Quasiamidostomum fulicae (Rudolphi, 1819) Lomakin, 1991, is a species of which the systematic position is still unclear, and it is reported in the literature under many synonyms. In the present study, an attempt has been made at establishing the ultimate systematic position of Quasiamidostomum fulicae against the backdrop of selected Amidostomatinae species. The parasites were identified based on measurements of external and internal structures. Ecological analysis of Q. fulicae was carried out using the quantitative indices (frequency, prevalence, mean intensity, relative abundance, and dominance index). Statistical analyses (discriminant analysis) were performed on measurement data. The intestines of 77 coots were examined. They yielded a total of 398 parasites, including 67 identified as Q. fulicae. Both males and females were located in the muscular gizzard. The morphometric analysis of Q. fulicae in this study showed the dimensions of all the internal organs to be in agreement with measurements reported by other authors. The discriminant analysis, used to find the differences between the examined nematode species (Amidostomoides acutum, A. petrovi, A. monodon, Amidostomum anseris, and Quasiamidostomum fulicae), gave highly significant results (P < 0.0001) with respect to both males and females. The results justify the separation of Q. fulicae from the genus Amidostomum.

OBJECTIVE To develop methods to identify and characterize activity monitor (AM) data signatures for jumps performed by cats. ANIMALS 13 healthy, client-owned cats without evidence of osteoarthritis or degenerative joint disease. PROCEDURES Each cat was fitted with the same AM, individually placed in an observation room, then simultaneously recorded by 3 video cameras during the observation period (5 to 8 hours). Each cat was encouraged to jump up (JU), jump down (JD), and jump across (JA) during the observation period. Output from the AM was manually annotated for jumping events, each of which was characterized by functional data analysis yielding relevant coefficients. The coefficients were then used in linear discriminant analysis to differentiate recorded jumps as JUs, JDs, or JAs. To assess the model's ability to distinguish among the 3 jump types, a leave-one-out cross-validation method was used, and the misclassification error rate of the overall categorization of the model was calculated. RESULTS Of 731 jumping events, 29 were misclassified. Overall, the mean misclassification error rate per cat was 5.4% (range, 0% to 12.5%), conversely indicating a correct classification rate per cat of 94.6%. CONCLUSIONS AND CLINICAL RELEVANCE Results indicated that the model was successful in correctly identifying JUs, JDs, and JAs in healthy cats. With advancements in AM technology and data processing, there is potential for the model to be applied in clinical settings as a means to obtain objective outcome measures.

Objective—To describe findings of 3.0-T multivoxel proton magnetic resonance spectroscopy (1H-MRS) in dogs with inflammatory and neoplastic intracranial disease and to determine the applicability of 1H-MRS for differentiating between inflammatory and neoplastic lesions and between meningiomas and gliomas. Animals—33 dogs with intracranial disease (19 neoplastic [10 meningioma, 7 glioma, and 2 other] and 14 inflammatory). Procedures—3.0-T multivoxel 1H-MRS was performed on neoplastic or inflammatory intracranial lesions identified with conventional MRI. N-acetylaspartate (NAA), choline, and creatine concentrations were obtained retrospectively, and metabolite ratios were calculated. Values were compared for metabolites separately, between lesion categories (neoplastic or inflammatory), and between neoplastic lesion types (meningioma or glioma) by means of discriminant analysis and 1-way ANOVA. Results—The NAA-to-choline ratio was 82.7% (62/75) accurate for differentiating neoplastic from inflammatory intracranial lesions. Adding the NAA-to-creatine ratio or choline-to-creatine ratio did not affect the accuracy of differentiation. Neoplastic lesions had lower NAA concentrations and higher choline concentrations than inflammatory lesions, resulting in a lower NAA-to-choline ratio, lower NAA-to-creatine ratio, and higher choline-to-creatine ratio for neoplasia relative to inflammation. No significant metabolite differences between meningiomas and gliomas were detected. Conclusions and Clinical Relevance—1H-MRS was effective for differentiating inflammatory lesions from neoplastic lesions. Metabolite alterations for 1H-MRS in neoplasia and inflammation in dogs were similar to changes described for humans. Use of 1H-MRS provided no additional information for differentiating between meningiomas and gliomas. Proton MRS may be a beneficial adjunct to conventional MRI in patients with high clinical suspicion of inflammatory or neoplastic intracranial lesions.

The aim of this study was carried out to obtain the discriminant variables between Pasundan and Ongole bulls through morphometrical measurements. Six morphometric traits of chest girth (CG), withers height (WH), body length (BL), chest depth (CD), hip height (HH) and rump width (RW) were performed in this study. Total of 110 bulls (72 Pasundan and 38 Ongole) from West Java Province of Indonesia were used in this study.

OBJECTIVE To predict histologic grade of meningiomas in dogs via texture analysis (TA) of MRI scans of the brain and spinal cord. SAMPLE 58 sets of MRI scans of the brain and spinal cord of dogs with histologically diagnosed meningioma. PROCEDURES MRI sequences were divided into a training set and a test set, and results of histologic assessment were obtained. Tumors were histologically grouped as benign (stage I) or atypical-anaplastic (stage II or III). Texture analysis was performed by use of specialized software on T2-weighted (T2W) and pre- and postcontrast T1-weighted (T1W) images. A set of 30 texture features that provided the highest discriminating power between the 2 histologic classes in the training set was automatically selected by the TA software. Linear discriminant analysis was performed, and the most discriminant factor (MDF) was calculated. The previously selected texture features were then used for linear discriminant analysis of the test set data, and the MDF was calculated. RESULTS For the training set, TA of precontrast T1W images provided the best diagnostic accuracy; a cutoff MDF of < 0.0057 resulted in a sensitivity of 97.4% and specificity of 95.0% for discriminating benign from atypical-anaplastic meningiomas. Use of postcontrast T1W and T2W images yielded poorer diagnostic performances. Application of the MDF cutoff calculated with the training set to the MDF calculated with the test set provided a correct classification rate of 96.8% for precontrast T1W images, 92.0% for postcontrast T1W images, and 78.9% for T2W images. CONCLUSIONS AND CLINICAL RELEVANCE Findings supported the potential clinical usefulness of TA of MRI scans for the grading of meningiomas in dogs.

