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We report on new measurements of absolute cross sections for single, double, and triple electron-impact ionization of singly charged lanthanum ions. The resulting single and double ionization cross sections are in fair agreement with results from previous experimental work. In the present work, we extended the experimental range by a factor of two to approx. 2000 eV. To the best of our knowledge, there have been no previous measurements of triple ionization. The present work in progress aims to provide vitally needed atomic data for the astrophysical modeling of kilonovae.

We report on new measurements of absolute cross sections for single, double, and triple electron-impact ionization of singly charged lanthanum ions. The resulting single and double ionization cross sections are in fair agreement with results from previous experimental work. In the present work, we extended the experimental range by a factor of two to approx. 2000 eV. To the best of our knowledge, there have been no previous measurements of triple ionization. The present work in progress aims to provide vitally needed atomic data for the astrophysical modeling of kilonovae. [ABSTRACT FROM AUTHOR]

The Group on Earth Observations Global Agricultural Monitoring Initiative (GEOGLAM) considers agricultural fields as one of the essential variables that can be derived from satellite data. We evaluated the accuracy at which agricultural fields can be delineated from Sentinel-1 (S1) and Sentinel-2 (S2) images in different agricultural landscapes throughout the growing season. We used supervised segmentation based on the multiresolution segmentation (MRS) algorithm to first identify the optimal feature set from S1 and S2 images for field delineation. Based on this optimal feature set, we analyzed the segmentation accuracy of the fields delineated with increasing data availability between March and October of 2018. From the S1 feature sets, the combination of the two polarizations and two radar indices attained the best segmentation results. For S2, the best results were achieved using a combination of all bands (coastal aerosol, water vapor, and cirrus bands were excluded) and six spectral indices. Combining the radar and spectral indices further improved the results. Compared to the single-period dataset in March, using the dataset covering the whole season led to a significant increase in the segmentation accuracy. For very small fields (< 0.5 ha), the segmentation accuracy obtained was 27.02%, for small fields (0.5 – 1.5 ha), the accuracy was 57.65%, for medium fields (1.5 ha – 15 ha), the accuracy was 75.71%, and for large fields (>15 ha), the accuracy stood at 68.31%. As a use case, the segmentation result was used to aggregate and improve a pixel-based crop type map in Lower Saxony, Germany.

Abstract Demyelinated lesions in human pons observed after osmotic shifts in serum have been referred to as central pontine myelinolysis (CPM). Astrocytic damage, which is prominent in neuroinflammatory diseases like neuromyelitis optica (NMO) and multiple sclerosis (MS), is considered the primary event during formation of CPM lesions. Although more data on the effects of astrocyte-derived factors on oligodendrocyte precursor cells (OPCs) and remyelination are emerging, still little is known about remyelination of lesions with primary astrocytic loss. In autopsy tissue from patients with CPM as well as in an experimental model, we were able to characterize OPC activation and differentiation. Injections of the thymidine-analogue BrdU traced the maturation of OPCs activated in early astrocyte-depleted lesions. We observed rapid activation of the parenchymal NG2+ OPC reservoir in experimental astrocyte-depleted demyelinated lesions, leading to extensive OPC proliferation. One week after lesion initiation, most parenchyma-derived OPCs expressed breast carcinoma amplified sequence-1 (BCAS1), indicating the transition into a pre-myelinating state. Cells derived from this early parenchymal response often presented a dysfunctional morphology with condensed cytoplasm and few extending processes, and were only sparsely detected among myelin-producing or mature oligodendrocytes. Correspondingly, early stages of human CPM lesions also showed reduced astrocyte numbers and non-myelinating BCAS1+ oligodendrocytes with dysfunctional morphology. In the rat model, neural stem cells (NSCs) located in the subventricular zone (SVZ) were activated while the lesion was already partially repopulated with OPCs, giving rise to nestin+ progenitors that generated oligodendroglial lineage cells in the lesion, which was successively repopulated with astrocytes and remyelinated. These nestin+ stem cell-derived progenitors were absent in human CPM cases, which may have contributed to the inefficient lesion repair. The present study points to the importance of astrocyte-oligodendrocyte interactions for remyelination, highlighting the necessity to further determine the impact of astrocyte dysfunction on remyelination inefficiency in demyelinating disorders including MS.

