Drinking water application of Denagard® Tiamulin for control of Brachyspira pilosicoli infection of laying poultry

Avian intestinal spirochaetosis (AIS) caused by Brachyspira spp., and notably Brachyspira pilosicoli, is common in layer flocks and reportedly of increasing incidence in broilers and broiler breeders. Disease manifests as diarrhoea,increased feed consumption, reduced growth rates and occasional mortality in broilers and these signs are shown in layers also associated with a delayed onset of lay, reduced egg weights, faecal staining of eggshells and non-productive ovaries. Treatment with Denagard® Tiamulin has been used to protect against B. pilosicoli colonisation, persistence and clinical presentation of AIS in commercial layers, but to date there has been no definitive study validating efficacy. Here, we used a poultry model of B. pilosicoli infection of layers to compare the impact of three doses of Denagard® Tiamulin. Four groups of thirty 17 week old commercial pre-lay birds were all challengedwith B. pilosicoli strain B2904with three oral doses two days apart. All birdswere colonised within 2 days after the final oral challenge and mild onset of clinical signs were observed thereafter. A fifth group that was unchallenged and untreated was also included for comparison as healthy birds. Five days after the final oral Brachypira challenge three groups were given Denagard® Tiamulin in drinking water made up following the manufacturer's recommendations with doses verified as 58.7 ppm, 113 ppm and 225 ppm. Weight gain body condition and the level of diarrhoea of birds infected with B. pilosicoli were improved and shedding of the organism reduced significantly (p = 0.001) following treatment with Denagard® Tiamulin irrespective of dose given. The level and duration of colonisation of organs of birds infected with B. pilosicoli was also reduced. Confirming previous findings we showed that the ileum, caeca, colon, and both liver and spleen were colonised and here we demonstrated that treatment with Denagard® Tiamulin resulted in significant reduction in the numbers of Brachyspira found in each of these sites and dramatic reduction in faecal shedding (p b 0.001) to approaching zero as assessed by culture of cloacal swabs. Although the number of eggs produced per bird and the level of eggshell staining appeared unaffected, egg weights of treated birds were greater than those of untreated birds for a period of approximately two weeks following treatment. These data conclusively demonstrate the effectiveness of Denagard® Tiamulin in reducing B. pilosicoli infection in laying hens.

A Variance-Minimizing Filter for Large-Scale Applications

A truly variance-minimizing filter is introduced and its per for mance is demonstrated with the Korteweg– DeV ries (KdV) equation and with a multilayer quasigeostrophic model of the ocean area around South Africa. It is recalled that Kalman-like filters are not variance minimizing for nonlinear model dynamics and that four - dimensional variational data assimilation (4DV AR)-like methods relying on per fect model dynamics have dif- ficulty with providing error estimates. The new method does not have these drawbacks. In fact, it combines advantages from both methods in that it does provide error estimates while automatically having balanced states after analysis, without extra computations. It is based on ensemble or Monte Carlo integrations to simulate the probability density of the model evolution. When obser vations are available, the so-called importance resampling algorithm is applied. From Bayes’ s theorem it follows that each ensemble member receives a new weight dependent on its ‘ ‘distance’ ’ t o the obser vations. Because the weights are strongly var ying, a resampling of the ensemble is necessar y. This resampling is done such that members with high weights are duplicated according to their weights, while low-weight members are largely ignored. In passing, it is noted that data assimilation is not an inverse problem by nature, although it can be for mulated that way . Also, it is shown that the posterior variance can be larger than the prior if the usual Gaussian framework is set aside. However , i n the examples presented here, the entropy of the probability densities is decreasing. The application to the ocean area around South Africa, gover ned by strongly nonlinear dynamics, shows that the method is working satisfactorily . The strong and weak points of the method are discussed and possible improvements are proposed.

Aspects of particle filtering in high-dimensional spaces

Nonlinear data assimilation is high on the agenda in all fields of the geosciences as with ever increasing model resolution and inclusion of more physical (biological etc.) processes, and more complex observation operators the data-assimilation problem becomes more and more nonlinear. The suitability of particle filters to solve the nonlinear data assimilation problem in high-dimensional geophysical problems will be discussed. Several existing and new schemes will be presented and it is shown that at least one of them, the Equivalent-Weights Particle Filter, does indeed beat the curse of dimensionality and provides a way forward to solve the problem of nonlinear data assimilation in high-dimensional systems.

An LDA and probability-based classifier for the diagnosis of Alzheimer's Disease from structural MRI

In this paper a custom classification algorithm based on linear discriminant analysis and probability-based weights is implemented and applied to the hippocampus measurements of structural magnetic resonance images from healthy subjects and Alzheimer’s Disease sufferers; and then attempts to diagnose them as accurately as possible. The classifier works by classifying each measurement of a hippocampal volume as healthy controlsized or Alzheimer’s Disease-sized, these new features are then weighted and used to classify the subject as a healthy control or suffering from Alzheimer’s Disease. The preliminary results obtained reach an accuracy of 85.8% and this is a similar accuracy to state-of-the-art methods such as a Naive Bayes classifier and a Support Vector Machine. An advantage of the method proposed in this paper over the aforementioned state of the art classifiers is the descriptive ability of the classifications it produces. The descriptive model can be of great help to aid a doctor in the diagnosis of Alzheimer’s Disease, or even further the understand of how Alzheimer’s Disease affects the hippocampus.

Dose response relationship of droplet applications for the leaf-specific weed control in vegetable crops

As a prelude to leaf-specific weed control using droplets targeted by a robotic weeder, amounts of herbicide required to control individual weed seedlings were estimated. Roundup Biactive was applied at doses equivalent to 1/128th to four times the recommended rate in addition to undiluted Roundup and water controls. Based on the mean ground cover of the seedlings, the recommended dose (1.5 l ha 1) was estimated and droplets were applied to individual plants by micropipette. All treatments contained 1% AS 500 SL, Agromix (adjuvant). Three weeks after application dry weights (DW) of each seedling was recorded. DW reductions of 50% were achieved in the five species tested at less than the recommended rate whereas only in one species was a 90% reduction obtained at that rate. In Galium aparine for example, 19.3 μg of glyphosate reduced DW per plant by 90% compared to the recommended dose of 8.4 μg.

Sparse density estimator with tunable kernels

A new sparse kernel density estimator with tunable kernels is introduced within a forward constrained regression framework whereby the nonnegative and summing-to-unity constraints of the mixing weights can easily be satisfied. Based on the minimum integrated square error criterion, a recursive algorithm is developed to select significant kernels one at time, and the kernel width of the selected kernel is then tuned using the gradient descent algorithm. Numerical examples are employed to demonstrate that the proposed approach is effective in constructing very sparse kernel density estimators with competitive accuracy to existing kernel density estimators.