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Статті в журналах з теми "Phenotying"

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Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 28 січня 2023

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1

Evans, R. T., A. Walker, and K. M. Bowness. "Improved accuracy of cholinesterase phenotyping after participation in a proficiency survey." Clinical Chemistry 33, no. 6 (June 1, 1987): 823–25. http://dx.doi.org/10.1093/clinchem/33.6.823.

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Abstract We report results of an investigation into the proficiency of cholinesterase (EC 3.1.1.8) phenotying, assessed in 13 laboratories between 1983 and 1986. Thirty-two specimens of serum were distributed for analysis: two, each in duplicate, from the eight genotypes that can be recognized by differential enzyme inhibitor numbers. The accuracy of genotype ascription was markedly improved over that observed in an earlier study in which 12 of these laboratories took part, although the proportion of clinically significant errors did not change. Consequently, although participation in a proficiency testing program can lead to a considerable enhancement of performance, we still recommend the use of reference centers for detailed cholinesterase studies.

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2

Hund, A., S. Trachsel, and P. Stamp. "Growth of axile and lateral roots of maize: I development of a phenotying platform." Plant and Soil 325, no. 1-2 (April 8, 2009): 335–49. http://dx.doi.org/10.1007/s11104-009-9984-2.

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3

Sagan, Vasit, Maitiniyazi Maimaitijiang, Paheding Sidike, Kevin Eblimit, Kyle Peterson, Sean Hartling, Flavio Esposito, et al. "UAV-Based High Resolution Thermal Imaging for Vegetation Monitoring, and Plant Phenotyping Using ICI 8640 P, FLIR Vue Pro R 640, and thermoMap Cameras." Remote Sensing 11, no. 3 (February 7, 2019): 330. http://dx.doi.org/10.3390/rs11030330.

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The growing popularity of Unmanned Aerial Vehicles (UAVs) in recent years, along with decreased cost and greater accessibility of both UAVs and thermal imaging sensors, has led to the widespread use of this technology, especially for precision agriculture and plant phenotyping. There are several thermal camera systems in the market that are available at a low cost. However, their efficacy and accuracy in various applications has not been tested. In this study, three commercially available UAV thermal cameras, including ICI 8640 P-series (Infrared Cameras Inc., USA), FLIR Vue Pro R 640 (FLIR Systems, USA), and thermoMap (senseFly, Switzerland) have been tested and evaluated for their potential for forest monitoring, vegetation stress detection, and plant phenotyping. Mounted on multi-rotor or fixed wing systems, these cameras were simultaneously flown over different experimental sites located in St. Louis, Missouri (forest environment), Columbia, Missouri (plant stress detection and phenotyping), and Maricopa, Arizona (high throughput phenotyping). Thermal imagery was calibrated using procedures that utilize a blackbody, handheld thermal spot imager, ground thermal targets, emissivityand atmospheric correction. A suite of statistical analyses, including analysis of variance (ANOVA), correlation analysis between camera temperature and plant biophysical and biochemical traits, and heritability were utilized in order to examine the sensitivity and utility of the cameras against selected plant phenotypic traits and in the detection of plant water stress. In addition, in reference to quantitative assessment of image quality from different thermal cameras, a non-reference image quality evaluator, which primarily measures image focus that is based on the spatial relationship of pixels in different scales, was developed. Our results show that (1) UAV-based thermal imaging is a viable tool in precision agriculture and (2) the three examined cameras are comparable in terms of their efficacy for plant phenotyping. Overall, accuracy, when compared against field measured ground temperature and estimating power of plant biophysical and biochemical traits, the ICI 8640 P-series performed better than the other two cameras, followed by FLIR Vue Pro R 640 and thermoMap cameras. Our results demonstrated that all three UAV thermal cameras provide useful temperature data for precision agriculture and plant phenotying, with ICI 8640 P-series presenting the best results among the three systems. Cost wise, FLIR Vue Pro R 640 is more affordable than the other two cameras, providing a less expensive option for a wide range of applications.

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4

Genkel, Vadim, Ilya Dolgushin, Irina Baturina, Albina Savochkina, Alla Kuznetsova, Lubov Pykhova, and Igor Shaposhnik. "Associations between Hypertriglyceridemia and Circulating Neutrophil Subpopulation in Patients with Dyslipidemia." International Journal of Inflammation 2021 (May 26, 2021): 1–8. http://dx.doi.org/10.1155/2021/6695468.

