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Добірка наукової літератури з теми "Influence functions"

Автор: Grafiati

Опубліковано: 5 березня 2024

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Статті в журналах з теми "Influence functions"

1

Pires, Ana M., and João A. Branco. "Partial Influence Functions." Journal of Multivariate Analysis 83, no. 2 (November 2002): 451–68. http://dx.doi.org/10.1006/jmva.2001.2055.

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2

Schioppa, Andrea, Polina Zablotskaia, David Vilar, and Artem Sokolov. "Scaling Up Influence Functions." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 8 (June 28, 2022): 8179–86. http://dx.doi.org/10.1609/aaai.v36i8.20791.

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We address efficient calculation of influence functions for tracking predictions back to the training data. We propose and analyze a new approach to speeding up the inverse Hessian calculation based on Arnoldi iteration. With this improvement, we achieve, to the best of our knowledge, the first successful implementation of influence functions that scales to full-size (language and vision) Transformer models with several hundreds of millions of parameters. We evaluate our approach in image classification and sequence-to-sequence tasks with tens to a hundred of millions of training examples. Our code is available at https://github.com/google-research/jax-influence.

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3

Burke, Murray D., and Lajos Horváth. "Estimation of influence functions." Statistics & Probability Letters 4, no. 2 (March 1986): 81–85. http://dx.doi.org/10.1016/0167-7152(86)90022-2.

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4

Zdravkovic, Suncica. "Cognitive Functions Influence Lightness Perception." i-Perception 3, no. 9 (October 2012): 706. http://dx.doi.org/10.1068/if706.

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5

Ronchetti, Elvezio. "Robust inference by influence functions." Journal of Statistical Planning and Inference 57, no. 1 (January 1997): 59–72. http://dx.doi.org/10.1016/s0378-3758(96)00036-5.

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6

H Paynter, R. J., and D. Nowell. "Improved Influence Functions For Uniform Triangular Dislocation Density Functions." Journal of Strain Analysis for Engineering Design 40, no. 7 (October 1, 2005): 729–33. http://dx.doi.org/10.1243/030932405x30821.

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There is increasing interest in the use of dislocation methods for stress analysis. In a recent paper, McKellar, Hills, and Sackfield presented a method for obtaining the stress field induced by a triangular area of constant dislocation density. The current paper describes a simplification of the formulae, which is more robust and easier to implement. This new formulation easily extends to any polygon.

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7

Huang, Yufen, Tzu-Ling Kao, and Tai-Ho Wang. "Influence functions and local influence in linear discriminant analysis." Computational Statistics & Data Analysis 51, no. 8 (May 2007): 3844–61. http://dx.doi.org/10.1016/j.csda.2006.03.001.

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8

Huang, Yufen, Mei-Ling Kuo, and Tai-Ho Wang. "Pair-perturbation influence functions and local influence in PCA." Computational Statistics & Data Analysis 51, no. 12 (August 2007): 5886–99. http://dx.doi.org/10.1016/j.csda.2006.11.005.

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9

Alhassan, Abukari. "Influence of Determinants of Diabetes on Sexual Quality using Discriminant Functions." Clinical Medical Reviews and Reports 3, no. 1 (February 20, 2021): 01–06. http://dx.doi.org/10.31579/2690-8794/063.

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Анотація:

This paper seeks to give a better understanding of the influence of age, creatinine level, duration of diabetes, glucose level and pulse rate on diabetic-induced sexual dysfunction among people. The study employed two-way MANOVA with balanced samples of 115 each. Prior to the analysis, univariate and multivariate normality, linearity, equality of error and covariance matrices, and multicollinearity assumptions were satisfied. Significance was attained for both sex and marital status (P < 0.05), though their interaction effect was not significant (P > 0.05). Wald’s test for individual groups confirmed the hypothetical decision of MANOVA, though independent T2 failed to report significance for marital status. Under the parameter estimation, it was ascertained that the difference between the groups was in terms of age and creatinine levels of people. The model is also considered credible since bootstrap estimates converged to the empirical estimates. Even though this paper did not exhaust all metabolic and comorbid factors relating to diabetes, it is hoped that it provides an insight into some circumstances in the evolution of diabetes.

