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Статті в журналах з теми "Non genomic effects"
Verhovez, Andrea, Tracy A. Williams, Silvia Monticone, Valentina Crudo, Jacopo Burrello, Maddalena Galmozzi, Michele Covella, Franco Veglio, and Paolo Mulatero. "Genomic and Non-genomic Effects of Aldosterone." Current Signal Transduction Therapy 7, no. 2 (May 1, 2012): 132–41. http://dx.doi.org/10.2174/157436212800376708.
Повний текст джерелаKowalik, M. K., D. Slonina, and J. Kotwica. "Genomic and non-genomic effects of progesterone and pregnenolone on the function of bovine endometrial cells." Veterinární Medicína 54, No. 5 (June 1, 2009): 205–14. http://dx.doi.org/10.17221/58/2009-vetmed.
Повний текст джерелаOrdóñez-Morán, Paloma, and Alberto Muñoz. "Nuclear receptors: Genomic and non-genomic effects converge." Cell Cycle 8, no. 11 (June 2009): 1675–80. http://dx.doi.org/10.4161/cc.8.11.8579.
Повний текст джерелаViñas, René, Yow-Jiun Jeng, and Cheryl S. Watson. "Non-Genomic Effects of Xenoestrogen Mixtures." International Journal of Environmental Research and Public Health 9, no. 8 (July 31, 2012): 2694–714. http://dx.doi.org/10.3390/ijerph9082694.
Повний текст джерелаJia, Wan-Yu, and Jian-Jiang Zhang. "Effects of glucocorticoids on leukocytes: Genomic and non-genomic mechanisms." World Journal of Clinical Cases 10, no. 21 (July 26, 2022): 7187–94. http://dx.doi.org/10.12998/wjcc.v10.i21.7187.
Повний текст джерелаMcEwen, Bruce S. "Non-genomic and genomic effects of steroids on neural activity." Trends in Pharmacological Sciences 12 (January 1991): 141–47. http://dx.doi.org/10.1016/0165-6147(91)90531-v.
Повний текст джерелаWeiss, Daniel J., and Erlio Gurpide. "Non-genomic effects of estrogens and antiestrogens." Journal of Steroid Biochemistry 31, no. 4 (October 1988): 671–76. http://dx.doi.org/10.1016/0022-4731(88)90017-9.
Повний текст джерелаSIMONCINI, T. "Genomic and non-genomic effects of estrogens on endothelial cells*1." Steroids 69, no. 8-9 (August 2004): 537–42. http://dx.doi.org/10.1016/j.steroids.2004.05.009.
Повний текст джерелаLecoq, L., P. Vincent, A. Lavoie-Lamoureux, and J. P. Lavoie. "Genomic and non-genomic effects of dexamethasone on equine peripheral blood neutrophils." Veterinary Immunology and Immunopathology 128, no. 1-3 (March 2009): 126–31. http://dx.doi.org/10.1016/j.vetimm.2008.10.303.
Повний текст джерелаBruscoli, Stefano, Rosa Di Virgilio, Valerio Donato, Enrico Velardi, Monia Baldoni, Cristina Marchetti, Graziella Migliorati, and Carlo Riccardi. "Genomic and non-genomic effects of different glucocorticoids on mouse thymocyte apoptosis." European Journal of Pharmacology 529, no. 1-3 (January 2006): 63–70. http://dx.doi.org/10.1016/j.ejphar.2005.10.053.
Повний текст джерелаДисертації з теми "Non genomic effects"
Guarino, Goffredo <1979>. "Genomic and non genomic effects of elevated concentration of anabolic steroids in human neuronal cells." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2008. http://amsdottorato.unibo.it/693/1/Tesi_Guarino_Goffredo.pdf.
Повний текст джерелаGuarino, Goffredo <1979>. "Genomic and non genomic effects of elevated concentration of anabolic steroids in human neuronal cells." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2008. http://amsdottorato.unibo.it/693/.
