Effects of metronidazole therapy on preterm labor in women with bacterial vaginosis

Regarding to prevalence of preterm labor and its consequences, there are different reports on relationship between bacterial vaginosis and preterm labor. This study was performed to evaluate the effect of metronidazole therapy on preterm labor in women with bacterial vaginosis. This randomized clinical trial was performed on 120 women suffering from bacterial vaginosis at 20-34 weeks of pregnancy, to evaluate the therapeutic effect of metronidazole to delay preterm labor in Shabih Khani maternity hospital in Kashan, Iran in 2002. Bacterial vaginosis was diagnosed based on clinical and laboratory findings. The patients were randomly divided into two groups. The patients in the case group received 500 mg metronidazole BID for 7 consecutive days, but the control group did not receive it. The demographic characteristics of the patients such as, pregnancy age, educational level and job of the spouse were similar at both case and control groups. Double-blind follow up of the patients at the whole stages of parturition and after delivery with respect to the delivery method, infection, and fever was done by other practitioner besides the main researcher. The results were analyzed statistically by chi-square, and Fischer's exact tests. 420 patients entered the study, of whom 120 (28.6) had bacterial vaginosis. The antibiotic and control groups were not significantly different for maternal age, job of the spouse, and education. No difference was observed in spontaneous preterm birth before 37 weeks of gestation in antibiotic-treated compared with control group. Treatment with metronidazole in symptomatic women with a bacterial vaginosis in the late second trimester does not decrease the incidence of preterm delivery. © 2009 Tehran University of Medical Sciences. All rights reserved

Comparison of culture and microscopic methods by PCR for detection of Mycobacterium tuberculosis in sputum

Background: It is difficult to diagnose Mycobacterium tuberculosis infection due to a lack of rapid, sensitive, and specific tests. Newer methods, which are easy and reliable, are required to diagnose TB at an early stage. Our aim is to evaluate the polymerase chain reaction (PCR) technique, using primers directed against the IS6110 gene, for the detection of M. tuberculosis in the sputum samples, and calculate the sensitivity and specificity of PCR. Patients and methods: A total of 248 sputum samples from patients suspected of mycobacterial diseases were studied. DNA was extracted by boiling method. IS6110 PCR method by a specific pair of primers designed to amplify 123bp and 245bp sequences of the insertion sequence, 6110, in the M. tuberculosis genome was used to analyze sputum samples. Results: Totally, 32 (12.9) samples had positive culture. PCR yielded a sensitivity of 93.8 and specificity of 99.1 for the diagnosis of TB, when diagnosis was confirmed by culture. There were 2 out of 32(6.3) PCR-positive cases among the patients with non-TB disease. Conclusion: We concluded that the performance of an IS6110 PCR assay is valuable in the rapid diagnosis of tuberculosis. © 2009 IDTMRC, Infectious Diseases and Tropical Medicine Research Center

Sensor-Based SLAM for Camera Tracking in Virtual Studio Environment

This paper addresses the problem of Camera Tracking in virtual studio environment. The traditional camera tracking methods are vision-based or sensor-based. However, the Chroma Keying process in virtual studio requires the color cues, such as blue screen, to segment objects from mages and videos. It limits the application of vision-based tracking methods in virtual studio since the background could not provide enough feature information. Therefore, in our research, we would try to apply the SLAM (simultaneously localization and mapping) methodology from mobile robots to the camera tracking area. We describe a sensor-based SLAM extension algorithm for 2D camera tracking in virtual studio. Also a technique call Map Adjustment is proposed to increase the accuracy and efficiency of the algorithm. The simulation results would be given in the conclusion. Keywords-SLAM, Particle Filter, Chroma Keying, Camera Trackin

An Experimental Analysis of Deep Learning Architectures for Supervised Speech Enhancement

Recent speech enhancement research has shown that deep learning techniques are very effective in removing background noise. Many deep neural networks are being proposed, showing promising results for improving overall speech perception. The Deep Multilayer Perceptron, Convolutional Neural Networks, and the Denoising Autoencoder are well-established architectures for speech enhancement; however, choosing between different deep learning models has been mainly empirical. Consequently, a comparative analysis is needed between these three architecture types in order to show the factors affecting their performance. In this paper, this analysis is presented by comparing seven deep learning models that belong to these three categories. The comparison includes evaluating the performance in terms of the overall quality of the output speech using five objective evaluation metrics and a subjective evaluation with 23 listeners; the ability to deal with challenging noise conditions; generalization ability; complexity; and, processing time. Further analysis is then provided while using two different approaches. The first approach investigates how the performance is affected by changing network hyperparameters and the structure of the data, including the Lombard effect. While the second approach interprets the results by visualizing the spectrogram of the output layer of all the investigated models, and the spectrograms of the hidden layers of the convolutional neural network architecture. Finally, a general evaluation is performed for supervised deep learning-based speech enhancement while using SWOC analysis, to discuss the technique’s Strengths, Weaknesses, Opportunities, and Challenges. The results of this paper contribute to the understanding of how different deep neural networks perform the speech enhancement task, highlight the strengths and weaknesses of each architecture, and provide recommendations for achieving better performance. This work facilitates the development of better deep neural networks for speech enhancement in the future

