Cyclic Nigerosyl-Nigerose as Oxygen Nanocarrier to Protect Cellular Models from Hypoxia/Reoxygenation Injury: Implications from an In Vitro Model

Heart failure (HF) prevalence is increasing among the aging population, and the mortality rate remains unacceptably high despite improvements in therapy. Myocardial ischemia (MI) and, consequently, ischemia/reperfusion injury (IRI), are frequently the basis of HF development. Therefore, cardioprotective strategies to limit IRI are mandatory. Nanocarriers have been proposed as alternative therapy for cardiovascular disease. Controlled reoxygenation may be a promising strategy. Novel nanocarriers, such as cyclic nigerosyl-nigerose (CNN), can be innovative tools for oxygen delivery in a controlled manner. In this study we analyzed new CNN-based formulations as oxygen nanocarriers (O₂-CNN), and compared them with nitrogen CNN (N₂-CNN). These different CNN-based formulations were tested using two cellular models, namely, cardiomyoblasts (H9c2), and endothelial (HMEC) cell lines, at different concentrations. The effects on the growth curve during normoxia (21% O₂, 5% CO₂ and 74% N₂) and their protective effects during hypoxia (1% O₂, 5% CO₂ and 94% N₂) and reoxygenation (21% O₂, 5% CO₂ and 74% N₂) were studied. Neither O₂-CNN nor N₂-CNN has any effect on the growth curve during normoxia. However, O₂-CNN applied before hypoxia induces a 15–30% reduction in cell mortality after hypoxia/re-oxygenation when compared to N₂-CNN. O₂-CNN showed a marked efficacy in controlled oxygenation, which suggests an interesting potential for the future medical application of soluble nanocarrier systems for MI treatment.     

Porous structures printed by fused deposition of granules processed from a novel polyamide/alumina-based system

Alumina ceramic porous structures were shaped by the fused deposition of alumina/polyamide 612 composite granules using an FDM 3D printer with a modified extruder to process granulated powder instead of a filament. The composite granules were prepared via thermally induced phase separation (TIPS). Firstly, in order to determine the proportion of polymer/solvent to be used in the TIPS process, different volume fractions of PA612 in dimethyl sulfoxide (DMSO) were studied (0.01 to 0.20), and the granules obtained were characterized by SEM, DSC, and Raman spectroscopy. Secondly, the addition of different ceramic loadings to the PA612/DMSO solution was studied with the aim of determining the amount to be used for preparing the composite granules by TIPS. In particular, the effect of the alumina content on the morphology and size of the obtained composite granules was studied. In addition, rheological properties (oscillatory and rotational tests) of the feed materials were studied. Finally, the printing conditions, including the nozzle temperature and nozzle diameter, and the extrusion process, were optimized in order to obtain porous structures with good quality. Alumina porous structures were successfully printed, debinded, and sintered. Adequate bonding between layers was achieved, and no defects at interfaces were detected.     

Change detection by probabilistic segmentation from monocular view

We present a method for foreground/background video segmentation (change detection) in real-time that can be used, in applications such as background subtraction or analysis of surveillance cameras. Our approach implements a probabilistic segmentation based on the Quadratic Markov Measure Field models. This framework regularizes the likelihood of each pixel belonging to each one of the classes (background or foreground). We propose a new likelihood that takes into account two cases: the first one is when the background is static and the foreground might be static or moving (Static Background Subtraction), the second one is when the background is unstable and the foreground is moving (Unstable Background Subtraction). Moreover, our likelihood is robust to illumination changes, cast shadows and camouflage situations. We implement a parallel version of our algorithm in CUDA using a NVIDIA Graphics Processing Unit in order to fulfill real-time execution requirements.     

