Displacement smoothing for the precise MRI-based measurement of strain in soft biological tissues

Displacement and strain are fundamental quantities that describe the normal and pathological mechanical function of soft biological materials. Non-invasive imaging techniques, including displacement-encoded magnetic resonance imaging (MRI), enable the direct calculation of biomaterial displacements during the application of extrinsic mechanical forces. However, because strain is derived from measured MRI-based displacements, data processing must be accomplished to minimise the propagation and amplification of errors. Here, we evaluate smoothing methods (including averaging filters, splines, finite impulse response filters and wavelets) that enable the calculation of strain in biomaterials from MRI-based displacements for minimal error, defined in terms of bias and precision. Displacement and strain precisions were improved using all smoothing methods studied. Precision generally increased with the number of smoothing iterations (i.e. repeated applications) of a chosen smoothing method. The bias depended on the smoothing method and tended to increase with the number of smoothing iterations. A Gaussian filter characterised complex and heterogeneous strain fields with maximum precision and minimum bias. The results suggest that the optimal choice of smoothing method to compute strain for a given biomaterial or tissue application depends on a careful consideration of trade-offs between the improved precision (with increased data smoothing) and the trending increase in bias.     

On the interpretation of the hazard ratio in Cox regression

We argue that the term “relative risk” should not be used as a synonym for “hazard ratio” and encourage to use the probabilistic index as an alternative effect measure for Cox regression. The probabilistic index is the probability that the event time of an exposed or treated subject exceeds the event time of an unexposed or untreated subject conditional on the other covariates. It arises as a well known and simple transformation of the hazard ratio and nicely reveals the interpretational limitations. We demonstrate how the probabilistic index can be obtained using the R-package Publish.     

Assessing the goodness of fit of logistic regression models in large samples: A modification of the Hosmer-Lemeshow test

Evaluating the goodness of fit of logistic regression models is crucial to ensure the accuracy of the estimated probabilities. Unfortunately, such evaluation is problematic in large samples. Because the power of traditional goodness of fit tests increases with the sample size, practically irrelevant discrepancies between estimated and true probabilities are increasingly likely to cause the rejection of the hypothesis of perfect fit in larger and larger samples. This phenomenon has been widely documented for popular goodness of fit tests, such as the Hosmer-Lemeshow test. To address this limitation, we propose a modification of the Hosmer-Lemeshow approach. By standardizing the noncentrality parameter that characterizes the alternative distribution of the Hosmer-Lemeshow statistic, we introduce a parameter that measures the goodness of fit of a model but does not depend on the sample size. We provide the methodology to estimate this parameter and construct confidence intervals for it. Finally, we propose a formal statistical test to rigorously assess whether the fit of a model, albeit not perfect, is acceptable for practical purposes. The proposed method is compared in a simulation study with a competing modification of the Hosmer-Lemeshow test, based on repeated subsampling. We provide a step-by-step illustration of our method using a model for postneonatal mortality developed in a large cohort of more than 300 000 observations.     

Applications of proportional odds ordinal logistic regression models and continuation ratio models in examining the association of physical inactivity with erectile dysfunction among type 2 diabetic patients

[Purpose]Many studies have observed a high prevalence of erectile dysfunction among individuals performing physical activity in less leisure-time. However, this relationship in patients with type 2 diabetic patients is not well studied. In exposure outcome studies with ordinal outcome variables, investigators often try to make the outcome variable dichotomous and lose information by collapsing categories. Several statistical models have been developed to make full use of all information in ordinal response data, but they have not been widely used in public health research. In this paper, we discuss the application of two statistical models to determine the association of physical inactivity with erectile dysfunction among patients with type 2 diabetes.[Methods]A total of 204 married men aged 20-60 years with a diagnosis of type 2 diabetes at the outpatient unit of the Department of Endocrinology at PSG hospitals during the months of May and June 2019 were studied. We examined the association between physical inactivity and erectile dysfunction using proportional odds ordinal logistic regression models and continuation ratio models.[Results]The proportional odds model revealed that patients with diabetes who perform leisure time physical activity for over 40 minutes per day have reduced odds of erectile dysfunction (odds ratio=0.38) across the severity categories of erectile dysfunction after adjusting for age and duration of diabetes.[Conclusion]The present study suggests that physical inactivity has a negative impact on erectile function. We observed that the simple logistic regression model had only 75% efficiency compared to the proportional odds model used here; hence, more valid estimates were obtained here.     

Indicators of sexual maturation and regression in the female cat flea, Ctenocephalides felis

Differences in the salivary glands, mesenteron epithelium and reproductive organs of female cat fleas, Ctenocephalides felis Bouché (Siphonaptera: Pulicidae), are related to the degree of reproductive maturation or regression. Contrary to previous ideas, blue bodies in the ovarioles are degenerate oocyte nuclei and their presence denotes failure of ripening oocytes to reach full maturity. A distinction between true corpora lutea and pseudo-corpora lutea is established, the presence of the former indicates successful oviposition, and of the latter, failure to complete maturation of eggs. Accurate indicators of sexual maturation and reproductive success are of potential value in assessing relative suitability of various hosts for a given flea species and therefore in assessing the degree of host specificity among fleas.     

