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The tremendous success of metal-halide perovskites, especially in the field of photovoltaics, has triggered a substantial number of studies in understanding their optoelectronic properties. However, consensus regarding the electronic properties of these perovskites is lacking due to a huge scatter in the reported key parameters, such as work function (Φ) and valence band maximum (VBM) values. Here, we demonstrate that the surface photovoltage (SPV) is a key phenomenon occurring at the perovskite surfaces that feature a non-negligible density of surface states, which is more the rule than an exception for most materials under study. With ultraviolet photoelectron spectroscopy (UPS) and Kelvin probe, we evidence that even minute UV photon fluxes (500 times lower than that used in typical UPS experiments) are sufficient to induce SPV and shift the perovskite Φ and VBM by several 100 meV compared to dark. By combining UV and visible light, we establish flat band conditions (i.e., compensate the surface-state-induced surface band bending) at the surface of four important perovskites, and find that all are p-type in the bulk, despite a pronounced n-type surface character in the dark. The present findings highlight that SPV effects must be considered in all surface studies to fully understand perovskites’ photophysical properties.
Anti-fat bias is widespread and is linked to the internalization of weight bias and psychosocial problems. The purpose of this study was to examine the internalization of weight bias among children across weight categories and to evaluate the psychometric properties of the Weight Bias Internalization Scale for Children (WBIS-C). Data were collected from 1484 primary school children and their parents. WBIS-C demonstrated good internal consistency (alpha = .86) after exclusion of Item 1. The unitary factor structure was supported using exploratory and confirmatory factor analyses (factorial validity). Girls and overweight children reported higher WBIS-C scores in comparison to boys and non-overweight peers (known-groups validity). Convergent validity was shown by significant correlations with psychosocial problems. Internalization of weight bias explained additional variance in different indicators of psychosocial well-being. The results suggest that the WBIS-C is a psychometrically sound and informative tool to assess weight bias internalization among children.
Weight-related teasing is a widespread phenomenon in childhood, and might foster the internalization of weight bias. The goal of this study was to examine the role of weight teasing and weight bias internalization as mediators between weight status and negative psychological sequelae, such as restrained eating and emotional and conduct problems in childhood. Participants included 546 female (52%) and 501 (48%) male children aged 7-11 and their parents, who completed surveys assessing weight teasing, weight bias internalization, restrained eating behaviors, and emotional and conduct problems at two points of measurement, approximately 2 years apart. To examine the hypothesized mediation, a prospective design using structural equation modeling was applied. As expected, the experience of weight teasing and the internalization of weight bias were mediators in the relationship between weight status and psychosocial problems. This pattern was observed independently of gender or weight status. Our findings suggest that the experience of weight teasing and internalization of weight bias is more important than weight status in explaining psychological functioning among children and indicate a need for appropriate prevention and intervention approaches.
Decoupling of optical properties appears challenging, but vital to get better insight of the relationship between light and fruit attributes. In this study, nine solid phantoms capturing the ranges of absorption (μa) and reduced scattering (μs’) coefficients in fruit were analysed non-destructively using laser-induced backscattering imaging (LLBI) at 1060 nm. Data analysis of LLBI was carried out on the diffuse reflectance, attenuation profile obtained by means of Farrell’s diffusion theory either calculating μa [cm−1] and μs’ [cm−1] in one fitting step or fitting only one optical variable and providing the other one from a destructive analysis. The nondestructive approach was approved when calculating one unknown coefficient non-destructively, while no ability of the method was found to analysis both, μa and μs’, non-destructively. Setting μs’ according to destructive photon density wave (PDW) spectroscopy and fitting μa resulted in root mean square error (rmse) of 18.7% in comparison to fitting μs’ resulting in rmse of 2.6%, pointing to decreased measuring uncertainty, when the highly variable μa was known.
The approach was tested on European pear, utilizing destructive PDW spectroscopy for setting one variable, while LLBI was applied for calculating the remaining coefficient. Results indicated that the optical properties of pear obtained from PDW spectroscopy as well as LLBI changed concurrently in correspondence to water content mainly. A destructive batch-wise analysis of μs’ and online analysis of μa may be considered in future developments for improved fruit sorting results, when considering fruit with high variability of μs’.