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10

Gustafson, Paul. "Model influence functions based on mixtures." Canadian Journal of Statistics 24, no. 4 (December 1996): 535–48. http://dx.doi.org/10.2307/3315332.

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Дисертації з теми "Influence functions"

1

Calder, P. "Influence functions in multivariate analysis." Thesis, University of Kent, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.375052.

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2

MULLOTH, SUDHAKARAN SUDHILAL, and VIBI JOHN. "Simulation of Removal Functions for Abrasive Grinding Tool : Numerical Simulation Of Tool Influence Function." Thesis, Högskolan i Halmstad, Maskinteknisk produktframtagning (MTEK), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-30760.

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A relatively large set of complex data can be described using a simple mathematical Prestonequation. However the parameter choices are an essential facet that determines the efficiencyof grinding. The depth and volume of removal contributes to a significant part of the grindingoperation that accounts for the manufacturing time. These interacting factors in the equationare studied to create, infer and compare the performance of three sets of grinding tools: padtool, brush tool and radial tool. The tools are generated using MATLAB and is provided as aninput for tool path generation. The geometry profiled by the tool influence function is generatedusing Preston equation and the analysis from the simulation shows that the pad tool can bepreferred in instances where maximum removal is required. The comparison amongst the toolswas made keeping the parameters (pressure and relative velocity) constant in the equation forthe different tools.

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3

Howes, Peter John. "Analysis of neural network mapping functions : generating evidential support." Thesis, Oxford Brookes University, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.287718.

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4

McLaren, Jennie Renée. "The influence of plant functional groups on ecosystem functions in a grassland in northern Canada." Thesis, University of British Columbia, 2010. http://hdl.handle.net/2429/22315.

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Human development, climate change, diseases and habitat degradation and loss are resulting in species extinction rates up to a thousand times faster than pre‐human levels. Biodiversity ecosystem functioning research examines how this loss of species and changes in the composition of plant communities are likely to influence numerous ecosystem functions. The effects of biodiversity loss on ecosystem properties may also be highly dependent on the identity of the organisms lost. I investigated the influence of plant functional group identity in determining ecosystem properties. I established a removal experiment in a grassland in northern Canada in 2003 with four treatments: a no‐removal control and independent removal of forbs, graminoids and legumes. As biodiversity loss is occurring in concert with environmental change, I crossed removals with a fertilizer and a mycorrhizal reduction (fungicide) treatment to determine the context dependency of effects. I showed that graminoids have the largest influence on ecosystem properties in this community, despite not being the most abundant group. Short‐term (4 years) biomass compensation for the removals showed no compensation for graminoid removal, but after 7 years there was full biomass compensation for this treatment. Light interception, soil moisture, and soil nutrients were all largely determined by the presence of graminoids in the plant community, and surprisingly legumes had very few effects on any ecosystem property. Graminoids also showed plant‐driven environmental effects on leaf decomposition, although no removal treatment resulted in changes in the decomposition of roots. Graminoids promoted decomposition of leaf litter through 2 mechanisms: influence on the decomposition microenvironment and changes in the litter composition. Finally, I have demonstrated that very few of the effects of functional group identity were context dependent on either fertilization or fungicide treatments. These results highlight the importance of considering plant functional group identity when predicting the effects of species loss, and indicate that plant identity, more so than dominance, determines effects on ecosystem properties.

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5

Kawa, Kevin Hideyuki, and Kevin Hideyuki Kawa. "Genetic and Neuroanatomic Factors that Influence Executive Functions in Aging." Diss., The University of Arizona, 2016. http://hdl.handle.net/10150/622974.

