Genome-wide meta-analysis of common variant differences between men and women

The male-to-female sex ratio at birth is constant across world populations with an average of 1.06 (106 male to 100 female live births) for populations of European descent. The sex ratio is considered to be affected by numerous biological and environmental factors and to have a heritable component. The aim of this study was to investigate the presence of common allele modest effects at autosomal and chromosome X variants that could explain the observed sex ratio at birth. We conducted a large-scale genome-wide association scan (GWAS) meta-analysis across 51 studies, comprising overall 114 863 individuals (61 094 women and 53 769 men) of European ancestry and 2 623 828 common (minor allele frequency >0.05) single-nucleotide polymorphisms (SNPs). Allele frequencies were compared between men and women for directly-typed and imputed variants within each study. Forward-time simulations for unlinked, neutral, autosomal, common loci were performed under the demographic model for European populations with a fixed sex ratio and a random mating scheme to assess the probability of detecting significant allele frequency differences. We do not detect any genome-wide significant (P < 5 x 10(-8)) common SNP differences between men and women in this well-powered meta-analysis. The simulated data provided results entirely consistent with these findings. This large-scale investigation across ~115 000 individuals shows no detectable contribution from common genetic variants to the observed skew in the sex ratio. The absence of sex-specific differences is useful in guiding genetic association study design, for example when using mixed controls for sex-biased traits.

Epidemiological data of seasonal variation in mood and behaviour

The aim of this study was to measure seasonal variation in mood and behaviour. The dual vulnerability and latitude effect hypothesis, the risk of increased appetite, weight and other seasonal symptoms to develop metabolic syndrome, and perception of low illumination in quality of life and mental well-being were assessed. These variations are prevalent in persons who live in high latitudes and need balancing of metabolic processes to adapt to environmental changes due to seasons. A randomized sample of 8028 adults aged 30 and over (55% women) participated in an epidemiological health examination study, The Health 2000, applying the probability proportional to population size method for a range of socio-demographic characteristics. They were present in a face-to-face interview at home and health status examination. The questionnaires included the modified versions of the Seasonal Pattern Assessment Questionnaire (SPAQ) and Beck Depression Inventory (BDI), the Health Related Quality of Life (HRQoL) instrument 15D, and the General Health Questionnaire (GHQ). The structured and computerized Munich Composite International Diagnostic Interview (M-CIDI) as part of the interview was used to assess diagnoses of mental disorders, and, the National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATPIII) criteria were assessed using all the available information to detect metabolic syndrome. A key finding was that 85% of this nationwide representative sample had seasonal variation in mood and behaviour. Approximately 9% of the study population presented combined seasonal and depressive symptoms with a significant association between their scores, and 2.6% had symptoms that corresponded to Seasonal Affective Disorder (SAD) in severity. Seasonal variations in weight and appetite are two important components that increase the risk of metabolic syndrome. Other factors such as waist circumference and major depressive disorder contributed to the metabolic syndrome as well. Persons reported of having seasonal symptoms were associated with a poorer quality of life and compromised mental well-being, especially if indoors illumination at home and/or at work was experienced as being low. Seasonal and circadian misalignments are suggested to associate with metabolic disorders, and could be remarked if individuals perceive low illumination levels at home and/or at work that affect the health-related quality of life and mental well-being. Keywords: depression, health-related quality of life, illumination, latitude, mental well-being, metabolic syndrome, seasonal variation, winter.

Hierarchical Bayesian modelling of early detection surveillance for plant pest invasions

Early detection surveillance programs aim to find invasions of exotic plant pests and diseases before they are too widespread to eradicate. However, the value of these programs can be difficult to justify when no positive detections are made. To demonstrate the value of pest absence information provided by these programs, we use a hierarchical Bayesian framework to model estimates of incursion extent with and without surveillance. A model for the latent invasion process provides the baseline against which surveillance data are assessed. Ecological knowledge and pest management criteria are introduced into the model using informative priors for invasion parameters. Observation models assimilate information from spatio-temporal presence/absence data to accommodate imperfect detection and generate posterior estimates of pest extent. When applied to an early detection program operating in Queensland, Australia, the framework demonstrates that this typical surveillance regime provides a modest reduction in the estimate that a surveyed district is infested. More importantly, the model suggests that early detection surveillance programs can provide a dramatic reduction in the putative area of incursion and therefore offer a substantial benefit to incursion management. By mapping spatial estimates of the point probability of infestation, the model identifies where future surveillance resources can be most effectively deployed.

