Effect of curing salt and phosphate on the activity of porcine muscle proteases

The effect of curing salt on the activity of porcine muscle proteases was evaluated, within the salt concentration range found in the manufacturing process of Spanish cured ham. Salt (NaCl) acts as a strong inhibitor of proteolytic activity; sodium nitrate and potassium nitrate do not affect cathepsin D activity; cathepsin L is inhibited at levels of the salts not found in cured ham, and Ca-dependent proteolytic activity is enhanced by nitrate concentrations below 800 ppm.

The appearance of phosphate precipitates in several areas of ham has led to the study of phosphate effect on enzymatic activity. Results show that phosphate is an inhibitor of proteolytic activity.     

Influence of carbon nanotubes structures embedded in UHMWPE on bacterial adherence

Because of their intrinsic properties, carbon nanotubes (CNTs) have been suggested for biomedical applications. We studied the anti-adherent performance of two ultra-high-molecular-weight polyethylene (UHMWPE) surfaces which contains 3% multi-wall CNT variant (MWCNT): Nanocyl/UHMWPE and Arc/UHMWPE. These surfaces were obtained by hot pressure forming after mechanical mixture. Additional nanoindentation studies were performed and hardness and stiffness were determined. Mechanical properties of the MWCNT/UHMWPE composites were also compared to raw UHMWPE and correlated with their anti-adherent performance. Comparing with UHMWPE, Nanocyl/UHMWPE was the least adherent surface. Bacterial adherence was also significantly reduced in Arc/UHMWPE for four strains.     

Inspection of an underwater structure using point‐cloud SLAM with an AUV and a laser scanner

This paper presents experimental results using a newly developed 3D underwater laser scanner mounted on an autonomous underwater vehicle (AUV) for real‐time simultaneous localization and mapping (SLAM). The algorithm consists of registering point clouds using a dual step procedure. First, a feature‐based coarse alignment is performed, which is then refined using iterative closest point. The robot position is estimated using an extended Kalman filter (EKF) that fuses the data coming from navigation sensors of the AUV. Moreover, the pose from where each point cloud was collected is also stored in the pose‐based EKF‐SLAM state vector. The results of the registration algorithm are used as constraint observations among the different poses within the state vector, solving the full‐SLAM problem. The method is demonstrated using the Girona 500 AUV, equipped with a laser scanner and inspecting a 3D sub‐sea infrastructure inside a water tank. Our results prove that it is possible to limit the navigation drift and deliver a consistent high‐accuracy 3D map of the inspected object.     

Small-angle synchrotron study of liquid crystalline poly[oxybis(trimethylene) p,p′-bibenzoate]

Summary Small-angle synchrotron experiments have been performed in two samples of poly[oxybis(trimethylene) p,p-bibenzoate], PDTMB. Owing to the very slow transformation of the mesophase of this polymer into the crystal, only the liquid crystalline phase is present in a PDTMB sample quenched from the melt, while about 30% crystallinity is found in the annealed specimen. The results indicate that the layer spacing peak for the mesophase is very close to a crystal diffraction. This seems to be a general fact in thermotropic polybibenzoates. Moreover, no long spacing has been detected in the quenched sample. On the contrary, the annealed specimen exhibits a long spacing centered at about 16 nm, corresponding to a rather small crystal thickness.     

MARGA: Multispectral Adaptive Region Growing Algorithm for brain extraction on axial MRI

Brain extraction, also known as skull stripping, is one of the most important preprocessing steps for many automatic brain image analysis. In this paper we present a new approach called Multispectral Adaptive Region Growing Algorithm (MARGA) to perform the skull stripping process. MARGA is based on a region growing (RG) algorithm which uses the complementary information provided by conventional magnetic resonance images (MRI) such as T1-weighted and T2-weighted to perform the brain segmentation. MARGA can be seen as an extension of the skull stripping method proposed by Park and Lee (2009) [1], enabling their use in both axial views and low quality images. Following the same idea, we first obtain seed regions that are then spread using a 2D RG algorithm which behaves differently in specific zones of the brain. This adaptation allows to deal with the fact that middle MRI slices have better image contrast between the brain and non-brain regions than superior and inferior brain slices where the contrast is smaller. MARGA is validated using three different databases: 10 simulated brains from the BrainWeb database; 2 data sets from the National Alliance for Medical Image Computing (NAMIC) database, the first one consisting in 10 normal brains and 10 brains of schizophrenic patients acquired with a 3 T GE scanner, and the second one consisting in 5 brains from lupus patients acquired with a 3 T Siemens scanner; and 10 brains of multiple sclerosis patients acquired with a 1.5 T scanner. We have qualitatively and quantitatively compared MARGA with the well-known Brain Extraction Tool (BET), Brain Surface Extractor (BSE) and Statistical Parametric Mapping (SPM) approaches. The obtained results demonstrate the validity of MARGA, outperforming the results of those standard techniques.     

FaRNet: Fast recognition of high-dimensional patterns from big network traffic data

Extracting knowledge from big network traffic data is a matter of foremost importance for multiple purposes including trend analysis, network troubleshooting, capacity planning, network forensics, and traffic classification. An extremely useful approach to profile traffic is to extract and display to a network administrator the multi-dimensional hierarchical heavy hitters (HHHs) of a dataset. However, existing schemes for computing HHHs have several limitations: (1) they require significant computational resources; (2) they do not scale to high dimensional data; and (3) they are not easily extensible. In this paper, we introduce a fundamentally new approach for extracting HHHs based on generalized frequent item-set mining (FIM), which allows to process traffic data much more efficiently and scales to much higher dimensional data than present schemes. Based on generalized FIM, we build and thoroughly evaluate a traffic profiling system we call FaRNet. Our comparison with AutoFocus, which is the most related tool of similar nature, shows that FaRNet is up to three orders of magnitude faster. Finally, we describe experiences on how generalized FIM is useful in practice after using FaRNet operationally for several months in the NOC of GÉANT, the European backbone network.     

KAMU: providing advanced user privacy in Kerberos multi-domain scenarios

In Next Generation Networks, Kerberos is becoming a key component to support authentication and key distribution for Internet application services. However, for this purpose, Kerberos needs to rectify certain deficiencies, especially in the area of privacy, which allow an eavesdropper to obtain information of the services users are accessing. This paper presents a comprehensive privacy framework that guarantees user anonymity, service access unlinkability and message exchange unlinkability in Kerberos both in single-domain and multi-domain scenarios. This proposal is based on different extensibility mechanisms already defined for Kerberos, which facilitate its adoption in already deployed systems. Apart from evaluating our proposal in terms of performance to prove its lightweight nature, we demonstrate its capability to work in perfect harmony with a widely used anonymous communication system like Tor.