The Peer’s Dilemma: A general framework to examine cooperation in pure peer-to-peer systems

The exploration of social dilemmas is being considered a major foundation for encountering the enforced necessities of cooperation in self-organizing environments. Such environments are characterized by self-interested parties and the absence of trusted third parties. Recent approaches apply evolutionary socio-inspired games to formally prove the existence and further prolongation of cooperation patterns within communities. For instance, the Prisoner’s Dilemma game has thus provided a rich opportunity to examine self-interested behaviors in pure peer-to-peer networks. However, assuming a total absence of coalitions, incentives and punishment mechanisms, several works argue against a durable maintenance of cooperation neither at single-shot nor repeated-scenarios. In this article, we formally and experimentally demonstrate a counterexample for the latter by applying evolutionary game theory and a particular instance of the Rock–Scissors–Paper game. Our framework proves that the cyclic dominance of certain type of nodes within a P2P system has an impact and introduces a strategic aspect to the evolution of the overall community.     

Nuclear Receptor Nur77 Controls Cardiac Fibrosis through Distinct Actions on Fibroblasts and Cardiomyocytes

Fibrosis is a hallmark of adverse cardiac remodeling, which promotes heart failure, but it is also an essential repair mechanism to prevent cardiac rupture, signifying the importance of appropriate regulation of this process. In the remodeling heart, cardiac fibroblasts (CFs) differentiate into myofibroblasts (MyoFB), which are the key mediators of the fibrotic response. Additionally, cardiomyocytes are involved by providing pro-fibrotic cues. Nuclear receptor Nur77 is known to reduce cardiac hypertrophy and associated fibrosis; however, the exact function of Nur77 in the fibrotic response is yet unknown. Here, we show that Nur77-deficient mice exhibit severe myocardial wall thinning, rupture and reduced collagen fiber density after myocardial infarction and chronic isoproterenol (ISO) infusion. Upon Nur77 knockdown in cultured rat CFs, expression of MyoFB markers and extracellular matrix proteins is reduced after stimulation with ISO or transforming growth factor–β (TGF-β). Accordingly, Nur77-depleted CFs produce less collagen and exhibit diminished proliferation and wound closure capacity. Interestingly, Nur77 knockdown in neonatal rat cardiomyocytes results in increased paracrine induction of MyoFB differentiation, which was blocked by TGF-β receptor antagonism. Taken together, Nur77-mediated regulation involves CF-intrinsic promotion of CF-to-MyoFB transition and inhibition of cardiomyocyte-driven paracrine TGF-β-mediated MyoFB differentiation. As such, Nur77 provides distinct, cell-specific regulation of cardiac fibrosis.     

Transcriptomic Profiles of CD47 in Breast Tumors Predict Outcome and Are Associated with Immune Activation

Targeting the innate immune system has attracted attention with the development of anti- CD47 antibodies. Anti-CD47 antibodies block the inhibition of the phagocytic activity of macrophages caused by the up-regulation of CD47 on tumor cells. In this study, public genomic data was used to identify genes highly expressed in breast tumors with elevated CD47 expression and analyzed the association between the presence of tumor immune infiltrates and the expression of the selected genes. We found that 142 genes positively correlated with CD47, of which 83 predicted favorable and 32 detrimental relapse-free survival (RFS). From those associated with favorable RFS, we selected the genes with immunologic biological functions and defined a CD47-immune signature composed of PTPRC, HLA-E, TGFBR2, PTGER4, ETS1, and OPTN. In the basal-like and HER2+ breast cancer subtypes, the expression of the CD47-immune signature predicted favorable outcome, correlated with the presence of tumor immune infiltrates, and with gene expression signatures of T cell activation. Moreover, CD47 up-regulated genes associated with favorable survival correlated with pro-tumoral macrophages. In summary, we described a CD47-immune gene signature composed of 6 genes associated with favorable prognosis, T cell activation, and pro-tumoral macrophages in breast cancer tumors expressing high levels of CD47.     

The Role of Circulating Biomarkers in Peripheral Arterial Disease

Peripheral arterial disease (PAD) of the lower extremities is a chronic illness predominantly of atherosclerotic aetiology, associated to traditional cardiovascular (CV) risk factors. It is one of the most prevalent CV conditions worldwide in subjects >65 years, estimated to increase greatly with the aging of the population, becoming a severe socioeconomic problem in the future. The narrowing and thrombotic occlusion of the lower limb arteries impairs the walking function as the disease progresses, increasing the risk of CV events (myocardial infarction and stroke), amputation and death. Despite its poor prognosis, PAD patients are scarcely identified until the disease is advanced, highlighting the need for reliable biomarkers for PAD patient stratification, that might also contribute to define more personalized medical treatments. In this review, we will discuss the usefulness of inflammatory molecules, matrix metalloproteinases (MMPs), and cardiac damage markers, as well as novel components of the liquid biopsy, extracellular vesicles (EVs), and non-coding RNAs for lower limb PAD identification, stratification, and outcome assessment. We will also explore the potential of machine learning methods to build prediction models to refine PAD assessment. In this line, the usefulness of multimarker approaches to evaluate this complex multifactorial disease will be also discussed.     

