Query Region Research Articles

BIT: Bayesian Identification of Transcriptional Regulators from Epigenomics-Based Query Region Sets.

Transcriptional regulators (TRs) are master controllers of gene expression and play a critical role in both normal tissue development and disease progression. However, existing computational methods for identification of TRs regulating specific biological processes have significant limitations, such as relying on distance on a linear chromosome or binding motifs that have low specificity. Many also use statistical tests in ways that lack interpretability and rigorous confidence measures. We introduce BIT, a novel Bayesian hierarchical model for in-silico TR identification. Leveraging a comprehensive library of TR ChIP-seq data, BIT offers a fully integrated Bayesian approach to assess genome-wide consistency between user-provided epigenomic profiling data and the TR binding library, enabling the identification of critical TRs while quantifying uncertainty. It avoids estimation and inference in a sequential manner or numerous isolated statistical tests, thereby enhancing accuracy and interpretability. BIT successfully identified critical TRs in perturbation experiments, functionally essential TRs in various cancer types, and cell-type-specific TRs within heterogeneous cell populations, offering deeper biological insights into transcriptional regulation.

Graphite: painting genomes using a colored de Bruijn graph.

The recent growth of microbial sequence data allows comparisons at unprecedented scales, enabling the tracking of strains, mobile genetic elements, or genes. Querying a genome against a large reference database can easily yield thousands of matches that are tedious to interpret and pose computational challenges. We developed Graphite that uses a colored de Bruijn graph (cDBG) to paint query genomes, selecting the local best matches along the full query length. By focusing on the best genomic match of each query region, Graphite reduces the number of matches while providing the most promising leads for sequence tracking or genomic forensics. When applied to hundreds of Campylobacter genomes we found extensive gene sharing, including a previously undetected C. coli plasmid that matched a C. jejuni chromosome. Together, genome painting using cDBGs as enabled by Graphite, can reveal new biological phenomena by mitigating computational hurdles.

Self-Supervised 3-D Semantic Representation Learning for Vision-and-Language Navigation.

In vision-and-language navigation (VLN) tasks, most current methods primarily utilize RGB images, overlooking the rich 3-D semantic data inherent to environments. To rectify this, we introduce a novel VLN framework that integrates 3-D semantic information into the navigation process. Our approach features a self-supervised training scheme that incorporates voxel-level 3-D semantic reconstruction to create a detailed 3-D semantic representation. A key component of this framework is a pretext task focused on region queries, which determines the presence of objects in specific 3-D areas. Following this, we devise an long short-term memory (LSTM)-based navigation model that is trained using our 3-D semantic representations. To maximize the utility of these 3-D semantic representations, we implement a cross-modal distillation strategy. This strategy encourages the RGB model's outputs to emulate those from the 3-D semantic feature network, enabling the concurrent training of both branches to merge RGB and 3-D semantic data effectively. Comprehensive evaluations on both the R2R and R4R datasets reveal that our method significantly enhances performance in VLN tasks.

Joint Representation Learning for Retrieval and Annotation of Genomic Interval Sets.

As available genomic interval data increase in scale, we require fast systems to search them. A common approach is simple string matching to compare a search term to metadata, but this is limited by incomplete or inaccurate annotations. An alternative is to compare data directly through genomic region overlap analysis, but this approach leads to challenges like sparsity, high dimensionality, and computational expense. We require novel methods to quickly and flexibly query large, messy genomic interval databases. Here, we develop a genomic interval search system using representation learning. We train numerical embeddings for a collection of region sets simultaneously with their metadata labels, capturing similarity between region sets and their metadata in a low-dimensional space. Using these learned co-embeddings, we develop a system that solves three related information retrieval tasks using embedding distance computations: retrieving region sets related to a user query string, suggesting new labels for database region sets, and retrieving database region sets similar to a query region set. We evaluate these use cases and show that jointly learned representations of region sets and metadata are a promising approach for fast, flexible, and accurate genomic region information retrieval.

