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Computational methods for the design of effective therapies against drug resistant HIV strains
(2005)
The development of drug resistance is a major obstacle to successful treatment of HIV infection. The extraordinary replication dynamics of HIV facilitates its escape from selective pressure exerted by the human immune system and by combination drug therapy. We have developed several computational methods whose combined use can support the design of optimal antiretroviral therapies based on viral genomic data
SIRT6 is a NAD(+)-dependent deacetylase that modulates chromatin structure and safeguards genomic stability. Until now, SIRT6 has been assigned to the nucleus and only nuclear targets of SIRT6 are known. Here, we demonstrate that in response to stress, C. elegans SIR-2.4 and its mammalian orthologue SIRT6 localize to cytoplasmic stress granules, interact with various stress granule components and induce their assembly. Loss of SIRT6 or inhibition of its catalytic activity in mouse embryonic fibroblasts impairs stress granule formation and delays disassembly during recovery, whereas deficiency of SIR-2.4 diminishes maintenance of P granules and decreases survival of C. elegans under stress conditions. Our findings uncover a novel, evolutionary conserved function of SIRT6 in the maintenance of stress granules in response to stress.
Despite energy efficiency measures, global energy demand has gradually increased due to global economic growth and changes in consumer behavior. Even if people are aware of the problem and want to change their energy consumption, they have difficulty acting on their attitudes. This is called the attitude-behavior gap. To narrow this gap and reduce energy consumption and CO2 emissions, behavioral interventions beyond technological advances must be considered. A promising intervention is nudging, which uses insights from behavioral economics to gently nudge individuals toward more sustainable choices. In this study, we investigate how modifying digital choice architectures with nudges can be used to influence consumer energy conservation behavior in smart home applications (SHAs). We conducted an online experiment with 391 participants to test the effectiveness of the following three digital nudges in an SHA: self-commitment, reminder, and social norm nudge. While the results of a structural equation model indicated no effect on bridging the gap between attitude and behavior, we found the potential to promote energy conservation with two nudge types. Thus, this paper makes substantial contribution to persuasive and information systems-enabled sustainability for a better world in the form of digital nudges for emerging technologies.
Diarylethene derivatives are photochromic molecular switches, undergoing a ring-opening/-closing reaction by illumination with light. The symmetry of the closed form is determined by the WoodWard Hoffinann rules according to which the reaction proceeds by corirotatory rotation -in that case. Here, we show by a cOrnbined approach of scanning tunneling microscopy (STM) and density functional theory (DFT) calculations that the Open isomer of 4,4'-(4,4'-(perfluorocydopent-1-ene-1,2-diyl)bis(5-methyl-thiophent-4,2,4-dipyridine) (PDTE) retains its open form upon adsorption on a Ag(111) surface. It caribe switched into a closed form, which we identify as the digrotatOly cydization product, by controlled manipulation 'With the STM tip, Evidence of an electric-field dependent switching-process 'is interpreted on the basis of a Simple electroStatic Model, which suggests that the reaction proceedS via an "upright" intermediate state. This pathway thus strongly differs from the switching reaction in solution.
Recurrences in past climates
(2023)
Our ability to predict the state of a system relies on its tendency to recur to states it has visited before. Recurrence also pervades common intuitions about the systems we are most familiar with: daily routines, social rituals and the return of the seasons are just a few relatable examples. To this end, recurrence plots (RP) provide a systematic framework to quantify the recurrence of states. Despite their conceptual simplicity, they are a versatile tool in the study of observational data. The global climate is a complex system for which an understanding based on observational data is not only of academical relevance, but vital for the predurance of human societies within the planetary boundaries. Contextualizing current global climate change, however, requires observational data far beyond the instrumental period. The palaeoclimate record offers a valuable archive of proxy data but demands methodological approaches that adequately address its complexities. In this regard, the following dissertation aims at devising novel and further developing existing methods in the framework of recurrence analysis (RA). The proposed research questions focus on using RA to capture scale-dependent properties in nonlinear time series and tailoring recurrence quantification analysis (RQA) to characterize seasonal variability in palaeoclimate records (‘Palaeoseasonality’).
In the first part of this thesis, we focus on the methodological development of novel approaches in RA. The predictability of nonlinear (palaeo)climate time series is limited by abrupt transitions between regimes that exhibit entirely different dynamical complexity (e.g. crossing of ‘tipping points’). These possibly depend on characteristic time scales. RPs are well-established for detecting transitions and capture scale-dependencies, yet few approaches have combined both aspects. We apply existing concepts from the study of self-similar textures to RPs to detect abrupt transitions, considering the most relevant time scales. This combination of methods further results in the definition of a novel recurrence based nonlinear dependence measure. Quantifying lagged interactions between multiple variables is a common problem, especially in the characterization of high-dimensional complex systems. The proposed ‘recurrence flow’ measure of nonlinear dependence offers an elegant way to characterize such couplings. For spatially extended complex systems, the coupled dynamics of local variables result in the emergence of spatial patterns. These patterns tend to recur in time. Based on this observation, we propose a novel method that entails dynamically distinct regimes of atmospheric circulation based on their recurrent spatial patterns. Bridging the two parts of this dissertation, we next turn to methodological advances of RA for the study of Palaeoseasonality. Observational series of palaeoclimate ‘proxy’ records involve inherent limitations, such as irregular temporal sampling. We reveal biases in the RQA of time series with a non-stationary sampling rate and propose a correction scheme.
In the second part of this thesis, we proceed with applications in Palaeoseasonality. A review of common and promising time series analysis methods shows that numerous valuable tools exist, but their sound application requires adaptions to archive-specific limitations and consolidating transdisciplinary knowledge. Next, we study stalagmite proxy records from the Central Pacific as sensitive recorders of mid-Holocene El Niño-Southern Oscillation (ENSO) dynamics. The records’ remarkably high temporal resolution allows to draw links between ENSO and seasonal dynamics, quantified by RA. The final study presented here examines how seasonal predictability could play a role for the stability of agricultural societies. The Classic Maya underwent a period of sociopolitical disintegration that has been linked to drought events. Based on seasonally resolved stable isotope records from Yok Balum cave in Belize, we propose a measure of seasonal predictability. It unveils the potential role declining seasonal predictability could have played in destabilizing agricultural and sociopolitical systems of Classic Maya populations.
The methodological approaches and applications presented in this work reveal multiple exciting future research avenues, both for RA and the study of Palaeoseasonality.