The agricultural sector has the potential to contribute to reaching both global and national climate targets. Lately, frequent discussions emerge among academics as well as policymakers regarding whether the agricultural sector should be subject to carbon pricing under different emission trading systems. Germany has set ambitious climate targets envisaging to reach carbon neutrality by 2045, and the EU plans reaching carbon neutrality by 2050. However, the current GHG emission mitigation trends are not in line with this goal. In this study, we quantitatively analyze the environmental and economic effects of the possible inclusion of the agricultural sector into a carbon pricing scheme, once for Germany only, and second for the EU. Moreover, we evaluate the role of already existing and novel technological mitigation options in the GHG emissions mitigation quest. Our findings demonstrate that even the unilateral action by Germany leads to net agricultural emissions reduction, although, the effect obtained by the EU-wide implementation of carbon pricing in agriculture is fivefold larger. The results also highlight the importance of stimulating the use and transferability of the technological options not only in mitigating GHG emissions but also in alleviating the emission leakage to third countries and easing the economic consequences of such a policy.

Abstract Background Severe acute pancreatitis is associated with high morbidity and mortality. Melatonin is known as the activator of antioxidant enzymes. The main purpose of this study was to evaluate the clinical effect of melatonin treatment in a pig model with induced acute pancreatitis. Methods In this study, acute pancreatitis was induced in 38 German domestic pigs (German Hybrid). After induction of acute pancreatitis, 18 animals were treated with melatonin. Intraoperative clinical data, postoperative blood parameters, fitness, and Porcine Well-being (PWB) score, and post-mortal histopathological data were analyzed in both study groups. Results The matching procedure created two groups (melatonin group and control group) which were very similar. The fitness and PWB score were postoperative significantly enhanced in the melatonin group as compared to the control group (p = 0.005 and p = 0.003). Additionally, histological analysis revealed that acinar necrosis, fat tissue necrosis, and edema were significantly reduced in the melatonin group as compared to the non-melatonin group (p = 0.025, p = 0.003, and p = 0.028). Conclusions Pigs, which were treated with melatonin, were characterized by higher fitness and PWB scores than those of the control group. Moreover, melatonin treatment reduces the acinar necrosis, fat tissue necrosis, and edema of pancreatic tissue. Thus, melatonin might be a useful therapeutic option in severe acute pancreatitis.

Summary: Pro-inflammatory caspase-1 is a key player in innate immunity. Caspase-1 processes interleukin (IL)-1β and IL-18 to their mature forms and triggers pyroptosis. These caspase-1 functions are linked to its enzymatic activity. However, loss-of-function missense mutations in CASP1 do not prevent autoinflammation in patients, despite decreased IL-1β production. In vitro data suggest that enzymatically inactive caspase-1 drives inflammation via enhanced nuclear factor κB (NF-κB) activation, independent of IL-1β processing. Here, we report two mouse models of enzymatically inactive caspase-1-C284A, demonstrating the relevance of this pathway in vivo. In contrast to Casp1−/− mice, caspase-1-C284A mice show pronounced hypothermia and increased levels of the pro-inflammatory cytokines tumor necrosis factor alpha (TNF-α) and IL-6 when challenged with lipopolysaccharide (LPS). Caspase-1-C284A signaling is RIP2 dependent and mediated by TNF-α but independent of the NLRP3 inflammasome. LPS-stimulated whole blood from patients carrying loss-of-function missense mutations in CASP1 secretes higher amounts of TNF-α. Taken together, these results reveal non-canonical caspase-1 signaling in vivo. : Reinke et al. show that enzymatically inactive caspase-1-C284A mediates non-canonical caspase-1 signaling. This pathway is RIP2 dependent and mediated by TNF-α but independent from IL-1 cytokines. Keywords: caspase-1, Rip2, NF-κB, TNF-α, non-canonical caspase-1 signaling, enzymatic activity

In this paper, we extend the analysis of farm structural change with respect to farm specialisation, size and exit in Norway by, first, explicitly incorporating the location information of farms generating a number of neighbouring farms within a certain range and, second, by predicting farm numbers in addition to farm group shares, which allows for consideration of the exit farm group. We use Norwegian single-farm full census data for the period 1996–2015. Four production specialisations and seven size classes represent farm groups, as well as a residual and an exit farm group at the regional level. The estimates indicate the explanatory power and importance of aggregated farm location information in the model. Simulation analysis showed that the farm groups develop differently, given a change in the number of neighbouring farms with respect to the farm numbers and farm group shares.

Genome-edited crops are on the verge of being placed on the market and their agricultural and food products will thus be internationally traded soon. National regulations, however, diverge regarding the classification of genome-edited crops. Major countries such as the US and Brazil do not specifically regulate genome-edited crops, while in the European Union, they fall under GMO legislation, according to the European Court of Justice (ECJ). As it is in some cases impossible to analytically distinguish between products from genome-edited plants and those from non-genome-edited plants, EU importers may fear the risk of violating EU legislation. They may choose not to import any agricultural and food products based on crops for which genome-edited varieties are available. Therefore, crop products of which the EU is currently a net importer would become more expensive in the EU, and production would intensify. Furthermore, an intense substitution of products covered and not covered by genome editing would occur in consumption, production, and trade. We analyzed the effects of such a cease of EU imports for cereals and soy in the EU agricultural sector with the comparative static agricultural sector equilibrium model CAPRI. Our results indicate dramatic effects on agricultural and food prices as well as on farm income. The intensification of EU agriculture may result in negative net environmental effects in the EU as well as in an increase in global greenhouse gas (GHG) emissions. This suggests that trade effects should be considered when developing domestic regulation for genome-edited crops.

