Growing industrial utilization of enzymes and the increasing availability of metagenomic data highlight the demand for effective methods of targeted identification and verification of novel enzymes from various environmental microbiota. Xylanases are a class of enzymes with numerous industrial applications and are involved in the degradation of xylose, a component of lignocellulose. The optimum temperature of enzymes is an essential factor to be considered when choosing appropriate biocatalysts for a particular purpose. Therefore, in silico prediction of this attribute is a significant cost and time‐effective step in the effort to characterize novel enzymes. The objective of this study was to develop a computational method to predict the thermal dependence of xylanases. This tool was then implemented for targeted screening of putative xylanases with specific thermal dependencies from metagenomic data and resulted in the identification of three novel xylanases from sheep and cow rumen microbiota. Here we present thermal activity prediction for xylanase, a new sequence‐based machine learning method that has been trained using a selected combination of various protein features. This random forest classifier discriminates non‐thermophilic, thermophilic, and hyper‐thermophilic xylanases. The model's performance was evaluated through multiple iterations of sixfold cross‐validations as well as holdout tests, and it is freely accessible as a web‐service at arimees.com.
Xylanases are a class of enzymes with numerous industrial applications and are involved in the degradation of xylose polysaccharide, which is present in lignocellulosic biomass. The optimum temperature of enzymes is the indicator of their thermal activity and is an essential factor to be considered when choosing an appropriate biocatalyst for a particular purpose. Therefore, insilico prediction of this enzymatic attribute is a significant cost and time-effective step in the effort to identify and characterize novel enzymes. The objective of this study was to develop an accurate computational method to predict the thermal activity status of xylanases from glycoside hydrolases families 10 and 11, the most prevalent known xylanase families. Here we present TAXyl (Thermal Activity Prediction for Xylanase), a new sequence-based machine learning method that has been trained using a selected combination of various physicochemical protein features. This ensemble of four supervised learning algorithms discriminates mesophilic, thermophilic, and hyper-thermophilic xylanases based on their optimum temperature with the process of soft-voting. TAXyl's performance was ultimately evaluated through multiple iterations of six-fold cross-validations, and it exhibited a mean accuracy of ~0.94, F1-score of ~0.91, and MCC of ~0.9. Additionally, the model was tested on previously unseen data and depicted relatively similar performance. To the best of our knowledge, this tool is the most accurate and practical prediction tool currently available and operating on this class of enzymes.TAXyl is freely accessible as a web-service at http://arimees.com/ and provides users with several features to facilitate the characterization of GH10 and GH11 xylanases.
Background: Prevalence of visual impairment is increasing all around the world and this impairment makes the medication management process more complicated. According to literature, Technology, particularly mobile technology has a significant impact on both medication adherence and improving quality of life for people with visual impairment. Due to aforementioned facts, the researchers decided to identify the user's goals for a mHealth-based medication management system for visually impaired individuals to help the developers to design and develop a medication management system for visually impaired individuals considering to improve their medication adherence and also preserve their independence. Methods: A goal directed approach, introduced by Cooper, has been used in order to extract the goals of visually impaired users who would employ a medication management system. One the most important concepts of this method are designing personas. The needed data for designing personas were extracted from two literature reviews and interviews with 14 visually impaired individuals and three experts of this area. Therefore the personas' different goals were defined. Results: As the results of this study, three personas were designed according to the extracted data and three categories of user goals including experience goals, end goals and life goals were determined for this system. Conclusion: This study could help the researchers and developers to design and develop an appropriate digital product for people with visual impairment, in order to increase their medication adherence. In addition, the findings of this study could help the policy makers of different areas to increase the quality of life for the people with visual impairment by eliminating the barriers of independent and effective medication adherence.nt..
Introduction: According to the substantial growth of mobile health applications in all aspects of health in the last decade, an mHealth app assessment has become more important for developers. On the other hand, the increasing rate of chronic diseases in all communities has encouraged the developers to develop applications for chronically ill patients. The aim of this study was to develop an assessment method for mobile health apps to fit specifically to chronic diseases mobile health apps. Methods: The methodology of this research had three steps. In the first step, the authors searched the literature for the assessment tools for chronic disease mobile applications. In the next step, the best tool for matching an application to the users' needs, which was also adaptable to Rinke Riezebos's scoring method, was selected. Then, in the last step of this methodology, a tool was first prepared and then adapted to the Riezebos's scoring method in order to attach it to this scoring method as a relevant subsection. Results:Step one showed limited tools have been designed specific to chronic disease applications. In step two, a framework, which was developed by Kelli Hale and the others, was selected as an appropriate tool. In this framework, the authors have used a behavioral theory content survey (BTS), which is a validated tool for assessing mobile apps according to behavior theory. In the last step, Riezebos's scoring method and the framework were combined as an extension of Riezebos's method. The BTS-based framework was mapped to the Riezebos's method in one of the subsections, which is about the application's content. Conclusion: The current study introduces an extension of Riezebos's method to assess chronic disease mobile applications. This extension uses a scoring method of Riezebos's peer-review tool with BTS for chronic disease mobile health apps developers.
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