Age, Growth, and Mortality of Atlantic Tripletail in the North-Central Gulf of Mexico

In the southeastern USA and the Gulf of Mexico (GOM), Atlantic Tripletail Lobotes surinamensis are increasingly targeted by recreational anglers, indicating that stock status should be assessed. A critical need for such assessments is age-specific data; however, previous studies have drawn conflicting conclusions regarding the most appropriate structure for aging. Moreover, growth parameters and mortality rates for GOM Atlantic Tripletail are unknown. Therefore, the goals of this study were to (1) evaluate sagittal otoliths and first dorsal spines as aging structures; (2) model combined and sex-specific growth; and (3) estimate mortality rates for GOM Atlantic Tripletail. From 2012 to 2019, Atlantic Tripletail (N = 230, including a near-record-size specimen) were collected from the north-central GOM via hook and line and were aged using otoliths and first dorsal spines. Total length ranged from 212 to 940 mm, and age ranged from 0.07 to 5.27 years. Otoliths produced higher percent agreement (95.0%) and lower average percent error (3.0%) between readers compared to spines (82.9% and 6.5%, respectively). The von Bertalanffy growth parameters differed slightly between the otolith-based data (mean asymptotic length [L∞] = 762.42 mm, Brody growth rate coefficient [k] = 0.69 year−1, and hypothetical age at which length equals zero [t0] = −0.58 year) and spine-based data (L∞ = 718.83 mm, k = 0.79 year−1, and t0 = −0.56 year). For both otolith- and spine-based sex-specific data, the best-fitting version of the von Bertalanffy growth function permitted L∞ to vary by sex. Chapman– Robson estimates of instantaneous total mortality rate and total annual mortality rate were 1.15 and 68.66%, respectively. Based on empirical, life history-based methods, the instantaneous natural mortality rate was estimated at 0.75–0.97 and the instantaneous fishing mortality rate was estimated at 0.18–0.45, suggesting low levels of exploitation. These growth parameters and mortality estimates will provide information for future stock assessments, thereby ensuring sustainability of the GOM stock of Atlantic Tripletail

