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The non-crossing requirement in non-concatenated ring polymers creates topological constraints, which impose important restrictions on the phase space of the system. For ring polymers interpenetration is costly entropically and compact structures evolve for high molecular weights - ring polymers are assumed to become mass fractals confining rings into territories. Other than the dynamics of linear or branched chains that predominantly takes place via the chain ends, rings do not feature ends and their dynamics is considered to be self-similar and thus fundamentally different to those of chains displaying ends. The present state of the art model for the description of the internal relaxations in dense ring systems was developed by Rubinstein et al. /1/. This self-consistent Fractal Loopy Globule (FLG) model is based on the conjecture that the overlap criterion /2,3/ in the packing model for entanglements also governs the rule for overlapping loops in polymer rings. The constant overlap of loops is conjectured to occur in a self-similar way over a wide range of length scales from the elementary loop size Ne up to ring size R. The dynamics of such rings in a melt is governed by topological constraints that dilute with progressing time, because with time loops of increasing sizes are relaxed and cease to be obstacles.Recently, combining results of SANS /4/ with PFG- NMR and NSE the unique topology driven self-similar internal ring dynamics predicted by the FLG model could be verified experimentally /5/: We find the center of mass diffusion taking place in three dynamic regimes from short to long times: (i) a strongly sub-diffusive regime, where the center-of-mass mean square displacement scales as t^\alpha (0.4 ≤ \alpha ≤ 0.6) , until it reaches roughly the value R_g^2; (ii) a second regime with a t^0.75 scaling that (iii) at roughly 2.5 R_g^2 crosses over to Fickian diffusion. While the second anomalous diffusion regime has been found in simulations and was predicted by theory, we attribute the first one to the effect of cooperative dynamics resulting from the correlation hole potential. The internal dynamics at scales below the elementary loop size is well described by ring Rouse motion. At larger scales the dynamics is self-similar and follows very well the predictions of the scaling models with preference for the FLG model.ReferencesReferences:1. T. Ge, S. Panyukov, and M. Rubinstein, Macromolecules 49, 708–722 (2016)2. T. A. Kavassalis and J. Noolandi, Macromolecules 22, 2709–2720 (1989)3. L. J. Fetters, D. J. Lohse, D. Richter, T. A. Witten, and A. Zirkel, Macromolecules (1994)4. M. Kruteva, J. Allgaier, M. Monkenbusch, L. Porcar, and D. Richter, ACS Macro Letters , 507–511 (2020)5. M. Kruteva,M. Monkenbusch, J. Allgaier, O. Holderer, S. Pasini, I. Hofmann and D. Richter, Phys. Rev. Lett. (2020)

Tinnitus is a common symptom of a phantom sound perception with a considerable socioeconomic impact. Tinnitus pathophysiology is enigmatic and its significant heterogeneity reflects a wide spectrum of clinical manifestations, severity and annoyance among tinnitus sufferers. Although several interventions have been suggested, currently there is no universally accepted treatment. Moreover, there is no well-established correlation between tinnitus features or patients’ characteristics and projection of treatment response. At the clinical level, this practically means that selection of treatment is not based on expected outcomes for the particular patient. The complexity of tinnitus and lack of well-adapted prognostic factors for treatment selection highlight a potential role for a decision support system (DSS). A DSS is an informative system, based on big data that aims to facilitate decision-making based on: specific rules, retrospective data reflecting results, patient profiling and predictive models. Therefore, it can use algorithms evaluating numerous parameters and indicate the weight of their contribution to the final outcome. This means that DSS can provide additional information, exceeding the typical questions of superiority of one treatment versus another, commonly addressed in literature. The development of a DSS for tinnitus treatment selection will make use of an underlying database consisting of medical, epidemiological, audiological, electrophysiological, genetic and tinnitus subtyping data. Algorithms will be developed with the use of machine learning and data mining techniques. Based on the profile features identified as prognostic these algorithms will be able to suggest whether additional examinations are needed for a robust result as well as which treatment or combination of treatments is optimal for every patient in a personalized level. In this manuscript we carefully define the conceptual basis for a tinnitus treatment selection DSS. We describe the big data set and the knowledge base on which the DSS will be based and the algorithms that will be used for prognosis and treatment selection. © 2021, Springer Nature Switzerland AG.

