Henry Stoddard and Joseph Crane to George C. Thomas, August 29, 1834

Henry Stoddard and Joseph Crane wrote from Dayton, OH to George C. Thomas, address not included. They sent a copy of the deed for land in Ohio owned by the late Susan Ursin Niemcewicz. People Included: M.D. Pettibonehttps://digitalcommons.kean.edu/lhc_1830s/1094/thumbnail.jp

Wireless Emergency Alerts: Trust Model Simulations

Trust is a key factor in the effectiveness of the Wireless Emergency Alerts (WEA) service. Alert originators must trust WEA to deliver alerts to the public in an accurate and timely manner. Members of the public must also trust the WEA service before they will act on the alerts that they receive. This research aimed to develop a trust model to enable the Federal Emergency Management Agency to maximize the effectiveness of WEA and provide guidance for alert originators that would support them in using WEA in a manner that maximizes public safety. This report overviews the public trust model and the alert originator trust model. The research method included Bayesian belief networks (BBNs) to model trust in WEA because they enable reasoning about and modeling of uncertainty. The report details the procedures used to run simulations on the trust models. For each trust model, single-factor, multifactor, random-input, and special-case simulations were run on each factor and group of factors investigated. The analysis of the simulations had two goals: to identify those simulations that predicted the highest levels of trust and those simulations that predicted the lowest levels of trust. This report includes the results for each trust Model

Gerrymandering in Redlands, California

In April 2016, the City of Redlands, California, began the process of creating council electoral districts to comply with voting rights acts and avoid litigation. Several maps prepared by a consultant were being considered when, in May 2017, a group of graduate students from the University of Redlands produced this map. The Council agreed that this map was as good as or better than the consultant’s maps, but it was too late to consider a new map giving the legislative requirements. It will be considered after the 2018 elections

Wireless Emergency Alerts: Trust Model Technical Report

Trust is a key factor in the effectiveness of the Wireless Emergency Alerts (WEA) service. Alert originators (AOs) must trust WEA to deliver alerts to the public in an accurate and timely manner. Members of the public must also trust the WEA service before they will act on the alerts that they receive. This research aimed to develop a trust model to enable the Federal Emergency Management Agency (FEMA) to maximize the effectiveness of WEA and provide guidance for AOs that would support them in using WEA in a manner that maximizes public safety. The research method included Bayesian belief networks to model trust in WEA because they enable reasoning about and modeling of uncertainty. The research approach was to build models that could predict the levels of AO trust and public trust in specific scenarios, validate these models using data collected from AOs and the public, and execute simulations on these models for numerous scenarios to identify recommendations to AOs and FEMA for actions to take that increase trust and actions to avoid that decrease trust. This report describes the process used to develop and validate the trust models and the resulting structure and functionality of the models

Controlling long-term SARS-CoV-2 infections is important for slowing viral evolution

The rapid emergence and expansion of novel SARS-CoV-2 variants is an unpleasant surprise that threatens our ability to achieve herd immunity for COVID-19. These fitter SARS-CoV-2 variants often harbor multiple point mutations, conferring one or more traits that provide an evolutionary advantage, such as increased transmissibility, immune evasion and longer infection duration. In a number of cases, variant emergence has been linked to long-term infections in individuals who were either immunocompromised or treated with convalescent plasma. In this paper, we explore the mechanism by which fitter variants of SARS-CoV-2 arise during long-term infections using a mathematical model of viral evolution and identify means by which this evolution can be slowed. While viral load and infection duration play a strong role in favoring the emergence of such variants, the overall probability of emergence and subsequent transmission from any given infection is low, suggesting that viral variant emergence and establishment is a product of random chance. To the extent that luck plays a role in favoring the emergence of novel viral variants with an evolutionary advantage, targeting these low-probability random events might allow us to tip the balance of fortune away from these advantageous variants and prevent them from being established in the population.https://www.nature.com/articles/s41598-021-02148-8First author draf

Description of Supported Employment Practices, Cross-System Partnerships, and Funding Models of Four Types of State Agencies and Community Rehabilitation Providers

