An Incremental Approach to Entity Resolution

We present a query-time entity resolution process that works in a highly parallel fashion. We use the application MobEx to showcase our process, which consists of a mobile client and a server, where the server takes the role of a mediator and carries out the resolution. Results are propagated to the client as early as possible. Resolution results that are produced later in the process are send as updates to the client and thus improve earlier results

Bootstrapping an Unsupervised Approach for Classifying Agreement and Disagreement

ABSTRACT People tend to have various opinions about topics. In discussions, they can either agree or disagree with another person. The recognition of agreement and disagreement is a useful prerequisite for many applications. It could be used by political scientists to measure how controversial political issues are, or help a company to analyze how well people like their new products. In this work, we develop an approach for recognizing agreement and disagreement. However, this is a challenging task. While keyword-based approaches are only able to cover a limited set of phrases, machine learning approaches require a large amount of training data. We therefore combine advantages of both methods by using a bootstrapping approach. With our completely unsupervised technique, we achieve an accuracy of 72.85%. Besides, we investigate the limitations of a keyword based approach and a machine learning approach in addition to comparing various sets of features

An approach for incremental entity resolution at the example of social media data

When querying data providers on the web, one has no guarantee that they will reply within a given time. Some providers may even not answer at all. This makes it infeasible to wait for a complete result before beginning with the entity resolution. In order to solve this problem, we present a query-time entity resolution approach that takes the asynchronous nature of the replies from data providers into account by starting the entity resolution as soon as first results are returned. Resolved entities are propagated from the entity resolution engine to the mobile client as early as possible. Resolution results that are produced later are send as updates to the client and thus improve earlier results

Investigating the Usability of a Mobile App for Finding and Exploring Places and Events

In our two-step field study, we developed and evaluated mobEx, a mobile app for faceted exploration of social media data on Android phones. mobEx unifies the data sources of related commercial apps in the market by retrieving information from various providers. The goal of our study was to find out, if the subjects understood the metaphor of a time-wheel as novel user interface feature for finding and exploring places and events and how they use it. In addition, mobEx offers a grid-based navigation menu and a list-based navigation menu for exploring the data. Here, we were interested in gaining some qualitative insights about which type of navigation approach the users prefer when they can choose between them. In this paper, we present the design and a preliminary analysis of the results of our study

Acquired resistance to anti-PD1 therapy in patients with NSCLC associates with immunosuppressive T cell phenotype

Immune checkpoint inhibitor treatment has the potential to prolong survival in non-small cell lung cancer (NSCLC), however, some of the patients develop resistance following initial response. Here, we analyze the immune phenotype of matching tumor samples from a cohort of NSCLC patients showing good initial response to immune checkpoint inhibitors, followed by acquired resistance at later time points. By using imaging mass cytometry and whole exome and RNA sequencing, we detect two patterns of resistance¨: One group of patients is characterized by reduced numbers of tumor-infiltrating CD8$^{+}$ T cells and reduced expression of PD-L1 after development of resistance, whereas the other group shows high CD8$^{+}$ T cell infiltration and high expression of PD-L1 in addition to markedly elevated expression of other immune-inhibitory molecules. In two cases, we detect downregulation of type I and II IFN pathways following progression to resistance, which could lead to an impaired anti-tumor immune response. This study thus captures the development of immune checkpoint inhibitor resistance as it progresses and deepens our mechanistic understanding of immunotherapy response in NSCLC

Rac1 Regulates the NLRP3 Inflammasome Which Mediates IL-1beta Production in Chlamydophila pneumoniae Infected Human Mononuclear Cells

Chlamydophila pneumoniae causes acute respiratory tract infections and has been associated with development of asthma and atherosclerosis. The production of IL-1β, a key mediator of acute and chronic inflammation, is regulated on a transcriptional level and additionally on a posttranslational level by inflammasomes. In the present study we show that C. pneumoniae-infected human mononuclear cells produce IL-1β protein depending on an inflammasome consisting of NLRP3, the adapter protein ASC and caspase-1. We further found that the small GTPase Rac1 is activated in C. pneumoniae-infected cells. Importantly, studies with specific inhibitors as well as siRNA show that Rac1 regulates inflammasome activation in C. pneumoniae-infected cells. In conclusion, C. pneumoniae infection of mononuclear cells stimulates IL-1β production dependent on a NLRP3 inflammasome-mediated processing of proIL-1β which is controlled by Rac1

Bootstrapping an Unsupervised Approach for Classifying Agreement and Disagreement

People tend to have various opinions about topics. In discussions, they can either agree or disagree with another person. The recognition of agreement and disagreement is a useful prerequisite for many applications. It could be used by political scientists to measure how controversial political issues are, or help a company to analyze how well people like their new products. In this work, we develop an approach for recognizing agreement and disagreement. However, this is a challenging task. While keyword-based approaches are only able to cover a limited set of phrases, machine learning approaches require a large amount of training data. We therefore combine advantages of both methods by using a bootstrapping approach. With our completely unsupervised technique, we achieve an accuracy of 72.85%. Besides, we investigate the limitations of a keyword based approach and a machine learning approach in addition to comparing various sets of features