Повний текст джерелаLi, Xiongjuan [Verfasser]. "Genomic and non-genomic effects of mineralocorticoid receptors and glucocorticoid receptors and their roles of pain modulation / Xiongjuan Li." Berlin : Medizinische Fakultät Charité - Universitätsmedizin Berlin, 2018. http://d-nb.info/1196803218/34.
Повний текст джерелаLA, SALA GINA. "Effetti degli estrogeni e dei distruttori endocrini sulle cellule germinali embrionali di topo e sulle cellule somatiche della gonade." Doctoral thesis, Università degli Studi di Roma "Tor Vergata", 2009. http://hdl.handle.net/2108/901.
Повний текст джерелаIn the recent years the increased presence of human made compounds that mimic the action of estrogens termed endocrine disrupters (ED) in environment and in food and the exposure to these compounds during fetal and neonatal period has been hypotized to be the cause of the raise of disorders of male reproductive function, such a decrease of sperm count, increase in the incidence of testicular cancer and cryptorchidism and hypospadias termed Testicular Dysgenesis Syndrome (TDS). For these reason, it is important to know how the estrogens and xenoestrogens, a class of ED, act during the fetal development and to know the mechanism by which these compounds exert their effects. AIMS: To study the expression of estrogen receptors (ERs) in the embryonic precursors of the adult gametes termed PGCs and to analyze the existence in such cells of intracellular molecular pathways modulable by estrogens and xenoestrogen lindane. To verify the presence of functional ER-beta in embryonic testicular somatic cells using an ERE-luc and AP1-Luc assay and to evaluate estrogenic activity of putative EDs on mammalian embryonic testis. RESULTS: The data described in this thesis highlights the existence of functional estrogen-dependent pathways in embryonic mouse gonads in particular in testis, both in germ and somatic cells. We found that E2 is able to activate via ER-beta multiple intracellular signalling in PGCs and that the xenoestrogens, lindane affect the survival in such cells through a direct pro-apoptotic action likely resulting from its adverse effect on AKT activity. Othermore, we described for the first time the existence of a functional ERα pathway in putative Leydig cells from early stage of testis development and describe an in vitro assay that can be used to evaluate estrogenic activity of compounds on mammalian embryonic testis. CONCLUSIONS: These results support the notion of the TDS origin during early stages of testis development. While data are accumulating showing direct effect of estrogens and EDs on gene expression and specific functions of somatic cells of the embryonic testes, in particular Leydig cells, such results on germ cells are lacking and further studies are needed to investigate the effects of these compounds on embryonic germ cell function including epigenetic regulation.
Gonzalez, Dieguez David. "Genomic selection accounting for non-additive genetic effects in pig and corn crossbreeding schemes." Thesis, Toulouse, INPT, 2020. http://www.theses.fr/2020INPT0078.
