String Search Research Articles

Parallel and private generalized suffix tree construction and query on genomic data

BackgroundSeveral technological advancements and digitization of healthcare data have provided the scientific community with a large quantity of genomic data. Such datasets facilitated a deeper understanding of several diseases and our health in general. Strikingly, these genome datasets require a large storage volume and present technical challenges in retrieving meaningful information. Furthermore, the privacy aspects of genomic data limit access and often hinder timely scientific discovery.MethodsIn this paper, we utilize the Generalized Suffix Tree (GST); their construction and applications have been fairly studied in related areas. The main contribution of this article is the proposal of a privacy-preserving string query execution framework using GSTs and an additional tree-based hashing mechanism. Initially, we start by introducing an efficient GST construction in parallel that is scalable for a large genomic dataset. The secure indexing scheme allows the genomic data in a GST to be outsourced to an untrusted cloud server under encryption. Additionally, the proposed methods can perform several string search operations (i.e., exact, set-maximal matches) securely and efficiently using the outlined framework.ResultsThe experimental results on different datasets and parameters in a real cloud environment exhibit the scalability of these methods as they also outperform the state-of-the-art method based on Burrows-Wheeler Transformation (BWT). The proposed method only takes around 36.7s to execute a set-maximal match whereas the BWT-based method takes around 160.85s, providing a 4× speedup.

Protocol for a Scoping Review of Factors That Influence Nutrition- and Mealtime-Related Practices of Caregivers of Children Ages 4–12 Years With Cerebral Palsy in India

Abstract Objectives To identify the factors that influence nutrition- and mealtime-related practices for caregivers of children ages 4–12 years with Cerebral Palsy (CP). Methods The proposed scoping review will be guided by the methodological framework proposed by Arksey and O'Malley. Scoping reviews are well-suited to new research areas to address questions that describe the conceptualization of a particular subject area and the scope of the literature. A review protocol will be established in collaboration with a GSU librarian; an exhaustive and comprehensive search will be carried out in the following databases: MEDLINE, CINAHL, EMBASE, PsycINFO, and Cochrane Library, to identify all relevant studies between 1990–2021. Studies will be limited to those pertaining to children with CP ages ≥ 1-year consuming foods orally and those published in English. Two reviewers will independently review the article's title and abstract (phase 1) and full texts (phase 2), include studies that meet the criteria, and conduct data extraction. Discrepancies will be resolved by a third reviewer. The string search for the scoping review will include broad terms ‘nutrition’, ‘mealtime-related practices’, and ‘children with cerebral palsy (4–12 years)’; additional search terms under nutrition will include nutrients, nutritional status, intake, energy, food, beverage, diet, liquid, fluid, semi-solid, malnutrition, stunting, and growth and under ‘mealtime-related practices’ will include feeding, swallowing, eating, drinking, food preparation, cooking, food consistency, food texture, and food type. Results N/A. Abstract is a study protocol abstract as denoted in the abstract title. Conclusions Results from the scoping review will be used to develop a Nutrition and Mealtime Management Questionnaire for caregivers of children ages 4–12 years with CP in India. Funding Sources Lewis Foundation Grant, Georgia State University.

In silico-aided molecular identification of potential pathogenic and prognostic differentially expressed genes (DEGs) associated with human trypanosomiasis

