Ueber den Zustand der Arzneikunde vor achtzehn Jahrhunderten : Antrittsvortrag

http://www.ester.ee/record=b3582025*es

Hemp (Cannabis sativa L.) as a Resource for Green Cosmetics: Yield of Seeds and Fatty Acids Composition of 20 Varieties under the Growing Conditions of Organic Farming in Austria

The interest in hemp (non-drug Cannabis sativa L.) for skin care and cosmetic use is due to the high content of oil, especially unsaturated fatty acids in seed with technological and therapeutic effects. In a field trial on an organic farm, seed weight and content of fatty acids of 20 hemp varieties were surveyed on three different harvest dates. The dry matter seed yields ranged from 27-149 g m2. The varieties Ferimon-12, Fedora-19, and Bialobreszie produced high seed yields on all three harvest dates but yields were not significantly different from a large group of other varieties. Contents of palmitic acid range from 3.1 to 4.1%, of stearic acid from 0.1 to 1.9%, of oleic acid from 3.7 to 9.2%, of linoleic acid from 44.8 to 60.2%, of α-linolenic acid from 18.2 to 27.4%, and of γ-linolenic acid from 1.6 to 4.7%. The genotype has no significant influence on fatty acid content. All 20 varieties tested show high quantities of fatty acid depending on the harvest date, so that no variety can be favored. Results confirm that hemp is a very good source of fatty acids for skin care and cosmetic use

Is the Protein Model Assignment problem under linked branch lengths NP-hard?

AbstractIn phylogenetics, computing the likelihood that a given tree generated the observed sequence data requires calculating the probability of the available data for a given tree (topology and branch lengths) under a statistical model of sequence evolution. Here, we focus on selecting an appropriate model for the data, which represents a generally non-trivial task. The data is represented as a so-called multiple sequence alignment. That is, each individual sequence of any one species (taxa) is arranged (aligned) in such a way, that the characters of all species at a given position (site) are assumed to share a common evolutionary history. It is well known, that an inappropriate model, which does not fit the data, can generate misleading tree topologies [3,4,26].More specifically, we consider the case of partitioned protein sequence alignments. This means that the sites of the alignment may be clustered together into different partitions. Each partition may have an individual model of evolution. Our objective is to maximize the likelihood of the per-partition protein model assignments (e.g., JTT, WAG, etc.) when branches are linked across partitions on a given, fixed tree topology. That is, branch lengths are not estimated individually for each partition. Linked branch lengths across partitions substantially reduce the number of free parameters.For p partitions and |M| possible substitution models, there are |M|p possible model assignments. Since the number of combinations grows exponentially with p, an exhaustive search for the highest scoring assignment is computationally prohibitive for |M|>1. We show that the problem of finding the optimal protein substitution model assignment under linked branch lengths on a given, tree topology, is NP-hard. Our results imply that one should employ heuristics to approximate the solution, instead of striving for the exact solution. Alternatively, the problem can be simplified by relaxing the assumptions

Efficient Detection of Repeating Sites to Accelerate Phylogenetic Likelihood Calculations

The phylogenetic likelihood function (PLF) is the major computational bottleneck in several applications of evolutionary biology such as phylogenetic inference, species delimitation, model selection, and divergence times estimation. Given the alignment, a tree and the evolutionary model parameters, the likelihood function computes the conditional likelihood vectors for every node of the tree. Vector entries for which all input data are identical result in redundant likelihood operations which, in turn, yield identical conditional values. Such operations can be omitted for improving run-time and, using appropriate data structures, reducing memory usage. We present a fast, novel method for identifying and omitting such redundant operations in phylogenetic likelihood calculations, and assess the performance improvement and memory savings attained by our method. Using empirical and simulated data sets, we show that a prototype implementation of our method yields up to 12-fold speedups and uses up to 78% less memory than one of the fastest and most highly tuned implementations of the PLF currently available. Our method is generic and can seamlessly be integrated into any phylogenetic likelihood implementation

Holly Bluff Gardens On-The-River Jordan

https://egrove.olemiss.edu/ms_pcards/1031/thumbnail.jp

Mississippi Gulf Coast Under Five Flags

https://egrove.olemiss.edu/ms_pcards/1077/thumbnail.jp