• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.5
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• pp.572-578
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• 2018

This paper considers Group Testing as one of combinatorial problems. The group testing first began to inspect soldier's syphilis infection during World War II and have long established an academic basis. Recently, there has been much interest in related areas because of the rediscovery of the value of the group testing. The group testing is the same as finding a few defect samples out of a large number of samples, which is similar to the inverse problem of Compressed Sensing. In this paper, we introduce the definition of the group testing, and specify the classes of the group testing and the bounds on performance of the group testing. In addition, we show a lower bound for the number of tests required to find defective samples using the theoretical theorem which is mainly used for relationship between conditional entropy and the probability of error in the information theory. We see how our result can be different from other related results.

• Communications for Statistical Applications and Methods
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• v.13 no.3
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• pp.787-794
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• 2006

In group testing, the test unit consists of a group of individuals and each group is tested to classify units from a population as infected or non-infected or estimate the infection rate. If the test group is infected, one or more individuals in the group are presumed to be infected. It is assumed in group testing that classification of group as positive or negative is without error. But, the possibility of false negatives as a result of dilution effects happens often in practice, specially in many clinical researches. In this paper, dilution effect models in group testing are discussed and estimation methods of infection rate are proposed when a dilution effect exists.

• Genomics & Informatics
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• v.14 no.4
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• pp.187-195
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• 2016

In genetic association studies with high-dimensional genomic data, multiple group testing procedures are often required in order to identify disease/trait-related genes or genetic regions, where multiple genetic sites or variants are located within the same gene or genetic region. However, statistical testing procedures based on an individual test suffer from multiple testing issues such as the control of family-wise error rate and dependent tests. Moreover, detecting only a few of genes associated with a phenotype outcome among tens of thousands of genes is of main interest in genetic association studies. In this reason regularization procedures, where a phenotype outcome regresses on all genomic markers and then regression coefficients are estimated based on a penalized likelihood, have been considered as a good alternative approach to analysis of high-dimensional genomic data. But, selection performance of regularization procedures has been rarely compared with that of statistical group testing procedures. In this article, we performed extensive simulation studies where commonly used group testing procedures such as principal component analysis, Hotelling's $T^2$ test, and permutation test are compared with group lasso (least absolute selection and shrinkage operator) in terms of true positive selection. Also, we applied all methods considered in simulation studies to identify genes associated with ovarian cancer from over 20,000 genetic sites generated from Illumina Infinium HumanMethylation27K Beadchip. We found a big discrepancy of selected genes between multiple group testing procedures and group lasso.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.4
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• pp.53-60
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• 2022

In a group-testing method, instead of testing a sample, for example, blood individually, a batch of samples are pooled and tested simultaneously. If the pooled test is positive (or defective), each sample is tested individually. However, if negative (or good), the test is terminated at one pooled test because all samples in the batch are negative. This paper considers a queueing system with a two-stage group-testing policy. Samples arrive at the system according to a Poisson process. The system has a single server which starts a two-stage group test in a batch whenever the number of samples in the system reaches exactly a predetermined size. In the first stage, samples are pooled and tested simultaneously. If the pooled test is negative, the test is terminated. However, if positive, the samples are divided into two equally sized subgroups and each subgroup is applied to a group test in the second stage, respectively. The server performs pooled tests and individual tests sequentially. The testing time of a sample and a batch follow general distributions, respectively. In this paper, we derive the steady-state probability generating function of the system size at an arbitrary time, applying a bulk queuing model. In addition, we present queuing performance metrics such as the offered load, output rate, allowable input rate, and mean waiting time. In numerical examples with various prevalence rates, we show that the second-stage group-testing system can be more efficient than a one-stage group-testing system or an individual-testing system in terms of the allowable input rates and the waiting time. The two-stage group-testing system considered in this paper is very simple, so it is expected to be applicable in the field of COVID-19.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.10
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• pp.1195-1200
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• 2019

This paper proposes a detection algorithm for group testing. Group testing is a problem of finding a very small number of defect samples out of a large number of samples, which is similar to the problem of Compressed Sensing. In this paper, we define a noiseless group testing and propose a probabilistic algorithm for detection of defective samples. The proposed algorithm is constructed such that the extrinsic probabilities between the input and output signals exchange with each other so that the posterior probability of the output signal is maximized. Then, defective samples are found in the group testing problem through a simulation on the detection algorithm. The simulation results for this study are compared with the lower bound in the information theory to see how much difference in failure probability over the input and output signal sizes.

