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OBJECTIVES: The aim of the present study was to evaluate variations in celiac trunk and hepatic artery with multi-detector computed tomography (MDCT). PATIENTS AND METHODS: Totally 820 patients who underwent angiography of the abdominal aorta were evaluated. Anatomical findings were grouped according to the Michels classification. RESULTS: Several variations and/or anomalies were noted in 33.2% of the patients (n=272). The most common abnormality was Michels type III (10.1%), followed by type V (7.3%), type II (4.7%) and others. Type X was not observed in our series. We have noted additional, previously unclassified variations in 12 cases (1.5%). CONCLUSIONS: Preoperative knowledge of variant anatomy may assist in the selection of treatment options and surgical planning, which in turn facilitates surgical dissection and helps avoiding iatrogenic injury. MDCT angiography allows detailed visualization of the vascular anatomy. [ABSTRACT FROM AUTHOR]

The relationships between genotype and placental traits, parity and litter weight (LW), and factors affecting these characteristics were investigated in this study. In total, 112 ewes (Romanov crossbred and local breeds) were utilized. One-way ANOVA was used for statistical comparison, and a Pearson correlation was used to determine the relationships between the variables. Significant differences in parity weight within genotype and breed have been determined. A negative correlation was revealed between placental weight (PW) and placental efficiency (r = -0.743, P < 0.01; and r = -0.732, P < 0.01). There was no relationship between litter sex and placental traits. Birth type had a significant effect on PW (P < 0.05), and significant differences within sex-birth type interactions occurred (P < 0.05). The results of the present study have shown a positive correlation between cotyledon density and placental efficiency among all genotypes and breeds that were used in the study. In conclusion, it has been determined that placental traits were affected by LW and Romanov crossbreed ewes had greater PW than local breeds. Further studies are required to investigate the relationship between parity and placental traits in sheep. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the potential of interpretable machine learning (ML) models to predict ascites improvement in patients undergoing transjugular intrahepatic portosystemic shunt (TIPS) placement for refractory ascites., Materials and Methods: In this retrospective study, 218 patients with refractory ascites who underwent TIPS were analyzed. Twenty-nine demographic, clinical, and procedural features were collected. Ascites improvement was defined as reduction of paracentesis need by 50% or more at one-month follow-up. Univariate statistical analysis was performed. Data was split into train and test sets. Feature selection was performed using a wrapper-based sequential feature selection (SFS) algorithm. Two ML models were built using support vector machine (SVM) and CatBoost algorithms. Shapley additive explanations values were calculated to assess interpretability of ML models. Performance metrics were calculated using the test set., Results: Refractory ascites improved in 168(77%) patients. Higher sodium (136mEq/L vs 134mEq/L,p=0.001) and albumin levels (2.91 g/dLvs2.68 g/dL,p=0.03), lower creatinine (1.01 mg/dL vs 1.17 mg/dL,p=0.04), model for end-stage liver disease (MELD) (13 vs 15,p=0.01) and MELD-Na (15 vs 17.5, p=0.002) scores were associated with significant improvement. While, main portal vein puncture was associated with a lower improvement rate (p=0.02). SVM and CatBoost models had accuracy ratios of 83% and 87%, with area under curve values of 0.83 and 0.87, respectively. No statistically significant difference was found between performances of the models in DeLong test (p=0.3)., Conclusion: According to this study, machine learning models may have potential in patient selection for TIPS placement by predicting the improvement in refractory ascites., (Copyright © 2024. Published by Elsevier Inc.)

Purpose: To investigate whether patients undergoing percutaneous liver mass biopsy (PLMB) can be safely discharged following a two-hour monitoring period., Methods: A multi-center prospective analysis was conducted for 375 patients (196 males and 179 females), mean age 63 ± 12.45 years (range 37-89) who underwent PLMB between August 2023 and March 2024. Patients were monitored for 24 h, and complications were classified as minor or major. The timing of complications was categorized into three groups: within the first two hours, between the 2nd and 24th hours, and within 1 week after 24 hours., Results: Minor complications occurred in 18.93% (71/375) and major complications in 2.13% (8/375). Most minor complications (80.2%, 57/71) appeared within the first two hours, 12.7% (9/71) between 2 and 24 h, and 7.1% (5/71) after 24 h. All major complications (62.5%, 5/8) except late-onset cases, occurred within the first two hours. No major complications occurred between 2 and 24 h. Late-onset major complications occurred in 37.5% (3/8) after 24 h., Conclusion: The two-hour monitoring period did not adversely impact patient management regarding minor complications and is safe for identifying all major complications except for late-onset ones. Extending the post-biopsy recovery period does not significantly improve patient care., (© 2024 Wiley Periodicals LLC.)