RFID-based hybrid Camera Tracking in Virtual Studio

This paper addresses the problem of Camera tracking in virtual studio environment. The traditional camera tracking methods can be classified into optical-based or electromechanical sensor-based. However, the electromechanical method is extensive time-consuming calibration procedures and cost too much; the optical method suffers from the error detection of references features and the chorma keying limitation in virtual studio. Therefore, in order to overcome those problems, we proposed a novel RFID-based hybrid camera tracking method in virtual studio application. Firstly, we designed a RFID passive tags based camera tracker. By using the triangular position algorithm, the accuracy could reach up to 5 centimeters. Secondly, we combined the optical based tracking method into RFID tracker with the aim to improve the orientation and position accuracy. Finally, the experiment results showed that this method could be a novel potential solution for camera tracking system in virtual studio applications. Keywords-RFID, camera tracking, chorma key, SLA

A Mixed Reality Approach for dealing with the Video Fatigue of Online Meetings

Much of the issue with video meetings is the lack of naturalistic cues, together with the feeling of being observed all the time. Video calls take away most body language cues, but because the person is still visible, your brain still tries to compute that non-verbal language. It means that you’re working harder, trying to achieve the impossible. This impacts data retention and can lead to participants feeling unnecessarily tired. This project aims to transform the way online meetings happen, by turning off the camera and simplifying the information that our brains need to compute, thus preventing ‘Zoom fatigue’. The immersive solution we are developing, iVXR, consists of cutting-edge augmented reality technology, natural language processing, speech to text technologies and sub-real-time hardware acceleration using high performance computing

Autoinflation compared to ventilation tubes for treating chronic otitis media with effusion

Background Otitis media with effusion (OME) is the most common cause of acquired hearing loss and surgery in children. Autoinflation has been suggested as an alternative treatment for OME. Objectives The aim of the study was to compare treatment outcome with a new autoinflation device versus ventilation tube (VT) surgery or watchful waiting in children with chronic bilateral OME from the waiting list for surgery. Methods Forty-five children performed autoinflation during four weeks, forty-five were submitted to VT surgery, and twenty-three were enrolled as control group. Tympanometry was performed in the autoinflation and the control groups and audiometry in all groups. Results An equivalent hearing improvement was achieved in the autoinflation and the VT group at one (p=.19), six (p=.23) and twelve (p=.31) months with no significant alteration in the control group. In the autoinflation group 80% of the children avoided surgery and no complications were reported compared to 34% complication rate in the VT group. Conclusion Autoinflation achieved an equivalent improvement in hearing thresholds compared to VT surgery for treating OME. Significance Autoinflation may be a reasonable first-line treatment for children with OME to potentially avoid surgery. Article Summary: The Moniri autoinflation device is well tolerated and an effective alternative to ventilation tubes for treatment of chronic otitis media with effusion in young children. What's known on this subject: Previous studies have shown that autoinflation may reduce effusion in children with otitis media with effusion; however limited compliance to treatment, lack of adequate hearing evaluation, short follow-up time and also lack of comparative data to ventilation tube surgery have been reported. What this study adds: A new device was developed to allow for the performance of autoinflation in young children. The effect is compared to ventilation tube surgery and equivalent improvement in hearing is achieved in the short and the long-term follow-up.info:eu-repo/semantics/publishedVersio

Mapping and Masking Targets Comparison using Different Deep Learning based Speech Enhancement Architectures

Mapping and Masking targets are both widely used in recent Deep Neural Network (DNN) based supervised speech enhancement. Masking targets are proved to have a positive impact on the intelligibility of the output speech, while mapping targets are found, in other studies, to generate speech with better quality. However, most of the studies are based on comparing the two approaches using the Multilayer Perceptron (MLP) architecture only. With the emergence of new architectures that outperform the MLP, a more generalized comparison is needed between mapping and masking approaches. In this paper, a complete comparison will be conducted between mapping and masking targets using four different DNN based speech enhancement architectures, to work out how the performance of the networks changes with the chosen training target. The results show that there is no perfect training target with respect to all the different speech quality evaluation metrics, and that there is a tradeoff between the denoising process and the intelligibility of the output speech. Furthermore, the generalization ability of the networks was evaluated, and it is concluded that the design of the architecture restricts the choice of the training target, because masking targets result in significant performance degradation for deep convolutional autoencoder architecture

A Comparative Study of Time and Frequency Domain Approaches to Deep Learning based Speech Enhancement

Deep learning has recently made a breakthrough in the speech enhancement process. Some architectures are based on a time domain representation, while others operate in the frequency domain; however, the study and comparison of different networks working in time and frequency is not reported in the literature. In this paper, this comparison between time and frequency domain learning for five Deep Neural Network (DNN) based speech enhancement architectures is presented. The comparison covers the evaluation of the output speech using four objective evaluation metrics: PESQ, STOI, LSD, and SSNR increase. Furthermore, the complexity of the five networks was investigated by comparing the number of parameters and processing time for each architecture. Finally some of the factors that affect learning in time and frequency were discussed. The primary results of this paper show that fully connected based architectures generate speech with low overall perception when learning in the time domain. On the other hand, convolutional based designs give acceptable performance in both frequency and time domains. However, time domain implementations show an inferior generalization ability. Frequency domain based learning was proved to be better than time domain when the complex spectrogram is used in the training process. Additionally, feature extraction is also proved to be very effective in DNN based supervised speech enhancement, whether it is performed at the beginning, or implicitly by bottleneck layer features. Finally, it was concluded that the choice of the working domain is mainly restricted by the type and design of the architecture used