HumanTop: a multi-object tracking tabletop

In this paper, a computer vision based interactive multi-touch tabletop system called HumanTop is introduced. HumanTop implements a stereo camera vision subsystem which allows not only an accurate fingertip tracking algorithm but also a precise touch-over-the-working surface detection method. Based on a pair of visible spectra cameras, a novel synchronization circuit makes the camera caption and the image projection independent from each other, providing the minimum basis for the development of computer vision analysis based on visible spectrum cameras without any interference coming from the projector. The assembly of both cameras and the synchronization circuit is not only capable of performing an ad-hoc version of a depth camera, but it also introduces the recognition and tracking of textured planar objects, even when contents are projected over them. On the other hand HumanTop supports the tracking of sheets of paper and ID-code markers. This set of features makes the HumanTop a comprehensive, intuitive and versatile augmented tabletop that provides multitouch interaction with projective augmented reality on any flat surface. As an example to exploit all the capabilities of HumanTop, an educational application has been developed using an augmented book as a launcher to different didactic contents. A pilot study in which 28 fifth graders participated is presented. Results about efficiency, usability/satisfaction and motivation are provided. These results suggest that HumanTop is an interesting platform for the development of educational contents.     

Approximate similarity search for online multimedia services on distributed CPU–GPU platforms

Similarity search in high-dimensional spaces is a pivotal operation for several database applications, including online content-based multimedia services. With the increasing popularity of multimedia applications, these services are facing new challenges regarding (1) the very large and growing volumes of data to be indexed/searched and (2) the necessity of reducing the response times as observed by end-users. In addition, the nature of the interactions between users and online services creates fluctuating query request rates throughout execution, which requires a similarity search engine to adapt to better use the computation platform and minimize response times. In this work, we address these challenges with Hypercurves, a flexible framework for answering approximate k-nearest neighbor (kNN) queries for very large multimedia databases. Hypercurves executes in hybrid CPU–GPU environments and is able to attain massive query-processing rates through the cooperative use of these devices. Hypercurves also changes its CPU–GPU task partitioning dynamically according to the observed load, aiming for optimal response times. In our empirical evaluation, dynamic task partitioning reduced query response times by approximately 50 % compared to the best static task partition. Due to a probabilistic proof of equivalence to the sequential kNN algorithm, the CPU–GPU execution of Hypercurves in distributed (multi-node) environments can be aggressively optimized, attaining superlinear scalability while still guaranteeing, with high probability, results at least as good as those from the sequential algorithm.     

Design of Lu2O3-reinforced Cf/SiC-ZrB2-ZrC ultra-high temperature ceramic matrix composites: Wetting and interfacial reactivity by ZrSi2 based alloys

The wettability and infiltration of molten ZrSi₂ and ZrSi₂-Lu₂O₃ alloys into C_f/SiC and B₄C-infiltrated C_f/SiC composites were investigated to understand the interfacial interactions that occur during the development of C_f/SiC-ZrC and C_f/SiC-ZrB₂-ZrC-Lu₂O₃ materials. A significant evaporation of Si from the liquid affected the wetting behaviour of the alloy when tested in a vacuum at 1670 °C. The better wetting and spreading of the alloy over the surface was observed for the composites with lower overall porosity (12 %). On the other hand, the formation of an outer dense layer, followed up by the uniform infiltrated region up to ～ 1 mm was observed for the C_f/SiC with higher porosity (21 %). The infiltrated alloy reacted with SiC matrix to form ZrC or with B₄C-infiltrated SiC matrix to form ZrB₂-ZrC-SiC. The Lu₂O₃ particles were not wetted by the melt, and were pushed away of the reaction zone by the solidification front.     

A Review of Element-Based Galerkin Methods for Numerical Weather Prediction: Finite Elements,Spectral Elements,and Discontinuous Galerkin

