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The aim of the present study was to determine correlations between udder skin surface temperatures and milk yield and estimated composition in dairy cows. The thermographic images of 34 Polish Holstein–Friesian black-and-white cows were taken in a milking parlor before and after milking. Partial correlation coefficients were calculated between the surface temperatures of the udder hind quarters and milk production traits controlling for age, parity, year, and milking time. Daily milk yield was weakly and nonsignificantly correlated with surface temperatures ( rp ranging from −0.19 to 0.21), except for the mean and maximum temperatures of the left hind quarter after milking ( rp = 0.40 and rp = 0.38, respectively). There were significant correlations of skin surface temperature with estimated fat content ( rp = −0.55 to 0.48), protein content ( rp = −0.39 to 0.42), fat yield ( rp = −0.42 to 0.54), and protein yield ( rp = 0.37 to 0.54). The estimated somatic cell count was significantly correlated with the minimum temperature ( rp = −0.54 to −0.36). The estimated urea content was significantly correlated with the minimum temperature ( rp = 0.52). A larger sample size is required in future research to confirm these preliminary results.

The aim of the present study was to analyse the mobility of Polish Konik horses in their natural environment. The study was conducted on a herd of 15 Polish Konik horses in 2018. The Global Positioning System (GPS) transmitter was used to track the horses' movements. Two habitats (forest and meadows), four seasons (autumn, winter, spring, and summer), and four times of the day (morning, midday, evening, and night) were distinguished. Season, habitat, and time of the day as well as the interaction among them significantly (p

Coat colour in dogs is influenced by many genes and their polymorphic forms. The main coat colour pattern depends on pigment type, i.e. eumelanin or pheomelanin, whose synthesis is regulated by tyrosinase encoded by the TYRP1 gene. However, other genes also contribute to the ultimate phenotype. One of them is PMEL responsible for the merle coat colour. This review focuses on the role of melanocytes in the phenotypic expression of coat colour, the merle phenotype and genotype, phenotypic diversity of the M allele carriers, health consequences for the carriers of the allele responsible for the merle coat colour and problems with the identification of the M allele carriers based on phenotype and dog breeds with the merle coat colour accepted by the FCI.

Simple Summary Routinely collected data on the performance of dairy cows are a valuable source of information on the beginning, course, and completion of their productive life. As a result, when using sufficiently accurate methods, one can analyze and optimize the milk production process at a herd level from the breeding and economic point-of-view. In this context, it is important to have a possibility to early predict culling reasons for cows, since, in the case of finding an effective method, it would be possible to modify breeding actions and farm management practices without anticipating the end of the animals’ productive lives. Therefore, the aim of the present study was to verify whether artificial neural networks and a general discriminant analysis may be an effective tool for predicting the culling reasons in cows based on routinely collected first-lactation data. It turned out that they were most effective in predicting culling due to old age and reproductive problems. It is significant because infertility is one of the conditions that are the most difficult to eliminate in dairy herds. Abstract The aim of the present study was to verify whether artificial neural networks (ANN) may be an effective tool for predicting the culling reasons in cows based on routinely collected first-lactation records. Data on Holstein-Friesian cows culled in Poland between 2017 and 2018 were used in the present study. A general discriminant analysis (GDA) was applied as a reference method for ANN. Considering all predictive performance measures, ANN were the most effective in predicting the culling of cows due to old age (99.76–99.88% of correctly classified cases). In addition, a very high correct classification rate (99.24–99.98%) was obtained for culling the animals due to reproductive problems. It is significant because infertility is one of the conditions that are the most difficult to eliminate in dairy herds. The correct classification rate for individual culling reasons obtained with GDA (0.00–97.63%) was, in general, lower than that for multilayer perceptrons (MLP). The obtained results indicated that, in order to effectively predict the previously mentioned culling reasons, the following first-lactation parameters should be used: calving age, calving difficulty, and the characteristics of the lactation curve based on Wood’s model parameters.

