Encapsulation process sterilizes and preserves surgical instruments

Ethylene oxide is blended with an organic polymer to form a sterile material for encapsulating surgical instruments. The material does not bond to metal and can be easily removed when the instruments are needed

Process for preparing sterile solid propellants Patent

Using ethylene oxide in preparation of sterilized solid rocket propellants and encapsulating material

Practical approach to diastolic dysfunction in light of the new guidelines and clinical applications in the operating room and in the intensive care

There is growing evidence both in the perioperative period and in the field of intensive care (ICU) on the association between left ventricular diastolic dysfunction (LVDD) and worse outcomes in patients. The recent American Society of Echocardiography and European Association of Cardiovascular Imaging joint recommendations have tried to simplify the diagnosis and the grading of LVDD. However, both an often unknown pre-morbid LV diastolic function and the presence of several confounders—i.e., use of vasopressors, positive pressure ventilation, volume loading—make the proposed parameters difficult to interpret, especially in the ICU. Among the proposed parameters for diagnosis and grading of LVDD, the two tissue Doppler imaging-derived variables e′ and E/e′ seem most reliable. However, these are not devoid of limitations. In the present review, we aim at rationalizing the applicability of the recent recommendations to the perioperative and ICU areas, discussing the clinical meaning and echocardiographic findings of different grades of LVDD, describing the impact of LVDD on patients’ outcomes and providing some hints on the management of patients with LVDD

IMAGE ORIENTATION WITH A HYBRID PIPELINE ROBUST TO ROTATIONS AND WIDE-BASELINES

The extraction of reliable and repeatable interest points among images is a fundamental step for automatic image orientation (Structure-From-Motion). Despite recent progresses, open issues in challenging conditions - such as wide baselines and strong light variations - are still present. Over the years, traditional hand-crafted methods have been paired by learning-based approaches, progressively updating the state-of-the-art according to recent benchmarks. Notwithstanding these advancements, learning-based methods are often not suitable for real photogrammetric surveys due to their lack of rotation invariance, a fundamental requirement for these specific applications. This paper proposes a novel hybrid image matching pipeline which employs both hand-crafted and deep-based components, to extract reliable rotational invariant keypoints optimized for wide-baseline scenarios. The proposed hybrid pipeline was compared with other hand-crafted and learning-based state-of-the-art approaches on some photogrammetric datasets using metric ground-truth data. Results show that the proposed hybrid matching pipeline has high accuracy and appeared to be the only method among the evaluated ones able to register images in the most challenging wide-baseline scenarios

Survival of microorganisms in desert soil exposed to five years of continuous very high vacuum

Microorganism survivability in desert algal soil crust under continuous very high vacuu

Extracorporeal CO2 removal in hypercapnic patients who fail noni nvasive ventialtion and refuse endotracheal intubation. a case series

Noninvasive ventilation (NIV) represents the standard of care for patients with exacerbation of chronic obstructive pulmonary disease. However, NIV fails in almost 40% of the most severe forms of acute hypercapnic respiratory failure and patients must undergo endotracheal intubation and invasive ventilation. Such transition from NIV to invasive ventilation is associated to increased mortality. Under these circumstances, patients may express a clear intention not to be intubated

Deep-image-matching: A toolbox for multiview image matching of complex scenarios

Finding corresponding points between images is a fundamental step in photogrammetry and computer vision tasks. Traditionally, image matching has relied on hand-crafted algorithms such as SIFT or ORB. However, these algorithms face challenges when dealing with multi-Temporal images, varying radiometry and contents as well as significant viewpoint differences. Recently, the computer vision community has proposed several deep learning-based approaches that are trained for challenging illumination and wide viewing angle scenarios. However, they suffer from certain limitations, such as rotations, and they are not applicable to high resolution images due to computational constraints. In addition, they are not widely used by the photogrammetric community due to limited integration with standard photogrammetric software packages. To overcome these challenges, this paper introduces Deep-Image-Matching, an opensource toolbox designed to match images using different matching strategies, ranging from traditional hand-crafted to deep-learning methods (https://github.com/3DOM-FBK/deep-image-matching). The toolbox accommodates high-resolution datasets, e.g. data acquired with full-frame or aerial sensors, and addresses known rotation-related problems of the learned features. The toolbox provides image correspondences outcomes that are directly compatible with commercial and open-source software packages, such as COLMAP and openMVG, for a bundle adjustment. The paper includes also a series of cultural heritage case studies that present challenging conditions where traditional hand-crafted approaches typically fail

