Characterization of nodularin variants in Nodularia spumigena from the Baltic Sea using liquid chromatography/mass spectrometry/mass spectrometry

Nodularin is a potent hepatotoxic cyclic pentapeptide produced by planktonic cyanobacterium Nodularia spumigena. Bloom and culture samples of the cyanobacterium collected and isolated from the Gulf of Gdańsk, southern Baltic Sea, were analyzed. Hybrid quadrupole-time-of-flight liquid chromatography/mass spectrometry/mass spectrometry (TOF-LC/MS/MS) with ionspray (ISP) and collision-induced dissociation (CID) were used to characterize nodularin and its analogues. The identification process was based on the comparison of recorded product ion spectra with the previously reported FAB-MS/CID (high-energy) mass spectra of the corresponding nodularin variants. Amino acid structures and sequences were derived from the fragmentation pattern of the [M+H](+) ions. Apart from unmodified nodularin with an arginine residue (NOD-R), three demethylated variants have been found. The sites of demethylation were located on aspartic acid [Asp(1)]NOD, the Adda residue [DMAdda(3)]NOD, and dehydrobutyric acid [dhb(5)]NOD. In two other nodularin variants an additional methyl group is located in the Adda [MeAdda]NOD and Glu [Glu(4)(OMe)]NOD residues. The linear NOD and the geometrical isomer of NOD-R, reported earlier in N. spumigena from New Zealand, have also been detected. Two of the total eight nodularin variants characterized in the present study, [dhb(5)]NOD and [MeAdda]NOD, have not been described earlier.     

Boundary value problems with eigenvalue depending boundary conditions

We investigate some classes of eigenvalue dependent boundary value problems of the form where A ? A⁺ is a symmetric operator or relation in a Krein space K, τ is a matrix function and Γ₀, Γ₁ are abstract boundary mappings. It is assumed that A admits a self‐adjoint extension in K which locally has the same spectral properties as a definitizable relation, and that τ is a matrix function which locally can be represented with the resolvent of a self‐adjoint definitizable relation. The strict part of τ is realized as the Weyl function of a symmetric operator T in a Krein space H, a self‐adjoint extension à of A × T in K × H with the property that the compressed resolvent P_K (à – λ)^–1|_K k yields the unique solution of the boundary value problem is constructed, and the local spectral properties of this so‐called linearization à are studied. The general results are applied to indefinite Sturm–Liouville operators with eigenvalue dependent boundary conditions (© 2009 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

The impact of sampling density upon cortical network analysis: regions or points

The choice of representation has a fundamental influence on the network analysis results of an empirical data set. The answers to two basic questions - how to define a node and how to define an edge between a pair of nodes - are not obvious in the network analysis of brain imaging data. We considered the first question in the case of magnetic resonance imaging (MRI)-based cortical thickness networks. We selected network nodes to represent vertices of a cortical surface mesh or cortical brain regions. The first network represents the maximal level of detail available in the analysis of cortical thickness networks, while the latter network represents the typical level of detail in the current network analysis studies. We compared the network analysis results between these two representations. The basic network measures behaved approximately as expected when the level of detail increased. However, the overall connectivity of nodes was greater in the vertex level, degree of clustering was smaller in the vertex level, and the node centralities were different between the levels. Further, many parameters of vertex-level network were more robust to the selection of the correlation threshold used to define the edges of network. We conclude that albeit many qualitative network properties were consistent between the two resolution levels, the vertex-level resolution revealed details that were not visible at the regional-level networks, and this additional detail could be useful for some applications. Finally, a similar methodology as the one used here could be used to study effects of the sampling density in other brain-imaging-based networks, for example, in resting-state functional MRI.