Changes In Higher Moments Research Articles

Amplified warming of North American cold extremes linked to human-induced changes in temperature variability

How global warming is impacting winter cold extremes is uncertain. Previous work has found decreasing winter temperature variability over North America which suggests a reduction in frequency and intensity of cold extremes relative to mean changes. However, others argue that cold air outbreaks are becoming more likely because of Arctic-induced changes in atmospheric circulation. Here we show that cold extremes over North America have warmed substantially faster than the winter mean temperature since 1980. This amplified warming is linked to both decreasing variance and changes in higher moments of the temperature distributions. Climate model simulations with historical forcings robustly capture the observed trends in extremes and variability. A pattern-based detection and attribution analysis shows that the changes in variability are detectable in observations and can be attributed to human influence. Our results highlight that human emissions are warming North American extreme cold temperatures beyond only shifting the winter mean temperature.

A Theory of Mechanical Stress-Induced H2O2 Signaling Waveforms in Planta

Recent progress in nanotechnology-enabled sensors that can be placed inside of living plants has shown that it is possible to relay and record real-time chemical signaling stimulated by various abiotic and biotic stresses. The mathematical form of the resulting local reactive oxygen species (ROS) wave released upon mechanical perturbation of plant leaves appears to be conserved across a large number of species, and produces a distinct waveform from other stresses including light, heat and pathogen-associated molecular pattern (PAMP)-induced stresses. Herein, we develop a quantitative theory of the local ROS signaling waveform resulting from mechanical stress in planta. We show that nonlinear, autocatalytic production and Fickian diffusion of H2O2 followed by first order decay well describes the spatial and temporal properties of the waveform. The reaction-diffusion system is analyzed in terms of a new approximate solution that we introduce for such problems based on a single term logistic function ansatz. The theory is able to describe experimental ROS waveforms and degradation dynamics such that species-dependent dimensionless wave velocities are revealed, corresponding to subtle changes in higher moments of the waveform through an apparently conserved signaling mechanism overall. This theory has utility in potentially decoding other stress signaling waveforms for light, heat and PAMP-induced stresses that are similarly under investigation. The approximate solution may also find use in applied agricultural sensing, facilitating the connection between measured waveform and plant physiology.

A theory of mechanical stress-induced H2O2 signaling waveforms in Planta

Recent progress in nanotechnology-enabled sensors that can be placed inside of living plants has shown that it is possible to relay and record real-time chemical signaling stimulated by various abiotic and biotic stresses. The mathematical form of the resulting local reactive oxygen species (ROS) wave released upon mechanical perturbation of plant leaves appears to be conserved across a large number of species, and produces a distinct waveform from other stresses including light, heat and pathogen-associated molecular pattern (PAMP)-induced stresses. Herein, we develop a quantitative theory of the local ROS signaling waveform resulting from mechanical stress in planta. We show that nonlinear, autocatalytic production and Fickian diffusion of H2O2 followed by first order decay well describes the spatial and temporal properties of the waveform. The reaction-diffusion system is analyzed in terms of a new approximate solution that we introduce for such problems based on a single term logistic function ansatz. The theory is able to describe experimental ROS waveforms and degradation dynamics such that species-dependent dimensionless wave velocities are revealed, corresponding to subtle changes in higher moments of the waveform through an apparently conserved signaling mechanism overall. This theory has utility in potentially decoding other stress signaling waveforms for light, heat and PAMP-induced stresses that are similarly under investigation. The approximate solution may also find use in applied agricultural sensing, facilitating the connection between measured waveform and plant physiology.

Structural Changes in Israeli Family Farms: Long-Run Trends in the Farm Size Distribution and the Role of Part-Time Farming

Israeli agriculture has experienced rapid structural changes in recent decades, including the massive exit of farmers, a resulting increase in average farm size, a higher farm specialization and a higher reliance on non-farm income sources. The higher farm heterogeneity makes it necessary to examine changes in the entire farm size distribution rather than the common practice of analyzing changes in the average farm size alone. This article proposes a nonparametric analysis in which the change in the distribution of farm sizes between two periods is decomposed into several components, and the contributions of subgroups of farms to this change are analyzed. Using data on Israeli family farms, we analyze the changes in the farm size distribution in two separate time periods that are characterized by very different economic environments, focusing on the different contributions of full-time farms and part-time farms to the overall distributional changes. We found that between 1971 and 1981, a period characterized by stability and prosperity, the farm size distribution has shifted to the right with relatively minor changes in higher moments of the distribution. On the other hand, between 1981 and 1995, a largely unfavorable period to Israeli farmers, the change in the distribution was much more complex. While the overall change in the size distribution of farms was smaller in magnitude than in the earlier period, higher moments of the distribution were not less important than the increase in the mean and led to higher dispersion of farm sizes. Between 1971 and 1981, the contributions of full- and part-time farms to the change in the size distribution were quite similar. Between 1981 and 1995, however, full-time farms contributed mostly to the growth in the average farm size, while the average farm size among part-time farms actually decreased, and their contribution to the higher dispersion of farm sizes was quantitatively larger. This highlights the need to analyze the changes in the entire farm size distribution rather than focusing on the mean alone, and to allow for differences between types of farms.

