Acdc - automated contamination detection and confidence estimation for single-cell genome data

A major obstacle in single-cell sequencing is sample contamination with foreign DNA. To guarantee clean genome assemblies and to prevent the introduction of contamination into public databases, considerable quality control efforts are put into post-sequencing analysis. Contamination screening generally relies on reference-based methods such as database alignment or marker gene search, which limits the set of detectable contaminants to organisms with closely related reference species. As genomic coverage in the tree of life is highly fragmented, there is an urgent need for a reference-free methodology for contaminant identification in sequence data.We present acdc, a tool specifically developed to aid the quality control process of genomic sequence data. By combining supervised and unsupervised methods, it reliably detects both known and de novo contaminants. First, 16S rRNA gene prediction and the inclusion of ultrafast exact alignment techniques allow sequence classification using existing knowledge from databases. Second, reference-free inspection is enabled by the use of state-of-the-art machine learning techniques that include fast, non-linear dimensionality reduction of oligonucleotide signatures and subsequent clustering algorithms that automatically estimate the number of clusters. The latter also enables the removal of any contaminant, yielding a clean sample. Furthermore, given the data complexity and the ill-posedness of clustering, acdc employs bootstrapping techniques to provide statistically profound confidence values. Tested on a large number of samples from diverse sequencing projects, our software is able to quickly and accurately identify contamination. Results are displayed in an interactive user interface. Acdc can be run from the web as well as a dedicated command line application, which allows easy integration into large sequencing project analysis workflows.Acdc can reliably detect contamination in single-cell genome data. In addition to database-driven detection, it complements existing tools by its unsupervised techniques, which allow for the detection of de novo contaminants. Our contribution has the potential to drastically reduce the amount of resources put into these processes, particularly in the context of limited availability of reference species. As single-cell genome data continues to grow rapidly, acdc adds to the toolkit of crucial quality assurance tools

A National Neuro-Ophthalmology Consensus Statement on the Terminology for Automated Visual Field Abnormalities

A multitude of terms have been used to describe automated visual field abnormalities. To date, there is no universally accepted system of definitions. Variability among clinicians creates the risk of miscommunication and the compromise of patient care. The purposes of this study were to 1) assess the degree of consistency among the nation's neuro-ophthalmologists in the description of visual field abnormalities, and 2) to create a national consensus statement with standardized terminology and definition

Economic Aspects of Food Waste-to-Energy System Deployment

Economic barriers are major considerations for the successful implementation and deployment of food waste-to-energy systems, along with technical, operational, and management aspects. For cities, municipalities, and medium and small food-related businesses in urban and semiurban areas that currently landfill the majority of food scraps and food-related waste, it would require the involvement of multiple stakeholders such as municipal decision makers, haulers, investors, and business managers to overcome challenges on costs, financing, revenue streams, and end-market development for energy and by-products. A project-specific financial feasibility analysis should incorporate a budget of all expenditures, including start-up capital costs and operating expenses, revenues, sources of funds, loan and interest repayments on borrowed capital, and investor income and disbursements. Anaerobic digesters, often suitable for high moisture-content organic wastes, have been commercially successful for food waste-to-energy applications and have potential for wider deployment for municipal and business waste generators. Digester systems codigesting food waste with other organic waste, such as livestock manure or wastewater sludge, are operational in large and medium scales at single-site livestock farms, centralized locations for multiple farms, and wastewater treatment plants or water resource recovery facilities. Systems operating solely on mixed food waste at centralized locations and at large food waste generators are increasing