Hens That Exhibit Poorer Feed Efficiency Produce Eggs with Lower Albumen Quality and Are Prone to Being Overweight

Feed efficiency (FE) is an important measure of productivity in the layer industry; however, little is known about how FE differs between individual hens during the egg-laying cycle and the implications for egg quality parameters. Individual 25-week-old ISA Brown hens were observed for 42 days, ranked into three FE groups (n = 48 per High (HFE), Medium (MFE) and Low (LFE) FE groups and then monitored later in the laying cycle from 35–40 weeks. The groups exhibited different feed to egg conversion ratios (p < 0.001) from 35–40 weeks. Average daily feed intake and body weight were highest (p < 0.001) in the LFE group compared to the MFE and HFE groups, while albumen height, Haugh unit and amino acid concentrations of the albumen were significantly higher in the HFE groups compared to the LFE cohort (p < 0.001). This study concludes that FE status established in early lay is a stable variable until at least 40 weeks of age, and overweight, mid-laying hens that had poor FE produced inferior egg albumen quality measurements and composition. The distinct traits of the highly efficient hens and the poor feed efficient hens may provide important information to improving productivity in egg production.

Seasonal Changes in Climate Variables in Rainfed Crop Areas in the Lerma-Chapala-Santiago Basin, Mexico

This paper shows the effects of changes in the spatial-temporal behavior and phase shift of climate variables on rainfed agriculture in the Lerma-Chapala-Santiago Basin in central Mexico. Specifically, changes in rainfall (R), maximum temperature (Tmax), and minimum temperature (Tmin) were analyzed over two 25-year periods (1960 to 1985 and 1986 to 2010). Climate surfaces were generated by interpolation using the thin-plate smoothing spline algorithm in the software ANUSPLIN. Climate data were Fourier-transformed and fitted to a sinusoidal curve model, and changes in amplitude (increase) and phase were analyzed. The temporal behavior (1960–2010) indicated that rainfall was the most stable variable at the monthly level and presented no significant changes. However, Tmax increased by 2°C in the final period, and Tmin increased by 0.7°C at the end of the final period. The basin was discretized into ten rainfed crop areas (RCAs) according to the extent of changes in the amplitude and phase of the climate variables. The central and southern portions (55% of the area) presented more significant changes in amplitude, mainly in Tmin and Tmax. The remaining RCAs were smaller (14.6%) but presented greater variation: the amplitude of the Tmin decreased in addition to showing a phase shift, whereas Tmax increased in addition to showing a phase shift. These results translate into a delay in the characteristic temperatures of the spring and summer seasons, which can impact the rainfed crop cycle. Additionally, rainfall showed an annual decrease of approximately 50 mm in all RCAs, which can affect the phenological development of crops during critical stages (emergence through flowering). These changes represent a significant threat to the regional economy and food security of Mexico.

Stable Variable Selection for High-dimensional Genomic Data with Strong Correlations

Background: High-dimensional genomic data studies are often found to exhibit strong correlations, which results in instability and inconsistency in the estimates obtained using commonly used regularization approaches including both the Lasso and MCP, and related methods. Result: In this paper, we perform a comparative study of regularization approaches for variable selection under different correlation structures, and propose a two-stage procedure named rPGBS to address the issue of stable variable selection in various strong correlation settings. This approach involves repeatedly running of a two-stage hierarchical approach consisting of a random pseudo-group clustering and bi-level variable selection. Conclusion: Both the simulation studies and high-dimensional genomic data analysis have demonstrated the advantage of the proposed rPGBS method over most commonly used regularization methods. In particular, the rPGBS results in more stable selection of variables across a variety of correlation settings, as compared to recent work addressing variable selection with strong correlations. Moreover, the rPGBS is computationally efficient across various settings.

Development of a Wilks feature importance method with improved variable rankings for supporting hydrological inference and modelling

Feature importance has been a popular approach for machine learning models to investigate the relative significance of model predictors. In this study, we developed a Wilks feature importance (WFI) method for hydrological inference. Compared with conventional feature importance methods such as permutation feature importance (PFI) and mean decrease impurity (MDI), the proposed WFI aims to provide more reliable variable rankings for hydrological inference. To achieve this, WFI measures the importance scores based on Wilks Λ (a test statistic that can be used to distinguish the differences between two or more groups of variables) throughout an inference tree. Compared with PFI and MDI methods, WFI does not rely on any performance measures to evaluate variable rankings, which can thus result in less biased criteria selection during the tree deduction process. The proposed WFI was tested by simulating monthly streamflows for 673 basins in the United States and applied to three interconnected irrigated watersheds located in the Yellow River basin, China, through concrete simulations for their daily streamflows. Our results indicated that the WFI could generate stable variable rankings in response to the reduction of irrelevant predictors. In addition, the WFI-selected predictors helped random forest (RF) achieve its optimum predictive accuracy, which indicates that the proposed WFI could identify more informative predictors than other feature importance measures.

Population Projection Revisions and Their Impact on Economic Growth – The Case of Malta

Typically, in short-term economic forecasts, population projections, and their related impact on the availability of labour, tend to be the most stable component. The scope of this paper is to show how in the case of Malta, the European Union’s smallest economy, migration flows have led to substantial revisions in population projections. Using the standard production function approach to estimate potential output growth, these revisions change very substantially expectations of economic expansion. Revisions in population projections are, in fact, estimated to have boosted Malta’s potential output growth in future years by as much as half a percentage point. While potential output is seen as a fairly stable variable for medium and large economies, it is more of a fluid concept for small open economies that are subject to large migration flows.

[In]stability in the use of a stable variable

The relationship between community-wide change and patterns of variation and change within the individual is one of the cornerstones of variationist theorising. But while sociolinguistic theory makes clear and testable predictions regarding the use of stable vernacular features across the life-span of the individual, we lack real-time evidence on the age-graded nature of stable variability. Indeed, whereas apparent time research highlights the diachronic stability of (ing), only two research projects have explored its use within the individual speaker. Both report on pre-adult speakers. Our research expands the window of analysis by adding a later age-bracket to the investigation of age-graded variability. We consider the variable realisation of (ing) in a group of individuals between early adulthood and retirement.

Option pricing in a subdiffusive constant elasticity of variance (CEV) model

In this paper, we extend the classical constant elasticity of variance (CEV) model to a subdiffusive CEV model, where the underlying CEV process is time changed by an inverse [Formula: see text]-stable subordinator. The new model can capture the subdiffusive characteristics of financial markets. We find the corresponding fractional Fokker–Planck equation governing the PDF of the new process. We also derive the analytical formula for option prices in terms of eigenfunction expansion. This method avoids the evaluation of PDF of an inverse [Formula: see text]-stable variable and also eliminates the need for numerical integration to calculate the option prices. We numerically investigate the sensitivities of the option prices to the key parameters of the newly developed model.

Monetary Policy and Rational Asset Price Bubbles: Comment

We revisit Galí’s (2014 ) analysis by extending his model to incorporate persistent bubble shocks. We find that, under adaptive learning, a stable bubbly steady state and the associated sunspot solutions under optimal monetary policy are not E-stable. When deriving the unique forward-looking minimum stable variable (MSV ) solution around an unstable bubbly steady state, we obtain results that are consistent with the conventional views: leaning against the wind policy reduces bubble volatility and is optimal. Such a steady state and the associated MSV solution are E-stable. (JEL E13, E32, E44, E52, G12)