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Zeitschriftenartikel zum Thema „Genetic data“

Autor: Grafiati

Veröffentlicht am 4. Juni 2021

Zuletzt aktualisiert am 25. Juli 2025

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1

Famuji, Tri Stiyo, Herman Herman, and Sunardi Sunardi. "Smart Contract Penyimpanan Data Genetika Manusia Berbiaya Murah pada Blockchain Ethereum." Jurnal Teknologi Informasi dan Ilmu Komputer 11, no. 3 (2024): 695–704. http://dx.doi.org/10.25126/jtiik.1137558.

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Genetika manusia merujuk pada informasi yang dikumpulkan tentang genom atau warisan genetik individu manusia. Data ini mencakup sekuens DNA, variasi genetik, mutasi, dan informasi lain yang terkait dengan sifat dan karakteristik genetik individu manusia. Data genetika manusia diperoleh melalui serangkaian proses, meliputi penguntaian genetik, pengujian genetik, analisis DNA, dan pemetaan genetik. Data genetika terutama pada manusia merupakan data yang bersifat privat yang harus dilindungi keamanan dan kerahasiaanya. Beberapa penelitian telah menggunakan teknologi Blockchain untuk menyimpan dat

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2

Volkova, T., E. Furta, O. Dmitrieva, and I. Shabalina. "Pattern Building Methods in Genetic Data Processing." Journal on Selected Topics in Nano Electronics and Computing 1, no. 2 (2014): 2–6. http://dx.doi.org/10.15393/j8.art.2014.3041.

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3

Taylor, Mark J. "Data Protection, Shared (Genetic) Data and Genetic Discrimination." Medical Law International 8, no. 1 (2006): 51–77. http://dx.doi.org/10.1177/096853320600800103.

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4

Butler, Amy W., Sarah Cohen-Woods, Anne Farmer, Peter McGuffin, and Cathryn M. Lewis. "Integrating Phenotypic Data For Depression." Journal of Integrative Bioinformatics 7, no. 3 (2010): 290–99. http://dx.doi.org/10.1515/jib-2010-136.

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Abstract The golden era of molecular genetic research brings about an explosion of phenotypic, genotypic and sequencing data. Building on the common aims to exploit understanding of human diseases, it also opens up an opportunity for scientific communities to share and combine research data. Genome-wide association studies (GWAS) have been widely used to locate genetic variants, which are susceptible for common diseases. In the field of medical genetics, many international collaborative consortiums have been established to conduct meta-analyses of GWAS results and to combine large genotypic da

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5

Ross-Ibarra, Jeffrey. "Genetic Data Analysis II. Methods for Discrete Population Genentic Data." Economic Botany 56, no. 2 (2002): 216. http://dx.doi.org/10.1663/0013-0001(2002)056[0216:gdaimf]2.0.co;2.

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6

Slatkin, Montgomery, Wayne P. Maddison, and B. S. Weir. "Genetic Data Analysis: Methods for Discrete Population Genetic Data." Systematic Zoology 40, no. 2 (1991): 248. http://dx.doi.org/10.2307/2992265.

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7

Chase, Gary A., and Bruce S. Weir. "Genetic Data Analysis: Methods for Discrete Population Genetic Data." Journal of the American Statistical Association 86, no. 413 (1991): 248. http://dx.doi.org/10.2307/2289745.

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8

Feytmans, E., and B. S. Weir. "Genetic Data Analysis: Methods for Discrete Population Genetic Data." Biometrics 47, no. 3 (1991): 1205. http://dx.doi.org/10.2307/2532683.

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9

Morton, N. E. "Genetic Data Analysis. Methods for Discrete Population Genetic Data." Journal of Medical Genetics 29, no. 3 (1992): 216. http://dx.doi.org/10.1136/jmg.29.3.216.

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10

Slatkin, M., and W. P. Maddison. "Genetic Data Analysis: Methods for Discrete Population Genetic Data." Systematic Biology 40, no. 2 (1991): 248–49. http://dx.doi.org/10.1093/sysbio/40.2.248.

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11

Lorey, Fred. "Human Genetics Data Applied to Genetic Screening Programs." Practicing Anthropology 20, no. 2 (1998): 30–33. http://dx.doi.org/10.17730/praa.20.2.n84728r821185380.

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The uses of human genetic data in genetic screening are multifaceted and dynamic, creating an ongoing stream of useful prevalence data, ethnicity data, and natural history information. Since the primary facility for generation of these data is a large public health genetic screening program, however, the results must be continually analyzed and evaluated in the context of testing parameters. For example, presumptive positive rates (initial screening test positives, only a portion of which will become diagnosed cases), false positive rates, detection rates, and analytical values must be constan

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12

Uzych, Leo. "Genetic Testing Data." Journal of Occupational & Environmental Medicine 38, no. 1 (1996): 13–14. http://dx.doi.org/10.1097/00043764-199601000-00001.

