Identification of sequence motifs significantly associated with antisense activity

<p>Abstract</p> <p>Background</p> <p>Predicting the suppression activity of antisense oligonucleotide sequences is the main goal of the rational design of nucleic acids. To create an effective predictive model, it is important to know what properties of an oligonucleotide sequence associate significantly with antisense activity. Also, for the model to be efficient we must know what properties do not associate significantly and can be omitted from the model. This paper will discuss the results of a randomization procedure to find motifs that associate significantly with either high or low antisense suppression activity, analysis of their properties, as well as the results of support vector machine modelling using these significant motifs as features.</p> <p>Results</p> <p>We discovered 155 motifs that associate significantly with high antisense suppression activity and 202 motifs that associate significantly with low suppression activity. The motifs range in length from 2 to 5 bases, contain several motifs that have been previously discovered as associating highly with antisense activity, and have thermodynamic properties consistent with previous work associating thermodynamic properties of sequences with their antisense activity. Statistical analysis revealed no correlation between a motif's position within an antisense sequence and that sequences antisense activity. Also, many significant motifs existed as subwords of other significant motifs. Support vector regression experiments indicated that the feature set of significant motifs increased correlation compared to all possible motifs as well as several subsets of the significant motifs.</p> <p>Conclusion</p> <p>The thermodynamic properties of the significantly associated motifs support existing data correlating the thermodynamic properties of the antisense oligonucleotide with antisense efficiency, reinforcing our hypothesis that antisense suppression is strongly associated with probe/target thermodynamics, as there are no enzymatic mediators to speed the process along like the RNA Induced Silencing Complex (RISC) in RNAi. The independence of motif position and antisense activity also allows us to bypass consideration of this feature in the modelling process, promoting model efficiency and reducing the chance of overfitting when predicting antisense activity. The increase in SVR correlation with significant features compared to nearest-neighbour features indicates that thermodynamics alone is likely not the only factor in determining antisense efficiency.</p

Exploring the Consistency, Quality and Challenges in Manual and Automated Coding of Free-text Diagnoses from Hospital Outpatient Letters

Coding of unstructured clinical free-text to produce interoperable structured data is essential to improve direct care, support clinical communication and to enable clinical research.However, manual clinical coding is difficult and time consuming, which motivates the development and use of natural language processing for automated coding. This work evaluates the quality and consistency of both manual and automated clinical coding of diagnoses from hospital outpatient letters. Using 100 randomly selected letters, two human clinicians performed coding of diagnosis lists to SNOMED CT. Automated coding was also performed using IMO's Concept Tagger. A gold standard was constructed by a panel of clinicians from a subset of the annotated diagnoses. This was used to evaluate the quality and consistency of both manual and automated coding via (1) a distance-based metric, treating SNOMED CT as a graph, and (2) a qualitative metric agreed upon by the panel of clinicians. Correlation between the two metrics was also evaluated. Comparing human and computer-generated codes to the gold standard, the results indicate that humans slightly out-performed automated coding, while both performed notably better when there was only a single diagnosis contained in the free-text description. Automated coding was considered acceptable by the panel of clinicians in approximately 90% of cases

GASKAP -- The Galactic ASKAP Survey

A survey of the Milky Way disk and the Magellanic System at the wavelengths of the 21-cm atomic hydrogen (HI) line and three 18-cm lines of the OH molecule will be carried out with the Australian Square Kilometre Array Pathfinder telescope. The survey will study the distribution of HI emission and absorption with unprecedented angular and velocity resolution, as well as molecular line thermal emission, absorption, and maser lines. The area to be covered includes the Galactic plane (|b|< 10deg) at all declinations south of delta = +40deg, spanning longitudes 167deg through 360deg to 79deg at b=0deg, plus the entire area of the Magellanic Stream and Clouds, a total of 13,020 square degrees. The brightness temperature sensitivity will be very good, typically sigma_T ~ 1 K at resolution 30arcsec and 1 km/s. The survey has a wide spectrum of scientific goals, from studies of galaxy evolution to star formation, with particular contributions to understanding stellar wind kinematics, the thermal phases of the interstellar medium, the interaction between gas in the disk and halo, and the dynamical and thermal states of gas at various positions along the Magellanic Stream.Comment: 45 pages, 8 figures, Pub. Astron. Soc. Australia (in press