Background: Growth ability and meat performance are of great economic importance in beef cattle. Therefore, breed comparison experiments are needed to provide valuable information on the suitability of different beef breeds for different production conditions. The aim of the present study was to compare the growth performance and meat quality of young Red Angus, Salers and Limousin bulls. Methods: The material comprised 30 bulls of Red Angus (RA), Salers (SL) and Limousin (LM) breeds. The parameters studied were 210-d bodyweight and daily gains from birth to weaning and to finishing, as well as from weaning to finishing. The finished bulls were slaughtered after 12-hour fasting and post-slaughter evaluation was carried out. The compositions of the samples of meat from the longest dorsal muscle (m. longissimus dorsi) were subjected to basic chemical analysis and sensory analysis. Result: The best results in the period of maternal nursing were obtained by the SL bulls and during intensive fattening, by the LM bulls. The differences existed in the chemical composition and sensory characteristics of meat from the bulls of the studied breeds. The RA breed demonstrated the highest content of intramuscular fat in the longissimus dorsi muscle and received higher scores for sensory characteristics.

The present study is planned to apply boosted classification trees for the detection of cows with potential conception problems. Nine hundred and eighteen artificial insemination records from Polish Holstein-Friesian Black-and-White cows were included. Each record consisted of nine predictor variables. The output variable was a conception difficulty class (good or poor). Sensitivity, specificity and accuracy on the test set were 82.93%, 84.46% and 83.91%, respectively. The most influential predictors of conception difficulty included calving interval, gestation length, body condition score, fat and protein content and age. Boosted classification trees could be applied as an on-farm decision support tool to identify cows with conception problems.

The aim of the present study was to: 1) check whether it would be possible to detect cows susceptible to mastitis at an early stage of their utilization based on selected genotypes and basic production traits in the first three lactations using ensemble data mining methods (boosted classification tress – BT and random forest – RF), 2) find out whether the inclusion of additional production variables for subsequent lactations will improve detection performance of the models, 3) identify the most significant predictors of susceptibility to mastitis, and 4) compare the results obtained by using BT and RF with those for the more traditional generalized linear model (GLZ). A total of 801 records for Polish Holstein-Friesian Black-and-White cows were analyzed. The maximum sensitivity, specificity and accuracy of the test set were 72.13%, 39.73%, 55.90% (BT), 86.89%, 17.81%, 59.49% (RF) and 90.16%, 8.22%, 58.97% (GLZ), respectively. Inclusion of additional variables did not have a significant effect on the model performance. The most significant predictors of susceptibility to mastitis were: milk yield, days in milk, sire’s rank, percentage of Holstein-Friesian genes, whereas calving season and genotypes (lactoferrin, tumor necrosis factor alpha, lysozyme and defensins) were ranked much lower. The applied models (both data mining ones and GLZ) showed low accuracy in detecting cows susceptible to mastitis and therefore some other more discriminating predictors should be used in future research.

WOS: 000461147800004 In this study, an attempt was made at predicting the values of selected reproductive parameters in Harnai sheep using different data mining algorithms (artificial neural networks - ANN, classification and regression trees - CART, chi-square automatic interaction detector - CHAID and multivariate adaptive regression splines - MARS) and indicating the most influential predictors of these traits. A total of 382 reproduction records including three predictors (month of lambing - MOL, age at first lambing - AFL and lambing weight - LW) and seven dependent (output) variables (services per conception - SPC, service period - SP, lambing interval - LI, twinning rate - TR, gestation length - GL, breeding efficiency - BE and fertility rate - FR) were used. A 10-fold cross-validation was applied to train and evaluate the models. The highest correlation coefficients (r) were found for LI (0.18 - 0.29; P 0.05) and FR (0.11 - 0.26; P

Background The aim of the present study was to verify the relationship between the level of neuroticism and selected aspects of life satisfaction in women undergoing breast augmentation surgery. Methods The study group included 109 women, aged 18–46 years, who completed the self-developed survey measuring selected psychological traits before and after (1 year) surgery. Four questions in this survey were related to the level of neuroticism and two pertained to the self-assessment of leading character traits and the level of life satisfaction. Three questions made up the lie scale. Results The studied women were constant in their truthfulness. No statistically significant difference in the level of neuroticism before and after surgery was noticed. However, an increase in the subjective life satisfaction after surgery was highly significant (p

The aim of the present study was to construct a rule-based module (RBM) for dystocia detection in dairy cattle and to verify its predictive performance. A total of 3041 calving records of Polish Holstein-Friesian Black-and-White heifers and cows were used. Three continuous and seven categorical predictors of dystocia were included in the three decision tree models, from which the rules for RBM were extracted. The system was equipped with a user-friendly text interface. The percentage of correctly detected easy, moderate and difficult calvings in heifers on the independent test set was 26.13%, 76.52% and 77.27%, respectively. The overall accuracy was 60.12%. The respective values for cows were: 59.18%, 69.01%, 0% and 62.03%. The predictive performance of the constructed system was satisfactory, except for the difficult category in cows.

