The risk of nosocomial infection with multidrug-resistant corynebacterium urealyticum after prostate cancer surgery

Corynebacterium urealyticum is a Gram-positive, lipophilic, multidrug resistant, and urease positive microorganism with diph-theroid morphology. C. urealyticum causes several diseases such as urinary tract infection, chronic urological disease, urinary tract infections, and bacteremia in immunocompromised individuals. This study reports a rare case with nosocomial infection and hematuria caused by multidrug-resistant C. urealyticum after prostate cancer surgery. © 2019, Archives of Clinical Infectious Diseases

Noninvasive, Transient and Selective Blood-Brain Barrier Opening in Non-Human Primates In Vivo

The blood-brain barrier (BBB) is a specialized vascular system that impedes entry of all large and the vast majority of small molecules including the most potent central nervous system (CNS) disease therapeutic agents from entering from the lumen into the brain parenchyma. Microbubble-enhanced, focused ultrasound (ME-FUS) has been previously shown to disrupt noninvasively, selectively, and transiently the BBB in small animals in vivo. For the first time, the feasibility of transcranial ME-FUS BBB opening in non-human primates is demonstrated with subsequent BBB recovery. Sonications were combined with two different types of microbubbles (customized 4–5 µm and Definity®). 3T MRI was used to confirm the BBB disruption and to assess brain damage

Closed-loop control of product properties in metal forming

Metal forming processes operate in conditions of uncertainty due to parameter variation and imperfect understanding. This uncertainty leads to a degradation of product properties from customer specifications, which can be reduced by the use of closed-loop control. A framework of analysis is presented for understanding closed-loop control in metal forming, allowing an assessment of current and future developments in actuators, sensors and models. This leads to a survey of current and emerging applications across a broad spectrum of metal forming processes, and a discussion of likely developments.Engineering and Physical Sciences Research Council (Grant ID: EP/K018108/1)This is the final version of the article. It first appeared from Elsevier via https://doi.org/10.1016/j.cirp.2016.06.00

Secure Untraceable Off-Line Electronic Cash System

AbstractEslami and Talebi (2011) [25] proposed an untraceable electronic cash scheme and claimed that their scheme protects the anonymity of customers, detects the identity of double spenders and provides the date attachability of coins to manage the bank database. In this paper, illustrating Eslami and Talebi’s scheme, as one of the latest untraceable electronic cash schemes, and showing its weaknesses (in fulfilling the properties of perceptibility of double spender, unforgeability and date attainability of coins) and its faults (related to exchange protocol), we propose a new untraceable electronic cash scheme which is immune to the weaknesses of the former. Our scheme contains anonymity, double-spending detection, unforgeability and date attachability properties and prevents forging. To do this, we described a special construction which injects the expiration date and the identity of the customer onto the coin and detects the identity in the case of double spending. Lastly, we show that the efficiency of our scheme is comparable with other schemes

Investigation of multiorientation and multiresolution features for microcalcifications classification in mammograms

Breast cancer is one of the most common cancers among women. One of the early signs of the disease is the appearance of microcalcifications clusters, which often show up as bright spots in mammograms. It is important to be able to distinguish between the shapes of these clusters to increase the reliability and accuracy of the diagnosis. In this paper, a new method to extract features to classify the microcalcification clusters using steerable pyramid decomposition is presented. The method is motivated by the fact that microcalcification clusters can be of arbitrary sizes and orientations. Thus, it is important to extract the features in all possible orientations to capture most of the distinguishing information for classification. The proposed method shows a clear improvement in the classification performance when compared to the wavelet transform; the most commonly used multiscale analysis technique at present. © 2011 IEEE.Aqilah Baseri Huddin, Brian Ng and Derek Abbotthttp://www.issnip.org/~issnip2011/index.ht

Iterative self-organizing atherosclerotic tissue labeling in intravascular ultrasound images and comparison with virtual histology.

Intravascular ultrasound (IVUS) is the predominant imaging modality in the field of interventional cardiology that provides real-time cross-sectional images of coronary arteries and the extent of atherosclerosis. Due to heterogeneity of lesions and stringent spatial/spectral behavior of tissues, atherosclerotic plaque characterization has always been a challenge and still is an open problem. In this paper, we present a systematic framework from in vitro data collection, histology preparation, IVUS-histology registration along with matching procedure, and finally a robust texture-derived unsupervised atherosclerotic plaque labeling. We have performed our algorithm on in vitro and in vivo images acquired with single-element 40 MHz and 64-elements phased array 20 MHz transducers, respectively. In former case, we have quantified results by local contrasting of constructed tissue colormaps with corresponding histology images employing an independent expert and in the latter case, virtual histology images have been utilized for comparison. We tackle one of the main challenges in the field that is the reliability of tissues behind arc of calcified plaques and validate the results through a novel random walks framework by incorporating underlying physics of ultrasound imaging. We conclude that proposed framework is a formidable approach for retrieving imperative information regarding tissues and building a reliable training dataset for supervised classification and its extension for in vivo applications