High field magnetic resonance imaging contributes to diagnosis of equine distal tarsus and proximal metatarsus lesions: 103 horses

Injury of the distal tarsus and proximal metatarsus commonly causes lameness. Magnetic resonance imaging (MRI) allows concurrent assessment of both the distal tarsal joints and suspensory ligament origin, and aids identification of lesions that may otherwise go undetected by other modalities. In this retrospective observational study, the medical records of a veterinary imaging center were searched for MRI exams of the distal tarsus and proximal metatarsus for the years 2012 through 2014. Studies for 125 limbs of 103 horses were identified and retrospectively evaluated by two board‐certified veterinary radiologists. Soft tissue and osseous changes were characterized and graded by degree of severity. The patients’ signalment, lameness severity, and results of diagnostic analgesia were recorded. Osteoarthritic changes of the distal intertarsal and tarsometatarsal joints were the most common findings. Other findings included bone marrow lesions, degenerative changes of the small cuboidal bones, subchondral cystic lesions, and intertarsal desmopathy. Suspensory ligament desmopathy was found in 53% of limbs. Fourty‐seven percent of limbs that responded to analgesia of the proximal suspensory ligament had more severe lesions in the distal tarsus. Bone marrow lesions of the third tarsal bone were the only MRI finding that correlated with grade of lameness in patients for which lameness grade was reported. The grade of lameness has a poor correlation with the severity of lesions found on MRI. The findings support the use of MRI for simultaneous evaluation of the proximal metatarsus and distal tarsus, particularly given the difficulty of lesion localization with diagnostic analgesia.     

Effects of Mowing and Tebuthiuron on the Nutritional Quality of Wyoming Big Sagebrush

Wyoming big sagebrush (Artemisia tridentata Nutt. ssp. wyomingensis Beetle & Young) is the most abundant and widely distributed subspecies of big sagebrush and has been treated through chemical application, mechanical treatments, and prescribed burning in efforts thought to improve habitat conditions for species such as greater sage-grouse (Centrocercus urophasianus) and mule deer (Odocoileus hemionus). Although the response of structural attributes of sagebrush communities to treatments is well understood, there is a need to identify how treatments influence the quality of sagebrush as winter food for wildlife. Our purpose was to identify how mowing and tebuthiuron treatments influenced dietary quality of Wyoming big sagebrush in central Wyoming. Two study areas were mowed in January and February 2014, and tebuthiuron was applied in two study areas in May 2014. We constructed 6 exclosures in each of these four study areas (24 total), which encompassed 30 × 30 m areas of treated and untreated sagebrush within each exclosure. Samples of current annual growth were collected from 18 sagebrush plants from treated and 12 plants from control portions of mowing exclosures during November 2013–2015 and tebuthiuron exclosures during November 2014–2015. Samples were analyzed for crude protein and plant secondary metabolites known to influence dietary selection of sagebrush by sage-grouse and other sagebrush-occurring herbivores. Our results suggest mowing and tebuthiuron treatments may slightly increase crude protein concentrations directly after treatments without immediate changes in plant secondary metabolites. Slight increases in dietary quality of sagebrush following treatments coupled with potential trade-offs with loss of biomass associated with treatments corroborates previous research that treating Wyoming big sagebrush may have little benefit for sage-grouse and other sagebrush-dependent wildlife. Future work should evaluate not only how treatments influence sage-grouse habitat use and reproductive success but also how treatments influence other wildlife species in fragile sagebrush ecosystems.     

Distribution and prevalence of T. bryosalmonae in Austria: A first survey of trout from rivers with a shrinking population

The first evidence of proliferative kidney disease (PKD) in an Austrian river (the River Kamp) was documented in 2016, and no information on the PKD infection status of trout in other rivers was available. Since then, brown trout (Salmo trutta fario) and rainbow trout (Oncorhynchus mykiss) have been collected from rivers in Upper and Lower Austria for different diagnostic purposes. In this study, we summarize the recent findings of a first survey concerning the distribution of Tetracapsuloides bryosalmonae, the causative agent of proliferative kidney disease (PKD), from these samples. Between September 2015 and October 2017, a total of 280 brown trout and 39 rainbow trout were collected from 21 rivers in the provinces of Upper and Lower Austria. T. bryosalmonae was detected by PCR of kidney tissue in 17 of 21 sampled rivers and in 138 of 280 brown trout as well as in 11 of 39 rainbow trout. Pathological signs of PKD (e.g., hypertrophy of the kidney) were observed in 33 analysed brown trout and six rainbow trout samples. No correlations between fish infected by T. bryosalmonae and the parameters size and age class, condition factor, geological origin of the streams and distribution within the river course were found, while positively tested fish are significantly increased at sampling sites exceeding water temperatures of 15°C for median periods of 115 days. The prevalence within the affected streams or stream sections is highly variable, and in single rivers, infection rates of up to 90% are confirmed.     