Lightweight verifiable blockchain top-[formula omitted] queries

Blockchain has been exploited in many applications as a fundamental technology to construct trust and share data among multiple participants. A user with limited resources who runs a light node fetches data records stored on the blockchain by requesting a full node that maintains the complete blockchain data. As a type of broadly used query, top-k queries which ask for data records with the highest k values in a certain region can be useful in many blockchain scenarios. However, untrusted full nodes may return incorrect top-k results. In this paper, we take the first step toward addressing the problem of verifiable blockchain top-k queries without the help of a trusted third-party. First, we design a novel Sorted Merkle tree-based authenticated data structure and based on it we propose a new index named inverted SMT index that supports verifiable top-k query with specific keyword conditions. Second, we propose a fundamental strawman design to support verifiable blockchain top-k queries with arbitrary query regions. We further introduce two improved schemes, named segmentation design and sliding-window design, that overcome the efficiency obstacle of the strawman design. Finally, we confirm the efficiency of our solutions through an extensive experimental evaluation.

Hierarchical matching and reasoning for multi-query image retrieval

As a promising field, Multi-Query Image Retrieval (MQIR) aims at searching for the semantically relevant image given multiple region-specific text queries. Existing works mainly focus on a single-level similarity between image regions and text queries, which neglect the hierarchical guidance of multi-level similarities and result in incomplete alignments. Besides, the high-level semantic correlations that intrinsically connect different region–query pairs are rarely considered. To address above limitations, we propose a novel Hierarchical Matching and Reasoning Network (HMRN) for MQIR. It disentangles MQIR into three hierarchical semantic representations, which is responsible to capture fine-grained local details, contextual global scopes, and high-level inherent correlations. HMRN consists of two modules: Scalar-based Matching (SM) module and Vector-based Reasoning (VR) module. Specifically, the SM module characterizes the multi-level alignment similarity, which consists of a fine-grained local-level similarity and a context-aware global-level similarity. Afterwards, the VR module is developed to excavate the potential semantic correlations among multiple region–query pairs, which further explores the high-level reasoning similarity. Finally, these three-level similarities are aggregated into a joint similarity space to form the ultimate similarity. Extensive experiments on the benchmark dataset demonstrate that our HMRN substantially surpasses the current state-of-the-art methods. For instance, compared with the existing best method Drill-down, the metric R@1 in the last round is improved by 23.4%. Our source codes will be released at https://github.com/LZH-053/HMRN.

PPT-LBS: Privacy-preserving top-k query scheme for outsourced data of location-based services

Location-based service (LBS) is enjoying a great popularity with the fast growth of mobile Internet. As the volume of data increases dramatically, an increasing number of location service providers (LSPs) are moving LBS data to cloud platforms for benefit of affordability and stability. However, while cloud server provides convenience and stability, it also leads to data security and user privacy leakage. Aiming at the problems of insufficient privacy protection and inefficient query in the existing LBS data outsourcing schemes, this paper presents a novel privacy-preserving top-k query for outsourcing situations. Firstly, to ensure data security of LSP and privacy of the user, the enhanced asymmetric scalar-product preserving encryption and public key searchable encryption have been adopted to encrypt outsourced data and LBS query, which can effectively lower the computational cost and realize the privacy protection search. Secondly, an efficient and secure index structure is constructed by using a coded quadtree and the bloom filter, so that the cloud server can quickly locate the user’s query region to improve retrieval efficiency. Finally, the formal security analysis is given under the random oracle model, and the performance is evaluated by experiments which demonstrates that our scheme is preferable to existing schemes.

Predicting the transmission trend of respiratory viruses in new regions via geospatial similarity learning

