Japan has found itself in a unique predicament. While governments around the world have responded to the spread of the new coronavirus by instituting draconian measures to curtail social interaction, Japan's central and local governments have no authority to implement or enforce such measures. Instead, they're relying on something else to convince citizens to socially distance: persuasion. The initial results of this approach have not not been encouraging. Prime Minister Shinzo Abe has set a target of reducing social interactions by 80 percent.
In the previous article of this series we examined the association construct from the perspective of Entity-Relationship data model. In this post we demonstrate how Topic Map data model represents associations. FROM suppliers INNER JOIN (parts INNER JOIN [catalog] ON parts.pid This will fetch all the rows of a result set where we are looking for the minimum catalogue price of a Red Fire Hydrant Cap and who is the supplier that manufactures this part. The reader will notice that apart from the deficiensy of the nested JOINs, (see here), we had to formalize our search in SQL language in order to get back our result.
Yes. Associations exist to serve groups of individuals who are involved in specific vocational and avocational activities. As long as you reside in a democratic society, employment still exists (the rise of automation notwithstanding) and/or hobbies and social causes of importance exist as well - you will find "associations" forming. Even if we stop calling them "associations' the functions will remain. People are still fundamentally people and we have been gathering in groups to discuss matters of interest since we began to use language. I'm not saying the next few years will be easy.
The Conservatives have put forward plans to change the structure of local party associations and the way that election candidates are chosen. The party wants to pilot associations spanning more than one constituency and also centralise certain membership functions and create a single candidate list for UK and European elections. Chair Lord Feldman said it would help channel resources into campaigning. It follows a review launched in the wake of last year's election victory. Despite the Conservatives winning their first majority since 1992, there were concerns that it struggled to match Labour's "ground campaign" in many parts of the country, in terms of mobilising activists to canvass and help get out the vote.
The recent adoption of Electronic Health Records (EHRs) by health care providers has introduced an important source of data that provides detailed and highly specific insights into patient phenotypes over large cohorts. These datasets, in combination with machine learning and statistical approaches, generate new opportunities for research and clinical care. However, many methods require the patient representations to be in structured formats, while the information in the EHR is often locked in unstructured texts designed for human readability. In this work, we develop the methodology to automatically extract clinical features from clinical narratives from large EHR corpora without the need for prior knowledge. We consider medical terms and sentences appearing in clinical narratives as atomic information units. We propose an efficient clustering strategy suitable for the analysis of large text corpora and to utilize the clusters to represent information about the patient compactly. To demonstrate the utility of our approach, we perform an association study of clinical features with somatic mutation profiles from 4,007 cancer patients and their tumors. We apply the proposed algorithm to a dataset consisting of about 65 thousand documents with a total of about 3.2 million sentences. We identify 341 significant statistical associations between the presence of somatic mutations and clinical features. We annotated these associations according to their novelty, and report several known associations. We also propose 32 testable hypotheses where the underlying biological mechanism does not appear to be known but plausible. These results illustrate that the automated discovery of clinical features is possible and the joint analysis of clinical and genetic datasets can generate appealing new hypotheses.