1,721,101 research outputs found
TBNET – COLLABORATIVE RESEARCH ON TUBERCULOSIS IN EUROPE
No full textNetworking is a key feature of scientific success. The Tuberculosis Network European Trialsgroup (TBNET) was founded in 2006 as a non-profit, non-governmental peer-initiated scientific organization to collaboratively address research priorities in the area of tuber-culosis in Europe. Today, TBNET is the largest tuberculosis research organization in Europe with nearly 500 members from 22 EU countries and 49 countries worldwide (www.tb-net.org). Apart from small multicenter basic research studies, a particular strength of TBNET is the performance of large collaborative projects, pan-European multicenter studies and database projects. In recent years, research from TBNET has substantially contributed to the understanding of the management, risk and prognosis of patients wit
Infection control, genetic assessment of drug resistance and drug susceptibility testing in the current management of multidrug/extensively-resistant tuberculosis (M/XDR-TB) in Europe: A tuberculosis network European Trialsgroup (TBNET) study.
Full text linkAIM: Europe has the highest documented caseload and greatest increase in multidrug and extensively drug-resistant tuberculosis (M/XDR-TB) of all World Health Organization (WHO) regions. This survey examines how recommendations for M/XDR-TB management are being implemented. METHODS: TBNET is a pan-European clinical research collaboration for tuberculosis. An email survey of TBNET members collected data in relation to infection control, access to molecular tests and basic microbiology with drug sensitivity testing. RESULTS: 68/105 responses gave valid information and were from countries within the WHO European Region. Inpatient beds matched demand, but single rooms with negative pressure were only available in low incidence countries; ultraviolet decontamination was used in 5 sites, all with >10 patients with M/XDR-TB per year. Molecular tests for mutations associated with rifampicin resistance were widely available (88%), even in lower income and especially in high incidence countries. Molecular tests for other first line and second line drugs were less accessible (76 and 52% respectively). A third of physicians considered that drug susceptibility results were delayed by > 2 months. CONCLUSION: Infection control for inpatients with M/XDR-TB remains a problem in high incidence countries. Rifampicin resistance is readily detected, but tests to plan regimens tailored to the drug susceptibilities of the strain of Mycobacterium tuberculosis are significantly delayed, allowing for further drug resistance to develop
TBNet: a context-aware graph network for tuberculosis diagnosis
No full textBackground and objectiveTuberculosis (TB) is an infectious bacterial disease. It can affect the human lungs, brain, bones, and kidneys. Pulmonary tuberculosis is the most common. This airborne bacterium can be transmitted with the droplets by coughing and sneezing. So far, the most convenient and effective method for diagnosing TB is through medical imaging. Computed tomography (CT) is the first choice for lung imaging in clinics because the conditions of the lungs can be interpreted from CT images. However, manual screening poses an enormous burden for radiologists, resulting in high inter-observer variances. Hence, developing computer-aided diagnosis systems to implement automatic TB diagnosis is an emergent and significant task for researchers and practitioners. This paper proposed a novel context-aware graph neural network called TBNet to detect TB from chest CT imagesMethodsTraditional convolutional neural networks can extract high-level image features to achieve good classification performance on the ImageNet dataset. However, we observed that the spatial relationships between the feature vectors are beneficial for the classification because the feature vector may share some common characteristics with its neighboring feature vectors. To utilize this context information for the classification of chest CT images, we proposed to use a feature graph to generate context-aware features. Finally, a context-aware random vector functional-link net served as the classifier of the TBNet to identify these context-aware features as TB or normalResultsThe proposed TBNet produced state-of-the-art classification performance for detecting TB from healthy samples in the experimentsConclusionsOur TBNet can be an accurate and effective verification tool for manual screening in clinical diagnosis.</div
TBNet: A Neural Architectural Defense Framework Facilitating DNN Model Protection in Trusted Execution Environments
Full text linkTrusted Execution Environments (TEEs) have become a promising solution to
secure DNN models on edge devices. However, the existing solutions either
provide inadequate protection or introduce large performance overhead. Taking
both security and performance into consideration, this paper presents TBNet, a
TEE-based defense framework that protects DNN model from a neural architectural