Tinnitus is the perception of a phantom sound and the patient's reaction to it. Although much progress has been made, tinnitus remains a scientific and clinical enigma of high prevalence and high economic burden, with an estimated prevalence of 10%–20% among the adult population. The EU is funding a new collaborative project entitled “Unification of Treatments and Interventions for Tinnitus Patients” (UNITI, grant no. 848261) under its Horizon 2020 framework. The main goal of the UNITI project is to set the ground for a predictive computational model based on existing and longitudinal data attempting to address the question of which treatment or combination of treatments is optimal for a specific patient group based on certain parameters. Clinical, epidemiological, genetic and audiological data, including signals reflecting ear-brain communication, as well as patients' medical history, will be analyzed making use of existing databases. Predictive factors for different patient groups will be extracted and their prognostic relevance validated through a Randomized Clinical Trial (RCT) in which different patient groups will undergo a combination of tinnitus therapies targeting both auditory and central nervous systems. From a scientific point of view, the UNITI project can be summarized into the following research goals: (1) Analysis of existing data: Results of existing clinical studies will be analyzed to identify subgroups of patients with specific treatment responses and to identify systematic differences between the patient groups at the participating clinical centers. (2) Genetic and blood biomarker analysis: High throughput Whole Exome Sequencing (WES) will be performed in well-characterized chronic tinnitus cases, together with Proximity Extension Assays (PEA) for the identification of blood biomarkers for tinnitus. (3) RCT: A total of 500 patients will be recruited at five clinical centers across Europe comparing single treatments against combinational treatments. The four main treatments are Cognitive Behavioral Therapy (CBT), hearing aids, sound stimulation, and structured counseling. The consortium will also make use of e/m-health applications for the treatment and assessment of tinnitus. (4) Decision Support System: An innovative Decision Support System will be implemented, integrating all available parameters (epidemiological, clinical, audiometry, genetics, socioeconomic and medical history) to suggest specific examinations and the optimal intervention strategy based on the collected data. (5) Financial estimation analysis: A cost-effectiveness analysis for the respective interventions will be calculated to investigate the economic effects of the interventions based on quality-adjusted life years. In this paper, we will present the UNITI project, the scientific questions that it aims to address, the research consortium, and the organizational structure. © 2021 Elsevier B.V.

Resumen del trabajo presentado al Workshop on Functional Polymers, celebrado en San Sebastián (España) del 19 al 21 de marzo de 2018., Nanoparticles are added to polymeric matrices to form nanocomposites with enhanced mechanical, optical or& electrical properties. The performance and macroscopic properties of polymer nanocomposites depend on the dynamics of the polymer matrix affected by the filler topology and concentration. However, despite the efforts, the confinement effects of nanoparticles on polymer motions are still not fully understood, especially in the case of soft nanoparticles. Recently, a notable dilation of the entanglement tube diameter in polyethyleneoxide (PEO) linear chains when mixed with poly(methylmethacrylate) (PMMA) single-chain nano-particles (SCNPs) was observed. In that case, however, the two components have a large dynamic asymmetry, being the PMMA SCNPs frozen compared to PEO. To isolate the topology effect we studied systems where the polymer matrix and the filler have the same chemistry (polyisoprene) and thus are dynamically symmetric. We have done a systematic variation of the filler topology and concentration and probed the polymer matrix chain dynamics using a combination of PFG-NMR and dielectric spectroscopy. We used two star polymers with 8 and 18-arms and compared them to the linear case of the same size, i.e. 2 arms. Our results show an acceleration&of&the& linear matrix chain mobility with the additive concentration, but less so with the additive topology.

We report a neutron-scattering investigation on the structure and dynamics of a single-component nanocomposite based on SiO2 particles that were grafted with polyisoprene chains at the entanglement limit. By skillful labeling, we access both the monomer density in the corona as well as the conformation of the grafted chains. While the corona profile follows a r−1 power law, the conformation of a grafted chain is identical to that of a chain in a reference melt, implying a high mutual penetration of the coronas from different particles. The brush crowding leads to topological confinement of the chain dynamics: (i) At local scales, the segmental dynamics is unchanged compared to the reference melt, while (ii) at the scale of the chain, the dynamics appears to be slowed down; (iii) by performing a mode analysis in terms of end-fixed Rouse chains, the slower dynamics is tracked to topological confinement within the cone spanned by the adjacent grafts; (iv) by adding 50% matrix chains, the topological confinement sensed by the grafted chain is lifted partially and the apparent chain motion is accelerated. We observe a crossover from pure Rouse motion at short times to topological confined motion beyond the time when the segmental mean squared displacement has reached the distance to the next graft.