Comparing Artificial Neural Networks, General Linear Models and Support Vector Machines in Building Predictive Models for Small Interfering RNAs

Exogenous short interfering RNAs (siRNAs) induce a gene knockdown effect in cells by interacting with naturally occurring RNA processing machinery. However not all siRNAs induce this effect equally. Several heterogeneous kinds of machine learning techniques and feature sets have been applied to modeling siRNAs and their abilities to induce knockdown. There is some growing agreement to which techniques produce maximally predictive models and yet there is little consensus for methods to compare among predictive models. Also, there are few comparative studies that address what the effect of choosing learning technique, feature set or cross validation approach has on finding and discriminating among predictive models.Three learning techniques were used to develop predictive models for effective siRNA sequences including Artificial Neural Networks (ANNs), General Linear Models (GLMs) and Support Vector Machines (SVMs). Five feature mapping methods were also used to generate models of siRNA activities. The 2 factors of learning technique and feature mapping were evaluated by complete 3x5 factorial ANOVA. Overall, both learning techniques and feature mapping contributed significantly to the observed variance in predictive models, but to differing degrees for precision and accuracy as well as across different kinds and levels of model cross-validation.The methods presented here provide a robust statistical framework to compare among models developed under distinct learning techniques and feature sets for siRNAs. Further comparisons among current or future modeling approaches should apply these or other suitable statistically equivalent methods to critically evaluate the performance of proposed models. ANN and GLM techniques tend to be more sensitive to the inclusion of noisy features, but the SVM technique is more robust under large numbers of features for measures of model precision and accuracy. Features found to result in maximally predictive models are not consistent across learning techniques, suggesting care should be taken in the interpretation of feature relevance. In the models developed here, there are statistically differentiable combinations of learning techniques and feature mapping methods where the SVM technique under a specific combination of features significantly outperforms all the best combinations of features within the ANN and GLM techniques

Improving model predictions for RNA interference activities that use support vector machine regression by combining and filtering features

<p>Abstract</p> <p>Background</p> <p>RNA interference (RNAi) is a naturally occurring phenomenon that results in the suppression of a target RNA sequence utilizing a variety of possible methods and pathways. To dissect the factors that result in effective siRNA sequences a regression kernel Support Vector Machine (SVM) approach was used to quantitatively model RNA interference activities.</p> <p>Results</p> <p>Eight overall feature mapping methods were compared in their abilities to build SVM regression models that predict published siRNA activities. The primary factors in predictive SVM models are position specific nucleotide compositions. The secondary factors are position independent sequence motifs (<it>N</it>-grams) and guide strand to passenger strand sequence thermodynamics. Finally, the factors that are least contributory but are still predictive of efficacy are measures of intramolecular guide strand secondary structure and target strand secondary structure. Of these, the site of the 5' most base of the guide strand is the most informative.</p> <p>Conclusion</p> <p>The capacity of specific feature mapping methods and their ability to build predictive models of RNAi activity suggests a relative biological importance of these features. Some feature mapping methods are more informative in building predictive models and overall <it>t</it>-test filtering provides a method to remove some noisy features or make comparisons among datasets. Together, these features can yield predictive SVM regression models with increased predictive accuracy between predicted and observed activities both within datasets by cross validation, and between independently collected RNAi activity datasets. Feature filtering to remove features should be approached carefully in that it is possible to reduce feature set size without substantially reducing predictive models, but the features retained in the candidate models become increasingly distinct. Software to perform feature prediction and SVM training and testing on nucleic acid sequences can be found at the following site: <url>ftp://scitoolsftp.idtdna.com/SEQ2SVM/</url>.</p

Understanding the circumgalactic medium is critical for understanding galaxy evolution

