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Manganese (Mn) as well as iron (Fe) are essential trace elements (TE) important for the maintenance of physiological functions including fetal development. However, in the case of Mn, evidence suggests that excess levels of intrauterine Mn are associated with adverse pregnancy outcomes. Although Mn is known to cross the placenta, the fundamentals of Mn transfer kinetics and mechanisms are largely unknown. Moreover, exposure to combinations of TEs should be considered in mechanistic transfer studies, in particular for TEs expected to share similar transfer pathways. Here, we performed a mechanistic in vitro study on the placental transfer of Mn across a BeWo b30 trophoblast layer. Our data revealed distinct differences in the placental transfer of Mn and Fe. While placental permeability to Fe showed a clear inverse dose-dependency, Mn transfer was largely independent of the applied doses. Concurrent exposure of Mn and Fe revealed transfer interactions of Fe and Mn, indicating that they share common transfer mechanisms. In general, mRNA and protein expression of discussed transporters like DMT1, TfR, or FPN were only marginally altered in BeWo cells despite the different exposure scenarios highlighting that Mn transfer across the trophoblast layer likely involves a combination of active and passive transport processes.
Myasthenia gravis is an autoimmune disease affecting neuromuscular transmission and causing skeletal muscle weakness. Additionally, systemic inflammation, cognitive deficits and autonomic dysfunction have been described.
However, little is known about myasthenia gravis-related reorganization of the brain. In this study, we thus investigated the structural and functional brain changes in myasthenia gravis patients.
Eleven myasthenia gravis patients (age: 70.64 +/- 9.27; 11 males) were compared to age-, sex- and education-matched healthy controls (age: 70.18 +/- 8.98; 11 males). Most of the patients (n = 10, 0.91%) received cholinesterase inhibitors.
Structural brain changes were determined by applying voxel-based morphometry using high-resolution T-1-weighted sequences. Functional brain changes were assessed with a neuropsychological test battery (including attention, memory and executive functions), a spatial orientation task and brain-derived neurotrophic factor blood levels.
Myasthenia gravis patients showed significant grey matter volume reductions in the cingulate gyrus, in the inferior parietal lobe and in the fusiform gyrus. Furthermore, myasthenia gravis patients showed significantly lower performance in executive functions, working memory (Spatial Span, P = 0.034, d = 1.466), verbal episodic memory (P = 0.003, d = 1.468) and somatosensory-related spatial orientation (Triangle Completion Test, P = 0.003, d = 1.200).
Additionally, serum brain-derived neurotrophic factor levels were significantly higher in myasthenia gravis patients (P = 0.001, d = 2.040). Our results indicate that myasthenia gravis is associated with structural and functional brain alterations. Especially the grey matter volume changes in the cingulate gyrus and the inferior parietal lobe could be associated with cognitive deficits in memory and executive functions.
Furthermore, deficits in somatosensory-related spatial orientation could be associated with the lower volumes in the inferior parietal lobe. Future research is needed to replicate these findings independently in a larger sample and to investigate the underlying mechanisms in more detail.
Klaus et al. compared myasthenia gravis patients to matched healthy control subjects and identified functional alterations in memory functions as well as structural alterations in the cingulate gyrus, in the inferior parietal lobe and in the fusiform gyrus.
Large-scale groundwater models are required to estimate groundwater availability and to inform water management strategies on the national scale.
However, parameterization of large-scale groundwater models covering areas of major river basins and more is challenging due to the lack of observational data and the mismatch between the scales of modeling and measurements.
In this work, we propose to bridge the scale gap and derive regional hydraulic parameters by spectral analysis of groundwater level fluctuations.
We hypothesize that specific locations in aquifers can reveal regional parameters of the hydraulic system.
We first generate ensembles of synthetic but realistic aquifers which systematically differ in complexity. Applying Liang and Zhang's (2013), , semi-analytical solution for the spectrum of hydraulic head time series, we identify for each ensemble member and at different locations representative aquifer parameters.
Next, we extend our study to investigate the use of spectral analysis in more complex numerical models and in real settings.
Our analyses indicate that the variance of inferred effective transmissivity and storativity values for stochastic aquifer ensembles is small for observation points which are far away from the Dirichlet boundary.
