Simulated nitrogen dynamics and nitrate leaching in a perennial grass ley

A soil nitrogen model was used for a 4-year simulation of nitrogen dynamics and nitrate leaching, both during grass ley growth and after ploughing a grass ley. Model results were compared with field measurements of soil mineral-N status and leaching. A soil water and heat model provided daily values for abiotic conditions, which were used as driving variables in the nitrogen simulation. Simulated values for mineral-N levels in the soil agreed well with field data for the first 3 years of the simulation. During the final year the model predicted considerably higher levels of soil mineral-N content compared with measurements. To reach the mineral-N level measured at the time of ploughing the ley, the simulated N-uptake by plants had to be increased by 8 g N m⁻². Simulations of nitrate leaching suggested that estimates of leaching based on measurements in tile-drained plots can be considerably underestimated. Accurate quantification of leaching in tile-drained plots often requires additional information on water-flow paths. A substantial increase in simulated and measured values for the mineral-N content of the soil occurred after ploughing the ley. In the simulation, most of the increase was due to a high crop residue input and the absence of a growing crop after ploughing. Litter accumulations in the soil during the 4-year period contributed little to the increase in soil mineral-N.     

Logistic Bayesian LASSO for identifying association with rare haplotypes and application to age-related macular degeneration

Rare variants have been heralded as key to uncovering "missing heritability" in complex diseases. These variants can now be genotyped using next-generation sequencing technologies; nonetheless, rare haplotypes may also result from combination of common single nucleotide polymorphisms available from genome-wide association studies (GWAS). The National Eye Institute's data on age-related macular degeneration (AMD) is such an example. Studies on AMD had identified potential rare variants; however, due to lack of appropriate statistical tools, effects of individual rare haplotypes were never studied. Here we develop a method for identifying association with rare haplotypes for case-control design. A logistic regression based retrospective likelihood is formulated and is regularized using logistic Bayesian LASSO (LBL). In particular, we penalize the regression coefficients using appropriate priors to weed out unassociated haplotypes, making it possible for the rare associated ones to stand out. We applied LBL to the AMD data and identified common and rare haplotypes in the complement factor H gene, gaining insights into rare variants' contributions to AMD beyond the current literature. This analysis also demonstrates the richness of GWAS data for mapping rare haplotypes-a potential largely unexplored. Additionally, we conducted simulations to investigate the performance of LBL and compare it with Hapassoc. Our results show that LBL is much more powerful in identifying rare associated haplotypes when the false positive rates for both approaches are kept the same.     

A new analytical approach to landscape genetic modelling: least-cost transect analysis and linear mixed models

Landscape genetics aims to assess the effect of the landscape on intraspecific genetic structure. To quantify interdeme landscape structure, landscape genetics primarily uses landscape resistance surfaces (RSs) and least-cost paths or straight-line transects. However, both approaches have drawbacks. Parameterization of RSs is a subjective process, and least-cost paths represent a single migration route. A transect-based approach might oversimplify migration patterns by assuming rectilinear migration. To overcome these limitations, we combined these two methods in a new landscape genetic approach: least-cost transect analysis (LCTA). Habitat-matrix RSs were used to create least-cost paths, which were subsequently buffered to form transects in which the abundance of several landscape elements was quantified. To maintain objectivity, this analysis was repeated so that each landscape element was in turn regarded as migration habitat. The relationship between explanatory variables and genetic distances was then assessed following a mixed modelling approach to account for the nonindependence of values in distance matrices. Subsequently, the best fitting model was selected using the statistic. We applied LCTA and the mixed modelling approach to an empirical genetic dataset on the endangered damselfly, Coenagrion mercuriale. We compared the results to those obtained from traditional least-cost, effective and resistance distance analysis. We showed that LCTA is an objective approach that identifies both the most probable migration habitat and landscape elements that either inhibit or facilitate gene flow. Although we believe the statistical approach to be an improvement for the analysis of distance matrices in landscape genetics, more stringent testing is needed.     

Developing a denoising filter for electron microscopy and tomography data in the cloud

The low radiation conditions and the predominantly phase-object image formation of cryo-electron microscopy (cryo-EM) result in extremely high noise levels and low contrast in the recorded micrographs. The process of single particle or tomographic 3D reconstruction does not completely eliminate this noise and is even capable of introducing new sources of noise during alignment or when correcting for instrument parameters. The recently developed Digital Paths Supervised Variance (DPSV) denoising filter uses local variance information to control regional noise in a robust and adaptive manner. The performance of the DPSV filter was evaluated in this review qualitatively and quantitatively using simulated and experimental data from cryo-EM and tomography in two and three dimensions. We also assessed the benefit of filtering experimental reconstructions for visualization purposes and for enhancing the accuracy of feature detection. The DPSV filter eliminates high-frequency noise artifacts (density gaps), which would normally preclude the accurate segmentation of tomography reconstructions or the detection of alpha-helices in single-particle reconstructions. This collaborative software development project was carried out entirely by virtual interactions among the authors using publicly available development and file sharing tools.     

