Glyphosate in waters and soils from genetically modified canola cultivation in Parkes, NSW, Australia

Investigations were conducted of farmland from the Parkes region of New South Wales, Australia, cultivated with genetically modified canola, involving the determination of glyphosate (N-(phosphonomethyl)glycine) concentrations in water and soils, and its sorption. The soils are classified as loam under the USDA system (clay 13.8-15.8%, silt 39-43%, sand 41.2-47.2%). Firstly, a low-cost fluorometric method was developed for the analysis of glyphosate in waters and soils, calibrated against analytical standards and spectrophotometric and enzyme-linked immunosorbent assay (ELISA) methods. Soil and water samples were then collected using the NEPM sampling protocol into glass containers, chilled and analysed within two weeks. The samples were collected in multiple episodes, taking account of glyphosate and pesticide crop applications. The soil and water physical and chemical properties were characterised, and glyphosate levels were determined. Field concentrations of glyphosate ranged between 0.01 - 0.067 mg/L in water and 0.10 - 0.575 mg/kg in soil. The aqueous levels lie below Australian and international drinking water guidelines, but reach a Canadian freshwater guideline. Glyphosate levels varied with time of application and rainfall events. Glyphosate sorption isotherms were also constructed by batch tests on several soils, and were fitted with Freundlich and Langmuir isotherms. Desorption tests indicated 25% to 58% of soil glyphosate is extractable by 0.1M KH2PO4

Offline Signature Verification by Combining Graph Edit Distance and Triplet Networks

Biometric authentication by means of handwritten signatures is a challenging pattern recognition task, which aims to infer a writer model from only a handful of genuine signatures. In order to make it more difficult for a forger to attack the verification system, a promising strategy is to combine different writer models. In this work, we propose to complement a recent structural approach to offline signature verification based on graph edit distance with a statistical approach based on metric learning with deep neural networks. On the MCYT and GPDS benchmark datasets, we demonstrate that combining the structural and statistical models leads to significant improvements in performance, profiting from their complementary properties

Dynamic self-referencing approach to whispering gallery mode biosensing and its application to measurement within undiluted serum

Biosensing within complex biological samples requires a sensor that can compensate for fluctuations in the signal due to changing environmental conditions and nonspecific binding events. To achieve this, we developed a novel self-referenced biosensor consisting of two almost identically sized dye-doped polystyrene microspheres placed on adjacent holes at the tip of a microstructured optical fiber (MOF). Here self-referenced biosensing is demonstrated with the detection of Neutravidin in undiluted, immunoglobulin-deprived human serum samples. The MOF allows remote excitation and collection of the whispering gallery modes (WGMs) of the microspheres while also providing a robust and easy to manipulate dip-sensing platform. By taking advantage of surface functionalization techniques, one microsphere acts as a dynamic reference, compensating for nonspecific binding events and changes in the environment (such as refractive index and temperature), while the other microsphere is functionalized to detect a specific interaction. The almost identical size allows the two spheres to have virtually identical refractive index sensitivity and surface area, while still having discernible WGM spectra. This ensures their responses to nonspecific binding and environmental changes are almost identical, whereby any specific changes, such as binding events, can be monitored via the relative movement between the two sets of WGM peaks.Tess Reynolds, Alexandre Franc, ois, Nicolas Riesen, Michelle E. Turvey, Stephen J. Nicholls, Peter Hoffmann, and Tanya M. Monr

Automatic human action recognition in videos by graph embedding

The problem of human action recognition has received increasing attention in recent years for its importance in many applications. Yet, the main limitation of current approaches is that they do not capture well the spatial relationships in the subject performing the action. This paper presents an initial study which uses graphs to represent the actor's shape and graph embedding to then convert the graph into a suitable feature vector. In this way, we can benefit from the wide range of statistical classifiers while retaining the strong representational power of graphs. The paper shows that, although the proposed method does not yet achieve accuracy comparable to that of the best existing approaches, the embedded graphs are capable of describing the deformable human shape and its evolution along the time. This confirms the interesting rationale of the approach and its potential for future performance. © 2011 Springer-Verlag

Modal analysis of holey fiber mode-selective couplers

Mode Division Multiplexing is currently investigated as a possible way to increase fiber system capacity. With this approach, different modes of the same fiber carry distinct information. One of the problems to be solved in these systems concerns coupling/decoupling of the various modes to/from the same fiber. In this presentation, the mode features of a mode mux/demux based on holey fibers are investigated, with particular emphasis on optimal device design. Some preliminary experimental results will also be presented

Levodopa‐induced dyskinesia are mediated by cortical gamma oscillations in experimental Parkinsonism

Background Levodopa is the most efficacious drug in the symptomatic therapy of motor symptoms in Parkinson's disease (PD); however, long‐term treatment is often complicated by troublesome levodopa‐induced dyskinesia (LID). Recent evidence suggests that LID might be related to increased cortical gamma oscillations. Objective The objective of this study was to test the hypothesis that cortical high‐gamma network activity relates to LID in the 6‐hydroxydopamine model and to identify new biomarkers for adaptive deep brain stimulation (DBS) therapy in PD. Methods We recorded and analyzed primary motor cortex (M1) electrocorticogram data and motor behavior in freely moving 6‐OHDA lesioned rats before and during a daily treatment with levodopa for 3 weeks. The results were correlated with the abnormal involuntary movement score (AIMS) and used for generalized linear modeling (GLM). Results Levodopa reverted motor impairment, suppressed beta activity, and, with repeated administration, led to a progressive enhancement of LID. Concurrently, we observed a highly significant stepwise amplitude increase in finely tuned gamma (FTG) activity and gamma centroid frequency. Whereas AIMS and FTG reached their maximum after the 4th injection and remained on a stable plateau thereafter, the centroid frequency of the FTG power continued to increase thereafter. Among the analyzed gamma activity parameters, the fraction of longest gamma bursts showed the strongest correlation with AIMS. Using a GLM, it was possible to accurately predict AIMS from cortical recordings. Conclusions FTG activity is tightly linked to LID and should be studied as a biomarker for adaptive DBS

Lasing of whispering gallery modes in optofluidic microcapillaries

Abstract not availableAlexandre François, Nicolas Riesen, Kirsty Gardner, Tanya M. Monro, and Al Meldru

Whispering-Gallery Mode lasers for biosensing: a rationale for reducing the lasing threshold

Abstract not availableAlexandre François, Nicolas Riesen, Hong Ji, Shahraam Afshar Vahida, Tanya M. Monr