The run.

The Run is a novella-length creative work that follows Dust, a Marine Corps Reservist who performs military funeral services all across Nebraska. In his civilian life, Dust is an English teaching assistant at Nebraska Technical University in Bellevue, here he is having a love affair with Inopine?üe, a married coworker. Dust's mounting disillusionment with the military combines with the stress of his ongoing assignations with Inopine?üe, whose emotionally-abusive and jealous husband, Dave, has begun to suspect the truth. Dust is accustomed to escaping these pressures by running the country roads outside of Bellevue, but when the forest through which he runs seems to grow strangely responsive to his presence, the various murky threads of Dust's life come together in a pattern he can neither decipher nor escape

Gradient Enhanced Surrogate Modeling Methods for NDE Applications

Over the past 15 years, there has been significant interest in the NDE community in surrogate modeling applied to both uncertainty propagation (UP) as well as inverse uncertainty quantification (UQ). There has been a general acceptance in the value of surrogates due to the quick model evaluations that can be made during an inverse problem or for fast Monte Carlo sampling, evidenced by the fact that surrogate modeling techniques now appear in multiple commercial NDE simulation tools. Many techniques have been explored such as regular grid methods, polynomial chaos, sparse grids, response surfaces, and Kriging models, among others. While these techniques all offer reduced computational burden for UP and inverse applications, they are still somewhat computationally expensive and require a significant amount of model evaluations. To overcome this, many other communities have adopted a gradient-enhanced approach to surrogate modeling. In many cases, when sensitivities (i.e. gradients with respect to parameters) are included in building a surrogatemodel, convergence of the surrogate can be greatly enhanced. In this presentation, several different gradient-enhanced methods will be presented as applied to NDE models. The convergence of the surrogates will be shown relative to the non- gradient-enhanced surrogate models, and the surrogates will be applied to both UP and inverse problems

Session 1: Innovation in Legal Services

This panel featured two “disrupters” who detailed their experiences innovating in the legal services space. The first panelist spoke about data-driven regulatory reform and the other spoke as an entrepreneur whose product introduces artificial intelligence (AI) into the legal recruiting process. Two additional panelists provided commentary regarding the second panelist’s presentation. The panel provided insight on the topics of: (1) the legal regulatory process at large; (2) how a data-driven and feedback-oriented sandbox provides an alternative regulatory process; (3) the legal hiring and recruiting process and (4) how AI allows law firms to consider alternative hiring metrics when assessing candidates and determining the likelihood of a candidate’s success at the firm. During the commentary, a client of the platform spoke about his experiences with the platform. An expert in legal hiring who has worked both on the academic side as well as with firms, asked additional questions about the platform. In the Q&A portions of each presentation, the panelists addressed questions from symposium organizers and attendees regrading equity and validity of their proposed “disruptions.

Differential regulation of the SMN2 gene by individual HDAC proteins

Spinal muscular atrophy (SMA) is an autosomal recessive neurodegenerative disorder that is the leading genetic cause of infantile death. SMA is caused by homozygous deletion or mutation of the survival of motor neuron 1 gene (SMN1). The SMN2 gene is nearly identical to SMN1, however is alternatively spliced. The close relationship to SMN1 results in SMN2 being a very power genetic modifier of SMA disease severity and a target for therapies. We sought to identify the regulatory role individual HDAC proteins use to control expression of full length protein from the SMN2 genes. We used quantitative PCR to determine the effects shRNA silencing of individual HDACs on the steady state levels of a SMN2-luciferase reporter transcripts. We determined that reduction of individual HDAC proteins was sufficient to increase SMN protein levels in a transgenic reporter system. Knockdown of class I HDAC proteins preferentially activated the reporter by increased promoter transcription. Silencing of class II HDAC proteins maintained transcriptional activity; however silencing of HDAC 5 and 6 also appeared to enhance inclusion of an alternatively spliced exon. This work highlights HDAC proteins 2 and 6 as excellent investigative targets. These data are important to the basic understanding of SMN expression regulation and the refinements of current therapeutic compounds as well as the development of novel SMA therapeutics

