What Do You Mean `Why?': Resolving Sluices in Conversations

In conversation, we often ask one-word questions such as `Why?' or `Who?'. Such questions are typically easy for humans to answer, but can be hard for computers, because their resolution requires retrieving both the right semantic frames and the right arguments from context. This paper introduces the novel ellipsis resolution task of resolving such one-word questions, referred to as sluices in linguistics. We present a crowd-sourced dataset containing annotations of sluices from over 4,000 dialogues collected from conversational QA datasets, as well as a series of strong baseline architectures.Comment: Accepted at the 34TH AAAI Conference on Artificial Intelligence (AAAI-2020

Ownership Variation in Violated Regulations and National Care Standards: Evidence From Social Care Providers

Social care services are increasingly provisioned in quasi-markets in which for-profit, public, and third sector providers compete for contracts. Existing research has investigated the implications of this development by analyzing ownership variation in latent outcomes such as quality, but little is known about whether ownership predicts variation in more concrete outcomes, such as violation types. To address this research gap, we coded publicly available inspection reports of social care providers regulated by the Care Inspectorate in Scotland and created a novel data set enabling analysis of ownership variation in violations of (a) regulations, and (b) national care standards over an entire inspection year (n = 4,178). Using negative binomial and logistic regression models, we find that for-profit providers are more likely to violate non-enforceable outcomes (national care standards) relative to other ownership types. We did not identify a statistically significant difference between for-profit and third sector providers with regard to enforceable outcomes (regulations)

Platelet-Cancer Interplay: Molecular Mechanisms and New Therapeutic Avenues

Although platelets are critically involved in thrombosis and hemostasis, experimental and clinical evidence indicate that platelets promote tumor progression and metastasis through a wide range of physical and functional interactions between platelets and cancer cells. Thrombotic and thromboembolic events are frequent complications in patients with solid tumors. Hence, cancer modulates platelet function by directly inducing platelet-tumor aggregates and triggering platelet granule release and altering platelet turnover. Also, platelets enhance tumor cell dissemination by activating endothelial cell function and recruiting immune cells to primary and metastatic tumor sites. In this review, we summarize current knowledge on the complex interactions between platelets and tumor cells and the host microenvironment. We also critically discuss the potential of anti-platelet agents for cancer prevention and treatment

Diagnosis trajectories of prior multi-morbidity predict sepsis mortality

Sepsis affects millions of people every year, many of whom will die. In contrast to current survival prediction models for sepsis patients that primarily are based on data from within-admission clinical measurements (e.g. vital parameters and blood values), we aim for using the full disease history to predict sepsis mortality. We benefit from data in electronic medical records covering all hospital encounters in Denmark from 1996 to 2014. This data set included 6.6 million patients of whom almost 120,000 were diagnosed with the ICD-10 code: A41 ‘Other sepsis’. Interestingly, patients following recurrent trajectories of time-ordered co-morbidities had significantly increased sepsis mortality compared to those who did not follow a trajectory. We identified trajectories which significantly altered sepsis mortality, and found three major starting points in a combined temporal sepsis network: Alcohol abuse, Diabetes and Cardio-vascular diagnoses. Many cancers also increased sepsis mortality. Using the trajectory based stratification model we explain contradictory reports in relation to diabetes that recently have appeared in the literature. Finally, we compared the predictive power using 18.5 years of disease history to scoring based on within-admission clinical measurements emphasizing the value of long term data in novel patient scores that combine the two types of data