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In the present set of experiments, we investigated the effects of age and COMT genotypes on traditional measures of executive functions, e.g., Wisconsin Card Sorting Test (WCST; Hart et al., 1988), a battery of executive functions based on the 3 factor model (shifting, updating, inhibition) described by Miyake et al. (2000) and developed at the University of Arizona (Alexander et al., 2012), and two fMRI tasks of executive functions (shifting, updating). The results of experiment 1 showed that COMT influenced performance on several traditional measures of executive functions, with Met homozygotes outperforming Val homozygotes. However, on the WCST we did not observe less perseverative errors in Met carriers as reported previously (Barnett, Jones, Robbins, & Muller, 2007; Bruder et al., 2005; Malhotra et al., 2002; Nagel et al., 2008). According to Miyake et al. (2000), however, such tasks as the WCST may actually involve multiple executive processes, making it difficult to tease apart the different types of executive functions being measured. Furthermore, COMT may be sensitive to some aspects of executive functions and not others. To this end, in experiment 2 we investigated associations between COMT and measures of executive functions from each of the 3 domains described in Miyake et al. (2000). According to the models proposed by Bilder et al. (2004) and Cools and D’Esposito (2011), the Val allele promotes cognitive flexibility, while the Met allele promotes cognitive stability. Contrary to what we expected, Met homozygotes actually performed better than Met/Val heterozygotes but no better than Val homozygotes on one measure of updating (flexibility). Upon closer examination of the processes involved in the updating task, however, the results may not necessarily be contradictory as the task may have required greater stability than previously thought. In the fMRI experiment, although behavioral performance was largely similar between age groups and COMT genotypes on the fMRI tasks, we observed differences in activation such that younger adults and Met homozygotes showed higher levels of activation relative to older adults and Val carriers, respectively. Our results suggest that these higher levels of activation may have been relied upon to maintain similar levels of performance. Additionally, across the 3 experiments the effects of COMT indicate that an overall Met advantage cannot be assumed. Rather, the benefits of one allele compared to the other should be investigated in terms of the specific cognitive processes involved in the task at hand. Thus, it is important for future studies to continue characterizing the unity and diversity of executive functions and investigate factors that may influence these patterns behaviorally and neurally, such as age and genetics.

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6

Thorpe, Kiaran Kay. "The influence of monitoring and executive functions on prospective memory /." Title page, table of contents and abstract only, 2003. http://web4.library.adelaide.edu.au/theses/09HS/09hst518.pdf.

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7

Smith, Arthur L. "Influence of denominator functions on the solutions of finite-difference schemes." DigitalCommons@Robert W. Woodruff Library, Atlanta University Center, 1989. http://digitalcommons.auctr.edu/dissertations/2072.

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We investigate the influence on the solutions of finite-difference schemes of using unconventional denominator functions in the discrete modeling of the derivatives for ordinary differential equations. The derived results are a consequence of using a generalized definition of the first derivative of a function. Two explicit examples, the linear decay equation and the Logistic differential equation, are used to illustrate in detail the various solution possibilities that can occur.

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8

Cochrane, Mary. "The influence of personality, dopaminergic genetic polymorphisms and schizophrenia on executive functions." Thesis, Goldsmiths College (University of London), 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.421783.

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9

Collins, Jennifer Rebecca. "Essential Functions: American Delsartism and Its Influence on Women’s Roles in Society." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1492699298734188.

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10

Charest, Joseph Leo. "Topographic and chemical patterning of cell-surface interfaces to influence cellular functions." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/24621.

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Thesis (Ph. D.)--Mechanical Engineering, Georgia Institute of Technology, 2007.
Committee Chair: Dr. William P. King; Committee Member: Dr. Andres J. Garcia; Committee Member: Dr. F. Levent Degertekin; Committee Member: Dr. Hang Lu; Committee Member: Dr. Todd C. McDevitt.

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Книги з теми "Influence functions"

1

Hartmann, Friedel, and Peter Jahn. Statics and Influence Functions. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-55889-5.

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2

Melnikov, Yu A. Influence functions and matrices. New York: M. Dekker, 1999.

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3

Hartmann, Friedel, and Peter Jahn. Statics and Influence Functions - from a Modern Perspective. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51222-8.

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4

1941-, Hampel Frank R., ed. Robust statistics: The approach based on influence functions. New York: Wiley, 1986.

