Insoluble Vascular Amyloid Deposits Trigger Disruption of the Neurovascular Unit in Alzheimer’s Disease Brains

Alzheimer’s disease (AD) is a neurodegenerative disease, characterized histopathologically by intra-neuronal tau-related lesions and by the accumulation of amyloid β-peptide (Aβ) in the brain parenchyma and around cerebral blood vessels. According to the vascular hypothesis of AD, an alteration in the neurovascular unit (NVU) could lead to Aβ vascular accumulation and promote neuronal dysfunction, accelerating neurodegeneration and dementia. To date, the effects of insoluble vascular Aβ deposits on the NVU and the blood–brain barrier (BBB) are unknown. In this study, we analyze different Aβ species and their association with the cells that make up the NVU. We evaluated post-mortem AD brain tissue. Multiple immunofluorescence assays were performed against different species of Aβ and the main elements that constitute the NVU. Our results showed that there are insoluble vascular deposits of both full-length and truncated Aβ species. Besides, insoluble aggregates are associated with a decrease in the phenotype of the cellular components that constitute the NVU and with BBB disruption. This approach could help identify new therapeutic targets against key molecules and receptors in the NVU that can prevent the accumulation of vascular fibrillar Aβ in AD.     

Apparatus for measuring physical adsorption on intact supported porous membranes

We present a non-destructive, reusable apparatus with which to measure physical adsorption isotherms of intact porous membranes. We developed a two-piece sample container to make reliable sorption measurements of porous membranes, the properties of which may differ from those of powders due to crystal intergrowth. This novel system employs a resealable container that can be immersed in a cryogenic bath, into which tubular, planar, or other porous membranes and films may be placed. Detailed sorption isotherms, including high-resolution adsorption in the low pressure (micropore-filling) region, are shown for MFI-type zeolite membranes grown on two types and configurations of α-alumina support.     

Chromate leaching from inhibited primers: Part I. Characterisation of leaching

Leaching and characterisation studies have been undertaken on two chromate-inhibited epoxy polyamide primers. Leaching was carried out in 5% (w/v) NaCl solutions at different pH values (1, 3, 5 and 7) and the amount of Cr released into solution was monitored over time. Cr release was initially high, but as the immersion time increased the leaching from the primers slowed. Prior to and after immersion, the primers were characterised by a number of techniques including electron microprobe analysis, X-ray microdiffraction, Raman spectroscopy, and positron annihilation lifetime spectroscopy. The unexposed primers were found to contain the inorganic phases SrCrO₄, BaSO₄ and TiO₂ (anatase or rutile). Upon immersion, water uptake by the primers was observed, together with a decrease in the amount of SrCrO₄ in the primers. These studies provide insights into the mechanism of chromate leaching from inhibited primers.     

Effect of catalyst type on the kinetics of the photoelectrochemical disinfection of water inoculated with E. coli

The rates of the photoelectrochemical disinfection of Escherichia coli at TiO₂ electrodes were measured as a function of concentration and applied potential. Two different TiO₂ photoelectrodes were used: a thermally treated titanium plate and a porous film prepared by a sol–gel hydrolysis technique. The kinetics of the disinfection process were found to depend upon the nature of the electrode material. For the thermal film they were first order, and half order for the sol–gel film. It was also found that the catalytic activity per unit surface area of catalyst is many orders of magnitude greater than that observed using TiO₂ slurries; this was attributed to the reduced rate of electron-hole recombination afforded by the application of a small potential bias (1 V vs Ag/AgCl), and hence the exploitation of the electric field enhancement (EFE) effect.     

Chasing the White “WHALE”: A Case Study in Using iPads to Promote Active Learning

In order to encourage more active learning in the library classroom, the college librarians at the Tidewater Community College/City of Virginia Beach Joint-Use Library turned one of their library instruction classrooms into a dynamic and flexible space using movable seating and iPads. This article explores the reasons for making such a change, the benefits of doing so, how the new classroom has affected librarians' teaching methods, and gives specific information about apps used for class activities.     

