Deep Learning Based Models for Detection of Diabetic Retinopathy

Diabetic retinopathy (DR) is an important disease that occurs because of damage to the retinal blood vessels in the human eye due to diabetes and causes blindness. If diagnosed correctly, the treatments to be applied increase the possibility of preventing vision loss or blindness. This study aims to present an evaluation of deep learning methods to detect diabetic retinopathy from retinal images. In this direction, the VGG16 model was considered, and two different versions of this model were obtained by making improvements. Besides, a model has been proposed, the first layers are dense, the next layers have decreasing convolution, and have fewer layers. According to the results, the VGG16 model, which reached 75.48% accuracy, reached 76.57% accuracy due to the dropout layer added to the classification layers, and 77.11% accuracy due to the dropout layer added to all blocks. The highest accuracy was obtained in the proposed model with 81.74%

Özel Yetenekli Öğrencilerin Akademik Benlik Algıları Üzerinde Büyük Balık-Küçük Gölet Etkisi

According to the big-fish-little-pond effect (BFLPE), equally able students would have lower academic self-concepts in high-ability settings than those who attend low- or mixed-ability settings. This study was an investigation of the BFLPE on math and science academic self-concepts of gifted students. Participants included 50 fifth- and sixth-grade gifted students who participated in an after-school program for gifted students (EPTS) at a university campus for five terms. Students’ academic self-concepts were measured using the Math and Science Academic Self-Concept Questionnaire both in the EPTS setting and in the school setting in three measurement points. Findings showed that gifted students’ academic self-concepts declined significantly from time 1 measurement to time 3 measurement. However, the level of their academic self-concepts was still high in the EPTS and very high in school. The main effect of setting showed that the overall academic self-concept in school (M = 34,24; SD = 2,26) was significantly higher than the mean of academic self-concepts in the EPTS (M = 31,49; SD = 3,87). They used the EPTS and school as two different frames of reference and thus held adaptable academic self-concepts, relatively low in the EPTS and relatively high in school.Benzer yetenek düzeyindeki öğrenciler düzey gruplaması olmayan karma sınıflarda yüksek akademik benlik algısına, düzey gruplaması olan homojen sınıflarda ise daha düşük akademik benlik algısına sahip olabilmektedirler. Bu durum büyük balık-küçük gölet etkisi (BBKGE) olarak tanımlanmaktadır. Bu çalışmada özel yetenekli öğrencilerin matematik ve fen bilimleri benlik algılarında BBKGE araştırılmıştır. Katılımcılar Anadolu Üniversitesi bünyesinde bulunan Üstün Yetenekliler Eğitim Programları (ÜYEP)’na devam eden 5. ve 6. sınıf 50 özel yetenekli öğrenciden oluşmaktadır. Bu program okul sonrası formatında olup üniversite kampüsünde yürütülmektedir. Öğrencilerin matematik ve fen bilimleri akademik benlik algıları hem ÜYEP’te hem de örgün öğretime devam ettikleri okullarda Akademik Benlik Algısı Ölçeği (ABAÖ) ile üç farklı zamanda ölçümlenmiştir. Öğrencilerin akademik benlik algılarının 1. ölçümden 3. ölçüme kadar geçen sürede anlamlı düzeyde azaldığı saptanmıştır. Ancak akademik benlik algıları ÜYEP’te halen yüksek, okullarında ise oldukça yüksek olmaya devam etmiştir. Okuldaki genel akademik benlik algısı (M = 34,24; SS = 2,26) ÜYEP’teki akademik benlik algısından (M = 31,49; SS = 3,87) anlamlı düzeyde yüksek bulunmuştur. Öğrenciler okul ve ÜYEP’i iki farklı referans çerçevesi olarak kullanmış ve bu nedenle uyarlanabilir akademik benlik algıları okulda nispeten yüksek, ÜYEP’te düşük saptanmıştır

Predicting sumoylation sites using support vector machines based on various sequence features, conformational flexibility and disorder

