An efficient distributed algorithm for generating and updating multicast trees

As group applications are becoming widespread, efficient network utilization becomes a growing concern. Multicast transmission represents a necessary lower network service for the wide diffusion of new multimedia network applications. Multicast transmission may use network resources more efficiently than multiple point-to-point messages; however, creating optimal multicast trees (Steiner Tree Problem in networks) is prohibitively expensive. This paper proposes a distributed algorithm for the heuristic solution of the Steiner Tree Problem, allowing the construction of effective distribution trees using a coordination protocol among the network nodes. Furthermore, we propose a novel distributed technique for dynamically updating the multicast tree. The approach proposed has been implemented and extensively tested both in simulation, and on experimental networks. Performance evaluation indicates that the distributed algorithm performs as well as the centralized version, providing good levels of convergence time and communication complexity.     

ILB®, a Low Molecular Weight Dextran Sulphate,Restores Glutamate Homeostasis,Amino Acid Metabolism and Neurocognitive Functions in a Rat Model of Severe Traumatic Brain Injury

In a previous study, we found that administration of ILB^®, a new low molecular weight dextran sulphate, significantly improved mitochondrial functions and energy metabolism, as well as decreased oxidative/nitrosative stress, of brain tissue of rats exposed to severe traumatic brain injury (sTBI), induced by the closed-head weight-drop model of diffused TBI. Using aliquots of deproteinized brain tissue of the same animals of this former study, we here determined the concentrations of 24 amino acids of control rats, untreated sTBI rats (sacrificed at 2 and 7 days post-injury) and sTBI rats receiving a subcutaneous ILB^® administration (at the dose levels of 1, 5 and 15 mg/kg b.w.) 30 min post-impact (sacrificed at 2 and 7 days post-injury). Additionally, in a different set of experiments, new groups of control rats, untreated sTBI rats and ILB^®-treated rats (administered 30 min after sTBI at the dose levels of 1 or 5 mg/kg b.w.) were studied for their neurocognitive functions (anxiety, locomotor capacities, short- and long-term memory) at 7 days after the induction of sTBI. Compared to untreated sTBI animals, ILB^® significantly decreased whole brain glutamate (normalizing the glutamate/glutamine ratio), glycine, serine and γ-aminobutyric acid. Furthermore, ILB^® administration restored arginine metabolism (preventing nitrosative stress), levels of amino acids involved in methylation reactions (methionine, L-cystathionine, S-adenosylhomocysteine), and N-acetylaspartate homeostasis. The macroscopic evidences of the beneficial effects on brain metabolism induced by ILB^® were the relevant improvement in neurocognitive functions of the group of animals treated with ILB^® 5 mg/kg b.w., compared to the marked cognitive decline measured in untreated sTBI animals. These results demonstrate that ILB^® administration 30 min after sTBI prevents glutamate excitotoxicity and normalizes levels of amino acids involved in crucial brain metabolic functions. The ameliorations of amino acid metabolism, mitochondrial functions and energy metabolism in ILB^®-treated rats exposed to sTBI produced significant improvement in neurocognitive functions, reinforcing the concept that ILB^® is a new effective therapeutic tool for the treatment of sTBI, worth being tested in the clinical setting.     

Characterisation of Progressive Skeletal Muscle Fibrosis in the Mdx Mouse Model of Duchenne Muscular Dystrophy: An In Vivo and In Vitro Study

Duchenne muscular dystrophy (DMD) is a rare genetic disease leading to progressive muscle wasting, respiratory failure, and cardiomyopathy. Although muscle fibrosis represents a DMD hallmark, the organisation of the extracellular matrix and the molecular changes in its turnover are still not fully understood. To define the architectural changes over time in muscle fibrosis, we used an mdx mouse model of DMD and analysed collagen and glycosaminoglycans/proteoglycans content in skeletal muscle sections at different time points during disease progression and in comparison with age-matched controls. Collagen significantly increased particularly in the diaphragm, quadriceps, and gastrocnemius in adult mdx, with fibrosis significantly correlating with muscle degeneration. We also analysed collagen turnover pathways underlying fibrosis development in cultured primary quadriceps-derived fibroblasts. Collagen secretion and matrix metalloproteinases (MMPs) remained unaffected in both young and adult mdx compared to wt fibroblasts, whereas collagen cross-linking and tissue inhibitors of MMP (TIMP) expression significantly increased. We conclude that, in the DMD model we used, fibrosis mostly affects diaphragm and quadriceps with a higher collagen cross-linking and inhibition of MMPs that contribute differently to progressive collagen accumulation during fibrotic remodelling. This study offers a comprehensive histological and molecular characterisation of DMD-associated muscle fibrosis; it may thus provide new targets for tailored therapeutic interventions.     

Mantaining Dynamic Matrices for Fully Dynamic Transitive Closure

In this paper we introduce a general framework for casting fully dynamic transitive closure into the problem of reevaluating polynomials over matrices. With this technique, we improve the best known bounds for fully dynamic transitive closure. In particular, we devise a deterministic algorithm for general directed graphs that achieves O(n ²) amortized time for updates, while preserving unit worst-case cost for queries. In case of deletions only, our algorithm performs updates faster in O(n) amortized time. We observe that fully dynamic transitive closure algorithms with O(1) query time maintain explicitly the transitive closure of the input graph, in order to answer each query with exactly one lookup (on its adjacency matrix). Since an update may change as many as Ω(n ²) entries of this matrix, no better bounds are possible for this class of algorithms. This work has been partially supported by the Sixth Framework Programme of the EU under contract number 507613 (Network of Excellence “EuroNGI: Designing and Engineering of the Next Generation Internet”), and number 001907 (“DELIS: Dynamically Evolving, Large Scale Information Systems”), and by the Italian Ministry of University and Research (Project “ALGO-NEXT: Algorithms for the Next Generation Internet and Web: Methodologies, Design and Experiments”). Portions of this paper have been presented at the 41st Annual Symp. on Foundations of Computer Science, 2000.     

