Artificial intelligence for dementia prevention

INTRODUCTION: A wide range of modifiable risk factors for dementia have been identified. Considerable debate remains about these risk factors, possible interactions between them or with genetic risk, and causality, and how they can help in clinical trial recruitment and drug development. Artificial intelligence (AI) and machine learning (ML) may refine understanding.// METHODS: ML approaches are being developed in dementia prevention. We discuss exemplar uses and evaluate the current applications and limitations in the dementia prevention field.// RESULTS: Risk-profiling tools may help identify high-risk populations for clinical trials; however, their performance needs improvement. New risk-profiling and trial-recruitment tools underpinned by ML models may be effective in reducing costs and improving future trials. ML can inform drug-repurposing efforts and prioritization of disease-modifying therapeutics.// DISCUSSION: ML is not yet widely used but has considerable potential to enhance precision in dementia prevention

The SECURE project – Stem canker of oilseed rape: : molecular methods and mathematical modelling to deploy durable resistance

N Evans et al, "The SECURE Project - Stem Canker of oilseed rape: Molecular methods and mathematical modeling to deploy durable resistance", in Vol 4 of the Proceedings of the 12th International Rapeseed Congress : Sustainable Development in Cruciferous Oilseed Crops Production, Wuhan, China, March 26 - 30, 2007. The proceedings are available online at: http://gcirc.org/intranet/irc-proceedings/12th-irc-wuhan-china-2007-vol-4.htmlModelling done during the SECURE project has demonstrated the dynamic nature of the interaction between phoma stem canker (Leptosphaeria maculans), the oilseed rape host (Brassica napus) and the environment. Experiments done with near-isogenic lines of L. maculans to investigate pathogen fitness support field data that suggest a positive effect of the avirulence allele AvrLm4 on pathogen fitness, and that the loss of this allele renders isolates less competitive under field conditions on cultivars without the resistance gene Rlm4. The highlight of molecular work was the cloning of AvrLm1 and AvrLm6. L. maculans is now one of the few fungal species for which two avirulence loci have been cloned. Subsequent research focused on understanding the function of AvrLm1 and AvrLm6 and on the analysis of sequences of virulent isolates to understand molecular evolution towards virulence. Isolates of L. maculans transformed with GFP and/or DsRed were used to follow growth of the fungus in B. napus near-isogenic-lines (NIL) with or without MX (Rlm6) resistance under different temperature and wetness conditions. The results greatly enhanced our knowledge of the infection process and the rate and extent of in planta growth on different cultivars. Conclusions from work to model durability of resistance have been tested under field conditions through a series of experiments to compare durability of resistance conferred by the major resistance gene Rlm6 alone in a susceptible background (EurolMX) or in a resistant background (DarmorMX) under recurrent selection over 4 growing seasons. A major priority of the project was knowledge transfer of results and recommendations to target audiences such as plant breeding companies and extension services. CETIOM developed a “diversification scheme” that encourages French growers to make an informed choice about the cultivars that are grown within the rotation based on the resistance genes carried by the individual cultivars. Use of such schemes, in association with survey data on the population structure of L. maculans at both national and European scales will provide opportunities for breeders and the industry to manage available B. napus resistance more effectively.Non peer reviewe

Calpain Cleavage Prediction Using Multiple Kernel Learning

Calpain, an intracellular -dependent cysteine protease, is known to play a role in a wide range of metabolic pathways through limited proteolysis of its substrates. However, only a limited number of these substrates are currently known, with the exact mechanism of substrate recognition and cleavage by calpain still largely unknown. While previous research has successfully applied standard machine-learning algorithms to accurately predict substrate cleavage by other similar types of proteases, their approach does not extend well to calpain, possibly due to its particular mode of proteolytic action and limited amount of experimental data. Through the use of Multiple Kernel Learning, a recent extension to the classic Support Vector Machine framework, we were able to train complex models based on rich, heterogeneous feature sets, leading to significantly improved prediction quality (6% over highest AUC score produced by state-of-the-art methods). In addition to producing a stronger machine-learning model for the prediction of calpain cleavage, we were able to highlight the importance and role of each feature of substrate sequences in defining specificity: primary sequence, secondary structure and solvent accessibility. Most notably, we showed there existed significant specificity differences across calpain sub-types, despite previous assumption to the contrary. Prediction accuracy was further successfully validated using, as an unbiased test set, mutated sequences of calpastatin (endogenous inhibitor of calpain) modified to no longer block calpain's proteolytic action. An online implementation of our prediction tool is available at http://calpain.org

