DFT based computational study on the molecular conformation, NMR chemical shifts and vibrational transitions for N-(2-methylphenyl) methanesulfonamide and N-(3-methylphenyl) methanesulfonamide

WOS: 000276653300018This paper presents a DFT quantum chemical investigation of the molecular conformation, NMR chemical shifts and vibrational transitions of N-(2-methylphenyl)methanesulfonamide and N-(3-methylphenyl)methanesulfonamide (C8H11NO2S) employing B3LYP exchange correlation. The vibrational wave-numbers were calculated and the complete assignments were performed on the basis of the total energy distribution (TED) of the vibrational modes, calculated with scaled quantum mechanics (SQM) method. The H-1 and C-13 NMR chemical shifts of the compounds were calculated in CDCl3 and DMSO using the GIAO method. Finally, calculations were compared with experimental values. (C) 2010 Elsevier B.V. All rights reserved

Tree-Seed Algorithm for Large-Scale Binary Optimization

Population-based swarm or evolutionary computation algorithms in optimization are attracted the interest of the researchers due their simple structure, optimization performance, easy-adaptation. Binary optimization problems can be also solved by using these algorithms. This paper focuses on solving large scale binary optimization problems by using Tree-Seed Algorithm (TSA) proposed for solving continuous optimization problems by imitating relationship between the trees and their seeds in nature. The basic TSA is modified by using xor logic gate for solving binary optimization problems in this study. In order to investigate the performance of the proposed algorithm, the numeric benchmark problems with the different dimensions are considered and obtained results show that the proposed algorithm produces effective and comparable solutions in terms of solution quality.Keywords: binary optimization, tree-seed algorithm, xor-gate, large-scale optimizatio

A rare cause of acute urinary retention in women: meatal condyloma accuminata, a case report

Acute urinary retention in women is a rarely seen phenomenon due to pharmacological, neuromuscular, anatomical, functional and infectious causes. Human papillomaviruses causing condyloma acuminata is one of the rarely reported viral infectious cause of acute urinary retention in case reports. A 45-year-old woman with acute urinary retention was found to have a round solid lesion on external urethral meatus. Histopathological examination revealed as condyloma acuminata. Urethral condyloma can be treated by local excision as an effective method for early improvement of voiding function. Even if the genital condyloma can be locally excised, patients should be referred to the gynecologists for cervical cancer screening.Pan African Medical Journal 2016; 2

FEATURE EXTRACTION AND RECOGNITION ON TRAFFIC SIGN IMAGES

FEATURE EXTRACTION AND RECOGNITION ON TRAFFIC SIGN IMAGESAbstractIt is vital that the traffic signs used to ensure the order of the traffic are perceived by the drivers. Traffic signs have international standards that allow the driver to learn about the road and the environment while driving. Traffic sign recognition systems have recently started to be used in vehicles in order to improve traffic safety. Machine learning methods are used in the field of image recognition. Deep learning methods increase the classification success by extracting the hidden and interesting features in the image. Images contain many features and this situation can affect success in classification problems. It can also reveal the need for high-capacity hardware. In order to solve these problems, convolutional neural networks can be used to extract meaningful features from the image. In this study, we created a dataset containing 1500 images of 14 different traffic signs that are frequently used on Turkey highways. The features of the images in this dataset were extracted using convolutional neural networks from deep learning architectures. The 1000 features obtained were classified using the Random Forest method from machine learning algorithms. 93.7% success was achieved as a result of this classification process.Keywords: Classification, Convolution neural network, Feature extraction, Random forest, Traffic signsTRAFİK İŞARETİ GÖRÜNTÜLERİNDE ÖZELLİK ÇIKARMA VE TANIMAÖzetTrafiğin düzenini sağlamak amacıyla kullanılan trafik levhalarını sürücülerin algılaması hayati önem taşımaktadır. Sürüş esnasında sürücünün yol ve çevre hakkında bilgi edinebilmesini sağlayan trafik levhaları uluslararası standartlara sahiptir. Trafik levhası tanıma sistemleri son zamanlarda trafik güvenliğini arttırmak amacıyla araçlarda kullanılmaya başlamıştır. Makine öğrenmesi yöntemleri görüntü tanıma alanında kullanılmaktadır.  Derin öğrenme yöntemleri, görüntüde yer alan gizli ve ilginç özellikleri çıkarak sınıflandırma başarısını arttırmaktadır. Görüntüler çok sayıda özellik içermektedir ve bu durum sınıflandırma problemlerinde başarıyı etkileyebilmektedir. Ayrıca yüksek kapasiteli donanım gereksinimini de ortaya çıkarabilmektedir. Bu sorunların çözülebilmesi için görüntüden anlamlı özelliklerin çıkarılmasında konvolüsyonel sinir ağları kullanılabilmektedir. Bu çalışmada Türkiye’deki karayollarında sıklıkla kullanılan 14 farklı trafik levhasına ait 1500 görüntü içeren bir veriseti tarafımızca oluşturulmuştur. Bu veriseti kullanılarak derin öğrenme mimarilerinden konvolüsyonel sinir ağları kullanılarak görüntülerin özellikleri çıkarılmıştır. Elde edilen 1000 özellik makine öğrenmesi algoritmalarından Random Forest yöntemi kullanılarak sınıflandırılmıştır. Bu sınıflandırma işlemi sonucunda %93.7 başarı elde edilmiştir.Anahtar Kelimeler: Konvolüsyonel sinir ağları, Özellik çıkarma, Random forest, Sınıflandırma, Trafik işaretleri