Background The aim of this study was to construct an electronic bowel sound recording system and determine its usefulness for the diagnosis of appendicitis, mechanical ileus and diffuse peritonitis. Materials and methods A group of 67 subjects aged 17 to 88 years including 15 controls was examined. Bowel sounds were recorded using an electret microphone placed on the right side of the hypogastrium and connected to a laptop computer. The method of adjustable grids (converted into binary matrices) was used for bowel sounds analysis. Results Significantly, fewer (p ≤ 0.05) sounds were found in the mechanical ileus (1004.4) and diffuse peritonitis (466.3) groups than in the controls (2179.3). After superimposing adjustable binary matrices on combined sounds (interval between sounds

The relationship between the SNP of the cytochrome P450 gene (CYP19-PvuII) and milk production traits of Black-and-White and Jersey cattle were analysed. A total of 437 cows were included in the study. A PCR-RFLP was used to genotype. The frequencies of genotypes and alleles for the Black-and-White cows were as follows: 0.8985 – AA, 0.0977 – AB, 0.0038 – BB, and 0.9474 – CYP19A, 0.0526 – CYP19B. In the Jersey, all cows were genotyped as CYP19AA (no polymorphism). There weren’t any associations between CYP19-PvuII polymorphism and milk production traits of the investigated cows.

In this study, the detection of dairy cows with difficult calvings using artificial neural networks (ANN) and classification functions (CF) is presented. The set of 15 classification variables was used. The dependent variable was the class of calving difficulty: difficult or easy. Perceptrons with one (MLP1) and two (MLP2) hidden layers as well as radial basis function (RBF) networks were analyzed. The prepared classifiers were characterized by good quality. The accuracy amounted to 75–92 %. Only the RBF network had somewhat worse quality. The level of correct detection by ANN was also high. The sensitivity on a test set was 67–80 % at specificity of 61- 81 %. In the case of CF, a considerable disproportion between sensitivity (6 %) and specificity (99 %) was found. The variables with the greatest contribution to the determination of calving difficulty class were calving season, CYP19-PvuII genotype, pregnancy length and, to a lesser degree, other variables. The performed analyses proved the usefulness of ANN for the detection of cows with difficult calvings, whereas the detection by CF was inaccurate.

Objective The aim of this study was to verify the usefulness of artificial neural networks (ANN), multivariate adaptive regression splines (MARS), naïve Bayes classifier (NBC), general discriminant analysis (GDA), and logistic regression (LR) for dystocia detection in Polish Holstein-Friesian Black-and-White heifers and cows and to indicate the most influential predictors of calving difficulty. Methods A total of 1,342 and 1,699 calving records including six categorical and four continuous predictors were used. Calving category (difficult vs easy or difficult, moderate and easy) was the dependent variable. Results The maximum sensitivity, specificity and accuracy achieved for heifers on the independent test set were 0.855 (for ANN), 0.969 (for NBC), and 0.813 (for GDA), respectively, whereas the values for cows were 0.600 (for ANN), 1.000 and 0.965 (for NBC, GDA, and LR), respectively. With the three categories of calving difficulty, the maximum overall accuracy for heifers and cows was 0.589 (for MARS) and 0.649 (for ANN), respectively. The most influential predictors for heifers were an average calving difficulty score for the dam’s sire, calving age and the mean yield of the farm, where the heifer was kept, whereas for cows, these additionally included: calf sex, the difficulty of the preceding calving, and the mean daily milk yield for the preceding lactation. Conclusion The potential application of the investigated models in dairy cattle farming requires, however, their further improvement in order to reduce the rate of dystocia misdiagnosis and to increase detection reliability.

