Effects of ambient temperature on cardiovascular regulation during sleep in hypocretin-deficient narcoleptic mice

Hypocretin 1 and 2 (HCRT, also called Orexin A and B) are neuropeptides released by neurons in the lateral hypothalamus. HCRT neurons widely project to the entire neuroaxis. HCRT neurons have been reported to participate in various hypothalamic physiological processes including cardiovascular functions, wake-sleep cycle, and they may also influence metabolic rate and the regulation of body temperature. HCRT neurons are lost in narcolepsy, a rare neurological disorder, characterized by excessive daytime sleepiness, cataplexy, sleep fragmentation and occurrence of sleep-onset rapid-eye-movement episodes. We investigated whether HCRT neurons mediate the sleep-dependent cardiovascular adaptations to changes in ambient temperature (Ta). HCRT-ataxin3 transgenic mice with genetic ablation of HCRT neurons (n = 11) and wild-type controls (n = 12) were instrumented with electrodes for sleep scoring and a telemetric blood pressure (BP) transducer (DSI, Inc.). Simultaneous sleep and BP recordings were performed on mice undisturbed and freely-behaving at 20 °C, 25 °C, and 30 °C for 48 hours at each Ta. Analysis of variance of BP indicated a significance of the main effects of wake-sleep state and Ta, their interaction effect, and the wake-sleep state x mouse strain interaction effect. BP increased with decreasing Ta. This effect of Ta on BP was significantly lower in rapid-eye-movement sleep (REMS) than either in non-rapid-eye-movement sleep (NREMS) or wakefulness regardless of the mouse strain. BP was higher in wakefulness than either in NREMS or REMS. This effect of sleep on BP was significantly reduced in mice lacking HCRT neurons at each Ta, particularly during REMS. These data suggest that HCRT neurons play a critical role in mediating the effects of sleep but not those of Ta on BP in mice. HCRT neurons may thus be part of the central neural pathways which mediate the phenomenon of blood pressure dipping on passing from wakefulness to sleep.

Constraint based methods for allocation and scheduling of periodic applications

This work presents exact algorithms for the Resource Allocation and Cyclic Scheduling Problems (RA&CSPs). Cyclic Scheduling Problems arise in a number of application areas, such as in hoist scheduling, mass production, compiler design (implementing scheduling loops on parallel architectures), software pipelining, and in embedded system design. The RA&CS problem concerns time and resource assignment to a set of activities, to be indefinitely repeated, subject to precedence and resource capacity constraints. In this work we present two constraint programming frameworks facing two different types of cyclic problems. In first instance, we consider the disjunctive RA&CSP, where the allocation problem considers unary resources. Instances are described through the Synchronous Data-flow (SDF) Model of Computation. The key problem of finding a maximum-throughput allocation and scheduling of Synchronous Data-Flow graphs onto a multi-core architecture is NP-hard and has been traditionally solved by means of heuristic (incomplete) algorithms. We propose an exact (complete) algorithm for the computation of a maximum-throughput mapping of applications specified as SDFG onto multi-core architectures. Results show that the approach can handle realistic instances in terms of size and complexity. Next, we tackle the Cyclic Resource-Constrained Scheduling Problem (i.e. CRCSP). We propose a Constraint Programming approach based on modular arithmetic: in particular, we introduce a modular precedence constraint and a global cumulative constraint along with their filtering algorithms. Many traditional approaches to cyclic scheduling operate by fixing the period value and then solving a linear problem in a generate-and-test fashion. Conversely, our technique is based on a non-linear model and tackles the problem as a whole: the period value is inferred from the scheduling decisions. The proposed approaches have been tested on a number of non-trivial synthetic instances and on a set of realistic industrial instances achieving good results on practical size problem.