438 research outputs found

    Improving Support Ticket Systems Using Machine Learning: A Literature Review

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    Processing customer support requests via a support ticket system is a key-element for companies to provide support to their customers in an organized and professional way. However, distributing and processing such tickets is much work, increasing the cost for the support providing company and stretching the resolution time. The advancing potential of Machine Learning has led to the goal of automating those support ticket systems. Against this background, we conducted a Literature Review aiming at determining the present state-of-the-art technology in the field of automated support ticket systems. We provide an overview about present trends and topics discussed in this field. During the Literature Review, we found creating an automated incident management tool being the majority topic in the field followed by request escalation and customer sentiment prediction and identified Random Forrest and Support Vector Machine as best performing algorithms for classification in the field

    Sieg Heil! War Letters of Tank Gunner Karl Fuchs, 1937-1941

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    Converting Poly(Methyl Methacrylate) into a Triple-Responsive Polymer

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    Multiresponsive polymers that can respond to several external stimuli are promising materials for a manifold of applications. Herein, a facile method for the synthesis of triple-responsive (pH, temperature, CO2) poly(N,N-diethylaminoethyl methacrylamide) by a post-polymerization amidation of poly(methyl methacrylate) (PMMA) is presented. Combined with trivalent counterions ([Fe(CN)6]3−) both an upper and lower critical solution temperature (UCST/LCST)-type phase behavior can be realized at pH 8 and 9. PMMA and PMMA-based block copolymers are readily accessible by living anionic and controlled radical polymerization techniques, which opens access to various responsive polymer architectures based on the developed functionalization method. This method can also be applied on melt-processed bulk PMMA samples to introduce functional, responsive moieties at the PMMA surface

    MausDB: An open source application for phenotype data and mouse colony management in large-scale mouse phenotyping projects

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    <p>Abstract</p> <p>Background</p> <p>Large-scale, comprehensive and standardized high-throughput mouse phenotyping has been established as a tool of functional genome research by the German Mouse Clinic and others. In all these projects, vast amounts of data are continuously generated and need to be stored, prepared for data-mining procedures and eventually be made publicly available. Thus, central storage and integrated management of mouse phenotype data, genotype data, metadata and linked external data are highly important. Requirements most probably depend on the individual mouse housing unit or project and the demand for either very specific individual database solutions or very flexible solutions that can be easily adapted to local demands. Not every group has the resources and/or the know-how to develop software for this purpose. A database application has been developed for the German Mouse Clinic in order to meet all requirements mentioned above.</p> <p>Results</p> <p>We present MausDB, the German Mouse Clinic web-based database application that integrates standard mouse colony management, phenotyping workflow scheduling features and mouse phenotyping result data management. It links mouse phenotype data with genotype data, metadata and external data such as public web databases, which is a prerequisite for comprehensive data analysis and mining. We describe how this can be achieved with a lean and user-friendly system built on open standards.</p> <p>Conclusion</p> <p>MausDB is suited for large-scale, high-throughput phenotyping facilities but can also be used exclusively for mouse colony management within smaller units or projects. The system is successfully used as the primary mouse and data management tool of the German Mouse Clinic and other mouse facilities. We offer MausDB to the scientific community as open source software to provide a system for storage of data from functional genomics projects in a well-structured, easily accessible form.</p

    Complex joint probabilities as expressions of determinism in quantum mechanics

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    The density operator of a quantum state can be represented as a complex joint probability of any two observables whose eigenstates have non-zero mutual overlap. Transformations to a new basis set are then expressed in terms of complex conditional probabilities that describe the fundamental relation between precise statements about the three different observables. Since such transformations merely change the representation of the quantum state, these conditional probabilities provide a state-independent definition of the deterministic relation between the outcomes of different quantum measurements. In this paper, it is shown how classical reality emerges as an approximation to the fundamental laws of quantum determinism expressed by complex conditional probabilities. The quantum mechanical origin of phase spaces and trajectories is identified and implications for the interpretation of quantum measurements are considered. It is argued that the transformation laws of quantum determinism provide a fundamental description of the measurement dependence of empirical reality.Comment: 12 pages, including 1 figure, updated introduction includes references to the historical background of complex joint probabilities and to related work by Lars M. Johanse

    The Novel Phosphodiesterase 9A Inhibitor BI 409306 Increases Cyclic Guanosine Monophosphate Levels in the Brain, Promotes Synaptic Plasticity, and Enhances Memory Function in Rodents.

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    N-methyl-d-aspartate (NMDA) receptor-dependent long-term potentiation (LTP) is an established cellular model underlying learning and memory, and involves intracellular signaling mediated by the second messenger cyclic guanosine monophosphate (cGMP). As phosphodiesterase (PDE)9A selectively hydrolyses cGMP in areas of the brain related to cognition, PDE9A inhibitors may improve cognitive function by enhancing NMDA receptor-dependent LTP. This study aimed to pharmacologically characterize BI 409306, a novel PDE9A inhibitor, using in vitro assays and in vivo determination of cGMP levels in the brain. Further, the effects of BI 409306 on synaptic plasticity evaluated by LTP in ex vivo hippocampal slices and on cognitive performance in rodents were also investigated. In vitro assays demonstrated that BI 409306 is a potent and selective inhibitor of human and rat PDE9A with mean concentrations at half-maximal inhibition (IC50) of 65 and 168 nM. BI 409306 increased cGMP levels in rat prefrontal cortex and cerebrospinal fluid and attenuated a reduction in mouse striatum cGMP induced by the NMDA-receptor antagonist MK-801. In ex vivo rat brain slices, BI 409306 enhanced LTP induced by both weak and strong tetanic stimulation. Treatment of mice with BI 409306 reversed MK-801-induced working memory deficits in a T-maze spontaneous-alternation task and improved long-term memory in an object recognition task. These findings suggest that BI 409306 is a potent and selective inhibitor of PDE9A. BI 409306 shows target engagement by increasing cGMP levels in brain, facilitates synaptic plasticity as demonstrated by enhancement of hippocampal LTP, and improves episodic and working memory function in rodents. SIGNIFICANCE STATEMENT: This preclinical study demonstrates that BI 409306 is a potent and selective PDE9A inhibitor in rodents. Treatment with BI 409306 increased brain cGMP levels, promoted long-term potentiation, and improved episodic and working memory performance in rodents. These findings support a role for PDE9A in synaptic plasticity and cognition. The potential benefits of BI 409306 are currently being investigated in clinical trials
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