Hierarchical Bayesian modelling of plant pest invasions with human-mediated dispersal

Hierarchical Bayesian models can assimilate surveillance and ecological information to estimate both invasion extent and model parameters for invading plant pests spread by people. A reliability analysis framework that can accommodate multiple dispersal modes is developed to estimate human-mediated dispersal parameters for an invasive species. Uncertainty in the observation process is modelled by accounting for local natural spread and population growth within spatial units. Broad scale incursion dynamics are based on a mechanistic gravity model with a Weibull distribution modification to incorporate a local pest build-up phase. The model uses Markov chain Monte Carlo simulations to infer the probability of colonisation times for discrete spatial units and to estimate connectivity parameters between these units. The hierarchical Bayesian model with observational and ecological components is applied to a surveillance dataset for a spiralling whitefly (Aleurodicus dispersus) invasion in Queensland, Australia. The model structure provides a useful application that draws on surveillance data and ecological knowledge that can be used to manage the risk of pest movement.

Interval Data Classification under Partial Information: A Chance-Constraint Approach

This paper presents a Chance-constraint Programming approach for constructing maximum-margin classifiers which are robust to interval-valued uncertainty in training examples. The methodology ensures that uncertain examples are classified correctly with high probability by employing chance-constraints. The main contribution of the paper is to pose the resultant optimization problem as a Second Order Cone Program by using large deviation inequalities, due to Bernstein. Apart from support and mean of the uncertain examples these Bernstein based relaxations make no further assumptions on the underlying uncertainty. Classifiers built using the proposed approach are less conservative, yield higher margins and hence are expected to generalize better than existing methods. Experimental results on synthetic and real-world datasets show that the proposed classifiers are better equipped to handle interval-valued uncertainty than state-of-the-art.

Single household domestic water heater design and control utilising PV energy: The untapped energy storage solution

Photovoltaic (PV) panels and electric domestic water heater with storage (DWH) are widely used in households in many countries. However, DWH should be explored as an energy storage mechanism before batteries when households have excess PV energy. Through a residential case study in Queensland, Australia, this paper presents a new optimized design and control solution to reduce water heating costs by utilizing existing DWH energy storage capacity and increasing PV self-consumption for water heating. The solution is produced by evaluating the case study energy profile and numerically maximizing the use of PV for DWH. A conditional probability matrix for different solar insolation and hot water usage days is developed to test the solution. Compared to other tariffs, this solution shows cost reduction from 20.8% to 63.3% This new solution could encourage solar households move to a more economical and carbon neutral water heating method.

Beyond fecundity control: which weeds are most containable?

1. Eradication is often the preferred strategy in the management of new weed invasions, but recent research has shown that the circumstances under which eradication can be achieved are highly constrained. Containment is a component of an eradication strategy and also a management objective in its own right. Just as for eradication, containment of a weed invasion should be attempted only if it is considered feasible. However, very little guidance exists for the assessment of containment feasibility for weeds. 2. Numerous factors have been proposed as influencing feasibility of containment, but those that relate to the potential for management of dispersal pathways and timely detection of new foci of infestation appear to be critical. Theory suggests that the rate of spread is largely driven by long-distance dispersal (LDD). However, LDD is generally unpredictable and often occurs for species that do not appear to be adapted for it. Furthermore, many (if not most) LDD events fail to give rise to new infestations. 3. As the probability of colonisation is related to the numbers of propagules immigrating ('propagule pressure') at a point in the landscape, dispersal pathways that move relatively large numbers of propagules simultaneously and/or repeatedly should most enhance weed spread. It is these pathways whose potential for management has the greatest bearing upon containment feasibility. A key impediment to containment is undetected spread; this need not occur through LDD and is more likely to occur through dispersal to lesser distances. 4. Synthesis and applications. Feasibility of containment should be viewed in terms of the effort required to reduce weed spread rate, as well as the effectiveness of relevant management actions. Where dispersal vectors are not readily manageable and the probability of detection via structured and/or unstructured surveillance is low, a much greater reliance upon fecundity control will be needed to contain a weed. A combination of empirical and theoretical approaches should be used to develop and refine estimates of containment feasibility. Such estimates will aid decision-making with regard to whether to attempt to reduce weed spread and assist in prioritisation of different weeds for containment.