Background: The molecular mechanism of human African trypanosomiasis remains to be fully understood. It is urgently required to identify genes that are associated with trypanosome development and prognosis and to elucidate the underlying molecular mechanisms. In the present study, we aimed to identify potential pathogenic and prognostic differentially expressed genes (DEGs) associated with human trypanosomiasis through bioinformatics analysis of genetic profiles from infected patients. Methods: The gene expression dataset of trypanosome infected (GSE85996) patients were obtained from the Gene Expression Omnibus (GEO). DEGs were identified using the LIMMA Package of R. The GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) analyses were conducted through Enrich. The protein-protein interaction (PPI) network of the DEGs was established through the STRING (Search Tool for the Retrieval of Interacting Genes database) server. Results: A total of 20 differentially expressed genes including 3 upregulated genes (STAT1, FBXW17 and LRRC15), and 17 downregulated genes (Setbp1, Rxfp1, 5031414D18Rik, Dnm3os, Rxfp1, Serpine2, Rnase2a, Tnfaip8l3, Serpine2, Adap1, Nrg1, P2ry14, Vegfd, Aldh1a2, P2ry14, Fgfbp3 and Aldh1a2) were identified. The PPI network of the DEG identified a total of 40 nodes, 254 edges, 12.7 average node degree, and a PPI enrichment p-value of < 1.0e-16. The enriched KEGG pathway of the DEG includes; Relaxin signalling pathway, retinol metabolism, erbb signalling pathway, neuroactive ligand-receptor interaction, TNF signalling pathway, and focal adhesion. However, the enriched GO were r FGFR signalling pathway, cardiac endothelial cell differentiation, cardiac muscle cell myoblast differentiation, plasminogen activation, mast cell chemotaxis, endocardial cell differentiation, and transmembrane receptor protein tyrosine kinase. Conclusion: The present study may provide a basis for an improved understanding of trypanosome infection in humans. The DEGs identified in this study could be utilized as new biomarkers for prognosis and potential new targets for the development of new drugs against human trypanosomiasis.

Web crawling of social media and related web platforms to analyze backyard poultry owners responses to the 2018-2020 Newcastle Disease (ND) outbreak in Southern California.

As social media becomes an ever-increasing staple of everyday life and a growing percentage of people turn to community driven platforms as a primary source of information, the data created from these posts can provide a new source of information from which to better understand an event in near real time. The 2018-2020 outbreak of Newcastle Disease (ND) in Southern California is the third outbreak of ND in Southern California within a 50-year time span. These outbreaks are thought to be primarily driven by non-commercial poultry (i.e. backyard and game fowl) in the region. Here we employed a commercial 'web crawling' tool between June of 2018 and July of 2020 which encompassed the majority of the outbreak in order to collect all available online mentions of 'virulent Newcastle Disease' (vND), the terminology commonly used by the California Department of Food and Agriculture (CDFA), United States Department of Agriculture (USDA), and the general public, in relation to the outbreak. A total of 2498 posts in English and Spanish were returned using a Boolean logic-based string search. While the number of posts was relatively small, their impact as measured by the number of visitors to the website and the number of people viewing the post (where provided) was much larger. Posts with negative sentiment were found to have a larger audience relative to posts with a positive sentiment. In addition, posts with negative sentiment peaked in May of 2019 which preceded the formation of the anti-depopulation group Save Our Birds (SOB). As the usage and impact of social media grows, the ability to utilize tools to analyze social media may improve both response and outreach-based strategies for various disease outbreaks including vND in Southern California which has a large non-commercial poultry population.

Quantum Algorithms for Some Strings Problems Based on Quantum String Comparator

We study algorithms for solving three problems on strings. These are sorting of n strings of length k, “the Most Frequent String Search Problem”, and “searching intersection of two sequences of strings”. We construct quantum algorithms that are faster than classical (randomized or deterministic) counterparts for each of these problems. The quantum algorithms are based on the quantum procedure for comparing two strings of length k in O(k) queries. The first problem is sorting n strings of length k. We show that classical complexity of the problem is Θ(nk) for constant size alphabet, but our quantum algorithm has O˜(nk) complexity. The second one is searching the most frequent string among n strings of length k. We show that the classical complexity of the problem is Θ(nk), but our quantum algorithm has O˜(nk) complexity. The third problem is searching for an intersection of two sequences of strings. All strings have the same length k. The size of the first set is n, and the size of the second set is m. We show that the classical complexity of the problem is Θ((n+m)k), but our quantum algorithm has O˜((n+m)k) complexity.

Review on String-Matching Algorithm

String-matching algorithm is one of the most researched algorithms in computer science which has become an important factor in many technologies. This field aims at utilizing the least time and resources to find desired sequence of character in complex data content. The most classical and famous string-search algorithms are Knuth-Morris-Pratt (KMP) algorithm and Boyer-Moore (DM) algorithm. These two algorithms provide efficient heuristic jump rules by prefix or suffix. Bitap algorithm was the first to introduce bit-parallelism into string-matching field. Backward Non-Deterministic DAWG Matching (BNDM) algorithm is a modern practical algorithm that is an outstanding combination of theoretical research and practical application. Those meaningful algorithms play a guiding role in future research in string-search algorithm to improve the average performance of the algorithm and reduce resource consumption.