Applications of electrified dust and dust devil electrodynamics to Martian atmospheric electricity

Atmospheric transport and suspension of dust frequently brings electrification, which may be substantial. Electric fields of 10 kVm-1 to 100 kVm-1 have been observed at the surface beneath suspended dust in the terrestrial atmosphere, and some electrification has been observed to persist in dust at levels to 5 km, as well as in volcanic plumes. The interaction between individual particles which causes the electrification is incompletely understood, and multiple processes are thought to be acting. A variation in particle charge with particle size, and the effect of gravitational separation explains to, some extent, the charge structures observed in terrestrial dust storms. More extensive flow-based modelling demonstrates that bulk electric fields in excess of 10 kV m-1 can be obtained rapidly (in less than 10 s) from rotating dust systems (dust devils) and that terrestrial breakdown fields can be obtained. Modelled profiles of electrical conductivity in the Martian atmosphere suggest the possibility of dust electrification, and dust devils have been suggested as a mechanism of charge separation able to maintain current flow between one region of the atmosphere and another, through a global circuit. Fundamental new understanding of Martian atmospheric electricity will result from the ExoMars mission, which carries the DREAMS (Dust characterization, Risk Assessment, and Environment Analyser on the Martian Surface)-MicroARES (Atmospheric Radiation and Electricity Sensor) instrumentation to Mars in 2016 for the first in situ measurements

Risk-based management of invading plant disease

- Effective control of plant disease remains a key challenge. Eradication attempts often involve removal of host plants within a certain radius of detection, targeting asymptomatic infection. Here we develop and test potentially more effective, epidemiologically motivated, control strategies, using a mathematical model previously fitted to the spread of citrus canker in Florida. - We test risk-based control, which preferentially removes hosts expected to cause a high number of infections in the remaining host population. Removals then depend on past patterns of pathogen spread and host removal, which might be nontransparent to affected stakeholders. This motivates a variable radius strategy, which approximates risk-based control via removal radii that vary by location, but which are fixed in advance of any epidemic. - Risk-based control outperforms variable radius control, which in turn outperforms constant radius removal. This result is robust to changes in disease spread parameters and initial patterns of susceptible host plants. However, efficiency degrades if epidemiological parameters are incorrectly characterised. - Risk-based control including additional epidemiology can be used to improve disease management, but it requires good prior knowledge for optimal performance. This focuses attention on gaining maximal information from past epidemics, on understanding model transferability between locations and on adaptive management strategies that change over time.Part of this work was funded by the USDA-APHIS Farm Bill; C.A.G. acknowledges support from USDA-APHIS

Fluorogenic Substrates for In Situ Monitoring of Caspase-3 Activity in Live Cells

The in situ detection of caspase-3 activity has applications in the imaging and monitoring of multiple pathologies, notably cancer. A series of cell penetrating FRET-based fluorogenic substrates were designed and synthesised for the detection of caspase-3 in live cells. A variety of modifications of the classical caspase-3 and caspase-7 substrate sequence Asp-Glu-Val-Asp were carried out in order to increase caspase-3 affinity and eliminate caspase-7 cross-reactivity. To allow cellular uptake and good solubility, the substrates were conjugated to a cationic peptoid. The most selective fluorogenic substrate 27, FAM-Ahx-Asp-Leu-Pro-Asp-Lys(MR)-Ahx, conjugated to the cell penetrating peptoid at the C-terminus, was able to detect and quantify caspase-3 activity in apoptotic cells without cross-reactivity by caspase-7.This work was supported by the Ramon Areces and Caja Madrid Foundations to AMPL and Spanish Ministry of Economy and Competitiveness to MLSG (graduate student fellowships FPI BES-2010-030257 and EEBB-I-13-07131)

Design of a plant producing 500,000 tonnes/annum synthetic oil products from natural gas, using Fischer-Tropsch technology

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