Estimation of parameters in a structured SIR model

[EN] In this paper, an age-structured epidemiological process is considered. The disease model is based on a SIR model with unknown parameters. We addressed two important issues to analyzing the model and its parameters. One issue is concerned with the theoretical existence of unique solution, the identifiability problem. The second issue is how to estimate the parameters in the model. We propose an iterative algorithm to study the identifiability of the system and a method to estimate the parameters which are identifiable. A least squares approach based on a finite set of observations helps us to estimate the initial values of the parameters. Finally, we test the proposed algorithms.The authors would like to thank the referees and the editor for their comments and useful suggestions for improvement of the manuscript. This work has been partially supported by Spanish Grant MTM2013-43678-P.Cantó Colomina, B.; Coll, C.; Sánchez, E. (2017). Estimation of parameters in a structured SIR model. Advances in Difference Equations. 33:1-13. https://doi.org/10.1186/s13662-017-1078-5S11333Strogatz, S, Friedman, M, Mallinck-Rodt, AJ, McKay, S: Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry, and Engineering. Perseus Books, Washington (1994)De La Sen, M, Quesada, A: Some equilibrium, stability, instability and oscillatory results for an extended discrete epidemic model with evolution memory. Adv. Differ. Equ. 2013, 234 (2013)Han, Q, Wang, Z: On extinction of infectious diseases for multi-group SIRS models with satured incidence rate. Adv. Differ. Equ. 2015, 333 (2015)Cantó, B, Coll, C, Sánchez, E: Structural identifiability of a model of dialysis. Math. Comput. Model. 50, 733-737 (2009)Cantó, B, Coll, C, Sánchez, E: Identifiability of a class of discretized linear partial differential algebraic equations. Math. Probl. Eng., 1-12 (2011)Craciun, G, Pantea, C: Identifiability of chemical reaction networks. J. Math. Chem. 44, 244-259 (2008)Malik, MB, Salman, M: State-space least mean square. Digit. Signal Process. 18, 334-345 (2008)Ding, F, Liu, PX, Liu, G: Multiinnovatiovation least-squares identification for system modeling. IEEE Trans. Syst. Man Cybern., Part B, Cybern. 18(3), 767-778 (2010)Ben-Zvi, A, McLellan, PJ, McAuley, KB: Identifiability of linear time-invariant differential-algebraic systems, I. The generalized Markov parameter approach. Ind. Eng. Chem. Res. 42, 6607-6618 (2003)Boyadjiev, C, Dimitrova, E: An iterative method for model parameter identification. Comput. Chem. Eng. 29, 941-948 (2005)Ben-Zvi, A, McLellan, PJ, McAuley, KB: Identifiability of linear time-invariant differential-algebraic systems, 2. The differential-algebraic approach. Ind. Eng. Chem. Res. 43, 1251-1259 (2004)Dion, JM, Commault, C, van der Woude, J: Generic properties and control of linear structured systems: a survey. Automatica 39, 1125-1144 (2003)Chou, IC, Voit, EO: Recent developments in parameter estimation and structure identification of biochemical and genomic systems. Math. Biosci. 219, 57-83 (2009)Schmitz, OJ: Ecology and Ecosystems Conservation. Island Press, Washington (2013