The aim of this study was to check the effectiveness of classification and regression trees (CART), support vector machines (SVM) and generalized linear model (GLZ) in the detection of difficult calvings in the Polish Holstein-Friesian Black-and-White heifers and to identify the most influential predictors of such calvings with special regard to oestrogen receptor alpha (ERα) and cytochrome P450 (CYP19) aromatase genotypes. A total of 529 calving records containing four continuous and four categorical predictors were analysed. Calving category (difficult vs. easy) was a dependent variable. The most significant predictors were as follows: gestation length, body condition score index and calving age (for CART), as well as ERα genotype (for GLZ). Sensitivity (percentage of correctly diagnosed difficult calvings) and specificity (percentage of correctly diagnosed easy calvings) on a test set were 70.0% and 94.7% (for CART) as well as 50.0% and 99.0% (for SVM and GLZ), respectively. The studied models were cha...

BACKGROUND The aim of this study was to assess the distribution and dynamics of temperature changes on the surface of selected body parts after systemic cryostimulation. The changes that occurred as a reaction to the 1st, 5th, and 10th session of a series of 10 sessions were also analyzed. METHODS The study group consisted of 24 students (12 women and 12 men, ∼21 yr of age) from the University School of Physical Education in Krakow. They were treated in a cryogenic chamber at the Rehabilitation Center in Krakow once daily for 10 d. The mean temperature in the chamber was -130°C ± 10°C and the session duration was 3 min. Thermovisual examination of temperature distribution in the selected parts of the upper and lower extremities was conducted before and immediately after a session on the 1(st) (S1), 5(th) (S2), and 10(th) (S3) day of treatment. All thermograms were digitally recorded using a Flir Therma CAM TM Sc500 camera. RESULTS On the 5th day of treatment, the examined group demonstrated the smallest mean temperature changes (4.57°C-17.31°C for the anterior part of the upper extremities in men and the posterior part of the lower extremities in women, respectively) before and after cryostimulation. The most significant temperature changes were observed in the group of women (6.80°C-20.08°C for the posterior parts of the upper extremities on S2 and the lower extremities on S3, respectively). CONCLUSION There is an important difference in response to cryogenic temperature between men and women in a series.

The aim of this study was to analyze age-related effects influencing retrobulbar blood flow parameters.The study enrolled healthy volunteers (162 eyes) with a mean age of 49.64 years (SD ± 17.50). Participants were divided into 5 groups (G1-G5) based on 12-year age spans (means from 25.41 to 73.87 years). Peak systolic velocity (PSV), end diastolic velocity (EDV), mean velocity (MV), pulsatility index (PI) and resistive index (RI) of the ophthalmic (OA), central retinal (CRA) and posterior ciliary arteries (PCAs) were measured by color Doppler ultrasonography (CDU). In addition, systemic blood pressure (BP), ocular perfusion pressure (OPP) and intraocular pressure (IOP) were evaluated. Statistical analysis was performed using the Shapiro-Wilk, Levene's, Spjotvoll-Stoline and analysis of variance tests (p ≤ 0.05 was considered statistically significant).In the oldest group, significantly lower PSV, EDV in the CRA and PCAs as well as MV in the CRA were confirmed. Significantly increased RI in the OA, CRA and PCAs in regard to age was recorded.CDU has considerable potential for evaluating hemodynamic changes in the retrobulbar circulation in relation to age. Age laboratory standards for a specific CDU device should be established for further analyses of blood flow in ocular diseases of vascular origin.

BACKGROUND This study verifies selected opinions on breast cosmetic surgery in the population of Polish women. MATERIALS AND METHODS A total of 78 women aged 20 to 48 years were surveyed between 2008 and 2010 before and after breast augmentation surgery using a questionnaire. The χ and Wilcoxon signed rank tests were used for statistical analysis. Most were married and single women aged approximately 30 years and residents of large cities. RESULTS The significance of professional success in the hierarchy of values and the level of self-assessment increased after surgery (P = 0.0000 and 0.0213, respectively). The distribution of responses concerning the expectations of surgery and the evaluation of their fulfillment changed significantly (P = 0.0031). In general, the satisfaction with one's life after surgery also increased (P = 0.0000). CONCLUSIONS A well-thought-out decision on breast cosmetic surgery positively affects at least several spheres of psychosocial functioning and fulfills most women's expectations.

