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G protein-coupled receptors (GPCRs) play a crucial role in modulating physiological responses and serve as the main drug target. Specifically, salmeterol and salbutamol which are used for the treatment of pulmonary diseases, exert their effects by activating the GPCR β2-adrenergic receptor (β2AR). In our study, we employed coarse-grained molecular dynamics simulations with the Martini 3 force field to investigate the dynamics of drug molecules in membranes in presence and absence of β2AR. Our simulations reveal that in more than 50% of the flip-flop events the drug molecules use the β2AR surface to permeate the membrane. The pathway along the GPCR surface is significantly more energetically favorable for the drug molecules, which was revealed by umbrella sampling simulations along spontaneous flip-flop pathways. Furthermore, we assessed the behavior of drugs with intracellular targets, such as kinase inhibitors, whose therapeutic efficacy could benefit from this observation. In summary, our results show that β2AR surface interactions can significantly enhance membrane permeation of drugs, emphasizing their potential for consideration in future drug development strategies.
Motivated by the question of the impact of selective advantage in populations with skewed reproduction mechanims, we study a Moran model with selection. We assume that there are two types of individuals, where the reproductive success of one type is larger than the other. The higher reproductive success may stem from either more frequent reproduction, or from larger numbers of offspring, and is encoded in a measure Λ for each of the two types. Our approach consists of constructing a Λ-asymmetric Moran model in which individuals of the two populations compete, rather than considering a Moran model for each population. Under certain conditions, that we call the "partial order of adaptation", we can couple these measures. This allows us to construct the central object of this paper, the Λ−asymmetric ancestral selection graph, leading to a pathwise duality of the forward in time Λ-asymmetric Moran model with its ancestral process. Interestingly, the construction also provides a connection to the theory of optimal transport. We apply the ancestral selection graph in order to obtain scaling limits of the forward and backward processes, and note that the frequency process converges to the solution of an SDE with discontinous paths. Finally, we derive a Griffiths representation for the generator of the SDE and use it to find a semi-explicit formula for the probability of fixation of the less beneficial of the two types.
Im November durfte er seinen 77. Geburtstag feiern, von Ruhestand ist aber noch keine Spur. Gerade bereitet er sich in seinem Büro über der Neuen Mensa am alten Campus in Bockenheim mit großem Elan auf die nächste Seminarsitzung vor. »Die Lehre an der Universität wie an Fachhochschulen war mir nie eine Last. Ich habe versucht, die Studierenden zu motivieren oder bei Bedarf gar zu provozieren, um zu kommunizieren, weil wir alle viel mehr von gegenseitigem Austausch haben«, sagt Ludwig Salgo. Zahlreiche Studierende der Sozialen Arbeit, aus der Rechtswissenschaft, aber auch aus der Erziehungswissenschaft haben seine Veranstaltungen besucht; etliche Juristinnen und Juristen haben bei ihm promoviert, sich für die pädagogische oder juristische Laufbahn qualifiziert. Sein zentrales Thema in Forschung und Lehre: das Verhältnis Eltern-Kind-Staat. Anfang 2012 ging er in den Ruhestand, doch die Lehre blieb vorerst seine große Passion. Jetzt beendet er seine Seniorprofessur an der Goethe-Universität zum Semesterende nach zehn Jahren: »Nun ist es genug«, sagt er. In der Fortbildung für Jugendämter, Verfahrensbeistände und Juristen, auch in Stiftungen und Verbänden, hier vor allem im Kinderschutzbund in Frankfurt, möchte er weiterhin aktiv bleiben, auch um für das Thema Kindeswohl zu sensibilisieren. Dass er dann mehr Zeit für seine große Leidenschaft haben wird, freut ihn: »Ich leide an der Melomanie«, sagt der passionierte Freund klassischer Musik. »Wie Nietzsche treffenderweise einmal bemerkt hat, wäre das Leben ohne Musik ein Irrtum.«
Highlights
• Seed size mediates seedling recruitment in tropical forests and pastures.
• Large-seeded species recruited better than small-seeded species in the forest.
• Recruitment of large-seeded species in pastures was limited by surface temperature.
• Large-seeded species should be protected against drought in regenerating pastures.
Abstract
Seedling recruitment is a key process of plant regeneration that often depends on plant functional traits, such as seed size. To optimize forest restoration efforts, we need to better understand how seedling recruitment of different seed sizes varies along environmental gradients with strong variation in abiotic and biotic factors. To understand these interacting effects, we conducted a sowing experiment with different-sized seeds in forests and pastures in the tropical mountains of southern Ecuador. We quantified seedling recruitment in relation to temperature, soil moisture and biotic pressures. We sowed seeds of five tree species of varying seed size at three elevations (1000, 2000 and 3000 m a.s.l.) in primary forest and pastures. We tested (1) how habitat type influences the recruitment of seedlings belonging to three small- and two large-seeded species, and (2) how abiotic and biotic factors limit seedling recruitment of species with different seed sizes. We found that seedlings of the two large-seeded species recruited better than seedlings of the three small-seeded species, but only in the forest habitat. Seedling recruitment of large seeds was primarily limited by high surface temperature, which explains lower recruitment of large seeds in pastures compared to forests. Our study shows that seed size can be a key trait mediating variability in seedling recruitment in tropical ecosystems. We conclude that restoration measures should aim to mitigate extreme temperatures in tropical pastures to aid the natural regeneration of large-seeded tree species.
Highlights
• Six Newton methods for solving matrix quadratic equations in linear DSGE models.
• Compared to QZ using 99 different DSGE models including Smets and Wouters (2007).
• Newton methods more accurate than QZ with comparable computation burden.
• Apt for refining solutions from alternative methods or nearby parameterizations.
Abstract
This paper presents and compares Newton-based methods from the applied mathematics literature for solving the matrix quadratic that underlies the recursive solution of linear DSGE models. The methods are compared using nearly 100 different models from the Macroeconomic Model Data Base (MMB) and different parameterizations of the monetary policy rule in the medium-scale New Keynesian model of Smets and Wouters (2007) iteratively. We find that Newton-based methods compare favorably in solving DSGE models, providing higher accuracy as measured by the forward error of the solution at a comparable computation burden. The methods, however, suffer from their inability to guarantee convergence to a particular, e.g. unique stable, solution, but their iterative procedures lend themselves to refining solutions either from different methods or parameterizations.
The hierarchical feature regression (HFR) is a novel graph-based regularized regression estimator, which mobilizes insights from the domains of machine learning and graph theory to estimate robust parameters for a linear regression. The estimator constructs a supervised feature graph that decomposes parameters along its edges, adjusting first for common variation and successively incorporating idiosyncratic patterns into the fitting process. The graph structure has the effect of shrinking parameters towards group targets, where the extent of shrinkage is governed by a hyperparameter, and group compositions as well as shrinkage targets are determined endogenously. The method offers rich resources for the visual exploration of the latent effect structure in the data, and demonstrates good predictive accuracy and versatility when compared to a panel of commonly used regularization techniques across a range of empirical and simulated regression tasks.