In high-value sweet cherry (Prunus avium), the red coloration - determined by the anthocyanins content - is correlated with the fruit ripeness stage and market value. Non-destructive spectroscopy has been introduced in practice and may be utilized as a tool to assess the fruit pigments in the supply chain processes. From the fruit spectrum in the visible (Vis) wavelength range, the pigment contents are analyzed separately at their specific absorbance wavelengths.
A drawback of the method is the need for re-calibration due to varying optical properties of the fruit tissue. In order to correct for the scattering differences, most often the spectral intensity in the visible spectrum is normalized by wavelengths in the near infrared (NIR) range, or pre-processing methods are applied in multivariate calibrations.
In the present study, the influence of the fruit scattering properties on the Vis/NIR fruit spectrum were corrected by the effective pathlength in the fruit tissue obtained from time-resolved readings of the distribution of time-of-flight (DTOF). Pigment analysis was carried out according to Lambert-Beer law, considering fruit spectral intensities, effective pathlength, and refractive index. Results were compared to commonly applied linear color and multivariate partial least squares (PLS) regression analysis. The approaches were validated on fruits at different ripeness stages, providing variation in the scattering coefficient and refractive index exceeding the calibration sample set.
In the validation, the measuring uncertainty of non-destructively analyzing fruits with Vis/NIR spectra by means of PLS or Lambert-Beer in comparison with combined application of Vis/NIR spectroscopy and DTOF measurements showed a dramatic bias reduction as well as enhanced coefficients of determination when using both, the spectral intensities and apparent information on the scattering influence by means of DTOF readings. Corrections for the refractive index did not render improved results.
Marine sedimentary archives are routinely used to reconstruct past environmental changes. In many cases, bioturbation and sedimentary mixing affect the proxy time-series and the age-depth relationship. While idealized models of bioturbation exist, they usually assume homogeneous mixing, thus that a single sample is representative for the sediment layer it is sampled from.
However, it is largely unknown to which extent this assumption holds for sediments used for paleoclimate reconstructions.
To shed light on
1) the age-depth relationship and its full uncertainty,
2) the magnitude of mixing processes affecting the downcore proxy variations, and
3) the representativity of the discrete sample for the sediment layer, we designed and performed a case study on South China Sea sediment material which was collected using a box corer and which covers the last glacial cycle.
Using the radiocarbon content of foraminiferal tests as a tracer of time, we characterize the spatial age-heterogeneity of sediments in a three-dimensional setup. In total, 118 radiocarbon measurements were performed on defined small- and large-volume bulk samples ( similar to 200 specimens each) to investigate the horizontal heterogeneity of the sediment. Additionally, replicated measurements on small numbers of specimens (10 x 5 specimens) were performed to assess the heterogeneity within a sample volume. Visual assessment of X-ray images and a quantitative assessment of the mixing strength show typical mixing from bioturbation corresponding to around 10 cm mixing depth.
Notably, our 3D radiocarbon distribution reveals that the horizontal heterogeneity (up to 1,250 years), contributing to the age uncertainty, is several times larger than the typically assumed radiocarbon based age-model error (single errors up to 250 years). Furthermore, the assumption of a perfectly bioturbated layer with no mixing underneath is not met.
Our analysis further demonstrates that the age-heterogeneity might be a function of sample size; smaller samples might contain single features from the incomplete mixing and are thus less representative than larger samples.
We provide suggestions for future studies, optimal sampling strategies for quantitative paleoclimate reconstructions and realistic uncertainty in age models, as well as discuss possible implications for the interpretation of paleoclimate records.
Background: Protein kinases constitute a particularly large protein family in Arabidopsis with important functions in cellular signal transduction networks. At the same time Arabidopsis is a model plant with high frequencies of gene duplications. Here, we have conducted a systematic analysis of the Arabidopsis kinase complement, the kinome, with particular focus on gene duplication events. We matched Arabidopsis proteins to a Hidden-Markov Model of eukaryotic kinases and computed a phylogeny of 942 Arabidopsis protein kinase domains and mapped their origin by gene duplication.
