Monthly model for genetic evaluation of laying hens. II. Random regression

1. We investigated the use of monthly production records for genetic evaluation of laying hens, derived from a test day model with random regression in dairy cattle and compared it with other models. 2. Records of 6450 hens, daughters of 180 sires and 1335 dams, were analysed using a model with restricted maximum likelihood (REML): traits considered were monthly and cumulative egg production. Five models were studied: (1) random regression with covariates derived from the regression of Ali and Schaeffer (Canadian Journal of Animal Science, 67: 637-644, 1987) (RRMAS), (2) random regression with covariates derived from quartic polynomial (RRMP4), (3) fixed regression with covariates derived from Ali and Schaeffer (FRM), (4) multiple trait (MTM) and (5) cumulative (CM). 3. The models were compared on the basis of Spearman rank correlations of individual breeding values and sire breeding values estimated from subsets of full-sib split data. The hens (about 10% per generation) which ranked highest on their estimated breeding values from different models were compared phenotypically with their full records. 4. The estimates of heritability resulting from RRMP4 were biased upward from the estimates obtained from MTM, so this model was discarded. The heritabilities for monthly productions from RRMAS and MTM showed a similar pattern. They were high for the 1st month of production, decreased to their lowest value at about month 5 of production and increased again to the end of lay. 5. Spearman rank correlations between animal breeding values estimated by monthly models (RRMAS, FRM and MTM) were high, between 0.91 and 0.98, whereas those between estimates of monthly models and CM were lower, from 0.85 to 0.87. The correlations estimated either from intermittent months of measurements (odd vs even months) or full records were generally high, from 0.93 to 0.99. Information from odd months of production could be sufficient for cost-efficient recording schemes. The RRMAS generally had the highest correlation of sire breeding values between subsets of full-sib records, followed by MTM, RM and CM. Monthly models selected hens with higher productivity than the cumulative model. 6. In conclusion, genetic evaluation based on monthly production may be better than using cumulative production and RRMAS appeared to be the best among the models tested here.     

Baclofen intoxication in a dog successfully treated with hemodialysis and hemoperfusion coupled with intensive supportive care

Objective: To describe a case of confirmed baclofen intoxication in a dog that was successfully treated with hemodialysis and hemoperfusion (HD/HP) and to report the serum baclofen kinetics. Case summary: A 2.5‐year‐old, 23 kg, spayed female Brittany Spaniel‐mix was treated after ingesting 21‐52 mg/kg of baclofen. The dog was comatose and was receiving manual ventilation at the time of presentation. Extracorporeal HD/HP was started 10 hours after admission. Within 3 hours of starting HD/HP the dog began initiating breaths and was extubated 18 hours after admission. Serial serum samples that were obtained during the first 24 hours of hospitalization were later analyzed for baclofen concentrations. The dog had elevated creatine phosphokinase and liver enzymes that correlated with an agitated recovery period. The dog had thrombocytopenia that resolved by 10 days after presentation. New or unique information provided: HD/HP shortened the baclofen serum elimination half‐life from 5 to 1.5 hours in the initial 2 hours of treatment. The intrinsic elimination rate constant (K_intr) for this dog was 0.138/hour and the total elimination rate constant (K_tot) during the first 2 hours of HD/HP treatment was 0.458/hour. In this dog, HD/HP was an effective method for rapidly decreasing serum baclofen concentration after an acute overdose.     

Plasma atrial natriuretic peptide concentration in warmblood horses with heart valve regurgitations

ObjectivesThis study measured plasma atrial natriuretic peptide (ANP) concentration in horses with heart valve regurgitations (HVR) with and without atrial and ventricular dilatation.BackgroundIn humans and small animals, plasma ANP concentration is increased in heart disease and correlates with the severity of clinical signs and heart enlargement.Animals, materials and methodsTen healthy horses (control) and 36 horses with HVR were evaluated by auscultation, electrocardiography, echocardiography, and determination of plasma ANP.ResultsControl horses demonstrated mean plasma ANP concentration of 21 ± 5.4 pg/mL. Of the 36 horses with HVR, 17 horses possessed normal echocardiographic heart size (group 1), 10 horses had a left atrial dilatation (group 2) and 9 horses had both left atrial and ventricular dilatation (group 3). Mean plasma ANP concentration of groups 1, 2 and 3 was 20.1 ± 5.6 pg/mL, 22.9 ± 11.0 pg/mL and 27.6 ± 17.4 pg/mL, respectively. The plasma ANP concentrations of HVR and control horses were not significantly different. The highest ANP concentrations were observed in horses with atrial and ventricular dilatation. No correlation between left atrial or ventricular size, weight, or sex and the plasma ANP concentration was found.ConclusionsNo significant differences in plasma ANP concentration was observed between groups. Further study, especially in horses with clinical signs of heart failure is needed.     