The Law and Policy of People Analytics

Leading technology companies such as Google and Facebook have been experimenting with people analytics, a new data-driven approach to human resources management. People analytics is just one example of the new phenomenon of “big data,” in which analyses of huge sets of quantitative information are used to guide decisions. Applying big data to the workplace could lead to more effective outcomes, as in the Moneyball example, where the Oakland Athletics baseball franchise used statistics to assemble a winning team on a shoestring budget. Data may help firms determine which candidates to hire, how to help workers improve job performance, and how to predict when an employee might quit or should be fired. Despite being a nascent field, people analytics is already sweeping corporate America. Although cutting-edge businesses and academics have touted the possibilities of people analytics, the legal and ethical implications of these new technologies and practices have largely gone unexamined. This Article provides a comprehensive overview of people analytics from a law and policy perspective. We begin by exploring the history of prediction and data collection at work, including psychological and skills testing, and then turn to new techniques like data mining. From that background, we examine the new ways that technology is shaping methods of data collection, including innovative computer games as well as ID badges that record worker locations and the duration and intensity of conversations. The Article then discusses the legal implications of people analytics, focusing on workplace privacy and employment discrimination law. Our article ends with a call for additional disclosure and transparency regarding what information is being collected, how it should be handled, and how the information is used. While people analytics holds great promise, that promise can only be fulfilled if employees participate in the process, understand the nature of the metrics, and retain their identity and autonomy in the face of the data’s many narratives

The Law and Policy of People Analytics

(Excerpt) Recently, leading technology companies such as Google and IBM have started experimenting with people analytics, a new data-driven approach to human resources management. People analytics is just one example of the phenomenon of big data, in which analyses of huge sets of quantitative information are used to guide a variety of decisions. Applying big data to workplace situations could lead to more effective work outcomes, as in Moneyball, where the Oakland A\u27s baseball franchise used statistics to assemble a winning team on a shoestring budget. People analytics is the name given to this new approach to personnel management on a wider scale. Although people analytics is a nascent field, its implementation could transform the ways that employers approach HR decisions. Data may help firms determine which candidates to hire, how to help workers improve job performance, and how to predict when an employee might quit or should be fired. In addition, people analytics could provide insights on more quotidian issues like location of the employee offices and use of break times. The data that drives these decisions may be collected in new ways: through the use of innovative computer games, software that monitors employee electronic communications and activities, and devices such as ID badges that record worker locations and the tone of conversations. Data may also be collected from sources outside the employer which have been gathered for different purposes, like real estate records, or for undefined purposes, like Google searches. While people analytics has great potential, no one has yet comprehensively analyzed the employment law or business ethics implications of these new technologies or practices. To date, most of the discussion centers on the uses for the data, not on its effects or its interactions with the law of the workplace. This Article seeks to survey these effects and interactions. Part I provides an overview, reviewing the history of employment testing, defining data mining, and describing the most current trends in people analytics. Part II describes the use of computer games and other technology to gather information. Part III examines the implications of people analytics on workplace privacy norms and laws. Part IV discusses the impact on equal-opportunity norms; while more and better information should lead to more merit-based decisions, disparate impact or unconscious bias could still operate to harm already-marginalized workers. Part V concludes with normative observations and preliminary policy notes. As the field of people analytics continues to develop, we must keep the values of employee voice, transparency, and autonomy as guiding principles

