406 research outputs found
Monomeric Garnet, a far-red fluorescent protein for live-cell STED imaging
The advancement of far-red emitting variants of the green fluorescent protein (GFP) is crucially important for imaging live cells, tissues and organisms. Despite notable efforts, far-red marker proteins still need further optimization to match the performance of their green counterparts. Here we present mGarnet, a robust monomeric marker protein with far-red fluorescence peaking at 670 nm. Thanks to its large extinction coefficient of 95,000 M-1 cm-1, mGarnet can be efficiently excited with 640-nm light on the red edge of its 598-nm excitation band. A large Stokes shift allows essentially the entire fluorescence emission to be collected even with 640-nm excitation, counterbalancing the lower fluorescence quantum yield of mGarnet, 9.1%, that is typical of far-red FPs. We demonstrate an excellent performance as a live-cell fusion marker in STED microscopy, using 640 nm excitation and 780 nm depletion wavelengths
The Relative Importance of Phytoplankton Light Absorption and Ecosystem Complexity in an Earth System Model
We investigate the relative importance of ecosystem complexity and phytoplankton light absorption for climate studies. While the complexity of Earth System models (ESMs) with respect to marine biota has increased over the past years, the relative importance of biological processes in driving climate-relevant mechanisms such as the biological carbon pump and phytoplankton light absorption is still unknown. The climate effects of these mechanisms have been studied separately, but not together. To shed light on the role of biologically mediated feedbacks, we performed different model experiments with the EcoGENIE ESM. The model experiments have been conducted with and without phytoplankton light absorption and with two or 12 plankton functional types. For a robust comparison, all simulations are tuned to have the same primary production. Our model experiments show that phytoplankton light absorption changes ocean physics and biogeochemistry. Higher sea surface temperature decreases the solubility of CO2 which in turn increases the atmospheric CO2 concentration, and finally the atmospheric temperature rises by 0.45°C. An increase in ecosystem complexity increases the export production of particulate organic carbon but decreases the amount of dissolved organic matter. These changes in
the marine carbon cycling, however, hardly reduces the atmospheric CO2 concentrations and slightly decreases the atmospheric temperature by 0.034°C. Overall we show that phytoplankton light absorption has a higher impact on the carbon cycle and on the climate system than a more detailed representation of the marine biota
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Climate pathways behind phytoplankton-induced atmospheric warming
We investigate the ways in which marine biologically mediated heating increases the surface atmospheric temperature. While the effects of phytoplankton light absorption on the ocean have gained attention over the past years, the impact of this biogeophysical mechanism on the atmosphere is still unclear. Phytoplankton light absorption warms the surface of the ocean, which in turn affects the air–sea heat and CO2 exchanges. However, the contribution of air–sea heat versus CO2 fluxes in the phytoplankton-induced atmospheric warming has not been yet determined. Different so-called climate pathways are involved. We distinguish heat exchange, CO2 exchange, dissolved CO2, solubility of CO2 and sea-ice-covered area. To shed more light on this subject, we employ the EcoGEnIE Earth system model that includes a new light penetration scheme and isolate the effects of individual fluxes. Our results indicate that phytoplankton-induced changes in air–sea CO2 exchange warm the atmosphere by 0.71 ∘C due to higher greenhouse gas concentrations. The phytoplankton-induced changes in air–sea heat exchange cool the atmosphere by 0.02 ∘C due to a larger amount of outgoing longwave radiation. Overall, the enhanced air–sea CO2 exchange due to phytoplankton light absorption is the main driver in the biologically induced atmospheric heating
Automated detection of Diabetic Retinopathy in Three European Populations
Objective: Currently 1/12 of the world’s population has diabetes mellitus (DM), many are or will be screened by having retinal images taken. This current study aims to compare the DAPHNE software’s ability to detect DR in three different European populations compared to human grading carried out at the Moorfields Eye Hospital Reading Centre (MEHRC). Participants: Retinal images were taken from participants of the HAPIEE study (Lithuania, n=1014), the PAMDI study (Italy, n=882) and the MARS study (Germany, n=909). Methods: All anonymized images were graded by human graders at MEHRC for the presence of DR. Independently, and without any knowledge of the human grader’s results, the DAPHNE software analysed the images and divided the participants into DR and no-DR groups. Main outcome measures: The primary outcomes were sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of the DAPHNE software with regards to the identification of DR or no-DR on retinal images as compared to the human grader as reference standard. Results: A total of 2805 participants were enrolled from the three study sites. The sensitivity of the DAPHNE software was above 93% in all three studies specificity was above 80%, the PPV was above 28% and the NPV was not below 98.8% in any of the studies. The DAPHNE software did not miss any vision-threatening DR. The areas under the curve (AUC) for all three studies were above 0.96. DAPHNE reduced manual human workload by 70% but had a total false positive rate of 63%. Conclusions: The DAPHNE software showed to be reliable to detect DR on three different European populations, using three different imaging settings. Further testing is required to see scalability, performance on live DR screening systems and on camera settings different to these studies
A missing link in the carbon cycle: phytoplankton light absorption under RCP emission scenarios
