Protochlorophyllide forms in non-greening epicotyls of dark-grown pea (Pisum sativum)

Low-temperature fluorescence emission spectra of 6.5-day-old dark-grown epicotyls of pea ( Pisum sativum ) revealed the presence of protochlorophyll(ide). The upper part of the epicotyl contained 30% of the protochlorophyll(ide) content per fresh weight found in pea leaves, whereas the lower part contained 3%. Three discrete spectral forms of protochlorophyll(ide) were clearly distinguished after Gaussian deconvolution of fluorescence excitation and emission spectra. Adding the satellite bands of the Qy_(0-0) transitions (the emission vibrational (Emv) bands with correlated amplitudes, gave the following delineation: Ex439–Em629–Emv684, Ex447–Em636–Emv700 and Ex456–Em650–Emv728. Sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS-PAGE) followed by immunodetection of whole tissue extracts of the epicotyl indicated the presence of NADPH-protochlorophyllide oxidoreductase (EC 1.3.1.33). Electron micrographs showed prolamellar bodies in at most 11 % of the plastid profiles of the epicotyl cells. These prolamellar bodies were smaller, and many of them showed less regular structure than those of the leaves. Taken together, the results indicate that the protochlorophyll(ide) in epicotyls is arranged in a different way than in leaves.     

Integrating sample similarities into latent class analysis: a tree-structured shrinkage approach

This paper is concerned with using multivariate binary observations to estimate the probabilities of unobserved classes with scientific meanings. We focus on the setting where additional information about sample similarities is available and represented by a rooted weighted tree. Every leaf in the given tree contains multiple samples. Shorter distances over the tree between the leaves indicate a priori higher similarity in class probability vectors. We propose a novel data integrative extension to classical latent class models with tree-structured shrinkage. The proposed approach enables (1) borrowing of information across leaves, (2) estimating data-driven leaf groups with distinct vectors of class probabilities, and (3) individual-level probabilistic class assignment given the observed multivariate binary measurements. We derive and implement a scalable posterior inference algorithm in a variational Bayes framework. Extensive simulations show more accurate estimation of class probabilities than alternatives that suboptimally use the additional sample similarity information. A zoonotic infectious disease application is used to illustrate the proposed approach. The paper concludes by a brief discussion on model limitations and extensions.     

Safety zones for a Sclerotinia sclerotiorum-based mycoherbicide: Accounting for regional and yearly variation in climate

Variation in the width of 'safety zones' for sheep and dairy pasture treated with a Sclerotinia sclerotiorum (Lib.) de Bary-based mycoherbicide was quantified using 10 years of climate data from each of five regions in New Zealand. Acceptable and risk-averse zones were defined as the maximum distances in any direction from a treated pasture where 'added:natural' ratios of air-borne S. sclerotiorum spores are 1:1 and 1:10, respectively. The 10-year mean 1:1 safety zone had zero width for sheep pasture at all locations, and was at most, 50 m wide for dairy pasture. The width of the 10-year mean 1:10 zone varied regionally from 314 to 443 m for sheep and from 175 to 280 m for dairy pasture, and the 90th percentile 1:10 zones were up to 41% wider. Linear relationships between safety zone width and mean wind speed were evident and these could be used to derive region-specific safety zones.     

Parameter estimation for discretely observed linear birth‐and‐death processes

Birth‐and‐death processes are widely used to model the development of biological populations. Although they are relatively simple models, their parameters can be challenging to estimate, as the likelihood can become numerically unstable when data arise from the most common sampling schemes, such as annual population censuses. A further difficulty arises when the discrete observations are not equi‐spaced, for example, when census data are unavailable for some years. We present two approaches to estimating the birth, death, and growth rates of a discretely observed linear birth‐and‐death process: via an embedded Galton‐Watson process and by maximizing a saddlepoint approximation to the likelihood. We study asymptotic properties of the estimators, compare them on numerical examples, and apply the methodology to data on monitored populations.     

