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: 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]

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.