Повний текст джерелаThis thesis explores and develops methodology to exploit dominance or/and epistasis genetic effects on genomic selection models in pig and maize crossbreeding schemes. The Chapter 2 consisted of estimating and exploiting within-breed dominance variance through mate allocation strategies to maximize the overall genetic merit of the traits age at 100 Kg (AGE), backfat depth (BD) and average piglet weight per litter (APWL), in a French Landrace pig population. Maximizing total genetic values instead of breeding values in matings gave to the progeny an average advantage of 0.79 days, 0.04 mm, and 11.3 g for AGE, BD and APWL, respectively, but slightly reduced the expected additive genetic gain (e.g. 1.8 % for AGE). These results indicate that genomic mate allocation can improve the performance of the offspring without dramatically compromising the additive genetic gain. In Chapter 3, the effectiveness of mate allocation strategies and genomic evaluations, accounting for additive and dominance effects, to improve crossbred (CB) performance were investigated by simulation in a two-way pig crossbreeding scheme. Effects of the sources of information used in the genetic evaluation (only purebred (PB) data or PB and CB data), of several narrow and broad-sense heritability values, and of several options for mate allocation to produce the CB (mating at random, minimizing expected future inbreeding, or maximizing the expected total genetic value of crossbred animals) were evaluated. Selecting PB animals for PB performance yielded a genetic gain of 0.2 genetic standard deviations of the trait “CB performance” per generation, whereas selecting PB animals for CB performance doubled the genetic response. Mate allocation strategy resulted in a slight increase of the CB performance. When the genetic correlation between PB and CB is low, selecting PB animals for CB performance using CB information is a more efficient strategy to exploit heterosis and increase performance at the CB commercial level. In Chapter 4, the theory of hybrid genetic evaluation models from single-cross of pure lines (as in maize) was revisited in a genomic context. Covariance between hybrids due to additive substitution effects and dominance and epistatic deviations were analytically derived. Using SNP genotypes, it is possible to split specific combining ability (SCA) into dominance and across-groups epistasis, and to split general combining ability (GCA) into within-line additive effects and within-line additive by additive epistasis. A publicly available maize data set of Dent × Flint hybrids was analyzed. The proposed model was compared to other genomic models in terms of variance components estimation and predictive ability, including a model assuming a common effect of genes across origins. The study confirms that most variation in hybrids is accounted for by GCA, and that variances due to dominance and epistasis are small and have similar magnitudes. Models based on defining effects either differently (as it is traditionally done in maize) or identically across origins (as it is done in single breeds in livestock) resulted in similar predictive abilities for hybrids
Strelzyk, Florian [Verfasser]. "Rapid, non-genomic effects of cortisol on the functioning of the human brain / Florian Strelzyk." Trier : Universität Trier, 2011. http://d-nb.info/1197697721/34.
Повний текст джерелаAlmeida, Filho Janeo Eustáquio de. "Genomic prediction of additive and non-additive effects in a pine breeding and simulated population." Universidade Federal de Viçosa, 2016. http://www.locus.ufv.br/handle/123456789/7540.
Повний текст джерелаMade available in DSpace on 2016-04-22T13:42:06Z (GMT). No. of bitstreams: 1 texto completo.pdf: 2115910 bytes, checksum: 9ac26b919d7cf610eba0b2e44061dcc7 (MD5) Previous issue date: 2016-02-17
Conselho Nacional de Desenvolvimento Científico e Tecnológico
A predição do mérito genético dos indivíduos é um dos maiores desafios no melhoremento de plantas e animais. A predição é difícil por que as características importantes possuem natureza complexa, onde alguns caracteres possuem poucos genes de efeito maior, enquanto que outros são controlados por um elevado número de genes de efeito pequeno, além disso, efeitos não-additivos como dominância e epistasia podem ser importantes para o controle da variação genética. Para obter altas acurácias na predição é importante usar o modelo que corresponde com a arquitetura genética da característica e adicionalmente a adequada partição das várias fontes de variação genética (aditiva, dominancia e epistasia) é desejada para várias aplicações como capacidade geral e específica de combinação. No capítulo 1 foi revisado os aspectos gerais da predição genômica (GP), a aplicação dessa abordagem com diferentes propósitos em características com distintas arquiteturas genéticas e no final alguns modelos estatísticos aplicado na GP. No capítulo 2 foi avaliado modelos de regressão genômica (WGR) aditivos e aditivo-dominante com diferentes prioris, essas são premissas sobre a presença ou não de marcas com efeito maior. Adicionalmente no capítulo 3 foi avaliado a inclusão da informação oriunda do pedigree na predição genômica, usando os modelos BayesA aditivo e aditivo-dominante e também com o RKHS, que teoricamente pode predizer os efeitos aditivo e não aditivos confundidos. Esses modelos foram aplicados na altura de árvores (HT) aos 6 anos de idade, diâmetro na altura do peito (DBH) e resistência a ferrugem, mesurados em 923 indivíduos de pinos oriundos de uma população estruturada em 71 irmãos