Flowability Properties of Commercial Distillers Dried Grains with Solubles (DDGS)

Distillers dried grains with solubles (DDGS), the major coproduct from the corn‐based fuel ethanol industry, is primarily used as livestock feed. Due to high protein, fiber, and energy contents, there is a high demand for DDGS. Flowability of DDGS is often hindered due the phenomenon of caking. Shipping and handling of DDGS has thus become a major issue due to bridge formation between the DDGS particles. The objective of this investigation was to measure flowability characteristics of DDGS samples from five ethanol plants in the north central region of the United States. Carr and Jenike tests were performed and the resulting data were mathematically compared with a previously developed empirical model. The largest particles had an average geometric mean diameter (GMD) of 1.19 mm, while the lowest particle size had an average GMD of 0.5 mm. Soluble solid levels were ≈10.5–14.8% (db). The effective angle of friction (δ) was 43.00–57.00°. Additionally, a few parameters exhibited fairly high linear correlations, including aerated and packed bulk densities (r = 0.97), geometric standard deviation and Carr compressibility (r = 0.71), geometric standard deviation and Hausner ratio (r = –0.70). Overall flowability assessment indicated that the commercial DDGS samples did have the potential for flow problems, although no samples exhibited complete bridging. Quantifying DDGS flowability is a necessary step toward overcoming this logistical challenge facing the fuel ethanol industry.     

Drying Kinetics of Distillers Wet Grains (DWG) Under Varying Condensed Distillers Solubles (CDS) and Temperature Levels

Distillers dried grains with solubles (DDGS) is a widely used animal feed. But transportation of DDGS is often troublesome because of its stickiness. DDGS is formed by combining condensed distillers solubles (CDS) with distillers wet grains (DWG) and then drying. As a first step toward understanding drying behavior, this study's objective was to investigate batch‐drying kinetic behavior of DWG with three CDS addition levels (10, 15, and 20% wb) and three drying‐temperature levels (100, 200, and 300°C). Multiple nonlinear mathematical models were used to fit experimental drying data for moisture content versus drying rate. A new comprehensive model was developed (R² = 0.89, SEM = 18.60) from a modified Chen and Douglas model to incorporate CDS and drying‐temperature terms. Drying temperature affected drying rate more significantly than did changes in CDS level; thus, drying temperature was the main effect and CDS was a subeffect. Increasing the drying temperature increased the drying rate significantly for all levels of CDS addition. This model can be used for predicting DWG drying behavior under broad operating conditions; it can be used to help the industry produce better DDGS, which may thus result in better DDGS handling and transport characteristics.     

Artificial Neural Network Modeling of Distillers Dried Grains with Solubles (DDGS) Flowability with Varying Process and Storage Parameters

Neural network (NN) modeling techniques were used to predict flowability behavior of distillers dried grains with solubles (DDGS) prepared with varying levels of condensed distillers solubles (10, 15, and 20%, wb), drying temperatures (100, 200, and 300°C), cooling temperatures (–12, 25, and 35°C), and storage times (0 and 1 month). Response variables were selected based on our previous research results and included aerated bulk density, Hausner ratio, angle of repose, total flowability index, and Jenike flow index. Various NN models were developed using multiple input variables in order to predict single‐response and multiple‐response variables simultaneously. The NN models were compared based on R², mean square error, and coefficient of variation obtained. In order to achieve results with higher R² and lower error, the number of neurons in each hidden layer, the step size, the momentum learning rate, and the number of hidden layers were varied. Results indicate that for all the response variables, R² > 0.83 was obtained from NN modeling. Compared with our previous studies, NN modeling provided better results than either partial least squares modeling or regression modeling, indicating greater robustness in the NN models. Surface plots based on the predicted values from the NN models yielded process and storage conditions for favorable versus cohesive flow behavior for DDGS. Modeling of DDGS flowability using NN has not been done before, so this work will be a step toward the application of intelligent modeling procedures to this industrial challenge.