Mechanism of tribo-chemical reactions of ionic liquids on titanium alloys

In this paper, the tribological, the tribo-chemical reaction mechanisms and desorption properties of three ionic liquids (ILs), [Bu3MeP][ Tf2N], [Bu3MeN][ Tf2N] and [Bu3MeP][ (MeO)2PO2], in contact with titanium and under vacuum conditions are studied with the CATRI © UHV Tribometer developed by IK4-TEKNIKER [1]. The two ILs containing the bis(trifluoromethanesulfonyl)amide anion presented lower coefficient of friction compared to that having the dimethyl phosphate anion. The tribodesorption study revealed that it is required an induction period to decrease the friction coefficient. The end of this period is accelerated in the case of trifluoromethane ionic liquids by the CF3+ release. Hence, the CF3+ reacts with the titanium surface generating a titanium fluoride tribolayer that could act like a catalyst to generate the tribodesorption of ionic liquid cation fragments (CH3+, C2H5+, C3H7+, C4H9+). The XPS analysis confirmed the generation of a boundary film, comprising of sulfide and inorganic fluoride, and being possibly the responsible of decreasing the friction coefficient. The [Bu3MeP][MeO)2PO2] ionic liquid required a long induction period, it did not form any tribolayer and no reduction of friction coefficient, yielding instead a high abrasion and adhesion mechanism. Thus, it can be concluded that bis(trifluoromethanesulfonyl)amide anion is more effective than dimethylphosphate in generating a surface protective film on the titanium surface under the selected test conditions and the testing methodology seems to be useful to understand the tribodesorption mechanism.The partners would like to acknowledge the financing to the Austrian Government financing of COMET K2 Excellence Centre of Tribology called X-Tribology to carry out this research collaborative activity. The authors also would like to acknowledge the financing of the EMAITEK Programme by the Basque Country

Genomic prediction of grain yield in a barley MAGIC population modelling genotype per environment interaction

Multi-parent Advanced Generation Inter-crosses (MAGIC) lines have mosaic genomes that are generated shuffling the genetic material of the founder parents following predefined crossing schemes. In cereal crops, these experimental populations have been extensively used to investigate the genetic bases of several traits and dissect the genetic bases of epistasis. In plants, genomic prediction models are usually fitted using either diverse panels of mostly unrelated accessions or individuals of biparental families and several empirical analyses have been conducted to evaluate the predictive ability of models fitted to these populations using different traits. In this paper, we constructed, genotyped and evaluated a barley MAGIC population of 352 individuals developed with a diverse set of eight founder parents showing contrasting phenotypes for grain yield. We combined phenotypic and genotypic information of this MAGIC population to fit several genomic prediction models which were cross-validated to conduct empirical analyses aimed at examining the predictive ability of these models varying the sizes of training populations. Moreover, several methods to optimize the composition of the training population were also applied to this MAGIC population and cross-validated to estimate the resulting predictive ability. Finally, extensive phenotypic data generated in field trials organized across an ample range of water regimes and climatic conditions in the Mediterranean were used to fit and cross-validate multi-environment genomic prediction models including GE interaction, using both genomic best linear unbiased prediction and reproducing kernel Hilbert space along with a non-linear Gaussian Kernel. Overall, our empirical analyses showed that genomic prediction models trained with a limited number of MAGIC lines can be used to predict grain yield with values of predictive ability that vary from 0.25 to 0.60 and that beyond QTL mapping and analysis of epistatic effects, MAGIC population might be used to successfully fit genomic prediction models. We concluded that for grain yield, the single-environment genomic prediction models examined in this study are equivalent in terms of predictive ability while, in general, multi-environment models that explicitly split marker effects in main and environmentalspecific effects outperform simpler multi-environment models