Daily body weight gains are an important indicator for the evaluation of beef production. The objective of this study was to verify the quality and detection abilities of selected data mining methods (artificial neural networks and decision trees) applied to the analysis of daily body weight gains in beef heifers. A total of 261 individuals (Charolais, Charolais A— Simmental and Charolais A— Hereford) were genotyped and their daily body weight gains were recorded. The genetic analysis included polymorphic variants of leptin, myostatin, and prion protein genes. The animals were divided into two categories: those with daily body weight gains equal to or lower than the mean for the analyzed population (99 heifers) and those with daily body weight gains higher than the mean (112 heifers). Heifer birth weight, breed, birth season, dam body weight at calving, calving interval, and genotypes were used as predictors. From among the analyzed models, the neural network with 8 neurons in a single hidden layer (root mean squared error equal to 0.4471) and the tree with an error coefficient of 0.4076 were selected. These classifiers correctly detected 20 out of 27 heifers with daily body weight gains below the mean. In the case of heifers with daily body weight gains above the mean, neural networks correctly classified 17 out of 23 individuals, whereas decision tree only 14. Breed, leptin genotype and combined genotype turned out to be the most important variables contributing to the determination of body weight gains class for both types of classifiers.

Summary Background The aim of this study was to determine the relationships between lens opacity, vascular and lipid factors and retrobulbar blood flow parameters in type-1 diabetic (DM) adolescents. Material/Methods Glycated haemoglobin (HbA1c), total cholesterol (TCH), high- and low-density cholesterol, triglycerides (TG) and apolipoprotein B (ApoB) were determined in 28 patients with (DM-1) and without (DM-0) lens opacity and 18 controls. In the ophthalmic, central retinal (CRA) and temporal posterior ciliary (TPCA) arteries, the systolic (PSV), end-diastolic and mean blood flow velocities as well as pulsatility and resistance (RI) indices were measured. Results Ten (35.71%) diabetic patients exhibited lens opacification. Higher TG and TCH levels in the DM-1 group and HbA1c level in the DM-0 and DM-1 groups were observed (P≤0.05). Diabetic patients had lower PSV and higher RI within CRA and TPCA (P≤0.05). Significant correlations between biochemical and blood flow parameters were found. Conclusions Glycaemic and lipid factors may play a vasoconstrictive role in retrobulbar endotheliopathy.

So far the studies on the prion protein (PRNP) gene have mainly been focused on the search for an association between detected polymorphisms in this gene and the susceptibility of cattle to bovine spongiform encephalopathy. The PRNP gene location on chromosome 13 suggests that the polymorphisms within this gene can be potential quantitative trait locus (QTL) markers for milk production traits. The aim of this study was the analysis of the potential effect of the insertion/deletion polymorphisms within the promoter region (23 bp) and intron 1 (12 bp) of the PRNP gene on milk production traits in Jersey cattle. The study material consisted of 171 Jersey cows. The following milk production traits were analysed: milk yield, milk fat and protein yield, milk fat and protein content. Statistically significant differences were found in milk fat content in the second lactation between animal groups representing the ins/del and del/del genotypes for the 23 bp polymorphism (P≤0.05). The ins/del 12 bp polymorphism wa...

Revisions and redescriptions of species and higher taxa have been known in parasitology since the first description of a parasite. Usually, they are based on standard morphometric methods or more modern genetic analysis. The former are not always sufficiently reliable, while the latter often require expensive equipment, pre-defined genetic markers, and appropriately prepared research material. They may be replaced by multivariate statistical methods, in particular discriminant analysis and cluster analysis, and Kohonen artificial neural networks included in data mining. This paper presents the examples of specific applications of these methods for the verification of the affinity of nematodes. The discriminant analysis showed that it was possible to statistically significantly discriminate individual nematode species, both for males and females, based on morphometric variables. This confirmed the previously assumed division of the species complex Amidostomum acutum into three distinct species. Similarly, hierarchical cluster analysis, used for the determination of coherent groups of nematode parasites, allowed the identification of relatively homogeneous clusters of nematode species depending on their circle of hosts, and groups of hosts.