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5

R, Maio Gregory, and Olson James M. 1953-, eds. Why we evaluate: Functions of attitudes. Mahwah, N.J: Lawrence Erlbaum, 2000.

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6

Niurka. Supreme influence: Change your life with the power of the language you use. New York: Harmony Books, 2013.

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7

Resonance of Ramanujan's mathematics. New Delhi: New Age International, 1996.

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8

Stonecipher, Harry W. Editorial and persuasive writing: Opinion functions of the news media. 2nd ed. Mamaroneck, New York: Hastings House, 1990.

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9

1939-, Pfaff Donald W., ed. Genetic influences on neural and behavioral functions. Boca Raton, Fla: CRC Press, 2000.

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10

European Centre for Research in Purchasing and Supply., ed. The Influence of the euro on the purchasing function. Vienna, Austria: European Centre for Research in Purchasing and Supply, 1998.

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Частини книг з теми "Influence functions"

1

Hartmann, Friedel. "Influence Functions." In The Mathematical Foundation of Structural Mechanics, 193–243. Berlin, Heidelberg: Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/978-3-642-82401-2_7.

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2

Cheng, Haobo. "Tool Influence Functions." In Independent Variables for Optical Surfacing Systems, 23–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-45355-7_2.

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3

Fini, Matteo, and Davide La Torre. "Generalized Influence Functions and Robustness Analysis." In Mathematical and Statistical Methods in Insurance and Finance, 113–20. Milano: Springer Milan, 2008. http://dx.doi.org/10.1007/978-88-470-0704-8_15.

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4

Nakamura, Kunio, Atsuko Akutsu, Ayumi Otake, and Hatsue Moritaka. "Influence of Heating Temperature on Cooking Curve of Rice." In Gels: Structures, Properties, and Functions, 127–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00865-8_18.

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5

Robins, James, Lingling Li, Eric Tchetgen, and Aad van der Vaart. "Higher order influence functions and minimax estimation of nonlinear functionals." In Institute of Mathematical Statistics Collections, 335–421. Beachwood, Ohio, USA: Institute of Mathematical Statistics, 2008. http://dx.doi.org/10.1214/193940307000000527.

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6

Gnarpe, Håkan, Judy Belsheim, Charlotte Blomqvist, and Lars Wesslen. "Influence of Different Tetracyclines on Granulocyte Functions." In Antibiosis and Host Immunity, 143–52. Boston, MA: Springer US, 1987. http://dx.doi.org/10.1007/978-1-4613-1901-6_16.

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7

Berman, Harvey Alan, and Mark W. Nowak. "Influence of Ionic Composition of the Medium on Acetylcholinesterase Conformation." In Multidisciplinary Approaches to Cholinesterase Functions, 149–56. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3046-6_20.

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8

Thoma, H., C. Battisti, M. Frey, J. Holle, W. Mayr, G. Schwanda, and H. StÖhr. "Substitution of Paralysed Functions in Para- And Tetraplegia." In The Influence of New Technology on Medical Practice, 177–82. London: Macmillan Education UK, 1988. http://dx.doi.org/10.1007/978-1-349-09609-1_27.

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9

Greenfield, Susan A. "Acetylcholinesterase as a Modulatory Neuroprotein and its Influence on Motor Control." In Multidisciplinary Approaches to Cholinesterase Functions, 233–42. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3046-6_31.

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10

Hartmann, Friedel, and Peter Jahn. "Basics." In Statics and Influence Functions - from a Modern Perspective, 1–75. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51222-8_1.

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Тези доповідей конференцій з теми "Influence functions"

1

Wind, H. G. "Influence Functions." In 22nd International Conference on Coastal Engineering. New York, NY: American Society of Civil Engineers, 1991. http://dx.doi.org/10.1061/9780872627765.251.

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2

Schinhaerl, Markus, Gordon Smith, Andreas Geiss, Lyndon Smith, Rolf Rascher, Peter Sperber, Elmar Pitschke, and Richard Stamp. "Calculation of MRF influence functions." In Optical Engineering + Applications, edited by James H. Burge, Oliver W. Faehnle, and Ray Williamson. SPIE, 2007. http://dx.doi.org/10.1117/12.730806.

