The influence of pulsed redox conditions on soil phosphorus

The effects of eleven pulsed reduction-oxidation cycles (20 and 2 days respectively) on soil phosphorus (P) dynamics are compared for 12 soils having contrasting properties and overfertilised with respect to P. Incubation conditions simulated transient waterlogging of the soil profile and involved repeated sampling and analysis of both the solution and solid phase P forms. An initial increase in P concentration occurred upto and including the fourth full cycle was followed by a sharp decline in concentration for all but one soil. Accompanying changes in the main extractable forms of P, which appeared to be cumulative, could be summarised as a general decline in the organic P fraction and an overall increase in amorphous associated inorganic forms of P. The fact that up to 60% of the total soil P was demonstrated to change its sensitivity for a particular extractant suggests that these operationally defined P forms can experience substantial transformations. There was also a suggestion that certain changes in P forms may not be reversible. While the laboratory conditions represent an extreme situation changes in timing and frequency of intense precipitation events, as predicted in many climate change scenarios, may increase the risk of episodic soil waterlogging. The potential onset of reducing conditions even for periods of less than twenty days will influence soil P dynamics and short-term bioavailable P. Various mechanisms are involved but the robustness of sequential extraction procedures and general soil test methods (e.g. Olsen) for quantifying and reliably distinguishing specific soil P forms/associations are questioned

Confluence reduction for Markov automata

Markov automata are a novel formalism for specifying systems exhibiting nondeterminism, probabilistic choices and Markovian rates. Recently, the process algebra MAPA was introduced to efficiently model such systems. As always, the state space explosion threatens the analysability of the models generated by such specifications. We therefore introduce confluence reduction for Markov automata, a powerful reduction technique to keep these models small. We define the notion of confluence directly on Markov automata, and discuss how to syntactically detect confluence on the MAPA language as well. That way, Markov automata generated by MAPA specifications can be reduced on-the-fly while preserving divergence-sensitive branching bisimulation. Three case studies demonstrate the significance of our approach, with reductions in analysis time up to an order of magnitude

Use of partial least squares regression to impute SNP genotypes in Italian Cattle breeds

Background The objective of the present study was to test the ability of the partial least squares regression technique to impute genotypes from low density single nucleotide polymorphisms (SNP) panels i.e. 3K or 7K to a high density panel with 50K SNP. No pedigree information was used. Methods Data consisted of 2093 Holstein, 749 Brown Swiss and 479 Simmental bulls genotyped with the Illumina 50K Beadchip. First, a single-breed approach was applied by using only data from Holstein animals. Then, to enlarge the training population, data from the three breeds were combined and a multi-breed analysis was performed. Accuracies of genotypes imputed using the partial least squares regression method were compared with those obtained by using the Beagle software. The impact of genotype imputation on breeding value prediction was evaluated for milk yield, fat content and protein content. Results In the single-breed approach, the accuracy of imputation using partial least squares regression was around 90 and 94% for the 3K and 7K platforms, respectively; corresponding accuracies obtained with Beagle were around 85% and 90%. Moreover, computing time required by the partial least squares regression method was on average around 10 times lower than computing time required by Beagle. Using the partial least squares regression method in the multi-breed resulted in lower imputation accuracies than using single-breed data. The impact of the SNP-genotype imputation on the accuracy of direct genomic breeding values was small. The correlation between estimates of genetic merit obtained by using imputed versus actual genotypes was around 0.96 for the 7K chip. Conclusions Results of the present work suggested that the partial least squares regression imputation method could be useful to impute SNP genotypes when pedigree information is not available

Imaging in the European Heart Journal: the team of deputy and associate editors Chiara Bucciarelli-Ducci, JoAo L. Cavalcante, and Nina Ajmone-Marsan

Cardiolog

Metal release under anaerobic conditions of urban soils of four European cities

Urban soil contamination may represent an environmental threat in view of their proximity to humans. The ecological homogenization of urban areas has been postulated, and as the sources of pollution are the same in most European cities, it is possible that soil contamination is another factor of convergence. The current climate change with consequent increase of extreme rain events may affect the mobility of potentially toxic elements (PTE) thus increasing the risks. If the soil is submerged, Eh decreases and causes the solubilization of Fe and Mn oxides, which are important carriers of PTE. We compared the release of Cu, Pb, and Zn from 48 soils of four cities (namely Glasgow, Ljubljana, Sevilla, and Torino) when submerged for up to 30 days. A decrease of the redox potential was observed in all soils after a few days and an increase of Mn and then Fe in solution. Cu, Pb, and Zn were consequently released to the solution according to the general soil contamination. Despite the marked differences in soil properties, the reaction to anaerobiosis appeared to be similar in all samples indicating that waterlogging of urban soil contaminated with PTE may pose a serious environmental risk and substantiating the hypothesis of ecological convergence

Multiple-breed genomic evaluation by principal component analysis in small size populations