Marine biota and biogeophysical mechanisms, such as phytoplankton light absorption, have attracted increasing attention in recent climate studies. Under global warming, the influence of phytoplankton on the climate system is expected to change. Previous studies analyzed the impact of phytoplankton light absorption under prescribed future atmospheric CO2 concentrations. However, the role of this biogeophysical mechanism under freely evolving atmospheric CO2 concentration and future CO2 emissions remains unknown. To shed light on this research gap, we perform simulations with the EcoGEnIE Earth system model (ESM) and prescribe CO2 emissions out to the year 2500 following the four Extended Concentration Pathway (ECP) scenarios, which for practical purposes we call Representative Concentration Pathway (RCP) scenarios. Under all RCP scenarios, our results indicate that phytoplankton light absorption leads to a shallower remineralization of organic matter and a reduced export efficiency, weakening the biological carbon pump. In contrast, this biogeophysical mechanism increases the surface chlorophyll by ∼ 2 %, the sea surface temperature (SST) by 0.2 to 0.6 °C, the atmospheric CO2 concentrations by 8 %–20 % and the atmospheric temperature by 0.3 to 0.9 °C. Under the RCP2.6, RCP4.5 and RCP6.0 scenarios, the magnitude of changes due to phytoplankton light absorption is similar. However, under the RCP8.5 scenario, the changes in the climate system are less pronounced due to decreasing ecosystem productivity as temperature increases, highlighting a reduced effect of phytoplankton light absorption under strong warming. Additionally, this work highlights the major role of phytoplankton light absorption on the climate system, suggesting highly uncertain feedbacks on the carbon cycle with uncertainties that may be in the range of those known from the land biota
A simple viability analysis for unicellular cyanobacteria using a new autofluorescence assay, automated microscopy, and ImageJ
<p>Abstract</p> <p>Background</p> <p>Currently established methods to identify viable and non-viable cells of cyanobacteria are either time-consuming (eg. plating) or preparation-intensive (eg. fluorescent staining). In this paper we present a new and fast viability assay for unicellular cyanobacteria, which uses red chlorophyll fluorescence and an unspecific green autofluorescence for the differentiation of viable and non-viable cells without the need of sample preparation.</p> <p>Results</p> <p>The viability assay for unicellular cyanobacteria using red and green autofluorescence was established and validated for the model organism <it>Synechocystis </it>sp. PCC 6803. Both autofluorescence signals could be observed simultaneously allowing a direct classification of viable and non-viable cells. The results were confirmed by plating/colony count, absorption spectra and chlorophyll measurements. The use of an automated fluorescence microscope and a novel ImageJ based image analysis plugin allow a semi-automated analysis.</p> <p>Conclusions</p> <p>The new method simplifies the process of viability analysis and allows a quick and accurate analysis. Furthermore results indicate that a combination of the new assay with absorption spectra or chlorophyll concentration measurements allows the estimation of the vitality of cells.</p
Systematically missing confounders in individual participant data meta-analysis of observational cohort studies.
One difficulty in performing meta-analyses of observational cohort studies is that the availability of confounders may vary between cohorts, so that some cohorts provide fully adjusted analyses while others only provide partially adjusted analyses. Commonly, analyses of the association between an exposure and disease either are restricted to cohorts with full confounder information, or use all cohorts but do not fully adjust for confounding. We propose using a bivariate random-effects meta-analysis model to use information from all available cohorts while still adjusting for all the potential confounders. Our method uses both the fully adjusted and the partially adjusted estimated effects in the cohorts with full confounder information, together with an estimate of their within-cohort correlation. The method is applied to estimate the association between fibrinogen level and coronary heart disease incidence using data from 154,012 participants in 31 cohort
Cancer incidence in type 2 diabetes patients - first results from a feasibility study of the D2C cohort
<p>Abstract</p> <p>Background</p> <p>A large prospective study in patients with type 2 diabetes (T2D), the German D2C cohort, is presently being enumerated to investigate risk factors of incident cancer in diabetic patients.</p> <p>Study setting</p> <p>A disease management program was offered, on a voluntary basis, to all T2D patients who were members of a statutory health insurance fund in Germany. This first feasibility report uses data from 26.742 T2D patients, who were 40 to 79 years old, resided in the Muenster District, and who were enrolled between June 2003 and July 2008. Cancer cases were identified through the regional Cancer Registry.</p> <p>Methods</p> <p>Invasive cancer cases were identified using probabilistic record linkage procedures and pseudonymised personal identifiers. Censoring date was December 31, 2008. We included only first cancers, leaving 12.650 male and 14.092 female T2D with a total of 88.778 person-years (py). We computed standardised incidence ratios (SIR) for external comparisons and we employed Cox regression models and hazard ratios (HR) within the cohort.</p> <p>Results</p> <p>We identified 759 first cancers among male T2D patients (18.7 per 1,000 py) and 605 among females (12.7 per 1,000 py). The risk of any incident cancer in T2D was raised (SIR = 1.14; 95% confidence interval [1.10 - 1.21]), in particular for cancer of the liver (SIR = 1.94 [1.15 - 2.94]) and pancreas (SIR = 1.45 [1.07-1.92]). SIRs decreased markedly with time after T2D diagnosis. In Cox models, adjusting for diabetes duration, body mass index and sex, insulin therapy was related to higher cancer risk (HR = 1.25 [1.17 - 1.33]). No effect was seen for metformin.</p> <p>Discussion</p> <p>Our study demonstrates feasibility of record linkage between DMP and cancer registries. These first cohort results confirm previous reports. It is envisaged to enhance this cohort by inclusion of further regions of the state, expansion of the follow-up times, and collection of a more detailed medication history.</p
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