Explanations for the evolution of body size in mammals have remained surprisingly elusive despite the central importance of body size in evolutionary biology. Here, we present a model which argues that the body sizes of Nearctic mammals were moulded by Cenozoic climate and vegetation changes. Following the early Eocene Climate Optimum, forests retreated and gave way to open woodland and savannah landscapes, followed later by grasslands. Many herbivores that radiated in these new landscapes underwent a switch from browsing to grazing associated with increased unguligrade cursoriality and body size, the latter driven by the energetics and constraints of cellulose digestion (fermentation). Carnivores also increased in size and digitigrade, cursorial capacity to occupy a size distribution allowing the capture of prey of the widest range of body sizes. With the emergence of larger, faster carnivores, plantigrade mammals were constrained from evolving to large body sizes and most remained smaller than 1 kg throughout the middle Cenozoic. We find no consistent support for either Cope's Rule or Bergmann's Rule in plantigrade mammals, the largest locomotor guild (n = 1186, 59% of species in the database). Some cold-specialist plantigrade mammals, such as beavers and marmots, showed dramatic increases in body mass following the Miocene Climate Optimum which may, however, be partially explained by Bergmann's rule. This study reemphasizes the necessity of considering the evolutionary history and resultant form and function of mammalian morphotypes when attempting to understand contemporary mammalian body size distributions.

In this study we report on the evolution of micro-cursoriality, a unique case of cursoriality in mammals smaller than 1 kg. We obtained new running speed and limb morphology data for two species of elephantshrews (Elephantulus spp., Macroscelidae) from Namaqualand, South Africa, which we compared with published data for other mammals. Elephantulus maximum running speeds were higher than those of most mammals smaller than 1 kg. Elephantulus also possess exceptionally high metatarsal:femur ratios (1.07) that are typically associated with fast unguligrade cursors. Cursoriality evolved in the Artiodactyla, Perissodactyla and Carnivora coincident with global cooling and the replacement of forests with open landscapes in the Oligocene and Miocene. The majority of mammal species, though, remained non-cursorial, plantigrade and small (<1 kg). The extraordinary running speed and digitigrady of elephant-shrews was established in the Early Eocene in the earliest macroscelid Prodiacodon, but was probably inherited from Paleocene, Holarctic stem macroscelids. Micro-cursoriality in macroscelids evolved from the plesiomorphic plantigrade foot of the possum-like ancestral mammal earlier than in other mammalian crown groups. Microcursoriality evolved first in forests, presumably in response to selection for rapid running speeds facilitated by local knowledge, in order to avoid predators. During the Miocene, micro-cursoriality was pre-adaptive to open, arid habitats, and became more derived in the newly evolved Elephantulus and Macroscelides elephant-shrews with trail running.

Up to now, the field of liquid biopsies has focused on circulating tumour DNA and cells, though extracellular vesicles (EVs) have been of increasing interest in recent years. Thus, reported sources of tumour‐derived nucleic acids include leukocytes, platelets and apoptotic bodies (AB), as well as large (LEV) and small (SEV) EVs. Despite these competing claims, there has yet to be a standardized comparison of the tumour‐derived DNA associated with different components of blood. To address this issue, we collected twenty‐three blood samples from seventeen patients with pancreatic cancers of known mutant KRAS G12 genotype, and divided them into two groups based on the time of patient survival following sampling. After collecting red and white blood cells, we subjected 1 ml aliquots of platelet rich plasma to differential centrifugation in order to separate the platelets, ABs, LEVs, SEVs and soluble proteins (SP) present. We then confirmed the enrichment of specific blood components in each differential centrifugation fraction using electron microscopy, Western blotting, nanoparticle tracking analysis and bead‐based multiplex flow cytometry assays. By targeting wild type and tumour‐specific mutant KRAS alleles using digital PCR, we found that the levels of mutant KRAS DNA were highest in association with LEVs and SEVs early, and with SEVs and SP late in disease progression. Importantly, we established that SEVs were the most enriched in tumour‐derived DNA throughout disease progression, and verified this association using size exclusion chromatography. This work provides important direction for the rapidly expanding field of liquid biopsies by supporting an increased focus on EVs as a source of tumour‐derived DNA.

Background The central role of proteins in diseases has made them increasingly attractive as therapeutic targets and indicators of cellular processes. Protein microarrays are emerging as an important means of characterising protein activity. Their accurate downstream analysis to produce biologically significant conclusions is largely dependent on proper pre-processing of extracted signal intensities. However, existing computational tools are not specifically tailored to the nature of these data and lack unanimity. Results Here, we present the single-channel Protein Microarray Analysis Pipeline, a tailored computational tool for analysis of single-channel protein microarrays enabling biomarker identification, implemented in R, and as an interactive web application. We compared four existing background correction and normalization methods as well as three array filtering techniques, applied to four real datasets with two microarray designs, extracted using two software programs. The normexp, cyclic loess, and array weighting methods were most effective for background correction, normalization, and filtering respectively. Conclusions Thus, here we provided a versatile and effective pre-processing and differential analysis workflow for single-channel protein microarray data in form of an R script and web application (https://metaomics.uct.ac.za/shinyapps/Pro-MAP/.) for those not well versed in the R programming language.