Galaxies evolve under the influence of gas flows between their interstellar medium and their surrounding gaseous halos known as the circumgalactic medium (CGM). The CGM is a major reservoir of galactic baryons and metals, and plays a key role in the long cycles of accretion, feedback, and recycling of gas that drive star formation. In order to fully understand the physical processes at work within galaxies, it is therefore essential to have a firm understanding of the composition, structure, kinematics, thermodynamics, and evolution of the CGM. In this white paper we outline connections between the CGM and galactic star formation histories, internal kinematics, chemical evolution, quenching, satellite evolution, dark matter halo occupation, and the reionization of the larger-scale intergalactic medium in light of the advances that will be made on these topics in the 2020s. We argue that, in the next decade, fundamental progress on all of these major issues depends critically on improved empirical characterization and theoretical understanding of the CGM. In particular, we discuss how future advances in spatially-resolved CGM observations at high spectral resolution, broader characterization of the CGM across galaxy mass and redshift, and expected breakthroughs in cosmological hydrodynamic simulations will help resolve these major problems in galaxy evolution.Comment: Astro2020 Decadal Science White Pape

Crop Updates 2007 - Farming Systems

This session covers forty papers from different authors: 1. Quality Assurance and industry stewardship, David Jeffries, Better Farm IQ Manager, Cooperative Bulk Handling 2. Sothis: Trifolium dasyurum (Eastern Star clover), A. Loi, B.J. Nutt and C.K. Revell, Department of Agriculture and Food 3. Poor performing patches of the paddock – to ameliorate or live with low yield? Yvette Oliver1, Michael Robertson1, Bill Bowden2, Kit Leake3and Ashley Bonser3, CSIRO Sustainable Ecosystems1, Department of Food and Agriculture2, Kellerberrin Farmer3 4. What evidence is there that PA can pay? Michael Robertson, CSIRO Floreat, Ian Maling, SilverFox Solutions and Bindi Isbister, Department of Agriculture and Food 5.The journey is great, but does PA pay? Garren Knell, ConsultAg; Alison Slade, Department of Agriculture and Food, CFIG 6. 2007 Seasonal outlook, David Stephens and Michael Meuleners, Department of Agriculture and Food 7. Towards building farmer capacity to better manage climate risk, David Beard and Nicolyn Short, Department of Agriculture and Food 8. A NAR farmers view of his farming system in 2015, Rob Grima, Department of Agriculture and Food 9. Biofuels opportunities in Australia, Ingrid Richardson, Food and Agribusiness Research, Rabobank 10. The groundwater depth on the hydrological benefits of lucerne and the subsequent recharge values, Ruhi Ferdowsian1and Geoff Bee2; 1Department of Agriculture and Food, 2Landholder, Laurinya, Jerramungup 11. Subsoil constraints to crop production in the high rainfall zone of Western Australia, Daniel Evans1, Bob Gilkes1, Senthold Asseng2and Jim Dixon3; 1University of Western Australia, 2CSIRO Plant Industry, 3Department of Agriculture and Food 12. Prospects for lucerne in the WA wheatbelt, Michael Robertson, CSIRO Floreat, Felicity Byrne and Mike Ewing, CRC for Plant-Based Management of Dryland Salinity, Dennis van Gool, Department of Agriculture and Food 13. Nitrous oxide emissions from a cropped soil in the Western Australian grainbelt, Louise Barton1, Ralf Kiese2, David Gatter3, Klaus Butterbach-Bahl2, Renee Buck1, Christoph Hinz1and Daniel Murphy1,1School of Earth and Geographical Sciences, The University of Western Australia, 2Institute for Meteorology and Climate Research, Atmospheric Environmental Research, Garmisch-Partenkirchen, Germany, 3The Department of Agriculture and Food 14. Managing seasonal risk is an important part of farm management but is highly complex and therefore needs a ‘horses for courses’ approach, Cameron Weeks, Planfarm / Mingenew-Irwin Group, Dr Michael Robertson, Dr Yvette Oliver, CSIRO Sustainable Ecosystems and Dr Meredith Fairbanks, Department of Agriculture and Food 15. Novel