Sub-atomic resolution X-ray crystallography and neutron crystallography: promise, challenges and potential

The International Year of Crystallography saw the number of macromolecular structures deposited in the Protein Data Bank cross the 100000 mark, with more than 90000 of these provided by X-ray crystallography. The number of X-ray structures determined to sub-atomic resolution (i.e. ≤1 Å) has passed 600 and this is likely to continue to grow rapidly with diffraction-limited synchrotron radiation sources such as MAX-IV (Sweden) and Sirius (Brazil) under construction. A dozen X-ray structures have been deposited to ultra-high resolution (i.e. ≤0.7 Å), for which precise electron density can be exploited to obtain charge density and provide information on the bonding character of catalytic or electron transfer sites. Although the development of neutron macromolecular crystallography over the years has been far less pronounced, and its application much less widespread, the availability of new and improved instrumentation, combined with dedicated deuteration facilities, are beginning to transform the field. Of the 83 macromolecular structures deposited with neutron diffraction data, more than half (49/83, 59%) were released since 2010. Sub-mm3 crystals are now regularly being used for data collection, structures have been determined to atomic resolution for a few small proteins, and much larger unit-cell systems (cell edges >100 Å) are being successfully studied. While some details relating to H-atom positions are tractable with X-ray crystallography at sub-atomic resolution, the mobility of certain H atoms precludes them from being located. In addition, highly polarized H atoms and protons (H+) remain invisible with X-rays. Moreover, the majority of X-ray structures are determined from cryo-cooled crystals at 100 K, and, although radiation damage can be strongly controlled, especially since the advent of shutterless fast detectors, and by using limited doses and crystal translation at micro-focus beams, radiation damage can still take place. Neutron crystallography therefore remains the only approach where diffraction data can be collected at room temperature without radiation damage issues and the only approach to locate mobile or highly polarized H atoms and protons. Here a review of the current status of sub-atomic X-ray and neutron macromolecular crystallography is given and future prospects for combined approaches are outlined. New results from two metalloproteins, copper nitrite reductase and cytochrome c′, are also included, which illustrate the type of information that can be obtained from sub-atomic-resolution (∼0.8 Å) X-ray structures, while also highlighting the need for complementary neutron studies that can provide details of H atoms not provided by X-ray crystallography

Predicting Securities Fraud Settlements and Amounts: A Hierarchical Bayesian Model of Federal Securities Class Action Lawsuits

This paper develops models that predict the incidence and amount of settlements for federal class action securities fraud litigation in the post-PLSRA period. We build hierarchical Bayesian models using data which comes principally from Risk metrics and identify several important predictors of settlement incidence (e.g., the number of different types of securities associated with a case, the company return during the class period) and settlement amount (e.g., market capitalization, measures of newsworthiness). Our models allow us to estimate how the circuit court a case is filed in as well as the industry of the plaintiff firm associate with settlement outcomes. They also allow us to accurately assess the variance of individual case outcomes revealing substantial amounts of heterogeneity in variance across cases

Predicting Securities Fraud Settlements and Amounts: A Hierarchical Bayesian Model of Federal Securities Class Action Lawsuits

This article develops models that predict the incidence and amount of settlements for federal class action securities fraud litigation in the post-PLSRA period. We build hierarchical Bayesian models using data that come principally from Riskmetrics and identify several important predictors of settlement incidence (e.g., the number of different types of securities associated with a case, the company return during the class period) and settlement amount (e.g., market capitalization, measures of newsworthiness). Our models also allow us to estimate how the circuit court a case is filed in as well as the industry of the plaintiff firm associate with settlement outcomes. Finally, they allow us to accurately assess the variance of individual case outcomes revealing substantial amounts of heterogeneity in variance across cases

Predicting Securities Fraud Settlements and Amounts: A Hierarchical Bayesian Model of Federal Securities Class Action Lawsuits

This article develops models that predict the incidence and amount of settlements for federal class action securities fraud litigation in the post-PLSRA period. We build hierarchical Bayesian models using data that come principally from Riskmetrics and identify several important predictors of settlement incidence (e.g., the number of different types of securities associated with a case, the company return during the class period) and settlement amount (e.g., market capitalization, measures of newsworthiness). Our models also allow us to estimate how the circuit court a case is filed in as well as the industry of the plaintiff firm associate with settlement outcomes. Finally, they allow us to accurately assess the variance of individual case outcomes revealing substantial amounts of heterogeneity in variance across cases

An Improved Algorithm for Generating Database Transactions from Relational Algebra Specifications

Alloy is a lightweight modeling formalism based on relational algebra. In prior work with Fisler, Giannakopoulos, Krishnamurthi, and Yoo, we have presented a tool, Alchemy, that compiles Alloy specifications into implementations that execute against persistent databases. The foundation of Alchemy is an algorithm for rewriting relational algebra formulas into code for database transactions. In this paper we report on recent progress in improving the robustness and efficiency of this transformation

OPTIMAX 2015 : multicultural team-based research in radiography, a holistic educational approach.

Following the successful OPTIMAX summer school held in Salford, 2013 and Lisbon, 2014 we organized OPTIMAX2015 summer school in Groningen. Fifty three people participated, comprising PhD, MSc and BSc students as well as tutors from the five European partners. Professional mix was drawn from engineering, medical physics/ physics and radiography. This summer school was hosted by the Hanze University of Applied Sciences Groningen in the Netherlands. It was funded by the partners. Two students from South Africa were invited by the Hanze University and one additional student from the United Kingdom who was funded by Nuffield. The summer school comprised of lectures and group work in which experimental research projects were conducted in five teams. Team project focus varied, two concentrating on CT reconstruction techniques and image quality, one on image quality high and low noise levels on DR systems, one on reliability and validity of detecting low dose radiation when using radiation detection applications and devices for smartphones. And one about ultrasound validity and reliability measuring rectus femoris muscle size. The summer school culminated in a poster market and conference, in which each team presented a poster and oral presentation on the conference. This book contains two parts, the first six chapters of this book shows the structure of organizing a summer school like OPTIMAX. The second part contains the oral papers in written format, in journal article style, and after editing they have been included within this book. At the time editing this book, several of the experimental papers has been commenced development work in order to make them fit for submission to conferences

Predicting Securities Fraud Settlements and Amounts: A Hierarchical Bayesian Model of Federal Securities Class Action Lawsuitsj els_1260 482..510

This article develops models that predict the incidence and amount of settlements for federal class action securities fraud litigation in the post-PLSRA period. We build hierarchical Bayesian models using data that come principally from Riskmetrics and identify several important predictors of settlement incidence (e.g., the number of different types of securities associated with a case, the company return during the class period) and settlement amount (e.g., market capitalization, measures of newsworthiness). Our models also allow us to estimate how the circuit court a case is filed in as well as the industry of the plaintiff firm associate with settlement outcomes. Finally, they allow us to accurately assess the variance of individual case outcomes revealing substantial amounts of heterogeneity in variance across cases

Perdeuterated GbpA Enables Neutron Scattering Experiments of a Lytic Polysaccharide Monooxygenase

publishedVersio