Search Results
Photodynamic opening of the blood-brain barrier using different photosensitizers in mice
In a series of previous studies, we demonstrated that the photodynamic therapy (PDT), as a widely used tool for treatment of glioblastoma multiforme (GBM), also site-specifically opens the blood-brain barrier (BBB) in PDT-dose and age-related manner via reversible disorganization of the tight junction machinery. To develop the effective protocol of PDT-opening of the BBB, here we answer the question of what kind of photosensitizer (PS) is the most effective for the BBB opening. We studied the PDT-opening of the BBB in healthy mice using commercial photosensitizers (PSs) such as 5-aminolevulenic acid (5-ALA), aluminum phthalocyanine disulfonate (AlPcS), zinc phthalocyanine (ZnPc) and new synthetized PSs such as galactose functionalized ZnPc (GalZnPc). The spectrofluorimetric assay of Evans Blue albumin complex (EBAC) leakage and 3-D confocal imaging of FITC-dextran 70 kDa (FITCD) extravasation clearly shows a revisable and dose depended PDT-opening of the BBB toEBACand FITCD associated with a decrease in presence of tight junction (TJ) in the vascular endothelium. The PDT effects on the BBB permeability, TJ expression and the fluorescent signal from the brain tissues are more pronounced in PDT-GalZnPc vs. PDT-5-ALA/AlPcS/ZnPc. These pre-clinical data are the first important informative platform for an optimization of the PDT protocol in the light of new knowledge about PDT-opening of the BBB for drug brain delivery and for the therapy of brain diseases. © 2019 by the authors.
CO2 emission mitigation and fossil fuel markets: Dynamic and international aspects of climate policies
This paper explores a multi-model scenario ensemble to assess the impacts of idealized and non-idealized climate change stabilization policies on fossil fuel markets. Under idealized conditions climate policies significantly reduce coal use in the short- and long-term. Reductions in oil and gas use are much smaller, particularly until 2030, but revenues decrease much more because oil and gas prices are higher than coal prices. A first deviation from optimal transition pathways is delayed action that relaxes global emission targets until 2030 in accordance with the Copenhagen pledges. Fossil fuel markets revert back to the no-policy case: though coal use increases strongest, revenue gains are higher for oil and gas. To balance the carbon budget over the 21st century, the long-term reallocation of fossil fuels is significantly larger—twice and more—than the short-term distortion. This amplifying effect results from coal lock-in and inter-fuel substitution effects to balance the full-century carbon budget. The second deviation from the optimal transition pathway relaxes the global participation assumption. The result here is less clear-cut across models, as we find carbon leakage effects ranging from positive to negative because trade and substitution patterns of coal, oil, and gas differ across models. In summary, distortions of fossil fuel markets resulting from relaxed short-term global emission targets are more important and less uncertain than the issue of carbon leakage from early mover action.
Bayesian Data Analysis for Revealing Causes of the Middle Pleistocene Transition
Currently, causes of the middle Pleistocene transition (MPT) – the onset of large-amplitude glacial variability with 100 kyr time scale instead of regular 41 kyr cycles before – are a challenging puzzle in Paleoclimatology. Here we show how a Bayesian data analysis based on machine learning approaches can help to reveal the main mechanisms underlying the Pleistocene variability, which most likely explain proxy records and can be used for testing existing theories. We construct a Bayesian data-driven model from benthic δ18O records (LR04 stack) accounting for the main factors which may potentially impact climate of the Pleistocene: internal climate dynamics, gradual trends, variations of insolation, and millennial variability. In contrast to some theories, we uncover that under long-term trends in climate, the strong glacial cycles have appeared due to internal nonlinear oscillations induced by millennial noise. We find that while the orbital Milankovitch forcing does not matter for the MPT onset, the obliquity oscillation phase-locks the climate cycles through the meridional gradient of insolation.
Comparing socioeconomic inequalities between early neonatal mortality and facility delivery: Cross-sectional data from 72 low- and middle-income countries
Facility delivery should reduce early neonatal mortality. We used the Slope Index of Inequality and logistic regression to quantify absolute and relative socioeconomic inequalities in early neonatal mortality (0 to 6 days) and facility delivery among 679,818 live births from 72 countries with Demographic and Health Surveys. The inequalities in early neonatal mortality were compared with inequalities in postneonatal infant mortality (28 days to 1 year), which is not related to childbirth. Newborns of the richest mothers had a small survival advantage over the poorest in unadjusted analyses (−2.9 deaths/1,000; OR 0.86) and the most educated had a small survival advantage over the least educated (−3.9 deaths/1,000; OR 0.77), while inequalities in postneonatal infant mortality were more than double that in absolute terms. The proportion of births in health facilities was an absolute 43% higher among the richest and 37% higher among the most educated compared to the poorest and least educated mothers. A higher proportion of facility delivery in the sampling cluster (e.g. village) was only associated with a small decrease in early neonatal mortality. In conclusion, while socioeconomically advantaged mothers had much higher use of a health facility at birth, this did not appear to convey a comparable survival advantage.
