Frustration in fuzzy protein complexes leads to interaction versatility

Disordered proteins frequently serve as interaction hubs involving a constrained variety of partners. Complexes with different partners frequently exhibit distinct binding modes, involving regions that remain disordered in the bound state. While the conformational properties of disordered proteins are well-characterized in their free states, less is known about the molecular mechanisms by which specificity can be achieved not with one but with multiple partners. Using the energy landscape theory concept of protein frustration, we demonstrate that complexes of disordered proteins exhibit a high degree of local frustration, especically at the binding interface. These suboptimal interactions lead to the possibility of multiple bound substates, each displaying distinct frustration patterns, which are differently populated in complexes with different partners. These results explain how specificity of disordered proteins can be achieved without a single common bound conformation and how the confliict between different interactions can be used to control the binding to multiple partners

Modeling health shocks

Most of the models on the life-cycle utilization of health care assume that individuals are able to foresee the development of their health perfectly. However, health shocks with significant impact (e.g. severe life-threatening conditions, the onset of chronic disease or accidents) should not be averaged into a mean value, as they have the potential to put the life-course onto a different trajectory. In this paper, we introduce a dynamic optimal control framework incorporating a stochastic health shock with individuals allocating their resources to consumption and different kinds of health care over their life-cycle. We distinguish between general health care and shock specific p revention, a cute and chronic care. This set-up enables us to analyse how the health risk shapes individual behaviour with respect to the different types of health care and how health shocks change the trajectories of consumption and savings. Newly developed transformation techniques allow us to investigate the optimal decisions made in anticipation of a potential health shock and the optimal reaction to all possible shock scenarios. We are able to obtain analytic expressions for the consumption and health care utilization profiles before and after the shock and identify the driving forces. Furthermore, we extend the value of life concept to other aspects of individual health. Finally, we illustrate our findings by calculating a numerical solution calibrated to an individual facing a potential cancer diagnosis in the US

Opening the DICE black-box

Integrated Assessment Models (IAMs) attempt to capture and describe the interactions of (i) human behaviour, (ii) economic activity, and (iii) and climate dynamics and impacts. However, IAMs are often treated as some sort of black-box when calculating solutions

An occurrence of apnea, bradycardia, and desaturation events resulting in a delay of discharge in late preterm and full term infants

In a 5-year retrospective study, we identified a population of late preterm and full term infants with apnea, bradycardia, and oxygen desaturation (ABD) events as the last discharge-delaying diagnosis

Modelling disaster risk behaviour on the household level

Disaster risk is a combination of natural hazard, exposure, and vulnerability. While the natural hazard can be seen as exogenously given on the household level, exposure and vulnerability are highly heterogeneous. A collection of empirical studies (see references) has investigated the impact of household characteristics (such as education, awareness, access to prevention measures, and time preference) on exposure and vulnerability to natural disasters. However, a theoretical model being able to replicate these findings is still missing in the literature. We propose such a dynamic household model, which consequently allows for better predictions and estimations regarding the impacts and the effectiveness of various community wide policy measures aiming to decrease disaster risk

CRASH2 in Germany [ISRCTN86750102]

ABSTRACT: In June 2005, the Study Centre of the German Surgical Society (SDGC) in Heidelberg, Germany, agreed to participate in the investigator initiated trial CRASH2. Regulatory and administrative affairs within Germany were assigned to the Coordination Centre for Clinical Trials (KKS) at the University of Heidelberg, Germany. For more than nine months the KKS and the SDGC have been trying to procure a separate insurance for CRASH2 in Germany. Unfortunately, these attempts have not been successful, yet. One major reason is the way in which German authorities and authorities of some other countries have interpreted the EU Directive (Directive 2001/20/EC) with regards to the need for "adequate" indemnity for clinical trials. The indemnity insurance for CRASH2 procured by the LSHTM for all participating hospitals throughout the world (except for the USA) did not comply with the limits required by the federal German drug law (AMG)

Tradeoffs in Model Performance and Effort for Long-Term Phosphorus Leaching Based on In Situ Field Data

