Validation of Transcranial Doppler with Magnetic Resonance Angiography in Acute Cerebral Ischemia

10.1111/j.1552-6569.2009.00412.xJournal of Neuroimaging212e34-e40JNER

Barley starch

This thesis examined barley amylopectin structure and looked for correlations between the structure and physical properties of starch. The structure of amylopectin and gelatinisation and retrogradation of starch were studied in 10 different barley cultivars/breeding lines with differing genetic background. Amylopectin is built up of thousands of chains of glucose monomers, organised into clusters. The detailed fine structure of amylopectin was studied by isolating clusters of amylopectin and their building blocks, which are the tightly branched units building up the clusters. Barley cultivars/breeding lines possessing the amo1 mutation had fewer long chains of DP≥38 in amylopectin and more large building blocks. The structure of building blocks was rather conserved between the different barley cultivars/breeding lines studied and was categorized into different size groups. These different building blocks were shown to be randomly distributed in the amylopectin molecule. The C-chains in amylopectin can be of any length and are a category of chains different from the B-chains. The backbone in amylopectin consists of a special type of B-chains which, when cleaved by α-amylase, become chains of a similar type to C-chains. Gelatinisation and retrogradation (recrystallisation of gelatinised starch) of barley starch was studied by differential scanning calorimetry. The amo1 mutation resulted in a broader gelatinisation temperature range and a higher enthalpy of retrogradation. Other structural features were also found to influence the physical properties of starch. Small clusters and denser structure of the building blocks resulted in higher gelatinisation temperature. Fast retrogradation was observed in barley which had amylopectin with shorter chains and many large building blocks consisting of many chains. Amylopectin structure was also studied in developing barley kernels. Three barley cultivars/breeding lines were grown in a phytotron and kernels were harvested at 9, 12 and 24 days after flowering. The results showed that amylopectin synthesized at later stages of development had a more tightly branched structure. Expression of the enzymes involved in starch biosynthesis is also known to change during endosperm development

Enhanced performance in fusion plasmas through turbulence suppression by megaelectronvolt ions

© 2022, The Author(s), under exclusive licence to Springer Nature Limited.Alpha particles with energies on the order of megaelectronvolts will be the main source of plasma heating in future magnetic confinement fusion reactors. Instead of heating fuel ions, most of the energy of alpha particles is transferred to electrons in the plasma. Furthermore, alpha particles can also excite Alfvénic instabilities, which were previously considered to be detrimental to the performance of the fusion device. Here we report improved thermal ion confinement in the presence of megaelectronvolts ions and strong fast ion-driven Alfvénic instabilities in recent experiments on the Joint European Torus. Detailed transport analysis of these experiments reveals turbulence suppression through a complex multi-scale mechanism that generates large-scale zonal flows. This holds promise for more economical operation of fusion reactors with dominant alpha particle heating and ultimately cheaper fusion electricity.N

Disruption prediction with artificial intelligence techniques in tokamak plasmas

In nuclear fusion reactors, plasmas are heated to very high temperatures of more than 100 million kelvin and, in so-called tokamaks, they are confined by magnetic fields in the shape of a torus. Light nuclei, such as deuterium and tritium, undergo a fusion reaction that releases energy, making fusion a promising option for a sustainable and clean energy source. Tokamak plasmas, however, are prone to disruptions as a result of a sudden collapse of the system terminating the fusion reactions. As disruptions lead to an abrupt loss of confinement, they can cause irreversible damage to present-day fusion devices and are expected to have a more devastating effect in future devices. Disruptions expected in the next-generation tokamak, ITER, for example, could cause electromagnetic forces larger than the weight of an Airbus A380. Furthermore, the thermal loads in such an event could exceed the melting threshold of the most resistant state-of-the-art materials by more than an order of magnitude. To prevent disruptions or at least mitigate their detrimental effects, empirical models obtained with artificial intelligence methods, of which an overview is given here, are commonly employed to predict their occurrence—and ideally give enough time to introduce counteracting measures