Background Sumoylation, which is a reversible and dynamic post-translational modification, is one of the vital processes in a cell. Before a protein matures to perform its function, sumoylation may alter its localization, interactions, and possibly structural conformation. Abberations in protein sumoylation has been linked with a variety of disorders and developmental anomalies. Experimental approaches to identification of sumoylation sites may not be effective due to the dynamic nature of sumoylation, laborsome experiments and their cost. Therefore, computational approaches may guide experimental identification of sumoylation sites and provide insights for further understanding sumoylation mechanism. Results In this paper, the effectiveness of using various sequence properties in predicting sumoylation sites was investigated with statistical analyses and machine learning approach employing support vector machines. These sequence properties were derived from windows of size 7 including position-specific amino acid composition, hydrophobicity, estimated sub-window volumes, predicted disorder, and conformational flexibility. 5-fold cross-validation results on experimentally identified sumoylation sites revealed that our method successfully predicts sumoylation sites with a Matthew's correlation coefficient, sensitivity, specificity, and accuracy equal to 0.66, 73%, 98%, and 97%, respectively. Additionally, we have showed that our method compares favorably to the existing prediction methods and basic regular expressions scanner. Conclusions By using support vector machines, a new, robust method for sumoylation site prediction was introduced. Besides, the possible effects of predicted conformational flexibility and disorder on sumoylation site recognition were explored computationally for the first time to our knowledge as an additional parameter that could aid in sumoylation site prediction

Performance evaluation of different classification techniques using different datasets

Nowadays data mining become one of the technologies that paly major effect on business intelligence. However, to be able to use the data mining outcome the user should go through many process such as classified data. Classification of data is processing data and organize them in specific categorize to be use in most effective and efficient use. In data mining one technique is not applicable to be applied to all the datasets. This paper showing the difference result of applying different techniques on the same data. This paper evaluates the performance of different classification techniques using different datasets. In this study four data classification techniques have chosen. They are as follow, BayesNet, NaiveBayes, Multilayer perceptron and J48. The selected data classification techniques performance tested under two parameters, the time taken to build the model of the dataset and the percentage of accuracy to classify the dataset in the correct classification. The experiments are carried out using Weka 3.8 software. The results in the paper demonstrate that the efficiency of Multilayer Perceptron classifier in overall the best accuracy performance to classify the instances, and NaiveBayes classifiers were the worst outcome of accuracy to classifying the instance for each dataset

Evaluation of Dermatology Consultations Requested from the Pediatric Clinic

Objective: The aim of this study is to evaluate the clinical, demographic, and diagnostic characteristics of pediatric patients consulting the dermatology department. Methods: Patients who were consulted to the dermatology department from pediatric clinic of XX Hospital between January 2021 and August 2021 were scanned retrospectively. The demographic data of the patients, the pediatric department requesting consultation, their complaints at admission, the reasons for asking for consultation, and the diagnoses they received as a result of the consultation were recorded. Results: A total of 296 patients, 150 (50.7%) females and 146 (49.3%) males, with a median age of 5.5 years (1.5-10.5 ) were evaluated. The outpatient clinic were seen to have made the most requests for consultation. The most frequent complaints on presentation were seen to be redness of the skin in 168 (56.8%) cases and itching in 36 (12.2%). The five most common diagnoses made as a result of the consultation were unspecified dermatitis in 47 (15.9%) cases, scabies in 34 (11.5%), insect bite in 17 (5.7%), atopic dermatitis in 13 (4.4%), and seborrheic dermatitis in 13 (4.4%). When the diagnoses were examined according to the age groups, unspecified dermatitis was usually seen in the 0-2, 6-11, and 12-18 years age groups and insect bite was more common in the 3-5 years age group. Conclusion: The establishment of effectively functioning consultation mechanisms not only facilitates a correct diagnosis for the patient and appropriate treatment, but also can shorten the length of hospital stay for pediatric patients and can reduce economic costs

Two-Stage Clustering Approach for the Household Electricity Load Profiles by Fuzzy Logic and Neural Network Techniques

In this paper, household electricity load profile (LP) clustering problem is addressed. LP clustering analysis has been utilized as predicted end-user LPs for demand or supply management strategies to maintain the stability of the power systems. The consumption dynamics of the LPs are formed by the combinations of technical and social factors. Hence, discovering the dynamic patterns of the LPs has been a challenging problem. For this problem, we have offered successive applications of Sugeno fuzzy-logic (SFL) and self-organizing map neural network (SOMNN) techniques. Firstly, the data sets of the LPs are clustered by fuzzy logic approach by the reference models which are generated with the common family-types per persons. Then, considering the extra input of the weighted occupancy profiles, SOMNN is performed to improve the clustering result according to the dataset. The proposed strategy has been simulated by MATLAB® and the related results are presented

