Stochastic effects on endemic infection levels of disseminating versus local contacts

The effects of two levels of mixing on endemic infection levels are shown to differ for identically conformed deterministic compartmental (DC) and stochastic compartmental (SC) models. Both DC and SC models give similar endemic levels when populations are large, immunity is short lived, and mixing is universal. But local transmissions and/or transient immunity decrease overall population infection levels in SC but not in DC models. DC models also fail to detect the greater effects of eliminating disseminating transmissions in comparison to eliminating local transmissions shown by SC models. These differences in model behavior arise because localities that encounter few infections from distant sites and that have stochastically low infection levels have decreased infection rates while localities with stochastically high levels of infection do not decrease the rate at which they lose infection. At the extreme this generates local stochastic die out with subsequent build up of susceptibility in SC but not DC models. This phenomenon should act upon all endemic infections that have changing geographic or social foci of infection. Neither standard epidemiological investigations nor sufficient-component cause models can capture these effects because they occur in the absence of differences between individuals.     

Permanent night workers´ sleep and psychosocial factors in hospital work. A comparison to day and shift work

ABSTRACT

We aimed to study whether permanent night workers sleep and psychosocial factors differ from day workers and shift workers. The participants (n = 9 312, 92% females, average age 45 years, most commonly nurses and departmental secretaries) were day workers (DW, n = 2 672), shift workers (SW, n = 6 486) and permanent night workers (PNW, n = 154). The Finnish Public Sector survey responses from six hospital districts from 2012 were combined to payroll data from 91 days preceding the survey. The data were analyzed using Pearson χ²-test, one-way ANOVA and multinomial logistic regression analysis. The PNWs reported slightly longer average sleep length than the SWs or the DWs (7:27 vs. 7:13 and 7:10 h, p < 0.001). The PNWs reported least often difficulties in maintaining sleep (p < 0.001) compared to the SWs and the DWs. The PNWs reported most often difficulties to fall asleep and fatigue during free-time (p-values <0.001). The DWs and PNWs experienced less often work-life conflict than the SWs (25 and 26 vs. 38%, p < 0.001). The PNWs were more often satisfied with autonomy at work and appreciation and fair treatment by colleagues than the DWs or the SWs (p < 0.001). The SWs and PNWs reported remarkably higher occurrence of verbal (p < 0.001, OR 3.71, 95% CI 3.23–4.27 and OR 7.67, 95% CI 5.35–10.99, respectively) and physical workplace violence (p < 0.001, OR 9.24, 95% CI 7.17–11.90 and OR 28.34, 95% CI 16.64–43.06, respectively) compared to DWs. Conclusively, PNWs reported contradictory differences in sleep quality compared to DWs and SWs. PNWs are more often satisfied with their colleagues and autonomy at work than DWs or SWs but face workplace violence remarkably more often.     

An allometric method for the projection of eelgrass leaf biomass production rates

We demonstrate that an allometric model for eelgrass leaf-growth rates can be derived from data on leaf architecture and growth form. Using this construct, we produced indirect assessments of growth rates of leaves that we call projections, which can be easily obtained in terms of allometric parameters and proxy values for leaf area, expressed as the product of leaf length and width. These projections of leaf-growth rates displayed a high level of correspondence with values observed in our data, as well as with other sets of reference data. A comparison with growth rates obtained by using the plastochrone index method showed that our model provides more accurate estimations while using a simpler methodology. Our results also show that whenever allometric parameters for the scaling of eelgrass leaf dry weight in terms of leaf area are available, the proposed model provides an accurate, cost-effective and non-destructive alternative to assessments based on traditional or plastochrone methods.     

A stochastic reaction scheme for drug/metabolite interaction

We present a simplified stochastic model to investigate the mechanisms of action of tramadol, a centrally acting analgesic, used for treating pain. The model accounts for the process of metabolization through the cytochrome CYP2D6 and the interactions between molecules and target receptors. The proposed formulation is stochastic in nature and allows to speculate on the role of finite-size fluctuations. Analytically, the master equation, governing the process under scrutiny, is derived and studied in the mean-field limit. The analysis of the associated asymptotic behavior proves interesting for its potential medical implications. The analysis of fluctuations is carried on via the van Kampen expansion. Numerical simulations are also performed to confirm the adequacy of our theoretical prediction.     