An interpretation of the condition for precautionary saving: the case of greater higher-order interest rate risk

This paper shows that an increase in interest rate risk via (m, n)th-order stochastic dominance induces precautionary saving if, and only if, the measure of $$(k+1)$$ th-degree relative risk aversion exceeds k for all $$k=m,\ldots , n$$ . This result has the following interpretation. On the one hand, the measures of $$(k+1)$$ th-degree relative risk aversion for all $$k=m,\ldots , n$$ capture the prudence effect with respect to a risk increase via (m, n)th-order stochastic dominance, which favors precautionary saving. On the other hand, the thresholds, $$k=m,\ldots , n$$ , measure the elasticity of the change in the kth moment of future income with respect to saving. The adverse changes in higher moments of future income for all $$k=m,\ldots , n$$ when saving increases give rise to the risk aversion effect with respect to a risk increase via (m, n)th-order stochastic dominance, which limits precautionary saving. The necessary and sufficient condition for precautionary saving simply states that the prudence effect dominates the risk aversion effect, thereby making precautionary saving prevail.

Renal Blood Oxygenation Level-Dependent Magnetic Resonance Imaging: A Sensitive and Objective Analysis.

The aim of this study was to determine a robust (sensitive and objective) method for analyzing renal blood oxygenation level-dependent magnetic resonance imaging data. Forty-seven subjects (30 with chronic kidney disease [CKD] and 17 controls) were imaged at baseline and after furosemide with a multiecho gradient recalled echo sequence. Conventional analysis consisted of regional segmentation (small cortex, large cortex, and medulla), followed by computing the mean of each region. In addition, we segmented the entire parenchyma and computed the mean (μ1) plus higher moments (μ2, μ3, and μ4). Two raters performed each of the segmentation steps, and agreement was assessed with intraclass correlation coefficients (ICCs). We used a measure of effect size (Cohen's d value), in addition to the usual measure of statistical significance, P values, for determining significant results. The mean of the renal parenchyma showed the highest agreement between raters (ICC, 0.99), and the higher parenchyma moments were on par with large cortical region of interest (ROI) ICC. The renal parenchymal mean also exhibited significant sensitivity to changes after furosemide administration in healthy subjects (P = 0.002, d = 0.84), in agreement with medullary ROIs (P = 0.002, d = 1.59). When comparing controls and subjects with CKD at baseline, cortical ROI showed a significant difference (P = 0.015, d = -0.69), whereas the parenchyma ROI did not (P = 0.152, d = 0.39). Post-furosemide data in all regions resulted in a significant difference (large cortex: P = 0.026, d = -0.51; medulla: P = 0.019, d = -0.61) with the renal parenchyma ROI resulting in the largest effect size (P = 0.003, d = -0.75). Higher moments of the renal parenchyma showed similar significant differences as well. Overall, our data support the use of the entire parenchyma to evaluate changes in the medulla after administration of furosemide, a widely used pharmacological maneuver. Changes in higher moments indicate that there is more than just a shift in the mean renal R2* and may provide clinically relevant information without the need for subjective regional segmentation. For evaluating differences between controls and subjects with CKD at baseline; large cortical ROI provided the highest sensitivity and objectivity. A combination of renal parenchyma assessment and large cortical ROI may provide the most robust method of evaluating renal blood oxygenation level-dependent magnetic resonance imaging data.

New criteria for selecting differentially expressed genes

One of the major concerns in detecting changes in higher moments is these changes may be due to outliers or process errors that are not biologically significant. For example, a larger variance observed in the expression levels may simply due to the larger variation in the data collecting process. Several outliers, which exhibit some extreme expression levels than the rest of the samples, may also increase the variance or skewness of the expression levels significantly. So it is very important to reduce the effect of outliers and process errors by proper experimental designs [27], such as technical replicates and biological replicates, before high sensitivity criterion, such as ADS, can be applied. We have presented and demonstrated the operation of two new criteria, ADS and the MDS, for identifying differentially expressed genes. These two criteria were compared with several commonly used criteria, namely WTS, WRS, FCS, and ICE. Experiments with simulated data show ADS to be more powerful than the WTS. When high-sensitivity screening is required, ADS appears to be preferable to WTS. When an FPR similar to WTS is desired, MDS should be used. The popular Wilcoxon rank sum is a more conservative approach that should be employed when the lowest FPR is desired, even at the expense of lower TPRs. ICE is a less desirable criterion because it does not perform well for data generated by the normal model. FCS gave results similar to those of WTS. Evaluation of these algorithms using real biological datasets showed that ADS and MDS flagged several biologically significant genes that were missed by WTS, besides selecting most of the genes that are also selected by WTS.