Formally self-dual codes over F₂[u]/‹u⁴›

In this work, Gray images of formally self-dual codes over the ring S₄ = F₂[u]/‹u⁴› and some of their construction methods are considered. As a result, a considerable number of good formally self-dual binary codes with large automorphism groups have been obtained from the Gray images of formally self-dual codes over S₄. Some have better minimum distances than the best known binary self-dual codes of the same lengths.Publisher's Versio

Generalization of the lee weight to Ζpᵏ

We introduce a new extension of the Lee weight to Ζpᵏ and later to Galois rings GR(pᵏ,m). The weight we define is a non-homogeneous weight and is different than the one that is generally labeled as "generalized Lee weight". Unlike the case of generalized Lee weight, we define a distance-preserving Gray map from (Ζpᵏ, extended Lee distance)to (Fppᵏ⁻¹, Hamming distance), thus making our extension practical for coding theory purposes.Publisher's Versio

A novel ensemble fuzzy classification model in SARS-CoV-2 B-cell epitope identification for development of protein-based vaccine

B-cell epitope prediction research has received growing interest since the development of the first method. B-cell epitope identification with the aid of an accurate prediction method is one of the most important steps in epitope-based vaccine development, immunodiagnostic testing, antibody production, disease diagnosis, and treatment. Nevertheless, using experimental methods in epitope mapping is very time-consuming, costly, and labor-intensive. Therefore, although successful predictions with in silico methods are very important in epitope prediction, there are limited studies in this area. The aim of this study is to propose a new approach for successfully predicting B-cell epitopes for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this study, the SARS-CoV B-cell epitope prediction performances of different fuzzy learning classification models genetic cooperative competitive learning (GCCL), fuzzy genetics-based machine learning (GBML), Chi's method (CHI), Ishibuchi's method with weight factor (W), structural learning algorithm on vague environment (SLAVE) and the state-of-the-art ensemble fuzzy classification model were compared. The obtained results showed that the proposed ensemble approach has the lowest error in SARS-CoV B-cell epitope estimation compared to the base fuzzy learners (average error rates; ensemble fuzzy=8.33, GCCL=30.42, GBML=23.82, CHI=29.17, W=46.25, and SLAVE=20.42). SARS-CoV and SARS-CoV-2 have high genome similarities. Therefore, the most successful method determined for SARS-CoV B-cell epitope prediction was used in SARS-CoV-2 cell epitope prediction. Finally, the eventual B-cell epitope prediction results obtained for SARS-CoV-2 with the ensemble fuzzy classification model were compared with the epitope sequences predicted by the BepiPred server and immunoinformatics studies in the literature for the same protein sequences according to VaxiJen 2.0 scores. We hope that the developed epitope prediction method will help design effective vaccines and drugs against future outbreaks of the coronavirus family, especially SARS-CoV-2 and its possible mutations. © 2021 Elsevier B.V.121E326This study was supported by The Scientific and Technological Research Council of Turkey-TÜBİTAK (Project Number: 121E326 ).This study was supported by The Scientific and Technological Research Council of Turkey-T?B?TAK (Project Number: 121E326)

A new approach for determining SARS-CoV-2 epitopes using machine learning-based in silico methods