Purpose: To develop and assess machine learning (ML) models' ability to predict post-procedural hepatic encephalopathy (HE) following transjugular intrahepatic portosystemic shunt (TIPS) placement., Materials and Methods: In this retrospective study, 327 patients who underwent TIPS for hepatic cirrhosis between 2005 and 2019 were analyzed. Thirty features (8 clinical, 10 laboratory, 12 procedural) were collected, and HE development regardless of severity was recorded one month follow-up. Univariate statistical analysis was performed with numeric and categoric data, as appropriate. Feature selection is used with a sequential feature selection model with fivefold cross-validation (CV). Three ML models were developed using support vector machine (SVM), logistic regression (LR) and CatBoost, algorithms. Performances were evaluated with nested fivefold-CV technique., Results: Post-procedural HE was observed in 105 (32%) patients. Patients with variceal bleeding (p = 0.008) and high post-porto-systemic pressure gradient (p = 0.004) had a significantly increased likelihood of developing HE. Also, patients having only one indication of bleeding or ascites were significantly unlikely to develop HE as well as Budd-Chiari disease (p = 0.03). The feature selection algorithm selected 7 features. Accuracy ratios for the SVM, LR and CatBoost, models were 74%, 75%, and 73%, with area under the curve (AUC) values of 0.82, 0.83, and 0.83, respectively., Conclusion: ML models can aid identifying patients at risk of developing HE after TIPS placement, providing an additional tool for patient selection and management., (© 2023. Springer Science+Business Media, LLC, part of Springer Nature and the Cardiovascular and Interventional Radiological Society of Europe (CIRSE).)

Purpose: To evaluate machine learning models, created with radiomics and clinicoradiomics features, ability to predict local response after TACE., Materials and Methods: 188 treatment-naïve patients (150 responders, 38 non-responders) with HCC who underwent TACE were included in this retrospective study. Laboratory, clinical and procedural information were recorded. Local response was evaluated by European Association for the Study of the Liver criteria at 3-months. Radiomics features were extracted from pretreatment pre-contrast enhanced T1 (T1WI) and late arterial-phase contrast-enhanced T1 (CE-T1) MRI images. After data augmentation, data were split into training and test sets (70/30). Intra-class correlations, Pearson's correlation coefficients were analyzed and followed by a sequential-feature-selection (SFS) algorithm for feature selection. Support-vector-machine (SVM) models were trained with radiomics and clinicoradiomics features of T1WI, CE-T1 and the combination of both datasets, respectively. Performance metrics were calculated with the test sets. Models' performances were compared with Delong's test., Results: 1128 features were extracted. In feature selection, SFS algorithm selected 18, 12, 24 and 8 features in T1WI, CE-T1, combined datasets and clinical features, respectively. The SVM models area-under-curve was 0.86 and 0.88 in T1WI; 0.76, 0.71 in CE-T1 and 0.82, 0.91 in the combined dataset, with and without clinical features, respectively. The only significant change was observed after inclusion of clinical features in the combined dataset (p = 0.001). Higher WBC and neutrophil levels were significantly associated with lower treatment response in univariant analysis (p = 0.02, for both)., Conclusion: Machine learning models created with clinical and MRI radiomics features, may have promise in predicting local response after TACE., Level of Evidence: Level 4, Case-control study., (© 2023. Springer Science+Business Media, LLC, part of Springer Nature and the Cardiovascular and Interventional Radiological Society of Europe (CIRSE).)