ARQMath Lab: An Incubator for Semantic Formula Search in zbMATH Open?

The zbMATH database contains more than 4 million bibliographic entries. We aim to provide easy access to these entries. Therefore, we maintain different index structures, including a formula index. To optimize the findability of the entries in our database, we continuously investigate new approaches to satisfy the information needs of our users. We believe that the findings from the ARQMath evaluation will generate new insights into which index structures are most suitable to satisfy mathematical information needs. Search engines, recommender systems, plagiarism checking software, and many other added-value services acting on databases such as the arXiv and zbMATH need to combine natural and formula language. One initial approach to address this challenge is to enrich the mostly unstructured document data via Entity Linking. The ARQMath Task at CLEF 2020 aims to tackle the problem of linking newly posted questions from Math Stack Exchange (MSE) to existing ones that were already answered by the community. To deeply understand MSE information needs, answer-, and formula types, we performed manual runs for tasks 1 and 2. Furthermore, we explored several formula retrieval methods: For task 2, such as fuzzy string search, k-nearest neighbors, and our recently introduced approach to retrieve Mathematical Objects of Interest (MOI) with textual search queries. The task results show that neither our automated methods nor our manual runs archived good scores in the competition. However, the perceived quality of the hits returned by the MOI search particularly motivates us to conduct further research about MOI.

Word Search on the "SITUK" Application Using the Levenshtein Distance Algorithm

Integrated System for Online Competency Certification Test (SITUK) is an application used to carry out the assessment process (competency certification) at LSP (Lembaga Sertifikasi Profesional) UPN (University of Pembangunan Nasional) “Veteran” Jawa Timur, each of which is followed by approximately five hundred (500) assessments. Thus the data stored is quite a lot, so to find data using a search system. Often, errors occur in entering keywords that are not standard spelling or typos. For example, the keyword "simple," even though the default spelling is "simple." Of course, the admin will get incomplete information, and even the admin fails to get information that matches the entered keywords. To overcome the problems experienced in conducting data searches on the SITUK application, we need a string search approach method to maximize the search results. One of the algorithms used is Levenshtein which can calculate the distance of difference between two strings. Implementation of the Levenshtein algorithm on the data search system in the SITUK application has been able to overcome the problem of misspelling keywords with the mechanism of adding, inserting, and deleting characters.

Prevalence and Temporal Trends Analysis of Screening and Diagnostic Instruments in Posttraumatic Stress Disorder: Text Mining Study.

BackgroundVarious instruments for patient screening and diagnosis have been developed for and applied in posttraumatic stress disorder (PTSD).ObjectiveThis study comprehensively investigates the prevalence and temporal trends of the most widely used instruments in PTSD-related studies.MethodsA total of 1345 files of registered clinical trials from ClinicalTrials.gov and 9422 abstracts from the PubMed database from 2005 to 2020 were downloaded for this study. The instruments applied in clinical trials were manually annotated, and instruments in abstracts were recognized using exact string matching. The prevalence score of an instrument in a certain period was calculated as the number of studies divided by the number of instances of the instrument. By calculating the yearly prevalence index of each instrument, we conducted a trends analysis and compared the trends in index change between instruments.ResultsA total of 4178 instrument synonyms were annotated, which were mapped to 1423 unique instruments. In the 16 years from 2005 to 2020, only 10 instruments were used more than once per year; the 4 most used instruments were the PTSD Checklist, the Clinician-Administered PTSD Disorder Scale, the Patient Health Questionnaire, and the Beck Depression Inventory. There were 18 instruments whose yearly prevalence index score exceeded 0.1 at least once during the 16 years. The changes in trends and time points of partial instruments in clinical trials and PubMed abstracts were highly consistent. The average time duration of a PTSD-related trial was 1495.5 days or approximately 4 years from submission to ClinicalTrial.gov to publication in a journal.ConclusionsThe application of widely accepted and appropriate instruments can help improve the reliability of research results in PTSD-related clinical studies. With extensive text data obtained from real clinical trials and published articles, we investigated and compared the usage of instruments in the PTSD research community.

