Oxidative discolouration in whole-head and cut lettuce: biochemical and environmental influences on a complex phenotype and potential breeding strategies to improve shelf-life

Lettuce discolouration is a key post-harvest trait. The major enzyme controlling oxidative discolouration has long been considered to be polyphenol oxidase (PPO) however, levels of PPO and subsequent development of discolouration symptoms have not always correlated. The predominance of a latent state of the enzyme in plant tissues combined with substrate activation and contemporaneous suicide inactivation mechanisms are considered as potential explanations for this phenomenon. Leaf tissue physical properties have been associated with subsequent discolouration and these may be influenced by variation in nutrient availability, especially excess nitrogen and head maturity at harvest. Mild calcium and irrigation stress has also been associated with a reduction in subsequent discolouration, although excess irrigation has been linked to increased discolouration potentially through leaf physical properties. These environmental factors, including high temperature and UV light intensities, often have impacts on levels of phenolic compounds linking the environmental responses to the biochemistry of the PPO pathway. Breeding strategies targeting the PALand PPOpathway biochemistry and environmental response genes are discussed as a more cost-effective method of mitigating oxidative discolouration then either modified atmosphere packaging or post-harvest treatments, although current understanding of the biochemistry means that such programs are likely to be limited in nature and it is likely that they will need to be deployed alongside other methods for the foreseeable future

Adaptive Lévy Walks in Foraging Fallow Deer

Background: Lévy flights are random walks, the step lengths of which come from probability distributions with heavy power-law tails, such that clusters of short steps are connected by rare long steps. Lévy walks maximise search efficiency of mobile foragers. Recently, several studies raised some concerns about the reliability of the statistical analysis used in previous analyses. Further, it is unclear whether Lévy walks represent adaptive strategies or emergent properties determined by the interaction between foragers and resource distribution. Thus two fundamental questions still need to be addressed: the presence of Lévy walks in the wild and whether or not they represent a form of adaptive behaviour. Methodology/Principal Findings: We studied 235 paths of solitary and clustered (i.e. foraging in group) fallow deer (Dama dama), exploiting the same pasture. We used maximum likelihood estimation for discriminating between a power-tailed distribution and the exponential alternative and rank/frequency plots to discriminate between Lévy walks and composite Brownian walks. We showed that solitary deer perform Lévy searches, while clustered animals did not adopt that strategy. Conclusion/Significance: Our demonstration of the presence of Lévy walks is, at our knowledge, the first available which adopts up-to-date statistical methodologies in a terrestrial mammal. Comparing solitary and clustered deer, we concluded that the Lévy walks of solitary deer represent an adaptation maximising encounter rates with forage resources and not a

Search for cardiac calcium cycling gene mutations in familial ventricular arrhythmias resembling catecholaminergic polymorphic ventricular tachycardia

<p>Abstract</p> <p>Background</p> <p>Catecholaminergic polymorphic ventricular tachycardia (CPVT) is a severe inherited cardiac disorder caused by mutations predominantly in the ryanodine receptor (<it>RyR2</it>) gene. We sought to identify mutations in genes affecting cardiac calcium cycling in patients with CPVT and in less typical familial exercise-related ventricular arrhythmias.</p> <p>Methods and Results</p> <p>We recruited 33 consecutive patients with frequent ventricular premature complexes (VPCs) without structural heart disease and often history of syncope or sudden death in family. Sixteen of the patients featured a phenotype typical of CPVT. In 17 patients, VPCs emerged also at rest. Exercise stress test and echocardiography were performed to each patient and 232 family members. Familial background was evident in 42% of cases (n = 14). We sequenced all the coding exons of the <it>RyR2</it>, <it>FKBP1B</it>, <it>ATP2A2 </it>and <it>SLC8A1 </it>genes from the index patients. Single channel recordings of a mutant RyR2 were performed in planar lipid bilayers. Two novel <it>RyR2 </it>missense mutations (R1051P and S616L) and two <it>RyR2 </it>exon 3 deletions were identified, explaining 25% of the CPVT phenotypes. A rare variant (N3308S) with open probabilities similar to the wild type channels <it>in vitro</it>, was evident in a patient with resting VPCs. No disease-causing variants were detectable in the <it>FKBP1B</it>, <it>ATP2A2 </it>or <it>SLC8A1 </it>genes.</p> <p>Conclusion</p> <p>We report two novel CPVT-causing <it>RyR2 </it>mutations and a novel <it>RyR2 </it>variant of uncertain clinical significance in a patient with abundant resting VPCs. Our data also strengthen the previous assumption that exon 3 deletions of <it>RyR2 </it>should screened for in CPVT and related phenotypes.</p