completos e genotipados com 4722 marcadores genéticos. Também foram simulados 6 características com distintas arquiteturas genéticas (poligenica e oligogênica com três leveis de dominância) para esses estudos. As populações simuladas usadas nessas características foram derivadas a partir de um programa de melhoramento padrão de pinos. No capítulo 2 para as caracteríticas oligogenica simuladas e para resistência a ferrugem o BayesA e BayesB forneceram as melhores acurácias para predição genotípica, porem as diferentes priores usadas em WGR produziram resultados similares para HT e para característica poligênicas simuladas. Contudo a inclusão da dominância nos modelos WGR aumentaram a acurácia apenas para características simuladas com elevado efeito de dominância e para HT. Quando o BayesB foi ajustado em uma geração para predizer na geração seguinte, a inclusão da dominância aumentou as acurácias apenas para características oligogenicas simuladas com elevada dominancia. Independente do modelo adotado, a acurácia da predição genotípica total decresceu com o aumento dos efeitos de dominancia nas características simuladas. Então esses resultados refletem que a predição da dominancia foi complexa quando comparado com a predição dos efeitos aditivos, e para a aplicações posteriores dos efeitos de dominância, algumas propriedades genéticas da população devem ser avaliadas como MAF e número de meios irmãos e irmãos completos. No capítulo 3, a inclusão do informação oriunda do pedigree no modelo genômico, não produziu acurácias mais elevadas quando comparado com os modelos que usaram apenas informações de marcadores, e ambos modelos foram substancialmente mais acurados que o modelo baseado apenas em informação de pedigree. Em HT, DBH e características poligênicas simuladas com efeitos aditivos e dominantes, os modelos baseados em RKHS mostraram acurácias ligeiramente superiores que o BayesA para predição genotípica total, enquanto que o BayesA foi a melhor opção para resistência a ferrugem e características oligogenicas. Para a predição dos valores de melhoramento o BayesA aditivo foi o melhor modelo.
The prediction of individual genetic merit is one of most important challenges in plant and animal breeding. Prediction is difficult because the important traits have a complex nature, where some traits have few genes with major effects, while others are controlled by a large number of genes with small effects. Non-additive effects such as dominance and epistasis can also be important for controlling the genetic variation. In order to achieve higher accuracies in the prediction, it is important to use the model that matches the genetic architecture of trait. The proper partition of the various sources of genetic variation (additive, dominance and epistasis) is desired for several applications, such as exploring the overall and specific combination ability. In Chapter 1, the general remarks of genomic prediction (GP) are reviewed, with the application of this approach with different proposals in distinct genetic architecture traits, together with some statistic models applied in GP. In Chapter 2, the additive and additive-dominance whole-genomic-regression (WGR) models are evaluated with different priors, together with assumptions regarding the presence or not of markers with major effects. Chapter 3 evaluates the inclusion of pedigree information in genomic prediction with additive- and additive-dominance BayesA and also with RKHS model that can theoretically predict confused additive and non- additive effects. These models were applied in tree height (HT), diameter at breast height (DBH) and rust resistance in 923 loblolly pine individuals at 6 years of age from a structured population of 71 full-sib families genotyped with 4722 genetic markers. Six traits were also simulated with distinct genetic architectures (polygenic and oligogenic traits with three dominance levels) for these studies. The simulated population for these traits was derived from a standard pine breeding program. In the oligogenic simulated traits and rust resistance in chapter 2, BayesA and BayesB provided greater accuracies for genotypic prediction; however, the different priors of WGR yielded similar results for HT and simulated polygenic traits. Therefore, the inclusion of dominance effects in WGR increases the accuracy only for simulated traits with high dominance effects and HT. When BayesB was fitted in one generation for predicting the next generation, the dominance inclusion increased the accuracies only for the oligogenic simulated trait with high dominance. Regardless of the model adopted, the accuracy of whole genotypic prediction decreased with the increase of dominance effects in simulated traits. Thus, these results reflect that dominance prediction is complex when compared to additive prediction, and for downstream applications of dominance effects, some genetic properties of the population should be evaluated, such as MAF and the number of half and full-sibs. In chapter 3, the inclusion of pedigree information in genomic model did not yield higher accuracies than models based in only marker information, and both models were substantially more accurate than models basedonly on pedigree. In HT, DBH and in polygenic traits simulated with additive-dominance effects, the RKHS-based models showed slightly higher accuracies than BayesA for whole genotypic prediction, while BayesA-based models were the best option for rust resistance and oligogenic simulated traits. For the prediction of breeding values, the BayesA additive was the best model.
