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In animals and humans, behavior can be influenced by irrelevant stimuli, a phenomenon called Pavlovian-to-instrumental transfer (PIT). In subjects with substance use disorder, PIT is even enhanced with functional activation in the nucleus accumbens (NAcc) and amygdala. While we observed enhanced behavioral and neural PIT effects in alcohol-dependent subjects, we here aimed to determine whether behavioral PIT is enhanced in young men with high-risk compared to low-risk drinking and subsequently related functional activation in an a-priori region of interest encompassing the NAcc and amygdala and related to polygenic risk for alcohol consumption. A representative sample of 18-year old men (n = 1937) was contacted: 445 were screened, 209 assessed: resulting in 191 valid behavioral, 139 imaging and 157 genetic datasets. None of the subjects fulfilled criteria for alcohol dependence according to the Diagnostic and Statistical Manual of Mental Disorders-IV-TextRevision (DSM-IV-TR). We measured how instrumental responding for rewards was influenced by background Pavlovian conditioned stimuli predicting action-independent rewards and losses. Behavioral PIT was enhanced in high-compared to low-risk drinkers (b = 0.09, SE = 0.03, z = 2.7, p < 0.009). Across all subjects, we observed PIT-related neural blood oxygen level-dependent (BOLD) signal in the right amygdala (t = 3.25, p(SVC) = 0.04, x = 26, y = -6, z = -12), but not in NAcc. The strength of the behavioral PIT effect was positively correlated with polygenic risk for alcohol consumption (r(s) = 0.17, p = 0.032). We conclude that behavioral PIT and polygenic risk for alcohol consumption might be a biomarker for a subclinical phenotype of risky alcohol consumption, even if no drug-related stimulus is present. The association between behavioral PIT effects and the amygdala might point to habitual processes related to out PIT task. In non-dependent young social drinkers, the amygdala rather than the NAcc is activated during PIT; possible different involvement in association with disease trajectory should be investigated in future studies.
We propose a computational method (with acronym ALDI) for sampling from a given target distribution based on first-order (overdamped) Langevin dynamics which satisfies the property of affine invariance. The central idea of ALDI is to run an ensemble of particles with their empirical covariance serving as a preconditioner for their underlying Langevin dynamics. ALDI does not require taking the inverse or square root of the empirical covariance matrix, which enables application to high-dimensional sampling problems. The theoretical properties of ALDI are studied in terms of nondegeneracy and ergodicity. Furthermore, we study its connections to diffusion on Riemannian manifolds and Wasserstein gradient flows. Bayesian inference serves as a main application area for ALDI. In case of a forward problem with additive Gaussian measurement errors, ALDI allows for a gradient-free approximation in the spirit of the ensemble Kalman filter. A computational comparison between gradient-free and gradient-based ALDI is provided for a PDE constrained Bayesian inverse problem.
More than half of Earth's freshwater resources are held by the Antarctic Ice Sheet, which thus represents by far the largest potential source for global sea-level rise under future warming conditions(1). Its long-term stability determines the fate of our coastal cities and cultural heritage. Feedbacks between ice, atmosphere, ocean, and the solid Earth give rise to potential nonlinearities in its response to temperature changes. So far, we are lacking a comprehensive stability analysis of the Antarctic Ice Sheet for different amounts of global warming. Here we show that the Antarctic Ice Sheet exhibits a multitude of temperature thresholds beyond which ice loss is irreversible. Consistent with palaeodata(2)we find, using the Parallel Ice Sheet Model(3-5), that at global warming levels around 2 degrees Celsius above pre-industrial levels, West Antarctica is committed to long-term partial collapse owing to the marine ice-sheet instability. Between 6 and 9 degrees of warming above pre-industrial levels, the loss of more than 70 per cent of the present-day ice volume is triggered, mainly caused by the surface elevation feedback. At more than 10 degrees of warming above pre-industrial levels, Antarctica is committed to become virtually ice-free. The ice sheet's temperature sensitivity is 1.3 metres of sea-level equivalent per degree of warming up to 2 degrees above pre-industrial levels, almost doubling to 2.4 metres per degree of warming between 2 and 6 degrees and increasing to about 10 metres per degree of warming between 6 and 9 degrees. Each of these thresholds gives rise to hysteresis behaviour: that is, the currently observed ice-sheet configuration is not regained even if temperatures are reversed to present-day levels. In particular, the West Antarctic Ice Sheet does not regrow to its modern extent until temperatures are at least one degree Celsius lower than pre-industrial levels. Our results show that if the Paris Agreement is not met, Antarctica's long-term sea-level contribution will dramatically increase and exceed that of all other sources. <br /> Modelling shows that the Antarctic Ice Sheet exhibits multiple temperature thresholds beyond which ice loss would become irreversible, and once melted, the ice sheet can regain its previous mass only if the climate cools well below pre-industrial temperatures.
Senescence represents a fundamental process of late leaf development. Transcription factors (TFs) play an important role for expression reprogramming during senescence; however, the gene regulatory networks through which they exert their functions, and their physiological integration, are still largely unknown. Here, we identify the Arabidopsis (Arabidopsis thaliana) abscisic acid (ABA)- and hydrogen peroxide-activated TF Arabidopsis thaliana ACTIVATING FACTOR1 (ATAF1) as a novel upstream regulator of senescence. ATAF1 executes its physiological role by affecting both key chloroplast maintenance and senescence-promoting TFs, namely GOLDEN2-LIKE1 (GLK1) and ORESARA1 (ARABIDOPSIS NAC092), respectively. Notably, while ATAF1 activates ORESARA1, it represses GLK1 expression by directly binding to their promoters, thereby generating a transcriptional output that shifts the physiological balance toward the progression of senescence. We furthermore demonstrate a key role of ATAF1 for ABA- and hydrogen peroxide-induced senescence, in accordance with a direct regulatory effect on ABA homeostasis genes, including NINE-CIS-EPOXYCAROTENOID DIOXYGENASE3 involved in ABA biosynthesis and ABC TRANSPORTER G FAMILY MEMBER40, encoding an ABA transport protein. Thus, ATAF1 serves as a core transcriptional activator of senescence by coupling stress-related signaling with photosynthesis- and senescence-related transcriptional cascades.
