Mortality after benign thyroid surgery in patients aged 80 years or older.

INTRODUCTION: A recent report from the United Nations showed that aged people are increasing worldwide. Few data exist on overall survival for patients 80 years or older undergoing benign thyroid surgery. Short- and long-term survival and risk factors for death in patients undergoing thyroid surgery for benign disease were evaluated, using a nationwide, population-based quality register. METHODS: Patients operated for benign thyroid disease, 2004 to 2017, were collected from the national quality register for thyroid surgery. Mortality data were retrieved from the Swedish National Board of Health and Welfare. Mortality at 30 days, 90 days, and 1 year after surgery, for patients 80 years or older, was calculated. Overall survival was calculated using the Kaplan-Meier estimate. Risk factors for mortality were assessed with Cox's multiple regression analysis. The standardized mortality ratio was calculated. RESULTS: There were 17,969 patients. Among them, 483 patients were 80 years or older, and of these, 397 (82.2%) were women. The mortality rate at 0-30 days, 31-90 days, and at 91-365 days after surgery was 0.4%, 0.2%, and 2.5%, respectively. The median (IQR) follow-up time was 4.5 (2.9-7.2) and the median (IQR) survival time was 8.0 (4.1-12.5) years. Apart from age, there was no other risk factor for death. The standardized mortality ratio (SMR) was 0.67 (0.49-0.91) for men and 0.76 (0.65-0.89) for women. CONCLUSION: Mortality after surgery for benign thyroid disease in patients 80 years or older was lower than the general population with no specific risk factors for death except for age.

Predicting self-assembled patterns on spheres with multicomponent coatings.

Patchy colloids are promising candidates for building blocks in directed self-assembly, but large scale synthesis of colloids with controlled surface patterns remains challenging. One potential fabrication method is to self-assemble the surface patterns themselves, allowing complex morphologies to organize spontaneously. For this approach to be competitive, prediction and control of the pattern formation process are necessary. However, structure formation in many-body systems is fundamentally hard to understand, and new theoretical methods are needed. Here we present a theory for self-assembling pattern formation in multi-component systems on the surfaces of colloidal particles, formulated as an analytic technique that predicts morphologies directly from the interactions in an effective model. As a demonstration we formulate an isotropic model of alkanethiols on gold, a suggested system for directed self-assembly, and predict its morphologies and transitions as a function of the interaction parameters.

Optimal networks of nature reserves can be found through eigenvalue perturbation theory of the connectivity matrix.

Conservation and management of natural resources and biodiversity need improved criteria to select functional networks of protected areas. The connectivity within networks due to dispersal is rarely considered, partly because it is unclear how connectivity information can be included in the selection of protected areas. We present a novel and general method that applies eigenvalue perturbation theory (EPT) to select optimum networks of protected areas based on connectivity. At low population densities, characteristic of threatened populations, this procedure selects networks that maximize the growth rate of the overall network. This method offers an improved link between connectivity and metapopulation dynamics. Our framework is applied to connectivities estimated for marine larvae and demonstrates that, for open populations, the best strategy is to protect areas acting as both strong donors and recipients of recruits. It should be possible to implement an EPT framework for connectivity analysis into existing holistic tools for design of protected areas.

A model-independent approach to infer hierarchical codon substitution dynamics.

BACKGROUND: Codon substitution constitutes a fundamental process in molecular biology that has been studied extensively. However, prior studies rely on various assumptions, e.g. regarding the relevance of specific biochemical properties, or on conservation criteria for defining substitution groups. Ideally, one would instead like to analyze the substitution process in terms of raw dynamics, independently of underlying system specifics. In this paper we propose a method for doing this by identifying groups of codons and amino acids such that these groups imply closed dynamics. The approach relies on recently developed spectral and agglomerative techniques for identifying hierarchical organization in dynamical systems. RESULTS: We have applied the techniques on an empirically derived Markov model of the codon substitution process that is provided in the literature. Without system specific knowledge of the substitution process, the techniques manage to "blindly" identify multiple levels of dynamics; from amino acid substitutions (via the standard genetic code) to higher order dynamics on the level of amino acid groups. We hypothesize that the acquired groups reflect earlier versions of the genetic code. CONCLUSIONS: The results demonstrate the applicability of the techniques. Due to their generality, we believe that they can be used to coarse grain and identify hierarchical organization in a broad range of other biological systems and processes, such as protein interaction networks, genetic regulatory networks and food webs.

Identification of metastable states in peptide's dynamics.

A recently developed spectral method for identifying metastable states in Markov chains is used to analyze the conformational dynamics of a four-residue peptide valine-proline-alanine-leucine. We compare our results to empirically defined conformational states and show that the found metastable states correctly reproduce the conformational dynamics of the system.

Stability of ecosystems enhanced by species-interaction constraints.

