Lund University

About: Lund University is a education organization based out in Lund, Sweden. It is known for research contribution in the topics: Population & Cancer. The organization has 42345 authors who have published 124676 publications receiving 5016438 citations. The organization is also known as: Lunds Universitet & University of Lund.

A pathology atlas of the human cancer transcriptome

Abstract: Cancer is one of the leading causes of death, and there is great interest in understanding the underlying molecular mechanisms involved in the pathogenesis and progression of individual tumors. We used systems-level approaches to analyze the genome-wide transcriptome of the protein-coding genes of 17 major cancer types with respect to clinical outcome. A general pattern emerged: Shorter patient survival was associated with up-regulation of genes involved in cell growth and with down-regulation of genes involved in cellular differentiation. Using genome-scale metabolic models, we show that cancer patients have widespread metabolic heterogeneity, highlighting the need for precise and personalized medicine for cancer treatment. All data are presented in an interactive open-access database (www.proteinatlas.org/pathology) to allow genome-wide exploration of the impact of individual proteins on clinical outcomes.

The use of phospholipid fatty acid analysis to estimate bacterial and fungal biomass in soil

Abstract: The cell content of 12 bacterial phospholipid fatty acids (PLFA) was determined in bacteria extracted from soil by homogenization/centrifugation. The bacteria were enumerated using acridine orange direct counts. An average of 1.40×10-17 mol bacterial PLFA cell-1 was found in bacteria extracted from 15 soils covering a wide range of pH and organic matter contents. With this factor, the bacterial biomass based on PLFA analyses of whole soil samples was calculated as 1.0–4.8 mg bacterial C g-1 soil C. The corresponding range based on microscopical counts was 0.3–3.0 mg bacterial C g-1 soil C. The recovery of bacteria from the soils using homogenization/centrifugation was 2.6–16% (mean 8.7%) measured by PLFA analysis, and 12–61% (mean 26%) measured as microscopical counts. The soil content of the PLFA 18:2ω6 was correlated with the ergosterol content (r=0.92), which supports the use of this PLFA as an indicator of fungal biomass. The ratio 18:2ω6 to bacterial PLFA is therefore suggested as an index of the fungal:bacterial biomass ratio in soil. An advantage with the method based on PLFA analyses is that the same technique and even the same sample is used to determine both fungi and bacteria. The fungal:bacterial biomass ratio calculated in this way was positively correlated with the organic matter content of the soils (r=0.94).

An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach

Abstract: Continuously indexed Gaussian fields (GFs) are the most important ingredient in spatial statistical modelling and geostatistics. The specification through the covariance function gives an intuitive interpretation of the field properties. On the computational side, GFs are hampered with the big n problem, since the cost of factorizing dense matrices is cubic in the dimension. Although computational power today is at an all time high, this fact seems still to be a computational bottleneck in many applications. Along with GFs, there is the class of Gaussian Markov random fields (GMRFs) which are discretely indexed. The Markov property makes the precision matrix involved sparse, which enables the use of numerical algorithms for sparse matrices, that for fields in R-2 only use the square root of the time required by general algorithms. The specification of a GMRF is through its full conditional distributions but its marginal properties are not transparent in such a parameterization. We show that, using an approximate stochastic weak solution to (linear) stochastic partial differential equations, we can, for some GFs in the Matern class, provide an explicit link, for any triangulation of R-d, between GFs and GMRFs, formulated as a basis function representation. The consequence is that we can take the best from the two worlds and do the modelling by using GFs but do the computations by using GMRFs. Perhaps more importantly, our approach generalizes to other covariance functions generated by SPDEs, including oscillating and non-stationary GFs, as well as GFs on manifolds. We illustrate our approach by analysing global temperature data with a non-stationary model defined on a sphere. (Less)