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3

Lee, Donghoon, Hyunsin Park, Trung Pham, and Chang D. Yoo. "Learning Augmentation Network via Influence Functions." In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020. http://dx.doi.org/10.1109/cvpr42600.2020.01097.

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4

Véricel, E., M. Croset, Ph Courpron, M. Dechavanne, and M. Lagarde. "BLOOD PLATELET FUNCTIONS: INFLUENCE OF AGING." In XIth International Congress on Thrombosis and Haemostasis. Schattauer GmbH, 1987. http://dx.doi.org/10.1055/s-0038-1644564.

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Aging has been associated with a high incidence of vascular diseases and it is suggested that platelet activation could contribute in the development of these diseases. The purpose of this study was to compare platelet functions from young adults (< 35 years) and elderly people (> 70 years). Aggregation of platelet rich plasma induced by arachidonic acid or epinephrine was significantly increased in the elderly. Similarly, an increase of platelet aggregation (platelets isolated from their plasma) induced by various agents (thrombin, U46619, arachidonic acid ...) was also noted. The same tendency was observed in whole blood aggregation. Platelet endogenous arachidonic acid metabolism under stimulation was evaluated. Production of thromboxane B2, measured by GLC, was significantly higher in the elderly people (510 ± 207 vs 242 ± 83 ng/10*platelets, p<0.02). On the other hand, platelet vitamin E, quantified by HPLC, was significantly decreased in elderly people (0.92 ± 0.21 vs 1.41 ± 0.55 nmoles/109 platelets, p<0.05). To further assess platelet and vascular function in vivo, we measured excretion of thromboxane B2 (TXB2), 2,3-dinor-TXB2 (M-TXB2), 6 keto-PGFlcr and 2,3-dinor-6 keto-PGFlα (M-6-k-PGFl±) in urine. These four metabolites were nearly all significantly increased in the older population (TXB2: 24.3 ± 2k.6 vs 3.1 ± 1.2 p<0.05; M-TXB2: 51.5 ± 43.2 vs 25.1 ± 14.5 NS; 6-k-PGFlα: 37.5 ± 29.3 vs 19.1 ± 4.2 p<0.05; and M-6- k-PGFlα: 193.6 ± 118.6 vs 116.4 ± 42.4 ng/mmole creatinine p<0.05). We conclude that changes in platelet functions reveal an enhanced platelet activity which may reflect a prethrombotic state in elderly people. The mechanisms of these modifications remain to be determined but the increased of specific peroxidation observed might be linked to the decrease of platelet vitamin E.

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5

Jain, Sarthak, Varun Manjunatha, Byron Wallace, and Ani Nenkova. "Influence Functions for Sequence Tagging Models." In Findings of the Association for Computational Linguistics: EMNLP 2022. Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.findings-emnlp.58.

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6

Lyla, N. L., and S. K. Sudakov. "INFLUENCE OF NICOTINE AND ETHANOL ON THE RISKED BEHAVIOR." In MODERN PROBLEMS IN SYSTEMIC REGULATION OF PHYSIOLOGICAL FUNCTIONS. NPG Publishing, 2019. http://dx.doi.org/10.24108/5-2019-confnf-48.

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7

Kahn, J., G. Kalai, and N. Linial. "The influence of variables on Boolean functions." In [Proceedings 1988] 29th Annual Symposium on Foundations of Computer Science. IEEE, 1988. http://dx.doi.org/10.1109/sfcs.1988.21923.

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8

Nguyen-Duc, Thang, Hoang Thanh-Tung, Quan Hung Tran, Dang Huu-Tien, Hieu Nguyen, Anh T. V. Dau, and Nghi Bui. "Class based Influence Functions for Error Detection." In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.acl-short.104.

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9

Hiddleston, Hoyt R., Dwight D. Lyman, and Eric L. Schafer. "Comparisons of deformable-mirror models and influence functions." In San Diego, '91, San Diego, CA, edited by Mark A. Ealey. SPIE, 1991. http://dx.doi.org/10.1117/12.48791.

