No abstract is provided for this article.
"Introductory Article: Anti-infectives: Present status of HIV protease inhibitors in the control of HIV infections." Expert Opinion on Investigational Drugs, 5(2), pp. 153–154
No abstract is provided for this article.
Despite the active development of SARS-CoV-2 surveillance methods (e.g., Nextstrain, GISAID, Pangolin), the global emergence of various SARS-CoV-2 viral lineages that potentially cause antiviral and vaccine failure has driven the need for accurate and efficient SARS-CoV-2 genome sequence classifiers. This study presents an optimized method that accurately identifies the viral lineages of SARS-CoV-2 genome sequences using existing schemes. For Nextstrain and GISAID clades, a template matching-based method is proposed to quantify the differences between viral clades and to play an important role in classification evaluation. Furthermore, to improve the typing accuracy of SARS-CoV-2 genome sequences, an ensemble model that integrates a combination of machine learning-based methods (such as Random Forest and Catboost) with optimized weights is proposed for Nextstrain, Pangolin, and GISAID clades. Cross-validation is applied to optimize the parameters of the machine learning-based method and the weight settings of the ensemble model. To improve the efficiency of the model, in addition to the one-hot encoding method, we have proposed a nucleotide site mutation-based data structure that requires less computational resources and performs better in SARS-CoV-2 genome sequence typing. Based on an accumulated database of >1 million SARS-CoV-2 genome sequences, performance evaluations show that the proposed system has a typing accuracy of 99.879%, 97.732%, and 96.291% for Nextstrain, Pangolin, and GISAID clades, respectively. A single prediction only takes an average of <20 ms on a portable laptop. Overall, this study provides an efficient and accurate SARS-CoV-2 genome sequence typing system that benefits current and future surveillance of SARS-CoV-2 variants.
No abstract is provided for this article.
SUMMARY This article describes an intelligent support system for the design of injection moulds. It integrates a commercial computer-aided design/computer-aided manufacture (CAD/ CAM) system, a relational database and an expert system. The rule-based approach makes it very easy to tailor the software to the specific design rules of a particular company. As well as knowledge (rules) to select various standard mould components, the system is able to support the design of non-standard components, such as slides and inserts. The system works in a very interactive way and its great flexibility makes it a powerful tool to create an initial design of a mould in a very short time. Using a neutral CAD programming interface, it can be integrated with a number of commercial CAD systems currently available on the market.