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Background. There is strong evidence to suggest that the negative influence of triglyceride-rich lipoproteins (TRLs) on atherosclerosis development and progression is at least partially mediated by their proinflammatory effects. However, the effect of hypertriglyceridemia (HTG) on the subpopulation composition of circulating neutrophils has not been studied so far. The aim of this study was to examine correlations between the level of triglycerides (TGs) and the subpopulation composition of circulating neutrophils in middle-aged patients with dyslipidemia without established atherosclerotic cardiovascular diseases (ASCVDs). Methods. Ninety-one patients with dyslipidemia, including 22 (24.2%) patients with HTG, were enrolled in the study. Phenotying of neutrophil subpopulations was performed through flow cytometry (Navios 6/2, Beckman Coulter, USA). For phenotyping of neutrophil subpopulations, conjugated monoclonal antibodies were used: CD16, PE-Cyanine7 (Invitrogen, USA); CD11b-FITC (Beckman Coulter, USA); CD62L-PE (Beckman Coulter, USA); and CD184 (CXCR4)-PE-CF594 (BD Biosciences, USA). Results. Following the correlation analysis, the TG level directly correlated with the number of circulating leukocytes (r = 0.443; p < 0.0001 ) and neutrophils (r = 0.311; p = 0.008 ). HTG patients displayed a significantly high number of circulating neutrophils with CD16hiCD11bhiCD62Lhi and CD16hiCD11bloCD62Lbr phenotypes. TG levels directly correlated with the number of circulating neutrophils having CD16hiCD11bhiCD62Lhi and CD16hiCD11bloCD62Lbr phenotypes. Following the linear regression analysis, statistically significant correlations between TG levels and neutrophil subpopulations having CD16hiCD11bloCD62Lbr and CD16hiCD11bbrCD62LloCXCR4hi phenotypes were established. Changes in TG levels could explain up to 19.1% of the variability in the number of studied neutrophil subpopulations. Conclusion. Among middle-aged patients without established ASCVDs, patients with HTG demonstrated a significantly higher overall number of neutrophils and neutrophils having CD16hiCD11bhiCD62Lhi (mature neutrophils) and CD16hiCD11bloCD62Lbr (immunosuppressive neutrophils) than patients with normal TG levels. The TG level was associated with an increase in the number of CD16hiCD11bloCD62Lbr and CD16hiCD11bbrCD62LloCXCR4hi (ageing neutrophils) neutrophils, adjusted for the sex and age of the patients.

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5

Washko, George R. "Chest Computed Tomography for Phenotying Chronic Obstructive Pulmonary Disease. A Pathway and a Challenge for Personalized Medicine." Annals of the American Thoracic Society 12, no. 7 (July 2015): 966–67. http://dx.doi.org/10.1513/annalsats.201504-239ed.

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6

Helali, Asadul Mazid. "Pharmacogenetics and polymorphism: future tools for optimizing therapeutic efficacy." Bangladesh Journal of Physiology and Pharmacology 26, no. 1-2 (August 12, 2014): 34–42. http://dx.doi.org/10.3329/bjpp.v26i1-2.19966.

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Individual variation in drug response is a major problem in clinical practice and drug development, which ranges from therapeutic failure to adverse drug reaction as well as drug interaction in multidrug therapy. Pharmacogenetics is relevant in this aspects and mainly concern with the study of genetic variation, which influence individuals responses to drug. Again, polymorphism (variation) in the genes which are involved in encoding drug metabolizing enzymes, transporters of drug and ion channels can play role in the adverse drug reaction in an individual or can interfere with the therapeutic efficacy. By studying pharmacogenetics one can apply genotyping of polymorphic alleles that encodes drug metabolizing enzymes to identify individuals drug metabolism phenotype and correlating this knowledge to dosing or drug selection, avoidance of many adverse effect of drug and therapeutic failure is possible as well as economic burden to the patient will be reduced. It is true that one drug will not effective for everyone and everyone will not response to a single drug in a similar fashion. It is almost impossible to test every drug in whole population in respect of investment and time. From this point of view, pharmacogenetic screening, such as phenotying test can be useful to identify patients who have inherent risk factor for a specific adverse drug reaction. Recently pharmacogenetic testing is performed for only a few drugs e.g. mercaptopurine, thioguanine, azathriopine and tacrine and the facility is also available in a limited number of teaching hospital but the days are not so far, when it may be considered as unethical if pharmacogenetics test is not done routinely before prescribing a drug to a patient. http://dx.doi.org/10.3329/bjpp.v26i1-2.19966 Bangladesh J Physiol Pharmacol 2010; 26(1&2) : 34-42

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7

ÁLVAREZ DE NEYRA KAPPLER, Susana. "EL FENOTIPADO FORENSE." IUS ET SCIENTIA 2, no. 4 (2018): 63–86. http://dx.doi.org/10.12795/iestscientia.2018.i02.05.

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8

Delage, Clément, Léa Darnaud, Bruno Etain, Marina Vignes, Tu-Ky Ly, Alexia Frapsauce, Marc Veyrier, et al. "Cytochromes P450 and P-Glycoprotein Phenotypic Assessment to Optimize Psychotropic Pharmacotherapy: A Retrospective Analysis of Four Years of Practice in Psychiatry." Journal of Personalized Medicine 12, no. 11 (November 8, 2022): 1869. http://dx.doi.org/10.3390/jpm12111869.

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Altered cytochromes P450 enzymes (CYP) and P-glycoprotein transporter (P-gp) activity may explain variabilities in drug response. In this study, we analyzed four years of phenotypic assessments of CYP/P-gp activities to optimize pharmacotherapy in psychiatry. A low-dose probe cocktail was administered to evaluate CYP1A2, 2B6, 2D6, 2C9, 2C19, 3A4, and P-gp activities using the probe/metabolite concentration ratio in blood or the AUC. A therapeutic adjustment was suggested depending on the phenotyping results. From January 2017 to June 2021, we performed 32 phenotypings, 10 for adverse drug reaction, 6 for non-response, and 16 for both reasons. Depending on the CYP/P-gp evaluated, only 23% to 56% of patients had normal activity. Activity was decreased in up to 57% and increased in up to 60% of cases, depending on the CYP/P-gp evaluated. In 11/32 cases (34%), the therapeutic problem was attributable to the patient’s metabolic profile. In 10/32 cases (31%), phenotyping excluded the metabolic profile as the cause of the therapeutic problem. For all ten individuals for which we had follow-up information, phenotyping allowed us to clearly state or clearly exclude the metabolic profile as a possible cause of therapeutic failure. Among them, seven showed a clinical improvement after dosage adaptation, or drug or pharmacological class switching. Our study confirmed the interest of CYP and P-gp phenotyping for therapeutic optimization in psychiatry.