Near optimal solutions to least-squares problems with stochastic uncertainty

In this paper, we consider least-squares (LS) problems where the regression data is affected by parametric stochastic uncertainty. In this setting, we study the problem of minimizing the expected value with respect to the uncertainty of the LS residual. For general nonlinear dependence of the data on the uncertain parameters, determining an exact solution to this problem is known to be computationally prohibitive. Here, we follow a probabilistic approach, and determine a probable near optimal solution by minimizing the empirical mean of the residual. Finite sample convergence of the proposed method is assessed using statistical learning methods. In particular, we prove that if one constructs the empirical approximation of the mean using a finite number N of samples, then the minimizer of this empirical approximation is, with high probability, an ε-suboptimal solution for the original problem. Moreover, this approximate solution can be efficiently determined numerically by a standard recursive algorithm. Comparisons with gradient algorithms for stochastic optimization are also discussed in the paper and several numerical examples illustrate the proposed methodology.     

A distributed simplex algorithm for degenerate linear programs and multi-agent assignments

In this paper we propose a novel distributed algorithm to solve degenerate linear programs on asynchronous peer-to-peer networks with distributed information structures. We propose a distributed version of the well-known simplex algorithm for general degenerate linear programs. A network of agents, running our algorithm, will agree on a common optimal solution, even if the optimal solution is not unique, or will determine infeasibility or unboundedness of the problem. We establish how the multi-agent assignment problem can be efficiently solved by means of our distributed simplex algorithm. We provide simulations supporting the conjecture that the completion time scales linearly with the diameter of the communication graph.     

Engineering the Design of Usable Hypermedia

This paper presents a systematic method for designing hypermedia that are easy to use for various types of users, along with its application to a specific case study. The design phase is supported by the use of task models. We have identified criteria that indicate how information in task models can be used to identify links, design presentations, and structure the data of the hypermedia considered. Different types of users imply different task models and thus different hypermedia designs. We then show how the design obtained was evaluated using both empirical testing and metrics for hypermedia navigation. We discuss the results obtained by these two evaluation methods and how they affected the original design.     

Enhancing augmented reality with cognitive and knowledge perspectives: a case study in museum exhibitions

ABSTRACT

In this paper, we present our results related to the definition of a methodology that combines augmented reality (AR) with semantic techniques for the creation of digital stories associated with museum exhibitions. In contrast to traditional AR approaches, we augment real-world elements by supplementing contents of a museum exhibition with additional inputs that provide new and different meanings. In this way we augment a cultural resource with respect to both its presentation and meaning. The methodology is framed in the cultural re-mediation theory and is grounded on a set of ontologies aimed at modelling a cultural resource and correlating it with external multimedia objects and resources. To provide an easy tool for the creation of museum narratives, the methodology makes use of a set of recognised practices widely adopted by museum curators that have been formalised through inference rules. The defined methodology has been experimented in a scenario related to Flemish paintings to validate the augmentation of cultural objects with two different approaches, the first basing on similarities and the second on dissimilarities.     

Non‐Covalent Functionalization of Graphene Using Self‐Assembly of Alkane‐Amines

A simple, versatile method for non‐covalent functionalization of graphene based on solution‐phase assembly of alkane‐amine layers is presented. Second‐order Møller–Plesset (MP2) perturbation theory on a cluster model (methylamine on pyrene) yields a binding energy of ≈220 meV for the amine–graphene interaction, which is strong enough to enable formation of a stable aminodecane layer at room temperature. Atomistic molecular dynamics simulations on an assembly of 1‐aminodecane molecules indicate that a self‐assembled monolayer can form, with the alkane chains oriented perpendicular to the graphene basal plane. The calculated monolayer height (≈1.7 nm) is in good agreement with atomic force microscopy data acquired for graphene functionalized with 1‐aminodecane, which yield a continuous layer with mean thickness ≈1.7 nm, albeit with some island defects. Raman data also confirm that self‐assembly of alkane‐amines is a non‐covalent process, i.e., it does not perturb the sp² hybridization of the graphene. Passivation and adsorbate n‐doping of graphene field‐effect devices using 1‐aminodecane, as well as high‐density binding of plasmonic metal nanoparticles and seeded atomic layer deposition of inorganic dielectrics using 1,10‐diaminodecane are also reported.     

Epigenetic and Genetic Factors Related to Curve Progression in Adolescent Idiopathic Scoliosis: A Systematic Scoping Review of the Current Literature

Adolescent idiopathic scoliosis (AIS) is a progressive deformity of the spine. Scoliotic curves progress until skeletal maturity leading, in rare cases, to a severe deformity. While the Cobb angle is a straightforward tool in initial curve magnitude measurement, assessing the risk of curve progression at the time of diagnosis may be more challenging. Epigenetic and genetic markers are potential prognostic tools to predict curve progression. The aim of this study is to review the available literature regarding the epigenetic and genetic factors associated with the risk of AIS curve progression. This review was carried out in accordance with Preferential Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. The search was carried out in January 2022. Only peer-reviewed articles were considered for inclusion. Forty studies were included; fifteen genes were reported as having SNPs with significant association with progressive AIS, but none showed sufficient power to sustain clinical applications. In contrast, nine studies reporting epigenetic modifications showed promising results in terms of reliable markers. Prognostic testing for AIS has the potential to significantly modify disease management. Most recent evidence suggests epigenetics as a more promising field for the identification of factors associated with AIS progression, offering a rationale for further investigation in this field.