Performance of slotted ALOHA satellite channels with finite buffer

The behaviour of the slotted ALOHA satellite channel with a finite buffer at each of the user terminals is studied. Approximate relationships between the queuing delay, overflow probabilities and buffer size are derived as functions of the system input parameters (i.e. the number of users, the traffic intensity, the transmission and the retransmission probabilities) for two cases found in the literature: the symmetric case (same transmission and retransmission probabilities), and the asymmetric case (transmission probability far greater than the retransmission probability). For comparison, the channel performance with an infinite buffer is also derived. Additionally, the stability condition for the system is defined in the latter case. The analysis carried out in the paper reveals that the queuing delays are quite significant, especially under high traffic conditions.

Complex carbohydrates: 2. The modes of binding of complex carbohydrates to concanavalin A - a computer modelling approach

The probable modes of binding of some complex carbohydrates, which have the trimannosidic core structure (Man3GlcNAc2), to concanavalin A (Con A) have been determined using a computer modelling technique. These studies show that Con a can bind to the terminal mannose residues of the trimannosidic core structure and to the internal mannosyl as well as to the terminal N-acetylglucosamine residues of the N-acetylglucosamine substituted trimannosidic core structure. The oligosaccharide with terminal mannose residues can bind in its minimum energy conformers, whereas the oligosaccharide with internal mannosyl and terminal N-acetylglucosamine residues can bind only in higher energy conformers. In addition the former oligosaccharide forms more hydrogen bonds with Con A than the latter. These results suggest that, for these oligosaccharides, the terminal mannose residue has a much higher probability of reaching the binding site than either the internal mannosyl or the terminal N-acetylglucosamine residues. The substitution of a bisecting N-acetylglucosamine residue on these oligosaccharides, affects significantly the accessibility of the residues which bind to Con A and thereby reduces their binding affinity. It thus seems that the binding affinity of an oligosaccharide to Con A depends not only on the number of sugar residues which possess free 3-, 4- and 6-hydroxyl groups but also on the accessibility of these sugar residues to Con A. This study also reveals that the sugar binding site of Con A is small and that the interactions between Con A and carbohydrates are extended slightly beyond the single sugar residue that is placed in the binding site.

Quantifying northward movement rates of eastern king prawns along eastern Australia

Movement rates of eastern king prawns, Melicertus plebejus (Hess), were estimated from historical and recent conventional tag-recapture information collected across eastern Australia. Data from three studies and 2,656 tag recaptures were used. Recaptured males and females both moved east–north-east in central Queensland and north–north-east in southern Queensland and New South Wales. Over a period of one year, the estimated transition matrix reflected the species strong northerly movement and the more complex longitudinal movement, showing a very high probability of eastern movement in central Queensland and almost negligible eastern or western movement in northern New South Wales. The high exchange probability between New South Wales and Queensland waters indicated that spatial assessment models with movement rates between state jurisdictions would improve the management of this single-unit stock.

A simple correction for multiple testing for single-nucleotide polymorphisms in linkage disequilibrium with each other

In this report, we describe a simple correction for multiple testing of single-nucleotide polymorphisms (SNPs) in linkage disequilibrium (LD) with each other, on the basis of the spectral decomposition (SpD) of matrices of pairwise LD between SNPs. This method provides a useful alternative to more computationally intensive permutation tests. Additionally, output from SNPSpD includes eigenvalues, principal-component coefficients, and factor "loadings" after varimax rotation, enabling the selection of a subset of SNPs that optimize the information in a genomic region.

