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Palaeogenomes of Eurasian straight-tusked elephants challenge the current view of elephant evolution
(2017)
The straight-tusked elephants Palaeoloxodon spp. were widespread across Eurasia during the Pleistocene. Phylogenetic reconstructions using morphological traits have grouped them with Asian elephants (Elephas maximus), and many paleontologists place Palaeoloxodon within Elephas. Here, we report the recovery of full mitochondrial genomes from four and partial nuclear genomes from two P. antiquus fossils. These fossils were collected at two sites in Germany, Neumark-Nord and Weimar-Ehringsdorf, and likely date to interglacial periods similar to 120 and similar to 244 thousand years ago, respectively. Unexpectedly, nuclear and mitochondrial DNA analyses suggest that P. antiquus was a close relative of extant African forest elephants (Loxodonta cyclotis). Species previously referred to Palaeoloxodon are thus most parsimoniously explained as having diverged from the lineage of Loxodonta, indicating that Loxodonta has not been constrained to Africa. Our results demonstrate that the current picture of elephant evolution is in need of substantial revision.
Within the framework of precision agriculture, the determination of various soil properties is moving into focus, especially the demand for sensors suitable for in-situ measurements. Energy-dispersive X-ray fluorescence (EDXRF) can be a powerful tool for this purpose. In this study a huge diverse soil set (n = 598) from 12 different study sites in Germany was analysed with EDXRF. First, a principal component analysis (PCA) was performed to identify possible similarities among the sample set. Clustering was observed within the four texture classes clay, loam, silt and sand, as clay samples contain high and sandy soils low iron mass fractions. Furthermore, the potential of uni- and multivariate data evaluation with partial least squares regression (PLSR) was assessed for accurate determination of nutrients in German agricultural samples using two calibration sample sets. Potassium and iron were chosen for testing the performance of both models. Prediction of these nutrients in 598 German soil samples with EDXRF was more accurate using PLSR which is confirmed by a better overall averaged deviation and PLSR should therefore be preferred.
Within the framework of precision agriculture, the determination of various soil properties is moving into focus, especially the demand for sensors suitable for in-situ measurements. Energy-dispersive X-ray fluorescence (EDXRF) can be a powerful tool for this purpose. In this study a huge diverse soil set (n = 598) from 12 different study sites in Germany was analysed with EDXRF. First, a principal component analysis (PCA) was performed to identify possible similarities among the sample set. Clustering was observed within the four texture classes clay, loam, silt and sand, as clay samples contain high and sandy soils low iron mass fractions. Furthermore, the potential of uni- and multivariate data evaluation with partial least squares regression (PLSR) was assessed for accurate determination of nutrients in German agricultural samples using two calibration sample sets. Potassium and iron were chosen for testing the performance of both models. Prediction of these nutrients in 598 German soil samples with EDXRF was more accurate using PLSR which is confirmed by a better overall averaged deviation and PLSR should therefore be preferred.
Quorum-sensing bacteria in a growing colony of cells send out signalling molecules (so-called “autoinducers”) and themselves sense the autoinducer concentration in their vicinity. Once—due to increased local cell density inside a “cluster” of the growing colony—the concentration of autoinducers exceeds a threshold value, cells in this clusters get “induced” into a communal, multi-cell biofilm-forming mode in a cluster-wide burst event. We analyse quantitatively the influence of spatial disorder, the local heterogeneity of the spatial distribution of cells in the colony, and additional physical parameters such as the autoinducer signal range on the induction dynamics of the cell colony. Spatial inhomogeneity with higher local cell concentrations in clusters leads to earlier but more localised induction events, while homogeneous distributions lead to comparatively delayed but more concerted induction of the cell colony, and, thus, a behaviour close to the mean-field dynamics. We quantify the induction dynamics with quantifiers such as the time series of induction events and burst sizes, the grouping into induction families, and the mean autoinducer concentration levels. Consequences for different scenarios of biofilm growth are discussed, providing possible cues for biofilm control in both health care and biotechnology.
