Modulation of swimming rhythmicity by 5-hydroxytryptamine during post-embryonic development in Xenopus laevis

During the first 24h of post-embryonic development in Xenopus laevis, a rapid change in the neural activity underlying swimming occurs in which the duration of ventral root discharge on each cycle increases from a single compound impulse to discrete bursts of activity. Moreover, this change in motor output progresses rostrocaudally, suggesting that it could result from the influence of a descending neural pathway upon the spinal rhythm-generating circuitry during early post-embryonic development. To begin to examine whether serotonergic neurons of brainstem raphe nuclei might have a role in this swimming development, we have studied the effects of 5-hydroxytryptamine (5HT) on fictive swimming in embryonic and larval animals. As previously demonstrated for other vertebrate locomotor rhythms, we find that bath-applied 5HT enhances the duration of motor activity on each cycle of larval fictive swimming. In addition, our results show that the sensitivity of the swimming rhythm to exogenous 5HT follows a strict rostrocaudal gradient. In young embryos (stages 32-36) 5HT does not affect the duration of ventral root impulses per cycle; by the time of hatching (stage 37/38), rostral but not caudal discharge is enhanced, and by stage 42 (24h post-hatching) 5HT can increase motor burst durations along most of the length of the animal. These reversible changes induced by bath-applied 5HT closely resemble the normal rostrocaudal development of burst discharge during swimming in animals some 12h older. As these developmental effects of 5HT are mimicked by bath application of its metabolic precursor, 5-hydroxytryptophan (5HTP), our results suggest that a functional endogenous 5HT system, presumably located in the brainstem raphe nuclei, develops rostrocaudally, is preceded by the development of 5HT receptors on target neurons of the spinal rhythm-generating circuit, and may have an important role in the ontogeny and subsequent modulation of Xenopus swimming motor output.</p

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