Looking under the hood: A comparison of techno-economic assumptions across national and global integrated assessment models
Integrated assessment models are extensively used in the analysis of climate change mitigation and are informing national decision makers as well as contribute to international scientific assessments. This paper conducts a comprehensive review of techno-economic assumptions in the electricity sector among fifteen different global and national integrated assessment models. Particular focus is given to six major economies in the world: Brazil, China, the EU, India, Japan and the US. The comparison reveals that techno-economic characteristics are quite different across integrated assessment models, both for the base year and future years. It is, however, important to recognize that techno-economic assessments from the literature exhibit an equally large range of parameters as the integrated assessment models reviewed. Beyond numerical differences, the representation of technologies also differs among models, which needs to be taken into account when comparing numerical parameters. While desirable, it seems difficult to fully harmonize techno-economic parameters across a broader range of models due to structural differences in the representation of technology. Therefore, making techno-economic parameters available in the future, together with of the technology representation as well as the exact definitions of the parameters should become the standard approach as it allows an open discussion of appropriate assumptions. © 2019 The Authors
Carbon lock-in through capital stock inertia associated with weak near-term climate policies
Stringent long-term climate targets necessitate a limit on cumulative emissions in this century for which sufficient policy signals are lacking. Using nine energy-economy models, we explore how policies pursued during the next two decades impact long-term transformation pathways towards stringent long-term climate targets. Less stringent near-term policies (i.e., those with larger emissions) consume more of the long-term cumulative emissions budget in the 2010–2030 period, which increases the likelihood of overshooting the budget and the urgency of reducing GHG emissions after 2030. Furthermore, the larger near-term GHG emissions associated with less stringent policies are generated primarily by additional coal-based electricity generation. Therefore, to be successful in meeting the long-term target despite near-term emissions reductions that are weaker than those implied by cost-optimal mitigation pathways, models must prematurely retire significant coal capacity while rapidly ramping up low-carbon technologies between 2030 and 2050 and remove large quantities of CO2 from the atmosphere in the latter half of the century. While increased energy efficiency lowers mitigation costs considerably, even with weak near-term policies, it does not substantially reduce the short-term reliance on coal electricity. However, increased energy efficiency does allow the energy system more flexibility in mitigating emissions and, thus, facilitates the post-2030 transition.
Statistical Properties and Predictability of Extreme Epileptic Events
The use of extreme events theory for the analysis of spontaneous epileptic brain activity is a relevant multidisciplinary problem. It allows deeper understanding of pathological brain functioning and unraveling mechanisms underlying the epileptic seizure emergence along with its predictability. The latter is a desired goal in epileptology which might open the way for new therapies to control and prevent epileptic attacks. With this goal in mind, we applied the extreme event theory for studying statistical properties of electroencephalographic (EEG) recordings of WAG/Rij rats with genetic predisposition to absence epilepsy. Our approach allowed us to reveal extreme events inherent in this pathological spiking activity, highly pronounced in a particular frequency range. The return interval analysis showed that the epileptic seizures exhibit a highly-structural behavior during the active phase of the spiking activity. Obtained results evidenced a possibility for early (up to 7 s) prediction of epileptic seizures based on consideration of EEG statistical properties.
The implications of initiating immediate climate change mitigation - A potential for co-benefits?
Fragmented climate policies across parties of the United Nations Framework on Climate Change have led to the question of whether initiating significant and immediate climate change mitigation can support the achievement of other non-climate objectives. We analyze such potential co-benefits in connection with a range of mitigation efforts using results from eleven integrated assessment models. These model results suggest that an immediate mitigation of climate change coincide for Europe with an increase in energy security and a higher utilization of non-biomass renewable energy technologies. In addition, the importance of phasing out coal is highlighted with external cost estimates showing substantial health benefits consistent with the range of mitigation efforts.
Network-based identification and characterization of teleconnections on different scales
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.
Constraining modelled global vegetation dynamics and carbon turnover using multiple satellite observations
The response of land ecosystems to future climate change is among the largest unknowns in the global climate-carbon cycle feedback. This uncertainty originates from how dynamic global vegetation models (DGVMs) simulate climate impacts on changes in vegetation distribution, productivity, biomass allocation, and carbon turnover. The present-day availability of a multitude of satellite observations can potentially help to constrain DGVM simulations within model-data integration frameworks. Here, we use satellite-derived datasets of the fraction of absorbed photosynthetic active radiation (FAPAR), sun-induced fluorescence (SIF), above-ground biomass of trees (AGB), land cover, and burned area to constrain parameters for phenology, productivity, and vegetation dynamics in the LPJmL4 DGVM. Both the prior and the optimized model accurately reproduce present-day estimates of the land carbon cycle and of temporal dynamics in FAPAR, SIF and gross primary production. However, the optimized model reproduces better the observed spatial patterns of biomass, tree cover, and regional forest carbon turnover. Using a machine learning approach, we found that remaining errors in simulated forest carbon turnover can be explained with bioclimatic variables. This demonstrates the need to improve model formulations for climate effects on vegetation turnover and mortality despite the apparent successful constraint of simulated vegetation dynamics with multiple satellite observations.