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Journal articles on the topic 'Expression and comparison'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 February 2022

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1

김충실. "Korean-Chinese sentence expression comparison." Bilingual Research ll, no. 51 (2013): 27–43. http://dx.doi.org/10.17296/korbil.2013..51.27.

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2

Geisse, Sabine, Hermann Gram, Beate Kleuser, and Hans P. Kocher. "Eukaryotic Expression Systems: A Comparison." Protein Expression and Purification 8, no. 3 (1996): 271–82. http://dx.doi.org/10.1006/prep.1996.0101.

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3

Irizarry, Rafael A., Zhijin Wu, and Harris A. Jaffee. "Comparison of Affymetrix GeneChip expression measures." Bioinformatics 22, no. 7 (2006): 789–94. http://dx.doi.org/10.1093/bioinformatics/btk046.

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4

Wishart, J., M. S. Phillips, A. Paterson, and V. C. Blok. "Comparison of gene expression in Solanum bulbocastanum infected with virulent and avirulent isolates of Meloidogyne chitwoodi." Plant Protection Science 38, SI 2 - 6th Conf EFPP 2002 (2017): 721–22. http://dx.doi.org/10.17221/10599-pps.

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Resistance to root knot nematode M. chitwoodi has been identified in the wild tuber-bearing Solanum species, S. bulbocastanum. Three pathotypes were identified suggesting at least two different genetic factors for virulence and resistance in the pathogen and the host species, respectively. Roots of S. bulbocastanum were infested with two isolates of M. chitwoodi differing in virulence. The infection process was monitored by histological examination of roots allowing time points to be identified. cDNA libraries were constructed from infected root tissue using Suppression Subtractive Hybridisation (SSH) to enrich for transcripts from either compatible or incompatible interactions, at three days and seven days post infection. Both plant and nematode genes, which may be important during the host/parasite interaction, were identified.

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Palejev, Dean. "Comparison of RNA-Seq Differential Expression Methods." Cybernetics and Information Technologies 17, no. 5 (2017): 60–67. http://dx.doi.org/10.1515/cait-2017-0055.

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Abstract There are many methods designed to find differentially expressed genes using RNA-seq data. Their outputs differ a lot, some genes are determined to be differentially expressed by most or all methods, and others - by very few or even by just one method. Here we derive a systematic approach to quantifying the proximity of such methods, allowing us also to discover patterns and to determine whether some of them are significantly different than others.

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Vavrová, Ľudmila, Katarína Muchová, and Imrich Barák. "Comparison of different Bacillus subtilis expression systems." Research in Microbiology 161, no. 9 (2010): 791–97. http://dx.doi.org/10.1016/j.resmic.2010.09.004.

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Neely, Gail J., Cherie A. Croak, Luci A. Kohn, and John Y. Cheung. "Comparison of Objective Measures of Facial Expression." Otology & Neurotology 23, Sup 1 (2002): S74—S75. http://dx.doi.org/10.1097/00129492-200200001-00193.

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8

Koutsoudaki, Mary. "Ο μύθος του Ορφέα στο θεατρικό έργο του Tennessee Williams". Σύγκριση 11 (31 січня 2017): 69. http://dx.doi.org/10.12681/comparison.10766.

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Τ Williams' use of Dionysicism is evident in his theater. A very interesting sample of this use can be found in Battle of Angels, Orpheus Descending, Camino Real and Suddenly Last Summer. These plays present various treatments of the ritual and the myth in the playwright's effort to give a universal meaning to the plights of modern man. The Orphic identity of Dionysus is predominant in Battle of Angels (1940), which was one of Williams' favorite plays. The use of Orphic Dionysicism expresses the playwright himself who believed that Battle of Angels was «coming directly from his heart as an expression of fundamental human hungers». It has been often labelled as «the root Williams play, a powerful mixture of sex, violence and religion». He revised it on and off for seventeen years before its mature version, Orpheus Descending, appeared in 1957. Both plays tell us about the advent of the Orphic hero to a city of the American South, the revival that he brings and his death-«sparagmos» that is commemorated by the inclusion of the snakeskin jacket in the local museum. In these plays, springsummer is followed by autumn-winter, and there is no continuation of the ritual, no god's resurrection and no coming of the following spring. Battle of Angels and Orpheus Descending could therefore be classified as Williams' non-regenerative plays in the sense that they do not follow the cyclical pattern of the Dionysiac death and rebirth.