The objective of our study was to evaluate the predictive ability of a multi-trait genomic prediction model that accounts for interactions between marker effects to estimate heritability and genetic correlations of traits including 305-day milk yield, milk fat percentage, milk protein percentage, milk lactose percentage, and milk dry matter percentage in the Polish Holstein Friesian cow population. For this aim, 14,742 SNP genotype records for 586 Polish Holstein Friesian dairy cows from Poland were used. Single-Trait-ssGBLUP (ST) and Multi-Trait-ssGBLUP (MT) methods were used for estimation. We examined 305-day milk yield (MY, kg), milk fat percentage (MF, %), milk protein percentage (MP, %), milk lactose percentage (ML, %), and milk dry matter percentage (MDM, %). The results showed that the highest marker effect rank correlation was found between milk fat percentage and milk dry matter. The weakest marker effect rank correlation was found between ML and all other traits. Obtained accuracies of this study were between 0.770 and 0.882, and 0.773 and 0.876 for MT and ST, respectively, which were acceptable values. All estimated bias values were positive, which is proof of underestimation. The highest heritability value was obtained for MP (0.3029) and the lowest heritability value was calculated for ML (0.2171). Estimated heritability values were low for milk yield and milk composition as expected. The strongest genetic correlation was estimated between MDM and MF (0.4990) and the weakest genetic correlation was estimated between MY and ML (0.001). The genetic relations with milk yield were negative and can be ignored as they were not significant. In conclusion, multi-trait genomic prediction can be more beneficial than single-trait genomic prediction.

The current study aimed to predict final body weight (weight of fourth months of age to select the future reproducers) by using birth weight, birth type, sex, suckling weight, age at suckling weight, weaning weight, age at weaning weight, and age of final body weight for the Romane sheep breed. For this purpose, classification and regression tree (CART), multivariate adaptive regression splines (MARS), and support vector machine regression (SVR) algorithms were used for training (80%) and testing (20%) sets. Different data mining and machine learning algorithms were used to predict final body weight of 393 Romane sheep (238 female and 155 male animals) were used with different artificial intelligence algorithms. The best prediction model was obtained by CART model, both training and testing set. Constructed CART models indicated that sex, suckling weight, weaning weight, age of weaning weight, and age of final weight could be used as an indirect selection measure to get a superior sheep flock on the final body weight of Romane sheep. If genetically established, the Romane sheep whose sex is female, age of final weight is over 142 days, and weaning weight is over 28 kg could be chosen for affording genetic improvement in final body weight. In conclusion, the usage of CART procedure may be worthy of reflection for identifying breed standards and choosing superior sheep for meat yield in France., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Tırınk et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

This study aimed to utilize the XGBoost and MARS algorithms to predict present weight from body measurements. The algorithms have the potential to model nonlinear relationships between body measurements and weight, and this study attempted to find a model that provided the most accurate predictions of present weight. The current study was conducted with 152 animals in order to achieve a certain goal. To compare the model performances, goodness-of-fit criteria such as R 2 , r, RMSE, CV, SD ratio , PI, MAPE, AIC were used. According to the results of this study, the XGBoost algorithm was the most reliable model for predicting present weight from body measurement. Even if the XGBoost algorithm was the most accurate model, the MARS algorithm was the reliable model for the same aim. In addition, it is hoped that the results of this study will help researchers and breeders better understand the relationship between body measurements and weight and ultimately be able to help individuals better manage their weight. As a conclusion, in the current study, the XGBoost algorithm is an effective, efficient, and reliable tool for accurately estimating present weight from body measurements. This makes it an invaluable tool in rural areas, where traditional weighing scales may not be available or reliable., (© 2023. The Author(s), under exclusive licence to Springer Nature B.V.)

