Circular nutrient solutions for agriculture and wastewater : a review of technologies and practices

This paper summarizes key findings from a series of systematic reviews and comprehensive efforts to collate evidence and expert opinions on circular solutions for recovery and reuse of nutrients and carbon from different waste streams in the agriculture and wastewater sectors. We identify established and emerging approaches for transformation towards a more circular nutrient economy with relevance to SDGs 6 and 14. The paper cites the example of the Baltic Sea Region which has experienced decades of fertilizer overuse (1950s–1990s) and concomitant urban sources of excessive nutrients. Regulations and incentive policies combining the nitrogen, phosphorus and carbon cycles are necessary if circular nutrient technologies and practices are to be scaled up. Pricing chemical fertilizer at levels to reflect society’s call for circularity is a central challenge. Highlights • Development of a circular nutrient economy in the EU is reviewed. • The socio-economic value of organic waste products from agriculture & municipalities needs to increase. • Opportunities are found in the new EU Circular Economy Package & Fertilizing Products Regulations. • Further implementation is possible with the Common Agriculture Policy (nutrient management tool) and Waste Framework Directive for recycling. • The Baltic Sea Region case is explored being sensitive to eutrophication with ongoing international efforts to introduce nutrient circularity

Wndchrm – an open source utility for biological image analysis

<p>Abstract</p> <p>Background</p> <p>Biological imaging is an emerging field, covering a wide range of applications in biological and clinical research. However, while machinery for automated experimenting and data acquisition has been developing rapidly in the past years, automated image analysis often introduces a bottleneck in high content screening.</p> <p>Methods</p> <p><it>Wndchrm </it>is an open source utility for biological image analysis. The software works by first extracting image content descriptors from the raw image, image transforms, and compound image transforms. Then, the most informative features are selected, and the feature vector of each image is used for classification and similarity measurement.</p> <p>Results</p> <p><it>Wndchrm </it>has been tested using several publicly available biological datasets, and provided results which are favorably comparable to the performance of task-specific algorithms developed for these datasets. The simple user interface allows researchers who are not knowledgeable in computer vision methods and have no background in computer programming to apply image analysis to their data.</p> <p>Conclusion</p> <p>We suggest that <it>wndchrm </it>can be effectively used for a wide range of biological image analysis tasks. Using <it>wndchrm </it>can allow scientists to perform automated biological image analysis while avoiding the costly challenge of implementing computer vision and pattern recognition algorithms.</p