Effect of diurnal cycling temperatures on cardiovascular- ventilatory function in statically-acclimated rainbow trout, Salmo gairdneri

Four groups of rainbow trout, Salmo gairdneri, were acclimated to 2°, 10°, and 18°e, and to a diurnal temperature cycle (100 ± 4°C). To evaluate the influence of cycling temperatures in terms of an immediate as opposed to acclimatory response various ventilatory-cardiovascular rate functions were observed for trout, either acclimated to cycling temperatures or acclimated to constant temperatures and exposed to a diurnal temperature cycle for the first time (10° ± 4°C for trout acclimated to 10°C; 18°+ 4°C for trout acclimated to l8°e). Gill resistance and the cardiac to ventilatory rate ratio were then calculated. Following a post preparatory recovery period of 36 hr, measurements were made over a 48 hour period with the first 24 hours being at constant temperature in the case of statically-acclimated fish followed by 24 hours under cyclic temperature conditions. Trout exhibited marked changes in oxygen consumption (Vo ) with temp- 2 erature both between acclimation groups, and in response to the diurnal temperature cycle. This increase in oxygen uptake appears to have been achieved by adjustment of ventilatory and, to some extent, cardiovascular activity. Trout exhibited significant changes in ventilatory rate (VR), stroke volume (Vsv), and flow (VG) in response to temperature. Marked changes in cardiac rate were also observed. These findings are discussed in relation to their importance in convective oxygen transport via water and blood at the gills and tissues. Trout also exhibited marked changes in pressure waveforms associated with the action of the resp; ratory pumps with temperature. Mean differenti a 1 pressure increased with temperature as did gill resistance and utilization. This data is discussed in relation to its importance in diffusive oxygen transport and the conditions for gas exchange at the gills. With one exception, rainbow trout were able to respond to changes in oxygen demand and availability associated with changes in temperature by means of adjustments in ventilation, and possibly pafusion, and the conditions for gas exchange at the gills. Trout acclimated to 18°C, however, and exposed to high cyclic temperatures, showed signs of the ventilatory and cardiovascular distress problems commonly associated with low circulating levels of oxygen in the blood. It appears these trout were unable to fully meet the oxygen requirements associated with c~ling temperatures above 18°C. These findings were discussed in relation to possible limitations in the cardiovascular-ventilatory response at high temperatures. The response of trout acclimated to cycling temperatures was generally similar to that for trout acclimated to constant temperatures and exposed to cycling temperatures for the first time. This result suggested that both groups of fish may have been acclimated to a similar thermal range, regardless of the acclimation regime employed. Such a phenomenon would allow trout of either acclimation group to respond equally well to the imposed temperature cycle. Rainbow trout showed no evidence of significant diurnal rhythm in any parameters observed at constant temperatures (2°, 10°, and 18° C), and under a 12/12 light-dark photoperiod regime. This was not taken to indicate an absence of circadian rhythms in these trout, but rather a deficiency in the recording methods used in the study.

Heuristics for the Critical Node Detection Problem in Large Complex Networks

Complex networks have recently attracted a significant amount of research attention due to their ability to model real world phenomena. One important problem often encountered is to limit diffusive processes spread over the network, for example mitigating pandemic disease or computer virus spread. A number of problem formulations have been proposed that aim to solve such problems based on desired network characteristics, such as maintaining the largest network component after node removal. The recently formulated critical node detection problem aims to remove a small subset of vertices from the network such that the residual network has minimum pairwise connectivity. Unfortunately, the problem is NP-hard and also the number of constraints is cubic in number of vertices, making very large scale problems impossible to solve with traditional mathematical programming techniques. Even many approximation algorithm strategies such as dynamic programming, evolutionary algorithms, etc. all are unusable for networks that contain thousands to millions of vertices. A computationally efficient and simple approach is required in such circumstances, but none currently exist. In this thesis, such an algorithm is proposed. The methodology is based on a depth-first search traversal of the network, and a specially designed ranking function that considers information local to each vertex. Due to the variety of network structures, a number of characteristics must be taken into consideration and combined into a single rank that measures the utility of removing each vertex. Since removing a vertex in sequential fashion impacts the network structure, an efficient post-processing algorithm is also proposed to quickly re-rank vertices. Experiments on a range of common complex network models with varying number of vertices are considered, in addition to real world networks. The proposed algorithm, DFSH, is shown to be highly competitive and often outperforms existing strategies such as Google PageRank for minimizing pairwise connectivity.