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9

Sarkar, Sayantan, Joseph Oakes, Alexandre-Brice Cazenave, Mark D. Burow, Rebecca S. Bennett, Kelly D. Chamberlin, Ning Wang, et al. "Evaluation of the U.S. Peanut Germplasm Mini-Core Collection in the Virginia-Carolina Region Using Traditional and New High-Throughput Methods." Agronomy 12, no. 8 (August 18, 2022): 1945. http://dx.doi.org/10.3390/agronomy12081945.

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Peanut (Arachis hypogaea L.) is an important food crop for the U.S. and the world. The Virginia-Carolina (VC) region (Virginia, North Carolina, and South Carolina) is an important peanut-growing region of the U.S and is affected by numerous biotic and abiotic stresses. Identification of stress-resistant germplasm, along with improved phenotyping methods, are important steps toward developing improved cultivars. Our objective in 2017 and 2018 was to assess the U.S. mini-core collection for desirable traits, a valuable source for resistant germplasm under limited water conditions. Accessions were evaluated using traditional and high-throughput phenotyping (HTP) techniques, and the suitability of HTP methods as indirect selection tools was assessed. Traditional phenotyping methods included stand count, plant height, lateral branch growth, normalized difference vegetation index (NDVI), canopy temperature depression (CTD), leaf wilting, fungal and viral disease, thrips rating, post-digging in-shell sprouting, and pod yield. The HTP method included 48 aerial vegetation indices (VIs), which were derived using red, blue, green, and near-infrared reflectance; color space indices were collected using an octocopter drone at the same time, with traditional phenotyping. Both phenotypings were done 10 times between 4 and 16 weeks after planting. Accessions had yields comparable to high yielding checks. Correlation coefficients up to 0.8 were identified for several Vis, with yield indicating their suitability for indirect phenotyping. Broad-sense heritability (H2) was further calculated to assess the suitability of particular VIs to enable genetic gains. VIs could be used successfully as surrogates for the physiological and agronomic trait selection in peanuts. Further, this study indicates that UAV-based sensors have potential for measuring physiologic and agronomic characteristics measured for peanut breeding, variable rate input application, real time decision making, and precision agriculture applications.

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10

James, Paula, and Barry S. Coller. "Phenotyping bleeding." Current Opinion in Hematology 19, no. 5 (September 2012): 406–12. http://dx.doi.org/10.1097/moh.0b013e32835673ab.

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11

Dijk, Derk-Jan. "Phenotyping sleep." Journal of Sleep Research 20, no. 4 (November 14, 2011): 495. http://dx.doi.org/10.1111/j.1365-2869.2011.00980.x.

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12

McGue, Matt. "Phenotyping Alcoholism." Alcoholism: Clinical and Experimental Research 23, no. 5 (May 1999): 757–58. http://dx.doi.org/10.1111/j.1530-0277.1999.tb04180.x.

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13

Fuchs, Helmut, Valérie Gailus-Durner, Thure Adler, Juan Antonio Aguilar-Pimentel, Lore Becker, Julia Calzada-Wack, Patricia Da Silva-Buttkus, et al. "Mouse phenotyping." Methods 53, no. 2 (February 2011): 120–35. http://dx.doi.org/10.1016/j.ymeth.2010.08.006.

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14

Cribb, Alastair E., Richard Isbrucker, Terry Levatte, Ban Tsui, C. T. Gillespie, and Kenneth W. Renton. "Acetylator phenotyping." Pharmacogenetics 4, no. 3 (June 1994): 166–70. http://dx.doi.org/10.1097/00008571-199406000-00009.

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15

Dorn, Gerald W., Jeffrey Robbins, and Peter H. Sugden. "Phenotyping Hypertrophy." Circulation Research 92, no. 11 (June 13, 2003): 1171–75. http://dx.doi.org/10.1161/01.res.0000077012.11088.bc.

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16

Vasnev, Andrey, Yuri Maslennikov, Mikhail Primin, Igor Nedayvoda, Oksana Sitnikova, Andrey Nogovitsin, and Yuri Gulyaev. "Magnetocardiographic phenotyping." Journal of Electrocardiology 44, no. 2 (March 2011): e37-e38. http://dx.doi.org/10.1016/j.jelectrocard.2010.12.102.

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17

Insel, Thomas R. "Digital Phenotyping." JAMA 318, no. 13 (October 3, 2017): 1215. http://dx.doi.org/10.1001/jama.2017.11295.

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18

Amarenco, P., J. Bogousslavsky, L. R. Caplan, G. A. Donnan, M. E. Wolf, and M. G. Hennerici. "The ASCOD Phenotyping of Ischemic Stroke (Updated ASCO Phenotyping)." Cerebrovascular Diseases 36, no. 1 (2013): 1–5. http://dx.doi.org/10.1159/000352050.

