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The mitochondrial ATP-binding cassette (ABC) transporters ABCB7 in humans, Atm1 in yeast and ATM3 in plants, are highly conserved in their overall architecture and particularly in their glutathione binding pocket located within the transmembrane spanning domains. These transporters have attracted interest in the last two decades based on their proposed role in connecting the mitochondrial iron sulfur (Fe–S) cluster assembly with its cytosolic Fe–S cluster assembly (CIA) counterpart. So far, the specific compound that is transported across the membrane remains unknown. In this report we characterized the ABCB7-like transporter Rcc02305 in Rhodobacter capsulatus, which shares 47% amino acid sequence identity with its mitochondrial counterpart. The constructed interposon mutant strain in R. capsulatus displayed increased levels of intracellular reactive oxygen species without a simultaneous accumulation of the cellular iron levels. The inhibition of endogenous glutathione biosynthesis resulted in an increase of total glutathione levels in the mutant strain. Bioinformatic analysis of the amino acid sequence motifs revealed a potential aminotransferase class-V pyridoxal-50-phosphate (PLP) binding site that overlaps with the Walker A motif within the nucleotide binding domains of the transporter. PLP is a well characterized cofactor of L-cysteine desulfurases like IscS and NFS1 which has a role in the formation of a protein-bound persulfide group within these proteins. We therefore suggest renaming the ABCB7-like transporter Rcc02305 in R. capsulatus to PexA for PLP binding exporter. We further suggest that this ABC-transporter in R. capsulatus is involved in the formation and export of polysulfide species to the periplasm.
The lack of soil data, which are relevant, reliable, affordable, immediately available, and sufficiently detailed, is still a significant challenge in precision agriculture. A promising technology for the spatial assessment of the distribution of chemical elements within fields, without sample preparation is laser-induced breakdown spectroscopy (LIBS). Its advantages are contrasted by a strong matrix dependence of the LIBS signal which necessitates careful data evaluation. In this work, different calibration approaches for soil LIBS data are presented. The data were obtained from 139 soil samples collected on two neighboring agricultural fields in a quaternary landscape of northeast Germany with very variable soils. Reference analysis was carried out by inductively coupled plasma optical emission spectroscopy after wet digestion. The major nutrients Ca and Mg and the minor nutrient Fe were investigated. Three calibration strategies were compared. The first method was based on univariate calibration by standard addition using just one soil sample and applying the derived calibration model to the LIBS data of both fields. The second univariate model derived the calibration from the reference analytics of all samples from one field. The prediction is validated by LIBS data of the second field. The third method is a multivariate calibration approach based on partial least squares regression (PLSR). The LIBS spectra of the first field are used for training. Validation was carried out by 20-fold cross-validation using the LIBS data of the first field and independently on the second field data. The second univariate method yielded better calibration and prediction results compared to the first method, since matrix effects were better accounted for. PLSR did not strongly improve the prediction in comparison to the second univariate method.
Medical imaging plays an important role in disease diagnosis, treatment planning, and clinical monitoring. One of the major challenges in medical image analysis is imbalanced training data, in which the class of interest is much rarer than the other classes. Canonical machine learning algorithms suppose that the number of samples from different classes in the training dataset is roughly similar or balance. Training a machine learning model on an imbalanced dataset can introduce unique challenges to the learning problem.
A model learned from imbalanced training data is biased towards the high-frequency samples. The predicted results of such networks have low sensitivity and high precision. In medical applications, the cost of misclassification of the minority class could be more than the cost of misclassification of the majority class. For example, the risk of not detecting a tumor could be much higher than referring to a healthy subject to a doctor. The current Ph.D. thesis introduces several deep learning-based approaches for handling class imbalanced problems for learning multi-task such as disease classification and semantic segmentation.
At the data-level, the objective is to balance the data distribution through re-sampling the data space: we propose novel approaches to correct internal bias towards fewer frequency samples. These approaches include patient-wise batch sampling, complimentary labels, supervised and unsupervised minority oversampling using generative adversarial networks for all.
On the other hand, at algorithm-level, we modify the learning algorithm to alleviate the bias towards majority classes. In this regard, we propose different generative adversarial networks for cost-sensitive learning, ensemble learning, and mutual learning to deal with highly imbalanced imaging data.
We show evidence that the proposed approaches are applicable to different types of medical images of varied sizes on different applications of routine clinical tasks, such as disease classification and semantic segmentation. Our various implemented algorithms have shown outstanding results on different medical imaging challenges.