use application of clopyralid in lupins, John Peirce, and Brad Rayner Department of Agriculture and Food 16. Long season wheat on the South Coast – Feed and grain in a dry year – a 2006 case study, Sandy White, Department of Agriculture and Food 17. Wheat yield response to potassium and the residual value of PKS fertiliser drilled at different depths, Paul Damon1, Bill Bowden2, Qifu Ma1 and Zed Rengel1; Faculty of Natural and Agricultural Sciences, The University of Western Australia1, Department of Agriculture and Food2 18. Saltbush as a sponge for summer rain, Ed Barrett-Lennard and Meir Altman, Department of Agriculture and Food and CRC for Plant-based Management of Dryland Salinity 19. Building strong working relationships between grower groups and their industry partners, Tracey M. Gianatti, Grower Group Alliance 20. To graze or not to graze – the question of tactical grazing of cereal crops, Lindsay Bell and Michael Robertson, CSIRO Sustainable Ecosystems 21. Can legume pastures and sheep replace lupins? Ben Webb and Caroline Peek, Department of Agriculture and Food 22. EverGraze – livestock and perennial pasture performance during a drought year, Paul Sanford, Department of Agriculture and Food, and CRC for Plant-based Management of Dryland Salinity 23. Crop survival in challenging times, Paul Blackwell1, Glen Riethmuller1, Darshan Sharma1and Mike Collins21Department of Agriculture and Food, 2Okura Plantations, Kirikiri New Zealand 24. Soil health constraints to production potential – a precision guided project, Frank D’Emden, and David Hall, Department of Agriculture and Food 25. A review of pest and disease occurrence in 2006, Mangano, G.P. and Severtson, D.L., Department of Agriculture and Food 26. e-weed – an information resource on seasonal weed management issues, Vanessa Stewart and Julie Roche, Department of Agriculture and Food 27. Review of Pesticide Legislation and Policies in Western Australia, Peter Rutherford, BSc (Agric.), Pesticide Legislation Review, Office of the Chief Medical Adviser, WA Department of Health 28. Future wheat yields in the West Australian wheatbelt, Imma Farré and Ian Foster, Department of Agriculture and Food, Stephen Charles, CSIRO Land and Water 29. Organic matter in WA arable soils: What’s active and what’s not, Frances Hoyle, Department of Agriculture and Food, Australia and Daniel Murphy, UWA 30. Soil quality indicators in Western Australian farming systems, D.V. Murphy1, N. Milton1, M. Osman1, F.C. Hoyle2, L.K Abbott1, W.R. Cookson1and S. Darmawanto1; 1UWA, 2Department of Agriculture and Food 31. Impact of stubble on input efficiencies, Geoff Anderson, formerly employed by Department of Agriculture and Food 32. Mixed farming vs All crop – true profit, not just gross margins, Rob Sands and David McCarthy, FARMANCO Management Consultants, Western Australia 33. Evaluation of Local Farmer Group Network – group leaders’ surveys 2005 and 2006, Paul Carmody, Local Farmer Group Network, Network Coordinator, UWA 34. Seeding rate and nitrogen application and timing effects in wheat, J. Russell, Department of Agriculture and Food, J. Eyres, G. Fosbery and A. Roe, ConsultAg, Northam 35. Foliar fungicide application and disease control in barley, J. Russell, Department of Agriculture and Food, J. Eyres, G. Fosbery and A. Roe, ConsultAg, Northam 36. Brown manuring effects on a following wheat crop in the central wheatbelt, , J. Russell, Department of Agriculture and Food, J. Eyres, G. Fosbery and A. Roe, ConsultAg, Northam 37. Management of annual pastures in mixed farming systems – transition from a dry season, Dr Clinton Revell and Dr Phil Nichols; Department of Agriculture and Food 38. The value of new annual pastures in mixed farm businesses of the wheatbelt, Dr Clinton Revell1, Mr Andrew Bathgate2and Dr Phil Nichols1; 1Department of Agriculture and Food, 2Farming Systems Analysis Service, Albany 39. The influence of winter SOI and Indian Ocean SST on WA winter rainfall, Meredith Fairbanks and Ian Foster, Department of Agriculture and Food 40. Market outlook – Grains, Anne Wilkins, Market Analyst, Grains, Department of Agriculture and Foo