Investigation of sainfoin (Onobrychis viciifolia) cultivar differences on nitrogen balance and fecal egg count in artificially infected lambs

Research in ruminant nutrition and helminth control with forages, which contain condensed tannins (CT), suggests that varying responses may depend not only on CT concentration but also on CT composition. An experiment was designed to test this by feeding 2 dried sainfoin cultivars (Visnovsky and Perly), which differed in CT properties, to lambs that were artificially infected with the abomasal blood-sucking nematode Haemonchus contortus. Twenty-four infected lambs received one of these 2 cultivars; the feeds were either untreated or treated with the CT-binding polyethylene glycol over 4 wk (n = 6). The 2 cultivars were also fed to 2 × 6 uninfected lambs. Nutrient digestibility, N balance, ADG, plasma urea together with indicators of infection [fecal egg count (FEC), abomasal worm count, per capita female fecundity, erythrocytic indices, and serum protein] were determined. The specific effects of sainfoin cultivar, CT, and infection were evaluated by contrast analysis. Digestibility of both NDF and ADF were lower (P < 0.001) with Perly compared to Visnovsky. The apparent nutrient digestibility was reduced (P < 0.001) by CT. However, no clear cultivar effects were evident on N excretion and retention. Condensed tannins reduced (P = 0.05) body N retention and shifted (P < 0.001) N excretion from urine to feces. Unlike cultivar and CT, infection decreased (P = 0.002) ADG. Plasma urea concentration was lower (P = 0.007) in Perly- compared to Visnovsky-fed lambs and was decreased (P < 0.001) by CT. Plasma concentrations of essential and semi-essential AA were increased (P < 0.001) by CT. The groups of infected lambs did not clearly differ in abomasal worm counts and erythrocytic indicators. In the last 2 to 3 wk of the experiment, FEC was lower (P ≤ 0.01) when feeding CT. The lack of substantial cultivar effects suggests that the differences in CT properties may have been too small to result in nutritional and anthelmintic effects. The present results indicate that sainfoin CT had a mitigating effect on FEC and, consequently, pasture infectivity. However, the reduction was too low to expect any significant benefits in an Haemonchus-dominated system. Therefore, the use of sainfoin for controlling H. contortus should only be one component within an integrated worm control system.

Foreign credit: travel writing and authenticity in the dutch translation of The memoirs and travels of Mauritius Augustus, Count de Benyowsky (1790)

This article explores the translation and reception of the Memoirs and Travels (1790) of Count Mauritius Augustus Benyowsky (1746-86) in the Netherlands, and examines the complications, tensions and problems that transfer between a major and a more minor European language involves. I analyse how the Dutch translator Petrus Loosjes Adriaanszoon positioned himself as a mediator between these very different source and target cultures and ask how he dealt with the problems of plausibility and ‘credit’ which had beleaguered the reception of the Memoirs and Travels from the outset. In this article I am concerned to restore minority languages to the discussion of how travel literature circulated in Western Europe at the close of the eighteenth century and to demonstrate how major/minor language translation was central to the construction of Dutch-language culture in the Low Countries in this period.

Geoadditive models for regional count data: an application to industrial location

We propose a geoadditive negative binomial model (Geo-NB-GAM) for regional count data that allows us to address simultaneously some important methodological issues, such as spatial clustering, nonlinearities, and overdispersion. This model is applied to the study of location determinants of inward greenfield investments that occurred during 2003–2007 in 249 European regions. After presenting the data set and showing the presence of overdispersion and spatial clustering, we review the theoretical framework that motivates the choice of the location determinants included in the empirical model, and we highlight some reasons why the relationship between some of the covariates and the dependent variable might be nonlinear. The subsequent section first describes the solutions proposed by previous literature to tackle spatial clustering, nonlinearities, and overdispersion, and then presents the Geo-NB-GAM. The empirical analysis shows the good performance of Geo-NB-GAM. Notably, the inclusion of a geoadditive component (a smooth spatial trend surface) permits us to control for spatial unobserved heterogeneity that induces spatial clustering. Allowing for nonlinearities reveals, in keeping with theoretical predictions, that the positive effect of agglomeration economies fades as the density of economic activities reaches some threshold value. However, no matter how dense the economic activity becomes, our results suggest that congestion costs never overcome positive agglomeration externalities.

