Systematic Functional Analysis of Bicaudal-D Serine Phosphorylation and Intragenic Suppression of a Female Sterile Allele of BicD

Protein phosphorylation is involved in posttranslational control of essentially all biological processes. Using mass spectrometry, recent analyses of whole phosphoproteomes led to the identification of numerous new phosphorylation sites. However, the function of most of these sites remained unknown. We chose the Drosophila Bicaudal-D protein to estimate the importance of individual phosphorylation events. Being involved in different cellular processes, BicD is required for oocyte determination, for RNA transport during oogenesis and embryogenesis, and for photoreceptor nuclei migration in the developing eye. The numerous roles of BicD and the available evidence for functional importance of BicD phosphorylation led us to identify eight phosphorylation sites of BicD, and we tested a total of 14 identified and suspected phosphoserine residues for their functional importance in vivo in flies. Surprisingly, all these serines turned out to be dispensable for providing sufficient basal BicD activity for normal growth and development. However, in a genetically sensitized background where the BicDA40V protein variant provides only partial activity, serine 103 substitutions are not neutral anymore, but show surprising differences. The S103D substitution completely inactivates the protein, whereas S103A behaves neutral, and the S103F substitution, isolated in a genetic screen, restores BicDA40V function. Our results suggest that many BicD phosphorylation events may either be fortuitous or play a modulating function as shown for Ser103. Remarkably, amongst the Drosophila serines we found phosphorylated, Ser103 is the only one that is fully conserved in mammalian BicD

Methoden zum gezielten Suchen von Farben und Farbkombinationen auf der Basis von GKS

Nach kurzen Bemerkungen über Farbwürfel der Farbenlehre wird gezeigt, welche der 4096 Farben eines Rasterbildschirmes zu den Ecken des Farbwürfels von Hickethier passen. In einer Farbtabelle sind zu landläufigen Farbbezeichnungen die Koordinaten im Farbwürfel angegeben, was zur ersten Orientierung dienen kann. Mit Hilfe von acht Programmen kann der Anwender im Farbwürfel Punkt für Punkt nach Farben suchen, verschiedene Farben zusammenstellen, ferner Farben auf Strecken, Fächern, gewinkelten Strecken, Kreisbögen und Kreisen im Farbwürfel betrachten und ausserdem kompensative Farben zusammenstellen. Die Lage dieser geometrischen Linien kann der Anwender selbst vorgeben. Zu jedem dieser Programme ist ein Beispiel angegeben. Dabei sind alle Eingangsgrößen numerisch angegeben und grafisch in einem Würfel dargestellt. Farbbilder mit den Koordinaten der Farben illustrieren den Output

X‑ray driven peanut trait estimation: computer vision aided agri‑system transformation

Background: In India, raw peanuts are obtained by aggregators from smallholder farms in the form of whole pods and the price is based on a manual estimation of basic peanut pod and kernel characteristics. These methods of raw produce evaluation are slow and can result in procurement irregularities. The procurement delays combined with the lack of storage facilities lead to fungal contaminations and pose a serious threat to food safety in many regions. To address this gap, we investigated whether X-ray technology could be used for the rapid assessment of the key peanut qualities that are important for price estimation. Results: We generated 1752 individual peanut pod 2D X-ray projections using a computed tomography (CT) system (CTportable160.90). Out of these projections we predicted the kernel weight and shell weight, which are important indicators of the produce price. Two methods for the feature prediction were tested: (i) X-ray image transformation (XRT) and (ii) a trained convolutional neural network (CNN). The prediction power of these methods was tested against the gravimetric measurements of kernel weight and shell weight in diverse peanut pod varieties1. Both methods predicted the kernel mass with R2 > 0.93 (XRT: R2 = 0.93 and mean error estimate (MAE) = 0.17, CNN: R2 = 0.95 and MAE = 0.14). While the shell weight was predicted more accurately by CNN ( R2 = 0.91, MAE = 0.09) compared to XRT ( R2 = 0.78; MAE = 0.08). Conclusion: Our study demonstrated that the X-ray based system is a relevant technology option for the estimation of key peanut produce indicators (Figure 1). The obtained results justify further research to adapt the existing X-ray system for the rapid, accurate and objective peanut procurement process. Fast and accurate estimates of produce value are a necessary pre-requisite to avoid post-harvest losses due to fungal contamination and, at the same time, allow the fair payment to farmers. Additionally, the same technology could also assist crop improvement programs in selecting and developing peanut cultivars with enhanced economic value in a high-throughput manner by skipping the shelling of the pods completely. This study demonstrated the technical feasibility of the approach and is a first step to realize a technology-driven peanut production system transformation of the future