Plants respond to low carbon supply by massive reprogramming of the transcriptome and metabolome. We show here that the carbon starvation-induced NAC (for NO APICAL MERISTEM/ARABIDOPSIS TRANSCRIPTION ACTIVATION FACTOR/CUP-SHAPED COTYLEDON) transcription factor Arabidopsis (Arabidopsis thaliana) Transcription Activation Factor1 (ATAF1) plays an important role in this physiological process. We identified TREHALASE1, the only trehalase-encoding gene in Arabidopsis, as a direct downstream target of ATAF1. Overexpression of ATAF1 activates TREHALASE1 expression and leads to reduced trehalose-6-phosphate levels and a sugar starvation metabolome. In accordance with changes in expression of starch biosynthesis-and breakdown-related genes, starch levels are generally reduced in ATAF1 overexpressors but elevated in ataf1 knockout plants. At the global transcriptome level, genes affected by ATAF1 are broadly associated with energy and carbon starvation responses. Furthermore, transcriptional responses triggered by ATAF1 largely overlap with expression patterns observed in plants starved for carbon or energy supply. Collectively, our data highlight the existence of a positively acting feedforward loop between ATAF1 expression, which is induced by carbon starvation, and the depletion of cellular carbon/energy pools that is triggered by the transcriptional regulation of downstream gene regulatory networks by ATAF1.
The influence of different parameters on the silicification procedure using lysozyme is reported. When polyethoxysiloxane (PEOS), an internally crosslinked silica reservoir, is used, regular structures with a narrow size distribution could be obtained only via introducing the silica precursor in two steps including initial dropping and subsequent addition of residual oil phase in one portion. We found that mixing sequence of mineralizing agents in the presence of a positively charged surfactant plays a key role in terms of silica precipitation when tetraethoxyorthosilicate (TEOS) is the oil phase. In contrast, well mineralized crumpled features with high specific surface area could be synthesized in the presence of PEOS as a silica precursor polymer, regardless of mixing sequence. Moreover, introducing sodium dodecyl sulfate (SDS) as a negatively charged surfactant resulted in regular silica sphere formation only in combination with hexylene glycol (MPD) as a specific co-solvent. Finally, it is demonstrated that by inclusion of different nanoparticles even more sophisticated hybrid materials can be generated.
Synthosomes are polymer vesicles with trans membrane proteins incorporated into block copolymer membranes. They have been used for selective transport in or out of the vesicles as well as catalysis inside the compartments. However, both the insertion process of the membrane protein, forming nanopores, and the spreading of the vesicles on planar substrates to form solid-supported biomimetic membranes have been rarely studied yet. Herein, we address these two points and, first, shed light on the real-time monitoring of protein insertion via isothermal titration calorimetry. Second, the spreading process on different solid supports, namely, SiO2, glass, and gold, via different techniques like spin- and dip-coating as well as a completely new approach of potential-assisted spreading on gold surfaces was studied. While inhomogeneous layers occur via traditional methods, our proposed potential-assisted strategy to induce adsorption of positively charged vesicles by applying negative potential on the electrode leads to remarkable vesicle spreading and their further fusion to form more homogeneous planar copolymer films on gold. The polymer vesicles in our study are formed from amphiphilic copolymers poly(2-methyl oxazoline)-block-poly(dimethylsiloxane)-block-poly(2-methyl oxazoline) (PMOXA-b-PDMS-b-PMOXA). Engineered variants of the transmembrane protein ferric hydroxamate uptake protein component A (FhuA), one of the largest beta-barrel channel proteins, are used as model nanopores. The incorporation of FhuA Delta 1-160 is shown to facilitate the vesicle spreading process further. Moreover, high accessibility of cysteine inside the channel was proven by linkage of a fluorescent dye inside the engineered variant FhuA Delta CVFtev and hence preserved functionality of the channels after spreading. The porosity and functionality of the spread synthosomes on the gold plates have been examined by studying the passive ion transport response in the presence of Li+ and ClO4- ions and electrochemical impedance spectroscopy analysis. Our approach to form solid-supported biomimetic membranes via the potential-assisted strategy could be important for the development of new (bio-) sensors and membranes.
Like almost all fields of science, hydrology has benefited to a large extent from the tremendous improvements in scientific instruments that are able to collect long-time data series and an increase in available computational power and storage capabilities over the last decades. Many model applications and statistical analyses (e.g., extreme value analysis) are based on these time series. Consequently, the quality and the completeness of these time series are essential. Preprocessing of raw data sets by filling data gaps is thus a necessary procedure. Several interpolation techniques with different complexity are available ranging from rather simple to extremely challenging approaches. In this paper, various imputation methods available to the hydrological researchers are reviewed with regard to their suitability for filling gaps in the context of solving hydrological questions. The methodological approaches include arithmetic mean imputation, principal component analysis, regression-based methods and multiple imputation methods. In particular, autoregressive conditional heteroscedasticity (ARCH) models which originate from finance and econometrics will be discussed regarding their applicability to data series characterized by non-constant volatility and heteroscedasticity in hydrological contexts. The review shows that methodological advances driven by other fields of research bear relevance for a more intensive use of these methods in hydrology. Up to now, the hydrological community has paid little attention to the imputation ability of time series models in general and ARCH models in particular.