Profiles of some clinical chemical parameters in growing dwarf and landrace kids.
Plasma creatinine, urea, bilirubin, glucose, cholesterol, calcium, magnesium, sodium, potassium, inorganic phosphorus and total serum proteins were analyzed in kids of Dwarf and Danish Landrace breeds from birth to 12 months of age. The purpose was to determine the reference ranges, age profiles and the influence of other factors. Comparisons between parametric (mean +/- standard deviation) and the corresponding nonparametric (5th and 95th percentile, median) values were calculated for each parameter, the results of which indicated no apparent differences. The levels were very much dependent on age. Creatinine, urea and total serum protein levels increased gradually with age. Glucose and cholesterol levels were high at birth and then decreased with age. The electrolyte concentrations were maintained within narrow limits. Significant differences were observed between kids of different ages (within the breeds), breeds (within similar age) and herds (within the same age and breed). Differences between female and male Landrace kids of the same ages were observed in plasma urea, creatinine, glucose and total serum proteins. It is concluded that age has a major influence on the clinical chemical reference values in young goats, followed by herd and breed, but the influence of sex was small and is negligible in most parameters.
Reference values for some haematological, haematochemical, and electrophoretic parameters in the Girgentana goat
The purpose of this study was to determine haematolog ical, haematochemical, and electrophoretic reference values for the Girgentana goat in order to form a basis for clinical interpretation. The study included 348 female Girgentana goats aged from 1 to 6 years. The animals were divided into 3 groups: goats aged 1-2 years (group A), goats aged 3-4 years (group B), and goats aged 5-6 years (group C). Blood samples were collected from each animal every morning at the same time (0800) by means of jugular venipuncture, and red blood cell (RBC), white blood cell (WBC), neutrophil (NEU), lymphocyte (LYM), monocyte (MONO), eosinophil (EOS), and basophil (BASO) counts, and haemoglobin (Hb), packed cell volume percent (PCV), mean corpuscular volume (MCV), mean corpuscular haemoglobin (MCH), mean corpuscular haemoglobin concentration (MCHC), and red cell distribution width (RDW) were recorded. Additionally, the concentration of some haematochemical parameters, including alanine-aminotransferase (ALT), aspartate-aminotransferase (AST), γ-glutamine-transferase (γ-GT), creatinine, β-hydroxybutyrate, urea, non- esterified fatty acids (NEFA), potassium, sodium, and chloride, and the electrophoretic profile (albumin, α1-globulins, α2-globulins, β-globulins, γ-globulins, total proteins and albumin:globulins ratio) were determined. Statistical analysis (one-way analysis of variance (ANOVA)), followed by Bonferroni's test, showed that there were significant differences only in RBC, MCV, EOS, ALT, AST, creatinine, γ-GT, sodium, urea, NEFA, γ-globulins, and the albumin:globulins ratio (P < 0.05 was considered statistically significant). The values determined in the present study contribute to the knowledge of the reference ranges for the Girgentana goat and can be used for monitoring health and disease diagnosis.
Color image based sorter for separating red and white wheat
A simple imaging system was developed to inspect and sort wheat samples and other grains at moderate feed-rates (30 kernels/s or 3.5 kg wheat/h). A single camera captured color images of three sides of each kernel by using mirrors, and the images were processed using a personal computer (PC). Real time image acquisition and processing was enabled on an ordinary PC under Windows XP operating system using the IEEE 1394 data transfer protocol, DirectX application software, and dual-core computer processor. Image acquisition and transfer to the PC required approximately 17 ms per kernel, and an additional 1.5 ms was required for image processing. After classification, the computer could output a signal from the parallel port to activate an air valve to divert (sort) kernels into a secondary container. Hard red and hard white wheat kernels were used in this study to test and demonstrate sorter capability. Simple image statistics and histograms were used as features. Discriminant analysis was performed with one, two, or three features to demonstrate classification improvements with increased numbers of features. The sorter was able to separate hard red kernels from hard white kernels with 95 to 99% accuracy, depending on the wheat varieties, feed-rate, and number of classification features. The system is an economical and useful instrument for sorting wheat and other grains with high accuracy.