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Antibodies against spike proteins of influenza are used as a tool for characterization of viruses and therapeutic approaches. However, development, production and quality control of antibodies is expensive and time consuming. To circumvent these difficulties, three peptides were derived from complementarity determining regions of an antibody heavy chain against influenza A spike glycoprotein. Their binding properties were studied experimentally, and by molecular dynamics simulations. Two peptide candidates showed binding to influenza A/Aichi/2/68 H3N2. One of them, termed PeB, with the highest affinity prevented binding to and infection of target cells in the micromolar region without any cytotoxic effect. PeB matches best the conserved receptor binding site of hemagglutinin. PeB bound also to other medical relevant influenza strains, such as human-pathogenic A/California/7/2009 H1N1, and avian-pathogenic A/MuteSwan/Rostock/R901/2006 H7N1. Strategies to improve the affinity and to adapt specificity are discussed and exemplified by a double amino acid substituted peptide, obtained by substitutional analysis. The peptides and their derivatives are of great potential for drug development as well as biosensing.
Arsenic-containing hydrocarbons (AsHC) constitute one group of arsenolipids that have been identified in seafood. In this first in vivo toxicity study for AsHCs, we show that AsHCs exert toxic effects in Drosophila melanogaster in a concentration range similar to that of arsenite. In contrast to arsenite, however, AsHCs cause developmental toxicity in the late developmental stages of Drosophila melanogaster. This work illustrates the need for a full characterisation of the toxicity of AsHCs in experimental animals to finally assess the risk to human health related to the presence of arsenolipids in seafood.
Die Qualität von Nutzpflanzen ist von zahlreichen Einflussfaktoren wie beispielsweise Lagerbedingungen und Sorteneigenschaften abhängig. Um Qualitätsmängel zu minimieren und Absatzchancen von Nutzpflanzen zu steigern sind umfangreiche Analysen hinsichtlich ihrer stofflichen Zusammensetzung notwendig. Chromatographische Techniken gekoppelt an ein Massenspektrometer und die Kernspinresonanzspektroskopie wurden dafür bislang verwendet. In der vorliegenden Arbeit wurde ein Gaschromatograph an ein Flugzeitmassenspektrometer (GC-TOF-MS) gekoppelt, um physiologische Prozesse bzw. Eigenschaften (die Schwarzfleckigkeit, die Chipsbräunung, das Physiologische Alter und die Keimhemmung) von Nutzpflanzen aufzuklären. Als Pflanzenmodell wurde dafür die Kartoffelknolle verwendet. Dazu wurden neue analytische Lösungsansätze entwickelt, die eine zielgerichtete Auswertung einer Vielzahl von Proben, die Etablierung einer umfangreichen Referenzspektrenbibliothek und die sichere Archivierung aller experimentellen Daten umfassen. Das Verfahren der Probenvorbereitung wurde soweit modifiziert, dass gering konzentrierte Substanzen mittels GC-TOF-MS analysiert werden können. Dadurch wurde das durch die Probenvorbereitung limitierte Substanzspektrum erweitert. Anhand dieser Lösungsansätze wurden physiologisch relevante Stoffwechselprodukte identifiziert, welche indikativ (klassifizierend) bzw. prädiktiv (vorhersagend) für die physiologischen Prozesse sind. Für die Schwarzfleckigkeitsneigung und die Chipseignung wurde jeweils ein biochemisches Modell zur Vorhersage dieser Prozesse aufgestellt und auf eine Züchtungspopulation übertragen. Ferner wurden für die Schwarzfleckigkeit Stoffwechselprodukte des Respirationsstoffwechsels identifiziert sowie Aminosäuren, Glycerollipide und Phenylpropanoide für das Physiologische Alter als relevant erachtet. Das physiologische Altern konnte durch die Anwendung höherer Temperaturen beschleunigt werden. Durch Anwendung von Keimhemmern (Kümmelöl, Chlorpropham) wurde eine Verzögerung des physiologischen Alterns beobachtet. Die Applikation von Kümmelöl erwies sich dabei als besonders vorteilhaft. Kümmelöl behandelte Knollen wiesen im Vergleich zu unbehandelten Knollen nur Veränderungen im Aminosäure-, Zucker- und Sekundärstoffwechsel auf. Chlorpropham behandelte Knollen wiesen einen ähnlichen Stoffwechsel wie die unbehandelten Knollen auf. Für die bislang noch nicht identifizierten Stoffwechselprodukte wurden im Rahmen dieser Arbeit das Verfahren der „gezielten An-/Abreicherung“, der „gepaarten NMR/GC-TOF-MS Analyse“ und das „Entscheidungsbaumverfahren“ entwickelt. Diese ermöglichen eine Klassifizierung von GC-MS Signalen im Hinblick auf ihre chemische Funktionalität. Das Verfahren der gekoppelten NMR/GC-TOF-MS Analyse erwies sich dabei als besonders erfolgversprechend, da es eine Aufklärung bislang unbekannter gaschromatographischer Signale ermöglicht. In der vorliegenden Arbeit wurden neue Stoffwechselprodukte in der Kartoffelknolle identifiziert, wodurch ein wertvoller Beitrag zur Analytik der Metabolomik geleistet wurde.
