The Trilogy of Personal Jurisdiction and the Importance of \u3cem\u3eFord\u3c/em\u3e

Litigants and judges alike have struggled to understand and resolve the parameters of personal jurisdiction, particularly in product liability cases. This results in significant costs and time which is likely to be of little benefit to anyone. Much of this confusion arises from two problems: (1) most of the early Supreme Court decisions on personal jurisdiction arose from contractual disputes; and (2) when the economy expanded after World War II, and new automobiles, commercial aircraft, appliances, and other complex products appeared, the Court’s attempts to resolve personal jurisdiction issues were unsuccessful. For over three decades, the Supreme Court failed to produce a clear majority opinion, while at the same time, these cases were becoming more common and complex. In the past decade, however, the Court has quietly produced a trilogy of virtually unanimous opinions that offer pathways to resolve personal jurisdiction disputes. These decisions will be particularly useful in product liability cases of all kinds, which often involve suit-related events occurring across multiple jurisdictions. Once lawyers and judges understand this clarified framework, it should become easier for plaintiffs to make better decisions about where to bring their case and enable both plaintiffs and defendants to spend less time and expense litigating personal jurisdiction disputes

Mobile command posts - addressing multi-jurisdictional response to major events

Discusses the benefits of having a mobile command post if it is well planned

Mouse obesity network reconstruction with a variational Bayes algorithm to employ aggressive false positive control

<p>Abstract</p> <p>Background</p> <p>We propose a novel variational Bayes network reconstruction algorithm to extract the most relevant disease factors from high-throughput genomic data-sets. Our algorithm is the only scalable method for regularized network recovery that employs Bayesian model averaging and that can internally estimate an appropriate level of sparsity to ensure few false positives enter the model without the need for cross-validation or a model selection criterion. We use our algorithm to characterize the effect of genetic markers and liver gene expression traits on mouse obesity related phenotypes, including weight, cholesterol, glucose, and free fatty acid levels, in an experiment previously used for discovery and validation of network connections: an F2 intercross between the C57BL/6 J and C3H/HeJ mouse strains, where apolipoprotein E is null on the background.</p> <p>Results</p> <p>We identified eleven genes, Gch1, Zfp69, Dlgap1, Gna14, Yy1, Gabarapl1, Folr2, Fdft1, Cnr2, Slc24a3, and Ccl19, and a quantitative trait locus directly connected to weight, glucose, cholesterol, or free fatty acid levels in our network. None of these genes were identified by other network analyses of this mouse intercross data-set, but all have been previously associated with obesity or related pathologies in independent studies. In addition, through both simulations and data analysis we demonstrate that our algorithm achieves superior performance in terms of power and type I error control than other network recovery algorithms that use the lasso and have bounds on type I error control.</p> <p>Conclusions</p> <p>Our final network contains 118 previously associated and novel genes affecting weight, cholesterol, glucose, and free fatty acid levels that are excellent obesity risk candidates.</p