Relationship between the somatic cell count in raw milk and the casein fractions of UHT milk

The relationship between somatic cell count (SCC) in raw milk and casein fractions of 15 batches of the corresponding ultra-high-temperature (UHT) milk was examined. Raw milk was collected, pasteurised and submitted to UHT treatment. Samples of the UHT milk were taken on days 8, 30, 60, 90 and 120 of storage at room temperature and their casein fractions analysed by high performance liquid chromatography. SCC ranged from 197,000 to 800,000 cells/mL. No correlation (p>0.05) was found between SCC and K-casein concentrations in raw or UHT milks. The alpha(s2) and P-casein concentrations in raw milk were negatively correlated with SCC (p<0.05). In UHT milk, negative correlations were observed for a,1-casein (p<0.05) and beta-casein (p<0.05) on the 8th day, and for alpha S-2-casein (p<0.01) on the 60th day of storage. Results indicate that higher SSC in raw milk is associated with substantial degradation of beta-casein and alpha(s)-casein, which may lead to quality defects in UHT milk during storage. Aust. J. Dairy Technol. 63, 45-49

Image processing technique to count the number of logs in a timber truck

This paper summarises the results of using image processing technique to get information about the load of timber trucks before their arrival using digital images or geo tagged images. Once the images are captured and sent to sawmill by drivers from forest, we can predict their arrival time using geo tagged coordinates, count the number of (timber) logs piled up in a truck, identify their type and calculate their diameter. With this information we can schedule and prioritise the inflow and unloading of trucks in the light of production schedules and raw material stocks available at the sawmill yard. It is important to keep all the actors in a supply chain integrated coordinated, so that optimal working routines can be reached in the sawmill yard.   

Making difference count: a demographic study of overseas born teachers

This article reports on the findings of a demographic study which is part of an Australian Research Council funded case study. It seeks to better understand the experiences and challenges facing teachers who are overseas born and educated non-native speakers of English. The investigation has been carried out in Victorian government metropolitan, regional city and rural secondary schools. A statewide survey has provided data about this previously `invisible' population of teachers, their demographic location, their qualifications, backgrounds and the nature of their teaching experiences. The findings raise a number of concerns in relation to overseas born teachers of ethnic difference in rural schools where cultural and professional isolation may be of particular concern. We explore some of the implications for teacher education in the light of increasing numbers of overseas born students entering teacher education courses.

Understanding environmental influences on nutrition and physical activity behaviors: where should we look and what should we count?

Research interest in the influence of environmental factors on nutrition and physical activity behaviors has surged internationally in recent years. This is evident from a rapidly expanding literature and facilitated by advances in methodological and analytical approaches to assessing multiple levels of influence on health behaviors. However, a number of conceptual challenges complicate research endeavours in this field. The purpose of this paper is to provide a 'state of the science' overview of evidence regarding environmental influences on nutrition and physical activity behaviors. We focus particularly on a number of key conceptual and methodological issues, including: a consideration of how the environment is defined; the selection and operationalization of environmental exposures; and the importance of integrating existing understanding of individual influences on behavior with the emerging data on the role of the environment. We draw on examples from the published literature including our own research studies to illustrate these issues. We conclude by proposing a research agenda to progress understanding of the influences of the environment on population nutrition and physical activity behaviors.

A nonparametric Bayesian Poisson Gamma model for count data

We propose a nonparametric Bayesian, linear Poisson gamma model for count data and use it for dictionary learning. A key property of this model is that it captures the parts-based representation similar to nonnegative matrix factorization. We present an auxiliary variable Gibbs sampler, which turns the intractable inference into a tractable one. Combining this inference procedure with the slice sampler of Indian buffet process, we show that our model can learn the number of factors automatically. Using synthetic and real-world datasets, we show that the proposed model outperforms other state-of-the-art nonparametric factor models.

Disease mapping and regression with count data in the presence of overdispersion and spatial autocorrelation: a Bayesian model averaging approach

This paper applies the generalised linear model for modelling geographical variation to esophageal cancer incidence data in the Caspian region of Iran. The data have a complex and hierarchical structure that makes them suitable for hierarchical analysis using Bayesian techniques, but with care required to deal with problems arising from counts of events observed in small geographical areas when overdispersion and residual spatial autocorrelation are present. These considerations lead to nine regression models derived from using three probability distributions for count data: Poisson, generalised Poisson and negative binomial, and three different autocorrelation structures. We employ the framework of Bayesian variable selection and a Gibbs sampling based technique to identify significant cancer risk factors. The framework deals with situations where the number of possible models based on different combinations of candidate explanatory variables is large enough such that calculation of posterior probabilities for all models is difficult or infeasible. The evidence from applying the modelling methodology suggests that modelling strategies based on the use of generalised Poisson and negative binomial with spatial autocorrelation work well and provide a robust basis for inference.