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9

Schuster, M., A. Einhauer, E. Wasserbauer, et al. "Protein expression in yeast; comparison of two expression strategies regarding protein maturation." Journal of Biotechnology 84, no. 3 (2000): 237–48. http://dx.doi.org/10.1016/s0168-1656(00)00355-2.

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10

Shah, Kausar Hussain, Bachar Almaghrabi, and Holger Bohlmann. "Comparison of Expression Vectors for Transient Expression of Recombinant Proteins in Plants." Plant Molecular Biology Reporter 31, no. 6 (2013): 1529–38. http://dx.doi.org/10.1007/s11105-013-0614-z.

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11

Vivian, John, Jordan M. Eizenga, Holly C. Beale, Olena M. Vaske, and Benedict Paten. "Bayesian Framework for Detecting Gene Expression Outliers in Individual Samples." JCO Clinical Cancer Informatics, no. 4 (September 2020): 160–70. http://dx.doi.org/10.1200/cci.19.00095.

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PURPOSE Many antineoplastics are designed to target upregulated genes, but quantifying upregulation in a single patient sample requires an appropriate set of samples for comparison. In cancer, the most natural comparison set is unaffected samples from the matching tissue, but there are often too few available unaffected samples to overcome high intersample variance. Moreover, some cancer samples have misidentified tissues of origin or even composite-tissue phenotypes. Even if an appropriate comparison set can be identified, most differential expression tools are not designed to accommodate comparisons to a single patient sample. METHODS We propose a Bayesian statistical framework for gene expression outlier detection in single samples. Our method uses all available data to produce a consensus background distribution for each gene of interest without requiring the researcher to manually select a comparison set. The consensus distribution can then be used to quantify over- and underexpression. RESULTS We demonstrate this method on both simulated and real gene expression data. We show that it can robustly quantify overexpression, even when the set of comparison samples lacks ideally matched tissue samples. Furthermore, our results show that the method can identify appropriate comparison sets from samples of mixed lineage and rediscover numerous known gene-cancer expression patterns. CONCLUSION This exploratory method is suitable for identifying expression outliers from comparative RNA sequencing (RNA-seq) analysis for individual samples, and Treehouse, a pediatric precision medicine group that leverages RNA-seq to identify potential therapeutic leads for patients, plans to explore this method for processing its pediatric cohort.

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12

Pacey, Philip D., and Willem Siebrand. "Intermolecular hydrogen tunneling in solids. Comparison between diabatic and adiabatic rate expressions." Canadian Journal of Chemistry 66, no. 4 (1988): 875–80. http://dx.doi.org/10.1139/v88-150.

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Rate expressions are derived for hydrogen transfer between two molecules in a solid, typical examples being hydrogen abstraction by methyl radicals in solid methane and in glassy methanol. These expressions are based on two-dimensional potential-energy surfaces describing the motion of the hydrogen atom along with that of the atoms between which it is transferred. A diabatic rate expression, based on the Golden Rule, is compared with an adiabatic rate expression, based on transition-state theory with a tunneling correction. In both cases, the two degrees of freedom must be treated quantum-mechanically rather than classically. For the adiabatic case, this leads to a new expression in which the barrier permeability is averaged over the wavefunction of the slow motion. The result differs from the Golden-Rule expression but yields similar rate constants. Numerical estimates are presented to illustrate the temperature and isotope dependence of these rate constants. The concept of a tunneling path is shown to break down at low temperature, so that the conventional one-dimensional tunneling approach becomes invalid.

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13

Espeland, Eric M., Steven N. Francoeur, and Robert G. Wetzel. "Microbial phosphatase in biofilms: A comparison of whole community enzyme activity and individual bacterial cell-surface phosphatase expression." Fundamental and Applied Limnology 153, no. 4 (2002): 581–93. http://dx.doi.org/10.1127/archiv-hydrobiol/153/2002/581.

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14

Santos, Alberto, Kalliopi Tsafou, Christian Stolte, Sune Pletscher-Frankild, Seán I. O’Donoghue, and Lars Juhl Jensen. "Comprehensive comparison of large-scale tissue expression datasets." PeerJ 3 (June 30, 2015): e1054. http://dx.doi.org/10.7717/peerj.1054.

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15

임시은. "Expression of Criticism -Comparison between Korean and Japanese-." Journal of Japanese Studies ll, no. 65 (2015): 351–73. http://dx.doi.org/10.15733/jast.2015..65.351.

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16

이영. "Research in Expression Comparison between Chinese-Korean Modals." Journal of Chinese Cultural Studies ll, no. 28 (2015): 435–58. http://dx.doi.org/10.18212/cccs.2015..28.017.