In this study, the effects of breed composition and predictor dimensionality on the accuracy of direct genomic values (DGV) in a multiple breed (MB) cattle population were investigated. A total of 3559 bulls of three breeds were genotyped at 54 001 single nucleotide polymorphisms: 2093 Holstein (H), 749 Brown Swiss (B) and 717 Simmental (S). DGV were calculated using a principal component (PC) approach for either single (SB) or MB scenarios. Moreover, DGV were computed using all SNP genotypes simultaneously with SNPBLUP model as comparison. A total of seven data sets were used: three with a SB each, three with different pairs of breeds (HB, HS and BS), and one with all the three breeds together (HBS), respectively. Editing was performed separately for each scenario. Reference populations differed in breed composition, whereas the validation bulls were the same for all scenarios. The number of SNPs retained after data editing ranged from 36 521 to 41 360. PCs were extracted from actual genotypes. The total number of retained PCs ranged from 4029 to 7284 in Brown Swiss and HBS respectively, reducing the number of predictors by about 85% (from 82% to 89%). In all, three traits were considered: milk, fat and protein yield. Correlations between deregressed proofs and DGV were used to assess prediction accuracy in validation animals. In the SB scenarios, average DGV accuracy did not substantially change when either SNPBLUP or PC were used. Improvement of DGV accuracy were observed for some traits in Brown Swiss, only when MB reference populations and PC approach were used instead of SB-SNPBLUP (+10% HBS, +16%HB for milk yield and +3% HBS and +7% HB for protein yield, respectively). With the exclusion of the abovementioned cases, similar accuracies were observed using MB reference population, under the PC or SNPBLUP models. Random variation owing to sampling effect or size and composition of the reference population may explain the difficulty in finding a defined pattern in the results

Milk Products in Bread Making

The topic was the usage of milk in bread making. Initial investigations in bread making using milk were aimed at learning how to prepare milk best suited for bread making and finding the proper quantities for optimum results. The convenience, economy, and uniformity of dry milk solids greatly increased their usage by bakers. These advantages have been further augmented by research and experimentation by the dry milk producers, which have resulted in a product which would appreciably improve bread quality. Milk may be altered both in physical and chemical properties by heating. The article goes on to give more information on milk usage in baking

BOVITA: a first overview on genome-wide genetic diversity of Italian autochthonous cattle breeds

Analysis of genomic data is increasingly becoming part of the livestock industry and is an invaluable resource for effective management of breeding programs in small populations. The recent availability of genome-wide SNP panels allows providing background information concerning genome structure in domestic animals, opening new perspectives to livestock genetics. BOVITA was established to join local efforts and resources for the genomic characterization of Italian local cattle breeds. Despite the growing diffusion of some cosmopolite specialized breeds, several autochthonous breeds are still bred in Italy. The main aim of the BOVITA is to investigate the genomic structure of Italian local cattle breeds, to provide information on their genetic status that will be useful for the management of the genetic variability, as a contribution to biodiversity conservation and prioritization actions. A total of about 800 animals (20-32 per breed) belonging to thirty Italian cattle breeds (Agerolese, Bar\ue0-Pustertaler, Burlina, Cabannina, Calvana, Chianina, Cinisara, Garfagnina, Italian Brown, Italian Holstein, Italian Simmental, Marchigiana, Maremmana, Modenese, Modicana, Mucca Pisana, Pezzata Rossa d\u2019Oropa, Piedmontese, Pinzgau, Podolica, Pontremolese, Pustertaler, Reggiana, Rendena, Romagnola, Rossa Siciliana, Sarda, Sardo-Bruna, Sardo-Modicana and Ottonese-Varzese) and two cosmopolitan breeds (Charolaise and Limousine) genotyped with the Illumina BovineSNP50 v2 BeadChip array were collected for the analysis. The genotypes of several breeds were detected in the frame of the project, whereas for some breeds these data are derived by previous studies. The dataset will be analyzed to: study several aspects of population genetic diversity, multi-dimensional scaling plot, population structure, linkage disequilibrium, and runs of homozygosity. In addition, comparative analysis of conserved haplotypes will be conducted to identify genomic segments under selection pressure. Such information also provides important insights into the mechanisms of evolution and is useful for the annotation of significant functional genomics regions. Data analysis will also be useful to select SNPs suitable for parentage test and breed genetic traceability. The analysis of the data will pinpoint the genetic distinctiveness of Italian breeds. Moreover, the obtained results contribute to a better characterization of history and genetic structure of Italian cattle breeds

Uncertainty quantification for kinetic models in socio-economic and life sciences

Kinetic equations play a major rule in modeling large systems of interacting particles. Recently the legacy of classical kinetic theory found novel applications in socio-economic and life sciences, where processes characterized by large groups of agents exhibit spontaneous emergence of social structures. Well-known examples are the formation of clusters in opinion dynamics, the appearance of inequalities in wealth distributions, flocking and milling behaviors in swarming models, synchronization phenomena in biological systems and lane formation in pedestrian traffic. The construction of kinetic models describing the above processes, however, has to face the difficulty of the lack of fundamental principles since physical forces are replaced by empirical social forces. These empirical forces are typically constructed with the aim to reproduce qualitatively the observed system behaviors, like the emergence of social structures, and are at best known in terms of statistical information of the modeling parameters. For this reason the presence of random inputs characterizing the parameters uncertainty should be considered as an essential feature in the modeling process. In this survey we introduce several examples of such kinetic models, that are mathematically described by nonlinear Vlasov and Fokker--Planck equations, and present different numerical approaches for uncertainty quantification which preserve the main features of the kinetic solution.Comment: To appear in "Uncertainty Quantification for Hyperbolic and Kinetic Equations