Inspirations of biomimetic affinity ligands: a review

Affinity chromatography is a well-known method dependent on molecular recognition and is used to purify biomolecules by mimicking the specific interactions between the biomolecules and their substrates. Enzyme substrates, cofactors, antigens, and inhibitors are generally utilized as bioligands in affinity chromatography. However, their cost, instability, and leakage problems are the main drawbacks of these bioligands. Biomimetic affinity ligands can recognize their target molecules with high selectivity. Their cost-effectiveness and chemical and biological stabilities make these antibody analogs favorable candidates for affinity chromatography applications. Biomimetics applies to nature and aims to develop nanodevices, processes, and nanomaterials. Today, biomimetics provides a design approach to the biomimetic affinity ligands with the aid of computational methods, rational design, and other approaches to meet the requirements of the bioligands and improve the downstream process. This review highlighted the recent trends in designing biomimetic affinity ligands and summarized their binding interactions with the target molecules with computational approaches

Genetic algorithm approach to parameter estimation of Kumaraswamy distribution using ranked set sampling

YÖK Tez ID: 540889Bu tez çalışmasında, Kumaraswamy dağılımının parametrelerinin tahmin edilmesi için en çok olabilirlik yönteminde genetik algortimanın kullanılması araştırlmıştır. Ayrıca basit rasgele örneklemeye alternatif olarak sıralı küme örneklemesi de incelenmiştir. Genetik algoritma, Kumaraswamy dağılımı parametrelerinin pozitif olma koşulunun hesaba katılması ve olabilirlik fonksiyonunun türev bilgisine ihtiyaç duymaması açısından kolaylık sağlamıştır. Bunun yanında sıralı küme örneklemesi tahmin edicileri basit rasgele örneklemeye kıyasla daha iyi sonuçlar vermiştir. Simülasyon çalışmasındaki hesaplamalar için R yazılımı kullanılmıştır.In this thesis, the estimation of parameters of the Kumaraswamy distribution has been investigated by using maximum likelihood method with genetic algorithm. In addition, ranked set sampling is also investigated as an alternative for simple random sampling. Genetic algorithm has two benefits for solving this problem. First benefit is that by using GA the pozitivity constraints for the parameters of the Kumaraswamy distribution are automatically satisfied. Second in GA use of derivatives is not needed. On the other hand ranked set sampling estimators give better results in comparison with simple random sampling estimators. R software was prefered for calculations in the simulation study

Sıralı Küme Örneklemesi ile Kumaraswamy Dağılımı Parametrelerinin Tahmin Edilmesinde Genetik Algoritma Kullanılması

Bu çalışmada, Kumaraswamy dağılımının parametrelerinin en çok olabilirlik yöntemi ile tahmin edilmesi genetik algoritma yaklaşımı kullanılarak araştırılmıştır. Ayrıca basit rasgele örneklemeye göre daha iyi sonuç verebileceği düşünülerek parametrelerin tahmin edilmesinde sıralı küme örneklemesi de incelenmiştir. Genetik algoritma yaklaşımı, Kumaraswamy dağılımı parametrelerinin pozitif olma koşulunun hesaba katılması nedeniyle tercih edilmiştir. Ek olarak genetik algoritma yaklaşımında en çok olabilirlik fonksiyonunun türev bilgisine ihtiyaç duyulmaması da hesaplamalarda kolaylık sağlamaktadır. Genetik algoritma kullanılarak elde edilen her iki örnekleme yöntemine ait olabilirlik tahmin edicilerinin performanslarının karşılaştırılması için yan, hata kareler ortalaması ve etkinlikleri hesaplanmıştır. Simülasyon çalışmasındaki hesaplamalar için R yazılımı ve ilgili paketler kullanılmıştır.In this paper, genetic algorithm approach is used to estimate parameters of the Kumaraswamy distribution with maximum likelihood method. In addition ranked set sampling is used since it is expected to give better results in comparison to simple random sampling. Genetic algorithm approach is chosen because it is relatively more convenient in terms of satisfying positivity constraints for the parameters of the Kumaraswamy distribution. Also there is no need to use derivatives in the genetic algorithm approach. Bias, MSE and efficiency is calculated to compare performaces of maximum likelihood estimators for ranked set sampling and simple random sampling obtained by using genetic algorithms. The R software and related packages are preferred for calculations in the simulation study

On Estimating Parameters of the Kumaraswamy Distribution with Ranked Set Sampling Using Genetic Algorithms