Ecosystem stability is a central question both in theoretical and applied biology. Dynamical systems theory can be used to analyze how growth rates, carrying capacities, and patterns of species interactions affect the stability of an ecosystem. The response to increasing complexity has been extensively studied and the general conclusion is that there is a limit. While there is a complexity limit to stability at which global destabilisation occurs, the collapse rarely happens suddenly if a system is fully viable (no species is extinct). In fact, when complexity is successively increased, we find that the generic response is to go through multiple single-species extinctions before a global collapse. In this paper we demonstrate this finding via both numerical simulations and elaborations of theoretical predictions. We explore more biological interaction patterns, and, perhaps most importantly, we show that constrained interaction structures-a constant row sum in the interaction matrix-prevent extinctions from occurring. This makes an ecosystem more robust in terms of allowed complexity, but it also means singles-species extinctions do not precede or signal collapse-a drastically different behavior compared to the generic and commonly assumed case. We further argue that this constrained interaction structure-limiting the total interactions for each species-is biologically plausible.

Bottom-up derivation of an effective thermostat for united atoms simulations of water.

In this article we derive the effective pairwise interactions in a Langevin-type united atoms model of water. The interactions are determined from the trajectories of a detailed molecular dynamics simulation of simple point charge water. A standard method is used for estimating the conservative interaction, whereas a new "bottom-up" method is used to determine the effective dissipative and stochastic interactions. We demonstrate that when compared to the standard united atoms model, the transport properties of the coarse-grained model is significantly improved by the introduction of the derived dissipative and stochastic interactions. The results are compared to a previous study, where a "top-down" approach was used to obtain transport properties consistent with those of the simple point charge water model.

A method for estimating the interactions in dissipative particle dynamics from particle trajectories.

We introduce a method for determining the functional form of the stochastic and dissipative interactions in a dissipative particle dynamics (DPD) model from projected phase space trajectories. The DPD model is viewed as a coarse graining of a detailed dynamics that displays a clear timescale separation. Based on the Mori-Zwanzig projection operator method we derive a consistency equation for the stochastic interaction in DPD. The consistency equation can be solved by an iterative bootstrapping procedure. Combined with standard techniques for estimating the conservative interaction, our method makes it possible to reconstruct all the forces in a coarse-grained DPD model. We demonstrate how the method works by recreating the interactions in a DPD model from its phase space trajectory. Furthermore, we discuss how our method can be used in realistic systems with finite timescale separation.

Effective thermostat induced by coarse graining of simple point charge water.

We investigate how the transport properties of a united atom fluid with a dissipative particle dynamics thermostat depend on the functional form and magnitude of both the conservative and the stochastic interactions. We demonstrate how the thermostat strongly affects the hydrodynamics, especially diffusion, viscosity, and local escape times. As model system we use simple point charge (SPC) water, from which projected trajectories are used to determine the effective interactions in the united atom model. The simulation results support our argument that the thermostat should be viewed as an integral part of the coarse-grained dynamics rather than a tool for approaching thermal equilibrium. As our main result we show that the united atom model with the adjusted effective interactions approximately reproduces the diffusion constant and the viscosity of the underlying detailed SPC water model.

Using force covariance to derive effective stochastic interactions in dissipative particle dynamics.

There exist methods for determining effective conservative interactions in coarse-grained particle-based mesoscopic simulations. The resulting models can be used to capture thermal equilibrium behavior, but the model system we study does not correctly represent transport properties. We suggest the use of force covariance to determine the full functional form of dissipative and stochastic interactions. We show that a combination of the RDF and a force covariance function can be used to determine all interactions in dissipative particle dynamics (DPD). Furthermore, we use the method to test whether the effective interactions in DPD can be adjusted to produce a force covariance consistent with the projection of a microscopic Lennard-Jones simulation. The results indicate that the DPD ansatz may not be consistent with the underlying microscopic dynamics. We discuss how this result relates to theoretical studies reported in the literature.

Deriving time-dependent diffusion and relaxation rate in porous systems using eigenfunctions of the Laplace operator.

Porous systems are investigated using eigendecomposition of the Laplace matrix. Three parameters; tortuosity, surface-to-pore volume ratio and relaxation rate are derived from the eigenvalue spectrum of the Laplace matrix and connected to the parameters in the Padé approximation, an expression often used to describe the time-dependent diffusion coefficient in porous systems. The Padé length is identified for systems with large pore to connector volume ratio. The results are compared with simulations.

Quasispecies and recombination.

Recombination is introduced into Eigen's theory of quasispecies evolution. Comparing numerical simulations of the rate equations in the non-recombining and recombining cases show that recombination has a strong effect on the error threshold and, for a wide range of mutation rates, gives rise to two stable fixed points in the dynamics. This bi-stability results in the existence of two error thresholds. However, we prove that, for low mutation rates the bi-stability breaks down and the unique equilibrium distribution is concentrated around the sequence with highest fitness.