Trehalose is required for the acquisition of tolerance to a variety of stresses in the filamentous fungus Aspergillus nidulans

Trehalose is a non-reducing disaccharide found at high concentrations in Aspergillus nidulans conidia and rapidly degraded upon induction of conidial germination. Furthermore, trehalose is accumulated in response to a heat shock or to an oxidative shock. The authors have characterized the A. nidulans tpsA gene encoding trehalose-6-phosphate synthase, which catalyses the first step in trehalose biosynthesis. Expression of tpsA in a Saccharomyces cerevisiae tps1 mutant revealed that the tpsA gene product is a functional equivalent of the yeast Tps1 trehalose-6-phosphate synthase. The A. nidulans tpsA-null mutant does not produce trehalose during conidiation or in response to various stress conditions. While germlings of the tpsA mutant show an increased sensitivity to moderate stress conditions (growth at 45 °C or in the presence of 2 mM H2O2), they display a response to severe stress (60 min at 50 °C or in the presence of 100 mM H2O2) similar to that of wild-type germlings. Furthermore, conidia of the tpsA mutant show a rapid loss of viability upon storage. These results are consistent with a role of trehalose in the acquisition of stress tolerance. Inactivation of the tpsA gene also results in increased steady-state levels of sugar phosphates but does not prevent growth on rapidly metabolizable carbon sources (glucose, fructose) as seen in Saccharomyces cerevisiae. This suggests that trehalose 6-phosphate is a physiological inhibitor of hexokinase but that this control is not essential for proper glycolytic flux in A. nidulans. Interestingly, tpsA transcription is not induced in response to heat shock or during conidiation, indicating that trehalose accumulation is probably due to a post-translational activation process of the trehalose 6-phosphate synthase

An observed 20-year time series of Agulhas leakage

We provide a time series of Agulhas leakage anomalies over the last 20-years from satellite altimetry. Until now, measuring the interannual variability of Indo-Atlantic exchange has been the major barrier in the investigation of the dynamics and large scale impact of Agulhas leakage. We compute the difference of transport between the Agulhas Current and Agulhas Return Current, which allows us to deduce Agulhas leakage. The main difficulty is to separate the Agulhas Return Current from the southern limb of the subtropical "supergyre" south of Africa. For this purpose, an algorithm that uses absolute dynamic topography data is developed. The algorithm is applied to a state-of-the-art ocean model. The comparison with a Lagrangian method to measure the leakage allows us to validate the new method. An important result is that it is possible to measure Agulhas leakage in this model using the velocity field along a section that crosses both the Agulhas Current and the Agulhas Return Current. In the model a good correlation is found between measuring leakage using the full depth velocities and using only the surface geostrophic velocities. This allows us to extend the method to along-track absolute dynamic topography from satellites. It is shown that the accuracy of the mean dynamic topography does not allow us to determine the mean leakage but that leakage anomalies can be accurately computed

Near-threshold measurement of the 4He(g,n) reaction

A near-threshold 4He(g,n) cross-section measurement has been performed at MAX-lab. Tagged photons from 23 < Eg < 42 MeV were directed toward a liquid 4He target, and neutrons were detected by time-of-flight in two liquid-scintillator arrays. Seven-point angular distributions were measured for eight photon energies. The results are compared to experimental data measured at comparable energies and Recoil-Corrected Continuum Shell Model, Resonating Group Method, and recent Hyperspherical-Harmonic Expansion calculations. The angle-integrated cross-section data is peaked at a photon energy of about 28 MeV, in disagreement with the value recommended by Calarco, Berman, and Donnelly in 1983.Comment: 10 pages, 3 figures, some revisions, submitted to Physics Letters

Outcome Prediction of Postanoxic Coma:A Comparison of Automated Electroencephalography Analysis Methods

BACKGROUND: To compare three computer-assisted quantitative electroencephalography (EEG) prediction models for the outcome prediction of comatose patients after cardiac arrest regarding predictive performance and robustness to artifacts. METHODS: A total of 871 continuous EEGs recorded up to 3 days after cardiac arrest in intensive care units of five teaching hospitals in the Netherlands were retrospectively analyzed. Outcome at 6 months was dichotomized as "good" (Cerebral Performance Category 1-2) or "poor" (Cerebral Performance Category 3-5). Three prediction models were implemented: a logistic regression model using two quantitative features, a random forest model with nine features, and a deep learning model based on a convolutional neural network. Data from two centers were used for training and fivefold cross-validation (n = 663), and data from three other centers were used for external validation (n = 208). Model output was the probability of good outcome. Predictive performances were evaluated by using receiver operating characteristic analysis and the calculation of predictive values. Robustness to artifacts was evaluated by using an artifact rejection algorithm, manually added noise, and randomly flattened channels in the EEG. RESULTS: The deep learning network showed the best overall predictive performance. On the external test set, poor outcome could be predicted by the deep learning network at 24 h with a sensitivity of 54% (95% confidence interval [CI] 44-64%) at a false positive rate (FPR) of 0% (95% CI 0-2%), significantly higher than the logistic regression (sensitivity 33%, FPR 0%) and random forest models (sensitivity 13%, FPR, 0%) (p < 0.05). Good outcome at 12 h could be predicted by the deep learning network with a sensitivity of 78% (95% CI 52-100%) at a FPR of 12% (95% CI 0-24%) and by the logistic regression model with a sensitivity of 83% (95% CI 83-83%) at a FPR of 3% (95% CI 3-3%), both significantly higher than the random forest model (sensitivity 1%, FPR 0%) (p < 0.05). The results of the deep learning network were the least affected by the presence of artifacts, added white noise, and flat EEG channels. CONCLUSIONS: A deep learning model outperformed logistic regression and random forest models for reliable, robust, EEG-based outcome prediction of comatose patients after cardiac arrest