Quorum-sensing bacteria in a growing colony of cells send out signalling molecules (so-called “autoinducers”) and themselves sense the autoinducer concentration in their vicinity. Once—due to increased local cell density inside a “cluster” of the growing colony—the concentration of autoinducers exceeds a threshold value, cells in this clusters get “induced” into a communal, multi-cell biofilm-forming mode in a cluster-wide burst event. We analyse quantitatively the influence of spatial disorder, the local heterogeneity of the spatial distribution of cells in the colony, and additional physical parameters such as the autoinducer signal range on the induction dynamics of the cell colony. Spatial inhomogeneity with higher local cell concentrations in clusters leads to earlier but more localised induction events, while homogeneous distributions lead to comparatively delayed but more concerted induction of the cell colony, and, thus, a behaviour close to the mean-field dynamics. We quantify the induction dynamics with quantifiers such as the time series of induction events and burst sizes, the grouping into induction families, and the mean autoinducer concentration levels. Consequences for different scenarios of biofilm growth are discussed, providing possible cues for biofilm control in both health care and biotechnology.
Today's perovskite solar cells (PSCs) are limited mainly by their open‐circuit voltage (VOC) due to nonradiative recombination. Therefore, a comprehensive understanding of the relevant recombination pathways is needed. Here, intensity‐dependent measurements of the quasi‐Fermi level splitting (QFLS) and of the VOC on the very same devices, including pin‐type PSCs with efficiencies above 20%, are performed. It is found that the QFLS in the perovskite lies significantly below its radiative limit for all intensities but also that the VOC is generally lower than the QFLS, violating one main assumption of the Shockley‐Queisser theory. This has far‐reaching implications for the applicability of some well‐established techniques, which use the VOC as a measure of the carrier densities in the absorber. By performing drift‐diffusion simulations, the intensity dependence of the QFLS, the QFLS‐VOC offset and the ideality factor are consistently explained by trap‐assisted recombination and energetic misalignment at the interfaces. Additionally, it is found that the saturation of the VOC at high intensities is caused by insufficient contact selectivity while heating effects are of minor importance. It is concluded that the analysis of the VOC does not provide reliable conclusions of the recombination pathways and that the knowledge of the QFLS‐VOC relation is of great importance.
The in‐depth understanding of charge carrier photogeneration and recombination mechanisms in organic solar cells is still an ongoing effort. In donor:acceptor (bulk) heterojunction organic solar cells, charge photogeneration and recombination are inter‐related via the kinetics of charge transfer states—being singlet or triplet states. Although high‐charge‐photogeneration quantum yields are achieved in many donor:acceptor systems, only very few systems show significantly reduced bimolecular recombination relative to the rate of free carrier encounters, in low‐mobility systems. This is a serious limitation for the industrialization of organic solar cells, in particular when aiming at thick active layers. Herein, a meta‐analysis of the device performance of numerous bulk heterojunction organic solar cells is presented for which field‐dependent photogeneration, charge carrier mobility, and fill factor are determined. Herein, a “spin‐related factor” that is dependent on the ratio of back electron transfer of the triplet charge transfer (CT) states to the decay rate of the singlet CT states is introduced. It is shown that this factor links the recombination reduction factor to charge‐generation efficiency. As a consequence, it is only in the systems with very efficient charge generation and very fast CT dissociation that free carrier recombination is strongly suppressed, regardless of the spin‐related factor.
Balancing foraging gain and predation risk is a fundamental trade-off in the life of animals. Individual strategies to acquire, process, store and use information to solve cognitive tasks are likely to affect speed and flexibility of learning, and ecologically relevant decisions regarding foraging and predation risk. Theory suggests a functional link between individual variation in cognitive style and behaviour (animal personality) via speed-accuracy and risk-reward trade-offs. We tested whether cognitive style and personality affect risk-reward trade-off decisions posed by foraging and predation risk. We exposed 21 bank voles (Myodes glareolus) that were bold, fast learning and inflexible and 18 voles that were shy, slow learning and flexible to outdoor enclosures with different risk levels at two food patches. We quantified individual food patch exploitation, foraging and vigilance behaviour. Although both types responded to risk, fast animals increasingly exploited both food patches, gaining access to more food and spending less time searching and exercising vigilance. Slow animals progressively avoided high-risk areas, concentrating foraging effort in the low-risk one, and devoting >50% of visit to vigilance. These patterns indicate that individual differences in cognitive style/personality are reflected in foraging and anti-predator decisions that underlie the individual risk-reward bias.
