Stomata-Zellen als Idioblasten

Zusammenfassung Den Schließzellen des Spaltöffnungsapparates als Idioblasten, Sonderlinge, der Epidermis fehlen bei verschiedenen Pflanzen eine Reihe von Inhaltskörpern, die in den anderen Epidermiszellen vorkommen. Ein neues Beispiel für diese Regel bildet das Auftreten langer stab-, nadel-, oder spindelförmiger Inhaltskörper in den gewöhnlichen Oberhautzellen der Blattunterseite vonScutellaria altissima und das Fehlen dieser Körper in den Schließzellen dieser Pflanze. Diese Gebilde haben eine Ähnlichkeit mit den Eiweißspindeln der Kakteen, doch ist die Eiweißnatur derScutellaria-Spindeln noch fraglich.Die Schließzellen machen in der Regel die in anderen Blattzellen vorsichgehende Umwandlung der Chloroplasten in Chromoplasten nicht mit. Eine Ausnahme von dieser Regel bilden die Stomazellen vonSelaginella helvetica, in denen sich in der kalten Jahreszeit die Chloroplasten in Chromoplasten umwandeln, ebenso wie in den übrigen Epidermiszellen.     

Isolation of Plant Gene Space-Related Sequence Elements by High C+G Patch (HCGP) Filtration: Model Study on Rice

For the isolation of gene space representative sequence elements, a new methodology—high C+G patch (HCGP) filtration—has been developed using rice as a model. The method is based on the fragmentation of the genomic DNA by methylation-sensitive HpaII and MspI restriction endonucleases having exclusively G/C base pair-containing recognition sites. These enzymes fragment the genome at high C+G content and hypomethylated regions. Cloning fragments spanning such regions in close vicinity (200–2,000 bp) revealed that about 60% of the clones represented gene space sequences resulting in twofold enrichment of these sequences, which is close to the theoretical maximum in rice. The sequence information of clones used in the present study was deposited in the NCBI database under the accession numbers EI 365676–EI 366364.     

Comparison of data science workflows for root cause analysis of bioprocesses

Root cause analysis (RCA) is one of the most prominent tools used to comprehensively evaluate a biopharmaceutical production process. Despite of its widespread use in industry, the Food and Drug Administration has observed a lot of unsuitable approaches for RCAs within the last years. The reasons for those unsuitable approaches are the use of incorrect variables during the analysis and the lack in process understanding, which impede correct model interpretation. Two major approaches to perform RCAs are currently dominating the chemical and pharmaceutical industry: raw data analysis and feature-based approach. Both techniques are shown to be able to identify the significant variables causing the variance of the response. Although they are different in data unfolding, the same tools as principal component analysis and partial least square regression are used in both concepts. Within this article we demonstrate the strength and weaknesses of both approaches. We proved that a fusion of both results in a comprehensive and effective workflow, which not only increases better process understanding. We demonstrate this workflow along with an example. Hence, the presented workflow allows to save analysis time and to reduce the effort of data mining by easy detection of the most important variables within the given dataset. Subsequently, the final obtained process knowledge can be translated into new hypotheses, which can be tested experimentally and thereby lead to effectively improving process robustness.

     

Mikrurgischer Kurs

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