Dynamics of liquid He-4 in confined geometries from Time-Dependent Density Functional calculations

We present numerical results obtained from Time-Dependent Density Functional calculations of the dynamics of liquid He-4 in different environments characterized by geometrical confinement. The time-dependent density profile and velocity field of He-4 are obtained by means of direct numerical integration of the non-linear Schrodinger equation associated with a phenomenological energy functional which describes accurately both the static and dynamic properties of bulk liquid He-4. Our implementation allows for a general solution in 3-D (i.e. no symmetries are assumed in order to simplify the calculations). We apply our method to study the real-time dynamics of pure and alkali-doped clusters, of a monolayer film on a weakly attractive surface and a nano-droplet spreading on a solid surface.Comment: q 1 tex file + 9 Ps figure

HDAC-mediated control of ERK- and PI3K-dependent TGF-β-induced extracellular matrix-regulating genes

Histone deacetylases (HDACs) regulate the acetylation of histones in the control of gene expression. Many non-histone proteins are also targeted for acetylation, including TGF-ß signalling pathway components such as Smad2, Smad3 and Smad7. Our studies in mouse C3H10T1/2 fibroblasts suggested that a number of TGF-ß-induced genes that regulate matrix turnover are selectively regulated by HDACs. Blockade of HDAC activity with trichostatin A (TSA) abrogated the induction of a disintegrin and metalloproteinase 12 (Adam12) and tissue inhibitor of metalloproteinases-1 (Timp-1) genes by TGF-ß, whereas plasminogen activator inhibitor-1 (Pai-1) expression was unaffected. Analysis of the activation of cell signalling pathways demonstrated that TGF-ß induced robust ERK and PI3K activation with delayed kinetics compared to the phosphorylation of Smads. The TGF-ß induction of Adam12 and Timp-1 was dependent on such non-Smad signalling pathways and, importantly, HDAC inhibitors completely blocked their activation without affecting Smad signalling. Analysis of TGF-ß-induced Adam12 and Timp-1 expression and ERK/PI3K signalling in the presence of semi-selective HDAC inhibitors valproic acid, MS-275 and apicidin implicated a role for class I HDACs. Furthermore, depletion of HDAC3 by RNA interference significantly down-regulated TGF-ß-induced Adam12 and Timp-1 expression without modulating Pai-1 expression. Correlating with the effect of HDAC inhibitors, depletion of HDAC3 also blocked the activation of ERK and PI3K by TGF-ß. Collectively, these data confirm that HDACs, and in particular HDAC3, are required for activation of the ERK and PI3K signalling pathways by TGF-ß and for the subsequent gene induction dependent on these signalling pathways

Common carotid intima media thickness and ankle-brachial pressure index correlate with local but not global atheroma burden:a cross sectional study using whole body magnetic resonance angiography

Common carotid intima media thickness (CIMT) and ankle brachial pressure index (ABPI) are used as surrogate marker of atherosclerosis, and have been shown to correlate with arterial stiffness, however their correlation with global atherosclerotic burden has not been previously assessed. We compare CIMT and ABPI with atheroma burden as measured by whole body magnetic resonance angiography (WB-MRA).50 patients with symptomatic peripheral arterial disease were recruited. CIMT was measured using ultrasound while rest and exercise ABPI were performed. WB-MRA was performed in a 1.5T MRI scanner using 4 volume acquisitions with a divided dose of intravenous gadolinium gadoterate meglumine (Dotarem, Guerbet, FR). The WB-MRA data was divided into 31 anatomical arterial segments with each scored according to degree of luminal narrowing: 0 = normal, 1 = <50%, 2 = 50-70%, 3 = 70-99%, 4 = vessel occlusion. The segment scores were summed and from this a standardized atheroma score was calculated.The atherosclerotic burden was high with a standardised atheroma score of 39.5±11. Common CIMT showed a positive correlation with the whole body atheroma score (β 0.32, p = 0.045), however this was due to its strong correlation with the neck and thoracic segments (β 0.42 p = 0.01) with no correlation with the rest of the body. ABPI correlated with the whole body atheroma score (β -0.39, p = 0.012), which was due to a strong correlation with the ilio-femoral vessels with no correlation with the thoracic or neck vessels. On multiple linear regression, no correlation between CIMT and global atheroma burden was present (β 0.13 p = 0.45), while the correlation between ABPI and atheroma burden persisted (β -0.45 p = 0.005).ABPI but not CIMT correlates with global atheroma burden as measured by whole body contrast enhanced magnetic resonance angiography in a population with symptomatic peripheral arterial disease. However this is primarily due to a strong correlation with ilio-femoral atheroma burden

Variability and coherence of the Agulhas Undercurrent in a High-resolution Ocean General Circulation Model

The Agulhas Current system has been analyzed in a nested high-resolution ocean model and compared to observations. The model shows good performance in the western boundary current structure and the transports off the South African coast. This includes the simulation of the northward-flowing Agulhas Undercurrent. It is demonstrated that fluctuations of the Agulhas Current and Undercurrent around 50–70 days are due to Natal pulses and Mozambique eddies propagating downstream. A sensitivity experiment that excludes those upstream perturbations significantly reduces the variability as well as the mean transport of the undercurrent. Although the model simulates undercurrents in the Mozambique Channel and east of Madagascar, there is no direct connection between those and the Agulhas Undercurrent. Virtual float releases demonstrate that topography is effectively blocking the flow toward the north