The aim of this study was to compare daily yield, milk composition and somatic cell count after increasing milking frequency from twice to three times a day. The study also includes the effect of other factors such as: the sequence of lactation (1, 2, ≥3) and the period of lactation (100 days, 101-200 days, 201-300 days, and more than 300 days). In conclusion, it should be noted that increasing the frequency of cow milking had a positive effect both on the milk yield (P≤0.01), causing a substantial increase, as well as on the cytological quality of produced milk. The somatic cell count (1000/mL) was lower in the group of cows milked three times a day. All the data regarding farming records of this farm came from the SYMLEK system maintained and made available by Polish Federation of Milk Producers and Cattle Breeders (PFHBiPM). Analysis of useful milk traits included: daily yield, fat, protein, dry matter and somatic cell count in milk., Cilj ovog istraživanja bio je usporediti dnevnu količinu mlijeka, sastav mlijeka i broj somatskih stanica nakon povećanja učestalosti mužnje s dva na tri puta. Studija je također uključila i druge čimbenike: redoslijed laktacije (1, 2, ≥3) i razdoblje laktacije (do 100 dana, 101-200 dana, 201-300 dana, i više od 300 dana). Zaključno, treba napomenuti da povećanje učestalosti mužnje krava ima pozitivan učinak i na količinu mlijeka (P≤0,01), što uzrokuje značajno povećanje, kao i na citološku kvalitetu proizvedenog mlijeka. Broj somatskih stanica (1000/mL) bio je manji u skupini krava koje su bile na mužnji tri puta. Svi podaci prikupljeni su iz sustava dokumentacije SYMLEK, osigurane iz zajedničke dokumentacije uzgojne grane privrede, koju vodi Poljsko udruženje stočara i proizvođača mlijeka (PFHBiPM). Analiza je uključivala: dnevnu količinu, količinu masti, proteina, suhe tvari kao i broj somatskih stanica u mlijeku.

The background of aggression is very complicated and the basis of its occurrence has not been well explained yet. It is thought that tendency to aggressiveness is an effect of both environmental and genetic factors. Aggression is a very undesirable behavioural trait in dogs living with humans. The aim of this study was to determine the relationship between two polymorphisms: DRD4 intron II VNTR and C/T substitution in exon I HTR2B genes and aggressive behaviour in dogs. The VNTR polymorphism in the DRD4 gene was detected by agarose gel electrophoresis following PCR amplification, whereas C/T substitution in the HTR2B gene was analysed using amplification created restriction site-polymerase chain reaction (ACRS-PCR). A total of 121 dogs of several breeds were analyzed. All animals were classified based on a veterinary interview and observation in two groups: aggressive (n = 21) and non-aggressive (n = 100). Significant differences in DRD4 genotype frequencies between aggressive and non-aggressive dogs were observed (P < 0.05). The study provides a clear evidence of an association of VNTR polymorphism within intron II of the DRD4 gene with the occurrence of aggressive behaviour in dogs. Moreover, the findings give good justification for further research aimed at evaluation of the possibility of using this genetic marker in Marker-assisted Selection.

Most available literature indicates that the most dominant nematode in Anatinae is a cosmopolitan species Amidostomum acutum (Lundahl, 1848). However, studies on wild duck helminthofauna in northwestern Poland suggest that these birds are attacked by not one but three different parasite species, previously described as a single species. Hence the aim of this study was the redescription of the species complex Amidostomum acutum, conducted on a representative sample of parasites and their hosts. The study material consisted of 6,430 nematode individuals, isolated from the digestive tracts of 1,005 wild ducks, representing 17 species. Unsupervised classification was performed using a Kohonen artificial neural network. The analysis confirmed the division of nematodes into three groups corresponding to three species, both for males and females. Taking into account the qualitative characteristics of the parasites, one can 100% accurately identify these species. The three groups of parasites were also significantly different in their ecology, manifested in their distinct host specificity.

