Optimization of the composition of crop collections for ex situ conservation

Many crop genetic resources collections have been established without a clearly defined conservation goal or mandate, which has resulted in collections of considerable size, unbalanced composition and high levels of duplication. Attempts to improve the composition of collections are hampered by the fact that conceptual views to optimize collection composition are very rare. An optimization strategy is proposed herein, which largely builds on the concepts of core collection and core selection. The proposed strategy relies on hierarchically structuring the crop gene pool and assigning a relative importance to each of its different components. Comparison of the resulting optimized distribution of the number of accessions with the actual distribution allows identification of under- and over-representation within a collection. Application of this strategy is illustrated by an example using potato. The proposed optimization strategy is applicable not only to individual genebanks, but also to consortia of cooperating genebanks, which makes it relevant for ongoing activities within projects that aim at sharing responsibilities among institutions on the basis of rational conservation, such as a European genebank integrated system and the global cacao genetic resources network CacaoNet

Virtual reality simulation for the optimization of endovascular procedures : current perspectives

Endovascular technologies are rapidly evolving, often - requiring coordination and cooperation between clinicians and technicians from diverse specialties. These multidisciplinary interactions lead to challenges that are reflected in the high rate of errors occurring during endovascular procedures. Endovascular virtual reality (VR) simulation has evolved from simple benchtop devices to full physic simulators with advanced haptics and dynamic imaging and physiological controls. The latest developments in this field include the use of fully immersive simulated hybrid angiosuites to train whole endovascular teams in crisis resource management and novel technologies that enable practitioners to build VR simulations based on patient-specific anatomy. As our understanding of the skills, both technical and nontechnical, required for optimal endovascular performance improves, the requisite tools for objective assessment of these skills are being developed and will further enable the use of VR simulation in the training and assessment of endovascular interventionalists and their entire teams. Simulation training that allows deliberate practice without danger to patients may be key to bridging the gap between new endovascular technology and improved patient outcomes

Single-shot compressed ultrafast photography: a review

Compressed ultrafast photography (CUP) is a burgeoning single-shot computational imaging technique that provides an imaging speed as high as 10 trillion frames per second and a sequence depth of up to a few hundred frames. This technique synergizes compressed sensing and the streak camera technique to capture nonrepeatable ultrafast transient events with a single shot. With recent unprecedented technical developments and extensions of this methodology, it has been widely used in ultrafast optical imaging and metrology, ultrafast electron diffraction and microscopy, and information security protection. We review the basic principles of CUP, its recent advances in data acquisition and image reconstruction, its fusions with other modalities, and its unique applications in multiple research fields

Big Data and the Internet of Things

Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this is resulting in an ecosystem of highly interconnected devices referred to as the Internet of Things - IoT. In conjunction, the advances in machine learning have allowed building models on this ever increasing amounts of data. Consequently, devices all the way from heavy assets such as aircraft engines to wearables such as health monitors can all now not only generate massive amounts of data but can draw back on aggregate analytics to "improve" their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski (eds.) Big Data Analysis: New algorithms for a new society, Springer Series on Studies in Big Data, to appea