Evidence for Metabolic Provisioning by a Common Invertebrate Endosymbiont, Wolbachia pipientis, during Periods of Nutritional Stress

Wolbachia are ubiquitous inherited endosymbionts of invertebrates that invade host populations by modifying host reproductive systems. However, some strains lack the ability to impose reproductive modification and yet are still capable of successfully invading host populations. To explain this paradox, theory predicts that such strains should provide a fitness benefit, but to date none has been detected. Recently completed genome sequences of different Wolbachia strains show that these bacteria may have the genetic machinery to influence iron utilization of hosts. Here we show that Wolbachia infection can confer a positive fecundity benefit for Drosophila melanogaster reared on iron-restricted or -overloaded diets. Furthermore, iron levels measured from field-collected flies indicated that nutritional conditions in the field were overall comparable to those of flies reared in the laboratory on restricted diets. These data suggest that Wolbachia may play a previously unrecognized role as nutritional mutualists in insects

A comparison of machine learning algorithms for chemical toxicity classification using a simulated multi-scale data model

<p>Abstract</p> <p>Background</p> <p>Bioactivity profiling using high-throughput <it>in vitro </it>assays can reduce the cost and time required for toxicological screening of environmental chemicals and can also reduce the need for animal testing. Several public efforts are aimed at discovering patterns or classifiers in high-dimensional bioactivity space that predict tissue, organ or whole animal toxicological endpoints. Supervised machine learning is a powerful approach to discover combinatorial relationships in complex <it>in vitro/in vivo </it>datasets. We present a novel model to simulate complex chemical-toxicology data sets and use this model to evaluate the relative performance of different machine learning (ML) methods.</p> <p>Results</p> <p>The classification performance of Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Naïve Bayes (NB), Recursive Partitioning and Regression Trees (RPART), and Support Vector Machines (SVM) in the presence and absence of filter-based feature selection was analyzed using K-way cross-validation testing and independent validation on simulated <it>in vitro </it>assay data sets with varying levels of model complexity, number of irrelevant features and measurement noise. While the prediction accuracy of all ML methods decreased as non-causal (irrelevant) features were added, some ML methods performed better than others. In the limit of using a large number of features, ANN and SVM were always in the top performing set of methods while RPART and KNN (k = 5) were always in the poorest performing set. The addition of measurement noise and irrelevant features decreased the classification accuracy of all ML methods, with LDA suffering the greatest performance degradation. LDA performance is especially sensitive to the use of feature selection. Filter-based feature selection generally improved performance, most strikingly for LDA.</p> <p>Conclusion</p> <p>We have developed a novel simulation model to evaluate machine learning methods for the analysis of data sets in which in vitro bioassay data is being used to predict in vivo chemical toxicology. From our analysis, we can recommend that several ML methods, most notably SVM and ANN, are good candidates for use in real world applications in this area.</p

Innovation by Experimenting in Public Services

Peer reviewe

A Genome-Wide Association Study of Diabetic Kidney Disease in Subjects With Type 2 Diabetes

Identification of sequence variants robustly associated with predisposition to diabetic kidney disease (DKD) has the potential to provide insights into the pathophysiological mechanisms responsible. We conducted a genome-wide association study (GWAS) of DKD in type 2 diabetes (T2D) using eight complementary dichotomous and quantitative DKD phenotypes: the principal dichotomous analysis involved 5,717 T2D subjects, 3,345 with DKD. Promising association signals were evaluated in up to 26,827 subjects with T2D (12,710 with DKD). A combined (T1D+T2D) GWAS was performed using complementary data available for subjects with T1D, which, with replication samples, involved up to 40,340 diabetic subjects (and 18,582 DKD cases). Analysis of specific DKD phenotypes identified a novel signal near GABRR1 (rs9942471, p=4.5×10-8) associated with 'microalbuminuria' in European T2D cases. However, no replication of this signal was observed in Asian subjects with T2D, or in the equivalent T1D analysis. There was only limited support, in this substantially enlarged analysis, for association at previously-reported DKD signals, except for those at UMOD and PRKAG2, both associated with 'EGFR'. We conclude that, despite challenges in addressing phenotypic heterogeneity, access to increased sample sizes will continue to provide more robust inference regarding risk-variant discovery for DKD.</p