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19

Huang, Yixiang, Pengcheng Xia, Liang Gong, Binhao Chen, Yanming Li, and Chengliang Liu. "Designing an Interactively Cognitive Humanoid Field-Phenotyping Robot for In-Field Rice Tiller Counting." Agriculture 12, no. 11 (November 21, 2022): 1966. http://dx.doi.org/10.3390/agriculture12111966.

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Field phenotyping is a crucial process in crop breeding, and traditional manual phenotyping is labor-intensive and time-consuming. Therefore, many automatic high-throughput phenotyping platforms (HTPPs) have been studied. However, existing automatic phenotyping methods encounter occlusion problems in fields. This paper presents a new in-field interactive cognition phenotyping paradigm. An active interactive cognition method is proposed to remove occlusion and overlap for better detectable quasi-structured environment construction with a field phenotyping robot. First, a humanoid robot equipped with image acquiring sensory devices is designed to contain an intuitive remote control for field phenotyping manipulations. Second, a bio-inspired solution is introduced to allow the phenotyping robot to mimic the manual phenotyping operations. In this way, automatic high-throughput phenotyping of the full growth period is realized and a large volume of tiller counting data is availed. Third, an attentional residual network (AtResNet) is proposed for rice tiller number recognition. The in-field experiment shows that the proposed method achieves approximately 95% recognition accuracy with the interactive cognition phenotyping platform. This paper opens new possibilities to solve the common technical problems of occlusion and observation pose in field phenotyping.

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20

Sahoo, Rabi N., C. Viswanathan, Gopal Krishna, Bappa Das, Swati Goel, Raju Dhandapani, Sudhir Kumar, Chandrapal Viswakarma, P. Swain, and SK Dash. "Next generation phenotyping for developing climate resilient rice varieties." Oryza-An International Journal on Rice 56, Special Issue (May 29, 2019): 92–105. http://dx.doi.org/10.35709/ory.2019.56.s.2.

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Present paper deals with different components of next generation phenomics for characterizing rice genotypes for water deficit stress. Major sensors used in the study were non-imaging hyperspectal remote sensing, thermal imaging at ground platform and RGB and multispectral imaging sensors from drone platform. Different spectral indices were evaluated along with new proposed index and different multivariate models were studied for non-invasive estimation of relative water content (RWC) and sugar content in rice plant using spectral reflectance data collected in spectral range 350 to 2500 nm. Spectral data were further used for spectral discrimination of rice genotypes. Crop water stress index derived from thermal images acquired for rice genotypes could well characterize the drought resistant and sensitive genotypes. Initial study on field phenotyping through drone remote sensing using multispectral and RGB sensor was also explored to capture differential response of genotypes, trait and heat map mapping. All developed protocols as reliable alternative to conventional methods are fast, economic and non-invasive and in use in plant phenomics centre for high throughput plant phenotyhping for water deficit stress studies.

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21

Sahoo, Rabi N., C. Viswanathan, Gopal Krishna, Bappa Das, Swati Goel, Raju Dhandapani Dhandapani, Sudhir Kumar, Chandrapal Viswakarma, P. Swain, and SK Dash. "Next generation phenotyping for developing climate resilient rice varieties." Oryza-An International Journal on Rice 56, Special (May 29, 2019): 92–105. http://dx.doi.org/10.35709/ory.2019.56.spl.2.

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Анотація:

Present paper deals with different components of next generation phenomics for characterizing rice genotypes for water deficit stress. Major sensors used in the study were non-imaging hyperspectal remote sensing, thermal imaging at ground platform and RGB and multispectral imaging sensors from drone platform. Different spectral indices were evaluated along with new proposed index and different multivariate models were studied for non-invasive estimation of relative water content (RWC) and sugar content in rice plant using spectral reflectance data collected in spectral range 350 to 2500 nm. Spectral data were further used for spectral discrimination of rice genotypes. Crop water stress index derived from thermal images acquired for rice genotypes could well characterize the drought resistant and sensitive genotypes. Initial study on field phenotyping through drone remote sensing using multispectral and RGB sensor was also explored to capture differential response of genotypes, trait and heat map mapping. All developed protocols as reliable alternative to conventional methods are fast, economic and non-invasive and in use in plant phenomics centre for high throughput plant phenotyhping for water deficit stress studies.

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22

Papageorghiou, Aris T. "Phenotyping pregnancy outcomes." BJOG: An International Journal of Obstetrics & Gynaecology 128, no. 7 (May 7, 2021): 1105–6. http://dx.doi.org/10.1111/1471-0528.16721.

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23

Gehan, Malia A., and Elizabeth A. Kellogg. "High-throughput phenotyping." American Journal of Botany 104, no. 4 (April 2017): 505–8. http://dx.doi.org/10.3732/ajb.1700044.

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24

Naeije, Robert, and Sergio Caravita. "Phenotyping long COVID." European Respiratory Journal 58, no. 2 (July 8, 2021): 2101763. http://dx.doi.org/10.1183/13993003.01763-2021.

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25

Silverstonea, Allen E., and Yvon Cayreb. "Phenotyping the prothymocyte." Survey of Immunologic Research 4, no. 1 (March 1985): 11–18. http://dx.doi.org/10.1007/bf02918581.

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26

de Souza, Natalie. "High-throughput phenotyping." Nature Methods 7, no. 1 (December 21, 2009): 36. http://dx.doi.org/10.1038/nmeth.f.289.