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17

Bowers, Len. "THE EXPRESSION AND COMPARISON OF WARD INCIDENT RATES." Issues in Mental Health Nursing 21, no. 4 (2000): 365–74. http://dx.doi.org/10.1080/016128400247988.

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18

Xu, X., Y. Zhao, and R. Simon. "Gene Set Expression Comparison kit for BRB-ArrayTools." Bioinformatics 24, no. 1 (2007): 137–39. http://dx.doi.org/10.1093/bioinformatics/btm541.

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19

Lizier, Michela, Pier G. Sarra, Roberto Cauda, and Franco Lucchini. "Comparison of expression vectors in Lactobacillus reuteri strains." FEMS Microbiology Letters 308, no. 1 (2010): 8–15. http://dx.doi.org/10.1111/j.1574-6968.2010.01978.x.

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20

Lai, R., L. Xie, X. Wu, et al. "Gene expression signature comparison for solid tumor chemotherapy." Journal of Clinical Oncology 29, no. 15_suppl (2011): e21045-e21045. http://dx.doi.org/10.1200/jco.2011.29.15_suppl.e21045.

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21

Hayward, David C., Nipam H. Patel, E. Jay Rehm, Corey S. Goodman, and Eldon E. Ball. "Sequence and Expression of Grasshopper Antennapedia: Comparison toDrosophila." Developmental Biology 172, no. 2 (1995): 452–65. http://dx.doi.org/10.1006/dbio.1995.8030.

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22

Huang, Wei, Xiaoyi Cao, and Sheng Zhong. "Network-based comparison of temporal gene expression patterns." Bioinformatics 26, no. 23 (2010): 2944–51. http://dx.doi.org/10.1093/bioinformatics/btq561.

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23

CHHABRA, S. K., V. K. VIJAYAN, R. GUPTA, and S. DE. "Expression of bronchodilator response: comparison of four indices." Respiratory Medicine 96, no. 8 (2002): 611–14. http://dx.doi.org/10.1053/rmed.2002.1327.

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24

Ikawa, S., A. Nakagawara, and Y. Ikawa. "p53 family genes: structural comparison, expression and mutation." Cell Death & Differentiation 6, no. 12 (1999): 1154–61. http://dx.doi.org/10.1038/sj.cdd.4400631.

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25

Feng, Xin, David Lorge Parnas, T. H. Tse, and Tony O'Callaghan. "A Comparison of Tabular Expression-Based Testing Strategies." IEEE Transactions on Software Engineering 37, no. 5 (2011): 616–34. http://dx.doi.org/10.1109/tse.2011.78.

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26

Ståhlberg, Anders, Mikael Kubista, and Michael Pfaffl. "Comparison of Reverse Transcriptases in Gene Expression Analysis." Clinical Chemistry 50, no. 9 (2004): 1678–80. http://dx.doi.org/10.1373/clinchem.2004.035469.

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27

Chen, Yu-Jie, Chao-Yi Teng, and Tzong-Yuan Wu. "Comparison of versatile of bicistronic baculovirus expression vectors." Journal of Biotechnology 136 (October 2008): S133. http://dx.doi.org/10.1016/j.jbiotec.2008.07.282.

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28

Chen, Yu-Jie, Chao-Yi Teng, and Tzong-Yuan Wu. "Comparison of versatile of bicistronic baculovirus expression vectors." Journal of Biotechnology 136 (October 2008): S205. http://dx.doi.org/10.1016/j.jbiotec.2008.07.433.

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29

Buciu, I., C. Kotropoulos, and I. Pitas. "Comparison of ICA approaches for facial expression recognition." Signal, Image and Video Processing 3, no. 4 (2008): 345–61. http://dx.doi.org/10.1007/s11760-008-0074-3.

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30

Stacke, Christina, Patrick Ziegelmüller, and Ulrich Hahn. "Comparison of expression systems for human fucosyltransferase IX." European Journal of Cell Biology 89, no. 1 (2010): 35–38. http://dx.doi.org/10.1016/j.ejcb.2009.10.003.

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31

Bringula, Rex P., Ian Clement O. Fosgate, Neil Peter R. Garcia, and Josf Luinico M. Yorobe. "Effects of Pedagogical Agents on Students’ Mathematics Performance: A Comparison Between Two Versions." Journal of Educational Computing Research 56, no. 5 (2017): 701–22. http://dx.doi.org/10.1177/0735633117722494.