Flora, Gagan Deep. "Non-genomic effects of the Pregnane X Receptor (PXR) and Retinoid X Receptor (RXR) in platelets." Thesis, University of Reading, 2018. http://centaur.reading.ac.uk/80709/.
Повний текст джерелаJohansson, Tobias. "Neurosteroids Induce Allosteric Effects on the NMDA Receptor : Nanomolar Concentrations of Neurosteroids Exert Non-Genomic Effects on the NMDA Receptor Complex." Doctoral thesis, Uppsala University, Department of Pharmaceutical Biosciences, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-8503.
Повний текст джерелаThe neurosteroids constitute a group of powerful hormones synthesized and acting in the central nervous system. They participate in a number of important central processes, such as memory and learning, mood and neuroprotection. Their effects emerge from rapid interactions with membrane bound receptors, such as the N-methyl-D-aspartate (NMDA) receptor, the gamma-amino-butyric acid receptor and the sigma 1 receptor. The mechanisms of action are separate from classical genomic interactions.
The aims of this thesis were to identify and characterize the molecular mechanisms underlying the effects of nanomolar concentrations of neurosteroids at the NMDA receptor.
The results show that the neurosteroids pregnenolone sulfate (PS) and pregnanolone sulfate 3α5βS) differently modulate the NMDA receptor, changing the kinetics for the NMDA receptor antagonist ifenprodil, through unique and separate targets at the NR2B subunit. The effects that appear to be temperature independent were further confirmed in a calcium imagining functional assay. A second functional study demonstrated that PS and 3α5βS affect glutamate-stimulated neurite outgrowth in NG108-15 cells.
Misuse of anabolic androgenic steroids (AAS) has powerful effects on emotional states. Since neurosteroids regulate processes involved in mood it can be hypothesised that AAS can interact with the action of neurosteroids in the brain. However, chronic administration of the AAS nandrolone decanoate did not alter the allosteric effects of PS or 3α5βS at the NMDA receptor, but changed the affinity for PS, 3α5βS and dehydroepiandrosterone sulfate to the sigma 1 receptor. The results also showed that the neurosteroids displace 3H-ifenprodil from the sigma 1 and 2 receptors without directly sharing the binding site for 3H-ifenprodil at the sigma 1 receptor. The decreased affinity for the neurosteroids at the sigma 1 receptor may be involved in the depressive symptoms associated with AAS misuse.
The NMDA receptor system is deeply involved in neurodegeneration and the NMDA receptor antagonist ifenprodil exert neuroprotective actions. The findings that neurosteroids interact with ifenprodil at the NMDA receptor may be an opportunity to obtain synergistic effects in neuroprotective treatment.
Caddy, Joanne. "The non-genomic effects of the PPAR-γ ligand rosiglitazone on intracellular calcium concentrations in mammalian monocytic and smooth muscle cells". Thesis, Cardiff Metropolitan University, 2010. http://hdl.handle.net/10369/921.
Повний текст джерелаКниги з теми "Non genomic effects"
Martin, Wehling, ed. Genomic and non-genomic effects of aldosterone. Boca Raton: CRC Press, 1995.
Знайти повний текст джерелаHodgkiss, Andrew. Psychiatric consequences of cancer treatments: hormone and cytokine treatments. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780198759911.003.0007.