Results: The phylogeny showed two major clades of receptor kinases and soluble kinases, each of which was divided into functional subclades. Based on this phylogeny, association of yet uncharacterized kinases to families was possible which extended functional annotation of unknowns. Classification of gene duplications within these protein kinases revealed that representatives of cytosolic subfamilies showed a tendency to maintain segmentally duplicated genes, while some subfamilies of the receptor kinases were enriched for tandem duplicates. Although functional diversification is observed throughout most subfamilies, some instances of functional conservation among genes transposed from the same ancestor were observed. In general, a significant enrichment of essential genes was found among genes encoding for protein kinases.
Conclusions: The inferred phylogeny allowed classification and annotation of yet uncharacterized kinases. The prediction and analysis of syntenic blocks and duplication events within gene families of interest can be used to link functional biology to insights from an evolutionary viewpoint. The approach undertaken here can be applied to any gene family in any organism with an annotated genome.
Explicit solution of the Lindblad equation for nearly isotropic boundary driven XY spin 1/2 chain
(2010)
Explicit solution for the two-point correlation function in a non-equilibrium steady state of a nearly isotropic boundary driven open XY spin 1/2 chain in the Lindblad formulation is provided. A non-equilibrium quantum phase transition from exponentially decaying correlations to long range order is discussed analytically. In the regime of long range order a new phenomenon of correlation resonances is reported, where the correlation response of the system is unusually high for certain discrete values of the external bulk parameter, e.g. the magnetic field.
State-of-the-art organic solar cells exhibit power conversion efficiencies of 18% and above. These devices benefit from the suppression of free charge recombination with regard to the Langevin limit of charge encounter in a homogeneous medium. It is recognized that the main cause of suppressed free charge recombination is the reformation and resplitting of charge-transfer (CT) states at the interface between donor and acceptor domains. Here, we use kinetic Monte Carlo simulations to understand the interplay between free charge motion and recombination in an energetically disordered phase-separated donor-acceptor blend. We identify conditions for encounter-dominated and resplitting-dominated recombination. In the former regime, recombination is proportional to mobility for all parameters tested and only slightly reduced with respect to the Langevin limit. In contrast, mobility is not the decisive parameter that determines the nongeminate recombination coefficient, k(2), in the latter case, where k2 is a sole function of the morphology, CT and charge-separated (CS) energetics, and CT-state decay properties. Our simulations also show that free charge encounter in the phase-separated disordered blend is determined by the average mobility of all carriers, while CT reformation and resplitting involves mostly states near the transport energy. Therefore, charge encounter is more affected by increased disorder than the resplitting of the CT state. As a consequence, for a given mobility, larger energetic disorder, in combination with a higher hopping rate, is preferred. These findings have implications for the understanding of suppressed recombination in solar cells with nonfullerene acceptors, which are known to exhibit lower energetic disorder than that of fullerenes.
The c-Fosc-Jun complex forms the activator protein 1 transcription factor, a therapeutic target in the treatment of cancer. Various synthetic peptides have been designed to try to selectively disrupt the interaction between c-Fos and c-Jun at its leucine zipper domain. To evaluate the binding affinity between these synthetic peptides and c-Fos, polarizable and nonpolarizable molecular dynamics (MD) simulations were conducted, and the resulting conformations were analyzed using the molecular mechanics generalized Born surface area (MM/GBSA) method to compute free energies of binding. In contrast to empirical and semiempirical approaches, the estimation of free energies of binding using a combination of MD simulations and the MM/GBSA approach takes into account dynamical properties such as conformational changes, as well as solvation effects and hydrophobic and hydrophilic interactions. The predicted binding affinities of the series of c-Jun-based peptides targeting the c-Fos peptide show good correlation with experimental melting temperatures. This provides the basis for the rational design of peptides based on internal, van der Waals, and electrostatic interactions.