Crop Updates 2006 - Cereals

This session covers twenty nine papers from different authors: PLENARY 1. The 2005 wheat streak mosaic virus epidemic in New South Wales and the threat posed to the Western Australian wheat industry, Roger Jones and Nichole Burges, Department of Agriculture SOUTH COAST AGRONOMY 2. South coast wheat variety trial results and best options for 2006, Mohammad Amjad, Ben Curtis and Wal Anderson, Department of Agriculture 3. Dual purpose winter wheats to improve productivity, Mohammad Amjad and Ben Curtis, Department of Agriculture 4. South coast large-scale premium wheat variety trials, Mohammad Amjad and Ben Curtis, Department of Agriculture 5. Optimal input packages for noodle wheat in Dalwallinu – Liebe practice for profit trial, Darren Chitty, Agritech Crop Research and Brianna Peake, Liebe Group 6. In-crop risk management using yield prophet®, Harm van Rees1, Cherie Reilly1, James Hunt1, Dean Holzworth2, Zvi Hochman2; 1Birchip Cropping Group, Victoria; 2CSIRO, Toowoomba, Qld 7. Yield Prophet® 2005 – On-line yield forecasting, James Hunt1, Harm van Rees1, Zvi Hochman2,Allan Peake2, Neal Dalgliesh2, Dean Holzworth2, Stephen van Rees1, Trudy McCann1 and Peter Carberry2; 1Birchip Cropping Group, Victoria; 2CSIRO, Toowoomba, Qld 8. Performance of oaten hay varieties in Western Australian environments, Raj Malik and Kellie Winfield, Department of Agriculture 9. Performance of dwarf potential milling varieties in Western Australian environments, Kellie Winfield and Raj Malik, Department of Agriculture 10. Agronomic responses of new wheat varieties in the Southern agricultural region of WA, Brenda Shackley and Judith Devenish, Department of Agriculture 11. Responses of new wheat varieties to management factors in the central agricultural region of Western Australia, Darshan Sharma, Steve Penny and Wal Anderson,Department of Agriculture 12. Sowing time on wheat yield, quality and $ - Northern agricultural region, Christine Zaicou-Kunesch, Department of Agriculture NUTRITION 13.The most effective method of applying phosphorus, copper and zinc to no-till crops, Mike Bolland and Ross Brennan, Department of Agriculture 14. Uptake of K from the soil profile by wheat, Paul Damon and Zed Rengel, Faculty of Natural and Agricultural Sciences, University of Western Australia 15. Reducing nitrogen fertiliser risks, Jeremy Lemon, Department of Agriculture 16. Yield Prophet® and canopy management, Harm van Rees1, Zvi Hochman2, Perry Poulton2, Nick Poole3, Brooke Thompson4, James Hunt1; 1Birchip Cropping Group, Victoria; 2CSIRO, Toowoomba, Qld; 3Foundation for Arable Research, New Zealand; 4Cropfacts, Victoria 17. Producing profits with phosphorus, Stephen Loss, CSBP Ltd, WA 18. Potassium response in cereal cropping within the medium rainfall central wheatbelt, Jeff Russell1, Angie Roe2 and James Eyres2, Department of Agriculture1, Farm Focus Consultants, Northam2 19. Matching nitrogen supply to wheat demand in the high rainfall cropping zone, Narelle Simpson, Ron McTaggart, Wal Anderson, Lionel Martin and Dave Allen, Department of Agriculture DISEASES 20. Comparative study of commercial wheat cultivars and differential lines (with known Pm resistance genes) to powdery mildew response, Hossein Golzar, Manisha Shankar and Robert Loughman, Department of Agriculture 21. On farm research to investigate fungicide applications to minimise leaf disease impacts in wheat – part II, Jeff Russell1, Angie Roe2and James Eyres2, Department of Agriculture1, and Farm Focus Consultants, Northam2 22. Disease resistance update for wheat varieties in WA, Manisha Shankar, John Majewski, Donna Foster, Hossein Golzar, Jamie Piotrowski, Nicole Harry and Rob Loughman, Department of Agriculture 23. Effect of time of stripe rust inoculum arrival on variety response in wheat, Manisha Shankar, John Majewski and Rob Loughman, Department of Agriculture 24. Fungicide seed dressing management of loose smut in Baudin barley, Geoff Thomas and Kith Jayasena, Department of Agriculture PESTS 25. How to avoid insect contamination in cereal grain at harvest, Svetlana Micic, Paul Matson and Tony Dore, Department of Agriculture ABIOTIC 26. Environment – is it as important as variety in sprouting tolerance? Thomas (Ben) Biddulph1, Dr Daryl Mares1, Dr Julie Plummer1 and Dr Tim Setter2, School of Plant Biology, University of Western Australia1 and Department of Agriculture2 27. Frost or fiction, Garren Knell, Steve Curtin and Wade Longmuir, ConsultAg Pty Ltd, WA 28. High moisture wheat harvesting in Esperance 2005, Nigel Metz, South East Premium Wheat Growers Association (SEPWA) Projects Coordinator, Esperance, WA SOILS 28. Hardpan penetration ability of wheat roots, Tina Botwright Acuña and Len Wade, School of Plant Biology, University of Western Australia MARKETS 29. Crop shaping to meet predicted market demands for wheat in the 21st Century, Cindy Mills and Peter Stone,Australian Wheat Board, Melbourn

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

Shifting the limits in wheat research and breeding using a fully annotated reference genome

Introduction: Wheat (Triticum aestivum L.) is the most widely cultivated crop on Earth, contributing about a fifth of the total calories consumed by humans. Consequently, wheat yields and production affect the global economy, and failed harvests can lead to social unrest. Breeders continuously strive to develop improved varieties by fine-tuning genetically complex yield and end-use quality parameters while maintaining stable yields and adapting the crop to regionally specific biotic and abiotic stresses. Rationale: Breeding efforts are limited by insufficient knowledge and understanding of wheat biology and the molecular basis of central agronomic traits. To meet the demands of human population growth, there is an urgent need for wheat research and breeding to accelerate genetic gain as well as to increase and protect wheat yield and quality traits. In other plant and animal species, access to a fully annotated and ordered genome sequence, including regulatory sequences and genome-diversity information, has promoted the development of systematic and more time-efficient approaches for the selection and understanding of important traits. Wheat has lagged behind, primarily owing to the challenges of assembling a genome that is more than five times as large as the human genome, polyploid, and complex, containing more than 85% repetitive DNA. To provide a foundation for improvement through molecular breeding, in 2005, the International Wheat Genome Sequencing Consortium set out to deliver a high-quality annotated reference genome sequence of bread wheat. Results: An annotated reference sequence representing the hexaploid bread wheat genome in the form of 21 chromosome-like sequence assemblies has now been delivered, giving access to 107,891 high-confidence genes, including their genomic context of regulatory sequences. This assembly enabled the discovery of tissue- and developmental stage–related gene coexpression networks using a transcriptome atlas representing all stages of wheat development. The dynamics of change in complex gene families involved in environmental adaptation and end-use quality were revealed at subgenome resolution and contextualized to known agronomic single-gene or quantitative trait loci. Aspects of the future value of the annotated assembly for molecular breeding and research were exemplarily illustrated by resolving the genetic basis of a quantitative trait locus conferring resistance to abiotic stress and insect damage as well as by serving as the basis for genome editing of the flowering-time trait. Conclusion: This annotated reference sequence of wheat is a resource that can now drive disruptive innovation in wheat improvement, as this community resource establishes the foundation for accelerating wheat research and application through improved understanding of wheat biology and genomics-assisted breeding. Importantly, the bioinformatics capacity developed for model-organism genomes will facilitate a better understanding of the wheat genome as a result of the high-quality chromosome-based genome assembly. By necessity, breeders work with the genome at the whole chromosome level, as each new cross involves the modification of genome-wide gene networks that control the expression of complex traits such as yield. With the annotated and ordered reference genome sequence in place, researchers and breeders can now easily access sequence-level information to precisely define the necessary changes in the genomes for breeding programs. This will be realized through the implementation of new DNA marker platforms and targeted breeding technologies, including genome editing