Moreover, the head time series has to cover a period which is roughly 10 times as long as the characteristic time of the aquifer. In deterministic aquifer models we infer equivalent, regionally valid parameters. A sensitivity analysis further reveals that as long as the aquifer length and the position of the groundwater measurement location is roughly known, the parameters can be robustly estimated.
Worldwide, companies are increasingly making claims about their current climate efforts and their future mitigation commitments. These claims tend to be underpinned by carbon credits issued in voluntary carbon markets to offset emissions. Corporate climate claims are largely unregulated which means that they are often (perceived to be) misleading and deceptive. As such, corporate climate claims risk undermining, rather than contributing to, global climate mitigation. This paper takes as its point of departure the proposition that a better understanding of corporate climate claims is needed to govern such claims in a manner that adequately addresses potential greenwashing risks. To that end, the paper reviews the nascent literature on corporate climate claims relying on the use of voluntary carbon credits. Drawing on the reviewed literature, three key dimensions of corporate climate claims as related to carbon credits are discussed: 1) the intended use of carbon credits: offsetting versus non-offsetting claims; 2) the framing and meaning of headline terms: net-zero versus carbon neutral claims; and 3) the status of the claim: future aspirational commitments versus stated achievements. The paper thereby offers a preliminary categorization of corporate climate claims and discusses risks associated with and governance implications for each of these categories.
The 2022 Kunming-Montreal Global Biodiversity Framework (GBF) and Paris Agreement (PA) are highly complementary agreements where each depends on the other’s success to be effective. The GBF offers a very specific framework of interim goals and targets that break down the objective of the Convention on Biodiversity (CBD) into a decade-spanning work plan. Comprised of 10 sections – including a 2050 vision and a 2030 mission, four overarching goals and 23 specific targets – the GBF is expected to guide biodiversity policy around the world in the coming years to decades. A similar set of global interim climate policy targets could translate the global temperature goal into concrete policy milestones that would provide policy makers and civil society with reference points for policy making and efforts to hold governments accountable. Beyond inspiring climate policy experts to convert temperature goals into policy milestones, GBF has the potential to strengthen the implementation of the PA at the nexus of biodiversity and climate (adaptation and mitigation) action. For example, the GBF can help to ensure that nature-based climate solutions are implemented with full consideration of biodiversity concerns, of the rights and interests of Indigenous Peoples and local communities, and with fair and transparent benefit sharing arrangements. In sum, the GBF should be mandatory reading for all climate policy makers.
We argue for a perspective on bilingual heritage speakers as native speakers of both their languages and present results from a large-scale, cross-linguistic study that took such a perspective and approached bilinguals and monolinguals on equal grounds.
We targeted comparable language use in bilingual and monolingual speakers, crucially covering broader repertoires than just formal language. A main database was the open-access RUEG corpus, which covers comparable informal vs. formal and spoken vs. written productions by adolescent and adult bilinguals with heritage-Greek, -Russian, and -Turkish in Germany and the United States and with heritage-German in the United States, and matching data from monolinguals in Germany, the United States, Greece, Russia, and Turkey. Our main results lie in three areas.
(1) We found non-canonical patterns not only in bilingual, but also in monolingual speakers, including patterns that have so far been considered absent from native grammars, in domains of morphology, syntax, intonation, and pragmatics.
(2) We found a degree of lexical and morphosyntactic inter-speaker variability in monolinguals that was sometimes higher than that of bilinguals, further challenging the model of the streamlined native speaker.
(3) In majority language use, non-canonical patterns were dominant in spoken and/or informal registers, and this was true for monolinguals and bilinguals. In some cases, bilingual speakers were leading quantitatively. In heritage settings where the language was not part of formal schooling, we found tendencies of register leveling, presumably due to the fact that speakers had limited access to formal registers of the heritage language.
Our findings thus indicate possible quantitative differences and different register distributions rather than distinct grammatical patterns in bilingual and monolingual speakers. This supports the integration of heritage speakers into the native-speaker continuum. Approaching heritage speakers from this perspective helps us to better understand the empirical data and can shed light on language variation and change in native grammars.
Furthermore, our findings for monolinguals lead us to reconsider the state-of-the art on majority languages, given recurring evidence for non-canonical patterns that deviate from what has been assumed in the literature so far, and might have been attributed to bilingualism had we not included informal and spoken registers in monolinguals and bilinguals alike.