Background
Semi-natural plant communities such as field boundaries play an important ecological role in agricultural landscapes, e.g., provision of refuge for plant and other species, food web support or habitat connectivity. To prevent undesired effects of herbicide applications on these communities and their structure, the registration and application are regulated by risk assessment schemes in many industrialized countries. Standardized individual-level greenhouse experiments are conducted on a selection of crop and wild plant species to characterize the effects of herbicide loads potentially reaching off-field areas on non-target plants. Uncertainties regarding the protectiveness of such approaches to risk assessment might be addressed by assessment factors that are often under discussion. As an alternative approach, plant community models can be used to predict potential effects on plant communities of interest based on extrapolation of the individual-level effects measured in the standardized greenhouse experiments. In this study, we analyzed the reliability and adequacy of the plant community model IBC-grass (individual-based plant community model for grasslands) by comparing model predictions with empirically measured effects at the plant community level.
Results
We showed that the effects predicted by the model IBC-grass were in accordance with the empirical data. Based on the species-specific dose responses (calculated from empirical effects in monocultures measured 4 weeks after application), the model was able to realistically predict short-term herbicide impacts on communities when compared to empirical data.
Conclusion
The results presented in this study demonstrate an approach how the current standard greenhouse experiments—measuring herbicide impacts on individual-level—can be coupled with the model IBC-grass to estimate effects on plant community level. In this way, it can be used as a tool in ecological risk assessment.
Simulating the impact of herbicide drift exposure on non-target terrestrial plant communities
(2019)
In Europe, almost half of the terrestrial landscape is used for agriculture. Thus, semi-natural habitats such as field margins are substantial for maintaining diversity in intensively managed farmlands. However, plants located at field margins are threatened by agricultural practices such as the application of pesticides within the fields. Pesticides are chemicals developed to control for undesired species within agricultural fields to enhance yields. The use of pesticides implies, however, effects on non-target organisms within and outside of the agricultural fields. Non-target organisms are organisms not intended to be sprayed or controlled for. For example, plants occurring in field margins are not intended to be sprayed, however, can be impaired due to herbicide drift exposure. The authorization of plant protection products such as herbicides requires risk assessments to ensure that the application of the product has no unacceptable effects on the environment. For non-target terrestrial plants (NTTPs), the risk assessment is based on standardized greenhouse studies on plant individual level. To account for the protection of plant populations and communities under realistic field conditions, i.e. extrapolating from greenhouse studies to field conditions and from individual-level to community-level, assessment factors are applied. However, recent studies question the current risk assessment scheme to meet the specific protection goals for non-target terrestrial plants as suggested by the European Food Safety Authority (EFSA). There is a need to clarify the gaps of the current risk assessment and to include suitable higher tier options in the upcoming guidance document for non-target terrestrial plants.
In my thesis, I studied the impact of herbicide drift exposure on NTTP communities using a mechanistic modelling approach. I addressed main gaps and uncertainties of the current risk assessment and finally suggested this modelling approach as a novel higher tier option in future risk assessments. Specifically, I extended the plant community model IBC-grass (Individual-based community model for grasslands) to reflect herbicide impacts on plant individuals. In the first study, I compared model predictions of short-term herbicide impacts on artificial plant communities with empirical data. I demonstrated the capability of the model to realistically reflect herbicide impacts. In the second study, I addressed the research question whether or not reproductive endpoints need to be included in future risk assessments to protect plant populations and communities. I compared the consequences of theoretical herbicide impacts on different plant attributes for long-term plant population dynamics in the community context. I concluded that reproductive endpoints only need to be considered if the herbicide effect is assumed to be very high. The endpoints measured in the current vegetative vigour and seedling emergence studies had high impacts for the dynamic of plant populations and communities already at lower effect intensities. Finally, the third study analysed long-term impacts of herbicide application for three different plant communities. This study highlighted the suitability of the modelling approach to simulate different communities and thus detecting sensitive environmental conditions.
Overall, my thesis demonstrates the suitability of mechanistic modelling approaches to be used as higher tier options for risk assessments. Specifically, IBC-grass can incorporate available individual-level effect data of standardized greenhouse experiments to extrapolate to community-level under various environmental conditions. Thus, future risk assessments can be improved by detecting sensitive scenarios and including worst-case impacts on non-target plant communities.
Introduction: Cystic fibrosis (CF) is a genetic disease which disrupts the function of an epithelial surface anion channel, CFTR (cystic fibrosis transmembrane conductance regulator). Impairment to this channel leads to inflammation and infection in the lung causing the majority of morbidity and mortality. However, CF is a multiorgan disease affecting many tissues, including vascular smooth muscle. Studies have revealed young people with cystic fibrosis lacking inflammation and infection still demonstrate vascular endothelial dysfunction, measured per flow-mediated dilation (FMD). In other disease cohorts, i.e. diabetic and obese, endurance exercise interventions have been shown improve or taper this impairment. However, long-term exercise interventions are risky, as well as costly in terms of time and resources. Nevertheless, emerging research has correlated the acute effects of exercise with its long-term benefits and advocates the study of acute exercise effects on FMD prior to longitudinal studies. The acute effects of exercise on FMD have previously not been examined in young people with CF, but could yield insights on the potential benefits of long-term exercise interventions.