Analysis of an intense bora event in the Adriatic area

International audienceNumerical simulations of a bora event, recently occurred in the Adriatic area, are presented. Two reference runs at different horizontal resolution (about 20km and 8km) describe the case. Initial conditions for the atmospheric model integration are obtained from ECMWF analyses. Satellite data are used for comparisons. A further run at horizontal resolution of 8km, using initial satellite sea surface temperatures, is performed to evaluate their impact on the low level wind over the Adriatic Sea. All the simulations are carried out with 50 layers in the vertical. Numerous aspects of the simulations are found to be in agreement with the understanding as well as the observational knowledge of bora distinctive characteristics. Satellite data and model results indicate that a more realistic simulation of the bora wind over the sea is achieved using the model with 8km horizontal resolution and that the low level wind in this case is sensitive, though weakly, to the difference between the used sea surface temperature fields. Simulation results also show that both wind intensity and the area around wind peaks tend to increase when relatively higher sea surface temperatures are used

PHOTOGRAMMETRY NOW AND THEN – FROM HAND-CRAFTED TO DEEP-LEARNING TIE POINTS –

Historical images provide a valuable source of information exploited by several kinds of applications, such as the monitoring of cities and territories, the reconstruction of destroyed buildings, and are increasingly being shared for cultural promotion projects through virtual reality or augmented reality applications. Finding reliable and accurate matches between historical and present images is a fundamental step for such tasks since they require to co-register the present 3D scene with the past one. Classical image matching solutions are sensitive to strong radiometric variations within the images, which are particularly relevant in these multi-temporal contexts due to different types of sensitive media (film/sensors) employed for the image acquisitions, different lighting conditions and viewpoint angles. In this work, we investigate the actual improvement provided by recent deep learning approaches to match historical and nowadays images. As learning-based methods have been trained to find reliable matches in challenging scenarios, including large viewpoint and illumination changes, they could overcome the limitations of classic hand-crafted methods such as SIFT and ORB. The most relevant approaches proposed by the research community in the last years are analyzed and compared using pairs of multi-temporal images

Evidences on the role of the lid loop of γ-glutamyltransferases (GGT) in substrate selection

\u3b3-Glutamyltransferase (GGT) catalyzes the transfer of the \u3b3-glutamyl moiety from a donor substrate such as glutathione to water (hydrolysis) or to an acceptor amino acid (transpeptidation) through the formation of a \u3b3-glutamyl enzyme intermediate. The vast majority of the known GGTs has a short sequence covering the glutamate binding site, called lid-loop. Although being conserved enzymes, both B. subtilis GGT and the related enzyme CapD from B. anthracis lack the lid loop and, differently from other GGTs, both accept poly-\u3b3-glutamic acid (\u3b3-PGA) as a substrate. Starting from this observation, in this work the activity of an engineered mutant enzyme containing the amino acid sequence of the lid loop from E. coli GGT inserted into the backbone of B. subtilis GGT was compared to that of the lid loop-deficient B. subtilis GGT and the lid loop-carrier E. coli GGT. Results indicate that the absence of the lid loop seems not to be the sole structural feature responsible for the recognition of a polymeric substrate by GGTs. Nevertheless, time course of hydrolysis reactions carried out using oligo-\u3b3-glutamylglutamines as substrates showed that the lid loop acts as a gating structure, allowing the preferential selection of the small glutamine with respect to the oligomeric substrates. In this respect, the mutant B. subtilis GGT revealed to be more similar to E. coli GGT than to its wild-type counterpart. In addition, the transpeptidase activity of the newly produced mutant enzyme revealed to be higher with respect to that of both E. coli and wild-type B. subtilis GGT. These findings can be helpful in selecting GGTs intended as biocatalysts for preparative purposes as well as in designing mutant enzymes with improved transpeptidase activity