Повний текст джерелаRucker, James J. H., and Peter McGuffin. Copy Number Variation in Neuropsychiatric Disorders. Edited by Turhan Canli. Oxford University Press, 2013. http://dx.doi.org/10.1093/oxfordhb/9780199753888.013.005.
Повний текст джерелаSlack, Jonathan. 5. Genes of small effect. Oxford University Press, 2014. http://dx.doi.org/10.1093/actrade/9780199676507.003.0005.
Повний текст джерелаLevinson, Douglas F., and Walter E. Nichols. Genetics of Depression. Edited by Dennis S. Charney, Eric J. Nestler, Pamela Sklar, and Joseph D. Buxbaum. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190681425.003.0024.
Повний текст джерелаMitchell, Colter. The Genetics of Human Behavior. Edited by Rosemary L. Hopcroft. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190299323.013.43.
Повний текст джерелаVermeulen, Roel, Douglas A. Bell, Dean P. Jones, Montserrat Garcia-Closas, Avrum Spira, Teresa W. Wang, Martyn T. Smith, Qing Lan, and Nathaniel Rothman. Application of Biomarkers in Cancer Epidemiology. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190238667.003.0006.
Повний текст джерелаFlinter, Frances. Ethical aspects of genetic testing. Edited by Neil Turner. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780199592548.003.0301_update_001.
Повний текст джерелаDalbeth, Nicola. Clinical features of gout. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780198748311.003.0005.
Повний текст джерелаLewis, Myles, and Tim Vyse. Genetics of connective tissue diseases. Oxford University Press, 2013. http://dx.doi.org/10.1093/med/9780199642489.003.0042.
Повний текст джерелаЧастини книг з теми "Non genomic effects"
Ramirez, V. D., and J. Zheng. "Non-Genomic Effects of Estrogens." In Estrogens and Antiestrogens I, 171–200. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-58616-3_9.
Повний текст джерелаFarhat, Michel, Sylvie Abi-Younes, Roberto Vargas, Raymond M. Wolfe, Robert Clarke, and Peter W. Ramwell. "Vascular Non-genomic Effects of Estrogen." In Sex Steroids and the Cardiovascular System, 145–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-662-02764-6_10.
Повний текст джерелаPupo, Marco, Marcello Maggiolini, and Anna Maria Musti. "GPER Mediates Non-Genomic Effects of Estrogen." In Methods in Molecular Biology, 471–88. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3127-9_37.
Повний текст джерелаMontesinos López, Osval Antonio, Abelardo Montesinos López, and Jose Crossa. "Bayesian Genomic Linear Regression." In Multivariate Statistical Machine Learning Methods for Genomic Prediction, 171–208. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89010-0_6.
Повний текст джерелаSharma, Sahil, and Cynthia M. Sharma. "Identification of RNA Binding Partners of CRISPR-Cas Proteins in Prokaryotes Using RIP-Seq." In Methods in Molecular Biology, 111–33. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1851-6_6.
Повний текст джерелаHau, Kwan-Leong, Amelia Lane, Rosellina Guarascio, and Michael E. Cheetham. "Eye on a Dish Models to Evaluate Splicing Modulation." In Methods in Molecular Biology, 245–55. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2010-6_16.
Повний текст джерелаGolz, Julia Carolin, and Kerstin Stingl. "Natural Competence and Horizontal Gene Transfer in Campylobacter." In Current Topics in Microbiology and Immunology, 265–92. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-65481-8_10.
Повний текст джерелаJuarez, Paul D., Darryl B. Hood, Min-ae Song, and Aramandla Ramesh. "Applying an Exposome-wide Association Study (ExWAS) Approach to Latino Cancer Disparities." In Advancing the Science of Cancer in Latinos, 17–32. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-14436-3_2.
Повний текст джерелаReynolds, Matthew P., and Hans-Joachim Braun. "Wheat Improvement." In Wheat Improvement, 3–15. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-90673-3_1.