Purpose: This study aimed to evaluate the potential of machine learning-based models for predicting carcinogenic human papillomavirus (HPV) oncogene types using radiomics features from magnetic resonance imaging (MRI)., Methods: Pre-treatment MRI images of patients with cervical cancer were collected retrospectively. An HPV DNA oncogene analysis was performed based on cervical biopsy specimens. Radiomics features were extracted from contrast-enhanced T1-weighted images (CE-T1) and T2-weighted images (T2WI). A third feature subset was created as a combined group by concatenating the CE-T1 and T2WI subsets. Feature selection was performed using Pearson's correlation coefficient and wrapper- based sequential-feature selection. Two models were built with each feature subset, using support vector machine (SVM) and logistic regression (LR) classifiers. The models were validated using a five-fold cross-validation technique and compared using Wilcoxon's signed rank and Friedman's tests., Results: Forty-one patients were enrolled in the study (26 were positive for carcinogenic HPV oncogenes, and 15 were negative). A total of 851 features were extracted from each imaging sequence. After feature selection, 5, 17, and 20 features remained in the CE-T1, T2WI, and combined groups, respectively. The SVM models showed 83%, 95%, and 95% accuracy scores, and the LR models revealed 83%, 81%, and 92.5% accuracy scores in the CE-T1, T2WI, and combined groups, respectively. The SVM algorithm performed better than the LR algorithm in the T2WI feature subset ( P = 0.005), and the feature sets in the T2WI and the combined group performed better than CE-T1 in the SVM model ( P = 0.033 and 0.006, respectively). The combined group feature subset performed better than T2WI in the LR model ( P = 0.023)., Conclusion: Machine learning-based radiomics models based on pre-treatment MRI can detect carcinogenic HPV status with discriminative accuracy.

Objective: To evaluate the association of body composition parameters with outcomes in Covid-19., Methods: 173 patients hospitalized for Covid-19 infection in 6 European centers were included in this retrospective study. Measurements were performed at L3-level and comprised skeletal muscle index (SMI), muscle density (MD), and adipose tissue measurements [visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), intramuscular adipose tissue (IMAT), visceral-to-subcutaneous-adipose-tissue-area-ratio (VSR)]. The association with mortality, the need for intubation (MV), and the need for admission to ICU within 30 days were evaluated., Results: Higher SAT density was associated with a greater risk of MV (OR = 1.071, 95%CI=(1.034;1.110), p < 0.001). Higher VAT density was associated with admission to ICU (OR = 1.068, 95%CI=(1.029;1.109), p < 0.001). Higher MD was a protective factor for MV and ICU admission (OR = 0.914, 95%CI=(0.870;0.960), p < 0.001; OR = 0.882, 95%CI=(0.832;0.934), p = 0.028). Higher VSR was associated with mortality (OR = 2.147, 95%CI=(1.022;4.512), p = 0.044). Male sex showed the strongest influence on the risk of ICU admission and MV. SMI was not associated with either parameter., Conclusion: In patients hospitalized for Covid-19 infection, higher VSR seems to be a strong prognostic factor of short-term mortality. Weak associations with clinical course were found for MD and adipose tissue measurements. Male sex was the strongest prognostic factor of adverse clinical course., Advances in Knowledge: VSR is a prognostic biomarker for 30-day mortality in patients hospitalized for Covid-19 disease.

The study's main goal was to compare several data mining and machine learning algorithms to estimate body weight based on body measurements at a different share of Polish Merino in the genotype of crossbreds (share of Suffolk and Polish Merino genotypes). The study estimated the capabilities of CART, support vector regression and random forest regression algorithms. To compare the estimation performances of the evaluated algorithms and determine the best model for estimating body weight, various body measurements and sex and birth type characteristics were assessed. Data from 344 sheep were used to estimate the body weights. The root means square error, standard deviation ratio, Pearson's correlation coefficient, mean absolute percentage error, coefficient of determination and Akaike's information criterion were used to assess the algorithms. A random forest regression algorithm may help breeders obtain a unique Polish Merino Suffolk cross population that would increase meat production.

Purpose: To create and evaluate the ability of machine learning-based models with clinicoradiomic features to predict radiologic response after transarterial radioembolization (TARE)., Materials and Methods: 82 treatment-naïve patients (65 responders and 17 nonresponders; median age: 65 years; interquartile range: 11) who underwent selective TARE were included. Treatment responses were evaluated using the European Association for the Study of the Liver criteria at 3-month follow-up. Laboratory, clinical, and procedural information were collected. Radiomic features were extracted from pretreatment contrast-enhanced T1-weighted magnetic resonance images obtained within 3 months before TARE. Feature selection consisted of intraclass correlation, followed by Pearson correlation analysis and finally, sequential feature selection algorithm. Support vector machine, logistic regression, random forest, and LightGBM models were created with both clinicoradiomic features and clinical features alone. Performance metrics were calculated with a nested 5-fold cross-validation technique. The performances of the models were compared by Wilcoxon signed-rank and Friedman tests., Results: In total, 1,128 features were extracted. The feature selection process resulted in 12 features (8 radiomic and 4 clinical features) being included in the final analysis. The area under the receiver operating characteristic curve values from the support vector machine, logistic regression, random forest, and LightGBM models were 0.94, 0.94, 0.88, and 0.92 with clinicoradiomic features and 0.82, 0.83, 0.82, and 0.83 with clinical features alone, respectively. All models exhibited significantly higher performances when radiomic features were included (P = .028, .028, .043, and .028, respectively)., Conclusions: Based on clinical and imaging-based information before treatment, machine learning-based clinicoradiomic models demonstrated potential to predict response to TARE., (Copyright © 2022 SIR. Published by Elsevier Inc. All rights reserved.)

