Relaminarization by steady modification of the streamwise velocity profile in a pipe

We show that a rather simple, steady modification of the streamwise velocity profile in a pipe can lead to a complete collapse of turbulence and the flow fully relaminarizes. Two different devices, a stationary obstacle (inset) and a device to inject additional fluid through an annular gap close to the wall, are used to control the flow. Both devices modify the streamwise velocity profile such that the flow in the center of the pipe is decelerated and the flow in the near wall region is accelerated. We present measurements with stereoscopic particle image velocimetry to investigate and capture the development of the relaminarizing flow downstream these devices and the specific circumstances responsible for relaminarization. We find total relaminarization up to Reynolds numbers of 6000, where the pressure drop in the downstream distance is reduced by a factor of 3.4 due to relaminarization. In a smooth straight pipe the flow remains completely laminar downstream of the control. Furthermore, we show that transient (temporary) relaminarization in a spatially confined region right downstream the devices occurs also at much higher Reynolds numbers, accompanied by a significant drag reduction. The underlying physical mechanism of relaminarization is attributed to a weakening of the near-wall turbulence production cycle

Cohabitation of settlements among crested porcupine (Hystrix cristata), red fox (Vulpes vulpes) and European badger (Meles meles)

In Italy, porcupines, badgers and red foxes share the same settlements. However, there is lack of informa-tion concerning their cohabitation. From 2012 to 2019, cohabitation by these three mammals was studied using camera-trapping and was found to occur only between porcupines and badgers, even in the presence of porcupettes. Cohabitation was associated with aggressive interaction between porcupines and badg-ers. Foxes were found to be scavengers of porcupine carcasses. Cohabitation among these semi-fossorial mammals and scavenging behaviour could play a role in disease transmission, including zoonotic diseases

Adhesion Improvement of Thermoplastics-Based Composites by Atmospheric Plasma and UV Treatments

The present work is concerned with adhesive bonding of thermoplastic composites used in general aerospace applications, including polyphenylene sulfide (PPS), polyetherimide (PEI) and polyetheretherketone (PEEK) carbon fibre composites. Three different surface treatments have been applied to the PEEK, PPS and PEI-based composites in order to enhance the adhesion: atmospheric plasma, ultraviolet radiation (UV) and isopropanol wiping as a control. Water contact angles and free surface energies were measured following the standard experimental procedure based on the employment of three different liquid droplets. Infrared spectroscopy and X-ray photoelectron spectroscopy (XPS) were subsequently performed to characterize the surface chemistry of the samples after treatment. The single lap joints were manufactured and bonded by an Aerospace grade epoxy-based film adhesive originally developed for use on metals but with the ability to bond treated thermoplastics to good strength (supplied by Henkel Ireland). Quasi-static (QS) tests were conducted. The lap shear strength was evaluated, and the failure mechanisms of the different joints were examined for the range of surface treatments considered. It was found that the performances of the PEEK and PPS joints were considerably improved by the plasma and UV treatments resulting in cohesive and delamination failures, while PEI was unaffected by the plasma and UV treatments and performed very well throughout

Vaccination against Clostridium difficile using toxin fragments: Observations and analysis in animal models

Clostridium difficile is a major cause of antibiotic associated diarrhea. Recently, we have shown that effective protection can be mediated in hamsters through the inclusion of specific recombinant fragments from toxin A and B in a systemically delivered vaccine. Interestingly while neutralizing antibodies to the binding domains of both toxin A and B are moderately protective, enhanced survival is observed when fragments from the glucosyltransferase region of toxin B replace those from the binding domain of this toxin. In this addendum, we discuss additional information that has been derived from such vaccination studies. This includes observations on efficacy and cross-protection against different ribotypes mediated by these vaccines and the challenges that remain for a vaccine which prevents clinical symptoms but not colonization. The use and value of vaccination both in the prevention of infection and for treatment of disease relapse will be discussed

Design and Experimental Characterization of a Niti-Based, High-Frequency, Centripetal Peristaltic Actuator

Development and experimental testing of a peristaltic device actuated by a single shape-memory NiTi wire are described. The actuator is designed to radially shrink a compliant silicone pipe, and must work on a sustained basis at an actuation frequency that is higher than those typical of NiTi actuators. Four rigid, aluminum-made circular sectors are sitting along the pipe circumference and provide the required NiTi wire housing. The aluminum assembly acts as geometrical amplifier of the wire contraction and as heat sink required to dissipate the thermal energy of the wire during the cooling phase. We present and discuss the full experimental investigation of the actuator performance, measured in terms of its ability to reduce the pipe diameter, at a sustained frequency of 1.5 Hz. Moreover, we investigate how the diameter contraction is affected by various design parameters as well as actuation frequencies up to 4 Hz. We manage to make the NiTi wire work at 3% in strain, cyclically providing the designed pipe wall displacement. The actuator performance is found to decay approximately linearly with actuation frequencies up to 4 Hz. Also, the interface between the wire and the aluminum parts is found to be essential in defining the functional performance of the actuator