The aims of these studies were to 1) develop and test the reliability of the FMD method and its applicability to study acute exercise effects; 2) compare baseline FMD and the acute exercise effect on FMD between young people with and without CF; and 3) explore associations between the acute effects of exercise on FMD and demographic characteristics, physical activity levels, lung function, maximal exercise capacity or inflammatory hsCRP levels.
Methods: Thirty young volunteers (10 people with CF, 10 non-CF and 10 non-CF active matched controls) between the ages of 10 and 30 years old completed blood draws, pulmonary function tests, maximal exercise capacity tests and baseline FMD measurements, before returning approximately 1 week later and performing a 30-min constant load training at 75% HRmax. FMD measurements were taken prior, immediately after, 30 minutes after and 1 hour after constant load training. ANOVAs and repeated measures ANOVAs were employed to explore differences between groups and timepoints, respectively. Linear regression was implemented and evaluated to assess correlations between FMD and demographic characteristics, physical activity levels, lung function, maximal exercise capacity or inflammatory hsCRP levels. For all comparisons, statistical significance was set at a p-value of α < 0.05.
Results: Young people with CF presented with decreased lung function and maximal exercise capacity compared to matched controls. Baseline FMD was also significantly decreased in the CF group (CF: 5.23% v non-CF: 8.27% v non-CF active: 9.12%). Immediately post-training, FMD was significantly attenuated (approximately 40%) in all groups with CF still demonstrating the most minimal FMD. Follow-up measurements of FMD revealed a slow recovery towards baseline values 30 min post-training and improvements in the CF and non-CF active groups 60 min post-training. Linear regression exposed significant correlations between maximal exercise capacity (VO2 peak), BMI and FMD immediately post-training.
Conclusion: These new findings confirm that CF vascular endothelial dysfunction can be acutely modified by exercise and will aid in underlining the importance of exercise in CF populations. The potential benefits of long-term exercise interventions on vascular endothelial dysfunction in young people with CF warrants further investigation.
“Mason without apron”
(2019)
While the lack of religion in Alexander von Humboldt’s work and the criticism he received is well known, his relationship with Freemasonry is relatively unexplored. Humboldt appears on some lists of “illustrious Masons,” and several lodges carry his name, but was he really a member? If so, when and where did he join a lodge? Are there any comments by him about Freemasonry? Who were the renowned Masons he was surrounded by? This paper examines these questions, but more importantly it analyzes what a membership might have meant for Humboldt’s scholarly work. It looks particularly at the unprecedented success he enjoyed in the United States in the early 19th century and the factors behind it. What could he have gained from these connections and how was he viewed by Masonic leaders and lodges in the trans-Atlantic world?
Este artículo estudia el papel de Alexander von Humboldt como promotor de una representación iconográfica del Nuevo Mundo. El objetivo de Humboldt era proporcionar una nueva imagen de América, basada en hechos reales, encontrados in situ, y no en la fantasía europea. Esta representación se sitúa entre su estricta visión científica, y su combinación con su elaborada sensibilidad artística, y, por lo tanto, utiliza tanto elementos textuales como visuales para transportar el conocimiento producido. En este trabajo se analizan las fuentes que inspiraron a Humboldt para desarrollar lo que hoy entendemos por «arte científico» así como los criterios que deberían aplicarse a este género. Partiendo de la importancia que tuvo la pintura de viaje para su gran proyecto americano, interesa de particular manera su interacción con los inicios del nuevo medio de la fotografía y las ventajas que él vio en este importante avance tecnológico.
Cocoa Bean Proteins
(2019)
The protein fractions of cocoa have been implicated influencing both the bioactive potential and sensory properties of cocoa and cocoa products. The objective of the present review is to show the impact of different stages of cultivation and processing with regard to the changes induced in the protein fractions. Special focus has been laid on the major seed storage proteins throughout the different stages of processing. The study starts with classical introduction of the extraction and the characterization methods used, while addressing classification approaches of cocoa proteins evolved during the timeline. The changes in protein composition during ripening and maturation of cocoa seeds, together with the possible modifications during the post-harvest processing (fermentation, drying, and roasting), have been documented. Finally, the bioactive potential arising directly or indirectly from cocoa proteins has been elucidated. The “state of the art” suggests that exploration of other potentially bioactive components in cocoa needs to be undertaken, while considering the complexity of reaction products occurring during the roasting phase of the post-harvest processing. Finally, the utilization of partially processed cocoa beans (e.g., fermented, conciliatory thermal treatment) can be recommended, providing a large reservoir of bioactive potentials arising from the protein components that could be instrumented in functionalizing foods.