The Law and Policy of People Analytics

Leading technology companies such as Google and Facebook have been experimenting with people analytics, a new data-driven approach to human resources management. People analytics is just one example of the new phenomenon of “big data,” in which analyses of huge sets of quantitative information are used to guide decisions. Applying big data to the workplace could lead to more effective outcomes, as in the Moneyball example, where the Oakland Athletics baseball franchise used statistics to assemble a winning team on a shoestring budget. Data may help firms determine which candidates to hire, how to help workers improve job performance, and how to predict when an employee might quit or should be fired. Despite being a nascent field, people analytics is already sweeping corporate America. Although cutting-edge businesses and academics have touted the possibilities of people analytics, the legal and ethical implications of these new technologies and practices have largely gone unexamined. This Article provides a comprehensive overview of people analytics from a law and policy perspective. We begin by exploring the history of prediction and data collection at work, including psychological and skills testing, and then turn to new techniques like data mining. From that background, we examine the new ways that technology is shaping methods of data collection, including innovative computer games as well as ID badges that record worker locations and the duration and intensity of conversations. The Article then discusses the legal implications of people analytics, focusing on workplace privacy and employment discrimination law. Our article ends with a call for additional disclosure and transparency regarding what information is being collected, how it should be handled, and how the information is used. While people analytics holds great promise, that promise can only be fulfilled if employees participate in the process, understand the nature of the metrics, and retain their identity and autonomy in the face of the data’s many narratives

Reconstructing Cardiac Electrical Excitations from Optical Mapping Recordings

The reconstruction of electrical excitation patterns through the unobserved depth of the tissue is essential to realizing the potential of computational models in cardiac medicine. We have utilized experimental optical-mapping recordings of cardiac electrical excitation on the epicardial and endocardial surfaces of a canine ventricle as observations directing a local ensemble transform Kalman Filter (LETKF) data assimilation scheme. We demonstrate that the inclusion of explicit information about the stimulation protocol can marginally improve the confidence of the ensemble reconstruction and the reliability of the assimilation over time. Likewise, we consider the efficacy of stochastic modeling additions to the assimilation scheme in the context of experimentally derived observation sets. Approximation error is addressed at both the observation and modeling stages, through the uncertainty of observations and the specification of the model used in the assimilation ensemble. We find that perturbative modifications to the observations have marginal to deleterious effects on the accuracy and robustness of the state reconstruction. Further, we find that incorporating additional information from the observations into the model itself (in the case of stimulus and stochastic currents) has a marginal improvement on the reconstruction accuracy over a fully autonomous model, while complicating the model itself and thus introducing potential for new types of model error. That the inclusion of explicit modeling information has negligible to negative effects on the reconstruction implies the need for new avenues for optimization of data assimilation schemes applied to cardiac electrical excitation.Comment: main text: 18 pages, 10 figures; supplement: 5 pages, 9 figures, 2 movie

Redox Signaling in Colonial Hydroids: Many Pathways for Peroxide

Studies of mitochondrial redox signaling predict that the colonial hydroids Eirene viridula and Podocoryna carnea should respond to manipulations of reactive oxygen species (ROS). Both species encrust surfaces with feeding polyps connected by networks of stolons; P. carnea is more ‘sheet-like’ with closely spaced polyps and short stolons, while E. viridula is more ‘runner-like’ with widely spaced polyps and long stolons. Treatment with the chemical antioxidant vitamin C diminishes ROS in mitochondrion-rich epitheliomuscular cells (EMCs) and produces phenotypic effects (sheet-like growth) similar to uncouplers of oxidative phosphorylation. In peripheral stolon tips, treatment with vitamin C triggers a dramatic increase of ROS that is followed by tissue death and stolon regression. The enzymatic anti-oxidant catalase is probably not taken up by the colony but, rather, converts hydrogen peroxide in the medium to water and oxygen. Exogenous catalase does not affect ROS in mitochondrion-rich EMCs, but does increase the amounts of ROS emitted from peripheral stolons, resulting in rapid, runner-like growth. Treatment with exogenous hydrogen peroxide increases ROS levels in stolon tips and results in somewhat faster colony growth. Finally, untreated colonies of E. viridula exhibit higher levels of ROS in stolon tips than untreated colonies of P. carnea. ROS may participate in a number of putative signaling pathways: (1) high levels of ROS may trigger cell and tissue death in peripheral stolon tips; (2) more moderate levels of ROS in stolon tips may trigger outward growth, inhibit branching and, possibly, mediate the redox signaling of mitochondrion-rich EMCs; and (3) ROS may have an extra-colony function, perhaps in suppressing the growth of bacteria