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10

Kaveh, Ashenayi, and James Vogh. "Influence of different nonlinearity functions on perceptron performance." In Midwest - DL tentative, edited by Rudolph P. Guzik, Hans E. Eppinger, Richard E. Gillespie, Mary K. Dubiel, and James E. Pearson. SPIE, 1991. http://dx.doi.org/10.1117/12.25814.

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Звіти організацій з теми "Influence functions"

1

Imbro, Dennis R., and Daniel J. Pless. Influence Metrics for Value and Utility Functions. Office of Scientific and Technical Information (OSTI), September 2019. http://dx.doi.org/10.2172/1561156.

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Xv, Xiaoyun, Xiao'ao Xue, and Yinghui Hua. Do strength trainings influence ankle functions in patients with chronic ankle instability ? A systematic review with meta-analyses. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, January 2021. http://dx.doi.org/10.37766/inplasy2021.1.0032.

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3

Xv, Xiaoyun, Xiao'ao Xue, and Yinghui Hua. Do strength trainings influence ankle functions in patients with chronic ankle instability ? A systematic review with meta-analyses. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, January 2021. http://dx.doi.org/10.37766/inplasy2021.1.0032.

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4

Filiz, Ibrahim, Jan René Judek, Marco Lorenz, and Markus Spiwoks. Einhorn, Yeti, Nessie und der neoklassische Markt – Legenden und empirische Evidenz. Sonderforschungsgruppe Institutionenanalyse, 2022. http://dx.doi.org/10.46850/sofia.9783947850020.

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The neoclassical market model still has a decisive influence on important economic policy decisions today. A central role in this model is played by the formation of equilibrium prices, where aggregate supply functions and aggregate demand functions meet. We examine whether equilibrium prices are actually formed. For this purpose, we analyse 2,217 prices for homogeneous products that were collected by students between October 2020 and May 2022 in stationary and online retail. In 143 of 146 cases, no equilibrium price is found. The percentage price range is regularly over 100%. The presumed steering function of an equilibrium price does not materialise. The establishment of market mechanisms for the efficient solution of economic problems must therefore be questioned.

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Ichimura, Hidehiko, and Whitney K. Newey. The influence function of semiparametric estimators. Cemmap, August 2015. http://dx.doi.org/10.1920/wp.cem.2015.4415.

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Ichimura, Hidehiko, and Whitney K. Newey. The influence function of semiparametric estimators. The IFS, January 2017. http://dx.doi.org/10.1920/wp.cem.2017.0617.

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7

Lers, Amnon, and Pamela J. Green. LX Senescence-Induced Ribonuclease in Tomato: Function and Regulation. United States Department of Agriculture, September 2003. http://dx.doi.org/10.32747/2003.7586455.bard.

