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PER FROST HENRIKSEN SOFTWARE
Merging or scaling software (Read, 1999 ). This allows them to be identified as outliers and rejected by standard diffraction-frame Will be larger than that predicted from Wilson statistics (Blessing, 1997 ). Measured intensity will be larger than those of symmetry-related reflections and/or When such a peak overlaps with a protein diffraction peak, the Often the case with frost), ice diffraction may manifest as discrete, isolated iceĭiffraction peaks. When only a small number of large ice crystals are present in the X-ray beam (as is Frost is typicallyĬomprised of an even smaller number of larger dendritic crystals. Producing `lumpy', anisotropic, quasi-continuous diffraction rings.
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Ice that forms from bulk-like solvent containing littleĬryoprotectant or that is cooled slowly tends to be comprised of fewer, larger crystals, That is rapidly cooled is typically highly polycrystalline, producing continuous and Ice that forms from solvent confined to the solvent cavities of the crystal, fromīulk-like solvent containing substantial cryoprotectant or from bulk-like solvent Ice may also appear as contaminating frost on the sample or sample-holder surface,ĭue to exposure to moist ambient air during handling or data collection, or from accumulatedįrost in the liquid nitrogen used to initially cool and to store the crystals (Pflugrath,Ģ004 ). In the crystal (Moreau et al., 2019 ) or in residual solvent on the crystal surface (Garman & Mitchell, 1996 ). Ice may form during crystal cooling in solvent present within solvent cavities Ice diffraction frequently contaminates diffraction data collected from biomolecularĬrystals at cryogenic temperatures (Rupp, 2009 Pflugrath, 2015 ). Positions at which structure-factor perturbations are observed, it is found that roughlyĢ5% of crystals exhibit ice with primarily hexagonal character, indicating that inadequateĬooling rates and/or cryoprotectant concentrations were used, while the remainingħ5% show ice with a stacking-disordered or cubic character.
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Solvent content and maximum solvent-cavity size. Sample of 89 827 PDB entries collected at cryogenic temperatures indicates that roughlyġ6% show evidence of ice contamination, and that this fraction increases with increasing Metric and improved algorithms, an analysis of structure-factor data from a random Resource for Reproducibility in Macromolecular Crystallography. Work of Thorn and coworkers, a revised metric is defined for detecting ice from deposited protein structure-factorĭata, and this metric is validated using full-frame diffraction data from the Integrated Increasing cryoprotectant concentration and increasing cooling rate. Mixture of hexagonal and cubic planes, with the cubic plane fraction increasing with Ice formed within solvent cavities during rapid cooling is comprised of a stacking-disordered Analysis of ice diffraction from crystals of three proteins shows that the Different solutions to all identified challenges are presented addressing technologies such as machine learning, middleware platforms, or intelligent data management.Diffraction data acquired from cryocooled protein crystals often include diffractionįrom ice. Challenges such as affordability, device power consumption, network latency, Big Data analysis, data privacy and security, among others, have been identified by the articles reviewed and are discussed in detail. During the implementation, different challenges are encountered, and here interoperability is a key major hurdle throughout all the layers in the architecture of an Internet of Things system, which can be addressed by shared standards and protocols. Current issues such as smart phones, intelligent management of Wireless Sensor Networks, middleware platforms, integrated Farm Management Information Systems across the supply chain, or autonomous vehicles and robotics stand out because of their potential to lead arable farming to smart arable farming. Lastly, it presents some future directions for the Internet of Things in arable farming. It provides an outline of the current and potential applications, and discusses the challenges and possible solutions and implementations. The review contributes an overview of the state of the art of technologies deployed. This review is addressing an analytical survey of the current and potential application of Internet of Things in arable farming, where spatial data, highly varying environments, task diversity and mobile devices pose unique challenges to be overcome compared to other agricultural systems. The Internet of Things is allowing agriculture, here specifically arable farming, to become data-driven, leading to more timely and cost-effective production and management of farms, and at the same time reducing their environmental impact.