Impact on Bacterial Micro-leakage in Exposed Root Canal Obturation Material in Teeth Irrigated with Different Solutions

Purpose: Determine the timeframe of bacterial penetration that occurs to the apex when obturation material (gutta percha) is exposed to bacteria for a set period of time (45 days) and to determine if bacterial penetration of the obturated root is influenced by the type of irrigant used during the final rinse (17% EDTA vs 2% Chlorhexidine vs full strength 5.25% NaOCl). Methods: Thirty-six extracted teeth, including six controls, were instrumented and irrigated with 5.25% NaOCl followed by a final rinse of either: 17% EDTA, 2% Chlorhexidine, or 5.25% NaOCl, and then obturated. Each root was suspended between two chambers: the coronal chamber inoculated with brain heart infusion broth and 〖10〗^8 colony-forming units of Enterococcus faecalis, the apical chamber with brain heart infusion broth. The latter was checked daily for turbidity, indicating bacterial leakage. Results: After excluding teeth with clear indications of experimental failure, 21 teeth were included in the analysis. Leakage rates were not significantly difference across the three groups (Chlorhexidine: 14%, EDTA: 67%, NaOCl: 50%; p-value=0.1581). Time to leakage was not significantly difference across the three groups (p-value=0.2470). Conclusion: Within the limitations of this study it was shown that leakage occurs between 4-42 days and that there was no significant difference between the different solutions in preventing leakage

Development of an Arabic text-to-speech system

Research on Text-to-speech technology has received the interest of professional researchers in many languages which is a consequence of wide range of applications where Text-To-Speech is implemented. However, Arabic language, spoken by millions of people as an official language in 24 different countries, gained less attention compared with other languages despite the fact that it has a religious value for more than 1.6 billion Muslim worldwide. These facts exhibit the need for a high quality, smallsize, and completely free Arabic TTS with the ability of future improvements. The vowelized written text of Arabic language carries the pronunciation rules with limited exceptions, so rulebased system with an exception dictionary for words that fail with those letter-to-phoneme rules may be a much more reasonable approach. This paper is a development of a rulebased text-to-speech Hybrid synthesis system which is a combination formant and concatenation techniques with acceptable naturalness. The simulation results of the system shows good quality in handling word, phrase, and sentence level compared to other available Arabic TTS systems. The accuracy of the overall system is 96%. Further improvements need to be done for stressed syllable position and intonation

Specific Cellular Immune Response and Cytokine Patterns in Patients Coinfected with Hepatitis C Virus and Schistosoma mansoni

Patients coinfected with hepatitis C virus (HCV) and Schistosoma mansoni show high incidence of viral persistence and accelerated fibrosis. To determine whether immunological mechanisms are responsible for this alteration in the natural history of HCV, the HCV-specific peripheral CD4+ T cell responses and cytokines were analyzed in patients with chronic hepatitis C monoinfection, S. mansoni monoinfection, or HCV and S. mansoni coinfection. An HCV-specific CD4+ proliferative response to at least 1 HCV antigen was detected in 73.3% of patients infected with HCV, compared with 8.6% of patients coinfected with HCV and S. mansoni. Stimulation with HCV antigens produced a type 1 cytokine profile in patients infected with HCV alone, compared with a type 2 predominance in patients coinfected with HCV and S. mansoni. In contrast, there was no difference in response to schistosomal antigens in patients infected with S. mansoni alone, compared with those coinfected with HCV and S. mansoni. These findings suggest that the inability to generate an HCV-specific CD4+/Th1 T cell response plays a role in the persistence and severity of HCV infection in patients with S. mansoni coinfectio

Domain Adapting Deep Reinforcement Learning for Real-world Speech Emotion Recognition

Computers can understand and then engage with people in an emotionally intelligent way thanks to speech-emotion recognition (SER). However, the performance of SER in cross-corpus and real-world live data feed scenarios can be significantly improved. The inability to adapt an existing model to a new domain is one of the shortcomings of SER methods. To address this challenge, researchers have developed domain adaptation techniques that transfer knowledge learnt by a model across the domain. Although existing domain adaptation techniques have improved performances across domains, they can be improved to adapt to a real-world live data feed situation where a model can self-tune while deployed. In this paper, we present a deep reinforcement learning-based strategy (RL-DA) for adapting a pre-trained model to a real-world live data feed setting while interacting with the environment and collecting continual feedback. RL-DA is evaluated on SER tasks, including cross-corpus and cross-language domain adaption schema. Evaluation results show that in a live data feed setting, RL-DA outperforms a baseline strategy by 11% and 14% in cross-corpus and cross-language scenarios, respectively

Novel Framework for Hidden Data in the Image Page within Executable File Using Computation between Advanced Encryption Standard and Distortion Techniques