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Natural leaf senescence, which occurs even when growth conditions are near optimal, has a negative influence on yield. Postharvest induced senescence contributes to the losses of quality in flowers, foliage, and vegetables. Strategies designed to control the senescence process in crop plants could therefore have great applied significance. However, the successful design of such strategies requires a better insight into the senescence machinery and control in higher plants. A main feature of senescence is the hydrolysis of macromolecules by hydrolases of various types such as ribonucleases (RNases) and proteases. Previously we had identified and characterized the tomato LX RNase gene demonstrating its transcript to be highly and specifically induced during senescence. This reported study was focused on LX but also had broadened our research to other senescence-associated nucleic acids degrading enzymes to learn about their function and the regulation of their encoding genes. Beside tomato we used parsley and Arabidopsis for the study of: the bi-functional nuclease which has a role in senescence. The study of different senescence- associated nucleases in few plant systems will allow a more general view on function and regulation of these enzymes in senescence. The specific original proposed objectives included: 1. Study the consequences of alterations in LX RNase level on tomato leaf senescence and general development; 2. Analyze stimuli which may participate in senescence-specific activation of the LX gene; 3. Clone the senescence-associated BFNI nuclease gene homologue from tomato. 4. Further characterize the sequences required for senescence-specific gene expression. Homozygous transgenic plants in which LX gene was either inhibited or over-expressed were generated. In both of these LX mutated plants no major phenotypic consequences were observed, which may suggests that LX is not essential for plant growth under optimal growth conditions. Lack of any abnormalities in the LX over-expressing lines suggests that special system exist to allow function of the RNase only when needed. Detailed analyses of growth under stress and consequences to RNA metabolism are underway. We have analyzed LX expression on the protein level demonstrating that it is involved also in petal senescing. Our results suggest that LX is responding to complex regulation involving developmental, organ dependent factors and responds differently to hormonal or environmental stimuli in the different plant organs. The cloned 1.4 kb promoter was cloned and its analysis revealed that probably not all required elements for senescence induction are included. Biochemical analysis of senescence-associated be-functional nucleases in the different plants, tomato, parsley and Arabidopsis, suggests they belong to a sub-class within the type I plant nucleases. The parsley PcNUC1/2 nuclease protein was purified from senescing leaves its and activity was studied in vitro revealing endo-, double strand, nucleolytic activity and exo-nucleolytic activity. Its encoding gene was cloned and found to be induced on the mRNA level. The promoter of the related Arabidopsis BFNI nuclease was shown in both tomato and Arabidopsis to be able and direct senescence-specific expression suggesting that, at least part, the gene is regulated on the transcriptional level and that the mechanism for this senescence-specific regulation is conserved between different plants. Few plants in which the BFNI gene is mutated were identified which are subjected now to detailed analysis. Our results suggest that the senescence-related nucleic acid degrading enzymes share similarities in both function and regulation between different plants and possibly have important functions in processes un-related to senescence. Still, the function of these enzymes, at least in some cases is not essential to plant development under optimal growth conditions. We are now at the stage which permits in depth investigation of the specific functions and mode of molecular regulation of senescence-associated nucleases with the aid of the research tools developed. The isolated senescence-specific promoter, shown to be active in heterologous plant system, could be utilized in agricultural-related biotechnological applications for retardation of senescence.

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Robins, James, Rajarshi Mukherjee, and Whitney K. Newey. Semiparametric efficient empirical higher order influence function estimators. The IFS, June 2017. http://dx.doi.org/10.1920/wp.cem.2017.3017.

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9

Goldstein, Steven A. Influence of Nutrition and Physical Forces on Bone Structure/Function Properties. Fort Belvoir, VA: Defense Technical Information Center, October 2004. http://dx.doi.org/10.21236/ada431879.

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Belokonova, Nadezhda, Elena Ermishina, Natalya Kataeva, Natalia Naronova, and Kristina Golitsyna. E-learning course "Chemistry". SIB-Expertise, January 2024. http://dx.doi.org/10.12731/er0770.29012024.

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The electronic training course ""Chemistry"" was created as an auxiliary resource to accompany the chemistry curriculum for the specialties of General Medicine, Pediatrics, and Dentistry. The purpose of studying the course is to form ideas about the structure and transformations of organic and inorganic substances that underlie life processes and influence these processes, in direct connection with the biological functions of these compounds. Course objectives: - formation of knowledge and skills about the basic laws of thermodynamics and bioenergy; about the structure and chemical properties of bioorganic compounds and their derivatives; - formation of knowledge necessary when considering the physical and chemical essence of processes occurring in the human body at the molecular and cellular levels; - developing the ability to carry out, when necessary, calculations of the parameters of these processes, which will allow a deeper understanding of the functions of individual systems of the body and the body as a whole, as well as its interaction with the environment; - training of a specialist who has a sufficient level of knowledge, skills, abilities, and is able to think independently and be interested in research work. The labor intensity of the course is 108 hours. The course consists of 3 didactic units. Each course topic contains theoretical material, a practice test to test your understanding of the theory, and a final test. Each final test on a topic is equivalent to a control event according to a point-rating system. Laboratory work is presented in the form of a video file and a test for it. In this way, an electronic form of completing a report for laboratory work is carried out. The materials presented in the course can be used by teachers as basic when testing students or as additional to those methodological developments that are currently used at the department.

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