Interactive Infographics\u27 Effect on Elaboration in Agricultural Communication

In public health, politics, and advertising, interactive content spurred increased elaboration from audiences that were otherwise least likely to engage with a message. This study sought to examine interactivity as an agricultural communication strategy through the lens of the Elaboration Likelihood Model. Respondents were randomly assigned a static or interactive data visualization concerning the production of peaches and blueberries in Georgia, then asked to list their thoughts in accordance with Petty and Cacioppo’s thought-listing measure. Respondents significantly exhibited higher elaboration with the interactive message as opposed to the static, extending the results of past research in other communication realms to agricultural communication as well. This increase in attitude and cognition encourages agricultural communicators to pursue the use of more interactive elements in their messaging

Interacting meaningfully with machine learning systems: Three experiments

Although machine learning is becoming commonly used in today's software, there has been little research into how end users might interact with machine learning systems, beyond communicating simple “right/wrong” judgments. If the users themselves could work hand-in-hand with machine learning systems, the users’ understanding and trust of the system could improve and the accuracy of learning systems could be improved as well. We conducted three experiments to understand the potential for rich interactions between users and machine learning systems. The first experiment was a think-aloud study that investigated users’ willingness to interact with machine learning reasoning, and what kinds of feedback users might give to machine learning systems. We then investigated the viability of introducing such feedback into machine learning systems, specifically, how to incorporate some of these types of user feedback into machine learning systems, and what their impact was on the accuracy of the system. Taken together, the results of our experiments show that supporting rich interactions between users and machine learning systems is feasible for both user and machine. This shows the potential of rich human–computer collaboration via on-the-spot interactions as a promising direction for machine learning systems and users to collaboratively share intelligence

Modelling interactions of acid–base balance and respiratory status in the toxicity of metal mixtures in the American oyster Crassostrea virginica

Author Posting. © The Author(s), 2009. This is the author's version of the work. It is posted here by permission of Elsevier B.V. for personal use, not for redistribution. The definitive version was published in Comparative Biochemistry and Physiology - Part A: Molecular & Integrative Physiology 155 (2010): 341-349, doi:10.1016/j.cbpa.2009.11.019.Heavy metals, such as copper, zinc and cadmium, represent some of the most common and serious pollutants in coastal estuaries. In the present study, we used a combination of linear and artificial neural network (ANN) modelling to detect and explore interactions among low-dose mixtures of these heavy metals and their impacts on fundamental physiological processes in tissues of the Eastern oyster, Crassostrea virginica. Animals were exposed to Cd (0.001 – 0.400 μM), Zn (0.001 – 3.059 μM) or Cu (0.002 – 0.787 μM), either alone or in combination for 1 to 27 days. We measured indicators of acid-base balance (hemolymph pH and total CO2), gas exchange (Po2), immunocompetence (total hemocyte counts, numbers of invasive bacteria), antioxidant status (glutathione, GSH), oxidative damage (lipid peroxidation; LPx), and metal accumulation in the gill and the hepatopancreas. Linear analysis showed that oxidative membrane damage from tissue accumulation of environmental metals was correlated with impaired acid-base balance in oysters. ANN analysis revealed interactions of metals with hemolymph acid-base chemistry in predicting oxidative damage that were not evident from linear analyses. These results highlight the usefulness of machine learning approaches, such as ANNs, for improving our ability to recognize and understand the effects of sub-acute exposure to contaminant mixtures.This study was supported by NOAA’s Center of Excellence in Oceans and Human Health at HML and the National Science Foundation

Toward harnessing user feedback for machine learning

There has been little research into how end users might be able to communicate advice to machine learning systems. If this resource--the users themselves--could somehow work hand-in-hand with machine learning systems, the accuracy of learning systems could be improved and the users' understanding and trust of the system could improve as well. We conducted a think-aloud study to see how willing users were to provide feedback and to understand what kinds of feedback users could give. Users were shown explanations of machine learning predictions and asked to provide feedback to improve the predictions. We found that users had no difficulty providing generous amounts of feedback. The kinds of feedback ranged from suggestions for reweighting of features to proposals for new features, feature combinations, relational features, and wholesale changes to the learning algorithm. The results show that user feedback has the potential to significantly improve machine learning systems, but that learning algorithms need to be extended in several ways to be able to assimilate this feedback.Author Keywords: Machine learning, explanations, user feedback for learnin

Interacting meaningfully with machine learning systems : three experiments

Although machine learning is becoming commonly used in today's software, there has been little research into how end users might interact with machine learning systems, beyond communicating simple "right/wrong" judgments. If the users themselves could somehow work hand-in-hand with machine learning systems, the accuracy of learning systems could be improved and the users' understanding and trust of the system could improve as well. We conducted three experiments to begin to understand the potential for rich interactions between users and machine learning systems. The first experiment was a think-aloud study, aiming to see how willing users were to interact with and about machine learning reasoning, and to help us understand what kinds of feedback users might give to machine learning systems. Specifically, users were shown explanations of machine learning predictions and asked to provide feedback to improve the predictions. The results were that users' feedback was rich, complex, and widely varied, ranging from suggestions for reweighting of features to proposals for new features, feature combinations, relational features, and wholesale changes to the learning algorithm. We then investigated the viability of introducing such feedback into machine learning systems: specifically, how to incorporate some of these types of user feedback into machine learning systems, and impact on the accuracy of the system. Taken together, the results of our experiments show that supporting rich interactions between users and machine learning systems is feasible for both user and machine. This shows the potential of rich human-computer collaboration via on-the-spot interactions as a promising direction for machine learning systems to work more intelligently, hand-in-hand with the user

Priorities for synthesis research in ecology and environmental science

ACKNOWLEDGMENTS We thank the National Science Foundation grant #1940692 for financial support for this workshop, and the National Center for Ecological Analysis and Synthesis (NCEAS) and its staff for logistical support.Peer reviewedPublisher PD

Priorities for synthesis research in ecology and environmental science

ACKNOWLEDGMENTS We thank the National Science Foundation grant #1940692 for financial support for this workshop, and the National Center for Ecological Analysis and Synthesis (NCEAS) and its staff for logistical support.Peer reviewedPublisher PD

Combining Asian and European genome-wide association studies of colorectal cancer improves risk prediction across racial and ethnic populations

Polygenic risk scores (PRS) have great potential to guide precision colorectal cancer (CRC) prevention by identifying those at higher risk to undertake targeted screening. However, current PRS using European ancestry data have sub-optimal performance in non-European ancestry populations, limiting their utility among these populations. Towards addressing this deficiency, we expand PRS development for CRC by incorporating Asian ancestry data (21,731 cases; 47,444 controls) into European ancestry training datasets (78,473 cases; 107,143 controls). The AUC estimates (95% CI) of PRS are 0.63(0.62-0.64), 0.59(0.57-0.61), 0.62(0.60-0.63), and 0.65(0.63-0.66) in independent datasets including 1681-3651 cases and 8696-115,105 controls of Asian, Black/African American, Latinx/Hispanic, and non-Hispanic White, respectively. They are significantly better than the European-centric PRS in all four major US racial and ethnic groups (p-values < 0.05). Further inclusion of non-European ancestry populations, especially Black/African American and Latinx/Hispanic, is needed to improve the risk prediction and enhance equity in applying PRS in clinical practice