Transesophageal monitoring of aortic blood flow during nonemergent canine surgeries

Objective: To establish baseline values for descending thoracic aortic blood flow parameters as determined with a transesophageal combined M‐mode and pulsed Doppler ultrasound‐based monitoring method. Design: Preliminary observational study. Setting: University small animal teaching hospital. Animals: The study population consisted of anesthetized canine patients undergoing nonemergent surgeries. Prospectively set criteria for inclusion were adequate body size for placement of the esophageal probe and a nonemergent reason for surgery. The criterion for exclusion was recent trauma. Interventions: Placement of the transesophageal probe. Measurements and main results: Data was collected during 15 surgeries. Data from three dogs was excluded from data analysis (two recently hit by motor vehicles, one recently having undergone a total hip replacement). Each parameter was stable across time within each individual dog. The ranges of the descending thoracic aortic parameters across the 12 nonemergent cases were as follows: blood flow, 0.038–0.085 L min^?1 kg^?1; blood flow per beat, 0.31–0.84 mL kg^?1; blood acceleration, 6–29 m s^2?1; blood peak velocity, 38–105 m s^?1; left ventricular ejection time interval 331–492 ms; and diameter, 0.30–0.93 mm kg^?1. Conclusions: The range of descending thoracic aortic blood flow parameters encountered in this small group of dogs during nonemergent surgeries was broad; however, each parameter was quite stable across time with little change occurring in any dog during monitoring.     

Factors that affect equine wound repair.

The rate and outcome of wound healing are determined by many factors,some of which are already in effect when the horse is first presented to the veterinarian. A thorough understanding of wound healing principles,coupled with clear client communication, should enable the practitioner to minimize the number of additional factors that may exacerbate the initial situation.     

Quantifying patch distribution at multiple spatial scales: Applications to wildlife-habitat models

Multiscale analyses are widely employed for wildlife-habitat studies. In most cases, however, each scale is considered discrete and little emphasis is placed on incorporating or measuring the responses of wildlife to resources across multiple scales. We modeled the responses of three Arctic wildlife species to vegetative resources distributed at two spatial scales: patches and collections of patches aggregated across a regional area. We defined a patch as a single or homogeneous collection of pixels representing 1 of 10 unique vegetation types. We employed a spatial pattern technique, three-term local quadrat variance, to quantify the distribution of patches at a larger regional scale. We used the distance at which the variance for each of 10 vegetation types peaked to define a moving window for calculating the density of patches. When measures of vegetation patch and density were applied to resource selection functions, the most parsimonious models for wolves and grizzly bears included covariates recorded at both scales. Seasonal resource selection by caribou was best described using a model consisting of only regional scale covariates. Our results suggest that for some species and environments simple patch-scale models may not capture the full range of spatial variation in resources to which wildlife may respond. For mobile animals that range across heterogeneous areas we recommend selection models that integrate resources occurring at a number of spatial scales. Patch density is a simple technique for representing such higher-order spatial patterns.     

The potential of mini-ridging for controlling intrarow weeds: estimating minimum lethal burial depth

Weed management using synthetic herbicides is undergoing a global decline, necessitating a re-evaluation of existing control measures and the development of novel weed management tools. ‘Mini-ridging’ is a non-discriminatory, physical weeding method that functions by burying weeds in the intrarow with a laterally shifted ridge of soil. In glasshouse trials using potted plants, we found that plant recovery after soil application was influenced by plant size, which in turn was influenced by plant species, developmental stage and/or age. The likelihood of plant recovery after soil application was negatively related to the depth of soil applied: very few plants survived total coverage by soil but, conversely, survival could be substantial if some parts of the plants were not covered. The results suggest that burial under a depth of 6 cm of soil would eliminate most plants regardless of species or growth stage. Larger plants would require the application of a greater total depth of soil to achieve this 6 cm of soil cover, and weed management would, therefore, tend to be more successful and more practical if weeds were targeted when still small. This research demonstrates the potential of plant burial as a simple and reliable means of non-chemical weed management, and re-emphasises that, for weed control to be effective, the applied soil layer must cover the whole plant.     

INVITED REVIEW—IMAGE REGISTRATION IN VETERINARY RADIATION ONCOLOGY: INDICATIONS,IMPLICATIONS, AND FUTURE ADVANCES

The field of veterinary radiation therapy (RT) has gained substantial momentum in recent decades with significant advances in conformal treatment planning, image‐guided radiation therapy (IGRT), and intensity‐modulated (IMRT) techniques. At the root of these advancements lie improvements in tumor imaging, image alignment (registration), target volume delineation, and identification of critical structures. Image registration has been widely used to combine information from multimodality images such as computerized tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) to improve the accuracy of radiation delivery and reliably identify tumor‐bearing areas. Many different techniques have been applied in image registration. This review provides an overview of medical image registration in RT and its applications in veterinary oncology. A summary of the most commonly used approaches in human and veterinary medicine is presented along with their current use in IGRT and adaptive radiation therapy (ART). It is important to realize that registration does not guarantee that target volumes, such as the gross tumor volume (GTV), are correctly identified on the image being registered, as limitations unique to registration algorithms exist. Research involving novel registration frameworks for automatic segmentation of tumor volumes is ongoing and comparative oncology programs offer a unique opportunity to test the efficacy of proposed algorithms.