The present study proposes and tests pathways by which racial discrimination might be positively related to bullying victimization among Black and White adolescents. Data were derived from the 2016 National Survey of Children's Health, a national survey that provides data on children's physical and mental health and their families. Data were collected from households with one or more children between June 2016 to February 2017.
A letter was sent to randomly selected households, who were invited to participate in the survey. The caregivers consisted of 66.9% females and 33.1% males for the White sample, whose mean age was 47.51 (SD = 7.26), and 76.8% females and 23.2% males for the Black sample, whose mean age was 47.61 (SD = 9.71).
In terms of the adolescents, 49.0% were females among the White sample, whose mean age was 14.73 (SD = 1.69). For Black adolescents, 47.9% were females and the mean age was 14.67(SD = 1.66).
Measures for the study included bullying perpetration, racial discrimination, academic disengagement, and socio-demographic variables of the parent and child.
Analyses included descriptive statistics, bivariate correlations, and structural path analyses.
For adolescents in both racial groups, racial discrimination appears to be positively associated with depression, which was positively associated with bullying perpetration. For White adolescents, racial discrimination was positively associated with academic disengagement, which was also positively associated with bullying perpetration. For Black adolescents, although racial discrimination was not significantly associated with academic disengagement, academic disengagement was positively associated with bullying perpetration.
High annotation costs are a substantial bottleneck in applying deep learning architectures to clinically relevant use cases, substantiating the need for algorithms to learn from unlabeled data.
In this work, we propose employing self-supervised methods. To that end, we trained with three self-supervised algorithms on a large corpus of unlabeled dental images, which contained 38K bitewing radiographs (BWRs). We then applied the learned neural network representations on tooth-level dental caries classification, for which we utilized labels extracted from electronic health records (EHRs). Finally, a holdout test-set was established, which consisted of 343 BWRs and was annotated by three dental professionals and approved by a senior dentist.
This test-set was used to evaluate the fine-tuned caries classification models. Our experimental results demonstrate the obtained gains by pretraining models using self-supervised algorithms. These include improved caries classification performance (6 p.p. increase in sensitivity) and, most importantly, improved label-efficiency.
In other words, the resulting models can be fine-tuned using few labels (annotations).
Our results show that using as few as 18 annotations can produce >= 45% sensitivity, which is comparable to human-level diagnostic performance.
This study shows that self-supervision can provide gains in medical image analysis, particularly when obtaining labels is costly and expensive.
Boreal forests cover over half of the global permafrost area and protect underlying permafrost. Boreal forest development, therefore, has an impact on permafrost evolution, especially under a warming climate.
Forest disturbances and changing climate conditions cause vegetation shifts and potentially destabilize the carbon stored within the vegetation and permafrost. Disturbed permafrost-forest ecosystems can develop into a dry or swampy bush- or grasslands, shift toward broadleaf- or evergreen needleleaf-dominated forests, or recover to the pre-disturbance state.
An increase in the number and intensity of fires, as well as intensified logging activities, could lead to a partial or complete ecosystem and permafrost degradation. We study the impact of forest disturbances (logging, surface, and canopy fires) on the thermal and hydrological permafrost conditions and ecosystem resilience.
We use a dynamic multilayer canopy-permafrost model to simulate different scenarios at a study site in eastern Siberia. We implement expected mortality, defoliation, and ground surface changes and analyze the interplay between forest recovery and permafrost. We find that forest loss induces soil drying of up to 44%, leading to lower active layer thicknesses and abrupt or steady decline of a larch forest, depending on disturbance intensity.
Only after surface fires, the most common disturbances, inducing low mortality rates, forests can recover and overpass pre-disturbance leaf area index values. We find that the trajectory of larch forests after surface fires is dependent on the precipitation conditions in the years after the disturbance. Dryer years can drastically change the direction of the larch forest development within the studied period.
We demonstrate a recycling system for synthetic nicotinamide cofactor analogues using a soluble hydrogenase with turnover number of >1000 for reduction of the cofactor analogues by H-2.
Coupling this system to an ene reductase, we show quantitative conversion of N-ethylmaleimide to N-ethylsuccinimide.
The biocatalyst system retained >50% activity after 7 h.
