage large sets of similarity search results, greatly simplifying genome-scale analyses. By focusing on taxonomic subsets of sequences, relational databases can reduce the size and redundancy of sequence libraries and improve the statistical significance of homologs. In addition, by loading similarity search results into a relational database, it becomes possible to explore and summarize the relationships between all of the proteins in an organism and those in other biological kingdoms. This unit describes how to use relational databases to improve the efficiency of sequence similarity searching and demonstrates various large-scale genomic analyses of homology-related data. It also describes the installation and use of a simple protein sequence database, seqdb_demo, which is used as a basis for the other protocols. The unit also introduces search_demo, a database that stores sequence similarity search results. The search_demo database is then used to explore the evolutionary relationships between E. coli proteins and proteins in other organisms in a large-scale comparative genomic analysis. © 2017 by John Wiley & Sons, Inc.

Bacillus mangrovi sp. nov., isolated from a sediment sample from a mangrove forest.

A facultatively anaerobic, endospore forming, alkali-tolerant, Gram-stain-positive, motile, rod-shaped bacterium, designated strain AK61T, was isolated from a sediment sample collected from Coringa mangrove forest, India. Colonies were circular, 1.5 mm in diameter, shiny, smooth, yellowish and convex with entire margins after 48 h growth at 30 °C. Growth occurred at 15-42 °C, with 0-3 % (w/v) NaCl and at pH 6-9. AK61T was positive for amylase activity and negative for oxidase, catalase, aesculinase, caseinase, cellulase, DNase, gelatinase, lipase and urease activities. The fatty acids were dominated by branched types with iso- and anteiso- saturated fatty acids with a high abundance of iso-C14 : 0, iso-C15 : 0, anteiso-C15 : 0 and iso-C16 : 0; the cell-wall peptidoglycan contained meso-diaminopimelic acid as the diagnostic diamino acid; and MK-7 was the major menaquinone. DNA-DNA hybridization between AK61T and Bacillus indicus MTCC 4374T and between AK61T and Bacillus indicus KCTC 3880 showed relatedness of 37.99 and 33.32 % respectively. The DNA G+C content of AK61T was 44 mol%. The results of a blast sequence similarity search based on 16S rRNA gene sequences indicated that Bacillus cibi and Bacillus indicus were the nearest phylogenetic neighbours, with a pair-wise sequence similarity of 97.69 and 97.55 % respectively. The results of phylogenetic analysis indicated that AK61T was clustered with Bacillus idriensis and Bacillus indicus. On the basis of its phenotypic characteristics and phylogenetic inference, AK61T represents a novel species of the genus Bacillus, for which the name Bacillus mangrovi sp. nov. is proposed. The type strain is AK61T (=JCM 31087T=MTCC 12015T=KCTC 33872T).

MathIRs: Retrieval System for Scientific Documents

Effective retrieval of mathematical contents from vast corpus of scientific documents demands enhancement in the conventional indexing and searching mechanisms. Indexing mechanism and the choice of semantic similarity measures guide the results of Math Information Retrieval system (MathIRs) to perfection. Tokenization and formula unification are among the distinguishing i features of indexing mechanism, used in MathIRs, which facilitate sub-formula and similarity search. Besides, the scientific documents and the user queries in MathIRs will contain math as well as text contents and to match these contents we require three important modules: Text-Text Similarity (TS), Math-Math Similarity (MS) and Text-Math Similarity (TMS). In this paper we have proposed MathIRs comprising these important modules and a substitution tree based mechanism for indexing mathematical expressions. We have also presented experimental results for similarity search and argued that proposal of MathIRs will ease the task of scientific document retrieval.

Context-awareness in similarity measures and pattern discoveries of trajectories: a context-based dynamic time warping method

Analyzing the spatial behaviors of moving-point objects (MPOs) and discovering their movement patterns have been of great interest to the geographic information science community recently. These interests can be explored through analyzing similarities in the MPO trajectories. Because movements of objects take place in various contexts, their trajectories are also highly influenced by such contexts. Therefore, it is essential to fully understand the contexts and to realize how they can be incorporated into movement analysis. This article first proposes a taxonomy for contexts. Then, a modified version of dynamic time warping called context-based dynamic time warping (CDTW) is introduced, to contextually assess the multidimensional weighted similarities of trajectories. Ultimately, the results of similarity searches are utilized in discovering the relative movement patterns of the MPOs. To evaluate the performance and effectiveness of our proposed CDTW method, we run several experiments on real datasets that were obtained from commercial airplanes in a constrained Euclidean space, taking into account contextual information. Specifically, these experiments were conducted to explore the role of contexts and their interactions in similarity measures of trajectories. The results yielded the robustness of CDTW method in quantifying the commonalities of trajectories and discovering movement patterns with 80% accuracy. Moreover, the results revealed the importance of exploiting contextual information because it can enhance and restrict movements.

Mobile local search with noisy locations

The deep penetration of mobile devices have led to the emergence of multiple mobile applications that seek to harness the positioning capabilities embedded in such devices. One of the most functional of these applications is local search. Local search is similar to a regular web search, yet is more powerful in a mobile setting since results are ranked both by prominence and locality. Undoubtedly popular, the current design of local search applications fails to cater equally to a privacy-aware user who desires finer controls in her location disclosure. Towards this end, we propose the design for a private local search (PriLS) application that enables a user to first learn the geographic variation in local search results and then use it to determine a noisy location that has little or no affect on the search results. Parametric studies and real-world evaluations show that PriLS can help identify geographic locations that would produce similar search results (compared to when the user’s location is used) with no noticeable delays in user experience. They also reveal that large areas typically exist where there is no change in the result of a local search query, and can be exploited to provide spatial privacy guarantees to a user.

