Respiratory muscle activity related to flow and lung volume in preterm infants compared with term infants

Infants with chronic lung disease (CLD) have a capacity to maintain functional lung volume despite alterations to their lung mechanics. We hypothesize that they achieve this by altering breathing patterns and dynamic elevation of lung volume, leading to differences in the relationship between respiratory muscle activity, flow and lung volume. Lung function and transcutaneous electromyography of the respiratory muscles (rEMG) were measured in 20 infants with CLD and in 39 healthy age-matched controls during quiet sleep. We compared coefficient of variations (CVs) of rEMG and the temporal relationship of rEMG variables, to flow and lung volume [functional residual capacity (FRC)] between these groups. The time between the start of inspiratory muscle activity and the resulting flow (tria)--in relation to respiratory cycle time--was significantly longer in infants with CLD. Although FRC had similar associations with tria and postinspiratory activity (corrected for respiratory cycle time), the CV of the diaphragmatic rEMG was lower in CLD infants (22.6 versus 31.0%, p = 0.030). The temporal relationship of rEMG to flow and FRC and the loss of adaptive variability provide additional information on coping mechanisms in infants with CLD. This technique could be used for noninvasive bedside monitoring of CLD.

Indigenous knowledge related to climate variability and change: insights from droughts in semi-arid areas of former Makueni District, Kenya

This article describes the indigenous knowledge (IK) that agro-pastoralists in larger Makueni District, Kenya hold and how they use it to monitor, mitigate and adapt to drought. It examines ways of integrating IK into formal monitoring, how to enhance its value and acceptability. Data was collected through target interviews, group discussions and questionnaires covering 127 households in eight villages. Daily rainfall data from 1961–2003 were analysed. Results show that agro-pastoralists hold IK on indicators of rainfall variability; they believe in IK efficacy and they rely on them. Because agro-pastoralists consult additional sources, the authors interpret that IK forms a basic knowledge frame within which agro-pastoralists position and interpret meteorological forecasts. Only a few agro-pastoralists adapt their practices in anticipation of IK-based forecasts partly due to the conditioning of the actors to the high rainfall variability characteristic of the area and partly due to lack of resources. Non-drought factors such as poverty, inadequate resources and lack of preparedness expose agro-pastoralists to drought impacts and limit their adaptive capacity. These factors need to be understood and effectively addressed to increase agro-pastoralists’ decision options and the influence of IK-based forecasts on their decision-making patterns. The limited intergenerational transfer of IK currently threatens its existence in the longer term. One way to ensure its continued existence and use is to integrate IK into the education curriculum and to link IK with formal climate change research through the participation of the local people. However, further studies are necessary to address the reliability and validity of the identified IK indicators of climate variability and change.

Adaptive Algorithms for Personalized Diabetes Treatment

Dynamic systems, especially in real-life applications, are often determined by inter-/intra-variability, uncertainties and time-varying components. Physiological systems are probably the most representative example in which population variability, vital signal measurement noise and uncertain dynamics render their explicit representation and optimization a rather difficult task. Systems characterized by such challenges often require the use of adaptive algorithmic solutions able to perform an iterative structural and/or parametrical update process towards optimized behavior. Adaptive optimization presents the advantages of (i) individualization through learning of basic system characteristics, (ii) ability to follow time-varying dynamics and (iii) low computational cost. In this chapter, the use of online adaptive algorithms is investigated in two basic research areas related to diabetes management: (i) real-time glucose regulation and (ii) real-time prediction of hypo-/hyperglycemia. The applicability of these methods is illustrated through the design and development of an adaptive glucose control algorithm based on reinforcement learning and optimal control and an adaptive, personalized early-warning system for the recognition and alarm generation against hypo- and hyperglycemic events.