Showing papers by "Giuseppe Prencipe published in 2023"
TL;DR: In this paper , a comparative map of molecules that control tenogenesis and to exploit systems biology to model their signaling cascades and physiological paths is drawn, and a data-driven computational framework based on three operative levels and a stage-dependent set of molecules and interactions (embryo-fetal or prepubertal) responsible for signaling differentiation and morphogenesis, shaping tendon transcriptional program and downstream modeling of its fibrillogenesis toward a mature tissue.
Abstract: There is high clinical demand for the resolution of tendinopathies, which affect mainly adult individuals and animals. Tendon damage resolution during the adult lifetime is not as effective as in earlier stages where complete restoration of tendon structure and property occurs. However, the molecular mechanisms underlying tendon regeneration remain unknown, limiting the development of targeted therapies. The research aim was to draw a comparative map of molecules that control tenogenesis and to exploit systems biology to model their signaling cascades and physiological paths. Using current literature data on molecular interactions in early tendon development, species-specific data collections were created. Then, computational analysis was used to construct Tendon NETworks in which information flow and molecular links were traced, prioritized, and enriched. Species-specific Tendon NETworks generated a data-driven computational framework based on three operative levels and a stage-dependent set of molecules and interactions (embryo–fetal or prepubertal) responsible, respectively, for signaling differentiation and morphogenesis, shaping tendon transcriptional program and downstream modeling of its fibrillogenesis toward a mature tissue. The computational network enrichment unveiled a more complex hierarchical organization of molecule interactions assigning a central role to neuro and endocrine axes which are novel and only partially explored systems for tenogenesis. Overall, this study emphasizes the value of system biology in linking the currently available disjointed molecular data, by establishing the direction and priority of signaling flows. Simultaneously, computational enrichment was critical in revealing new nodes and pathways to watch out for in promoting biomedical advances in tendon healing and developing targeted therapeutic strategies to improve current clinical interventions.
TL;DR: In this article , the authors consider the case of unknown k and show that gathering with one more agent is deterministically unfeasible under the conditions that at least one must be known.
Abstract: Consider a set of k identical asynchronous mobile agents located in an anonymous ring of n nodes. The classical Gather (or Rendezvous) problem requires all agents to meet at the same node, not a priori decided, within a finite amount of time. This problem has been studied assuming that the network is safe for the agents. In this paper, we consider the presence in the ring of a stationary process located at a node that disables any incoming agent without leaving any trace. Such a dangerous node is known in the literature as a black hole, and the determination of its location has been extensively investigated. The presence of the black hole makes it deterministically unfeasible for all agents to gather. So, the research concern is to determine how many agents can gather and under what conditions. In this paper we establish a complete characterization of the conditions under which the problem can be solved. In particular, we determine the maximum number of agents that can be guaranteed to gather in the same location depending on whether k or n is unknown (at least one must be known). These results are tight: in each case, gathering with one more agent is deterministically unfeasible. All our possibility proofs are constructive: we provide mobile agent algorithms that allow the agents to gather within a predefined distance under the specified conditions. The analysis of the time costs of these algorithms show that they are optimal. Our gathering algorithm for the case of unknown k is also a solution for the black hole location problem. Interestingly, its bounded time complexity is Θ(n); this is a significant improvement over the existing O(nlogn) bounded time complexity.