True religion : Christianity and the making of radical ideology in England, 1816-1834

Noting the importance of religion to the lives of many leading radicals of the late eighteenth and early nineteenth centuries, Edward Royle and James Walvin remarked: "The religious dimension to secular affairs is probably one of the most unfamiliar aspects of the period under consideration". The early nineteeenth century saw the enormous growth of evangelical non-conformist denominations and sects. A debate has raged for many years over the nature of the impact upon the common people of this religious revival, and over the social and political effects of Methodism in particular. While Elie Halevy's famous thesis that it was Methodism which prevented the outbreak of a French-style revolution in Britain has usually been regarded as at best an overstatement, it has become an axiom of modern historiography that Methodism was a stabilising influence in early-industrial England. On the one hand there is E.P. Thompson's caustic characterisation of the attraction of Methodism to working-class people as the "chlliasm of despair".[33 On the other, Alan Gilbert argues that the inherent social deviance of Methodism and Dissent produced a moderate radicalism which acted as "the political equivalent of the safety valve"

Identification of cash management opportunities in the Navy Industrial Fund

http://archive.org/details/identificationof00wardNAN

NCBO Ontology Recommender 2.0: An Enhanced Approach for Biomedical Ontology Recommendation

Biomedical researchers use ontologies to annotate their data with ontology terms, enabling better data integration and interoperability. However, the number, variety and complexity of current biomedical ontologies make it cumbersome for researchers to determine which ones to reuse for their specific needs. To overcome this problem, in 2010 the National Center for Biomedical Ontology (NCBO) released the Ontology Recommender, which is a service that receives a biomedical text corpus or a list of keywords and suggests ontologies appropriate for referencing the indicated terms. We developed a new version of the NCBO Ontology Recommender. Called Ontology Recommender 2.0, it uses a new recommendation approach that evaluates the relevance of an ontology to biomedical text data according to four criteria: (1) the extent to which the ontology covers the input data; (2) the acceptance of the ontology in the biomedical community; (3) the level of detail of the ontology classes that cover the input data; and (4) the specialization of the ontology to the domain of the input data. Our evaluation shows that the enhanced recommender provides higher quality suggestions than the original approach, providing better coverage of the input data, more detailed information about their concepts, increased specialization for the domain of the input data, and greater acceptance and use in the community. In addition, it provides users with more explanatory information, along with suggestions of not only individual ontologies but also groups of ontologies. It also can be customized to fit the needs of different scenarios. Ontology Recommender 2.0 combines the strengths of its predecessor with a range of adjustments and new features that improve its reliability and usefulness. Ontology Recommender 2.0 recommends over 500 biomedical ontologies from the NCBO BioPortal platform, where it is openly available.Comment: 29 pages, 8 figures, 11 table

Cluster, Classify, Regress: A General Method For Learning Discountinous Functions

This paper presents a method for solving the supervised learning problem in which the output is highly nonlinear and discontinuous. It is proposed to solve this problem in three stages: (i) cluster the pairs of input-output data points, resulting in a label for each point; (ii) classify the data, where the corresponding label is the output; and finally (iii) perform one separate regression for each class, where the training data corresponds to the subset of the original input-output pairs which have that label according to the classifier. It has not yet been proposed to combine these 3 fundamental building blocks of machine learning in this simple and powerful fashion. This can be viewed as a form of deep learning, where any of the intermediate layers can itself be deep. The utility and robustness of the methodology is illustrated on some toy problems, including one example problem arising from simulation of plasma fusion in a tokamak.Comment: 12 files,6 figure

