Can I, or Should I? Science and Ethics

Purpose: Students will engage in a group discussion about the important relationship that exists between scientific advancement and ethical considerations. Optionally, students learn about an amusing historical example of ethics going unchecked in the face of scientific progress, with what could have been glaringly obvious and disastrous effects, as an illustration of why such advancements must be checked by ethics

Choose Your Own Adventure : Fire Response!

Purpose: Present students with scenarios of various common fire emergencies and test their knowledge on the proper ways to respond to such emergencies, citing professionally published guidelines as resources

The Five Stages of Team Development

Purpose: Provide students an introduction to existing business/psychology teachings on group dynamics and the five distinct stages of team development likely to be experienced in any group setting

Noise is not error : detecting parametric heterogeneity between epidemiologic time series

© Copyright © 2018 Romero-Severson, Ribeiro and Castro. This is an open-accessarticle distributed under the terms of the Creative Commons Attribution License (CCBY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.Mathematical models play a central role in epidemiology. For example, models unify heterogeneous data into a single framework, suggest experimental designs, and generate hypotheses. Traditional methods based on deterministic assumptions, such as ordinary differential equations (ODE), have been successful in those scenarios. However, noise caused by random variations rather than true differences is an intrinsic feature of the cellular/molecular/social world. Time series data from patients (in the case of clinical science) or number of infections (in the case of epidemics) can vary due to both intrinsic differences or incidental fluctuations. The use of traditional fitting methods for ODEs applied to noisy problems implies that deviation from some trend can only be due to error or parametric heterogeneity, that is noise can be wrongly classified as parametric heterogeneity. This leads to unstable predictions and potentially misguided policies or research programs. In this paper, we quantify the ability of ODEs under different hypotheses (fixed or random effects) to capture individual differences in the underlying data. We explore a simple (exactly solvable) example displaying an initial exponential growth by comparing state-of-the-art stochastic fitting and traditional least squares approximations. We also provide a potential approach for determining the limitations and risks of traditional fitting methodologies. Finally, we discuss the implications of our results for the interpretation of data from the 2014-2015 Ebola epidemic in Africa.This work was funded by NIH grants R01-AI087520 and R01-AI104373; grants FIS2013-47949-C2-2-P and FIS2016-78883-C2-2-P and PRX 16/00287 (Spain); and PIRSES-GA-2012-317893 (7th FP, EU).info:eu-repo/semantics/publishedVersio

Noise Is Not Error: Detecting Parametric Heterogeneity Between Epidemiologic Time Series

Mathematical models play a central role in epidemiology. For example, models unify heterogeneous data into a single framework, suggest experimental designs, and generate hypotheses. Traditional methods based on deterministic assumptions, such as ordinary differential equations (ODE), have been successful in those scenarios. However, noise caused by random variations rather than true differences is an intrinsic feature of the cellular/molecular/social world. Time series data from patients (in the case of clinical science) or number of infections (in the case of epidemics) can vary due to both intrinsic differences or incidental fluctuations. The use of traditional fitting methods for ODEs applied to noisy problems implies that deviation from some trend can only be due to error or parametric heterogeneity, that is noise can be wrongly classified as parametric heterogeneity. This leads to unstable predictions and potentially misguided policies or research programs. In this paper, we quantify the ability of ODEs under different hypotheses (fixed or random effects) to capture individual differences in the underlying data. We explore a simple (exactly solvable) example displaying an initial exponential growth by comparing state-of-the-art stochastic fitting and traditional least squares approximations. We also provide a potential approach for determining the limitations and risks of traditional fitting methodologies. Finally, we discuss the implications of our results for the interpretation of data from the 2014-2015 Ebola epidemic in Africa

Clinton Street MAX Visioning

This report documents the initial analysis and visioning process performed in the area surrounding the Clinton Street Station, which is nestled between Hosford-Abernethy and Brooklyn neighborhoods and the Central Eastside Industrial District. This project focuses on the future of the Clinton Street Station and how its development will impact the surrounding area over the next 50 years. This task involved acknowledging and balancing the current needs of the various stakeholders. The purpose of this report is to act as a tool for the Hosford-Abernethy Neighborhood Association (HAND) to more adequately understand the opportunities and constraints that the future station area holds. In order to begin imagining the future of the area, a thorough understanding and analysis is presented. Following this, design principles that shape the vision for the future are described. The document concludes with next steps and implementation recommendations. This project was conducted under the supervision of Donald J. Stastny and Edward Starkie

Evaluation of different recall periods for the US National Cancer Institute’s Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE)

