After the outbreak of severe acute respiratory syndrome (SARS-2002/2003) and middle east respiratory syndrome (MERS-2012/2014) in the world, new public health crisis, called new coronavirus disease (COVID-19), started in China in December 2019 and has spread all over countries. COVID-19 coronavirus has been global threat of the disease and infected humans rapidly. Control of the pandemi is urgently essential, and science community have continued to research treatment agents. Support therapy and intensive care units in hospitals are also efective to overcome of COVID-19. Statistic forecasting models could aid to healthcare system in preventation of COVID-19. This study aimed to compose of forecasting model that could be practical to predict the spread of COVID-19 in Italy, Spain and Turkey. For this purpose, we performed Auto Regressive Integrated Moving Average (ARIMA) model on the European Centre for Disease Prevention and Control COVID-19 data to predict the number of cases and deaths in COVID-19. According to the our results, while number of cases in Italy and Spain is expected to decrease as of July, in Turkey is expected to decline as of September. The number of deaths in Italy and Spain is expected to be the lowest in July. In Turkey, this number is expected to reach the highest in July. In addition, it is thought that if studies in which the sensitivity and validity of this method are tested with more cases, they will contribute to researchers working in this field.

Bu çalışmanın amacı regresyon modellerinde çoklu doğrusal bağlantı olması durumunda işlevini yitiren en küçük kareler (EKK) metodu ile yanlı tahmin ediciler olan Ridge Regresyon (RR) ve Temel Bileşenler Regresyonu (TBR) yöntemlerini karşılaştırmaktır. Bu amaçla Tokat Gaziosmanpaşa Üniversitesi Araştırma ve Uygulama Çiftliğinde yetiştirilen sütten kesim döneminde bulunan 59 baş Saanen oğlaklarının canlı ağırlıkları (CA) ve bazı vücut özelliklerinden vücut uzunluğu (VU), cidago yüksekliği (CY), sağrı yüksekliği (SY), göğüs derinliği (GD), göğüs genişliği (GG) ve göğüs çevresi (GÇ) ölçümleri kullanılmıştır. Metotların tahmin performansını değerlendirmek için belirtme katsayısı (R2) ve hata kareler ortalaması (HKO) değerleri incelenmiştir. Çalışmada, canlı ağırlık tahmininde kullanılan cidago yüksekliği (CY) ve sağrı yüksekliği (SY) arasında çoklu doğrusal bağlantı olduğu görülmüş ve bu sorun RR ve TBR ile ortadan kaldırılmıştır. İncelenen yöntemlerin R2 ve HKO değerleri karşılaştırıldığında; Saanen oğlaklarının canlı ağırlık tahmininde RR yaklaşımının diğer yöntemlerden daha iyi sonuç verdiği belirlenmiştir.

The aim of this study is to evaluate the model performance in the classification of FAMACHA© scores using Support Vector Machines (SVMs) with a focus on the estimation of the FAMACHA© scoring system used for early diagnosis and treatment management of parasitic infections. FAMACHA© scores are a color-based visual assessment system used to determine parasite load in animals, and in this study, the accuracy of the model was investigated. The model’s accuracy rate was analyzed in detail with metrics such as sensitivity, specificity, and positive/negative predictive values. The results showed that the model had high sensitivity and specificity rates for class 1 and class 3, while the performance was relatively low for class 2. These findings not only demonstrate that SVM is an effective method for classifying FAMACHA© scores but also highlight the need for improvement for class 2. In particular, the high accuracy rate (97.26%) and high kappa value (0.9588) of the model indicate that SVM is a reliable tool for FAMACHA© score estimation. In conclusion, this study demonstrates the potential of SVM technology in veterinary epidemiology and provides important information for future applications. These results may contribute to efforts to improve scientific approaches for the management of parasitic infections.