The outbreak and spread of COVID-19 remind us again of the devastating attack that human-to-human transmitted respiratory infectious diseases (H-HRIDs) bring to global economics and public health. Predicting the trend of H-HRIDs yields important suggestions for making response strategies. Existing methods predict the future trend of H-HRIDs based on the prophase epidemiological data. However, it is also crucial to predict the potential outbreak trend of H-HRIDs in regions without prophase epidemiological data based on those with epidemiological data available. This prediction can provide constructive pharmaceutical strategies and non-pharmaceutical interventions such as pre-allocating the limited preventions to regions with different potential trends, especially at the early stage of an H-HRID. In this paper, transform the problem of predicting the early-stage trend of an H-HRID in new regions without prophase epidemiological data into the problem of learning regions’ geospatial features via a Contrastive learning-based Hierarchical Graph Convolutional Neural Network (CHGCN). Specifically, CHGCN first generates training data from regions with epidemiological data available via a Relaxed Graph Edit Distance (RGED) algorithm. CHGCN then uses a constructive learning schema to learn region embeddings, where regions with similar geospatial features and H-HRID trends exhibit similar embeddings. Once trained, CHGCN searches for the region with epidemiological data available that has the highest embedding similarity to the given query region without prophase epidemiological data, so as to achieve early-stage H-HRID trend prediction for the query region. Experimental results on COVID-19 demonstrate that the proposed CHGCN achieves state-of-the-art performance in predicting the early-stage trend of H-HRIDs, compared with the baselines.

CEDA: Learned Cardinality Estimation with Domain Adaptation

Cardinality Estimation (CE) is a fundamental but critical problem in DBMS query optimization, while deep learning techniques have made significant breakthroughs in the research of CE. However, apart from requiring sufficiently large training data to cover all possible query regions for accurate estimation, current query-driven CE methods also suffer from workload drifts. In fact, retraining or fine-tuning needs cardinality labels as ground truth and obtaining the labels through DBMS is also expensive. Therefore, we propose CEDA, a novel domain-adaptive CE system. CEDA can achieve more accurate estimations by automatically generating workloads as training data according to the data distribution in the database, and incorporating histogram information into an attention-based cardinality estimator. To solve the problem of workload drifts in real-world environments, CEDA adopts a domain adaptation strategy, making the model more robust and perform well on an unlabeled workload with a large difference from the feature distribution of the training set.

AdaLiftOver: high-resolution identification of orthologous regulatory elements with Adaptive liftOver.

Elucidating functionally similar orthologous regulatory regions for human and model organism genomes is critical for exploiting model organism research and advancing our understanding of results from genome-wide association studies (GWAS). Sequence conservation is the de facto approach for finding orthologous non-coding regions between human and model organism genomes. However, existing methods for mapping non-coding genomic regions across species are challenged by the multi-mapping, low precision, and low mapping rate issues. We develop Adaptive liftOver (AdaLiftOver), a large-scale computational tool for identifying functionally similar orthologous non-coding regions across species. AdaLiftOver builds on the UCSC liftOver framework to extend the query regions and prioritizes the resulting candidate target regions based on the conservation of the epigenomic and the sequence grammar features. Evaluations of AdaLiftOver with multiple case studies, spanning both genomic intervals from epigenome datasets across a wide range of model organisms and GWAS SNPs, yield AdaLiftOver as a versatile method for deriving hard-to-obtain human epigenome datasets as well as reliably identifying orthologous loci for GWAS SNPs. The R package and the data for AdaLiftOver is available fromhttps://github.com/keleslab/AdaLiftOver.

Smart-Area-Selection Based Location Privacy Enhancement

Protecting location privacy from location-based service (LBS) providers is a challenging problem. Existing mechanisms either use a trusted third party (TTP) or location perturbation techniques. In TTP-based mechanisms, when the security of a TTP is compromised, the user’s privacy is breached. On the other hand, in location perturbation-based mechanisms, privacy is achieved at the expense of query accuracy. To overcome these problems, in this article, a novel smart-area-selection (SAS)-based location privacy protection algorithm is proposed. The proposed approach does not require the user to trust any party including LBS. The proposed efficient mechanism achieves privacy without compromising the accuracy of the query. Furthermore, our SAS-based mechanism uses a user-specific geographical coordinate system and public key encryption for additional security. In the existing location privacy protection mechanisms, when LBSs are accessed continuously, more location information can be extracted by a smart adversary. A specially designed query region spreading algorithm is used in the proposed mechanism to overcome this problem. The validity of the proposed SAS-based location privacy protection mechanism is demonstrated by simulation results.