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27

Uray, Thomas, Andrew Lamade, Jonathan Elmer, Tomas Drabek, Jason P. Stezoski, Amalea Missé, Keri Janesko-Feldman, et al. "Phenotyping Cardiac Arrest." Critical Care Medicine 46, no. 6 (June 2018): e508-e515. http://dx.doi.org/10.1097/ccm.0000000000003070.

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28

Schiattarella, Gabriele G., Dan Tong, and Joseph A. Hill. "Ventricular Phenotyping Reviews." Circulation 138, no. 8 (August 21, 2018): 749–50. http://dx.doi.org/10.1161/circulationaha.118.036938.

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29

Hammond, Peter. "3D facial phenotyping." Molecular Cytogenetics 7, Suppl 1 (2014): I2. http://dx.doi.org/10.1186/1755-8166-7-s1-i2.

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30

Reynolds, Matthew, Scott Chapman, Leonardo Crespo-Herrera, Gemma Molero, Suchismita Mondal, Diego N. L. Pequeno, Francisco Pinto, et al. "Breeder friendly phenotyping." Plant Science 295 (June 2020): 110396. http://dx.doi.org/10.1016/j.plantsci.2019.110396.

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31

Struijker-Boudier, Harry A. J., Bart F. J. Heijnen, Yan-Ping Liu, and Jan A. Staessen. "Phenotyping the Microcirculation." Hypertension 60, no. 2 (August 2012): 523–27. http://dx.doi.org/10.1161/hypertensionaha.111.188482.

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32

Landau, Ruth, and Debra Schwinn. "Genotyping Without Phenotyping." Anesthesia & Analgesia 116, no. 1 (January 2013): 8–10. http://dx.doi.org/10.1213/ane.0b013e318275355a.

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33

Steege, John F. "Pelvic Pain Phenotyping." Obstetrics & Gynecology 113, no. 2, Part 1 (February 2009): 258–59. http://dx.doi.org/10.1097/aog.0b013e3181969bcd.

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34

Engelmann, Lukas, and Ger Wackers. "Digital phenotyping – Editorial." Big Data & Society 9, no. 2 (July 2022): 205395172211137. http://dx.doi.org/10.1177/20539517221113775.

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35

Ubbens, Jordan, Mikolaj Cieslak, Przemyslaw Prusinkiewicz, Isobel Parkin, Jana Ebersbach, and Ian Stavness. "Latent Space Phenotyping: Automatic Image-Based Phenotyping for Treatment Studies." Plant Phenomics 2020 (January 20, 2020): 1–13. http://dx.doi.org/10.34133/2020/5801869.

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Анотація:

Association mapping studies have enabled researchers to identify candidate loci for many important environmental tolerance factors, including agronomically relevant tolerance traits in plants. However, traditional genome-by-environment studies such as these require a phenotyping pipeline which is capable of accurately measuring stress responses, typically in an automated high-throughput context using image processing. In this work, we present Latent Space Phenotyping (LSP), a novel phenotyping method which is able to automatically detect and quantify response-to-treatment directly from images. We demonstrate example applications using data from an interspecific cross of the model C4 grass Setaria, a diversity panel of sorghum (S. bicolor), and the founder panel for a nested association mapping population of canola (Brassica napus L.). Using two synthetically generated image datasets, we then show that LSP is able to successfully recover the simulated QTL in both simple and complex synthetic imagery. We propose LSP as an alternative to traditional image analysis methods for phenotyping, enabling the phenotyping of arbitrary and potentially complex response traits without the need for engineering-complicated image-processing pipelines.

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36

Laughlin, Maren R., K. C. Kent Lloyd, Gary W. Cline, and David H. Wasserman. "NIH Mouse Metabolic Phenotyping Centers: the power of centralized phenotyping." Mammalian Genome 23, no. 9-10 (September 1, 2012): 623–31. http://dx.doi.org/10.1007/s00335-012-9425-z.

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37

Kagawa, Rina, Yoshimasa Kawazoe, Yusuke Ida, Emiko Shinohara, Katsuya Tanaka, Takeshi Imai, and Kazuhiko Ohe. "Development of Type 2 Diabetes Mellitus Phenotyping Framework Using Expert Knowledge and Machine Learning Approach." Journal of Diabetes Science and Technology 11, no. 4 (December 7, 2016): 791–99. http://dx.doi.org/10.1177/1932296816681584.

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Background: Phenotyping is an automated technique that can be used to distinguish patients based on electronic health records. To improve the quality of medical care and advance type 2 diabetes mellitus (T2DM) research, the demand for T2DM phenotyping has been increasing. Some existing phenotyping algorithms are not sufficiently accurate for screening or identifying clinical research subjects. Objective: We propose a practical phenotyping framework using both expert knowledge and a machine learning approach to develop 2 phenotyping algorithms: one is for screening; the other is for identifying research subjects. Methods: We employ expert knowledge as rules to exclude obvious control patients and machine learning to increase accuracy for complicated patients. We developed phenotyping algorithms on the basis of our framework and performed binary classification to determine whether a patient has T2DM. To facilitate development of practical phenotyping algorithms, this study introduces new evaluation metrics: area under the precision-sensitivity curve (AUPS) with a high sensitivity and AUPS with a high positive predictive value. Results: The proposed phenotyping algorithms based on our framework show higher performance than baseline algorithms. Our proposed framework can be used to develop 2 types of phenotyping algorithms depending on the tuning approach: one for screening, the other for identifying research subjects. Conclusions: We develop a novel phenotyping framework that can be easily implemented on the basis of proper evaluation metrics, which are in accordance with users’ objectives. The phenotyping algorithms based on our framework are useful for extraction of T2DM patients in retrospective studies.

