Evaluation of the role of lower urinary tract disease in cats with urine-marking behavior

OBJECTIVE: To determine whether findings of urinalyses could be used to reliably distinguish gonadectomized cats with urine-marking behavior from those with no problem urination. DESIGN: Case control study. ANIMALS: 58 gonadectomized cats (47 males and 11 females) with urine-marking behavior (ie, marking of vertical surfaces) and 39 (26 males and 13 females) without problem urination or urinary tract-associated conditions. PROCEDURE: Urine was collected by cystocentesis from all cats. Findings of urinalyses of cats with urine-marking behavior were analyzed statistically for sex-related differences and differences between cats that marked vertical surfaces only and those that marked both vertical and horizontal surfaces; findings of urinalyses of control cats were compared between sexes. Subsequently, results of urinalyses of cats with urine-marking behavior were compared with those of control cats. RESULTS: With regard to variables measured via urinalysis, there were no differences between male and female cats within either group. Among cats with urine-marking behavior, there were no differences between those that only marked vertically and those that marked vertically and horizontally. Analyses of data from all cats with urine-marking behavior and control cats revealed no differences that could be associated with urine marking. CONCLUSIONS AND CLINICAL RELEVANCE: These data suggest that urine-marking behavior by gonadectomized cats is an aspect of normal behavior. Clinicians are advised to focus on behavioral history of house-soiling cats to differentiate between urine-marking behavior and inappropriate urination; for the latter, urinalysis is appropriate to rule out lower urinary tract disorders.     

Comparison of four methods for detection of oestrus in dairy cows with resynchronised oestrous cycles

OBJECTIVE: To compare the sensitivity and positive predictive value of four methods for detection of oestrus in dairy cows with resynchronised oestrous cycles. PROCEDURE: Oestrous cycles in cows in three herds were synchronised for a first round of artificial insemination (AI) and then resynchronised for a second round of AI. Sensitivity and positive predictive value of four aids (pedometers, radiotelemetric transmitters [HeatWatch; HW], tail-paint and heatmount detectors) that were used to detect the resynchronised oestrus were compared. Milk progesterone concentration and pregnancy testing at 12 weeks were used as the reference standard for cows being in oestrus. RESULTS: The mean sensitivity and positive predictive value for detecting the resynchronised oestrus, for each aid that was used, was > 80%. Tail-paint was significantly more sensitive at detecting oestrus compared to heatmount detectors (P = 0.002), but not significantly more sensitive than pedometers (P = 0.07) or HW (P = 0.55) for detecting oestrus (91.3, 85.7; 81.4 and 88.4%, respectively). Positive predictive value of HW for detecting oestrus was greater than tail-paint (P = 0.014) and heatmount detectors (P = 0.024) but not pedometers (P = 0.25; 100, 91.7, 92.9 and 87.5%, respectively). Positive predictive value of heatmount detectors was greater than pedometers in herd C (93.4% vs 73.3%; P = 0.035) but not in herds A (95.0% vs 90.0%; P = 0.56) or B (90.8% vs 100%; P = 0.10). No other significant differences in sensitivity or positive predictive value of detection of oestrus were found between aids. CONCLUSION: Tail-paint, heatmount detectors, pedometers and HW provide a high sensitivity (> 80%) and positive predictive value (> 85%) of detecting oestrus in dairy cows with resynchronized oestrous cycles.     

Microbial cross-contamination by airborne dispersion and contagion during defeathering of poultry

1. A readily identifiable strain of Escherichia coli K12 was used as a 'marker' organism to determine the sources, routes and patterns of microbial cross-contamination during mechanical defeathering of broiler chicken carcases. 2. Inoculation of scald water with the marker organism led to a relatively even pattern of carcase contamination during subsequent defeathering. Microbial cross-contamination was greater by this route of inoculation than by either surface inoculation of a 'seeder' carcase or oral inoculation of a live bird one day before slaughter. 3. Dispersal of the marker organism was strongly influenced by the mechanical action of the defeathering machines. Forward transmission of the marker occurred by aerosol or large airborne droplets and particulates such as feathers. Moving carcases through the defeathering machines when these were non-operational clearly reduced backward transmission of the marker. 4. Although microbial dispersal was unaffected by increasing the spacing between individual carcases or installing a water curtain at the entry and exit of the defeathering machines, shielding of carcases with aluminium baffles reduced counts of the marker organism from contaminated carcases by > 90%. 5. The results imply that microbial cross-contamination of broiler chicken carcases during defeathering occurs mainly via the airborne route, which could be contained by physical means.     

Use of artificial neural networks to estimate production variables of broilers breeders in the production phase

1. Although the poultry industry uses state-of-the-art equipment and up-to-date services, in Brazil it generally makes decisions involving all its production variables based on purely subjective criteria. This paper reports the use of artificial neural networks to estimate performance in production birds belonging to a South Brazilian poultry farm. 2. Recorded data from 22 broiler production breeder flocks were obtained, from April, 1998 to December, 1999, which corresponded to 689 data lines of weekly recordings. 3. These data were processed by artificial neural networks using the software NeuroShell 2 version 4.0 (Ward Systems Group). The artificial neural network models generated were compared and selected based on their largest determination coefficient (R2), lowest Mean Squared Error (MSE), as well as on a uniform scatter in the residual plots. The authors conclude that it is possible to explain the performance variables of production birds, with the use of artificial neural networks. 4. The method allows the decisions made by the technical staff to be based on objective, scientific criteria, allows simulations of the consequences related to these decisions, and reports the contribution of each variable to the variables under study.