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13

Rischitelli, Gary. "Genetic Testing Data." Journal of Occupational & Environmental Medicine 38, no. 1 (1996): 14. http://dx.doi.org/10.1097/00043764-199601000-00002.

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14

Ahluwalia, Maninder. "Protecting genetic data." New Scientist 247, no. 3295 (2020): 23. http://dx.doi.org/10.1016/s0262-4079(20)31409-3.

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15

The Lancet Oncology. "Consolidating genetic data." Lancet Oncology 6, no. 6 (2005): 351. http://dx.doi.org/10.1016/s1470-2045(05)70177-7.

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16

Rutledge, Louis C. "Genetic Data Analysis." Annals of the Entomological Society of America 84, no. 6 (1991): 639. http://dx.doi.org/10.1093/aesa/84.6.639a.

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17

Mick Richardson, P. "Genetic data analysis." Biochemical Systematics and Ecology 18, no. 5 (1990): 387. http://dx.doi.org/10.1016/0305-1978(90)90013-6.

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18

Manning, Al. "Analysis of genetic and performance data in dairy herds." Livestock 30, no. 1 (2025): 18–22. https://doi.org/10.12968/live.2025.0028.

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Herd performance is a function of genetics and management. Understanding the environment and genotype is therefore essential when providing proactive herd health advice. This review explains how genetic data are calculated and used, and offers insight into assessing genetic expression by analysing performance data. Optimal management enables cows to express their genetic potential. Where genetics are not being expressed, vets and consultants should provide advice to improve management.

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19

Weir, B. S. "Genetic Data Analysis II." Biometrics 53, no. 1 (1997): 392. http://dx.doi.org/10.2307/2533134.

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20

Ott, Jurg. "Genetic data analysis II." Trends in Genetics 13, no. 9 (1997): 379. http://dx.doi.org/10.1016/s0168-9525(97)81169-9.

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21

Coupland, Robin, Sophie Martin, and Maria-Teresa Dutli. "Protecting everybody's genetic data." Lancet 365, no. 9473 (2005): 1754–56. http://dx.doi.org/10.1016/s0140-6736(05)66563-4.

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22

Jorde, L. B. "Genetic Data Analysis: Methods for Discrete Population Genetic Data. Bruce S. Weir." Quarterly Review of Biology 66, no. 4 (1991): 488–89. http://dx.doi.org/10.1086/417362.

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23

Spector-Bagdady, Kayte, Amanda Fakih, Chris Krenz, Erica E. Marsh, and J. Scott Roberts. "Genetic data partnerships: academic publications with privately owned or generated genetic data." Genetics in Medicine 21, no. 12 (2019): 2827–29. http://dx.doi.org/10.1038/s41436-019-0569-z.

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24

Chikhi, L. "Genetic markers: How accurate can genetic data be?" Heredity 101, no. 6 (2008): 471–72. http://dx.doi.org/10.1038/hdy.2008.106.

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25

Deckard, Jamalynne, Clement J. McDonald, and Daniel J. Vreeman. "Supporting interoperability of genetic data with LOINC." Journal of the American Medical Informatics Association 22, no. 3 (2015): 621–27. http://dx.doi.org/10.1093/jamia/ocu012.

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Abstract Electronic reporting of genetic testing results is increasing, but they are often represented in diverse formats and naming conventions. Logical Observation Identifiers Names and Codes (LOINC) is a vocabulary standard that provides universal identifiers for laboratory tests and clinical observations. In genetics, LOINC provides codes to improve interoperability in the midst of reporting style transition, including codes for cytogenetic or mutation analysis tests, specific chromosomal alteration or mutation testing, and fully structured discrete genetic test reporting. LOINC terms foll

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26

Ur Rashid, Shabir, and Mrigana Walia. "Data Gathering Optimization Using ACO and Genetic Algorithm in WSN." International Journal of Science and Research (IJSR) 10, no. 3 (2021): 262–65. https://doi.org/10.21275/sr21226092807.

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27

Lyutov, N. L. "Genetic discrimination and protection of personal genetic data: Adapting legal standards to advances in genetics." Journal of Physics: Conference Series 2210, no. 1 (2022): 012001. http://dx.doi.org/10.1088/1742-6596/2210/1/012001.

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Abstract Genetics has advanced to the point where genetic data on an individual could mark them as predisposed to hereditary illness or unsuitable for certain kinds of jobs. There is widespread apprehension that workers with ‘problematic’ genetics will be singled out by employers and insurance companies for treatment as second-class citizens with restrictions placed on their rights. The article takes up the legal issues involved in defining the concept of genetic data, in regulating genetic information as a type of personal information, and in applying genetic antidiscrimination laws in variou

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28

Atramentova, L., and H. Ehyakonandeh. "Molecular genetic data in terms of associative and population genetics." 36, no. 36 (August 25, 2021): 35–40. http://dx.doi.org/10.26565/2075-5457-2021-36-4.