Multitask Learning on Graph Neural Networks: Learning Multiple Graph Centrality Measures with a Unified Network

The application of deep learning to symbolic domains remains an active research endeavour. Graph neural networks (GNN), consisting of trained neural modules which can be arranged in different topologies at run time, are sound alternatives to tackle relational problems which lend themselves to graph representations. In this paper, we show that GNNs are capable of multitask learning, which can be naturally enforced by training the model to refine a single set of multidimensional embeddings $\in \mathbb{R}^d$ and decode them into multiple outputs by connecting MLPs at the end of the pipeline. We demonstrate the multitask learning capability of the model in the relevant relational problem of estimating network centrality measures, focusing primarily on producing rankings based on these measures, i.e. is vertex $v_1$ more central than vertex $v_2$ given centrality $c$?. We then show that a GNN can be trained to develop a \emph{lingua franca} of vertex embeddings from which all relevant information about any of the trained centrality measures can be decoded. The proposed model achieves $89\%$ accuracy on a test dataset of random instances with up to 128 vertices and is shown to generalise to larger problem sizes. The model is also shown to obtain reasonable accuracy on a dataset of real world instances with up to 4k vertices, vastly surpassing the sizes of the largest instances with which the model was trained ($n=128$). Finally, we believe that our contributions attest to the potential of GNNs in symbolic domains in general and in relational learning in particular.Comment: Published at ICANN2019. 10 pages, 3 Figure

The factor H binding protein of Neisseria meningitidis interacts with xenosiderophores in vitro.

The factor H binding protein (fHbp) is a key virulence factor of Neisseria meningitidis that confers to the bacterium the ability to resist killing by human serum. The determination of its three-dimensional structure revealed that the carboxyl terminus of the protein folds into an eight-stranded ߠbarrel. The structural similarity of this part of the protein to lipocalins provided the rationale for exploring the ability of fHbp to bind siderophores. We found that fHbp was able to bind in vitro siderophores belonging to the cathecolate family and mapped the interaction site by nuclear magnetic resonance. Our results indicated that the enterobactin binding site was distinct from the site involved in binding to human factor H and stimulates new hypotheses about possible multiple activities of fHbp.Full Tex

NarE: a novel ADP-ribosyltransferase from Neisseria meningitidis.

Mono ADP-ribosyltransferases (ADPRTs) are a class of functionally conserved enzymes present in prokaryotic and eukaryotic organisms. In bacteria, these enzymes often act as potent toxins and play an important role in pathogenesis. Here we report a profile-based computational approach that, assisted by secondary structure predictions, has allowed the identification of a previously undiscovered ADP-ribosyltransferase in Neisseria meningitidis (NarE). NarE shows structural homologies with E. coli heat-labile enterotoxin (LT) and cholera toxin (CT) and possesses ADP-ribosylating and NAD-glycohydrolase activities. As in the case of LT and CT, NarE catalyses the transfer of the ADP-ribose moiety to arginine residues. Despite the absence of a signal peptide, the protein is efficiently exported into the periplasm of Neisseria. The narE gene is present in 25 out of 43 strains analysed, is always present in ET-5 and Lineage 3 but absent in ET-37 and Cluster A4 hypervirulent lineages. When present, the gene is 100% conserved in sequence and is inserted upstream of and co-transcribed with the lipoamide dehydrogenase E3 gene. Possible roles in the pathogenesis of N. meningitidis are discussed

Deep Graph Matching via Blackbox Differentiation of Combinatorial Solvers

Building on recent progress at the intersection of combinatorial optimization and deep learning, we propose an end-to-end trainable architecture for deep graph matching that contains unmodified combinatorial solvers. Using the presence of heavily optimized combinatorial solvers together with some improvements in architecture design, we advance state-of-the-art on deep graph matching benchmarks for keypoint correspondence. In addition, we highlight the conceptual advantages of incorporating solvers into deep learning architectures, such as the possibility of post-processing with a strong multi-graph matching solver or the indifference to changes in the training setting. Finally, we propose two new challenging experimental setups. The code is available at https://github.com/martius-lab/blackbox-deep-graph-matchingComment: ECCV 2020 conference pape