Disciplinary Learning From an Authentic Engineering Context

This small-scale design study describes disciplinary learning in mathematical modeling and science from an authentic engineeringthemed module. Current research in tissue engineering served as source material for the module, including science content for readings and a mathematical modeling activity in which students work in small teams to design a model in response to a problem from a client. The design of the module was guided by well-established principles of model-eliciting activities (a special class of problem-solving activities deeply studied in mathematics education) and recently published implementation design principles, which emphasize the portability of model-eliciting activities to many classroom settings. Two mathematical modeling research questions were addressed: 1. What mathematical approaches did student-teams take when they designed mathematical models to evaluate the quality of blood vessel networks? and 2. What attributes of mature mathematical models were captured in the mathematical models that the student-teams designed? One science content research question was addressed: 1. Before and after the module, what aspects of angiogenesis did students describe when they were asked what they knew about the process of blood vessel growth from existing vessels? Participants who field-tested the module included high school students in a summer enrichment program and early college students enrolled in four general-studies mathematics courses. Data collected from participants included mathematical models produced by small teams of students, as well as students’ individual responses before and after the module to a prompt asking them what they knew about the process of new blood vessel growth from existing vessels. The data were analyzed for mathematical model type and science content by adopting methods of grounded theory, in which researchers suspend expectations about what should be in the data and, instead, allow for the emergence of patterns and trends. The mathematical models were further analyzed for mathematical maturity using an a priori coding scheme of attributes of a mathematical model. Analyses showed that student-teams created mathematical models of varying maturity using four different mathematical approaches, and comparisons of students’ responses to the science prompt showed students knew essentially nothing about angiogenesis before the module but described important aspects of angiogenesis after the module. These findings were used to set up an agenda for future research about the design of the module and the relationship between disciplinary learning and authentic engineering problems

Effect of atorvastatin on spermatogenesis in rats: A stereological study

Purpose: To investigate the effects of oral atorvastatin on spermatogenesis in a rat model.Methods: Rats were equally assigned into control and study groups, the latter receiving atorvastatin (20 mg/kg/day). At the end of 12 weeks, spermatogenetic activity was evaluated using stereological and optical fractionator methods. Serum follicle-stimulating hormone (FSH), total testosterone (TT), and luteinizing hormone (LH) levels were measured using micro–ELISA kits. Total cholesterol, triglyceride (TG), low-density lipoprotein cholesterol (LDL - C), and high-density lipoprotein cholesterol levels were also measured by enzymatic colorimetric assays.Results: Testicular stereological analysis revealed that atorvastatin reduced Sertoli cell numbers (p < 0.001), spermatogonia (p < 0.001), spermatocytes (p < 0.001), and seminiferous tubule diameters (p < 0.001). LDL – C (p = 0.01) and TG (p = 0.01) values were significantly lower in the study group compared with the control group. There was no significant difference in FSH (p = 0.44), LH (p = 0.48),and TT (p = 0.06) levels between the groups.Conclusion: The findings show that atorvastatin causes deleterious effects on rat spermatogenesis. It should therefore be used with caution in clinical practice owing to its potential adverse effects, especially on male fertility. Keywords: Statin, Atorvastatin, Spermatogenesis, Stereology, Testi