Crop Updates 2008 - Weeds

This session covers twenty nine papers from different authors: 1. BOXER® GOLD, a new pre-emergent herbicide option for WA wheat and barley growers for the control of Annual Ryegrass and Toad Rush, Craig A. Ruchs, Syngenta Crop Protection Australia Pty Ltd 2. Efficacy of Boxer Gold in the control of annual ryegrass in wheat, Dr Abul Hashem, Dr Catherine Borger, Department of Agriculture and Food, Mr Ken McKee, Field Development Manager, Syngenta Crop Protection Australia Pty Ltd 3. Alternative herbicides to avoid trifluralin resistance, Catherine Borger and Abul Hashem, Department of Agriculture and Food 4. Exiting new herbicides for ryegrass control in wheat, Peter Newman, Department of Agriculture and Food 5. Herbicide options for resistant wild radish in wheat, Peter Newman, Department of Agriculture and Food 6. A near-complete control of wild radish with three new herbicide products, Aik Cheam and Siew Lee, Department of Agriculture and Food 7. An investigation of diflufenican resistance mechanism/s in wild radish, Meagan Pearce, Dr Michael Walsh and Prof. Stephen Powles, Western Australian Herbicide Resistance Initiative, School of Plant Biology, University of WA 8. Synergistic effects of Group C and GroupF herbicides on resistant and susceptible wild radish populations, Kent Stone, Dr Michael Walsh and Prof. Stephen Powles, Western Australian Herbicide Resistance Initiative, School of Plant Biology, University of WA 9. Precept® for the management of wild radish resistant to PDS inhibiting herbicides, Mike Clarke and Andrew Loorham, Bayer Cropscience Pty Ltd, Dr Michael Walsh, WAHRI, University of Western Australia 10. Evolution of glyphosate resistance in annual ryegrass: Effects of cutting rates, Roberto Busi and Stephen B. Powles, Western Australian Herbicide Resistance Initiative, School of Plant Biology, The University of Western Australia 11. Metribuzin and other herbicides pre-sowing of lupins, Peter Newman, Department of Agriculture and Food 12. Crop topping lupins with glufosinate gives poor control of ryegrass seed set, Peter Newman, Department of Agriculture and Food 13. Brome grass has developed multiple resistance to Group B and C herbicides, Dr Abul Hashem, Dr Catherine Borger and Dr Shahab Pathan, Department of Agriculture and Food 14. Effect of sowing methods, Logran® and Metribuzin on weeds and wheat grain yield, Alexandra Douglas and Abul Hashem, Department of Agriculture and Food 15. Effect of alternative Group K herbicides on control of on-row annual ryegrass in wide row lupins, Dr Abul Hashem1, Ray Fulwood2 and Chris Roberts1, 1Department of Agriculture and Food, 2Farmer, Meckering, Western Australia 16. Control and seed production of annual ryegrass in wide row lupins within the Western Australian wheatbelt, Abul Hashem1,6, Alex Douglas1,6, Shahab Pathan1, Glen Riethmuller1,6 and 1,6Sally Peltzer, Department of Agriculture and Food, 6CRC Australian Weed Management 17. Effective weed control in wide row lupins, Glen Riethmuller, Abul Hashem and Shahab Pathan, Department of Agriculture and Food, and CRC Australian Weed Management 18. Slender iceplant control, Lorinda Hunt1, John Borger1, Meir Altman1,4 and Dr Ed Barrett-Lennard1,4, Department of Agriculture and Food, Western Australia1, University of Western Australia and Future Farm Industries CRC4 19. Chemical and non-chemical weed control – a European perspective, Glen Riethmuller, Department of Agriculture and Food 20. Mouldboard ploughing shows promise on sand, Peter Newman, Stephen Davies and Sally Peltzer, Department of Agriculture and Food 21. Weed seed head trimming, Glen Riethmuller and Abul Hashem, Department of Agriculture and Food 22. A survey of summer weed incidence and distribution across the WA wheatbelt, Pippa Michaela, Bill McLeodb, Catherine Borgerb and Alex Douglasb, aCurtin University of Technology, bDepartment of Agriculture and Food 23. Herbicide tolerance of field pea varieties, Harmohinder Dhammu and Mark Seymour, Department of Agriculture and Food 24. Herbicide tolerance of current/new wheat varieties, Dr Harmohinder Dhammu, Department of Agriculture and Food 25. Herbicide tolerance of new oat varieties, Harmohinder Dhammu, Vince Lambert and Chris Roberts, Department of Agriculture and Food 26. Herbicide tolerance of saltbush and bluebush, Lorinda Hunt1, John Borger1, Meir Altman1,4 and Dr Ed Barrett-Lennard1,4, Department of Agriculture and Food1, University of Western Australia and Future Farm Industries CRC4 27. A review of 2,4-D formulations and vapour drift, John H. Moore, Department of Agriculture and Food 28. Movement of 2,4-D butyl ester and the dose response of three formulations of 2,4-D on canola, John H. Moore, Department of Agriculture and Food 29. Pathways to registration – Improving pesticide research outcomes, Dr Rohan Rainbow, Manager Crop Protection, Grains Research and Development Corporatio

Artificial intelligence (AI) to enhance breast cancer screening: protocol for population-based cohort study of cancer detection

Introduction Artifi cial intelligence (AI) algorithms for interpreting mammograms have the potential to improve the effectiveness of population breast cancer screening programmes if they can detect cancers, including interval cancers, without contributing substantially to overdiagnosis. Studies suggesting that AI has comparable or greater accuracy than radiologists commonly employ € enriched\u27 datasets in which cancer prevalence is higher than in population screening. Routine screening outcome metrics (cancer detection and recall rates) cannot be estimated from these datasets, and accuracy estimates may be subject to spectrum bias which limits generalisabilty to real-world screening. We aim to address these limitations by comparing the accuracy of AI and radiologists in a cohort of consecutive of women attending a real-world population breast cancer screening programme. Methods and analysis A retrospective, consecutive cohort of digital mammography screens from 109 000 distinct women was assembled from BreastScreen WA (BSWA), Western Australia\u27s biennial population screening programme, from November 2016 to December 2017. The cohort includes 761 screen-detected and 235 interval cancers. Descriptive characteristics and results of radiologist double-reading will be extracted from BSWA outcomes data collection. Mammograms will be reinterpreted by a commercial AI algorithm (DeepHealth). AI accuracy will be compared with that of radiologist single-reading based on the di €erence in the area under the receiver operating characteristic curve. Cancer detection and recall rates for combined AI-radiologist reading will be estimated by pairing the first radiologist read per screen with the AI algorithm, and compared with estimates for radiologist double-reading. Ethics and dissemination This study has ethical approval from the Women and Newborn Health Service Ethics Committee (EC00350) and the Curtin University Human Research Ethics Committee (HRE2020-0316). Findings will be published in peer-reviewed journals and presented at national and international conferences. Results will also be disseminated to stakeholders in Australian breast cancer screening programmes and policy makers in population screening