Background: The neuroimaging findings of brain stem atrophy resembling progressive supranuclear palsy (PSP) radiology are common in idiopathic normal pressure hydrocephalus (iNPH) subjects. Besides, recent studies report the existence of iNPH-like MRI findings in PSP subjects. We aimed to comparatively investigate the neuroimaging indices of iNPH and PSP in our patient groups in a detailed methodology., Methods: Ultimately, 19 probable PSP patients and 18 patients with a definite diagnosis of iNPH were enrolled. The subjects were recruited retrospectively from those who had been admitted between 2017 and 2021 to the Movement Disorders Polyclinic and the Neurosurgery Clinic of the Diskapi Yildirim Beyazit Training and Education Hospital. MRI-based DESH score, Evans index, and the callosal angle (CA) have been calculated in all the individuals. Besides, quantitative MRI parameters of PSP were evaluated in every subject. Statistical analyses were performed using IBM SPSS Statistics 26., Results: The comparative analyses regarding the radiological parameters of PSP did not reveal any difference between patient groups. On the other hand, the comparisons of the neuroimaging parameters of iNPH, yielded differences in the CA, CA score, and the total DESH score. However, the ROC curve analyses did not reveal a discriminative power at a value of "very good" or "excellent" in any of the indices., Conclusions: We found that the neuroimaging features of iNPH and PSP highly overlapped between these patient groups. These results may provide indirect evidence regarding the coexistence of PSP and iNPH pathophysiology that has been deliberated in several recent reports., Competing Interests: There are no conflicts of interest., (Copyright: © 2022 Annals of Indian Academy of Neurology.)

Anatolian buffalo is an important breed reared for meat and milk in various regions of Türkiye. The present study was performed to estimate body weight (BW) from several body measurements, such as tail length (TL), shoulder height (SH), withers height (WH), body length (BL), chest circumference (CC), shank diameter (SD) and birth weight (BiW). The data set was taken from Muş Province of Türkiye. In this respect, 171 Anatolian buffaloes were used. To estimate the BW, different proportions of the training and test sets were used with the MARS algorithm. The optimal MARS was determined at a proportion of 70-30%. The MARS model displays the heaviest BW that can be produced by Anatolian buffalo according to tail length, body length, chest circumference and shoulder height. In conclusion, it could be suggested that the MARS algorithm may allow animal breeders to obtain an elite population and to determine the body measurements affecting BW as indirect selection criteria for describing the breed description of Anatolian buffalo and aiding sustainable meat production and rural development in Türkiye., Competing Interests: The authors declare no conflicts of interest, and none of the authors of this paper have a financial or personal relationship with other people or organizations that could inappropriately influence or bias the content of the paper.

Background: We read with great interest the article by Atalay et al. in which they illustrate the frequent presence of hummingbird signs in their patient group with idiopathic normal pressure hydrocephalus (iNPH) [1]. The results are substantially interesting; however, we believe that some points may be further deliberated for a better understanding of this crucial study., Discussion: The authors declare that there are no commercial, financial, and other relationships in any way related to the subject of this article that might create any potential conflict of interest.