Land-based climate mitigation measures have gained significant attention and importance in public and private sector climate policies. Building on previous studies, we refine and update the mitigation potentials for 20 land-based measures in >200 countries and five regions, comparing “bottom-up” sectoral estimates with integrated assessment models (IAMs). We also assess implementation feasibility at the country level. Cost-effective (available up to $100/tCO2eq) land-based mitigation is 8–13.8 GtCO2eq yr−1 between 2020 and 2050, with the bottom end of this range representing the IAM median and the upper end representing the sectoral estimate. The cost-effective sectoral estimate is about 40% of available technical potential and is in line with achieving a 1.5°C pathway in 2050. Compared to technical potentials, cost-effective estimates represent a more realistic and actionable target for policy. The cost-effective potential is approximately 50% from forests and other ecosystems, 35% from agriculture, and 15% from demand-side measures. The potential varies sixfold across the five regions assessed (0.75–4.8 GtCO2eq yr−1) and the top 15 countries account for about 60% of the global potential. Protection of forests and other ecosystems and demand-side measures present particularly high mitigation efficiency, high provision of co-benefits, and relatively lower costs. The feasibility assessment suggests that governance, economic investment, and socio-cultural conditions influence the likelihood that land-based mitigation potentials are realized. A substantial portion of potential (80%) is in developing countries and LDCs, where feasibility barriers are of greatest concern. Assisting countries to overcome barriers may result in significant quantities of near-term, low-cost mitigation while locally achieving important climate adaptation and development benefits. Opportunities among countries vary widely depending on types of land-based measures available, their potential co-benefits and risks, and their feasibility. Enhanced investments and country-specific plans that accommodate this complexity are urgently needed to realize the large global potential from improved land stewardship.
Honey traceability is an important topic, especially for honeydew honeys, due to the increased incidence of adulteration. This study aimed to establish specific markers to quantify proteins in honey. A proteomics strategy to identify marker peptides from bracatinga honeydew honey was therefore developed. The proteomics approach was based on initial untargeted identification of honey proteins and peptides by LC-ESI-Triple-TOF-MS/MS, which identified the major royal jelly proteins (MRJP) presence. Afterwards, the peptides were selected by the in silico digestion. The marker peptides were quantified by the developed targeted LC-QqQ-MS/MS method, which provided good linearity and specificity, besides recoveries between 92 and 100% to quantify peptides from bracatinga honeydew honey. The uniqueness and high response in mass spectrometry were backed by further complementary protein analysis (SDS-PAGE). The selected marker peptides EALPHVPIFDR (MRJP 1), ILGANVK (MRJP 2), TFVTIER (MRJP 3), QNIDVVAR (MRJP 4), FINNDYNFNEVNFR (MRJP 5) and LLQPYPDWSWTK (MRJP 7), quantified by LC-QqQ-MS/MS, highlighted that the content of QNIDVVAR from MRJP 4 could be used to differentiate bracatinga honeydew honey from floral honeys (p < 0.05) as a potential marker for its authentication. Finally, principal components analysis highlighted the QNIDVVAR content as a good descriptor of the analyzed bracatinga honeydew honey samples.
Measuring the variability of incoming neutrons locally would be usefull for the cosmic-ray neutron sensing (CRNS) method. As the measurement of high energy neutrons is not so easy, alternative particles can be considered for such purpose. Among them, muons are particles created from the same cascade of primary cosmic-ray fluxes that generate neutrons at the ground. In addition, they can be easily detected by small and relatively inexpensive detectors. For these reasons they could provide a suitable local alternative to incoming corrections based on remote neutron monitor data. The reported measurements demonstrated that muon detection system can detect incoming cosmic-ray variations locally. Furthermore the precision of this measurement technique is considered adequate for many CRNS applications.