Balancing foraging gain and predation risk is a fundamental trade-off in the life of animals. Individual strategies to acquire, process, store and use information to solve cognitive tasks are likely to affect speed and flexibility of learning, and ecologically relevant decisions regarding foraging and predation risk. Theory suggests a functional link between individual variation in cognitive style and behaviour (animal personality) via speed-accuracy and risk-reward trade-offs. We tested whether cognitive style and personality affect risk-reward trade-off decisions posed by foraging and predation risk. We exposed 21 bank voles (Myodes glareolus) that were bold, fast learning and inflexible and 18 voles that were shy, slow learning and flexible to outdoor enclosures with different risk levels at two food patches. We quantified individual food patch exploitation, foraging and vigilance behaviour. Although both types responded to risk, fast animals increasingly exploited both food patches, gaining access to more food and spending less time searching and exercising vigilance. Slow animals progressively avoided high-risk areas, concentrating foraging effort in the low-risk one, and devoting >50% of visit to vigilance. These patterns indicate that individual differences in cognitive style/personality are reflected in foraging and anti-predator decisions that underlie the individual risk-reward bias.
Cooperation is — despite not being predicted by game theory — a widely documented aspect of human behaviour in Prisoner’s Dilemma (PD) situations. This article presents a comparison between subjects restricted to playing pure strategies and subjects allowed to play mixed strategies in a one-shot symmetric PD laboratory experiment. Subjects interact with 10 other subjects and take their decisions all at once. Because subjects in the mixed-strategy treatment group are allowed to condition their level of cooperation more precisely on their beliefs about their counterparts’ level of cooperation, we predicted the cooperation rate in the mixed-strategy treatment group to be higher than in the pure-strategy control group. The results of our experiment reject our prediction: even after controlling for beliefs about the other subjects’ level of cooperation, we find that cooperation in the mixed-strategy group is lower than in the pure-strategy group. We also find, however, that subjects in the mixedstrategy group condition their cooperative behaviour more closely on their beliefs than in the pure-strategy group. In the mixed-strategy group, most subjects choose intermediate levels of cooperation.
Cooperation is — despite not being predicted by game theory — a widely documented aspect of human behaviour in Prisoner’s Dilemma (PD) situations. This article presents a comparison between subjects restricted to playing pure strategies and subjects allowed to play mixed strategies in a one-shot symmetric PD laboratory experiment. Subjects interact with 10 other subjects and take their decisions all at once. Because subjects in the mixed-strategy treatment group are allowed to condition their level of cooperation more precisely on their beliefs about their counterparts’ level of cooperation, we predicted the cooperation rate in the mixed-strategy treatment group to be higher than in the pure-strategy control group. The results of our experiment reject our prediction: even after controlling for beliefs about the other subjects’ level of cooperation, we find that cooperation in the mixed-strategy group is lower than in the pure-strategy group. We also find, however, that subjects in the mixedstrategy group condition their cooperative behaviour more closely on their beliefs than in the pure-strategy group. In the mixed-strategy group, most subjects choose intermediate levels of cooperation.
Diverse communities can adjust their trait composition to altered environmental conditions, which may strongly influence their dynamics. Previous studies of trait-based models mainly considered only one or two trophic levels, whereas most natural system are at least tritrophic. Therefore, we investigated how the addition of trait variation to each trophic level influences population and community dynamics in a tritrophic model. Examining the phase relationships between species of adjacent trophic levels informs about the strength of top-down or bottom-up control in non-steadystate situations. Phase relationships within a trophic level highlight compensatory dynamical patterns between functionally different species, which are responsible for dampening the community temporal variability. Furthermore, even without trait variation, our tritrophic model always exhibits regions with two alternative states with either weak or strong nutrient exploitation, and correspondingly low or high biomass production at the top level. However, adding trait variation increased the basin of attraction of the high-production state, and decreased the likelihood of a critical transition from the high- to the lowproduction state with no apparent early warning signals. Hence, our study shows that trait variation enhances resource use efficiency, production, stability, and resilience of entire food webs.
Diverse communities can adjust their trait composition to altered environmental conditions, which may strongly influence their dynamics. Previous studies of trait-based models mainly considered only one or two trophic levels, whereas most natural system are at least tritrophic. Therefore, we investigated how the addition of trait variation to each trophic level influences population and community dynamics in a tritrophic model. Examining the phase relationships between species of adjacent trophic levels informs about the strength of top-down or bottom-up control in non-steadystate situations. Phase relationships within a trophic level highlight compensatory dynamical patterns between functionally different species, which are responsible for dampening the community temporal variability. Furthermore, even without trait variation, our tritrophic model always exhibits regions with two alternative states with either weak or strong nutrient exploitation, and correspondingly low or high biomass production at the top level. However, adding trait variation increased the basin of attraction of the high-production state, and decreased the likelihood of a critical transition from the high- to the lowproduction state with no apparent early warning signals. Hence, our study shows that trait variation enhances resource use efficiency, production, stability, and resilience of entire food webs.
