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This highlights trophic evolution as a key element in enhancing reef fish diversification.Authors have explored the photo-physical properties of Ho3+-Yb3+ doped BaTiO3 nanocrystals and proposed an intuitive method to probe temperature and crystal phase structure of the matrix. Structural phase change of doped crystals was analyzed in terms of their X-ray diffraction, and it was confirmed through second harmonic generation. We give insights on upconversion of energy of light-emission in Ho3+-Yb3+ BaTiO3 nanocrystals upon a 980 nm laser-light excitation and subsequently, the excited state dynamics were studied with the help of dependence of upconversion luminescence on excitation power and measuring-temperature. To understand the nature of occupancies of the Ho3+ ions at the Ti- and Ba-sites, we performed site-selective, time-resolved spectroscopic measurements at various crystal phases. Based on the lifetime analysis, it is inferred that the Ho3+ ions are present at two types of sites in barium titanate lattice. One of those is the 6-coordinated Ti-site of low symmetry, while the other one is the 12-coordinated Ba-site of higher symmetry. The upconversion emission of the nanocrystals are found to be temperature-sensitive (12 to 300 K), indicating possible use as a self-referenced temperature probe. An analysis of the temperature dependent emissions from 5F4 and 5S2 levels of Ho3+ ions, gives a maximum value of temperature sensitivity ~ 0.0095 K-1 at 12 K. Furthermore, we observe a sharp change in the luminescence intensity at ~180 K due to a ferroelectric phase change of the sample. The correlation of upconversion luminescence with the results of X-ray diffraction and second harmonic generation at different crystal phases implies that the frequency upconversion may be used as a probe of structural change of the lattice.Worldwide, maize (Zea mays L.) is considered an important food and fodder crop. Compost as a soil amendment and potassium (K) could enhance the maize yield. Therefore, two field experiments were carried out in the two seasons 2017 and 2018 to study the effects of compost at three levels and four forms of potassium fertilization on the yellow maize hybrid 'Pioneer SC 30N11' yield components. To conduct the field trials, a split plot system in three replications was established. Three compost levels (0, 5 and 10 ton/ha) were in the main plots, and four potassium forms (untreated, nano-potassium fertilizer, humic acid and potassium sulfate) were in the subplots. Plot size was 10.50 m2, with 5 ridges with 3 m length and 0.7 m width. The results indicated that the application of compost (as organic manure) and the potassium forms significantly affected the plant height, ear length, grains number/rows, grains number/ear, 100- grain weight, straw and biological yields, grain protein and K contents in both seasons. Increasing the compost from 5 to 10 ton/ha increased the yield, its components, protein and K contents. The foliar application of nano-potassium followed by humic acid increased all the studied characteristics. The interaction between compost manure (10 ton/ha) and nano-potassium (500 cm3/ha) or humic acid (10 ton/ha) recorded the highest mean values for all parameters during both harvest seasons.Genomic selection has become a reality in plant breeding programs with the reduction in genotyping costs. Especially in maize breeding programs, it emerges as a promising tool for predicting hybrid performance. The dynamics of a commercial breeding program involve the evaluation of several traits simultaneously in a large set of target environments. AZD1775 concentration Therefore, multi-trait multi-environment (MTME) genomic prediction models can leverage these datasets by exploring the correlation between traits and Genotype-by-Environment (G×E) interaction. Herein, we assess predictive abilities of univariate and multivariate genomic prediction models in a maize breeding program. To this end, we used data from 415 maize hybrids evaluated in 4 years of second season field trials for the traits grain yield, number of ears, and grain moisture. Genotypes of these hybrids were inferred in silico based on their parental inbred lines using single nucleotide polymorphisms (SNPs) markers obtained via genotyping-by-sequencing (GBS). Because genotypic information was available for only 257 hybrids, we used the genomic and pedigree relationship matrices to obtain the H matrix for all 415 hybrids. Our results demonstrated that in the single-environment context the use of multi-trait models was always superior in comparison to their univariate counterparts. Besides that, although MTME models were not particularly successful in predicting hybrid performance in untested years, they improved the ability to predict the performance of hybrids that had not been evaluated in any environment. However, the computational requirements of this kind of model could represent a limitation to its practical implementation and further investigation is necessary.Recombination is a central biological process with implications for many areas in the life sciences. Yet we are only beginning to appreciate variation in the recombination rate along the genome and among individuals, populations and species. Spurred by technological advances, we are now able to bring variation in this key biological parameter to centre stage. Here, we review the conceptual implications of recombination rate variation and guide the reader through the assumptions, strengths and weaknesses of genomic inference methods, including population-based, pedigree-based and gamete-based approaches. Appreciation of the differences and commonalities of these approaches is a prerequisite to formulate a unifying and comparative framework for understanding the molecular and evolutionary mechanisms shaping, and being shaped by, recombination.To estimate dementia incidence rates using Australian administrative datasets and compare the characteristics of people identified with dementia across different datasets. This data linkage study used a cohort of 267,153 from the Australian 45 and Up Study. Participants completed a survey in 2006-2009 and subsequent dementia was identified through pharmaceutical claims, hospitalisations, aged care eligibility assessments, care needs at residential aged care entry and death certificates. Age-specific, and age-standardised incidence rates, incidence rate ratios and survival from first dementia diagnosis were estimated. Estimated age-standardised dementia incidence rates using all linked datasets was 16.8 cases per 1000 person years for people aged 65+. Comparing incidence rates to the global published rates suggested 77% of cases were identified but this varied by age with highest coverage among those aged 80-84 years (92%). Incidence rate ratios were inconsistent across datasets for sex, socio-economic disadvantage, size of support network, marital status, functional limitations and diabetes.