Image segmentation is a cost-effective way to obtain information about the sizes and structural composition of agricultural parcels in an area. To accurately obtain such information, the parameters of the segmentation algorithm ought to be optimized using supervised or unsupervised methods. The difficulty in obtaining reference data makes unsupervised methods indispensable. In this study, we evaluated an existing unsupervised evaluation metric that minimizes a global score (GS), which is computed by summing up the intra-segment uniformity and inter-segment dissimilarity within a segmentation output. We modified this metric and proposed a new metric that uses absolute difference to compute the GS. We compared this proposed metric with the existing metric in two optimization approaches based on the Multiresolution Segmentation (MRS) algorithm to optimally delineate agricultural parcels from Sentinel-2 images in Lower Saxony, Germany. The first approach searches for optimal scale while keeping shape and compactness constant, while the second approach uses Bayesian optimization to optimize the three main parameters of the MRS algorithm. Based on a reference data of agricultural parcels, the optimal segmentation result of each optimization approach was evaluated by calculating the quality rate, over-segmentation, and under-segmentation. For both approaches, our proposed metric outperformed the existing metric in different agricultural landscapes. The proposed metric identified optimal segmentations that were less under-segmented compared to the existing metric. A comparison of the optimal segmentation results obtained in this study to existing benchmark results generated via supervised optimization showed that the unsupervised Bayesian optimization approach based on our proposed metric can potentially be used as an alternative to supervised optimization, particularly in geographic regions where reference data is unavailable or an automated evaluation system is sought.

Background. Nintedanib is a small molecule tyrosine kinase inhibitor that blocks the action of the platelet-derived growth factor receptor (PDGFR), the vascular endothelial growth factor receptor (VEGFR) and the fibroblast growth factor receptor. All of these receptors have been shown to be involved in the development of cardiac allograft vasculopathy (CAV) after heart transplantation. We therefore hypothesized that blocking these tyrosine kinase receptors with nintedanib could prevent CAV. Methods. CBA/JRj (H2k) mice underwent an abdominal aortic transplantation with a graft derived from fully allogeneic C57BL/6JRj (H2b) mice. Nintedanib was given daily from the first day after transplantation until harvest on day 14 for polymerase chain reaction analysis of intragraft cytokine expression or harvest on day 30 for histological analysis of the graft. Results. Nintedanib treatment resulted in significantly reduced neointima formation in the aortic graft compared with untreated control allografts. Interestingly, the immigration of smooth muscle cells into the neointima was markedly reduced while graft infiltrating macrophages and T cells were not altered in nintedanib-treated animals. The expression of the growth factor PDGF was significantly reduced in the nintedanib group going along with a distinctly reduced expression of the corresponding receptors PDGFR α and -β. Conclusions. Treatment with nintedanib caused a significant reduction of CAV development after aortic transplantation in mice. We hypothesize the attenuated neointima formation in nintedanib-treated animals to be mediated by a direct inhibition of intimal smooth muscle cell proliferation via reduced expression of PDGF and the appropriate receptors PDGFR α + β.

Flood estimation and flood management have traditionally been the domain of hydrologists, water resources engineers and statisticians, and disciplinary approaches abound. Dominant views have been shaped; one example is the catchment perspective: floods are formed and influenced by the interaction of local, catchment-specific characteristics, such as meteorology, topography and geology. These traditional views have been beneficial, but they have a narrow framing. In this paper we contrast traditional views with broader perspectives that are emerging from an improved understanding of the climatic context of floods. We come to the following conclusions: (1) extending the traditional system boundaries (local catchment, recent decades, hydrological/hydraulic processes) opens up exciting possibilities for better understanding and improved tools for flood risk assessment and management. (2) Statistical approaches in flood estimation need to be complemented by the search for the causal mechanisms and dominant processes in the atmosphere, catchment and river system that leave their fingerprints on flood characteristics. (3) Natural climate variability leads to time-varying flood characteristics, and this variation may be partially quantifiable and predictable, with the perspective of dynamic, climate-informed flood risk management. (4) Efforts are needed to fully account for factors that contribute to changes in all three risk components (hazard, exposure, vulnerability) and to better understand the interactions between society and floods. (5) Given the global scale and societal importance, we call for the organization of an international multidisciplinary collaboration and data-sharing initiative to further understand the links between climate and flooding and to advance flood research.