The hurried development of multimedia and internet allows for wide distribution of digital media data. It becomes much easier to edit, modify and duplicate digital information. In additional, digital document is also easy to copy and distribute, therefore it may face many threats. It became necessary to find an appropriate protection due to the significance, accuracy and sensitivity of the information. Furthermore, there is no formal method to be followed to discover a hidden data. In this paper, a new information hiding framework is presented.The proposed framework aim is implementation of framework computation between advance encryption standard (AES) and distortion technique (DT) which embeds information in image page within executable file (EXE file) to find a secure solution to cover file without change the size of cover file. The framework includes two main functions; first is the hiding of the information in the image page of EXE file, through the execution of four process (specify the cover file, specify the information file, encryption of the information, and hiding the information) and the second function is the extraction of the hiding information through three process (specify the stego file, extract the information, and decryption of the information).Comment: 6 Pages IEEE Format, International Journal of Computer Science and Information Security, IJCSIS 2009, ISSN 1947 5500, Impact Factor 0.42

Multitask Learning from Augmented Auxiliary Data for Improving Speech Emotion Recognition

Despite the recent progress in speech emotion recognition (SER), state-of-the-art systems lack generalisation across different conditions. A key underlying reason for poor generalisation is the scarcity of emotion datasets, which is a significant roadblock to designing robust machine learning (ML) models. Recent works in SER focus on utilising multitask learning (MTL) methods to improve generalisation by learning shared representations. However, most of these studies propose MTL solutions with the requirement of meta labels for auxiliary tasks, which limits the training of SER systems. This paper proposes an MTL framework (MTL-AUG) that learns generalised representations from augmented data. We utilise augmentation-type classification and unsupervised reconstruction as auxiliary tasks, which allow training SER systems on augmented data without requiring any meta labels for auxiliary tasks. The semi-supervised nature of MTL-AUG allows for the exploitation of the abundant unlabelled data to further boost the performance of SER. We comprehensively evaluate the proposed framework in the following settings: (1) within corpus, (2) cross-corpus and cross-language, (3) noisy speech, (4) and adversarial attacks. Our evaluations using the widely used IEMOCAP, MSP-IMPROV, and EMODB datasets show improved results compared to existing state-of-the-art methods.Comment: Under review IEEE Transactions on Affective Computin

Kinetics of Intrahepatic Hepatitis C Virus (HCV)-Specific CD4+ T Cell Responses in HCV and Schistosoma mansoni Coinfection: Relation to Progression of Liver Fibrosis

The kinetics of intrahepatic hepatitis C virus (HCV)-specific CD4+ T cell responses and their role in progression of fibrosis have not previously been characterized. Subjects with HCV/Schistosoma mansoni coinfection have a more rapid progression of HCV liver fibrosis than do those with HCV infection alone. The present prospective longitudinal study compared the liver histology, HCV-specific intrahepatic and peripheral CD4+ T cell proliferative responses, and cytokines (enzyme-linked immunospot) in 48 subjects with unresolved acute HCV infection with or without S. mansoni coinfection, at 6-10 months after acute infection and at the end of follow-up ( months), and the findings were correlated 96±8.7 to the rate of progression of fibrosis per year. Coinfected subjects had significant worsening of fibrosis, compared with subjects with HCV infection alone. At baseline, subjects with HCV infection alone had stronger multispecific intrahepatic HCV-specific CD4+ T helper 1 responses than did coinfected subjects, who had either no responses or weak, narrowly focused responses, and, over time, these T cell responses were maintained only in the liver. The rate of progression of fibrosis and virus load inversely correlated with intrahepatic HCV-specific CD4+ T cell response. The present prospective analysis indicates that enhancement of progression of liver fibrosis is associated with failure to develop early, multispecific, HCV-specific CD4+ Th1 responses, suggesting that novel therapeutic approaches inducing strong cellular immune responses might limit subsequent liver damage in individuals with chronic hepatitis

Survey of deep representation learning for speech emotion recognition

Traditionally, speech emotion recognition (SER) research has relied on manually handcrafted acoustic features using feature engineering. However, the design of handcrafted features for complex SER tasks requires significant manual eort, which impedes generalisability and slows the pace of innovation. This has motivated the adoption of representation learning techniques that can automatically learn an intermediate representation of the input signal without any manual feature engineering. Representation learning has led to improved SER performance and enabled rapid innovation. Its effectiveness has further increased with advances in deep learning (DL), which has facilitated \textit{deep representation learning} where hierarchical representations are automatically learned in a data-driven manner. This paper presents the first comprehensive survey on the important topic of deep representation learning for SER. We highlight various techniques, related challenges and identify important future areas of research. Our survey bridges the gap in the literature since existing surveys either focus on SER with hand-engineered features or representation learning in the general setting without focusing on SER