Privacy-aware Character Pattern Matching over Outsourced Encrypted Data

Providing a method to efficiently search into outsourced encrypted data, without forsaking strong privacy guarantees, is a pressing concern rising from the separation of data ownership and data management typical of cloud-based applications. While several existing solutions allow a client to look up the occurrences of a substring in an outsourced document collection, the practical application requirements in terms of privacy and efficiency call for the improvement of such solutions. In this work, we present a privacy-preserving substring search protocol with a polylogarithmic communication cost and a limited computational effort on the server side. The proposed protocol provides search pattern and access pattern privacy, for both exact string search and character-pattern search with wildcards. Its extension to a multi-user setting shows significant savings in terms of outsourced storage w.r.t. a baseline solution where the whole dataset is replicated. The performance figures of an optimized implementation of our protocol, searching into a remotely stored genomic dataset, validate the practicality of the approach exhibiting a data transfer of less than 50 kiB to execute a query over a document of 40 MiB, with execution times on client and server in the range of a few seconds and a few minutes, respectively.

Penerapan Algoritma Apostolico Giancarlo Pada Pencarian Fasilitas Umum di Provinsi Aceh Berbasis Android

Public Facilities are facilities built by the government for the benefit of the general public. The problem that is often faced in Aceh Province is the difficulty of knowing the location of Public Facilities in Aceh Province due to the difficulty of the signal, many Public Facilities are not yet registered and mapped on Google Maps. Mobile Phone is one of the fastest growing technologies. Mobile Phone was originally used to send letters and make calls, but has now evolved into a smartphone that has the function of searching for a location. Apostolico Giancrlo works by comparing each index of keyword characters with the String character being examined. The search for strings in the Apostolico Giancarlo algorithm starts with pre-processing keywords. Pre-processing of keywords. This pre-processing is done to look at the index number of strings that are checked for each keyword character

A Formalisation of Digital Forensic String Search Specifications

Computer forensic tools are tools concerned with the identification, recovery, preservation and analysisof evidence in digital media involved in criminal investigations and illegal cases. String searchtools are a special class of such forensic tools, which are used to search for specific queries in theinvestigated medium. However, one main issue we find in literature mainly is that computer forensictools, including string search tools, are generally specified in an informal and oftentimes ambiguousand inconstant manner. We adopt in this short paper formal specification approaches, in particularEvent-B, in order to define clearly the behaviour and properties of string search tools, a class of computerforensic tools, and investigate whether there are any requirements that cannot be achieved. Ourresults show that some basic and optional requirements as stated in literature related to string searchtools could be not be consistent and some are unnecessarily restrictive.

Leveraging Case Narratives to Enhance Patient Age Ascertainment from Adverse Event Reports.

Missing age presents a significant challenge when evaluating individual case safety reports (ICSRs) in the FDA Adverse Event Reporting System (FAERS). When age is missing in an ICSR's structured field, it may be in the report's free-text narrative. This study aimed to evaluate the performance and assess the potential impact of a rule-based natural language processing (NLP) tool that utilizes a text string search to identify patients' numerical age from unstructured narratives. Using FAERS ICSRs from 2002 to 2018, we evaluated the annual proportion of ICSRs with age missing in the structured field before and after NLP application. Reviewers manually identified patients' age from ICSR narratives (gold standard) from a random sample of 1500 ICSRs. The gold standard was compared to the NLP-identified age. During the study period, the percentage of ICSRs missing age in the structured field increased from 21.9 to 43.8%. The NLP tool performed well among the random sample: sensitivity 98.5%, specificity 92.9%, positive predictive value (PPV) 94.9%, and F-measure 96.7%. It also performed well for the subset of ICSRs missing age in the structured field; when applied to these cases, NLP identified age for an additional one million ICSRs (10% of the total number of ICSRs from 2002 to 2018) and decreased the percentage of ICSRs missing age to 27% overall. NLP has potential utility to extract patients' age from ICSR narratives. Use of this tool would enhance pharmacovigilance and research using FAERS data.