Sea surface temperature (SST) patterns can – as surface climate forcing – affect weather and climate at large distances. One example is El Niño-Southern Oscillation (ENSO) that causes climate anomalies around the globe via teleconnections. Although several studies identified and characterized these teleconnections, our understanding of climate processes remains incomplete, since interactions and feedbacks are typically exhibited at unique or multiple temporal and spatial scales. This study characterizes the interactions between the cells of a global SST data set at different temporal and spatial scales using climate networks. These networks are constructed using wavelet multi-scale correlation that investigate the correlation between the SST time series at a range of scales allowing instantaneously deeper insights into the correlation patterns compared to traditional methods like empirical orthogonal functions or classical correlation analysis. This allows us to identify and visualise regions of – at a certain timescale – similarly evolving SSTs and distinguish them from those with long-range teleconnections to other ocean regions. Our findings re-confirm accepted knowledge about known highly linked SST patterns like ENSO and the Pacific Decadal Oscillation, but also suggest new insights into the characteristics and origins of long-range teleconnections like the connection between ENSO and Indian Ocean Dipole.
Sea surface temperature (SST) patterns can – as surface climate forcing – affect weather and climate at large distances. One example is El Niño-Southern Oscillation (ENSO) that causes climate anomalies around the globe via teleconnections. Although several studies identified and characterized these teleconnections, our understanding of climate processes remains incomplete, since interactions and feedbacks are typically exhibited at unique or multiple temporal and spatial scales. This study characterizes the interactions between the cells of a global SST data set at different temporal and spatial scales using climate networks. These networks are constructed using wavelet multi-scale correlation that investigate the correlation between the SST time series at a range of scales allowing instantaneously deeper insights into the correlation patterns compared to traditional methods like empirical orthogonal functions or classical correlation analysis. This allows us to identify and visualise regions of – at a certain timescale – similarly evolving SSTs and distinguish them from those with long-range teleconnections to other ocean regions. Our findings re-confirm accepted knowledge about known highly linked SST patterns like ENSO and the Pacific Decadal Oscillation, but also suggest new insights into the characteristics and origins of long-range teleconnections like the connection between ENSO and Indian Ocean Dipole.
Dendritic hPG-amid-C18-mPEG core-multishell nanocarriers (CMS) represent a novel class of unimolecular micelles that hold great potential as drug transporters, e. g., to facilitate topical therapy in skin diseases. Atopic dermatitis is among the most common inflammatory skin disorders with complex barrier alterations which may affect the efficacy of topical treatment.
Here, we tested the penetration behavior and identified target structures of unloaded CMS after topical administration in healthy mice and in mice with oxazolone-induced atopic dermatitis. We further examined whole body distribution and possible systemic side effects after simulating high dosage dermal penetration by subcutaneous injection.
Following topical administration, CMS accumulated in the stratum corneum without penetration into deeper viable epidermal layers. The same was observed in atopic dermatitis mice, indicating that barrier alterations in atopic dermatitis had no influence on the penetration of CMS. Following subcutaneous injection, CMS were deposited in the regional lymph nodes as well as in liver, spleen, lung, and kidney. However, in vitro toxicity tests, clinical data, and morphometry-assisted histopathological analyses yielded no evidence of any toxic or otherwise adverse local or systemic effects of CMS, nor did they affect the severity or course of atopic dermatitis.
Taken together, CMS accumulate in the stratum corneum in both healthy and inflammatory skin and appear to be highly biocompatible in the mouse even under conditions of atopic dermatitis and thus could potentially serve to create a depot for anti-inflammatory drugs in the skin.