Molybdenum cofactor (Moco) biosynthesis is a complex process that involves the coordinated function of several proteins. In recent years it has become obvious that the availability of iron plays an important role in the biosynthesis of Moco. First, the MoaA protein binds two (4Fe-4S] clusters per monomer. Second, the expression of the moaABCDE and moeAB operons is regulated by FNR, which senses the availability of oxygen via a functional NFe-4S) cluster. Finally, the conversion of cyclic pyranopterin monophosphate to molybdopterin requires the availability of the L-cysteine desulfurase IscS, which is a shared protein with a main role in the assembly of Fe-S clusters. In this report, we investigated the transcriptional regulation of the moaABCDE operon by focusing on its dependence on cellular iron availability. While the abundance of selected molybdoenzymes is largely decreased under iron-limiting conditions, our data show that the regulation of the moaABCDE operon at the level of transcription is only marginally influenced by the availability of iron. Nevertheless, intracellular levels of Moco were decreased under iron-limiting conditions, likely based on an inactive MoaA protein in addition to lower levels of the L-cysteine desulfurase IscS, which simultaneously reduces the sulfur availability for Moco production. IMPORTANCE FNR is a very important transcriptional factor that represents the master switch for the expression of target genes in response to anaerobiosis. Among the FNR-regulated operons in Escherichia coli is the moaABCDE operon, involved in Moco biosynthesis. Molybdoenzymes have essential roles in eukaryotic and prokaryotic organisms. In bacteria, molybdoenzymes are crucial for anaerobic respiration using alternative electron acceptors. This work investigates the connection of iron availability to the biosynthesis of Moco and the production of active molybdoenzymes.
The ability of some plant species to dominate communities in new biogeographical ranges has been attributed to an innate higher competitive ability and release from co-evolved specialist enemies. Specifically, invasive success in the new range might be explained by release from biotic negative soil-feedbacks, which control potentially dominant species in their native range. To test this hypothesis, we grew individuals from sixteen phylogenetically paired European grassland species that became either invasive or naturalized in new ranges, in either sterilized soil or in sterilized soil with unsterilized soil inoculum from their native home range. We found that although the native members of invasive species generally performed better than those of naturalized species, these native members of invasive species also responded more negatively to native soil inoculum than did the native members of naturalized species. This supports our hypothesis that potentially invasive species in their native range are held in check by negative soil-feedbacks. However, contrary to expectation, negative soil-feedbacks in potentially invasive species were not much increased by interspecific competition. There was no significant variation among families between invasive and naturalized species regarding their feedback response (negative vs. neutral). Therefore, we conclude that the observed negative soil feedbacks in potentially invasive species may be quite widespread in European families of typical grassland species.
Ecologists carry a well-stocked toolbox with a great variety of sampling methods, statistical analyses and modelling tools, and new methods are constantly appearing. Evaluation and optimisation of these methods is crucial to guide methodological choices. Simulating error-free data or taking high-quality data to qualify methods is common practice. Here, we emphasise the methodology of the 'virtual ecologist' (VE) approach where simulated data and observer models are used to mimic real species and how they are 'virtually' observed. This virtual data is then subjected to statistical analyses and modelling, and the results are evaluated against the 'true' simulated data. The VE approach is an intuitive and powerful evaluation framework that allows a quality assessment of sampling protocols, analyses and modelling tools. It works under controlled conditions as well as under consideration of confounding factors such as animal movement and biased observer behaviour. In this review, we promote the approach as a rigorous research tool, and demonstrate its capabilities and practical relevance. We explore past uses of VE in different ecological research fields, where it mainly has been used to test and improve sampling regimes as well as for testing and comparing models, for example species distribution models. We discuss its benefits as well as potential limitations, and provide some practical considerations for designing VE studies. Finally, research fields are identified for which the approach could be useful in the future. We conclude that VE could foster the integration of theoretical and empirical work and stimulate work that goes far beyond sampling methods, leading to new questions, theories, and better mechanistic understanding of ecological systems.
Density regulation influences population dynamics through its effects on demographic rates and consequently constitutes a key mechanism explaining the response of organisms to environmental changes. Yet, it is difficult to establish the exact form of density dependence from empirical data. Here, we developed an individual-based model to explore how resource limitation and behavioural processes determine the spatial structure of white stork Ciconia ciconia populations and regulate reproductive rates. We found that the form of density dependence differed considerably between landscapes with the same overall resource availability and between home range selection strategies, highlighting the importance of fine-scale resource distribution in interaction with behaviour. In accordance with theories of density dependence, breeding output generally decreased with density but this effect was highly variable and strongly affected by optimal foraging strategy, resource detection probability and colonial behaviour. Moreover, our results uncovered an overlooked consequence of density dependence by showing that high early nestling mortality in storks, assumed to be the outcome of harsh weather, may actually result from density dependent effects on food provision. Our findings emphasize that accounting for interactive effects of individual behaviour and local environmental factors is crucial for understanding density-dependent processes within spatially structured populations. Enhanced understanding of the ways animal populations are regulated in general, and how habitat conditions and behaviour may dictate spatial population structure and demographic rates is critically needed for predicting the dynamics of populations, communities and ecosystems under changing environmental conditions.