Повний текст джерелаZhou, Li-bin, Yan Du, Zhuo Feng, Tao Cui, Xia Chen, Shan-wei Luo, Yu-ze Chen, et al. "Comparative study of mutations induced by carbon-ion beams and gamma-ray irradiations in Arabidopsis thaliana at the genome-wide scale." In Mutation breeding, genetic diversity and crop adaptation to climate change, 451–58. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781789249095.0046.
Повний текст джерелаТези доповідей конференцій з теми "Non genomic effects"
Adeel, Z., K. Kaczmarek, P. Ramos-Ramirez, and O. Tliba. "Non-Genomic Effects of Glucocorticoids Differentially Modulate Glucocorticoid Receptor Site-Specific Phosphorylation." In American Thoracic Society 2020 International Conference, May 15-20, 2020 - Philadelphia, PA. American Thoracic Society, 2020. http://dx.doi.org/10.1164/ajrccm-conference.2020.201.1_meetingabstracts.a2375.
Повний текст джерелаRazi, Abolfazl, Nilanjana Banerjee, Nevenka Dimitrova, and Vinay Varadan. "Non-linear Bayesian framework to determine the transcriptional effects of cancer-associated genomic aberrations." In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2015. http://dx.doi.org/10.1109/embc.2015.7319885.
Повний текст джерелаHe, Jiaxiu. "A study of the effects of non-specific filtering on the gene expression data prior to statistical testing." In 2011 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS). IEEE, 2011. http://dx.doi.org/10.1109/gensips.2011.6169482.
Повний текст джерелаInoue, K., M. Nishio, Y. Inoue, M. Takeda, and H. Hirooka. "651. Genomic prediction with non-additive genetic effects for carcass weight and beef marbling in Japanese Black cattle." In World Congress on Genetics Applied to Livestock Production. The Netherlands: Wageningen Academic Publishers, 2022. http://dx.doi.org/10.3920/978-90-8686-940-4_651.
Повний текст джерелаGoetz, MP, M. Kuffel, KE Reinicke, Z. Huang, AM Bode, J. Cheng, T. Hoskin, et al. "Abstract P5-09-08: A comparison of the non-genomic effects of endoxifen and tamoxifen in aromatase inhibitor resistant breast cancer: Differential effects on the estrogen receptor co-regulator SRC3 (AIB1) and identification of PKC and PI3K as endoxifen substrates." In Abstracts: Thirty-Sixth Annual CTRC-AACR San Antonio Breast Cancer Symposium - Dec 10-14, 2013; San Antonio, TX. American Association for Cancer Research, 2013. http://dx.doi.org/10.1158/0008-5472.sabcs13-p5-09-08.
Повний текст джерелаZambalde, Erika Pereira, Ana Carolina Rodrigues, Rubens Silveira Lima, Enilze Maria Souza Fonseca Ribeiro, and Jaqueline Carvalho Oliveira. "TLNC-UC.147, A NOVEL LONG RNA (lncRNA) FROM AN ULTRACONSERVED REGION AS POTENTIAL BIOMARKER IN LUMINAL A BREAST CANCER." In Scientifc papers of XXIII Brazilian Breast Congress - 2021. Mastology, 2021. http://dx.doi.org/10.29289/259453942021v31s1052.
Повний текст джерелаYounes, Nadin, Atiyeh Abdallah, and Marawan Abu madi. "A Whole-Genome Sequencing Association Study of Low Bone Mineral Density Identifies New Susceptibility Loci in the Phase I Qatar Biobank Cohort." In Qatar University Annual Research Forum & Exhibition. Qatar University Press, 2021. http://dx.doi.org/10.29117/quarfe.2021.0115.
Повний текст джерелаYoo, Seung Soo, Hyo-Gyoung Kang, Jin Eun Choi, Sun Ha Choi, So Yeon Lee, Shin Yup Lee, Jaehee Lee, Seung Ick Cha, Chang Ho Kim, and Jae Yong Park. "The effects of polymorphisms identified in genome-wide association studies of never-smoking females on the prognosis of non-small cell lung cancer." In ERS International Congress 2016 abstracts. European Respiratory Society, 2016. http://dx.doi.org/10.1183/13993003.congress-2016.pa2873.