ClimBar : An Integrated Approach to Evaluate and Utilize Genetic Diversity

European agriculture anticipates an unprecedented combination of stress factors, production threats and quality needs due to climate change. Various regions of Europe will be affected differently. Barley & wheat domestication, and landrace formation in Europe, were under very different climates than those emerging now. Alleles needed for sustainable, resilient, quality yields in a changed climate are likely not combined in current haplotypes of elite barley cultivars. These alleles are likely found in diverse landraces and wild relatives in the Mediterranean basin and Fertile Crescent -- areas that prefigure expected climate change. New precision, high-throughput phenotyping tools are essential to find trait-allele associations needed for future-climate breeding. Combining genetics, genomics, modelling, molecular biology, morphology, and physiology, ClimBar takes an interdisciplinary approach to develop a strategy for breeding an increased resilience to climate change in barley. ClimBar, a new project under the framework of FACCE ERA-NET Plus Joint Programming Initiative on Climate Smart Agriculture, will identify genome regions, genes, and alleles conferring the traits needed to breed resilient barley varieties adapted to the climatic conditions predicted for 2070 in different European environments. Adapted, resilient germplasm created using ClimBar data, tools and models will provide food-chain security, economic stability and environmental sustainability. Website: http://plen.ku.dk/english/research/plant_soil/breeding/quality/climbar

Making sense of entertainment

This contribution explores the relationship of emotion and cognition in entertainment experience. Drawing on the reflective model of aesthetic experience (Cupchik, 1995) and the concept of appreciation (Oliver & Bartsch, 2010), we propose a multi-level view of affective processing that includes simple affect schemata as well as more elaborate forms of sociomoral reasoning that build on this basic layer of emotional meaning. To better understand how affective factors can stimulate or impede cognitive elaboration processes, we review research on motivated cognition that has dealt with the influence of arousal, valence, and personal relevance on cognitive depth. The role of affect in defensive information processing (i.e., the motivated neglect or denial of information) is also considered. Specifically, we discuss how research on motivated cognition can help explain thought-provoking entertainment experiences, and the potential of such experiences to stimulate self-reflection and personal growth

Openness in product and process innovation

Electronic version of an article published as International Journal of Innovation Management, Vol. 16, Iss. 4, 2012, art. 1250020, pp. 1-24. DOI: 10.1142/S1363919612003812 © Imperial College Press. http://www.worldscientific.com/doi/abs/10.1142/S1363919612003812.Open innovation has generally been explored in terms of improved innovation performance vis-à-vis product/service innovation performance. However, process innovation is often ignored in the open innovation literature. In this study, we assess the impact of openness on innovation in products/services, and also on process innovation, drawing on a large-scale sample of Australian firms. In essence, we find that open innovation models are useful for firms seeking to innovate in processes as well as products and services. However, we find that openness to external information sources may, after a time, lead to decreasing marginal returns as measured by innovation performance. We also observe that, within our sample, the proposed complementarities between internal and external knowledge are generally only evident as precursors to the introduction of new products and services, and may not be as beneficial in stimulating process innovations. It is also shown by our study that investment in absorptive capacity has a declining marginal effect on the innovation performance of new processes, but not on the introduction of new products and services.Fang Huang and John Ric

Barley grain (1,3;1,4)-β-glucan content:effects of transcript and sequence variation in genes encoding the corresponding synthase and endohydrolase enzymes

The composition of plant cell walls is important in determining cereal end uses. Unlike other widely consumed cereal grains barley is comparatively rich in (1,3;1,4)-β-glucan, a source of dietary fibre. Previous work showed Cellulose synthase-like genes synthesise (1,3;1,4)-β-glucan in several tissues. HvCslF6 encodes a grain (1,3;1,4)-β-glucan synthase, whereas the function of HvCslF9 is unknown. Here, the relationship between mRNA levels of HvCslF6, HvCslF9, HvGlbI (1,3;1,4)-β-glucan endohydrolase, and (1,3;1,4)-β-glucan content was studied in developing grains of four barley cultivars. HvCslF6 was differentially expressed during mid (8-15 DPA) and late (38 DPA) grain development stages while HvCslF9 transcript was only clearly detected at 8-10 DPA. A peak of HvGlbI expression was detected at 15 DPA. Differences in transcript abundance across the three genes could partially explain variation in grain (1,3;1,4)-β-glucan content in these genotypes. Remarkably narrow sequence variation was found within the HvCslF6 promoter and coding sequence and does not explain variation in (1,3;1,4)-β-glucan content. Our data emphasise the genotype-dependent accumulation of (1,3;1,4)-β-glucan during barley grain development and a role for the balance between hydrolysis and synthesis in determining (1,3;1,4)-β-glucan content, and suggests that other regulatory sequences or proteins are likely to be involved in this trait in developing grain.Guillermo Garcia-Gimenez, Joanne Russell, Matthew K. Aubert, Geoffrey B. Fincher, Rachel A. Burton, Robbie Waugh, Matthew R. Tucker, Kelly Housto