An Indexing Method of Continuous Spatiotemporal Queries for Stream Data Processing Rules of Detected Target Objects

Real-time performance is important in rule-based continuous spatiotemporal query processing for risk analysis and decision making of target objects collected by sensors of combat vessels. The existing Rete algorithm, which creates a compiled node link structure for executing rules, is known to be the best. However, when a large number of rules are to be processed and the stream data to be performed are large, the Rete technique has an overhead of searching for rules to be bound. This paper proposes a hashing indexing technique for Rete nodes to the overhead of searching for spatiotemporal condition rules that must be bound when rules are expressed in a node link structure. A performance comparison evaluation experiment was conducted with Drool, which implemented the Rete method, and the method that implemented the hash index method presented in this paper. For performance measurement, processing time was measured for the change in the number of rules, the change in the number of objects, and the distribution of objects. The hash index method presented in this paper improved performance by at least 18% compared to Drool.

A combinatorial indexing strategy for epigenomic profiling of plant single cells

Understanding how cis-regulatory elements facilitate gene expression is a key question in biology. Recent advances in single-cell genomics have led to the discovery of cell-specific chromatin landscapes that underlie transcription programs. However, the high equipment and reagent costs of commercial systems limit their applications for many laboratories. In this study, we profiled the Arabidopsis root single-cell epigenome using a combinatorial index and dual PCR barcode strategy without the need of any specialized equipment. We generated chromatin accessibility profiles for 13,576 Arabidopsis thaliana root nuclei with an average of 12,784 unique Tn5 integrations per cell and 85% of the Tn5 insertions localizing to discrete accessible chromatin regions. Comparison with data generated from a commercial microfluidic platform revealed that our method is capable of unbiased identification of cell type-specific chromatin accessibility with improved throughput, quality, and efficiency. We anticipate that by removing cost, instrument, and other technical obstacles, this combinatorial indexing method will be a valuable tool for routine investigation of single-cell epigenomes and usher new insight into plant growth, development and their interactions with the environment.

A novel wastewater-based epidemiology indexing method predicts SARS-CoV-2 disease prevalence across treatment facilities in metropolitan and regional populations

There is a need for wastewater based epidemiological (WBE) methods that integrate multiple, variously sized surveillance sites across geographic areas. We developed a novel indexing method, Melvin’s Index, that provides a normalized and standardized metric of wastewater pathogen load for qPCR assays that is resilient to surveillance site variation. To demonstrate the utility of Melvin’s Index, we used qRT-PCR to measure SARS-CoV-2 genomic RNA levels in influent wastewater from 19 municipal wastewater treatment facilities (WWTF’s) of varying sizes and served populations across the state of Minnesota during the Summer of 2020. SARS-CoV-2 RNA was detected at each WWTF during the 20-week sampling period at a mean concentration of 8.5 × 104 genome copies/L (range 3.2 × 102–1.2 × 109 genome copies/L). Lag analysis of trends in Melvin’s Index values and clinical COVID-19 cases showed that increases in indexed wastewater SARS-CoV-2 levels precede new clinical cases by 15–17 days at the statewide level and by up to 25 days at the regional/county level. Melvin’s Index is a reliable WBE method and can be applied to both WWTFs that serve a wide range of population sizes and to large regions that are served by multiple WWTFs.

A reference-based multi-lattice indexing method integrating prior information correction and iterative refinement in protein crystallography

A new multi-lattice indexing method based on the principle of whole-pattern matching given cell dimensions and space-group symmetry is presented for macromolecular crystallography. The proposed method, termed the multi-crystal data processing suite (MCDPS), features a local correction for prior information accompanied by iterative refinement of experimental parameters, both of which are numerically and experimentally demonstrated to be critical for accurately identifying multiple crystal lattices. Further analysis of data reduction and structure determination with conventional single-crystal programs reveals that the processed multi-lattice data sets are comparable in quality to typical single-crystal ones in terms of crystallographic metrics. Importantly, it is confirmed that careful exclusion of overlapping reflections prior to scaling is necessary to guarantee an accurate data reduction result. The potential for multi-lattice indexing in solving the general macroscopic twinning problem is also explored.

A Hybrid Spatio-Temporal Data Indexing Method for Trajectory Databases

In recent years, there has been tremendous growth in the field of indoor and outdoor positioning sensors continuously producing huge volumes of trajectory data that has been used in many fields such as location-based services or location intelligence. Trajectory data is massively increased and semantically complicated, which poses a great challenge on spatio-temporal data indexing. This paper proposes a spatio-temporal data indexing method, named HBSTR-tree, which is a hybrid index structure comprising spatio-temporal R-tree, B*-tree and Hash table. To improve the index generation efficiency, rather than directly inserting trajectory points, we group consecutive trajectory points as nodes according to their spatio-temporal semantics and then insert them into spatio-temporal R-tree as leaf nodes. Hash table is used to manage the latest leaf nodes to reduce the frequency of insertion. A new spatio-temporal interval criterion and a new node-choosing sub-algorithm are also proposed to optimize spatio-temporal R-tree structures. In addition, a B*-tree sub-index of leaf nodes is built to query the trajectories of targeted objects efficiently. Furthermore, a database storage scheme based on a NoSQL-type DBMS is also proposed for the purpose of cloud storage. Experimental results prove that HBSTR-tree outperforms TB*-tree in some aspects such as generation efficiency, query performance and query type.