Efficient trajectory compression and range query processing

Nowadays, there are ubiquitousness of GPS sensors in various devices collecting, transmitting and storing tremendous trajectory data. However, such an unprecedented scale of GPS data has posed an urgent demand for not only an effective storage mechanism but also an efficient query mechanism. Line simplification in online mode, searving as a mainstream trajectory compression method, plays an important role to attack this issue. But for the existing algorithms, either their time cost is extremely high, or the accuracy loss after the compression is completely unacceptable. To attack this issue, we propose $\epsilon \_$Region based Online trajectory Compression with Error bounded (ROCE for short), which makes the best balance among the accuracy loss, the time cost and the compression rate. The range query serves as a primitive, yet quite essential operation on analyzing trajectories. Each trajectory is usually seen as a sequence of discrete points, and in most previous work, a trajectory is judged to be overlapped with the query region R iff there is at least one point in this trajectory falling in R. But this traditional criteria is not suitable when the queried trajectories are compressed, because there may be hundreds of points discarded between each two adjacent points and the points in each compressed trajectory are quite sparse. And many trajectories could be missing in the result set. To address this, in this paper, a new criteria based on the probability and an efficient Range Query processing algorithm on Compressed trajectories RQC are proposed. In addition, an efficient index \emph{ASP\_tree} and lots of novel techniques are also presented to accelerate the processing of trajectory compression and range queries obviously. Extensive experiments have been done on multiple real datasets, and the results demonstrate superior performance of our methods.

Encoding histopathology whole slide images with location-aware graphs for diagnostically relevant regions retrieval.

Content-based histopathological image retrieval (CBHIR) has become popular in recent years in histopathological image analysis. CBHIR systems provide auxiliary diagnosis information for pathologists by searching for and returning regions that are contently similar to the region of interest (ROI) from a pre-established database. It is challenging and yet significant in clinical applications to retrieve diagnostically relevant regions from a database consisting of histopathological whole slide images (WSIs). In this paper, we propose a novel framework for regions retrieval from WSI database based on location-aware graphs and deep hash techniques. Compared to the present CBHIR framework, both structural information and global location information of ROIs in the WSI are preserved by graph convolution and self-attention operations, which makes the retrieval framework more sensitive to regions that are similar in tissue distribution. Moreover, benefited from the graph structure, the proposed framework has good scalability for both the size and shape variation of ROIs. It allows the pathologist to define query regions using free curves according to the appearance of tissue. Thirdly, the retrieval is achieved based on the hash technique, which ensures the framework is efficient and adequate for practical large-scale WSI database. The proposed method was evaluated on an in-house endometrium dataset with 2650 WSIs and the public ACDC-LungHP dataset. The experimental results have demonstrated that the proposed method achieved a mean average precision above 0.667 on the endometrium dataset and above 0.869 on the ACDC-LungHP dataset in the task of irregular region retrieval, which are superior to the state-of-the-art methods. The average retrieval time from a database containing 1855 WSIs is 0.752ms. The source code is available at https://github.com/zhengyushan/lagenet.

Systematic Exploration in Tissue-Pathway Associations of Complex Traits Using Comprehensive eQTLs Catalog.