The emergence of machine learning-based in silico tools has enabled rapid and high-quality predictions in the biomedical field. In the COVID-19 pandemic, machine learning methods have been used in many topics such as predicting the death of patients, modeling the spread of infection, determining future effects, diagnosis with medical image analysis, and forecasting the vaccination rate. However, there is a gap in the literature regarding identifying epitopes that can be used in fast, useful, and effective vaccine design using machine learning methods and bioinformatics tools. Machine learning methods can give medical biotechnologists an advantage in designing a faster and more successful vaccine. The motivation of this study is to propose a successful hybrid machine learning method for SARS-CoV-2 epitope prediction and to identify nonallergen, nontoxic, antigen peptides that can be used in vaccine design from the predicted epitopes with bioinformatics tools. The identified epitopes will be effective not only in the design of the COVID-19 vaccine but also against viruses from the SARS family that may be encountered in the future. For this purpose, epitope prediction performances of random forest, support vector machine, logistic regression, bagging with decision tree, k-nearest neighbor and decision tree methods were examined. In the SARS-CoV and B-cell datasets used for education in the study, epitope estimation was performed again after the datasets were balanced with the synthetic minority oversampling technique (SMOTE) method since the epitope class samples were in the minority compared to the nonepitope class. The experimental results obtained were compared and the most successful predictions were obtained with the random forest (RF) method. The epitope prediction performance in balanced datasets was found to be higher than that in the original datasets (94.0% AUC and 94.4% PRC for the SMOTE-SARS-CoV dataset; 95.6% AUC and 95.3% PRC for the SMOTE-B-cell dataset). In this study, 252 peptides out of 20312 peptides were determined to be epitopes with the SMOTE-RF-SVM hybrid method proposed for SARS-CoV-2 epitope prediction. Determined epitopes were analyzed with AllerTOP 2.0, VaxiJen 2.0 and ToxinPred tools, and allergic, nonantigen, and toxic epitopes were eliminated. As a result, 11 possible nonallergic, high antigen and nontoxic epitope candidates were proposed that could be used in protein-based COVID-19 vaccine design (“VGGNYNY”, “VNFNFNGLTG”, “RQIAPGQTGKI”, “QIAPGQTGKIA”, “SYECDIPIGAGI”, “STFKCYGVSPTKL”, “GVVFLHVTYVPAQ”, “KNHTSPDVDLGDI”, “NHTSPDVDLGDIS”, “AGAAAYYVGYLQPR”, “KKSTNLVKNKCVNF”). It is predicted that the few epitopes determined by machine learning-based in silico methods will help biotechnologists design fast and accurate vaccines by reducing the number of trials in the laboratory environment. © 2022 Elsevier LtdTürkiye Bilimsel ve Teknolojik Araştirma Kurumu, TÜBITAK: 121E326This study was supported by Turkish Scientific and Technical Research Council, Turkey-TÜBİTAK (Project Number: 121E326).This study was supported by Turkish Scientific and Technical Research Council, Turkey -TÜBİTAK (Project Number: 121E326 )

Epidemiological investigation of 673 patients who resorted to the emergency department for mild head trauma complaints

Aim: Mild head trauma (MHT) or mild traumatic brain injury (MTBI) is an injury whose incidence is increasing in emergency services.This retrospective study carried out an epidemiological evaluation of patients with MHT, who underwent head computed tomography(HCT) with a 15-point score on the Glasgow Coma Scale (GCS).Material and Methods: This study retrospectively evaluated 673 patients with MHT, who were examined by the department ofneurosurgery in the emergency department of Istinye University, Canakkale Anatolian Hospital between 2015 and 2019. The caseswere evaluated because of age, gender, cause of trauma, HCT findings, duration of admission to the emergency department, andother body traumas associated with head trauma.Results:390 (57.95%) patients were male, while 283 (42.05%) were female. The mean age and standard deviation were calculatedas 23.72 ± 24.87 years. Of the 673 cases, 494 (73.40%) were admitted to the emergency department due to non-high falls. Aftertrauma, 642 (95.39%) patients were admitted to the emergency department within the admitted to the emergency department withinthe first two hours after injury. 656 (97.48%) of the patients were treated in the emergency department. 105 (15.60%) patients hada scalp incision and underwent a small surgical procedure. The most common accompanying body trauma detected was that ofthe maxillofacial region in 26 (3.86%) patients. HCT pathology was detected in 20 (2.97%) patients. These pathologies included; 14(2.08%) non-surgical intracerebral hemorrhage, 2 (0.30%) skull base fractures, 1 (0.15%) traumatic subdural hematoma, 1 (0.15%)traumatic epidural hematoma, 1 (0.15%) pneumocephalus and 1 (0.15%) cerebral edema.Conclusion: Head trauma is an important issue in this country. Brain CT may not be necessary in patients with a GCS score of 15.After a short observation, if patients live near the medical center, they can be sent home to return the next day for further evaluation

THE DISCOURSE OF NATIONAL WILL AS A POPULIST CONSTRUCTION PRACTICE IN THE POLITICS OF AKP

This article explores the discourse of national will in Justice and Development Party (AKP) politics as a practice of public construction, and the relation between this discourse and populism. Drawing upon Laclau’s critical insights it is aimed to discuss the distance of the discourse to populism, and the construction of this process. The establishment of the discourse into the national will, and the continuity of social antagonisms constructed by this discourse in the center-right movements also considered through the discussion. Methodological populism is applied to the speeches of Recep Tayyip Erdoğan in which the discourse of national will is emphasized. In this respect the sample is compiled from “The Rallies of Respect for National Will” (2013), Local Elections of 2014, General Elections of 2015 and the gatherings of İzmir, Ankara, and Diyarbakır.</p