Polysulfobetaines in aqueous solution show upper critical solution temperature (UCST) behavior. We investigate here the representative of this class of materials, poly (N,N-dimethyl-N-(3-methacrylamidopropyl) ammonio propane sulfonate) (PSPP), with respect to: (i) the dynamics in aqueous solution above the cloud point as function of NaBr concentration; and (ii) the swelling behavior of thin films in water vapor as function of the initial film thickness. For PSPP solutions with a concentration of 5 wt.%, the temperature dependence of the intensity autocorrelation functions is measured with dynamic light scattering as function of molar mass and NaBr concentration (0–8 mM). We found a scaling of behavior for the scattered intensity and dynamic correlation length. The resulting spinodal temperatures showed a maximum at a certain (small) NaBr concentration, which is similar to the behavior of the cloud points measured previously by turbidimetry. The critical exponent of susceptibility depends on NaBr concentration, with a minimum value where the spinodal temperature is maximum and a trend towards the mean-field value of unity with increasing NaBr concentration. In contrast, the critical exponent of the correlation length does not depend on NaBr concentration and is lower than the value of 0.5 predicted by mean-field theory. For PSPP thin films, the swelling behavior was found to depend on film thickness. A film thickness of about 100 nm turns out to be the optimum thickness needed to obtain fast hydration with H 2 O.
The importance of plasmonic heating for the plasmondriven photodimerization of 4-nitrothiophenol
(2018)
Metal nanoparticles form potent nanoreactors, driven by the optical generation of energetic electrons and nanoscale heat. The relative influence of these two factors on nanoscale chemistry is strongly debated. This article discusses the temperature dependence of the dimerization of 4-nitrothiophenol (4-NTP) into 4,4′-dimercaptoazobenzene (DMAB) adsorbed on gold nanoflowers by Surface-Enhanced Raman Scattering (SERS). Raman thermometry shows a significant optical heating of the particles. The ratio of the Stokes and the anti-Stokes Raman signal moreover demonstrates that the molecular temperature during the reaction rises beyond the average crystal lattice temperature of the plasmonic particles. The product bands have an even higher temperature than reactant bands, which suggests that the reaction proceeds preferentially at thermal hot spots. In addition, kinetic measurements of the reaction during external heating of the reaction environment yield a considerable rise of the reaction rate with temperature. Despite this significant heating effects, a comparison of SERS spectra recorded after heating the sample by an external heater to spectra recorded after prolonged illumination shows that the reaction is strictly photo-driven. While in both cases the temperature increase is comparable, the dimerization occurs only in the presence of light. Intensity dependent measurements at fixed temperatures confirm this finding.
The importance of plasmonic heating for the plasmondriven photodimerization of 4-nitrothiophenol
(2019)
Metal nanoparticles form potent nanoreactors, driven by the optical generation of energetic electrons and nanoscale heat. The relative influence of these two factors on nanoscale chemistry is strongly debated. This article discusses the temperature dependence of the dimerization of 4-nitrothiophenol (4-NTP) into 4,4′-dimercaptoazobenzene (DMAB) adsorbed on gold nanoflowers by Surface-Enhanced Raman Scattering (SERS). Raman thermometry shows a significant optical heating of the particles. The ratio of the Stokes and the anti-Stokes Raman signal moreover demonstrates that the molecular temperature during the reaction rises beyond the average crystal lattice temperature of the plasmonic particles. The product bands have an even higher temperature than reactant bands, which suggests that the reaction proceeds preferentially at thermal hot spots. In addition, kinetic measurements of the reaction during external heating of the reaction environment yield a considerable rise of the reaction rate with temperature. Despite this significant heating effects, a comparison of SERS spectra recorded after heating the sample by an external heater to spectra recorded after prolonged illumination shows that the reaction is strictly photo-driven. While in both cases the temperature increase is comparable, the dimerization occurs only in the presence of light. Intensity dependent measurements at fixed temperatures confirm this finding.
Ecological and physiological factors lead to different contamination patterns in individual marine mammals. The objective of the present study was to assess whether variations in contamination profiles are indicative of social structures of young male sperm whales as they might reflect a variation in feeding preferences and/or in utilized feeding grounds. We used a total of 61 variables associated with organic compounds and trace element concentrations measured in muscle, liver, kidney and blubber gained from 24 sperm whales that stranded in the North Sea in January and February 2016. Combining contaminant and genetic data, there is evidence for at least two cohorts with different origin among these stranded sperm whales; one from the Canary Island region and one from the northern part of the Atlantic. While genetic data unravel relatedness and kinship, contamination data integrate over areas, where animals occured during their lifetime. Especially in long-lived animals with a large migratory potential, as sperm whales, contamination data may carry highly relevant information about aggregation through time and space.