Numerical weather prediction (NWP) is in a period of transition. As resolutions increase, global models are moving towards fully nonhydrostatic dynamical cores, with the local and global models using the same governing equations; therefore we have reached a point where it will be necessary to use a single model for both applications. The new dynamical cores at the heart of these unified models are designed to scale efficiently on clusters with hundreds of thousands or even millions of CPU cores and GPUs. Operational and research NWP codes currently use a wide range of numerical methods: finite differences, spectral transform, finite volumes and, increasingly, finite/spectral elements and discontinuous Galerkin, which constitute element-based Galerkin (EBG) methods. Due to their important role in this transition, will EBGs be the dominant power behind NWP in the next 10 years, or will they just be one of many methods to choose from? One decade after the review of numerical methods for atmospheric modeling by Steppeler et al. (Meteorol Atmos Phys 82:287–301, 2003), this review discusses EBG methods as a viable numerical approach for the next-generation NWP models. One well-known weakness of EBG methods is the generation of unphysical oscillations in advection-dominated flows; special attention is hence devoted to dissipation-based stabilization methods. Since EBGs are geometrically flexible and allow both conforming and non-conforming meshes, as well as grid adaptivity, this review is concluded with a short overview of how mesh generation and dynamic mesh refinement are becoming as important for atmospheric modeling as they have been for engineering applications for many years.     

Standard goal programming with fuzzy hierarchies: a sequential approach

This paper proposes a pragmatic model for multi-objective decision-making processes involving clusters of objectives which have a decisional meaning for the decision maker (DM). We provide the DMs with a comfortable tool that allows them to express their preferences both by comparing criteria of the same cluster and via the comparison between the different clusters. In standard goal programming the importance of the goals is modeled by the introduction of preferential weights or/and the incorporation of pre-emptive priorities. However, in many cases the DM is not able to establish a precise preference structure. Even in the case of precise weights the solution does not match necessarily the relative weights or, in the case of precise pre-emptive priority, the result could be very restrictive. In order to overcome these drawbacks, in this paper the normalized unwanted deviations are interpreted in terms of achievement degrees of the goals and fuzzy relations are used to model the relative importance of the goals. Thus, we show how several methodologies from the fuzzy goal programming literature can be tailored for solving standard GP problems. We apply this new modeling to problems where there is a “natural” clustering between goals of the same class. We address this situation by solving two phases; in the first one each class is handled separately taking into account the hierarchy of their goals and, in the second phase, we integrate the results of the first phase and the imprecise hierarchy of the different classes. We formulate a new goal programming model called as sequential goal programming with fuzzy hierarchy model. Because many real situations involve decision making in this environment, our proposal can be a useful tool of broad application. A numerical example illustrates the methodology.     

An optimization framework for “build-or-buy” decisions in software architecture

Building a software architecture that meets functional requirements is a quite consolidated activity, whereas keeping high quality attributes is still an open challenge. In this paper we introduce an optimization framework that supports the decision whether to buy software components or to build them in-house upon designing a software architecture. We devise a non-linear cost/quality optimization model based on decision variables indicating the set of architectural components to buy and to build in order to minimize the software cost while keeping satisfactory values of quality attributes. From this point of view, our tool can be ideally embedded into a Cost Benefit Analysis Method to provide decision support to software architects. The novelty of our approach consists in building costs and quality attributes on a common set of decision variables related to software development. We start from a special case of the framework where the quality constraints are related to the delivery time and the product reliability, and the model solution also devises the amount of unit testing to be performed on built components. We generalize the framework formulation to represent a broader class of architectural cost-minimization problems under quality constraints, and discuss advantages and limitations of such approach.     

Outlier detection using default reasoning

Default logics are usually used to describe the regular behavior and normal properties of domain elements. In this paper we suggest, conversely, that the framework of default logics can be exploited for detecting outliers. Outliers are observations expressed by sets of literals that feature unexpected semantical characteristics. These sets of literals are selected among those explicitly embodied in the given knowledge base. Hence, essentially we perceive outlier detection as a knowledge discovery technique. This paper defines the notion of outlier in two related formalisms for specifying defaults: Reiter's default logic and extended disjunctive logic programs. For each of the two formalisms, we show that finding outliers is quite complex. Indeed, we prove that several versions of the outlier detection problem lie over the second level of the polynomial hierarchy. We believe that a thorough complexity analysis, as done here, is a useful preliminary step towards developing effective heuristics and exploring tractable subsets of outlier detection problems.