The collection of expression quantitative trait loci (eQTLs) is an important resource to study complex traits through understanding where and how transcriptional regulations are controlled by genetic variations in the non-coding regions of the genome. Previous studies have focused on associating eQTLs with traits to identify the roles of trait-related eQTLs and their corresponding target genes involved in trait determination. Since most genes function as a part of pathways in a systematic manner, it is crucial to explore the pathways’ involvements in complex traits to test potentially novel hypotheses and to reveal underlying mechanisms of disease pathogenesis. In this study, we expanded and applied loci2path software to perform large-scale eQTLs enrichment [i.e., eQTLs’ target genes (eGenes) enrichment] analysis at pathway level to identify the tissue-specific enriched pathways within trait-related genomic intervals. By utilizing 13,791,909 eQTLs cataloged in the Genotype-Tissue Expression (GTEx) V8 data for 49 tissue types, 2,893 pathway sets reported from MSigDB, and query regions derived from the Phenotype-Genotype Integrator (PheGenI) catalog, we identified intriguing biological pathways that are likely to be involved in ten traits [Alzheimer’s disease (AD), body mass index, Parkinson’s disease (PD), schizophrenia, amyotrophic lateral sclerosis, non-small cell lung cancer (NSCLC), stroke, blood pressure, autism spectrum disorder, and myocardial infarction]. Furthermore, we extracted the most significant pathways for AD, such as BioCarta D4-GDI pathway and WikiPathways sulfation biotransformation reaction and viral acute myocarditis pathways, to study specific genes within pathways. Our data presented new hypotheses in AD pathogenesis supported by previous studies, like the increased level of caspase-3 in the amygdala that cleaves GDP dissociation inhibitor and binds to beta-amyloid, leading to increased apoptosis and neuronal loss. Our findings also revealed potential pathogenesis mechanisms for PD, schizophrenia, NSCLC, blood pressure, autism spectrum disorder, and myocardial infarction, which were consistent with past studies. Our results indicated that loci2path′s eQTLs enrichment test was valuable in unveiling novel biological mechanisms of complex traits. The discovered mechanisms of disease pathogenesis and traits require further in-depth analysis and experimental validation.

QRB-tree Indexing: Optimized Spatial Index Expanding upon the QR-tree Index

Support for region queries is crucial in geographic information systems, which process exact queries through spatial indexing to filter features and subsequently refine the selection. Although the filtering step has been extensively studied, the refinement step has received little attention. This research builds upon the QR-tree index, which decomposes space into hierarchical grids, registers features to the grids, and builds an R-tree for each grid, to develop a new QRB-tree index with two levels of optimization. In the first level, a bucket is introduced in every grid in the QR-tree index to accelerate the loading and search steps of a query region for the grids within the query region. In the second level, the number of candidate features to be eliminated is reduced by limiting the features to those registered to the grids covering the corners of the query region. Subsequently, an approach for determining the maximal grid level, which significantly affects the performance of the QR-tree index, is proposed. Direct comparisons of time costs with the QR-tree index and geohash index show that the QRB-tree index outperforms the other two approaches for rough queries in large query regions and exact queries in all cases.

Low Complexity Sequential Search With Size-Dependent Measurement Noise

This paper considers a target localization problem where at any given time an agent can choose a region to query for the presence of the target in that region. The measurement noise is assumed to be increasing with the size of the query region the agent chooses. Motivated by practical applications such as initial beam alignment in array processing, heavy hitter detection in networking, and visual search in robotics, we consider practically important complexity constraints/metrics: time complexity, computational and memory complexity, and the complexity of possible query sets in terms of geometry and cardinality. Two novel search strategy, dyaPM and hiePM, are proposed. Pertinent to the practicality of our solutions, dyaPM and hiePM are of a connected query geometry (i.e. query set is always a connected set) implemented with low computational and memory complexity. Additionally, hiePM has a hierarchical structure and, hence, a further reduction in the cardinality of possible query sets, making hiePM practically suitable for applications such as beamforming in array processing where memory limitations favors a small and predefined query sets. Through a unified analysis with Extrinsic Jensen Shannon (EJS) Divergence, dyaPM is shown to be asymptotically optimal in search time complexity (asymptotic in both resolution (rate) and error (reliability)). On the other hand, hiePM is shown to be near-optimal in rate. In addition, both hiePM and dyaPM are shown to outperform prior work in the non-asymptotic regime.

A FRAMEWORK FOR GENERIC SPATIAL SEARCH IN 3D POINT CLOUDS

Abstract. Modern data acquisition with active or passive photogrammetric imaging techniques generally results in 3D point clouds. Depending on the acquisition or processing method, the spacing of the individual points is either uniform or irregular. In the latter case, the neighbourhood definition like for digital images (4- or 8-neighbourhood, etc.) cannot be applied. Instead, analysis requires a local point neighbourhood. The local point neighbourhood with conventional k-nearest neighbour or fixed distance searches often produce sub-optimal results suffering from the inhomogeneous point distribution. In this article, we generalize the neighbourhood definition and present a generic spatial search framework which explicitly deals with arbitrary point patterns and aims at optimizing local point selection for specific processing tasks like interpolation, surface normal estimation and point feature extraction, spatial segmentation, and such like. The framework provides atomic 2D and 3D search strategies, (i) k-nearest neighbour, (ii) region query, (iii) cell based selection, and (iv) quadrant/octant based selection. It allows to freely combine the individual strategies to form complex, conditional search queries as well as specifically tailored point sub-selection. The benefits of such a comprehensive neighbourhood search approach are showcased for feature extraction and surface interpolation of irregularly distributed points.

