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Preparation and application of highly porous aerogel-based bioactive materials in dentistry

Frontiers of Materials Science 2014年第1期8卷 46-52页

作者： Andrea KUTTOR Melinda SZALOKI TLinde RENTE Farkas KERENYI Jozsef BAKO Istvan FABIAN Istvan LAZAR attila jenei Csaba HEGEDUES Department of Prosthetic Dentistry and Biomaterials Faculty of Dentistry University of Debrecen 4012 Debrecen Hungary Aerogel Research Group Department of Inorganic and Analytical Chemistry Faculty of Science University of Debrecen 4010 Debrecen Hungary Department of Dental Biochemistry Faculty of Dentistry University of Debrecen 4012 Debrecen Hungary Department of Biophysics and Cell Biology Faculty of Medicine University of Debrecen 4012 Debrecen Hungary

In this study, the possibility of preparation and application of highly porous silica aerogel-based bioactive materials are presented. The aerogel was combined with hydroxyapatite and *** phosphate as bioactive and osteoinductive agents. The porosity of aerogels was in themesoporous region with a maximum pore diameter of 7,4 and 12.7 nm for the composite materials. The newly developed bioactive materials were characterized by scant electron microscopy. The in vitro biological effect of these modified surfaces was also tested on SAOS-2 osteogenic sarcoma cells by confocal laser scanning microscopy.

关键词： aerogel sol-gel technique bioactive matedal SAOS-2 cell

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The Effect of Speech Fragmentation and Audio Encodings on Automatic Parkinson’s Disease Recognition

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Journal of Biomedical Science and Engineering 2022年第1期15卷 6-25页

作者： Dávid Sztahó attila Zoltán jenei István Valálik Klára Vicsi Department of Telecommunications and Media Informatics Budapest University of Technology and Economics Budapest Hungary Department of Neurosurgery St. John’s Hospital Budapest Hungary

Parkinson’s disease is a neurological disease which is incurable according to current clinical knowledge. Therefore, early detection and provision of appropriate treatment are of primary importance. Speech is one of the biomarkers that enable the detection of Parkinson’s disease affection. Numerous researches are based on recordings from controlled environments;nonetheless fewer apply real circumstances. In the present study, three objectives were examined: recording fragmentation (paragraph, sentences, time-based), variable encodings (Pulse-Code Modulation [PCM], GSM-Full Rate [FR], G.723.1) and majority voting on 8 kHz records using multiple classifiers. Support Vector Machine (SVM), Long Short-Term Memory (LSTM), i-vector and x-vector classifiers were evaluated in contrast with SVM as baseline. The highest results in accuracy and F1-score were achieved using i-vector models. Although variable encodings generally caused decrease in Parkinson-disease recognition, decline was within 2% - 3% at best. Moreover, fragmentation did not yield a clear outcome though some classifiers performed with the very similar efficiency along the differently fragmented sets. Majority voting did produce a slight increase in classification performance compared to as if no aggregation is used.

关键词： Parkinson’s Disease Speech Support Vector Machine Neural Network i-Vector x-Vector

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