New splitting method for wood fracture characterization

Summary A testing procedure with a new and simple specimen shape is presented which is appropriate to characterize fracturing of inhomogeneous and complex materials like wood. With this, the fracture energy of spruce wood is determined in the TL and RL direction. The size effect, i.e. influences of specimen dimensions on K_IC and G_f (specific fracture energy) are investigated. Stress and deformation distribution in the newly developed specimens are analysed with FE methods. The measured load-displacement curves are approximated by bilinear softening diagrams and FE analysis. Based on these results, it is tried to interpret typical deviations from LEFM's behaviour by mechanisms like microcracking, crack branching or crack tip bridging.The authors thank Dr. A. Teischinger for supplying the testing material and Dipl. Ing M. Elser for preparation of the diagrams. Financial support of the Fonds zur Förderung der wissenschaftlichen Forschung, Wien is gratefully acknowledged.     

Effects of selection felling and gap felling on forest arthropod communities: a case study in a spruce-beech stand of southern Bavaria

The effects of selection felling and gap felling on arthropod communities were studied over 3 years in a spruce-beech stand in southern Bavaria, Germany. The arthropods were sampled in three strata, using pitfall traps on the forest floor as well as flight-interception traps near the forest floor and in the tree crowns. Coleoptera, Araneae, Opiliones, Heteroptera, Isopoda, Diplopoda and Neuropterida were determined to species level and assigned to ecological guilds. In general, the effects of both treatments on arthropod communities were small. An increase of eurytopic species and species of open woodland as well as of indwellers of deadwood was observed, mainly in the first year after felling. However, forest species dominated the communities in all plots, strata and years. In contrast to selection fellings, gap fellings might favour potential pest species. Nevertheless, in the studied managed spruce-dominated forest site out of the natural growth range of spruce, negative ecological effects such as the repression of forest arthropod species are not expected by small scale fellings.     

Stress wave sorting of red maple logs for structural quality

Existing log grading procedures in the United States make only visual assessments of log quality. These procedures do not incorporate estimates of the modulus of elasticity (MOE) of logs. It is questionable whether the visual grading procedures currently used for logs adequately assess the potential quality of structural products manufactured from them, especially those for which MOE is of primary concern. The purpose of this study was to investigate the use of stress wave nondestructive evaluation techniques to sort red maple logs for the potential quality of lumber obtained from them. Ninety-five red maple logs were nondestructively evaluated using longitudinal stress wave techniques and sorted into four stress wave grades. The logs were then sawn into cants and lumber. The same procedure was used to obtain stress wave times in the cants and lumber. The lumber specimens were then dried and graded using a transverse vibration technique. The results of this study showed that good relationships existed between stress wave times measured in logs, cants, and the lumber produced from the logs. It was found that log stress wave grades have positive relationships with the lumber grades. Logs with high stress wave grades produced high-grade lumber. These findings indicate that the longitudinal stress wave technique has potential in sorting logs and cants for the production of high MOE products.The Forest Products Laboratory is maintained in cooperation with the University of Wisconsin. This article was written and prepared by U.S. Government employees on official time, and it is therefore in the public domain and not subject to copyright.     

Utilization of Weather Data in Predicting Bread Loaf Volume by Neural Network Method

The unique ability of wheat (Triticum aestivum L.) flour to produce cohesive dough has helped to make it the most widely used cereal crop for bread and other baked food products. Measurement of end‐use qualities, such as loaf volume, is ideally carried out through assessing loaves of bread; however, this is resource intensive. Predictive testing methods are more often utilized to identify wheat genotypes with potentially acceptable loaf volume, although more accurate predictive methods would be beneficial. Our objectives were to study the influence of weather observations on bread loaf volume and flour and dough quality data, to use a neural network (NN) model to predict loaf volume with select input data, and to compare the best multiple regression models identified with their NN counterparts. Weather data collected at 20 days after heading (DAH) showed the highest correlations with bread loaf volume when compared with prior intervals. A NN model containing maximum, minimum, and nighttime temperatures produced the highest coefficient of determination for predicting loaf volume. Our results showed that the NN model explained up to 20% more loaf volume variation than a similar regression model. Weather parameters representing conditions at 20 DAH played a significant role in loaf volume prediction.