Probabilistic climate change projections using neural networks

Anticipated future warming of the climate system increases the need for accurate climate projections. A central problem are the large uncertainties associated with these model projections, and that uncertainty estimates are often based on expert judgment rather than objective quantitative methods. Further, important climate model parameters are still given as poorly constrained ranges that are partly inconsistent with the observed warming during the industrial period. Here we present a neural network based climate model substitute that increases the efficiency of large climate model ensembles by at least an order of magnitude. Using the observed surface warming over the industrial period and estimates of global ocean heat uptake as constraints for the ensemble, this method estimates ranges for climate sensitivity and radiative forcing that are consistent with observations. In particular, negative values for the uncertain indirect aerosol forcing exceeding -1.2Wm-2 can be excluded with high confidence. A parameterization to account for the uncertainty in the future carbon cycle is introduced, derived separately from a carbon cycle model. This allows us to quantify the effect of the feedback between oceanic and terrestrial carbon uptake and global warming on global temperature projections. Finally, probability density functions for the surface warming until year 2100 for two illustrative emission scenarios are calculated, taking into account uncertainties in the carbon cycle, radiative forcing, climate sensitivity, model parameters and the observed temperature records. We find that warming exceeds the surface warming range projected by IPCC for almost half of the ensemble members. Projection uncertainties are only consistent with IPCC if a model-derived upper limit of about 5K is assumed for climate sensitivit

Fatal scuba diving incident with massive gas embolism in cerebral and spinal arteries

CT and MRI have the potential to become useful adjuncts to forensic autopsy in the near future. The examination of fatal injuries facilitates a profound experience in the clinical-radiological examination of these cases; the more severe findings in corpses with autopsy verification can help one to understand the tiny signs seen in clinical cases of surviving victims. We present the case of a 44-year-old male diver who died from severe decompression sickness after rapid ascent from approximately 120m. Post-mortem CT and MRI studies of the brain and spinal cord revealed extensive gas inclusions in cerebral arteries, spinal arteries and cerebrospinal fluid (CSF) spaces, while the intracranial venous sinuses remained unaffected. These findings were confirmed at autopsy. Appropriate imaging techniques can help forensic pathologists to aim their autopsies at findings that might otherwise remain undetecte

NCAM 180 Acting via a Conserved C-Terminal Domain and MLCK Is Essential for Effective Transmission with Repetitive Stimulation

SummaryNCAM 180 isoform null neuromuscular junctions are unable to effectively mobilize and exocytose synaptic vesicles and thus exhibit periods of total transmission failure during high-frequency repetitive stimulation. We have identified a highly conserved C-terminal (KENESKA) domain on NCAM that is required to maintain effective transmission and demonstrate that it acts via a pathway involving MLCK and probably myosin light chain (MLC) and myosin II. By perfecting a method of introducing peptides into adult NMJs, we tested the hypothesized role of proteins in this pathway by competitive disruption of protein-protein interactions. The effects of KENESKA and other peptides on MLCK and MLC activation and on failures in both wild-type and NCAM 180 null junctions supported this pathway, and serine phosphorylation of KENESKA was critical. We propose that this pathway is required to replenish synaptic vesicles utilized during high levels of exocytosis by facilitating myosin-driven delivery of synaptic vesicles to active zones or their subsequent exocytosis

Imminent ocean acidification in the Arctic projected with the NCAR global coupled carbon cycle-climate model

© 2009 The Authors. This article is distributed under the terms of the Creative Commons Attribution 3.0 License. The definitive version was published in Biogeosciences 6 (2009): 515-533, doi:10.5194/bg-6-515-2009Ocean acidification from the uptake of anthropogenic carbon is simulated for the industrial period and IPCC SRES emission scenarios A2 and B1 with a global coupled carbon cycle-climate model. Earlier studies identified seawater saturation state with respect to aragonite, a mineral phase of calcium carbonate, as a key variable governing impacts on corals and other shell-forming organisms. Globally in the A2 scenario, water saturated by more than 300%, considered suitable for coral growth, vanishes by 2070 AD (CO2≈630 ppm), and the ocean volume fraction occupied by saturated water decreases from 42% to 25% over this century. The largest simulated pH changes worldwide occur in Arctic surface waters, where hydrogen ion concentration increases by up to 185% (ΔpH=−0.45). Projected climate change amplifies the decrease in Arctic surface mean saturation and pH by more than 20%, mainly due to freshening and increased carbon uptake in response to sea ice retreat. Modeled saturation compares well with observation-based estimates along an Arctic transect and simulated changes have been corrected for remaining model-data differences in this region. Aragonite undersaturation in Arctic surface waters is projected to occur locally within a decade and to become more widespread as atmospheric CO2 continues to grow. The results imply that surface waters in the Arctic Ocean will become corrosive to aragonite, with potentially large implications for the marine ecosystem, if anthropogenic carbon emissions are not reduced and atmospheric CO2 not kept below 450 ppm.This work was funded by the European Union projects CARBOOCEAN (511176-2) and EUROCEANS (511106-2) and is a contribution to the “European Project on Ocean Acidification” (EPOCA) which received funding from the European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 211384. Additional support was received from the Swiss National Science Foundation and SCD acknowledges support from the US National Science Foundation (NSF) grant ATM-0628582