Investigating the use of metadata record graphs to analyze subject headings in the digital public library of America

PurposeThis study furthers metadata quality research by providing complementary network-based metrics and insights to analyze metadata records and identify areas for improvement.Design/methodology/approachMetadata record graphs apply network analysis to metadata field values; this study evaluates the interconnectedness of subjects within each Hub aggregated into the Digital Public Library of America. It also reviews the effects of NACO normalization – simulating revision of values for consistency – and breaking up pre-coordinated subject headings – to simulate applying the Faceted Application of Subject Terminology to Library of Congress Subject Headings.FindingsNetwork statistics complement count- or value-based metrics by providing context related to the number of records a user might actually find starting from one item and moving to others via shared subject values. Additionally, connectivity increases through the normalization of values to correct or adjust for formatting differences or by breaking pre-coordinated subject strings into separate topics.Research limitations/implicationsThis analysis focuses on exact-string matches, which is the lowest-common denominator for searching, although many search engines and digital library indexes may use less stringent matching methods. In terms of practical implications for evaluating or improving subjects in metadata, the normalization components demonstrate where resources may be most effectively allocated for these activities (depending on a collection).Originality/valueAlthough the individual components of this research are not particularly novel, network analysis has not generally been applied to metadata analysis. This research furthers previous studies related to metadata quality analysis of aggregations and digital collections in general.

Influenza A virus infects pulmonary microvascular endothelial cells leading to microvascular leakage and release of pro-inflammatory cytokines.

ObjectiveTo investigate the replication of influenza A virus A/Puerto Rico/8/34 (H1N1) in pulmonary microvascular endothelial cells and its effect on endothelial barrier function.MethodsHuman pulmonary microvascular endothelial cells were infected with influenza A/Puerto Rico/8/34 (H1N1) virus. Plaque reduction assay, real-time quantitative PCR, immunofluorescence staining, and western blot were used to elucidate the replication process of virus-infected endothelial cells. In addition, real-time quantitative PCR was used to detect the relative expression levels of mRNA of some inflammatory factors. The endothelial resistance assay was used to determine the permeability of the endothelial monolayer. Excavation and analysis of data from open databases, such as the GeneCards database, DAVID Bioinformatics Resources, STRING search tool, and DGIdb database determined the genes, proteins, and signal pathways related to microvascular leakage caused by the H1N1 virus, and predicted the drugs that could be effective for treatment.ResultsIn vitro experiments showed that the influenza virus can infect endothelial cells, leading to a significant increase in the permeability of pulmonary microvascular endothelial cells and the release of pro-inflammatory cytokines, but does not efficiently replicate in endothelial cells. A total of 107 disease-related target genes were obtained from the Gene-cards database. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that these genes mainly affected the pathways related to “Inflammatory bowel disease” (IBD), “Chagas disease” (American trypanosomiasis), “Influenza A”, and also played a key role in anti-inflammation and regulation of immunity. After enrichment analysis, 46 hub genes were screened. A total of 42 FDA-approved drugs corresponding to the hub genes were screened from the DGIdb database, and these could be formulated for topical application. In addition, these drugs can be used to treat other diseases, including cancer, inflammatory diseases, immune system disorders, and cardiovascular diseases.ConclusionH1N1 influenza virus affects the barrier function of endothelial cells indirectly. Combined with bioinformatics tools, we can better understand the possible mechanism of action of influenza A (H1N1) virus causing pulmonary microvascular leakage and provide new clues for the treatment of pulmonary microvascular leakage.

Penerapan Algoritma A-star dan Brute Force pada Aplikasi Jakvel (Jakarta Travel) Berbasis Android

The city of Jakarta has several main tourist attractions that are interesting to visit. As the Capital of the State of Indonesia, it will certainly be ogled and visited by local and foreign tourists. However, there is limited information, tourists from outside the Jakarta area lack complete information on tourism in the city of Jakarta and the roads that must be taken to tourist sites, due to the number of roads and heavy traffic, this makes it a problem when you want to vacation in the city of Jakarta. Therefore, the author designed an application called Jakvel based on Android, this application provides information and looks for the shortest path to tourist sites for tourists who want to vacation in Jakarta. In this study using the A-star and Brute Force algorithms. The A-star algorithm will be applied to find the shortest path to tourist sites and the brute force algorithm is used to search for words (strings) in selecting tours. With this application, it is hoped that it will make it easier for tourists outside the Jakarta area to find out tourist information and help find the shortest path to locations in the city of Jakarta. After testing of 10 tourist sites to determine the shortest path by comparing the A-star algorithm with Google Maps based on Android, with a 70% success rate in finding the shortest path using the A-star algorithm and the results of string search testing using the Brute Force algorithm have a percentage of 100% accurate