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In studies on associative genetics of various multifactorial diseases, it is most often found that the minor allele’s frequency in the group of patients is higher than in the group of healthy people. Due to reduced adaptation, the minor allele manifests itself as a disease. In the group of patients, the number of homozygotes by major allele is reduced, the number of heterozygous carriers of the provocative allele is increased, and the frequency of homozygotes by the provocative allele is significantly increased. The aim of this article was to analyze the unusual result for SNP 1298A/C of the M

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29

Rajavarma, V. N., and S. P. Rajagopala. "Feature Selection in Data-Mining for Genetics Using Genetic Algorithm." Journal of Computer Science 3, no. 9 (2007): 723–25. http://dx.doi.org/10.3844/jcssp.2007.723.725.

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30

Diaconescu, Ioana, and Sorin Hostiuc. "Pharmacogenomics: Ethical Issues in Data Management." Studia Universitatis Babeş-Bolyai Bioethica 66, Special Issue (2021): 69. http://dx.doi.org/10.24193/subbbioethica.2021.spiss.40.

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"Pharmacogenomics uses a DNA sequence in order to create a “genetic map” that determines which drugs are the most efficient for a specific disease, in a particular patient. The needed information for developing personalized therapies needs, besides genetic data, various non-genetic factors might interfere with some mechanisms of drug action, and they should also be considered. The assumption that the genetic data is more important than any other type of non-genetic medical information may severely alter the reliability of pharmacogenomics. In order to decrease the risk for non-genetic factors

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31

Suksut, Keerachart, Kittisak Kerdprasop, and Nittaya Kerdprasop. "Support Vector Machine with Restarting Genetic Algorithm for Classifying Imbalanced Data." International Journal of Future Computer and Communication 6, no. 3 (2017): 92–96. http://dx.doi.org/10.18178/ijfcc.2017.6.3.496.

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32

Pearse, Devon E., and Keith A. Crandall. "Beyond FST: Analysis of population genetic data for conservation." Conservation Genetics 5 (June 12, 2004): 585–602. https://doi.org/10.5281/zenodo.13520807.

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(Uploaded by Plazi for the Bat Literature Project) Both the ability to generate DNA data and the variety of analytical methods for conservation genetics are expanding at an ever-increasing pace. Analytical approaches are now possible that were unthinkable even five years ago due to limitations in computational power or the availability of DNA data, and this has vastly expanded the accuracy and types of information that may be gained from population genetic data. Here we provide a guide to recently developed methods for population genetic analysis, including identification of population structu

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33

Pearse, Devon E., and Keith A. Crandall. "Beyond FST: Analysis of population genetic data for conservation." Conservation Genetics 5 (June 7, 2004): 585–602. https://doi.org/10.5281/zenodo.13520807.

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(Uploaded by Plazi for the Bat Literature Project) Both the ability to generate DNA data and the variety of analytical methods for conservation genetics are expanding at an ever-increasing pace. Analytical approaches are now possible that were unthinkable even five years ago due to limitations in computational power or the availability of DNA data, and this has vastly expanded the accuracy and types of information that may be gained from population genetic data. Here we provide a guide to recently developed methods for population genetic analysis, including identification of population structu

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34

Pearse, Devon E., and Keith A. Crandall. "Beyond FST: Analysis of population genetic data for conservation." Conservation Genetics 5 (July 3, 2004): 585–602. https://doi.org/10.5281/zenodo.13520807.

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(Uploaded by Plazi for the Bat Literature Project) Both the ability to generate DNA data and the variety of analytical methods for conservation genetics are expanding at an ever-increasing pace. Analytical approaches are now possible that were unthinkable even five years ago due to limitations in computational power or the availability of DNA data, and this has vastly expanded the accuracy and types of information that may be gained from population genetic data. Here we provide a guide to recently developed methods for population genetic analysis, including identification of population structu

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35

Pearse, Devon E., and Keith A. Crandall. "Beyond FST: Analysis of population genetic data for conservation." Conservation Genetics 5 (July 10, 2004): 585–602. https://doi.org/10.5281/zenodo.13520807.

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(Uploaded by Plazi for the Bat Literature Project) Both the ability to generate DNA data and the variety of analytical methods for conservation genetics are expanding at an ever-increasing pace. Analytical approaches are now possible that were unthinkable even five years ago due to limitations in computational power or the availability of DNA data, and this has vastly expanded the accuracy and types of information that may be gained from population genetic data. Here we provide a guide to recently developed methods for population genetic analysis, including identification of population structu

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36

Greytak, Ellen M., David H. Kaye, Bruce Budowle, CeCe Moore, and Steven L. Armentrout. "Privacy and genetic genealogy data." Science 361, no. 6405 (2018): 857.1–857. http://dx.doi.org/10.1126/science.aav0330.