川西地区生态系统服务对可持续发展目标的影响

可持续发展目标(Sustainable Development Goals, SDGs)的实现往往会因为生态保护或人类福祉之间可能存在的权衡关系而受到阻碍。将生态系统服务(Ecosystem services, ESs)纳入到可持续发展目标的决策中能够避免各方利益的冲突,促进SDGs的实现。然而,在生态环境脆弱的山区,ESs对SDGs的贡献分析仍然不足。以川西地区为研究区,对2000—2020年11个可持续发展目标进行了量化,利用生态系统服务和权衡的综合评估(Integrated Valuation of Ecosystem Services and Tradeoffs, InVEST)模型和定量指标法估算了碳固存、土壤保持和食物生产三种重要生态系统服务,并使用空间面板数据模型研究ESs对SDGs的影响及其空间溢出效应。结果表明：(1)可持续发展目标水平整体提升,但SDG1(无贫穷)和SDG3(良好健康和人类福祉)表现较差,分值低于5分。从空间上看,与环境相关的SDGs在川西北高原表现更好,与社会经济相关的SDGs在川西东部和川西南山地地区表现更好。(2)川西地区碳固存和食物生产服务呈现线性增长趋势,土壤保持服务呈现波动增长趋势,分别增长了0.23×10～8t、8.93×10～5t和1.23×10～8t。(3)与土壤保持和食物生产相比,碳固存对SDGs表现出更强烈的直接影响和空间溢出效应。其中县域碳固存对本县域和邻近县域的SDG11(可持续城市和社区)和SDG1具有显著的促进作用,对SDG2(零饥饿)呈现显著负向影响。研究结果可为区域联合管理提供科学依据,推动可持续发展目标的实现。     

Parameter estimation in cardiac ionic models

We examine the problem of parameter estimation in mathematical models of excitable cell cardiac electrical activity using the well-known Beeler–Reuter (1977) ionic equations for the ventricular action potential. The estimation problem can be regarded as equivalent to the accurate reconstruction of ionic current kinetics and amplitudes in an excitable cell model, given only action potential experimental data. We show that in the Beeler–Reuter case, all ionic currents may be reasonably reconstructed using an experimental design consisting of action potential recordings perturbed by pseudo-random injection currents.

The Beeler–Reuter model was parameterised into 63 parameters completely defining all membrane current amplitudes and kinetics. Total membrane current was fitted to model-generated experimental data using a ‘data-clamp’ protocol. The experimental data consisted of a default action-potential waveform and an optional series of perturbed waveforms generated by current injections. Local parameter identifiability was ascertained from the reciprocal condition value (1/λ) of the Hessian at the known solution. When fitting to a single action potential waveform, the model was found to be over-determined, having a 1/λ value of 3.6e−14. This value improved slightly to 1.4e−10 when an additional 2 perturbed waveforms were included in the fitting process, suggesting that the additional data did not overly improve the identifiability problem. The additional data, however, did allow the accurate reconstruction of all ionic currents. This indicates that by appropriate experimental design, it may be possible to infer the properties of underlying membrane currents from observation of transmembrane potential waveforms perturbed by pseudo-random currents.     

Fixed or random? On the reliability of mixed‐effects models for a small number of levels in grouping variables

Biological data are often intrinsically hierarchical (e.g., species from different genera, plants within different mountain regions), which made mixed‐effects models a common analysis tool in ecology and evolution because they can account for the non‐independence. Many questions around their practical applications are solved but one is still debated: Should we treat a grouping variable with a low number of levels as a random or fixed effect? In such situations, the variance estimate of the random effect can be imprecise, but it is unknown if this affects statistical power and type I error rates of the fixed effects of interest. Here, we analyzed the consequences of treating a grouping variable with 2–8 levels as fixed or random effect in correctly specified and alternative models (under‐ or overparametrized models). We calculated type I error rates and statistical power for all‐model specifications and quantified the influences of study design on these quantities. We found no influence of model choice on type I error rate and power on the population‐level effect (slope) for random intercept‐only models. However, with varying intercepts and slopes in the data‐generating process, using a random slope and intercept model, and switching to a fixed‐effects model, in case of a singular fit, avoids overconfidence in the results. Additionally, the number and difference between levels strongly influences power and type I error. We conclude that inferring the correct random‐effect structure is of great importance to obtain correct type I error rates. We encourage to start with a mixed‐effects model independent of the number of levels in the grouping variable and switch to a fixed‐effects model only in case of a singular fit. With these recommendations, we allow for more informative choices about study design and data analysis and make ecological inference with mixed‐effects models more robust for small number of levels.