Building a biomedical ontology recommender web service

<p>Abstract</p> <p>Background</p> <p>Researchers in biomedical informatics use ontologies and terminologies to annotate their data in order to facilitate data integration and translational discoveries. As the use of ontologies for annotation of biomedical datasets has risen, a common challenge is to identify ontologies that are best suited to annotating specific datasets. The number and variety of biomedical ontologies is large, and it is cumbersome for a researcher to figure out which ontology to use.</p> <p>Methods</p> <p>We present the <it>Biomedical Ontology Recommender web service</it>. The system uses textual metadata or a set of keywords describing a domain of interest and suggests appropriate ontologies for annotating or representing the data. The service makes a decision based on three criteria. The first one is <it>coverage</it>, or the ontologies that provide most terms covering the input text. The second is <it>connectivity</it>, or the ontologies that are most often mapped to by other ontologies. The final criterion is <it>size</it>, or the number of concepts in the ontologies. The service scores the ontologies as a function of scores of the annotations created using the National Center for Biomedical Ontology (NCBO) <it>Annotator web service</it>. We used all the ontologies from the UMLS Metathesaurus and the NCBO BioPortal.</p> <p>Results</p> <p>We compare and contrast our Recommender by an exhaustive functional comparison to previously published efforts. We evaluate and discuss the results of several recommendation heuristics in the context of three real world use cases. The best recommendations heuristics, rated ‘very relevant’ by expert evaluators, are the ones based on coverage and connectivity criteria. The Recommender service (alpha version) is available to the community and is embedded into BioPortal.</p

Around-the-Clock Media Coverage and the Timing of Earnings Announcements.

We reexamine the descriptive ability of the conventional wisdom that earnings announcements made after trading and on Friday are dominated by bad news in light of the 24/7 media coverage and other technological changes of the 1990’s. We find that the change in media coverage has facilitated a significant change in earnings announcement times: only 27% of earnings announcements are now made during trading as opposed to 67% in prior research. However, our finding of continued dominance of bad news in Friday announcements in particular strongly suggests that the conventional wisdom is not solely the result of managers’ desire to take advantage of limited media coverage. Instead, managers appear to be taking advantage of other aspects of investors’ behavior, such as their anticipating negative Friday announcements earlier in the week, and the relatively quiet (in terms of trading) weekend period to manage stock price responses to their companies’ financial news.strategic planning ; bad news ; earnings announcements ; earnings disclosures

Green Chemistry to the Resque of Disasters of the 1900 -2020 Period

There is uncertainty about several aspects of the Covid-19 origin story that scientists are trying hard to unravel, including which species passed to humans. They are trying hard because knowing how a pandemic starts is a key to stopping the next one. Green chemistry emerged from a variety of existing ideas and research efforts - characterization is one major analytical technique in our laboratories and so Scientists moved rapidly to characterize 2019-nCoV and widely disseminated their findings amongst the international research community as quickly as possible including the basic Viral Structure and Mechanism of Infection. Coronaviruses are large, enveloped, positive-stranded RNA viruses. They have the largest genome among all RNA viruses, typically ranging from 27 to 32 kb. The genome is packed inside a helical capsid formed by the nucleocapsid protein (N) and further surrounded by an envelope. One important example of this is the homology models of the novel coronavirus cysteine protease produced by Martin Stoermer etal (2020) . It is also established that disasters during the century 1900- 2020 were avoidable if the principles of green chemistry were applied to prevent future pandemics. Keywords: Coronaviruses, Covid-19, Green chemistry, RNA, Pandemic, Characterization DOI: 10.7176/JEES/11-2-03 Publication date: February 28th 202

Analysis of long branch extraction and long branch shortening.

RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are.BACKGROUND: Long branch attraction (LBA) is a problem that afflicts both the parsimony and maximum likelihood phylogenetic analysis techniques. Research has shown that parsimony is particularly vulnerable to inferring the wrong tree in Felsenstein topologies. The long branch extraction method is a procedure to detect a data set suffering from this problem so that Maximum Likelihood could be used instead of Maximum Parsimony. RESULTS: The long branch extraction method has been well cited and used by many authors in their analysis but no strong validation has been performed as to its accuracy. We performed such an analysis by an extensive search of the branch length search space under two topologies of six taxa, a Felsenstein-like topology and Farris-like topology. We also examine a long branch shortening method. CONCLUSIONS: The long branch extraction method seems to mask the majority of the search space rendering it ineffective as a detection method of LBA. A proposed alternative, the long branch shortening method, is also ineffective in predicting long branch attraction for all tree topologies