Machine learning enables detection of early-stage colorectal cancer by whole-genome sequencing of plasma cell-free DNA.

Background: Blood-based methods using cell-free DNA (cfDNA) are under development as an alternative to existing screening tests. However, early-stage detection of cancer using tumor-derived cfDNA has proven challenging because of the small proportion of cfDNA derived from tumor tissue in early-stage disease. A machine learning approach to discover signatures in cfDNA, potentially reflective of both tumor and non-tumor contributions, may represent a promising direction for the early detection of cancer.

Quantitative criticism of literary relationships.

Authors often convey meaning by referring to or imitating prior works of literature, a process that creates complex networks of literary relationships ("intertextuality") and contributes to cultural evolution. In this paper, we use techniques from stylometry and machine learning to address subjective literary critical questions about Latin literature, a corpus marked by an extraordinary concentration of intertextuality. Our work, which we term "quantitative criticism," focuses on case studies involving two influential Roman authors, the playwright Seneca and the historian Livy.

Actin-myosin network influences morphological response of neuronal cells to altered osmolarity.

Acute osmotic fluctuations in the brain occur during a number of clinical conditions and can result in a variety of adverse neurological symptoms. Osmotic perturbation can cause changes in the volumes of intra- and extracellular fluid and, due to the rigidity of the skull, can alter intracranial pressure thus making it difficult to analyze purely osmotic effects in vivo. The present study aims to determine the effects of changes in osmolarity on SH-SY5Y human neuroblastoma cells in vitro, and the role of the actin-myosin network in regulating this response.