The objective of this study was to develop a model using equine data from geographically limited surveillance locations to predict risk categories for West Nile virus (WNV) infection in horses in all geographic locations across the province of Saskatchewan. The province was divided geographically into low-, medium-, or high-risk categories for WNV, based on available serology information from 923 horses obtained through 4 studies of WNV infection in horse populations in Saskatchewan. Discriminant analysis was used to build models using the observed risk of WNV in horses and geographic division-specific environmental data as well as to predict the risk category for all areas, including those beyond the surveillance zones. High-risk areas were indicated by relatively lower rainfall, higher temperatures, and a lower percentage of area covered in trees, water, and wetland. These conditions were most often identified in the southwest corner of the province. Environmental conditions can be used to identify those areas that are at highest risk for WNV. Public health managers could use prediction maps, which are based on animal or human information and developed from annual early season meteorological information, to guide ongoing decisions about when and where to focus intervention strategies for WNV.

OBJECTIVE To examine ultrasonographic predictors of ovarian development in European eels (Anguilla anguilla) undergoing hormonal treatment for assisted reproduction. ANIMALS 83 female European eels. PROCEDURES Eels received weekly IM injections of salmon pituitary extract (first injection = week 1). Ultrasonography of the ovaries was performed twice during hormonal treatment (weeks 7 and 11). Eels were identified on the basis of body weight as having an adequate response by weeks 14 to 20 or an inadequate response after injections for 21 weeks. Eels were euthanized at the end of the experiment and classified by use of ovarian histologic examination. Ovarian cross-sectional area and size of eel (ie, length3) were used to classify eels (fast responder, slow responder, or nonresponder) and to calculate an ultrasonographic-derived gonadosomatic index. Gray-level co-occurrence matrices were calculated from ovarian images, and 22 texture features were calculated from these matrices. RESULTS The ultrasonographic-derived gonadosomatic index differed significantly between fast responders and slow responders or nonresponders at both weeks 7 and 11. Principal component analysis revealed a pattern of separation between the groups, and partial least squares discriminant analysis revealed signals in the ovarian texture that discriminated females that responded to treatment from those that did not. CONCLUSIONS AND CLINICAL RELEVANCE Ovarian texture information in addition to morphometric variables can enhance ultrasonographic applications for assisted reproduction of eels and potentially other fish species. This was a novel, nonlethal method for classifying reproductive response of eels and the first objective texture analysis performed on ultrasonographic images of the gonads of fish.

To evaluate renal function and obtain reference values for measurements of urinary excretion of various substances, quantitative urinalysis was performed in healthy, growing kittens from 4 to 30 weeks after birth. Endogenous creatinine clearance, 24-hour urine protein excretion, and urine protein-to-creatinine ratio were determined. Additionally, fractional excretion to creatinine clearance was calculated for calcium, inorganic phosphorus, sodium, potassium, and chloride. Mean +/- SD endogenous creatinine clearance values (range, 3.80 +/- 0.48 to 4.74 +/- 0.61 ml/min/kg) were significantly (P < 0.0001) higher in kittens 9 to 19 weeks old, compared with younger (range, 1.39 +/- 0.85 to 3.59 +/- 0.86 ml/min/kg) and older kittens (range, 2.69 +/- 0.40 to 3.46 +/- 0.37 ml/min/kg). Mean values for all kittens for 24-hour urine protein excretion (range, 2.54 +/- 1.81 mg/kg at 4 weeks to 11.39 +/- 7.61 mg/kg at 14 weeks) and for urine protein-to-creatinine ratio (range, 0.14 +/- 0.03 to 0.34 +/- 0.18) varied from week to week of age. The urine protein-to-creatinine ratio in kittens greater than or equal to 9 weeks old correlated well (R2 = 0.861) with 24-hour urine protein excretion. Urinary fractional excretion of calcium, inorganic phosphorus, sodium, potassium, and chloride in kittens varied among age groups, being significantly (P < 0.01) different for potassium and calcium in young kittens (4 to 6 weeks) and older kittens (greater than or equal to 7 weeks).

Objective: The objective of this study was to assess the veracities of most admired strategy dis¬criminant analysis (DA), in comparison to the artificial neural network (ANN) for the anticipation and classification of milk production level in Holstein Friesian cattle using their performances. Materials and Methods: A total of 3,460 performance records of imported and locally born Holstein Friesian cows were gathered during the period from 2000 to 2016 to compare two alter¬native techniques for predicting the level of production based on performance traits in dairy cattle with the use of statistical software (Statistical Package for the Social Sciences, version 20.0). Results: The findings of the comparison indicated that ANN was more impressive in the expec¬tancy of milk production level than did an imitator statistical method based on DA. The accuracy of the ANN model was high for the winter season (79.5%), whereas it was 47.3% for DA. The cur¬rent findings were assured via the areas under receiver operating characteristic curves (AUROC) for DA and ANN. AUROC curves were smaller in the condition of the DA model across different calving seasons compared with the ANN model. The inaccuracies of variations were significant at a 5% significance level utilizing paired sample t-test. Conclusion: ANN model can be used efficiently to predict the level of production across the differ¬ent calving seasons compared to the DA model. [J Adv Vet Anim Res 2020; 7(3.000): 429-435]