Gaussian curvature analysis allows for automatic block placement in multi-block hexahedral meshing

Musculoskeletal finite element analysis (FEA) has been essential to research in orthopaedic biomechanics. The generation of a volumetric mesh is often the most challenging step in a FEA. Hexahedral meshing tools that are based on a multi-block approach rely on the manual placement of building blocks for their mesh generation scheme. We hypothesise that Gaussian curvature analysis could be used to automatically develop a building block structure for multi-block hexahedral mesh generation. The Automated Building Block Algorithm incorporates principles from differential geometry, combinatorics, statistical analysis and computer science to automatically generate a building block structure to represent a given surface without prior information. We have applied this algorithm to 29 bones of varying geometries and successfully generated a usable mesh in all cases. This work represents a significant advancement in automating the definition of building blocks.     

The mechanism of high affinity pentasaccharide binding to antithrombin,insights from Gaussian accelerated molecular dynamics simulations

Abstract

The activity of antithrombin (AT), a serpin protease inhibitor, is enhanced by heparin and heparin analogs against its target proteases, mainly thrombin, factors Xa and IXa. Considerable amount of information is available on the multistep mechanism of the heparin pentasaccharide binding and conformational activation. However, much of the details were inferred from ‘static’ structures obtained by X-ray diffraction. Moreover, limited information is available for the early steps of binding mechanism other than kinetic studies with various ligands. To gain insights into these processes, we performed enhanced sampling molecular dynamics (MD) simulations using the Gaussian Accelerated Molecular Dynamics (GAMD) method, applied previously in drug binding studies. We were able to observe the binding of the pentasaccharide idraparinux to a ‘non-activated’ AT conformation in two separate trajectories with low root mean square deviation (RMSD) values compared to X-ray structures of the bound state. These trajectories along with further simulations of the AT-pentasaccharide complex provided insights into the mechanisms of multiple conformational transitions, including the expulsion of the hinge region, the extension of helix D and the conformational behavior of the reactive center loop (RCL). We could also confirm the high stability of helix P in non-activated AT conformations, such states might play an important role in heparin binding. ‘Generalized correlation’ matrices revealed possible paths of allosteric signal propagation to the binding sites for the target proteases, factors Xa and IXa. Enhanced MD simulations of ligand binding to AT may assist the design of new anticoagulant drugs.

Communicated by Ramaswamy H. Sarma     

Comparison of three formal methods used to estimate the functional axis of rotation: an extensive in-vivo analysis performed on the knee joint

Estimating the main axis of rotation (AoR) of a human joint represents an important issue in biomechanics. This study compared three formal methods used to estimate functional AoR, namely a cylindrical fitting method, a mean helical axis transformation, and a symmetrical axis approach. These methods were tested on 106 subjects undergoing navigated total knee arthroplasty. AoR orientation in 3D and in the frontal and coronal planes provided by each method was compared to the transepicondylar axis direction. Although all the methods resulted effective, significant differences were identified among them, relatively to the orientation in 3D and in the frontal plane projection. This was probably due to the presence of secondary rotations during the first degrees of knee flexion.     

The shape of the competition and carrying capacity kernels affects the likelihood of disruptive selection

Many quantitative genetic and adaptive dynamic models suggest that disruptive selection can maintain genetic polymorphism and be the driving force causing evolutionary divergence. These models also suggest that disruptive selection arises from frequency-dependent intraspecific competition. For convenience or historical precedence, these models assume that carrying capacity and competition functions follow a Gaussian distribution. Here, we propose a new analytical framework that relaxes the assumption of Gaussian competition and carrying capacity functions, and investigate how alternative shapes affect the likelihood of disruptive selection. We found that the shape of both carrying capacity and competition kernels interact to determine the likelihood of disruptive selection. For certain regions of the parametric space disruptive selection is facilitated, whereas for others it becomes more difficult. Our results suggest that the relationship between the degree of frequency dependence and the likelihood of disruptive selection is more complex than previously thought, depending on how resources are distributed and competition interference takes place. It is now important to describe the empirical patterns of resource distribution and competition in nature as a way to determine the likelihood of disruptive selection in natural populations.