Corrigendum to �Cauda equine syndrome due to Brucella spondylodiscitis and epidural abscess formation: A case report� (Interdisciplinary Neurosurgery: Advanced Techniques and Case Management (2019) 17 (42�44), (S2214751918302676), (10.1016/j.inat.2019.01.011))

The authors' regret: Acknowledgements must be deleted because it is not related to our manuscript. The authors apologise for any inconvenience caused. © 201

Antibiotic resistance pattern and distribution of Vietnamese extended-spectrum- β lactamase (VEB-1) gene in Acinetobacter baumannii isolated from hospitalized patients in Kashan Shahid Beheshti hospital during 2013-2014

Background: Acinetobacter baumannii are widely distributed pathogens in hospitals. They have the ability to have various mechanisms of resistance. Multiple drug resistant (MDR) strains of A. baumannii have created therapeutic problems worldwide. The aim of the present study was to determine the antimicrobial susceptibility and detection of blaOXA51 and VEB-1 genes of A. baumannii isolated from clinical specimens in teaching hospital. Materials and Methods: A descriptive cross-sectional study was performed on 124 A. baumannii strains isolated from patients in Beheshti hospital, Kashan, Iran, during 2013-2014. At the species level, the isolates were identified by conventional biochemical tests and then confirmed by the Microgen kit (GNA). An antibiotic susceptibility test was performed for 17 antimicrobial agents according to the CLSI guidelines. Multiple drug resistant was defined as presence of resistance to three or more classes of antibiotics. The presence of blaOXA51 and VEB-1 genes was investigated using the polymerase chain reation. Results: Acinetobacter baumannii isolates demonstrated the highest resistance to ceftriaxone, ceftazidime and cefotaxime. All isolates were sensitive to colistin and polymyxin. All isolates were positive for blaOXA51. Thirty-two isolates (25.8) were positive for the VEB-1 gene. Conclusion: This study highlights the high frequency of MDR isolates. The VEB-1 gene, which produces extended spectrum beta lactamase enzymes and inactivates third generation cephalosporins, was positive in more than 25 of the samples

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

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

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

Sensitivity of levofloxacin in combination with ampicillin-sulbactam and tigecycline against multidrug-resistant Acinetobacter baumannii

Background and Objectives: The selection of alternative treatment options with antibiotic combinations may be used for successful managing of multidrug-resistant Acinetobacter baumannii. The aim of this study was to determine the synergistic effects of ampicillin-sulbactam combined with either levofloxacin or tigecycline against MDR A. baumannii. Materials and Methods: A total 124 of A.baumannii isolates collected from clinical samples of hospitalized patients which assessed for antibiotic susceptibility using disk diffusion method. E-test was used on 10 MDR A. baumannii isolates to determine the minimum inhibitory concentration (MIC) of ampicillin-sulbactam, levofloxacin and tigecycline. Any synergistic effects were evaluated at their own MIC using E-test assay at 37°C for 24 hours. Synergy was defined as a fractional inhibitory concentration index (FICI) of ≤0.5. Results: Levofloxacin plus ampicillin-sulbactam combination was found to have synergistic effects (FIC index: ≤0.5) in 90% of the isolates, but there was no synergistic effect for ampicillin-sulbactam/tigecycline and tigecycline/ levofloxacin combination. The antagonist effect in 50% of isolates (FIC index: >2) showed in combination of levofloxacin/tigecycline. Conclusion: The emergence of multidrug A. baumannii isolates requires evaluating by combination therapy. The combination of levofloxacin plus a bactericidal antibiotic such as ampicillin-sulbactam is recommended. Results should be confirmed by clinical studies. Keywords: Acinetobacter baumannii, Etest Methods, Microbial Drug Resistance, Synergistic effec

Lecania makarevicziae, a new lichen species from Iran

A new for science species Lecania makarevicziae differing from L. pallida in having knobby to squamulose, blastidiate thallus, in having larger apothecia, in having a dark violet-brown disc, in having a plane disc, in having (1–2–)3-septate ascospores with slightly distinct constrictions at the septum, as well as in the lack of dense bluish pruina on apothecium disc, from Iran and Turkmenistan, is described, compared with closely related taxa

Cauda equnia syndrome due to Brucella spondylodiscitis and epidural abscess formation: A case report

Brucellosis is an infection with a widening clinical disease spectrum, has been reported as the causative agent of lumbar spine complications but rarely accompanying CES Injury. We report a female patient with Brucella spondylodiscitis affecting the lumbosacral region resulting in CES due to epidural abscess formation. Brucella spondylodiscitis should be suspected in patients with unexplained neurological features and low back pain in endemic regions. © 201