This work aimed to compare the fitting performance of the random regression models applied to the different order orthogonal Legendre polynomials on the race completion speed (m/s) of Arabian racing horses. Legendre polynomial function for additive genetic, permanent environmental variances and heritability values with the L(2,2), L(2,3), L(3,2) and L(3,3) models (where L(i,j) means L(order of fit for additive genetic effects, order of fit for permanent environmental effects)) was estimated. A total of 233,491 race speed records (m/s) of Arabian horses were taken from the Jockey Club of Turkey between 2005 and 2016. The mean and standard deviation of heritability values were estimated as 0.294 ± 0.0746, 0.285 ± 0.0620, 0.302 ± 0.0767 and 0.290 ± 0.1018 for L(2,2), L(2,3), L(3,2), and L(3,3), respectively. The steady decreasing trend of permanent environmental variances for L(2,2) provided stationery for heritability values. According to Akaike information criterion (AIC) and Bayesian information criterion (BIC) values, the L(2,2) model could be reliably used to estimate heritability values for the racing speed of Arabian horses in the presence of repeated observations.

In vitro maturation (IVM) of mammalian oocytes, which influences subsequent in vitro development of embryos, is affected by the macromolecule content in culture media for the success of oocyte maturation competence, in which the cytoplasmic and nuclear reprogramming events occur. The insulin-like growth factor family (IGFs) promotes the maturation of bovine oocytes and the expansion of cumulus cells and also inhibits apoptosis. This study was, therefore, designed to examine the effects of macromolecules (bovine serum albumin, BSA; fetal calf serum, FCS; and polyvinyl alcohol, PVA) on in vitro nuclear maturation, total cellular protein, glutathione peroxidase (GPx) enzyme activity, and the gene expression level of IGF1, IGF2, and their receptor in bovine oocytes. Oocytes obtained from bovine ovaries were cultured in bicarbonate-buffered medium 199 supplemented with 4 mg/mL BSA, 10% FCS, 1 mg/mL PVA, and without macromolecule supplement (control) during 22 h in the air with a humidified atmosphere and 5% CO2 at 38.5 °C temperature. Supplementation of BSA and FCS increased (χ2 = 9.84; p < 0.05) the percentages of oocytes that reached metaphase II compared to the control and PVA. The amount of protein per ml of cell extracts of oocytes matured in FCS supplemented culture media was higher (p < 0.05) than the oocytes in the PVA and control. The levels of GPx enzyme activity in cell extracts isolated from oocytes in each experimental group did not change over time, but the GPx enzyme activity in oocytes matured in PVA-supplemented culture media was lower (p < 0.05) than in oocytes in the other experimental groups. Transcript for the IGF1 gene was not detected in all experimental groups, but the supplementation of BSA and FCS significantly elevated the transcript level of the IGF2 gene. In addition, the maturation of oocytes with BSA-supplemented media increased the transcript level of the IGF1R gene, whereas the transcript level of the IGF2R gene was similar among macromolecule supplementation groups. The current study concluded that BSA and FCS could improve in vitro bovine oocyte development due to supporting nuclear maturation and increasing the total cellular protein content, GPx enzyme, and transcript activity.

Sheep farmers' perceptions of climate change and its impacts and the adaptation strategies they consider to address these risks are of great importance in ensuring the resilience of farming practice. This study focused on sheep farmers' perception of climate change and the risks and actions taken to mitigate these impacts. A total of 68 surveys were carried out among sheep farmers (39 transhumance and 29 semi-intensive farmers) by two different representative production systems in Türkiye. Variables regarding the socio-economic profile, climate change impacts, and adaptation strategies were identified and analyzed. Principal component analysis and a Pearson Chi-square test were used to evaluate the data. Both farmers' groups accepted and perceived climate change, showing good awareness and perception. The farmers' attitudes towards adaptation to climate change were associated with production systems. Transhumance farmers had limited adaptation and coping strategies compared to semi-intensive farmers. Transhumance farmers focused mainly on selling livestock (mostly to cope with degraded natural grassland/feed deficiency) as an adaptive strategy. In contrast, semi-intensive farmers focused on modifying their farm management and feed operations, such as changing the feed ratio and supplement use, improving water and feed storage, and considering crop feed production. The knowledge obtained from this study could be helpful for farmers and policymakers who develop long-term small ruminant production strategies that consider the effects of climate change and adapt them to different farming systems in the Türkiye.