Answering why-not questions on top-[formula omitted] augmented spatial keyword queries

The top-k augmented spatial keyword query (TkASKQ) retrieves k objects with the highest scores based on a scoring function, which considers spatial proximity, textual similarity and attribute matching simultaneously. As far as we know, no work has been conducted on answering why-not questions on TkASKQ queries (WTkASKQ). This paper takes the first step to address WTkASKQ queries by adopting a Query Refinement model. Specifically, we propose a hybrid indexing structure, AkC, which adopts a two-level partitioning scheme, to efficiently organize the textual, attribute, and spatial information of objects. Based on AkC, several filtering strategies are proposed to prune unqualified objects for query processing. To limit the number of refined queries to be explored, we construct new refined queries by sequentially extracting new keywords and attribute–value pairs from missing objects and adding them to the original keyword and attribute–value sets, respectively, so as to efficiently obtain the best refined query with minimal modification cost. In addition, we discuss the applicability of the methods in handling why-not questions on augmented regional queries, ordinary top-k SKQ queries and complex scoring queries. Experimental result shows that our AkC-based method has higher query efficiency compared with other baseline methods.

Where Were the Birds Staying Last Week?

A stay region of a moving object is a region in which it spends a significant amount of time. Given the location of an object through a certain time interval, i.e. its trajectory, our goal is to find its stay regions. We consider axis-parallel square stay regions of fixed side length, which contain the longest continuous portion of the trajectory. We present an algorithm for answering time-windowed stay region queries, to find a stay region in any given time interval. The algorithm has an approximation factor of [Formula: see text] and answers each query with the time complexity [Formula: see text], in which [Formula: see text] is the number of vertices of the input trajectory. The time complexity of the preprocessing step of the algorithm is [Formula: see text]. We also present two approximation algorithms for finding stay regions of whole trajectories.

RETRACTED ARTICLE: Data mining technology of computer testing system for intelligent machining

This paper aims at how to turn the stored data into useful information, dig out the deep information of process operation, and improve the process monitoring ability by using this information. Based on the research topic of large-range linear accelerometer detection technology and data mining, this paper improves the traditional data mining methods to different degrees according to the characteristics of different detection objects, and puts forward some new detection data analysis and processing and fault diagnosis and prediction methods. Through the analysis of the density-based algorithm and the grid-based algorithm, this paper proposes an equivalent rule of dense cell recognition and density-based object search and thus proposes a grid-based and density-based clustering algorithm CLIGRID. This algorithm achieves fast clustering by clustering in stages and selecting seed objects to extend the class, thus reducing the number of regional queries and I/O overhead. Based on the DBSCAN algorithm of parameter selection difficult and hard to find the problem of large cluster density difference, put forward the multidimensional value of worshiping improved algorithms of DBSCAN algorithm using grid map density distribution of density matrix, automatically determine the density level classification, through a multidimensional hierarchical clustering process with multiple density more careful in the level of clustering results, solved by using global epsilon value clustering quality deterioration. The comparison results show that the efficiency of CLIGRID algorithm is significantly higher than DBSCAN algorithm, and the execution time of the algorithm is basically linear with the number of data points. Experiments show that when the data volume is less than 10,000, the efficiency of the algorithm is slightly lower than DBSCAN algorithm, because the representative core unit occupies a large proportion (64%) in all the dense units, while the seed objects in the representative core unit participate in the operation in the two stages of clustering, which increases the running time. With the increase in the data set and the decrease in the proportion of core elements (26%), the effect of grid clustering on the improvement of algorithm efficiency gradually becomes apparent.