Background: Chronic spontaneous urticaria (CSU) is a common and disabling disease. Assessments of IgE and C‐reactive protein (CRP) are recommended in the diagnostic work‐up, but the role and clinical relevance of these biomarkers are not well characterized. Moreover, it remains unknown if elevated levels of IgE or CRP are linked to CSU microRNA (miRNA) signatures or interleukin 31 (IL‐31).Methods: We measured IgE and CRP serum levels in 47 CSU patients (and 45 healthy controls) and determined CSU disease activity using the urticaria activity score (UAS7). Expression levels of miR‐155 and miR‐221 were assessed by RT‐PCR, and IL‐31 levels were determined by ELISA.Results: Total IgE and CRP levels were independently increased in CSU patients. IgE and CRP levels were highest and lowest in patients with high and mild disease activity. IgE levels correlated with miR‐155 levels, whereas CRP levels correlated with miR‐221 levels. miR‐155 and miR‐221 were significantly overexpressed in CSU patients. ROC analyses linked miRNA‐155 and CSU with a sensitivity of 79% and specificity of 87%, and miRNA‐221 and CSU with a sensitivity of 75% and specificity of 91%. High CRP and miR‐221 expression levels were linked to elevated levels of IgG anti‐TPO and IL‐31.Conclusion: IgE and CRP are useful biomarkers for disease activity in CSU, with distinct miRNA profiles. High CRP and miR‐221 levels may point to autoimmune CSU and a role for IL‐31.

In agricultural activities, there is an increasing need for organic products as alternatives to chemical pesticides. Wood vinegar (WV, pyrolysis liquid) from these alternative potential products is produced as a result of the carbonization process in which it is obtained from organic wastes together with useful products such as energy and biochar. This study was carried out to determine the insecticidal effects of WVs produced from broiler chicken litter and nutshells on the harmful almond leaf wasp Cimbex quadrimaculata Müller, 1766 (Hymenoptera: Cimbicidae). The study was carried out in an almond orchard in Diyarbakır (Eğil district) and Elazığ (Keban district) in the 2019-2021 production seasons. WV mixed solutions (1%, 3%, 5%, 7%, 10% and 25%) were applied with a back sprayer at the beginning and end of leafing in contaminated almond orchards. The effects of the applications were determined by impact and visual control methods on the 1st, 7th, 14th and 21st. In the analysis of the obtained data, first of all, normality test was performed. In the analysis of the obtained data, the Kruskal-Wallis test was applied. In conclusion; It has been determined that WV-added solutions have a insecticidal effect against harmful almond leaf bees, and the highest decrease in the number of larvae occurs at doses of 10% to 25%. It is important to test these vinegars in different locations with more detailed studies and to conduct detailed bioecological studies depending on the climatic factors and population ecology of the pest in terms of alternative control of the pest. The data of this study contain basic data for almond integrated pest management (IPM) studies.

Mühendislik uygulamalarında teknik bir sistemin güvenilirliği, o sistemi oluşturan bileşenlerin güvenilirliklerine bağlıdır. Bazı özel sistemlerde iki veya daha fazla bileşen aynı işleve sahip olabilirler ve bu bileşenler sistemde farklı pozisyonlara atanabilirler. Bu gibi sistemlerde aynı bileşenlerin konumlarını değiştirerek sistemin güvenilirliği artırılabilir. Bu çalışmada seri ve paralel sistemlerin genel hali olan doğrusal ardışık n-den k-çıkışlı sistemler incelenmiş ve sistemi oluşturan bütün bileşenlerin aynı işleve sahip olduğu durum için sistemin optimizasyonu Birnbaum önem tabanlı genetik algoritma yaklaşımı ile sağlanmaya çalışılmıştır. Ayrıca, önermiş olduğumuz yaklaşımın uygulamasını göstermek için petrol taşıma sistemi üzerinde bir durum çalışmasına yer verilmiştir.

The high variability and nonlinear relationships between environmental variables (such as temperature, relative humidity, and altitude) in ecological datasets prevent classical statistical models from obtaining accurate predictions. This study aimed to compare and investigate the performance of AI-based machine learning methods in analyzing complex ecological data structures. An agricultural dataset containing meteorological and vegetation variables was used as the representative case study. This dataset is based on population observations of Cimbex quadrimaculata in Diyarbakır (Eğil) and Elazığ (Keban) provinces in Türkiye between 2020 and 2022. Three different modeling approaches (binary classification, multiclass classification, and regression) were applied to the same data. This three-approach design enabled a systematic comparison of model performance, generalizability, and explainability on the same dataset using different definitions of the target variable. For classification tasks, the model performance was evaluated using accuracy, F1 score, and AUC metrics under a stratified 10-fold cross-validation scheme. Regression models, on the other hand, were assessed within a nested cross-validation framework using R², root mean square error (RMSE), mean absolute error (MAE). Ensemble-based boosting AI algorithms (Gradient Boosting, XGBoost, and LightGBM) demonstrated high accuracy and generalizability in characterizing the highly nonlinear relationships, nested effects, and non-additive interactions among multiple variables. Furthermore, the SHAP analysis improved the interpretability of the models and revealed that temperature- and humidity-related variables were consistently among the most influential predictors in the model predictions. Comparative performance evaluations of machine learning models showed that Gradient Boosting (94.3% accuracy, 0.983 AUC) and XGBoost (84.6% accuracy) were the strongest predictors in binary classification scenarios and overall analyses, respectively. In regression analyses, LightGBM and Random Forest algorithms stood out with cross-validation performances of approximately R² ≈ 0.73. In particular, the success of ensemble-based learning methods in capturing multidimensional relationships in ecological datasets explains the high predictive accuracy and robustness of these models across complex ecological data structures.

This study was carried out to determine the insecticidal effects of different plant extracts on confused flour beetle, Tribolium confusum Jacquelin du Val, 1863 (Coleoptera: Tenebrionidae) and time-varying LD50 and LD90 values. LD50 and LD90 of the formulations were determined by the probit analysis method. The studies were conducted under controlled conditions in a climate cabin and air conditioning room at 25 ± 2°C and 60% humidity 2019 to 2020 in Fırat University Bioengineering Department in Turkey. In this study, Grainpro AITC-ALPHA (Monoterpen, Allyisothiocyanate,Cocoate methyl esters), Grainpro AITC-GAMMA (Gamma terpene, Allyisothiocyanate, Cocoate methyl esters-Eugenol), Grainpro GAMMA-EUG (Gamma terpinene, Eugenol, Cocoate methyl esters), and Grainpro AITC-EUC (Eucalyptus oil, Allyisothiocyanate, Cocoate methyl esters) plant formulations were used in doses (In all doses, different µL drug doses prepared in 1 ml were mixed in 1 liter of water and applied by obtaining 0.2 µL, 0.4 µL, 0.6 µL, 0.8 µL, 1 µL doses for each formulation). The two upper and two lower doses of the most effective drug obtained were then tested and their effectiveness was determined. It was determined that the insecticidal efficacy of the pesticides increased as the pesticide dose increased and two lower and two upper doses of the most effective dose of 1 µL were subjected to efficacy trials. Among these formulations, 1 µL and 1.10 µL doses of Gainpro AITC-EUC formulation were the most effective on the pest.