Source of support: Departmental sources Summary Background: The frequency of foreign body retention in the abdominal cavity ranges from 1 in 100 to 1 in 3000 surgeries performed. Worldwide literature describes only a few cases of the migration of misplaced surgical gauze into the colon. Case Reports: The first case is a 60-year-old patient following laparoscopic cholecystectomy, who excreted (on his own) a cotton sheet 30×65 cm after 26 weeks, which did not possess a radiological locator. The latter fact caused diagnostic difficulties in interpreting ultrasonography, CT-scans and abdominal X-rays. Colonoscopy after 4 months following the excretion of the sheet showed flat, stretched ul ceration of the colonic wall near the hepatic turn. The second case is a 76-year-old who had undergone several abdominal surgeries, including a classical cholecystectomy and extirpation of the uterus along with related tissues, as a result of cancer and with subsequent radiotherapy. The reason for the last intervention was an occlusion, which required a resection due to abscesses inside the peritoneal cavity. Abdominal pain continued after the surgery. Uroscopy and abdominal X-rays were performed 3 months later, which confirmed the presence of foreign matter in the abdominal cavity. Conclusions: Most foreign objects that have migrated into the colon will be excreted autonomously, which warrants a conservative assessment. Radiologically-tagged materials should be used, which will greatly ease identification in cases of suspected retention of surgical materials in the abdominal cavity.

The aim of this study was to relate polymorphic variants of CYP19, CYP21 and ER1 genes to reproductive traits in 472 Polish Holstein-Friesian cows. High frequencies of one of the homozygous genotypes were found. The ER1/SnaBIAA homozygotes were not identified. In the first and third lactation, an average calving-to-conception interval (CLVC) in cows of ER1/SnaBIGG genotype was significantly shorter (P ≤ 0.05) than in heterozygous cows. In the cows of ER1/BglIGG genotype, significantly shorter CLVC (P ≤ 0.05) was observed compared to heterozygotes in the first lactation, whereas in the third lactation, CLVC in homozygous cows was significantly longer (P ≤ 0.05) than in heterozygous ones. It was also found that homozygous cows were characterized by significantly longer calving interval (CLVI; P ≤ 0.05) compared to heterozygotes in the third lactation. Longer CLVCs in CYP19AA cows were found, compared to heterozygotes, and this difference was significant in the first and third lactation (P ≤ 0.05). Similarly, the average CLVIs were longer in CYP19AA homozygotes than in heterozygous cows; however, significance was proven only in the third lactation (P ≤ 0.05). Description of the molecular mechanisms regulating reproduction, and thus identification of the individuals of genotypes with optimal potential may facilitate the employment of selected reproductive model by a breeder.

In the present study, the detection of cows with artificial insemination (AI) difficulties using selected statistical and machine learning methods is presented. Cows were divided into two classes: those that conceived after one or two services (''GOOD'') and those that required more than two services per conception (''POOR''). The best performance was exhibited by one of the artificial neural networks (ANN) and the multivariate adaptive regression spline (MARS) method (AIC, BIC, RMS and accuracy); whereas logistic regression (LR) and classification functions (CF) were of somewhat lower quality. The detection of cows with AI difficulties, performed on the basis of the test set comprising new instances, showed that the ANN and MARS were more precise in comparison with the statistical methods. Sensitivity and specificity were over 85% for the perceptron with two hidden layers (MLP2) and MARS and approximately 80% or lower for LR and CF. From among variables determining the AI category, the average calving interval and cow body condition index were the most important. Other significant variables were lactation number, pregnancy length, sex of calf from previous calving and cow age. The prognoses obtained using ANN and MARS can be used for the appropriate preparation of cows for AI.

Szatkowska, I., Dybus, A., Grzesiak, W., J“drzejczak, M. and Muszy½ska, M. 2009. Association between the growth hormone releasing hormone (GHRH) gene polymorphism and milk production traits of dairy cattle. J. Appl. Anim. Res., 36: 119-123. The association between the GHRH/HaeIII gene polymorphism and milk production traits of Polish Holstein and Jersey cows were analysed. A total of 427 animals were included in the study. A PCR-RFLP method was used for genotyping. The frequencies of genotypes and alleles for the Polish Holstein cows were 0.078 - AA, 0.339 - AB, 0.583 - BB and 0.248 - GHRH A , 0.752 - GHRH B . For the Jersey cows, the frequencies were: 0.032 - AA, 0.427 - AB, 0.541 - BB and 0.246 - GHRH A , 0.754 - GHRH B . In all lactations, the Jersey cows with AA genotype exhibited the highest milk fat content. In the 2 nd and 3 rd lactations the AA Jersey cows had lower milk yields compared with the AB/BB cows. However, Polish Holstein cows did not exhibit such effects of genotypes on milk yield/traits. It still remains unclear that the analysed polymorphism could influence to the physiology of milk fat production.

