Gamma ray-induced mutations have been widely used to improve existing crop germplasm and create novel genetic variation. In the current study, a multi-year experiment was carried out to induce and isolate mutants with desirable agro-morphological traits and improved grain hardness through evaluation of induced mutant populations generated in soft-textured wheat variety HPW 89 irradiated with gamma ray dose of 250, 300 and 350 Gy. Mutagen sensitivity studies revealed a higher frequency of biological damage and seedling mortality for doses beyond 300 Gy in the M1 generation. However, the mutagenic treatments in the M2–3 populations significantly altered the magnitude of the biometrical traits. Results from the variability and association studies among traits showed that biological yield per plant, 1000-grain weight, spike length, grains per spike and plant height may be prioritized for higher genetic gain and could be used as selection criteria parameters. Multivariate analysis indicated induction of heterogeneity among mutant populations. Overall, 250–300 Gy doses were found ideal for a successful wheat mutation programme and 293 agro-morphologically superior wheat mutants were identified, out of which 108 had semi-hard grain texture based on single kernel characterization system. Among these, nine mutants were found to have the highest grain hardness index due to induced changes in one or both puroindoline genes. Hence, these mutants identified for several traits along with harder grain texture will serve as important genetic resource in future wheat-breeding programmes.

Improvement in genetic gains of crops could be achieved by phenomics' characterization of agronomic, physiological and stress-related traits. Molecular and strategic breeding programmes require broad range of foreground and background phenotypic information for crop improvement. The current experiment was performed on 123 advanced soybean (Glycine max L.) genotypes including seven local lines belongs to four different maturity groups (000-lV) to estimate the endogenous potential of various yield-related traits. The experimental trial was repeated for two cropping seasons. Four traits out of six, yield per plant (YPP), number of seeds per plant, number of pods per plant and plant height (PH), showed maximum variation (CV%) that directly correlate with variability in the subjected population. PH, number of pods, 100-seed weight and YPP showed strong positive correlation in both years. Among the principal components, factors 1 and 2 showed maximum contribution in phenotypic variability ranges from 19 to 48.5% and 26 to 47.7% in the first and second years, respectively. Number of pods showed significant positive correlation with genotypes in both years. Dendrogram showed two distinct groups of soybean genotypes. Genetic variation and association among the accessions is indispensable for effective conservation and utilization of germplasm. Principal component analysis helps to identify the diverse genotypes that will be used as a parent for various breeding programmes. These phenotypic data will be used for detection of heat stress-related quantitative trait loci with genotypic data in genome-wide association studies experiments.

Traditional techniques for calculating outstanding claim liabilities such as the chain-ladder are notoriously at risk of being distorted by outliers in past claims data. Unfortunately, the literature in robust methods of reserving is scant, with notable exceptions such as Verdonck & Debruyne (2011, Insurance: Mathematics and Economics, 48, 85–98) and Verdonck & Van Wouwe (2011, Insurance: Mathematics and Economics, 49, 188–193). In this paper, we put forward two alternative robust bivariate chain-ladder techniques to extend the approach of Verdonck & Van Wouwe (2011, Insurance: Mathematics and Economics, 49, 188–193). The first technique is based on Adjusted Outlyingness (Hubert & Van der Veeken, 2008. Journal of Chemometrics, 22, 235–246) and explicitly incorporates skewness into the analysis while providing a unique measure of outlyingness for each observation. The second technique is based on bagdistance (Hubert et al., 2016. Statistics: Methodology, 1–23) which is derived from the bagplot; however; it is able to provide a unique measure of outlyingness and a means to adjust outlying observations based on this measure.

Furthermore, we extend our robust bivariate chain-ladder approach to an N-dimensional framework. The implementation of the methods, especially beyond bivariate, is not trivial. This is illustrated on a trivariate data set from Australian general insurers and results under the different outlier detection and treatment mechanisms are compared.

Nick Martin was a doctoral student of mine at the University of Birmingham in the mid 1970s. In this review, I discuss two of Nick’s earliest and most seminal contributions to the field of behavior genetics. First, Martin and Eaves’ (1977) extension of the model-fitting approach to multivariate data, which laid the theoretical groundwork for a generation of multivariate behavior genetic studies. Second, the Martin et al.’s (1978) manuscript on the power of the classical twin design, which showed that thousands of twin pairs would be required in order to reliably estimate components of variance, and has served as impetus for the formation of large-scale twin registries across the world. I discuss these contributions against the historical backdrop of a time when we and others were struggling with the challenge of figuring out how to incorporate gene-by-environment interaction, gene–environment correlation, mate selection and cultural transmission into more complex genetic models of human behavior.

