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.     

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.     

Lipidomic Approaches to Study HDL Metabolism in Patients with Central Obesity Diagnosed with Metabolic Syndrome

The metabolic syndrome (MetS) is a cluster of cardiovascular risk factors characterised by central obesity, atherogenic dyslipidaemia, and changes in the circulating lipidome; the underlying mechanisms that lead to this lipid remodelling have only been partially elucidated. This study used an integrated “omics” approach (untargeted whole serum lipidomics, targeted proteomics, and lipoprotein lipidomics) to study lipoprotein remodelling and HDL composition in subjects with central obesity diagnosed with MetS (vs. controls). Compared with healthy subjects, MetS patients showed higher free fatty acids, diglycerides, phosphatidylcholines, and triglycerides, particularly those enriched in products of de novo lipogenesis. On the other hand, the “lysophosphatidylcholines to phosphatidylcholines” and “cholesteryl ester to free cholesterol” ratios were reduced, pointing to a lower activity of lecithin cholesterol acyltransferase (LCAT) in MetS; LCAT activity (directly measured and predicted by lipidomic ratios) was positively correlated with high-density lipoprotein cholesterol (HDL-C) and negatively correlated with body mass index (BMI) and insulin resistance. Moreover, many phosphatidylcholines and sphingomyelins were significantly lower in the HDL of MetS patients and strongly correlated with BMI and clinical metabolic parameters. These results suggest that MetS is associated with an impairment of phospholipid metabolism in HDL, partially led by LCAT, and associated with obesity and underlying insulin resistance. This study proposes a candidate strategy to use integrated “omics” approaches to gain mechanistic insights into lipoprotein remodelling, thus deepening the knowledge regarding the molecular basis of the association between MetS and atherosclerosis.     

Engineering the Thermoelectrical Properties of PEDOT:PSS by Alkali Metal Ion Effect

Engineering the electrical properties of poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate)(PEDOT:PSS)holds great potential for various applications such a...     

Lipidomics of Bioactive Lipids in Alzheimer’s and Parkinson’s Diseases: Where Are We?

Lipids are not only constituents of cellular membranes, but they are also key signaling mediators, thus acting as “bioactive lipids”. Among the prominent roles exerted by bioactive lipids are immune regulation, inflammation, and maintenance of homeostasis. Accumulated evidence indicates the existence of a bidirectional relationship between the immune and nervous systems, and lipids can interact particularly with the aggregation and propagation of many pathogenic proteins that are well-renowned hallmarks of several neurodegenerative disorders, including Alzheimer’s (AD) and Parkinson’s (PD) diseases. In this review, we summarize the current knowledge about the presence and quantification of the main classes of endogenous bioactive lipids, namely glycerophospholipids/sphingolipids, classical eicosanoids, pro-resolving lipid mediators, and endocannabinoids, in AD and PD patients, as well as their most-used animal models, by means of lipidomic analyses, advocating for these lipid mediators as powerful biomarkers of pathology, diagnosis, and progression, as well as predictors of response or activity to different current therapies for these neurodegenerative diseases.