生态过程模型敏感参数最优取值的时空异质性分析——以BIOME-BGC模型为例

生态过程模型是当前研究陆地生态系统水循环、碳循环有力的工具,但此类模型参数众多,参数的合理取值对模型模拟结果有重要影响.以往研究对模型参数的敏感性以及参数的优化取值有诸多的分析和讨论,但有关参数最优取值的时空异质性关注较少.本文以BIOME-BGC模型为例,在常绿阔叶林、落叶阔叶林、C3草地3种植被类型下,通过构建敏感性判别指数,筛选出模型的敏感参数,并在每种植被类型下选取两个试验站点,使用模拟退火算法结合实测通量数据构建目标函数,获取各站点敏感参数逐月的最优取值,然后构建时间异质性判别指数、空间异质性判别指数和时空异质性判别指数对模型敏感参数最优取值的时空异质性进行定量分析.结果表明:BIOME-BGC模型在3种植被类型下遴选出的敏感参数大部分一致,少数有差异,但参数的敏感性强弱在不同植被类型下的表现不尽相同;BIOME-BGC模型敏感参数的最优取值,大都具有不同程度的时空异质性,但不同植被类型下,敏感参数最优取值的时空异质性表现各异;敏感参数中与植被生理、生态相关的参数,其时空异质性相对较小,而与环境、物候相关的参数,其时空异质性普遍较大;在3种植被类型下,模型敏感参数最优取值的时间异质性与空间异质性表现出显著的线性相关性;依据其最优取值的时空异质性,可对BIOME-BGC模型敏感参数进行类型划分,以便在实践应用中采取不同的参数率定策略.本研究结论有助于加深对生态过程模型参数特性及最优取值的理解,可为实践应用中模型参数的合理取值提供一种思路和参考.     

Predicting the distribution of invasive crayfish (Pacifastacus leniusculus) in a Kusiro Moor marsh (Japan) using classification and regression trees

Summer distributions of the invasive signal crayfish (Pacifastacus leniusculus) were investigated in relation to physicochemistry in a Kusiro Moor marsh and its inflows and outflows in northern Japan. Maximum crayfish abundance and biomass were 1.04 individuals/m² and 3.56 g dry mass (DM)/m² in littoral marsh habitats, and 5.84 individuals/m² and 13.48 g DM/m² in stream habitats. Classification tree analysis was used to predict crayfish occurrence at 102 sites from all habitats (i.e. littoral marsh, pelagic marsh and stream) while regression tree analyses were used to predict crayfish abundance at littoral marsh and stream sites separately. The classification tree showed that crayfish occurrence was primarily determined by undercut bank volume regardless of habitat identity. When undercut bank volume was <0.0054 m³, crayfish were predicted to be absent at marsh sites, but expected to occur at stream sites where pH and water temperature exceeded 6.5 and 14.3°C, respectively. The regression tree using only littoral marsh sites showed that undercut bank volume, followed by dissolved oxygen level, determined the splits of the tree. Crayfish abundance was highest when undercut bank volume was >0.61 m³, and moderately high when dissolved oxygen was >9.09 mg/l and undercut bank volume was <0.61 m³. On the other hand, the regression tree using only stream sites showed that water temperature was the major predictor that determined the splits. We discuss the roles of physicochemical factors as limiting factors of the distribution pattern of the invasive crayfish.     

GAM模型和BRT模型在长江口鱼类群落多样性预测中的比较

长江口为西太平洋最大的河口,评估其鱼类群落多样性分布能够为长江口生态系统的修复和管理提供科学依据.本研究基于2012—2014年长江口渔业监测数据,分别使用GAM模型和BRT模型建立各站点水域鱼类群落多样性指数与环境和时空因子之间的关系.结合线性回归方程,采用交叉验证的方式对模型的预测能力和拟合效果进行评价,并绘制了2014年长江口鱼类群落多样性指数和丰富度指数的空间分布图.结果表明: 盐度、pH和叶绿素a对多样性指数贡献最高,pH、溶解氧和叶绿素a是对丰富度指数贡献率最高的环境因子.BRT模型对于多样性指数和丰富度指数的拟合和预测结果均优于GAM模型.空间分布预测显示,相较于GAM模型,BRT模型能够对长江口小面积水域间的鱼类群落多样性作更好的区分,河口外侧水域的鱼类群落多样性明显高于河口内侧水域,而北支水域的多样性高于南支水域.     

基于Probit回归模型的经济发达地区土地利用变化驱动力分析——以南京市为例

基于1996、2002及2010年的遥感影像,借助RS和GIS技术,分析南京市1996—2010年土地利用变化特征,并采用Probit回归模型定量分析土地利用变化驱动因素.结果表明： 1996—2010年,南京市土地利用变化特征主要表现为耕地和林地面积不断减少,建设用地、园地和草地面积持续增加,综合土地利用变化率呈不断上升趋势,整体处于发展状态;通过对耕地和林地变化的回归分析发现,耕地变化在1996—2002年主要受距最近农村居民点距离和农业人口密度变化的影响,在2002—2010年主要受地均GDP变化、距最近农村居民点距离和距最近道路距离的影响;而林地变化在1996—2002年主要受高程和距最近农村居民点距离的影响,在2002—2010年主要受地均GDP变化、人口密度变化和距最近道路距离的影响.影响研究区土地利用变化的因素早期主要是自然和空间距离因素,而近年主要是社会经济和人口因素.