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37

Židanavičiutė, J. "LOGIT ANALYSIS OF GENETIC DATA." Mathematical Modelling and Analysis 13, no. 1 (2008): 135–44. http://dx.doi.org/10.3846/1392-6292.2008.13.135-144.

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A new framework of genetic sequence statistical analysis based on generalized logit model is introduced. Logit analysis is applied to assess the dependence structure (interactions) between DNA nucleotides and to test hypothesis about Markov order of these dependencies. The procedure proposed seeks the non‐coding subsequences which are homogeneous but yet non‐Markov. It has been shown, that even homogeneous DNA regions can not be treated as the first order Markov sequences.

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38

Check Hayden, Erika. "Data barriers limit genetic diagnosis." Nature 494, no. 7436 (2013): 156–57. http://dx.doi.org/10.1038/494156a.

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39

Elkan, C. "Access to genetic sequence data." Science 255, no. 5045 (1992): 663. http://dx.doi.org/10.1126/science.1738833.

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40

Lawson, Daniel John, and Daniel Falush. "Population Identification Using Genetic Data." Annual Review of Genomics and Human Genetics 13, no. 1 (2012): 337–61. http://dx.doi.org/10.1146/annurev-genom-082410-101510.

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41

Sariyar, Murat, Stephanie Suhr, and Irene Schlünder. "How Sensitive Is Genetic Data?" Biopreservation and Biobanking 15, no. 6 (2017): 494–501. http://dx.doi.org/10.1089/bio.2017.0033.

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42

Drechsler, R., and N. Göckel. "Genetic algorithm for data sequencing." Electronics Letters 33, no. 10 (1997): 843. http://dx.doi.org/10.1049/el:19970600.

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43

Otlowski, Margaret F., Sandra D. Taylor, and Kristine K. Barlow-Stewart. "Genetic discrimination: Too few data." European Journal of Human Genetics 11, no. 1 (2003): 1–2. http://dx.doi.org/10.1038/sj.ejhg.5200910.

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44

Bhasin, Harsh, and Neha Singla. "Cellular-genetic test data generation." ACM SIGSOFT Software Engineering Notes 38, no. 5 (2013): 1–9. http://dx.doi.org/10.1145/2507288.2507303.

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45

Sorani, Marco D., John K. Yue, Sourabh Sharma, et al. "Genetic Data Sharing and Privacy." Neuroinformatics 13, no. 1 (2014): 1–6. http://dx.doi.org/10.1007/s12021-014-9248-z.

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46

Bowman, K. O., and M. A. Kastenbaum. "Overdispersion of aggregated genetic data." Mutation Research/Environmental Mutagenesis and Related Subjects 272, no. 2 (1992): 133–37. http://dx.doi.org/10.1016/0165-1161(92)90041-j.

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47

Giles, Barbara E. "Genetic biodiversity: analysing the data." Trends in Ecology & Evolution 9, no. 9 (1994): 317–19. http://dx.doi.org/10.1016/0169-5347(94)90150-3.

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48

S. Waples, Robin. "Guidelines for genetic data analysis." J. Cetacean Res. Manage. 18, no. 1 (2023): 33–80. http://dx.doi.org/10.47536/jcrm.v18i1.421.

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The IWC Scientific Committee recently adopted guidelines for quality control of DNA data. Once data have been collected, the next step is to analyse the data and make inferences that are useful for addressing practical problems in conservation and management of cetaceans. This is a complex exercise, as numerous analyses are possible and users have a wide range of choices of software programs for implementing the analyses. This paper reviews the underlying issues, illustrates application of different types of genetic data analysis to two complex management problems (involving common minke whale

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49

Kuru, Taner, and Iñigo de Miguel Beriain. "Your genetic data is my genetic data: Unveiling another enforcement issue of the GDPR." Computer Law & Security Review 47 (November 2022): 105752. http://dx.doi.org/10.1016/j.clsr.2022.105752.

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50

Sukhorolskyi, Petro, and Valeriia Hutsaliuk. "Processing of Genetic Data under GDPR: Unresolved Conflict of Interests." Masaryk University Journal of Law and Technology 14, no. 2 (2020): 151–76. http://dx.doi.org/10.5817/mujlt2020-2-1.

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Over the last decades, developments in the fields of genetics and bioinformatics caused a marked increase in the processing of human genetic data by various companies and institutions. This results in the adoption of several international documents and the emergence of legal norms on the protection of genetic data. The paper examines how and to what extent the interests and rights of the data subject with regard to the processing of genetic data are protected in the European Union. It is concluded that under the GDPR this task is implemented through classifying genetic data as sensitive, relia

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