Optimizing genetics online resources for diverse readers.

PurposeClear and accurate genetic information should be available to health-care consumers at an individualized level of comprehension. The objective of this study is to evaluate the complexity of common online resources and to simplify text content using automated text processing tools.MethodsWe extracted all text from Genetics Home Reference and MedlinePlus in bulk and analyzed content using natural language processing. We applied custom tools to improve the readability and compared readability before and after text optimization.ResultsCommonly used educational materials were more complex than the recommended reading level for the general public. Genetic health information entries from Genetics Home Reference (n = 1279) were written at a median 13.0 grade level. MedlinePlus entries, which are not exclusively genetic (n = 1030), had a median grade level of 7.7. When we optimized text for the 59 actionable conditions by prioritizing medical details using a standard structure, the average reading grade level improved.ConclusionFactors that increase complexity are long sentences and difficult words. Future strategies to reduce complexity include prioritizing relevant details and using more illustrations. Simplifying and providing standardized online health resources would benefit diverse consumers and promote inclusivity

Resource Constrained Structured Prediction

We study the problem of structured prediction under test-time budget constraints. We propose a novel approach applicable to a wide range of structured prediction problems in computer vision and natural language processing. Our approach seeks to adaptively generate computationally costly features during test-time in order to reduce the computational cost of prediction while maintaining prediction performance. We show that training the adaptive feature generation system can be reduced to a series of structured learning problems, resulting in efficient training using existing structured learning algorithms. This framework provides theoretical justification for several existing heuristic approaches found in literature. We evaluate our proposed adaptive system on two structured prediction tasks, optical character recognition (OCR) and dependency parsing and show strong performance in reduction of the feature costs without degrading accuracy

Brand Diversity and Brand Similarity Impacts on Brand Evaluations

This research examined the joint impact of brand diversity and brand similarity upon brand evaluations. The results revealed that low-diversity brands are favored over high-diversity brands, whereas high-similarity brands are favored over low-similarity brands. High-diversity narrow brands are favored over high-diversity broad brands, whereas low-diversity narrow and broad brands are favored identically. Additionally, low-diversity narrow brands are favored over high-diversity narrow brands, whereas low-diversity broad brands are favored over high-diversity broad brands. The findings of extant research that narrow brands are preferred over broad brands are true only when the quality diversities of both brands are high

Real-time decoding of question-and-answer speech dialogue using human cortical activity.

Natural communication often occurs in dialogue, differentially engaging auditory and sensorimotor brain regions during listening and speaking. However, previous attempts to decode speech directly from the human brain typically consider listening or speaking tasks in isolation. Here, human participants listened to questions and responded aloud with answers while we used high-density electrocorticography (ECoG) recordings to detect when they heard or said an utterance and to then decode the utterance's identity. Because certain answers were only plausible responses to certain questions, we could dynamically update the prior probabilities of each answer using the decoded question likelihoods as context. We decode produced and perceived utterances with accuracy rates as high as 61% and 76%, respectively (chance is 7% and 20%). Contextual integration of decoded question likelihoods significantly improves answer decoding. These results demonstrate real-time decoding of speech in an interactive, conversational setting, which has important implications for patients who are unable to communicate

Nox2 redox signaling maintains essential cell populations in the brain.

Reactive oxygen species (ROS) are conventionally classified as toxic consequences of aerobic life, and the brain is particularly susceptible to ROS-induced oxidative stress and damage owing to its high energy and oxygen demands. NADPH oxidases (Nox) are a widespread source of brain ROS implicated in seizures, stroke and neurodegeneration. A physiological role for ROS generation in normal brain function has not been established, despite the fact that mice and humans lacking functional Nox proteins have cognitive deficits. Using molecular imaging with Peroxyfluor-6 (PF6), a new selective fluorescent indicator for hydrogen peroxide (H(2)O(2)), we show that adult hippocampal stem/progenitor cells (AHPs) generate H(2)O(2) through Nox2 to regulate intracellular growth signaling pathways, which in turn maintains their normal proliferation in vitro and in vivo. Our results challenge the traditional view that brain ROS are solely deleterious by demonstrating that controlled ROS chemistry is needed for maintaining specific cell populations

The \u27Nayirah\u27 Effect: The Role of Target States’ Human Rights Violations and Victims’ Emotive Images in War Support

When a target state violates human rights, how does the identity of the victims and the presence of emotive imagery affect the level of public support for interventionist war? How does the perceived race and gender of victims affect this relationship? We employ a survey experiment to study whether and when information about a target state’s human rights violations affects public attitudes toward the use of force. Specifically, we manipulate a fictional victim’s race (light-skinned vs. dark-skinned) and gender (male vs. female), and explore how these variations affect support for interventionist war. In our experiment, we find that war support is stronger when a target state violates human rights. More importantly, public support for intervention was affected by the characteristics of the victims of human rights abuse. Support for interventionist war was found to be greatest among those participants who viewed images of light-skinned or female victims, though a white male image was found to me most impactful. Our causal mediation analysis showed that subjects viewing light-skinned or female images had less concern about the costs of intervention. Our findings suggest that the racial and gender characteristics of the victims of human rights abuse plays a substantial role in determining individual support for war