A large landslide (frozen debris avalanche) occurred at Assapaat on the south coast of the Nuussuaq Peninsula in Central West Greenland on June 13, 2021, at 04:04 local time. We present a compilation of available data from field observations, photos, remote sensing, and seismic monitoring to describe the event. Analysis of these data in combination with an analysis of pre- and post-failure digital elevation models results in the first description of this type of landslide. The frozen debris avalanche initiated as a 6.9 * 10(6) m(3) failure of permafrozen talus slope and underlying colluvium and till at 600-880 m elevation. It entrained a large volume of permafrozen colluvium along its 2.4 km path in two subsequent entrainment phases accumulating a total volume between 18.3 * 10(6) and 25.9 * 10(6) m(3). About 3.9 * 10(6) m(3) is estimated to have entered the Vaigat strait; however, no tsunami was reported, or is evident in the field. This is probably because the second stage of entrainment along with a flattening of slope angle reduced the mobility of the frozen debris avalanche. We hypothesise that the initial talus slope failure is dynamically conditioned by warming of the ice matrix that binds the permafrozen talus slope. When the slope ice temperature rises to a critical level, its shear resistance is reduced, resulting in an unstable talus slope prone to failure. Likewise, we attribute the large-scale entrainment to increasing slope temperature and take the frozen debris avalanche as a strong sign that the permafrost in this region is increasingly at a critical state. Global warming is enhanced in the Arctic and frequent landslide events in the past decade in Western Greenland let us hypothesise that continued warming will lead to an increase in the frequency and magnitude of these types of landslides. Essential data for critical arctic slopes such as precipitation, snowmelt, and ground and surface temperature are still missing to further test this hypothesis. It is thus strongly required that research funds are made available to better predict the change of landslide threat in the Arctic.
A novel idea for an optimal time delay state space reconstruction from uni- and multivariate time series is presented. The entire embedding process is considered as a game, in which each move corresponds to an embedding cycle and is subject to an evaluation through an objective function. This way the embedding procedure can be modeled as a tree, in which each leaf holds a specific value of the objective function. By using a Monte Carlo ansatz, the proposed algorithm populates the tree with many leafs by computing different possible embedding paths and the final embedding is chosen as that particular path, which ends at the leaf with the lowest achieved value of the objective function. The method aims to prevent getting stuck in a local minimum of the objective function and can be used in a modular way, enabling practitioners to choose a statistic for possible delays in each embedding cycle as well as a suitable objective function themselves. The proposed method guarantees the optimization of the chosen objective function over the parameter space of the delay embedding as long as the tree is sampled sufficiently. As a proof of concept, we demonstrate the superiority of the proposed method over the classical time delay embedding methods using a variety of application examples. We compare recurrence plot-based statistics inferred from reconstructions of a Lorenz-96 system and highlight an improved forecast accuracy for map-like model data as well as for palaeoclimate isotope time series. Finally, we utilize state space reconstruction for the detection of causality and its strength between observables of a gas turbine type thermoacoustic combustor.
Deforestation is currently a widespread phenomenon and a growing environmental concern in the era of rapid climate change.
In temperate regions, it is challenging to quantify the impacts of deforestation on the catchment dynamics and downstream aquatic ecosystems such as reservoirs and disentangle these from direct climate change impacts, let alone project future changes to inform management.
Here, we tackled this issue by investigating a unique catchment-reservoir system with two reservoirs in distinct trophic states (meso- and eutrophic), both of which drain into the largest drinking water reservoir in Germany.
Due to the prolonged droughts in 2015-2018, the catchment of the mesotrophic reservoir lost an unprecedented area of forest (exponential increase since 2015 and ca. 17.1% loss in 2020 alone).
We coupled catchment nutrient exports (HYPE) and reservoir ecosystem dynamics (GOTM-WET) models using a process-based modeling approach. The coupled model was validated with datasets spanning periods of rapid deforestation, which makes our future projections highly robust.
Results show that in a short-term time scale (by 2035), increasing nutrient flux from the catchment due to vast deforestation (80% loss) can turn the mesotrophic reservoir into a eutrophic state as its counterpart.
Our results emphasize the more prominent impacts of deforestation than the direct impact of climate warming in impairment of water quality and ecological services to downstream aquatic ecosystems. Therefore, we propose to evaluate the impact of climate change on temperate reservoirs by incorporating a time scale-dependent context, highlighting the indirect impact of deforestation in the short-term scale. In the long-term scale (e.g. to 2100), a guiding hypothesis for future research may be that indirect effects (e.g., as mediated by catchment dynamics) are as important as the direct effects of climate warming on aquatic ecosystems.
