Der Zug des Sperbers(Accipiter nisus) auf Helgoland

Zusammenfassung Der Sperber ist auf Helgoland nur Durchzügler. Im langjährigen Mittel werden pro Jahr im Winter 0,4, auf dem Heimzug 2,1 und im Herbst 23,0 Fänglinge festgestellt. Der Zugablauf läßt eine Trennung der Zugzeiten der Alt- und Jungvögel einerseits sowie der und andererseits erkennen. Altvögel stellen 14,1%, 40,1% der Fänglinge. Anhand der Flügelmaße und Gewichte läßt sich mit fortschreitender Zugzeit keine klinale Variation nachweisen. Ältere Literaturangaben, Helgoland betreffend, werden teils korrigiert, teils präzisiert. Der geringe Anteil der Altvögel bzw. wird auf deren geringer ausgeprägten Zugtrieb zurückgeführt.

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The migration of the sparrow-hawk on Heligoland

Summary On Heligoland the Sparrow-Hawk occurs as a passage visitor only. Mean numbers (17 years) of birds trapped are 0.4 in winter, 2.1 during spring and 23.0 in autumn. Adults and juveniles as males and females as well show different periods of migration. Among birds trapped there are 14.1% adults and 40.1% females. No clinal variation of wing length and weight according to time of passage could be found. Former literature concerning Heligoland is partly corrected, partly rendered more precise. The low proportions of adults and females respectively are caused by weaker migratory urge.

     

Uncertainty–guided learning with scaled prediction errors in the basal ganglia

To accurately predict rewards associated with states or actions, the variability of observations has to be taken into account. In particular, when the observations are noisy, the individual rewards should have less influence on tracking of average reward, and the estimate of the mean reward should be updated to a smaller extent after each observation. However, it is not known how the magnitude of the observation noise might be tracked and used to control prediction updates in the brain reward system. Here, we introduce a new model that uses simple, tractable learning rules that track the mean and standard deviation of reward, and leverages prediction errors scaled by uncertainty as the central feedback signal. We show that the new model has an advantage over conventional reinforcement learning models in a value tracking task, and approaches a theoretic limit of performance provided by the Kalman filter. Further, we propose a possible biological implementation of the model in the basal ganglia circuit. In the proposed network, dopaminergic neurons encode reward prediction errors scaled by standard deviation of rewards. We show that such scaling may arise if the striatal neurons learn the standard deviation of rewards and modulate the activity of dopaminergic neurons. The model is consistent with experimental findings concerning dopamine prediction error scaling relative to reward magnitude, and with many features of striatal plasticity. Our results span across the levels of implementation, algorithm, and computation, and might have important implications for understanding the dopaminergic prediction error signal and its relation to adaptive and effective learning.     

Cytosolic RNA:DNA hybrids activate the cGAS–STING axis

Intracellular recognition of non‐self and also self‐nucleic acids can result in the initiation of potent pro‐inflammatory and antiviral cytokine responses. Most recently, cGAS was shown to be critical for the recognition of cytoplasmic dsDNA. Binding of dsDNA to cGAS results in the synthesis of cGAMP(2′–5′), which then binds to the endoplasmic reticulum resident protein STING. This initiates a signaling cascade that triggers the induction of an antiviral immune response. While most studies on intracellular nucleic acids have focused on dsRNA or dsDNA, it has remained unexplored whether cytosolic RNA:DNA hybrids are also sensed by the innate immune system. Studying synthetic RNA:DNA hybrids, we indeed observed a strong type I interferon response upon cytosolic delivery of this class of molecule. Studies in THP‐1 knockout cells revealed that the recognition of RNA:DNA hybrids is completely attributable to the cGAS–STING pathway. Moreover, in vitro studies showed that recombinant cGAS produced cGAMP upon RNA:DNA hybrid recognition. Altogether, our results introduce RNA:DNA hybrids as a novel class of intracellular PAMP molecules and describe an alternative cGAS ligand next to dsDNA.     

The plant glycosyltransferase clone collection for functional genomics

The glycosyltransferases (GTs) are an important and functionally diverse family of enzymes involved in glycan and glycoside biosynthesis. Plants have evolved large families of GTs which undertake the array of glycosylation reactions that occur during plant development and growth. Based on the Carbohydrate‐Active enZymes (CAZy) database, the genome of the reference plant Arabidopsis thaliana codes for over 450 GTs, while the rice genome (Oryza sativa) contains over 600 members. Collectively, GTs from these reference plants can be classified into over 40 distinct GT families. Although these enzymes are involved in many important plant specific processes such as cell‐wall and secondary metabolite biosynthesis, few have been functionally characterized. We have sought to develop a plant GTs clone resource that will enable functional genomic approaches to be undertaken by the plant research community. In total, 403 (88%) of CAZy defined Arabidopsis GTs have been cloned, while 96 (15%) of the GTs coded by rice have been cloned. The collection resulted in the update of a number of Arabidopsis GT gene models. The clones represent full‐length coding sequences without termination codons and are Gateway^® compatible. To demonstrate the utility of this JBEI GT Collection, a set of efficient particle bombardment plasmids (pBullet) was also constructed with markers for the endomembrane. The utility of the pBullet collection was demonstrated by localizing all members of the Arabidopsis GT14 family to the Golgi apparatus or the endoplasmic reticulum (ER). Updates to these resources are available at the JBEI GT Collection website http://www.addgene.org/ .     

