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A Kettunen

Publications and source records attributed to A Kettunen.

4 recordsLinked to original sources
PubMed2001

Percent G+C profiling accurately reveals diet-related differences in the gastrointestinal microbial community of broiler chickens.

Broiler chickens from eight commercial farms in Southern Finland were analyzed for the structure of their gastrointestinal microbial community by a nonselective DNA-based method, percent G+C-based profiling. The bacteriological impact of the feed source and in-farm whole-wheat amendment of the diet was assessed by percent G+C profiling. Also, a phylogenetic 16S rRNA gene (rDNA)-based study was carried out to aid in interpretation of the percent G+C profiles. This survey showed that most of the 16S rDNA sequences found could not be assigned to any previously known bacterial genus or they represented an unknown species of one of the taxonomically heterogeneous genera, such as Ruminococcus or Clostridium. The data from bacterial community profiling were analyzed by t-test, multiple linear regression, and principal-component statistical approaches. The percent G+C profiling method with appropriate statistical analyses detected microbial community differences smaller than 10% within each 5% increment of the percent G+C profiles. Diet turned out to be the strongest determinant of the cecal bacterial community structure. Both the source of feed and local feed amendment changed the bacteriological profile significantly, whereas profiles of individual farms with identical feed regimens hardly differed from each other. This suggests that the management of typical Finnish farms is relatively uniform or that hygiene on the farm, in fact, has little impact on the structure of the cecal bacterial community. Therefore, feed compounders should have a significant role in the modulation of gut microflora and consequently in prevention of gastrointestinal disorders in farm animals.

Animal Feed↗
PubMed2000

Stability of Fourier coefficients in relation to changes in respiratory air flow patterns.

The characterization of respiratory air flow patterns is a subject of continuous interest. The pattern of the air flow curve is an individual characteristic which remains constant over long periods of time. Basically two different approaches have been proposed to determine the individual parameters of the respiratory flow, namely the graphical characterization, which originated from visual indices and subsequently led to harmonic analysis, and the optimal control modeling approach. It has been suggested that respiratory personality can be accurately characterized by the Fourier coefficients of the air flow curve. The first four Fourier sine components should reproduce the shape of the cycle and remain constant in different environmental conditions for one individual. This paper evaluates the possibilities of the Fourier approximation approach to characterize the respiratory personality. The Fourier sine coefficients which were assumed to be invariable were found to be very sensitive to changes in respiratory times. Both the amplitude and the angle of the Fourier sine transformation varied when duration of inspiration and expiration changed.

Fourier Analysis↗
PubMed1999

Solving large test-day models by iteration on data and preconditioned conjugate gradient.

A preconditioned conjugate gradient method was implemented into an iteration on a program for data estimation of breeding values, and its convergence characteristics were studied. An algorithm was used as a reference in which one fixed effect was solved by Gauss-Seidel method, and other effects were solved by a second-order Jacobi method. Implementation of the preconditioned conjugate gradient required storing four vectors (size equal to number of unknowns in the mixed model equations) in random access memory and reading the data at each round of iteration. The preconditioner comprised diagonal blocks of the coefficient matrix. Comparison of algorithms was based on solutions of mixed model equations obtained by a single-trait animal model and a single-trait, random regression test-day model. Data sets for both models used milk yield records of primiparous Finnish dairy cows. Animal model data comprised 665,629 lactation milk yields and random regression test-day model data of 6,732,765 test-day milk yields. Both models included pedigree information of 1,099,622 animals. The animal model ¿random regression test-day model¿ required 122 ¿305¿ rounds of iteration to converge with the reference algorithm, but only 88 ¿149¿ were required with the preconditioned conjugate gradient. To solve the random regression test-day model with the preconditioned conjugate gradient required 237 megabytes of random access memory and took 14% of the computation time needed by the reference algorithm.

Algorithms↗