The Human Phenotype Ontology in 2017.

Deep phenotyping has been defined as the precise and comprehensive analysis of phenotypic abnormalities in which the individual components of the phenotype are observed and described. The three components of the Human Phenotype Ontology (HPO; www.human-phenotype-ontology.org) project are the phenotype vocabulary, disease-phenotype annotations and the algorithms that operate on these. These components are being used for computational deep phenotyping and precision medicine as well as integration of clinical data into translational research. The HPO is being increasingly adopted as a standard for phenotypic abnormalities by diverse groups such as international rare disease organizations, registries, clinical labs, biomedical resources, and clinical software tools and will thereby contribute toward nascent efforts at global data exchange for identifying disease etiologies. This update article reviews the progress of the HPO project since the debut Nucleic Acids Research database article in 2014, including specific areas of expansion such as common (complex) disease, new algorithms for phenotype driven genomic discovery and diagnostics, integration of cross-species mapping efforts with the Mammalian Phenotype Ontology, an improved quality control pipeline, and the addition of patient-friendly terminology

TCP performance re-visited

Detailed measurements and analyses for the Linux-2.4 TCP stack on current adapters and processors are presented. We describe the impact of CPU scaling and memory bus loading on TCP performance. As CPU speeds outstrip I/O and memory speeds, many generally accepted notions of TCP performance begin to unravel. In-depth examinations and explanations of previously held TCP performance truths are provided, and we expose cases where these assumptions and rules of thumb no longer hold in modernday implementations. We conclude that unless major architectural changes are adopted, we would be hard-pressed to continue relying on the 1GHz/1Gbps rule of thumb.