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

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

Self-organization of the in vitro attached human embryo

Implantation of the blastocyst is a developmental milestone in mammalian embryonic development. At this time, a coordinated program of lineage diversification, cell-fate specification, and morphogenetic movements establishes the generation of extra-embryonic tissues and the embryo proper, and determines the conditions for successful pregnancy and gastrulation. Despite its basic and clinical importance, this process remains mysterious in humans. Here we report the use of a novel in vitro system1,2 to study the post-implantation development of the human embryo. We unveil the self-organizing abilities and autonomy of in vitro attached human embryos. We find human-specific molecular signatures of early cell lineage, timing, and architecture. Embryos display key landmarks of normal development, including epiblast expansion, lineage segregation, bi-laminar disc formation, amniotic and yolk sac cavitation, and trophoblast diversification. Our findings highlight the species-specificity of these developmental events and provide a new understanding of early human embryonic development beyond the blastocyst stage. In addition, our study establishes a new model system relevant to early human pregnancy loss. Finally, our work will also assist in the rational design of differentiation protocols of human embryonic stem cells to specific cell types for disease modelling and cell replacement therapy