Hepatit B enfeksiyonu olan hastalarda yaşanılan zorlukların değerlendirilmesi

Objectives: Although stigma is well defined in people with a chronic disease or condition, it has not been studied much in individuals infected with hepatitis B virus (HBV). The study is one of the first descriptive individual studies conducted on this subject in our country. Our aim in this study was to evaluate the stigma experiences and concerns of individuals living with HBV, their sharing of their illness with the environment, and the state of being affected by their social relationships. Materials and Methods: Patients with hepatitis B surface antigen positivity who were admitted to the infectious diseases outpatient clinic were surveyed through face-to-face interviews. Epidemiological data, stigma experiences and anxiety states, people with whom they shared their illness, the reasons for not sharing, the impairment of social relations were questioned. Results: It was found that 19.5% of 390 individuals infected with HBV who participated in our study were "exposed" to stigma in various ways, and 27.4% were "worried" about experiencing this condition. In research, 19.9% of women, 41.4% of university graduates, and 34.8% of divorced or widowers were found to experience higher stigma (p=0.002, p=0.02 and p<0.001, respectively). It was determined that 56.7% of the participants did not share their illnesses, and this need increased with stigma experiences and anxiety. It was found that individuals mostly shared their disease status with their first-degree relatives (p<0.001). Conclusion: The fact that individuals infected with HBV experience different forms of stigma or experience anxiety suggests that there is a need to investigate these conditions and develop treatment interventions

Singlet oxygen formation during accelerated and hyperaccelerated corneal cross-linking: in vitro study

BACKGROUND: To evaluate the singlet oxygen (1 O2) production of oxygen assisted %0.1 riboflavin and ultraviolet-A (UVA) crosslinking therapy (with and without oxygen assistance), in combination with standard, accelerated and hyper-accelerated procedures via an important quantitive marker of 1 O2 which is the photo-oxidation of 1,3 diphenylisobenzofuran (DPBF). METHODS: %0.1 riboflavin-containing wells were irradiated with UVA light (365-nm wavelength) with or without 2-4-6-8 L/min oxygen flow assistance. Measurements of decrease in absorbance of DPBF were made in 30 mW (hyper-accelerated), 9 mW (accelerated), and 3 mW UV-A (standard) applications, and with additional 2-4-6-8 L/min oxygen flow in 30 mW and 2 L/min oxygen flow in 9 mW. A total of 8 different UV-A irradiance with and without oxygen supplementation groups were formed. RESULTS: 2 L/min oxygen assisted accelerated UV-A irradiance group has shown a greater decrease in DPBF absorbance compared to Dresden protocol. (p = 0.014) Also, Dresden protocol has shown a greater decrease in DPBF compared to all groups except accelerated crosslinking with 2 L/min oxygen. (p < 0.001) Oxygen assisted hyper-accelerated crosslinking groups were showed greater reduction in DPBF absorbance compared to standard crosslinking without oxygen groups. (p < 0.001). CONCLUSION: Oxygen supplementation may increase the singlet oxygen generation to the similar levels of Dresden Protocol’s in accelerated group. Also, more singlet oxygen generation with oxygen supplementation compared to standard UV-A application might be considered to be promising in terms of shortening the crosslinking therap

A microdeletion event at 19q13.43 in IDH-mutant astrocytomas is strongly correlated with MYC overexpression

MYC dysregulation is pivotal in the onset and progression of IDH-mutant gliomas, mostly driven by copy-number alterations, regulatory element alterations, or epigenetic changes. Our pilot analysis uncovered instances of relative MYC overexpression without alterations in the proximal MYC network (PMN), prompting a deeper investigation into potential novel oncogenic mechanisms. Analysing comprehensive genomics profiles of 236 “IDH-mutant 1p/19q non-co-deleted” lower-grade gliomas from The Cancer Genome Atlas, we identified somatic genomic alterations within the PMN. In tumours without PMN-alterations but with MYC-overexpression, genes correlated with MYC-overexpression were identified. Our analyses yielded that 86/236 of astrocytomas exhibited no PMN-alterations, a subset of 21/86 displaying relative MYC overexpression. Within this subset, we discovered 42 genes inversely correlated with relative MYC expression, all on 19q. Further analysis pinpointed a minimal common region at 19q13.43, encompassing 15 genes. The inverse correlations of these 15 genes with relative MYC overexpression were re-confirmed using independent scRNAseq data. Further, the micro-deleted astrocytoma subset displayed significantly higher genomic instability compared to WT cases, but lower instability compared to PMN-hit cases. This newly identified 19q micro-deletion represents a potential novel mechanism underlying MYC dysregulation in astrocytomas. Given the prominence of 19q loss in IDH-mutant gliomas, our findings bear significant implications for understanding gliomagenesis