Bu araştırma Tokat Gaziosmanpaşa Üniversitesi araştırma çiftliğinde 3 katlı bir kafes sisteminde gerçekleştirilmiştir. Her kafes bölmesinde 6 tavuk yerleştirilmiş ve her katta 5 tekerrür oluşturulmuştur. 24-42. haftalar arasında toplam 90 Atak-S tavuğundan 9:00 - 13:00 ve 16:00 saatlerinde mevcut yumurtalar toplanmıştır. Deneme süresince 90 tavuktan toplam 1442 yumurta elde edilmiştir. Kafes pozisyonu ile yumurtlama zamanı arasındaki ilişkinin belirlenmesi için uyum analizi uygulanmıştır. Verilerin analizinde SPSS paket programı kullanılmıştır. Sonuçlara göre farklı katlardaki tavukların farklı saatlerde yumurtlamaları istatistiksel olarak önemli bulunmuştur. Uyum analizi sonuçlarına göre değişkenliğin tek boyuttaki açıklama gücü %99,7 olup ikinci boyuttaki açıklama gücünün %0,3 olduğu belirlenmiştir. Birinci boyuttaki kafes katları bakımından değişkenlik incelendiğinde birinci katın açıklama gücünün %65,4 olduğu ikinci boyutta ise ikinci (%56,9) ve üçüncü katın (%41,8) açıklama gücünün daha yüksek olduğu belirlenmiştir. Birinci boyuttaki yumurta toplama saatleri bakımından değişkenlik incelendiğinde saat 13:00‘da toplama için açıklama gücünün %52,8 olduğu ikinci boyutta ise saat 16:00‘daki toplama için açıklama gücünün %79,6 olduğu belirlenmiştir. Sonuç olarak birinci kattaki tavukların genellikle saat 9:00 sıralarında, 2. ve 3. kattaki tavukların ise 13:00 ile 16:00 saatleri arasında yumurtladığı belirlenmiştir. Yumurtaların ortam sıcaklığına göre değişiklik göstermekle birlikte üreticiler tarafından genellikle sabah saatlerinde toplandığı bilinmektedir. Bu nedenle yumurta toplama sıklığının hem ekonomik olarak hem de tüketici sağlığı açısından yeniden düzenlenmesi önem arz etmektedir.

Bovine mastitis is a complex inflammatory disease that has a substantial negative influence on milk productivity and costs the dairy industry greatly. Identifying strong molecular biomarkers for early diagnosis is still a key area of research. In this study, integrated analyses of transcriptomic data and machine-learning approaches were used to identify candidate bovine mastitis-associated genes. The GSE15020 and GSE24217 data sets were used as discovery cohorts, while the third dataset GSE50685 served as an independent validation set. Differential gene expression analysis following quality control, normalization and batch effect adjustment using the ComBat method identified 1143 significant genes in the discovery cohort. Candidate biomarkers were subsequently ranked and selected using Random Forest and XGBoost algorithms. All candidate genes were evaluated in an independent dataset, where ACSL6 showed the highest discriminative power (p < 0.001) relative to all other candidates and PTX3 also showed potential as a good classifier. The robustness of the model was evaluated using ROC curve, confidence intervals, and permutation tests. Functional enrichment analysis showed that the DEGs were mainly involved in immune response, inflammatory pathways and defense response. Despite the limited sample size of the validation cohort, findings of present study support ACSL6 as a potential biomarker for bovine mastitis. However, larger independent cohorts and experimental validation are needed to support its diagnostic relevance and biological significance. This analysis collectively demonstrates that the combination of transcriptomic profiling and machine learning is a valuable means to identify candidate biomarkers for bovine mastitis.

Traditional methods are limited by the accuracy and objectivity of broiler meat quality evaluation, due to both the complexity of its multivariate structure and conflict inherent in the uncertainty of data. A Mamdani-type fuzzy logic-based decision support system was designed for meat quality classification based on the dataset of 48 male Ross 308 broiler chickens, slaughtered at the age of 42 days. In this structure, pH24, cooking loss (CL), drip loss (DL) and lightness (L*) were used as input variables, and quality was classified into low/high/medium groups as output variable. The model performance was thoroughly evaluated by examining the results against those provided by human experts, using Accuracy, Cohen’s kappa, and Weighted kappa statistics to achieve meaningful measures. Results indicated that the Accuracy rate attained by the model was between 81% to 85%, along with a Kappa coefficient of over 0.70, evidencing moderate agreement. Importantly, the weighted kappa analysis demonstrated that most classification errors happened between adjacent quality classes. Overall, the developed fuzzy logic–based model effectively addresses the complexity and vagueness of meat quality prediction, providing reliable classifications that are simple to interpret.