Boosting Escherichia coli’s heterologous production rate of ectoines by exploiting the non-halophilic gene cluster from Acidiphilium cryptum

Extremophiles - The compatible solutes ectoine and hydroxyectoine are synthesized by many microorganisms as potent osmostress and desiccation protectants. Besides their successful implementation...     

A Mechanochemical Model for Embryonic Pattern Formation: Coupling Tissue Mechanics and Morphogen Expression

Motivated by recent experimental findings, we propose a novel mechanism of embryonic pattern formation based on coupling of tissue curvature with diffusive signaling by a chemical factor. We derive a new mathematical model using energy minimization approach and show that the model generates a variety of morphogen and curvature patterns agreeing with experimentally observed structures. The mechanism proposed transcends the classical Turing concept which requires interactions between two morphogens with a significantly different diffusivity. Our studies show how biomechanical forces may replace the elusive long-range inhibitor and lead to formation of stable spatially heterogeneous structures without existence of chemical prepatterns. We propose new experimental approaches to decisively test our central hypothesis that tissue curvature and morphogen expression are coupled in a positive feedback loop.     

Multi-objective optimal control of dynamic bioprocesses using ACADO Toolkit

The optimal design and operation of dynamic bioprocesses gives in practice often rise to optimisation problems with multiple and conflicting objectives. As a result typically not a single optimal solution but a set of Pareto optimal solutions exist. From this set of Pareto optimal solutions, one has to be chosen by the decision maker. Hence, efficient approaches are required for a fast and accurate generation of the Pareto set such that the decision maker can easily and systematically evaluate optimal alternatives. In the current paper the multi-objective optimisation of several dynamic bioprocess examples is performed using the freely available ACADO Multi-Objective Toolkit (http://www.acadotoolkit.org). This toolkit integrates efficient multiple objective scalarisation strategies (e.g., Normal Boundary Intersection and (Enhanced) Normalised Normal Constraint) with fast deterministic approaches for dynamic optimisation (e.g., single and multiple shooting). It has been found that the toolkit is able to efficiently and accurately produce the Pareto sets for all bioprocess examples. The resulting Pareto sets are added as supplementary material to this paper.     

Noise Suppression and Surplus Synchrony by Coincidence Detection

The functional significance of correlations between action potentials of neurons is still a matter of vivid debate. In particular, it is presently unclear how much synchrony is caused by afferent synchronized events and how much is intrinsic due to the connectivity structure of cortex. The available analytical approaches based on the diffusion approximation do not allow to model spike synchrony, preventing a thorough analysis. Here we theoretically investigate to what extent common synaptic afferents and synchronized inputs each contribute to correlated spiking on a fine temporal scale between pairs of neurons. We employ direct simulation and extend earlier analytical methods based on the diffusion approximation to pulse-coupling, allowing us to introduce precisely timed correlations in the spiking activity of the synaptic afferents. We investigate the transmission of correlated synaptic input currents by pairs of integrate-and-fire model neurons, so that the same input covariance can be realized by common inputs or by spiking synchrony. We identify two distinct regimes: In the limit of low correlation linear perturbation theory accurately determines the correlation transmission coefficient, which is typically smaller than unity, but increases sensitively even for weakly synchronous inputs. In the limit of high input correlation, in the presence of synchrony, a qualitatively new picture arises. As the non-linear neuronal response becomes dominant, the output correlation becomes higher than the total correlation in the input. This transmission coefficient larger unity is a direct consequence of non-linear neural processing in the presence of noise, elucidating how synchrony-coded signals benefit from these generic properties present in cortical networks.     

T cell responses against microsatellite instability-induced frameshift peptides and influence of regulatory T cells in colorectal cancer

High-level microsatellite-unstable (MSI-H) colorectal carcinomas (CRC) represent a distinct subtype of tumors commonly characterized by dense infiltration with cytotoxic T cells, most likely due to expression of MSI-H-related frameshift peptides (FSP). The contribution of FSP and classical antigens like MUC1 and CEA to the cellular immune response against MSI-H CRC had not been analyzed so far. We analyzed tumor-infiltrating and peripheral T cells from MSI-H (n = 4 and n = 14, respectively) and microsatellite-stable (MSS) tumor patients (n = 26 and n = 17) using interferon gamma ELISpot assays. Responses against 4 FSP antigens and peptides derived from MUC1 to CEA were compared with and without depletion of regulatory T cells, and the results were related to the presence of the respective antigens in tumor tissue. Preexisting FSP-specific T cell responses were detected in all (4 out of 4) tumor-infiltrating and in the majority (10 out of 14) of peripheral T cell samples from MSI-H CRC patients, but rarely observed in MSS CRC patients. Preexisting T cell responses in MSI-H CRC patients were significantly more frequently directed against FSP tested in the present study than against peptides derived from classical antigens MUC1 or CEA (p = 0.049). Depletion of regulatory T cells increased the frequency of effector T cell responses specific for MUC1/CEA-derived peptides and, to a lesser extent, T cell responses specific for FSP. Our data suggest that the analyzed FSP may represent an immunologically relevant pool of antigens capable of eliciting antitumoral effector T cell responses.