Nanoengineering hybrid supramolecular multilayered biomaterials using polysaccharides and self-assembling peptide amphiphiles

Developing complex supramolecular biomaterials through highly dynamic and reversible noncovalent interactions has attracted great attention from the scientific community aiming key biomedical and biotechnological applica-tions, including tissue engineering, regenerative medicine, or drug delivery. In this study, the authors report the fabrication of hybrid supramolecular multilayered biomaterials, comprising high-molecular-weight biopolymers and oppositely charged low-molecular-weight peptide amphiphiles (PAs), through combination of self-assembly and electrostatically driven layer-by-layer (LbL) assembly approach. Alginate, an anionic polysaccharide, is used to trigger the self-assembling capability of positively charged PA and formation of 1D nanofiber networks. The LbL technology is further used to fabricate supramolecular multilayered biomaterials by repeating the alternate deposi-tion of both molecules. The fabrication process is monitored by quartz crystal microbalance, revealing that both materials can be successfully combined to conceive stable supramolecular systems. The morphological properties of the systems are studied by advanced microscopy techniques, revealing the nano-structured dimensions and 1D nanofibrous network of the assembly formed by the two molecules. Enhanced C2C12 cell adhesion, proliferation, and differentiation are observed on nanostructures having PA as outermost layer. Such supramolecular biomaterials demonstrate to be innovative matrices for cell culture and hold great potential to be used in the near future as prom-ising biomimetic supramolecular nanoplatforms for practical applications

Clinical Study Comparison of Efficiencies of Michigan Neuropathy Screening Instrument, Neurothesiometer, and Electromyography for Diagnosis of Diabetic Neuropathy

Aim. This study compares the effectiveness of Michigan Neuropathy Screening Instrument (MNSI), neurothesiometer, and electromyography (EMG) in detecting diabetic peripheral neuropathy in patients with diabetes type 2. Materials and Methods. 106 patients with diabetes type 2 treated at the outpatient clinic of Ankara Numune Education and Research Hospital Department of Endocrinology between September 2008 and May 2009 were included in this study. Patients were evaluated by glycemic regulation tests, MNSI (questionnaire and physical examination), EMG (for detecting sensorial and motor defects in right median, ulnar, posterior tibial, and bilateral sural nerves), and neurothesiometer (for detecting alterations in cold and warm sensations as well as vibratory sensations). Results. According to the MNSI score, there was diabetic peripheral neuropathy in 34 (32.1%) patients (score ≥2.5). However, when the patients were evaluated by EMG and neurothesiometer, neurological impairments were detected in 49 (46.2%) and 79 (74.5%) patients, respectively. Conclusion. According to our findings, questionnaires and physical examination often present lower diabetic peripheral neuropathy prevalence. Hence, we recommend that in the evaluation of diabetic patients neurological tests should be used for more accurate results and thus early treatment options to prevent neuropathic complications

Postmarketing Safety Study Tool: A Web Based, Dynamic, and Interoperable System for Postmarketing Drug Surveillance Studies

Postmarketing drug surveillance is a crucial aspect of the clinical research activities in pharmacovigilance and pharmacoepidemiology. Successful utilization of available Electronic Health Record (EHR) data can complement and strengthen postmarketing safety studies. In terms of the secondary use of EHRs, access and analysis of patient data across different domains are a critical factor; we address this data interoperability problem between EHR systems and clinical research systems in this paper. We demonstrate that this problem can be solved in an upper level with the use of common data elements in a standardized fashion so that clinical researchers can work with different EHR systems independently of the underlying information model. Postmarketing Safety Study Tool lets the clinical researchers extract data from different EHR systems by designing data collection set schemas through common data elements. The tool interacts with a semantic metadata registry through IHE data element exchange profile. Postmarketing Safety Study Tool and its supporting components have been implemented and deployed on the central data warehouse of the Lombardy region, Italy, which contains anonymized records of about 16 million patients with over 10-year longitudinal data on average. Clinical researchers in Roche validate the tool with real life use cases.Publisher's Versio