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Wang, Yongjian, Weiliang Wen, Sheng Wu, Chuanyu Wang, Zetao Yu, Xinyu Guo, and Chunjiang Zhao. "Maize Plant Phenotyping: Comparing 3D Laser Scanning, Multi-View Stereo Reconstruction, and 3D Digitizing Estimates." Remote Sensing 11, no. 1 (December 31, 2018): 63. http://dx.doi.org/10.3390/rs11010063.

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High-throughput phenotyping technologies have become an increasingly important topic of crop science in recent years. Various sensors and data acquisition approaches have been applied to acquire the phenotyping traits. It is quite confusing for crop phenotyping researchers to determine an appropriate way for their application. In this study, three representative three-dimensional (3D) data acquisition approaches, including 3D laser scanning, multi-view stereo (MVS) reconstruction, and 3D digitizing, were evaluated for maize plant phenotyping in multi growth stages. Phenotyping traits accuracy, post-processing difficulty, device cost, data acquisition efficiency, and automation were considered during the evaluation process. 3D scanning provided satisfactory point clouds for medium and high maize plants with acceptable efficiency, while the results were not satisfactory for small maize plants. The equipment used in 3D scanning is expensive, but is highly automatic. MVS reconstruction provided satisfactory point clouds for small and medium plants, and point deviations were observed in upper parts of higher plants. MVS data acquisition, using low-cost cameras, exhibited the highest efficiency among the three evaluated approaches. The one-by-one pipeline data acquisition pattern allows the use of MVS high-throughput in further phenotyping platforms. Undoubtedly, enhancement of point cloud processing technologies is required to improve the extracted phenotyping traits accuracy for both 3D scanning and MVS reconstruction. Finally, 3D digitizing was time-consuming and labor intensive. However, it does not depend on any post-processing algorithms to extract phenotyping parameters and reliable phenotyping traits could be derived. The promising accuracy of 3D digitizing is a better verification choice for other 3D phenotyping approaches. Our study provides clear reference about phenotyping data acquisition of maize plants, especially for the affordable and portable field phenotyping platforms to be developed.

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Chawade, Aakash, Joost van Ham, Hanna Blomquist, Oscar Bagge, Erik Alexandersson, and Rodomiro Ortiz. "High-Throughput Field-Phenotyping Tools for Plant Breeding and Precision Agriculture." Agronomy 9, no. 5 (May 22, 2019): 258. http://dx.doi.org/10.3390/agronomy9050258.

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High-throughput field phenotyping has garnered major attention in recent years leading to the development of several new protocols for recording various plant traits of interest. Phenotyping of plants for breeding and for precision agriculture have different requirements due to different sizes of the plots and fields, differing purposes and the urgency of the action required after phenotyping. While in plant breeding phenotyping is done on several thousand small plots mainly to evaluate them for various traits, in plant cultivation, phenotyping is done in large fields to detect the occurrence of plant stresses and weeds at an early stage. The aim of this review is to highlight how various high-throughput phenotyping methods are used for plant breeding and farming and the key differences in the applications of such methods. Thus, various techniques for plant phenotyping are presented together with applications of these techniques for breeding and cultivation. Several examples from the literature using these techniques are summarized and the key technical aspects are highlighted.

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Watt, Michelle, Fabio Fiorani, Björn Usadel, Uwe Rascher, Onno Muller, and Ulrich Schurr. "Phenotyping: New Windows into the Plant for Breeders." Annual Review of Plant Biology 71, no. 1 (April 29, 2020): 689–712. http://dx.doi.org/10.1146/annurev-arplant-042916-041124.

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Plant phenotyping enables noninvasive quantification of plant structure and function and interactions with environments. High-capacity phenotyping reaches hitherto inaccessible phenotypic characteristics. Diverse, challenging, and valuable applications of phenotyping have originated among scientists, prebreeders, and breeders as they study the phenotypic diversity of genetic resources and apply increasingly complex traits to crop improvement. Noninvasive technologies are used to analyze experimental and breeding populations. We cover the most recent research in controlled-environment and field phenotyping for seed, shoot, and root traits. Select field phenotyping technologies have become state of the art and show promise for speeding up the breeding process in early generations. We highlight the technologies behind the rapid advances in proximal and remote sensing of plants in fields. We conclude by discussing the new disciplines working with the phenotyping community: data science, to address the challenge of generating FAIR (findable, accessible, interoperable, and reusable) data, and robotics, to apply phenotyping directly on farms.

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Xu, Rui, and Changying Li. "A Review of High-Throughput Field Phenotyping Systems: Focusing on Ground Robots." Plant Phenomics 2022 (June 17, 2022): 1–20. http://dx.doi.org/10.34133/2022/9760269.

