Comparison of statistical approaches for the analysis of proteome expression data of differentiating neural stem cells

Comparative proteomic studies often use statistical tests included in the software for the analysis of digitized images of two-dimensional electrophoresis gels. As these programs include only limited capabilities for statistical analysis, many studies do not further describe their statistical approach. To find potential differences produced by different data processing, we compared the results of (1) Student's t-test using a spreadsheet program, (2) the intrinsic algorithms implemented in the Phoretix 2D gel analysis software, and (3) the SAM algorithm originally developed for microarray analysis. We applied the algorithms to proteome data of undifferentiated neural stem cells versus in vitro differentiated neural stem cells. We found (1) 367 spots differentially expressed using Student's t-test, (2) 203 spots using the algorithms in Phoretix 2D, and (3) 119 spots using the algorithms in SAM, respectively, with an overlap of 42 spots detected by all three algorithms. Applying different statistical approaches on the same dataset resulted in divergent set of protein spots labeled as statistically "significant". Currently, there is no agreement on statistical data processing of 2DE datasets, but the statistical tests applied in 2DE studies should be documented. Tools for the statistical analysis of proteome data should be implemented and documented in the existing 2DE software.     

On the possibility of identifying human subjects using behavioural complexity analyses

Background: Identification of human subjects using a geometric approach to complexity analysis of behavioural data is designed to provide a basis for a more precise diagnosis leading towards personalised medicine. Methods: The approach is based on capturing behavioural time-series that can be characterized by a fractional dimension using non-invasive longer-time acquisitions of heart rate, perfusion, blood oxygenation, skin temperature, relative movement and steps frequency. The geometry based approach consists in the analysis of the area and centroid of convex hulls encapsulating the behavioural data represented in Euclidian index spaces based on the scaling properties of the self-similar normally distributed behavioural time-series of the above mentioned quantities. Results: An example demonstrating the presented approach of behavioural fingerprinting is provided using sensory data of eight healthy human subjects based on approximately fifteen hours of data acquisition. Our results show that healthy subjects can be factorized to different similarity groups based on a particular choice of a convex hull in the corresponding Euclidian space. One of the results indicates that healthy subjects share only a small part of the convex hull pertaining to a highly trained individual from the geometric comparison point of view. Similarly, the presented pair-wise individual geometric similarity measure indicates large differences among the subjects suggesting the possibility of neuro-fingerprinting. Conclusions: Recently introduced multi-channel body-attached sensors provide a possibility to acquire behavioural time-series that can be mathematically analysed to obtain various objective measures of behavioural patterns yielding behavioural diagnoses favouring personalised treatments of, e.g., neuropathologies or aging.     

Beiträge Zur Cytologie Des Phloems. Entwicklungsgeschichte Der Siebröhrenglieder und Geleitzellen BeiVicia faba L.

Ohne ZusammenfassungMit 14 Textabbildungen.     

Morphology‐based phylogenetic binning to assess a taxonomic challenge: a case study in Graphidaceae (Ascomycota) requires a new generic name for the widespread Leptotrema wightii

Molecular phylogenetic analysis presents two challenges when it is transformed into formal classifications: the taxonomic challenge (whether and how to distinguish monophyletic sister clades or how to deal with paraphyletic grades) and the nomenclatural challenge (naming clades, i.e. placing name‐giving types accurately on a tree). One approach to the latter is morphology‐based phylogenetic binning, which places specimens based on phenotypic features on a molecular tree and assigns uncertainty values to alternative placement options. Here, we use the example of the lichenized fungal genus Leptotrema to demonstrate how morphology‐based phylogenetic binning can help to clarify taxonomic and nomenclatural issues when naming phylogenetically defined entities. Leptotrema is known for a common and widespread species, L. wightii, and phylogenetic analyses have been based exclusively on this species, including the recognition of a separate tribe, Leptotremateae. However, the genus name Leptotrema and the tribal name Leptotremateae are based on the name L. zollingeri, which was initially considered to be a synonym of L. wightii, but has recently been shown to represent a distinct species. As L. zollingeri differs considerably in phenotypic features from L. wightii, it can be questioned whether the two are at all related or whether L. zollingeri is actually closer to the genera Myriotrema and Ocellularia in tribe Ocellularieae. The solution to this problem is not trivial, as it affects the correct use of the names Leptotrema and Leptotremateae. Morphology‐based phylogenetic binning indeed demonstrated that L. zollingeri clusters with the Myriotrema album group in tribe Ocellularieae with high support. Hence, in contrast with current use, the name Leptotrema becomes available for the M. album group and Leptotremateae becomes a synonym of Ocellularieae. As a consequence, the new names Sanguinotrema and Sanguinotremateae are introduced to accommodate L. wightii and the tribe including this species and the genus Reimnitzia. Although the studied case is specific to lichen fungi, the approach can be used in a much broader context with any kind of taxon or organism. © 2015 The Linnean Society of London, Botanical Journal of the Linnean Society, 2015, 179 , 436–443.