Arsenic-containing hydrocarbons are one group of fat-soluble organic arsenic compounds (arsenolipids) found in marine fish and other seafood. A risk assessment of arsenolipids is urgently needed, but has not been possible because of the total lack of toxicological data. In this study the cellular toxicity of three arsenic-containing hydrocarbons was investigated in cultured human bladder (UROtsa) and liver (HepG2) cells. Cytotoxicity of the arsenic-containing hydrocarbons was comparable to that of arsenite, which was applied as the toxic reference arsenical. A large cellular accumulation of arsenic, as measured by ICP-MS/MS, was observed after incubation of both cell lines with the arsenolipids. Moreover, the toxic mode of action shown by the three arsenic-containing hydrocarbons seemed to differ from that observed for arsenite. Evidence suggests that the high cytotoxic potential of the lipophilic arsenicals results from a decrease in the cellular energy level. This first in vitro based risk assessment cannot exclude a risk to human health related to the presence of arsenolipids in seafood, and indicates the urgent need for further toxicity studies in experimental animals to fully assess this possible risk.
In the present work, we use symbolic regression for automated modeling of dynamical systems. Symbolic regression is a powerful and general method suitable for data-driven identification of mathematical expressions. In particular, the structure and parameters of those expressions are identified simultaneously.
We consider two main variants of symbolic regression: sparse regression-based and genetic programming-based symbolic regression. Both are applied to identification, prediction and control of dynamical systems.
We introduce a new methodology for the data-driven identification of nonlinear dynamics for systems undergoing abrupt changes. Building on a sparse regression algorithm derived earlier, the model after the change is defined as a minimum update with respect to a reference model of the system identified prior to the change. The technique is successfully exemplified on the chaotic Lorenz system and the van der Pol oscillator. Issues such as computational complexity, robustness against noise and requirements with respect to data volume are investigated.
We show how symbolic regression can be used for time series prediction. Again, issues such as robustness against noise and convergence rate are investigated us- ing the harmonic oscillator as a toy problem. In combination with embedding, we demonstrate the prediction of a propagating front in coupled FitzHugh-Nagumo oscillators. Additionally, we show how we can enhance numerical weather predictions to commercially forecast power production of green energy power plants.
We employ symbolic regression for synchronization control in coupled van der Pol oscillators. Different coupling topologies are investigated. We address issues such as plausibility and stability of the control laws found. The toolkit has been made open source and is used in turbulence control applications.
Genetic programming based symbolic regression is very versatile and can be adapted to many optimization problems. The heuristic-based algorithm allows for cost efficient optimization of complex tasks.
We emphasize the ability of symbolic regression to yield white-box models. In contrast to black-box models, such models are accessible and interpretable which allows the usage of established tool chains.
Background
The flowering plant Primula veris is a common spring blooming perennial that is widely cultivated throughout Europe. This species is an established model system in the study of the genetics, evolution, and ecology of heterostylous floral polymorphisms. Despite the long history of research focused on this and related species, the continued development of this system has been restricted due the absence of genomic and transcriptomic resources.
Results
We present here a de novo draft genome assembly of P. veris covering 301.8 Mb, or approximately 63% of the estimated 479.22 Mb genome, with an N50 contig size of 9.5 Kb, an N50 scaffold size of 164 Kb, and containing an estimated 19,507 genes. The results of a RADseq bulk segregant analysis allow for the confident identification of four genome scaffolds that are linked to the P. veris S-locus. RNAseq data from both P. veris and the closely related species P. vulgaris allow for the characterization of 113 candidate heterostyly genes that show significant floral morph-specific differential expression. One candidate gene of particular interest is a duplicated GLOBOSA homolog that may be unique to Primula (PveGLO2), and is completely silenced in L-morph flowers.
Conclusions
The P. veris genome represents the first genome assembled from a heterostylous species, and thus provides an immensely important resource for future studies focused on the evolution and genetic dissection of heterostyly. As the first genome assembled from the Primulaceae, the P. veris genome will also facilitate the expanded application of phylogenomic methods in this diverse family and the eudicots as a whole.