Human-Induced Neural and Mesenchymal Stem Cell Therapy Combined with a Curcumin Nanoconjugate as a Spinal Cord Injury Treatment

We currently lack effective treatments for the devastating loss of neural function associated with spinal cord injury (SCI). In this study, we evaluated a combination therapy comprising human neural stem cells derived from induced pluripotent stem cells (iPSC-NSC), human mesenchymal stem cells (MSC), and a pH-responsive polyacetal–curcumin nanoconjugate (PA-C) that allows the sustained release of curcumin. In vitro analysis demonstrated that PA-C treatment protected iPSC-NSC from oxidative damage in vitro, while MSC co-culture prevented lipopolysaccharide-induced activation of nuclear factor-κB (NF-κB) in iPSC-NSC. Then, we evaluated the combination of PA-C delivery into the intrathecal space in a rat model of contusive SCI with stem cell transplantation. While we failed to observe significant improvements in locomotor function (BBB scale) in treated animals, histological analysis revealed that PA-C-treated or PA-C and iPSC-NSC + MSC-treated animals displayed significantly smaller scars, while PA-C and iPSC-NSC + MSC treatment induced the preservation of β-III Tubulin-positive axons. iPSC-NSC + MSC transplantation fostered the preservation of motoneurons and myelinated tracts, while PA-C treatment polarized microglia into an anti-inflammatory phenotype. Overall, the combination of stem cell transplantation and PA-C treatment confers higher neuroprotective effects compared to individual treatments.     

Bayesian Model Uncertainty In Smooth Transition Autoregressions

Abstract. In this paper, we propose a fully Bayesian approach to the special class of nonlinear time‐series models called the logistic smooth transition autoregressive (LSTAR) model. Initially, a Gibbs sampler is proposed for the LSTAR where the lag length, k, is kept fixed. Then, uncertainty about k is taken into account and a novel reversible jump Markov Chain Monte Carlo (RJMCMC) algorithm is proposed. We compared our RJMCMC algorithm with well‐known information criteria, such as the Akaikes? information criteria, the Bayesian information criteria (BIC) and the deviance information criteria. Our methodology is extensively studied against simulated and real‐time series.     

Scandinavian Thins on Top of Cake: New and Improved Algorithms for Stacking and Packing

We show how to compute the smallest rectangle that can enclose any polygon, from a given set of polygons, in nearly linear time; we also present a PTAS for the problem, as well as a linear-time algorithm for the case when the polygons are rectangles themselves. We prove that finding a smallest convex polygon that encloses any of the given polygons is NP-hard, and give a PTAS for minimizing the perimeter of the convex enclosure. We also give efficient algorithms to find the smallest rectangle simultaneously enclosing a given pair of convex polygons.     

Domain-specific discrete event modelling and simulation using graph transformation

Graph transformation is being increasingly used to express the semantics of domain-specific visual languages since its graphical nature makes rules intuitive. However, many application domains require an explicit handling of time to accurately represent the behaviour of a real system and to obtain useful simulation metrics to measure throughputs, utilization times and average delays. Inspired by the vast knowledge and experience accumulated by the discrete event simulation community, we propose a novel way of adding explicit time to graph transformation rules. In particular, we take the event scheduling discrete simulation world view and provide rules with the ability to schedule the occurrence of other rules in the future. Hence, our work combines standard, efficient techniques for discrete event simulation (based on the handling of a future event set) and the intuitive, visual nature of graph transformation. Moreover, we show how our formalism can be used to give semantics to other timed approaches and provide an implementation on top of the rewriting logic system Maude.     

Top-<Emphasis Type="Italic">k</Emphasis> overlapping densest subgraphs

Finding dense subgraphs is an important problem in graph mining and has many practical applications. At the same time, while large real-world networks are known to have many communities that are not well-separated, the majority of the existing work focuses on the problem of finding a single densest subgraph. Hence, it is natural to consider the question of finding the top-k densest subgraphs. One major challenge in addressing this question is how to handle overlaps: eliminating overlaps completely is one option, but this may lead to extracting subgraphs not as dense as it would be possible by allowing a limited amount of overlap. Furthermore, overlaps are desirable as in most real-world graphs there are vertices that belong to more than one community, and thus, to more than one densest subgraph. In this paper we study the problem of finding top-k overlapping densest subgraphs, and we present a new approach that improves over the existing techniques, both in theory and practice. First, we reformulate the problem definition in a way that we are able to obtain an algorithm with constant-factor approximation guarantee. Our approach relies on using techniques for solving the max-sum diversification problem, which however, we need to extend in order to make them applicable to our setting. Second, we evaluate our algorithm on a collection of benchmark datasets and show that it convincingly outperforms the previous methods, both in terms of quality and efficiency.