Adaptive augmented Lagrangian methods: algorithms and practical numerical experience

In this paper, we consider augmented Lagrangian (AL) algorithms for solving large-scale nonlinear optimization problems that execute adaptive strategies for updating the penalty parameter. Our work is motivated by the recently proposed adaptive AL trust region method by Curtis et al. [An adaptive augmented Lagrangian method for large-scale constrained optimization, Math. Program. 152 (2015), pp. 201–245.]. The first focal point of this paper is a new variant of the approach that employs a line search rather than a trust region strategy, where a critical algorithmic feature for the line search strategy is the use of convexified piecewise quadratic models of the AL function for computing the search directions. We prove global convergence guarantees for our line search algorithm that are on par with those for the previously proposed trust region method. A second focal point of this paper is the practical performance of the line search and trust region algorithm variants in Matlab software, as well as that of an adaptive penalty parameter updating strategy incorporated into the Lancelot software. We test these methods on problems from the CUTEst and COPS collections, as well as on challenging test problems related to optimal power flow. Our numerical experience suggests that the adaptive algorithms outperform traditional AL methods in terms of efficiency and reliability. As with traditional AL algorithms, the adaptive methods are matrix-free and thus represent a viable option for solving large-scale problems.     

Subroutine based detection of APT malware

Statistical detection of mass malware has been shown to be highly successful. However, this type of malware is less interesting to cyber security officers of larger organizations, who are more concerned with detecting malware indicative of a targeted attack. Here we investigate the potential of statistically based approaches to detect such malware using a malware family associated with a large number of targeted network intrusions. Our approach is complementary to the bulk of statistical based malware classifiers, which are typically based on measures of overall similarity between executable files. One problem with this approach is that a malicious executable that shares some, but limited, functionality with known malware is likely to be misclassified as benign. Here a new approach to malware classification is introduced that classifies programs based on their similarity with known malware subroutines. It is illustrated that malware and benign programs can share a substantial amount of code, implying that classification should be based on malicious subroutines that occur infrequently, or not at all in benign programs. Various approaches to accomplishing this task are investigated, and a particularly simple approach appears the most effective. This approach simply computes the fraction of subroutines of a program that are similar to malware subroutines whose likes have not been found in a larger benign set. If this fraction exceeds around 1.5 %, the corresponding program can be classified as malicious at a 1 in 1000 false alarm rate. It is further shown that combining a local and overall similarity based approach can lead to considerably better prediction due to the relatively low correlation of their predictions.     

Evaluating the species energy relationship with the newest measures of ecosystem energy: NDVI versus MODIS primary production

Ecosystem energy has been shown to be a strong correlate with biological diversity at continental scales. Early efforts to characterize this association used the normalized difference vegetation index (NDVI) to represent ecosystem energy. While this spectral vegetation index covaries with measures of ecosystem energy such as net primary production, the covariation is known to degrade in areas of very low vegetation or in areas of dense forest. Two of the new vegetation products from the MODIS sensor, derived by integrating spectral reflectance, climate data, and land cover, are thought to better approximate primary productivity than NDVI. In this study, we determine if the new MODIS derived measures of primary production, gross primary productivity (GPP) and net primary productivity (NPP) better explain variation in bird richness than historically used NDVI. Moreover, we evaluate if the two productivity measures covary more strongly with bird diversity in those vegetation conditions where limitations of NDVI are well recognized.Biodiversity was represented as native landbird species richness derived from the North American Breeding Bird Survey. Analyses included correlation analyses among predictor variables, and univariate regression analyses between each predictor variable and bird species richness. Analyses were done at two levels: for all BBS routes across natural landscapes in North America; and for routes in 10 vegetation classes stratified by vegetated cover along a gradient from bare ground to herbaceous cover to tree cover. We found that NDVI, GPP and NPP were highly correlated and explained similar variation in bird species richness when analyzed for all samples across North America. However, when samples were stratified by vegetated cover, strength of correlation between NDVI and both productivity measures was low for samples with bare ground and for dense forest. The NDVI also explained substantially less variation in bird species richness than the primary production in areas with more bare ground and in areas of dense forest. We conclude that MODIS productivity measures have higher utility in studies of the relationship of species richness and productivity and that MODIS GPP and NPP improve on NDVI, especially for studies with large variation in vegetated cover and density.     

ICCD: Interactive Continuous Collision Detection between Deformable Models Using Connectivity-Based Culling

We present an interactive algorithm for continuous collision detection between deformable models. We introduce multiple techniques to improve the culling efficiency and the overall performance of continuous collision detection. First, we present a novel formulation for continuous normal cones and use these normal cones to efficiently cull large regions of the mesh as part of self-collision tests. Second, we introduce the concept of “procedural representative triangles” to remove all redundant elementary tests between nonadjacent triangles. Finally, we exploit the mesh connectivity and introduce the concept of “orphan sets” to eliminate redundant elementary tests between adjacent triangle primitives. In practice, we can reduce the number of elementary tests by two orders of magnitude. These culling techniques have been combined with bounding volume hierarchies and can result in one order of magnitude performance improvement as compared to prior collision detection algorithms for deformable models. We highlight the performance of our algorithm on several benchmarks, including cloth simulations, N-body simulations, and breaking objects.