Despite their predominance worldwide, few studies have been conducted to look at the impact of sheep production systems relying on transhumance practices in arid and continental conditions, on farm-level greenhouse gas (GHG) emissions. Using Turkey as an example, this paper examines on farm-level GHG emissions calculated for two contrasting sheep production systems under arid and continental climate conditions. Production and management data were obtained through face-to-face interviews carried out on 10 transhumance and 15 semi-intensive meat sheep farms in Turkey. A total of seven GHG emission estimates were then calculated for each farm with the Agricultural Resource Efficiency Calculator (AgRECalc©) tool; i) total Carbon Dioxide (CO2) from energy use (kg CO2e), ii) total Carbon Dioxide equivalent (CO2e) from methane (kg CO2e), iii) total CO 2 e from nitrous oxide (kg CO 2 e), iv) whole farm and enterprise CO 2 e emissions (kg CO 2 e), v) net emission from land use (kg CO 2 e), vi) whole farm CO 2 e emissions per kg of farm output (kg CO 2 e/kg output), vii) product CO 2 e emissions (meat): kg CO 2 e / kg live weight, and viii) farm output (kg of sheep). Multivariate analyses (using R software) were carried out to compare both farm types and their respective carbon emissions. The total farm output per ewe was lower in the transhumance farms (7.4 kg/ewe) than in the semi-intensive farms (7.7 kg/ewe). The kg CO 2 e per kg of output was also lower for the transhumance farms (46.2 kg CO 2 e) than for the semi-intensive ones (56.5 kg CO 2 e). This trend was similar for the amount of CO 2 e per kg of live weight produced (20.8 kg and 25.4 kg for the transhumance and the semi-intensive farms, respectively). Despite overall net emissions from land use being greater on average for the transhumance farms, once measured per hectare, they were found to be lower than those for the semi-intensive farms. This study provides a reference point for different sheep production systems' GHG emission impact in continental rangelands in Turkey., (Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.)

Purpose: This study aims to evaluate the potential of machine learning algorithms built with radiomics features from computed tomography urography (CTU) images that classify RB1 gene mutation status in bladder cancer., Method: The study enrolled CTU images of 18 patients with and 54 without RB1 mutation from a public database. Image and data preprocessing were performed after data augmentation. Feature selection steps were consisted of filter and wrapper methods. Pearson's correlation analysis was the filter, and a wrapper-based sequential feature selection algorithm was the wrapper. Models with XGBoost, Random Forest (RF), and k-Nearest Neighbors (kNN) algorithms were developed. Performance metrics of the models were calculated. Models' performances were compared by using Friedman's test., Results: 8 features were selected from 851 total extracted features. Accuracy, sensitivity, specificity, precision, recall, F1 measure and AUC were 84%, 80%, 88%, 86%, 80%, 0.83 and 0.84, for XGBoost; 72%, 80%, 65%, 67%, 80%, 0.73 and 0.72 for RF; 66%, 53%, 76%, 67%, 53%, 0.60 and 0.65 for kNN, respectively. XGBoost model had outperformed kNN model in Friedman's test (p = 0.006)., Conclusions: Machine learning algorithms with radiomics features from CTU images show promising results in classifying bladder cancer by RB1 mutation status non-invasively., Competing Interests: The authors declare no conflict of interest., (© 2022 The Authors. Published by Elsevier Ltd.)

In barns equipped with an automatic milking system, the profitability of production depends primarily on the milking efficiency of a cow (ME; kg/min) defined as cow milk yield per minute of box time. This study was carried out on 1823 Polish Holstein−Friesian cows milked by the automatic milking system (AMS) in 20 herds. Selected milking parameters recorded by the AMS were analyzed in the research. The aim of the study was to forecast ME using two statistical techniques (analysis of variance and decision trees). The results of the analysis of variance showed that the average ME was 1.67 kg/min. ME was associated with: year of AMS operation (being the highest in the first year), number of cows per robot (the highest in robots with 61−75 cows), lactation number (highest for multiparas), season of calving (the highest in spring), age at first calving (>36 months), days in milk (151−250 days) and finally, rear quarter to total milk yield ratio (the highest between 51% and 55%). The decision tree predicted that the highest ME (2.01 kg/min) corresponded with cows that produced more than 45 kg of milk per day, were milked less than four times/day, had a short teatcup attachment time (<7.65 s) and were milked in robots that had an occupancy lower than 56 cows.