Bu çalışmada, Kumaraswamy dağılımının parametrelerinin en çok olabilirlik yöntemi ile tahmin edilmesi genetik algoritma yaklaşımı kullanılarak araştırılmıştır. Ayrıca basit rasgele örneklemeye göre daha iyi sonuç verebileceği düşünülerek parametrelerin tahmin edilmesinde sıralı küme örneklemesi de incelenmiştir. Genetik algoritma yaklaşımı, Kumaraswamy dağılımı parametrelerinin pozitif olma koşulunun hesaba katılması nedeniyle tercih edilmiştir. Ek olarak genetik algoritma yaklaşımında en çok olabilirlik fonksiyonunun türev bilgisine ihtiyaç duyulmaması da hesaplamalarda kolaylık sağlamaktadır. Genetik algoritma kullanılarak elde edilen her iki örnekleme yöntemine ait olabilirlik tahmin edicilerinin performanslarının karşılaştırılması için yan, hata kareler ortalaması ve etkinlikleri hesaplanmıştır. Simülasyon çalışmasındaki hesaplamalar için R yazılımı ve ilgili paketler kullanılmıştır.In this paper, genetic algorithm approach is used to estimate parameters of the Kumaraswamy distribution with maximum likelihood method. In addition ranked set sampling is used since it is expected to give better results in comparison to simple random sampling. Genetic algorithm approach is chosen because it is relatively more convenient in terms of satisfying positivity constraints for the parameters of the Kumaraswamy distribution. Also there is no need to use derivatives in the genetic algorithm approach. Bias, MSE and efficiency is calculated to compare performaces of maximum likelihood estimators for ranked set sampling and simple random sampling obtained by using genetic algorithms. The R software and related packages are preferred for calculations in the simulation study

Effects Of Erdosteine On Oxidative-Antioxidative Equilibrium And On Cataract Formation In Rat Pups With Selenite-Induced Cataract

Aim: To investigate whether erdosteine supplementation following selenite exposure affects oxidant-antioxidant equilibrium and prevents cataract formation in rat pups. Methods: Thirty-nine Wistar-albino rat pups were divided into 3 groups. In Group 1 (n=16) only s.c saline was injected. In Group 2 (n=10) subcutaneous (s.c.) sodium selenite (30 nmol / g body weight) was injected on postpartum day 10. In Group 3 (n=13) s.c. sodium selenite (30 nmol/g body weight) were injected on postpartum day 10 and oral erdosteine (10 mg/kg body weight, daily for one week) was administered by gavage thereafter. The development of cataract was assessed weekly. The density of cataract was graded by slit-lamp biomicroscopy. On day 21, the blood was collected and lenses were removed. Oxidative stress index (OSI) and total antioxidant capacity (TAC) were determined in the lenses of the rat pups. Paraoxonase-1 (PON 1) activity was determined in the sera. Results: All of the lenses of rat pups in Group 1 remained clean. All rat pups developed dense nuclear opacity in Group 2. Eight out of 13 rat pups developed slight nuclear opacity in Group 3. Differences among the groups were statistically significant (p<0.05). Group 2 lenses had higher mean OSI level than that of Group 3 lenses (p=0.003). Group 2 lenses lower mean TAC levels than that of Group 3 (not significant). Mean PON 1 level of Group 2 was lower than that of Group 3 (p<0.05). Conclusion: Erdosteine diminishes the incidence of cataract due to its protection of the antioxidant defense system

An Outranking Approach for MCDM-Problems with Neutrosophic Multi-Sets

KILIC, Adil/0000-0002-0082-2544; Sahin, Memet/0000-0002-1066-1641WOS: 000502521500017In this paper, we introduced a new outranking approach for multi-criteria decision making (MCDM) problems to handle uncertain situations in neutrosophic multi environment. Therefore, we give some outranking relations of neutrosophic multi sets. We also examined some desired properties of the outranking relations and developed a ranking method for MCDM problems. Moreover, we describe a numerical example to verify the practicality and effectiveness of the proposed method

Orbita Breast Metastasis

Breast carcinomas are among the most frequent metastatic lesions of the orbit. Diagnosing the disease earlier is of great importance in maximising the life quality of the patient. We report a 35-year-old-female with a retroorbital mass due to the metastases from her left breast invasive ductal carcinoma. Pre-existing malignant diseases should be considered in differential diagnoses of external ophthalmoplegia, central retinal vein occlusion, optic nerve meningioma, and proptosis. The ophthalmologists should be aware that eye, particularly orbita breast metastases may be easily overlooked and a late diagnosis would not work

Atypical Fibroxanthoma Of The Eyelid

Atypical fibroxanthoma (AFX) is probably a neoplasm of fibrohistiocytic lineage. The tumor arise in the skin and has strikingly atypical properties. We report a case of AFX that was excised from the left lower eyelid of a twelve-year-old girl. The nodular mass was reported as AFX. Though this tumor has the capability to recur aggresively, no recurrence was noted in the present case. Malignant fibrous histiocytoma, atypical fibrous histiocytoma, squamous cell carcinoma, sarcoma, dermatofibroma protuberans, and reticulohistiocytoma should be included in differential diagnosis