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This experimental study investigated the effects of the use of two versions of a pedagogical agent named personal instructing agent (PIA) on the mathematics performance of students. The first version exhibits synthetic facial expressions while the second version does not exhibit facial expression (i.e., neutral facial expression). Two groups of students with the same levels of prior knowledge in mathematics utilized two different versions of PIA. The first group—the facial group—utilized a PIA that provides textual and facial expressions feedback (happy, sad, surprise, and neutral facial expressions). The second group—the nonfacial group—used the same software except that PIA only exhibited neutral facial expression. The study showed that the mathematics scores of the students in the facial group significantly improved as compared with those who are in the nonfacial group. The posttest scores of the facial group were found significantly higher than those of the nonfacial group. The study showed that PIA that exhibited synthetic facial expressions improved students’ mathematics learning. It is concluded that synthetic facial expressions and textual feedback of pedagogical agent can be utilized to help students learn to solve mathematics problems. Limitations and recommendations are also presented.

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32

Dabernat, Sandrine, Monique Larou, Karine Massé, et al. "Organization and expression of mouse nm23-M1 gene. Comparison with nm23-M2 expression." Gene 236, no. 2 (1999): 221–30. http://dx.doi.org/10.1016/s0378-1119(99)00288-7.

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33

Mitsiadis, Thimios A., Odile Gayet, Nian Zhang,, and Patrick Carroll. "Expression of Deltex1 during mouse embryogenesis: comparison with Notch1 , 2 and 3 expression." Mechanisms of Development 109, no. 2 (2001): 399–403. http://dx.doi.org/10.1016/s0925-4773(01)00534-2.

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34

Kinchella, Jade, and Kun Guo. "Facial Expression Ambiguity and Face Image Quality Affect Differently on Expression Interpretation Bias." Perception 50, no. 4 (2021): 328–42. http://dx.doi.org/10.1177/03010066211000270.

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We often show an invariant or comparable recognition performance for perceiving prototypical facial expressions, such as happiness and anger, under different viewing settings. However, it is unclear to what extent the categorisation of ambiguous expressions and associated interpretation bias are invariant in degraded viewing conditions. In this exploratory eye-tracking study, we systematically manipulated both facial expression ambiguity (via morphing happy and angry expressions in different proportions) and face image clarity/quality (via manipulating image resolution) to measure participants’ expression categorisation performance, perceived expression intensity, and associated face-viewing gaze distribution. Our analysis revealed that increasing facial expression ambiguity and decreasing face image quality induced the opposite direction of expression interpretation bias (negativity vs. positivity bias, or increased anger vs. increased happiness categorisation), the same direction of deterioration impact on rating expression intensity, and qualitatively different influence on face-viewing gaze allocation (decreased gaze at eyes but increased gaze at mouth vs. stronger central fixation bias). These novel findings suggest that in comparison with prototypical facial expressions, our visual system has less perceptual tolerance in processing ambiguous expressions which are subject to viewing condition-dependent interpretation bias.

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35

Rahmah, Nunung Nur, Keiichi Sakai, Kenji Sano, and Kazuhiro Hongo. "Expression of RECK in endothelial cells of glioma: comparison with CD34 and VEGF expressions." Journal of Neuro-Oncology 107, no. 3 (2011): 559–64. http://dx.doi.org/10.1007/s11060-011-0778-z.

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36

Motley, Michael T., and Carl T. Camden. "Facial expression of emotion: A comparison of posed expressions versus spontaneous expressions in an interpersonal communication setting." Western Journal of Speech Communication 52, no. 1 (1988): 1–22. http://dx.doi.org/10.1080/10570318809389622.

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37

Nie, Xuguang, Keijo Luukko, Inger Hals Kvinnsland, and Päivi Kettunen. "Developmentally regulated expression ofShh andIhh in the developing mouse cranial base: Comparison withSox9 expression." Anatomical Record Part A: Discoveries in Molecular, Cellular, and Evolutionary Biology 286A, no. 2 (2005): 891–98. http://dx.doi.org/10.1002/ar.a.20231.

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38

Mansor, Muhammad Naufal, and Mohd Nazri Rejab. "Neural Network Performance Comparison in Infant Pain Expression Classifications." Applied Mechanics and Materials 475-476 (December 2013): 1104–9. http://dx.doi.org/10.4028/www.scientific.net/amm.475-476.1104.