Effective and efficient similarity search in scientific workflow repositories

Scientific workflows have become a valuable tool for large-scale data processing and analysis. This has led to the creation of specialized online repositories to facilitate workflow sharing and reuse. Over time, these repositories have grown to sizes that call for advanced methods to support workflow discovery, in particular for similarity search. Effective similarity search requires both high quality algorithms for the comparison of scientific workflows and efficient strategies for indexing, searching, and ranking of search results. Yet, the graph structure of scientific workflows poses severe challenges to each of these steps. Here, we present a complete system for effective and efficient similarity search in scientific workflow repositories, based on the Layer Decomposition approach to scientific workflow comparison. Layer Decomposition specifically accounts for the directed dataflow underlying scientific workflows and, compared to other state-of-the-art methods, delivers best results for similarity search at comparably low runtimes. Stacking Layer Decomposition with even faster, structure-agnostic approaches allows us to use proven, off-the-shelf tools for workflow indexing to further reduce runtimes and scale similarity search to sizes of current repositories.

Rethinking Molecular Mimicry in Rheumatic Heart Disease and Autoimmune Myocarditis: Laminin, Collagen IV, CAR, and B1AR as Initial Targets of Disease.

Rationale: Molecular mimicry theory (MMT) suggests that epitope mimicry between pathogens and human proteins can activate autoimmune disease. Group A streptococci (GAS) mimics human cardiac myosin in rheumatic heart disease (RHD) and coxsackie viruses (CX) mimic actin in autoimmune myocarditis (AM). But myosin and actin are immunologically inaccessible and unlikely initial targets. Extracellular cardiac proteins that mimic GAS and CX would be more likely.Objectives: To determine whether extracellular cardiac proteins such as coxsackie and adenovirus receptor (CAR), beta 1 adrenergic receptor (B1AR), CD55/DAF, laminin, and collagen IV mimic GAS, CX, and/or cardiac myosin or actin.Methods: BLAST 2.0 and LALIGN searches of the UniProt protein database were employed to identify potential molecular mimics. Quantitative enzyme-linked immunosorbent assay was used to measure antibody cross-reactivity.Measurements: Similarities were considered to be significant if a sequence contained at least 5 identical amino acids in 10. Antibodies were considered to be cross-reactive if the binding constant had a Kd less than 10-9 M.Main results: Group A streptococci mimics laminin, CAR, and myosin. CX mimics actin and collagen IV and B1AR. The similarity search results are mirrored by antibody cross-reactivities. Additionally, antibodies against laminin recognize antibodies against collagen IV; antibodies against actin recognize antibodies against myosin, and antibodies against GAS recognize antibodies against CX. Thus, there is both mimicry of extracellular proteins and antigenic complementarity between GAS-CX in RHD/AM.Conclusion: Rheumatic heart disease/AM may be due to combined infections of GAS with CX localized at cardiomyocytes that may produce a synergistic, hyperinflammatory response that cross-reacts with laminin, collagen IV, CAR, and/or B1AR. Epitope drift shifts the immune response to myosin and actin after cardiomyocytes become damaged.

Accessing biomedical literature in the current information landscape.

Biomedical and life sciences literature is unique because of its exponentially increasing volume and interdisciplinary nature. Biomedical literature access is essential for several types of users including biomedical researchers, clinicians, database curators, and bibliometricians. In the past few decades, several online search tools and literature archives, generic as well as biomedicine specific, have been developed. We present this chapter in the light of three consecutive steps of literature access: searching for citations, retrieving full text, and viewing the article. The first section presents the current state of practice of biomedical literature access, including an analysis of the search tools most frequently used by the users, including PubMed, Google Scholar, Web of Science, Scopus, and Embase, and a study on biomedical literature archives such as PubMed Central. The next section describes current research and the state-of-the-art systems motivated by the challenges a user faces during query formulation and interpretation of search results. The research solutions are classified into five key areas related to text and data mining, text similarity search, semantic search, query support, relevance ranking, and clustering results. Finally, the last section describes some predicted future trends for improving biomedical literature access, such as searching and reading articles on portable devices, and adoption of the open access policy.

FmcsR: mismatch tolerant maximum common substructure searching in R

The ability to accurately measure structural similarities among small molecules is important for many analysis routines in drug discovery and chemical genomics. Algorithms used for this purpose include fragment-based fingerprint and graph-based maximum common substructure (MCS) methods. MCS approaches provide one of the most accurate similarity measures. However, their rigid matching policies limit them to the identification of perfect MCSs. To eliminate this restriction, we introduce a new mismatch tolerant search method for identifying flexible MCSs (FMCSs) containing a user-definable number of atom and/or bond mismatches. The fmcsR package provides an R interface, with the time-consuming steps of the FMCS algorithm implemented in C++. It includes utilities for pairwise compound comparisons, structure similarity searching, clustering and visualization of MCSs. In comparison with an existing MCS tool, fmcsR shows better time performance over a wide range of compound sizes. When mismatching of atoms or bonds is turned on, the compute times increase as expected, and the resulting FMCSs are often substantially larger than their strict MCS counterparts. Based on extensive virtual screening (VS) tests, the flexible matching feature enhances the enrichment of active structures at the top of MCS-based similarity search results. With respect to overall and early enrichment performance, FMCS outperforms most of the seven other VS methods considered in these tests. fmcsR is freely available for all common operating systems from the Bioconductor site (http://www.bioconductor.org/packages/devel/bioc/html/fmcsR.html). thomas.girke@ucr.edu. Supplementary data are available at Bioinformatics online.