Zygosity diagnosis in the absence of genotypic data: An approach using latent class analysis

For zygosity diagnosis in the absence of genotypic data, or in the recruitment phase of a twin study where only single twins from same-sex pairs are being screened, or to provide a test for sample duplication leading to the false identification of a dizygotic pair as monozygotic, the appropriate analysis of respondents' answers to questions about zygosity is critical. Using data from a young adult Australian twin cohort (N = 2094 complete pairs and 519 singleton twins from same-sex pairs with complete responses to all zygosity items), we show that application of latent class analysis (LCA), fitting a 2-class model, yields results that show good concordance with traditional methods of zygosity diagnosis, but with certain important advantages. These include the ability, in many cases, to assign zygosity with specified probability on the basis of responses of a single informant (advantageous when one zygosity type is being oversampled); and the ability to quantify the probability of misassignment of zygosity, allowing prioritization of cases for genotyping as well as identification of cases of probable laboratory error. Out of 242 twins (from 121 like-sex pairs) where genotypic data were available for zygosity confirmation, only a single case was identified of incorrect zygosity assignment by the latent class algorithm. Zygosity assignment for that single case was identified by the LCA as uncertain (probability of being a monozygotic twin only 76%), and the co-twin's responses clearly identified the pair as dizygotic (probability of being dizygotic 100%). In the absence of genotypic data, or as a safeguard against sample duplication, application of LCA for zygosity assignment or confirmation is strongly recommended.

Resistance fluctuation at the mobility edge

Following a Migdal-Kadanoff-type bond moving procedure, we derive the renormalisation-group equations for the characteristic function of the full probability distribution of resistance (conductance) of a three-dimensional disordered system. The resulting recursion relations for the first two cumulants, K, the mean resistance and K ~ t,he meansquare deviation of resistance exhibit a mobility edge dominated by large dispersion, i.e., K $ ’/ K=, 1, suggesting inadequacy of the one-parameter scaling ansatz.

Multiaction learning automata possessing ergodicity of the mean

Multiaction learning automata which update their action probabilities on the basis of the responses they get from an environment are considered in this paper. The automata update the probabilities according to whether the environment responds with a reward or a penalty. Learning automata are said to possess ergodicity of the mean if the mean action probability is the state probability (or unconditional probability) of an ergodic Markov chain. In an earlier paper [11] we considered the problem of a two-action learning automaton being ergodic in the mean (EM). The family of such automata was characterized completely by proving the necessary and sufficient conditions for automata to be EM. In this paper, we generalize the results of [11] and obtain necessary and sufficient conditions for the multiaction learning automaton to be EM. These conditions involve two families of probability updating functions. It is shown that for the automaton to be EM the two families must be linearly dependent. The vector defining the linear dependence is the only vector parameter which controls the rate of convergence of the automaton. Further, the technique for reducing the variance of the limiting distribution is discussed. Just as in the two-action case, it is shown that the set of absolutely expedient schemes and the set of schemes which possess ergodicity of the mean are mutually disjoint.

Characterization of north-eastern Australian environments using APSIM for increasing rainfed maize production

Recurring water stresses are a major risk factor for rainfed maize cropping across the highly diverse agro-ecological environments of Queensland (Qld) and northern New South Wales (NNSW). Enhanced understanding of such agro-ecological diversity is necessary to more consistently sample target production environments for testing and targeting release of improved germplasm, and to improve the efficiency of the maize pre-breeding and breeding programs of Qld and New South Wales. Here, we used the Agricultural Production Systems Simulator (APSIM) – a well validated maize crop model to characterize the key distinctive water stress patterns and risk to production across the main maize growing regions of Qld and NNSW located between 15.8° and 31.5°S, and 144.5° and 151.8°E. APSIM was configured to simulate daily water supply demand ratios (SDRs) around anthesis as an indicator of the degree of water stress, and the final grain yield. Simulations were performed using daily climatic records during the period between 1890 and 2010 for 32 sites-soils in the target production regions. The runs were made assuming adequate nitrogen supply for mid-season maize hybrid Pioneer 3153. Hierarchical complete linkage analyses of the simulated yield resulted in five major clusters showing distinct probability distribution of the expected yields and geographic patterns. The drought stress patterns and their frequencies using SDRs were quantified using multivariate statistical methods. The identified stress patterns included no stress, mid-season (flowering) stress, and three terminal stresses differing in terms of severity. The combined frequency of flowering and terminal stresses was highest (82.9%), mainly in sites-soils combinations in the west of Qld and NNSW. Yield variability across the different sites-soils was significantly related to the variability in frequencies of water stresses. Frequencies of water stresses within each yield cluster tended to be similar, but different across clusters. Sites-soils falling within each yield cluster therefore could be treated as distinct maize production environments for testing and targeting newly developed maize cultivars and hybrids for adaptation to water stress patterns most common to those environments.