Organic solar cells (OSCs) have progressed rapidly in recent years through the development of novel organic photoactive materials, especially non-fullerene acceptors (NFAs). Consequently, OSCs based on state-of-the-art NFAs have reached significant milestones, such as similar to 19% power conversion efficiencies (PCEs) and small energy losses (less than 0.5 eV). Despite these significant advances, understanding of the interplay between molecular structure and optoelectronic properties lags significantly behind. For example, despite the theoretical framework for describing the energetic disorder being well developed for the case of inorganic semiconductors, the question of the applicability of classical semiconductor theories in analyzing organic semiconductors is still under debate. A general observation in the inorganic field is that inorganic photovoltaic materials possessing a polycrystalline microstructure exhibit suppressed disorder properties and better charge carrier transport compared to their amorphous analogs. Accordingly, this principle extends to the organic semiconductor field as many organic photovoltaic materials are synthesized to pursue polycrystalline-like features. Yet, there appears to be sporadic examples that exhibit an opposite trend. However, full studies decoupling energetic disorder from aggregation effects have largely been left out. Hence, the potential role of the energetic disorder in OSCs has received little attention. Interestingly, recently reported state-of-the-art NFA-based devices could achieve a small energetic disorder and high PCE at the same time; and interest in this investigation related to the disorder properties in OSCs was revived. In this contribution, progress in terms of the correlation between molecular design and energetic disorder is reviewed together with their effects on the optoelectronic mechanism and photovoltaic performance. Finally, the specific challenges and possible solutions in reducing the energetic disorder of OSCs from the viewpoint of materials and devices are proposed.
In organic solar cells, the resulting device efficiency depends strongly on the local morphology and intermolecular interactions of the blend film. Optical spectroscopy was used to identify the spectral signatures of interacting chromophores in blend films of the donor polymer PM6 with two state-of-the-art nonfullerene acceptors, Y6 and N4, which differ merely in the branching point of the side chain. From temperature-dependent absorption and luminescence spectroscopy in solution, it is inferred that both acceptor materials form two types of aggregates that differ in their interaction energy. Y6 forms an aggregate with a predominant J-type character in solution, while for N4 molecules the interaction is predominantly in a H-like manner in solution and freshly spin-cast film, yet the molecules reorient with respect to each other with time or thermal annealing to adopt a more J-type interaction. The different aggregation behavior of the acceptor materials is also reflected in the blend films and accounts for the different solar cell efficiencies reported with the two blends.
The question if a given partial solution to a problem can be extended reasonably occurs in many algorithmic approaches for optimization problems.
For instance, when enumerating minimal vertex covers of a graph G = (V, E), one usually arrives at the problem to decide for a vertex set U subset of V (pre-solution), if there exists a minimal vertex cover S (i.e., a vertex cover S subset of V such that no proper subset of S is a vertex cover) with U subset of S (minimal extension of U).
We propose a general, partial-order based formulation of such extension problems which allows to model parameterization and approximation aspects of extension, and also highlights relationships between extension tasks for different specific problems.
As examples, we study a number of specific problems which can be expressed and related in this framework. In particular, we discuss extension variants of the problems dominating set and feedback vertex/edge set.
All these problems are shown to be NP-complete even when restricted to bipartite graphs of bounded degree, with the exception of our extension version of feedback edge set on undirected graphs which is shown to be solvable in polynomial time.
For the extension variants of dominating and feedback vertex set, we also show NP-completeness for the restriction to planar graphs of bounded degree.
As non-graph problem, we also study an extension version of the bin packing problem. We further consider the parameterized complexity of all these extension variants, where the parameter is a measure of the pre-solution as defined by our framework.
Classical linguistic theory assumes that formal aspects, like sound, are not internally related to the meaning of words. However, recent research suggests language might code affective meaning such as threat and alert sublexically. Positing affective phonological iconicity as a systematic organization principle of the German lexicon, we calculated sublexical affective values for sub-syllabic phonological word segments from a large-scale affective lexical German database by averaging valence and arousal ratings of all words any phonological segment appears in. We tested word stimuli with either consistent or inconsistent mappings between lexical affective meaning and sublexical affective values (negative-valence/high-arousal vs. neutral-valence/lowarousal) in an EEG visual-lexical-decision task. A mismatch between sublexical and lexical affective values elicited an increased N400 response. These results reveal that systematic affective phonological iconicity - extracted from the lexicon - impacts the extraction of lexical word meaning during reading.