The paper reviews the various factors affecting dystocia in cattle. It is based mainly on the recent studies found in the literature of the subject but refers occasionally to some older papers as well. The factors are grouped into four main categories: direct factors, phenotypic factors related to calf and cow, non-genetic and genetic factors. The first group includes malpresentations and uterine torsion. The second one includes: calf birth weight, multiple calvings, perinatal mortality, cow pelvic area, cow body weight and body condition at calving, gestation length. The non-genetic factors are: cow age and parity, year and season of calving, place of calving, maintenance practises, disorders, calf sex and nutrition. Other non-genetic factors are the level of hormones in the periparturient period, in vitro production of embryos and embryo cloning. Finally, the genotypes of cow, bull and calf, inbreeding, muscular hypertrophy, selection and quantitative trait loci form the fourth group of genetic factors.

The analysis comprised purebred Charolais bull calves (CH) and crossbred bull calves born from Hereford (CH × HE) and Simmental (CH × SI) cows. Body weights and daily gains were measured in subsequent periods of the experiment and the post-slaughter evaluation was carried out. Carcass lean weight and dressing percentage were evaluated, as well as the lean content of five prime cuts. Total weight of the five prime cuts was measured and its share in the half-carcass weight was determined. The rib-eye area was also measured. The CH × SI crossbreds reached significantly higher body weights at weaning, 210-day body weights and weights at finishing, as well as significantly higher daily gains in all the periods ( P < 0.05, P < 0.01) and were characterized by the heaviest carcasses and most cuts, particularly the round. The cross - breds had the highest total weight of five prime cuts and total lean weight, highest weight of the round and lean weight in the round. Other relationships were found analyzing the share of each cut in the carcass. The share of tenderloin and rump in the carcass of CH × SI was significantly lower compared to the purebred bulls ( P < 0.01) and CH × HE (P < 0.05). They also had a significantly lower share of the shoulder, shank and shin, as compared to the other groups, as well as a significantly higher ( P < 0.05) share of the neck, as compared to the purebred bulls. The purebred bulls also achieved good results and had the highest share of the prime cuts and the largest mean rib-eye area.

The study is focused on the capability of artificial neural networks to forecast milk yield for both full and standardised lactations. We used a dataset of 108,931 daily milk yields (dataset A) collected from three lactations of dairy cows managed in a production farm. Using the actual data on daily milk yields and the data recorded on official milk recording test days, a number of neural networks were designed and parameters of Wood's model were estimated. The quality of each network and regression model was measured using coefficients of determination, relative approximation errors (RAE), and root mean square errors (RMS). In order to test the prognostic parameters of the models, we randomly selected a subset of cows from the studied population, which produced in a dataset of 28,576 daily yields (dataset B). For those cows, daily and lactation yield forecasts were generated, which were next compared with their actual (observed) yield records and with the yields calculated by SYMLEK (ZETO Olsztyn Sp. z o.o., www.zeto.olsztyn.pl). The results have shown that the quality parameters of the designed neural networks were better than those of the regression model, for both the daily yields and test-day data (higher coefficients of determination and lower RAE and RMS). The prognostic parameters estimated for the forecasts of the neural networks were characterised by lower errors of prediction for both the daily yields and test-day data and exhibited higher coefficients of correlation between the predicted and the actual data (or the yields produced by SYMLEK). The predictions by the neural networks were more accurate than those by Wood's models. Furthermore, the predictions by both analysed models were closer to reality than the values estimated with the SYMLEK system. Application of neural networks does not require the data meeting the assumptions that must otherwise be met in a regression model. Large datasets are not needed to design a quite reliable neural network and, what is more, it is much easier to work with such a model than with a regression model.