Ecological impacts from invasive plants that have been identified include reductions in biodiversity, changes in resource cycling, and disruptions of ecosystem function. To mitigate these negative ecological impacts, managers work to remove invasive plants. However, removal does not necessarily immediately lead to a return to the uninvaded ecological state. Similarly, the accumulation rate of ecological impacts following invader establishment is almost entirely unknown for most species, hindering identification of optimal management times. The accumulation and loss (so-called legacy effects) of impacts following invader establishment and removal represent an “invasion shadow.” To begin to understand invasion shadows, we measured the changes in biotic and abiotic ecological impacts during establishment and following removal of the forest understory invader Japanese stiltgrass. We found that when the abiotic metrics were considered, seeded areas became more functionally similar to the invaded landscape and removed areas became more similar to the uninvaded landscape. However, while the plant community did not change in a 3-yr period during a new invasion, following invader removal, it became less similar to both the invaded and uninvaded landscape altogether, suggesting legacies. Surprisingly, all changes occurred almost immediately and persisted following invader establishment and removal. Our results show, at least in a 3-yr period, that ecosystems can respond to changes in invader abundance, and in some cases simply removing the invader could result in long-term changes to the resident plant community.

Cover crops represent a potentially important biological filter during weed community assembly in agroecosystems. This filtering could be considered directional if different cover-crop species result in weed communities with predictably different species composition. We examined the following four questions related to the potential filtering effects of cover crops in a field experiment involving five cover crops grown in monoculture and mixture: (1) Do cover crops differ in their effect on weed community composition? (2) Is competition more intense between cover crops and weeds that are in the same family or functional group? (3) Is competition more intense across weed functional types in a cover-crop mixture compared with cover crops grown in monocultures? (4) Within a cover-crop mixture, is a higher seeding rate associated with more effective biotic filtering of the weed community? We found some evidence that cover crops differentially filtered weed communities and that at least some of these filtering effects were due to differential biomass production across cover-crop species. Monocultures of buckwheat and sorghum–sudangrass reduced the number of weed species relative to the no-cover-crop control by an average of 36 and 59% (buckwheat) and 25 and 40% (sorghum–sudangrass) in 2011 and 2012, respectively. We found little evidence that competition intensity was dependent upon the family or functional classification of the cover crop or weeds, or that cover-crop mixtures were stronger assembly filters than the most effective monocultures. Although our results do not suggest that annual cover crops exert strong directional filtering during weed community assembly, our methodological framework for detecting such effects could be applied to similar future studies that incorporate a greater number of cover-crop species and are conducted under a greater range of cover-cropping conditions.

Agroecosystems are inherently complex, and practices aimed at managing one component of the system can have unintended consequences for other components of the system. Management decisions, therefore, can be improved by assessing and understanding the multivariate nature of agricultural systems and the multifunctional character of particular agricultural management practices. The act of simultaneously assessing and evaluating multiple characteristics or functions in agriculture also can be a valuable education and extension activity, because it draws on active and experiential methods of learning and because the process effectively reveals important functions and tradeoffs associated with agroecosystems and their management. Here we introduce a tool (the spider plot) and present a case-study exercise in which we used this tool to evaluate the multiple characteristics and functions of different cover crops within a field day workshop format. We also provide examples of how this approach could be used to assess other management practices or properties of agroecosystems and communicate multivariate concepts within a weed science classroom or extension environment.

The dynamics of soil seed banks in crop rotations of corn, soybean, and winter wheat were investigated to determine whether weed seed inputs associated with the winter wheat phases of the rotation were present in the readily germinable fraction of the seed bank in subsequent phases. Two studies were conducted, each in chisel-plowed systems. In one study, we compared seed banks in plots after 8 yr of corn grown continuously and with rotation that included winter wheat and soybean. A second study followed seed bank composition and abundance for 3 yr in plots that were planted to corn and soybean in successive years after planting to winter wheat. Seed banks were measured by direct germination in a heated greenhouse. In the first study, seed banks in plots planted to continuous corn (never planted to winter wheat) and the corn phase of the rotation (winter wheat planted 3 yr earlier) did not differ in species composition or abundance despite significant differences in seed banks in plots that had been planted to winter wheat the previous season. In the second study, seed bank abundance and composition in plots planted to winter wheat in 2001 rapidly changed after planting of corn and soybean in 2002 and 2003, respectively. Data from the two experiments suggest that seed banks in annual row crops experience rapid change in composition and abundance and can be strongly influenced by the most recent crop. This could limit our ability to infer longer term trends associated with changes in management practices from studies of soil seed banks.

Consider the problem of drawing random variates (X1, …, Xn) from a distribution where the marginal of each Xi is specified, as well as the correlation between every pair Xi and Xj. For given marginals, the Fréchet-Hoeffding bounds put a lower and upper bound on the correlation between Xi and Xj. Any achievable correlation between Xi and Xj is a convex combination of these bounds. We call the value λ(Xi, Xj) ∈ [0, 1] of this convex combination the convexity parameter of (Xi, Xj) with λ(Xi, Xj) = 1 corresponding to the upper bound and maximal correlation. For given marginal distributions functions F1, …, Fn of (X1, …, Xn), we show that λ(Xi, Xj) = λij if and only if there exist symmetric Bernoulli random variables (B1, …, Bn) (that is {0, 1} random variables with mean ½) such that λ(Bi, Bj) = λij. In addition, we characterize completely the set of convexity parameters for symmetric Bernoulli marginals in two, three, and four dimensions.