Empirical species distribution models (SDMs) constitute often the tool of choice for the assessment of rapid climate change effects on species vulnerability. Conclusions regarding extinction risks might be misleading, however, because SDMs do not explicitly incorporate dispersal or other demographic processes. Here, we supplement SDMs with a dynamic population model 1) to predict climate-induced range dynamics for black grouse in Switzerland, 2) to compare direct and indirect measures of extinction risks, and 3) to quantify uncertainty in predictions as well as the sources of that uncertainty. To this end, we linked models of habitat suitability to a spatially explicit, individual-based model. In an extensive sensitivity analysis, we quantified uncertainty in various model outputs introduced by different SDM algorithms, by different climate scenarios and by demographic model parameters. Potentially suitable habitats were predicted to shift uphill and eastwards. By the end of the 21st century, abrupt habitat losses were predicted in the western Prealps for some climate scenarios. In contrast, population size and occupied area were primarily controlled by currently negative population growth and gradually declined from the beginning of the century across all climate scenarios and SDM algorithms. However, predictions of population dynamic features were highly variable across simulations. Results indicate that inferring extinction probabilities simply from the quantity of suitable habitat may underestimate extinction risks because this may ignore important interactions between life history traits and available habitat. Also, in dynamic range predictions uncertainty in SDM algorithms and climate scenarios can become secondary to uncertainty in dynamic model components. Our study emphasises the need for principal evaluation tools like sensitivity analysis in order to assess uncertainty and robustness in dynamic range predictions. A more direct benefit of such robustness analysis is an improved mechanistic understanding of dynamic species responses to climate change.
SDM performance varied for different range dynamics. Prediction accuracies decreased when abrupt range shifts occurred as species were outpaced by the rate of climate change, and increased again when a new equilibrium situation was realised. When ranges contracted, prediction accuracies increased as the absences were predicted well. Far- dispersing species were faster in tracking climate change, and were predicted more accurately by SDMs than short- dispersing species. BRTs mostly outperformed GLMs. The presence of a predator, and the inclusion of its incidence as an environmental predictor, made BRTs and GLMs perform similarly. Results are discussed in light of other studies dealing with effects of ecological traits and processes on SDM performance. Perspectives are given on further advancements of SDMs and for possible interfaces with more mechanistic approaches in order to improve predictions under environmental change.
Models are useful tools for understanding and predicting ecological patterns and processes. Under ongoing climate and biodiversity change, they can greatly facilitate decision-making in conservation and restoration and help designing adequate management strategies for an uncertain future. Here, we review the use of spatially explicit models for decision support and to identify key gaps in current modelling in conservation and restoration. Of 650 reviewed publications, 217 publications had a clear management application and were included in our quantitative analyses. Overall, modelling studies were biased towards static models (79%), towards the species and population level (80%) and towards conservation (rather than restoration) applications (71%). Correlative niche models were the most widely used model type. Dynamic models as well as the gene-to-individual level and the community-to-ecosystem level were underrepresented, and explicit cost optimisation approaches were only used in 10% of the studies. We present a new model typology for selecting models for animal conservation and restoration, characterising model types according to organisational levels, biological processes of interest and desired management applications. This typology will help to more closely link models to management goals. Additionally, future efforts need to overcome important challenges related to data integration, model integration and decision-making. We conclude with five key recommendations, suggesting that wider usage of spatially explicit models for decision support can be achieved by 1) developing a toolbox with multiple, easier-to-use methods, 2) improving calibration and validation of dynamic modelling approaches and 3) developing best-practise guidelines for applying these models. Further, more robust decision-making can be achieved by 4) combining multiple modelling approaches to assess uncertainty, and 5) placing models at the core of adaptive management. These efforts must be accompanied by long-term funding for modelling and monitoring, and improved communication between research and practise to ensure optimal conservation and restoration outcomes.