General statistical model shows that macroevolutionary patterns and processes are consistent with Darwinian gradualism

Abstract: Macroevolution posed difficulties for Darwin and later theorists because species’ phenotypes frequently change abruptly, or experience long periods of stasis, both counter to the theory of incremental change or gradualism. We introduce a statistical model that accommodates this uneven evolutionary landscape by estimating two kinds of historical change: directional changes that shift the mean phenotype along the branches of a phylogenetic tree, and evolvability changes that alter a clade’s ability to explore its trait-space. In mammals, we find that both processes make substantial independent contributions to explaining macroevolution, and are rarely linked. ‘Watershed’ moments of increased evolvability greatly outnumber reductions in evolutionary potentials, and large or abrupt phenotypic shifts are explicable statistically as biased random walks, allowing macroevolutionary theory to engage with the language and concepts of gradualist microevolution. Our findings recast macroevolutionary phenomena, illustrating the necessity of accounting for a variety of evolutionary processes simultaneously

Towards a model for understanding the key factors in KMS implementation

Researchers and practitioners have reported on the factors said to influence the design and implementation of Knowledge Management Systems (KMS). Factors such as strategy, culture, information technology, people and organisational structure have arisen as key dimensions to be considered in KMS implementation. However, researchers have tended to explore these factors in isolation, and have, by and large, achieved little in the way of success in establishing relationships between them. Using a research framework derived from the literature, this paper investigates the key factors affecting the implementation of KMS and explores the links between these factors. A field study approach incorporating 12 large organisations that have implemented KMS is adopted to study the phenomenon. Significantly, feedback from KM practitioners provided direct guidance on the model’s practical relevance and led to a refined and extended model of KMS implementation factors. Hence, the findings provide a foundation for understanding the key factors that organisations face as they implement Knowledge Management Systems

Optimising Clinical Trial Design in Older Cancer Patients

Cancer is predominantly a disease of older patients, with over half of those aged over 65 years of age being diagnosed with cancer at some stage. Despite comprising a significant proportion of the patients that we see in clinical practice, there is a lack of representation of older patients in cancer clinical trials. This is mainly due to restrictive trial inclusion criteria that prevent older patients from participating. Also, trial endpoints, such as overall survival, may not represent the most important and most meaningful endpoints for older patients. The latter may place more significance on quality of life and other outcomes such as functional independence. Baseline assessment using Comprehensive Geriatric Assessment, may provide a better framework for quantifying patient outcomes for varying degrees of fitness or frailty. This short communication makes the case for more age appropriate endpoints, such as quality of life, toxicity and functional independence, and that novel trial designs are necessary to inform evidence-based care of older cancer patients

Distinct microbiome composition and metabolome exists across subgroups of elite Irish athletes

peer-reviewedObjectives: The gut microbiome has begun to be characterised in athlete groups, albeit, to date, only across a subset of sports. This study aimed to determine if the gut microbiome and metabolome differed across sports classification groups (SCGs) among elite Irish athletes, many of whom were participating in the 2016 Summer Olympics. Methods: Faecal and urine samples were collected from 37 international level athletes. Faecal samples were prepared for shotgun metagenomic sequencing and faecal and urine samples underwent metabolomic profiling. Results: Differences were observed in the composition and functional capacity of the gut microbiome of athletes across SCGs. The microbiomes of athletes participating in sports with a high dynamic component were the most distinct compositionally (greater differences in proportions of species), while those of athletes participating in sports with high dynamic and static components were the most functionally distinct (greater differences in functional potential). Additionally, both microbial (faecal) and human (urine) derived metabolites were found to vary between SCGs. In particular cis-aconitate, succinic acid and lactate, in urine samples, and creatinine, in faeces, were found to be significantly different between groups. These differences were evident despite the absence of significant differences in diet, as determined using food frequency questionnaires, which were translated into nutrient intake values using FETA. Conclusions: Differences in the gut microbiome and metabolome between groups, in the absence of dietary changes, indicates a role for training load or type as a contributory factor. Further exploration of this hypothesis has the potential to benefit athletes, aspiring athletes and the general public