Phosphorus and N are critical nutrients for agriculture but are also responsible for surface water enrichment that leads to toxic algal growth. Although P loading to surface waters has traditionally been thought to occur primarily in surface runoff, contributions from subsurface transport can also be significant. The primary objectives of this research were to evaluate several methods of representing macropore flow and transport in a finite element model using plot-scale infiltration and leaching data and to compare several models of various levels of complexity to simulate long-term P leaching. To determine flow and transport parameters, single- and dual-porosity models in HYDRUS-2D were calibrated with infiltration, Cl−, and P data from a 22-h plot-scale leaching experiment on a silt loam mantle with gravel subsoil. Both homogeneous and heterogeneous gravel profiles were simulated. The dual-porosity model with heterogeneous hydraulic conductivity best matched experimental data, with physical nonequilibrium (dual porosity) being more important than two-dimensional (2D) heterogeneity. Long-term (9 yr) P leaching to the water table (3 m below the soil surface) at the field site was simulated with both one-dimensional (1D) and 2D models using the calibrated parameters. There was little difference between analogous 1D and 2D models, suggesting that HYDRUS-1D may be sufficient to model long-term P leaching. Overall, the most important elements for accurately simulating P leaching in this silt loam and gravel soil profile were found to be (i) field-measured hydraulic conductivity of the limiting soil layer, (ii) calibrated dispersivity, and (iii) dual-porosity, in some circumstances

Should I stay or should I go: Modelling disaster risk behaviour using a dynamic household level approach

In the last decades, many parts of the world faced an increase in the number of extreme weather events and worsening climate conditions endangering the livelihood of households in developing countries that rely on their local environment. While various empirical studies have identified key factors of exposure and vulnerability to disaster risk, we still lack a conceptual understanding of how these forces interact and how they impact household decision making. To gain insight into these mechanisms we set up a dynamic household model where households face environmental hazards. To respond to the risk, households can either relocate to a safer area or undertake preventive measures. Both actions require material and immaterial resources, which constrain the household's decision. Households are assumed to be heterogeneous with respect to key empirically identified factors for individual disaster risk: education, income, risk awareness, time preference and their access to preventive measures. This paper provides analytical insights into the short-run decision making of households derived from the theoretical framework as well as an extensive numerical investigation. To parameterize and calibrate the model we use data from Thailand and Vietnam. The roles of household characteristics on the short-term decision-making and long-run outcomes of households' well-being and disaster risk is discussed. We conclude the paper with an extensive evaluation of different policy interventions including housing and prevention cost subsidies as well as income transfers with respect to their heterogeneous effects on different sub-populations

Should the COVID-19 lockdown be relaxed or intensified in case a vaccine becomes available?

Immediately after the start of the COVID-19 pandemic in Early 2020, most affected countries reacted with strict lockdown to limit the spread of the virus. Since that time, the measures were adapted on a short time basis according to certain numbers (i.e., number of infected, utilization of intensive care units). Implementing a long-term optimal strategy was not possible since a forecast when R&D will succeed in developing an effective vaccination was not available. Our paper closes this gap by assuming a stochastic arrival rate of the COVID-19 vaccine with the corresponding change in the optimal policy regarding the accompanying optimal lockdown measures. The first finding is that the lockdown should be intensified after the vaccine approval if the pace of the vaccination campaign is rather slow. Secondly, the anticipation of the vaccination arrival also leads to a stricter lockdown in the period without vaccination. For both findings, an intuitive explanation is offered

Chasing up and locking down the virus: Optimal pandemic interventions within a network

During the COVID-19 pandemic countries invested significant amounts of resources into its containment. In early stages of the pandemic most of the (nonpharmaceutical) interventions can be classified into two groups: (i) testing and identification of infected individuals, (ii) social distancing measures to reduce the transmission probabilities. Furthermore, both groups of measures may, in principle, be targeted at certain subgroups of a networked population. To study such a problem, we propose an extension of the SIR model with additional compartments for quarantine and different courses of the disease across several network nodes. We develop the structure of the optimal allocation and study a numerical example of three symmetric regions that are subject to an asymmetric progression of the disease (starting from an initial hotspot). Key findings include that (i) for our calibrations policies are chosen in a “flattening-the-curve,” avoiding hospital congestion; (ii) policies shift from containing spillovers from the hotspot initially to establishing a symmetric pattern of the disease; and (iii) testing that can be effectively targeted allows to reduce substantially the duration of the disease, hospital congestion and the total cost, both in terms of lives lost and economic costs