• Journal of Veterinary Clinics
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• v.20 no.3
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• pp.351-356
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• 2003

The aim of this experiment was to study the calcium sulfate on early bone consolidation in distraction osteogenesis in the canine mandible. Twelve dogs were used and divided into control group (group A, 4 heads), sodium hyaluronate injection group (group B, 4 heads), mixture of calcium sulfate and sodium hyaluronate injection group (group C, 4 heads). Each group were subdivided into 3 weeks testing group (totally 6 heads) and 5 weeks testing group (totally 6 heads). Mandibular distraction was started at the 5th day after the mandibular osteotomy and continued for 10 days by 1 mm a day. After the distraction on the 10th day of mandibular distraction, 0.5 m1 saline in group A, 0.5 m1 sodium hyaluronate in group B, and 0.5 ml mixture of calcium sulfate and sodium hyaluronate in group C were each injected in mandibular distraction lesion. And X-ray examination, bone mineral density, and histopathological findings were examined. The radiological findings were the most radiopaque in group C, and the most radiolucent in group A in both 3 and 5 week testing group. The level of bone mineral density was also the highest in group C, and the lowest in group C in both 3 and 5 week testing group. The histopathological findings of new bone formation were the most remarkable in group C in both 3 and 5 week testing group. New bone formation of group A was not present in both 3 and 5 week testing group. In conclusion, calcium sulfate is a kind of material that can stimulate early bone formation and can shorten the duration of bone consolidation. And it can be effective in clinical usage.

• Communications for Statistical Applications and Methods
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• v.11 no.3
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• pp.619-629
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• 2004

Binomial group testing or composite sampling is often used to estimate the proportion, p, of positive(infects, defectives) in a population when that proportion is known to be small; the potential benefits of group testing over one-at-a-time testing are well documented. The literature has focused on maximum likelihood estimation. We provide two Bayes estimators and compare them with the MLE. The first of our Bayes estimators uses an uninformative Uniform (0, 1) prior on p; the properties of this estimator are poor. Our second Bayes estimator uses a much more informative prior that recognizes and takes into account key aspects of the group testing context. This estimator compares very favorably with the MSE, having substantially lower mean squared errors in all of the wide range of cases we considered. The priors uses a Beta distribution, Beta ($\alpha$, $\beta$), and some advice is provided for choosing the parameter a and $\beta$ for that distribution.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.2
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• pp.117-123
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• 2022

Currently the number of COVID-19 cases is increasing rapidly around the world. One way to restrict the spread of COVID-19 infection is to find confirmed cases using rapid diagnosis. The previously proposed group testing problem assumed without measurement noise, but recently, false positive and false negative cases have occurred during COVID-19 testing. In this paper, we define the noisy group testing problem and analyze how much measurement noise affects the performance. In this paper, we show that the group testing system should be designed to be less susceptible to measurement noise when conducting group testing with a low positive rate of COVID-19 infection. And compared with other developed reconstruction algorithms, our proposed algorithm shows superior performance in noisy group testing.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.6
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• pp.445-451
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• 2021

Due to the recent spread and increasing damage of COVID-19, the most important measure to prevent infection is to find infected people early. Group testing which introduced half a century ago, can be used as a diagnostic method for COVID-19 and has become very efficient method. In this paper, we review the fundamental principles of existing group testing algorithms. In addition, the sparse signal reconstruction approach proposed by compressed sensing is improved and presented as a solution to group testing. Compressed sensing and group testing differ in computational methods, but are similar in that they find sparse signals. The our simulation results show the superiority of the proposed sparse signal reconstruction method. It is noteworthy that the proposed method shows performance improvement over other algorithms in the group testing schemes. It also shows performance improvement when finding a large number of defective samples.

• Women's Health Nursing
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• v.14 no.4
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• pp.314-322
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• 2008

Purpose: Dysmenorrhea is one of the most common gynecologic complaints of young women. This study investigated the changes in the menstrual cycle during test evaluations according to perfectionism and testing anxiety among high school girls in order to provide information for care of dysmenorrhea. Method: Data collection was done from July 14 to 18, 2008 by self administered questionnaires with 300 high school girls. Result: The subjects experienced amenorrhea(35.3%), changes in the menstrual cycle(66.3%), changes in menstrual duration(50.0%), changes in amount of menstruation(47.7%), and increases in menstrual pain during testing evaluations. There were significant differences in changes of menstrual patterns, physical symptoms, and menstrual pain during test evaluations between a high perfectionist group and low perfectionist group. Also, there were significant differences in changes of menstrual patterns, physical symptoms, and menstrual pain during test evaluations between a high testing anxiety group and low testing anxiety group. Perfectionism and testing anxiety showed a positive relationship with changes in menstrual patterns, physical symptoms, and menstrual pain. Conclusion: As perfectionism and testing anxiety seem to contribute to changes in the menstrual cycle during test evaluations, school health-care providers should screen routinely for dysmenorrhea and give more attention to health education on this topic to girls.