A note on sliced inverse regression with regularizations

Summary .   In Li and Yin (2008, Biometrics 64, 124–131), a ridge SIR estimator is introduced as the solution of a minimization problem and computed thanks to an alternating least-squares algorithm. This methodology reveals good performance in practice. In this note, we focus on the theoretical properties of the estimator. It is shown that the minimization problem is degenerated in the sense that only two situations can occur: Either the ridge SIR estimator does not exist or it is zero.     

Using multiple landscape genetic approaches to test the validity of genetic clusters in a species characterized by an isolation‐by‐distance pattern

Bayesian clustering methods are typically used to identify barriers to gene flow, but they are prone to deduce artificial subdivisions in a study population characterized by an isolation‐by‐distance pattern (IbD). Here we analysed the landscape genetic structure of a population of wild boars (Sus scrofa) from south‐western Germany. Two clustering methods inferred the presence of the same genetic discontinuity. However, the population in question was characterized by a strong IbD pattern. While landscape‐resistance modelling failed to identify landscape features that influenced wild boar movement, partial Mantel tests and multiple regression of distance matrices (MRDMs) suggested that the empirically inferred clusters were separated by a genuine barrier. When simulating random lines bisecting the study area, 60% of the unique barriers represented, according to partial Mantel tests and MRDMs, significant obstacles to gene flow. By contrast, the random‐lines simulation showed that the boundaries of the inferred empirical clusters corresponded to the most important genetic discontinuity in the study area. Given the degree of habitat fragmentation separating the two empirical partitions, it is likely that the clustering programs correctly identified a barrier to gene flow. The differing results between the work published here and other studies suggest that it will be very difficult to draw general conclusions about habitat permeability in wild boar from individual studies.     

Viscoelastic properties of waxy maize starch and selected non-starch hydrocolloids gels

Polysaccharides play a significant role in food systems as texture forming agent. Their solutions posses some peculiar rheological behaviors. Some information about structure of these biopolymers and relaxation processes occurring in their solutions may be obtained by investigation of linear viscoelasticity properties. The aim of this research was to investigate viscoelastic properties of systems consisting of waxy maize starch–hydrocolloid–water. To achieve this aim two techniques were applied: rheological measurements in frequency domain and structural studies by means of AFM method. The results were interpreted on basis of time–temperature superposition principle and phenomenological theory of viscoelasticity. Thermal stability of analyzed systems allowed applying time-temperature superposition principle. Calculation of a_T parameter enabled to obtaine the master curves. Continuous Maxwell model was applied to analyze phase separation in examined systems. Relaxations spectra obtained by Tikhonov regularization method were heterogeneous, indicating on non-homogenous structure of system.     

A new cost‐distance model for human accessibility and an evaluation of accessibility bias in permanent vegetation plots in Great Smoky Mountains National Park,USA

Question: Can a new cost‐distance model help us to evaluate the potential for accessibility bias in ecological observations? How much accessibility bias is present in the vegetation monitoring plots accumulated over the last three decades in Great Smoky Mountains National Park? Location: Great Smoky Mountains National Park, North Carolina and Tennessee, USA. Methods: Distance, slope, stream crossings, and vegetation density were incorporated into a least‐cost model of energetic expenditure for human access to locations. Results: Estimated round‐trip energy costs for the park ranged from 0 to 1.62 × 10⁵ J kg^?1. The estimated round‐trip energetic expenditure for the surveys ranged from 53 to 1.51 × 10⁵ J kg^?1. Their distribution was more accessible than the random expectation. Ten (17%) of the vegetation types in the park are significantly under‐sampled relative to their area, and 16 (29%) are over‐sampled. Plots in 18 of the 40 vegetation types exhibited a significant positive correlation with accessibility. Conclusions: The least‐cost model is an improvement over previous attempts to quantify accessibility. The bias in plot locations suggests using a least‐cost model to test for bias in cases in which human accessibility is confounded with other sources of ecosystem variation.     

Shrinkage‐based Diagonal Discriminant Analysis and Its Applications in High‐Dimensional Data

Summary High‐dimensional data such as microarrays have brought us new statistical challenges. For example, using a large number of genes to classify samples based on a small number of microarrays remains a difficult problem. Diagonal discriminant analysis, support vector machines, and k‐nearest neighbor have been suggested as among the best methods for small sample size situations, but none was found to be superior to others. In this article, we propose an improved diagonal discriminant approach through shrinkage and regularization of the variances. The performance of our new approach along with the existing methods is studied through simulations and applications to real data. These studies show that the proposed shrinkage‐based and regularization diagonal discriminant methods have lower misclassification rates than existing methods in many cases.