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Infant pain is a non-stationary made by infants in response to certain situations. This infant facial expression can be used to identify physical or psychology status of infant. The aim of this work is to compare the performance of features in infant pain classification. Fast Fourier Transform (FFT), and Singular value Decomposition (SVD) features are computed at different classifier. Two different case studies such as normal and pain are performed. Two different types of radial basis artificial neural networks namely, Probabilistic Neural Network (PNN) and General Regression Neural Network (GRNN) are used to classify the infant pain. The results emphasized that the proposed features and classification algorithms can be used to aid the medical professionals for diagnosing pathological status of infant pain.

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39

Dino, Hivi I., and Maiwan B. Abdulrazzaq. "Comparison of Four Classification Algorithms for Facial Expression Recognition." Polytechnic Journal 10, no. 1 (2020): 74–80. http://dx.doi.org/10.25156/ptj.v10n1y2020.pp74-80.

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Facial expression recognition (FER) has achieved an extreme role in research area since the 1990s. This paper provides a comparison approach for FER based on three feature selection methods which are correlation, gain ration, and information gain for determining the most distinguished features of face images using multi-classification algorithms which are multilayer perceptron, Naïve Bayes, decision tree, and K-nearest neighbor (KNN). These classifiers are used for the mission of expression recognition and for comparing their proportional performance. The main aim of the provided approach is to determine the most effective classifier based on minimum acceptable number of features by analyzing and comparing their performance. The provided approach has been applied on CK+ dataset. The experimental results show that KNN is proven to be better classifier with 91% accuracy using only 30 features.

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40

Lim, Jin-Soo, and Dong-Hoon Lim. "Comparison of clustering methods of microarray gene expression data." Journal of the Korean Data and Information Science Society 23, no. 1 (2012): 39–51. http://dx.doi.org/10.7465/jkdi.2012.23.1.039.

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41

Galjart, N. J., N. Gillemans, D. Meijer, and A. d'Azzo. "Mouse "protective protein". cDNA cloning, sequence comparison, and expression." Journal of Biological Chemistry 265, no. 8 (1990): 4678–84. http://dx.doi.org/10.1016/s0021-9258(19)39616-4.

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42

Liu, H., I. Koukoulas, M. C. Ross, S. Wang, and E. M. Wintour. "Quantitative Comparison of Placental Expression of Three Aquaporin Genes." Placenta 25, no. 6 (2004): 475–78. http://dx.doi.org/10.1016/j.placenta.2003.10.008.

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43

Haibe-Kains, Benjamin, Christine Desmedt, Fanny Piette, et al. "Comparison of prognostic gene expression signatures for breast cancer." BMC Genomics 9, no. 1 (2008): 394. http://dx.doi.org/10.1186/1471-2164-9-394.

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44

Stekel, D. J. "The Comparison of Gene Expression from Multiple cDNA Libraries." Genome Research 10, no. 12 (2000): 2055–61. http://dx.doi.org/10.1101/gr.gr-1325rr.

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45

Chen, Chien‐Ming, Tsan‐Huang Shih, Tun‐Wen Pai, Zhen‐Long Liu, Margaret Dah‐Tsyr Chang, and Chin‐Hwa Hu. "Gene expression rate comparison for multiple high‐throughput datasets." IET Systems Biology 7, no. 5 (2013): 135–42. http://dx.doi.org/10.1049/iet-syb.2012.0060.

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46

Devi, S., Dr M. Rajalakshmi, S. Saranya, B. Jeevanandan, and A. Ramya. "Real Time Emotion Analysis Usingbrainwavesin Comparison to Facial Expression." IOP Conference Series: Materials Science and Engineering 994 (December 11, 2020): 012039. http://dx.doi.org/10.1088/1757-899x/994/1/012039.

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47

Braasch, Jonas, and Thomas D. Rossing. "A comparison of different expression devices in pipe organs." Journal of the Acoustical Society of America 116, no. 4 (2004): 2514. http://dx.doi.org/10.1121/1.4785028.

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48

Rubinstein, George. "On Subject Expression in Russian Infinitival Clauses of Comparison." Slavic and East European Journal 32, no. 3 (1988): 428. http://dx.doi.org/10.2307/309181.

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49

Fabre, Emmanuelle E., Pascal Bigey, Cécile Orsini, and Daniel Scherman. "Comparison of promoter region constructs forin vivo intramuscular expression." Journal of Gene Medicine 8, no. 5 (2006): 636–45. http://dx.doi.org/10.1002/jgm.878.

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50

Murray, Sandra A., and Stacey Y. Pharrams. "Comparison of gap junction expression in the adrenal gland." Microscopy Research and Technique 36, no. 6 (1997): 510–19. http://dx.doi.org/10.1002/(sici)1097-0029(19970315)36:6<510::aid-jemt8>3.0.co;2-l.

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