The adaptive response of humans to exercise: Impact of exercise intensity, fibre-type specific responses and molecular patterns of response

In an attempt to improve the current understanding of the adaptive response to exercise in humans, this dissertation performed a series of studies designed to examine the impact of training intensity and mode on aerobic capacity and performance, fibre-type specific adaptations to training, and individual patterns of response across molecular, morphological and genetic factors. Project #1 determined that training intensity, session dose, baseline VO2max and total training volume do not influence the magnitude of change in VO2max by performing a meta-regression, and meta-analysis of 28 different studies. The intensity of training had no effect on the magnitude of increase in maximal oxygen uptake in young healthy participants, but similar adaptations were achieved with lower training doses following high intensity training. Project # 2 determined the acute molecular response, and training-induced adaptations in aerobic performance, aerobic capacity and muscle phenotype following high-intensity interval training (HIT) or endurance exercise (END). The acute molecular response (fibre recruitment and signal activation) and training-induced adaptations in aerobic capacity, aerobic performance, and muscle phenotype were similar following HIT and END. Project # 3 examined the impact of baseline muscle morphology and molecular characteristics on the training response, and if muscle adaptations are coordinated. The muscle phenotype of individuals who experience the largest improvements (high responders) were lower before training for some muscle characteristics and molecular adaptations were coordinated within individual participants. Project # 4 examined the impact of 2 different intensities of HIT on the expression of nuclear and mitochondrial encoded genes targeted by PGC-1α. A systematic upregulation of nuclear and mitochondrial encoded genes was not present in the early recovery period following acute HIT, but the expression of mitochondrial genes were coordinated at an individual level. Collectively, results from the current dissertation contribute to our understanding of the molecular mechanisms influencing skeletal muscle and whole-body adaptive responses to acute exercise and training in humans.

An Exploration of the challenges associated with software logging in large systems

Over the past few years, logging has evolved from from simple printf statements to more complex and widely used logging libraries. Today logging information is used to support various development activities such as fixing bugs, analyzing the results of load tests, monitoring performance and transferring knowledge. Recent research has examined how to improve logging practices by informing developers what to log and where to log. Furthermore, the strong dependence on logging has led to the development of logging libraries that have reduced the intricacies of logging, which has resulted in an abundance of log information. Two recent challenges have emerged as modern software systems start to treat logging as a core aspect of their software. In particular, 1) infrastructural challenges have emerged due to the plethora of logging libraries available today and 2) processing challenges have emerged due to the large number of log processing tools that ingest logs and produce useful information from them. In this thesis, we explore these two challenges. We first explore the infrastructural challenges that arise due to the plethora of logging libraries available today. As systems evolve, their logging infrastructure has to evolve (commonly this is done by migrating to new logging libraries). We explore logging library migrations within Apache Software Foundation (ASF) projects. We i find that close to 14% of the pro jects within the ASF migrate their logging libraries at least once. For processing challenges, we explore the different factors which can affect the likelihood of a logging statement changing in the future in four open source systems namely ActiveMQ, Camel, Cloudstack and Liferay. Such changes are likely to negatively impact the log processing tools that must be updated to accommodate such changes. We find that 20%-45% of the logging statements within the four systems are changed at least once. We construct random forest classifiers and Cox models to determine the likelihood of both just-introduced and long-lived logging statements changing in the future. We find that file ownership, developer experience, log density and SLOC are important factors in determining the stability of logging statements.