A group of 221 male healthy volunteers of Indian Army were the subjects of the study. The baseline parameters of skeletal health were measured during their residency at an altitude of 3542 m. These subjects were then taken to an extreme altitude (EA, 5400-6700 m) where they stayed for about 4 months. The study parameters were repeated following their de-induction (DI) to 3542 m. On random selection, a subgroup was constituted from the above mentioned volunteers for detailed investigations on various bone turnover markers. Results of this study indicate a loss of body weight after DI from EA. The bone impairment was detected at the proximal phalanx, which is known to undergo early morpho-structural changes associated with bone resorption. The intact parathyroid hormone (i-PTH) levels showed a significant increase, while alkaline phosphatase (ALP) and bone specific alkaline phosphatase (BAP) activities declined significantly after DI from EA. This elevation in i-PTH might be required for maintenance of blood Ca level. 25 (OH) Vitamin D3 (25VitD) and calcitonin (CT) also showed a significant decline, which may suggest a negative impact on bone formation during sojourn at EA. The causes of deterioration of skeletal health at EA although are poorly understood but may be due to acute hypoxemia arising from extreme hypobaric hypoxia prevalent at extreme altitude.
Formulating mathematical models for accurate approximation of malicious propagation in a network is a difficult process because of our inherent lack of understanding of several underlying physical processes that intrinsically characterize the broader picture. The aim of this paper is to understand the impact of available information in the control of malicious network epidemics. A 1-n-n-1 type differential epidemic model is proposed, where the differentiality allows a symptom based classification. This is the first such attempt to add such a classification into the existing epidemic framework. The model is incorporated into a five class system called the DifEpGoss architecture. Analysis reveals an epidemic threshold, based on which the long-term behavior of the system is analyzed. In this work three real network datasets with 22002, 22469 and 22607 undirected edges respectively, are used. The datasets show that classification based prevention given in the model can have a good role in containing network epidemics. Further simulation based experiments are used with a three category classification of attack and defense strengths, which allows us to consider 27 different possibilities. These experiments further corroborate the utility of the proposed model. The paper concludes with several interesting results.
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