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Manual assessments of plant phenotypes in the field can be labor-intensive and inefficient. The high-throughput field phenotyping systems and in particular robotic systems play an important role to automate data collection and to measure novel and fine-scale phenotypic traits that were previously unattainable by humans. The main goal of this paper is to review the state-of-the-art of high-throughput field phenotyping systems with a focus on autonomous ground robotic systems. This paper first provides a brief review of nonautonomous ground phenotyping systems including tractors, manually pushed or motorized carts, gantries, and cable-driven systems. Then, a detailed review of autonomous ground phenotyping robots is provided with regard to the robot’s main components, including mobile platforms, sensors, manipulators, computing units, and software. It also reviews the navigation algorithms and simulation tools developed for phenotyping robots and the applications of phenotyping robots in measuring plant phenotypic traits and collecting phenotyping datasets. At the end of the review, this paper discusses current major challenges and future research directions.

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Guo, Wei, Matthew E. Carroll, Arti Singh, Tyson L. Swetnam, Nirav Merchant, Soumik Sarkar, Asheesh K. Singh, and Baskar Ganapathysubramanian. "UAS-Based Plant Phenotyping for Research and Breeding Applications." Plant Phenomics 2021 (June 10, 2021): 1–21. http://dx.doi.org/10.34133/2021/9840192.

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Unmanned aircraft system (UAS) is a particularly powerful tool for plant phenotyping, due to reasonable cost of procurement and deployment, ease and flexibility for control and operation, ability to reconfigure sensor payloads to diversify sensing, and the ability to seamlessly fit into a larger connected phenotyping network. These advantages have expanded the use of UAS-based plant phenotyping approach in research and breeding applications. This paper reviews the state of the art in the deployment, collection, curation, storage, and analysis of data from UAS-based phenotyping platforms. We discuss pressing technical challenges, identify future trends in UAS-based phenotyping that the plant research community should be aware of, and pinpoint key plant science and agronomic questions that can be resolved with the next generation of UAS-based imaging modalities and associated data analysis pipelines. This review provides a broad account of the state of the art in UAS-based phenotyping to reduce the barrier to entry to plant science practitioners interested in deploying this imaging modality for phenotyping in plant breeding and research areas.

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Kyryk, V. M. "PHENOTYPING AND SORTING OF MURINE BONE MARROW HAEMATOPOIETIC STEM CELLS USING FLOW CYTOMETRY." Biotechnologia acta 7, no. 6 (2014): 51–56. http://dx.doi.org/10.15407/biotech7.06.051.

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Yao, Lili, Rick van de Zedde, and George Kowalchuk. "Recent developments and potential of robotics in plant eco-phenotyping." Emerging Topics in Life Sciences 5, no. 2 (May 20, 2021): 289–300. http://dx.doi.org/10.1042/etls20200275.

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Automated acquisition of plant eco-phenotypic information can serve as a decision-making basis for precision agricultural management and can also provide detailed insights into plant growth status, pest management, water and fertilizer management for plant breeders and plant physiologists. Because the microscopic components and macroscopic morphology of plants will be affected by the ecological environment, research on plant eco-phenotyping is more meaningful than the study of single-plant phenotyping. To achieve high-throughput acquisition of phenotyping information, the combination of high-precision sensors and intelligent robotic platforms have become an emerging research focus. Robotic platforms and automated systems are the important carriers of phenotyping monitoring sensors that enable large-scale screening. Through the diverse design and flexible systems, an efficient operation can be achieved across a range of experimental and field platforms. The combination of robot technology and plant phenotyping monitoring tools provides the data to inform novel artificial intelligence (AI) approaches that will provide steppingstones for new research breakthroughs. Therefore, this article introduces robotics and eco-phenotyping and examines research significant to this novel domain of plant eco-phenotyping. Given the monitoring scenarios of phenotyping information at different scales, the used intelligent robot technology, efficient automation platform, and advanced sensor equipment are summarized in detail. We further discuss the challenges posed to current research as well as the future developmental trends in the application of robot technology and plant eco-phenotyping. These include the use of collected data for AI applications and high-bandwidth data transfer, and large well-structured (meta) data storage approaches in plant sciences and agriculture.

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Vit, Adar, and Guy Shani. "Comparing RGB-D Sensors for Close Range Outdoor Agricultural Phenotyping." Sensors 18, no. 12 (December 13, 2018): 4413. http://dx.doi.org/10.3390/s18124413.

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Phenotyping is the task of measuring plant attributes for analyzing the current state of the plant. In agriculture, phenotyping can be used to make decisions concerning the management of crops, such as the watering policy, or whether to spray for a certain pest. Currently, large scale phenotyping in fields is typically done using manual labor, which is a costly, low throughput process. Researchers often advocate the use of automated systems for phenotyping, relying on the use of sensors for making measurements. The recent rise of low cost, yet reasonably accurate, RGB-D sensors has opened the way for using these sensors in field phenotyping applications. In this paper, we investigate the applicability of four different RGB-D sensors for this task. We conduct an outdoor experiment, measuring plant attribute in various distances and light conditions. Our results show that modern RGB-D sensors, in particular, the Intel D435 sensor, provides a viable tool for close range phenotyping tasks in fields.

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Jiang, Yu, and Changying Li. "Convolutional Neural Networks for Image-Based High-Throughput Plant Phenotyping: A Review." Plant Phenomics 2020 (April 9, 2020): 1–22. http://dx.doi.org/10.34133/2020/4152816.