Повний текст джерелаHammerman, James. "Statistics education on the sly: exploring large scientific data sets as an entrée to statistical ideas in secondary schools." In Next Steps in Statistics Education. International Association for Statistical Education, 2009. http://dx.doi.org/10.52041/srap.09802.
Повний текст джерелаLiu, Qiao, Chen Chen, Annie Gao, Hang Hang Tong, and Lei Xie. "VariFunNet, an integrated multiscale modeling framework to study the effects of rare non-coding variants in genome-wide association studies: Applied to Alzheimer's disease." In 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2017. http://dx.doi.org/10.1109/bibm.2017.8217995.
Повний текст джерелаЗвіти організацій з теми "Non genomic effects"
Fridman, Eyal, Jianming Yu, and Rivka Elbaum. Combining diversity within Sorghum bicolor for genomic and fine mapping of intra-allelic interactions underlying heterosis. United States Department of Agriculture, January 2012. http://dx.doi.org/10.32747/2012.7597925.bard.
Повний текст джерелаWeller, Joel I., Derek M. Bickhart, Micha Ron, Eyal Seroussi, George Liu, and George R. Wiggans. Determination of actual polymorphisms responsible for economic trait variation in dairy cattle. United States Department of Agriculture, January 2015. http://dx.doi.org/10.32747/2015.7600017.bard.
Повний текст джерелаZhao, Bingyu, Saul Burdman, Ronald Walcott, and Gregory E. Welbaum. Control of Bacterial Fruit Blotch of Cucurbits Using the Maize Non-Host Disease Resistance Gene Rxo1. United States Department of Agriculture, September 2013. http://dx.doi.org/10.32747/2013.7699843.bard.
Повний текст джерелаGur, Amit, Edward Buckler, Joseph Burger, Yaakov Tadmor, and Iftach Klapp. Characterization of genetic variation and yield heterosis in Cucumis melo. United States Department of Agriculture, January 2016. http://dx.doi.org/10.32747/2016.7600047.bard.
Повний текст джерелаAbbott, Albert G., Doron Holland, Douglas Bielenberg, and Gregory Reighard. Structural and Functional Genomic Approaches for Marking and Identifying Genes that Control Chilling Requirement in Apricot and Peach Trees. United States Department of Agriculture, September 2009. http://dx.doi.org/10.32747/2009.7591742.bard.
Повний текст джерелаWeller, Joel I., Ignacy Misztal, and Micha Ron. Optimization of methodology for genomic selection of moderate and large dairy cattle populations. United States Department of Agriculture, March 2015. http://dx.doi.org/10.32747/2015.7594404.bard.
Повний текст джерелаSoller, Moshe (Morris), Hans Cheng, and Lyman Crittenden. Mapping the Chicken Genome, Including Loci Affecting Traits of Economic Importance. United States Department of Agriculture, September 1994. http://dx.doi.org/10.32747/1994.7568779.bard.
Повний текст джерелаPalmer, Guy, Varda Shkap, Wendy Brown, and Thea Molad. Control of bovine anaplasmosis: cytokine enhancement of vaccine efficacy. United States Department of Agriculture, March 2007. http://dx.doi.org/10.32747/2007.7695879.bard.
Повний текст джерелаCohen, Yuval, Christopher A. Cullis, and Uri Lavi. Molecular Analyses of Soma-clonal Variation in Date Palm and Banana for Early Identification and Control of Off-types Generation. United States Department of Agriculture, October 2010. http://dx.doi.org/10.32747/2010.7592124.bard.
Повний текст джерелаKuiken, Todd, and Jennifer Kuzma. Genome Editing in Latin America: Regional Regulatory Overview. Inter-American Development Bank, July 2021. http://dx.doi.org/10.18235/0003410.
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