The genetic influences on aging-related phenotypes, including cognition and depression, have been well confirmed in the Western populations. We performed the first twin-based analysis on cognitive performance, memory and depression status in middle-aged and elderly Chinese twins, representing the world's largest and most rapidly aging population. The sample consisted of 384 twin pairs with a median age of 50 years. Cognitive function was measured using the Montreal Cognitive Assessment (MoCA) scale; memory was assessed using the revised Wechsler Adult Intelligence scale; depression symptomatology was evaluated by the self-reported 30-item Geriatric Depression (GDS-30)scale. Both univariate and multivariate twin models were fitted to the three phenotypes with full and nested models and compared to select the best fitting models. Univariate analysis showed moderate-to-high genetic influences with heritability 0.44 for cognition and 0.56 for memory. Multivariate analysis by the reduced Cholesky model estimated significant genetic (rG = 0.69) and unique environmental (rE = 0.25) correlation between cognitive ability and memory. The model also estimated weak but significant inverse genetic correlation for depression with cognition (-0.31) and memory (-0.28). No significant unique environmental correlation was found for depression with other two phenotypes. In conclusion, there can be a common genetic architecture for cognitive ability and memory that weakly correlates with depression symptomatology, but in the opposite direction.

Asthma and hay fever are likely to share hundreds if not thousands of genetic risk variants. Despite this, the extent to which the power to identify shared risk variants could be improved by considering information from both diseases when designing or analyzing genetic studies has not been studied in detail. Simulations were performed to quantify the power to detect an association between case-control status and a bi-allelic risk variant shared between asthma and hay fever across a range of disease and genetic models, as well as different ascertainment and analytical strategies. For a fixed sample size, when designing a new genome-wide association study (GWAS), selecting for genotyping cases with both asthma and hay fever (A+H+), and controls with neither disease (A-H-) was the study design that provided the greatest power to identify a shared risk variant. On the other hand, when analyzing an existing GWAS, power was greatest across a wide range of scenarios, when cases were defined as individuals who suffered from either disease (A+ or H+) and controls as those who suffered from neither (A-H-). Bivariate analysis of asthma and hay fever provided comparable but slightly decreased power. In conclusion, new GWAS can be designed and existing GWAS reanalyzed more efficiently to identify risk variants for allergic disease by using ascertainment or analytical strategies that consider both asthma and hay fever information.

This study aimed at using a multivariate approach to describe the body structure of Djallonke sheep in northern Ghana and to determine which approach explains better the variation in body composition. Live weight (LW) and linear body measurements including heart girth (HG), neck girth (NG), chest depth (CD), height at withers (HW), rump height (RH), body length (BL) and pin-bone width (PBW) were obtained from 172 sheep aged between two and three years. The fixed effects of sex and age were tested using the general linear model (GLM) while the Nearest Neighbor method of Hierarchical Cluster Analysis was used to group body traits into clusters. Principal Component Factor Analysis was used to describe the variation in body traits where extracted factors were varimax rotated to enhance interpretability. The analysis of variance revealed significant (P < 0.01) differences in the morphological traits of the two sexes with higher values recorded for the male in all traits examined except in PBW, which was insignificant (P > 0.05). Age had no significant influence (P > 0.05) on the body traits. The sheep weighed 26.92 ± 0.89 kg averagely and had averages of other body measurements to be: 71.74 ± 1.23, 40.52 ± 0.79, 27.73 ± 0.52, 60.72 ± 0.86, 59.61 ± 0.87, 58.87 ± 1.06 and 12.81 ± 0.23 cm for HG, NG, CD, HW, RH, BL and PBW, respectively. The product moments of correlation were positive and significant (P < 0.05, 0.01; r = 0.18–0.99) for all pairs of traits. The body traits were categorized mainly into two clusters with the first cluster comprising the HG, HW, RH and BL while NG, CD and PBW formed the second cluster. The grouping of the traits was slightly different in Factor analysis where two underlying principal components (PC) were extracted to discern the variance structure of the Djallonke sheep. The first principal component which consisted of CD, HW, RH, BL and PBW explained 61.26 percent and the second, 12.92 percent thereby giving a maximum of 74.17 percent generalized variance in body measurements. The traits loaded on the first principal component are closely associated with bone growth hence describing the general body size conformation while the traits (HG and NG) on the second component seem to describe only the thoracic region. It can be concluded that both the Hierarchical Cluster analysis and the Factor analysis grouped body traits similarly but the later is to be recommended because of the additional ability of indicating the amount of variation explained by the developed factors.

This paper presents a joint use of the T2 chart and total inertial tolerancingfor process control. Here, we will show an application of these approaches in the case ofthe machining of mechanical workpieces using a cutting tool. When a cutting tool inmachining impacts different manufactured dimensions of the workpiece, there is acorrelation between these parameters when the cutting tool has maladjustment due to badsettings. Thanks to total inertial steering, the correlation structure is known. Thispaper shows how T2 charts allow one to take thiscorrelation into account when detecting the maladjustment of the cutting tool. Then thetotal inertial steering approach allows one to calculate the value of tool offsets inorder to correct this maladjustment. We will present this approach using a simpletheoretical example for ease of explanation.