Production and evaluation of (multimodal) answers to medical questions

This paper describes two experiments carried out to investigate the production and evaluation of multimodal answer presentations in the context of a medical question answering system. In a production experiment participants had to produce answers to different types of questions. The results show that about one in four produced answers using multiple media. In an evaluation experiment, users had to evaluate different types of multimodal answer presentations. Answers with an informative visual were evaluated as more informative and more attractive than answers with a mere illustrative visual

Pten inhibition dedifferentiates long-distance axon-regenerating intrinsically photosensitive retinal ganglion cells and upregulates mitochondria-associated Dynlt1a and Lars2.

Central nervous system projection neurons fail to spontaneously regenerate injured axons. Targeting developmentally regulated genes in order to reactivate embryonic intrinsic axon growth capacity or targeting pro-growth tumor suppressor genes such as Pten promotes long-distance axon regeneration in only a small subset of injured retinal ganglion cells (RGCs), despite many RGCs regenerating short-distance axons. A recent study identified αRGCs as the primary type that regenerates short-distance axons in response to Pten inhibition, but the rare types which regenerate long-distance axons, and cellular features that enable such response, remained unknown. Here, we used a new method for capturing specifically the rare long-distance axon-regenerating RGCs, and also compared their transcriptomes with embryonic RGCs, in order to answer these questions. We found the existence of adult non-α intrinsically photosensitive M1 RGC subtypes that retained features of embryonic cell state, and showed that these subtypes partially dedifferentiated towards an embryonic state and regenerated long-distance axons in response to Pten inhibition. We also identified Pten inhibition-upregulated mitochondria-associated genes, Dynlt1a and Lars2, which promote axon regeneration on their own, and thus present novel therapeutic targets

Why Gender and Age Prediction from Tweets is Hard: Lessons from a Crowdsourcing Experiment

There is a growing interest in automatically predicting the gender and age of authors from texts. However, most research so far ignores that language use is related to the social identity of speakers, which may be different from their biological identity. In this paper, we combine insights from sociolinguistics with data collected through an online game, to underline the importance of approaching age and gender as social variables rather than static biological variables. In our game, thousands of players guessed the gender and age of Twitter users based on tweets alone. We show that more than 10% of the Twitter users do not employ language that the crowd associates with their biological sex. It is also shown that older Twitter users are often perceived to be younger. Our findings highlight the limitations of current approaches to gender and age prediction from texts

Project Brainstorm: Using Neuroscience to Connect College Students with Local Schools

Neuroscience can be used as a tool to inspire an interest in science in school children as well as to provide teaching experience to college students

Making effective use of healthcare data using data-to-text technology

Healthcare organizations are in a continuous effort to improve health outcomes, reduce costs and enhance patient experience of care. Data is essential to measure and help achieving these improvements in healthcare delivery. Consequently, a data influx from various clinical, financial and operational sources is now overtaking healthcare organizations and their patients. The effective use of this data, however, is a major challenge. Clearly, text is an important medium to make data accessible. Financial reports are produced to assess healthcare organizations on some key performance indicators to steer their healthcare delivery. Similarly, at a clinical level, data on patient status is conveyed by means of textual descriptions to facilitate patient review, shift handover and care transitions. Likewise, patients are informed about data on their health status and treatments via text, in the form of reports or via ehealth platforms by their doctors. Unfortunately, such text is the outcome of a highly labour-intensive process if it is done by healthcare professionals. It is also prone to incompleteness, subjectivity and hard to scale up to different domains, wider audiences and varying communication purposes. Data-to-text is a recent breakthrough technology in artificial intelligence which automatically generates natural language in the form of text or speech from data. This chapter provides a survey of data-to-text technology, with a focus on how it can be deployed in a healthcare setting. It will (1) give an up-to-date synthesis of data-to-text approaches, (2) give a categorized overview of use cases in healthcare, (3) seek to make a strong case for evaluating and implementing data-to-text in a healthcare setting, and (4) highlight recent research challenges.Comment: 27 pages, 2 figures, book chapte