There is little knowledge about the carcass potential of Turkish native goat breeds raised under different rearing conditions. It is necessary to compare the carcass characteristics of these breeds to minimize the effect of the rearing conditions. Therefore, this study aims to compare the growth, slaughter, and carcass characteristics of male kids at weaning age to eliminate the impact of rearing conditions. Kids born to Angora ( n = 6), Hair ( n = 6), Honamlı ( n = 6), and Kilis ( n = 6) Turkish native goat breeds, ranging in age from 2-3 years, were slaughtered at 90 days of weaning age and carcass characteristics were determined immediately. There were differences ( p < 0.05) among breeds in terms of birth weight (BW) and daily weight gain (DWG) from birth to weaning age. Honamlı kids had higher BW and DWG than those of other breeds ( p < 0.05). Similarly, carcass weights (hot-cold) and yields (hot-cold) of Honamlı kids were higher compared to kids born to other breeds ( p < 0.05). Additionally, Honamlı and Hair kids had higher longissimus-dorsi (LD), and semitendinosus (ST) muscle weights compared to Kilis and Angora kids. Interestingly, Kilis kids had lower BW and DWG than those of other breeds ( p < 0.05). There were significant differences ( p < 0.05) among breeds in terms of non-carcass parts and organ weights. Positive correlations were calculated between cross-sectional area and weight (r = 0.793; p < 0.01), length and weight (r = 0.723; p < 0.01), and depth and weight (r = 0.698; p < 0.01) in LD muscle of all kids. A similar correlation trend was calculated for the ST muscle (cross-sectional area and weight; r = 0.699; p < 0.01, length and weight; r = 0.751; p < 0.01, and depth and weight; r = 0.528; p < 0.05) in all kids. In conclusion, the present study results showed that Honamlı kids could be used for fattening material due to their good carcass quality compared to other native breeds.

Objective: The aim of the study was to evaluate the interobserver agreement and diagnostic accuracy of COVID-19 Reporting and Data System (CO-RADS), in patients suspected COVID-19 pneumonia., Methods: Two hundred nine nonenhanced chest computed tomography images of patients with clinically suspected COVID-19 pneumonia were included. The images were evaluated by 2 groups of observers, consisting of 2 residents-radiologists, using CO-RADS. Reverse transcriptase-polymerase chain reaction (PCR) was used as a reference standard for diagnosis in this study. Sensitivity, specificity, area under receiver operating characteristic curve (AUC), and intraobserver/interobserver agreement were calculated., Results: COVID-19 Reporting and Data System was able to distinguish patients with positive PCR results from those with negative PCR results with AUC of 0.796 in the group of residents and AUC of 0.810 in the group of radiologists. There was moderate interobserver agreement between residents and radiologist with κ values of 0.54 and 0.57., Conclusions: The diagnostic performance of CO-RADS for predicting COVID-19 pneumonia showed moderate interobserver agreement between residents and radiologists., Competing Interests: The authors declare no conflict of interest., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

The automatic milking system (AMS) provides a large amount of information characterizing the course of each milking cow, which is not available in the conventional system. The aim of our study was to estimate heritability and genetic correlations for milk yield (MY), milking frequency (MF), and speed (MS) for 1713 Polish Holstein-Friesian primiparous cows milked in barns with an AMS. Daily heritability indicators estimated using second-order Legendre polynomials and Random Regression Models showed high variation during lactation, ranging 0.131-0.345 for MY, 0.153-0.322 for MF, and 0.336-0.493 for MS. The rates of genetic correlation between traits ranged: 0.561-0.929 for MY-MF, (-0.255)-0.090 for MF-MS, (-0.174)-0.020 for MY-MS. It is possible to carry out effective selection for milking speed, which provides an opportunity to increase the number of cows per milking robot, and thus increase the profitability of production in the herd. The results proved that selection for milk yield and daily milking frequency is also feasible. The research showed a high, positive genetic correlation between milking frequency and milk yield, which allows us to conclude that preferring breeding cows with a natural tendency to frequent visits to the milking robot should indirectly improve the genetic basis of milking.