Perbandingan Algoritma N-gram dan Algoritma Knuth Morris Pratt untuk Mengukur Tingkat Akurasi Plagiarisme pada Dokumen Abstrak Skripsi Berbasis Website


 
 
 
 Plagiarism is a crime that often occurs in the academic world, plagiarism occurs because of theft of other people's work that is illegally recognized as if the work is his own. N-gram is an algorithm by cutting as many characters as N-characters in a sentence or word. While the Knuth Morris Pratt (KMP) algorithm is a string search algorithm, this algorithm is used to maintain information that is used to carry out the number of shifts whenever there is no matched patency in the text. The purpose of this study is to create a system to measure the comparison of the accuracy of the N-gram algorithm with a website-based KMP on a thesis abstract document. This research uses the waterfall system development method which has stages, namely analysis, design, coding, and testing. The KMP test results are better than N-gram where kmp has an average percentage of 3.8% while the N-gram 3.5% results are obtained from an average of 10 trials and 5 documents tested.
 
 
 

Q‐frame hash comparison based exact string matching algorithms for DNA sequences

AbstractThe importance of string matching is due to its applications in many fields, such as medicine and bioinformatics. Various string matching algorithms are developed to speed up the search. Especially, hash‐based exact string matching algorithms are among the most time‐efficient ones. The efficiency of hash‐based approaches depends on the hash function. Hence, perfect hashing plays an essential role in hash‐based string matching. In this study, two q‐frame hash comparison‐based exact string matching algorithms, Hq‐QF and HqBM‐QF, are proposed. We have used a collision‐free perfect hash function for DNA sequences in the proposed algorithms. In the first approach, after hash values match for the last qcharacters, the character comparisons in the Hash‐q algorithm are replaced with q‐frame hash comparison. In the second approach, we improved the first approach by utilizing the shift size indicated at the th entry in the good suffix shift table. Since the number of character comparisons is minimized, the worst‐case time complexity of the proposed algorithms is . In both approaches, q‐frame hash comparisons replace most character comparisons as a trade‐off. The results show that the proposed approaches are more efficient than the Hash‐q algorithm in terms of runtime efficiency and the number of character comparisons.

The WM-q multiple exact string matching algorithm for DNA sequences

The string matching algorithms are among the essential fields in computer science, such as text search, intrusion detection systems, fraud detection, sequence search in bioinformatics. The exact string matching algorithms are divided into two parts: single and multiple. Multiple string matching algorithms involve finding elements of the pattern set P in a given input text T. String matching processes should be done in a time-efficient manner for DNA sequences. As the volume of the text T increases and the number of search patterns increases, the total runtime increases. Efficient algorithms should be selected to perform these search operations as soon as possible. In this study, the Wu-Manber algorithm, one of the multiple exact string matching algorithms, is improved. Although the Wu-Manber algorithm is effective, it has some limitations, such as hash collisions. In this study, the WM-q algorithm, a version of the Wu-Manber algorithm based on the perfect hash function for DNA sequences, is proposed. String matching is performed using different block lengths provided by the perfect hash function instead of using the fixed block length as in the traditional Wu-Manber algorithm. The proposed approach has been compared with E. Coli and Human Chromosome1 datasets, frequently used in the literature, using multiple exact string matching algorithms. The proposed algorithm gives better results for performance metrics such as the average runtime, the average number of characters and hash comparisons.

Designing a word recommendation application using the Levenshtein Distance algorithm

Good scriptwriting or reporting requires a high level of accuracy. The basic problem is that the level of accuracy of the authors is not the same. The low level of accuracy allows for mistyping of words in a sentence. Typing errors caused the word to become non-standard. Even worse, the word became meaningless. In this case, the recommendation application serves to provide word-writing recommendations in case of a typing error. This application can reduce the error rate of the writer when typing. One method to improve word spelling is Approximate String Matching. This method applies an approach to the string search process. The Levenshtein Distance algorithm is a part of the Approximate String-Matching method. This method, firstly, is necessary to go through the preprocessing stage to correct an incorrectly written word using the Levenshtein Distance algorithm. The application testing phase uses ten texts composed of 100 words, ten texts composed of 100 to 250 words, and ten texts composed of 250 to 500 words. The average accuracy rate of these test results was 95%, 94%, and 90%.