Hybrid-learning for social design

Underlying causes of conflict, inequity, and injustice remain deeply entrenched in the lives of people ranging from impoverished villages to overpopulated megalopolises. To help address these complex issues, social design brings together designers from varying disciplines to address the needs of the community. While universities across the world recognize the need to introduce social design pedagogy into their curriculum, many programs remain confined within Western post-graduate education. In response, two multidisciplinary professors initiated a team-taught \u27Design for Social Change\u27 course in an undergraduate design program in Dubai, UAE. Open to students across disciplines, the course followed a hybrid-learning approach to planning, conducting, and evaluating learning activities. The methodology empowered students to determine their project interest, cooperatively build research, and value their diverse skills. This paper introduces the notion of hybrid-learning, collabor-active team-teaching in an interdisciplinary classroom, and applies the methodology to a social design course in the MENA region. This paper has been presented as part of the Tasmeem Exploration Platform during Tasmeem Conference, Doha, 2013

Imatinib Reverses Doxorubicin Resistance by Affecting Activation of STAT3-Dependent NF-κB and HSP27/p38/AKT Pathways and by Inhibiting ABCB1

Despite advances in cancer detection and prevention, a diagnosis of metastatic disease remains a death sentence due to the fact that many cancers are either resistant to chemotherapy (conventional or targeted) or develop resistance during treatment, and residual chemoresistant cells are highly metastatic. Metastatic cancer cells resist the effects of chemotherapeutic agents by upregulating drug transporters, which efflux the drugs, and by activating proliferation and survival signaling pathways. Previously, we found that c-Abl and Arg non-receptor tyrosine kinases are activated in breast cancer, melanoma, and glioblastoma cells, and promote cancer progression. In this report, we demonstrate that the c-Abl/Arg inhibitor, imatinib (imatinib mesylate, STI571, Gleevec), reverses intrinsic and acquired resistance to the anthracycline, doxorubicin, by inducing G2/M arrest and promoting apoptosis in cancer cells expressing highly active c-Abl and Arg. Significantly, imatinib prevents intrinsic resistance by promoting doxorubicin-mediated NF-κB/p65 nuclear localization and repression of NF-κB targets in a STAT3-dependent manner, and by preventing activation of a novel STAT3/HSP27/p38/Akt survival pathway. In contrast, imatinib prevents acquired resistance by inhibiting upregulation of the ABC drug transporter, ABCB1, directly inhibiting ABCB1 function, and abrogating survival signaling. Thus, imatinib inhibits multiple novel chemoresistance pathways, which indicates that it may be effective in reversing intrinsic and acquired resistance in cancers containing highly active c-Abl and Arg, a critical step in effectively treating metastatic disease. Furthermore, since imatinib converts a master survival regulator, NF-κB, from a pro-survival into a pro-apoptotic factor, our data suggest that NF-κB inhibitors may be ineffective in sensitizing tumors containing activated c-Abl/Arg to anthracyclines, and instead might antagonize anthracycline-induced apoptosis

A novel forest state assessment methodology to support conservation and forest management planning

A new forest state assessment methodology to complement existing conservation and forestry data has been developed. The aim is to provide tools for strategic planning including spatial distribution of conservation priorities. The method is point-based using a dense systematic sampling grid and provides more detailed information than vegetation maps or forest subcompartment descriptions, but requires less effort than forest inventories. Indicators include canopy composition and structure, deadwood, herbs, microhabitats, disturbances, shrubs and regeneration. The results can inform managers about the structural and compositional diversity of forest stands in the form of thematic maps and can provide the basis for analysis of habitat suitability for forest-dwelling organisms. A smartphone application has been developed to enable electronic data collection. PostGIS and Python scripts were used in the data flow. In this paper, we outline the development of the assessment protocol, and present the sampling design and the variables recorded. The main advantages of the survey methodology are also shown by case-studies based on data collected during the first field season in 2014. The protocol has been designed for low mountain forests in Hungary, but it can be modified to fit other forest types