19 research outputs found
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