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Plant phenotyping has been recognized as a bottleneck for improving the efficiency of breeding programs, understanding plant-environment interactions, and managing agricultural systems. In the past five years, imaging approaches have shown great potential for high-throughput plant phenotyping, resulting in more attention paid to imaging-based plant phenotyping. With this increased amount of image data, it has become urgent to develop robust analytical tools that can extract phenotypic traits accurately and rapidly. The goal of this review is to provide a comprehensive overview of the latest studies using deep convolutional neural networks (CNNs) in plant phenotyping applications. We specifically review the use of various CNN architecture for plant stress evaluation, plant development, and postharvest quality assessment. We systematically organize the studies based on technical developments resulting from imaging classification, object detection, and image segmentation, thereby identifying state-of-the-art solutions for certain phenotyping applications. Finally, we provide several directions for future research in the use of CNN architecture for plant phenotyping purposes.

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Martinez-Martin, Nicole, Henry T. Greely, and Mildred K. Cho. "Ethical Development of Digital Phenotyping Tools for Mental Health Applications: Delphi Study." JMIR mHealth and uHealth 9, no. 7 (July 28, 2021): e27343. http://dx.doi.org/10.2196/27343.

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Background Digital phenotyping (also known as personal sensing, intelligent sensing, or body computing) involves the collection of biometric and personal data in situ from digital devices, such as smartphones, wearables, or social media, to measure behavior or other health indicators. The collected data are analyzed to generate moment-by-moment quantification of a person’s mental state and potentially predict future mental states. Digital phenotyping projects incorporate data from multiple sources, such as electronic health records, biometric scans, or genetic testing. As digital phenotyping tools can be used to study and predict behavior, they are of increasing interest for a range of consumer, government, and health care applications. In clinical care, digital phenotyping is expected to improve mental health diagnoses and treatment. At the same time, mental health applications of digital phenotyping present significant areas of ethical concern, particularly in terms of privacy and data protection, consent, bias, and accountability. Objective This study aims to develop consensus statements regarding key areas of ethical guidance for mental health applications of digital phenotyping in the United States. Methods We used a modified Delphi technique to identify the emerging ethical challenges posed by digital phenotyping for mental health applications and to formulate guidance for addressing these challenges. Experts in digital phenotyping, data science, mental health, law, and ethics participated as panelists in the study. The panel arrived at consensus recommendations through an iterative process involving interviews and surveys. The panelists focused primarily on clinical applications for digital phenotyping for mental health but also included recommendations regarding transparency and data protection to address potential areas of misuse of digital phenotyping data outside of the health care domain. Results The findings of this study showed strong agreement related to these ethical issues in the development of mental health applications of digital phenotyping: privacy, transparency, consent, accountability, and fairness. Consensus regarding the recommendation statements was strongest when the guidance was stated broadly enough to accommodate a range of potential applications. The privacy and data protection issues that the Delphi participants found particularly critical to address related to the perceived inadequacies of current regulations and frameworks for protecting sensitive personal information and the potential for sale and analysis of personal data outside of health systems. Conclusions The Delphi study found agreement on a number of ethical issues to prioritize in the development of digital phenotyping for mental health applications. The Delphi consensus statements identified general recommendations and principles regarding the ethical application of digital phenotyping to mental health. As digital phenotyping for mental health is implemented in clinical care, there remains a need for empirical research and consultation with relevant stakeholders to further understand and address relevant ethical issues.

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Montag, Christian, Paul Dagum, and Jon D. Elhai. "On the Need for Digital Phenotyping to Obtain Insights into Mental States in the COVID-19 Pandemic." Digital Psychology 1, no. 2 (October 27, 2020): 40–42. http://dx.doi.org/10.24989/dp.v1i2.1857.

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Highlights Digital phenotyping provides real-time insight into population mental health in a crisis such as COVID-19. Digital phenotyping empowers policy makers with population level information to help fight a pandemic like COVID-19. User privacy and informed consent is paramount in building trust with digital phenotyping.

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Gonzalez, Frank J., and Jeffrey R. Idle. "Pharmacogenetic Phenotyping and Genotyping." Clinical Pharmacokinetics 26, no. 1 (January 1994): 59–70. http://dx.doi.org/10.2165/00003088-199426010-00005.

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Orsolini, L. "Digital phenotyping in psychiatry." European Psychiatry 64, S1 (April 2021): S18. http://dx.doi.org/10.1192/j.eurpsy.2021.71.

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Digital phenotyping represents a new approach aimed at measuring the human behavior by using smartphones and personal device sensors, smartphone apps, keyboard interaction, and various features of subject’s voice and speech. Data collected by a digital phenotyping smartphone application are divided into two categories: a) active data (i.e., those usually collected by using a survey modality) which require an ‘active participation’ from the subject to be generated; and, b) passive data (for instance, those data collected by using Global Positioning System (GPS) traces), usually collected without any participation or action from the subject. Digital phenotyping may theoretically enhance clinicians’ ability to early identify, diagnose and manage any mental health conditions and favoured a more personalized diagnostic and therapeutic approach to several mental conditions. The innovative and insightful approach applied by the digital phenotyping appears to find an interesting and useful application in the field of psychiatry. The digital phenotyping is in line with the new paradigm of the precision psychiatry, i.e. the new approach performed to help clinicians in customizing a psychiatric treatment for each patient, by integrating information about individual phenotypes and genotypes with biographical, clinical and biological data. A precision psychiatry approach would ideally allow clinicians to tailor clinical decision-making and stratify patients to each available treatment according to each one’s likelihood of treatment response and prognosis. Our aims are at providing a comprehensive panorama on evidence-based applications of digital phenotyping in psychiatry.DisclosureNo significant relationships.

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