perspective. Specifically, TBNet generates a novel Two-Branch substitution
model, to respectively exploit (1) the computational resources in the untrusted
Rich Execution Environment (REE) for latency reduction and (2) the
physically-isolated TEE for model protection. Experimental results on a
Raspberry Pi across diverse DNN model architectures and datasets demonstrate
that TBNet achieves efficient model protection at a low cost
TBNet: a context-aware graph network for tuberculosis diagnosis
No full textBackground and objectiveTuberculosis (TB) is an infectious bacterial disease. It can affect the human lungs, brain, bones, and kidneys. Pulmonary tuberculosis is the most common. This airborne bacterium can be transmitted with the droplets by coughing and sneezing. So far, the most convenient and effective method for diagnosing TB is through medical imaging. Computed tomography (CT) is the first choice for lung imaging in clinics because the conditions of the lungs can be interpreted from CT images. However, manual screening poses an enormous burden for radiologists, resulting in high inter-observer variances. Hence, developing computer-aided diagnosis systems to implement automatic TB diagnosis is an emergent and significant task for researchers and practitioners. This paper proposed a novel context-aware graph neural network called TBNet to detect TB from chest CT imagesMethodsTraditional convolutional neural networks can extract high-level image features to achieve good classification performance on the ImageNet dataset. However, we observed that the spatial relationships between the feature vectors are beneficial for the classification because the feature vector may share some common characteristics with its neighboring feature vectors. To utilize this context information for the classification of chest CT images, we proposed to use a feature graph to generate context-aware features. Finally, a context-aware random vector functional-link net served as the classifier of the TBNet to identify these context-aware features as TB or normalResultsThe proposed TBNet produced state-of-the-art classification performance for detecting TB from healthy samples in the experimentsConclusionsOur TBNet can be an accurate and effective verification tool for manual screening in clinical diagnosis.</div
The Tuberculosis Network European Trials Group (TBNET): new directions in the management of tuberculosis.
No full textUNLABELLED: The Tuberculosis Network European Trials Group (TBNET) is the largest clinical research organisation in Europe. Educational activities include the TBNET Academy and the European Advanced Course in Clinical Tuberculosis. Four of their publications are reviewed to show how the clinical management of tuberculosis is changing. KEY POINTS: Most tuberculosis (TB) in contacts is found at their first visit.In contacts of pulmonary TB patients, the likelihood of later TB is ≤3%.Genetic tests can indicate when another antimycobacterial drug in the same class might be effective (e.g. rifabutin when there is rifampicin resistance or which injectable to choose).The short-course "Bangladesh" regimen can only be rarely used in Europe.Treatment completion in multidrug-resistant TB should not be included as a successful outcome
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
E-TBNet: Light Deep Neural Network for Automatic Detection of Tuberculosis with X-ray DR Imaging
No full textCurrently, the tuberculosis (TB) detection model based on chest X-ray images has the problem of excessive reliance on hardware computing resources, high equipment performance requirements, and being harder to deploy in low-cost personal computer and embedded devices. An efficient tuberculosis detection model is proposed to achieve accurate, efficient, and stable tuberculosis screening on devices with lower hardware levels. Due to the particularity of the chest X-ray images of TB patients, there are fewer labeled data, and the deep neural network model is difficult to fully train. We first analyzed the data distribution characteristics of two public TB datasets, and found that the two-stage tuberculosis identification (first divide, then classify) is insufficient. Secondly, according to the particularity of the detection image(s), the basic residual module was optimized and improved, and this is regarded as a crucial component of this article’s network. Finally, an efficient attention mechanism was introduced, which was used to fuse the channel features. The network architecture was optimally designed and adjusted according to the correct and sufficient experimental content. In order to evaluate the performance of the network, it was compared with other lightweight networks under personal computer and Jetson Xavier embedded devices. The experimental results show that the recall rate and accuracy of the E-TBNet proposed in this paper are better than those of classic lightweight networks such as SqueezeNet and ShuffleNet, and it also has a shorter reasoning time. E-TBNet will be more advantageous to deploy on equipment with low levels of hardware
- …