Aims—The U.S. National Cancer Institute recently developed the Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE). PRO-CTCAE is a library of questions for clinical trial participants to self-report symptomatic adverse events (e.g., nausea). The objective of this study is to inform evidence-based selection of a recall period when PRO-CTCAE is included in a trial. We evaluated differences between 1-week, 2-week, 3-week, and 4-week recall periods, using daily reporting as the reference. Methods—English-speaking patients with cancer receiving chemotherapy and/or radiotherapy were enrolled at four U.S. cancer centers and affiliated community clinics. Participants completed 27 PRO-CTCAE items electronically daily for 28 days, and then weekly over 4 weeks, using 1-week, 2-week, 3-week, and 4-week recall periods. For each recall period, mean differences, effect sizes, and intraclass correlation coefficients were calculated to evaluate agreement between the maximum of daily ratings and the corresponding ratings obtained using longer recall periods (e.g., maximum of daily scores over 7 days vs. 1-week recall). Analyses were repeated using the average of daily scores within each recall period rather than the maximum of daily scores. Results—127 subjects completed questionnaires (57% male; median age 57). The median of the 27 mean differences in scores on the PRO-CTCAE 5-point response scale comparing the maximum daily versus the longer recall period (and corresponding effect size), was −0.20 (−0.20) for 1-week recall; −0.36 (−0.31) for 2-week recall; −0.45 (−0.39) for 3-week recall; and −0.47 (−0.40) for 4-week recall. The median intraclass correlation across 27 items between the maximum of daily ratings and the corresponding longer recall ratings for 1-week recall was 0.70 (range: 0.54–0.82); 2-week recall: 0.74 (range: 0.58–0.83); 3-week recall: 0.72 (range: 0.61–0.84); and 4-week recall: 0.72 (range: 0.64–0.86). Similar results were observed for all analyses using the average of daily scores rather than the maximum of daily scores. Conclusions—1-week recall corresponds best to daily reporting. Although intraclass correlations remain stable over time, there are small but progressively larger differences between daily and longer recall periods at 2, 3, and 4 weeks, respectively. The preferred recall period for the PRO-CTCAE is the past 7 days, although investigators may opt for recall periods of 2, 3, or 4 weeks with an understanding that there may be some information loss

Teechain: a secure payment network with asynchronous blockchain access

Blockchains such as Bitcoin and Ethereum execute payment transactions securely, but their performance is limited by the need for global consensus. Payment networks overcome this limitation through off-chain transactions. Instead of writing to the blockchain for each transaction, they only settle the final payment balances with the underlying blockchain. When executing off-chain transactions in current payment networks, parties must access the blockchain within bounded time to detect misbehaving parties that deviate from the protocol. This opens a window for attacks in which a malicious party can steal funds by deliberately delaying other parties' blockchain access and prevents parties from using payment networks when disconnected from the blockchain. We present Teechain, the first layer-two payment network that executes off-chain transactions asynchronously with respect to the underlying blockchain. To prevent parties from misbehaving, Teechain uses treasuries, protected by hardware trusted execution environments (TEEs), to establish off-chain payment channels between parties. Treasuries maintain collateral funds and can exchange transactions efficiently and securely, without interacting with the underlying blockchain. To mitigate against treasury failures and to avoid having to trust all TEEs, Teechain replicates the state of treasuries using committee chains, a new variant of chain replication with threshold secret sharing. Teechain achieves at least a 33X higher transaction throughput than the state-of-the-art Lightning payment network. A 30-machine Teechain deployment can handle over 1 million Bitcoin transactions per second

Mode equivalence and acceptability of tablet computer-, interactive voice response system-, and paper-based administration of the U.S. National Cancer Institute’s Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE)

Abstract Background PRO-CTCAE is a library of items that measure cancer treatment-related symptomatic adverse events (NCI Contracts: HHSN261201000043C and HHSN 261201000063C). The objective of this study is to examine the equivalence and acceptability of the three data collection modes (Web-enabled touchscreen tablet computer, Interactive voice response system [IVRS], and paper) available within the US National Cancer Institute (NCI) Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) measurement system. Methods Participants (n = 112; median age 56.5; 24 % high school or less) receiving treatment for cancer at seven US sites completed 28 PRO-CTCAE items (scoring range 0–4) by three modes (order randomized) at a single study visit. Subjects completed one page (approx. 15 items) of the EORTC QLQ-C30 between each mode as a distractor. Item scores by mode were compared using intraclass correlation coefficients (ICC); differences in scores within the 3-mode crossover design were evaluated with mixed-effects models. Difficulties with each mode experienced by participants were also assessed. Results 103 (92 %) completed questionnaires by all three modes. The median ICC comparing tablet vs IVRS was 0.78 (range 0.55–0.90); tablet vs paper: 0.81 (0.62–0.96); IVRS vs paper: 0.78 (0.60–0.91); 89 % of ICCs were ≥0.70. Item-level mean differences by mode were small (medians [ranges] for tablet vs. IVRS = −0.04 [−0.16–0.22]; tablet vs paper = −0.02 [−0.11–0.14]; IVRS vs paper = 0.02 [−0.07–0.19]), and 57/81 (70 %) items had bootstrapped 95 % CI around the effect sizes within +/−0.20. The median time to complete the questionnaire by tablet was 3.4 min; IVRS: 5.8; paper: 4.0. The proportion of participants by mode who reported “no problems” responding to the questionnaire was 86 % tablet, 72 % IVRS, and 98 % paper. Conclusions Mode equivalence of items was moderate to high, and comparable to test-retest reliability (median ICC = 0.80). Each mode was acceptable to a majority of respondents. Although the study was powered to detect moderate or larger discrepancies between modes, the observed ICCs and very small mean differences between modes provide evidence to support study designs that are responsive to patient or investigator preference for mode of administration, and justify comparison of results and pooled analyses across studies that employ different PRO-CTCAE modes of administration. Trial registration NCT Clinicaltrials.gov identifier: NCT0215863