In 2005, the National Institute on Disability and Rehabilitation Research (NIDRR) awarded the VR Rehabilitation Research and Training Center (RRTC) to the Institute for Community Inclusion (ICI) at the University of Massachusetts Boston and its partners, InfoUse in Berkeley, California and the Center for the Advancement and Study of Disability Policy. In 2010, NIDRR provided supplemental funds to the ICI so that the VR-RRTC could include a focus on the provision of supported employment (SE) services. This SE research would focus on vocational rehabilitation (VR)agency partnerships with other state entities, and sources and models for long-term funding (extended services). The design called for embedding supported employment questions in ongoing surveys of multiple state agencies and case studies of SE coordination and funding models in several states to illuminate issues identified through these surveys. Accordingly, the ICI included an SE module into ongoing surveys of four state agencies known to deliver public employment services to people with disabilities. These surveys and the response rates achieved for each included: a) the state VR agency (89 percent); b) the state intellectual and developmental disability (IDD) agency (82 percent); c) the state mental health (MH)agency (55 percent); and d) the state welfare agency (47 percent).1 The SE supplement also included additional analysis of data obtained from an ongoing survey of community rehabilitation programs (CRPs) relevant to supported employment (37 percent response rate). The ICI then conducted case studies of SE partnerships in five states. These case studies were designed to help us better understand the range of practices that VR systems might use to ensure more successful transitions to long-term support through other resources. This report presents the findings from the SE supplement, the central focus of which was to identify the role and impact of VR agencies within the larger SE delivery system. The remainder of this executive summary highlights the findings in three areas the supplement was designed to address: providers of SE services, SE partnerships, and SE funding

Controlling long-term SARS-CoV-2 infections can slow viral evolution and reduce the risk of treatment failure.

The rapid emergence and expansion of novel SARS-CoV-2 variants threatens our ability to achieve herd immunity for COVID-19. These novel SARS-CoV-2 variants often harbor multiple point mutations, conferring one or more evolutionarily advantageous traits, such as increased transmissibility, immune evasion and longer infection duration. In a number of cases, variant emergence has been linked to long-term infections in individuals who were either immunocompromised or treated with convalescent plasma. In this paper, we used a stochastic evolutionary modeling framework to explore the emergence of fitter variants of SARS-CoV-2 during long-term infections. We found that increased viral load and infection duration favor emergence of such variants. While the overall probability of emergence and subsequent transmission from any given infection is low, on a population level these events occur fairly frequently. Targeting these low-probability stochastic events that lead to the establishment of novel advantageous viral variants might allow us to slow the rate at which they emerge in the patient population, and prevent them from spreading deterministically due to natural selection. Our work thus suggests practical ways to achieve control of long-term SARS-CoV-2 infections, which will be critical for slowing the rate of viral evolution.DGE-1762114 - National Science FoundationPublished versio

Computational identification of antibody-binding epitopes from mimotope datasets

Introduction: A fundamental challenge in computational vaccinology is that most B-cell epitopes are conformational and therefore hard to predict from sequence alone. Another significant challenge is that a great deal of the amino acid sequence of a viral surface protein might not in fact be antigenic. Thus, identifying the regions of a protein that are most promising for vaccine design based on the degree of surface exposure may not lead to a clinically relevant immune response.Methods: Linear peptides selected by phage display experiments that have high affinity to the monoclonal antibody of interest (“mimotopes”) usually have similar physicochemical properties to the antigen epitope corresponding to that antibody. The sequences of these linear peptides can be used to find possible epitopes on the surface of the antigen structure or a homology model of the antigen in the absence of an antigen-antibody complex structure.Results and Discussion: Herein we describe two novel methods for mapping mimotopes to epitopes. The first is a novel algorithm named MimoTree that allows for gaps in the mimotopes and epitopes on the antigen. More specifically, a mimotope may have a gap that does not match to the epitope to allow it to adopt a conformation relevant for binding to an antibody, and residues may similarly be discontinuous in conformational epitopes. MimoTree is a fully automated epitope detection algorithm suitable for the identification of conformational as well as linear epitopes. The second is an ensemble approach, which combines the prediction results from MimoTree and two existing methods